From 4f4d05bac617a3b159e599bd8c08a8ac86b4191d Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 10 Nov 2023 12:53:15 -0600
Subject: [PATCH 01/90] add `.gitignore`

---
 .gitignore | 133 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 133 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..91f9376
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,133 @@
+# User data
+.DS_Store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution, packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.idea/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete*.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+scratchpaper.*
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+./.env
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# datajoint
+dj_local_c*.json
+dj_*.y*ml
+temp*
+temp/*
+
+# docs
+/docs/site
+/docs/src/tutorials/*ipynb
+
+# emacs
+**/*~
+**/#*#
+**/.#*
+
+# Codespaces
+example_data
+
+#nwb export
+*nwb
+
+# vscode
+*.code-workspace
+.vscode

From e9d4b4bc0557a4e815f98c076a20b5af3fe46e93 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 10 Nov 2023 17:14:22 -0600
Subject: [PATCH 02/90] add DevContainers

---
 .devcontainer/Dockerfile          | 48 +++++++++++++++++++++++++++++++
 .devcontainer/devcontainer.json   | 29 +++++++++++++++++++
 .devcontainer/docker-compose.yaml | 22 ++++++++++++++
 docker-compose-db.yaml            | 15 ++++++++++
 setup.py                          | 44 ++++++++++++++++++++++++++++
 5 files changed, 158 insertions(+)
 create mode 100644 .devcontainer/Dockerfile
 create mode 100644 .devcontainer/devcontainer.json
 create mode 100644 .devcontainer/docker-compose.yaml
 create mode 100644 docker-compose-db.yaml
 create mode 100644 setup.py

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
new file mode 100644
index 0000000..1bc4731
--- /dev/null
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,48 @@
+FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f
+
+ENV PATH /usr/local/bin:$PATH
+ENV PYTHON_VERSION 3.9.17
+
+RUN \
+    adduser --system --disabled-password --shell /bin/bash vscode && \
+    # install docker
+    apt-get update && \
+    apt-get install ca-certificates curl gnupg lsb-release -y && \
+    mkdir -m 0755 -p /etc/apt/keyrings && \
+    curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /etc/apt/keyrings/docker.gpg && \
+    echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null && \
+    apt-get update && \
+    apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin -y && \
+    usermod -aG docker vscode && \
+    apt-get clean
+
+RUN \
+    # dev setup
+    apt update && \
+    apt-get install sudo git bash-completion graphviz default-mysql-client s3fs procps -y && \
+    usermod -aG sudo vscode && \
+    echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers && \
+    pip install --no-cache-dir --upgrade black pip nbconvert && \
+    echo '. /etc/bash_completion' >> /home/vscode/.bashrc && \
+    echo 'export PS1="\[\e[32;1m\]\u\[\e[m\]@\[\e[34;1m\]\H\[\e[m\]:\[\e[33;1m\]\w\[\e[m\]$ "' >> /home/vscode/.bashrc && \
+    apt-get clean
+
+COPY ./ /tmp/element-moseq/
+
+RUN \
+    # pipeline dependencies
+    apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
+    # clean up
+    rm -rf /tmp/element-moseq && \
+    apt-get clean
+
+ENV DJ_HOST fakeservices.datajoint.io
+ENV DJ_USER root
+ENV DJ_PASS simple
+
+ENV IMAGING_ROOT_DATA_DIR /workspaces/element-moseq/example_data
+ENV DATABASE_PREFIX neuro_
+
+USER vscode
+CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd"
\ No newline at end of file
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000..11a5e14
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,29 @@
+{
+	"name": "Environment + Data",
+	"dockerComposeFile": "docker-compose.yaml",
+	"service": "app",
+	"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
+	"remoteEnv": {
+		"LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}"
+	},
+	"onCreateCommand": "mkdir -p ${DATA_MOUNTPOINT} && pip install -e .",
+	"hostRequirements": {
+		"cpus": 4,
+		"memory": "8gb",
+		"storage": "32gb"
+	},
+	"forwardPorts": [
+		3306
+	],
+	"customizations": {
+		"settings": {
+			"python.pythonPath": "/usr/local/bin/python"
+		},
+		"vscode": {
+			"extensions": [
+				"ms-python.python@2023.8.0",
+				"ms-toolsai.jupyter@2023.3.1201040234"
+			]
+		}
+	}
+}
\ No newline at end of file
diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml
new file mode 100644
index 0000000..bd0a1ea
--- /dev/null
+++ b/.devcontainer/docker-compose.yaml
@@ -0,0 +1,22 @@
+version: "3"
+services:
+  app:
+    cpus: 4
+    mem_limit: 8g
+    build:
+      context: ..
+      dockerfile: ./.devcontainer/Dockerfile
+    extra_hosts:
+      - fakeservices.datajoint.io:127.0.0.1
+    devices:
+      - /dev/fuse
+    cap_add:
+      - SYS_ADMIN
+    security_opt:
+      - apparmor:unconfined
+    volumes:
+      - ..:/workspaces/element-moseq:cached
+      - docker_data:/var/lib/docker # persist docker images
+    privileged: true # only because of dind
+volumes:
+  docker_data:
diff --git a/docker-compose-db.yaml b/docker-compose-db.yaml
new file mode 100644
index 0000000..1d453c8
--- /dev/null
+++ b/docker-compose-db.yaml
@@ -0,0 +1,15 @@
+# MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build
+version: "3"
+services:
+  db:
+    restart: always
+    image: datajoint/mysql:${MYSQL_VER}
+    environment:
+      - MYSQL_ROOT_PASSWORD=${DJ_PASS}
+    ports:
+      - "3306:3306"
+    healthcheck:
+      test: [ "CMD", "mysqladmin", "ping", "-h", "localhost" ]
+      timeout: 15s
+      retries: 10
+      interval: 15s
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..72d0e3c
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+from os import path
+from setuptools import find_packages, setup
+import urllib.request
+
+pkg_name = "element_moseq"
+here = path.abspath(path.dirname(__file__))
+
+with open(path.join(here, "README.md"), "r") as f:
+    long_description = f.read()
+
+with open(path.join(here, pkg_name, "version.py")) as f:
+    exec(f.read())
+
+setup(
+    name=pkg_name.replace("_", "-"),
+    version=__version__,  # noqa: F821
+    description="Keypoint Moseq DataJoint Element",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    author="DataJoint",
+    author_email="info@datajoint.com",
+    license="MIT",
+    url=f'https://github.com/datajoint/{pkg_name.replace("_", "-")}',
+    keywords="neuroscience keypoint-moseq science datajoint",
+    packages=find_packages(exclude=["contrib", "docs", "tests*"]),
+    scripts=[],
+    install_requires=[
+        "datajoint>=0.13.0",
+        "ipykernel>=6.0.1",
+        "ipywidgets",
+        "plotly",
+    ],
+    extras_require={
+        "moseq": ["moseq @ git+https://github.com/dattalab/keypoint-moseq.git"],
+        "elements": [
+            "element-animal>=0.1.8",
+            "element-event>=0.2.3",
+            "element-interface>=0.6.0",
+            "element-lab>=0.3.0",
+            "element-session>=0.1.5",
+        ],
+    },
+)

From 3ca04675e3d572a41f341d17e2985bd7e3fdeaf8 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 10 Nov 2023 17:27:48 -0600
Subject: [PATCH 03/90] fix dockerfile and devcontainer

---
 .devcontainer/Dockerfile        | 15 ++++++++++-----
 .devcontainer/devcontainer.json |  3 ---
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 1bc4731..9a91441 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -31,18 +31,23 @@ COPY ./ /tmp/element-moseq/
 
 RUN \
     # pipeline dependencies
-    apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
+    apt-get update && \
+    apt-get install -y gcc ffmpeg graphviz && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements,moseq] && \
     # clean up
-    rm -rf /tmp/element-moseq && \
+    rm -rf /tmp/element-moseq/ && \
     apt-get clean
 
 ENV DJ_HOST fakeservices.datajoint.io
 ENV DJ_USER root
 ENV DJ_PASS simple
 
-ENV IMAGING_ROOT_DATA_DIR /workspaces/element-moseq/example_data
+ENV DATA_MOUNTPOINT /workspaces/element-moseq/example_data
+#ENV DLC_ROOT_DATA_DIR $DATA_MOUNTPOINT/inbox
+#ENV DLC_PROCESSED_DATA_DIR $DATA_MOUNTPOINT/outbox
 ENV DATABASE_PREFIX neuro_
 
 USER vscode
-CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd"
\ No newline at end of file
+CMD bash -c "sudo rm /var/run/docker.pid; sudo dockerd"
+
+ENV LD_LIBRARY_PATH="/lib:/opt/conda/lib"
\ No newline at end of file
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 11a5e14..e3f89ea 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -3,9 +3,6 @@
 	"dockerComposeFile": "docker-compose.yaml",
 	"service": "app",
 	"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
-	"remoteEnv": {
-		"LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}"
-	},
 	"onCreateCommand": "mkdir -p ${DATA_MOUNTPOINT} && pip install -e .",
 	"hostRequirements": {
 		"cpus": 4,

From d039df77cda3792dc8e1a8b3dbfccf316054fbba Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 1 Dec 2023 07:41:07 +0100
Subject: [PATCH 04/90] update setup.py

---
 setup.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/setup.py b/setup.py
index 72d0e3c..e6e7b93 100644
--- a/setup.py
+++ b/setup.py
@@ -27,18 +27,17 @@
     scripts=[],
     install_requires=[
         "datajoint>=0.13.0",
+        "pydot",
         "ipykernel>=6.0.1",
         "ipywidgets",
-        "plotly",
     ],
     extras_require={
-        "moseq": ["moseq @ git+https://github.com/dattalab/keypoint-moseq.git"],
+        "default": ["moseq @ git+https://github.com/dattalab/keypoint-moseq.git"],
         "elements": [
-            "element-animal>=0.1.8",
-            "element-event>=0.2.3",
-            "element-interface>=0.6.0",
             "element-lab>=0.3.0",
+            "element-animal>=0.1.8",
             "element-session>=0.1.5",
+            "element-interface>=0.6.0",
         ],
     },
 )

From 029501c8dd8a6af7be4370b10a1436cde887f378 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 1 Dec 2023 07:47:07 +0100
Subject: [PATCH 05/90] add README

---
 README.md | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)

diff --git a/README.md b/README.md
index 8b13789..867d843 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,78 @@
+# DataJoint Element for Motion Sequencing with Keypoint-MoSeq
+
+DataJoint Element for motion sequencing with
+[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html).  DataJoint Elements collectively standardize
+and automate data collection and analysis for neuroscience experiments.  Each Element is
+a modular pipeline for data storage and processing with corresponding database
+tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline.
+
+## Experiment Flowchart
+
+![flowchart]()
+
+## Data Pipeline Diagram
+
+![pipeline]()
+
+## Getting Started
+
++ Please fork this repository.
+
++ Clone the repository to your computer.
+
+  ```bash
+  git clone https://github.com/<enter_github_username>/element-moseq
+  ```
+
++ Install with `pip`:
+
+  ```bash
+  pip install -e .
+  ```
+
++ [Interactive tutorial on GitHub Codespaces](https://github.com/datajoint/element-moseq#interactive-tutorial)
+
++ [Documentation](https://datajoint.com/docs/elements/element-moseq)
+
+## Support
+
++ If you need help getting started or run into any errors, please open a GitHub Issue 
+or contact our team by email at support@datajoint.com.
+
+## Interactive Tutorial
+
++ The easiest way to learn about DataJoint Elements is to use the tutorial notebooks within the included interactive environment configured using [Dev Container](https://containers.dev/).
+
+### Launch Environment
+
+Here are some options that provide a great experience:
+
+- (*recommended*) Cloud-based Environment
+  - Launch using [GitHub Codespaces](https://github.com/features/codespaces) using the `+` option which will `Create codespace on main` in the codebase repository on your fork with default options. For more control, see the `...` where you may create `New with options...`.
+  - Build time for a codespace is a few minutes. This is done infrequently and cached for convenience.
+  - Start time for a codespace is less than 1 minute. This will pull the built codespace from cache when you need it.
+  - *Tip*: Each month, GitHub renews a [free-tier](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts) quota of compute and storage. Typically we run into the storage limits before anything else since Codespaces consume storage while stopped. It is best to delete Codespaces when not actively in use and recreate when needed. We'll soon be creating prebuilds to avoid larger build times. Once any portion of your quota is reached, you will need to wait for it to be reset at the end of your cycle or add billing info to your GitHub account to handle overages.
+  - *Tip*: GitHub auto names the codespace but you can rename the codespace so that it is easier to identify later.
+
+- Local Environment
+  > *Note: Access to example data is currently limited to MacOS and Linux due to the s3fs utility. Windows users are recommended to use the above environment.*
+  - Install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+  - Install [Docker](https://docs.docker.com/get-docker/)
+  - Install [VSCode](https://code.visualstudio.com/)
+  - Install the VSCode [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
+  - `git clone` the codebase repository and open it in VSCode
+  - Use the `Dev Containers extension` to `Reopen in Container` (More info is in the `Getting started` included with the extension.)
+
+You will know your environment has finished loading once you either see a terminal open related to `Running postStartCommand` with a final message of `Done` or the `README.md` is opened in `Preview`.
+
+Once the environment has launched, please run the following command in the terminal:
+```
+MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d
+```
+
+### Instructions
+
+1. We recommend you start by navigating to the `notebooks` directory on the left panel and go through the `tutorial.ipynb` Jupyter notebook. Execute the cells in the notebook to begin your walkthrough of the tutorial.
+
+1. Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to `Stop Current Codespace` but when running Dev Container on your own machine the equivalent option is `Reopen folder locally`. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity.  Once the Codespace is no longer being used, we recommend deleting the Codespace.
 

From a3f149cfd87ef033ccd3e47be48c6306739ed72e Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 11 Dec 2023 15:10:32 +0100
Subject: [PATCH 06/90] minor change in setup.py

---
 setup.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/setup.py b/setup.py
index e6e7b93..84ecd30 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,6 @@
     scripts=[],
     install_requires=[
         "datajoint>=0.13.0",
-        "pydot",
         "ipykernel>=6.0.1",
         "ipywidgets",
     ],

From 30907d54758dba10871e4284657ebadf13dd0b6a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 21:59:43 +0100
Subject: [PATCH 07/90] test container only with element-moseq[elements]

---
 .devcontainer/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 9a91441..f30ae5b 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,7 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements,moseq] && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean

From d01b394c96cc19460ccfd2e58f67c77c09c70b7a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 22:01:14 +0100
Subject: [PATCH 08/90] test container without DATA_MOUNTPOINT

---
 .devcontainer/Dockerfile        | 2 +-
 .devcontainer/devcontainer.json | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index f30ae5b..1b55788 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -42,7 +42,7 @@ ENV DJ_HOST fakeservices.datajoint.io
 ENV DJ_USER root
 ENV DJ_PASS simple
 
-ENV DATA_MOUNTPOINT /workspaces/element-moseq/example_data
+#ENV DATA_MOUNTPOINT /workspaces/element-moseq/example_data
 #ENV DLC_ROOT_DATA_DIR $DATA_MOUNTPOINT/inbox
 #ENV DLC_PROCESSED_DATA_DIR $DATA_MOUNTPOINT/outbox
 ENV DATABASE_PREFIX neuro_
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index e3f89ea..922a2fd 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -3,7 +3,7 @@
 	"dockerComposeFile": "docker-compose.yaml",
 	"service": "app",
 	"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
-	"onCreateCommand": "mkdir -p ${DATA_MOUNTPOINT} && pip install -e .",
+	"onCreateCommand": "pip install -e .",
 	"hostRequirements": {
 		"cpus": 4,
 		"memory": "8gb",

From d75905855fa18c6c7551e4b83015f2dac9e27a82 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 22:14:32 +0100
Subject: [PATCH 09/90] test without version.py

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 84ecd30..c6a4f36 100644
--- a/setup.py
+++ b/setup.py
@@ -9,8 +9,8 @@
 with open(path.join(here, "README.md"), "r") as f:
     long_description = f.read()
 
-with open(path.join(here, pkg_name, "version.py")) as f:
-    exec(f.read())
+# with open(path.join(here, pkg_name, "version.py")) as f:
+#    exec(f.read())
 
 setup(
     name=pkg_name.replace("_", "-"),

From 36dcdc2679185c27f92375ec6c27f94748287b3a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 22:27:24 +0100
Subject: [PATCH 10/90] test with no version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index c6a4f36..faee0ca 100644
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@
 
 setup(
     name=pkg_name.replace("_", "-"),
-    version=__version__,  # noqa: F821
+    # version=__version__,  # noqa: F821
     description="Keypoint Moseq DataJoint Element",
     long_description=long_description,
     long_description_content_type="text/markdown",

From 94c4dc62dd6e1e477414570ec8aeef88aa09544e Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 22:49:41 +0100
Subject: [PATCH 11/90] test default installation

---
 .devcontainer/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 1b55788..fbb00e4 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,7 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements,default] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean

From ae1cbf481a31af9b94310c084786169ad09360f6 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 23:11:33 +0100
Subject: [PATCH 12/90] change default kpms installation

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index faee0ca..2b13e59 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,7 @@
         "ipywidgets",
     ],
     extras_require={
-        "default": ["moseq @ git+https://github.com/dattalab/keypoint-moseq.git"],
+        "default": ["keypoint_moseq"],
         "elements": [
             "element-lab>=0.3.0",
             "element-animal>=0.1.8",

From 644505d84b7445189ea0aa67f17ae2c1612f3315 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 23:28:42 +0100
Subject: [PATCH 13/90] test moseq installation using https

---
 setup.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 2b13e59..e430236 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,9 @@
         "ipywidgets",
     ],
     extras_require={
-        "default": ["keypoint_moseq"],
+        "default": [
+            "keypoint-moseq @ -U git+https://github.com/dattalab/keypoint-moseq"
+        ],
         "elements": [
             "element-lab>=0.3.0",
             "element-animal>=0.1.8",

From 2689bb8c99999c2a3c66a11ccf85f6f49c56dc44 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 23:32:26 +0100
Subject: [PATCH 14/90] delete -U

---
 setup.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index e430236..78c02ed 100644
--- a/setup.py
+++ b/setup.py
@@ -31,9 +31,7 @@
         "ipywidgets",
     ],
     extras_require={
-        "default": [
-            "keypoint-moseq @ -U git+https://github.com/dattalab/keypoint-moseq"
-        ],
+        "default": ["keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq"],
         "elements": [
             "element-lab>=0.3.0",
             "element-animal>=0.1.8",

From 66cddfcbaf0fd9875b14a8b888edef80e7ea179e Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 23:48:16 +0100
Subject: [PATCH 15/90] Update README

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 867d843..af0cbc7 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # DataJoint Element for Motion Sequencing with Keypoint-MoSeq
 
 DataJoint Element for motion sequencing with
-[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html).  DataJoint Elements collectively standardize
+[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html). Keypoint-MoSeq  processes keypoint timeseries data generated by DeepLabCut, SLEAP, or alternative pose estimation methods. DataJoint Elements collectively standardize
 and automate data collection and analysis for neuroscience experiments.  Each Element is
 a modular pipeline for data storage and processing with corresponding database
 tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline.

From 45c9e1a7e11887f6ed58c4a8f2bd63727706fdd4 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 15 Dec 2023 23:48:58 +0100
Subject: [PATCH 16/90] from default to kpms_default and add mchips

---
 .devcontainer/Dockerfile | 2 +-
 setup.py                 | 5 ++++-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index fbb00e4..6c8df97 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,7 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements,default] && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements,kpms_default] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean
diff --git a/setup.py b/setup.py
index 78c02ed..9af6356 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,10 @@
         "ipywidgets",
     ],
     extras_require={
-        "default": ["keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq"],
+        "kpms_default": [
+            "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq"
+        ],
+        "kpms_apple_mchips": ["jax==0.3.22", "jaxlib==0.3.22", "keypoint_moseq"],
         "elements": [
             "element-lab>=0.3.0",
             "element-animal>=0.1.8",

From 2022f3509118df36e837d9de38c3c8c7fb2adc11 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Sat, 16 Dec 2023 00:05:33 +0100
Subject: [PATCH 17/90] add jax and jaxlib to fix installation

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 9af6356..a57dc2e 100644
--- a/setup.py
+++ b/setup.py
@@ -29,12 +29,13 @@
         "datajoint>=0.13.0",
         "ipykernel>=6.0.1",
         "ipywidgets",
+        "jax==0.4.1",
+        "jaxlib==0.4.1",
     ],
     extras_require={
         "kpms_default": [
             "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq"
         ],
-        "kpms_apple_mchips": ["jax==0.3.22", "jaxlib==0.3.22", "keypoint_moseq"],
         "elements": [
             "element-lab>=0.3.0",
             "element-animal>=0.1.8",

From bb8e543e7f13c1f205db904a5d71bef73d266f41 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Sat, 16 Dec 2023 00:17:58 +0100
Subject: [PATCH 18/90] update description of MoSeq in README

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index af0cbc7..8a2f2d8 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
 # DataJoint Element for Motion Sequencing with Keypoint-MoSeq
 
 DataJoint Element for motion sequencing with
-[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html). Keypoint-MoSeq  processes keypoint timeseries data generated by DeepLabCut, SLEAP, or alternative pose estimation methods. DataJoint Elements collectively standardize
+[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html) ([scientific article](https://doi.org/10.1101/2023.03.16.532307)). Serving as an open-source tool, Keypoint-MoSeq utilizes an advanced generative model to capture animal behavior. This machine learning software excels in automatically identifying and interpreting behavioral modules, or "syllables," from keypoint data without requiring manual intervention. The keypoint timeseries data can originate from DeepLabCut, SLEAP, or other pose estimation algorithms.
+
+DataJoint Elements collectively standardize
 and automate data collection and analysis for neuroscience experiments.  Each Element is
 a modular pipeline for data storage and processing with corresponding database
 tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline.

From 03f59487683f1d8663821d5089ca8f1fdcdf8cc1 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Sat, 16 Dec 2023 00:53:47 +0100
Subject: [PATCH 19/90] update setup

---
 setup.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/setup.py b/setup.py
index a57dc2e..d1c59ee 100644
--- a/setup.py
+++ b/setup.py
@@ -25,16 +25,12 @@
     keywords="neuroscience keypoint-moseq science datajoint",
     packages=find_packages(exclude=["contrib", "docs", "tests*"]),
     scripts=[],
-    install_requires=[
-        "datajoint>=0.13.0",
-        "ipykernel>=6.0.1",
-        "ipywidgets",
-        "jax==0.4.1",
-        "jaxlib==0.4.1",
-    ],
+    install_requires=["datajoint>=0.13.0", "ipykernel>=6.0.1", "ipywidgets"],
     extras_require={
         "kpms_default": [
-            "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq"
+            "tensorflow==2.12.0",
+            "'jax[cuda]==0.4.1' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html",
+            "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq",
         ],
         "elements": [
             "element-lab>=0.3.0",

From 97047db0036d0f00b15b0c7da644a10ccc4c689e Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Sat, 16 Dec 2023 01:01:09 +0100
Subject: [PATCH 20/90] add necessary dependency

---
 setup.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/setup.py b/setup.py
index d1c59ee..00248fc 100644
--- a/setup.py
+++ b/setup.py
@@ -28,6 +28,7 @@
     install_requires=["datajoint>=0.13.0", "ipykernel>=6.0.1", "ipywidgets"],
     extras_require={
         "kpms_default": [
+            "ffmpeg",
             "tensorflow==2.12.0",
             "'jax[cuda]==0.4.1' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html",
             "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq",

From 9080fe3d43c1652d501869edc8aa5c2f45a7e92b Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 18 Dec 2023 22:28:20 +0100
Subject: [PATCH 21/90] WIP pipeline

---
 element_moseq/__init__.py |  21 ++++
 element_moseq/train.py    | 230 ++++++++++++++++++++++++++++++++++++++
 element_moseq/version.py  |   4 +
 3 files changed, 255 insertions(+)
 create mode 100644 element_moseq/__init__.py
 create mode 100644 element_moseq/train.py
 create mode 100644 element_moseq/version.py

diff --git a/element_moseq/__init__.py b/element_moseq/__init__.py
new file mode 100644
index 0000000..7dbc508
--- /dev/null
+++ b/element_moseq/__init__.py
@@ -0,0 +1,21 @@
+import os
+import datajoint as dj
+
+if "custom" not in dj.config:
+    dj.config["custom"] = {}
+
+# overwrite dj.config['custom'] values with environment variables if available
+
+dj.config["custom"]["database.prefix"] = os.getenv(
+    "DATABASE_PREFIX", dj.config["custom"].get("database.prefix", "")
+)
+
+dj.config["custom"]["kpms_root_data_dir"] = os.getenv(
+    "KPMS_ROOT_DATA_DIR", dj.config["custom"].get("kpms_root_data_dir", "")
+)
+
+dj.config["custom"]["kpms_processed_data_dir"] = os.getenv(
+    "KPMS_PROCESSED_DATA_DIR", dj.config["custom"].get("kpms_processed_data_dir", "")
+)
+
+db_prefix = dj.config["custom"].get("database.prefix", "")
diff --git a/element_moseq/train.py b/element_moseq/train.py
new file mode 100644
index 0000000..53f47d8
--- /dev/null
+++ b/element_moseq/train.py
@@ -0,0 +1,230 @@
+import datajoint as dj
+import matplotlib.pyplot as plt
+import cv2
+from typing import Optional
+
+import inspect
+import importlib
+import os
+from pathlib import Path
+from element_interface.utils import find_full_path, dict_to_uuid
+
+schema = dj.schema()
+_linking_module = None
+
+
+def activate(
+    train_schema_name: str,
+    *,
+    create_schema: bool = True,
+    create_tables: bool = True,
+    linking_module: str = None,
+):
+    """Activate this schema.
+
+    Args:
+        train_schema_name (str): schema name on the database server
+        create_schema (bool): when True (default), create schema in the database if it
+                            does not yet exist.
+        create_tables (bool): when True (default), create schema tables in the database
+                             if they do not yet exist.
+        linking_module (str): a module (or name) containing the required dependencies.
+
+    Dependencies:
+    Functions:
+        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies)
+                                 with all behavioral recordings, as (list of) string(s).
+        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed
+                                      data. Defaults to session video subfolder.
+    """
+
+    if isinstance(linking_module, str):
+        linking_module = importlib.import_module(linking_module)
+    assert inspect.ismodule(
+        linking_module
+    ), "The argument 'dependency' must be a module's name or a module"
+    assert hasattr(
+        linking_module, "get_kpms_root_data_dir"
+    ), "The linking module must specify a lookup function for a root data directory"
+
+    global _linking_module
+    _linking_module = linking_module
+
+    # activate
+    schema.activate(
+        train_schema_name,
+        create_schema=create_schema,
+        create_tables=create_tables,
+        add_objects=_linking_module.__dict__,
+    )
+
+
+# -------------- Functions required by element-moseq ---------------
+
+
+def get_kpms_root_data_dir() -> list:
+    """Pulls relevant func from parent namespace to specify root data dir(s).
+
+    It is recommended that all paths in DataJoint Elements stored as relative
+    paths, with respect to some user-configured "root" director(y/ies). The
+    root(s) may vary between data modalities and user machines. Returns a full path
+    string or list of strings for possible root data directories.
+    """
+    root_directories = _linking_module.get_kpms_root_data_dir()
+    if isinstance(root_directories, (str, Path)):
+        root_directories = [root_directories]
+
+    if (
+        hasattr(_linking_module, "get_kpms_processed_data_dir")
+        and get_kpms_processed_data_dir() not in root_directories
+    ):
+        root_directories.append(_linking_module.get_kpms_processed_data_dir())
+
+    return root_directories
+
+
+def get_kpms_processed_data_dir() -> Optional[str]:
+    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
+
+    Method in parent namespace should provide a string to a directory where KPMS output
+    files will be stored. If unspecified, output files will be stored in the
+    session directory 'videos' folder, per DeepLabCut default.
+    """
+    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
+        return _linking_module.get_kpms_processed_data_dir()
+    else:
+        return None
+
+
+# ----------------------------- Table declarations ----------------------
+
+
+@schema
+class VideoSet(dj.Manual):
+    definition = """
+    -> Session
+    videoset_id: int
+    ---
+    -> Device
+    videoset_path: varchar(255) #file path of the video, relative to root data directory
+    """
+
+    class VideoIndex(dj.Part):
+        definition = """
+        -> master
+        video_id: int
+        ---
+        video_path: varchar(255) # filepath of video, relative to root data directory
+        """
+
+
+@schema
+class RecordingInfo(dj.Imported):
+    """Automated table with video file metadata.
+
+    Attributes:
+        px_height (smallint): Height in pixels.
+        px_width (smallint): Width in pixels.
+        nframes (int): Number of frames.
+        fps (int): Optional. Frames per second, Hz.
+        recording_datetime (datetime): Optional. Datetime for the start of recording.
+        recording_duration (float): video duration (s) from nframes / fps."""
+
+    definition = """
+    -> VideoSet.VideoIndex
+
+    ---
+    px_height                 : smallint  # height in pixels
+    px_width                  : smallint  # width in pixels
+    nframes                   : int  # number of frames 
+    fps = NULL                : int       # (Hz) frames per second
+    recording_datetime = NULL : datetime  # Datetime for the start of the recording
+    recording_duration        : float     # video duration (s) from nframes / fps
+    """
+
+    def make(self, key):
+        """Populates table with video metadata using CV2."""
+
+        file_path = (VideoSet.VideoIndex & key).fetch("video_path")
+
+        nframes = 0
+        px_height, px_width, fps = None, None, None
+
+        file_path = (find_full_path(get_kpms_root_data_dir(), file_path)).as_posix()
+
+        cap = cv2.VideoCapture(file_path)
+        info = (
+            int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+            int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+            int(cap.get(cv2.CAP_PROP_FPS)),
+        )
+        if px_height is not None:
+            assert (px_height, px_width, fps) == info
+        px_height, px_width, fps = info
+        nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        cap.release()
+
+        self.insert1(
+            {
+                **key,
+                "px_height": px_height,
+                "px_width": px_width,
+                "nframes": nframes,
+                "fps": fps,
+                "recording_duration": nframes / fps,
+            }
+        )
+
+
+@schema
+class KeypointsSet(dj.Manual):
+    """Input data containing keypoints and the parameter set used during pose estimation inference (E.g. DeepLabCut)."""
+
+    definition = """
+    -> VideoSet
+    kpset_id                    : int
+    ---
+    kpset_path                  : varchar(255)  # keypoints path of the pose estimation method, relative to root
+    config_path                 : varchar(255)  # config file path of the pose estimation method, relative to root
+    kp_description=''           : varchar(300)  # Optional. User-entered description.
+    """
+
+    class BodypartsParamSet(dj.Part):
+        """Body parts to use in the model
+
+        Attributes:
+            anterior_bodyparts(longblob): list of strings of anterior bodyparts
+            posterior_bodyparts(longblob): list of strings of posterior bodyparts
+            use_bodyparts(longblob): list of strings of bodyparts to be used
+        """
+
+        definition = """
+        -> master
+        bodypartset_id       : int
+        ---
+        anterior_bodyparts          : varchar(100)  # list of strings of anterior bodyparts
+        posterior_bodyparts         : varchar(100)  # list of strings of posterior bodyparts
+        use_bodyparts               : longblob      # list of strings of bodyparts to be used
+        """
+
+
+@schema
+class PoseEstimationMethod(dj.Lookup):
+    definition = """ # Parameters used to obtain the keypoints data based on a specific pose estimation method        
+    -> KeypointsSet
+    method_id                   : int
+    ---
+    format='deeplabcut'         : enum('deeplabcut', 'sleap') # pose estimation method   
+    extension='h5'              : enum('h5', 'csv') 
+    """
+
+
+@schema
+class KpmsProject(dj.Manual):
+    definition = """
+    -> Session
+    kpms_project_id         : int
+    ---
+    kpms_project_path       : varchar(255) # kpms project path
+    project_description     : varchar(300)  # User-friendly description of the kpms project
+    """
diff --git a/element_moseq/version.py b/element_moseq/version.py
new file mode 100644
index 0000000..652faa3
--- /dev/null
+++ b/element_moseq/version.py
@@ -0,0 +1,4 @@
+"""
+Package metadata
+"""
+__version__ = "0.1.0"

From e68c61e5008b254391802454e1bbe27ea97d26c9 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 18 Dec 2023 22:28:42 +0100
Subject: [PATCH 22/90] WIP notebooks

---
 notebooks/testing.ipynb        | 1499 ++++++++++++++++++++++++++++++++
 notebooks/tutorial_pipeline.py |   95 ++
 2 files changed, 1594 insertions(+)
 create mode 100644 notebooks/testing.ipynb
 create mode 100644 notebooks/tutorial_pipeline.py

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
new file mode 100644
index 0000000..392f265
--- /dev/null
+++ b/notebooks/testing.ipynb
@@ -0,0 +1,1499 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
+    "    os.chdir(\"..\")\n",
+    "assert os.path.basename(os.getcwd()) == \"element-moseq\", (\n",
+    "    \"Please move to the \" + \"element directory\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datajoint as dj\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.config.load(\"dj_local_conf.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2023-12-18 22:09:52,236][INFO]: Connecting milagros@db.datajoint.com:3306\n",
+      "[2023-12-18 22:09:53,727][INFO]: Connected milagros@db.datajoint.com:3306\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DataJoint connection (connected) milagros@db.datajoint.com:3306"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dj.conn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2023-12-18 22:10:01,266][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tutorial_pipeline import lab, subject, session, train, Device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train.BodypartsParamSet.drop()\n",
+    "# train.PoseEstimationMethod.drop()\n",
+    "# train.KeypointsSet.drop_quick()\n",
+    "# train.RecordingInfo.drop_quick()\n",
+    "# train.VideoSet.VideoIndex.delete(force=True)\n",
+    "# train.VideoSet.drop()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dj.Diagram(subject) + dj.Diagram(session) + dj.Diagram(train)\n",
+    "#dj.Diagram(session) + dj.Diagram(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"523pt\" height=\"184pt\" viewBox=\"0.00 0.00 523.12 183.68\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 179.68)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-179.68 519.12,-179.68 519.12,4 -4,4\"/>\n",
+       "<!-- train.KeypointsSet -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>train.KeypointsSet</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ train.VideoSet\rkpset_id             \r------------------------------\rkpset_path           \rconfig_path          \rkp_description=&quot;&quot;    \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"150.25,-105.12 36.75,-105.12 36.75,-70.56 150.25,-70.56 150.25,-105.12\"/>\n",
+       "<text text-anchor=\"start\" x=\"44.75\" y=\"-84.56\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.KeypointsSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.KeypointsSet.BodypartsParamSet -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>train.KeypointsSet.BodypartsParamSet</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ train.KeypointsSet\rbodypartset_id       \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"187,-27.28 0,-27.28 0,-7.28 187,-7.28 187,-27.28\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-14.78\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.KeypointsSet.BodypartsParamSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.KeypointsSet&#45;&gt;train.KeypointsSet.BodypartsParamSet -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>train.KeypointsSet-&gt;train.KeypointsSet.BodypartsParamSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M93.5,-70.24C93.5,-56.61 93.5,-37.84 93.5,-26.99\"/>\n",
+       "</g>\n",
+       "<!-- train.PoseEstimationMethod -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>train.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ train.KeypointsSet\rmethod_id            \r------------------------------\rformat=&quot;deeplabcut&quot;  \rextension=&quot;h5&quot;       \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"344,-34.56 205,-34.56 205,0 344,0 344,-34.56\"/>\n",
+       "<text text-anchor=\"start\" x=\"213\" y=\"-14.78\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.PoseEstimationMethod</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.KeypointsSet&#45;&gt;train.PoseEstimationMethod -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>train.KeypointsSet-&gt;train.PoseEstimationMethod</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M137.78,-70.07C166.35,-59.25 203.32,-45.24 231.62,-34.52\"/>\n",
+       "</g>\n",
+       "<!-- train.RecordingInfo -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>train.RecordingInfo</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ train.VideoSet.VideoIndex\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"438.5\" cy=\"-17.28\" rx=\"76.62\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"438.5\" y=\"-13.01\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">train.RecordingInfo</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.VideoSet.VideoIndex -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>train.VideoSet.VideoIndex</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ train.VideoSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"400.12,-97.84 270.88,-97.84 270.88,-77.84 400.12,-77.84 400.12,-97.84\"/>\n",
+       "<text text-anchor=\"start\" x=\"278.88\" y=\"-85.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.VideoIndex</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.VideoSet.VideoIndex&#45;&gt;train.RecordingInfo -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>train.VideoSet.VideoIndex-&gt;train.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M348.97,-77.88C365.65,-66.77 394.37,-47.65 414.78,-34.07\"/>\n",
+       "</g>\n",
+       "<!-- train.KpmsProject -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>train.KpmsProject</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ session.Session\rkpms_project_id      \r------------------------------\rkpms_project_path    \rproject_description  \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"509.75,-175.68 399.25,-175.68 399.25,-141.12 509.75,-141.12 509.75,-175.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"407.25\" y=\"-155.12\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.KpmsProject</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.VideoSet -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>train.VideoSet</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ session.Session\rvideoset_id          \r------------------------------\r→ Device\rvideoset_path        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"381,-175.68 290,-175.68 290,-141.12 381,-141.12 381,-175.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"298\" y=\"-155.12\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.VideoSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- train.VideoSet&#45;&gt;train.KeypointsSet -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>train.VideoSet-&gt;train.KeypointsSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M289.7,-144.42C250.23,-133.24 193.09,-117.05 150.64,-105.03\"/>\n",
+       "</g>\n",
+       "<!-- train.VideoSet&#45;&gt;train.VideoSet.VideoIndex -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>train.VideoSet-&gt;train.VideoSet.VideoIndex</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M335.5,-140.8C335.5,-127.17 335.5,-108.4 335.5,-97.55\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x14050ae20>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dj.Diagram(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">device</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">videoset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>Camera1</td>\n",
+       "<td>./input_data/videos</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   device      videoset_path \n",
+       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
+       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.VideoSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subject and Session tables\n",
+    "subject.Subject.insert1(\n",
+    "    dict(\n",
+    "        subject=\"subject1\",\n",
+    "        sex=\"F\",\n",
+    "        subject_birth_date=\"2020-01-01\",\n",
+    "        subject_description=\"test\",\n",
+    "    ),\n",
+    "    skip_duplicates=True,\n",
+    ")\n",
+    "\n",
+    "#Definition of the dictionary named \"session_keys\"\n",
+    "session_keys = [\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2021-06-03 14:43:10\"),\n",
+    "]\n",
+    "\n",
+    "#Insert this dictionary in the Session table\n",
+    "session.Session.insert(session_keys, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">device</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">modality</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">description</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>Camera1</td>\n",
+       "<td>Pose Estimation</td>\n",
+       "<td>Panasonic HC-V380K</td></tr><tr><td>Camera2</td>\n",
+       "<td>Pose Estimation</td>\n",
+       "<td>Panasonic HC-V770K</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 2</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*device     modality       description   \n",
+       "+---------+ +------------+ +------------+\n",
+       "Camera1     Pose Estimatio Panasonic HC-V\n",
+       "Camera2     Pose Estimatio Panasonic HC-V\n",
+       " (Total: 2)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recording_key = dict(subject=\"subject1\",\n",
+    "            session_datetime=\"2021-06-02 14:04:22\",\n",
+    "            videoset_id=1)\n",
+    "train.VideoSet.insert1({**recording_key, \n",
+    "                            \"videoset_path\":\"./input_data/videos\",\n",
+    "                             \"device\":\"Camera1\"},\n",
+    "                            skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">device</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">videoset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>Camera1</td>\n",
+       "<td>./input_data/videos</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   device      videoset_path \n",
+       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
+       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.VideoSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "video_files = [\"./input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "               \"./input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "               \"./input_data/videos/21_12_2_def6b_2.top.ir.mp4\"]\n",
+    "\n",
+    "train.VideoSet.VideoIndex.insert(\n",
+    "    ({**recording_key, \"video_id\":v_idx, \"video_path\":Path(f)}\n",
+    "    for v_idx, f in enumerate(video_files)),skip_duplicates=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">device</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">videoset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>Camera1</td>\n",
+       "<td>./input_data/videos</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   device      videoset_path \n",
+       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
+       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.VideoSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">video_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_height</p>\n",
+       "                            <span class=\"djtooltiptext\">height in pixels</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_width</p>\n",
+       "                            <span class=\"djtooltiptext\">width in pixels</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">nframes</p>\n",
+       "                            <span class=\"djtooltiptext\">number of frames</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">fps</p>\n",
+       "                            <span class=\"djtooltiptext\">(Hz) frames per second</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\">Datetime for the start of the recording</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">video duration (s) from nframes / fps</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *videoset_id   *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
+       "+---------+ +------------+ +------------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.RecordingInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train.RecordingInfo.populate()\n",
+    "# train.RecordingInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'subject': 'subject1',\n",
+       " 'session_datetime': '2021-06-02 14:04:22',\n",
+       " 'videoset_id': 1}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "recording_key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">keypoints path of the pose estimation method, relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">config_path</p>\n",
+       "                            <span class=\"djtooltiptext\">config file path of the pose estimation method, relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kp_description</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>./input_data/videos/</td>\n",
+       "<td>./data/inbox/input_data/config.yaml</td>\n",
+       "<td>first test</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   *kpset_id    kpset_path     config_path    kp_description\n",
+       "+----------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1              1            ./input_data/v ./data/inbox/i first test    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.KeypointsSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Insert data in KeypointsSet table\n",
+    "kpset_key = {**recording_key,\"kpset_id\":1}\n",
+    "train.KeypointsSet.insert1({**kpset_key,\n",
+    "                            \"kpset_path\":\"./input_data/videos/\",\n",
+    "                            \"config_path\":\"./data/inbox/input_data/config.yaml\",\n",
+    "                            \"kp_description\":\"first test\"},\n",
+    "                            skip_duplicates=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">keypoints path of the pose estimation method, relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">config_path</p>\n",
+       "                            <span class=\"djtooltiptext\">config file path of the pose estimation method, relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kp_description</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>./input_data/videos/</td>\n",
+       "<td>./data/inbox/input_data/config.yaml</td>\n",
+       "<td>first test</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   *kpset_id    kpset_path     config_path    kp_description\n",
+       "+----------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1              1            ./input_data/v ./data/inbox/i first test    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.KeypointsSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">method_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">format</p>\n",
+       "                            <span class=\"djtooltiptext\">pose estimation method</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">extension</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>h5</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   *kpset_id    *method_id    format         extension    \n",
+       "+----------+ +------------+ +------------+ +----------+ +-----------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1              1            1             deeplabcut     h5           \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.PoseEstimationMethod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Insert data in PoseEstimationMethod table\n",
+    "train.PoseEstimationMethod.insert1({**kpset_key,\n",
+    "                                    \"method_id\":1, \n",
+    "                                    \"format\":\"deeplabcut\",\n",
+    "                                    \"extension\":\"h5\"}, \n",
+    "                                    skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">method_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">format</p>\n",
+       "                            <span class=\"djtooltiptext\">pose estimation method</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">extension</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>h5</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *videoset_id   *kpset_id    *method_id    format         extension    \n",
+       "+----------+ +------------+ +------------+ +----------+ +-----------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1              1            1             deeplabcut     h5           \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.PoseEstimationMethod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">videoset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodypartset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of anterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of bodyparts to be used</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *videoset_id   *kpset_id    *bodypartset_i anterior_bodyp posterior_body use_bodyparts \n",
+       "+---------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.KeypointsSet.BodypartsParamSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "OperationalError",
+     "evalue": "(1241, 'Operand should contain 1 column(s)')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[25], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Insert data in BodypartsParamset table\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m train\u001b[39m.\u001b[39;49mKeypointsSet\u001b[39m.\u001b[39;49mBodypartsParamSet\u001b[39m.\u001b[39;49minsert1({\n\u001b[1;32m      3\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkpset_key,\n\u001b[1;32m      4\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mbodypartset_id\u001b[39;49m\u001b[39m\"\u001b[39;49m:\u001b[39m1\u001b[39;49m,\n\u001b[1;32m      5\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39manterior_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m\"\u001b[39;49m\u001b[39mnose\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m      6\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mposterior_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m\"\u001b[39;49m\u001b[39mspine4\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m      7\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39muse_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m'\u001b[39;49m\u001b[39mspine4\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine3\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine2\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine1\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m      8\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mhead\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mnose\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mright ear\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mleft ear\u001b[39;49m\u001b[39m'\u001b[39;49m]\n\u001b[1;32m      9\u001b[0m     })\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/table.py:337\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minsert1\u001b[39m(\u001b[39mself\u001b[39m, row, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    331\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[39m    Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m    333\u001b[0m \n\u001b[1;32m    334\u001b[0m \u001b[39m    :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \u001b[39m        as one row.\u001b[39;00m\n\u001b[1;32m    336\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minsert((row,), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/table.py:440\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    425\u001b[0m     query \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m{command}\u001b[39;00m\u001b[39m INTO \u001b[39m\u001b[39m{destination}\u001b[39;00m\u001b[39m(`\u001b[39m\u001b[39m{fields}\u001b[39;00m\u001b[39m`) VALUES \u001b[39m\u001b[39m{placeholders}\u001b[39;00m\u001b[39m{duplicate}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    426\u001b[0m         command\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mREPLACE\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m replace \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mINSERT\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    427\u001b[0m         destination\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfrom_clause(),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    438\u001b[0m         ),\n\u001b[1;32m    439\u001b[0m     )\n\u001b[0;32m--> 440\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49mquery(\n\u001b[1;32m    441\u001b[0m         query,\n\u001b[1;32m    442\u001b[0m         args\u001b[39m=\u001b[39;49m\u001b[39mlist\u001b[39;49m(\n\u001b[1;32m    443\u001b[0m             itertools\u001b[39m.\u001b[39;49mchain\u001b[39m.\u001b[39;49mfrom_iterable(\n\u001b[1;32m    444\u001b[0m                 (v \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m r[\u001b[39m\"\u001b[39;49m\u001b[39mvalues\u001b[39;49m\u001b[39m\"\u001b[39;49m] \u001b[39mif\u001b[39;49;00m v \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m) \u001b[39mfor\u001b[39;49;00m r \u001b[39min\u001b[39;49;00m rows\n\u001b[1;32m    445\u001b[0m             )\n\u001b[1;32m    446\u001b[0m         ),\n\u001b[1;32m    447\u001b[0m     )\n\u001b[1;32m    448\u001b[0m \u001b[39mexcept\u001b[39;00m UnknownAttributeError \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    449\u001b[0m     \u001b[39mraise\u001b[39;00m err\u001b[39m.\u001b[39msuggest(\n\u001b[1;32m    450\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m     )\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:340\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m    338\u001b[0m cursor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_conn\u001b[39m.\u001b[39mcursor(cursor\u001b[39m=\u001b[39mcursor_class)\n\u001b[1;32m    339\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute_query(cursor, query, args, suppress_warnings)\n\u001b[1;32m    341\u001b[0m \u001b[39mexcept\u001b[39;00m errors\u001b[39m.\u001b[39mLostConnectionError:\n\u001b[1;32m    342\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m reconnect:\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:296\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m    294\u001b[0m         cursor\u001b[39m.\u001b[39mexecute(query, args)\n\u001b[1;32m    295\u001b[0m \u001b[39mexcept\u001b[39;00m client\u001b[39m.\u001b[39merr\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 296\u001b[0m     \u001b[39mraise\u001b[39;00m translate_query_error(err, query)\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:294\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[39mif\u001b[39;00m suppress_warnings:\n\u001b[1;32m    292\u001b[0m             \u001b[39m# suppress all warnings arising from underlying SQL library\u001b[39;00m\n\u001b[1;32m    293\u001b[0m             warnings\u001b[39m.\u001b[39msimplefilter(\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 294\u001b[0m         cursor\u001b[39m.\u001b[39;49mexecute(query, args)\n\u001b[1;32m    295\u001b[0m \u001b[39mexcept\u001b[39;00m client\u001b[39m.\u001b[39merr\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    296\u001b[0m     \u001b[39mraise\u001b[39;00m translate_query_error(err, query)\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/cursors.py:153\u001b[0m, in \u001b[0;36mCursor.execute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[1;32m    151\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmogrify(query, args)\n\u001b[0;32m--> 153\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_query(query)\n\u001b[1;32m    154\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executed \u001b[39m=\u001b[39m query\n\u001b[1;32m    155\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/cursors.py:322\u001b[0m, in \u001b[0;36mCursor._query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    320\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_db()\n\u001b[1;32m    321\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_result()\n\u001b[0;32m--> 322\u001b[0m conn\u001b[39m.\u001b[39;49mquery(q)\n\u001b[1;32m    323\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get_result()\n\u001b[1;32m    324\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrowcount\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:558\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m    556\u001b[0m     sql \u001b[39m=\u001b[39m sql\u001b[39m.\u001b[39mencode(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoding, \u001b[39m\"\u001b[39m\u001b[39msurrogateescape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    557\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_execute_command(COMMAND\u001b[39m.\u001b[39mCOM_QUERY, sql)\n\u001b[0;32m--> 558\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_query_result(unbuffered\u001b[39m=\u001b[39;49munbuffered)\n\u001b[1;32m    559\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:822\u001b[0m, in \u001b[0;36mConnection._read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m    820\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    821\u001b[0m     result \u001b[39m=\u001b[39m MySQLResult(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m     result\u001b[39m.\u001b[39;49mread()\n\u001b[1;32m    823\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m result\n\u001b[1;32m    824\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mserver_status \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:1200\u001b[0m, in \u001b[0;36mMySQLResult.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m   1199\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m         first_packet \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49m_read_packet()\n\u001b[1;32m   1202\u001b[0m         \u001b[39mif\u001b[39;00m first_packet\u001b[39m.\u001b[39mis_ok_packet():\n\u001b[1;32m   1203\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_read_ok_packet(first_packet)\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:772\u001b[0m, in \u001b[0;36mConnection._read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m    770\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m    771\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 772\u001b[0m     packet\u001b[39m.\u001b[39;49mraise_for_error()\n\u001b[1;32m    773\u001b[0m \u001b[39mreturn\u001b[39;00m packet\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/protocol.py:221\u001b[0m, in \u001b[0;36mMysqlPacket.raise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m    220\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39merrno =\u001b[39m\u001b[39m\"\u001b[39m, errno)\n\u001b[0;32m--> 221\u001b[0m err\u001b[39m.\u001b[39;49mraise_mysql_exception(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data)\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/err.py:143\u001b[0m, in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[39mif\u001b[39;00m errorclass \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    142\u001b[0m     errorclass \u001b[39m=\u001b[39m InternalError \u001b[39mif\u001b[39;00m errno \u001b[39m<\u001b[39m \u001b[39m1000\u001b[39m \u001b[39melse\u001b[39;00m OperationalError\n\u001b[0;32m--> 143\u001b[0m \u001b[39mraise\u001b[39;00m errorclass(errno, errval)\n",
+      "\u001b[0;31mOperationalError\u001b[0m: (1241, 'Operand should contain 1 column(s)')"
+     ]
+    }
+   ],
+   "source": [
+    "# Insert data in BodypartsParamset table\n",
+    "train.KeypointsSet.BodypartsParamSet.insert1({\n",
+    "    **kpset_key,\n",
+    "    \"bodypartset_id\":1,\n",
+    "    \"anterior_bodyparts\":[\"nose\"],\n",
+    "    \"posterior_bodyparts\":[\"spine4\"],\n",
+    "    \"use_bodyparts\":['spine4', 'spine3', 'spine2', 'spine1',\n",
+    "    'head', 'nose', 'right ear', 'left ear']\n",
+    "    })"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kpms_test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py
new file mode 100644
index 0000000..9e5da95
--- /dev/null
+++ b/notebooks/tutorial_pipeline.py
@@ -0,0 +1,95 @@
+import datajoint as dj
+from collections import abc
+from element_lab import lab
+from element_animal import subject
+from element_session import session_with_datetime as session
+
+from element_moseq import train
+
+from element_animal.subject import Subject
+from element_lab.lab import Source, Lab, Protocol, User, Project
+
+
+__all__ = [
+    "Subject",
+    "Source",
+    "Lab",
+    "Protocol",
+    "User",
+    "Project",
+    "Session",
+]
+
+if "custom" not in dj.config:
+    dj.config["custom"] = {}
+
+db_prefix = dj.config["custom"].get("database.prefix", "")
+
+
+# Declare functions for retrieving data
+def get_kpms_root_data_dir() -> list:
+    """Returns a list of root directories for Element Keypoint-MoSeq"""
+    kpms_root_dirs = dj.config.get("custom", {}).get("kpms_root_data_dir")
+    if not kpms_root_dirs:
+        return None
+    elif not isinstance(kpms_root_dirs, abc.Sequence):
+        return list(kpms_root_dirs)
+    else:
+        return kpms_root_dirs
+
+
+def get_kpms_processed_data_dir() -> str:
+    """Returns an output directory relative to custom 'kpms_output_dir' root"""
+    from pathlib import Path
+
+    kpms_output_dir = dj.config.get("custom", {}).get("kpms_processed_data_dir")
+    if kpms_output_dir:
+        return Path(kpms_output_dir)
+    else:
+        return None
+
+
+# Activate "lab", "subject", "session" schema -------------
+
+lab.activate(db_prefix + "lab")
+
+subject.activate(db_prefix + "subject", linking_module=__name__)
+
+Experimenter = lab.User
+Session = session.Session
+session.activate(db_prefix + "session", linking_module=__name__)
+
+# Activate equipment table ------------------------------------
+
+
+@lab.schema
+class Device(dj.Lookup):
+    """Table for managing lab equipment.
+
+    In Element DeepLabCut, this table is referenced by `model.VideoRecording`.
+    The primary key is also used to generate inferred output directories when
+    running pose estimation inference. Refer to the `definition` attribute
+    for the table design.
+
+    Attributes:
+        device ( varchar(32) ): Device short name.
+        modality ( varchar(64) ): Modality for which this device is used.
+        description ( varchar(256) ): Optional. Description of device.
+    """
+
+    definition = """
+    device             : varchar(32)
+    ---
+    modality           : varchar(64)
+    description=null   : varchar(256)
+    """
+    contents = [
+        ["Camera1", "Pose Estimation", "Panasonic HC-V380K"],
+        ["Camera2", "Pose Estimation", "Panasonic HC-V770K"],
+    ]
+
+
+# Activate Keypoint-MoSeq schema -----------------------------------
+
+train.activate(db_prefix + "train", linking_module=__name__)
+# model.activate(db_prefix + "model", linking_module=__name__)

From 9bea9d0cddbb3cd2ba9a3dbed81842714845dbb3 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 2 Jan 2024 07:33:36 +0100
Subject: [PATCH 23/90] WIP: test schema `pca` and basic structure `model`

---
 element_moseq/model.py         |  139 ++++
 element_moseq/pca.py           |  375 +++++++++
 element_moseq/train.py         |  230 ------
 notebooks/testing.ipynb        | 1370 ++------------------------------
 notebooks/tutorial_pipeline.py |    6 +-
 5 files changed, 599 insertions(+), 1521 deletions(-)
 create mode 100644 element_moseq/model.py
 create mode 100644 element_moseq/pca.py
 delete mode 100644 element_moseq/train.py

diff --git a/element_moseq/model.py b/element_moseq/model.py
new file mode 100644
index 0000000..1589a7b
--- /dev/null
+++ b/element_moseq/model.py
@@ -0,0 +1,139 @@
+import datajoint as dj
+import matplotlib.pyplot as plt
+import cv2
+from typing import Optional
+
+import inspect
+import importlib
+import os
+from pathlib import Path
+from element_interface.utils import find_full_path, dict_to_uuid
+
+schema = dj.schema()
+_linking_module = None
+
+
+def activate(
+    model_schema_name: str,
+    *,
+    create_schema: bool = True,
+    create_tables: bool = True,
+    linking_module: str = None,
+):
+    """Activate this schema.
+
+    Args:
+        model_schema_name (str): schema name on the database server
+        create_schema (bool): when True (default), create schema in the database if it
+                            does not yet exist.
+        create_tables (bool): when True (default), create schema tables in the database
+                             if they do not yet exist.
+        linking_module (str): a module (or name) containing the required dependencies.
+
+    Dependencies:
+    Functions:
+        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies)
+                                 with all behavioral recordings, as (list of) string(s).
+        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed
+                                      data. Defaults to session video subfolder.
+    """
+
+    if isinstance(linking_module, str):
+        linking_module = importlib.import_module(linking_module)
+    assert inspect.ismodule(
+        linking_module
+    ), "The argument 'dependency' must be a module's name or a module"
+    assert hasattr(
+        linking_module, "get_kpms_root_data_dir"
+    ), "The linking module must specify a lookup function for a root data directory"
+
+    global _linking_module
+    _linking_module = linking_module
+
+    # activate
+    schema.activate(
+        model_schema_name,
+        create_schema=create_schema,
+        create_tables=create_tables,
+        add_objects=_linking_module.__dict__,
+    )
+
+
+# -------------- Functions required by element-moseq ---------------
+
+
+def get_kpms_root_data_dir() -> list:
+    """Pulls relevant func from parent namespace to specify root data dir(s).
+
+    It is recommended that all paths in DataJoint Elements stored as relative
+    paths, with respect to some user-configured "root" director(y/ies). The
+    root(s) may vary between data modalities and user machines. Returns a full path
+    string or list of strings for possible root data directories.
+    """
+    root_directories = _linking_module.get_kpms_root_data_dir()
+    if isinstance(root_directories, (str, Path)):
+        root_directories = [root_directories]
+
+    if (
+        hasattr(_linking_module, "get_kpms_processed_data_dir")
+        and get_kpms_processed_data_dir() not in root_directories
+    ):
+        root_directories.append(_linking_module.get_kpms_processed_data_dir())
+
+    return root_directories
+
+
+def get_kpms_processed_data_dir() -> Optional[str]:
+    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
+
+    Method in parent namespace should provide a string to a directory where KPMS output
+    files will be stored. If unspecified, output files will be stored in the
+    session directory 'videos' folder, per DeepLabCut default.
+    """
+    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
+        return _linking_module.get_kpms_processed_data_dir()
+    else:
+        return None
+
+
+# ----------------------------- Table declarations ----------------------
+
+
+@schema
+class Kappa(dj.Lookup):
+    definition = """
+    kappa_id             : int
+    ---
+    kappa_min_value      : int
+    kappa_max_value      : int
+    kappa_description='' : varchar(1000)
+    """
+
+
+@schema
+class InitializingTask(dj.Manual):
+    definition = """
+    -> Session
+
+    """
+
+
+@schema
+class Initializing(dj.Computed):
+    definition = """
+    -> InitializingTask
+"""
+
+
+@schema
+class Prefitting(dj.Computed):
+    definition = """
+    -> Initializing
+    """
+
+
+@schema
+class FullFitting(dj.Computed):
+    definition = """
+    -> Prefitting
+    """
diff --git a/element_moseq/pca.py b/element_moseq/pca.py
new file mode 100644
index 0000000..6e2ffb5
--- /dev/null
+++ b/element_moseq/pca.py
@@ -0,0 +1,375 @@
+import datajoint as dj
+import matplotlib.pyplot as plt
+import cv2
+from typing import Optional
+import numpy as np
+from datetime import datetime
+
+import inspect
+import importlib
+import os
+from pathlib import Path
+from element_interface.utils import find_full_path, dict_to_uuid
+
+schema = dj.schema()
+_linking_module = None
+
+
+def activate(
+    pca_schema_name: str,
+    *,
+    create_schema: bool = True,
+    create_tables: bool = True,
+    linking_module: str = None,
+):
+    """Activate this schema.
+
+    Args:
+        pca_schema_name (str): schema name on the database server
+        create_schema (bool): when True (default), create schema in the database if it
+                            does not yet exist.
+        create_tables (bool): when True (default), create schema tables in the database
+                             if they do not yet exist.
+        linking_module (str): a module (or name) containing the required dependencies.
+
+    Dependencies:
+    Functions:
+        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies)
+                                 with all behavioral recordings, as (list of) string(s).
+        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed
+                                      data. Defaults to session video subfolder.
+    """
+
+    if isinstance(linking_module, str):
+        linking_module = importlib.import_module(linking_module)
+    assert inspect.ismodule(
+        linking_module
+    ), "The argument 'dependency' must be a module's name or a module"
+    assert hasattr(
+        linking_module, "get_kpms_root_data_dir"
+    ), "The linking module must specify a lookup function for a root data directory"
+
+    global _linking_module
+    _linking_module = linking_module
+
+    # activate
+    schema.activate(
+        pca_schema_name,
+        create_schema=create_schema,
+        create_tables=create_tables,
+        add_objects=_linking_module.__dict__,
+    )
+
+
+# -------------- Functions required by element-moseq ---------------
+
+
+def get_kpms_root_data_dir() -> list:
+    """Pulls relevant func from parent namespace to specify root data dir(s).
+
+    It is recommended that all paths in DataJoint Elements stored as relative
+    paths, with respect to some user-configured "root" director(y/ies). The
+    root(s) may vary between data modalities and user machines. Returns a full path
+    string or list of strings for possible root data directories.
+    """
+    root_directories = _linking_module.get_kpms_root_data_dir()
+    if isinstance(root_directories, (str, Path)):
+        root_directories = [root_directories]
+
+    if (
+        hasattr(_linking_module, "get_kpms_processed_data_dir")
+        and get_kpms_processed_data_dir() not in root_directories
+    ):
+        root_directories.append(_linking_module.get_kpms_processed_data_dir())
+
+    return root_directories
+
+
+def get_kpms_processed_data_dir() -> Optional[str]:
+    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
+
+    Method in parent namespace should provide a string to a directory where KPMS output
+    files will be stored. If unspecified, output files will be stored in the
+    session directory 'videos' folder, per DeepLabCut default.
+    """
+    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
+        return _linking_module.get_kpms_processed_data_dir()
+    else:
+        return None
+
+
+# ----------------------------- Table declarations ----------------------
+
+
+@schema
+class KeypointSet(dj.Manual):
+    definition = """
+    -> Session
+    kpset_id                : int
+    ---
+    kpset_path              : varchar(255)  #Path relative to root data directory where the videos and their keypoints are located.
+    kpset_description=''    : varchar(300)  # Optional. User-entered description.
+
+    """
+
+    class VideoFiles(dj.Part):
+        """IDs and file paths of each video file."""
+
+        definition = """
+        -> master
+        video_id        : int
+        ---
+        video_path      : varchar(255) # Filepath of each video, relative to root data directory
+        """
+
+    class PoseEstimationMethod(dj.Part):
+        definition = """ 
+        # Parameters used to obtain the keypoints data based on a specific pose estimation method        
+        -> master
+        ---
+        format                       : varchar(20)   # deeplabcut, sleap
+        extension                    : varchar(20)   # h5, csv
+        -> Device
+        """
+
+
+@schema
+class RecordingInfo(dj.Imported):
+    """Automated table with video file metadata.
+
+    Attributes:
+        px_height (smallint): Height in pixels.
+        px_width (smallint): Width in pixels.
+        nframes (int): Number of frames.
+        fps (int): Optional. Frames per second, Hz.
+        recording_datetime (datetime): Optional. Datetime for the start of recording.
+        recording_duration (float): video duration (s) from nframes / fps."""
+
+    definition = """
+    -> KeypointSet.VideoFiles
+    ---
+    px_height                 : smallint  # height in pixels
+    px_width                  : smallint  # width in pixels
+    nframes                   : int  # number of frames 
+    fps = NULL                : int       # (Hz) frames per second
+    recording_datetime = NULL : datetime  # Datetime for the start of the recording
+    recording_duration        : float     # video duration (s) from nframes / fps
+    """
+
+    @property
+    def key_source(self):
+        """Defines order of keys for make function when called via `populate()`"""
+        return KeypointSet & KeypointSet.VideoFiles
+
+    def make(self, key):
+        """Populates table with video metadata using CV2."""
+
+        file_path = (KeypointSet.VideoFiles & key).fetch1("video_path")
+
+        nframes = 0
+        px_height, px_width, fps = None, None, None
+
+        file_path = (find_full_path(get_kpms_root_data_dir(), file_path[0])).as_posix()
+
+        cap = cv2.VideoCapture(file_path)
+        info = (
+            int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+            int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+            int(cap.get(cv2.CAP_PROP_FPS)),
+        )
+        if px_height is not None:
+            assert (px_height, px_width, fps) == info
+        px_height, px_width, fps = info
+        nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        cap.release()
+
+        self.insert1(
+            {
+                **key,
+                "px_height": px_height,
+                "px_width": px_width,
+                "nframes": nframes,
+                "fps": fps,
+                "recording_duration": nframes / fps,
+            }
+        )
+
+
+@schema
+class Bodyparts(dj.Manual):
+    """Body parts to use in the model
+
+    Attributes:
+        anterior_bodyparts(longblob): list of strings of anterior bodyparts
+        posterior_bodyparts(longblob): list of strings of posterior bodyparts
+        use_bodyparts(longblob): list of strings of bodyparts to be used
+    """
+
+    definition = """
+    -> KeypointSet
+    bodyparts_id              : int
+    ---
+    anterior_bodyparts          : blob  # list of strings of anterior bodyparts
+    posterior_bodyparts         : blob  # list of strings of posterior bodyparts
+    use_bodyparts               : blob  # list of strings of bodyparts to be used
+    """
+
+
+@schema
+class PCATask(dj.Manual):
+    definition = """# Manual table for defining a data loading task ready to be run
+    -> KeypointSet
+    -> Bodyparts
+    ---
+    project_path=''             : varchar(255)              # KPMS's project_path in config relative to root
+    task_mode='load'            : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
+    """
+
+    class FormattedDataset(dj.Part):
+        definition = """
+        -> master
+        ---
+        config                  : longblob # stored full config file
+        coordinates             : longblob
+        confidences             : longblob             
+        bodyparts               : longblob
+        data                    : longblob
+        metadata                : longblob
+        """
+
+    @classmethod
+    def generate(cls, key):
+        # kpms bodyparts
+        anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
+            Bodyparts & key
+        ).fetch1(
+            "anterior_bodyparts",
+            "posterior_bodyparts",
+            "use_bodyparts",
+        )
+
+        # kpms project path
+        project_path = get_kpms_root_data_dir()
+
+        task_mode = (PCATask & key).fetch1("task_mode")
+
+        # pose estimation params
+        format, extension = (KeypointSet.PoseEstimationMethod & key).fetch1(
+            "format", "extension"
+        )
+
+        video_path = (KeypointSet.VideoFiles & key).fetch("video_path")
+
+        kpset_path = (KeypointSet & key).fetch1("kpset_path")
+        kpset_path = find_full_path(get_kpms_root_data_dir(), kpset_path)
+
+        import keypoint_moseq as kpms
+
+        # define config file for kpms with anonymous function
+        kpms_config = lambda: kpms.load_config(project_path)
+
+        # setup kpms project, create a new project dir and config.yml
+        if task_mode == "trigger":
+            if format == "deeplabcut":
+                kpms.setup_project(
+                    project_path, deeplabcut_config=kpset_path.as_posix()
+                )
+            else:
+                kpms.setup_project(project_path)
+
+        # elif task_mode == "load":
+
+        # update kpms config file
+        kpms.update_config(
+            project_path,
+            video_dir=video_path,
+            anterior_bodyparts=anterior_bodyparts,
+            posterior_bodyparts=posterior_bodyparts,
+            use_bodyparts=use_bodyparts,
+        )
+
+        # load data (e.g. from DeepLabCut)
+        coordinates, confidences, bodyparts = kpms.load_keypoints(
+            filepath_pattern=video_path, format=format, extension=extension
+        )
+
+        # format data for modeling
+        data, metadata = kpms.format_data(coordinates, confidences, **kpms_config())
+
+        cls.insert1(
+            dict(
+                **key,
+                config=kpms_config(),
+                coordinates=coordinates,
+                confidences=confidences,
+                bodyparts=bodyparts,
+                data=data,
+                metadata=metadata,
+            )
+        )
+
+    auto_generate_entries = generate
+
+
+@schema
+class PCAFitting(dj.Computed):
+    definition = """
+    -> PCATask
+    ---
+    pca_fitting_time     : datetime  # Time of generation of the PCA fitting analysis 
+    """
+
+    def make(self, key):
+        from keypoint_moseq import (
+            load_pca,
+            fit_pca,
+            save_pca,
+            print_dims_to_explain_variance,
+            plot_scree,
+            plot_pcs,
+        )
+
+        task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
+        data, config = (PCATask.FormattedDataset & key).fetch1("data", "config")
+
+        project_path = find_full_path(get_kpms_root_data_dir(), project_path)
+
+        if task_mode == "load":
+            pca = load_pca(**data, **config())
+
+        elif task_mode == "trigger":
+            pca = fit_pca(**data, **config())
+            save_pca(pca, project_path)
+            creation_time = datetime.strftime("%Y-%m-%d %H:%M:%S")
+
+        print_dims_to_explain_variance(pca, 0.9)
+        plot_scree(pca, project_dir=project_path)
+        plot_pcs(pca, project_dir=project_path, **config())
+
+        self.insert1(**key, pca_fitting_time=creation_time)
+
+
+@schema
+class LatentDimension(dj.Lookup):
+    definition = """
+    latent_dim                : int
+    ---
+    latent_dim_description='' : varchar(1000)
+    """
+
+
+@schema
+class UpdateLatentDimension(dj.Computed):
+    definition = """
+    -> PCAFitting
+    -> PCATask
+    -> LatentDimension
+    """
+
+    def make(self, key):
+        # update latent_dim in config_file
+        from keypoint_moseq import update_config
+
+        project_path = (PCATask & key).fetch1("project_path")
+        latent_dim = (LatentDimension & key).fetch1("latent_dim")
+        update_config(project_path, latent_dim=latent_dim)
diff --git a/element_moseq/train.py b/element_moseq/train.py
deleted file mode 100644
index 53f47d8..0000000
--- a/element_moseq/train.py
+++ /dev/null
@@ -1,230 +0,0 @@
-import datajoint as dj
-import matplotlib.pyplot as plt
-import cv2
-from typing import Optional
-
-import inspect
-import importlib
-import os
-from pathlib import Path
-from element_interface.utils import find_full_path, dict_to_uuid
-
-schema = dj.schema()
-_linking_module = None
-
-
-def activate(
-    train_schema_name: str,
-    *,
-    create_schema: bool = True,
-    create_tables: bool = True,
-    linking_module: str = None,
-):
-    """Activate this schema.
-
-    Args:
-        train_schema_name (str): schema name on the database server
-        create_schema (bool): when True (default), create schema in the database if it
-                            does not yet exist.
-        create_tables (bool): when True (default), create schema tables in the database
-                             if they do not yet exist.
-        linking_module (str): a module (or name) containing the required dependencies.
-
-    Dependencies:
-    Functions:
-        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies)
-                                 with all behavioral recordings, as (list of) string(s).
-        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed
-                                      data. Defaults to session video subfolder.
-    """
-
-    if isinstance(linking_module, str):
-        linking_module = importlib.import_module(linking_module)
-    assert inspect.ismodule(
-        linking_module
-    ), "The argument 'dependency' must be a module's name or a module"
-    assert hasattr(
-        linking_module, "get_kpms_root_data_dir"
-    ), "The linking module must specify a lookup function for a root data directory"
-
-    global _linking_module
-    _linking_module = linking_module
-
-    # activate
-    schema.activate(
-        train_schema_name,
-        create_schema=create_schema,
-        create_tables=create_tables,
-        add_objects=_linking_module.__dict__,
-    )
-
-
-# -------------- Functions required by element-moseq ---------------
-
-
-def get_kpms_root_data_dir() -> list:
-    """Pulls relevant func from parent namespace to specify root data dir(s).
-
-    It is recommended that all paths in DataJoint Elements stored as relative
-    paths, with respect to some user-configured "root" director(y/ies). The
-    root(s) may vary between data modalities and user machines. Returns a full path
-    string or list of strings for possible root data directories.
-    """
-    root_directories = _linking_module.get_kpms_root_data_dir()
-    if isinstance(root_directories, (str, Path)):
-        root_directories = [root_directories]
-
-    if (
-        hasattr(_linking_module, "get_kpms_processed_data_dir")
-        and get_kpms_processed_data_dir() not in root_directories
-    ):
-        root_directories.append(_linking_module.get_kpms_processed_data_dir())
-
-    return root_directories
-
-
-def get_kpms_processed_data_dir() -> Optional[str]:
-    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
-
-    Method in parent namespace should provide a string to a directory where KPMS output
-    files will be stored. If unspecified, output files will be stored in the
-    session directory 'videos' folder, per DeepLabCut default.
-    """
-    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
-        return _linking_module.get_kpms_processed_data_dir()
-    else:
-        return None
-
-
-# ----------------------------- Table declarations ----------------------
-
-
-@schema
-class VideoSet(dj.Manual):
-    definition = """
-    -> Session
-    videoset_id: int
-    ---
-    -> Device
-    videoset_path: varchar(255) #file path of the video, relative to root data directory
-    """
-
-    class VideoIndex(dj.Part):
-        definition = """
-        -> master
-        video_id: int
-        ---
-        video_path: varchar(255) # filepath of video, relative to root data directory
-        """
-
-
-@schema
-class RecordingInfo(dj.Imported):
-    """Automated table with video file metadata.
-
-    Attributes:
-        px_height (smallint): Height in pixels.
-        px_width (smallint): Width in pixels.
-        nframes (int): Number of frames.
-        fps (int): Optional. Frames per second, Hz.
-        recording_datetime (datetime): Optional. Datetime for the start of recording.
-        recording_duration (float): video duration (s) from nframes / fps."""
-
-    definition = """
-    -> VideoSet.VideoIndex
-
-    ---
-    px_height                 : smallint  # height in pixels
-    px_width                  : smallint  # width in pixels
-    nframes                   : int  # number of frames 
-    fps = NULL                : int       # (Hz) frames per second
-    recording_datetime = NULL : datetime  # Datetime for the start of the recording
-    recording_duration        : float     # video duration (s) from nframes / fps
-    """
-
-    def make(self, key):
-        """Populates table with video metadata using CV2."""
-
-        file_path = (VideoSet.VideoIndex & key).fetch("video_path")
-
-        nframes = 0
-        px_height, px_width, fps = None, None, None
-
-        file_path = (find_full_path(get_kpms_root_data_dir(), file_path)).as_posix()
-
-        cap = cv2.VideoCapture(file_path)
-        info = (
-            int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-            int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
-            int(cap.get(cv2.CAP_PROP_FPS)),
-        )
-        if px_height is not None:
-            assert (px_height, px_width, fps) == info
-        px_height, px_width, fps = info
-        nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        cap.release()
-
-        self.insert1(
-            {
-                **key,
-                "px_height": px_height,
-                "px_width": px_width,
-                "nframes": nframes,
-                "fps": fps,
-                "recording_duration": nframes / fps,
-            }
-        )
-
-
-@schema
-class KeypointsSet(dj.Manual):
-    """Input data containing keypoints and the parameter set used during pose estimation inference (E.g. DeepLabCut)."""
-
-    definition = """
-    -> VideoSet
-    kpset_id                    : int
-    ---
-    kpset_path                  : varchar(255)  # keypoints path of the pose estimation method, relative to root
-    config_path                 : varchar(255)  # config file path of the pose estimation method, relative to root
-    kp_description=''           : varchar(300)  # Optional. User-entered description.
-    """
-
-    class BodypartsParamSet(dj.Part):
-        """Body parts to use in the model
-
-        Attributes:
-            anterior_bodyparts(longblob): list of strings of anterior bodyparts
-            posterior_bodyparts(longblob): list of strings of posterior bodyparts
-            use_bodyparts(longblob): list of strings of bodyparts to be used
-        """
-
-        definition = """
-        -> master
-        bodypartset_id       : int
-        ---
-        anterior_bodyparts          : varchar(100)  # list of strings of anterior bodyparts
-        posterior_bodyparts         : varchar(100)  # list of strings of posterior bodyparts
-        use_bodyparts               : longblob      # list of strings of bodyparts to be used
-        """
-
-
-@schema
-class PoseEstimationMethod(dj.Lookup):
-    definition = """ # Parameters used to obtain the keypoints data based on a specific pose estimation method        
-    -> KeypointsSet
-    method_id                   : int
-    ---
-    format='deeplabcut'         : enum('deeplabcut', 'sleap') # pose estimation method   
-    extension='h5'              : enum('h5', 'csv') 
-    """
-
-
-@schema
-class KpmsProject(dj.Manual):
-    definition = """
-    -> Session
-    kpms_project_id         : int
-    ---
-    kpms_project_path       : varchar(255) # kpms project path
-    project_description     : varchar(300)  # User-friendly description of the kpms project
-    """
diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index 392f265..1885085 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,296 +36,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-12-18 22:09:52,236][INFO]: Connecting milagros@db.datajoint.com:3306\n",
-      "[2023-12-18 22:09:53,727][INFO]: Connected milagros@db.datajoint.com:3306\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DataJoint connection (connected) milagros@db.datajoint.com:3306"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dj.conn()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2023-12-18 22:10:01,266][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from tutorial_pipeline import lab, subject, session, train, Device"
+    "# dj.schema('vathes-team_devlab_milagros_moseq_testing3_pca').drop()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train.BodypartsParamSet.drop()\n",
-    "# train.PoseEstimationMethod.drop()\n",
-    "# train.KeypointsSet.drop_quick()\n",
-    "# train.RecordingInfo.drop_quick()\n",
-    "# train.VideoSet.VideoIndex.delete(force=True)\n",
-    "# train.VideoSet.drop()\n"
+    "from tutorial_pipeline import lab, subject, session, pca, model, Device"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#dj.Diagram(subject) + dj.Diagram(session) + dj.Diagram(train)\n",
-    "#dj.Diagram(session) + dj.Diagram(train)"
+    "# pca.KeypointSet.VideoFiles.delete_quick()\n",
+    "# pca.KeypointSet.PoseEstimationMethod.delete_quick()\n",
+    "# pca.PCAInfo.delete_quick()\n",
+    "# pca.KeypointSet.delete()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"523pt\" height=\"184pt\" viewBox=\"0.00 0.00 523.12 183.68\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 179.68)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-179.68 519.12,-179.68 519.12,4 -4,4\"/>\n",
-       "<!-- train.KeypointsSet -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>train.KeypointsSet</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ train.VideoSet\rkpset_id             \r------------------------------\rkpset_path           \rconfig_path          \rkp_description=&quot;&quot;    \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"150.25,-105.12 36.75,-105.12 36.75,-70.56 150.25,-70.56 150.25,-105.12\"/>\n",
-       "<text text-anchor=\"start\" x=\"44.75\" y=\"-84.56\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.KeypointsSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.KeypointsSet.BodypartsParamSet -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>train.KeypointsSet.BodypartsParamSet</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ train.KeypointsSet\rbodypartset_id       \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"187,-27.28 0,-27.28 0,-7.28 187,-7.28 187,-27.28\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-14.78\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.KeypointsSet.BodypartsParamSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.KeypointsSet&#45;&gt;train.KeypointsSet.BodypartsParamSet -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>train.KeypointsSet-&gt;train.KeypointsSet.BodypartsParamSet</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M93.5,-70.24C93.5,-56.61 93.5,-37.84 93.5,-26.99\"/>\n",
-       "</g>\n",
-       "<!-- train.PoseEstimationMethod -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>train.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ train.KeypointsSet\rmethod_id            \r------------------------------\rformat=&quot;deeplabcut&quot;  \rextension=&quot;h5&quot;       \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"344,-34.56 205,-34.56 205,0 344,0 344,-34.56\"/>\n",
-       "<text text-anchor=\"start\" x=\"213\" y=\"-14.78\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.PoseEstimationMethod</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.KeypointsSet&#45;&gt;train.PoseEstimationMethod -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>train.KeypointsSet-&gt;train.PoseEstimationMethod</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M137.78,-70.07C166.35,-59.25 203.32,-45.24 231.62,-34.52\"/>\n",
-       "</g>\n",
-       "<!-- train.RecordingInfo -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>train.RecordingInfo</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ train.VideoSet.VideoIndex\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"438.5\" cy=\"-17.28\" rx=\"76.62\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"438.5\" y=\"-13.01\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">train.RecordingInfo</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet.VideoIndex -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>train.VideoSet.VideoIndex</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ train.VideoSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"400.12,-97.84 270.88,-97.84 270.88,-77.84 400.12,-77.84 400.12,-97.84\"/>\n",
-       "<text text-anchor=\"start\" x=\"278.88\" y=\"-85.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">train.VideoSet.VideoIndex</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet.VideoIndex&#45;&gt;train.RecordingInfo -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>train.VideoSet.VideoIndex-&gt;train.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M348.97,-77.88C365.65,-66.77 394.37,-47.65 414.78,-34.07\"/>\n",
-       "</g>\n",
-       "<!-- train.KpmsProject -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>train.KpmsProject</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ session.Session\rkpms_project_id      \r------------------------------\rkpms_project_path    \rproject_description  \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"509.75,-175.68 399.25,-175.68 399.25,-141.12 509.75,-141.12 509.75,-175.68\"/>\n",
-       "<text text-anchor=\"start\" x=\"407.25\" y=\"-155.12\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.KpmsProject</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>train.VideoSet</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ session.Session\rvideoset_id          \r------------------------------\r→ Device\rvideoset_path        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"381,-175.68 290,-175.68 290,-141.12 381,-141.12 381,-175.68\"/>\n",
-       "<text text-anchor=\"start\" x=\"298\" y=\"-155.12\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">train.VideoSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.KeypointsSet -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.KeypointsSet</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M289.7,-144.42C250.23,-133.24 193.09,-117.05 150.64,-105.03\"/>\n",
-       "</g>\n",
-       "<!-- train.VideoSet&#45;&gt;train.VideoSet.VideoIndex -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>train.VideoSet-&gt;train.VideoSet.VideoIndex</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M335.5,-140.8C335.5,-127.17 335.5,-108.4 335.5,-97.55\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x14050ae20>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dj.Diagram(train)"
+    "dj.Diagram(pca)\n",
+    "#+ dj.Diagram(model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">device</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">videoset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>Camera1</td>\n",
-       "<td>./input_data/videos</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   device      videoset_path \n",
-       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
-       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.VideoSet()"
+    "pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,459 +121,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">device</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">modality</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">description</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>Camera1</td>\n",
-       "<td>Pose Estimation</td>\n",
-       "<td>Panasonic HC-V380K</td></tr><tr><td>Camera2</td>\n",
-       "<td>Pose Estimation</td>\n",
-       "<td>Panasonic HC-V770K</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 2</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*device     modality       description   \n",
-       "+---------+ +------------+ +------------+\n",
-       "Camera1     Pose Estimatio Panasonic HC-V\n",
-       "Camera2     Pose Estimatio Panasonic HC-V\n",
-       " (Total: 2)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Device()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "recording_key = dict(subject=\"subject1\",\n",
+    "kpset_key = dict(subject=\"subject1\",\n",
     "            session_datetime=\"2021-06-02 14:04:22\",\n",
-    "            videoset_id=1)\n",
-    "train.VideoSet.insert1({**recording_key, \n",
-    "                            \"videoset_path\":\"./input_data/videos\",\n",
-    "                             \"device\":\"Camera1\"},\n",
+    "            kpset_id=1)\n",
+    "pca.KeypointSet.insert1({**kpset_key, \n",
+    "                            \"kpset_path\":\"/input_data/videos\",\n",
+    "                            \"kpset_description\":\"testing pca schema\"},\n",
     "                            skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">device</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">videoset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>Camera1</td>\n",
-       "<td>./input_data/videos</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   device      videoset_path \n",
-       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
-       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.VideoSet()"
+    "pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "video_files = [\"./input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
-    "               \"./input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
-    "               \"./input_data/videos/21_12_2_def6b_2.top.ir.mp4\"]\n",
+    "video_files = [\"/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "               \"/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "               \"/input_data/videos/21_12_2_def6b_2.top.ir.mp4\"]\n",
     "\n",
-    "train.VideoSet.VideoIndex.insert(\n",
-    "    ({**recording_key, \"video_id\":v_idx, \"video_path\":Path(f)}\n",
-    "    for v_idx, f in enumerate(video_files)),skip_duplicates=True\n",
+    "pca.KeypointSet.VideoFiles.insert(\n",
+    "    ({**kpset_key, \"video_id\":v_idx, \"video_path\":Path(f)}\n",
+    "    for v_idx, f in enumerate(video_files)),\n",
+    "    skip_duplicates=True\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">device</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">videoset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">file path of the video, relative to root data directory</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>Camera1</td>\n",
-       "<td>./input_data/videos</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   device      videoset_path \n",
-       "+----------+ +------------+ +------------+ +---------+ +------------+\n",
-       "subject1     2021-06-02 14: 1              Camera1     ./input_data/v\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.VideoSet()"
+    "pca.KeypointSet.VideoFiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">video_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height</p>\n",
-       "                            <span class=\"djtooltiptext\">height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width</p>\n",
-       "                            <span class=\"djtooltiptext\">width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes</p>\n",
-       "                            <span class=\"djtooltiptext\">number of frames</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps</p>\n",
-       "                            <span class=\"djtooltiptext\">(Hz) frames per second</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Datetime for the start of the recording</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">video duration (s) from nframes / fps</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *videoset_id   *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
-       "+---------+ +------------+ +------------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.RecordingInfo()"
+    "pca.KeypointSet.PoseEstimationMethod.insert1(\n",
+    "    {**kpset_key,\n",
+    "     \"format\":\"deeplabcut\",\n",
+    "     \"extension\":\"h5\",\n",
+    "     \"device\":\"Camera1\"},\n",
+    "     skip_duplicates=True\n",
+    ")"
    ]
   },
   {
@@ -813,664 +190,81 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# train.RecordingInfo.populate()\n",
-    "# train.RecordingInfo()"
+    "pca.KeypointSet.PoseEstimationMethod()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject1',\n",
-       " 'session_datetime': '2021-06-02 14:04:22',\n",
-       " 'videoset_id': 1}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "recording_key"
+    "# pca.RecordingInfo.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">keypoints path of the pose estimation method, relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">config_path</p>\n",
-       "                            <span class=\"djtooltiptext\">config file path of the pose estimation method, relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kp_description</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>./input_data/videos/</td>\n",
-       "<td>./data/inbox/input_data/config.yaml</td>\n",
-       "<td>first test</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   *kpset_id    kpset_path     config_path    kp_description\n",
-       "+----------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1              1            ./input_data/v ./data/inbox/i first test    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.KeypointsSet()"
+    "pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Insert data in KeypointsSet table\n",
-    "kpset_key = {**recording_key,\"kpset_id\":1}\n",
-    "train.KeypointsSet.insert1({**kpset_key,\n",
-    "                            \"kpset_path\":\"./input_data/videos/\",\n",
-    "                            \"config_path\":\"./data/inbox/input_data/config.yaml\",\n",
-    "                            \"kp_description\":\"first test\"},\n",
-    "                            skip_duplicates=True)\n",
+    "# Insert data in PCAInfo table\n",
+    "bodypart_key = {**kpset_key,\n",
+    "                \"bodyparts_id\":1}\n",
+    "pca.Bodyparts.insert1({**bodypart_key,\n",
+    "                \"anterior_bodyparts\":\"nose\",\n",
+    "                \"posterior_bodyparts\":\"spine4\",\n",
+    "                \"use_bodyparts\":['spine4', 'spine3', 'spine2', 'spine1',\n",
+    "                'head', 'nose', 'right ear', 'left ear']},\n",
+    "                    skip_duplicates=True)\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">keypoints path of the pose estimation method, relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">config_path</p>\n",
-       "                            <span class=\"djtooltiptext\">config file path of the pose estimation method, relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kp_description</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>./input_data/videos/</td>\n",
-       "<td>./data/inbox/input_data/config.yaml</td>\n",
-       "<td>first test</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   *kpset_id    kpset_path     config_path    kp_description\n",
-       "+----------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1              1            ./input_data/v ./data/inbox/i first test    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train.KeypointsSet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">method_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">format</p>\n",
-       "                            <span class=\"djtooltiptext\">pose estimation method</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">extension</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>h5</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   *kpset_id    *method_id    format         extension    \n",
-       "+----------+ +------------+ +------------+ +----------+ +-----------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1              1            1             deeplabcut     h5           \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train.PoseEstimationMethod()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Insert data in PoseEstimationMethod table\n",
-    "train.PoseEstimationMethod.insert1({**kpset_key,\n",
-    "                                    \"method_id\":1, \n",
-    "                                    \"format\":\"deeplabcut\",\n",
-    "                                    \"extension\":\"h5\"}, \n",
-    "                                    skip_duplicates=True)"
+    "pca.DataLoadingTask.insert1({**bodypart_key,\n",
+    "                            \"task_mode\":\"load\"},\n",
+    "                            skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">method_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">format</p>\n",
-       "                            <span class=\"djtooltiptext\">pose estimation method</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">extension</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>h5</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *videoset_id   *kpset_id    *method_id    format         extension    \n",
-       "+----------+ +------------+ +------------+ +----------+ +-----------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1              1            1             deeplabcut     h5           \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.PoseEstimationMethod()"
+    "pca.DataLoadingTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">videoset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodypartset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of anterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of posterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of bodyparts to be used</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *videoset_id   *kpset_id    *bodypartset_i anterior_bodyp posterior_body use_bodyparts \n",
-       "+---------+ +------------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train.KeypointsSet.BodypartsParamSet()"
+    "pca.DataLoadingTask.generate(bodypart_key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "OperationalError",
-     "evalue": "(1241, 'Operand should contain 1 column(s)')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[25], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Insert data in BodypartsParamset table\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m train\u001b[39m.\u001b[39;49mKeypointsSet\u001b[39m.\u001b[39;49mBodypartsParamSet\u001b[39m.\u001b[39;49minsert1({\n\u001b[1;32m      3\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkpset_key,\n\u001b[1;32m      4\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mbodypartset_id\u001b[39;49m\u001b[39m\"\u001b[39;49m:\u001b[39m1\u001b[39;49m,\n\u001b[1;32m      5\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39manterior_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m\"\u001b[39;49m\u001b[39mnose\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m      6\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mposterior_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m\"\u001b[39;49m\u001b[39mspine4\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m      7\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39muse_bodyparts\u001b[39;49m\u001b[39m\"\u001b[39;49m:[\u001b[39m'\u001b[39;49m\u001b[39mspine4\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine3\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine2\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mspine1\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m      8\u001b[0m     \u001b[39m'\u001b[39;49m\u001b[39mhead\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mnose\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mright ear\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mleft ear\u001b[39;49m\u001b[39m'\u001b[39;49m]\n\u001b[1;32m      9\u001b[0m     })\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/table.py:337\u001b[0m, in \u001b[0;36mTable.insert1\u001b[0;34m(self, row, **kwargs)\u001b[0m\n\u001b[1;32m    330\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minsert1\u001b[39m(\u001b[39mself\u001b[39m, row, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    331\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[39m    Insert one data record into the table. For ``kwargs``, see ``insert()``.\u001b[39;00m\n\u001b[1;32m    333\u001b[0m \n\u001b[1;32m    334\u001b[0m \u001b[39m    :param row: a numpy record, a dict-like object, or an ordered sequence to be inserted\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \u001b[39m        as one row.\u001b[39;00m\n\u001b[1;32m    336\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minsert((row,), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/table.py:440\u001b[0m, in \u001b[0;36mTable.insert\u001b[0;34m(self, rows, replace, skip_duplicates, ignore_extra_fields, allow_direct_insert)\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    425\u001b[0m     query \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m{command}\u001b[39;00m\u001b[39m INTO \u001b[39m\u001b[39m{destination}\u001b[39;00m\u001b[39m(`\u001b[39m\u001b[39m{fields}\u001b[39;00m\u001b[39m`) VALUES \u001b[39m\u001b[39m{placeholders}\u001b[39;00m\u001b[39m{duplicate}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m    426\u001b[0m         command\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mREPLACE\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m replace \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mINSERT\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    427\u001b[0m         destination\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfrom_clause(),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    438\u001b[0m         ),\n\u001b[1;32m    439\u001b[0m     )\n\u001b[0;32m--> 440\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49mquery(\n\u001b[1;32m    441\u001b[0m         query,\n\u001b[1;32m    442\u001b[0m         args\u001b[39m=\u001b[39;49m\u001b[39mlist\u001b[39;49m(\n\u001b[1;32m    443\u001b[0m             itertools\u001b[39m.\u001b[39;49mchain\u001b[39m.\u001b[39;49mfrom_iterable(\n\u001b[1;32m    444\u001b[0m                 (v \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m r[\u001b[39m\"\u001b[39;49m\u001b[39mvalues\u001b[39;49m\u001b[39m\"\u001b[39;49m] \u001b[39mif\u001b[39;49;00m v \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m) \u001b[39mfor\u001b[39;49;00m r \u001b[39min\u001b[39;49;00m rows\n\u001b[1;32m    445\u001b[0m             )\n\u001b[1;32m    446\u001b[0m         ),\n\u001b[1;32m    447\u001b[0m     )\n\u001b[1;32m    448\u001b[0m \u001b[39mexcept\u001b[39;00m UnknownAttributeError \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    449\u001b[0m     \u001b[39mraise\u001b[39;00m err\u001b[39m.\u001b[39msuggest(\n\u001b[1;32m    450\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mTo ignore extra fields in insert, set ignore_extra_fields=True\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m     )\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:340\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, query, args, as_dict, suppress_warnings, reconnect)\u001b[0m\n\u001b[1;32m    338\u001b[0m cursor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_conn\u001b[39m.\u001b[39mcursor(cursor\u001b[39m=\u001b[39mcursor_class)\n\u001b[1;32m    339\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute_query(cursor, query, args, suppress_warnings)\n\u001b[1;32m    341\u001b[0m \u001b[39mexcept\u001b[39;00m errors\u001b[39m.\u001b[39mLostConnectionError:\n\u001b[1;32m    342\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m reconnect:\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:296\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m    294\u001b[0m         cursor\u001b[39m.\u001b[39mexecute(query, args)\n\u001b[1;32m    295\u001b[0m \u001b[39mexcept\u001b[39;00m client\u001b[39m.\u001b[39merr\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m--> 296\u001b[0m     \u001b[39mraise\u001b[39;00m translate_query_error(err, query)\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/datajoint/connection.py:294\u001b[0m, in \u001b[0;36mConnection._execute_query\u001b[0;34m(cursor, query, args, suppress_warnings)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[39mif\u001b[39;00m suppress_warnings:\n\u001b[1;32m    292\u001b[0m             \u001b[39m# suppress all warnings arising from underlying SQL library\u001b[39;00m\n\u001b[1;32m    293\u001b[0m             warnings\u001b[39m.\u001b[39msimplefilter(\u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 294\u001b[0m         cursor\u001b[39m.\u001b[39;49mexecute(query, args)\n\u001b[1;32m    295\u001b[0m \u001b[39mexcept\u001b[39;00m client\u001b[39m.\u001b[39merr\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m    296\u001b[0m     \u001b[39mraise\u001b[39;00m translate_query_error(err, query)\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/cursors.py:153\u001b[0m, in \u001b[0;36mCursor.execute\u001b[0;34m(self, query, args)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[1;32m    151\u001b[0m query \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmogrify(query, args)\n\u001b[0;32m--> 153\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_query(query)\n\u001b[1;32m    154\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_executed \u001b[39m=\u001b[39m query\n\u001b[1;32m    155\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/cursors.py:322\u001b[0m, in \u001b[0;36mCursor._query\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m    320\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_db()\n\u001b[1;32m    321\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_result()\n\u001b[0;32m--> 322\u001b[0m conn\u001b[39m.\u001b[39;49mquery(q)\n\u001b[1;32m    323\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get_result()\n\u001b[1;32m    324\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrowcount\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:558\u001b[0m, in \u001b[0;36mConnection.query\u001b[0;34m(self, sql, unbuffered)\u001b[0m\n\u001b[1;32m    556\u001b[0m     sql \u001b[39m=\u001b[39m sql\u001b[39m.\u001b[39mencode(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoding, \u001b[39m\"\u001b[39m\u001b[39msurrogateescape\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    557\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_execute_command(COMMAND\u001b[39m.\u001b[39mCOM_QUERY, sql)\n\u001b[0;32m--> 558\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_query_result(unbuffered\u001b[39m=\u001b[39;49munbuffered)\n\u001b[1;32m    559\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_affected_rows\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:822\u001b[0m, in \u001b[0;36mConnection._read_query_result\u001b[0;34m(self, unbuffered)\u001b[0m\n\u001b[1;32m    820\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    821\u001b[0m     result \u001b[39m=\u001b[39m MySQLResult(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m     result\u001b[39m.\u001b[39;49mread()\n\u001b[1;32m    823\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m result\n\u001b[1;32m    824\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mserver_status \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:1200\u001b[0m, in \u001b[0;36mMySQLResult.read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mread\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m   1199\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1200\u001b[0m         first_packet \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection\u001b[39m.\u001b[39;49m_read_packet()\n\u001b[1;32m   1202\u001b[0m         \u001b[39mif\u001b[39;00m first_packet\u001b[39m.\u001b[39mis_ok_packet():\n\u001b[1;32m   1203\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_read_ok_packet(first_packet)\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/connections.py:772\u001b[0m, in \u001b[0;36mConnection._read_packet\u001b[0;34m(self, packet_type)\u001b[0m\n\u001b[1;32m    770\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m    771\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39munbuffered_active \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 772\u001b[0m     packet\u001b[39m.\u001b[39;49mraise_for_error()\n\u001b[1;32m    773\u001b[0m \u001b[39mreturn\u001b[39;00m packet\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/protocol.py:221\u001b[0m, in \u001b[0;36mMysqlPacket.raise_for_error\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m    220\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39merrno =\u001b[39m\u001b[39m\"\u001b[39m, errno)\n\u001b[0;32m--> 221\u001b[0m err\u001b[39m.\u001b[39;49mraise_mysql_exception(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data)\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/pymysql/err.py:143\u001b[0m, in \u001b[0;36mraise_mysql_exception\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[39mif\u001b[39;00m errorclass \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    142\u001b[0m     errorclass \u001b[39m=\u001b[39m InternalError \u001b[39mif\u001b[39;00m errno \u001b[39m<\u001b[39m \u001b[39m1000\u001b[39m \u001b[39melse\u001b[39;00m OperationalError\n\u001b[0;32m--> 143\u001b[0m \u001b[39mraise\u001b[39;00m errorclass(errno, errval)\n",
-      "\u001b[0;31mOperationalError\u001b[0m: (1241, 'Operand should contain 1 column(s)')"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Insert data in BodypartsParamset table\n",
-    "train.KeypointsSet.BodypartsParamSet.insert1({\n",
-    "    **kpset_key,\n",
-    "    \"bodypartset_id\":1,\n",
-    "    \"anterior_bodyparts\":[\"nose\"],\n",
-    "    \"posterior_bodyparts\":[\"spine4\"],\n",
-    "    \"use_bodyparts\":['spine4', 'spine3', 'spine2', 'spine1',\n",
-    "    'head', 'nose', 'right ear', 'left ear']\n",
-    "    })"
+    "pca.DataLoadingTask.FormattedDataset()"
    ]
   }
  ],
diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py
index 9e5da95..aa48271 100644
--- a/notebooks/tutorial_pipeline.py
+++ b/notebooks/tutorial_pipeline.py
@@ -4,7 +4,7 @@
 from element_animal import subject
 from element_session import session_with_datetime as session
 
-from element_moseq import train
+from element_moseq import pca, model
 
 from element_animal.subject import Subject
 from element_lab.lab import Source, Lab, Protocol, User, Project
@@ -91,5 +91,5 @@ class Device(dj.Lookup):
 
 # Activate Keypoint-MoSeq schema -----------------------------------
 
-train.activate(db_prefix + "train", linking_module=__name__)
-# model.activate(db_prefix + "model", linking_module=__name__)
+pca.activate(db_prefix + "pca", linking_module=__name__)
+model.activate(db_prefix + "model", linking_module=__name__)

From 69787e88209cffb4255bd411433e431975a06dc2 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 2 Jan 2024 07:37:08 +0100
Subject: [PATCH 24/90] Testing notebook

---
 notebooks/testing.ipynb | 1311 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 1246 insertions(+), 65 deletions(-)

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index 1885085..d491511 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,57 +27,217 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dj.config.load(\"dj_local_conf.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-02 07:27:12,131][INFO]: Connecting milagros@db.datajoint.com:3306\n",
+      "[2024-01-02 07:27:13,535][INFO]: Connected milagros@db.datajoint.com:3306\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DataJoint connection (connected) milagros@db.datajoint.com:3306"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dj.conn()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dj.schema('vathes-team_devlab_milagros_moseq_testing3_pca').drop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-02 07:27:13,795][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+     ]
+    }
+   ],
    "source": [
     "from tutorial_pipeline import lab, subject, session, pca, model, Device"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pca.KeypointSet.VideoFiles.delete_quick()\n",
-    "# pca.KeypointSet.PoseEstimationMethod.delete_quick()\n",
-    "# pca.PCAInfo.delete_quick()\n",
-    "# pca.KeypointSet.delete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"785pt\" height=\"316pt\" viewBox=\"0.00 0.00 785.29 316.16\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 312.16)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-312.16 781.29,-312.16 781.29,4 -4,4\"/>\n",
+       "<!-- pca.Bodyparts -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>pca.Bodyparts</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"368.38,-237.6 275.88,-237.6 275.88,-203.04 368.38,-203.04 368.38,-237.6\"/>\n",
+       "<text text-anchor=\"start\" x=\"283.88\" y=\"-217.04\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.PCATask -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>pca.PCATask</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"402.75,-167.04 315.5,-167.04 315.5,-132.48 402.75,-132.48 402.75,-167.04\"/>\n",
+       "<text text-anchor=\"middle\" x=\"359.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M331.08,-202.72C336.9,-191.94 344.45,-177.95 350.25,-167.2\"/>\n",
+       "</g>\n",
+       "<!-- pca.PCAFitting -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>pca.PCAFitting</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"175.12\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"175.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.UpdateLatentDimension -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>pca.UpdateLatentDimension</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"175.12\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"175.12\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M175.12,-66.42C175.12,-54.83 175.12,-37.23 175.12,-25.67\"/>\n",
+       "</g>\n",
+       "<!-- pca.LatentDimension -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>pca.LatentDimension</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"108.25,-96.48 0,-96.48 0,-61.92 108.25,-61.92 108.25,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-76.7\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M84.66,-61.99C110.7,-48.16 146.66,-29.07 164.37,-19.67\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>pca.KeypointSet</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"572,-308.16 468.25,-308.16 468.25,-273.6 572,-273.6 572,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"476.25\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.Bodyparts -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>pca.KeypointSet-&gt;pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M471.69,-273.11C440.32,-262.25 399.69,-248.18 368.7,-237.45\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.PCATask -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>pca.KeypointSet-&gt;pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M473.7,-273.19C454.14,-264.61 432.04,-252.74 415.12,-237.6 392.67,-217.5 375.57,-186.38 366.47,-167.36\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"616.25,-230.32 424,-230.32 424,-210.32 616.25,-210.32 616.25,-230.32\"/>\n",
+       "<text text-anchor=\"middle\" x=\"520.12\" y=\"-216.82\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M520.12,-273.28C520.12,-259.65 520.12,-240.88 520.12,-230.03\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet.VideoFiles -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>pca.KeypointSet.VideoFiles</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"771.5,-230.32 634.75,-230.32 634.75,-210.32 771.5,-210.32 771.5,-230.32\"/>\n",
+       "<text text-anchor=\"start\" x=\"642.75\" y=\"-217.82\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M564.89,-273.11C600.97,-259.59 650.31,-241.11 679.24,-230.27\"/>\n",
+       "</g>\n",
+       "<!-- pca.RecordingInfo -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>pca.RecordingInfo</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"703.12\" cy=\"-149.76\" rx=\"74.16\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"703.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M315.05,-132.34C272.74,-116.57 211.66,-93.81 186.55,-84.46\"/>\n",
+       "</g>\n",
+       "<!-- pca.PCATask&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>pca.PCATask-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M327.67,-131.99C310.47,-122.35 289.07,-109.58 271.12,-96.48 252.42,-82.82 249.65,-77.06 232.12,-61.92 215.48,-47.55 195.81,-31.12 184.47,-21.7\"/>\n",
+       "</g>\n",
+       "<!-- pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>pca.PCATask.FormattedDataset</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"438,-89.2 280.25,-89.2 280.25,-69.2 438,-69.2 438,-89.2\"/>\n",
+       "<text text-anchor=\"middle\" x=\"359.12\" y=\"-75.7\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M359.12,-132.16C359.12,-118.53 359.12,-99.76 359.12,-88.91\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M703.12,-210.36C703.12,-199.47 703.12,-180.88 703.12,-167.35\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x12ec36940>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dj.Diagram(pca)\n",
     "#+ dj.Diagram(model)"
@@ -85,16 +245,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_description</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    kpset_path     kpset_descript\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,16 +388,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_description</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>/input_data/videos</td>\n",
+       "<td>testing pca schema</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    kpset_path     kpset_descript\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            /input_data/vi testing pca sc\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,16 +510,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">video_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">video_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>0</td>\n",
+       "<td>/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>2</td>\n",
+       "<td>/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 3</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
+       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            0            /input_data/vi\n",
+       "subject1     2021-06-02 14: 1            1            /input_data/vi\n",
+       "subject1     2021-06-02 14: 1            2            /input_data/vi\n",
+       " (Total: 3)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.KeypointSet.VideoFiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,16 +640,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">format</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">extension</p>\n",
+       "                            <span class=\"djtooltiptext\">h5, csv</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">device</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>h5</td>\n",
+       "<td>Camera1</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    format         extension     device     \n",
+       "+----------+ +------------+ +----------+ +------------+ +-----------+ +---------+\n",
+       "subject1     2021-06-02 14: 1            deeplabcut     h5            Camera1    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.KeypointSet.PoseEstimationMethod()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,16 +758,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of anterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">list of strings of bodyparts to be used</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
+       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,41 +889,564 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Manual table for defining a data loading task ready to be run</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">project_path</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/kpms_project</td>\n",
+       "<td>load</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1            1              /kpms_project  load         \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pca.DataLoadingTask.insert1({**bodypart_key,\n",
+    "pca.PCATask.insert1({**bodypart_key,\n",
+    "                             \"project_path\":\"/kpms_project\",\n",
     "                            \"task_mode\":\"load\"},\n",
     "                            skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
+      "File \u001b[0;32m~/Documents/datajoint-elements/element-moseq/element_moseq/pca.py:264\u001b[0m, in \u001b[0;36mPCATask.generate\u001b[0;34m(cls, key)\u001b[0m\n\u001b[1;32m    261\u001b[0m video_path \u001b[39m=\u001b[39m (KeypointSet\u001b[39m.\u001b[39mVideoFiles \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch(\u001b[39m\"\u001b[39m\u001b[39mvideo_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    263\u001b[0m kpset_path \u001b[39m=\u001b[39m (KeypointSet \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mkpset_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 264\u001b[0m kpset_path \u001b[39m=\u001b[39m find_full_path(get_kpms_root_data_dir(), kpset_path)\n\u001b[1;32m    266\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mkeypoint_moseq\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mkpms\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[39m# define config file for kpms with anonymous function\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/element_interface/utils.py:42\u001b[0m, in \u001b[0;36mfind_full_path\u001b[0;34m(root_directories, relative_path)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[39mif\u001b[39;00m (_to_Path(root_dir) \u001b[39m/\u001b[39m relative_path)\u001b[39m.\u001b[39mexists():\n\u001b[1;32m     40\u001b[0m         \u001b[39mreturn\u001b[39;00m _to_Path(root_dir) \u001b[39m/\u001b[39m relative_path\n\u001b[0;32m---> 42\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m     43\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mNo valid full-path found (from \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     44\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(root_directories, relative_path)\n\u001b[1;32m     45\u001b[0m )\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos"
+     ]
+    }
+   ],
    "source": [
-    "pca.DataLoadingTask()"
+    "pca.PCATask.generate(bodypart_key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">config</p>\n",
+       "                            <span class=\"djtooltiptext\">stored full config file</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">data</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">metadata</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  config     coordinate confidence bodyparts  data       metadata  \n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pca.DataLoadingTask.generate(bodypart_key)"
+    "pca.PCATask.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
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+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
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+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*latent_dim    latent_dim_des\n",
+       "+------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "pca.DataLoadingTask.FormattedDataset()"
+    "pca.LatentDimension()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
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+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
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+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
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+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *latent_dim   \n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.UpdateLatentDimension()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 6bf3a03da0b997ecc2fadfee2644ce81a7e1b4be Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 2 Jan 2024 07:39:32 +0100
Subject: [PATCH 25/90] testing notebook

---
 notebooks/testing.ipynb | 122 ++++++++++++++++++++++++----------------
 1 file changed, 73 insertions(+), 49 deletions(-)

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index d491511..4c0c7c9 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -25,34 +25,6 @@
     "from pathlib import Path"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-01-02 07:27:12,131][INFO]: Connecting milagros@db.datajoint.com:3306\n",
-      "[2024-01-02 07:27:13,535][INFO]: Connected milagros@db.datajoint.com:3306\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DataJoint connection (connected) milagros@db.datajoint.com:3306"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dj.conn()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -72,15 +44,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"785pt\" height=\"316pt\" viewBox=\"0.00 0.00 785.29 316.16\">\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1010pt\" height=\"316pt\" viewBox=\"0.00 0.00 1009.62 316.16\">\n",
        "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 312.16)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-312.16 781.29,-312.16 781.29,4 -4,4\"/>\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-312.16 1005.62,-312.16 1005.62,4 -4,4\"/>\n",
        "<!-- pca.Bodyparts -->\n",
        "<g id=\"node1\" class=\"node\">\n",
        "<title>pca.Bodyparts</title>\n",
@@ -161,9 +133,9 @@
        "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M473.7,-273.19C454.14,-264.61 432.04,-252.74 415.12,-237.6 392.67,-217.5 375.57,-186.38 366.47,-167.36\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
        "<polygon fill=\"none\" stroke=\"none\" points=\"616.25,-230.32 424,-230.32 424,-210.32 616.25,-210.32 616.25,-230.32\"/>\n",
        "<text text-anchor=\"middle\" x=\"520.12\" y=\"-216.82\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
        "</a>\n",
@@ -175,9 +147,9 @@
        "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M520.12,-273.28C520.12,-259.65 520.12,-240.88 520.12,-230.03\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet.VideoFiles -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node14\" class=\"node\">\n",
        "<title>pca.KeypointSet.VideoFiles</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
        "<polygon fill=\"none\" stroke=\"none\" points=\"771.5,-230.32 634.75,-230.32 634.75,-210.32 771.5,-210.32 771.5,-230.32\"/>\n",
        "<text text-anchor=\"start\" x=\"642.75\" y=\"-217.82\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
        "</a>\n",
@@ -208,9 +180,9 @@
        "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M327.67,-131.99C310.47,-122.35 289.07,-109.58 271.12,-96.48 252.42,-82.82 249.65,-77.06 232.12,-61.92 215.48,-47.55 195.81,-31.12 184.47,-21.7\"/>\n",
        "</g>\n",
        "<!-- pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>pca.PCATask.FormattedDataset</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
        "<polygon fill=\"none\" stroke=\"none\" points=\"438,-89.2 280.25,-89.2 280.25,-69.2 438,-69.2 438,-89.2\"/>\n",
        "<text text-anchor=\"middle\" x=\"359.12\" y=\"-75.7\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
        "</a>\n",
@@ -221,26 +193,85 @@
        "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M359.12,-132.16C359.12,-118.53 359.12,-99.76 359.12,-88.91\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<!-- model.Prefitting -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>model.Prefitting</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ model.Initializing\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"845.12\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Prefitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.FullFitting -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>model.FullFitting</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ model.Prefitting\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"845.12\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.Prefitting&#45;&gt;model.FullFitting -->\n",
        "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>model.Prefitting-&gt;model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M845.12,-136.82C845.12,-124.23 845.12,-104.54 845.12,-92.01\"/>\n",
+       "</g>\n",
+       "<!-- model.Kappa -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>model.Kappa</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"kappa_id             \r------------------------------\rkappa_min_value      \rkappa_max_value      \rkappa_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"1001.62,-308.16 928.62,-308.16 928.62,-273.6 1001.62,-273.6 1001.62,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"936.62\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.Kappa</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.InitializingTask -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>model.InitializingTask</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ session.Session\r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"910.5,-308.16 779.75,-308.16 779.75,-273.6 910.5,-273.6 910.5,-308.16\"/>\n",
+       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-286.61\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.InitializingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.Initializing -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>model.Initializing</title>\n",
+       "<g id=\"a_node15\"><a xlink:title=\"→ model.InitializingTask\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"845.12\" cy=\"-220.32\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Initializing</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.InitializingTask&#45;&gt;model.Initializing -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>model.InitializingTask-&gt;model.Initializing</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M845.12,-273.28C845.12,-260.92 845.12,-244.33 845.12,-233.25\"/>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
        "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
        "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M703.12,-210.36C703.12,-199.47 703.12,-180.88 703.12,-167.35\"/>\n",
        "</g>\n",
+       "<!-- model.Initializing&#45;&gt;model.Prefitting -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>model.Initializing-&gt;model.Prefitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M845.12,-207.38C845.12,-194.79 845.12,-175.1 845.12,-162.57\"/>\n",
+       "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x12ec36940>"
+       "<datajoint.diagram.Diagram at 0x12f664190>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dj.Diagram(pca)\n",
-    "#+ dj.Diagram(model)"
+    "dj.Diagram(pca) + dj.Diagram(model)"
    ]
   },
   {
@@ -1440,13 +1471,6 @@
    "source": [
     "pca.UpdateLatentDimension()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 6bc6f8d999bd2d61837cd53457d230f4a6ed04b6 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 2 Jan 2024 08:17:35 +0100
Subject: [PATCH 26/90] WIP: redefine schema `model` and connect to `pca`

---
 element_moseq/model.py  |  34 ++-
 element_moseq/pca.py    |   3 +-
 notebooks/testing.ipynb | 539 ++++++++++++----------------------------
 3 files changed, 188 insertions(+), 388 deletions(-)

diff --git a/element_moseq/model.py b/element_moseq/model.py
index 1589a7b..1af707c 100644
--- a/element_moseq/model.py
+++ b/element_moseq/model.py
@@ -100,40 +100,52 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 
 @schema
-class Kappa(dj.Lookup):
+class InitializationParamSet(dj.Lookup):
     definition = """
-    kappa_id             : int
+    paramset_id             : int
     ---
-    kappa_min_value      : int
-    kappa_max_value      : int
-    kappa_description='' : varchar(1000)
+    num_iterations          : int
+    kappa                   : int
+    paramset_description='' : varchar(1000)
     """
 
 
 @schema
-class InitializingTask(dj.Manual):
+class FittingParamSet(dj.Lookup):
     definition = """
-    -> Session
-
+    paramset_id             : int
+    ---
+    num_iterations          : int
+    kappa                   : int
+    paramset_description='' : varchar(1000)
     """
 
 
 @schema
-class Initializing(dj.Computed):
+class Prefitting(dj.Computed):
     definition = """
-    -> InitializingTask
-"""
+    -> InitializationParamSet
+    -> pca.PCAFitting
+    ---
+    model_name              : varchar(20)
+    """
 
+    def make(self, key):
 
 @schema
 class Prefitting(dj.Computed):
     definition = """
     -> Initializing
     """
+        return
 
 
 @schema
 class FullFitting(dj.Computed):
     definition = """
     -> Prefitting
+    -> FittingParamSet
     """
+
+    def make(self, key):
+        return
diff --git a/element_moseq/pca.py b/element_moseq/pca.py
index 6e2ffb5..624da9b 100644
--- a/element_moseq/pca.py
+++ b/element_moseq/pca.py
@@ -317,6 +317,7 @@ class PCAFitting(dj.Computed):
     -> PCATask
     ---
     pca_fitting_time     : datetime  # Time of generation of the PCA fitting analysis 
+    pca                  : longblob
     """
 
     def make(self, key):
@@ -346,7 +347,7 @@ def make(self, key):
         plot_scree(pca, project_dir=project_path)
         plot_pcs(pca, project_dir=project_path, **config())
 
-        self.insert1(**key, pca_fitting_time=creation_time)
+        self.insert1(**key, pca_fitting_time=creation_time, pca=pca)
 
 
 @schema
diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index 4c0c7c9..2c9a5c8 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -34,7 +34,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-01-02 07:27:13,795][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+      "[2024-01-02 08:13:28,851][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
      ]
     }
    ],
@@ -44,228 +44,224 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1010pt\" height=\"316pt\" viewBox=\"0.00 0.00 1009.62 316.16\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 312.16)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-312.16 1005.62,-312.16 1005.62,4 -4,4\"/>\n",
-       "<!-- pca.Bodyparts -->\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"971pt\" height=\"387pt\" viewBox=\"0.00 0.00 971.38 386.72\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 382.72)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-382.72 967.38,-382.72 967.38,4 -4,4\"/>\n",
+       "<!-- model.InitializationParamSet -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>pca.Bodyparts</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"368.38,-237.6 275.88,-237.6 275.88,-203.04 368.38,-203.04 368.38,-237.6\"/>\n",
-       "<text text-anchor=\"start\" x=\"283.88\" y=\"-217.04\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
+       "<title>model.InitializationParamSet</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"140.5,-167.04 0,-167.04 0,-132.48 140.5,-132.48 140.5,-167.04\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.InitializationParamSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCATask -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>pca.PCATask</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"402.75,-167.04 315.5,-167.04 315.5,-132.48 402.75,-132.48 402.75,-167.04\"/>\n",
-       "<text text-anchor=\"middle\" x=\"359.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
+       "<!-- model.Prefitting -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>model.Prefitting</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ model.InitializationParamSet\r→ pca.PCAFitting\r------------------------------\rmodel_name           \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"70.25\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"70.25\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Prefitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
+       "<!-- model.InitializationParamSet&#45;&gt;model.Prefitting -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M331.08,-202.72C336.9,-191.94 344.45,-177.95 350.25,-167.2\"/>\n",
+       "<title>model.InitializationParamSet-&gt;model.Prefitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M70.25,-132.51C70.25,-120 70.25,-103.01 70.25,-91.83\"/>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting -->\n",
+       "<!-- pca.LatentDimension -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>pca.PCAFitting</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"175.12\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"175.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
+       "<title>pca.LatentDimension</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"396.38,-167.04 288.12,-167.04 288.12,-132.48 396.38,-132.48 396.38,-167.04\"/>\n",
+       "<text text-anchor=\"start\" x=\"296.12\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- pca.UpdateLatentDimension -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>pca.UpdateLatentDimension</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"175.12\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"175.12\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"356.25\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"356.25\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M175.12,-66.42C175.12,-54.83 175.12,-37.23 175.12,-25.67\"/>\n",
+       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M345.57,-132.51C348.12,-120 351.59,-103.01 353.88,-91.83\"/>\n",
        "</g>\n",
-       "<!-- pca.LatentDimension -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>pca.LatentDimension</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"108.25,-96.48 0,-96.48 0,-61.92 108.25,-61.92 108.25,-96.48\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-76.7\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
+       "<!-- pca.Bodyparts -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>pca.Bodyparts</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"560.5,-308.16 468,-308.16 468,-273.6 560.5,-273.6 560.5,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"476\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- pca.PCATask -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>pca.PCATask</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"574.88,-237.6 487.62,-237.6 487.62,-203.04 574.88,-203.04 574.88,-237.6\"/>\n",
+       "<text text-anchor=\"middle\" x=\"531.25\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M84.66,-61.99C110.7,-48.16 146.66,-29.07 164.37,-19.67\"/>\n",
+       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M518.37,-273.28C521.04,-262.5 524.51,-248.51 527.17,-237.76\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node4\" class=\"node\">\n",
        "<title>pca.KeypointSet</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"572,-308.16 468.25,-308.16 468.25,-273.6 572,-273.6 572,-308.16\"/>\n",
-       "<text text-anchor=\"start\" x=\"476.25\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"736.12,-378.72 632.38,-378.72 632.38,-344.16 736.12,-344.16 736.12,-378.72\"/>\n",
+       "<text text-anchor=\"start\" x=\"640.38\" y=\"-358.17\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- pca.KeypointSet&#45;&gt;pca.Bodyparts -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
        "<title>pca.KeypointSet-&gt;pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M471.69,-273.11C440.32,-262.25 399.69,-248.18 368.7,-237.45\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M642.66,-343.67C616.22,-333 582.12,-319.25 555.7,-308.6\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet&#45;&gt;pca.PCATask -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
        "<title>pca.KeypointSet-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M473.7,-273.19C454.14,-264.61 432.04,-252.74 415.12,-237.6 392.67,-217.5 375.57,-186.38 366.47,-167.36\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M655.92,-343.66C640.79,-334.11 622.26,-321.43 607.25,-308.16 582.34,-286.13 557.78,-256.15 543.59,-237.8\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"616.25,-230.32 424,-230.32 424,-210.32 616.25,-210.32 616.25,-230.32\"/>\n",
-       "<text text-anchor=\"middle\" x=\"520.12\" y=\"-216.82\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
+       "<!-- pca.KeypointSet.VideoFiles -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>pca.KeypointSet.VideoFiles</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"752.62,-300.88 615.88,-300.88 615.88,-280.88 752.62,-280.88 752.62,-300.88\"/>\n",
+       "<text text-anchor=\"start\" x=\"623.88\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M520.12,-273.28C520.12,-259.65 520.12,-240.88 520.12,-230.03\"/>\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M684.25,-343.84C684.25,-330.21 684.25,-311.44 684.25,-300.59\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.VideoFiles -->\n",
+       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
        "<g id=\"node14\" class=\"node\">\n",
-       "<title>pca.KeypointSet.VideoFiles</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"771.5,-230.32 634.75,-230.32 634.75,-210.32 771.5,-210.32 771.5,-230.32\"/>\n",
-       "<text text-anchor=\"start\" x=\"642.75\" y=\"-217.82\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
+       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"963.38,-300.88 771.12,-300.88 771.12,-280.88 963.38,-280.88 963.38,-300.88\"/>\n",
+       "<text text-anchor=\"middle\" x=\"867.25\" y=\"-287.38\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M564.89,-273.11C600.97,-259.59 650.31,-241.11 679.24,-230.27\"/>\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M729.02,-343.67C765.1,-330.15 814.44,-311.67 843.37,-300.83\"/>\n",
        "</g>\n",
-       "<!-- pca.RecordingInfo -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>pca.RecordingInfo</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"703.12\" cy=\"-149.76\" rx=\"74.16\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"703.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
+       "<!-- model.FullFitting -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>model.FullFitting</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ model.FittingParamSet\r→ model.Prefitting\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"133.25\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"133.25\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
+       "<!-- model.FittingParamSet -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>model.FittingParamSet</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"254.12,-96.48 138.38,-96.48 138.38,-61.92 254.12,-61.92 254.12,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"146.38\" y=\"-76.7\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.FittingParamSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- model.FittingParamSet&#45;&gt;model.FullFitting -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M315.05,-132.34C272.74,-116.57 211.66,-93.81 186.55,-84.46\"/>\n",
+       "<title>model.FittingParamSet-&gt;model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M180.35,-61.99C168.1,-49.5 151.65,-32.72 141.78,-22.66\"/>\n",
        "</g>\n",
        "<!-- pca.PCATask&#45;&gt;pca.UpdateLatentDimension -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
        "<title>pca.PCATask-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M327.67,-131.99C310.47,-122.35 289.07,-109.58 271.12,-96.48 252.42,-82.82 249.65,-77.06 232.12,-61.92 215.48,-47.55 195.81,-31.12 184.47,-21.7\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M499.07,-202.73C481.84,-193.24 460.65,-180.56 443.25,-167.04 426.39,-153.95 382.78,-108.31 364.5,-88.97\"/>\n",
        "</g>\n",
-       "<!-- pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>pca.PCATask.FormattedDataset</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"438,-89.2 280.25,-89.2 280.25,-69.2 438,-69.2 438,-89.2\"/>\n",
-       "<text text-anchor=\"middle\" x=\"359.12\" y=\"-75.7\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
+       "<!-- pca.PCAFitting -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>pca.PCAFitting</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \rpca                  \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"221.25\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"221.25\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M359.12,-132.16C359.12,-118.53 359.12,-99.76 359.12,-88.91\"/>\n",
+       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M487.34,-211.16C436.9,-201.58 351.75,-184.71 279.25,-167.04 263.2,-163.13 244.86,-157.84 233.18,-154.37\"/>\n",
        "</g>\n",
-       "<!-- model.Prefitting -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>model.Prefitting</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ model.Initializing\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"845.12\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Prefitting</text>\n",
+       "<!-- pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>pca.PCATask.FormattedDataset</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"610.12,-159.76 452.38,-159.76 452.38,-139.76 610.12,-139.76 610.12,-159.76\"/>\n",
+       "<text text-anchor=\"middle\" x=\"531.25\" y=\"-146.26\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.FullFitting -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>model.FullFitting</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ model.Prefitting\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"845.12\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M531.25,-202.72C531.25,-189.09 531.25,-170.32 531.25,-159.47\"/>\n",
        "</g>\n",
        "<!-- model.Prefitting&#45;&gt;model.FullFitting -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
        "<title>model.Prefitting-&gt;model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M845.12,-136.82C845.12,-124.23 845.12,-104.54 845.12,-92.01\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M78.75,-69.54C90.84,-57.21 112.87,-34.74 124.89,-22.49\"/>\n",
        "</g>\n",
-       "<!-- model.Kappa -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>model.Kappa</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"kappa_id             \r------------------------------\rkappa_min_value      \rkappa_max_value      \rkappa_description=&quot;&quot; \r\">\n",
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-       "<text text-anchor=\"start\" x=\"936.62\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.Kappa</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- model.InitializingTask -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>model.InitializingTask</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ session.Session\r\">\n",
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-       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-286.61\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.InitializingTask</text>\n",
+       "<!-- pca.RecordingInfo -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>pca.RecordingInfo</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"684.25\" cy=\"-220.32\" rx=\"74.16\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"684.25\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.Initializing -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>model.Initializing</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"→ model.InitializingTask\r\">\n",
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-       "<text text-anchor=\"middle\" x=\"845.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Initializing</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M232.3,-143.15C257.74,-130.23 320.21,-98.51 345.43,-85.7\"/>\n",
        "</g>\n",
-       "<!-- model.InitializingTask&#45;&gt;model.Initializing -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>model.InitializingTask-&gt;model.Initializing</title>\n",
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+       "<!-- pca.PCAFitting&#45;&gt;model.Prefitting -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>pca.PCAFitting-&gt;model.Prefitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M209.9,-143.61C181.78,-130.84 109.15,-97.86 81.32,-85.23\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
        "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M703.12,-210.36C703.12,-199.47 703.12,-180.88 703.12,-167.35\"/>\n",
-       "</g>\n",
-       "<!-- model.Initializing&#45;&gt;model.Prefitting -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>model.Initializing-&gt;model.Prefitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M845.12,-207.38C845.12,-194.79 845.12,-175.1 845.12,-162.57\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M684.25,-280.92C684.25,-270.03 684.25,-251.44 684.25,-237.91\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x12f664190>"
+       "<datajoint.diagram.Diagram at 0x10735efa0>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -872,26 +868,20 @@
        "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
        "                            <span class=\"djtooltiptext\">list of strings of bodyparts to be used</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
-       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 1)"
+       "*subject    *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -920,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1000,25 +990,20 @@
        "                            <p id=\"nonprimary\">task_mode</p>\n",
        "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/kpms_project</td>\n",
-       "<td>load</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1            1              /kpms_project  load         \n",
-       " (Total: 1)"
+       "*subject    *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1029,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1041,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1051,7 +1036,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
+      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
       "File \u001b[0;32m~/Documents/datajoint-elements/element-moseq/element_moseq/pca.py:264\u001b[0m, in \u001b[0;36mPCATask.generate\u001b[0;34m(cls, key)\u001b[0m\n\u001b[1;32m    261\u001b[0m video_path \u001b[39m=\u001b[39m (KeypointSet\u001b[39m.\u001b[39mVideoFiles \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch(\u001b[39m\"\u001b[39m\u001b[39mvideo_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    263\u001b[0m kpset_path \u001b[39m=\u001b[39m (KeypointSet \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mkpset_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 264\u001b[0m kpset_path \u001b[39m=\u001b[39m find_full_path(get_kpms_root_data_dir(), kpset_path)\n\u001b[1;32m    266\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mkeypoint_moseq\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mkpms\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[39m# define config file for kpms with anonymous function\u001b[39;00m\n",
       "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/element_interface/utils.py:42\u001b[0m, in \u001b[0;36mfind_full_path\u001b[0;34m(root_directories, relative_path)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[39mif\u001b[39;00m (_to_Path(root_dir) \u001b[39m/\u001b[39m relative_path)\u001b[39m.\u001b[39mexists():\n\u001b[1;32m     40\u001b[0m         \u001b[39mreturn\u001b[39;00m _to_Path(root_dir) \u001b[39m/\u001b[39m relative_path\n\u001b[0;32m---> 42\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m     43\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mNo valid full-path found (from \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     44\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(root_directories, relative_path)\n\u001b[1;32m     45\u001b[0m )\n",
       "\u001b[0;31mFileNotFoundError\u001b[0m: No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos"
@@ -1064,123 +1049,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
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-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">config</p>\n",
-       "                            <span class=\"djtooltiptext\">stored full config file</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">data</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">metadata</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  config     coordinate confidence bodyparts  data       metadata  \n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+ +--------+ +--------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pca.PCATask.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1256,6 +1134,9 @@
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
        "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -1264,13 +1145,13 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti pca       \n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1281,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1362,7 +1243,7 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1373,104 +1254,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-      "text/html": [
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-       "    \n",
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-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
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-       "        .djtooltip .djtooltiptext {\n",
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-       "            width: 120px;\n",
-       "            background-color: black;\n",
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-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *latent_dim   \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca.UpdateLatentDimension()"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From b468fd41a93f1e92174672721811c8d9adc10038 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 2 Jan 2024 08:35:28 +0100
Subject: [PATCH 27/90] WIP: restructure `model` schema

---
 element_moseq/model.py  |  41 ++-
 notebooks/testing.ipynb | 580 ++++++++++++++++++++++++++++------------
 2 files changed, 448 insertions(+), 173 deletions(-)

diff --git a/element_moseq/model.py b/element_moseq/model.py
index 1af707c..f1f7589 100644
--- a/element_moseq/model.py
+++ b/element_moseq/model.py
@@ -122,30 +122,61 @@ class FittingParamSet(dj.Lookup):
 
 
 @schema
-class Prefitting(dj.Computed):
+class PreFittingTask(dj.Manual):
     definition = """
     -> InitializationParamSet
     -> pca.PCAFitting
     ---
     model_name              : varchar(20)
+    task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
     """
 
-    def make(self, key):
 
 @schema
-class Prefitting(dj.Computed):
+class PreFitting(dj.Computed):
     definition = """
-    -> Initializing
+    -> PreFittingTask
+    ---
+    model                   : longblob
     """
+
+    def make(self, key):
+        # model = kpms.init_model(data, pca=pca, **config())
+        # model = kpms.update_hypparams(model, kappa=kappa)
+        # model, model_name = kpms.fit_model(model, data, metadata, project_dir,ar_only=True, num_iters=num_ar_iters)
         return
 
 
 @schema
-class FullFitting(dj.Computed):
+class FullFittingTask(dj.Manual):
     definition = """
     -> Prefitting
     -> FittingParamSet
+    ---
+    model_name              : varchar(20)
+    task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
+    """
+
+
+@schema
+class FullFitting(dj.Computed):
+    definition = """
+    -> FullFittingTask
+    ---
+    model                   : longblob    
     """
 
     def make(self, key):
+        # task_mode == "trigger"
+        # modify kappa to maintain the desired syllable time-scale
+        # model = kpms.update_hypparams(model, kappa=1e6)
+
+        # run fitting for an additional 200 iters
+        # model = kpms.fit_model(
+        #    model, data, metadata, project_dir, model_name, ar_only=False,
+        #    start_iter=current_iter, num_iters=current_iter+500)[0]
+        # task_mode == "load"
+        # model_name = '2023_11_27-16_06_07'
+        # model, data, metadata, current_iter = kpms.load_checkpoint(
+        # project_dir, model_name, iteration=num_ar_iters)
         return
diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index 2c9a5c8..eefd845 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -34,7 +34,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-01-02 08:13:28,851][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+      "[2024-01-02 08:32:38,675][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
      ]
     }
    ],
@@ -44,224 +44,252 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"971pt\" height=\"387pt\" viewBox=\"0.00 0.00 971.38 386.72\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 382.72)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-382.72 967.38,-382.72 967.38,4 -4,4\"/>\n",
-       "<!-- model.InitializationParamSet -->\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"622pt\" height=\"528pt\" viewBox=\"0.00 0.00 622.25 527.84\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 523.84)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-523.84 618.25,-523.84 618.25,4 -4,4\"/>\n",
+       "<!-- pca.PCAFitting -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>model.InitializationParamSet</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"140.5,-167.04 0,-167.04 0,-132.48 140.5,-132.48 140.5,-167.04\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.InitializationParamSet</text>\n",
+       "<title>pca.PCAFitting</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \rpca                  \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"213.12\" cy=\"-290.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"213.12\" y=\"-286.61\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.Prefitting -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>model.Prefitting</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"→ model.InitializationParamSet\r→ pca.PCAFitting\r------------------------------\rmodel_name           \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"70.25\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"70.25\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.Prefitting</text>\n",
+       "<!-- pca.UpdateLatentDimension -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>pca.UpdateLatentDimension</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"136.12\" cy=\"-220.32\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"136.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.InitializationParamSet&#45;&gt;model.Prefitting -->\n",
+       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>model.InitializationParamSet-&gt;model.Prefitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M70.25,-132.51C70.25,-120 70.25,-103.01 70.25,-91.83\"/>\n",
-       "</g>\n",
-       "<!-- pca.LatentDimension -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>pca.LatentDimension</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"396.38,-167.04 288.12,-167.04 288.12,-132.48 396.38,-132.48 396.38,-167.04\"/>\n",
-       "<text text-anchor=\"start\" x=\"296.12\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M203.99,-281.75C189.17,-268.55 160.06,-242.63 145.25,-229.44\"/>\n",
        "</g>\n",
-       "<!-- pca.UpdateLatentDimension -->\n",
+       "<!-- model.PreFittingTask -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>pca.UpdateLatentDimension</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"356.25\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"356.25\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
+       "<title>model.PreFittingTask</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ model.InitializationParamSet\r→ pca.PCAFitting\r------------------------------\rmodel_name           \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"477.38,-237.6 348.88,-237.6 348.88,-203.04 477.38,-203.04 477.38,-237.6\"/>\n",
+       "<text text-anchor=\"middle\" x=\"413.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.PreFittingTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- pca.PCAFitting&#45;&gt;model.PreFittingTask -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M345.57,-132.51C348.12,-120 351.59,-103.01 353.88,-91.83\"/>\n",
+       "<title>pca.PCAFitting-&gt;model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M225.04,-285.8C251.86,-276.6 318.27,-253.83 364.53,-237.98\"/>\n",
        "</g>\n",
-       "<!-- pca.Bodyparts -->\n",
+       "<!-- model.FullFitting -->\n",
        "<g id=\"node3\" class=\"node\">\n",
-       "<title>pca.Bodyparts</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"560.5,-308.16 468,-308.16 468,-273.6 560.5,-273.6 560.5,-308.16\"/>\n",
-       "<text text-anchor=\"start\" x=\"476\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
+       "<title>model.FullFitting</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ model.FullFittingTask\r------------------------------\rmodel                \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"477.12\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"477.12\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCATask -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>pca.PCATask</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"574.88,-237.6 487.62,-237.6 487.62,-203.04 574.88,-203.04 574.88,-237.6\"/>\n",
-       "<text text-anchor=\"middle\" x=\"531.25\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
+       "<!-- pca.LatentDimension -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>pca.LatentDimension</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"108.25,-308.16 0,-308.16 0,-273.6 108.25,-273.6 108.25,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
+       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M518.37,-273.28C521.04,-262.5 524.51,-248.51 527.17,-237.76\"/>\n",
+       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M73.56,-273.63C90.42,-259.54 114.08,-239.75 126.93,-229\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>pca.KeypointSet</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"736.12,-378.72 632.38,-378.72 632.38,-344.16 736.12,-344.16 736.12,-378.72\"/>\n",
-       "<text text-anchor=\"start\" x=\"640.38\" y=\"-358.17\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
+       "<!-- pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>pca.PCATask.FormattedDataset</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"437,-300.88 279.25,-300.88 279.25,-280.88 437,-280.88 437,-300.88\"/>\n",
+       "<text text-anchor=\"middle\" x=\"358.12\" y=\"-287.38\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.Bodyparts -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M642.66,-343.67C616.22,-333 582.12,-319.25 555.7,-308.6\"/>\n",
+       "<!-- model.PreFitting -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>model.PreFitting</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ model.PreFittingTask\r------------------------------\rmodel                \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"413.12\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"413.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.PreFitting</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.PCATask -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M655.92,-343.66C640.79,-334.11 622.26,-321.43 607.25,-308.16 582.34,-286.13 557.78,-256.15 543.59,-237.8\"/>\n",
+       "</g>\n",
+       "<!-- model.PreFittingTask&#45;&gt;model.PreFitting -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>model.PreFittingTask-&gt;model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M413.12,-202.72C413.12,-190.36 413.12,-173.77 413.12,-162.69\"/>\n",
        "</g>\n",
        "<!-- pca.KeypointSet.VideoFiles -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>pca.KeypointSet.VideoFiles</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"752.62,-300.88 615.88,-300.88 615.88,-280.88 752.62,-280.88 752.62,-300.88\"/>\n",
-       "<text text-anchor=\"start\" x=\"623.88\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"255.5,-442 118.75,-442 118.75,-422 255.5,-422 255.5,-442\"/>\n",
+       "<text text-anchor=\"start\" x=\"126.75\" y=\"-429.5\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M684.25,-343.84C684.25,-330.21 684.25,-311.44 684.25,-300.59\"/>\n",
+       "<!-- pca.RecordingInfo -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>pca.RecordingInfo</title>\n",
+       "<g id=\"a_node16\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"187.12\" cy=\"-361.44\" rx=\"74.16\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"187.12\" y=\"-357.17\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"963.38,-300.88 771.12,-300.88 771.12,-280.88 963.38,-280.88 963.38,-300.88\"/>\n",
-       "<text text-anchor=\"middle\" x=\"867.25\" y=\"-287.38\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M187.12,-422.04C187.12,-411.15 187.12,-392.56 187.12,-379.03\"/>\n",
+       "</g>\n",
+       "<!-- model.InitializationParamSet -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>model.InitializationParamSet</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"595.38,-308.16 454.88,-308.16 454.88,-273.6 595.38,-273.6 595.38,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"462.88\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.InitializationParamSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M729.02,-343.67C765.1,-330.15 814.44,-311.67 843.37,-300.83\"/>\n",
+       "<!-- model.InitializationParamSet&#45;&gt;model.PreFittingTask -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>model.InitializationParamSet-&gt;model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M498.58,-273.63C480.99,-262.86 457.97,-248.77 440.25,-237.93\"/>\n",
        "</g>\n",
-       "<!-- model.FullFitting -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>model.FullFitting</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ model.FittingParamSet\r→ model.Prefitting\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"133.25\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"133.25\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
+       "<!-- pca.KeypointSet -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>pca.KeypointSet</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"409,-519.84 305.25,-519.84 305.25,-485.28 409,-485.28 409,-519.84\"/>\n",
+       "<text text-anchor=\"start\" x=\"313.25\" y=\"-499.29\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.FittingParamSet -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>model.FittingParamSet</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"254.12,-96.48 138.38,-96.48 138.38,-61.92 254.12,-61.92 254.12,-96.48\"/>\n",
-       "<text text-anchor=\"start\" x=\"146.38\" y=\"-76.7\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.FittingParamSet</text>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M315.54,-484.79C281.92,-471.23 235.92,-452.68 209.08,-441.85\"/>\n",
+       "</g>\n",
+       "<!-- pca.Bodyparts -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>pca.Bodyparts</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"366.38,-449.28 273.88,-449.28 273.88,-414.72 366.38,-414.72 366.38,-449.28\"/>\n",
+       "<text text-anchor=\"start\" x=\"281.88\" y=\"-428.73\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.FittingParamSet&#45;&gt;model.FullFitting -->\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.Bodyparts -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>model.FittingParamSet-&gt;model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M180.35,-61.99C168.1,-49.5 151.65,-32.72 141.78,-22.66\"/>\n",
+       "<title>pca.KeypointSet-&gt;pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M348.17,-484.96C342.35,-474.18 334.8,-460.19 329,-449.44\"/>\n",
        "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- pca.PCATask -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>pca.PCATask</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"401.75,-378.72 314.5,-378.72 314.5,-344.16 401.75,-344.16 401.75,-378.72\"/>\n",
+       "<text text-anchor=\"middle\" x=\"358.12\" y=\"-357.17\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.PCATask -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M499.07,-202.73C481.84,-193.24 460.65,-180.56 443.25,-167.04 426.39,-153.95 382.78,-108.31 364.5,-88.97\"/>\n",
+       "<title>pca.KeypointSet-&gt;pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M364.42,-484.98C368.46,-474.88 373.07,-461.59 375.12,-449.28 377.66,-434.13 377.53,-429.89 375.12,-414.72 373.18,-402.47 368.83,-389.18 365.01,-379.06\"/>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>pca.PCAFitting</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \rpca                  \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"221.25\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"221.25\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
+       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"614.25,-442 422,-442 422,-422 614.25,-422 614.25,-442\"/>\n",
+       "<text text-anchor=\"middle\" x=\"518.12\" y=\"-428.5\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
+       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M487.34,-211.16C436.9,-201.58 351.75,-184.71 279.25,-167.04 263.2,-163.13 244.86,-157.84 233.18,-154.37\"/>\n",
+       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M396.51,-484.79C428.35,-471.23 471.91,-452.68 497.34,-441.85\"/>\n",
        "</g>\n",
-       "<!-- pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>pca.PCATask.FormattedDataset</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"610.12,-159.76 452.38,-159.76 452.38,-139.76 610.12,-139.76 610.12,-159.76\"/>\n",
-       "<text text-anchor=\"middle\" x=\"531.25\" y=\"-146.26\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
-       "</a>\n",
+       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M329.32,-414.4C335.3,-403.62 343.05,-389.63 349.01,-378.88\"/>\n",
        "</g>\n",
+       "<!-- model.FullFittingTask -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>model.FullFittingTask</title>\n",
+       "<g id=\"a_node15\"><a xlink:title=\"→ model.FittingParamSet\r→ model.PreFitting\r------------------------------\rmodel_name           \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"542.12,-96.48 412.12,-96.48 412.12,-61.92 542.12,-61.92 542.12,-96.48\"/>\n",
+       "<text text-anchor=\"middle\" x=\"477.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.FullFittingTask</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M531.25,-202.72C531.25,-189.09 531.25,-170.32 531.25,-159.47\"/>\n",
        "</g>\n",
-       "<!-- model.Prefitting&#45;&gt;model.FullFitting -->\n",
+       "<!-- model.PreFitting&#45;&gt;model.FullFittingTask -->\n",
        "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>model.Prefitting-&gt;model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M78.75,-69.54C90.84,-57.21 112.87,-34.74 124.89,-22.49\"/>\n",
+       "<title>model.PreFitting-&gt;model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M421.23,-140.08C431.32,-129.27 448.78,-110.57 461.5,-96.94\"/>\n",
        "</g>\n",
-       "<!-- pca.RecordingInfo -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>pca.RecordingInfo</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"684.25\" cy=\"-220.32\" rx=\"74.16\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"684.25\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
+       "<!-- model.FittingParamSet -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>model.FittingParamSet</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"599,-167.04 483.25,-167.04 483.25,-132.48 599,-132.48 599,-167.04\"/>\n",
+       "<text text-anchor=\"start\" x=\"491.25\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.FittingParamSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- model.FittingParamSet&#45;&gt;model.FullFittingTask -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M232.3,-143.15C257.74,-130.23 320.21,-98.51 345.43,-85.7\"/>\n",
+       "<title>model.FittingParamSet-&gt;model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M525.96,-132.51C515.9,-121.74 502.75,-107.65 492.63,-96.81\"/>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting&#45;&gt;model.Prefitting -->\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
        "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>pca.PCAFitting-&gt;model.Prefitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M209.9,-143.61C181.78,-130.84 109.15,-97.86 81.32,-85.23\"/>\n",
+       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M322.65,-343.67C290.46,-328.45 244.95,-306.93 224.2,-297.12\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<!-- pca.PCATask&#45;&gt;pca.UpdateLatentDimension -->\n",
        "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M684.25,-280.92C684.25,-270.03 684.25,-251.44 684.25,-237.91\"/>\n",
+       "<title>pca.PCATask-&gt;pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M314.3,-351.69C258.02,-340.08 166.24,-319.69 155.12,-308.16 135.42,-287.72 134.22,-251.8 135.11,-233.21\"/>\n",
+       "</g>\n",
+       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M358.12,-343.84C358.12,-330.21 358.12,-311.44 358.12,-300.59\"/>\n",
+       "</g>\n",
+       "<!-- model.FullFittingTask&#45;&gt;model.FullFitting -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>model.FullFittingTask-&gt;model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M477.12,-61.66C477.12,-50.36 477.12,-35.66 477.12,-25.58\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x10735efa0>"
+       "<datajoint.diagram.Diagram at 0x171c20eb0>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -272,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +390,7 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -373,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -400,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -509,7 +537,7 @@
        " (Total: 1)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +669,7 @@
        " (Total: 3)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -652,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -765,7 +793,7 @@
        " (Total: 1)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -776,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -785,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +909,7 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -910,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1003,18 +1031,18 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.PCATask()"
+    "pca.PCATask()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1026,7 +1054,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Manual table for defining a data loading task ready to be run</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">project_path</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/kpms_project</td>\n",
+       "<td>load</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1            1              /kpms_project  load         \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +1173,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
+      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
       "File \u001b[0;32m~/Documents/datajoint-elements/element-moseq/element_moseq/pca.py:264\u001b[0m, in \u001b[0;36mPCATask.generate\u001b[0;34m(cls, key)\u001b[0m\n\u001b[1;32m    261\u001b[0m video_path \u001b[39m=\u001b[39m (KeypointSet\u001b[39m.\u001b[39mVideoFiles \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch(\u001b[39m\"\u001b[39m\u001b[39mvideo_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    263\u001b[0m kpset_path \u001b[39m=\u001b[39m (KeypointSet \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mkpset_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 264\u001b[0m kpset_path \u001b[39m=\u001b[39m find_full_path(get_kpms_root_data_dir(), kpset_path)\n\u001b[1;32m    266\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mkeypoint_moseq\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mkpms\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[39m# define config file for kpms with anonymous function\u001b[39;00m\n",
       "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/element_interface/utils.py:42\u001b[0m, in \u001b[0;36mfind_full_path\u001b[0;34m(root_directories, relative_path)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[39mif\u001b[39;00m (_to_Path(root_dir) \u001b[39m/\u001b[39m relative_path)\u001b[39m.\u001b[39mexists():\n\u001b[1;32m     40\u001b[0m         \u001b[39mreturn\u001b[39;00m _to_Path(root_dir) \u001b[39m/\u001b[39m relative_path\n\u001b[0;32m---> 42\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m     43\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mNo valid full-path found (from \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     44\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(root_directories, relative_path)\n\u001b[1;32m     45\u001b[0m )\n",
       "\u001b[0;31mFileNotFoundError\u001b[0m: No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos"
@@ -1049,16 +1186,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">config</p>\n",
+       "                            <span class=\"djtooltiptext\">stored full config file</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">data</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">metadata</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  config     coordinate confidence bodyparts  data       metadata  \n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pca.PCATask.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1151,7 +1395,7 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1162,7 +1406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1243,7 +1487,7 @@
        " (Total: 0)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }

From 4eba5667b8fe8226e3ae9b363e7512760db6794f Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 8 Jan 2024 12:56:19 +0100
Subject: [PATCH 28/90] test element-moseq[elements] in codespaces

---
 .devcontainer/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 6c8df97..1b55788 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,7 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements,kpms_default] && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean

From fe6be5474ca308654365e5e241fc65945dcb9c02 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 8 Jan 2024 12:58:06 +0100
Subject: [PATCH 29/90] test element-moseq[elements] in codespaces

---
 .devcontainer/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 6c8df97..1b55788 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,7 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements,kpms_default] && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean

From 1b2362de923ab5de1feec99742b4ea2d0e14455a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 8 Jan 2024 13:09:05 +0100
Subject: [PATCH 30/90] test modif dockerfile in codespaces

---
 .devcontainer/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 1b55788..50113ab 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -32,7 +32,7 @@ COPY ./ /tmp/element-moseq/
 RUN \
     # pipeline dependencies
     apt-get update && \
-    apt-get install -y gcc ffmpeg graphviz && \
+    apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \
     pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \

From 848b8897f8662b5b04d2ff96ceee13ca7edb12cb Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Mon, 8 Jan 2024 16:12:51 +0100
Subject: [PATCH 31/90] test codespaces

---
 .devcontainer/Dockerfile | 2 +-
 setup.py                 | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 50113ab..a643a53 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -32,7 +32,7 @@ COPY ./ /tmp/element-moseq/
 RUN \
     # pipeline dependencies
     apt-get update && \
-    apt-get install gcc g++ ffmpeg libsm6 libxext6 -y && \
+    apt-get install gcc g++ ffmpeg -y && \
     pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
diff --git a/setup.py b/setup.py
index 00248fc..5205dd2 100644
--- a/setup.py
+++ b/setup.py
@@ -28,8 +28,6 @@
     install_requires=["datajoint>=0.13.0", "ipykernel>=6.0.1", "ipywidgets"],
     extras_require={
         "kpms_default": [
-            "ffmpeg",
-            "tensorflow==2.12.0",
             "'jax[cuda]==0.4.1' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html",
             "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq",
         ],

From 4b71c99cf0b3adf14646e01ffc4680360a0e7565 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 26 Jan 2024 21:00:16 +0100
Subject: [PATCH 32/90] fixed import keypoint-moseq in ipython codespace

---
 .devcontainer/Dockerfile | 3 ++-
 setup.py                 | 6 +++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index a643a53..e74176f 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -33,7 +33,8 @@ RUN \
     # pipeline dependencies
     apt-get update && \
     apt-get install gcc g++ ffmpeg -y && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
+    pip install "jax[cpu]==0.3.22" -f https://storage.googleapis.com/jax-releases/jax_releases.html && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements,kpms_default] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean
diff --git a/setup.py b/setup.py
index 5205dd2..6422765 100644
--- a/setup.py
+++ b/setup.py
@@ -25,11 +25,11 @@
     keywords="neuroscience keypoint-moseq science datajoint",
     packages=find_packages(exclude=["contrib", "docs", "tests*"]),
     scripts=[],
-    install_requires=["datajoint>=0.13.0", "ipykernel>=6.0.1", "ipywidgets"],
+    install_requires=["datajoint>=0.13.0"],
     extras_require={
         "kpms_default": [
-            "'jax[cuda]==0.4.1' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html",
-            "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq",
+            "panel==1.3.4",
+            "keypoint-moseq",
         ],
         "elements": [
             "element-lab>=0.3.0",

From e55b69d3edff660eca8fcdc9073c3adf46087775 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 9 Feb 2024 03:50:50 +0100
Subject: [PATCH 33/90] fix RecordingInfo.populate()

---
 element_moseq/pca.py | 54 +++++++++++++++++++++++---------------------
 1 file changed, 28 insertions(+), 26 deletions(-)

diff --git a/element_moseq/pca.py b/element_moseq/pca.py
index 624da9b..c83137e 100644
--- a/element_moseq/pca.py
+++ b/element_moseq/pca.py
@@ -164,35 +164,37 @@ def key_source(self):
     def make(self, key):
         """Populates table with video metadata using CV2."""
 
-        file_path = (KeypointSet.VideoFiles & key).fetch1("video_path")
+        file_paths,video_ids = (KeypointSet.VideoFiles & key).fetch("video_path","video_id")
 
-        nframes = 0
-        px_height, px_width, fps = None, None, None
+        for fp, video_id in zip(file_paths,video_ids):
+            nframes = 0
+            px_height, px_width, fps = None, None, None
 
-        file_path = (find_full_path(get_kpms_root_data_dir(), file_path[0])).as_posix()
+            file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
 
-        cap = cv2.VideoCapture(file_path)
-        info = (
-            int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-            int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
-            int(cap.get(cv2.CAP_PROP_FPS)),
-        )
-        if px_height is not None:
-            assert (px_height, px_width, fps) == info
-        px_height, px_width, fps = info
-        nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        cap.release()
-
-        self.insert1(
-            {
-                **key,
-                "px_height": px_height,
-                "px_width": px_width,
-                "nframes": nframes,
-                "fps": fps,
-                "recording_duration": nframes / fps,
-            }
-        )
+            cap = cv2.VideoCapture(file_path)
+            info = (
+                int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+                int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                int(cap.get(cv2.CAP_PROP_FPS)),
+            )
+            if px_height is not None:
+                assert (px_height, px_width, fps) == info
+            px_height, px_width, fps = info
+            nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            cap.release()
+
+            self.insert1(
+                {
+                    **key,
+                    "video_id": video_id,
+                    "px_height": px_height,
+                    "px_width": px_width,
+                    "nframes": nframes,
+                    "fps": fps,
+                    "recording_duration": nframes / fps,
+                }
+            )
 
 
 @schema

From 9f41acece8761c35b0dce94ef069340177e43060 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 28 Feb 2024 02:53:23 +0100
Subject: [PATCH 34/90] readers WIP

---
 element_moseq/readers/kpms_reader.py | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 element_moseq/readers/kpms_reader.py

diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py
new file mode 100644
index 0000000..d38b332
--- /dev/null
+++ b/element_moseq/readers/kpms_reader.py
@@ -0,0 +1,26 @@
+import re
+import logging
+import numpy as np
+import pandas as pd
+from pathlib import Path
+import pickle
+from ruamel.yaml import YAML
+from element_interface.utils import find_root_directory, dict_to_uuid
+from .. import model
+from ..model import get_dlc_root_data_dir
+from datajoint.errors import DataJointError
+
+logger = logging.getLogger("datajoint")
+
+# def read_yaml(fullpath: str, filename: str = '*') -> tuple:
+#     """ Return contents of yaml in fullpath. If available, defer to DJ-saved version
+    
+#     Args: 
+#         fullpath (str): String or pathlib path. Directory with yaml files
+#         filename (str, optional): Filename, no extension. 
+    
+#     Returns:
+#         Tuple of (a) filepath as pathlib.PosixPath and (b) file contents as dict    
+#     """
+    
+    
\ No newline at end of file

From ba85e310c568ce96e5742e24978739d0a7cfe765 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 28 Feb 2024 02:54:27 +0100
Subject: [PATCH 35/90] from `model.py` to `kpms_model.py` [WIP]

---
 element_moseq/{model.py => kpms_model.py} | 44 +++++++++++------------
 1 file changed, 22 insertions(+), 22 deletions(-)
 rename element_moseq/{model.py => kpms_model.py} (83%)

diff --git a/element_moseq/model.py b/element_moseq/kpms_model.py
similarity index 83%
rename from element_moseq/model.py
rename to element_moseq/kpms_model.py
index f1f7589..6dfe9f7 100644
--- a/element_moseq/model.py
+++ b/element_moseq/kpms_model.py
@@ -158,25 +158,25 @@ class FullFittingTask(dj.Manual):
     """
 
 
-@schema
-class FullFitting(dj.Computed):
-    definition = """
-    -> FullFittingTask
-    ---
-    model                   : longblob    
-    """
-
-    def make(self, key):
-        # task_mode == "trigger"
-        # modify kappa to maintain the desired syllable time-scale
-        # model = kpms.update_hypparams(model, kappa=1e6)
-
-        # run fitting for an additional 200 iters
-        # model = kpms.fit_model(
-        #    model, data, metadata, project_dir, model_name, ar_only=False,
-        #    start_iter=current_iter, num_iters=current_iter+500)[0]
-        # task_mode == "load"
-        # model_name = '2023_11_27-16_06_07'
-        # model, data, metadata, current_iter = kpms.load_checkpoint(
-        # project_dir, model_name, iteration=num_ar_iters)
-        return
+# @schema
+# class FullFitting(dj.Computed):
+#     definition = """
+#     -> FullFittingTask
+#     ---
+#     model                   : longblob
+#     """
+
+# def make(self, key):
+# task_mode == "trigger"
+# modify kappa to maintain the desired syllable time-scale
+# model = kpms.update_hypparams(model, kappa=1e6)
+
+# run fitting for an additional 200 iters
+# model = kpms.fit_model(
+#    model, data, metadata, project_dir, model_name, ar_only=False,
+#    start_iter=current_iter, num_iters=current_iter+500)[0]
+# task_mode == "load"
+# model_name = '2023_11_27-16_06_07'
+# model, data, metadata, current_iter = kpms.load_checkpoint(
+# project_dir, model_name, iteration=num_ar_iters)
+# return

From a675c580eab1e51b55f3594ba9d5136ff4bb9adb Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 28 Feb 2024 03:05:25 +0100
Subject: [PATCH 36/90] from `pca.py` to `kpms_pca.py` [WIP]

---
 element_moseq/kpms_pca.py | 510 ++++++++++++++++++++++++++++++++++++++
 element_moseq/pca.py      | 378 ----------------------------
 2 files changed, 510 insertions(+), 378 deletions(-)
 create mode 100644 element_moseq/kpms_pca.py
 delete mode 100644 element_moseq/pca.py

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
new file mode 100644
index 0000000..97a68a3
--- /dev/null
+++ b/element_moseq/kpms_pca.py
@@ -0,0 +1,510 @@
+import datajoint as dj
+import matplotlib.pyplot as plt
+import cv2
+from typing import Optional
+import numpy as np
+from datetime import datetime
+
+import inspect
+import importlib
+import os
+from pathlib import Path
+from element_interface.utils import find_full_path, dict_to_uuid
+
+schema = dj.schema()
+_linking_module = None
+
+
+def activate(
+    pca_schema_name: str,
+    *,
+    create_schema: bool = True,
+    create_tables: bool = True,
+    linking_module: str = None,
+):
+    """Activate this schema.
+
+    Args:
+        pca_schema_name (str): A string containing the name of the pca schema.
+        create_schema (bool): If True (default), schema  will be created in the database.
+        create_tables (bool): If True (default), tables related to the schema will be created in the database.
+        linking_module (str): A string containing the module name or module containing the required dependencies to activate the schema.
+
+    Dependencies:
+    Functions:
+        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies) with all behavioral recordings, as (list of) string(s)
+        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed data. Defaults to session video subfolder.
+    """
+
+    if isinstance(linking_module, str):
+        linking_module = importlib.import_module(linking_module)
+    assert inspect.ismodule(
+        linking_module
+    ), "The argument 'dependency' must be a module's name or a module"
+
+    assert hasattr(
+        linking_module, "get_kpms_root_data_dir"
+    ), "The linking module must specify a lookup function for a root data directory"
+
+    global _linking_module
+    _linking_module = linking_module
+
+    # activate
+    schema.activate(
+        pca_schema_name,
+        create_schema=create_schema,
+        create_tables=create_tables,
+        add_objects=_linking_module.__dict__,
+    )
+
+
+# -------------- Functions required by the element-moseq ---------------
+
+
+def get_kpms_root_data_dir() -> list:
+    """Pulls relevant func from parent namespace to specify root data dir(s).
+
+    It is recommended that all paths in DataJoint Elements stored as relative
+    paths, with respect to some user-configured "root" director(y/ies). The
+    root(s) may vary between data modalities and user machines. Returns a full path
+    string or list of strings for possible root data directories.
+    """
+    root_directories = _linking_module.get_kpms_root_data_dir()
+    if isinstance(root_directories, (str, Path)):
+        root_directories = [root_directories]
+
+    if (
+        hasattr(_linking_module, "get_kpms_processed_data_dir")
+        and get_kpms_processed_data_dir() not in root_directories
+    ):
+        root_directories.append(_linking_module.get_kpms_processed_data_dir())
+
+    return root_directories
+
+
+def get_kpms_processed_data_dir() -> Optional[str]:
+    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
+
+    Method in parent namespace should provide a string to a directory where KPMS output
+    files will be stored. If unspecified, output files will be stored in the
+    session directory 'videos' folder, per DeepLabCut default.
+    """
+    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
+        return _linking_module.get_kpms_processed_data_dir()
+    else:
+        return None
+
+
+# ----------------------------- Table declarations ----------------------
+
+@schema
+class PoseEstimationMethod(dj.Lookup):
+    """Table for storing the pose estimation method used to obtain the keypoints data.
+
+    Attributes:
+        format (str)                : Pose estimation method.
+        pose_estimation_desc (str)  : Pose estimation method description.
+    """
+
+    definition = """ 
+    # Parameters used to obtain the keypoints data based on a specific pose estimation method.        
+    format                          : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
+    ---
+    pose_estimation_desc            : varchar(1000)    # Optional. Pose estimation method description
+    """
+
+    contents = [
+        ["deeplabcut", "`.csv` and `.h5/.hdf5` files generated by DeepLabcut analysis"],
+        ["sleap", "`.slp` and `.h5/.hdf5` files generated by SLEAP analysis"],
+        ["anipose", "`.csv` files generated by anipose analysis"],
+        ["sleap-anipose", "`.h5/.hdf5` files generated by sleap-anipose analysis"],
+        ["nwb", "`.nwb` files with Neurodata Without Borders (NWB) format"],
+        ["facemap", "`.h5` files generated by Facemap analysis"],
+    ]
+
+
+@schema
+class KeypointSet(dj.Manual):
+    """Table for storing the keypoint sets and their associated videos.
+
+    Attributes:
+        kpset_id (int): Unique ID for each keypoint set.
+        kpset_config_path (str): Path relative to root data directory where the config file is located.
+        kpset_videos_path (str): Path relative to root data directory where the videos and their keypoints are located.
+        kpset_description (str): Optional. User-entered description.
+    """
+
+    definition = """
+    -> Session
+    kpset_id                        : int
+    ---
+    kpset_method                    : varchar(15)   # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
+    kpset_config_path               : varchar(255)  # Path relative to root data directory where the config file is located
+    kpset_videos_path               : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
+    kpset_description=''            : varchar(300)  # Optional. User-entered description
+
+    """
+
+    class VideoFiles(dj.Part):
+        """IDs and file paths of each video file.
+
+        Atribbutes:
+            video_id (int): Unique ID for each video.
+            video_path (str): Filepath of each video, relative to root data directory.
+        """
+
+        definition = """
+        -> master
+        video_id                    : int
+        ---
+        video_path                  : varchar(255) # Filepath of each video, relative to root data directory
+        """
+
+
+@schema
+class RecordingInfo(dj.Imported):
+    """Automated table to store video metadata.
+
+    Attributes:
+        KeypointSet.VideoFiles (foreign key)    : Unique ID for each video.
+        px_height (smallint)                    : Height in pixels.
+        px_width (smallint)                     : Width in pixels.
+        nframes (int)                           : Number of frames.
+        fps (int)                               : Optional. Frames per second, Hz.
+        recording_datetime (datetime)           : Optional. Datetime for the start of recording.
+        recording_duration (float)              : Video duration (s) from nframes / fps.
+    """
+
+    definition = """
+    -> KeypointSet.VideoFiles
+    ---
+    px_height                 : smallint  # Height in pixels
+    px_width                  : smallint  # Width in pixels
+    nframes                   : int       # Number of frames 
+    fps = NULL                : int       # Optional. Frames per second, Hz
+    recording_datetime = NULL : datetime  # Optional. Datetime for the start of the recording
+    recording_duration        : float     # Video duration (s) from nframes / fps
+    """
+
+    @property
+    def key_source(self):
+        """Defines order of keys for the make function when called via `populate()`"""
+        return KeypointSet & KeypointSet.VideoFiles
+
+    def make(self, key):
+        """
+        Make function to populate the RecordingInfo table.
+
+        Args:
+            key (dict): Primary key from the RecordingInfo table.
+
+        Returns:
+            dict: Primary key and attributes for the RecordingInfo table.
+
+        Raises:
+        High-Level Logic:
+        1. Fetches the file paths and video IDs from the KeypointSet.VideoFiles table.
+        2. Iterates through the file paths and video IDs to obtain the video metadata using OpenCV.
+        3. Inserts the video metadata into the RecordingInfo table.
+
+        """
+
+        file_paths, video_ids = (KeypointSet.VideoFiles & key).fetch(
+            "video_path", "video_id"
+        )
+
+        for fp, video_id in zip(file_paths, video_ids):
+            nframes = 0
+            px_height, px_width, fps = None, None, None
+
+            file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
+
+            cap = cv2.VideoCapture(file_path)
+            info = (
+                int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
+                int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                int(cap.get(cv2.CAP_PROP_FPS)),
+            )
+            if px_height is not None:
+                assert (px_height, px_width, fps) == info
+            px_height, px_width, fps = info
+            nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            cap.release()
+
+            self.insert1(
+                {
+                    **key,
+                    "video_id": video_id,
+                    "px_height": px_height,
+                    "px_width": px_width,
+                    "nframes": nframes,
+                    "fps": fps,
+                    "recording_duration": nframes / fps,
+                }
+            )
+
+
+@schema
+class Bodyparts(dj.Manual):
+    """Table for storing the bodyparts used in the analysis.
+
+    Attributes:
+        KeypointSet (foreign key)       : Unique ID for each keypoint set.
+        bodyparts_id (int)              : Unique ID for each bodypart.
+        anterior_bodyparts (longblob)   : List of strings of anterior bodyparts
+        posterior_bodyparts (longblob)  : List of strings of posterior bodyparts
+        use_bodyparts (longblob)        : List of strings of bodyparts to be used
+    """
+
+    definition = """
+    -> KeypointSet
+    bodyparts_id                : int
+    ---
+    anterior_bodyparts          : blob  # List of strings of anterior bodyparts
+    posterior_bodyparts         : blob  # List of strings of posterior bodyparts
+    use_bodyparts               : blob  # List of strings of bodyparts to be used
+    """
+
+
+@schema
+class PCATask(dj.Manual):
+    """
+    Table to define the PCA task.
+
+    Attributes:
+        KeypointSet (foreign key)       : Unique ID for each keypoint set.
+        Bodyparts (foreign key)         : Unique ID for each bodypart.
+        pca_task_id (int)               : Unique ID for each PCA task.
+        project_path (str)              : KPMS's project_path in config relative to root
+        task_mode (str)                 : 'load': load computed analysis results, 'trigger': trigger computation
+    """
+
+    definition = """ 
+    -> KeypointSet
+    -> Bodyparts
+    pca_task_id: int
+    ---
+    project_path='' : varchar(255)             # KPMS's project_path in config relative to root
+    task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
+    """
+
+@schema
+class FormattedDataset(dj.Imported):
+    """
+    Table for storing the formatted dataset.
+    """
+
+    definition = """
+    -> PCATask
+    ---
+    config                  : longblob # stored full config file
+    coordinates             : longblob
+    confidences             : longblob             
+    bodyparts               : longblob
+    data                    : longblob
+    metadata                : longblob
+    """
+
+    def make(self, key):
+        """
+        Make function to format keypoint coordinates and confidences for inference.
+        
+        Args:
+            key (dict): Primary key from the PCATask table.
+
+        Returns:
+            dict: Primary key and attributes for the PCATask table.
+
+        Raises:
+        
+        High-Level Logic:
+        
+        """
+
+        anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
+        Bodyparts & key
+        ).fetch1(
+            "anterior_bodyparts",
+            "posterior_bodyparts",
+            "use_bodyparts",
+        )
+        project_path = (PCATask & key).fetch1("project_path")
+        task_mode = (PCATask & key).fetch1("task_mode")
+        method = (KeypointSet & key).fetch1("kpset_method")
+        format = (PoseEstimationMethod & {'format':method}).fetch1("format")
+        kpset_config_path, kpset_videos_path = (KeypointSet & key).fetch1("kpset_config_path","kpset_videos_path")
+            
+        from keypoint_moseq import (setup_project,
+                                    load_config,
+                                    check_config_validity,
+                                    update_config,
+                                    load_keypoints,
+                                    format_data)   
+        
+        if task_mode == "trigger":
+            config = setup_project(
+                    project_path, deeplabcut_config=kpset_config_path
+                ) # setup a project directory for deeplabcut, sleap or nwb config, and generate a `config.yml` file with project settings. Overwrite by default is false. If the project dir already exists, pick a different project dir name. 
+
+
+        # TO-DO: Here there should be the creation of a DJ_config file to update the new bodyparts
+        elif task_mode == "load":
+            config_kwargs_dict = dict(
+            video_dir = kpset_videos_path,
+            anterior_bodyparts = anterior_bodyparts,
+            posterior_bodyparts = posterior_bodyparts,
+            use_bodyparts = use_bodyparts)
+            
+        ## The following function `update_config` does the following: (1) config = load_config(...), (2) config.update(kwargs), (3) generate_config(project_dir, **config)
+            update_config(
+            project_path,
+            **config_kwargs_dict
+        )
+
+        # To check load_config, this is the function:
+        # check_if_valid = True
+        # build_indexes = True
+        # config_path = os.path.join(project_path, "config.yml")
+
+        ## Proposal: Instead of updating the new bodyparts in the original kpms_config file, the previous function will be splitted to create a new config file instead named `dj_config.yml` with the new bodyparts
+            # config=load_config(project_path, check_if_valid=False, build_indexes=False)
+            # config.update(config_kwargs_dict)
+            # generate a new config file, not overwriting the original config file
+
+
+        # load data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
+        coordinates, confidences, bodyparts = load_keypoints(
+            filepath_pattern=kpset_videos_path, format=format
+        )
+        
+        # coordinates: dict
+        #     Dictionary mapping filenames to keypoint coordinates as ndarrays of
+        #     shape (n_frames, n_bodyparts, 2[or 3])
+
+        # confidences: dict
+        #     Dictionary mapping filenames to `likelihood` scores as ndarrays of
+        #     shape (n_frames, n_bodyparts)
+
+        # bodyparts: list of str
+        #     List of bodypart names. The order of the names matches the order of the
+        #     bodyparts in `coordinates` and `confidences`.
+
+        # formatted_bodyparts is not necessarily the same as use_bodyparts
+
+
+
+        # format data for modeling
+        # Data are transformed as follows:
+        #     1. Coordinates and confidences are each merged into a single array
+        #        using :py:func:`keypoint_moseq.util.batch`. Each row of the merged
+        #        arrays is a segment from one recording.
+        #     2. The keypoints axis is reindexed according to the order of elements
+        #        in `use_bodyparts` with respect to their initial orer in
+        #        `bodyparts`.
+        #     3. Uniform noise proportional to `added_noise_level` is added to the
+        #        keypoint coordinates to prevent degenerate solutions during fitting.
+        #     4. Keypoint confidences are augmented by `conf_pseudocount`.
+        #     5. Wherever NaNs occur in the coordinates, they are replaced by values
+        #        imputed using linear interpolation, and the corresponding
+        #        confidences are set to `conf_pseudocount`.
+
+        # TO-DO: fix issue with the config["anterior_idxs"] since they are iterated as characters, not words, and rise an error: ` 'n' is not in list`
+        data, metadata = format_data(
+            **config, coordinates=coordinates, confidences=confidences
+        )
+        # data: dict with the following items
+        #     Y: jax array with shape (n_segs, seg_length, K, D)
+        #         Keypoint coordinates from all recordings broken into fixed-length segments.
+
+        #     conf: jax array with shape (n_segs, seg_length, K)
+        #         Confidences from all recordings broken into fixed-length segments. If no input is provided for confidences, then data["conf"]=None.
+
+        #     mask: jax array with shape (n_segs, seg_length)
+        #         Binary array where 0 indicates areas of padding
+        #         (see keypoint_moseq.util.batch).
+
+        # metadata: tuple (keys, bounds)
+        #     Metadata for the rows of Y, conf and mask, as a tuple with a array of recording names and an array of (start,end) times. See
+        #     jax_moseq.utils.batch for details.
+
+
+        # TO-DO: store data and metadata in files (Not allowed to store this jax data type as longblob in the table)
+
+        self.FormattedDataset.insert1(
+            dict(
+                **key,
+                config=config,
+                coordinates=coordinates,
+                confidences=confidences,
+                bodyparts=bodyparts,
+                # data=data, -----> should be saved as a file
+                # metadata=metadata,-----> should be saved as a file
+            )
+        )
+
+
+@schema
+class PCAFitting(dj.Computed):
+    definition = """
+    -> FormattedDataset
+    pca_fitting_id       : int 
+    ---
+    pca_fitting_time     : datetime  # Time of generation of the PCA fitting analysis 
+    pca                  : longblob
+    """
+
+    def make(self, key):
+        from keypoint_moseq import (
+            load_pca,
+            fit_pca,
+            save_pca,
+            print_dims_to_explain_variance,
+            plot_scree,
+            plot_pcs,
+        )
+
+        task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
+        config = (PCATask.FormattedDataset & key).fetch1( "config")
+
+        # data = ---> Load data from the file saved in the previous table
+        
+        project_path = find_full_path(get_kpms_root_data_dir(), project_path)
+
+        if task_mode == "load":
+            pca = load_pca(**data, **config())
+
+        elif task_mode == "trigger":
+            pca = fit_pca(**data, **config())
+            save_pca(pca, project_path)
+            creation_time = datetime.strftime("%Y-%m-%d %H:%M:%S")
+
+        print_dims_to_explain_variance(pca, 0.9)
+        plot_scree(pca, project_dir=project_path)
+        plot_pcs(pca, project_dir=project_path, **config())
+
+        self.insert1(**key, pca_fitting_time=creation_time, pca=pca)
+
+
+@schema
+class LatentDimension(dj.Lookup):
+    definition = """
+    latent_dim                : int
+    ---
+    latent_dim_description='' : varchar(1000)
+    """
+
+
+@schema
+class UpdateLatentDimension(dj.Computed):
+    definition = """
+    -> PCAFitting
+    -> LatentDimension
+    """
+
+    def make(self, key):
+        # update latent_dim in config_file
+        from keypoint_moseq import update_config
+
+        project_path = (PCATask & key).fetch1("project_path")
+        latent_dim = (LatentDimension & key).fetch1("latent_dim")
+        update_config(project_path, latent_dim=latent_dim)
diff --git a/element_moseq/pca.py b/element_moseq/pca.py
deleted file mode 100644
index c83137e..0000000
--- a/element_moseq/pca.py
+++ /dev/null
@@ -1,378 +0,0 @@
-import datajoint as dj
-import matplotlib.pyplot as plt
-import cv2
-from typing import Optional
-import numpy as np
-from datetime import datetime
-
-import inspect
-import importlib
-import os
-from pathlib import Path
-from element_interface.utils import find_full_path, dict_to_uuid
-
-schema = dj.schema()
-_linking_module = None
-
-
-def activate(
-    pca_schema_name: str,
-    *,
-    create_schema: bool = True,
-    create_tables: bool = True,
-    linking_module: str = None,
-):
-    """Activate this schema.
-
-    Args:
-        pca_schema_name (str): schema name on the database server
-        create_schema (bool): when True (default), create schema in the database if it
-                            does not yet exist.
-        create_tables (bool): when True (default), create schema tables in the database
-                             if they do not yet exist.
-        linking_module (str): a module (or name) containing the required dependencies.
-
-    Dependencies:
-    Functions:
-        get_kpms_root_data_dir(): Returns absolute path for root data director(y/ies)
-                                 with all behavioral recordings, as (list of) string(s).
-        get_kpms_processed_data_dir(): Optional. Returns absolute path for processed
-                                      data. Defaults to session video subfolder.
-    """
-
-    if isinstance(linking_module, str):
-        linking_module = importlib.import_module(linking_module)
-    assert inspect.ismodule(
-        linking_module
-    ), "The argument 'dependency' must be a module's name or a module"
-    assert hasattr(
-        linking_module, "get_kpms_root_data_dir"
-    ), "The linking module must specify a lookup function for a root data directory"
-
-    global _linking_module
-    _linking_module = linking_module
-
-    # activate
-    schema.activate(
-        pca_schema_name,
-        create_schema=create_schema,
-        create_tables=create_tables,
-        add_objects=_linking_module.__dict__,
-    )
-
-
-# -------------- Functions required by element-moseq ---------------
-
-
-def get_kpms_root_data_dir() -> list:
-    """Pulls relevant func from parent namespace to specify root data dir(s).
-
-    It is recommended that all paths in DataJoint Elements stored as relative
-    paths, with respect to some user-configured "root" director(y/ies). The
-    root(s) may vary between data modalities and user machines. Returns a full path
-    string or list of strings for possible root data directories.
-    """
-    root_directories = _linking_module.get_kpms_root_data_dir()
-    if isinstance(root_directories, (str, Path)):
-        root_directories = [root_directories]
-
-    if (
-        hasattr(_linking_module, "get_kpms_processed_data_dir")
-        and get_kpms_processed_data_dir() not in root_directories
-    ):
-        root_directories.append(_linking_module.get_kpms_processed_data_dir())
-
-    return root_directories
-
-
-def get_kpms_processed_data_dir() -> Optional[str]:
-    """Pulls relevant func from parent namespace. Defaults to KPMS's project /videos/.
-
-    Method in parent namespace should provide a string to a directory where KPMS output
-    files will be stored. If unspecified, output files will be stored in the
-    session directory 'videos' folder, per DeepLabCut default.
-    """
-    if hasattr(_linking_module, "get_kpms_processed_data_dir"):
-        return _linking_module.get_kpms_processed_data_dir()
-    else:
-        return None
-
-
-# ----------------------------- Table declarations ----------------------
-
-
-@schema
-class KeypointSet(dj.Manual):
-    definition = """
-    -> Session
-    kpset_id                : int
-    ---
-    kpset_path              : varchar(255)  #Path relative to root data directory where the videos and their keypoints are located.
-    kpset_description=''    : varchar(300)  # Optional. User-entered description.
-
-    """
-
-    class VideoFiles(dj.Part):
-        """IDs and file paths of each video file."""
-
-        definition = """
-        -> master
-        video_id        : int
-        ---
-        video_path      : varchar(255) # Filepath of each video, relative to root data directory
-        """
-
-    class PoseEstimationMethod(dj.Part):
-        definition = """ 
-        # Parameters used to obtain the keypoints data based on a specific pose estimation method        
-        -> master
-        ---
-        format                       : varchar(20)   # deeplabcut, sleap
-        extension                    : varchar(20)   # h5, csv
-        -> Device
-        """
-
-
-@schema
-class RecordingInfo(dj.Imported):
-    """Automated table with video file metadata.
-
-    Attributes:
-        px_height (smallint): Height in pixels.
-        px_width (smallint): Width in pixels.
-        nframes (int): Number of frames.
-        fps (int): Optional. Frames per second, Hz.
-        recording_datetime (datetime): Optional. Datetime for the start of recording.
-        recording_duration (float): video duration (s) from nframes / fps."""
-
-    definition = """
-    -> KeypointSet.VideoFiles
-    ---
-    px_height                 : smallint  # height in pixels
-    px_width                  : smallint  # width in pixels
-    nframes                   : int  # number of frames 
-    fps = NULL                : int       # (Hz) frames per second
-    recording_datetime = NULL : datetime  # Datetime for the start of the recording
-    recording_duration        : float     # video duration (s) from nframes / fps
-    """
-
-    @property
-    def key_source(self):
-        """Defines order of keys for make function when called via `populate()`"""
-        return KeypointSet & KeypointSet.VideoFiles
-
-    def make(self, key):
-        """Populates table with video metadata using CV2."""
-
-        file_paths,video_ids = (KeypointSet.VideoFiles & key).fetch("video_path","video_id")
-
-        for fp, video_id in zip(file_paths,video_ids):
-            nframes = 0
-            px_height, px_width, fps = None, None, None
-
-            file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
-
-            cap = cv2.VideoCapture(file_path)
-            info = (
-                int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-                int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
-                int(cap.get(cv2.CAP_PROP_FPS)),
-            )
-            if px_height is not None:
-                assert (px_height, px_width, fps) == info
-            px_height, px_width, fps = info
-            nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-            cap.release()
-
-            self.insert1(
-                {
-                    **key,
-                    "video_id": video_id,
-                    "px_height": px_height,
-                    "px_width": px_width,
-                    "nframes": nframes,
-                    "fps": fps,
-                    "recording_duration": nframes / fps,
-                }
-            )
-
-
-@schema
-class Bodyparts(dj.Manual):
-    """Body parts to use in the model
-
-    Attributes:
-        anterior_bodyparts(longblob): list of strings of anterior bodyparts
-        posterior_bodyparts(longblob): list of strings of posterior bodyparts
-        use_bodyparts(longblob): list of strings of bodyparts to be used
-    """
-
-    definition = """
-    -> KeypointSet
-    bodyparts_id              : int
-    ---
-    anterior_bodyparts          : blob  # list of strings of anterior bodyparts
-    posterior_bodyparts         : blob  # list of strings of posterior bodyparts
-    use_bodyparts               : blob  # list of strings of bodyparts to be used
-    """
-
-
-@schema
-class PCATask(dj.Manual):
-    definition = """# Manual table for defining a data loading task ready to be run
-    -> KeypointSet
-    -> Bodyparts
-    ---
-    project_path=''             : varchar(255)              # KPMS's project_path in config relative to root
-    task_mode='load'            : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
-    """
-
-    class FormattedDataset(dj.Part):
-        definition = """
-        -> master
-        ---
-        config                  : longblob # stored full config file
-        coordinates             : longblob
-        confidences             : longblob             
-        bodyparts               : longblob
-        data                    : longblob
-        metadata                : longblob
-        """
-
-    @classmethod
-    def generate(cls, key):
-        # kpms bodyparts
-        anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
-            Bodyparts & key
-        ).fetch1(
-            "anterior_bodyparts",
-            "posterior_bodyparts",
-            "use_bodyparts",
-        )
-
-        # kpms project path
-        project_path = get_kpms_root_data_dir()
-
-        task_mode = (PCATask & key).fetch1("task_mode")
-
-        # pose estimation params
-        format, extension = (KeypointSet.PoseEstimationMethod & key).fetch1(
-            "format", "extension"
-        )
-
-        video_path = (KeypointSet.VideoFiles & key).fetch("video_path")
-
-        kpset_path = (KeypointSet & key).fetch1("kpset_path")
-        kpset_path = find_full_path(get_kpms_root_data_dir(), kpset_path)
-
-        import keypoint_moseq as kpms
-
-        # define config file for kpms with anonymous function
-        kpms_config = lambda: kpms.load_config(project_path)
-
-        # setup kpms project, create a new project dir and config.yml
-        if task_mode == "trigger":
-            if format == "deeplabcut":
-                kpms.setup_project(
-                    project_path, deeplabcut_config=kpset_path.as_posix()
-                )
-            else:
-                kpms.setup_project(project_path)
-
-        # elif task_mode == "load":
-
-        # update kpms config file
-        kpms.update_config(
-            project_path,
-            video_dir=video_path,
-            anterior_bodyparts=anterior_bodyparts,
-            posterior_bodyparts=posterior_bodyparts,
-            use_bodyparts=use_bodyparts,
-        )
-
-        # load data (e.g. from DeepLabCut)
-        coordinates, confidences, bodyparts = kpms.load_keypoints(
-            filepath_pattern=video_path, format=format, extension=extension
-        )
-
-        # format data for modeling
-        data, metadata = kpms.format_data(coordinates, confidences, **kpms_config())
-
-        cls.insert1(
-            dict(
-                **key,
-                config=kpms_config(),
-                coordinates=coordinates,
-                confidences=confidences,
-                bodyparts=bodyparts,
-                data=data,
-                metadata=metadata,
-            )
-        )
-
-    auto_generate_entries = generate
-
-
-@schema
-class PCAFitting(dj.Computed):
-    definition = """
-    -> PCATask
-    ---
-    pca_fitting_time     : datetime  # Time of generation of the PCA fitting analysis 
-    pca                  : longblob
-    """
-
-    def make(self, key):
-        from keypoint_moseq import (
-            load_pca,
-            fit_pca,
-            save_pca,
-            print_dims_to_explain_variance,
-            plot_scree,
-            plot_pcs,
-        )
-
-        task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
-        data, config = (PCATask.FormattedDataset & key).fetch1("data", "config")
-
-        project_path = find_full_path(get_kpms_root_data_dir(), project_path)
-
-        if task_mode == "load":
-            pca = load_pca(**data, **config())
-
-        elif task_mode == "trigger":
-            pca = fit_pca(**data, **config())
-            save_pca(pca, project_path)
-            creation_time = datetime.strftime("%Y-%m-%d %H:%M:%S")
-
-        print_dims_to_explain_variance(pca, 0.9)
-        plot_scree(pca, project_dir=project_path)
-        plot_pcs(pca, project_dir=project_path, **config())
-
-        self.insert1(**key, pca_fitting_time=creation_time, pca=pca)
-
-
-@schema
-class LatentDimension(dj.Lookup):
-    definition = """
-    latent_dim                : int
-    ---
-    latent_dim_description='' : varchar(1000)
-    """
-
-
-@schema
-class UpdateLatentDimension(dj.Computed):
-    definition = """
-    -> PCAFitting
-    -> PCATask
-    -> LatentDimension
-    """
-
-    def make(self, key):
-        # update latent_dim in config_file
-        from keypoint_moseq import update_config
-
-        project_path = (PCATask & key).fetch1("project_path")
-        latent_dim = (LatentDimension & key).fetch1("latent_dim")
-        update_config(project_path, latent_dim=latent_dim)

From 8af783ab94046303b8681359480ed50b04c7bd39 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 28 Feb 2024 03:06:20 +0100
Subject: [PATCH 37/90] update `tutorial_pipeline.py` w new schema names

---
 notebooks/tutorial_pipeline.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py
index aa48271..0ffc7af 100644
--- a/notebooks/tutorial_pipeline.py
+++ b/notebooks/tutorial_pipeline.py
@@ -4,7 +4,7 @@
 from element_animal import subject
 from element_session import session_with_datetime as session
 
-from element_moseq import pca, model
+from element_moseq import kpms_pca, kpms_model
 
 from element_animal.subject import Subject
 from element_lab.lab import Source, Lab, Protocol, User, Project
@@ -91,5 +91,5 @@ class Device(dj.Lookup):
 
 # Activate Keypoint-MoSeq schema -----------------------------------
 
-pca.activate(db_prefix + "pca", linking_module=__name__)
-model.activate(db_prefix + "model", linking_module=__name__)
+kpms_pca.activate(db_prefix + "kpms_pca", linking_module=__name__)
+kpms_model.activate(db_prefix + "kpms_model", linking_module=__name__)

From c18000fe61c05d2a92488821c650c68163963710 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 04:01:37 +0100
Subject: [PATCH 38/90] Create functions in `readers/kpms_reader.py`

---
 element_moseq/readers/kpms_reader.py | 159 +++++++++++++++++++++++----
 1 file changed, 139 insertions(+), 20 deletions(-)

diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py
index d38b332..01948fd 100644
--- a/element_moseq/readers/kpms_reader.py
+++ b/element_moseq/readers/kpms_reader.py
@@ -1,26 +1,145 @@
-import re
+import os
 import logging
-import numpy as np
-import pandas as pd
-from pathlib import Path
-import pickle
-from ruamel.yaml import YAML
+import yaml
+# from ruamel.yaml import YAML
 from element_interface.utils import find_root_directory, dict_to_uuid
-from .. import model
-from ..model import get_dlc_root_data_dir
 from datajoint.errors import DataJointError
 
 logger = logging.getLogger("datajoint")
 
-# def read_yaml(fullpath: str, filename: str = '*') -> tuple:
-#     """ Return contents of yaml in fullpath. If available, defer to DJ-saved version
-    
-#     Args: 
-#         fullpath (str): String or pathlib path. Directory with yaml files
-#         filename (str, optional): Filename, no extension. 
-    
-#     Returns:
-#         Tuple of (a) filepath as pathlib.PosixPath and (b) file contents as dict    
-#     """
-    
-    
\ No newline at end of file
+def _build_yaml(sections, comments):
+    text_blocks = []
+    for title, data in sections:
+        centered_title = f" {title} ".center(50, "=")
+        text_blocks.append(f"\n\n{'#'}{centered_title}{'#'}")
+        for key, value in data.items():
+            text = yaml.dump({key: value}).strip("\n")
+            if key in comments:
+                text = f"\n{'#'} {comments[key]}\n{text}"
+            text_blocks.append(text)
+    return "\n".join(text_blocks)
+
+
+def generate_dj_config(project_dir, **kwargs):
+    """Generate a `dj_config.yml` file with project settings. Default settings
+    will be used unless overriden by a keyword argument.
+
+    Parameters
+    ----------
+    project_dir: str
+        A file `dj_config.yml` will be generated in this directory.
+
+    kwargs
+        Custom project settings.
+    """
+
+    def _update_dict(new, original):
+        return {k: new[k] if k in new else v for k, v in original.items()}
+
+    hypperams = _update_dict(
+        kwargs,
+        {
+            "error_estimator": {"slope": -0.5, "intercept": 0.25},
+            "obs_hypparams": {
+                "sigmasq_0": 0.1,
+                "sigmasq_C": 0.1,
+                "nu_sigma": 1e5,
+                "nu_s": 5,
+            },
+            "ar_hypparams": {
+                "latent_dim": 10,
+                "nlags": 3,
+                "S_0_scale": 0.01,
+                "K_0_scale": 10.0,
+            },
+            "trans_hypparams": {
+                "num_states": 100,
+                "gamma": 1e3,
+                "alpha": 5.7,
+                "kappa": 1e6,
+            },
+            "cen_hypparams": {"sigmasq_loc": 0.5},
+        },
+    )
+
+    hypperams = {k: _update_dict(kwargs, v) for k, v in hypperams.items()}
+
+    anatomy = _update_dict(
+        kwargs,
+        {
+            "bodyparts": ["BODYPART1", "BODYPART2", "BODYPART3"],
+            "use_bodyparts": ["BODYPART1", "BODYPART2", "BODYPART3"],
+            "skeleton": [
+                ["BODYPART1", "BODYPART2"],
+                ["BODYPART2", "BODYPART3"],
+            ],
+            "anterior_bodyparts": ["BODYPART1"],
+            "posterior_bodyparts": ["BODYPART3"],
+        },
+    )
+
+    other = _update_dict(
+        kwargs,
+        {
+            "recording_name_suffix": "",
+            "verbose": False,
+            "conf_pseudocount": 1e-3,
+            "video_dir": "",
+            "keypoint_colormap": "autumn",
+            "whiten": True,
+            "fix_heading": False,
+            "seg_length": 10000,
+        },
+    )
+
+    fitting = _update_dict(
+        kwargs,
+        {
+            "added_noise_level": 0.1,
+            "PCA_fitting_num_frames": 1000000,
+            "conf_threshold": 0.5,
+            #         'kappa_scan_target_duration': 12,
+            #         'kappa_scan_min': 1e2,
+            #         'kappa_scan_max': 1e12,
+            #         'num_arhmm_scan_iters': 50,
+            #         'num_arhmm_final_iters': 200,
+            #         'num_kpslds_scan_iters': 50,
+            #         'num_kpslds_final_iters': 500
+        },
+    )
+
+    comments = {
+        "verbose": "whether to print progress messages during fitting",
+        "keypoint_colormap": "colormap used for visualization; see `matplotlib.cm.get_cmap` for options",
+        "added_noise_level": "upper bound of uniform noise added to the data during initial AR-HMM fitting; this is used to regularize the model",
+        "PCA_fitting_num_frames": "number of frames used to fit the PCA model during initialization",
+        "video_dir": "directory with videos from which keypoints were derived (used for crowd movies)",
+        "recording_name_suffix": "suffix used to match videos to recording names; this can usually be left empty (see `util.find_matching_videos` for details)",
+        "bodyparts": "used to access columns in the keypoint data",
+        "skeleton": "used for visualization only",
+        "use_bodyparts": "determines the subset of bodyparts to use for modeling and the order in which they are represented",
+        "anterior_bodyparts": "used to initialize heading",
+        "posterior_bodyparts": "used to initialize heading",
+        "seg_length": "data are broken up into segments to parallelize fitting",
+        "trans_hypparams": "transition hyperparameters",
+        "ar_hypparams": "autoregressive hyperparameters",
+        "obs_hypparams": "keypoint observation hyperparameters",
+        "cen_hypparams": "centroid movement hyperparameters",
+        "error_estimator": "parameters to convert neural net likelihoods to error size priors",
+        "save_every_n_iters": "frequency for saving model snapshots during fitting; if 0 only final state is saved",
+        "kappa_scan_target_duration": "target median syllable duration (in frames) for choosing kappa",
+        "whiten": "whether to whiten principal components; used to initialize the latent pose trajectory `x`",
+        "conf_threshold": "used to define outliers for interpolation when the model is initialized",
+        "conf_pseudocount": "pseudocount used regularize neural network confidences",
+        "fix_heading": "whether to keep the heading angle fixed; this should only be True if the pose is constrained to a narrow range of angles, e.g. a headfixed mouse.",
+    }
+
+    sections = [
+        ("ANATOMY", anatomy),
+        ("FITTING", fitting),
+        ("HYPER PARAMS", hypperams),
+        ("OTHER", other),
+    ]
+
+    with open(os.path.join(project_dir, "dj_config.yml"), "w") as f:
+        f.write(_build_yaml(sections, comments))

From 7d7ad374cf91073cc33844d615418bca786ab399 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 04:02:34 +0100
Subject: [PATCH 39/90] first draft of `element_moseq/kpms_pca.py`

---
 element_moseq/kpms_pca.py | 236 +++++++++++++++++---------------------
 1 file changed, 105 insertions(+), 131 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 97a68a3..40290ea 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -4,13 +4,25 @@
 from typing import Optional
 import numpy as np
 from datetime import datetime
-
 import inspect
 import importlib
 import os
+import yaml 
 from pathlib import Path
-from element_interface.utils import find_full_path, dict_to_uuid
+from element_interface.utils import find_full_path
+from .readers.kpms_reader import generate_dj_config
+from keypoint_moseq import (
+            setup_project,
+            load_config,
+            load_keypoints,
+            format_data,
+            load_pca,
+            fit_pca,
+            save_pca,
+            check_config_validity
+        )
 
+        
 schema = dj.schema()
 _linking_module = None
 
@@ -286,23 +298,21 @@ class PCATask(dj.Manual):
     ---
     project_path='' : varchar(255)             # KPMS's project_path in config relative to root
     task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
+    variance_threshold : float                 # Variance threshold to be explained by the PCA model
     """
 
 @schema
-class FormattedDataset(dj.Imported):
+class FormattedDataset(dj.Imported): # --> TO-DO: change name for a more intuitive option
     """
-    Table for storing the formatted dataset.
+    Table for storing the formatted dataset and update the config.yml by creating a new dj_config.yml in the project path (output_dir)
     """
 
     definition = """
     -> PCATask
     ---
-    config                  : longblob # stored full config file
     coordinates             : longblob
     confidences             : longblob             
-    bodyparts               : longblob
-    data                    : longblob
-    metadata                : longblob
+    formatted_bodyparts     : longblob
     """
 
     def make(self, key):
@@ -333,21 +343,12 @@ def make(self, key):
         method = (KeypointSet & key).fetch1("kpset_method")
         format = (PoseEstimationMethod & {'format':method}).fetch1("format")
         kpset_config_path, kpset_videos_path = (KeypointSet & key).fetch1("kpset_config_path","kpset_videos_path")
-            
-        from keypoint_moseq import (setup_project,
-                                    load_config,
-                                    check_config_validity,
-                                    update_config,
-                                    load_keypoints,
-                                    format_data)   
         
         if task_mode == "trigger":
             config = setup_project(
                     project_path, deeplabcut_config=kpset_config_path
-                ) # setup a project directory for deeplabcut, sleap or nwb config, and generate a `config.yml` file with project settings. Overwrite by default is false. If the project dir already exists, pick a different project dir name. 
-
+                ) 
 
-        # TO-DO: Here there should be the creation of a DJ_config file to update the new bodyparts
         elif task_mode == "load":
             config_kwargs_dict = dict(
             video_dir = kpset_videos_path,
@@ -355,156 +356,129 @@ def make(self, key):
             posterior_bodyparts = posterior_bodyparts,
             use_bodyparts = use_bodyparts)
             
-        ## The following function `update_config` does the following: (1) config = load_config(...), (2) config.update(kwargs), (3) generate_config(project_dir, **config)
-            update_config(
-            project_path,
-            **config_kwargs_dict
-        )
-
-        # To check load_config, this is the function:
-        # check_if_valid = True
-        # build_indexes = True
-        # config_path = os.path.join(project_path, "config.yml")
-
-        ## Proposal: Instead of updating the new bodyparts in the original kpms_config file, the previous function will be splitted to create a new config file instead named `dj_config.yml` with the new bodyparts
-            # config=load_config(project_path, check_if_valid=False, build_indexes=False)
-            # config.update(config_kwargs_dict)
-            # generate a new config file, not overwriting the original config file
-
+            # Update the config with new video_dir and bodyparts and save it as `dj_config.yml`        
+            config = load_config(project_path, check_if_valid=True, build_indexes=False)
+            config.update(**config_kwargs_dict)
+            generate_dj_config(project_path, **config)
 
-        # load data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
-        coordinates, confidences, bodyparts = load_keypoints(
+        # load keypoints data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
+        coordinates, confidences, formatted_bodyparts = load_keypoints(
             filepath_pattern=kpset_videos_path, format=format
         )
         
-        # coordinates: dict
-        #     Dictionary mapping filenames to keypoint coordinates as ndarrays of
-        #     shape (n_frames, n_bodyparts, 2[or 3])
-
-        # confidences: dict
-        #     Dictionary mapping filenames to `likelihood` scores as ndarrays of
-        #     shape (n_frames, n_bodyparts)
-
-        # bodyparts: list of str
-        #     List of bodypart names. The order of the names matches the order of the
-        #     bodyparts in `coordinates` and `confidences`.
-
-        # formatted_bodyparts is not necessarily the same as use_bodyparts
-
-
-
-        # format data for modeling
-        # Data are transformed as follows:
-        #     1. Coordinates and confidences are each merged into a single array
-        #        using :py:func:`keypoint_moseq.util.batch`. Each row of the merged
-        #        arrays is a segment from one recording.
-        #     2. The keypoints axis is reindexed according to the order of elements
-        #        in `use_bodyparts` with respect to their initial orer in
-        #        `bodyparts`.
-        #     3. Uniform noise proportional to `added_noise_level` is added to the
-        #        keypoint coordinates to prevent degenerate solutions during fitting.
-        #     4. Keypoint confidences are augmented by `conf_pseudocount`.
-        #     5. Wherever NaNs occur in the coordinates, they are replaced by values
-        #        imputed using linear interpolation, and the corresponding
-        #        confidences are set to `conf_pseudocount`.
-
-        # TO-DO: fix issue with the config["anterior_idxs"] since they are iterated as characters, not words, and rise an error: ` 'n' is not in list`
-        data, metadata = format_data(
-            **config, coordinates=coordinates, confidences=confidences
-        )
-        # data: dict with the following items
-        #     Y: jax array with shape (n_segs, seg_length, K, D)
-        #         Keypoint coordinates from all recordings broken into fixed-length segments.
-
-        #     conf: jax array with shape (n_segs, seg_length, K)
-        #         Confidences from all recordings broken into fixed-length segments. If no input is provided for confidences, then data["conf"]=None.
-
-        #     mask: jax array with shape (n_segs, seg_length)
-        #         Binary array where 0 indicates areas of padding
-        #         (see keypoint_moseq.util.batch).
-
-        # metadata: tuple (keys, bounds)
-        #     Metadata for the rows of Y, conf and mask, as a tuple with a array of recording names and an array of (start,end) times. See
-        #     jax_moseq.utils.batch for details.
-
-
-        # TO-DO: store data and metadata in files (Not allowed to store this jax data type as longblob in the table)
-
-        self.FormattedDataset.insert1(
+        self.insert1(
             dict(
                 **key,
-                config=config,
                 coordinates=coordinates,
                 confidences=confidences,
-                bodyparts=bodyparts,
-                # data=data, -----> should be saved as a file
-                # metadata=metadata,-----> should be saved as a file
+                formatted_bodyparts=formatted_bodyparts
             )
         )
 
 
 @schema
 class PCAFitting(dj.Computed):
+    
     definition = """
-    -> FormattedDataset
-    pca_fitting_id       : int 
+    -> PCATask
     ---
-    pca_fitting_time     : datetime  # Time of generation of the PCA fitting analysis 
-    pca                  : longblob
+    pca_fitting_time = NULL    : datetime  # Time of generation of the PCA fitting analysis 
     """
 
     def make(self, key):
-        from keypoint_moseq import (
-            load_pca,
-            fit_pca,
-            save_pca,
-            print_dims_to_explain_variance,
-            plot_scree,
-            plot_pcs,
-        )
 
         task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
-        config = (PCATask.FormattedDataset & key).fetch1( "config")
-
-        # data = ---> Load data from the file saved in the previous table
-        
-        project_path = find_full_path(get_kpms_root_data_dir(), project_path)
+        config = load_config(project_path, check_if_valid=True, build_indexes=False)
+        coordinates, confidences = (FormattedDataset & key).fetch1(
+            "coordinates", "confidences"
+        )
 
-        if task_mode == "load":
-            pca = load_pca(**data, **config())
+        data, metadata = format_data(
+            **config, coordinates=coordinates, confidences=confidences
+        )
 
-        elif task_mode == "trigger":
-            pca = fit_pca(**data, **config())
-            save_pca(pca, project_path)
+        if task_mode == "trigger":
+            pca = fit_pca(data, **config)
+            pca_path = os.path.join(project_path, "pca_{}.p".format(key["pca_fitting_id"]))
+            save_pca(pca, pca_path) # The model is saved
             creation_time = datetime.strftime("%Y-%m-%d %H:%M:%S")
 
-        print_dims_to_explain_variance(pca, 0.9)
-        plot_scree(pca, project_dir=project_path)
-        plot_pcs(pca, project_dir=project_path, **config())
-
-        self.insert1(**key, pca_fitting_time=creation_time, pca=pca)
+            self.insert1(**key, pca_fitting_time=creation_time)
 
 
 @schema
-class LatentDimension(dj.Lookup):
+class LatentDimensionSet(dj.Lookup):
+    """
+    This table is used to store the number of latent dimensions to be used in the analysis (manually inserted or computed from the variance threshold by the DimsExplainedVariance table).
+    """
     definition = """
     latent_dim                : int
     ---
     latent_dim_description='' : varchar(1000)
     """
+@schema
+class DimsExplainedVariance(dj.Computed):
+    """
+    This is an optional table to compute and store the latent dimensions that explain a certain specified variance threshold.
+    """
+    definition = """
+    -> PCATask
+    ---
+    variance_percentage     : float 
+    dims_explained_variance : int
+    """
+    
+    def make(self, key):
+        variance_threshold, project_path = (PCATask & key).fetch1("variance_threshold","project_path")
+        pca = load_pca(project_path)
+        cs = np.cumsum(pca.explained_variance_ratio_)
+        # explained_variance_ratio_ndarray of shape (n_components,)
+        # Percentage of variance explained by each of the selected components.
+        # If n_components is not set then all components are stored and the sum of the ratios is equal to 1.0.
+        if cs[-1] < variance_threshold: 
+                dims_explained_variance = len(cs)
+                variance_percentage = cs[-1]*100
+                LatentDimensionSet.insert1(dict(latent_dim = dims_explained_variance,
+                                        latent_dim_description= (f"All components together only explain {cs[-1]*100}% of variance.")))
+
+        else:
+                dims_explained_variance = (cs>variance_threshold).nonzero()[0].min()+1
+                variance_percentage = variance_threshold*100
+                LatentDimensionSet.insert1(dict(latent_dim= dims_explained_variance,
+                                        latent_dim_description= (f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components.")))
+        
+        self.insert1(dict(**key, 
+                          variance_percentage = variance_percentage,
+                          dims_explained_variance=dims_explained_variance))
 
-
+        
 @schema
-class UpdateLatentDimension(dj.Computed):
+class UpdateLatentDimensionTask(dj.Manual):
+    """
+    This table allows the user to choose between the manual or computed latent dimension from the LatentDimension table for a PCA model.
+    """
     definition = """
-    -> PCAFitting
-    -> LatentDimension
+    -> PCATask
+    latent_dim : int
     """
 
+@schema
+class UpdateLatentDimension(dj.Computed):
+    """
+    This table updates the chosen latent dimension in the UpdateLatentDimensionTask in the `dj_config.yml` file.
+    """
+    definition="""
+    ->UpdateLatentDimensionTask
+    """
+    
     def make(self, key):
-        # update latent_dim in config_file
-        from keypoint_moseq import update_config
-
         project_path = (PCATask & key).fetch1("project_path")
-        latent_dim = (LatentDimension & key).fetch1("latent_dim")
-        update_config(project_path, latent_dim=latent_dim)
+        latent_dim = (UpdateLatentDimensionTask & key).fetch1("latent_dim")
+
+        # Update the `dj_config.yml` with a possible new latent_dir    
+        config_path = os.path.join(project_path, "dj_config.yml")
+        with open(config_path, "r") as stream:
+            config = yaml.safe_load(stream)
+        check_config_validity(config)
+        config.update(dict(latent_dim = int(latent_dim)))
+        generate_dj_config(project_path, **config)

From 3ee1d09068ee292ce59803b92b8b11715627d846 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 04:07:39 +0100
Subject: [PATCH 40/90] update testing notebook

---
 notebooks/testing.ipynb | 1981 +++++++++++++++++++++++++++++++--------
 1 file changed, 1577 insertions(+), 404 deletions(-)

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index eefd845..e5e9e6d 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -9,10 +9,7 @@
     "import os\n",
     "\n",
     "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
-    "    os.chdir(\"..\")\n",
-    "assert os.path.basename(os.getcwd()) == \"element-moseq\", (\n",
-    "    \"Please move to the \" + \"element directory\"\n",
-    ")"
+    "    os.chdir(\"..\")"
    ]
   },
   {
@@ -22,285 +19,380 @@
    "outputs": [],
    "source": [
     "import datajoint as dj\n",
-    "from pathlib import Path"
+    "from pathlib import Path\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.config.load(\"dj_local_conf.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.conn()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.list_schemas()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_pca\").drop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-01-02 08:32:38,675][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+      "[2024-03-08 03:53:37,997][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
      ]
+    },
+    {
+     "data": {
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i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/jquery/jquery.slim.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/plotlyplot/plotly-2.18.0.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/tabulator-tables@5.5.0/dist/js/tabulator.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/luxon/build/global/luxon.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [\"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/tabulator-tables@5.5.0/dist/css/tabulator_simple.min.css\"];\n  var inline_js = [    function(Bokeh) {\n      inject_raw_css(\".tabulator{position:relative;border:1px solid #999;font-size:14px;text-align:left;overflow:hidden;-webkit-transform:translateZ(0);-moz-transform:translateZ(0);-ms-transform:translateZ(0);-o-transform:translateZ(0);transform:translateZ(0)}.tabulator[tabulator-layout=fitDataFill] .tabulator-tableholder .tabulator-table{min-width:100%}.tabulator[tabulator-layout=fitDataTable]{display:inline-block}.tabulator.tabulator-block-select{user-select:none}.tabulator .tabulator-header{position:relative;box-sizing:border-box;width:100%;border-bottom:1px solid #999;background-color:#fff;color:#555;font-weight:700;white-space:nowrap;overflow:hidden;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none}.tabulator .tabulator-header.tabulator-header-hidden{display:none}.tabulator .tabulator-header .tabulator-header-contents{position:relative;overflow:hidden}.tabulator .tabulator-header .tabulator-header-contents .tabulator-headers{display:inline-block}.tabulator .tabulator-header .tabulator-col{display:inline-flex;position:relative;box-sizing:border-box;flex-direction:column;justify-content:flex-start;border-right:1px solid #ddd;background:#fff;text-align:left;vertical-align:bottom;overflow:hidden}.tabulator .tabulator-header .tabulator-col.tabulator-moving{position:absolute;border:1px solid #999;background:#e6e6e6;pointer-events:none}.tabulator .tabulator-header .tabulator-col .tabulator-col-content{box-sizing:border-box;position:relative;padding:4px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-header-popup-button{padding:0 8px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-header-popup-button:hover{cursor:pointer;opacity:.6}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title-holder{position:relative}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title{box-sizing:border-box;width:100%;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;vertical-align:bottom}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title.tabulator-col-title-wrap{white-space:normal;text-overflow:clip}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title .tabulator-title-editor{box-sizing:border-box;width:100%;border:1px solid #999;padding:1px;background:#fff}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title .tabulator-header-popup-button+.tabulator-title-editor{width:calc(100% - 22px)}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-sorter{display:flex;align-items:center;position:absolute;top:0;bottom:0;right:4px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{width:0;height:0;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #bbb}.tabulator .tabulator-header .tabulator-col.tabulator-col-group .tabulator-col-group-cols{position:relative;display:flex;border-top:1px solid #ddd;overflow:hidden;margin-right:-1px}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter{position:relative;box-sizing:border-box;margin-top:2px;width:100%;text-align:center}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter textarea{height:auto!important}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter svg{margin-top:3px}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter input::-ms-clear{width:0;height:0}.tabulator .tabulator-header .tabulator-col.tabulator-sortable .tabulator-col-title{padding-right:25px}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable.tabulator-col-sorter-element:hover{cursor:pointer;background-color:#e6e6e6}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter{color:#bbb}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-bottom:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-top:none;border-bottom:6px solid #bbb}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter{color:#666}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-bottom:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-top:none;border-bottom:6px solid #666}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter{color:#666}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-top:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-bottom:none;border-top:6px solid #666;color:#666}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical .tabulator-col-content .tabulator-col-title{writing-mode:vertical-rl;text-orientation:mixed;display:flex;align-items:center;justify-content:center}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-col-vertical-flip .tabulator-col-title{transform:rotate(180deg)}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-col-title{padding-right:0;padding-top:20px}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable.tabulator-col-vertical-flip .tabulator-col-title{padding-right:0;padding-bottom:20px}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-col-sorter{justify-content:center;left:0;right:0;top:4px;bottom:auto}.tabulator .tabulator-header .tabulator-frozen{position:sticky;left:0;z-index:10}.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-left{border-right:2px solid #ddd}.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-right{border-left:2px solid #ddd}.tabulator .tabulator-header .tabulator-calcs-holder{box-sizing:border-box;background:#fff!important;border-top:1px solid #ddd;border-bottom:1px solid #ddd}.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row{background:#fff!important}.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle,.tabulator .tabulator-header .tabulator-frozen-rows-holder:empty{display:none}.tabulator .tabulator-tableholder{position:relative;width:100%;white-space:nowrap;overflow:auto;-webkit-overflow-scrolling:touch}.tabulator .tabulator-tableholder:focus{outline:none}.tabulator .tabulator-tableholder .tabulator-placeholder{box-sizing:border-box;display:flex;align-items:center;justify-content:center;width:100%}.tabulator .tabulator-tableholder .tabulator-placeholder[tabulator-render-mode=virtual]{min-height:100%;min-width:100%}.tabulator .tabulator-tableholder .tabulator-placeholder .tabulator-placeholder-contents{display:inline-block;text-align:center;padding:10px;color:#ccc;font-weight:700;font-size:20px;white-space:normal}.tabulator .tabulator-tableholder .tabulator-table{position:relative;display:inline-block;background-color:#fff;white-space:nowrap;overflow:visible;color:#333}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs{font-weight:700;background:#f2f2f2!important}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-top{border-bottom:2px solid #ddd}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-bottom{border-top:2px solid #ddd}.tabulator .tabulator-footer{border-top:1px solid #999;background-color:#fff;color:#555;font-weight:700;white-space:nowrap;user-select:none;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none}.tabulator .tabulator-footer .tabulator-footer-contents{display:flex;flex-direction:row;align-items:center;justify-content:space-between;padding:5px 10px}.tabulator .tabulator-footer .tabulator-footer-contents:empty{display:none}.tabulator .tabulator-footer .tabulator-calcs-holder{box-sizing:border-box;width:100%;text-align:left;background:#fff!important;border-bottom:1px solid #ddd;border-top:1px solid #ddd;overflow:hidden}.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row{display:inline-block;background:#fff!important}.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle{display:none}.tabulator .tabulator-footer .tabulator-calcs-holder:only-child{margin-bottom:-5px;border-bottom:none}.tabulator .tabulator-footer>*+.tabulator-page-counter{margin-left:10px}.tabulator .tabulator-footer .tabulator-page-counter{font-weight:400}.tabulator .tabulator-footer .tabulator-paginator{flex:1;text-align:right;color:#555;font-family:inherit;font-weight:inherit;font-size:inherit}.tabulator .tabulator-footer .tabulator-page-size{display:inline-block;margin:0 5px;padding:2px 5px;border:1px solid #aaa;border-radius:3px}.tabulator .tabulator-footer .tabulator-pages{margin:0 7px}.tabulator .tabulator-footer .tabulator-page{display:inline-block;margin:0 2px;padding:2px 5px;border:1px solid #aaa;border-radius:3px;background:hsla(0,0%,100%,.2)}.tabulator .tabulator-footer .tabulator-page.active{color:#d00}.tabulator .tabulator-footer .tabulator-page:disabled{opacity:.5}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-footer .tabulator-page:not(.disabled):hover{cursor:pointer;background:rgba(0,0,0,.2);color:#fff}}.tabulator .tabulator-col-resize-handle{position:relative;display:inline-block;width:6px;margin-left:-3px;margin-right:-3px;z-index:10;vertical-align:middle}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-col-resize-handle:hover{cursor:ew-resize}}.tabulator .tabulator-col-resize-handle:last-of-type{width:3px;margin-right:0}.tabulator .tabulator-alert{position:absolute;display:flex;align-items:center;top:0;left:0;z-index:100;height:100%;width:100%;background:rgba(0,0,0,.4);text-align:center}.tabulator .tabulator-alert .tabulator-alert-msg{display:inline-block;margin:0 auto;padding:10px 20px;border-radius:10px;background:#fff;font-weight:700;font-size:16px}.tabulator .tabulator-alert .tabulator-alert-msg.tabulator-alert-state-msg{border:4px solid #333;color:#000}.tabulator .tabulator-alert .tabulator-alert-msg.tabulator-alert-state-error{border:4px solid #d00;color:#590000}.tabulator-row{position:relative;box-sizing:border-box;min-height:22px}.tabulator-row,.tabulator-row.tabulator-row-even{background-color:#fff}@media (hover:hover) and (pointer:fine){.tabulator-row.tabulator-selectable:hover{background-color:#bbb;cursor:pointer}}.tabulator-row.tabulator-selected{background-color:#9abcea}@media (hover:hover) and (pointer:fine){.tabulator-row.tabulator-selected:hover{background-color:#769bcc;cursor:pointer}}.tabulator-row.tabulator-row-moving{border:1px solid #000;background:#fff}.tabulator-row.tabulator-moving{position:absolute;border-top:1px solid #ddd;border-bottom:1px solid #ddd;pointer-events:none;z-index:15}.tabulator-row .tabulator-row-resize-handle{position:absolute;right:0;bottom:0;left:0;height:5px}.tabulator-row .tabulator-row-resize-handle.prev{top:0;bottom:auto}@media (hover:hover) and (pointer:fine){.tabulator-row .tabulator-row-resize-handle:hover{cursor:ns-resize}}.tabulator-row .tabulator-responsive-collapse{box-sizing:border-box;padding:5px;border-top:1px solid #ddd;border-bottom:1px solid #ddd}.tabulator-row .tabulator-responsive-collapse:empty{display:none}.tabulator-row .tabulator-responsive-collapse table{font-size:14px}.tabulator-row .tabulator-responsive-collapse table tr td{position:relative}.tabulator-row .tabulator-responsive-collapse table tr td:first-of-type{padding-right:10px}.tabulator-row .tabulator-cell{display:inline-block;position:relative;box-sizing:border-box;padding:4px;border-right:1px solid #ddd;vertical-align:middle;white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.tabulator-row .tabulator-cell.tabulator-frozen{display:inline-block;position:sticky;left:0;background-color:inherit;z-index:10}.tabulator-row .tabulator-cell.tabulator-frozen.tabulator-frozen-left{border-right:2px solid #ddd}.tabulator-row .tabulator-cell.tabulator-frozen.tabulator-frozen-right{border-left:2px solid #ddd}.tabulator-row .tabulator-cell.tabulator-editing{border:1px solid #1d68cd;outline:none;padding:0}.tabulator-row .tabulator-cell.tabulator-editing input,.tabulator-row .tabulator-cell.tabulator-editing select{border:1px;background:transparent;outline:none}.tabulator-row .tabulator-cell.tabulator-validation-fail{border:1px solid #d00}.tabulator-row .tabulator-cell.tabulator-validation-fail input,.tabulator-row .tabulator-cell.tabulator-validation-fail select{border:1px;background:transparent;color:#d00}.tabulator-row .tabulator-cell.tabulator-row-handle{display:inline-flex;align-items:center;justify-content:center;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none}.tabulator-row .tabulator-cell.tabulator-row-handle .tabulator-row-handle-box{width:80%}.tabulator-row .tabulator-cell.tabulator-row-handle .tabulator-row-handle-box .tabulator-row-handle-bar{width:100%;height:3px;margin-top:2px;background:#666}.tabulator-row .tabulator-cell .tabulator-data-tree-branch{display:inline-block;vertical-align:middle;height:9px;width:7px;margin-top:-9px;margin-right:5px;border-bottom-left-radius:1px;border-left:2px solid #ddd;border-bottom:2px solid #ddd}.tabulator-row .tabulator-cell .tabulator-data-tree-control{display:inline-flex;justify-content:center;align-items:center;vertical-align:middle;height:11px;width:11px;margin-right:5px;border:1px solid #333;border-radius:2px;background:rgba(0,0,0,.1);overflow:hidden}@media (hover:hover) and (pointer:fine){.tabulator-row .tabulator-cell .tabulator-data-tree-control:hover{cursor:pointer;background:rgba(0,0,0,.2)}}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse{display:inline-block;position:relative;height:7px;width:1px;background:transparent}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse:after{position:absolute;content:\\\"\\\";left:-3px;top:3px;height:1px;width:7px;background:#333}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand{display:inline-block;position:relative;height:7px;width:1px;background:#333}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand:after{position:absolute;content:\\\"\\\";left:-3px;top:3px;height:1px;width:7px;background:#333}.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle{display:inline-flex;align-items:center;justify-content:center;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none;height:15px;width:15px;border-radius:20px;background:#666;color:#fff;font-weight:700;font-size:1.1em}@media (hover:hover) and (pointer:fine){.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle:hover{opacity:.7;cursor:pointer}}.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle.open .tabulator-responsive-collapse-toggle-close{display:initial}.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle.open .tabulator-responsive-collapse-toggle-open{display:none}.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle svg{stroke:#fff}.tabulator-row .tabulator-cell .tabulator-responsive-collapse-toggle .tabulator-responsive-collapse-toggle-close{display:none}.tabulator-row .tabulator-cell .tabulator-traffic-light{display:inline-block;height:14px;width:14px;border-radius:14px}.tabulator-row.tabulator-group{box-sizing:border-box;border-bottom:1px solid #999;border-right:1px solid #ddd;border-top:1px solid #999;padding:5px 5px 5px 10px;background:#ccc;font-weight:700;min-width:100%}@media (hover:hover) and (pointer:fine){.tabulator-row.tabulator-group:hover{cursor:pointer;background-color:rgba(0,0,0,.1)}}.tabulator-row.tabulator-group.tabulator-group-visible .tabulator-arrow{margin-right:10px;border-left:6px solid transparent;border-right:6px solid transparent;border-top:6px solid 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before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"2a0add99-b645-459a-9881-f0441c3f3968\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"23fbdb09-a73b-4f41-bacc-5ebeddf7e70b\",\"attributes\":{\"plot_id\":\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\",\"comm_id\":\"de177822eac14690be912c6724c01980\",\"client_comm_id\":\"373319e9836b4b9fbd979e153a6f4604\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"2a0add99-b645-459a-9881-f0441c3f3968\",\"roots\":{\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\":\"b204cace-626d-43d3-a469-aaf10d5a7f49\"},\"root_ids\":[\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined) && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined))\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "6a106c71-1b1c-4de8-bf82-6f3d46cd30cc"
+      }
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from tutorial_pipeline import lab, subject, session, pca, model, Device"
+    "# from element_moseq import kpms_pca\n",
+    "from tutorial_pipeline import lab, subject, session, kpms_pca"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"622pt\" height=\"528pt\" viewBox=\"0.00 0.00 622.25 527.84\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 523.84)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-523.84 618.25,-523.84 618.25,4 -4,4\"/>\n",
-       "<!-- pca.PCAFitting -->\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"846pt\" height=\"316pt\" viewBox=\"0.00 0.00 845.56 316.16\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 312.16)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-312.16 841.56,-312.16 841.56,4 -4,4\"/>\n",
+       "<!-- kpms_pca.LatentDimensionSet -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>pca.PCAFitting</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rpca_fitting_time     \rpca                  \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"213.12\" cy=\"-290.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"213.12\" y=\"-286.61\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.PCAFitting</text>\n",
+       "<title>kpms_pca.LatentDimensionSet</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"192.68,-308.16 40.18,-308.16 40.18,-273.6 192.68,-273.6 192.68,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"48.18\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.LatentDimensionSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.UpdateLatentDimension -->\n",
+       "<!-- kpms_pca.KeypointSet -->\n",
        "<g id=\"node2\" class=\"node\">\n",
-       "<title>pca.UpdateLatentDimension</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ pca.PCAFitting\r→ pca.PCATask\r→ pca.LatentDimension\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"136.12\" cy=\"-220.32\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"136.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">pca.UpdateLatentDimension</text>\n",
+       "<title>kpms_pca.KeypointSet</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_method         \rkpset_config_path    \rkpset_videos_path    \rkpset_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"349.93,-308.16 210.93,-308.16 210.93,-273.6 349.93,-273.6 349.93,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"218.93\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- kpms_pca.KeypointSet.VideoFiles -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet.VideoFiles</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"363.43,-230.32 197.43,-230.32 197.43,-210.32 363.43,-210.32 363.43,-230.32\"/>\n",
+       "<text text-anchor=\"start\" x=\"205.43\" y=\"-217.82\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFiles</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFiles -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>pca.PCAFitting-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M203.99,-281.75C189.17,-268.55 160.06,-242.63 145.25,-229.44\"/>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFiles</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M280.43,-273.28C280.43,-259.65 280.43,-240.88 280.43,-230.03\"/>\n",
        "</g>\n",
-       "<!-- model.PreFittingTask -->\n",
+       "<!-- kpms_pca.PCATask -->\n",
        "<g id=\"node6\" class=\"node\">\n",
-       "<title>model.PreFittingTask</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ model.InitializationParamSet\r→ pca.PCAFitting\r------------------------------\rmodel_name           \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"477.38,-237.6 348.88,-237.6 348.88,-203.04 477.38,-203.04 477.38,-237.6\"/>\n",
-       "<text text-anchor=\"middle\" x=\"413.12\" y=\"-216.04\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.PreFittingTask</text>\n",
+       "<title>kpms_pca.PCATask</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.KeypointSet\r→ kpms_pca.Bodyparts\rpca_task_id          \r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \rvariance_threshold   \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"530.68,-167.04 408.18,-167.04 408.18,-132.48 530.68,-132.48 530.68,-167.04\"/>\n",
+       "<text text-anchor=\"start\" x=\"416.18\" y=\"-146.48\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.PCAFitting&#45;&gt;model.PreFittingTask -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.PCATask -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>pca.PCAFitting-&gt;model.PreFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M225.04,-285.8C251.86,-276.6 318.27,-253.83 364.53,-237.98\"/>\n",
-       "</g>\n",
-       "<!-- model.FullFitting -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>model.FullFitting</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ model.FullFittingTask\r------------------------------\rmodel                \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"477.12\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"477.12\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.FullFitting</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M313.88,-273.18C331.83,-263.66 353.99,-250.97 372.43,-237.6 390.91,-224.2 393.44,-218.29 410.43,-203.04 423.77,-191.06 438.91,-177.65 450.51,-167.41\"/>\n",
        "</g>\n",
-       "<!-- pca.LatentDimension -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>pca.LatentDimension</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"108.25,-308.16 0,-308.16 0,-273.6 108.25,-273.6 108.25,-308.16\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.LatentDimension</text>\n",
+       "<!-- kpms_pca.Bodyparts -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_pca.Bodyparts</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"547.31,-237.6 419.56,-237.6 419.56,-203.04 547.31,-203.04 547.31,-237.6\"/>\n",
+       "<text text-anchor=\"start\" x=\"427.56\" y=\"-217.04\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.LatentDimension&#45;&gt;pca.UpdateLatentDimension -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>pca.LatentDimension-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M73.56,-273.63C90.42,-259.54 114.08,-239.75 126.93,-229\"/>\n",
-       "</g>\n",
-       "<!-- pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>pca.PCATask.FormattedDataset</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ pca.PCATask\r------------------------------\rconfig               \rcoordinates          \rconfidences          \rbodyparts            \rdata                 \rmetadata             \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"437,-300.88 279.25,-300.88 279.25,-280.88 437,-280.88 437,-300.88\"/>\n",
-       "<text text-anchor=\"middle\" x=\"358.12\" y=\"-287.38\" font-family=\"arial\" font-size=\"10.00\">pca.PCATask.FormattedDataset</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M330.09,-273.11C361.66,-262.44 402.39,-248.69 433.93,-238.04\"/>\n",
        "</g>\n",
-       "<!-- model.PreFitting -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>model.PreFitting</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ model.PreFittingTask\r------------------------------\rmodel                \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"413.12\" cy=\"-149.76\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"413.12\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">model.PreFitting</text>\n",
+       "<!-- kpms_pca.RecordingInfo -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>kpms_pca.RecordingInfo</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"280.43\" cy=\"-149.76\" rx=\"97.2\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"280.43\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.PreFittingTask&#45;&gt;model.PreFitting -->\n",
+       "<!-- kpms_pca.KeypointSet.VideoFiles&#45;&gt;kpms_pca.RecordingInfo -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>model.PreFittingTask-&gt;model.PreFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M413.12,-202.72C413.12,-190.36 413.12,-173.77 413.12,-162.69\"/>\n",
+       "<title>kpms_pca.KeypointSet.VideoFiles-&gt;kpms_pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M280.43,-210.36C280.43,-199.47 280.43,-180.88 280.43,-167.35\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.VideoFiles -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>pca.KeypointSet.VideoFiles</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"255.5,-442 118.75,-442 118.75,-422 255.5,-422 255.5,-442\"/>\n",
-       "<text text-anchor=\"start\" x=\"126.75\" y=\"-429.5\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">pca.KeypointSet.VideoFiles</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- pca.RecordingInfo -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>pca.RecordingInfo</title>\n",
-       "<g id=\"a_node16\"><a xlink:title=\"→ pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"187.12\" cy=\"-361.44\" rx=\"74.16\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"187.12\" y=\"-357.17\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">pca.RecordingInfo</text>\n",
+       "<!-- kpms_pca.FormattedDataset -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_pca.FormattedDataset</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"110.43\" cy=\"-79.2\" rx=\"110.43\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"110.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.VideoFiles&#45;&gt;pca.RecordingInfo -->\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>pca.KeypointSet.VideoFiles-&gt;pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M187.12,-422.04C187.12,-411.15 187.12,-392.56 187.12,-379.03\"/>\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M407.8,-136.62C400.6,-135.22 393.35,-133.81 386.43,-132.48 315.92,-118.89 235.21,-103.63 179.36,-93.13\"/>\n",
        "</g>\n",
-       "<!-- model.InitializationParamSet -->\n",
+       "<!-- kpms_pca.UpdateLatentDimensionTask -->\n",
        "<g id=\"node8\" class=\"node\">\n",
-       "<title>model.InitializationParamSet</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"595.38,-308.16 454.88,-308.16 454.88,-273.6 595.38,-273.6 595.38,-308.16\"/>\n",
-       "<text text-anchor=\"start\" x=\"462.88\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.InitializationParamSet</text>\n",
+       "<title>kpms_pca.UpdateLatentDimensionTask</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.PCATask\rlatent_dim           \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"467.56,-96.48 239.31,-96.48 239.31,-61.92 467.56,-61.92 467.56,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"247.31\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.UpdateLatentDimensionTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.InitializationParamSet&#45;&gt;model.PreFittingTask -->\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.UpdateLatentDimensionTask -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>model.InitializationParamSet-&gt;model.PreFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M498.58,-273.63C480.99,-262.86 457.97,-248.77 440.25,-237.93\"/>\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.UpdateLatentDimensionTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M441.05,-131.99C422.94,-121.28 399.57,-107.47 381.51,-96.8\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>pca.KeypointSet</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_path           \rkpset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"409,-519.84 305.25,-519.84 305.25,-485.28 409,-485.28 409,-519.84\"/>\n",
-       "<text text-anchor=\"start\" x=\"313.25\" y=\"-499.29\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.KeypointSet</text>\n",
+       "<!-- kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_pca.DimsExplainedVariance</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rvariance_percentage  \rdims_explained_variance \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"586.43\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"586.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.VideoFiles -->\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.VideoFiles</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M315.54,-484.79C281.92,-471.23 235.92,-452.68 209.08,-441.85\"/>\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.DimsExplainedVariance</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M498.05,-131.99C523.22,-117.24 558.47,-96.58 575.87,-86.39\"/>\n",
        "</g>\n",
-       "<!-- pca.Bodyparts -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>pca.Bodyparts</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"366.38,-449.28 273.88,-449.28 273.88,-414.72 366.38,-414.72 366.38,-449.28\"/>\n",
-       "<text text-anchor=\"start\" x=\"281.88\" y=\"-428.73\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">pca.Bodyparts</text>\n",
+       "<!-- kpms_pca.PCAFitting -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_pca.PCAFitting</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"771.43\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"771.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.Bodyparts -->\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.PCAFitting -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M348.17,-484.96C342.35,-474.18 334.8,-460.19 329,-449.44\"/>\n",
-       "</g>\n",
-       "<!-- pca.PCATask -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>pca.PCATask</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ pca.KeypointSet\r→ pca.Bodyparts\r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"401.75,-378.72 314.5,-378.72 314.5,-344.16 401.75,-344.16 401.75,-378.72\"/>\n",
-       "<text text-anchor=\"middle\" x=\"358.12\" y=\"-357.17\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">pca.PCATask</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M530.91,-134.8C604.2,-118.16 721.54,-91.53 759.31,-82.95\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.PCATask -->\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M364.42,-484.98C368.46,-474.88 373.07,-461.59 375.12,-449.28 377.66,-434.13 377.53,-429.89 375.12,-414.72 373.18,-402.47 368.83,-389.18 365.01,-379.06\"/>\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M480.04,-202.72C477.84,-191.94 474.98,-177.95 472.79,-167.2\"/>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet.PoseEstimationMethod -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ pca.KeypointSet\r------------------------------\rformat               \rextension            \r→ Device\r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"614.25,-442 422,-442 422,-422 614.25,-422 614.25,-442\"/>\n",
-       "<text text-anchor=\"middle\" x=\"518.12\" y=\"-428.5\" font-family=\"arial\" font-size=\"10.00\">pca.KeypointSet.PoseEstimationMethod</text>\n",
+       "<!-- kpms_pca.UpdateLatentDimension -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>kpms_pca.UpdateLatentDimension</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_pca.UpdateLatentDimensionTask\r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"353.43\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"353.43\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.UpdateLatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- pca.KeypointSet&#45;&gt;pca.KeypointSet.PoseEstimationMethod -->\n",
+       "<!-- kpms_pca.UpdateLatentDimensionTask&#45;&gt;kpms_pca.UpdateLatentDimension -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>pca.KeypointSet-&gt;pca.KeypointSet.PoseEstimationMethod</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M396.51,-484.79C428.35,-471.23 471.91,-452.68 497.34,-441.85\"/>\n",
-       "</g>\n",
-       "<!-- pca.Bodyparts&#45;&gt;pca.PCATask -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>pca.Bodyparts-&gt;pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M329.32,-414.4C335.3,-403.62 343.05,-389.63 349.01,-378.88\"/>\n",
-       "</g>\n",
-       "<!-- model.FullFittingTask -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>model.FullFittingTask</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"→ model.FittingParamSet\r→ model.PreFitting\r------------------------------\rmodel_name           \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"542.12,-96.48 412.12,-96.48 412.12,-61.92 542.12,-61.92 542.12,-96.48\"/>\n",
-       "<text text-anchor=\"middle\" x=\"477.12\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">model.FullFittingTask</text>\n",
-       "</a>\n",
+       "<title>kpms_pca.UpdateLatentDimensionTask-&gt;kpms_pca.UpdateLatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M353.43,-61.66C353.43,-50.36 353.43,-35.66 353.43,-25.58\"/>\n",
        "</g>\n",
-       "</g>\n",
-       "<!-- model.PreFitting&#45;&gt;model.FullFittingTask -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>model.PreFitting-&gt;model.FullFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M421.23,-140.08C431.32,-129.27 448.78,-110.57 461.5,-96.94\"/>\n",
-       "</g>\n",
-       "<!-- model.FittingParamSet -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>model.FittingParamSet</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"paramset_id          \r------------------------------\rnum_iterations       \rkappa                \rparamset_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"599,-167.04 483.25,-167.04 483.25,-132.48 599,-132.48 599,-167.04\"/>\n",
-       "<text text-anchor=\"start\" x=\"491.25\" y=\"-147.26\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">model.FittingParamSet</text>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"format               \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"533.06,-308.16 367.81,-308.16 367.81,-273.6 533.06,-273.6 533.06,-308.16\"/>\n",
+       "<text text-anchor=\"start\" x=\"375.81\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- model.FittingParamSet&#45;&gt;model.FullFittingTask -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>model.FittingParamSet-&gt;model.FullFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M525.96,-132.51C515.9,-121.74 502.75,-107.65 492.63,-96.81\"/>\n",
-       "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCAFitting -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M322.65,-343.67C290.46,-328.45 244.95,-306.93 224.2,-297.12\"/>\n",
-       "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.UpdateLatentDimension -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M314.3,-351.69C258.02,-340.08 166.24,-319.69 155.12,-308.16 135.42,-287.72 134.22,-251.8 135.11,-233.21\"/>\n",
-       "</g>\n",
-       "<!-- pca.PCATask&#45;&gt;pca.PCATask.FormattedDataset -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
-       "<title>pca.PCATask-&gt;pca.PCATask.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M358.12,-343.84C358.12,-330.21 358.12,-311.44 358.12,-300.59\"/>\n",
-       "</g>\n",
-       "<!-- model.FullFittingTask&#45;&gt;model.FullFitting -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>model.FullFittingTask-&gt;model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M477.12,-61.66C477.12,-50.36 477.12,-35.66 477.12,-25.58\"/>\n",
-       "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x171c20eb0>"
+       "<datajoint.diagram.Diagram at 0x2a1d53610>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dj.Diagram(pca) + dj.Diagram(model)"
+    "dj.Diagram(kpms_pca)\n",
+    "# + dj.Diagram(model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -371,11 +463,17 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located.</span>\n",
+       "                            <p id=\"nonprimary\">kpset_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_config_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_videos_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">kpset_description</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -384,24 +482,24 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    kpset_path     kpset_descript\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    kpset_method   kpset_config_p kpset_videos_p kpset_descript\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.KeypointSet()"
+    "kpms_pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,34 +514,41 @@
     "    skip_duplicates=True,\n",
     ")\n",
     "\n",
-    "#Definition of the dictionary named \"session_keys\"\n",
+    "# Definition of the dictionary named \"session_keys\"\n",
     "session_keys = [\n",
     "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
     "    dict(subject=\"subject1\", session_datetime=\"2021-06-03 14:43:10\"),\n",
     "]\n",
     "\n",
-    "#Insert this dictionary in the Session table\n",
+    "# Insert this dictionary in the Session table\n",
     "session.Session.insert(session_keys, skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpset_key = dict(subject=\"subject1\",\n",
-    "            session_datetime=\"2021-06-02 14:04:22\",\n",
-    "            kpset_id=1)\n",
-    "pca.KeypointSet.insert1({**kpset_key, \n",
-    "                            \"kpset_path\":\"/input_data/videos\",\n",
-    "                            \"kpset_description\":\"testing pca schema\"},\n",
-    "                            skip_duplicates=True)"
+    "kpset_key = dict(subject=\"subject1\", \n",
+    "                 session_datetime=\"2021-06-02 14:04:22\", \n",
+    "                 kpset_id=1)\n",
+    "\n",
+    "kpms_pca.KeypointSet.insert1(\n",
+    "    {\n",
+    "        **kpset_key,\n",
+    "        \"kpset_method\":\"deeplabcut\",\n",
+    "        \"kpset_config_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
+    "        \"kpset_videos_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
+    "        \"kpset_description\": \"testing kpms pca schema\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -514,58 +619,70 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located.</span>\n",
+       "                            <p id=\"nonprimary\">kpset_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_config_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_videos_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">kpset_description</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
-       "<td>/input_data/videos</td>\n",
-       "<td>testing pca schema</td> </tr> </tbody>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
+       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    kpset_path     kpset_descript\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            /input_data/vi testing pca sc\n",
+       "*subject     *session_datet *kpset_id    kpset_method   kpset_config_p kpset_videos_p kpset_descript\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.KeypointSet()"
+    "kpms_pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "video_files = [\"/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
-    "               \"/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
-    "               \"/input_data/videos/21_12_2_def6b_2.top.ir.mp4\"]\n",
+    "video_files = [\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
+    "]\n",
     "\n",
-    "pca.KeypointSet.VideoFiles.insert(\n",
-    "    ({**kpset_key, \"video_id\":v_idx, \"video_path\":Path(f)}\n",
-    "    for v_idx, f in enumerate(video_files)),\n",
-    "    skip_duplicates=True\n",
+    "kpms_pca.KeypointSet.VideoFiles.insert(\n",
+    "    (\n",
+    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
+    "        for v_idx, f in enumerate(video_files)\n",
+    "    ),\n",
+    "    skip_duplicates=True,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -646,15 +763,15 @@
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>0</td>\n",
-       "<td>/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>1</td>\n",
-       "<td>/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>2</td>\n",
-       "<td>/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 3</p></div>\n",
@@ -663,39 +780,24 @@
       "text/plain": [
        "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
        "+----------+ +------------+ +----------+ +----------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            0            /input_data/vi\n",
-       "subject1     2021-06-02 14: 1            1            /input_data/vi\n",
-       "subject1     2021-06-02 14: 1            2            /input_data/vi\n",
+       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
        " (Total: 3)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.KeypointSet.VideoFiles()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca.KeypointSet.PoseEstimationMethod.insert1(\n",
-    "    {**kpset_key,\n",
-    "     \"format\":\"deeplabcut\",\n",
-    "     \"extension\":\"h5\",\n",
-    "     \"device\":\"Camera1\"},\n",
-    "     skip_duplicates=True\n",
-    ")"
+    "kpms_pca.KeypointSet.VideoFiles()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -753,62 +855,47 @@
        "        }\n",
        "    </style>\n",
        "    \n",
-       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method</b>\n",
+       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method.</b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">format</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">extension</p>\n",
-       "                            <span class=\"djtooltiptext\">h5, csv</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">device</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">pose_estimation_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Pose estimation method description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>h5</td>\n",
-       "<td>Camera1</td> </tr> </tbody>\n",
+       "            <tbody> <tr> <td>anipose</td>\n",
+       "<td>`.csv` files generated by anipose analysis</td></tr><tr><td>deeplabcut</td>\n",
+       "<td>`.csv` and `.h5/.hdf5` files generated by DeepLabcut analysis</td></tr><tr><td>facemap</td>\n",
+       "<td>`.h5` files generated by Facemap analysis</td></tr><tr><td>nwb</td>\n",
+       "<td>`.nwb` files with Neurodata Without Borders (NWB) format</td></tr><tr><td>sleap</td>\n",
+       "<td>`.slp` and `.h5/.hdf5` files generated by SLEAP analysis</td></tr><tr><td>sleap-anipose</td>\n",
+       "<td>`.h5/.hdf5` files generated by sleap-anipose analysis</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 6</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    format         extension     device     \n",
-       "+----------+ +------------+ +----------+ +------------+ +-----------+ +---------+\n",
-       "subject1     2021-06-02 14: 1            deeplabcut     h5            Camera1    \n",
-       " (Total: 1)"
+       "*format        pose_estimatio\n",
+       "+------------+ +------------+\n",
+       "anipose        `.csv` files g\n",
+       "deeplabcut     `.csv` and `.h\n",
+       "facemap        `.h5` files ge\n",
+       "nwb            `.nwb` files w\n",
+       "sleap          `.slp` and `.h\n",
+       "sleap-anipose  `.h5/.hdf5` fi\n",
+       " (Total: 6)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.KeypointSet.PoseEstimationMethod()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pca.RecordingInfo.populate()"
+    "kpms_pca.PoseEstimationMethod()"
    ]
   },
   {
@@ -884,17 +971,26 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"primary\">video_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of anterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">px_height</p>\n",
+       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of posterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">px_width</p>\n",
+       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">list of strings of bodyparts to be used</span>\n",
+       "                            <p id=\"nonprimary\">nframes</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">fps</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -903,8 +999,8 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "*subject    *session_datet *kpset_id    *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
+       "+---------+ +------------+ +----------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
@@ -915,7 +1011,7 @@
     }
    ],
    "source": [
-    "pca.Bodyparts()"
+    "kpms_pca.RecordingInfo()"
    ]
   },
   {
@@ -924,21 +1020,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Insert data in PCAInfo table\n",
-    "bodypart_key = {**kpset_key,\n",
-    "                \"bodyparts_id\":1}\n",
-    "pca.Bodyparts.insert1({**bodypart_key,\n",
-    "                \"anterior_bodyparts\":\"nose\",\n",
-    "                \"posterior_bodyparts\":\"spine4\",\n",
-    "                \"use_bodyparts\":['spine4', 'spine3', 'spine2', 'spine1',\n",
-    "                'head', 'nose', 'right ear', 'left ear']},\n",
-    "                    skip_duplicates=True)\n",
-    "\n"
+    "kpms_pca.RecordingInfo.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -996,7 +1083,7 @@
        "        }\n",
        "    </style>\n",
        "    \n",
-       "    <b>Manual table for defining a data loading task ready to be run</b>\n",
+       "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
@@ -1009,52 +1096,81 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"primary\">video_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">project_path</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                            <p id=\"nonprimary\">px_height</p>\n",
+       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">px_width</p>\n",
+       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">nframes</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">fps</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>0</td>\n",
+       "<td>576</td>\n",
+       "<td>640</td>\n",
+       "<td>71524</td>\n",
+       "<td>30</td>\n",
+       "<td>None</td>\n",
+       "<td>2384.13</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>576</td>\n",
+       "<td>640</td>\n",
+       "<td>107507</td>\n",
+       "<td>30</td>\n",
+       "<td>None</td>\n",
+       "<td>3583.57</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>2</td>\n",
+       "<td>576</td>\n",
+       "<td>640</td>\n",
+       "<td>107520</td>\n",
+       "<td>30</td>\n",
+       "<td>None</td>\n",
+       "<td>3584.0</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 3</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *kpset_id    *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
+       "+----------+ +------------+ +----------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            0            576           640          71524       30      None           2384.13       \n",
+       "subject1     2021-06-02 14: 1            1            576           640          107507      30      None           3583.57       \n",
+       "subject1     2021-06-02 14: 1            2            576           640          107520      30      None           3584.0        \n",
+       " (Total: 3)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.PCATask()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pca.PCATask.insert1({**bodypart_key,\n",
-    "                             \"project_path\":\"/kpms_project\",\n",
-    "                            \"task_mode\":\"load\"},\n",
-    "                            skip_duplicates=True)"
+    "kpms_pca.RecordingInfo()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1112,7 +1228,7 @@
        "        }\n",
        "    </style>\n",
        "    \n",
-       "    <b>Manual table for defining a data loading task ready to be run</b>\n",
+       "    <b></b>\n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
@@ -1128,65 +1244,68 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">project_path</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/kpms_project</td>\n",
-       "<td>load</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  project_path   task_mode    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1            1              /kpms_project  load         \n",
-       " (Total: 1)"
+       "*subject    *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.PCATask()"
+    "kpms_pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pca\u001b[39m.\u001b[39;49mPCATask\u001b[39m.\u001b[39;49mgenerate(bodypart_key)\n",
-      "File \u001b[0;32m~/Documents/datajoint-elements/element-moseq/element_moseq/pca.py:264\u001b[0m, in \u001b[0;36mPCATask.generate\u001b[0;34m(cls, key)\u001b[0m\n\u001b[1;32m    261\u001b[0m video_path \u001b[39m=\u001b[39m (KeypointSet\u001b[39m.\u001b[39mVideoFiles \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch(\u001b[39m\"\u001b[39m\u001b[39mvideo_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    263\u001b[0m kpset_path \u001b[39m=\u001b[39m (KeypointSet \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mkpset_path\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 264\u001b[0m kpset_path \u001b[39m=\u001b[39m find_full_path(get_kpms_root_data_dir(), kpset_path)\n\u001b[1;32m    266\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mkeypoint_moseq\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mkpms\u001b[39;00m\n\u001b[1;32m    268\u001b[0m \u001b[39m# define config file for kpms with anonymous function\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniconda/envs/kpms_test/lib/python3.9/site-packages/element_interface/utils.py:42\u001b[0m, in \u001b[0;36mfind_full_path\u001b[0;34m(root_directories, relative_path)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[39mif\u001b[39;00m (_to_Path(root_dir) \u001b[39m/\u001b[39m relative_path)\u001b[39m.\u001b[39mexists():\n\u001b[1;32m     40\u001b[0m         \u001b[39mreturn\u001b[39;00m _to_Path(root_dir) \u001b[39m/\u001b[39m relative_path\n\u001b[0;32m---> 42\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m     43\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mNo valid full-path found (from \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m)\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     44\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39m for \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(root_directories, relative_path)\n\u001b[1;32m     45\u001b[0m )\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: No valid full-path found (from ['/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox', PosixPath('/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox')]) for /input_data/videos"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pca.PCATask.generate(bodypart_key)"
+    "# Insert data in PCAInfo table\n",
+    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
+    "kpms_pca.Bodyparts.insert1(\n",
+    "    {\n",
+    "        **bodypart_key,\n",
+    "        \"anterior_bodyparts\": [\"nose\"],\n",
+    "        \"posterior_bodyparts\": [\"spine2\"],\n",
+    "        \"use_bodyparts\": [\n",
+    "            \"spine4\",\n",
+    "            \"spine3\",\n",
+    "            \"spine2\",\n",
+    "            \"spine1\",\n",
+    "            \"head\",\n",
+    "            \"nose\",\n",
+    "            \"right ear\",\n",
+    "            \"left ear\",\n",
+    "        ],\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1260,23 +1379,130 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">config</p>\n",
-       "                            <span class=\"djtooltiptext\">stored full config file</span>\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
+       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.Bodyparts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts</p>\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">data</p>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">metadata</p>\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">project_path</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">variance_threshold</p>\n",
+       "                            <span class=\"djtooltiptext\">Variance threshold to be explained by the PCA model</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -1285,19 +1511,37 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  config     coordinate confidence bodyparts  data       metadata  \n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+ +--------+ +--------+ +--------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   project_path   task_mode     variance_thres\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +-----------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.PCATask.FormattedDataset()"
+    "kpms_pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCATask.insert1(\n",
+    "    {\n",
+    "        **bodypart_key,\n",
+    "        \"pca_task_id\": 1,\n",
+    "        \"project_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project\",\n",
+    "        \"task_mode\": \"load\",\n",
+    "        \"variance_threshold\": 0.90,\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
@@ -1376,23 +1620,36 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca</p>\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">project_path</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">variance_threshold</p>\n",
+       "                            <span class=\"djtooltiptext\">Variance threshold to be explained by the PCA model</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project</td>\n",
+       "<td>load</td>\n",
+       "<td>0.9</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti pca       \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   project_path   task_mode     variance_thres\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +-----------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              1              /Users/milagro load          0.9           \n",
+       " (Total: 1)"
       ]
      },
      "execution_count": 27,
@@ -1401,13 +1658,38 @@
     }
    ],
    "source": [
-    "pca.PCAFitting()"
+    "kpms_pca.PCATask()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'subject': 'subject1',\n",
+       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       " 'kpset_id': 1,\n",
+       " 'bodyparts_id': 1,\n",
+       " 'pca_task_id': 1}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = (kpms_pca.PCATask).fetch1(\"KEY\")\n",
+    "key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1468,10 +1750,28 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <p id=\"primary\">subject</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
@@ -1481,27 +1781,900 @@
        "        "
       ],
       "text/plain": [
-       "*latent_dim    latent_dim_des\n",
-       "+------------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   coordinate confidence formatted_\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pca.LatentDimension()"
+    "kpms_pca.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ACTION REQUIRED: `anterior_bodyparts` contains n which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains o which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains s which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains e which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains s which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains p which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains i which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains n which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains e which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains 4 which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading keypoints: 100%|████████████████| 10/10 [00:01<00:00,  9.31it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kpms_pca.FormattedDataset.populate(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   coordinate confidence formatted_\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1              1              =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.FormattedDataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   pca_fitting_ti\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ACTION REQUIRED: `anterior_bodyparts` contains n which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains o which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains s which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains e which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains s which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains p which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains i which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains n which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains e which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains 4 which is not one of\n",
+      "  the options in `use_bodyparts`.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "kpms_pca.PCAFitting.populate(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   pca_fitting_ti\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*latent_dim    latent_dim_des\n",
+       "+------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.LatentDimensionSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.LatentDimensionSet.insert1({\"latent_dim\":3, \"latent_dim_description\":\"testing\"}, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>3</td>\n",
+       "<td>testing</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*latent_dim    latent_dim_des\n",
+       "+------------+ +------------+\n",
+       "3              testing       \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.LatentDimensionSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.DimsExplainedVariance.populate(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>90.0</td>\n",
+       "<td>4</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   variance_perce dims_explained\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              1              90.0           4             \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.DimsExplainedVariance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TO-DO: Plotting before choosing the latent dimensions to use \n",
+    "# plot_scree(pca, project_dir=project_path) # --> both functions to plot outside the pipeline\n",
+    "# plot_pcs(pca, project_dir=project_path, **config())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.UpdateLatentDimensionTask.insert1({\n",
+    "    **key,\n",
+    "    \"latent_dim\": 5}, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>5</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   *latent_dim   \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              1              5             \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_pca.UpdateLatentDimensionTask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.UpdateLatentDimension.populate(key)"
+   ]
   }
  ],
  "metadata": {

From dc8c1530e4a3556cf6cfdb051589b47231646076 Mon Sep 17 00:00:00 2001
From: Thinh Nguyen <thinh@datajoint.com>
Date: Fri, 8 Mar 2024 12:37:57 -0600
Subject: [PATCH 41/90] code review - pipeline restructure

---
 element_moseq/kpms_model.py |  40 +++++--------
 element_moseq/kpms_pca.py   | 108 +++++++++++-------------------------
 2 files changed, 48 insertions(+), 100 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index 6dfe9f7..10f940c 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -99,33 +99,13 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 # ----------------------------- Table declarations ----------------------
 
 
-@schema
-class InitializationParamSet(dj.Lookup):
-    definition = """
-    paramset_id             : int
-    ---
-    num_iterations          : int
-    kappa                   : int
-    paramset_description='' : varchar(1000)
-    """
-
-
-@schema
-class FittingParamSet(dj.Lookup):
-    definition = """
-    paramset_id             : int
-    ---
-    num_iterations          : int
-    kappa                   : int
-    paramset_description='' : varchar(1000)
-    """
-
-
 @schema
 class PreFittingTask(dj.Manual):
     definition = """
-    -> InitializationParamSet
     -> pca.PCAFitting
+    laten_dim               : int
+    kappa                   : int
+    num_iterations          : int
     ---
     model_name              : varchar(20)
     task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
@@ -138,9 +118,17 @@ class PreFitting(dj.Computed):
     -> PreFittingTask
     ---
     model                   : longblob
+    suggested_laten_dim: int
+    suggested_kappa: int
+    suggested_num_iterations: int
     """
 
     def make(self, key):
+        # latent_dim = (PreFittingTask & key).fetch1("latent_dim")
+        # config = load_config()
+        # check_config_validity(config)
+        # config.update(dict(latent_dim = int(latent_dim)))
+
         # model = kpms.init_model(data, pca=pca, **config())
         # model = kpms.update_hypparams(model, kappa=kappa)
         # model, model_name = kpms.fit_model(model, data, metadata, project_dir,ar_only=True, num_iters=num_ar_iters)
@@ -150,8 +138,10 @@ def make(self, key):
 @schema
 class FullFittingTask(dj.Manual):
     definition = """
-    -> Prefitting
-    -> FittingParamSet
+    -> pca.PCAFitting
+    laten_dim               : int
+    kappa                   : int
+    num_iterations          : int
     ---
     model_name              : varchar(20)
     task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 40290ea..5b89259 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -150,14 +150,13 @@ class KeypointSet(dj.Manual):
     -> Session
     kpset_id                        : int
     ---
-    kpset_method                    : varchar(15)   # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
+    -> PoseEstimationMethod
     kpset_config_path               : varchar(255)  # Path relative to root data directory where the config file is located
-    kpset_videos_path               : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
+    kpset_videos_dir                : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
     kpset_description=''            : varchar(300)  # Optional. User-entered description
-
     """
 
-    class VideoFiles(dj.Part):
+    class VideoFile(dj.Part):
         """IDs and file paths of each video file.
 
         Atribbutes:
@@ -169,7 +168,7 @@ class VideoFiles(dj.Part):
         -> master
         video_id                    : int
         ---
-        video_path                  : varchar(255) # Filepath of each video, relative to root data directory
+        video_path                  : varchar(1000) # Filepath of each video, relative to root data directory
         """
 
 
@@ -188,7 +187,7 @@ class RecordingInfo(dj.Imported):
     """
 
     definition = """
-    -> KeypointSet.VideoFiles
+    -> KeypointSet
     ---
     px_height                 : smallint  # Height in pixels
     px_width                  : smallint  # Width in pixels
@@ -272,6 +271,7 @@ class Bodyparts(dj.Manual):
     -> KeypointSet
     bodyparts_id                : int
     ---
+    bodyparts_desc=''           : varchar(1000)
     anterior_bodyparts          : blob  # List of strings of anterior bodyparts
     posterior_bodyparts         : blob  # List of strings of posterior bodyparts
     use_bodyparts               : blob  # List of strings of bodyparts to be used
@@ -292,13 +292,10 @@ class PCATask(dj.Manual):
     """
 
     definition = """ 
-    -> KeypointSet
     -> Bodyparts
-    pca_task_id: int
     ---
     project_path='' : varchar(255)             # KPMS's project_path in config relative to root
     task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
-    variance_threshold : float                 # Variance threshold to be explained by the PCA model
     """
 
 @schema
@@ -380,52 +377,47 @@ def make(self, key):
 class PCAFitting(dj.Computed):
     
     definition = """
-    -> PCATask
+    -> FormattedDataset
     ---
-    pca_fitting_time = NULL    : datetime  # Time of generation of the PCA fitting analysis 
+    pca_fitting_time=NULL    : datetime  # Time of generation of the PCA fitting analysis 
     """
 
     def make(self, key):
 
         task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
-        config = load_config(project_path, check_if_valid=True, build_indexes=False)
-        coordinates, confidences = (FormattedDataset & key).fetch1(
-            "coordinates", "confidences"
-        )
-
-        data, metadata = format_data(
-            **config, coordinates=coordinates, confidences=confidences
-        )
 
         if task_mode == "trigger":
+            config = load_config(project_path, check_if_valid=True, build_indexes=False)
+            coordinates, confidences = (FormattedDataset & key).fetch1(
+                "coordinates", "confidences"
+            )
+
+            data, metadata = format_data(
+                **config, coordinates=coordinates, confidences=confidences
+            )
+
             pca = fit_pca(data, **config)
             pca_path = os.path.join(project_path, "pca_{}.p".format(key["pca_fitting_id"]))
             save_pca(pca, pca_path) # The model is saved
-            creation_time = datetime.strftime("%Y-%m-%d %H:%M:%S")
-
-            self.insert1(**key, pca_fitting_time=creation_time)
+            creation_time = datetime.utcnow()
+        else:
+            creation_time = None
 
+        self.insert1(**key, pca_fitting_time=creation_time)
 
-@schema
-class LatentDimensionSet(dj.Lookup):
-    """
-    This table is used to store the number of latent dimensions to be used in the analysis (manually inserted or computed from the variance threshold by the DimsExplainedVariance table).
-    """
-    definition = """
-    latent_dim                : int
-    ---
-    latent_dim_description='' : varchar(1000)
-    """
+    
 @schema
 class DimsExplainedVariance(dj.Computed):
     """
     This is an optional table to compute and store the latent dimensions that explain a certain specified variance threshold.
     """
     definition = """
-    -> PCATask
+    -> PCAFitting
+    variance_threshold : float                 # Variance threshold to be explained by the PCA model
     ---
     variance_percentage     : float 
     dims_explained_variance : int
+    latent_dim_description: varchar(1000)
     """
     
     def make(self, key):
@@ -436,49 +428,15 @@ def make(self, key):
         # Percentage of variance explained by each of the selected components.
         # If n_components is not set then all components are stored and the sum of the ratios is equal to 1.0.
         if cs[-1] < variance_threshold: 
-                dims_explained_variance = len(cs)
-                variance_percentage = cs[-1]*100
-                LatentDimensionSet.insert1(dict(latent_dim = dims_explained_variance,
-                                        latent_dim_description= (f"All components together only explain {cs[-1]*100}% of variance.")))
-
+            dims_explained_variance = len(cs)
+            variance_percentage = cs[-1]*100
+            latent_dim_description= f"All components together only explain {cs[-1]*100}% of variance."
         else:
-                dims_explained_variance = (cs>variance_threshold).nonzero()[0].min()+1
-                variance_percentage = variance_threshold*100
-                LatentDimensionSet.insert1(dict(latent_dim= dims_explained_variance,
-                                        latent_dim_description= (f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components.")))
+            dims_explained_variance = (cs>variance_threshold).nonzero()[0].min()+1
+            variance_percentage = variance_threshold*100
+            latent_dim_description= f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
         
         self.insert1(dict(**key, 
                           variance_percentage = variance_percentage,
-                          dims_explained_variance=dims_explained_variance))
-
-        
-@schema
-class UpdateLatentDimensionTask(dj.Manual):
-    """
-    This table allows the user to choose between the manual or computed latent dimension from the LatentDimension table for a PCA model.
-    """
-    definition = """
-    -> PCATask
-    latent_dim : int
-    """
-
-@schema
-class UpdateLatentDimension(dj.Computed):
-    """
-    This table updates the chosen latent dimension in the UpdateLatentDimensionTask in the `dj_config.yml` file.
-    """
-    definition="""
-    ->UpdateLatentDimensionTask
-    """
-    
-    def make(self, key):
-        project_path = (PCATask & key).fetch1("project_path")
-        latent_dim = (UpdateLatentDimensionTask & key).fetch1("latent_dim")
-
-        # Update the `dj_config.yml` with a possible new latent_dir    
-        config_path = os.path.join(project_path, "dj_config.yml")
-        with open(config_path, "r") as stream:
-            config = yaml.safe_load(stream)
-        check_config_validity(config)
-        config.update(dict(latent_dim = int(latent_dim)))
-        generate_dj_config(project_path, **config)
+                          dims_explained_variance=dims_explained_variance,
+                          latent_dim_description=latent_dim_description))

From 4a3294a1d2af2b101559eedd73cfdc5ff0922da2 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 20:05:56 +0100
Subject: [PATCH 42/90] revert update on dockerfile-> will update it later

---
 .devcontainer/Dockerfile | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index e74176f..1b55788 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -32,9 +32,8 @@ COPY ./ /tmp/element-moseq/
 RUN \
     # pipeline dependencies
     apt-get update && \
-    apt-get install gcc g++ ffmpeg -y && \
-    pip install "jax[cpu]==0.3.22" -f https://storage.googleapis.com/jax-releases/jax_releases.html && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements,kpms_default] && \
+    apt-get install -y gcc ffmpeg graphviz && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean

From 47087f2cde9cd38493a49c27896f66147491b2dd Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 21:24:14 +0100
Subject: [PATCH 43/90] from 'format' to 'format_method'

---
 element_moseq/kpms_pca.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 5b89259..6174212 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -109,18 +109,19 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 # ----------------------------- Table declarations ----------------------
 
+
 @schema
 class PoseEstimationMethod(dj.Lookup):
     """Table for storing the pose estimation method used to obtain the keypoints data.
 
     Attributes:
-        format (str)                : Pose estimation method.
+        format_method (str)                : Pose estimation method.
         pose_estimation_desc (str)  : Pose estimation method description.
     """
 
     definition = """ 
     # Parameters used to obtain the keypoints data based on a specific pose estimation method.        
-    format                          : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
+    format_method                          : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
     ---
     pose_estimation_desc            : varchar(1000)    # Optional. Pose estimation method description
     """
@@ -337,10 +338,8 @@ def make(self, key):
         )
         project_path = (PCATask & key).fetch1("project_path")
         task_mode = (PCATask & key).fetch1("task_mode")
-        method = (KeypointSet & key).fetch1("kpset_method")
-        format = (PoseEstimationMethod & {'format':method}).fetch1("format")
-        kpset_config_path, kpset_videos_path = (KeypointSet & key).fetch1("kpset_config_path","kpset_videos_path")
-        
+        format_method = (KeypointSet & key).fetch1("format_method")
+        kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
         if task_mode == "trigger":
             config = setup_project(
                     project_path, deeplabcut_config=kpset_config_path
@@ -360,7 +359,7 @@ def make(self, key):
 
         # load keypoints data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
         coordinates, confidences, formatted_bodyparts = load_keypoints(
-            filepath_pattern=kpset_videos_path, format=format
+            filepath_pattern=kpset_videos_dir, format=format_method
         )
         
         self.insert1(

From 15bbe27e1c790bef75997afe7c2756596ee9a48b Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 21:27:15 +0100
Subject: [PATCH 44/90]  `project_path` to `project_dir` for consistency

---
 element_moseq/kpms_pca.py | 44 +++++++++++++++++++--------------------
 1 file changed, 21 insertions(+), 23 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 6174212..7815ed2 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -142,8 +142,8 @@ class KeypointSet(dj.Manual):
 
     Attributes:
         kpset_id (int): Unique ID for each keypoint set.
-        kpset_config_path (str): Path relative to root data directory where the config file is located.
-        kpset_videos_path (str): Path relative to root data directory where the videos and their keypoints are located.
+        kpset_config_dir (str): Path relative to root data directory where the config file is located.
+        kpset_videos_dir (str): Path relative to root data directory where the videos and their keypoints are located.
         kpset_description (str): Optional. User-entered description.
     """
 
@@ -152,9 +152,9 @@ class KeypointSet(dj.Manual):
     kpset_id                        : int
     ---
     -> PoseEstimationMethod
-    kpset_config_path               : varchar(255)  # Path relative to root data directory where the config file is located
+    kpset_config_dir               : varchar(255)  # Path relative to root data directory where the config file is located
     kpset_videos_dir                : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
-    kpset_description=''            : varchar(300)  # Optional. User-entered description
+    kpset_desc=''            : varchar(300)  # Optional. User-entered description
     """
 
     class VideoFile(dj.Part):
@@ -288,14 +288,14 @@ class PCATask(dj.Manual):
         KeypointSet (foreign key)       : Unique ID for each keypoint set.
         Bodyparts (foreign key)         : Unique ID for each bodypart.
         pca_task_id (int)               : Unique ID for each PCA task.
-        project_path (str)              : KPMS's project_path in config relative to root
+        output_dir (str)                : KPMS's output directory in config relative to root
         task_mode (str)                 : 'load': load computed analysis results, 'trigger': trigger computation
     """
 
     definition = """ 
     -> Bodyparts
     ---
-    project_path='' : varchar(255)             # KPMS's project_path in config relative to root
+    output_dir='' : varchar(255)             # KPMS's output directory in config relative to root
     task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
     """
 
@@ -336,7 +336,7 @@ def make(self, key):
             "posterior_bodyparts",
             "use_bodyparts",
         )
-        project_path = (PCATask & key).fetch1("project_path")
+        output_dir = (PCATask & key).fetch1("output_dir")
         task_mode = (PCATask & key).fetch1("task_mode")
         format_method = (KeypointSet & key).fetch1("format_method")
         kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
@@ -347,15 +347,10 @@ def make(self, key):
 
         elif task_mode == "load":
             config_kwargs_dict = dict(
-            video_dir = kpset_videos_path,
-            anterior_bodyparts = anterior_bodyparts,
-            posterior_bodyparts = posterior_bodyparts,
-            use_bodyparts = use_bodyparts)
-            
-            # Update the config with new video_dir and bodyparts and save it as `dj_config.yml`        
-            config = load_config(project_path, check_if_valid=True, build_indexes=False)
+            # ---- Build and save DLC configuration (yaml) file ----
+            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
             config.update(**config_kwargs_dict)
-            generate_dj_config(project_path, **config)
+            generate_dj_config(output_dir, **config)
 
         # load keypoints data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
         coordinates, confidences, formatted_bodyparts = load_keypoints(
@@ -382,11 +377,10 @@ class PCAFitting(dj.Computed):
     """
 
     def make(self, key):
-
-        task_mode, project_path = (PCATask & key).fetch1("task_mode", "project_path")
+        task_mode, output_dir = (PCATask & key).fetch1("task_mode", "output_dir")
 
         if task_mode == "trigger":
-            config = load_config(project_path, check_if_valid=True, build_indexes=False)
+            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
             coordinates, confidences = (FormattedDataset & key).fetch1(
                 "coordinates", "confidences"
             )
@@ -396,8 +390,10 @@ def make(self, key):
             )
 
             pca = fit_pca(data, **config)
-            pca_path = os.path.join(project_path, "pca_{}.p".format(key["pca_fitting_id"]))
-            save_pca(pca, pca_path) # The model is saved
+            pca_path = os.path.join(
+                output_dir, "pca_{}.p".format(key["pca_fitting_id"])
+            )
+            save_pca(pca, pca_path)  # The model is saved
             creation_time = datetime.utcnow()
         else:
             creation_time = None
@@ -416,12 +412,14 @@ class DimsExplainedVariance(dj.Computed):
     ---
     variance_percentage     : float 
     dims_explained_variance : int
-    latent_dim_description: varchar(1000)
+    latent_dim_desc: varchar(1000)
     """
     
     def make(self, key):
-        variance_threshold, project_path = (PCATask & key).fetch1("variance_threshold","project_path")
-        pca = load_pca(project_path)
+        variance_threshold, output_dir = (PCATask & key).fetch1(
+            "variance_threshold", "output_dir"
+        )
+        pca = load_pca(output_dir)
         cs = np.cumsum(pca.explained_variance_ratio_)
         # explained_variance_ratio_ndarray of shape (n_components,)
         # Percentage of variance explained by each of the selected components.

From afd6c45101a4efd5db67b3620dd1bbec48090241 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 21:28:16 +0100
Subject: [PATCH 45/90] `RecordingInfo` to av. metadata for one `kpset_id`

---
 element_moseq/kpms_pca.py | 67 ++++++++++++++++++++-------------------
 1 file changed, 35 insertions(+), 32 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 7815ed2..b9d9444 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -175,10 +175,10 @@ class VideoFile(dj.Part):
 
 @schema
 class RecordingInfo(dj.Imported):
-    """Automated table to store video metadata.
+    """Automated table to store the average metadata from the videoset associated with a kpset_id.
 
     Attributes:
-        KeypointSet.VideoFiles (foreign key)    : Unique ID for each video.
+        KeypointSet (foreign key)               : Unique ID for each video set.
         px_height (smallint)                    : Height in pixels.
         px_width (smallint)                     : Width in pixels.
         nframes (int)                           : Number of frames.
@@ -190,18 +190,18 @@ class RecordingInfo(dj.Imported):
     definition = """
     -> KeypointSet
     ---
-    px_height                 : smallint  # Height in pixels
-    px_width                  : smallint  # Width in pixels
-    nframes                   : int       # Number of frames 
-    fps = NULL                : int       # Optional. Frames per second, Hz
+    px_height_average                 : smallint  # Height in pixels
+    px_width_average                  : smallint  # Width in pixels
+    nframes_average                   : int       # Number of frames 
+    fps_average = NULL                : int       # Optional. Frames per second, Hz
     recording_datetime = NULL : datetime  # Optional. Datetime for the start of the recording
-    recording_duration        : float     # Video duration (s) from nframes / fps
+    recording_duration_average        : float     # Video duration (s) from nframes / fps
     """
 
     @property
     def key_source(self):
         """Defines order of keys for the make function when called via `populate()`"""
-        return KeypointSet & KeypointSet.VideoFiles
+        return KeypointSet & KeypointSet.VideoFile
 
     def make(self, key):
         """
@@ -221,39 +221,42 @@ def make(self, key):
 
         """
 
-        file_paths, video_ids = (KeypointSet.VideoFiles & key).fetch(
+        file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
             "video_path", "video_id"
         )
 
-        for fp, video_id in zip(file_paths, video_ids):
-            nframes = 0
-            px_height, px_width, fps = None, None, None
+        px_height_list = []
+        px_width_list = []
+        nframes_list = []
+        fps_list = []
+        recording_duration_list = []
 
+        for fp, video_id in zip(file_paths, video_ids):
             file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
 
             cap = cv2.VideoCapture(file_path)
-            info = (
-                int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
-                int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
-                int(cap.get(cv2.CAP_PROP_FPS)),
-            )
-            if px_height is not None:
-                assert (px_height, px_width, fps) == info
-            px_height, px_width, fps = info
-            nframes += int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+            px_height_list.append(int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+            px_width_list.append(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
+            fps_list.append(int(cap.get(cv2.CAP_PROP_FPS)))
+            nframes_list.append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))
             cap.release()
 
-            self.insert1(
-                {
-                    **key,
-                    "video_id": video_id,
-                    "px_height": px_height,
-                    "px_width": px_width,
-                    "nframes": nframes,
-                    "fps": fps,
-                    "recording_duration": nframes / fps,
-                }
-            )
+        px_height_average = int(np.mean(px_height_list))
+        px_width_average = int(np.mean(px_width_list))
+        fps_average = int(np.mean(fps_list))
+        nframes_average = int(np.mean(nframes_list))
+        recording_duration_average = int(np.mean(nframes_list) / np.mean(fps_list))
+
+        self.insert1(
+            {
+                **key,
+                "px_height_average": px_height_average,
+                "px_width_average": px_width_average,
+                "nframes_average": nframes_average,
+                "fps_average": fps_average,
+                "recording_duration_average": recording_duration_average,
+            }
+        )
 
 
 @schema

From f9c0b5c64a1f34931914cca9bf5ee2538c3a341a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 21:29:49 +0100
Subject: [PATCH 46/90] delete unused dependencies in `kpms_model`

---
 element_moseq/kpms_model.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index 10f940c..b81cb9d 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -1,13 +1,10 @@
 import datajoint as dj
-import matplotlib.pyplot as plt
-import cv2
 from typing import Optional
 
 import inspect
 import importlib
-import os
 from pathlib import Path
-from element_interface.utils import find_full_path, dict_to_uuid
+from element_interface.utils import find_full_path
 
 schema = dj.schema()
 _linking_module = None

From d8137da01eb5267339af52ee8f177ba97d5d0f22 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Fri, 8 Mar 2024 21:30:45 +0100
Subject: [PATCH 47/90] markdown clarification in `tutorial_pipeline`

---
 notebooks/tutorial_pipeline.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/tutorial_pipeline.py b/notebooks/tutorial_pipeline.py
index 0ffc7af..efdcb67 100644
--- a/notebooks/tutorial_pipeline.py
+++ b/notebooks/tutorial_pipeline.py
@@ -89,7 +89,7 @@ class Device(dj.Lookup):
     ]
 
 
-# Activate Keypoint-MoSeq schema -----------------------------------
+# Activate element-moseq schemas -----------------------------------
 
 kpms_pca.activate(db_prefix + "kpms_pca", linking_module=__name__)
 kpms_model.activate(db_prefix + "kpms_model", linking_module=__name__)

From 13e248082ad72074d28fbe303e102c079e47b3ef Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 00:03:32 +0100
Subject: [PATCH 48/90] Update `FormattedDataset` logic

---
 element_moseq/kpms_pca.py | 85 ++++++++++++++++++++++++++-------------
 1 file changed, 56 insertions(+), 29 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index b9d9444..3d9632c 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -7,22 +7,22 @@
 import inspect
 import importlib
 import os
-import yaml 
+import yaml
 from pathlib import Path
+
 from element_interface.utils import find_full_path
-from .readers.kpms_reader import generate_dj_config
+from .readers.kpms_reader import generate_dj_config, load_dj_config
 from keypoint_moseq import (
-            setup_project,
-            load_config,
-            load_keypoints,
-            format_data,
-            load_pca,
-            fit_pca,
-            save_pca,
-            check_config_validity
-        )
+    setup_project,
+    load_config,
+    load_keypoints,
+    format_data,
+    load_pca,
+    fit_pca,
+    save_pca,
+)
+
 
-        
 schema = dj.schema()
 _linking_module = None
 
@@ -302,8 +302,9 @@ class PCATask(dj.Manual):
     task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
     """
 
+
 @schema
-class FormattedDataset(dj.Imported): # --> TO-DO: change name for a more intuitive option
+class FormattedDataset(dj.Imported):
     """
     Table for storing the formatted dataset and update the config.yml by creating a new dj_config.yml in the project path (output_dir)
     """
@@ -318,8 +319,8 @@ class FormattedDataset(dj.Imported): # --> TO-DO: change name for a more intuiti
 
     def make(self, key):
         """
-        Make function to format keypoint coordinates and confidences for inference.
-        
+        Make function to generate/update dj_config.yml and to format keypoint coordinates and confidences for inference.
+
         Args:
             key (dict): Primary key from the PCATask table.
 
@@ -327,45 +328,71 @@ def make(self, key):
             dict: Primary key and attributes for the PCATask table.
 
         Raises:
-        
+
         High-Level Logic:
-        
+
         """
 
         anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
-        Bodyparts & key
+            Bodyparts & key
         ).fetch1(
             "anterior_bodyparts",
             "posterior_bodyparts",
             "use_bodyparts",
         )
-        output_dir = (PCATask & key).fetch1("output_dir")
-        task_mode = (PCATask & key).fetch1("task_mode")
-        format_method = (KeypointSet & key).fetch1("format_method")
-        kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
+        output_dir, task_mode = (PCATask & key).fetch1("output_dir", "task_mode")
+        format_method, kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
+            "format_method", "kpset_config_dir", "kpset_videos_dir"
+        )
+
         if task_mode == "trigger":
-            config = setup_project(
-                    project_path, deeplabcut_config=kpset_config_path
-                ) 
+            # create an output_dir if it does not exist, and create a config file with the default values from the pose estimation config
+            setup_project(
+                output_dir, deeplabcut_config=kpset_config_dir + "/config.yaml"
+            )  # creates KPMS default config file from dlc data
+            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
+
+            # update the config dict with the video_dir and bodyparts used in the pipeline
+            config_kwargs_dict = dict(
+                video_dir=kpset_videos_dir,
+                anterior_bodyparts=anterior_bodyparts,
+                posterior_bodyparts=posterior_bodyparts,
+                use_bodyparts=use_bodyparts,
+            )
+            config.update(**config_kwargs_dict)
+
+            # save the updated config dict to a different file named `dj_config.yml`
+            generate_dj_config(output_dir, **config)
 
         elif task_mode == "load":
+            config = load_dj_config(output_dir)
+
+            # update the config dict with the video_dir and bodyparts used in the pipeline
             config_kwargs_dict = dict(
-            # ---- Build and save DLC configuration (yaml) file ----
-            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
+                video_dir=kpset_videos_dir,
+                anterior_bodyparts=anterior_bodyparts,
+                posterior_bodyparts=posterior_bodyparts,
+                use_bodyparts=use_bodyparts,
+            )
             config.update(**config_kwargs_dict)
+
+            # update the updated config dict to the file `dj_config.yml`
             generate_dj_config(output_dir, **config)
 
+        else:
+            raise ValueError("task_mode should be either 'load' or 'trigger'")
+
         # load keypoints data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
         coordinates, confidences, formatted_bodyparts = load_keypoints(
             filepath_pattern=kpset_videos_dir, format=format_method
         )
-        
+
         self.insert1(
             dict(
                 **key,
                 coordinates=coordinates,
                 confidences=confidences,
-                formatted_bodyparts=formatted_bodyparts
+                formatted_bodyparts=formatted_bodyparts,
             )
         )
 

From 53edbcba4ac333c8f00fc4a4e7b39954d4372fb1 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 00:04:25 +0100
Subject: [PATCH 49/90] Update `PCAFitting` computation logic

---
 element_moseq/kpms_pca.py | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 3d9632c..5bbd1a1 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -399,7 +399,6 @@ def make(self, key):
 
 @schema
 class PCAFitting(dj.Computed):
-    
     definition = """
     -> FormattedDataset
     ---
@@ -410,7 +409,7 @@ def make(self, key):
         task_mode, output_dir = (PCATask & key).fetch1("task_mode", "output_dir")
 
         if task_mode == "trigger":
-            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
+            config = load_dj_config(output_dir, check_if_valid=True, build_indexes=True)
             coordinates, confidences = (FormattedDataset & key).fetch1(
                 "coordinates", "confidences"
             )
@@ -419,18 +418,22 @@ def make(self, key):
                 **config, coordinates=coordinates, confidences=confidences
             )
 
-            pca = fit_pca(data, **config)
-            pca_path = os.path.join(
-                output_dir, "pca_{}.p".format(key["pca_fitting_id"])
-            )
-            save_pca(pca, pca_path)  # The model is saved
+            pca = fit_pca(**data, **config)
+
+            # save the pca model to a file
+            pca_path = os.path.join(output_dir, "pca.p")
+            save_pca(
+                pca, output_dir
+            )  # `pca.p` as the first pca model stored in the output_dir
+
             creation_time = datetime.utcnow()
+
         else:
             creation_time = None
 
-        self.insert1(**key, pca_fitting_time=creation_time)
+        self.insert1(dict(**key, pca_fitting_time=creation_time))
+
 
-    
 @schema
 class DimsExplainedVariance(dj.Computed):
     """

From 8a8139d8d038f4677a93adb449d269cd31f4a447 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 00:05:11 +0100
Subject: [PATCH 50/90] Update `DimsExplainedVariance` computation logic

---
 element_moseq/kpms_pca.py | 38 ++++++++++++++++++++++----------------
 1 file changed, 22 insertions(+), 16 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 5bbd1a1..6ad2da2 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -439,34 +439,40 @@ class DimsExplainedVariance(dj.Computed):
     """
     This is an optional table to compute and store the latent dimensions that explain a certain specified variance threshold.
     """
+
     definition = """
     -> PCAFitting
-    variance_threshold : float                 # Variance threshold to be explained by the PCA model
     ---
     variance_percentage     : float 
     dims_explained_variance : int
     latent_dim_desc: varchar(1000)
     """
-    
+
     def make(self, key):
-        variance_threshold, output_dir = (PCATask & key).fetch1(
-            "variance_threshold", "output_dir"
-        )
+        output_dir = (PCATask & key).fetch1("output_dir")
+        variance_threshold = 0.90
+
         pca = load_pca(output_dir)
         cs = np.cumsum(pca.explained_variance_ratio_)
         # explained_variance_ratio_ndarray of shape (n_components,)
         # Percentage of variance explained by each of the selected components.
         # If n_components is not set then all components are stored and the sum of the ratios is equal to 1.0.
-        if cs[-1] < variance_threshold: 
+        if cs[-1] < variance_threshold:
             dims_explained_variance = len(cs)
-            variance_percentage = cs[-1]*100
-            latent_dim_description= f"All components together only explain {cs[-1]*100}% of variance."
+            variance_percentage = cs[-1] * 100
+            latent_dim_desc = (
+                f"All components together only explain {cs[-1]*100}% of variance."
+            )
         else:
-            dims_explained_variance = (cs>variance_threshold).nonzero()[0].min()+1
-            variance_percentage = variance_threshold*100
-            latent_dim_description= f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
-        
-        self.insert1(dict(**key, 
-                          variance_percentage = variance_percentage,
-                          dims_explained_variance=dims_explained_variance,
-                          latent_dim_description=latent_dim_description))
+            dims_explained_variance = (cs > variance_threshold).nonzero()[0].min() + 1
+            variance_percentage = variance_threshold * 100
+            latent_dim_desc = f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
+
+        self.insert1(
+            dict(
+                **key,
+                variance_percentage=variance_percentage,
+                dims_explained_variance=dims_explained_variance,
+                latent_dim_desc=latent_dim_desc,
+            )
+        )

From fd2ccca1dccd274388ba19c14cb1336e4c54ca62 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 00:06:05 +0100
Subject: [PATCH 51/90] Add `load_dj_config` function to readers

---
 element_moseq/readers/kpms_reader.py | 31 ++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py
index 01948fd..48245d5 100644
--- a/element_moseq/readers/kpms_reader.py
+++ b/element_moseq/readers/kpms_reader.py
@@ -1,12 +1,15 @@
 import os
 import logging
 import yaml
+
 # from ruamel.yaml import YAML
+import jax.numpy as jnp
 from element_interface.utils import find_root_directory, dict_to_uuid
 from datajoint.errors import DataJointError
 
 logger = logging.getLogger("datajoint")
 
+
 def _build_yaml(sections, comments):
     text_blocks = []
     for title, data in sections:
@@ -143,3 +146,31 @@ def _update_dict(new, original):
 
     with open(os.path.join(project_dir, "dj_config.yml"), "w") as f:
         f.write(_build_yaml(sections, comments))
+
+
+def load_dj_config(output_dir, check_if_valid=True, build_indexes=True):
+    """
+    Load a project dj_config file from output_dir
+    """
+    from keypoint_moseq import check_config_validity
+
+    config_path = os.path.join(output_dir, "dj_config.yml")
+
+    with open(config_path, "r") as f:
+        config = yaml.safe_load(f)
+
+    if check_if_valid:
+        check_config_validity(config)
+
+    if build_indexes:
+        config["anterior_idxs"] = jnp.array(
+            [config["use_bodyparts"].index(bp) for bp in config["anterior_bodyparts"]]
+        )
+        config["posterior_idxs"] = jnp.array(
+            [config["use_bodyparts"].index(bp) for bp in config["posterior_bodyparts"]]
+        )
+
+    if not "skeleton" in config or config["skeleton"] is None:
+        config["skeleton"] = []
+
+    return config

From cd27d1e10424fcf42f84fcfd035c067e02403692 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 00:15:17 +0100
Subject: [PATCH 52/90] Update notebook after testing the new`pca` logics

---
 notebooks/testing.ipynb | 1345 +++++++++++++++------------------------
 1 file changed, 507 insertions(+), 838 deletions(-)

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
index e5e9e6d..2a9e65f 100644
--- a/notebooks/testing.ipynb
+++ b/notebooks/testing.ipynb
@@ -20,64 +20,22 @@
    "source": [
     "import datajoint as dj\n",
     "from pathlib import Path\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dj.config.load(\"dj_local_conf.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dj.config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dj.conn()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dj.list_schemas()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_pca\").drop()"
+    "import numpy as np\n",
+    "\n",
+    "# from keypoint_moseq import setup_project , load_config\n",
+    "# from element_moseq.readers.kpms_reader import load_dj_config, generate_dj_config"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-03-08 03:53:37,997][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+      "[2024-03-12 00:13:11,809][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
      ]
     },
     {
@@ -124,12 +82,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='6a106c71-1b1c-4de8-bf82-6f3d46cd30cc'>\n",
-       "  <div id=\"b204cace-626d-43d3-a469-aaf10d5a7f49\" data-root-id=\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\" style=\"display: contents;\"></div>\n",
+       "<div id='4734f2b1-ddd4-443b-b41a-63bdf1b717bc'>\n",
+       "  <div id=\"e4c5556c-13eb-4c3f-bf46-edf3a5c94dca\" data-root-id=\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"2a0add99-b645-459a-9881-f0441c3f3968\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"23fbdb09-a73b-4f41-bacc-5ebeddf7e70b\",\"attributes\":{\"plot_id\":\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\",\"comm_id\":\"de177822eac14690be912c6724c01980\",\"client_comm_id\":\"373319e9836b4b9fbd979e153a6f4604\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"2a0add99-b645-459a-9881-f0441c3f3968\",\"roots\":{\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\":\"b204cace-626d-43d3-a469-aaf10d5a7f49\"},\"root_ids\":[\"6a106c71-1b1c-4de8-bf82-6f3d46cd30cc\"]}];\n",
+       "  var docs_json = {\"554d882e-5148-4324-b896-d009a3085866\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"2c64e15c-79c1-411b-9fda-5a0a520a1257\",\"attributes\":{\"plot_id\":\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\",\"comm_id\":\"ab464bf2e7bb4fa7bba9c4f786f2bb72\",\"client_comm_id\":\"228a095720284b0b957f8ed697cd7a5a\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"554d882e-5148-4324-b896-d009a3085866\",\"roots\":{\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\":\"e4c5556c-13eb-4c3f-bf46-edf3a5c94dca\"},\"root_ids\":[\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -193,206 +151,207 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "6a106c71-1b1c-4de8-bf82-6f3d46cd30cc"
+       "id": "4734f2b1-ddd4-443b-b41a-63bdf1b717bc"
       }
      },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# from element_moseq import kpms_pca\n",
-    "from tutorial_pipeline import lab, subject, session, kpms_pca"
+    "from tutorial_pipeline import lab, subject, session, kpms_pca, kpms_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. PCA MODEL AND LATENT DIMENSIONS\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "<!-- kpms_pca.LatentDimensionSet -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>kpms_pca.LatentDimensionSet</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"latent_dim           \r------------------------------\rlatent_dim_description=&quot;&quot; \r\">\n",
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-       "<text text-anchor=\"start\" x=\"48.18\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.LatentDimensionSet</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"574pt\" height=\"519pt\" viewBox=\"0.00 0.00 573.63 519.20\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 515.2)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-515.2 569.63,-515.2 569.63,4 -4,4\"/>\n",
        "<!-- kpms_pca.KeypointSet -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
+       "<g id=\"node1\" class=\"node\">\n",
        "<title>kpms_pca.KeypointSet</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\rkpset_method         \rkpset_config_path    \rkpset_videos_path    \rkpset_description=&quot;&quot; \r\">\n",
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-       "<text text-anchor=\"start\" x=\"218.93\" y=\"-287.61\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"362.7,-370.08 223.7,-370.08 223.7,-335.52 362.7,-335.52 362.7,-370.08\"/>\n",
+       "<text text-anchor=\"start\" x=\"231.7\" y=\"-349.53\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet.VideoFiles -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>kpms_pca.KeypointSet.VideoFiles</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"363.43,-230.32 197.43,-230.32 197.43,-210.32 363.43,-210.32 363.43,-230.32\"/>\n",
-       "<text text-anchor=\"start\" x=\"205.43\" y=\"-217.82\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFiles</text>\n",
+       "<!-- kpms_pca.RecordingInfo -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_pca.RecordingInfo</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_datetime=null \rrecording_duration_average \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"97.2\" cy=\"-282.24\" rx=\"97.2\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"97.2\" y=\"-277.97\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFiles -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFiles</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M280.43,-273.28C280.43,-259.65 280.43,-240.88 280.43,-230.03\"/>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M245.25,-335.03C213.07,-323.77 171.03,-309.07 139.98,-298.21\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kpms_pca.PCATask</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.KeypointSet\r→ kpms_pca.Bodyparts\rpca_task_id          \r------------------------------\rproject_path=&quot;&quot;      \rtask_mode=&quot;load&quot;     \rvariance_threshold   \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"530.68,-167.04 408.18,-167.04 408.18,-132.48 530.68,-132.48 530.68,-167.04\"/>\n",
-       "<text text-anchor=\"start\" x=\"416.18\" y=\"-146.48\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"373.57,-292.24 212.82,-292.24 212.82,-272.24 373.57,-272.24 373.57,-292.24\"/>\n",
+       "<text text-anchor=\"start\" x=\"220.82\" y=\"-279.74\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.PCATask -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.PCATask</title>\n",
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+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M293.2,-335.2C293.2,-321.57 293.2,-302.8 293.2,-291.95\"/>\n",
        "</g>\n",
        "<!-- kpms_pca.Bodyparts -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node10\" class=\"node\">\n",
        "<title>kpms_pca.Bodyparts</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"547.31,-237.6 419.56,-237.6 419.56,-203.04 547.31,-203.04 547.31,-237.6\"/>\n",
-       "<text text-anchor=\"start\" x=\"427.56\" y=\"-217.04\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"519.07,-299.52 391.32,-299.52 391.32,-264.96 519.07,-264.96 519.07,-299.52\"/>\n",
+       "<text text-anchor=\"start\" x=\"399.32\" y=\"-278.97\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
        "<g id=\"edge3\" class=\"edge\">\n",
        "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M330.09,-273.11C361.66,-262.44 402.39,-248.69 433.93,-238.04\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M332.83,-335.03C358.03,-324.36 390.52,-310.61 415.7,-299.96\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.RecordingInfo -->\n",
+       "<!-- kpms_pca.PCATask -->\n",
        "<g id=\"node4\" class=\"node\">\n",
-       "<title>kpms_pca.RecordingInfo</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.KeypointSet.VideoFiles\r------------------------------\rpx_height            \rpx_width             \rnframes              \rfps=null             \rrecording_datetime=null \rrecording_duration   \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"280.43\" cy=\"-149.76\" rx=\"97.2\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"280.43\" y=\"-145.48\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
+       "<title>kpms_pca.PCATask</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"516.45,-228.96 393.95,-228.96 393.95,-194.4 516.45,-194.4 516.45,-228.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-207.41\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet.VideoFiles&#45;&gt;kpms_pca.RecordingInfo -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet.VideoFiles-&gt;kpms_pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M280.43,-210.36C280.43,-199.47 280.43,-180.88 280.43,-167.35\"/>\n",
-       "</g>\n",
        "<!-- kpms_pca.FormattedDataset -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>kpms_pca.FormattedDataset</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"110.43\" cy=\"-79.2\" rx=\"110.43\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"110.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"455.2\" cy=\"-141.12\" rx=\"110.43\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-136.84\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
        "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M407.8,-136.62C400.6,-135.22 393.35,-133.81 386.43,-132.48 315.92,-118.89 235.21,-103.63 179.36,-93.13\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-194.08C455.2,-183.3 455.2,-169.31 455.2,-158.56\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.UpdateLatentDimensionTask -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>kpms_pca.UpdateLatentDimensionTask</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.PCATask\rlatent_dim           \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"467.56,-96.48 239.31,-96.48 239.31,-61.92 467.56,-61.92 467.56,-96.48\"/>\n",
-       "<text text-anchor=\"start\" x=\"247.31\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.UpdateLatentDimensionTask</text>\n",
+       "<!-- subject.Subject -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>subject.Subject</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"419.07,-511.2 321.32,-511.2 321.32,-476.64 419.07,-476.64 419.07,-511.2\"/>\n",
+       "<text text-anchor=\"start\" x=\"329.32\" y=\"-490.65\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.UpdateLatentDimensionTask -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.UpdateLatentDimensionTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M441.05,-131.99C422.94,-121.28 399.57,-107.47 381.51,-96.8\"/>\n",
+       "<!-- session.Session -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>session.Session</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"422.07,-440.64 318.32,-440.64 318.32,-406.08 422.07,-406.08 422.07,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"326.32\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- kpms_pca.DimsExplainedVariance -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>kpms_pca.DimsExplainedVariance</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rvariance_percentage  \rdims_explained_variance \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"586.43\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"586.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
+       "</g>\n",
+       "<!-- subject.Subject&#45;&gt;session.Session -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>subject.Subject-&gt;session.Session</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M370.2,-476.32C370.2,-465.54 370.2,-451.55 370.2,-440.8\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"299.82,-440.64 134.57,-440.64 134.57,-406.08 299.82,-406.08 299.82,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"142.57\" y=\"-420.86\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M235.21,-406.11C247.15,-395.34 262.77,-381.25 274.79,-370.41\"/>\n",
+       "</g>\n",
+       "<!-- session.Session&#45;&gt;kpms_pca.KeypointSet -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.DimsExplainedVariance</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M498.05,-131.99C523.22,-117.24 558.47,-96.58 575.87,-86.39\"/>\n",
+       "<title>session.Session-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M351.56,-405.76C339.54,-395.06 323.97,-381.2 311.93,-370.48\"/>\n",
        "</g>\n",
        "<!-- kpms_pca.PCAFitting -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
+       "<g id=\"node9\" class=\"node\">\n",
        "<title>kpms_pca.PCAFitting</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rpca_fitting_time=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"771.43\" cy=\"-79.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"771.43\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"455.2\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.PCAFitting -->\n",
+       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M530.91,-134.8C604.2,-118.16 721.54,-91.53 759.31,-82.95\"/>\n",
-       "</g>\n",
-       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M480.04,-202.72C477.84,-191.94 474.98,-177.95 472.79,-167.2\"/>\n",
+       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-123.58C455.2,-112.28 455.2,-97.58 455.2,-87.5\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.UpdateLatentDimension -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>kpms_pca.UpdateLatentDimension</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_pca.UpdateLatentDimensionTask\r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"353.43\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"353.43\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.UpdateLatentDimension</text>\n",
+       "<!-- kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_pca.DimsExplainedVariance</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"455.2\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.UpdateLatentDimensionTask&#45;&gt;kpms_pca.UpdateLatentDimension -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>kpms_pca.UpdateLatentDimensionTask-&gt;kpms_pca.UpdateLatentDimension</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M353.43,-61.66C353.43,-50.36 353.43,-35.66 353.43,-25.58\"/>\n",
-       "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>kpms_pca.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"format               \r------------------------------\rpose_estimation_desc \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"533.06,-308.16 367.81,-308.16 367.81,-273.6 533.06,-273.6 533.06,-308.16\"/>\n",
-       "<text text-anchor=\"start\" x=\"375.81\" y=\"-288.38\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
-       "</a>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-62.04C455.2,-51.54 455.2,-36.27 455.2,-25.77\"/>\n",
        "</g>\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-264.64C455.2,-253.86 455.2,-239.87 455.2,-229.12\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x2a1d53610>"
+       "<datajoint.diagram.Diagram at 0x105670880>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dj.Diagram(kpms_pca)\n",
-    "# + dj.Diagram(model)"
+    "(\n",
+    "    dj.Diagram(subject.Subject)\n",
+    "    + dj.Diagram(session.Session)\n",
+    "    + dj.Diagram(kpms_pca)\n",
+    "    #    + dj.Diagram(kpms_model)\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -463,16 +422,16 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap</span>\n",
+       "                            <p id=\"nonprimary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_config_path</p>\n",
+       "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
        "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_videos_path</p>\n",
+       "                            <p id=\"nonprimary\">kpset_videos_dir</p>\n",
        "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_description</p>\n",
+       "                            <p id=\"nonprimary\">kpset_desc</p>\n",
        "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
@@ -482,13 +441,13 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    kpset_method   kpset_config_p kpset_videos_p kpset_descript\n",
+       "*subject    *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
        "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,278 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpset_key = dict(subject=\"subject1\", \n",
-    "                 session_datetime=\"2021-06-02 14:04:22\", \n",
-    "                 kpset_id=1)\n",
-    "\n",
-    "kpms_pca.KeypointSet.insert1(\n",
-    "    {\n",
-    "        **kpset_key,\n",
-    "        \"kpset_method\":\"deeplabcut\",\n",
-    "        \"kpset_config_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
-    "        \"kpset_videos_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
-    "        \"kpset_description\": \"testing kpms pca schema\",\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_config_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_videos_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_description</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
-       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    kpset_method   kpset_config_p kpset_videos_p kpset_descript\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.KeypointSet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "video_files = [\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
-    "]\n",
-    "\n",
-    "kpms_pca.KeypointSet.VideoFiles.insert(\n",
-    "    (\n",
-    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
-    "        for v_idx, f in enumerate(video_files)\n",
-    "    ),\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">video_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">video_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>0</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>2</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 3</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
-       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
-       " (Total: 3)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.KeypointSet.VideoFiles()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -859,7 +547,7 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">format</p>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
        "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">pose_estimation_desc</p>\n",
@@ -878,7 +566,7 @@
        "        "
       ],
       "text/plain": [
-       "*format        pose_estimatio\n",
+       "*format_method pose_estimatio\n",
        "+------------+ +------------+\n",
        "anipose        `.csv` files g\n",
        "deeplabcut     `.csv` and `.h\n",
@@ -889,18 +577,38 @@
        " (Total: 6)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PoseEstimationMethod()"
+    "kpms_pca.PoseEstimationMethod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpset_key = dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\", kpset_id=1)\n",
+    "\n",
+    "kpms_pca.KeypointSet.insert1(\n",
+    "    {\n",
+    "        **kpset_key,\n",
+    "        \"format_method\": \"deeplabcut\",\n",
+    "        \"kpset_config_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
+    "        \"kpset_videos_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
+    "        \"kpset_desc\": \"testing kpms pca schema\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -971,61 +679,70 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">video_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height</p>\n",
-       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width</p>\n",
-       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
+       "                            <p id=\"nonprimary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
+       "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
+       "                            <p id=\"nonprimary\">kpset_videos_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
+       "                            <p id=\"nonprimary\">kpset_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
+       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
-       "+---------+ +------------+ +----------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.RecordingInfo()"
+    "kpms_pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.RecordingInfo.populate()"
+    "video_files = [\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
+    "]\n",
+    "\n",
+    "kpms_pca.KeypointSet.VideoFile.insert(\n",
+    "    (\n",
+    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
+    "        for v_idx, f in enumerate(video_files)\n",
+    "    ),\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1099,78 +816,48 @@
        "                            <p id=\"primary\">video_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height</p>\n",
-       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width</p>\n",
-       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
+       "                            <p id=\"nonprimary\">video_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>0</td>\n",
-       "<td>576</td>\n",
-       "<td>640</td>\n",
-       "<td>71524</td>\n",
-       "<td>30</td>\n",
-       "<td>None</td>\n",
-       "<td>2384.13</td></tr><tr><td>subject1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>1</td>\n",
-       "<td>576</td>\n",
-       "<td>640</td>\n",
-       "<td>107507</td>\n",
-       "<td>30</td>\n",
-       "<td>None</td>\n",
-       "<td>3583.57</td></tr><tr><td>subject1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>2</td>\n",
-       "<td>576</td>\n",
-       "<td>640</td>\n",
-       "<td>107520</td>\n",
-       "<td>30</td>\n",
-       "<td>None</td>\n",
-       "<td>3584.0</td> </tr> </tbody>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 3</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *video_id    px_height     px_width     nframes     fps     recording_date recording_dura\n",
-       "+----------+ +------------+ +----------+ +----------+ +-----------+ +----------+ +---------+ +-----+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            0            576           640          71524       30      None           2384.13       \n",
-       "subject1     2021-06-02 14: 1            1            576           640          107507      30      None           3583.57       \n",
-       "subject1     2021-06-02 14: 1            2            576           640          107520      30      None           3584.0        \n",
+       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
+       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
        " (Total: 3)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.RecordingInfo()"
+    "kpms_pca.KeypointSet.VideoFile()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1241,17 +928,23 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">px_height_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">px_width_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">nframes_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                            <p id=\"nonprimary\">fps_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -1260,52 +953,33 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "*subject    *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_date recording_dura\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.Bodyparts()"
+    "kpms_pca.RecordingInfo()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Insert data in PCAInfo table\n",
-    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
-    "kpms_pca.Bodyparts.insert1(\n",
-    "    {\n",
-    "        **bodypart_key,\n",
-    "        \"anterior_bodyparts\": [\"nose\"],\n",
-    "        \"posterior_bodyparts\": [\"spine2\"],\n",
-    "        \"use_bodyparts\": [\n",
-    "            \"spine4\",\n",
-    "            \"spine3\",\n",
-    "            \"spine2\",\n",
-    "            \"spine1\",\n",
-    "            \"head\",\n",
-    "            \"nose\",\n",
-    "            \"right ear\",\n",
-    "            \"left ear\",\n",
-    "        ],\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "kpms_pca.RecordingInfo.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1376,49 +1050,57 @@
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">px_height_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">px_width_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">nframes_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                            <p id=\"nonprimary\">fps_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "<td>576</td>\n",
+       "<td>640</td>\n",
+       "<td>95517</td>\n",
+       "<td>30</td>\n",
+       "<td>None</td>\n",
+       "<td>3183.0</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  anterior_b posterior_ use_bodypa\n",
-       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
+       "*subject     *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_date recording_dura\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            576            640            95517          30             None           3183.0        \n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.Bodyparts()"
+    "kpms_pca.RecordingInfo()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1492,17 +1174,17 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">project_path</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_threshold</p>\n",
-       "                            <span class=\"djtooltiptext\">Variance threshold to be explained by the PCA model</span>\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -1511,34 +1193,43 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   project_path   task_mode     variance_thres\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +-----------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCATask()"
+    "kpms_pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.PCATask.insert1(\n",
+    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
+    "kpms_pca.Bodyparts.insert1(\n",
     "    {\n",
     "        **bodypart_key,\n",
-    "        \"pca_task_id\": 1,\n",
-    "        \"project_path\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project\",\n",
-    "        \"task_mode\": \"load\",\n",
-    "        \"variance_threshold\": 0.90,\n",
+    "        \"anterior_bodyparts\": [\"nose\"],\n",
+    "        \"posterior_bodyparts\": [\"spine2\"],\n",
+    "        \"use_bodyparts\": [\n",
+    "            \"spine4\",\n",
+    "            \"spine3\",\n",
+    "            \"spine2\",\n",
+    "            \"spine1\",\n",
+    "            \"head\",\n",
+    "            \"nose\",\n",
+    "            \"right ear\",\n",
+    "            \"left ear\",\n",
+    "        ],\n",
     "    },\n",
     "    skip_duplicates=True,\n",
     ")"
@@ -1546,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1620,75 +1311,50 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">project_path</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's project_path in config relative to root</span>\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_threshold</p>\n",
-       "                            <span class=\"djtooltiptext\">Variance threshold to be explained by the PCA model</span>\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project</td>\n",
-       "<td>load</td>\n",
-       "<td>0.9</td> </tr> </tbody>\n",
+       "<td></td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   project_path   task_mode     variance_thres\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +-----------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              1              /Users/milagro load          0.9           \n",
+       "*subject     *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1                             =BLOB=     =BLOB=     =BLOB=    \n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.PCATask()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject1',\n",
-       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       " 'kpset_id': 1,\n",
-       " 'bodyparts_id': 1,\n",
-       " 'pca_task_id': 1}"
-      ]
-     },
-     "execution_count": 28,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "key = (kpms_pca.PCATask).fetch1(\"KEY\")\n",
-    "key"
+    "kpms_pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1762,17 +1428,11 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory in config relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -1781,77 +1441,40 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   coordinate confidence formatted_\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.FormattedDataset()"
+    "kpms_pca.PCATask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ACTION REQUIRED: `anterior_bodyparts` contains n which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains o which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains s which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains e which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains s which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains p which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains i which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains n which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains e which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains 4 which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading keypoints: 100%|████████████████| 10/10 [00:01<00:00,  9.31it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "kpms_pca.FormattedDataset.populate(key)"
+    "kpms_pca.PCATask.insert1(\n",
+    "    {\n",
+    "        **bodypart_key,\n",
+    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project2\",\n",
+    "        \"task_mode\": \"trigger\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1925,50 +1548,66 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory in config relative to root</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project2</td>\n",
+       "<td>trigger</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   coordinate confidence formatted_\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1              1              =BLOB=     =BLOB=     =BLOB=    \n",
+       "*subject     *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1            1              /Users/milagro trigger      \n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.FormattedDataset()"
+    "kpms_pca.PCATask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'subject': 'subject1',\n",
+       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       " 'kpset_id': 1,\n",
+       " 'bodyparts_id': 1}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = (kpms_pca.PCATask & \"task_mode = 'trigger'\").fetch1(\"KEY\")\n",
+    "key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2042,11 +1681,14 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -2055,70 +1697,53 @@
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   pca_fitting_ti\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCAFitting()"
+    "kpms_pca.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ACTION REQUIRED: `anterior_bodyparts` contains n which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains o which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains s which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains BODYPART1 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
       "\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains e which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains s which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains p which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains i which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains n which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains e which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains 4 which is not one of\n",
-      "  the options in `use_bodyparts`.\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains BODYPART3 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
       "\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading keypoints: 100%|████████████████| 10/10 [00:00<00:00, 16.01it/s]\n"
+     ]
     }
    ],
    "source": [
-    "kpms_pca.PCAFitting.populate(key)"
+    "kpms_pca.FormattedDataset.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2192,37 +1817,46 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *pca_task_id   pca_fitting_ti\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
+       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCAFitting()"
+    "kpms_pca.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2284,11 +1918,20 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
@@ -2297,33 +1940,33 @@
        "        "
       ],
       "text/plain": [
-       "*latent_dim    latent_dim_des\n",
-       "+------------+ +------------+\n",
+       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
        "\n",
        " (Total: 0)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.LatentDimensionSet()"
+    "kpms_pca.PCAFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.LatentDimensionSet.insert1({\"latent_dim\":3, \"latent_dim_description\":\"testing\"}, skip_duplicates=True)"
+    "kpms_pca.PCAFitting.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -2385,47 +2028,50 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_description</p>\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>3</td>\n",
-       "<td>testing</td> </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>2024-03-11 23:13:55</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*latent_dim    latent_dim_des\n",
-       "+------------+ +------------+\n",
-       "3              testing       \n",
+       "*subject     *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              2024-03-11 23:\n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.LatentDimensionSet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpms_pca.DimsExplainedVariance.populate(key)"
+    "kpms_pca.PCAFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2499,35 +2145,29 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">variance_percentage</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>90.0</td>\n",
-       "<td>4</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   variance_perce dims_explained\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              1              90.0           4             \n",
-       " (Total: 1)"
+       "*subject    *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2538,29 +2178,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TO-DO: Plotting before choosing the latent dimensions to use \n",
-    "# plot_scree(pca, project_dir=project_path) # --> both functions to plot outside the pipeline\n",
-    "# plot_pcs(pca, project_dir=project_path, **config())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.UpdateLatentDimensionTask.insert1({\n",
-    "    **key,\n",
-    "    \"latent_dim\": 5}, skip_duplicates=True)"
+    "kpms_pca.DimsExplainedVariance.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -2634,47 +2261,89 @@
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pca_task_id</p>\n",
+       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">latent_dim</p>\n",
+       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
        "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>5</td> </tr> </tbody>\n",
+       "<td>90.0</td>\n",
+       "<td>3</td>\n",
+       "<td>>=90.0% of variance exlained by 3 components.</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pca_task_id   *latent_dim   \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              1              5             \n",
+       "*subject     *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              90.0           3              >=90.0% of var\n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.UpdateLatentDimensionTask()"
+    "kpms_pca.DimsExplainedVariance()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "kpms_pca.UpdateLatentDimension.populate(key)"
+    "# Plotting before the user's choice of the latent dimensions to use in the next step\n",
+    "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
+    "from element_moseq.readers.kpms_reader import load_dj_config\n",
+    "\n",
+    "output_dir = (kpms_pca.PCATask & key).fetch1(\"output_dir\")\n",
+    "config = load_dj_config(output_dir, check_if_valid=False, build_indexes=False)\n",
+    "\n",
+    "pca = load_pca(output_dir)\n",
+    "\n",
+    "plot_scree(pca, project_dir=output_dir)\n",
+    "plot_pcs(pca, project_dir=output_dir, **config)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From af6c094fe5c51512956dd50be243f41e00afdd1f Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 02:43:47 +0100
Subject: [PATCH 53/90] update `PCAFitting` to save data & metadata files

---
 element_moseq/kpms_pca.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 6ad2da2..2493ed9 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -9,6 +9,7 @@
 import os
 import yaml
 from pathlib import Path
+import pickle
 
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import generate_dj_config, load_dj_config
@@ -421,11 +422,19 @@ def make(self, key):
             pca = fit_pca(**data, **config)
 
             # save the pca model to a file
-            pca_path = os.path.join(output_dir, "pca.p")
             save_pca(
                 pca, output_dir
             )  # `pca.p` as the first pca model stored in the output_dir
 
+            # save the data and metadata to files
+            data_path = os.path.join(output_dir, "data.pkl")
+            with open(data_path, "wb") as data_file:
+                pickle.dump(data, data_file)
+
+            metadata_path = os.path.join(output_dir, "metadata.pkl")
+            with open(metadata_path, "wb") as metadata_file:
+                pickle.dump(metadata, metadata_file)
+
             creation_time = datetime.utcnow()
 
         else:

From c55844ae96580c53dc1b8cf371eb2b5714d32e69 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 02:45:36 +0100
Subject: [PATCH 54/90] Fix attrib `PreFittingTask` and `FullFittingTask`

---
 element_moseq/kpms_model.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index b81cb9d..f8690ac 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -1,10 +1,18 @@
 import datajoint as dj
 from typing import Optional
-
+import pickle
 import inspect
 import importlib
 from pathlib import Path
 from element_interface.utils import find_full_path
+from .readers.kpms_reader import load_dj_config, generate_dj_config
+from keypoint_moseq import (
+    load_config,
+    format_data,
+    load_pca,
+    init_model,
+)
+from element_moseq.kpms_pca import PCATask
 
 schema = dj.schema()
 _linking_module = None
@@ -99,10 +107,10 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 @schema
 class PreFittingTask(dj.Manual):
     definition = """
-    -> pca.PCAFitting
-    laten_dim               : int
-    kappa                   : int
-    num_iterations          : int
+    -> kpms_pca.PCAFitting
+    latent_dim               : int
+    kappa                    : int
+    num_iterations           : int
     ---
     model_name              : varchar(20)
     task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
@@ -135,8 +143,8 @@ def make(self, key):
 @schema
 class FullFittingTask(dj.Manual):
     definition = """
-    -> pca.PCAFitting
-    laten_dim               : int
+    -> kpms_pca.PCAFitting
+    latent_dim               : int
     kappa                   : int
     num_iterations          : int
     ---

From aa151c21671e635591ecc006df8a98c5c8b808c4 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 12 Mar 2024 18:51:58 +0100
Subject: [PATCH 55/90] Update `Prefitting` computation logic

---
 element_moseq/kpms_model.py | 99 ++++++++++++++++++++++++++++---------
 1 file changed, 75 insertions(+), 24 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index f8690ac..6abc3ad 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -3,6 +3,7 @@
 import pickle
 import inspect
 import importlib
+import os
 from pathlib import Path
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import load_dj_config, generate_dj_config
@@ -11,6 +12,8 @@
     format_data,
     load_pca,
     init_model,
+    update_hypparams,
+    fit_model
 )
 from element_moseq.kpms_pca import PCATask
 
@@ -112,8 +115,8 @@ class PreFittingTask(dj.Manual):
     kappa                    : int
     num_iterations           : int
     ---
-    model_name              : varchar(20)
     task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
+    prefitting_desc=''      : varchar(1000)
     """
 
 
@@ -122,29 +125,76 @@ class PreFitting(dj.Computed):
     definition = """
     -> PreFittingTask
     ---
-    model                   : longblob
-    suggested_laten_dim: int
-    suggested_kappa: int
-    suggested_num_iterations: int
+    model_name              : varchar(255)
     """
 
     def make(self, key):
-        # latent_dim = (PreFittingTask & key).fetch1("latent_dim")
-        # config = load_config()
-        # check_config_validity(config)
-        # config.update(dict(latent_dim = int(latent_dim)))
-
-        # model = kpms.init_model(data, pca=pca, **config())
-        # model = kpms.update_hypparams(model, kappa=kappa)
-        # model, model_name = kpms.fit_model(model, data, metadata, project_dir,ar_only=True, num_iters=num_ar_iters)
-        return
-
-
+        output_dir = (PCATask & key).fetch1("output_dir")
+        task_mode, latent_dim, kappa, num_iterations = (PreFittingTask & key).fetch1("task_mode","latent_dim","kappa","num_iterations")
+
+        if task_mode == "trigger":
+            # Update `dj_config.yml` with the latent dimensions 
+            config = load_dj_config(output_dir,check_if_valid=True, build_indexes=True)
+            config.update(dict(
+                            latent_dim=int(latent_dim),
+                            kappa=int(kappa)
+                            )
+                        )
+            generate_dj_config(output_dir, **config)
+            
+            # Load the pca model and the data 
+            pca = load_pca(output_dir)
+
+            data_path = os.path.join(output_dir, 'data.pkl')
+            with open(data_path, 'rb') as data_file:
+                data = pickle.load(data_file)
+
+            metadata_path = os.path.join(output_dir, 'metadata.pkl')
+            with open(metadata_path, 'rb') as data_file:
+                metadata = pickle.load(data_file)
+                
+            # Initialize the model 
+            model = init_model(data=data, metadata=metadata, pca=pca, **config)
+
+            # update with chosen kappa
+            model = update_hypparams(model, kappa=float(kappa))
+            #model has jaxlib.xla_extension.Array, so it cannot be stored as longblob.
+            
+            # # fit AR-HMM model:
+            # - generates a name for the model and a corresponding directory in project_dir
+            # - saves a checkpoint every 25 iterations from which fitting can be restarted
+            # - plots the progress of fitting every 25 iterations, including
+                # 1. the distributions of syllable frequencies and durations for the most recent iteration
+                # 2. the change in median syllable duration across fitting iterations
+                # 3. a sample of the syllable sequence across iterations in a random window
+            model, model_name = fit_model(model, 
+                                        data, 
+                                        metadata, 
+                                        output_dir,
+                                        ar_only=True, 
+                                        num_iters=num_iterations
+                                        )
+
+            model_path = os.path.join(output_dir + "/" + model_name + "/" + "model.pkl")
+            with open(model_path, "wb") as data_file:
+                pickle.dump(model, data_file)
+
+            self.insert1({**key,
+                        "model_name":model_name})
+        
+        elif task_mode == "load":
+            pass
+        
 @schema
 class FullFittingTask(dj.Manual):
+    """
+
+    The full model will generally require a lower value of kappa to yield the same target syllable durations.
+    """
+
     definition = """
     -> kpms_pca.PCAFitting
-    latent_dim               : int
+    latent_dim              : int
     kappa                   : int
     num_iterations          : int
     ---
@@ -153,13 +203,14 @@ class FullFittingTask(dj.Manual):
     """
 
 
-# @schema
-# class FullFitting(dj.Computed):
-#     definition = """
-#     -> FullFittingTask
-#     ---
-#     model                   : longblob
-#     """
+@schema
+class FullFitting(dj.Computed):
+    definition = """
+    -> FullFittingTask
+    ---
+    model_name              : varchar(255)
+    model                   : longblob
+    """
 
 # def make(self, key):
 # task_mode == "trigger"

From d42d87e110034e6b815d2e08d44c30cf514c9538 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 06:00:57 +0100
Subject: [PATCH 56/90] fix markdown typo in `kpms_pca.py`

---
 element_moseq/kpms_pca.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 2493ed9..473694d 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -475,7 +475,7 @@ def make(self, key):
         else:
             dims_explained_variance = (cs > variance_threshold).nonzero()[0].min() + 1
             variance_percentage = variance_threshold * 100
-            latent_dim_desc = f">={variance_threshold*100}% of variance exlained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
+            latent_dim_desc = f">={variance_threshold*100}% of variance explained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
 
         self.insert1(
             dict(

From d7c85760d968189327bd8468bc0964b777c08ff5 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 14:16:37 +0100
Subject: [PATCH 57/90] add images

---
 images/flowchart.drawio |  81 +++++++++++++++
 images/pipeline.svg     | 224 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 305 insertions(+)
 create mode 100644 images/flowchart.drawio
 create mode 100644 images/pipeline.svg

diff --git a/images/flowchart.drawio b/images/flowchart.drawio
new file mode 100644
index 0000000..2bb0137
--- /dev/null
+++ b/images/flowchart.drawio
@@ -0,0 +1,81 @@
+<mxfile host="app.diagrams.net" modified="2023-12-21T20:58:51.013Z" agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" etag="qaNWZB8tkVXOWCzf9reb" version="22.1.11" type="google">
+  <diagram id="4ApZs3ESX_rJ877tGElo" name="docs">
+    <mxGraphModel grid="1" page="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" pageScale="1" pageWidth="1600" pageHeight="1240" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="0lQ9qycPSiAqUX2RLHM0-32" value="" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;" vertex="1" parent="1">
+          <mxGeometry x="348" y="310" width="323" height="200" as="geometry" />
+        </mxCell>
+        <mxCell id="agbY6gUAcdcyBiqxeYTd-104" value="Synchronize data modalities &amp;amp; exploratory analysis" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=none;" vertex="1" parent="0lQ9qycPSiAqUX2RLHM0-32">
+          <mxGeometry x="41.5" width="240" height="200" as="geometry" />
+        </mxCell>
+        <mxCell id="0lQ9qycPSiAqUX2RLHM0-21" value="Visualize&lt;br style=&quot;font-size: 28px;&quot;&gt;&lt;br style=&quot;font-size: 28px;&quot;&gt;&lt;br style=&quot;font-size: 28px;&quot;&gt;&lt;br style=&quot;font-size: 28px;&quot;&gt;&amp;nbsp;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=bottom;" vertex="1" parent="1">
+          <mxGeometry x="686" y="310" width="323" height="200" as="geometry" />
+        </mxCell>
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EBnbcP3eV8IzOL5sRf+PDV+mKD3KojRMy9VQtjL98q0i44qSBSHYRsWZGmQlkQePpiSh4nJmK72g+2T/ipYLZ2wkSakFx3JcL0DEfbyvSxejxOyNEhLIg+PgadgsvXVOw5hZr3sIuRpQnzuZHwtywUqcCuXqG5Q6JRmWapkNWEv1z2lvVQtQuOSZcMHVj2W7NYuddewsqyBfxphL9d5Vv5iuFOeslx3wl6yswLsSqdj9d2Mr6eKizF7CQ7PjQzXUcfJlaUd3l7neX+G91AliLAXd3il2NgipJB6llH2vDHDe6gSRNiLG6FLAHDaAEcw6tf/fqoGEfbiXidLrbuTcK2jQNcuVbl/7qg8PjYvNw5NL1tZGIusPj1+2N9CxkQRdjUD6mDWelBSenb331HaEHMcbbagGe1CjpV0/+LRA/LJe/YvW1kYi6ye//i4XOmBbyD0mwm7OuzB2uFBnI5/I4xx+y6yK/EL/GixRypqb9CSA/I/uGuvv2FA1KZfJ102KG/dsEv+06Z9PvS37Z4xqW7ndMKubihosYMRPADvhi4wHjsx73cib0nNAqyoxfmU15LHQR4HPfZKwnsNAN71zEWSCjgYyDhd4kdOk9YWbzyEQRiC1hnQ5t16PShDxpcEvwoIgzSWH3ISdnUHWRRAf5FEj+KNZ3h/Y0LcjXAErfTxivNi8WuA8Afxvsby91Yi7OrOMoAEndZ9PVr/YOllJ+N7KxNadXQ0EX8jLFGdBI6w5z96cf7Azw/q2rq70r1nhF3dWYu+ggU5Lwpd+Ma3eA+36kWX99C8dV8MJQl7MWcJRYLW3O3+e4WkzJWsQ8iZI5VYpFUPt+6f7MbuGoqwl+QsU8KQqQn/CmQNfypVi9af5JLVJToLvOMxjxtTi2BfHLkm7MWtRViSVy2C3RHCruQLJXqQyLgZSi2BfbFVF8Ke2xiccKVTItD/t8bz5nl0155Z+cLWUXlVCVvfnOx1cjEwdZteW9+40nePCHt+B6UBF/Y93/iIaB49P3nYX07vHSVsaob0JdKYSGdqIlcifoEJe37jIgadzPBFdTLdBk00vXBsMf342qvUVxZGq/6RTSNylRfCaFQF2T9BhrAXcHghpCCPaxTsEFYDDjYixi7aeUHHe/DLgF8ITVp1NEKxu+oRdnWHY3TAbxzsWA0YW8x/7ZED8m+u25ULeLwW7/nSA2N+/K9Bq477kbhXKmFXdzjliBYlS75dOw3PdGrZAS1a6ZMzjKgi7MGvAb4keC+OoYGSKkwJewk2LuUYJcCK1hnhyFlbRnzoo7I0gBzb3Hx+66gf/uBXQRPQIcJegxsv6ipDCEMQd183OO1Dj7Rk/7Q8tP7YTOHOPTN+vK/ZpgiuEPbKbeQIapyQpUFaEi19/4RrbKSgIeSBXCHstdiKcMZwuULYa7NRI6gWyhXCXpvZujcrVwh7rWbr3qwIe43OvMUMVYkIe412Mt4UqhoR9prtZrotVBUi7DWbcXtzYm1MzdZi/caWKlijh7DX5HXZ7gtVgQh7zWZGpjkR9pqNuFGL5TJaKMLegNm6N6PI6XiBCXt1fiHL3aFKFWFvyFpMymyZCHsDfn+mW0OVLcLegBmzNyPC3oA5sNSMCHsDZuqxGRH2ms1JHM2JsNdsxuvNibDXbFY9NifCXqNPzXhTqGpE2Gv0JzLeFKoa+fudxpmwl+tbMt4UqhqxEKwmv05YItC0CHtN/kzWO0JVpsR7RNjL85asd4SqTIS9Br9FGMI0LVcIey3+RsYbQlUnbOxG2Guwm/GGUNWJmxHU4I9lvh1UlRoQwl65H8l8O6gq1bObdZQJezGzwlEfoQCPsFdoN/OtoKrWKiHslfncHDeCql6JdTFC2JXtCGcj6abERU2FsCvbzXETqOqFhif1vhH2/DZyJ2vLlZpjF8Ke26tz3QKqLm0Uwl6qHWGcrqtSc+xC2HOB7ua6/FSdSk07CmHP7G05Lz5Vr1LTjkLYM/nCvFeeqlUorc50Lwl7spl50V+ZMjFC2BO9Kvdlp5rQT4SwF7IjzLyYIixfQtgVDdDd3JfcAA0eWpCNO6flvEcPyCfu2S9nbh7xjccXbBuX23fPyL7ZI02fZl69XQg7QQ8EyK9yD8mqTfvkpZfskOMG3Ej/i8sH5bP3jcqNQ9OmQO9KjvtL2C0Hffv4YTn/8XE56bLBWMj7fcr6nfLtpybkuQnt55Cnzk4Km7BbDDpg/dojBzJD3m+8F18WjYXyDcKewygNdVWutM5CGILWWRX0wPhVQBikqdBIEfaMBuhWjo4i7kY4UhR2hD+I9zVU5vx64LbDbuXoKFp1dDSLgh4YHVsNW/fUOaf9bjPs1pbr3rVnVl59RfYOaZpffukOuWlopumP1S9HCHsm40K5atdYf/3Nk8Vj9X7/1eNajbGlrv4V5bbCPqB4kY3QZ+7bXzrsq+/d3/THCitXFiZwG2F3VK+wKfrPm0dKhx3H1Ei4h4Q9g63slIZVBez/ZYs2sOcaSAq7jbC7atfYHH1+a3mZmMA4piZK3CQsyW2D3doMTFg/2D5ZOuzfemqi6Y8FuVLg/rcN9lbsUbp1ZE5OvXqoNNCRxkQ6UwMpdUwDtwl2R/UKm6aJ+aPy5wVqYvqNASoNqiBdybDqV5LbBPtatWtspjYNz8pbN+wqDDp+IVB6oIGUO6aB2wS7q3aNzdT0wjH58fNT8is/i69fz+Lve/G/Bq065Ahhz+RW7o7x86kFfwaSCuT4kuC9OIYGulVK4KAtsLd252nAitY5T4cV4Q9+FTQBHUJjRdgz+LdUr7AtQhiCuPvPHhhNhN65dki+/ti4H+8jDNJE26UkFvLC7khn1r1yYr8Bf0/xIlsnZGmQlrz4mUn50/tH5eN375eztozIf/Ue/91zU/Lw6LxOkAcqlG4MOy/sbvcEogrn8UXQ7UtwsmetKpioXHKlRB7ywB4efYxa/H1ASkgPlewz8l5dSiuV1qpLTtjdvhMJt+JO9zm3zJMrwQxhzJUrJaQbw84Ke9QWiGtDfw+3+llHufCeTKuvKvp1nrUYDaGUVHqUkBX2qNHHcNzuhp5Pi9vxZdjQfW2mdbUV/eXUy0nprFJbdSkIO3S6LG/1k2Z9oyV3q/xAstSqa1GmRykp07YxeV0U9nGJXgA0qmPRv2+oW8UH6pqxutmq5Be/KOxxwhcgiN3RekeV1oZj/jL9x8JY3WS5UlEjmBX2NQonjQ4GgI6blu5U8IEQvrygcK6UPqqqEcwMe9kzfKrKx/+fks+Tql9VNIKNwl7FB/pCyedI1a/cS9rlcROwKy1wk2J8eRinm69SR0z7XVUHNUllhzBWrsLbUlXxi98o7GV/ezcIZYMqDWEkB+wXlfihyiwRaNW8UsuVe1XevM4K+7oSP1ShGeIhE3S7VGWdVGOwE3SqX65UDLrkgL3MxYWKtuwE3T7VMg8CsKOQa3Wf+2sTyoRdtYOKWUfWL0raUlVZ/doDe9xwPvLhQSvslvjB8P85OU90pVi69xHlKy8PyrAntdoB8G6Zn0x6v0hJfouwNbddVQwyJoYxSXJL+1i9AsRR32iEK5+RTk2z1ptwUqWotnnLQQe1qdVtXel82DVd47E2q95Ttajy/Lr0wZ7WulNUVapt+ZVw6rHMUVKKyqqyBhlzwd5kOEO1U67UBLpEwO5IfCqSospW5cVfkgA73Ip9hygtVOsKcnHlAozfqTpU2XzTKMfBnjSQFPc8ReWVFrDDUelIdmCpMqUN7HB/aS9nBVFlCuGyNrCv6js5ZmqoMlXJMndxToPdqfjDUiHNHDkmvzy4IFv2zsp1g9Oy3j3kG4/v3Tcng4cWZP6odjtjFJErGsF+QrWflYIA+QuTh+WmoRl/2/QvPTDmb/9y5uaO8fgrD47Jd5+elDt2z8gvpqyC/jTRCHa32s/abo3OHZX7R+b8He0+dc9+edeNu+W1V+2UE9ctbeyFx6es3ynvuXmPfG5rZ/+jx8bm/T2SLFDthWBJ5jS4ijTuwbp5eFbOf3xc3uuBvOKKwdRtG19z5U75nduG5ZtPjMvdXrgzNmc88LWNomaBHTl3dkxLFsKQR0fn5RvbxuWdXmv+8kuz70R90mWD/i8ANuV9wPtVQBhksMIrPjcOO6yyii+VIMTdP3x2Sj7gtdKAN+8O1PhyvO+WPfIDL/xBvG+4ainzzbOBGAeUShJadXQ0EX8jLFHZbh1G2IM4Hx1bw1v3WvLteWB3hJOeS9Gu6SN+i4yWWRX0wO++aY985+kJ2XFQm63XVVTLPNS8m/6yQKwEIfuy5qEx+fWErdWzGlkapCWRhzdc2sHeP6JKKWjjzml/K/WXXpK9UxpnHAPHwjENV+X59rywr6j287ZDGBXFYFFR0APjWDim4dIOdtit8hO3QWXCfrznj3rHunoHYU+zCuyM2wsK2RNkUfLk1uP8Mi+M+YR3rBvMD2MqBV0UYeeyGwWFwaTzHj0gzrXFO6inep3crz485g8uGaxaRlFVYOeIakHtmz2yOKBUFHaUGaCuZmj6SNMfq4gq3UspsArsMAeYCgjjP/fsm5NzHhyTN6xXH1TCgBQGpjBAZXgVpCMaw84VCAoKLfFPXzwoH75zr7zy8vzlAnjPGbcPy4+en/JLDwxW7Ws9MpRpQM9OHJaLn5mU9986nAt4vBbvQX37Ewfm2apXDDtDmRJ0cOGYPOx1VhFzf+iOvfIvr0oPaRD24NcAXxK8F8cwWFqsG5PFzMqUIMD6pNc6/9gLR754/6jfaX3TtUPyikt3+Dl0AI4U5Zuv3+WHLec+NOaHP/hVMBx0V2ps1aUg7AxlShKYxdzTTcOzfpbmvz96QD59735/sAiDT8jJf/2xcT8+3zoyZ3rmJdBaqRF0KQg7zAGmEoUsDdKSmHJ389CMPyqK0VYMQiE2t2BWUiBXagZdSoCdoQylolo2DOt3UdhhdlSpPKq1Uxp2GbCzdaeyClsIOWIw7LBb9lWhrNR50hDoUiLsHFGl0rRdGgRdSoSdrTuVJ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+        </mxCell>
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+          <mxGeometry x="994" y="330" width="60" height="160" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+</mxfile>
diff --git a/images/pipeline.svg b/images/pipeline.svg
new file mode 100644
index 0000000..103d8da
--- /dev/null
+++ b/images/pipeline.svg
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+<g id="a_node1"><a xlink:title="→ subject.Subject
session_datetime     
">
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kpset_id             
------------------------------
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kpset_videos_dir     
kpset_desc=&quot;&quot;        
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pre_kappa            
pre_num_iterations   
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model_name_initialization=&quot;&quot; 
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subject_nickname=&quot;&quot;  
sex                  
subject_birth_date   
subject_description=&quot;&quot; 
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+<g id="node6" class="node">
+<title>kpms_pca.KeypointSet.VideoFile</title>
+<g id="a_node6"><a xlink:title="→ kpms_pca.KeypointSet
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------------------------------
video_path           
">
+<polygon fill="none" stroke="none" points="239,-424.72 78.25,-424.72 78.25,-404.72 239,-404.72 239,-424.72"/>
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+</g>
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->
+<g id="edge4" class="edge">
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+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M158.62,-467.68C158.62,-454.05 158.62,-435.28 158.62,-424.43"/>
+</g>
+<!-- kpms_pca.Bodyparts -->
+<g id="node8" class="node">
+<title>kpms_pca.Bodyparts</title>
+<g id="a_node8"><a xlink:title="→ kpms_pca.KeypointSet
bodyparts_id         
------------------------------
bodyparts_desc=&quot;&quot;    
anterior_bodyparts   
posterior_bodyparts  
use_bodyparts        
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="384.5,-432 256.75,-432 256.75,-397.44 384.5,-397.44 384.5,-432"/>
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+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->
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+<!-- kpms_pca.RecordingInfo -->
+<g id="node13" class="node">
+<title>kpms_pca.RecordingInfo</title>
+<g id="a_node13"><a xlink:title="→ kpms_pca.KeypointSet
------------------------------
px_height_average    
px_width_average     
nframes_average      
fps_average=null     
recording_datetime=null 
recording_duration_average 
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+</g>
+</g>
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->
+<g id="edge6" class="edge">
+<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M228.45,-470.24C289.74,-457.92 378.05,-440.16 437.06,-428.3"/>
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+<!-- kpms_model.FullFittingTask -->
+<g id="node5" class="node">
+<title>kpms_model.FullFittingTask</title>
+<g id="a_node5"><a xlink:title="→ kpms_pca.PCAFitting
full_latent_dim      
full_kappa           
full_num_iterations  
------------------------------
full_fitting_desc=&quot;&quot; 
task_mode            
sort_syllables       
results_as_csv       
visualizations       
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="403.25,-158.4 238,-158.4 238,-123.84 403.25,-123.84 403.25,-158.4"/>
+<text text-anchor="start" x="246" y="-137.84" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.FullFittingTask</text>
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+</g>
+<!-- kpms_model.FullFitting -->
+<g id="node15" class="node">
+<title>kpms_model.FullFitting</title>
+<g id="a_node15"><a xlink:title="→ kpms_model.FullFittingTask
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full_fitting_duration=null 
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+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="320.62" cy="-74.88" rx="12.96" ry="12.96"/>
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+<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->
+<g id="edge7" class="edge">
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+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-123.58C320.62,-112.28 320.62,-97.58 320.62,-87.5"/>
+</g>
+<!-- kpms_pca.PCAFitting -->
+<g id="node7" class="node">
+<title>kpms_pca.PCAFitting</title>
+<g id="a_node7"><a xlink:title="→ kpms_pca.FormattedDataset
------------------------------
pca_fitting_time=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="320.62" cy="-207.36" rx="12.96" ry="12.96"/>
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+<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->
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+<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M308.9,-202.24C284.44,-193.66 226.81,-173.43 185.15,-158.8"/>
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+<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->
+<g id="edge9" class="edge">
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+</g>
+<!-- kpms_pca.DimsExplainedVariance -->
+<g id="node14" class="node">
+<title>kpms_pca.DimsExplainedVariance</title>
+<g id="a_node14"><a xlink:title="→ kpms_pca.PCAFitting
------------------------------
variance_percentage  
dims_explained_variance 
latent_dim_desc      
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="522.62" cy="-141.12" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="522.62" y="-136.84" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_pca.DimsExplainedVariance</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->
+<g id="edge10" class="edge">
+<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M332.36,-202.63C367.98,-191.3 474.61,-157.39 510.61,-145.94"/>
+</g>
+<!-- kpms_pca.PCATask -->
+<g id="node9" class="node">
+<title>kpms_pca.PCATask</title>
+<g id="a_node9"><a xlink:title="→ kpms_pca.Bodyparts
------------------------------
output_dir=&quot;&quot;        
task_mode=&quot;load&quot;     
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="381.88,-361.44 259.38,-361.44 259.38,-326.88 381.88,-326.88 381.88,-361.44"/>
+<text text-anchor="middle" x="320.62" y="-339.89" font-family="arial" font-size="12.00" fill="darkgreen">kpms_pca.PCATask</text>
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+<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->
+<g id="edge11" class="edge">
+<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-397.12C320.62,-386.34 320.62,-372.35 320.62,-361.6"/>
+</g>
+<!-- kpms_pca.FormattedDataset -->
+<g id="node11" class="node">
+<title>kpms_pca.FormattedDataset</title>
+<g id="a_node11"><a xlink:title="→ kpms_pca.PCATask
------------------------------
coordinates          
confidences          
formatted_bodyparts  
">
+<ellipse fill="#00007f" fill-opacity="0.250980" stroke="#00007f" stroke-opacity="0.250980" cx="320.62" cy="-273.6" rx="110.43" ry="17.28"/>
+<text text-anchor="middle" x="320.62" y="-269.33" font-family="arial" font-size="12.00" fill="#00007f" fill-opacity="0.627451">kpms_pca.FormattedDataset</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->
+<g id="edge12" class="edge">
+<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-326.56C320.62,-315.78 320.62,-301.79 320.62,-291.04"/>
+</g>
+<!-- kpms_model.GenerateResults -->
+<g id="node10" class="node">
+<title>kpms_model.GenerateResults</title>
+<g id="a_node10"><a xlink:title="→ kpms_model.FullFitting
------------------------------
grid_movies_sampled_instances 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="320.62" cy="-12.96" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="320.62" y="-8.69" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.GenerateResults</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->
+<g id="edge13" class="edge">
+<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-256.06C320.62,-244.76 320.62,-230.06 320.62,-219.98"/>
+</g>
+<!-- kpms_pca.PoseEstimationMethod -->
+<g id="node12" class="node">
+<title>kpms_pca.PoseEstimationMethod</title>
+<g id="a_node12"><a xlink:title="format_method        
------------------------------
pose_estimation_desc 
">
+<polygon fill="#000000" fill-opacity="0.125490" stroke="none" points="165.25,-573.12 0,-573.12 0,-538.56 165.25,-538.56 165.25,-573.12"/>
+<text text-anchor="start" x="8" y="-553.34" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.PoseEstimationMethod</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->
+<g id="edge14" class="edge">
+<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-dasharray="5,2" stroke-opacity="0.250980" d="M100.64,-538.59C112.58,-527.82 128.2,-513.73 140.22,-502.89"/>
+</g>
+<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->
+<g id="edge15" class="edge">
+<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-62.04C320.62,-51.54 320.62,-36.27 320.62,-25.77"/>
+</g>
+</g>
+</svg>
\ No newline at end of file

From 071104434e9f8899748a3270c31224ebc41d5719 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Milagros=20Mar=C3=ADn?= <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 14:28:53 +0100
Subject: [PATCH 58/90] Update flowchart.drawio

---
 images/flowchart.drawio | 67 ++++++++++++++++++-----------------------
 1 file changed, 29 insertions(+), 38 deletions(-)

diff --git a/images/flowchart.drawio b/images/flowchart.drawio
index 2bb0137..198b9d5 100644
--- a/images/flowchart.drawio
+++ b/images/flowchart.drawio
@@ -1,80 +1,71 @@
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;rotation=-90;fontSize=28;" vertex="1" parent="1">
+          <mxGeometry x="491" y="68" width="68" height="196.97" as="geometry" />
+        </mxCell>
       </root>
     </mxGraphModel>
   </diagram>

From ae9acae5b0df37c6ed70af9dcf092595ae05a588 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 15:36:46 +0100
Subject: [PATCH 59/90] update docstring of `readers` and black formatting

---
 element_moseq/readers/kpms_reader.py | 51 +++++++++++++++-------------
 1 file changed, 27 insertions(+), 24 deletions(-)

diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py
index 48245d5..243be32 100644
--- a/element_moseq/readers/kpms_reader.py
+++ b/element_moseq/readers/kpms_reader.py
@@ -1,41 +1,35 @@
 import os
 import logging
 import yaml
-
-# from ruamel.yaml import YAML
 import jax.numpy as jnp
-from element_interface.utils import find_root_directory, dict_to_uuid
-from datajoint.errors import DataJointError
 
 logger = logging.getLogger("datajoint")
 
 
-def _build_yaml(sections, comments):
-    text_blocks = []
-    for title, data in sections:
-        centered_title = f" {title} ".center(50, "=")
-        text_blocks.append(f"\n\n{'#'}{centered_title}{'#'}")
-        for key, value in data.items():
-            text = yaml.dump({key: value}).strip("\n")
-            if key in comments:
-                text = f"\n{'#'} {comments[key]}\n{text}"
-            text_blocks.append(text)
-    return "\n".join(text_blocks)
-
-
 def generate_dj_config(project_dir, **kwargs):
     """Generate a `dj_config.yml` file with project settings. Default settings
     will be used unless overriden by a keyword argument.
 
-    Parameters
-    ----------
-    project_dir: str
-        A file `dj_config.yml` will be generated in this directory.
+    Args:
+        project_dir (str): Directory containing the `dj_config.yml` that will be generated.
+        kwargs (dict): Custom project settings.
 
-    kwargs
-        Custom project settings.
+    Returns:
+        config (dict): configuration settings
     """
 
+    def _build_yaml(sections, comments):
+        text_blocks = []
+        for title, data in sections:
+            centered_title = f" {title} ".center(50, "=")
+            text_blocks.append(f"\n\n{'#'}{centered_title}{'#'}")
+            for key, value in data.items():
+                text = yaml.dump({key: value}).strip("\n")
+                if key in comments:
+                    text = f"\n{'#'} {comments[key]}\n{text}"
+                text_blocks.append(text)
+        return "\n".join(text_blocks)
+
     def _update_dict(new, original):
         return {k: new[k] if k in new else v for k, v in original.items()}
 
@@ -150,8 +144,17 @@ def _update_dict(new, original):
 
 def load_dj_config(output_dir, check_if_valid=True, build_indexes=True):
     """
-    Load a project dj_config file from output_dir
+    Load a project `dj_config.yml` file from `output_dir`
+
+    Args:
+        project_dir (str): Directory containing the `dj_config.yml` that will be loaded.
+        check_if_valid (bool): default=True. Check if the config is valid using :py:func:`keypoint_moseq.io.check_config_validity`
+        build_indexes (bool): default=True. Add keys `"anterior_idxs"` and `"posterior_idxs"` to the config. Each maps to a jax array indexing the elements of `config["anterior_bodyparts"]` and `config["posterior_bodyparts"]` by their order in `config["use_bodyparts"]`
+
+    Returns:
+        config (dict): configuration settings
     """
+
     from keypoint_moseq import check_config_validity
 
     config_path = os.path.join(output_dir, "dj_config.yml")

From bd4bcbdae78a51c7782a3cd4c93961d81d483858 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 15:39:46 +0100
Subject: [PATCH 60/90] add `flowchart.svg`

---
 images/flowchart.svg | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 images/flowchart.svg

diff --git a/images/flowchart.svg b/images/flowchart.svg
new file mode 100644
index 0000000..3b212dd
--- /dev/null
+++ b/images/flowchart.svg
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- Do not edit this file with editors other than draw.io -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" width="1340px" height="505px" viewBox="-0.5 -0.5 1340 505" content="&lt;mxfile host=&quot;app.diagrams.net&quot; modified=&quot;2024-03-13T13:29:24.600Z&quot; agent=&quot;Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36&quot; etag=&quot;SGjXs2hsbq0Zb6v7va9y&quot; version=&quot;24.0.5&quot; type=&quot;device&quot;&gt;&#10;  &lt;diagram id=&quot;4ApZs3ESX_rJ877tGElo&quot; name=&quot;docs&quot;&gt;&#10;    &lt;mxGraphModel dx=&quot;954&quot; dy=&quot;615&quot; grid=&quot;1&quot; gridSize=&quot;10&quot; guides=&quot;1&quot; tooltips=&quot;1&quot; connect=&quot;1&quot; arrows=&quot;1&quot; fold=&quot;1&quot; page=&quot;1&quot; pageScale=&quot;1&quot; pageWidth=&quot;1600&quot; pageHeight=&quot;1240&quot; math=&quot;0&quot; shadow=&quot;0&quot;&gt;&#10;      &lt;root&gt;&#10;        &lt;mxCell id=&quot;0&quot; /&gt;&#10;        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bWQyWQ2Pxxvr6+tH6+vrn3N0dAxSV1fXeFkoSLNwYLFYbAaDQRseHq5rbW296u/vX+ru7r4qSk5QlmIRAIGgWP4Ga+WAQG1trcbU1BQSBv9gYmLip6qqSuANXJIawHhLCisrKwtDQ0Ofj4yMfG5tbd3Luwr58uXLpKSkpE4zMzN02RNmL4yMjJRcuXIlaT8jEKKoksrKyva29vbxJCIx29ra2ldVVVVV2mcWdt8HwWKxWHQ6fWxqauoxOgnh5ORU7+fnt44ZPGRUSAIgEBTS7WC0LBIoKioy1NbWPmZvb/9Nc3PzADwe/7W1fHGKg5fCCXMXFhampqambra2tl5+6623/ubrvrKyMiQsLOwp1kGWd3qhqqrqm1FRUZ+Iy38UCkV7amrKx8PDI9PAwCDB0NDQXprFwu6LtHh/39jY2JidnX2+sLBQ1NHRUeTn59cJ11KLqwfJdj0gEGTbf9B6BSBAJpONbGxsTpqYmFy0sLBwU1FRkfhdyzsb5TiLi4tDfX19N2ZmZj7PzMwcepU70HHLmpqa3x8+fPh7WAUCmqGg0WjjT58+PZyWljYuCbdTKBTd+fn5CHd39xMmJibhOjo6Zng8fjvMs7T/eHtCNjc310dGRupWV1dvDQ0NlR84cGCMN8sj7TZA+8RPAASC+JlDjUBgzwSePHnif+DAgY+trKy8eIOrpAcktNa9tLQ0XlFR8Udzc/PPQ0JCaG8y6IsvvjA5cuRIs7GxsYUwAqG3t/eqm5vbW3uGt48Jnz59ajwwMJAQEBDwFlqCIBAIOtJ8iQUSWKjf7D6pgSI3dnd3P2YwGF+Mjo7WnD17dkFJSWn7kgv4AQFEAAQC9AMgIKUEHj9+7BkQEFBFJBJ1JSkKdh1Z5ExOTg6Xl5f/9ty5c58pKSnt6VhddXX1scOHDxcJYwNqQ1lZWWpsbOw9aXNXVVWV2YsXL3L8/f3ftrW1dVFSUsIjW4XZayEuG3kzC2hTaXV19X0Gg/EXXV3d+sjIyE24E0JcXpDeekAgSK9voGUKTKCwsNDEz8+PamZmZsnDIMwAKwxKNIh0dnbWj4+P/8f8/Hy5IMF5Ll26pJyRkXHFw8PjjDDtf/HixVBdXV1Qdnb2nDC27FdedGTy888/V7exsbFhMBhZLq6u52xtbA6giyb3q05RlcsTCag8NpvNXFlZmW1tbX1Io9H+LysrqxlmFURFWvbKAYEgez6DFss5ATSoRkVFvR8aGvpzZKq4v0R5MwZsNnvr+fPnTxYXF/9nYWGhNjs7W+Ajc7m5ufbx8fG1enp6JsIIhMbGxg8CAgJ+JCNftUrFxcVEAoHgqqmpedzS0jLV1NT0gNIOAGkPxMR7vNYZ62sryyvDHR0dBXNzczdVVFTgWmo5f/e8bB4IBAVzOJgr/QQ+++wz2/T09CYdHR1D1FphBlYs1q6uri6NjY3d7+np+bOXlxfV0dHxaxciCVJmTU3NO8HBwR8LMyiimw4pFMrh6OjoOkHqloa0aGbh9u3bhiQSKcjMwiLTxMgoxsDAAG1ulImZBcQQzTCgPkGj0VqGhoZu0+n00oyMDHQtNUcaGEMb9o8ACIT9YwslAwFMBBobG8/7+fmhNX5M+bFmoi8trU5NThZ0d3f/KSMjo0nYqWV0J4S1tXWBnZ1dElahgwankZGRXiqVGpidnb2E1TZpyMe7VROHw8W6uroeJ5FI/jvXUvMmF8QuBgXhwtvgSKPRpufn56vm5ubuTE1NVWdlZc3IyMyOIOZCWtikCH0ACEgXgc7OThTqt8DFxSVB3EsLfX19L+ZptP+3uLDwgMvlDiUnJwsVWOfmzZse8fHxVTo6OnpYBQJa7iguLn7/2LFj78vTIITEwr1795zU1dUzPTw80kgkkquWlpa2uEWhIL1/d+yLnb9z5+fnJ4eHhwvW1tbI09PTHadOnVqCmQVBqEp3WphBkG7/QOsUjMCVK1e8MjIyarS0tIiSGiyYTCazp6enZXh4+IqGhkbh8PDw3MWLF5mCuqKsrOznUVFRvxTGDhQRsLm52TsoKKhL0PplJT2ZTEZ3Z7irqqp+MzQ0NFlbW9sM3QnBO5bI+1PcglEQfkjIoWup29vbyaqqqtcXFxcHsrKyNuRJ1AnCQ17SgkCQF0+CHXJBoIFK/Yn/wYO/QV9okhoQdh1r3L4IqK2trb6np+fDgICAYkdHx+W9gEbBkaanpxtMTU399pL+dWn6+/t7nj596nvhwoUNYcqRhbxoVuGjjz5SMTMzO6SqqnoxIDAwVZdE2p59QT9J9Ye9sNt9eye6oOvFxMRodVXVFSsrq6uRkZGjIBT2QlH60oBAkD6fQIsUlMD4+LgGh8OpsLa2DpA2BOhLHl0ENDA0UN3+rP3q0aNHH1tZWTFe187c3NyAlJSUMqzT5jtH7zi3bt368alTpz5Ae+Wkjcl+tgfNKrBYLD1ra2t/HA53xtPTM4FIJG6LBV4IZWFmZvaz7byykbpcX19fnZub62hsbPzC0NCw4OjRoxPiqBvqEA0BEAii4QilAAGhCVy7di0sKyurRF1d/ctbGaXsxzsvv8Vkbq2urIyMj48/6u3tvWtjY9MQEhLyNbFQWVn5p9CwsL9D2ywFHch4a90MBoPe2NjoGhUVNS1lKMTaHHR99yeffGLp7u4eqqOjc8rCwiKUSCSiyI1ibQeWynh9hs1mc1ZXV2fn5uYa2trablhaWj7mF4ETS32QR7QEQCCIlieUBgQwE2hqbv6j38GD38FcwD5nfOmCpu3aNjc3GTMzM23z8/OFPT09D3x9fXvd3NzQvQk9enp6doKKA94XMqqrv7+f4uLicmSfzZKp4tEm1ra2NkdHR8dEY2PjdENDQ08CgbC9uXH3ngVpM4o364H+5HA4nJmZmcHFxcXHAwMD+VpaWk0xMTEyfUJF2niLqj0gEERFEsoBAkIQaGtr0zIzM2sxNDR0wjKoClG1SLKiNej19fWVsbGx2oWFhadBQUE/V1VVRScyBP7tXATFvnPnzsWcnByx3dwocEMlnKG4uFh9bm7O283N7YSlpWWCrq6uvZqamjpqlizMLqA+w2KxmJOTk89pNFrh2NhYoZWVVR9cSy3hjrWrehAI0uMLaIkCE7h69Wpydnb2HTU1te3d67L2230ZEBrg8Xg8ZhNQfhqNNtHV1eUbHh4ulaGVMRu3TxmRwOzo6Ah3dHQ87erqGq2hoWGgoqKyfR042rwhjT3q5RkpJBZ6e3ubFhYWrq+vrz+amZmZUITNqfvUJURSLAgEkWCEQoCAcASamppuHzx48LgsfPkJZ+necvf09Nx0dXXN2VtqSLWbQGlpqdbkzGSiu4v733l6eQWpqqioo/UHJBJkoX/tzCBtn54ZHBy87Orq+iAvL2/p0qVLELlRzF0dBIKYgUN1QOBlArOzs9oEAmFQS0vLWJiQxPJCFl0nffv27eOnTp0qkBebJGHH5cuXVd3d3Y3odHqCubn5O56env68WQVJtGevde6eWUBHJjc2Nla7u7ufdnV1feTj4/PE29t7ba9lQTrhCIBAEI4f5AYCQhP4/PPPc86cOXMVj8dvTwkL+tvZ+PW1r0NZXKbg7XifmZnp7u3tDYmKiqILygLSv5pAUVGRJpvNtjUxMYnX1dXNdnBwOIjH41V5/USScTf24jO0hLW5uclkMBiTQ0ND5X19fV9ERUXVmpubCxXtcy91K3IaEAiK7H2wXeIEqFSqKg6HKzt06FAY1sagl/vy8vLK1tbWiqGhobksigNkO08gdHV1/c7T0/MfsfKAfG8mgPYrjI6Oetra2qaSdHVTzM3MnJWVlVE0R6lFx5tV4P25sbGxsbi42Ds5OXmvt7e34ODBg8/d3d23pNYAGW0YCAQZdRw0Wz4IUCgUy8OHD/erqqoShLGoqanpTzMzM/+lqqoa5+TklK2np3cQ3YGAlp6l/euQZzf6SmQymVtFRUXRWVlZ1cLwgLx7I/DkyRODxcXFUG9v71QdHZ0jBgYGVng8/iulIO19B/WZtbW11dnZ2YbFxcXbfX195aqqqqPZ2dkgFvbWBd6YCgSCCCBCEUAAK4H79+9/OzEx8U9fXeeHoaCNjY31O3fuxJ89e3Z7UEUhe2/duuViaGh43M3NLZ1EIjlqampqYyharFnQYDQ0NNQ0Pj5+JCoqalWslUNlOCqVatjf33/skN+hHGMj40NaWlp6SCxI84wUb9aJFwNidXWVPjw8XLa+vp7b29tbd/78+TlhbyVV5K4BAkGRvQ+2S5RAbW2thpaWVrmnp2cQlpcwb7p1dHS0AYfDRdrZ2f3NfQUoCt+9e/e8VFRU3gkKCkrV1tY2UVFRQcsa2z9p2hSJ7GlpafmZn5/fbyTqGKgcV1ZWZjE3N3ciIDDwrLmZmYuqqqo6bzZKmvrMm1y1tLREb25uLlZSUvp0cnKy6fTp06tw06RgnRsEgmC8IDUQEBmBvLw815SUlA4lJSVM67+8C3JaW1v/yc/P79dvahiaVXj//ffxvr6+gRoaGt/yDwg4pkMk6qI80rL2jG6RJJPJPmfOnJHbmxtF1nnEVBC6E8LS0tJufHz8XEBAwFlLKysbvDKaVJDGyAp/hcLbuLvTTg6NRltoaGjI39zcvJyRkdEKl0ftrQOBQNgbJ0gFBEROoLa29h+Dg4P/C2vBvOiFRUVFoadOnWrfazloVuH27dv6xsbGQerq6mfc3d3jtLW1Sbz8vJkJcQkHntDp6OigrK+vJ718r8Ne7YJ0+0cACcxPP/1U287Ozg2Px5+0t7dPNbewsMNxuV+FeJYmsfkqEqhfM5lMxvLy8ov6+vo7XC43Nzk5uRtmFV7fb0Ag7N8zBSUDgdcSKC4u1nF0dKQcOHDgIFZMaGDt7e0tXl9fT/Pz82NiKYdCoaiMjIygi4AiiUQiCtl7WEtLiyjOzWm8KIwVFRXvHDly5FMsdkAe8RG4dOmS8qFDh/R1dHQOkkikbHNz80R9fX1TNK0gLlEpjLW8/ra8skJfXlpq7+vrK6TRaPeys7MHhClXHvOCQJBHr4JNUk+gsLAwPD4h4YmaqupX+wEEbTR60TU1NaG9BSIZVPv7+9Xr6uocXV1dE42MjDJMTEw8X3WzpKinl5EY2dra2nzw4IFzZmbmqKAcIL1kCRQUFFjp6OhE2djYZOvr6weQSCSjl1sk6j4jjMW7L45C7eJwONzFxcXZxcXF6vb29g8zMjKqlZSUWMLUIS95QSDIiyfBDpki0NbW9jsPD4/3hPniWlpaWsrPz/e5cOHCiKiNHx4eJlRXV/u5ubsft7SwSCSRSDbq6upq+7H8sCN0yolEYgKcZRe1J8VXHtqvgMPh7AwNDdOdnJwyiESih5aWljavj0uTSHjdEgS6yry9re3PS0tLv4AbJnE4EAjie36gJiCwTaCkpETfzc2tysLCwl0YgdDQ0HArMDDwzH5/7XR2dmpTqdRINze3c87OzuEEAsFAVVUVU9THV3UBJpPJKigoyMzOzr4HXUQ+CHC5XOVbt255GhgYvOXj43NMR0fHSk1NTahYH+Igw1t+aG1t/Wx5efl7in7cFgSCOHod1AEEdhG4e/duSmpqah6KXoflq4r3FV9VVZUSGRlZJE64SCy0trYmOTk5fc/Hx8cPhetFpyWxCB3eGfbR0dF2KpV6MDs7my1OW6Au8RBAm2Jv3LgRbGBgcDEoKChJU1MTbYhVRjd+inOviyDWstlsXGtr68/9/f1/JUg+eUsLAkHePAr2SD2Bnp6eXCcnp1OooVgEAvrKQcsLJSUlB06dOjUvCYPRRUCenp7GdDo90cLC4m13d/dDO2Jhz81BgwOdTp9obGyMi4+P79xzRkgokwTQEsTa2hrR0tIyUF1d/Rve3t6xRCJRB8szsN8AOFwubnFxcfrJ48cRJ0+e7Nvv+qS1fBAI0uoZaJdcEqBQKKaenp6NKKQtVgPR182TJ0+uLS8vX5CGr250EZCSkpK9oaFhvJ6eHjoC54NHn4evEUC8Y42zs7MDnZ2dp6Kjo5uwsoB8skkA3UHS2dlp7OjoGK6pqXnKwcEhgnd6RloCMSEB+/Tp0/fCwsI+kE3KwrcaBILwDKEEILBnAnfv3r2YkpLyJxUVle0BFMsPBRQqLi4+mpaWJnX3FdTU1BBnZma8nJycUnWzZZ9FAAAgAElEQVR1dZNMTEycXt6vsLCwMD05OXl7eHj4f1NTU+FoGZZOIEd5Ojs71VpbWw84OzvHkkikTCsrq4MEAkFTGkwcGhp64ODgcEwa2iKJNoBAkAR1qFNhCQwODpbb2dlFYZlW5e09oNFo4x0dHU5RUVF/E1pZmsCizZhLS0sHra2tD2toaBDRzMHk5GTD5uZmc0ZGxggEqJEmb0lHW7hcrnpubq6vu7s7OmaboK+v76SmprZ9ekYSMwvT09ONZmZmgdJBR/ytAIEgfuZQo4ISKCoqsg4PD28hEokGWAQCwoYG2UePHv1vfHz89yFcrIJ2JAUxe2dDbKSHh8dJa2vrSC0tLRM1NTWRnZ7ZC0YQCA9MlZBKSEpKmt4LMEgDBIAANgI3btz4SXZ29q+VlZUxBbJH4mBjY2Pt3r17YTk5Oa3YWgG5gIDsEUBLV/39/Uk+Pj5vOzk5BaqpqaH4Cl9dS43lFM1eKIyMjFSgGb+9pJXHNDCDII9eBZukjgDawe3v799mY2PjjnX2AG1OnJmZaTM3N/eF2QOpczE0SAwEUJjnlJQUg8HBwVRHZ+d3PdzcDvGEAtbn6k3Nbm5u/qWfn9+/isE0qawCBIJUugUaJW8EKisrHQMDA9vV1dUxB4tBMwhPnjz5l7i4uH+XNz5gDxAQlACFQiEwmUwrPB6famFhcdLBwcFbWVlZhScUhBEM6FljMBhrBQUFUWfOnFHYUzYgEATtlZAeCAhIAH31eHl5/UdKSsqP8Xg8puUFVCWdTl+gUCghGRkZCnsuW0D0kFxBCFAoFO319XV3MzOzVAMDgzQrKytXYQXC8PDwk83NzSRFDv8NAkFBHiAwU3IErl+/rhMREfHcwsLCCstLi3d6YWRk5DGVSk2QhtgHkqMJNQOBNxMoLCyMOXbs2CNh9iWgZ45Kpf59QEDAh4rMGwSCInsfbBcLgfLy8sCIiIh6VBmWlxaa7uRyuVwKhXIxJibm/8TSaKgECMgmAaWWlpZfe3t7/wTLs8YzeWlpabGoqCjg7NmzCh2nAwSCbD4E0GoZIcDlcpWqq6t/Fxoa+kPUZCwzCEggLCwsTFVUVARlZ2ePyYjp0EwgIHYCRUVFhl5eXjVWVlbOWJ41XoNbW1vv+Pr65uz3RWhiByRghSAQBAQGyYGAIASuXr1qHBsb22piYmIuSL7daZFA6O3tverm5vYW1jIgHxBQBAL37t07lpycjPkCM95tjg0NDZnBwcF5isDsTTaCQFD0HgD27yuBysrKxLCwsPtKQnzOoJcWhUJJj46OLtjXxkLhQEDGCfT09FxzcnI6g3W2DuWbn5+fe/z4seepU6dmZByH0M0HgSA0QigACLyaAJfLVW5ra/vcy8vrrBD6AIUnHm5oaAjKyMiYBdaiI4CuIc7Pz3dlMBi+aL2ayWTitLW1G9LT0wcgDLToOIurJDKZbH3kyJE6AwMDzLN1KNZIRUXFJzQa7SJsBsbhQCCIq/dCPQpHIDc31yY2Nha9sMyEEQiNjY2/DwgIeA+CI4muC+Xl5QV7enr+xMjIKJxIJOqhktHO9ZWVlQU6nd4yMjJSSKPRHmZkZAwDd9Fx38+SHj58+G5sbOxfsEYqRW1js9mcysrKqKNHj1btZ1tlpWwQCLLiKWinzBGorKw8FxoaegXrJTO80wu1tbVh4eHhT2UOgJQ2+P79+0nBwcGf6unpGb9OuHE4HC6NRpuZn5+nTE9Pk5eXl+tSUlJmQSxIp1PRbN3IyMgTW1tbzGGRkUCcn5+fLCkpcTp37tyadFoq3laBQBAvb6hNQQig6euBgYE8e3v7FGQyliNXSCCMjo4OVFZW+l+4cIGuIOj21czHjx9b+/v7NxGJxG1x8AaB8FU7UJrJycmRFy9e3NvY2LizuLjYkZaWtgRiYV9dJVDhhYWFzpGRkXVoNgjrbB06SpyXl/fH58+f/+DSpUscgRogp4lBIMipY8EsyRL44osvXBITE5+SSCR9rC1BAqG4uPjfk5OT/wVrGZDvrwTQfRgkEulmbGzscaxc0MzC+Pj4UEdHxw0jI6Mb3d3dQxcuXNhEKxRYy4R8whMoKir6ZVJS0s9RSYIKBF4gso2NjfVHjx75p6WldQnfIvkoAQSCfPgRrJAyAmVlZT+NjIr6tbIS5sjKOBaLxaZSqZ7BwcHdUmaeTDYHRbTMysqaVlNT0xDGACTc0I/L5bJHR0cHGxoaPjYzM7sRFRX1QphyIS92ArOzsz2GhoaYYx8gkTAxMfHMysrKF3sr5C8nCAT58ylYJGECaHlhamqqztTU1F/Qr5ndTR8cHOytrq72uXDhwoaETZKL6quqqryCg4NbVFRU8KI0iMPhcLa2tlYmJiY6qFTqF4aGhnnR0dEKf0ROlIzfVFZ+fr5PQkJCHdaL0JA4QMsLRUVF/5KWlvYrcbVbFuoBgSALXoI2yhSBmzdv+icmJpYRiUQi1oajQefWrVs/y8nJ+S2sdWOl+PV8FRUVwaGhoTV4PF5ZNCV+vRQ00KBd8EtLS9MLCwvV7e3tZENDw6qoqKj5/agPytyexVEuLi7+U0JCwrewinHkN/rS0nx1VVVwamqqQodWfrlPgUCApwwIiJhAeXn5/0ZGRn4XFYvlpYVeWOvr68tNTU3uMG0tOucUFxcbxcTETKioqKiKrtS/loT8hvzNW9Nms9nsubm5IRqN9qC7u7vQzMysOTQ0dGU/6lbUMouLi3UCAgIGDAwMjLAyQP4aHh5+aG9vnwRi/OsUQSBg7VWQDwi8ggCXy1Wdn5/v0dfXtxfmeOPg4GClk5NTJEAWHYHi4mJ1BweHRicnJy/Rlfrmknamr9HMAnN8fLxjaWmJPDAwUGxvbz/o5+e3Lq52yGs95eXlRyMiItDNjZhnhdCsz8OHD7+RnJz8mbxywmoXCASs5CAfEHgFgdu3b4ckJyeXq6mpqWOdPeBwOOybN29++8yZM3Bzo4h72ZUrV47n5ORcV1FRweQfQZrz8owCb2aBw+Ewe3p6Gubn569vbGyUcrncqcTERHQSAn4CEKBQKCpcLjc3IiIiWwh9gJuZmRl79uxZUHx8/JQA1StEUhAICuFmMFIcBNAxOl1d3S9iYmKysdaHBhEajfZiZGTE18/PD9ausYJ8Q77qp0+/CA4MPInuxxBmYBGmaRwuFy2g47a2traeP39e19/ff9nMzKwoMjJyDaa590b2ypUrFmnp6V1EbW0drGIcPW+9vb2furm5vbO3WhUrFQgExfI3WLuPBPLz83VjYmLGtLS0MG9ORC+s/v5+srOz84l9bKpCF11aWqqlpqb2LyGHD/9ATVVVTRIweEcleQMbOtLKZDJX29raKoaGhj729PSkeHt7QzS/Nwm96uqcw4cPf4GeGSxCj7eptKCg4FhWVtZDSfQDaa8TBIK0ewjaJzMESkpKUqNjYvLwWN5WO1YymUxWYWHhiaysLIW/anY/HU+lUlWXlpY8iUTiWQsLizRTU1Pb3fUJ4UKhmo2EA5PJ3FpeXp4cHh4uHRwcvO7k5NTg5+fHFKpgOcuM9pOYmZnd9fHxScJqGpvDwY2Njj7v6+uLiI+Pp2EtR57zgUCQZ++CbWIjgAacjY2NW8HBwenCDC6Tk5N93d3dIdHR0Qtia7wCV8TlcpVu3LhhbGxsHGxmZpZlbGwcpa+vbyrMhT/C4OTtU+D9yWAw1peWlnpQmOfx8fF8W1vbbhALONzNmzddExMTG4Q5SowYt7S0/Lufnx9EKn1NpwWBIMzTDHmBwA4BdNVsbFxchw6RiHk9FBXV1dX1Bw8Pjx8AWMkQIJPJdvr6+rEODg6ZJBLpEAqV/fIUNpb1blFYs7y8vDQzM9NAo9Hu9vX1PT579uyYkpISWxRly1oZLS0tP/Tx8flvYXzBZDKZjx49Cj527FizrNkvrvaCQHgFaS6XqzIwMCDSaGvicqgo6tna2uK6u7tviaIsRSmjrq7urcDAwM+FWQ9lsVjMwsLC2KysrApF4SatdqIAPPn5+c4ETc2TfgcPphMIBHttbW3NL0NbYA+fLYy9vCOTqIyVlZX5wcHBss3NzVu9vb1158+fn1MUsVBbW6thbGz8xN7ePkQYX3R1dTXNzs4eiYqKWhXGL/KcFwQC7wuws1PNeG7Oe3l5OUNfX99JTU1NV1F3E3O5XObc3Fwf+qDV0NCo6e/vH3733XcZisqD3wugs7NTjc1mF3h6eibwS/u6f0cv/6GhoZbp6elICKaDleL+5UPhfAkEwtv+/v6pOjo6Zng8XpU3OAkzSImixTQabb6tre0Bh8O5Mjo6Sh0fH1+T59sIb9y4EZKamvqEQCBoYGHPZrO3NzWWl5d/Pzo6+n9E4QN5LQMEAg6He/LkiYment7nXl5eMUpKSnisAW7kqZPsnOHmopC/s7OzE9XV1Z85OTn9wdfXF64dfsnRn376qduJEyeaNDU10Rcmph/anNbW1vazgwcP/gZTAZBp3wmg/Qp1dXWEubk5fzU1tXeDg4OTiUSijjB7TkTRaN6JCA6Xy1mi0+cqKyvvEgiEvyQmJj6XR1Hf1tb2e09Pz+9jEQeIN+KFYo3cu3fPITMzc1QUPpDXMhReIJSUlJi5uroWWVlZHZTY3KEM9C50ze3a2tosEgqampq/gmm5vzqttbX1R15eXr8VZqBAO9cLCgr8srOzO2SgOyh8E9GFXB999JGes7NzsIaGxlkXF5cYIpGouxsM1gFMWLhI3G9sbDAWFxeHW1tb72xtbd1sb2/vlYdZhc7OTm09Pb1GMzMzV6yckECgUqlVCwsLsRCg6s0UFVogoHXG7u7uu07Ozql4ZWXJLCxi7eUSyIdePEgozMzMPG9vb/+7+Pj4WiUlpS/vvlXQ38566GM7O7vDwgiEjo6OitHR0aTk5GQIvytjfQmdYOno6LBwcXeJ1CXqnrC0tDyMYmFISiDwvpJ590KsrKws0un09s7OzttbW1sP0tPTR2QM8VfNvXHjRkp6evptdXV1zPErmEwmu6Cg4HR2dvYtWeUgrnYrtEC4e/fuwdjY2EpNTU1tYV7u4nKWNNTDO35Fp9MXOzs7vx8aGnpdkUXCjRs3wtLS0krV1dUxrYfyeFZXV38jIiLiE2nwMbQBOwG0H4VKpbq4urommpiYZBgbG3vw+sbu0Mv7LR54dSFLeH0M/XVhYWFmcXGxrL+/P39tbe1pVlbWjCwtQ6AbMt3d3bOwvq8Ri7W1tUUqlWofFRUFy6V8urpCC4SbN2++n5WV9a+IEdYOh/1VIts50UYfBoOx3NLS8l0KhXJdHqYvsXiktbX1Ax8fH8zHEnmBccrKypySkpJgPRSLE6Q0D5VK1ezt7fVzdnY+bmFhEaenp2erpqamhvWkiyjM3JkF3C5qenp6cGFh4f7CwkL+1NRU++nTpxdFUcd+ldHQ0GDg4uLyXFtbG8WpELga9KwhYdbU1FT48OHDDEV9ZwkCTmEFAtpw1NDQUBIQEBCLgO23ohfEKbKQlnfkanV1dammpiYlKSmpShbaLco2trW1aRkbGzeZmpq67v5iE6QO9NJqbm6u0NLSioOjpYKQk620qK+0tbUdcXNzO+fk5BSuqampj8fjVcRtxa7ZhK9mFrhcLmdsbGxgeHiYrKSkdIfJZA7ExsauS9vMQm5u7jvZ2dmXlZWVMQUrRbZvbW1tksnkjHPnzhWLm70s1qewAuHSpUvKqamp5T4+PhGy6DhpaTMa4BYWFgZwOJyvsbGxQp0nvnbtWhIKiSzsemheXl7OyZMnb0uLT9Gz4ebmpnLgwAESh8NZu3///tYvfvELtrQNGNLCS9B2zM7OahcXFyd7enp+28PDwx+Px6ObJbf3QGH5Mha0/tel552GGBwc7G1vb79saWn5SVBQ0LKoyhemHNQnT5w8WeXi7HwYazlIICwtLQ1PTk66gBjfG0WFFQhoBqGwsPDPqampF/eGClK9igB6qXC5XC6VSs0rKSnJVqRpO2HXQxHPdQaD3tjQYCUtp0LIZHKwnZ3dP7u7uwcrKyujgYuNZomam5sfLCwsfG5qatocFRXFgqdBeAJoc+PCwoIpi8VKsrKyOu/m5uaHx+MlFqCNt/yAtMrO8ebempqab544ceKp8NYKV0JLS4u5q6trH4FA0MJaErKvtrb2T6Ghod/BWoai5VNYgYAcfe3atR+ePn36v9HfYYlBuK6Pjundv3//W+np6Z8rwpdmdXW1nru7e4eurq4Flr7Dm+qtr68vfPTokcTXQ9FgtcZg/NshX99va2lpkXb3ht33AqysrAx1dHQULi4uftHV1dWjSIJQuCfkzbmLioo0mUymjbGx8TFTU9MT1tbWXioqKqq7l66w9DNh2oyupKYvLk5WVFQkZ2ZmtghTlpB5lW7duvVeZmbmf+HxeMynzVZXV1fv378fnZOT0yBkexQmu0ILhI8//vg758+f/190MYu4Hz5562FoJmFlZQVtWgw/cuRIm7zZ97I9169fP5Odnf2ZioqKCpa+w1sPLSgoSD958qREr5pFs2klJSU/PXLkyPt4FRVV/EsbwHaH+N3hwEW+ptPpLWjdmk6nl6SlpaF7ART6yKuo+nxxcbEOk8v0sbe2TyGRSInm5ubOyl++pERVxZ7KQRuRUZ1DQ0O1L168iJDUzNHVq1e1AgMDGx0dHd2EYTAxMdHY3t4eDrEP9uT+7UQKLRCuX79+ODMz84m6ujpBmI63d9zyn7Kvr6+2r68vNTk5eV5erUXroWlpaU+8vb2jsNqIBl0ajTbAYDC8rKysGFjLEUW+hw8f2oaHhz/T1NT82szBXspGdswvLEzTFhaqZmdnyYODg08vXLgwvZe8kIY/gdraWv3p6ekwNze3bAMDg3CSrq6Zys4yhLjeWZtbW5sVFEpIfHy8RGYRnjx54hUZGdmq9KVK4g/tpRS8vRVNTU3/FBQU9GuBC1DgDAotEIqKihyPHDnSoqGhoS2uh03e+xp6GNvb23/b2Nj4zxcvXpTLO+zr6+tNfHx8BtTV1bWF8WdjY+NfAgMDvy1MGcLmRbMHZDL5V8ePH/8Zlg1yvJcvagfaizI/P/9iaGTk3tbGxq2JiYnn/f39S7AMIayXvsyPor6ura2l+Pj4nDA0NPTW1tbWE8e11Ggm4datW++fPn36kmgs2XspqH9WVFT8MiIi4p9RLizv6Z3NiYuFhYVh58+f79x77ZBSoQUChUKx9fX1bdPR0cF0RS90n78lgAYMNpvNKi8vPx0fH4925nPljVNubu73Tp48+Xv0QYPVtvX19bXbt2/HnT9/XqIbwD777DPCwYMHGzw9Pb2wvHxfZz96KY+Pj492dXWR1dXVc0dHR3svXLiwgZUX5Ps6gbKyMpvZ2dmTQUFBp83MzBxVVVXVd980KWpfFhYWftjW1vZdcYs9MpmsHxAQ0GBjY3MAax9AfXFgYOC+o6NjmqLceImV1cv5FFogoFDLS0tLAzo6OnaifKBE5RxZLIe3E5pGo6ELnsIzMzOHZNGO17WZTCZruLq61ru5uXkJoQ9wk5OTLXQ6PVjSx62QQAgJCWlxdHR0FeUzwAtKs7N/gTU6NjZUX1d3xdbWNjckJATtV5A74SiJfk4mk/EkEslxbW3t7YCAgJOmpqZWqF+K0pfIh2iW6eTJkz8Xt42VlZVHQkNDy4R51tAMSH19/enQ0NAvxN1+Wa9PoQUCct7CwsKgnp6evSgfKFnvFKJoP5puHh0drbS1tY1XUlLaFEWZ0lAGhUJxCQsL68S6aYy34a+xsfFfgoOD/13SNu3MIDR6enp67vczgPrExsbG6tTUVAeVSr1FJBLzEhMTX0iagTzUj/bFHDhwQNvCwsJNWVk5x9nZOdPY2NgcTdHvnprH4mMmk8nJy8s7d/LkyVxxs3r27Nmfvby8voWl3by2zs3PTz0qLQ04c+YM9DUBHajwAmF0dPSplZVViDAdUEDmCpEcDYQsFotVW1v7QWRk5E/kxeiysrKfRkRE/Br1FyxfNYjLysrKUlFRUdiZM2ckfnMjGkDu3Lnzq8zMzJ+K6yjPznW7nOXl5dm5ubmnHR0dtywsLMpCQkJo8tJPJGkH8um1a9eMbGxs/HR1dU9aWlrG6OrqmqI2YemzdDp9rru720Xc/iGTyRbh4eFPjY2NbYR5P1Op1E/8/PzehVM2gvdKhRcI3d3dN5ycnE5ieXAEx604OXhn59EX4/3797Oys7NLZN16MplM8jl4sMrRwcELa2jlnfXQUiaTmSLp5QWeP0pKSg6Eh4e3EAgE4u6vzf3y1+5wv6gONofDnp2ZGaTT6aU9PT339PX1qXCRjmjoo2XUa9eu2VhZWUVYWltnGBsaHtbW1tZ/ecDd/d88/6D/x2QymU+ePPl5QkLCb8W9LEShUM6Eh4dfRcIVi0DYnq3D4XA11dVxERERj0RDVLFKUXiB0NDQcN3f3/80lg6oWF0Fu7XT09Mvenp6gqOiomR6iq+4uDgoJiamRkVFBXO0O/TSam5ufsff3/9T7ERFmxN9cVZWVv4hODj475FtkhLLaGYBzTqh0w90Ov3uyMhIkbW1db+fnx9cgS0Cl3O5XPzdu3cdSSRSmqura4aOjo6rtra2Nk/s8t6BvGUwdG9BZ2fn5ZWVlffEHQMBCZvBwcHb9vb2GVhFK7JjZmbmxb1793wvXrwot8euRdA1XluEwguEgoKCH6WkpCB1vJ+cFbps9OKvr6+/t7q6eiouLm5NFmGgQbSlpeU3vr6+Pxamr9Dp9PmCggL/CxcujEgTBxRJkcvlXvb09DyJricWZ9t4A9T2BlcuF4eexO2vVxZrq7evr3VuZuYKg8EoVVZWnoAgN6LxDBILN2/e9DM1NT3j4eGRqK2tbYbH41XRpzoSBkvLy8P1dXUfpKenoy94sYfWzsvLc4qMjKzR1dU1Qv0Ci2hF753S0tI/NDY2vifu0xei8ZLkS1F4gXDjxo2zJ06cQA+B5L0hpy3gnWyora39Xnh4+P+TxaOPJSUl+uj0gpWVlaMwfaWpqenWyMjI6ezsbLa0uRuJoOvXryf7+fldcnR09MLhcNuzCcLYK6yNvL7D5XLZbW1ttYODg3+2srIqDQ4OXhT3lLewtkhrfiQOp6amHGg0mjOHw9GysLBootPpQ5Lso48fP/7p0aNHt/f6YP2xWCx2fX39obCwMLmP7IqVEb98Ci8Q/vznP5985513vlBVVcXeE/lRVvB/3xXLf7WsrCwmJSWlXtaQPHjw4FhcXFyhktJ2PDfMzUdXY4eFhRVhLkAMGYuLi9U1NDTs1tfXj7u4uJy1tbV1FEdAnleZtvu4JPp3tF9hc2Njpbe3twZNf8fExJSbm5vDEoQY+oW4qkDLCxMTE0/Nzc2DhBEIIyMjw01NTe7Z2dkSjVQqLm77UY/CC4SbN286JSYmNhCJRN39AAxl/pUAOo/84sWLlra2tsTU1NQZWWLT3d39qbOz8wXUZiwvLTTQ0Wi0ucePH3ueOnVKVmxXKi4uJhIIBFctLa1Mc3PzNFMzswPbN5fsfNlhYSEKv+/cZbG1vr4+PTg4+Linp+eWq6vrU9ivIAq6ki3j5s2b/omJiWXa2tpErP0LHaklk8m/PHHixCWYacLuT4UXCA8ePLAJDg5u0dPT08eOEXLuhQB6qbPZbG5vb+9Vd3f3d2QlqllRUZFFYGBgvYGBgSWyE8sMAhIIFRUVnx85cuQbsmL3bp+i5Yfbt28b6uvrB5qammYaGRlF6xsYWKgIcbveXvrM69LsDvGM0mxtbW0sLCx0T01N3evq6ir08/PrlJZTIsLYqYh5yyiUP0aGh38HiQOsAoHBYKxUVlZ6JSQkSNVeH1nzp8ILhIaGBgMbGxuqiYmJraw5T1bby2AwGDU1Nd+LjY39WBZsKCoqupCQkPAxHo/HvLbAYrE4VVVVUUePHq2SBZvf1EYkFvLy8iwIBEI0ukSIRCL5k0gkg/0K9Ssor9XV1ZXJ6WkqnUYjDw8Plx84cGDYz89PLu8FEZSNtKdH+yGsra17DQwMtqPbCioQeCcwRkZGah0cHA5Lu73S3j6FFwjIQdPT0w0mJiYB0u4seWkf+vpbWlqarq2tjTl27NhzabYLDYbDw8Mltra2sYK+rHbbNT8/P/3w4cMD586dk8lTHK/zEeJz4+4NZ0MdwwxXV9c0XV1dtBwh0cvPeKchcFwubnV1dWloaKhsY2Pj+uDgYL2lpeWcuI/sSXP/lra23bp1KzwtLe3Rzt0SmAQCh8PhkMnk7546depDabNP1toDAgGHw42MjNTb2NgEyprzZL29AwMDba2trcHSvIkIXejl4+ND1dXVRV/ImH5owLqbf/fDzo5OsV92g6nBGDOhewHU1NTc2Fz22xFhERlEItEUj8eroSWZ3UcZsSzRYGzS17LR6XR6U1NTiYqKysfz8/PUrq6uFTj+Jgqyoilj516JG7GxsVlYS0T9jEajTQ0MDPgGBQXJyl4frObuez4QCDgcrqen5/+cnZ2/se+0oYKvEeBwONz79+//JSUl5bvSui5/+/btn6Snp/8Gj8ccGwm3ubnJqKysDIiLi5Pq2RIRdk+ly5cvq5ibm/vh8fhvBgYGpuvq6n61CVhSAoF3mobL5XIWFxcXGhoa7jIYjI8yMzOfwUY2EXofY1G5ubl6GRkZYwQCAfM16sjHKMCSo6NjNsZmQLZdBEAg4HC4zs7O37m5ub0HPUO8BHiR8yorK7NjY2PzxVv73mobHR1ttrKyOohSY1li2Inm1mlqaoquU+bsrVb5SYW+Clkslp6lpeUhZWXlc56englEIlEPWfhyBD9xWL07RDb6OxJvy8vLY01NTQUsFuvq1tZWryTP/4uDgbTWUVpamnb06NE8vBAbX1Hsg4KCguysrKw8abVTltoFAgGHQ1H+3g8MDPxXWXKcPLSVFwSHRqO9aGpqOpqUlNQnTXbduXPHJTExkQz/BNwAACAASURBVEogELSwigN03Kq4uPjfkpOTL0mTbZJoC4re98knn1i6urqGoUuELCwswohEoo6kZhR4IoX3J7pEi06nPxsYGMhfWloqysjIGIaZBfH0lM7OTrWl5eW7gQEBx7D0B97s0PT0dO/IyEiIuC+WEg8l8dcCAgGHwxUWFmakpKTcFT9+xa6R91CjmYS+vr7CmZmZM1FRUavSQAVtvsvPz/+vtLQ0NLOE+bKY5eVlWmVlZUhqamqvNNglLW1AA0JbW5ujvb19jJmZ2XFDQ0NvDQ2Nr00tYxFlWOzbtfSwfYSVw+VyaQsLM3Q6vXpkZKRgeXmZkpGRMQ1iAQvdveXJz893iI+P70BhvrH4nefDrq6uDzw8PGA2eG/Y+aYCgYDD4a5fv56Qk5NTjEW58iUMCfgS2ImPwKZSqb8oLS39tTRsHLt69apWdHR0j5mZ2XbsAyw/ZNf4+HiZjY1NNJb8ipIHiQUqlerj5uaWY2lpGU8ikezU1dXVkf2SeiZ5x+XQSsj0zMz47MzM/YWFhTs0Gq0tKysLwjyLuHPW1dV9Mygo6COsxSJ/MZnMrXv37kVnZWVVYy0H8n2dAAgEHA730UcfhZw9e7ZMXV2dgEW9QqcSjgBP/a+sri5VV1WdOHbsWKlwJQqfm0KhBIWFhT0VJvYB2oRZWlr6vcTERHT/BPz2QKCtrU2ro6PjsKOj4zk3N7ejBALBUEVFRQVl3b1/YA9FCZWE1yd314s2N7548WJoaGjolpqa2p2JiYmB7OxsqZjxEspYCWeura3VIJFID93c3CKwNgXNQg4ODjYwGIyj3t7ecnWUGCsTUeQDgYDD4RCEwMDAFn19fTMQCKLoVtjKQC/lqamp/vHx8TBJHlG6dOmSckBAwP/Ex8dvR3MT9McbXGg02kRbW1vg0aNHJwQtA9LjcFQqVbO7uzvWycnpe97e3sF4PF6ddyeEpGYWkF/QYIT2loyNjY00NTX9n4WFRe7k5OQEbG7E1muvXbvmnZWV1aiurq6GrYQvxWNLS8t7fn5+H2AtA/L9LQEQCDsC4eDBgy0mJiYgECT4lOxsWuR2d3c/8PDwSJHUmu/Vq1eN4+LiOgwNDY2xDES8L93+/v5cR0fHs5KyQ4KuFGnVFApFhcPhmDAYjHgzM7Pznp6eQaqqqtuzCuL+7d6vsPN3LovFYoyOjj5vbm6+YmxsfDc6OhrO3wvgmGfPnv2rl5fX+1jEOK8aJpO5SSaTD545c6ZLgKohKR8CIBBwONzw8DABh8OVW1tbB2MZEKCXiZYAi8Vi3rt378cZGRn/I4nBtba2Ni44OLgEq1U8ofP48ePjCQkJcNwKK8hX5EPT0bOzs3ampqaxJBLppIODw0E8Hq/KG1wQe0k9wyic9vr6+vzU1BSVSqVetbCwKIuKipoXoflyV1RNTQ3R3NycYmNjg47BCmwfb69IW1tbmZ6e3jE7O7sNgQuBDK8lAAJhB01fX9+DAwcOJAqjYqGfiY4ACpFbU1MTk5CQ0CS6UvmXhI7iNbe0fHzo4MHz/FO/PsXExER/c3NzmKzdWimMzeLOi/YrDAwMeDo4OKTq6emlmJmZOeHxeBUsA40o2r5rY+P2tdS0hYWxhYWF0q6urju6urrUmJiYJVHUI09l5OXlxSQmJj5QVVVVxeI33oxOdXX1WxEREVfliY002AICYccLHR0d+e7u7mkgEKShW365pjg4ONgyNDSUEBcXNyuuVt24ccMqOjq60dDQ0BRrnajt7e3tH3h7e/9IEjMgWNsty/lKSkr0V1dXD3t4eaXr6ugcMTA0tFJWUkI3U2+bJcmZha2tra2ZmZme+fn5e52dnfnGxsa9cXFxsJHuyyB1l93c3N7F2vfQfpCtrS1GaWmpc1pa2jjWciDfqwmAQNjh0tbW9s+enp7/DgJBOh4V3tdYS0vLn+bm5t5LTEzcFEfLnj59ej4oKOgzLF8zvPahy2LKysqOxMbGVoqjzVDH1wlQqVTDoaGhY76+vtnGxsYBmpqaeug0iqSebV5fRvWjo3iDg4PUxcXF3Kmpqcfe3t5jjo6OYunb0tZPqqqqjJycnJpMTExssLYNCYS6uroHBAIhHW7sxErx9flAIOywaWlp+aGPj89/S+olInrXykeJTCaThQbtqKio3P22CIUF9vHxyTtw4ADaIClwdbzpzpGRke62traQ9PR0usCFQAaREqBQKJYzMzPZgYGBZ83MzFzQLYEo6hVvIykWP4uigah+FOb5+fPntVNTU1c2NjZKjIyMFhXppskbN26cOn78+FUVFRXMF52wWCxWYWFh6vHjx4tF4Rco4+sEQCDs8ECDUHBw8GeSemFAx3w1AfSFQKPRpsvKyuJPnjzZtp+crl+/7nbs2LEaHR0dPSz9gCcQKioqfnHkyJF/28+2QtmCEUDiz9LS0m58fPxcQEDAWWtraxskFLD4WbCaX516d5wFFC9jc2uL0UylVkxMTHwYFBRUpgib7fr6+kocHBzihJmtW11dnevo6LAJCQlhiMIvUAYIhFf2gTt37gSmp6fXC9NZoXPtH4GBwcHKrc3NY+7u7vsWmKa+vv6nAQEBv8Y6aOx8lXIrKiq8oqKiFOXmxv1z+j6UjEJof/rpp9q2trauysrK2Y6OjplmZma2vKqQ7yW5X4HJZDLX19dnnz9//mR0dPRKSEhInTyKhUePHpkHBAS0E4lEA6zvXOSnckr59aNHjp6DvT778LDshABQQtMISUlJ0/tThWyU+tlnn/mfO3euEWtnlQ0rZbeVTCaT3dzc/L9BQUHv7cfLgMvlqr548aLC0tIyBCslNNsxMDDQ2dfXF5CcnLyOtRzIJx4CKCDWoUOH9HV0dA4SSaRsS3PzRH19fVM0rSCp98DuOAtoCWJpaWloeHi4aHh4+I6zs3O7vKyz37x584eZmZm/xbo3hLdEU1BQEJeTkwOhlffpkYElhh2wn332mW1GRgZVR0fHYJ9YQ7FCENh5IawODw9/qKSkJPDgOzs7O8/hcC6/bo335s2bQYmJiY+JRCLmu+hZLBY3Ly/vH0+cOIGiuXGFMBeySoBAQUGBlY6OTpSVtVWmoYFhMIlEMkLN4M0q8P6+n03jCYTdpy9QfWtrayvz8/PN09PT9wYGBorPnDkzoKSkxN7PtuxX2Wi5x9/fn2pjY+ODlSniNDMzg65R91VSUmLuV1sVvVwQCDs9gEwm6x8+fLjF3Nwc845aRe9M+2k/b1PZ7rXbvdaHvuxLSkour6+v//3rwuFWV1f/4fDhw/+AdXkBtWVzc3OVSqU6h4aGTu61bZBO+gigAQyHw9np6+tnuLi4pBOJRA8tLS1t3qyCMH1EGGtRP0Y/dEPoxMREOZ1OvzM4OFijoaExLUthnsvLy52Dg4OfEQgEFKAO0w+xqKmp+c+IiIifYioAMu2JAAiEHUwUCkXX2tq6yd7e/sCeyEEisRPgiQRBK0ZHyx5XVBxOio2lviovuk3QyMiow8DQ0EkZw+kFXpnd3d11rq6uoUpKSl++yeEn8wS4XK7yrVu3PA0MDN7y8fE5RiQSrdClbjzDsPZJLGDQoPiyOKHT6XPPnz9/wOFwvpidnW3OysqiS3P/Q3tA7t69+x/p6ek/4d2rgYXF8vLy0v379yNPnz79DEt+yLM3AiAQdjihjtve3p7r5eWVszd0kEpWCMzNzY18+OGHjpcuXWK9qs3Xr18/mpmZ+UBNTQ1dBiSwWejFjWIfXLt27eLbb7/9scAFiDgD6ssdHR26IyMjBzY2NuzV1dVfGBsbT4yPj4/L0pemiLEIXRyKspmbmxtkZGT0zYCAgBRtbW3Sl9sVlCW6sZEnHBYWFuZra2tvqaqqftLY2Njxuv4uNAghCrh+/bpOWFhYt6WlpTmWZ41X9ejoaIWNjc2R/diPJIR5cpcVBMIulzY0NFwJCAg4J3deVnCDCgoKPkhPT//Rq/YFoMH06dOnN0JCQk5g3b2O8i0vL88MDAx4+Pn5STT2fk9PD/HFixe/CwgIyCIQCNpoAEM3D3I4HCYK/0ylUq/p6ureiouLg6hzGJ8LNKtw+/Ztop6eXpC6uvo73t7ecUQiUUdSSw+7AzEhscBisbZoNNr406dPPzU0NPwwKipKauJx1NTUBAUFBdUhcSAMr/Ly8u8dPXr0jxhdCNn2SAAEwi5QT58+/V1ISMh7e2QHyaScAHpZrq+vrzx+/DgkIyPjlccOS0tLtYKCg4d1iMTtDWlYfqie7u7uOx4eHllY8osqD1rbdXZ2LjAxMXHG4/GvjPSENlKur6/T5ubnnz1ra/uCqKV1X5yhrEVlq7SUQ6VSVTs7O40dHR0jtLS0TtrZ2UVqa2sTJR2ICfFBs1qTk5Ntz549+15ycnKNpJmhUyORkZF/CAsL+y5qC5YZBMR1YWFhuq6uLiQlJWVY0jbJe/0gEHZ5uK6u7nRQUNB1eXe6otiHXiYTExMN6Oji69Zl79y5k5aSknJbmOuDNzc3mfn5+SdycnLyJcU2Pz9f19nZ+Y6Tk9NR1AY8/tXB6Xgb3XYGEPbi4uLk4uJieUdHB1lbW7s+Pj6eJikbZL1etJelo6PDAQX/IZFImVZWVgcJBIKmJO1is9m42dnZ/tra2pTjx4/3SLItZDLZNCIigmpkZGQhzGxdX1/fF87Ozugaddjrs88OBYHwdYFwIigo6OY+M4fixUSAzWZzS0tLf5SUlISOHf7Nj0wmq5mamhaGhobGCzPdOTMz09/d3R0gyancurq6twICAj7H8lWGwKCQtWgJgr5Mvzc+Ol6ora3dERUVtW9BqcTUBSRWDZfLVc/NzfV1dXXNMDExSTA0NHRSU1NTQw3COjgKY0xPT0+ui4sLCigksUG1srIyIyws7K4wzxqbzebU1tZmhoeHFwjDA/LujQAIhF2cqqqqIsLCwir2hg5SSTsBtMO7vLw8KDMzc+hVbS0qKrKIjo4eQLvShXlpdXZ2/snDw+M7kuKBjuW5u7vnubq6YrpDgjdo8Y6QotsHx8bG2mk02u3x8fFCS0vLMQhli927nZ2d2q2trZEeHh4nrK2to7S1tU2EmbHC0hI6nb5QVVV1ODU1tRdLfmHzoD7q4uJyzcPDI0eYZ216enqYQqEEnzp1akbYNkF+/gRAIOxilJuba3/q1KlB/tgghbQTQIPd8PDwfQcHh+TXtbWiouJsWFjYVfTCwvLSQtP16Ajl7du3486ePSsxYUkmk41CQkJQDA9LLHa8jg+yj81mszs7Oxump6evaGlpFXV3d89fvHgRAtNgfACQWGhsbDzm4+PztpOTU4CamhoRHYNAfkO8X7c0hLG6r7Khsm/fvv3WyZMnrwpbFpb86N0aHx/foKenZyhMH21oaPifoKCg72NpA+QRnAAIhF3MPvzwQ/tvfetbKEKZ4Ff5Cc4ecuwTAd6u7pKSknNJSUnXXlUNhUIh6Ojo3Pfx8dles8cyNY/qGRkZaVpZWYny9vZe2ydz+BaLBEJoaGiLiYmJJRY73iQQ0L+hxwEt17DZ7I22trb6np6eD2NiYorNzc0FjmjJ1xgFSYA27KWkpBgMDg6mOjo7v+vh5naId2RyPxAggZCXl/etrKysy/tRPr8ya2pqLgYHB/8FqxhH5aPnraqqKiQyMrKOX33w76IhAAJhF8crV64YJCQkPDc0NETx2EVDGEoROwH0MpydnR1ra2sLio+Pn3pVA8hksnNycnIrgUDQwNpAVE9HR8fPfHx8foO1DFHkQ1+laurqFfZ2dodEKRBe1za0X2FjY2Oxv7+/sre393NHR0eKn58fiAWMzkRilU6nW+jp6aWYmpqesre391FRUVHhFSeKExGor5LJ5HM5OTmvFMwYm76nbBQKRcXGxuahnZ1d9J4yvCIRav/o6Cg6puufnZ29hLUcyCcYARAIu3iRyWTtgwcPttjZ2TmK40UrmKsg9V4JoBdqV1fX/7m7u198XSCVpqam7x86dOj3wghBBoPBuHfvXvB+X0O9F7ufPXv2n15eXj8Wxp691MNLwwvOs7m5ubm8vDzy4sWLx93d3bmnT59uhtj4gpD8eloKhaK9urrqZmFhkaavr59ibmHhgldW3j6SIsw7aW1tbb2goCD2zJkzT7G3DltOMpnsEx8fX0EkEknYStg+sol79OjR+/Hx8e9DcCSsFAXPBwJhF7OioiJNa2vrak9Pz4PietEK7jLIwY8Ah8Nhl5aWJiYmJj56VVoU+8De3v6xg4NDsDB+7uzsrFheXk6Uhg18xcXFvocPH6bwXsLC2MWP78v/ztvcyGAw1ubm5p7Nz88XdXV1PXBzc+uVl9sHBWUiivQlJSX6Gxsb/o6Ojtn6+vpHjYyMrNDth7svdOIX6pn372NjYzVDQ0MxUVFRG6JomyBl1NXV/SYwMPAnwvRJJovFelpT4xkVFSXRo5qC2C0PaUEg7PIiipBWXl7+cWRk5AVh1Lo8dAxZtmFsbKy3ubn5cEZGxsKr7CCTycHJycnlwp5eqK+v/2ZwcLDEQysjG3f67g/DwsL+E21626/Nbvz6BfrSQ4MSg8FYGh0drV5bW7s9MDBQ6ezsPAligR+91/87elFvbW3Fe3l5Zenr6/sRiUS02W87zPObfiw2G7e2ujpfWVl5NjU1tQR7C7DlRLEh9PX1qSYmJp7CCISBgYGO/v5+/8TExE1sLYFcWAiAQHiJ2v379z9ITEz8gTCdGYsjII/oCKAvlpCQkJ+9rsRnz5792svLa/sWOKx+3tzcZOTn57vl5OSMiK7lwpWEovoNDw//KC4u7sfa2tq6WG0TphW8r9uvbt/E4XCrKyv0rq6uRwwG4+r09HRDTk7OoqxeVSwMG1HlLS0ttVpdXc308/M7Z2Bg4KSpqan5uo3Vy8vLC42NjRdjYmLuiqp+QcrJzc2NSk9Pf4D2+mDtjzv3nPzo/PnzvxekbkgrPAEQCC8xvHv37qW09PRfCHOr35vcwlu7fdXUIO/lyu+r4FW3uvHrCjtlc/ml4/37Tvv2nH7nK3bPA66gwWJQRDgU5EUJxXB/hRG7r4F+8uRJUFxcXPOrbKVSqSRjY+M6KysrV37Ts69jhfLV19eXkkikFHd39629MhVXukePHjlubW39KCgoKF1HR8cATUvv9zG6vdiGuC0uLi41NjbeY7FYf2YwGOj2QSasKe+F3t+mQfeIlJWV2c3Nzb0bERFx0tjY2ALNKuy8H5iNjY0VNBrtB8eOHevGVoPwuWrr668GBgScRc8sFoGA+szq2tri8tKSo6Wl5StnBIVvJZTwOgIgEF4iU1paGhMdHV3Cd+4OY5/a2NjYGh4e7h0bG6tksVhfO0/OYr3yssFX1rRzAQ86WrenyGh4PJ6ppqaGjnAiQf7GIxrKyspcZWVlBrp6XhAzd2283lM2QdIrKSmtqqurL7LweGX1N5TOZDLZb7q1MDc3NzErK6tARUVFFRWD5aXF4XC4T548yY6Li7uzJ0MlkAgtOeTm5mobGxv7q6qqnvfy8jqmr6+vK4GmfFUl7/gp+h9sNnuLTqdPNjY25rNYrM9SU1Ofg1DA5h0kFD766CMNFxcXEy6Xa8lkMpmzs7O95ubmK1FRUXt/qWCr/rW5uFwuYWl5eZiorW3K76PnVYXw+kt/f3+5i4vL9nFk+ImXAAiEl3iXlZWFR0ZGUkQtEJhMJquzszN3enr6A2Vl5cHY2Nh1eCGKt7Oj2jo7O6+5urqewSIMeK1dXV1FF0DZvW6Pg/itenON6JjZ9PS0sbW1dQiBQMg6cOBAjLa2tp40zCqgr92NjY3VpaWlvmfPnhVsbGzcYrFYg3AttbT1IsHbk5ubm5qdnX2bJ8YFLQEJBPTevHPnzjunT5+WSIAnQdssb+lBILzk0crKSs/Dhw+34kW4y4vN/v/t3QdUVUmeP3CSBEFQRJKigkQJIpJBkgEEkSCigKAY2umZ3Z6entmZnd4J7s5O2OmZ6Z3w32kD0oCKgmQEARWVHARJkkQUA4igRCW//ynbN6M2yn333sd7cL+c0+f0OVb9qupTxXs/bqianCgrK/vZ2NjYn0XxFPF8W7R0x5Ofn69mYWFRpaqqqkM3Bql37dq1lKdPn+6ai19iJFm4d++eromJyRZVVVVyToCdsrKyEhMPunXffF5BguxmKSEh0d/f39vX11fe0tJyYWhoKHfnzp0P6cZHPdEJkHWmoKCQZWdnt4VuL8j6ePbsWUdbW5uVnZ0dbi/QhWRQDwnCO3hnzpxZsnv37ifS0tKvLkEz/SG3DcrLy0/39/cfwhO4TDWZ1T979uy+oKCgk3T2wed/mZGtlZOSkraHhobmMeuN6GuTJ8xv3bplbmRk5K+lpeWjoqJirKCg8OphsikeT0J6hifkhTUCYk1u4/T29nb29vbmPX78+MKzZ8/Kdu3a1YOrbsJSZzfujRs3tGxtbdvk5ORob0RG1kF9ff3XFhYWkez2DtGoCiBBeEfq2LFjKgcOHOiWkZF5dfIa05/R0dGxoqKitZs2bcIZD0wxGdZvbm7O09fX30x3u1dyOby/v/9BZ2envjg+nMiEp7W1Va6wsNDBzMwsQk9PjxwotFxWVpaVJFnQfvHvPZN54v9/V1dX26NHj5KHh4fTRkZG6ry9vQV6PkbQPqA8M4Hs7OyDW7ZsOSEtLU17S1pyaFhSUpJfaGjorL+eyWz086c2EoR35jIhIUHB0dGxcfny5avYmObm5ub62traDcHBwWL3tDsb45srMaqqqpYZGRk1L1y4cAmdPvO/qIqKik64uLh8RCfGXKlDtm6uqqryNDQ0PGBhYeEkLS29iL9BD3nYTNA3UNgcNznut6Ojo7WhoSFBTU0t/vnz53e3bds2hisLbCozi1VcXEwuROVaWFg40304kfSgo6OjZnh42NnU1BTHjjObEtq1kSB8+wrCAhcXl1JjY2Mr2qpvVExNTT3t7++/T5TnsLMxjrkeIyYm5vvh4eF/Iq+B0RkL+VJ88eLFi4yMjC0hISHFdGLMtTrkQKGwsDClioqKAAMDg8MWFha2UlJSC8jjOUwe8mTiwL/V83pDpsm7d+82V1dXR2loaJx1d3fvYhIbddkRuHDhgvGOHTvqyXNcdBMEcourvq7uV5aWlr9gp1eIQkcACcI7auThmoULF163tbV1pAP6bp3z58//effu3WTjJYH2FGCjbcT4RiArK0tOV0+vwtjIyJyuCflievLkSbWmpqatpKSkyF4do9t/pvWI4dTU1HIFBQUfDQ2NMCMjow3S0tL/OFCIxBfVlQXy3u7Lly8HOzs7q0pKSuJtbGySTUxM8FAb00mnWb+oqOgnjo6OtA8wI8nf6OjocFpamltISEglzW6gGgsCSBDeQSTvFCdcuPCn4KAgVs4cLykpKRgdHfUQ5fvILKyTOR2ioKDA0MHBgfxFQ/ueOvnyKykp+bWTk9PP5jQGC50nZ1kMDg6u0dXV9VVVVQ3S1tY2Ja+y0X22g4Uu/eNZBR6PN0n2V3j69GlRfX39OR0dnXx7e3s8r8AGMoUYhYWFizQ1Na+uWbPGmkLxaYuQBKG1tTW/s7PTG2990VVkpx4ShGkcT5069R+RkZH/zQbx7du3qwoLC+2PHDny1qZIbMRGjJkFSMKXnZ39U09Pz/+WkpIS+IEp/iXtgYGB/osXL7qFhYXdmrlV7pTIyspSHh8ft9PX199JDhRSU1PTlZGRkea78R80nI1bEm/upklmgGxE1tvb29zb25vb0NCQpq6uXuXu7o772UJcnmlpaS7btm27vGDBAkbJeEVFxcd2dnZfCbGrCE1BAAnCNEjHjx//7sGDB//KxmZJ9+7da25sbFyHVxwprEYhFElISFCxsrIq19PTM6TzJcV/OPH+/ft5urq63ly8vUB1Wsg+E93d3a7r168PW7p0qcOiRYvUZWRk/vHMBx1/qm1/qBz5i3RiYmLs/v37twYGBhLv3buXra2tfVccTuFkY3ziFKO2tvaPpqamn9F59oA/joGBgaGcnJz1wcHBd8RpbFzsCxKEaWb93Llz6wICAspkZWU/tKsvpfUyMjLy4sSJE1qffPIJLnNSEmO30OXLl208PDzK6d4f559hUVxc/F1nZ2f8RUNxei5duqTV19cXYGdnF7Zs2TIzBQWFReQKDt3zLyg2+95L1m8mJ2NjYyNNTU0lfX19caOjo5f7+/uf4C0jJsLf1CUJor6+fuHy5cuNmCSDlZWVqQsXLtw9314lZi48+xGQIExjnpOTY+zq6lpNjgNmOiWTk5PjUVFRy44cOdLPNBbqCy5ATna0t7f/ieA1v6lBvtD6+vqeZmZm2kVERLTTjcPletevX9d5/Pjxfjt7+/06OjqrpCQlpYkHk78y2fAkc0v2Kamurr7x8OHDv6qoqFzBFuj0ZS9cuLDT398/gVx5pZMg8G8RFRYW+ru4uKTR7wlqsiWABGEayeLi4uUWFha3FRUVlZlCk22WS0tL1zg7O3cwjYX6ggkkJycvtbGxKV6xYoWhYDX/WZp8aNXV1cWnpKTsPXr0KKWDsei2NZ/rkWdBrl27JicrK6v/vL9/r5Wl5R4tLS1W9hoR1O3dZxVe79w4OTIy0n/r1q0rjx49Ounk5FSgo6NDDizDD0WB5ubm8wYGBsGkON0EYXBwcDAtLU0/IiKim2KzKCZEASQI0+CSk/BevHjxSEFBQZPOQn8zJNnYpaioaKOrqysn3p0X4loVOHRubq63h4dHBnkdW+DKryuQ+9dlZWWBjo6OKXRjoN7bAiRZiI2NVV29erXpwoULd61YscJPXV39H+djiPJtCDLfY2Njo4ODgw/a29vzmpqa4iIiIirw7MmHV3FqaqqOs7Nz2dKlS7Xorndif/ny5fOysrJ78dYXXUV26yFBeI/n8+fPH6moqGizkSBcv37dfdOmTTfYnTpE+5AA+RJqaGg4vnbt2kNM5rD76dPHuTk5ze5GBwAAIABJREFU1uHh4Z0QZ1+AbMa0du1adXV1dQd1dfXdGhoabouXLNGQkhT4hRNWOvfm1QXy/y9fvhzu6+u7/eDBg9S2traM9evXN+Pe+LepU1NTv7d9+/a/MEnGyTHV2dnZnn5+fvmsTCaCMBZAgvAewnv37lWsXLnSmsmXCwk9OTnJy8vLC922bds5xrOFAJQFkpKSVjg5ORWqq6uvojOHb2yt/FVXV9e/zMWTGyljiUlBktQlJiau0dDQ8Fy1apX/4sWLrRctWrSY3z1RXlkg64G86trd3V3a09OT0N7efiU0NPQBdkh99ZyOZEdHx3UdHZ2NdH/XyBw/e/bsQV1dnSH2PhCTX0gJCQkkCO+Zi8bGxgxDQ8PtTB+kIh8sqampXwQGBv5YfKZ9/vckJydn36ZNm6Ilyd7KNE4l5CcIhYWFrq6urrj6M8tLhsfjSZ8/f95kyZIleywsLHYoKyuvkZeXX0hnLtnoOn898GP19/c/vXPnTt74+PjZ+/fvV4aEhJCTJifZaGuuxbh48aLhRheXcsWFC1Xozg+5vXDp0qU/e3t7Y9dZMVoASBDeMxkFBQXJzs7OAUzninywZGZm/u+OHTt+wDQW6lMTIH/RtLe3p+jq6vpRq/HtUuQD6+nTp51paWkmeAOFriI79cgzQeeTz9uqqagdsrS03LZo0aJlb27EQ/cVVnZ6JyHR09PztKamJoPH40V3dnZW37179yWXHmhNSkr6r8DAwJ/T9SS/ayMjI0PZ2dlOQUFBtXTjoB77AkgQ3mOamJj4o8DAwC/oZsT8sOTD68aNG+d4PF44HrxhfwFPFzErK2uNnZ1dqaqqqhrdFsmHVnJy8h+DgoL+Dedo0FVktx5J/EpKSuS7urocFBUVP3J2dvaVI1cVXj+vQOfyNhs9JGuF/PB4vKnnz593Xb169cKiRYuOlZeXN833ROHo0aMyhw8fbtTW1tana0k+I7u6uqq1tLQ24HeNrqJw6iFBeI9rXFzckdDQ0K/YSBDqGxrqb1VX20dERAwLZxoR9U2Bixcv/runl9dvpGlsrcyPMzo6OlpeXm7l4uJyG7riJ0AOVXvw4AF5E8JRQUFhn5GRkbuioqLKu0mCKJIG8oU3Njb2oqenp626ujpJQkLi/Pbt25vn45dfbm6upYuLS5mcnJws3VVCvMh26D4+PrQPeKLbNup9WAAJwnt8jh8/vmffvn2nZWVlX23qQveH/HVx79691ps3b1oFBwdjH3i6kBTrkcvRjzo7S7S/OXWRYq1vF3v06FFza2urGa760CactYoNDQ2ydXV1Onp6eh6LFi0KXrVqlYOcnJwi0+Se6QBeP7fAGxwcfN7X11fd0NCQKC8vn+nh4fF4PiQL5HctIyPjrz4+Pt9lYv38+fOeq1evOgYFBbUyNUd9dgWQILzH89y5c4be3t4VSkpKyky+aEj4np6eruTkZPMjR470sDt9iPauQHJystmWLVuKyCZXdOaN/6Gelpb2m8DAQM6f3DjXVhg5lvrJkycm69at27F06VJfdXV1U3l5eQVRbPHMt+O/Ojk1NcUjJ0329PTkk2dkeDxegbe399O5Zszvb0JCgqqHh0fTkiVLltFJEPgu9+7du6irq7sDb4SI30pAgvCeOcnMzNRzcnKqUlFR+dZlS0GnkdybjIqK0j98+DC26hUUT7Dykunp6b/39vb+EfnAopMgkCs+w8PDffn5+TZ+fn44LEYwf7EqXVlZubClpcXGxMRk9/Lly7cuWrRopays7AI6X2ZsDYz/pUg+Ezo7O+90d3dndnd3p0pLS9du2bJlTm3HfuPGDU9nZ+dLxIbu7xpJmnJzc/f5+PjEsWWMOOwJIEF4j2VDQ4OqhobGLVVVVR06i//dsOfPn9ffs2dPG3tTh0jvCiQkJMi6uLg0qaur6zL50Hrw4MH11atXu0F4/gg0NDQoVVVVbbawsDiop6dnr6CgsERaWprR7UM6Ou9u80xi8Hi8yfb29uaOjo5zk5OTqSMjI22+vr4v6MSfrToJCQnS2traJx0cHPbT3Z/i9cOJHRUVFQ5+fn6PZ6vvaIe6ABKED1j19PTUqaqqmrGRIBQWFtpv3LixjPrUoKSgArm5udZubm5FCxYsoP3A1OTkpERKSsr3du3a9X+Cto/yc0Ogurp6cV1dna+ZmdkRMzMza2lpabJeXj2wIqqrC/w3IUi+0NzcXF9XV3dSV1c3ISsrq1sc34Q4deqUTmBgYLWKispSurNOxtzU1PSVqanpx3RjoJ5wBZAgfMD34cOHtcuXLzdnYwpKS0v3ODg4nGcjFmJ8W4D8RSMnJ3fM19f3IJOErqenp7O2ttZm06ZNj+A8vwXIFaclS5Zojo+P71i9evVBQ0NDCyZbBTPRen1g1KsE5fX/T01MTAzfvn37cl9f3w82bdp0n0l8tuuWlZWF2NranmUYl3fx4sVt27dvz2EYB9WFJIAE4QOwTU1NZwwNDUOZfOHww5NfKHt7e2y3LKSFfObMmSVeXl6tS5YsWUpnvviXftva2hL19fV3z4enzIVEPS/D5uTkKE5NTa1RVVXdvmzZsl0rV640l5KSenULQpQbMb3e4rn7ypUrhwMDAzPEYV2St0Z4PN6FtWvX+tL5XeMvoHv37jU2Nzc7e3l5PZuXi2oeDAoJwgcmsba+/pjZ2rUfMfkl4IcvKSv73NHe/rfzYM2I5RDy8/O9XF1ds+l+mJN65OTNS5cu7fb19b0gloNEp2ZFICUlZbGMjIzl6tWr/RcvWeyjraW9RorBnhpMO00uxT9//vxpdXW165YtWxqZxmNaPzY21iwgIKBwIYOtlUkfysrKfmVnZ/dLcUh6mJrM1/pIED4ws9evX/+di4vLT9iY/JKSklhHR8d9bMRCjLcFyKY5cnJypxwcHMLp2pAEgTxVXl9f7+Tp6Ymz6OlCzqN6ZOfGa9euLe3t7XU1NzcPUlNTc1FRUSFHwEu9+UcDG39AUGGrqqo6MTAw8F1R781RWVn5Qysrqz+QPtMZO0l4pqamJq9cuWLr5eVVRWXsKCMaASQIH3BPTEzcFRQUlMDG1FRXV8dZWVlFsBELMd4WiI+P19m6dWu1qqoq7QemSIJQX1//pYWFxWfwhcB0AqmpqTo8Hs/f0tJyl6qqqrmSktJiuk/w0xF+9vz5s4uZmdYREREie126tbVVTlZW9sqKFSucyBjoPNRJfteamppu1dfXb8TmcXRWwuzVQYLwAevo6OjA8PDwJDbehmpqairp6OjY4unpie2WWV7fxcXF5PmOs3T+muF3hdxeuHjxorufnx9ObmR5fuZbOHJlITs7W6+/v3/vxo0bdy9btkxPRkZGlpwcSvevaipG5C/vpKQku+Dg4HIq5YVRJjY21nnXrl055GRNuvHJ3geXLl36gY+Pz5/pxkC92RFAgvAB5zNnzmzw9/e/tnDhQiWm0/HgwYN7paWlG4KDg/FADlPMN+qTY4Fv376dZGxs7Efnrxl+qLa2tob29nanubZZDYuUCEVDgLw9o6ysbDw6Onpgg7X1Hi1NTW0m6/BDXSAJQnp6ul1AQIDIEoSqqqo/W1pafsIkGZ+YmJgoLy/Xd3JyEqs3M2hM/7yvggThA1OclJS0wsPDo2rx4sXLmK6EJ0+e3L9x4wY5jwEJAlPMN+qfPn16ra+vb7GSkhKts+jJhy75sCstLf2Fo6Pjr1jsGkJxSICcS5CYmLhIY/nydRITE6EmJiZkq2fyvMKrqwr8rZ6ZfLGSv7zj4+Pt9u7dWyEKWrLZlJqaWtWyZcsMmIyjpqamvKWlhdxeGBPFONAmdQEkCB+wys3N1bawsKjS0NDQoE46fUly3vm5c+dsIiMjm5jGQv1/CpSXl/9ww4YNrx6YovOXG/+BKbLJko+Pzy3YQoCpwNGjR6VWrVqlvmbNGjtVVdVgTU3NTUuWLHn1GUJnjfL78+jRo7vFxcX2wcHBIjm/ISYmxm/Pnj0JZCMyugnCxMTEZEJSwoGwPWGxTJ1RX/gCSBA+YEze91VWVi5YsWKFLdOpIF9Ep0+fttu3b5/ILg8yHYO41ScPTMnIyOStWrVqI90PLDIvzc3N1fX19S54YErcZnju9+f1lYVVWlpa3tra2v5qamq25AA4/oZIVNYtf4+OkpKSnzs6Ov5aVK8FVldXJ1tYWATQfTCTjGNoaKj7yZMnBgYGBgNzf3bn/wiQIMwwx/fu3ctftWoV4335yS9Henq6k7+/f/H8X1azM8LTp0/b+Pv7X1FUVFxEt8XJyUleTk7Opz4+Pn+hGwP1IEBFgDyvICEhsVZdXT3A0NDQX0VFxVBeXl5xpisLZPvvO3fuXL19+3ZwYGBgL5W22C5TWVmpYmho2KqkpLSMSlLzbvuvT0mVqK2tTVm/fn0g2/1DPOEIIEGYwbW5pSXD0MBgO9PjYkn90tLSI46OjseFM5Xci1paWvqFra3tj+h8YPG1xsbGxiorKw3xwBT31o8oR1xZWbmgpaXFTltbO2LdunVeioqKGtOdITIxMTHe0tJyo6+vL9TJyUlk+3PExsbu37Nnz0lywBWd2yTk829sbGz8/Pnzu/bt25cmSnu0TV0ACcIMVo2Njf9pZGT0C1KMyRcR+QW5desW2WDkT9SnByXfJ0BuL6ioqNxSU1MzZjIvtbW1ZePj4xutra3HoQ0BUQiQZIFsOayhofGJubm53RJVVbXhoaHh9vb2mtu3b/8tLCwsSVS3FYgHObPC0NCwcN26dTZ0fcjn39OnT+9MTk6u09bWFuuTKumOcT7WQ4Iww6xWV1f/ct26dUfZSBDq6+t/amFh8bv5uJBme0wxMTGbg4ODM+Tk5OTpJgiTk5OTZ8+ePRQREfH1bPcf7UHgXQFyC0JBQUHu8ePHC5YsWTL5/Pnz0SNHjog8cb1y5coqZ2fnVllZ2QV0Z+31H0h/tbKy+oRuDNSbfQEkCDOYX7l2LdJt48ZTdB/M4YcnvyCNjY0XTE1Nd83+NM+/FsvKy0/bWFuH0U0OyMOJL1++7GlqatK3trbun39CGBEE2BHIyMj4zMfH5w/8VzYFicp/wPLly5cv0tLSPENDQwsFqY+yohVAgjCDf2JioltgYGA+GwnCnTt3sgwNDX1EO+Vzv/XKysqFa9asaVVRUdGmkyDwP7Ru1dSkW61f7zf3RTACCAhHIC0tbZGRkVGhgYGBBZ1nD0ivyO9bR0dH0fj4+CYDA4NR4fQUUYUhgARhBtXo6OiN4eHh16TIcW7f7HlC64f8kty7d++6rq7uFklJSZFfNqQ1CDGpFBcX50/ex5aRkaF1yZP/wFRycnJIaGhokpgMC92AgNgJ5OXlrffw8KjgH30taAfJlTryU11d/e/W1tb/I2h9lBetABKEGfzPnDmzysfHp1xZWVmdSYJAflE6Ozsf3bx5c4Ofn98T0U773G2dnNwoLy+fZW9vv4XuKPgPTPX09Kw3NTUdohsH9SAwnwXImRNlZWX/aWdn93O64yS/a4ODg/0XL150Cg0NbaAbB/VEI4AEYQb3hISEZc7OzlWampormCQI5Belp6ens6KiwsrHx6dLNNM991vNyclRd3d3v79gwQJ5uqMhc1FbW3vc0tLyCN0YqAeB+S6QkJCgamNjU7x69WojumMlv2u3b9/OMDU1DZSUlJygGwf1RCOABGEG90uXLqkaGxuX6ejo6NO9B0eaIFcQBgcHn+fm5joEBwc3i2a6536raWlpB3x8fMj72LTv97x8+fJlamrqVjwwNffXA0YgPIHs7GzPrVu3ZpO7q3RbIZ975eXlux0cHBLoxkA90QkgQZjBnlxma2lpSTIwMHi1xSjdH5JJk8NWvv76a+9Dhw5dohuHab329nb5goICRw0NDT89PT0zVVVVQ0lJSbLDm1j/8B8slJKSUlZRUVFkMhcTExNTfX19PVJSUpNM4og12BzrnKS0tISsjAyjvUbm2JDFvruSkpIL5eXlVZh2dHh4uFdKSkrsD2YinzEjIyNTPT09zXfv3q158eLF6cDAwFuSkpLfPEjBwR8kCBQmvaamJtHc3DyI6ZcJWYCJiYk+u3fvzqLQLKtFyLHImZmZvuvWrft86dKl5vLy8rT3D2C1YzSCMZ0H0iQ/4aDRPKqwLEDmk+lOpSx3CeE4KsD/XJji8SSGh4YG2tvbkx8+fPjZ9u3bn3ORBAkChVmvqqr6g6Wl5Q/Z+GK6evXqdzdt2vR3Cs2yVoQkBxUVFb8yNTX9VwUFBaU3P5DxwcwaMwJBAALzRICfKJCzWtra2tIVFRVDdHR0Xs6T4VEeBhIEClTV1dWH161bd5yNBOHmzZt/tLa2/hGFZlkpQk6Ty83N/dzNze2XMjIyMkz3c2ClUwgCAQhAYA4IkERhfHx8IjEx8dDevXtj5kCXWe0iEgQKnGVlZQdtbGxOspEgNDY2frl27drPKDTLSpHCwsKV1tbWjXJycgv5WTEb42ClcwgCAQhAQIwF+J+ZpaWluQ4ODl6iPBNDFExIECio5+Xl2W/atKmEjS/Wtra2bH19fW8KzbJSJCsra/fWrVvjmTz1z0pHEAQCEIDAHBUoKi7OcnJ03I4EYY5OoDC7HRsbaxYeHl7HRhsPHz4s19HRsWMjFpUYaWlpf9y+fftnTF7RpNIOykAAAhCYrwKnT5/+cXh4+BfzdXzvGxeuIFCY8RMnTphERkbWSktLy1Ao/sEiXV1dFZqamnazkYk2NDTIDg0NZdjY2Gxl4+oH07GjPgQgAIG5JEBuMUxOTk7FxMQcOXTo0Mm51Hc2+ooEgYJiTEzMUk9PzyoNDY2VFIp/sMiTJ08e1dfXb9i8ebPQt1tOSEhQ2rBhQ/Xq1asZbfLEdMyoDwEIQGAuCpAEgZxEefHiRfvg4GBWriLPJQckCBRmKyEhQcXW1vbmqlWr1lAo/sEiz58/f1ZSUkK2W77PNNZM9U+cOLFi7969TfLy8oozlcW/QwACEIDA2wKvz5IYqKqqWufu7n6Paz5IECjMODleWE5O7rKZmZkDheIfLDIwMNCXnZ1tt2fPnhamsWaqn5ycbBIQEHB7pnL4dwhAAAIQ+LYASRD6+/vvL1682ICLp/AiQaD4W1FQUHDc2dn5MMXi7y02NjbGi4mJCf3oo4/OMY01U/3S0tKddnZ2F2Yqh3+HAAQgAIHpE4QnT540a2lpGXPRBwkCxVm/fPnyV5s2bWJ8+h85vCQhISEiJCQkjmLTtIuVl5d/bGNj83+0A6AiBCAAAQ4LkM/r1tbWC8bGxru4yIAEgeKsX7169RNXV9c/M92JkFyySklJ+WTnzp1/pdg07WKlpaXRdnZ2+2kHQEUIQAACHBYgCUJlZWWcnZ1dBBcZkCBQnPWCggJfJyendKavC5IFV1ZWFufo6CjUBVdcXKygoqJyae3atS4Uh4hiEIAABCDwhgD5vE5JSfk4KCjoKy7CIEGgOOtFRUVbHRwccsgVACabDpEF19jYGG9mZhZKsWlaxRISElSdnJyqtLW1V9EKgEoQgAAEOC4wMTHBi4uLCztw4EA8FymQIFCc9fz8fGNXV9dGNhKE9vb2HH19fS+KTdMq9vXXX5vu2bOnQk5OToFWAFSCAAQgwHGBoaGh/oyMDKfQ0NAGLlIgQaA469HR0ZoRERGdpDjTKwhPnz5t09TU1KfYNK1iGRkZzt7e3gVM+kqrYVSCAAQgME8Eenp6eqqqqqw8PT0fzJMhCTQMJAgUuf7+97+rHzp06KG0tPQCJs8hkFsMAwMDd5csWcJ406UPdf3mzZufrl+//ksmfaVIg2IQgAAE5qVAd3d3lbq6uq2kpOTkvBzgDINCgkBx1mNjYxVdXV3rdHR0dJl86ZIEob+/v+vhw4drLSwsnlNsXuBiNTU1vzY3N/+cSV8FbhQVIAABCMwjgYcPH5bq6Ogw3iBvrpIgQaA4c/n5+fIaGhpVRkZGJkwu27/eunOopKRkvZeX1x2KzQtcrKWlJUNfX58cTypwXVSAAAQgAAEJsgfCV4aGhh9z1QIJAsWZJycj9vX1XXZwcNjI5Ev39eEfw5cvX97g5+fXTLF5gYrl5eWpGBoaFuno6Jgy6atAjaIwBCAAgXkmUFVV9fsNGzb8ZJ4Ni/JwkCBQpOLxeJLJyclfBAQE/JDJly5JEMbGxsbPnDkTdPDgwXSKzQtULCkpaYWrq2ulqqqqBpO+CtQoCkMAAhCYZwI5OTm7vby8EubZsCgPBwkCZSoJidOnT/8qNDT0Z0y+dEmCwJOQkPg6OvoHBw8e/F8Bmqdc9NSpUx5hYWE5CxYskGHSV8oNoiAEIACBeSRAPqenpqZ4sbGx2w4cOJAzj4Ym0FCQIAjAdebMmYO7d+8+Li0tLSVAtWmLnj59+ofh4eF/YhpnuvppaWm7duzYwdmsVximiAkBCHBH4PUpjj15eXm2wcHB7dwZ+dsjRYIgwMwnJSVZ+fr6li5YsGCBANWmLZqdk3N2m6fnXklJSXJBgdWf6urqLywtLX/EalAEgwAEIMARAfK22dOnTztv3rxp5ePj08WRYX9rmEgQBJj53NxcS1dX13JZWVnGCUJNTc211NTUTUePHp0SoAuUira2tibo6+tz8vQxSkAoBAEIQOADAiRB6OjouNbb27vV2tp6nKtYSBAEmPny8nLNdevW3ZGVlVUUoNq0RVtaWori4+NdhJEgdHZ2lmlqatoy7SPqQwACEOCiwOTkJHnFMcPExGQHF8fPHzMSBAFmPyEhQXbHjh3dcnJyKgJUm7ZoZ1fX/ePHjukfPXp0gmmsN+tfLi3VsNDTu7ls2bLlbMZFLAhAAAJcESDPINy+ffu/zczMfs6VMU83TiQIAsz+sWPHFoSEhHQuWrRoqQDVpi06NjbW9/XXX6sfOXKE1ctXcXFxRn5+fqWLFi1azLSPqA8BCECAiwIkQaiurv7Bhg0bhPKm2VwxRYIgwEzxeDyptra28jVr1mwQoNq0RUdGRweqq6o0HR0dXzKN9Wb9U6dOhYSHh5+RkZHBFopswiIWBCDAGQGSIOTl5W319PTM48ygpxkoEgQBZv/o0aNSO3fuvGpmZuYqQLVpi46Ojo5WVlauc3Z2ZnU3xdTU1B/5+vp+wWQ7aKZjQ30IQAACc1mAJAgxMTG2kZGRFXN5HEz7jgRBAEGym+LVq1eTPDw8AgSoNm3R8fHx8WvXrtlu3br1FtNYb9avqa09Y2ZqGooEgU1VxIIABLgk0NfX9yQ9PX3Dvn37HnFp3O+OFQmCgLMfHx//+e7du39NqjHZpXB8fHwiMzPTPTAwsFDALnyweEdHR+mKFSvsmPSNzf4gFgQgAIG5JtDR0XG/rKzMKjg4+Nlc6zub/UWCIKBmVFTUp/v37/+SfAEz+RIm23iePn36R/v27WNtN0VyC+T73//+HRUVFUZHUgtIguIQgAAE5pVAXV1d1tjYmD+X90AgE4oEQcBlHR0d7R8SEpJANktimCBIxMfH/3rv3r0/E7AL7y2ek5OjY29vX6OkpLQEtxjYUkUcCECAawIlJSWxjo6O+7g2btxiYDjjycnJhp6enlUKCgqKTBIE8hBMSkrK3wIDAz9ha7vl2NhY66CgoBvy8vIKTPrGkAjVIQABCMxpgdLS0p84ODj8fk4PgoXO4wqCgIhZWVlrnJycbi1atEiJyZcwSRAqb94s73n61MXb23tUwG5MWzwqKupf9+/f/xdcPWBDEzEgAAGuCpSWlu5xcHA4z9Xx88eNBEHAFdDV1aUoJyfXrKKispxJgkD2+m5ubm4kD8JERkaOCNiN6YpLpqSk/MnPz+9T8o9M+sZCXxACAhCAwJwVuH79uqObm1vJnB0ASx1HgkAD8vnz520qKip6TL6EyRWE+/fvtxUUFKyLiIgYptGNt6qQBxQDAgOvWpibM96jgWlfUB8CEIDAXBQgf7iRz/X4+Pg1YWFhd+fiGNjsMxIEGpqPHj1q0NTUXMvkUj5ZiGNjYyPx8fErDxw48JRGN95NEGS+853vtGpoaKxmGgv1IQABCHBVoL+/vzcxMdH08OHDT7hqwB83EgQaK6C5uTlJX18/kEmCQK4gkJ+YmBityMhIxueNFxcXK1hZWXXJyckp0xgSqkAAAhDgvAD5XL57925rVVUV2QNhiOsgSBBorICampqz5ubmIUxuMZArCOQnOzt7zfbt2xlfysrPzzd2cHC4JScnJ0djSKgCAQhAgPMCrx8ev9Lz9KkPWw+Pz2VUJAg0Zu/y5ct/cHd3/yHTKwhkMRYXF2/ZuHHjZRrdeKtKUlKS+44dO3JlZGRkmMZCfQhAAAJcFCB/uGVnZ//v9u3bf8DF8b87ZiQINFZBfHx8+O7du2OZXEHg32IoLCzc7uLicpFGN96qEhsb+59hYWG/YJK0MO0D6kMAAhCYywIkQcjMzPzcz8/vt3N5HGz1HQkCDcmTJ0+G79+/P1ZaWppG7W+q8BOEgoKCMFdX17O0A30TSzorK+uUl5dXBBIEJpKoCwEIcFlgampqKjc3d9O2bduucdmBP3YkCDRWQXR0tE1QUNBVJSUlJRrV36qSm5v7paen52dM4kRHR8vb2dlVGBkZmSFBYCKJuhCAAJcFJiYnp4oKC+3d3Nw4fcwzEgQGvwUXL15c5ejoWLV48WJVBmFeVb127doxd3f37zCJc+bMmSXbtm27Q/rD5LYHkz6gLgQgAIG5LjA5OTmenp6uFRgY2DvXx8JG/3EFgYZiVlbWCmtr65vLli1Tp1H9rSrl5eVn79+/HxEcHDxJN1ZMTMzyvXv3PiT1cQWBriLqQQACXBcYfvly8Hx8/PKDBw8Oct2CjB8JAo1VQO75d3Z2lmtpaVnRqP5WlaampoaWlhYHPz8/2guysLDQ2cHBoYDpEdRMx4L6EIAABOayQEtLS11NTY11cHDw2FweB1t9R4JAU/Lhw4dly5cvt6VZ/R/VOjo62srKyjYEBwf3042vjyS2AAAgAElEQVR17dq1ABcXl2RSH7cY6CqiHgQgwHWBioqKkuHhYRd3d/cJrlvgCgKDFXD37t0cXV3drQxCvKra09PzIC8vzyo0NLSHbqycnJw/b9my5RMkCHQFUQ8CEICABDmD4bchISH/ISkp+c1Wtxz/wRUEmgugsbHxT8bGxow30yCv1URFRVl89NFHDXS6Qm53lJWVnbazs9tDpz7qQAACEIDAN6+eR0VF/fjw4cNfwOMbASQINFdCZVXVHzasX/9DmtX/UY0sytOnT5tHRETU04mVlZWlbGRkVKGnp2dIpz7qQAACEICAhMT4+PhocnKy2549e0rhgQSB0RrIy8uL3Lx58ylGQV5vmHTt2jU3Dw+P63RiRUVFae/cufOWiorKMjr1UQcCEIAABCQkRkZGXhYVFVlu3ry5BR5IEBitgcTExG07d+7MYvpQILmCUFFRcdjOzu4knQ6Rqw+hoaG1TPtBp23UgQAEIDBfBIaHh3uGhoZWa2pqDs+XMTEdB24x0BSMioraum/fvmwpKSkpJl/OJEGorq4+smHDhuN0ulJUVBRkb2+fiP0P6OihDgQgAIFvBAYHBx8pKyuvgMc/BZAg0FwN8fHxOl5eXpUqKirqTBIEcjhITU3N/1hZWf07na7cunXriIWFxVdM+kCnXdSBAAQgMF8EyB9qd+7cuWZoaOg+X8bExjiQINBUJHAbNmyoUldX12Ly5UwShNra2gvr16/fRacrt27dirOwsNjLpA902kUdCEAAAvNBgCQH5L+ampp0Kysrv/kwJrbGgASBpmR+dfXi1YsXF61cuXItk8v7ZGE2NzdfMDExEThBaGhokJWSkso0MjLaggSB5kSiGgQgwGkBfoKQnp7+i4CAgF9xGuOdwSNBoLkaeDyeZFtb20VdXd1tTBKEyakpiXvt7TdHR0fdTE1NhwTpTkZGhpqVldVNLS2tlUgQBJFDWQhAAAL/FJicnJQ4e/bsRxERESfg8k8BJAgMVkNjY2OGoaHhdiYJArnF0NnZ+bC4uNgqODj4qSDdOXXq1Npdu3aVKCoqKiNBEEQOZSEAAQj8U+Dly5cvkpKStoSHhxfDBQkCK2ugvr7+f0xMTH7MNEF49uzZo+Li4g1+fn5PBOlYUlKSu7+//1Uc0iSIGspCAAIQeFugv7+/v6ysbL2np2c7bJAgsLIGbt269Zm5ufkfmSQIpCOjo6MjZ86c2Xjw4MFKQTpWWlr6qY2NzZdIEARRQ1kIQAAC/xQgzyD09va2TU5OrsMeCG+vDNxiYPCbUlpa+n1bW9v/ZXJ5nyxO8hMTE+MeGRl5TZDuNDQ0/MbExOSnJAbTJEWQdlEWAhCAwHwS6O7urtfQ0DCfT2NiYyxIEBgoXrx40WXbtm3XmSYI5As+MzNzq5+fX54g3Wlra8vT1dXdTOow6YMgbaIsBCAAgfkm0NrammJoaBg438bFdDxIEBgIxsTE2O7du7eMyV/v/FdsKioqfmRvb/9Hqt15/PjxwqmpqeLly5evo1oH5SAAAQhA4G0B8hlcX19/zMLC4juweVsACQKDFfHVV1+tjYiIqFBQUFhINww/Qaivrz+6bt26/6QaJysra4WNjU2VmpoaDmmiioZyEIAABN4RIJ/Bubm5n3p5ef0ZOEgQWFsDMTExS7du3Vqlqam5km5QfoLQ3Nz8+7Vr1/6Eapyvv/7aZffu3Tny8vLyVOugHAQgAAEIvC1AXjU/d+7czrCwsGTYIEFgbQ0kJCSo2tnZ3Vy5cuVqukH5Dym2tbXlGhgYeFKNk5iYuGfnzp3xePaAqhjKQQACEPi2wNDQ0GBiYqLbgQMHquCDBIG1NdDe3i4/PDycbWpq6kY3KEkQyJf8gwcPrq1cuZLyQSHFxcV/s7e3/x4SBLryqAcBCEBAQqKnt7e7tqbGatOmTY/ggQSB1TVQUVERa21tHU43KP8KwqNHj8oGBgZcTE1Nx6jEampqSjA0NNyFBIGKFspAAAIQmF7g4cOHtQMDA06CbnXPBU88pMhwlgsLC6OdnJz2Mwwj8eTJkye1tbVWW7dufUwlVldXV426uroFEgQqWigDAQhA4G0B/h9n9+/fv6arq0v56i2XHJEgMJzt4uLi7zk4OPyNYRiJ5319T0tLSqy8vb0fzhSruLhY1cDA4JaamprOTGXx7xCAAAQgML0ASRJaWlpOGhsbH4bRtwWQIDBcFaWlpXvs7OziGYaRGB4eHkxISKD0oExGRoaBi4tLpbKysjLTdlEfAhCAABcF6L5iziUrJAgMZ7uoqMjP0dExlWEYCXLcaExMTNDBgweTZooVFRUVvHfv3jMLFiyQwS2GmbTw7xCAAAS+LUBebyQ/169f3+fh4RELI1xBYH0NpKWlGe3YsaOJjcBJSUlBQUFBMyYI586d+zQ4OPjVIU34gQAEIAABwQXIFYQpHk8i4fx5t9DQ0OuCR5j/NXAFgeEcnzp1Si8yMrKNYZhX1XNzcz/39PT87UyxKioqUq2srPyYbPE8Uxv4dwhAAALzWYBcQXjx4kXfhQsXHCIjI1n5I2++eSFBYDijUVFR2sHBwY1KSkqMngcg2Wx1dfWxDRs2zLgf+J07d4r09PQccQWB4eShOgQgwFkBclu3u7v7YWFhoVVwcPBTzkJ8YOBIEBiuiqioqEWbN2+uXrly5RqGoSSamppOmJiYfDRTnIGBgUdKSkraSBBmksK/QwACEJhegFxBuHv3btndu3c3eXp6DsPp2wJIEBiuipycHMVVq1bdNDQ0NGLyhU2uILTfu1e0Rk/P+UNdqqqqWmZoaNiiqKi4mGHXUR0CEIAAZwXIZ+7t27cvmJmZ7eIswgwDR4LAcGVUVlYuGBsbS7K3t/dlmiB0P31ar6mhYf6hLiUnJ6/z8fEplZWVxSFNDOcO1SEAAe4KkAShpqbmi/Xr1/+YuwofHjkSBBZWRlZW1pdeXl6fMkkQSDd6enoa1NTUyO6I37x/M83PiRMnvnfgwIG/SOEJRRZmDiEgAAEuC1RVVX20YcOGE1w2+NDYkSCwsDKSk5O/CAgI+BGTUCSbffbsWW9DQ8N6V1fXB++LdebMmf8KDQ39OZO2UBcCEIAA1wXIZ25JSYm/k5NTGtct3jd+JAgsrIysrKzdnp6e8VJSUow2JhgYHOwvuHHDavv27Xen69axY8cW2NjYFK5fv96WhW4jBAQgAAHOCpAE4ezZsxZ79+6t4yzCDANHgsDCyrhy5YqLm5tbPpPL/mSxvnjxYjA1NdXpfQs2Ojpa3s3NrX716tWM35hgYdgIAQEIQGDOCpDt7ePj49cdPny4fc4OQsgdR4LAAvCNGzdsHR0dS6SlpaXohiMJwsTExFRMTEzk4cOHp93289ixYyoHDhx4IiMjI0e3HdSDAAQgAAEJiQcPHtwtLS0leyD0w2N6ASQILKyMy5cva7i5uT2UlpaWoRuOf3DI2bNnvxMeHn5sujhXr141cnFxaZCWlpam2w7qQQACEICAhER9fX3p6OjoJmtr6xfwQIIgtDVA/rI/ePDgU2lp6QV0G+EnCCkpKT8NCgr63XRxLl++7O7q6npZRkaG9pUKuv1DPQhAAALzSaCgoOCEi4vLjBvTzacxCzoWXEEQVGya8gkJCUqenp4dysrKS5iEI0lCcXFxkrOzc9B0cVJTU3/i6+v7O7zhyEQZdSEAAS4L8E9xzM/P/83mzZv/g8sWM40dCcJMQhT+PSEhQdbS0rLCwMDAgkLx9xYhCUJzc3OmiYmJ73SFMjMz/+rt7f0vTPdbYNJH1IUABCAwlwXI5yz5KSoq2rFx48aMuTwWYfcdCQILwuT1QwcHh1Jzc3MrJuFebbfc3l68Zs0ap3fjZGRkLNTV1S1cu3bteiQITJRRFwIQ4LIA/3ZuSUmJh7Ozcz6XLWYaOxKEmYQo/HtCQoL04sWLz23ZsmXaWwMUQrwqQhZuX1/fI1VV1RXv1klJSVlsZ2dXo6mpuRIJAlVRlIMABCDwtgA/QcjIyFjp7+//3k3p4CYhgQSBpVUQExPz84iIiP9iGm54eLhLSUlJ6904J06c0D1w4ECrlJQU3mBgioz6EIAAZwVIgjA2NjYWHR2t8/HHH3dzFoLCwJEgUECiUuTkyZM/PnDgwP8w/et+eHj4+d27d9dYWFg8f7Pdq1ev2rm7u5dS6QvKQAACEIDA+wXa29vbCwsLzSMiInDM8wcWChIEln6L4uLivHbv3p2+YMEC2q86kq6MjIyM3LhxY72np2fTm10rLi4OcXBwOMtSdxEGAhCAACcFyFsMtXV1tzofP7b39vYe5SQCxUEjQaAINVOxjIwM4y1btlTLyckxOoZ5bGxsJCUlxWrPnj2Nb7ZZUlLya3t7+89n6gf+HQIQgAAE3i9AbjGkpKT8NTAw8PuSkpLfvNKAn2kFkCCwtDDS0tKMSIKgoKCgwCTk+Pj4xNmzZ3fu378//c04FRUVMdbW1hFMYqMuBCAAAa4LkCsI5FTciIiIX3LdYqbxI0GYSYjiv/N4PPkXL150LFy4cBnFKtMWI9ltVFTUTw8fPvyP3RSzsrKUDQwNi/XXrDFlEht1IQABCHBdYGJiYiI1NXXHrl27srluMdP4kSDMJCTAvw8PDz9euHDht95AECDEq6JxcXG/fPONiLi4OC0vL6+bampqjGML2heUhwAEIDCfBMbGxsbz8/NtvLy8aubTuIQxFiQILKp2dnY2a2hoGDJ9kyE7O/v80NBQWHBw8CTpXlRUlPX+/fvLmBwnzeIwEQoCEIDAnBUYGRkZbmhoWG1tbd0zZwcxSx1HgsAidFNTU7ahoaEX0wShtra2qrS01P7IkSPjpHv5+fnbXVxcMnAGA4uThVAQgAAnBV6+fNkXFxenzv985SQCxUEjQaAIRaXYzerqDEsLi+1Mv8hbWlpqBgcHbaytrV8lCFVVVT+0tLT8A9PEg8oYUAYCEIDAfBbo6Oi4XVZWZsG/Qjufx8p0bEgQmAq+UT89Pf3L7du3f8r0i7y7u/tpTk6OLn8Tj/r6+q9NTEz2MU08WBwqQkEAAhCYkwK3b9++npiY6HH06NGpOTmAWew0EgQWsePj47+ze/fuvzNNECYmJ8dPRUUtO3LkSD+Px5Nsa2vL1tPT82Qal8WhIhQEIACBOSXAP8UxMzPzmK+v78fYA2Hm6UOCMLMR5RKnTp2KjIiIiJKWlpakXGmagpOTk+Pp6elagYGBvVlZWcvMzc0rtLW1V+EKAhNV1IUABLgswE8QYmNjP9u/f/+XXLagOnYkCFSlKJSLi4uzCgwMvKGgoKDI5K/9ycnJyfLy8vWOjo510dHRxoGBgcWLFi1awiQmhe6jCAQgAIF5K0AShNHR0bEzZ84EHjp06OK8HSiLA0OCwCJmZmam3saNG6uUlZVVmISdmpqaKigocHNzcys4ffr0tpCQkItSUlKMrkow6Q/qQgACEJjrAmQHxZGRkaGrV69a+fr6ts718cxG/5EgsKicn5+/wszMrHrp0qVqTP7a5/F4U5cvX966devWK1euXPnY3d39/5jEY3GICAUBCEBgTgqQKwgDAwNPGxoazJycnHDMM4VZRIJAAYlqER6PJ9Xb21u7dOlSRlsiT01N8dLT0/8tICDgjw0NDX9cu3btZ1T7gHIQgAAEIPBtAZIgDA4OtqmoqOjDh5oAEgRqTpRLdXZ21mloaJgx+YufLGTyyqS/v/9n7e3t11avXu1KuQMoCAEIQAAC3xIgn6sdHR3Fq1evdgIPNQEkCNScKJdqa2vL09PT20y5wjQFyb2yvMuX/+65deunjx8/LtfW1l7HJB7qQgACEOC6APlcbWxsPGdmZhbCdQuq40eCQFWKYrnGxsbjxsbGhykWn7YYyXRv3rx5fWho6JCZmVmJmpqaGpN4qAsBCECA6wIkQbh69eoXW7Zs+THXLaiOHwkCVSmK5SoqKv5ubW39HYrFpy1GFnLrnTstlRUV3w0ICEhbuHChIpN4qAsBCECA6wLkc/XcuXMRYWFhcVy3oDp+JAhUpSiWy8rK+hcvL6+/MnkGgSzkhw8ftl2/fv3LsLCwv2GDJIr4KAYBCEDgPQJkD4S4uLiAw4cPZwGJmgASBGpOlEudP39+565duy4wSRBIYyMjI4OXL1/O8vb23o0EgTI/CkIAAhCYVqC/v/9ZUVGRlY+Pz30QURNAgkDNiXKp48eP74iMjEyRkZGRolzpPQXLysoabW1tTZgmG0z7gfoQgAAE5rpAV1fXg7t371pjDwTqM4kEgboVpZLx8fGrvb29K5SVlRk/WNjb28tTVVWVRIJAiR6FIAABCLxX4FFnZ8UKbW07CQkJHpioCSBBoOZEudSVK1eWW1hYVKmpqalTrvSegvzDRZAgMJVEfQhAgOsCbW1tWfr6+j5cdxBk/EgQBNGiUDY/P3+xgYFB6fLly40oFP9gEZIgIDlgqoj6EIAABCQkGhoavjQzM8OutAIsBiQIAmBRLfrgwYP8FStWuFEtj3IQgAAEICBcgRs3bvzM1dX118JtZX5FR4IghPlsbW29smbNGg/89S8EXISEAAQgQEPgwoUL3rt27cqmUZWzVZAgCGHqGxsb/2RkZPQDcosArygKARghIQABCFAUIPvKjI+Pj8XHx2+OjIwsoFgNxSQkJJAgCGEZ1NXV/dLU1PQoCY2rCEIARkgIQAACFAXIH2o9PT1dBQUFVjt37uykWA3FkCAIZw0Ul5X9m4Ot7e/xkKFwfBEVAhCAAFWB16c4Ng8MDDhYWFg8p1oP5XAFQShrIC0tbYuvr28uriAIhRdBIQABCFAWIAnC3bt3s/X19b0pV0LBVwK4xSCEhRAdHe0WERGRT24v4BaDEIAREgIQgABFAfIMQlNTU5ypqWkExSoo9loACYIQlkJUVNS6PSEhRQry8opIEIQAjJAQgAAEKAqQKwj19fU/tLCw+BPFKiiGBEF4ayAhIWGZu7s72U1xhfBaQWQIQAACEJhJgCQIZWVlHzs4OHw1U1n8+9sCuIIghBVBEgQHB4cqbW3tFXjNUQjACAkBCECAogBJENLS0uwCAgLKKVZBMVxBEN4ayM/Pl9HU1Mw0NDT0RIIgPGdEhgAEIDCTwNjY2GhsbKzt4cOHa2cqi3/HFYRZWQP19fWJa9euDcIzCLPCjUYgAAEITCvQ1dXVUVJSYhUYGNgLIsEEcItBMC/KpauqqmIsLS0jkCBQJkNBCEAAAqwLtLW11bW0tDh7e3sPsB58ngdEgiCkCb5169a/WlhY/AUJgpCAERYCEIAABYGqqqozVlZW4ZKSkjwKxVHkDQEkCEJaDuXl5Yetra2PI0EQEjDCQgACEKAgUFRUdMzZ2fk7FIqiyDsCSBCEtCRKSkoC7ezskpAgCAkYYSEAAQhQECgrK4uwt7ePo1AURZAgzM4aSEhIMA8KCqpFgjA73mgFAhCAwHQCpaWlOx0cHJKhI7gAriAIbkapxsmTJ80OHjxIXquRpFQBhSAAAQhAgHWBzMxME19f3ybWA3MgIBIEIU1yVFSU9s6dO6tVVFTUhdQEwkIAAhCAwAcEJicnx0+cOGH48ccf3wOU4AJIEAQ3o1QjISFBxc7OrmrlypV6lCqgEAQgAAEIsCZAdlDs7Oy8n5ubaxkZGdnHWmAOBUKCIKTJTktLW2RsbFxmaGhoIqQmEBYCEIAABD4g0NjYeLuystI2IiJiGFCCCyBBENyMUg0ejydVXl4ea2trG0apAgpBAAIQgABrAuSY5/z8/Nhnz54dCA4OnmQtMIcCIUEQ4mTn5+d/5ebmdkSITSA0BCAAAQhMI0AShPT09D8FBAT8EED0BJAg0HOjVCs3N/d3mzdv/gledaTEhUIQgAAEWBOYmpri5eTkhHh7e59nLSjHAiFBEOKE37hxY6ezs/MF0gSSBCFCIzQEIACBdwQmJyenrl+/7r5p06YbwKEngASBnhulWjdu3PBxdnbORIJAiQuFIAABCLAmMD4+PllUVGTs7u5+h7WgHAuEBEGIE15UVORgb29fTK4e4AqCEKERGgIQgMA7AhOTk2OnoqLUjxw50g8cegJIEOi5UaoVExOzfO/evQ9JYSkpKUp1UAgCEIAABJgLdHd3P0pNTTU8cuTIC+bRuBkBCYIQ5z06OlozIiKiE1cQhIiM0BCAAASmEWhsbKwqKCiwP3LkyDiA6AkgQaDnRqkW2U3R1dW1edmyZRq4xUCJDIUgAAEIsCJw6dKlC4ODg3uwBwJ9TiQI9O1mrJmfny+/fPnyKn19fRMkCDNyoQAEIAAB1gRiY2N/sW/fvl+xFpCDgZAgCHHSo6Oj5a2srMrMzc0tkCAIERqhIQABCLwhMDExMRkbG7vv4MGDZwBDXwAJAn27GWsmJCRIKygoRPn4+OxDgjAjFwpAAAIQYEVgbGxsJCMjwzooKKiBlYAcDYIEQcgTf+bMmV+HhIR8jgRByNAIDwEIQOC1wMuXLwcrKipMXV1dHwCFvgASBPp2lGqeOnXqZ/v37/8vSWQIlLxQCAIQgAATAXIGw4sXL7qUlJS0JSUleUxicb0uEgQhr4Dk5GQPX1/fHBkZGRkhN4XwEIAABDgvwOPxJLq7u9s0NTX1OY/BEAAJAkPAmapnZWWt9/DwKJOTk1swU1n8OwQgAAEI0BcgyQH5aWlpyTY2NvamHwk1iQASBCGvg4KCAkMbG5taOTk5OSE3hfAQgAAEOC9AkoRr167FeHh47Oc8BkMAJAgMAWeqnpOTo+jq6vpYTk5Oeaay+HcIQAACEGAmQBKEs2fP/svevXv/H7NIqI0EQchr4NixYwvCw8O7FRQUFgu5KYSHAAQgwHkBcopjbGxsxKFDh85yHoMhABIEhoAzVSd7ITg6OjYtX74cD8zMhIV/hwAEIMBAgFw9GBoaGsjNzSV7ILQyCIWqeAZB+Gvg6NGjUsHBwfkmJiYuwm8NLUAAAhDgtsCzZ896a2trLd3d3V+dpIsf+gK4gkDfjlJNkiD4+Phctra2dqdUAYUgAAEIQIC2QG9vb8PSpUvNsQcCbcJ/VESCwNxwxgjJycm/DwgI+LcZC6IABCAAAQjQFiC3GB4+fFi5cuVKG9pBUBEJwmyugdjY2E/Dw8O/nM020RYEIAABLgo0NTV9bWJiEsnFsbM9ZlxBYFt0mnjHjx8/fODAga+kpKSksOPyLICjCQhAgLMCxcXFf3ZycvqUswAsDhwJAouY7wt17ty5dTt27CiWl5dfiARhFsDRBAQgwEkBcoshJSVlz86dO89zEoDlQSNBYBl0unAZGRkGbm5uVYqKikpIEGYBHE1AAAKcFJiYmJiMiYkJOHToUAYnAVgeNBIElkGnC3flypXlVlZWtSoqKqpIEGYBHE1AAAKcFBgYGHh648YNa19f3w5OArA8aCQILINOF47H40n29/ffUVZW1kOCMAvgaAICEOCUAP+Qpu7u7s6WlpYNLi4unZwCENJgkSAICfbdsN3d3a1Lly7Vl5KSmqUW0QwEIAABbgk8evSosLi42C04OHiSWyMXzmiRIAjH9VtR29raLuvq6m7CFYRZAkczEIAAZwT4VxDutLXlGBoYeHFm4EIeKBIEIQPzw99ubDxrbGQUggRhlsDRDAQgwBkBkiCQ/5qamn5ramr6OWcGLuSBIkEQMjA/fHFxcYy9vX0EEoRZAkczEIAAZwT4CcLNmzc/t7W1/S1nBi7kgSJBEDIwP3xSUtJ3/f39/x+eQZglcDQDAQhwRmBqakqC/PGVlpa2OSAg4ApnBi7kgSJBEDIwP3xsbGx4WFhYLBKEWQJHMxCAAGcEyBWEyampqZivv3Y/dOjQDc4MXMgDRYIgZGB++JMnT/ru3bs3SU5ObsEsNYlmIAABCHBCgCQIz54968zLy7MOCQl5zIlBz8IgkSDMAjJpIikpSW/z5s2VysrKS2apSTQDAQhAgBMC5BbDo0eP2ltaWmw2b97cy4lBz8IgkSDMAjJpIi8vb+X69eurly5dqjpLTaIZCEAAApwQIFcQWlpasjo7O/3c3d0nODHoWRgkEoRZQCZNtLa2KisqKlZqaWkZzFKTaAYCEIAAJwRIgnDr1q14KyurUE4MeJYGiQRhlqBJM3fu3EnR09PzJ4sZDyvOIjyaggAE5rUAucVQV1f3r5aWln+b1wOd5cEhQZhF8PT09GBvb++zkpKS0kgQZhEeTUEAAvNagCQIlZWV++3s7GLm9UBneXBIEGYRPCMjY6GRkVH6mjVrNiFBmEV4NAUBCMxLAf4GSUNDQ0M5OTnrg4OD78zLgYpoUEgQZhk+Ly/P3NTUNFVLS0tvlptGcxCAAATmlcDk5DdnMhUXF/+iq6vrNzikid3pRYLArueM0cjRz/n5+bZGRkbRWlpaJvwK/C2YSUaM7ZhnZEQBCECAgwL8z0f+4Uzj4+OjdXV1cYODgz9wd3cf4iCJUIeMBEGovO8Pnp2dvXr16tVfrFixwktRUVGJlERyIKLJQLMQgMCcEhgdHR199uxZQ0VFxe/9/PwuSEpK4nhnIcwgEgQhoAoSMi4uzlFNTS1g1apVFsuWLTOSlpaWFaQ+ykIAAhDgggCPx5vq6+tru3fv3u0nT56kbdy48bqOjs5LLoxdVGNEgiAqebQLAQhAAAIQEGMBJAhiPDnoGgQgAAEIQEBUAkgQRCWPdiEAAQhAAAJiLIAEQYwnB12DAAQgAAEIiEoACYKo5NEuBCAAAQhAQIwFkCCI8eSgaxCAAAQgAAFRCSBBEJU82oUABCAAAQiIsQASBDGeHHQNAhCAAAQgICoBJAiikke7EIAABCAAATEWQIIgxpODrkEAAhCAAAREJYAEQVTyaBcCEIAABCAgxgJIEMR4ctA1CEAAAhCAgKgEkCCISh7tQgACEIAABMRYAAmCGE8OugYBCEAAAhAQlQASBFHJo10IQAACEICAGAsgQRDjyUHXIAABCEAAAqISQIIgKgl2mBUAAANXSURBVHm0CwEIQAACEBBjASQIYjw56BoEIAABCEBAVAJIEEQlj3YhAAEIQAACYiyABEGMJwddgwAEIAABCIhKAAmCqOTRLgQgAAEIQECMBZAgiPHkoGsQgAAEIAABUQkgQRCVPNqFAAQgAAEIiLEAEgQxnhx0DQIQgAAEICAqASQIopJHuxCAAAQgAAExFkCCIMaTg65BAAIQgAAERCWABEFU8mgXAhCAAAQgIMYCSBDEeHLQNQhAAAIQgICoBJAgiEoe7UIAAhCAAATEWAAJghhPDroGAQhAAAIQEJUAEgRRyaNdCEAAAhCAgBgLIEEQ48lB1yAAAQhAAAKiEkCCICp5tAsBCEAAAhAQYwEkCGI8OegaBCAAAQhAQFQCSBBEJY92IQABCEAAAmIsgARBjCcHXYMABCAAAQiISgAJgqjk0S4EIAABCEBAjAWQIIjx5KBrEIAABCAAAVEJIEEQlTzahQAEIAABCIixABIEMZ4cdA0CEIAABCAgKgEkCKKSR7sQgAAEIAABMRZAgiDGk4OuQQACEIAABEQlgARBVPJoFwIQgAAEICDGAkgQxHhy0DUIQAACEICAqASQIIhKHu1CAAIQgAAExFgACYIYTw66BgEIQAACEBCVABIEUcmjXQhAAAIQgIAYCyBBEOPJQdcgAAEIQAACohJAgiAqebQLAQhAAAIQEGMBJAhiPDnoGgQgAAEIQEBUAkgQRCWPdiEAAQhAAAJiLIAEQYwnB12DAAQgAAEIiEoACYKo5NEuBCAAAQhAQIwFkCCI8eSgaxCAAAQgAAFRCSBBEJU82oUABCAAAQiIsQASBDGeHHQNAhCAAAQgICoBJAiikke7EIAABCAAATEWQIIgxpODrkEAAhCAAAREJYAEQVTyaBcCEIAABCAgxgJIEMR4ctA1CEAAAhCAgKgEkCCISh7tQgACEIAABMRYAAmCGE8OugYBCEAAAhAQlQASBFHJo10IQAACEICAGAsgQRDjyUHXIAABCEAAAqISQIIgKnm0CwEIQAACEBBjASQIYjw56BoEIAABCEBAVAJIEEQlj3YhAAEIQAACYiyABEGMJwddgwAEIAABCIhK4FWCEBcXt05VVfWJqDqBdiEAAQhAAAIQEC+BZ8+eafx/NdJ7u1l8ZSsAAAAASUVORK5CYII=;&quot; 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y="108" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Run pre-fitting of...</text></switch></g></g><g><path d="M 646 400 L 646 330 Q 646 320 652 328 L 700 392 Q 706 400 700 408 L 652 472 Q 646 480 646 470 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 984 100 L 984 30 Q 984 20 990 28 L 1038 92 Q 1044 100 1038 108 L 990 172 Q 984 180 984 170 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 646 100 L 646 30 Q 646 20 652 28 L 700 92 Q 706 100 700 108 L 652 172 Q 646 180 646 170 Z" fill="#fdfcfc" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><rect x="0" y="300" width="323" height="200" rx="30" ry="30" fill="#1ba1e2" stroke="#006eaf" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 321px; height: 1px; padding-top: 400px; margin-left: 1px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 28px; font-family: &quot;Roboto Slab&quot;; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Run full fitting and generate and store results</div></div></div></foreignObject><text x="162" y="408" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Run full fitting and ge...</text></switch></g></g><g><path d="M 132.91 310 L 132.91 240 Q 132.91 230 138.91 238 L 186.91 302 Q 192.91 310 186.91 318 L 138.91 382 Q 132.91 390 132.91 380 Z" fill="#ffffff" 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From bc017782583022e4f15a6e6786f5d7f0dfa906ff Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 16:13:40 +0100
Subject: [PATCH 61/90] Add docstrings in tables and `make` in `kpms_pca`

---
 element_moseq/kpms_pca.py | 160 ++++++++++++++++++++++++--------------
 1 file changed, 103 insertions(+), 57 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 473694d..edc7f78 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -1,15 +1,13 @@
-import datajoint as dj
-import matplotlib.pyplot as plt
-import cv2
-from typing import Optional
-import numpy as np
-from datetime import datetime
+import datetime
 import inspect
-import importlib
 import os
-import yaml
 from pathlib import Path
 import pickle
+from typing import Optional
+
+import cv2
+import datajoint as dj
+import numpy as np
 
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import generate_dj_config, load_dj_config
@@ -116,15 +114,15 @@ class PoseEstimationMethod(dj.Lookup):
     """Table for storing the pose estimation method used to obtain the keypoints data.
 
     Attributes:
-        format_method (str)                : Pose estimation method.
-        pose_estimation_desc (str)  : Pose estimation method description.
+        format_method (str): Pose estimation method.
+        pose_estimation_desc (str): Pose estimation method description.
     """
 
     definition = """ 
     # Parameters used to obtain the keypoints data based on a specific pose estimation method.        
-    format_method                          : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
+    format_method           : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
     ---
-    pose_estimation_desc            : varchar(1000)    # Optional. Pose estimation method description
+    pose_estimation_desc    : varchar(1000)    # Optional. Pose estimation method description
     """
 
     contents = [
@@ -143,9 +141,10 @@ class KeypointSet(dj.Manual):
 
     Attributes:
         kpset_id (int): Unique ID for each keypoint set.
+        PoseEstimationMethod (foreign key): Unique format method varchar used to obtain the keypoints data.
         kpset_config_dir (str): Path relative to root data directory where the config file is located.
         kpset_videos_dir (str): Path relative to root data directory where the videos and their keypoints are located.
-        kpset_description (str): Optional. User-entered description.
+        kpset_desc (str): Optional. User-entered description.
     """
 
     definition = """
@@ -153,15 +152,15 @@ class KeypointSet(dj.Manual):
     kpset_id                        : int
     ---
     -> PoseEstimationMethod
-    kpset_config_dir               : varchar(255)  # Path relative to root data directory where the config file is located
+    kpset_config_dir                : varchar(255)  # Path relative to root data directory where the config file is located
     kpset_videos_dir                : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
-    kpset_desc=''            : varchar(300)  # Optional. User-entered description
+    kpset_desc=''                   : varchar(300)  # Optional. User-entered description
     """
 
     class VideoFile(dj.Part):
         """IDs and file paths of each video file.
 
-        Atribbutes:
+        Attributes:
             video_id (int): Unique ID for each video.
             video_path (str): Filepath of each video, relative to root data directory.
         """
@@ -176,27 +175,25 @@ class VideoFile(dj.Part):
 
 @schema
 class RecordingInfo(dj.Imported):
-    """Automated table to store the average metadata from the videoset associated with a kpset_id.
+    """Automated table to store the average metadata of the videoset associated with a kpset_id.
 
     Attributes:
         KeypointSet (foreign key)               : Unique ID for each video set.
-        px_height (smallint)                    : Height in pixels.
-        px_width (smallint)                     : Width in pixels.
-        nframes (int)                           : Number of frames.
-        fps (int)                               : Optional. Frames per second, Hz.
-        recording_datetime (datetime)           : Optional. Datetime for the start of recording.
-        recording_duration (float)              : Video duration (s) from nframes / fps.
+        px_height_average (smallint)            : Average height of the video set (pixels).
+        px_width_average (smallint)             : Average width of the video set (pixels).
+        nframes_average (int)                   : Average number of frames of the video set (frames).
+        fps_average (int)                       : Optional. Average frames per second of the video set (Hz).
+        recording_duration_average (float)      : Average video duration (s) of the video set (nframes / fps).
     """
 
     definition = """
     -> KeypointSet
     ---
-    px_height_average                 : smallint  # Height in pixels
-    px_width_average                  : smallint  # Width in pixels
-    nframes_average                   : int       # Number of frames 
-    fps_average = NULL                : int       # Optional. Frames per second, Hz
-    recording_datetime = NULL : datetime  # Optional. Datetime for the start of the recording
-    recording_duration_average        : float     # Video duration (s) from nframes / fps
+    px_height_average                 : smallint  # Average height of the video set (pixels)
+    px_width_average                  : smallint  # Average width of the video set (pixels)
+    nframes_average                   : int       # Average number of frames of the video set (frames)
+    fps_average = NULL                : int       # Optional. Average frames per second of the video set (Hz)
+    recording_duration_average        : float     # Average video duration (s) of the video set (nframes / fps)
     """
 
     @property
@@ -206,20 +203,17 @@ def key_source(self):
 
     def make(self, key):
         """
-        Make function to populate the RecordingInfo table.
+        Make function to populate the `RecordingInfo` table.
 
         Args:
-            key (dict): Primary key from the RecordingInfo table.
-
-        Returns:
-            dict: Primary key and attributes for the RecordingInfo table.
+            key (dict): A dictionary containing key information for the session
 
         Raises:
+
         High-Level Logic:
         1. Fetches the file paths and video IDs from the KeypointSet.VideoFiles table.
         2. Iterates through the file paths and video IDs to obtain the video metadata using OpenCV.
-        3. Inserts the video metadata into the RecordingInfo table.
-
+        3. Calculate the average video metadata and insert it into the RecordingInfo table.
         """
 
         file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
@@ -267,16 +261,17 @@ class Bodyparts(dj.Manual):
     Attributes:
         KeypointSet (foreign key)       : Unique ID for each keypoint set.
         bodyparts_id (int)              : Unique ID for each bodypart.
-        anterior_bodyparts (longblob)   : List of strings of anterior bodyparts
-        posterior_bodyparts (longblob)  : List of strings of posterior bodyparts
-        use_bodyparts (longblob)        : List of strings of bodyparts to be used
+        bodyparts_desc(varchar)         : Optional. User-entered description.
+        anterior_bodyparts (blob)       : List of strings of anterior bodyparts
+        posterior_bodyparts (blob)      : List of strings of posterior bodyparts
+        use_bodyparts (blob)            : List of strings of bodyparts to be used
     """
 
     definition = """
     -> KeypointSet
     bodyparts_id                : int
     ---
-    bodyparts_desc=''           : varchar(1000)
+    bodyparts_desc=''           : varchar(1000) # Optional. User-entered description.
     anterior_bodyparts          : blob  # List of strings of anterior bodyparts
     posterior_bodyparts         : blob  # List of strings of posterior bodyparts
     use_bodyparts               : blob  # List of strings of bodyparts to be used
@@ -289,49 +284,56 @@ class PCATask(dj.Manual):
     Table to define the PCA task.
 
     Attributes:
-        KeypointSet (foreign key)       : Unique ID for each keypoint set.
         Bodyparts (foreign key)         : Unique ID for each bodypart.
-        pca_task_id (int)               : Unique ID for each PCA task.
-        output_dir (str)                : KPMS's output directory in config relative to root
+        output_dir (str)                : KPMS's output directory relative to root
         task_mode (str)                 : 'load': load computed analysis results, 'trigger': trigger computation
     """
 
     definition = """ 
     -> Bodyparts
     ---
-    output_dir='' : varchar(255)             # KPMS's output directory in config relative to root
-    task_mode='load' : enum('load', 'trigger') # 'load': load computed analysis results, 'trigger': trigger computation
+    output_dir=''               : varchar(255) # KPMS's output directory relative to root
+    task_mode='load'            : enum('load', 'trigger') # default = 'load': load computed analysis results, 'trigger': trigger computation
     """
 
 
 @schema
 class FormattedDataset(dj.Imported):
     """
-    Table for storing the formatted dataset and update the config.yml by creating a new dj_config.yml in the project path (output_dir)
+    Table for storing the formatted dataset and update the `config.yml` by creating a new `dj_config.yml` in the project path (`output_dir`)
+
+    Attributes:
+        PCATask (foreign key)           : Unique ID for each PCATask.
+        coordinates (longblob)          : Dictionary mapping filenames to keypoint coordinates as ndarrays of shape (n_frames, n_bodyparts, 2[or 3])
+        confidences (longblob)          : Dictionary mapping filenames to `likelihood` scores as ndarrays of shape (n_frames, n_bodyparts)
+        formatted_bodyparts (longblob)  : List of bodypart names. The order of the names matches the order of the bodyparts in `coordinates` and `confidences`.
     """
 
     definition = """
     -> PCATask
     ---
-    coordinates             : longblob
-    confidences             : longblob             
-    formatted_bodyparts     : longblob
+    coordinates             : longblob # Keypoint coordinates
+    confidences             : longblob # Keypoint confidences                 
+    formatted_bodyparts     : longblob #Formatted bodyparts
     """
 
     def make(self, key):
         """
-        Make function to generate/update dj_config.yml and to format keypoint coordinates and confidences for inference.
+        Make function to generate and update `dj_config.yml` and to format keypoint coordinates and confidences for inference.
 
         Args:
             key (dict): Primary key from the PCATask table.
 
-        Returns:
-            dict: Primary key and attributes for the PCATask table.
-
         Raises:
+            ValueError: If `task_mode` does not match either 'load' or 'trigger'
 
         High-Level Logic:
-
+        1. Fetches the anterior_bodyparts, posterior_bodyparts, use_bodyparts, output_dir, and task_mode from the PCATask table.
+        2. Fetches the format_method, kpset_config_dir, and kpset_videos_dir from the KeypointSet table.
+        3. Based on the task_mode, it either triggers the computation or loads the computed analysis results.
+        4. If task_mode='trigger', it creates an output_dir if it does not exist, creates a `dj_config` file with the default values from the pose estimation config, and update it with the video_dir and bodyparts used in the pipeline.
+        6. If task_mode='load', it loads the `dj_config` file and updates it with the video_dir and bodyparts used in the pipeline.
+        7. Then, it loads keypoint tracking results from one or more files and formats them for PCA analysis.
         """
 
         anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
@@ -400,6 +402,13 @@ def make(self, key):
 
 @schema
 class PCAFitting(dj.Computed):
+    """Table for storing the PCA fitting analysis.
+
+    Attributes:
+        FormattedDataset (foreign key)  : Unique ID for FormattedDataset.
+        pca_fitting_time (datetime)     : Time of generation of the PCA fitting analysis.
+    """
+
     definition = """
     -> FormattedDataset
     ---
@@ -407,6 +416,20 @@ class PCAFitting(dj.Computed):
     """
 
     def make(self, key):
+        """
+        Make function to fit PCA model and save the PCA model, data, and metadata to files.
+        Args:
+            key (dict): Primary key from the FormattedDataset table.
+
+        Raises:
+
+        High-Level Logic:
+        1. Fetches the task_mode and output_dir from the PCATask table.
+        2. If task_mode='trigger', it loads the `dj_config` file and formats the keypoint coordinates and confidences for PCA analysis.
+        3. Then, it fits the PCA model and saves the PCA model, data, and metadata to files.
+        4. If task_mode='load', it does not perform any computation and sets the pca_fitting_time to NULL.
+        5. Finally, it inserts the pca_fitting_time into the PCAFitting table.
+        """
         task_mode, output_dir = (PCATask & key).fetch1("task_mode", "output_dir")
 
         if task_mode == "trigger":
@@ -447,17 +470,40 @@ def make(self, key):
 class DimsExplainedVariance(dj.Computed):
     """
     This is an optional table to compute and store the latent dimensions that explain a certain specified variance threshold.
+
+    Attributes:
+        PCAFitting (foreign key)           : Unique ID for each PCAFitting.
+        variance_percentage (float)        : Percentage of variance explained by the selected components.
+        dims_explained_variance (int)      : Number of components required to explain the specified variance.
+        latent_dim_desc (varchar)          : Description of the latent dimensions that explain the specified variance.
     """
 
     definition = """
     -> PCAFitting
     ---
-    variance_percentage     : float 
-    dims_explained_variance : int
-    latent_dim_desc: varchar(1000)
+    variance_percentage      : float # Percentage of variance explained by the selected components.
+    dims_explained_variance  : int # Number of components required to explain the specified variance.
+    latent_dim_desc          : varchar(1000) # Description of the latent dimensions that explain the specified variance.
     """
 
     def make(self, key):
+        """
+        Make function to compute and store the latent dimensions that explain a certain specified variance threshold.
+
+        Args:
+            key (dict): Primary key from the PCAFitting table.
+
+        Raises:
+
+        High-Level Logic:
+        1. Fetches the output_dir from the PCATask table.
+        2. Loads the PCA model from the output_dir.
+        3. Computes the cumulative sum of the explained variance ratio and determines the number of components required to explain the specified variance.
+        4. If the cumulative sum of the explained variance ratio is less than the specified variance threshold, it sets the dims_explained_variance to the number of components and variance_percentage to the cumulative sum of the explained variance ratio.
+        5. If the cumulative sum of the explained variance ratio is greater than the specified variance threshold, it sets the dims_explained_variance to the number of components and variance_percentage to the specified variance threshold.
+        6. Inserts the variance_percentage, dims_explained_variance, and latent_dim_desc into the DimsExplainedVariance table.
+        """
+
         output_dir = (PCATask & key).fetch1("output_dir")
         variance_threshold = 0.90
 

From 6a6b5b866ffe698dce2cdf29b4a8699f888c1904 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:22:16 +0100
Subject: [PATCH 62/90] add import dependency in `kpms_pca`

---
 element_moseq/kpms_pca.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index edc7f78..4370b25 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -1,9 +1,10 @@
-import datetime
+from datetime import datetime
 import inspect
 import os
 from pathlib import Path
 import pickle
 from typing import Optional
+import importlib
 
 import cv2
 import datajoint as dj

From 87ad0f70f57a351c8c1a6694da0d330f4a131a9f Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:22:51 +0100
Subject: [PATCH 63/90] blackformatting `kpms_pca`

---
 element_moseq/kpms_pca.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 4370b25..58d81c6 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -419,6 +419,7 @@ class PCAFitting(dj.Computed):
     def make(self, key):
         """
         Make function to fit PCA model and save the PCA model, data, and metadata to files.
+        
         Args:
             key (dict): Primary key from the FormattedDataset table.
 

From a2c2fdbc28d909606d3dc9ee335033452fbe537a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:23:19 +0100
Subject: [PATCH 64/90] Add `kpms_model` computation and logic

---
 element_moseq/kpms_model.py | 420 ++++++++++++++++++++++++++++--------
 1 file changed, 332 insertions(+), 88 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index 6abc3ad..a92d8e7 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -1,21 +1,30 @@
-import datajoint as dj
-from typing import Optional
-import pickle
+from datetime import datetime
 import inspect
-import importlib
 import os
 from pathlib import Path
-from element_interface.utils import find_full_path
+import pickle
+from typing import Optional
+import importlib
+
+import datajoint as dj
+
+from element_moseq.kpms_pca import PCATask, FormattedDataset
 from .readers.kpms_reader import load_dj_config, generate_dj_config
 from keypoint_moseq import (
-    load_config,
-    format_data,
     load_pca,
     init_model,
     update_hypparams,
-    fit_model
+    fit_model,
+    load_checkpoint,
+    reindex_syllables_in_checkpoint,
+    extract_results,
+    save_results_as_csv,
+    generate_trajectory_plots,
+    generate_grid_movies,
+    plot_similarity_dendrogram,
+    apply_model,
 )
-from element_moseq.kpms_pca import PCATask
+
 
 schema = dj.schema()
 _linking_module = None
@@ -109,120 +118,355 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 @schema
 class PreFittingTask(dj.Manual):
+    """Table to specify the parameters for pre-fitting the AR-HMM model and to optionally initialize the model.
+
+    Attributes:
+        kpms_pca.PCAFitting (foreign key)   : PCA fitting task
+        pre_latent_dim (int)                : Number of latent dimensions for the AR-HMM pre-fitting
+        pre_kappa (int)                     : Kappa value for the AR-HMM pre-fitting
+        pre_num_iterations (int)            : Number of iterations for the AR-HMM pre-fitting
+        pre_fitting_desc(varchar)           : Description of the pre-fitting task
+        model_initialization (Yes or No)    : Whether to initialize a new AR-HMM model and output_dir
+        model_name_initialization (varchar) : Default name of the chosen initialized model to be pre-fitted (optional)
+    """
+
     definition = """
     -> kpms_pca.PCAFitting
-    latent_dim               : int
-    kappa                    : int
-    num_iterations           : int
+    pre_latent_dim               : int
+    pre_kappa                    : int
+    pre_num_iterations           : int
     ---
-    task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
-    prefitting_desc=''      : varchar(1000)
+    pre_fitting_desc=''      : varchar(1000)
+    model_initialization="Yes"     :enum("Yes","No") # 'Yes' initialize a new AR-HMM model and output_dir, 'No' will directly pre-fit the model
+    model_name_initialization='' : varchar(100) # Optional. Name of the initialized model to be pre-fitted. Only needed if model_initialization = False
     """
 
 
 @schema
 class PreFitting(dj.Computed):
+    """Table for storing the pre-fitted AR-HMM model and its duration of generation.
+
+    Attributes:
+        PreFittingTask (foreign key)        : Pre-fitting task
+        model_name (varchar)                : Name of the model
+        pre_fitting_duration (time)         : Duration of generation of the full fitting model
+    """
+
     definition = """
     -> PreFittingTask
     ---
-    model_name              : varchar(255)
+    model_name=''                : varchar(100) # Name of the model
+    pre_fitting_duration=NULL    : time  # Duration of generation of the full fitting model 
     """
 
     def make(self, key):
+        """
+        Make function to pre-fit the AR-HMM model and store the model and its duration of generation.
+
+        Args:
+            key (dict) : dictionary with the primary key of the `PreFittingTask` table
+
+        Raises:
+
+        High-level Logic:
+        1. Fetch the `output_dir` and parameters specified in the `PreFittingTask` table
+        2. Update `dj_config.yml` with the latent dimensions and kappa
+        3. Load the pca, data, and metadata from files
+        4. Initialize the model if `model_initialization` is set to "Yes", else load the model from the specified `model_name_initialization`
+        5. Update the model with the chosen kappa
+        6. Fit the model for `pre_num_iterations` and save the model in the `output_dir`. This will also:
+            - generates a name for the model and a corresponding directory in `output_dir`
+            - saves a checkpoint every 25 iterations from which fitting can be restarted
+            - plots the progress of fitting every 25 iterations, including:
+                -- the distributions of syllable frequencies and durations for the most recent iteration
+                -- the change in median syllable duration across fitting iterations
+                -- a sample of the syllable sequence across iterations in a random window
+        7. Insert the autogenerated `model_name` and `pre_fitting_duration` into the `PreFitting` table
+        """
+
         output_dir = (PCATask & key).fetch1("output_dir")
-        task_mode, latent_dim, kappa, num_iterations = (PreFittingTask & key).fetch1("task_mode","latent_dim","kappa","num_iterations")
+        model_initialization, pre_latent_dim, pre_kappa, pre_num_iterations = (
+            PreFittingTask & key
+        ).fetch1(
+            "model_initialization", "pre_latent_dim", "pre_kappa", "pre_num_iterations"
+        )
 
-        if task_mode == "trigger":
-            # Update `dj_config.yml` with the latent dimensions 
-            config = load_dj_config(output_dir,check_if_valid=True, build_indexes=True)
-            config.update(dict(
-                            latent_dim=int(latent_dim),
-                            kappa=int(kappa)
-                            )
-                        )
-            generate_dj_config(output_dir, **config)
-            
-            # Load the pca model and the data 
-            pca = load_pca(output_dir)
+        # Update `dj_config.yml` with the latent dimensions and kappa
+        config = load_dj_config(output_dir, check_if_valid=True, build_indexes=True)
+        config.update(dict(latent_dim=int(pre_latent_dim), kappa=int(pre_kappa)))
+        generate_dj_config(output_dir, **config)
+
+        # Load the pca, data, and metadata
+        pca = load_pca(output_dir)
 
-            data_path = os.path.join(output_dir, 'data.pkl')
-            with open(data_path, 'rb') as data_file:
-                data = pickle.load(data_file)
+        data_path = os.path.join(output_dir, "data.pkl")
+        with open(data_path, "rb") as data_file:
+            data = pickle.load(data_file)
 
-            metadata_path = os.path.join(output_dir, 'metadata.pkl')
-            with open(metadata_path, 'rb') as data_file:
-                metadata = pickle.load(data_file)
-                
-            # Initialize the model 
+        metadata_path = os.path.join(output_dir, "metadata.pkl")
+        with open(metadata_path, "rb") as data_file:
+            metadata = pickle.load(data_file)
+
+        if model_initialization == "Yes":
             model = init_model(data=data, metadata=metadata, pca=pca, **config)
+        else:
+            model_name = (PreFittingTask & key).fetch1("model_name_initialization")
+            model = load_checkpoint(output_dir, model_name)[0]
+
+        # update with the chosen kappa
+        model = update_hypparams(model, kappa=int(pre_kappa))
+
+        start_time = datetime.utcnow()
+
+        model, model_name = fit_model(
+            model,
+            data,
+            metadata,
+            output_dir,
+            ar_only=True,
+            num_iters=pre_num_iterations,
+        )
+
+        model_path = os.path.join(output_dir + "/" + model_name + "/" + "model.pkl")
+        with open(model_path, "wb") as data_file:
+            pickle.dump(model, data_file)
+
+        end_time = datetime.utcnow()
+        duration_seconds = (end_time - start_time).total_seconds()
+        hours, remainder = divmod(duration_seconds, 3600)
+        minutes, seconds = divmod(remainder, 60)
+        duration_formatted = "{:02}:{:02}:{:02}".format(
+            int(hours), int(minutes), int(seconds)
+        )
+
+        self.insert1(
+            {
+                **key,
+                "model_name": model_name,
+                "pre_fitting_duration": duration_formatted,
+            }
+        )
+
 
-            # update with chosen kappa
-            model = update_hypparams(model, kappa=float(kappa))
-            #model has jaxlib.xla_extension.Array, so it cannot be stored as longblob.
-            
-            # # fit AR-HMM model:
-            # - generates a name for the model and a corresponding directory in project_dir
-            # - saves a checkpoint every 25 iterations from which fitting can be restarted
-            # - plots the progress of fitting every 25 iterations, including
-                # 1. the distributions of syllable frequencies and durations for the most recent iteration
-                # 2. the change in median syllable duration across fitting iterations
-                # 3. a sample of the syllable sequence across iterations in a random window
-            model, model_name = fit_model(model, 
-                                        data, 
-                                        metadata, 
-                                        output_dir,
-                                        ar_only=True, 
-                                        num_iters=num_iterations
-                                        )
-
-            model_path = os.path.join(output_dir + "/" + model_name + "/" + "model.pkl")
-            with open(model_path, "wb") as data_file:
-                pickle.dump(model, data_file)
-
-            self.insert1({**key,
-                        "model_name":model_name})
-        
-        elif task_mode == "load":
-            pass
-        
 @schema
 class FullFittingTask(dj.Manual):
-    """
-
+    """This table is used to specify the parameters for fitting the full model.
     The full model will generally require a lower value of kappa to yield the same target syllable durations.
+
+    Attributes:
+        kpms_pca.PCAFitting (foreign key)    : PCA fitting task
+        full_latent_dim (int)                : Number of latent dimensions for the full fitting
+        full_kappa (int)                     : Kappa value for the full fitting
+        full_num_iterations (int)            : Number of iterations for the full fitting
+        full_fitting_desc(varchar)           : Description of the full fitting task
+        task_mode ('trigger' or 'load')      : 'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca
+        sort_syllables (bool)                : Whether to sort syllables by frequency (reindexing; Optional)
+        results_as_csv (bool)                : Whether to save results as csv (Optional)
+        visualizations (bool)                : Whether to save visualizations (Optional)
+
     """
 
     definition = """
     -> kpms_pca.PCAFitting
-    latent_dim              : int
-    kappa                   : int
-    num_iterations          : int
+    full_latent_dim              : int
+    full_kappa                   : int
+    full_num_iterations          : int
     ---
-    model_name              : varchar(20)
-    task_mode='load'        : enum('load', 'trigger')  # 'load': load computed analysis results, 'trigger': trigger computation
+    full_fitting_desc=''         : varchar(1000)
+    task_mode                    : enum('trigger', 'load') # 'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca
+    sort_syllables               : bool # Whether to sort syllables by frequency (reindexing)
+    results_as_csv               : bool # Whether to save results as csv (Optional)
+    visualizations               : bool # Whether to save visualizations (Optional)
     """
 
 
 @schema
 class FullFitting(dj.Computed):
+    """This table is used to fit the full model and store the model and its duration of generation.
+
+    Attributes:
+        FullFittingTask (foreign key)        : Full fitting task
+        full_fitting_duration (time)         : Duration of generation of the full fitting model
+    """
+
     definition = """
     -> FullFittingTask
     ---
-    model_name              : varchar(255)
-    model                   : longblob
+    full_fitting_duration=NULL    : time  # Duration of generation of the full fitting model 
     """
 
-# def make(self, key):
-# task_mode == "trigger"
-# modify kappa to maintain the desired syllable time-scale
-# model = kpms.update_hypparams(model, kappa=1e6)
-
-# run fitting for an additional 200 iters
-# model = kpms.fit_model(
-#    model, data, metadata, project_dir, model_name, ar_only=False,
-#    start_iter=current_iter, num_iters=current_iter+500)[0]
-# task_mode == "load"
-# model_name = '2023_11_27-16_06_07'
-# model, data, metadata, current_iter = kpms.load_checkpoint(
-# project_dir, model_name, iteration=num_ar_iters)
-# return
+    def make(self, key):
+        """
+        Make function to fit the full model and store the model and its duration of generation.
+
+        Args:
+            key (dict): Primary key from the FullFittingTask table.
+
+        Raises:
+
+        High-level Logic:
+        1. Fetch the `output_dir` and parameters specified in the `FullFittingTask` and `PreFitting` tables
+        2. If `task_mode` is 'trigger':
+        - load the initialized model checkpoint
+        - update it with `full_kappa` to maintain the desired syllable time-scale
+        - fit the model for `full_num_iterations` and save the model in the `output_dir`
+        3. If `task_mode` is 'load':
+        - load the chosen model with its most recent checkpoint, and load the corresponding pca object
+        - apply the model to new keypoint data and save the results
+        """
+
+        output_dir = (PCATask & key).fetch1("output_dir")
+        try:
+            selected_key = (
+                PreFitting
+                & "pre_latent_dim = {}".format(key["full_latent_dim"])
+                & "pre_kappa = {}".format(key["full_kappa"])
+            )
+            model_name, pre_num_iterations = selected_key.fetch1(
+                "model_name", "pre_num_iterations"
+            )
+        except:
+            print("No prefitting model found")
+
+        task_mode, full_kappa, full_num_iterations = (FullFittingTask & key).fetch1(
+            "task_mode", "full_kappa", "full_num_iterations"
+        )
+
+        if task_mode == "trigger":
+            pre_model, data, metadata, current_iter = load_checkpoint(
+                output_dir, model_name, iteration=pre_num_iterations
+            )
+
+            model = update_hypparams(pre_model, kappa=int(full_kappa))
+
+            start_time = datetime.utcnow()
+
+            full_model = fit_model(
+                model,
+                data,
+                metadata,
+                output_dir,
+                model_name,
+                ar_only=False,
+                start_iter=current_iter,
+                num_iters=full_num_iterations,
+            )[0]
+
+            model_path = os.path.join(
+                output_dir + "/" + model_name + "/" + "full_model.pkl"
+            )
+            with open(model_path, "wb") as data_file:
+                pickle.dump(full_model, data_file)
+
+            end_time = datetime.utcnow()
+            duration_seconds = (end_time - start_time).total_seconds()
+            hours, remainder = divmod(duration_seconds, 3600)
+            minutes, seconds = divmod(remainder, 60)
+            duration_formatted = "{:02}:{:02}:{:02}".format(
+                int(hours), int(minutes), int(seconds)
+            )
+
+            self.insert1({**key, "full_fitting_duration": duration_formatted})
+
+        else:
+            # load the most recent model checkpoint and pca object
+            model, data, metadata = load_checkpoint(output_dir, model_name)
+            pca = load_pca(output_dir)
+
+            # load the keypoints data
+            # data_path = os.path.join(output_dir, 'data.pkl')
+            # with open(data_path, 'rb') as data_file:
+            #     data = pickle.load(data_file)
+
+            # metadata_path = os.path.join(output_dir, 'metadata.pkl')
+            # with open(metadata_path, 'rb') as data_file:
+            #     metadata = pickle.load(data_file)
+
+            results = apply_model(model, pca, data, metadata, output_dir, model_name)
+
+            # TO-DO: Save the results in files/table
+
+
+@schema
+class GenerateResults(dj.Computed):
+    """
+    This table is used to extract the model results from the checkpoint file and save them to `{output_dir}/{model_name}/results.h5`.
+    The results are stored as follows:
+        results.h5
+        ├──recording_name1
+        │  ├──syllable      # syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).
+        │  ├──latent_state  # inferred low-dim pose state (x). Low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.
+        │  ├──centroid      # inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.
+        │  └──heading       # inferred heading (h). The heading of the animal in each frame, as estimated by the model.
+
+    Attributes:
+        FullFitting (foreign key)               : Unique ID for full fitting task
+        grid_movies_sampled_instances(longlbob) : Dictionary mapping syllables to lists of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+    """
+
+    definition = """
+    -> FullFitting
+    ---
+    grid_movies_sampled_instances : longblob # Dictionary mapping syllables to lists of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+    """
+
+    def make(self, key):
+        """
+        Make function to extract the model results from the checkpoint file and save them to `{output_dir}/{model_name}/results.h5`.
+
+        Args:
+            key (dict): Primary key from the FullFitting table.
+
+        Raises:
+
+        High-level Logic:
+        1. Fetch the `output_dir`, `model_name`, and parameters specified in the `FullFitting` table
+        2. Sort syllables if `sort_syllables` is set to True. This function permutes the states and parameters of a saved checkpoint so that syllables are labeled in order of frequency (i.e. so that 0 is the most frequent, 1 is the second most, and so on).
+        3. Load the most recent model checkpoint
+        4. Extract the model results
+        5. If `results_as_csv` is set to True, save the results as csv
+        6. If `visualizations` is set to True, generate visualizations
+        7. Insert the `grid_movies_sampled_instances` into the `GenerateResults` table
+        """
+
+        output_dir = (PCATask & key).fetch1("output_dir")
+        model_name = (PreFitting & key).fetch1("model_name")
+        sort_syllables, results_as_csv, visualizations = (FullFittingTask & key).fetch1(
+            "sort_syllables", "results_as_csv", "visualizations"
+        )
+
+        # sort syllables
+        if sort_syllables:
+            reindex_syllables_in_checkpoint(output_dir, model_name)
+
+        model, data, metadata, current_iter = load_checkpoint(
+            output_dir, model_name, iteration=None
+        )
+
+        results = extract_results(model, metadata, output_dir, model_name)
+
+        if results_as_csv:
+            save_results_as_csv(results, output_dir, model_name)
+
+        if visualizations:
+            coordinates = (FormattedDataset & key).fetch1("coordinates")
+            config = load_dj_config(output_dir)
+
+            # Generate plots showing the median trajectory of poses associated with each given syllable.
+            generate_trajectory_plots(
+                coordinates, results, output_dir, model_name, **config
+            )
+
+            # Generate video clips showing examples of each syllable.
+            grid_movies_sampled_instances = generate_grid_movies(
+                results, output_dir, model_name, coordinates=coordinates, **config
+            )
+
+            # Plot a dendrogram representing distances between each syllable’s median trajectory.
+            plot_similarity_dendrogram(
+                coordinates, results, output_dir, model_name, **config
+            )
+
+            self.insert1(
+                {**key, "grid_movies_sampled_instances": grid_movies_sampled_instances}
+            )

From 397a6f06aeb799ae1af51b9c2d5723c2c27c4dfb Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:28:32 +0100
Subject: [PATCH 65/90] add `docs` [WIP]

---
 docs/.docker/Dockerfile                       |  17 ++
 docs/.docker/apk_requirements.txt             |   1 +
 docs/.docker/pip_requirements.txt             |  12 ++
 docs/docker-compose.yaml                      |  54 ++++++
 docs/mkdocs.yaml                              | 178 ++++++++++++++++++
 .../.overrides/.icons/main/company-logo.svg   |  11 ++
 .../.icons/main/project-logo-black.svg        |  22 +++
 docs/src/.overrides/404.html                  |  19 ++
 .../assets/images/company-logo-blue.png       | Bin 0 -> 41770 bytes
 .../.overrides/assets/stylesheets/extra.css   | 101 ++++++++++
 docs/src/.overrides/partials/nav.html         |  33 ++++
 docs/src/api/make_pages.py                    |  27 +++
 docs/src/citation.md                          |  13 ++
 docs/src/concepts.md                          |  38 ++++
 docs/src/index.md                             |  43 +++++
 docs/src/partnerships.md                      |   7 +
 docs/src/pipeline.md                          |  55 ++++++
 docs/src/roadmap.md                           |   9 +
 docs/src/tutorials/index.md                   |  18 ++
 19 files changed, 658 insertions(+)
 create mode 100644 docs/.docker/Dockerfile
 create mode 100644 docs/.docker/apk_requirements.txt
 create mode 100644 docs/.docker/pip_requirements.txt
 create mode 100644 docs/docker-compose.yaml
 create mode 100644 docs/mkdocs.yaml
 create mode 100644 docs/src/.overrides/.icons/main/company-logo.svg
 create mode 100644 docs/src/.overrides/.icons/main/project-logo-black.svg
 create mode 100644 docs/src/.overrides/404.html
 create mode 100644 docs/src/.overrides/assets/images/company-logo-blue.png
 create mode 100644 docs/src/.overrides/assets/stylesheets/extra.css
 create mode 100644 docs/src/.overrides/partials/nav.html
 create mode 100644 docs/src/api/make_pages.py
 create mode 100644 docs/src/citation.md
 create mode 100644 docs/src/concepts.md
 create mode 100644 docs/src/index.md
 create mode 100644 docs/src/partnerships.md
 create mode 100644 docs/src/pipeline.md
 create mode 100644 docs/src/roadmap.md
 create mode 100644 docs/src/tutorials/index.md

diff --git a/docs/.docker/Dockerfile b/docs/.docker/Dockerfile
new file mode 100644
index 0000000..340dea5
--- /dev/null
+++ b/docs/.docker/Dockerfile
@@ -0,0 +1,17 @@
+FROM datajoint/miniconda3:4.10.3-py3.9-alpine
+ARG PACKAGE
+WORKDIR /main
+COPY --chown=anaconda:anaconda ./docs/.docker/apk_requirements.txt ${APK_REQUIREMENTS}
+COPY --chown=anaconda:anaconda ./docs/.docker/pip_requirements.txt ${PIP_REQUIREMENTS}
+RUN \
+    umask u+rwx,g+rwx,o-rwx && \
+    /entrypoint.sh echo "Dependencies installed" && \
+    rm ${APK_REQUIREMENTS} ${PIP_REQUIREMENTS} && \
+    git config --global user.name "GitHub Action" && \
+    git config --global user.email "action@github.com"&& \
+    git config --global pull.rebase false && \
+    git init
+COPY --chown=anaconda:anaconda ./${PACKAGE} /main/${PACKAGE}
+COPY --chown=anaconda:anaconda ./docs/mkdocs.yaml /main/docs/mkdocs.yaml
+COPY --chown=anaconda:anaconda ./docs/src /main/docs/src
+COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/
\ No newline at end of file
diff --git a/docs/.docker/apk_requirements.txt b/docs/.docker/apk_requirements.txt
new file mode 100644
index 0000000..0899c29
--- /dev/null
+++ b/docs/.docker/apk_requirements.txt
@@ -0,0 +1 @@
+git
\ No newline at end of file
diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt
new file mode 100644
index 0000000..3842f28
--- /dev/null
+++ b/docs/.docker/pip_requirements.txt
@@ -0,0 +1,12 @@
+mkdocs-material==9.1.17
+mkdocs-redirects
+mkdocstrings
+mkdocstrings-python
+mike
+mdx-truly-sane-lists
+mkdocs-gen-files
+mkdocs-literate-nav
+mkdocs-exclude-search
+mkdocs-markdownextradata-plugin
+mkdocs-jupyter
+mkdocs-section-index
\ No newline at end of file
diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml
new file mode 100644
index 0000000..5fbd23e
--- /dev/null
+++ b/docs/docker-compose.yaml
@@ -0,0 +1,54 @@
+# MODE="LIVE|QA|PUSH" PACKAGE=element_calcium_imaging UPSTREAM_REPO=https://github.com/datajoint/element-calcium-imaging.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build
+version: "2.4"
+services:
+  docs:
+    build:
+      dockerfile: docs/.docker/Dockerfile
+      context: ../
+      args:
+        - PACKAGE
+    image: ${PACKAGE}-docs
+    environment:
+      - PACKAGE
+      - UPSTREAM_REPO
+      - MODE
+      - PATCH_VERSION
+    volumes:
+      - ../docs:/main/docs
+      - ../${PACKAGE}:/main/${PACKAGE}
+      - ../notebooks:/main/notebooks
+    user: ${HOST_UID}:anaconda
+    ports:
+      - 80:80
+    command:
+      - sh
+      - -c
+      - |
+        git config --global --add safe.directory /main
+        set -e
+        export ELEMENT_UNDERSCORE=$$(echo $${PACKAGE} | sed 's/element_//g')
+        export ELEMENT_HYPHEN=$$(echo $${ELEMENT_UNDERSCORE} | sed 's/_/-/g')
+        export PATCH_VERSION=$$(cat /main/$${PACKAGE}/version.py | grep -oE '\d+\.\d+\.[a-z0-9]+')
+
+        cp /main/notebooks/tutorial.ipynb /main/docs/src/tutorials/
+
+        if echo "$${MODE}" | grep -i live &>/dev/null; then
+            mkdocs serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80 2>&1 | tee docs/temp_mkdocs.log
+        elif echo "$${MODE}" | grep -iE "qa|push" &>/dev/null; then
+            echo "INFO::Delete gh-pages branch"
+            git branch -D gh-pages || true
+            echo "INFO::Fetch upstream gh-pages"
+            git fetch $${UPSTREAM_REPO} gh-pages:gh-pages && git switch gh-pages || git switch --orphan gh-pages && git commit --allow-empty -m "init commit"
+            echo "INFO::mike"
+            mike deploy --config-file ./docs/mkdocs.yaml -u $$(grep -oE '\d+\.\d+' /main/$${PACKAGE}/version.py) latest
+            mike set-default --config-file ./docs/mkdocs.yaml latest
+            if echo "$${MODE}" | grep -i qa &>/dev/null; then
+                mike serve --config-file ./docs/mkdocs.yaml -a 0.0.0.0:80
+            elif echo "$${MODE}" | grep -i push &>/dev/null; then
+                echo "INFO::Push gh-pages to upstream"
+                git push $${UPSTREAM_REPO} gh-pages
+            fi
+        else
+            echo "Unexpected mode..."
+            exit 1
+        fi
\ No newline at end of file
diff --git a/docs/mkdocs.yaml b/docs/mkdocs.yaml
new file mode 100644
index 0000000..c187218
--- /dev/null
+++ b/docs/mkdocs.yaml
@@ -0,0 +1,178 @@
+# ---------------------- PROJECT SPECIFIC ---------------------------
+
+site_name: DataJoint Documentation
+site_url: http://localhost/docs/elements/element-moseq
+repo_url: https://github.com/datajoint/element-moseq
+repo_name: datajoint/element-moseq
+nav:
+  - Element Calcium Imaging: index.md
+  - Data Pipeline: pipeline.md
+  - Tutorials:
+      - tutorials/index.md
+      - Tutorial Notebook: tutorials/tutorial.ipynb
+  - Concepts: concepts.md
+  - Key Partnerships: partnerships.md
+  - Roadmap: roadmap.md
+  - Citation: citation.md
+  - API: api/ # defer to gen-files + literate-nav
+  - Changelog: changelog.md
+
+# --------------------- NOTES TO CONTRIBUTORS -----------------------
+# Markdown in mkdocs
+# 01. Redering concatenates across single line breaks. This means...
+#     A. We have to be careful to add extra line breaks around paragraphs,
+#        including between the end of a pgf and the beginning of bullets.
+#     B. We can use hard wrapping to make github reviews easier to read.
+#        VSCode Rewrap extension offers a keyboard shortcut for hard wrap
+#        at the ruler, but don't add breaks in [multiword links](example.com)
+# 02. Instead of designating codeblocks with bash, use console. For example..
+#     ```console
+#     cd ../my_dir
+#     ``` 
+# 03. Links across docs should ...
+#     A. Not involve line breaks.
+#     B. Use relative paths to docs in the same repo
+#     C. Use lowercase and hyphens not spaces: [sub headings](./doc#sub-heading)
+#
+# Files
+# 01. Add a soft link to your changelog with the following
+#     ```console
+#     ln -s ../../CHANGELOG.md ./docs/src/changelog.md
+#     ```
+#
+# Site rendering
+# 01. Deploy locally to localhost with the command
+#     ```console
+#     MODE="LIVE" PACKAGE=element_{ELEMENT} \
+#     UPSTREAM_REPO=https://github.com/datajoint/element-{ELEMENT}.git \
+#     HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build
+#     ```
+# 02. The API section will pull docstrings.
+#     A. Follow google styleguide e.g., 
+#        https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html
+#        With typing suggestions: https://docs.python.org/3/library/typing.html
+#     B. To pull a specific workflow fork, change ./docs/src/api/make_pages.py#L19
+# 03. To see your fork of the workflow-{element} in this render, change the
+#     URL in ./docs/src/api/make_pages.py#L19 to your fork.
+# 04. To deploy this site on your fork, 
+#     A. declare a branch called gh-pages
+#     B. go to the your fork > settings > pages 
+#     C. direct pages to render from the gh-pages branch at root
+#     D. push a tag to your fork with the format test*.*.*
+#
+# ---------------------------- STANDARD -----------------------------
+edit_uri: ./edit/main/docs/src
+docs_dir: ./src
+theme:
+  font:
+    text: Roboto Slab
+    code: Source Code Pro
+  name: material
+  custom_dir: src/.overrides
+  icon:
+    logo: main/company-logo
+  favicon: assets/images/company-logo-blue.png
+  features:
+    - toc.integrate
+    - content.code.annotate
+  palette:
+    - media: "(prefers-color-scheme: light)"
+      scheme: datajoint
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
+plugins:
+  - markdownextradata: {}
+  - search
+  - mkdocstrings:
+      default_handler: python
+      handlers:
+        python:
+          options:
+            members_order: source
+            group_by_category: false
+            line_length: 88
+  - gen-files:
+      scripts:
+      - ./src/api/make_pages.py
+  - literate-nav:
+      nav_file: navigation.md
+  - exclude-search:
+      exclude:
+        - "*/navigation.md"
+  - mkdocs-jupyter:
+      ignore_h1_titles: True
+      include: ["*.ipynb"]
+  - section-index
+markdown_extensions:
+  - attr_list
+  - md_in_html
+  - toc:
+      permalink: true
+  - pymdownx.emoji:
+      options:
+        custom_icons:
+          - .overrides/.icons
+  - mdx_truly_sane_lists
+  - pymdownx.superfences:
+      custom_fences:
+        - name: mermaid
+          class: mermaid
+          format: !!python/name:pymdownx.superfences.fence_code_format
+  - pymdownx.tabbed:
+      alternate_style: true
+  - pymdownx.highlight:
+      linenums: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.arithmatex:
+      generic: true
+  - pymdownx.magiclink # Displays bare URLs as links
+  - pymdownx.tasklist: # Renders check boxes in tasks lists
+      custom_checkbox: true
+extra:
+  PATCH_VERSION: !ENV PATCH_VERSION
+  generator: false # Disable watermark
+  version:
+    provider: mike
+  social:
+    - icon: main/company-logo
+      link: https://www.datajoint.com
+      name: DataJoint
+    - icon: fontawesome/brands/slack
+      link: https://datajoint.slack.com
+      name: Slack
+    - icon: fontawesome/brands/linkedin
+      link: https://www.linkedin.com/company/datajoint
+      name: LinkedIn
+    - icon: fontawesome/brands/twitter
+      link: https://twitter.com/datajoint
+      name: Twitter
+    - icon: fontawesome/brands/github
+      link: https://github.com/datajoint
+      name: GitHub
+    - icon: fontawesome/brands/docker
+      link: https://hub.docker.com/u/datajoint
+      name: DockerHub
+    - icon: fontawesome/brands/python
+      link: https://pypi.org/user/datajointbot
+      name: PyPI
+    - icon: fontawesome/brands/stack-overflow
+      link: https://stackoverflow.com/questions/tagged/datajoint
+      name: StackOverflow
+    - icon: fontawesome/brands/youtube
+      link: https://www.youtube.com/channel/UCdeCuFOTCXlVMRzh6Wk-lGg
+      name: YouTube
+extra_css:
+  - assets/stylesheets/extra.css
+
+extra_javascript:
+  - https://js-na1.hs-scripts.com/23133402.js  # HubSpot chatbot
+  - javascripts/mathjax.js
+  - https://polyfill.io/v3/polyfill.min.js?features=es6
+  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
diff --git a/docs/src/.overrides/.icons/main/company-logo.svg b/docs/src/.overrides/.icons/main/company-logo.svg
new file mode 100644
index 0000000..e876313
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+++ b/docs/src/.overrides/.icons/main/company-logo.svg
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 15.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 width="260.479px" height="365.463px" viewBox="0 0 260.479 365.463" enable-background="new 0 0 260.479 365.463"
+	 xml:space="preserve">
+<g>
+	<path d="M4.638,361.434c0,0,124.071-124.072,135.896-357.434c0,0,29.555,189.053,115.266,256.988
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+</g>
+</svg>
diff --git a/docs/src/.overrides/.icons/main/project-logo-black.svg b/docs/src/.overrides/.icons/main/project-logo-black.svg
new file mode 100644
index 0000000..76bebb1
--- /dev/null
+++ b/docs/src/.overrides/.icons/main/project-logo-black.svg
@@ -0,0 +1,22 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 208.15 210.1">
+    <defs>
+    </defs>
+    <title>Asset 3</title>
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diff --git a/docs/src/.overrides/404.html b/docs/src/.overrides/404.html
new file mode 100644
index 0000000..e4c84db
--- /dev/null
+++ b/docs/src/.overrides/404.html
@@ -0,0 +1,19 @@
+{% extends "main.html" %}
+
+<!-- Content -->
+{% block content %}
+<h1>🚧 Not Found 👷</h1>
+<p>
+    Unfortunately, we could not find what you were looking for.
+    <br>
+    <br>
+    Usually there are two possibilities for this:
+    <br>
+<ul>
+    <li>We are currently updating some of our content.</li>
+    <li>There is a mistake in the address or link you are using.</li>
+</ul>
+<br>
+Please make sure you are navigating to the correct address.
+</p>
+{% endblock %}
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diff --git a/docs/src/.overrides/assets/stylesheets/extra.css b/docs/src/.overrides/assets/stylesheets/extra.css
new file mode 100644
index 0000000..13d1c0a
--- /dev/null
+++ b/docs/src/.overrides/assets/stylesheets/extra.css
@@ -0,0 +1,101 @@
+:root {
+    --dj-primary: #00a0df;
+    --dj-secondary: #ff5113;
+    --dj-background: #808285;
+    --dj-black: #000000;
+    --dj-white: #ffffff;
+}
+
+/* footer previous/next navigation */
+.md-footer__inner:not([hidden]) {
+    display: none
+}
+
+.md-typeset figure img {
+    display: inline;
+}
+
+/* footer social icons */
+html a[title="DataJoint"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="Slack"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="LinkedIn"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="Twitter"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="GitHub"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="DockerHub"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="PyPI"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="StackOverflow"].md-social__link svg {
+    color: var(--dj-primary);
+}
+html a[title="YouTube"].md-social__link svg {
+    color: var(--dj-primary);
+}
+
+[data-md-color-scheme="datajoint"] {
+    /* ribbon */
+    /* ribbon + markdown heading expansion */
+    --md-primary-fg-color: var(--dj-black);
+    /* ribbon text */
+    --md-primary-bg-color: var(--dj-primary);
+
+    /* navigation */
+    /* navigation header + links */
+    --md-typeset-a-color: var(--dj-primary);
+    /* navigation on hover + diagram outline */
+    --md-accent-fg-color: var(--dj-secondary);
+
+    /* main */
+    /* main header + already viewed*/
+    --md-default-fg-color--light: var(--dj-background);
+    /* primary text */
+    --md-typeset-color: var(--dj-black);
+    /* code comments + diagram text */
+    --md-code-fg-color: var(--dj-primary);
+
+    /* footer */
+    /* previous/next text */
+    /* --md-footer-fg-color: var(--dj-primary); */
+}
+
+[data-md-color-scheme="slate"] {
+    /* ribbon */
+    /* ribbon + markdown heading expansion */
+    --md-primary-fg-color: var(--dj-primary);
+    /* ribbon text */
+    --md-primary-bg-color: var(--dj-white);
+
+    /* navigation */
+    /* navigation header + links */
+    --md-typeset-a-color: var(--dj-primary);
+    /* navigation on hover + diagram outline */
+    --md-accent-fg-color: var(--dj-secondary);
+
+    /* main */
+    /* main header + already viewed*/
+    /* --md-default-fg-color--light: var(--dj-background); */
+    /* primary text */
+    --md-typeset-color: var(--dj-white);
+    /* code comments + diagram text */
+    --md-code-fg-color: var(--dj-primary);
+
+    /* footer */
+    /* previous/next text */
+    /* --md-footer-fg-color: var(--dj-white); */
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper .Table Td {
+    color: var(--dj-black)
+}
\ No newline at end of file
diff --git a/docs/src/.overrides/partials/nav.html b/docs/src/.overrides/partials/nav.html
new file mode 100644
index 0000000..8b179b4
--- /dev/null
+++ b/docs/src/.overrides/partials/nav.html
@@ -0,0 +1,33 @@
+{% set class = "md-nav md-nav--primary" %}
+{% if "navigation.tabs" in features %}
+{% set class = class ~ " md-nav--lifted" %}
+{% endif %}
+{% if "toc.integrate" in features %}
+{% set class = class ~ " md-nav--integrated" %}
+{% endif %}
+<nav class="{{ class }}" aria-label="{{ lang.t('nav.title') }}" data-md-level="0">
+    <label class="md-nav__title" for="__drawer">
+        <a href="{{ config.extra.homepage | d(nav.homepage.url, true) | url }}" title="{{ config.site_name | e }}"
+            class="md-nav__button md-logo" aria-label="{{ config.site_name }}" data-md-component="logo">
+            {% include "partials/logo.html" %}
+        </a>
+        {#-
+        Add DataJoint home link to navigation header, otherwise unchanged
+        -#}
+        <a href="https://datajoint.com/docs/elements/" title="DataJoint Elements">
+            ⬅ Home
+        </a>
+    </label>
+    {% if config.repo_url %}
+    <div class="md-nav__source">
+        {% include "partials/source.html" %}
+    </div>
+    {% endif %}
+    <ul class="md-nav__list" data-md-scrollfix>
+        {% for nav_item in nav %}
+        {% set path = "__nav_" ~ loop.index %}
+        {% set level = 1 %}
+        {% include "partials/nav-item.html" %}
+        {% endfor %}
+    </ul>
+</nav>
\ No newline at end of file
diff --git a/docs/src/api/make_pages.py b/docs/src/api/make_pages.py
new file mode 100644
index 0000000..199ee4a
--- /dev/null
+++ b/docs/src/api/make_pages.py
@@ -0,0 +1,27 @@
+"""Generate the api pages and navigation.
+NOTE: Works best when following the Google style guide
+https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html
+https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings
+"""
+
+import mkdocs_gen_files
+from pathlib import Path
+import os
+
+package = os.getenv("PACKAGE")
+
+element = package.split("_", 1)[1]
+
+nav = mkdocs_gen_files.Nav()
+for path in sorted(Path(package).glob("**/*.py")):
+    if path.stem == "__init__" or path.stem == "version":
+        continue
+    with mkdocs_gen_files.open(f"api/{path.with_suffix('')}.md", "w") as f:
+        module_path = ".".join(
+            [p for p in path.with_suffix("").parts if p != "__init__"]
+        )
+        print(f"::: {module_path}", file=f)
+    nav[path.parts] = f"{path.with_suffix('')}.md"
+
+with mkdocs_gen_files.open("api/navigation.md", "w") as nav_file:
+    nav_file.writelines(nav.build_literate_nav())
diff --git a/docs/src/citation.md b/docs/src/citation.md
new file mode 100644
index 0000000..9471de2
--- /dev/null
+++ b/docs/src/citation.md
@@ -0,0 +1,13 @@
+# Citation
+
+If your work uses the following resources, please cite the respective manuscript and/or Research Resource Identifier (RRID):
+
++ DataJoint Element MoSeq - Version {{ PATCH_VERSION }}
+  + Yatsenko D, Nguyen T, Shen S, Gunalan K, Turner CA, Guzman R, Sasaki M, Sitonic D,
+    Reimer J, Walker EY, Tolias AS. DataJoint Elements: Data Workflows for
+    Neurophysiology. bioRxiv. 2021 Jan 1. doi: https://doi.org/10.1101/2021.03.30.437358
+
+  + [RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894)
+
++ Keypoint-MoSeq
+  + [Manuscripts](https://www.biorxiv.org/content/10.1101/2023.03.16.532307v1)
diff --git a/docs/src/concepts.md b/docs/src/concepts.md
new file mode 100644
index 0000000..8b0eba0
--- /dev/null
+++ b/docs/src/concepts.md
@@ -0,0 +1,38 @@
+# Concepts
+
+## Keypoint-MoSeq: Pose Dynamics for Advanced Motion Sequencing
+
+<!-- In the domain of animal behavior analysis, Keypoint-MoSeq has emerged as a highly efficient alternative to conventional methods. This machine learning software is specifically designed to automatically identify and comprehend behavioral modules or "syllables" in observed animal behavior without requiring human intervention. Its primary objective is to accurately quantify and analyze animal movements and actions from keypoint data extracted from standard video recordings. 
+
+Keypoint-MoSeq utilizes a sophisticated generative model. This model effectively distinguishes genuine behavior patterns from noise within the keypoint data, ensuring precise identification of sub-second behavior transitions. The resulting syllables can establish correlations between neural activity and behavior, a task often daunting for traditional clustering methods.
+
+The Keypoint-MoSeq PCA (`kpms_pca`) pipeline includes [x]. -->
+
+
+Element MoSeq uses the keypoint data extracted from the mentioned pose estimation method and the behavior segmentation with Keypoint-MoSeq software for the management of data and
+its analysis.
+
+## Acquisition tools
+
+### Hardware
+
+The primary acquisition systems are video cameras that record standard video recordings capturing animal behavior.
+
+### Software
+
+- [Keypoint-MoSeq](https://github.com/dattalab/keypoint-moseq)
+- [DeepLabCut]()
+
+<!-- ## Data Export and Publishing
+
+Element MoSeq supports exporting of all data into standard Neurodata Without Borders (NWB) files. This makes it easy to share files with collaborators and
+publish results on [DANDI Archive](https://dandiarchive.org/).
+[NWB](https://www.nwb.org/), as an organization, is dedicated to standardizing data
+formats and maximizing interoperability across tools for neurophysiology.
+
+To use the export functionality with additional related dependencies, install the
+Element with the `nwb` option as follows:
+
+```console
+pip install element-moseq[nwb]
+``` -->
\ No newline at end of file
diff --git a/docs/src/index.md b/docs/src/index.md
new file mode 100644
index 0000000..d576894
--- /dev/null
+++ b/docs/src/index.md
@@ -0,0 +1,43 @@
+# Element MoSeq
+
+DataJoint Element for Motion Sequencing with 
+[Keypoint-MoSeq](https://github.com/dattalab/keypoint-moseq){:target="_blank"}, 
+from keypoint data extracted with [DeepLabCut](x){:target="_blank"}. DataJoint Elements collectively standardize and automate
+data collection and analysis for neuroscience experiments. Each Element is a modular
+pipeline for data storage and processing with corresponding database tables that can be
+combined with other Elements to assemble a fully functional pipeline.
+
+## Experiment Flowchart
+
+![flowchart](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/flowchart.svg)
+
+## Data Pipeline Diagram
+
+![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline.svg)
+
+## Getting Started
+
++ Please fork the [repository](https://github.com/datajoint/element-moseq){:target="_blank"}
+
++ Clone the repository to your computer
+
+  ```bash
+  git clone https://github.com/<enter_github_username>/element-moseq
+  ```
+
++ Install with `pip`
+
+  ```bash
+  pip install -e .
+  ```
+
++ [Data Pipeline](./pipeline.md) - Pipeline and table descriptions
+
++ [Tutorials](./tutorials/index.md) - Start building your data pipeline
+
++ [Code Repository](https://github.com/datajoint/element-moseq/){:target="_blank"}
+
+## Support
+
++ If you need help getting started or run into any errors, please contact our team by 
+email at support@datajoint.com.
diff --git a/docs/src/partnerships.md b/docs/src/partnerships.md
new file mode 100644
index 0000000..4143b52
--- /dev/null
+++ b/docs/src/partnerships.md
@@ -0,0 +1,7 @@
+# Key partnerships
+
+Several labs have developed DataJoint-based data management and behavior analysis pipelines for advanced motion sequencing. Our team collaborated with several of them during their projects. Additionally, we interviewed these teams to understand their experiment workflow, pipeline design, associated tools, and interfaces.
+
+These teams include:
+
+- 
\ No newline at end of file
diff --git a/docs/src/pipeline.md b/docs/src/pipeline.md
new file mode 100644
index 0000000..4655c0f
--- /dev/null
+++ b/docs/src/pipeline.md
@@ -0,0 +1,55 @@
+# Data Pipeline
+
+Each node in the following diagram represents the analysis code in the pipeline and the
+corresponding table in the database.  Within the pipeline, Element MoSeq
+connects to upstream Elements including Lab, Animal, Session, and Event. For more 
+detailed documentation on each table, see the API docs for the respective schemas.
+
+The Element is composed of two main schemas, `kpms_pca` and `kpms_model`.
+
+## Diagrams
+
+- [x].
+
+     ![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline.svg)
+
+
+## Table descriptions
+
+### `lab` schema
+
+- For further details see the [lab schema API docs](https://datajoint.com/docs/elements/element-lab/latest/api/element_lab/lab/)
+
+| Table | Description |
+| --- | --- |
+| Device | Scanner metadata |
+
+### `subject` schema
+
+- Although not required, most choose to connect the `Session` table to a `Subject` table.
+
+- For further details see the [subject schema API docs](https://datajoint.com/docs/elements/element-animal/latest/api/element_animal/subject/)
+
+| Table | Description |
+| --- | --- |
+| Subject | Basic information of the research subject |
+
+### `session` schema
+
+- For further details see the [session schema API docs](https://datajoint.com/docs/elements/element-session/latest/api/element_session/session_with_datetime/)
+
+| Table | Description |
+| --- | --- |
+| Session | Unique experimental session identifier |
+
+### `kpms_pca` schema
+
+- For further details see the [kpms_pca schema API docs](https://datajoint.com/docs/elements/element-moseq/latest/api/element_moseq/kpms_pca/)
+
+[x]
+
+### `kpms_model` schema
+
+- For further details see the [kpms_model schema API docs](https://datajoint.com/docs/elements/element-moseq/latest/api/element_moseq/kpms_model/)
+
+[x]
\ No newline at end of file
diff --git a/docs/src/roadmap.md b/docs/src/roadmap.md
new file mode 100644
index 0000000..1da2e32
--- /dev/null
+++ b/docs/src/roadmap.md
@@ -0,0 +1,9 @@
+# Roadmap
+
+Through our interviews and direct collaboration on key projects, we identified
+the common motifs to create Element MoSeq. Major features include:
+
+- [x]
+
+Further development of this Element is community driven. Upon user requests and based on
+guidance from the Scientific Steering Group we will continue adding features to this Element.
diff --git a/docs/src/tutorials/index.md b/docs/src/tutorials/index.md
new file mode 100644
index 0000000..d622431
--- /dev/null
+++ b/docs/src/tutorials/index.md
@@ -0,0 +1,18 @@
+# Tutorials
+
++ Element MoSeq includes an [interactive tutorial on GitHub Codespaces](https://github.com/datajoint/element-moseq#interactive-tutorial), which is configured for users to run the pipeline.
+
++ DataJoint Elements are modular and can be connected into a complete pipeline.  In the interactive tutorial is a example Jupyter notebook that combine five DataJoint Elements - Lab, Animal, Session, Event, and MoSeq.  The notebook describes the pipeline and provides instructions for running the pipeline.  For convenience, this notebook is also rendered on this website:
+   + [Tutorial notebook](tutorial.ipynb)
+
+## Installation Instructions for Active Projects
+
++ The Element MoSeq described above can be modified for a user's specific experimental requirements and thereby used in active projects.  
+
++ The GitHub Codespace and Dev Container is configured for tutorials and prototyping.  
+We recommend users to configure a database specifically for production pipelines.  Instructions for a local installation of the integrated development environment with a database can be found on the [User Guide](https://datajoint.com/docs/elements/user-guide/) page.
+
+
+## POSE ESTIMATION METHOD
+
++ Behavioral segmentation analysis using keypoint data extracted with DeepLabCut is currently supported for single animal.

From bff0c614526bd81f0979e08b9ca5bcf70509f2b9 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:38:20 +0100
Subject: [PATCH 66/90] tutorial {WIP}

---
 notebooks/testing.ipynb  | 2371 --------------------------------------
 notebooks/tutorial.ipynb |  665 +++++++++++
 2 files changed, 665 insertions(+), 2371 deletions(-)
 delete mode 100644 notebooks/testing.ipynb
 create mode 100644 notebooks/tutorial.ipynb

diff --git a/notebooks/testing.ipynb b/notebooks/testing.ipynb
deleted file mode 100644
index 2a9e65f..0000000
--- a/notebooks/testing.ipynb
+++ /dev/null
@@ -1,2371 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
-    "    os.chdir(\"..\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datajoint as dj\n",
-    "from pathlib import Path\n",
-    "import numpy as np\n",
-    "\n",
-    "# from keypoint_moseq import setup_project , load_config\n",
-    "# from element_moseq.readers.kpms_reader import load_dj_config, generate_dj_config"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2024-03-12 00:13:11,809][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.3.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    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{'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"plotly\"], function(Plotly) {\n\twindow.Plotly = Plotly\n\ton_load()\n      })\n      require([\"tabulator\"], function(Tabulator) {\n\twindow.Tabulator = Tabulator\n\ton_load()\n      })\n      require([\"moment\"], function(moment) {\n\twindow.moment = moment\n\ton_load()\n      })\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 12;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['Plotly'] !== undefined) && (!(window['Plotly'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/plotlyplot/plotly-2.18.0.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Tabulator'] !== undefined) && (!(window['Tabulator'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/tabulator-tables@5.5.0/dist/js/tabulator.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['moment'] !== undefined) && (!(window['moment'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/luxon/build/global/luxon.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.4/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.3.4/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/jquery/jquery.slim.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/plotlyplot/plotly-2.18.0.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/tabulator-tables@5.5.0/dist/js/tabulator.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/luxon/build/global/luxon.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.2.min.js\", \"https://cdn.holoviz.org/panel/1.3.4/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [\"https://cdn.holoviz.org/panel/1.3.4/dist/bundled/datatabulator/tabulator-tables@5.5.0/dist/css/tabulator_simple.min.css\"];\n  var inline_js = [    function(Bokeh) {\n      inject_raw_css(\".tabulator{position:relative;border:1px solid #999;font-size:14px;text-align:left;overflow:hidden;-webkit-transform:translateZ(0);-moz-transform:translateZ(0);-ms-transform:translateZ(0);-o-transform:translateZ(0);transform:translateZ(0)}.tabulator[tabulator-layout=fitDataFill] .tabulator-tableholder .tabulator-table{min-width:100%}.tabulator[tabulator-layout=fitDataTable]{display:inline-block}.tabulator.tabulator-block-select{user-select:none}.tabulator .tabulator-header{position:relative;box-sizing:border-box;width:100%;border-bottom:1px solid #999;background-color:#fff;color:#555;font-weight:700;white-space:nowrap;overflow:hidden;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none}.tabulator .tabulator-header.tabulator-header-hidden{display:none}.tabulator .tabulator-header .tabulator-header-contents{position:relative;overflow:hidden}.tabulator .tabulator-header .tabulator-header-contents .tabulator-headers{display:inline-block}.tabulator .tabulator-header .tabulator-col{display:inline-flex;position:relative;box-sizing:border-box;flex-direction:column;justify-content:flex-start;border-right:1px solid #ddd;background:#fff;text-align:left;vertical-align:bottom;overflow:hidden}.tabulator .tabulator-header .tabulator-col.tabulator-moving{position:absolute;border:1px solid #999;background:#e6e6e6;pointer-events:none}.tabulator .tabulator-header .tabulator-col .tabulator-col-content{box-sizing:border-box;position:relative;padding:4px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-header-popup-button{padding:0 8px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-header-popup-button:hover{cursor:pointer;opacity:.6}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title-holder{position:relative}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title{box-sizing:border-box;width:100%;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;vertical-align:bottom}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title.tabulator-col-title-wrap{white-space:normal;text-overflow:clip}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title .tabulator-title-editor{box-sizing:border-box;width:100%;border:1px solid #999;padding:1px;background:#fff}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-title .tabulator-header-popup-button+.tabulator-title-editor{width:calc(100% - 22px)}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-sorter{display:flex;align-items:center;position:absolute;top:0;bottom:0;right:4px}.tabulator .tabulator-header .tabulator-col .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{width:0;height:0;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #bbb}.tabulator .tabulator-header .tabulator-col.tabulator-col-group .tabulator-col-group-cols{position:relative;display:flex;border-top:1px solid #ddd;overflow:hidden;margin-right:-1px}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter{position:relative;box-sizing:border-box;margin-top:2px;width:100%;text-align:center}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter textarea{height:auto!important}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter svg{margin-top:3px}.tabulator .tabulator-header .tabulator-col .tabulator-header-filter input::-ms-clear{width:0;height:0}.tabulator .tabulator-header .tabulator-col.tabulator-sortable .tabulator-col-title{padding-right:25px}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable.tabulator-col-sorter-element:hover{cursor:pointer;background-color:#e6e6e6}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter{color:#bbb}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-bottom:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=none] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-top:none;border-bottom:6px solid #bbb}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter{color:#666}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-bottom:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=ascending] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-top:none;border-bottom:6px solid #666}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter{color:#666}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter.tabulator-col-sorter-element .tabulator-arrow:hover{cursor:pointer;border-top:6px solid #555}}.tabulator .tabulator-header .tabulator-col.tabulator-sortable[aria-sort=descending] .tabulator-col-content .tabulator-col-sorter .tabulator-arrow{border-bottom:none;border-top:6px solid #666;color:#666}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical .tabulator-col-content .tabulator-col-title{writing-mode:vertical-rl;text-orientation:mixed;display:flex;align-items:center;justify-content:center}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-col-vertical-flip .tabulator-col-title{transform:rotate(180deg)}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-col-title{padding-right:0;padding-top:20px}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable.tabulator-col-vertical-flip .tabulator-col-title{padding-right:0;padding-bottom:20px}.tabulator .tabulator-header .tabulator-col.tabulator-col-vertical.tabulator-sortable .tabulator-col-sorter{justify-content:center;left:0;right:0;top:4px;bottom:auto}.tabulator .tabulator-header .tabulator-frozen{position:sticky;left:0;z-index:10}.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-left{border-right:2px solid #ddd}.tabulator .tabulator-header .tabulator-frozen.tabulator-frozen-right{border-left:2px solid #ddd}.tabulator .tabulator-header .tabulator-calcs-holder{box-sizing:border-box;background:#fff!important;border-top:1px solid #ddd;border-bottom:1px solid #ddd}.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row{background:#fff!important}.tabulator .tabulator-header .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle,.tabulator .tabulator-header .tabulator-frozen-rows-holder:empty{display:none}.tabulator .tabulator-tableholder{position:relative;width:100%;white-space:nowrap;overflow:auto;-webkit-overflow-scrolling:touch}.tabulator .tabulator-tableholder:focus{outline:none}.tabulator .tabulator-tableholder .tabulator-placeholder{box-sizing:border-box;display:flex;align-items:center;justify-content:center;width:100%}.tabulator .tabulator-tableholder .tabulator-placeholder[tabulator-render-mode=virtual]{min-height:100%;min-width:100%}.tabulator .tabulator-tableholder .tabulator-placeholder .tabulator-placeholder-contents{display:inline-block;text-align:center;padding:10px;color:#ccc;font-weight:700;font-size:20px;white-space:normal}.tabulator .tabulator-tableholder .tabulator-table{position:relative;display:inline-block;background-color:#fff;white-space:nowrap;overflow:visible;color:#333}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs{font-weight:700;background:#f2f2f2!important}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-top{border-bottom:2px solid #ddd}.tabulator .tabulator-tableholder .tabulator-table .tabulator-row.tabulator-calcs.tabulator-calcs-bottom{border-top:2px solid #ddd}.tabulator .tabulator-footer{border-top:1px solid #999;background-color:#fff;color:#555;font-weight:700;white-space:nowrap;user-select:none;-moz-user-select:none;-khtml-user-select:none;-webkit-user-select:none;-o-user-select:none}.tabulator .tabulator-footer .tabulator-footer-contents{display:flex;flex-direction:row;align-items:center;justify-content:space-between;padding:5px 10px}.tabulator .tabulator-footer .tabulator-footer-contents:empty{display:none}.tabulator .tabulator-footer .tabulator-calcs-holder{box-sizing:border-box;width:100%;text-align:left;background:#fff!important;border-bottom:1px solid #ddd;border-top:1px solid #ddd;overflow:hidden}.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row{display:inline-block;background:#fff!important}.tabulator .tabulator-footer .tabulator-calcs-holder .tabulator-row .tabulator-col-resize-handle{display:none}.tabulator .tabulator-footer .tabulator-calcs-holder:only-child{margin-bottom:-5px;border-bottom:none}.tabulator .tabulator-footer>*+.tabulator-page-counter{margin-left:10px}.tabulator .tabulator-footer .tabulator-page-counter{font-weight:400}.tabulator .tabulator-footer .tabulator-paginator{flex:1;text-align:right;color:#555;font-family:inherit;font-weight:inherit;font-size:inherit}.tabulator .tabulator-footer .tabulator-page-size{display:inline-block;margin:0 5px;padding:2px 5px;border:1px solid #aaa;border-radius:3px}.tabulator .tabulator-footer .tabulator-pages{margin:0 7px}.tabulator .tabulator-footer .tabulator-page{display:inline-block;margin:0 2px;padding:2px 5px;border:1px solid #aaa;border-radius:3px;background:hsla(0,0%,100%,.2)}.tabulator .tabulator-footer .tabulator-page.active{color:#d00}.tabulator .tabulator-footer .tabulator-page:disabled{opacity:.5}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-footer .tabulator-page:not(.disabled):hover{cursor:pointer;background:rgba(0,0,0,.2);color:#fff}}.tabulator .tabulator-col-resize-handle{position:relative;display:inline-block;width:6px;margin-left:-3px;margin-right:-3px;z-index:10;vertical-align:middle}@media (hover:hover) and (pointer:fine){.tabulator .tabulator-col-resize-handle:hover{cursor:ew-resize}}.tabulator .tabulator-col-resize-handle:last-of-type{width:3px;margin-right:0}.tabulator .tabulator-alert{position:absolute;display:flex;align-items:center;top:0;left:0;z-index:100;height:100%;width:100%;background:rgba(0,0,0,.4);text-align:center}.tabulator .tabulator-alert .tabulator-alert-msg{display:inline-block;margin:0 auto;padding:10px 20px;border-radius:10px;background:#fff;font-weight:700;font-size:16px}.tabulator .tabulator-alert .tabulator-alert-msg.tabulator-alert-state-msg{border:4px solid #333;color:#000}.tabulator .tabulator-alert .tabulator-alert-msg.tabulator-alert-state-error{border:4px solid #d00;color:#590000}.tabulator-row{position:relative;box-sizing:border-box;min-height:22px}.tabulator-row,.tabulator-row.tabulator-row-even{background-color:#fff}@media (hover:hover) and (pointer:fine){.tabulator-row.tabulator-selectable:hover{background-color:#bbb;cursor:pointer}}.tabulator-row.tabulator-selected{background-color:#9abcea}@media (hover:hover) and (pointer:fine){.tabulator-row.tabulator-selected:hover{background-color:#769bcc;cursor:pointer}}.tabulator-row.tabulator-row-moving{border:1px solid #000;background:#fff}.tabulator-row.tabulator-moving{position:absolute;border-top:1px solid #ddd;border-bottom:1px solid #ddd;pointer-events:none;z-index:15}.tabulator-row .tabulator-row-resize-handle{position:absolute;right:0;bottom:0;left:0;height:5px}.tabulator-row .tabulator-row-resize-handle.prev{top:0;bottom:auto}@media (hover:hover) and (pointer:fine){.tabulator-row .tabulator-row-resize-handle:hover{cursor:ns-resize}}.tabulator-row .tabulator-responsive-collapse{box-sizing:border-box;padding:5px;border-top:1px solid #ddd;border-bottom:1px solid #ddd}.tabulator-row .tabulator-responsive-collapse:empty{display:none}.tabulator-row .tabulator-responsive-collapse table{font-size:14px}.tabulator-row .tabulator-responsive-collapse table tr td{position:relative}.tabulator-row .tabulator-responsive-collapse table tr td:first-of-type{padding-right:10px}.tabulator-row .tabulator-cell{display:inline-block;position:relative;box-sizing:border-box;padding:4px;border-right:1px solid #ddd;vertical-align:middle;white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.tabulator-row .tabulator-cell.tabulator-frozen{display:inline-block;position:sticky;left:0;background-color:inherit;z-index:10}.tabulator-row .tabulator-cell.tabulator-frozen.tabulator-frozen-left{border-right:2px solid #ddd}.tabulator-row .tabulator-cell.tabulator-frozen.tabulator-frozen-right{border-left:2px solid #ddd}.tabulator-row .tabulator-cell.tabulator-editing{border:1px solid #1d68cd;outline:none;padding:0}.tabulator-row .tabulator-cell.tabulator-editing input,.tabulator-row .tabulator-cell.tabulator-editing select{border:1px;background:transparent;outline:none}.tabulator-row .tabulator-cell.tabulator-validation-fail{border:1px solid #d00}.tabulator-row .tabulator-cell.tabulator-validation-fail input,.tabulator-row .tabulator-cell.tabulator-validation-fail select{border:1px;background:transparent;color:#d00}.tabulator-row 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.tabulator-cell .tabulator-data-tree-control:hover{cursor:pointer;background:rgba(0,0,0,.2)}}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse{display:inline-block;position:relative;height:7px;width:1px;background:transparent}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-collapse:after{position:absolute;content:\\\"\\\";left:-3px;top:3px;height:1px;width:7px;background:#333}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand{display:inline-block;position:relative;height:7px;width:1px;background:#333}.tabulator-row .tabulator-cell .tabulator-data-tree-control .tabulator-data-tree-control-expand:after{position:absolute;content:\\\"\\\";left:-3px;top:3px;height:1px;width:7px;background:#333}.tabulator-row .tabulator-cell 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-      "text/html": [
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-       "  var render_items = [{\"docid\":\"554d882e-5148-4324-b896-d009a3085866\",\"roots\":{\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\":\"e4c5556c-13eb-4c3f-bf46-edf3a5c94dca\"},\"root_ids\":[\"4734f2b1-ddd4-443b-b41a-63bdf1b717bc\"]}];\n",
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-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
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-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
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-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
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-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined) && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined))\n",
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-       "        if (attempts > 200) {\n",
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-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "4734f2b1-ddd4-443b-b41a-63bdf1b717bc"
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tutorial_pipeline import lab, subject, session, kpms_pca, kpms_model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. PCA MODEL AND LATENT DIMENSIONS\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "<!-- kpms_pca.KeypointSet -->\n",
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-       "<title>kpms_pca.KeypointSet</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
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-       "</g>\n",
-       "</g>\n",
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-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>kpms_pca.RecordingInfo</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_datetime=null \rrecording_duration_average \r\">\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
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-       "</g>\n",
-       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
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-       "</g>\n",
-       "<!-- kpms_pca.Bodyparts -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>kpms_pca.Bodyparts</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
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-       "<text text-anchor=\"start\" x=\"399.32\" y=\"-278.97\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
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-       "</g>\n",
-       "<!-- kpms_pca.PCATask -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>kpms_pca.PCATask</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
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-       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-207.41\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.FormattedDataset -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
-       "<title>kpms_pca.FormattedDataset</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"455.2\" cy=\"-141.12\" rx=\"110.43\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-136.84\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
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-       "</g>\n",
-       "<!-- subject.Subject -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>subject.Subject</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
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-       "<text text-anchor=\"start\" x=\"329.32\" y=\"-490.65\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- session.Session -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>session.Session</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
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-       "<text text-anchor=\"start\" x=\"326.32\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- subject.Subject&#45;&gt;session.Session -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>subject.Subject-&gt;session.Session</title>\n",
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-       "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kpms_pca.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
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-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
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-       "</g>\n",
-       "<!-- kpms_pca.PCAFitting -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>kpms_pca.PCAFitting</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
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-       "</g>\n",
-       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
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-       "</g>\n",
-       "<!-- kpms_pca.DimsExplainedVariance -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>kpms_pca.DimsExplainedVariance</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"455.2\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"455.2\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-62.04C455.2,-51.54 455.2,-36.27 455.2,-25.77\"/>\n",
-       "</g>\n",
-       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M455.2,-264.64C455.2,-253.86 455.2,-239.87 455.2,-229.12\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
-      "text/plain": [
-       "<datajoint.diagram.Diagram at 0x105670880>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(\n",
-    "    dj.Diagram(subject.Subject)\n",
-    "    + dj.Diagram(session.Session)\n",
-    "    + dj.Diagram(kpms_pca)\n",
-    "    #    + dj.Diagram(kpms_model)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
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-       "        .Table{\n",
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-       "        .Table td{\n",
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-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
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-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
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-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
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-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
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-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">format_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_videos_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.KeypointSet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Subject and Session tables\n",
-    "subject.Subject.insert1(\n",
-    "    dict(\n",
-    "        subject=\"subject1\",\n",
-    "        sex=\"F\",\n",
-    "        subject_birth_date=\"2020-01-01\",\n",
-    "        subject_description=\"test\",\n",
-    "    ),\n",
-    "    skip_duplicates=True,\n",
-    ")\n",
-    "\n",
-    "# Definition of the dictionary named \"session_keys\"\n",
-    "session_keys = [\n",
-    "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
-    "    dict(subject=\"subject1\", session_datetime=\"2021-06-03 14:43:10\"),\n",
-    "]\n",
-    "\n",
-    "# Insert this dictionary in the Session table\n",
-    "session.Session.insert(session_keys, skip_duplicates=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
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-       "        .Table th{\n",
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-       "        .Table td{\n",
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-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method.</b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">format_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pose_estimation_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Pose estimation method description</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>anipose</td>\n",
-       "<td>`.csv` files generated by anipose analysis</td></tr><tr><td>deeplabcut</td>\n",
-       "<td>`.csv` and `.h5/.hdf5` files generated by DeepLabcut analysis</td></tr><tr><td>facemap</td>\n",
-       "<td>`.h5` files generated by Facemap analysis</td></tr><tr><td>nwb</td>\n",
-       "<td>`.nwb` files with Neurodata Without Borders (NWB) format</td></tr><tr><td>sleap</td>\n",
-       "<td>`.slp` and `.h5/.hdf5` files generated by SLEAP analysis</td></tr><tr><td>sleap-anipose</td>\n",
-       "<td>`.h5/.hdf5` files generated by sleap-anipose analysis</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 6</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*format_method pose_estimatio\n",
-       "+------------+ +------------+\n",
-       "anipose        `.csv` files g\n",
-       "deeplabcut     `.csv` and `.h\n",
-       "facemap        `.h5` files ge\n",
-       "nwb            `.nwb` files w\n",
-       "sleap          `.slp` and `.h\n",
-       "sleap-anipose  `.h5/.hdf5` fi\n",
-       " (Total: 6)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.PoseEstimationMethod()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpset_key = dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\", kpset_id=1)\n",
-    "\n",
-    "kpms_pca.KeypointSet.insert1(\n",
-    "    {\n",
-    "        **kpset_key,\n",
-    "        \"format_method\": \"deeplabcut\",\n",
-    "        \"kpset_config_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
-    "        \"kpset_videos_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
-    "        \"kpset_desc\": \"testing kpms pca schema\",\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
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-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
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-       "        .Table td{\n",
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-       "        .Table tr:nth-child(odd){\n",
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-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">format_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_videos_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">kpset_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
-       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.KeypointSet()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "video_files = [\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
-    "]\n",
-    "\n",
-    "kpms_pca.KeypointSet.VideoFile.insert(\n",
-    "    (\n",
-    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
-    "        for v_idx, f in enumerate(video_files)\n",
-    "    ),\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
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-       "        .Table td{\n",
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-       "        .Table tr:nth-child(odd){\n",
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-       "            color: #000000;\n",
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-       "        .Table tr:nth-child(even){\n",
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-       "        .djtooltip {\n",
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-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
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-       "            text-align: center;\n",
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-       "        #primary {\n",
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-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
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-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">video_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">video_path</p>\n",
-       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>0</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>2</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 3</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
-       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
-       " (Total: 3)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.KeypointSet.VideoFile()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
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-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_date recording_dura\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.RecordingInfo()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpms_pca.RecordingInfo.populate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Height in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Width in pixels</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of frames</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Frames per second, Hz</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Datetime for the start of the recording</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Video duration (s) from nframes / fps</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>576</td>\n",
-       "<td>640</td>\n",
-       "<td>95517</td>\n",
-       "<td>30</td>\n",
-       "<td>None</td>\n",
-       "<td>3183.0</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_date recording_dura\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            576            640            95517          30             None           3183.0        \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.RecordingInfo()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.Bodyparts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
-    "kpms_pca.Bodyparts.insert1(\n",
-    "    {\n",
-    "        **bodypart_key,\n",
-    "        \"anterior_bodyparts\": [\"nose\"],\n",
-    "        \"posterior_bodyparts\": [\"spine2\"],\n",
-    "        \"use_bodyparts\": [\n",
-    "            \"spine4\",\n",
-    "            \"spine3\",\n",
-    "            \"spine2\",\n",
-    "            \"spine1\",\n",
-    "            \"head\",\n",
-    "            \"nose\",\n",
-    "            \"right ear\",\n",
-    "            \"left ear\",\n",
-    "        ],\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td></td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1                             =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.Bodyparts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
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-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">output_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's output directory in config relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.PCATask()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpms_pca.PCATask.insert1(\n",
-    "    {\n",
-    "        **bodypart_key,\n",
-    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project2\",\n",
-    "        \"task_mode\": \"trigger\",\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">output_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's output directory in config relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'load': load computed analysis results, 'trigger': trigger computation</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project2</td>\n",
-       "<td>trigger</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1            1              /Users/milagro trigger      \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.PCATask()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject1',\n",
-       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       " 'kpset_id': 1,\n",
-       " 'bodyparts_id': 1}"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "key = (kpms_pca.PCATask & \"task_mode = 'trigger'\").fetch1(\"KEY\")\n",
-    "key"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
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-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
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-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.FormattedDataset()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ACTION REQUIRED: `anterior_bodyparts` contains BODYPART1 which is not\n",
-      "  one of the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains BODYPART3 which is not\n",
-      "  one of the options in `use_bodyparts`.\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading keypoints: 100%|████████████████| 10/10 [00:00<00:00, 16.01it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "kpms_pca.FormattedDataset.populate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    \n",
-       "    <style type=\"text/css\">\n",
-       "        .Table{\n",
-       "            border-collapse:collapse;\n",
-       "        }\n",
-       "        .Table th{\n",
-       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
-       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
-       "        }\n",
-       "        .Table td{\n",
-       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
-       "        }\n",
-       "        .Table tr:nth-child(odd){\n",
-       "            background: #ffffff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        .Table tr:nth-child(even){\n",
-       "            background: #f3f1ff;\n",
-       "            color: #000000;\n",
-       "        }\n",
-       "        /* Tooltip container */\n",
-       "        .djtooltip {\n",
-       "        }\n",
-       "        /* Tooltip text */\n",
-       "        .djtooltip .djtooltiptext {\n",
-       "            visibility: hidden;\n",
-       "            width: 120px;\n",
-       "            background-color: black;\n",
-       "            color: #fff;\n",
-       "            text-align: center;\n",
-       "            padding: 5px 0;\n",
-       "            border-radius: 6px;\n",
-       "            /* Position the tooltip text - see examples below! */\n",
-       "            position: absolute;\n",
-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
-       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.FormattedDataset()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
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-       "                            <p id=\"primary\">kpset_id</p>\n",
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-       "                            <p id=\"primary\">bodyparts_id</p>\n",
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-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
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-       "        \n",
-       "        <p>Total: 0</p></div>\n",
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-       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
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-    "kpms_pca.PCAFitting.populate()"
-   ]
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-   "execution_count": 31,
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-       "                            <p id=\"primary\">subject</p>\n",
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-       "                            <p id=\"primary\">bodyparts_id</p>\n",
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-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>2024-03-11 23:13:55</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
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-       "*subject     *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              2024-03-11 23:\n",
-       " (Total: 1)"
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-     "execution_count": 31,
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-    "kpms_pca.PCAFitting()"
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-       "    \n",
-       "    <b></b>\n",
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-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 0</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.DimsExplainedVariance()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kpms_pca.DimsExplainedVariance.populate()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
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-   "outputs": [
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-       "\n",
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-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>90.0</td>\n",
-       "<td>3</td>\n",
-       "<td>>=90.0% of variance exlained by 3 components.</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              90.0           3              >=90.0% of var\n",
-       " (Total: 1)"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kpms_pca.DimsExplainedVariance()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
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",
-      "text/plain": [
-       "<Figure size 300x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x300 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting before the user's choice of the latent dimensions to use in the next step\n",
-    "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
-    "from element_moseq.readers.kpms_reader import load_dj_config\n",
-    "\n",
-    "output_dir = (kpms_pca.PCATask & key).fetch1(\"output_dir\")\n",
-    "config = load_dj_config(output_dir, check_if_valid=False, build_indexes=False)\n",
-    "\n",
-    "pca = load_pca(output_dir)\n",
-    "\n",
-    "plot_scree(pca, project_dir=output_dir)\n",
-    "plot_pcs(pca, project_dir=output_dir, **config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "kpms_test",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
new file mode 100644
index 0000000..12621a9
--- /dev/null
+++ b/notebooks/tutorial.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DataJoint Element for Motion Sequencing with Keypoint MoSeq\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Open-source Data Pipeline for Motion Sequencing in Neurophysiology**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Welcome to the tutorial for the DataJoint Element for motion sequencing analysis. This tutorial aims to provide a comprehensive understanding of the open-source data pipeline by `element-moseq`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "![pipeline](../images/flowchart.svg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "if os.path.basename(os.getcwd()) == \"notebooks\":\n",
+    "    os.chdir(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datajoint as dj\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.list_schemas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_model\").drop()\n",
+    "dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_pca\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tutorial_pipeline import lab, subject, session, kpms_pca, kpms_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. FIT PCA MODEL AND LATENT DIMENSIONS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    dj.Diagram(subject.Subject)\n",
+    "    + dj.Diagram(session.Session)\n",
+    "    + dj.Diagram(kpms_pca)\n",
+    "    + dj.Diagram(kpms_model)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.KeypointSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subject and Session tables\n",
+    "subject.Subject.insert1(\n",
+    "    dict(\n",
+    "        subject=\"subject1\",\n",
+    "        sex=\"F\",\n",
+    "        subject_birth_date=\"2020-01-01\",\n",
+    "        subject_description=\"test\",\n",
+    "    ),\n",
+    "    skip_duplicates=True,\n",
+    ")\n",
+    "\n",
+    "# Definition of the dictionary named \"session_keys\"\n",
+    "session_keys = [\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2021-06-03 14:43:10\"),\n",
+    "]\n",
+    "\n",
+    "# Insert this dictionary in the Session table\n",
+    "session.Session.insert(session_keys, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PoseEstimationMethod()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpset_key = dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\", kpset_id=1)\n",
+    "\n",
+    "kpms_pca.KeypointSet.insert1(\n",
+    "    {\n",
+    "        **kpset_key,\n",
+    "        \"format_method\": \"deeplabcut\",\n",
+    "        \"kpset_config_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
+    "        \"kpset_videos_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
+    "        \"kpset_desc\": \"testing kpms pca schema\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.KeypointSet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "video_files = [\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
+    "]\n",
+    "\n",
+    "kpms_pca.KeypointSet.VideoFile.insert(\n",
+    "    (\n",
+    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
+    "        for v_idx, f in enumerate(video_files)\n",
+    "    ),\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.KeypointSet.VideoFile()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.RecordingInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.RecordingInfo.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.RecordingInfo()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.Bodyparts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
+    "kpms_pca.Bodyparts.insert1(\n",
+    "    {\n",
+    "        **bodypart_key,\n",
+    "        \"anterior_bodyparts\": [\"nose\"],\n",
+    "        \"posterior_bodyparts\": [\"spine4\"],\n",
+    "        \"use_bodyparts\": [\n",
+    "            \"spine4\",\n",
+    "            \"spine3\",\n",
+    "            \"spine2\",\n",
+    "            \"spine1\",\n",
+    "            \"head\",\n",
+    "            \"nose\",\n",
+    "            \"right ear\",\n",
+    "            \"left ear\",\n",
+    "        ],\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.Bodyparts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCATask.insert1(\n",
+    "    {\n",
+    "        **bodypart_key,\n",
+    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_testing\",\n",
+    "        \"task_mode\": \"trigger\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = (kpms_pca.PCATask & \"task_mode = 'trigger'\").fetch1(\"KEY\")\n",
+    "key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.FormattedDataset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.FormattedDataset.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.FormattedDataset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `PCAFitting` computation will fit a PCA model to aligned and centered keypoint coordinates\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCAFitting.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.DimsExplainedVariance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.DimsExplainedVariance.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.DimsExplainedVariance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting before the user's choice of the latent dimensions to use in the next step\n",
+    "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
+    "from element_moseq.readers.kpms_reader import load_dj_config\n",
+    "\n",
+    "output_dir = (kpms_pca.PCATask & key).fetch1(\"output_dir\")\n",
+    "config = load_dj_config(output_dir, check_if_valid=False, build_indexes=False)\n",
+    "\n",
+    "pca = load_pca(output_dir)\n",
+    "\n",
+    "plot_scree(pca, project_dir=output_dir)\n",
+    "plot_pcs(pca, project_dir=output_dir, **config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. KPMS Model prefitting and full fitting\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. **Initialization**: Auto-regressive (AR) parameters and syllable sequences are randomly initialized using pose trajectories from PCA\n",
+    "2. **Fitting an AR-HMM**: AR parameters, transition probabilities and syllable sequences are iteratively updated through Gibbs sampling\n",
+    "3. **Fitting the full model**: all params, including both AR-HMM as well as centroid, heading, noise-estimates and continuous latent states (i.e. pose trajectories) are iteratively updated through Gibss sampling. Step useful for noisy data.\n",
+    "4. **Extracting model results**: the learned states of the model are parsed and saved to disk for visualization and downstream analysis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    dj.Diagram(subject.Subject)\n",
+    "    + dj.Diagram(session.Session)\n",
+    "    + dj.Diagram(kpms_pca)\n",
+    "    + dj.Diagram(kpms_model)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adjust `kappa` hyperparameter to achieve the desired distribution of syllable durations. Higher values of kappa lead to longer syllables.\n",
+    "\n",
+    "Let's chose a kappa value that yields a median syllable duration of 12 frames.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fps = kpms_pca.RecordingInfo.fetch1(\"fps_average\")\n",
+    "kappa_min = (12 / fps) * 1000 #ms\n",
+    "kappa_max = 1e6 #ms \n",
+    "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
+    "print('kappa range = {} ms'.format(kappa_range))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For rodents it's recommended a target duration of ~400ms\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The kappa value in the config is only used during model initialization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prefitting_key = {\n",
+    "    **key,\n",
+    "    'pre_latent_dim': 4,\n",
+    "    'pre_kappa': 10000,\n",
+    "    'pre_num_iterations': 2,\n",
+    "    'pre_fitting_desc': \"initialization of model\",\n",
+    "    'model_initialization': 'Yes',\n",
+    "} \n",
+    "\n",
+    "kpms_model.PreFittingTask.insert1(prefitting_key, skip_duplicates=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prefitting_keys = [{\n",
+    "#     **key,\n",
+    "#     'latent_dim': 3,\n",
+    "#     'kappa': int(i),\n",
+    "#     'num_iterations': 50,\n",
+    "#     'pre_fitting_desc': f\"Prefitting {c}\"\n",
+    "# } for c, i in enumerate(kappa_range, start=1)]\n",
+    "\n",
+    "# prefitting_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for prefitting_key in prefitting_keys:\n",
+    "#     kpms_model.PreFittingTask.insert1(prefitting_key, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.PreFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.PreFitting.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.PreFitting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.FullFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fullfitting_key = ({**key,\n",
+    "                      'full_latent_dim': 4,\n",
+    "                      'full_kappa': 10000,\n",
+    "                      'full_num_iterations':2,\n",
+    "                      'full_fitting_desc':\"Testing full fitting model and generate results\",\n",
+    "                      'task_mode':'trigger',\n",
+    "                      'sort_syllables':True,\n",
+    "                      'results_as_csv':True,\n",
+    "                      'visualizations':True})\n",
+    "\n",
+    "kpms_model.FullFittingTask.insert1(fullfitting_key, skip_duplicates=True)                                  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.FullFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.FullFitting.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.FullFitting()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Generate results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.GenerateResults.populate()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kpms_test",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From aafbc4a3f004fa1faef5469a324ba8b1a85dd3bb Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:38:29 +0100
Subject: [PATCH 67/90] Update README

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 8a2f2d8..92415cc 100644
--- a/README.md
+++ b/README.md
@@ -10,11 +10,11 @@ tables that can be combined with other Elements to assemble a fully functional p
 
 ## Experiment Flowchart
 
-![flowchart]()
+![pipeline](../images/flowchart.svg)
 
 ## Data Pipeline Diagram
 
-![pipeline]()
+![pipeline](../images/pipeline.svg)
 
 ## Getting Started
 

From 0ead1ea82db724db9507f0a6032c4b18eea8063a Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:38:47 +0100
Subject: [PATCH 68/90] Update logic in `GenerateResults`

---
 element_moseq/kpms_model.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index a92d8e7..4d6805c 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -430,7 +430,18 @@ def make(self, key):
         """
 
         output_dir = (PCATask & key).fetch1("output_dir")
-        model_name = (PreFitting & key).fetch1("model_name")
+        try:
+            selected_key = (
+                PreFitting
+                & "pre_latent_dim = {}".format(key["full_latent_dim"])
+                & "pre_kappa = {}".format(key["full_kappa"])
+            )
+            model_name, pre_num_iterations = selected_key.fetch1(
+                "model_name", "pre_num_iterations"
+            )
+        except:
+            print("No prefitting model found")
+            
         sort_syllables, results_as_csv, visualizations = (FullFittingTask & key).fetch1(
             "sort_syllables", "results_as_csv", "visualizations"
         )

From b1e2b2607eec56cdb83b558c75c585aaeca0b33f Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 13 Mar 2024 17:45:43 +0100
Subject: [PATCH 69/90] update `tutorial.ipynb` (WIP)

---
 notebooks/tutorial.ipynb | 3834 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 3704 insertions(+), 130 deletions(-)

diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index 12621a9..b18db59 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -22,17 +22,104 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![pipeline](../images/flowchart.svg)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The package is designed to seamlessly integrate the **PCA fitting**, **model fitting** through **initialization**, **fitting an AR-HMM**, and **fitting the full model** into a data pipeline and streamline model and video management using DataJoint.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![pipeline](../images/pipeline.svg)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By the end of this tutorial, you will have a clear grasp of how to set up and integrate the `Element MoSeq` into your specific research projects and your lab.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "\n",
+    "Please see the [datajoint tutorials GitHub repository](https://github.com/datajoint/datajoint-tutorials/tree/main) proceeding.\n",
+    "A basic understanding of the following DataJoint concepts will be beneficial to your understanding of this tutorial:\n",
+    "\n",
+    "1. The `Imported` and `Computed` tables types in `datajoint-python`.\n",
+    "2. The functionality of the `.populate()` method.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### **Tutorial Overview**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Setup\n",
+    "- _Activate_ the DataJoint pipeline\n",
+    "- _Insert_ example data into subject and session tables\n",
+    "- _Insert_ the keypoint data from pose estimation and the body parts in the DataJoint pipeline\n",
+    "- _Fit a PCA model_ to aligned and centered keypoint coordinates and _select_ the latent dimension\n",
+    "- _Fit the AR-HMM and the full model_\n",
+    "- _Generate and store_ the model results and visualizations\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Setup**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "![pipeline](../images/flowchart.svg)"
+    "This tutorial loads the keypoint data extracted by DeepLabCut of a single freely moving mouse in an open-field environment. The goal is to link this point tracking to pose dynamics by identifying its behavioral modules (\"syllables\") without human supervision. The modeling results are stored as a `.h5` file (and optionally a directory of `.csv` files) that contain the following information:\n",
+    "\n",
+    "- Behavior modules as \"syllables\": the syllable label assigned to each frame (i.e. the state indexes assigned by the model)\n",
+    "- Centroid and heading in each frame, as estimated by the model, that capture the animal's overall position in allocentric coordinates\n",
+    "- Latent state: low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.\n",
+    "\n",
+    "The results of this Element example can be combined with **other modalities** to create a complete customizable data pipeline for your specific lab or study. For instance, you can combine `element-moseq` with `element-deeplabcut` and `element-calcium-imaging` to characterize the neural activity along with natural sub-second rhythmicity in mouse movement.\n",
+    "\n",
+    "#### Steps to Run the Element-MoSeq\n",
+    "\n",
+    "The input data for this data pipeline is as follows:\n",
+    "\n",
+    "- A DeepLabCut (DLC) project folder with its config file, video set and keypoint data.\n",
+    "- Selection of the anterior, posterior, and use bodyparts.\n",
+    "  This tutorial includes this DLC project folder with example data and the results as well in `example_data` directory.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start this tutorial by importing the packages necessary to run the data pipeline.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,39 +131,199 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import datajoint as dj\n",
     "from pathlib import Path\n",
-    "import numpy as np\n"
+    "import numpy as np"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "dj.list_schemas()"
+    "If the tutorial is run in Codespaces, a private, local database server is created and made available for you. This is where we will insert and store our processed results.\n",
+    "\n",
+    "Let's connect to the database server.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-03-13 17:32:11,291][INFO]: Connecting root@localhost:3306\n",
+      "[2024-03-13 17:32:11,319][INFO]: Connected root@localhost:3306\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DataJoint connection (connected) root@localhost:3306"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dj.conn()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_model\").drop()\n",
-    "dj.schema(\"vathes-team_devlab_mila_kpmstest_kpms_pca\")"
+    "### **Activate the DataJoint pipeline**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial presumes that the `element-moseq` has been pre-configured and instantiated, with the database linked downstream to pre-existing `subject` and `session` tables. Please refer to the `tutorial_pipeline.py` for the source code.\n",
+    "\n",
+    "Now, we will proceed to import the essential schemas required to construct this data pipeline, with particular attention to the primary components: `kpms_pca` and `kpms_model`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-03-13 17:32:11,328][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+     ]
+    },
+    {
+     "data": {
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before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
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+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
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+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
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+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
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+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined) && ( root['Plotly'] !== undefined) && ( root['Tabulator'] !== undefined))\n",
+       "  }\n",
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+       "    embed_document(root);\n",
+       "  } else {\n",
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+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "94587c3b-f52d-44fc-b1d8-cf67301da0d3"
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from tutorial_pipeline import lab, subject, session, kpms_pca, kpms_model"
    ]
@@ -85,14 +332,251 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. FIT PCA MODEL AND LATENT DIMENSIONS\n"
+    "We can represent the tables in the `kpms_pca` and `kpms_model` schemas as well as some of the upstream dependencies to `session` and `subject` schemas as a diagram.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"605pt\" height=\"652pt\" viewBox=\"0.00 0.00 604.82 651.68\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 647.68)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-647.68 600.82,-647.68 600.82,4 -4,4\"/>\n",
+       "<!-- kpms_pca.PCAFitting -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>kpms_pca.PCAFitting</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-207.36\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-203.09\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_pca.DimsExplainedVariance</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"118.62\" cy=\"-141.12\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"118.62\" y=\"-136.84\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M308.89,-202.63C273.27,-191.3 166.64,-157.39 130.64,-145.94\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"403.25,-158.4 238,-158.4 238,-123.84 403.25,-123.84 403.25,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"246\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M320.62,-194.58C320.62,-184.44 320.62,-169.7 320.62,-158.42\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"585.5,-158.4 421.75,-158.4 421.75,-123.84 585.5,-123.84 585.5,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"429.75\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M332.35,-202.24C356.81,-193.66 414.44,-173.43 456.1,-158.8\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"228.12,-502.56 89.12,-502.56 89.12,-468 228.12,-468 228.12,-502.56\"/>\n",
+       "<text text-anchor=\"start\" x=\"97.12\" y=\"-482.01\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"239,-424.72 78.25,-424.72 78.25,-404.72 239,-404.72 239,-424.72\"/>\n",
+       "<text text-anchor=\"start\" x=\"86.25\" y=\"-412.22\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M158.62,-467.68C158.62,-454.05 158.62,-435.28 158.62,-424.43\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.Bodyparts -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>kpms_pca.Bodyparts</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"384.5,-432 256.75,-432 256.75,-397.44 384.5,-397.44 384.5,-432\"/>\n",
+       "<text text-anchor=\"start\" x=\"264.75\" y=\"-411.45\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M198.25,-467.51C223.45,-456.84 255.95,-443.09 281.12,-432.44\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.RecordingInfo -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>kpms_pca.RecordingInfo</title>\n",
+       "<g id=\"a_node15\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_duration_average \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"499.62\" cy=\"-414.72\" rx=\"97.2\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"499.62\" y=\"-410.45\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M228.45,-470.24C289.74,-457.92 378.05,-440.16 437.06,-428.3\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.GenerateResults -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_model.GenerateResults</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-62.04C320.62,-51.54 320.62,-36.27 320.62,-25.77\"/>\n",
+       "</g>\n",
+       "<!-- subject.Subject -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>subject.Subject</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"284.5,-643.68 186.75,-643.68 186.75,-609.12 284.5,-609.12 284.5,-643.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"194.75\" y=\"-623.13\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- session.Session -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>session.Session</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"287.5,-573.12 183.75,-573.12 183.75,-538.56 287.5,-538.56 287.5,-573.12\"/>\n",
+       "<text text-anchor=\"start\" x=\"191.75\" y=\"-552.57\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- subject.Subject&#45;&gt;session.Session -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>subject.Subject-&gt;session.Session</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M235.62,-608.8C235.62,-598.02 235.62,-584.03 235.62,-573.28\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"165.25,-573.12 0,-573.12 0,-538.56 165.25,-538.56 165.25,-573.12\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-553.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M100.64,-538.59C112.58,-527.82 128.2,-513.73 140.22,-502.89\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.FormattedDataset -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_pca.FormattedDataset</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"320.62\" cy=\"-273.6\" rx=\"110.43\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-269.33\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-256.06C320.62,-244.76 320.62,-230.06 320.62,-219.98\"/>\n",
+       "</g>\n",
+       "<!-- session.Session&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>session.Session-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M216.99,-538.24C204.97,-527.54 189.4,-513.68 177.36,-502.96\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCATask -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_pca.PCATask</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"381.88,-361.44 259.38,-361.44 259.38,-326.88 381.88,-326.88 381.88,-361.44\"/>\n",
+       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-339.89\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-326.56C320.62,-315.78 320.62,-301.79 320.62,-291.04\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-123.58C320.62,-112.28 320.62,-97.58 320.62,-87.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node16\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"503.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"503.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M503.62,-123.58C503.62,-112.28 503.62,-97.58 503.62,-87.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-397.12C320.62,-386.34 320.62,-372.35 320.62,-361.6\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x14543da60>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(\n",
     "    dj.Diagram(subject.Subject)\n",
@@ -102,22 +586,366 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As evident from the diagram, this data pipeline encompasses several tables associated with different keypoint-MoSeq components like pca, pre-fitting of AR-HMM, and full fitting of the model. A few tables, such as `subject.Subject` or `session.Session`, while important for a complete pipeline, fall outside the scope of the `element-moseq` tutorial, and will therefore, not be explored extensively here. The primary focus of this tutorial will be on the `kpms_pca` and `kpms_model` schemas.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"576pt\" height=\"581pt\" viewBox=\"0.00 0.00 575.88 581.12\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 577.12)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-577.12 571.88,-577.12 571.88,4 -4,4\"/>\n",
+       "<!-- kpms_pca.PCAFitting -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>kpms_pca.PCAFitting</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-207.36\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"303\" y=\"-203.09\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>kpms_pca.DimsExplainedVariance</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"101\" cy=\"-141.12\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"101\" y=\"-136.84\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M291.27,-202.63C255.65,-191.3 149.02,-157.39 113.02,-145.94\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"385.62,-158.4 220.38,-158.4 220.38,-123.84 385.62,-123.84 385.62,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"228.38\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M303,-194.58C303,-184.44 303,-169.7 303,-158.42\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"567.88,-158.4 404.12,-158.4 404.12,-123.84 567.88,-123.84 567.88,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"412.12\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M314.73,-202.24C339.18,-193.66 396.81,-173.43 438.48,-158.8\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"534.5,-502.56 395.5,-502.56 395.5,-468 534.5,-468 534.5,-502.56\"/>\n",
+       "<text text-anchor=\"start\" x=\"403.5\" y=\"-482.01\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.RecordingInfo -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_pca.RecordingInfo</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_duration_average \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"124\" cy=\"-414.72\" rx=\"97.2\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"124\" y=\"-410.45\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M395.17,-470.24C333.89,-457.92 245.58,-440.16 186.56,-428.3\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.Bodyparts -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>kpms_pca.Bodyparts</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"366.88,-432 239.12,-432 239.12,-397.44 366.88,-397.44 366.88,-432\"/>\n",
+       "<text text-anchor=\"start\" x=\"247.12\" y=\"-411.45\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M425.37,-467.51C400.17,-456.84 367.67,-443.09 342.5,-432.44\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"545.38,-424.72 384.62,-424.72 384.62,-404.72 545.38,-404.72 545.38,-424.72\"/>\n",
+       "<text text-anchor=\"start\" x=\"392.62\" y=\"-412.22\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M465,-467.68C465,-454.05 465,-435.28 465,-424.43\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"303\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.GenerateResults -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>kpms_model.GenerateResults</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"303\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-62.04C303,-51.54 303,-36.27 303,-25.77\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.FormattedDataset -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_pca.FormattedDataset</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"303\" cy=\"-273.6\" rx=\"110.43\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"303\" y=\"-269.33\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-256.06C303,-244.76 303,-230.06 303,-219.98\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCATask -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_pca.PCATask</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"364.25,-361.44 241.75,-361.44 241.75,-326.88 364.25,-326.88 364.25,-361.44\"/>\n",
+       "<text text-anchor=\"middle\" x=\"303\" y=\"-339.89\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-326.56C303,-315.78 303,-301.79 303,-291.04\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-397.12C303,-386.34 303,-372.35 303,-361.6\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-123.58C303,-112.28 303,-97.58 303,-87.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"486\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"486\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M486,-123.58C486,-112.28 486,-97.58 486,-87.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"547.62,-573.12 382.38,-573.12 382.38,-538.56 547.62,-538.56 547.62,-573.12\"/>\n",
+       "<text text-anchor=\"start\" x=\"390.38\" y=\"-553.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M465,-538.59C465,-527.82 465,-513.73 465,-502.89\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x1073b1df0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "kpms_pca.KeypointSet()"
+    "(\n",
+    "    dj.Diagram(kpms_pca)\n",
+    "    + dj.Diagram(kpms_model)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Insert example data into subject and session tables\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's delve into the `subject.Subject` and `session.Session` tables and include some example data.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">subject_nickname</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">sex</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">subject_birth_date</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">subject_description</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td></td>\n",
+       "<td>F</td>\n",
+       "<td>2020-01-01</td>\n",
+       "<td>test</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     subject_nickna sex     subject_birth_ subject_descri\n",
+       "+----------+ +------------+ +-----+ +------------+ +------------+\n",
+       "subject1                    F       2020-01-01     test          \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subject.Subject()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a new entry for a subject in the `Subject` table:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Subject and Session tables\n",
     "subject.Subject.insert1(\n",
     "    dict(\n",
     "        subject=\"subject1\",\n",
@@ -126,8 +954,22 @@
     "        subject_description=\"test\",\n",
     "    ),\n",
     "    skip_duplicates=True,\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create session keys and input them into the `Session` table:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Definition of the dictionary named \"session_keys\"\n",
     "session_keys = [\n",
     "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
@@ -138,18 +980,221 @@
     "session.Session.insert(session_keys, skip_duplicates=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Confirm the inserted data:\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-03 14:43:10</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 2</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet\n",
+       "+----------+ +------------+\n",
+       "subject1     2021-06-02 14:\n",
+       "subject1     2021-06-03 14:\n",
+       " (Total: 2)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "session.Session()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
+   "source": [
+    "### Insert the keypoint data from pose estimation and the body parts in the DataJoint pipeline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b>Parameters used to obtain the keypoints data based on a specific pose estimation method.</b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pose_estimation_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Pose estimation method description</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>anipose</td>\n",
+       "<td>`.csv` files generated by anipose analysis</td></tr><tr><td>deeplabcut</td>\n",
+       "<td>`.csv` and `.h5/.hdf5` files generated by DeepLabcut analysis</td></tr><tr><td>facemap</td>\n",
+       "<td>`.h5` files generated by Facemap analysis</td></tr><tr><td>nwb</td>\n",
+       "<td>`.nwb` files with Neurodata Without Borders (NWB) format</td></tr><tr><td>sleap</td>\n",
+       "<td>`.slp` and `.h5/.hdf5` files generated by SLEAP analysis</td></tr><tr><td>sleap-anipose</td>\n",
+       "<td>`.h5/.hdf5` files generated by sleap-anipose analysis</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 6</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*format_method pose_estimatio\n",
+       "+------------+ +------------+\n",
+       "anipose        `.csv` files g\n",
+       "deeplabcut     `.csv` and `.h\n",
+       "facemap        `.h5` files ge\n",
+       "nwb            `.nwb` files w\n",
+       "sleap          `.slp` and `.h\n",
+       "sleap-anipose  `.h5/.hdf5` fi\n",
+       " (Total: 6)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.PoseEstimationMethod()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,16 +1214,120 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_videos_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">Path relative to root data directory where the videos and their keypoints are located</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">kpset_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
+       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.KeypointSet()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,25 +1348,232 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">video_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">video_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>0</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>2</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 3</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
+       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
+       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
+       " (Total: 3)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.KeypointSet.VideoFile()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_height_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average height of the video set (pixels)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_width_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average width of the video set (pixels)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">nframes_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average number of frames of the video set (frames)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">fps_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Average frames per second of the video set (Hz)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average video duration (s) of the video set (nframes / fps)</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_dura\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.RecordingInfo()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,25 +1582,234 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_height_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average height of the video set (pixels)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">px_width_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average width of the video set (pixels)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">nframes_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average number of frames of the video set (frames)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">fps_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Average frames per second of the video set (Hz)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
+       "                            <span class=\"djtooltiptext\">Average video duration (s) of the video set (nframes / fps)</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>576</td>\n",
+       "<td>640</td>\n",
+       "<td>95517</td>\n",
+       "<td>30</td>\n",
+       "<td>3183.0</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_dura\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            576            640            95517          30             3183.0        \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.RecordingInfo()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.Bodyparts()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,32 +1836,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td></td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1                             =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.Bodyparts()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fit a PCA model to aligned and centered keypoint coordinates and select the latent dimension\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">default = 'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.PCATask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "kpms_pca.PCATask.insert1(\n",
     "    {\n",
     "        **bodypart_key,\n",
-    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_testing\",\n",
+    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial\",\n",
     "        \"task_mode\": \"trigger\",\n",
     "    },\n",
     "    skip_duplicates=True,\n",
@@ -305,18 +2080,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">default = 'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial</td>\n",
+       "<td>trigger</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
+       "subject1     2021-06-02 14: 1            1              /Users/milagro trigger      \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.PCATask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'subject': 'subject1',\n",
+       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       " 'kpset_id': 1,\n",
+       " 'bodyparts_id': 1}"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "key = (kpms_pca.PCATask & \"task_mode = 'trigger'\").fetch1(\"KEY\")\n",
     "key"
@@ -324,27 +2213,252 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
+       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.FormattedDataset()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The directory `/Users/milagros/Documents/datajoint-elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial` already exists. Use\n",
+      "`overwrite=True` or pick a different name\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains BODYPART1 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains BODYPART3 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading keypoints: 100%|████████████████| 10/10 [00:00<00:00, 28.63it/s]\n"
+     ]
+    }
+   ],
    "source": [
-    "kpms_pca.FormattedDataset.populate()"
+    "kpms_pca.FormattedDataset.populate(key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
+       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
+       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.FormattedDataset()"
    ]
@@ -358,63 +2472,474 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.PCAFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.PCAFitting.populate()"
+    "kpms_pca.PCAFitting.populate(key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>2024-03-13 16:32:23</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              2024-03-13 16:\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.PCAFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "        .djtooltip .djtooltiptext {\n",
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+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
+       "                            <span class=\"djtooltiptext\">Percentage of variance explained by the selected components.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of components required to explain the specified variance.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Description of the latent dimensions that explain the specified variance.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.DimsExplainedVariance()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.DimsExplainedVariance.populate()"
+    "kpms_pca.DimsExplainedVariance.populate(key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
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+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
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+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
+       "                            <span class=\"djtooltiptext\">Percentage of variance explained by the selected components.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of components required to explain the specified variance.</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Description of the latent dimensions that explain the specified variance.</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>90.0</td>\n",
+       "<td>4</td>\n",
+       "<td>>=90.0% of variance explained by 4 components.</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              90.0           4              >=90.0% of var\n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_pca.DimsExplainedVariance()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 300x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Plotting before the user's choice of the latent dimensions to use in the next step\n",
     "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
@@ -433,13 +2958,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. KPMS Model prefitting and full fitting\n"
+    "### Fit the AR-HMM and the full model\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "KPMS Model prefitting and full fitting involves:\n",
+    "\n",
     "1. **Initialization**: Auto-regressive (AR) parameters and syllable sequences are randomly initialized using pose trajectories from PCA\n",
     "2. **Fitting an AR-HMM**: AR parameters, transition probabilities and syllable sequences are iteratively updated through Gibbs sampling\n",
     "3. **Fitting the full model**: all params, including both AR-HMM as well as centroid, heading, noise-estimates and continuous latent states (i.e. pose trajectories) are iteratively updated through Gibss sampling. Step useful for noisy data.\n",
@@ -448,16 +2975,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"362pt\" height=\"166pt\" viewBox=\"0.00 0.00 361.50 166.40\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 162.4)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-162.4 357.5,-162.4 357.5,4 -4,4\"/>\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"88.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"88.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.GenerateResults -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_model.GenerateResults</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"88.62\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"88.62\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M88.62,-62.04C88.62,-51.54 88.62,-36.27 88.62,-25.77\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"171.25,-158.4 6,-158.4 6,-123.84 171.25,-123.84 171.25,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"14\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M88.62,-123.58C88.62,-112.28 88.62,-97.58 88.62,-87.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"353.5,-158.4 189.75,-158.4 189.75,-123.84 353.5,-123.84 353.5,-158.4\"/>\n",
+       "<text text-anchor=\"start\" x=\"197.75\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"271.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"271.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M271.62,-123.58C271.62,-112.28 271.62,-97.58 271.62,-87.5\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x29237cbe0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "(\n",
-    "    dj.Diagram(subject.Subject)\n",
-    "    + dj.Diagram(session.Session)\n",
-    "    + dj.Diagram(kpms_pca)\n",
-    "    + dj.Diagram(kpms_model)\n",
-    ")"
+    "dj.Diagram(kpms_model)"
    ]
   },
   {
@@ -471,11 +3071,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kappa range = [4.e+02 2.e+04 1.e+06] ms\n"
+     ]
+    }
+   ],
    "source": [
-    "fps = kpms_pca.RecordingInfo.fetch1(\"fps_average\")\n",
+    "fps = (kpms_pca.RecordingInfo & key).fetch1(\"fps_average\")\n",
     "kappa_min = (12 / fps) * 1000 #ms\n",
     "kappa_max = 1e6 #ms \n",
     "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
@@ -498,96 +3106,717 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'subject': 'subject1',\n",
+       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       "  'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 400,\n",
+       "  'pre_num_iterations': 2,\n",
+       "  'pre_fitting_desc': 'Prefitting 1',\n",
+       "  'model_initialization': 'Yes'},\n",
+       " {'subject': 'subject1',\n",
+       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       "  'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 20000,\n",
+       "  'pre_num_iterations': 2,\n",
+       "  'pre_fitting_desc': 'Prefitting 2',\n",
+       "  'model_initialization': 'Yes'},\n",
+       " {'subject': 'subject1',\n",
+       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
+       "  'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 1000000,\n",
+       "  'pre_num_iterations': 2,\n",
+       "  'pre_fitting_desc': 'Prefitting 3',\n",
+       "  'model_initialization': 'Yes'}]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "prefitting_key = {\n",
+    "prefitting_keys = [{\n",
     "    **key,\n",
     "    'pre_latent_dim': 4,\n",
-    "    'pre_kappa': 10000,\n",
+    "    'pre_kappa': int(i),\n",
     "    'pre_num_iterations': 2,\n",
-    "    'pre_fitting_desc': \"initialization of model\",\n",
+    "    'pre_fitting_desc': f\"Prefitting {c}\",\n",
     "    'model_initialization': 'Yes',\n",
-    "} \n",
+    "} for c, i in enumerate(kappa_range, start=1)]\n",
     "\n",
-    "kpms_model.PreFittingTask.insert1(prefitting_key, skip_duplicates=True)\n"
+    "prefitting_keys"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# prefitting_keys = [{\n",
-    "#     **key,\n",
-    "#     'latent_dim': 3,\n",
-    "#     'kappa': int(i),\n",
-    "#     'num_iterations': 50,\n",
-    "#     'pre_fitting_desc': f\"Prefitting {c}\"\n",
-    "# } for c, i in enumerate(kappa_range, start=1)]\n",
-    "\n",
-    "# prefitting_keys"
+    "kpms_model.PreFittingTask.insert(prefitting_keys, skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
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+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
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+       "        .djtooltip {\n",
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+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">model_initialization</p>\n",
+       "                            <span class=\"djtooltiptext\">'Yes' initialize a new AR-HMM model and output_dir, 'No' will directly pre-fit the model</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">model_name_initialization</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Name of the initialized model to be pre-fitted. Only needed if model_initialization = False</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>400</td>\n",
+       "<td>2</td>\n",
+       "<td>Prefitting 1</td>\n",
+       "<td>Yes</td>\n",
+       "<td></td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>20000</td>\n",
+       "<td>2</td>\n",
+       "<td>Prefitting 2</td>\n",
+       "<td>Yes</td>\n",
+       "<td></td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>1000000</td>\n",
+       "<td>2</td>\n",
+       "<td>Prefitting 3</td>\n",
+       "<td>Yes</td>\n",
+       "<td></td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 3</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera pre_fitting_de model_initiali model_name_ini\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              4              400           2              Prefitting 1   Yes                          \n",
+       "subject1     2021-06-02 14: 1            1              4              20000         2              Prefitting 2   Yes                          \n",
+       "subject1     2021-06-02 14: 1            1              4              1000000       2              Prefitting 3   Yes                          \n",
+       " (Total: 3)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# for prefitting_key in prefitting_keys:\n",
-    "#     kpms_model.PreFittingTask.insert1(prefitting_key, skip_duplicates=True)"
+    "kpms_model.PreFittingTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
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+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
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+       "        }\n",
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+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
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+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">Name of the model</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera model_name     pre_fitting_du\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "kpms_model.PreFittingTask()"
+    "kpms_model.PreFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/milagros/miniconda/envs/kpms_test/lib/python3.9/site-packages/keypoint_moseq/fitting.py:589: UserWarning:\n",
+      "\n",
+      "'kappa' with <class 'int'> will be cast to <class 'float'>\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_32_42\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 67%|████████████████████████▋            | 2/3 [00:28<00:13, 13.85s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 3/3 [00:40<00:00, 13.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_31\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 67%|████████████████████████▋            | 2/3 [00:09<00:04,  4.53s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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RtGHDBjx9+lQlBgAqcajTsmVLJCYmYvHixUrp8+bNg0wmQ4sWLTSKTROzZ8/GnDlz8N1332HEiBE6K5eIiOhzwjuliIiIUhgzZgy2b9+ONm3aIDw8HH/99ZfS9JRv9/ruu++wYcMGNG7cGCNGjEB0dDRmz56NihUrwt/fX8o3f/58/PLLL6hTpw7Mzc1Vymzfvj0sLCxQtmxZlC1bVm1cbm5uGt0hlZKm8f3www84duwYmjdvjuLFiyM8PBybNm3CmTNnMGzYMJQqVSpTy121ahUePnwoPWp45MgRTJs2DUDy29MUd6NUrFgR3t7eqFKlCmxtbXHnzh388ccf+PDhg8aDQe/YsQOXLl0CAHz48AGXL1+WlvXFF1+gUqVKSvkVj9dl5dE9TeM+dOgQhg8fjo4dO8Ld3R3x8fH477//sHnzZlSvXj3DN8WllNmyFi9ejIiICDx79gxAcj09efIEQHIHqbW1NQBgy5Yt8Pf3R1BQkFYDnnt6emL//v2YO3cunJ2d4ebmhlq1amHmzJkIDg5GrVq10L9/f3h4eCA8PBznz5/H/v37ER4enmHZrVu3xpQpU+Dv74+6deviypUrWL16tUqnZMmSJWFjY4OlS5fCysoKFhYWqFWrltpxqtq0aYPGjRvj+++/x4MHD1C5cmXs3bsX27Ztw8iRI5UGNc+KLVu24Ntvv4W7uzvKlSun0uabNm2KwoUL62RZREREeZogIiIiiZeXlwCQ5l9qV69eFc2aNRPm5ubCxsZG9OjRQzx//lwpj5+fX7pl3r9/P92YAIghQ4ZotT6axLd3717RunVr4ezsLIyMjISVlZWoV6+eCAoKEklJSZleZnp1GBwcLOULCAgQ1atXF7a2tqJAgQLC2dlZdO3aVVy+fFnjZaVXt0FBQUp5ExMTRZEiRUS1atUyvU4paRr33bt3Ra9evUSJEiWEmZmZMDU1FeXLlxcBAQEiOjo6U8vMbFkuLi4a7W9BQUFq60odLy8vUb58eaW0mzdvioYNGwozMzMBQPj5+UnTXrx4IYYMGSKKFSsmjIyMhKOjo/D29hbLli2T8gQHBwsAYsOGDSrLe/funRgzZoxwcnISZmZmol69euLEiRPCy8tLeHl5KeXdtm2b8PDwEAUKFFBaHz8/P+Hi4qKUNyoqSowaNUra393d3cXs2bNV9vW02p2Li4vSeqoTEBCQbptP2Q6IiIjyM5kQmRipkYiIiIiIiIiISAc4phQREREREREREeU4jilFRESUx7x8+RKJiYlpTjc2NoadnZ1Ol6nJm+Xs7OxgbGys0+XmpPDwcMTHx6c53dDQEPb29jpdZmJiYoYDf1taWsLS0lKnyyUiIiLKDfj4HhERUR7j6uqKhw8fpjndy8sLhw4d0ukyV6xYoTQ4ujrBwcFo1KiRTpebkxo1aoTDhw+nOd3FxQUPHjzQ6TIfPHigdkDulAICAjB58mSdLpeIiIgoN2CnFBERUR5z7NgxxMXFpTnd1tYWnp6eOl1maGgorl27lm4eT09P2Nra6nS5OencuXN48+ZNmtPNzMxQr149nS7z3bt3OHr0aLp5SpQoofLGOSIiIqLPATuliIiIiIiIiIgox3GgcyIiIiIiIiIiynHslCIiIiIiIiIiohzHTikiIiIiIiIiIspx7JQiIiIiIiIiIqIcx04pIiIiIiIiIiLKceyUIiIiIiIiIiKiHMdOKSIiIiIiIiIiynHslCIiIiIiIiIiohzHTikiIiIiIiIiIspx7JQiIiIiIiIiIqIcx04pIiIiIiIiIiLKceyUIiIiIiIiIiKiHMdOKSIiIiIiIiIiynHslCIiIiIiIiIiohzHTikiIiIiIiIiIspx7JQiIiIiIiIiIqIcx04pIiIiIiIiIiLKceyUIiIiIiIiIiKiHMdOKSIiIiIiIiIiynHslCIiIiIiIiIiohzHTikiIiIiIiIiIspx7JQiIiIiIiIiIqIcx04pIiIiIiIiIiLKceyU+oxMnjwZMplMKc3V1RW9e/fO9mU/ePAAMpkMK1askNJ69+4NS0vLbF+2gkwmw+TJk3NseZS91O3P+qRuH8/JGBs1aoRGjRpJnw8dOgSZTIaNGzfmyPJ79+4NV1fXHFkW5Q6pvz8U+9yhQ4f0FlNqOfUdl1m5sa6IiEj3ZDIZhg4dmmG+FStWQCaT4cGDB9kfFFEeo7NOKUVDU/c3btw4XS2GcsC///6bazt3cnNsuVHqdmlqagpnZ2f4+vpi4cKFiIqK0mt8sbGxmDx5cr66cHv27BkmT56Mixcv6jsUFbk5tvwqZRs+evSoynQhBIoVKwaZTIbWrVvrIcL87ZdfflHqqCaiz9eVK1fQsWNHuLi4wNTUFEWKFEHTpk2xaNEipXzTp0/H1q1btV7O9evXMXnyZHZeUL48T6b8qYCuC5wyZQrc3NyU0ipUqKDrxZCGbt26BQODzPU9/vvvv/j5558z1fnj4uKCuLg4GBkZZTLCzEkvtri4OBQooPNd+rOgaJcfPnzA8+fPcejQIYwcORJz587F9u3bUalSJb3EFRsbi8DAQABQugsIACZMmJDrO7S1ifHZs2cIDAyEq6srqlSpovF8e/fuzWR0mZdebL/99huSkpKyPQZSz9TUFGvWrEH9+vWV0g8fPownT57AxMQk22No2LAh4uLiYGxsnO3Lyit++eUXFCpUSOVuLdYV0efl+PHjaNy4MYoXL47+/fvD0dERjx8/xsmTJ7FgwQIMGzZMyjt9+nR07NgR7dq102pZ169fR2BgIBo1asQ7lD8jPXv2RNeuXTP1fZ3eeTLR50TnV/AtWrRA9erVNcr77t07GBsbZ7rThDSX3RcqCQkJSEpKgrGxMUxNTbN1WRnR9/Jzs9Ttcvz48Th48CBat26NL774Ajdu3ICZmVmWl5Nyf8iqAgUK5PpOxpyIMTY2Fubm5nq/uM3uDmdKX8uWLbFhwwYsXLhQaZ9bs2YNPD098erVq2yPwcDA4LM+zgoh8O7dO50cCz/3uiLKb3744QdYW1vjzJkzsLGxUZoWFhamn6AoTzE0NIShoaG+wwAAxMTEwMLCQt9hEElyrDdIMb7C2rVrMWHCBBQpUgTm5uZ4+/YtAODUqVNo3rw5rK2tYW5uDi8vLxw7dkylnKNHj6JGjRowNTVFyZIl8euvv6qM66Ju7BcFdeMOPX36FH369EHhwoVhYmKC8uXLY/ny5WrjX79+PX744QcULVoUpqam8Pb2xt27d1WWc+rUKbRs2RK2trawsLBApUqVsGDBAgBAUFAQZDIZLly4oDLf9OnTYWhoiKdPn6Zbn+rqQZ3U4218+PABgYGBcHd3h6mpKQoWLIj69etj3759AJLHjfn555+lulL8AZ/qdc6cOZg/fz5KliwJExMTXL9+Pd06v3fvHnx9fWFhYQFnZ2dMmTIFQgiVuk19a2rqMtOLTZGWetteuHABLVq0gFwuh6WlJby9vXHy5EmlPIrHY44dO4bRo0fD3t4eFhYWaN++PV6+fKl+A3wGmjRpgokTJ+Lhw4f466+/pPTUYxcppB5TKL39IT4+HpMmTYKnpyesra1hYWGBBg0aIDg4WGl+e3t7AEBgYKC0PRXbUN14TQkJCZg6daq0LFdXV3z33Xd4//69Uj5XV1e0bt0aR48eRc2aNWFqaooSJUrgzz//1KhuIiIi0Lt3b1hbW8PGxgZ+fn6IiIhQyacuxn379qF+/fqwsbGBpaUlypQpg++++w5A8r5eo0YNAIC/v7+0zop9vFGjRqhQoQLOnTuHhg0bwtzcXJo3re2SmJiI7777Do6OjrCwsMAXX3yBx48fq9SHunF3UpaZUWzqxpSKiYnBmDFjUKxYMZiYmKBMmTKYM2eOUvsGPo23sHXrVlSoUEE6zu7evVslJlKvW7dueP36tXSsBoD4+Hhs3LgR3bt3VztPUlIS5s+fj/Lly8PU1BSFCxfGwIED8ebNG6V8QghMmzYNRYsWhbm5ORo3boxr166plKfuWP3ff/+hU6dOKF68OExMTFCsWDGMGjUKcXFxSvMqxhh8+vQp2rVrB0tLS9jb22Ps2LFITEzMcP01jTGtcd7UjeWhOE7s2bMH1atXh5mZmfRdGhQUhCZNmsDBwQEmJibw8PDAkiVLlMp0dXXFtWvXcPjwYam9pGxP6r7XNmzYAE9PT5iZmaFQoUL46quvVL7vs1pXRKR7ISEhKF++vEqHFAA4ODhI/5fJZIiJicHKlSul44Li+/fhw4f4+uuvUaZMGZiZmaFgwYLo1KmT0nFpxYoV6NSpEwCgcePGUhkpjyW7du1CgwYNYGFhASsrK7Rq1Urt8TC1jK4BFG7evImOHTvCzs4OpqamqF69OrZv365S3rVr19CkSROYmZmhaNGimDZtGpYvX65yrE1rzFd15yYREREYOXKkdF5RqlQpzJo1S+lO7ZTnn8uWLZPOCWvUqIEzZ86oLOfmzZvo3Lkz7O3tYWZmhjJlyuD7779XyqPJtWBGMjrHUfc9dPbsWfj6+qJQoUIwMzODm5sb+vTpI61neufJAHDw4EFpX7CxsUHbtm1x48YNpeUqvhevX7+O7t27w9bWFvXr19fJ9SiRruj8J/7IyEiVX2wLFSok/X/q1KkwNjbG2LFj8f79exgbG+PgwYNo0aIFPD09ERAQAAMDA+mE8L///kPNmjUBJD/L3axZM9jb22Py5MlISEhAQEAAChcurHW8L168QO3ataWLJnt7e+zatQt9+/bF27dvMXLkSKX8M2fOhIGBAcaOHYvIyEj8+OOP6NGjB06dOiXl2bdvH1q3bg0nJyeMGDECjo6OuHHjBnbu3IkRI0agY8eOGDJkCFavXo2qVasqlb969Wo0atQIRYoUSTPmrNTD5MmTMWPGDPTr1w81a9bE27dvcfbsWZw/fx5NmzbFwIED8ezZM+zbtw+rVq1SW0ZQUBDevXuHAQMGwMTEBHZ2dmk+1pOYmIjmzZujdu3a+PHHH7F7924EBAQgISEBU6ZMyTDelDSJLaVr166hQYMGkMvl+Pbbb2FkZIRff/0VjRo1wuHDh1GrVi2l/MOGDYOtrS0CAgLw4MEDzJ8/H0OHDsW6desyFWde0rNnT3z33XfYu3cv+vfvr1UZ6vaHt2/f4vfff0e3bt3Qv39/REVF4Y8//oCvry9Onz6NKlWqwN7eHkuWLMHgwYPRvn17fPnllwCQ7qOE/fr1w8qVK9GxY0eMGTMGp06dwowZM3Djxg1s2bJFKe/du3fRsWNH9O3bF35+fli+fDl69+4NT09PlC9fPs1lCCHQtm1bHD16FIMGDUK5cuWwZcsW+Pn5ZVgX165dQ+vWrVGpUiVMmTIFJiYmuHv3rtTBXq5cOUyZMgWTJk3CgAED0KBBAwBA3bp1pTJev36NFi1aoGvXrvjqq68ybNc//PADZDIZ/ve//yEsLAzz58+Hj48PLl68mKk7PjSJLSUhBL744gsEBwejb9++qFKlCvbs2YNvvvkGT58+xbx585TyHz16FJs3b8bXX38NKysrLFy4EB06dMCjR49QsGBBjePMr1xdXVGnTh38/fffaNGiBYDkC5PIyEh07doVCxcuVJln4MCBWLFiBfz9/TF8+HDcv38fixcvxoULF3Ds2DHp7rdJkyZh2rRpaNmyJVq2bInz58+jWbNmiI+PzzCuDRs2IDY2FoMHD0bBggVx+vRpLFq0CE+ePMGGDRuU8iYmJsLX1xe1atXCnDlzsH//fvz0008oWbIkBg8enO5yshJjem7duoVu3bph4MCB6N+/P8qUKQMAWLJkCcqXL48vvvgCBQoUwI4dO/D1118jKSkJQ4YMAQDMnz8fw4YNg6WlpXSBk157VWyLGjVqYMaMGXjx4gUWLFiAY8eO4cKFC0oXu1mpKyLSPRcXF5w4cQJXr15Nd1iSVatWSefYAwYMAACULFkSAHDmzBkcP34cXbt2RdGiRfHgwQMsWbIEjRo1wvXr12Fubo6GDRti+PDhWLhwIb777juUK1cOAKR/V61aBT8/P/j6+mLWrFmIjY3FkiVLUL9+fVy4cCHdx/0yugYAks9j6tWrhyJFimDcuHGwsLDA+vXr0a5dO2zatAnt27cHADx//hyNGzdGQkKClG/ZsmVZutM0NjYWXl5eePr0KQYOHIjixYvj+PHjGD9+PEJDQzF//nyl/GvWrEFUVBQGDhwImUyGH3/8EV9++SXu3bsnfb9dvnwZDRo0gJGREQYMGABXV1eEhIRgx44d+OGHHwBk/lpQHW3OccLCwqTruXHjxsHGxgYPHjzA5s2bASDD8+T9+/ejRYsWKFGiBCZPnoy4uDgsWrQI9erVw/nz51X2hU6dOsHd3R3Tp0+HECLL16NEOiV0JCgoSABQ+yeEEMHBwQKAKFGihIiNjZXmS0pKEu7u7sLX11ckJSVJ6bGxscLNzU00bdpUSmvXrp0wNTUVDx8+lNKuX78uDA0NRcpVuX//vgAggoKCVOIEIAICAqTPffv2FU5OTuLVq1dK+bp27Sqsra2lWBXxlytXTrx//17Kt2DBAgFAXLlyRQghREJCgnBzcxMuLi7izZs3SmWmXL9u3boJZ2dnkZiYKKWdP38+zbhT0rQehBDCxcVF+Pn5SZ8rV64sWrVqlW75Q4YMUSlHiE/1KpfLRVhYmNppKWP38/MTAMSwYcOktKSkJNGqVSthbGwsXr58KYT4VLfBwcEZlplWbEKobtt27doJY2NjERISIqU9e/ZMWFlZiYYNG0ppin3Xx8dHaRuNGjVKGBoaioiICLXLywsU63bmzJk081hbW4uqVatKn728vISXl5dKPj8/P+Hi4iJ9Tm9/SEhIUGonQgjx5s0bUbhwYdGnTx8p7eXLlyrbTSEgIEBpW1+8eFEAEP369VPKN3bsWAFAHDx4UEpzcXERAMSRI0ektLCwMGFiYiLGjBmjviI+2rp1qwAgfvzxR6X1adCggcr+mDrGefPmCQDSvq3OmTNn0mznXl5eAoBYunSp2mkpt4ui3RQpUkS8fftWSl+/fr0AIBYsWCClpT4OpFVmerGl3v6Kepo2bZpSvo4dOwqZTCbu3r0rpQEQxsbGSmmXLl0SAMSiRYtUlkWfpGzDixcvFlZWVtL3UqdOnUTjxo2FEMnbOOWx/b///hMAxOrVq5XK2717t1J6WFiYMDY2Fq1atVI6/n333XcCgNJ+o+5YnfL7XGHGjBlCJpMpfUcpvg+mTJmilLdq1arC09Mz3TrITIyp26SCoh7v378vpSmOE7t371bJr269fH19RYkSJZTSypcvr/Z4mbqu4uPjhYODg6hQoYKIi4uT8u3cuVMAEJMmTZLSslJXRJQ99u7dKwwNDYWhoaGoU6eO+Pbbb8WePXtEfHy8Sl4LCwu137nqjisnTpwQAMSff/4ppW3YsEHteXFUVJSwsbER/fv3V0p//vy5sLa2VklPTZNrAG9vb1GxYkXx7t07KS0pKUnUrVtXuLu7S2kjR44UAMSpU6ektLCwMGFtba1yrE3rPC/1ucnUqVOFhYWFuH37tlK+cePGCUNDQ/Ho0SMhxKfzz4IFC4rw8HAp37Zt2wQAsWPHDimtYcOGwsrKSun7SLFOCppeC6ZF03Oc1N9DW7ZsyfAcPb3z5CpVqggHBwfx+vVrpeUaGBiIXr16SWmK78Vu3bqplJGV61EiXdL543s///wz9u3bp/SXkp+fn1Iv+sWLF3Hnzh10794dr1+/xqtXr/Dq1SvExMTA29sbR44cQVJSEhITE7Fnzx60a9cOxYsXl+YvV64cfH19tYpVCIFNmzahTZs2EEJIy3716hV8fX0RGRmJ8+fPK83j7++vNLaL4m6Ce/fuAUh+XOz+/fsYOXKkyi2+KR8p6NWrF549e6b0ONPq1athZmaGDh06pBlzVuvBxsYG165dw507dzLMm5YOHTpIt5NqIuVrUhW/QsTHx2P//v1ax5CRxMRE7N27F+3atUOJEiWkdCcnJ3Tv3h1Hjx6VHh1VGDBggNI2atCgARITE/Hw4cNsizM3sLS0zNJb+NTtD4aGhlI7SUpKQnh4OBISElC9enWVNqWpf//9FwAwevRopfQxY8YAAP755x+ldA8PD6l9Asm/OJUpU0Zqq+ktp0CBAkp3IxgaGioNYpoWRZvftm2b1oOCm5iYwN/fX+P8vXr1gpWVlfS5Y8eOcHJykuoru/z7778wNDTE8OHDldLHjBkDIQR27dqllO7j4yP9Wgwk/9Inl8sz3B70SefOnREXF4edO3ciKioKO3fuTPPRvQ0bNsDa2hpNmzZV+m7z9PSEpaWl9N2zf/9+xMfHY9iwYUrHP01+GQag9H0eExODV69eoW7duhBCqH0kYNCgQUqfGzRokOE+kNUY0+Pm5qb2uzPleinuAPfy8sK9e/cQGRmZ6eWcPXsWYWFh+Prrr5XGmmrVqhXKli2rcvwCtKsrIsoeTZs2xYkTJ/DFF1/g0qVL+PHHH+Hr64siRYqofbRNnZTHlQ8fPuD169coVaoUbGxsNDo32rdvHyIiItCtWzel47qhoSFq1aqldE2hTkbXAOHh4Th48CA6d+6MqKgoqfzXr1/D19cXd+7ckR7n+vfff1G7dm3paRYg+TyrR48emlSFWhs2bECDBg1ga2urtH4+Pj5ITEzEkSNHlPJ36dIFtra20ufU12QvX77EkSNH0KdPH6VrJuDTNZk214LqaHOOozhn3LlzJz58+JDhMlIKDQ3FxYsX0bt3b9jZ2Sktt2nTpmrPAVN/pwDaX48S6ZrOH9+rWbNmugOdp34zn+LAmN6jMZGRkXj//j3i4uLg7u6uMr1MmTJaXYC9fPkSERERWLZsGZYtW6Y2T+rBC1Mf1BQHQ8UYHSEhIQAyfuNg06ZN4eTkhNWrV8Pb2xtJSUn4+++/0bZtW6ULTHUxZ6UepkyZgrZt26J06dKoUKECmjdvjp49e2bq7Wupt2F6DAwMlDqFAKB06dIAkK2vun358iViY2OlRzFSKleuHJKSkvD48WOlx7gy2rafq+joaKXxEDIrrf1h5cqV+Omnn3Dz5k2lL9vM7D8pPXz4EAYGBihVqpRSuqOjI2xsbFQ6D1NvTyB5m2a0PR8+fAgnJydYWloqpavbl1Lr0qULfv/9d/Tr1w/jxo2Dt7c3vvzyS3Ts2FHjFzoUKVIkU4Oapz4WyGQylCpVKttfJf3w4UM4OzurHK8UjxjoanvQJ/b29vDx8cGaNWsQGxuLxMREdOzYUW3eO3fuIDIyMs22rfhuU2yn1PuRvb290sl+Wh49eoRJkyZh+/btKtsydeeNqampSge2pm0yKzGmJ63j0bFjxxAQEIATJ04gNjZWaVpkZCSsra0ztRzFOqg7jpQtWxZHjx5VStO2rogo+9SoUQObN29GfHw8Ll26hC1btmDevHno2LEjLl68CA8Pj3Tnj4uLw4wZMxAUFISnT58qjb+oSWe34pqpSZMmaqfL5fJ058/oGuDu3bsQQmDixImYOHGi2jLCwsJQpEgRPHz4UGUYDECzc6W03LlzB5cvX07zh+/MXpMpOoTSuybT5lpQHW3Ocby8vNChQwcEBgZi3rx5aNSoEdq1a4fu3btn+KKq9L5TypUrhz179qgMZq7u+07b61EiXcvxV1ulftZYcTfB7Nmz03w9uqWlpcpAxulRN8gpAJUBQhXL/uqrr9LsFEvdWZPWWxNSfrFowtDQEN27d8dvv/2GX375BceOHcOzZ8/w1VdfZaqczGrYsCFCQkKwbds27N27F7///jvmzZuHpUuXol+/fhqVoYs3E6Wk6fbKbrratnnJkydPEBkZqdTRI5PJ1K5zWttD3f7w119/oXfv3mjXrh2++eYbODg4wNDQEDNmzJA6brWV1v6Smj62p5mZGY4cOYLg4GD8888/2L17N9atW4cmTZpg7969Gr11RdftC0i/jeXUm2DyY/vKDt27d0f//v3x/PlztGjRQu2gu0Dy95uDgwNWr16tdnpm7nZNS2JiIpo2bYrw8HD873//Q9myZWFhYYGnT5+id+/eKncL5sS+ltnvE3XtLSQkBN7e3ihbtizmzp2LYsWKwdjYGP/++y/mzZun9V2QmZFb3tBERKqMjY1Ro0YN1KhRA6VLl4a/vz82bNiAgICAdOcbNmwYgoKCMHLkSNSpUwfW1taQyWTo2rWrRscVRZ5Vq1bB0dFRZXpGbwPO6BpAUf7YsWPTfPoi9Q+DWaHuuqxp06b49ttv1eZX/KitoIvzCm2uBdXRJhaZTIaNGzfi5MmT2LFjB/bs2YM+ffrgp59+wsmTJ1V+HM0qdd93+roeJUpN7+9bV9zqKJfL4ePjk2Y+xRsT1N1yeuvWLaXPip7y1G/LSv3Lvb29PaysrJCYmJjusjNDsT5Xr17NsMxevXrhp59+wo4dO7Br1y7Y29tn+AheZuohLXZ2dvD394e/vz+io6PRsGFDTJ48WeqU0vSiXxNJSUm4d++e0hfJ7du3AUAagE/T7ZWZ2Ozt7WFubq62Tm7evAkDAwMUK1ZMo7I+Z4oB41Pud7a2tmpvN87MY4wbN25EiRIlsHnzZqVtlvqELTP7mouLC5KSknDnzh3pbhwgeYDKiIgIuLi4aFxWRss5cOAAoqOjlU4ING1fBgYG8Pb2hre3N+bOnYvp06fj+++/R3BwMHx8fHTavgCoHAuEELh7967SSZStra3atwc+fPhQ6U7GzG6P/fv3IyoqSunXtJs3b0rTSffat2+PgQMH4uTJk+m+hKFkyZLYv38/6tWrl25Hp2I73blzR2lfePnyZYZ35Vy5cgW3b9/GypUr0atXLyk99WP7WZWZGFN+n6TssMvM8WvHjh14//49tm/frvTrt7pHYzRtM4p1uHXrlspdDrdu3WJ7IcqjFE+HhIaGSmlpHRc2btwIPz8//PTTT1Lau3fvVL6f05pfcY3h4OCg9XVLetcAiuOrkZFRhuW7uLhofC2i7hwkPj5eqc6A5PWLjo7W2TWZYn2uXr2aZp7suBbMrNq1a6N27dr44YcfsGbNGvTo0QNr165Fv3790twXUn6npHbz5k0UKlRI6S6p9GhzPUqkazofUyqzPD09UbJkScyZMwfR0dEq01++fAkguSfX19cXW7duxaNHj6TpN27cwJ49e5TmkcvlKFSokMqzx7/88ovSZ0NDQ3To0AGbNm1Se8BSLDszqlWrBjc3N8yfP1/lAJy6t7xSpUqoVKkSfv/9d2zatAldu3bN8FeOzNSDOq9fv1b6bGlpiVKlSindiaY4iKm7iNXG4sWLpf8LIbB48WIYGRnB29sbQPKB1dDQMMPtlZnYDA0N0axZM2zbtk3pMaYXL15gzZo1qF+/foa3OX/uDh48iKlTp8LNzU1pDICSJUvi5s2bSvv/pUuXpDfIaULxi1HKff7UqVM4ceKEUj5zc3MAmu1rLVu2BACVt6/MnTsXQPLYLLrQsmVLJCQkKL3+PTExEYsWLcpw3vDwcJU0xR2gijam6/b1559/Ko0JtnHjRoSGhkpvaAOSt+nJkyeV3lS2c+dOPH78WKmszMTWsmVLJCYmKrVvAJg3bx5kMpnS8kl3LC0tsWTJEkyePBlt2rRJM1/nzp2RmJiIqVOnqkxLSEiQtrGPjw+MjIywaNEipfaaup2po66dCyGwYMECDddGM5mJUXHRlvL7RPF6dk2pW6/IyEgEBQWp5LWwsNCovVSvXh0ODg5YunSp0vftrl27cOPGDZ0dv4goewQHB6u960UxbEbKx6jSOi4YGhqqlLFo0SKVO4bS+i729fWFXC7H9OnT1Y5BlNF1S0bXAA4ODmjUqBF+/fVXlQ6j1OW3bNkSJ0+exOnTp5Wmq7s7t2TJkirn+MuWLVNZ786dO+PEiRNqr2ciIiKQkJCQ7vqlZm9vj4YNG2L58uVK10zAp+N7dlwLaurNmzcq+0Pqc8a0zpOdnJxQpUoVrFy5Umna1atXsXfvXumcWRPaXI8S6Zre9zgDAwP8/vvvaNGiBcqXLw9/f38UKVIET58+RXBwMORyOXbs2AEACAwMxO7du9GgQQN8/fXXSEhIwKJFi1C+fHlcvnxZqdx+/fph5syZ6NevH6pXr44jR45Id+ikNHPmTAQHB6NWrVro378/PDw8EB4ejvPnz2P//v1qLzIzWp8lS5agTZs2qFKlCvz9/eHk5ISbN2/i2rVrKgfaXr16YezYsQCg8a2SmamH1Dw8PNCoUSN4enrCzs4OZ8+excaNG5UGI/f09AQADB8+HL6+vjA0NETXrl0zUw0SU1NT7N69G35+fqhVqxZ27dqFf/75B9999530+Ii1tTU6deqERYsWQSaToWTJkti5c6faZ7gzE9u0adOwb98+1K9fH19//TUKFCiAX3/9Fe/fv8ePP/6o1frkVbt27cLNmzeRkJCAFy9e4ODBg9i3bx9cXFywfft2pYF3+/Tpg7lz58LX1xd9+/ZFWFgYli5divLly6sMDp+W1q1bY/PmzWjfvj1atWqF+/fvY+nSpfDw8FDqfDYzM4OHhwfWrVuH0qVLw87ODhUqVFD7/H/lypXh5+eHZcuWISIiAl5eXjh9+jRWrlyJdu3aoXHjxlmvKABt2rRBvXr1MG7cODx48AAeHh7YvHmzRuM9TJkyBUeOHEGrVq3g4uKCsLAw/PLLLyhatCjq168PIPnkzMbGBkuXLoWVlRUsLCxQq1YtrcfasrOzQ/369eHv748XL15g/vz5KFWqFPr37y/l6devHzZu3IjmzZujc+fOCAkJwV9//aU0KGdmY2vTpg0aN26M77//Hg8ePEDlypWxd+9ebNu2DSNHjlQpm3QnvTEYFby8vDBw4EDMmDEDFy9eRLNmzWBkZIQ7d+5gw4YNWLBgATp27Ah7e3uMHTsWM2bMQOvWrdGyZUtcuHABu3btQqFChdJdRtmyZVGyZEmMHTsWT58+hVwux6ZNm3Q+7lFmYmzWrBmKFy+Ovn374ptvvoGhoSGWL18Oe3t7lYuStDRr1gzGxsZo06YNBg4ciOjoaPz2229wcHBQuVDz9PTEkiVLMG3aNJQqVQoODg5qx3sxMjLCrFmz4O/vDy8vL3Tr1g0vXrzAggUL4OrqilGjRmlfQUSU7YYNG4bY2Fi0b98eZcuWRXx8PI4fP45169bB1dVV6QUlnp6e2L9/P+bOnQtnZ2e4ubmhVq1aaN26NVatWgVra2t4eHjgxIkT2L9/PwoWLKi0rCpVqsDQ0BCzZs1CZGQkTExM0KRJEzg4OGDJkiXo2bMnqlWrhq5du0rHtn/++Qf16tVT+aEoJU2uAX7++WfUr18fFStWRP/+/VGiRAm8ePECJ06cwJMnT3Dp0iUAwLfffotVq1ahefPmGDFiBCwsLLBs2TK4uLiovSYbNGgQOnTogKZNm+LSpUvYs2ePyvH7m2++wfbt29G6dWv07t0bnp6eiImJwZUrV7Bx40Y8ePAgw++l1BYuXIj69eujWrVqGDBgANzc3PDgwQP8888/uHjxIgDdXwtqauXKlfjll1/Qvn17lCxZElFRUfjtt98gl8ulTqX0zpNnz56NFi1aoE6dOujbty/i4uKwaNEiWFtbY/LkyZmKRZvrUSKd0tVr/DJ69bzi9cgbNmxQO/3ChQviyy+/FAULFhQmJibCxcVFdO7cWRw4cEAp3+HDh4Wnp6cwNjYWJUqUEEuXLlX7CujY2FjRt29fYW1tLaysrETnzp1FWFiY2tdqvnjxQgwZMkQUK1ZMGBkZCUdHR+Ht7S2WLVuWYfyK15Kmfm3m0aNHRdOmTYWVlZWwsLAQlSpVUvvq89DQUGFoaChKly6ttl7Somk9pH7d6rRp00TNmjWFjY2NMDMzE2XLlhU//PCD0ittExISxLBhw4S9vb2QyWRSmYp1nT17tko86urBz89PWFhYiJCQENGsWTNhbm4uChcuLAICApRePSpE8itPO3ToIMzNzYWtra0YOHCguHr1qkqZacUmhPpXzp4/f174+voKS0tLYW5uLho3biyOHz+ulCetfVfd68/zGsW6Kf6MjY2Fo6OjaNq0qViwYIF4+/at2vn++usvUaJECWFsbCyqVKki9uzZI/z8/ISLi4uUJ739ISkpSUyfPl24uLgIExMTUbVqVbFz506VMoQQ4vjx49K+nHIbqtufP3z4IAIDA4Wbm5swMjISxYoVE+PHj1d6dbEQyfu9utcee3l5qX19e2qvX78WPXv2FHK5XFhbW4uePXuKCxcuqOyPqWM8cOCAaNu2rXB2dhbGxsbC2dlZdOvWTeX1xtu2bRMeHh6iQIECSmV6eXmJ8uXLq40pdeyK/fPvv/8W48ePFw4ODsLMzEy0atVK5dXHQgjx008/iSJFiggTExNRr149cfbsWbX1kVZs6rZdVFSUGDVqlHB2dhZGRkbC3d1dzJ49W+lVy0Ikt80hQ4aoxJT6+ESqMvpuVUhrn1+2bJnw9PQUZmZmwsrKSlSsWFF8++234tmzZ1KexMREERgYKJycnISZmZlo1KiRuHr1qsr2UXdMvH79uvDx8RGWlpaiUKFCon///tKrsNV9H6Smrp2ro2mMQghx7tw5UatWLWFsbCyKFy8u5s6dq/Iq7vTqTAghtm/fLipVqiRMTU2Fq6urmDVrlli+fLlKGc+fPxetWrUSVlZWAoDUntL6/li3bp2oWrWqMDExEXZ2dqJHjx7iyZMnSnmyWldEpHu7du0Sffr0EWXLlhWWlpbC2NhYlCpVSgwbNky8ePFCKe/NmzdFw4YNhZmZmQAgHaPevHkj/P39RaFChYSlpaXw9fUVN2/eVHsc++2330SJEiWEoaGhyrEkODhY+Pr6Cmtra2FqaipKliwpevfuLc6ePZvuOmhyDSCEECEhIaJXr17C0dFRGBkZiSJFiojWrVuLjRs3KuW7fPmy8PLyEqampqJIkSJi6tSp4o8//lA5TiYmJor//e9/olChQsLc3Fz4+vqKu3fvql3vqKgoMX78eFGqVClhbGwsChUqJOrWrSvmzJkjxZne+ae6a4GrV6+K9u3bCxsbG2FqairKlCkjJk6cqJRHk2vBtGh6jpP6e+j8+fOiW7duonjx4sLExEQ4ODiI1q1bq2zHtM6ThRBi//79ol69esLMzEzI5XLRpk0bcf36daX5Fd8dL1++THMdtL0eJdIVmRB5f5TZyZMnIzAwME8OmPvq1Ss4OTlh0qRJab7pgoiIiIiIKDdbsWIF/P39cf/+fWnsWMr9eD1K+qb3MaXyuxUrViAxMRE9e/bUdyhERERERESUj/B6lPRN72NK5VcHDx7E9evX8cMPP6Bdu3b8NYGIiIiIiIhyBK9HKbdgp5SeTJkyBcePH0e9evU0eqsXERERERERkS7wepRyi89iTCkiIiIiIiIiIspbOKYUERERERERERHlOHZKERERERERERFRjmOnFBERERERERER5Ti9DHSelJSEZ8+ewcrKCjKZTB8hUD4mhEBUVBScnZ1hYJB/+mXZ7kjf2PbY9kg/2PbY9oiIiHKapucfeumUevbsGYoVK6aPRRNJHj9+jKJFi+o7jBzDdke5BdsekX6w7REREVFOy+j8Qy+dUlZWVgCSg5PL5foIgfKxt2/folixYtJ+mF+w3ZG+se2x7ZF+5Pe2N/lxd7wrtgZ/Rp6Xpp1974mFJsMx07r4x5TGyjMvL4nHg6whvwxcLZucZAYgDEDz5ZFo32ENTqEW6uM/aZaf7Eeg7IMQ7LBoDQBoYn0CkedtcK5kOXi2uQEs+1SWojwAiPv4b8jHf+sAOBbpi95r1mNc90nojA1Yb/1Mmm98Q8D6yB30jtyEbYltAQAj7MrBNLI3mmEvqrW9gYhL1rB5cwdY4qC0Wiu6d8ZN6z2fytoAWHc6Jn2OnFcPKApgETBix0ypfIUHYeWAlgBmAZgJYDWwuizwPHIwpvSZiRHLZ2DB5PHAvHmInDkZAGAdHAnsCcN5uKOabyQiZ1gnF7YXQDugvdMabFnSHQBQe/BB3FjmiWPfWCstt8KK5H97t1+CLdYP8dh4JgDA8IkMzk4RmBQ+DnWtl6C8MVCsdyQAYNzsSfg1cRB2GbaAP5ZjtXUTOIYl3zHnvCkCAHBncPJyfohMLm/BknGAx6fl3vByRbm9DxDWCbgaWRtR1ifRa3Zy+Xe+scYJAL3GJX+OPG4NjAVQGjhRFkiM9JTKqd/xnNL6hOwDqjlEYsSdGVhg7QEcaItx3tYYYgzILwPWZX/Cmsh/cNv6oDTPqHkAagPoAay+B0yLPI+4RDOYGcYhI9MxHgDQfvmnbS+mADZjIuE8LAQ3PKol1+/1JRmWZY1IrLCO+PipLyJvuqebP6Qs4Bgmg0W4gHXZn3AychFqW89RyhO5vSOsLSKxoHp/jLBuBIzroVLOsZnJ26oeIhA5zyY5llERiLz58f9TIhE5yVplPuspkcAagUhPm0+Jq5P/uVoWaPYsBM/alExO6wHgKwCV1K83AOAPILKSdfJ+MhhY/VT9en+N82rTt0R+jfa19wA35mFL5G4AQPvaexBZNDn2LRt90du6KiIPzFSd+TGwureaZZWLxOQb1igBwCqyNrahHVZYN8OfqIa2Bz7lG1F9JlZYNwMAyO444oRDXdS2Xi1Nnxn5G8aHTYefw58AgA0xnRDjXBContxueh9YghV9Bkv5I/2sYX0rEse+sUY9RAAAlkT6J8+LTgCAg9+0wp/LPm2XXssjPwU0+1M5tQcfxI3ankAQ0LncCqwvnryij+OsIR/2MX/F5H+EV/K/9+ycEQontLLuDqAxsLwk2ndYg/9hllL9JFo/wPrIcQCAKR4zYV0hEufXl0O14jcQecoa1iMiEWlmjXIrk7fZs6olgbZAZOUU+1NtACc/fbTuvTE5OdIQe/zaw3rrRkSO6Ygmk/7BOeszSN6Z/kjO7DAev4RZ4+uPx46TAyqj9jeXlGJU2V3OzgOqj0LkrE8xrPZO3tadT67A+tq9cf6GNaqNiwRmJn97WDyzxjDnkpj5cVv0jlwKAB+3efDHlSj/sbSXOAZ31MMx4EAFbPS2Rsdxn7bNuJnWGL8dsCn3DBHTnREz6+Oxc24EHs9VbWepFYvfCItnVRHj/BuAmRmef+ilU0pxC7VcLucJOulNfruVn+2Ocgu2PSL9yK9tz1RuDAHAQG4pTZO/l8HUxASfuoYslWc2l0MuA+RWn6aYAYj5OM1IbgYDWMJYmh+QywCZ3AqWFoaKFMgtAUu5IeQFAFgpL0UxpyK3+cd/rQCYy40AMzlM5SawhAFMUswnL5Ccy1huClli8om+6cf1tIQBUCA5dsAKMFM+5pjLjZTLMlded7nZx0AKQKl8aXrcx4A/5oFV8nqYyk0AIzlM5KaAiRyAKeSmH2cykgOIS16KUXKdSEFbAQXk5lJeQ7klYCZPvTU+xgkYyc0AmHxcP8AwudJhKjeBBZK3AYyT19lUbgJZohUsDQ1gCEtYApArZvxYL4q1M/4YgNwUgMWn5VrKDSA3T+44tJAXQGKqec2TF5Q8b4GP81ol/5MoN5TKkRspr48lABh8rC+YAxZymHyMX24FAGYwlxeAaYp55GYfZzRMrnMDuSVkieYwMDRERsxh9KmMj4QsOXYDuSXkH5+qMUqZIQ3GeA9Ie5HVx3jTpqh3iw8CgBks5Qb4tLcnk1sAsJDDTG4MwEyq09TlfMydYj3kn5ZvLFcfi7EcgPjYbqSwpTJlciupfaJA8uJT7gMpovxYXor9xABIu8ZS78XJLOQFAMPkNmKhCMpQLu0j5nIjACbJdZKaeRrLM5Tj454EC3kBGMMUgCXMAaVykvfz5LhkVvKP2+JTnKZyY8ji5FJ7kBlaJa93Admn+Y0+bRu5BVK01+R0s48rUkCxjY3lylvbPMW2/bjryk0/tn1DOWAJGMvNANnHdiUD5Ird7WNB4mMRlnIDWMAQybViKR2bLVMNm50IfGxrSN7XjT6uu+zjPmOUXP/Sd4SBHDD5dNwBkKLBSyuSvJ7yAh+3nTnkJoCh3ALSwU3RTgzkydvt47HDUm4gHackKr0ypsnH8hTbzwwADOXJ9WP4sd5N5ZC2qdzq4xKTyzaWDsKWn+pIsR/j3ce5LAELudKxDB8jl1sk7ydy44/H2o955BqdSphDJrf6WBcZn3/kn4EFiIiIiIiIiIgo12CnFBERERERERER5Ti9PL6nUCFgDwxMzPFgZit9hkGUb7mO+0fpM9siEVHekpgkcPp+OMKi3sHByhQ13exgaJC/HtMjIiKivEuvnVJEREREpJ3dV0MRuOM6QiPfSWlO1qYIaOOB5hWc9BgZERERkWbYKUWUzyjuUCQiorxr99VQDP7rPESq9OeR7zD4r/NY8lU1dkwRERFRrscxpYiIiIjykMQkgcAd11U6pABIaYE7riMxSV0OIiIiotyDd0oRkYRjTBER5X6n74crPbKXmgAQGvkOp++Ho07JgjkXGBEREVEm8U4pIiIiojwkLCrtDilt8hERERHpCzuliIiIiPIQBytTneYjIiIi0hd2ShERERHlITXd7OBkbQpZGtNlSH4LX003u5wMi4iIiCjTOKYUEaWJY0wREeU+hgYyBLTxwOC/zkMGKA14ruioCmjjAUODtLqtiIiIiHIH3ilFRERElMc0r+CEJV9Vg6O18iN6jtamWPJVNTSv4KSnyIiIiIg0lyvulFLcjcG7MIiIiIg007yCE5p6OOL0/XCERb2Dg1XyI3u8Q4qIiIjyilzRKUVEREREmWdoIEOdkgX1HQYRERGRVvj4HhERERERERER5Th2ShERERERERERUY5jpxQREREREREREeU4dkoREREREREREVGOY6cUERERERERERHlOHZKERERERERERFRjmOnFBERERERERER5bgC+g6AiPIO13H/KH1+MLOVniIhIiIiIiKivE6rO6ViYmJ0HQcRaYBtj4iI8pNq1apBJpNBJpPB0NAQEydO1HdIREREpENadUoVLlwYffr0wdGjR3UdDxGlg22PiIjyixYtWuDChQuoX78+Fi1aBCsrK0ybNg1HjhzRd2hERESkI1p1Sv31118IDw9HkyZNULp0acycORPPnj3TdWxElArbHhER5Rd79+6FnZ0d/vvvPwwdOhRhYWEAgH79+uk5MiIiItIVrTql2rVrh61bt+Lp06cYNGgQ1qxZAxcXF7Ru3RqbN29GQkKCruMkIrDtERFR/hAeHo6kpCQ0atRISjM2NoalpSUePXqkv8CIiIhIp7L09j17e3uMHj0aly9fxty5c7F//3507NgRzs7OmDRpEmJjY3UVJxGlwLZHRESfszNnzgAASpUqpZRuaWmJDx8+qJ3n5cuXuHHjhvR369atbI+TiIiIsiZLb9978eIFVq5ciRUrVuDhw4fo2LEj+vbtiydPnmDWrFk4efIk9u7dq6tYieij3NL2+DY+IiJKLS4uDkIImJubAwAePnyILVu2wMPDA82aNcu25daoUQMPHz7MtvKJiIhI97TqlNq8eTOCgoKwZ88eeHh44Ouvv8ZXX30FGxsbKU/dunVRrlw5XcVJRGDbIyKi3K9t27b48ssvMWjQIERERKBWrVowMjLCq1evMHfuXAwePDjDMmrUqAEAuHv3rlJ6dHQ0jIyM1M5z5swZvHr1SilvzZo1s7AmRERElN206pTy9/dH165dcezYMemkITVnZ2d8//33WQqOiJSx7RERUW53/vx5zJs3DwCwceNGFC5cGBcuXMCmTZswadIkjTql7OzsYGBggMOHD0tp8fHxiI6Ohru7u9p57O3tYW9vL31++/ZtFteEiIiIsptWnVKhoaHSLdlpMTMzQ0BAgFZBEZF6bHtEmXPnzh0EBwcjLCwMSUlJStMmTZqkp6iIPm+xsbGwsrICkPwGvS+//BIGBgaoXbt2ph6va9asGXbv3g0vLy907doV3333HQBg2bJl2RI3ERER5TytOqWsrKwQGhoKBwcHpfTXr1/DwcEBiYmJOgmOiJSx7RFp7rfffsPgwYNRqFAhODo6QiaTSdNkMhk7pYiySalSpbB161a0b98ee/bswahRowAAYWFhkMvlGpeza9cuVK1aFUeOHMGRI0dgYGCA7777TumNfERERJS3adUpJYRQm/7+/XsYGxtnKSAiShvbHpHmpk2bhh9++AH/+9//9B0KUb4yadIkdO/eHaNGjYK3tzfq1KkDIPmuqapVq2aqrAsXLmRHiERERJRLZKpTauHChQCSf2H+/fffYWlpKU1LTEzEkSNHULZsWd1GSERse0RaePPmDTp16qTvMIjynY4dO6J+/foIDQ1F5cqVpXRvb2+0b99ej5ERERFRbpOpTinFoJVCCCxduhSGhobSNGNjY7i6umLp0qW6jZCI8kzbcx33j9LnBzNb6SkSIqBTp07Yu3cvBg0apO9QiPIdR0dHODo6KqXxTXhERESUWqY6pe7fvw8AaNy4MTZv3gxbW9tsCYqIlLHtEWVeqVKlMHHiRJw8eRIVK1ZUeY388OHD9RQZ0ectJiYGM2fOxIEDB9S+ZODevXt6ioyIiIhyG63GlAoODtZ1HESkAbY9Is0tW7YMlpaWOHz4sNJr5YHkR2HZKUWUPfr164fDhw+jZ8+ecHJyUnrJABEREVFKGndKjR49GlOnToWFhQVGjx6dbt65c+dmOTAiSsa2R6QdxR2GRJSzdu3ahX/++Qf16tXTdyhERESUy2ncKXXhwgV8+PBB+n9a+GsYkW6x7RFlneLNlWwnRNnP1tYWdnZ2+g6DiIiI8gCNO6VSPjbER4iIcg7bHpH2/vzzT8yePRt37twBAJQuXRrffPMNevbsqefIiD5fU6dOxaRJk7By5UqYm5vrOxwiIiLKxbQaUyq7pHxzF9/aRUREWTF37lxMnDgRQ4cOlR4jOnr0KAYNGoRXr15h1KhReo6Q6PP0008/ISQkBIULF4arq6vKSwbOnz+vp8iIiIgot9G4U+rLL7/UuNDNmzdrFQwRqcrLbS9lRzPAzmbKWYsWLcKSJUvQq1cvKe2LL75A+fLlMXnyZHZKEWWTdu3a6TsEIiIiyiM07pSytrbOzjiIKA1se0TaCQ0NRd26dVXS69ati9DQUD1ERJQ/BAQE6DsEIiIiyiM07pQKCgrKzjiIKA1se0TaKVWqFNavX4/vvvtOKX3dunVwd3fXU1RE+ce5c+dw48YNAED58uVRtWpVPUdEREREuU2uGlOKiIhIVwIDA9GlSxccOXJEGlPq2LFjOHDgANavX6/n6Ig+X2FhYejatSsOHToEGxsbAEBERAQaN26MtWvXwt7eXr8BEhERUa6hcadUtWrVcODAAdja2qJq1arpvlabA1gS6Q7bHpF2OnTogFOnTmHevHnYunUrAKBcuXI4ffo079ggykbDhg1DVFQUrl27hnLlygEArl+/Dj8/PwwfPhx///23niMkIiKi3ELjTqm2bdvCxMQEAAewJMpJbHtE2vP09MRff/2l7zCI8pXdu3dj//79UocUAHh4eODnn39Gs2bN9BgZERER5TYad0qlHLSSA1gS5ZzPqe3xbXyU3d6+fQu5XC79Pz2KfBmZPHkyAgMDldLKlCmDmzdvahck0WcuKSkJRkZGKulGRkZISkrSQ0RERESUW2VpTKmzZ89KA1h6eHjA09NTJ0ERUfrY9ojUs7W1RWhoKBwcHGBjY6P2cVchBGQyGRITEzUut3z58ti/f7/0uUABDslIlJYmTZpgxIgR+Pvvv+Hs7AwAePr0KUaNGgVvb289R0dERES5iVZn1U+ePEG3bt1w7NgxpQEs69ati7Vr16Jo0aK6jJGIPmLbI0rfwYMHYWdnBwAIDg7WWbkFChSAo6Ojzsoj+pwtXrwYX3zxBVxdXVGsWDEAwOPHj1GhQgU+TktERERKtOqU6tevHz58+IAbN26gTJkyAIBbt27B398f/fr1w+7du7McmOIxHz7eQ/RJTrQ9orzMy8tL+r+bmxuKFSumcreUEAKPHz/OVLl37tyBs7MzTE1NUadOHcyYMQPFixdXm/f9+/d4//699DmjxwiJPjfFihXD+fPnsX//fukx13LlysHHx0fPkREREVFuo1Wn1OHDh3H8+HHpohhIHl9j0aJFaNCggc6CIyJlbHtEmnNzc5Me5UspPDwcbm5uGj++V6tWLaxYsQJlypRBaGgoAgMD0aBBA1y9ehVWVlYq+WfMmKEyBhVRfiOTydC0aVM0bdpU36EQERFRLqZVp1SxYsXw4cMHlfTExERp7AAi0j22PSLNKcaOSi06OhqmpqYal9OiRQvp/5UqVUKtWrXg4uKC9evXo2/fvir5x48fj9GjR0uf3759Kz3CRPS5WrhwIQYMGABTU1MsXLgw3bzDhw/PoaiIiIgot9OqU2r27NkYNmwYfv75Z1SvXh1A8sDLI0aMwJw5c3QaIBF9wrZHlDFFh5BMJsPEiRNhbm4uTUtMTMSpU6dQpUoVrcu3sbFB6dKlcffuXbXTTUxMYGJionX5RHnRvHnz0KNHD5iammLevHlp5pPJZOyUIiIiIonGnVK2trZKvzjHxMSgVq1a0huIEhISUKBAAfTp0wft2rXTWYAcW4ryO321PaK86sKFCwCS75S6cuUKjI2NpWnGxsaoXLkyxo4dq3X50dHRCAkJQc+ePbMcK9Hn4v79+2r/T0RERJQejTul5s+fn41hEFFa2PaIMkfx1j1/f38sWLAAcrk8S+WNHTsWbdq0gYuLC549e4aAgAAYGhqiW7duugiX6LMzZcoUjB07VukuRQCIi4vD7NmzMWnSJD1FRkRERLmNxp1Sfn5+2RlHhhR3TAG8a4ryF323veyUsl0DbNukW0FBQTop58mTJ+jWrRtev34Ne3t71K9fHydPnoS9vb1Oyif63AQGBmLQoEEqnVKxsbEIDAxkpxQRERFJZEIIkZUC3r17h/j4eKW0jH6Vfvv2LaytrVFs5HoYmJinm1cdXrhSVij2v8jIyCzfQaFPmW17WW13OYFt+/Omj7Z39uxZrF+/Ho8ePVJpL5s3b86RGD6XYw7lXTm9DxoYGODFixcqHbcHDx5Ely5d8PLly2yPAfi03uMApB7lbXCqz4XXqymgnubLkn2fpdPpNAWsUH1ZQ24Q2FrN+u68k/OBqHFVNAcAVPAPyZkFuqpPFqc133ZvD6imWfdPrmNRIf1yag08lGH5f8kaSf93b6lZHE3fqS/3tMxW6XNN8SbdZZ9u4yX9X/RKXhdZbAbtpXrak8R11fq43Dn94jKj8mQBTL6suwI/Q53FBY3zTsKUdKdXGPapnYqvNGszS2qrph0Xy6T/j8NM6f+JsnsAALMUedNrA0oy+v3kdfI/slaXNCww2Xrxvdr0Ofg0tETqdpYd7BIKZpjn9RdF052u7tiVkvXhSKB2xucfBhlGokZMTAyGDh0KBwcHWFhYwNbWVumPiLIH2x6R5tauXYu6devixo0b2LJlCz58+IBr167h4MGDsLa21nd4RJ8dW1tb2NnZQSaToXTp0rCzs5P+rK2t0bRpU3TurMOrRyIiIsrztHr73rfffovg4GAsWbIEPXv2xM8//4ynT5/i119/xcyZMzMugIi0wrZHpLnp06dj3rx5GDJkCKysrLBgwQK4ublh4MCBcHJy0nd4RJ+d+fPnQwiBPn36IDAwUKnz19jYGK6urqhTp44eIyQiIqLcRqtOqR07duDPP/9Eo0aN4O/vjwYNGqBUqVJwcXHB6tWr0aNHD13HSURg2yPKjJCQELRqlfxIqLGxMWJiYiCTyTBq1Cg0adIEgYGBeo6Q6POiGAPRzc0NdevWhZGRkZ4jIiIiotxOq06p8PBwlChRAkDyGDbh4eEAgPr162Pw4NRP6eueYnBkjj9D+Y2+215248DnpEu2traIiooCABQpUgRXr15FxYoVERERgdjYWD1HR/T58vL6NIaMNmOPEhERUf6h1ZhSJUqUwP379wEAZcuWxfr1yaND7tixAzY2NjoLjoiUse0Raa5hw4bYt28fAKBTp04YMWIE+vfvj27dusHb21vP0RF9vmJjYzn+IREREWlEq04pf39/XLqUPMr8uHHj8PPPP8PU1BSjRo3CN998o9MA0+M67h+VOyuIPme5pe0R5QWLFy9G165dAQDff/89Ro8ejRcvXqBDhw74448/9Bwd0efrm2++wcGDB7FkyRKYmJjg999/R2BgIJydnfHnn3/qOzwiIiLKRbR6fG/UqFHS/318fHDjxg2cP38epUqVQqVKlXQWHBEpY9sj0kxCQgJ27twJX19fAMmvqB83bpyeoyLKHzj+IREREWlKq06p1FxdXeHq6qqLoogoE9j2iNQrUKAABg0ahBs3bug7FKJ853Mf/5CIiIh0R6vH9wDgwIEDaN26NUqWLImSJUuidevW2L9/vy5jIyI18lPbUzyiy0d1SRs1a9bExYsX9R0GUb7D8Q+JiIhIU1p1Sv3yyy9o3rw5rKysMGLECIwYMQJyuRwtW7bEzz//rOsYiegjtj0izX399dcYPXo0Fi9ejBMnTuDy5ctKf0SUPTj+IREREWlKq8f3pk+fjnnz5mHo0KFS2vDhw1GvXj1Mnz4dQ4YM0VmAmkh5BwVfIU+fs9zW9ohyM8Ug58OHD5fSZDIZhBCQyWRITEzUV2hEn7XU4x/evHkT586d4/iHREREpEKrTqmIiAg0b95cJb1Zs2b43//+l+WgiEg9tj0izSkeHyKinPPhwwc0b94cS5cuhbu7OwDAxcUFLi4ueo6MiIiIciOtHt/74osvsGXLFpX0bdu2oXXr1lkOiojUY9sj0pziQjitPyLSPSMjIz4eS0RERBrT+E6phQsXSv/38PDADz/8gEOHDqFOnToAgJMnT+LYsWMYM2aM7qPMBMWjfHyMjz4XeaXtEeU2f/75Z7rTe/XqlUOREOUvX331Ff744w/MnDlT36EQERFRLqdxp9S8efOUPtva2uL69eu4fv26lGZjY4Ply5djwoQJuotQS+ycos9FXmt72UndG/jYxiktI0aMUPr84cMHxMbGwtjYGObm5uyUIsomCQkJWL58Ofbv3w9PT09YWFgoTZ87d66eIiMiIqLcRuNOKY7NQaQfbHtE2nnz5o1K2p07dzB48GC+AYwoG129ehXVqlUDANy+fVtpmkwm00dIRERElEtpNdB5SkIIALn3JINv5qPPVW5ve0S5kbu7O2bOnImvvvoKN2/e1Hc4RJ+l4OBgfYdAREREeYRWA50DyWN1VKxYEWZmZjAzM0OlSpWwatUqXcZGRGqw7RFlTYECBfDs2TN9h0H02bt79y727NmDuLg4AJ9+TCEiIiJS0OpOqblz52LixIkYOnQo6tWrBwA4evQoBg0ahFevXmHUqFE6DVJXOM4U5XV5te0R6cP27duVPgshEBoaisWLF0vth4h07/Xr1+jcuTOCg4Mhk8lw584dlChRAn379oWtrS1++uknfYdIREREuYRWnVKLFi3CkiVLlAaJ/eKLL1C+fHlMnjyZF8ZE2YRtj0hz7dq1U/osk8lgb2+PJk2a8KKYKBuNGjUKRkZGePToEcqVKyeld+nSBaNHj2b7IyIiIolWnVKhoaGoW7euSnrdunURGhqa5aCISD22PVWp38jHOyFJISkpSd8hEOVLe/fuxZ49e1C0aFGldHd3dzx8+FBPUREREVFupFWnVKlSpbB+/Xp89913Sunr1q2Du7u7TgLLTnyMj/KqvN72cgI7qfK30aNHa5yXr6Unyh4xMTEwNzdXSQ8PD4eJiYkeIiIiIqLcSqtOqcDAQHTp0gVHjhyRxuU4duwYDhw4gPXr1+s0wOzEN/NRXvO5tD2i7HLhwgWlz+fPn0dCQgLKlCkDIPn19IaGhvD09NRHeET5QoMGDfDnn39i6tSpAJIfnU1KSsKPP/6Ixo0b6zk6IiIiyk206pTq0KEDTp8+jblz52Lr1q0AgHLlyuH06dOoWrWqLuMjohTY9ojSl/JV9HPnzoWVlRVWrlwJW1tbAMCbN2/g7++PBg0a6CtEos/ejz/+CG9vb5w9exbx8fH49ttvce3aNYSHh+PYsWP6Do+IiIhykUx3Sn348AEDBw7ExIkT8ddff2VHTESkBtseUeb89NNP2Lt3r9QhBQC2traYNm0amjVrhjFjxugxOqLPV4UKFXD79m0sXrwYVlZWiI6OxpdffokhQ4bAyclJ3+ERERFRLpLpTikjIyNs2rQJEydOzI54iCgNbHva4RhT+dfbt2/x8uVLlfSXL18iKipKDxER5Q+PHj1CsWLF8P3336udVrx48QzLcHd3x71796QXFpiYmGDRokXo37+/zuMlIiIi/THQZqZ27dpJjw4RUc5h2yPSXPv27eHv74/NmzfjyZMnePLkCTZt2oS+ffviyy+/1Hd4RJ8tNzc3tR3Cr1+/hpubm0ZlPHz4EPXq1cPChQsxa9YsCCEwYMAA3L9/X9fhEhERkR5pNaaUu7s7pkyZgmPHjsHT0xMWFhZK04cPH66T4IhIGdte1vHOqfxj6dKlGDt2LLp3744PHz4AAAoUKIC+ffti9uzZeo6O6PMlhIBMJlNJj46OhqmpqUZlxMfHK32uV68e6tevj/nz52PBggU6iZOIiIj0T6tOqT/++AM2NjY4d+4czp07pzRNJpPxwpgom7DtEWnO3Nwcv/zyC2bPno2QkBAAQMmSJVU6c4lIN0aPHg0g+fto4sSJMDc3l6YlJibi1KlTqFKlilZlK9pwendavXz5Eq9evZI+R0dHa7UsIiIiyjladUqlvHVaCAEAan8Ry0sUd0/wrgnKzT7HtkeU3SwsLFCpUiV9h0H02btw4QKA5O+nK1euwNjYWJpmbGyMypUrY+zYsZkuNz4+Hv3794ehoSFGjhyZZr4aNWrg4cOHmS6fiIiI9EerTikg+Y6NefPm4c6dOwCSHysaOXIk+vXrp7PgiEgV2x4REeVGwcHBAAB/f38sXLgQVlZWOinX0dER8fHx2LFjR7r5zpw5o3KnVM2aNXUSAxEREWUPrTqlJk2ahLlz52LYsGGoU6cOAODEiRMYNWoUHj16hClTpug0yJzEO6YoN/uc2x4REeVtKV8g4Ofnl2a+zZs3a1xmwYIF8ebNG6xduxatW7dON6+9vT3s7e2lz2/fvtV4OURERKQfWnVKLVmyBL/99hu6desmpX3xxReoVKkShg0b9llcGKccDJkdVJRb5Ie2l9M48DnpS2KSwOn74QiLegcHK1PUdLODoQEfx6W8y9raWmdlJSYmwsHBAeHh4QgKCkKXLl10VjYRERHlHlp1Sn348AHVq1dXSff09ERCQkKWgyIi9dj2sh87qSgn7L4aisAd1xEa+U5Kc7I2RUAbDzSv4KTHyIi0FxQUpLOy7O3t8ebNGwwdOhTFixfHwYMHAQBlypRBkSJFdLYcIiIi0i8DbWbq2bMnlixZopK+bNky9OjRI8tBEZF6bHtEed/uq6EY/Nd5pQ4pAHge+Q6D/zqP3VdD9RQZUe7x5s0bAMDixYvh7e0t/aW8U5iIiIjyviwNdL53717Url0bAHDq1Ck8evQIvXr1kl4JDABz587NepREJGHby1m8c4p0KTFJIHDHdQg10wQAGYDAHdfR1MORj/JRvqZ4wywRERF93rTqlLp69SqqVasGAAgJCQEAFCpUCIUKFcLVq1elfHxVPZFuse0R5W2n74er3CGVkgAQGvkOp++Ho07JgjkXGBERERGRHmjVKaV45S8R5Sy2Pf3jnVOUFWFRaXdIaZOPiIiIiCgv0/rxvfwk9UVoSrwgJcrf2ElFmeFgZarTfEREREREeZlWA50TERFR5tV0s4OTtSnSesBWhuS38NV0s8vJsIiIiIiI9IJ3SmUR75IgIiJNGRrIENDGA4P/Og8ZoDTguaKjKqCNBwc5JyIiIqJ8gZ1SOqbuUT92VBHlH+yopow0r+CEJV9VQ+CO60qDnjtamyKgjQeaV3DSY3RERERERDmHnVJERNmInVSkTvMKTmjq4YjT98MRFvUODlbJj+zxDikiIiIiyk/YKUVEpEfstMq/DA1kqFOyoL7DICIiIiLSG3ZKERHloPTe5km5188//4zZs2fj+fPnqFy5MhYtWoSaNWvqOywiIiIiojyNb98jIiJKx7p16zB69GgEBATg/PnzqFy5Mnx9fREWFqbv0IiIiIiI8jTeKZUDOPg5EWkqs3dS8ViS/ebOnYv+/fvD398fALB06VL8888/WL58OcaNG6fn6IiIiIiI8i52SukJx5EhIsr94uPjce7cOYwfP15KMzAwgI+PD06cOKHHyIiIiIiI8j52SuUS6d0dwQ4rIiL9ePXqFRITE1G4cGGl9MKFC+PmzZtq53n//j3ev38vfX779m22xkhERERElFexUyoPSNlhxQ4qIkqJd13mPjNmzEBgYKC+wyAiIiIiyvXYKZXH8AKUiNKT0ZhUPGZkTqFChWBoaIgXL14opb948QKOjo5q5xk/fjxGjx4tfX779i2KFSuWrXESEREREeVF7JTK4/jYHxFlBju2M8fY2Bienp44cOAA2rVrBwBISkrCgQMHMHToULXzmJiYwMTEJAejJCIiIiLKm9gp9RnjY39ElBF2UmVs9OjR8PPzQ/Xq1VGzZk3Mnz8fMTEx0tv4iIiIiIhIO+yUyid44UlEmuDjf6q6dOmCly9fYtKkSXj+/DmqVKmC3bt3qwx+TkREREREmcNOqXxK3YVnfrzYJKLMyajTKj1J72N1GEnOGjp0aJqP6xERERERkXbYKUUSjk9FRERERERERDmFnVKkEd5ZRURERERERES6xE4p0pomj/Gw44qIiIiIiIiI1GGnFGWrrIw/o2vsICMiIiIiIiLKPfTSKSWEAJC3B72lvOft27dK/yr2w/yC7Y70TbHv5de2pzj2EOW0/P69917NtKhUn83UfTWmzpSe+Oxp3+pizxU+qFvf6BwPQ53ot0nJ/8mmbaLinfrktx80L+Ktuqb5Mf63cenPm/g2JsPyU26Z9OJKGUfa5Rplbvkp9pW3inYWl8G2SWdXequmrep0z3v3VtclfnY+ZLRTphCNpPQzpGinGuzKAAB1S49PEVPKZSam+hfIRNvMKB5pX8zc/hKbRgCJSgs0UptHl8Rb4wzzZFRXao9dKcVodv4hE3o4Q3ny5AmKFSuW04slUvL48WMULVpU32HkGLY7yi1CQkJQokQJfYeRY9j2KLfIb9979+7dQ8mSJfUdBhERUb6W0fmHXjqlkpKS8OzZMwghULx4cTx+/BhyuTynw8gxb9++RbFixbieuYQQAlFRUXB2doaBgYG+w8kxinZnZWUFmUym73DylLyyb+d2kZGRKF68ON68eQMbGxt9h5Nj0mt73LcyxjrKWEZ1lF+/9yIiImBra4tHjx7B2tpa3+HkOWx7Wcc6zBrWX9axDrOG9Zc1mp5/6OXxPQMDAxQtWlS6nVwul+eLjcz1zD3y48mpot2R9vLCvp0X5KeLYkCztsd9K2Oso4ylV0f59XsPSF537jvaY9vLOtZh1rD+so51mDWsP+1pcv6Rv64MiIiIiIiIiIgoV2CnFBERERERERER5Ti9dkqZmJggICAAJiYm+gwj23E9ifI27tu6wXpUxTrJGOsoY6wj9VgvWcP6yzrWYdaw/rKOdZg1rL+coZeBzomIiIiIiIiIKH/j43tERERERERERJTj2ClFREREREREREQ5jp1SRERERERERESU4/TWKfXzzz/D1dUVpqamqFWrFk6fPq2vUHRixowZqFGjBqysrODg4IB27drh1q1bSnkaNWoEmUym9Ddo0CA9Ray9yZMnq6xH2bJlpenv3r3DkCFDULBgQVhaWqJDhw548eKFHiMmypgmbZj7dubNnDkTMpkMI0eOlNJYj598bt+FunTkyBG0adMGzs7OkMlk2Lp1q75DynU0OW7lZ2xf6mXUtoQQmDRpEpycnGBmZgYfHx/cuXNHKU94eDh69OgBuVwOGxsb9O3bF9HR0Tm4Fvqjq/OFR48eoVWrVjA3N4eDgwO++eYbJCQk5OSq6MWSJUtQqVIlyOVyyOVy1KlTB7t27ZKms+4yR9vzrPxch7q4ls3P9Zcd9NIptW7dOowePRoBAQE4f/48KleuDF9fX4SFhekjHJ04fPgwhgwZgpMnT2Lfvn348OEDmjVrhpiYGKV8/fv3R2hoqPT3448/6inirClfvrzSehw9elSaNmrUKOzYsQMbNmzA4cOH8ezZM3z55Zd6jJYoY5q0Ye7bmXPmzBn8+uuvqFSpklI66zHZ5/hdqEsxMTGoXLkyfv75Z32Hkmtpeu6RH7F9pS2jtvXjjz9i4cKFWLp0KU6dOgULCwv4+vri3bt3Up4ePXrg2rVr2LdvH3bu3IkjR45gwIABObUKeqWL84XExES0atUK8fHxOH78OFauXIkVK1Zg0qRJ+lilHFW0aFHMnDkT586dw9mzZ9GkSRO0bdsW165dA8C6ywxtz7NYh1m7lmX9ZQOhBzVr1hRDhgyRPicmJgpnZ2cxY8YMfYSTLcLCwgQAcfjwYSnNy8tLjBgxQn9B6UhAQICoXLmy2mkRERHCyMhIbNiwQUq7ceOGACBOnDiRQxESZV3qNsx9O3OioqKEu7u72Ldvn9Kxj/X4SX74LtQVAGLLli36DiPXU3fukV+xfWkmddtKSkoSjo6OYvbs2VJaRESEMDExEX///bcQQojr168LAOLMmTNSnl27dgmZTCaePn2aY7HnFtqcL/z777/CwMBAPH/+XMqzZMkSIZfLxfv373N2BXIBW1tb8fvvv7PuMiEr51n5vQ6zei2b3+svO+T4nVLx8fE4d+4cfHx8pDQDAwP4+PjgxIkTOR1OtomMjAQA2NnZKaWvXr0ahQoVQoUKFTB+/HjExsbqI7wsu3PnDpydnVGiRAn06NEDjx49AgCcO3cOHz58UNq+ZcuWRfHixT+r7Uufv9RtmPt25gwZMgStWrVSqi+A9aiQX74LKWelde6R37B9ae/+/ft4/vy5Ut1ZW1ujVq1aUt2dOHECNjY2qF69upTHx8cHBgYGOHXqVI7HrG/anC+cOHECFStWROHChaU8vr6+ePv2rXTHUH6QmJiItWvXIiYmBnXq1GHdZUJWzrNYh1m7lmX96V6BnF7gq1evkJiYqLQRAaBw4cK4efNmToeTLZKSkjBy5EjUq1cPFSpUkNK7d+8OFxcXODs74/Lly/jf//6HW7duYfPmzXqMNvNq1aqFFStWoEyZMggNDUVgYCAaNGiAq1ev4vnz5zA2NoaNjY3SPIULF8bz58/1EzBRJqlrw9y3Nbd27VqcP38eZ86cUZnGekyWH74LKWelde6RH7F9aU9xHFZXd4ppz58/h4ODg9L0AgUKwM7OLl8dxwHtzxeeP3+uto4V0z53V65cQZ06dfDu3TtYWlpiy5Yt8PDwwMWLF1l3GsjqeVZ+r8OsXsvm9/rLDjneKZUfDBkyBFevXlV6NhWA0rP2FStWhJOTE7y9vRESEoKSJUvmdJhaa9GihfT/SpUqoVatWnBxccH69ethZmamx8iIdCOtNkwZe/z4MUaMGIF9+/bB1NRU3+EQ5Rs8bhHlPLY77ZQpUwYXL15EZGQkNm7cCD8/Pxw+fFjfYeUJPM/KOl7L5j45/vheoUKFYGhoqDKC/YsXL+Do6JjT4ejc0KFDsXPnTgQHB6No0aLp5q1VqxYA4O7duzkRWraxsbFB6dKlcffuXTg6OiI+Ph4RERFKeT6X7Uufv7TaMPdtzZw7dw5hYWGoVq0aChQogAIFCuDw4cNYuHAhChQogMKFC7Me8fl/F1LOysy5R37A9qU9Rf2kV3eOjo4qA8YnJCQgPDw8X9VvVs4XHB0d1daxYtrnztjYGKVKlYKnpydmzJiBypUrY8GCBaw7DejiPCu/12Fqmb2WZf3pXo53ShkbG8PT0xMHDhyQ0pKSknDgwAHUqVMnp8PRGSEEhg4dii1btuDgwYNwc3PLcJ6LFy8CAJycnLI5uuwVHR2NkJAQODk5wdPTE0ZGRkrb99atW3j06FGe3r70+cuoDXPf1oy3tzeuXLmCixcvSn/Vq1dHjx49pP+zHj/f70LKWdqce+QHbF/ac3Nzg6Ojo1LdvX37FqdOnZLqrk6dOoiIiMC5c+ekPAcPHkRSUpL0g+vnTBfnC3Xq1MGVK1eUOvf27dsHuVwODw+PnFmRXCQpKQnv379n3WlAF+dZ+b0OU8vstSzrLxvoY3T1tWvXChMTE7FixQpx/fp1MWDAAGFjY6M0gn1eM3jwYGFtbS0OHTokQkNDpb/Y2FghhBB3794VU6ZMEWfPnhX3798X27ZtEyVKlBANGzbUc+SZN2bMGHHo0CFx//59cezYMeHj4yMKFSokwsLChBBCDBo0SBQvXlwcPHhQnD17VtSpU0fUqVNHz1ETpS+jNiwE921tpX7zKOsx2ef4XahLUVFR4sKFC+LChQsCgJg7d664cOGCePjwob5DyzU0OW7lV2xfacuobc2cOVPY2NiIbdu2icuXL4u2bdsKNzc3ERcXJ5XRvHlzUbVqVXHq1Clx9OhR4e7uLrp166avVcpRujhfSEhIEBUqVBDNmjUTFy9eFLt37xb29vZi/Pjx+lilHDVu3Dhx+PBhcf/+fXH58mUxbtw4IZPJxN69e4UQrDttZPY8K7/XYVavZfN7/WUHvXRKCSHEokWLRPHixYWxsbGoWbOmOHnypL5C0QkAav+CgoKEEEI8evRINGzYUNjZ2QkTExNRqlQp8c0334jIyEj9Bq6FLl26CCcnJ2FsbCyKFCkiunTpIu7evStNj4uLE19//bWwtbUV5ubmon379iI0NFSPERNlLKM2LAT3bW2lPlliPX7yuX0X6lJwcLDaNunn56fv0HINTY5b+Rnbl3oZta2kpCQxceJEUbhwYWFiYiK8vb3FrVu3lMp4/fq16Natm7C0tBRyuVz4+/uLqKgoPaxNztPV+cKDBw9EixYthJmZmShUqJAYM2aM+PDhQw6vTc7r06ePcHFxEcbGxsLe3l54e3tLHVJCsO60oc15Vn6uQ11cy+bn+ssOMiGEyN57sYiIiIiIiIiIiJTl+JhSRERERERERERE7JQiIiIiIiIiIqIcx04pIiIiIiIiIiLKceyUIiIiIiIiIiKiHMdOKSIiIiIiIiIiynHslCIiIiIiIiIiohzHTikiIiIiIiIiIspx7JQiIiIiIiIiIqIcx04pHVmxYgVsbGykz5MnT0aVKlUyVYZMJsPWrVvTnP7gwQPIZDJcvHhRqxiz26FDhyCTyRAREaHvUEgLeXkfTh27Pri6umL+/Pk5sqyePXti+vTp0ufY2Fh06NABcrk8T7bB3bt3o0qVKkhKStJ3KKShRo0aYeTIkfoOQ0lGxx8iIiIiyn3YKUVElAlpdYCdOXMGAwYMyPblX7p0Cf/++y+GDx8upa1cuRL//fcfjh8/jtDQUFhbW2d7HLrUvHlzGBkZYfXq1foOhTS0efNmTJ06FUDOdsgCaXeYh4aGokWLFjkWBxHR56B3795o166d3pbPH9qIiJ1S+VxiYiIPmkQA4uPjszS/vb09zM3NdRRN2hYtWoROnTrB0tJSSgsJCUG5cuVQoUIFODo6QiaTqcyX1fXLbr1798bChQv1HQZpyM7ODlZWVjotM6v7qKOjI0xMTHQUDRFR3ieTydL9mzx5MhYsWIAVK1boJT7+0EZEADullGzcuBEVK1aEmZkZChYsCB8fH8TExODIkSMwMjLC8+fPlfKPHDkSDRo00KjsM2fOoGnTpihUqBCsra3h5eWF8+fPq+RT/NJrZmaGEiVKYOPGjemWe/XqVbRo0QKWlpYoXLgwevbsiVevXqWZX3GXx/bt2+Hh4QETExM8evRIo/hkMhl+//13tG/fHubm5nB3d8f27dvTXFZsbCxatGiBevXq5blfOfKq/LAPA8n7cfHixWFubo727dvj9evXStPV/eo3cuRINGrUSPrcqFEjDB06FCNHjkShQoXg6+sLAJg7dy4qVqwICwsLFCtWDF9//TWio6MBJD+i6u/vj8jISKUTOkD1bpFHjx6hbdu2sLS0hFwuR+fOnfHixQtpuuJuj1WrVsHV1RXW1tbo2rUroqKi0lzvxMREbNy4EW3atFFaj59++glHjhyBTCaT1tHV1RVTp05Fr169IJfLpbu4/ve//6F06dIwNzdHiRIlMHHiRHz48EElruXLl6N48eKwtLTE119/jcTERPz4449wdHSEg4MDfvjhB6XYIiIi0K9fP9jb20Mul6NJkya4dOmSNP3SpUto3LgxrKysIJfL4enpibNnz0rT27Rpg7NnzyIkJCTN9afcQ/H4XqNGjfDw4UOMGjVKahMKR48eRYMGDWBmZoZixYph+PDhiImJkaZrs4+uWLECgYGBuHTpkrQ8xcVU6sf3rly5giZNmkjHwwEDBkhtGfh0nJgzZw6cnJxQsGBBDBkyRKk9EBHlZaGhodLf/PnzIZfLldLGjh0La2trvQ2BwB/aiAhgp5QkNDQU3bp1Q58+fXDjxg0cOnQIX375JYQQaNiwIUqUKIFVq1ZJ+T98+IDVq1ejT58+GpUfFRUFPz8/HD16FCdPnoS7uztatmypcgE6ceJEdOjQAZcuXUKPHj3QtWtX3LhxQ22ZERERaNKkCapWrYqzZ89i9+7dePHiBTp37pxuLLGxsZg1axZ+//13XLt2DQ4ODhrHFxgYiM6dO+Py5cto2bIlevTogfDwcLWxNW3aFElJSdi3b5/ex/vJD/LLPnzq1Cn07dsXQ4cOxcWLF9G4cWNMmzZNo3VIbeXKlTA2NsaxY8ewdOlSAICBgQEWLlyIa9euYeXKlTh48CC+/fZbAEDdunVVTurGjh2rUm5SUhLatm2L8PBwHD58GPv27cO9e/fQpUsXpXwhISHYunUrdu7ciZ07d+Lw4cOYOXNmmvFevnwZkZGRqF69upS2efNm9O/fH3Xq1EFoaCg2b94sTZszZw4qV66MCxcuYOLEiQAAKysrrFixAtevX8eCBQvw22+/Yd68eSpx7dq1C7t378bff/+NP/74A61atcKTJ09w+PBhzJo1CxMmTMCpU6ekeTp16oSwsDDs2rUL586dQ7Vq1eDt7S0dH3r06IGiRYvizJkzOHfuHMaNGwcjIyNp/uLFi6Nw4cL477//0t9olKts3rwZRYsWxZQpU6Q2ASTvQ82bN0eHDh1w+fJlrFu3DkePHsXQoUOV5s/sPtqlSxeMGTMG5cuXl5aXul0BQExMDHx9fWFra4szZ85gw4YN2L9/v8ryg4ODERISguDgYKxcuRIrVqzQ2x0DRES65ujoKP1ZW1tDJpMppVlaWqr8kNeoUSMMGzYMI0eOhK2tLQoXLozffvsNMTEx8Pf3h5WVFUqVKoVdu3YpLSuzPzLyhzYikggSQghx7tw5AUA8ePBA7fRZs2aJcuXKSZ83bdokLC0tRXR0tBBCiKCgIGFtbS1NDwgIEJUrV05zeYmJicLKykrs2LFDSgMgBg0apJSvVq1aYvDgwUIIIe7fvy8AiAsXLgghhJg6dapo1qyZUv7Hjx8LAOLWrVtqlxsUFCQAiIsXL6YZW3rxTZgwQfocHR0tAIhdu3YJIYQIDg4WAMSNGzdEpUqVRIcOHcT79+/TXQ7pTn7Zh7t16yZatmyplNalSxel2P38/ETbtm2V8owYMUJ4eXlJn728vETVqlXTXD+FDRs2iIIFC0qfU9eTgouLi5g3b54QQoi9e/cKQ0ND8ejRI2n6tWvXBABx+vRpIURy/Zqbm4u3b99Keb755htRq1atNGPZsmWLMDQ0FElJSemumyKedu3aZbh+s2fPFp6entJndXH5+voKV1dXkZiYKKWVKVNGzJgxQwghxH///Sfkcrl49+6dUtklS5YUv/76qxBCCCsrK7FixYp0Y6lataqYPHlyhjGT/nl5eYkRI0YIIZT3fYW+ffuKAQMGKKX9999/wsDAQMTFxUnzabuPqjs2ARBbtmwRQgixbNkyYWtrKx3fhBDin3/+EQYGBuL58+dCiOTjhIuLi0hISJDydOrUSXTp0iXDmIiI8pq0zl9SnzN5eXkJKysrMXXqVHH79m0xdepUYWhoKFq0aCGWLVsmbt++LQYPHiwKFiwoYmJihBBCvHnzRtjb24vx48eLGzduiPPnz4umTZuKxo0bpxnP+fPnBQDpmCyEEK9fvxb9+/cXderUEaGhoeL169dCiOTvC7lcLubMmSPu3r0r7t69K4RIPo88duyYuH//vti+fbsoXLiwmDVrllReQECAsLS0FB07dhTXrl0T27dvF8bGxsLX11cMGzZM3Lx5UyxfvlwAECdPnpTm8/HxEW3atBFnzpwRt2/fFmPGjBEFCxaU4ilfvrz46quvxI0bN8Tt27fF+vXrVa6tChcuLIKCgjTbOET5HO+U+qhy5crw9vZGxYoV0alTJ/z222948+aNNL137964e/cuTp48CSD5EYLOnTvDwsJCo/JfvHiB/v37w93dHdbW1pDL5YiOjsajR4+U8tWpU0flc1p3mVy6dAnBwcGwtLSU/sqWLQsA6fbMGxsbo1KlSlrFl3I+CwsLyOVyhIWFKeVp2rQpSpUqhXXr1sHY2DjNOEi38ss+fOPGDdSqVSvdZWrK09NTJW3//v3w9vZGkSJFYGVlhZ49e+L169eIjY3VuNwbN26gWLFiKFasmJTm4eEBGxsbpbpwdXVVGpfHyclJpT2lFBcXBxMTE7W3squT8o4qhXXr1qFevXrSL6QTJkxQ2Yap4ypcuDA8PDxgYGCglKaI9dKlS4iOjkbBggWVtuX9+/el7Th69Gj069cPPj4+mDlzptrta2Zmlql6ptzr0qVLWLFihdL+4Ovri6SkJNy/f1/Kp+0+mpEbN26gcuXKSse3evXqISkpCbdu3ZLSypcvD0NDQ+lzRm2QiCg/qFy5MiZMmAB3d3eMHz8epqamKFSokHQeOGnSJLx+/RqXL18GACxevBhVq1bF9OnTUbZsWVStWhXLly9HcHAwbt++rXYZDx8+hKGhIRwcHKQ0Ozs7mJubw9jYGI6OjrCzs5OmNWnSBGPGjEHJkiVRsmRJAMCECRNQt25duLq6ok2bNhg7dizWr1+vtJykpCQsX74cHh4eaNOmDRo3boxbt25h/vz5KFOmDPz9/VGmTBkEBwcDSH70/PTp09iwYQOqV68Od3d3zJkzBzY2NtKQFI8ePYKPjw/Kli0Ld3d3dOrUCZUrV1ZarrOzMx4+fJjFLUGUPxTQdwC5haGhIfbt24fjx49j7969WLRoEb7//nucOnUKbm5ucHBwQJs2bRAUFAQ3Nzfs2rULhw4d0rh8Pz8/vH79GgsWLICLiwtMTExQp06dLD0THR0djTZt2mDWrFkq05ycnNKcz8zMTOWiVtP4Uj5uAySP4ZF6oPRWrVph06ZNuH79OipWrJjZ1SIt5ad9OCMGBgYQQiilqRsnJnWH3IMHD9C6dWsMHjwYP/zwA+zs7HD06FH07dsX8fHxOh/IXJP2lFKhQoUQGxuL+Ph4jTp8U6/fiRMn0KNHDwQGBsLX1xfW1tZYu3YtfvrppwzjSi/W6OhoODk5qd2fFI/uTp48Gd27d8c///yDXbt2ISAgAGvXrkX79u2lvOHh4bC3t89wvSj3i46OxsCBA5UGr1UoXry49H9t91FdyWwbJCLKD1L+CG1oaIiCBQsqndMXLlwYAJR+nFL8yJhaSEgISpcurZKuqx/aFi5ciJCQEERHRyMhIQFyuVwpj7of2gwNDTX6oS11vKl/aFu1ahV8fHzQqVMnqaNMgT+0EWmOnVIpyGQy1KtXD/Xq1cOkSZPg4uKCLVu2YPTo0QCAfv36oVu3bihatChKliyJevXqaVz2sWPH8Msvv6Bly5YAgMePH6t9zvrkyZPo1auX0ueqVauqLbNatWrYtGkTXF1dUaBA1jalpvFpYubMmbC0tIS3tzcOHToEDw+PLMVGmssP+3C5cuWUxjJSLCMle3t7XL16VSnt4sWLKhegqZ07dw5JSUn46aefpJOV1L+4GRsbIzExMcMYHz9+jMePH0t3S12/fh0RERFZag9VqlSRylL8PzOOHz8OFxcXfP/991KaLn7Fq1atGp4/f44CBQrA1dU1zXylS5dG6dKlMWrUKHTr1g1BQUFSp9S7d+8QEhKS5r5CuZe6NlGtWjVcv34dpUqVylRZmuyjmrbBFStWICYmRur4OnbsGAwMDFCmTJlMxURElN9k9OOUoiMp5Y9Tmf2RkT+0EZECH9/76NSpU5g+fTrOnj2LR48eYfPmzXj58iXKlSsn5fH19YVcLse0adPg7++fqfLd3d2xatUq3LhxA6dOnUKPHj1gZmamkm/Dhg1Yvnw5bt++jYCAAJw+fVplYFaFIUOGIDw8HN26dcOZM2cQEhKCPXv2wN/fP8MTdm3j09ScOXPQo0cPNGnSBDdv3tS6HNJcftmHhw8fjt27d2POnDm4c+cOFi9ejN27dyvladKkCc6ePYs///wTd+7cQUBAgEonlTqlSpXChw8fsGjRIty7dw+rVq2SBkBXcHV1RXR0NA4cOIBXr16p/RXMx8cHFStWRI8ePXD+/HmcPn0avXr1gpeXl9pf+jRlb2+PatWq4ejRo1rN7+7ujkePHmHt2rUICQnBwoULsWXLFq3jUfDx8UGdOnXQrl077N27Fw8ePMDx48fx/fff4+zZs4iLi8PQoUNx6NAhPHz4EMeOHcOZM2eU9s2TJ09Kd99R3uLq6oojR47g6dOnUkf1//73Pxw/flx6IcGdO3ewbdu2NI8FCprso66urrh//z4uXryIV69e4f379yrl9OjRA6ampvDz88PVq1cRHByMYcOGoWfPntIv/EREpBvVqlXDtWvX4OrqilKlSin9pTVMRMof2rSR8kcMxWN2uv6hLfW6FCpUSMqn+JFt7969+PLLLxEUFCRN4w9tRJnDTqmP5HI5jhw5gpYtW6J06dKYMGECfvrpJ7Ro0ULKY2BggN69eyMxMVHpThBN/PHHH3jz5g2qVauGnj17Yvjw4UrPUCsEBgZi7dq1qFSpEv7880/8/fffad5Z4ezsjGPHjiExMRHNmjVDxYoVMXLkSNjY2CjdkqrL+DJj3rx56Ny5M5o0aZLm8+SkO/llH65duzZ+++03LFiwAJUrV8bevXsxYcIEpTy+vr6YOHEivv32W9SoUQNRUVEarW/lypUxd+5czJo1CxUqVMDq1asxY8YMpTx169bFoEGD0KVLF9jb2+PHH39UKUcmk2Hbtm2wtbVFw4YN4ePjgxIlSmDdunUZxpCRfv36YfXq1VrN+8UXX2DUqFEYOnQoqlSpguPHj0tvPMsKmUyGf//9Fw0bNoS/vz9Kly6Nrl274uHDh9Jt8q9fv0avXr1QunRpdO7cGS1atEBgYKBUxt9//40ePXro/BFJyn5TpkzBgwcPULJkSelX4UqVKuHw4cO4ffs2GjRogKpVq2LSpElwdnZOtyxN9tEOHTqgefPmaNy4Mezt7fH333+rlGNubo49e/YgPDwcNWrUQMeOHeHt7Y3FixfrbsWJiAiAdj8y8oc2IpLoe6T1vKZPnz6iTZs2+g6DSGvch/O22NhYUaxYMXH8+HF9h6IzL1++FHZ2duLevXv6DoWIiOizlJm37ynerqqg7i2rSPHGUyGEuH37tmjfvr2wsbERZmZmomzZsmLkyJEqbwxO6ZdffhG1a9dWSkvrjcKply9E8luLCxYsKCwtLUWXLl3EvHnzMnyTtLo3NKde57dv34phw4YJZ2dnYWRkJIoVKyZ69OghHj16JN6/fy+6du0qihUrJoyNjYWzs7MYOnSo9GZZIYQYMGCAGDhwYJrrTUTKZEKkGg2Y1IqMjMSVK1fQtGlTbN++HU2bNtV3SESZwn3483Ho0CFERUWhTZs2+g5FJ86ePYuQkBB06dJF36EQERFRDomLi0OZMmWwbt26z+auolevXqFMmTI4e/Ys3Nzc9B0OUZ7ATikNNWrUCKdPn8bAgQMxb948fYdDlGnch4mIiIgoN+EPbUTETikiIiIiIiIiIspxHOiciIiIiIiIiIhyHDuliIiIiIiIiIgox7FTioiIiIiIiIiIchw7pYiIiIiIiIiIKMexU4qIiIiIiIiIiHIcO6WIiIiIiIiIiCjHsVOKiIiIiIiIiIhyHDuliIiIiIiIiIgox7FTioiIiIiIiIiIctz/AZ2X2vWxmjZ4AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 3/3 [00:13<00:00,  4.48s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 67%|████████████████████████▋            | 2/3 [00:07<00:03,  3.73s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 3/3 [00:11<00:00,  3.87s/it]\n"
+     ]
+    }
+   ],
    "source": [
-    "kpms_model.PreFitting.populate()"
+    "kpms_model.PreFitting.populate(key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">Name of the model</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>400</td>\n",
+       "<td>2</td>\n",
+       "<td>2024_03_13-17_32_42</td>\n",
+       "<td>0:00:40</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>20000</td>\n",
+       "<td>2</td>\n",
+       "<td>2024_03_13-17_33_31</td>\n",
+       "<td>0:00:13</td></tr><tr><td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>1000000</td>\n",
+       "<td>2</td>\n",
+       "<td>2024_03_13-17_33_50</td>\n",
+       "<td>0:00:11</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 3</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera model_name     pre_fitting_du\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              4              400           2              2024_03_13-17_ 0:00:40       \n",
+       "subject1     2021-06-02 14: 1            1              4              20000         2              2024_03_13-17_ 0:00:13       \n",
+       "subject1     2021-06-02 14: 1            1              4              1000000       2              2024_03_13-17_ 0:00:11       \n",
+       " (Total: 3)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_model.PreFitting()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "        .Table{\n",
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+       "        .Table tr:nth-child(even){\n",
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+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
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+       "            text-align: center;\n",
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+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
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+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
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+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
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+       "            visibility: visible;\n",
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+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">full_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">sort_syllables</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to sort syllables by frequency (reindexing)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">results_as_csv</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to save results as csv (Optional)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">visualizations</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to save visualizations (Optional)</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject    *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d task_mode     sort_syllables results_as_csv visualizations\n",
+       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_model.FullFittingTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
     "fullfitting_key = ({**key,\n",
     "                      'full_latent_dim': 4,\n",
-    "                      'full_kappa': 10000,\n",
-    "                      'full_num_iterations':2,\n",
-    "                      'full_fitting_desc':\"Testing full fitting model and generate results\",\n",
+    "                      'full_kappa': 1000000,\n",
+    "                      'full_num_iterations':6,\n",
+    "                      'full_fitting_desc':\"Full fitting model and generate results and visualizations\",\n",
     "                      'task_mode':'trigger',\n",
     "                      'sort_syllables':True,\n",
     "                      'results_as_csv':True,\n",
@@ -598,27 +3827,293 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">full_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">task_mode</p>\n",
+       "                            <span class=\"djtooltiptext\">'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">sort_syllables</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to sort syllables by frequency (reindexing)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">results_as_csv</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to save results as csv (Optional)</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">visualizations</p>\n",
+       "                            <span class=\"djtooltiptext\">Whether to save visualizations (Optional)</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>1000000</td>\n",
+       "<td>6</td>\n",
+       "<td>Full fitting model and generate results and visualizations</td>\n",
+       "<td>trigger</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d task_mode     sort_syllables results_as_csv visualizations\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              4              1000000        6              Full fitting m trigger       1              1              1             \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_model.FullFittingTask()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 80%|█████████████████████████████▌       | 4/5 [01:21<00:18, 18.81s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 5/5 [01:40<00:00, 20.03s/it]\n"
+     ]
+    }
+   ],
    "source": [
     "kpms_model.FullFitting.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">subject</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">full_fitting_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>1000000</td>\n",
+       "<td>6</td>\n",
+       "<td>0:01:40</td> </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 1</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*subject     *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d\n",
+       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2021-06-02 14: 1            1              4              1000000        6              0:01:40       \n",
+       " (Total: 1)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kpms_model.FullFitting()"
    ]
@@ -627,14 +4122,93 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. Generate results\n"
+    "### Generate and store the model results and visualizations\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reindexing: 100%|█████████████| 3/3 [00:00<00:00,  9.18model snapshot/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved results to /Users/milagros/Documents/datajoint-elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/results.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Saving to csv: 100%|████████████████████| 10/10 [00:01<00:00,  6.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving trajectory plots to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/trajectory_plots\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating trajectory plots: 100%|██████| 29/29 [00:06<00:00,  4.34it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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329FodL/Hs9f9999vu91u+/bbbz/otiJy7Kj2Nu4Nb3iDHQgEbMC+4oor7Ewms99tReTYU61tXDabtZcvX27/7//+r23btn3nnXfagB2Px/d77CJy7KnWNs62bftTn/qUfd9999lPPPGE/e///u+2z+ezv/SlL+332EWOoEPKO3XLUo4Zr33ta+np6eGWW27h4osv5q677uKEE07gu9/9LlC+E3fzzTcD0N/fz+9//3uuu+66Q97/n//8Zy644ALa29uJRCK85S1vYXh4mHQ6PbmN2+3mpJNOmny8aNEiamtrWbdu3bT9jY+Ps2XLFt7+9rdP3kUMh8P867/+K1u2bHlen8HatWu58sor+cQnPsErX/nK57UPETk6VXsb94UvfIEnnniC3/zmN2zZsoX3v//9z3kfInL0qtY27iMf+QiLFy/mzW9+8yE/R0SOPdXaxgF8/OMf54wzzuD444/nwx/+MB/60If4z//8z+e0D5GXkoJROab4/X4uvPBCPv7xj/PAAw/w1re+lU984hMA/PVf/zVbt27lwQcf5Ac/+AHd3d2cddZZh7Tf7du3c/nll7N8+XJ+8Ytf8Pjjj/PVr34VKBecfj721nz51re+xerVqyd/1q5dy0MPPfSc9/fss89ywQUX8I53vIOPfexjz+uYROToVs1tXEtLC4sWLeJVr3oV3/jGN/ja177mGHImIse+amzj7rjjDn72s5/hdrtxu91ccMEFADQ0NEy+dxF5eajGNm4mp5xyCrt3756xrIDI0Uiz0ssxbcmSJfz6178GoL6+nquuuoqbb76ZBx98kLe97W2HvJ/HH3+cUqnE5z73ucnaTz/96U+nbVcoFHjsscc4+eSTAdiwYQOJRILFixdP27a5uZm2tja2bt3Km970pufx7iqeeeYZzj//fK699lo+/elPv6B9icixo1rauH2VSiUAnVCLvMxVQxv3i1/8gkwmM/n40Ucf5brrruPee+9l7ty5z3u/InL0q4Y2biarV68mFovh8/le1P2KHC4KRuWYMDw8zNVXX811113H8uXLiUQiPPbYY/zHf/wHV1555eR2119/PZdffjnFYpFrr732kPc/b948LMviK1/5CldccQX3338/X//616dt5/F4uPHGG/nyl7+M2+3mPe95D6eeeurkl8++PvnJT/J3f/d3RKNRLr74YnK5HI899hjxeHxymOjOnTsZGRlh586dFItFVq9ePXlM4XCYtWvXcv7553PRRRfx/ve/n76+PgBM06SxsfGQ36OIHL2quY373e9+R39/PyeddBLhcJhnnnmGD37wg5xxxhl0dXUd+ocoIketam7j9g0/h4aGAFi8eDG1tbWH/B5F5OhVzW3crbfeSn9/P6eeeip+v58//elP/Nu//Rsf+MAHnsMnKPISO9RipEe4QKqIQzabtf/hH/7BPuGEE+xoNGoHg0F74cKF9sc+9jE7nU5PblcqlezOzk770ksvnbaPgxW0/vznP2+3trbagUDAvuiii+zvf//7juL4ewtO/+IXv7DnzJlj+3w++xWveIW9Y8eOyX3sW9Datm37hz/8ob1y5Urb6/XasVjMPvvss+1f/vKXk+uvvfZaG5j2c+edd07uc6b1nZ2dz/vzFJGjSzW3cXfccYd92mmn2dFo1Pb7/fb8+fPtD3/4w5qYRORlpJrbuH1p8iWRl59qbuN+//vf2ytXrrTD4bAdCoXsFStW2F//+tftYrH4/D9QkRfPIeWdhm3bh5yhvrAIVuTwS6VStLe3c/PNN/Oa17zmpT4cEZEXldo4EXk5UxsnIi9nauNEjjjjUDbSUHp5WSiVSgwNDfG5z32O2tpaXvWqV73UhyQi8qJRGyciL2dq40Tk5UxtnMjRTcGovCzs3LmT7u5uOjo6+O53v4vbrX/aIvLyoTZORF7O1MaJyMuZ2jiRo5uG0ouIiIiIiIiIiMjLySENpXcd7qMQEREREREREREROdooGBUREREREREREZGqo2BUREREREREREREqo6CUREREREREREREak6CkZFRERERERERESk6igYFRERERERERERkaqjYFRERERERERERESqjoJRERERERERERERqToKRkVERERERERERKTqKBgVERERERERERGRqqNgVERERERERERERKqOglERERERERERERGpOgpGRUREREREREREpOooGBUREREREREREZGqo2BUREREREREREREqo6CUREREREREREREak6CkZFRERERERERESk6igYFRERERERERERkaqjYFRERERERERERESqjoJRERERERERERERqToKRkVERERERERERKTqKBgVERERERERERGRqqNgVERERERERERERKqOglERERERERERERGpOgpGRUREREREREREpOooGBUREREREREREZGqo2BUREREREREREREqo6CUREREREREREREak6CkZFRERERERERESk6igYFRERERERERERkaqjYFRERERERERERESqjoJRERERERERERERqToKRkVERERERERERKTqKBgVERERERERERGRqqNgVERERERERERERKqOglERERERERERERGpOgpGRUREREREREREpOooGBURERERERERkf/P3n3HyXHXh/9/Tdu+d3u9n+7Uu9Vsy71hg8Fgqik2JWAghpAfEMiXhEBCEkihhQSSEDDN9GJjXDC4W7ZkWy4qVm+n6333tu9O+/2xq9tb6XSSsaxivZ+Pxz1OOzszO7u6nfnM+/P+vD9CnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQgghhBBCCCGEEGcdCYwKIYQQQgghhBBCCCHOOhIYFUIIIYQQQgghhBBCnHUkMCqEEEIIIYQQQgghhDjrSGBUCCGEEEIIIYQQQghx1pHAqBBCCCGEEEIIIYQQ4qwjgVEhhBBCCCGEEEIIIcRZRwKjQgghhBBCCCGEEEKIs44ERoUQQghxWsjlchw8eJChoaFTfShCCCGEEEKIs4B+qg9ACCGEEGe3oaEh/uPLn2BidAOtdcPEkw7b9/mprl3Mhz7yT6y98DJUVfpyhRBCCCGEECeW4rru8a573CsKIYQQQhyP3t5e/u5Tr+LLn9hFXU1puevC7X9U+f3jtcxd8g4+9ZmvouvSnyuEEEIIIYQ4LspxrSSBUSFOnP3793PfPT8mm0myeOmFXP3q16Np2qk+LCGEOG3d8oHL+be/eJSK8PTPf//XKqpnLnn/u/ngLX83uTwajTI4OIiu63R0dGAYxkk6YiGEOD2YpkksFiWbSaGq4PMFqaiskvOhEEIIUXBcgVFJvRDiBBgbG+Pzn3kHrdUv8KYrBwn44dltAf78fZ1cfs3HiMcGSCW6ABevv5XXv+mDdHR0nOKjFkKIU2vfvn101O84alAU4KbrHT7+pX46528kk8kwPjbKH3//IyKBAVobEuTyOo8/WE2oeiFXX/MWKisrUZTjagMJIcQZybIsRkeGyGRiVFc4VNcVJo5IZZMMDowTCtdRVVVzzP0IIYQQQjJGhXjJUqkUt7z/Yr7yyU3UH9YGdV342695WLW8lbddFwEgFodf/7EaX/V13PTe/49kMsnY2Bher5eGhga5oRdCnDV+cOt/sKLuE6xYPPN6H/+il7e/aRn9+U8yMfIk775ulMMTonZ3Kdz9SIiOOcvo6FzBytUXyvlUCPGKY9s2Q4N92PYEzXUw3bik4ZiGL9hERUXVST8+IYQQ4jQiGaNCnAzf+86/88kbNx8RFAVQFPjSJ/N8+PMDvPV1hSymSAXc/NZx7nnkN/zj57Yxf46fppo06axO32g18xddzCWXvUZu6IUQr3iO43A8cyopCoSDDs8+fh9f+EsLY5rWy/wOl0vXJHGU3QTcUf54Xz/XvOatci4VQryiJJNxbCtBYy2kUjZjE2YhfcUFf0CjJqJTF7HpHRmVwKgQQghxHGSKVyFeom2b7mLF4qMnVCsKXLwqx/qNybLlr7s8hWJu4+2vHuWyc9Nce0mcm9/URcD+NXf8+taX+7CFEOKUO2/t1TzyzMzDPW0bMjmdvT1+5rRNHxQ9ZNUSlxd2JlnYmaepcgc7t28F4EWMjhFCiNNaJp1AVRx6+7OkM1laG2w6m206W2yC3jwH+zIkMzZeI08mkznVhyuEEEKc9iRjVIiXyO+ZOOY6yxc4bNqT4aLzygvpzZtlcbAvT0erZ3LZmiVZohue4r7fN1BfV0NNbRPtszok60kI8YqzdNky/vMr88jlxvB6p1/n9j8qvObyOjZsruQj7555f6oChmEDsGRejp/cfS+jw1tQcDFtDzW1nSxcvAKPxzPzjoQQ4jTkOA5gk07naKq3CfnKnw8HIBBw6erLURlWMU0Tv99/So5VCCGEOFNIxqgQL5HtHHvmz3gSgoEjq0BVVdokUs4Ryy9dk6Zr9+9oCNzDeM+P+e2vv8WeXdtOyPEKIcTp5JOf+S4f/eIs0tMkNj3+DPz0bpXhaBWt7edzXImfxXU0BarDGS5ZMcHFK+JcsXqUxopneOyRO8lmsyf0PQghxMniOg66dmRQ9BANaK51iSVMNG26CqRCCCGEmEoyRoV4iXTfAhLJXYRDR1/njgcM/uGvIkcs7xnUWHnOkV9DrwFBv0VLPbTUZ1mxcIBHnvkdtuOwcNGyE3j0Qghxai1ctITPfOGPfOJLH8bjbOTcpSnyJmzcAovmwD98zObAkInpGWLL7ggdLbGj7ss0wXJKgQBFLcRJD+Xb11e5nL9kmI1PP8ScOcsYG+0HIFxRS2WkGk3TMAwDTdMkq1QIcdpRVZV0Jk991cy9RH4v2LZDIBA4SUcmhBBCnLkkMCrES/Tem/+er/3oKf7+I0PTPr9zH/QN2lSEyxO0bRuGxwzqa47yNZzS5lUUuHxNkt8+8jDz5i+WDAAhxCvK3Hnz+fb3H2ZkZITf3flbqvIf4r+/AIdOdZt393He6qd5aPO1pDIxgkcZGfrIRpXzV1QC4LpgWuoRU1GGA+Dme0nHUixod1GAgdFudmxVCYcD2I4Pw+PB56uguraFqqrql+19CyHEi6VpOsaxByuhqqqUYRJCCCGOgwylF+IlOmfFKuav+jx/95+NjEVLyx0H7nkYvvo9+M4XHW6/d2DyOdeF//4JNNZOHxTN5sE9rN9CUWB+W5SdO7exZ88Otm19lq4D+7Asq7RdNsvOHVt4/pmHef7Zh9m6+Uni8fhxv5d4PE5fz0H6+rpJp9PHvZ0QQpwIdXV1fODmDxI3L2Jq/09rfZRZzRZNtSl+dHcL47Hy7VwXnngO9h5UWTyvEDXd1aXQ0Tp9ttS8WSaOncGjgaFBe4PL+StsLDPFrOYsFaE0hjpOamIPfX0HX6Z3K4QQL14wFCaTm3kd04F0OsuObU+zc9sGdmx7kgP7d0kZESGEEGIayouYqVWmdBViBjt37OAH3/0C6fh2HDvH+Mhu3voauP5VhaynrbvACCwkmghw/xPwmkugIgxVNXNoqC3v+r/3Mehsb2TR3PICUtEE/GF9DZes8eP3OUwkNboHKmloXkS4sprB3i3MbU9TGSysn8nB/j4fqtHGoiUrj3rs8XicnoM7CPszRCpMbAfGJ7zkrSCdc5bg85WOwzRNxkaHyefTaLpBdXWDFPYXQpxQ2154ltrkGhpqS8t+9YcGFi1qp3rBHTyx7m7i0V2EfMOouMSTsHwh+HwKC+fNYnAUnnjO4IbX103bAzwSg9FYmEVzys+xpg1bdxmsWhpiT49CS2OQoVGVUKSTurr6l/U9CyHE8TBNk97ubXQ2TR/kzFkuO/dlaK63qa0slRJJZuDgoJ/m1sWSCS+EEOJscVxDJyQwKsTL5P7ff5819e+nqrK07H9+5qehaSGvv0rBKCaE3vmgj+tf0wEUsp6e3gIDI1BdFeLS82vL9jkShb29YS5YUT67/fYDHgZG/Fx5gWfab37PkIGpLmL2nIVHPJdIxOnt2szcjizGYRvnTNjdE2L+glV4PB76+7owzRi1lTm8HrBsGI8Z5J0gra1zMI5nbJcQQhyHH/3HXN5z3b7Jx7/5o8qbXncOv3/m9bzuLV/Asiyef/Zp2oLforF4qnRc+PFd1XS2VxAI6KxeNn0wc9telbraymlLmWzdqzK3MwyuSv+wQUuzj+5+L22zFkonkBDitDDQ342hDFFbaR/x3La9GWa3WfinaZLZwM4DfubOPxev1/vyH6gQQghxah1XYFSG0gvxMrn62j/j149eVbbsw2/P8MLu5GRQFOBVF2T53q+y3P5H+O6vwXbgja+CBR1JuvvLx0pt36szr+PIoaGLO/N49CQ5c/r+i7YGk7GRgziOc8Rzvd27mDvryKAoFCaBmtOapLd7L/29XQQ8o3Q05Aj5wFDBb0BLnUlDJMbe3Zvo7t7F8PAApmkexyckhBBH17b0S2SmJES9/nKHB9eN0xy+j2Qyia7rnHv+heztLXUgqQrMaYtzyRpoqrPI5I4859nA8Lh21PrOIb9LNuvg94Bpu/h0ULBIJKLTri+EECdbY1MbOaeeg0NeUrlC9orpQPeAg88zfVAUCjPWtzVmGOzvPpmHK4R4hcpmszz/7AYevv/XPPLQHWx88hESicSpPiwhXjQJjArxMnrXh+/kzodK2Z2qCu+7bg8//V1qclkwAPPaBqhsupHrr4QLiyPeG2qgp28Yq3hfn0xD/wjURqafeGlBp83ersxRj6U2kmJsbKxsWSaTwaNnMGY4EwQ8kM3GsMwYVaEjMxMAAl6orbYJ+9KEvKMMDHRJcFQI8ZJcftXbuOvR0nBPjwcGh/pZucjisfu/Mbk8WHMd9pT45/L5Fgf7czTXwd6u1NRdYgOPPwML5waP+ro5U8HQiz1FU/uaHDmnCSFOD4qi0NIyi6aWJUykm+kainCgVyedzdPcMPO2FX5Ip8dPzoEKIV6RXNdlx7bNPPbwz2is2MTla4a4fGU/C9q2sfXZX/Hcs0+c6kMU4kWRwKgQL6NgMEjryjs42Fda1t4MPqWL8VgpyHjJ6hzJ8YfYsH1B2fYXrLDZ8OwYA6Pw+3WwbL5FJn9kBhRAbRXE4ke/cfd5LfL5fNmyVCpFOHCMCv6AR88TCc1csD9SAbEJi4AHGquzDA/2HnO/QghxNIqioNd+qmzZay812fRCkjrf78hkCh1BS1dczgt7PJPrhIPQdbCQ3ZnJZOkZgoFR2LoXHnsaZrdx1NaPCyRSKiG/RioLXo9K3gLHVZGKQkKI043H46G5pZ3OzoXM6lyG7bjo0/efl1EUOZ8JIf40pmny1FNPsGfX41x4TpKmWmdyrHJFAC48J0NQ28bzz204pccpxIshgVEhXmarz72Kh7e/j6mj2N98TY5v/7g8cHje3IeZv+T1PLmlVMNOVWBOa4JN2/O8/kpYOhe27YpN+zqWBZp69K90OmMQCJQPw9c0Dds5dgvasuBYpfUMhckzikcDTcvK7KdCiJfkdW/8JH98olQHr6YKnt/aw7lLLR754zcBMAyDuHVe2XbtjVlSGYcFHSZjmfM50KvRWAtXnAdtDRCLlmeSHrK/DxprC0HWniGFxgYPg2MKVREfrjSZhBCnMa/Xi25UkT764CEALBdcd/pSIkIIcTSmafLIw3dx753fRMvdT0fjAE9sHOXuh8aJJsoTdxbMMhkb2UM8LmWIxJlBWvlCnAQ3ffA7/PTeprJlf37DGD+/c3TycVM9HNj8GWo7/5LxidJ6zXUQ8g5P1iWd05ZlcPTIzNC9PSodrb4jlh8yHg8RiUTKloVCIWLJmYvvO0DO1HHd46hbPOWaWBG0SCbjx95GCCGOwuv1MpS7sWzZxasz9PTnqNJuJ5crZLwvXXkDAyOldTrbYPO2CSrD4Fpd1NQvpC5Ser62ymQiVTphZU3Yvh9iE9DY4OVAP2iqRjKlkEqrBAIefL7ySe+EEOJ00zl7Mf3DM9/ejUY1autaT9IRCSFeCSzL4p67fsyCxme4/vJRzl1isXy+y6svsrhqbZaHnhglGi8vudbWkKbnYBcvYrJvIU4ZCYwKcRLous7a6+5ny67SV66qEpprehkaKQ1vv2btKAd2r2NL15qy7S9YYfHUs4Uet6owDAxGywZ15izY06VQWzN9tf3dByEQiqAo5cFNwzDw+qqZSB096Nk/AtW1rYzHZs4uiKfBHyhlnyoKuO70w/6FEOJ4vfbN/8bz20vnqHmz4MHHell7Tp5H7/8/AKqqa9jTO6tsO0NL4LjgcbfTOmsNo1M6nOoisG2PxpMvBHluO+zaD831sHwRvLArR2UIfJ5CAz9SAfGkQSAYetnfqxBCvBQejwfd18jAUUqIJrMwOhGmprb+5B6YEOKM47ouL2zdyj13/Yrvf++bzGncQ1OddcR6fi+87nKLdU/HypaHAjbpVIxkMnmSjliIP50ERoU4SebOXcLuic+TnVLS89JzHX7+232TPWm6Dk3+HzJvyVt5dkcp+1PXoLVhYjJTdOl8i+17CxeZ8QSsewaWzXN4ekuAvhGNQ/1143F4bjt4DKj09047IVL7rHn0j1YzElPKgq2mCz1DkElDdXUNGdNP9shr4eS6g6MKNZFS8DSTVfH5jj7BiRBCHI+a2lo2dV1Vtmz+rAkSSZuA/fPJ81p9x1vJTimjfM5Ch51708xtyzE2PsBIrDwQ0FAV4/wLXktNjY9zFkAkVCgJEglnqa6A+hoXj2ETCdkYuo1hHGWaZyGEOI3MnbuYWCLMzi6FaKLQeZ7MwL5e6B2Cioow6gyll4QQ4pmNj/PPn7+Jvc/8BbND/8qK1tt44KHH+Oatm8lNM9+Fz4BwwC4bUp9IaUQqLUaHd3PwwB6ZmFec1uSqKMRJ9JZ3fp7bfr+0bNmfvyPDT24fmHy8dJ7Nxkf+lnDTR5hIlNZrb4Lde4dwXDA08OoJ7l/vcLAPLl4Ns1tdImETPOeyZW8LG7ZWMBaDpQugoxla60x6u7cccUy6rjN/4TlY6gK2H6hg50Ef2w/A3i7w+wrZWbHR7bS1z6dnKMh4XJk6Yp5kFrr7IBhw0dVCVpcDpLIeQiHJsBJCvHQXX/sNekqnSS5cBXffP8DFq7I8+sD3AFiwcCWbd5U6YzwGjI7F8Bow2vcYlXVryE/p3GlvytPftx/VO6/stRpqHOIpG68OyWQOvxfyuYkjJq8TQojTkaIozJ2/mvraEOmsj91dMBaDyrANdob+3t3s2bNjshSJEEJM9fSTj7HxkX/i7z64kze+KsmiuXDuMvjEezNcd+kA//7NZ7DsI4fHd7RY9A+VzisDo17mtIfobDKpj4xxsGu3BEfFaUsCo0KcRIqicP277+fRjaXMI68Hzpk3yJ796cllV63ezsTECM/tW162/QUrLZ7eFANgbpuLR0+zcmGhlw5gVmM/hqGwctVFrL3w1eiGH8+UuZUqfftJp4+cdETTNJqa21iy7HwWLrmEysoGFnVCbWXh+ZrKBNlMmo6OhVjqLPZ0e9h1UOFAHySShaBtfTXEkhYOMDgKwWmG7gshxJ9i3rzFPPjs4rJlId8oluXiyd6GaZooioLju7JsnQWdeUaiFhF/N3X1rRwcLNVUNjSIj26ksWkug+Ol5lDAC2PRwuwl4aBN1nIJePOkU2Mv4zsUQogTxzAM0NuprzOorvQwHk0yMpKktTHLqsVZanxb2bH1AXbu2CT1/4QQk1zX5Q93f4s/f/so093GdbTCq9bGeGhd3xHPOS6oxfnpdxzQaGqsQS3uxO+FukiC6NjoEdsJcTqQwKgQJ1l9QyNm9XcYj5WWLVsATzy9F9MsNE7DIcj0/zMrzv8Qm3eV38g3VMUYjRbSns6ZH2f7/lLPW8gH0aEHcBwHRVGorDuf9JSEgOqwy0DPU8c8xtqGBUSnlIPxGxAf345hGNTXNzKrYxEVFQ2EgtBUWzguQ4VcPk/fCFSEwLVjR92/EEK8WPPXfJnElPPStZc6/OGRES5eleHxR34MwDmr38Ce7lLTpqEGdu6O0dFisW/vJtAXle2zsXqMXC5HxiyfHC8UMMnbLhUBGB/PURmGdHJUAghCiDNGVU0TYxNeegdizG03WTTbIeQDjwbVlbBifoqw5wC7dm4+1YcqhDhNPPPMk5y3pH/aoOgha1c4PLu594jl+3t0Kio9bNgEgyMaC+eUlzCqCEIyKYFRcXqSwKgQp8CrXv1efrPu6rJl77ne4rbfdE0+vnRNhnX3fxmj5mYSpWRSOlvhhZ1DuC5EwpBKJsqGts9uS7N/77MAVFfXMzBeV/Y6zdWjjI0Oznh8Xq+XdL6xbFl1ZZJ4vDABlM/np76xDcttKHvtmkqXCr9FyAdBX5ZMJjPzByGEEMfpgouu5e7HGiYf6zpMxAZRFBcmfoBt2wQCAQai5ZmlkXAK24Z0dANtnasZnpIdWhWG3oNPU9e4jOSU01VdBAaHs4XX0cxCFoSaIZ2WCQSEEGcGRVHI5EM01+UJB6Zfp6XOJJfuk2H14hXBdV0cx2F4eJitW7eyb98+6dB8kfbv2cziOekZ11EUyGSzOFM+2kQaBsd0hkZUli+EeR0WplP+2SuAopTPXC/E6UICo0KcIu/60B389sHw5GNVhavXRtm4KT65bGXnH/AHqnhm18KybS9cYfLM1sIUyysWZXnimdIFzFDA7z5LthiUbG47n/GJUref3wvx8adxnJlnjK9rmE90So1TvwGJ8R2TjzVNo7a+lVi8NFb/UNYogE+HTLL/mJ+DEEIcD0VRCLd9FntKm/q6Kyyeei7OJauTPP7oLwCYvfgdZfWZl8xz2bwjTnPtEJZlMZYs7/Txshd/IMhIrOKwV8zhAjVVMB4zqQo7pBLDL8+bE0KIl0F0tI+m+pkDQ+2NKbq7956kIxLixHJdl43r1vGVT3yCd69ezXXt7fztBRew7/3v55n3v59/fuc7uesnPznVh3nG8Hj8ZHOQStt856eD/MVnd/GJv9/JX3x2N7ffO4ZlFc4nhu5yz4PjAEQTcMf9GtdcGmHVYgj6oLHOYXAodgrfiRAvjgRGhThFgsEg7avupGtKiZa2Jujv308yVbjzb2uEbRv+ijUXf5wX9pbqknoMiASjRBM2hgqt9YmyWQBb6m32734QAH8gQDw/p+y1W+uz9PXumvH4PF4vaau5bFlNZYqJ2PjkY8MwsLXyIENVpUs8VRjq7/emyWazx/oohBDiuLzm9bfw+3Wl1KeKEOzc04euQ37sOziOQ2tbJ1v31U6uoyqQy8ZprIb9ex6npvE8slNq/7c2mfT17MFfsRhzStC1qdZlNGqiAWY+h1cHMx+TiQOEEGcMRTHRj1HuPRSCdDI+80pCnIZc1+V7X/0q37rxRsb/53/47PPP872+Pm7av58Htm4lsXcvn9uzB/9//ze3/uu/nurDPSOsvegafnGvn4/8zU5WzOvnm59P8fW/TfOff5ckYHTzgU/t4mCfRShg0D+U5o4H4LltsHqJQzSuT+5HVyCZGCnbt+kAqu8kvyMhjo8ERoU4hVadewWP7Hg/U5M3r7/K4ed37Jt8/OoLhnj6iZ/jBN9LekqMcd4s2LS1MKS+s8Vmy47yIZ6ttQcZLQ6Zb2lfRs9wKbBqqKDb2445y3J943zGE6UWtc+AZGxH2TqRSCPRROlUYqiQzRX26zcgnRhACCFOBF3Xias3ly274rws+7qyXL46zobH7wAgWHMd9pTz6vL5Ft39Ochuor6hhZ7BUnDVUCAde5b6hlYGR6ecJzVIpgqZ9zURl3jKJuQ3SSZLnUNCCHE6c91j3+qZedANz0k4GiFOnEwmw50//jF//PKX+VxPD1/K5VgENAKXA9/M5fAODvJ/AwO8KptFvesufv6d79Df1yfD62dQVVXFhqe28+1/ynHulDmAVRVec6nLv386zUc/u5/rXlXLhSstmmpSXLUW5s926eq1yvYV9Kawp3zWgyNQU1OedCPE6UICo0KcYjfd/G1+cm/5ReKtr05y/6OF4tSGAbXad2mbtZyntpdnfl6wIs+m7YUxoysXJNl5oBTorAjASO8fcV0XwzDQgyvIT82GqnHoO/jsjMdmGAYZq6VsWW0kTTQ6UraOpTSUrROpcElkCi/m8yTJS+0qIcQJct1b/4knN5VKeMxqgcee7MEwIDXw37iuy9IVl7NtSpZ9OAgHDkbpbEkwPDyA4ltats/muhjJxASu3lm2vKbSJpW18RmQSOapDEI6MSw3VUKIM0K4spFoYuaU0e4BnfqG9pN0REK8dNlsltHeXu75wQ+4cXSUOUdZ792Ow7axMdKOw9uyWfp+/3ui997L4/fdh21Lrcvp/PJn3+UzH0rg807/fEMtXH5elmQiRWUYsrnC52ioEPQlSE1J4mmqcxgeTmA60DcKugap1NhJeBdCvHgSGBXiFNN1nQuve4DNO0tfx0gF+PUeBocLgc5zFlo8cu+nWH3RJ9l5oDRMwecBnzFOPGVTEYJkMsHUy/zc1gRd+7cC0NjYQd9wmKlqQj0k4jMPn6pvnMvolBqlXh3SE+XD8CNVzUSTpXU8GmQyhWBowAPJuGSNCiFOjIqKCnYOva5s2fJ5CcZjJpetifHU+nswDIMJ8/yyddoas6iKQ9/+R2jvOIf+0dI5tyIAfd1P0dC8qOx8VxGE4dFCKz8ctMhZLoaWJpNJvYzvUAghToz2WXPZ2x3COkpfTjoHqYwX15KZosWZIz42xlP33cfEtm287hhzJrw9n+fXo6OEAFIpluTzzD14kE3r15+UYz3TbNzway5ebc24zodusPjpHTvpH1GpjpQ6oVvq0vQMltpQhgb9Q8P0DkDYD021YDBMJjPz5E5CnAoSGBXiNDBn7iL2Jb9AZkov28VrXO65f89kZtIVK7ewb+/zZPR3kp0yAn7RbJdnNxcmBFmxMMf6qRMxaaDlN5DP51EUhZrGtWUz3FcEYaR/w4zHZhgGeVrLltVVZYiOD5etY7n1ZetUhl2S2UKY1qMnpC6fEOKEufK6/2DvwdLj1Uvh3gf68Xog2v2fuK7LkhVvY2BKeavZbbBl+wQ+bTe6rjORKT+vBfUuVFUlkaktW+7zmphOIXg6Fs1RVeESnxh8Od+eEEKcEF6vl7kLzuX5HeGyCTUdoH8EnnlBo7m5nkjYKmvXCXG6yufzpMbH8YyNETILdcBnshTYly7c/BzqH2iybdLd3XJvMg1NzaMcoy5xRRj6BlNs3RWmtdk/uXxWi8vIaHmZtupQktYml4pg4XFtpcv46B757MVpRwKjQpwm3vT2z/KT+5aVLbvpDTl+fVdhZvdIBcQO/AOLl1/Bhq1tZeudtyzH1p1JdBXaG+NlEzG1N1rs2/UIABWVVYwmmsq2bamfYHiwe8Zjq6ufw8hEeUZoOr6rbDhppLqVidRhmaXpwsUx5IPEhGSNCiFOjPZZnazbuqpsWW3lOPm8w+Vrxnj26QeorqllT2/58FBNTTCnLc/Bg7upbz6/bMhXa6NNb/cOInXLyUxp1zfUuJNZo7pqompgm1Fp1AshzghV1bUsX3klI4lFPLlZ4fkd8Px2sB1obzFw3Ryu6xIb28/+ffvo7+/HsmbOGBPiVDFNk64dOzjPNEkfK4IHxICgrhMFTG9pfHh7PE5v98z3P2cjV6nkWBXQunqhqkJl/vx5dA+UPlNNAa+RLJvgsrnBpqsnU7Z92J9hIib3heL0IoFRIU4TiqJw/Xvu5+GnSgXwvR5YPHuI3fsKPZ1XnJ/kj3d9iRUXfpq9PaU+0qAfFHeMVMZmVpPD1h3lw+MbK/cQjRZqujS3nctwtPTV9+qQTTw3YyPYMAwsygMM9dVZouNDZevk7PJMq4qwQ6qYNWpoExJIEEKcMOdc/HWiE6XH11zscs+Dw/h9MLz/67iuS/2st5Zl2J+z0GFfV5rowGPU1NbTO6W8iArkkpuoqqphcDxQttw0C3cJ1REYj5pUBGySyejL+waFEOIE8Xq9zF+4jIaGClYugtWLoa0BXMdmZGScxx5/jkR0C3r+PpLDd/PEI79iy6YnpZ6yOO2oqkoum6USCDY1sfcY6/8UsDIZfum6LO/v5+B4YQJFr+OQz2Rm3vgs9Ma3fZzf3B+acZ3/uk3h/93SwERsGFUvHzHYWJOhu7/02KtDLjNCbsptZkUArPwIyeQEQpwuJDAqxGmkrr4Bt+5Wxqbcby+ZBxuf34tpFhqny1vvJhGPErXeSn5KnHHpPJenny+MG125KM3OA6XuvqowDB78AwBen4+su7CsFmlrQ57+nm0zHlttfSfDsVIw1lAhk9xd1miuqmknPqX0nk+HZLpwkGEfJCdkmJYQ4sRYteZS7nm8lD2vqmBmh3Bdl8tWDbH5+XUsWLSKzbuCk+t4DRgZjVET7iWXy+EJLWdqdbLW+gTj4yN4/AvKzpGNNQ7RhIWuQD6fIxyAZEKyHYQQZxZFi5Q9Hh1PkU0P8Kq1KZbPM2lvhPmzTC5bPU7Et4Wnnnzo1ByoEEdhGAb+6mpGFIXPXnEFnw+FOFr4fgDoAT6dSDC6ezdX2zZDd91FTzRKzOMhVFV1Eo/8zHD5Fdfw8KY17Ds4/fOPPgWK4jIwlCYXf4yOWa2MTLlvbW+CgeHy4fR+I0YsUV70oDJkks8MkM+XryvEqSKBUSFOM1decxN3PPHqsmU3vt7iJ7d3AYUZmDev+ySrz7+O9Vsay9ZbsyTLjj0pwgFIp+JlN/azW6J0H9wJQEvbIvqGSkMfVMCv7SE7Q8+pYRjYSltZEKG+KsfY6GDZOlmrpmy7ipBNOl/YSlPHJWtUCHHC1M39AlPb1NddYfPYkzGCAejd+RUURcHxXVm2zfzOPEFfjv17n6G1fQl9Q6XGesgHQ71PUd/YwfBYqYnk80AsVhhOXxNxSaRsfEZGJhA4jdi2zfDwMCMjIzjHmIxDiLOV11fK7jJtiE0kOW+pxXQDktsbTAJaF8NDUlNZnD50XWf+ypVsCoWYHQ7znmuv5X2hEFPjeC7wMPD/gC8CBvDZVIrbtm9nQS5H3113sXl8nLq6ulPxFk5riqLwjf++h/+6/fX8y3ca6R2AfB527IW/+Qo8thH+/a9hbHQ/qxYl2bh5iN4hX9k+Ap4k+SkZos31Fq5jkpxSvshvgJVPkEr2yTVbnBYkMCrEaehdH7qdOx6oKFv2qrVRnn6+MOTg1Rf28eiDt7HkvL/mQF/paxwOQjY7Sibncs5CkyefK1Xa9+pgJddhmiaapuGvXFM2xLQu4tDf8/SMx1Vb38FotDxrNJ8uzxqN1Mwqm+DJb0AiWXihCj8kEzLzqRDixLj62vdyz2Ol4fABP3QVx3BdtrKPF7Y+yTmr38DentJ5srEGduyOkZ14GsMwSJqzyvZZ4evFdV2ydvnkTJUVJlnTwWdAPJknEobYeN/L+O7E8cjn8/zw61/nX9/xDu5617v458sv501z5vDeyy5j06ZNp/rwhDithCvrSBcHFHX1ZlnQYU8bFD1kQWeW3bueOynHdqJlMhlGhgcYHuxleLCPRDyObdvH3lCc9uobG9FnzWKbrvOaefP41/e/n++uXMn76+r4cE0N729u5pvt7fw9EJmy3Qfyef6wbRttqRTNt9/OUz/4gQTlphEIBPiPb/2Od/z5en786F/yue++g6/+ZAUffx987i8KI3Ree5nLg4/tpWfPHUxkZpdt31yf5eCU5pHfA2Oj/aQz5VmjVZU2rp0kGZd7Q3HqSWBUiNNQIBCg49zfcaC3tKy1EYaHDpBIWngMqHT+G4/hZSj9eswp7byVi1yefG4ETYG2hiSxZOmCP7s5z77djwNQV99C/1j5EJL6yBDR6NEvToZh4OgdZZmodVV5RkdLxWQ8Hg8Zs7psu3DAJmMWjkN1xiRrVAhxQqiqihn8WNmyay7MsWN3inAIDmz9NwKBAP3ji8vWiYRTNNWMEo/HaWpdS3xKZ05LnU3Pwa00NC9jYkppkOowDI0UIgohv4XruLh2TM5np1Aul+NfbrmFC376U2Y/9RTPPv44b9y+nR91dfGlxx7j3rVrufmSSxgaGjr2zs5w+/fv55lnnqG/v//YK4uzVjAYZCJZuP0bHTdpOkbCnFcH10nNvNJpxjRNhgf7yCYGiPiS1Icy1ATTkB9heKiP3LFmlhGnPU3TuPA1r2F/Rwe/DwQgFOLz117LNz74Qd55yy284X3v47abbmLHa1/LC4dte4Nts337dqonJuDWW1n/i1+ckvdwJujs7OQzf/cN/u1rP+PbP3iG+zaUT/5743VpcslNLFh6JYNTbh9b6qFvqHwUokeNEan0EU2WlhkquHYaQ5sgkzmzzjPilUcCo0Kcplauvox1u25mauf2dVc4/Py3+wFYtdjioXv+mvMueisbNpdPerRyYZo9BzK0N7ps3Rkre64msIN4vDA5U33LWqLJUq5AyAexoadmLLZfW9fOSFSffGyoYGX2lG1TWT2rbLhEwAuJRCFrtDLokkyOHd+HIIQQx3DdWz7LoxuNycdN9fDks4VepYuXd7Nj+3N0Ln4H8SmN8SXzXPr64xzY8xiRqir6RyNl+7QzWwgEgozHyzuPVCWHA1SGYCyapzJsk0zIJEynym1f/zrv3rKFLcPDjPX389+5HFcAlUAL8Le5HP/++ON8+pprSCaTx9jbmenOH9/Ghy4+lzuuXcvOG67g1qvO5ZZXXcKTjz56qg9NnIYURSFnFYa9KmphZvpjco898/fpJBYdJexNUxWwMYp3upoCYZ9DbSjH+NigdGi9AsRHR7lm0SIuv/RSDi5cyAPNzTzT0cHCSy9l7ty55IDrVqwg/a538ZhaHvK42nXJ7tqFMjKC+Y1vsPH3vz81b+IMomkaV7z5YTZuKX2WwQCcu7iPn/3wrxkYL78XDfrSmFPOL031OQZHLFzHgznlNrOmwiWZTGFm5XspTi0JjApxGrvx5v/lJ/eWD+e84TVJ/vBIYZKly5Y/y7Yt65m/6tP0DJYarpEwxGIj5ExYuTDL7q5S73hdxKVn330AhEJhYqny3r/WhhQD/fuOeky6rqPoHVhTLmq1VSajI6X0Vq/XSzoXKdsuGLDJHrpCWmNYloUQQrxUgUCAgxNvKVt27tIUQyN5qiph76Z/oa2tky17S412VYFsJo5qbinso2JV2TmtrTHN8HA/oeql5Kd0TjXWuQyP5Yr7yOPzQjIuw+lPBdM0Gdm4kVm2zQPDw/zFUYbIVgOf3LKFmxcu5N+uuIIvv+ENfO2mm/ivj32Mb3/hC/zoW9/i9l/9igceeIBnnnmGvXv3MjQ0RCwWI5VKYZrmaTsz93/+w+fo+7uP8O3BZ/grZ4SbtCSfs/r5Ztfj3P3hd/KHO24/1YcoTkMuhVJNbY0+uvpmvhVMpMHrr5lxndNJPp8HJ4vfmP55Q4VKX570K7Sj5Gxh2zZ2KoUX8BsGa+fN49rVq7li2TIaw2GWLVpEbM0aelSV8zo6aL75Zm43yv8oVgNNBw6Q7O0l9o//yNb160/JezmTtM+aw5DyNRJTvj6rFkOF8wvwnlO2bltDjq6e0rUz5IPo+ABev0E0Vt7Z4jOyeA2LZELqGYtTR3kRjb3Ts1UoxCvcgf27iL2wmJWLS91uTzwLc+YupbHew/1PVnD5G//I00/8nLXz70Ob0sZ9ZGOAyy+o5/mdOksX1U/2nGfyELWupbllduHmsvcummtKgcq+EZ369tdjGNO3LG3bZqTvMRqrS9sMjOs0tF6GWuyVzWYz2JnNBEtzPDEc1aiv9mEBqWwjlVVS9FwI8dINDgwwvrmFxXNLTZUf3VnFe27oZCwG474fkIwNck7rD1CL7fF4EgajTYRbP05dXSM9u79HZ1MpW2FHdzMLl7+Rnr130t5QKsh8oF+ls7US04VY3IeqGgSrzsHn85+stysoDB3f9IEPEO7pIbF/P28+Rnv2FuB/plluAXnALP7kj/LbOvRbUTAVBVtVsVUVS1WxNQ1H13EP/RhG4WfKY4rL8HhQDv32eMAwUH0+FI8HxesllkwSHRtBVcDVDdTKCLOWLCXS0IDu96P5fGh+P7f/7GdYd/2Y/4pMn2HjuvDBwDy+tXErXq932nXE2amnezttkRdwgQfXx7hsjYmhTb/uuk1elpzzJqqrT6/gqGmaWJaJZeZw7RyubaJgkc1ZVATBp5evb7mQyTo4rotHV0nk/NQ3tU2/c3Hay+fzDL3wAm3HmNF8fyJBZv16luTzjKZS/PY73+HmdPmkiaPA72trqZ07l3m33srcxYun35mY9C+fmcPf3Lx/8rHjwD9/by0fePdVtDSUrsUPb4xwxdrA5OMdBzTq2l6PRxvBq2fxTvmeDkUNqqsryFq1hMPlo3WEeImOa9iDfuxVhBCnUufsBdyx8Z9YmP0s/uKkfxethu/9Zg9/9s7FXL02zj13/Tuvuf4zbLj/SS5eEZvcdtm8NAd6spyzwMeTmxJcuKowSYnfA73DD2M1tGMYBq6+FNPeNNkwbqmzONC9ic455057TJqmoXrmYLq7MIqnmtoqi9HhXuob2wHw+fwMRysJeicmtwv4bXKWi1dXcKxhHKdmMpAqhBB/qsamJn7/ywtZPPeJyWVtDTEyWYeaiMoTj/0L197wQ17Y8BOWzy8EkipC8PyOKEn7EZqabiLnzAZ2TW5fFRos1KIz5gLbJ5fXVTsk0jbhgEY+n6O+3mB4tIeW1vkn6+2e1Xp6elj/u9/R39NDcybDgUyGi4+jk993lOU6L7Ix7LqFn1M8YccDGnx4hriOosCN8S5+88Mf8K4PffjkHZg47QVD9Zg2GBqsWhLmkY0TXLjCJjjlS2LasGmHh9raNux8DNetRlFO7pB60zQxTRPbyuHa2WLw00ZVLXQFdB38KoXxj8V+/JwJ2pQgr+vCaNwin7cI+ApD6pMZSGUtEvE44YqK6V5anOYURcE+jr/HUDiM/upXs/GRR1gN3PjRj/Kf3/8+Hx0d5dCfSS3w1tFRbsvnyd98M/6f/5yW9vaX8/DPeB/+9NP89lf1vPFVxfkjVHj/dU/yyJPncOP1pU4Uvy+NTWDys26us+nu20FLSwuZjElDVWmkR2XYJJe10LRRcrmAdOiJk04iEkKcAd54w9/wk/vKhyjc9Pocv7yrMMnCwoY7GOjvpnPppxgYKa1TE4GhkRFsG2Y1JokmShegOa1Z9u95CoDm1nn0Dgem7p6Ir4tU6uhDjWrrWxmdWmtUASe/r2zGz3Bk1uTsp1AYRjERL05eEnJIJsaP7wMQQohjOO+qbzA0pfj/FWtd7r6/MCzr/MW76O09wES+vLOntTGL7uzCtm1aZp1HNFF6rrHaoafreZpaFjAcK6/FPDpemFSgOuKSyzqo7oTUxnqZmabJbV//Oi988Yu8bv16bunu5qCiENY0jqfK6yuteEtch+ZjRHQv101u//d/YvuWzSfnoMQZIRyuZKLYvKuu1Dh/RYR1z2g8+gw8tRXWPQcPbNCpb+xgVnsDupomFhslk8mc0POc67qYpkk6nSIRHyceHWBitJv46AGS43uwk10Ydh9hdZQKI0mlL0eFzyLkAZ/BZMf8VLoCtnVo/zAYzeMzLFpqoCoIFQGoC0NHrU1yoo/4hNSIPhMZhoHr8RzzvJ4C6oDWK65gY0sLGAYfvflmvtnZydS8UT9wczzO4PPPs+6GGxgdGZl+hwKA6poaXuh9DV1TKgm1NkKl+30mJkqTLs1ty7P/YKkTsTIEuttDLu/H7/eUzUfh0yGdTuL3QDbVX3Y/KcTJIIFRIc4AiqLwxvfez0NPeiaXeTywdPYQu/almdPu8uyjf0VzSwd7R67AmZI8s3a5zYZnR2lpcNm2q5S9qQIVni2kUkkURSFSdz6pKUHMqrDLYO+TRz0mVVXRfXPLCmgXska7Jx/7/QGS2XDZdn6vTd52MQA7P3za1m4TQpx8ruuSyWRIp9Mv+gZ8ydLV/OHJOWXLVHcYx3FpqIWtG77IkpVvL+s8mtMGifgYXfu3Ew5XMBg9bLiouR1d10nlGssWB3wmpgN+o9DZE6mwScTlBvvl9Jv/+z8u2bSJaxMJQoBPUQj6/ayqquK3Hs+M244B44rCnbrOvarKHxSFBxWFx4D1wEZgE7AN2AN0Af3ACDABpCkMoz/TuED7aB/1b1zBrxdW8+OP/hl7du481YclTjHDMMhkS9+ZiqBKc6PGJWvgnIWwdiV0toeY1RImmcwRCkM2MUo21k9stJfhgR4yhw1HPppS8DNJfGKcifFS8DMV3YuT6sJr9xNWx447+DmTYAASxWBLKutgaA7hadLFFQUaKywmosNS8/4MFWlqYkQ/eu9QjMJIAT/Q5LrMWbWKFxYvJqaq/OU73sFPV61iavhTBT6czWI++yx3vva1xCcmptutKLr5o9/l9vtryicJvjzPbT/9xeTj2ggc7M+WbacqOczUs+SsStKZ8hoeVZU2sUSWUMAiGZfgtDi5pMaoOKMdanC5rothGK/4YdkPP/BTllXcSG11adlP79Z56+uXYjsqz/T+NWsvfjPPPPQxLjinlPo0NAY5u4maai+9I9Us6Ci1ErcdbGbJijcBsG/Xw8xpKl2IMnlIuZdQW9c07fG4rstgz6M0VZduGQejOrVNl6AXGyvpdArF3FpWCP9QrVHThYzVSkXFzLVkXNdlwxPrePbJ+9F0gytf/TYWLlp07A9MiDOcbduoqopt24yOjpLPZvAHgtTU1p60892+ffv43lf/gfTAXnBdfPWdvO+v/oEFCxaUree6Ll0H9rPzuSdxbBNPIMSKtZdRV3fsWsLxiQkO7N6BnS9kGhgeD4bHhy9UQWNLGz7f0QZCl3v4gV+ytvHtk2VHcnl47LlZXH1ZDf1DYDf8mgMvfJtLV/ZMbvP0VhUtcAGrL/oIvT17aPD/cbKsSM6Ccev1BIMVaNk/lg017Rny0dbkJ5qEYDDE0JiPto6Vx3Wc4sUZGRnhwc99jnckEmXLE47Dl7u7Se/cyXvHx1k2zbYu8PGKCs7/wAdoq62dZo1iW8KySGazJPN5EpkMuWwWM53GzGZxslnsXA43l0PJ5VDzeXTLQiv+6LaNbtsYjoPHddEcB911cV0Xpzjs3jk0BL+4nCk/iuOgAKrrorhu4XfxsTblt1Zss3tw+bUKf9sKbUeZZAbg/jTEHHhbqLRsyIJH/bWY17yZi/7y/9Exe/aL+J8QrxQHdj9AZ2Np1M7WXTGWzTUxLdh7MMvBfi9zZrfTMauORMaHpuqQN9BUBa9HJZbV8YdrCIUrJtvilpnHsgpD3nEKNT811UbXCpMeqSdoJL7rQt6GnOmSs8B1SjtWVHAdC12xSGagqZqj1k8FmEgruN4WIlVS0/BMNNjXhzk4SINpcijUb1KoG5oDZlFeXDALbB8aomrjRjpdl9uffJLlDz3E3MP2e5eq0rV8OX+2bh2hUAgxvVu/8Vr0/AO8902l+8BUGh58/kre8Lq1ADy3I8CqpZHJ58fiEAjU0D3cRF19LdgJqqdUtBiPQ7iyBscBW2kiEJDPX7xkx3X1kcCoOCM5jsNAbzexoUE8Th4VyKkG/kgNrR2dR5006JXgu//xWm6+7vdly75/RxV/9o5ONm41WHjxH4mOD+LPfIG6Ke289c9rnLe6je17dRbNr59sKCazkFbeQH1jG5lMhtTo3dRWlr7uXYM+2uded9QgzMhIHxHv9smJnWxgJDGbxqZS5tZw/1bqI6nJx/FUIZvU0BTGkzrVdUcPcj69YR3f/upfcumc/VyyII5pw92b6tgdW8Dn/u1ntLa2HucnJ8Tpoaenh3/96/+Pvj078YfDvOfTf8/c2bN5/L67GBsaIBjwUxGuAMNDU3UEF8i40FHhp8GnkkRnyPUSqm2iY978l7XR/r9f+WcG7vsmH+0Yor5YbWMsA1/Z5kdZ9Q6++F+3AvDC1i3c94vvESKNT3NZ2hRkQX2IzckQyeAsXnX923AcB1VVMQwD0zTp7TpAbGyYZCJOyAOLaw28GpgujGVhJKsRCgYwFS9tcxfi9x97ciPXdfn1/9TytmtKN/y33Wnw7hsKIbPfPb6C+ed8hI7Q1/AV76JyJmzc1sCay7+Ex+PhwPZbmdNSmtBhR3c9i855Gwd23UtnU6m8SPegQltzBAUYGPfg83nxV6w47iCuODbXddnwwAM8c9993DA4SOM06yQch+/293Pfpk28N5Hgba57qNwg3cBXAwFevXIlr50162U/3pzrkrBtko5D0rbJWBZZy8K0bUzLwrZtHMcB20Z1HFTHwXBdPK6L13HwA6HiT5jp66J+RYdPNMJgNs9/PreLfzvK5EuOCzcPw7frj55112fBumAD7mtv4JKPfZrWNpmM5mzRfeA52mv2Tj7eumuciViUaCzLgk6LUBCiEzr7evzU1M2ic9YS/LoCLqRN0HQNRzEI+VU0zcHQwDNDAHImrluYHMmyXWyr0JlgO4UOBLs4BEoBNNVBVUBTCz+6NnPQs28cWqqP/jwUaqmOZSM0ykRMZ6xUKsVYfz92tpiZqOtEGhsxYzFCQ0MEptlmx8QE5vr1LLcs1u3eje83v+Hcw2IijysKjy9aRO2730l6bBRF1Yi0tHL1De+ksXG6q9HZ56kNj6CO/wVubhvnLS8tf267QqT5g8zuqCWagNGJBubNKn1Z9/UHaagLMJFbjUefIFKRL7tODU/4qK8Kksiq+ILtr+j7enFSSGBUnPlM06Rr3x4mBvrQbLPQ3ewLguahVU1Sq7lMrb0dt+CgWsHC5atesSfRdDrNfT9q5s2vKg3x6BuC3tE5nL+qkl8+sJIrX/fPbH3uV1yxojQU3nHhiU1hLjm3hg1bg1ywonLyuV3dAeYtey+qqtJ14Hk6avZMPme6MDixjLb26YOXrusy0P0YzTWlQMJwVKOq8ZLJ/4NUKoFmbyubJXQ4plFfVcgazdnthMKVh++a5599mh9++S189abesmL6APE0fOxHC/nKdx87row0IU41x3H44BuuRt25gY80ZlgUhKgJ3x3QeSQd4lvvvII5VX660rA+qdMQqSAfrGZRXSXtlV6eT2rUhQPMiRTCJfvSCjEtiL+6jta5C6g4wZNI3Hv7L+n+3i38+ezpawF/d4fGY96LuOjyq+nI7eGyZgufVjjXbB1TWD9s8NYV9eQVg4dGK5nX1oDtQtIx8Bg6S2ug1utiA0Mphd60wtz6IDX+wokib8OuuEptRZCkFmbewiXHddx3/OJfeNPqv518PBaF7tH5rFwaomcA1OY76N3xT5y/rDQU9LFnPdS0v48lyy5j1/bHWNC0dfK5kZhCsOF9TMSGqA1snOwEytkQT4epq9QZHIfamjBD4xFa2ha+yE9aTMdxHO79xS+Ys3MnmwYHeWsyyUxX9aRt86mDB8nt20eFZeEqCnXV1bx/6VJaAtPdGp9+HCDtOJOB1Ttcm4t9FqZpkcvbWKbNzlSWld4sl/vhhweGiA4N8rGQjTalLZR14ONRnTqfwYVqliu8Lr5jJJgfNOHxiiaU697BlR/7NI1N048UeTlZlsXAwADxWIxAKERra+srsi1nmibJiRhmvhDIUVSdcGUE33F0/pwIrusyONhLU3DD5LJf3b2XVYvSzGk78nbv+R0G48k5XLVmxeSytAWjSZXKSg+Vh329jhboxHVxORToLNQd1JTCxC3HE+h8sfrGoOXw6ii2i+2ApioYWuE6M56tprGp5cS9sDhtpBIJUrt3U2OaHP6n1ZdK0b1+Pedms+weGKD7Rz/iNYfVtdwG3DF3Np9+7fl4dYUJGx50A9hLz+WtN3/wpE9Idjr6xf9dTMg4yCUre6mY0k//49+FeMe7/gJdV3nk2RouP680mdLOLo2Fc6o5OKjir1gJ9ij1VaVzTyIDhrcKn64STXuJVLWdkM/atm0GB/oYHuwB1yFUWU37rLnHPdHToQ5+ccaRwKg4s+VyOTatX8ccotTqpT8/04HdaRW/z8vsyJH5FEkL9lh+IhVhQEHxeKltajljh0LkcjnSxVpOoVAIwzB45KHf0a5cz+wpHdz3PKJyyYVLyZk6O4c/Q8ecZfTt/Sprl5du/gdGwFGbUVQv/mA9VeFCM8F04eDoecydfy6maTLYdTdtDVOGx4+pVDW//qgXjtGRASq9L0wGDBxgaKKDppZ5k+sM92+hPlI6lokUBAIBDFVhLGFQU39kMOHPb7yYb77zCfSjNJT7x+DbW97PF/791hk/QyFOBx98w9Vc2/8Qb64/cjbrviy8Z0+Iu/78dQSKaTcPjGkEK6uI+iKc31ZDjU/jiQmN5U0RwsUv23MJldbKIH2Oj/rOeeiGQT6fJ5fLkc1myeVyZDIZspkMViZFPp3ATCex0knsTBozk8TJprGzaZxsGjeXwc1ncPNZ/vD889z+6tRRhz+6Lrz5Pp2mtlncsKaFypCf6pCP6qCHkE8n5yh8f5eHD1zQzOODOis7a6nyq1gu7Iyq2JqPc1qCk/uzXNg8pjKvPkSFt/AZjGTBVH3EHS+VNfUEQxUYPj+6rh81YJLL5Xj055Vcc1GOiQT84rfwwDqNlcurecub6tjZfwl1rZdxwby7J7cZHIM9fYu55JrPkk6nSQ39gLpI6bqza2ApcxdczGDX72ipK50bD/TrdLaGMV2YSPqwbA91TWvQDu/JES/K3u3b2fPMM+j79nG1ZXFPLMaaWIyGGbZxgR8Af3ZyDvGk+K0Gl1VD1ZQ/J9uFL2w+wGdDObwKPDgU41ddQzQ6OeoVl25HZTM+/nlxC6sjARzXZcPIBHsHh2nOp7jM5+I5xi3CXhOe8FaTW3khy69+LZH6evAF0AJBtGAIIxzGG6rAHwjg8/lecvDSNE02Pf0kI117adZy1Ok2WVely/IRbu1g1QWl8jxnIsdxmIiNExsdwcpnsW0br8egKeLHpymYDiRsDdsIUFvX8KIDAKZp4jgOjuNgWyauZeG6Fth28beLgg04qDiogGVa4NlKRQAGR7Ls2r2Xy849+mQn96wLsHj+lVQGfQS8Gj5dIZ6HWBZCAfWkBDpfrIEY1IQLmazRhEU0YaFrDpoKeatQK9/r9RGq7jxj7xHEsdm2zVh397TZo1vGx0ju2s6K0RixiQke+u53uSmXK1unD7i1rZnPvPlSYqZDznUZ1v0MrL2a695100l7H6erX/7sK7zh3J/xo1/u4kM3lEYHOg786J6lvO/db+D5XT5WLi6lbw9HoTJSi0dX2LSrCo/XRyRs01DvRS+e/wbHdRprKzFdyJo1hCuOkf59DNHoOLu3P01rJE1jxEJTYDylcHA0QKR+Lp1zystD5fN5stnC+drMpcE1UXBxUVB0L5WVNXiOUd9cnDYkMHomMU2TO3/2Ux762W1MDPSR13TWvOY63v3Rj9Hc3HyqD+8lc12XH3//e/zq6/9IIBvHBuyKBq5994e46cO34Pf7cRwH0zSLdYpMdm95jiXOGMGjtIV3pBQaKkNU+45cYVsSKg0FBQVQyKOA1483GEbRdBRdR9UNVMODpnvQdB1d19E0bXKo58vNdV3y+TyKokx7Ys1kMhzcuxMtnySkmjgoxB0PeAKENJuH7/1nbnz9nWWZlN/5VYgP3jSf+9aFWLHmP+ga6Gd+40+pnpKMue5ZnQvPa+WpTV4uXFPqSu8b0ahpey8+v5/BgS6qfU+XDYs6MNRK57wLj/pe+rvX0VJTakwMxzSqGkpZo8lkHMPZjnfKf9fIhE7AsNnfNUhPX5JsdA+Z6C7sVBex0R7GR/v5hxtnPvV89IcL+M+fbJNAxBluy6ZN/OhL/0h6/27SiTjpUCXX/+WneNd73jPtTeKha9fp2lufz+cZHx9nZGSEkZER9u3dw2Nf/EtuW5I/6jZ3D8Mvx/2srjFQXQfNdbEdB4/iouFiKA4GLrri4lFcDKUwZNijungV8KjgVcFX/O1VwaeV/v1iDObgOxPwuem/8pO+9jy8cTbMriwMSRzPwXgWojlIWipJSyPuGLialwnXh8frx/EEwVdBzqgkVNNIe0sz7U3VNFVX4vMH2JXwsrK1cJPquvDCONQGwHEVbDRs1EIhOc1A03QU3YPi8eLxBfAHKzA8Xn7xgz8nuu+H9O6AGxfDokYYT8NPt+psjvl518d/RqP3x6xcXPpgHnwqxKpL/5Wqqip2bP4Vi9qHJ5/b1+ehc/EH6O56no7afZPLJ1Lg8Vbi96j0jSjU1oRI5DqorZNhdn+qXVu3ktu4kdHBQdakUlQA47bNwwMDvGWGSVK2ARsNgzkn8VrgFH8sCo1kR1EKjeWp5yVFQVEKrRFVUdAUpTDzaXHdLIUgkuI6ZEyLKrV0zYs7LmnyvC1cfh3sSuX44Z4+3ufLM6vYXBnJmezI2tznBLigsRYySWrNDKs9pUCo5bg8MRKja3CYNjPFpb7CLN4z2ZGH9d4qrOXnccWll9FZVYGhFjoysm6hzEcOFVPTsTQd2+MFjx98frRgED0YQg9X4guF8fv9eL3esjZWKpnkufXrUEe6uaDCOqIjZsBU2eVr5dJXv+6MzNQxTZPufbupIEnEWzhnu0A8DwMZjWDQR2OlDwNI2iqmUUGoMoJlWTi2jWObuI4FloXrOuDYKK4NuCi4k4HOQwHJw1vEDmA7hYC647jYuLhOIYszZW6jpc7hD48c5NLVMfwzJE0NjsGOPfNYsWQhiZyC7ao0RbyMZBTapi/d+7Kx3cJ7sqxD76vwKbiogAqqjqIYWK6CmY2hOFlwTeorKcuszltwYFijrWMxgWDwqK8nXhmmyx59bmSYlVqWnX0j1Hb1481m+cF3vsNfHlbPOg78bU0Vq1bMJaAp9NoKPd4wb/vU3zJv8WIikchxZx0eL9u2GR8fJx6PM9LXjWtb+ENh5i1eRvAk/L1ms1kGerrIJhNYjoXhCdDaOeeITgTTNHn41xdxwYocD697gTdcWer87xuCnokbWLJ0LoPj9bS36OzdD9EYxNMGfl+QjiqDoBeylsJIVqeiKsDc2ZXkLLDsSkJ+nYwJinH89eYPl0wm2bH5UVZ0pKYtLbNnyIOv5hza2jvJ5XKMDvejksOnFzrC07lCR0ptxIOhQt6B8aROZVXjcZV5EqecBEbPFLu2b+dLN72Nd4zu4tUeG1WBURtuTWk8E6zj3De/nY/907/g9/uJRqNks1kMw6C6uhrbthnq7yM2OIjr2KCo1LbPor6h4ZQEilzXxbIsLMsqBDhzOXLpJH9xwxt4TXYH72608RUPa18K/q3HIN1+Dp/+5P8j4PGguoVGnmlDCpOVR46unpR34JkJhaaAXmgHKSoKCpqqkHAUvKpKpUdBVxUMVUFX1cmMxsOZxUaj5RYbkS7sPNjLnXfdyf79ewgGA8xpb8UbqefyV11HTW0tE+kMHp+fSCRSaIQVA66KpqMWA6yapqGpauEmyXUKjVHboufgATLxUfxYhfqBGISr6mlqLATBc2aO4b6DLArnj6jZFDehJ6Uzp9bDL+54M+99U+nmPZ6Exze3UllRz+9+X09tdQt5LcvHb7YJ+gtv3nbg6a2VrFhaRc9wFfM7Sif07d2zWHzOdQAc2Pl7OptLDYN4CghefdSJksbHBgkbW8s+456xFlwlRG/3Psb6NjNycB2G2Y2SH8avxKj0pWiK2LTXQfiwbtz7nys0Wl933lH/BAD4p9tb+Mi/bqampmbmFafI5/PExkcx88WeTUUnXFlDOBw+bQNtr2T/8dm/Ifvz/+MWd5zK4t97yoH/Tek8EGrmtX/2Aepa2giEQqjpJF7bwgbw+ojUN9K+YCHNf2JtPNM0SSaTJJNJUqkU8YkJYgP9JIYGSI4MkR4ZxhobwZoYg8QESiqJkk2j5bIYZg6PbeFxbHyug09x8OESUCCoQFCFkAIhFX6WghWzYU3k6MfiuPCx7fCt4xsx/rLalYSHgFtWzLzej3bA0hpYVf+nv5bjQqwYUB3PwUReJWFrpF0POdVPXg+h+EKo/gh6MIK3ooZgpJbK2gZCkWoCoTChcBhV9+KiYCsa//ftb3Ku+yvevPTI1+uLwYdvD3LlRctJ6xp1TXV4FD+uCXnCVDQuYunqi1jQ8tRkR47pwFD6GmprW8mM303VlEDVgQEPnS1BMnmwCTI+4SVSPRt/IPSKHAb8coqOjbHprru4IpXinvFxXpctzWb789FR1iaTdEyzXRr4ocfDzc3NMw63PxqnuI80kAHyikJeVbEUBUcpRpwUZfKa7tE0vLqOX9MIKAp+VcWhEOTMFH/nVRVHUbCL2yuqiqaqGJpGv64xN6hSoakYqsLDQzHO100GUjleiMaZp9i06w6mCz/uGuVNepL2wyJeMdPmNz1jbI2nqdIU8q5LVSDA21uraSt2GNsubI0m6RmPUp1Ls8Zb6EQBMB2Xx4ai9A4N02mludh37MlxtubhaV815sIVLFlzLqFQEI9uEPJ5qfXqBLWjBy7NskCqhqlp2JpO2oH+XJbX1Doc7fK7K63hWX01nXPmTL/CSzBZ97XILU6Odej+aOpv13GgGFgEt9iuo7AMp9CT41D4XfgHQ8ND1KppRpM57OLLRAI6NaHCX2pXXMUXCNJa6UEBBtMQ8YKmgUFhIhnXAdt1cZzijwsOUyfzOnT0zuTdn0qh6akqxaBp8c946vfjQHwfnS157nlwL6+7JMWx3PNwI6+7+Fyg0AY/OK7iC/hoq3tp7abjDXSi6WiaF1UzJpMZjnWOHRjow0z20l47/W2s6UD3WJDZ85ZK++8scHj26PMjg6zUCx3WfbEE2e0HaDctvnDrd/n86BhT01ZM4NaqIO+/eB4eVWXchn/OBvjg9a+nIhwk66rkVQVL0bAMLwQCqBVV+GrqidTXU1lZidfrPebfmeu67N25nVhfN7HhAUJunoUBm5AGSRt22WGoaWbNJZefsEz6XC5Hb28viqLQ1NTErhc2kxofxKe6eHSFWdU+VFWjK6ngeiuZv2R5WSD4e9/6AO9/wyae3RKnJrSXjimVKe5bp7P2ko/x5LZ6glqYBVVQGwLLgf2jKn1JjXNnh6kIFG4A+iYUxtwgbe3VDI+Cx2Og6xqqHsTQCzeLvkDgiDbW1BhE6cfEtvIcPLCT5a3j+Gf4uDZ2RVi++jKG+rtoiuSPyHbPmjAcV2mqKQRHTRdGEx7q6lvO6BENZwkJjJ4JotEof3X5Wv4ntXuysTrVE1m421tLbuVFXHn9m5lVEaBWcciqBqOGDxOFhX6NKqXQALJcGLFhQPOyeM15f3LPytSTimnmsbI53HwWM5fFzGaxcjmsvAlmDiwTbBvFcVBdZ8qMqoVe7H/5xtd4f3oD500T5HRd+OReA2vhJXz1wx9iXzrHcDZX6NFWoTHoYV61H89RWuvPTzBj8NR0C40s0ylkdJg22CjFRmWhv73wuxi8VCCaSPBf//kVOq29vLszQ8gDz40o3HHQw4XzG4j6mhmmlssX1pNxVIbMAMvmzWft4vml1z080Fp8fcuF/dEkiyIm1VP+a1wX+lMKPWkvs6orGc6ZLKpyj1rIPpqDhOPBSQ4zHn0HqxaXGvWPbYSn7p/DeW0acxs8TOS9/GaHwcoLI1z36sIwhO4B8PhbGIn6WDivYfLkH09DLHM5kUgd6VyGgPYUFVMClvv6w3TOfzWqquK6LuPj43Qf7GK4dysTgy8QG3gSvzuIx40S9iSpDeVpr4XGP2H0w/rt0D0C77hs5vU+8cN6vvi9AwSOs4ZcIj7BRHSAmqCJv9jiMV1IplXSZoD6pldmTbPT1Z0//TGjn/8oH1Di0z5/R1rljlAr775wFbvxMaF7iFRUsijoIZvNsjuRxg/YmkbIY5CLjmPGozjxCUjG0TIptHwWPZ/DY+XxOTZetxDIDChuIYBZDF6GVAgoJ27m3Kn+bgI+swJCx2g7fWIHfP3oc5GdNBMm/Msw/Osxvn//8BTcsgwaTlEJx0R+akBVIeEYjJsenhhL83/vOrJkwSF/2KGQ9LTzhvNq+N/1OmsWNHLB/EIWRDIPt28LUTu/g1ddUsGeAzA2CrmcjidQjzcAa5abJJLQ0wPpLHgMA3/QIGf6MHQfum7gYFBZXUdTc6vccB+H0eFherdto27nTloch7uLgdFDn5wJ/Hx4mNpcjktsm1Bx2UZVZbNhcEN9PTWahkkhOHkoyJlTFCxVxZ4S5FRUFVXTMDQNQ1XxahpBTcOvqviK2Z0A+Sn7MhWFfDHQ6U4JlKqKgqHr+A5tr6rHDM5uVmFxoDQpUtZ2eHIkQQ0Wc3WH3lSOnkyOMVchFAozuH8fLekoF2l5gsUbsmctnU16iLdffD5V/kKDwnFchhMpBkZGSI6NQmKCCjNLi2JTrcELsRQHx8aJFIOk/uLr52yHR4fGGRgeYZ6d4cLjaDo+l4OtwVqql63k8pXnkNM9RF3IKDq2oqHqOgGvQcTrocrQ0Y8SM+22IOeFeYeNZHZc2DeRY+94CtuFccXHBSuW4vf6cFWFQ61MVym23w51Qitq4d8c+n9WURQVVYVC97mCqhT2X3wloHiTc2iZWzx3TF3oulNXAOfwG6NDwdLCLwWXvOMSSyep0G2aAu5kDeaxHAxmVDqr/ZiojOcUfFrhGKdTbJ6W39kp093pKVN+KWWLDvsnCgrjqUHmzU5y9/37uO6yJDMxHXjgsSauvWjN5LKsBQeiGo01evGzcHGdYpjYLQyldSc/kqnvoPBvRVFR1OLRHMqkZup5+9CWUz/70vLCltPt3QXFBQXG4i6zasGY4do7OKERqJ5PRcUMNxTiFeVQ9mjPQB8r1exku29nLEVy5wGeHjhI7sGHeP/+Xg7/q/hewMNbLltI2nHZFM/yKzvAu9euxl9TS0djHY3BIzMIcy6kHUi7kEUlr+pYuoHjD6CGK/FVVeOvriVYGaF77y5qk/3sHxxjoS9P3ZQLSiKbx7Rt0lqQ7qo5XHjVNS/pc0gmk9z2rS8T2/c8s/UxHMdha8yDGaznHde9itUdVWQs6Eqq2BgsaQzQlVRIqiEWLFs1OeKxr6+P4W1vYOUi+NGvDnLj68bKRzT+ponzLvoAy8ONR3SA5W1Yd0DjwgUR/J7CWWDrgIJj+KgIKOAWMtwtx0VVXQxNQaHQkaapLqrqoCkuquKiqYXvuqaBroKhFs5Bz+2H1cV+tazpkMm5eAyF4JThVPuHDbJGJw0Rl3iyNDrF79OoqTAwtELmaDKvU1+pF/8/FPDUEz7BNf7FCSeB0TPBV/7mr3ndz77Mohla0R8eUZgd9qN4vGiqiqIohaFPrjvZMa0phxoHTDbkHBdUTWOyK9kttuKKPczgokxp9Ckwua5SbNyVN6JKjcOpTZuSQ/sDpfiPrO3yvJvn1hVHf39jeXjfNp0LmoKomoGjqqhaoWfY0TRyqqcw9MrjRTE8KB4fhseH1+cloXhprfDi9/nweX34fD78/sLzusfA0HU8uoaha4XfmlZapqlHDMtKZbLc8vnP89VlB6mbJjP+a5s15sxqRQnXE66u54p5lbgurB/WOcAsLlpxDsVWHooGKiqKqqAqKpqi0JPKsyBiUnGU0RYDaYVk3gd+H/MiR//MADaPQW1A4cFHf8Fbr/oufh/87w9h0zOwPAL1QYUdwzqDKS+3vLqVHfEwtefUcdEFEZ56DjZtVqgKe4imNM4/v5lVK+swNIVtez00hJeRyubY3rUTn7KX2HA36VgfZmoE8gkqPXFqghlaahzaasHzMsQRTQv+v2/Df3/06OvYNrz13yv58F9/h9dc97Zj7jObzTI23EVzpTltZko6BxNmJU3N7S/hyMWxWJbF0NAQ/T09fPnP3sUvzANHzRQCuHEQ/ioC4WLw8lAm5ssRwHy5fDkONyyGWccYcfOx7fBfi1/8/k2nMOFKrvhz+L9Np9AxY7oKpluoNme7ChbFAIOiYBf/jaLgKiq/Hcrwk9c6R+1hN2145x/gpkUqfs2l0uNS5YVqH1R5OWog5OV26w445xxYM+vo6zgO3PBjjT+7qomFjRX8ZEuYv37zbHzFSJXrwrc2GLTMP4cLOjw0hovbuXAwprF1SGV2UwVza1V8emH9oSQcGFNprAmStQ18XgNH86IGaujsnHf0gxGkkknyQ0Ps2LSJcwcHMYAt6TSBWIy5h63bk8+zPh4nb9ukFAVvJMLq6mo8xeu7X1ULmZyqynSX2syUn3wxaOoAbvEuTtN19ENZoapKoBjsPJF/zgcUCPmg7rDv1nDWYl8yg+U6GKqG6/VzQahwXAfHJ9iwcw9mPg+axorOWSxpbkA9jhNhNJOld3iM+MgQTiKGP5smEY+TjEapzKU51+tMBkkztsMjA2MMj4ywyMly3nEESZ/KwXP+GsLLVnH9mhWEvaVGQcaBUQfirkpeUUHX8egG4WKW6S5bZW4VhKd8FtGszYMHoyzwmSwMuhgqRPPwZEIn4wmxav4cmiNBPMdxkrHdUkaiTfHHKZZBmNJJXgq1qaVSCCrFUKpSiLcqauFmXCmMTpr8DSiqcsQQzS3RLK3+8o7wQ0wXdsUU5tUF6U8rdIaP/TmfaH2JBC0dUZ58boi2+kFaZsj837pHx6esZN6s8jIhB2LQWndqa4nO5MAwdB5jREPWgrFMAy3tnSfnoMRpwbZtdj//HBUDXbQU57KImzbrDg7SvX0rtwRyfOT2x/ibrl6mjknaBXzWA4srdVbpFlFH4UHbR6i6huvOWUTSE4CKSlqamljR2kTYc/zZhNli8HTAhrynlPzzwJbdPLl1J9VOBp/qMmR7sKuaeNsnP0dHRweqqk7+HOqIPZRBeaj+sOM42LaN6zg4jkViIs7/fukzfKx5H02HdUztn4Bv7a1i+YVX8Jbz5hDyqgykFRKOlzm1fraMgs/nQ/cFsB0HHIf1j/0d73/zOLbt8uNfvcB731Sqx55Kw71PX8ElC95AY+WRN43RNOwc87GyM4RXV8jZsHsclp+Ar6Rpw44eaKqy2NGVwlBtQn6XbE4hmVOZ0xKgvcHD8AQciNUxu8VPJFTouHRdSGRhaEKhucZH0KfSM6bQUutFVQrXlrFUgPpGmbztNCeB0VPFcRzi8TjRaJTY0BDRAwc4+MILjG7fjr1/P8bwMKFEgupslrt0mx8dYwToU1nYlYf3nIGdEb9OQm0bXH6M+kN/SjAg50DWLlxEDgUBDj3Ou4Uf01GKmZqFYICFguUqOBT+baNgKyo2Ko6i8tR4jk+em5txWOi7HjD44MVtfG+HzrmtXnRNRdcUcraGohv4vB4qvB40XUNRFDRFRVMLje60oxA0KDa6p3b3Fy9iKMTzCh690HteWEUFFBS12KNebJhnbYWAUchs2RG/m7GRGFdWwNXzy483b8Gnfmfwnld18svdlTQ2hmkKujhWnrGJDLlckmw+jatkqK+0CHuz1IZMmqsLQc/Iy1yPPp6Gg8MwGNUYTweYyFfi6nXYRjNPbB7klks2cfHi6ScE+L/fa7TPamfL8EJu/Ni3CVeECpMN4OK4dildARvXdUimMjRELLwzNOAHYga1TXNO26xR13XZvXMnmx55BDOXY9bixZx/+eWnRQFw13UZGxujr7eXt9wtXQABAABJREFUkV27iO/cSXL3brL796P29xOIxYik09RZFs0U/upvrYDPHeOm5ZsxeFUAFp76t3hcLBeSDiSLv1NOYTKT7QH4wgwTlr8Qh893ebi40Y+tFDKhcq6Goqo4aiELylU0XFVFUfVi5psGmoauHQrmKBiqWjgvqSq6pqGrh+oeFr8Oh513Jv/pFs9DxcX9sSQ7D2zh1svzHD5C1nHh/z1p8M4LO3kuFqIhCJ1hF1VxcCyLfN5iImuSyubJ5kzyplWYFMSxUV0LHQev6uDXHEK6S8QL1V6o8TPj9/N4fO4Z+Os3QfgYAZ1P3A1f/ziMTsCWA7B/yIdHr6Eq1MCCtiZCoQp+sb2aT7zlyBTeVA6e7vVwxdLyXBLTgU39KsvawyTyKrGcAZqfxo7FhMMvb+TDNE0ymRRmLo+qKQSCFSe85tnLZbivj6p0mme3bWNFXx8+Ctmgdw4Pc71lTZuBaQK/1XXeWF9PjtIQdqs4BH7qEHa1OITdq2n4VBW/quKdkhl6ojgUskyt4vGZgK0Up71RiiNUFAVHVRnBYq3v6E3rPSbkVQOXQlb7oeHZCsVh0u7kQOPCoGOlUF9Sp1A3VFOYcZKlVN6kZ3Sc6PAQXV0HsQZ7aM2lOM/rFF4PSFk2D/WPEh0bY6mbZdVx/Dk9kYVN/hrsuYtZtXgxzZUhWv3eI0bA2C6M2xB1FdKKhmtoaF4dzWfwwmiSN9ZOf63emVIZU4PMrQszbmlkFA+qz09lwIde7AD36xp+vXA+fLkcK+BqAjEblsxQ5SduwpjpAcNL5ymY+yeeNfE0DODaNnf/cSdvuebIGq9Q6Ki+88EK3nzFpaiH9WIOJKEyDIHT9FRzPIHRnAXDmQbaJDB61rEsiw3338caJ02x4hi/3NOL3bWXy+0JfrptPyvHB6h//iBLge3AN/zwjaZCPfepnsnB950K/mv1HPIojNkw7kDMVUgqOjnDixYIogcC6MEQgXAFFZWVVIeC1AT8+KakNa/Pw6qawmt85w/rmRXby9URqyyJYCAHXxmp4uY3voZgIFDW8e0oWrHutYaiKaBoqKqGrhU6/QxN4Wd33c17q1+g8SjlSvfG4I6JDtqXXsj1q1vx6QpPDkKDv3BvZVv2ZN1jx3XYur+Liy+8l8Za6OnPMdC3jfOWl/b3/HaFdVtvYe2idpZ3BPEdVuD64X1wznzI5SFvKeSswr2y4xavegpoioaqqRi6imFo+LwqPkNh3/4+tm7fjcfwcNlF51BbXQqaJDM2j25O41EzXL5cKSv55rjwQreKZvgJBoPo/npa6448mZkudA0pdDT6GUso1ER8k/sZTkpg9AxwXI2BM6ogguM4PP3EEzz285+jJJPYhsGSq67iiteXZst+OYIZruuSSqWIRqNER0ZI9vSQ6e8nPzSEPTICsRi5sTHGRkbIj4+jJBJ4cjkC+TwRy6LRcXgTEJlm3+uP40a/VYd1mRP8pk6SCRcWHkdj6VjF/6dzaEKRmQe+uIf9ntn2iWPXynvjLBN3ZD+3rSw0gnP2YT9pyCUK/zaLmVqmC5ajoLoKlgu2UwzKuhTrKBVudw4N6c8UhxgxNRiqqqiKgqpqaJqCV1NRisGPXJ/OrLojg6IAHh2+dr3JW3+0h+svU6n1u9SHHdrqoLGKow7dOhHyJvSOQu8YRJMqqZyO5Rpomge/30ukwk9DrZ/22R7me1WcYjKz44LrDPDOC1ze/y8hekfivPVid3J2+lQW/vdeDd1fy2vWVrFgaIiH7vo/3n3jDTBDUCWTOnbQJew3SUzEqK6tO3EfxAkyNDjIz7/yFTw7d9KQTrMwGETfuJHv/OY3LHnjG7n82mtfttdOJBL09fUxtG8f49u2Mb5lC5l9+yYDnlXpNLWWRROwADjnOPa5l/LZlo+mSoPE0UdF/8nsaQKYabc4/BaFrKuSUxRMCplOpqJiqiq2WqiNZ2s6jm7gaB5sjwFeH47Hj+rz4/H7CQT8BIIhguEwnlAFG277LgfSw3ROM+zcduFzB7187T2vpbOqkFa6Jw1bzDBOpJHr5tUSsxT25b1c1FJB1oFtaZU5VUGqfDopC/otHd3np3NKVqo59ca9eCNfmDCmECw9lCVaOAcdGoKq4Goaiqbz/PPP8YbPf4Sb5ya4tt1FUeDBXoVfd3l439pWKsMh+sbC3Ly2BduBRM5mImsTz9qQMnmuz6GuJkBr0ME2s7hmDs3O48HEq9gENIew4eBRXZK2xVjeIpYxSWZNMrk8uXyhRpRr2yiuhYaDR7Hxaw5B3aXSUwioVvsgXLyeVhowljp2YNQsXhZqK+HKFXAlWQrzz/YRS8LmAwrBuI+vf7ca1dtBe/syLlkxl9pKH0Ev1AYshuMW9RWlppShwoJah67hHAtb/CRzJpUBD33de2luqKQQwtJA1VAUHUUzUFUDrVgvT9f1P2nY/UR0nFw2TsBbGJFg25CMJpnAQ1VN/Wnb0TOpGPyc1dLC7sFBlts2BnBRVRV3R6NcbllUTVk9Ctzn8bCouZnxQAC/phE5jiHsx2JO+clT/N5MCWjaMDlkW4HJ7Bz1UP1QRZn88R7jeCbyWfaYWeYZR7ZPRmxIqgYrIiF2mdB5HG1F1y10ytg4WI5LzoE4hSGItu3gUKhPWbjGurgeA29TgMbGVhqXrQIcLMvi3h27SGzaSHt8mLUeeH17A7Q3EDdt7uwbIT4+xgpyLDtK2+4iH1zkjmHvXscTWx/n0WA13jkL6JzViWV40H0eqvwB2oJe6nSVOlzAAteCbI71E3BpTfm12nFh21iG3WNpXCCmp1ndEKIhYAMZTDfDcBQSroame3D9PhyvzrhNMVCg4BYDBIU6rzoBXcVvaEetPX8sWjH4fLRU4r4sNIcgmbfJWi4+XSF0WHS4woCD8TwNSp6BWLEAlVI4Rl3T8BkqXuOl/10fTdCnMxaH+mqNOe2V3PPoBFecZxGaco2KxuGRJ0NctmrVEUFRKPzdKafznFhuIWNspozWeEYjFJZh9GcjXdeZvXARL+zeRSSfZpbm0BkOQE2Eu3rSuLhc2V7D8wEP69bv5UeGyzebmbb83RovDKUT/Lp3jBvaamnRoRAuc5m8smSShUbmWGm7qAM9diGAmlJ0srqHCcNHtMLPQculMTfINQ2lBBHTdtFVaPIqfK4hylfvuZ83XrCCXC6Lmcth5vPYZg4nn8exTFzbRLEsFNtCcSw0tzCxWz5t0tDq0h01SeVtWis9hH2lL8rcCEzsHSQ6Oswftyh0Vum0+FQqLQWfAZ7D2ljzlzXy641e3nZtjrZmLxueaWXh7F4qip0+Kxe7PLv1hxzYfT33r9e5ZFUHly4t3XTrGlQHgeChz+zY9+5Pbenm27f+kqXNI1w4P0UuDf/2xWpidhtve/uNjMZyVPpMqsMupq1w9+Mq89sCLCk2xFUFls9y2LAry1gqxEWrp7/YGgo0RlzGJixctXRGzppgnAaJKeLEOO0zRm3bZve2bbzw+OM8eMcdvD4W4xrHmZzVcZOq8vP6eq695RYaW1sJVlZS29CA/xj1BjOZTCHQOTZGoreXTF8fucFB7NFR3PFxtIkJjGQSXzZLMJcj4jiYlsVoJsNwOk0yncbJ5fCaJtW2TZvr0gnTDtuayUe98K1jZIxuyBSyjd59BmaMPpCBiVp4S/PM631kG/z3aTDhyCcOwtevmnmdbWOwfgA+OM2kHqfKx9bDV94F3hlaz7c9Ax1L4JJlJ+51h6KFOqCjcYV4RiNnGyiqgdfrpSLkp6HGT1u9l5pK7U+usffl37gsboxxzxMjeHWrkDSjerjx1Y2cu7iUYvGVPy7mDW98B08++RSKU5hEwBOo4dKLL6apoXBL3TcOLdWFRsV4wiJvOuiqQnWlgbcYnc+YkLRrqatvemkfzgk2MTHBx179atIvvMCl2SyNjsNWTeM5v5+rFi2ic+FCam+8kUuuObLekOu65PN5stksmUyGbDZLLpUiH4+TGhtjsLub4e5uUnv3ku7pgZER/BMThNNpqkyTGtumkUID70SehpLAPwThK8f4qD8/Bu8JQ0AtBjDdQhAzC+RRyKGQp5AllqMQwLRVFVPRsFUNR9NwNB1HM8AwUA0PmtfAZ3gJ+Lx4PB68Hg+G14tueAo16XQdTTOwNA2zGAS1DC/4/HiCIcIVldRFwtSFglQH/Xi0UnDMLGas5zkUqChki43Hk/x/H7uZj1RHeWOdMzk77pYEfO6Al/dcdT5vWdpK1ob1Expbcj4am1tY1lxNn+3F1AwaKkMoioJH1+mM+AkZKlET+vI6zZV+xmwNn2Hgqlqxvp6OquvoxqHJKooTxR2aJE7TJifqO1rwbHx8nE++761Y/S/Q6LW4eE4ll86p4g+jVfSEl9LcPoeKiV1cWh0j4i10Cq0b1HkmFmTuwsW8cY5yROfXUBq2T3i4dG4lrm0zMJFl05BFImfjZBM4mQRqPgnZCdxUFC0fx+NkCagWftUmpDuEPeDXHBzbJmfaRNMmiUyerrEMPdkR/um6ozdbtg/AH4bgE2+d+W9vqkQaNh+A/cM+xjPVZNV2rNByrr54CdUVYQIeg5qggc9Q2HBQoanGwFANxjJevMEg4ZAHj8dDRch7RCab6RSCmY4LtlOYdKRQtEYrTjxy6P/MQNPKg6gT0SiONU6V/8jeg7wNYykvtXVNp3VwdLiri3rTxHQcfv/447w6m51sT8Udh2cTCRL5PJrrEldVasNhgg0NXDjlPdmUBzYtCkFNi2KWJkzWBoVSUFMrBjZ1VUVXFDxTgpsvZ7WOUWDQzpHJ52jAIay6ZF0YdFU8hsHCcAAH2G3CbL046z2l28VDj2FqHccju4PLl5VniZevW14kyQW6ursZWP8IkYO7WavmCBcDYNG8xUN9I6THx1ij5ll0jPtCy4XHcgrdoVo6Fizkwo4WRl2VQVslpemohodwwE9bwMdjrsb1U5JvDkzk+OP+Mc4JWCwLFd7x1qTKUykPrz1nLvPqjrwq5RwYyiukXQ3N46E24CPi1cqyrXJOoeZfbtrgqYZX1/Bpf3rwdGvMJGtmCRkOfs0layskLJXWiJemcOkD2xmFhVXT7yNnF0ZC5Www0Qod6uigFn50XcfQFdRCyj8w9X9vqil/BWUltFwSdg8dLRZDwyPYlsnzL4zguoX4f94Ew9vIBYuXUVMxfU/TnlGoipSCKeXVPtXDlh86wsPWUZgSXS09f2jkFJO50UzWkT3UplQUtTCy6tAIK1UpfsdVVFQy2Qxuto+myPQjjxwXDoz46Zy37IjyWuLsMD48TDA1wXgyxcH+Acx8nt5tW7jMSfCvD27gP0JpVAXu64+yde8BPj1DFrjjwluHFN7UGMbr8RL2+6kL+GgNeKn36tN2LszkXxLw6UWQNC3+d9MAXWMJKlSbnKtgaR7etbSRbVYF1zdAw4sIQuxIwm3dI4yMjTI/bFLpcdkb1zA1H395WSudNYXv+493wvJaSFoqUdtLzgijBKqpqKqjOhIGVStloxoqG/bt4KrLXuAXv4EqIGHv5lN/kSSdgS9+DVJDsLZRwa8rPNYdYkJt5h8+cSOtDVU81gWXLp/xsMts2d7Nt7/zHf7j/dEjagj3jsJHb23iR1+7icrg1Ama4PkDGhkrwEXLSqN4Ell4YneY11wyTYbRFAeGNXRPgLa6wjl8JKETqZW5Kc4AZ/5Q+kwmwz0//CGdfX18b906PptI0Az05fMMWxZ1uk6rx0MO+Mf6et79kY8wkM0Si0bxOw7KxATW8DDu+DjqxARGIoEvnSaQz1PpOFRTyDY8dDm3XJfufJ6D6TRj6TSZTAZyOYKWRa1t0wG0ctTO4T/Jf6hwVTMsmyGz5YMjCosqQ8RR8WvlN5iHGhqHmkRu2QlXmebPYJplxca/e8Q6yuTDwmrljZpDIy+VKdsoU7ZTUHBcl3Wjg/x2VamI8eF2JOBvD3i4ssmH4jg4xdmddRx0wMBBV1wMxcVT/O1VwFPMGPVN/dEKv/1/4lDMjx2A/7p65nWe6IfuJLxz5nPnSfWJZ+Dr75x5nd4o/OIA/NUNx7fPZKYQ9BwYh4m0Rjqv42BgeLwE/T5qqvy01ftoqjUw/pSU3+P0ldvhU28+9np/9aM6XntxMxcvzE0GiOMZ+MOmEA2zzuPSi8+jdwwUN49pWVSHXbx6YYjYeLLQiG6q85HOAt5WKquOcqdyitx44YW88amneJtTHvywgU8bBvaCBcxavpyVlZV4LAs3nyeVShFLJkmMj5OamMBOJNDTafzZLBWWRZVt0+C6NAMnMz82B/QDA8A3dfh2O5M33IfLu/CBeIBAeydrq0OEvR68XoN9jsF5QQ2vpuPXNRStEAh0VQ1bKQRKc4pCHoW8qmGhFmdBNrA9XvD6MAIhKsIhqioqqKkIEvH78BsG+mE3R7ZbDHYWj8ehOImbAoXAVaH2XCHLR8FAxaMrGChHDEvMWhbfvO2nPHrf7QSdHFlXpaJzEWsvuwIjnyafy5J2VGrbOpg9dz5evw9X1VnaGGE4Y+G3s0xkDxUCAQuVsEejMeDloOOnY+Hil23kxNZNm3jodz/DzqXwhiJce8OfMWduoQLk0NAQj//xd6TjUTTDy5pLr2HegoWMDA2x/dn1VDoT1OhpTEejzw6jV9QTDIZw8oVZx33hCLPmLjjuSdQA8vk8o6Oj9PX10du1l2TfXrJjvVjJMX7/7B3811UOs6aZ+M124F23KVx5vk5nk8WyTpemP2GCOChkr2/pgr2DXkbTVaSVNvw1S1gwfz7V1RXUVgcIBfzYjkbG0rBsHTQ/Pn8An9eD4fERDBj4X0SBvkNBVLs4+iCVc2maIdkpmQdHq6Wi8vQ6p0013NNDdTZLKp+nt7ubA93dXHRYlmgWeE7TaA4GafR6eU5VafN4Ct+7YlBTLwY09ROQPXo8JgOwxR+n+HgyiFlsNzlQmhyIQ00shSHXZJkOo6ZFxrLRFYVGn4FRPHH0WYDXR1rRaTOKcwyV9oAOvMj77OPmFDt1Cv922dvdw/5HHySwbxtryFBZPE2OZPM80j9CLjrOWtVk7jGCpHkXHs2p9FXUMXfhQi6c1TwZLBi1oMdWSBkars9D1mPQE0/xnkbriJrFtgvfH9CZP3s257XX4Jth2HzagaGcShYdr9egPuQjdBzRzkPB06xdGKJqohay7ZXCiB1D1whoKgFDw6uWZn/vTuRI5LMsqnbKrgGuC3smFAyvj85i4dEdUVj0J341JwOnjoqt6jiaFww/mjeA7g/jC4QKNfpnuCYc2PMwrbUjxGIj2JZFVWh4sg3VNwKJzKW4toqDTm1Yp2FKfcCJnELaaKap5RgZHqdYd9cewkaMqkB5cNRyoXfMQ1V9J5Wn8flRvLxM02R0oI9a26QvmcGfT/P0lheYO9pDz8GDGHu3cYXP4QcHhjkn2svKYwQgPzECX5/SqLZc6Legz4YxVyWpaORVHcXjwef1FIOnflr9HuoOC55+JQs3tZj89UN7+Hx7lrlThr3nbPhmr0amooE1HY285kU05D+1vps1deO8fa5Tdg1J5OGTGzx88lVzWNTo57ad8Ko2aDpsuH3ShL0TMJLzkNODeMMRGupq0TWdH23YwN9fBX4DklmLP8S3cve9Ln93Hsw57BgnMvDh39Xw3ps/AL4IjTWga4fazgqqqkzGFlTlUB+Kguu6fOFL3+T7t3QfdWK1zQfg4YMX8vH3X37Ecw9v1Vkyp5r6qtLGf9wS4JpLZp79dN+QRlVlmOqwxkRGxdErqao6Rr1AcTo48wOjd956K5fv38+3u7qo37uXjrExfjY4SHsuR5vj0KeqHPB4eHtjI3WRCFuAt8OMjeGUbbM/l6MvnSaWTpPPZtHzecLFIe+zgRk6gv4keaALOKgoDOk6MY+HbDCIWvH/s3fe4XGU596+p+3O9l1Jqy5ZxbIk23I32MYGDBjT0yChBFJIvvSenJOcc76clnKSk5yU76SShISQEEJCQodQDBhs495lWZIlq/eyu9o2uzPfHyNLWkuyDcHEJnNfly553nlnZnetfeed3/s8z8+L4vezuaWeX/nGJmqbTOW5uMA2Rw6Jkkq+uLCMP0TF8y5y9DeHGunt2MNnS6eLoyENbjui8pkb1nNZWYD2GBwZgw1nMMYY4w51cS1NPGX+JFK6+e9kikQqRVJLkdA0kkmNeFJD0zS0VIpUKk06lULXzXQC0inQdZ7tCfHzK1METiFUf/x5uLwE/HawSeMCrTTzj018YwrSf3oHfPfWU/c5PgQPtpkRUqk0dA5Cez/0jYqMRmXCcYVk2k7SUFEdDnweB8GAHacqIQgnG24ZE/q5MP4gYG4bE22M/zZ/JttPBABMHjO5wH/iXCd+iyJs2gcXLYAFp/BDau6Gh/fk8JmbZv7DeXiHk9zqG3B4chCN+ET0R7ZPIdsnIwoQiUN/SMLm8JJbUHFOrf7t2bWLx1at4l9SMy8wGMDbBIF1Nht5uk5WKkXOuOCZzxtXM0UHejBFz0FBICyKRGWZuKIQVxRSqorudGJ3Osl2OvE7nQxrGo8d3skvA/Fp9fDSBnx2VGFt9VwSeUW8u8xMuXk6BmM2J8OySrZDJa7YcHk8aC4fC4IBvA47TrsNx7iJykzMFtV5opyFKJh1fSXBrNdpF83anVPNNTRjspbg1BT1tAAGpqELglm/2PyDNmsjiZKMJCuIsvlbkmUEWUZUZHOfZEZYn/gbjMVi9Bw/Rp6UpjOSIFtO45WZqDUYSkGvYaf4VQqLbyTDw8OMjIwgyzL5+fln9fv1u999kY0bv8HnPwA35MJ1NUzUSD3YBd/dbOej18xhWYUbwzBoH9T4zydDXFgTQpVj5LiTLCg1KHmNqwXxpFmztLHHxkDMT1wswp1TS211ObLNiSA6UOxufB4noqySxoYhOvB43DhUBw7VHHfPxFwsFDPHSbucprWtm1g0js/npbQod2IcNgzoj6jkFpy74kU4FMLo72csGiXe20soHCYyMkIqmUQ0DAxAkiRq3W4CksQeUaTA7aZAOrMbbPoUP1NFzIkF5hM/nLjPnShrI0xGmY7/loVx8x1BOCMx9oQdhQ4MpFOEknGqFWPa//ewDr3IVLodtKQgcEL4y1gcn3iF0y+U0X3KTXni1+TD54nXMxmJOn4DFs3oPEQRQZAQZHMBqr29neNPPIT78E5WpCMExr9fPbEkL3T2kR4Z5iJZY85pPpC4DpuSIn3+PGpqa7mgOC8ju+SeMXhX2ewlcLQ0/GbQwYZ5efRpMorDQZnfdVrRM5SCfk0kKcg4VDt5LhuO1+gWF9dNg6m4LqKJEgkUBhNxVuXNXv9lz4BITYEHzRAZjHPWjJdOCKdJXSQ1i3A61N9MrqeeWHSQkdEE5QWTOb5N7SJzsy8GTKG8axT6R5MU59gQbG7CRoA5FfPO+UhLwzDo6ekgGh7GbUsgiToJTSahqwTzSixHaQsSiQTDfb3EI2GEaASnlmTrgYO4hvvYvm0bVycGONDZS+1gOytOU6bn0/3w3Vc5f9BmEE9TkkyTaONAPMrP5mtkzbLo9G/HZIJz5vKx6sz5nzF+75woUzZuzLylO0z9cAsfXzTzGJVIwwc3q3zzrVV8dy98rM68P53ITDDPDQbGZLaCYS7a/r+DBh9aD3Z50uv5O4+389GVYRbPUoYzFIMPPOLnHddUkNIS6KkEhp5AMJKIaIhoKFIKm5TGLqdRFZ2BkEFjJ3zxNME+H/pJLj/5xgemtY8l4aV6BxsvmFwQeXq/kw2nEEbTBjR2SxTmBoimbDidnnN6wdkigzMSRs/ZGqPd3d0Ee3vpjMVI9vZi9PSwrauLH6VSkxGbumny8J3jxynUNDqDQWTDYCCd5lg8Ts/YGOFolHQigS2ZxJ9OU2wYVAKvYzYxACGgGegURYbsdpJOJ6rXiz8ri4KcHCo9HubNUoPimvx8btu+mZulMG936sgCdKfg51GZfocPf3EFt88tNGvbAR2pyQmsPvEwfmLQm55mZUxKWeNpV8JE+tWJ7ckkm0yTDkOYoogLmecSTvTNSM06aeIuCJTOX0R9SuTmw/v5WEGMC7wwlobf9Io8Nurkk1ev5rIyc2B5dkhkTsDJ1oRpMNQShTKnmSY05TllItpCQECwgWBj4kFGAGzjPyf6iSeMjMb3n3hQmMi4EUyLo+reQb7+7AN884KxGf+v6ofAGwjQllVFzaIiavMcZppySuep4wYxyUWOQySuacQTaeoHUxQpSTRNI51Kkk4m0dMp9HQSI5WCdApDN2u+oJs19AQ9ZcYkGGlEQzeTpwwdCfNHRkcSdKTx6FlZ0OkLp4kkwH2KVcw/7oeGRB5P7JpD0OenNNfNRQvtGQ8hw1F4vhPetnb285xNpt5gzTqjBjflp/nBH44zv2RmJ3mAXz4r84V3z35zunpZlB8/9yI3vvVKgj6zpq1mwOBoikOtEvOKHbhVgXA8jS45zilRFODuL3+Zr84iioL593ynYUAiwfVn6TUMYgqe/cCoJBGRJKKyTMJmI2G3k3I6weXC5XbjdrkQVBXBbseuqgTsdiRZJi2KGJJEWpYxJIkxRUFRFC6oW8gNf7qftxPiLWoaUYBn4wJPanaWl5fS7gvy6TmmKBrRoQU7bynK5aDkYn6OjyZUFH8At9tNniKQNMwSThFDwAywnT2q0zE+DmjGlOgv44RgYoqcKURigHGyyCmKiLJkCpvjYqdNVhCVE9vj6eqi+JprRwI4HA7y51TQ09GG3SEyqmn0xc17oS6IuPxZlBed26k8gUCAwBsQhd3cXM+CBd8kEICf/h6eeBhu+xHYdQfJMZEL53r41vty8bvM6U/aEHjsqJP3biijNFeldSBCKB7luWNhogeHUYVRFKJkuZPUFhuU55/mBQCqDS6ohguqk0Af0EdS28OB49DYrTAQ9WPYiygoqaF2XjG64CCWlBjuNAVSRDey6sPtySYn24fL5UC1y2x+4UWefPyPpFIaNbVLuPXW29AEN4f27mJkoJ2q3Ah+e4rBVoU9uz1UVc1nQe3c8XHzb1IN6Yxxezz0j42BphGRJBa63ewH/KkUxYYxITiGgT2iiOZwoMkynTOJmKI4vuA2KWJO/bFhuoqfmAdNFUd1ps6dJj+1qdGfYI4TMxXoPjHnmKhBOv4aYNwwaUq7COZYMiZxOBHDTRqXYC7UjCCgygpVTju9Oih2leAs6uCJyM4TEasn3k9amFIzWJTMf0tmdD2ijCibhpHi+DglvYqxKjinnGVrTcGspbGRrX+4F9vOzSy3j/KuucVAMR3RBPd19sHoMBcrKYpmeOJQRbha1SHeTXRXN49tlRjKymfB/Fqq84LY7MI0UTSaTLG3a5iUbrAgz4cLGQ86Re4UEKZ/IExjUkayq5T4XQTU6Z+bVwavfMIqK8nQCHSkzNIrLodKnsuO7Qy1vhMZSyf+ghqjGtWniOAGqPTotA4mSNh8FFbW0p/WSMXD6IkoaHFEPYlCClU0M1tOdrs/U04s0k/agiVBD5s3yBgk+oGxFEkVJAni8cx5RjJhfghaKsWTL++hf6STnJwEbe0CvUNe8ouWUDyn8pwXRgVBoKCgBD2viLGxMXRdx2Wz4XA4Tn+wxd8FdrudvOISs55+YwOFdoVVdQtpGR5loSeLh3ZspyeqUN/dwwpVm/U8JxbdXy2KAHMUxheTTtyNNI4mY/zZyayiKMBnSlJ86tARXg6NZxQIk8UnTkRZisKJexD8pgN+cApLArsEq7Li7NxzgI/lgNBttp84Xpjp9/i//6MYhMbJdoD0CLOKogBeB8zzjrC+bDe5/tN9UiZ/2gKrTx3cCYBDjqJpaZSTopRcNkilJiPIIwlIC4pZM3mW8XZkTMDjy0NyZpPrcJzz457Fq+ecjRh9+sEHuWjXLh7s66O1vp7uHTv4QTI5a/87gZWiSI2uMwco5ZQeLK+JHuAY0KcoRFQV3eVC9fkIZGVREghQ5vGgShIJIIo574hipo0mBYGkKKIJApogmK6pokh6XCSIiSLP1O8j3tRg1tGy2agqLWZ5Xg7XFGbhlkXuH5MRCovxuV2oig2vIjNgiIiKQo5NQsScrEd08/yqzQaqA4fLPRnxMMVaeCJUf8YRYJYv+0xdZxxApjdGYzG+/62vYXQfQ5FErlo0l3cuLJy4/KGIQJPu5i1VZszuWBoeHZCZ47UTlpwE8/IxjPR4DmEaDN10HdfToKcxdLM9nU6OF2tLm06thmktIgGiYCAYJxxcT7SPV3Ibj1J8ZvcB9u1+nn+oC0+kDqR1eLhV4LetbubV1PL2pfksL5msb7l3QKQx6uTGxTkT7yeswbYe2HAWg3RSOqR0g+1HO3l5y+/40uUzf0diSXjrr7x8/s7r2HAKV+wH9oEnS8bvBknSkWUDVTawK+Cwmw/8Dhuob7D+8uLeEPVNfdy5MTOdLq3DL56RSUs5fPgG/ynP8ctNTm5719tRTsq5iKeguVtmfpmDlA6d4QBl5dVn4V28dt5fXc0vjh49ZZ8w8E3gP1/luccw7WZ6gGFRJDwueMZsNmLjizxprxebz4fb50Px+5E8HrxuN36nE7uqmg/ailm/U1AUJEXBbrPhstlwyjI2WTYdoUURVRSxzVC7zzAMfv2Xp3j4iUfQE3FcHg8VOQGuLMxmTbb5XTuagueSdi4uzeWA4CQvGCRpU8kN+KnyOWnFRpbDQY5sjoXTRc5JoyFDEEEQMUQBQxQRx6M5JUkGSUZUJCTJfC/iSeLBCQHhb0EsFiMSHgUD7A4HDofznBZE30h0Xeeee8p573vbJtoGBqC+vox167LYuhmeexh8SZGSAPTGRFrCCj5/kItKnRR7IZyAvV0Kfq+DsqIsDncbBGwjhEKdxMd6iY71YSOEzBgBZ5LqIp2q12hGmkrDoePQ0KXQH/WBvYjKymouWlaCbLczFIZQTKT+2DC/ve+PvG3lEDeuSWKTYXezyE+fC6I5l/CJ25ZQGjTvun7PZKTptkYVybeQnNwChkI6wdxCCgqLztm/F13X6evuZqStjdxIhADQn0rRGY+PO7KDKsvk2O1EVBXNbscvCNMEyglxcvz31AXSyTVbYVKoPPFvpsyLpjBTtcZX02e2ybOBWR4A0WAknsCvaxi6WffaM+40OKCDLimkVSduVQVJRBBlkM36wIJyou6sNC3S/G/B8ZZjHP79vcjbX2BpcoSc8Yl461icLR29yOERLlHS5J0mLCOsw7NJmaZgAVeuraWuOJt4Ks3XHt9BaLCfNc4oCrAjptIm+bnxuqt4a03+tOnsiAatcRldsVHsd5HrPP1noxswoMFgSiIlKvhdKvlO5YzrjO6JwNIzWEDZ3CNSsfACCotLZhWizdrgMRJjkbMinAIk0wa9tkGyPUM0tYRYVBWZ2LfviMr8whX88qGnufLiPuYUZf41H+8WeWpbFe+984vYLAMSizcJo6Oj9DUdpUw0SxYNJjQiqbSZvv3ZT/H9wQOzGof+aEymvaiKGjGFPTyMOxYmJ5WgSIYCCU5R8WNGfhiGy+dBtfvU/T51GL43/8zO+ZlW+M4Vp+7TGoI/H4NPLzmzc87GSAK+ewz+7YZT93v0IDjK4PIzvN7jO8xn9qtWnLrf+76n8vPvfAZxhhScx3bZuHaNmWm4rVEGZwXVZT4CzumRtHEN2kdclFfUIsvnbFyhxeyc3xGjyVgMB+bDclt3Nx87hSgK8DngSV3n0r/imingOGbK+4CiEHI6SbjdGIEASk4OWYEAPrcbhyyjiCK6JGFIEjFFodFup9FmA7sd0W5HcThQHA5sTieSqiK53bj8fjyBAE63WfNHUhSz+P+4AQD33oO24yVucaYy64ga8HxcosvpY2FxKTa3F7fHRUq2UZETREvrxGJRdF1HlETy7So+h52Ew0N+aek59RD0w9Vruf/H32VOpIM1qjn56onD5pCMbHdMiKKJNDw+IJNXXIqRX8qlay99Ve/DMAxS4xF2ryZSyzAMbkuluKCxke9+7yuMHjlAYmyU/rE02XPms/a2dRiJEIeTo4Q646R0geNRicocJzcu9mdMyrf1irhdDg6EzVqrhmEwGDewiUyIsVNjcMfjOkAwkKasxJ3oKwimiCuK477G40KuKAhUlRXzsy0Cd++B9yzOfE4cGoNPP+zjfz92Mw93xFgYSlEwQ9ZQ4wCkBTdXLTBvErs7IDsIpUFTgIxrBrGETn9cJx5Kk0zqJFNpUqk0qbSOrps/GGasjSiYkX+yaCDLOjbJwKaAKoNqB6fNFFvPJF304iVe/G6Z/35wgIArSa7PYCAkMBhRqCrPpqrkNDMGwC4l+eNTu7ls9Xxysyf7qzL4XSmGI2myPBKGPntk5t+KdHpm04CMPmQuZyQxa3h2Y6a1hySJiCwTVRTiDgea203a78eek4M/GMSbm4snEKDc7SbL5UKUZdOwZHycEyUJefwHQTAfxEURmyShCgKqJKEwnlZOZu29E1FMUUEgMkXIOBHdJY2LE9e/7e289e3vQBEEEokEW/fvYV9LE3sSCZIION0efIUB6j0eSnPzKPC6yXfYUESRxpSA7FBJqQ4GZLMQvCiKcCKaU5HNaM7xiM6ZUtbPBxwOhxXlMgu///0Xue22toy2Bx7w8JGPmAVEV6+DpG4jK6sQVYW1WTLZ2TL1h2HrUzlEMPA7JS5a5KQhJDPiUynyenGmi1iTtQBVgVDMYMuxKD39XYSNbnYOdLKpqRsbo4hGFL8jQVWBTnXxjAGFGcgSLK6AxRUaphXPAOn0Po7sgCOdMr0RH+FUHnsOt3HvJyOoU3SH5XN1fjK3lx88+gzf/0kDwfxSli0oJpq0kZ/tZMMqP6uq4vx+y15IdpHWJeo7D9Jw0IXTnc3SlRefc2UXRFEkr7AQbWyMtkQCXdPIlWVy3ZPj9TDQJcvk2u3E8/JwZGdP3t+n3ICFWf490/bUNmOGPsKU7TOdS0wIrqJ4ymPchkFbYwNzJIHBhEgspSOgM6oDgkiWqhCRVfLKyrHbX6295xvPnPIK5vzjlwFob23liT/8BumVTSxVh7m1eg4wh8ZwjGc6erGPjbLeliZ7BoHBI8Jb1RSE2xl5tJ37UzK/Top8pzrJvPLJfm8nyogW5aMPPIB4zUaKCvNZnOedKGHkV2CJYi6TjY1FOTAoE0ah2OeixGObMS5AFCDXBrk2Mw5XS8XpGxAZEVXiNjeKw4lHAllPougpHKKBU+RVGzS5ZZ1g7zY623ahufPwl8zDH8jO+Hux2UzDNrwzh6Amk0li8TgjY+Fx4TQGWmxCOLWLBupphFObJBCJQK4f0ifNf/S0zOMv7eSqS/soKZgu8c8p0Ll69VH+8JtvccnFlyFILiSbG5vDh8PhxG63n/F3xsLiXMHn8yFV19Lacgx3KoHfDl67wogg8bFvfY9PfPpj/NdIPcVTVBTDgN8Zbjqufhsf//w/IMRjqEYaVQCbnqZ3cIhdXV0MtB4j3NZCsqcTBvuwh4ZxxyMZ4unU56Ikpp/G6Xi9v2Wv1/kkwQziOR2aDu5XMYauqIKv3X9qYTSVhoGwDS1lYD+pVtdYAmRZwjCgvlNCsvnIKywjkpYJD4yQ40ngsJnlWgYjMrGUk7LyKksUfZNzzkaMbn7ySWo3b+bp/n5eePhhftzbe9pjPg189zR9xjBT3tsliT5VZdTrJZmdjVhcjK+igorSUkoCAfx2O16bDbskIWA+5Mcxo0ATgkBSEEjJMrrNBg4Hst+PMxDAk5WFw/HaUnENw+Ch39xL0ysvMycdI0/QCRkCRw0FMSePS69/C9V1i8zUMEkiMjKCFosgJZJEUylEQ0cXRFKCiDsrG38gcM4+8Le2tLD92ScZHR4iMjrCijwnC52m4/jhhJ2WlErFouUsXnkhbvfpRa83mlAoxLOPP0z2aD3rCqcH3TaOCrTFnVw+b3IyG03BjkQJl1x1mmWzV4lhGOzZs4WiorVsfwkeuw/yADkt0JMsRQrWcdtH/gXVJuL1f5eXnwF3HNYWg1+FgQhsbpVBUnnbhTnIonmDv38/XDAf3Cp4HGak6F//WiGRglgiTTxpEI/rxLU0mpYeF1nHBdZ0Gt3Q0XUDAx0JA0kCm6QTT2okkilyfBL5WTLhKDR1wdWnWTW8+1mFWzYW8cRulUvXLCbgmxSXNB2O9cjMK3HQOuShfO6Cv/7Nvo589Lrr+PfHHjulQdLdwFMuFwvz8iArCyU3l+y8PHKKivD7/WS73bhV1XQgF8drxokixniJDsZFwhOLNeK48CmJYkYNvqkmIhNpq1PSVGXBND6RAflEuuhJnIjmPCGe6ozX95ySgmqIIrpoRnQKokgknSYcGkVNaxTK4BPNaNBBHXp1kRyXStKfS+ncudaD2N8hzc31xGILWLhwcqry2GMCF19ch8djTmTb26G5eQ6XXjr53e/vh8HBXAIBJ9u3VJGM91NWYbBgsRNVNf+Odu3yEBpyY5OHcftEqmocOJ0inZ3Q36OQShp0dgwTGmhD1lowYm2MjXZiM0YQ9DG8aoLKfJ35JWa66qvhP34L79vAKeudfugHKv/1iVq++7DKp24uYWRM5rkDKu+5Lp+eIegJe1lZ40Y34FC7SDytEkllseLC9edkbb1wKES4s5PBwUH0ZBLnuOFcUhTxSBKK3Y6WnU3Jm+S7HovF6G8/Tp6uYRfMiEUBc2wcEGTUYB6BrNfoDnaO0Nnezv4/3Iu0dROLY4PkjX8P6kNRdnX24oyMcpldxz/L9+M7IVg/F5bMkqI+osGXBwv47s3r2R8RiMhuFhX48c5SfiCRhmMJmQHsuB0qCwP2MxY2Ezr0ahJRpx8prwx/cQWyopCIhNHGQvR1d1FqH8Fvg6aBGH3hJCIGOgJ5XjuV2SqRFPTHYJ5/ynlT0JdUSHkKCJRU438dSo+YEadxEqcRTg+NDLKwepADhwZZWpuYOH7XAT/7D7XyvhtHTnmdux/08+63XY8yHumcNswIq3gSkrqCjh1EB5LNg0314hg3hbJSUS3OdUKhEGMjIwC4/X48Xi+Dg4N8/0v/wMCOlymJhxgTRHqzi9j4gY/wjve8d+JYwzAIh8OMDfSTGgshp1LI42KpSuaCRTqdpmdwkO3tXdi7mumqP0LfgT1EQiPU+sa4vXj215g24OOH4P/Nn1IWxjip5N6U7X9qge9tNANDZuMnByGowpLgZImzjN8ztY1Pv07e/+3D8Os7Z09RB/jg72H1BQo2WcIww3/MclWShCxL2BQZmyJjt8k4VZnWfplHXuzi3981OmvWzs+ekjjQV8GGKy7j6tXZTEma5el9MjZnDrrgpCDXh6YUU7NgOYqikEgkGBjoJZmIIUkyWdl556QWYfGqOKPJ4jkrjIZCIV75yU+Y29fHl3/zG37d3X3aYz6NKYz2Y6a896oqEZ+PdDCIragIf0UFxRUVlOfl4Txp5T2JKZrGx0VPTRRJyTKG3Y7gcqEEArizsvD4fGa056t9unkVxONxtm9+kZ7OTjw+L6suvWzW2myaphGNREgmEkiShMvrPS+iCqZiGAbHW1vpbDqKIImUVc+nsOg15ia+gRiGwfaXnqe3aR/zbMP4lBSjmsiBIZl8r5215Z6Jm8BQHHZFc7how/VnJUrn5z9fxp137pnYfuQR6Oi4nfe976eoqlkl/Pnnv8qllx4GTDHg2Scg3O6gyG9ndbWHgGvyb3rzMYlgtpd5BQ6O9kFLTERVbaTicVxCEoEUOilschqHzUCVNVz2NB7nqeucvp5oOsQTOtG4zu+f7eNj1yRnjT4dGYO/7HfzzstyiGnwfH0OV1+6MKPPkU6ZgqCThFxKbl7BG/AOzpw9e/Zwz6pVfGeWyPkkcIeqUvmud/Hxa64hLQjExyOW9AmB0YyelETRNBEaX/WcqMM3Hrl5QtxUToidZ/gaT4icJyJG04ybmZyIDp1yHVkwxU5dECYiSvXx+qMoCoKqItpspCMRgqQQBBjV0oTiSWLxGEIyQVo3o2h9ikxKUYirHipqas7q2GxxbqLrOr/+dTnvec9ktGh/Pxw5Us66dea9M5GA3/0uj/e8Z/JemkzCjh0+LroowI4dZTgcOSxc2JVx7qYmO4qyhtzcg0wN1A2FIJFwEQyaK0YDAxCNZlFcvBRRFDEMg67OTloathLqeoXowAFGB1pRjGFIj+G2JSjPS7NwDrM6qgJ87Afwg4+d+v3f97xAUclc6qo8/PI5N5+5pZiWHjjc7efq1X6e3KNyzepJYW3bUQl/wM+IlsfKCy8+J78z4VCIkd5ejLExEskkgq5P1CfOLS4mkJPzphBFT5BMJhnq7yUdjSEYOoYAKHYCwVycLtdpjz+f6O7sZN8ffouw5RkWjQ1QIJvzqYOjY+zp7MMXC3GZXccz/hBrGPDhMPxk+anP+4VjDr7wrqvJdZtzntYYtKRU5uZlUeKZfWVX0810+z7Jia44qHJL5J/GXGUqUR16UwpxVxb2wkoCxWUc3r6Z6HA3C/0p8p2mIGAY0DUGh0dl3B4fy3JF7LN89+Mp6NNs6J5CsuZU450lYvSv5YRw2nDoJVbWtLDnUB9LayajRje97CelHWHD2vgpz/PsVjs1VVdTlOc/o+vGkuZPUpdJYwPRiSi7UVQPDqcHu93+qiKzNE2jq6OVaHgQQUhjICDJLgqLK3B7zpKzlcXfPalUisHBQVRVxec7/Xc0kUiQCIVIRyOk4jFSySR6SkMxdGwYOAXo1KF8fMj/6X1/4L1qFx/91ePcVT46q7D4QJ/My1XXsnzZUlS7HUmxYbPbURQbdpuCosjYFAVZVpAVhb0HDxJ54qt8pHpmP41kGj641c+33n8ZD7S5CJQtwB7rw6mN4tDj+OQkeU7w2KZ4dwim98jXX+ji/avDFHjNbUGAX7/YR77YxdXzZ5aSWgbhyy8Hedf188azDnUk0cAmG6iyjsNu4LKD2zH+o0JrL2w9rPPslkb+z5VjGfVGtRTc/bTEqJaN5C5n/eoK9rQ4uf2aPGQBdjYrDMRzWVhbRjztIS15qJg73yoH8ubm/E6l93q9SHPnQiSCWljIru5uTjUn2g+Eqqv5w/XXs3zOHJZnZ5spn+NEx38SgkCvIJCSJNKKgqGqiF4vaiCANyeHbJcLRVH+phNuVVW5eMOVZ9RXURR8b4ChxdlEEATKysspKy8/fedzCEEQuHDderRVa2k+epTO0BAuj59VXi8th/fx0nAIER1NsOHLL2XdumUTIuXrSXPzES6/fE9GW3d3Ftdc868T1+vp6aCm5vDE/mAQcgpV7FIeWTYzpTOmQcco7OyUkVU/GLm0tiSZu0BiY6WKKEI06qG9vRa/Pxev15uR0tvSsgO7r5u2UILoWJTkWIyxUQm7nEZPjmIXY6hyGqeq43GAS331tXamooigOEQ8DpENFwT448uD3HhRatrEIaHB71+Suely83viUMBIRYgnU6g2cwhMpk3j3b6wnbLKmZ3t/5aUlZRgXH89//XQQ3whlcoQK0eBz9hsyEuXctXGjRQEAkSAbkWhTFX/qkFew6yRfCIlPgVwktgpjUeMKoLp2u4aF1MTZEaEpsfLj+jj7vQ2hwObw2GWJpklsj1it9M/PIhXT+FTJCTRDrJEUrNhaCkQYFSUySkqpiT4Gi3ELc577r//H6el0D/0kIcPfGDy3vi733m4447Me+WLL6pcfnmAI0fsqGoZ1dWZdXw7OyVstkvw+/dmiKKxGITDDoqKbBPb0aiTgoJFExFQgiBQVFxMUfFNwE0AE2LpsYathDpfoXnoIPu2tyLpIxipCE4lTnlumoVlYFdMIcV+BkkfcwsNGvvjXLzUAyQIx3TK80WeOxAlmfJPi8paOCfNwfYxkowwOjxEVs65993xeL24PR7GxsZIxmIIkoTb4zlns2D+Wmw2G/lFZkFyTdP+KrO2c52CoiIKPvUF+NQX6Onu5qk/3of+0l9YlO7njgVuMwtmZIyDXT0EohGWKToFZ1A9ZK07xoHuES6vMgt8ljmgjDgjo10816UQ8PtYnOuetoCqiFDlTFFFCN0I0T4ksk32kVB9BG1QICUJnOLPzilCuU0DrReO9zLatIWRsMCGOUaGgZMgQJEbstUUT/eM0efPIk/QsM2wLqHKUConIdVKrKGVNs2O4Ssiu7T6dRX6TqTqq64coAX5pNciCgqCePq4mBOO1WeKw3YiC+lE/kgUs5wIJEJmrWctLZMyFAzJgSi7kO0eVIcXVVUzxoF4PE7jkT3MyYowZ8qadiI1xvHWUfzBKnLzz63Fbos3B7Isk5eXd8b97XY79mAQCKJpGvFIBD0SRk4lsAGRVIp4OEJcT6OKsOHitTzw1CPctnYJ/7TlFb46Jz5t/NoVlng650K+8W//DqJIGjMIQpRtZv3/EzXyZTuyYtajrr30ar7SeoxHOu7j+sJMcTSqwWe3u7jz2lU8MVrM6htvZukFqxmLRBjs7SRLSWInzeBIiGO9vQz2d5OKDOHQwqhaiIvnFfHfWxr57CqDivHHqfdemsv7fzQC0hhXzcuMHD3UDd98JYsf/eeluE9RA1pLGURiaYbGdA616fSNpNjd2seP/qGKe57o45fPDeO0pdEN0HQb71ifx5rFPv73cTtLKwT8rhh3PRqhoKSGkopFLCooQRRFSrJzzsqzucX5yTkbMQpmFMgLjzzCwL59/Plb3+LecHhWuffjwSDv/cY38Ljd2AUBQ5IYkSRUnw/Z58OZnY0vEMBut79pJ9YW5xaGYaDr+lmPxrn77st43/s2TWzv3AlNTVdy881PTbQ9//yXufTS5onttjbweIoJBGQ6O2HL8zKBLDt5BQqqy09Z2XuJREZQlE1MzR44dsxLRcVV017D8HAfqrovQzzo7FQpKLiIdDpFJLKHQMAcQjRNp7s7gZ4GLR5mZCCKDKTiI5AOoQgxVFsKp80UUN2OM6vddeBYjJf2DrFyboraEoO0AbuaJBq7ZW5YFyQ/a1IifOmIQvX8ZQT95gtuG4BIys+c8lpc51i6RCKRoLepidGBAZ584AGeve8+5kUi5Ok6xyWJLo+H8tWreftNN7E8GMQuSRwVRaqdTmwzPFgnyaz/mT5RW++EoDM1clQUTcFzBrOkcX/biejQE47zyDKC3Y7sdKI6HMjjzvOvFU3TCI+MkIrHAQNBlnG4PUiyWSPUWuH9+6ap6RDxeN20FPpLLlmE222OvZs2ySxdWo5/Sp7uli0Sy5eXkEhAQ8M6FixoZmow/+CgQDh8GS7XQYLByQJZmgYdHXbKy83Oug4dHTJ+/xK83le/SGkYBt1dXRxr2MpoxzYSQwcID7cjpYfRtQjP7Yrwy8+d+hyPvgKSu4KrV/t5YqdASekcFparPLFLwhUopCg/l8rCzFD+zUdsKI5s8koWUF5xbpnNWfx90tfby94/3kf6padZGOqhZDySdFPvKH/ubuH7C079GPJgD2z31PJf1y2dcb+mw66ITL/i56ICF1n20wvP3QnolrMY8+fjVhR82ii5YhL3KaZ1LTHAAeWnCCBrDNtQF27A5XAy2nEUW7SPXDV12rnOWAoGNRUCJWSXVL1u85XGxgNU5bzIkaZuasonP+fD9XPZvn8H733H6CmP//mD2VxxxW0oUgqJJDYpZRp2vs7hN1rajDSNaxJpQ0EXVfoHo9QWJ2eMvjUMaOhRmVO1wqrNbXHOomkayViMVCRMZCyCNjZCmWp+D/c1NLN9y2a8vY08u2Mvi5UIdWqSkbTAw4lc8i+8gs997Tu4/f5X7cXxqx9+hy2P/JpaoQOPEGffkEyXkcWqS65k7uKVXHzVW8jOzs54naMjwyTGwgAIooQvKydjHBocHKCrayN7tkJHPWTbIBwS6ZeOUVs3xMtPQKECWgIG414q5xby8dsX4XS8+sHihT2j9PX1cdPa9MR7mqgXbsD3H5G5Zm0xVSWm8PnzF/K546P/ZelAf5+c36n0U4lEInzzc58j+dvf8pVIJCMCKgV8w+ej9tZbKbniClaOr6QOCgLxuXMpKiv7W7xkC4s3hO7uToaHi5k/xYnw7rt9zJt3Nxdd9DYA2toa8Xj+jamBxS++6OTii3MBaGwUKCnJR1UFxsYgFLqG/PwyWlsfo7w8OnHMyIiAJG3E48msSadpGoODL5GfP5l+FQ6DIFyI2+2lp6ee/PzRKf0hErETCMjEYgCVOBzTywuk02mi0SgjI8MM9XcSGTqOFutHNMawiUnschpV0XHYdNwOsw7qcASe3zOGTYgjirCw0k1loX1aFOnT+21cuGI5XredSBya+lwsWLTynLxZdra2khUO0xOPY4tE6BoZIdLeztHOTrxOJzXl5SRcLkqcTuKyTEiWyXc4kKbUAUUUkccFzxNp8vIMYudUZhU+JQlBVZEdDuxO0xH9XPzcLN786LrOvfeWcccd7RNt/f3Q0FDO2rXmgNfWBgMDpSxbNjnGNDRAVlYxwaDMyy9XMn9+fGLhBiASgZ6eNTgcrRQVaVOuB83NCnPnuifGlM5OAVGcQ0FB1ev2vibF0m38+Dtf4n/vaMR/Cv3j4z+28+1P12K3iTyyXWRuRSm1c1Se2C3Rnyjh9qsKp42Bm4/YUNRs8krmU15Z87q9dguL14OB/n72PPg7tBefYv5IN5/aX8+fF8ZOWaPus/Xw6TJ4UCtg7aoLWJE3ewmCI1GJRiWXgMdNnVPDdwa3sIEkdEo+knlzUbwB1PAAjuggeYoZ3XWCzaOwpuTUGTGaDq8ky1m74RrAHMsGB/qIdDaOi6Tp04qkkSQM6g6EQCk5pVV/VZmmluYDlGW9SGtbF+VTKlltO7CE5saDbLioidzsmY/tH4bn963jpps/PNGWTqdJJBJEx8KkEiHSWgT0GBIJbGJqSsToX0coBsNhmJM7e59oErqjhVRWnVu14y0sZiIcCtF3/Bhe4gTHx6VYUmPrnv10dnTQ3N1HOJ6iduWF3PLu9+D6K0utGIZBc3MzY2NjlJaWzlq670x5+ukfsGHDL8bPDXv3QmHjXDan/8yNtyQxDBgdhe98u5yNl7ybyHAUhxxHTsVx2jT8zhQ5HjOr8Ex45KUhGlqGeMdFKcrzzGsePA5/3qZw1Zo8VtZOTp5eOqKSv+JLzJ079696jxbnJed3Kv1U3G43//g//8P95eV87J57yOvtpTCZpNtmoy83l3ffeCNZixfjH6+tGRIEegMB5hWfolKxhcWbgKee+jjvfe/kdn09ZGVVcuGF10+0tbb+jIsvnuzT1ARLl5ozXE2DVMo7YTLS2lrCggXltLfXU1w8KYoCDA+XUl4+3aijt/cQxcWTomg6DeFwCYWFXsLhEfz+zEiDoSGB3Fxz6InFfGRlzTyZlyQJj8eDx+OhpKQUWJ2xP5FIEAoNEwv1MRDuZfP+RhaWRkGSefvFs08UDAP6Qwp21U7LgI2E7mXBooXnrLinxWI4ALcsIzscLFFVOt1uAhUVAKh2O36bzazvabez2OWa0fDoZKZFjp4sfKoqdpcLu6K8qdM6Lc5f7r//C9x6a3tG29QU+kQCXnklh5tumhxj+vsBcgkGZfbudTJnTiJDFE0moaOjDru9PUMUBWhuFikvnxRFh4dB03zMmfP6TrIFQaCwqIjConcQLFzAV/5jA//97o4ZRaHn9gpUFPuwj+fsHm6XuWqdORdq7JJ45zU5044bS4IkimhpEdXx5qpfafHmICcYZMOHPgEf+gSDAwOoG1fx9OAxrsyZOUbjeAxsIpQ64NOObna/8gj/XbCODTUlLPFMt0SucaapoZveUdgdLyJZWEVucoTi1AjBWeqk59ggh1EY2EWoG47rLmKFVSQq5iNEhkn3teOKjwLp05YJUkSIDHbS09lOfpGZ0hnMzSeYm2+KpH09RLoaUWP95Kj6jCKp2wZuYhBrIHSggVbdiZg9h2DJ3FcfHamniCcNXFOmY5EYuHIcLA5eyv1/CXPzVb0ET9JMBkbgD8+U8d4PvCejXZIknE7nuFibmWpsGAbxeJzhWIRELEQ6aYqmopFAkTRUxUC1gXwGU47BMOT7T93HaQNtKHz6k1lYnAM4nE7c/gCx0AgNsSQBKY0iKMxfupySRRewRpbxuh3Ivty/WhQFc77xegqFuv7wlHNDe70dvxZj7cbkRJvDAVddcwurVy+krU2ltNRBT4+MKBbg8w0wNBSmtX+Iga5BxPQoJEMIqTAuOYlHTZDthcD4W79+bRZ183z88rEhPPY4BlBV4uLztwdwnOREr0hpUqkUFhazcV4IowAul4t3fOhDLFi6FFtPD/LQEEGPB3w+WiQJXVXRHQ7qbTZsWVlUzZ37qgp3W1icbwwPD7FgwcMZbdu2ucnNvW7ib7+p6QBLl3Zk9OnudjN3rpkHdvCgzJIl5t2lrU2isvJKEokEinKIqTphZ6dCcfGyaa9hdHSA7Oz+jLaeHhv5+VWk02ni8WamlsOKRMDrVU0znVERj+e1132y2+0Eg/kQNOuJ+YI1HDv4FJUlOkc6wtQUT38YAtjbKuMLVtIWyie/uAzPOV6Y/8TzkF+WaUulsKfTlHk8TP1gDaBdEMi12Sb6a5jiZ4bwOW7AhN2OMh7x6bTZLOHT4ryjqekQixZ9h6m3+cceE7n55oqJ7QcecHDbbZM1gxMJaGz0sWaNk74+AcPwUzxlnNB1OHq0FLs9Snl5ptlIS4vInDmeieslEhAOqxQWLj6r353qmhquuuN/+cjPPs0nr2hl/hyzfXTMNBfoG/Px1Q+bIV4tPZDlc6LIAl3DkJ8bID9reljW4eMSit2FXc0iZ3z8tLA4V8nOyWHppVfy6JO/RSDEhpPE0fowfP0Y/HBKQOAyj86S8Av8YZOfvVd9hBp9kCVKBPWkFPg8O+TRSay9kz0pH41LN9DlcaO3HiQ3MUiRfWYh1qtAHWMwuJdY315aUnYSwUocSy5HazhIPN01ca14yuBA1xi9oQSCAKIoUJPvwS+B/+jDNBx0Yy9fQUmlaR4oiiLB/EKC+YWkUimG+roZ627CkRgkR9VnFAy9NvAShbF6RvfV04MbOXsOwZLKM6qfl0rHiUR13FOF0SgUFnoRRQ8br30LTzz3UwDKSwADmjpycHqrec+d731VQqwgCDgcjvFjMusbG4ZBMpkkHI2ixUdIJSIY6SiiEUeWNFTZQLVPunmndTKcpi0szndkWcbh9mIzUhQIOn1jccJpHVEQCbhkPDaZIez4z8Fnl7a2FpYvH5jYTqehqS2LXSMN/PuUtZMXX5S57LI6wmHIybGN93Vis4Wx22UKCgIUFAToy59Hbq45fvX1KeTm1pBKpRgeHubIQA+RoeMkwp2kYn3k5TfxkatPLXq2Dfm4ssCqN2wxO+eVcugLBLjgyivp7emh7/hxenQdfzDI4qIi4vE4giCQ63afs5FfFhavJ4888jnuuGPyob61FSorS6mt/SgwbvTR9TOmLgQePiywYoUZLTo8DPn5fgTBfMiHdaiqyrFjm6momDxvMgmiuGTa90rTNKLRA0w1YjSFzyVIkkR//3GysjIjrqJRmdxcEU0DQSh4Xb+rBUUlxJOXcHjP83TFUozGYiyr0FHGH04SKdjVLNMZLuDGW9953giBaUFAAxSgVFXpjMcRdR2/YSBhWhaMiCIoCqqq0mcJnxZvcnRdZ8uWq7jjjknRoq8P/P45E3VFn3tO4i1vKc6IlnzpJZXLLgugaXDokJ/16zPPW18fwG73UVHRk9He2SmQl+fBNsVJpbdXxuOpfUNq3F5x1VtYdsE67v7JN/mX399DliNMlkfm3Vfls6jKVDLq2+CBrQ6+eHsevaPwm80ePnZzpqGhYUB9p0hEs2OzeZk7p9KaL1mcF1x7+/9hV/8R2tob+ODRYUqUFIpo0JaQKc7ysHKOwP5IH2v8k8eIArzTM8Lgpq/zR+8Kkh/+N9j3AvOTneSc9LV1SLBGGkU/9AcOxhQic1dTdMOt1LcdI350N4GxHkrt+jTjkxPHzpcSEDpM8sXDxBMyO1AoynMgSyI7jo9wQTDFStNbi3gadvZqDKZsKHkBqp0RtO7naW3egl5Qx5z5yybGFVmWyS0sgcISNE1joLebWHcjLm2IbNWYMTLVZwcfEYgcYnjvIbrxIAfLyS0ux26fORw2EunBlk4TnKK1xGIgGzb8fpnDB7p51zUGT74wwvMvjRGNwYq1V3Ltde96XY1LBEEwDWrsdmB6Sq+macSiUYZjo6STYVKMMBqLZrzuaccYYJxfj7sWf+d4AwFCwHBkFJ8LckVz4XbMkE3D0dz8c/LefeDAz7j22sntnTsFPvE5Hw88kBmg09VViSSJDA6KlJWZc7Z02oXHE5noo2ngnGLGJIp+wBwTg8EgwWAQqJvY33/fXfSHnic4PbERgLgGUaEAn+8UxZ8t/u45L2qMWlhYZDI2Nsb27QHWr58UHu++20FOzg1cf/3vAKivf4Wysu9nGCK9/LKXiy7KAmDHDpWVK81/HzqUw4IF72J4eABFee4kwyUPFRVXT3sNnZ0HKCqaFBB0Hbq7CykqWkA8HkPT9mVEi/b3g9/vQlFgYMBOTs681+OjmMbw0CA7tz1LLNRHKDyKS9VRZAEtZcORVc4ll113XhXhHx4aItXZmRFXkTAMRtNpdMNAFQRCikKwsvK8el8WFq+V3/72s7zznZnRoj//uZc77zRXgY4fh2i0lNrayfCnl1+WWbGiGLsdtm61c+GF2Uw1a29sVBGERcyZ05IRLT8wICCKbrKmmLd1d4Oul1BU9MbX5kyn0zz84G84tOspstVBEvEoHQMCudkuVi7K51ifB7u7gHVrL6SppQObMEbAbRBLCPSOSEg2J4HsQirn1ZoR9xYW5wkP/eYeBp66h3e6hkim0qQNg4Bd5pWIwgP9Mh/ODrGloYXrPWPkzqD/7QgJHF7/cS7/4Oc49uT9FPQcoMoxc2YJQFtcoCUwj/Jrb6OguJTWIwcJH96JZ6SDMvvpjZKGk/BsGN42d+Z6o21haI67WD8380G9Y0wi4qukpG71rOZKmqYx0N1BvLcJtzZC0HH6R7ShhMAoHmx5leQWl00IK8PD/fR0PIWodVNdOjLRv/6YhL/kegoKVH72w7voOr6d6y7VWDoemXugQeLhFwq49MqPs/biDae9/tlA0zSaDm+ntjA6a5/uEQnZv5Bg8BSFSC0szkHMhYAIyUQCUZBwejznrIO6ruts3nwxl1wSm2h75BEXa9bk0t//R2pqTvSDp5/+CBs3XjqRRq9p0NWVz5w5YxPHDgxAdrYLQRhfpJFrTikGRyIRfn3XV7htVTvekx6F4hr8+qU83nLrP5KblzfzCSze7Lx5zJcsLCwyue++z3HLLf8zsd3XB8ePz0OWf8rSpZdgGAZbtnyciy4ameizd6/A/PlzsNmgpUUgLy8Pp1Okp0fE7X43Lpd7muHS6KiAIFyJ15s5cQ+FhpDlXRkuzt3dNnJy1qAoCj09B8jPn7zBJRKQSNjxemWiURDFuajq2RPxEokEbS2NhEd6EDDQkcjKLaG4pOycXGU9FYZh0NrQQI6mMVNQRD8Qd7spKS+fYa+FxZuLpqZDJBJ1LJjiUP3YYyKXXlqHyyURj8Mzz2Rz3XWTSwlHjgjk5BSRkyPT1ibgdueSlTWZU9veLpFMrqGw8EjGQlIoBCMjLkpLbRltQ0Ne5sy54G8ahW0YBpFIBMMwUBSFrq4uszZpYSGaphEODWLoZlqqIcgkkxpOpxO/34/H47EiyC3OS44eOcJjv76LVG8bIjoJu5eVV72V9VdfS+OBvTT98us4j++jv72Vm3Kn1/rUDfjNgIPCf/4VqzZcw74nH8R28HkWq/FZhc5RDfaLQXyXvJ2FF65F13XaW5oYPvAKzoFWypTktBR9gE1DsKQEArPULAV4rE3m8tog6gz58QNxgX6lgLyFa8nKCc5wtImmafR1tpHsbcajj5Kjnv5xbSAuEBZ9KLnliPYOwsMdJMOd1M2drMW557BCIr2YkdgIB7f/lM+9PzmtXrFhwE8fyGHVZV9h8dKVp73u2aC7sw0tfIzSbG3avlAMOkezqV6wBFG0cu4tLM4We/e+RE3Npzih24bD0Nk5h/r6Y7ztbfsm+r3yisCiRfeQSNiw2304HCLDw5BK+QkG0xP9+vqkiTT64WGBQGDhaV9DKBTiz/f/BIfezsLCQSQRjvT4GUjkc83bP0h+vrUY/HeMJYxaWLwZSSaTPPOMn2uumVyV+9Wv7BQXX8hllz2PIAjs37+JmpqfcSLLU9dhxw4/F17oJ5WC+noPdXUeNA1aWy+gqmolnZ1HyM3dnxEt1dJSSnn5qozra5rGwMDLFBRMTkLHxiCdXoHXG2BkpB+XqznjPD09Evn55g1uaMhPVlbJ6//BvInRNI3u1lZIJvHrOhJm/dCQKCI7neSXliJJMzyZWVi8iUin0/zmN2XcccdkWlZfHzQ1lbNmjZl2ed99KjffXDbxAN/fD8PDucyb5yQeN2uM1tVNGhYMDAiMjq4jGDyC1zs5zYnFoLXVSW3tpKphRjXYycu74JyN2rCw+Hun4cBe6u/6KqN7N1Ob7uWCGTIn+5Pwy3gRb/nZM1RWVXFgy4tEXvozixnAM0vWtabD3oSKVnc5S696Gw6HwyxZ1N5G376t2HqamCPHGK/mwSNDcP1pPE1awtCkBblkjoJtFt0uokF7OoC3ejWFJWWnXNRIJpP0dx4n2duMzwiTdQYiaX8M2tNhRLGHJdWTC+N7DqksLazmP372O770oePMtqacTsM371nCl77889Ne62zR093BYF87Wc4YLnuaVBr6IyqiLUBZRfV5tyBuYXG+8cgjd3L99Xsntp95RuLyy6u4995Huf320ET7PfcUc8cd36alRaS83Bycu7oUgkF1YozRNIjHVTweczAdGFDJyak649cyPDxMY8NhdD1NWcU8SxC1gDeTK72FhcUkjz76Ld761klRNBSCefOKCYffhiAIpNNpotH7mFr6btcukWXL/IBpuLRokRl72NTkobp6OYlEAkk6eJLhkkxx8fJp1+/rOzrNrXlkJJ+iogCappFOt2acJxSCwHjIxPCwiMdj3aBeLYqiUFpVRSwWY6i/Hz2dRrbZKAgG35AahxYW5wL33/95brsts1bVI494ufNOUxTdtEnk+utLJkTRRAKamnysXm2Gtu/da2PVqklRNBKB4eEL8fsbMkRRTYOmJid1dZmhXt3dMh7PPEsUtbA4h6muW0L19x/g6MH9bPv+/2X37md5h3+M4JRbZdAGX7B1su39tfw+eyUf+vmjBNetp7XxKLuf/C2VI00UnyQqKiKsdMSh6TGOfvNJegoXUXP9bRSVzqGo1HRF6+/toWnvVsSOBuD0TugeBZz5C+ifU0GocRsl8gjuk57M3ArUKsMkjj1O02En8pylFM9dMKPYZ7PZKCqvgvIqEokE7R0tpPpb8BOeNXI16IDjIR2BdOYOXWY0EsPnHmTPwQi/+VMPAqazdDIlc93lQa6+zI8kCeR4Ounv7x+v+/fGk19QTG5eIUODg4zGIsh2G+VF1vzIwuKNIB6PU1x8IKNN0zzEYimqqkIZ7R7POgBkeXJASiSkjOfG0VHIzp4M9pCkV1cXNBAIcMGqi17VMRYWYAmjFhbnFel0GvhGRm28hx9WWLYswMKFdwKwe/cTrFgxmcauaSBJfhTFFClzcvyIIgwNCeTnX4MoinR27sgwXNI0EMXF0ybeodAwPl9XRlt3t0xurlk8Zni4ldzcdMZ5EgkFr1dA00CWC62V+78Ch8NBUWnp3/plWFi84Rw9up8lS77H1MDoRx4RuXncYOj4cSgqKp4wX4JJsyXzeIFFi7Im9iWT0N5eh9fbRjCY6Uzf1OSkqipTRejpAUEIkpVlLexYWJwPzFu4iHk/fYjmI4e5918/SkHTS7wzN51hoLTKDytTO7jvqkKGlr+FW7/2Yy75xL8xPDzM5kd/h//YNuqc052O5znTzBvZQ//P9/CiWkLehncxr24Jwbx8ghvfBkDz3d8Hek/5GofjEG7YQqq4mNoNtzHQ30f7wZfJS3WRddL6i12CKleUdP/LtLVuJ5lby5wFK2ddqLHb7ZRU1kBlDfF4nPb2ZlKDx8kigm/K8KbpYAiAcFK9VUNmaDTKwfpG9HgfX/9sGud4qZFUCv741Bif/Xcv3/5yBQU5UQYGBv5mwiiAKIrkBIOc7HRvYWFxdtmx48+sWzf57NfZCcuWZbN5cxsbppQfPnIE1q27jOFhyJ2pEPQ4qZQ0scCdTIKqnsJdzcLidcQquGJhcR7x1FN3cf31k6tv8TgUFBTS1LQUj8eDpmlI0p8yTEl27pRYssS06WtosFNcbK6g9/XVEgjkMDw8QG5uptjZ0eEhP78ioy2VShGJ7M8wZopGwelchKIoRKMR3O7BjGOGhwWys83rjY6qeDzTXUYtLCwsTkU6nWbbtquZP38ygqu3F7KzyybqijY0ZDNvXqbZ0rp1+QiCWetKUQI4neaUR9ehoWEOqjoyLfq9uVklGLQzVWuIRCAWc1NYWHt236iFhcXrTmXNfD5z//Ms//Vevp6sZddo5n5JgHfnp3nnsQe576oK7v2HD5JKJll3+0eo/tJd7Kh7F1vjHuLp6ecO2uBivZ3Sx7/Ftq98hB1/eQRNM8eUYFk1fbHpx0zlyDBcXpgk++C97PjTj7HZVWrXvw3b6juol+bSOTa9RI4kQLlbozq6n+EX7qb+5ScIh0IznH0SVVUpqVpA+aprsC2+nkN6Dq1xkVDSTNVXJANJzIyQlQWFHXsPsHheH59536QoCiDL8K5rDW7aOMp3f9bJwIgdv99/6jdrYWHxpiQUuj9je+9ehbw8hZ6epoxAnt27s8nJ8TI8LGG3m8pnIkFGzfeT3ejjcawsHYs3DEsYtbA4TzAMg9HRf81IN3j4YYnly7OoqfkEALt2/Ylly+IT++NxcLsDiCK0tUFNjSlMNjaqVFauxTAMRka2ZYidoRBkZ6+eVseqt7eR/PxkRtvQUC4+X/a4EUhjhhlTLAZOpx1RNGuQejxWXVELC4tXz+9+91luuy1z8ebRR72sWeMH4JFHVDZsyDRbqq7OnygncuSISnn55MS6vj4LVZUpL49PPSWtrQqy7CQnZ7JN02BgwEZ+/mKrjq+FxXlM1fyF/POzh9G/+QLfGsxhMHM6Q74dPpEXoeKln/HgTUv40//9CMODg6y86gZW/cuPOH7N53lRKKY/Of3cDglWq2FW7vsdR772ATbf+yPm1i7mxVAuiVmM7w8OQa7TTNF3K7BSaWf0iW+x78UnUFUHtWs2Erzs/TS6l3IsYiM1Q7nQAqdOrX6M1Cu/pn7TH+jv7T7t56BpMcpr05St9iHX+eix2xBFkOXMF6qINp575Vk+eusMivA4a5bB0WOjtPXlU1BQcNprW1hYvLkYGOinrq4to83p9JNO6xQWDmW0i+IFANjtmYaWHs+kHBUOM1FbFCCVUi2zSIs3DEsYtbA4T3jhhT/wlrf0TWynUuB05rN1a5B58+qIx+O4XI9nHLNzp8zChW50HYaH3Xg8IuEw+HxXjjsZH6W4OJpxzOBgKV6vP6MtEgnh9XZkrPz19Mjk5c0HYHi4i0AgkXFMKCRNpLUmElnY7daKn4WFxavj6NH9LFv2/zJS6B9+eDKFftMmkauvLskwWxLFXHJyzLD5AwckliyZTKFvbHQiy4VUVGRGWHV3i6TTOZSWGtPaXa65OBxOLCwszn9WXnQxn3+ln5fe/WPu659eg3KNH+509hB7/Gfs+PDFPPR/P0xPVyfVi5Zy8ee/gf7+b/GifwmN0ZkXSuqcKdZ1v4R21+cJhPq5r9XD3mE72rju2DMGdx1I81xTmFQ0wpg2OeaUuNLUjb7E4Qe+SVtzAzabjaqla5hz5ftpz7uYhjHnjJGrARVq5V5chx/kyF9+TfuxRmYy19U0jbGxfROL2E6nSFGlE10wsCuZwmgqYeD3DnA6M/cLF2nY7QWMDA/PeE0LC4s3L7t2/ZKpFb4OHIALLwywfXsP69ZNjik9PXDBBRvp789Mo4/HxYwxJhabTKMHEARr7mXxxmEJoxYW5wnt7V/IiMh89FGRq6/ORVVvA2Dnzvuoq8t0ig8GsxEEOHRIYsECM52+ra2M3NwSkskkonjgtIZL6XSa0dG9eKaUeInFwG5fiKIoaFoSQejIOM/wMGRlTTVcyn2dPgULC4u/F9LpNK+8cjW1tZMP2z09EAyW43JJtLZCcXHJxAJMPG6aLc2bZ+Z8DgxATk5gYmxqa5PR9VoqKnozhNbBQYFUqoTCwnhGe38/GEY2wWDR2X6rFhYWbzBvee+HuHFLhJ9Uv4fdJ2WiyyLcmpdiebiJgb/8hv5/vIaH//n/0NneRl5BARd/8AsUfeHHbJl7DTui6oToOZUS1eDyVAtvHzvASGsv97d6+XG9nT8eHGGF2sNNBYNk6wP8YWcnD+4bJDV+DlGARZ4xAvvvYcef7iIcDiFJEuXVdVRf9T6G515PfTyb0AyRq04Zahwh8tv/QtNTv6D5wM6J1H6Avr4jFBRk1kwdGVEwDD0jVT6eBMEwcKjT66ueTMALK+epqOGn6Tj8Z1qOvMxAfy+6PkuorIWFxZsGUXwyY/vIERWnU6Sx8WhGSaKXXnJRUVHAyIiCokwqn4IwKUVpGqiqkrFts7064yULi78GSxi1sDgP2LHjWa677nhGWyqVy+7dCmvWvIuxsTFycp7P2L9rl0x1tWM8jd2PLENLi8LcuVcA0NGxg4KC0xsu9fU1kZeXGQ06MJBDIGCmrg4ONhMIZDo6gx1FsQyXLCwsXjv33fcZbr01M4X+sce8rF7tIx6HxsYcqqomn+Zfflll1SqzXEg6De3tLgoKzIiw/n6RWGwZpaVtGYs4oRDE46WoagjHFGEgGoVIxElh4YKz9wYtLCz+piiKwoe+80vy7jvOj8U6hjNLDlOgwp3ZEULNB9BeeZTUf7yVh//pTtqPt+B0OlnztttY9i8/pX7d/2FzOofQDDqiV4ZL1RHyuvZTnWzgY1UjLM3WKHBBTQDeU51ikTfCPTv6mBpw6VFgpdLK8GPfYv9Lfxk334SC4lJqN9xMeunN1Bsl9Menp5kqIlS54lSOvEL3sz/nyPZN9Pf34Pf3ZPTr7IRkcgSDNK4pC++RKBTn+mnrPn35kENNXuaW56PKUBLQKPd24ou/QE/9g7Qc3kRPd1uGOGthYfHmoLHxMBdcMDKxrWlQVmaWV/N6+zL6xuOLMddKMqP07fZJU4xwGAKByTEnkQCbbXaTJguL1xtLGLWwOA84dOgTBKb4Fj31lMD11+fT07MWu93Orl2/pKZmckY+OgolJWahvMOHbZSV2YnHQZYvwW63Mzo6RDDYmXGNmQyXIpEwLldbhplTX59Mfv5CAMLhYfz+TCeDoSERv988YGTEZhkuWVhYvGoaGvaxYsX/Tkuhv+UWc4x67DGVK66YLAb60kuTZksA+/bJLFpkRhqEQjA4uJyCgtYM8TMeh56eIjQtylQz5XQaensVgsFF1qKOhcXfAUUlpXz40f0c+OQD/DE03QF5bQBukLvZvPsgc469hPi1G3n4H9/L8WNNSJLEojWXsO6L32PkXf/GC2o17SeJlbE09BiwfpZS63N9BuUenaeOg35SNnqpO82C4Rc4+Ptv0t7SONEeyMqm9uIbcF30Xo4oNbTNYNQEUOpKU5M4jLHnj7Tt7mBk2Aw1NRd/YpSXaySTaVxTxsaxGIym7AhKkO6+GU8LmEJIR28+pUU5Ge2KBIUBnXJ/Pzn6Noab/0zLoafpbGskmZwh1NXCwuK848iRn+GbEtC5bZvAsmUeDh8e5pJLJp9JIxGYP/8qBgYESkomhdFoFNzuyQfMWEzKSKvXNMWag1m8oVjCqIXFOc6hQ7vYuLE+o21wMIfhYZElSz7B6OgIJSXbMvbv3WujvFyluxuqqkxhsrk5j5KSagzDYGhoa0ZqvGm4tCqjwLWu64yO7sXrnewXj4OizEdRFFKpFPH4sWnuzV6vHUE48e+y1+1zsLCw+PsgnU6zffvV1NRkptDn5pbjdIps2iRy1VWTdUXr6wVqayfNljo7obw8G0kyIw7a2xeSnd2G15sZ2d7dHURVJYqKMsO8uroEHI4y3O7pAomFhcWbl4uvv5Hrnxvg3iUfZn8kU9xURHh3nkZ231Ge3NvIxcO7sX3jXTz8D3fQ0ngUgNKKKi75xJfxfPz/sblwHfvHZHQDto7C2uJTX3tNME6f4OeA9yI6o5mPZ5IAiz0RfHt/yY6HfkYkEpnY53Q6qVl1OQWX30mj7wKaIrYZU/tzHVBLFHFXK/XPHGf/nlHKy+O0tSTo7EgiTXm70bhI3FfC7e9azBe/7SEcmX6+VAo+840Crr/xs3QOS2izeDTJAuR6DcoDwxTJe4gcf4iWg0/Q3nKIWCx26g/FwsLinCSVSpGdvTWjraXFgyTB3r1HM4J5XnjBxtKl8xgZcU17ZnQ4zIHHzFrMFEENw6ovavHGYgmjFhbnONu3f5SpZp+bN8P11xeyc2cpJSVl7NnzM8rLJ2fBAwMwd665et/T48Lvl+jqkigvvxqArq5GiovHMq4xOFiC15sZ2dnb20xubqZrc39/FoFAHgDDwx1kZWWmR0WjMg6HSDoNsZgfu91KgbCwsHh13Hffp7j11kx35cce87FqlY+WFigpKcHlMqOj+vtBlnPJzjajDpJJGBnxEghIpNNw9GgZfv8QwWCmUtDS4sHhyCIrK5yRWj80BKlUFvn5ZWf1PVpYWJyb2Gw23v21HxG4p5lf2BYzelIWeLEKd2aH2bf/INuO9XBd7CDOb93Cw5+/jeYj5iK2PxBg3W0fpvaff8aupTdzMC5Tepp1FrsEQjLC4kuuwbPxc+zQShg76dpeBVZKLQw++t8c2PLMRHo9mGUBqhatpGLjnXQXX86RqIfYTKn9dqi1J1ic7OWFPw+QaA1TnZupamoJgX07DrBsocJX/ukybv8CfPvn0HAMmo/Dj+8T+NBXlvDej/2Ba976QQpq30ZIvZSWUDHtw8qM1z1BlsugPCtMif0QWtcjtB54lLbGPYTDodkPAiKRCH19fQxbJk8WFn9z9uzZxMqVk9Hfw8NQUWEaXcpyZkbiwEA16bTA6KjEkSNw9KiZ2ahpkzJUJJLpRm+ex1qctnhjkU/fxcLC4m9FS0sjl1yy/aS2AKtWSeTkfID+/j6qqvZk7D940M6ll9o4dEhk4UIfmgax2AUUFrpmNFzq6pIpLl6RcY5oNILT2ZrRr79fIj+/DoBYLIaq9kwzKgkETCF0dBQCgQIsLCwsXg1HjuxlxYofZowtDz0kcsst5cRi0NycwxVXmDmf8TgcPernoosmc0D37bOxYoUbgMOHs3G70xQVZaoLDQ12srIKSKf7cLsn2+NxGB11UFxcd/beoIWFxXlBSVk57//zXjY9eB+j//Mh3uoNZ+y/JMsgqXdx75Z+ls4r5wb7Efq/824eds+j9s5/omp+HYqisHLD9Rzr6CCpv4TjNOEo8YQ5Vnl9fla+7cO0Nh7m2I4HqfNmRlbOcadIDW7iwO93Elx9E0VllRP7RFGktLIGKmvo6eqgcf9fKJFjBE5ap+4cg7Isg6pc2NuTuXCkpwTyba14nOCwOfj8ByDbB89uNUuNNPZews9+/dxElpEoimQHc8kOmkabo6PD9PY1ISa6yXLFcc+yRu51gNcRBRqJDjbSdtxOWsnDn1uF35+FIAj09/fR2nwQtxzBbU8STkk0xZ3k5M6hvLLm1B+ohYXFWaGn51cZz4hbtypcc41KZ+cYq1ZN+lKkUjBnzgb6+wUWLRKx209k7MDQkEFuroGiCMTjcsZ8LBoFh2NKg4XFG4AVMWphcQ7z/PMfpWJK2c89e+Daa4vZtk1l5cqrOHToJxRNMUzu7oYFC4LEYqCqXhQFmpr8VFQsBaCzcycFBZORAWY9/LqMGi5mqv2+jLoxiQQYRhV9fT00N9dz/Ph+pmTdk0hAMmmnr4/xWqbFyLK17mJhYXHmpNNpdu68JiOFvrsb8vPNFPrHH1e5/PLJWnabNqmsWeOf2G5uFqitzUIQoKHBid3up7w8M+r9+HGZ8vI5RKPD5OdnXr+nRyY7e6FV08rCwmKC9W+/hauf7efXdR/g0Fhmer1NhDvyNHw9R/nF9iZsRoobtKMEvv9eHv70Ozl6YB8Ayy66jG2Dp04L7YiAJ3Kc3c8/MdFWVjWfmpv+kb3u1dPS62UBlnjCePb8gh0P383Y2NjJpyRthFh4ZQnSBeXU46InOrmvPQpzx2srG0JmxKiRFkEeAaDxWIi6eVA7Fz56G1y7Hq689j0ZpZdOxucLUFa1ktKFNyDkXktbrIaWQRfD01/iBE4blGYlKPe04Qw/S9fhP7N/x9N0Nm1lWckAtYVxSrJ1KvM0Vs4ZRY7Vc/jA7tlPaGFhcVYYGxujsvJIRlt3t5l1uGVLI3PmTLa//LLIRRctJx6XOZFEqChQWgpVVQZNTXE0DQzDliG0JhKiNRezeMOxhFELi3OU3t4eVqx4NqNt3z4v2dkK4fBV9PV1sWBB5o3p6FEHwaDMoUMKlZVOBgYECguvQRAERkeHyc7uyOjf0eGhoGDuSdc9Rl5e9KR+KvH4AIFAFxUVo1RUGITDcOyYmbo6PCxRVCQTCJhCxslp+RYWFhan47e//SS33JKZQv/EEz4uvNDHpk0iV189WVd00yaZyy+fNFsaG4OhIR9ut0hLi0Q8XkxlZWZqZleXQCiUR3NzmMLCzFzPzk4BTfPj9frP1tuzsLA4T7Hb7dz+jbtw/aKBX0gLCZ+UKl7qgPdnhdi9/wAPN/SSrRjcoDcT/OH7eeRTN6KNhWk0CtnVGePbz3Xw7Wda+fYzrXz3+Q4a++MYBjx5HK4tg+rme3nul/894eSuKApLLr0O94bPsiNRSHSm9Hqxib6Hv8nBbZvQTetnwuFRfL5ORBG8XoW5qwoJVwRpUB00R8CuMLnALZ1UlFQXsTlMFXPvwW58UzJaj7YILFl6+Rl/di6Xi9LKRZTXXYuz9C10aItoGfbRHxamGU1NfN4y5Pk1EtFhFpcmMuqfnqAkS0NMtjM8PHzGr8XCwuKvZ/v2+5k/f/LL29ICpaXmc18i0ZbR9/jxOYCMyzU9bNzlApcrRV+fjqpmfskNwzGtv4XF2cYSRi0szlGefPIT1NVN3niOHoUrrijh2DFYteqjNDT8JMNJua0NFi3Koa8PysuzSKdhcLAOny8wHgW6JcNIaSbDpVgsit1+7KQUegGfT6G0NIHTaU6kbTYoKIDCQvOG6PebNzynE+x2ZoxcsLCwsJiNI0f2cMEFP5oxhb6lBYqLS3A6zZ2HDgkMD+cz9Xn48GGVRMJJczPIshdZHmb/fnPhBszay5GIh6ysKIIQp7nZTNUCGBkBXXdjt9smxAgLCwuLkymrrOL9jxxg+wfu4eHQ9DTP9VkGG4VO7tlykEMDYwQUuN5ooeAnH+L4tk3sb+rko9URPlcX53N1ce6sjLD5cDv//HQ/FxWAxwYuBS4T9/LKDz9Nf1/PxLl9gQAr3/Exemtv5lBInXbtck+K6r5n2Hf/N+lsayEU2juRmppOQ2trmKoqqF7ppvDSbAx58hFQOEkYTWsCuTnmWNg35TUAdPXbKJyaqvQqsNvtFM+poXzBRvwVb6WXlbQM59A9KqKdJJJ2D0FRDpwiMJXyYILjxw6/ptdiYWHx2ojH/5ixffCgi/nzBUZHk9TVZTq1ZWWtp79fIDt75ujPggIYGtI4OclQkqz6ohZvPJYwamFxDjI6OkJ19Z8y2l5+2UVxsZ1Dh2qJRIZYvLglY39rq4tAQKKjw0l2tkRTk5PKytUAdHU1zWC4VJwR2WkYBgMDezOcBM1oUBc5OcyIqkIwCJHIZBpWVhYMDvbMfICFhYXFSaRSKXbuvIbq6swU+oKCckCkuTmHqiozeqCvD2y2XK6+Wmb/fjhwAA4dElm0KMDy5dDaqlBSIrNgAVRVwd695iKQrruYN0+iqAhqaqCiwox4D4Wgv18lP9+Fw2G+FgsLC4tTcfk7b+fKZ/q5p/Y91J+UXm8X4T15Gs6uBn6xo5lwMs3mUdhQPMY7KxJ0DMfpDpmio8cG769Js9QTomswM8J9bWCEkfv/kcM7X85oL6+pY95NX2SP60K6o5nXVkRY6gnj2vkzunbvJDYeXtraGqGiIjNdPpyaFEMVW6YqOTwap2S8TLyqjGbuC3sRxb/+8VFRFAqKyilfcBnBqrcyoqylZbSAzmGZRBqGIxD0nvocdhmMlOVsb2HxRtHd3cmSJZPPeLoOY2P5FBXBiy+2sHjxZN+9e+Hii9eRSMgZi95TORGI45gSIJpIgN1uCaMWbzyWMGphcQ7yyCP/wKpVk5PY9na46KJSolGYM+ejtLb+NEPAbGyEZcuyOHJEYMECP6EQZGVdhSzL44ZL+zOiQLu7JYqLV05sp9Np+vtbyc/PFE87O1UKC0+xXA/4/RCeklcmSZBOW+KChYXFmfG7332SW2/NXEx54gkfF1zg44knHBN1ReNxOHbMT1WVA4cDNmyAri4wDC92u4DDAYoy+bDvdkNRERw/rpCba8s4v80G1dXQ3Aygjo+Ppx7rLCwsLE6gqip3fPuX2O86zC+EGiInTXvKnfD+wCjb9uznj/UdPLSjmX/9cz1/2dnA3Zvq+eBv63lg7wAA7yjXeKZdpPMkkbXKl6Jgzw958fc/nuZAv3T9DagbPsuOeOE0F3i/DVYKQ/Q8tJUtf2mmuFibECaGh1N0dQ2TWwRD41HzNttJEaN6GHm8f35OZp1mTajg9UaWZYK5hZTXrqNw/tuIui5jTHNxRt7zlkG9hcUbxt69v6Bgirfunj0CubmmiDk0dCyj77ZtQRoanDgcs7ivYXpdiKKU8Ywaj4uo6vSoeAuLs40ljFpYnGNEo1EKC3+Z0fbMMw7mzXPw8ss+HA4ny5Z1Zezv6fGgKBKC4MNuh87OuQSD5p1rJsMlXV9EPB6noWEv9fUv09j4Mr29TbS2Qmx88X1wUACCE8WyZ+PkwIFUCiTJMl6ysLCYGcMw2LLlRX70o//kq1/9FAUFP8oYR/78ZzOF/plnRK66arKu6Msvq1x4oT/jXGvXQkfHpAOquTAzub+gYDKd/mQUBfx+AUlKEY8bJBIyNptt5s4WFhYWM1Axr4b3P1bP1vf9gsdC0w2WSuwGcrSPf188yrdWJ/nYwjT/tCzFT9fGSA118I1n2hEFqHAlGFr1KXYPZwoCATtcFN3Miz/6B0KhzOjNQCCLlTd+jK5575wlvV5nZbydww/W0905RkdHgmRymMpKnYVLYPcAhGKgqpPqopYGu2oqpqOhJNVlk6JpNAaB3Iv/mo/rtAiCQCArh7KqJfSMnNp8JZYEWbUiyyws3ghSqRSC8DTGlMWIrq4AXV09fO5zL/L888N88pPw+9+b8y5RXI7fn6S9PYGuz3zOri5wOjPriabT1jzM4m+DJYxaWJxjPPLIv3PZZZN17gYGYNmyEgAM42309Nw9UTcKzHp7K1ZkceiQzLx5To4dszF37mUAhEIzGS65sdk8dHfvoby8n9raKDU1CerqoLjYTC81a+7Nw2azZ4gMZ8LwMGRl5b2m925hYfHmZtu2F/jAB5Zy6NBVXH75l7nhhu/zyivw0Y+aafJdXVBYWE53t0h/fymPPy5iGPDSSzIXX5w/rd6cy5WZ/m4uzEzuF0UQxdlDivx+g3Q6RSRiYLN5kGbL97KwsLA4BRtueR/r/9LPr6pu4+iU5Jv/7oQfbgDvSc/6ggC3VOmoiSG2t0VwyykCwQKqP/B9nhsrRZsy95JEWO/t4fgvP82x+gPTrl05fzHzbvoiLxtV9JyUWW6m1ydRtzXRsasVr9dUKGw2WHsFvNSj456i50ai4PWbi027Dw5SVTa571AjFOWo9HW3v5aP6FWRl1dAd8g5rfboVJp6Vcoq55/112Jh8ffMvn07+cY3PsJ///e1NDaO8V//BT/9KbS1GTzwwFGGh5/hX/+1g7vvhu9+1yypdvvtUFVVRU9PjLy8FL290+u3x2IQDsvE4zqdnWZ2ZH8/wPQFJguLNwIrrMvC4hxC0zRcrv/NaHv8cTt33OHmwAGRrKyFLFiwLWP/6KiXsTGBkpIAsRjY7ZehKAqGYTA4uJXy8sm+oRB4vSvo62ugpiYxTWRwOGDePDh4UGTx4mLi8TiDg0MUFc1uSBIOg8NhigljY5BI2HC7p5sSWFhY/H2zY8dL/P73t/HTn3ZmiJd1dTA4CJ/5DGRlefnKV3xs357DLbeo7NkD998PV16Zn5FqNRVBMJ+cIxGw26d3Mk6TaplKwdiYk6Ki4Kk7WlhYWJwCp9PJe753L031X+Luz76NucONXFgM9lOst3ygNs0/7+ghuzyb63w+XC4X6z/8NV5+6NdU9z5FcEowVZ0/Se8L/8WW5utYfe3NGeaZiUSMukuKSCcD7HjuEAvtYzimXDdgh1XEaf5zL8m5XmpWuGhrs1G9sAxJ7GFwFFwO6OpNUVFsqrINDR2sXzR5jtYuieuuyEIbeZj6nmrmLrwEZbaB+a9EFEXmzV/Jvvqt1JWMZXyGaQOae+24subh8VgRoxYWZ4tHHrmX4eFf8pnPDDM1oaa3F97znla+9rURli2bnGSJIlxxhZnN8+EP/56bb55LQYGPXbsS5OQoKIqZudjXByMjMvPmqfT36wQCOk6nSCQCg4NjeDzaWRtbLCxmw4oYtbA4h3j00e9wzTXRie1wGCorTffP1tYVJJO/zyhQvWePwMqVAVpbVYJBhZaWQoqKKgHo7m6iuDjTHXBwsJhQaJTi4visTp+KAvn5AoODAzidTpJJ10R6/cloGvT0CPh8Mj090NenUFz8+tefsrCwOP+5664v8N//3TljEf7sbPjyl8EwbHzzmzLr15t1RZcuheFhGb9/5nVcw4BUSkTTYMcOiYULXRn7QyFwOGZfAx4aEtB1J0VF5cgn26JaWFhYvAbm1i7gvY83cH/JlVx1mimRSwE9ncQlJHj2q+8mNDqKIAisfesdDK/5LIdGMselPCes6H+UTT/9V2LjkzNd1xkd3YfXC26fg+AF8+icW0J9aPqYVukxmNs9yvZ7+xkd8rPzxT28shkadsHzz8DWHXECPrNvIj6QcWzfoB2HquB1QG2ggWO77mV4qP+1f1CnIRDIonrhOur7itjT5uZIt8qBdid724N4C1ZQUVVz1q5tYfH3TmtrK52dv+aOOzJFUQBB0Fi6NJIhik5FVeFTn+rl+eefRVFAkgSam2WOHJE5dkzG4XAyf74Dm03A5zMIhczFGLcb8vIS9Pd3nu23Z2ExDUsYtbA4R9B1nXT6axm19h5+WGHNGh/DwyDLF7Jy5aRraToNqZSf1laBBQsCtLdLFBevp7n5CIcP72FwcF/G+bu7JYqKVpBIjHC6BfacnDRDQ90AFBdX0tXlob9fQhsPHDUMU3BoapIQBDe9vSqqWkxZWbW1wmdhYTGNI0eOUFPTNKszKcDcuZBIRFAUKSPKs6bGoL195kKhx46ZUVrPPy9TV+fF7Z4cQHUd6utFyspmLpScTEI4LFFYWGaJohYWFq8rgiBQPn/xGVm6DcTgnVXw1uwOdn31BtpbmgGYV7ecwtu/wwujQfQpY6JNgssczRz48Sc4tH8Px4/Xk5sbJ52G48fDlJXp+PM82C7MZY/dSd8M6fV2XSN6dC83Vo+yoRLWlMBVVTAvd3Jx3u/OXFwPxwIZ29X5UdJdD9B4aAv6bEUE/0o8Xi9Lll/EwuUbKKm5nHlLNrB81XryC4rOyvUsLCxMHnnkJ9x888CM+/7whwHe/e7ZswnBXNgeGjqEYYAkidTUOKipcVBd7SArS5oI0BFFs/b8CVQVBCFOcrYC8RYWZwlLGLWwOEf4y19+wQ03TBbWTyQgGCxAEAS2bs0jO/vljFTS3btF6up8pFJeJEmgvb2Q48dfIitrP3PmNJKbq3PggOlYf8Jw6UyNRWQZDMOc5CqKQnl5LbJcSUeHj5YWF62tHmKxEubOXcLcuQsoK5uH3x/ISOuysLCwOEFDw0GWLp15gj2VQCBNRUWS48cnDZVU1SAanR6VMDgIL77oAeYyf342WVmT4mYoBLt2ubDbiwmHp6ux8TgcOiQTDFYSCGS/tjdlYWFhcQpWX3Edzw54T9knloKYplM8XoFofTDCyE9vYe/WTYAZNXnRR77Fi8oqQonMYy/IjuHZ/G2O7X2ao0fh6NEI5eVp2triGMYIlZWwdIMf6dJcdkZl4uN1S0MadCfhkiqQTpq2KS5TjNB1gzmFmcJHkmpCJ4msOW6DMucejmy/j3A4xNlCURRcLhf20zmCWlhYvC7E4234/TPvGxhIUlh4+nO4XCn6+wUCAcesfUIhcLsz52lut8bYWGSWIywszg5WiISFxTmAYRgMDf1LRqrCQw9J3HhjNroOg4NL2bixdWKfpoEsB6ivl1myxMWePSq1tQkCgUnxwOWCvDxoa4O9exVWrJgLgCjaSCaZlhYxlVAInE7fxLYoigQCWQQCWa/be7awsPj7wen0EgrJQOqU/WIxAZcLtCmOG11dTo4dy0PTYhQUJEgkoL7eTypVxK23vgNRFOnu7mDPnlZEMYVhgMORw8KFNaiqSkdHK4cO9aCqcURRJxIRMQwHVVULrXrIFhYWZ43Va9fxs/+s4Lb0XpRZouV/fQQ+MC/Jb7e1c+sq02izLitN55Of59m2D3H5u/4Psixz6a2fYP+2hbh2/oJK72R0ZqnHIHtoB89v7uDS25fT2BijpCSGa0pVkeygzNiqAuo7XThamugZS7FqhhR/LQ3ZOeYYfex4mLp5k/vau2Hphe8h7l9Pf8eTVAYno7kUAebnj9Bz7DcMOFdTXrXkNX9mFhYW5wanMq4sKLBz/DgEArN2wTBM34mmJpmVK2eea2kajI1J5OdnDpBmFOmrdP+1sPgrsSJGLSz+hgwODvK///vv3HbbSpqaemlqMtvTadixI5/vflfghRdk5s3ryEhB3blToqzMQ36+n/5+EafTmyGKTqW0FCRJnkhJyM8vp7Pz1Onu3d0qBQUlr8t7tLCwsLjoorU888ycU/bRNEgmbbS2yhQWmis36TSEQkXccsvncLlu5tixK+jvv45Vqz7M1Vffit1uR1EUSkvLWb58PUuXbmDZsg3U1i7F4XAgCAIlJeXMn7+KgoILCQYvpKZmLYsWrbJEUQsLi7OKIAh88us/5VMHS4nPsCb0WAt0RODacrgh2M8vNrdMpJQWueHC5p/wp29/jnTaFAjmLVnNwPKb2DKsZpzHpcC1zm52/Php/N7hDFEU4MgRG1lZ81m6uoryd1xJr24nMIPxc8dQkjnjGeo793WTPUX0ONIssPzCK8gtKKFk8Xs41D+H6EmZtPk+nULxZQ5te2Ci/qmFhcX5STrtnyihdjI33pjDb3976mfJbdtEsrMvJhAoIBabnlGoadDRIZCXN/080aiI3W6501u8sVjCqIXF3wDDMPj61z/BN7+5nEsv/TfuvnsXd94Jf/wjfOIT8Mc/Cnz967ncfjv87ncGLS1J+sfr28fj4PUG2L5dxe220dCQTWXlqW2Xy8vjtLWZqqvX6yWVCtLfP3P4QlubjMdTbNUKtbCweN1wOp0Eg5eze/fs5TZ+8hOJa67JwzDsuN0iug6//30Wl1zyLkRRpLJyHhdeuJ6lS1e/aidiQRBwOp243W5rbLOwsHjDWLJ8JZ/44WN8fmAD//dICfe2uPnf5mxu3OSjLQz/fqHZz22D20qG+cmmJvTxgqJuBW5IPc9DX76ZsUiE7u7jLKhzsOIjN7IpVUDsJLH10mCC8B/3cGiHOWHUNDh40ENl5QLcbnOxyW6XcHpnFhx641Hs48NjT3d3xr7OPoWCggIAbDYbC1Zex7D9StqGMueSdgkW5PcxUP8r2o83vObPzcLC4m9LXd3VPPHEzMnFgYCM1+vjmWdmntOFQvC971Xy3vd+gXnzsgmHVVpaJPr6YGAA2toEOjokgkH7tDT6dBricRuuk1d4LCzOMsLUYren4Yw7WlhYnJqvfe3jXHDBL7jiiukr6q2t8MlPKvz5zwsRRYGWFnjqqQB5eXmk05CbK1NSUozfn8+WLQoFBZUsWzZ8yuvpOuzfX8SSJRcBpjDb3t7E2FgfgUAMu90gGpUZGVHJyiq2okUtLCxedzRN48MfvpLrrnuRt7xFnzCaGxuDH/1IQpKCDA2V8f73lzIw4GLPniAXX/xuamrq/rYv3MLCwuJ1oL+/n7a2NlwuF9XV1dz7nx/juo4fEZgSAJrW4SdHnXxg/Txs8mT8ytP9PhzXfYG1F08KFVuf3Edpyx6KTtIPRhKwyxmkcNlqampKM+q/d3UNsvWFbWzI1/FmBp6yuaubdReZhUx/eNdDfPSWsYl9//OrHD77n9Md6GOxGMf2PcrcYB/2k9bboxq0jJRStXjjGde4t7CwODd48snfsX//r7nqqj4WLco0VzMM+MEPJA4d6qGwsJ/3vz9OUZHpj/HAAy6efrqar33tYWw2Eb8/gqKYCzWxWIp02mBkJE1BgWm0NJV0Gnp7ZbzeYtzuV7cAbmFxCs7IBMUSRi0s3mAGBgb49reX8/Wvt83a5w9/EPF4yti40Q/A175m50tfKufwYXj2WSfr11dSV+ehvl6hr6+ASy45dcpSIgFHj86hru7CjHZN0xgZGSGVSqKqTvx+v2WgZGFhcdZIp9Pcc8+PePDBr5KTE8ZmS5NM2igsrEYUq5k7dyVer5Py8sUsWrQcUbQSWywsLN68/OH//SsX7f0PCk4SN39Ub+c9l9bitE2OgbsGZOzvvIOFy+ehaXDgQDd2sYvEC/UsC2SGj6Z12Bz2s+yOd+D1OTEMg/37j1BU1IQkwc7HYcNJdUZ3RtpYUWcKIA8++DvefuWkGPJfv7qAL/7nK7O+j5bGfajRLRT4prvTN/fb8BRfRa616G5hcd7w2GP3cPXVDdx77xG6uvpZvjyK32/Q3CzR1KRw7bU5LF3qpq1tjF/96hBDQ2PouoTPdz3//u8/QBAEYrEo8Xg3gUDmuKBppoET6LjdIAgQi4nE4zYCgXxLFLV4vbGEUQuLc5Hvfe9f2Ljxq9TUzN5H0+Bzn/Pw/e9XAfCjH8ncfnsFbrfIXXfJXHppHVVVDjQNnnsuhwsuEGetMQrQ2GgjO3stWVk5r/fbsbCwsHhN9Pb2cuDALgxDZ+HC5RNpmhYWFhZ/Tzx+z/9S/ewnqPRltv+sQeHGNTX4nZPlP46H4diSa3CXlLNwYQyHA8bGUmy/5xBr1bFpJk8HRmTEdVcSTXWwZEmIE5VEtm0GpR+W5ZuiRDiuE8lpoyAIkTGNliMPUFdt9k0k4Vebvsj/+djXT/k+wuEQbYceYV7eCMpJj6GhGHTGq5m78BKrnImFxXnA008/wEUX7cXpBE3T+da3jrFmTZziYoXKSvusx/361wu5/fYvTWz39LSTlRWf0fQ3Hoe+PnC7/aiqE6fTSp+3OCuckTBqhWJYWLzBHD9ez7x5p+6jKCBJk258pgu9+Z2+9NIUzzzTNdHP4XDQ1ORCn75ID5g3nZERryWKWlhYnFPk5eVxxRXXsGHDdZYoamFh8XfLNXd8nPZ3/JZDQ5nPbh+o1nhsWz09oUkH+DkeWHb4cTq3Pondbi6Iu1wyl354MU9STP9JCUR1/hQ5rzyO1nMceUq5wFXroE9R+WNTHi+3u9h8LElB0Ny3+8AQNVOiSQ83QXb2qc3zADweL/MvvJXW6DIGIpnvxeuA2kADx3bdy/Dg9JR8CwuLc4u6urXs3WsKlYoiUlLiZc0a9ylFUU0DUczMj8/OzmdoSCUUEifMnHQdIhEYHraTnz+HrKygJYpa/M2xhFELizcYrzeLwcFT9zEMSKXMSaWuQzgso6rmdk4OaJpZA2psDOx2D2VlK9i920UkknmOgQGBvXt9LFy4+qy8FwsLCwsLCwsLi7+OS2+4hbEPPsH2k4wxb6tKsW1fPc0Dk4qnzw7XRpv50789QiyWIpmEffucVK2+hvZFH+DQSGZEZp4TVgyMsukn3cSiOpEI7Ny5kCuu+iI33vFJ5q3/IHJu/kT/g/UdTA3qPNYuctncVna//CdOl2koCAJVC1YjFd5EQ48T/aTu1flR9O4HOHpwC/psK/oWFhZ/c/LzCxgcLKa/33z+XLLEw86dpw68277dwbJlV2S0KYpCbm4RopjL8LCTvj6VgQEHup5LTk6BVX/Y4pzBEkYtLN5gbrrpU/zmN6eO3tyxAxYvNnOq/vIXkTVrAhP7RkZAHl/2b2hwMm/eEoLBXObPX09b21x27w6wZ4+X3buzGB2tY/nyy3A4HGft/VhYWFhYWFhYWPx1XHDJRlz/sIXnezKFgreWpWlpbGB3x6QZkiLBOzwDbPryAzz7tIKqLqGwcAnLVq/H/47/5Ok+e4YoaZPgMmeCAz/p4OC+daxYcctESnswGMRlm3ShT0QzIzp7B+0EPBIL/TvZ+fyvz0jQDGQFqVxxOw2jNYROimLNdhuUu/bQsP23hEOhM/14LCws3mA2bryZbdvmsWWLi4oKB/v22QiHZ+4bCsHBg0XU1Cyctk8URdxuD7m5ReTmlpCbW4zX67PKalicU1jCqIXFG8yCBQtpaFhMV9fM+5NJ+OEP7bzznUEefVSkrc3L+vWThae2bpUJBv309krE44UEAqZo6nQ6mT9/GcuWbWDp0qtYvvwKKitrrJuOhYWFhYWFhcV5wIKlF1D6lb082unMaL+iWCfZc5Snj2YKidfkxsh68W4EzcDr9dHScpB4/Ges/3QBL9pchBKZ578gW6do373s2vTYRJuu6/hdk6lMfnck45hw1JyD2mRYWtDAzmd/inYiJ/YUyLJM7ZLLiQfeQnN/ptirCFCbP8pYy29oadx72nNZWFi88dhsNq6//g7Ky9/L5s2LCQZX8cMf+tmxQyE9XvEtnYZdu+z89rcV3HLL5ywTX4vzFst8ycLib8Do6Cif+tRGbrppH1ddFUcaz5zauRP+7d9UamrmkpXl4+KLs1i7dtKZr7ERtmzx4PXWUlAwnxUr1k9Ej1pYWFhYWFhYWJz/PPH4fYz94oPcWDiW0b5/EA5Szq1LAhntzaMC20uu5NpbBLzeyfYDr0Rw7BhgrjejO9EUbBPqWPWuT/L8M08wePxBVDssmG/Q3XIfl0+pwPSNu5byj++/OuP4HcdzqLv4I6hqZj3B2dA0jaN7n6LcfxznSev1yTQ09ufiyltBe+tBSI2/Z9nN3JplFBQUntE1LCwszj7pdJp9+3Zw8OAmBEHDMBTq6tazaNFKJEk6/QksLN54LFd6C4tzmVQqxWOPPcDTT/8UWQ6TStlZuPBa5s1bzq5d97NxY4KFC0EUzQLVmzdL9PSoiOI83vrWD+Lz+U5/EQsLCwsLCwsLi/OG4eFhurufZ2S4mabvfoU7CkYz9jeNwpORMj6+KiujfSgOW/JKuPaDCzgRtNXcLBMavYLY5t+yJpAZbbqjF7aPSFxSbVCdo5PS4bH9Y6xd/xD540ZMPf3w8O5/5cbVGlkneaPsafNQtfpjuN0ezpTOtkbS/c9Rmp2aaDMM2NEEqg1qi80yAWAKpkc6VTR7FcsvvPiMr2FhYWFhYTEFSxi1sDhfaWo6wtNP30c63Y2qphFFGZerGJ9vLuvXvx27fXZHQAsLCwsLCwsLi/OTI0cOoCgvUVmpMzaW4Ld3/hcfzM2s+9k1Bj/vLef/XpIZOZpMwyNagKs/t5JDh/Korb0Tt9tDMpnk5d98m1XGQRyyGXnamoIbFmVe+3cvH+XmO3ZObD+3FRZc2UM01INj6AHy/ZmPg4c6VQqXfoRA4NS186cSi8U4tv8x5ub0YpegvgNUBcrzZu7f2GMD/2qqZqhdaGFhYWFhcRosYdTC4nzGMAw6OtoZGOhElu1UVFTjcrlOf6CFhYWFhYWFhcV5R2dnI5L0DPn5kwZHyWSKu97/P3zY34Y0xR1iKA7f7ynhS6uD2E+qqvSnbi8X/9Pvyc4JZrTvePZh8hvu58lO+MA6OLkc4HeefoHPfLhzYvuePyvc/rkEgiDQ0daI3n4vpTmpjGMaexR81R8kN7/oVb3XlsZ9qGNbONKus75u9n6GAc8fzWX9xpte1fktLCwsLCywhFELCwsLCwsLCwsLC4tzi4GBfhoadqHrpsWzKHpwOmUqKxsyaoQCdHYKgJtHvvIt3icewD6ljF8kCd8dKOJDdbkEnZnPfi/3qxTceRcV1fMz2l95eTMjO37Mxtrpr+sHLz/Ex+6YrGv6P7/K5rP/OTCx3dfTyWjDXVTlZ5ovtQ3ICMXvpmRO1av4FKCzs5Peow+zrPzUTve7jzupWnYjHs+Zp+1bWFhYWFhwhsKo5UpvYWFhYWFhYWFhYWHxBrB371aOH3+U5cubWbeuj3Xr+li+vBldb6C5ObNvY6OIxxOgqEjlQz/8Z36YXkEkObnfbYMv5HXyi4M9HBnKjGG5KBgncc972DHFgR7AEEVKMsuTTpAbjGdsJ/SyzP35ReQs+iSHOjNNl0pzUig9v6K5Ye9p3/9UbDYbjpPDXWdAkXRSqdRp+1lYWFhYWLwWLGHUwsLCwsLCwsLCwsLiLNPUdBhVPcjy5VGmGrqrKixfDg4HNDaCpsGOHQ5UNQePR0LTdHbubOHD37mSP5csZ2iKfmmX4PN53TzZnOCl3kzL91q/zpwXv8yTv/oOmqaxdevvGRj8KeEk04gn01SWpie2NQ38+ZdP6xcIZFF2wWfY05YZvZnvN8gae4D6/ZvP+PNwu92MRk9fNz8Us+F0Os/4vBYWFhYWFq8GSxi1sLCwsLCwsLCwsLA4y7S3H6K6egZVcpzqamhvh6amapYsuR3DWMbmzXYOHjzCypVhHA549z9v5PlF6+iazHhHEuHT+Yc51DbAg13+jHPmOuGSrnu591+vYMWKh9i4McXhTKN7AHY0DrJgSib8kWOwZPl1M75Ol9vN/Is/w47jmTVMAy4oNp5k3/YnTvtZADgcDpKiHy09e59ECnQ5yzIetbCwsLA4a1jCqIWFhYWFhYWFhYWFxVkkFArh80WmGR5NRRDA47FRVHQBiqIQiXRSVbWTpUsza3pe+8F1PFJ5M80nCZwfym8m3F7PPf1zM8RGhwzvC0R4/MvNxKIpCqrgYF/msfu7OrDbJreb20SSx39LOBye8bXa7XaWrP8Y29tKSU3J4veoUO18iZ2bH+BMvCzqllzElkYP2gxdNQO2NHqp+//s3Xd8VeXhx/HvXcnN3iEJhBDC3kP2EAQHMtxb616tttVWrb+6OhzVVltX6wR3tSq4FwqCCMjegRBGQkjI3nff8/sjmnBJCBFDAO/n/Xrxas9znvOc5+Qlh9zvfcaQsYdsBwCAw0UwCgAAAABHkNvtVmho65sMSVJ4uEl1dXVaseI5de/+mVJTA68pKjJp+/apuu7O11Vw3pvaUB74ce7ytEKF5HyiJ4ojVRm4ZKjOiPdo9f07lJnl1IIiuz7ZZpfj+6U7XZaSgLqFpaE6sW+1Nn32e1VVVrTYV5vNphOmXKs1hX3k3m8JULtNGpy4Vt99NeeQa4PGxSeo3/DTtHhrkjYXhKjaKVU7pE0FoVq8NVmDR05TbFxcq20AAPBTsCs9AAAAABxBDodDa9f+T2PGtDwC8wfz51uVklKsAQOczc5t2BCuTp2uUHJy58aylYu+kO/paRqVHDgf/aO9VtWfPFojCurV7YDN3DeUm+Wd+TfFJKdr6YIP5HdWyVH9sK67oOmef3suUXdcdIokacWOcPWY9KDi4hNb7LNhGFq79D31jlqh8P1GnfoNaWV+qoZOul42m63Fa/dvo6hwr4r27pJMJqWmZSolNbXVawAAOIQ27UpPMAoAAAAAR9iiRe9rzJh8tZQRGoa0YUONUlJqlJwceM7hkDZuzNKgQZe0uNbm/I/ekmn2xZqSFhiOLigyq/7s8UpZW6fhCYEf5QpqpS19r9PUC6+XYRj6fI5Fp05oqvPwc310+0XDGo/X7LSry/j7lZSUctDn27j6S3U1faXoA/ZJWrk7Vv0n3KSwsLCDXgsAwBFAMAoAAAAAx4KyshJt3vyJRo6skd9vKDTUJLNZcjj82rixXIMHuxUSEnhNQYFZNTWnqU+fcc3a27Nnp3Jzn9HQofkqK67Wpj98qhlpgVPXlxebtG7UH9StcLlOSaoMOFfrkT6xTFR9Ym+d1OtGpX8/QLOsQnr9g5N189mBmyutzwtV8og/KSU1/aDPmLPpO8XWv6+k6MCPjuvzIpQ56iZFRUcf4qcEAEC7IRgFAAAAgGNBdvZ6bdjwtazWcoWF+eVwmGSxWJSR4dLgwc0/aq1dG6kuXa5UYmLgKM2iogJlZz+rgQO3KyFhv/K99Vr06491fpo7oP7GcpP2njlbtes+0RmRObJ8vyxpTqX0yg6pc3yFrr9lZ2P9r5dLS7b+VqcMrtYJWYFT+jfvsSpm8H3qnJ550OfclbtJtuL/qnN84Pqo2XtDlTTwBiUkJh/kSgAA2hXBKAAAAAAcbYsWfaTIyNUaMsQp8377JZWWSgsWSNOmSZGRDWV1ddLmzX00ZMiFAWtzlpWVaMOGZ9Wnz2alHGRG+5Jv/Nr8yOe6NqM8oDy3Ssqe8rg85Xs0pW6+3H7p6a3SH06X3t6Yp4uuKm2s++Z7VnXXCK21zdDgLrs1snttQFtb91oV1vf/1DWz90Gft7Bgp5w75igzOXAE685iq+zdr1ZicqoqKho2dYqLizvkGqQAABwGglEAAAAAOJpyc3NUVvZfjRzZfEMlSaqtbQhHZ86U8vIscrlmqmfPEY3nq6oqtXbt88rKWqsuXVr+SJadbVVZ2SQNH36R/H6/XrtmuK5NzA6os7dO+nbIPUpI6ar1Hzyt88ZJaTHSy9nZ+sWl9Y31Xn45Ur/o10uPfNdFv7jjGe1Y9CeN6Vkd0Nb2IossWbcrs+eAgz53aUmRSjc+oz77jWB1e6XP1kgeI0adEzwySdpXHaHwuAyNn3R6i2uoAgBwmNoUjJoPXQUAAAAAcDg2bVqsoUNbDkWlhpGiYWHSkiVRior6dWMoWldXq0WLnlR5+c068cQ1LYaiO3aYtXjxeKWnP61x466U3W5XeHi4Lp+zTs/UjJRvv9nsaRHSlA1/1p4tq1QS1klpMQ3lEXGugDbDfA27J52UVqj1q1do0LRH9E1OXECdHik+mXb9TTlbVh/0uRKTUpQ2/DfakN/QntsrffCdNLq3dPbIKo3KqtfIrHrNHFqiwfEr9cG7s+VyuQ7aHgAARwLBKAAAAAAcIRZLVYs70e+vd28pLGyS4uIS5XQ6tXjx8yoo+KUmTlyqzEx/s/p5eSYtWnSCEhOf0oQJNyoiIiLgfEhIiK55/lu9YJwq136b1cfZpbML/y3H9qUyDENur1/pXZoq+HxSZ3vDBklJET5VlhYpIjJSw2c8oq+3BW7G1C3Jr7C9j2rLhmUHfa7o6Fj1GHuLVu2O1ZLN0oQBUlIL+y8lRkuTeuZr8YKPW/9BAQDQzghGAQAAAOAoMpsln8+jb799Tbm512vChAXq1cvXrF5hobRw4UBFRf1LEyfeouhWdnm3WCy64IE39Wj9KNXutx9ThE16sE+lnnpjm/71caFe/a90yx+l196VNm2T+nduCFn3VpsV3ylNkhQWFqZRsx7Sgm2pAffokmAotvRJbVyz6KD9CAsPV78JN6mk2qrkVjalT4ySHJW75PF4Dl4JAIB2Zj3aHQAAAACAnyuPJ0yGIeXlufXtt1Xyen0yDCktLUzjxkXLajVp6dI6ZWW9qBEj3C22UVIibdrUS/36XadJk1JbrPODuro6rVgxV7W1c9W79y7dOVt6/aEknZpbooSwhjrL9kmr9tTpglF1+l0fyWSSFu2QHpwnXTGxWqcOidNXO/waM7BpEye73a7xZz2or+berZN65TeWp8YZslQ/ozXfuTR05Mkt9qm2tlbpnUIleVs8/4OUmDqVlZUp5WC7SwEA0M7YfAkAAAAAjpA1a5ZpxYrZyshwaMIEn8IbltzUzp3Shx9a1K2bT9OnK2C3+h9UVkrr1nVTjx7XqnPnbge9h9Pp1MqVH6mi4n/q0WO7+vZt/tHt3acLNXJlocqd0st7pIfPaX5Pw5Du/sSqjMxMRaZGaepwaaPpJk2edmVjHY/Ho0Xz/qLJPXJl3m9bi7IaaZf5Ig0fN6PZvUtKSpS7/DmN7lnX2o9KK3aEKX3oNQSjAID2wK70AAAAAHA0zZs3W717L1Dfvs2nxvv90ksvNexIn5jYVF5bK61enaqMjOuUkdGrxXY9Ho9WrfpCJSX/VdeuWzR4cPO1SA/0wsOlWvJ6nv79Cyn0IOueen3Sua9HaO6jvWUySWXV0lrv9Zoy87rGOj6fTwvnPqATs7Jl3e9jZ2WdtNV7lkadeG6zvn7yzlOaNbSk1f59sCZRp51zk2yHWpQVAIBDa1MwylR6AAAAADgCHA6Hamo2tBiKSg0jNs88U/ryS+nccyWHQ1q5MlEpKVdp4sTBzep7vV6tXbtIhYWvKTV1o0aM8Mpiab0PW7aYtH17d8XGnqvTL5ug9V/2V6it4qD1rRZpWJpbhWVupSWGKCFaGlb7jD6f69IpZ90sqWH90sln/1EL33tEE7qul+37PsRGSP0cc/Xtl26NnXJxY5s2m01RCd1VXF2q5OiWx9uU1khhsd0IRQEAHYoRowAAAABwBCxbtkjR0c+pX7/WR3POni316hWruLjL1LfvKJlMTYNc/H6/Nm5crt27X1FS0moNH+455C73O3ZIW7Z0VXj4mRox4hwVFxdq2bJ/aN++/8m2qlw3jWr9+tkrbOo9LktjB4Q3llXXS0sqL9K0837fWGYYhha+/0+N67xSIfsNualzSqurpmrCqU1T8N1ut95/50Wd2GOPkg4IR0urpYU56Zp5zpUKDQ1tvXMAALQNU+kBAAAA4GiZP3+e+vb9nzp3br3e88/H6Oqrn2oMRA3DUHb2WuXmvqyYmO80YoRTdnvrbeTnSxs2pCokZIZOOOEClZQU6bvv/iGb7T1NnFiqlJSGTZye/qV078TW23rk22TFnHCeLhu2VGEhTeW1Tmlh8dmacdEfG8sMw9Dij/+jkcnfyL5fYOvwSN8VT9DE069vfC63260lCz9RTflOdYquk0lSUXWEIuIyNH7S6YSiAID2RDAKAAAAAEfL6tXLZTI9rQEDvPr660pt3lwvk0k64YRojRoV2bj50ezZGbriivuVm5ut7Ow5ioj4ViNH1isiovX2i4qkNWuSZLGcpqFDL1FZ2T6tWvUPhYa+r4kTy5Wc3Pya68+V/j2h5c2epIYNmC58t6veXLhbH7z+F53UaZ4i9gtl613S/IIZmnnpfQEjWxd/+qKGx3+p8P2CVJdH+rZwpCbN/HVAXY/Ho4qKhun8cXFxTJ8HABwJBKMAAAAAcLR4PB498si18vm2aupUj4YPN+T3S0uWmLVokU0XXZSi2lqzVq8OV1ZWnkaMqFF0dOttlpVJq1fHyeudoiFDfqHy8hKtWfMP2e0fatKkyoBNnFry2YfSupel2ye0fP65ZZI7Whp/zSINHj5BH7/5N02If0tRTbPq5XRLn+4+WWdc/mBA4Ll0/msaEPmxovYLUj0+aVHeEE0+43cyHyyNBQCg/RGMAgAAAMDRsn79Cs2f/3vdckutTAd8PPN6pb/8pWHTpYEDW2+nqkpasSJKHs8k9ev3C9XUVGrNmkcUEfGJJk2qUnx869e73dLXX9u0d+9YZWT8Wrmbd2jrZ//UTcMK1PX7a/dWSU99K3XJkG6cJX2xzq6Bl29XSmpnffbuPzUq8hXF7jeC1e2VPtw+UWdd9WhAOLr86/+pj22eYvYLUr2GtGB7X5109p2yHGq3KAAA2gfBKAAAAAAcLQ88cJXuuGPdQXeOr6uTnnlGuvXWls9991246urGqU+fy1VXV6P16/+uyMjPNHlytWJjW7+3yyUtXBiioqLxysz8tcaOnS6rtWmHpIKCAr34xF9VlrdRkqHCwlw9fHmRMjo1tfHKklSdd9cO2e12zX//3xoW8rzio5rOe7zS+9vG6KyrHw8YDbpyyQfqbvxX8ZFNdX2G9NW2LMVmTtOOrWskSb36DdegIScQlgIAjgSCUQAAAAA4GgoLC/Xpp5fryivLWq336KPSL38p2e2S0ymtWGFXZeVIZWVdJoejXps2PaaYmM81aVKtYmJav6fTKS1YEKri4onKyvqNxow5rc2ho9vt1ut/6qkrxucFlD+zdIyu+9MSmUwmLfjoBQ0wP62k/ab7e/3SvE0n6Kxrnw6415rln6mL6+XGujl7pa/WSUOyTBqc2fDRcmN+qFbtTtRJ065Qzz792tRPAADaiGAUAAAAAI6GNWvWqKzsV5o61dVqvVdekRISrPL5TlBGxqVyOl3Kzv6n4uLma9KkOkVFtXq5HA5pwQK7SkomqVevWzRq1NTDXsuztKRYK57N1LRh9Y1lLo/0v9036tKbn5YkLfrsVfXyPKaUuKbrfH5p7sZBOvPa5wJGpW5YvVBJVc/J7ZW+3ihdMkkyH/Ax1WdIr3+ToBNn3aKuGZmH1W8AAFpAMAoAAAAAR8P27dv19deXq6xsu/bsqZPZbMhksurCC1M0cmRE47qc//hHjIYM+a327HlOCQlfafLkQ+9GX1cnLVgQrrKyk9Snz281YsTkdtvYaNP67+RZPEZDuvsby/ZVSOsjn9fJs66WJC356n/KrHtIafutber3S3M39NWsa2cH7DK/Zf1Sff3Rk7r6FMl2kMGrHp/02ooBuuKGO9vlGQAAUBuDUeuhqwAAAAAAfozc3PX66qu1uu++evXs2VBWVye99lqd3nwzSmefnaDt24vVo0e9xoxZoPDw1turqZEWLIhQZeVU9et3q6ZPnxCw6VF76T9opObnPa+iiqsaR4V2ipM67bpOm9YNUP/BozTupPO0fFGIfKV/VnpiQx2zWTpn8Ba98+ylmnntKwoJCZEkdckcoOioKNksNQe9p80ihatINTU1ijrUEFkAANoRI0YBAAAAoB1t27ZVzz47VY88sqfZbvSS9M030oIF0t13t95OdbW0YEGkqqtPUf/+t2ro0LFHJAxtyatP3qRz05+SPaSp7NM1YRp+zU4lJTfs0LRiySdKLL5bmcmBHxXnrummade8Jrvdrry8POUu/qsm969r9X4LNkWqx8S7lJ6e3u7PAgAISm36B7N95lsAAAAAACRJL7xwt/74x5ZDUUkaP14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",
+      "text/plain": [
+       "<Figure size 1360x3625.68 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing grid movies to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/grid_movies\n",
+      "Using window size of 128 pixels\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating grid movies: 100%|███████████| 30/30 [00:59<00:00,  2.00s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving dendrogram plot to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/similarity_dendrogram\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "kpms_model.GenerateResults.populate()"
    ]

From b50860ecc4c2b951fc8ac693d4d8f8f96595be83 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Sun, 17 Mar 2024 20:13:27 +0100
Subject: [PATCH 70/90] update `kpms_pca.py`

---
 element_moseq/kpms_pca.py | 332 +++++++++++++-------------------------
 1 file changed, 112 insertions(+), 220 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 58d81c6..d2836b0 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -1,8 +1,6 @@
-from datetime import datetime
+from datetime import datetime, timezone
 import inspect
-import os
 from pathlib import Path
-import pickle
 from typing import Optional
 import importlib
 
@@ -11,16 +9,7 @@
 import numpy as np
 
 from element_interface.utils import find_full_path
-from .readers.kpms_reader import generate_dj_config, load_dj_config
-from keypoint_moseq import (
-    setup_project,
-    load_config,
-    load_keypoints,
-    format_data,
-    load_pca,
-    fit_pca,
-    save_pca,
-)
+from .readers.kpms_reader import generate_kpms_dj_config, load_kpms_dj_config
 
 
 schema = dj.schema()
@@ -112,11 +101,11 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 @schema
 class PoseEstimationMethod(dj.Lookup):
-    """Table for storing the pose estimation method used to obtain the keypoints data.
+    """Table for storing the pose estimation methods supported by the keypoint loader of keypoint-moseq package.
 
     Attributes:
         format_method (str): Pose estimation method.
-        pose_estimation_desc (str): Pose estimation method description.
+        pose_estimation_desc (str): Pose estimation method description with the formats supported.
     """
 
     definition = """ 
@@ -138,7 +127,7 @@ class PoseEstimationMethod(dj.Lookup):
 
 @schema
 class KeypointSet(dj.Manual):
-    """Table for storing the keypoint sets and their associated videos.
+    """Table for storing the keypoint data and videos directory to train the model.
 
     Attributes:
         kpset_id (int): Unique ID for each keypoint set.
@@ -149,7 +138,6 @@ class KeypointSet(dj.Manual):
     """
 
     definition = """
-    -> Session
     kpset_id                        : int
     ---
     -> PoseEstimationMethod
@@ -159,7 +147,7 @@ class KeypointSet(dj.Manual):
     """
 
     class VideoFile(dj.Part):
-        """IDs and file paths of each video file.
+        """IDs and file paths of each video file that will be used to train the model.
 
         Attributes:
             video_id (int): Unique ID for each video.
@@ -174,87 +162,6 @@ class VideoFile(dj.Part):
         """
 
 
-@schema
-class RecordingInfo(dj.Imported):
-    """Automated table to store the average metadata of the videoset associated with a kpset_id.
-
-    Attributes:
-        KeypointSet (foreign key)               : Unique ID for each video set.
-        px_height_average (smallint)            : Average height of the video set (pixels).
-        px_width_average (smallint)             : Average width of the video set (pixels).
-        nframes_average (int)                   : Average number of frames of the video set (frames).
-        fps_average (int)                       : Optional. Average frames per second of the video set (Hz).
-        recording_duration_average (float)      : Average video duration (s) of the video set (nframes / fps).
-    """
-
-    definition = """
-    -> KeypointSet
-    ---
-    px_height_average                 : smallint  # Average height of the video set (pixels)
-    px_width_average                  : smallint  # Average width of the video set (pixels)
-    nframes_average                   : int       # Average number of frames of the video set (frames)
-    fps_average = NULL                : int       # Optional. Average frames per second of the video set (Hz)
-    recording_duration_average        : float     # Average video duration (s) of the video set (nframes / fps)
-    """
-
-    @property
-    def key_source(self):
-        """Defines order of keys for the make function when called via `populate()`"""
-        return KeypointSet & KeypointSet.VideoFile
-
-    def make(self, key):
-        """
-        Make function to populate the `RecordingInfo` table.
-
-        Args:
-            key (dict): A dictionary containing key information for the session
-
-        Raises:
-
-        High-Level Logic:
-        1. Fetches the file paths and video IDs from the KeypointSet.VideoFiles table.
-        2. Iterates through the file paths and video IDs to obtain the video metadata using OpenCV.
-        3. Calculate the average video metadata and insert it into the RecordingInfo table.
-        """
-
-        file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
-            "video_path", "video_id"
-        )
-
-        px_height_list = []
-        px_width_list = []
-        nframes_list = []
-        fps_list = []
-        recording_duration_list = []
-
-        for fp, video_id in zip(file_paths, video_ids):
-            file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
-
-            cap = cv2.VideoCapture(file_path)
-            px_height_list.append(int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
-            px_width_list.append(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
-            fps_list.append(int(cap.get(cv2.CAP_PROP_FPS)))
-            nframes_list.append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))
-            cap.release()
-
-        px_height_average = int(np.mean(px_height_list))
-        px_width_average = int(np.mean(px_width_list))
-        fps_average = int(np.mean(fps_list))
-        nframes_average = int(np.mean(nframes_list))
-        recording_duration_average = int(np.mean(nframes_list) / np.mean(fps_list))
-
-        self.insert1(
-            {
-                **key,
-                "px_height_average": px_height_average,
-                "px_width_average": px_width_average,
-                "nframes_average": nframes_average,
-                "fps_average": fps_average,
-                "recording_duration_average": recording_duration_average,
-            }
-        )
-
-
 @schema
 class Bodyparts(dj.Manual):
     """Table for storing the bodyparts used in the analysis.
@@ -282,10 +189,10 @@ class Bodyparts(dj.Manual):
 @schema
 class PCATask(dj.Manual):
     """
-    Table to define the PCA task.
+    Staging table for the PCA task and its output directory.
 
     Attributes:
-        Bodyparts (foreign key)         : Unique ID for each bodypart.
+        Bodyparts (foreign key)         : Bodyparts Key
         output_dir (str)                : KPMS's output directory relative to root
         task_mode (str)                 : 'load': load computed analysis results, 'trigger': trigger computation
     """
@@ -293,21 +200,21 @@ class PCATask(dj.Manual):
     definition = """ 
     -> Bodyparts
     ---
-    output_dir=''               : varchar(255) # KPMS's output directory relative to root
-    task_mode='load'            : enum('load', 'trigger') # default = 'load': load computed analysis results, 'trigger': trigger computation
+    kpms_project_output_dir=''               : varchar(255) # KPMS's output directory relative to root
     """
 
 
 @schema
-class FormattedDataset(dj.Imported):
+class LoadKeypointSet(dj.Imported):
     """
-    Table for storing the formatted dataset and update the `config.yml` by creating a new `dj_config.yml` in the project path (`output_dir`)
+    Table to create the `kpms_project_output_dir`, and create and update the `config.yml` by creating a new `dj_config.yml`.
 
     Attributes:
         PCATask (foreign key)           : Unique ID for each PCATask.
         coordinates (longblob)          : Dictionary mapping filenames to keypoint coordinates as ndarrays of shape (n_frames, n_bodyparts, 2[or 3])
         confidences (longblob)          : Dictionary mapping filenames to `likelihood` scores as ndarrays of shape (n_frames, n_bodyparts)
         formatted_bodyparts (longblob)  : List of bodypart names. The order of the names matches the order of the bodyparts in `coordinates` and `confidences`.
+        average_frame_rate (float0      : Average frame rate of the trained videos
     """
 
     definition = """
@@ -315,26 +222,28 @@ class FormattedDataset(dj.Imported):
     ---
     coordinates             : longblob # Keypoint coordinates
     confidences             : longblob # Keypoint confidences                 
-    formatted_bodyparts     : longblob #Formatted bodyparts
+    formatted_bodyparts     : longblob # Formatted bodyparts
+    average_frame_rate      : float    # Average frame rate of the trained videos
     """
 
     def make(self, key):
         """
-        Make function to generate and update `dj_config.yml` and to format keypoint coordinates and confidences for inference.
+        Make function to:
+        1. Generate and update the `dj_config.yml` with both the `video_dir` and the bodyparts.
+        2. Create the keypoint coordinates and confidences scores to format the data for the PCA fitting.
 
         Args:
             key (dict): Primary key from the PCATask table.
 
         Raises:
-            ValueError: If `task_mode` does not match either 'load' or 'trigger'
 
         High-Level Logic:
-        1. Fetches the anterior_bodyparts, posterior_bodyparts, use_bodyparts, output_dir, and task_mode from the PCATask table.
-        2. Fetches the format_method, kpset_config_dir, and kpset_videos_dir from the KeypointSet table.
-        3. Based on the task_mode, it either triggers the computation or loads the computed analysis results.
-        4. If task_mode='trigger', it creates an output_dir if it does not exist, creates a `dj_config` file with the default values from the pose estimation config, and update it with the video_dir and bodyparts used in the pipeline.
-        6. If task_mode='load', it loads the `dj_config` file and updates it with the video_dir and bodyparts used in the pipeline.
-        7. Then, it loads keypoint tracking results from one or more files and formats them for PCA analysis.
+        1. Fetches the bodyparts, output_dir and keypoint method, and keypoint config and videoset directories.
+        2. Creates the `kpms_project_output_dir` (if it does not exist), and generates the kpms default `config.yml` with the default values from the pose estimation (DLC) config.
+        3. Create a copy of the kpms `config.yml` named `kpms_dj_config.yml` that will be updated with both the `video_dir` and bodyparts
+        4. Load keypoint data from deeplabcut, sleap, anipose, sleap-anipose, nwb, or facemap. The coordinates and confidences scores will be used to format the data for modeling.
+        5. Calculate the average frame rate of the videoset chosen to train the model. The average frame rate can be used to calculate the kappa value.
+        6. Insert the results of this `make` function into the table.
         """
 
         anterior_bodyparts, posterior_bodyparts, use_bodyparts = (
@@ -344,184 +253,167 @@ def make(self, key):
             "posterior_bodyparts",
             "use_bodyparts",
         )
-        output_dir, task_mode = (PCATask & key).fetch1("output_dir", "task_mode")
+        kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
         format_method, kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
             "format_method", "kpset_config_dir", "kpset_videos_dir"
         )
 
-        if task_mode == "trigger":
-            # create an output_dir if it does not exist, and create a config file with the default values from the pose estimation config
-            setup_project(
-                output_dir, deeplabcut_config=kpset_config_dir + "/config.yaml"
-            )  # creates KPMS default config file from dlc data
-            config = load_config(output_dir, check_if_valid=True, build_indexes=False)
-
-            # update the config dict with the video_dir and bodyparts used in the pipeline
-            config_kwargs_dict = dict(
-                video_dir=kpset_videos_dir,
-                anterior_bodyparts=anterior_bodyparts,
-                posterior_bodyparts=posterior_bodyparts,
-                use_bodyparts=use_bodyparts,
-            )
-            config.update(**config_kwargs_dict)
-
-            # save the updated config dict to a different file named `dj_config.yml`
-            generate_dj_config(output_dir, **config)
+        from keypoint_moseq import setup_project, load_config, load_keypoints
 
-        elif task_mode == "load":
-            config = load_dj_config(output_dir)
-
-            # update the config dict with the video_dir and bodyparts used in the pipeline
-            config_kwargs_dict = dict(
-                video_dir=kpset_videos_dir,
-                anterior_bodyparts=anterior_bodyparts,
-                posterior_bodyparts=posterior_bodyparts,
-                use_bodyparts=use_bodyparts,
-            )
-            config.update(**config_kwargs_dict)
+        setup_project(
+            kpms_project_output_dir, deeplabcut_config=kpset_config_dir + "/config.yaml"
+        )
 
-            # update the updated config dict to the file `dj_config.yml`
-            generate_dj_config(output_dir, **config)
+        kpms_config = load_config(
+            kpms_project_output_dir, check_if_valid=True, build_indexes=False
+        )
 
-        else:
-            raise ValueError("task_mode should be either 'load' or 'trigger'")
+        kpms_dj_config_kwargs_dict = dict(
+            video_dir=kpset_videos_dir,
+            anterior_bodyparts=anterior_bodyparts,
+            posterior_bodyparts=posterior_bodyparts,
+            use_bodyparts=use_bodyparts,
+        )
+        kpms_config.update(**kpms_dj_config_kwargs_dict)
+        generate_kpms_dj_config(kpms_project_output_dir, **kpms_config)
 
-        # load keypoints data from deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap
         coordinates, confidences, formatted_bodyparts = load_keypoints(
             filepath_pattern=kpset_videos_dir, format=format_method
         )
 
+        file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
+            "video_path", "video_id"
+        )
+        fps_list = []
+        for fp, video_id in zip(file_paths, video_ids):
+            file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
+            cap = cv2.VideoCapture(file_path)
+            fps_list.append(int(cap.get(cv2.CAP_PROP_FPS)))
+            cap.release()
+        average_frame_rate = int(np.mean(fps_list))
+
         self.insert1(
             dict(
                 **key,
                 coordinates=coordinates,
                 confidences=confidences,
                 formatted_bodyparts=formatted_bodyparts,
+                average_frame_rate=average_frame_rate,
             )
         )
 
 
 @schema
 class PCAFitting(dj.Computed):
-    """Table for storing the PCA fitting analysis.
+    """Automated fitting of the PCA model.
 
     Attributes:
-        FormattedDataset (foreign key)  : Unique ID for FormattedDataset.
-        pca_fitting_time (datetime)     : Time of generation of the PCA fitting analysis.
+        LoadKeypointSet (foreign key)   : LoadKeypointSet Key.
+        pca_fitting_time (datetime)     : datetime of the PCA fitting analysis.
     """
 
     definition = """
-    -> FormattedDataset
+    -> LoadKeypointSet
     ---
-    pca_fitting_time=NULL    : datetime  # Time of generation of the PCA fitting analysis 
+    pca_fitting_time=NULL    : datetime  # datetime of the PCA fitting analysis
     """
 
     def make(self, key):
         """
-        Make function to fit PCA model and save the PCA model, data, and metadata to files.
+        Make function to format the keypoint data, fit the PCA model, and store it as a `pca.p` file in the output directory.
         
         Args:
-            key (dict): Primary key from the FormattedDataset table.
+            key (dict): LoadKeypointSet Key
 
         Raises:
 
         High-Level Logic:
-        1. Fetches the task_mode and output_dir from the PCATask table.
-        2. If task_mode='trigger', it loads the `dj_config` file and formats the keypoint coordinates and confidences for PCA analysis.
-        3. Then, it fits the PCA model and saves the PCA model, data, and metadata to files.
-        4. If task_mode='load', it does not perform any computation and sets the pca_fitting_time to NULL.
-        5. Finally, it inserts the pca_fitting_time into the PCAFitting table.
+        1. Fetch the `kpms_project_output_dir` from the PCATask table.
+        2. Load the `kpms_dj_config` file that contains the updated `video_dir` and bodyparts, \
+            and format the keypoint data with the coordinates and confidences scores to be used in the PCA fitting.
+        3. Fit the PCA model and save it as `pca.p` file in the output directory.
+        4.Insert the creation datetime as the `pca_fitting_time` into the table.
         """
-        task_mode, output_dir = (PCATask & key).fetch1("task_mode", "output_dir")
-
-        if task_mode == "trigger":
-            config = load_dj_config(output_dir, check_if_valid=True, build_indexes=True)
-            coordinates, confidences = (FormattedDataset & key).fetch1(
-                "coordinates", "confidences"
-            )
-
-            data, metadata = format_data(
-                **config, coordinates=coordinates, confidences=confidences
-            )
-
-            pca = fit_pca(**data, **config)
-
-            # save the pca model to a file
-            save_pca(
-                pca, output_dir
-            )  # `pca.p` as the first pca model stored in the output_dir
 
-            # save the data and metadata to files
-            data_path = os.path.join(output_dir, "data.pkl")
-            with open(data_path, "wb") as data_file:
-                pickle.dump(data, data_file)
+        kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
 
-            metadata_path = os.path.join(output_dir, "metadata.pkl")
-            with open(metadata_path, "wb") as metadata_file:
-                pickle.dump(metadata, metadata_file)
+        from keypoint_moseq import format_data, fit_pca, save_pca
 
-            creation_time = datetime.utcnow()
+        kpms_default_config = load_kpms_dj_config(
+            kpms_project_output_dir, check_if_valid=True, build_indexes=True
+        )
+        coordinates, confidences = (LoadKeypointSet & key).fetch1(
+            "coordinates", "confidences"
+        )
+        data, _ = format_data(
+            **kpms_default_config, coordinates=coordinates, confidences=confidences
+        )
 
-        else:
-            creation_time = None
+        pca = fit_pca(**data, **kpms_default_config)
+        save_pca(pca, kpms_project_output_dir)
 
-        self.insert1(dict(**key, pca_fitting_time=creation_time))
+        creation_datetime = datetime.now(timezone.utc)
+        self.insert1(dict(**key, pca_fitting_time=creation_datetime))
 
 
 @schema
-class DimsExplainedVariance(dj.Computed):
-    """
-    This is an optional table to compute and store the latent dimensions that explain a certain specified variance threshold.
-
-    Attributes:
-        PCAFitting (foreign key)           : Unique ID for each PCAFitting.
-        variance_percentage (float)        : Percentage of variance explained by the selected components.
-        dims_explained_variance (int)      : Number of components required to explain the specified variance.
-        latent_dim_desc (varchar)          : Description of the latent dimensions that explain the specified variance.
-    """
+class LatentDimension(dj.Imported):
+    #     """
+    #     Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) necessary for the model fitting.
+    #     The analysis aims to select each of the components that explain the 90% of variance (fixed threshold).
+
+    #     Attributes:
+    #         PCAFitting (foreign key)           : PCAFitting Key.
+    #         variance_percentage (float)        : Variance threshold. Fixed value to 90%.
+    #         latent_dimension (int)             : Number of principal components required to explain the specified variance.
+    #         latent_dim_desc (varchar)          : Automated description of the computation result.
+    #     """
 
     definition = """
     -> PCAFitting
     ---
-    variance_percentage      : float # Percentage of variance explained by the selected components.
-    dims_explained_variance  : int # Number of components required to explain the specified variance.
-    latent_dim_desc          : varchar(1000) # Description of the latent dimensions that explain the specified variance.
+    variance_percentage      : float            # Variance threshold. Fixed value to 0.9
+    latent_dimension         : int              # Number of principal components to explain the variance.
+    latent_dim_desc          : varchar(1000)    # Automated description of the computation result.
     """
 
     def make(self, key):
         """
-        Make function to compute and store the latent dimensions that explain a certain specified variance threshold.
+        Make function to compute and store the latent dimensions that explain a 90% variance threshold.
 
         Args:
-            key (dict): Primary key from the PCAFitting table.
+            key (dict): PCAFitting Key.
 
         Raises:
 
         High-Level Logic:
-        1. Fetches the output_dir from the PCATask table.
-        2. Loads the PCA model from the output_dir.
-        3. Computes the cumulative sum of the explained variance ratio and determines the number of components required to explain the specified variance.
-        4. If the cumulative sum of the explained variance ratio is less than the specified variance threshold, it sets the dims_explained_variance to the number of components and variance_percentage to the cumulative sum of the explained variance ratio.
-        5. If the cumulative sum of the explained variance ratio is greater than the specified variance threshold, it sets the dims_explained_variance to the number of components and variance_percentage to the specified variance threshold.
-        6. Inserts the variance_percentage, dims_explained_variance, and latent_dim_desc into the DimsExplainedVariance table.
+        1. Fetches the output directory from the PCATask table and load the PCA model from the output directory.
+        2. Set a specified variance threshold to 90% and compute the cumulative sum of the explained variance ratio.
+        3. Determine the number of components required to explain the specified variance.
+            3.1 If the cumulative sum of the explained variance ratio is less than the specified variance threshold, \
+                it sets the `latent_dimension` to the total number of components and `variance_percentage` to the cumulative sum of the explained variance ratio.
+            3.2 If the cumulative sum of the explained variance ratio is greater than the specified variance threshold, \
+                it sets the `latent_dimension` to the number of components that explain the specified variance and `variance_percentage` to the specified variance threshold.
+        4. Insert the results of this `make` function into the table.
         """
+        from keypoint_moseq import load_pca
+
+        kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
+        pca = load_pca(kpms_project_output_dir)
 
-        output_dir = (PCATask & key).fetch1("output_dir")
         variance_threshold = 0.90
+        cs = np.cumsum(
+            pca.explained_variance_ratio_
+        )  # explained_variance_ratio_ndarray of shape (n_components,)
 
-        pca = load_pca(output_dir)
-        cs = np.cumsum(pca.explained_variance_ratio_)
-        # explained_variance_ratio_ndarray of shape (n_components,)
-        # Percentage of variance explained by each of the selected components.
-        # If n_components is not set then all components are stored and the sum of the ratios is equal to 1.0.
         if cs[-1] < variance_threshold:
-            dims_explained_variance = len(cs)
+            latent_dimension = len(cs)
             variance_percentage = cs[-1] * 100
             latent_dim_desc = (
                 f"All components together only explain {cs[-1]*100}% of variance."
             )
         else:
-            dims_explained_variance = (cs > variance_threshold).nonzero()[0].min() + 1
+            latent_dimension = (cs > variance_threshold).nonzero()[0].min() + 1
             variance_percentage = variance_threshold * 100
             latent_dim_desc = f">={variance_threshold*100}% of variance explained by {(cs>variance_threshold).nonzero()[0].min()+1} components."
 
@@ -529,7 +421,7 @@ def make(self, key):
             dict(
                 **key,
                 variance_percentage=variance_percentage,
-                dims_explained_variance=dims_explained_variance,
+                latent_dimension=latent_dimension,
                 latent_dim_desc=latent_dim_desc,
             )
         )

From fcd8e692503856963ca1c9b04bb2842ba179ad8b Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Tue, 19 Mar 2024 19:00:15 +0100
Subject: [PATCH 71/90] update the video train logic in `LoadKeypointSet`

---
 element_moseq/kpms_pca.py | 44 +++++++++++++++++++++++++--------------
 1 file changed, 28 insertions(+), 16 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index d2836b0..aa6e262 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -3,7 +3,7 @@
 from pathlib import Path
 from typing import Optional
 import importlib
-
+import os
 import cv2
 import datajoint as dj
 import numpy as np
@@ -193,14 +193,13 @@ class PCATask(dj.Manual):
 
     Attributes:
         Bodyparts (foreign key)         : Bodyparts Key
-        output_dir (str)                : KPMS's output directory relative to root
-        task_mode (str)                 : 'load': load computed analysis results, 'trigger': trigger computation
+        kpms_project_output_dir (str)   : KPMS's output directory relative to root
     """
 
     definition = """ 
     -> Bodyparts
     ---
-    kpms_project_output_dir=''               : varchar(255) # KPMS's output directory relative to root
+    kpms_project_output_dir=''          : varchar(255) # KPMS's output directory relative to root
     """
 
 
@@ -241,7 +240,8 @@ def make(self, key):
         1. Fetches the bodyparts, output_dir and keypoint method, and keypoint config and videoset directories.
         2. Creates the `kpms_project_output_dir` (if it does not exist), and generates the kpms default `config.yml` with the default values from the pose estimation (DLC) config.
         3. Create a copy of the kpms `config.yml` named `kpms_dj_config.yml` that will be updated with both the `video_dir` and bodyparts
-        4. Load keypoint data from deeplabcut, sleap, anipose, sleap-anipose, nwb, or facemap. The coordinates and confidences scores will be used to format the data for modeling.
+        4. Calculate the `filepath_patterns` that will select the videos from `KeypointSet.VideoFile` as the training set.
+        4. Load keypoint data for the selected training videoset. The coordinates and confidences scores will be used to format the data for modeling.
         5. Calculate the average frame rate of the videoset chosen to train the model. The average frame rate can be used to calculate the kappa value.
         6. Insert the results of this `make` function into the table.
         """
@@ -254,36 +254,45 @@ def make(self, key):
             "use_bodyparts",
         )
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
+        kpms_project_output_dir = get_kpms_processed_data_dir()/ kpms_project_output_dir
+
         format_method, kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
             "format_method", "kpset_config_dir", "kpset_videos_dir"
         )
+        
+        file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
+            "video_path", "video_id"
+        )
+
+        kpset_config_dir = find_full_path(get_kpms_root_data_dir(), kpset_config_dir)
+        kpset_videos_dir = find_full_path(get_kpms_root_data_dir(), kpset_videos_dir)
 
         from keypoint_moseq import setup_project, load_config, load_keypoints
 
         setup_project(
-            kpms_project_output_dir, deeplabcut_config=kpset_config_dir + "/config.yaml"
+            kpms_project_output_dir, deeplabcut_config=kpset_config_dir / "config.yaml"
         )
 
         kpms_config = load_config(
-            kpms_project_output_dir, check_if_valid=True, build_indexes=False
+            kpms_project_output_dir.as_posix(), check_if_valid=True, build_indexes=False
         )
 
         kpms_dj_config_kwargs_dict = dict(
-            video_dir=kpset_videos_dir,
+            video_dir=kpset_videos_dir.as_posix(),
             anterior_bodyparts=anterior_bodyparts,
             posterior_bodyparts=posterior_bodyparts,
             use_bodyparts=use_bodyparts,
+            
         )
         kpms_config.update(**kpms_dj_config_kwargs_dict)
-        generate_kpms_dj_config(kpms_project_output_dir, **kpms_config)
+        generate_kpms_dj_config(kpms_project_output_dir.as_posix(), **kpms_config)
+
+        filepath_patterns = [(kpset_videos_dir / (os.path.splitext(os.path.basename(path))[0] + '*')).as_posix() for path in file_paths]
 
         coordinates, confidences, formatted_bodyparts = load_keypoints(
-            filepath_pattern=kpset_videos_dir, format=format_method
+            filepath_pattern=filepath_patterns, format=format_method
         )
 
-        file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
-            "video_path", "video_id"
-        )
         fps_list = []
         for fp, video_id in zip(file_paths, video_ids):
             file_path = (find_full_path(get_kpms_root_data_dir(), fp)).as_posix()
@@ -336,11 +345,12 @@ def make(self, key):
         """
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
+        kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir
 
         from keypoint_moseq import format_data, fit_pca, save_pca
 
         kpms_default_config = load_kpms_dj_config(
-            kpms_project_output_dir, check_if_valid=True, build_indexes=True
+            kpms_project_output_dir.as_posix(), check_if_valid=True, build_indexes=True
         )
         coordinates, confidences = (LoadKeypointSet & key).fetch1(
             "coordinates", "confidences"
@@ -350,7 +360,7 @@ def make(self, key):
         )
 
         pca = fit_pca(**data, **kpms_default_config)
-        save_pca(pca, kpms_project_output_dir)
+        save_pca(pca, kpms_project_output_dir.as_posix())
 
         creation_datetime = datetime.now(timezone.utc)
         self.insert1(dict(**key, pca_fitting_time=creation_datetime))
@@ -399,7 +409,9 @@ def make(self, key):
         from keypoint_moseq import load_pca
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
-        pca = load_pca(kpms_project_output_dir)
+        kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir
+
+        pca = load_pca(kpms_project_output_dir.as_posix())
 
         variance_threshold = 0.90
         cs = np.cumsum(

From cb6e0326e3ff41d3015685653e232e19e9dd3db2 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 03:19:48 +0100
Subject: [PATCH 72/90] `images`:update flowchart, add new pipeline images

---
 images/flowchart.drawio        |  28 +--
 images/flowchart.svg           |   2 +-
 images/pipeline.svg            | 348 ++++++++++++++++++++-------------
 images/pipeline_kpms_model.svg | 172 ++++++++++++++++
 images/pipeline_kpms_pca.svg   | 112 +++++++++++
 5 files changed, 511 insertions(+), 151 deletions(-)
 create mode 100644 images/pipeline_kpms_model.svg
 create mode 100644 images/pipeline_kpms_pca.svg

diff --git a/images/flowchart.drawio b/images/flowchart.drawio
index 198b9d5..c1b9bd3 100644
--- a/images/flowchart.drawio
+++ b/images/flowchart.drawio
@@ -1,11 +1,11 @@
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+<mxfile host="app.diagrams.net" modified="2024-03-20T02:14:13.855Z" agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36" etag="KMCC7C6eRLDUaixhcEwU" version="24.0.5" type="device">
   <diagram id="4ApZs3ESX_rJ877tGElo" name="docs">
-    <mxGraphModel dx="954" dy="615" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1600" pageHeight="1240" math="0" shadow="0">
+    <mxGraphModel dx="954" dy="581" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1600" pageHeight="1240" math="0" shadow="0">
       <root>
         <mxCell id="0" />
         <mxCell id="1" parent="0" />
-        <mxCell id="UdrtFf_ipJ74pKheC8B5-1" value="Enter keypoint data and body parts&amp;nbsp; into pipeline" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=top;" vertex="1" parent="1">
-          <mxGeometry x="354.5" y="10" width="310" height="200" as="geometry" />
+        <mxCell id="UdrtFf_ipJ74pKheC8B5-1" value="Load keypoint data and body parts into pipeline" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=top;" parent="1" vertex="1">
+          <mxGeometry x="17.91" y="10" width="310" height="200" as="geometry" />
         </mxCell>
         <mxCell id="0lQ9qycPSiAqUX2RLHM0-32" value="" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;" parent="1" vertex="1">
           <mxGeometry x="348" y="310" width="323" height="200" as="geometry" />
@@ -23,12 +23,12 @@
           <mxGeometry x="1024" y="310" width="323" height="200" as="geometry" />
         </mxCell>
         <mxCell id="3o6Un45pMF5JGRha6qpf-5" value="Enter metadata &lt;br&gt;into pipeline" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;" parent="1" vertex="1">
-          <mxGeometry x="11.410000000000025" y="10" width="323" height="200" as="geometry" />
+          <mxGeometry x="348" y="10" width="323" height="200" as="geometry" />
         </mxCell>
         <mxCell id="3o6Un45pMF5JGRha6qpf-4" value="" style="triangle;whiteSpace=wrap;html=1;fillColor=#fdfcfc;rounded=1;strokeColor=#1BA1E2;strokeWidth=2;fontSize=28;" parent="1" vertex="1">
           <mxGeometry x="318" y="30" width="60" height="160" as="geometry" />
         </mxCell>
-        <mxCell id="3o6Un45pMF5JGRha6qpf-6" value="&amp;nbsp; &lt;span style=&quot;background-color: initial;&quot;&gt;Format dataset,&amp;nbsp;&amp;nbsp;&lt;/span&gt;&lt;div&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;&amp;nbsp;run PCA fitting, and select latent dimensions&lt;/span&gt;&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=middle;" parent="1" vertex="1">
+        <mxCell id="3o6Un45pMF5JGRha6qpf-6" value="&lt;div style=&quot;&quot;&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;&amp;nbsp; Fit&amp;nbsp;&lt;/span&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;PCA,&amp;nbsp;&lt;/span&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;select latent dimension &amp;amp; kappa,&amp;nbsp;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;div style=&quot;&quot;&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;and fit the model&lt;br&gt;&lt;/span&gt;&lt;/div&gt;&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=middle;" parent="1" vertex="1">
           <mxGeometry x="690" y="10" width="320" height="200" as="geometry" />
         </mxCell>
         <object label="" width="10" id="wCUkDDBLQIwaV8VIMWGC-7">
@@ -36,7 +36,7 @@
             <mxGeometry relative="1" as="geometry" />
           </mxCell>
         </object>
-        <mxCell id="3o6Un45pMF5JGRha6qpf-14" value="&lt;span style=&quot;font-size: 28px;&quot;&gt;&amp;nbsp; &amp;nbsp;Run pre-fitting of&amp;nbsp; &amp;nbsp;&lt;/span&gt;&lt;div&gt;&lt;span style=&quot;font-size: 28px;&quot;&gt;AR-HMM and select kappa&amp;nbsp;&amp;nbsp;&lt;/span&gt;&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=middle;align=center;" parent="1" vertex="1">
+        <mxCell id="3o6Un45pMF5JGRha6qpf-14" value="&lt;span style=&quot;font-size: 28px;&quot;&gt;&amp;nbsp;Film new videos &amp;amp;&amp;nbsp;&lt;/span&gt;&lt;div&gt;&lt;span style=&quot;font-size: 28px;&quot;&gt;load metadata&lt;/span&gt;&lt;span style=&quot;background-color: initial;&quot;&gt;&amp;nbsp;&lt;/span&gt;&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;verticalAlign=top;align=center;horizontal=1;" parent="1" vertex="1">
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         </mxCell>
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@@ -48,8 +48,8 @@
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           <mxGeometry x="656" y="30" width="60" height="160" as="geometry" />
         </mxCell>
-        <mxCell id="wCUkDDBLQIwaV8VIMWGC-2" value="Run full fitting and generate and store results" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;" parent="1" vertex="1">
-          <mxGeometry x="10" y="310" width="323" height="200" as="geometry" />
+        <mxCell id="wCUkDDBLQIwaV8VIMWGC-2" value="Pair videos with&amp;nbsp;&lt;div&gt;models &amp;amp; run&amp;nbsp;&lt;/div&gt;&lt;div&gt;inference&lt;/div&gt;" style="rounded=1;whiteSpace=wrap;html=1;fontSize=28;fontFamily=Roboto Slab;fontSource=https%3A%2F%2Ffonts.googleapis.com%2Fcss%3Ffamily%3DRoboto%2BSlab;container=1;labelBackgroundColor=none;fillColor=#1ba1e2;fontColor=#ffffff;strokeColor=#006EAF;" parent="1" vertex="1">
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@@ -63,8 +63,14 @@
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;&quot; 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GrdSYZNKGnR6b+SEQDiAhR6T2ixAsZUmcJIWD9qBbZN33twD3ZL1dbchk+tLritqf+rHqk1BvUjsowrLlxYNoZ53Q2xdXB8N2tlSMeW+d4e125ry/G8vJeiTOfHJH6UJRjWO9lq4q594zlBd8tKPB0Hpdl2rvghOxuTbVO1X5pW1MQehMRAoQw+cMfrZKWQwAnmRc3Vs24/vfyf4HG4bhc81kfj89S49ByQ6De1MwIyNTGFUTUh4iWE9ExQojqUx8zI2hxtORjibQGh0RsxhXrq/xD395sf3mzPWBaWuIl1DrpkqD1fzw+y3V4qiEJZGnvb3ME3tjm9P+4x11tCZh5eBJd389GpZ4QnfZjUajQEwppBPNFPax7nx6RCutFMl72Z4WfOidoyarXgBAH7YzQ/oCh9WEiGkpEsCTgNP1ixEUqlQPQ7h4F64gQAkoQlJ5JRPQoEZVLZQiBmXEta8p81034du9DxZ6QrUeiPnjPwKTxF/Wy/hxv7mBkPryxzTX00VVlP222BxLSjBrH3GMynz4mx9xkAiMM0MW/lt784XbH48ykOa9bwsOvjU2/O9YDpxSEWCPaRp9Xw4yJ6G3EYCAtCzEY+5EsMXN2/0/y89ZX+E2TOpids0ZmDemRJDa3lW7LdCVd7ZRTuTFkyIUYfBeIWM8XQtWfaCtjG4t2MvMJRPSldBm8JYS4JJrnFjFbX1xdNnZBgW/u/AJPmC8ly6QtmTk46b/TOlveSTXrNhERCHTgtoCPeGKeIzig1BXsyoL690nWn2fTizBj7JN/VNJzG+zubJPWsOikbK1WiHCa9bf5bpqxpIwKPEEammbgDhZtebZJt9kR0Nx+QS/znmMyjJi//5VuCCg3JwghSuS3fQcRPSL7spuIJgshVjAzroNSAYH/fmq80gqjH7O3ecbetKx4XqE7mJFm1Hg/PTbzgbHZ5pidyqMZ64Zes6TYc8tJPxTdX+IJWlKMGu9DQ1JvvLqP9ZWmWGy+3sWpj/1R8O38As+wnom6HXOOzJo8NMuA+JeYilIQYgpn230YM4+RKVnwdSKYplLyNyAQEcVNPhVC5KOHj62tePWfqyoux6J1XK55/tcTs05tC9HZUgm4jIiGENE6InqbiMCxfrkMMEPwF/qPUyKUni+I6J1IMFrbHV3V8mgRYOZlRDQMRU2J6Mhoxn5lkafXkxvs9/1S6D5jhzNoNGkFndvdurRngvaOuw5Pxsk2XAmVmW9FGIJsC5QQbNIBZobb4iIkXOF3dn+I1lb4H8s0as7sadODSda3vtKff/EvJajHEnZPRAQLeG6CLtDFot05MsP4/QODU8ZY9eIwKPdyXmOOH09En9dgpMXtz8mAylci75X3PENEt9e1eTFzKtzpeJYQArEOLSrzdlelPbvO9cMvhe7DE/Ua36W9Ep/514jU21u0EY182cyVZXe+sslxV4E7aO1l0299elTmtFNyDVDOGiWf73JfcO6CwnfsAabr+tlef25k6vTmqLirFIRGDU/7uknSRqPGxXkH6BnMmPiDxRPmyAU3LSnd9cpmh6WLVef55YSce1KNmudjRUoSa3TlCQqR6K/KzR/zHv2BwgPyE1TEPNC3gMCxq4UQWEj3E2aG2Rem6FXR+P6kmRenSygoecizjzezaKyxb0vPY2awtkYynaAQI+bpgILx/E+e48VrF5ddVu4LaTChbHpBl/dKfMA2IvXee4XA3KkWOQ/xDeHEDrfFVXKTxs8wD8G1EOFrgeUOP8Of3xD38OGfVHZEB0pINFOHr3c7xv1Y6L3n63x31m5nIDx38Z8ByXpaOxnehrCgSWhDONu79vSV/65r3iNN7oaaSgIzD5TWiV6y7ScKIZo1v7828LPz7PPOW1ByOjp1Qkfz8g8mZB2fJERZW5hjGPt7Vlf8+8FVFReivX2TDMH1U3KzYeFpaPuxXh/x5V7XkmKv/rhcc/Db47KSa7rBGvq8g10f1wqC/GCxeEc+GsQg/KBiEGI5BcInG5yYPiEirCxb5Oka3yEIqJDHjTGoSXkdJpQK85AjhHif4MQ1QQiBxS4uREaew+eK9DREpsOEi35hE6jmXpc/Q/ux4KH9OH1dIPuP62BNmSmEeLZ65WXGc98Bu6q0QiDqHff/WPujl2x68BnfL6l7ARr8xNdBaVFKQlxMF3wHNxPRv2RrQGxUZ6lcLPZ5duo9Y0nRAz8Xec6q8IVEtlnrs+m1i6f3sXx6U/9kMC4iwPEvIgOt36tdul0qB4gbgJKOGIeIYJ6cLS14fykz5GHucd/K8rN/LfReWOAOdt/uDBhvG2AT/zw8Ocz9UkNwL9wPoEmHrzqiMGCuP0lEf8i/s+T3AQsD5jS+f1Dew/VS85uB4gG8Xm+uzanGt6a/a1nZ355cb38tGGKa3MWy/f1RmUebzS1vxWjKTGXmrCnzi9/6Lt91givIdFkv65eX9jBdMjY7MeqKi1AOntlgv++m30vvwtp7Y3/bq48MS72yKe1qywoCJig2r8hkhv+um1IQYjsdmBkLExYJLEaXSmUBL4FJEadk1MCYViMP/EANQPT0BUIInI5bTeRJbTwRzSAipKUhyAuCRfL/iOhDGYmOSPUIiQ0sIwiAjQSkdZcLM+h2kcYGVwQiyR8jIvh64a+tnYsOxQNKAzaXalptZgZ2yIuvruUh24NT4klCCJwQlbQyAswMciQsttjcjxBCYNP8i8zb4Tr75qWlD+5xBXvDzTYoxeA6u6v1rtOyNLMHZVlLDuZbZmYo2zD7g2Wxprwo68yg1gzmYqSCLebG+INZ5xBXs9Pr7fr8WleOTkNTLDoxOsQ0toNFSwNTDDQizYi4Bf6t2DPnxIyEW8zmcDAjyhtDwOUwRaZHQpEGBgiexHeB7xjfP9aByPeDWApYzSBglcU3BH/6warLN2lkf9zjOuOW5RXPryz15vRPMjiu6p80ZUYfKw6KbU7m73Z1vG9dxbc/FXjBJ+O7qV/SP+8enIzxjkr+vdk1+tkNFR8tL/VmHZVl2njNANv4czsnIF6qWSTeLQjI+625qEJBwORUWQyNnA4ySA+ELlfL0tlgiATxC6wHiD1ASec6N3hmxqIF8pfHZRluenBNRdfBqYaUkztaIjb6n4joNhnVXdgcfrEDdV0qBvDZImgJp66IYNNHAM91QojqtExp8kfe+X6m4MhN8nk4JeHkj7kHgRKBRRMnLch/iAjKBEyvKE8eOfF9hudKK9jzkpUPCgQsNVA4sEEAT8RCYAMobuSQqttigABOd0T0kTy9Q4G8pC4rwIvr7ce8udU+b0mJN8WoEaHxuaa8LydkQZloEAmOVBRgboalCorijUKIdcwM9xOUT3xnsFx1aEx8T7nb3XXKgvK38+yBnuW+ULrdz3os9l0Tdfz40OT7RmaYbssxa7V6jXhYCPFAjTkPVxziEKr9FFI5RnbD34UQnzEzrGGwpiGgGaffztG42BoyTPj2PtrqyKhkzVkPril7fqs9IPQaETox1/zkJxMy/9GS60pD2h3NtWtLXWNO+KHk03xXMC3brOVXx2SeckpH41f1KVnA5Mz5JXd/le+8PxCi0IkdTc9+Mj7rjuas8RPXCkJtsBtbrCmaQTtUrmHmw+UmBdN45PR8qrTSzBFC4LQbtTy3oeLsH/K9sx8fnqLtZYscesImeigZ30nT6DdRP7CRF8o4Ciy4UA5y5GNwikdw1rtE9JMQAqlcDRZmhokVZa5rLpp4zkJpWcHmDhMsNn+4w+BLxkkQVgNUA4QCBisGlCec1pAlgjbB7QGFAkFqiHPY7xQmlQtYQfCdwmzdbHStDQalnd0gN2bMFZjUEasCKxDGJiwoff7C+sq/vZ/num9RsTcDJs3Leyd+OLGT9a6pnUxwy8VEpNIK5R1WuyVCiDlNefBmu7f/g6urTrL7+KZv97hyUUwInodcs5Ym5Zr3dkjQPXZF9+R3OiX9z5cvmRqhNCBgd5ukjP41osTKbw1xCrC24BuDD7y6Rk007ZUHFVjiULemel6jts76Cs8R7293H/9Tgfe4JcXe4b4Qa3LM2tD4bOPHTw/LvDbTKmLGFBhNW2N9Dcb4H8srrvtgh+PJrfaA7vAUg/2CHubzbx2QguDPA1piinw8eOzn+Us3V/lxT9H841NHpJrNzRosqhSEWI9+nD+Pma+XJ4S6xj63NrlKfd1ZtoctN60ufN0XDJ3z84nZtf2euB0fMza/j+t7VlN+z8zYeBFoGXEnYNE+i4g2NPV0IxdtnJbgOz5SvgPKBtwpiEbHBgI8wd0PUzH2D5ipsdDDxYHMCQgCv+DbxfWwdECBwkYAhepcIQR8xNXCzC/JSHT8DKlp2LSQWaEkxggw8+lENE8G9UEZfKLmYn3/yrK7Xtrs+OceV9AAUsQ3x2Z8O6Z7wuRuzVClUc4lzKFQfafKaGDAZvzhtvLEcr/+vM0O371PrqsKU0Bjwhq0msDVva3rHh2ZOs0kBNwHkbkMKxeUXaQ6/4WiXrrNYD2D3CWEiKRQ1tkkfENFnuB5K0r9E3a5A112O4M5D60pzx6cYgjO6Ju4aES6cdmKUv8R3xd6On6wzdEjRGRGCWXslr0T9c6bB1hvndQn6a3mwDsaDGN9Dcbk/AXF183d5XrGG2Qak2ksvbineeKVvVPqJJ4r8Xr7jvi88Kc8ezArSS/o6+Nzh43OMIAHo1lFKQjNCm98PVy6CGBqR9ErmN2xeYOdDfMAp5UjGtPiWRsqL7hjeflriXqN8Zo+idv+MSgZriH45xE/opOnZERsz45FpgMzY1M9UVoLsJDhBI5NGL5T8DdgsXo8Fu+qjYckyoE52l4HUx1MKLcQ0X0yqBMnUPyBewIZE72F2FcjXm4C58tAMGAEM/MgIUSYqIaZj5FplhHXBn6MhRoKBgLDNjRmrNQ9dSPAzDCZQ3nGSRgug0idBTF3h+OMW5ZWzNnhDGiyzFrX1C6WOTcckTa9Vx0bZ7zji41pcZF79OPrKm/cZA8es77Sn44CbN0Sdd7jcszvjUw3/vuUXtYN2fWUtZZVdLGGYO5Cee1am3Ydh4d388uPznf6R9h9oRnzCzyp3jqdeX9FLVGvCeVaNHkDU4yrnxie/EA3q6HdMbZuKHbmzvzD+fy8Hc7TA0x0UkdL5Yk5xjOu65+0oEbRL/HmFvtpz6933L2qzDvcpBN8cQ/rR7NGp8MV1OyiFIRmhzh+XsDMyPdHZDY2MpzoEc08XW6sCDRqVMVKULXevrT8uyf+rBzVJUHn+/dR6fcenWV6C2RfRARTJU4iMEUigA/+Q/AqNFikWRKBgqiUN6iO1C08E/EPL7cW2UsNrgX4cWt+X4h/2K/KmuwPApTQZggsIHAp4PSI0xny16EULJWWiwhmiERHrjpM4k0SqagkHoruC2ZGlg5cQXADIWYFyhhST8Pl4yGvbK48/6m19hc3VPkTcy1avrJv4qXTByZ/mH2ALIUmDUYL3szM5tlb7IO/KfDe+dlu90ll3mA4H6mnTV+SbtZsurSb9dfL+yQ+eLB5wcywuMDyEj7oCyHCrhbMqS2VvrMfWVd5yZ+VwdGTOpiSNlX6ae4OJ9n0GhqbaVrZK0m3VUPCHyLWLin2nra5KhDOkDipo2mdVa9ZWOoNLRufbf79kp5WWACjVCtaEMAYvWq705nzynrfM0+trzzbE2SUiC6Z3Mky57FhKVgTjPeurrz9gzzHdRur/FawcE7rlrBwXKr2gmsHprUI46VSEGI00PH+GGkmR84tAuyg+eOD3o+rvSl9cPl5bPoHO39yBUK6LJPWMWd89rhxGXpo/YiWxsYYCTaFiRyBWThJw1cPCwAWBywyjgMxF8oTC6KvETQYEQRIwSwayXIB8x38x60uzIwURmSGoH+IUUDwJ5j09hNmRiwISgFD4YGr4QOpFCCeAoocrDHYvJCH/ppMvYx8t1C0EAeB5wLrbyPUuz8XOMRXe/+XkpFj5tDlfRO9GUTVnPvMjI0RbcQYITDuLkkQhWuaLSq91Qdn3yaGbAJY0HACrikLhRDhgky/F7i7XbM6F97pAAAgAElEQVSs/MPlJd5hFp0mePsA2633DklBFky7EWzmy4v9N7y8ufKa2Xmuju5gyBKUIz8wxRC4ZUDiNy4fP7jeo9k6Kc0aOKJzeI6GJYVoknZfRhBV+IJTPt5h35Fk1F+8xxkaVhkIjbl9gE2jq5FqWeYNUapRM04IgTmtRCLwxx9/GGY5sv775ibHya4gh9eyEelG+CxDS0q84RR/s1YEbupv+/OhoSmg/m5ULFVjAFcKQmNQa+Z78NHGeoGWFLI4cWLC4YQNy0HMBG2+Y3nZ/722yXFdhT+kOTzFsHx0tv6UF0ZkhH31kic+Uv4W5ltYK0BygkI0KKuLkzLahxSwdbVPDcyMdENo1VA0oBigBgQC91BaN+5EWhLAjoesG/gK/1NXtLG0IqACG05jtb9HKFDn7HTQn+sqnJ0/y/dcNsBmmHxZL6verPvLp4trEewVPm3tdAa0K0u92nfznLTDHiCLTlRlmrUbt9j9v888PGXJKZ0sLt2+IDOcACMKFszr85HKKYSA4tEuRQZ/gtEQ1gMI5iGUZ/T9X0KIzVVVnH76bwVv/lbsO9kVCImr+yZ+fG6XtCvH5bTPbJO8PDbtNHuPv+n30iN1Opr0Z7l/EAIaITkWLXe06Ow5Zm1hgu5/AYK9bPq02w9L6p+gE/Rjgcd51aISo1kndA8NSaWTO5rDwZB1COJ4blJZO/sjs63c3eX6FZUXlHlDk9eV+UaAIRFi0QnqnWQoPCrT9K8zMiufO7ZbN7hQW0yUgtBiUP/vRTIWAMRDSBdEDAAiUZFPjEh4LNrwcWPBAi96k5nCpBkZPlacarGBnCyEiHnd9F8LOHPWpuIP39vmHIf967HhKR/f3D8Jle8wz9DPX2T61oFQR9uQLvY9/MGR1DGZeQHKZ5zG8eUgngHWguoo81YYxnpfGW2wmVQSYDGASwaxB2H5do878NZWx7aFBe5kh59N7iDb0Hmw5Z3UwYL8e5gcD9gOnAQL3AHaUOmny38tpe3OAGFcTulkcbx1ZHooSa+JBHTWfgbiGxAPEdf41jsAB7iAmaGQIuMEgaKYV6i3gO+sDH3GeNy3quK/D66pCPuGp3azVjw8In1QT8u++JD2LOj725vcOazxH1PiFVNmrio7yyU3q9r9zrVo6dPxmTQszUiVvlCYAho/SzdqI8oB0jRBPLVdcojAahPmWhFCIItHSQ0EsF58U+LKLqriYwNBHic0xBzkxRvtgfnDhqXkTxUCh4AWFaUgtCDcciNAJS+YcqtzAg/ShJUwLwshmlTGtFaON6Luz2kuH72LucNRn+9ZuaLUlwG14MVRqS9c1dt2KwIGZfoUiJjg48UiDQxAQIQ0QCgQqIUQMfkiTfJeKDNEhBM23BAwb94thABZUbuTRSXevkUO3wXLywP/KPMG6bVNDvIiggwJ5zpBmWatJ8RUIgR5B6UYtnS06KrGZBpdp3dKyDPpwgsvBC4LbPwIEAU3Q1jWlPvovtUVtNcVpNfGpFP/ZD1h3V9W4qWPd7mKz+2a8PKgVMNVYh8JFARlhmGxOVbGfOB0DdbHOhkC29JgMDO+QVikAC7icBDQGra84Bt9ZF3l359YW3lvhS+kPzLbtH7hpOyjGspz0JbwOFBbsWGV+2igOxg64c9Kf/onOxxd9/c7CbqwR8JhI9ON/WptJNjIYNlDFk8krgkZRUj7RTYQAnJ7xOLw0x5wjuc+KAWhBUdHbpDwv6GufETgi8fmFzHz4hvEz3A0xKK1WEbFI2cemjiKB/3Fl32wbkj3Ak5MsFogePDy2hHHsYRhzjbHcY+srXxlZbmvayeLtvKMLqYrnjkiM+yrlIsw2gFGTPwNszhOZrCaICgPGQDY5GoLlAMEWELZaDcnWyzCe33Ud9LX+XdpSAwt9wZ77XQFw8qjUSNoQIrem2PWvieYFk/umljUL4HyLUadKyXVsO1AKV/ScoGCPQjmRHzBSDyvyh8inAazzfuYyxcXe+nsn4oo3xWEVaJiUKrBcPfAJEu/5GpWbcROIGYF7QH+4MlA5kWbFWZGdg0sWZEYhFE1v6clxZ5Tp/1U9GqeM5iVbdZUXdLdetkjI9L2Sz9ts51vhoYzM+YDgmsjdPhQ9hGngZz+6hgneTi6RmbhoCXfCCFgMVUSxwgoBaGFBkeSizwlzfx4K05nXxERrAPYHMP13mXgFBj5cNIHQ19tQZri8IYwiTEzTPYT5ImpD3yszd3tC34uvvnTna4nqvwhzYkdzKVfTszKiWZjlzg9KP3D2KmgJME3DgXp21jHZjQ3Dgd6Pjbx57aQwcyOLx5fW3nMZrs/vMBqNYJ1ggLndLUsf3RI6kvZCdr3o8HtIO/pJE9xmGOR7x2Yriz2BR+9/JeSu7/c7T4sRKSFsaJ3op6+m5RFnRP2GXKgrdZaJKBwvBvLcZCbB5QQKMnAAQqgL/IOqfCECaiiea98HpqNTqCvyOVnGZiIDBBYSdA1ZJZU11vIYzad9Oke1/pyn0BI//vjMmZP62Y9UAGz1po6cfVe6S5F4DCwRsYBYoPqlFrFqmBlaFSxorgCoJ03Jq4VBFlsBzn7EXM8zNA4Abc5qmUZrY5aBWBqQ9ra6ZHyyXXNMWZG8B5Mcug/BP73SEQ8YgjwjHqFmcH+F2Hy2yuEwPubXRCkd+uysneeXW8/I8hM1/exffCPw1JvjIYFTSoJKMyFviNSH0GLMEu2C8EGdvuykmlLSwL/WlDoycFOZdUJGpyqXzM4xbjwn4OT/ptu0oGXokHsdAdZmGGxQgYISvXidaB+fgYpbEhR3e0ITrpuSenEIk9wqCfIhw1PM1gu65VIfmZaVeajjhYdynoTgtHkeCA1EwGiTQ6YkqmGCOaEORqKMaxpcDuBk2C2tDIhNgNuJijUCPb8S/aNVArgVhkh3VWoHwClEgoBrFdQipFCCqsKBC6T4yKWtLxyTn5iY+lbszbYJ6MIzmU9re89PyoNlrYm97FdTNoYdYKZMQY4KEWIxd6IRumL0evj8jFy7sK9CmUenBKrG3IAbM5OxbuCgHrZ2NAi5necKHHSbosKwvtygQbmoAO+p74Pg5lBdQpTKOQUIsLJGljAN/9QNPnBzHyHLP6CZ5wnhMCi2yKy2W7PuHWJa8Mnu1ypJq3g8dnGd96ZmH192iFMGYwS0XevrJj78qaqoaWeUDJOqmd1SdidadJcd14vy3Jd3p+Fw4cPj7kLRWZVRPL782qz40Ep+7nElbFwl79DRTD49zXl/jNWl/tEqSdICXoN3djPRncPSiKZtoZNE8VyzhdCIBCtUSKVZgTLQvGvHZOD7BcE7mLhRMZNJAYFFjdQXy+H0ihTNVGYCxY5KMOwDtROXYQVAbETUJTw/aFi59+EEFAewnLz0tJL39hsn1XhY+NhKYZfZg5KOntqN2ubpvRt1KA0803SzQoGUaTYwkqKOdRulP+GwMfMiMeA4ot5Dnp4/BsuV7hRIyyVDXlkzK+NdwUBlKcR0zs6jwUAPq82oyBIsxpYryI+eCw63Rt6OpS+U8QR4GSNdEAELx70dCMtMKgGh4BAnLoGtGQOLQbsqT8qR76zzTFvZamvg1kr6NKeia/dPyr1lkNJSQAHxeIKT6f31zvHran0P7mg0JOBDy/VqPHf2t82+8peyf9IswicHOJGkAr4yib7w0+sq5xU4Qv1KfOG9E+PTKVzuyVQhinibg6zQyJtDbU2YJnApo7FPr8+twgzI/gRvA4RNxrmJ3gcEHuDzJfIoSCCCTb5mj8DXkiXhetsv1LkMlYnzFgprW44mUWuAZkPKmiG3Wz4Ph9eXXHMvF2ur5eX+gwdLFrvQ0PSrr2op+WQP9k2x2SUpc9hBUbNF6yFsOKgYNkhIXI/gDKAolxItY1YiGv2H3gc2RTlO1ZgxrWCULuTbaVYE9wDkVKxzAz6YmiDoAcGQdFkIQQixBskkugIp3+Q5uA5g+vLbmBmWB2wgCOqHb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y="108" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Run pre-fitting of...</text></switch></g></g><g><path d="M 646 400 L 646 330 Q 646 320 652 328 L 700 392 Q 706 400 700 408 L 652 472 Q 646 480 646 470 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 984 100 L 984 30 Q 984 20 990 28 L 1038 92 Q 1044 100 1038 108 L 990 172 Q 984 180 984 170 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 646 100 L 646 30 Q 646 20 652 28 L 700 92 Q 706 100 700 108 L 652 172 Q 646 180 646 170 Z" fill="#fdfcfc" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><rect x="0" y="300" width="323" height="200" rx="30" ry="30" fill="#1ba1e2" stroke="#006eaf" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 321px; height: 1px; padding-top: 400px; margin-left: 1px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 28px; font-family: &quot;Roboto Slab&quot;; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Run full fitting and generate and store results</div></div></div></foreignObject><text x="162" y="408" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Run full fitting and ge...</text></switch></g></g><g><path d="M 132.91 310 L 132.91 240 Q 132.91 230 138.91 238 L 186.91 302 Q 192.91 310 186.91 318 L 138.91 382 Q 132.91 390 132.91 380 Z" fill="#ffffff" 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JE35E0R0ExGh+t1FQggcPmApsBLRG0R0Rs1AtQP14fdiL01bUEyndDTTEyNSyawVtK7CR6f9UER5jkBYoxiTaXJ3sGg3aoQoPaebuWRTpd+7rNS7KkGrdSYZNKGnR6b+SEQDiAhR6T2ixAsZUmcJIWD9qBbZN33twD3ZL1dbchk+tLritqf+rHqk1BvUjsowrLlxYNoZ53Q2xdXB8N2tlSMeW+d4e125ry/G8vJeiTOfHJH6UJRjWO9lq4q594zlBd8tKPB0Hpdl2rvghOxuTbVO1X5pW1MQehMRAoQw+cMfrZKWQwAnmRc3Vs24/vfyf4HG4bhc81kfj89S49ByQ6De1MwIyNTGFUTUh4iWE9ExQojqUx8zI2hxtORjibQGh0RsxhXrq/xD395sf3mzPWBaWuIl1DrpkqD1fzw+y3V4qiEJZGnvb3ME3tjm9P+4x11tCZh5eBJd389GpZ4QnfZjUajQEwppBPNFPax7nx6RCutFMl72Z4WfOidoyarXgBAH7YzQ/oCh9WEiGkpEsCTgNP1ixEUqlQPQ7h4F64gQAkoQlJ5JRPQoEZVLZQiBmXEta8p81034du9DxZ6QrUeiPnjPwKTxF/Wy/hxv7mBkPryxzTX00VVlP222BxLSjBrH3GMynz4mx9xkAiMM0MW/lt784XbH48ykOa9bwsOvjU2/O9YDpxSEWCPaRp9Xw4yJ6G3EYCAtCzEY+5EsMXN2/0/y89ZX+E2TOpids0ZmDemRJDa3lW7LdCVd7ZRTuTFkyIUYfBeIWM8XQtWfaCtjG4t2MvMJRPSldBm8JYS4JJrnFjFbX1xdNnZBgW/u/AJPmC8ly6QtmTk46b/TOlveSTXrNhERCHTgtoCPeGKeIzig1BXsyoL690nWn2fTizBj7JN/VNJzG+zubJPWsOikbK1WiHCa9bf5bpqxpIwKPEEammbgDhZtebZJt9kR0Nx+QS/znmMyjJi//5VuCCg3JwghSuS3fQcRPSL7spuIJgshVjAzroNSAYH/fmq80gqjH7O3ecbetKx4XqE7mJFm1Hg/PTbzgbHZ5pidyqMZ64Zes6TYc8tJPxTdX+IJWlKMGu9DQ1JvvLqP9ZWmWGy+3sWpj/1R8O38As+wnom6HXOOzJo8NMuA+JeYilIQYgpn230YM4+RKVnwdSKYplLyNyAQEcVNPhVC5KOHj62tePWfqyoux6J1XK55/tcTs05tC9HZUgm4jIiGENE6InqbiMCxfrkMMEPwF/qPUyKUni+I6J1IMFrbHV3V8mgRYOZlRDQMRU2J6Mhoxn5lkafXkxvs9/1S6D5jhzNoNGkFndvdurRngvaOuw5Pxsk2XAmVmW9FGIJsC5QQbNIBZobb4iIkXOF3dn+I1lb4H8s0as7sadODSda3vtKff/EvJajHEnZPRAQLeG6CLtDFot05MsP4/QODU8ZY9eIwKPdyXmOOH09En9dgpMXtz8mAylci75X3PENEt9e1eTFzKtzpeJYQArEOLSrzdlelPbvO9cMvhe7DE/Ua36W9Ep/514jU21u0EY182cyVZXe+sslxV4E7aO1l0299elTmtFNyDVDOGiWf73JfcO6CwnfsAabr+tlef25k6vTmqLirFIRGDU/7uknSRqPGxXkH6BnMmPiDxRPmyAU3LSnd9cpmh6WLVef55YSce1KNmudjRUoSa3TlCQqR6K/KzR/zHv2BwgPyE1TEPNC3gMCxq4UQWEj3E2aG2Rem6FXR+P6kmRenSygoecizjzezaKyxb0vPY2awtkYynaAQI+bpgILx/E+e48VrF5ddVu4LaTChbHpBl/dKfMA2IvXee4XA3KkWOQ/xDeHEDrfFVXKTxs8wD8G1EOFrgeUOP8Of3xD38OGfVHZEB0pINFOHr3c7xv1Y6L3n63x31m5nIDx38Z8ByXpaOxnehrCgSWhDONu79vSV/65r3iNN7oaaSgIzD5TWiV6y7ScKIZo1v7828LPz7PPOW1ByOjp1Qkfz8g8mZB2fJERZW5hjGPt7Vlf8+8FVFReivX2TDMH1U3KzYeFpaPuxXh/x5V7XkmKv/rhcc/Db47KSa7rBGvq8g10f1wqC/GCxeEc+GsQg/KBiEGI5BcInG5yYPiEirCxb5Oka3yEIqJDHjTGoSXkdJpQK85AjhHif4MQ1QQiBxS4uREaew+eK9DREpsOEi35hE6jmXpc/Q/ux4KH9OH1dIPuP62BNmSmEeLZ65WXGc98Bu6q0QiDqHff/WPujl2x68BnfL6l7ARr8xNdBaVFKQlxMF3wHNxPRv2RrQGxUZ6lcLPZ5duo9Y0nRAz8Xec6q8IVEtlnrs+m1i6f3sXx6U/9kMC4iwPEvIgOt36tdul0qB4gbgJKOGIeIYJ6cLS14fykz5GHucd/K8rN/LfReWOAOdt/uDBhvG2AT/zw8Ocz9UkNwL9wPoEmHrzqiMGCuP0lEf8i/s+T3AQsD5jS+f1Dew/VS85uB4gG8Xm+uzanGt6a/a1nZ355cb38tGGKa3MWy/f1RmUebzS1vxWjKTGXmrCnzi9/6Lt91givIdFkv65eX9jBdMjY7MeqKi1AOntlgv++m30vvwtp7Y3/bq48MS72yKe1qywoCJig2r8hkhv+um1IQYjsdmBkLExYJLEaXSmUBL4FJEadk1MCYViMP/EANQPT0BUIInI5bTeRJbTwRzSAipKUhyAuCRfL/iOhDGYmOSPUIiQ0sIwiAjQSkdZcLM+h2kcYGVwQiyR8jIvh64a+tnYsOxQNKAzaXalptZgZ2yIuvruUh24NT4klCCJwQlbQyAswMciQsttjcjxBCYNP8i8zb4Tr75qWlD+5xBXvDzTYoxeA6u6v1rtOyNLMHZVlLDuZbZmYo2zD7g2Wxprwo68yg1gzmYqSCLebG+INZ5xBXs9Pr7fr8WleOTkNTLDoxOsQ0toNFSwNTDDQizYi4Bf6t2DPnxIyEW8zmcDAjyhtDwOUwRaZHQpEGBgiexHeB7xjfP9aByPeDWApYzSBglcU3BH/6warLN2lkf9zjOuOW5RXPryz15vRPMjiu6p80ZUYfKw6KbU7m73Z1vG9dxbc/FXjBJ+O7qV/SP+8enIzxjkr+vdk1+tkNFR8tL/VmHZVl2njNANv4czsnIF6qWSTeLQjI+625qEJBwORUWQyNnA4ySA+ELlfL0tlgiATxC6wHiD1ASec6N3hmxqIF8pfHZRluenBNRdfBqYaUkztaIjb6n4joNhnVXdgcfrEDdV0qBvDZImgJp66IYNNHAM91QojqtExp8kfe+X6m4MhN8nk4JeHkj7kHgRKBRRMnLch/iAjKBEyvKE8eOfF9hudKK9jzkpUPCgQsNVA4sEEAT8RCYAMobuSQqttigABOd0T0kTy9Q4G8pC4rwIvr7ce8udU+b0mJN8WoEaHxuaa8LydkQZloEAmOVBRgboalCorijUKIdcwM9xOUT3xnsFx1aEx8T7nb3XXKgvK38+yBnuW+ULrdz3os9l0Tdfz40OT7RmaYbssxa7V6jXhYCPFAjTkPVxziEKr9FFI5RnbD34UQnzEzrGGwpiGgGaffztG42BoyTPj2PtrqyKhkzVkPril7fqs9IPQaETox1/zkJxMy/9GS60pD2h3NtWtLXWNO+KHk03xXMC3brOVXx2SeckpH41f1KVnA5Mz5JXd/le+8PxCi0IkdTc9+Mj7rjuas8RPXCkJtsBtbrCmaQTtUrmHmw+UmBdN45PR8qrTSzBFC4LQbtTy3oeLsH/K9sx8fnqLtZYscesImeigZ30nT6DdRP7CRF8o4Ciy4UA5y5GNwikdw1rtE9JMQAqlcDRZmhokVZa5rLpp4zkJpWcHmDhMsNn+4w+BLxkkQVgNUA4QCBisGlCec1pAlgjbB7QGFAkFqiHPY7xQmlQtYQfCdwmzdbHStDQalnd0gN2bMFZjUEasCKxDGJiwoff7C+sq/vZ/num9RsTcDJs3Leyd+OLGT9a6pnUxwy8VEpNIK5R1WuyVCiDlNefBmu7f/g6urTrL7+KZv97hyUUwInodcs5Ym5Zr3dkjQPXZF9+R3OiX9z5cvmRqhNCBgd5ukjP41osTKbw1xCrC24BuDD7y6Rk007ZUHFVjiULemel6jts76Cs8R7293H/9Tgfe4JcXe4b4Qa3LM2tD4bOPHTw/LvDbTKmLGFBhNW2N9Dcb4H8srrvtgh+PJrfaA7vAUg/2CHubzbx2QguDPA1piinw8eOzn+Us3V/lxT9H841NHpJrNzRosqhSEWI9+nD+Pma+XJ4S6xj63NrlKfd1ZtoctN60ufN0XDJ3z84nZtf2euB0fMza/j+t7VlN+z8zYeBFoGXEnYNE+i4g2NPV0IxdtnJbgOz5SvgPKBtwpiEbHBgI8wd0PUzH2D5ipsdDDxYHMCQgCv+DbxfWwdECBwkYAhepcIQR8xNXCzC/JSHT8DKlp2LSQWaEkxggw8+lENE8G9UEZfKLmYn3/yrK7Xtrs+OceV9AAUsQ3x2Z8O6Z7wuRuzVClUc4lzKFQfafKaGDAZvzhtvLEcr/+vM0O371PrqsKU0Bjwhq0msDVva3rHh2ZOs0kBNwHkbkMKxeUXaQ6/4WiXrrNYD2D3CWEiKRQ1tkkfENFnuB5K0r9E3a5A112O4M5D60pzx6cYgjO6Ju4aES6cdmKUv8R3xd6On6wzdEjRGRGCWXslr0T9c6bB1hvndQn6a3mwDsaDGN9Dcbk/AXF183d5XrGG2Qak2ksvbineeKVvVPqJJ4r8Xr7jvi88Kc8ezArSS/o6+Nzh43OMIAHo1lFKQjNCm98PVy6CGBqR9ErmN2xeYOdDfMAp5UjGtPiWRsqL7hjeflriXqN8Zo+idv+MSgZriH45xE/opOnZERsz45FpgMzY1M9UVoLsJDhBI5NGL5T8DdgsXo8Fu+qjYckyoE52l4HUx1MKLcQ0X0yqBMnUPyBewIZE72F2FcjXm4C58tAMGAEM/MgIUSYqIaZj5FplhHXBn6MhRoKBgLDNjRmrNQ9dSPAzDCZQ3nGSRgug0idBTF3h+OMW5ZWzNnhDGiyzFrX1C6WOTcckTa9Vx0bZ7zji41pcZF79OPrKm/cZA8es77Sn44CbN0Sdd7jcszvjUw3/vuUXtYN2fWUtZZVdLGGYO5Cee1am3Ydh4d388uPznf6R9h9oRnzCzyp3jqdeX9FLVGvCeVaNHkDU4yrnxie/EA3q6HdMbZuKHbmzvzD+fy8Hc7TA0x0UkdL5Yk5xjOu65+0oEbRL/HmFvtpz6933L2qzDvcpBN8cQ/rR7NGp8MV1OyiFIRmhzh+XsDMyPdHZDY2MpzoEc08XW6sCDRqVMVKULXevrT8uyf+rBzVJUHn+/dR6fcenWV6C2RfRARTJU4iMEUigA/+Q/AqNFikWRKBgqiUN6iO1C08E/EPL7cW2UsNrgX4cWt+X4h/2K/KmuwPApTQZggsIHAp4PSI0xny16EULJWWiwhmiERHrjpM4k0SqagkHoruC2ZGlg5cQXADIWYFyhhST8Pl4yGvbK48/6m19hc3VPkTcy1avrJv4qXTByZ/mH2ALIUmDUYL3szM5tlb7IO/KfDe+dlu90ll3mA4H6mnTV+SbtZsurSb9dfL+yQ+eLB5wcywuMDyEj7oCyHCrhbMqS2VvrMfWVd5yZ+VwdGTOpiSNlX6ae4OJ9n0GhqbaVrZK0m3VUPCHyLWLin2nra5KhDOkDipo2mdVa9ZWOoNLRufbf79kp5WWACjVCtaEMAYvWq705nzynrfM0+trzzbE2SUiC6Z3Mky57FhKVgTjPeurrz9gzzHdRur/FawcE7rlrBwXKr2gmsHprUI46VSEGI00PH+GGkmR84tAuyg+eOD3o+rvSl9cPl5bPoHO39yBUK6LJPWMWd89rhxGXpo/YiWxsYYCTaFiRyBWThJw1cPCwAWBywyjgMxF8oTC6KvETQYEQRIwSwayXIB8x38x60uzIwURmSGoH+IUUDwJ5j09hNmRiwISgFD4YGr4QOpFCCeAoocrDHYvJCH/ppMvYx8t1C0EAeB5wLrbyPUuz8XOMRXe/+XkpFj5tDlfRO9GUTVnPvMjI0RbcQYITDuLkkQhWuaLSq91Qdn3yaGbAJY0HACrikLhRDhgky/F7i7XbM6F97pAAAgAElEQVSs/MPlJd5hFp0mePsA2633DklBFky7EWzmy4v9N7y8ufKa2Xmuju5gyBKUIz8wxRC4ZUDiNy4fP7jeo9k6Kc0aOKJzeI6GJYVoknZfRhBV+IJTPt5h35Fk1F+8xxkaVhkIjbl9gE2jq5FqWeYNUapRM04IgTmtRCLwxx9/GGY5sv775ibHya4gh9eyEelG+CxDS0q84RR/s1YEbupv+/OhoSmg/m5ULFVjAFcKQmNQa+Z78NHGeoGWFLI4cWLC4YQNy0HMBG2+Y3nZ/722yXFdhT+kOTzFsHx0tv6UF0ZkhH31kic+Uv4W5ltYK0BygkI0KKuLkzLahxSwdbVPDcyMdENo1VA0oBigBgQC91BaN+5EWhLAjoesG/gK/1NXtLG0IqACG05jtb9HKFDn7HTQn+sqnJ0/y/dcNsBmmHxZL6verPvLp4trEewVPm3tdAa0K0u92nfznLTDHiCLTlRlmrUbt9j9v888PGXJKZ0sLt2+IDOcACMKFszr85HKKYSA4tEuRQZ/gtEQ1gMI5iGUZ/T9X0KIzVVVnH76bwVv/lbsO9kVCImr+yZ+fG6XtCvH5bTPbJO8PDbtNHuPv+n30iN1Opr0Z7l/EAIaITkWLXe06Ow5Zm1hgu5/AYK9bPq02w9L6p+gE/Rjgcd51aISo1kndA8NSaWTO5rDwZB1COJ4blJZO/sjs63c3eX6FZUXlHlDk9eV+UaAIRFi0QnqnWQoPCrT9K8zMiufO7ZbN7hQW0yUgtBiUP/vRTIWAMRDSBdEDAAiUZFPjEh4LNrwcWPBAi96k5nCpBkZPlacarGBnCyEiHnd9F8LOHPWpuIP39vmHIf967HhKR/f3D8Jle8wz9DPX2T61oFQR9uQLvY9/MGR1DGZeQHKZ5zG8eUgngHWguoo81YYxnpfGW2wmVQSYDGASwaxB2H5do878NZWx7aFBe5kh59N7iDb0Hmw5Z3UwYL8e5gcD9gOnAQL3AHaUOmny38tpe3OAGFcTulkcbx1ZHooSa+JBHTWfgbiGxAPEdf41jsAB7iAmaGQIuMEgaKYV6i3gO+sDH3GeNy3quK/D66pCPuGp3azVjw8In1QT8u++JD2LOj725vcOazxH1PiFVNmrio7yyU3q9r9zrVo6dPxmTQszUiVvlCYAho/SzdqI8oB0jRBPLVdcojAahPmWhFCIItHSQ0EsF58U+LKLqriYwNBHic0xBzkxRvtgfnDhqXkTxUCh4AWFaUgtCDcciNAJS+YcqtzAg/ShJUwLwshmlTGtFaON6Luz2kuH72LucNRn+9ZuaLUlwG14MVRqS9c1dt2KwIGZfoUiJjg48UiDQxAQIQ0QCgQqIUQMfkiTfJeKDNEhBM23BAwb94thABZUbuTRSXevkUO3wXLywP/KPMG6bVNDvIiggwJ5zpBmWatJ8RUIgR5B6UYtnS06KrGZBpdp3dKyDPpwgsvBC4LbPwIEAU3Q1jWlPvovtUVtNcVpNfGpFP/ZD1h3V9W4qWPd7mKz+2a8PKgVMNVYh8JFARlhmGxOVbGfOB0DdbHOhkC29JgMDO+QVikAC7icBDQGra84Bt9ZF3l359YW3lvhS+kPzLbtH7hpOyjGspz0JbwOFBbsWGV+2igOxg64c9Kf/onOxxd9/c7CbqwR8JhI9ON/WptJNjIYNlDFk8krgkZRUj7RTYQAnJ7xOLw0x5wjuc+KAWhBUdHbpDwv6GufETgi8fmFzHz4hvEz3A0xKK1WEbFI2cemjiKB/3Fl32wbkj3Ak5MsFogePDy2hHHsYRhzjbHcY+srXxlZbmvayeLtvKMLqYrnjkiM+yrlIsw2gFGTPwNszhOZrCaICgPGQDY5GoLlAMEWELZaDcnWyzCe33Ud9LX+XdpSAwt9wZ77XQFw8qjUSNoQIrem2PWvieYFk/umljUL4HyLUadKyXVsO1AKV/ScoGCPQjmRHzBSDyvyh8inAazzfuYyxcXe+nsn4oo3xWEVaJiUKrBcPfAJEu/5GpWbcROIGYF7QH+4MlA5kWbFWZGdg0sWZEYhFE1v6clxZ5Tp/1U9GqeM5iVbdZUXdLdetkjI9L2Sz9ts51vhoYzM+YDgmsjdPhQ9hGngZz+6hgneTi6RmbhoCXfCCFgMVUSxwgoBaGFBkeSizwlzfx4K05nXxERrAPYHMP13mXgFBj5cNIHQ19tQZri8IYwiTEzTPYT5ImpD3yszd3tC34uvvnTna4nqvwhzYkdzKVfTszKiWZjlzg9KP3D2KmgJME3DgXp21jHZjQ3Dgd6Pjbx57aQwcyOLx5fW3nMZrs/vMBqNYJ1ggLndLUsf3RI6kvZCdr3o8HtIO/pJE9xmGOR7x2Yriz2BR+9/JeSu7/c7T4sRKSFsaJ3op6+m5RFnRP2GXKgrdZaJKBwvBvLcZCbB5QQKMnAAQqgL/IOqfCECaiiea98HpqNTqCvyOVnGZiIDBBYSdA1ZJZU11vIYzad9Oke1/pyn0BI//vjMmZP62Y9UAGz1po6cfVe6S5F4DCwRsYBYoPqlFrFqmBlaFSxorgCoJ03Jq4VBFlsBzn7EXM8zNA4Abc5qmUZrY5aBWBqQ9ra6ZHyyXXNMWZG8B5Mcug/BP73SEQ8YgjwjHqFmcH+F2Hy2yuEwPubXRCkd+uysneeXW8/I8hM1/exffCPw1JvjIYFTSoJKMyFviNSH0GLMEu2C8EGdvuykmlLSwL/WlDoycFOZdUJGpyqXzM4xbjwn4OT/ptu0oGXokHsdAdZmGGxQgYISvXidaB+fgYpbEhR3e0ITrpuSenEIk9wqCfIhw1PM1gu65VIfmZaVeajjhYdynoTgtHkeCA1EwGiTQ6YkqmGCOaEORqKMaxpcDuBk2C2tDIhNgNuJijUCPb8S/aNVArgVhkh3VWoHwClEgoBrFdQipFCCqsKBC6T4yKWtLxyTn5iY+lbszbYJ6MIzmU9re89PyoNlrYm97FdTNoYdYKZMQY4KEWIxd6IRumL0evj8jFy7sK9CmUenBKrG3IAbM5OxbuCgHrZ2NAi5necKHHSbosKwvtygQbmoAO+p74Pg5lBdQpTKOQUIsLJGljAN/9QNPnBzHyHLP6CZ5wnhMCi2yKy2W7PuHWJa8Mnu1ypJq3g8dnGd96ZmH192iFMGYwS0XevrJj78qaqoaWeUDJOqmd1SdidadJcd14vy3Jd3p+Fw4cPj7kLRWZVRPL782qz40Ep+7nElbFwl79DRTD49zXl/jNWl/tEqSdICXoN3djPRncPSiKZtoZNE8VyzhdCIBCtUSKVZgTLQvGvHZOD7BcE7mLhRMZNJAYFFjdQXy+H0ihTNVGYCxY5KMOwDtROXYQVAbETUJTw/aFi59+EEFAewnLz0tJL39hsn1XhY+NhKYZfZg5KOntqN2ubpvRt1KA0803SzQoGUaTYwkqKOdRulP+GwMfMiMeA4ot5Dnp4/BsuV7hRIyyVDXlkzK+NdwUBlKcR0zs6jwUAPq82oyBIsxpYryI+eCw63Rt6OpS+U8QR4GSNdEAELx70dCMtMKgGh4BAnLoGtGQOLQbsqT8qR76zzTFvZamvg1kr6NKeia/dPyr1lkNJSQAHxeIKT6f31zvHran0P7mg0JOBDy/VqPHf2t82+8peyf9IswicHOJGkAr4yib7w0+sq5xU4Qv1KfOG9E+PTKVzuyVQhinibg6zQyJtDbU2YJnApo7FPr8+twgzI/gRvA4RNxrmJ3gcEHuDzJfIoSCCCTb5mj8DXkiXhetsv1LkMlYnzFgprW44mUWuAZkPKmiG3Wz4Ph9eXXHMvF2ur5eX+gwdLFrvQ0PSrr2op+WQP9k2x2SUpc9hBUbNF6yF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;&quot; 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style="font-size: 28px;"> Film new videos &amp; </span><div><span style="font-size: 28px;">load metadata</span><span style="background-color: initial;"> </span></div></div></div></div></foreignObject><text x="1169" y="35" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Film new videos &amp;...</text></switch></g></g><g><path d="M 638 400 L 638 330 Q 638 320 644 328 L 692 392 Q 698 400 692 408 L 644 472 Q 638 480 638 470 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 976 100 L 976 30 Q 976 20 982 28 L 1030 92 Q 1036 100 1030 108 L 982 172 Q 976 180 976 170 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><path d="M 638 100 L 638 30 Q 638 20 644 28 L 692 92 Q 698 100 692 108 L 644 172 Q 638 180 638 170 Z" fill="#fdfcfc" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><rect x="-0.09" y="300" width="310" height="200" rx="30" ry="30" fill="#1ba1e2" stroke="#006eaf" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 308px; height: 1px; padding-top: 400px; margin-left: 1px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 28px; font-family: &quot;Roboto Slab&quot;; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Pair videos with <div>models &amp; run </div><div>inference</div></div></div></div></foreignObject><text x="155" y="408" fill="#ffffff" font-family="Roboto Slab" font-size="28px" text-anchor="middle">Pair videos with...</text></switch></g></g><g><path d="M 124.91 310 L 124.91 240 Q 124.91 230 130.91 238 L 178.91 302 Q 184.91 310 178.91 318 L 130.91 382 Q 124.91 390 124.91 380 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" transform="rotate(90,154.91,310)" pointer-events="all"/></g><g><path d="M 300 400 L 300 330 Q 300 320 306 328 L 354 392 Q 360 400 354 408 L 306 472 Q 300 480 300 470 Z" fill="#ffffff" stroke="#1ba1e2" stroke-width="2" stroke-miterlimit="10" pointer-events="all"/></g><g><image x="1099.5" y="365.5" width="138" height="138" 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-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M137.62,-123.58C137.62,-112.28 137.62,-97.58 137.62,-87.5"/>
+<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.LatentDimension -->
+<g id="edge3" class="edge">
+<title>kpms_pca.PCAFitting-&gt;kpms_pca.LatentDimension</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M655.27,-140.71C683.32,-132.01 754.32,-109.97 802.44,-95.04"/>
 </g>
-<!-- subject.Subject -->
-<g id="node3" class="node">
-<title>subject.Subject</title>
-<g id="a_node3"><a xlink:title="subject              
------------------------------
subject_nickname=&quot;&quot;  
sex                  
subject_birth_date   
subject_description=&quot;&quot; 
">
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-<text text-anchor="start" x="194.75" y="-623.13" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">subject.Subject</text>
+<!-- kpms_model.VideoRecording.File -->
+<g id="node2" class="node">
+<title>kpms_model.VideoRecording.File</title>
+<g id="a_node2"><a xlink:title="→ kpms_model.VideoRecording
file_id              
------------------------------
file_path            
">
+<polygon fill="none" stroke="none" points="203.38,-362.8 41.12,-362.8 41.12,-342.8 203.38,-342.8 203.38,-362.8"/>
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 </a>
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-<!-- subject.Subject&#45;&gt;session.Session -->
-<g id="edge3" class="edge">
-<title>subject.Subject-&gt;session.Session</title>
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+<!-- kpms_model.PreFitting -->
+<g id="node3" class="node">
+<title>kpms_model.PreFitting</title>
+<g id="a_node3"><a xlink:title="→ kpms_model.PreFittingTask
------------------------------
model_name=&quot;&quot;        
pre_fitting_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="643.25" cy="-12.96" rx="12.96" ry="12.96"/>
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+</a>
 </g>
-<!-- kpms_pca.KeypointSet.VideoFile -->
-<g id="node6" class="node">
-<title>kpms_pca.KeypointSet.VideoFile</title>
-<g id="a_node6"><a xlink:title="→ kpms_pca.KeypointSet
video_id             
------------------------------
video_path           
">
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-<text text-anchor="start" x="86.25" y="-412.22" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.KeypointSet.VideoFile</text>
+</g>
+<!-- kpms_model.VideoRecording -->
+<g id="node4" class="node">
+<title>kpms_model.VideoRecording</title>
+<g id="a_node4"><a xlink:title="→ session.Session
→ kpms_pca.PoseEstimationMethod
recording_id         
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="209,-440.64 35.5,-440.64 35.5,-406.08 209,-406.08 209,-440.64"/>
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 </a>
 </g>
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-<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->
+<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.VideoRecording.File -->
 <g id="edge4" class="edge">
-<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>
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+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M122.25,-405.76C122.25,-392.13 122.25,-373.36 122.25,-362.51"/>
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-<!-- kpms_pca.Bodyparts -->
-<g id="node8" class="node">
-<title>kpms_pca.Bodyparts</title>
-<g id="a_node8"><a xlink:title="→ kpms_pca.KeypointSet
bodyparts_id         
------------------------------
bodyparts_desc=&quot;&quot;    
anterior_bodyparts   
posterior_bodyparts  
use_bodyparts        
">
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-<text text-anchor="start" x="264.75" y="-411.45" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_pca.Bodyparts</text>
+<!-- kpms_model.InferenceTask -->
+<g id="node13" class="node">
+<title>kpms_model.InferenceTask</title>
+<g id="a_node13"><a xlink:title="→ kpms_model.VideoRecording
→ kpms_model.Model
------------------------------
inference_output_dir=&quot;&quot; 
inference_desc=&quot;&quot;    
num_iterations=null  
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="383,-370.08 221.5,-370.08 221.5,-335.52 383,-335.52 383,-370.08"/>
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-<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->
+<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.InferenceTask -->
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-<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>
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-<!-- kpms_pca.RecordingInfo -->
-<g id="node13" class="node">
-<title>kpms_pca.RecordingInfo</title>
-<g id="a_node13"><a xlink:title="→ kpms_pca.KeypointSet
------------------------------
px_height_average    
px_width_average     
nframes_average      
fps_average=null     
recording_datetime=null 
recording_duration_average 
">
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+<!-- kpms_model.FullFitting -->
+<g id="node7" class="node">
+<title>kpms_model.FullFitting</title>
+<g id="a_node7"><a xlink:title="→ kpms_model.FullFittingTask
------------------------------
model_name           
full_fitting_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="460.25" cy="-12.96" rx="12.96" ry="12.96"/>
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-<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->
+<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->
 <g id="edge6" class="edge">
-<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M228.45,-470.24C289.74,-457.92 378.05,-440.16 437.06,-428.3"/>
+<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M460.25,-61.66C460.25,-50.36 460.25,-35.66 460.25,-25.58"/>
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-<!-- kpms_model.FullFittingTask -->
-<g id="node5" class="node">
-<title>kpms_model.FullFittingTask</title>
-<g id="a_node5"><a xlink:title="→ kpms_pca.PCAFitting
full_latent_dim      
full_kappa           
full_num_iterations  
------------------------------
full_fitting_desc=&quot;&quot; 
task_mode            
sort_syllables       
results_as_csv       
visualizations       
">
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-<text text-anchor="start" x="246" y="-137.84" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.FullFittingTask</text>
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+<g id="node6" class="node">
+<title>kpms_pca.KeypointSet</title>
+<g id="a_node6"><a xlink:title="kpset_id             
------------------------------
→ kpms_pca.PoseEstimationMethod
kpset_config_dir     
kpset_videos_dir     
kpset_desc=&quot;&quot;        
">
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-<!-- kpms_model.FullFitting -->
-<g id="node15" class="node">
-<title>kpms_model.FullFitting</title>
-<g id="a_node15"><a xlink:title="→ kpms_model.FullFittingTask
------------------------------
full_fitting_duration=null 
">
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+<!-- kpms_pca.KeypointSet.VideoFile -->
+<g id="node8" class="node">
+<title>kpms_pca.KeypointSet.VideoFile</title>
+<g id="a_node8"><a xlink:title="→ kpms_pca.KeypointSet
video_id             
------------------------------
video_path           
">
+<polygon fill="none" stroke="none" points="561.62,-362.8 400.88,-362.8 400.88,-342.8 561.62,-342.8 561.62,-362.8"/>
+<text text-anchor="start" x="408.88" y="-350.3" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.KeypointSet.VideoFile</text>
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-<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->
 <g id="edge7" class="edge">
-<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-123.58C320.62,-112.28 320.62,-97.58 320.62,-87.5"/>
+<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M481.25,-405.76C481.25,-392.13 481.25,-373.36 481.25,-362.51"/>
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-<!-- kpms_pca.PCAFitting -->
-<g id="node7" class="node">
-<title>kpms_pca.PCAFitting</title>
-<g id="a_node7"><a xlink:title="→ kpms_pca.FormattedDataset
------------------------------
pca_fitting_time=null 
">
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+<!-- kpms_pca.Bodyparts -->
+<g id="node12" class="node">
+<title>kpms_pca.Bodyparts</title>
+<g id="a_node12"><a xlink:title="→ kpms_pca.KeypointSet
bodyparts_id         
------------------------------
bodyparts_desc=&quot;&quot;    
anterior_bodyparts   
posterior_bodyparts  
use_bodyparts        
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="707.12,-370.08 579.38,-370.08 579.38,-335.52 707.12,-335.52 707.12,-370.08"/>
+<text text-anchor="start" x="587.38" y="-349.53" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_pca.Bodyparts</text>
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-<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->
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-<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>
-<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M308.9,-202.24C284.44,-193.66 226.81,-173.43 185.15,-158.8"/>
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-<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->
+<!-- kpms_model.Model -->
+<g id="node9" class="node">
+<title>kpms_model.Model</title>
+<g id="a_node9"><a xlink:title="model_name           
------------------------------
latent_dim           
kappa                
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="362.38,-440.64 242.12,-440.64 242.12,-406.08 362.38,-406.08 362.38,-440.64"/>
+<text text-anchor="start" x="250.12" y="-420.09" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.Model</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.Model&#45;&gt;kpms_model.InferenceTask -->
 <g id="edge9" class="edge">
-<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>
-<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M320.62,-194.58C320.62,-184.44 320.62,-169.7 320.62,-158.42"/>
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+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M302.25,-405.76C302.25,-394.98 302.25,-380.99 302.25,-370.24"/>
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-<!-- kpms_pca.DimsExplainedVariance -->
-<g id="node14" class="node">
-<title>kpms_pca.DimsExplainedVariance</title>
-<g id="a_node14"><a xlink:title="→ kpms_pca.PCAFitting
------------------------------
variance_percentage  
dims_explained_variance 
latent_dim_desc      
">
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+<!-- kpms_model.Inference.GridMoviesSampledInstances -->
+<g id="node10" class="node">
+<title>kpms_model.Inference.GridMoviesSampledInstances</title>
+<g id="a_node10"><a xlink:title="→ kpms_model.Inference
syllable             
------------------------------
instances            
">
+<polygon fill="none" stroke="none" points="248.5,-221.68 0,-221.68 0,-201.68 248.5,-201.68 248.5,-221.68"/>
+<text text-anchor="start" x="8" y="-209.18" font-family="arial" text-decoration="underline" font-size="10.00">kpms_model.Inference.GridMoviesSampledInstances</text>
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 </g>
 </g>
-<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->
+<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->
 <g id="edge10" class="edge">
-<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M332.36,-202.63C367.98,-191.3 474.61,-157.39 510.61,-145.94"/>
+<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M643.25,-61.66C643.25,-50.36 643.25,-35.66 643.25,-25.58"/>
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 <!-- kpms_pca.PCATask -->
-<g id="node9" class="node">
+<g id="node14" class="node">
 <title>kpms_pca.PCATask</title>
-<g id="a_node9"><a xlink:title="→ kpms_pca.Bodyparts
------------------------------
output_dir=&quot;&quot;        
task_mode=&quot;load&quot;     
">
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-<text text-anchor="middle" x="320.62" y="-339.89" font-family="arial" font-size="12.00" fill="darkgreen">kpms_pca.PCATask</text>
+<g id="a_node14"><a xlink:title="→ kpms_pca.Bodyparts
------------------------------
kpms_project_output_dir=&quot;&quot; 
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="704.5,-299.52 582,-299.52 582,-264.96 704.5,-264.96 704.5,-299.52"/>
+<text text-anchor="middle" x="643.25" y="-277.97" font-family="arial" font-size="12.00" fill="darkgreen">kpms_pca.PCATask</text>
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 <!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->
 <g id="edge11" class="edge">
 <title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-397.12C320.62,-386.34 320.62,-372.35 320.62,-361.6"/>
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-<!-- kpms_pca.FormattedDataset -->
-<g id="node11" class="node">
-<title>kpms_pca.FormattedDataset</title>
-<g id="a_node11"><a xlink:title="→ kpms_pca.PCATask
------------------------------
coordinates          
confidences          
formatted_bodyparts  
">
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-<text text-anchor="middle" x="320.62" y="-269.33" font-family="arial" font-size="12.00" fill="#00007f" fill-opacity="0.627451">kpms_pca.FormattedDataset</text>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M643.25,-335.2C643.25,-324.42 643.25,-310.43 643.25,-299.68"/>
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+<!-- kpms_model.Inference -->
+<g id="node19" class="node">
+<title>kpms_model.Inference</title>
+<g id="a_node19"><a xlink:title="→ kpms_model.InferenceTask
------------------------------
inference_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="302.25" cy="-282.24" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="302.25" y="-277.97" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.Inference</text>
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-<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->
+<!-- kpms_model.InferenceTask&#45;&gt;kpms_model.Inference -->
 <g id="edge12" class="edge">
-<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-326.56C320.62,-315.78 320.62,-301.79 320.62,-291.04"/>
-</g>
-<!-- kpms_model.GenerateResults -->
-<g id="node10" class="node">
-<title>kpms_model.GenerateResults</title>
-<g id="a_node10"><a xlink:title="→ kpms_model.FullFitting
------------------------------
grid_movies_sampled_instances 
">
-<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="320.62" cy="-12.96" rx="12.96" ry="12.96"/>
-<text text-anchor="middle" x="320.62" y="-8.69" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.GenerateResults</text>
+<title>kpms_model.InferenceTask-&gt;kpms_model.Inference</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M302.25,-335.2C302.25,-322.84 302.25,-306.25 302.25,-295.17"/>
+</g>
+<!-- kpms_pca.LoadKeypointSet -->
+<g id="node18" class="node">
+<title>kpms_pca.LoadKeypointSet</title>
+<g id="a_node18"><a xlink:title="→ kpms_pca.PCATask
------------------------------
coordinates          
confidences          
formatted_bodyparts  
average_frame_rate   
">
+<ellipse fill="#00007f" fill-opacity="0.250980" stroke="#00007f" stroke-opacity="0.250980" cx="643.25" cy="-211.68" rx="108.47" ry="17.28"/>
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 </a>
 </g>
 </g>
-<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->
+<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.LoadKeypointSet -->
 <g id="edge13" class="edge">
-<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-256.06C320.62,-244.76 320.62,-230.06 320.62,-219.98"/>
+<title>kpms_pca.PCATask-&gt;kpms_pca.LoadKeypointSet</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M643.25,-264.64C643.25,-253.86 643.25,-239.87 643.25,-229.12"/>
+</g>
+<!-- subject.Subject -->
+<g id="node15" class="node">
+<title>subject.Subject</title>
+<g id="a_node15"><a xlink:title="subject              
------------------------------
subject_nickname=&quot;&quot;  
sex                  
subject_birth_date   
subject_description=&quot;&quot; 
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="171.12,-581.76 73.38,-581.76 73.38,-547.2 171.12,-547.2 171.12,-581.76"/>
+<text text-anchor="start" x="81.38" y="-561.21" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">subject.Subject</text>
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+<!-- session.Session -->
+<g id="node16" class="node">
+<title>session.Session</title>
+<g id="a_node16"><a xlink:title="→ subject.Subject
session_datetime     
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="174.12,-511.2 70.38,-511.2 70.38,-476.64 174.12,-476.64 174.12,-511.2"/>
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+</g>
+</g>
+<!-- subject.Subject&#45;&gt;session.Session -->
+<g id="edge14" class="edge">
+<title>subject.Subject-&gt;session.Session</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M122.25,-546.88C122.25,-536.1 122.25,-522.11 122.25,-511.36"/>
+</g>
+<!-- session.Session&#45;&gt;kpms_model.VideoRecording -->
+<g id="edge15" class="edge">
+<title>session.Session-&gt;kpms_model.VideoRecording</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M122.25,-476.32C122.25,-465.54 122.25,-451.55 122.25,-440.8"/>
 </g>
 <!-- kpms_pca.PoseEstimationMethod -->
-<g id="node12" class="node">
+<g id="node17" class="node">
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-<g id="a_node12"><a xlink:title="format_method        
------------------------------
pose_estimation_desc 
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-<text text-anchor="start" x="8" y="-553.34" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.PoseEstimationMethod</text>
+<g id="a_node17"><a xlink:title="format_method        
------------------------------
pose_estimation_desc 
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+<polygon fill="#000000" fill-opacity="0.125490" stroke="none" points="460.88,-511.2 295.62,-511.2 295.62,-476.64 460.88,-476.64 460.88,-511.2"/>
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+<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_model.VideoRecording -->
+<g id="edge16" class="edge">
+<title>kpms_pca.PoseEstimationMethod-&gt;kpms_model.VideoRecording</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M317.58,-476.67C277.51,-465.94 225.14,-451.91 184.71,-441.09"/>
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 <!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->
-<g id="edge14" class="edge">
+<g id="edge17" class="edge">
 <title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>
-<path fill="none" stroke="#000000" stroke-width="0.75" stroke-dasharray="5,2" stroke-opacity="0.250980" d="M100.64,-538.59C112.58,-527.82 128.2,-513.73 140.22,-502.89"/>
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+</g>
+<!-- kpms_pca.LoadKeypointSet&#45;&gt;kpms_pca.PCAFitting -->
+<g id="edge18" class="edge">
+<title>kpms_pca.LoadKeypointSet-&gt;kpms_pca.PCAFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M643.25,-194.14C643.25,-182.84 643.25,-168.14 643.25,-158.06"/>
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+<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.GridMoviesSampledInstances -->
+<g id="edge19" class="edge">
+<title>kpms_model.Inference-&gt;kpms_model.Inference.GridMoviesSampledInstances</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M290.57,-276.74C261.57,-265.57 185.83,-236.4 147.37,-221.59"/>
+</g>
+<!-- kpms_model.Inference.MotionSequence -->
+<g id="node20" class="node">
+<title>kpms_model.Inference.MotionSequence</title>
+<g id="a_node20"><a xlink:title="→ kpms_model.Inference
video_name           
------------------------------
syllable             
latent_state         
centroid             
heading              
">
+<polygon fill="none" stroke="none" points="457.62,-221.68 266.88,-221.68 266.88,-201.68 457.62,-201.68 457.62,-221.68"/>
+<text text-anchor="start" x="274.88" y="-209.18" font-family="arial" text-decoration="underline" font-size="10.00">kpms_model.Inference.MotionSequence</text>
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 </g>
-<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->
-<g id="edge15" class="edge">
-<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>
-<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M320.62,-62.04C320.62,-51.54 320.62,-36.27 320.62,-25.77"/>
+</g>
+<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.MotionSequence -->
+<g id="edge20" class="edge">
+<title>kpms_model.Inference-&gt;kpms_model.Inference.MotionSequence</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M310.09,-272.28C321.66,-259.06 343.14,-234.52 354.59,-221.43"/>
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diff --git a/images/pipeline_kpms_model.svg b/images/pipeline_kpms_model.svg
new file mode 100644
index 0000000..b2cfe54
--- /dev/null
+++ b/images/pipeline_kpms_model.svg
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+<polygon fill="white" stroke="none" points="-4,4 -4,-368.16 841.88,-368.16 841.88,4 -4,4"/>
+<!-- kpms_model.VideoRecording -->
+<g id="node1" class="node">
+<title>kpms_model.VideoRecording</title>
+<g id="a_node1"><a xlink:title="→ session.Session
→ kpms_pca.PoseEstimationMethod
recording_id         
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="510,-223.04 336.5,-223.04 336.5,-188.48 510,-188.48 510,-223.04"/>
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+</a>
+</g>
+</g>
+<!-- kpms_model.VideoRecording.File -->
+<g id="node8" class="node">
+<title>kpms_model.VideoRecording.File</title>
+<g id="a_node8"><a xlink:title="→ kpms_model.VideoRecording
file_id              
------------------------------
file_path            
">
+<polygon fill="none" stroke="none" points="504.38,-145.2 342.12,-145.2 342.12,-125.2 504.38,-125.2 504.38,-145.2"/>
+<text text-anchor="start" x="350.12" y="-132.7" font-family="arial" text-decoration="underline" font-size="10.00">kpms_model.VideoRecording.File</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.VideoRecording.File -->
+<g id="edge1" class="edge">
+<title>kpms_model.VideoRecording-&gt;kpms_model.VideoRecording.File</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M423.25,-188.16C423.25,-174.53 423.25,-155.76 423.25,-144.91"/>
+</g>
+<!-- kpms_model.InferenceTask -->
+<g id="node9" class="node">
+<title>kpms_model.InferenceTask</title>
+<g id="a_node9"><a xlink:title="→ kpms_model.VideoRecording
→ kpms_model.Model
------------------------------
inference_output_dir=&quot;&quot; 
inference_desc=&quot;&quot;    
num_iterations=null  
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="324,-152.48 162.5,-152.48 162.5,-117.92 324,-117.92 324,-152.48"/>
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+</a>
+</g>
+</g>
+<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.InferenceTask -->
+<g id="edge2" class="edge">
+<title>kpms_model.VideoRecording-&gt;kpms_model.InferenceTask</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M379.22,-187.99C351.22,-177.32 315.11,-163.57 287.14,-152.92"/>
+</g>
+<!-- kpms_model.Model -->
+<g id="node2" class="node">
+<title>kpms_model.Model</title>
+<g id="a_node2"><a xlink:title="model_name           
------------------------------
latent_dim           
kappa                
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="303.38,-223.04 183.12,-223.04 183.12,-188.48 303.38,-188.48 303.38,-223.04"/>
+<text text-anchor="start" x="191.12" y="-202.49" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.Model</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.Model&#45;&gt;kpms_model.InferenceTask -->
+<g id="edge3" class="edge">
+<title>kpms_model.Model-&gt;kpms_model.InferenceTask</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M243.25,-188.16C243.25,-177.38 243.25,-163.39 243.25,-152.64"/>
+</g>
+<!-- kpms_model.Inference.GridMoviesSampledInstances -->
+<g id="node3" class="node">
+<title>kpms_model.Inference.GridMoviesSampledInstances</title>
+<g id="a_node3"><a xlink:title="→ kpms_model.Inference
syllable             
------------------------------
instances            
">
+<polygon fill="none" stroke="none" points="248.5,-20 0,-20 0,0 248.5,0 248.5,-20"/>
+<text text-anchor="start" x="8" y="-7.5" font-family="arial" text-decoration="underline" font-size="10.00">kpms_model.Inference.GridMoviesSampledInstances</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.Inference -->
+<g id="node4" class="node">
+<title>kpms_model.Inference</title>
+<g id="a_node4"><a xlink:title="→ kpms_model.InferenceTask
------------------------------
inference_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="243.25" cy="-68.96" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="243.25" y="-64.69" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.Inference</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.GridMoviesSampledInstances -->
+<g id="edge4" class="edge">
+<title>kpms_model.Inference-&gt;kpms_model.Inference.GridMoviesSampledInstances</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M232.27,-62.7C211.9,-52.95 168.06,-31.97 142.94,-19.95"/>
+</g>
+<!-- kpms_model.Inference.MotionSequence -->
+<g id="node7" class="node">
+<title>kpms_model.Inference.MotionSequence</title>
+<g id="a_node7"><a xlink:title="→ kpms_model.Inference
video_name           
------------------------------
syllable             
latent_state         
centroid             
heading              
">
+<polygon fill="none" stroke="none" points="457.62,-20 266.88,-20 266.88,0 457.62,0 457.62,-20"/>
+<text text-anchor="start" x="274.88" y="-7.5" font-family="arial" text-decoration="underline" font-size="10.00">kpms_model.Inference.MotionSequence</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.MotionSequence -->
+<g id="edge5" class="edge">
+<title>kpms_model.Inference-&gt;kpms_model.Inference.MotionSequence</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M254.23,-62.7C274.6,-52.95 318.44,-31.97 343.56,-19.95"/>
+</g>
+<!-- kpms_model.PreFittingTask -->
+<g id="node5" class="node">
+<title>kpms_model.PreFittingTask</title>
+<g id="a_node5"><a xlink:title="→ kpms_pca.PCAFitting
pre_latent_dim       
pre_kappa            
pre_num_iterations   
------------------------------
pre_fitting_desc=&quot;&quot;  
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="654.12,-364.16 490.38,-364.16 490.38,-329.6 654.12,-329.6 654.12,-364.16"/>
+<text text-anchor="start" x="498.38" y="-343.61" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.PreFittingTask</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.PreFitting -->
+<g id="node10" class="node">
+<title>kpms_model.PreFitting</title>
+<g id="a_node10"><a xlink:title="→ kpms_model.PreFittingTask
------------------------------
model_name=&quot;&quot;        
pre_fitting_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="572.25" cy="-276.32" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="572.25" y="-272.05" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.PreFitting</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->
+<g id="edge6" class="edge">
+<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M572.25,-329.28C572.25,-316.92 572.25,-300.33 572.25,-289.25"/>
+</g>
+<!-- kpms_model.FullFittingTask -->
+<g id="node6" class="node">
+<title>kpms_model.FullFittingTask</title>
+<g id="a_node6"><a xlink:title="→ kpms_pca.PCAFitting
full_latent_dim      
full_kappa           
full_num_iterations  
------------------------------
full_fitting_desc=&quot;&quot; 
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="837.88,-364.16 672.62,-364.16 672.62,-329.6 837.88,-329.6 837.88,-364.16"/>
+<text text-anchor="start" x="680.62" y="-343.61" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_model.FullFittingTask</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.FullFitting -->
+<g id="node11" class="node">
+<title>kpms_model.FullFitting</title>
+<g id="a_node11"><a xlink:title="→ kpms_model.FullFittingTask
------------------------------
model_name           
full_fitting_duration=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="755.25" cy="-276.32" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="755.25" y="-272.05" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_model.FullFitting</text>
+</a>
+</g>
+</g>
+<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->
+<g id="edge7" class="edge">
+<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M755.25,-329.28C755.25,-316.92 755.25,-300.33 755.25,-289.25"/>
+</g>
+<!-- kpms_model.InferenceTask&#45;&gt;kpms_model.Inference -->
+<g id="edge8" class="edge">
+<title>kpms_model.InferenceTask-&gt;kpms_model.Inference</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M243.25,-117.66C243.25,-106.36 243.25,-91.66 243.25,-81.58"/>
+</g>
+<!-- subject.Subject -->
+<g id="node12" class="node">
+<title>subject.Subject</title>
+<g id="a_node12"><a xlink:title="subject              
------------------------------
subject_nickname=&quot;&quot;  
sex                  
subject_birth_date   
subject_description=&quot;&quot; 
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="472.12,-364.16 374.38,-364.16 374.38,-329.6 472.12,-329.6 472.12,-364.16"/>
+<text text-anchor="start" x="382.38" y="-343.61" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">subject.Subject</text>
+</a>
+</g>
+</g>
+<!-- session.Session -->
+<g id="node13" class="node">
+<title>session.Session</title>
+<g id="a_node13"><a xlink:title="→ subject.Subject
session_datetime     
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="475.12,-293.6 371.38,-293.6 371.38,-259.04 475.12,-259.04 475.12,-293.6"/>
+<text text-anchor="start" x="379.38" y="-273.05" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">session.Session</text>
+</a>
+</g>
+</g>
+<!-- subject.Subject&#45;&gt;session.Session -->
+<g id="edge9" class="edge">
+<title>subject.Subject-&gt;session.Session</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M423.25,-329.28C423.25,-318.5 423.25,-304.51 423.25,-293.76"/>
+</g>
+<!-- session.Session&#45;&gt;kpms_model.VideoRecording -->
+<g id="edge10" class="edge">
+<title>session.Session-&gt;kpms_model.VideoRecording</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M423.25,-258.72C423.25,-247.94 423.25,-233.95 423.25,-223.2"/>
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+</g>
+</svg>
\ No newline at end of file
diff --git a/images/pipeline_kpms_pca.svg b/images/pipeline_kpms_pca.svg
new file mode 100644
index 0000000..4c8dbc0
--- /dev/null
+++ b/images/pipeline_kpms_pca.svg
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+<polygon fill="white" stroke="none" points="-4,4 -4,-453.28 357.1,-453.28 357.1,4 -4,4"/>
+<!-- kpms_pca.PCAFitting -->
+<g id="node1" class="node">
+<title>kpms_pca.PCAFitting</title>
+<g id="a_node1"><a xlink:title="→ kpms_pca.LoadKeypointSet
------------------------------
pca_fitting_time=null 
">
+<ellipse fill="#ff0000" fill-opacity="0.125490" stroke="#ff0000" stroke-opacity="0.125490" cx="108.47" cy="-83.52" rx="12.96" ry="12.96"/>
+<text text-anchor="middle" x="108.47" y="-79.25" font-family="arial" font-size="12.00" fill="#7f0000" fill-opacity="0.627451">kpms_pca.PCAFitting</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.LatentDimension -->
+<g id="node6" class="node">
+<title>kpms_pca.LatentDimension</title>
+<g id="a_node6"><a xlink:title="→ kpms_pca.PCAFitting
------------------------------
variance_percentage  
latent_dimension     
latent_dim_desc      
">
+<ellipse fill="#00007f" fill-opacity="0.250980" stroke="#00007f" stroke-opacity="0.250980" cx="108.47" cy="-17.28" rx="107.49" ry="17.28"/>
+<text text-anchor="middle" x="108.47" y="-13.01" font-family="arial" font-size="12.00" fill="#00007f" fill-opacity="0.627451">kpms_pca.LatentDimension</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.LatentDimension -->
+<g id="edge1" class="edge">
+<title>kpms_pca.PCAFitting-&gt;kpms_pca.LatentDimension</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M108.47,-70.74C108.47,-60.6 108.47,-45.86 108.47,-34.58"/>
+</g>
+<!-- kpms_pca.LoadKeypointSet -->
+<g id="node2" class="node">
+<title>kpms_pca.LoadKeypointSet</title>
+<g id="a_node2"><a xlink:title="→ kpms_pca.PCATask
------------------------------
coordinates          
confidences          
formatted_bodyparts  
average_frame_rate   
">
+<ellipse fill="#00007f" fill-opacity="0.250980" stroke="#00007f" stroke-opacity="0.250980" cx="108.47" cy="-149.76" rx="108.47" ry="17.28"/>
+<text text-anchor="middle" x="108.47" y="-145.49" font-family="arial" font-size="12.00" fill="#00007f" fill-opacity="0.627451">kpms_pca.LoadKeypointSet</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.LoadKeypointSet&#45;&gt;kpms_pca.PCAFitting -->
+<g id="edge2" class="edge">
+<title>kpms_pca.LoadKeypointSet-&gt;kpms_pca.PCAFitting</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M108.47,-132.22C108.47,-120.92 108.47,-106.22 108.47,-96.14"/>
+</g>
+<!-- kpms_pca.Bodyparts -->
+<g id="node3" class="node">
+<title>kpms_pca.Bodyparts</title>
+<g id="a_node3"><a xlink:title="→ kpms_pca.KeypointSet
bodyparts_id         
------------------------------
bodyparts_desc=&quot;&quot;    
anterior_bodyparts   
posterior_bodyparts  
use_bodyparts        
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="172.35,-308.16 44.6,-308.16 44.6,-273.6 172.35,-273.6 172.35,-308.16"/>
+<text text-anchor="start" x="52.6" y="-287.61" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_pca.Bodyparts</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PCATask -->
+<g id="node4" class="node">
+<title>kpms_pca.PCATask</title>
+<g id="a_node4"><a xlink:title="→ kpms_pca.Bodyparts
------------------------------
kpms_project_output_dir=&quot;&quot; 
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="169.72,-237.6 47.22,-237.6 47.22,-203.04 169.72,-203.04 169.72,-237.6"/>
+<text text-anchor="middle" x="108.47" y="-216.05" font-family="arial" font-size="12.00" fill="darkgreen">kpms_pca.PCATask</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->
+<g id="edge3" class="edge">
+<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M108.47,-273.28C108.47,-262.5 108.47,-248.51 108.47,-237.76"/>
+</g>
+<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.LoadKeypointSet -->
+<g id="edge4" class="edge">
+<title>kpms_pca.PCATask-&gt;kpms_pca.LoadKeypointSet</title>
+<path fill="none" stroke="#000000" stroke-width="2" stroke-opacity="0.250980" d="M108.47,-202.72C108.47,-191.94 108.47,-177.95 108.47,-167.2"/>
+</g>
+<!-- kpms_pca.KeypointSet -->
+<g id="node5" class="node">
+<title>kpms_pca.KeypointSet</title>
+<g id="a_node5"><a xlink:title="kpset_id             
------------------------------
→ kpms_pca.PoseEstimationMethod
kpset_config_dir     
kpset_videos_dir     
kpset_desc=&quot;&quot;        
">
+<polygon fill="#00ff00" fill-opacity="0.188235" stroke="#00ff00" stroke-opacity="0.188235" points="339.97,-378.72 200.97,-378.72 200.97,-344.16 339.97,-344.16 339.97,-378.72"/>
+<text text-anchor="start" x="208.97" y="-358.17" font-family="arial" text-decoration="underline" font-size="12.00" fill="darkgreen">kpms_pca.KeypointSet</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->
+<g id="edge5" class="edge">
+<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M230.84,-343.67C205.64,-333 173.14,-319.25 147.97,-308.6"/>
+</g>
+<!-- kpms_pca.KeypointSet.VideoFile -->
+<g id="node7" class="node">
+<title>kpms_pca.KeypointSet.VideoFile</title>
+<g id="a_node7"><a xlink:title="→ kpms_pca.KeypointSet
video_id             
------------------------------
video_path           
">
+<polygon fill="none" stroke="none" points="350.85,-300.88 190.1,-300.88 190.1,-280.88 350.85,-280.88 350.85,-300.88"/>
+<text text-anchor="start" x="198.1" y="-288.38" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.KeypointSet.VideoFile</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->
+<g id="edge6" class="edge">
+<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-opacity="0.250980" d="M270.47,-343.84C270.47,-330.21 270.47,-311.44 270.47,-300.59"/>
+</g>
+<!-- kpms_pca.PoseEstimationMethod -->
+<g id="node8" class="node">
+<title>kpms_pca.PoseEstimationMethod</title>
+<g id="a_node8"><a xlink:title="format_method        
------------------------------
pose_estimation_desc 
">
+<polygon fill="#000000" fill-opacity="0.125490" stroke="none" points="353.1,-449.28 187.85,-449.28 187.85,-414.72 353.1,-414.72 353.1,-449.28"/>
+<text text-anchor="start" x="195.85" y="-429.5" font-family="arial" text-decoration="underline" font-size="10.00">kpms_pca.PoseEstimationMethod</text>
+</a>
+</g>
+</g>
+<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->
+<g id="edge7" class="edge">
+<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>
+<path fill="none" stroke="#000000" stroke-width="0.75" stroke-dasharray="5,2" stroke-opacity="0.250980" d="M270.47,-414.75C270.47,-403.98 270.47,-389.89 270.47,-379.05"/>
+</g>
+</g>
+</svg>
\ No newline at end of file

From d7743cdcfb4181f2ae005b894f330d8acba39601 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 03:20:47 +0100
Subject: [PATCH 73/90] add `.github`

---
 .github/ISSUE_TEMPLATE/bug_report.md      | 39 ++++++++++++++++
 .github/ISSUE_TEMPLATE/config.yml         |  5 ++
 .github/ISSUE_TEMPLATE/feature_request.md | 57 +++++++++++++++++++++++
 .github/workflows/release.yaml            | 27 +++++++++++
 .github/workflows/test.yaml               | 37 +++++++++++++++
 5 files changed, 165 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md
 create mode 100644 .github/ISSUE_TEMPLATE/config.yml
 create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md
 create mode 100644 .github/workflows/release.yaml
 create mode 100644 .github/workflows/test.yaml

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000..31fe9fc
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,39 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: 'bug'
+assignees: ''
+
+---
+
+## Bug Report
+
+### Description
+
+A clear and concise description of what is the overall operation that is intended to be
+performed that resulted in an error.
+
+### Reproducibility
+Include:
+- OS (WIN | MACOS | Linux)
+- DataJoint Element Version
+- MySQL Version
+- MySQL Deployment Strategy (local-native | local-docker | remote)
+- Minimum number of steps to reliably reproduce the issue
+- Complete error stack as a result of evaluating the above steps
+
+### Expected Behavior
+A clear and concise description of what you expected to happen.
+
+### Screenshots
+If applicable, add screenshots to help explain your problem.
+
+### Additional Research and Context
+Add any additional research or context that was conducted in creating this report.
+
+For example:
+- Related GitHub issues and PR's either within this repository or in other relevant
+  repositories.
+- Specific links to specific lines or a focus within source code.
+- Relevant summary of Maintainers development meetings, milestones, projects, etc.
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000..b3d197d
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+  - name: DataJoint Contribution Guideline
+    url: https://datajoint.com/docs/community/contribute/
+    about: Please make sure to review the DataJoint Contribution Guidelines
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..1f2b784
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,57 @@
+---
+name: Feature request
+about: Suggest an idea for a new feature
+title: ''
+labels: 'enhancement'
+assignees: ''
+
+---
+
+## Feature Request
+
+### Problem
+
+A clear and concise description how this idea has manifested and the context. Elaborate
+on the need for this feature and/or what could be improved. Ex. I'm always frustrated
+when [...]
+
+### Requirements
+
+A clear and concise description of the requirements to satisfy the new feature. Detail
+what you expect from a successful implementation of the feature. Ex. When using this
+feature, it should [...]
+
+### Justification
+
+Provide the key benefits in making this a supported feature. Ex. Adding support for this
+feature would ensure [...]
+
+### Alternative Considerations
+
+Do you currently have a work-around for this? Provide any alternative solutions or
+features you've considered.
+
+### Related Errors
+Add any errors as a direct result of not exposing this feature.
+
+Please include steps to reproduce provided errors as follows:
+- OS (WIN | MACOS | Linux)
+- DataJoint Element Version
+- MySQL Version
+- MySQL Deployment Strategy (local-native | local-docker | remote)
+- Minimum number of steps to reliably reproduce the issue
+- Complete error stack as a result of evaluating the above steps
+
+### Screenshots
+If applicable, add screenshots to help explain your feature.
+
+### Additional Research and Context
+Add any additional research or context that was conducted in creating this feature request.
+
+For example:
+- Related GitHub issues and PR's either within this repository or in other relevant
+  repositories.
+- Specific links to specific lines or a focus within source code.
+- Relevant summary of Maintainers development meetings, milestones, projects, etc.
+- Any additional supplemental web references or links that would further justify this
+  feature request.
diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml
new file mode 100644
index 0000000..4a5f2cb
--- /dev/null
+++ b/.github/workflows/release.yaml
@@ -0,0 +1,27 @@
+name: Release
+on:
+  workflow_dispatch:
+jobs:
+  make_github_release:
+    uses: datajoint/.github/.github/workflows/make_github_release.yaml@main
+  pypi_release:
+    needs: make_github_release
+    uses: datajoint/.github/.github/workflows/pypi_release.yaml@main
+    secrets:
+      TWINE_USERNAME: ${{secrets.TWINE_USERNAME}}
+      TWINE_PASSWORD: ${{secrets.TWINE_PASSWORD}}
+    with:
+      UPLOAD_URL: ${{needs.make_github_release.outputs.release_upload_url}}
+  mkdocs_release:
+    uses: datajoint/.github/.github/workflows/mkdocs_release.yaml@main
+    permissions: 
+      contents: write
+  devcontainer-build:
+    uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main
+  devcontainer-publish:
+    needs: 
+      - devcontainer-build
+    uses: datajoint/.github/.github/workflows/devcontainer-publish.yaml@main
+    secrets:
+      DOCKERHUB_USERNAME: ${{secrets.DOCKERHUB_USERNAME}}
+      DOCKERHUB_TOKEN: ${{secrets.DOCKERHUB_TOKEN_FOR_ELEMENTS}}
\ No newline at end of file
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
new file mode 100644
index 0000000..842b9db
--- /dev/null
+++ b/.github/workflows/test.yaml
@@ -0,0 +1,37 @@
+name: Test
+on:
+  push:
+  pull_request:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 8 * * 1"
+jobs:
+  devcontainer-build:
+    uses: datajoint/.github/.github/workflows/devcontainer-build.yaml@main
+  tests:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        py_ver: ["3.9", "3.10"]
+        mysql_ver: ["8.0", "5.7"]
+        include:
+          - py_ver: "3.8"
+            mysql_ver: "5.7"
+          - py_ver: "3.7"
+            mysql_ver: "5.7"
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{matrix.py_ver}}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{matrix.py_ver}}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8 "black[jupyter]"
+      - name: Run style tests
+        run: |
+          python_version=${{matrix.py_ver}}
+          black element_moseq --check --verbose --target-version py${python_version//.}
+          black notebooks --check --verbose --target-version py${python_version//.}
+

From 24673ec9155608a1dce1c0fe4875388e490a60c2 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 03:25:09 +0100
Subject: [PATCH 74/90] add files for the element

---
 .markdownlint.yaml      |  17 ++++
 .pre-commit-config.yaml |  59 +++++++++++
 CODE_OF_CONDUCT.md      | 132 +++++++++++++++++++++++++
 CONTRIBUTING.md         |   5 +
 LICENSE                 |  21 ++++
 cspell.json             | 210 ++++++++++++++++++++++++++++++++++++++++
 6 files changed, 444 insertions(+)
 create mode 100644 .markdownlint.yaml
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 CODE_OF_CONDUCT.md
 create mode 100644 CONTRIBUTING.md
 create mode 100644 LICENSE
 create mode 100644 cspell.json

diff --git a/.markdownlint.yaml b/.markdownlint.yaml
new file mode 100644
index 0000000..0e9ceeb
--- /dev/null
+++ b/.markdownlint.yaml
@@ -0,0 +1,17 @@
+# Markdown Linter configuration for docs
+# https://github.com/DavidAnson/markdownlint
+# https://github.com/DavidAnson/markdownlint/blob/main/doc/Rules.md
+MD009: false # permit trailing spaces
+MD007: false # List indenting - permit 4 spaces
+MD013:
+    line_length: "88" # Line length limits
+    tables: false # disable for tables
+    headings: false # disable for headings
+MD030: false # Number of spaces after a list
+MD033: # HTML elements allowed
+    allowed_elements:
+        - "figure"
+        - "figcaption"
+MD034: false # Permit bare URLs
+MD031: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling
+MD046: false # Spacing w/code blocks. Conflicts with `??? Note` and code tab styling
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..e991fd6
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,59 @@
+default_stages: [commit, push]
+exclude: (^.github/|^docs/|^images/|^notebooks/|^tests/)
+# Current tests/__init__ violates many flake8. Excluding pending change to conftest
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-added-large-files # prevent giant files from being committed
+      - id: requirements-txt-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+        description: Forces to replace line ending by the UNIX 'lf' character.
+
+  # black
+  - repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+      - id: black
+      - id: black-jupyter
+        args:
+          - --line-length=88
+
+  # isort
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+        description: Sorts imports in an alphabetical order
+
+  # flake8
+  - repo: https://github.com/pycqa/flake8
+    rev: 4.0.1
+    hooks:
+      - id: flake8
+        args: # arguments to configure flake8
+          # making isort line length compatible with black
+          - "--max-line-length=88"
+          - "--max-complexity=18"
+          - "--select=B,C,E,F,W,T4,B9"
+
+          # these are errors that will be ignored by flake8
+          # https://www.flake8rules.com/rules/{code}.html
+          - "--ignore=E203,E501,W503,W605,E402"
+          # E203 - Colons should not have any space before them.
+          #        Needed for list indexing
+          # E501 - Line lengths are recommended to be no greater than 79 characters.
+          #        Needed as we conform to 88
+          # W503 - Line breaks should occur after the binary operator.
+          #        Needed because not compatible with black
+          # W605 - a backslash-character pair that is not a valid escape sequence now
+          #        generates a DeprecationWarning. This will eventually become a SyntaxError.
+          #        Needed because we use \d as an escape sequence
+          # E402 - Place module level import at the top.
+          #        Needed to prevent circular import error
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..0502528
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,132 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+[Support@DataJoint.com](mailto:support@datajoint.com).
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
+
+[homepage]: https://www.contributor-covenant.org
+[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
+[Mozilla CoC]: https://github.com/mozilla/diversity
+[FAQ]: https://www.contributor-covenant.org/faq
+[translations]: https://www.contributor-covenant.org/translations
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..2bd0f49
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,5 @@
+# Contribution Guidelines
+
+This project follows the
+[DataJoint Contribution Guidelines](https://datajoint.com/docs/about/contribute/).
+Please reference the link for more full details.
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..6872305
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 DataJoint
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/cspell.json b/cspell.json
new file mode 100644
index 0000000..325eacf
--- /dev/null
+++ b/cspell.json
@@ -0,0 +1,210 @@
+// cSpell Settings
+//https://github.com/streetsidesoftware/vscode-spell-checker
+{
+    "version": "0.2", // Version of the setting file.  Always 0.2
+    "language": "en", // language - current active spelling language
+    "enabledLanguageIds": [
+        "markdown",
+        "yaml",
+        "python"
+    ],
+    // flagWords - list of words to be always considered incorrect
+    // This is useful for offensive words and common spelling errors.
+    // For example "hte" should be "the"
+    "flagWords": [],
+    "allowCompoundWords": true,
+    "ignorePaths": [
+        "./element_moseq.egg-info/*",
+        "./images/*"
+    ],
+    "words": [
+        "acorr",
+        "aggr",
+        "Alessio",
+        "Andreas",
+        "apmeta",
+        "arange",
+        "arithmatex",
+        "asarray",
+        "astype",
+        "autocorrelogram",
+        "Axona",
+        "bbins",
+        "bdist",
+        "Binarize",
+        "bouton",
+        "Brody",
+        "Bruker",
+        "bshift",
+        "Buccino",
+        "catgt",
+        "cbar",
+        "cbin",
+        "cdat",
+        "chans",
+        "Chans",
+        "chns",
+        "Clust",
+        "clusterings",
+        "cmap",
+        "cnmf",
+        "correlogram",
+        "correlograms",
+        "curations",
+        "DANDI",
+        "decomp",
+        "deconvolution",
+        "DISTRO",
+        "djbase",
+        "dtype",
+        "ecephys",
+        "Eftychios",
+        "electrophysiogical",
+        "elif",
+        "Ephys",
+        "fluo",
+        "fneu",
+        "Fneu",
+        "gblcar",
+        "gfix",
+        "Giovannucci",
+        "Hakan",
+        "hdmf",
+        "HHMI",
+        "hstack",
+        "ibllib",
+        "ifnull",
+        "imax",
+        "Imax",
+        "IMAX",
+        "imec",
+        "imread",
+        "imro",
+        "imrotbl",
+        "imshow",
+        "Inan",
+        "inlinehilite",
+        "iplane",
+        "ipynb",
+        "ipywidgets",
+        "iscell",
+        "Kavli",
+        "kcoords",
+        "Klusta",
+        "Kwik",
+        "lfmeta",
+        "linenums",
+        "masky",
+        "mathjax",
+        "mdict",
+        "Mesoscale",
+        "mesoscope",
+        "mkdocs",
+        "mkdocstrings",
+        "Moser",
+        "mtscomp",
+        "nblocks",
+        "nchan",
+        "Nchan",
+        "nchannels",
+        "ndarray",
+        "ndepths",
+        "ndim",
+        "ndimage",
+        "Neuralynx",
+        "NEURO",
+        "neuroconv",
+        "Neurodata",
+        "Neurolabware",
+        "neuropil",
+        "Neuropil",
+        "Neuropix",
+        "neuropixel",
+        "NeuroPixels",
+        "nfields",
+        "nframes",
+        "npix",
+        "nplanes",
+        "nrois",
+        "NTNU",
+        "nwbfile",
+        "NWBHDF",
+        "oebin",
+        "openephys",
+        "openpyxl",
+        "Pachitariu",
+        "paramsets",
+        "phylog",
+        "plotly",
+        "Pnevmatikakis",
+        "PSTH",
+        "pykilosort",
+        "pymdownx",
+        "pynwb",
+        "pyopenephys",
+        "pyplot",
+        "pytest",
+        "quantile",
+        "Reimer",
+        "repolarization",
+        "Roboto",
+        "roidetect",
+        "rois",
+        "ROIs",
+        "RRID",
+        "Rxiv",
+        "Sasaki",
+        "sbxreader",
+        "scipy",
+        "sdist",
+        "sess",
+        "SGLX",
+        "Shen",
+        "Siegle",
+        "Sitonic",
+        "spikeglx",
+        "spkcount",
+        "spks",
+        "Stereotaxic",
+        "Sutter",
+        "tcat",
+        "tickvals",
+        "tofile",
+        "Tolias",
+        "tqdm",
+        "usecs",
+        "usedb",
+        "Vidrio's",
+        "vline",
+        "vmax",
+        "Vmax",
+        "voxel",
+        "xanchor",
+        "xaxes",
+        "xaxis",
+        "xblock",
+        "xcoords",
+        "xcorr",
+        "xlabel",
+        "xlim",
+        "xoff",
+        "xpix",
+        "XPOS",
+        "xtick",
+        "yanchor",
+        "Yatsenko",
+        "yaxes",
+        "yaxis",
+        "yblock",
+        "ycoord",
+        "ycoords",
+        "ylabel",
+        "ylim",
+        "yoff",
+        "ypix",
+        "YPOS",
+        "yref",
+        "yticks",
+        "zpix"
+    ]
+}
\ No newline at end of file

From d57b52f10830a8f5c35d2166248b457da5201491 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 03:26:29 +0100
Subject: [PATCH 75/90] black formatting README

---
 README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/README.md b/README.md
index 92415cc..53ecddf 100644
--- a/README.md
+++ b/README.md
@@ -77,4 +77,3 @@ MYSQL_VER=8.0 docker compose -f docker-compose-db.yaml up --build -d
 1. We recommend you start by navigating to the `notebooks` directory on the left panel and go through the `tutorial.ipynb` Jupyter notebook. Execute the cells in the notebook to begin your walkthrough of the tutorial.
 
 1. Once you are done, see the options available to you in the menu in the bottom-left corner. For example, in Codespace you will have an option to `Stop Current Codespace` but when running Dev Container on your own machine the equivalent option is `Reopen folder locally`. By default, GitHub will also automatically stop the Codespace after 30 minutes of inactivity.  Once the Codespace is no longer being used, we recommend deleting the Codespace.
-

From 3360062e212cfd40e360462073d3c683de3255cc Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 03:28:18 +0100
Subject: [PATCH 76/90] add CHANGELOG

---
 CHANGELOG.md | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 CHANGELOG.md

diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 0000000..f98f9d6
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,18 @@
+# Changelog
+
+Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and 
+[Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention.
+
+## [0.1.0] - 2024-03-20
+
++ Add - `docs` 
++ Add - `kpms_reader` reader
++ Add - `element_moseq` containing `kpms_pca` and `kpms_model` modules
++ Add -  `flowchart` and `pipeline` images 
++ Add - `tutorial.ipynb` consistent with the DataJoint Elements
++ Add - `tutorial_pipeline.py` script for notebooks to import and activate schemas
++ Add - spelling, markdown, and pre-commit config files
++ Add - GitHub Actions that call reusable workflows in the `datajoint/.github` repository
++ Add - `LICENSE`, `CONTRIBUTING`, `CODE_OF_CONDUCT`, `CHANGELOG`
++ Add - `README` consistent with the DataJoint Elements
++ Add - `setup.py` with `extras_require` feature 

From 90a65ac9e92476af118add89318d6525a7545f50 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 06:40:51 +0100
Subject: [PATCH 77/90] update `tutorial.ipynb`

---
 notebooks/tutorial.ipynb | 3709 +++++++++++++++++++++++---------------
 1 file changed, 2303 insertions(+), 1406 deletions(-)

diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index b18db59..6193d6f 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# DataJoint Element for Motion Sequencing with Keypoint MoSeq\n"
+    "# DataJoint Element for Motion Sequencing with Keypoint-MoSeq\n"
    ]
   },
   {
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The package is designed to seamlessly integrate the **PCA fitting**, **model fitting** through **initialization**, **fitting an AR-HMM**, and **fitting the full model** into a data pipeline and streamline model and video management using DataJoint.\n"
+    "The package is designed to seamlessly integrate the **PCA fitting**, **model fitting** through **initialization**, **fitting an AR-HMM**, and **fitting the full keypoint-SLDS model** into a data pipeline and streamline model and video management using DataJoint.\n"
    ]
   },
   {
@@ -76,10 +76,10 @@
     "- Setup\n",
     "- _Activate_ the DataJoint pipeline\n",
     "- _Insert_ example data into subject and session tables\n",
-    "- _Insert_ the keypoint data from pose estimation and the body parts in the DataJoint pipeline\n",
+    "- _Insert_ the keypoint data from the pose estimation and the body parts in the DataJoint pipeline\n",
     "- _Fit a PCA model_ to aligned and centered keypoint coordinates and _select_ the latent dimension\n",
-    "- _Fit the AR-HMM and the full model_\n",
-    "- _Generate and store_ the model results and visualizations\n"
+    "- _Fit the AR-HMM and Keypoint-SLDS Models_\n",
+    "- _Run the inference_ task and _visualize_ the results\n"
    ]
   },
   {
@@ -93,11 +93,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This tutorial loads the keypoint data extracted by DeepLabCut of a single freely moving mouse in an open-field environment. The goal is to link this point tracking to pose dynamics by identifying its behavioral modules (\"syllables\") without human supervision. The modeling results are stored as a `.h5` file (and optionally a directory of `.csv` files) that contain the following information:\n",
+    "This tutorial loads the keypoint data extracted by DeepLabCut of a single freely moving mouse in an open-field environment. The goal is to link this point tracking to pose dynamics by identifying its behavioral modules (\"syllables\") without human supervision. The modeling results are stored as a `.h5` file and a subdirectory of `.csv` files that contain the following information:\n",
     "\n",
     "- Behavior modules as \"syllables\": the syllable label assigned to each frame (i.e. the state indexes assigned by the model)\n",
     "- Centroid and heading in each frame, as estimated by the model, that capture the animal's overall position in allocentric coordinates\n",
-    "- Latent state: low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.\n",
+    "- Latent state: low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, and are modified to reflect the pose dynamics and noise estimates inferred by the model.\n",
     "\n",
     "The results of this Element example can be combined with **other modalities** to create a complete customizable data pipeline for your specific lab or study. For instance, you can combine `element-moseq` with `element-deeplabcut` and `element-calcium-imaging` to characterize the neural activity along with natural sub-second rhythmicity in mouse movement.\n",
     "\n",
@@ -105,9 +105,10 @@
     "\n",
     "The input data for this data pipeline is as follows:\n",
     "\n",
-    "- A DeepLabCut (DLC) project folder with its config file, video set and keypoint data.\n",
-    "- Selection of the anterior, posterior, and use bodyparts.\n",
-    "  This tutorial includes this DLC project folder with example data and the results as well in `example_data` directory.\n"
+    "- A DeepLabCut (DLC) project folder with its configuration file as `.yaml` file, video set as `.mp4`, and keypoint tracking as `.h5` files.\n",
+    "- Selection of the anterior, posterior, and use bodyparts for the model fitting.\n",
+    "\n",
+    "This tutorial includes the keypoints example data in `example_data/inbox/dlc_project`.\n"
    ]
   },
   {
@@ -137,7 +138,10 @@
    "source": [
     "import datajoint as dj\n",
     "from pathlib import Path\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "\n",
+    "from element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir \n",
+    "from element_interface.utils import find_full_path"
    ]
   },
   {
@@ -158,8 +162,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-03-13 17:32:11,291][INFO]: Connecting root@localhost:3306\n",
-      "[2024-03-13 17:32:11,319][INFO]: Connected root@localhost:3306\n"
+      "[2024-03-20 05:59:24,965][INFO]: Connecting root@localhost:3306\n",
+      "[2024-03-20 05:59:25,009][INFO]: Connected root@localhost:3306\n"
      ]
     },
     {
@@ -195,14 +199,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[2024-03-13 17:32:11,328][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
+      "[2024-03-20 05:59:41,609][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n"
      ]
     },
     {
@@ -249,12 +253,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='94587c3b-f52d-44fc-b1d8-cf67301da0d3'>\n",
-       "  <div id=\"dcf6da07-a10e-4ca8-a6f9-885f15327ef1\" data-root-id=\"94587c3b-f52d-44fc-b1d8-cf67301da0d3\" style=\"display: contents;\"></div>\n",
+       "<div id='ce094051-92c7-46b7-8716-4784ac4589f1'>\n",
+       "  <div id=\"da2d14ad-2d93-4177-8632-fd2cf3c46e66\" data-root-id=\"ce094051-92c7-46b7-8716-4784ac4589f1\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"cae30a99-7be2-45f9-a2fd-1679dc2bd9a3\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"94587c3b-f52d-44fc-b1d8-cf67301da0d3\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"8f934d16-f434-4f7d-9880-8753052d73a0\",\"attributes\":{\"plot_id\":\"94587c3b-f52d-44fc-b1d8-cf67301da0d3\",\"comm_id\":\"d845f0d0ef3744609f7ce545be5e1b1e\",\"client_comm_id\":\"8d1d532a7a364a669a9f604990c8ed61\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"cae30a99-7be2-45f9-a2fd-1679dc2bd9a3\",\"roots\":{\"94587c3b-f52d-44fc-b1d8-cf67301da0d3\":\"dcf6da07-a10e-4ca8-a6f9-885f15327ef1\"},\"root_ids\":[\"94587c3b-f52d-44fc-b1d8-cf67301da0d3\"]}];\n",
+       "  var docs_json = {\"9104f357-dc6d-4dca-99b4-9306adec1f0d\":{\"version\":\"3.3.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"ce094051-92c7-46b7-8716-4784ac4589f1\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"352c6a6d-6fc5-47f2-9fb6-58954a755eb1\",\"attributes\":{\"plot_id\":\"ce094051-92c7-46b7-8716-4784ac4589f1\",\"comm_id\":\"ac065597e8b74a4c9c0e45ba31a3f907\",\"client_comm_id\":\"82529fc5a36948c6a5b8e3ed037951c2\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"9104f357-dc6d-4dca-99b4-9306adec1f0d\",\"roots\":{\"ce094051-92c7-46b7-8716-4784ac4589f1\":\"da2d14ad-2d93-4177-8632-fd2cf3c46e66\"},\"root_ids\":[\"ce094051-92c7-46b7-8716-4784ac4589f1\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -318,7 +322,7 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "94587c3b-f52d-44fc-b1d8-cf67301da0d3"
+       "id": "ce094051-92c7-46b7-8716-4784ac4589f1"
       }
      },
      "output_type": "display_data"
@@ -337,242 +341,312 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
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-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-647.68 600.82,-647.68 600.82,4 -4,4\"/>\n",
-       "<!-- kpms_pca.PCAFitting -->\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"857pt\" height=\"590pt\" viewBox=\"0.00 0.00 857.12 589.76\">\n",
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+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-585.76 853.12,-585.76 853.12,4 -4,4\"/>\n",
+       "<!-- kpms_model.Model -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>kpms_pca.PCAFitting</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-207.36\" rx=\"12.96\" ry=\"12.96\"/>\n",
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+       "<title>kpms_model.Model</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"model_name           \r------------------------------\rlatent_dim           \rkappa                \r\">\n",
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+       "<text text-anchor=\"start\" x=\"419.75\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.Model</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.DimsExplainedVariance -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>kpms_pca.DimsExplainedVariance</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"118.62\" cy=\"-141.12\" rx=\"12.96\" ry=\"12.96\"/>\n",
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+       "<!-- kpms_model.InferenceTask -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>kpms_model.InferenceTask</title>\n",
+       "<g id=\"a_node21\"><a xlink:title=\"→ kpms_model.VideoRecording\r→ kpms_model.Model\r------------------------------\rinference_output_dir=&quot;&quot; \rinference_desc=&quot;&quot;    \rnum_iterations=null  \r\">\n",
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+       "<text text-anchor=\"middle\" x=\"471.88\" y=\"-348.53\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.InferenceTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<!-- kpms_model.Model&#45;&gt;kpms_model.InferenceTask -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M308.89,-202.63C273.27,-191.3 166.64,-157.39 130.64,-145.94\"/>\n",
+       "<title>kpms_model.Model-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M471.88,-405.76C471.88,-394.98 471.88,-380.99 471.88,-370.24\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFittingTask -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
-       "<title>kpms_model.FullFittingTask</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"403.25,-158.4 238,-158.4 238,-123.84 403.25,-123.84 403.25,-158.4\"/>\n",
-       "<text text-anchor=\"start\" x=\"246\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "<!-- kpms_pca.LoadKeypointSet -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_pca.LoadKeypointSet</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \raverage_frame_rate   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"264.88\" cy=\"-211.68\" rx=\"108.47\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"264.88\" y=\"-207.41\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.LoadKeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
+       "<!-- kpms_pca.PCAFitting -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_pca.PCAFitting</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.LoadKeypointSet\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"264.88\" cy=\"-145.44\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"264.88\" y=\"-141.16\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.LoadKeypointSet&#45;&gt;kpms_pca.PCAFitting -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M320.62,-194.58C320.62,-184.44 320.62,-169.7 320.62,-158.42\"/>\n",
+       "<title>kpms_pca.LoadKeypointSet-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M264.88,-194.14C264.88,-182.84 264.88,-168.14 264.88,-158.06\"/>\n",
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-       "<!-- kpms_model.PreFittingTask -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>kpms_model.PreFittingTask</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"585.5,-158.4 421.75,-158.4 421.75,-123.84 585.5,-123.84 585.5,-158.4\"/>\n",
-       "<text text-anchor=\"start\" x=\"429.75\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"405.5,-511.2 240.25,-511.2 240.25,-476.64 405.5,-476.64 405.5,-511.2\"/>\n",
+       "<text text-anchor=\"start\" x=\"248.25\" y=\"-491.42\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
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-       "</g>\n",
        "<!-- kpms_pca.KeypointSet -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
+       "<g id=\"node4\" class=\"node\">\n",
        "<title>kpms_pca.KeypointSet</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
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-       "<text text-anchor=\"start\" x=\"97.12\" y=\"-482.01\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"kpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"187.38,-440.64 48.38,-440.64 48.38,-406.08 187.38,-406.08 187.38,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"56.38\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M274.29,-476.67C242.21,-465.94 200.27,-451.91 167.89,-441.09\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>kpms_model.VideoRecording</title>\n",
+       "<g id=\"a_node19\"><a xlink:title=\"→ session.Session\r→ kpms_pca.PoseEstimationMethod\rrecording_id         \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"738.62,-440.64 565.12,-440.64 565.12,-406.08 738.62,-406.08 738.62,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"573.12\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.VideoRecording</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_model.VideoRecording -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_model.VideoRecording</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M400.84,-476.67C452.34,-465.94 519.65,-451.91 571.61,-441.09\"/>\n",
+       "</g>\n",
        "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
        "<g id=\"node9\" class=\"node\">\n",
        "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
        "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"239,-424.72 78.25,-424.72 78.25,-404.72 239,-404.72 239,-424.72\"/>\n",
-       "<text text-anchor=\"start\" x=\"86.25\" y=\"-412.22\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"198.25,-362.8 37.5,-362.8 37.5,-342.8 198.25,-342.8 198.25,-362.8\"/>\n",
+       "<text text-anchor=\"start\" x=\"45.5\" y=\"-350.3\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
        "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M158.62,-467.68C158.62,-454.05 158.62,-435.28 158.62,-424.43\"/>\n",
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        "</g>\n",
        "<!-- kpms_pca.Bodyparts -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>kpms_pca.Bodyparts</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"384.5,-432 256.75,-432 256.75,-397.44 384.5,-397.44 384.5,-432\"/>\n",
-       "<text text-anchor=\"start\" x=\"264.75\" y=\"-411.45\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
+       "<g id=\"a_node15\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"343.75,-370.08 216,-370.08 216,-335.52 343.75,-335.52 343.75,-370.08\"/>\n",
+       "<text text-anchor=\"start\" x=\"224\" y=\"-349.53\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
        "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M198.25,-467.51C223.45,-456.84 255.95,-443.09 281.12,-432.44\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M157.5,-405.59C182.7,-394.92 215.2,-381.17 240.37,-370.52\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.RecordingInfo -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
-       "<title>kpms_pca.RecordingInfo</title>\n",
-       "<g id=\"a_node15\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_duration_average \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"499.62\" cy=\"-414.72\" rx=\"97.2\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"499.62\" y=\"-410.45\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"163.75,-96.48 0,-96.48 0,-61.92 163.75,-61.92 163.75,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M228.45,-470.24C289.74,-457.92 378.05,-440.16 437.06,-428.3\"/>\n",
+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"81.88\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"81.88\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFitting -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>kpms_model.FullFitting</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M81.88,-61.66C81.88,-50.36 81.88,-35.66 81.88,-25.58\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M253.15,-140.32C228.69,-131.74 171.06,-111.51 129.4,-96.88\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node16\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"347.5,-96.48 182.25,-96.48 182.25,-61.92 347.5,-61.92 347.5,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"190.25\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_model.GenerateResults -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>kpms_model.GenerateResults</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"320.62\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M264.88,-132.66C264.88,-122.52 264.88,-107.78 264.88,-96.5\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.LatentDimension -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>kpms_pca.LatentDimension</title>\n",
+       "<g id=\"a_node20\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rlatent_dimension     \rlatent_dim_desc      \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"472.88\" cy=\"-79.2\" rx=\"107.49\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"472.88\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.LatentDimension</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-62.04C320.62,-51.54 320.62,-36.27 320.62,-25.77\"/>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.LatentDimension -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.LatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M276.65,-140.8C304.58,-132.18 376.12,-110.08 424.65,-95.09\"/>\n",
        "</g>\n",
        "<!-- subject.Subject -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>subject.Subject</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"284.5,-643.68 186.75,-643.68 186.75,-609.12 284.5,-609.12 284.5,-643.68\"/>\n",
-       "<text text-anchor=\"start\" x=\"194.75\" y=\"-623.13\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"subject              \r------------------------------\rsubject_nickname=&quot;&quot;  \rsex                  \rsubject_birth_date   \rsubject_description=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"700.75,-581.76 603,-581.76 603,-547.2 700.75,-547.2 700.75,-581.76\"/>\n",
+       "<text text-anchor=\"start\" x=\"611\" y=\"-561.21\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">subject.Subject</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- session.Session -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node17\" class=\"node\">\n",
        "<title>session.Session</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"287.5,-573.12 183.75,-573.12 183.75,-538.56 287.5,-538.56 287.5,-573.12\"/>\n",
-       "<text text-anchor=\"start\" x=\"191.75\" y=\"-552.57\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
+       "<g id=\"a_node17\"><a xlink:title=\"→ subject.Subject\rsession_datetime     \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"703.75,-511.2 600,-511.2 600,-476.64 703.75,-476.64 703.75,-511.2\"/>\n",
+       "<text text-anchor=\"start\" x=\"608\" y=\"-490.65\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">session.Session</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
        "<!-- subject.Subject&#45;&gt;session.Session -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
        "<title>subject.Subject-&gt;session.Session</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M235.62,-608.8C235.62,-598.02 235.62,-584.03 235.62,-573.28\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M651.88,-546.88C651.88,-536.1 651.88,-522.11 651.88,-511.36\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kpms_pca.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"165.25,-573.12 0,-573.12 0,-538.56 165.25,-538.56 165.25,-573.12\"/>\n",
-       "<text text-anchor=\"start\" x=\"8\" y=\"-553.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
+       "<!-- kpms_model.Inference -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_model.Inference</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_model.InferenceTask\r------------------------------\rinference_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"486.88\" cy=\"-282.24\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"486.88\" y=\"-277.97\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.Inference</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M100.64,-538.59C112.58,-527.82 128.2,-513.73 140.22,-502.89\"/>\n",
+       "<!-- kpms_model.Inference.GridMoviesSampledInstances -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_model.Inference\rsyllable             \r------------------------------\rinstances            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"849.12,-221.68 600.62,-221.68 600.62,-201.68 849.12,-201.68 849.12,-221.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"608.62\" y=\"-209.18\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.GridMoviesSampledInstances</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- kpms_pca.FormattedDataset -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>kpms_pca.FormattedDataset</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"320.62\" cy=\"-273.6\" rx=\"110.43\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-269.33\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.GridMoviesSampledInstances -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M499,-277.75C534.81,-267.43 640.86,-236.88 693.92,-221.6\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference.MotionSequence -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>kpms_model.Inference.MotionSequence</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_model.Inference\rvideo_name           \r------------------------------\rsyllable             \rlatent_state         \rcentroid             \rheading              \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"582.25,-221.68 391.5,-221.68 391.5,-201.68 582.25,-201.68 582.25,-221.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"399.5\" y=\"-209.18\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.MotionSequence</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-256.06C320.62,-244.76 320.62,-230.06 320.62,-219.98\"/>\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.MotionSequence -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.MotionSequence</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M486.88,-269.3C486.88,-255.64 486.88,-233.6 486.88,-221.44\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rmodel_name           \rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"264.88\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"264.88\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording.File -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>kpms_model.VideoRecording.File</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"733,-362.8 570.75,-362.8 570.75,-342.8 733,-342.8 733,-362.8\"/>\n",
+       "<text text-anchor=\"start\" x=\"578.75\" y=\"-350.3\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.VideoRecording.File</text>\n",
+       "</a>\n",
        "</g>\n",
-       "<!-- session.Session&#45;&gt;kpms_pca.KeypointSet -->\n",
-       "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>session.Session-&gt;kpms_pca.KeypointSet</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M216.99,-538.24C204.97,-527.54 189.4,-513.68 177.36,-502.96\"/>\n",
        "</g>\n",
        "<!-- kpms_pca.PCATask -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
+       "<g id=\"node18\" class=\"node\">\n",
        "<title>kpms_pca.PCATask</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"381.88,-361.44 259.38,-361.44 259.38,-326.88 381.88,-326.88 381.88,-361.44\"/>\n",
-       "<text text-anchor=\"middle\" x=\"320.62\" y=\"-339.89\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "<g id=\"a_node18\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\rkpms_project_output_dir=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"334.12,-299.52 211.62,-299.52 211.62,-264.96 334.12,-264.96 334.12,-299.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"272.88\" y=\"-277.97\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-326.56C320.62,-315.78 320.62,-301.79 320.62,-291.04\"/>\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M278.18,-335.2C277.08,-324.42 275.65,-310.43 274.55,-299.68\"/>\n",
        "</g>\n",
        "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
        "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-123.58C320.62,-112.28 320.62,-97.58 320.62,-87.5\"/>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M264.88,-61.66C264.88,-50.36 264.88,-35.66 264.88,-25.58\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.PreFitting -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
-       "<title>kpms_model.PreFitting</title>\n",
-       "<g id=\"a_node16\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"503.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"503.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
-       "</a>\n",
+       "<!-- session.Session&#45;&gt;kpms_model.VideoRecording -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>session.Session-&gt;kpms_model.VideoRecording</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M651.88,-476.32C651.88,-465.54 651.88,-451.55 651.88,-440.8\"/>\n",
        "</g>\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.LoadKeypointSet -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.LoadKeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M270.94,-264.64C269.68,-253.86 268.05,-239.87 266.79,-229.12\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
-       "<g id=\"edge14\" class=\"edge\">\n",
-       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M503.62,-123.58C503.62,-112.28 503.62,-97.58 503.62,-87.5\"/>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.VideoRecording.File -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.VideoRecording.File</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M651.88,-405.76C651.88,-392.13 651.88,-373.36 651.88,-362.51\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M320.62,-397.12C320.62,-386.34 320.62,-372.35 320.62,-361.6\"/>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.InferenceTask -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M607.84,-405.59C579.84,-394.92 543.73,-381.17 515.76,-370.52\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.InferenceTask&#45;&gt;kpms_model.Inference -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>kpms_model.InferenceTask-&gt;kpms_model.Inference</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M475.51,-335.2C478.25,-322.68 481.93,-305.82 484.36,-294.75\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x14543da60>"
+       "<datajoint.diagram.Diagram at 0x111e19280>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,214 +669,284 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "image/svg+xml": [
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-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-577.12 571.88,-577.12 571.88,4 -4,4\"/>\n",
-       "<!-- kpms_pca.PCAFitting -->\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1060pt\" height=\"519pt\" viewBox=\"0.00 0.00 1059.87 519.20\">\n",
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+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-515.2 1055.87,-515.2 1055.87,4 -4,4\"/>\n",
+       "<!-- kpms_model.Model -->\n",
        "<g id=\"node1\" class=\"node\">\n",
-       "<title>kpms_pca.PCAFitting</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_pca.FormattedDataset\r------------------------------\rpca_fitting_time=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-207.36\" rx=\"12.96\" ry=\"12.96\"/>\n",
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+       "<title>kpms_model.Model</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"model_name           \r------------------------------\rlatent_dim           \rkappa                \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"274.5,-440.64 154.25,-440.64 154.25,-406.08 274.5,-406.08 274.5,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"162.25\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.Model</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.DimsExplainedVariance -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
-       "<title>kpms_pca.DimsExplainedVariance</title>\n",
-       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rdims_explained_variance \rlatent_dim_desc      \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"101\" cy=\"-141.12\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101\" y=\"-136.84\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.DimsExplainedVariance</text>\n",
+       "<!-- kpms_model.InferenceTask -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>kpms_model.InferenceTask</title>\n",
+       "<g id=\"a_node19\"><a xlink:title=\"→ kpms_model.VideoRecording\r→ kpms_model.Model\r------------------------------\rinference_output_dir=&quot;&quot; \rinference_desc=&quot;&quot;    \rnum_iterations=null  \r\">\n",
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+       "<text text-anchor=\"middle\" x=\"214.38\" y=\"-348.53\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.InferenceTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.DimsExplainedVariance -->\n",
+       "<!-- kpms_model.Model&#45;&gt;kpms_model.InferenceTask -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.DimsExplainedVariance</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M291.27,-202.63C255.65,-191.3 149.02,-157.39 113.02,-145.94\"/>\n",
+       "<title>kpms_model.Model-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M214.38,-405.76C214.38,-394.98 214.38,-380.99 214.38,-370.24\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFittingTask -->\n",
-       "<g id=\"node10\" class=\"node\">\n",
-       "<title>kpms_model.FullFittingTask</title>\n",
-       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"385.62,-158.4 220.38,-158.4 220.38,-123.84 385.62,-123.84 385.62,-158.4\"/>\n",
-       "<text text-anchor=\"start\" x=\"228.38\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "<!-- kpms_pca.LoadKeypointSet -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_pca.LoadKeypointSet</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \raverage_frame_rate   \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"736.38\" cy=\"-211.68\" rx=\"108.47\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"736.38\" y=\"-207.41\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.LoadKeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
+       "<!-- kpms_pca.PCAFitting -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_pca.PCAFitting</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.LoadKeypointSet\r------------------------------\rpca_fitting_time=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"736.38\" cy=\"-145.44\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"736.38\" y=\"-141.16\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_pca.PCAFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.LoadKeypointSet&#45;&gt;kpms_pca.PCAFitting -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M303,-194.58C303,-184.44 303,-169.7 303,-158.42\"/>\n",
+       "<title>kpms_pca.LoadKeypointSet-&gt;kpms_pca.PCAFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M736.38,-194.14C736.38,-182.84 736.38,-168.14 736.38,-158.06\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.PreFittingTask -->\n",
-       "<g id=\"node11\" class=\"node\">\n",
-       "<title>kpms_model.PreFittingTask</title>\n",
-       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"567.88,-158.4 404.12,-158.4 404.12,-123.84 567.88,-123.84 567.88,-158.4\"/>\n",
-       "<text text-anchor=\"start\" x=\"412.12\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_pca.PoseEstimationMethod</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
+       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"567,-511.2 401.75,-511.2 401.75,-476.64 567,-476.64 567,-511.2\"/>\n",
+       "<text text-anchor=\"start\" x=\"409.75\" y=\"-491.42\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
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        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M314.73,-202.24C339.18,-193.66 396.81,-173.43 438.48,-158.8\"/>\n",
-       "</g>\n",
        "<!-- kpms_pca.KeypointSet -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
+       "<g id=\"node4\" class=\"node\">\n",
        "<title>kpms_pca.KeypointSet</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ session.Session\rkpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"534.5,-502.56 395.5,-502.56 395.5,-468 534.5,-468 534.5,-502.56\"/>\n",
-       "<text text-anchor=\"start\" x=\"403.5\" y=\"-482.01\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"kpset_id             \r------------------------------\r→ kpms_pca.PoseEstimationMethod\rkpset_config_dir     \rkpset_videos_dir     \rkpset_desc=&quot;&quot;        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"643.88,-440.64 504.88,-440.64 504.88,-406.08 643.88,-406.08 643.88,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"512.88\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.KeypointSet</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.RecordingInfo -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>kpms_pca.RecordingInfo</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.KeypointSet\r------------------------------\rpx_height_average    \rpx_width_average     \rnframes_average      \rfps_average=null     \rrecording_duration_average \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"124\" cy=\"-414.72\" rx=\"97.2\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"124\" y=\"-410.45\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.RecordingInfo</text>\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M505.7,-476.67C519.84,-465.9 538.34,-451.81 552.57,-440.97\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>kpms_model.VideoRecording</title>\n",
+       "<g id=\"a_node17\"><a xlink:title=\"→ session.Session\r→ kpms_pca.PoseEstimationMethod\rrecording_id         \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"481.12,-440.64 307.62,-440.64 307.62,-406.08 481.12,-406.08 481.12,-440.64\"/>\n",
+       "<text text-anchor=\"start\" x=\"315.62\" y=\"-420.09\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.VideoRecording</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.RecordingInfo -->\n",
+       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_model.VideoRecording -->\n",
        "<g id=\"edge4\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.RecordingInfo</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M395.17,-470.24C333.89,-457.92 245.58,-440.16 186.56,-428.3\"/>\n",
+       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_model.VideoRecording</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M463.05,-476.67C448.91,-465.9 430.41,-451.81 416.18,-440.97\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.Bodyparts -->\n",
+       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
        "<g id=\"node8\" class=\"node\">\n",
-       "<title>kpms_pca.Bodyparts</title>\n",
-       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
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-       "<text text-anchor=\"start\" x=\"247.12\" y=\"-411.45\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
+       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"654.75,-362.8 494,-362.8 494,-342.8 654.75,-342.8 654.75,-362.8\"/>\n",
+       "<text text-anchor=\"start\" x=\"502\" y=\"-350.3\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
        "<g id=\"edge5\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M425.37,-467.51C400.17,-456.84 367.67,-443.09 342.5,-432.44\"/>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M574.38,-405.76C574.38,-392.13 574.38,-373.36 574.38,-362.51\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet.VideoFile -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
-       "<title>kpms_pca.KeypointSet.VideoFile</title>\n",
-       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_pca.KeypointSet\rvideo_id             \r------------------------------\rvideo_path           \r\">\n",
-       "<polygon fill=\"none\" stroke=\"none\" points=\"545.38,-424.72 384.62,-424.72 384.62,-404.72 545.38,-404.72 545.38,-424.72\"/>\n",
-       "<text text-anchor=\"start\" x=\"392.62\" y=\"-412.22\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.KeypointSet.VideoFile</text>\n",
+       "<!-- kpms_pca.Bodyparts -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>kpms_pca.Bodyparts</title>\n",
+       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_pca.KeypointSet\rbodyparts_id         \r------------------------------\rbodyparts_desc=&quot;&quot;    \ranterior_bodyparts   \rposterior_bodyparts  \ruse_bodyparts        \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"800.25,-370.08 672.5,-370.08 672.5,-335.52 800.25,-335.52 800.25,-370.08\"/>\n",
+       "<text text-anchor=\"start\" x=\"680.5\" y=\"-349.53\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.Bodyparts</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.KeypointSet.VideoFile -->\n",
+       "<!-- kpms_pca.KeypointSet&#45;&gt;kpms_pca.Bodyparts -->\n",
        "<g id=\"edge6\" class=\"edge\">\n",
-       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.KeypointSet.VideoFile</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M465,-467.68C465,-454.05 465,-435.28 465,-424.43\"/>\n",
+       "<title>kpms_pca.KeypointSet-&gt;kpms_pca.Bodyparts</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M614,-405.59C639.2,-394.92 671.7,-381.17 696.87,-370.52\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFitting -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>kpms_model.FullFitting</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"303\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"635.25,-96.48 471.5,-96.48 471.5,-61.92 635.25,-61.92 635.25,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"479.5\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_model.GenerateResults -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>kpms_model.GenerateResults</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"303\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"303\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"553.38\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"553.38\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
        "<g id=\"edge7\" class=\"edge\">\n",
-       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-62.04C303,-51.54 303,-36.27 303,-25.77\"/>\n",
-       "</g>\n",
-       "<!-- kpms_pca.FormattedDataset -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
-       "<title>kpms_pca.FormattedDataset</title>\n",
-       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_pca.PCATask\r------------------------------\rcoordinates          \rconfidences          \rformatted_bodyparts  \r\">\n",
-       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"303\" cy=\"-273.6\" rx=\"110.43\" ry=\"17.28\"/>\n",
-       "<text text-anchor=\"middle\" x=\"303\" y=\"-269.33\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.FormattedDataset</text>\n",
-       "</a>\n",
-       "</g>\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M553.38,-61.66C553.38,-50.36 553.38,-35.66 553.38,-25.58\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.FormattedDataset&#45;&gt;kpms_pca.PCAFitting -->\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.PreFittingTask -->\n",
        "<g id=\"edge8\" class=\"edge\">\n",
-       "<title>kpms_pca.FormattedDataset-&gt;kpms_pca.PCAFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-256.06C303,-244.76 303,-230.06 303,-219.98\"/>\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.PreFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M724.65,-140.32C700.19,-131.74 642.56,-111.51 600.9,-96.88\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
-       "<title>kpms_pca.PCATask</title>\n",
-       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\routput_dir=&quot;&quot;        \rtask_mode=&quot;load&quot;     \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"364.25,-361.44 241.75,-361.44 241.75,-326.88 364.25,-326.88 364.25,-361.44\"/>\n",
-       "<text text-anchor=\"middle\" x=\"303\" y=\"-339.89\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node15\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"819,-96.48 653.75,-96.48 653.75,-61.92 819,-61.92 819,-96.48\"/>\n",
+       "<text text-anchor=\"start\" x=\"661.75\" y=\"-75.92\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.FormattedDataset -->\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_model.FullFittingTask -->\n",
        "<g id=\"edge9\" class=\"edge\">\n",
-       "<title>kpms_pca.PCATask-&gt;kpms_pca.FormattedDataset</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-326.56C303,-315.78 303,-301.79 303,-291.04\"/>\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_model.FullFittingTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M736.38,-132.66C736.38,-122.52 736.38,-107.78 736.38,-96.5\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
+       "<!-- kpms_pca.LatentDimension -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>kpms_pca.LatentDimension</title>\n",
+       "<g id=\"a_node18\"><a xlink:title=\"→ kpms_pca.PCAFitting\r------------------------------\rvariance_percentage  \rlatent_dimension     \rlatent_dim_desc      \r\">\n",
+       "<ellipse fill=\"#00007f\" fill-opacity=\"0.250980\" stroke=\"#00007f\" stroke-opacity=\"0.250980\" cx=\"944.38\" cy=\"-79.2\" rx=\"107.49\" ry=\"17.28\"/>\n",
+       "<text text-anchor=\"middle\" x=\"944.38\" y=\"-74.92\" font-family=\"arial\" font-size=\"12.00\" fill=\"#00007f\" fill-opacity=\"0.627451\">kpms_pca.LatentDimension</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCAFitting&#45;&gt;kpms_pca.LatentDimension -->\n",
        "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-397.12C303,-386.34 303,-372.35 303,-361.6\"/>\n",
+       "<title>kpms_pca.PCAFitting-&gt;kpms_pca.LatentDimension</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M748.15,-140.8C776.08,-132.18 847.62,-110.08 896.15,-95.09\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<!-- kpms_model.Inference -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>kpms_model.Inference</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_model.InferenceTask\r------------------------------\rinference_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"214.38\" cy=\"-282.24\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"214.38\" y=\"-277.97\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.Inference</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference.GridMoviesSampledInstances -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ kpms_model.Inference\rsyllable             \r------------------------------\rinstances            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"457.62,-221.68 209.12,-221.68 209.12,-201.68 457.62,-201.68 457.62,-221.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"217.12\" y=\"-209.18\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.GridMoviesSampledInstances</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.GridMoviesSampledInstances -->\n",
        "<g id=\"edge11\" class=\"edge\">\n",
-       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M303,-123.58C303,-112.28 303,-97.58 303,-87.5\"/>\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M224.94,-275.16C245.98,-263.03 293.24,-235.8 317.84,-221.63\"/>\n",
        "</g>\n",
-       "<!-- kpms_model.PreFitting -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
-       "<title>kpms_model.PreFitting</title>\n",
-       "<g id=\"a_node14\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"486\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"486\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "<!-- kpms_model.Inference.MotionSequence -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>kpms_model.Inference.MotionSequence</title>\n",
+       "<g id=\"a_node13\"><a xlink:title=\"→ kpms_model.Inference\rvideo_name           \r------------------------------\rsyllable             \rlatent_state         \rcentroid             \rheading              \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"190.75,-221.68 0,-221.68 0,-201.68 190.75,-201.68 190.75,-221.68\"/>\n",
+       "<text text-anchor=\"start\" x=\"8\" y=\"-209.18\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.MotionSequence</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.MotionSequence -->\n",
        "<g id=\"edge12\" class=\"edge\">\n",
-       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M486,-123.58C486,-112.28 486,-97.58 486,-87.5\"/>\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.MotionSequence</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M203.81,-275.16C182.77,-263.03 135.51,-235.8 110.91,-221.63\"/>\n",
        "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod -->\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rmodel_name           \rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"736.38\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"736.38\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording.File -->\n",
        "<g id=\"node12\" class=\"node\">\n",
-       "<title>kpms_pca.PoseEstimationMethod</title>\n",
-       "<g id=\"a_node12\"><a xlink:title=\"format_method        \r------------------------------\rpose_estimation_desc \r\">\n",
-       "<polygon fill=\"#000000\" fill-opacity=\"0.125490\" stroke=\"none\" points=\"547.62,-573.12 382.38,-573.12 382.38,-538.56 547.62,-538.56 547.62,-573.12\"/>\n",
-       "<text text-anchor=\"start\" x=\"390.38\" y=\"-553.34\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_pca.PoseEstimationMethod</text>\n",
+       "<title>kpms_model.VideoRecording.File</title>\n",
+       "<g id=\"a_node12\"><a xlink:title=\"→ kpms_model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"475.5,-362.8 313.25,-362.8 313.25,-342.8 475.5,-342.8 475.5,-362.8\"/>\n",
+       "<text text-anchor=\"start\" x=\"321.25\" y=\"-350.3\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.VideoRecording.File</text>\n",
        "</a>\n",
        "</g>\n",
        "</g>\n",
-       "<!-- kpms_pca.PoseEstimationMethod&#45;&gt;kpms_pca.KeypointSet -->\n",
+       "<!-- kpms_pca.PCATask -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>kpms_pca.PCATask</title>\n",
+       "<g id=\"a_node16\"><a xlink:title=\"→ kpms_pca.Bodyparts\r------------------------------\rkpms_project_output_dir=&quot;&quot; \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"797.62,-299.52 675.12,-299.52 675.12,-264.96 797.62,-264.96 797.62,-299.52\"/>\n",
+       "<text text-anchor=\"middle\" x=\"736.38\" y=\"-277.97\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_pca.PCATask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_pca.Bodyparts&#45;&gt;kpms_pca.PCATask -->\n",
        "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>kpms_pca.PoseEstimationMethod-&gt;kpms_pca.KeypointSet</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-dasharray=\"5,2\" stroke-opacity=\"0.250980\" d=\"M465,-538.59C465,-527.82 465,-513.73 465,-502.89\"/>\n",
+       "<title>kpms_pca.Bodyparts-&gt;kpms_pca.PCATask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M736.38,-335.2C736.38,-324.42 736.38,-310.43 736.38,-299.68\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M736.38,-61.66C736.38,-50.36 736.38,-35.66 736.38,-25.58\"/>\n",
+       "</g>\n",
+       "<!-- kpms_pca.PCATask&#45;&gt;kpms_pca.LoadKeypointSet -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>kpms_pca.PCATask-&gt;kpms_pca.LoadKeypointSet</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M736.38,-264.64C736.38,-253.86 736.38,-239.87 736.38,-229.12\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.VideoRecording.File -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.VideoRecording.File</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M394.38,-405.76C394.38,-392.13 394.38,-373.36 394.38,-362.51\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.InferenceTask -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M350.34,-405.59C322.34,-394.92 286.23,-381.17 258.26,-370.52\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.InferenceTask&#45;&gt;kpms_model.Inference -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>kpms_model.InferenceTask-&gt;kpms_model.Inference</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M214.38,-335.2C214.38,-322.84 214.38,-306.25 214.38,-295.17\"/>\n",
        "</g>\n",
        "</g>\n",
        "</svg>"
       ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x1073b1df0>"
+       "<datajoint.diagram.Diagram at 0x14d72c430>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -818,7 +962,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Insert example data into subject and session tables\n"
+    "### **Insert example data into subject and session tables**\n"
    ]
   },
   {
@@ -830,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -910,8 +1054,8 @@
        "            <tbody> <tr> <td>subject1</td>\n",
        "<td></td>\n",
        "<td>F</td>\n",
-       "<td>2020-01-01</td>\n",
-       "<td>test</td> </tr> </tbody>\n",
+       "<td>2024-01-01</td>\n",
+       "<td>test subject</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
@@ -920,11 +1064,11 @@
       "text/plain": [
        "*subject     subject_nickna sex     subject_birth_ subject_descri\n",
        "+----------+ +------------+ +-----+ +------------+ +------------+\n",
-       "subject1                    F       2020-01-01     test          \n",
+       "subject1                    F       2024-01-01     test subject  \n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -942,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -950,8 +1094,8 @@
     "    dict(\n",
     "        subject=\"subject1\",\n",
     "        sex=\"F\",\n",
-    "        subject_birth_date=\"2020-01-01\",\n",
-    "        subject_description=\"test\",\n",
+    "        subject_birth_date=\"2024-01-01\",\n",
+    "        subject_description=\"test subject\",\n",
     "    ),\n",
     "    skip_duplicates=True,\n",
     ")"
@@ -966,14 +1110,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Definition of the dictionary named \"session_keys\"\n",
     "session_keys = [\n",
-    "    dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\"),\n",
-    "    dict(subject=\"subject1\", session_datetime=\"2021-06-03 14:43:10\"),\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2024-03-15 14:04:22\"),\n",
+    "    dict(subject=\"subject1\", session_datetime=\"2024-03-16 14:43:10\"),\n",
     "]\n",
     "\n",
     "# Insert this dictionary in the Session table\n",
@@ -989,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1058,8 +1202,8 @@
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-03 14:43:10</td> </tr> </tbody>\n",
+       "<td>2024-03-15 14:04:22</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-16 14:43:10</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 2</p></div>\n",
@@ -1068,12 +1212,12 @@
       "text/plain": [
        "*subject     *session_datet\n",
        "+----------+ +------------+\n",
-       "subject1     2021-06-02 14:\n",
-       "subject1     2021-06-03 14:\n",
+       "subject1     2024-03-15 14:\n",
+       "subject1     2024-03-16 14:\n",
        " (Total: 2)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1086,12 +1230,47 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Insert the keypoint data from pose estimation and the body parts in the DataJoint pipeline\n"
+    "Let's define a `key` to use throughout the notebook:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'subject': 'subject1', 'session_datetime': '2024-03-15 14:04:22'}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "key = session_keys[0]\n",
+    "key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Insert the keypoint data from the pose estimation and the body parts in the DataJoint pipeline**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `PoseEstimationMethod` table contains the pose estimation methods and file formats supported by the keypoint loader of `keypoint-moseq` package. In this tutorial, the keypoint input data are `.h5` files that have been obtained using `DeepLabCut`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1333,7 @@
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">format_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">pose_estimation_desc</p>\n",
        "                            <span class=\"djtooltiptext\">Optional. Pose estimation method description</span>\n",
@@ -1183,7 +1362,7 @@
        " (Total: 6)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,21 +1371,26 @@
     "kpms_pca.PoseEstimationMethod()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Insert keypoint input metadata into the `KeypointSet` table:\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpset_key = dict(subject=\"subject1\", session_datetime=\"2021-06-02 14:04:22\", kpset_id=1)\n",
-    "\n",
     "kpms_pca.KeypointSet.insert1(\n",
     "    {\n",
-    "        **kpset_key,\n",
+    "        \"kpset_id\": 1,\n",
     "        \"format_method\": \"deeplabcut\",\n",
-    "        \"kpset_config_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data\",\n",
-    "        \"kpset_videos_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos\",\n",
-    "        \"kpset_desc\": \"testing kpms pca schema\",\n",
+    "        \"kpset_config_dir\": \"dlc_project\",\n",
+    "        \"kpset_videos_dir\": \"dlc_project/videos\",\n",
+    "        \"kpset_desc\": \"Example keypoint set\",\n",
     "    },\n",
     "    skip_duplicates=True,\n",
     ")"
@@ -1214,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1276,17 +1460,11 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">format_method</p>\n",
-       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,</span>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"nonprimary\">kpset_config_dir</p>\n",
        "                            <span class=\"djtooltiptext\">Path relative to root data directory where the config file is located</span>\n",
@@ -1297,26 +1475,20 @@
        "                            <p id=\"nonprimary\">kpset_desc</p>\n",
        "                            <span class=\"djtooltiptext\">Optional. User-entered description</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>deeplabcut</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos</td>\n",
-       "<td>testing kpms pca schema</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            deeplabcut     /Users/milagro /Users/milagro testing kpms p\n",
-       " (Total: 1)"
+       "*kpset_id    format_method  kpset_config_d kpset_videos_d kpset_desc    \n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1325,21 +1497,28 @@
     "kpms_pca.KeypointSet()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add the video files in `KeypointSet.VideoFile` that will be used to fit the model:\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "video_files = [\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
-    "    \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
+    "    \"dlc_project/videos/21_11_8_one_mouse.top.ir.Mp4\",\n",
+    "    \"dlc_project/videos/21_12_2_def6a_1.top.ir.mp4\",\n",
+    "    \"dlc_project/videos/21_12_2_def6b_2.top.ir.mp4\",\n",
     "]\n",
     "\n",
     "kpms_pca.KeypointSet.VideoFile.insert(\n",
     "    (\n",
-    "        {**kpset_key, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
+    "        {\"kpset_id\": 1, \"video_id\": v_idx, \"video_path\": Path(f)}\n",
     "        for v_idx, f in enumerate(video_files)\n",
     "    ),\n",
     "    skip_duplicates=True,\n",
@@ -1348,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1410,12 +1589,6 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
@@ -1425,34 +1598,28 @@
        "                            <p id=\"nonprimary\">video_path</p>\n",
        "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
+       "            <tbody> <tr> <td>1</td>\n",
        "<td>0</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>dlc_project/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>1</td>\n",
        "<td>1</td>\n",
+       "<td>dlc_project/videos/21_12_2_def6a_1.top.ir.mp4</td></tr><tr><td>1</td>\n",
        "<td>2</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/inbox/input_data/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
+       "<td>dlc_project/videos/21_12_2_def6b_2.top.ir.mp4</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 3</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *video_id    video_path    \n",
-       "+----------+ +------------+ +----------+ +----------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            0            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            1            /Users/milagro\n",
-       "subject1     2021-06-02 14: 1            2            /Users/milagro\n",
+       "*kpset_id    *video_id    video_path    \n",
+       "+----------+ +----------+ +------------+\n",
+       "1            0            dlc_project/vi\n",
+       "1            1            dlc_project/vi\n",
+       "1            2            dlc_project/vi\n",
        " (Total: 3)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1461,9 +1628,43 @@
     "kpms_pca.KeypointSet.VideoFile()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's insert the body parts to use in the analysis:\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca_task_key = {\"kpset_id\": 1, \"bodyparts_id\": 1}\n",
+    "kpms_pca.Bodyparts.insert1(\n",
+    "    {\n",
+    "        **pca_task_key,\n",
+    "        \"anterior_bodyparts\": [\"nose\"],\n",
+    "        \"posterior_bodyparts\": [\"spine4\"],\n",
+    "        \"use_bodyparts\": [\n",
+    "            \"spine4\",\n",
+    "            \"spine3\",\n",
+    "            \"spine2\",\n",
+    "            \"spine1\",\n",
+    "            \"head\",\n",
+    "            \"nose\",\n",
+    "            \"right ear\",\n",
+    "            \"left ear\",\n",
+    "        ],\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1525,64 +1726,75 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average height of the video set (pixels)</span>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average width of the video set (pixels)</span>\n",
+       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average number of frames of the video set (frames)</span>\n",
+       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Average frames per second of the video set (Hz)</span>\n",
+       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average video duration (s) of the video set (nframes / fps)</span>\n",
+       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td></td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_dura\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
+       "+----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
+       "1            1                             =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.RecordingInfo()"
+    "kpms_pca.Bodyparts()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 17,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Fit a PCA model to aligned and centered keypoint coordinates and select the latent dimension**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To perform the model fitting, a PCA model and the precise dimension of the pose trajectory are required for fitting the keypoint-MoSeq.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "kpms_pca.RecordingInfo.populate()"
+    "The `PCATask` table serves the purpose of specifying the PCA task.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1644,62 +1856,68 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_height_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average height of the video set (pixels)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">px_width_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average width of the video set (pixels)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">nframes_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average number of frames of the video set (frames)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">fps_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Average frames per second of the video set (Hz)</span>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">recording_duration_average</p>\n",
-       "                            <span class=\"djtooltiptext\">Average video duration (s) of the video set (nframes / fps)</span>\n",
+       "                            <p id=\"nonprimary\">kpms_project_output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "            <tbody> <tr> <td>1</td>\n",
        "<td>1</td>\n",
-       "<td>576</td>\n",
-       "<td>640</td>\n",
-       "<td>95517</td>\n",
-       "<td>30</td>\n",
-       "<td>3183.0</td> </tr> </tbody>\n",
+       "<td>kpms_project_tutorial</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    px_height_aver px_width_avera nframes_averag fps_average    recording_dura\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            576            640            95517          30             3183.0        \n",
+       "*kpset_id    *bodyparts_id  kpms_project_o\n",
+       "+----------+ +------------+ +------------+\n",
+       "1            1              kpms_project_t\n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.RecordingInfo()"
+    "kpms_pca.PCATask()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining and inserting a PCA task requires:\n",
+    "\n",
+    "1. Select a keypoint set\n",
+    "2. Select the body parts to use\n",
+    "3. Specify the output directory for the KPMS project\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_pca.PCATask.insert1(\n",
+    "    {\n",
+    "        **pca_task_key,\n",
+    "        \"kpms_project_output_dir\": \"kpms_project_tutorial\",\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1761,82 +1979,49 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                            <p id=\"nonprimary\">kpms_project_output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  kpms_project_o\n",
+       "+----------+ +------------+ +------------+\n",
+       "1            1              kpms_project_t\n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.Bodyparts()"
+    "kpms_pca.PCATask()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "bodypart_key = {**kpset_key, \"bodyparts_id\": 1}\n",
-    "kpms_pca.Bodyparts.insert1(\n",
-    "    {\n",
-    "        **bodypart_key,\n",
-    "        \"anterior_bodyparts\": [\"nose\"],\n",
-    "        \"posterior_bodyparts\": [\"spine4\"],\n",
-    "        \"use_bodyparts\": [\n",
-    "            \"spine4\",\n",
-    "            \"spine3\",\n",
-    "            \"spine2\",\n",
-    "            \"spine1\",\n",
-    "            \"head\",\n",
-    "            \"nose\",\n",
-    "            \"right ear\",\n",
-    "            \"left ear\",\n",
-    "        ],\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "Before running the PCA fitting, the keypoint detections and body parts need to be formatted. The resulting coordinates and confidences scores will be used to format the data for modeling.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1898,69 +2083,93 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">bodyparts_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. User-entered description.</span>\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">anterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of anterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">posterior_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of posterior bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">use_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">List of strings of bodyparts to be used</span>\n",
+       "                            <p id=\"nonprimary\">average_frame_rate</p>\n",
+       "                            <span class=\"djtooltiptext\">Average frame rate of the trained videos</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td></td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 0</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  bodyparts_desc anterior_b posterior_ use_bodypa\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1                             =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 1)"
+       "*kpset_id    *bodyparts_id  coordinate confidence formatted_ average_frame_\n",
+       "+----------+ +------------+ +--------+ +--------+ +--------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.Bodyparts()"
+    "kpms_pca.LoadKeypointSet()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Fit a PCA model to aligned and centered keypoint coordinates and select the latent dimension\n"
+    "Populate the `LoadKeypointSet` table will:\n",
+    "\n",
+    "1. Create the output directory, if it does not exist, with the kpms default `config.yml` file that contains the default values from the pose estimation\n",
+    "2. Generate a copy as `dj_config.yml` and update it with both the video directory and the bodyparts\n",
+    "3. Create and store the keypoint coordinates and confidences scores to format the data for the PCA fitting\n",
+    "4. Calculate the average frame rate of the videoset chosen to train the model. This will be useful to calculate the kappa value in the next step.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The directory `/Users/milagros/Documents/datajoint-elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial` already exists. Use\n",
+      "`overwrite=True` or pick a different name\n",
+      "ACTION REQUIRED: `anterior_bodyparts` contains BODYPART1 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
+      "\n",
+      "ACTION REQUIRED: `posterior_bodyparts` contains BODYPART3 which is not\n",
+      "  one of the options in `use_bodyparts`.\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading keypoints: 100%|██████████████████| 3/3 [00:00<00:00, 10.94it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kpms_pca.LoadKeypointSet.populate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2022,65 +2231,70 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">output_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
+       "                            <p id=\"nonprimary\">coordinates</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">confidences</p>\n",
+       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
+       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">default = 'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">average_frame_rate</p>\n",
+       "                            <span class=\"djtooltiptext\">Average frame rate of the trained videos</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>30.0</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  coordinate confidence formatted_ average_frame_\n",
+       "+----------+ +------------+ +--------+ +--------+ +--------+ +------------+\n",
+       "1            1              =BLOB=     =BLOB=     =BLOB=     30.0          \n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCATask()"
+    "kpms_pca.LoadKeypointSet()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `PCAFitting` computation will format the aligned and centered keypoint coordinates, fit a PCA model, and save it as `pca.p` file in the output directory.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.PCATask.insert1(\n",
-    "    {\n",
-    "        **bodypart_key,\n",
-    "        \"output_dir\": \"/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial\",\n",
-    "        \"task_mode\": \"trigger\",\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "kpms_pca.PCAFitting.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2142,78 +2356,60 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">output_dir</p>\n",
-       "                            <span class=\"djtooltiptext\">KPMS's output directory relative to root</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">default = 'load': load computed analysis results, 'trigger': trigger computation</span>\n",
+       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
+       "                            <span class=\"djtooltiptext\">datetime of the PCA fitting analysis</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
+       "            <tbody> <tr> <td>1</td>\n",
        "<td>1</td>\n",
-       "<td>/Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial</td>\n",
-       "<td>trigger</td> </tr> </tbody>\n",
+       "<td>2024-03-20 04:59:56</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
        "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  output_dir     task_mode    \n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+\n",
-       "subject1     2021-06-02 14: 1            1              /Users/milagro trigger      \n",
+       "*kpset_id    *bodyparts_id  pca_fitting_ti\n",
+       "+----------+ +------------+ +------------+\n",
+       "1            1              2024-03-20 04:\n",
        " (Total: 1)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCATask()"
+    "kpms_pca.PCAFitting()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, we still need to determine the specific dimension of the pose trajectory to utilize for fitting the keypoint-MoSeq model. A helpful guideline is to consider the number of dimensions required to explain 90% of the variance, or a maximum of 10 dimensions, whichever is lower.\n",
+    "\n",
+    "The computation of `LatentDimension` will automatically identify the components that explain 90% of the variance, aiding the user in making the final decision regarding an appropriate latent dimension for model fitting.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'subject': 'subject1',\n",
-       " 'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       " 'kpset_id': 1,\n",
-       " 'bodyparts_id': 1}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "key = (kpms_pca.PCATask & \"task_mode = 'trigger'\").fetch1(\"KEY\")\n",
-    "key"
+    "kpms_pca.LatentDimension.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2275,99 +2471,312 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
+       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
+       "                            <span class=\"djtooltiptext\">Variance threshold. Fixed value to 0.9</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
+       "                            <p id=\"nonprimary\">latent_dimension</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of principal components to explain the variance.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
+       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Automated description of the computation result.</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>90.0</td>\n",
+       "<td>4</td>\n",
+       "<td>>=90.0% of variance explained by 4 components.</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
-       "+---------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  variance_perce latent_dimensi latent_dim_des\n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+\n",
+       "1            1              90.0           4              >=90.0% of var\n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.FormattedDataset()"
+    "kpms_pca.LatentDimension()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To aid the user in selecting the latent dimensions for model fitting, two plots are created below: a cumulative scree plot and a visualization of each Principal Component (PC). In this visualization, translucent nodes/edges represent the mean pose, while opaque nodes/edges represent a perturbation in the direction of the PC.\n",
+    "The plots are stored in the output directory.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The directory `/Users/milagros/Documents/datajoint-elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial` already exists. Use\n",
-      "`overwrite=True` or pick a different name\n",
-      "ACTION REQUIRED: `anterior_bodyparts` contains BODYPART1 which is not\n",
-      "  one of the options in `use_bodyparts`.\n",
-      "\n",
-      "ACTION REQUIRED: `posterior_bodyparts` contains BODYPART3 which is not\n",
-      "  one of the options in `use_bodyparts`.\n",
-      "\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading keypoints: 100%|████████████████| 10/10 [00:00<00:00, 28.63it/s]\n"
-     ]
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x300 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "kpms_pca.FormattedDataset.populate(key)"
+    "# Generate and store plots for the user to choose the latent dimensions in the next step\n",
+    "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
+    "from element_moseq.readers.kpms_reader import load_kpms_dj_config\n",
+    "\n",
+    "kpms_project_output_dir = (kpms_pca.PCATask & pca_task_key).fetch1(\"kpms_project_output_dir\")\n",
+    "kpms_project_output_dir = get_kpms_processed_data_dir()/kpms_project_output_dir\n",
+    "\n",
+    "kpms_dj_config = load_kpms_dj_config(kpms_project_output_dir.as_posix(), check_if_valid=False, build_indexes=False)\n",
+    "pca = load_pca(kpms_project_output_dir.as_posix())\n",
+    "\n",
+    "plot_scree(pca, project_dir=kpms_project_output_dir.as_posix())\n",
+    "plot_pcs(pca, project_dir=kpms_project_output_dir.as_posix(), **kpms_dj_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The chosen dimension for the next steps in the analysis will be `latent dimension = 4`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Fit the AR-HMM and keypoint-SLDS Models**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The pre-fitting and full-fitting processes for the KPMS Model involve the following steps:\n",
+    "\n",
+    "1. **Initialization**: Auto-regressive (AR) parameters and syllable sequences are randomly initialized using pose trajectories from PCA\n",
+    "2. **Fitting an AR-HMM**: AR parameters, transition probabilities and syllable sequences are iteratively updated through Gibbs sampling\n",
+    "3. **Fitting the full model**: All parameters, including both AR-HMM and centroid, heading, noise-estimates, and continuous latent states (i.e., pose trajectories) are iteratively updated through Gibbs sampling. This step is particularly useful for noisy data.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       "<!-- kpms_model.PreFitting -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>kpms_model.PreFitting</title>\n",
+       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"81.88\" cy=\"-135.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"81.88\" y=\"-130.93\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Model -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>kpms_model.Model</title>\n",
+       "<g id=\"a_node2\"><a xlink:title=\"model_name           \r------------------------------\rlatent_dim           \rkappa                \r\">\n",
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+       "<text text-anchor=\"start\" x=\"197.75\" y=\"-202.49\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.Model</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.InferenceTask -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>kpms_model.InferenceTask</title>\n",
+       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_model.VideoRecording\r→ kpms_model.Model\r------------------------------\rinference_output_dir=&quot;&quot; \rinference_desc=&quot;&quot;    \rnum_iterations=null  \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"330.62,-152.48 169.12,-152.48 169.12,-117.92 330.62,-117.92 330.62,-152.48\"/>\n",
+       "<text text-anchor=\"middle\" x=\"249.88\" y=\"-130.93\" font-family=\"arial\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.InferenceTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Model&#45;&gt;kpms_model.InferenceTask -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>kpms_model.Model-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M249.88,-188.16C249.88,-177.38 249.88,-163.39 249.88,-152.64\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>kpms_model.FullFittingTask</title>\n",
+       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \r\">\n",
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+       "<text text-anchor=\"start\" x=\"542.25\" y=\"-202.49\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFitting -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>kpms_model.FullFitting</title>\n",
+       "<g id=\"a_node6\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rmodel_name           \rfull_fitting_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"616.88\" cy=\"-135.2\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"616.88\" y=\"-130.93\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M616.88,-188.16C616.88,-175.8 616.88,-159.21 616.88,-148.13\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>kpms_model.Inference</title>\n",
+       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_model.InferenceTask\r------------------------------\rinference_duration=null \r\">\n",
+       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"249.88\" cy=\"-68.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
+       "<text text-anchor=\"middle\" x=\"249.88\" y=\"-64.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.Inference</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.InferenceTask&#45;&gt;kpms_model.Inference -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>kpms_model.InferenceTask-&gt;kpms_model.Inference</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M249.88,-117.66C249.88,-106.36 249.88,-91.66 249.88,-81.58\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference.GridMoviesSampledInstances -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<g id=\"a_node7\"><a xlink:title=\"→ kpms_model.Inference\rsyllable             \r------------------------------\rinstances            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"255.12,-20 6.62,-20 6.62,0 255.12,0 255.12,-20\"/>\n",
+       "<text text-anchor=\"start\" x=\"14.62\" y=\"-7.5\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.GridMoviesSampledInstances</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.GridMoviesSampledInstances -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.GridMoviesSampledInstances</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M238.89,-62.7C218.52,-52.95 174.68,-31.97 149.57,-19.95\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference.MotionSequence -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>kpms_model.Inference.MotionSequence</title>\n",
+       "<g id=\"a_node9\"><a xlink:title=\"→ kpms_model.Inference\rvideo_name           \r------------------------------\rsyllable             \rlatent_state         \rcentroid             \rheading              \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"464.25,-20 273.5,-20 273.5,0 464.25,0 464.25,-20\"/>\n",
+       "<text text-anchor=\"start\" x=\"281.5\" y=\"-7.5\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.Inference.MotionSequence</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.Inference&#45;&gt;kpms_model.Inference.MotionSequence -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>kpms_model.Inference-&gt;kpms_model.Inference.MotionSequence</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M260.86,-62.7C281.23,-52.95 325.07,-31.97 350.18,-19.95\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording.File -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>kpms_model.VideoRecording.File</title>\n",
+       "<g id=\"a_node8\"><a xlink:title=\"→ kpms_model.VideoRecording\rfile_id              \r------------------------------\rfile_path            \r\">\n",
+       "<polygon fill=\"none\" stroke=\"none\" points=\"511,-145.2 348.75,-145.2 348.75,-125.2 511,-125.2 511,-145.2\"/>\n",
+       "<text text-anchor=\"start\" x=\"356.75\" y=\"-132.7\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"10.00\">kpms_model.VideoRecording.File</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>kpms_model.PreFittingTask</title>\n",
+       "<g id=\"a_node10\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \r\">\n",
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+       "<text text-anchor=\"start\" x=\"8\" y=\"-202.49\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M81.88,-188.16C81.88,-175.8 81.88,-159.21 81.88,-148.13\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kpms_model.VideoRecording</title>\n",
+       "<g id=\"a_node11\"><a xlink:title=\"→ session.Session\r→ kpms_pca.PoseEstimationMethod\rrecording_id         \r\">\n",
+       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"516.62,-223.04 343.12,-223.04 343.12,-188.48 516.62,-188.48 516.62,-223.04\"/>\n",
+       "<text text-anchor=\"start\" x=\"351.12\" y=\"-202.49\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.VideoRecording</text>\n",
+       "</a>\n",
+       "</g>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.InferenceTask -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.InferenceTask</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M385.84,-187.99C357.84,-177.32 321.73,-163.57 293.76,-152.92\"/>\n",
+       "</g>\n",
+       "<!-- kpms_model.VideoRecording&#45;&gt;kpms_model.VideoRecording.File -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>kpms_model.VideoRecording-&gt;kpms_model.VideoRecording.File</title>\n",
+       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"0.75\" stroke-opacity=\"0.250980\" d=\"M429.88,-188.16C429.88,-174.53 429.88,-155.76 429.88,-144.91\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<datajoint.diagram.Diagram at 0x29c332580>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dj.Diagram(kpms_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the pre-fitting step (fitting an AR-HMM), a pre-fitting task needs to be defined and inserted:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
        "        .Table td{\n",
        "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
        "        }\n",
@@ -2414,65 +2823,445 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of latent dimensions to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\">Kappa value to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of iterations to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">User-defined description of the pre-fitting task</span>\n",
+       "                        </div> </th> </tr> </thead>\n",
+       "            <tbody> <tr>  </tr> </tbody>\n",
+       "        </table>\n",
+       "        \n",
+       "        <p>Total: 0</p></div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "*kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera pre_fitting_de\n",
+       "+----------+ +------------+ +------------+ +-----------+ +------------+ +------------+\n",
+       "\n",
+       " (Total: 0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_model.PreFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This task requires the following inputs:\n",
+    "\n",
+    "1. The keypoint set, body parts, and latent dimension (extracted in the section above).\n",
+    "2. A kappa value for the model pre-fitting.\n",
+    "3. The number of iterations for the model pre-fitting.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Kappa hyperparameter**:\n",
+    "An important decision for the user is to adjust the kappa hyperparameter to achieve the desired distribution of syllable durations. Higher values of kappa result in longer syllables.\n",
+    "\n",
+    "As a reference, let's choose a kappa value that yields a median syllable duration of 12 frames (400 ms), a duration recommended for rodents.\n",
+    "\n",
+    "During the model pre-fitting, it's advisable to explore different values of kappa (`kappa_range`) until the syllable durations stabilize.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['kappa = 400.00 ms', 'kappa = 2000.00 ms', 'kappa = 10000.00 ms']\n"
+     ]
+    }
+   ],
+   "source": [
+    "fps = (kpms_pca.LoadKeypointSet & pca_task_key).fetch1(\"average_frame_rate\")\n",
+    "kappa_min = (12 / fps) * 1000 #ms\n",
+    "kappa_max = 1e4 #ms \n",
+    "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
+    "kappa_range = np.round(kappa_range).astype(int)\n",
+    "print(['kappa = {:.2f} ms'.format(x) for x in kappa_range])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Number of Iterations**: Typically, stabilizing the syllable duration requires 10-50 iterations during the model pre-fitting stage, while stabilizing the syllable sequence after setting kappa may take 200-500 iterations during the model full-fitting stage.\n",
+    "\n",
+    "For tutorial purposes, we will opt for a very low number of iterations (`num_iterations = 5`) to ensure the notebook runs quickly, taking just a few minutes.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus, we will insert different entries (`prefitting_keys`) in the `PreFittingTask` with various kappa values until the target syllable time-scale is achieved.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "# \n",
+       "kpset_id             : int                          # \n",
+       "bodyparts_id         : int                          # \n",
+       "pre_latent_dim       : int                          # Number of latent dimensions to use for the model pre-fitting\n",
+       "pre_kappa            : int                          # Kappa value to use for the model pre-fitting\n",
+       "pre_num_iterations   : int                          # Number of iterations to use for the model pre-fitting\n",
+       "---\n",
+       "pre_fitting_desc=\"\"  : varchar(1000)                # User-defined description of the pre-fitting task"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_model.PreFittingTask.heading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 400,\n",
+       "  'pre_num_iterations': 5,\n",
+       "  'pre_fitting_desc': 'Testing Pre-fitting task 1'},\n",
+       " {'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 2000,\n",
+       "  'pre_num_iterations': 5,\n",
+       "  'pre_fitting_desc': 'Testing Pre-fitting task 2'},\n",
+       " {'kpset_id': 1,\n",
+       "  'bodyparts_id': 1,\n",
+       "  'pre_latent_dim': 4,\n",
+       "  'pre_kappa': 10000,\n",
+       "  'pre_num_iterations': 5,\n",
+       "  'pre_fitting_desc': 'Testing Pre-fitting task 3'}]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prefitting_keys = [{\n",
+    "    **pca_task_key,\n",
+    "    'pre_latent_dim': 4,\n",
+    "    'pre_kappa': int(i),\n",
+    "    'pre_num_iterations': 5,\n",
+    "    'pre_fitting_desc': f\"Testing Pre-fitting task {c}\"\n",
+    "} for c, i in enumerate(kappa_range, start=1)]\n",
+    "\n",
+    "prefitting_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.PreFittingTask.insert(prefitting_keys, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show the contents of the `PreFittingTask` table.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    \n",
+       "    <style type=\"text/css\">\n",
+       "        .Table{\n",
+       "            border-collapse:collapse;\n",
+       "        }\n",
+       "        .Table th{\n",
+       "            background: #A0A0A0; color: #ffffff; padding:4px; border:#f0e0e0 1px solid;\n",
+       "            font-weight: normal; font-family: monospace; font-size: 100%;\n",
+       "        }\n",
+       "        .Table td{\n",
+       "            padding:4px; border:#f0e0e0 1px solid; font-size:100%;\n",
+       "        }\n",
+       "        .Table tr:nth-child(odd){\n",
+       "            background: #ffffff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        .Table tr:nth-child(even){\n",
+       "            background: #f3f1ff;\n",
+       "            color: #000000;\n",
+       "        }\n",
+       "        /* Tooltip container */\n",
+       "        .djtooltip {\n",
+       "        }\n",
+       "        /* Tooltip text */\n",
+       "        .djtooltip .djtooltiptext {\n",
+       "            visibility: hidden;\n",
+       "            width: 120px;\n",
+       "            background-color: black;\n",
+       "            color: #fff;\n",
+       "            text-align: center;\n",
+       "            padding: 5px 0;\n",
+       "            border-radius: 6px;\n",
+       "            /* Position the tooltip text - see examples below! */\n",
+       "            position: absolute;\n",
+       "            z-index: 1;\n",
+       "        }\n",
+       "        #primary {\n",
+       "            font-weight: bold;\n",
+       "            color: black;\n",
+       "        }\n",
+       "        #nonprimary {\n",
+       "            font-weight: normal;\n",
+       "            color: white;\n",
+       "        }\n",
+       "\n",
+       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
+       "        .djtooltip:hover .djtooltiptext {\n",
+       "            visibility: visible;\n",
+       "        }\n",
+       "    </style>\n",
+       "    \n",
+       "    <b></b>\n",
+       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "        <table border=\"1\" class=\"Table\">\n",
+       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">coordinates</p>\n",
-       "                            <span class=\"djtooltiptext\">Keypoint coordinates</span>\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of latent dimensions to use for the model pre-fitting</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">confidences</p>\n",
-       "                            <span class=\"djtooltiptext\">Keypoint confidences</span>\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\">Kappa value to use for the model pre-fitting</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">formatted_bodyparts</p>\n",
-       "                            <span class=\"djtooltiptext\">Formatted bodyparts</span>\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of iterations to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">User-defined description of the pre-fitting task</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "            <tbody> <tr> <td>1</td>\n",
        "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>400</td>\n",
+       "<td>5</td>\n",
+       "<td>Testing Pre-fitting task 1</td></tr><tr><td>1</td>\n",
        "<td>1</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td>\n",
-       "<td>=BLOB=</td> </tr> </tbody>\n",
+       "<td>4</td>\n",
+       "<td>2000</td>\n",
+       "<td>5</td>\n",
+       "<td>Testing Pre-fitting task 2</td></tr><tr><td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>10000</td>\n",
+       "<td>5</td>\n",
+       "<td>Testing Pre-fitting task 3</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 3</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  coordinate confidence formatted_\n",
-       "+----------+ +------------+ +----------+ +------------+ +--------+ +--------+ +--------+\n",
-       "subject1     2021-06-02 14: 1            1              =BLOB=     =BLOB=     =BLOB=    \n",
-       " (Total: 1)"
+       "*kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera pre_fitting_de\n",
+       "+----------+ +------------+ +------------+ +-----------+ +------------+ +------------+\n",
+       "1            1              4              400           5              Testing Pre-fi\n",
+       "1            1              4              2000          5              Testing Pre-fi\n",
+       "1            1              4              10000         5              Testing Pre-fi\n",
+       " (Total: 3)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_model.PreFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When populating the `PreFitting` table, the fitting of different AR-HMM models for each kappa defined in the `PreFittingTask` will be automatically computed. This step will take a few minutes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/milagros/miniconda/envs/kpms_test/lib/python3.9/site-packages/keypoint_moseq/fitting.py:589: UserWarning:\n",
+      "\n",
+      "'kappa' with <class 'int'> will be cast to <class 'float'>\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_00_08\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|██████████████████████████████▊      | 5/6 [00:31<00:06,  6.21s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
       ]
      },
-     "execution_count": 28,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6/6 [00:37<00:00,  6.30s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_00_51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|██████████████████████████████▊      | 5/6 [00:10<00:01,  1.90s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6/6 [00:12<00:00,  2.09s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|██████████████████████████████▊      | 5/6 [00:08<00:01,  1.74s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6/6 [00:10<00:00,  1.77s/it]\n"
+     ]
     }
    ],
    "source": [
-    "kpms_pca.FormattedDataset()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `PCAFitting` computation will fit a PCA model to aligned and centered keypoint coordinates\n"
+    "kpms_model.PreFitting.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2534,55 +3323,148 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
        "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of latent dimensions to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_kappa</p>\n",
+       "                            <span class=\"djtooltiptext\">Kappa value to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"primary\">pre_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\">Number of iterations to use for the model pre-fitting</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">Name of the model as \"kpms_project_output_dir/model_name\"</span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">pre_fitting_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Time duration of the model fitting computation</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>400</td>\n",
+       "<td>5</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_00_08</td>\n",
+       "<td>0:00:37</td></tr><tr><td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>2000</td>\n",
+       "<td>5</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_00_51</td>\n",
+       "<td>0:00:12</td></tr><tr><td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>10000</td>\n",
+       "<td>5</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_07</td>\n",
+       "<td>0:00:10</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 3</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera model_name     pre_fitting_du\n",
+       "+----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
+       "1            1              4              400           5              kpms_project_t 0:00:37       \n",
+       "1            1              4              2000          5              kpms_project_t 0:00:12       \n",
+       "1            1              4              10000         5              kpms_project_t 0:00:10       \n",
+       " (Total: 3)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCAFitting()"
+    "kpms_model.PreFitting()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can define a FullFitting task based on the selected `latent_dimension = 4`, the chosen `kappa = 10000`, and `num_iterations = 5` based on the previous exploration.\n",
+    "\n",
+    "Again and for tutorial purposes, we will opt for a very low number of iterations (`num_iterations = 5`) to ensure the notebook runs quickly, taking just a few minutes.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "# \n",
+       "kpset_id             : int                          # \n",
+       "bodyparts_id         : int                          # \n",
+       "full_latent_dim      : int                          # \n",
+       "full_kappa           : int                          # \n",
+       "full_num_iterations  : int                          # \n",
+       "---\n",
+       "full_fitting_desc=\"\" : varchar(1000)                # "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_model.FullFittingTask.heading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.PCAFitting.populate(key)"
+    "# modify kappa to maintain the desired syllable time-scale\n",
+    "full_fitting_key = ({**pca_task_key,\n",
+    "                      'full_latent_dim': 4,\n",
+    "                      'full_kappa': 10000,\n",
+    "                      'full_num_iterations':5,\n",
+    "                      'full_fitting_desc':\"Fitting task with kappa = 10000 ms\"\n",
+    "})\n",
+    "\n",
+    "kpms_model.FullFittingTask.insert1(full_fitting_key, skip_duplicates=True)                                 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's add a second FullFitting task:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_fitting_key_2 = ({**pca_task_key,\n",
+    "                      'full_latent_dim': 4,\n",
+    "                      'full_kappa': 5000,\n",
+    "                      'full_num_iterations':5,\n",
+    "                      'full_fitting_desc':\"Fitting task with kappa = 5000 ms\"\n",
+    "})\n",
+    "\n",
+    "kpms_model.FullFittingTask.insert1(full_fitting_key_2, skip_duplicates=True)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2644,50 +3526,138 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <p id=\"primary\">full_latent_dim</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"primary\">full_kappa</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pca_fitting_time</p>\n",
-       "                            <span class=\"djtooltiptext\">Time of generation of the PCA fitting analysis</span>\n",
+       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                        </div></th><th><div class=\"djtooltip\">\n",
+       "                            <p id=\"nonprimary\">full_fitting_desc</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "            <tbody> <tr> <td>1</td>\n",
        "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>5000</td>\n",
+       "<td>5</td>\n",
+       "<td>Fitting task with kappa = 5000 ms</td></tr><tr><td>1</td>\n",
        "<td>1</td>\n",
-       "<td>2024-03-13 16:32:23</td> </tr> </tbody>\n",
+       "<td>4</td>\n",
+       "<td>10000</td>\n",
+       "<td>5</td>\n",
+       "<td>Fitting task with kappa = 10000 ms</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 2</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  pca_fitting_ti\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              2024-03-13 16:\n",
-       " (Total: 1)"
+       "*kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d\n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "1            1              4              5000           5              Fitting task w\n",
+       "1            1              4              10000          5              Fitting task w\n",
+       " (Total: 2)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.PCAFitting()"
+    "kpms_model.FullFittingTask()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_20\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|██████████████████████████████▊      | 5/6 [00:52<00:08,  8.86s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6/6 [01:00<00:00, 10.08s/it]\n",
+      "Reindexing: 100%|█████████████| 2/2 [00:00<00:00, 20.27model snapshot/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
+      "elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_02_24\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|██████████████████████████████▊      | 5/6 [00:39<00:07,  7.79s/it]"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x250 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 6/6 [00:46<00:00,  7.77s/it]\n",
+      "Reindexing: 100%|█████████████| 2/2 [00:00<00:00, 26.64model snapshot/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "kpms_model.FullFitting.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -2749,61 +3719,112 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
+       "                            <p id=\"primary\">kpset_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
+       "                            <p id=\"primary\">bodyparts_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <p id=\"primary\">full_latent_dim</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"primary\">full_kappa</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
-       "                            <span class=\"djtooltiptext\">Percentage of variance explained by the selected components.</span>\n",
+       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of components required to explain the specified variance.</span>\n",
+       "                            <p id=\"nonprimary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">Name of the full-fitted model (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Description of the latent dimensions that explain the specified variance.</span>\n",
+       "                            <p id=\"nonprimary\">full_fitting_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Time duration of the full fitting model</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>5000</td>\n",
+       "<td>5</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>0:01:00</td></tr><tr><td>1</td>\n",
+       "<td>1</td>\n",
+       "<td>4</td>\n",
+       "<td>10000</td>\n",
+       "<td>5</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_02_24</td>\n",
+       "<td>0:00:46</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 2</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter model_name     full_fitting_d\n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "1            1              4              5000           5              kpms_project_t 0:01:00       \n",
+       "1            1              4              10000          5              kpms_project_t 0:00:46       \n",
+       " (Total: 2)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.DimsExplainedVariance()"
+    "kpms_model.FullFitting()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Run the inference task and visualize the results**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The models along with their pertinent information will be registered in the DataJoint pipeline as follows:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_pca.DimsExplainedVariance.populate(key)"
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 10000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
+    "kpms_model.Model.insert1({\n",
+    "    \"model_name\" : model_name,\n",
+    "    \"latent_dim\" :latent_dim,\n",
+    "    \"kappa\" : kappa}, skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 5000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
+    "kpms_model.Model.insert1({\n",
+    "    \"model_name\" : model_name,\n",
+    "    \"latent_dim\" :latent_dim,\n",
+    "    \"kappa\" : kappa}, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check the `Model` table to confirm that the new models have been registered:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2865,75 +3886,96 @@
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">User-friendly model name (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
+       "                            <p id=\"nonprimary\">latent_dim</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"nonprimary\">kappa</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">variance_percentage</p>\n",
-       "                            <span class=\"djtooltiptext\">Percentage of variance explained by the selected components.</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">dims_explained_variance</p>\n",
-       "                            <span class=\"djtooltiptext\">Number of components required to explain the specified variance.</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">latent_dim_desc</p>\n",
-       "                            <span class=\"djtooltiptext\">Description of the latent dimensions that explain the specified variance.</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>90.0</td>\n",
+       "            <tbody> <tr> <td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
        "<td>4</td>\n",
-       "<td>>=90.0% of variance explained by 4 components.</td> </tr> </tbody>\n",
+       "<td>5000</td></tr><tr><td>kpms_project_tutorial/2024_03_20-06_02_24</td>\n",
+       "<td>4</td>\n",
+       "<td>10000</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 2</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  variance_perce dims_explained latent_dim_des\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              90.0           4              >=90.0% of var\n",
-       " (Total: 1)"
+       "*model_name    latent_dim     kappa    \n",
+       "+------------+ +------------+ +-------+\n",
+       "kpms_project_t 4              5000     \n",
+       "kpms_project_t 4              10000    \n",
+       " (Total: 2)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_pca.DimsExplainedVariance()"
+    "kpms_model.Model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Optional: Model comparison to select a model\n",
+    "\n",
+    "The expected marginal likelihood (EML) score can be used to rank models. The model with the highest EML score can then be selected for further analysis.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████| 2/2 [00:02<00:00,  1.07s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best model: kpms_project_tutorial/2024_03_20-06_01_20\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "/Users/milagros/miniconda/envs/kpms_test/lib/python3.9/site-packages/keypoint_moseq/viz.py:2895: UserWarning:\n",
+      "\n",
+      "Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
+      "\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 300x200 with 1 Axes>"
+       "(<Figure size 400x350 with 1 Axes>, <Axes: ylabel='EML score'>)"
       ]
      },
+     "execution_count": 80,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Eekr2nYSoqGHmhBTeAoD09CRSUyNp184+cuncuUOZNm19NUtfpOzoVCjQzgURijtV/NkzXH5hOP2tlytMz7Bq2T/5XUY++Yc6iuxGVMyJ/fsT6N59JADbtxvp31/O4omGxnE7sWrVG8TGvl7tu1atepvY2D85O7gGRVEU9syfz5V336XzwYO0dLDbd1GjYX/HjoT/4Q/0nTEDnU7nYE01Zzab2T3/Z1J++IKIE3vprTWjc3AMNU+BnQY/8noPo9UTv6NDvwG3wcHWmyX7TkJU1PByQgpvUWrJkt9z990fAJCRca2B1qzYj1JdHRq9Ow3tPnyi9sz/85NMOfAfh/N+Km5E6zd/olXn7nh7e7s4shtRMScyM6PRaJoREACXLkGjRsUYjcZrrUCIO4zjdmL37mU0azaRsLCqt6e6csVAauoKunUb7sI471wXjx/nwF/+QuiKFfQvLq5ybWE+sDk0FPWhhxj48sv4BQQ4JQ5FUTi8cT3nv/6EgH2b6WvLw9PBhY4WFXaq7qR07EOTB5+i592TqgzOdnTXdhKO7sUnoil9R09Aq72drpiUfSchKmp4OSGFtyh1daC10aPtA60tWRLG+PHVDbSWAFy9dtUf6OKSGMXtzWq1cnLfHuKWzsO0Zwv+cWfI0+UxMdrx8odzIcQNtBqIsxjIMAZSFBiJNrI1Pq270aj7QGLadsDD48a6JymKwu4NqyjIzaTHkLH418oOZ8WcOHCgB9262bDZ4Pz5I7Rq1bEWPkOI20nVdkJVVebMGc69926osKSqwty5o5g2beUdfMbT+fLz8tjy7rso33zDoORkfB0ss91oJGnkSLrMmkWLLl1cHSJnjxzm2H/+gduWFfQuSCO4mpPrB6w6LsTYB2drOWIMu/76G3rFbaCZ3kyWTcPmwJ60/f3HtO7ex7UbcAtstni2bfsPRUUnUBQfmjV7mnbtetd1WELUoYZVT0jhLSo4dGglMTFj8PUFRYFVq15jzJjXHSypAnuAopLXHYEgV4UpbhOJly9zYsk8cjavxv30YZrlpNBWr6Itt1+9XAtjmzt+/+YM6OYHPte4/8KVYoizGsnyCMIUFIU+qi2+7XoQ0W0A0a3a4ObmVmH5vWsXkbxgFv2Vg/joYWdxY7Laz+Du5967xR3+ijmxZs0kRo7MBmDTps+5665f38K6hbgdOW4n0tOT+fHHltx1Vz4tWsDx4wbi4u5m2LDPCQgIrrtw67nszEw2z5qFYfNmtMXFWDp2JPqpp2g3aBC75s4l6b336HTokMOu5Bc0Gg507EijP/6RvjNm1JszxSkJCez9z0dYV82nS9pFmlXzW/+NCo+2qjp9qXtbhv+w/4YPvtaFnJwsVqyYwpgxG/Erubj+7FkPjh59ksmTP3TKZyZevsC+1X9Dn7cDDQpWrx60H/w8Ma07O+XzxJ0tIeEiR47MAVTatZtM06a1MT5Dw6onpPAWVcyZ05fp02sy0FoacLzkuRfQA/sACaIhKiws5Mj61SSvWox6cCdhyXF01Jjwuc7+3TIT9G8FAW5V5/070UgPX4WmBgvhN9hT26ZCfBFcUd3J9gzBFNyMLJ8Q2ueto19AboVl8yywtfMbjHnyLzf2IVWU5cTChY8yadJFABYvfoIJExx3pxfizua4ndiyxZNmzYq4dAmysj5hwoTf1lmEt4OC/HyWjxzJPTt3Vmhld7q5cU5Vuc9iqdKVPA/YHBYGDz3EoD/9CV8/R0Op1R95ubns+u8X5Mz/Hy0vHaOzwT442wkLuEVCCwc9UM0KbJz6EaN+9ayLo71xCxc+xsSJ31D5+G5amo5Tp/7HwIEzavXzkhPiODh3HKPbHKswfev5ZjQaNo8WbbvX6ueJO5eqqixe/CKRkV/TrVsmGg0cPuzHuXP3M3nyP2uhl1LDqSfkPt6iimHDFnLiRBPatbPRpYuVuXPvqWagtRDAF8gFCoBk7PfiE7erCydPcHrRHDQ2G42HjqZjv/4Ol1MUhTNHDnFh6TwKd27C9+JJ2hRn06f8L4qD7oNWFY5btcQFRmBp25XAoaPpOWYiqz/+C0MPzCZUb7/u06LAMr9uTF4yn0ZRzcjLy+PwsUOkHNpO/ulDkHAaj6wEgs1ZRLtZCXZQtOs0EO0J0RQDlyHjMksvQb+uVZf1MQAHf8Fq/VOVawpvTFlOqGoY9oFDwGI5fQvrFOJ2VrWdyM31olWrIsLDwdMTdLr76jbE28Cmjz9mSqWiG6Cv2Uw6ZfeGVYAdRiPJsbF0nTWLcZ06uTbQW+Dj68uI3/4efvt7LBYL2xbMJen7z0k5tIPf+DgeId1NC+mHdwD1u/A2mUx4e6+rUnQDhITY2LNnIVC7hffe1X9nfKWiG2Bg80ss3fIBLdr+WKufJ+5cmzZ9ycCBHxIUVDY2R+fOOURH/5s1a6IZNeqFW/yEhlNPSOEtqggODmfHjt/Rrp19oLVhwzZw/PjmagZaaw4cLHl+CfuohLc2GqpwPVVVWfTn39Ny0VeMNtvPBl/839/5aeBkpn72X3Kysji2bCEZG5bjduIgkZmJdNArtCm/E+Hg1yTeCqeNfuREt8Wj713EjJ9Kx85d6Vypm+P0v37B7tXj2bd1CVpLEWp0F0Y++BReXl4A+Pj40LnvQOg7sMpnZGdnc+DwPtIO76DgzCFIPItXdiIh1hyaG234lbttkZt79f8PwnKP8dO7zzPxmbfw8XV0ZWRN2XPCaGwG2HuOGAwN5x6VQlRVsZ04eTKJ3iWXtZ465c6AAY56VInytLt2VduyhmPvqBnfuTMRL75Iv+nT601X8ptlMBgYMP0+mH4f+zatI+GtWBq7VR2QT1HBfdtc5k08Tse/fE3rbvXzeum8vDwCAjKqna/TVT/vZunz9lRbu+jzdtX654k7V2HhvApF91W+vpCb+z2q+nwtnPVuGPXEHVd4K4rCmg8+wLZsGfq0NMxRUQTcdx8DHnigrkO7rYwd+zdWrpzN6NGZBAXB9u3TadfO0UBrfkAwkI79nnxXgKY1/pxL545xdNOH6PP2gkaH1acvPWNfIrxxzdchbt2m775h0C8fE6SW/bBGKyYiNv3EF1G/MNPDyl3lf/8c3IM3X4Ejqhsp4c3QdOlD+KgJdBw+ihElxfO1aDQa+sTeDbF333Ds/v7+dBs8HAZXHAlZVVUy0tPZe2gPaYd3UnT+MGlnNmPvgFlVrgmmJPyTpGf+xRpDR0LHvUC/cffdxG117DkRGFh27ZOfX9oNrkOIO0nFdiIlZWHpnPT0xnUV1B3DajAQdvIkvZpXM1jGba774GEs+KYfU9K3Vpm3MQOGBYO/+ThZL/Zhrm9nur0+mxYdHXRtqkMBAQHs3h0DHHU432IJqfXPNJnlNpZ1TVVVdi5dTO7ZExgbNWHgtJm32Kuubuh0idXO8/Q8xNatXqSmdsbffxo9ejyMv//NDFh7a/XE7eL2+9e/jl8ef5xx33xD6a7+yZMkbtrEqitXiH355boM7bai0+lo1Oh7cnPtA62NG5fCypVvVDPQWgyQgX2AhHggAoeVWSUXTh8mfs0Uxrc4X27qYZbP2U736Uul+HahgtULKhTdVxk10FRjJaBS7amocMqq4YJfGMWtOuF/1yjajp9Kv6goF0V8fRqNhuCQEIJHjIURYwHYtmwuqUvvI9RorbCsqkK2CTwN0Nyg0pwjsP4hdv38OFcaD6PjA2/QunPPG/j0GKKi2lFUBB4eEB5egM1mu+V74wpx+yprJyyWPaVTFUUGeaoJpU8fbMuWOTz/kzZoEH3v0KIb7L/l3V76Fwtff5hROQfx1NnH8FhhCiYeC4N0OQAEGGBa0WHSn+vGL4Hd6fHGbKLb1o+7Sdh/+6eSl3cUn0rXqh886EvLlkOw31Lp+vtONXFo1woyEo5CB8fzLd71s2fAneTK2bPsfO5RhpzaQbBGIV+B5V99SMu3/km7flV779VnNlsEZddgV6SqMGhQEfYefrtISnqenTuDKSwcQGTko3TrNvoGDjbcXD1xO6m7wdVsNjCbwWiEWuoSdWzHDjyHDSOmuOpRvk2RkXQ7dgzfW+pC2vCUH2ht/34d7u4HiYhoQkCV2y+dAa4eEWsMtLzmelVV5ZePpzKt9QKH85cm/Ybxj/6zmjfbQDGD1g00d1Ah44ScqKnlI7oz9tIBh/NWF0AnNzjp5k1WVCvceg+i2biptO3Z+7Y7cquqKgv//gKdT39Gc0/770S+GeaauqLxDqVF+mb6BxdXGHUdoNACW3ICMXW+lwGPvkZQSOh1P0tRTnHyZDvat1cxmSAl5QJRUdXcN+1WSU6I24K9nVizZjwjR+YDsGHDJwwd6oSB1e6wnCjIz2f5qFHcs2NHheu8D4WEoMyeTbexY+ssNlexWCxsmfMtloSzqL4hDLj/Sby9vdkx/3uyPnuBWEMa+nI/E6lm2BTciz5v/peolm3qLvASqqqyZMkfCQj4gQEDkigogC1bICFhJE888TI12XeqiTXz/kb74pfQaeDAeRhT6ZjxlnPRRAyfR4u23W75s+qFethOqKrK/IlDmHpic5V5yxq1Y/ia/bi7X+Pat3pm48Yv6NTpqSrdzXNyYOVKuPfe6t975IiW8+dj0OnG0KHDr4mJaXedT7uxeuKW1EE74frCW1Hg3DlITrY/1+uhcWNo1gyHo07cgJUvvcTov/3N4Twb8PGECTw6e3Yt3bO3YUhPTyY1tQmJR2yYjkKkCvlqAGl+d9Fl6ts0bdG2ZEkzsBuwgQqqtRNpaYVcjj9JxpX9FKSfwJZ/DjdrAj76DEK8C7mYrDK+moOuK8/0YPSzeytOVBUovACmJHuyaHRgjADPmFv+7tQpJ+ZETZzbv59NI/vwq0Crw/k/BjRn7Jp9+Pn7Oz0WVzm+bydxW+aitRbjFt2TQZMeRK/X2+/vvWoeCYvfo1PRAVr5V/15TCiAPUpLfIf/loH3/LrK7crKmNmwwZ+hQ+23yNi+9Uv693sIdLV49FZyQtxWzJjN2zhwYBidO9v/KVMST9I0phaLojs4J3L272fL+++jP3QIjcmEtWNHov/wB9oPdjT+SsOiqipb53xD/hd/ZJQxE125f+okE2xt1J9+b35Lk+gWdRdkiaysDD79tA+DBp2jb19YtKgdU6f+236Sz9YZNN431U6oqsov/5rJ2CY/41VS0+05DQcy+9HULwONasLq3pp2vaYR037wHZETrmwnrFYrWVlZZKWlkXf5MgVXrlCUmIg5JQVrWhpKRgZkZ6PLySEhO52JXtk0dvDPaFLhI2NjWnTqjKFxUzyjW+Dfqh1hrdsR3qgRBkP9O8NrP2j0Eo0bf0337hloNHDokD/nz9/PiBFvsX//j2Rk/ERQ0AF69SqguisMi4pg714PUlO74O9/TzXd0s2o6i4O7TlIRnIqXXo+SHBYzB2z7+T6wvv4cUhzcL1jZCTcYleplS++yOj33nM4zwasA9oDB8PCsI0cQec/vkh0h2r64YhS/3hlMPf5bCG0UiItvtyGgJHvYso9Q0HaMax5xzGY4/HR5xLqayYqRMXXs/r1rthb9UjsVQ4L7/xTYEquurB7BHg5uMHn7cKJOXE9K15/nbazZpGqVYloDJGVashcVcPO377LqGf/6NQ46qOC/Hy2fP8Blu1fMcDjCoEODk4fytBzLqgfLaa9Suf+w6qMgbB4cVMmTIgHYNnPIxk34GVwDwT/VqCvhaPdd1BOHNm0ibhvv8WQkIDVzY3QAQPo1bdvxYWcnBOqqrJzyUJy9+1ANRrpP/NhfJvGQB1fIrB/4xqS189DZy5EiWrPwId+g0/l/qpOlJ+fz87lc1AUG31GT7upg3CKovDvN2bglr4cf2MBGblg8oDpD/xAREwHyYnrqcN2AoCcLCjIAzcjBATXeU5UR1VVNv3vM0yzX2Gke3aF3ksJxbA9cjAD3/yWRlHN6ixGgI0bP2XIkKcB2LrVyIBuP6DJSwGrBxB9w+1EcVERCz/sx4xuh0qn7TvvhnvflXRoHVHvc8JisbD2/fdR165Fl5uLtW1bmj3+OB2udVDpJnLCYrGQnZ1do+LZkJ+PsbAQT5MJb4sFP0UhAPvY29ezygCx17haclUBxFbapzapkGSFNI2eHKMXhT7+WAJCIbwxxibN8IppiX/r9oQ3b0loWNhNXbpWUFDA5tmfoDmzF1WrRddhEEMefPIaJxAqSkyM4/DhnwGF9u2nEhVV9Wz0pUunOXbsS8zmZTRrdo4uXWzVdkRITIRDh0IoKhpAVNSjdO0ay5nDOzmz6Dl6Gg8S6q2wL8WXBM/BTPjVaxhC2ju1nVCNjVi/fh2mK0vQ2zKwGJoS2v4Beg2aeOufWcK1hXdhIewpubbr/Hn7hQE6nb1riMEAPXvau4ro9fbp1T1W8y94ZOtWfIcPp5nZXGXeZqAL9kv3ryoAdnl7kzVwIM2efpqusbGuuQbTXAxZyWAxgYcP+Ic5txFTVchNgtxksFnA3RcCosB4/UGvsrOzWfdGV6bGXKoyT1FgRTKMG3TjIRWbYd42uH+o4/lLrzzE+PvftB+JQgFbEeTstx+l4uqfHjybARoI6GvvKnK7cXJOVCc/N5dlQ4cyZf/+0qtnFrmBIcxIrJsJLbDHK5iEMfcz6Z0Pa2G0yuuo5zlx5dJ59n77On6nFzEwMB9DpdAsNtiS6UNO6wn0fnQWjUu6lH//v74EaXahKYAjp4L59a/+hZ9fmL3hCO0J2pIVqYr9u64q2HuN2MpNK8mBq8/vwJzY/J//EP7HP9I6t+z+6vFubhwfM4bRY8e6JCdSr1xh4//NZPjJbQRpVGwqbPIMxWPmr+n39Avg4+J7IJuLUTOTWPDBa/Q+9DNNDBYArAqs8OlEl7fnEtWq9XVWcgOqyYk1P36GYfu/GOgeh1YD2wsbk9v9McY+9UaNV60oCv94oQe/HngQb4+y6dsOwpELjXjwD1/j7RcgOVGdOmonALBY4PQRe9F9ld4ArTrUSU7UtJ1QVZX133yM8r/XGOmZW2FefDHsajacwW/OJqxxk+o/7xb2na4nPz+PjAxfmjYFkwni9vyRVtGNSs6+tQG8atxOpCTGse/HoYztUjYy+srD/nSZsohG4cH1PidsNhs/T53KPYsWUT6SI0FBFH/+Ob2mTq2wvMViITsxkayNG8lLTaXgxAmKsrIw5+djzctDKSwEQFdQgKGwEGNRkb14tlpvqHi+FXu1EBUJYQ5O0ppV2FgIo27ya1SgQKIN0rVu5Lp7UeTpg9UvEG14Y4wxbfBu0YbANh0Ij2lOUFBQ6R0OMtPSWP/M3UzK2lV6SYbJBvMbj2DSvxfj4eFxjU8tcYM5YbPZOHx4PZcufY3RuJlOnVKIjKx+9Zs3Qc4iPXc3r9gD02yFFcWTmPjIH53aTsz7/h+MbLIUX8+y0vhimicXfP/KsInPXP//Tw24tvBOTYUTJ+zPjxyxV27ltWxJtf0TytNqyxqTSg3LTy+9xMQlSyj/9UnS6fg+LIwemRn0LTbh6FiJAhzQ67nUuTMBDzxAn8cew8vbu3T+wY0bSFoxH62pCE3bLgx+5Imbuz4jJw3iT9i/vFe5uUN0ZzDW4EtfA4qisGXxDxQeWYXWWoTFK4z+Q0cRGOBftpBWBxGdwcP+85Ofn8+ZQ7tJPryRovN70WecIcicjM1UTGRHiAl2/Fmr4iHWQfFstkBcGiRn6ckp9sKkCUbj0QQPvygCwlrSOLIlBXmZpB2YxcCWKRXeu/pEUzqO/CsREeXug2HJgaKLFT9E4wY+JdeK+HQCt9vwljQuyInKj0d37SLzwQcZXNI4AezX67H8+Y+069ebnVs2o4Q1oevEewhvco2dktrigpyoIvkk5FX83lXOCUdUReHQllWcn/cOLbJ20cXB7TUyimF7QSPSIvriU7CSya2K0OvApsD6OE9Cus+ka/eu4NMEPAJLGoOb+Bm+Q3KioKCAne3bMzyu6i3XthsMXB4wgGBvbzRaLRqtFm14OBoPDzQ6HVqdzj796nO9Hq1WW/pcYzCgMxrRGI1oDQa0RiNaNze0RiM6Nze0BkPp46bXnue+ExurxqD3w+tP7xM+bCyqRoOqqqiqiqIopc+rvFYUVEVBsVlLn6OqKFYrqqqAopbNV1W4uoyioCg21LwsSI3jxLGjjDj1ExFuVb8fs7370PnpN9HoDej0ejQ6PTqDGxqdDp3eDZ1ej1anQ6fTodVqr/+YdgZtQRparbb0QNvOrZtpsutdIt0rjpuSatKzu9e7dBk5jYLcbIqz0zDlZWLOy8CSn405PxtrUQ62ojyU4nzOxJ3hiYknCHBwkn7RenALG8uY8WMkJ6pTB+1E6WP8OcjJLCv29Xr7etyM0LWv666pvcl2QlEU1n35AdofZzHcK7/CvItFGva1iGXIW98QHBZe9c032U6gqqDY7H+qzfFzxcbSLd0ZPyEVgMWz+zChW3uwXQHFFzz6gc7tujlx/NghCg7/kV4tLaXTft4TzYRHP8XD0/22yIlN//sf3R580GEx/J3RSKC/P+5Xi+cbPPNcW8xAVslfnk5HoUFPsZsbJnd3bF6eKN7eaPz80DVviSEsDLewMOJ/+ZJH009VWdccfTAMjkWXmoQ2PQljTgbehXkEWooI16qE1NI5hxwbJCmQoTOS6+HDKSz8LjKnSk9qmworhr/K+BdmXX+l1eSELawDW9cspTjuEIrRl653P0qjJuUG2y3JidzsTPbt/4HM7F8IDj1Mz56FFX66ln0FYwsd9/ZedRqKg414uutR0aGoGlS0qGhQVS0KGkBrn6baH9GUPNdo0Gh09mmqCqoFNPb3abRa0OhJytQyvOVx2jSpmmfrT8fQ94mjeHpeoxtvDbm28M7OhkOH7Bt9+HDV+e3aQQ27O1THZrOx+qefUHftQp+VhTk8HN+77mJwK/shljOHD3B82x7cLsTTo6CIsGrWcxE4EtEIzZBBZNoKGbpvFVHYf9hMKixt3IGBb39CWOPGJQ2Pxv6o1dq/MZqSx/LTFAXO77f/6FpM9g/S6ezb7B0AUe3LNSolj2qlR9Sq08rNU2w2fv7bs4zJW4C/sWz2yuzGtBv1MEH+/pyJu0xKwgWK0i6jL0wjyJZOjKeZiLLjDKUS8iEtCLpUM1j1t0e0hDTzp1gJRKN44O4RSkBANhEhKo1D3NF79wBDNHgEgW/Vw1znz53lxJ5f0BedAo0Wq2d7ug6YQZPKBZ+tCApOV5ym0YNPyaUCfj1Bf+tHoV3OBTlxlaqqLP7nP+m1cCER5ab/EuDPkPsnEuzjBdHR9u9kk2jwD6z4fdaW+06X/347mlaPcqLKNFMeJB21H60tyi65NswA7l72o7ahrSruKNmsZTtMatkOr9lsYcvqxeTtX0Rv9wQiyn39rAqsyIO7e1T9d1hxwZ8hD7yMR0AjhzlRY3dITqz5/HOG/N//ORy3VAVWAmOc9NlW7AddLwKmxtDJwf67qsKCfPvZCQ1lf1pNpdcl02rTcg2MreaS1L3Z0NQDQo3Vv9+m2u9AYFPt21ntaxxMBw6oMLOL43UvPA9jm4FbDXYSf8iF+6Y7nnfkPBw605QHn/xdte1Ejd0hOVGFC9uJCmw2OFdy+6vUBCi2j1dxO7YTNpuVNV99jPvyTxjiVXbQGeBckYaDMcMZ9uLfCQwMsk+spXbiWhbueIdJD60FYPHcACY0GwhqyVlr9+7X3HcC2LR2EU2LPyE6zL6NhcWw9Oxg7nnkL2X3cb8NcmLFww8z5r//dTjvElAIXG84rpooXzzn63QUGAxlxbOnJzZvbzQ+Puj8/DD4++MeFIRncDDeISH4BQUR4OuLt5sbmvPH7Cu8Tk7ExV1i70d/ZXjCcfy1UKTAupAYop96kQ69+lTJCVWjIbegkKSEK2RcPE9u3EWKk+KxpSajy0zDPT8bn+J8Aq1mGunUKnebuZ4VHjCmmnMpvyRBuI87eQYvTB4B2HyD0QVF4B4ehW+T5gRFtyI82B+/gktoFGuFnEjOLWbLL58x2uM0Pm72lNuT60dB90cZOm4KKDZUxUZqeiZx506RfukkRakX0eQk4mVNpVm7QqK6g0cHWDUfYqsZszctD+I9oXstdvSq8P/nGpe/WqywWTOb4eMevuXPce2QxP7+4Olp7zbVpYv9R11R7I9+ftCmjf251Wr/u/r8eo/ljh3odDrG3H8/3H9/2eeaTXDxJACtwsNoNd5+v9/04GBWbtpJ0YFjtE5Ion259UQD0YlJbPt5Dn0iIbTc/ymjBqYmHmPBB28y+aW/1Hz78zMhPd7+w5xqPwKpGoxsPH+FwvNH0CsWrH4h+HcfxoChw6+zMsc2rF3O2PwF+JXbGdNoYExAAl/PfpuxzaD71d9Yj5K/a3DXwaGLjgvvzEINMX3/xKCBw+wTsk+ALQfUA6BNAtUA+NoHLdB7gc6zZNRArf1Ro6N5+3CadxgM6Eqmlc2zTyv3Wu8DtkL7tJIjWQDofeu84bhpLsgJgMysLNY9+yxTL1/m6vmJFGD7wH5MHdYXzdX7fV6tHKzmsp0KZ3KQE7i5Q3hJ61BcaN/ZqU15yfY/mxmy7ddf4+ENAeFQkG7vOqW9fovm5mZg+PipMH4q6emZLF78X4wXNzI4KI/d6TCsl+P3DY/KZtO2vYycNMNhTpTmQQPJCaWwsNqbhVwtaJ3l6s96ihZ6VFPAajTgrbX/udq1rrYIcoNs67ULb53G/nezGRR3jcPynvqaFd0AxmvUhD5eUGxzu2Y70dByogoXtRNV2Mp19yy/6G3YTuiA0bGjsA4fzoq53+G9fQ6DfOxFfAsPlRZJaznzRBfWRw5gxOMv4K8rgrxkMrOyuHz+OFGh/gQEB99wO3EtLUPHY7GsxWCAdl2zKErQ4eHBdfedVLQs/G4Ww8J/wa/k7FFiJhzlD0x/9nlut5zQ2Kr//rgBOeVeVy6e80uKZ7O7O1YPDxQvLzQBAeiiojAEB+MeHIxnaCjeYWH24tnbm1CjEY2i3FxOmE1lz6+TE00bNybyb5+wffMmChIuYwgMYtSIUbgZDPZeJJX/P2C/HNYvwAcCukC3Lg5DUFWVzMR4jp0+RGZCEvlxZynOyUUpKERvNuNeXIif1UyQaiVCV9Z2Xatzio8eBnoXA8WgZED2OcgGzpctk26GY2bIUgzkafSY9e7YPH1IMufy27bZZduhgd5+ORzc9TGfr/uB5h4FhOoLifFR6XW1LdACV8dUSwNWAcfBlgdUc0v7pHwIbVz9Ntyqax0312nB5uAy5pvh+nsBtW8PR49CcbF9r0Kng4AA6NTp5o/Y2mzXbmAsFjDlgqnYPmy8qQhsNoIjmjB6xhT408uYdAa2rlxD8oLFNDpxkt5mMwYgx6Ni0V2e/56NbJi2BQxuFLm5YzZ6oXh6g48vOt8A3AICcQ8KwSc0DN+wcIIMKgGKgq5cH4rFm7YwMGs/QYaSDM44T9KKfaxISWTMjAcBMFsspGVkkZaSRG5yAvlpiZgyU1FyM9AUZGMozsPdUoivaiIdK35dHcfbJgCM1bQT6UVwLs9AmsYfk08TPBq3JaR1T1p26E7PpDhWLH+A0TFl3T9yimC1eSr3Tv0N6PT2H3f/EMjeBGpPoBDc2oE+1P4jH94bdNfYQ6wJv+6Qd9TegFyl8wTv2jgWWoecnBN7Fy5EefRRppX70djm7o7PSy8wuX1rSEsqzQm8fO1H6wNDwMPLvnOnKiXdhJSS1yo31Q3Ukas7bOX7FZV/XsMzBzfk6vo15Vuhku3RVI5Fa9+50ujsj1p9yaOuwvTgoGgmvNgLtDpOHj3Inn8+w13Gci1WOW56KCjUQsRYyQmgzdixHH7jDTqXu777qjjgnJ8fy0pGedXo9WhU1b7TpKpoFQWNau/VoFXV0nmlz8tPh9Ln2qvTS6aZVYVjxdDLQS8ytWTAm+MWjf1bUnI2WNUA2Kep2KfZZ9u/P0rJdDRly6gly4MGVVM27er7VI39A+3/aThrsTBKVRx2uzucB97uRpLzuNrZrqSTHSXP7Y+6q681lM7XauyFiLakMNdS8ljpddE10i/PDIcytJgULWZVhwU9ZgxYNQYUnRGbzoiid0c1eHC+IBWbLd7hgYQjp6Fl72ng21naiWupR/tOt3M7odfrGDPzESz3PMCyn77Bf/d8BvjY28ZWniqtMrZy8tXtLAnvht7DQjPzWZq5F3L8mAfJHjHETr0fby/3m2onKj9v36gDu/e/QJ8+Flq2hC0HdQzqMvya+05ms5l5H9zF9I47S/PpyCU91s7zGdXvbscbXc9zQtenD8Xff+/wMtDNISEEvfgiO6Oi8I6MxD8igoDAQEK9vNAUFpblxFXe3vUqJ7SqysDhI2s1JzQaDUE+ngTFNIXoSGhZci2o0QPCSirTqLbYtHrSs7I5fzmBrLiLJKz+GXAwyB5QWINjZ8Fu9j/7/eYtQBGJRVkkV3MpatdghYS8VEZU17W4hM0fTH3BsyVEHYezB6FlUNXl9uW2oq33fVzWNbX3ylIsqDYLimJBsVpQlZLnNguqYkVVLKg2K6pa8lyxoipWUK2oNhOqOcP+b6gqqKqN+KwcOqafpomD7dl+IYJe902tOuMm1M19vFUVMjLsyeLlZW88nC3pMsQ72BH2DYC2nSuFp3Js+3bO/vOf5G9YyIP+lqrvA46bwEcLUTU8nZBjgwwFstGSr9ERh442jYrp6V912fWZGvK0RiL1FoJ1NkLdwKMGB1dXmSG2mt/SYxn2QaCK0ZOGPxb/pri3GkhopyG07D6w2tusbVrzBW28fs3eLVCcAW4hk/Ftczd33f1gxUG3rJmQsgSKs0HjBoa29gYosA14VHMI60apKlgy7d2ndJ5gCMDhXuntxgk5YbPZWPDwwwz9/nuu/oZZgXmdOzNm0yZ8i/JqnBMO4628k6WWe155R6y6+cUFcOGQfccqK9neJrm52btqublDTMlRJI2m3L+zpmxaTeZVnmYthisH7HGYcu07QgYjuHuDdwhEdCq3k3Tj362iwkL+Nas39/seo5GDa1qzC2FpiobAHq8yZsbrtz5w3R2QE3MffZQJs2dTvuSyAXNiY5n55ZcuaSfmPTqNKVt+qfK/brNHCG3/+SOhg6/TC8kJOZFy/hQn5n3KELeKZ0dyrbB1wDOM/fVzJVNuPCdUVbXfvchmQzEXYos/gKLYsBXloKhabDo9mzdvZmTq9/hU2oc12WBt+78wroYDrOXm5LDio+7c26vi701iOvxvc0v+8NqXaIPbSTtxPfV836mK26CdMJvNrPr0XUI3z6aPt7k07J/z4d4uFb82igpzzX2490+f3HI7cdW8ed2ZOvWA/fl3LZk64HfV7jtlpqew5T/dmNgtsXTaumO+tJq6i6jotpVXXVE9zgmTycTCkSOZvmVLhbOOcR4eXPzgA+76v/+r/s2SEzXOif2b1uE9+zlaayuOdbCfYAoe/Bs+3l5kXzpHQeIFTGlXICsFQ34GnsW5+CqFhGitRLiDsdw5i3250KwtBFfTe3ZVnH10d4sNLuVBosWTHEMwZt8oin19CIzawci7ctCXO8H5zdcauml1dAm397gxWWBFQgu6T3mdqK4jndZOqHp/fvpoGve0nI+hXDwZeTp2mF5k/ANv18rH1k3hXVeSrkDyZXtXEY0WQsIhqvk1+/MtfelZxs//xOG8Zfn22wHob/K3a6UVRlfzW6mosCYdYm/w+zU3C+7p4fj3dE5qI0a88BmB/n6gN0LjLuB2/cGrFn7ZmklDzgDwy8Y+3PP4TscLFu8HJQ8sRaBGgCEMPILtDYhwqeS4OHYMGMDkK1dKp8VrNBx9+WXGvPVWWbF3EzlR6+JPQnZK1elR7cA/1DmfmXEJMi9VnHYDOVGdU8e3k3hoNEN65fHLf2Ba06rLzDsFk0suWl56uBGtx82jTad+N/2ZdwKr1cqKV17BbfFiQi9fJj08nILYWGL//veajbRaC3Kyslj51AP03r+WaNVMkQLrA5rS6OmX6P7g43WWEyePHeXMwm9om3GMQK2VQ14xFPW/l3F/eOvWD9qU5yAnFK2BX/77JT0S59Dc035W6XKRG9tCJnPPrP+h19f8tz0h/jybf/4tvkVrCPG1cSYBTqYaeevfF9B6hUo7UZ81gHbCVFzMqr/9kYj1n6NVLYS3gMYODpyezzdQ9KtVdOg39JY/E2DNmncZOfJlADasd2do910O953OndxH8qrBDGhTdtZ67v4WjH5mHz6+frUSS13Kzc5m45//jH79evR5eZjbtCHokUfod999dR2aY7dpTuxc+CNp8/9Fi4R9WDVaLjXtS5P7fk+34WOv+3EWi4WUk3tJPbKdrCvxFKZcISsxgUbmA4yIrHrKXFXhveTO+HTtR1Cztoyb8CheXl5ciT/LjpXTiO19CN9yY0tdSYZdZ+5m1NTvSD6/nDPbFqKz5qPx68igu/8Pj8BIp7cTZrOZ1T+/hjZ9JXpbGmZDMzyaTmfYxN/WWnvbsApvsH8TzOayETuvI+70aRLvH0bf7IQK03NUDatn/oGOU2aQG3eRgoR4ihIvY0lLxpaRCtmZ6POyMRTk4WkqwNtiwle1EqSFAK29S99KG4xuU32Yq9JhVLD9uopUi4Zs3CjQe1Hs4YfFOxCNfxj6kMa4hzfFu0kMAc1ao2i1JP/rHoa7n6uwvpRiA0c7/57hk2bYr03yi6jRzehTUxKxXWhMoxB7D5sjeSvo3mt01QVtaWA6bn+u9QZj93pzNLWh2fr11/g9+SSdrGXX563z9SVy9Wpa9+lT9Q03mBO1TlUh7TJkJtkHzvH0gZAo8HXQ16g2FWaXXOttuaGccERVVZbMeZ4ekR/RuKRbVXIyzPvWwOhIaB5oIT7XjeVx4bRqdplhXct+dlOzYWPSGMY+/jPeLrw3c31ksVjIysrC39+/xvcVrW1Hd2znyp7tuPkFMPDe+3FzUeFfgYOcOJtRQHaRmfY9+9TKyKoOVZMTx/ftJG77IlAVGvUcTdf+N1l02NLYsOIfdAz9K0EBsGmvN0Pvy6vNLRDO0kDaiaLCQj5/eCDPhR+odpnPT+gIadYWQ5uhNB86k7ZdepYNZnaDMlJPYlXbERYG+fmQkX6aps0q3lt7x7rvCLr0CK0b27vUmy0w/0ws055b5ppb4LqYqqrOv4VpbbhNc0JVVeLj49HpdFUHMq6JSu3EvH++waS02egqpcDn2X40HRbAXd0vYbPBlkOtOHnBnXuGHSGq3Ai/efmwak9n+sbOpUlUq3pTTzjre9jwCu+bcHjdGi59+DrdzuzBX7WxJ7gZhWNnMu7VGzvjoCgKubm5ZKSmkh1/idM7t9B5w3u096jalX1HoSfZj31Cq94DCA1vhI+PT40/68zhvZz8/jWapGzBT1PIGc+OuPV9mOEPPnf9N1ey6IfHmNj7GwDW7fRh2P05VeNQVTDtBaXkSKyxI+icXDSJKiwWC/MnTWLc8uVcPYhYDCwaOJAJq1bh4ayd9QYuIz2ZjfMHM3nImdLBS9IyYfOxsUye9jwnjpwkIb6IsOhedOk9iPS0ZNZ/M5ExLXbjU+6fZPdZI3lR7zJswrO3x06HEDeipJ24dPEcjbkPgwGOnNbScYxVvu+iXln5zQeMPPD7KoUE2LvMbkqAESUDzloVOJqlI14fidKsPxED7qHzwFE1u91sSU6s3DCS0WPsQ4gtXfo848d/ULrIkm9foL/HhwT52l+n5cAu8x8Z/+DfbnUzhagVxcXFLH37V7ROWEon71xyzTC7oAmTn0ghslHF+uZ8HGTlQo+O9kvjl28NJ6rrt3TpPsq+QAOoJ6TwriFVVTm6Zze56Wl0vWsoXjW5Z2YNzH3xUcYfml3h+u0cm4YN/Z9h0l8+uqV1Jycnk5ebS0zz5jd1VFRVVdb/4MfwPvYzEgt3P8ak+76quqA1Gcwl9yrU+YGxmtHdhNNcOnaM40OGMDY9vXTaGa2W+PfeY/gLL9RhZHe2XVt/wpD5EN3blzUu2w964Bn5BV07lRxJriYn9m9fTMa2hxjZqWzMVqsNFh+Optv0xUS36Oj0+IVwmZJ2wmKxcuXACKIjISsH1OgMAgMD6zo6IUrlZGez//mODPW+UmXeiktwV2PwvEbnqHM5cNoaSlGjbgT3mkTn4VMJcPQdL8mJ+cv+wpRpWwFYsKAlkyefwWazMffDWKa0XodbyWeduqIlK+Y7+g6rp92vRYN26cwpTu/ZgGdgKJnpnzGh3waHyy1dD35+nlj83mFo7DOVxoq68+sJKbzrmM1mY9W/3kbdvQx9djLWoCYYBkxh5BPP1/lZgP17VtDJZywGAySmgqFFIiGhjSoupCpg2gNKyaiSxi6g83d1qA3amvffJ/qll2iplI3suiwkhI6bNtG0Xf0YtfROY7FYWPDfe4jtvhg/n6vTYP7mroy5dx2+7mdqlBNWq5Wls39DJ90XNG9U9lN8JR125dzL+F/NxliTMydC1GeV2omdK0bRt4t9IKsj+Vvp1GVAXUYnRBVb532Nz6o/0MUzq3Ta/oJgcmLfxc3dg5Sd8zFc3kOMmkC7ALX0blKOpBbCkQJfsgPb490plrYjZtI0OqY0J/bs30nGlT9huwDmHFB8x5GQtJdnx5Vdv7vlpCeNx2yleZtuztxsIWrFqu+iie13yeG8OasCmfREUtXLyRpIPSGFt6jWL1/24Z4huwFYsLEVkx8/XXUhawKYz9qf6wLB2MmFETZsRYWFLBo5kknbt5fehiMPWDF+PJPnz8dgqOX7XwsALl04yvHNQxk7sKx3wYXLGk5nv8boSa/dVE4kXrnA9v9NYHy7Y7iXa4u2nPBE2/HfDBj5cC1vhRAuVC4nVG0An/5tINGN89Bq4UzqGB568gf8/P3rNkYhKjl7/BCnln+NviAZm08E7cY9QUyb9hWWsVgsHNuzlbjNP6Gc3UpE8QW6BlqqvXUrQIEFDmW7kewRgaFFF65kZvBA+FZ8yt3W4VwGXNHBXT1gwf4mDHnqAAGBtTSasxBOtvy7boztd9DxvF2DGHv/5qozGkg9IYW3cKggP5/LO/xoE2M/i7rp4hfcNeLxigupNijeDWrJ/aHdu4O2YQ8O5SqnduwgITaWYXllAxMd1uvJ++ILBjzySB1GdmdbvfhtWvj8heZRZb0LVmwNos3AdcS06HLLObF97XfYjv4fg9qWjVxbbIalJ9rT/4ElRDSJqa1NEcI1yuWEqqr8Mvd7Brb+hkYlg+7abLBiZwc63/ULUc2qGW1UiNuEqqpcOH2C0+t+oPDYGoKyT9HFt4CAajouHc4AvxbQzMG9g1edg+Tgftz30iY5kC5uK0vmvMiYzu9R+cYXxSbYeOavjJ70UsUZDaiekMJbOLRiwauM6fQWALsOu9FjQkHVW8dY4sFywf5cFwLG9gjnUlWVZS+/TOf33iOqXOouiIqi35YthDd1cP8qccvy83JZ/sMwJg3ax9XeUXn5sHzfOCY/OL+sy1Qt5ISpuJilXz9KH9+faFJuZ+xCsobDlicY/+i/bug2TkLUqXI5sW37Ptr4vUhwoFJlscU7JzLhgYWujk4Ip0tNSebImp/J3r8M79SDtDVm0rSkpliZCqP7O35fsQW2t/yRYeNnuC5YIWqByWRi3ux7iO2+jKAA+75qcpqODcfuYfqj31cdd6oB1ROy9yYcUrLLBlG7kjuSPpV39FUrWOPtzzUaMES7MLqGKTcrixWDBzP16NHSxM0ANj74IJNnz77p25mIazt8YDW5ZyczfXjZWegDJwwU+33Nvb96oGzBWsoJo7s7U5/+kUvn/8T8nydwd6cLGPQQE64Sw39Y++HPBPb/L937T7iVzRLC+SrlRE76PoKjqxbdAOFeG0hPTyc42MGpPyFuY6Fh4Qx/4HfwwO9AtVKQsZHtm7eQvGcrScX7gAKH79NrwWY1uTJUIWqF0WhkxhOL2Ll1AbmXNoBGS1DEaGb8aoyDOyM1rHpCzniLKs6dPURYQVd8vO1n9VK9D9O8RaVrLSwX7EeoAPTh4CZdBGtTcXEx544fJyA0lMaRkRxatYqCyZPpX1RUusxuoxH9nDl0nyAFmDMoisLCHx5nYKtvCA26Og3mb2jOXZO3EBIaUfENTsgJVVXZsOQTfOL+SK+W5tLpeYWw8lxvhj26iKCQcIqKijh/5gSBwWFENL6J+3IK4QyVcmLVwheJ7b7E4aJnL4G22Vmat2jhuviEcLVKObFz62lidk8kzEGv2o1JUfR4+Rg+Pndml1shgAZXT8gpMlHFoa1/xqfkRtCbD4RXLbpVs30QBACNFvTNXBrfnUxVVZb/5S9s6dQJvx49SG3Ths8iIzGPHl1adCvAnDZtaHX5shTdTpKcFMeir6KZ0res6E5MhSX7fsPUX5+tWnQ7KSc0Gg3DJjxL+0czmHtqDKnZ9uk+njCt024S5zXmwz/2ZevnHfE/0oOUJW1Y9M9xXDp3vFY+X4ib5iAnLJo2VHeo/1xSO6LkUhlxJ3OQE95B3ixPsd9Ksry0Aj2FLR6Rolvc2RpgPSFdzUUFVquVSL81pa+1/o9XXcgSZx8IAUAXAVq53VFtWf7qqwx6+218S15HFhbStbCQFUA29g5p+373O6Z9+GGd327uTqCqKju2zCcneSE68jBrWmFRAmjp+zqTh1pLl9u4x5vQDkuZOPMuxytyck54eXsz7bnlnDq6g51LpzK+cxJaLVxMVniizy68PezLNQkuoCvLWbroEkG/3ik7baLuOMiJXoN+y8aN8xja80KFRbNytGh87pMBpMSdrVJOFJtUUo9NYuYTsHwxFF3R4ucZhtW7OV4dZjB26lN1G68QztYA6wnpai4q2LTmC+6K+TUApy5oieqfi6eXV9kCSjGYdoOqgkYH7r1B41bN2sSNKCoqYnvHjgw/f77KPAX4VK9nyNq1tL/rLpfHdidSVZX5/3uGwa0/JSSo7LrTTbshJBDat7SPwLloW3/uvn91xTwoz8U5oaoqq+bMIiD5TQqKbAzrUnUZmw1W5b7F2JmvOC0OIap1jZw4dXwXp/e9SovQrQT4mjhyoRU2z5mMmfwXOZgo7lwOcmLOVxOZPmQlAKkZkGJcS4fOwyQPRMPQQOsJOeMtKsiM+zuU3LHoaFwv2oysVGxYL1HaV1DfpEEkiaucPnSI9g6KbrBfE9JswgQpumvRvl2r6RvzeYWiG+Cu3rB4Heh1Wi5b3+feXz9/7RW5OCc0Gg2j732NDas70SV7ssNldDrQFZ10ahxCVOsaOdGmfR/atF/LpYvnSMtOZ8i0rhiNRsfrEeJOUSkndu9YxOieK0tnbzk5k6kPD6+b2ISoCw20npBrvAVg72K+fs1C/IxnURSwWKB5l9cqLqQUgDXZ/lyjB32k6wO9gwWEhpLh4VHtfG1AgAujufOlxi+gcbjV4bzWMZBiWMTwMdcpuuswJ6KatSY9r/qGyqb1dlksQpSqYU40i25B5659pOgWd75KOZFX6IuS9BC+JT/Ry7cGM/H+/9ZdfEK4WgOuJ6TwFmxa8xlrfuxGe5/JtImBFZtg9gIjXXuMsi+gqqAUgflM2Zv0UfZkEbWmafPmnB840OG8BIOBkClTXBzRnU2rKax2XrA/eHhU07Uc6kVOtGjdjpP5AxzOi0tzo1Hbe1wWixD1ISeEqFeqyYkVP4+lb5diAOITNbQZsBp95Vu2CnEnknZCupo3dDu3/EQz4+9p1q+sCGkcDodOqhw5sI7OndvZBz9Qsu2JovUDQwzoG9dd0Hewdu+8w5pLlxhx5gxXr/JK1OvZ9fTTTImNrdPY7jhunbBaf8DR/s6hc83pP6mv4/dZE+pNTrQd9g7r1t/PsFbnuHpZYEKGgYPWZ5jYZ5jL4xENVD3KCSHqhWpyYuP6RUwYsLt0sQMJzzBxULe6i1MIV5F2ApDCu8FLj/uWvn2rnvnr0tbMst3/pHPbkq621kT7o5IDaiHSWcI5WnbvTuC2bSz/5BMMp09j8/YmaPJkpowbV9eh3XEGDn+K5XN/ZMKgwxWm5+RpMBln4uGo2781Ecxny55DneZE6469CQrfxvJVn2AwncWm8SGk9VQmDhjt8lhEA1XPckKIOldNTqSlXCbI9gLuJVdXLNjQlEmP/6NuYhTClaSdKCWFdwNnUE9df56SB0q+/bnGDTRGUDJBF+SCCBueoJAQxr35Zl2Hccfz9vam+/BfWLTpJcK8NhLok82FlLaoXtMZM/lVx2+yxtsf61FOBIeEMe6Bt13+uUIA9TInhKhTDnJCxcCmVX/inhH2cUVOXdDSe8x6GcFcNAzSTpSSwruBs+FX/Tyl5IyfNalsoi4c0NqTpwElirgzNYlqSZMH55OZmUl2djbDh0RWfy9h1WK//QVITggBkhNCVFZNTqxctZnJQy8D9sFr44rfYlST5nURoRCuJe1EBQ3r/L6owmoYiqJUnW42g2rsASig9QQNoHEHXcnI2hoZiVbcOQIDA4mJiam+6AZAb7/XpOSEECUkJ4SoqGpOXL6STavQeeh09iUWbu7IqLtfrtMohXAdaSfKk8K7gRs27k1+Xj+UgnKXeefkwS9bJjBy3O8Brf3+eoZ2YIgCNPbRB3UhdRWyEHVDowFdIyQnhCghOSFERZVyQtG1Yf/Of9Giqf1+xQeOGxh+z7q6jVEIV5J2ogKNql69e7loqCwWC1vWfYs5bxsqWrwChzNgyL3otID5BNgyyhbWGMCtPej86ypcIeqOapOcEKI8yQkhKiqXEwvnfsikAUsBKCyC/WlfMXDoY3UcoBAuJu1EKSm8xfUpeaDkAgbQBYNGOkqIBk5yQoiKJCeEqODUsXX45IygcZj99c/rB3Lvr7fUbVBC1CVpJ6TwFkIIIYQQorZYLBbW/xBG7IAsALbu96Db2FS8vL3rODIhRF1qeIcahBBCCCGEcJKF/7u3tOjOygHP6J+l6BZCSOEthBBCCCFEbTiwdynDOy0ofb320ES697q7DiMSQtQXUngLIYQQQghxiwoLCii4cC+B/vbXq7f7M/GBOXUakxCi/pDCWwghhBBCiFu09IexDOxuvz9rYio07bEcNze3Oo5KCFFfSOEthBBCCCHETbJYLGzbMJtxfTaXTtt5/le0ad+vDqMSQtQ3Mqq5EEIIIYQQN0BRFFYt+iu23AW46S6Tk52GuxuMHgQrtjdi/KNX0Grl/JYQooy+rgMQQgghhBDidrLox2cY2fHfeHuVTTOb4dsFMHLmOim6hRBVyK+CEEIIIYQQNZSYcIkon58qFN0Abm4wpI+O3JysuglMCFGvSeEthBBCCCFEDR3Zv5Du7TIdzmvR1EbCxVUujkgIcTuQwlsIIYQQQoga0mgMKEr181W5klMI4YAU3kIIIYQQQtRQn4H3s/VgY4fzDp/2o13XGS6OSAhxO5DCWwghhBBCiBry8/fH4v0ccYkeFaanZei5lPckTZu1qqPIhBD1mdxOTAghhBBCCCGEcCI54y2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeEthBBCCCGEEEI4kRTeQgghhBBCCCGEE0nhLYQQQgghhBBCOJEU3kIIIYQQQgghhBNJ4S2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeEthBBCCCGEEEI4kRTeQgghhBBCCCGEE0nhLYQQQgghhBBCOJEU3kIIIYQQQgghhBNJ4S2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeEthBBCCCGEEEI4kRTeQgghhBBCCCGEE0nhLYQQQgghhBBCOJEU3kIIIYQQQgghhBNJ4S2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeEthBBCCCGEEEI4kRTeQgghhBBCCCGEE0nhLYQQQgghhBBCOJEU3kIIIYQQQgghhBNJ4S2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeEthBBCCCGEEEI4kRTeQgghhBBCCCGEE0nhLYQQQgghhBBCOJEU3kIIIYQQQgghhBNJ4S2EEEIIIYQQQjiRFN5CCCGEEEIIIYQTSeHtJA8//DAajQaNRoObmxstWrRg1qxZWK3W0mVUVeWLL76gd+/eeHt74+/vT48ePfjoo48oLCy85vq//fZbOnXqhLu7O6GhoTz99NPO3iQhbomzcuLbb78tXW/lv9TUVFdtnhA3zJntxN69exk2bBj+/v4EBAQwatQoDh8+7IrNEuKmOTMn1q9fT79+/fDx8SE8PJwXX3yxwnqFqA+cmQPPPPMM3bt3x2g00qVLF4fLHDlyhIEDB+Lu7k5kZCTvvfdebW9igyaFtxPFxsaSlJTE2bNneeGFF3j99dd5//33S+c/8MAD/O53v2PChAls3LiRQ4cO8eqrr7J48WLWrFlT7Xo//PBDXnnlFV566SWOHz/OunXrGDVqlCs2SYhb4oycmD59OklJSRX+Ro0axeDBgwkNDXXVpglxU5yRE/n5+cTGxhIVFcXu3bvZtm0bPj4+jBo1CovF4qpNE+KmOCMnDh8+zJgxY4iNjeXgwYPMmTOHJUuW8NJLL7lqs4SoMWfVDwCPPvoo06dPdzgvNzeXkSNH0rRpU/bv38/777/P66+/zhdffFGr29egqcIpHnroIXXChAkVpo0YMULt06ePqqqqOmfOHBVQFy1aVOW9iqKo2dnZDtebmZmpenh4qOvWrav1mIVwJmflRGWpqamqwWBQv/vuu1uOWQhnclZO7N27VwXU+Pj40mlHjhxRAfXs2bO1twFC1DJn5cTLL7+s9ujRo8K0JUuWqO7u7mpubm7tBC9ELXDFvtJrr72mdu7cucr0Tz/9VA0ICFBNJlPptBdffFFt3br1jW2EqJac8XYhDw8PzGYzAD/88AOtW7dmwoQJVZbTaDT4+fk5XMfatWtRFIWEhATatm1LkyZNmDZtGpcvX3Zq7EI4Q23kRGXfffcdnp6eTJ06tVZjFcIVaiMnWrduTVBQEF9//TVms5mioiK+/vpr2rZtS7NmzZwZvhC1rjZywmQy4e7uXmW9xcXF7N+/v/aDFqIWOWNfyZGdO3cyaNAg3NzcSqeNGjWK06dPk5WVddPrFWWk8HYBVVVZt24dq1evZujQoQCcPXuW1q1b3/C6Lly4gKIovPPOO3z00UfMmzePzMxMRowYUZqUQtR3tZkTlX399dfMnDkTDw+PW16XEK5Smznh4+PDpk2b+P777/Hw8MDb25tVq1axcuVK9Hp9bYcuhFPUZk6MGjWKHTt28NNPP2Gz2UhISGDWrFkAJCUl1WrcQtQWZ+4rOZKcnExYWFiFaVdfJycnO+UzGxppgZ1o2bJleHt7Y7FYUBSFmTNn8vrrrwP2ZLoZiqJgsVj45JNPGDlyJAA//fQT4eHhbNy4Ua71FvWaM3KivJ07d3Ly5En+97//3fK6hHAFZ+REUVERjz32GP379y8tNP7+978zduxY9u7dKwelRL3mjJwYOXIk77//Pk8++SQPPPAARqORV199la1bt6LVyjkoUb84e19J1B0pvJ1oyJAhfPbZZ7i5uREREVHhTEOrVq04derUDa+zUaNGALRr1650WkhICMHBwcTHx9960EI4kTNyoryvvvqKLl260L1791sNVQiXcEZO/Pjjj1y6dImdO3eWFhU//vgjAQEBLF68mHvvvbfW4heitjmrnXj++ed57rnnSEpKIiAggEuXLvHyyy8TExNTW6ELUSucva9UnfDwcFJSUipMu/o6PDzcKZ/Z0MhhPify8vKiRYsWREVFVeneN3PmTM6cOcPixYurvE9VVXJychyus3///gCcPn26dFpmZibp6ek0bdq0FqMXovY5Iyeuys/PZ+7cuTz22GO1GrMQzuSMnCgsLESr1aLRaEqnXX2tKErtboAQtcyZ7YRGoyEiIgIPDw9++uknIiMj6datW63GL8StcmYOXEvfvn3ZsmVLhbtfrF27ltatWxMQEHDT6xVlpPCuI9OmTWP69OnMmDGDd955h3379hEXF8eyZcsYPnw4GzdudPi+Vq1aMWHCBJ599ll27NjBsWPHeOihh2jTpg1Dhgxx8VYIUXtuNieumjNnDlarlfvvv99FEQvhXDebEyNGjCArK4unn36akydPcvz4cR555BH0er20E+K2divtxPvvv8/Ro0c5fvw4b775Ju+++y6ffPIJOp3OhVsgxK25lRw4d+4chw4dIjk5maKiIg4dOsShQ4dKx4iaOXMmbm5uPPbYYxw/fpw5c+bw8ccf8/zzz7tq8+58dTCSeoPg6HYAldlsNvWzzz5Te/bsqXp6eqq+vr5q9+7d1Y8//lgtLCys9n05OTnqo48+qvr7+6uBgYHqpEmTKtw2Roj6yJk5oaqq2rdvX3XmzJm1GLEQzuXMnFizZo3av39/1c/PTw0ICFCHDh2q7ty5s5a3QIja5cycGDJkiOrn56e6u7urvXv3VlesWFHL0Qtx65yZA4MHD1aBKn8XL14sXebw4cPqgAEDVKPRqDZu3Fh99913a2nLhKqqqkZV5Sp9IYQQQgghhBDCWaSruRBCCCGEEEII4URSeAshhBBCCCGEEE4khbcQQgghhBBCCOFEUngLIYQQQgghhBBOJIW3EEIIIYQQQgjhRFJ4CyGEEEIIIYQQTqSv6wCEEEIIIYS4U+3aNYe0tNkYDGex2fyx2YYzcuQbuLu713VoQggXkvt4CyGEEEII4QQbNvyL5s3/SNOmRaXTbDb4+ecx3HvvEnQ6XR1GJ4RwJelqLoQQQgghRC2zWCyYTJ9WKLoBdDoYN24F27fPqaPIhBB1QQpvIYQQQgghatm+fevp0+ekw3l+flBQsNHFEQkh6pIU3kIIIYQQQtQyrVaLolxrCY2rQhFC1ANSeAshhBBCCFHLuncfyu7dHRzOy8zU4OMzwsURCSHqkhTeQgghhBBC1DK9Xo+397OcO+ddYbrZDCtXTqRfvyl1FJkQoi7IqOZCCCGEEEI4yf79S9m793GaNUvBZoPjx6N57rnTGAyGug5NCOFCch9vIYQQQgghnKR79/EkJEwiNvZzAEwmLym6hWiApKu5EEIIIYQQTuThEV363M0tsw4jEULUFTnjLYQQQgiXUFWVfftWk5FxEDe3RgwceJ+c+RMNgo9Py9Lnnp65dRiJEKKuSOEtasACFAMeyFdGCJCcEKKy6+dESsplNm9+mEGDNtOzp43CQli9+gOioj6kUycZ3VncaSrmRFBQGxQFtFrw8yuq49iEqAv2nFBVdzSahnnAVQZXE9dgA84AqYCK/cqEcKAFcpWCaJgkJ4SoqOY58csvY7jnnpVV1rBqVQz9+h3E19fX6dEK4XyOcyIvL4zCQn/CwiAuDho1MuHm5lankQrhGjZSU7ewa9cH6HSH0OnMWCxtiYh4nu7dJ9R1cC4lhbe4hmNAeslzK/bGQws0AlrXVVBC1CHJCSEqqllOnDy5D4NhIC1aFFdZg80Gq1f/lTFjXnJ+uEI4neOcUNVwjh9vR4cOKiYTpKXF06RJZN2FKYSL5ORsZ8OGR5g06WyF6SdP+pCX9w29ek2to8hcT07RiGoUYW84FCAFOElZQ5IMmOsoLiHqiuSEEBXVPCfi4vY7LLoBdDrQaK44NVIhXKP6nNBoUsjKMgJgNEJa2tlq1iHEnaSIrVu/YMKEqt/3tm3zSEn5hIZ0DlgKb1GNgpJHE5CEvetUCvajtypQWEdxCVFXJCeEqKjmOdGkSScuXHB8TZ+igKo2cmqkouZMJhPrvv2SVe+/xa5lSxrUTvGtu3ZO5Od7lS6Zk3Pa5dEJ4XoF6PVn0FZTcTZuvJ/U1FTXhlSHpPAW1TCWPHoAgSXPrzYg5ecL0VBITghRUc1zws8vgE2bLA7Xsn59U/r1e8pJMYobcXjdataO6Er/N58g9otXaf67Sfwy4S6SLl2s69BuE9fOieJi/9IlCwrOuzAuIeqKEVWtvty0WPTo9Q1nkFopvEU1fICrA92EA5qS5+mAF/ZGRYiGRHJCiIpqlhP5+bmcOtWHUaPgl18gI8O+lNkMK1e2JCjoX/j7B7g0clFVbk4OKX9+inFJJ/Eo2TsM0ShMO7mFHX98sm6Du21cOydstrDSJU2meNeGJkSd8AF6YnF83JXU1L4EBQW5NKK6JIW3uIZ2gDfgBoSUTPMseS1EQyQ5IURF184JRVFYsaIXI0bk0LgxjBgB8+Y9xqpVr7F587+5667DdOs2rm5CFxVs//pThqVfcDiv2+HNHN+z28UR3a6qzwmttuySCkVJcn1oQtSBIUNe59//9kBRKk7fvTuU6OiGNahmwzm3L26CO9ADyMF+a5gT2BuSLCAfe8MiREMiOSFERdfOiblz7+Pee+3XstpssG7dI/z611/VVbDiGjQpieg0judFKyZWHT1E+169XRvUban6nHBzCy9dSqdrONe1ioYtIyOTu+8uYvly++uionA8PLrSps1EWrbsUbfBuZgU3qIG/Er+AM6VPJ4HOtdNOELUOckJISqqmhMrV/6eiROXlC4xd25P7r33a9eHJmrk1PFTjFBxWHyftUJYm3auD+q2VjUnPD19Sucajdkuj0iIurBjx2+55x6IiYHVq/0YN24PGs3VMQ4a1r6TdDUXNyCCsutYs4DMOoxFCOeIizvPwYPbKSysySjlkhNCVGTPif37t9Gly39wd7dPXbYsjMmTt6DRVHNKVdQZq9XKT6NHM2P5OtbnOl7mcDGce+FRiouKXBvcHaGsnfDzK7uW1ds7r47iEcJ18vPzaNduVelrq/W3aDSNaaj7TlJ4ixugBaLLvb6A/ZYxQtz+zp07yKJFseTldaRRowFs396RJUteRql8UVIFkhNCVKQlPt6CwfA6jUouZ92920ivXvswGt3rNjRRRUZyMktjYpixahVhQGgmzM2G4pKfvRQbzM2D/u5wT8Y5VvVrSX5uNdW5qEZZOxES0gRzye3t/f1NdReSEC6yfv2faN/e/oOyd6+B4cP/TEPed5LCW9ygEOwjFIL9mtaUaywrxO0hKyuDc+dmMHHiajp0KCI8HEaMuMDQoe+yYsWr13m35IQQV+Xn53LmzFg6dbIBcPGiBh+fnwkNbVLHkYnKjm/cyNlmzZh0+XLptLjQRvTddY5tf/6CVY/P4txHCzB360ejkgsTJ+YnsKVfc7LS0uoo6tuVvZ0ICwskOdk+JTRUIT8/v06jEsKZbDYbfn7flL6Oj5+C0Xj1lnsNc99JCm9xgzRA83KvLwJVzwgmJcWzbNnLrFr1CMuWvUJKSoKrAhTihu3Y8U9GjjxdZbq3N7i7/0zRNbtX1iwnhLjT2Ucw78nw4fYzorm5EBf3DO3ahSE5Ub+sfucdfIcNo4/JftbVDPw0ZAhj4uKIbN6c4Y88TuwfX6X/3ZO4b/k2fuoai63khNQYUzoHBrYg5bLcDqvm7O2E0ehGWpp91zskBJKTZd9I3Lk2b/6Mu+6yX7Z36ZKGwYP/UW5uw9x3ksJb3AR/4Op1SibgSoW5u3fP5cKF3owd+y6xsd8yduw7nDnTi717F7o4zobtxN7dLPvD06z89UyWznqF1CS5dUl1dLqTaKv5NWzf/gLHju29zhr8uVZOCNEQzJ07kWnTzgD2EczXrp3EXXdNRHKi/rBarfw8diyDXnmFSNVeSScCa195hRkbNmAwGKq8R6PRcO+cFcwfci+mkuJ7mC2XM0Pbcvn0KRdGf7vzB4LIybGf8dPpICND/v+JO1dBwdulz/fu7UtwcHilJfxpaPtOUniLmxSD/WgVQDxgAaCgoIDs7Jfo3z+Zq2PoaDQwcGAiaWkvUVxcXBfBNjhrPnoP7YPDGLfoU0Zv+Ilx373DsYn9OLZlU12HVi8pim+189LTISFhGIsWPU1R0bUGXHOcE0I0BCtWvMbEiUtLX8+d24vJk79DcqL+yExJYUnz5ty7YkXpsEb73NxIXb6csW+9dc33ajQapn35E8vv/jWFJSelBlJI8pgunD90wLmB31FiKCz0LH2Vm3uyDmMRwnkOHVrLiBH26ypycqBLl0+qWbJh7TvVy8LbZrOxZc4cVn3wAYc2barrcIRDXsDVI1dWIA6Abdu+ZujQiw7fMWzYGbZt+84l0Z06uo9Vv3zA+mXfYr46kkkDcfHUSUK/fJs21oLSaRoNDM28xPm/voSqNowBLG5EaOgUrlypeqYH4Px5mDjRysSJn3LypB8LFjxCXp6jwYUc54QQd7p9+xbSteus0hHMly4NY/LkzWg03khO1A/HN23idLNmTI4v6x6+JDycyLNn6TJmTI3XM/nDz9n44EvklBTfPbUm8if35sS2zbUd8h3KC7M5oPRVUdHROoxFCOc5d+6F0jZhw4YYWrbsXs2SDWvfqd4V3sc3b2ZZz550ufdeYn//e4JGjWLOyJGkSzfZeqgZZV+hBKAYRcnEQU81AIxGsFrTnRpRQX4+P/9jMtq9g4n1/j39rI+w7p+d2btlgVM/tz458fO3dDE7HnW2z+ndfHBXN1a+/SqHt2+VHgglevSIZe/e33L5ctmXV1FgzhxvfMpuu0q3blYmT/6WS5cCmTdvOtnZGZXW1IzKOSHEnSw+/iRG47TSEcx37TLSp8/+ciOYN0Nyom6tefddfIYOpW/J770Z+OmuuxgdH09YVNQNr2/sa39l92/+Spp9/Dw666zoHhjKgZVLr/1GAYCilA00aLFcRHJC3GkuXz7LwIH2g0pWK4SFXbtHTUNqJ+pV4Z2fn0/8448z4eBBrnb8jDSbmb52LZsef7xOYxOOGIHIkucqcIHAwB6kpOgcLn3lioHQ0F5OjWjVd79hWquFtGpk7xLsYYQx7U5hPfo0V+IvOPWz6wtdQfX3Bg3VQqdzhxj97Vt0fngQSW082NDKhwUD27HgV/ey4Yt/cfHc2QZ5VnzSpA9ISVnK8uW/YtWqe1mx4jVGjoyje/d05s2bxrFjZd/rjh1tTJ06l5SUUH755W7S0q4eGKyaE0LcqfLycjhzpg8dO1oBuHBBg5/fWkJCGpdbSnKirthsNn4aN46BL79MVMlvehKw9k9/YsbGjQ6v566pkc+9xIk/f0ZiSfHdWq8Q8NQEdv3yYy1EfmfT6SLLvcpEckLcaXbteoawMPvz9ev96dv33uu8o+G0E/q6DqC8rZ99xoizZx3O67xhA6f276dN9+q6Koi6EYl9aBYLkEqvXoOYM2cw9967ocJSqgrbtg1l+vRhToskIz2NcNtyh4Nk9Y1JZtnGz2jy0PtO+/z6QteqA0UKeDj4/7DXBJ3cyl5HGyBazYfkk/a/zXMo/hscsWlJ9A6kMLIFhg7dCB04nNYDBhMQGHhDsSiKgqIo6PX16qemWj16jAJGVZk+deoc8vJyWbDgWZo1+55u3eyFRuvWCq1bL+XChWVs3DiMgQO/pFGjijlhzxGfKusU4namKAorV/Zk2jR775qcHIiP/xd33TXQwdKSE66WmZrKxl69mBFX1m3zgMGAZuFCxo4dWyufMfjRJ9nt60/xH2YSo1eJ1qu4v3gfm3NzGPzY/9XKZ9yJ3N2blj7X63OQnBB3kvz8PBo3XsXOdRAWCTbbs2iuDvp0TQ2jnahfe8Px8dUG1LKoiFX79knhXe/osXcRsR8w0WguMHz4D3z6aSQDBlhp0QKOH3cnLm4so0Z9XsPkuzGFBQVsX/0fjmz6hAf7Vd+VXW9pGLft6DP9fn5+6bc8EljxtgwWFbY17UhUcBDucWcIzU6lrdaKT6UC3V0LnbUKnU3pcC4dzu2CRZ+SboMdGEkLaoQ1pg1e3foQOSSWll264ubmVmEdl8+e4cAXs3A7sQ291URR0840nvIU3Ufd7ezNdxofH18mT55NUdG/WbToD4SHf0WfPvbxA2JiVGJi1nHlSjRz5w6gT5+38PLIZOeiheizX0fRhuPRIpbB4x9EW93w6ULcRubMmciMGfbffZsN1q17jClTnqpm6YrtBJwHujg7xAbrxJYtZI8axZRylxItDQuj5+7dhDdteo133rjeU+/loI8Pp568mzZ6hUY6MLz9FGtyshn5/Mu1+ll3Cm/vslsoeXhcHYtFckLc/sxmM5//rjd9FOhogMvFUBS0g7gWJ2jaqt113t0w2gmNWo/6lC5/801G/+UvDvu/x+l0ZG/cSOeBjo6mi7qlAHsB+72Os7OjMJmaYzLBsWMQHr6Dbt361uonXi228858TYfgk7RqrJKcCVfSoUcrx+9ZlvIM4x7+uFbjqI/mTp1K//nz2eMDzXwg0ggnAyJIu2sC4/76UWmRrKoqKUlJnN28joztG7GdPIRP4iWiinJoqVfR1eAYiaLCBSvEGX3JCY9C06YTuradUTd8xQRrxd4rx1V/ip6dTY/YiU7YatczmUysWfNn/Pz+zaBBFe/zvXcnnP/BjemtzKWj++cWwTLLTO59+X9SfGP//plMJoxGo1MOyAnnWbHiNYYOLRtM7aefenHvvbuu8+9YsZ2ATsCN9aCpTXfq92/te+/R6qWXaFqya2cB5g0axJQ1a3AzGp32uSd3bMV8/1A66+y9gXJssOWBPzD+jfec9pm3q2PHdtGhg32faOdOA337rimZU7c5IRouVVU5tH0zqRfPENO1Ny07dL7hdVitVj7+7URG5CynYxCU/1ldVNyZUR/vxMPDo/oVAPWtnXCGuim8TSZISoLiYvDygvBwMBjIysjgYNeuDL18ucpbvgc6rFxJl9hYl4d7O7LZbGxb+jNFlw5gM3jReezDNGkW48RPTAOOA7B58zoGD3675LkngwcXXON9NVdYUMD2VZ+Sd+ZLOoSco1Xjql/dedtgSv+KCQ9wKD4A78GbaNGmU63EUuuqyYkbdWrHDgL696fk0hr+M24s3X//e1p27oKfv3+N1mG1Wjl/4jhxG1aRt287unMnCEpPoIWtmEY16COz1AbjWlf9NwBYFHYXE2dvrPH23A6sVitr1ryF0fgBw4blA7DorzDRu+qyecWwp8WXDJv0q9oLQDGDKRlshaDzBGM4aN2u/746YrFYWPWXv6Bdtgyv5GTyGzdGM2kSo199teIBiVrKCXHrEuIvcGD5vzHknSU1N4egzlsYe4993tKl4YwcebHcYGrXUtZO2Eey7UHZbWRq0TVyQlEUVv78FmryQry4QqEahi1oLKPve+uWrnl2iWvkhM1m45fJk7l7yRKu3qwqGdj34ouMe/ddl4R34fBB0if3pZfWBEChAqvufoLJ//iPSz7/dpGamoqHRxg+PnD6NLRsub7kt69ucuK2Vi4nMgoL2fHjj+j37wetFmufPgz6/e/xCwi4/noasIsnjnLwvd/SJ3EHEXoLp6xeHG0xguGzviQgOLja95lMJpITE0k9f5K9K77CL3sjQ2KyMVng2EUIt0HPklt0m2ywqddHjHro2RpEVDfthFUXzPqlX2JLWY1Wycfi3oZ2A5+meesbPwhxLa4vvDMz7adBlXLdYA0G6NwZvL3Zt2gRGc8/z7CLF9EDOcBaoBeQpdfju38/0Z3qafFUT6SnJLNu1j2Mtm3Dz81+ffW+/ECyB/+JEQ+/4MRPPgBqNvPnvsiU6fsAmP9LJ6bcc/im1+jozHZligK7zrqRYOuLX8uHyTj1HSOabibYV0FRYOvZRmhavcGgMfV0gL7r5ERNKYrC8kaNGJ+aCsB6Hx8GpqXV2lmOvLw8Tu/cRvLmtRQf2YdH/BnCc9Npo7XhVa5eWukGo6vpzbg5E/L0npiDm6Fp3oXAHkNoNWg04RERt/VZJ1VVib90kc0Lnycqehn63TYGNHa87BfHm9D9oa/o1GPore/oW7Ih7yiotrJpGj34dASD362t20l+vu8+Jv/4I+V3+QqBFb/+NVM//9w+oZZyQty6Y3s2kbnkYQaFlV0rnJIP27ygcS8jzZufrzSY2vXY2wmKssEUCppw8AwHt1r6d71OTsz7/ClGh32GV7njBGYLzL8wnRm/+7l2YnCGa+REVlER63v1YuqlS6WzDhgMMH8+3caPd2mYV86e4eKYrgzEPsCpWYVFg6dxz1c/39a/8bXJZrNx4YKeli0hKwsUZT1BgdRZTty2yuVESmoqu195hfHnzpWWaCqwoFcvhixbRmBISF1GWm+ZzWaW39eHSQUHK0xXVfjCszfNR91D4ZVzWFLiITMRQ14GXsXZ+ClFhOqshBthjxmiBkBUpf/Fh+PAkALtSo57rGrya2L/+HkNI3NtO2Gz2fj5m7eZ0nYj7uV2TnZfaox799l07jWidj4XVxfeigK7doHZDPn59lNiWi3odODrC717g0ZDfn4+2778EtLTSTWZGPjBB0SXrGKrpydtz50j+Oq9S25EdiYkxkNBHrgZITQCwhs7PjXnbIoCNivoDbX++Qtemc7kvLlVpp8u8ML60Bza9x0GhpqcnbhBSjqkzWfZ7j8y7m77gDvLfprGuLHvgW/Nryu7WmznnvmKjsGnqi+2z+hJMLUnqvNMeo76fenZMlVV2bV5MTmJe1F1vvQe/hiBQdUftatTNcyJmlj83HNM+OgjAHKBs4sX0/3u61xTfYs5oaoqiZcvc27jGjJ3bUE9dZj8lOM82MzmcPmNGdDbHzzLDXyfb4WTxXqSPcIwN26NV8cBNBk0htadujn3DJSq2n/UbRZw961xTqiqStyFc5zfvpTsYxswJB+lkTWRdn5WvAxwJR+yQqFjNbXIqksQOxxOxGs4l9UEm29/IjrMoEvvURhrepBEVUGxQvZOUIpAVUoaEA3ovUDnAf69a7YuFzq+axfGIUNo4eA2dnsMBo5NmUJ4s2a45eXh7uODu9GIZ0AA3n5++Pj44BMWhr6/gy4ttamW2onCwkKuXL5MWHg4fn43uXN7C+1EcXExW375EiX1LDaPQHpO/jWh4ddpNyvlhKo3svj1QUwM3lZl0bhsmGcaTYdO/VAVG6CiKjZU1Qaq6vgRFVXJh8LzqDYLoEFV/dAAqt4TVe8BqPbvMwqqqthjKvdag2qfjlrynS+Zr9pfq9ZsNKoNUMqma7Sg0ZCZr6ND8An6tq76+3QhxUhh5w106Nrvhv4/u0S5diL98mV2Ll2K4coVFA8PcmNiaPL99wwofz13aKj9eu5mzWrn828wJ1KvXObw8I6MsOUAYFNhbtdR3Dt35a0X307cd3LoJtuJ69m9243evS0AnDi2jnYh58BSBBiANoAOfKNvaN+pikrtRH5ePpvXLUJnuoCqNeIW3IfBU2bdNoOfVlBp32nJv//N3atWVVlMBZb+7nfc/Y9/1O7n3yb1hMViITMzk8zkJHLjLpJ/JY7i5AQsqUnY0lM5f/EUvw2+jIeDmxGdLbDnbpvr1LpLPODuu6qZtx3uDrU//zjNi+ad/VDRoKKFCo8aQFvutQK2fPt3GC0qHoAWdO6gd7dP02jt0yo9ajQlzzW6cs+1gK7k90cDliw0KKDRoEHDuUvJ/Kr/Mfy8qm7D0rN3Mf6Z2uup6drCOyMDjtrv68bRo/YRWcpr3dp+NkOvtxceJY+b58+n05tvcrWzyPKgIIbs2oWnr2+VZUsfK3/5M1Lh3ImqMQWHQ/M2tb6p1bLZIOk8ZKeAYgM3dwiOhOAbOWNQkdls5sKp41zZvZEre7cQk7mMQRGOC59PE6LpO+03tOs7HGNkx9r7kVBVyDqNJWMj56xP0bYtWCxwfvPfadM8BoI72Rss1VbypwC20teF+fls3zCX3EuL6BB2idbVFts6Egqb0bT1YHp074rWt4N9pm83MPhWeU+9d5M5gV5f4XnC5csUDR5Mi5J0ntO/P9NXr66TnFj+8VvErnrV4TXi85NgSg2OmSkqnCuES1p/8oKj0bfuQWjvEbTpP+yGR1Z3qDgPko6D9erOqgZ8wyC0Yh95RVG4cOYkF7YvIe/EZoypx4iwJdPO34Z7NfsqigorcmFcj6rzVBWWXoG7h1Sdd/qKhjNpIZg9O9OoVSxduw/Aw92APU+UcrlTUmxY86HwXMWVaN3Au2QAk3qYEytfe43Rs2ZVO38V4OhiIgv2g0l5QL5GQ5FWS7Fej8lgwOTmhtVoxObhgerlBd7eaHx80Pr5oQ8IwBAQgDEkBPeQEDwaNcIrPByfwEB8/P3x8PSsWAjUQk6YzWaWv/I8fpuXEZUSR1JAGKl9RjL8rx9fs8tjalIi++Z8ha44D/foDgzo0wtdXvpNtRPnjx/i2McPEms4ilFn/95tLwiD8e8yYNLDFZbNzc3lysVzpJ/cSe6p7ZjS4yAvFaMpm6y8HPp3txBTzcmi5ZdhrIPvcn21ah/EOsjL0vnZrxI7o/rvZ50paSfOnTnDhT//mRFpaaVn9dKADcA0wArM69yZKZ9/jpuHR5V2okaPtdROZKWlsWNIe8aa0kqn/diiL9OXbUWnc3zL0Wtywr7TddWwnbgZa9f6M2KE/cDE9lXv0b+NH6ipYEkDokHf1N679jr7TmUHXa/dTqSkZbFjzTfc3eUKV//3FxTDonNjmfa7hfX/MovKKu07rXj7bcakpDhcdFmbNoz74QeH+06uzIlbVVxcTEZ6OpkJV8iLv0jBlTiKzx3HmnwFJTcbTX4+elMRRlMRHqYifK0m/FEI0lFlEN2rVqkQW824SACr0iDWwe+/okKaGRJNcKUVjO/q+P2rD8AoHzicDT6DIMaJ6Xorlu+BsdXc7fjkFQPuw04QHdOiVj7LtYe5yhcVlQsMKDlqY6syb/DQoSw9c4YRc+bgDozNyGDu0KFMmT27+h/wyslz/oT9iGV+jv1HW6eD4BBIS7afYXT3tD9q7UfG0ZR7Xu20cs+vTr+e+OOQl1n22lwMiWcBFYKbXPOtWVlZnD+wi9T9Wyg6exBd0ln881KI0hTQygPaaOBYPgRc47vR3HqRrttf4NwKuGDyJde/Fe4xvWnSM5Z2XbrjptfZ//8oJT/cNqv9sfw0xcFrVYGsoxw5c4Tu99s/6+gR6BKUDkU6yCwC74gKsRQWFLF98xJy41bSITyeEY3VKmMolJ7ZLm5D05a96HN3+7LrQCv8/3Z8oKHeu8mcqGznAw8wtaTo3qvXM/rFF2Hv3ooLuSgnhjz8W+buW8f09M1oy/0TbXaLpvHfP2VrVhoZe9djO3cA/4xLRJNHjGfFULUaaOUFrciGgoNw4CAc+JL0j2G72UiaT2NsTTvg12UQ0YPHEN2ytcMBy1RVZduyueQdXIrWnI8ltC197vkNIUWX7NteuqCCLSOesydOcOnkAfJPbcUj4xRNSKetv0KLqz8z17hU7HwOXCj2JtezMZdVNxJyTtDYr+K/26JTkO3pw/5zBXSOVtCX+/lq3USldZNU7BfXrOXCCjiZFECxW1tCYwbTrcdAvLzKD0xScdT6kg0u96L+5YRqMKDi+GotB1tTygAElfyhqmU5YTLdVBx5QDZwGSjUainS6Sg2GDDrtFgMBqx6HarRDdXdCL6+aL280EU2RR8UjFtgIO7BwXiEhuARGopPYBA+vj74+Pqi0+tZ9NyTTNr0MwYN4AYtClJQ1/2Pn9OSuPeX1WgcfE83ffcZbj+9zmhS0WggbwMs+LkDA//vFcLDw2+onVBVlUP/eZYp7kdLp2k0MMA7hfU/Psk3iz8hSMnE25JJoKaACE+FduXzTwuUnKDfqULQNc523ET5VKc0GvvXprrdBqWe3eilVMnv//FvvmFCWlqFWSHAMGAZwPTpzHjySfs14A56ldRILbUTAQYdg1ftY+H4fkzKt99RZOa5nfw0pAtTVuy0XwLlon2nm6LYIPFIlXaC7AT7adTAqGvvF11n36kozx37xZSQl7ABAoaAdj8oacBBcB8AhlCH+043uCEA7Nq6kkndr1SY4+UOU1svZ93Cjxg97Q+38Bl1oNK+k/Ya+0jFp06x6I03GPnkk3iWDO6lKApX0tLwMBoJud74N7W876QC+QWFZGRlkXUlnrzEyxQlJVCckog1PRWyM9DkZGEoyMG9qABPcxG+NjOBGpUgLTSuydisGuwN53UoVnsRrXWQgmkmOK0JoNAYgRrQCF1IYzw8Dfg2akxQZFMahYfRxdOdxM8fAq5UXQFgtcGJHEhpBJ3radENjrf/Kk+DhcKiwlr7LNee8TabYedOezGRklK282Sz2b+QHTqUFRpWa5Vi4+fXXmPali2lo57/2KULMz788Ppdl8wmuHjS/jwlAUwlo+XFRINWZ+8eElhL13+UK8ZPnD7JpT3r0VgteMR0ZGD/gehSztuXy0q0L+dmhIBgexeRZp1QFIXLCQnEH91P5pnDWBPO456dSLAlh+ZGK8HXGQsjzwoH3GFwM8fzl12EcdGO553LgfPFPuR7RWJs0oHIzv1o26Ydbm41PBKaeZhFh39k4hN7AFg8x8iEtk+Cvhl4BINvkyrFdvVntvUkmjsR1fk+evQebL83d94x+wKl3Up09gFCNDoI6Gd/vN3cYk4ArJs7l8GffYYBMAPbnnmGoZMmXedznZsTBUVFbFzyM9r4I2hsFixhLeg88h6aRscA5Q5WmQoovnScUxcukHh0N4Wpl/EozKARhbT1VBx2f6qyKQqcKtSQoPOnIDAS92btiOjcl9YdOrHs+w8Znr+MIHf790xVYW12BJED7kWxWog7vpPCzES8rNlEuRfR2l9FX4NG7Uw2XDSV5ErjdjTu0Jd2HTpgLHdbtQ1r51N0diExXgnkWyBJD94toXPMrwgIbMuJ+BzOXzyEkn2QJj6JdIm2YbjGfn9cKpxI9KfQ0Jbg6OF07TkUX23Jdbe3SU4kXLxIcvfudM/KqjJvtZsbqS1a4FNcjD4nB4PZjNFsxmiz4WGz4aWq+GK/q6dnlXfXHRP2Qj4POKOx9zjs6mDg1hQb/M8vmugmEeg8vNF5+WDw9SXDbKbVyXn09KxaKP0nL4yYNp2wmE1YUbGai1G1BtTiArTmQrTmIvRWEwabGTfFjLtq5XKhhX4doYmDzg6qCivjYEyzmm1bgQV2KjC8Q9V5Zit8ctiLls290ZR2FaTkUVP6aP9cLRpNyTRbsb33NwWo2OyLaLxAYwStG6rOWLqs/VFbuv7SboLluhZqNPbuiRqNvYsiNvsAhxqtrnRZjc4TNDoKFX8CC5cS27XqTtTBOH9CRu+jSdPmVebVObOZpGXLSLrvPrpVU1B/160bD/7rX9W2Ezf3ubfeThQXm1jyu18xLb9sx/wXzwjGffw1Hh7lum1XPpFRqZ0g8Yx9uWr2ncqU7FOojh6rmYdadVp+GuTEg80M2ZftRbe7F/iH2be9UYdbOus9f+8TTJlpv/PHoi97MbHjNGAraEpuierRo8K+0/Vp7L/3Gh1otBSbrJw6eZLECzvJS9qBofgMk/s5Pry5/MJIxj69+qa3pU5U2nda8uGH3F35ZEOJJcDdwCGdjnODB+PVsTW23euJTj5Pod6NxOiOtJ70IG3atq/B51bMCaW4kOxiExmBgeRkZ5NnUykqLsKSlYE1JwvyctEV5mEoLsTdXIy3zYyvYiVQC0E60DuxZ7pVhUwbZKqQo9GTr3ej2M0Ds4cXipcP+PlR5O5NYMI2xvhV/U1cYGzHpLe/LDtQXk1OLNq1mQmNd1ZJB5MF3t4UQeueg4mICEVVbaiqUvJYcmnS1UuKFPuj/bUNilJRVSuqmgAUoaoq6INR8QKdEVXvDlcvOyr5U0v+yi5XKsl5VUUtn+PYwFpg72oOgMrZuCx+f3d2hZMhVy0/2YnYZw/cXE8dB1w/uNr58+Bg1HJatoTGlQ6HlD+zYbWiWq38NHQoMw8cAOzdqhZOnco9f/1rxcKk8mNxEZwvGSEv5QqYShquFs0BDUREgX/tDle/4L//puPpJbQ02u/zm2uB5cZ2jJk8Ez8Pd4qunOV8ShqJqWnkF2RDdio+lnwaUUhLTxXjDdxtKNcKZ4u0pOp8KPQJ5aKpkGdbJmCo9B3ZkKAjrtCN9n4mOgYqeNTg4P7ZbDhv8iHfOxL3Jh2J6tSftm3bYjAa7T/wWvtfTm4Bmxd/RFbyMkJDrFiDIb2wKY/cPYtCk4bthw+Te3kpHUPOXmeAtH407fUcPfqPq3oGsygOCi9WDdIzBjyiav4/rL65iZzYt3Qpyd9/j3rpEtkHDtAMGAj80qIF96xdW30u1GFOOJSbBpkJ9qPGqSX/tm4eqGERxKdkcD4ti+zzx9EknyeoMI1WbibCa3gZ9KI06N8ZQhxUaT+cghmtr32UE8CmwOkcDXEWXwq8IvGM6khkx760adMWQ+UqWaurkBNodNiyz3DhwkFOJb/P+GH2H/l5K3sydcwfIKgteISARoeqajh98hhnD8zBnLqJCPfzdIsxY7zGMa/EDDh6xY88TXMCowbQtecwAgJKqi3PGFJzdOxa+QGGgn2AFqtvXwbd/Qf8XPHveg3LXn2VHn/7G+GWsjNJ8e7unHrjDUb+8Y/2CQ5yQlEUCps0Ic/Hh9zMTApTUylKTaU4LQ1zWhrWjAwsGRkoOTmoublo8vPRFhSgKyzEYDJhMJlwt1hwt1rxtNnwgdK/2jo8scoAsde4JHNVAcRWuoZsmWK/C4Ajh3Mh0ACR17sDS/nPyIJRPaqvCVbFlcVoUyCxAJKKdGTb3CjUeGHTuKHz8MEzIIyAqFaciTvJuIjt+FWK4ZdzENH/Nfp3vav6YBzkBHnnwRwHtougTQQiwKuXfVyCcjlReiCppJAAXbnX10jc67QTa+d/QKvCV2kaXHYLwJRsA3usf2D8A29Xv946dmrlSoxjxpSOd1PZqkmTiF2wwP6i0r5TlcfrtQ+13E5YrFbmPf9/zMgsuzRmsSGIoZ98i49PDQZLqqadILykfYxqC4ZavlVaXhLkpdjPbGaXHOD08IaAcPvziI727/SNKsmD+Tt+z5T7dwIw/78dmdL5oZvKCUXVcOnSBS6dWEv25a1o8o7ir79MTEgBTUuurS02w65TcFc14xKvOt+f2N9UHceh3ivXTpw4fJjCWbPokZlZYZHNQATQsuT1Vj1ERkCzSiextvhHEvW32Xh6eZOenETulXgKku1noc2Z6Sg5mZCXja4wD2NhHh42M16KBX9UgnTgr73+/sStMKmQboNMRUOeVk+BzkCxXo/VzQObXoPG3R2Djy9u4RF4Bvjh064r/hFNCAr0x8fL85onKHdtWo9p6WcM0meg0YBFgdXaKFo++CKt25W793Y1OZGt92H16q+Z0Pwi7iX7LDlF8OkxP/7w8nz0jqrZ8upBO1FQUMSq759jStfTFRa7kuHOGZ/3GDrht9fehhtQN7cTS0yEhISyW2JERkINRxy0WCwsbtWqdPTOAmDLG28w+i9/qf5NqgrHD0BWBhQV2i8+ttnAy9P+D9W+O+i0JQNRlBvM5epzpdwgLdVNU0qOrigK2zasptWyvxBqUKqE8XGCG919FZoarETdwM4UwJViuGhxI9PNH0tgY9ybtCCoZUdi2nUmNDiwtAujyWJhybd/o2XmVrr45ZNnhs15EYS36kmXtq3YGneRpOJCrNnK/7d35+FRlWfjx7+zTyb7SkICJIEQZA07hDXIvqogUqjWulTf1mrV1i62fdtf1bf+uvja1rYu1WoVUWSTLYLsmwn7EghhCZAEkhCybzOZmfP+cSIQMhOTOEkkc3+ui4uZc86c86Bzz3Pu5zwLvvYaDEVZdFUK6Rfk+Mpk3KnAmTIN2fYQKoMT8UkcS3D/FHI2/zcLo9IbfM8/yrBQ7hvIhDvy3SbbaWeM5DaVbN/Kmg+1ueCoUZfEMMeAqUvTn7kdtCAmtrz6KnHPP0981Y1l2q6iturOzM4mqjkT6rRzTDQ4x837K4vh6gV1e8F5tWxmH4iIVp+E9RjQoF9oeWUVmcePUHDiALZLmfiWXqY7FfT2afykekM1zHRzo3H4KnTxga433fPVOeBUqYYcZwi1Qb2w9BpOj+FT6H3HQPRGU8NK4cvXWn39NjfdJa3FkLeUNZs+Yt5k9cbmaKaWhP47sfRIdlsxKIrCuawMMtPexXplE5HG0wyOtWJpYl6fwlI4esmPMt0AnMEpGK+t5K4BmQ3+l6/MGE7KQ+sJCe3YGV53L11K6fLl6AsKqIuOJmLxYkbe2kvja9QTzWGz2aioqKCivJyKwkJqCgqoPXIA6+U86oqLsZdVYK+sRLFaUWpq0Sqgq65BX1uLwWrFaLNirlOTeF+nE3/goA5SeoDZxc+YosDGaph5S+KdqoXpbh6yltZBZiWMamKIw63OVEFNFAyMaLyvzgGv5PZi4NCxBEb1JKJbAtFRkZgd1VCSXd+V9qL6Hffxh+BIFJ2JDV/sQ5eXSpwxm/xqhRIfGDEVzuebSRp/Er+A4BbHBIq63jPGPqAxgykQwpI8M+/IV9QT6TvXUHjyffT2POy6LgTGL2DctCVf/7ptqK6uji39+jH9zJlG+xRg/S9/yezf/c6zF/VgPeG021n2+P0sunDweoKyURfIyDdXEBIS7NF6ooHr3yfNjfeutt26r7rEbUygN0P3oY2TBa27941j4uOPHyJIeQdnBRw7H8lT9z+HyVh/bTcxUXztGqePbePq+a3Yig/i6zxPdEAxidHOJhtpAdamwRw3821+mvsocx99o+kTfFPdVE8cz8jgwsqV+B05gqLRUDVqFBGPPkruBx/Qa/lyBjocrAuCOW7m2n2/HOb6QUALHny1RqUTrjmgBC0VOgPVBjNWkwW7xQ/FLwBNQDCG4BDMoeFYwrrg3yWSkPBwQgMD8TEa2iQmioqLSf/sU3RVZSghUYyfdTcWH3OzY8Lq1LHj1FlyT6wgKiwTSxQYA/SMuDMbncHY6nun9qwnCku17Et9FXPldiy6Ckq5g8DeDzJ+pgeXf6WjEu+vqaKsjL1xcUyr765YCJx9912SH3jA/Ydqa+DUEbWbyHUa6HUHhLq4Q2mhyspKTqfvI3/n5+RseIfHw666PO7zIhgbDGY38WBzwplqDZd1AVQEdoWYPvj2GUrU8In0GjAYi6X5nSwvnTvDqb2f4VNXRvLoURzP2k9u/htMGp2NrwXOXPDh0IVpzFz4Ln7+/lzKPkfWrjWUn9iKIf8Y0Y4r9GtiEikANLA6H+a5mWj40wswd/KN961KtsV1lZWVpPfrx6RLlxrtO2A0EnbsGLGJbh6f3aqNY6JZnE44tRfqrOpNnaKoXyS9HgLDoUd9H1dXN0b1HA4HZ0+e4OKuDVQc2YU+9yRdKq9wVWdjzgDXl71QDoeKwOgXijUoFr/YQcQPSqbniCloQz3Ye6IuG6pOUX31ImdOP8GgPmpj3Or073PX4tdadKoL2Vmc3PceNbkbCdedYnBsDf5ufg7W7IN5oxtvVxRYW/A0c7/755b+S7xDK2PC6XRSVVVFUVERRxdO4q7SC42O2WE3cXbIOAKdDjRVlWhqqtDVVpNdVshTPatd/n5uL4Y8fRC+PhacRjOKJQCCuqD1DUTnH4TeLxhDYCim4DB8QiLwCY3ALziMXX97im/b1jY657qavkx6dX/jesTphIvpN00idZOwXhAcg81mIycnh6sFWXRXZtK1Po/96PMR3Pd4mtv/No3UxwSVBeAwgLEnWLqAf4/WPUH0IhtffJGRv/41ITcvKQak9unD8J07CW2LJZM8WE8oisKyxXO4d//6611st2gD6Lf5GJHdm+gq4oF6osWaEROtdTxtCyffX8Td8UUY9VBbB5vye9B78hL6JN6BVelGVs5V8nKOUVWYjqH2FOHmfHpH2Qh1MYTElfJqyMwzUFAVgdWUSEG5hemxm+jZxdbguLQLMYRO/JRedwxu9b/nm6a2thaNRtNglZDqqipWPvkkcRvfZoyLmavBdY+kr1LigGtOKENLhd5MjdmCzdcfu38QBIaiDYvAEB6JOSoav5geBHSPIySqKyEhIRiNxq88v1vf0Jioqa7m4m5/+sSrv1G7ct5lXEoTuZk7HVhP2O12rFYrllsnX/WQ2zLxBsi/eJELiYmMqp9c55xWS+2WLfSbONH9h+x2KCq4Mf1/eKTaQtQCNpuNrKOHyduxmcpDX6A7d4rQa5fp6aila32Cmmp0393wUBnEmNVxHWetOq7qA6gN6oo+cQRBg5LpPnwC3ePiPTaWQC10DVczdpORsYSJoxo2CCgK/Gv9VPoPe4yaqsvUVOVjtxZitxXhtBfjsBbgTyFh5koiAuoICVDw8wOtGTCpfzb8C2a6mftjw2mYNl1NtvOcycSOfIahybMk2W6lTa+/zqTHH3c7BdCGX/+amb/9bfNP6IGY+NoqS+HiCXUymi/5+EPcQHX8Xit99PIzLMx9xWVds6Ysnhm/XorRWd/dVKuDoG4QGtvq6zWi2KA2TZ1UR6NhxUd/Zf7YFQDsPmhm1PyKr7WMS27OBU7seY/KnPWEajIYElt1fSmMDfth5nDXn9uQNYKZT7UgUfI2XzMm9i3/EPOLTzG46sZv7Vm9L+f/6zdMffLHjY4vuJxH5mOjmeC4pWu9Aiujp7HgyZ+pG1oQExVlZWz8/cP0K/yMfr6VFNTo2GMaRf9H/kzvJDdTt9pqoCATatUJn5qKiVVLn+DuEWrDkdUGu7L/yORZz35luW6NCUwj6ysT0RyKorDp97/H+f77JJ48SYmfH7kTJtD/hRfomZTUdhf2YD2hKAqfPH4/c7d8gKn+t3k3PnRfe5Dufe5w/8E2qiea1IKYaC6r1crm3wxhdtfGs2K/fchITB8jPcMr6dnMFXPtDsjK05BbFkQFceiDBxMaO5GEAZPpEhnZ4NidG96k/PTrJPofotZu5IJtLD1G/4yBwye7OXvnUlNTw97RcdxZ5Xr281WV0McIFToDlQY1ia7zDcDhHwTBYejDumCIiMKnawx+kdEEBgQQajETFBaOLjK609w7NakZMfHJm8NYkHJQfb1tEAsePdKya3TyeuK2TbwBzuzfj330aO6on0jkkMFA+PHjdGvuE78mOJ1Oss9kcXH7ZkrTd0FWBoGFOfSwVhCvb3osx1oF5riZnn9ZVRixz71BQu/ehAb4gcUf/Nt+zOW65b9kVtKLLpOQ/cegWxREtrKxfONbMMPNbIUrT5qIWfyxPNn2kNS//IVpTz3lclZogI0//zkzXnqpXcvkEQ4HlF1VW289FBPFRUWk/2Ic082ZDbZfs2rZP/wFpj/yc7UScdSBydfzrai2M2BXZ/NFH01xRRAlR7vQs7v6k5ua8QLT5z3vsctduZzL8b3vU35hHZrSvcwf4/qnfePpwcz40SGPXVc0dvbYUTLffxN9fg724HC6LbifQeMmuD3+yNZULv3jZ0wsPUqAATIc/mQkTmf2c7/HYtC1OibOnTrBmcN7CI2OZ9j4yc1rvW9GTCiKwso34pl/5wUAMs9r8e97muiYr1hu5ZaYwJjQ9PHCpbq6OrLPniUgOFid9f42tPonTzBl5Wv41t8WHHAaCVq+h15DmljvrQ3qiWbxYD2xcdlrpFx+4vpY2JuV18ARB4xPcv3ZvCI4V+hDcV00Dt/+BEQn06PvdHom9G32wxqn08n5c2cxmcx0634bz43TSqsfvo+7dn7caHutE1KfeJl5P/pJmzzlbDPfwJjYs+NDxkQvBiDrgoboUeX4+jVjLofr5+7c9cRtnXgDHFi1iq733MOXD1yXW3zQPPU4fooDQ2J/xi95sMn1CRVF4UpeHud2buHavh04Th3F7/IFYqpLSNArGJsZf0UOyMJEUUgXckMiGVp3nJE+NQ2OqXDArhm/ZeaPmhiP3kZSlz/I9MHvutxXWQVHTsHYJuq7m9lsUFKu/qms1nNwq8JjCa5nUV1bfRdzfrqqtcUWt7hy6RJ5Q4Yw7Nq1RvsumUwUp6aS1FSvDy+TfeoYx/79PF3zdxBMBVnmfmiHL2Hawz9r28rVWQvWtPruXzowjwSNkY/fnMTClG0AfLYnkKkPlLRJOdb+YwFzYle43pf3MHMeecvj1xRfj8PhYM+6lVRfKyBh1ER69nUxlfg3SP6VixQd7kn/3upv/6qtMcx75KL7BlY3MSG81/oXnmfM2y8RVH/fftyhR/vvTfQbn9KxBfOw/Mu5HPzkb9gOr6Ty2hnun+L+2NQCGDcUTuXqya8Kp9bQG5/w4UQmTCFxQDJ+LUlgRCM5Z7I49uh8ZuaeuDF02Qkrx9zNorc//lq90ITK6XSSttKH0UnqsIaNJ37LjLuamfd4QT1x2yfeAFteeYVhzzzDQQP4hcFwi9o7ocoJG3sNZ/Q/PiQ6viclxcVk7dtF4a4tWE8cwifnLJHlRSRqHfg182FspRNOO3RcCQyntlsvTAOGEDF2Er1HjyM45EZLU9ra5RT950XGlRzFVw97NVEUj1nC3F/8/w5pTVv38Y+ZPeRPLvcdPgmZ2b4EBPhhd/piVwJAG4hGF4LeGIbeFIGPbyQ+vl3xDYgmKCiKoOBg/Pz80Gg0XMpcz/n/fI+JkZcbnPfQtQgMM5YyYOSd7fFP9Born3ySqX/9KzdXv3XA8vnzWfzJJx1VrG+0wsJCysvK6BEb22RDnMfYMsGer7429ACDOhdxzsUsjFcS6RKmNlTvyX2b8Xd+1+OXzzi8g5r0RQzrnt9g+65zsURPWU184iCPX1N4n22fvcao6Cf4clWoFfseZv79bhp13MSE8G6b//IHBv3vc0TUJ99Zdi2Vr61iyMy5HVuwr0FRFI59sZ2z6/6Ob/ZWRvgVE2IGfGFHAAzRg7+LnrP5FbDO+CQzv/UTorpG315PXm8jJdeuseefr6I/dRin0Yxx7BQmPvCQJN0e9MnbM1gwPhWANdu7M++Ri837oBfUE50i8QZ4ffG3GLVzGYNcDLH4e62ZwQYnCdgIa2YvIZsCWXYNeZZgKrvGouubROioCfSacCeRXbs26wfR4XCw77N1VJeVMGL6PIKCWzA1rYddupBFwZFkhvdv/KR0xa65zP/umtad2FkF1gOcy8wi47MPMRafQYeD2uBh9Ej5IQNHNdG0K1rF6XSS+tJLOFeuxC8nh6qICBwzZjD9pZe+3mQdwjPqY0JtsdWDeZT6d73lbwzm3klHAFizvSvzHslrk2KcOLiN7C9ewac2HQUdtZbRJI57jt793YzxFaIVlr0+jkV3qjP25xdBkc8W+g+a1PCgr4gJ4d12/vsN4n/7GDH1X4mLdg15//MuyYvu79iC3SQ/X00G3HXtr6qsJG3tuxTvfI/o0iMMD7U1XGljADAGHDpY8wbcE9v4HMsLxrHgdzsk4Ra3vZMn9hGvScZsgsJr4Ox+iciobk1/yEvqiU6TeK//xdPMWv6/LvedsIK/Fnq4eNDlVOC8HS6a/CmL6AaJAwgaPoYeE6cQl9C7U41L3r3139gLn2f8kMtotVBaDpsOjGX8nA+IjGrlWB9rBjjqJxEyxOPQRqMoirQctgNFUbBarZhMJqmov0luiQkMDWPr5Ik9dLOPxd8PamohszaVwcOmtVlx6urq0Gg0EpOiTVRXVZH+aRcmDleXN9yW7sfIeflYfG+aHvgrYkKItJUfEf7st4jXq7eklx1w+ud/I+XRH3RouQ5tW0/u2j8RU/QFGkUhN2wkXWc/zdA753HxXBbHVv4V5/G1DNReJMAIexXQR6qrIGmvwKRuoJ2sDlX90v7DcHBTIPN7lhPuq5BfqWd39RhGPfQmMXGdazyr8F6b3gtmanIpAKvTHuGuJW82/QEvqSc6TeKd+v0HmL75Py73lTngpA26G+Cczofi0CjsPfviP2QU0RMm03vQYK95Ulh8rYi0nW+hUUrxDU5izISFrW9ccFZArTpzIRpj/VgMWQ5GeLFmxsSqN+O5OyUbgE+29mHB9061ZymF8KhjhzcTaZtKRKj6ftmWCSx6bLv6RuoJ0UxHN6difGw2d+jUeQOuOeDA919g2o89NwllS2Sk7aDurXtJ8mm4Gkx6kYETpXoWdq/Br/7WMacGjveCGffcWLXJaoWPN8J9M+HLW8y1OyJIGPkBiXE60nd9QWlBPqHx0xk6dro0oItOZdX7D3H3qHcA+GxPENO+U+L+YC+qJzpN4r3uxV8x650XXK+FqlhwvPwOI6fNlIkpPMl6FBz1gWRMaNikK4Q3amZMpO1ZweDgBRiNUFIGRf4HSeg9pB0LKoRnrXjvu8xP/jcA1TWwP/+fTJjymNQTokUy9+2hdkkKSbo6AMqdsH3xs8z93R/bvSzr/nsRs0s+crlvbTbMuWn46Wod3OXi4XxdHXy+FxJiNWRcfYo5C/+Etu64xITo9K5cvoghL5awYLV33wXNF9zRb6Trg72onug0/ahHPfIE20IbL57tUODajEXcOX+hJN2e5Ci9ESRaM+iaueikEJ1VC2JiRPI9bP5CXb9Pr4NP3rmP9H2bcTqd7VBQITzvriVvsXqbur6IxQeCrE9QeDlD6gnRIn1GjyFgdTppigmAAC1MWfonVjz5SLuVoeByHuv+5ydUp610e4xBC1drYENROB/5L8Q40PV6rAYDXL5qwSfxDPMWvYJWKZeYEF4hqmsP9h5Tx3X7mCEj7f+5PtDL8olOk3iHdelC+MtvszpuGMWK+tj7iCmQFZMWM+sPr3Vw6TqhuvM3XuvjQNNpvkpCtE4LYkKj0WCK/BXrtsKBE/D0t88Sp5vKhv8MZ/8+WX5P3H50Oh3Dpm3jdLb6vR/Ux86ONdO53qlO6gnRTPEDk4hJPc5OjTpPgI8W5mz4Fx89uIC26qR55sRRVj33HTZMjkD3QAyzd/0RP2ed2+OLjV3ghSvMXFrIrJ//C3+L6yVVAaK7Dyc6pqf6Ru6dhBcxhDx0/XWU/xbXDxe8LCY61b9uwMRJzE1NI/PVNaT+7J+ErNrPwjc+wGx2sW6DaD1HETjL1ddaX9BFdGx5hOhorYgJm62CMUMhZRSYTRAeArPHHEJf/DhZpw62cYGF8LyY7r25WPsSNnX5Vubfmcuala9JPSFaLLpXAn22ZbJJHwSAUQMLdq/gwwVTPdIzyOl0cnBrKp88NputE/2JfjaJu0+8x0zjVcLqx2Pbq9UJlm+lKOA7YiHhXdQZzv38/Ci1DnB7rTpt/T65dxJeZtzkZzlzUX0YOnqQlbQ9tyx564Ux0akSbwCtVkvyrDlMf/gxuifI7JAepyhQl33jvSEOlwPrhfAWrYgJp9OJUr6c4MDG+wYnFnL6yOseLqQQ7WPqnJ+yaudQALRaGBa3gtNnr0k9IVosIjqGEbvPss6s3ozrNLD42Ocsm5mMw+H+CbM7VquVHcve5pMlyaRP9CHpDzNYkLOeSZZKLDfN41Rhh001QZT3ncW/SgdgvynPtzthuWM0Ex7+TYNzh8V+j3M5jYcz7jgUR9LoJ+XeSXglP39/jp5XG560WsjL/MONnV4aE7K+jGgZR6G61h6ANgB0YR1bHiE6Witiori4mIiAs273Gzjvdp8Q33Qz7l3K3q0DSB5sIyYSPtv7LWJ7XcEkvc9ECwWFhpKy9ywrJ/bnnvJLACw+l8aylEHcs/kgTkXB4XDge/PydTcpKy3li/dfo+rzpSSWZDLBtz6LviVHzrfCfl1XHCNmM/g7zzA1IRGAmpoaNn/wV8jeCzhRYkcza/GTja43evxi9m53krH7H3QLOUyNzcy12jH0HvJLuvVIAHuB3DsJrxQR/zTwXQD6djtETXU1PhaL1+YTnWZWc9EOFAWs6eCsUd+bBoEuuGPLJERHamVM1NbWsntFIpNHXnK5f136PcxevMKTJRWifdTHxMH0zcRZfkVIkLp52bYZLHp0Q4cWTdy+rLW1rJ6UxH1XTwNwxg6f+1pI6OqHUbFT2mMI0ff+kKHT5pJ74TyH3n0F9q1hsC2Hbm7ae05XazgZkIBp4n2MvP+HhIa7niCtJRRFoaCgAJPJRHBw8Jcb5d5JeC273c6htRZGDFDnTNh06mWmzv6J18aEPPEWzee4ciNIdMFeEyRCuNXKmDCbzZQ7UlCUdxv1rCou1eIXPs/DBRWindTHxNBhY/n4g8ksTPkcgFnDN7J3x/skT/h2BxdQ3I5MZjPzd57gw2kjGXP+EHnB8F+R1UC1esDlzzn40g7e/JWJewMrmWtAHUx5S9K9v1LPxagkQmc9xKh7HyTRx8ej5dRoNERGRjbcKPdOwovp9XouFo9jBFsBqMp/HRzf9tqYkCfeommKAxwF6nT/thOg8QetD5iHqF1DhPA2HoqJkuIiPlt+D3NG78LXom67dMVI2vlHWPDA39B4wVgn0Um4iQm7fiCp7/dh9virABw4YSR+XA4hoZ1/Ah3RNpxOJ6+MjOHZ8Csu939aAHO73HhvdcK+GgtFvcbQ/d4fMHTKbHQ6ncvPepTcOwlx3bHD2+ljSsFohCtXQd/ldcIjunllTEjiLdxTrGA9orZKOQrBflndbhoIlkkdWjQhOoSHY6Kuro5dW97DVpGGExPRPRcwaMgEz5ZZiLb0FTGRfe4YyqUk4mIUtn0BZ3P86BabhJ04Yvs+zIAk+b6Lltn4QDIziva53LfpKgwNhHRnCDVJ0+j77adJHDysfRsy5d5JiAYUZy1bl4Zy5yi1h8qa3WOYN2+BV8aEdDUX7tWdq+8KUt9y+yXFplYsGlOHFU2IDuHhmDAYDEya/jDwsEeLKUS7+YqYiOs5kPVHnmf/sReYNg4mja4EdgO7yTi3jr3b/0LyROl+LppP0Rvd7qsw+lP95hFmxMW3Y4luIfdOQjSgsZ+nzJYMqEOPjM7DwAKvjIlOt5yY8BDFCQ61eyCOQrXbFKjjMDTmG/uE8BYSE0I01MyYiI6dzND+EHRLb8J+PUsoy3kZu93ejoUWtztnv/E43fTVNCbPo1tHJt1STwjRUH1MDB/7ICVl6qbxw6o5k13ulTEhibdww6nOxIlTXeAeQAPootTXitwoCW8jMSFEQ82LicvZq+nVw/UZxg08wb7d69q8pKLzSHnsOT4OG4/jpuRbUSDVpy9J3/tVxxUMkHpCiFupMdEtJordh9XWV18LHD2Uqu72spiQrubCNY0etP7grABDIjjyAR1o6rt46UI6tHhCtDuJCSEaamZMaLC5PYXZBLU15e1QWNFZ+Pr5Mecf69n41p/RntyNxlFHXdwQRj74I7rEdOvYwkk9IURDN8WE1ncG8BEA4b6HUBQFjZfFhEyuJtxzFIPteH3r7U10YWDq3zFlEqIjSUwI0VAzYmLnlqUMDF7SqKs5wPYD3Rg6KwN/f/92KKwQ7UDqCSEaqo+JspJyco7M5UIuaDRQWjuA0O4PkjL9B5hM3jHOW7qaC/d0IWAcpP6t0avT/hviwNi3o0smRMeQmBCioWbExNiURWxIn4LT2fCjJWVaKnUPSNItOhepJ4RoqD4m7Iovew4bmTEBZqXAkhnHmZTwLCvenkV1dXVHl7JdyBNvIYQQQrSpyooKPv/0p5gcn+NrKqasJh5T6H1Mmf2MrFkvhBBeYM3Sx5g7/A1u/cl3OGDDseeZc+8LHVOwdiRjvIUQQgjRpvz8/blryd9xOBzU1tZisVgk4RZCCC9iqNvRKOkG0OlAa93R/gXqAJJ4CyGEEKJd6HQ6fH19O7oYQggh2pkW993JdU3s60xkjLcQQgghhBBCiDZj1w9wu69O5x0TD0riLYQQQgghhBCizXRNeJzM7KBG2w+cjKT3oO+3f4E6gEyuJoQQQgghhBCiTX2xaxkF5/5GXPgB6uw6LpePpnvf5xg0dGpHF61dSOIthBBCCCGEEKLNKYpCXl4eer2eyMjIji5Ou5LEWwghhBBCCCGEaEMyxlsIIYQQQgghhGhDkngLIYQQQgghhBBtSBJvIYQQQgghhBCiDUniLYQQQgghhBBCtCFJvIUQQgghhBBCiDYkibcQQgghhBBCCNGGJPEWQgghhBBCCCHakCTeQgghhBBCCCFEG5LEWwghhBBCCCGEaEOSeAshhBBCCCGEEG1IEm8hhBBCCCGEEKINSeIthBBCCCGEEEK0IUm8hRBCCCGEEEKINiSJtxBCCCGEEEII0YYk8RZCCCGEEEIIIdqQJN5CCCGEEEIIIUQb+j+hxMsvtZLCXwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
-       "<Figure size 1000x300 with 10 Axes>"
+       "<Figure size 400x350 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2941,236 +3983,152 @@
     }
    ],
    "source": [
-    "# Plotting before the user's choice of the latent dimensions to use in the next step\n",
-    "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
-    "from element_moseq.readers.kpms_reader import load_dj_config\n",
+    "model_names = (kpms_model.FullFitting).fetch(\"model_name\")\n",
     "\n",
-    "output_dir = (kpms_pca.PCATask & key).fetch1(\"output_dir\")\n",
-    "config = load_dj_config(output_dir, check_if_valid=False, build_indexes=False)\n",
+    "checkpoint_paths = []\n",
+    "for model_name in model_names:\n",
+    "    checkpoint_paths.append(get_kpms_processed_data_dir()/Path(model_name)/\"checkpoint.h5\")\n",
+    "checkpoint_paths    \n",
     "\n",
-    "pca = load_pca(output_dir)\n",
+    "from keypoint_moseq import expected_marginal_likelihoods, plot_eml_scores\n",
+    "eml_scores, eml_std_errs = expected_marginal_likelihoods(checkpoint_paths=checkpoint_paths)\n",
+    "best_model = model_names[np.argmax(eml_scores)]\n",
+    "print(f\"Best model: {best_model}\")\n",
     "\n",
-    "plot_scree(pca, project_dir=output_dir)\n",
-    "plot_pcs(pca, project_dir=output_dir, **config)"
+    "plot_eml_scores(eml_scores, eml_std_errs, model_names)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Fit the AR-HMM and the full model\n"
+    "Thus, we select the best ranked model for the inference task:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kpms_project_tutorial/2024_03_20-06_01_20\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = best_model\n",
+    "print(model_name)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "KPMS Model prefitting and full fitting involves:\n",
-    "\n",
-    "1. **Initialization**: Auto-regressive (AR) parameters and syllable sequences are randomly initialized using pose trajectories from PCA\n",
-    "2. **Fitting an AR-HMM**: AR parameters, transition probabilities and syllable sequences are iteratively updated through Gibbs sampling\n",
-    "3. **Fitting the full model**: all params, including both AR-HMM as well as centroid, heading, noise-estimates and continuous latent states (i.e. pose trajectories) are iteratively updated through Gibss sampling. Step useful for noisy data.\n",
-    "4. **Extracting model results**: the learned states of the model are parsed and saved to disk for visualization and downstream analysis.\n"
+    "Insert the video set to be used for inference into the `VideoRecording` table as well.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"362pt\" height=\"166pt\" viewBox=\"0.00 0.00 361.50 166.40\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 162.4)\">\n",
-       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-162.4 357.5,-162.4 357.5,4 -4,4\"/>\n",
-       "<!-- kpms_model.FullFitting -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>kpms_model.FullFitting</title>\n",
-       "<g id=\"a_node1\"><a xlink:title=\"→ kpms_model.FullFittingTask\r------------------------------\rfull_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"88.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"88.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.FullFitting</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_model.GenerateResults -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>kpms_model.GenerateResults</title>\n",
-       "<g id=\"a_node2\"><a xlink:title=\"→ kpms_model.FullFitting\r------------------------------\rgrid_movies_sampled_instances \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"88.62\" cy=\"-12.96\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"88.62\" y=\"-8.69\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.GenerateResults</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_model.FullFitting&#45;&gt;kpms_model.GenerateResults -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>kpms_model.FullFitting-&gt;kpms_model.GenerateResults</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M88.62,-62.04C88.62,-51.54 88.62,-36.27 88.62,-25.77\"/>\n",
-       "</g>\n",
-       "<!-- kpms_model.FullFittingTask -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>kpms_model.FullFittingTask</title>\n",
-       "<g id=\"a_node3\"><a xlink:title=\"→ kpms_pca.PCAFitting\rfull_latent_dim      \rfull_kappa           \rfull_num_iterations  \r------------------------------\rfull_fitting_desc=&quot;&quot; \rtask_mode            \rsort_syllables       \rresults_as_csv       \rvisualizations       \r\">\n",
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-       "<text text-anchor=\"start\" x=\"14\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.FullFittingTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_model.FullFittingTask&#45;&gt;kpms_model.FullFitting -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>kpms_model.FullFittingTask-&gt;kpms_model.FullFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M88.62,-123.58C88.62,-112.28 88.62,-97.58 88.62,-87.5\"/>\n",
-       "</g>\n",
-       "<!-- kpms_model.PreFittingTask -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>kpms_model.PreFittingTask</title>\n",
-       "<g id=\"a_node4\"><a xlink:title=\"→ kpms_pca.PCAFitting\rpre_latent_dim       \rpre_kappa            \rpre_num_iterations   \r------------------------------\rpre_fitting_desc=&quot;&quot;  \rmodel_initialization=&quot;Yes&quot; \rmodel_name_initialization=&quot;&quot; \r\">\n",
-       "<polygon fill=\"#00ff00\" fill-opacity=\"0.188235\" stroke=\"#00ff00\" stroke-opacity=\"0.188235\" points=\"353.5,-158.4 189.75,-158.4 189.75,-123.84 353.5,-123.84 353.5,-158.4\"/>\n",
-       "<text text-anchor=\"start\" x=\"197.75\" y=\"-137.84\" font-family=\"arial\" text-decoration=\"underline\" font-size=\"12.00\" fill=\"darkgreen\">kpms_model.PreFittingTask</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_model.PreFitting -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>kpms_model.PreFitting</title>\n",
-       "<g id=\"a_node5\"><a xlink:title=\"→ kpms_model.PreFittingTask\r------------------------------\rmodel_name=&quot;&quot;        \rpre_fitting_duration=null \r\">\n",
-       "<ellipse fill=\"#ff0000\" fill-opacity=\"0.125490\" stroke=\"#ff0000\" stroke-opacity=\"0.125490\" cx=\"271.62\" cy=\"-74.88\" rx=\"12.96\" ry=\"12.96\"/>\n",
-       "<text text-anchor=\"middle\" x=\"271.62\" y=\"-70.6\" font-family=\"arial\" font-size=\"12.00\" fill=\"#7f0000\" fill-opacity=\"0.627451\">kpms_model.PreFitting</text>\n",
-       "</a>\n",
-       "</g>\n",
-       "</g>\n",
-       "<!-- kpms_model.PreFittingTask&#45;&gt;kpms_model.PreFitting -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
-       "<title>kpms_model.PreFittingTask-&gt;kpms_model.PreFitting</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" stroke-width=\"2\" stroke-opacity=\"0.250980\" d=\"M271.62,-123.58C271.62,-112.28 271.62,-97.58 271.62,-87.5\"/>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>"
-      ],
       "text/plain": [
-       "<datajoint.diagram.Diagram at 0x29237cbe0>"
+       "['dlc_project/videos/21_12_10_def6a_3.top.ir.mp4',\n",
+       " 'dlc_project/videos/22_04_26_cage4_1_1.top.ir.mp4',\n",
+       " 'dlc_project/videos/21_12_10_def6a_1_1.top.ir.mp4',\n",
+       " 'dlc_project/videos/22_27_04_cage4_mouse2_0.top.ir.mp4',\n",
+       " 'dlc_project/videos/22_04_26_cage4_0.top.ir.mp4',\n",
+       " 'dlc_project/videos/21_11_8_one_mouse.top.ir.Mp4',\n",
+       " 'dlc_project/videos/21_12_2_def6b_2.top.ir.mp4',\n",
+       " 'dlc_project/videos/21_12_10_def6b_3.top.ir.Mp4',\n",
+       " 'dlc_project/videos/22_04_26_cage4_0_2.top.ir.mp4',\n",
+       " 'dlc_project/videos/21_12_2_def6a_1.top.ir.mp4']"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dj.Diagram(kpms_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Adjust `kappa` hyperparameter to achieve the desired distribution of syllable durations. Higher values of kappa lead to longer syllables.\n",
-    "\n",
-    "Let's chose a kappa value that yields a median syllable duration of 12 frames.\n"
+    "# Extract all the video names relative to the root directory\n",
+    "kpset_videos_dir = (kpms_pca.KeypointSet & pca_task_key).fetch1(\"kpset_videos_dir\")\n",
+    "kpset_videos_dir = find_full_path(get_kpms_root_data_dir(), kpset_videos_dir)\n",
+    "root_dir = get_kpms_root_data_dir()[0]\n",
+    "video_extensions = ['.mp4']\n",
+    "video_names = [file.relative_to(root_dir) for file in kpset_videos_dir.rglob('*') if file.suffix.lower() in video_extensions]\n",
+    "video_names = [str(name) for name in video_names]\n",
+    "video_names"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "kappa range = [4.e+02 2.e+04 1.e+06] ms\n"
-     ]
+     "data": {
+      "text/plain": [
+       "# \n",
+       "subject              : varchar(8)                   # \n",
+       "session_datetime     : datetime                     # \n",
+       "format_method        : char(15)                     # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.\n",
+       "recording_id         : int                          # "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "fps = (kpms_pca.RecordingInfo & key).fetch1(\"fps_average\")\n",
-    "kappa_min = (12 / fps) * 1000 #ms\n",
-    "kappa_max = 1e6 #ms \n",
-    "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
-    "print('kappa range = {} ms'.format(kappa_range))\n"
+    "kpms_model.VideoRecording.heading"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 51,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "For rodents it's recommended a target duration of ~400ms\n"
+    "recording_key = {\n",
+    "    **key, \n",
+    "    \"recording_id\": 1, \n",
+    "    \"format_method\":\"deeplabcut\"\n",
+    "    }\n",
+    "kpms_model.VideoRecording.insert1(recording_key,\n",
+    "                                  skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The kappa value in the config is only used during model initialization.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'subject': 'subject1',\n",
-       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       "  'kpset_id': 1,\n",
-       "  'bodyparts_id': 1,\n",
-       "  'pre_latent_dim': 4,\n",
-       "  'pre_kappa': 400,\n",
-       "  'pre_num_iterations': 2,\n",
-       "  'pre_fitting_desc': 'Prefitting 1',\n",
-       "  'model_initialization': 'Yes'},\n",
-       " {'subject': 'subject1',\n",
-       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       "  'kpset_id': 1,\n",
-       "  'bodyparts_id': 1,\n",
-       "  'pre_latent_dim': 4,\n",
-       "  'pre_kappa': 20000,\n",
-       "  'pre_num_iterations': 2,\n",
-       "  'pre_fitting_desc': 'Prefitting 2',\n",
-       "  'model_initialization': 'Yes'},\n",
-       " {'subject': 'subject1',\n",
-       "  'session_datetime': datetime.datetime(2021, 6, 2, 14, 4, 22),\n",
-       "  'kpset_id': 1,\n",
-       "  'bodyparts_id': 1,\n",
-       "  'pre_latent_dim': 4,\n",
-       "  'pre_kappa': 1000000,\n",
-       "  'pre_num_iterations': 2,\n",
-       "  'pre_fitting_desc': 'Prefitting 3',\n",
-       "  'model_initialization': 'Yes'}]"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prefitting_keys = [{\n",
-    "    **key,\n",
-    "    'pre_latent_dim': 4,\n",
-    "    'pre_kappa': int(i),\n",
-    "    'pre_num_iterations': 2,\n",
-    "    'pre_fitting_desc': f\"Prefitting {c}\",\n",
-    "    'model_initialization': 'Yes',\n",
-    "} for c, i in enumerate(kappa_range, start=1)]\n",
-    "\n",
-    "prefitting_keys"
+    "Insert video files into the `VideoRecording.File` table:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_model.PreFittingTask.insert(prefitting_keys, skip_duplicates=True)"
+    "for idx,video_name in enumerate(video_names):\n",
+    "    kpms_model.VideoRecording.File.insert1(dict(\n",
+    "                                **recording_key,\n",
+    "                                file_id = idx,\n",
+    "                                file_path = video_name),\n",
+    "                                skip_duplicates=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -3228,7 +4186,7 @@
        "        }\n",
        "    </style>\n",
        "    \n",
-       "    <b></b>\n",
+       "    \n",
        "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "        <table border=\"1\" class=\"Table\">\n",
        "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
@@ -3238,84 +4196,166 @@
        "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_kappa</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_num_iterations</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pre_fitting_desc</p>\n",
+       "                            <p id=\"primary\">recording_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_initialization</p>\n",
-       "                            <span class=\"djtooltiptext\">'Yes' initialize a new AR-HMM model and output_dir, 'No' will directly pre-fit the model</span>\n",
+       "                            <p id=\"primary\">file_id</p>\n",
+       "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_name_initialization</p>\n",
-       "                            <span class=\"djtooltiptext\">Optional. Name of the initialized model to be pre-fitted. Only needed if model_initialization = False</span>\n",
+       "                            <p id=\"nonprimary\">file_path</p>\n",
+       "                            <span class=\"djtooltiptext\">Filepath of each video, relative to root data directory.</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
+       "<td>0</td>\n",
+       "<td>dlc_project/videos/21_12_10_def6a_3.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>400</td>\n",
-       "<td>2</td>\n",
-       "<td>Prefitting 1</td>\n",
-       "<td>Yes</td>\n",
-       "<td></td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
        "<td>1</td>\n",
+       "<td>dlc_project/videos/22_04_26_cage4_1_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>20000</td>\n",
        "<td>2</td>\n",
-       "<td>Prefitting 2</td>\n",
-       "<td>Yes</td>\n",
-       "<td></td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>dlc_project/videos/21_12_10_def6a_1_1.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
+       "<td>3</td>\n",
+       "<td>dlc_project/videos/22_27_04_cage4_mouse2_0.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
        "<td>4</td>\n",
-       "<td>1000000</td>\n",
-       "<td>2</td>\n",
-       "<td>Prefitting 3</td>\n",
-       "<td>Yes</td>\n",
-       "<td></td> </tr> </tbody>\n",
+       "<td>dlc_project/videos/22_04_26_cage4_0.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>5</td>\n",
+       "<td>dlc_project/videos/21_11_8_one_mouse.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>6</td>\n",
+       "<td>dlc_project/videos/21_12_2_def6b_2.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>7</td>\n",
+       "<td>dlc_project/videos/21_12_10_def6b_3.top.ir.Mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>8</td>\n",
+       "<td>dlc_project/videos/22_04_26_cage4_0_2.top.ir.mp4</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>9</td>\n",
+       "<td>dlc_project/videos/21_12_2_def6a_1.top.ir.mp4</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 3</p></div>\n",
+       "        <p>Total: 10</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera pre_fitting_de model_initiali model_name_ini\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              4              400           2              Prefitting 1   Yes                          \n",
-       "subject1     2021-06-02 14: 1            1              4              20000         2              Prefitting 2   Yes                          \n",
-       "subject1     2021-06-02 14: 1            1              4              1000000       2              Prefitting 3   Yes                          \n",
-       " (Total: 3)"
+       "*subject     *session_datet *format_method *recording_id  *file_id    file_path     \n",
+       "+----------+ +------------+ +------------+ +------------+ +---------+ +------------+\n",
+       "subject1     2024-03-15 14: deeplabcut     1              0           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              1           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              2           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              3           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              4           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              5           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              6           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              7           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              8           dlc_project/vi\n",
+       "subject1     2024-03-15 14: deeplabcut     1              9           dlc_project/vi\n",
+       " (Total: 10)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_model.PreFittingTask()"
+    "kpms_model.VideoRecording * kpms_model.VideoRecording.File"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `InferenceTask` table serves the purpose of specifying an inference task:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "# \n",
+       "subject              : varchar(8)                   # \n",
+       "session_datetime     : datetime                     # \n",
+       "format_method        : char(15)                     # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.\n",
+       "recording_id         : int                          # \n",
+       "model_name           : varchar(64)                  # User-friendly model name (output_dir/model_name)\n",
+       "---\n",
+       "inference_output_dir=\"\" : varchar(1000)                # Optional. Sub-directory where the results will be stored\n",
+       "inference_desc=\"\"    : varchar(1000)                # Optional. User-defined description of the inference task\n",
+       "num_iterations=null  : int                          # Optional. Number of iterations to use for the model inference."
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kpms_model.InferenceTask.heading\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Defining and inserting a inference task requires:\n",
+    "\n",
+    "1. Define the subject and session datetime\n",
+    "2. Define the video recording\n",
+    "3. Define the pose estimation method used for the video recording\n",
+    "4. Choose a model\n",
+    "5. Specify the output directory and any optional parameters\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kpms_model.InferenceTask.insert1({\n",
+    "        **recording_key,\n",
+    "        \"model_name\"   : model_name,\n",
+    "        \"inference_output_dir\": \"inference_output\",\n",
+    "        \"inference_desc\": \"Inference task for the tutorial\",\n",
+    "        \"num_iterations\": 5, # for tutorial purposes.\n",
+    "}, skip_duplicates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -3383,310 +4423,153 @@
        "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_latent_dim</p>\n",
+       "                            <p id=\"primary\">recording_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_kappa</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">User-friendly model name (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_num_iterations</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"nonprimary\">inference_output_dir</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Sub-directory where the results will be stored</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">Name of the model</span>\n",
+       "                            <p id=\"nonprimary\">inference_desc</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. User-defined description of the inference task</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pre_fitting_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
+       "                            <p id=\"nonprimary\">num_iterations</p>\n",
+       "                            <span class=\"djtooltiptext\">Optional. Number of iterations to use for the model inference.</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>inference_output</td>\n",
+       "<td>Inference task for the tutorial</td>\n",
+       "<td>5</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera model_name     pre_fitting_du\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *format_method *recording_id  *model_name    inference_outp inference_desc num_iterations\n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t inference_outp Inference task 5             \n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_model.PreFitting()"
+    "kpms_model.InferenceTask()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Populating the `Inference` table will automatically extract learned states of the model (syllables, latent_state, centroid, and heading) and stored in the inference output directory together with visualizations and grid movies. The following function will take a few minutes.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/milagros/miniconda/envs/kpms_test/lib/python3.9/site-packages/keypoint_moseq/fitting.py:589: UserWarning:\n",
-      "\n",
-      "'kappa' with <class 'int'> will be cast to <class 'float'>\n",
-      "\n"
+      "Loading keypoints: 100%|████████████████| 10/10 [00:00<00:00, 28.18it/s]\n",
+      "Applying model: 100%|█████████████████████| 5/5 [01:37<00:00, 19.51s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
-      "elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_32_42\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|████████████████████████▋            | 2/3 [00:28<00:13, 13.85s/it]"
+      "Saved results to /Users/milagros/Documents/datajoint-elements/element-\n",
+      "moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_20/inference_\n",
+      "output/results.h5\n"
      ]
     },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x250 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████| 3/3 [00:40<00:00, 13.56s/it]\n"
+      "Saving to csv: 100%|████████████████████| 10/10 [00:01<00:00,  6.71it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
-      "elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_31\n"
+      "Saving trajectory plots to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_20/inference_output/trajectory_plots\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 67%|████████████████████████▋            | 2/3 [00:09<00:04,  4.53s/it]"
+      "Generating trajectory plots: 100%|██████| 42/42 [00:09<00:00,  4.41it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1200x250 with 4 Axes>"
+       "<Figure size 1360x4360 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████| 3/3 [00:13<00:00,  4.48s/it]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
-      "elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50\n"
+      "Writing grid movies to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_20/inference_output/grid_movies\n",
+      "Using window size of 144 pixels\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 67%|████████████████████████▋            | 2/3 [00:07<00:03,  3.73s/it]"
+      "Generating grid movies: 100%|███████████| 42/42 [01:27<00:00,  2.09s/it]\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x250 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100%|█████████████████████████████████████| 3/3 [00:11<00:00,  3.87s/it]\n"
+      "Saving dendrogram plot to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_20-06_01_20/inference_output/similarity_dendogram\n"
      ]
-    }
-   ],
-   "source": [
-    "kpms_model.PreFitting.populate(key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
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-       "            z-index: 1;\n",
-       "        }\n",
-       "        #primary {\n",
-       "            font-weight: bold;\n",
-       "            color: black;\n",
-       "        }\n",
-       "        #nonprimary {\n",
-       "            font-weight: normal;\n",
-       "            color: white;\n",
-       "        }\n",
-       "\n",
-       "        /* Show the tooltip text when you mouse over the tooltip container */\n",
-       "        .djtooltip:hover .djtooltiptext {\n",
-       "            visibility: visible;\n",
-       "        }\n",
-       "    </style>\n",
-       "    \n",
-       "    <b></b>\n",
-       "        <div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-       "        <table border=\"1\" class=\"Table\">\n",
-       "            <thead> <tr style=\"text-align: right;\"> <th> <div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">subject</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">session_datetime</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_kappa</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">pre_num_iterations</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">model_name</p>\n",
-       "                            <span class=\"djtooltiptext\">Name of the model</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">pre_fitting_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
-       "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>400</td>\n",
-       "<td>2</td>\n",
-       "<td>2024_03_13-17_32_42</td>\n",
-       "<td>0:00:40</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>20000</td>\n",
-       "<td>2</td>\n",
-       "<td>2024_03_13-17_33_31</td>\n",
-       "<td>0:00:13</td></tr><tr><td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
-       "<td>1</td>\n",
-       "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>1000000</td>\n",
-       "<td>2</td>\n",
-       "<td>2024_03_13-17_33_50</td>\n",
-       "<td>0:00:11</td> </tr> </tbody>\n",
-       "        </table>\n",
-       "        \n",
-       "        <p>Total: 3</p></div>\n",
-       "        "
-      ],
+      "image/png": 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",
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *pre_latent_di *pre_kappa    *pre_num_itera model_name     pre_fitting_du\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              4              400           2              2024_03_13-17_ 0:00:40       \n",
-       "subject1     2021-06-02 14: 1            1              4              20000         2              2024_03_13-17_ 0:00:13       \n",
-       "subject1     2021-06-02 14: 1            1              4              1000000       2              2024_03_13-17_ 0:00:11       \n",
-       " (Total: 3)"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "execution_count": 43,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "kpms_model.PreFitting()"
+    "kpms_model.Inference.populate()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -3754,80 +4637,55 @@
        "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_kappa</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_num_iterations</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">full_fitting_desc</p>\n",
+       "                            <p id=\"primary\">recording_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sort_syllables</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to sort syllables by frequency (reindexing)</span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">results_as_csv</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to save results as csv (Optional)</span>\n",
+       "                            <p id=\"primary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">User-friendly model name (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">visualizations</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to save visualizations (Optional)</span>\n",
+       "                            <p id=\"nonprimary\">inference_duration</p>\n",
+       "                            <span class=\"djtooltiptext\">Time duration of the inference computation</span>\n",
        "                        </div> </th> </tr> </thead>\n",
-       "            <tbody> <tr>  </tr> </tbody>\n",
+       "            <tbody> <tr> <td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>0:01:44</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 0</p></div>\n",
+       "        <p>Total: 1</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject    *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d task_mode     sort_syllables results_as_csv visualizations\n",
-       "+---------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
-       "\n",
-       " (Total: 0)"
+       "*subject     *session_datet *format_method *recording_id  *model_name    inference_dura\n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 0:01:44       \n",
+       " (Total: 1)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_model.FullFittingTask()"
+    "kpms_model.Inference()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 45,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "fullfitting_key = ({**key,\n",
-    "                      'full_latent_dim': 4,\n",
-    "                      'full_kappa': 1000000,\n",
-    "                      'full_num_iterations':6,\n",
-    "                      'full_fitting_desc':\"Full fitting model and generate results and visualizations\",\n",
-    "                      'task_mode':'trigger',\n",
-    "                      'sort_syllables':True,\n",
-    "                      'results_as_csv':True,\n",
-    "                      'visualizations':True})\n",
-    "\n",
-    "kpms_model.FullFittingTask.insert1(fullfitting_key, skip_duplicates=True)                                  "
+    "The `MotionSequence` table contains the results for the inference (syllables, latent_state, centroid, and heading):\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -3895,115 +4753,161 @@
        "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_kappa</p>\n",
+       "                            <p id=\"primary\">recording_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_num_iterations</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">User-friendly model name (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">full_fitting_desc</p>\n",
+       "                            <p id=\"primary\">video_name</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">task_mode</p>\n",
-       "                            <span class=\"djtooltiptext\">'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca</span>\n",
+       "                            <p id=\"nonprimary\">syllable</p>\n",
+       "                            <span class=\"djtooltiptext\">syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">sort_syllables</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to sort syllables by frequency (reindexing)</span>\n",
+       "                            <p id=\"nonprimary\">latent_state</p>\n",
+       "                            <span class=\"djtooltiptext\">inferred low-dim pose state (x). Low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">results_as_csv</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to save results as csv (Optional)</span>\n",
+       "                            <p id=\"nonprimary\">centroid</p>\n",
+       "                            <span class=\"djtooltiptext\">inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">visualizations</p>\n",
-       "                            <span class=\"djtooltiptext\">Whether to save visualizations (Optional)</span>\n",
+       "                            <p id=\"nonprimary\">heading</p>\n",
+       "                            <span class=\"djtooltiptext\">inferred heading (h). The heading of the animal in each frame, as estimated by the model.</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
-       "<td>4</td>\n",
-       "<td>1000000</td>\n",
-       "<td>6</td>\n",
-       "<td>Full fitting model and generate results and visualizations</td>\n",
-       "<td>trigger</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_12_10_def6a_1_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_12_10_def6a_3.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_12_10_def6b_3.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_12_2_def6a_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>22_04_26_cage4_0_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>22_04_26_cage4_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>22_04_26_cage4_1_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
-       "<td>1</td> </tr> </tbody>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>22_27_04_cage4_mouse2_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
        "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>Total: 10</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d task_mode     sort_syllables results_as_csv visualizations\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +-----------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              4              1000000        6              Full fitting m trigger       1              1              1             \n",
-       " (Total: 1)"
+       "*subject     *session_datet *format_method *recording_id  *model_name    *video_name    syllable   latent_sta centroid   heading   \n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +--------+ +--------+ +--------+ +--------+\n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_11_8_one_mo =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_12_10_def6a =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_12_10_def6a =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_12_10_def6b =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_12_2_def6a_ =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 21_12_2_def6b_ =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 22_04_26_cage4 =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 22_04_26_cage4 =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 22_04_26_cage4 =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 22_27_04_cage4 =BLOB=     =BLOB=     =BLOB=     =BLOB=    \n",
+       " (Total: 10)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_model.FullFittingTask()"
+    "kpms_model.Inference.MotionSequence()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 47,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Outputs will be saved to /Users/milagros/Documents/datajoint-\n",
-      "elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|█████████████████████████████▌       | 4/5 [01:21<00:18, 18.81s/it]"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x250 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████| 5/5 [01:40<00:00, 20.03s/it]\n"
-     ]
-    }
-   ],
    "source": [
-    "kpms_model.FullFitting.populate()"
+    "The `GridMoviesSampledInstances` table contains the sampled instances for the grid movies. The sampled instances is a dictionary mapping syllables to lists of instances shown in each grid movie (in row-major order).\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -4071,147 +4975,140 @@
        "                            <p id=\"primary\">session_datetime</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">kpset_id</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">format_method</p>\n",
+       "                            <span class=\"djtooltiptext\">deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">bodyparts_id</p>\n",
+       "                            <p id=\"primary\">recording_id</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_latent_dim</p>\n",
-       "                            <span class=\"djtooltiptext\"></span>\n",
+       "                            <p id=\"primary\">model_name</p>\n",
+       "                            <span class=\"djtooltiptext\">User-friendly model name (output_dir/model_name)</span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_kappa</p>\n",
+       "                            <p id=\"primary\">syllable</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
        "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"primary\">full_num_iterations</p>\n",
+       "                            <p id=\"nonprimary\">instances</p>\n",
        "                            <span class=\"djtooltiptext\"></span>\n",
-       "                        </div></th><th><div class=\"djtooltip\">\n",
-       "                            <p id=\"nonprimary\">full_fitting_duration</p>\n",
-       "                            <span class=\"djtooltiptext\">Duration of generation of the full fitting model</span>\n",
        "                        </div> </th> </tr> </thead>\n",
        "            <tbody> <tr> <td>subject1</td>\n",
-       "<td>2021-06-02 14:04:22</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>0</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
        "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
        "<td>1</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>2</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>3</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
        "<td>4</td>\n",
-       "<td>1000000</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>5</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
        "<td>6</td>\n",
-       "<td>0:01:40</td> </tr> </tbody>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>7</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>8</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>9</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>10</td>\n",
+       "<td>=BLOB=</td></tr><tr><td>subject1</td>\n",
+       "<td>2024-03-15 14:04:22</td>\n",
+       "<td>deeplabcut</td>\n",
+       "<td>1</td>\n",
+       "<td>kpms_project_tutorial/2024_03_20-06_01_20</td>\n",
+       "<td>11</td>\n",
+       "<td>=BLOB=</td> </tr> </tbody>\n",
        "        </table>\n",
-       "        \n",
-       "        <p>Total: 1</p></div>\n",
+       "        <p>...</p>\n",
+       "        <p>Total: 42</p></div>\n",
        "        "
       ],
       "text/plain": [
-       "*subject     *session_datet *kpset_id    *bodyparts_id  *full_latent_d *full_kappa    *full_num_iter full_fitting_d\n",
-       "+----------+ +------------+ +----------+ +------------+ +------------+ +------------+ +------------+ +------------+\n",
-       "subject1     2021-06-02 14: 1            1              4              1000000        6              0:01:40       \n",
-       " (Total: 1)"
+       "*subject     *session_datet *format_method *recording_id  *model_name    *syllable    instances \n",
+       "+----------+ +------------+ +------------+ +------------+ +------------+ +----------+ +--------+\n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 0            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 1            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 2            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 3            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 4            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 5            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 6            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 7            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 8            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 9            =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 10           =BLOB=    \n",
+       "subject1     2024-03-15 14: deeplabcut     1              kpms_project_t 11           =BLOB=    \n",
+       "   ...\n",
+       " (Total: 42)"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kpms_model.FullFitting()"
+    "kpms_model.Inference.GridMoviesSampledInstances()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "### Generate and store the model results and visualizations\n"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Reindexing: 100%|█████████████| 3/3 [00:00<00:00,  9.18model snapshot/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved results to /Users/milagros/Documents/datajoint-elements/element-\n",
-      "moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/results.h5\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Saving to csv: 100%|████████████████████| 10/10 [00:01<00:00,  6.60it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving trajectory plots to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/trajectory_plots\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating trajectory plots: 100%|██████| 29/29 [00:06<00:00,  4.34it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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tUQE9/H64vNKW/FDlDJ/Ybf3i7zIMQ3V1daqrK5dkyPBKNlugQoJMsli8cnnM8nisCguLUnDIDw9acdgiGAUA4MfYsX27nrrjj2rKWy+byylHUKjSjj5WV916m6Kje3/zBwA48lWUFysqvKXb1NFvOTxSbX2bEnsfyKmdRW0alOyRSVKby9CO3a2KCvcoPkaymdvXAy2tkBwuKSXRK4vFpIq6BHmchcpI8t2CvrTaqrC4OQoNDe/xWl6vV5vXv6kR6b4bIm0rCFZa9kXd1ghtbm7Wro33a+Rg3+us3RquYRNuUHnpbjWX3qucDN+d13N32BSR9icNTBnkU+5yufTuy5fq7GO2+JQvW9OqloYdOnaK7/TZJ94epctvXL3XYGRfMgxDG9ev1qbF7WuFjkgs0sj0/p1b3SAt3RaiSk3U5p11Omdys55+p0C/PduhrC572KzeLv37XbvSx1you//x3/1yH8AP4XA49Oz9I/Tzs32/wFi2VvJah2pb8RBddsFISdKOQpOiIuMUE2nVlt2SDCkwMEQZKT1/c1BRJzk0SSmp6aqqrJSrZZOSYtw91pWkilqTvLZhSuxjzWGHw6GCgm2KDGlTZLhHNnP7pnAVtSY1NpkVFhosi8WkyHCrmlptCgyKUVhYz89HHLEIRoEjTW1trXZsy5NhGMrIHKK4uJ6HIDidThUW7lJjQ40sZrMSBwxSbGxct2+kXS6XDMOQxWKRxdLD13WAH/vs3Xf0wS3X6g/OYsV0+fXY7pTuihyiO+d9pPSMjIPXQQDAQdXc3CS3o1wRwd4ejzu9UlV1mwZ036ekQ22jWy1tTiXHtn/UMiTV1ntUWeOUIam1TUqKcyuxy3dxDo9U1ZguV0ue0hN9R1oWV9oUlXhcryOjXC6Xdua+pOw03+nrubujlT3qvG7vBxsa6lSy9QFlZ/iOjF2ZF6sxk3+l6uoylW35s8Zk+x7fXmCVJfpmDRo80qfc7XbrnZev0JkzN/ls9rJ4RbMs3u2aOtb3tXzs7Wm6+qZFMpv339YY7aNCX1FV3guKM63W9Ow2hfdjVKgkrdxu0tbqDFmTTtXAYWfI1VSgVUve1rFDCzRusFRV59Z/3ytTQVmjbBZDDrdZo7Ii9dMT4vTowkH6v3ve2m/3BfwQra2tevmhkbrizJ0+5UtWmxQUOlTrdmToip+MlSRtzTepuileQXaLxma3j4SubwlTSkL3kd5b9gQoKXW2IiIitCVvrTIHVvX6pZLUvmTI9sJoZeeM7/G42+1Wfn6e0hKbexx12tQmVdValJRgV229ZMgikzlIMbED2QTKvxCMAkeKuro6vfvW4wqyFSnUtlsff7FFxWUuBdgTdNPvHtW4ceM66ubv3KK6ml1KSWxWVIQhj0cqLrOpsj5Uw0ZMUUhIiBoa6tTa0iirxSOzSXK7TTJbgxURGcUfCkBSVVWV/jhzgh5x7FFPM9yavNLZbTH63ZPPa/bJJx/4DgIADjrDMFRZUaKw4JYe1xCtbZIaGp1KS+w5OP3W9iJDVrNHCdEOBX8zaLOpTaqsliLCJKfLpfCQNgV3WSavqU1q9Q5Ta/0qpSb4hpKFFTbFJp+goKCe072W5maV735ZGQOcHWVeSXl70jR8zCnd6tdUV6q28CFlDvQd3bVi8wCNn3aNGuprtW31H3TUyCbffpSZ1GS7VjnDp/iUezwezXv55zpjxlp1zWEXfNWoyKAdGjus82On2y098+HJuvKG+ftsyrlhGNqwfrVyFz8hVX2oUYlFGpHev3OrG6SlW0NUa5uioIGnKzTYKk/t14owdignuVVxEdL8FdLgAVL2wL7b+ucHA3XTPe/86PsB9rWWlha9+ugwXXb6Hp/yhctNik0YqmUbB+rKn06SySTl7jArNSVeYcHtX15UN0gub4QSYzofWC5DWrHOrElTT5fNZtOWvDXKTvFdw7gnWwojlZ0zscdjVZUVsnjzFRXWe0xVWG5SbIxdQTazGtukhiargkMTFBXFrC8/QjAKHAkaGhr00nP36Oxjd+vefy1VUky1Lj2zTQmxUnGZdP/zdjV6Z+mBR95RSfEeedpylZns7NaO0yOt3RKm1LQRCg1yKjTI6/OUaHNL1fVWRUcnym63s94R/Nrff3uzTn/lnxrSx1rtTzSYtDEmRcmnX6Cb7rz7kPxSweFwyNHWJpkkuz2IxecBYB9zu92qq62S19OqkGC3zCbJ5W4f6Rlkl1wut2xWt8J72Xy81SVV1QUrITFFlZWlcjpb5HW7FWBtVmKsZPsmOMwvdmlgQptsXQZO1jaaJPsYNVUtVkqC7zT0gvIAxaec2OsanbW1lWqrflNJMZ3b3jvd0u6qMRqSM61b/fKyIjmqnlRqYmc46jWkVVsGaeL0y9XU1KR1S27VjLG+W9aXV0slrT/T2Akn+JR7vV69/dK1Om36Ctm6zJT/6MtapcflKzuzs8zhlF5acJEuv/alHu+lP+rr67X081dVufl5xZtWa9r3GBW66ptRoS2hMxUYmqZIy05FmXcqJ7lNsT3Myt20R9pZKp0+ue92752frd//5YffE7A/NTU26s2nhuuSUwt9yj9fZlJKSraWrEvQJRdNkcVs0sbtFg1Kj+vYeKmsWrIFRikmwiq3IW3IkzJSpOrGOGUOnaUdOzYrJbZE9j7eOjs90p6yBGUNHdXj8fyduUpLalRfY8nbXFJlrVUpCe3fOJVUSWZblBIT9/KtBY4kBKPAkeDVlx/RceOX6A93L9BvLilTVnr3Oms3S79/IE23/uHfOnq8q3uFbzQ0S3vKEzQyu3OtFpchVde65HF7ZLNJHq9Zbk+A7EFhiomJ369Tl4BD1fXHTNN/Cr/qs06TV7q3VroxUsp3SRUBwWoMj5YzPlnWtMEKGTpcMaPGK3XIUA0cOPCALlfR2tqqyrISmdwuhVpNCrCY1SKrDFuAomLjD8kQFwAOZy6XS62tLfJ6PDIkBdmqZbe2f4AqqXQqJMiriBDfT2hNbVJtQ/uoUHvoUJ8vryrKdio2vNLnQ//OQpcyktt8yspqrAqOHK/Gys+VHOf7HnB3mV1JaSd0Wzv0W6XF+bJ7P1BUl1n3TW1STdsspWYM71a/uGinzE3PKim2cwSsyyut2zFcE6ZcKIfDoWWf/VGzJ5T4nFfbKG2tOEOTp53rU+71evX2yzfotKlL1fXP0nufVGvk4D1K77IuZ1Oz9PaKXyot6xwV7d6uqNhEHXNc7/dmGIY2blijTYsel6o+1Mjvu1bo1hCVuEar1ZallOg2JdoLNDzF4fNa9cYwpLtfkW67QD3OOpGkvAKTNukanXvhlf3rFHAQNDY06J1nsvXTuaU+5Z8sMSkrK1uL1sTownOmK8Bm1rqtVg0dHKuggPYnVEG5SbWNUXK6LRqcKkWFta//WVSTpbiELFWWrlZGUvfBPN/KL5XCosYoNrbnpePy8zcqI6G5x2PfMiTtLrUoY0D7F0TNbVJ1Y7BSUzP7PA9HFIJR4HDndDr1v+duVU7yRq1fu1BXnd976Pn3pyzKLZysh/7xc4WG9PwmUZJWbw7S+DHZktpD0dJyp+Kivd2mgDW2SQ3NoUoakEY4Cr9z46zJ+nfx8j7reAzplmrpn32sHSdJZW5pj0eqsIWoMSJG7oSBsqRlKiR7hOJGj1da1lAlJSX96OC0tbVVD913rxbPe1Uet1PRA1I0durRGjFqtJIGpiosNEQhATa5bUGKTRpAOAoA+1FDQ5WCbLWymdo/RNU1etTY5JE1wCLD65HX65U9UIqMkGySqhrtio3P6jjf7XartnK94iI63/u1uaSKKpdSE3zXFS2sDFZU7Eg1Vn3WbTOT/NIgJWec0OuMgfyda5UY8pWCurx1rGowyWs/VfEJKd3r529SqPsVxUV1fjR0uKTNRRM0duKZcrlc+vKjuzV7/A5Zurx9bG6V1uTP1vTZV/jMSjIMQ/NeuVknT1qowC5dfOP9Ck0bVaSk+PafX39X+vhDaWayTdmxLlW0WPVR8SBlTDhNN952n0wmU/taoZ+/qqrNLyhOqzQ95/uNCt1UPlAVzgyFh4VoZHKNRqS6FNHP8yWpxdE+WrSyMUDF1QFqaWvV9ad6uoWjlfXSQwuG6da7nux1RC9wqKivq9MHLwzVhSf5btr24ZcmjRw5TEvWROjUudMVEmTV6s1WjciJV+A3o8B3FNkUEhqupJjOh0GLQ2pwT1RjQ5Niw8t7nArf0CKVlEthoXbFD5zc43vWfo0YdUuVNZ0jRp0eqbwmVCmp7BHgRwhGgcNdeXm5Vn15mxZ+8ZFu/2WRQvp4c1ZdK937RLSyhp+uay4/sdd6a7cGauzIYZKk0mqnosK9svey2WdDq+T0JCg2Lv7H3AZw2LnxrFN19/r5Cunj3daKNmmjU7riR25uWeKWdrtNqgwMUXNkbHtwmj5YYdkj24PTwVlKTEzs8wuKT+a/o8duvFy/iK7TnOj2kTwLaqQHS+wyJQ/W1BPmatr0ozU4ZYBaZZEtNFJJA7t/4EV37aPAWuVxO2Q2WRUcGkqoDKBfamsKFRXsG2LWNgUqNCJJ9bW7FRvWecxlSC3OREVEdo6OamlukuHcpK7fd1fWS25Xq5Kiu0xpl1RcFauomEw1Vn3qMz1ekvJLQjQw8/hen115mxZocNJmn2n6xVUWhcWfr/CIqG71t29ZqdjAeYoK6yxrdUg7KmZq5Njj5fV6teCjf2jGyPUK6HLJNqe0bPMEzTz+ep+/aYZh6J1Xf68TJ3wie5d7/d+8Ep0wpUzvfiQ5tkg/P6p73z/fEaAnN6fqtKkOjUws7Peo0JpGaXFekHbVDZTTFKWJQ7yamOlV2PcIQhtbpdxCq6pc6dpW4NIpR5k0eKBd5m8+hq/a0qQPv6rUuEEuDUvxqs0pLdwWr2bzEP38hrsVFhbW9wWAQ0RtTY0+eXmozj+xyqd8/gKzxo3J0cpNoZo5c4YiwwO0MteqMcPjO5YBydsdoOTEMIV3eVNd22iSgmepuqpChrtGCbFtCgqUHA6p9JtLpA+UbCapsDJaKRndN2CqrKyQ1chXVGhfa4xKsTFBCvrm4dbilJqdcYqLS/yRrwgOIwSjwOGuurpaX35wixZ/OV///l3FXuvf+JcwRcVP0q3/d4Vs1p5Hn63Js2vc6By5DKmiqk3JexntVlhp18CUwaw5Cr+yfOlSrb34ZF1jaei1zuUV0rkh0pAAKdWqPnfW/KG8hlTslvZ4TKqyh7YHp4kpCsgYotDsEYofNV71jfV65zcX6qH0xm6jUgxD+v1Oq4wBg2XLGqczfnqZxqYmqMhhVlzaIAUF9bLoHSRJ9bU1crQ1KDjIJZtV8nqlpjarZLIrMjKWgBRAn1wul1qbihQe1BliuiS1tkYoMDhCrpZtCu0yYLCxTQoMGaKAgM50sLJit6JCymTt8nwvrAhSSGCloruMtHJ5parGNIWExamt9nPFR/lu+LSzJEypmXN6fG4ZhqHcdfM0It13CvzOokAlD/6J7D38rcjbuEgpkR8rtMuhxhapqPFE5QyfIcMwtOizxzQxa4mCu9yj2yN9uS5bM0+4VVZr5zfzhmFo/hu3a86Y+QrqUv+xFwqUu6hKD87t1oUODyyWjp4hjRncex1JWr3DpLV7olTZEqXBA4N04iSbwoL6/8e7oUXKLbSp2p0ub9g42UJSFGIqV2P1TplcNTq5hz1iDENat6NFu4patb08WKdfco9ycnL6fU3gUFFVWaGFr2frnON91xF+5zOzJk8apg1bgzVqzDQlxAVpxUabxo6K6/iyZf12u7IHh3WMJJWkkmqrohJPkslkUkVFsRyOVlnNNtmthUqK+e6zLV1JyVk+13W5XNq9e4vSE5s7Qtiumh3to07TU4Nl++YNcnGlFJc4lDX3/QvBKHC4MwxDD/zjV8rf8rb+8X+lsvYyslNqX+T/j/+J0PARI3XSyZcoMaH7MLZWp7R1T5TGDE9XY6tXbrdzr2slldVYFRM/mAAAfsUwDJ2dkqRbjHJN7mH0yBNNFtniEnVpRryqnR79uaxNJ1ta1djSKqfDIbPLqRCvW9HyKNUqpVjl86F2X/EY0tV10qMTpIBeBpS6vdJ1+SE6cfwwbUufoZ9ferHsNotKvXYlpabzu92L2toamY1aRQR330261S01NAUrPmEAXxoB6JPD0SaPo9BnR3mHR/IoUS5nm4IDy3y+WKtqtCsmrvMLaa/Xq6qy9YqP7Nx53uWRKupiFGHf7RNMtrqkZvcw2Wx2ORsWKC7yu+FohFIzj+nxue9yubR906sall7vU567O0JDRpzf4zkb1n6krPjFPtPwaxulKteZyhoyQZL01aKXNWLA+wr/zvvNL1alaNpxd3RbI3T+W3dr1vC3FfrN394nni3V2IZSTUztdvkODa3SXUukv//Ct7ymUfoyN0D5VREKDgnV2TPDFRfZ/2Vr6pql3MIAVbvS1WobogB7qGLtVQo1V2pgjENx37zVLqiU8sukmSP7bu+L3FANnfUnJScn910ROERVlJdp6bxsnXms73PirU8smjE1R1t325WaPkWpA8O0fGOAJoyJ1be/cau3BGnM8FB1/Q3MLwnWwMwTfJ4vDfU1MjlXK6zLs63FKTk0WlHRvrMY29raVFi4XZGhbYoM98hman8+VtVJbW3SwCSput6ixGh7+/qidVJkdKLCI3petxRHJIJR4Egw7+0XVbz1CQ0IX6ozj+/+Af1bz71l1uDMTOXmJ+rMsy5RXKzv9BxD0qpcs5IHDlFiXJAamjwyDNfeg9Faq2LiCEbhX0qLi1U5cKDet0hlgdKpEVKcVdruNukzd6BmJcfpgrT2N1VLWqWv3FaNCpACDa8iTV7FmtvrB8pQpdOt3c0OVTS1qKG1VS6HQ1Z3e3AaI6/SrFKyVbL8gHytxiP90yPd032PDB935Nt0zbRs/aM2Qb+67U6lRYaqyCFFhtjlsQYrICJS9qBgQr5vuFwu1VYXKT7S3WudhlazzLYEhYb2YycOAH6tqalegeYKn6nqzU7JZk9VQ11B9yn1jkRFRHV+cG9tbZG7dYPCuoykrGs2qdUVq+jgXT6jsOpbJJN9gjwetzxNCxUb4fsRbkdxlNKzZvuM1vxWW1ubira/qMEDHT7lm3YP0PAxZ/T4N2LN8rc1Im2VAro0V1lrUov1QqV9s4HT6hXzlRr2P8V9Z1b+ojWxGj/zXoWEhPiUv/biHTKVPKWW3Bp9srpFL17d+yZG37rxA+nf10mrt0trdger1ROqWeNiNDLT3u+/bTWNUm6RXRVtA9VsJCo0JEDJEfVKCm9Wcmzvf6ebHdK7y6ULj+67/Ze+itdZl93LbA0c1spKi7Vifo5Om93oU/76hxYdM3OY9pQEKDRqkjIHRWnVpkBNHBMjsySPpDWbgzRhZKhPUrWjpH2n+q6/pxVluxURtF2BXVLUmkaT7BGTFRzs+77L6/Wqvr5OdXUVMjxuuVxNGhCvjmDV4Zaq6gIlk1WxUVJza4DCIgfx2dZ/EIwCRwLDMHTfvddrxdIX9d97ahXRw3JEVTXSLf8I0dP/GKI//CdRl116idIHmjqmFTQ0S9v3mGWYIxQWniCrxSGbVXK5vDKbzIqJsik8pOdv0Asr7UoemMkGTPArz556qi6dP1+S1CLpWUnNQxM0PjpUs+LCZf7mzdtWl/SmJ0S/HZaiT1qkDJuUYZXaPB5VOlyqbXOpyeFQm8stj9sls9stm9ejIHkUJkPRZkOG263SFofKm5vV1NImt8Mhi9upUK9bseb24HSARR1rlnWV55QWBUs/38sa8k8WSkOzBmu3LU6JF9ygcdmZcpgCFGgxKyTQJqvFpFavWbKHyR4R6fdTjOpra2WzVPmM8Poul6Ta+mDFJzDyB8De1deWKiSoSV3jyPpWq+xBSXK1bveZUt/UJgUED1FAl9GUVZWFiggq9glXy2pD5PFI8VG+o04r6y0Ki56ilpZ6qW2xz5R7r6SdxbEaNGRmj5v+NTTUqrHsVSXHda5T6vJKO0qHKWfE7G71DcPQqq9e1pgs3zVKSyrNMsIuU/LAQZKkTRsWKUpPKDne9yPlsg1hyp5wj3Zuy9PW95+UZeunGh1Qqpzo9uO3LJfuPF+y7yXDOPdFiy47Z6Bmjo1USFD/RoVWNkibi+wqa4pXizdCMeFWZSW1KSPB2Ov1vuuFBdI506SgXv5uNLdJ8/Im6CeX3fj9GgYOQcVFe7Tu4+GaO9N3V/hX3rfohGOGqazaJpdpnHJy4rVus10TRkfLpPbRnJt2BGvssM4vQ1weqaRuiNIyxvi0VVSwQUnR5T4jTEtrbIpNmtZnqNnc1Ci5cn3WZq5ukMLD26fU1zZJskQrKvrAvn+rq6vT2689q6qKAiUNzNIZ51zMl+sHBsEocKSora3VQ//5sxZ++oxuurxJJ86QzGbJ45He+0J669Mg/evPWdpZGKjC+jkaPnys2lxG+2+tIbkNq4YOCtHOwmDlZNl8NltyeaWKKpMCAgMUF+k7eqDFKTU5YhUfn3Rgbxg4iMpLS1WenKxRXf4+PhoaqrphQxRrl4bYJadMWuOxKTY0WD/LSJRT0guOQJ2REqfPm7yaFOiV02PI7fXI7fXK4/HKY3jl9XhlGF4Zxv+zd95xcpz1/X9Pn+3l9vZ6092pd8mybLkXjAvGxoDBmJ5gyI+ENFqAAElIBRIISWgJxWCajQGDKe5NltUlq0un622v7O5t32m/P/Z0RTqdJGMbl32/Xvu6m9mZ2dkyzzzP5/l8v18H27LJFk0migWK+QJOsYBgFFFMA802cDk2btvENIpkcnmMXI58LodZLCCbBl7HRLJttmnwmTOkK/vX49DugQOEqXrdu9l00SVUVkVxRAWvrpK3QZBlPKqMZUNBVBDdAfRXaaGhkZFBXGoSo2ghySIel8xcw+xYUiNaNU98Z5kyZcpMYts2yUQPIbcxa30850ZEPWNIveM4DA/spTqUm9rGsCGVbyCT6qOhcnZO7P5RjWjdBSQSMWRj86wCJRZwfCBK68JL5pz4jg33IeV/QYV/ep9cAYbSF9LStuaU7R3HYeuT/8t5iztnTeL1DMroVe8jGi0JEEcP70JO/Tst9RYjo3me+t1xkrt7qEknuKjKxj3H7ebhPoh54S1z5O88wYFB+PV4FX/19vmFjuFESQgdTAUo2F6aqiXWtooEPfPuNifZInQNQzwjY6NiobLrSJ4/uTaPdtL7yBvw3SerecPbP04kcobk/mXKvEzo6e5g/8Mrufbi7Kz1d90nc/1rlhKfkBmaWM66VfXsOeJi/YqSZTxvQEevm2Xt0xdepgBpcwNVNc1T6yzLYqBnCw2Vs4/fO+Kjvvn8eZ3gI8OdhLzDs9JYDcVLIfUAI0kRX3ABuv7Cu7cty+Jzn/oA6d4HePP6LmoroHtE5CfbW6hd+nr+6m8+X47YemEpC6NlyrySyGaz/PzeO7n7zr+mpjKNLIFlg6op/PPHl/PMHoXfbVvMJz92G2NjE7OSVvePeYiNulnUpuPW5m4beoYEKiP6VNW+nAGjCTfVNY2vSmGkzKuXb990E+/6+c+nlo8B8tKlNOs6DwOFJXVUazLLfDqqJDBkwr0FlVubq/CoEo9m4ZrnMMg6GcuZDKm0bAazBQbSORLpNLlsBiefQyoUUIw8P9qzh7tXZud0lEKp8MMd++Hry0vLmxPwhG8559/yTpqWriAYCGCKMkGXSt4SMBDRNRm3JJKxwZR1FH8Il9vzqui4JZNxhge78LiLuFQH0xJIZ0Vcbo3qCtes3lUsoRGtLgujZcqUOTsMw6CQ6cGrT6dGMhzIGxEK+dEzhtTn83mKqT34PdN9vIkMyJ6VxGPbqIsUZ71e74if+uYNjMR60e1nZu1nONAzVMOChRfN2bb3dO4nrD86y8maSENOvI6aulPDFEzTZMfmr3H+0pMKOPXKhJs+iM8fYvvmx9lz9xfw9TzMef487cEzf2aOA29/DL7xzrndmI4Dt31f5SufXkxFYPYE/+A47O9TGZzwguhiVZvOypZz79MaNvSNQGxComAquHSdmko3tRF11r33/h0y3cMmzaExltelcIB9fX4GstXc+Mb3E62qOufXLlPmpUxX5xGOPrGKqy/Mz1p/588VbrpuCemczOHeRVywoYUDHR7WLAsAJVf84KiP9qbpBmZ8QkD2XYU/MJ1zo1gsMj78FNWhGQXsHIgl6qlrOL0rwLIsxob3zsrNXDChUNTwe+RSPua8l0j0DCFXzwN/+sev5y1LfsWmJdYpzz2w28WW9Dv51Of+5wU/j1cxZWG0TJlXGhMTE3zvK+fzJ28+NLXuP76j0xlbzRXXfpBLLr2Wro6dNFWNEy7ddzAs2H20mtqodkr40kzyJvQPS4QCOrmCiiS7qYhUl0XRMq8qYsPDDNTUsHrGvfFbHg/vXrQIC7inooJCTSUZGTyKjCmKVLh0rqgO4pVFtuYhiUyFLGAj4CBgAY4g4AhgCyKOIIBQes4RxdL/oogjiCCKWKKII8ogyQiSiCgpCIqCpKnImgvd58MXCuMPhXF5PDzx4G/Z99l38dG67Jzv6Ws9UK/D9bPz1fNUQmBreDWrb34Hi1euwq27KIgSIV0DUSBtAqKMV5VBZDLU3oPmD51SLOOVQjw+SirRTX2VwckeqvgEpHIajTWlsKe8CdlikHC4nMC/TJkyZ08ul0GwBmZF7+QMsIRqHKNrVh7RdB4U98JZbe746CA+vXtW2PpwXMcfXkR6/GkqAzNC4B2Ixaupa1zB0GAnHmEbvhkFBQ0bemL1LGi/YE5x9MjBp2iO7J6VP3RoXEQLv5FQ6NS2zzAMdm/5CuctGQVgcDDFlt/tJbb1EO3WGBdGzVnv+3RYNmyLwc64hhoN0NTo5WsPdfLF6x0aw9PbJbLwuQcVcp4GvvLhIH2jcKBPYSTlwuV2ccFyHzXhs3jBkxhKQO+ISDqvoOoa0bCbg/06N54//3794xCTb6Cyup6jh/aAILBwyepysaUyr2g6jh2ga8tartw4Ozfxt3+q8KbXL6VoSDxzoJlLNy3mSI+XVYtLVcviaUjn/TRUTbdv/SMKFXXXouvTDWE6PYGVfobAjKjzE4XmIpWnv7ay2TR2YR/eGV3W8QnwTYbUj6VAdzfh8Z5asPj54jv/+1UGn/ozPvYm47TbfPontbzv77eW24kXjrIwWualTT6fxzRNBEFAVdWXhABn2zb5fB6Xy/WSdEYVi0Xu/M/1vPf1z06tu/PnGjnxWt73F/cC0Hn0UaqCMdyTN4FUFkYzq6irjM/q2M5FR7+Ez+smFFn4kvg+ypR5sfnOG9/IO++5Z2r5OMDSpSzQdX4rSVzV3s4YkF9cQ71Hm+UUyTvw44LOtYtbkSQBCwELEUcAAwFLEDCRsEQBR5RKwqeqIGkudE1FVzVkVUGQVQRVRVB1ZLcLRdXOeD3+33/8G9v+57N8vDZD42RUUF8OvtQnMWEKfLrFpFafe9/NCYHtlWtZfvO7WLNqJZKikhckArqGJgpkbCjYIqoi4VEkijbkRXky1N73imkrTNOku3MfC+ryp+1BDYwKFC03igSWIyIICpruJRSqfMV8DmXKlHnhmZgYxaXEZ4XOp3IiJj682vC8IfUAQ/3PUh2azu1nOJDI1qJpPshvxz9D/CwYkCy0Ea1uYbC/A5+yY5YL1LCgb7SRlvaNc57r/t2/YnFT1+xK0oMqVU234T6paJJhGDzx4P3s/8knqEkeZ70vR/NZag5DGXg6JjKBl8bqCja2BHhkQKS+BVbWwTfG9jDUazF4EDQHxsZEqoN+3ntFDd/e6+HKi6KsXewl7Ds3ITSVg64YTORkEHT8fhdN1S787tnTY7/aDtevn/9YPaMw7r6J1WvmifsvU+YVyNEjzzKwfT2XbpjtWv+/e1TecvNSHEfkoa21XHHZSroHfCxbWCqaMZIAhyDRiuk+VMeAl8bWq2f1q0ZH+vHIB3DN6GolMyB5zsc7j7A5Eusk5Jk7pN4A4gmFimj7nPmWf1/27NnDR++4lB/+VZLgPKlEe2LwvY7/x9/83Vee93MoA5ylMHruU2hlyvyeZDMZJsbHUC0L1TGxgLikILs8BCsq/iBFfnZu3cq3/+XT0H8YLyYJyU3Fio184G//kdra2hf9fE6HoijYzuzLVhQdXPr0vIVpFtFnzIzliiDJIsJZfKyiCLLsKg/wy7wqGR0ZYcW9985a95jbzbt1nThQW1ODBDzk83K9a7YoOmTCr4sqS9asZigYIujW8Go6PlVBE6VTqukWHSgCtiTjKCqi7kLWS9feXJWCz8R7/vzDXPOm2/jbP3kPw3u2UKHYNPo07righn7cfOJ4llXjHbw5nDtFIL0w6HChsYOnvr6T71WtZ8kb/ogLVi/DyFskbQm/WyMs2xiOTSJrgCjhUUHNjpFNj5GVdRRfCJfn5R1qn4iPE/YXpnpPDpBIGuQNE12VCfoVKsMOfYN56qp1SiVMCuSMAoODaaqqml6xTtoyZco8v/j9EeLjeULu6XyhPpdNPJsnmdZnhdQHvHkmEiMEQtO2/3BkIfHk7qm8oYoALmkASVlJprCUnDEtIGgKuM1jxONeaupa6e+zEIXdUxPoigR1kR46O2RaWk9V/haveC2H9v6EZc1jU+taaoocOvpjWpfdztDgIFt/9r9ktt9Dc+4QF0ctrvACZ6gpYtqwZQiOZzVcviDrWyq4efHsG9TFNfC/B6E+YHLxlRaLF5fWGwY8/S/NXNIWZDgFLU1+rjoveIZPHYomdI/A6ISM6Si4XS7qqtwsXySfseq9ZZUctso8/eljMT9rL28/43mUKfNKo33hCizraZ7csZGL1k27I99zS5Fv/uQAt79xKddcOMCvHixyxeXrOdghsqTVQ2UQ+keTxFNBQpOTGq21aTqOP03rokumjhOprKO/d4JosG9q4ijggeH4DlTlwlmF6mZSEWlidCgxK6Q+5LOYyJj4PTIu3WBiYoxQKDrn/meL4zh0Hj/Os8/cQ7LrfrzGHpbWJFhSw7yiKEBjFEaePP57vX6Z35+yMFrmRSWTTpMdHaHSMZEEpvR7v22QTScYNQ0qq2te1MH1T+/8Fnv/8+P8c2gY94zwnKGjh/nkzU/x4W//gkVLzlDZ5EVCEARM66QZLQeWtkuMjY5SEYmAXZwVAmoYEm5/gHRmnJDv9MZvYzLdlSiXq+OVeXXyqw9+kHfa03nfuoCLmpoAeMzl4iavl22iyJUVEbZOQFwCXZXJIJLX3Ky6cC1rWxqJWRCdvEwtBwqA4YjYsoKgKYi6G1V34VGU57Wtq6ur439//lts22bLE4+x8/GH+DUOy8+/iG9ccTX7dm7nns/8MZ5YB6/1nyqQbgo6bCps46n/2c7/VW9g+ZvuYNPKxRh5i5gt4dNVQrIIWOSKFuO2gCzLeMU8dnKQdFzE1tzogRCadhp76kuYbDY5lZu5ZyDNwHCKsN/E43YYTogcPCpRX+sDMTBrP5cCNRUFhob7aGhs/UOcepkyZV6GeH1VTKT78Lumc+d53QXSWS+pfH4qpF4RQGKYQiEwNfmiahoZpYWidRx18n7j1SE2fphIzSqGBzPIvulwe68LxlN7yGY2Ule/kL4eC4FncU1qCaoEtaHjdHVKNLfMLq4kSRLty97AkUPfZ2FDlkLB4OnHD9Dz+NM82/9BLgxlucULBCcf89CXhmdiEhnRQ3NNhI3r/Fwkn15pHMrCeErgpwfi/NEfTa/fsR02NPqxHfjWToX3vHnugkZ9YzA4LpI3FRTNRVXITXOTSvtZmsMMG8aSkMgJuDSRHccsNi6ce9tMATJ2lGAweHYHL1PmFcbiJWvZbz7B07sv4oLV0+3aH72pyDd+cpB3vGkJr798lHsf2sKll5zP0S6B9mY3dRGHroEEshTC5y5dnI3RIXo699LYsnLqOLX1i+nrmphVaK4qZNI7sJ3axo1zuj5FUcQbbCc9I6RekyGTLWDYEj5dYCQxgmGEzskYVCwW2b1zC8d3/Rgj9ihR5RjrFhR4fQiYTpGKZZfyIM/X3TdMGB4exrKsF8S5WubsKIfSl3nRsG2bWF8fUatw2iIhE46IGIni9flelHM6fPgwX7hpI19vScz5fN6C9+dW863Hdr5knFBf/Mx5/OXt26eWv/dzhZtuvoV9Azez7vyb6Tn2a1rrpp0GXUM6dS3X0tO1f94Q0cEx0DUdl3/xrLwuZcq8GhgfG+N4NMr6GcLot9xu3r14MQeByrY2FFlmuKqKhS4XMWBPcx0el05e9xKuirKsvhYZgSEb3LpeCoV3uVC0M4fCv1hYlsVdf/dXRPf+hoHeHl7rz1Fzmsv9yYTAzurzWXPbB9m4rBXDhrQt4dVV3JMDWQPImmCJIm5FQZcECjYURBncAVyTofaO45DJZMimEmBP5r8TZTz+AB7PS2Mypqeng5rQCJ09SQQmaG+Y3e1xgEOdIulcgPNWVZ+y/0hSxONvxu1+HipvlSlT5lVBoZDHKvTinlFUqGBBKusm4Bk/c0j9wH6qg6mpZdOBeLqKSLSZvq5dNFROuzwBBsdlKqo3oaoqPV3PEvUeRJ/x2nkDYhNLaGxZMWu/4x3HePon/0Vh5w9YTIwNUYd59MwpihY8PQTdeR2vP8iGlgrqQ3M7u2wHjiahOytj6DqRGj/LmrzoqsjfPfkL/u5z6altv/dVlfON5fzwWYXXX13DynY38Qz0xARSeRlB1gn5XTTVuPCcpujoTJJZGE9BIiOBICFIMpqq4PeqVPhl9Mkv4pFdSYKuHKub7VlCRzwDDx6s5rU3vgvfizSGKVPmpcre3U+RP34pG1bOLjb09R/rvOvWxaiqyL0Pejjv/AsxrAgt9aUcUEd7ReprQri0UuOSzkPW3jiryKVhGIwObqYmPB2ybwGD41XUN67kdIzEugh5huYMqS9akMz4qKxqPu3+4+PjbN98P8OHfoqS3kpLcIA1CxzUM3Tv73oEaivgstOfGnc9KlH0X4JUcyO3v/tDLxnN4RVEOcdomZcW6XQae2QYv2CfdhvDgVFkfKEgsqYjyfILJig8/cSjfO4D7+S/Kntocp1+u+/HfdT/wz1ceuXVL8h5nCuf+/h6PvHeHVPLd/5c5u1vezNP7K5mzUWfZaTvAVpqp0MYOof8tCx8LfHxEdLJHuqrjFNah3gaUhmJoF/HF15ebpDLvOr47tvexjvuumtquQfIL15Mm9vNb0Mhrq2q4hGXi8urqrCBX4eDBNvbEFSdxuooVQE/igAZB8xghEAodNrXeilw9OB+9vzj+2kzRtjV0cNrfacXSJ9KiGyv2cjGd/8l61rrMGxI2RJuXcErT89s523I2iDJCm5VQqG0bMgaOVvARRG3YnOiRTeAtCFiqT4ilb9fCNPzQTIRZ2xkH709PaxZCn7P3PeeLc+KrFjaiuekCiIFCxLpMFXV5eT5L2WmRPpkshQbCwiyjDcUwuWapzNQpswLRDo9gSoOo84QGjNFyOah8qQq9ZlCFcEZIZ+GYZAe303INy1AZAqAugxNczHY+wwNlaXCfLZt8/BD2/jxt36BG7BEmcqFy7njAxdQVTE9oZMrQF+8le6DR+l/6NsE+jdzQShF1Yy8pfPRmYTtYxJ5yceCuggbGn0o0qn9SsuGIwnoycocz+msXxFgWbMHtzpbce0bL7JN/yk33zw9fviLO6K0Vq5mSXsEVXPj9bhpqNap8J0+JL5gwugETORECoaIg4ysKHhdKmG/gt8tcngAfC6oC899DIBfPGNj2hI+JYMs2aQKGr5wM+df9Brc7rP8kMqUeYWza8djOH1XsnbZSeLoj1y8+62LURSBXz6qs2T5JhS1msba0oTJwU6J1pbwlBN+NCmgBq7C75/uV2ezafLJLYRnREIWLEjmSrmU58K2bUaH9pxSpT5f1Ah4ZOJpcHnb0F0uHMfh6JHD7N96N6me3+Cz9rG8Nkn7OXbvigY8cwS+cA/8+OPMKaJm8vDOL/n58Rev5+F9Cr3ZRSxaeh5rzruo3Cd5/igLo2VeWoyPjOBNJ1BP+mkalkXX0DDpdAZdV/FV1VKvKxgOmIApiNiiBIqCoGnIqv57O7AO7t/Plz7wNpJHn+UHa04v1AKkTfgz8WK+/suHn1Puv+ebz35kNZ9+356p5e//QuZtt72Z470C7vp/JpfcSkvtdPhC53AVLe2XAqUcerHhI/jcoOtgWpCYEPC6ZaojKvG0TriynBupzKuL+Pg4R6JRzremO2/fcrl495IlPChJXNreziFBYEFDAx5RZLssU3vLTdT6ZzsdDQdGJZVITe1LxiE6H7Ztc/cXPsOqQ7/CyGbYdqyba/15qk+TJvPJhMi22gu4/H1/zaqGago2TNgiLk3Fp0wLpDaldtNExKXJ5BFRJPBOfiQ5yyGdt3AARRKQJAW8YfyBP5yYPDo6wpbNv8bIdVMdMckXBcYSCk31YVYvr2DG2yOegmO9fs5bNbuHbNgwOhGkpqbhRT77MmeLbduMDQ+jZrO47dki/YQsI/p8hCJzh+SWKfNCkowP4XGlZuU4i6dEZDl7xir1E8kxNOEo2oydR5IKwchKHMdhfOgpgu4sH/qjT3G5fZA3NOSnRIdnx+DfOut536feS2XEz+7fPIRxYAcL7Rjro85pI7xmkjfhqSHoy7sIhEJsXBCm2q+esp1pw8E49OVkHNlNVdjP0moPLkXgrg54wwbmrFj/nV2dvOadT1NTU1rOZODRH7yF125agDSHazWeKbk/s0UZ0xIRJBldUwn6FCr8yqz2/ASGBbEJSGTddA2bXLmiiD7HbXwoKdGVbmPjpqumisi6XK5y+GuZMnOwfesDyMOvZfWS2WPtr/3IzXtvW4QsCzywWaG2cRPBUD11VaV2Y3+HxMK28FQqkN4RhWjdtWgzIhrj4zFUZw+eGX3WVA4cdR3+wNwzG6erUq8oEvt27+bAjgdRs3up1jpY12oQPkfz99gEbD8mEEuqyJqPo7Egt17pxSzk+eJdnXzkDQUW1U9vv+c4/M2dLv7zk1exoN5H0YQfPuni6o1VPNVRxZXXvZ3QS9xo8TKhLIyWeWkRHxnBnU5wIqLFcWDfsQ4SsUHqi1mCgo0hihwWXCjBEOuWL0WZo2KQ7ZQGMQaThUskGUFREXUdWdWQZXlOAXN0dJRHv/NNUj+/iwWd+9ikOXw4Df++dv7zNmz4+GF4TY3KwMJLWfLuj7Lhkiv+YK7Kz3x4BZ+5Y9/U8g9+KfPWt7wZgCcO3UJDjU1zzfQNqHu0haYFpeqYtm2THtuKKEOxUCq2JMo6flepQxfPhQmFy26nMq8uvvuOd/COO++cWu4HUosWUevx0FFXx2Kfj6ORCCu9XsaAo+vXs3T1clyUcr85DmSBCUklFK162aWi6O7sYMtn/pg3+JIcGkuz/WgP1wZOL5A+kRDZWreJ6z/wERbXRig6kDAFdFXFr85ue4vAhAmSIuPSJMbSBopo41NAFEoz/BMG2LJOY8sfZlJmaHCALU/exWsvis8aCDsObN7tcPCYzMKFdaxdFsA7WaX4yV0qF22YnU90IgeWUEMoVBbWXqrER0ZQEwlOl+wgLooo0RcvnU+ZMiewbZtkopeQe3ZF5+G4SDiQPSmkXqOisn12SP3gIaoDienjAaPJSqI1rWQyaT7y7mv4oP40S4KnDucsG975oMAn1zssnscpOZMjCdg1JmMoPhbWR1jX4EU6SUU1rJIQ2p9XcGQ3NRU+llZ70ORT+89HE9AnwOVz5O/8t12P8uGPDUwtP/iAwAVVH2M8LTCRESlaIggyiqrgcalEAgpeXZw3p1+mCLGkQMGQkESVgE9lNOulYXGpf7/liV/TFErQXFVEESBrwKEBN3mhlg0XXvmSMEqUKfNy4JnN9+NJ3MDyhdNtj23DN37i4Y/ethBJEnhiu4wrdAH1dU1UR0ri6LPHVJYtCkzVzegY8NHUdvWsa2+w/ygRX9esgmgjSRFfxYXo+txuy5FYF2b2IHu2Pkms43G0/EEWhMdZvcBBOcfL+nAf7O+RSBV0/IEAK9oCtNbpdMUEjg176U342HNojM+9o0ixaPKtXw5zvG8CVXYomiKWFOT6y5fxuosrp45556Mab7+2hpwB9z9bzxve8v5yJOfvT1kYLfPSIpfLkR8awItNomhy8MhhBnZuJT02itcycYCMrLK4Jors89PpyLQ2NrK4rQ2/+8xWcmuyynPOtDEnf66dHcc4cPddKFseYW1qiKUnTWD/VRI+sxp88zSEj41BfwFumyxOnzHhoYyHxOprWffHf8Oy1WtOv/MLwN99eCl/e8fBqeUf3y/x5jffCsADWxtYvGQdDVXTl2tfYgX1jaXiUYVCATu/a6pS6dBojr7+FI5jEQz5qKrbgN/vf/HeTJkyf2CSiQQHIhEumMMt+jNd56bmZh7RNC6ftKo8EAxy/g03IMoyKUDSNRAFNLcXt9f7snCKzoXjOPz8q59n4bYfstQLz46k2XG0h9eeSSCtv5ib//SjLKgMYTgQNwU0RcWvybN6IQaldrTGA9ocDp9YHiR/NRWVlac++QLiOA73/Ogr+OQd7Njdi6aa2Db0D+VJxAeo8KXQNZuxpEbebuKv//ztLFtcw1NzCKP9IyrRmgUv29/AKx3TNBnv7ydaLJ5+G2Bc14k2lF2/ZV58DMMgn+nBp09PbhsOJBIWlUFj1rq5QuqTo7uJBKbvZbki5OxWvvpvnyP24Ff5jwumQ0hPpiMJPzkKHzu1KD0AGQOeGhQYNFyEQyEuaKsg4jlpIsyC/eMwVFBA8VAX8bGkyj1nGP0JChZ0p2C0KPP0iMO7N1qEZ0Sjd44WeKRwP+95T25q3Ve+FOHNl36SkF+et0L8TMbTMJYWMe1S7tCKgIbfLU2Jp7YDO3sjrN94BVC6N/T19tB9fD+OY6KoHhYtXVt2b5Up8xzY/MTPCGbewNK26fGpZcE37/byx7e3I4oC2/eJ5NjA4kXtVIZKkaNHOlWWtgcQKE32dA7W0Lro4lnH7u3cQUPl+Kx1/SM60fqNKIqCbdscOrifg9vuJtP3OwLOflbWp2g5NVX8vOSLsLMDumIKluCmOhpi3WI/PrfMoX6BrrEA4/kAoqSxvElgWZOAKsNw3ORrP+2jvabIa9fZ6Coc7IHf7lJZvSjMtRcEZ73Odx/ReMd1pTHHzk6dikW309Qyd3qAMmdNWRgt89LCcRy6Dh9GcwyUfI7vfPs73ESCNnnmNvBMQWSv5OZNDRHywD7JgxmMcNnFFyPIEoZtY9oO2Hapyhs2ogOSAIJpsnvrMxz9+Y/x79vKxU6GynmiW3bm4Sk3/Ok8hYTfvw++tAS0OY4zXoSHimGKF97CRXd8fM6Gy7IsnnnmGcZiMeoaG1mzZs3vNfPzTx9fyMffe3Rq+Z7fStxyS0kY3X1IpqrpOmoqpo8fy24iOpnzLpVKoYv7MQoGW7d14lfztFUbyDIMJyS6xnw0tq6ltX3Jcz6/MmVeTtz5nvfw9m99a2p5EBhftAjV48HX2kpaUahoaCAkSeySJHzXXENbNIoBFKqq8L7CJhIG+/t49G/fy836KLoEe2Npdhyb30H6eEJka8OlvPVDH6Mu7C8N5E0BWVEJTgqkoyboKnjnmYTqzavUNre9qCGJx44e4Zv//SGuv2SUTWttRBF+fN8IR44N8uH3mmgzw60S8Of/HOG9f/xhNN8KNq6ZjocaSUjIerTsFn0Jk81mMQcH8dvzp8+JiSLuaBTd5Sq7wsq86ORyWQSrf1ZIeboAjp2fI6S+HU2bXpmaiCM7h9Ekm2PHejn45BaeuesnjAwP8ucbbZZVzP/aH3wUvnLZ9PK+MXg2IWNrfpbUR1hd50Gc4QrNm7BvHGJFFVF1U1/pZ1Gle85Q9RMMZaAvJ5ITFBSvi6qom/oKFUWCgaTDdzf3sqnB5vw6UCW4r3sAtekxrrlmehj6o29cza1Xv/60r2HYMJKEVF7CtGWKtgvFHSGdzrGurTjLfQulscezvTo1C86nMlo1/4dUpkyZ58QTj/yEqHkri1qmr2XThP+9x8f73l4qKrfvqMBgch3r1iwlHJApmnC838XiBaXUVUULhpJLaGyeLg5nGAaxvi3UVZbyMWezeXbseJaDu57BXTxEres461pNAudYFzOWgB3HBEZSGprLR2tDkFXtXvKGwP5emf5EkImiF69bYf0ikZbo/GP7B3bkeWpvgkq/w4JanSvWB9FOaozGUvDYQR9vuLTUWBdMuHtrlEuvuoma2vpyyo7nTlkYLfPSYrCnGy0zQViEb/zwx1w3fpy604w5dhUE0v4wF1eWwtlGTXjA1FhfFwVJwhYkHEkCSSZdKLBryxbMLY/QNtzJJarFWRSgpMuAZxyZn5ki76ozuCZyUgViBz7TIZC1BP662abqNKLACfry8LhQi3zVO7j8vX9BRSTCZ/7qA+x/6CfcUDNBg8viYFpju93KGz/wN7zuTW87m4/tFL746Tb+8u0dU8s/e1DipptKwmi+CEeGLmLl4skG1YK8eA2BQACAeHwEt3SEx584wkULs1PO0Zns6nYTrLuAlrbFz+n8ypR5uZBKpdgdDnOxOZ2T91u6zruXLuVXwSCvqa5mVzDIhmCQOLBv6VLOO+88dEEgruuEXqGuMsdx+M13v0bto99k1WRi+72xNDsnBdLTtYWPJ0R2NF3BbR/6CFVBXynnat5ky869DI6NsaCplvbFyxEnHT42In5dJuyWkYB4EaRwA/5A8EV5nwCf/dT7uO01O2hvLi33DxX54lcP84WPG3NuXyzCWz/SwL9+4YdUV/koGiLpnIrPV0EwdJYxqGX+IKTTaZzBQc4UJD8K+Cm5nE1NQ/B6cfl8ZSdwmReNiYlxXMrYLAFvJGET9BdPCakPhFo4cugQxx7/NcmdjyB17qYuH2OVxyY0+ZP9py54z4WcsXjSnz0Gr20WGLHcRMJhLmwNEXJPd9RzZkksjRVVZM1NY9RPe8SNfJqxetaErgmIGzJpR6PocnPpMhd+19w2z98clmhY34EowI6nwDEhI+7mttsOcGIOcmQEkvv/lrbmabds1oCRhEDOkDAdBUfQcbtd6JpCyK8wmlIR3M34A0EO7dtO1JumJlQqRDqakhlIuGlYsILqmnIqqTJlXkgeffAu6oW30dY0vc4w4Fs/8/OmG5rYd7jAQAwcfQPXXn0eAa9Ergj9MTdtTSVlM5WDvHAhldHS5PTg4CBbH7uHkcN34reP0BZJsLKF07ZLp2N/Nxzsk8gUXYRCpbD45hqNobjAoUGdoVSQvOWiNixx/hKZ4DkKrek8fPO3cMd1zDn+Bvj+ozLXbKoh4i+dvOPAD550Ea2qxR9pYc36i8p9kedGWRgt89Ihn8sxcryDBtmhYJp862tf5f1KetY2WcNk69AYRctmaUWAey03F7lFHEr2edMRsBBAgIFkiq4jh/ENdXO+nWHNGUTLE2zNw2FB46A7xK3rI6wKqziOw3/uHODgcIIr/EUiisORnMivEhrvW1HNDa0hjsZzPNM5jDuf4Kqwjf8MJpKDafhMn8obFhnc2n7qpfPPe124XvNh/uzjnzkn96jjOHz9X1u545bOqXX3PSzyuhvfMrX82M56Lr1oHQDJDOjh108l6R8d6aG/awe1+gCV84wOHzlUwaWveTOieJYxSmXKvAz53vvex+3f+MbU8hAwsnAho34/m9rb2aIoXFxfjwA84PWy9NprqfN4yABqc/MrvnMyOjLCA598LzfJ/UymIT4rgfSxhMjOlisxI9Uc3/U4VwUGqNENOnM6j6VrufzaN/DWN9+CAySKMF6UaAzrmBaM226idY0vymeby+X4/D/cyKfePx2C9Xf/3sO7bhqlsfb0+931Sw2x+qu85urrUVQNr9dXzv/0MqBQKJAZGCA8YyJkLmJABTBzTJUHirKMo+uogQCarpfvj2VeUOLj/YTc2Vnr+oYzjPQdpPPJJ0g9uwOt/zj1hXFWee15U0J9fxCaFsBF8+h+jgPvfkznW7cvmWrP0kZJCB0zNRTdTVNlgLaIPmfBI9uB/jQMZiXyKLhcOjUhN7KiMlaQSCGzb0zgxmV5ot5T9z8+JrBj2OHG27umnPrbtuU4ePBJ3vGOkantfvFzhU2tXySRlijaMjYKouLDtBQiQZGQX0GfI77+yJBGQ9t56LrOyMgwI0O92A6Ew1VU19aVnVhlyrxIPPibb7FAew8LJr0FY3H4h89DLiZwVVtp3eO9Cl3ZCGvWrMCvl/IIty5fwY3XL+fo0T52PvMMdrKLiHSEVY0ZGqOnf725yOZhxzHoGVNxRDe10RDrlvjxuSWODUHHsJtYygeyh6VNEqtbpXmd8HMxGIe+MZG8oaLrOnVVHhRJ4J5HhnnDBQbRwPS2OQN+uVWmuS7IeUumG8ihOHSO+Wit93F4UCdQtYyVazae24mUgbMURssxQmVecAzDINnXh1csCYQH+wZYbudImTa/7B/naHyC3Uc78GZT3KgWcAkO/4eLvYqPSy/bwMoKP7bjsKUvxo4DB4kkYlyqmLxJBs4wds7a8HhBYFhxU1NZwfmVYVyOiGDJ/HDEYmnQQREF/mxdHYZVy+ahNMmCCbbGijoPlRHIWg7tIRftoWYcx2HPSIa93UNEzBRXhB30ORpKWYCVFUVuPU0tkY+tzPHuu/+D74UC3HDruwiFz85plEqlCJ8UuXvyjJgmT3cg8wUBvzqdWNWxCiTGEqw6gxm0PphisL+fuleoI65MmXQ6TcO3vz1r3W90nTd6veSrqhgRBBZGowjAs6JI3fr1RD2eUtG3SOQVL4oCRCorectXf85j99yF75f/wTq/zcqol5XRpeyNpfn1sR6uC+SJniSQXhq0eWLnAyxvgQ+vnl5/IXnexnG+/sj/8PHuIT70gfdR5VVQRIuhZIGwV8MlFMmO9qGHamZVH30h2L1rB5etS8xaNxbPziuKAtx8VYHP/N9vectb3vWCnVuZ55disUhybIxUNstIPo9o26UOsCgS1HUCqopIKU+5AJwsq+iAbpqQTmOk02QBS9cRvV70l3Fu4TIvXSTZx6OPbmZ0+0Nk9+/ANXicRiPOSq/DmhP9Ptfk4wzcHIWPH5xfGH1qEC5si7BlWGTMVNF0D81RPxtW6Mw1BzBRgK60QNKUEWWNkM9NbZUL0xBIWjKoKoWATkWFRrVaGpNqfQbPJPJ4RmFVJXg1GM/CnmEZzaNRWz86K33JoUNxBGFi1uv29zYz3tiIHIwQ9NWjuTzEBnuo944SmscRWx8uMNDXRWv7EqLRaqLRc0wwWKZMmeeFq177bn73KwNp4A68Lvjzj8IXroKob9pI9MaVRfriA3z0/ji3X1bFmDHBxK4H2DxkcF67w4pzDGocGINdxwXG0jq620d7Y4gLNrg5zxY40CvQPe7h3l1eXLrOhsUq17af28SnYUFXDEaSMhYqAZ+b5lo359Weepy3XVPDw9vjDI0XUEQbSRYQJYVLVwdprJpdDGVXp8xl6zy4VOiMGRTSMfL5/MuuyOvLhbIwWuYFJzs6imKVwtuzls1QJoudK/D5rh5uVfM8eOAo/6RmWDQjn/n15MjZOd7xi9/S5la5yMlyme5wocgZO4EDJmw2JHJuHy21lYQ8XmxJw9LddAd9FDU3KysEzrfy/P2zg1wbKnB+ABRJYFHEx89GZTSvj7UVVSythP5Uno7RJLlMlmqpyPKwl9XRNmzHYXN/kmP9MZqENJeESnlOAb49DH9+8fzn+YH2CbbtvI+HVInrb3sfLteZe7dDQwOEQ7MbWVkGw2Sqkl5rQ4HxRI5w0EXRkGc5mRyniChYnImgp8hIcrQsjJZ5xfLzj3yEtxnT4dIxYE1jIw9qGjcGAmz2+bhIVZkARhobWVlbiwLENY1gMPgHOusXH0EQuOyNbyN+xbX88DPv5warA6/ElEC6J5bmV8d6uH6GQHo0A6IPbmqb+5jvW5jhg9vu55Et66irb+KixTWM5C0SBVBFE59mYsZ7Sbkj+AIvXKGLfC6L3zU736R4FnPKigyWNb/rsMxLh4l4nNTwMEouh1gs0ug4U90Iw7JIGAbdikKF201XIoFUKDDmODiCgKxp1IdCiEDn8DCZiQlE28YRBERNo6mmBtPlIifL4PGg+nxoul52EL/KGBsbY3x8nMrKyud0f0in0+zd9gy9T/yK/L7NeIeO0GzHudAL6oku3xnC4E+mNwd70wJxSQWfC8Fn85Njad7UdmqO3ZEcfP6Aj39+1zoWVXlOqeZu2tCbguG8TFFQcLtc1Ifd1AVlpLzMmO2iV69gyM5z6RJxTkcpgKBned11EI/Dvt0w0gc1Xh8Xn+/Bo4k81T2dJspxIBpN4XbPLhjlcb8Oj68CWSigW4dxGRAzmFcUBXCrYCTS82/0ApHNZknF4ziAx+fD9wrLTV6mzLnymuvfx69/UeRXX/5T/u1KiM4RxVgfgr+/Jsd3Dnbx2Xef/bFtG57thqMDEhnDTTjoZ/WiINdfqRFPw/5emaOJALu3uIgGFS5aobH2HNvXiRx0DQkk8wqyrBGp8NDcpNN+BlfpyATEUi6q6qP4q3W6+8e5fEWWxjnS0+/tEgn6XLgmbwKL6iwO9o8xNNhHc8tpOthlfi/KwmiZF5RsKoUrnSYHmA6M5YtUuHW+29XPv3qLfKcjxh9JGRapp+7rEuG7IYO/GDW4/gwW+T0F2OeoWP4gnppKdJ+fgMdDNOin1aszsyDmUQOGDVhZofOpTS081pfgC4MpBCCoK9y8MkKVW+bOftBFaA/ptIdKMzOOA53JPFtGUxSyGUJekZvXBnGJNg91jzMwPMISJUcWqDyDzrmhCv5ty3G+cnWMh375U5atWAmCiCcQJlQxtyNtbKQD90klnWXJ4Ui3wLLW0kxbZQi2HOxi47olWM5Jl7hTpFCcv/AEQNEUUNzl2agyr0wymQy13/zmrHX36zqXer2sr6vjGUni/IpSnt5tbjety5cTVFWygKeq6lUpeoTCYd7y5R/z1C9/inT3P7PRX5pgWRX1smpSIL33SA83h/J8Zxj+7KL5j/f+BePc89SviFxzC08dhiXNNaQMi7qARCKXR9NU9Pwo8Xwab7j6BXHkVdXU88wDCuuWTwvkiqqSmMgSnGfc+uQOhbXnXfW8n0+Z559cNktmaIhQschIsUiz48yKp1KASsDM5+lOJlloWcy88+UMg8OZDLlslqX5PDMi3yjk8xzJZgnW1NBQUQHJJEYySVoUsXUdyedDc7nKbtJXME88+Dvu+uI/EI13UkWOftwkKtt57yc/x9oNc4c7JhIJ9j7zFANP3E/x4Bb8sWO0MsEGD6XJf4A5Qs3n43gWDmUFxh0VQXdTGfCzvMVHZ1zhtrUCYR3e5jh8+fEB/urpOG9vK7K8AhIF+NZhmf25IN/8f5cQmazuFM+X3KBpS0FWNSJ+D3X1OlIeYoZCDpUjjk5I1Qh7oUmH/nSRQPj0oiiAGCyJnKEQXHw57Nys0RLwMRQXyRYForX5qW0PHcqiaUk2bJjxPo/DNeefT1Uge/KhS8VYX2K35nw+T+/Ro5gTE9jFIgBxWUbweqltbcVfFkjLvEqxLIto7UayMTfV/lOv5xMsiMDo2GwD0Mmkc6Ww+L5xFSQvddUB1i/2s2qVRE8MDg+pPH7MTfGIh0WNOuevUc45LL5/HAbGJHKmgsvloqHKzYolyrxtTioPg+MS8axC3lRQVI26iExrg4iuANiYy4M8vlNgR0eBdQtM3DrEU3CoX6Y64uKC5dOKsdcFRdPCMAqnfc0yvx9lYbTMC4ZhGFixGG5AkCQGCwaSY9Pd2c11ShGXADtH4rx/nn6BS4QmGY4bsGDG2KLowBN56JTcJIKV+NobsSsq8YYj6J6SW8N04JjjcNQBAQccGwGHomPSNZHhogoHRYKrmoJc1RSc9bpFq5Qv6eQ0RYIAC4I6C4I6UInjQM9Ege2jKQphN63hSnoyJr1jBykF5Z0eywYjk6QjlsJQ+2jRWnGAZDJFx8gQja2LcLtnT2Glkx0ET6pgJ0sOYwkPMD0TbuQGgCU4M4Z4+XyeweN70WSDvMFkozw3XaM+1l3cMu/5lynzcuXnH/84t81wi44Cqxob2ef3c4GqUl1ZiQIcEEXCy5YRDoVwADMUwq2dZULjVyibbngDExdfyY///k95bXb/VL7lEwLp9qE0R44fJeqePy358gr4x51dvPsNOfaOj2I3RPGppX2CKmTMIjlLIaTlmRjtwQzW4HKd45T+GZCEJLsOSLzj9QYn0su95XVR/ut7CT7xJ3Pv4zjwg9828+VvvOt5PZcyLwzxWIxK02SgWKTmJFH0BAYwnsuxzHFOCaF3AUYqxVrD4OQrXwNWGAa7BwcJ+Xx4VRUFUGwbslmsbJYCkFUUBI8Hze+fyvdd5uXPj//v6xz/+qf4z2AMecrYPkax2MtnPngLY5/6L1Zv3MTezY8x/NSvsQ5vIzh2nIVihos8M9zpZ6oGNgPbgSMZOJITSaIiuzxUhfysaPdy3YxqHoYNx7JQpYn87z6bD68vuf8/dGkdyVw1P9g5wvd3Z5EliYyvgU+8cSlHszIH8ypet5vGsE5bpUx/ViZhqQyikjJkFkRUGk8zZy5Sah9PRzxj0bhk2mlvmlDIVJB0V2JrCrGxIS7bNL39vn3jFIvjs0Lrd+3wcstlp0YR+FwQz0B4HkE5nQdNf/GEyEKhwNHduyGdpsZxqJhcnzBNBgoFurNZmlasKIujZV7x5PN59j+7h+4tvyZ9+Am0kQPU2jFs2+ay+jPvv6gCekdgQU1puXcEth0VSRU03B4/i5qDXLTRjSMIHO4TOD7i4ac7vMiqzIbFLq6+4NxU0KIJncOlwmwOGoGAm5YaF3V1p5/1KZowMA7jaZmiJeNxa9RX6rQ3S/OKp7IAV6wL8PBeh7Fcjv6khd8j89oLXadoENk8gITXFzyn91Pm7CkLo2VeMNJDQ4TsaXeiZVl4cDiwfRsf1ywsx8HvnDkccZOr5Aj1i/BYQeS4K4i5ZA2Lr3s9mzZdTEVFJYKiIEkSgigiSRLijL+iKCLLs0PKv/z3n2RbspvzAnO7J38+KmO4vDyYNgg4BapkkxptbqG0KaDRFNCACI4DTw+k8fUNcXB8mCXzpA69vxve1pjkqZ/fSejS91C0bFRJJKiCRylw5PB+Kqtr8YcqpnKJGNkeFK+IYcAJE4osg+yJMFMYra1Mky9YIJT2GxnuJ91zP6vqi4ylYfdx2Lho7vMaTYtI7ppy/pIyr0iy2SzRr31t1rpfaRoL/H6uqqnhGbeby3SdNDBUXc2ipiZ8ksSELOOvqJj7oK8y/IEAb/78d9n20K8pfPczXBSYbsfXV3upCcyeqJkLy4aoW2BH7wQNtS4O943R3jzd5ngkKNoG41mbkFuhkOhnIh/EF4w8L47deHyEkdhxWhtN/vNO+LN3gCjCngMj2Db88mG44YrZ+zgO/OPXq7j+DZ8uC1wvAxzHwSkUUADbcZgjMAWAoWKR2jlEUYC4aRI0zVNE0ZksNAw6BgdZ0dQ0a73EZPSzYUAiQTGRYEIUcVwuFL8fVdeR5XI3/KWOaZrs2rqFA08/hpXP0tU/hIlA7NGf8s3m+CnbqyJ8rmKA297/Bj61wOHKmcLnOWhglgMH0nAsL5ISNFSXh9pwgBUNHhbr07+btFVKX7I7J1OQVFwuNwnZQ9NinTdVCzx4OMH/7Bvl9oUmPhUCLon3b6rmcBz+Y7/O+69bT1O1F2+hFCqfs2R6bI1mt86iivkH9TOp9sChcag5zXvszWVYGZxe7u4WWNzWQtBTcl0NjfdNTVDZNlRVZejvT806RiaxdNay4UAuD5IIvaMQ8pzeNdoX12le2DT3k88zuVyOzb/7HUYshmxZxCQJXdNYWlFBSFUJOg7Hczm6Dx5kxfnnvyjnVKbMi0EymWTv9qcZ2PYb8h1P440fpVmOsywM62RKMyhVpW33jEDHmYMYyRnw620SPtVDhSvAmsYAnQmV998E+3sEjo652DHgJuT3cslKjWVLzq2PmMxC57BAKq8gKzpVFW4WLNBZdBod1HYgloDRlETRklFUlXBAp65eofk5dk9rKwR8Ljd18+gGh/skZHclVVU1z+1FypyRco+szAtCKh7Hmy+FxDhAslikEoEJQLBNFKHUsFhnUSSsaMOP1UqG3/chbv/gh/B6zzHGaA7e/9G/5b//6bP0xvq4JmzimbwSRopwf1zDrm7l9be/l2BFBYmxURIDPWzpPEa6vxMhNYZWTBN0ClQpFlXadG5RQYAL67zcV93Il/eP898XGXN20kwb7j4G/3clSGKCrc/+O/92dDfv+eBfUR0JoQgCUZdFOjkGZhG7Iorb7cE2B1FkoRRScEIYlaCpMULvUBcNk7nkWxtg77EBgtWLOLp/MxF20VJZeq7CC4kAPHUQVi0oJb+HUqqD7phCz0Q1F1126e/9GZcp81Lk55/4BG8tTru5x4HWhgbEaJRDksTGaClvx3Zdp27RIoJuN3lAr6l5VYbQz8d5V15LesNF3P3Pf82V49sJTbZJi4I+dsbSrJ0nBcp9XSAFqqnSLFwUGMlmqZANhlI2FT4dhZLA4BMtxjIOAZeKy0iQGMrhjdT8XuHJhmGQT3exe9v93HGryeFO+If/hqAnyS1XxqmNwjd+BHd8EjasgppKONwT4mB3G7fe/kmuvPrG5/zaZV48LMtCOGFhm8fKlrEsTmdaGTIMGuezwVESPzPxOB2FAorPhy8YxO9ycbJHRQVU24ZMBiOTIQ9Ymobg9eLy+coh9y9BJiYm+P6X/oULrH5cvYN0jyW5OWBw974ePhw5VRQ9gSDABxsdtiZh6Vk4Qos2PJuCroJERtLQ3R4awgFWNHlYoU7/kkaLcCQH41kFW9bwetw0Bt2sbJxdKX6kAPcOw/pquGpRkOYKnW/uG8UoFJEEh4IjEfB52bCuHccbpd9xUVOpUiM/t3tc2oRkAcYyJRHDNcdP2fHnZy0nR/20zggFk/Xhqf/37cuwdKmJz2fM2kfXrqA/LoIgIksSuirhdon4fQKyanJ4IE9rrcPMwCrTgd4RlUCk+UWZ8E8mEjz8k5/A0BCKaSIDWSCvKDydSrGstpYmr5cWx2FPNkvv7t1oHg+y14vi8aDpOoqilPsbZV7yDA4Osu+Zx4jt/C1W93aC6U7aXRkuDJYmK85UHG5JGL6+A96z6fTbABwb0/nam5YgTtrsiyYMxRW+/kgdH7xZZ8M5hsX3jsJAXKJgKrjdbpqq3axaIp92UiWRhaGEQK4gI0gqAa9OdUSluvrcr1HDgXQWUjmBnCFSMCVMR8eSghzuGCISKKLN8X7G0jCWUWisrkecqxpemeeFsjBa5nmnUCggjY5OFYxPmCYB20YSRYZtgeqqagZGDlErC2QlBdspzlvw4j69kn944GlaW1uft3NUVZUPfuKz7NqxjW/e/3PE3ERp3OT2s/raK9mw6eKpDlRldQ2V1TW0ry3N6uZyOcaHh5joPUY8OUZPYoz8+AhGKo5WTBOgyGtaargnNc7fbO3mk2tNPDM6iWM5+Jun4S9WM9WR3VDlsNp6mO99eguVq64kUNeGXtWKU7WYtQtdjI6NoKoaqjiOopSE0RMoCjg2dPZpNFRP5x0ZH+vGNvexrD7FSdH3eFwCQaWGZ4ctitksogSmpdC8aAOXntdSbnTLvCLJ5XKE//u/Z627T9MIVVRweTBIX0UFOnBYEPC0tRGqqkIXRTKBAP6yg3pOvD4fb/zc19j1xCPs+d9PcJm/wG2LovzRo2P85DXFOTuahgX3dsCmxq0k4yuxNB+C10ERoVqzGJnI4vXouCQRBYioNvFcHpeuEZIKpEd7MHxVuJ/jJNnoaC/HO/ayaeVhdA1WLYYF9RYPPtpJ3aST4X1vgWPdsHn/efxmZyMf+NO/5y+WLHnuH1SZFx1ZlnFO3MtEEazTFx48XRfEdpxTBM650ByH1kwGMhmKQ0OMCAI5VQW3G1cwSMjnQ5Omj6RMPigUoFAgPzZGTpZxdB01EEB3ueYURnK5HOlkAse2EWUFXyBQdi+/QDiOww/+6wu83jrKt7YcQxnoozqXoi9fZDRvs+i8+fe/MAg/GTx1fc6CPSnoKcrkJA23x0tzRYDlC9ysk6f7Xr052JUVSGUUBFUn6HXTXO1lg1smZZVE0qQtMIREX0FCVBU0t4LPq1HpU9APZ3ikP8nldRZtEZ2/mBGzmijAD7q83HbxIjT57Pt7GbO0b9aSsEQJSZHQ3QrBoEKdJhIs2DywP87lrTa+GT/LRNaicdF0x9WywKfWTh83V6ChKTe13N09TiyW5KoZqZz37IGrLrmKcMAz57lF/DKK6GJft41XB02xKZgipqNTVddMOFx51u/zueI4Dr/74Q8JDg5yyUlO84xp8oBhsMOyqGhvxyvL+E0Ta2QEY3wcUxAoShI5SUKQJJAkLEVB0HVEtxvZ40F3u1EUpew0L/OiYts2xzs6OLTlQeLPPoTQv5tIvo+l/gJXn5j4CUw+zoGBDBRzsG8AltfOvc3WbljaEJgSRQEeOipy66Yq9if0edN3AOQN6BqGsbSMI+iEAm5aal00NMx9188bMDgOEzkZGxm3W6c6rLO45ezbScuBVK70yBsSpiVStCVsdFBCuHyV6BUh/KoEVgHbylMs5HBrMo/v72NB1KS5ykYSoGDC4X6RrhGZFYuipK38mU+gzHOm3LKWeV6xLIv8wMBU25izbTTTRAEsANvhoiuv4ke7tvAXcp7r6iv5QV+Wt3nnbtn6TZDPu+h5FUVPIMsy551/Aeedf8E57edyuahrbqGuuQXHcUiMjzLeeRRS44i2gWhbePM51vmbeWzzI7z/6R78Yo6A6tCbERk23FzepDJhxCj5aUuoErynLcuB3vvY1dvOrVeuIxXfzO4dGmlXNZ0t5+FSJkqO0RmT6IoMmYxBzggDg+SLcPAIDA2OUecfQD4pgdWhQZXa1oW4BQ2v10MiBWG/TroA3sDz/zmXKfNS4Wd/+7ez3KJxIFxfz8q6OrbrOpd7PGSBgWiU1qYm/JpGRpbxlEPoz8iaiy8nt/4h7v3Xj6EfepKLljbwl0/38LdrDUIzNOWhDHz6GfjrNbAiUuBLv/sOVTd/kragQedQnMbqIJU6TBRy5AWNkKvUTQmpkC4WSIkKPg0K6UGSeR/eUCWSdPZ2gUKhgFMcpOfQXbz1+un29657j3PHm6djumwbNj/byvJVS2lY8WaWlEXRlyWiy0WhUEAXRdKWNWdNGwnIMbexxSdJxIH5AtcMZt7JS87QaseZEj2Jx4kDw7KM5XIhe70EgkF8uj4lyOqAbpqQTmOk02QAS9eRvF60yQmAeCyGYhQIOhaKAMUCTGQzTGg6ocrKsljye2LbNgf3PcuD995Nbu92lOOHaBwfYMwp8jF1OjIIHR6eP4U8UHKNWg48GYd+Q6Ygu/D6PCyoCLC2zcXGyZlxw4bjOdiSksgICoqm4/N68EfdBCQZxZYwRAlLkulTFboEjaRH4cplMgvmGatHQ376kjLfOJzkomiRRq9D3oItIwoxw83tl51eFM2aEC9A1pSwEBElmaSlUFslU1Mrcbq587Qh0rxYYH8KCjHwq5AriIwbWW6YUYuqp0fA662ib1zGsEU6uru46rWl50wTKiqyHDs2PuvYe/dGWHXD3KLoCQJeiWA4SF1DO4ZhIMvyizpx0HHsGNLgIFeZ5imTLR7getPkp+PjPDYwwNrKSlAUfJJExYlJG9PEAAqU2iRHELBEEQQBS5IwJQlJFLFFEVuWsVUVye1GcrtRvF70SbdpmVcv4+Pj7Nu3D0mSWLNmzSm1Ks6EYRgc2Pcsx7f8htTBx1Fi+6g2h1gZsrjhxE3yHLvElg2H4nBsQiRpq8iah6qwj+WNPr7ULnHrjw7w2euKrK2bvd/TnfCdXT6+8kfTqungBGzr1/jUBV6M7pLouXDGfvE0dI0IpPMqiqpTFfHQ1qqyeI42y3RgOA7jaQnDktE0lYqgTmODMqto81zYTilvcTIH+aKIZUsgiNjIWI6KqLgQRRU0FV2VqdBtZNFGlUFVEqhSYtbx+osQjsosbmyiZyDD44cSmIaNhUh9NMi1l3hRBNjfl6Xj6LOEI7WEQuWxyfNNuRdV5nklPTaGxyzNChtAvljkRJr0sXyeJgc6kEnJPh7N5bmxNsRth/pQbZM3+mbnBjpkwD/WrOU/v/GtF/19nC2CIBCqqCRUUUmxWGSsvxtG+6kWDW669rWMmTbrlG6OxzJYts0720IsqnJxaDjLvqNdfPPYUd7YkCE4o9+2NAyLnaP88J4elm+4hPOaK4FurMFuDtTnT3WMyrBtt4nXV8WPfzFIPgmrIrDODX37DvDoUzob1tT8b9IAAAEAAElEQVTT3lpBZyLEwhUtKKLA+EQp1YsglN2hZV755PN5Al/60qx1v1BVQvX1ZF0u1leW3CQ7NI1IczOeQAAEAaWq6pyEt1czLpeLmz/9Jf7+L97HR6t20NXWxj89PUg2l0MTbJKGgGMW+ez5UD+pUH1oSZp/v+8LXPGnnyCZtOg281RFK/GrMkW7wFDKnAqt98qQtw3iOYegS0a2U6RjOVwVdajq6TJIzmZ0+AgP/+5H3HRpYmrdg4+P8eZrZuey+/59LtqWXcj+7lXc/s5rn6dPqMyLTTgaJZbNEnUceiyLJts+JddorabRZ1m0z2E9qdE09uTz1NinT4TWz/zCKUAICJkmpFKQSpEfHGRIEMhrGqLXW3KVejwoojjtJs3neXb3bp7avJmCKvPaKy9nUWPDlL1VFSCCSS6fJj4ClTWvzrxjhmEw2NvD2NAAAN5QmMYFbXMKYoZhcPzIIWJ9PVimSSKdo9hxiPTmR1AP7GSRmeb9Kmgn+qKnaVaiQGcWWubRHJ5JQGW0ko3r6pEn3U5ZC45l4LGkTEpQKSguJLePSJUXRVWpVBTcmkrYpRBxq3OGVDoO/Gz81Jz3M7EdyKPx9otrKZg2247F2D+eQZMl1q6IUhcqnXhu0gGaNkUsJERJRtdkvJqC1yUhWWA4AhYCQUeiN2FSG5jbyGA7cHxMYMl6i1ColFo3l4OO/ZXUyKOzth0fCbCufXrSvnewY+r/PXtSnHeew8BAZtY+2fRaCiZo84xcDQdAQlXVs74nPJ9se/DBOUXREyjAMsNgf38/4yMjoCgMSRIxWUaQZRxJAllGVlVkVUXXNPyKgluSkEwTi5JomqVkNrFFEUsUkUQRZ1I4RRAwJQlLVREn3aaKx4PqcqGqark/8wplaGiIf/6rP8I1so/1+iCmDd8a8VMILeSz/30nCxYsOGWfdDrN3p1b6dv2G3JHNuMeP0yDOMaKsMOqE/r6PCmR5iJnwr4x6M5IZNDQXR7qKwOsWOyhRZI4loT+nEzCVtiVd1Hj8nDDm3t5lDG+/iA0uiCVhoMjLiqCAT731hpkSSCVh18ekOhK6vzVjXUIAshiqVr8eErCKKp0jru4bpObNUvnbiTG0zCcFMkXJSRJJeBzUV2pUjfPrTOVh4ks5Ioipl1K46HIpbyiCBKWJIEmo0kSHpdCwCuhnZKS5Mz1VABwQBIEWuq8tNTNHRElCTYLKjL0jXfhWBbhyDl+QWXmpSyMlnneyGUyaMnk1I8qWSwSOfG/aeK3LLKWRWF4mJt9QR7tGOFp2eG9aZNEBu5IQFiFoiyS8oapv/EW/vtfPv+85BR9MVBVlZqWdpzmNpLxcbL9x9l08RVsefR+7jgvi3vG1ba4yo2kLeEnZpSHMkdwD3VzbdN0Z1MU4LYFBbqOP8C39jXx1tdsZKLoUFUBjiNQmOGkV2Rweg5zpKBxwxJon5Fbvj1icZmT4WcHO+nJNPC6a6dvjJYjYAOyXOokldMZlXklc8+nPsXbZlitk4DV2MjFNTV0hUL4JIljgoC3uZmq+nr8qkrW6yVwjrPtZcAbDCEKsDDq4l9fX2pzTMtBlgQOD2fp6j5M/Ywogb9oH+GL//UvvOODH2U4aZIVDSbcIWqCPqo1i9hEFt9kaL0ugiSajGVsAh6VgGSSGeum6Ini9c8fxzUxEae3Zz/NkafxTd5WRsaKKEIPoRm77jkE/bm3UCu9jtvfeVM519vLGE3TiDQ2MtzXR1AQ6M3ncVkWYUpO0TSQcLsp6jqxZJKoOXsAowCay8X+XI6ltn2K4DEKHDEMnGyWQ44DjoMgiizxeKiax8GpAzWOA/k85POYo6NMABOTrtLuWIzvfeELrOnt5dJEAhO453++Sl9bGx/5989TGYkgiQK6IOASIFPIUygUXnVh9SOxYTr37qRRzLBathEEiA8Nsb+/i3DLQppb24FSiPPmJx9n83e/ibZ/G4HhflqKaa5UwXdCYJQmH2fBrTr8+zH48srTb/O/Qyq3bazl/rhKUtAxdT+BUJDmxgAL3ToRt4Ln5DxHJzFhQNyADCJFRBxZQlRlPB6b3WN5VlfMLVJujUksqy25KzVZZH17NfE8ZEyRFBIHJiQcSUFWFFS3jCxIyIJA0pDwhSQETUTXJAInDfD1sQJ7h7MsrQRlxmdVtGDvoIiom4Qm3RCKAqOjAs21FZju2eGfPmV2ESSXLzb1/8DAOA0NBqtXT6e+KBahrf5aRpNQN49JKpGWCIWrTr/BC4ydTDIzYH/INMnZNkFJIjQpSC4GegoFlhVKqa8MIEWpLcoKAoYkUSo+XQqpH5IkTFmGyRB6WVURJ4UZVdPQVRWvouCW5SmnqQVYky5TURQRJovTGqJIUZKwZRlHVUsh+i4XiteLpmllt+nLlMHBQT721iv4j4WHmGkivHXBKB3JUf7f9WtZe+XNXHjZVSQPPIbZuQ3/RAeteooNIbhQBDTOPMN3EvE8PDsGA3mZgujC5/XQXBlg5WoXTUWRjgmBcUMmIWnsKripD7tZWqOzckbbYdsOg9E4r72uFKkzNgb/8bkq7r2vloEBgfvugc5noU72c83qMG+tng5BOjIqcfWaGqKB0r124hBEJou/ZYowMAaZgoyDgtetUR3RWVJx6oxSujApfhZEDFsAQUKWZVyaQsAr41NEyAsULQGQUBUFn1vG45LO6Cw9W4KeUkGnwDxDDtMB2ykVxKsPGRwfHcAXCJWv2+cRwTlTcoZpznrDMq8+DMMg19ODf9JVMWFZuAwDBcg7DoVsFj8wkE7z7BNP4Nm2jYuHh7lneJhb+vunjtMBJN7xDoTFi1n78Y//Qd7L88nBHU8TMYf4wf1PELCTtPkMTFvgQEolEoly00UrONY7yJ7f/oh0/0Gur5qgZo5IoXu7ZHbIS/G1yBRzJre9cS+tjaXn+obg8IM3oVe52XTqhCBQchh8fXeE9/y/N6NMds5icRGv14Wi6igCZIvg9re/QJ9EmTJ/OAqFAg94vdwwQ/T4rqLQcuWVWFVVXFpXRx7YGolQtWwZVQ0NSKqKq7Gx3OF4DvzmFz8l/OS/sKFy7lnyvf0pikNHWT9jott24Es9jbz5vX9GHC/NVUGGDReNNZUoosCEAZakEZpRjXm8CB5dR5NK4ahp0YM3VDnnd+Y4Dh2Hn+KRX/89f/zG0al13/zeQf74zdOD9nwB7tv+fq667oN4gwvL3/8rBMdxSKVSZBIJioUCpmmiyDIun49gRQWyLNN77BiZWIyKXA7VcciLInGXC19NDbZtM3DkCFWZDAHbpgD0yTKDiQTLCwWabXuqqn0R2CtJ5Nxumtxu3LZNADjbX9KR/n6++E//xJdHRk4xLCaAP1mwgM/e+S0CoRAZ00GXRbyyTFaUcEkijihhSxKJdIZ8NoMgSYSiVYQrIq+o3/NEIkHHtidZpWWn8tQ7jsPoRIY9Rzt5+qkdyN0dVIwO0GJkWK06VD4Hs5zhwN4idCKR8eh4K3y01gfYPpQiPzbM/6u3Zk0s2w58ulundt3F3HHleacNOwfIWxA3IW0K5BwJSxBBklAVmZit0latUOsT5xx8P94xAUaO8yotJjOOkDLg6WEZS/HQUBnAQMaRVBRVw+tWUSeP7VJFdFnkZGPTwQS0VZ/ejTqUKiJFM/R2gGiBIkC+KCMqCgsagwxOHGPxsuzU9of2uzCyIivOOzq1rrtboMZ1LeqksjqeSGH7vk0kUnKZ7t59iOHhYW64YXps8NSTIosat5DMQlXAwjtHyu+cAQNJHwvalv7BJrN++G//xlsSCbal0+xJpagzTXyOw4gokpBlLgmFWKBp3A9cf47HNiiJp2kgLwjkJankGpUkLFHEkuUpwfOE21Sd/OvRNBRZxpFlipREUyZFU0UU0U4Ip5NuU1tVEVwuRJcLdYbb9HT1B+LxOPv27sUsFmlesIDq2loEQUDTtFnfRS6XY3wkhmWYaC6dcGX0FdUm/aH487fdwKc9v5qVtmgm+0bhriPwjxc+99foS8P+cYFRQ8GWXQT9XhZV+2kO6/SkBfoyIhlHQVR1Qj43CyIeKj2nL2h0gm09wyz7y4c44UEYGYFHH13Km940/WZ++Q0/NzTOTkKaM+HHe12887LpiZCf7IHWeh1NVnFrOglbZs1khrisAck0ZIsihlUSP6VJ8dPvlvG6RCy7VGgpkxdwkJBECV1X8LsldPX5ia7Mm1A0SpNJpgm2I+A4Ig4CoxMWSxsc1NPcpwbiICs60WDpmolnBUylicpo9fNybq9wzuqmUHaMlnleSMdihCZF0YLjIE6KoiYwkcsRBUYMAyWfRx8aIpFIAJCPxWYd52m/n9vr63l2/foX9fxfKGSPH6+V5k/eci1bj4+RTyWp9cGF0QCyJDKah3G9jsvf/iEe/s39bOt/muLxLt64YHaRiJubTTZl93Jfp4/K5go+93nwCaWrPGUCUhf/9SeLgbkbbkGADVUJdu86xnnrF51YS6EIbu3EUinHVrnwUplXGt//8Id5zwxRNAWMNTdzcTiMr6oKAdipqkTr64lWV+OWZayqqnKH/TlyxWtv4F9/dSfnRXrm7BSvrPPxueOt6GMdLJ90N4gCfLC+h69+96tcdesf0z/iUBdxGBroxxuuJOTWKNgFhidD62VKEQYThTymouJRREJkmBgtYIVqTqk+PDTQxdNP/oKbrpgO6bz3N0PcfuNsJ9P37m/lbXd8AEcqi+KvJARBwO/34/f7T7tNY3s7RnMziXicbD6P6nKxMByeCj1tWLCAkViM5MQEsqZhHj7MBcPDVJ4UZq8C6y2LXdksyWAQ1eViyDSxi0Vs00S3bYKOc9pivV/+v//jP+YQRQGCwOePH+efPvVZ3v3xjxHyulEUiSQCGUFC87gwTIPYeJwoJvWT199IcpRDRxWqopXoLheOIIEsIcgKoqohqWrJjSbL84pK8XiczsOHsC2LugWt1MwTvu84Dts2b+bpX/8S2zRpW72Wq2+6+TlVBjcMg9jgIImRAQrpNEOxEYaPHaU63sehgUH6unsQYkMEchPUWgVWKA5Xnbh8Zc56tGM7cNiAw5ZIUtbRPR40f4DxSi/vaZ7dN1od9fDr4yp3HI7RohSp1hwGbZ1uKcjbXncRlyxrxXJKEzhJE7K2RFEQQJCRFRldUfB7ZMK6Qs0c3S5XDmw4rSPpklY/QykXPzqSxiM7IIgIqos1y5ppCHtRz8HKVLShYIEuwWgKak5jvs9JRVoqobKyJGJ2dYksDNWgSAKGaeMXc7O2VwiRkw7OWjcyHMBfJzGRL4kCB44d47KrS8/t2jXB2rXw/e8nZu3TebyeTSt1XK6SC8ydLxLxGmhyqZjfaEomZ7ppbG7/gzr8A/X13NfVRSCV4r2WNT0St21M0+TukRH6KiqIuuYp1X0aFCZTckDJ7XCiT3OSy90CMpT6OVlBICuKJCedqKYoYsoytqJgaxqapuF1udB0HVXTkBUFcTKcXxRFBFFElCRsUSQFGJNuU3QdQdcpAo/c9zP8mQnahCICsG/7kzwqqDQ3N+ONRNDDERRRJJ9Jg21RpcpEVZncBHQNDqAGgzQuaCtHZpwj2WyWI4cOsv+x32IdfoTQPKLn8giM7S21b/MVO4bSNkcTcCQpkrBURNVNZcjHslofm1oVOiYEYnmZAgrdopuC4GZBs4s27dzGjgULhrPwdP4o581wST78oMIb3zx9jxgZgZWe2TbxnAnf3Sbzho3T6yfyEPS6WdsYBODoaGnS5mifii7LaIpM3JBYOmkqShcglRUoGAJjKYmJjIxHlQnqMmNZWFzPWWNYUDCgaIJpg2WXIjKZdHcKoogsisiyiKYIuNwi/jnEz3DA4XB/hvZaB/2ke1YsWXK/tkWm+6Vel8NAMgWUhdHni7IwWub3Jp1M4s2WZogtIF0oTOVlHsvnqXQcTMDM5xmNxahIpQgVChzK5bhqRmirDQTOP58hYMEF51YQ6aVKXWML3fvHWeTNc2FrBeO5AD3xPPuSgAO6LhMOahTIc831r2NwcB37HvwR3+4+zMXBOK0zOqdRN2Q6UowaKf7rSnBNjppsG355YC9//uUh/vNDl6GcJpl+W4XJ957eSnNzNZWRAIIoYDkzthVKxbPKwmiZVxLFYpGKkyrR/1RRuG7FCoaCQVpkmU5BwN/QgLu6GllVybrdhF4mKTxeiqiqyqW3foCv/uhfeH9r4hRx9JE+qK0IEFeaOZrooj1YWq9I8MfRY9z58++y9pq3MiZaeHwhpPQwXSkfjVUhKjWL0Yksfo+OLon4FchZReKWTEiX8UsmuXgvKb0CUVXJpJI4tsXxY08S9TxKZbj0Wp09OVqqB3HN0Gie2CHzujd+nVwhSLhydtG6Mq8OFEWhMjp3zi5BEIhWVUFVFcVika4nnjhFFJ3Jcsvid8PDyJEIqixjTub8y4siQ5aFaRjIpok06SoLALFEgkh/P/NJh7VA6uGHObBrF6bfjxEKYUWjSPV1iG2t1NTVsKkqjN/jnhIbagWHSqfIkeFhohUhhkZGMQ0DEIiEg1SHw9gIZAFbEHFEEUeSESQZVIVswWDLb35JIDHGQjOFCHRt9bDFG+aCG15P9UkCaeexY3z7s59k00Qf7yeHDDy77zG+eM8PWHjdjVxy5ZVAyQWnqC4EScLMZTFSKdLJOP39/Yz0dGH0dSOMx9DSSbyFHG4jx3gyRX4iiZRNUzQLrJVs3npCRT7HTAJdBuw3BcYkDdnlwR8IEPZ7CUgqSDIjgkLQp3A4mWW0WCRyklp97YIw17SE+YtDArUrlrKuooJrK0IIksxBu5SrM+SXaZbFeZ1TBXvy4ZScREUECo5AX9ym6fRaPqKksG5ZGysa5o4vt5yS6Jm3SoN3E4GCBaoiIYoCoiyiyCKqJqKrAk2CwJ6eNH7dwXPSZ2lY4ApM99kVBVTHgzIpwA6NJaltnw4ujMfBtoPU1s3OFeqSWwl6PVOfxyH5+NRzw8PjyDKEw7MFVsnZBEAqL9PQ3IJt2wyPDGKZBpIoEa6spsb7h2+zm1evZvdvfsPrLOuU52TgVtPki+PjnLdgAXsdB9008dg2PkrFmZ6P7J8S4J984Dgwo7DTyaQpCagZICuVhPuiKJZE1MniTo6mobjd6B4PHrcbt6ahKwqGbfPA5idpTI/Sl8qQpDTWa/S5eV2Fn/s7jnGeUySST9JlK6wOeUGA0bzBobxEu89Fu2Qxkhzj6L48C1eseB7e/fOPaZp0dXVhGAY+n4+DO7ZQGBtGcGwsRadm4XJkHEzTIBiJ0tzadlaTqo7jUJhMp3BiUmqubWKxGPt2PEPv5gfJHt6BFjtOZX6cZqXIYg8kE3B55Sm7nkKjD8byUDlDky9asG8culISKUdDc7mprQiwos1DwJHpTAkkTBlD0ujATYPbzbJanVXn8EON52EoCxOGRBEZUVZw6yohr051nUpt6Keztu/q9DMzDe4Tj8sUhjUSBZBEODIikyjKvGFjhMrJEHrHgSc6ZS5c4pta7kvKXLo0NOXYH8nAxIRI17CCLsv4dZlq76ntsmGVxGPDAdMoVYU3zFIBKdsuuTtBQETAdERMRCIBEU0X8EjC75WWzq0JtDV46B3Oky9aBNyl180WRYJembY6dfbx7bO0QZY5a8rCaJnfC6NYRIjFpkLEkoZBcPL/uGkStCxEYLhQIOI4HOrpQUkmuRC4s7+fd8441iOiyDVr17I/GqX6FSJKuN1uIs2LONR1mEY9T9glE3Z5MR0YLUjELDchl4THSKAJKnpE4sZ3fYi7f/4rjsS28cihDt690EQS4aE+UCvgzy6d/RqiCDcut2kKDfPP39/Op965Yc5zMSyo9yeg67s8/rQP27+KRcsv5ER1gfJkbZlXIl9973v54IxBShoYaWsj5vWyKRikAPQEAkTDYXzBIIIs4z2NMFLm7Ln4itfgdnv5zJ1fob7QTZsnz1hB4plRBckx+fR6E5cc5oEDNlqqh8bJMa0uw1u9+7n30XtZfPH1VDs2eXeQOj909eWprooS1SWS+RwFSSOgy7gkkB2T0YxN0KMii5BMjaEpENLgYMcxDu36Nu+9pfQ7sCyHxzcf4503T59vYgKyyidQvdV4g6/OIjZlzp6hwUHqJybm3UYB/MUiiwYGpmI5CkCeUh7A4mToakGSmAC6RJEdx4+zPJU63SGnWApcGY9TE49Dd/es5yaAQWC3IJDUNTJeL0bQDxUVTITCKFVRFjTV0lBbRXU4RDo/wdaBIWqrq7Asi97ePiiWnNS2rFIRreTQrl3c6CRxi0yNHGrIYKQy3PeD77DkNdcTrKjAth1GYjF++nef4JP2MKoABydy/HZwnKJtszY0Rv+PRrln5zaq/F7yiXGYSKBk0+jFHD7LoAKTtRL4BYd9ySwd4wky6TSSkadOtLhYpRQCPlWl6uyImbDbEOgXVJKaG9MXJFBRgeTxIno9qJVBWuorWFgxLfYZFtx9eJTl+TT/3tHFTeE86/3T/aXeHHx73Mu7bryYVc01FJySCFmwSwProiPSbwiYBliIWIKALUhkbIGIR8CrisiqhCaLaJJAUBFRxFIuuV1jCvWiwcHEOEuCpwptORN2JzRWtYcYypXyxiOUKpiLwrRDSVdFfLIwNeDrzJw+T6ftQFWDQ28ClCxUuUGVS/n6uuIGK2YEc2UyIAp+YhOl10zkx2iYIWiMxlSKxjBVM1J+9vYKtNS3kDdK6U8MA3zBUuRYoeBQX5/n+PE8558/PeGQSsGyha+nYIKFPuU4bmxqPfsv/0Vi36OPct1JAuSwafJwPM6EZVGlKGwKheg7dgyvopCUJMZlueTklCQMJvOD2ja2beNQEj4kSj11lyjimQx9l0URTRRRBQFNEKb+P5eBvXfyAUwLqHMw5UAFsqJIXBTZZRrkxwZpUItcpU1fE8ezBe5OpLmyoYonuwe4dmETlYLJwSQsCbipkkTCjsWRVI6lfjeVIkzks3QeO0qkqhpRFEtuVUGYcuv/IaI3TNPkrq/+J/27NrPYGkG0DPZnJCSXl/dctJRKj4blwKGdPRzIK1zTUkFiWGLz/p1UL1zBohVzJyA2TZORwQEm4mMUEmPEkwlGBofpO3aIVPdhxFgXnuwoYTNDrWzQ7na4cqbB2DP5mEQSShMgZ6JowfYYDOVkRFXH4/XQVBlg8TI3vpxIX0Yk66hkFJ0jgpsFUTcbW84cCn/i2ENZGCsIZC0JW1RQVBWfS6c6qLK4Rpo6jmFDvADJIjzSMcGm22akMspD++LQrGMHgkE2vAYOHYJHfhdkU4uP1zVJnPAAxbPwZKfM4sYAIY+E48D2PonmKvesNCb9cZFqrx+/SyA8Tx7PwRToqBzrk2mOiHhkAeU0ddwGUxD0gT758zRtwCnlnjzxlThML0x9TaV05FPrZma1dBCojriIJcDntdEVAV2dW3BNZCX8wcipT5R5zpSF0TLPGcdxSA8OTlWdT1sWbstCAnK2jVQooAFFx0ErFumfmEAaGiKeSFC0bZpPGlT0tbWhShKu889/sd/KC0qkMorX52ewt4tCumQVdUSJSG0DyyNRRFHEMAzGhvpw0ctodoKrrruRwb41PHz3Xfw408vyYox7uuHLbz/966yqc/jm9iFyBRPXHGU7t/XCqiVQ6YdKfwp4kmP7n+LxdB2R1k20tC069aBlyryMyWQyNN5116wEEz9RFF6/fDnuaBQR2KUoNNfX46mrQ1FVxGg559XzxbqNF7Ju44X09PTQ3d1NhWnyl4sXI2Jz3+ffx3J1iKuXRrhvj4Wa6ad6srPvVeEGewcPbtNwb7wCj23RVQzQHvUQGx0g6QpRE/SStwvE0iYV3lKe5LBqE8/kKYo6YQ9oEgyOJti/906uvjAxdV4/+Fkvt99ozDrXHz+0nrffcQuOXA6hL3NmjGIRbR636AlkSkLHiTZIAQq2jZtSsV8RpgSJeC7H97ZuZelZHDcDp3WVnnCLLXIcyOVLj5FROHp81nYnBNShSQH1mMtF3uNBC/nRImE8VZV4a6t4VNC5JSDhcqvM9Kc4DtiWzcXmGHd//7vUyw5WscCWfYf4sDjOaLHIZ/Z2ssTJc5NuoQuwpWeMzccHcPqG+VprkBO1h2zH4Ugmz+HxJD2pFAcKOZoEk1UqrDkhxp7DiGXChp0FOI7KmO6lGIzgq66lKlpJo99P2O+lwqWjyBKHjFLo+OI5DIeKBG9dGuHewQrqvdXc09fHXT2jBDURR1LwROu46uoL8Ab99CsqI47EkkoJvywinUZRyBgQyzoMJ9K0BM2p/KAnMB3YMyZSHdDRVS8dRXhkKMliX5GIq+QAPZqUGTF1Nq5cgF8/x/ZqHhFlLGdQ2Qi1dZDNwvAgJEdlXLqMJ5hjpqltLKbQWFWyszqOQ8ZKzzpWMR8hZx2etW5oqIKKpQuRVZXE6CBGppe2tpKQuGNHko0b4d57x7nllul9Nm9W2LhqMcMpnZrac4hv/QOQj8U4EeiVdxz+p78fbz7PNaZJCOgFfpNIMOLzcUs0+nu7vYqUJlsKQHxyuUjJiW0JAoYgYE8+LMARBByxJGSfeIiiiDgpQp74qwhCSXiVJFRBQBVF3IJAUBTRbJuibfPA6BC3y1kswyYjyHgnlaoFCrxbNvi/3hjVtTJOx0F0Sm3fICURzwZ0R+AoYFMKO7YRSCLgCGALpWUHofRXOLFenPxfnHoftiiDJJUeogSKjCApCIqKoChIqoqoqMiahqzpyJqOquuok7lYZVlGURSUyeJWiqIgiiL/9tE/58bEVhalJhjKFKl0K3yszkPKzPDp+5K88/J1rKnyscxrU6kUeLovwdXNAbzZQZ56tJvND/2WaMCDkB7HGBvCGB+imBhjfHQUOx1Hzqdx23mCgkGjYnO1BwIK4Jp8nCVr/HB3L7xp4fzbdWY1PrlxCf+4Q+TCOpkCKp2Ci44JD69fpNN+usTCM4gXYCgz2/3p0lXCXp3qOoXGyaTFEwYk8pA2RXrzIj2FUj5PSVYQZQVZV8Al0dV9N9fOmId+7DGR179+uiE+eBA2bAjj84FjS/zlR4McPwa/3gt2AbJZFUSF9S0+gh6RQzGRwZTEgioXTZXTlvehlIAlutBcHnriOXy6M2ce5YIFYxmZaNCNbELesgidpnk1HEgXRDwulZ60TiAYBmwc54Q6ak0qnqWijSI2CKUCgaIAguggCpDOQdArIEkQTxrIooXfC7WKzfB4npB37gbbcCCR02hrCJ7xeytz9pSF0TJnjWEY5NJpcqkUguNg2TaSaZKnNJtpGQZeSjfksWyWXD7PgONgWBYKUBwbI5hK4c7n+U0sxk0zjt0PbLrsMjoliQVr1774b+4FRtd1WtoXn/Z5RVGobmiBhhZSExPEew/jNmUqoxHeerHD0YEKzI6DyGcIX7i8Oc2nvvsrNixsYGF9EyuaQ0iSSKYIPSm45qRwi7Zqhzb6MKwfse9BhaS0ivZ1t1BXV0rCYlkWu3du5dkdDyORw3YkovVLufjy6/G+Qly9ZV65fPnmm/noDJEhA2SXLGEwFOISVaUHcNXVkQ4E8LrdmG43oXlyEJZ5bjQ2NtLY2Dhr3aY/+wo9d32c+7o7uW5FFffusrhCGiI8qfaEdLg0u5mnd+qsP38j+YLBfsNkSW2AojlOx0CWxupKKtRSaH3Q40KTBCpUSBTzWLZKzjTZtfv/8KsdNE52vp89mOaClaOzQrV++aiLW9/xZXLFIGF/uV0rc2ZCFRX0uFzU53LzblegJAgYwNZUipF8npDjIABxQSCs62z0+ZjIZvnSb37DRxIJPqsovHcyxHIuHEoCQ+i0W5wdcwqo4/GSejOD9ZOv9zgwLopMSBJ5WcZSFERVxa1p1Os6DZpGi6JwwC0iBi0+tvMYX/blCc641m6WbW5y8vz1UDefyk6wVDJQ8llqHYNVKiyWKKnF5yAM5G3YXYSjqEx4/MiVNQTrGglURqhUXVR4vNhuH60hFVWV0ORSIQ23IuGWRS4W4N4jozS4i3jm6GMNFUAPR/nAa5ad8VzSWXCdQafMWiWBqiHiY/doGrdoUeexkQQYzYuMmRJN0RARv44CVAZ8FE2LjoExulIZZFmiuaWS1f5zz1MJoIqQNsA7x3kaaoET80JuN+gugeaFEUzbYawwNGvbQsFLPFNyOMWTaepbph2HuRyEfDWEXdtn7aPLrQjFAdIpcMtwcHgPjZOCzthYAlGETCYxa5++3nas9bXUVARfNpNWDvCF3l5uz2ZRikV+NDLCcLFIlary1miUQ5bFdxyHd8200z4H1MnHnEkEHGe2Fe05YDEtvuaB5OT/D6TTjGfG+JVgEBRh2BLIKypvaKyk1aujCXCBVGTbQD8HXTKqKCAIAoIgllI4ICKIIIsigiAiSQIKIrJUEmRVUUARBBRx8oGIIszIj+lMntxzxHAmnbnO9KNIyclvAQ8e60I4+BRfHhjmQm+RRs3meEHgu7s0Lmys4O8XV/HxB57hkho3XsFEtQ3cWOzfaROUHRYVDDzJDKMTafK5HIpZICSaNOsOrW5QZll1nxt5C45koKcgMpR26Eg4tAbn3vahPoGLF1aQtUQCAR9Xr6sDYCQLO0Zmt1mGBYMz3Z/CpPvTrVEV0FhULWE6MJ6HlCGQsyTijkg8IyFLMkgqkqwge2VEQUQXRKTJHJ9uVcKnSWiTAurW2N5Z53nooIdrrpmeLjh2TGfJkpKCORxzI8uwcHHpsXOnytq1EXI56DoO24+7UWWVhQ0KNZMXRLYIHeMKWXw0LWhFVlQ8ygCHxxM0+sA/qZ06DsRzAv05D0vWrEHXdQzDoPPYAVQph+ekZsdwoC8uUBVUSZo6dfVN6M8hb7BlWUieItnkIGGvRTSkEE+LDI5b6CqIkkrPaJGa8Gwht2BC37hGbf2Ccm7e55myMFrmrCg5Gofw5vNEKd04UpZF1rIYBhzb5sTtvWtiAieXo2FGcYEJ0+TQ0BB2KsV64MjIyKzjPxIIcHskws76elrU03jWXyX4/H58y87DMFaTueeHOM4IdRUakeCJ4dXpCehw2fIMN5x/iEz+EE/uE+iKuTk6Vkuodik7jrlY0yqeknxbkWBNiwFsJ9m9nUce1Ri1FhMbS7OxqY+3n1+YKgDQN3aMH3zjGa684Q4WtJddpmVemvT39rL4oYdmuUV/JMusWbGC8yIRDKArEKApGCRcV4etqrirywnMXyzqmloo3vxJqh/4Z37bPcj5i+A3+yxeVzmCb/IWEHXD2sTDPP2MyGsu2cCxpMEB22ZhTYhWd56egX584Uqq3BrxXJaCquFXZfwqTGRyPLH1u6h0cOVFJUdSPm9z6HAHb7p2+jwGhiHS+m/YYhB/OYS+zFkSDofZ4/dj5XKnzQsYB0xV5ZAksau/n4vyeTadtE2/YfDLYpFD27fzV4kEAUGgJRBgayzG3Elx4E5JolBfz+dkGTmTQcvl8BYK+A2DsGVRBdQAz1eA3ZSACqWk5vZkDPRJovAJB+qFwEdk+Ewds0TREwgCfD5occfIKO+Icnrr6xyYDuwvwjEUMp4AdkUUqa6RiuoawoEQVeEI0coQPrfOBDKSy01z0EUyV2B4aJiVvlPzLAIsqI7ws1iSZVqBZS4TRSoJmLsyKuOKnyvWLiFjTosp9uTDEU78LXWQbMdhwgD/PPrdaF6gLepBEQTqwj4yRYvYRBYcqIhqtLtP7QOrssSSxvlTvBTt0sOwS+foUHLWIYKAgCSXBCifX6A/kWdBBVOOXYB43iRSNy2k2TYUc17igkA8maKpbfq5dBpqwrV4J881Nn4UbUZO0sEBmdzEIMsWTK/r74e2xlZcCqRyEPKA4ZSq1edyNs3NeWzboaZmdkG8UPhWwhVnkUTxJYCrtpb4kSMcS6dZmc3y1c5O1HSatxsG9ZTmHP5nfBzL68XX2srDmkZAlpFEEVkQUIUTgqCIIgiolMRDZfK5F7sCgMRsA6PjOHzu2GF29hyl0TE4KJTSbby1pZrVHi9fOVbgNc21rAy6WanB3pTBFeLkuOVEfPEZDPGmUxrpGM6kK29yuWiXXLg5yyFnO+RPPCwb03awLAvbmUxBMNlOCY4Djo1o24iOjYiNZDuIjo3sOEg4SNgojoMMKI6NDPyis5/rtBgfWzhTfXW4nTw/HBrkm7tNFlXUEM1MkMmkGU5nsAp53HaRqGyx0ANXaJyz+3MuRoolAbTfkIg7KllJx9R8uHx+qpt8uDWN2xD5yNM7+MyaJCtOavgf7hP4+YCf/7i5iv85IPP61dMbjOZhLC/wxLDKYF6htaLk/qypU/DbAokCZEyRoiNSFGT6TInejIIgKWi6guCWUQURSZLRVAm3Ks8SPosWZMzJHMe2QNISGMtK2EhMZHKsXD9bC2honJ7uMwyoqSnl/IjF4LzzpotuFApQUVFSP10ukFWNjRd58PthaACeHYB83o0vUEXz6sX4fL7JYxo40iECLTDYD/1xMA0JWfHhD1exuLVmavJFURT8IYGxHMRyEFBL8RKZgkDOENF0naTtJlJd+5xEUQBJknC5XKTTLjLFDB7VIeSVCHok8qaDS1NJ5XS6RorIko0klgo7SbKHuqaG51TIsMz8lIXRMmdFYnSUYD6PDuQch5FikaDjUENpcr8IJIDeXA4hl/v/7N13nBx3ff/x1+xsL7e315t0d+qS1WzLvdsYd2yKA6bEYBwIBifAjxIIhEBCCEnoIVRjIEDoxTbFFPdeZVm2epeu3+3e7d622d35/bGn2xvdqbhIlrzv5+NxD93OzM7OrqTvzrzn+/18WbDPXcre4WGyfX2MjoywcXycC6dMulQEYqeeigXEzjzzSL2lo57H4+Gk817NE1s+zwntNiMZk4MFo6v74FUry7+H/HDOMptzGAc2USxu4ukd8OMnIgyMx2hsbub8kzpobXQ26NEgnHdcju/f8RRXn2jz7MY+PvyLTRSLJZobIlx75RKuO7ef79z2DRqv/+TkF47I0eS7r3oV/zClt2gGKK5YQVNTEx7gUbebBe3tjDc0kHO78Tc0HDO9UV4uuhctZX3mRk7wfJlkKkpTh8kvthd4fXt8cnjprAjkRv7Mj3+X5W1XnM2TIxabKNLZVMfsMIwl+9mZqmFWUy2JXJrHevLYFNm9/Q5WLdnN+Njw5PDPH/5y22Sd0b3+8NhFXPO2s8Eze8bJD0T2Z/Fpp/HQn//MaanUtMAiAzxWX89ZV13FI3fcwbnbtzNT7N4GjO7ahTllCO7729t5bzZLXzLJ5bY9ue888H2Xi75YjO/VTxSJjE6fPjxTKtFrWWzI5Uhks6RyOfL5PPbEZE+BYpGaYpEmDl+A+mN3eUjt/hgGnOCD1TlYeYAJkzbmYV3JTSIQwYo14OropKV7Dk1tbdTNasdbW8+YVWROoERs4r/veAE2Z10Ybi/NkQDjgCvgwY4WuTM+wspgfnJ45FgB1qQ9mDUxzluwlL6hYW7bvRsjV8Tt9bJw+RyWx6LloZAuA6+rXMNx0AJ/AGr2aTLqSzYbBtIsipZmHKrZl4GQz4NnSi+fkNeku2Hm86giE7U4SxOzHe8NPCf+VRguF6bhwu02cHvKtSZHswbttfv/TC0bahpgewKaAxDxlS/4064csSmZ7NCQQV20HtMFmHHHMPqRIQ+zp8xEZXqHHK+Rz8bIlzY7lg301dK+sPIcl12irn4YgEcfTXD22fDUU2mmzr3a1wcnnHCAGlJHmXOuuoo/rl7Nzl272L1lC28bHWXllPXzgU9ZFk/E43xz82Y6SyXOn7J+7zD4vT+ZGZYVgQLlofJ7/yxN/MnE7/bE8HmgPJEaTA6dNyYKL7oMA/bW8pxYbk4MrTcnlpkTs2mbLhdu4AurH2NVpocfh0t4J/4Jp0tZbto0zt01dXxw8Sz+cWsvVnczpaLN4HiRe8ZLFIslChOh5d7gstyhtYRRKoFt48LGsG3Mid9NbNzYE6WEbTwG+IxyR0ufAT4X+I3yfGsHm2kdqFQB2edmjW3b5EsTP9jsyljUZYd4+5yZu6S+oaXEB9b3c6HVz8l1EDQpd9l9AZdCRRu2pmFLxqC36CFhBCgEIgSi9XR3N7K4OcrygIewZ/+TuF047yL+686n+PjjO1lZZ2EYBrszbk7pjvGJS5r56rMelsxqoLu+0uA+NuTm6pPa8bsN/m+TgRkNkPa4uXvUS13QTTjiwTRMwhPBZ8DjZvu4yaqmcmmGrFW+gWQVDSwMkraLZN6glDcZKQQIhiOYXpu5MWNaOehb7/4pV1xaefzYY/DKV1aC0YceMjj99PKHumaNn1e8otIAbdxosnRp5X2MjTUwf34egPZZEAwb+P0XENgnsIzHe6mvL2Ga0NldDliz2ZVEo3XTPk/LsgiF0rS0lEPaVAoSCZNQnRfDClNf3/6iBZP19c0kRoYYT44T8pXnFSnaBmnLiz8UoqWjgcJE7WLTNDVJ8mGkqwA5KMuyYCIUtYChXI5WnDXvvUC4VGJwfHxaKGoDrmSSaDKJN5vlkZ4e3jpl/R2myStXrmSLx8P8heqBONUFF1/JR971eebWG3Q2R9gymGXufm6cF4qwKQHz22deb5qwcg6snJOkXEZ9Jzt6HuVX97qJ56JEY60sWTCbhV1RhlMG+VyaD/373bxyRZJPv76AzwPb++F/vrcTf6SDa1+3invuuI3LrrzmML17kefnsT//mcVr1jjOf3/s8TBv5UrmBQLsAWhtJRkI0NLQgBUKEY690IGp8nwsOv4U1mTeTsfam5i1Yh6/t/3cvGkNfzNnFM/EX+C8KORH7uMrP0zyjqsvYmuijx3FAi2NDdSH3HgLYzywIUXA56KztsCazWu4+Mwd3H7nLq4qT37NfY/EufycUcdr//h3tVzz1n8jk6+jriaEyHPR0t6Off753P3AA7SMjdGSz1MAdgSDjNfXc/oFF+B2u0ls3kzrfiY2+UMigT04yFlTbhabhsGX5s7lVyMj3DA0RLBQoARs8Xh4d3Mz188Qhk4VcLmY4/Mxx+eDA5QGOVwBagGIHMKsxUu9sMWqBKO7C/B0wcWgL0y2th7aOmmevxBXSzsnzemgs62RkH96ijqYK7ExWcCdL+IywONxM7cjStjjPIj22jBpq4kNu/tZm0qDUS5ztGJJK5GJOp1tkTZO6G47pPc506Wp12UwtyHA+qEsdd4iTcFyr8ykBb3jBl6fh85aHxblScIteyLwxKBolwOskuEq7900cRkmpunC7THYOxNymTEZ9BSLeQo2GMWJiVgokS2Cfz9/B4lcntZZYDXA8BAMJyGXd9M519mVr1jsINI0vxwQ5J9yrLOKrSRLDdh2gfFUiubmfGWdBXapnaYW5zBZk3nkJ2ZRNgzYunM38+aX/1+MjZXb5mefHWbFispzHnooxFVXde/37+Bo09jYSNPFF3PHQw+xJJVyhKJTnQDMHh3ljzt3EvR6MSZ6gxqGgWviZ+rvromgcurvXpeLAOX2wtz3zym/uw+w3vUchuF+faCXq0p9XB5xXuMFXXBjpMiPUsP8aneQSxrqSe7ZzTl+m+NtyGdtcqVSOXicDCHLj3PFElbJplAsUSgVKZRKWKUSxYllRbs00QvUxi7ZlOy9oaqNvbdUgG1j2KWJP8u3DQy7HLyXK3OUQ9ZyqWIbc+JPD+UQxGOAd8rPr8bhg0sO/Fm8txNu3jO9PNnBJAuwfhx25U0SrgAEa4jW1dPe0sSCplrmByvtW6YIqSKkCjBegvUZF9m0izwGFiaWYVIy3ZQMNyXTg+HxsPyiBaw0DNZs2kL/wB4azTwjPvjRTh+vOqGBzil3Pp4ZNohFgvjdBjuTMLcpwsrZYYo2DPZ5OWne9O+Zgg3r0z6ezNbQ4M7RGDRocDtHIVo2bEwFWb5qFT6fj/jICDt7d9DszRGeSJ2yJejL/s6x7yef8LFqVaXRGh0NY5rlnuter7PugG0HJgPiPXtMGhudIwESiTq6u6f34iwUtjvKKA0Pe2lrmx6Kll+/j4aJLz2PB7xeaGvzAQalUtuL2lvT5XJR19BULlmYGSdfsDDdXmKRoKMHqxx+CkbloDKpFKGJOxWJQoF6pk8EagHjmQyuiXqiU/WNj5MbGiKfTLK4WMTaZ8bV3vnz8Zgm6XnzJmcglLKHH/45H/2PIl/5D+jsbuO9vx/iu6+1qd/nGr5Ygr/9pZvmzhq+d2eW2mCWFV0lug5SvqizCTqbCsAwMMzo+FruuN3g0S1+7nwyz88/WqRmyux9Xc3wL2/KcPsTW/nJrSb1C+a92G9Z5DnL5/Pc/otf8PQf/oA3mWTro4/y5Sm9RbNA9oQTOLWxEQvYGomwtK6OQlsbWbebaNuhXQjL4bH89PN5dHyMRdt/xqtXzeKxhhq+cPc9/L/FyXJvJWBJHWQHn+IXv85ywmnnEPIU2dNrMd7QRF/ex4qOEmFfiUfXb+DkE7bx2FMjXHxm+WJ9dKxAamw7zVMuuDdug6aF/8N4LkCsXiUU5PlpnTWLlr/6K3p7etjd04PLNJk/dy7RifBycHCQxlRqxuembZuRVIr5M9QBdBkGr62v57V7e4YCXwJH77LnIsPEjNITv2eAnMtF3u/HCgTwulxEgZKrPKmJbRgYpknOMNgKPGlZJHM5rEwGI53GzGbx5nL4czl8lkVDoUDTxCiieiB3CKUNR4rwiO1jKNREqXU2zYuX0DpvHt1dncxubyXoK1/Ej2QLDI5nCfim7zRTgL6ChxM6oo5h4fsT9Hg4vvuFT+ATMCGZZ/JC3/EabhdLW4LEM0XWjOTxmi7cPh+1zXV4/AESXj8erx+P10vI7X5B573pdAqPpxe3G8ZTkE6B6YL+VJa26Azn6ja4AuUQ3uOBllYYMH14rQJTO1eNjBg0NJRnfR8e7qWxsfJ9mkpBc/PSyTrz/QNbaJ1yQ76/36TksmmfMiy/txe6F7wC2+ulWCrh9mfZ3X8b85dCKlVi7txyTd1i0Tkp68jI8c/7s3mpnHfFFfzXu9/NZw8wyzvAO22bLwwNceXzfJ0SE6UdDvCzt9fpgbYpTfnT8WMYkyPfLdvmbj/83wH+61wTLHHNxh28s38HHuCRvWEjzuAxtE8Q+bwY8IJnrtqPUQMWHOQ+aUcARg4wgG9XBjZlDIZsH1lvCDsYxVfbQLS+gaa6KB0uD42GSQE3BdPDmOnhUa8Pl2fv5FBBwkE/NQEvYZ+POtPAdBmYRvm7wXQZuKeGkaWJEgR2+cbUKSuWUrQNehJJ7n/iEc5sHGdWbfn/cCIH9/S6ybkCvG5FHUNZeGzExxVLw9g2PDJgsrB55mnbd417mLf0RBqamtmyfi1DhQQtZgGXMVGnM2+wJx+gc+EyfBO1NWJ1dYQjEYYG+hgcH6NULDGeTjN/obNHebS2dvL3oSFYurScSj77rIvTTqsEoz09MG9e5fHAQCPLllX+r+VyEI0um3bsY2Nx6usrtbtLJXC7O/f7d1gqDTgej48bNDUZxONuYrEXWB9hP8qTgNUeln3LoVEwKgdVtO3Jk6t8qcRMN8iGs1kagJEZ1iVGRhjp62M0Hmf3wACvnrJuF3DWueeSBlrPOedFPvJjWzabxeP5Ok1N8PH/grvvKvKhy23+6WvQWoDLu8Hnhgd6PDwwHOZv/+NEtm+YTXYki+nezY5MnGefTjA0MorfTDO/tcDyLjjQOXg0BK9YabN5T4Z/fyuOUHSqi04o8oebdlPTdeCJJ0QOt3Q6zWff9S6uWLeOy4tFSrbNr3t6HF9uP/F4OOPkkwm4XDzmdtPZ1kaf309LKITZ2Ii3yusaHw1OuvAqHrglyfFDf2BVV5Tuhov4j5/9gY8sq4RKJzRCrm8DY8/CtvA8Tlu1nO09Fv7GZsK+EGu27mDO/PUUSwVC3kH2di776S1b+JurKxfphQI8uuFNvPq1Sxm1QrohJy+IYRi0tbfT1j7zcA1jPxOgPDw2xumFAlG/n/9zuzmtMHP9SyiPvOkB7jVNioZBcWJ2aXvvcNiJH5fLhdvtnpjAxIXX7cZnmgRdLgKmSY1h0DJRs/DF8r8+uCAGGatA73iWDWNJtt35CNnSOP4DjPi71VfPjf/5JXwds1m4v6l/gTq/m7zt5+lUngZ3kYi7PPR0yDIoGiYLY/5DCkX3ZdkTc7iUJsKhicflQMg1ubxECZMpM7+4wHDBaLZEY9Ce8ULKAAy3m1lz59DUcnhqF5dKJQqFQYIT52nhSHlS7lyuFq+3j96Bcl28sLc8ejhbhP7xIrOmdMC0LDCMFqLRtY59ZzJN1NV5Jn7f5DhvHBnxM3t2JZgoFrc4nptM1pPPP+lY1tfXwPHHV8Ya+/1+TE/5eY8+Gue88yCfLzFnTt7xvKamNz6nz+RoURsOU3uQbeopTwj5fE30K54Wfr9oprRba4Fz93M9sJdhwHFeOM4DTcdwurC3I+rBmshCCdYmYWvOZJAAY74oRBvxN3XQ1NqGHW1kcUsd/kCAsN/PNsvDqQ0eRy/d3ESJDItKbeCibUyE1uWyCAlclGwXpb1/26Ybw+PH5fPjDoTxBEP4AwF8Ph9er5eAaU72LmwFll/1Fp585CFueuhOUiP9eF1wyuwwEb+bW3e68Xq9XLKklt6MyaZMBAJBQj5n6luyYce4h3RkNt3t5XR80bLjicfjbN29AztvgQGRumYWtbRO693o8XhobZ81+fiee27hjDMrYeaOHXDGGZUxCY895ubii8sncDt3Blm6tLKvvj4vbW3lL5Z4HPx+D253ccp6P52d0+sxJ5ObHQMohocNYrGZv7MzmTQ1NZXPoFQCj2fvdYImaH05O4abLjlSvD4feZeLwEQdGMu2GbIsshPFrYt2eWhClvIJ5dQv01yxSG0mQ3Z0FFc6za4B5x2Yu2Ix3lJXx9PBIEs7Xvhd/JeTv/zlv7jsskoR+i1b93DddXDWWbB9O/zwO+DzLuasd3Rw3akNPPKQCfk5XHFuF/FUhsFEgt2b1xFo2MJx7Uk6G4vcvz7Jrt4EFFO01+U5aX65Fum+1myHv73swMf3utNS/OCpdS/mWxZ5zr760Y/yzrVrabVtNuVy/H5wkDdM6S2aA7avXMm1kQh9QKmpCb/fT/2sWYz7/dRO6ZElL63TX/UW7v5xktMz91Mf9vL/3nw5X/jxrbxvceXy8bQWuHPPRpbOc/HYfcPMW34Sw3vy/GnQQ9e8NZRKJe68fzeve2X5e+h3fxngDZc4Lz//99Y2rv3rD+JywXAqS6lUUs0mOSxqa2sZDAYhnZ62bsiyOA3wu92k/X4SqdR+w5RHgDOamrigbuZhfy+lfLF82hfwuJlTG2ZObZiPveI0vnDPPXwklJ/xOY8V3DSffg6z5s1lqHjwIMLjdtMWdWMXiwxbRTAMaiMe3KaLFAbFolFOBY29Ew6Z5fTSNDFcJrhMDLeJy3RjuNyYbjcu041rol6b1zRnrN1mWRbpdJrU6Ai2lcftKmFjUMRDXSzIruE+ZgVz0y6mxgswYofpbDh8kwaNjycIBJxhumVFgQFCIfDNgmQS9iTANMOYHi+haA9T32Ii4SWf76dxymGOjUF9/fyJ/VkEg3HHa9h2JeDI5XLU1TlLlLjdc6mvdw6993hW7nOcFm1tiYn3UX7+o48mOXnKjGMbN8Kpp159kE/h6FQ3ezaFdesOeJF94BkDji7jQPQQ7h/WuGD8EHqLHy0sG/L7/HQbcMcQXHCA/7oPJSA4ay4PrTyZYHs3JzfXsqwxSNGGnVmDbXkPZ3ZE8U78X4sXwJN102N7Jnp8luu4Gm6DnXmT9ogfl8tFomAyO+on4DYpALtzLtq75r6gYdsej4eTzziLk884CyiPYhjo66HkcrPIhKHe3Txh2zR1zebsufOxbZstG55h40g/PsplXCxXkNbOuXTPdvawjMVixJ5HGapt277G2WdXHt9/v5s3vrFSRsDtrgXK7dfixZUgMpuFhin1mHt6YnR3Oydrc7nmT3u9bDZLJJJwLMvnm/c7PD2Z3EPTlGx1dBTCYTe5HAQCumZ4OVMwKgcVDAbp93ioyeUo2jbbczlai0XHRAIW5dlIS6ZJulRi743FXYkEdiIB4+P4UyleOaVHRBFoOPXU8u9LlmC8iD0YjnXDw4N0d/9m8nE6XaKrKzH5uKsL2robedvbysOMBgdhx9o5vPrMuXjcLmrDHrpbajh50Wwe74HO9hwPPrqahLWGE5aNsHgWFIs2T29Ns+mpOJl0kmggyykLbFrqwHsILcOcFvBvPjzDCUQOxdDQEDz+OL/v6+PheJxl+TzhYpEvU54h+UbgCdPErq2lAGwNh1leX8+eaBS320101iy1O0eZs1//Lu78bopzSk/h9bh41+sv55u/uJV3zK8ES+e12/x+w3qWH7eE7Lb7+UtvA1lvik1PFPBQYihb4r93wrln54mFdhOZUp7qsaddXPCKb08GID6zSKFQUK9hOSw8Hg/+zk7iQ0Pse/noN02SlCdlf3tLC5/ftYu/y+Wm1fJ8ErgrHOaDzyEU3TuMdu9PnvIQS2vvny4XhYmep0XTxDZNii4XuUKBvZ0jbZeLvMsk63bjj9XhC4fB48Hl82F4PBg+Hy6fD9/unazdsYZlnsoNqVPam9iw+Dg+8ew6/j6QpW4iVCnY8PNCgHsWrOLLn/wYbrebXNoiWcgfcCb3EXy0z1uIaZrEJnrFHom22+PxEI1GiUajlEolihPDo/deUCeDIXb27MRnZwm5CpRsGLV9mP4aZnccvgndLMvCMEaYel0/NuaiVCpSU1P+e3C7IRwGl6uFaLSR4eE91NYWp+wDDKONYPBpx76TyRjt7eVzu6GhPTQ1OYfR7w1NAXp7N9PVVUnCBgchl8swf35l2cAAdHWd5HiNLVueZsECm9HRIosWlcPzbduGOeOMyjarVzfwV3/1Yk0NdmRd8o53cMvdd/OabHa/2/wcCEej/NzjwVUqT0Jk2Hb5d9vG3Pt4yjLXlB9z7++UewTP9OM+wLqZfvanG/htFiZniNuPHRa0TOwoWyq3O3m7XFpj3wDSAvKGi8LET9HlougyKbpMCi43JXOihqbbg+12Y5seSh4PttsDHi94fRgeb7n4o9eH4fXh8vkrP34/Ln8Ac+LHEwhhBgK4A0HcwSCeQAiv3z/Z03Lvz+ttmw+/6mzOtTdjztDE2DZ8czDE6646laGuk5izdBk9PTsYGbUAg46on7ObfZND3S0bNqddLG8K4XO7HPtZnzaorwmRwoVhuOiO+fC6DJIFGC66aGyf/aLPPN7Y2EjjlDshCxYdN22bxcuOx7btiXbGeFHrW9q2TSx2r2NZqVQJP9etg1NOKX9brl7t4ayzKudmmzZVJl3K5cCyagkGK6OK+vpctLRML/E2MrKFqRWzkkmoq5u5FJxt27hczptBlmXi8TAxjF4zwb+cKRiVgzIMg3BtLcPDw2SyWbqKRfYtf+8BZgNbfT422DZLCwU8QHFsjIGeHhIjIwz39LBqynP+bJq8Yvly4kDnueceoXdzbLj33o9z1VWVk9Gf/rSXt761cqLZ1wennHLC5OOnH+jgr86d/uUWz5SHw4eCPl5xzinAKaTGc9zxxFOM9a9hUfMwV18YolSCz/0KjvfnuOXJBH2JXsqDyPavdwTmLlj5Qt+qyPN25623smvdOpbE43yz5Pz3mgHeB6xoaCAzPs6TbjeNLS0MmSZdra2MxWL4XuQTTnnhDMPg3Gs/wJ3f/iQX+Dfj95q88apL+d9bbuMtcysXmZfMtvn1mnX01C5haccQVyx3zkw7lLL5zu2b+NA7KsvG07Bz9L2smjI6oXQM9W6RY9OZl1zCbb29XNjT4+gRelIkwgPj41xZKFDndvO+2bP5QX8/2UyGOaUSeWC92814YyNXnHYa9/t8uLxeDK8Xw+fD8HoxfT5MfzkEMP1+3BN/evz+iXpl5Z/QxJ9ut/ugYV1/by8927ZQKpbo6phFR2fnAXtUn2BZfPe/Pktd/wbazUo7/NcrF7O2o40bHt9ArddNwOshH63nkne+hw+vWE6ylCdMifqgh+2JIl5XccYJg3otN7XNrQSDBxnLe5i5JkoVTBWpqSFSs5R0Ok0mPY7LdNMRDh/2iTLS6WGi0UrjVZ67qw7T3OMY9p5KuYlG6ykWi9h2j2NdIuEhn48ztQJEOg3RaGUS1Gx28z6Tlfjp7KzcacpknKOGRkZqyOdXO5b19MRYudI5ecrQ0H0sXgyPPRbngokJ8txuZy3ebPb0/X8AR7mLrryS6xYt4sLVq2ecrHwM+MPxx3PzY4+9KKMVbNumWCw6fgqFguOxVSyS3Wcbx0+hQNGyKObzlCyr8jPxeON/fppsct1+y2PEi/BMoJYfve9TrDjlVLw+nyN0DE8JH71e71FdwubdX7iJ977/r/lUdAdTq3yMWfCRnSGazriU2kuv5dLzLgTKNypWP3Q/Dbk4HR4L98TgyaGiwYaMh4aakKOndqoAu4p+wrNnE62NYdslxkaG6S8UoASBaJSOWP1LOuGOYRiH5YbxM8+s5uyzK6N4xsZg+fJKULt5s5/Fi8v/NgoFZ7HXqZMu7dwZoK3NORook5k+jL9QKOB29zuWjY3VTN782VcymSAarXyPWRYEAnv3qWH0L3cKRuWQhGtqyOdyBIeGpoWiU7UbBrnaWjbm87jicWZbFuNjY6SSSebvM+lS/4IFeEyTHbW1rNSM0JO2bVvHKac8Ovm4VLIJBIYc29x+e5hrry1357/3bj9nLZxeoN4qwvoBOGGhc3k45OP8s04GTiY5nuNX9zzG+MBq3K4Us1t8zG5pZuNuiw27B1h4gOoGP3ygiTfduIxn1jxMU0snjU2awESOrLtvv52l8TiXlaaH+AHgy8Dfjo3RXiqxxePh1aEQAy0tjJkmda2Hp+6bvHAul4uz3/Yx7vzmP3JeZA/hgJfLL7uMH//uVt4wpQbdVd0lvrdpHeecswSX4TwZvmNLD+99a86x7Lu3LOSGd1zrWJYvlI7qCzQ59gUCAS6/7jruueUWCnv2MGviXGh3fT2b8nmGBgZoAGKmyY1tbWRsm758HsMwGOvo4E3/8i+TE90cCc2trTQ/h/bR4/Hw1g98mF9/9yayWzaybHwQHyW2eyP0zJ/HZz72BbrnO4c32rZNKplkID4MpRL+2iC70mn8Vp56l4XLgEzJIGH4CDe2HPXnF8Fg8IgFt/l8Dq/XeT6dTrspFsepq3OGpW53Cy6Xi5GR3mm9RU2zHa/XWVs0Hg/T3h6Z2MYiFHL2nDKMyjD6YrFIKOQ8N7XtTurr/+RYZpor2JfbvRGAXK48jH5srMiSJZURZaUSdHS8ZT+fwNHPNE3+9Te/4d2XXcY71q3jjGIRg3IN2/vcbr61eDGfufXWF62Ei2EYh3TT44XoWL6c9//V5XzJ2jmtpm+mBG9PR/jgd3/EKy6+5LAdw5Fyypln0/CjP/Pv//pRUhueJGrnGC4YFJvn8oav/AMXXHiho8e6x+Nh1ZnnMNjfx9qtm6BgYRsuom2tnDJnLqlUiq27tmMX8oALX20N3bM6Hb1Bo9HaI/9GXwJPPfXfjpqhd9/t4vLLy22nZUFLS/nadts2OPnkShC5Zw/Mn1/5HkwmW5k/v9IOplLQ0DB90qWRkR7q6iptXz4PodD+Jw5Op3c7apGOjkJ9vUk2q2H01UDBqByyXCZD/UFmWfRRrpfVffzxPH3LLfSPjeEZHyfR18fUr8odwDkTvUTNE06YYU/Va+3aj3HFFZXHP/zhMK9/feVzT6ehq2slALt3A6VT2TjioisKIW+5gHfvmEFP1s2iE+sZSGTJj4zTGLGo2aeDXCTk49WXnMEzWxbzox/9H1C+Y/+2y1v40FcSfP2GPJ4ZWonHNptYgaUs7vTh94yxa2Q9e3IZ2qdW9Rc5zPY8+SSfmyEU3csLnJ7Pc+f4OCeWSmw1DLyhENGODoVhRzmPx8Npb/8U937zw5xVO0TK9rFn7mX8avttvLqzUp3t2vlFvv/r9bz2dUsITXQ329SfZslZ/Uzt7PCn+9289rXfcFzMJPMQ8LsYHxuhJnb4agGKBAIBLnr968lmswxM1Fpf0NTEebbN9z/7WU7esYPl+TwmEDAM3D4ff2hu5pXvetcRDUWfL4/Hw9V/87dkMhk2rF9PKptl+ezZXLK/CakMY6K3pbMHTiaTITE0SKlYxBcK0VEbe0l7TR2N0ukBpkzgTD4PptmAaW53bJdMeqiri02UANjN1MwskXBTKIzT2jplToAchMOVO+nlYfSV9fsOox8Y2ENra9GxPp83WLCgNGUf0NV1iuO4xsfH6ehIMDxcYOnSclv+4INxLrqoss2TTxqccsqxHbDNmj2bbzzyCD/79rf58U9+gjubxfL7OfWaa/jm29/+og+RPtwWL1vGO77/c9554zs5c2gr5xdGKQG3uqI80TyX//je/zFvwYKX+jBfNHPnzeOz3/0ptm2TyWTw+/0HDLINw6CppXXGydaeby3OlyPT/J3j8chIaPK87OGHDU47rXxjZuNGP93dlc+7v99He3v58c6dJrGYs0zF4GCE7u7pPTota4ej7Rse9tLaOnPAaVkWgYCzF6ptuzEMyGQ0jL4aKBiVQ1YqFjmke5sTdUmiPT3s6O1lbGQEY3DQscnddXX8dSxGr2Ew97TTDsvxHosef/zPnHfedseyTGbAcYF/221err56FpYFO3fO46zzoqxf72drtpVCKkO+kGR2V5CTWwO4XOD3exlxNzGQNtk12k/QTNIStQhMudY4bm4dzW2dDI09Q0MN1EfdvPcNc7j+K9u4/sIcZx5XnhghmYbv3x1iY2Ix//yP17N2a5Ku1hCz62FD324yDS0EAqo7KkdGc6l0wB7sAK8oFrktnaalWMRtmqTHX8g8sHIk+f1+Tnj7v/HAN9/PU4Mp/u7iAP1Dl/CHX/2WizsqF+QXt1i88/NPU1Pvw+Oy6c1kOf8C6O6AUBCGRmBjz1u5oKFyYZKyYCgNs1rcWIUE2WwYv19tlxxefr+f2bNnO5a9/WMfY/Ujj/C/d9yBmc1SMk2aly7lmksuOSZC0akCgQArj58+guW5PD8wa/bBN6xSmcw4waAzEBgf92Pbw0wtQZvPg99fDqVHRwdm7C1aLK537GdoyE97e2Un+w6jHxoK0NVV+fc4OvoMUzsW9/b6KRSecQwZ3r27lpUrnWHF5s2PsmIF/PGPcV75yvKy/n5nz9QNGzo48UTnMNpjUSAQ4K9vvJG/vvHGl/pQXhQrTzqJmx58nIfvv5/f3P57DJfBOVe+hr9/GXdwMQzjJS/j8XLR19fLySf3TT4uFGD27Eod4UQihGmW26+OjkpB20wGmpoqRSni8TYWL660g+Xh7kumvd7YWJz6+sp2pRK4XPv/fhkdHaBhSlnjTAYikb0XywcpsCsvCwpG5ZAFwmHSg4P4D9BDqwhgmvRu3kxbscjA6CgbBwZ49ZRJlwpA80QY2tvURKu+cIDysKShoc9w4omVZT/84Tivfa3zJNjjWYxhGDzwQIizzlrCtm0eursvwufzMTi4g1is13F3LJk08QUiBMP+ybtdY6NZ+nr7cBeSNEcLeE245qpz+NpNe/jQVQl8Hlg2L8g3/mExP/3LED/7doKhMRe+SDs3vvMtvHv5XABqwyUe3zpOOBChoy7Lzh2bmTd/CYn4CGPxAcp/2waG209DY9sxd5EnR7dD6XFhA53RKCUg6nJRLBZJDg1RE9VJzrEgFAphrXgNp277Ph4XdDSFyV12EXf/4Q+c01riiUH42mb45FUl5jZmJp/32C541/vg3z8FX/5+C5dddjI9CfB6IFcEvxcaontn04bRZB9e74FrKYocDm63m1Wnn86q04/dmopy+Nm2TT4/wNSvrkwGfL46DGOrY9tk0k99fQTbtikUdjsmaUokTKBIfX3lvNyywOebO+Xx9GH0LpeztpLHs9vxOJ9vIxZ7cJ/nLGVfo6MPAVAsVmazD4fTjm1s+/xpz5Ojg2EYnHrmmZx65pkv9aHIMeb++7/Ja19befzgg3DGGeUGbWQEjjuuPHLnqadMTjyxcqN682aTpUu9k9t5vR683sq1cV+fh/b2SpmPvZLJzY4aysPDBnV1+68RVyj0OR4nkwZNTS6yWQgGNYy+GugKQA5ZtK6OuM/HgeaqSLhcRJuaSK9eTU8ySTCVorRrF1PnUr3dNLlg2TJKQEi9RSfdddfNXHBBYvJxsQjDwwPUT2mLb7/dxaWXLmLLFoPjjjuN3btdNDe/Ep/PNzHUo3efIQPgdgcJBJoIhSonnjVRP3Wt82hfchVj/nPYNtpMwR3ljde8ln/7VRN/eMJLJg9uj4uli1pp7T6Zt13/Hm7+6j9zwvLKybPLgIVtJXb05Qh5oZQZYtP6J7DTW5ldn6S7IUN3Q5r28Ajx/g0M9O05nB+hVJlUbS2Zg2zza6+XNy5fzh63m1ggUO76POVGjRz9dm5cw4opJ7dzO2ppPP8V/GkXfGUDfO1NMHefkfCrZsFXLoIb3g9NzVdRMNyY/gDBILQ3QGMUIn5Ipcv/FkL+AqnR4SP4rkREDl0qlSAYdH535fM1ZDJ9TB2ok8lAMFi++E8kBqmtndIxoQCm2YFlbXWEpUNDburrW6c83k19/b7D6CvDpOPxEVpbK/We83nIZgPMnl2csg10dk4/xw8EttDfb7FyZfm4+vrynHhi5XnZLMyb99aDfRwicoxJp3/seLxtmx+vtxxFPfKIm+7u8hiwsbEAznvUwclJl3p7G2lrc9ZYLhan39TO5bJEIgnHsny+eb+lWbLZLDU1lmOZy1XeNpPx4PMdbHyavBwoGJVDZpomsbY2ejyeGcPRcSARieDx+2keGWFHby/rBgdZmnLONDm8aBFul4sdbjdzVq48Eod+1MtkMoRCNzlCzZ/8pMgllzjv2A8OdmPbJiMji8nlwng8pxAMhrBtm9HRDUSmTH+Zy0Gp5MPv76RYHHIMx89mwedrxeVy0dDYTPfic2icfxWhWZdz7d+8H3f7q/n2fYv5zG0L2Wadx/vecx0Xnj29gD5ATQDS2fIJbr4AHXVFGiI25pTi7B43zKqzsNJ9jI2NveDPSwTghIsv5qYDlG7IAmvq6+lua8MOh0m5XARUr+4YVHTMOA8QqYvwHaueG84H937KxUYDcGGXi0jARVNjDLc9Rr7ode7LLrddbgM8JMhmDxa1i4gcWZZlASOOMDOZdOHxhBw3vQEymTCBQGCit+iuab1FDcOkoaEyMV2pBIbR5ai/nM1umTaMPhSqDG0fHFzL1MFevb0msNHxnJ07I9MmlBkZGaa7O83q1cOTw/AffjhOZ2dlm4cfNjnxRPVGFHk5SafTLFiwwbGstrbS88c0yz1HBwdhxYrayeW7dsG8eeXRhtksFIt1jl7z8bhBS8v0YfTDw9sckyglk1BXt/9JlxKJXY42bWwMotG9F+UaYVYtFIzKc1LX2Ehw9my2BoMMmCYpYBTY7vMxXF/P7HnzeOA3v+HWZ5/lmaEh1m3ZwtT7xVuB8yYmXYp3dKio/oQ///nfOfXUyt338XEYGIgzdSLXJ5+ESy5ZwYMPRunomE8220Zra7lWSjze66ijUl5m4na3UiikCYedvQxyuci0Ychut5uWttnMWXoh51zxPl5z7T9x4aVXc/mFJ+L3HfjvyQBSOQAX4QOMbm6qsRgeVK9ReXEs6+xkdNUqfjJDT/Yx4D21tVx/+eX8KRDgxNZWNhSLNIdC2Idx5lZ58YWjTQxPufbvGc2xu2M79eEsJ3Xu/3kAbzu5xP0PPc6Cec1E/BYld5TClH8sYT+k8+XyMAEP5JJ9FA8yyaCIyJGUTseJRCplrCwLoI5stoepp3Lj4xAOtwGQSAxRW1vpAVUsgmG0kU5vdtwoHxoyqa+vDC+deRi9c5iqbW93PE6lmqit3ffcbvow+i1bHpio4VfpMJFIOF9r5875h3V2dRE58u6992ecdFLl5OvZZ+HMM8vB6IYNcMop5bGlTz/tpaGh8v9/cNBHIFC+abNjR5D6emfnmkSiYdr1bKFQwO3udSwbG4vsdw4M27YxTWc7lM2aeDwGmQyEQnUzPk9efvTNI89ZrLGR2oYGxkZHSWcyGKZJ+8Rsez/44heZu3Yt1xQK9Ofz3DMx++pef4pGeWc0igXEzjrrJTj6o8/gYB8LF/7Wsey227ysWrXLseypp5rx+YJ0dZ0G1NDWVi52ns/nMYyd+/QKAJerlmCwllJpl2NIwtiY66C1UjweD+2zF5C3YCz9FI2R/RdQKNpQwmDnoEFrg3e/2wF4TChamvxGXjz/+PrX8+P2dt7xwAN0jIxQUyyyxecj1dTEGRddxGhLCye0teExTbw+Hxm3m0idTnKOFbZtg383d+6E1y2CvjGLbS3bOeMMm5/+98GfH/BATdiDxzTwmDCWHSddNKgJlts0jwnJVIHgRFIQ9hcYHxumJtZ0ON+WiMghsaw8Xu+oY1k67cY0TSKRnGN5LhclFPLtt7eoxxMmGHSegxWLbY5OCkNDuw84G30mk6GxsTKUtVSCVCrMggWVG/CjozB79vSauZnMY+zeneeEE8rb2rZNQ4Ozl77Xe+l+PwsROTb19d3kuBZdvdrDG99YjqE2bvSzcKFJqQQ1NZWhj5kMNDeXH5dKkM12Mnfu0OT6bBai0ek3YOLxXurqKje483kIBvffWzSZTFBb67zx5PO5J47BQ12dhtFXCwWj8rwYhkG0thZqawHYs3Mnv/7mN7l0+3a67fIJ1S1btvDGKbX8LKB2YnKBLV4vCxYsQOD++z/GVVdVTkL7+mB4OMPrX19Ztns3rFx5IqOjy2ltbSCb7Z688zU4uM5RXNqyIJ320djYRSrVQyzmXOd2Nx1yT92O2d2sfXwzjZHUfrfpSxiYpg+3O4jfZ+x3u70Mu0hvz04iNTHC4chBtxfZn4Lfjz06yhvOPJM3nHkmexIJRnM5gpEIYZ+PWtPkKdOk1edjm2nSWltLJhymdaLdkqObbdv8+c+f4/LLN/C7X8GdWy3MRds4++xy2ziUKpLOQ/AA92M2DkB9a2UWUqMQp+BrojzWoczrLmGVwOMC0wAPo4yOenC53Hi9XtWWEpGXTCrV7ziPy+fB620ik9nJ1Pksk0mIRMrj08fGhqmtrYxCKpXAtltIJjc6zhdHRgwaGip142HmYfRdXZVh9L2965kzp7K+v9/ANHc4QtgdO8IsX+68AWnbNpHITp5+eoRLLikv27Qpy6mnVs51EwlYtuytB/5AROSYUiqVaG5+xLHMMMrD0y0LmpvLbcW6dQYrVlSuCzdtcrN8efkEb9cuN5FI2lFyrr/fT2fnPgXmgXx++z7zbXhpadl/h6Dx8d3Tht3X1rodxynVQUPp5QV7+uGH2fP73xPt7Z0MRQEymzdTO2W73xsGxYkzssz8+Zr9F9i06WlOO+1Jx7IHHojR2vqMY9lf/hJlcHAeCxcuJZsN0NhYPpEdGembdrd9aMhFbe18crkU4XDesS6V8hMMHnoY6fF4CNfOZufwzEFqKgebejzU1jfT0hBkPDfjZg4lDPyuNFZ6gHh86OBPENmPjsWL2TblCq69tpYlzc10BYM0mCZFIGWaBE2TlM+Hu7WV1tmzHbXU5Oh1551f5ayznsTjgdPPK1Ccv42zzyth2zY//OEu3vCOLP+39sD7uPnpJq5542uwJsbP1/gLmL4I2SnVRcJ+SI4XsGwYSkIyDUZ+CCPXx3iih/7eXWQyqj0qIkdWJpMmFHKWSRof92NZOSIRZ4mkQqF+8qZ3Lje9t2gwWEdNTWKf/TtvlB/KMPp83lknMB6vJRp1zlBfKk2v+dfbu4d58/K43eXeprZt88c/DtDTU56oCeDBB30sXjy9B5iIHLsef/xezj67coE4MACrVpUDzUcfNTjxxHIq2dfnd7RbhlEZ+j421klLi7Oesss1vYPV2FhiWmk5w9j/eb9lWfj9zl70hYKJy8XEMHrNRl9NlEzJC9K7Zw+FZ56h1NPDidlKQ7Q5nebMpHPWuC1tbeQti3Gg9ZxzjvCRHl0sy2LdujWsXv0+mpsry9evh6GhAldcUTnhTaXAME5k8eLzCAbd+P1LcLvdWJZFqbSdqZ2Zkklwu1vxeHzAsOMLplx7quU5H+v8RUvJeRfw5M4Ig2MG2QIks/DMHj8bBpo5/ZxL6Zx3AoQ6GU2Z2PsfdU86B36vSaFoEwuWKOaSZBU4yPM0d/FidrS0sMecPvtOFrjPNFkWDvO410vnokU0trQoFD2KZbNZxsfHyWaz3HXXtzjttAfx+2FkpMDTT2/jFa8oYVk23/zmJl7zmkEuvRxWl+Cpnpn39/M1bmoXnMmCWTUMJso3iXxuyI0Pkc7t083ULjE8BiE/NEXLk8qF/VAXKlAXzpJM9JHJpGd4FRGRF59t2+Tz/Y56oOUZ55soFnsd53ejoy7C4XLQMDY2QizmvEtdLDYTj29yTNA5NuYcIg/lYfQNDZWTuGQSGhoq21iWRSTiDE5HRyN0dVXOWcfGoKNj+jD6nTvvY2gox4knFvjBDwZ45zvXMzY2zKOPwn/+J/zd38HTT8/Xd7TIy8z69V91TGx0330m8+eXQ8/h4RCmWW5r5s+vndxm506YP7/cJX5oyMDj8Tr20dfnoqXF2dsdIJnc5Ki7PDhoOGoo7yseH3D0Fs1mIRwuN7qZjAev98Al4uTlRUPp5QXZ/MQTnJrJcF+hwJTROfxi9Wo+PHU74NUnnsi9wNZQiGVTx/JUmb6+PTzzzF0UCk9w2WXOE8xNm9rxeP7sGAJwyy0+Fi36Kzo6QuzaVcusWU0T+3mGWVNu5BcKkEgE6OjoIJHoIRarnNxaFpRK9c97sqv5C4/Dshawe+d2BhIJ3B4fnYvnEJ4yjqumJsawP8iu4RSz6m32PbfNF2BP3EVzzEPRLq+MBgskkgn8B5hZXGR/XC4XZ1x6KY/fcQcb+/uJZTJ4bJsRwyBvmtTGYmwLheju6iIy9Q6EHFVSqSSpVByvt4jbXeCRR/7MySffRyAA8XiBxx/fxoUXFonHLX71qw28852VnvCf/zb80/vga78yeOcqm6YIbBmCnz8D4/5mvvJP1wPgceexij48JriKCdzhORTsHtwT7VQkUCKZKxHwTL9f7DEgFi4wnBgiEJg9bb2IyIttfHyUYNDZKzSfj1AqJaip2XcipsbJ87tsdofjQj8edxGJNJHJOHt6JpO1tLc7z72y2c2OOoDDw0HHMPq+vu20tVVeOx4Ht7t/n2H0IZYtmz681bKe5NlnR3jyyV0sWTLCN79ZcqxPp+Fv/3Y3a9Y8zvLlJ874mYjIsScQ+LPjcSpVvnaMx+G44xoAeOopN2eeWenpMzTkY/bs8glaf38TTU3Djn1kMq3TrmlzuRzhcMKxLJ9vPuC1b7HY5ygdkkwaNDaWG0HDqD34m5OXFQWj8sKMjeEBGgIBek2T0aEhfrx+Peu3b+c/gLcArcAfAgHeEw5zp8tFcWn1DpMZGxtjw4Y7OPPMBHff/YDj7teDD7rYtSvPm95U6UFZLMLu3WfwxjfOIR6HxsbyZzc42ENDg3OowPCwQUvLErLZcUIhZ8+m8XEP0WiMF8Lj8dA9d/4BtwlFajGsPFsHLGqCJcJ+sEsQHzfIWy5mN/sYGjNoqit/C3lcYBetA+5T5EC8Xi+LVq3CiMcppFLkLItgqYTXNKkNBnF5vSSDQYKh0MF3Jkfc2FicQiFBc3MBw4BHH72LE0+8j1AIRkcLPPLINi66qMjmzWmeeWYj113nvJiOx2HJ3HlEF8zim7ffz9zGPO2N8O/vhp8+UqA0cX+oMQy98RytDT6i/iJZt4f0ONRMtMEeE7ALwMy9AzwG+EyLTCaz35lNRUReDOURQc5RP6mUQTBYTzq90XEhn0qZRKPlcGFsLE5trbO3aKHQzMjINtraKsvSaYhGF017zVAo4Vi27zD6VGqd47UHB4PU1Gx3bFMsOvdbXlYkFuvlqad6iMVGeM1rStO2CQbh299O8Ld/+zfcdNPj6jkq8jKwdetmzjyz0gkom4UFC8o3Th55xM1FF5W7d7pclY424+PQ2lru3l4eVNhKY+OeyfWpFDQ2Lpv2WsPDWx3tXCoFdXX7n3Qpm80Qiex7DVqOxtJpCIc1jL7aKBiVF0W7z8fb/vIXzh8a4v35PDFgO/A1oAi0zp/PLqA2EKCrimejf/bZh5k9e5hvfvMhbrih0uupUID7759Nbe0dRKfUef79703e8573UixCNttFLBYgm81i27uYem2eToPH041hGFjWoGNdNgt+/5EZQlxb10h/T4rZLXlS6QLx8RIGEKtxE/S5SGXBbZrM0ClL5HmLNTQQd7koeTxELQsPUAJSbjf4/cQaGnSRdRSyLItsNkFTU7lX1OOPP8C8eXdRUwPJZJEHHtjOJZcUeeCBOC7XNq680vn8Z5+FHfev4i3nLeDBp0e55MxGXrWycvLcEh6iWKr0nHe78uwa8lEoQaG0EZfpw+ux8XvK/zY8ZmUSppn4PSXy2ayCURE5rDKZ+LReobZdRyo1QCzmXG6arZM1+6f3FjWIRlsZHb3Hsf94PEx7u7Pe/NDQbpqb9z+M3rZt/P6+fZ4TYdUqZ+mn9nbnMPre3l6+9rVPMjq6ma1be/nJT6aHont5vXDWWVt58MF7Of30s/e7nYgcGx555Ku84Q2Vx/fcY3DBBeW2x+UqX/Bu2wYrVlQars2b3axYUb5JvXNnhEhk0LHPwcEI3d01jmXFYhG3u9exbHQ0Mq1X/FQjIzsdQWp50qW9Pe+9BIPPb5SlHLsUT8gLUvB6KZRK/L+vfY3/6enh7/N56gAD6AY+BVwI7Ewmuc3rpam1ldrYC+u5eCxLJHbz6KMDnHpqZdbPdLrEhz88yCOP3M9jjyW47bZyT1GA3bvPJxj0MzDgoalpDoVCgcHBddTXOwt5joyEqKtrIp0eIRx2Dr3K5SL4pxZcOYy8Xi/1TR30xr2YbjetdV5a67143C4GRiGZddFYO6XQfwkwpteHFHmuYnV1xNrbKTU1ka6tJd/QQG1HB42t04fbyNEhlUpOTiCyevWjdHX9kVgMUqki9967jUsuKfDLX/bS0rKNU091Pveuu7xs3PANzl9ZLr7v95kcNzfKUGWyeU6cW2TztsqJcmMNYOfoboL5LQXaasfpG87QFy/3GIj4IZV2tp9T2YChsyYROYwsy8LtHnUsy2Tc+P0RPJ4Rx/Jk0kMkUjvx+yjRqHMkUaHQxMjILhobK+eMuRyEwwunve5Mw+iDU4Y1DQ3109pqTdkeXK6Eowbqtm0BGhvLtexLpRLf/OZn+M//vJqrr36UL34xx7x5lmOk1EwuvHCUBx645cAbicgxoVj8leNxb28Q0zTYtAlOPrmcB2zf7iMUqjQ+bne5kSgUoFCYS0tLpSORZUEgcNy01xkZ6SUWKzq2Cwan1yCdyu0eczzOZEw8EzfKDaN6s4pqplN8eUFaFy7kR2vXcvmuXeyvaui5gNXfz3mhEJFTTjmCR3d06evr49FHV7Nnz6MsWVK+Y/6znw3x/vc/y2tfu4uf/KSf//5vKJXguuvgBz+Aa665YaLH53GYpsnw8C5isdw+Q5kMmpsXkcvl8HhGHSe2Y2MugsEjOxQgGAzR2jGHgruF7UNu9oy4GEq6iIS8tNZ5cU3puJfKuQhFovvfmchz4PF4qIlGiTU2Eo3FFIge5Swrg9sNDz74OG1tv6W+HtLpInfeuY2LL7a46aatXHBBL3PmOJ/3k5/UM3fuel515fU8vbt808fvczO7xccTWyoNTGMUnnjiUcdzfWa+fEMGCHigq9HGLuXZNVhgzzAkMwX2DOYZSRYnt9srnXPjD6gkg4gcPqnUgCM8tCxwuxtJJnuYUtadfB58vrbJ0RCZjHMyznjcoLa2A9jp2P/QkJ9otM6x7FCG0Y+MrHVMarJnj4dodI9jm0KhErj+5CdfZ9u2W/nsZzMsW8a0uvMi8vIWj8dZvnyHY1lzc/madP16H9Gom3weWlpqJ9dv3w7z5pUbul27PPj9446bL319Hpqapk+mlM9vc5QeGRryUlvbcIBjGyEadQapHs/UYfQKRquRglF5QeYuXszv7ruPKwv772UDcGM6zf889hjzzzzzCB3Z0WPXrp185jPv5Je/fBMXXHAfK1eO8I1vwN/9XZzt2/fw9a/nOf308kmj2w2vehV8+9vwhz94GRkZY3CwlliskWQyjm0POE6Mczlwu+fg8XjIZPocQ+jLJ9MHLjp9uHg8HurqGmlqnoXXF6Qp5iXgdTY3qbyLgh0kGFTQIFKNisUiDz+8mq6uW2lqgkzG5s9/3s755+f41rfWce21CUdpkWIRvv71JVx22XZmzerG5XKR8a8CIOR3kckZDIw5a4SWxtZiVc59aYjAUMJZg68lCtl8gbY6mF0PbXUlPKZF73CO3MRXm1WCEl7NUCoih002myEYdNaIT6V8mKYbv39sn+V+QqGaid/HiEYzjvWW1UQiMUB9fWHKMvD5pvei2nc2+rEx5zB6AJdr15TjtFmzpkg2m6d3olN+Og2treVh9PF4nPXr/8Rb35pzhBW27SHtfHvT/PGPUc4448oDbyQiR7177/0ey6aUAn30UTjrrBiWBU1N5ZszTz3lYuHCysXryIh/8gZPOj2HhoaEY5+lUtdk6ZC9kslR6uudveUNY/YBS2il07scN5JSKYhEysFoNutTx4oqpWBUXhCPx4MxOnrQYrXdwFgiwfonnjgSh3XU2LVrB9/97o28972PccMNCU4/Hc47D97/fhgb6+MDHyjO+DyPB77ylTxf/vJNNDYuI5/PkUptn3EIfSzWSCoVJxTKO9alUgGCwTAvpVA4TCjaxFDKz0jaTTILoxkX/UkvFjU0NB6Z2qcicnSxLIutW1fT1fUbWlvLF9q3376VVatS3HLLM7zznVncU75Yxsbgq1+9kL/5mzWEp9wdWnjCqxlOQtDvYjwLLo+zzYvaG+hLlNsYy4a+BAyO5Fm/I8OuwTxWsXxTKugtkbXK7athlIfVt9ba9MfzZC0YSvmI1Tcd9s9FRKpXLtfvuFjPZCAYbCKV2uPoRZrNQiBQGaeVTjt7i46OGsRiHVjWln16Ubmpr2+d9rr7DqMfGXEOo0+lkjQ3p7Es+NOfEtxxxyBLl+6mvh527IBf/Qoee8xLc3O5YN8DD/wet3uAhfuM2L/mmhZuumn/5ZPyebjvvjmcemr1daIQebmJx7/veLxhg49QyOTRRw1OPLF81zubDU62PakUtLWV648ODhp4vQHHzfGREYPm5sXTXmdsbJOjN/vQkEF9/fRepXtZlkUgsO+NJHPynNPlUm/RaqVgVF6w8WwW+yDbDAFR02TXI49gWdUzC/kPfvBvfPCDu9l3ro61azOcfHL+gEOLYrFyIX3TNEkkthIMOu+8x+PQ0LBw4vN0zl46Pg7hcPOL+l6er2AwRHPrLELRNgx/K55QKw1NHcTqGhWKilSp++77LW1tP6WjwyaXs/n977fR1TXCunXPcs01zhEI27fDb37zNq644suYpvOiuqm5hWf7Ggj5XaRz0N4SpTRlCPy8hiFSGZPBMdi4C/weWNYJC9sKhL05Nu1K0R+38Jg2hZJz7LzHhLC3RDztp6FRtWpF5PBJpcYIBp3nx/l8mFKpRCjk7GY5Ph4iEAhO/J6kpsa5PpdrIJVK0NBQ6R1fKoHL1TXtvMuyLMLhhGOZyzXb8bi39xmCQbjttmFWrEhz6aUF5s+H2bPh1FPhyithdNRm+/bNAMTj/dPOewFOOSVMMhnjZz9zYe9z4TA+Djfe2MX73/8dnRuKHOPy+TxdXWscy/z+cuA4MhLC7YbBQVi0qHZy/ebNblpayudZg4OtBIPOyd5GRxumzZmRy+UIh+P7LGs64Pna8PAeIlPmnsvnIZ32kEyW26FQqHa/z5WXNwWj8oJYlkV9czMPHGS7m0yT4xcsYEkiwfo1aw6y9ctDb28vjY3bmWneo92788ydO3Nv0amam/Ps2rWeUinluGtWnqG0E4/Hy/j4AOGw7VhXKjUcdRfxPp+PcDhMMBicFm6ISPW4555b6ej4Od3dNpZl89vfbiMY3IPHs4kLLnBeLT/yiIunnvokb3nLO4DstH3t3L6FuNWO31sORk9YGOHZKSX1VnTDM8+uY2gMlsyCunC5R6hhQCwEi9ttUuM5RlNFPOb0U6JIGGyKR117KiIvH4VCgVJp0HGDO5WCYLCBdHqP4zyyXP+ufcp2zvPMsTGIxWaTTm9y1OYbGjKpq5vei2poaJdjNFJ5GP28iddK8+ST97N16xp+85s0S5bkaJqh47zLBZddZrF69e0Ts9eHyeVclGaYgP6jH51NodDOO94R4DOfcfPVrwb58Ie7+OhHL+GDH7ydpUtXHvTzEpGj24MP/o4zzqhc5+7YAaefXk88DkuWlGt/rlvnprGxMjTI49l7s6d8E6elpfL8bBZqa5dOe52RkW2O6+NUCurq5s14TLZtk81mKRadbe3oKLS3m+TzMDjomjZUX6qH/ublBXG73czr7uYbfj+Z/WyzE1gdCnFcczPNhQIjPT1H8hBfMps3b2TZsqEZ19XVuenvP/gd8cFBCAYz1NU5Q9ThYT+xWAuZzPi0elTptIdwuPZ5H7eIyOHyyCP30tT0E+bPt7EsuOWWbaTTW1i5chfH7TPR6C23+Ojt/QqXXnrhjPsaGOhlfPAZLjytg819BlkLIkGTjT2VGy8eN/Rt+jPz2mae/MMwoLvZJjWex++ZvoHHALt0sDERIiLPXzodJxyupIjlG9x15HIZIhHnDaFsNopvYtx8Op2ipmZ8n/X15HIZ6uqcy4vFthlv8GSzW2YcRr9+/WqeeOLndHWt5qKLLNzuNAsW7P89uFzQ1RVnx47tnHLKxQSDzTz00Mznuddc08g3v7mIsbGTWLXqDj760dV86Uu/Y968A7yAiBwztm//uqMc0sMPu2lr8/HII27mzPFTKkFkyuS727bB/Pl7J12qwefb45houK/PTyzW6HiNYrGIafY6lo2ORiZ70+9l2zbx+DD9/buIx3cSiezbMcmDxwP19VBfX2J4uA+pTgpG5QUxDAP/vHl8cM4c3un3cy9MDqu3gJ+5XPxzKMSipUtZWVdHFvDM1IXyZcg0TQqFmf+LrVoV5IEHfDOu2yuTgfHxNpLJEn19TBasTyahtnYhxWIRyxpw9AjIZsHnU91OETk6DA4OsmHDBnbt2sW3vvU5HnnkX3nyyQEefTTFz3++haGhtbzudYM071P542tfq6ej49ucddZ8BgYK2DaUSs6e5ru2rmd+W5aAz83uRBSrUG73UnnnGE5vdjWeA5ztuAyoCRZJ56b34s8XwXQfrIq2iMjzY1kWpplwhJOZjJtQqJZ8vsdxjpdMQiRSqRGaSm13DFkfG4Pa2k5GRzc4lo+MGDQ0TJ90aX/D6Hfs2EQu9wRnnpkkNlFuz+0uHXRm+fb2LP3925k1axaRyPH89rc+hmbuH8B995nMmXMFp5xyCtGpXb5E5Jhm2zax2H2OZZZVnijOMMr/19etM1i6tDKePR734/WWbwrBElpanJ1+3O7pN01GRvqIxZyTywWDznbOtm2GhvpwuUaw7TijowmGh6G3t7z9+DjU1FRuGEUiYFmpqir7JxU625cX7FXvehd/XLeOb3i9/HxoiJ8mErhtm6LLxRVNTVwfi7GzvR2PYbA2EmHlqlUv9SEfEcuWreDb327ljDP2TFvnchmcdFIdv/xlH695zQxjjYDPfjbGFVf8NfPnl2ef7+mB2looFNqIRAKMjQ0QDjtr8eVyEaLR6gieReTotXHjM9x7789pbBzA5xvlllu24vUOc8IJJY47LsCaNTAwsIH3v985aVw2C9/97lzmz/9/eL0RIhEYHi6QTHoIheqmbJfFa4zjnrhQT9qzoPAIYBOJRIDU5Latgd2MpQvUBPd/yhP02+TyRYI+Z/iaSLmpbWzcz7NERF6Y8fEBamsrjy0L3O4GxsdHiUScF+eFQt1kr89MJk0kknKsz2br8HiK1NQkHMszmSbq6ioX/5ZlsXv3Nvr7t+Dz2fT1QWdneV1Dwzwefvi3nHuucxyYbR/8hnuhAKZZfp1rrnkvN92U5r/+6z5WrMhw6aVFampg50740Y/8hELnc+ON/3DQfYrIsWXt2ic5++xKj/WxMVi+vJHNm+Hkk8t3WoaH/ZPD2ZNJ6Ogoh6S7dnnxeFKOGzt9fS6am6ff2Mnnt+4zuZyHlpYGxzbj4yny+RFSqTSNjUVaJ+4rpVKwe3e5g1dXl7NtC4fzpNPjRKO1z/MTkGOVglF5weYvXEjf+97HF7/8Zd7scvGWiQJESeBWj4dNTU28a/58EsB4WxuxWHXM9haJRDDN4+jt3TPZEE913XXNvOtdFk89NcqNN+ZpmGjLN2+GL32pkfPPfysLFpTrqfh80NVVXtfZ2UIul8PjGXX0MBgbcxEM1h/+NyYicgBr1jzKhg3f5W1vS7BzZ4YbbribJUuSnHuuhcsFP/yhmx07Cnzxi87nDQzA7befxd/8zV/x4INu8vkipVL5gnxgIExnZ6W4nWVZ+Lzlm0o7+3Okc0U8lgsosag7yuh4L9FQedvj51j84e5n+atLlu/3mHOWQWSfbqWpHBRdoWnF/kVEXgzZbAa/f9+JlXxEIkFyufX71MFzEQ5X2sBkcpuj3mcyCdFoJ0ND62ivlCBlbAzq6+dPPh4ZGWTTpseYMydFV5eNYZRHKG3fDqOjHhYuzFFbm5rWO9QwTHI5C98BBjtt2FDD8ceXz1tDoRDvfvenWLPmCe644+f8y7+sx7KKtLQs4G1v+xAtLS2H/DmJyLFjzZqvsmxZ5fE997i47LIgt93m44or3KRS0NVVO7l+yxY3K1eWG7t8fiH19f2O/WUy08uAJJNj1NU5y4wYxuxpIyZHRvpwuVJ0dztLIoXD5Z+dO21GRwtEo5VIzDTBspwdj6Q6KBiVF8VZF1/McSefzPc+9zkGH3uMmkIBv8/HpXPnMr+mhq1uN0+2tnLZ1Ve/1Id6RF1//cf57Gd7ePObn2H+/EqjbNtw++0efL4T8HgivOc9qzGMcQKBIAsWLOfjH38DhhHDsipfBIYBra0wPNxHIGDP0MOgWROEiMhLyrIsHn74J1x/fYJk0uKGG/7ETTelHDeHLr20QDwOf//38J//Cc3N8MwzcNttl/LhD1/Krl3Q3OwjlbLp6zOw7Xo6Oxc62jefz0cm5yJfKPGXh7Zz+aosD9xXDjYXzg5w9wNw3kQO2tEA8dWPEx9bRKzGy75sG0bHPTQ3ubGKUChCIu3B8IRoamk7rJ+XiFSvXG7AMXFINgt+fxNjY8NEIpXRRIUClEqNjt6i4XDSsa9MJobPZxAMDjqWJ5O1tLeXu1+l02m2bn2EVavGHfX7AgFYvBh6e4ts2rSWWGz6MNKVKyM8/LDF2WfPPHFoeUbnFmqnnJyapsnxx5/E8cefdEifh4gc+0zzd47HIyMhikWDxsbyqJ+nnzY57bTKHRafr3wXu6/PhccTobGxUuMzmYTGxmXsa2xsk+MG0NCQQX39rGnbZTJJ5s3bf5349nbYudMZjObzLrxe3RCvRgpG5UVTV1fH+z79aRKJBE/edRfje/awqVRivc9H5/HHc+XKlVUX3AWDQf7hH77OL37xbX760/uoqRkjm7WIx2vo7j6ZN7xhBSecYOL14rjLNTYGu3a5WbjQ+XmFw9Dfn6ChwVlDL5UKEIuFj8h7EhHZn8cfv59TTx3EMODzn3+C//mf1Iw95mMx+MIX4DOfgYsvNigWX8PSpcdRKsHmzSZnnuln9WoXY2Nhli2bflLs9XopmhEeWL2Zi4/P0lgDLpcHyONyGfTEvUBlmL47/TR7BlLEauqm7Wt33E1j2xxSJTeFvIXb7aW+pQ6vd3qIKiLyYhgfHyMYdJYSyeVCBIMmLtegY+KSZNJFTU39lMc7HL1FUymoqelkaGgLbVPu5aTTEI0umny8bdszLF7sDEWnam0tsX17nGTSQ3mmgIr2dg89PUHuuy/NKacUHb1Z+/rg3nvbuPTS1x3y+xeRl5++vl5WraoEm4UCzJ7dwKOPGqxaVb4LZBiV2qJbtsC8eeVgNB5vJxTa4djf0FAN3d3l7YvFIsPDA4yM9FEojADQ1AQeD+RyjdMyhmw2SyBQ3G97B+XeoW53iVzOxuczsG0YH/cSi4We/4cgxywFo/Kiq62t5byrrnqpD+OoEQgEePObb8S230Mmk8E0zckZRQcHB1i3bgORiEU0Wh42OjJiYJpuFizwM9OcH6WSBVSC0fFxCIebp28oInKEbd26hquvLmBZJQYGBujq2v+29fXl9mtoaCEdHTFGRgwefthk3rwwQ0NQLIaZN2/Ffp/f1X0cf1p7H+cuLD82XFMaTFeIjbvz/O8dkMrCrqF17CrcRfvfvIpYpLxdOg97Rnz4ox20tU/vaSAicjgUi0WKxSFHuDg+DsFgI6nUALGYc4Z602zFnLi6z2YzhEKjjv2l07XEYiYeT49jeTwepr29EkLkciOEDnK939WVY80aL7adnjac/qSTIuzY4eP3v0+Ry0E47Cef99PQsJArrjhbZUdEqtTatWu4+eZ/YmTkXmKxcu/3tjZYvhwuvjjKH/8YwuMpl+xYurRm8nmjo+Vao6kUeL3zaW19enJdeTKlJRPbxdm5cx3NzVnmzi2HnYkEbNwINTXOciF7FQoFPJ799xbdy+OxsaxyMDo46KampkGTGFcpBaMiR4hhGASDwcnHyeQY+fwIxx1XSy5XoKcnSyhUZO5cN/vrWGtZ4HK5iMfB7y8PfyqVGqquJ66IHJ32nkz29GSYPz9/kK3hjDMMRkYMWlrcJJN+Tj45Qjjs4cknQ5x00lkHvNB2e7001ZZfr1iy2bSnyL9vBpdp86d78uxZDn9/JTREoVQq8ofHv8nf3vBHrr3h08yZOxePN0zHvC4CgcB+X0NE5MU2Ph4nFKoMSS8WoVisBcDtHnFsm0x6HLX5x8a2O3qLjoyUGB0t0Nf3CIFACZ+vHBSUg8uFjn0ZxsyTfU4VCNjU1c1m9eo8xx+fnrZ+9mwvvb2z6Op6JU1NLbhcrhn2IiLV4pZbfsDDD3+If/7nXiKV+zDs2AHve59BZ2eeRYvKE2ns3Omjq6vcZoyNwaxZ5ZB0164oLpezt2hfn4f29g5SqRQ9PWtZvDjr6DBUV1cefbR5s0Emk3JcYwO43W6sidrzB2JZBpblYs8eL8FgPdFodcyFItPp20zkJWBZFolED21tFh4PhMNuurtD5POe/YaiAIODBs3NfpqayifS8bhJOFx7xI5bRORAFi06hdWrfbjdBpZ1KLMYG6xf76e3t4lzz53F8HANmza1csopFx5S7yOXy0UuX+Iz33qMpR0pPvA6KKZ28/HXj/Ohq8uhaHk7uPSkEt+/YSs/v/kTNLXOZe78xQpFReSIsiwLlyvuGN45Pu4iGIyRTDqDBcsCr7dt8oZTNpslGCz3Fi0UYNOmDMPDWTo6elm+PM3s2TAyAuvWwcCAn2jUWTqkVDr4Zd/4uIu2ti7C4VXcc0+EoaHKur4+F3fdFaW19QxaWtoUiopUuZ6eHu6446N8+tPOtgugsxP+939tPv7x7cyd68OyoLGxUlR5yxY3jY1uLAvicTfF4hhr18KaNeUyIKVSFy6Xi927NzFvXnbGUZSGAfPm2fT3b5u2zu/3Y1kBijOXRQbK19Lj4x6glaamOcRiDfvfWF721GNU5CWQSIxQV2c5hil5PAaBgJuBAcvRG2Cv0VEoFLyEw+X/trEY9PUZFItF3DN9W4iIHGHLl5/It77VyMqVWTZv9gPZA25/770e6uvn0919Pul0A52dnYTDh1YvORKJkMiE+MZP7+XNZw3T1QRj40WGRkY5e+nMz/F54IOXbOTmr3+W//fRzz7Hdyci8sKk00OOCZfKQ+UbKRaL+P3OIfLJpJ+6usqw06m9RbduzdDUVHBMxOnzQVdXOVTYuNFFR4ftGBIaCDSSTI5OCzCm6usLsXx5Cx7PLGbNms+GDatZt648oVNdXTtnnLFU9ZdFBIDvfOfTvO99u/a7PhSCiy7KceutPbS3z+L448u9OkslKJXKdT127nRx6qku9t5nyWRg9WpYtKgLy7KA8QN2GjIMCASypFKpaeePDQ1t9PRsZdas6RPKAfT0eGht7VYvUQEUjIq8JPL5cRobpy9vafEyOOhiyxaLmpoSfn+5V0AiYeL1eujocBaHCoUKZDJpIpGa6TsTETnCTNPkla+8nh/84L857rhZ3HPPGGefPfMwpjVrYHCwm69//QfPqxyIYRj4o7MoWQm6JsKC39wzwutOP/AQ/sWz4L/v/vNzfj0RkRcil8vi86Ucy8bHvdTW1jA8vI36yvxKZLMQCJSnXc7n82QyGTyexMRzSrjdRUcoOlUwCE1NeUZGhqivr5xsdncvZu3aflauTM4YNOza5SEanT3ZHvv9flasOPV5v18ReXkbHHyczs4Db/OmNxW59tr1XHPNQgYH4YQTYGzMwOUKce+90NjoZ2rn80AATjwRnnrqCZYuPR2f7wBdPicEgxa5XG5aMBqNxrCsDrZt66GxMcfe1akUDA76iETaFYrKJAWjIi+J/ReDbmx0U1fnZmysSF+fTX29l44O74wnsV4vpNO5w3icIiLPzZw58wkEPsQf/9jBF7/4NTKZHbzylSVHD/kHHjD4wheW8dOfPvyCaiRn0uNctKLSK3UgnuPcBQd/ntd14J6sIiIvlnKvJ0inB9hbLrRUgtHRIqlUgVRqE7adxTAgEinPsjw+HsTrLbBt27OYZg6PJ0+hUB4qn8/n6eo68KQijY0FtmzZ7QhGA4EACxacyuOPP8ysWWlaWgqYJiSTsH17EI+ng4ULlxy2z0FEXl5su3DQbfx+KBYLtLW5OfFEeOAByOfdXHYZDA3BunUFFi1yPsfrhWg0RTo9PlEn9MDyeZNAYOZzyYaGZqLROoaGehkcHJ84pjAdHS2ao0McFIyKvARcLi+5XJqJyemnMU3w+02i0XJIuj+lEoC53/UiIi+F1tZ2rr32g1x99bu56abP8dOf/pKWliFcLujvr2fFitfwox995AWflGbTY0ztMN9S52PXIMyaoUf+VPmSaouKyOGVzWYZGxsG8rhcJUqlEgMD5R6d8XiecLhES0u51rxtl3sx9fZCbS3Ydg0jI5vp6Mg7boyXSrB2rc3ByiOXnzM9tIhGY5x00oX09e1hzZodgI3fH2Hu3IXTJi8RETmQTCZMOl1u0/bnkUdgwYIQDzwwzKmn1nLhhXD77SX6+ixaWjwEg0USiSK1tc7r2ZaWHP39vRSLfiwre8Dh9GNjfjo69l8jxOPx0No6+7m+PakyCkZFXgKxWCPxeIqWlv3faRsactHQcOD/oqmUm2j00OrxiYgcacFgkBtv/DjwccbHxymVSoTDYUfduxeifdZ8tm3y0tFQHj5/5dl1fPxr/Zy+ZP/D6dfugPkrLnpRXl9EZCbp9Dip1ACxWMFxQW9ZsHt3ntbWcrmkvfb2FvX7YfduF8XiHubOzbNvU+lylYeaWhYHDApKJbDtmdtZ0zRpb59Ne7uCAhF5/i677Ea+8537ec979n89+3//52XVqiYWLCjx8MNJzjorwtlnF7nrriSXXFLHokVFnn02w0knOa9nDaN8w6ilpZvdu5+hu3vm87reXje1tW0v2nmlVC9NJyjyEvD7/bhcNcTjM/8XHBlx4fPVkUy6sfczWiqXA/CpCL6IHBNCoRCRSORFPXk97cxzuW9z2+TjcNCktSnKHatnfo1MDj53+0Le+o4PvmjHICIylWVZjI4OUl9fmBZeFgo2oZAzFJ3K4wGvN099fXZaKLpXU5OX3t4DH8PwsEEs1vrcD15E5BC96lWv5ne/a+X++2durL7wBZNzz22mr8/LGWd42LYtDZRv7hQnposPhSCbnV5HdGjIQyzWTCzWgN/fzYYNftLpyvpsFrZs8ZLNttHefpBCpyKHQD1GRV4iTU3tDA152L07QSiUx+22KRRcpFJewuFampoaSaXGGBwcpra2wN78s1QqzziaSvkdtaNERKqN2+1m5Zmv5xcPf53XnlKe0fmDb+7gn79d4v51o1x/UYHWOigW4dbHfPxy9Xw+/h8/p3Z/s5aIiLxA5UkxLcwZKh2Njhaoqzvw8/P5Aq0HyDSjUZPdu91kMoUZh9RbVnl2+UWLmp7bgYuIPAcul4t3vvNf+dGPPsSPf5zg4otz1NfDxo0u7rvPxxVXNBMONxCJRHG5cNzsMYxyz590Gnw+Z2NZKMDQUJiurvJ1bmvrbGprG+np2Y5llSewM00/ra3d0yZcEnm+DHt/3dGmO+QNReTQFYtF0uk0hUIBj8dDMBjENWV6vlwuRzIZp1TaO4TAwO+vIRgM4Xbr3oaIyG9/8388/eBPecXiHha0FRjPwv/e6eGuxwdoa2shHI5xwWXXcukVr8WcKa0QEXmRDAzsoaEh7Zhpea89e/K0tZX22xsUYNu2LJ2dxRmfv1c+b/P003na24vU1xfxeMo3zoeHDfr6Qsydu0I1Q0XksLNtm3/8x79m6dJniURGGR+36Oz0c/zxIW67zSQer+H661vo64Mnnohy2WUxbBtuucXLlVc28OSTJl1dUWKx8rmZZcHTTwfp7DxFHYDkxXJIQ9UUjIqIiMgxL5fLceeff8vOrc/gD4Q5+xWvpqur66U+LBGpMgMDe2hqSu9nXYGamsJ+h9Lv3cbjyU3OYD+TbBYGBhoJBMIMD+8Giti2QW1tKw0NzZptWUSOmHg8zne+83cUizswzQK2DaWSmwsvrOP440MA/OhHbi6/vJWaGpOdO6G/v4Z588Lcf3+Q446rxe0uMjbmI5sNM2fOCmKxg3StFzl0CkZFRERERESOlHh8BL9/eMZh7rkcjIxkDzhUPh6HoSGLefOmT760186dHhoblxA42PT0IiJHwO23/4xQ6I+ceeb0m0J33WVSLEa44IJaxsbg9tu9LFjQRiLRzKmnXkQqlaJYLBIKhQiFQi/B0cvLnIJRERERERGRI8WyLOLx3TQ1zTxTc29vnnC4RCQyfV0uBwMDAcLhWpLJHbS2Wo4JnGwb+vtNoIWWllmH5w2IiDwP99//J9atu4MFCwaIxbL097vZsMFk8eIIS5aEefJJH4ODATo7F7Nw4Ym0tGiCODkiFIyKiIiIiIgcSWNjcQqFBLFYYVqvz9FRF6OjJTyeArFYAZ+vPNlIImGSzwdoaurA4/GQTI4xNLQL08zh9xexLBfZrJdYrIW6OtXeE5Gjj23bbNmyhWQyQTgcxbYLDA3txuPxMm/ecmIHqhEicngoGBURERERETnSUqkxUqkEPl8Bt7tIoeAil/MQDEaoqYmRy+UYHR3CsvK4XG6i0foZJ0zK5XJYloVpmho6LyIi8twoGBUREREREXmpZLNZCoUCpmni9/sxDjQlvYiIiLyYFIyKiIiIiIiIiIhI1TmkYNR1uI9CRERERERERERE5GijYFRERERERERERESqjoJRERERERERERERqToKRkVERERERERERKTqKBgVERERERERERGRqqNgVERERERERERERKqOglERERERERERERGpOgpGRUREREREREREpOooGBUREREREREREZGqo2BURERERERERER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mF01lpAeWNKltw0v6/KL8UFSSKkqkL1zSovt/8VUZhnHgGwwAAA4JglGgAL3+7C81vmpw+dXVbs0//lRJUvfGp/P23dZfLa/XezCbBwAA8J4UFRXpk/96jz5xT5O2duVvW98q/eM9U3XqeTfq/fNb5drNNyK3U7p4zjY9+9SjB77BAADgkGAoPVCAohsfkCoHl5uTM+R2524H3uTyvH2zQYaPAQCAsee4E09W3fdf1O03zVapr1cet5Q2pLh7hu74xYu6+ydf0dy55ojHOH6qoe8++7TOu/Dyg9NoAABwUBGMAgXGsizVeZblrSua8D5JUiqV0vhwd9626snnHrS2AQAA7E/19fW64JRaffCE3oF1v1m3QJWVlXI6rBEnZ5Jykzc5ZR3YRgIAgEOGofRAgVny1t918ozBWlltPdJxZ9wkSVq5/E1Nq7cHtq1tkWYefdJBbyMAAMD+4tgp2HQ4XJKkkrIGdfSO/Nlt3VJ5VeOBaRgAADjkCEaBArPq1f9V0D+4/Nr6YjU2NUmS2tY+mVdna1NPqUKhnWYjAAAAGFN26vG5PRhddMXN+tPiymH2H/SnN2t0yZU3HaiGAQCAQ4xgFCg0XU/mLfZ5hvQI7V+Sty3tnXUQGgQAAHDg7DwU3unMVROrq6uTb9x5enZ5cNjP/e2dkMoaL1Rl5cjhKQAAGLuoMQoUkFgspplVzXnramd/UJJkmqZqi1rytpVOPHPUx47H41qzaoVM09D4iZNUW1v73hsMAADwXtnD9xiVpBs+8ln94qeWXrn/fi2YKVWXSq3d0utbGzXnhMt0/RUfPrhtBQAABxXBKFBAXn3+zzp38uDykg3SKTe8X5K0ds0KzWwY/OKwrVuaecrpezxmMpnUQ7//hezEes2u65TfJb3xblidyVqdd+mNahg/Yb9fBwAAwOjtVGPUOfgVyOFwaM6cOTpu9v16c620tUtq73PpX775e7lcrp0PBAAAjjAEo0ABaV32S2nu4PKKjvGat72GaPOKJzSzbnDbmragzqyoGPF46XRa9/78W7rimHWqKhlcP3N8n4xsn371wPd03hWfJRwFAACHjNMx/FD6HWKda+Sulk6cnlv+2/IwoSgAAAWCGqNAAXjh2af0qQ+ep+f/+pQ+89/SJ/5benaJpIpzB/Yxet7I+0zMOXWPx332b3/R+TPX54WiO3hc0ocWdurJv9zzXpoOAADw3uw8lN6ZH3qm+zfmLzurDnSLAADAYYIeo8ARzLZt/ecX/kF1Hb/Xd+f3yXdibn3GlH71prTY0aZrbVuSVO7ZkvfZUN2pezz+to1v6YKF9m63e1xSVaBNbW1t1BwFAACHhGOEofSSpEx+jXWHv04HUjKZ1NIli5WM9amiql5HzTmaHqoAABwiBKPAEey+//2Rjo7/Vu87Lpa33uuWbjlRqlnzvO7+nzt01oVX6KgGY2B7T1SacvQ5Ix7bMAwVuRN7bMPkqj5tbd5MMAoAAA4JhyP/Je7OQ+m9Vnfesq+k6YC0wzRNPfzgvYp3vqtjJ3Sq3m+pbZ1X975QrRnzztHJp52754MAAID9iqH0wBHKtm099/D/6fIZsd3us2haXK898WutX/aESooG16/Y6lVDQ8OIx3c6nTKze26HaTrkcntG22wAAID9auceo0NnpZekEm80f7lqxn5vg23b+s09P9Tc8Av64Cntmjne0vgq6fgpGd182lalm/+o559+dL+fFwAAjIxgFDiCtLS06PFH79avf/YB/eA/G9XkXCKHY+TPHFW8TeuXPZG3LmI1yrGHD7pcLmUcFcrufiS9JGlZa5WmTZs+muYDAADsdzsHo07XYI/ReDyu6hJzYDmalKrrJu/3NqxZvVITAis0qcYcdvuZR8W0ecXTSqVS+/3cAABg9xhKD4xRkUhES956Tq0bH1Y28ZoqQhs1Z2pKF0yXNF1auU56cd2ej1PiNWSnN2noexJ/1UmjasP8k87TC8vW6Myjh09HO/okV2iygsHgqI4HAACwvzm081D6wZEsLS0tGl8+uK2lW2pcOG6/t+H1Fx/VB+aNXIJowZQu/f2VZ3X6WRfu9/MDAIDhEYwCY0AikdCSt17RlnV/USb6kkr9azWjMaYzmyRNHP4zDbXS2t49H/vNrbZuO9/SjmA0kZYmzjpvdA0zo4rEbL2wXDrlKMk1pJPpxnannlg9STfe+pHRHQsAAOAAcDh2P5S+q2WVpnoHN3XHvZru8+3/RmRj8u2hslBjTVaLl43irTYAANhvCEaBA2Tz5s36w29/pEhPu8ZPnKGrr/u4ysrK9vg5wzC0bNlb2rDyL0r0PK+ge6UmN/TohKnSyXsxf1FxSMoGpf6kVBIYfp94WspW9uuvy1vl947XhGrp3Wa35p8+bY/Hz2QyUstv9f6Tpbc3Sr98SsraDgWCFUpZQY2ffJxu/tgieb3ePR4LAADgQNmlx+iQofSx7rVSzeC2mFFyQNpgaw+1jSRlMpLbcwBCWQAAsFsEo8B+lkgk9KXbr1O5/3Vdf1GrqiukdZul//evd6m8/lLd/m//PVC/07IsrVmzSquXPaLe9qfkd7yjiTUdmjvD1vwT9/7cfVHp7ZUutXbXy3Qfo9lnHaeP/fpH+r/L2+XfqZdC2pA+85R0+4+k6ppO/dcXTbkzjSqrKtXxLtfwJxji78/8Wgub0pKkY5qko8ZL75of1uxjz5THw2RLAADg8LDzrPSOIbPSp/s35gWjGWflAWlDWc1UtUbWqm6Ed+Svrw9q/olnHpDzAwCA4RGMAvuRbdv69D9erNs/9JymDBniPnemNHfmJj358s91601LdNqJZXKbb2tcxTbNnZHVjGP2/lzJlLRklUNb2muU1hyFq8/R9DmLdNo1M+R0DtYL7X77DX3yV3/RMU3SeUdJDof08EqXlqayuv0OafKU3H7/+t2I7v1WVr64Tw8/cJ8uueL63Z47Eomowfls3rq/b6zUqVeeu8dJmwAAAA4m586TLw2pMerItOVvCzQckDacfvYleuDu13XjaV3DToyZyEhru+t05pSpB+T8AABgeASjwH707NNP6Kx5i/NC0aHOOyWl5//+gs6fL1VXjP64piktXyut31quuDlTReWna9LMS3XspcdowR6Gqjd2vaLPL5SWdEpPvCy9ZTUpXFenc+avVe24zoH9/H7pli/163//3zKtfOHXWnj2xSotLR32mEue+7HObBr8khGJSw3zP0YoCgAADjs79xgdOpTer568bf7wpAPShpKSEk2YdbZ+/dzvdNlJ+WWOtvVIDy9t0FUfvu2AnBsAAOwewSiwH/35gf/Wf30qNuI+N75f+vWfpc/evPt91myUVm4MqS81Td6SUzRx6mWae/YCzSsq2qv2bNm8SQuKuyRJ86qkWeXSv+sU3TprhSZVjNcD33fplI+1qXZ77VK3W/qHL8f1w28+pwd/87+6+R//eddjblyruZUr89Yt7ZiuM06ZuVdtAwAAOBh2rjE6dCh9iTf/ua24avoBa4cnuVrnHiM9tlhKpiXDcsgVmKCaiUfrQ7deomAweMDODQAAhkcwCuxHTvVpT3MNTZkobR0yaqu5VVq21q/u2CS5ik5UXdMlmnfcGZp27p4nahqJbdt66u5v6+bQ4LoX2qTiCe9qUkWuZ+cVM8bp0btcOupD29TYOLjfp/41pTv+6z8Vi92qUKg475ibFv9Ypw0Z5bW506m5p//je2orAADAgeJ0DD+UPhqNqiZsDqzvjUu1MycfkDaYpqlSLVdtqXT1wty6F1tO1MKLPnVAzgcAAEaHYBTYj8ysR7atYWtH7dAfk1q7Q/r10xeqsuFCHX3M+bpo4bj3fO6+vj6tXvmGultflJ1artJgi9zL3pLqB/fZFiqSz5PfuIun1uiZ37iVev9mzZgxuP6fP9+vX/1quhYtWq6ysnJJ0rI3n9MJjZ15n99inqaF5QdmogIAAID3aufnsh1D6bdt3arJ5YPrW3ukKTuG0exnq5a/oZkNgyFsX0KaPPfyA3IuAAAwegSjwH4077jL9PrSV3TivOxu9/nVw+W6/at/07xj5u/zeZLJpFavWqr2rS/IiC1VsX+LmsYldEK9BoLQnl5T4UAm73PVJ1Yq5JDebZOOGvLcf9akCr36Z6feTm7UMUMmgvrQh1r1xz9O1amnvqOKimqlm++Tv2lw+9ubfDrhwg/v83UAAAAcaDv3GN0xlL67bZVmDBnp0x3za6bHowOhZ8Oj8gwpX7qiJawFJ48/IOcCAACjRzAK7EfXfvDj+vhNd+mYWauHHVLf0S29s2G2PrkXoahhGFq3bpW2bnpBqb43FXRvVENNv2Y3SPNG6Kj54uNdumxIL4g3OqTTPlGm7oj087ukr9Xk96BYMLFMbz/j1quJtVpwyuD6K6/s0SOPzFCm7yt6/1HJwXZlJbvmKvl8vlFfCwAAwMHm3GXypVz4GetaIw0ZtBPPlhyQ8yeTSdUXbcpfWX7qATkXAADYOwSjwH5UVFSkL3z5d/qHr31An752rebNyj2IW5b01Cte3ffE0frejx7c7ecty9LmzRu1ad2LivW8IZ9jrerKezS10dbM2XvXlr63e6QhwekbyXKl276iqTNOVsOUO/WT5/+mj54qeYfcBY6qKda3fjld8eQanXPO4JeIRYti+tuTt2v15vM0fWIubf37hlKdcuUFe9coAACAg2x3Q+mN2Ja89Yaz+oCcf/mbf9PxtYPPVdt6pKOOvfiAnAsAAOwdglHgPbIsS39/7SW99vJfZFmm6sZN1X98+2965M/36hcP/1UeV0YZM6gzzr1Jv/jlB+XZPkTLtm21tbZq3ZpX1Nf5qlzZVaoOd2paY1ZNM/Zw0mGk0tKajQ61RSoUzTRqivl23vbaCz6rU09/nyzL0tSeVzWuTLrzcSlQJAUDTnUaTjnKTF31qaD8wRl69NHVuvjiwaFn555n6ZWXn9CStWerqb5a4+Z9XI6RiqkCAAAcBnbpMbp98iVnpjV/fVG9DoR06zPSkIkrN0TGaWE4fEDOBQAA9g7BKPAeNG/ZrF/8z+1aeMxWffLKpDxuaePWF/Wn+/6qmgmL9KO7Xh3YNxKJ6M03nlFP24tSeoXKQi2aOtHQwsmS9nICVNOU1m2RtnWElbSaFCg5TvVNCzXzjOk62uPRkw/8SifV/Hxg/9URaeEVH5UkvfHc41pYEZPXJc0slyIp6Y05n9f5xy1Qa+sHNHN2RlJAfv8s/eEPK3XVVYP1Uk8+xdbbbz2t3z3zAd36ub3swgoAAHAI7DqUPvcVyO/oyVsfKN3/M9J3d3VqWnX+xJWh+nP3+3kAAMC+IRgF9lEkEtH//vSf9MVbtsg/pMxmU4P06Q+26a/P36f/+vprmjk5rRJ/syY1pHTSeEn7UGd/01ZpU2tQscx4eYPzVDv+dE09YY5mBALD7t/65M/lHNKmNzMNuq46Nzys628/lndI7dHF/SU697Kr5HA41Nn5dXV2fkFVVVJdnVdnnz1Ld9+9UjfdNDiL6jHzbfkDv9ezzy7SmWd+cO8vBgAA4CBy7BSMulwe2batsD+etz5cvQ9DdvZg1VuP6JQhI/RXbXNp1jmn7/fzAACAfUMwCuyjh/74C91yeX4oOtRFpye1dOVynX+S5NmLf2ltXdK6LT71Jerl9M9WZf1pmn7UsWpcMLoJAbLZrOq6Xh+YnV6SHHMvlyStWva2Tg225e3vOvHDA0PijznmbD388EW6+OK/yumUyss9uvLKWfrhD1brU7elBz4zc6Ytv/9D+utfI7rook+N/uIAAAAOMteQYDSblZwul3p7e1UTHiwZ1BOV6uZOGu7j+8y2bbmjr0lDgtGOzBTNYOJKAAAOGwSjwD5q2/amxu9h7qFT5kuvL5VOOXb47X1RafVGt7r7a2R7Z6q0+hRNnbFApx5Ttc/tev3FZ3RaTWawnXHphKty4eXaB+/QjKLBfV/t9OrUT1+f9/kLL/yKHn30LV1ySS5ALS5268M3zNQd316nf749NrBfU5Pk99+mBx+M6P3v//d9bi8AAMCBNLTHqGVLLpdLrS1bNbVscJ+WHmlG1b4/fw2nefN6za4ffHbKmFL1tEv26zkAAMB7QzAK7COvO7XHfcbVSMvX5v53MiWt3uRUe0+FDOd0lVScqKapp+r4i8bv10mM1v7lR1ow5F/2S31lunLqNLVu26p51sq8fSOTL5bX681b53a7dcIJd2nJkss0b16uJ0U47NRlV0zV//tmrz7/hY1ybz9+XZ10xhlf0W9+06PrrvvvPbbNMAxls1n5fD4mbgIAAAeFc8gjR9bKBaPdras0a0hFokjCL7d7/3412rTsz5owpITSu80+zb1s7n49BwAAeG8IRoF9ZFrePe7TGZHWbWvUMyvO1/hJCzX7zKmat58fuoeybVslm5/JG0afnHKeJGnxb7+rS0KD61dEnDrlo58c9jhFRcXa/EatGhtbVFqaWzdlikMnnNigu+6aqptvflI7RoGVl0uXXPJ93X13j2688d5dAk/btrVi+RKtWfF3BZxRuZxSPONXRe1UnXDymfIxnAwAABwgtm3L5RxczlqS0+lUvGdt3vNSPFu6X8+bzWYVtpblrYt558npdO7mEwAA4FDgNzOwj0rKZqorMvI+Ly2ZoE9+7j6ddf6tmjp15n7vibCzFcuW6vSKwSFbsYx01PtvUzQaVVP3y3n7ri87SeEdqedO3nz2p7rs+BI9++dQ3vqzz46rurpev/nNdYoNnkbFxdK11/5Kd911iSxrsF6Xbdt6+omHlG17WJfNbdYFR/fq3Nm9unx+m6YWvaiHH/g/pVJ77nkLAACwLyzLyusxam3vMWrGNuftZ7qqtT+tenexZjUYA8v9SWnS3Mv26zkAAMB7RzAK7KPLr/yY/vfBOg3JAfO89a5PNQ0L5ff7D1qbljzwQ5UNOd1znQEdc/wCvfS7H2t2+WBDW2LS/Gs+P+wxWrdt0czwEknSxbPq9djD+WHuhRcuVVPTefrznz/x/9m76/A4rnPx499lXu1KWjGTJcuyZWaKHbDjMDUNY9uUktv29tdbxltubyGFNE0bLCRxwGEwxsy2LFlgMcNKWsb5/bGOdtcCy44TJ+n5PE+fJ3PmzMyZ7aPxmXfOeQ/2mMCwVgt33vkSf/7zcoLByCr21VWHSJYfYXqOL+6lBCDNAqvK2tj4+vqzvl9BEARBEISJhMPhMUeMKgLdcfUUhmzOpf6Gl1ApotvH2s1kZuWe02tMJBAIcPjgHl5a/xgvrX+Une9sxOPxfGDXFwRBEISPChEYFYSzlJ6ezporv80P/pxFdX30T8nhhCc2WHinZhU333b/B9om5bENcdv9GUsIBoOYj78QV76XKWRm54xsBwIBdm3fwnP/eIQXHvsh0skYqlolI8WXQ3V19P70esjM/B1LlnyRt9/+Fl0xi9wrlXDvvdt4+OHZ+Hw+ao/tZnpOdDX7U1n1IPO143a738NdC4IgCIIgjC0UCqGICVC+m2NUK4+f9qOzFJ6za3o8HtK1jXFlkmXROTv/6bQ0nuD5f/4O/dAG1pbVcOnU4+TI3+Kt5x/k4L6dH1g7BEEQBOGjQOQYFYT3YHrlXAqLn+TVl//Fhu07kMvDqDRJrL3sboqKiz/QtrS1trLQ2DOyHQxD7trPsG3DUyy3RYOTDj8UXvnAyPY7m9+gds/rzEnp4QJLEG8x7DoIfT64dgW4tMtw912N0/lzjCdn1hcXh3nttU+zbt3LvPmmlWnTHiD35CAIuRw+9anDPPxwORbVbaNGip6qMNlOS3MzpWVl5+iXEARBEARBiAiFQvFT6aXIiFGL1hVXz5J27vohR/e/xdz06EydTjuUzV53zs4/kd6eHg7veoar5wzG3XdGokRG4gDba9+mRquntHz6B9IeQRAEQfiwE4FRQXiPDAYD11x3B3DHeW3H9n//nutN0e1t3QoWrb6UTd9Yg9wWLd/qSGXtzLkA7NjyFu5jz3BHhWNkv1kDl02FQQ/8ZYOMO79yKwkJCTz77CauvnrvSL2LLx5k/fovc9VVv+Xtt614vXcwZYo0sv+uuxr4y59/hn3oLqwJ+nHbrVBAIBw8B7+AIAiCIAhCvFFT6UMwNDREmiXaZ+kdgvSiczfN3df5NhRFt+v701hqtZ6z809kz47XWT1tcNwP0wuLXTx3cJsIjAqCIAjCSWIqvSB8TPj3/ituu9k8kyO7trLUOjhSFgiB9YJPARAMBqne/RoXFjgYi0UHl5bC/t3bAFiz5te8+aYprs6qVdvZvv1pLrjgNgYGnuPgwfhHyt33Onir8Y909Q6P2+7WARNp6Znj7hcEQRAEQThboVBoVI7Rvt5uUizRsk67DJvNNurYs2G32ylKjs9fqs9cfU7OfTqBQADJ14NWNX4dmQzSjXa6YnMhCYIgCMJ/MDFiVBA+BgYHB5lOfC6r5JV30fnqb5ltiZZt6tGgVXSw9bXv0t7eSYVl7KDou4qTJd6p2gUXXopOp6Og4EEaGm6lsDAyysJsBrP5p3R0zGPhwss5cOBtduxYzcKF0RGg197g5YXn/oh9+wUcrDsBshBWYwK3rVtMemoCQ4EkEhMTz9lvIQiCIAiC8K5wOExVM7y+JxIULMkBh+oEipi1luxuHXL5uRkvUr33BRbFxFjrOhWUr1x5Ts59Oj6fD70qcNp6FoMXh8NBWlraB9AqQRAEQfhwEyNGBeEjTpIk3njqT0xPjpbt64FB53YWalvjK5cZWVq+jaUzarHqHeRYJj63TAZKKbqCaUHBVBob7yV2UdNp00Ls338vgUCAmTOXY7Hs5O23NSP7GxvhmfVBDAWv87M/1POrPzdy95cP8ujmv3HPj95k/rLL3sPdC4IgCIIgjO3IoQM8cPMFdNbDRSmwKhkO74dHf/t9Gtu9I/XcYcs5u6Z8aEfcdqe3AK1We87OPxGNRoM3oDhtPYdXjV4/fpojQRAEQfhPIkaMCsI5JkkSNTVV7N6+AblsGCQZclUyS5ZdSW5e/lmd0+Px0Nvbg72/CbejmaCvA1moH41yGIPOw/DmFyElWv8YJhI6G0gyRsv29MpYekM0eqrTwmQWgw8T38Feteoe1q/fxtVXV42UrVvXy3PPfYMrr/wJZWWz0WoPsWHDLJYtc/Od78Af/hBZzf5dGRnwze+6eOutw/ztb//LV7/6f2f6kwiCIAiCIIzrePUxfv//ruKhi5pRx7zxTEmFT/k6+dzvB/jBA8Vk2tSElKnn5JqtLY2UZ0Vn4wRCYCu69JycezJUKhU+mRV/0B53z6dqG7QyJyPjA2uXIAiCIHyYicCoIJxDkiTxr6f+SIpxPzde5EZ9MseT29PEWzuPU1O9iovXXDfqOL/fT29vLwP9LbgdzfjdHRDqQaUYwqB1k5gQJNUKOelAevyxPl+IgdBgfDuKsqkIe+PKelJMaNXRTPwzyuHVFyA/afz7GfKCPil+MQKZTMZFF/2eLVvWsmxZNLK6bNmb7NnzCnPnriErq4ATuz/LV//7l3zpSyHGG5SwapWXl17agMPxA0wm09iVBEEQBEEQztBvf/BFfrW6ecwAoUEDv1jj48ePt/HTBwpQGLJHVzoLjYeeY1nMqY62qJl++axzcu7JkCQJo3KQzUdgdWVk5s+pDjbryJ8yB9lYOwVBEAThP5AIjArCOfTmG88xNXsPFcW+uHK9Di5b4eCNHa/y9D87SUsGKdiNSj6ITu3CYgpgS4TMFOJGfk7G1o2trIwZ6NAwBJjM5Cf4R8rqBiFleQp7j+nwBcyEZUko1Wl0uA7TMdxHhnn0eSUJnj0m4+Lbrhi1z2g0YbP9itbWT5F98gUgMRGamr5Db+8s2uq2s6pSxb9eTmL69J4J23/99Y2sX/93br31c2d244IgCIIgCGPo7e0lwVuNTj1+nUQDBBxuPL4whsSi8StOUigUwhQ6HFfmUE1HoTj91PZz5dCul1leOkBDJ7y4G+ZNgTRLZN+gG/Y0mDCkzGbRrAUfWJsEQRAE4cNOBEYF4RwJh8O0ntjFhVf6xq2zaoGXR57exbXLz91127fVo4gZkbl1UM/0tPjE+4c1M7hq9V/iFhbw+/3IDnyalw7ArHyozAbFycED/W54uR4qV0s0Nr5NRsbNo65bVjaHV165ibS0J1CdHBk7a1aQ9evvZFHGDECG3iCdtv05ORJbt5444/sWBEEQBEGI5XK52PjGo+ze/EvKTO2nrZ9nCdHRG8BaWvaer117bD/TsqMfpR0eyJ9+5Xs+72S1tdSRo96OSg6lmZBjg82H5QTUWSiUCnT6JOasWoHVav3A2iQIgiAIHwUiMCoI50hzczMFmf0T1pHLwGwElwcMusmf2+WBXjsMOtR4/HqCYSsyZSoqbQa2gachJjDabZrBrORocLbHDbNv+dqo1VZ3bXiMpUk+5lrhYD88uhUCegtBmYO0vBBr7oDkZDhx4jXs9kvH7EhfcskDPPfcTq66qmGk7PLLu3j5KSWXzZ2G33v6URIdHZCcnDP5H0MQBEEQBOEkh8PBxjceZrD1YcpyqlhXIZGnhu1Pnf7YziEZR5vlLF713vshvfUbKItJJV/VbmLBwrPLLX+m3C4XrhNPkhWTbikUgtJ515FfPP0DaYMgCIIgfFSJwKggnCM+nw+D1n/aejot+HzxgVGvH/rsYB9W4vLqCYQsoEhBrc/AYMolMTmbjGk28tTxc8J2b9vEitToNXs9kKXxAtG8UbuC+VxWUBx33EBfLzmdm8AECjnMtoFblciSz/+G+vrDpKb+FPPJ6fUFBWE2bvw/Vq78zqh7kclkrFz5J3buvJQFCyLBWIUC5q5u4/CBFArT8zl2rIupU8f/Pf75zzy+/vXbT/u7CYIgCIIgAAwODrLpjYcY7niEaXk1XD5dgpj439Qi+G3vxOeQJGgdUjDLruKhX3+X//rmz9FoNGfVHq/XS7o2fvZLOGHhWZ3rTEmSRNX2R5ibE+0PhiU4PjyDOZUiKCoIgiAIpyMCo4JwjthsNqr3G5nL4IT1OnvlHDmRhlxlQ6lJx2DKxZKUh63ERpbuDIaRAjXP/YZ5quj2xh49NyyKBkVdAche98VRxx1e/ztWmMIj23YfZK25D5lMRnHxDDZtKmHFitqR/bNn11FXd5Di4spR57JYrOj1P6a7+wFST+Y6TUuDJstBplcs5ac/7eNPf6pjrHeNHTvUmM0XY7FYzui+BUEQBEH4zzIwMMCm1/+Aq/vvzCis48qZwMyx68rlMGM2vFEHFxaPXedfVVCwxMfKfD9zgzt58Gff4IFv/Oys2nb0wEbmpEf7VV12KJtz+Vmd60wd2fsalVmdcWUHWy1Urrz6A7m+IAiCIHzUyU9fRRCEyUhKSmLQlUYwNH4dhxuMifNYvvZnLL3wv1m47Bamz1xGTk4OujMMigIYG9+O227yG1HG/FVvHUpixrwlcXXqjh1iFvVxZQflpeQXR/NrzZjxBU6ciJ7IbIa+vj8SCo19cxUVS3n5uSJidy9YGKZfVs9Xv/os99wzlTfeUBM++c7Q3w+/+EUyzz13Df/zP78/k1sWBEEQBOE/RF9vL8889V0e/00hnbuTuHrON7jl0jqml45/TFUdPP5yAQmFX+XJTfDYTgjE9E+8AfjDbmhIhu//DF4KHifTHMToOExnZ+e4552Ip/3NuO26/tQPJJdne2sjGWxFFdP3a+lTUjDnbpRKMf5FEARBECZDBEYF4RxaufqTPPVyEqHw6H2+APzjlVQuWvPJc3KtmmNHWZY4NLLtDsCCvISR7VAYDEvvQiaLjiANh8P0vPEnzDGjN+uHFMy6Jn5FeKvVSlfXxSOBTIB58xzs3v3vMdtSfWgrNy4q5KXn4oO7V1/dTlPT6zz88EEGB//I/fdfyAMPXMAvfvFJ1q7dzE9+8uQHulqrIAiCIAgfbt1dXfz78a/zxG/y6TuQwjVzv8PNa09QPsHC8Ydr4LGXinl23/dJm9fHzV9oQGmHvy6HVBfc/zh87gn49L/V3PQCVNwN//PDyLG33xfgkRP1XFrQy+vP/+OM22u32ylK6oor06avOuPznCmPx8Nw/WMkm6NlTh/4k67EIhZYEgRBEIRJE58SBeEcyssvJLD8Mzy8/u9UFPZSWuAlHIaDx42caE9l3dWfJikp6Zxca//Tv+WTMXHIjR1y1iwyjmy/06dl0SXXxB2z963nmZ8wFFfWnXshRZbRHeh58z7Bnj1bmT/fCURyhyYnv8zw8BrM5mgA1jE8jNH1FtpkGQsz57N/32ZmzY6sRq9SwbRpT1Bfv4zrrruD66674z3ftyAIgiAIHy8d7e1se+v/CA79g3lTW7luwemPOXBMxpHGKZjS72DlRfcyfZ1lZN/Q0BDyrb9BlgMXnfzf35s03PbJcrqG/BzxVAHSSP1bv+Lk2R+3M6jpPuO21+zbwMLk6HZdp5yKle9vYFSSJI5ue4S5Ob648mr7NOYuHye/gCAIgiAIYxKBUUE4x4pLyigq/l+OHT3MturDyGVyyqbNY9UV4yS5Okvyoy9AWnS7PaBHLo+ODvWUXxs3jcrldGKqfg6lJXrM7gE98278xJjnVyqVWCz34HD8CpMpUlZcHGLjxt+ycuU3RurV7HqMuTlBAGxWLbXHKhjIP0xiYmR/djZs2fIA2dkvYzSa3ttNC4IgCILwsdDa0sw7b/8KyfFvFlZ0cP2i0x+z94iMYy1TsWTdxYoL72Lm5dHhkh3tbVQd+DcBx0b6Gw9zQ6ZnZJ8rALkVmQCkJajpqiqiKruO8vLIfp0OVtzbwyN/OvOp9LLBdyAmMNrpLaBYqz3j85yJo/veZEZGe1zZgRYTlSuufV+vKwiCIAgfRyIwKgjvA5lMRnnFDMorZrwv5+/s6GC+LjptKxSGogzbyPbBfgVL7rs37pg96//ECktwZNsdBMOSO1CpVIxnypQ5bNpUwIoV0ZVWKyurOXGiioKCcuqq9lCR3hF3jNo4ly1bUrjyymi+rWXL3Dz77Ge56qq/x03tFwRBEAThP0fjiQZ2bvolMvezLJ7RxSeWnP6YnQflHG+vICn3HlZceBtzjJHZMeFwmKoj+2g6/i+00m6mlwxz4clu12PPdqJOj55jfauCoph0Q5WZJl5+NovU1DaSTwY1MzNhzRWPc/z4A0yZUjmp+2lrbaI80zGyHQhBcuGlkzr2bHW2t5AmbUYd8xbX2q8gb87dE/bpBEEQBEEYmwiMCsJH0OZ//p6ro7Pm2d4FS2ZHO/ytqSuoNBhGtttbmihz7Qd99Jg9viyWzzn98Izp079AU9N/kZcXSThqtcKxYw+SmvpTZP0b0KZG6zb1KildcDOVWh0vvXSQSy/tG9m3Zk0Vb731EKtX33vqJQRBEARB+Ijp6+tj/dN/pb+vnaycUq665lYMMX2Pd9XXH2f35l+g9D7Hkspeblw28XnDYdhxUE59ZyUpBZ9m+dqbWKCPdGA8Hg87tr5IX9tzJBurqCwNUD4//vjDR5xcYvHHlQUrU/lHlYKylDAJJ9MQrS1O4W+/cXPTNwd4N544e3aYF19cis3WRGLi6VMfNR5+jqWZ0e1jbSqmrZt92uPOltfrZeD43ynPjKYBcPnBm3AZ2dbkCY4UBEEQBGE8MkmSTl8rYtIVBUE498LhME8+/Ee2Pvc35G0HMCmDDPpgfirINVruWDkVgKZh0H/uJVJSI/PsJUliy4NfZrkpOsK00wWyK39KWnrmmNc61bZtj7BkSXQEaDAILz6dx1Uxaaz8QagPXMLUyqUAdHS0MDBwLdOmRZeCPXFCRn//r9HrrSQkJJCVlRV3HYfDgdfrxWAwoNfrEQRBEAThwyUQCPDdb9xF2L2ZGy5qITUZTrQq+PebeeSWXMsXv/S/HD9exd6tv0Dtf5Hls/tJPU3MLhSCd/YraOydTVrRZ1h+wSfQnpyO3tvTw6F9z+AbfJO81GbKiyd+JXnkGzXckeAe2X69TUHJL+vZ8cTPOX5kB3MK4cIy0Cih1yHx59Yavv49T9w5Hn64kFtvrZ5wBGY4HObQ83czMz+a53NL6yyWXfaliW/2LEmSxN43/8zcnJa48j0dpcxdecv7ck1BEARB+Iib1HRVERgVhA9YMBgkHA6jUqkmPa08HA7zwG1Xscb9Ghdn+Hj3MEmCLR3wmyoN/7x9KkqFjBe807n8m4+MHHtk52aKq/+MNmbx983K+Sz/5Bcm3eZAIMDBg/cxd270RaOmGjJ92Zh0kRPvbUlm9ur74+5p+/anmTbtfzGb4dAheOklsFhk5OXp6O/X0NSUxtKlN5KXW0Jj7SHMagdadQiHT40vZKasYj4paemj2iMIgiAIwgdPkiS+eN8V3LX2FWaUBUftf+5NBRs2Kvjxl/wkJ058rmAQtu5V0jIwj8wpn2XpimvQaDRIkkR9XTV1Vf9EFdhOecEAGakTnwvAH4A3toXIe/0Q5THXftR0Czd86yEOf2sZc5KCHOyDzd3QiQWNLYXrb6/hyJEj3HhjKO58Dz20hnvueXnc69VU7afA84uRKe1OLwykfY+cvMLTN/YsVB3YSJH6TTQx8/0OthjI4HgIAAEAAElEQVQpX/FlMYVeEARBEMY2qYCLmEovCB+Qurpqjh3Zhko2iEIJXp8Ga1IRcxesRKfTTXjsH3/xA672vcryzPipYTIZLM8Eo8rHLze1c/fiLMqujY5UCAQC+HY+ijbmBeHIoIp599xzRm1XqVQYjXfhcv2Wd2fJlZbBxpf6WFmUSrtdTuGcW0cFehctupZnn91ESsoOqqrgq18FhUIC3IAbSbLz9NM/oWrfNO67upzo4T4CkoO9R17H519Gdk7+GbVXEARBEIRzb/eu7UzP3TJmUBTgytUhdh8KER5nOEUgAFv2qmgbXEhO2edZcsMVqFQq/H4/h/Zvpqt5PRb1IWZO9VI85/TtsQ/BgRojbmkOOSXX0XvsN1yaeGhk/5F+GZd8/Wdsf/lfrEyOtHmmDbKM4Lr5T+TmF7J+/Z0sXRpg06ZjrFgRbfiNN77Cv//9Da677gdjXrun9kVKY7onR9uNLFjw/gRFuzvbSAq8hSZmMk37gJzsmXeJoKggCIIgvEciMCoIH4B3tr6KKrSXS5d4UMqj5b32bjY8f4K1l90xZl4uiIzOOPjGM9xX6h9zP8DsFPhL7TBb3RlcUTZtpHzni4+xNNE7sh0Iga/iutMGYsdSVraATZueZ8WK6BSu6Qs8NFb5cOguJNM6di6u1at/xI9/vIof/jDMqQNkZTK47joXv/v1Ebr780hLjv4GKhksKHKyqWo7aelZouMvCIIgCOfZPx//Cd+/d2jCOndeC4+uhy/fHdn2+WHzbjWdjsXkTfsCy29ch1KpxG63s23Tk7j6XiYz6QSzSsIoJpHhp6EFalvTkOmXM7XyOi64NhKd9Hq99NY/DRnRunuNi7kjNZV9O5+IWzl+tzeDSwuKAFiz5kG2b19DQkIhtbX1lJRE6hiNMG/eD9m2bRZLllwd1wafz0eqpiGuLGRecPrGnwWfz0fPsb9RkRUN2nr84DCuJTM55X25piAIgiD8JxGBUUF4nzU2NiD372PeDM+ofTYrXLKok41vPc26y28b8/jW1lamKLtPe52ZiQE8Uy4b2bb395HVuRFM0Tq7HFYWr1h75jdxUnLi1bS2/prs7Mh2UhIc9vWxdNGqcY/ZsuVlrrtONiooGuu6G908//hB7r1mcVy5TAaFKQ6aTtRSPKX8rNstCIIgCMJ7J4UGMJwmBXhRLrR0witbNPS4l1E4/YusuuUS5HI5TU0NvPHSz5B5tlCa18PK0tNfMxiEw8cVtNuLMKdcSuWcdRQuThhV76W//pxrMgIj2z1umPvZX1FXXcUCY+9IeSgM5iW3jmzrdDoKC/9EIHAz1dWZ2GztWK2Rfbm50Nl5PfX1hygqivZDqg5sYlZGdOp9zxCUzbr89DdzFg5ve4y5WfF9yKN9RcxdufB9uZ4gCIIg/KeRn76KIAjvxdFDW5hT7h53v0kPylAje3ZsoO74Adrb23E4HITDkVXgA4EAGnlo3OPfJZdByfTovLODz/6WfFN4ZNvug8w1n510XtNTBQIBpJ7XaTpkjCtfsiLAgQOvjHvc8eO7qKycuP2pqeDwjj0CJSsxRHdny5j7BEEQBEH44ARDCk63PIHbA829pVx8u5ubPv0yBmMCLz/zJTY9uxyr+wbWzHqaSxb3kDfB6FCHE7bs1fHi9oUc6vs5Fave4bIbn2T5qptISBgdFJUkifDbv4ore82Vz7SZc6h5/ndYtdHy3f1qFlx4ZVzdvLwpdHU9wMqVqaxfbyEYkylgwYIQR44sZmhocKTM1fZG3PG1PTYSk06/iv2ZOnZwKxWpjXFlh1sMVC65+ZxfSxAEQRD+U4kRo4LwPpNJg6hO85dWXhKgb2AHxUmR6e7OPmhtkuHxqnB6FBwZPH0w84DLyvVFkWlhDdVHmE193P6D8lJWFpedUdtDoRAtTU04hu10tBxh9RQ34VAy+/a4mT03EnRVqUCvfxqX64Jx0gGcXSA21uTXiBMEQRAE4f1SOedydh96h/kTfPD850tyFs2U86NvreXiRXYqS4PMnMTgxrYuqDqRTFi9hOKK61h65ZRJf8zd8vKzrEsZGNn2hSD9+u/jcrnIHdwLMTHL/uyVY6bnWbr0kzz77E5uv13iL3+p5t57o6mIrrpqiIceWsiddx7F6XBQmNQZd6wmffyZM2ert6cTq/d1tNZoWaddTvqMO0R6IUEQBEE4h0RgVBDeZ5Pp0ivlcHKAKCoFWE1gNUlAJK/oc1NSaXf2kmkc+/gBL3gSU2mo+icoEnBsfZ1CW3T/iWE5M2/63Bm1+8Ce7bTU7CXfaMei8SMLw8vvQJoNdLpEPJ4+3k1VWl4eZOPGP7By5ZdHnWfatKXs3fsOc+eOvVADQHs7WA3WsfcNKEhLzzmjtguCIAiCcO7dePNnuO/OPzN7Wi3KMd4ihhywdZ+Wh3+h4VeP9FJaEPmAOpZwGKrq5LT05qFLvIgZs6/i4rnJY1c+jeYn/4flMcHPl7rMXHnFjbz1jz9xYVJ09ky7E+bc/Plxz7Nu3c959dV13HhjCf/6VxXXXx8NAN91Vw3f+/YsUrVLMCpBqYZlFeANypi2fPVZtXs8fr+fzkOPMD0n2nZvAAb1F1OWkn5OryUIgiAI/+lEYFQQ3meBkA5JYsIcmy1dkJU6/v77/3s1d11UxV8ukrCdsm7SoA/u2Wnk90+tIi21id3bB1mYGJ+L6rjORF79b2kO6gijB5kZhToRtTYJvTEVk8mC0WgcGYGwc+ubaPp2cEWRN+48FWlwrBva+ox0b5Oz+sKekX3Tph2gtbWB7Oz4FVkvuGAdP/rRY8ye3Yx8nOQd/3rSwC2rZ4wqlySo7zGxZHXJ+D+OIAiCIAgfCL1ezwP/7wlu/NKF/PD+QUpOrsouSbDnMPzuCS0//loBMpmMdSvh1S1w3Zro8R4v7K/WMOCZhi37KiqXrKRCqx37YpNUU3WY1ZrauLLgks9FRpsefBpiPhQfCOezLn38wKJarWbmzIdparqGefOmsG1bNUuWRKatyOXwX185zJt/lLh6RgUOH7x9GLoCCXzqkjNf1HIih955grk5rriyI90FzF215Jxe570aHh6m+ug+/O5+AOQqIyVTZ2Oz2U5zpCAIgiB8eMikyc9RFZNZBeEsHDywE4P0CsU5Y4+YDIbhny+pmF6mQaPyodMGSDCA0RDJGwrw42++xS3Hd/GzVtAbYEVeJEHwi60q2n1afvSXmykpsuLyhGla30q5JfrnuqdfTuWNOeNO5w9L4PSAwwFun5JBp5rOej+Xl40/wvO1WhUFi25Ar/8hmZnRa23ZkszSpb8eNfVt377tbNnyfe67rweNJloeCsGjf9MScldy17qpccHjgAR76w1kTVlOdk7+uG0RBEEQBOGD9b1vfBK99BaNLQ7UKgmfX87MCgufvNKGQa8AIsHSX/4VbrkCDtVbCMgXkF92HVNKpyMf70vpWXjkvlXcoXx7ZHtzp5I5fxvkxPGjFLx6H4aTI1YDITgw/4fMW3nJac+5e/cGioq+TW3tEDZbA4Ux33wbGqDvteXML4wkSd3cqEFTfgsLlq48J/dTc2Q7ubyETh0tO9Kmp2TJl9HEdqLOs5pjh7C3H6Ai24XxZGzbG4DqDh1BTSFz5i8767z2giAIgnCOTOofIjFiVBDeZ9Mq5vDi88cwGZpIS4rPyRUMw6vbLKy48AYysyLTxYPBIC6Xi/bhQdzOHnZteYPE3QfITIVfF0OvD355TMbChXl8dZqRfzRpCIUjf+973uxjRUxQ1B0E/czECXOcymVg1kf+B0G27g6yMHvie1qYE2BPfQ0qwyIyM98ZKV+woI8DB15j1qz4l47ZsxdhMv2C//u/36PTNZGc5GRwQMLea+SiedNJSsnmzUNgMWvRa8I4fBp8ITNlFfNJSRNTxgRBEAThw0Sn0/HlW7MmrOPzQ58jj1Da77hw5vvzb/lAfz+VgxshZgZ+U96VLDcYaHzpQSpiBqNu79exdPlFkzrvvHnreP75HVx22as891waNlsXZnNkX2EhdFZsoblpHbkpJpbl+fjLnjeZv2TFew4E9vV2Y3K+gi4xWtY1KCO1/PYPVVC0va0FT+9+FhbHLy6qVcHMXA+NvbUcO2qivGLWeWqhIAiCIEyeCIwKwvtMqVSy7vJb2LxpA3uPNVGYaUetCtHRZ6R/KJG5C9eSmZkdVz8hIYGEhAQkKYfff+OrfC/RP7I/SQ3zZqRz+bRITs7Plof54RMDrLxsFmW+JtBHr71jSM28gnESk45jeAhseRPXMWvB2zfA4pV3cuDAXmbO9AGgVoNS+Q88nuXodPHTykpKpvLf//17jh58B7VjCykVaiymyBvLsAeUlauwJGXi8/nI1+vR6/WjrisIgiAIwvmXlTuHuqaDFOeNX+ftXQau++RXSZ9g6vp79crv/oebkqMfhGsHYfWPfsmg3U6ptwpiAqPukjVjjlSVJInBwUG6u1pxDNbjdTVDsJsUnZ2n/6Xi+k9k8NBDHu66a2gkJdCSpRL/bn2NJM/lGHVqsjQDdHd3k5aWdtb3EggEaDv4Vypj8or6gtCvXk15WuZZn/f9UF+9n0VF7nH359v8bKyppay88pyODhYEQRCE94MIjArCB0ClUrH6wqvw+Xy0tLTgD/oprUw5bQ6mxsZGCpt3YUmJlr02IOPSZdECjRKsYQfB40dIi1251AWl136HoNFMy7Adt6sHv7ePkN8O4WEUMhcqpRedxo/JIGE2RhZ+gsj0evkEgx4kCSTkaLVa5PKb8Xof5t0UYdOnB9i8+c8sX/7FUce1t9SRrTlIgsU8UuYJQJ98AQV5pQCYTKYJfxNBEARBEM6vdVfczK9//Arf+FT7mDnUPV7YVZXNdz45931rQyAQIOnIo5ARLduunMPtWdm88tefsiZmBk3DEJRcdQPV1Qdx2hvwulog1IVGMYjJ4CYlMUypFThlHciE+lx27mjgzjsLePjhY9xzj29k33Wf9PPnX7zJ3VPXYFQH8Hji87ufqYPbnmJujjOu7HBnHnNXr3hP5z3X/H4/KoZRnWZwbKrJQV9fHykpKRNXFARBEITzTARGBeEDpNFoKC4unnT9Q3t3c605/ou83ZaMWhn/9d0sOSmVdxL7J12bMJ/l2bkAWK1WoGDMa0iShMvlos8xjHOoF4XxMHV9NUyZIGbbOqQgNbsIgOnTV7J160ssW9Y1sn/KlN10dDSTkZE7UtbT1Y566E0SYuKe/hA0e6ZROn32RD+DIAiCIAgfIiaTiXVXf42f/+0H3HlVF0mW6L6WDvjr83l86rM/HXNquSRJ9PX1EQqFSExMRK1Wj6ozGS8/9nsuS48uEmn3Qu713+LY0f2ojq6HmIGq+/1aVgW/TeIYwc+J5GYq+evfM8nNa+P666fw7LNHufrq6IjOO784yN9/sh2z6TIqrGdw4lPUVu2mPPl4XFlVu5aKJbec9TnfL4FAALUydNp6OlUQn8932nqCIAiCcL6JwKggfIh1/eMXZMZMA3tnEC5bkzGqXq/DR/LJxQ4AjgyqmHfPPZO6hkwmw2g0YjQakdLS6a/azZEOKEwC5Rizn8IS7O5K4LIL5o0cX1z8Rbq6vsa7M8jS0mDz5v8jPf0XyGQy7PZ+Ap0vkmmNvkwEJagbyGPqnGWTaqcgCIIgCB8eM2cvJDX9If71zJ9w2KtRyP34Qzoyc+bxwFfvJCEhIa5+OBxm41sv0li/k/TkYdTKEJ19BtT6AtZedgtms3mcK8Wfo7+/n57uFoae/zby6PdXnu/RcUXBP2g47mCpLZqCyBOEklXJJFrO/B4NOtAbjexdb+WSe+xMn17Crl01zJ8f2a9UwpX3NfPL/63nGstZXACwD/SiG9qAPila1jMsI7H0NrRa7fgHnieSJDHkOH3Ac9CjJc94ZumcBEEQBOF8EIFRQfiQ6mhrY2X/XjBEy+oNFhZrFXH1PEFQKNXoTs5pCoTAW3HNqByfk3HgzWeZrWgiOxleOgIXTAFTTJ/c5YeNLZCUmxm3CEB6eg6bN88nLW3XSNn8+d0cOvQ2xUXzGax/lnxb/OiCmu4Ups5bI1YsFQRBEISPqIyMDD7z+e+etp4kSTz52G+ZWXiQVVf5Y/Y4sDu6eOLvLdx4y9ewWCyEQiEGBgbo6W7GOdSA392CLNSDRjVIgsGDLUli+HgHV6UPj5wlGIaUi6ZhNUPnjj7mxAzefKdfwarpEwfo7EPQPSDD4dLjC1mR5Klo9NmYrEWkZB2kv/pNNjzl5qpbobMzn+bmRnJPBmWtVrj5rlfZt+8tZs9edSY/H4FAgKZ9DzMzJ9pH8gehW76CioycMzrXB6G3ux1Xy0to1SG8gchiS2MJSzDoN52csSQIgiAIH24iMCoIH1Kv/+Dz3B4TFD3qgEtWxK8AK0nwYJWSK2dGl2Pd7bCwcPmlZ3y9qp0bKXPtR6WETAPolbDpGAT0FuQ4kSuDaBJgwRro7q4jHA7HJdSfN+8eDh8+wPTpkRcerRYk6QmaDp6gPMsfd61jXQlMmX2lSMgvCIIgCP8Bdu7YRFnWIcqL/KP2WU1w86VtPPzn/2LxbAUJBg+pSRLlycStNh/r+DMHWRiz7+UOBZd+rZCevgAzlPGjGcNFFmQyGBiE7gE5TrceX8iCJE9Da4gEP20p2ZQUW0f1S1wuF6E3f83UZHjpaDabN55g+Uor//63m+Tkbgwn+2lTpsCmTWtpaztOVlbepH+Xg9v/xdwcR1zZoY4s5qw+swDrB6G+eg9J4T3k2SSsRth5HBaXMyrXqCTBnkY9JWXzzk9DBUEQBOEMicCoIJxHXq+X6mMHGB5sRS4LIqFEb0wnI6OAsqMvQMyssqedRm4Oq0kl0uk8NKDgH41arqmwUmyLDOu0+yDj4vvOOODYUHWAjPY30UUHgaKQQ+mSKymeMZfGxioyMp7k3UGiCQleGhoOUFwczQ2q0+kIhW7A73+Md9OFzZzpY8vLR4GSkXp1XToKKq9FpRpnmIEgCIIgCB8rxw5v4s4rxp9+bdJDVqqPoiywJoxbDYDGpmFWqAfjylxTi1Ao5Gx7sZurY3KZHx0Azew7qLFPJTUth9IpljOaqbL7mQdZaQkC8Dmzkg2bs2jMa+G66zL585893HtvdNTqihV+nnxyPlde2Yherz/tuetq9lNmPRZXVt2hYdqS2z9Us2kCgQDHD2ygJKkD9clJSwk6KM+Ftw5CfpqC/JQQMjl0Diho6DFRUDqfzKwP34hXQRAEQRiLGK4lCOeJ3W5n2+ZnyU3az/KZvSyttLOsspfitMM8/v9uZb45mo+z2QNXlmWzpw6+ejCFnw8u4kD2J3hgRRZzc6LTww7KppA/pfyM2tHR0oj+2LNYY4Ki3hA0JS+jeEZkNdm8vKnU1kaHr6pU4HS+PepclZUXs3Nn/KpNRZV9dA1EFpBq7FWRPu26D2XOLEEQBEEQzr1wOIxGOTTm6vWxyougvuX053vjL/vJi/lwvLNbRtqKb3Ks/8vY+r1xdU+YZ7P8ghspLZuB1Wo9o4BjU10N070HR7blMjDbCmlouBePB+64o5C//jV+4ahPfrKHxx5bhiRJE57bbh9A0/88xpi+V+8wWEpuOatUSO+XQfsAjfseZ1pKNCj6rn6XkcWrbkGXeTH7OkrY01aMz7ScJauvJze/6Pw0WBAEQRDOghgxKgjnQTgcZu/u11g+y476lL9CrSpA5YktEDNi4m2njjuSdbicWq56cD1qtZodv/08qdZoB//EsJyZN33+jNox0NeLb/vfyDdGg7CBMBxRT2Pu0otHymQyGXL5YuD1kbLi4kE6O1tIT8+Jq1dQ8AV6e7+J7WR8NCMDNlXVEpbNxlJ0NUZjzFAOQRAEQRD+A0wcKIwVlqB/EHoGFDjdevxhKzJlOhpDDgplGhWOf0DMgMya9DXcvvomtr/+HAtt0VGpwz6YctVnz6q1oVCI9pd/y+KYFJltLhllVz9ASloG69fv5uqrD3LllVN48cUqLrssZqX6O/fx6KN3cdttfx3z3MFgkKa9DzMzJzhSFghBJ8uYnpl/Vu19P7Q2VqMa3kRJWjiu3BuAhsE8SmZfhEqlwmQ2k5Pz4Wm3IAiCIJwpMWJUEM6DtpYm8jOGRwVFATb85Q0uSoh2lgf8MCM/i0AYgpd+Bq1Wy763XmSeeTDuuM7sC7GcQZJ7l9NJ1xt/JN8YjCs/EMxm9sU3jKpfUrKIurpog41G6Oh4ZfR57e0c3RPfjvlL3DS60rBax0kWJgiCIAjCx5JcLscbNHOaQZQcOq7GyTrqh/8HdcaDTF30d+Zf+EeWXvy/LFn1BeYuuJLjb/6LhanRQF3TMCz//K8BGNj8CMqYN5vtjmSmlM84qzbvfv0Z5icMxpU1Ji8nNT0TmUzGmjW/49VX9SQmqigpKWHfvmg9lQouvfQRXnnlwTHPfXD708zIjj/3wfZ0KuZcdFZtPddCoRDVB94gMfA2aQnxQdFeh5xOFlM+91KREkkQBEH42BCBUUE4D1pajpObGRhVHgqFkb0aP0X9JbuaWWkm3gwks+zqm3C7XOirn0UVM6Vpz4CeeWtvnPT1/X4/tS8+yFRT/JSzvc5EZl5215g5SlUqFU5nRVxZdnYLw8PR/FqNxw+QrT3M/IISqo5Gz6HTgVb3In7/6EUXBEEQBEH4eJsydRlH6safqOb2gCdUzMoLb6RkSjkJCQnIZDLC4TB1tdW89MKjvLD+ITQ74oONm8LTyC8sprW5kbnqtrh96rmfOKu2DtrtJNdviAuy7hnQs+DK20e2dTodpaUPc/w4TJmiZ3g4l/b2aP3kZMjN/RyHDm2NO3dD7UGmJBxBHjOjv6ZDzbTFd34o8oq63S5q9vyDEksthvgsATT0qlGkX0N+ceV5aZsgCIIgvF9EYFQQzgNJCo5axRPgjad3cJkxGqz0hCArMwN7ALhgBfW1u3jnXw9SbomO8vQEQb/49kl/uQ+FQhx6/iFmmobiyo849JRfcd+E5ykqupjOzmjDU1KgoeFVANpbakkKb0evAr1Wgbszh0BM7Hf2bC+7dv19Um0UBEEQBOHjY/GSVeyrnUZdy+jgqNMDj25I5/KrPxVf7nTy7NN/wjvwby5acBxV77NclhWdKu/0Q8ltPwHg4DO/IzVmev2+PiWzVl/FwQM72LppPVs3r2fHO68z0N9/2rYeePrXFCeERrYdfjCtuHdU/ygvr4T9+y/H4YCVK5PYvDkFjye6f+pUiZ6ei3jphX/x0rNP8OLTj9FT80xcXtF+J5gKb0Y3icWa3m89na30VT9Oedogipg+akCCo102sipuITE55fw1UBAEQRDeJyIwKgjngV5vYdgVXyZJEgP/eAlVzF/lC30KVuRa2aJNYs1FZozhrVT4DsYdt8uTQfncxZO6riRJ7H/lCWZpO+LK6x1Ksi+577QJ/00mEx0duXFlZnMVne1NqIfewhxz+PT8DLZtiy4M5fFAX98m/va3n7F+/cO0tDRPqs2CIAiCIHy0yeVybr3jv6jtvoS/PpfF27uMbN2n48lXbDy/dQ433Px1kpKSRuqHw2Feffkx1ixso6LIh0oOA2+8Ezdb5pkOEwsvWIPP5yOte1vc9RqM0zm090WyrftZOqODpdM7qCyqo7n2Bfbs3Dju4kg1h/Ywh/q4sr3hIkor545Z//rrv8HTT0eSwt94YyaPPmqM23/hhV56u+/iAuthLkmrQRcI88xW6B+O5HRvDSwiM6dw0r/j+0GSJOqqdqKyv0BOUnx6pWEPNDpnUD7/OjRi4UxBEAThY0p2ulUTY0w+a7ogCBNyOp0cPfAsC6Y5Rsq2v32Uij/9EdPJwRQhCV6SMqnISuXlnErMagVOuweLMsyyTMg0QqcbBiorUerTkKlysCaXkZxsG3c61sFNGygb3oEmJvja7pajWHwPaZk5Yx5zqq6udgyGBzHFrKG0440EFhZHV60PSnC8vwCjrQCD4XscOABdXbBkCWRlRYKkO3aYaG3N5Prrv4jZbB7jSuPz+/20NDcwPNCFTBYCmZqM7GJSUtM+FFPRBEEQBEEYWygUoqOjg2AwSEpKCgaDYVSd2poq/INPM60okoLnwL4Wcp95nMSTsbmwBL/RXUjlhZ+ks6aaG71vjhzb64HXMi9jTqUBhRyQyVAq5MjkMhRy6B1Q4Q6XUlpWiUKhGPkfQNVD9zMv0T1yrvphBUk3/R/WCXK479z5Av39P+TSS4P4/WGefLKK22+PT5f0x59l8qnFtyKTyfAF4cUaSM+0sWjNF89rv8Xv91O7fwMlts5Rq8632RUoUi4m/UO0IJQgCIIgnKFJ/SMrVqUXhPPAaDSi0ubT2FFDfkak01//52dZFPMX+XKfnLXzU/i708w92d5IhzUjMqXrjVawDoI8I4nlU/TAMHAUb+AoTdVKvMFktIZi0jKKR0aB1ux7hyL7DjQxM8H6vOCfcQP5kwyKAqSlZXLgQCIzZw5E7yfFiSQZeLdvX9OdytR5FyOXy/nLX9IoL+/illui51Cp4OKLHQwP1/DEEz/hzju/hUajYTLsA33UVe+h0OakKDvyvSYgQVtPP3ubEqmcvUgsCCAIgiAIH1IKhYLs7OwJ69TW7uHiBdG85AeffIuZCdH9r3couOPns9h5qA5L6zawRfftdum4+YoAMDjmudOSIBzqJOTbSCgEwRD4w7B/Sz8XxQRFQ2GoM5iQGn9Nd4McSZIRlmSEwwpAjkSkTBOWo5Dnc+hQHTNmyLn00im88koVa9ZE+iguFwS07dzws1+TmaojGJCjDNmoXPJ5Fp/HoKjd3kff8eeZluYdta+6y0T2tKswGk1jHCkIgiAIHy8iMCoI58msOUs4fFDFloMNBOyHuTjYAzFf613GZA445NywJDXuK75JDVcXwoYmyCyIX+Vdq4L89CDQBXQx0LuVtkE9vb1aSuxHMcbMgnIGoC9vDaVTpp1x243GVQQC/+bd+OOUshBNB7zkp2qp6kxgypwrkMvl+P1+FAqJhQvHPo/ZDKtXt7Jz59ssX77mtNf1er3UV+9mVp4TZcy7hEoG+ckBEj09VB3eQ+XsRWd8T4IgCIIgfDjICIykFmrvGGaR1Bm3v6+4ggSTgo6OQa6MWUgyFAbznIlHOKrkjEom1tMXpMzjgJiZ8DvtSi662YJCFj+9fCx7qpLx+1309XVgs6nJzi7h8OHjpKfDl74E998Pn/+8B4gkIe3r6+M73/4atpcTWbv2htOe/1xrOVGF1rmF4rT4Vec9ATgxXMCUOReiVIrXREEQBOE/g8gxKgjniUwmY8bMBSxccj21j7xKasyAyY0DcOmUDIZsSRg1ijGPvyQH9mzvmvAaiSYwaYfI7T9KckxQ1B+GasNsSucsOau25+WVU30s2mFWq2FQNkxtl57CmdeOjNjcv3878+ZNvNBBUVGIEyd2Tuq6TSeOU5IWHxSNlaCTUIT7cbvdY1cQBEEQBOHDT6YhcDIe+ebDbzLFEt11sA/W3LEctw9kDXVYY/o3u/vkLFg68WjUU0kSHHu9m+yYoOiAFzKWJMctQjSRytIEFHILO3aYCYVg2jQD3d05fPWr8ItfwKxZ8fWTk+G3vxvi9de/Qmtr6xm1970IBoNU7X8VW2gTKeb4oGiPQ06PfBnls9eIoKggCILwH0X8qycI51lnexsXOg5DzIKkrcpEvE4VFy1IHvc4pRxU3hAHTkxHHm7HpBsi3RZEp47WGXKEcOzrozhmJlRIgkMhLVPKO6k/8jBB0jBaSklNyxl3Crrb7aah9hhetwOtwUzQeQKlpAOiOVILS4MMDq5AG5Ocv6+vjSlTJh5pIZOBUumbsM5IO4Z7SDjNrP+cRA/tLScoLj3zkbCCIAiCIJx/pVMXUtXQREm2k7yOWkiP7jtgyKEyycBrWyVmKQfijjuhz0JTp0KjkkAmIZeDDAmFAuQnt+VyUMhBoYj8r77ayQJTfE7QQzI9K/Mnv9iQSgH+YTNr12awYYOHK64IkJVloKBAhs029jINMhl8+cutPPzw9/n2t/88+R/nLLmcTlqOrqcsdRj5KQHfuh4NyUVXkJJoG/tgQRCED0g4HMbtduH3e5EBOp0R7WkWCBaE90oERgXhPHvr+5/jjpig6IFhWFacyb6k1MiiARPQycMkmYdJMCfh9tqoapGhlPnRqvrRKe0MH+yjIiG+Q77fpWLWxYkoALPeCzQRoomeJjlDbjMKdS7JaVOxWKyEQiH27tiM5GynyOLAqAGHC+oHQKY00tjoIP/kjDWzGerrN5GTE53Cptcn4HDABGsWAJzM13V6MsKnraNRgd89Ol+WIAiCIAgfDRqNjh/9ajPDLQcoCocxKmB2CnS6YOEdF9Jrl9FwsJ+Lk6N9nDYnrPr0gzQ1VVGSezwufdCpNu6GJbPA5Q1iONGKNjG67+iAnKwL5tHSpyQUChMOhwlLYZo6jGRnFyCFg4SlIOGwH6QgkhQiHPKj0gY4fOhVli3LZdeuBrZu7efWWydeuzYrC3p69rzXn+u0utqbCXS/QllaKK48EIbanlSKZ12OWq0e5+j3nyRJYvFMQRBwOh04HXb02gBGTRhJArfTyeCQksTE1PP6nBI+3kRgVBDOo/6+PmbUvgIxIzoPBU1oFRkYkmzAxCMpB4NyMlOVKOSQYAiTngStPRokxQK6dzzH3IT4DvCBISUzLrJxahhSAaQnh0lnEBjE4ztE0zEVNXUqKlO8pGdGA5I6FaQYoX1IxrEaHfn5npF9qakNuFyukRVmZ81ayhtvvElOTt+499DbCwkJxRPeJ4Df58PtcSNJMFHf2eGRodef2Sr3giAIgiCcf+FwmO/efw+ymlf5dmYHOQvA6YcnauGPR2BGsYWZngLqqjIpcu+Iywl6IJzPZenpGIxG9uzsYsmsoZE8pbEO10J2OqiUcOjNBpYnRvtK/hB4inPQyYJ4fQqyMwzoNJGTOHxJlJaPkzT9pB07ZFitL2IwZNLX14TROGH1CMlJddUBikqmnfPFIyVJov7YDlLkB0hLjN836IHe8Eymzl94XoKSPV2dtDYfRx52g0wiKKmxJGWQl18iFtEUhP9ALpcTj7uPVGswbhlxjSlIQArS29uOyZyMSqWKm6EoCOeCyDEqCOfRyz/+CrNM0dEEDW4ozc9myqe+Q31XgPAEAw0GfaC0WmlqdxOIGUiZlRKgcduzzDXGTy87NqxBXbGaho4kWnsUccecSqcBkz5Aus5NunHsipkJoPGa6e2NKcuUqKt7Y2TbYrEQDBbR3j72oyYchg0bbCxffsX4jQHsAz301r9MhjVA7/CEVWmzG8jMzpu4kiAIgiAIHzo/+PJnWNXzBN8p7SDn5Edjoxo+NQ1+uBA2NGjImXIzeTmlLLYOjhwXCEHaqnsAMJlMVMy8mM17k2hoUxGUQAJ67DK2HjQj089HUs9kxz4/Zb74POg7HVrmLMqgMCtMSY4Hp7Of6oZBWrv9+LwD9HRPnA802VzK4QMypk0zolKlsH//xPcbDAJhPdkpfTTUHz3DX2tiPp+PY7ueIU93gIRTZqG2DCjwJlxGcfmi8xIUra05wlDnLqZn9TAz18nMHBdzc+1YOcb+PZvx+/0feJsEQTi/HMMDJJnjg6IAbn+YI0c7eGfry7y24Y+8+OxvWf/0I7Q0N56XdgofT2LEqCCcJx6Ph8ztj4MlWrbFpcc65QJM215knTXMv47DDVNGj5D0huDRJiM33jMVW2KAps4hkqxGTHoF+zbVsMg0GFe/0akk9cJPk2RLASLJ93t7Oxnqr0ERbiPZ4sZqir9GXQPMPE2qqdlZCvYdM2NbHo1W6vUHCQQuHfnaf801n+Kxx5xMnVrPnDnekZXs29rglVdSWL78bqzjzLWXJInWE4excJxMa2TK1756MBlAN8bTq92uQp+QKaZZCIIgCMJHTG9vL94jr7CsbOzZMil6uH/KILu3bULV8BYVMQOGtvfrWLrswpHt5GQbF1x4PS3NjeyqqiUshbFYU5i3aDoajYZwOEzX6/8gxRI9R6cLii8sjXspt1nAZgngDw7hdcvp73iTgb5s8ouWotHErJoJ9HZ3oj36LL0BM8wc4hvfmM7ddzeyevX46X3Wr1exdvUVGHWgUQ7hcDgwmUzj1p8se38v/Q3PU54W/1uGJajuSiBv+pUYDJMZznru9XR1IvM0UJw6OviZbJTQqgaoPrqXGbMWnYfWCYJwPng8HjSqwKhRew5PiG9952ccPfgCBq2fQFCGSpPK5ZcuZvuWRhrS57Fy9brz0mbh40UERgXhPHnhV9/lBkt0YaIeHyRk5KJMymCF5ygKAwQk+MshqEiFqYkQAvbaVRx1GLjsE3OwJUbeCvLSw/QPDbNjbx+VoW5UMXPluzwy1AtvGwmKAiiVStLTs0lPj6zc6vF4aOyoxeuqQ6fsI80WIhgA7WmeEAYNuDqzcLuPoT+ZJ7WoKMDx47spK1sMgFqt5o47vkpV1SGeeuo1ZDI34bCMlJSpfOITa8d9AfB6vbTXbSYncQjVybcUlRxmFMDBBkgyK8m1BVErYNgDzf16VMZMSkunn8H/C4IgCIIgfBg89dBvuTV14hGZF6X7uO8ff+K/KnwQExh1l6xFLo9/pZbL5eTlF5KXXzjqPPveeoEFpvjRorvDaZQHjfiDYdSn9H/USijOCQNuegaPc/xQIypDJaVT5yCTyXC5XPS9/RBlphC2oJWjh4eZNl3BqlXl/PKXh/iv/xq9EOWxY/CPp3SUT93H1KnFZGSk0dLVhMlUMfEPdRpN9UcweLZRlBI/48cdgCZnCaXzLkChmFxu9/dDa/NxpmeNPyLUqAH8A3i9XjFdVhD+QwQCAdTq+GeWNxjkyitWs3ZxN9/9dQjzyVfGPYeH+OEfmsnIXcAFK0Ps35/KrFlzz0OrhY8TERgVhPMgGAyifuHXEJPv6ZVBDcFZi7jJdZR3+6tTjaBVyHgncS5HPMPIZQpsxflcVW7AaoofgTDQOUC5rx1dTFomux9cU6+hMLcAiOTuGhocYGioHwkJrdZAcnIaOp2O/MIZwAwkSaKvrxen9yXCknvUyqWxAmEwGS3U1iZQWTkERFZ79fu3IknR6VlyuZyKiplUVMyc1O/T19NJoH8HhUmjXyS6hrRMm7OCQCBAbUs9wWAQg8FMyYxi0YEWBOGsBQIBXE4nLrcTuVyB1ZoonimC8AFqO1FLQcLEdeQy8PW1UGKJfuxtGJKx4K5PT/o6juFhTDXrUcVca/+Alpzl1+IO9NPaM4jf58ZmDZBsGX18igVSLH58wd3UHtxHUFGGp2o3c0yR0ZlaJQweMcJ0B7ffPoXf/z7EjTfWcPXVPioqJAYHYf16NUqlgSefykUm6+In//sYn7r1XkLB5Anb7vF46OvtIBgKIEOG1ZqCOcGCTCaL9IsOv0mB+QS6U745dw/L8ZmWMXVm+aR/p/eLLOwe+eA9nvQEN709XWTn5H0gbRIE4fySy+WEA/Flt958I9//XAeLZsWXz50Oz/zOxy3/vZMjR5IYdr/BzJlzxAJuwnsiAqOCcB68+NBvuNwand7kDIIzOY/rzW60iuhDvccP/ks/xy2zF4yUNTYeJ9nYQiCoorXbSXaqRGurg4TmVswxOaQ8QehIX015eSQY6XI56eg4gdXoIyslhFIGTu8gbS196PQpJCWnIJfLUSgUhHxekvHSaofcU5L1x2qyq8gpmIrbayMYfB7lySfKlCkOWlvryck5/aJKscLhMM31B7CpGkg+5eXIE4ROVxbZpXNHpuknJiad0fkFQRBO5XQ6aW6sxu93IpOBViPHYtbQ3tpOWNKSk1syasqsIAjnXlJqOt0dkD3BTHJJgqB7CIgGRo+pS7kscYLOyin2Pv0bViZEP7y6g6BZfCflMxfi8Xhoba7HL3PSMaSk2zGMVn6CnDR/3GwcAI0SSvJC7HlrM/NM8VPWNeFMqqvrKSsLct99U2lqUmE297NpkweTSclXv2olMTH6GvbZz/fzxKOvcsnaL47Z5lAoRHvbCWTSMLbEAFpl5OP04PAw9b1akpMz6Tq+gfI0x6hj67p1JJdcQar1/PaZAoEAnW21hIKu09aVyyP3LAjCfwaNRoPdqcKki0RHe3oH0CtqRgVF36VQwE+/7ONT39tJbl4xzU1N5OXnf4AtFj5uRGBUED5gkiTh/Ns3UcT0T1/oV5I/rxCbNhoUdQShZd4nmBMTFAVIT8+lsa6H0nwvBr2ZA1V9JDU3khKTKioQhoMkkpeTA0SmpXd11JGX7otbodWoBWO6j87+Ntqa+lBrNfQ0t5DnrWNmEmzsgIwERr0MAPhC0ORKYFVOLpKUQ23tK5SVRaZGabXQ1/fqGQVGPW43nQ2byUl0oDzlg1/vsJyQeTYFZeIfPEEQzl4gEKC/v59QKITJZKK3uxVCPRRlBdGcHG0/7IbWrgDpNi16fYjGE1UUFlWIVZIF4X12/d1f4NHP/JOvmzrHrbOrR86ivGjk1BOE3DX3TfoadccOMTNUHfcGtNufy4q5kfQ/Op2OktL4qezh8Eo6OxoZ7N1NetIAiTGB20O7h5mpjg+K1g3L0ExbQk+PjbKyrdTVeVmwQOLiiy3EJZaPkZgIg45WUlJzxtzf0d6ERT+A2RBdlVMlB5slhM/djrNxG2Vp8dNQ/SGo68ugeOal5zX3eiAQoLOlGoW3nixLkO5JHGN3q0lOmXywWxCEjzaVSoVCqcftH0Kvht8/+EfuvGbsfNPvykoHvWoQl8tBXe1RERgV3hMRGBWED9iGx//KNRb3yHYgDE2GLL6Wbxkp84fgYN5Kll542ajjtVotyWkl1DbXYrMMoWusJcccv3z9Ab+RBRdl4/Pvo6G2C5UmhcyU+KBorPQkibpWD+76KsrldtQnB0fNSYLNdTA9i7jAa5cDDnTrWbx6zUhOL59vNrBjpE5+fhd2ex9W68TTwgB6OltgaDcFSfGd+rAEzQN6UgtWoD9PiwQIgvDh0t7eSl3tQULBIKlpeUwpnTYStOzr66O2ei+hkBOQodUlUzp1Flqtltqaw/i8PdgsLpTKMK0NKoadUFqoRaOKdofMephaIHG8yUuWRk9ump+2tkby80vO0x0Lwn+G/Px8DjsSqLN3UjzGmozeIHx3v4bFM2y83QrLMmHbgJnVcxZO6vzBYJC+1/9AcUy8rckhp+L6ByY8Ti6Xk5lVSGZWIU6nk/1Vb5Cg7yLgdFLodhL7zaTLA/oZSRRnHKOxI8zx4zK6u4Pk5ITHv8BJJlMQr9dFQkL8lBm/348UGo4LigJISNQebyNd14P5lBii3QXN7hLKZ604bx91AoEA7U1H0QQbyEkIw8lZTRYD9DkgeZyRwUEJBr0GisdZmFMQhI8nizWJ/r4AwZCXvv4O0k6zCDCAThsmEJQYtWqTIJwhERgVhA9Yx28eQB/zoN/QJ+eqVZUjK8+HJdiaMI0LPnHXuOew2VJRKtWc2PA7Zlvj83DucWiZeUkBMkCrhsK0dupbewgak2GczrHT5Sd44jjlxvhk+AkaSDUl0K4qZH/DEQy6AGEgOQNyLBbMZvNI3SlTVtHauovs7Ejn32qF/ftfwWq9Zdz7CAaDtNbvJVXbgt4cv8/tg25fLjlT55zXRQIEQTh/Gurr2LPrNQJ+D2qtmWDAS1G2k9mFbpQKaOs+yEvPbaZ46nLcbgfKYD0zp3hGci0Pu7s4vLcJT9DM7KkhrKZocCI9KUBQgiM1IaQsA4kJ0S6RDMjPkmju8FGSpyPoGyIQCIhRo4LwPnK5XNwUOsHP3oGl+XDdlEi+TkmKzGB55LiW31yVR2Gygp1d8N3dMH31xZPOK7fr5adYaI1ONQ9L0J59EYttk3j7PsloNDJr/lU01lWTUP04Jn10nyMAw1kWSjIiozPzM+Rs3Z+MKdFNb68MkMY+6UkBn5yQ+xiBgC3uWdPf301iQvzIKa8/REN1LSVpo/N1Nvcp0KWUMyXbQldnC2npOR/os8vn89HRfBh9uJk88+iAcF4K7K+P9FGNp2QpCUpwpEVPQVHlB9NYQRA+NBQKBcm2dNwuJ0kpZdS3vE7JaQaBOlwKlCoTycmTf44LwlhEYFQQPkAvPPF3rk2Iz/90VJPBVcnRnvVmWSbL7/nKhB39YDBI/RuPMtcavwDTgUENpopSTu18F2UH6B3swuGykp4cP/Kyq2uIQO0JyozxnVdPEI4GU5h+weVoNBpqE/yUlBwd2V9XZ4+rr9Pp6O0tJjv7+EhZcnLduKuKOp0Oehs3k5s4eoGnriE5isQF5OdnjfsbCILw8TU8PMw/n/g1RZltXLnUhVYNfYOwabeSllYtc6Ylo5BBcU6Qouxe3tq9AY3axNI58S//Zj0srnSy56gHr9cEpvj9ShlUlIY5cMxDYkX88CWNMprjTq8N4/f7RWBUEN5Hz3zrc9ya6OcyK2y1w1fegGalmQxDmKWFFv5yUzIaZWRY0KJ0mJ4M/3vwAFcFgyiVE7/S9Pf2ktH6OvKYP/PddiPzPvmJM27n8KCd0N5/kB5zrkAYDoQMLCzRx9WdWZpPu7OXffvUXHTR+NNCvV4gnEyGTaK1/TDZebOj5w540cRcq9/uZrCjlvL0+BycIQlquvQUlE5Hp40EZy0mNz09nSQkJKLX60dm+bwfvB4Pnc2HMMpayTeNHQT2h6B9UEdW4TTqezuR+QdIS3Ajl8OgW82g10hB0QySziBYLQjCx4dcLsdoMvPf/+8H3H3jX1i7fPycxMfqwBOwkZ6RR0lJxbj1BGEyxKBjQfiASJJE46++QFJMmqfX+2XcvnLeyPY7vgQWfO67E3bwJUli/0uPMVvXE1de61CTt/azmC02Ovu0DJ/y74jNImHRD9DQ0kcgHOmw1td0ojlRT7YhPija75XRlVxIxpSpDA31A2Aw5BKbBz8hwUMgEL98YG7uGuwx8dKcnBC1tRtH3UNn+wk87a+RnxQfFA1I0NBvIiHvUmypIigqCP+JAoEAT/z9p9yw+jgr50WCogDJFrj2oiAVxU6ef7V/pL5MBqvmeekbGBp3PFbl1BB1J9xj7lPKINEcon8oOGqfTH7yM5MM3O6xjxcE4b3rbG9n+u5Hgcjf9LJEmJ6dwy0XzeKPN5Rw05yUkaDou4wquMTUwraNb572/Ief+RX5MSPGB32QdOFnzvhjh8/n48Qrf6TIFP+82OGwMHdFEZ19JqqbFAw6T7ZRr6S7NZmCAhM7doz/wfuxv5tYd9FqAFKtA/T2tI7sUyk0vNvdamzqQRqoptAWHxR1+WB/azKlM2aPBEUhkks+FHAQDnTT293K8HD8R+1zwe1201j9Du62DeSbW7CNERT1BqGx38CQejF509aRmZ1P5axFTKlcjd8wD5d6Nsl5y5m7cLUIigqCgNFoJC33Yp58cexwlcsN9/9Iw9IVF5NXUIHBYPiAWyh83IgRo4LwAXn24T9ypWk4rmyPPI2LEiKjKQ96NEz9/A/Q6XRjHT7iwJvPMVNWHxdQbHUpsa68F2tiEhrtHLxDOwmhor3XSaYt2kHVqaEww01DqxdXu5spSgenLrbc5FJgKJ9GfnIC7X0gw0cgECA5OR27HZJPpgy1WqG/v4e0tMyRY5OSbOzfn4rVGk2tr1TuIRS6CIVCQSAQoKVuJ5nGTrSn5JZy+KAvWEx++Yz3dUSDIAgfHi0tLfz1z99joOcIbtcgXb0esrOzyEyFPz2u5KLlScyYGj8CqzQfDtd4GHaGMBsjaTZkMshKC9LW5Sc7bfQiIyo5KJQhAqGxF5NLskr0DwVJSojvFknhyLR6t0+GxjDxNFhBEM7eK/99E3fGBC7bvNBSdgGfTD084XGLbH5++eYLrLjwknHrVO15h3mq5riyA/JSVpZXnlEbw+Ewh1/4C3NNzrjy/Q4LC6+/n76eFlTqA5Slaxl2hahpcqNR+6goySMr2MdrrwVpanJw2WUhjCcn73R2wuOPGkhJmkdqWmqkUArTVLudo3sklLIgYUmGPyzDZAxQmeVEG/9IpHNQhlOVS3ZhOopTZhvJAIUijFkXxqzzY3cOMDQECQnvPX+n0+mkt2U/FlU3+QljPx89QegaMmFKm0l+Ttqo/VqtluycvPfcFkEQPn5+/btnuOu21Wzes40HbvNRWgg+HzzzGry+DUoKdCyea6Vi+pzz3VThY0AERgXhAxAIBGj+4/9wTUq0bNcg3Lk6smBAnUdJ6u3fxmqNz54vSRKtLc3093Sh1mgJDPdS6twblyq01yNDmnMjtrR0APR6Iz7fdNShQ+gTzTS0ucjJCI4svOR0hwk0DTDdGD/aICxBjVtH/vwZ6LRq/CGQy0GjChEIBNDr9bS1qUlOjuQhVanA4WiKC4wCJCZehNf7GO/Oni8u9tHQsJ+0tGIGmrdQmBQ//R+gw65Ek7KQfFv6mf60giB8RP3zyT+yd8v3uf/mDjJPvi87XfD39e2091r40v25PP+ml8PVCdwS+/AEFs8Osn2/g0uWWUbKUhLDDA4FxwyMAqjVEAiGUSlGf3iRJEal9PAEQKlSMuwGpUKBSiGm0QvC++Hovt1c1LkZYrLuvJa2DIvFjOE0f3YKOcjD/nH3+3w+fO/8FUNM96pmSMmc2754xu3c//rTzNR2xJXVODSUXPZpVCoV6ZmF+LyZNDTvIDOpl9JCE0HJREenl+raZD7zGaipsfDQQ314vUFAg9mcwNWXzKMwx4zL20xVjZKmJhlzMwKkZMfcRwgOd0JTN5TGTKg53qUiJbcEx6COJMs4r3UxMUurMUzP4BABvfGsU4M4hofpa91HkraXmHVD47h90O20YMmcSX6OGAEqCMLZ+fNfX6P66DZ+9vNv0Na8k4qSIOtWwiM/gRfeHiLg7Wfv7k0sWTb+xzFBmAwRGBWE99Hx48d55Mffpn3/NvT2Qb5mhzuyoMQAW8M2vmzW0umTwVX3k56TG3dsXc0xju7YSK5ygFSVF28IGu3Qq4AVmZERUI4A2IvXUVJYGnes1ZpCZ0ceycYmCnJMtHZ6sJi8uB0BAg1DlBrjv+x7g1AdSqRi2VSUskjQYGAIrGYV/mA0WhAIWIHoaNBQqHPUPefmTuHIESPTp0dGVKhU4Bh+g2RZE3lJ8dcNhKHFbiWreCmaMfKQCoLw8bRn93bq9n+bn305PiWI0QCfvTnMW9vtPPSEii/clckLbw6xdbeOpfOiw8xTEsE+GD+V1eMFjWb8hdq8HlCpxh6N3tEjJzcrGiQISHCiVUaKTUN3vwK9zojeaBzzWEEQ3pu9X7+J22O6AAcdMtY+9CQvPPVXBjyQOEH3wB8C1ONPody1/i8sS4x+kA2EYLj0SkrN5nGPGUvV7s2Uew8Rm+mozaUgaeXdGI3RZ5NGq6Vwykp6ulroH95LZnKQnAwtZuNsmptfo7RUS+nJyObmzVqWL0+jal+kn2XQwkBfkOW5oxcl0ihgbhbsaYW+BDDpoa7XRFpuEX3DcgwGCdWpX3eIPMtOzZxm0Adwu51nPGp0aNDOQPs+kvUD5I9zqNMHve5EEjNmkZ+fOHYlQRCESVIoFGTnlPDwQ39lw8svsyTvfiwnH9+XLJX46WMHWLCoEBCBUeG9EfNVBeF98pvvfZ1/37KMr7T+k8cy2/nTNPhSPvyjA75dCzdcsIjBAHQvuY3iiplxx9ZWHaFtxwtcmdzBLKuXTCMUJsC6PKhIgg1NkdFMjclLKKlcMOb10zOK6RpIRgbkpOtoORFE0zxI9inTQfu9cExuJqssh3A40qm2O0GS5GhVMrw+xcioAoUifkSnSjU6V5VMJkOhWEI4Jm1paZkLrzd+6tmwR0anr4yCaatFUFQQ/sM8+vB3+fIdPePuX7VIorpukGBQYt0FIbbsGojbP+gAoz6+C1PXpCQ/65RowkmeACiUSsaKi3r94PIoMOoUBCXoHogk9JcrdAQDMvQ6OWqtWSy8JAjvg43PPsU14fq4skNzbyU9M5O1N9zOv5sm7h+81a1n+eVjL6DU2dpCsX17XNlORxJzVl1xRm1sqasmteV1dDFBUbsPAjOuw5aWMeYxKWk52LLW0dCVjtsHFrOWpvrUuDpFRV76+4P4w5FFmYbdoJVGB0VjVaTD0UY43JWDIakECTl5WWCzgMMTGlV/cAjMpvhxMGoV+P2jZ++Mxz7QS+PRN1Da3yDfOoBpjPYNe6DRbiOcdDH55atJsIqgqCAI54ZOn4gvCOvWruWlTdHnmUYNRmUNSP14vZN/pgnCWERgVBDeB+sf/zvmVx/kG8k9cYstJavhW8WQooFdzT1UTbmEyhUXxh0bDoep2r2RFckOxlqYPk0PpRZ4y5nF9KVrJmxHWuY0Wro1VO1po9hvxzoqn6iMcJGFWTM1SKEOTjT309obmVavUUdGksoUmpGAgMmUR+x6SwkJfny+0auslpQsoL4++g+XwQCdruj0s1a7ipB1BTmFFcjGuklBED62gsEgilAd2gle/gFWzvezY58LuRzMhgBuT/Rryzv7lSyYFR3x1dypxBs0Mta6dQEJtu3TYk6wcGrYwOGG/dVGjAn5VJ2A+pZIvtKpRZCWBOnJICNIsm10bjxBEN6bUChE368/S2zc7vUhDdd+//cABL0uenu91I6zXlCnC/ZIJcyeN/oDsSRJ1D33K9JjBpP2uCHvsi+cUR7zvt5u2PcPkmPis94QtKWtIL904lWQ1Wo1hSVLcbGY1m4V00tm0NYW3Z+ZCUeP9mO0BGjqVNDaDnmWidujVYIkN1M57xIsCTJsCaCSgVoJHk+0gxYAegfBH5Rj0p1yvxLIOH3fq7+3i8Yjr6Id3kh+oh3DGM9su0tG42Aq8tQ15JevxGxOOO15BUEQzoRao8EbiLyLttpnx+1bOsvJYH87u3e8dT6aJnyMiMCoILwPXnnkd9yWMDju/vty4OmjbSy+5pZR+xpqayhR28cMir6rxAph+ekzYfh8Xlx1JyhXuomdYRqWoMqpIHV2IrakyD80KRaJ3JRefK52EowSKlWYfrsciyV55LikpJS4VecTE6GvL346vSRJdLXVYu+K7xznFLjpdwRoGLCRWnwp1kSRc0oQ/hN5PB5MhsBp6yVZJIYckenyeh34InNC6ewFh1uNLVFJIAj7j8HOoynMmnMZm/ZaaO2WE5QiU2brW+Gt7TCt8hKS0uZz4LiNfcfgSD3srYKWTsjIymVqeSWJFiWluZBijaQq8fgibTTqJDESQRDeB+v/7wdcbYh2KoJhcF7/LQwGA4FAgOM/+xxfzYGXq+GRI5EZLgAOPzzVauFv7nl86Ye/GfMD6/7Nr7DAGD8q/ZhxDtn5RZNun8ftpvvNP5NjjKbtCEtwRFlGxeILJzgyns2WSVreOuzeYmqr4/s+BQUeEhKC9A/pae9JQjmJb8VKRWQmTyCcHFdu1Ido6grT3gs9vaDVgk4zelEkp1uOwTh+KoHennYaj7yMwbOF/KRhdGOkbR5wyWgcykSTuZb8qcvj0gkIgiCcawqVlSBwzQ3fp7ohWj6nAo4e2YnXMfFCfYJwOiLHqCCcY83NzRS7W5FNMBpKJoMZugDt7e1kZsYvXtTd3kK5dvyFBAAUMlCEJn5R725vwX/kZcoM8WOkvEFokJuwlCYxMOwh0xYdhaXTQEHmMM0tHlJTc1EpAyhjhmCpVCqGhnSkpHiAyOJMbnczkAdEFjloq91KjmUAa2YqnZ39pJ+cfW9LgXeOu1m8dOWE7RYE4ePNaDTSP6Q/bb3jjXKWLoo8SHsH5GjVCrbuhW37NeTlpfHS5jBSGMqLoVhlpLC4lJy8Qupqj1G/exsaDWSlwepF0O3ykZldQHp6No01r5KZOoxKEVmxubG7F4AwZiBmyr4UCYYYDNDR20p2Tsm5/ikE4T+W0+nE+syPUMR8Q33ancR1n/sqAK/8+jtcro0ETe/PjqxS/4uaNBLzitAZE7jo87dwY3HxmOd2OZ1oD/8TtSVadsiuYf69n550+4LBIEdf+BNzTfF9rX2eFGZdc+Okz/MulUpFQfEiwjIrHR0/IOPkDPzsbNiypR/J005O2EyfE6wTPB4lCcInO5iWxGwcjl5Musg+vQZUCsiwMTIe1CWXCIQZSSMSkMAXUGHV6U85r0R3VyuevsNkWNzYksa+fq9DhlPKJi13Bok63Rn/DoIgCGdDqzXgdcooLsrjd88YKSuMpmjLTW0l4OvB4/GgE88l4SyJwKggnGNDQ0MkMXp6+amSZT4GBwdHBUblCgXB8DgHxRg9BiCq4cgeErt3k3rKegT9Xhi22SgvimTN77WrqWl2U5jtG+k0y4H8jAA9gw24fJmYvB50MR3oYNAKeEa2Q6EuAAb6evB0v0NhUmSUlUqn4niTkfT06D9c6Zlt+P1+1OqxV40WBOHjTyaTkZg6n67eWtLGGTguSXDouJb77tTR0wd9gzqefg3mTocV83XoEmdQmX9gpL6zPjI/VaVSMbV8Bqrwfoqz3NH9ww1AAQBydRJqxfDIPgWR/1ZpU4gNjGo1EgEpMk1VztA5untBEACe+fqnuS0h+hF4OAjpX/4DCoWCo7u3s6D+VYj5wHw8ZORbDz+DdhI5yXc/8yArLdFRnt4QMPfmSb8wS5LEgZcfZ66xL678iENPxZX3olCMv8jb6RQVlbFxYykZGTUjZTk5bjpbnJTozGzugiIb484aah1SkJEXWXBTrVYz6E/ApIs+n0x6D06vHpP25IJOarA7g1hNSrxB6B+SY4tJDSJJEl0dTfgGjpBp8aIaJyDaMyzHJc8lI7cCm8gLLwjCB0ypVBKSDIATjNcQCv2ddx/Fa5YGeGZrDTu3v8HKVZef13YKH11iKr0gnGOZmZlUDZ0+stmIkfT0+MWMnE4ngbqDHOuf+FhXAFSm0RGFQCBA1eYXyerfPSqfaLNLRU9aJVmF0aVEbVZISDDT1GHCecoA1BSLRKqljaYTuwiFoqNOVar4QK5GM0xz/UGUw5vItMZPj00zZDA8DMPDsHEjHDsW5IUXfsmLL/6V2toqwuFJRIAFQfjYue+LP+brvy3CO8Y3JEmC7/9OwfXr0nC44BePpvG1+2zccgWU5kNmyiAGcxmBmMHwaUlunM7oR5gQKfEnDUVTfuiMGXG5Ro06L4FAALPZFvccNBnA6YrU1KhC+MfIpywIwplrb21h5oEn48qeVU5h+eXX4Xa5sP/pv0mJ6cO0eSHri7+eVFC0sa6aGb5DcWU73RlMXzj52SqHt77KdOriz+tUknXRp9Ceg9FIU6feTVdXdDsvD5y+XrzBMIUm2N0ceQ6eyu6GYwMacguiI2VNllw8MZOMzHoYcsTPFAqGgvTYwe0Boz4SUA2FQrS31NF69AWS2UNekhfVGPHeziE5Ta4iLAWXk18yVyyWKQjCeaPVJ+ILwR13PMDGndGvR+kpMNi3D8/wkfPYOuGjTgRGBeEckiSJl779VRw9w/gniPl5QzCcWkpiYnTVzo7mRjqf+gXLNYMMucE5QQq+7XYTMxdfEFfmcjqpf+spyuUto/KJHnObsS27iSnlC+nqM8Ydl54YRKnS4gnY6OyPH6Kg10BJdi8tda8yNGTnePURutsG8Md0wi0WP0nyGsyn9JX7nDJk1kXs3Gli82aorIR16+Daax1cckkDPt/TvPDC3wkETp9rUBCEj5eMjAy++u0X+fQPZvL3500MOcDjjeQD/fQ3YcBhpLXHws8egtvu+RnDzuiK8Dnp4HYP0N4d7cLYrNDWcnRkW2csiLueWecaedZYLDaGHTH7TDA42I9Wq8XpjkkdIo8uZmI0QP9AB4IgvHevfeWTTDdGI3/NHpj3oyci+370Xyw1xMxKkeDgtKuYMn3mac8bCoXoePl3JMb0R1qdMqZe81+TXuixvuoAeX3b0MS8IfV6ZKjm34w1KXn8AyfJ6/XSX7ufIzvic3IWFrqoHnBgVkFYoeL1Ewqq+6DXCe1DsLUZajwmVq3MoLfr+MhxOp0ehzt+epBe7ScQE1g16SHBJJFoAq0qREtDFR3VL5CiOEBOkg/VGD9Nu11Ji6eU5KIryCueJWb6CIJw3mk0Grx+FQa9nn01GXH7Kor68Lvb4z6SC8KZEIFRQThHvF4vP129kOtfeZjP6eH/HY0EJU8VkuD/DWTx6e/8ZKSsavtGVK8/TLEmEnG8wAqvnoBOd/yx3hBs7DViq1hOSkp0RFRPeysD2x6nzOCIrx+EalkBJatuRK83IJfLScuaQ+dAfBaN7LQgHi9YrRk0tKviOtQKQIGL2v1vkBKoZn76MP0xs8usVrC74oObjf1atOkXozMm4/WGWLcuUu9dKhVUVPhZtOgEb7+9foJfVRCEj6uSKaU88uQ+Cuc9z6e/k8j3fw/9g/Dbb0Fhjo9Vi+B798NQfwOdA9a4Y4f7dmF3xpe5h6pG/jslLQ97zOMwOTFMf19kIRa1Wo3THX3JV8rA42wHIER8sEIKR6bjapUQ9A++xzsWBOHwru1c0vNOXNlbWRcwtXI2O195jtX2vXH7XvGnsOazXxv3fA6Hg1fW/5PHfvtj/vC9L+McHsQdnUVPU+oKUtLSxz0+Vld7C/qqZ0mIiQG6AtBfuJas/LHzmU5WOBym4cg+ejf9jak0MU1dSG8v9PTAP/4BmzfDvv5qnm48TNvgCXLylFhKoc8M7hTImgaVlamoFHISTV3YB7pHzq0zZuOPGSRqMUv09Uf7ZVoVOFwBWlo76Ws+TKa2iuzEwKiAaEiCVruKVl85KSWXkVM4HZVKhSAIwoeFQplACCif+xUGo1mRuGSpxKFDe9iz443z1jbho03kGBWE90iSJB77w29o+vl3+Lp8EIUcpmvgWi/csQ8uy4K1J2e9vz6s4zlZHp/+5R+YVjmTQCDAwecep8JZjzZm2phRAdMMSg6pytjT34ci7COMHLkhicqLLiAzM2ukbsORPSR27SbllHyiA14ZQxkLKS+PH2WhUqkwJ83FPrwTqykSAVXKIDUpyJBLSX5uOs1tdmwWB0YtdPeDawDmRi+Jc0gBGdFeuN3rJxsNniB0urLILp2LSqXijTf+zcqV7nFzZaWkhIFmXC4XBoNh7EqCIHxsyWQylixdSU/7A1w955sj5eWFXjJSJEBGf9fbWJJKge0j+1XUI1evAaJ5RzSK6IhOnU5Hx5AGqyky/V0lB8dgPWnpkVQgQRKA3pH6oUDka49KnQJEV8nWaSWCUuQZqVYG8Pt8qDUTrKwnCMK4JEli/zdv5vaYEZ37HHIuffhxBvr60Dz9v5hiugK1bjmV33xw3Jye2ze+Qf32DVxg7SMrAUiApmH4Zy3MSIagUs+CG2+bVNscw8M4tjxCsSk63ScQhuPG2cyatehsbndET2c7A4dep1DnRnXy/tLNeh5/SUMQH1dfDWYzQBhwMjjo5N//7OeC1YWUTYt8rOnsBIdjmIGglowULc6ho/h8FjQaDSazhZ5ODSkJkeedHFDKA0ioCIbCdLT3ogwOkmMNwxhdrYAEXXYVMmMZ6VOK4hbdFARB+DDRaI143H2sW7uWJx78MjddHvkSplGDWVPDsP0wcNX5baTwkST+5ROE9+jX3/06ur/+km9p4vPPFSqhwGrjBVUi+7LnIAMWr72Sh6+4CoVCwZDdzoln/8RclSMyLDPG/qCFohvvoTTBMu51A4EAtTteoyjcjOaUaewtLhW6GZeSn5455rEGgxGfbxpu/xH0J0dGaJSgVXtxuuTkZ1nptWtxuPpoa5OYnRp/fMijgJgsfWFlgJ5hOWHzHArK8kbKvd4uTPEDsEaZOnWQ48cPM2vWwokrCoLwsTV30a20t3yTzJNrgiycCQeqnMyZbkItVWHLuJ1AaPtIDrzsVAdubQn+4G7UJ3syGTYvQ4ODJFgsAPilJCAaLA3520f+W6lJITYwqla4ADBbUnA5j2M4+Ux9N8+oxahAqw4zNNyDzZb9fvwEgvCx99a/H+M6GuPKqhbeway0NJ67/3quMkSHevpC0LbyXi7IzR/zXEcO7MG+51luzR6OK88zw+1l8HQDJC9YO6kRj4FAgPqXHmSmyR9XfiCQzdzLzv4F2+1207jndXLD7aM+XnuDYfp74fNfAvkp8/csFrjjLjcPPdTA3XfPQKWSIZOBzSIRDHk40eon3Wagq20fwXAijsEBgqEAUqZESpIWGWA0hqir78CkHCY3YezlOgNh6LRrUFimkjm1EPmpDREEQfiQUalUuMNGwEnLwCxg98i+pbOcVHU1MDhox2KxjnsOQRiL+BdQEN6Dne9sw/bwz/j0KUHREwHYV5DFt1dkU16Ux//86g/86C+Pc+nV16JQKGg+foyBp3/NTFX81HdPCPaYplBxy/2YJwiKjuQTlTXH5RMFqHabSV52E7ZxgqLvSkxMY8iTRyAmF6pZBz6/G29AwmbVIVOmokGG/JQRn+pQ/ItGgiWMKvUC0jLy4srl8vgFAMai04HP5zptPUEQPr6yc3LYfdQ8sq3TQm19ZIX4nNR+0jPyaWqLPogyU8A53ElrV/T7rtUEba3RxPtaXV7cNfSa4ZEF30zmDHwx020TTEHcbjdarRaH55Q8o97IlFS9HvyeAQRBOHPBYJDB334BQ8yQjFeHtFz7nd/w9uMPsTZ4Iq7+y6oiVn7y7nHPt/vNDaxNHR5zn0wGV+ZD0/Fjp22XJEkceOFhZpri+2MHHGZmXn7XpHOTxgqFQtQd2MHQ1kcpl7djPGUYii8EL9QPs+aK4Kig6LuUSli82Mu+fZEPOAoFBEOgUkBhZojGlmG6W9oxu45QkdDGDEs3ro4+du3ppKa2F0dnOyVJQ6SPERQNhCIpj/pkc8medjmZ2cUiKCoIwkeGVm/FF4JrP/EDqhui5XMqoP74O+zZ+eb5a5zwkSX+FRSEs1R7+BD9N67kZm0wrvyAD9or8llbHskBerWuj5f+FVl9VZIkDr71EuZtT5CviT+u2yejqewS5l5164QjHHo6WukfJ59oFQUUn8wnOhlpGUV09cd/UbMlQP+AC18wzGCvE71ydKfaqtTgjVm9OTlZwuPxjqoXCp1+ymlfnxKrNfW09QRB+HgbDs6PL5AiQY/iXKg7fpCewfiFTxwDuxl2J8aV+RzVI/+dlFoYt8p8SmKIgYHI1HuzOYHB4WjAw6iFQXtk5fqQFL9AXTgUeVarZKBQ+PGJ1ekF4Yw9+4vvcLVxaGQ7EAbPjd+lv7uLnM0PxX3k3e9Ssvwbvx83KNnb20uK1Ddumh6IBBCNvp7TLsRx4M1nmKlqjSurc6gpWveZs8qv2dnaxIk3/k6BYz/p+tEfhxucanqyV+HVyCkunvjj8bRpYerqIs8shQLCJ5eqb+kAtR/mZUnYDBIKWeR+CxLDzE4NMGT3YNKP7rv5gtDYr8ZOPolZc0nPzD+rwK8gCML5pNFo8fqVFBfl8daO+D5bfnorQwMHRz6EC8JkicCoIJyFfRvfov/yOVx6SnBzi0+GfEExS/OjwcZivUR7XTU+n489T/6R8tbtWE/pa9f4NIQvuZeyBUsnvG7DkT2oql4kxxDfmR7wyuhMWUT50jVnlBtKJpORljWD9t7oXHynO4R/wMHQ0TZyQk58/tHHJegV9EVnoWIygdvdN6peWtpU2tomfszU1iZSVFQ26TafS4FAAI/HM7JatSAI509a/s1x26X5AVzuEAoFdLe+DuryuP06+QkU2vgFUXSqzpH/NpvN9A5EH7ZaFdj7I0ML5HI5noA+7lifuwt4N89olFYjjSQOUchDuByjn3WCIIxveGiIlBd+Gjf75GmvjUvvvZ/DP76PYn30BXY4CIHrv0Fi8vgrwA8PD5OoGP0x9lQWhW/CwGj13m1McR5AFdNN6XDLMS27E5PJPO5xY3E6HFRtfJqE+pcoNnhQnBJv7PXKqNFOJWf17WQXliKTSRMGduHdKfaRAKdMBj39MsJAdw8UJ419jEoBU23QFM0igvdkQHRIlo0tu4wUmwUZp//9BEEQPqzkSkukb2a6hlDMa/GapQGaGg5hH+gf71BBGJMIjArCGXr7qcfQ3nEhC1XxQdENXjlZy0uZkRafVDMYBn8wSO2jP2deqC2uAx4Mwx5s5HzyS6Rn54x7zUAgQNWWDWT178aqjR8F0OJSEZp2BfmnLLI0WSqVisS0OTQ0B2k8MgyNw+TLA6ToQK8Etz+yUmksuRxcw/Fz+IPBDk41Y8YCdu5MY7z3kj179OTkzP/AE/0PDw9TU3WAEzU76WrazYmandRU7Wd4yH76gwVBeF8sXHolh2qi25VlsGNfZISZzH+IlKzFBGIeu7npLnSmQjwxH2+yUv0M9Ec7w75g/Ij4kC86MkySxY82lZ9cdMmUkII75pxmIzidkV63UQvBgJhOLwhn4pmv38uKhOgHyMEAZH/lz7z+h5+wRt0bV/etpLnMv/iyCc9nMBjocgQnrAMwGNJgNBrH3NfaWEdy46sYYj5UD/nBU341aZmTzyMcCASo2bMV947HKVd2oz+lO+MJQpXfhmb+zZTOXTkyCtVgsGE/TZejqwsslsgHHIUCjEmrOVxnIlU38XEmDbi94PFDQ6+KQVk2+UWFpCQb8fsjv5taFRQfhQVB+MjSao14fHDHHQ+wcWf0K1N6CniGdrN3j5hOL5wZERgVhDPw/C9+TNH/3Eq5Kj5S+KRPybyLplJgHd1b3exQM03jo0Ljjit3BOFwykxm3/wF9BOsyO5ynSaf6NLT5xMdTyAQoLWuit4Dr5HjdZCvDWFUx9fJNUBN1+hjJW98Y2Sy0aOoNBoNl1xyG2+9lc+2bQaGhsDjgcZGJS++mIxSuYLKygVn1faz1d/XS2fzQQqT+piS5iU/2c+UNC+Fyf10txyht2eMmxUE4X1nNps51pgxsi2XQ0dnJAiZlthFdnYB9S3R505KEgzbG2mLyTNq0kNn2+GRbYU2PsChUUQjEVpDetw+ncZDMBhEp9Mx7IrPM+o+mWdUpwHCATGdXhAmqaXxBPMO/zuu7FnNVJIzs5l17Lm4UaRbXHou/tovJzzf8OAgtY/8L/bhINLYawoBkTyaTk3KmIFRe38fwV2PY9NFT+ALQ7NtKYVn8JG5rbGO5jf/Rqn3MCm6+GmbYQnqnFoGCi6hfNX1mM3xI1CXLr2SjRstE57/7bc1LF0aeSYqFNBZtQOF3YlxEjP8w5IMrzaVwuJ05PLo80ypDBMAtErweiZOMyAIgvBhpVKpCIUNGPR69lZnxO2bXtxHX+c+8fFHOCNiVXpBmARJknjyK5/nkmd/T9IpfzU/cau579JSTNrRf07+ELw9pOb7K+OnZrb5FHjmXcGsGbNHyux2O9UHdxF0DCAjTBglcr2FdF8TZeb4qfPeIDQo85my6qKzGm3pcjrpaTiMztVBti4M44w+cAXBIckJ6XTsbvNTnBTg3dhv2KsBosOqdDonkiSNyldlNBq54oq76e7u4uDBvQQCAZKTM7jkkllnlb/rvQgEAnS11TI1wzdqCptKDkWpPo511GGxJn3gbRMEAUKaC4DHR7YN2sjCbFMLw9TXV2MfTgGi0+Vd9j3ITba4Mp+rGlgJgDWpEG9gH9qTf862xADDw8OYzWYsllSGh8B8cka91SThcAxhtSadzDM6OHLOd/OMnmwlLkcfGs3ZfZAShP8kb/73J7nTGA1ANnpg9k//Ssdv/4vSmG/CXT5I+vRP0ev1Y5wlormuhsGnfsoynQ+rCV4+AZcWjq4nSfB8l4UlV189ap/H46HttT9RYY4fcXqYEuYsuXhS9zQ8ZKdtz+sUKPrQjjEgtcsjZzh5OvkL543bl7DZbCiVC9izZzNz53pG7X/nHR06XRIWS+RrtVwO6Yph3BoFvtOva4lCqcRqjuR516pDBMISKrkMgw5c7hAqvYJwyAF89FZuDgaD9Pf1MmTvRUIiwZJMsi31A599JAjC+aXRJeIPuZg27ysMDt+P5eT3p0uWSvzgL1uxD/SRkpo+8UkE4STxL4ggnEY4HObhW67mpp3Po48ZJBmU4OGAnqXTSnioVc7d+WCO+Yvq8cEfe/TctWxG3KqjR3x60q+4i6yUtJGylsYGWg+9w6wEB7qYPqrd18++IUjRwMn+LXavDHvGAsrLZ53RfUiSRF9vF87GoyRJdvI1jBsQ7fWCU6kiKVdHoV5B5wDocq00NLUj90gggdKgwuWCdwe7JieHGBwcxGodu5OdmppGauq6M2rzudbV2UZ6gnvcvF4yGWQleunsaCEnd4y3LUEQ3ldF0+7A538czcmR63Omhejs9pOeqqat6nXMCdOIDYLqVU0o9Qviygzq7pGPNImJSbTVKshJjU6Fb+xuwGyeiU6nY6BDhVkfGVGgUoBjoAOrNQmV2kZsYFSjjuQZVQAqVYhQeBAQgVFBmMj+dzazdmAXxKzDuDH3IqwvP8FVhvjRinuK13LZ3IXjnuvA269ge+cJZpwc5VlhBMcgPHIEVudB9sksRo3DMjYO2ph54XUUT4nPXx4KhTj64p+Ya46fwbPHlczMa2467UJEgUCA+n1bsDmOM1U7eriqKwhNZJC36ELSxpnCH+uyy25m40YTf/rTZiore0lMlOjvl3HkiIpMZS6L1kbTfcjlkVGoKXo4OgjpE6RA9QZBFhOQNRugd8iPzaJBAQSDkaeZQu4jHA5/pFaktw/00d5ynBSTl/ykyCjdQUcvx482kZ5VQmKy7Ty3UBAmx+N24xwaQgoHQSZHq9OjMxrFwIwzoNVqGbIrWbd2LU88+GVuujzywUujBqv+GLt2vs5lV9x2nlspfFSIwKggTMDv9/PIpcu4q3EXyph+oysMT8kSuKeyALlMxvGAjj+aZiP1tiL3uXF7PaRZLXzmomJshsgbQSAEBzXZTL/hDjSa6FuC2+2m8eA7LEtyjArYWTWwNAO2dcIFmdDqVqKbsY6CM5g6HwgE6GppQOquJVXtxaYep14YurwqpMR8wiYX+WnRF4f0RGjt9pJoU5CfH/lHR5IUtLbKMRgiHVO9Hrq7W8cNjH7QwuEwLpcTl6OfkG8AecjJ0JCfrNOkDkvQQWfP4AfSRkEQ4s2Zt4x3/qVg+bxIIDM3E57bOMCVl6QRdO0hveLHeP1voD35HCvM9NInz8HlAcPJDz3Z6UF6e7tJSUlDLpfj9pmB6BR6n6cJiEyX9YeNcftCgW6gAlNCKh5PHbqT10kwgcsVwmxQYNSDwx2ZTh/7LBcEIUqSJI5+51ZmxfyJ7HEoyL77dua88XOIefd/xZvIJV/81pjnCYVCbHn098zt2oVRG7/PrFGz4NrPcbSpgU3tJwBIy5vCjasuQacb/eV3/ytPMlsXn9O0yqFl2pWfnnC0oSRJtNZXE6p/hzKjH05pR1CCBreehIpVlGeOny/+VF6vl+LcKWTrNDQNv0k4HMRsVnLnnVpq3rYQu6iyXA7hsIRKDgoJepyQMkbsVZLgWI8Ka1YqEB1aqpAFCaBBBahVYQJSJHjg83nR6cYfpfthMjw8RHdbNVMzvHEpGGxmiSSTm7rOahQKJQkfkn6oIIwlEAhg7+tB5nOjlsIoFaBVyPE43fQ5hrHaUtCO8fwSxiZXWAjTR1NvJbB3pHzZbCd7mt4hEPikCDYLkyICo4IwDsfwME+trORTw40Q0wHrC8FrWht3l2TjkeApfQY3/+0Z1Go11Tu3Yjn8KumndJrtAWjOX8yclWtGjUioOXyA6cbRQdF3aRSQYYBddj3TV18/YT7SWG63m+6Gw2gdbWRqw8jH+TfWHYTukBFtZhmZmbnI5XL8fj9dXe+QlhidapaWEqKlKzpCQiYDh0MDRKeA+XytwPRJte9ckiQJr9eLY6iPgKcfWciBRuHFpAWTipEXsCEnp10FVhCE80epVNLcWwJUj5QNDQ0CaSSZWsnKzqNmh5JpxZFnkzUB6puOE1KoKc2PpPbQa6Ch9SApKZcAIFdnEhv8VMmj+ZAVSlvcPqXMAYBOp6O7T4lOHTx5DPR5gpgNClRyCAYDuJ1iOr0gxAqFQrz24vO88dQj9LU2kt/TQks25JzsfxyedxsVL/0Ga0w35oRHxtSvPjjmi6vT4WDPH77HMjpQnPLGsjWQRMXnv4vFaqVs5uxRx4bDYRpqj9PWWIdMJkMKeFkYqiEm3SbNTiVpqz81ZhD1XfaBPjr3vUaRehD1GIHIdrcCd/osihbPRqFQjK4QQ5IkhgYHGeyoRzbchVXmIVsDaMEZtjJzYbQ/5dcPEQ5HA3wqFbQF9XiNxRSV5HH44G6GfL3kWkB98rLDXqgbkJOUpkZrSCMQakd1cp/FDINDARITVBi1YHcFsRqVeDzDH5nAaHtzPUVp8UHRd8llUJDqo76tngTr3A++cYIwSX1dnQRcDtSyMEP+IP5wiDAyTHotyXro7+0mKT1TBPMmSasz4nb3cf1NP6K64SLKTk74m1MBz256m76+dtLT885rG4WPBhEYFYQxHK+uZvvaudyrdMWVNwXgsCWDm/LSaAvCK1ml3P2HxwkGg+x59lGmDR1Hd0pQtNGnRL7sBiqnTB3zWi57F/+fvfcOdyQtz7x/FVXK0sm5z+ncPd3T3ZNzYBjyAAP4I4MNBu86YGxwWFjWXi829q6N1+AAtsEmL2AyYzLD5NjT0zn3OX1y0lGWKr/fH6WTQ3dPHkb3demSVCqVSiXVW897P/dzP+lzVFytT8BjUtd5kaIzU5PkBw7Q4M2sWS4/Y0rk9RYaNl5M35Lsuq7rxBouJVt6aG7fNAmWVlvZtsFCYlSWMzwTsCyLYiGLXZlGuAV0qUIsJGjRgTWOpaFB0YS4sfo6JQtCxvmRz3XUUcdTD6PhNhYSo60NVYQQXLTRZXCwn2y5DRiee72a24uSaAfmO8671ZNAQIwmUhtx/ENotfGrIWlTrVYJh8NE4504/om51xLReSWou8xn1GG2JliSfISXp15OX0cdAc6cPs2fvvN1vM47yf9JVlFTMGLAZ4bABy5rNoi5Za6IznuTOz6cuvqdvGTjpmXbGxnoZ+LzH+XmsLlouenB/ckdXP/rH1yVOBjsP8Ojd97BplCWyyMWAjiRgzsKcF0HtIZh2gTpsjfT2Nyy4jYsy+LMo3fSVjnN9hVihoINQ1o362+4lc41iFXHcchOjVOe6EevZGjUHXpVlqlOtUqShfFUotXEXdI3pHXLpXR0BGUvbZ1hYlE4PAC4YNmQShpsvzhEWJMZnzlFxkvQlg42IgOesBFoSBCU76Iu+sznIizLolzMUy5OIXlZtDWS25oEqmximiaGsUagV0cdzxKy2Rn8ch7Ns6k6LilF4Pgujg+Vgs2ZskpXQ4p8Nksyna6To+cBTdOo+BE2bezlk1+PsG3DfMXjxvZBHrj3R7zuV37jWdzDOp4vqBOjddSxBPsfeZj+11/Hr4UWR6SHbJhu6+XV7Q08bEuc3LaLX/3LT1Es5Dn9jX/hcjUXGNAtwD4nwfo3vHfVsh7HcTCLhTXJPABFBt9d3W3fcRwmhwdwxo7Sppk0qKx4drsCJqoqTrqXtm3baVgjcIzFEmTMHVTtQ3PlpLXQem4dIRaXoUajZTzPO6dq4kLgOA6lUhGzNIlv51CpEtVdmgzgAkUO7Q1wZhy2da2+zmjWoGdT75PZ5TrqqONJYPeVv8b0zP+mqWavd/UeweETVXZsifDQoZ8Qi+1iITEaDw2hx25mITEaC0/Peec1NrUwNSDT0RTUpaZjcHZ8gHV920gkU+TGJJpTgRo+GYXxmUna2rtRtCaW+oz6BKNgxBAI3Ho5fR11APl8nj9726v4VOLYIi/2TgP+xyb46TT8q7uOf3NPLoqT/pMeXv1rv71sewfuvZPEnZ/lkiWd3idtOHPxa3nRbb+y6r6MDg9x8Off4LUtuUXKwkub4eJG+P4AXNkCuQ0vZfvGrcveL4Rg4NgBlLMPsi3qLiMwHR9OV+M07r6Vi9pWbuphmiZTIwO404NEvTwNhqBFYs2YpSvSQC43TioVPO9ZJ9i/32Fd7zwxIsTChlHTNDXDrKXm0aMxtnbPr9vW4NI/ZuOkpDkyMRWHQsklGVMJG0GDUE11cRznWSdgZit/SsUsTjWDcItomER0jwYDNJVFXrWrIaK52LZdJ0breE7AcRwcy8I1q3iWSb5YIOTZ5Kan+cl9D1EuFWhr7+Dy7ZuIhg0cX2XA8WhsAtfKYwE+MsgyQlaRFBVUFVkNoWgaqqqiquo5/ZF/2REKN2D7FUT09XjeF5idhr78BofP/vAHmLe9A8Oo2xPUsTbqxGgddSzA4/feTf6tL+K1ocUk5P0mGL0buakxwfcclcPd6/nd//VJxgZO4/7iK+wJLe5uanpwML6J3a9564rBphCCgSMH4MRDhB0H2wd9De/7qapMQ0fbsuXVapXxU4cIFQZpC3soq4z5VRfG3QhG5zbaOnvPm7hsbGpnZDBHS2p4TlG1EJFIiEIBErUmAI2NPtnsDE1P0Pze8zzK5TKV4lTgC+qXiWgOsTCkDZZNUM6Fig1lU8YmjKQlCYWbiDVWGMycpbvBWVZWPzSjEk521gPqOup4FtHbu54ffS7MbTcHSqZkHH5w3zQ7tvRQzT3Alh0fpVy9Y85TdEO3xYRop1AOmowAdLe5jI+P0NHRjaZpFKtRoDj3GdXSGWAbmqZRtULAvCrNrIwA3SSSrVStU4RrQ3gyDqWaz2g0BDNFF0fUy+nrqONzn/gbPqgtJkUX4sVN8K0T/XjeNmZnrAfKKtf8xT8tmtD7vs+9X/lXdg/eRWLJZfhYVUV57fu56uI9a+7Lo3f9iJc351Yst9ZkeFkPfGMozrrGAvd898uAwJVDtPdtoaGxicz+n7DRKKKtUDgyVFZwu69ky/bdi/ZbCEEhlyM3ehoKY6RmS+RX8XSf+74CMpZESYqjtnSTHz5DKhWMRZoGplkBkguOTxBrOo5DNLpY6ZlIbGEsM0x747wit7OlxOh4mHXt6tz3t2wbUAlrkC25pOMqJbOCpiV5puD7PpVKhUpxBsfMInlFNMUkpovAB38FAllVwHaXL18K25Pr3enreMbhOA6u6+KaVXzTBMdG9jw0fDRqfQ8cl+zoWf790/9IZWQAe3IIw7c5KeDLboi+K2/mQ//1VykLj1xOIZSIocgSiuyjIqPhIvtATbfjAL4faL49ZIQsIyQFZAVJ0ZBUDUXXUFQNWZafdPLDcRzyuRl824RamljSdBLJhmc9QWwYYQpZlXf/+gf4+Xe/yK3XBcnu9hbwyvczPTVMV/fyyoQ66liI+pWjjjpquPsbXyP5gTdzo75YofDDqsTGLVtoi0X4lhbjVVd1YVVSPPbzH7JtYi8NS64FU5bE5EUv5vJrblrxc8aHBsnv/RnrlQqaBoYBJ2fgoqbV9+2kGeeqBaX42ZkpcqcPkfKm6QuJVVUIWUsipzSR2rCTvoY1PmANdHRvpf/kNOs7TCQWzzRCIUEmo5JIBNGqYcDIyOB5EaNCCKrVKqX8NI6ZQfZKgS9oGBI655xQLIXtQsGUsL0QQkmgR5qINTTQvKTErRGYHDc4Mn6WuG4SUlwsT6Foh2ls7qbtAhpb1VFHHU8dqpUK2cw0tllmbGYrsG/uNdcOSM2UcYaurh4O3qNy8eZg3EnE4Njpgzh6iO19FhDYZkydfXyu7BSlnYXEqMLk3GOPNAu72gs/UMFHIhEmMyrh9HKfUQg6O6tSvZy+jjqO3/UD3neOype3Ntt8+9QMb9veTNmDwms+wMWt8wnfaqXCA//0Ua53zwbqwAW4z0qy9b/+Txqb144tCoUCcTuzYiJ3FmEVopLJJcpZorViHiFgaHCS/Y8Lbupi2fuzFoyH17P+ppsJ1RKnQQOVCcrj/WiVaZp0h3UrlMgvhe3BlKViG2mMlvU0tXXQXCMspvftBY7PrRuNWoveO6sYzeezpFLznu+FAjQ2duK6zRQqD5GoxYS6AolIlaoVJRwKvlQyLiiZHjFDAYJyes8tspCAfSrhui6VSoVqKYNrZZG9MrpiEjOgReWcFVOzCGtgusFvtZo4TgioOCEikeeHZ2odzz/4vo9j2ziWhW9VEJaD5Dmowg9mSK6HLwTC97GFT9X1QAgk3+PkmTN87d8/RWLkMH1Whvf0ekRrY53tm3zu+Hf5rfcf4h//78fJFGVc1cdBDspUaqpRSVJQFCl4LoEkySiyhCz76MhosovsERT32UAlIFBdnzn1qZBnyVMVVA1Z1c+pPjWrVXLTE6Q1h9ACftXxbbKTJrGGZiLR8zyZnyZISoqo7vLgoRZuvW5ibvnuLdPc9fPv8pZ3/P4LXllbx9qoE6N11AF8/x/+lu3/5/dZvySZ9rWKwg07tlFVdR5tbeb2TY0ArPPyhE7eR0Pj4gj4hKUTe8k7uGhd37LPyGezjDz4E3qdKdoWnHltOpwpwFgZ2ldQKBzMGzSv34GiKIyePY01epQ2tULfKuXynoDxqoKd7KFt6w76nmRnQ0mS6Oi5nJGxe5GXEKOuU8KsqATBdQDHGQGWN0IIfEFnsMs1X1ClSjwkaNE478B47jN8KFXBdHV8JYYaaiCaaKKhNYq81Ah1BbS0tdPS1k6xWMS2bcKaRnc8Xr9g1vGchOM4jI+NYJbKhMJh2jq70PULzBw8x1EqFshNT1AuFdDx0WO3sJAYXd9lYVk+W9ebjI+Pk690AQNzr1vFfajJ3kXLfOvU3ONoYj0+J5gdHVKxCrZto+s6utHGQmLUUMsIIZAkCUdEgfz8Nj2X2XpOTfUIh716OX0dL3joTvWcycx1Ybi/FBB9P47v4vZX/39zr42PDDH42f/Fi4wyLLiE2x7cE9nMde/74/M6xzKZDGm5cs71Wgwf2/GI1joTSRL0xARxFR6dhKtrfK3tw2krSdslL2VbUzOmaTJ0+hhuZpComycdErTInNPWp2hD1jPw4q2ketbT0dC0YryRSOzB847PlYF2drqUyz7RaHBQPC+ItSqVIZoW5LrzeY3u7qCsZry4nrB/Zo7cTcdhaKJCR3sMBQipkC86xAyFSBiqjkBmMQH7RDFnfVTO4Ns5ZL+CodrEDEgsaIR5Qdv0oGQG8Z6mqQxmTNY1+SuuO5pTaWzufnJfoo46anAcB9s08cwqwraQXBfZd5F8ge/7uL6H6/t4rge+hyR8ZAGS8FERRCSQfIHl+5iOw4++/03asgN0exne2bO4MlGX4T2dPt2ZM/yvj3+CD//xH1IoFjEUBVmWUGQZVZFRFBnJkfCQcJEQgIOMJEtUZQWkYF1JkhALyNNgGxIhGVR8FN8JRJ8L1KeeH3Cp/grq01K1TLPhzp3CVU8wXajywIOPkJkaJ93aya0vfxWpdMNTaqd2ITDCMUrVaS6+6o/I5n+fdC3X89LrBH/6z/9B5Q3vJRqNPyv7VsfzA3VitI4XPL7yoQ9wy1c+HhB0C/DZqs7rd23lMBqNO7q5qWk+8rWBlDIfvXsCHvOb2P7m9xCNLWb5zGqV/gd/QVvuDNs1FgX9EPh+psJhHiuG0AsmW+I2huKTdzWG3QRtfVsJyz7jD36H1rCLuooawXRh3AmjtW+lZWfvU+oXZRgGHu2Y9ulFy0Ma+P7iMlSJySA4LuYxS9P4Tg6NKpFZX9AL7GvkiyAorjgqHlFkPU0k3kSsIUb6SX7HeLx+gazj2cPMzAzHDx9E+D59mwOvu2OPP4ZUzaMIH19WcTWDqKawTjFpVQQVT+Kxo/txVIOG1jYaW9pobGp61v3hngwcx2Fs+CyqW6HbEERUCF91Cyf6/5rNtRzTFRfDA48XuP7KFL84/FPC8d0sJEET4RH0+CsWLUtFs7iui6qqNLd0kRmF5lTwWmMKRieG6epeTzLdTrW6j3CNd0nHfYrFIolEAkVrZiExqus+ApCAWBQsx8Nx6uX0dbywkS1Xz3ltHzGhOaJzf9ngxX/+d3PLjzxyP/oPPsUV4cVEQcaG49tezotuf+s5k5aWZXH03p/iHH0YP3TuemvTk9DV5dtMG0HMYbowWpHJJjax4fJLyY2epnDywfkS+XOQfItK5Bu7aezso+c8VIzd3VsYGZHo6REUCmCacPp0mauuitdUksF387zJRe9z3dTc49a29QwPTNHdPK+Q72gVjE+YdLYGAWQ04mE6PoYmM1NyiEelC25YZNs2pUIOq5LBd/IoVIloLjED0iHOyw90KSw3iPdsL4SvRFH0VBDvNUZIaxrtwNDgaU6OD9PdaGPUfgPThZEZHS3SQWe96qeOC4TjOLiOg2NWEJaF5NhInovwfTzfx3LdgPz0PGTho+AjC4EiBJovkGsEqeP6lC2T4clphkZGqI4OIzLjONkZSrksw8UClalhPnzx6j0jXtbo88+P/pz/eN+juIqGo2jYqoGrRfAiCZRkM+nmVlo7OuntaqelqYlYLIaiqsiSjKLKuMhIckCWCgKCVJJBkmUsSUaSgtcBBBIgIckSshyoT3UZFHxU4YIXJHbiyvyQN1N1+cyn/4lHfvAVXts6w+64z8mCxHv/uoFrXvfrvO9D/+tZiUk1TaNSivCaV72KL/7DH/K21wTjZUiH5thBJicH6Ovb+YzvVx3PH9SJ0TpesBBC8Nl3vYk33v01YgvOBE/Av9ph3rF7Cz+WQtxwxTrSxuJT5URV5Yp4II8oeXCsaReXvOL1i7JkjuMw8PgjRIYPsE3zVgyiT1saw1qKja0yLzF8PF/j8JTDdFmhqauXHqqkSydpMFYvl89bMKM0kOy9mN5Vuqs+FWjr3MTpk8MsIkEVQVRarKhKJC0yAz+nKSmTDgMXKFit2FAyFRzCyFqSUKSJeHuaxPOY+KnjySObzXLs0Udwchkk38dXNVJdPWzZtft54QnrOA6ZTAZN01AUhe/826eI5YbZSRZJgvvv0Oi3FDa3N4MeIh4OcU1nAtOReDSrYLQlSagKCVXQFnKYtFxOjQ5iF3JMjcXYuG0H4SepDl8LrusiRFC66Xkevu8H956L73t4no9wPfCDyQTCD8yvhA/4AVOAAB8kArVPEI77FEyHlGbTvCCn1NaQ4u77U2zuywGgqjAwmOH6K1MUp+5m681/RqH0bRK192zscRhxGpjJQ0NNJdDV6jE2OkR3Tx+hUIhcKUJzKlCTyUCpcBpYTzQaZXxKJhwK9svQYTo3SiKRIJFooWov9hkt1nxGNRlypouiFqiX09fxQsXX//xP6D57hmMR2LpG9cfXpnV+89pGiu/6c+LxOEII7vuPL3DRiR8FscICnKyquK/4r1xz2VUrbstxHKqlEsV8jqH9D9E4fpTdYRcRh2/PrL2/voCiKxNdxdi9Jw6PTGtc2ZvCLs1QPPgTeuOcZ4m8gm00YLT00dTWOVcif77QdZ3+/gSPP54nHoemJhCiyLe/bdLVZdDQ4OL7PuFwecn75ptASZJES8cepqbvozkZEDAyIEsOh48LFAVCIRk9LNPZHEbCR5WhahVXvZZWq1VKxdySpkguqRDITyDOA6jaULIlHM9AKDE0o4FIvIF0a2TNyp/ung2Uy20MDZ/BsyuBQk4N076uj1js2S3lreO5Ddd1cV0Xp1rFtwIfUFwbx/VwXAffdcHzA/JTePheUBLveS4lyyRfLDM6Nsb46CjW2AjW9ARWNoMoFtDNEiHLJOzaGJ5DxHdpxieNT6sCHSqYAv5OW90KYhavavK5JJxjd2LJCz6QhcIUFA9AwYWiCxMeVIVE1ZeoChlTKFgomJKKKeu4moFnxJHjaUINLSSb20i3dZJsbiOZTJCIRYlF40SjEVRVwZGk2jko1USlEqoskbUDlerH/uwjXDb+Lf7g+nnl9tXtgndsyfAv9/w1H3zvGf73p76AruvPeCVeKNyA41c4OrQNODi3/MbLytzxnS/yG7/10ee1kKCOpxd1YrSOFyQcx+Gzr7mFd524Z5GXVNWHL4gEb9y1gR9HErzykk7UJfHZqQo0xSNoCoxYMpXLXs1ley6fe10IwdDJYziH72WDaiOvMP6OOQrTTRtx3CrXJ6vIUnBx0WS4tF3DF7B/epCGeK3p0BL4AiZMGTPeReuWi+l7mv2UbNvGNKuUi4vLI1QFUrEw2Syka15dTU1wqN+iLXXuSNmq+YIGgXECPdpIvLGRlucB0VXH0wvHcdj7wP3s++mPcLMZXM9DNsLcvL6LnakYkgvTx6e4d3CAq17+qufshKhQKPDlf/w78icep0MUMX04lLW5tjXM6zY1zAXIm6IOlufw+YkxbtvchqK6fO+Uw6s2NHJD0uOu8QIvWpeeayjSEhLMuDbNqslo2eP0kUOs33YRQgg8z8PzPPBcPN9DeEGZ1xxh6dXUCr4PiMD/StSIS6jpC8SccUbNzWrusTS3TMw1mJYQCKhRnmKOBxWzj2sQQpA3bYrVKkrtAywfUg06i9pVA6a1C7hr7nlIDciAiHqMUHWa00Mae7YFdWDRMGSGHsMJhWlIBk1JNBUyQ4/T3RPITj2agbNz25O8oHxekiRMJwYU5l5zrAlgK5FolMkZhXA6OGa6DJkFPqMIj4jhYpnmnPdgHXU8H+A4DsVcDrNaQUIimkwSi8fPy44GApLh3976Wt706B3cEocPHodP7Vm5keT9OUglEhzc/BJec+1NWJbFvZ/6C24wT6EtKcF/yIzR++v/g9aOlZMNgcJ8kInHHyQ1cYKrIu4cMSdJ0KXB0RnY1rDyfu+dVtjatrq8VZOhJWGgq7A5CY9NQfcqxSUlG2Y8Ay/eQqpnw6ol8ueLwcF+slmTV74yaL40D4djxzwOHNhHQ8NG0ul5QqJSgWSya9F2QqEQangHVWc/CDjVDw0GbE65aAqULBjJOJyuOPR0J6hYPsIvI0QTlXKZcmkGp5pF8kpocpVoaPWmSOeDkgkVW8HBQFLiaJEGYg0NNBnGEzpe0WiUTVvqyq9fRmSzWe74+tc4fHA/pmnR29PDnquv4bJrrj1v71ghxFwZvG9W8SwLxzRxHAtcN4iHPJeq5VCuVLBMi0I2w/DYOJnxUczRYarTU0jFLHq5iGFVMRyLiO8Q81168EhJgnYF2hUILxzzVrE5A5hxa5YS50BChfLqolISanBbPELOxnA+C+3NFsEBJmq3Q7NJIih6Ack66QafW/ZlKkKmioopazhaGD8URYkmyUohGHqEt1y/sp3Fe7Z7vOeu73B63/1093TjyzqSbqAZMTRdf9obo802YXrnr/8NR069hO0bg+WX7oCv/+wbVKt/jKaln9Z9qOP5izoxWscLDuVymS/fehm/MXWMhZaZWQ++pzdy8/oevmS0saGnYU4hBcEFZG9e4ogT5m3b0xy2wrTc9m462+Yz9VNjo0w/8jM2UEBf4eKXdWA81UvvVS9idN/DXByZWbFzqizBjkY4MA2XLphrWx6M2wZy62badm54yrJejuNgmSZmtYRTLuHbJbBNZN9GxcGQBWEVQu7ii62sCMIhhfEZhXSNONA0sP0ySyUEjrfAF1RNoBlBSXxTW7Tu7VnHIpTLZf7tr/6CjUPHeIUGXTEDRYcxt8q3DxZ4pKWNN23qokmRudbM8OBPf8LNr7197v2zakYhAh+opbdZ8lAIge95wc33AtN830P4HsINlDl4AaEovNpyz6uRiz5C+AjPRdSeM3cvwPfIF0r8y79/mtT4UTzHYUKWedn6Rt69LsmD+RKf2Gvyvks75sjRkAJvb3X56pkM79zZyi2yzV2DeV7al6Jbd9k7XqQ9HiKiKkQ0mV4DDmaKdIUkLDPL2H1D+JKELykIWQ68oWQVSQluqiqjK4FP1UrUh1QjRpe+NhtuS4CQAvJB1NabI0rlxeTpClWqzFRtjk5kUSQfXYZkTKMjFdRbDpdc8qZOb2q+/rKn8xW47l3MxtEXb3WZybls6qkgmSWKpWZgdG59O/8I5dgGgh6ttX23T+E4DpqmYcTWs5AYjYfLc6/JSiMLiVFlgQre8aOLXlvoMxo2BIosKFUyhIy6arSO5weyMzOUMlOkPJskwVS6WM4zKGu0dHUTia5dF5+dmeFbL7uSd8+cYpZH/QMD3r0X3toLL2kK4pgZGz47pjCpJLjl8h3c/IGPMjU5zql//p/cEiosGmwcH+7Re7n6g/99RfV7tVqlVCpx/N4f0zpxgssj7opE3fYo3JExmPEElzVa1HoOUXXh3nGVWDTM1pbVE7fTpkxLQzDoyBKEleC9YXVhiXwMpbGLpo719JzjWJ0vhBA8+uj3ec1rLFay6Nu61WdmZpRTp/Zx+XwunmxWpbNzeWIw3dDC4EAr+akJtjQFjZhmEQvBlpBgvFjh1CmXsBE0bXFyp4gZ4oKaIi2EK6AyZ30UVP3o4QZiLSla6j7MdZwDP/jWN/nUn/0PxHA/e2SbCHCfp3LHZxu44hW38Wvvez/rN29ZNGdwHAfHsnCqFcxyGatcolIqUSyXMEslzGKBXCbD5MQEM6PDlCfGIJtBL+UxzAphxyRaIzxb8NiCoF0NCE9jYTD0FEy3mhUYLJ97vcMluOYZ4O5kCZJacFuMWqUPLkGVYM2Ww4M/PQMfvGbt7f7RLpvf/b3f4Qtf/iqS4qB5VVQ7jyWggopQDRQjSsgIPz3qTSXJ5o29fOJrEbZvnPec3tw5yOjIKRKJy9d4cx0vZNSJ0TpeUJienOQHN+/kPfZif6YhFx6Nt9Pb2clwey9XvPYt2PEk//K9/0ccGwBXUtjRGuctTVH2ah1c/MZ3YdSC92KhwNADP6OnOsq2Fc6qqg/9aiPtN97KtoZGTNNEsfJoa4gqNTlQXlTdgFTMyGnivTvoaW67YCLR84IGIWa5jF0t4FllsCvIno3iOxiyj6FAbGEwvtK1SpJwnHklg6KCb4JV1ghaIAbQDZNCdTY4jiEbKaKxZmKN0SftC1rHLzdyuRzvevGNaGdPMi65/FTAcXRa29v5k2t28RtR+MzUON8NhXhTbythGVIzoxz+t38gjsCbJUDFgse+wPUDFaXnenOKSt/38HyB8F08VyBEjQSdKwUXSMJHEvP3CoG6UkagiGBer9YUlkpNQakSrP9nP/gZ14eyvLvdpTkUTPy/PVLk1w6H+PMbN2B6Ej8bzPHidam5728ooLhVfnJinHjYYNSuciyh0ZuKsLdg0hUxMR2olKDiQcqXKVYlfGSQJRRZRVcVDE0hqsqENSU4lT2wzaCZiC2Ce1cEZVEe4KPgQWDWr8ggyUiygiwraDUyVZVrPlU1P6o55agkIUkSihQ8nlWe6jWPKxAcGpkmrFhc1uQTUgLueKLqsHfQYltrlL64wlDZZqoi0xwJxoir9lzJo4ckrtodbG/rerjjniyvfHEzP3rwQUJGHwuJ0cbUDKp6IzA+vyyRJ3/2MELRUGyfbAnStQl/c4NPJjNJW1sn4VgHPv1zPE3MMOdIU0VvYiExushn1IBsyUPUu9PX8RyFEIJisUg5nwcZVC2En83QjTuXHFaABgniwmF0cICmnl6iqxB+Jw4e4NjrruNdFBcll9sVuK4tSS6V4P0DOSQEBTTaW9v4rU1hnN//BANHD+J/5xNcHV6cZM058Pi6G7j6de9A+D6lYrFmyyGoVspkJsfJHD9AW+4s10W8FQnRigeH3TihrZezXdUonj7Ij8/OIMs+CJAlmVg0zsalhvIL4IugHH7HAn8lXQn8RuVYI0ZLL0YshT18FlEqMXjiAEJSaeropqm59UkleU+fPsHGjdkVSdFZXHqpw3e/+ziXXz6vELXteTmrEGK+XNhxqFYlepKLSdGFaItDbsqmMwnaBfZLmW+KpOFLUWQ9iRFrJJZO1K2P6rhg3H/PPfyf9/0X/pIprkgtfMXloDXKh7/+Jf6hUOD17/p1nGIOM5fDLubJZbKMjo6QGR+F6Un0QhbDLBNxTKKuTVy4pITHJXJN4alCaOFp+jT/VX0BUx6MejDpS0xYgmkbmlZpVmf78FBJJV6IEso5GLiEJR9D8onIgqgiiCmBYjReuzeewV5HeQEd50iabExBd+kwP/2dPYyFeum88hXsuO7ltLZ31PxOTRS7gFOSqSIjFB1JixIKR9BDoSctlgmH45SrGTLOrXjed+bG1Ffc6PDpL/4dH/rTf6uX09exIqSFirhz4LxXrKOO5yLOnDjOvpfs5vWauWj5URsGm9dRbGnH2rSD+FU30NbWSs+pu2kLLf7b5xwY6LmKXS9+FZIkYVkWZx6+m6bpkzRry08RR8AZN0Li0ptoau9iZnKcysgZ3Pw0qB6bUmvv8+kcmNE2ei+6hGh09SuREALLsqhWqziVAm61hHAqSK6J6rtouBhqoHx4Mjhb8GjZWWBWzFEoQGg6xWBuiqa+HMePg+NAuSyzYcON9PVtftrLJur45cH09DTvumoPn3CH6V0QswgB367K/Luc5i9ecj3pWIQfVeDSUKBMVKndS0GMO/tcW7BMOUec5fpg+j5Vz8f0BabnY7o+tudh+x6u5+N6tQ6kNbXorPo08NIUyLPkqRB8+cRp3tOa4drU8s8qOPDbpwz+6WVb+dhwmD+5thdtwQ4+lIOkOu/XV3Jh0oYZR6YoFCw1hGqESccTNCZjtMYMwivMaoOSqKDRiCUkfKRal1EJRVEIKQqGphLVZcKKvKJ63RXBOGb7gYdeQKYGhv0egKSABLIsMVmsMlMuE1Z8JAksTyIZiSOFQvQlHBpXqDR3fNg3LbOrK4EmSxzNK1zUOs96fOlbb+Ctrx6Ze/65b8d45xs38/2f7+TKi9+KGvn3uc6jpg2PH38D63sfpSU9v/3hoRvoa+sA4HTmATZ0O3PbO3Cig7bW7XiyhqE8SrrGMTgCcvYNNDe3US6VUJwH5hp92B5YToR4JDjmYxkFRTao2AbphiaSyXqZ1i8jLMuimM3i16wojHicSCTynL7GlQoFJgbPEnVMEngIYMaXsJHpiIQIr3DS2wJGfJlUMjUvCQ8cN3jg+9+i9WO/y2Xa4jLKkzYc6evgNRe3Ldveg1n4kb6R666/iYtH7g1KshegvyozsfOVbNmxe5HqHCBvVhk4tJeO/CAbIivXlpY9OO7FiW/aiVspEytn6DZWLvO0fbgzB9d2QWzJvNgX8OCEQm9LnI4F9a77cgbbr3kpoVCIoYF+7Mwg3YY9Rza6AqZMhbyaZtP2i5d1ZHYch0qlgvB9jHB4VR/PX/zie1x66YOcqyfkt78d4bWv3TT3/NjeCA1KBFnyURCoMnO3U3nY1rH29mYqYEnQvor1AIBda4pkLWyKFGskHInWCYY6nhK8/ord/Lfx/Vy2irD4qA1/UNC5JmmQ9mwSvksKjyZZ0KZA21LC82mGLwJvzzEXpoREBpWcFqIUSSCa24lt2Exy205aL9pF36bNdHd3c+LwAb74V3/CqQd+zhe2WUSWhG2uD79+Js47/uE/eNGtL1n2mbNjydjYGKdOHGfw9CmmhwYoTo3h5aeQKwVUu4zmmGiehe47aL5LCBedgFwN4ROWxdwtrkBcnS/PX8V6eQ6/ewr+70vX9kn1Bfz+PfB/b5hfNlaGhyYVTvutNOy+latf+jq61/Vi+7PNoQJfU0lWEHIISTdQw1E0TX9CY0w+O4IiMjzwvd3ceu38/Px//mMz7/vIIdLpp68nRx3PSZzX6PDcjebqqOMpxI++9hWUD7yN1y8Jlh82odS9geGWddxw4y42NYYpmcd4YN8xhjRoW3CBHrBUxHVvYPe2nXieR//Bx9DP7GWbvnJjpX5bw11/CZFoDHPoFNmTj9KoCVolKKtw9tyNU7HlEOu2B6TorM+nVS7imiV8qwyOieLb6LgYcnBxmythlXjKMqG2B6ZHYMrtMEeMqio4ns/whE0RuPhiiESCUubBwbu58879XHbZS0mn14i463jBolqtcubYMYYffRTr5Em+9PUv86+MsFTQI0lwe8SnUprhT+7fzwcu305/UdCpu5ieh+162K6L6/vYro/ne3i1bqK+7+N7PpLwgmjN95HxUYWP5AsUEXQX1SSBKoJOnIoEqggUoUL4QWU8AiFqNwRCUHsOLHkt7/jE3OKKpCgEHlPvbTP5+vFpUpEWjh07zoSrYCo6IcNgXIqxuzHMbJ1pTA1u6+fKmxygBNUp7BJMOTDtSJSEiqvoKKEwRjRKIh6jORam2dCRpNnAcDG54NhQrQYBvulLuMh4SIFiVFFwPZ+jQ8MUi3lkSaKnuZmrt/RiqLMRfXB8Hpoo0mjYXNrpLyJYJyoZHprW2dWYXPFYaDJsSvoMzphsag6j4jGaLSIkBVmSsf3LgHlitCERlEXp+gDNqTiPntK5bGeg6jd0ENYJJqZCtKStue2PZ/uJJ5owVAXbigK5ue3pao4WzcbDZqSskI7XLEEkyIwdBccmZIQplyWMdE0Jq0Am784Ro5rqEQHCuklhepj8zDTtXb11wuCXBL7vk5mcRK5WSbous/URlWqVKU0j3dr6rDWAcxwH3/dxHAfPtvEsC99xEJ6HY5pUykXWKT7agnMyqggc4TFQrtIdDWMsIUd1CXThkyzOW/0IIfh/f/dxrv/h5+ha8rf+uSkRv3wDr+la2ikkwFVp+NbZca4Zu5fwElL00XKIppvexKZ0I6JSJqxrSJKE7TgcP7qf5Ew/16+iEC17cMSNIXVtJWwVaZoZIK2xapOkURPy6FzSFmXfVBld9uhLeCgyTFZlxqsKm1sji0hRxweMNKFQiJnpabyZs2yIOou2q0rQHvYwnAxnTh7DCEcol4oI38N3XXRFEMIB3ydbG4Y1VUbGR/KDDteS8LAyw5yPVmUh72pZ0K4oJI2VSWPpPOxiwxrk56tNFzRFCiGU+FxTpNQ5miLVUccTxfj4OGLwDJetkRTYpsM6YfNB1V7RquypgreA8JwUEjMo5NQQpXACt6Wd6MattO++jPYdu+jbuIkdra3o+iryzyXYecnlvOfPPs6n/+ZjvPqH3+bWWIU3NLsoEvwgq/JDu5nf/Nt/XpEUhaDrejKZJJlMsnXr1nN+3qxoplAokJmaIjc5Tm56gtzkBOO5DNVCHruUxS3mcSslHLOKsCoojonm2Sieg+bXbvhowiPrWDw45nL1GgmXnw3Bte2Ll7VH4bV9HjAKxc8x88XPcdeExHErhVh3NTe99m1cvGM7jidj2yauWcAtypiyjKToyLqBEgpUpecTW+lGA6qo8MCBBm69NjO3fM+WaU4c38eVV730nNuo44WHumL0CcJxHIQQKIqyLDv8fIdlWdi2jSRJhEKh5/Xkrlwq8cXf+69c9qMvcumSLORPqhJO32ayW3bwxuu2LGuy9EAWmnXYGIXHnTjrbn8P6YZGRvpPUXn8bvrU6oo+epOOzFComVQ4RANV0qukHx4pwmVtq2fdhIC7JzQ2dzSgCgdDERgqiyY4TxUcAaY7S4xo+LIOuoGsx9CiMYxwjFDNJL9Q+CYNNZ7TtuHRu2zWbSvS2bVcoeE4cPfdKa699vXPi87hdTx9qFarHNm/n4mHHsI9cwYjn8fP5fCKRbIzMwxMTzNZHOETzatfajwB75qAP2kMSj8VKbiXFzxebdkzib8vwos2wfY1gnwh4M374aqWEA0Rg0Q0SmcqxuaGMDFdYbAKg5ZMCQ1ZN0jHomxoiNAUVs/Z0XQWvoAZB6ZsKAkVS9YRWgg9HCEWi9EYj9IYDaGtJBUFHjo7yZHRYW5qN+lLBPt8Kg8/HTXo6+qjt7UFJImMAy0xe1UFfNaE4yWDq3pXPyAPjEv0NIQp+RrrEvJcadj+YydY3/vbxGvq2akZKFjbcX2DjpaPs+/kHdxwxTxxeu8jKaKxrezZOV9Ov/+owa6uW3GAUxMTbNsyPPfa4LhMe/xaNEmiP3eWvu55hqB/yKAr0Yflw4ydoad9nhAZmVLorClbbQ+cQniuy3XFhWkzQte6vjqR8DyGEIJCLkd2YgLNdUEIhCwTVxQSioJEkKLIaBqNnZ0rxkqBZUdwbZxVmItZr+NZD2Mhat7Fwb0365HsebVGIT74AYkmAXLtXgHkmo+yBhi1fZpFv2nSpXqrxgyWgEFPIqWp1DqlMRvqm6KmvhJgOw7f+uP38+6zjxBZ8nf+Akmmt2/h99atnen96oTCVZvXsS4REAiegJ+baVJXv5JEWCOpBs3bpqo+maEzrKuMsX4NhehRN0Yl2UYjFhsMb7EX4AI4AgZNGV8z6ElFCNUuBrYPI1WfgbKLK3wualZoj2uLxlZPwGPTGrFkM0ZII1eusKPBXTMGOzwD65OBWvNCY7XBqRyToUNcdsXqx7JahV/8IsXLX74OgLExibZcatVrwtHcuRWjuSqMVCTisRCaESOW2kgsFqt7v9fxtMP3fQ7t3ctXPvlJIv/5RT6SXptq+EwBrgjBzidgV+sJGJ9VePoSM5JCTtEpGTGsxjZCG7bQfdmVrL/8Krp61tHa2vq0zn+z2Sw//tGPeOAH30ZXVa575e3cdvvrnrXzznEcLMuiUipRymUoF/K45TxepYRbrVCtVshkczx47Aynf/YFvvESa+UqIx/e/EP4zC2QuIDfqWjDgxMSx8pRsuntXPmqt3PLDdeiqipVL4izAtsnGSGrKHoY1Yigh2OEVim/L2T7+eLnP8Obr/vzucoiy4aP/NMN/Mlf/ieRSKQ+zr1wUFeMPh3ITk8zMzGBallIQuDIMlo8Tmt3N6FQaL4bMEFm5/l0wpmmSXZyEqdaRqoF8q4ko0eitLS3P68IUiEEe3/0n+z96//Ji049wqYlg/M3qgrmhou4+GXX8YrO1IrbuCIF352EbNMGdr/57RRzOY5+/8v0+Vk6VzgUBRdOOyF6UjEuNXwWNgBZCa1aQDKsRiYMFCVSEY2obJ9XF8O14ImgA7zpg42KJ+ugGcihKKoRIxSNYxhh4ueR9XTdeQ9B3xc4amVFUhQCL9Jdu3IcO/Y4u3df9eS+RB1PGFNTUwwNDRGPx9m4cePTPi6Nj49zz09+wtmf/Qzr2DHkyUn0YpFYtUrMskh7Ho1C0AW01t7zDeCGc1g0KhKkFOhT1y7jebYx7kPPGv7BEOx/ewje32EBFpDHKcLpCRgwJbK+iqOGMMJhmhIxOvwoblXjQUsm56v4aoh4NEJ3MkpPIoSyAjEgS4GPVeBl5dZuFfCykIdSBoZsyPsKVTQ8NYRqGBiRGDnbo1Ac4de2OIv2eVMKNiZNvnTyFLLvcHlHI2OewsaVBaEApA1wcg62K9BXyiYBhiro1CsUbZiekXBQUVWFdev6eOBxlZdcF5AFzQ3wix9neP0rO7n74X0ItYuFitLGhgKyaGOhz2hTo4nluoRUlc5EipI5TKyWp2lp9BkbyRAOR7CtUHB8atANG00OVKclK0RAg9VeUz0KZZjJBXa0jm2SjumkoioRFeJqhcz0BLF4ClVVn9brp+ME+/V8ukY/1yGEYHxoCKNYpNv3mU1/e75PwfcZ8zzadR0NSDkOmcFBIpJU8yMO6s5nT0mfeVLUnxUj+IFPLbNkJ7WmZQSCR5m1Cz4c32ewVMJ2bQx8fKCCTEjVSek6nhAokr8mOReqKeNTvrN8vdrz6ZkZfvq+d/E79sTiRkkCPtu0mWv/8E85+NW/O8fRhLAssGtxZdGFR4zNtF51DVujoMsC03E5euoMqcI4F0f8FRWiJQ8OmQbVaAO9MYk9RmXVpFfZgyFLIRKN0Ndq4AnIOFB0FFxJQw3pxFIKF6WgaDkMVioIWdASCX6fiYrESElmQ9KgSa/i+VUs6dxkZ2MYyg40PYE8cHdTkkceC7NrT5HVTuX771PZvXu+BNQsSIuuhY4I+v+5flDiD0EH+tgaBMVkRaKhMUZ7c5R8WUUI+3k1d6nj+YdsNssvvvQl9n3966T27uX6cpnT5+FAoxCMpwvh1gjPURemhUQOhZysUQpFMFPNKL2baN1zORuvuoZNW7ayq6XlOXGtTKfTvPFNb+KNb3rTs70rQBA/aJpGLBajpW25Jcosrhsf57eP7uO37j3ARy9zaFwQ605W4COP6ngtG/iHjEyoMkmDKNBpWFzZCok1pphxHW7tFtxKCXiY6gMPc9+34UjRYCK0ju5rX8cbX/8aYpEIYGO6NnY+TyUHxZpPqWrEMKJxND0QdQlivO4N7+SO//dXvO01QQwZ0qEtuZfC5GlmCGPE4jQ2ttST2HUAdWJ0TZimSS6bRfg+sUSCYjaLPDFBr+exUCNqWhb9+TyptjZk10WtBX+2ohAKh4mn089JDyohBMVCgdzkJJ7nYjsOEVmiZUEnPkf45Mt5hk9X6Nrw1HVBfzox0t/P6X/9vxz52Q95zcQJ2pfs8r9XNcqXXs+Om65mZ+Pq2UlFAj1ksPHFt3Pyrh/QVRoKGistGTstH06bCi3pBHuMtX/nkgtTrowrqZT1EFHF4VDGZkPSJ1x7a9WFMwUZXddZnwqRMzkvYtT0oOqBI1Q8WcVXDWQtghKOoUUShMNh0k+BqfVCYrS/32LzVmfN9Zua4PDhIaBOjD7TOLRvH3/3vveROnaM7eUy2XCYoz093Piud/G23/mdJ7TNQqHA8NAQ48ePkz14kKl9+6gcOYIxPk7KNGlyXdqF4MXAhTgtepzfBen5oM9vkWHEhC1rGNSLmm/nQmhy4Cu6NSYISLhaybwzxfggnCjDpKtQkXWUkIGIRRmPRxnXwxTRsRSdSDhCWzLKhpSBsQoJOYvZEn3wGC5U+NqBMaqux2UdDfRrKd67Z+X3SxK8cYPDZ06OoOkwbCoUWtMkV5NtAR1hj0fPZmmOhUjHQjRG5pWvszatEATHcX3++5suDI72AGfmtlWt5pHlTiqlg1y94zVMZh6ipTF4bUOPz2OPVxibhvamYFl7M5w+OcWm9nYS4RD9MwqxjppPpAaWk6UnFUYVBpYHodqfLBn3GZooEzV0dEnHdJjzGU3EYWLMpzMho8kgEBRMi4EJh86GEHFDZiQ3jernkGUZWV7QnktWkSQNWQ2hqPoTIk5936dYzFOtFGtts8BDIWTEiMcTz8mY4/mEzNQU0WKRhL/4JFUIxjXF9xm3bSKyjA8I38cFqBGjs2Rn0IwtuH+i0ZMAbCGwfB/X97E8j/FSic2qS3TRz+wz7blMl23ajPCq5NpC6FIw9q606uEjhxn877/NryqLfdkzHnzv8lfwoje9jUNf+BfGqs58hmsVDFgKN0d0hkx4vOlSGno30q17+J5g37EzpErj7AmvRYiG8KNpNrbJNauVlWO3KQemPZ2meJSGqEpZyAx5GnFDpzmu0rbCkNYS0uiJJRmvWByZCSbPTYbGZa36HPHqi/OrPFClYN0nAoHE1eu38P1vHuZlr67OWRZBMD7u3atQKMZob59/wXIaGU/2oqg6iqajajqqpqEpCmFVxXAcThy8l+3NYllVFEDeAklXSKegbHp4PiheGWh8Yl+ijjpWged5PHL33dz/pS+R+8EPuGR8nA/7PrMpxzvMc20B7jLhuBblO5EW6FxHYuceeq+8lp27dnPJunX1694zgLa2Nt75mx/gy5/5JO996DjNSpmo4lP2ZIpSDNG8gdvf+KtsXdeGV5zm7Nkhjpw9xsPj/aj5YRpEnq6QyRUtYhGpuhRhFW7shBsxgeM4Qx/j0Q9/jMN5nWFaMLa+iDe+9a30dbYT0OUmlmViVaYxBbiomL5MQ0ziwJk+4OTctm+8rMzjex/i5TdeRdE2GRsxae/sqZOjddSJ0ZVg2zb9R44gF4s0mCYyMK2q5IH1ur6sdN4ANjgOp0ZH2ZxKzU/cXZeKZTFj2zS0tj6nBuxqtcrwmVPEbZN2PFSgDEz7EjOKSkfNCEqToEkCQziMDQ7S3tMDgKqqz7mMsmmaPPTv/8yWIw/y0M/u4jfMURILDrkv4FOWgfrat9DXt54mvbL6xmqISILsj7/E9qi07GzxBJwxZYxIjO3tq6fjpx0oejKqptMYC9FXi06PVWFTg0rB0jk1Y+K6PkgSiiLR22SQCCkUF3COth+Uu1u+hCvr+IqOpEeQQ1H0SAIjEiURCj3t1g7eguo20/TO2SgAQJbPw1C1jqcUP/re9/jKW97Cp0sl5v6d1SrMzPC1D32Ij504wX/75Cfn1hdCkMlkGB4cZPLIEYpHj2KeOoXT3480Okp0ZoZG06RDCNYB25/i/b0U+HoJrlkjUBICZnwYdoPJvEdwHvq1+2XLmH/NR8KXauotpNpzCTF7LwX3SBJCkhGzywie+3KwrpBkfEnCl4PO7b4kI2Q5eI8ig6zgJQSfHj7Lx7eu/r9/KAeXrGzJtyLaQrOexx6BGr0KZKnm4XgZpi2ZAipZ1SATjrDfiKKG4+hGGCMSqE43psKklrQvna7YvPeb9xDxctzQ5pIA/uM+lVE3TpO0mzfsWdmgXlMgKtnsarS4SlO449g0L93aRHwVclSRoCvq0ROtYDoVhiYkKp4STOhDIVL6ytdHQ4XG8BUsJEa7W008T+DJAzionB0J0dIYeIrqGnheP1NTIdqbgmWyBDPlAcZycXwUSpbOQkW/K+WBDpKGxnReoqUhYDZiBhRFiQY1hu1DrgR6GnJFsEywXAsI/rASkDQgovsMZE16W8KoqkQ64uKIwMvV8YL/IshIkoziyai+jGcGZc2+ryBQQVaQJL1GnGrLiFPf95meHCeilmmNsYBgdqk4FpmpKg1Nzw1VzPMRQgiq+TxN/lJt0jwSQN73SdQUn08WTo34dITArZXZS37QzE0DdCBKQLAeN6tsVV3CK5xqTQr4wmXKtghJGgVfzLkKC1/UvJJnaUVBxRPIMvNl9CIg+e/94X+y5fN/w8v1xSzfEUdi7+2/QdixCH/mr7ldhb+1g4qZxCohbskFSw5xytZpuPhqbmtpIGuWefzQaXrdDHsiYvY0Wvw+Dw5VQ4hokp3tKrFVtu8JGDIl8pJBJBYjHAohhXTShkrLef44mgzdsRDdq0grVSlQYZ4LRQeiKmSt2nWndi0SSDg+iMBZFJDwkfFlBVVWkFQFIesYvTu4qvcq7rzz5wgxQSxWwHEEpZLM1q1RNm+el+Y7DrR07KChoXnV/SmV8nT0Co4OQlcMUkYwXjgejBWhgsrGDWFUCSaLDrG4gutZeJ73S2cTVsezg8nJSX7yz//M/m98g75jx7jdNOlbso4GtNhwyoaNq6gKR1woJRq4+Z/+nZe86rane7frWAOvesObSKRSPP7ow5zY/xBlu4qkaOzYupurr72eq266dc4+7VLHITM9yXj/CazMEIXMFCdPnuDRs0dgaIBGP0uXUeXyZp/26OqfqSlwdRtc3WYDw3je5znwt5/nc1mNQS+F1bqbF9/+Fq69fBeaohJUSAWWb5dc+4ccOfUetm+ER/bDT++En973fv7101t573t/g6uuvprcTIaGptXH0jpeGKh7jC6B4zgc27uXjcXisjjNAU5JEp3hMIkVAoYhaqqA2ixF1DIPHuDPKkZqk21mJ+AsOLCyvPggL8hcSJI01xxUgkCRIEnz4sUFv+NsGddqEL5P1jRZv4r31LQPjqLRbiy+Og26EF5YrynBnHxSDgK/uSc14xFJloPnCkjIyJKEJEsgy8hS0IUOSQ6OhywhSUpgFi8pwXeW5do2asdAkhZldGbJ5gN33Yn4zhfZLqr8n+/+kA/KuUXdCS0Bn3Tj9N32Bl7d107Jg4MK3NC06mEC4I6Myks2tKAtmYAMmRKmFqYvHV3mM+r4QSMUU6gYhkZTRF/Rw+9oGTY2rl2a1V8ASVWJhVRcFGw5hKfG6N24+VkjpgcG/oPe3mCGcOyYSTJZpL197ffcdVcLN974K8/A3tUBQXf3d2zYwPcKhVUVlh/TNJz169lYLBLN5WisVukUgg7gCdg3XTAsYBgYAzKyTF5R+Lri8bUOf8UJP8DPqvB/Q13c+Lo3cMOWDfTGwuiKjCLXborMY7bCtsYEY2hsWyHA9kXQddkmKMEKCswlPAl8FHyJYExSFCRFQVFVFDnwkpZlORinFQVUDUk30CMRjFpn6oUTyQ/9+tv4lZNfZU90OTladuH1J1O8/T2/iRg8DMPHiedH6PSLbI2yKgFwoRiowOkqTHsqlhLCVsNU9ChSNEU6lcQIhfnHX9zHp68tLbP06M/D+x6M8I5XXMOvrEKO/ugsNBpgouKrUaaUZl6/yrqPTMDWBoivwNWZLkyYEkVHRdM1GpcoSkcmM4job9JVq+4yLThwZhN6KMG29Ae4b+AhXnT91Nz27nkkTiy8lT2755cdPKazs+kaAE7OTLNp28z8dx2W0b2t6IpKSZ6ir3v+N+sfiNAXDy4Ux/IlPF+QDkFYAcuDbEUiamh0N4XmroBFG0qOhq/odK4hvFqNMNVVGV0PlKiOD27tdd9XEShUHZ9YyCG2ygSy6kDVS5JubMZ1g+9SJ0nPH6ZpMnXyJJ5pzsVkhizTpGlz8R3AFBBjRU5vGRzA9X3MBU3hACTfRxUCnYD8XPgr+QRKUdv3sWrvc3yfEu6KY9sshIAjDmyubexCvC6FEHzpH/+eFz/wPdqWjEM/dTQyr383bcf3c6NqzS2f8OCfqvBHW4LzYiGqHnx0QKOpZz2//rqXo6oqx86OkbJm6AuvHKeWPDhY0VFjSXantWXx1yxMH/pNhaoep7MpRUNYX3Xdc8GZLT8nuC4sJTV9YMYRbEhBZJVTyRdwOKewtSWMKkvLCPNJM1BBxZccVwdw3KD6yBUSnpDxkHFQGM0eZteuGaLRWkL9mM7WrQE5Ojkp0dBw+5qii5GRe+nsrOI4MDEG2QyocgJZDRGKKPS2lufWLVmgqhGqpkYo2kMksgZLUUcda8BxHO75z//k3n/7N+R77uHKbJZbFtiLrISHgI+EJD7VJli/5BwbdeE3i2F2v/lX+fDH/65+PXuOwDRNjh05SDGfIxKOsH7LdtLptWvFhBCUSyXGhvrJDZ3ALEwxeHaY00f2YY8dp9GdZF2owp5Gn741LJpWwuEM7M+qDNkxcvH17Lj+Nm6+5RakWJK/+9+XMzlgc3Ur3LYlaKJ512mJz+8NEe25jL/423+lp2/LkzgadTzHcV6RUJ0YXYL+kydpHBhgNSGPA5xUVbaHl4fCZaAIrO7M8cQxKxwMgrRZ1RNzxOrsMhY8FrDIiC+Yd8kUfZ8WXRBf4y9y0oFUrTtowOlJWLV/gBHwmsyGfTXasrYsWC7VPrpGj85td6V9nlcvLH5t4VtF7YlAWrTuTCbD8B3f4FpzmoJp8rnv/oAPGNVFhtB5D/5eaeKdb30LXcn52tZvV+G2ztXLoyoe3F0K87K++UF+2oEp32B9Y2zOxB+g6sO0LeHKKjHDoCGsrll25QgYM0HSoHsVxaUj4ERe5aLW8Nz3PZUP/Pp8SQFZB1VFUnRQQqi6jqIbqJo+5xXzVKNSqTAx8Z/09QX/NtMUPPBAnptvXr2cfmoKRkcvZdeuein90w3f97nnxz/mH//0T7n94Yd50xrjexH4U+Bvnob9KBEkiiaArCxTUFVKmkYlFMJOJFCbm0l0d9Pb00Nvby+d6TTxUIjTY2N86C//B/8WLRJdEkEfsOHDVpJr3/qrfOCWa1ac7A+5EqYWYkMszAEHGlQlUHzKEkhKkJBRFHRVIaSq6HKQfBFIgdJUAo9A/YmsQEhHMww0I4IWCi0jP9eC4zj84TvfSO+Zu3lnPENKCybb38uG+A95Ex/90nfp7Vusm7Btm9OnTjGw9z5yBx/EHTiEMT1Ak5lhk+HR9RT1L8s5gcr0H0fhQ9fBloaV1+vPw/v3NfH137sVfYUB7esn4eXrmCPoshYcz6nYeoKW1lY2tKXQFAnbhwfGgrKo80FAlMoUHAVN02iKh7hn/3u4/aXzKv8vfb+BX3l1Lyf2vwUHnz1XPz732skByEzexOVXD8yNw+PTkLSvI6yrTJctYj1nmXU+KZngT/SRMDTOVEus3zA/nvUPyaTkdoqWC5JNzwox/1QZcpbGptb5H+jENBiGRkNSIRJSVmxSsBrORZhmitC2xoTB8WEk46PIEqrsIwR4qMTjjaTOMWl5ocN1Xc6ePEmkUKCpptaEYLyclCSaQ6G5xPgMQcVQhOXl7p7nIQiIT0UIVIKEU2jJunZtfX/Wc9TzghL82meHCMjS2f2YAoQOLedInpy0oVe7MFLUtGw+95E/5FcnjixKLAN8Xkrj7byc1zvTJFZgN75ZhUdUne0Rj6sSgUb1oaLCgK3zzp3t/JQOdm9Yd16EaDSe4OKG1ZnfrAPDrk5jUzMdqSj2AlLTE0GiS0gKHhLICr6sBOO5rIKqgqIG1wNVwUcFKYhjZeEhIZB9D2o3yffA95Hx8T2PsWKZrSkffcllwBdwOi/RlAyTNla+RoxUoCG0nDxeC8dyp9m6Jzf//JjG1q0pAM6cNmhqvolQKISu68uS5TMz48RiB1loHT8y0kVn5zYgIDW86uNEQ1CsQnYGTFtCUmQSyVZa23vOf0frqAMYHBjgjr//e85+5ztsOXuWVzkOa2nwCsD3FYUj69dz48c+xp4bb+S/vOqlaKePcqtqoSK429E5G07y+vd/kHe/7/11UvSXEI7jkJ2eYuTUYaqZYbJTkxw6dIDs6cdoMMfo0UvsbvTYeoHhy6kc7Mso7M0aHCpW+dqv+kSWXFqEgN//jka182X82cf+CklLkEg2EQo9ExKROp5B1JsvPRFUpqeXSfwXQgNCrstAqYQmB0pHWZJQZBlPlrFkGVuS0KTl2eIngyd8GVhKjHgeZYk1SVEIyrFc16FptQBuQTm1s+AjFhK3YoXb7C4trEgSC94bPK/tnLR8nVnq1XFdjtx9J9sGj3CLKnhsOs/en/2YP4gsJujGXPiX+Dr+4O1vIqwu/jKXavCjSXh5y/JGLo4HP5pRuWldQJGXPRiwNbrTcbbVDOgKLsy4MrKqkQqH6E6sfLBsv1ZKJiR8SUFRZAxNpT0tM5A3may6tCzh2W0PThYk1jXOD8wSoCrQtKiclvn6YXv+82wfKgI8oeDJclCeqaqg6EiKjqKHUPUQqhYYVJ+vNUI+P7zouWFIeF6UoaEC3d0rd6V/7LE4N964+5zbruPJoVqt8vV/+Ad2zszgnj3LredIesVZbmJ/PsgQKD2nJIm8LFNRVSqaRlXXMcNhzGQSpbGRxnXraO7pYWtbG+tTKaLnEWRs7OjgLz/0UX717z9OR36c6xQbU8CP3RDFdCu/95u/weW79nB/tUKf4tGuCjQJCh4M+DK6prMlFmbKA8WIoCcSNCjBqeEJ8OeIT3BlBVfVUHQdTTeCpkO6fkHk51rQNI2//fI3OXTwIH/1t3+BOTOJr2jc8q5f43Ove8OKn6HrOtu2b2fb9u3Ae+aWCyEYHx/n7gOPMfnYPVRP7EMZO0mqOE6PXGVzFPQLUEqltKCMPzazOikK0JeERgp89sExfvXK9kWepY4HJYdFqsV0CK5qdYEZnOoMJ/bBsGUwrrSz+dLrOGwVMKwZ2iLeEm/ExTBUWBebTf85mE6Fyal1wNG5dWSK6BqMZI9yzZYbGZ2EjppYtbcb7Mkqo5PQXfM+bGuC++8fpquplaihMTUt0d02XzJ/ploiYaQJC41s0WFwKOBGXNenxDRCM7i4a+WD3ByFkuVQNPU5KwFNhZ4mB8dzyJfAcSUkSUbVVGKGgrZG5kyTQFt0ugTHwhHgWIEHeLYYlOVKkoymyoQWKEzHMjYtMZ/Igm34wiFfGWV8rEJb+3ky1L9EcByHydFRCpOTSJ6HUBRS7e00Lek8PHb2LM2FAokl42cciApBv2VhahqSEJg1YrO8QPEZEgKp1mjJrak87ZqJblmIOcJTJ1CaXoCbxhyejlqR8alpfvLHv8NveJlFH2AJ+LtQBy/qbuMyb3qZx/qQC4/HE7ziynZep0oMFGz2T5YAuHZTlLelgj9hdqTMbimDtIK8tujCgYpOSzrB1c2rE6JDpsSEkiTU3IEaieGmG8imGtA0DaXmqfl02zzFqlVOHjtExC/TrDvIEuQcmYmKTHdKW5UUFQQJn/AKHqprY/XvIpsKkcwRHBfyAlwUfEkHNYSkh7AZoGHB+D41JdHSsnHuuWEYjGXiTE4WMWRoj4AWEzi+x3RpjP6TZbp6N9aJqDrWhG3b3PHFL/Lw5z5Hw759XF8s8l/P8Z77gHuTSYx3vpO3f+QjvLmxce68/Y8H91Iul7nzzjuZmpridy65hB07dtStHX6JoWkaLe0dtLR3zC27qVxm+MxJssMnKWfG+cWpk3z28ftJlvrp0QrsTLlc3MSaSeeNKdiY8tj/SJnPvIllpCgE8/+/ebXDyz5zJwndxAi5VIszzMyA64dQjTTxRKo+Dr5AUCdGF8BxHHT33F6IScAXgpaFZos12LVbiVr2eta3Tp4tI5dRJQldlgnJMvpTTKCeD/yar5QmS4vKwhbCkCB7nqzJhSgSgPOI6tcidARHTp6gct8vuFWxQIVvnh7Ge+Q+3hNZ/HuctOG763bxkde+chHxaQo46qkUmtoIh1S+MjLKrqjD+rDAB05UZM5YKtd3p4jpCscqCg2JOFvTGjN2ULql6jpNMZ3eJW72VRdKPjgiUCxoqkJYV0hGlBUVpBuTYUYrNvszLgndR5UEFTdQOfQ1GUSW1umfh25blxeSJLOsqbP46WokqiQjpKBMeI5E1QIlqqaH8P0JgpnR/J+jb10jp05ojIzk2bHDJhYLfEgHBxROnQyxrbuXybEhunuf/m7oL1QIIfjO5z7HFbkc4UqF+Kz66ALgEyg8h4GMJFFQFCxFwdF1LF3HDofxIhHUWAwjFsOPx3Gamkhs3MjGDRvoCIdpcBzSNZLgQpAFipKEp6qE+vr49098ikIuxw/37cN2HN5/0UV09fTgKgoZRaE5HmWkVCLjBG79YUVhUyJEWJHJ+jCCSlMsiq1qFIwQih78j/WQgaoHiupnymR9x86dfOyzX3lS25Akifb2dtrbXwkvfeWi10qlEoeOHmH4sXspHH4YMXiUyMwQbW6OrRFB4yo/xkAVtp+HndKVTTaFI3fz2YEIJT1NS2s7GzubOFGJsblh9RBCU+CiRrgIE+hn9Hg/pwoqOa2JU907SDa1EXWLxNwcLYZzTqK0L3U5C4nRbRscSmWPijeA6b+IwZEQHS3W3GdnrRGc6Qjdrfbce8JGhp5wEsc3Gc/L0DZ/zbCkMkKkmS64qNWAMJ5TlNoWJyYdBjMGPY0rE/wdSeifsdjaEV507mkKpCMQDN4evvComIFq1/UUVFUhaiiEdXlZgm7ZMa0RpvOE52LCtOJBrgxtaQgtOZ6yBOmoz0w5RyEfIZF84ShHK+Uyp/fvp6tcpoMgBPGBTD7PseFhNu3ejWEYOI6DtwIpCjUPUCGI+T55yyLm+8hC4AmBqBGhHgHhaRAQqU8UPjDhOIw7wf9ZkaBbM0irKjFg2GdNFRaACZR9EFItWS0WVOpINQW9BEKSOHLwIP7HPsjbNXvRNiZc+LTRwR9vaF2WeHEE/MhWybR08ZKNxty50pvQ6U0szrZMWJA2NA6VYOeCA1N04VBVp7MhwbUtKw9Ujg8DXgh6d9K9bRfNgKIoz9okNRwOc9GeyykWi2Qmx/F9Qbw1zXpVpTh+hvn6rsUYrUAkpDHjSlieh+uBoYCCh6ZASF45uSWJpYNCEHt5HkT8CCpBzjvgm+f9pyeLDi0b5uczvg+lKQNROIakGch6BC0coVTw6Yox1/wTAr/V9pig6uYZHDjB+o3b67FbHctw+OBBvv/Xf03pZz9jx/g4H/Y81ug3yThwh65z5tJLeePHP87v7dmDvkrCPBqN8qpXvepp2e86nh+IRqNs2bkbdu4G4AbHYXpqkuGThyhNDnJi+Cxfeeh+opkjdCkz7Eg6XNLMsiZzng9ZAa1rZCFlGa5fV+a97/1VXnXb67jpxhfR0pwCLBwxjpkdJ+fICMnAiDURi8XrjZp+SVEnRhdAkiSWU53L4bM6tzerGpjDrN/nEhN/h4CbqtQeO5IUkKizPqPyvG+eXiNQ5drn1lw75x7D+SlKZzyPCdsGyWNSEli1cqOOsE5CW5yJc2CZd+azjZlCkf0/+k+uqEwSVYLSqX95/CgbTz7OrZHFE5lHLTh+2S184Por55aNuzCq6jS1xukKh3i8BE3Y3LC1iZNZk/uKFooEvekwr4vrnDFlylKUeNqgLGn4kk5jWqe5dtBLLkxa4CAhKQqaqhINqzQp0jknuLMqW8+HtKET0XWmXZ+mCLSp0oqepJZ/fl1RLwSrk6jmYhK1DFVXEG81yWQW74Tv2rQ39dAasTiydwzLdUBIdDU28qKdKRRZYrKcI5fLndN7po4nhuHBQZicZIsQjCsKnQ0N/Gh6mreu0TwkD5xVVT6fTmOFQviRCNFwmFg4jBwKIUIhCIcJx2KkkkmkpiZSGzcSW7eOlBCE8nmilkWyViaKba/6WQvhADmgXPPqDOs6aU0jXTtpfILEEo2NvORlL8NTFBRdR9Y0UopCRFEoAWokQrlQIIJHAsGMgBlHxlNUehsS5FSNrvUbfumzvLFYjEsuv4JLLr9i0XLP8zh79iyP7XuIzP77sE7uRxk7SbOZYUPIDYiS82DPZeDlPT47GktAieHSEI/tk/Ecg4eVFPtSHUTTDbSkYtzSaxBe5XB3RKEj6gLjlAvjnBiUGHdDOOFGxlu7cfU4EoJW3aJjBUXp1dsuZv9R2BVUgbJ7G/zkoRwdbRoxZ4b8TJTAuba23+EMmC0EBc8B1Gigstdk0JygAZMAbBckzWTvcJaWFPSklhxjHS7p8jk8bjKtKzQtNQkk6GQvcJksFzEdBXyFkqkRDS0mPGUpUKjGDJgdZG0XcgUwXQWfoGQ+GVOXleuuhlnC1PagYi0nRRciGfEZz8+8YIhR13U5feAAW8vlRXGSDDQLQaJY5MTjj9O5YQP5yUmUQoER3wchkGvkp0pNDUrgKXqOBuxPChXf52ClTLvisUMLFPGWD/2OxzFHo7uxibJrY/vuqirxrA/x7l6i63qxLItqtYpbKuFUiviVCsKsgm2i2A73/uf32PXdz3CRtngwOGDDkaYe/kf3ciP2QzaMx5t4xYYmSgK+PwVv6V79O92ZVXntliS/yAfEaNGFE6bGuJbkhk59mecmBOsMqyniO69g43MwqRqPx4kv6TxpO530T43Roltz41fFhTEzaKwUUlRcwAjJNEXUOVGBI8BxPSYqHrJwkXwXSfioko/trfwj53OQjqzsreIAftJk4aVvcgK6wyoqZfDKUM1QKEJMWUyKLkRYhYgoMT4+RnNzy3OqgWwdzw4qlQpf/cQnOPbVr9J5/DivrlbZtsb6HvATSeLB1la2fehDvO5NbyLR0FBXf9ZxwdA0jfaOTto75iteXlkuM3DyGDODx5iYGOHPH3kA7+xeusQk2xMWl7cGgqWW88hUXtYluLjpAA2ZA9zxT3/BZDFOWeqmd+v1XH/TK9jY14EkVXC8QYqTYHsKQo4RSzYRiVxwKUAdz1HUPUaX4PAjj7A1l1u1YQnAMWADT6K8/QLg1G4W4EoSrqriqypC0xC6DqEQciiEZBioioKmKCAEwvMCFUPtPpfLIhdyrJPdRYSnI+C0K9ESDdOwgBztdyA630MJAUGzJKSaDf2s4+c8SSwvuJdWuM2+dqHknuN7PHzf/XSc2k+fGvwNs5bL5x9+nKvHTnDFktjwp1UJ/VX/Hzds2RD4dLoyTjzMxrY4sQXO/ELAg3nYGIXmBT/mqAljREinUqSiBnFdpeoH5fQOCr4kI6squqqiyNIC64AF24b5ozPbzAWg1kBqtplWoCIOVslWTDojHpFVYs/+kkTM0LE9kAmC5uD0DXqcKggUOSC0tVXUB08Gk5ZNy1aL/v4SfX3zqojTx3Sichtta1x4HAFnMjLpVBhkDUnWkZQQiqajqsZcKdyzSWKZpolt20iSRCQSeV4Fbnd+5zu4jz3Gra6LAP7knnsYevRR/rlaXXWc+oiqol17LVe1tJDWNKqaRjkUwopGUZubSW3aROO2bUQBb3ISNZcj6TgXXPppEVBTpiwjaxoRXSelqnP75RD495mShFAUfFVFrdk7hFWVkKJg1dZzZRmh66iGQck0UR0T1XXIWwEp22iECGsqk75MON1Ay7m6gr1AMTMzw5H9+/jb33w137i1sua6v/UL+Pj1AfG3Eqaq8MikxIRjUFKSiFgzyXQjXY1xruiJEg+tPRD5ImgyN2pqqKEosXgMQnGKRLCFSkSDzWmFlAFfefjDvPk1+bn3fv47CW5/5UamDr6Wsu+x88aTc6/1D8Hpk5dz4y1TzF7apnNgTF1EzFAZKzpM6zmmpwLPPyFgpqjS06SztSuKtsL3dTw4OKFxSd/Kuphj07B1AUFkuVCogO3JyHKQOIuFz+036gmoWlB1FGxPBRR0XSKig66y4r4BZMugKJA4hxftSE6jo3vLc45seqIQQpDJZKiWy2i6TmNT09y1ZHxkBOXoUZrXiHWHCTq+P1NUsU8wLtqALUl4ioKv63iaxtnJcS6XTFY6bbK+xNmGDvq27+Dk/n2022WE7+N7Pp7wg5MJH4VZItfHkCAkLS83FELw+U9/ilce/MUyy6Q7qjK9GzdxUWpx852iDz/zDK7r7aLJmA9UflgFOQovWaHv2t1ZBSsc59Z1KX44BWlDpacxRXtMp+TCoRJclZpff9yWGNObWHfVzc/LDsG2bTM9MY5llkEIdCNCU2s7qqpimSalmUnSIXfRdVkABUfGDyVIpBtwHAfHquLbJsOjj3HRroG5dY8fV9iypYH+0ypJpwNVDsbmhf+XMcuidbM917/VcSB32qA5tDgaOFuCjgbWbFjl+DBcgKYUuL6E5wfWTAK1FsuFUFQdRQ2j6qE5f/tnY2xxHAfXdZEkiVAo9Eszvj0XcO+dd/KTv/kb1IceYk8mw0vOURl0CvhBOEzhttt424c/TMumTYRX6M1RRx1PJTzPY3x0lKHjj5MdHeDo/r0MHbiXcuk0//yWtd/7lcdg3Q64ZvvC7cGBATg2rDKaj5JzWmnpuYIrr38ll+7ehqLIVB0wbXB8reZP2lj3J31uou4x+kTQ3NXFeKlE5yol9SWgrCgMKwrSAlWBBmi1C8Wswf5TgVnT/aASTwQRjuNAtbpovRUDbVVF1IJtp5hnu7r8O2kSbFYFh0tV9ESQ8aj44CCj6TpVX1BCIZxIEo6G8b2a+lX4CF8gRI2A9YOgXAgfUTOrF7USM+bua4G78OeVtCJ4LgmA4Lkkai2WBAwPDeE9fBdXSyZy7d96PF/hzr2P8dKZQbYumQD+R1Xh4re9m8bGJh6TDEbCMV7Zqa84EZUkuDQBD+WguSEw9D/jhmlqbaMjHMbVwlSjSbxUI4lEkvgK5vZPJZKOw8DpEzR6RVKheaLZ9GG8IpE0dOKGynAZkqHgtzOU5UGt4wscX2D6As8VuPi13wMWkahSUHJwviSqEwr+P8vKBxRB4hx/eE0CXfVpiZWXveY64JoBiVDyJXxfxg/2ECQVSdZB1lCUEIo231xKVdWnpJShXC4xOTqIIpuENRchJKbHdYxImtb2rudHuYRtzw3mErCzs5MNU1P8zpkz/I1ts3B6K4DPKgpjnZ3sWrcOb+dOnC1b6N21i0g4TGVkBG9yEr1YpOHw4fPquLwQJSAvSdiKgqqqJHSdNkWZuyJVCUrnHUVBVhRQVRRVRVMUDFVFVxRMwJZlKopCNRIhFIsRjkTQdX3u94jZNlOjI5hmlUiNBCkjU5IUwukkTa1Pp67r+Y2Ghgauu/kWfnjrmzmU+Qw7VumcfiIbNAtZjRQFaA7DK9YJZks489Y4D4/B6NkQX94fx4s0k0430NYQY2NrnK6kvkxBuSEJG5KBlnjGzHFyWsFTQnSkE6yLGkzPwKAdYnyqD3h87r2xcJl4FB6a6eeajbsZHIOeGhfe0wmlMwVGJqC1EY6ehFIJRkbPcllfFyN5kw2tsHPdwm/jMlZwufeoy3XbkssISE0B33cpWx7RJQelYEF4yTgYUqE5AbX+4jieTSYPtiMhSyphQyMWVZZVZyhzqtJZ2X4wPlZMiaII4fkarucTNWpjuBoQplAzOhGQK7nkiy5IszUuEo1JjUQkOBeFEL8UxMFIfz+jp07RWCoRcxwsWeZANEqiq4uNO3aQGx9n8zkEAK3AGZ4aYtQkiMOs2ThM0xCaBoaBHApBKASGEXhhAsLzwLYRlkV2apINWCuSogBpWTA8MYI1PcIuZYmNUXBhPy9UTIsv/Pmf8q7cmWX/8c9WNW7bsYVmYzHtcY8lY7S08tqm+a5fWQ/2C41QQwN5zeNfxopsDLk0qh4ZV+a0pXFRS5wbOoJEgiQrXNzZTLj2h4+ps53YYcjRsNq3sG73FbQ9jwkUXdfp6F65aVEkGkWW28jmZpB8GxUfgYyNQiSeJJ0Ijq2iKBhGENxO5lKLtjF7ytp2FM9I4ONh+4Ky5+ELH9fzURvnSVGAqXGJ9tDyFKnP2qQos69LEDdg1g4kuFnL1nWq4JQCL1XXV6i1EUNIGkg1ayY1hKo9tU1CLctiYnQQzy1jqB6+D6anE0800dza9ksxzj0byGaz/Puf/ilTd9zB+qEh3m3brNWOqwr8pyyzb9MmXv6xj/GmK64g1dLyS1+1U8dzB4qi0NHVRSQmSL94KzdVX8LIyDS//tpbcbzqqkllgLvPwidfv3R7sGcD7NngEtTZ5ekfP8H+O7/MQ98MM2M2Ekrv4JKrX8kN112JpjtUCxmKtoRPCDWcIh6v+5M+n1AnRhdAiMBjcsA0GTFNNCFQZZk+wyCmKGSB4USC9vZ2rGIRyfdRZRlf11HjcXzfp2JZmJaF47rYrhtkjCUJLAvJcZAdB8Xz0BeQqE8F7SITeAyFgy8CrhvcTJN+YF2NQKy4Pv1lE8sNum/6SDSENFoMg2ypTIsMaSmYIMhW8BiA0tRTsJeL4QlwhI9dI/KcmurB8wXFSpWhh+/nGnNxB9Q7RzKcObSfV5XH6VqSrvyMZbDj3f+FmbZ2quk0ti3YWDPHXw26HASH+8wIiW2Xsal7HZFI5FkpGdI0jd4Nm5menOD41BghRSAhocjQmtRxfJnhikxrTx+RSATXdalUKuSKOYRdQcVFlQRhRZr3Jj2H2aPjCxwhML0aiVoLrH3m1b2KBDIe4eZggr40yJTlJycmV6XAIysYjRYG3itALAi+PfB8CU8sVTBocwG4UlOjqqq64oWpVCoyNXqKrkZryQShSr5icvasSW/vpud8YK0YBjML9vEN69fzR9PTXC3LfGh4mFC1Sq/vk5UkDus60e5u/uTSS7n7ssvYdsUViEyG0EMPkRSC5YWTa2OhP6iuaaQ1jc7arGy2LH4cAiWooiCramA9IcsYNaWwTUCUVkMh3ESCcDxO4hyKD13X6VjXS7lcplosIHyBrGnEk8l6tvY88d//99/z9pc8xP8Qh9i55Ic/loXfeqSRP/zrz/D5R76PdfhH9PkjXNXiL2q4tBTJENzaDcHE2aLiTPPIJAwN6gyocVyjgcbGNNFYjI6GOFtaI4s63jcYcKXhARUcr8LxfphxQ0SjUXY3bMO0Hseo/byX7fAYHbfIeoNMV3YyXNXpaQ/Uw4oMOZHh+N4EUcVnR3NQHbC+u8RjA6eQ1RAdG5e3dm9PgKrYHDhb5tL10WWvhzVBxS8wPSOD0EhHQsQNhbMzsKFr7eOtKdAcg2CcCxozFfJQdSQkSSUc0ohFlBWD95AKoZggoN5MHAFVE0xLoWrK+Oi4qOiKy1TWJB726GmZrdAQOB5M5T1KVQ1PMZ4fCZ9zYPDkScwjR7jcWkDS+D7dxSJjJ09yyDTROHeMpbGgOSSBP7zr+3hCBEp1IbCFCDrI17xERa355lz1x+zN95FqCXPJ85CFQJktya+V5Suw6H4WEzK0nqMib70GZ51zd6VfDcOTk9zz0Q/zGxQW6SeqPnzej/Frl2xEX/DfGHXhETXOSzZ3zBGaIy6ckgw2NjdwU0Qn78Fe4DW9MQYKNgXbo1dXuCmhL/F3lzGWlAz5SAxtvJ7uTdteEKXaRjiMEe4M/Gw9D1mW0fXVB1RJUpY8D+4TqS00ta3Dtm0cy8SulPCtChVzkvULhLa2DVIlTk7RcX0P4QskfCREEIP751aMnm/0oyk1NbsOy6yZFsIHu1xL9vjg+QoeyoIYTkVSjIBEVQ00XV+1osg0TYYGjtHTaC6yEBHCIVs2GTpbonvdc8+K4bkK3/f59he/yL5Pf5r44cNckc9zA2uPoY8BP00mSf+X/8Jtb387t3Z0kKjbZtXxLKFUyhKLWTgOmKbNt771ad72O1U+eRf8/tUrv+fHx8HXYHAS1p+j0KyvDfrafKAMlMkUBnn08A/4/N0hpspJ3NBGLrr0pdx884uISybVzDg5b9aftJFwOHpeRKnrusxWddeJ1WcO9VL6GlzX5eBDD5GenKTTcebKXCrAUVnGTaXo2LCBtq4uNE3DcRx830eSpEVlI47j4DhBmfFa2dDZ9SzLwq1UcCsV/GoVLAtsG9lxUF0XVQhCzCtHnwiOSbA1DJOWw1CxwhbFJ1aLtYSAaR9O+yrxaJSLjAubLAkRNCUIyE0fRwhcL+jE6vsCH79WZx4EYrKoqRURqFLNk1UKlA+KBL4vePjocRqHTrBJnf/LOT5879QQ2ZNHeJ2dIb1k4vhxP8WO3/og2xpStKhB6Vi/DUoMes4hPri/HObS1771OUWmOI7D9OQkVqWAjMBDwogmaGpeO/vqOA7VahWzmAO7goyLLgkM5YmX1jsCJssmrRtyqCqMjJh0ds4rlgcHVBSrg841aqxny7H62p7YPjxR+CIof3XcwM/Vq6kYBAqgkS1UWN/urdpAbKqooMT6aGi4ULrwmcVgfz+Pf+1rXFkozPnfOULwv44eRZqcRLdtirYNus7NLS1sTaX4fkMD77rmGsIXYBmwmj9oaHb8Y3FZfEhV0VQVW5YRsoxWU4fasoyvqkjxOLF0GsMw6hOXZwmmafLRP/ptTt3zHfbEC0jAo1mD5Kar+OvPfo1kcp489H2fQwf2cfTHX6a47w46zVNc1eyRPkfp9kLYHjw2BWdKGq4WQ0RSpNMNyKEo6USMrW0xGlfpxDSQ9+lPf5Wbr5q/Nvz9VzqIRjt4dcfNPGZmufzaCnc/APksZPMSbfEUt12aILzkJB/MwpHpCC/bnVrxs+46rXDt9oZlRv77RmHnxnkP7qoLkzkZk0YiqV5UHHxnBp0SiYhY0+9zKRwBpQpU7KA5jqGrJCIq2nkaflcdGM0EKtXEKgTbWA4cpZWenr7z37HnIBzH4cBPfsKlpdKq6xxVFEqRCE2WNW+oW/N8nyUzJTFvgqPWHqtLbs/UlGSfDHuWc/GLIAQ8bsGe8zjnbBFUnJhIOLLC/oMHifzTR7lJW1w9NOLC3ZEW3rSxc24cdgX82FbZ1NXJpkQQRJ1wYFyLsKO5gYYlf+zvVuGl7auryyctOObFuKFrcaBwl5PmxtvPUeP4Asbp04+wYcPDc89PnlRoaWnAMF6zLGa1LJNK5R4WclKDAyph0YKOj6EuLrnPmh6Wb9K2RseciRLIIUjFnkCj1acAbk3n4Xgrl/JncxXWt9hoq4yzk0UFLdZH+jkewz3b6D9zhq/8t/+Gfd99bJqY4GWuyyqFJEAQB35HVTlzzTW87c/+jERXF8nOzjmlcx11PBuwLBPfHyIchulpm3vu+QGRyB/y0pcKPvsPcPbn8DuXQVNtzDMd+JcHFe4YWsd/+/NPMjZ4iP6j9+LNPEZ3apKL1zns6oMLySNXLdh7Ck5P6EwUY5Toom/rjdx0yytp7+ygaoHjqyBHiSYaCIcjixLVlmVRyM8gPAtZEoFDDhqxZJpI5BwBQh1r4byuYHVitIbDe/fS2t9P0yrNSg4ZBp3XXUe6oWHF158ueJ6HbdvBrVLBK5fxq1WEacICBarmeXONn5YG8cckWKf7HMoWuVTzV1RQVn142FXZnYxgC4Hreri+CFS0wgvmFLP+VSLwFw0mDUFzgFly88kETgNTGYYee5SrZZP+XIHPPnKQcj6P8H0mHI8m4fGXMZPEgsDbFvAPsV5++/d+D01dHJFPejClwUXnMF2+20px3e1v/qVQ0KyEgCytYBbzi8jSpb5Ua2HEnqGzL1BijY1ZtLfP+xIOD8kUc91sbBCrKg9GClDxNZKxCOAi1W6K5KPIAlVZoDZ4hlA0oWRCe2r1dVwBQ5k4fZsuesb264nA932+/4UvYA4McKttz5WEOsCPs1lGKxXSvo+qqszoOk3Nzbxs+3b0c5CiJpCVJExJQqn5gyYX+INWCYjQ2bL4qKoiKwpVSULIgRevoqq4qoowDIyGBsKRSD37+RyE67qcPXsWIQTr1q07r99ICMHJE8c59OP/R27vd2nKHeGKRou2C4jdPB8OZOBEQcVSo4SiaaLJFEKPEI5EWdcUZ32jgVIbW74w8H1ef3uBr34bDh6G/kGF67b0IuktDJcNLtrSzKu2qbTWxv0zGfj5KZUrNzeyc93iLNk9ZxS29DTSklw+qz44Bras05YK0xLX0ZSgRPT4DOxahVN0PJguyZh+I0ayl2iigUpxGs+cRqNM3PAIn0PFvxC+mB2nJIRQCOlq0JRpFaLUETA8CX217Ijjg+sIZAVCtfd4AgYycTY8x8e0c6H/+HFiBw6s6R1qEXjC73rG9urJ4TEZdkeW+4EuRNmHMzZ0amAh4yoKnqrXPOfDKEYEJRpFi8YIRWOEQiFCoRDf+8TfsPVv/5DNS5os7bWg2N7LTe3zse1RB4ZijdzS2YwEHHAkyuEYe5ob5qtRaqj4cNaVmZZVxjyP17Z6y5qG5Rz4aV7nto2NhBZ8ubIHBxKbufrmW5/wMftlR3//Pvr67p97fvq0jKa109Pz2mXrjow8TmfnfHVXtQqOcxmJRBARVCoVyoUswiqh+i5VKpi2R2cEjBWIRdOF4YJCd0MIy/WZMQVxI6ju8kXgoq9IPpIMqhLEf6oKurL2f/ipQtWBTB661uA8ny8x3LMBx3H4zMc+xug3vkHz6dNcXS5z2Tnecxdwd3s7l33kI1x6ww3I6TTJ5uZ6TFfHsw7XdSmXh0gmXfJ5l+HhU+zdezvveMd8BeLx4/D7v64QMiPIkqDox7niRW/g/X/0JzQ2zqcCqtUqp44fpv/kXk4fuZ/8yAO0hofZ3lnlis3LbZPWgu/DwbOBT+lYPkLOaaGx63Kuvuk1bNmyBcdTcEXgT2qEY5SLGRpj7tx8umz65EoungAJBUlWSaSbiccTdUHJhaFOjJ4vHMfh0E9/yp5icdV1bOBRWaZrNkN7/sftmUPNs1MIEfxYC55PI9hiiEWk4lIcs6FJYZkR/9ONsmVzz0OPcHl1mkYFvnnkNA8/vp8PhE2aFyhbf1yBr5Xgk80QkYNGAF9ddwnvftevrTg4CAG/cODmNfz7qz7si2zkmhe/9Gn6ds9NOI5DtVLBLM2TpRoBWWos+f0FMKWN01JrrDA56dDSMq/SGRuTiEa7mRqBnsTisiwBTJfB8lWa4iFyNNDW3rlo+77v47puYJzv2HiOiefZ4NsI3wbhIuGh4CHJPlqNSFVVlnnzXQjGcpAIQ/QcF7j+aYO+Tbuf+Ac9jahWq5SyWYRtc+LAASqjo+QzGSK2TZ/rIgP9qsqEqnJNWxvTmsZOwyC+ShKgBOQkCafmD5rUdeI1f9DZsvgyQVm8pijEVRUhy5QBUfMLlVQVDAMlHieSStUVBC8wnB0Y4PGffJ3so98hPrmPS5MVei+wY9fhDBzJK1SkCPFkilgihadFQA3zHwcPYUjTvG0HXNkTKFC/fVDiB8dD/OYr+7jjZIrfe3UP6ej8QCYEfPVxlWsuaqGnaZ6ZLNtw/2CEW3ellu3D0QloaguUUpM5KJYVJoshWpsMNnWcu4GSLyBTkig5cdRoN01t6xFCUMxP4FamUSgRD7nnHH+WomhC0ZRwPQUjpJKIqhi1jGS+EhyPWEgwPGnieR4hDVwfbFemManRltY4Ox2io3fH83oyu++++9g1MnLOMvl9wJ5nYofOAZ9aiX7t5s3eyzJCkvBkmQzQpDhEhIcA4qqyLNl4UIRou/waUqnz8y2zLIvP/NqbePOD315WafO9qszmTZvZkgzkxWUBP3ENru3tJBnSeMxWUBIJdjcmF+3HqAuTqEh6iPZYhOaQRknAoOlzMJ+nUXVZbzj4Ao6bQXLspp404QVl9L6Ae804u1/6WhKJC23p98JBf/9B+vrunnt++rSMLG+nr+/GRetVKmU8737iC4QAw8NxurquWnG709PDJJPDAAyegYQKKSOI3xwfchUo2ArdjSF0RaJSK3tvWkFo4HiBSsr2wPflWq9RH9cTtdJ9gSQLFPxaMtybS4brF6CoX4rJfEBQxM8RYjyXY7inGsVikc9+5jOc2L+fWCTCzl27eNnrXkdT0zx7fM9dd3HXRz+KcuAAW6aneanvs1YucwT4rmFQff3redPv/i5KIkG4pYVoIvG8alJaxy838vkx4vESluVTKuX5/Odv5AMfWGzj8alP6Wzd+h0iRgpJho2btpA+D9sHx3E4e/YMAyf2cfzwg0yeuZuUfJrNrUWu3CxYYL19XhiYgP39MsNZg5lqA0rqIi675jYu3nMFuh5GkhXCIYVixcO2Tf7pU//Cg3d9i5gW9OmwSbDnqlfwwQ/91Xntfx1AnRg9f4wMDSE/+CDtQmAKwclKhbzrIssy6wyDDk1DIvBRueTZ3tkniPMp0ar4QTf6i56hivKK6/GzfQfpmxpkhx78vY5l8nz+Rz/jL2Lmiu857cCn8vAHKfjZnlt48+tvX7ZO3oMZJCxZYQaZsOKyJ75cCewKuM9MsPvW20imUk/lV3tewnEcqqUSZqUAdhUZDw0f27VJ9c4wOwebnHRpbCwyGw9NTUFzcw/VKkyMgnACJarvS1ieTCKi0hwLot+hkkbnus1PSp07680VdCA18V0b37URvgW+g1Sbcqr4KLI/r0ZdEoCP5yB+XsRomL5Nzz3NUSGbxcrliLsuGdNk8tQpdjsOI7bNqUqFfM13b0cyyXrDQAJGCa4M7QQT9TxQlGU8RZnzB43UfpulZfGGqpJQVVxJogxzDd4UXUeOxQilUhh1NWgdSzA+Ps7en3yT6Ye+RXj0US6O5th6gXHc6Tzsn5F5YEoj0WjzkZctD0csB37rmzrvv30zPxts5HdfsTgBY7rwtYMR3nHj4tbZ/2+/zJUbU/Q0zatSAe4dgMsvWl4aXLZgNKtQssOoisLGTv28VKCFKmQrBsLoIN2ygWQyjWmaFHKTONUpZK9ALOSec5K/FGULClUJx1PwhIKqKuQKVTZ1iEUqMCFgNAu2p6OG4rR271jT2/C5jv0PPMD2oaFzlrlfKDHqsIC0nL2XJDxJwpdl/Np4KGQZX5nN0KlBlwZVRdK0wEdZ15F1HVnTUEIhFE1DrXkqq6q6zDPRcRwef+A+MqdP0klQ6j6DRETT2ZWKoSuzXek72X31Ncv2u1qtkpuepjI5hpOZgkIOZybDPZ/4S95rji1LIn6mqnP7zi001Jry3GdJKE2t7GhKsc9TSaVT7EjGkCSoCjjrSJQVHTkcJhaL4Ydj+PEkarqJcGs7yaYmYrEYkiSRzWYZHx1BkmWy42OomUG2qRViavA/HHdljnoJtl19E+2dncu+Sx3zGBg4Qm/vnXPP+/tldP3FdHZuWrTe8PAjdHXl5p4HDhNXEYstZzKLxSyKcpxIzW5DCJiegsKUMlcymo6qJMPq3ExytATNqfOr6nH8oEuz6wHIqKqCEVIQYpY8VfGEji/VbMh8F8+zEJ4NvgMEcZyKhyL7aCpzcdxC1InReZTLZT709rcz+OijaNPTXOE4hIXgMU1juqODl7znPVRPn6byi1/QPjzMDabJljW29/+zd99hcl713cbv6bO7s71oV7vqki1ZlovcwN1ggw3GtNBLAEPooeUlQEgggRQCIYSEJICDgQChmhJabINtjHuTm2yr9+29TJ/n/WPUVt3Gssrcn+vay96ZZ2bPrGbPPM/3nPM7eeD/QiHuOfFEXvp3f0fnCSdAdTWptjaqalzOq6PL1NQE0Wg3oRCMjU3x+c9fwqc+NTxtCfxPfhImk/kir371u//gn5fP5+nr6WbDmodZ9fi9bH7iZmLpR5jfPMyZC4ssOEid0j0Nj8M9q0NsGkwwMFlHJjafE069nBOWns1ffex9LJuxgQ9clad9+8KOxzfDF34WY6i4iC9ccz0z/Rw9FAajh2rj2rVU33cfvZOTDGezLC4WaaF8Qrw2HGZLJMLZtbWsjUQ4jUMvRH40eSACpx+kqH8+gMdycMqTCEbLGyiVl7TngAJhiuEIxXCEUiRCEI2XE6lYgkgiQRCN093fR989t5PavIYLYgUKxRK92QKJSJh/vvlOPpretNfMht39vwE4/Yoree0LL2eiBINBhNFIjFHCVFdHWVQXoy4eLte1zMdYM1EkkZ9icXSCtli54PymXIS1QT2nnH8xbe1PsgerILlcju7uh5kzZ2jnbevXl+joGGXHRMDhYUilZhGLhcjnYbg7RioWJUyI5B5XY90TkEyGCEJVhBN1VNfUH9aL8yAIdpuNmqWYz1IslkPUXCZDqTDJrAMsw8qXYMtQPfMWLTlsbXwq0uk0Ez091Obz9GXLr2Vi82ZOKxQO+LjNwGA4TFU0Sn0sRlMsVt4cjn0vi6+JRMhTniWaj0QoRqNEqqpI1NeTqKsjWVV13Jag0OExNDTEPb/9Ob23/5D45jtZHOvnlOaDL70MAnj77fCVN+3/mJ4x+NLdTTR0LOKdL5xH9R6Flb95b5TXXNi1c5CkFMAv18Dzz4K13SHSU0lmNdZSlYjyYD+ce5A/+1IA/WMhBiZi5Iox2pvitDdGOdjqpnQBBsZjFMJtpJrn09TcRiQS2V5baoDcZB/h4hjV8Ry1ySe3LPXhjbC4a/8BxqYBmCw0cOLSM47pv92erVvJ3X03s7fXdN+XMeDxWIzWqiqIRilFImSy2fLmgokE1Q0NxJLJcoAZjxNJJKaFlzsCzMM9Kyqfz/O7n/+M0yd6adpjM8PhItxbjNHV1kq6oYV5J53M+MAA2f5uikODRCbHSGTT1FKkPrzrvbKhp5d7v/RP/FFkatrzTZTgf4I63nTyAmLhED0FuDOS4oxZM1kXqWJ2cyPzapP0FGAbUcarakl0zqF17gJS7R3Ub68J/WSMjY3x+AP3kZ0aA0K0dM5m4ZKlDqIdgoceuot8/l6mdvtnPPnk106bJTQ+PkokcvfOoBNgy5YmurrO2Ov5MpkM6fSDNDbuVsM/D1s2xZlZE9tnjdjhDIwXYqSSEIvkScaeXNmjUlAOSrMFCAgTjZSD0nikfI6VLZTD0iAcJxytIhavIhYrb7hUKpV27slQyOcoFdIUtw+E53NZSoX0Ac/hSgGsH0yxYNHJh97gY8zU1BTvf+ELia1YwUtHR7l0j+v6MeBNoRBXBgFv5MC7Lj8B/LyujvZ3v5vnveIVkEwSraujpqWF+FG0D4O0Qz6fJ5PZRG1tiYGBHJ/5zEv4m79ZQ9VulZNuvx3uuus9fOAD/3pY2pDNZhkd6mPrljWsfvwh1j5yM7mh+5hd38spc3KcNg+ezGlEJleuU/o/t4Q5c1GJN1269zFBAH/5rShbi+fzte/+1mX1B2cweqj6+/tZ9etf0zwxweJ91BjNArdGIqQaGnjWMXohcX8YllUfuAZof6Fcl7M1GqIQjlAMhQmiUYrRKEQTEIsRjicIJxKE41VEq5PEElXEEgni8TixWGzahVY+n2d4aJCxTRvI9mxjaMtGYiNDTPT3cnJxklChwHc29EA2y6xIiYki/O99D/KjltwBX8s9Gbjppe/kFe94N81ds6itrSUUCjE+Pk5vTw/DPVvKsyMiUTrnLaJ1xgyy2SyrVz7MSH8fhELMnLeAOfMWVMQuqH+o7u6b6OjYFbitX19Pa+sGUtuLV09MQCTSSVVVhN7uEG3R6v32Pt0T0NK4a7l9pgDpXJggXE000UBVdeoZvVhau/pRZjeO7/ckv3skQnXTQurrj66lCv3d3TRMTNCdy1GXz5d3Zu3t5cxs9oA9/wPhMF11dRRCIWrYe1l8VThMdvvt2e0zQqOpFMnGRpK1tUfVBmU6PkxMTHDPLdez5dbvE1l/GwvZyvLWYK9Nj+7thYdC8JbzDvx87/pRgkvPXcqieXNYtsfOe9c9DLXNURritZzUWce2sRCZBCybu+uYsTSs3Bxlinram6uY2VCi4SCDijtk8uU+YzQdJRGPM7c9TtVBdr3Ll2BgPEym1ESibi6t7bN29oH5fJ6xkUEyE32ECqNUxTKkDvA5PpaGoXGY27bv+6G87PXRrdWcdsZBfpFHuSAIuPv661k+Okq6WCQHpEIhktvPQQLggepqljz3uVRVVR3wuY60e279HQvWP0JTZN+n2cNFuDcfYWmsREMooPoAb6mxXIlf3XMvXT//Fuclpp/PbszD3bUzeMWCTooBXJ+NUtMxk6ChifmtLaRjCQZTjSQWn8KiM8+mtvYgBdp1WD366L0MDNzP6afn2VFtoL8fHn20jrlzn8XcueVZo1u33k5n5+TOx42OQiJxPsnk9Pd9Pp9nePhh2tqmn2P39ERobk7S113eo6A+Xt5sJF+Ewakw0ao62ju6CIVCFItFspk02fQYQWGSKFmSiWCvurIHkylAJgslwoRCYZKJCMloiFCo3CfmClDcHpaGIgli8Rqi0ehe54cHO4frHw9RiM7c2f7j0afe8x5m/fKXJDds4NX7uaafAj4AfHkf900C/xuJsPbcc3ntJz9J/YwZFGMxEo2NVB1iyQ7pSBke3kxjY4bh4QL/8i/v493vvoHW3UrorVoF3/rWC/jkJ//3GRkMDoKgPHlltJ/RoW2sWbOKlStuZXTr72lPbuakrinOXgTVBxlfLJXgnV+CL793/8eks/DGf6nnr/7lZpadctrT+jqOQ4f0AWAqBDQ0NDCeyXDefjZeSgBnFIvcmsvx8MKFRCOR8lc0SiQcZsf0kCf9kbuvD+k/9IN7z8eHQoSAxNgo3f1bmB3Z92sE2BSrpvXUUxidmiJRXU1HR8chfyCWSiVGhoYY2bKJXPcWIqODpHJposUC4yPj1OSmqB2fgNFBnhUP2JrL8dVVW/hAdZZV6QlWrulh5sAEs5IHz9/rwlDbPpN5J00vpl5bW1s+kV+0aK/HJJNJli0/65Bei3aZmpqkoWFXKJrPQyo1m3x+w87bolHI5Urk8xHqSe7376BIefn27rXKklFIRndUr5xgagQmClGI1hCraiCRqDqsJ2Uzu+azYeMTzG7OTNs9OghgYCJCMdJy1IWiAEEuR7pYpLZUYjifpwvI1dSwKZdjzn5OjMeAaDRaXhofj1MVj1MXCpFne23RSIShWIxofT3Vzc3UVh3e370EkEqluOSFL4MXvgwoz2i6645b2HjTdwnW/I7Z+Q2c1Vpi8yQsXHDw5wuTZdXWEWgI2DhSz7PntdFcW/7jnszDi04pEIsMs753mMd743S115EvpHbOIq2rgmedUAAG2TIYYuXGFKGaGdTFoTaapb1x781ldkjGYF5rkXJvl2VwAjaMRsmXorQ2JWlriBHZo4OMhaGjvgQMAAMMrL+P8VyKaM1sWmbMo7m1HVrbgXKwMTE+Qnq8F3IjxKNpIhSJRKA6GWFkEloOUq4xFoFE/NgPCEKhEFVNTVy/aRMdxSKJIGB1KEQhEmFxKkVPTQ0zli17RkPRHasTcrkcue0z+YtTUxTTaQpTU5SyWYJsFrZvnhnK5yGXY6SQoal6V79dKMHmTI5sMaA+FqY9ESNRKtIa3jsULwawLZ1jYGKKaD7HPbf8hosfvY35e4xh3ZmB4qx5vKKtkSfycE+yidb5c4l3zSaxYDGh085k4ew5nHCMDv4fb9aufQx4kIsumj4jurUVLrpojLvu+j3V1SlisSgtLZPTjpmYaKe+fu/3/eDgGtrb9wxFQzQ3J4nFoHM2DA3B4FQ9QVAinkjSPrt52nlAJBKhuiZFdU15ZDwIArLZLMPpMYLsOCEyJGMlqg5y6lA+94PyWWGJfLHA+CQUghAhIsRjYaoTBaKhAjBFPj9MPgPpYoSAGKFIkmiimhkdc9i0dTWzm7N7haNjUzCRidHUmKO3ZwvNLe3H3TlNLpdj8sEHuauvj387wESnamAR5RmhO5bQ3wPc2N7O2R/7GJdfdBHFaJRQMkm8uZnq2tpjekWBKsP4+DCpVIZ0usQPfvBVXve66aFoXx9cc82p/M3f/OgZez+HQiGqq6uprp5DW8cc5i8+m/MveQnpyQEyE0Ns2rKFr993Oz1rbqY+vGpnndK2hunPc/8aeNaB6l1QLiXS2Zjm5ht/bDD6NDEYBbZu2sSiYvGAxzQC9ePjLHngAcoVcMpLx/NAIRKhsL32VCkapRSNEkSjEItBPA7xOKFEorxUK5kknEwSqaoiWlVFIpEgFovt/DpcS7Zmlkrc9/sMDeP91IX3/vC8azzPcH6Qmlv+j/pSjqlQhNuTdTTOnc+ys86ZNtIaBAHjY2MMbtlEdttmwsMD1GQmqQsKjA6OMJrOkAlFaI6GmBcqMLNQ4PGeXs6J5EluXzX92QfWcOrmDdzcO8alxRI7IsvrCuVQ6kD58MORak44fe8lQnr6jY5upmO3SgMjIyEaGpoZGdl1WzQK4+MBkxMJ2pL7/+AZTh98hKw6AdWJAjBKvjhKZhjGS3FC0RTJ6kaSVVVP66h/VVUVs+Yuprd7M8XCJPFokVIpRK4Uo75hBl2tB5h6dSQFARPFIq3ACOVZHrNSKe6YmiKUzdIVBNM2JRkE1sZinF5Tw6pIhJmJBAORCMVEgnhDA7UtLdQnk54I64hLJpOcd8nzOe+S8oZ4hUKBFffeyf1f/Swnj/3soI8PReBdL1rHL++LEqppozc/wm0P1bCwZQZEkzsvnufNgHkzcpSCAR5fN8DARJIZLfUsmlW1czlyV3NAV/M4+dI4a3qiDMaWki0uozSxjVBmC43VE7QeYFJdcwqaU+XtdvLFDFu7Q4ymoySTcTqak6T20V+2pAJaGAceZaznUfonk5SSHTS2LqSuvoHGplZStQ2semwFmclxahMZgiBgeCJEqRShYcHBg8BjPxaF+269lbrHH+cFudy015MrFrllYoKFZ51F55w5h/x8OwPNXI58Lkd+aor81BTFqSlKmQylTKYcaubzkM0SyucJFwqECwWipRLRUolYqUSM8mB6NRy0/inAMJDd/rkYBHDX0Dh9kxnmhgpUhQI2BWHuDKI0pFKMJpK0RGCyWGLjeJrMVJraIMecKISmcvz397/PeyY3UbvHD74uHeHUxScws6aK/8kmKJx8BsuufBkLlp/prNCjUBAEbNr0MJdcsu9a+6EQnHHGFHfccQ8LFjSy+94bQ0MhWlv3vpru799Ec/PotNuGhqCxsWpn/fhMBkKhdmZ2HWDH0r3aEiKZTG4vr1A+X8rlcoxNTVDIjRIqZYhHCiTj7DUotLtYBGLVUJ7rXShv6JTeu05pdWzHwFOGfHGEQgHq61JsGIBoqEAqWaRYCjGeCRONRulorSEZK5EsZhge7KOt/fiqxbd161aW5XLcWyhwsKvHS4Hrgd8nEky95CW86n3v4+2pFKFYjKC6muqWFqqqD3F5hHSEZbMZotEBAG666XcsW/bP0+ZFpdPwuc/N4v/9vxuO6CawkUiE2rp6auvqgQV0zD2NpaddQH7qrZRyYwwPD/Pr+x5g3R3Xk8g+wvzmQZbPLzI0ATMOYV5OUypgcnzssL+OSmEwCkyOjtJ+kGAUIEz5FxYFpl16FIvlL4DcgZeB7ylLOVxNU94IpRAOU9w+66EYiRBsr4tFLLYzaA3F44STyfKS9h0hazJJLJncuaR9x9fOtofDnPbs81h5372ERwbpKkwRD8F4EObhdIHWzBSXR3LlFxkGKLIgP8ymVQ9x++g4s2bPYmrbRkID/VSlJ2gOFZm3/ZouCOD/1m+iu3+QU8kwLxIwUAjxUCnKI6Ewl8WyXBSFzWMT3Hr3SsJPbOLj2Rz7Kgt0Zr48s+HZ+7m2CwK4oXk+//H8ytpF/kgJgv5p3+dytYyMbKFYDLGjukY0CoODAQsWROnpLs92qY3vuvjOBzCSgVwpSn0szPBkiYACsQgkE/tfFhoLQSwJ5SGIIfLZIcbHoECSUKKe6ur6p2VpdzKZZPa8RTs3dQqHw0f9piRBOEzA9A48BCxvbWXF8DC9mQyxUokwkA2HaYhGOb2qik2hEMmODqrnz6fRZfE6BkSjUc581vmcsvxs3vjcel61fN9hAZQ3Ioomoa4GXn1hAdjGjSu6GR1rINw1Sl20hrsfbeHMk+p3hp/hEJw0CyBDNp/hwZVhJnJJ5nY2MKut3A/EwrBkZgF4kNHJB1k/2ECs+WwSc05nQ/9m0kOrSNJPe0N+vzOlYhGY3RLA9qHVsfQkq3oj5EsRGmqraGuK77UDeV0S6pIZYD2ZsfVs3RojSws9AxMs6xqnbo8P0ZGpAg+tLXLOktR+l5bmAwgOKbI7evV2dxNZtYpF+zjfigMX53LceNttFAcHCbLZXaHm9mAzlM8TLhaJFgpEisVyqBkEJIAkUAcHDRkOh9/0jTK7kOZZyd1X9pQIghy/GR/ljskJZoVLzAwVOGn7yej68QzXb+1m8/U/588j49M2mygG8LVsgledciKPBjFuWfZcXv9XnzrqP98q3eDgIC0t4wc8JhaDUGiQpqYE7DYMms3Ooqlp+r/v2NgQqdQ2dp8sOTEB8XiSRKLcEebzMDZWTVvboYei+xOPx4nHm4Cm7c+dZyo9RSE7CsUpouGD1ymNTdtsqUQxKJHN5pnYo05pVQyqYhCPpigWS4yM54iEA2Y0hgiFYWQ8S7EYprkhRiycJZNOkzzKS2scTvc9//l88bOfpRAOE4nHIZWiqqnJ+qE6phSLRTKZburq4MEHN5DPv4NnP3vX/aUS/P3fN/DGN/6E1tY/vE97OsViMRobW6CxfALXODPLjFnLKFx+JaFggkIuyz0rnuC7v/g6pzXdwQvOOvBK2i2DIV700nOeiaZXBINRIF5dTTocLv8lHcDhKLKa2P610442HEJQu6fdZ7JOAvlQqBy0bt89tRQOk4zFKEUibCyVyrusxuOEJ8Y5Lb7vQHcWeZ5Y9QgrVtxJJIB4OMypjSlqtm93GwTwrYdXs3RqkMt3q2c1JxpwBnluGM7wzTueoGv1Oi6eTPPag7yG5wXw7gH4cgfM3Me783PhVl74gT93ZtszIJvNkkrtGUK0UFPTy/Bw+cQ6k4FkEmprw8RiMLMTxkZhwyBEwmEi4QhEEtQ1NNNSW0smk6GYHiFUmiIZL5HNlZjIF4ASiWh5xuj+7JpRkAEyZCZ7GR7eUZ+0nqrq2j9omdSegwlHs2RtLVOTk6RLJSKUfyNJIBkKcUpDA+syGeq2zyiNUh50WZVM0jRvHh1dXUey6dJT8o1vvJFzrshw/Rp43sK97w8C+PvfwptfOP32S08LgGEe3zzM8LZqZtR08pvb6yHWxAWnN5PcbWQmEYPT55eAKcbTU9z5YIRiqJoT59bTUlf+QKqvhtOqR4Dr2fTIjQxlO2mcczGz51zF6MgQW7Y9TjG9kYbEGG11wX43Tqqrgrqq8uynfJCjrx9GpiLE4wlaG5I01ExPDZJR6GrM8/CmbpZ1lR+/p4bqgKVdeVZtnmLp3H3P/OkfjdIyY9a+G3WMWHX//Zyd2X9AHgPmTUxQde+9HO3zw1KUa4h2p/NUZTOckNz7PDQUgkurinx7MM1P772Tx9esA6C1oY4Xz51J9s7f8c6q6RvvjRbhB5F6XnTqfP531jKu+sd/49nODj0mpNNpamr2v6nYDslknmKxxI5gtK8vQkvL/GnHZDJpgmD1tI1IcjnI5eI0Ne3qY4aHw7S0zHta2r+n8rlVPVAPlFcBZLMZJtKjBIUpoqEMVfEDB6WR0I4VRbBj+X0mn2d4qhyUprNhisUCM1sgsdvU1Nok5IslekayNNfD+MTocRWMdnV18XAySSYapZDNHvCi/qfV1bzsLW+B6mqidXXWD9Uxa2Kin1SqwObNo9x555W84x3TPze/8IUkZ5/9r5x88vIj1MJDl0gkSLR2AOXlmcPDg5x2RjMNTV38w8dfy/8LRva7inZ0EjYO1fK8F7z0mWvwcc5gFJg9bx5333svXaOj+z1mEiiGQnQHAVHKYWaM8i/waPlYiW3/2nk5FAS7ZrPuZ+fWR8Iwp2bfzzdZLHH9tmG6yHNhvEQiBJMluL8/y13hGLOTUcZGR0mND7O8alenNJzJ8717HiNYuZYLJ9N87CDtHgdujIaZbG/g7EUz+FZVnL98cD3JdI4/jmeoDcMDoSS/aJrPCz/4Ea567euf3C9GT8nIyDZmzNj9e4jFhhgbS7NxIzQ2QlUVDAzA0NAA4XCYjo46iqU4XXPn7/OEq6qqiqqqKoIgIJNJUyyMEI2lScbL75+xbJFCoUiIElWJHTWo9m1/9UmDSLk+aTJ5/NbIrE6lmKqpYXh0lNZ4nN5MZmdt0epwmKXV1YwUi2zN5ykFAZnqapYsX37c/j50fPve9z7GK1/5Perq4BMfgkfvgLecBjvK6K3th7++MU6oOcX6gVGWzC7urBm6w+JZsHjWFCMTq/nlfVFq62fwwIMz2DZex0XL22ipn/6A2ip41glFYJz+sXFuWxslmkhx8vw6arYvgZ/dXGI2m8ml/5tVv0uQiS9h7pJLaGy6gHw+z9ZtG5gcfIJYsZsZdTlS+xn4iYWgsxE6G4vAFJO5KdZuCpMrRampStDenCAeDZEPyh/ldVWwdSDP2q1ThCgBASUizOuoYlZrnMe2lMgX9w4ahibCjBeaOKF1xr6aceyYnORg85u6KNfTeyaD0dxuX1l2K7O0fSO74vYSS7lwmKkgIF8oQCZDbmCAm6fGeEmqxHCuyC19I4xlyiUCQpEIZzTW8q077uGB++7jnckMH68KiIRg3cA2vrT2cTojTFvGtDYPK+pn0jl/EcN/8Vled6b11Y8lNTU1bNkSp7yWbP+y2RCJRLkvKhSgVJo37TM+n88zPr6S1tZd0zqKRRgaitDevuu44WGoq1vwjE04iEajRKMpanarU5rJbA9Kc5OEyFAVLx3w/A/KNZ2TMYASPfkSTSlI7CNcjUWgvQH6RvPEkk9+0snRLBaL0fysZ7F482a+t2EDr9tPndFJYMWMGbzx5JNJzZvn5BIds9LpCZLJcSYmcnzzm8/j4x+fPrHrm9+MUl39Aa688tjMChobm2lsbCYar2P+SRfzqe/9gr969d4ZTrEIf/qVOJdd9U43xn0auSv9dvfccgtdjz1Gxx4zNXe86N/X1nLqS15CbW0thUJhZy2qQj5fLrCfTlPc/t8gl9tVZD+f3/kVKRSgWCRaLBIpFolsX761I9CMb/96Jv0+Auel9q7pWQzgus39nB/KkC8WGc8WyGTz5LIFCvkihVyRUr7I6qDAlbVFNg6Nc/vDa4mv38w5k1M86yA/Nwv8Nhyir7WWZYvaOb2pZlrtyEIAf1F3AvOWnkx6fJwTzzib57/4xYetBqv2tnXrHXR2Tuz8ftOmKFBgamqUE08sTXvPBAGsWhUlCFpZsOBZTyqAK+/gN0k+M0q4lCaZCIiFIFuETKZAsVQiEipRnTjwjILd5YPyqslcsVyfNFFTDkoP5WSwUCgQBAGRSOSoPnnM5/N0b9xITTpNqVAgk8vRHkxfJJsBtiSTzDzhBKqtHaVj0PXXX8PSpW+jc7eE64Yb4N//oYbWmiTxZIgZC5r4k/efTF1dkvvuq2ViYDMD9/+e5580Rmv9vp+3WIQbVoQYTDdxwoIOVvc1sGxRK8sOUqNzYz9s7I9TW1vH0vkp4ntcvA9PwYaBBuKt57BwyTk7T1jHxsbo3bqS4sQGaqPDtDWU9ltGZE99ozA0GSFfilMKJcllM1THspzQtes58gGs3RZmeCpOY1MTmVA7tYk81fEcpVKE0UyCmrpWZs155gKQw+XW73+fC/r7D3jMBOVg9FCrkWfYFWrmQyHy4TDFSIRCJAI7Qs3dasaHdi9lVF1NtLqaeDJJNBolm80yOTpKpqeHbH8/wdAQofFxYpOTJHM56opFGpg+qP6LKHSUJnmkf5grEgVat7+vJovwgfvXkF35IF9tyLKvfbN+OFGedfq2erg1E2KgcwHJV72Vy977IaJR5z8ci2666QdcfHHffmcK5XJw113VXHBBeQVId3eUtrYLd54jB0FAb+9K2tunL8nv7g7R1lbNjlPpqSnIZjvKSzuPIrlcjszkGIX8OKFShsT2OqX7moFfDKBnCDqbD/yc3SMQSbbQ1tZx4AOPMZlMhg+99KWE77qLF4yMcMUe1/XDwJubmvjoN75B19KldM47PDODpcMtn8+TyWwikSjyqU9dxSc/+QS7xwK//nWI++9/FR/72P8cuUY+TfL5PKseW8E//+NfMbHtFj54VZqzTywvLP7lPSH+7ZdJTr3gDXz6H/7NSS+H5pDOuA1GtysWi9x5ww3UbdlCy+QkD46Pk98+y3IiEiE1bx4XvfjF1NUdZMvXJ2nHLqY7Cv/ns1kKmQyFdJpSOk0plyvXxyqvfSl/FQo7a2SFCwUipRKR7cX/4+wKWWPsXScrAKaKRQazWYYyGR7LZGgrZihlspDLE80VSOQLVJWKpEoBOxa/7D4W0ZvP88T4OMPj42zo6yPe3c0JExNcxIGnIJeAW4ENNTXcOXMGn1tcT81+1hn+qlRF3Qc+xXmXXPLUfrH6g5RnGtxMU9Ou2zZtijI4OMLpp+9/idcDD1SxZMllT7nQdalUIpOeIp8dJcIUiVh5NlUQQKYQkMkWCYIi0UhA1UFqVE17PUVI56AQJAnFa6mqbtirjRMTE4wO90OQJRyCUhAiHKmiqWXGUT0a19/TQ3p4mHgux2QuB6US4e11ihMNDczo6jqq2y/tzz33/B+p1OUsWbLrttFR+MY3ZrJs2YVccsmugK9YhN/9rppLLilvAPLQQ1Gy6QkeueGnLG9ay6kHuBZ8aD3ct76GExZ00jvVSiHUzIvPT5E4wLlmKYBVW6FvPElbSz0n7LZp0w6bBsIM5GbTPPciZs9dtHPwL5/P09/XzVjfSiL5blpSUzQe4rjFo5shHoVF+7m239QfYv1gHaefexWxWIx0Ok0kEiGVSh03A4u3/uIXnLNhw86B5DwwXiwSAuojEcLASmAyHqc+maS050aYiQThqirCySTRmhpiVVUkkklisRiJRIJoNLrPTf7Kg3hpRoeHmejrI9PbS2FwEEZGiI6PE89kqMnnqQee7KL1bxdzxEb7eGX19CXxN/SM8OPb7+ajwQCzDvB+fFcfnFcTJvOs53LFv1zDzFmzn2QLdDRZtepRJid/x+mn711aIQjg9ttjnHhiBy0tSfJ5GBk5idbWXaNH/f0baGjomVZXdGAA6uqqicd31RUdHk7R1nb0B2X5fJ5MepJCdgwKU0S3b+YaC0OuCMPjMKPhwM8xPAkku2jcfbeq48TExAT/+IEP8PANNxDu62N5Pk91EPBAPE5u8WI+/pnPUNfVRcvs2VTX7GeZoHSUGx7eTH19hn/8x/fxnvf8ilRq13333Qc/+9lFfPzjNxw3QeHY6Aj93etYt+4JvnHtV+nbuopSAHNPPIf3vf/PmDt/KbVPcy51HDMYfSruv/NONl5/Pc/PZtn9OmUU+G1LC+e95jW0tbcftp+fz+d3hqW5XK4cmuZyFNNpCrvNTC1ls7tmpuZyFLJZpiYmmJqcpDAxQZBOE8pmieZyxItF4sUiyVKJVKlEHeWwM3WwxlAOMzdkMqwbHyczOUldNktpfJyNvb00j43x3CDgYNV67gNWJJNMdnYy65xzqDv/fJInncSNX/on/iy7lZo93qp3BUnuOvVi3vvXn35adyDXoRsY6Kax8ZGdI3ETE7B5c5H29jEOdE45Ogrd3SeyePGpf3AbCoUC2cwUhcwokXCGZAyi298OxQAy2RK5fLlGXywK1fuZTbAv2SKks2FKoSSReD35YolCepDm2sK0DVByRegdjdEyYw5VR3FdqkKhwNT4OLl8nkg0Sk0qRSwW8+9Hx6zVqx+mv/80zj13VzCQy8GXv9xIR8fz+aM/mj7r8Te/SXDJJR2Ew7B+fYi6ulaam8tDdQP943zvP3/O3NDdXH5anv3lgwOj8OsHYtQ3thNPdbFlcg5nnBDmlLkH7ltyBXh0c4iJbBVzZtYzqy0xbaZXtgBrehPkkkuZu+Q5e12YT05O0rt1FdnRNdSEB2lvKBLfRxuDAG5+BC4+ee9VHrv71QNxLr3yTcfNxcGeerq76f3lLzkxneaOsTHGs1lagoBSKMRgKERTMklm7lwufdWrnlQfWB4QHGd0cJB0dze5PWZ7VuXz1O5jtufT4QuDfby9eoqqPZr7949sZOMD9/Kf9dkDPv66CXj8NX/KR//pC/b7x4GtW1czPn4H/f1TLFlSpKWl/Pe/bVuYlSujLFnSQldXavuxSWbOPH/nv/voaD/x+NppdUUnJiAUSlKzW+3inp4ora0nHJMDJvl8nlw2Sy49SqEwQSGXo+MgeefQJMRr55FKHcrVz7FnbHiYyZ4e1q5fz5Zt22hvaGDOrFlEUylikQjh+npa29vtH3RMGh8fJpkc4L//+z+54orP07Hb4PC6dfC1r53Mhz70u+Nu4COdTtPfs4ViYZIQAQEhIrEaWmd0HdXXpUehQ+r4XGOzm3Q6zcbbbuMl2exev7164MqBAX7y/e9z6RvewBMrV/LgDTcQGx6GQoF0IkHrwoUsXrSIUKGwc5ZnkMuVh2V3/LdQIFwolGd7bv+KFovlHe+LRaJBQBx21jENikXyxSJThQLpfJ58oUBhe93QSLFIrFSiqlSicXsb/9DdVPNBwONTU2wdH6c4NUVrLsdJAJkMt/f0kBkd5dJikYsP8jyrgFsSCQY6O2g75QRmX/0eLrvyRdOOmbtgAf/xz58lsXktc7PjTIWjrK5tZfEll/HeN73FD+8jKJvdMi08GB8PkcnkDhiKAtTXw7p1I09LG6LRKNFUHaTqyrMFsmmKmRGi4SxVMahJhrfX+ouRL8HEbvVJkzGoOkBdikQEEtXlTVYyhSkmJqCzae/j4hFob8jT3buFWXMWHrXvyWg0St1xdjKgytXTs5VVq87mhS+cPlvqy1+uIZXaOxS99dYo559fDkUHBgCadoaiAJlsHW9437upqnoXv/rJbxh9+Me84ORhGveY1tdSD6+/OE+huJlf37eVqvxqRnpn8e1Np9JQX8cpnYPMad17jDgehdPnBcAUk5kp7n44QiGo5oS59bTWR0lEYWlnFrif4fX388B9TSRaz2HB4rNIJBLU1NQw/4TTgdMplUoMDPQy3P04ocxmGqvHad3ezoks1NccOBQFaG8IGB8fp6lpH53acaC9o4MN8+fzo1tv5XnZLHvuObupUOCu0VGKxeLOpeRBEDA1NcXoyAiTvb3TZntGJiZIZDKkts/2PNzz5yYoL2/dVCoxlMsRSadJ5NJUbZ/IFQSwcnSSX9z9OOF1GwmT3bFvzX4tiMHonLlH7WeUnpxcbhuLFyeZOzfOE0+keeABSCZbiMViXHxxiNj2EdxsgWOZZQABAABJREFUFqqrF+/8d0+npwiF1k0LRbNZyOUS0zZbGhqCxsZ5x2QoCrs2y6zZHnJu3rSaIMgcsG9M5+PUHserZ+oaGwmFwyysqWHpokXljiQUIh+LEa2upqGlxf5Bx6RsNkskMsBvfnMj55wzPRQdHoavf302r3nNt4+7UBTKe3PMnrcIKK+qPNZLIR3tnDG6m9tvuIF5t9xCxwF+Jw8D9w8P0z4xwXOKxZ2zBgLK9axuTSZ5Y3v7fjcGCIB0EDBaLDKaz5MuFMhuDzwplQgXCkSDgGSpRHUQ0ED5fPhw1R6dKJV4bHKSvrExIuk0nYUCiynPhujN5bilr4/S0BAXFgrMPMhzbQOuT8QZnT+LF527lPn1KfIBXFPVydv++u/2W+tqfHycbdu2kUgkmDNnjh/cR1j5wvwm2tp2hRLbtsXo7R3j9NP3vxPwDg88MIPTT7/osLUvn8+TzUxRzI4Si2Sp3sfUnVwR0tkixWKRSKi8kdO+ZmAB9IxAYwoSBxgmGpoME0vNorbWJQvS4TQ+PsbPfjaP171uaNrtX/lKgmLxKt75zul/8A88EGbBgk7q6iKk07B6dS2nnLIr8RwYgFKpgba2XY9bty7MYN8mHv71V3lWx+OcdIBVx/evgYc2p+ia1clE7CyC+rNoi63lhLYBWg/SHQyMwxNbo0RiKZYuqKO2avoJ7Yb+CEP5WbTMu3i/Ay+ZTIburWvJDK2imOmjVCpwypwD/9zHtsVpXvhi2traDnzgMez6H/yAs1asYH+XQRtDIe6vr2deJEJscpLqXI7aUol6Du+GmXnKK4zGw2Gm4nHyNTWUamuZSCSYnJwk3N9PTV8fzWNjzC4WqaG8Muc7CTgjNMWv7l9DeM0GTp+Y4ALK+42/Jwn/1nXgn/vzdBi++H2ufNnLD+Or0zNl06afMHv2rk1FNmyYR3v7yeRyv2f3lZNbt9bQ2XkuUD43Ghl5kNbWXeUYCgXo74/Q0bGrdNDEBBSLHdTXH111Rf8Qw0P9hHN91FfvXXoAyqWURvMNtHfMeoZb9swLgoD01BSFXI5QNEpye5kQ6VhULBaZmNjE2rUPMTr6Ui65ZFdGk83CZz/bwhln/AtXXPHaI9hKHQOcMfpkDW3YwLkHCYqDdJq28XGeX5r+4RsCFgN1mQxf7+7m9Koq8oUCpUKB0I6ZoNtnd9YBDcCRKP/dn8+zYmKCsYkJUpkMC4tFdt+vdKxY5Bf9/UwMDHB2LscrD/J8I8Cvq6tZOX8OL1u+kDe17LogHSzCdckZXPm2dx1wA4Da2lpOPPHEP+Rl6Wk0NjZCY+Ou93c2C7W1caamkgwMZGg5wLn08HCIVOogFfD/QOWZAvVQW19e+pieoJQbnxaSxiMQr46wY/50Jh8wPLW9PmkooGq3jZyKxQOHogC1yRID46MGo9JhlM/n+c53lvP2t08PRf/nf6Jks8/j7W+ffnG3ejV0dLRTVxehWIQVK6p49rN3fQZNTMD4eC3z5u16XHd3iJkza5k1axldc/6TcGiC//7ul2ibupHLTs2z52D88oWwfOEEvcNPcP2D66mtv5Xh+pNYUfV6klNpYhN3s7Rrgtp9lFVuqYWWxQVghM39I6wYiJGqqeekBSkSUZjbWmQuG8hOfp2VNyXJV53M3MUX09DYSLFY5OEH72X9qgeIhyeBELlSNQQBy2aHDjgzanQqwdzaJ1vl8tiRzWbJb9my31AUYE4QcP/ICKdyiGfDh2iCcvA5EYuRTSYpplJQX0+0pYVEWxs17e1EIhHG165l/MEHCdato66/n1MKBRr2eK4AWDk5yW83biSzfj3rxsZ4VxCw51upMQ89BWg/wOfUzxvn8i8vvPJpfKU6EsbGxli37hHy+RyDg+XSuHPmQE1NF4ODK6dtQjc1BXV1S4FyGDY4+Djt7dNr1Pb3R5gxY9c7KpuFqakUbW3HTygK0NDYQs+2NKXJceqrS9NKn0xkYDhTTXvH4SuDdjQJhULlOqLWEtVxYGJikJGRbaxe/Qpe9arpGc0Xv5hi5sy3G4rqaWMwuptDOXm+b3SU15f2PSIJMBOoyWZZls0etPbm0ylDOaQcAyajUaaiUTLxONuAqdFRagYH6RwdZUk+z2V7PDZbKnHT0BC9fX2cnMnwkoP8rDRwYyzKylNP5QX/8Z+86owzGBwc5KYf/4gHNq4jWshTiMWpn7+IP3rJy47Lqe3Hs8nJLTQ07Pp+eBja28PEYikeemiS5ub8Pi/KgwDWravm5JMXPWNtLYekjUDjrpA0O0Y8mqNqtwwlGQuRjEWBKKUAMvkSE5NFCqUi+/9r3sOhz66X9CQFQcBXv3oJ73rX2mm3//KXIUZGLuT1r68lvtvSiW3bIByeQXt7+ca7745z9tm7PmuyWdi4sZqlS3eFAqOjkEymiETC9PREmTUrASR4w/v/iq1bP8qPf3sbudVf4IWn9lK3x2ZIMxrhDRfnyOU38st7NzPafR/1rQtIzH4NPU2XsmLV/5HKP8RJXdl9bto0qxVmteaBAR7fMEDfWIKWxnpOnFO9fal9hu7BO/nZN3/NVLZELg/RCFx4ai1L51URCkExGOSh9VF+fVecy89p2Gc/nCtCPtJ4XNee6u3tZdbY2EGPa6IcZB5qRLyv2Z5BbS2hxkbibW1UtbdT39JCW20tndtnYKXTaTY+8QRDDzzAyO9+R6q3l5nZLKfs52cEwMqpKe7csoXh9euZNzLCG4rFA66U/5MifLwXvjwTIvv4N/95JsqSV7/KTfaOcY8/voJicRMnnpjZuRR+YgJWrYJUaoCZM6cPGA0NNdDVVX7n9PdvoLl5ctr9/f3Q0pLYOdhTKMDwcJTm5oNMPz4GhUIh2mfOYmRkmK2jg4Qpb8ZWJEJVVT3tHY3OmpSOMen0JLlcDz/72Qt573unb/z75S/HicdfzFve8ukj1Dodj1xKv5v/++53ueDhh9nf5rAl4BtbtvDmQmE/R5Q9BBSB0//A9uQpB52jwEQ0SjaZJFddTam2FhoaiDY1kWhro6qjg9qWFmpSKbauX8/WG26gcO+9NG/axNJCYZ8XBcUg4LbRUdb39jJvcpLzKS/Z2p8CcEskzKZ5swg950Lqn3URL3jt6z0RPw51d99ER8eu9/jWrVE6O6vo6YlQLOYYGupj8eL8tN1O83lYtSpJQ8MyOjuP/A6n+XyedHqcIDtGPJqfFpLuPKZU3sk0X8jRVlc64A73YxkoxmbS2Hh4Z8NKleq//uu1vOEN/zMt/LzjDnj00TO57LJFzJmzKxEaGYENG5o57bTyp9uKFVEWL24hmSx/ihWL8OCDSZYv3/Xpl83CwEAVHR1JNm2KMmdO1c5gcWICcrm5NDW1EwQB99z5W1Ze/2HO7byfE3abobWnux6Hx7praWqdS2jG5Zx12QcYHeml94mf0xJdzeLOwgE3bcoXYeWmEKOZKkYmI2SzaZ6/vEBd1a7773wizIaBJK+9rI3I9g/pRzaFSZfqOOvE6Wcr+QB+/3gdpz3ryuN6QHLTpk2MXnsty3K5Ax73O+A0yrXXoRySjgCTO2Z71taWZ3s2N5OcMYOaGTOob2igurp6n2UN8vk8m9evp++++8g9/jhV27bRPjXFTA5c270EPDI5yYM9PfRt2EDH4CAXH0J5ok3ADYk462Z3MF4dZ3TLJt5eleXZiXKd2b4CfCGTIHTla/j0V75mGaJj2Pr1q4hEHmX27Pw+73/44TDt7TW0tpbns4yPQyRyHtXV1QwP91NVtZbkblONy/cnqa7e9c7s74fa2oUkk8fvoMkOxWKRIAgOuFpN0tGrvBnieq655oV8+MNrpt33gx9E2Lz5Mt773p854KFD5a70T1ZvTw+rvvY1Lpic3Of9eeAb27bx1oOcjD9G+QT8rP3cvyPsHItESMfj5KuqyFVXE25sJNzURLy1leSMGaTa22lobCS1fYfpPU1NTfHoXXfRf8MNhB56iJm9vSwplfZbjzQIAh6YmODh3l5mjI/znO0bPR3IneEwDy5ZwtgF53HCGcuJ16RY+uzzmGUt0OPS5OQE4fAdO2cr5PMwMVENRKiuriaRCLF1a5pt2yZJJCapqiqSyUTI5WqYPXspra1HX027fD5PemqUIDdOIloguf1PaSxNeYOpUompbG7nBid7CgLYMhKnfeZ8P4Clw+D73/8LLr/876bVzlu5Eu666yROOeUUzjhj17BdJgP33VfHeeeVNxZavz5EQ0MbjY27AoD7749z2mn105bFr10bY8GCFOvXR5g9u3rn5nL5PHR31zB79rK92rV1y2Z+873/R1fhOp5zyr4DC4AtA/CbhxJU1c0k2XYeJ1zwQRYsOpmVK25jdNP/Mat2M/Pa9n8KtbobHtsEV52z//sf767jRdtfcxDAf99azSkntjOnuVz3ectQkoHJOk4582KaW/bcjuj4kk6nufnf/50rhoYOeNx343FOPPlkkm1tJNvbaWhp2e/51J6KxSLbtm6l5/77yaxcSWzzZlrGx5kTBIdUo7QIPDwxwRP9/WzbtInmgQHOyWY5WNGgIeBX0SiD8zt54Xmnkq9t4I7qFlInnca6B+7hkVtvYbxnG+FwmJq5C/jIv3+VpUuXHkKLdLQKgoB7772es84a3e8x+Tw89FCMM87YsRN9K52dpzE1NUGh8Mi0vjOTgampBE1Nu0LBsTEIguOrrqik49fQ0Ca++c038+53/3baRJybbgpx333ncPXVvzyuB4D1tLPG6JM1o72dh5YsYfVDD7FoH+Hng9XVpGtrYXDwgM+zBoimUvy+ro6gro5wY2O5/tSMGdS0t1Pf3ExrXR2JsTF+9KUvMfLIIyQzGXLxOIlFi3jJZZcxd97es+4GBwd57OabGbn5ZhKPPcbc4WHO4MAzPQFWpdPc3dNDanSU55ZKLD/I8Y8C9y9eTMM73sFz3vpWnmWdmooxOrqZmbtNYxkZgbq6CFNTCRKJEOk01NcvprOzjmw2SzabJR6Pk0zuo8DeUSIWixGrbwFayOfzjE2MQGGCbKFAQwJi4TBjU2HG0qWdM7V2CALoGYW6ujZDUekwuP76r3L++X+3x4YicPfdc+nqWjYtFC0W4fbbq7nkknJA2N8P0WjTtFD00UfjnHzy9FB0/fows2en2LQpRFfXrlAUYNu2CIlEwz7b1tk1izd+6Luk02mu+8Hnya/5Z648fZCaPbq7rhb44+dkyeTW84t7NnL3t29gZeuptJ/6Ts57yafJZDLcc/8N5HpvZmFrPzP2WDd9+0p47cX7/x0t6oDbHkuTL0AsWp4tOKslQtPcS9ky3AcBzDhxFsvaKqOGXlVVFcycyfjQ0M4VMXlgpFAgEgrRGInQEw7TefbZnH7FFQd9viAIGOjvZ8uDDzLx0ENENm6kcWSEOcUiT2arlgLlMHTtwAA9W7dS09fHsnSaVx3kcVPA9dEoWy++mAv/4R84s7qah35/C3fn83QtWsxrL7ywvDrnDW94Eq3RsWJkZISmpqkDHhOLQTRaJJ8PmJgI0dKyhHw+z9TUY9Pqvufz5eXyHR27Lu/SachmU7S2GopKOvqNjw/z059+mje9aXoo+vDDcO+9J3HFFf9uKKrDwmB0D5e+5CXcUVPDYytXsmh4mFShwHA8zrqmJuaddRYLN25k089+xv42sS0AfQsXcvVf//UBf86aJ57gfz78Yd65ZQu7n6qMb9rENY88wnkf+Qjts2ez5oYbmLr9dlJr13Li9iXvh2JrNsvvensJjYzwnEKB1x/k+I3AbbNmEX/Tm7jkT/+UNxxohx0dt4rFnmnf53JRhocjtLdHyechl5tBfX05wUgkEsdcKYVYLEassRVoZWign2JxhFgY2hvjDIwV2DpYpDoZEAlDtgCZXJhwJEm9H8DS0+6ee/6PWbP+ZNpgzOgo/PrX7dTUnMNll01foHzLLXEuuqiNUKi88Uh/fz0nnbSrD1q/PkJn5/RapN3dIdrba+ntDRGP10wLTPv7oaoqST5/4FUgVVVVvOyNf0EQfIzbf/dLVt/0US6a8zDz9sghk3F4+XkloJffP3o9T/zqTjbfuYiGE1/PBZf/CdXVL2FocJDb7/854dE7OKlznFSyHHQeqJQHwNJZeZ7YlOHk+eVUNhqB2to6Zs8+yBb1x6kLr7qKXw4McGF3N78fGmIsk6G9VKIYCrElEiHX0cHbn/OcfT52dHSUTY88wtiKFbBuHXUDA8wuFJ5S+aMM8JvxcYZGR8lu20akp4e5ExO8lAMvry8AN4XDrDnrLE77m7/hqksvJbzbm/PEJUueQmt0LMrlciQSxYMeF49DPh8wNdVJfX2Mvr5HaG+f/ri+vggzZ+7qE/N5GB+P0dh4/NUVlXT8yeWy3HDDv/Oc53x12n4XW7bADTfMY/HiP+Pkk//QYoXSvrmUfj/y+Twb1q5lcnyc+qYm5sybRzgcJpPJcO2nPsWrN2zYa0fUAvCdlhYueO97mbdw4X6fu1gs8tevfjV/uW7dPpdkBcBfAu+CnTWoNmSzrMlmqQmHOaumhug+lrEPFwpc19cHw8M8J5vlYJUeB4DftrRQfOUrOf/DH2bWnMq8wFLZ8HA/0egKdmxmXCrBpk1JOjvrgRDj4w00NT2Z+TNHt1wux9jQVlpSu+qpFgJIZ0qUgoBYNEwkEmIyV0/TUVgiQDqWrVr1EIODy3n2s3dd2Ody8LWvNRKLPZ+rr56+FuK226KccUYXyWR5E5EHHqjmrLMadt7f3R2mUGhg1qxdcdTYGBQKtRQKUWprU6TTIQYHYdas8nOEw0mi0Qjd3THmzFn8pNq/ccM6bv7+h5gX/l8uXLr/UGNDL9zyaJJQVRcNC67i7Od9iPaO8if71i2bWHX/j9mw6i6ed2qBcDjEjKboPuuSrtwMk6VWzlpcXsHxvytauPxl76nomez9/f1c+4lP8KbRUfbsoR+LRrl72TL+6J3vZNOqVQzffz/F1atJ9fXRlc3yVIoNlIBuYHMyyapQiEhvL6F168ht3Urr2BjP3ceO8nu6MxTiwZNOYt6f/zmXvPrVFf3vp7KxsTG2bbuZxYszBzzugQfCdHY20Nh4EcPDm2hs7J82m6qvL0RjYxWx2K4OpL8/TCo1j6qq/e2eIElHh1KpxM03f4uGhj9m+W7LW8fG4Npr26mtfSNvectnjlwDdSyzxujhMjk5yXX/+Z8kNm3itIEBYsCaVIoNM2Zw6RvewLxFB96V+9YbbqD0yU9yUWb/J0GrgfuBRRMTfGXrVuZls5xcKDAWCnFrPM78+nre19nJw0HAbzIZqvv6uHBoiL2rpE03AfwmlWLsyis586MfZckp+9s7VZUkn8+zZcudzJu36z05PAzhcAv19VEGBqppbp5/3NWV7evdRn1iksQ+5s6XAhiYiNLY0uXFq/Q06unZyv33L+IFL0hPu/0//iNFOHw5b3tbfNrMzgceCDF/fhf19eXQ884745x9dsvOY4aHobu7kZNO2vWHnMtBb2+SmpoqEokaamrCO2/fuBGamuI0N8fI52HLlhjz5j25YHSHyclJfvm9fyC08V954emjVO1nEv1kBn5xT4SJUht1XRdy0kV/xqOPruRbX/krUqE+4rGAsXSc5tZOLjmni1c8p5XoblMOb1gR5sQFHcxuizE6BXf3nM5lV/zRU2rz8eLb//IvvODuu/capN7hIWAdcBUHLzm0L/3AlkSCibY2SvPmMVgqEbnzTlixgrGtW6kaGeHSYpGGgzzPY8Adc+fS/L73cenb3kaN5Ym0h3vuuZEzzxxif6dY2SysXBmjvf1kkslaqqs3sPuCnbExiESqdvZzsOMcrp36+uO75rCk48O99/6WwcHn8fzn7xpsLhTgS19qIJF4AW9/+7eOu+tQPWMMRg+3kZERHnvwQQq5HLMXLmTOPuqC7svnP/Qh3nfzzQdcZgXwvvFxwhs28Nl8fq+aB7eEw/xTdTUfmZzk3IP8G+aB38bj9DznOSz9yEc448IL7Vi00+joMBs3PkIolNk5+2DGDBgfjzB7ditDQzFqaxccl+FgPp9ncKCHmliW6kRAZPufRSYPI+kodfWtVNekjmwjpePI+PgYP//5fF7zmum1ur/61QSl0vP44z+unba78urVkErNpKOjvD5+xYooS5a0kkiU/1jTaVi5sokzzpj+ibp2bYyWlhShUDV1ddPvK9cmTdDYGGVsDCYnm+joOMD284cgCAJ+d+N1bLj1L3juoifoOkA1mt+sgC/+b5RXnFfkNRcFO2ueZvPwjRsjPNbTSFvXEj78+llEwuVax/91Y4K3XtXBZA5++eAMXvDSqys6YJucnOSnH/84r+3r2+8xAXAN8FYOfkY8BmyMRhlrbiaYN4+6006j/cQTWXvXXQz88IdUP/gg/T09lAYHubBQ4GALk7cAN82YQfItb+Hi97+f1jZXHWj/enu3MjBwHyedlNkrHC0U4KGHwjQ21tPaeg5BsHLnyh4ob7Y0MZGgpWXXlcLkJExNpWhpmev5vqSjUjo9xfj4MFCkv7+HFSuew+teN30D7H//92qi0Yt585t/clxeh+oZ4+ZLh1tDQwPPvuiiJ/24oFg8aCgaBAHrNm/mJ/n8Po+9qFRi68QE4wd4jlsjEdaefTZzP/QhnvviFxON+s+t6davfwLYxpIlhZ2haHmXZhgdDTEyEqa29vjdjT0Wi9HaNpOpqUkGJ0bZMf4TjVXR3Npw3L5u6UjI5/N897tn8La3TQ9Fv/OdKLHYuVx11fRQtLsbYMbOUHTt2hBz5zbvDEXzeXjkkeZpGzRBebOl1tYUxWLVtI2ZdmhshM2bCzQ0RBkaCtPZ+YeHVqFQiIsuezkXXfZy1qx+jGt/+EFOTF7PuYtLex378Ab4fy8rcP4em4knYvAnVxT57i1DbJncyHW3VPFHF7fw/duiLJzTwG9W1jMVtHL5i/+ookNRgA0bNnDiQXalDwENlOuA7r6vXhrYGA4z1NhIcfZsUsuWMfPUUzl5xgyy2Sz3/epXbPzylxl/7DH6enuZGBhgYTbLcw/SpmHghro68q98Jed95CO8YcGCP+AVqpLMmNFJoZDn3nsfY86cSVpaShSL0NsbYuvWMPPnV1MsLiSbfYLm5l2Py+dhcDBGZ+f02fJTUzEaGroMRSUddYIgYHCwn0hknMbGEuPjk9xyy8t417umh6Lf/GaMePxMrrrqv7we0zPCpOwI6Fy8mNW33sqBFtz/anycFx8kQH0F8D7g+bvd9kAoxENLl9L6nvdwyRvfyAVVVft5tCpdT89W4vFtdHYWpt0ei8Hs2dDdXWRsLEFDQ3w/z3B8iEQi1NbWUVtbd/CDJT0lQRBwzTWX8M53rpl2+y9+EQbO4MILZ9DUtOv2sTHo6Wni9NPLn2F9fZBMNtPQsOtT8eGHm1i2LDxt2X1PT4iGhlqy2SStrfs+xdkxRtjTE6a+vuNpP+FeuGgJCz/6K8bHx/nu//wNsa3/wZVnTJKIQbEIT2yB979k/49/1YUl3vHvAwzlJxgonkzXgrNonn8Cs+cuoL6+fv8PrCBBEBAu7R0678t6YKyujlxXF1UnnUT78uUs6uoisn2qbjqd5t5f/IK7r7uOmU88weTgIIN9fcxIp3npQZ47DdyYTDL8ghdw+kc/yivPPPMPel2qXJ2dc2lr62Tz5nWsX/8YiURAa2ucM8+M09sbJxwepbl5X5st7TpHK5VgdDRMKmUJIElHp6GhAW688QfceuuvGR/P0NGxks98Ztu0Y372szCZzKmceeanaWtr388zSU8vg9Ej4IWvex1f+cUv+NC2bfs95of5PH+Vzx/weWJAAlgD3DlvHtVXX81z3v1uTt99GzdpPwYHN3PSSYX93t/REfDoowMEwQJnHUj6g3zta6/j6qtvm3bb7bdDJrOUhQvnMX/+rnQzm4WHHqrl/PPLgxWTkzA01MDixbsCgAceiDN3bmTaDNOxMYAaMpkkHR37DwXyechmIzQ3z6Subn8VKv9wtbW1vPpPPkup9Bl+++v/Ycttf0ln1XrOPuHAjwuFoLMpT6ImzKuv/jiNjYevjceq2bNn86umJk4fGJh2ey4IGCwWSQKN0SjbmptZ+o530FlbS1fXrhl0U1NT3PuznzH64x8zZ80aIqOjDPb2UjMxwXM58I7yReCWaJTNF1zAog9/mBc+73nTdpSXnqpYLEYiUeScc3bNCM/lIJutZ/bs6fsS9PVFaGtLTlt6PzwM8XgrVVWWAJJ0dMnn8/zbv/0Vt9/+FV75ymGuvjpgyxb4v/+DL34R3vve8vnP7bfD0NBJtLa+keXLLzjSzVYFMRg9Ampra5n/8pfz4298g5eWr+Sm+X1VFcH8+RQ2bjzocw23tFDz4IO8fubMgx4r7ZBOp4nH0/st9L9DKpVhcnKSVMqTbElPzfe//xe84hX/Q3y3yeePPgo9PYuoqlrC2WfvCpVKJbjttiouuaS8XjSfhyeeqGb58l27Kq9eHaGpqX7aDNN8HoaHk4RCs4hGs8D+B30GByPMnLmAurpnZvZlOBzm0he8Dl7wOr7z7W+QGHszByvbXpMMKIbDDkrtR11dHdmuLsYHBqgFhgoFvtPXRzqdpiMIGCgUuGHzZpqKRULf/S65cJjHWltpXryY84OARRs20DQ5yYbubhgf59JSiXMP8jPvCYV49PTT6Xz/+7noVa8iHj++V1PomZPP5xkY6COTmaBU2koux87+sqcnTmdnZmctYoDRUaiujk/blX5sDEKhFLW1ByhwLElHQCaT4e///r3MnfsNfvCDXRO/li+Hq64qh6N//dfw6lfDmjULKZUu5Mor33EEW6xKZDB6hLz0TW/i+tpa/vb732f5li3MzGQYjMW4q7OThc97Hh8691x+/pzn8P49ZkPsbhxoOvdcOgxF9SSNj4+QSOw/ONghGg0oFosHPU6S9uX//u8rnH/+31G3W6WKLVvgscfmkMst52Uvmz437+abY1x44Yydgzb33RfnnHMadt6/eXOYXK6JRXvUonnssRiNjbPp6jqZDRueYHJyhH2V4UynYXKymhkzjkzpjOdeejn/ekMHsP8VIwAb+yI01ba6dP4AXvTWt/Ktv/97nrdxI9/ctIk/zeVoBvrzeT60ejVfyWSYtqVWby83PvII/xGL8epikeeWSpx8kJ/xBHD3woXUvuMdXPr2t3OWg4R6GgVBwObNa5ia6qOlZYq6uvIM0fXryyU/Zs6EZDI6bVApnYZcLkFr664BpUwGCoU4tbWdzl6WdNR5+OF7GBn5MW9+875Xwz7/+XDvvfC9781gdHQ+733vuygdYrkc6eliMHoEPe/lL+fSl76UB1esoG/rVupbWvizM8/cWRfoiyecsHM2xL78Z3Mzr//4x5+5Buu4kM/nKBQ2kcmEONispampKM3NiWemYZKOK3ff/WvmzHkHu4/djY7C737XzuTkObztbdND0dtvj/LsZ8/cWQP0gQeiLF/esjMk7e+H4eFmlu6xadH69WFqa1OkUnMIhUJ0dS1gy5Z1xOMTtLTkSSTKYcPAQJRstobZsxcesZmYM2bMYJglZHLbSO5nwmHfCBTDNZxwyiXOGD2AxsZGXvOxj/GJ176Wv83l2BFZfnrjRj6fybCveXOXApl8vjywvJ/n3Qbc0tFB5A1v4OIPfpA3zJhxOJovsWHDE9TUdDN79q6B6pqa8gZxw8Pw+ONw+um7+sl8HoaGEtM2W8rny+dqiUQHsZizmCUdXSYnJ/n5z7/Ce94zeMDj/uRP4AtfyFEs5ohEcJBHzziD0SMsHA5z+vLl5bnke/jL736X91x2Gf/wxBN07HZ7EfhqYyPJd7yD5Wed9Yy1Vce+fD5PX9/jzJgRMDYWIZ8vsL/6/Pk8FArVJHcv4idJh2DVqgcpla5k8eJdgy/ZLPzwh40Ui+dy9dXTTz8efDDEkiUdVFWVg8DVq0MsWNCyc6bU+Dhs2NDEySczbUlpX1+ItrYU4TD09PTQ2NhILBZj3rwTmZqaore3m2IxTyQSoamp46goC/KnH/ki7/3oFXzpzZuI79H/jk3B+76aYNbJl/OK1/7JkWngMWR8fJzTCoWdoWhvPk9tOr3PUHSHFwJvB16z220jwI0NDWRf9jLO/ehHec3ChYepxVJZOp2mUOinrW3fq3caG2FkBMbGitTVlTu9np4YXV3T+87R0TDRaAM1NW4gKenoMzY2yKZNj+210mdPra0wNZUnlcqTzebcQE7POIPRo9isWbP47O9+x7/9+Z8zePvtdExMMB6JMDpnDi/70Id43ktecqSbqGPM8PAWGhuniEahqyvB6tVFFi0K9gpHCwVYsyZJV9dBPsUkaQ/d3VtYu/bZXHHF9DIcX/96iljsXN7whuS0cHPtWmhp6aCxccfFP6RSzdTVlWcLZDKwcWM1DQ0RHnkE5s2DlhaYmIBQqIaamgjZLJRK02fAV1dXM3v2gsP7Yp+CExefxPv/5mf8ySffzMktj/GC5RmKRfjxXTHuXFPHRVdczQc+/CkvCg7Bit//nnMHd81CuWtiguccZOPKEFALTAI3V1Ux8LzncfrHPsbLzzrLGbp6xvT0bGTmzMwBj+nshA0bctTVVdHTE6G9Pb7XZkuRSDWpVOthbq0kPTWlUolstkSxOH1ge09BUK4zX/4IP9AWiNLhYTB6lGtra+Nvrr2WYrHIyMgIVVVVVFdXH/yB0h5GR/uBPna8fVKpMO3tVTz6aI6WFmhuLs9aGB6OMjSUZNasxdTWOgNB0qEbHx/jlltO59WvTk+7/atfTVBXdxaXXlpHVdWu23t6oFBoo7Mzvv3xMDbWwAknlL8vFGDt2gQnn1wuGFoswkMPlU+eM5kks2eXw8PR0TC1tcfO7u1Ll53K1390PyseuJ+bbv4Fo2NjnPPaS/noc55jIPoklIpFdl9sF1AOPg9mKpHgnu9/nyuuvNLlejoistn0Pusg7y4eL/d1w8OhvTZbmpqCcDhBMtlBNOrlnKSjU3V1HSeeeC4/+9lDvPSl+9+34p57YObMOrq7G2hoaHsGWyiV+Ul6jIhEIjQ3Nx/pZugYNTU1zsTEJjo7p8+oymbjLFhwEoVCQE9PHwD19c0sXdrizBlJT0oul+N73zuTt751+qaB3/lOlPb25ZxwQhutrbv6lfFx2Lq1iTPOKI/W5PPwu9/VcNJJuwb/Hn88xkkn7RqgiUTglFPgzjtDnHtuOWENAujvT7J48bF3In3a6cs57fS9S+no0Cw95xzuaWhg0cgIAGfW1HBNLMYlB5g1GgCFpUu5+KqrnplGSvsQCoXI59lvOSMo923FIuTzSRobdwX4+TxkszFisVYSCcsdSTp61dc38NznvoTPf/6HvPCFvdM2k9uhVIL/+I847e0zOfnki2hoMPPQM89hcuk4EwQBU1NT9PVtY/Pm9axfv5re3vU0N0+/UOzvh2Syi9raRhobm5g3bzHz5i2mqanVUFTSkxIEAV/72nN461tXT7v9F78I09Z2Mk1NszjxxF1Lo3I5WLGiljPO2BV6PvBAgiuuqGdoqLwr8+OPRzjxxAb2nNAXiZSX0o+NFQkCWLMmTlvbAiIHWqOl49LCRYtY09VFbvv3nfE4fYkEYwd4zE2xGOe+/vXPRPOk/WpqmsnAwIHnpwwPAyRpa5veCY6OhoFaUqljZ5a8pMoUDodZunQ5l1zyJ7z+9Q1s3jz9/r4+eOc7Y0SjCwiCJbzqVe9w5YyOiFAQHHhX6t0c8oGSjox8Ps/AQA+Tk8MUCllqa4skEjt2LYW6uh3FrWF0tJmODmuISnrqxsfHGR4e5he/+ABvfet102Y/3XYb5HJLGB9fxlVXTQ8Arr8+yWWXte+sl/fAA1FOPrmNWKw8S+r66+H885upqdn3+G1fH2zbVkcs1kBHxwKamqyxV6nWr1nDtz/wAT60bRtVwJZcjr9YvZovZLPsGRvdF41yzXOfy7/94hcG6Tqi8vk8jz56F0uXpvc5a7RQgEceibJ0aTOx2K7B6qGhcl3R6urZhgeSjhmrVz/BHXf8hN/85vuMj68hlcqSzYaZmEjR1bWEWbNO5kMf+hxVu9dbkp4ehzTjy2BUOo709GxmbGyAVCpLRwfsOfGzr6+8LKtQSNLZeYq11SQ9JTfc8Ct+9KMv09Q0RCy2gQ99aDN1u5UkXrkSursX8tBDJ9LYWM8LX1gelAG48cYYU1OdzJsHy5aVd6Bvb59Bbe2u/mjVqhBVVXXMmrWPNVfA1q0hMpnlLFhw9G2upGfe5o0b+Z9//EdqN2xg4cgIW4pFfrRpE7OyWZ6dz5OLRLivo4N5V1zBBz/zGWsy6ojq7+9mYqKXZDLNwMAkc+YE0/rPyUlYty5CR0c9LS27+sDxcYAksVgXyaThgaRjS39/P3fe+Wui0QmGh0cYHJygWExw+unP5qKLLjvSzdPxy2BUqiTpdJqennXABPPm7f/Pdf16mDHjFDfxkvSk3XDDT/j61z/GSSetZcmSHJs2hVixIuDKK+GP/qh8zJYt8NBDc4hGT+B5z2slnYb//m946Uth7doIp57aSVVVmFtuKc9i7+hoob19egA6NATbtlXv3HRpTw88kGLp0kuJ76tYlSrW0NAQW7ZsIZFIsHDhQiYmJli7di2xWIwlS5YYiOqI6+vrplTaQnt7eROSXC5g27Yc6XQRKJHNhkkkIsTjdcycGd25WV0mA7lcDGijrq7piLVfkv5Q+XyeiYkJEomE16N6JhiMSpWkr28bo6M9dHTkSKX2f1w6DQMDM5g1a94z1zhJx7wf/OCrrFv3MT784YG9ZqNfe225b3nta+Fv/7adZcsW8cY3ztp5fzoN11wDr399F42N5XCqUIAf/jDKq1+996ZJPT0wMlLL4sV7bywyOhpi06bZLFt2ztP7AiXpMMrn82zZ8ijz5mX2e8zQEGQyMVpa4gwOQjIJqRSMj0cJhVI0Ns7a72MlSdJeDikYdR2tdJzIZrNA8YChKEBVFRQKU89ImyQdH8bHx7n55n/gz/9871AU4M1vLs8U/drX6vngB2eSzU6vfVdVBTNmRAiCXQ+ORqG5GdLp0l7Pt3p1kny+lt03Fw8C6O4Os2pVC4sXu5O7pGPLyMgQDQ3ZAx5TXw/ZbIl4HDo6yhvV9fVBJBInlWp/hloqSVJlcU2RdJwIh8sbSQTB3rVFJekP8e1v/ytXX73hgMe8613wX/8VpaMDRkf3vvhfvLjIpk05mpp21carqQnIZAJ2r7U/PAyRSCft7ct55JHHgUkACoUYra1zOP30eS6JlnTMyWSmaGs78AK8PfcEa2qCnp4w2WyS+no3W5Ik6XDwykI6TtTXN5HJDDI2VqS+fv/HTUxAMlm3/wMkaQ+PP34b73jH3jM7d9fVtSsQ3dfgTLG490X/6GiIqqry4pUggM2bI6xZM4Pzz38O8Xic1ta9l9lL0rEoEolQOnA3updYDMLhEpAnn8+7E70kSYeBwah0nEilUvT3J+nry1FbG7CvDefLS1GjzJ3rcixJT0bokGajl0rQ3w81NYm97lu5MspLXrKrZmguB4ODddxzTwsQUCrFmTlzMRdccIIX/5KOOw0NLQwN9dPZmd/vMek0RKPTO9pwGMLhgCexL4QkSXoSDEal40h7+1y2bFnNxo1puroCds8W8nnYsiVCc/McQwdJT8o551zFzTdfzyWX7P+C/rHHYNasGn7wgygvfWnztPsGBiAcTlBTU77gL5XgN79p4LnPfTUdHZ2Hte2SdDSorq6mt7eaTGaU5N77yhEE0NMToqMjPu32UmlHmSTrJEmSdDi4+ZJ0HKmqqmLWrBOABtaujbBuXYjNm0Pb/z9Bc/OJNDW1HulmSjrGvOxlf8w3vzn3gMtA/+3fYjQ2zqS1tYGOjl2DL1u2wHe+k+DFL24hCGDt2ig//nELixe/xFBUUkXp6lrI5s01jI6Ww84dMhnYsCFEQ0OcZDI07fZIJEKxGHFQW5KkwyT0JJZluH5DOobkcjkymQzFYpFUKuUJtaQ/yO9/fyPXXfdmPv3pLVRX77o9l4NPfCJOd/cZnHfeiwmFJqivHyUaDRgerqapaSkzZsxiaGgLAF1diznppNPskyRVpHw+z/DwAJOTAwRBgWKxQCIBra3xnTWXy8dBdzfU1iaJRmdQW2t9eEmSnqRDWm5hMCpJkg7Jo48+yFe/+hdEIo/S3p6htzfKwEAnL3/5/+NFL3r5zuPS6TSFQoGamhrC+yp4LEkCYHR0mOHhbTQ2lqiuLi+dHxuDTCZMMhknEqmnqcmN6CRJegoMRiVJ0tNvamqKkZER6urqSKVSR7o5knRM2zGLdGpqjFAoIBqNEYslSaUaqK6uOdLNkyTpWGUwKkmSJEmSJKniHFIw6vo2SZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRSZIkSZIkSRXHYFSSJEmSJElSxTEYlSRJkiRJklRxDEYlSZIkSZIkVRyDUUmSJEmSJEkVx2BUkiRJkiRJUsUxGJUkSZIkSZJUcQxGJUmSJEmSJFUcg1FJkiRJkiRJFcdgVJIkSZIkSVLFMRiVJEmSJEmSVHEMRiVJkiRJkiRVHINRVYyLL76Y97///Tu/nzt3Ll/4whcO+fFf//rXaWhoOOAxn/zkJznttNOeUvsk6Q9hHyfpeGYfJ+l4Zh8nHTkGozpm9Pf38853vpPZs2eTSCRob2/n+c9/PrfddtuRbtpTtmHDBq6++mrmzZtHVVUVCxYs4BOf+AS5XG6fx69Zs4ba2tqDfuhJOvZUah+3YcMGQqHQXl933nnnEWy5pKdbpfZxAEEQ8LnPfY4TTjiBRCJBZ2cnf/u3f3uEWi3pcKjUPu6Tn/zkPs/jampqjmDLpScneqQbIB2ql7/85eRyOb7xjW8wf/58ent7+c1vfsPg4OCRbtpT9vjjj1Mqlfjyl7/MwoULeeSRR3jb297G5OQkn/vc56Ydm8/nec1rXsMFF1zA7bfffoRaLOlwqfQ+7sYbb2Tp0qU7v29ubn6mmyvpMKrkPu5973sf119/PZ/73OdYtmwZQ0NDDA0NHcGWS3q6VWof92d/9me84x3vmPa45z73uZx11llHosnSU+KMUR0TRkZGuPXWW/nMZz7DJZdcwpw5czj77LP56Ec/ylVXXcVb3vIWrrzyymmPyefztLW18V//9V+H9DM+//nPs2zZMmpqapg1axbvete7mJiY2Ou4n/zkJyxatIhkMsnzn/98Nm/efMDnveaaa1iyZAnJZJLFixfz7//+7zvvu/zyy7n22mt53vOex/z587nqqqv4sz/7M6677rq9nufjH/84ixcv5pWvfOUhvR5Jxw77uHIQ2t7evvMrFosd0uuSdPSr5D7uscce4z/+4z/46U9/ylVXXcW8efM444wzuOyyyw7pdUk6+lVyH5dKpaadv/X29rJy5UquvvrqQ3pd0tHAYFTHhFQqRSqV4ic/+QnZbHav+9/61rfy61//mu7u7p23/fznP2dqaopXvepVh/QzwuEwX/ziF3n00Uf5xje+wW9/+1s+/OEPTztmamqKv/3bv+Wb3/wmt912GyMjI7z61a/e73N++9vf5q/+6q/427/9Wx577DH+7u/+jr/8y7/kG9/4xn4fMzo6SlNT07Tbfvvb3/KDH/yAL33pS4f0WiQdWyq9jwO46qqraGtr4/zzz+dnP/vZIb0mSceGSu7j/vd//5f58+fz85//nHnz5jF37lze+ta3OmNUOo5Uch+3p2uuuYYTTjiBCy644JBel3RUCILgUL+kI+qHP/xh0NjYGCSTyeDcc88NPvrRjwYPPvjgzvtPOumk4DOf+czO71/0ohcFb3rTm3Z+f9FFFwXve9/7dn4/Z86c4J//+Z/3+/N+8IMfBM3NzTu/v/baawMguPPOO3fe9thjjwVAcNdddwVBEASf+MQnglNPPXXn/QsWLAi+853vTHveT33qU8Gzn/3sff7M1atXB3V1dcFXvvKVnbcNDAwEs2bNCm655Zad7aivr99vuyUdmyq1j+vv7w/+6Z/+KbjzzjuDu+++O/jzP//zIBQKBT/96U/323ZJx55K7ePe/va3B4lEIjjnnHOC3/3ud8FNN90UnHbaacEll1yy37ZLOvZUah+3u3Q6HTQ2Nk57ndIRdkh5pzNGdcx4+ctfzrZt2/jZz37G5Zdfzs0338zy5cv5+te/DpRH4q699loAent7+dWvfsVb3vKWQ37+G2+8kec+97l0dnZSW1vLG97wBgYHB5mamtp5TDQanVYvZfHixTQ0NPDYY4/t9XyTk5OsXbuWq6++eucoYiqV4tOf/jRr167d6/itW7dy+eWX84pXvIK3ve1tO29/29vexmtf+1ouvPDCQ34tko49ldrHtbS08MEPfpBzzjmHs846i3/4h3/g9a9/PZ/97GcP+bVJOvpVah9XKpXIZrN885vf5IILLuDiiy/mv/7rv7jpppt44oknDvn1STq6VWoft7sf//jHjI+P88d//MeH/Lqko4HBqI4pyWSSyy67jL/8y7/k9ttv501vehOf+MQnAHjjG9/IunXruOOOO/jWt77FvHnzDnkK/4YNG7jyyis55ZRT+NGPfsR99923c9n6/naIP5gdNV+++tWvsmLFip1fjzzyyF67LW/bto1LLrmEc889l6985SvT7vvtb3/L5z73OaLRKNFolKuvvprR0VGi0Shf+9rXnlLbJB2dKrGP25dzzjmHNWvWPKV2STp6VWIf19HRQTQa5YQTTth525IlSwDYtGnTU2qbpKNTJfZxu7vmmmu48sormTFjxlNqk3SkuCu9jmknnXQSP/nJT4Dyxh0veclLuPbaa7njjjt485vffMjPc99991Eqlfinf/onwuHyeMH3v//9vY4rFArce++9nH322QA88cQTjIyM7DzB3d2MGTOYOXMm69at43Wve91+f/bWrVu55JJLOOOMM7j22mt3/vwd7rjjDorF4s7vf/rTn/KZz3yG22+/nc7OzkN+jZKOPZXQx+3LihUr6OjoONSXJ+kYVQl93HnnnUehUGDt2rUsWLAAgFWrVgEwZ86cQ36Nko49ldDH7bB+/Xpuuukm68TrmGQwqmPC4OAgr3jFK3jLW97CKaecQm1tLffeey//+I//yItf/OKdx731rW/lyiuvpFgsPqkp/AsXLiSfz/Ov//qvvOhFL+K2227jP//zP/c6LhaL8d73vpcvfvGLRKNR3vOe9/CsZz1r54fPnv76r/+aP/3TP6W+vp7LL7+cbDbLvffey/DwMB/84AfZunUrF198MXPmzOFzn/sc/f39Ox/b3t4OsNcH2b333ks4HObkk08+5Ncn6ehWyX3cN77xDeLxOKeffjoA1113HV/72te45pprDvn1STq6VXIfd+mll7J8+XLe8pa38IUvfIFSqcS73/1uLrvssmmzSCUduyq5j9vha1/7Gh0dHVxxxRWH/Lqko8ahFiN9hgukStNkMpngIx/5SLB8+fKgvr4+qK6uDk488cTg4x//eDA1NbXzuFKpFMyZMyd4wQtesNdzHKyg9ec///mgo6MjqKqqCp7//OcH3/zmNwMgGB4eDoJg16ZHP/rRj4L58+cHiUQiuPTSS4ONGzfufI49C1oHQRB8+9vfDk477bQgHo8HjY2NwYUXXhhcd911O58T2OfX/rj5knT8qeQ+7utf/3qwZMmSoLq6OqirqwvOPvvs4Ac/+MEf8NuUdLSp5D4uCIJg69atwcte9rIglUoFM2bMCN70pjcFg4ODT/G3KeloU+l9XLFYDLq6uoKPfexjT/E3KB02h5R3hoIgOOQM9Q8NYaXDbWJigs7OTq699lpe9rKXHenmSNLTyj5O0vHMPk7S8cw+TnrGhQ7lIJfS67hQKpUYGBjgn/7pn2hoaOCqq6460k2SpKeNfZyk45l9nKTjmX2cdHQzGNVxYdOmTcybN4+uri6+/vWvE4361pZ0/LCPk3Q8s4+TdDyzj5OObi6llyRJkiRJknQ8OaSl9OHD3QpJkiRJkiRJOtoYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIpjMCpJkiRJkiSp4hiMSpIkSZIkSao4BqOSJEmSJEmSKo7BqCRJkiRJkqSKYzAqSZIkSZIkqeIYjEqSJEmSJEmqOAajkiRJkiRJkiqOwagkSZIkSZKkimMwKkmSJEmSJKniGIxKkiRJkiRJqjgGo5IkSZIkSZIqjsGoJEmSJEmSpIoTPdINkCRJkiTtbWBggPVrHmVr9zb6e9eRiIeJRJOcefblnHDiYkKh0JFuoiRJx7RQEASHeuwhHyhJkiRJemomJia44dffZ3xsC709m1m6MMvFZxUJh4r8/t5BHlwZJR9Zwnve9zekUqkj3VxJko5GhzR6aDAqSZIkSUeJTCbDj77/nyycNcTKx7dxxQVpmutLfOZf76O3t4cLz5ykpqrEfSuT3PvYDP7usz9l6cmnHulmS5J0tDEYlSRJkqRjyU2/+RWR/B3M60zz6OO9XHZunnd/5Gbe9apeTlk8/ZIsl4N3fqqD93/0Fyw75fQj1GJJko5KhxSMuvmSJEmSJB0lNm96iLNPLXLHfWNccGaR7/1kLS97Tt9eoShAPA7/8ZfdfOEf334EWipJ0rHPYFSSJEmSjhLJeI5kDLK5IjVVcMvt67js/NJ+j4/HYWHHCn70o+8+g62UJOn4YDAqVaggCJicnGRkeJjx8XGKxeKRbpIkSVLF27HuLxaLMJmG6kSWg20+f/n5ea777qfp7e097O2TJOl4YjAqVZB8Ps/ExAQ9PVvZvGkV6ckNhIMt5NMb2LRpFd3dWxkfH6evt4f+/n7y+fyRbrIkSVJFyeSS5Evw7OV13HZfhNIh7PSQL8CFy8f48Q++fPgbKEnScSR6pBsg6fALgoCR4QHyuUmKxQzRcJ7ZbZAPAnp6cxQKRaBIb3cPE1UFctks45PlUDQgQqp2Bv+fvfsOs+MqDz/+nT6337t7txdp1Xux5CZ3G9vgSjMlQOgJIQGSUNJIoSQkv4SSEEoIEHo1GNuAjTHuRbYkW5Kt3rb3vb1Mn98fdyXtWtJKxpZkS/N5nvtsuTNzz2yZe+Y973lP15ylRKPR03sigUAgEAgEAme4Jcsv5Zmdv+KcJSr3PaZSNsJYVglVPfY+v35Q5j1vTfD9u7eduoYGAoFAIHAGCDJGA4GzwPj4CIpUIJWw8VybhmQtKNrdZ1KfdKhPOhhGhYVdFplMkYZkhfOX21yw3ObC5QZzmnvYs+MBhocGTvepBAKBQCAQCJzRzllzHvsG29jXL/Dmmxupa1rJV3907HyWsQkYz4Vob1GRRfMUtjQQCAQCgZe/IDAaCJzhjGoViQpRzSOXd0jGa98fGjFpb3QJa9A3ZDJvts/2XRWWL3BorKvVt6qaHtmiiyh4rFpYYbD/aSqVymk9n0AgEAgEAoEzmSAIXHzZdewZmM/PfqNzy83n8vizc/nmrSLec9Zg2t8Lf/T3Cv/04S4ALEc/DS0OBAKBQODlK5hKHwic4YqlHMlobWEly/ZoSIDtg+t4hDQolF1iIZdq1SWs1wKlQ2M2u/dX0FSXSMinXBUwLYnOdpsD+3ewdNma03xWgUAgEAgEAmcmy7LAK7NyxSI8p5mNm/v58Adfy3//z208sbUPVS6TTsHwGLQ1wTWX6qTrZPqGoLl97elufiAQCAQCLytBYDQQOMP5voMyZSVTFzBNl0i4Vsm/WPZIxGBoxKK92WN/n8FEpsK6Ne60/WzfZdOzLlWrNwiMBgKBlzTTNPE8D1mWURTldDcnEAgEnpd8foJkzGZg0KazReTay9vZurmXm1+1Cl2MMTS4l9dclSURA1GEW39TwTDhm7+YxQc/+u7T3fxAIBAIBF5Wgqn0gcCZbspKptGQRKk8/WlRBM8Hx/XxfZ++wQrnLpseFAVQBDh/uYtljmOaQf2qQCDw0lMo5Bke6qVcGMSqDpLP9jM63Ec1KAESCAReRmzLQlZAkgRcF3RFxKGB11w3h/1DbWzvbiQSqvXhAFYvdvnwv9i89V3/RiqVOr2NDwQCgUDgZUbwff/4W9Wc8IaBQOClI5sZQ1dyhJRatujAsElLg0//YIWuNp+q7dM/WEaTDQYGx5jXWaEheXh/H+gbtOgdNBCAWExHiFzC8uXBVK1AIPDSkc2Og58nGfaYOq5TtWF03EWUJERRQtUixGIJHMdBkiR0XUcQhGMeNxAIBE614eEBUtEMpuEwkRmnqxUKJZNq4XEaUvDQ43uQyj/lknMP73P7/e10rvkRq8+56PQ1PBAIBAKBl5YT6uQHU+kDgTNcNJZkYryEnnKQgHSdwsiEhSCKVG2XkCLgehJ1KY1tO5xpQdGhcZvHN+aZ1eqydF6tTunenio7dvyWpsZ2GpuaT8s5BQKBwFSGYeDYRRri01clGc06uJ5Dcxo02aZseAyNTVDKQSgEtgO5goesaMTjdTQ0NKOq6uk5iUAgEJiUSNSRzxVoTMK+bhmzySEe1dixQ6WpzkKUYvQMR7mE0qF9cvkcyoFfB4HRQCAQCASep2AqfSBwhlMUhWgszXhOxvYhrIqk6zQUNcyBfomqBXM6dA70S4jS4UvCWNbhyaez3HiVxZqlLqkYKJJHV5vF668u8eiD32difPw0nlkgEAjUFItZklFn2vcyBQdFdmipA02GYtVldNxgVqvL7DYXo1qhXMzTmi4wq3GMsLiLA3ueYN/eHbiue5rOJBAIBCAUCmG7OoYDszuTbN8rUayCLzUCkIhHkdWWafskYxUkZ9PpaG4gEAgEAi9rQcZoIHAWiEZjyLJCrpDFcSpIoociqyQTEqPZIp7jUZcKMTwSI1M0qIvB+qcKvPISB0WEzdsrbHqmQDziENYhUwCEUX537895w5v+6HSfXiAQOMv5Xu1adZAHVAyX9obJ54GRcYvZbT6yAAf6TSIhi9lT4gphDVIxg2xxgH17bBYsWnEqTyEQCASmaWxqZ2iol1ioQmNDgt6hElp4DkNj/SycG6G/bw6l8h6ikdr25y332NHXR6VSIRwOn97GBwKBwElgWRblUh7HdZFllWg0FiyyGXhRBIHRQOAsoes6ut5CqZgnrIwiAi4S5YpOSIeRUYM1K1rYtj3POUsMNNVBU+Ceh3MIXoFbrnHQNAFNrZXpKFUdvnnreg4cuIqurrmn9+QCgUBgikrVI6IfLo2eLzkkIh6yAFXLx3FtGo+xPkkq5pMvZSgUCsTj8VPU4kAgEJhOURTa27vIZCaw3DH0kIQkh+ke1GhpMInGmli/ReAV62rXupZGePzZLM9seZjzL7z2NLc+EAgEXjy2bZPLjiNgEA07SBI4DuSyeSQxQqq+IagXH3hBgsBoIHCW0fQwlgm6DBK1NxVFgEhYJhVVaGxs4sktPdQnPXbsLfPgI7sxKmPcensexxZoaKjnxqtbuOjcGH/yJpOv/fxrvP+D/xq8GQUCgdNGEBUsz0CdzBp1XB9pSg+nVHFpmAyEDo9atDUefT1JH59ndoyxc/cAorgeTQ8hynWsWn05be0dJ/ckAoFA4DlEUSSdbiAWi5MZ76elrsKG7FxgO7oepWcsDuQPbV8s5BjrvxsIAqOBQODM4Ps+2YlR4tEK+pS+naJASHEomUUmxiHd0Hj6Ghl42QsCo4HAWUZRFKolCV2u1dATBAlwiUYlihVYOLeR0QmDwZF+fnrrg4juHpbOM7nkSgfXg9880s9//s9uNm9bxZ++o50lXRn27tnF/AWLTu+JBQKBs1Y8niKfr9IQr9UZlRUByzz6tqbtEdaO/H656vKLu55hSVeR117tIovg+0UKpRE2P9tLb895XHjR1SfxLAKBQODoNE0j3dhJubST5csW09e/nflzouzc1sbUwGhjykDyNp+2dgYCgcCLrVqpoKnGtKDoVFHNo2qWsSwrWEAz8HsLAqOBwFnIRwMqAKiqhO27KIJA1hSIhX0Wzm3mb7+5HsXfzpc+YaFPCSJctMZmPGPzrr/dwLJFEVYthYeffSIIjAYCgdNG0zRkJUG2lCMRdYloItm8QF2slhkaC8sUig4NKRAA269lyh9UtTx+fe9uLlpdYHaLRzbvcu8jWUplk2jIp2pCrtiDaXlcfkWQiRUIBE49RVEoOjp1UegfCXPhigqh2Dwmstupn8yIv2CVz6bdw+RyOZLJ5GltbyAQCLwYSuUcqZg34zbxsEOpmKeuvuEUtSpwpglWpQ8EzkKSHD30ua6JVI3a54JYGyspl/OMDW/li38/PSh6ULoOvvj3Vb7wP08jCeD5M79ZBQKBwMmWStWhaGlGsxrjBZAlmbHJRKp4VCJflrB9qEsqjGen77tzXwVFyjO7xWNo1ObWXw3xinUl3vlam1te5fCHr3Z435vKDB/4Eesfu+/Un1wgEAgAipbGBvTofABaWppYv0U69HwqAZlMhq1P//Y0tTAQCARebN60weyj0WRwXOvUNCdwRgoCo4HAWUjTwxi1GaeH6oxCrc6o6cDDDz/O0rllwqFjH2NWG2hyjq17VObOW3WymxwIBALHFY3FaW7pRJZlUgkJkBkcB8OG5gaF/kGBZEJmPCNhT47n2B6MTxTparPxfbj9t+O8/TUOqRjk8hYDQxVM00VV4E3XWezb8XPGx8dP63kGAoGzUzgSp1IWWbp4Pj1D0N4aZyybnLaNaRQY7fvl6WlgIBAIvEhs2yabzWBUDDIF91C/7ajb+iAIQWgr8PsLptIHAmehY9UZDasi4zno6x9lzTL7uMeZ1+ny+OYwH7525cltcCAQCDwPoiCjyw56Usb2ZIbHDERRQpR19vUahEI62/ZUmNPhg++iyS6a6rJrv8HSeTa/e6iHn/1yJ4lIiWjE40CfhEeSf/qr83nlRfDQ/b/gNbe853SfZiAQOMtIkoTrR1HlAoNjES5YDne5HcDEoW3amgwcZc/pa2QgEAi8QONjw5hGnljEorG+lsQzPmGDKJKuV1Ges32pKhKJJE5LWwNnhiAwGgicpabWGVUO1RkFz5OYO2c25VEBOPrKzQcVywI3X/kWRDEYoQsEAi8dgqgDtRohilgrGdKQ8ACRibxCPOJTNSIMjpQwLdB0laExmfHxEn37n0ZjF1/7hMPhS5vNRHaY93z8bv7glnVUhb7TdGaBQOBsF4o2YLoFoslFeGyiqWUB/cObaW+uPX/BSnj4mXEOHDhAV1fX6W1sIBAIPE9jY0PIZGhLT0kRlSGqQ8XyGB21aG1UOTi73vbANFVSdeHT0t7AmSGIZgQCZ6nn1hk1Jldw1lSZm266jF8/FJlxf9+H3tFmzj1/3clsZiAQCDxvshziWDOuBECRIBQWaGmAFQskRClE37DM/u5xyvk9/OlbpwZFa+pT8IPPmnzrB08yMZE52acQCAQCRxUKhalUZRYtmEPvgMCCeS1seOZw/lQ4BBNjGZ5+4nunsZWBQCDw/Nm2jW3kSUWP3osLqxDSPUpVDx8oWTCe16hPN5/ahgbOOEFgNBA4S02tMyoD9uTM+XBExnYU2matY9Ozx97/R78SufSqt5/0dgYCgcDzpagalnP4a2lqb2cyxcCxfRSl9mUqKZNKJXnyyS188A+PXbw/pMNNVxo8uWHTSWl3IBAInBA5iSJJDE1EWTgnwkimbtrTvlsEYwOeFyyOGQgEXj5yuQmS8ZnLucWiMJ5zGMmC5SRJp1tQlOdOrg8Enp8gMBoInKUURcGyD69kWqszCooApiXyr//89/y/7yzl7ocE/Ckz6l0XvvlT2HZgNhdduPpUNzsQCASOS1EUHOfwEqbTCvILte/btocyWVBobkcIXdeoT5RJxGY+9g2Xu/j2AIVC4cVudiAQCJyQaDRN2YS6hiWIEhj+9CnzczosEuEhRgZ7TlMLA4FA4PmzLANdPd5WPp5j43sGICFJ0vF2CASOKwiMBgJnsVqd0RpFkTg4PidKEooi8x//8QW29L+JG98X4S/+GT70KfizT8DQKHziY+dhVnpPT8MDgUDgODz/cBl1SRKOeN52fJTJXpAAICiI4sx1lWvHgojukcvlXpyGBgKBwPOkKAqmrTN3bicHBgSWLV3Crv2Hn1+7DCwjy57dD5y2NgYCgcDzJYoi7jES3R2gb8Skf6hKKmaAWyCf3cW+vVsZHx89pe0MnHmCxZcCgbNYrc5obQEmTRcxqqDoEI0oVC2bSEjnhhtfybpzW1iS/hx1yVqyVd8QbNtdBmfotLY/EAgEjsVHgcnhHlEScAGJQzPpcZ/T826sr6NsxDHMEXSNY3rwSYFEspFY7DippYFAIHASyVoaRIOxTIK1qxx+c6fGwjm1gvGKAhMTGVx9E6Xi64kG16tAIPAyEIvVkS8XScem99F8oK/foC7pMjphMDrmEI/66AqUjDIDfTlsy6SlteP0NDzwshdkjAYCZ7GpdUYVDtcZ1WWBUlUgFpMRBY0Lz53Fs3sOzUClowUefaIfTcqelnYHAoHA8YhS6NDnsiTiuNOfd73p2aGxeIzzLryRr/342FOyXBfufkSjvnklqVTqRW1vIBAIPB+RSJxKGRqalxIJSwxnGqY9L4tFsPfR173xNLUwEAgEnp9oNErVULGfkzWaLzmEQi79g1Va6m0Wdfm0NkBLHcyf5bOoy2Z4YC+VSuX0NDzwshcERgOBs9iRdUYPXxJ8X0QRwRfCCJJE98D0otaFwjDxaCUo7B8IBF6SZEXHnox9SlItqDnVc69dpuXyltev5qnds/nlfUcez3HgI/+q0DlrCa+64b0nqdWBQCBwYiRJwvXjzOlqZ1+viBxeyNTL2uI5DiFlFF0exzTN09fQQCAQeB4amzoZmtApT7lsZXMO1apJR4tDNHTkPpoMSxY47Nuz9dQ1NHBGCabSBwJnuVqd0dromiLL2FgogK4r2J5LKKRTKMFEIQGMH9ovouVpqvfIZDKk0+nT0vZAIBA4FllWcIzalFJFgOrBwOjBufRHlBN1KFVs/u+Lb+B1b/sid95X4vyVkIjBU9thcCxCqnEl6676C85Ze+GpO5FAIBA4hlC0AdctkCmmuOqypWzZ8TtWL609t3Ix/OqRDEZ5iN7uZ5i/cO3pbWwg8BK3e/dufvXtb2KUyyw+/0JuuOUNyHIQLjnVNE2jpWU2uVyGiewokgyO62PZDl0tM+wngSxWMQwDXddPXYMDZ4QgYzQQOMvV6ozWaLqIadQ+j4YkShWIRHSKJQFPaJ+2X0vaIBWH0ZFgxdNAIPDSU1uZ/nA357kZozA9Y1QSHCqVKoIgcOl5Il/5JKxeAo31UDEiXHrtJ/j7T/+Sa1/1+pPf+EAgEDgBoVCYUlWmtX0ZXZ1RntkzPZVqYiJDX882HLMPK8gaDQSOamxsjD+59kruuPESXv39f+Ndt/832t++iz89bzm3fefbp7t5ZyVFUWhoaCIWlWhLgyh66MrxF8hMJRxKpdIpaGHgTBMMgQTOOLZt47ouoiiiKAqCcORqxIHDND2MYYIu17KqSjag10ZNbFsiFvXJjYnMnrsay9qMqtb2mzfLY3jMopDfA6w5jWcQCAQCR+ehAEcPBjx3BXpFdsgVTPJFl4ZkBVHkUObVo1vTvPM9Hz7JrQ0EAoHnT5CTdHbY7HpGZLzYAhxenj6slbGN/aSTNmMj+2jrXHJK2uQ4Dtuf2UomM0w0XseKleegHuxABgIvIaVSiY/ccA2fHdtMWqK26ALwKkxeVdzJv3/6o9ypyNz45rec1naerXwkwEWWJKznDHAbtofngaKKKMLB7QVEMcj9Czx/QWA0cMawbZvx0WGMYgHHMPDx8ASZWF2a1vb2IEB6DIqiUC1J6PLku40gcjCTSpZkQoqLaSu84rIlbH8GVi2ubbZkLvzkviE6F/SdnoYHAoHAcQhTAqO+X7u2HXwnEITDgVEf0DUH0zQYGDZpa3KmHUcMrT4l7Q0EAoHnKxpNUy2PkyvX09y2EtvejzIZ3Fmx0KUvM0Yq5rKrp5t001w0TTup7Xnovl+xb9uDLG8dZU7cJjcg8aMnGqjvOIfrbvqDoD8eeEn5v8/+Ox8e2UJaOfrzH/HGeN/n/43r3/jmIOB2WqiARVODyo49IuDSN2wyPGahax6yBNUqKKrMvK4Q+WKEuS2x093owMtQEBgNnBFs22awez9WuUBMdKnXatmPtu8ynh1gZ2aceUuWoSjHeNc7y02tMyrLEg4eMhCLSpQMcHyNZCLE3h6RVYtrQVNFgYHBfmbNGzl9DQ8EAoEZiLIOTJ9SdfCWXJqy+LxhecTCPgI2O/eOcn7X4ecGR2DRitee9LYGAoHA70NRFPKWRsesZSzqHGbDJlh3Tu25BV2woyfDzt0DRBIK2YlumlsXnrS23POrnxIt38M7Lzm8MnRng8uK2cNs77+Xn/6gyBve8r6T9vqBwNE4jkM2k2FibJTs8BCy5yJJEmghnvjJ9/jADFO0BQEuHtjB1/7zC7zrT/8syHw+xQSxdu+uKyLRiMZT2yeoi5usXeozdYilYlps2eYQSzYE9/uB30sQGA2cEUYH+7FKBTpUF33Kza7r+VQrJqZZZNMjD1DX2Ex711zC4fDpa+xLUK3OaK0Tq+sShmET1UGRBHKGgCRFgDxD4xGgeGg/WciiK9nT0uZAIBA4HlkJYfu1gbKpWUoeIE95r8iXXJqToCouzz7bw2unrK20pwcuve7GU9bmQCAQeL5UvYFE1GD3VpXt+6OsO6fE7gOwbRc8/PQotvwU19/QSd9IH6n62Scla7RYLDLe8wjXrKsc9fkl7RYHxjbT19dHR0fHi/76gcDRDHQfID88xHBfL9VCnoneHjKPrSf0zDYWjY1Rr/vQOvMxVkkOj37mwzz1+Y9woK4Nd9UFNF33WtZe/UpSqdSpOZGzlCgevlbFIiKebTK79chAdliD1Us8tu7Jn8rmBc4gQWA0cMJs2+aBu+9i8333IHoeseZWbnzHu2lpmWF5uFPANE2sSomEND0oOlSy2T04TLWQQ7YNssUyuxwfUQ+x5uLLWbJi1Wlr80vNEXVGp84iFSTi8Si2D1W7iamB0YZkhVTMwLbtYHQuEAi85CiKglkGRaWW9kHto+X4qFN6QNWKA0kIay6u1T/tGAMjMpclk6eqyYFA4GXGMAy+83//x66tG0g3tfDWd7/vlAf+IpE4paxA0Whgb38jf/znJeaH4bwWuFYzuf37t7HxsR7e+J4PkJ04QHProhe9DQ/ceweXLRqfcZvLFhf55f2/4E1/+IEX/fUDgefq3beHgY1P8MhPbiW+cROzB4Z4tesSn7LNrcdfz4ecB3UiXKD5XFDuh0dvhUdvZedH4f5QkuKClSSvup4VN76O2V1dL0q5CM/zDt1fnc1T+CVFx3Zd9uztZ+eePjpaPMrVOiIh6YhtNRmioSKFQoF4PH6UowUCxxYERgMnZO+uXXznE3/HZZlu1J4BdmXzSL7H/f/5b/hdC/n3n9x2SjqBvu9jmibVYhGjkMOrlPEcG9t1SUUOb5epOmzde4BZFNk7kUHHYZ1msTlTYHDA4N7dz/ArRWft5Vez4py1KOEomh5CVdWzsvbRc+uMHqzFBxDSZUJqmGIJUg1Lgb2H9utsdUjFPMbHx2hpOc5wayAQCJxikiThuDLgIEm1a7sA2JaPPmU2nOO62K5HJOwTDeWmHWMsFz1l7Q0EAi8fpVKJj773LXRvuo9rWqq8scmlNAyfuunzFOoW89Wf/Y7kKRpUkSQJy4tSrmiMbhvhf24GZUrc4NrFBnvHHuMf/3Gcf/rsf5Oq73rBWaOO43DgwG5G9z0Mpe0M7R2n7ZUz7xPVwa4GM40CJ8/o6ChP/OhHjP/4x6SeeooLDYMLZ9hesyHvQuLIONshd5Th40dJDF2kwiI3BzsehB0PMvqFj3E3OmOdC9Avuop5N7+BFWvWIssnHnLJZjJsfeJRvMIEqu9i+iJCLMmy8y6iobHxhI9zptix7SmGetezqKvC5ec4VA145IlxBCHMFRd3oEjTg8bN9QbjY4NBYDTwvAWB0cBxZTIZvvM3f8m7Sr38w9N7+WjI4EOHahqbDAxv4u8uWsV7v/UTLrryqhf1tS3LopDLYeYyYFQRXQfFd9EESFPLbkSAAwKHVqPzfHhmYIx2iuwYmuDymEmuavLrXWO8IuFAOctv9wzT3dvL4z/7LoYvseD8S3j/B/6cWDyGJyuIso4WDiNpIZRQGEVVT2pGpOM42LaN7/soinJasi+n1hlVFAkXDwmI6BLZvESxLHLFJavpHbydzskY6IqFsGHrBM0L9geB0UDgJSSbydC3bzeubaPoOp3zFp61ncTaiqa1wKgNIIBt+8T0w9uIoku+6OC6DvXx6rT9q968U9ncQCDwMlAsFnn/G66lfnQjv7zGZuq9+ZUdVfbln+IPrlzJzx/bha7rxz7QC+D7Po7jYFkWpmlSLHl864uf55s3l6cFRQ+a1wAfWLqXH33nu7z3Qx00tzy/rFHf9xkaHKB398PYmaepV4eZ1+Iyv7n2fHcf2B4ox0lu8zl7s98CL75CocD6O+5g+Ac/ILJ+PauzWZ5P8ZurHfjEBHzuGDHHZ00Y8WD4OMFTgEYZXoUBg1vhp1up/vjzrLdFehs68M+5iNYbXsfaK68mFjv64kADfX3sfvReLghXCEWgZFqM5MtI9gjP3DNKx5pLmL94yfM4u5e3DU88iGQ9zE1XTOmXJaCtyWJo3OIHPze5/uou0snp983+CWQBBwLPFQRGAzOybZvPfOhP+XNziL/fsp//jhvEn9OfaZPhm1KGt73jjWT+9hMkG9LI4QhyJIoaiaKGI+i6jqZpqKqKpmnouo4sy9OyMw3DoJDNYOeyCKaB7Dmo+MQESB9aRriWx2h6PiXPw3ZdHNfF93wOFCwM08IxDPKZDAmhwnzVQsbj3n1jvLXJ5t+f6GVs/y5anSwfn+WSnswYembgTv7pHQ9y0/s/wuWXXooklnDLIq4kYiLiIoAog6qiaDqSGkYKhV5wlqnrukyMjmIYJRQ8dEWkLMj4oko8VX/SOtNHI8kRnltnNKLXsqscV6JqSCyak+bu2wQ6J2u7pBLw7PZ+Ys37gItPWVsDgcCRfN/Hsiy2PvkYSTOHNNLLrT+7jVw2S0VUWXPd63nXn/35SV+R+KVGEHXARBZFPAcQwHW9adtois/IaJXRoRyN9fa051pmX3/qGhsIBF4W/vc/Pok8uJnPXjI9KHrQ3AT8zZxe3v/ay3jVlZfiWVVcq4pvG5MPExwT36l9FFzr0EP0HETPrj18F8l3kHGQcJHxUHCRBQ9F8NEk0CRQJegtwkqFowZFD7pglsf/++5PuTNZoGXedXQtWsfcefOO2d/MZbPs27me8vATxOhlXrPJBSngKNlzy2fD5n1w7vxjv/6BEZG22Stm/NkGAjMxTZMnf/c7ur/zHZSHHmLJ0BDXPI/9R4H1okgmFKIumWRZXR13Wnk+lhniwxGbpsnoiOvDnVWBu/woX79sLr8refwumydWyiEbFbqwOUcDdYZbwJAIF2seFHrggR544Ac8Y8F2NU521iJCF17BoqtfSfvsOYiKwtaH7+WaWIXe8Rxfu+thxvt7yYwMEPUtbF8kryd5+8f/lVv+4K1n/AxH0zQZGdzEDZdUj/p8SxpWLjJYv2mUpYvSdLXV+rbjOZV069mXWRt44YLAaGBGP/jv/yI21Eu/VeYi0TwiKHqQKMBf+xP81998kMtjCr4g4COAIOALIp4o4CHgCQcf4qGHK0p4oogvSfiShCfJ+JKCK8n4soIrSSDLCKIEoogkigiiiCjKyLKEJMsoooggy0iCiCAJqJLEMAKNusC9psd1zR5f3zKMPLyPV2oZrm+fflO8PAbfjxb4s//9DxrqUqxeMBdVlnBtAdMFEPAQQRSoCiK+KCGIUm1qhCiBpCCpCqKiI+lhlFAYTdNmzPwsl0qMDvURER3qJChaDuNlsGybmCpSyOdomzWHUCj04v1CZ6DpEQxzbFqd0YPVCRRVxvEUwKZ3WAOMQ/s51hiO0X+0QwYCgZOsXCqRmxgD28KyLbr37WONbvCJz/0XqYFn+EC6REOolsVz24+38pYffJ3P3fY7Ojs7T3fTTxlR0oE8ogxW9eBgz+H3ANuDSMint6/Cnj0DXLjwcKpB1YCVa2469Y0OBAIvWb29vTx+98+5JGkcNSh60MUt8L37n+SW3iePvoEAKJOPF8GmUVh3Akmgs5MG715+O6J4O6XdsPNBGMiGyDuNmFIHFb8Bx5NoTPqsWyKxplGGruMfd9ls+NT3DB7YMMaO3YNMTIxRFwVdVzl/ZSe3XDuPe7e38db3Xf2CzzXw8lKtVrn7Jz9h7yOP4JomY67L+TfeyA033XTcBXFd12XzE0+w61vfwr/3Xub29HCh53HJCb52EXhcEBjWdSKJBKvr6rhpykBAFViUSHN9R4z/3jdEvlhFwscSZV7V3sCXW5JIgkBYEVnbVM95s+oBMF2Pp3Ml9o/ncMpFmj2LtapP6jhZpctVWE4Bep6EnicZ/N6/8ZSnMphqQl60iK+kG+jZv4/QyD7W2hneMc9Dmzym41X47r+/k4/85k7+49s/PqODo89u3ciKeTOX3Vg6z6dvqET/QIiGOgVNE8kVI8xP1R33+OVSiXIpD/7BhTVkIrEEkWhQPulsFQRGA0cwDAPHcXjmqU0Iu7ciuDa3943zN2F3xv2W66AXPd6imcd/EX/yATDzYQFwfLAmH+bBzzn8vYMPh4MPARdwfIFOAZ72BULAesflb9cc/TUEAf6lrcD7v/RF3vfe9xCORJE1HRSNsK4iSxKyLKHJMmFZRJYkLAdMt/Z6BzNLfVGsBUolBVmRkRUZBKkWQJVlZFnBBYxKkfawx3jJYrTiYBkGrmPToPuoLhhWlr1bxkm2dNIx6wR6pC/QTHVGoxEZhDBQIVNMAUOH9ktGioj+6ElvXyBwqo2OjjIyMkIsFmPWrFkvuQ5oZmwMOzdOo+xgOA47B0eYq9l84gtf4nX5J1nXdvjiqojwhrTFq5y9/PHrr+HrDzx13JuRM4Wq6lh2LaOq6tdiEZ53ODBaKjtEw+DYBqNjg8y/9vC+u7thwZUv/iIlgUDg5aFcLnPvr+9k64/+h3DvFjq9AovCLtebsOI4XTNBgPAprIykSGA6x9/O9Q6vRRcNwao5sIoq0DP5qCkbsHcPPLteomBoWH4ENZQgmayjvaWeue0xyrZM93iEsjiH+x/fQ6O2n8ee3MI7r6xy1ara6/g+PLp9nDd/rJsP/cP3T9mAf+ClYde2bfzg4x/n+t5eNnV3UyiVWO045H/8Y/44FqPjhhv42698hehkQMr3fXbv2MHGr3+d6i9/Sdf+/Vzouhzj9u0IJvAk0KNpyLEYS+rreUU4jHiMPlwIyNkwu07jUytmH/O4T5nwR4nDX2uSyPn1cc6vjx9ud9Hg/vEsxWKRhFVlheIx5zjXgFYZWrGg3Aeb+ih58NkCzGuFt7RP31YW4Z2tLvfsuY3P/e1f8t6//CtELYSs1pJxJOk4UdmXkfHxAVas9mbcRpFAFD1WLnLZsaeCIDcxb+HaGffxfZ+J8REkv0wq4h0qxWf7FqWqQaVaIp1uesn1+QMnXxAYDVAul6mUy5TyeSq5CXTbwqpU+O23v8O5e/bS7dvszpSINBz/WCfrD0oWao8Tv433n/MR7q9AsmXmvRIKMLiX3/3gG7TOW0hrczN+OE4yGUfRo0QjITRFo6wq6KqCh4iNgC9IiJKIKkuEZImwKCG4AoZdC5zaCDgHp+RLMhVBoSWhMJTziEouFdNAc6vkygYDJbeW0YRASJXxx226HYfZc2eYm/QieW6dUa+WJ4sigC9EgHHU8FymBkZbGy1Cav6kty0QOFV++Ytf8LW/+TCRQgbFc5HiCZpWncsVb/wDrn3t609387Btm2wmw0T3XoT8BAcyWbLZHIO5PHp2gOrOjaxbcPQRp5gMfyrt4kv/8BHe+O73oScbiCWTZ/SNqqIolKu1wKjrgiIz9a2BStUhFQFRsFDFCfQplQYO9MusPEsCyIHA2a5SqbDx8UfY8MOvwbaHabMzLNYdro/CzSIwpR+8vQTVEwhCeiex1p0xOThvebWPHRH44Ra4YsGx97FdsIXDgdGZRHRYOQdWznGp9Q0rwBhQC5rueQr6szoFp4HeUZG0WubeDQW+8SGLuiklFAUBLl4Kq+dmeN9/f5RLL7/qtNTSD5x62WyWn/zt3/IXPT18cPduPlmtMnvK8+/N5Tjwve/x5gceYO3s2SR37GBuNss6z+MtJ/gaHvA0sEtR8GMx5tbVcUEsxiUz/JHngQFgRBTJSBKqDPcbNlceoys06EC/oHKnJxN2LBK+S73o0yRBw2QsUhAEFsZDLIwfPsioaXP3eJ7RbA7NqDBPcFip1e5pjyUqwoAA/zDD0g3XpBx+8Ns7iL7/fYhWERswPXAQ8SUFQVEQVB1FCyFJ0kv6/23bs1v59Z1fB2cIUfSwnCQXXPJ6ZFnFtCb7bDPwPYhHYCIncOEl60gkjlLnY4pCLocqlImHpgddFQFSIY+iUaaQy5I4gazTwJklCIyexYqFAqN9PYRsg3K5guZYLJZg1969PPN/P+KDhQK/GRrivNFRGgSfrXFYNUPJy8rMgzqn3YQHzSdQsrNN9fg7bRe9e3bx1FMSfYQpRZuo75xDZ1srYjhOOBYjGo0iaWFCIQ1ZVgjrKnUhBckXqBhQccH2RVxEkARkSSIiy4QFkZAi4RVA9qDoQM4w0X2DZSlvWtH6ouWyfdwmYktUW9tPevDiuXVGq6ZNZDJIEAolsF1Ys+ZciqVHiE3ONFg812f/eAbDME5pTdRA4MXm+z7vf+2N1D16D9+M2qQn42H7zCJfvG+Ex/dtY3RggLd94EMv+LU8z6NUKlEoFMjn8+QzE1RGhqiODmOOj2KMjWCOj+JmxhGKOaRKCdWsotoGIccm5DlEfJe4AO0SLBUhJsJXivAnxxlDWZeE//fDrzPXnWDhqrWobR3kBBlb1nC1KFI0iZpKE02kiEQiL/tRc0EQ8LzayvST32FqZNS2a0Fk2zGIhYrT9h3NBR3jQOB08zwP0zQRRfFFq5FsGAZbN21g6+3fo/DEb2guD7JYt7kwCpeKHLWG5lRX1sHn98JFMwQvDAcGy/DDfQo+tZJSPrVVQ31BAEQEsfa1IAqIwmSpKEFAFCUkSUASRSRJRJFEZKk2W0mRRVRJQNVFNElEkwVUSaBVECn3ddObKdB5jEvX1x6DeFzlh/d7xCIuc5p95raA9jzjJoeCphhAHwCPbIOEzLSg6HP3ect5e7jzth/x2je87fm9YOBl6Y5vfYu39/fzuf5+PvGcoOhBXcAX+vv5Tn8/J9q72gVslSSqkQjt9fVckEiwRjx8A2VTy30eAfKyjCVJmIpCUdcR4nE602lSySTDusgqwWBiMMPverqRjCKXav60gYO9NvxvqJ3XvfUWRNvCN8oIhkHZMtiYzTOSzeIWsoiVEqplEnYtop5LUnBpFBWuaE2jtaUBqLoe6yeK9GZyeJUSrZ7JuVqt/3bQsAOzoscfvDjPG+K2H/+A9mWrmTt3LgldIyx64JtgmWCVsEu1ckFVRHxRAaVW9k2eXOvjhQZMDy4cLAjC73WsH33/y9j5n/Fnr88RmryFdF14aOMOHtmyHJ0OLlp99BqjAGNZSMRrC4ZEotHjBkV936dqFGmOHTtoEdM9hosl4n7qZd//DTw/QWD0LFUsFBjdt5tZOBieR8Wx8UeH+cW3fsglQ8PIIyM8MjzMGyenG+Z9+GQWVs2QcfmdskBdays/jGp4rovvefiuB76L53r4njv58MHzwPcQPA/R95F9DxEf2feR8FHwkfEnSy9NfhRqBa4PPjQBVKZ/b6YRuDYJ9pTh4uPc55bc2rTTuWGYG3apVacp0tO/l007RPr8MKVImmRLJ80trYQTcYRwnPpEnFFNR9VDSLKKJMuEdZVGXUGXBcAGD6pVyJTA8gRsBHKuiCi4LG30KRgez45XqToeEUVkXkOIVfXw9ESW/t1baWxqRQrH0ULhkzL6d0SdUZtDgdGmxii5PKw7dxYb7od159S+P7cT7lo/yIKRIWadgin/gcDJYNs2//T+P+LiJ+7mLYnp2ZZzFfhC0uaTffvZ+ZNv8+DylSSSyUNBzXI2gzE+gp2ZwMll8HIZKOURigWkSgnZKKMaVTTHIGRbhD2HqO8SFyEhwmwRIie6SO/BmnTHMOxB53HGJwQB5qg2r931EwrbfsLTRYFuMYnZMJvGZWs595xzaG6oo+LAqCdgihquFkaMJlCTaSLJeiKRyO81ZcvzPCbGR5kYHsB3XRwf0i1tNDW1IIon+kN4/nxUwDlUIkTgcKfY9z18oLt7mIhuTNtP1JedtDYFAoGZmabJsxufpDTaT2agj5FsHj2VZt0VVzN/ydITvmk1TZNnn36K3ffexugjvyI9sY+FqsmqGJwnUiuoHjneUQ6zPOg1YF+2tuBR5zECgZ/dIrFgyVK09ighySEiucQUn5QG9TrE1BN/zRP176/q4o9u28NHr65wzpRpuI4L394gURbr+NfXdgDw+YfhvLUuO8YM+kcqFIoVbMtAkyySEYeOdC1oqp9gO29fD586TrzzmlUm7/j6F7j+5jecdQsBno2GN22iyfPIlkozlqmdC0xQmwZ/tL+KAWCDKJIPh2lIpbggleJyWWYIyIgiD0oSrqIgqypRXSet69QrCiFZxlBVkuEwndEo44pCVleR4yEqkQgRX2JTJsMFc6Osa05x954e7hgbp0VwCMky+VCc9mVL+KebriekHvsfwbBdMtUK2VKVajGPXSmRy+U4MJEhn5nAK+RRqiU0z0RPhalP1BPFJYHH1kqVoUyWcrFIzDZowiN6AtejsGugVjKstgbofnqIA1IENZUmnkyhazqKKhPRVEKSONnnMcE2wS5hV8DxwEDEE2VQVERFQ9Z0ZEWZ8R7Ttm1y2QlK+SzSob6UhKKHiaVSRKPHuCA+xxOPP4xU/SlvurEw7fuSBFecb9Jcv5nv3W2walE7kaPkBfk+PPa0zFUXR7EcQDxyI9u2sS0Tx6riuwaeZ6EKx8/k0iQXy7KCa9RZJgiMnqUG9+1jLg6yAM8OjND9f9/i4p4+lNFRHh4a4mbPY+otbwKYV4Xv5uBtySOPt9ESeDQ1iw/c+CpESUQSRERRqD0mR8ZtQcARRBzAQ8QTBBAlRFFEEESQJDxZwhel2uJLsoKoqchaCCUWJ5KqQ4snESNRZFUFQaDquuQtC8uyKBQK5EaH8SolqJYpFwts37IVc3yYSnYCKzvGRGaYP+w4dlHTERPqjvFeMCsEs0IeUAJK9I91s2m/yD4vRDFUR7ypjbrmVlLJJISjRGJxEuEQOTWEqqm4oow4mVlar8uEVBHweabkMTsJjx7Io+KyMO4SUqBkwTN9JoKsUJeIk8tkkD0DNRQnEYtSETXEUAw9En3RgqS1OqMiulx705haZzQWkhkfkmhIwv5+hXXnHF65uVwaIjO6NwiMBl62nnzwPnb8+nb+OXLs68PHog7v3vw04bddQViElsnApv584nkneTZTowgDxmRZkBlYk/3CuAyXpXwuIwtWFp56mv7H4FdlmQG1DqtxDo3L1rJu9QranARC/gDGAcg4UPUVLEWHcBwtmSaabiQaPXw9sm2bYi6LYxmAi217jA0PUSdVMUslqoZNsVRmYNdmJAHQQtQ1z2bxyjUvev1TQVI5mA0vAIJ0OGNUklzKVQ/PGiAWsqbtl0if96K2IxAInBjDMHj4rjuoPHUfP7/zV8yxx1ik2fQ7Mp/638+x7Lo38LF/+Y8jgqOWZfHslqfZ/8AvmXj8N0QGtrNQLrMiBmtE4DlT4o/H9uDZEuypSgz5Ohm9jlDrXC5eN5/kxm38zcbN/MWSCmubDu9juvAfW2R6k+fwthtewcIkpPTa4L3vQ9lyGC+YPJurMJYrUS6XcSwDyTVRsNEFh6jsEZU9khrU6ZBUmXGhp4NiusQHLl3AN8c28z87IS7UzmFoVOVdl7fx7lWHM6rSEfCRWDU/wqr5R0ZiShWXXQNTgqa2gSJaJMOHg6ahKXED2zl+EFUUISqPceet3+D6177zjC7jEgDZttlvmix3jl93YjlwAFgEZKktmNQXCmEkk6QbGkhEIjQoCiFNo0fXadF1lsrytPvVPqBflpnQdbRolGZdx9V1aG3EnzObBYkYI8UyrueRCOms1TS6HYGhiQwTfT3MaZnHylSK1rY2ZFEkHAohCgIZ/3CJtsmc70MTTwQAWUKKqTTEktDScmjbg9tDLVvRMCwy5TLDQ0MMjwwzmBnFzOWwy0XsSplBo0KfM8G2nbs43iIce8s+5q1f5os7r+amV17FOSmPwUyJbGaIcjiGGIoQjURRVQUkqTaDUZQRZRldFmv33wgokoPsWch2CXdKwPTg/bigabV7caX2zz3U201SMkiHOXRuhusxYeQpjZs4tk3yBKah3/fb7/DRtxWO+fzieQ6qOMId97dyyRqR9inX2HwRHtoos2JxgmhIZNt+la6ulZRKBTy7gu9aiNgokocmT9Z6VqBqg2Ef8yUPE7xptegDZ4cgMHoWGhsbI+pZlCpFfvnlr3P+rj3kJyZ4bGCA17juUe/b80A4FuWhVJJfZSd4q2qwSPYZceFHQgI6uvjgTTcgywpIAqIgHZryo8oSmiThi7Vp5bYoYgkCDhKOJOOrKoTCaNEYeiKFXl9PLFlHNBr9vTKILMvCcRwEQeAWXWdwcJAHfn0nW9c/THXzY3y1v4/3tR/5ZmN58IE9GmtSIg9nq5yXAG2Gl2/XoV33gDJQZijbx6ZekR5PJ6+niDY2E69vJJ5IEU4kEUJREtEItq5h6DqirGAhUfRlHsu5nJu2qZvM9NozXuXhXeO4roOqyghKhFhDK/OSrUR9i+L4OAVHxlfDJBMJREVD0KPokQTqDCOaJ8JH52AAQZ5SZxTAsCTAZWQiSq3bUhNS8lRKB17Q6wYCp0u1WuVXt97KK5zcjNvpIjRJsEKrZayfbo4Pee/wo+RBWoD/7YHPz5DouCEHq+LHfr52bXOAUaiO4j25nu33weOmxni4Abd5Lg2LVrJs4QIakzFSFJCNAaxBKLhQcQUMZCxJQVQ1PC2MrOlUSgVmhUz2jZZo1S125wucn3aRBI/BggOUyO3r54dPPMKcpas5b90lRCLPI41rBrIcwiWHIAgIU2YXuEBI88lkLeLhPI3R6e8NTe0nuu5tIBB4oVzXJTM2RiE7xv7t2+i7/3b2/vZnfGuOPWVaqYXv9/OVu/6bv50Y4+KrrmXwyfuwdm0gNrafhVItCHrOwWhJ/Ym/vuPBthLsNSSGfI2MkoR0O2suXsR1c1vI+xITjowryowLGm95zSx+fk+M/7dvO8KzE8Rlh4ojMO5FuPGVr+BNy1fiRxJE0z5DJZtCpTaQj+egyiptzRFWdTUQmuFurGy69GaqbMpWyeUKmEYFzzYQXQsVm5DgEpJqQdSEWstGjanwrj+Dc84B2wbHgZ1f6GJ11/Trac6oTW8/lmhYYuX8CCuPEjQtV132DBgMjFZ4Zl+FmGYwUawwnPFpniEmUjYgrIe4ZukA9939U65/zR8e57cSeDmzdB2EqXM0js0AvhcKEWpoYO6sWaxNp7lC0wjNcC9YAXYLAgVNIxaNMjccxpYkJqJRKh0d1Le3E9E12lQIT15D2hO1jEbbh25XoCMaIhFpJza7ndDJ7ttFInTWp6Cz/Yin+ioGv7r1e4w+u5F4LMaElaP+GLd0VRfyDny63WN89Dd84/OP0nXV61iwcAHz0zEs02C4nMMqR7A1HUfWCIV0VEUlosr4loiJgE+tfIck1e7bmVy8GMCfvPsTygKuKGAikDMdQoLNRMVh4uApaTKpsExrGAYqBlYxixmOzJhtads2ujSIPMO1z/N8NGEfTzw2wvatSWbPWUoqlUBRFEK6ysXnx0jFRAbGJcazKebNNdAwmJxuelSyBI5x9OemchwReabGBc5IwW/8LDTY082mL/wXa7bvoD2bZUN/P691HI7WNyoD342EqV56Pu9a0MLbJSg7Ll/e2c/3siUuWjSfT19yPpFQiLIHJb82ymQJArYgYUkyZUVDDIUIR6Io0ShqOEo8FkULR5FDYRRVfVGnhauqOi042NbWxlve+z7e8t73cf9v7+G//vqDbNx5gD9rtVgVr3WEfzEuc7s8n7+/4/ssX7Ua27Z54J67efaH/014z5MsEnKcl4DQDDNHW3S4Qfc4WKB+tDDAhn6BIV+noCUI1zcTr6tDjKVorEvi67Fa/T5VR9V1VEHD80W+/sAeyrkMTmGEctWorTCqxzlnyRweMzKsWjAbCxldV0lLDma+QNYSsESdSCxBKBxFUCNo0Tja71Hzc2qd0ZAuYVg24ckfp+eHAAvDbWNqYLSp3sCzBp73awUCp1NmYoJb//VfGf/Od5DHxpjVdPxVMuqkWgBSe4ELf1oHg5ruZFDThyoCBgLW5ACSJ9RG7AVZQhJlZEVCkxR0VSakKsRkmYQqE5clOhQJRaz16H+4tZtN+QxrEke+bsWFTx8QeWWzwu2jFqtjPp3HSdgRBVgWg2UxE+iHQj+Vxx/k6d/Ak7bGqJZGbJvP3GUraG9pQw2HiUXDdIR1NEcCN0ulCGVXYEcG1jT5PLyvwPn1Nrdt6GPXru3IVgHXcbF9geXtCerdZ/nFxruQGhfwmre+5wVPZ1IUDduktiptbQ282s+j6hINQ09fFV0psWD24X36hmDeglUv6HUDgcCJsW2bvv17SdkF6myDXdlhfn3HL7h12ZHpPYIA72+1+Oi932X+ru9y/cG43fMIgro+7CjBHkNkxNPJKjGsZDNzls/h4oWdNCQiDBkCJV9CUDTGwjqtMY22yRjNzjIkEvBXb30lj+w7D6OYJ6X5pBJxRi2FzroQ57RFeGgQQrJAR1KtpX1O4XiQrTj0Vy0qho3nOEi4hCSHeg2SGkQ0icUtURa3RJkp1dV0YaJs05upcO8zvby/azMAigL798Ocpun9QduDPWMSv9kSO5T25ftMfn4oJ47DT3K46OGU7XwJ2uf67DqQ5RN/XOGbd+3ib99w7OzAHzyo8cabLiQeAtUfplAoEI/PMFIXeFlbdNVVGNu2sVWWa8UjZ7A1Huer112HcpykmAPAoCwjhEK0x2LMU1V6RJHxSITB+fOZ29LCHFGkBGQ1DVWWGHdMXNcjOpnBWUHAFUU6Ihq6KNSyJH1OfmB0Bs0hndaFK6grj7KxWuUv92zny/NNIs+J1lge/PkO+PDkJL20Cn/VUmL9Y9/mtsc6uO6mVyMn0rSkYtR5Nj05CVUPobshyr6EpWooqoYnSkiyTFyVUUQR0wLDAw8JQaytjRFWJLTJvmXB8alWq4RUlxZdQJdr14ii7dKdsWhP6tRrInnXIjvcS/wYQV0fqFQMNOXYqZt33Zfh53cNc/U6izde7VGuws9/u5F7utv48F+8m46WMLkibN6uoYebWbt6IdoJRLUUsbZYk+0xbU2PqWwPfOHFjU0EXh6CwOhZpFAo8NOPfpQlmzaxOJdjS28vr7VtokfZ1gR+EA4Tvvka/njtYkZMl9+O5ahaLoIgMGvt+bSnm1nYlGavqiOFwkSiEaLRKKnJYCeSgifLCIqKGKqtjKeo6kmtI3c8V1x9DZe/YgdPPP443/jSZ5no6yHV2Mh1f/QuvnXzaw7VzFMUhauvv5Grr78RAMdx2LT+MTZ894vIzz7IfHecC+L+EW9WUzVqcH2DD1SBKhOVYTYMC4z4OtvVOGqqgVRdCiuUpC6V4qmBGE9kLRLGAGP9g1TG+vDtKgJQcGV+1XuAc9eupi6qc+GCJjzfZiJXpuSKyLJKS9RBMqtki1D2FEQ9RjIZR1SjqNEkuq6fUD2uI+qMWhwKjMpKBMjTPms1rvssB0sMzu1wGc71/d6/l0DgVCmVStz+pS+x/6tfpau/nxsdhxZgB3CvCUe9IE4x6tYGooedw5maZR8qvkAVEUMQsAQRW5BwxdrDl2QEWQJZRpJlZEUlJMtEFJmIIhGSJWRBIMrBMne1/9ODGRYHb1YP3qT61JYRykw+fAT8yf6lD6zonM3XRjwW54u8vcUlpdSCAL8eE/jJhMYXrpnDQyWd1XWgeDZ3D+cZz+URzCqtksU58eNPxQ9LcFEKLsIEBqA0wNhDD/BUQeCAozEmxTESzdS1dRCvS5NIJdFCUeLRCI+NhpidlvjyXU8TyezkY3NNkjo4rs99ews8sbOX+7bE+O8/WE7BnuA7/53hHR/86xfUSVUUBaMkIkgCrusfWuW0VHZpqYP9+weJCmWap8Qd9vaIXH7xDIW1A4HAC2LbNvlcjuLwACO7tpPtOUC2t5dcfw+/fXI9f90xc2rPh2bBN/rhH4+z4Jzn1wKZu6siE75GVo5ixBpomNPOotmdvLIzTc4TGbclbFGmomkY0RDz0hKCULt+Wn5tYCnn1G6cJQF2ZuCKDoVbVjdRMBrIGS6aLLAuIiMIMFoBVYKiDSH5yDr4sggNUZmG6JGdyZLl0Vd2KJVNLNsG30EXPOKKRzp85IwmTYLWuEJrPMFAViE1ZQ2S/l5YGJ4+mve1x2XOWbmKSy9J0/AixCV/8KvdDOZ7KJgxntiZ5fxFR26zvReeHmjjvSvaAFjUnGffnu2sXnPBC29A4CXplbfcwj/ddhvpnh52mCaLj7HdNsBqajpqULQI7BFFSqpKMhplfiRCSBAYlmV6dZ2e5mbOnT+fpZMZfnmgWleHKMuo1SoOHm1hHduHquviA02ieCjgB1OXY3x+7MkZ9d6Uh3/oIeIJ1OpHHFxwTRBrX4siCCKCdPhrUZS57Pqb+Y1t0+XJIMi8cecOLopUeXXaRQTuzCg8UFC4KGIwNzw9D/eCJKz1+vjBT77CYPMyXnf1FYyqMbRIhDbNZShbxpR1ZiXDlCoVSp6Mpqv4nkbWBRsJSakFSsOSCDhUDch6ULQ9xiomccXFcqCvBI4v0ZxQSekSIcWnO2fSVR/CqjrIMviyiK6IHC2PIBzVyeePnrzzmweybNrSz9c+5UxbgGrZghJDo7v483/7Ih/+yN8RiUQ4b+VqVE0lZx5eE+O5vx/HAdsV8VEQJB09pjFRylAfc1CEI7efKMkk657HKFvgjBEERs8C42Nj3PHxjzPrqadYVSjwbE8PN5kmFx5lWxu4Tdexr7iCpZeto6yI/NI0mS8KnNeSZsIV6fZlWuJh1LlLWL5wARbgSnLt5l/TkfUQiqa9ZEdaBEHggnXruGDduhPeR5Zlzr/4Us6/+FKgNt1r69Ob2PSjr+FuuJs5xiAXJHxiM/xH1avwyvThQGnOHOGJnQIjvka/HkeI1WNpcZ7NDaGXhvjYcpvWySBN2YZv7drB3XcN44fTnD+/CVGAhhA04AEGpYrBuCXiijKpsEZMtCmOZ8g6EraoE02mCEfiKJEEeih8zAD1THVGQ3qtQTdcu4zde2Dx3No+S+fDAz/fxw0n/BMNBE6dYrHI3d/4Btv/939p3bePdabJW56zzWLgC2Xw6469EmjOhd1KlC8tv5A/aAzTrKrM1+RaFuJpYPu1a7bj127cHb8WMN1WdpFiCldG8/z7jhGqlo2PwJVd9XzrghTjtsD6YZV0MozvmMh1IWbXN5BWfFo1n8G8wX2jeQrFAmHHoEtzWBED9ThjWg0qXJv2qU2IM4BR9nRvZft2kVFPo6RGkRL1NKaSbPNUVKnCB1Z7+L7P/zw6zJbeDNe0Wrwy7tNdFHjn57t55doFXHiOxCMP3MtFl12FJEm/16JPrutSroLrCIQ0n8jkYI9t1zKbirm9IJam7TOcSQYrkgYCLwLf98lms2x7+ml6N62n9OzT+P3dKJlRIuU8SbNCMw4XypCc/PfeY8D5yZmP2x6qTSd9rp2lWhA0g0ZRjlCN1BFva6a+uZVr5rSSjur0m5BzZQxRY5MYIhENoUgSigCiKJETBYpIyKKAJkkokoAuCcQk8VCAszReprdUpTPqE9dF4lMKTpsubM3KXDQ3geNCxvYwbAfLdnE8j6LhEVNcJEAWfVTRIyzVauFpEkRVkaiqQmp62pXt1TJDcxUbw7TwXQcZj4jsUK9DQoUiQ9P2mRiWD00t7cvCNzcozJ49myVz07gvUhm9N1+3gG/9wqV9lsxPntjFnRuK/OEVNrObYDAD/3dfhDLtfO7vbz60jyyC45xIwb/Ay1UoFGLRFVewa3iYvzIM/qVU4rmVfp4FPtfQwOXn1FZ19YB9wIiiIIVCzI7FWK4o9AJ5VeVAKIQfieA3NHB+e/uhYGpWEHAaGxF0HSmTIWqahKgt3OT4tWQPRT56/6HggaoqZGYMXkqIk7N4BFFCkGrrZBx8SIKANPm9F+IN734fw8Ov5t6f/ZCWVfvZs7+Hfxofpbm5idd/4LX80bIlPHT/A/zj97/MzXRz7pTZQbIIf9js0FfdzC++vx9x7kpuvOhceqohSnKIufVxSrkq445KQzJCne8wka9Q9mszEesVDcc0GS0fDpS6CFRtkxX1/rRAouM79JZcDEejJapQr3rkKw6SKNOqO9hApVLrqwqChKJIaIqIQq2fnU4vZnBkgNYptUN93+fWXw0dERQ9qKURPvL2fp7a+ABXXfMGIrqKDeDXro2WA64n44sqgqShqGFkXSH0nJiEqqpMZMfQJJuQ4oEAVUvEdBWSdQ3ov8eMy8DLXxAYPYP1HTjAA5/5DOktW1iTz/N0Tw8rDIM1R9nWA+5QVQqXXsriSy5hVirFJkXkskitL7WvbJJ1XepUmVmaymNijIvPPR83EiV0mrNATwdJkli99jxWr60tzOF5Htuf2cqWn3+L0mN3MqvQzQVxj+QMseGkMj2IUHBG+XYPVEPw6efEbCMK/Okyj4X9Gb7wq19w7tIukrEoLckQcb32Jh9VIap6gIXtWYxnBAxPRtcU2nQHs1QmnxGooCDpcVJ19SjRJJoeOiKIfbDOqO1Brdte6zlHozFMB+pTER7aL7J47sGAKZilforFIrHYia1GGAicLL7vMzo8zOM//jHbv/994jt2sLJc5pbj7He9DZ+agH9IH/mc48OHCirnnLOKC1obsBQY8MG1a/WYPARcBNzJWlq+IOAh4gsCrkCtoy0IeJNZA74ggiggCLXF54TJz0Wp9lE62NGWRGSx9hAlCUmQkGURSRSQRQlJBFmUUASBZx2BZfUiF49l2LvN4fY+eM+aMHMmp5l6PjyeE/hVXmPJksXUd9ThmiaeY4HjMOZa7K9UMf0q6FVSjoXi2WRci5/mililHJJRIuGbLAl7HKXs3BHmR2B+xOPggJDljbG1F8yqjBvVuc2Ks36kyoWJPF+++PAd+rlNPrfMq/DDvc9yzyMmyliUlrhOoq6eSDgEolyrfyXJCKIEkoqkKEiygiRJ065pnucxMTpMTPaQFIGBnEPdZCKoOxkVUKVhjGp1WtsNb/bxTzAQeImqVqsM9/fjWBbRZJLG5mYkSWLH9u38/KtfopjLMmf5St70x3/yokxltm2bvr4+Djz7DINPPEJ522aEwW4iuQlSZokW32apDJccLS5xlEwfn1q2/PHGJkoO3DkmkkXFVCNUQkkiTQ1oqTTz2ls5v6OerCMw4sgYkkY2EkNNJZgb1RFf4LjHqvoIG0d9hkoWi1Muca12c76/INBXljlvVoyQLIIMMU3k4G1XXwlEHZpCtcxTSQQJcH2Pku0zZrnYjovjuriuC76PiIck1AKouqLQWa8Qlg8vUuf7UDQ9DpRtCnJ2WjsHx2Q+OwETE+C5jbz7+kXMbw3z652w/DizJE6UIMA7X7OY8dwcfvlAC09uy/CPP+8logss6Krn3e88l87W6fVd+rM6TUs7X5wGBF6yKhMTfHjOHHoaG/nUpk342Szn2DaaKLJb0+hsbubLq1fzbUni4XCYumiUeeEwzUCvJDGsKFjRKO2qii1JVNNplrW1oQgCDjAhSXhNTejJJHZ/P9FSiakVgmLUAp91xxhTNX0gEqVh9ktnAdnm5mbe+qd/QalYID8+hujYSPjYCJQ0nave+Wdc+94/55ff/Qb3ffGv+KN0gdSUW7mOEHwgVODe7ke4a3Q/Ysdirl67AnOiQp+jkk7GCZUtduckwqEws+I6+A7j2cNB0sawBrjsMqAt7LBt0MDzXARqiyhHdZmuOp39RZO6sExCE9hfdND12nVOoVbKo8bFxsWs1spRIYhc/8pb+PLXd/Kx9/QTn7wOPfJkicvPNWe87p+7wucbtz3BZa/4IwAsG5AjuGodoeiJxSR0PURzSyfVapVqtQQ+6LEIqdCLu/Bn4OUlCIyeYXzfZ+fGjWz56lcJb9vGqkKBDT09rKhUeMcx9vm1LDN64YUsvvJKLq6vZ0xR2B2LousajxbzhH2HBk1HRWA3EsTquPaGm1+yGaGngyiKLFu5imUrvwB8ofZ72L6Ne+74PvlH7qBtYjcXRB3qZlgXKS7DLhP+47Jjb/OKdp+v7hhhuK8btamOnBtjBA1kjeakTmyy8KEiQkvYp5ZPZpMpVMg7IpKk0hJVUbDIDo+T82QcKUQ0WU80lUYP11aUFsQQo9kKggeaKFGZrDMai+lkR6ExBQOjIWpVaGs0JcPw8ACx2FHmTwUCJ5lhGAzt2cPOe+9lxy9+gbB1Kwvyef7S949aP/kgB7hPENhRX0/jihWIXon37NjGO+QyF+m1xXlurwh809S55fy1FOYvp/2aVyCpGoqqoKgamiwjSiIutWntrjBZn02ojZJLolAL3omTAVGplm0gSRKCJNeqwU/WE0UUEaZkRPq+j+u6eNMeDkapSLVYQHVNcuUqURzqHZvuQoZzOxsYzM5ibVjj0bExbu0vIwlgIzIrnSTV1MnlV1yGIgm1Ykued+hjg+eC72FZNmXDxKhWcIwqdVaVYr5IsZhjvFzgt8U8Px4bRS1NUOeW6ZAtzon7NBxn7TdVhLUJWJuoLbbUM1FicwVevfro2795nsNfb+gm3ThEz0M/wo+10tTWiZpsJBxLooYihHUNSRQQJQFRFGvTXqG2Aisihge65CAJPqM5C2XKSqOS6GI6Pm0NRYb7p69Ir0VXzHwygTOCbduMj49jVCqEIhEaGhp+r6zklwrTNNm+aSNiIUubU0EF8oLEA6bH9/7zcywf3se7rTFSEmx78Ed84jv/w6zXvJkPfOLTM2ZIO47DwMAAPTu3M75lE6Wdz2Lt34U2OkiykqPFtZitwFVH+9H9HmtCXiDD/eNw5QwryO8uQ5/eQGbJCqLpJq6e20IqpDFkCuRR8bUQPYkkrckojcqJ/U69yWnztjeZle+B4wuT02QnF5ERascSJIGm+jSm67ElX6DquPgiiLLG5fPlo9awsz04UJS4cHa8dn33fFzfx/LA9X08CWTVQ/J9PM/Hw8f3AL/2ueP7FDyfUcvFMl1818b3HETfQ8RDFhVC8eK016xv0HjbX8L3vxflLXNrWXllCyqOxj2boyyfc2Q7KybkqwKuK6FqCsmojCpPPyHXh6oJhi1guQIeGshRll52E01Lx5gX28L85qPXlfR86M/Vsbajk2w2S1/3TlzHAERS6TYaGlsYHe7DNMqASKKukXS6Mbj3eBmKxOPsEEUujUb58WWX4XgefZXaWgYd4TCyKGICxVCIuY2NjMoyu3Sd1kiEpZKEAeyTJLY3NbG8uRlZELCBMVlGamsjnExS7O1FyudpPMrrx4EBF8YFqBOYNiBS9WFM0mlpO3IxpJeCaCxONBbHtm1830cQhGn/A6951/vw3vFHfOPTf0fyjn/nlsbp/2+vqPe5yB3gx9vG2FQYIJPs5LKVS2gwLPZkJMRwhAbVpXukjCvrdKTCNEoOru8wmqmwt2hRsGz68FjUoDGrXp38+XlkTYen+x3mNUYYLFh0JTVkXGJ+mdGihCzJxPTD9e8VOFTGCDxiisYrLn89//WdL9DWBJeeC+ufLvOqi45f2EBTqihqkqrjU7ZVUo0NRyb5+D6lYpFyOY/vQzgUJRqPT3t/D4VChELHKbQfOGsEgdEzhOM4bL33Xvp++EP8vXtZms+zobubZeUy7zrGPvdKEr1r17L42mtZXl9PTtfZ11BPtL2dlniC1s5ZZMbGGOw+wEi1ihqJcMHKVUFG4AkQBIHFS5exeOlngM/g+z57d+/mgV/+kPwjd5Ae2sYFUWtaEMHxQNZAP85/5WtmGWxc/zDxNYupZBOo0QThSAxDiDBoy4iKRmsiRGRKAao6Heomp9xXTINRQ8ARZKIhnaTmUMwXyU70YQoKciiBrIdoTnBoykTGBMOEalnCrEg0plxy5QamBkbrExXyE3tgfhAYDZx8vu8zPjJCZscO+jdu5MB995F76ik6JyZ4u+sedw2OjcD6WIyupUu5ZvZsFogi2xWZhCbyngVd7Nizh58MjuD4cOmqTn62YBbfcqJccv0NxNpaEalNrxJEEVEQEGQJTahNtfIlCUQJQZYRZAVRkQ9lMsqy/KJNz65WKhTzWWK2RbVcQrIshvd4jNpZbl45hy0DSYalOM0NFhJQRqbYNJsPffDD0xaoe75s28ZxHGzbwjUMSoU8/b09/PDxxxjecB+R4V20WlkWaLV6pTMtWvetYfijo9V1meKPF1T59KbHWB5eykJ5Anv/bnqqGkU1TWvnHBINzYQiCURVIxoOIcsyrn+wZpiI4wn4ushw0aYlBt1jFrlSLR6sKD77u/MsnuNiFqZP6Uw0nP97/4wCL30H9u1j52OPIVcqqL6PJYrImsaueJy69nYWrVr1sgrCVMplevfuYaS3m1WCRUKCg8XddNvk4//w9/xbeT/NMod6/+eoPucYB7j1B//FZ22bq9/4B/Ts2EZ26ybMfTsRBvvQs6Mky3maXJPZMlx6tD7KDKsAn6iqB91OLYCRQ2JAlPlxr8Xlaf+YmZ2f6VV57xtvYNGsDgxZYzwchWSc9miIZgRsD0yvli2WNQ5n9/tMTpcVqF3DEZAkAUkUkSURVRRQJIHQlGnzR2N7tb6bDaRjERy/Vn+6PeKwebTMwqQ3bQGSggnb8yLhSJRRU+JwZUP/4BJHCLXxHJj8cLAnN3UpJIFaFhxMz6gVJrePV6YHRkMhHc8Dz6wVHq3acNdOmQsXpxg2RbqOW0rZI1O2mChKuL5EJKTTWKeiHfWHY2A4w2TjPg+sD5EMl46oYer68JstcVZdcC1bnl5PiFEWpk00uZb5OlIY5an1zzC/U6erUcbzYaI8wc5nu5k1ZwnxROoorxt4KfF9n5HhYfZs3oxvGOxQFC40TRRAFkW6otNTlR8WRaL19dS1tNA6+UddAbbKMn5LC0saG1Go/a+NqSpyezvxRIJCfz/++PhRA6JQm6tSDodJt7VhVKsMZMYRHQcRcEQJLRqlJX1kUO2lZqb2iaLIe//hMxgf+0f+4z2v5freu1g85ccbkuAdTRa7sjtYPzZMtzXGo6Fm1i6ex9Koy/6hAnlCdKXjZCcq5HwVT1EZrVSZm3Rpqa9dqXqKJveNSaxqj5GOyKQ0WFHnsH28QiSuYvu1oHNtjTkXcCkakHUFREkmpilo0uFrhgxUC0/z8ffXFrvcsBWe2SNx8arj/zxMR6IjKZMpmViCdMQK8pVKmfHRQWKaRb1eGwyvmAUG+8ZI1jUTCxZ8CxyF4PsnXG74961LHDiJqtUqm3/+cyq//jXF3l5m5XJs7unhwmKRY4WnHhZFdq1cycLrrqOpsZFqOITY2kKyox1RVlA1lVC6iWhw0ThpfN/nwP79bP7lDyk8cid1/VtZrBp8twifvHjmfe/thScqTVy1vI1x4tSnG7DlGGI4QSiWoCEZIxbSGDElZFWjLRWqTeU6CtuDcQMMT0ZVVNIxFdsTyNsCli+DGiKWiFI0a8X+oyrs7t/AwtlV/t9/3snH3vqbQ8f63WNgJz7DtTf9VVCXL3BUpmlSyGbx7VoASgmFiCYSJ9whrVardD/7LOauXVgDAxzYuJGeJ56geWSEiy2LoyS9TNMN3KvrhObN47rFixlWFLojEbyuLuoWLsB2LXL9PfT3D0G5wHzJRcejx5cZDye44IZXs3jJUkRFRpQVRFlGluUXpabUC+E4Dr7vI0kSruvy5AO/Q80PsVipEJWg6sJOS6MQbeSCK699wSu8n4hsNsPEUC/Z/dt5+nd34+54gvpcL0sUgyXRwxkbf7EPPn/t8Y/3xntk/u3VC0hGdDZPiJQI05JO0JCKcqCkMOFHUeo7mNvVRTyRwhYVkFQiIY2QKmMjIkjQM5JD9Ezqw+D5HonGPL+7/0lesepeeru3sHRyEZdyBQ6IW1i2LMgaPRNteOgh9O3bWey60zIEhoFtqkpdMonT0MCqiy467TfMvu/jOLVCmq7r4vs+nufhed6hjPJKqcTQ3j2EjBKSUSZXqmA7tcwhURLY+MjDLLnr+1ytH7ug5PtG4WNJmPP7j5nMyPSh24Z+F7JIVEUZX9HQdY1EOERLJERM18kIEqO+TJ8gsz4zRmKim3+eazN17SDbg3/tkdkYnsvHP/kpGsMqoljLGFdEEVkUUERhMsApHjOwavvgerVSKfZkrWbPB5daKRRvMvO/tnjKwTqDk2VPRAlJEql6AqZrMzdeq783ULAQXZOk4tGdMalYDiIeHiIRTcaUQqTjUUKKgCzWFmSSxePXcH4+frfvYq56xeHMsfvu66K1NcXeLSsJmx2UHIkLF6bYOCDT3iSQiLg0pWq1TU/UWBFyJQkXmVhEpzGl8tykXNvxuP/JPly7yrwWE02B/gmZvgmNzs52fDHKvBaHxqPcbtg+bN4nsnxuDH3yh2P7sHswRNeCcyiXS2TGBxE8G0EE15NI1DXR1NRy1pX3eqkxDIPH77qLxokJ5psmPaZJ39AQPaOjvMVxjkggf1oU2RiJcPmCBczXNArAPlVFbmlhYX09qiBgAnldR+/sRI9GKQwMEC6VONbE5ypQ0nXiHR1oz6kXaU/2QV/MgeqXkn27dnDPH1/L27U+wkf5n759TCTS3Ikfb2Ak2szC2Z2sak5QcKHblBl1RHpLFWbFfUK6jidplE0XQagFYR01wWUL04dKue3MiRTFGK11ceK6cMz1NioOlF0BBImIrhCWRe564CO86tLxQ9t8/06VZ7Zu5V8/Ujnm+RWK8JH/vJCv/fv/1L62BAypjsbmVqD29zc23E1zyj5icSXPh5G8TKyunWg0SPQ6i5zQP3oQGH2ZymazPPO97xFZv56BgQFaMhm29fWxOpdj5TH22SAIPL10KfOvv5661la8WIzorHaaOtprtexEkYogI0ZjpNJHKbIXOKk2bdzIv/zBhfzslUdZSWCKL22FtY2wu6gwZ1YH7Y1JtuZ0WpvTeEqMihhBjqSIxOLMaYxiIjFhSiiaTnsydIwR/pqsCXlbRJAUUmENTZHIW2D4Mq6kEglrjOV2sXRejkc2DDA//hmaJv9Uhsfgu/e+llff8knmLlgcdEwDh/i+z4E9exg7cADVtkEQ0MNhmpubMXWdaDpNdEomuu/72LZNuVSif/dujP37iRYKKMUiu595hu1PPEGyr4+V1SrnHue1s8Bdskxl1iwuX7aMQjRKfyKBtmIFnUuX0pKIEFVkxgyL8WKJXGaCZsfErJQYK5VAVvHrG1l83gW0tHec1J/Tiymfz7N769MYlTKKrjN/6UrqT/F1PTM2Rj4zSkvIpT9XZVbMp1os89uHn+CxH3+HRUYP948afP/GmesIej785cPw4dXw4IhKvK6Bq5c0oEgiWydgwNCIxuLMaUwwbKmMOSHsUB3p5g46OztQQ2FsX2KkCnPqHeon76TGig51HUV+8uMfsqB1B8u7dnEwkfapbbDk2mpQgP8MdGDPHir3389S++iLvmSBnbqOn0gQaWqq9Ye8WqkJz3XxPQ9cd7IEhX/ouUMP36897/uHn/d9hMnPhYOfT/koQu3jZJkH0a9lEAqTz9XWND6cQfjcrwEE1+V7/b3sf2Yz7t596JkMoXyJeLHEvU6ZH7fP/H/2WBX22vCHv+eYuOVDrw29UwKfnqKi6hrJcJjmSIjZEY2kIjPiQo8Do8gUJQ0rHCGaqmNxU5qGWJTtElyWhlu39vHAhqdxRnqJegYtqseYLTDoapTTXXzsrz5GMt1Eu14ri3IwyHkwsIkg4IkgcnDV51pNZ0kSJ7NDJWRJOFzH+fc47wEL6nSPwbxBZ8xHAXpyJpZl0xr2iSi1P4NxE4YNmc50nESodqHxAcfzsX0fx/VxHB/H93A9vzZtllqmqIiPKPhIk0FUGZDloyfp5gpFRkPXsGBB7etMBjKZJezdG2Vi+Fwun7OQ4bLMaElmaUcMQRIZLYmAT1y3sJwKiYhNU9I/aimAo3F8GMtDoSqBoBCP6aSTyqGgRNV06R4sYTsuDakQqYTGju4qgxmHZFigs0mnvUE94u+zUIXBnM6izsNTXasW7ByOEI9ItNb5hCZ/CLYP2QJkyjpzFyw97QMaZyvf97nvtts4f2iIg0mLHvCz8XHWlss8lMkQtm2aPY+qINAry8yKRMjV13NFQwO9qorW2sqcVIqQKFIFipEI4VmzUHWd0uAgWrHIsUqcV4GiohCbNeusniLt+z53fesrSP/7F1ybtI54PmfDz7IhLlsyh11uBCPZzN6yx0BmjNlRm3RYpK8sI6ph6ptbaEhEuWl5PZYLG8ckes0wf7BuDoIARQt2FHXmpUOUUWlOaMedQGB6MFoyKWX/8tBCvgAPbGzmV795hve+ro8Fxyj5+g9fTHDJ1V/k6vNXHfpeX0EkHAsjIFIsV2hNOkcM1Bzk+jCUD9PeOffoGwTOREFg9Ew00NvLnm9/m/adO9k9PExyYoK9fX0symQ47xj7bAWeWLiQ2TfdRLK9HS2ZJDRrFunly7GrVfBqo8qCrBBNJs/qN5LT7U1XreU/uzbRdIwhUN+HP74fvnZl7eu8Cbf3qszv6mBFe4JHhiWUWB2L2+rYX1aw5ShavI66RJw5DWGyBkxYMroeoi2po84wbb/qwLgh4CAT0TVSEYWiDd35cc5ZnsG0fR6++wO8YspCUR//0nJueeNniTfOpmvO/BfvBxN42XIch8d/9zsi2SxdQGoyYF4A9qgqrW1tSPX1iLpONZ+nNDxMaXgYtVQiadvETJPNO3bw7JNPouzfz7xSiSt8f8Y6MCZwjyAw0NzMyuXLobmZkcZGWs8/n67Zs5E0DU/XqbguuDYtUq1sRNXzGTNMMsUSlXIFVxBpnTuXztldwU3W76larTI23A+2QbViUKe5JHWwDIO//qd/YaG5g1VyPxe3HvsYv+2tDdq8Ycol5ZFB2GdEWdrVwpqOKIIg0FuEHXkZT47Q2ZzCkXQmHJ2qHCMcr0eub2bpvCZScR1BENg5UuLZ3d0o1k8R3F5uunzg0PF/do/KTe8tBb/3M9ADt93GJYODiMA+02TANAGYret0qioC8ADgl0o8vmkT7sQEsutiKgrR5mYuW7OGWHjyTfq5kRxBqGUgCUItyDb5vEitxI4girWApiAc+ihObisKAtLk9yVBQJrc7+A2nuMwMDFBX38/E93dVPr78UZHUTIZYuUyTabJLDjqlNK/0OHzxymhN+HCl/Pw93VHf97xodepPTJIVA4GPrVaxmdTJMzsiEZKkQ5lYfU70OcIjAkSRVnHjURJ1NWzrLGerlgYUYSsA+OIVCQFSwnhR6LszRe4IlqgRYPtYyW+u34nVjmPbTu4koLW2MEbX/caGpua0ToXEo1Ga0FPwPdrgWjf9/A9D9dx8X0X1/XwPXdKUPvg42CA2psMNnuHgs4CtYWRRGqLJAnCkcHIIQvS0Vp25EjBQhY9orKP6fiMFC1830OTRQRZZ25DCOUFZqjZgOf6mK6P6Xg4Xu2ca/dzPtu27+DyG99+aJBn0yZYvnwV996bpJC/lLbmRhYuTSEKMpUKDPYqdCSj2B6ULYm6qEJDXGY8VyJfzIBfIB6yaEgwY1mBaW30YTQHxaqEJKokEzr1cRlRgGcOlMkXTBa2u6SjtYB297BAz5jMmsUxUpHp0YxN+2XWLJye2bW1W2T53MhRA/0lA0ZKCebOX/x8f7SBF8hxHLZs2EBkyxYWudNrXeY9j3smJlhr29R7HhOOgyqKmLLME4pCS0sLzZ2dtMXjxCSJMlCOxYjOno2iKBSHhtAKhZkDoqKI3tZGPJk8yWf68lEsFvnph9/KVXvuYNZRbu+fysNmIc0dWUhKJRbEHSwPBqsKF82r5+olDXx1Z4jXru3gmXKMt62tvcPsyQsMC2kuWdxKxYFnJgQ660I0xDTGDBFDitAQgugMnfUnn9nI2pX/x8E8mt3dcPdjC9DYyfonN/PmG+Dqiw+/zWZy8PlvJ2nquoXXv+aPaY4ezj0uWOCKENVhNA9tx3gfO2gkL5No6AoGv88eJ/TuFdQYfRnwfZ89mzcz/MMfsmhgAHt8nL6xMbJ9fcTGx/nDY+y3C3hozhzab76Z5V1dxOrriXZ0EOvqItbUFNzwvQR97rt38GfXreC76yaOqDXq+/DpDfCmKcGBhAZ/ON8iZ+7j1odVFs/t4Lyky4b942S9KOfOaaRSydGb0RgYTBBL1tFRH6NZdxiaKJKxFWJhnZakfkRmQEiGjmhtASfbs8lmoexJWEbt2qIpAj2DKrVlTib3UYapFvNYbh+ds+a8rBewCLxwu7ZtY/f69bRaFhHfZ78oMmRZ1Pk+86NRzonF2NjdDfv2oZXL6LZNk+syx3XZuGcPT27ahLVrF625HO/0PI436eUR4Jm6OlqWLiXW1UXb7NlE162jqaWFhaEQQixGKJFA02uBMdM0GR8ZZqBcQvc9IggkdR1RDyN0hGlsbw+uky9QKBSis2s+pmliGAaVcon+chFk+NN/+Czf+Pyn+d3Dd3Bhs4F0lOwkx4Pv7YKvXzn9+xe3wsWUKFh7+NlDErae4qolzVzbDpCnZOd5elCg7IVI1yWIM0omu4/tfVG0SIyqGGFTf5Hu3U+TjpuYXpmbLj98/Gw2zO0//gY33vLOU1J6IHBquK6LXCwyaFlsyOeZ5zic4/v4wN5KhY2yzMWJBNWRETY/+ih/Ui4ztZrhlpERPv3oo3Sl0yxoa+P169aRDL/wVWw9oGLb9GSz9A8NkenvxxgehokJtHyeRKVCs20zC5hhfcZjck4gtWFiMsn1oSpM+BJlScaTFRRNJxYOTWZ86lymStOmn7o+9DnQ5wo85MuU0PFDMerS9axsrOfCqI7nQ9atBVSLssZoOMZIqh69rZOGjlk0p1IoioI8WZ6kY3CAB3/1C1rz45xbF+UzN67FduGZisyzpsZFK5fQEI8wqKXoap1hVOVF4Lounucd+mi6Dp7j4rsO5WIOUbTIGR4NukhXnU7V9anaLpoMS+IRFFGgaNeChc+d1vn7UAAkoVar7yjz77cYe5haQnp4WGDFCpFEIsHatQnyeYnGxloH0zRBD+kUC9AoRZili4fuHpvrYjTXxRivgC97jFSLFIsZRLdEMmyRjnHMEgWKAG0pIOUCVSy3Sv8wdI9I1Mc8Ll58+A9SAea3+MxutHloW54LlycJT6mRLwpH/vGqyrH/oKM6jORL2LYdvH+fQq7rsmfrVgq7d7PiOUFRgIQo8uqGBrZVKmytVjF9H1EQiMZirGpuRm9upisUogiMJpPEZ80iJcsUh4bw8nmOFecyqAVEpVSKusbGI2pNnu1isRjv+trtPLNhPd/9q9fxJnVw2v3evDB8Ye84/3YRLJzyQ/Z9h7t7B/jcvSX+6pouvrdjiOXzFPZNmMyt15if8NnRV8R2PLKGSHvEJy1VGBqv4ssazTGPiapOPt6KYpfR/QpRhWlZ+aPZJ5k6uXB/n8x73ihw+y+28bVPw8/vgff/IximQCjaRizRwTv/8PUo2iLKFYeiLBHTDy86bHq196MTWWdPld1DJWoCgYOCq8dLmOu6bL3vPiq/+AWrczlGslmeHhkh099Pw9gYbzlGtu8B4HcdHTTefDNLFyygrqmJSFsbqcZGaGggGoykvWS1trbyzz96hPe8/w1cquzmDbNNVBEeGpb4aaaL5mXLMHK/oTY2elhSg7fPt8gY+/j2fRpL53VyQ6dP31iRZ7ManS0NLEmbbJsYY8dIiB2ROtJ1KeY1RdAkm6HREhlbIRnTaY5rRwRJFREawwAucdGnakFIhZFsEhg9tF1drMTsZIU9GZHBgV6aW4LA0tlq/f33o+3axQ2uiwBsGR3lmw89RGbvXvxcDh/IaBprLruMT155JaLnsbm3l8c2baKwfTvpTIbrbJu247zODuDhaBR54UIali2jc+lS5l94IammJuR4nFAiccwRYU3TaOuchWEYlIpFSpaFoqo0JZPB3+2LTNM0NE0jkUhM+/6/f/3H/PrOX/CeT76bf1uZmbzO1AyX4W+2tTBvRTuPjT7NZS1HdmLjKrx+jguMs/PAOHdN6LQ0N3LlgnouaQWo4PkVto0MUaiqqOEYifokj+wvYTgT/N2lDo0x+O5uY9pxR8cjtLf00LvrEZINHYRizUQisTOyHtnZxPM8KqbJllyOmx0HCci4Lk8UixQch7gsc3elwr716/lEuXwoSGT7Pv/U3Q2lEv9o27QcOED3hg188sEHCS1cyDte9Spsz8O0bRzLqj0cB8e2cS0Lz7apGAajhQK5XA4vm0UqlQhVKsRNk7Rt0+L7zAaWnoTz7rRhjwXzZ6gf+p2KxOtXzmdlPHTUv3Pbr01/32aKTIgyZSWEEI3RmE6zuqmOS0Iqrg8ZFzLIVMJRRpJpMk2txGbNoaGlhfnPWXDlWFpa27jpLe9k/97d3PnEowhVE10SWDSnhdel6xj2dIZDSeYuPPlZgZIkIUnSUd8TQvEEEyODVI0ytuehiBCSBELS4dsrG8g6CqmmNgqOjetY+I6F59jgO4iei+B7SII/rebo71uMqGDsmv51QWbbNkgkwtTXq2Qyh6MGmYxAc7OMU1SI6Ue+ou1BpiLQUBdCiGio4Xp8fBzbYUcmj2tk0MUS9TGb+hl+taoErXVwYMhl6TEq0igSrJnnsqO7ypqFh/MCPe/Iv0Xfn7ksRDrmks2M09h03FWlAi+Swd5emjIZxj3vmNOoFWBVOMyqcJi7JYkrUik0QWCTotAcCjFaX09y1ix0QaA4NIR6nIBoSRQhFCLe2npEHdHAdMvPvYCl9/bx8y98ipaff5qL4rX+1Kd64F+ugPbn/P8KArxqlo/j5fntjnEEVWVFyua3B/LMra9ljc6PmuwbLVKUEqxurF27agk1BqN5AxuVpFehrKWJzlpBvlLGLo6iWCWiqk8kvH/aa/pEeOjRfbzuGhtFgTdeD6+5Gn6x4V284TXXAFAxwS3FCOs++ydMQnoYmdq1ShZrmf3uCQwEur6AFpR8CzxHEBg9TcbGxth4331UCgXq29o4/4orDk1hNwyDp2+/HeWee1hpGGwtFrlveJhyfz/R4WHefIyA6ABwd0sLqZtvZumyZaSbmlBaW2lvaKAsyyitrUHmy8vAgkWL+O7vtvDoQw/y+Z99F9u2WP26K/nqG9+KoiiYpsmPv/RPJB77Aq9snX4zX6fD2+ebTFT38K37NFbOn8WNHVCx+3lgs4QUSbFuXj2WW2Fb3zCPDEQIx+tpTSdZWu/jYTE4KpC1FepiIVoTKtJzOp8FR8IuQqgebH8WUwOjrQ0mE9kM7ckQAz278M1xQtEkmh5D0WsBqiCwcOYbGhjA3LuXCyazBh7o6eEnv/wl1X37+LBlsXZyu2KpxNdvv53rHniAGyIRkmNjrDEMlh/n+MPAPZpGvquLhnPPpXPdOpZcdBGxpiZC8fjznhqj63owneY0uu7GV7N0xWr+818/Tm7HRlTPwBJD1C08j0/+9FN0dHRgGAZ3/uhr5O/5Ty5R9zPrKHUQF6VgUcrA8Xq5b30fQ16c8+a3sLg5zPI0LMcCJvhVzwTpMLxtSoHacGJ6vUnNDdE3MIoT3skN1zVi2z3khkXQ0kRjdUcESQzDwLZtRFEkFAoFNZZfohRFYXh8nDc7Dh7wk7ExVNPkIschCUwAX9m3j7eVStPmXX10/37ekc+zasr36oE12Sz3PfEE39qzh79ub6fPNBk3DPLVKlXDANNEdxzirssc3+dK4GQta+kAvUA/MCFJFGSZEV2hM6nTGlb49PAY36xzjnhPB9hnQyUUZVUijOHDAQuGPJGMqFDRQkixBK0NaVanU8zTZWy/lmGaE1Wq4Rj9qTSjLe3EOmfT0NxCw4tQkikcDrNsxSoWLl6KaZqMDfVjGSajuk5re+dLouyTqqrUN7WSGR+jv5ijXvOIKYfn7JVdmLBVGlo7CJ1AZrFt27XFtBwHz7ZwHRPfsacEUZ3JICrHDKJaXvdzjqkyOKhQV6cgSQLSlKCtYYiUy1A2NCx9egKq4cBAQaCtSSdylKBpa0MUJocuq6bDgdFxqsUxFEo0xG2SzzndwXFoP06Z62QYShULJidMZ8sQf85cXMMBWZ45JUyWoOIcvYZw4MXn+z6FkRE6Ju9PHWYOMBhAQlHQBIGMICDU1RFdswbghAKiZVFEVlXU+npiqVRwX3GCRFHk9X/5j4y99U/4+odex+WDj4B2ZFB0qhtm+7zv0QnecmkDByYMPP/woEVM8dkwXGbN/MQRZTZqg9wWOcPCLlbIlsagbhaNnQsQRZHuA/tYPO9wko9pQVtrnG1bHpuW8f6z34Z549tfcejrkQmRrkSt/1Uf9smVHNJRmZwhEAlrFE0wbQt38jp5LFVbpf4l8B4SeGkJAqOnmGEY3PqVrxDZt48Ls1liwLAg8PN77yWyciV1pRL1Tz7JhY7DjkqFXw8OYg0MoAwN8XrP42hdgTHgzoYGojfdxNLVq2lqbcVrbKSzsREfKEejRBoagukFLyOCIHDxZZdz8WWXH/Gcpmm88S8/g/H+f+RH/5+9+46Tq673P/6aujO7s73vppKEJIQeOtKVIlWQq4IoIHrv9We/livqtXvvtWG9105TuaKCBQuiFGnSAwQS0pNN2Wwvs7uzU39/TEiypBCFJMC8ng/zeDAz33Pme9bdmXPe5/v9fL/5Ceoe+Canto1PaFMfh0tnjdM9toRrbo9xyL5TeM2kBNDDw4t62JBNcNg+zbRWZ1mbHGTF0ijPrKqivqGBmU3VTKktkMmnWdMZZCAboSYRZ2pNhGAA+tOwYRUsWgwVlQeTGn+I2Ka8fda0PE8uW8H5J7exsXOMutgAg0ODDPeXEYtXUpGogECMUDRBWTzhqLxXqIX33cdh6WKJhUw+z4/vuIPQ8uX8IJ2e8BlWCbw/l+Oovj5u6+vjfTvZ5wjwx1CI1ZMmkTjuOGadfTZnHHcclbW1hpqvAFOnTuXz/3s9ULzIeu6FTiwW4+xL3wOXvoc1q1fx4x98isqnf8FpzSPblB0JB+HUKQVgkK7BQW5YFCZS1cCp85qpioVY0A8fPWtL+1y+QEtLbsIopGUdEc6anqF/YICugXFqK6PUVuSBLob6uximkvKqFnLZLP19G4kG0kTCWQqFIH09EWLlVTQ0NHvB9hIzNDREUy5HBLi2q4uTR0fZegBbO1A9MsLsrZ57dGSEfZPJCaHo1k4uFLitt5cHe3s5EthvN/U9TTH47AD6QyHGwmFy0WKNz6p4nKZ4nGmxGMeFilPdx4H/icMbN4VR+zZU8i+LVvG+eJp5m76zswX45WiQa7IVnH/UodxQXcfkpkYOaaxhbiRIpgDdORgMlTFeXsXK+kaiLZOonjqdhqYmWvbAZ28kEiESiZCYNWe3v9c/IhqN0tLWTibTRH9/L4Mjw5uDymhFJa2t9bt8rvPsse6KHYWoRDsntAuFyggGK8hkomSzkNiqLl8oFGFwMED79ACdGyGTguCmJZ/C4SDtrVFiu1BYNF4WZvrkFqAFgJHRNCu7u0mN9FIWGqapMsdIChpqnv+4gsFiuJbJwZL1IQ7ed8vvWK4AS9cGmDF554M8RtMQq9y10cl64bLZbHFhTWByTQ2rUylmbFpIbnuWBIPMiMfZEAyytrGRA486iuHOTqIDAzsNREeDQcrCYYLl5ZS3tHgN8Q9qbGriihvu5uv/+VmOv+8/dto2EICacIZ0Nk9003qCz+oZgwPq8owMD/J0toZZ1dsu2FZTBjVlGcayGTrXP8WqrlVUT5nH6uV3c/JWX5ZPPAMDw92cd8qW69lsFsbCF0y40TyeKoNNE4+q47BmIEskGiVeWUd9U3GEeHhogL7h9TRWblvSAWBgNEh5ebXnZ9qGSdkelM/nuf7LX+bsRYtoBhaMjvLQ+DixYJBzcjkWb9xIFzAtleLXGzaQXbeO/Lp1nLfpJP65BoBf1dYSPftsDjjySBpaWwk1N1NbV7e5cDXNzVRX7a7xCdqbYrEYb/zQlxgb+ww3fP1Kmh75H05pnbjyYGMcLp2VYuPIEq6+I8Zhs6dwWFsCSLK2J8lvl5YxqaWR49oT5As9LOrp4dENcfLxOtqb65nZlGB6aJx0fpxVG4L05aJ0JHMc3A7TGqEtMosnl8Dhm4b4zZ4O//enx3nDa15FPpslOZamrTZKIJBicCzFUG8v47kY1TVVhHIxxrJhCFcQjVdSVuZo0leCTCZDLpncXJfv5sWLiaxaxUefE4pu7WjgFxQXZNr60yoH3BkI8HhTE4UTT+T4//f/OPPwww1CX+Ge73NgytRpvPmz15DP/4j7/vw7lt34OfZPPsRhTdvOpmgqhzfNzAKdPPxUJ48NlpOKt0ChGgIBHlya5Ht/Xk9ZDH5xLYwVoHkKTKuNUFM2zsrBYbLjSYaIkSdCTWWUqhjAMP39w4ykoKVu60VJ8tQxzlCql871aVraJvu59hIyODhISz7PinSa9lSK7c3qDTxnVs7/bdzIJ7ZTM29r/w+4FjjlBfQtBaymOOJzYFPwmY9GKYvFiMfj1MTjtJSVMSMUIr0p+ExTHJ2VCgQYCQZ4MhAgGwhQCEIuEKAzn2WsUCAegMPrEsw8fA7XrdzIdwaShCmQDgQZa5vGTW88m95CgMFwjHSimiV1jcTaJlM9ZToNjY20RXcyB19AMdRsatoSDu6J99teMFRZ1T/hcSwWZ/r0Grq64vT3Q11d8dIvlYKamiipVJTycpgyHdZ1VFIe3xJNjeaK/6BAMeYqPPu/TfKwOf8qsKVJgYqGeioaik90j47SlVpCeWo9z1csPF+AVd1BOrqDzJtRTlk4QKFQHD26ti9MIVxO/nkWDu4fiTCjvXqnbbR7TEkkuD0apS2VYnvj8YaA1eEwyepqKpuaaEskSC9ZssNAdJzilPnycJhIOAyNjdR6Xfui2PfAQwk88PztwkFY0Bfi1GllNMW3nH+vHIa2QJLD9iljaLyLhcPTqC4L0BJOUv6chCkehumJPJn8MBtWPEDvml9PuIvY2RNlsOcx4vO3PPfr22Nc9IbXbn6cSkN9+Zb/74NAOhtkPFhDY+OWz92qqhp6xsfpHOyjLpEluuniI5MrhqK5YA3NDY279DNSaTEY3YMWPvYYB6xYwdKhIb7W2cn4+vWE0mkqy8p4uKWFsooKYtks4+vXM752La/LZtne5X8SuKmyksCZZ3Lg8cdT29JCRUsLwaoqqjetqDoQiVDR1kbEk9lXvHg8zpv+/SpGRz/PT7/277Q+9h1Oap04hai5HC6bmWLD0BKuXhzniLlTmNdawaTEOGPZtfz50RCB8lqOnVnP/pExRjLrWLi6k9tXl1Ne08iM1jqmV0UZ6E1x9FRo2XRi214X49FFIQ4/oHjRGAoB+ZUk01CbKFAfH6Fj4yiBYIyW+hjV8Tz5wigDI6N0DYVJF2K0NNYQSg+STAbJB2OEIpWUxeLeCX6ZGk0mJ0zr+9vKlQRHRpjyPNudD9wCXAQ8BtxdU8PoKadwwec/zymzZ+98Y5WkYDDIq049m1edejaDg4P84rqvkrn7e5xS1TmhVumzDmuCw5pGGcuu4Lc/C3JbXxlttWm+eU6O+FZflQs3wMf+uJSpUyaTCfWTG+skEK4nnQ0zUIhBIEpNZZShkTxhxuhYnyMaCdFYFycaLv72V8XypDPDJJNJKiufb9kw7SnRaJRUKMTTAwO8bgcjmiLhMMNsyW8y2ezzLvw2Ceh5njajwCpgbSBAdyjEYDTKWFkZ2YoKQokEVVVVtFZUUF1WRmswSCQUIhwIEA2FKAsGCQWD5AIBxgIBMsEghUCAfDBIKhhkJACDkSxveE5eMCc5zreXdfCBRJZgAGqjYd47e0vl5h9mKqg+7yIGzzqX5oYGJvm9+7KWz+dpbk5u9RgGB+PMmhVjaKicoaEgjY3FGzW9vQHi8RC1tcUPv56eEE0t++62c69JU/blb3fcyNTGgR22GUlDcrycsqppHNpWzobuQbqGi7UQKxPlTJ1cxTgJOjauZUZLjuh2rmLX9QWoqW1z8c89KBwOk45EIJUiCBzT1sa969czNZ1m2qaaoxlgSShEZ3k5B8yaRSSRoAqo3MECOOMUp8zHw2ESwSBjFRVUNDZ6bfAiOvjQQ/nOaDuns26n7TpTIcbHgjzQHWZqfXEAzkNdsE8VHFA/zsPLuzl4eiMHBVexdLCcntnHkevtoCbbQ23ZxJsYkSBMKs+xrnniyPY168Z4yxlbptbn87B+5HUEthot2tUXYErlltHiw+NQWddIU+u2qxE0NDYzNlZFb183+dymUajBCDW1jVRUVGzTXgKD0T1qwW230dzZydUPPsi0oSEuz2RopXii/H/d3fTHYhwyOspp2Szb+5NNAb+sqCBz+ukceOKJ1EyZQm1TE6GKCohEqAqFGAdSlZVUNTVZ46zElJeXc9GV3yCZ/AI//uqHmLzwh5zQMjEgba2Ay2aNsX7wGa5eHOfouVOY01LBayYVFy95ZHEP67MJ5u/TxJHNUWCYztFhljy9hseCNRSqG9m3vZpnK1olyoKs766geA+4qKaynxW9MHNKsZj+lPoCMMaG3hSpXJTWhhh1iSB1iSyZQpKBoSQ9qTD5UIL25iqijJJKwmguQiBUQVm80qLqLyO5bJZgPE7f6Ch1QK5QILKTKVXPagF+EA7T8drX8sZvfIP3TJ262/uqV47q6mpe/+5Pw7s/zaKFj/OHqz9J6+o/cHJLmvBzvgrjYdinIk9zaowrX7PtvvZvhe+/fox3/OZhrv5KNY8/+Qcy8f15zXEH0z+aZCQVZkkvQIG22hx15cWRDCs7xghHo0xrqyIUgOryPF0DPQajLyFNTU3cX1VFLp/f7mgmgNfW13PL6Chv2jRKNEdxLNzOxv2OUxw49ySwIRhkIBIhFYsRSCSIVVVRXVNDS00NDYkE9ZEIoxSXUBwPBBgPBskGAmQCAQrBIJlgkHQoRDAYJBQKEQ6FiIZCxEIhykMhqoJBYoHA5hH4fw7D66rhFx09LM6MMGerFbunJ8p4zbQ2Pr6qkzOjGY6JFggE4OlskN9UtDH/skt5zbmve2E/VL1krF+/nmnTtjxeuxYikeLIyerqajKZLWFhOh0CwtTVQSYD4fC03Ro6RSIRKmqnsK4/SXvttmFYoQCPrqzkwMNOJBzJES3LMnNKsQ5EJg/DqRC5QIKmxiZisXKWr1lGVTxLfVWBUACS49AzGKKyto2mFhdd2pMCgQBVzc30JZPUFQqUh8OcPGUKa5NJ/jYwUGwUDBKpr+f4+npWhMNM28G+xoGhQICKSITqYLC4RkZzMzW7UKNXf5/W1lZ6a+YynF5H5Q7GUT3UCetSZXxwTo5VwSTH1MH1C4aY01bFQZvKtBzRmOHRVV3MndLA7IpR+pfdysaa/YnufwYrVywknlxLS3zLdcCydRs5aM6WWRi9A1AeWsrW1S9+d2eUY+efxZKnIVWA5kYYSUUnjDjvGoH2fXY88jMejxNvf75hGdIWBqN7UG54mKv+9je+0N/PoVs93wAclk7zl3Sau2GbUDQD3ByLkXz1qzn4tNOonjKFhtZWymMxBoNBEtEoESAZCBBsbqbai7CSlkgkePN//C/Dw1/k+q98gGlPX8NxLRNPQtsq4LKZY3T0PcOPFpXzqv2msG9zOfObYD5J1m+aZt/e3MghkxK0lGeBHp4Z6OFvd8QYL69j35lNlMXCrO9pZOtgtKUuxe9uvZOzTjuAA2bWb36+tbYAjNM3Ms6G0Qj1tXGq4iEaq6CxKksmN0BvzwAj6SiRaBWtzQkiwQEy6QGGkkEKwRjhaCXRsm1Hk46NjZEcHqCQKwbBgVCEyiprT+4NoUiEptZWnhwe5oR0mlnt7Ty4CzdplgBnX3MN51988e7vpF7R5u5/EHO/8isymQx/+uV1dN3yXxwVWMac2i1trlkG//H6He+jqRLm1fWxZt0gJ86rpntoAb+4aQWnnvpaxlIxWmqYsApzPAK1FVl6klmeWJKhpioOBMEblC8pgUCASQcfzENPPEGe7a/8PSsW448VFTw8PMxhhQJH19ZyZzLJSTvZ709DIapf9So2zJtHPJEgHosRisUIl5cTKS8nWl5OvKKCYCxGeVkZ9WVlhINBMkC6UCAdCJAFcqEQ+UiEQiRCIBolUFZGOBYjHIlsrhNf2DSNOJ/PUygUaH3yCZ554HYumNzAr9cFeGx0jGOiWeqD0J+HxWVVVB80md6jjuUrD95PoFBg+kEH857Xv4Fyw4ZXlJUrn+a447Y8XrUKmptr6e6GlpY4GzZsuewLhcKUlxfPpfr6ymlu3v1TSw+efyx/uzdFz/AaZremiG06leseDrBwbRX77n8c7ZOnkEqlGBjq3+qcLkyisnbzgltVVdVU7T+fvr5e1vV2kS/kSVTWMG1WkyMK95LWyZNZ0tdHoL+f2kKBIMVp9VMSCcaAZcEgM8rLWRMMEo1GSQNbZ3FpitexsUiEXLBY6XakupryujrXyNiNPvKVH/Hui0/hG/stpeo54egz/fC/C+H9cwapC6fpH11HPjeDk+v76M1FYat5rYc2ZHlybTdTWhuojYWoHlvI0ntW0Dj/TCpr5rNm5WLYuJTW8iwrup9g30O2vM/Nfxzm/JNHJ7x3cuQMDp387HcedAxCfxIKzcXX1w9BdQKGuxcTaTvA3xG9KAKF56nTspVdbijo6+1l9eLFZDMZahobWfXMM/zkW9/ikL/+lffupFbVp4ArKE7LygG/jkbpO/FEDn7ta6nZZx+aWlqoisUYBgqbRolmgGQ0SqKtzRMCbWNoaIhffem9zFpyPUc3b/93b/UQ3N5dznHzpjKzccs4mrEs3NsZohCv4VWz6olvqqqdysGTvUGeGI7xyLJF/M+n7928zX2PQu3I67l9VQUHHnYQx83fdooDFO/udw+GSCTiNFY/Z4XnLPQNBxjJlJGorKGhJkYkWFwoIp2BTD5KIFJOLFZFcngIcsNUxnObi35n8jA4GiZUVklt7fMsg6oXVSaTYf2yZaxevJh4Vxdz02n+6b/+iy+uX8/+O9nu4vp6frRuHWVlO19UQfp7DA31UlXVx+JF6/jrdV+jdslfOL0xycceh29ctPNtn9oAP1nTztvedCQz2osXAE+sCrBq9EDOfvXB9AxmWLxygGwuT01VjP1nVBMJBVjZFaCxrpyySJAN/QFiVVNoanYE00tFoVDgmq9/nUMefpiDd3AOnAf+q7ubVCrFSSMjfOfpp7kulWJ7n06DwHvnzOHqH/1oQj3Z9KZ/mUCAXDBIYevAc1NwGonHiUajRCKRf7gWbS6X43c//THzN66gPZQlmc3zYO8wg+ks8UiI/obJnHLJpTS17Jnal9p7vvOdd/Mv//KtzY9vuinEYYedx9BQjHnzjmbt2iomTy5jbAw2bqxm6tRqRkYgFDpoc+i4J3R3d7F00aPkMsWps9V1bew79yBvZr/MZbNZVi1dylhnJ5W5HMFCgeFAgHwoRCIeh1CI5miUQDDIMNDExEC0fNONxC6gatIkYq4avkd0dHTwpQ+9g8q+p5kf62Q0neOB9TnqYvDRwyAWhh8uKeOyk/bj1x1xzjt8Ggv7w4Tr2phbN/H24tLBANV1DTQlitd1/akAG2vmMevQVxEIBOhcu5pHbr+Cs08Z2LzNt360iHddvGUa/Z/uCXPc/KuJxyYmtSt7AyRDdZSXV1BXBbWJYk3irrE6Wtr33X0/IL0S7NIJlsHoi2xkZISH/vQnqgcGmDE6Sgj4XV8fx6ZS/PMPf8gNGzfutFZVB3AdcGA4zPpjj+XAc8+lYdYsWltbSUSjZIDBQICqsjKiFKfXp6urqWxsdIEH7dRAfz+/+fJ7mb38pxzZtP2AdOUQ3NldwQn7T2WfhoknqI/1wNp0gkP3aaK9OkqhAN99Bs6cN0669ZfM2DRbYTgJt9x0PG84ZhJfvzvKsSceRXtLgvb67Yf241nYMBAkEonRUl9G6Dm/xqNp6B0OkMrFqampoaYyujkA7R+FYKC4MuH29I8FCccaqay0UPue1LVhA4GuLpZ0dJDs7ub+e+7hoZ/+lJ+lUmxvndifhMN0f/jDvO/zn9/jfdUr2+DgKqqrM/T1pQgEOonH89zzl/u59tNf4PpLMjvddnk3/L6vhdOOb6Mv3cJR+9cAkM7CX54sg8RcWmpyLHh6Hdl0mlQ2QDhWw9mnHsFYvpZZk4ofTGt6I9S3zrGu1UtIJpPhfz/yEf5548bthp0jwPXNzVz6utfx4MKFPLVkCbffcAMf7+rioE3nzQXgb8Eg35w2jY984xvUtbVRVlW1ZaRoWdnftcr4C5HNZrn3tj8yuGIZU1JDRPI5NkQrGKtt5KgzzqKx0YUmSsHnPz+bj31syebH3/9+GVdccQEPPBBlv/0OIxSqp6IiyLp1AaCB9vY4XV2NNDXN2Hud1itK1/r1VPb0kM3ltgQHgQBlwSBlW12nrgPiQFk0SsVzZlZ0h8PUTJrkYJ89rLu7m0WLFhEMBnni65fzztalm1/L5uH/1jdw4eFT+OtgA6/Zr4G7eqtobWpk3+qJ15RrkhCoqGdyTfHbNVeApaPlNB92FrGKBCv/dhz7zSz+djyxKElz1RKatxrD8pNfn8bF51y+Tf9yBbhvdYT992uldqvJDuksjASmUtvgDWjt0C6FZI47fhGNj49z329/y6t6ezfXrlo6Ps6c8XHagcpc7nkL+E8G7orFOO6zn2XO3LlMamsjvml4eBLIhsPUh8MEgOFgkHBLC1VebGkX1NTW8pbPX0d/39e49ovvYr/VP+Pwxom1H6dXwfSqEZZ3Ps3VCys46YCpTKsvBqSHNMAhJNnQm+S3S6IUKhqZN6mSyQ1l3LIswIwpxS+5ygTctXgVR85s4Lz94ekNKzlw2mQWrwqRo4yZk4qrjD6rLAzTGvJkC6Ns2DhGnjJaG2JENrUpj0J5fQEYZSg1yoZ1ITKUU1dTTSofpnXTwqOZPAwks6TH8xAoQCFAJBoklR4wGN3DGlta6Mxk2DccJtXcTN20aeRiMc6+9louGRvj4nyeMuAZ4Cvl5Uy5/HI+9rnP7e1u6xUmk8kQjWZIpQqk0ykaGmDDhnFefeZ+/OyH08jnl+50pvu9q2D/+RXs2w69Q53cck+KM49tIRqG0w8e5yd3LiA7EOCNRxc2L97UPzLMLX/pJl5/ELMmHQFAS3WGzq71VEyftfsPWrskEonwTx/6EFdfdRWndHYyM5cjQDHsXBwKcWdtLW8880xikQjHH3IIrzrkEI446yxuvfFG/ve++wil0+TLyzns9NP5xpveRKaqitqWlr024i0cDnPCGWeRyWTYsGEDuVyOI+rrqXL15pJSUbFhwuOhoRiBAGQyUfr7A0ydumnWTypES0sZfX0hamutwacXTy6XIxwIbL523ZE8ULeDz8v8pvrK2rMaGxs330Tbd9ZfuePd0zippbhwUTgIr6rq4Yl1NcysgRU9CU5ogFuHm8gGytivanzzfqYkYONYL0u7a5jVWE4oAHMqRul/7Of8ctUgF526ZazdfQ928C9v3NKHux4Mce4p25/OEwpANJRjtFBNZHyQIHmCwQCxSIB0ajUjyQQVCcsJ6h9nMPoiSafTPHrffRy8VSgKsDiZ5PRNowsS8fguFfCPNjVxxCmnENv0pZKhOFWrsqyMRCBQnDpfVkaitdW7afq71dbVccHHv01y+L1cc9WnOXDtrRzaMDEgnVENM6pHWLr+aa5+MsEpB05lSl3xzl9rBZxdkSaVXce9y0L8tCNBZyhK8be36IgZg9z3ZAc94+UE6uKceuxk5rTlgFE29I3RPRyhtaF8whT6cAAm1ReAFF2DKUbSEZrryikv25JcVMWgKpYDhukfGWZ8LMzKZIKqqgQjqRz1iQKNm4ckFhhL5+kaHGZ4eNgFUPagQCBA65QpjI6MkO3poa22lsve9S4u+dd/5SfXXsuFt9xCKBDgoJNO4j8/8xnq6+uff6fS3ymVSlJZCf39OVKpUcJhyOWKK6rue9ih/GHJMs6cs/3JMIUC3Lkajmro5sT5VdRXBTjtgAFu+kuKM0+Yyr1PB5g3DQ6ZPnH72gq45PgUv37ocRYva2XOzMlEw5DLjm33fbT3tLS2culnPsP9f/4zt99zD/HxcXLhMPvuuy9X7Lcfka0uzHuA9ro63vK+95H+4AeJlJUVFz8KBAhVVFBdWfmSOB+LRCJMmWLQVWoKhQL33XcXlZVjz3m+eOc4GCwnldpyLpVOR4lGg6TTU18Sv7d65SiLxRgHdvZblYMdLhCcAiLxuAsI72VNzS08c9lPWP+L19O2afzVtCpYtGY5sxr356HVHbTVzOTEsmX8LfZqFgxv5ODK4c3bN8chOj7AUxtyzGstXn+tGyuQSd69+Yb0kuWjnHnCxM+s1euO44TDdxCYFyCdKbB86VLCFEepRqIRYrEYDdVlRLKLiZYd7Gea/mFOpX+BMpkMqxYvJjswwIrFizkznZ7w+u96ejY/99NHH6Xp1lt59U5+5leHQlR87nP805lnAjAKjIfD1GwaJZoC0jU1VDY0OHVe/5Dx8RS5XAfPrrmwYnkXd377kxzaeTsHN2z/d/OZfrivP8FrDpzKpNotEw+H0/Dlp2CfaSt46+UDm5//yc+jXDx9fxZugO8/VsNZp82jpq6R6ZOqaagsXmyOZWB1V4hwNMaUlnKi27k5PDgGfcNhaqrj1Ca2fx8nV4D+ERgZj1BZUUFNVYzgVn8amQJs6IvQOmmGX5ZSCRkcXEN19Tj9/SlGRzdSXZ0Fhkgk4MYb7+DPv/wx75uZZL/nlF4sFODjf4RTXgXH7g/X3lnF28+fsfk79w+PhFk2MJV3n7Pjz5NMHn587yQue3Nxxe+VPeVMn3Xg7jpUvUCZTIY1S5bQkExSVShsvoGdBXpCIYbLy6ltbCRRVWUdRL2kPProfdxyy9c55JDVhEIP8trXbjmPu/baQ3nrW+fw2GP7EI9PYc6cYk3R7u5GysvraGo6aC/2XK9EmUyGruXLac/suFRNP0AkQu1zRoWmgd6yMhoc+POScfVHL+GtyR9PuK76/pI4V5w8h1+tS/C6+ZPpTcH6g/+F1KpHOLy8e8L2oxl4ZrSCGU3VPJWEodS1nPaqYqh59Q2Luez8LYsu/W1BgBlTfkBj7bZFtzI5WLABGmphUiNEAsVztZ4kLO8M0lSXoBCMUVFZT/Pk/c1I9FxOpd/dMpkMSxYsYOrAACtHRqjczqJKW8dMFxx4IJc/+CDH9fVtt6bVEPCbtjZueu1ryW56XF5WRu2mP+6hYJBIaytVriKqf1AmkyGV2kD1punno6N5WlqruPyrV7Gxs4fvfOZTHDN4NwfWTwxIZ9fC7Noki9Y8xdVPVnLagVNoqynjljXw5qNhaXcCGNjcvqG5eEK0fys0LxrhsKldrNzYz8Knqogn6qitrmKfSeXMac8BI6zrGWFgNEprfQV1VVs+lqrjUB3PMpYZZvnaIBUVcZpromz9fRcKQEMCGhIZMvkBenpgPBulqipBVXkU8gUC+TFWLX+KcCgEBIklaolEooRCIRKJhCdg0itMJpMhEhknk4FAIEd1dYGennGmTSu+Xihs4JvXzeRzH1lD7+/6OH8mVJbBgh64bwVcfiacsCkzuPTEIb5/0/LN4egZ87P8+bHlfOknCXLZDJFQnkwuSHVljNed2EhLXZhIEKIMkhrPEIlGAKcFvpRFIhGm7LsvvV1d9PT1Ecxmiy9Eo9Q0N9PS4CJ+eulZsOBB7r3303z84z2Mj+dZsmTLudvYGNx3X4ZzzslTV1dJPl+88ujrC1BXFycSsbSHXnyRSIRoTQ39vb3U5vPbvJ4ChmMxorEYfdkssWyWAjAaDlOIRmloavKc/CXkks9ezfUX3cVbWzq2PLfPGD9/ZD1nHNjOfcv7OWZGLf0Pfp+6cz/Lgw//iUNCqzavBVEegXmVI9yxKkc+OsjB+xezklUdKY4/bOJK9I88dTA1jWEaa7ftx8KNMGcqVG51XzIQgMZKqEnkeXRZkuaGEKHcIN2dq6ipbyefzxMKhfx90i5zxOgLsG7VKuLLllGRz7NoaIih3l6Of/ZkepPbBgY4cnSUZ6s8Lerq4r9++Uv+rb+fZ8eOFID7gW9MncqXv/996ltaSIXDVIfDBClOpU/GYiRaWvzj1gvS37+B2tokAOk0pFIpqqqKd902bkyRSiUZHFjCI1d/lyN7H2Ne3fb3s7AXHhisYmVsCp97fZSlG8ZoOH4RNVXQ0wcdndCybn/a6qIs2giL84287qTidOlFHdA9kqCyqhZClewzqZLqimJoMJqBNd0hotE4k5vjRJ6TJWRysK4vSD5QxuTm2OYv3u1JZ6E3CUNjEWorozTVBBkezbG2K004nKMyViBHiKGxMmIVDUyZNtOaRtIrRDI5SHl5F0NDWYaG+pk8eYSOjgGmbKqF/Nvffpuzz06SyRR44IHH6O2C0WFomwZLF3yIM+q/RPtWFR7SGbj6jireccEMVm0McP3t8PbToXWrz8juIfjBrRHOOaGNedPj3HhvGaeffQnpQAXB+HTqGlwA5+Xi2XNjR53opeyzn30zH/vYIoJBWLx4jPb2RTxbNejpp6FQOIgbb2zkPe85m2CwkdraEIsXR6ir25+mJoNR7T49nZ2MDw5SlU5TRnH6/GA4TC4Wo2nTwkqpVIr0+DgEAsTjca9xX6LWrlnFmg/P4pimLRnHM/2QadgXyhLUt8+gtTrCQ/3l7HfF13j6r7/lgPSTxLa6pFo5BHf3Pcpbzu0C4OoblnDZ+cnNry94Gloqv8CD3Y2ccWDVhOu/oRR0jMC8nVSJ6U1C30icsWyCeCJOWThEKBwknQ0RilbS0jqZaDS64x3olc4Ro7vbQGcnbYUCazMZWvJ5RkMhxrLZCTVGD6us5L50mtM3BaZzm5r4+mWXcd0DD/DdFSsI53JsjMV49QUX8P03vIFMeTlEo9RuKsAxBmTr66mt20FCJe2iZHKQRKL4JZTJQDKZ4tlfq+7uFOl0krKyHg44sJbQ2/+VRKKTq796A0f1L2Luc+7e7V8P+9cP8UTPQq65qZpTjmvnc1+DkR6YsqkWzdNrn2H+jDredEIrPeu2TKmZOxnmkmR1d5LV3TE2BBtYno1TXV3J9NbYplqkSTq6kyRTUVoaEtQmit+QkRBMa8wDY6zvSTE4WkZ7cxlV8W0T0mgYWmugtSbDWCbDyvWQTMGcycUpGEU52mtG6R/tYMniUWbPPci6RtIrQC43RDAI+XyOQmGcgYEszc3FsGtsLE9Dw+jm/25shFe9qrjdxo0wffq7ePT+Fgo9/8akTQMFoxG4/OQhvvvLZawbncknLgoQfc4ZVGMVfPiCDJ/9vw3s89bp9A4HKARjDI1VMrXN7/CXEwNRvdQtXbqUOXPWba7X19Exxpw5W15fuzbAqaeGyOdHuOGGRVxxRfHGTDZbTm3ttD3fYZWUhpYWMvX1JAcGGEmnCQWD1NTWTihFEovFLE3yMjBpyjQWXfBdev/0Nuo3hRyza+HmZcs49agDuP2Z1ZwxfyaH145yxzWf4fh/+QIL76lin757qYzAskF4cGCA2uohANZ3jnPUQckJ7/HYwnm85cwq4qkCTw5AJA+1EcjmYXV/gMNm73x8Xn0CVndnKSvPMbO5OFOoGMdnGMukWL1yhCnTZlNWtr05u1KRCcALEMpmCQC5QoEIMKeiggXPGXFWGwoxOZHgtnCYZ8sL18RivOeEE/jkZZdx3Ac/yPd+8QsuvuwyMpWVVMVixDed5QyFQgQmTaLSUFQvUDo9TjDYRSRSHB06OJjeHIp2dY0zPl4MRZuaCjz99CBz5qSZOq2Oy77x/0h87tN8fWAmi/u33e+BDXBx4yAf/s7TXNgE33ktXHlC8d+PL85wQvtG3n/1MoYG+7jz0V7yW32vTW2E4/dLUV+2luG+NeTH1rNoWScLlg6STOWZXAdz29JECn0sXtXHyg0pMlvNymmrLTC3PUUmNcRTq0ZY2ZVnPLttHwHiERjdJhTdora8QG1ZH10bO//xH7Kkl4RMJkMolCKTgUikQDyeZWAgxbPnw8uWDTBtWvHDZHQ0T3yru5ljYxCPxzn7DR/g8bKv0bFVuaxIGN528jDl+WWEg9s/SQ8F4fyj09xybz/JbIKhXBOTHY0u6UW2YsViZs0a2vy4v3/itNSBgeJnTl1djte9boibb36MZBLq6+c6Mk97RCQSobaxkZb2dhpbWw1BX8Zec+Hl3Fxx9oTnXjc9z0/uWcaZU7L84cn1ABxfsZa7b/xfDjrhtayb8lq6xuDR3gIH7j9AQ3Vxkd4/3dnB3Blb9vP0Mjh6/wvpGAgybZ8yDj0KZhwM6XoYLiQIlkXYlcGeAQqEAvDc+5rxCEytG2X92hUv5EegEuCI0Rdi019eRSjEUCDA9FCIlooK7hkZ4aBcjmfXwJ5dXs5oWRk3JJM0BIOECgWy4TB1ra2cud9+pCIRwtEodZsC0QyQLC8nYZ0VvQiy2SxjY+s31xUdGMhSXV0MBbq704yPD1NW1kNdXYFFiwbYb7/M5hEImQysXQ/Hv/+19Ayv4kc3PMBxIxuZVbNl/197Ej58Jhwyedv3PnQy/PsJSW5ev5ZjZwT5w1/7mT6llf2mV2xu01gFJ8wbZzjVyYKVYWprahgeGGFJf5SG2komN5cxpy1HvjDM2s4ko5kILQ0JajZNv69PFKhPpEmOp1m1IUI+UE59ZZDaBJunYgyOQWX59kPRLf3I88zGdbS0tv2jP2pJLwHpdDEEHRvLMT4+TnU15HJb7posX76GAw4o/vfYWJ7EVnX+R0ehoaGYlJ75T+/ldz8PUOh+L1M2zYKPhOFD5w7zpeuWkCNO90CaAAHOOKaBUw6rIhgMcMA0+OFfRrn0XW9m8tStzv4l6UWQy+VYuPChCZ9d4+OpCW1SqeL1QyYToK0NAoF+OjqyzJ27k/mokrQDl/z3z/npxZO5qG3LHeOLpo7w2yc6OXoWLFhbxcGTEswfuZ9H79yHQ098LX/Z2EWi6o+URVNMn12guzfNwXOGJuz3nr9NZdbsCOO5EKccUwzPy8uhUAgRq4/SmA2SHE1TW8EO5QqQJ0C+sP2b0NEwhBgjlUoZ0GuHDEZfgEIsRmZ0lIZwmFWhENOyWaaXlVEfibBodJTxTdPnC4EADbEYh++3Hwc0N2/ePgWMBoNURqM8G3+OAvmGBmprt1N5WPoHjIx0U1lZ/F0cHs4Rj2eJRKCnJ834+BCxWDc1NbB48QD7779lyvvgYI5ly3rIZruYPn2YmpoErzr+FB57pJ8v/ec9nF8xzLRKWD4GH9pOKPqsg9rhu48MAwXOPCxL33AHP78txgmHtdNUuyX4r4zBcXOzpLM9PLqin1CkivryNAuXhShQzqxpCaY0AKQZSvWxeFWIWLycSU0xwgFIlMHslgzj2UHW94fp6EnQVBshFskyNg7bWeRwglAAgqT/4Z+zpJeGbHaIiori510yOUoul6ahYcsIz1Rqzeb/fnYq/ZbHxRGjzzrzwvfwh1+GoPtdTGksjrj/4i9gbHyEK04bYWozZLLwq/uHueyzZXz67dOZ1hYjUlbOwYcetkeOV1JpefzxBzjttBx//GM5Rx01wu9+18+vfjXMI49APg+VlVBXFyKdhkKhuGDloYeO8tRT/cydu7d7L+nlqKysjCM+dwePfeZADmkoDrBJRGFafj2DySpS+XUM1M2gpjxM9LGfcN0zz7Bx/Spqq5KMLBxg1unwi1vW8tZzt+xz+Rp4zQFn8LeuLFNnw7PjwXI5KC8vIxiMUl1dzpLHR6mt2MG0QKBzIEAsGqWhZsfRVk0szfDwkMGodsip9C9A89SpbAiHCQCTYzGeDgYpAFXBIEckEhxXU8NxNTUcXl1Nb2UliYaGzStY9QO5aJS6rULRwXCY0OTJJAxF9SIZGRkiHk8SDBYDgEAgQywGfX2ZCaHokiV9E0LRNWvGWb9+I4ODaznqqGFqaorPP/30CJlcFx/6xSz42H58sTvOnPbn78f+DRmWrCmOZqirhAuPSdHfvZyf3tpJKjNxSmo0DEftm2P+tH6WLl/N0EA3M5uH6OnewCOLelnfm6YqBnPackyuGWbd+h4Wrx5maLS40mFZGKY3Zjlw8gCBTC8jYzlGx8Ps+jpzkl6uiquQjlIoQCiUJxxOMzY2vvlkO5uFsrItox3GxiZOpU+l2Gba+xkX/D+eTnyb1V3wtV/BoTPgs2+BqZvuc0bCcOFxef7nX8f41A+Ws3pjjknTD7JOpaTdYvXqx5k3D2Kxai69dBn5/GpuvLHA174G3/gGvPe9sHr1KB/9aB+nn15cRS4eh1Doee4QS9JOzJwzj7Wv/hLDW40jOagBHn5qKYc05LjjyQ7+bxksHYezmh7mQ8f0cPFsCBVGGBjMMGfawIT93XVvO9Pb6ojHYN+mDMnh4rVcd3eAiooyqqsjxGJBQuVxNgxu/5xqNA2ru0IEI+U0Vu9kpm0AKOR3/LpKnsHoC1DX0EB+0iTWhMPURyLUxeM8HArREQiQBIaAp0MhHo7FOHjqVGpCIQaAnmCQRCxGxVZT5wcSCconTaLMuxh6kaTTaWAj0WhxSnwqlSaRgP7+LCMjg8Tj3VRVFUPRefNym7d78slhUqlOBgc7OPnkLYHCX/7SQ2VlB0ccUUwYZ+wb44LPTicfff6PkXAwz01/WcniVSObn5vdDhe9aoAHHlvOz24f2ia4DAbg4OkFjp01xNq1q1m5ppt9Gkepjvbw+DNdPLUySTYHUxsLzGlNUcgUa5Gu2ZgiVyhOm59Ul2da7RB1sR7WdI4wOj7xC3EsnWf1xnFWbkjRNZClgH9/0stZKjW6aRp9nkgkTzyeJ5HYMsqgry9LZeXwVu3zbJ2Djo9v//Ps9PPfyYKyr7FsPZy+g4GgFTH4t3PH+dS1Q5z/pne+KMcjSVvL5/PEYkkCAVi/fhUf/vAw551XmPA51tQE3/hGnkBgPeFwMcFYtSrMtGkH7qVeS3qlOPvyD3BD/oQJz71pZo7r7lnBmZPHCaS7ed0hUFdefC1AgX1nZPjtbes48qAt26zthGNnnU7/GFRXhmlIQMczxUWZxsbCDA8HiMWKYejcebWsT9by+JoYQ2PF2TupLCxeF+ThZTHiiWb2nRLfpr7o1obHI1Qkql7Un4VeWQxGX6Bps2ZRPm8eT8Vi9MXjlFdXszaR4PGKChZXVxNpa+OoadOoDIdJAAPBIA1bjRIdAcabm6lpbbWeqF40uVyO0dF1VFQUpyMMDqaorS3WF00mB0gkuqmoyLN8+ZZQNJOBBx/sBToJBNZx1FHF59PpAjfd1MHRR/cw+TlT5m+5Jcnioee/+/bURvi3c1NU5p/hml89zaPPbAkmTpiX5bxD13PznRu4+f48mdy22+/bBsfPGWF0qIOHn+ykMTHKvLYh1q9fz6OL++gayFIdL44iba0ZZs2mUaTJVHFnTVUF9p+UJjM+yMp1SboHMjy0aJDFq4YoD49SEx9jOJlkcKCLrs4N/9gPXdJel8kMEolAKpWlp2eUsbHxCVPlOztHaW4e3/w4lZo4NSud3vEiSaOZcv7p+J2PAj1gOgyPjDNlinX8JO0ehUKAwcEU6fTazfWSt+cTn0hz9dVPUCjAwoUJDjjg4D3WR0mvXBdf9Tt+ua56wnMXtA9x5zM9HBXtZdn6sc3PP7kuyeSWHFNb+ia0v+3OJmZPbeaBjjCHzi2uzNIST9PTk6O6OkY6vWVafDAIU6bNZN+DXsP69BweW9/K0xvbqWw7mmNPOpP2qfswNLrj87NMDlLZGOXl5S/G4esVyhqjL4KGpibGh4ZoGxsjWyiQBwpAOBCY8AMObfqXBOJAMhwm3tZG9NmlcqUXSTK5pa5of3+a2loYGsoyPFwMRePxPCtWDDB3bjE4HBrKsXx5D5lMDzNmDFJfnHlFd3eGBx9czfnnTwwPRkfzfO1rHbz73b18sQNW98HUuu33Zd0AlFdBeaz479KTU/QOLeW6X0fZZ+okjj2omrJIgPOPGKRrYJCf3TWT+roajp+TpOI5Azjb66C9LkV/cj1/fSRKa3Mth06HobEUjy0OE48nmDG5nOmNBSBF/0iKRRvCJBLltDeUEQ7m6ezuY2hVhroEpHKwsD/EtPYYM5rL2KdpmCdXPQQcTlNL64v3f4ik3a5QKBAMFkt2BAJ5crkUMD6hzdq1PZx44pbHnZ0T78RkMjs+LVrfsZRj25+/Jsek1ppd7bIk/V2CwSDpdII//nEJ55wzuNO21dWQSg3xq1+VMXPm4Zb3kPSiqKioYPbHbmXRVUczt7Z4XlQXg+rBNQQylax5Yi3tDTOIR4NszA2y9C/ruOi1W7bf2AOHTj2Dv62GMUJUlQfZ0A/LOmF06QDxijEC0UpaWlqIx4sDx8LhcuLxOHPmbjvyvb6+iTUrBgiHhqiMTTxPy+RgTW8ZrZOn774fiF4RDEZfJIFwmCwQ2clJxygQDwZJArnKShKNjdvUMpNeqGJd0WFCIRgcLK5APzKSY2Cgn+rqHqLRPKtXbwlFOzrGGRnppa+vk+OOSxGNFvfz5JNJxsfXceaZE79gHn98lMcfX8WVVxYDiA99Gv7fG+G/job2mol92TAI7/0tfP+DE5+vr4K3nJRmeHQFP70lQmNjK685sp6mmgBvPnIZT68J8psnTqa5psCBretoqJy4fW0Cjt8vzWh6I/c+FiZRWcNBMxIUGGDF6kGS6RjT2quoTYSorciSKQyxaEWANV0Bjts/T2KrexH5Qo5Fa7MsGM5y8MwKDpic4uEVC2lsbvEiQnoZSaXGiMXypFIF4nEYHk5TUzNxRPv4eAdbDxjo6Zn4eiaz45kbDU2T2dAXYErTzsPRTN6bnZJ2nxkzjuA3v7l5lxZSGhmBffY5gKamE56/sSTtov0PPZKfH/Zxpj3zWeKbEqUjm+G6BUu46IT9+e2da3ndqVMIlo/QGOidsO0vb62nbXI7c9ugIp/h7kVQXwlH7QuRUB4Yo39kjAfuGmDGfpNpaa0hEolv24lNQqEQk6fPYuOGDnp6BqmIZggECoylI+QCMVonT3W0qJ6XweiLpLqxkf6REZryO55WPAg0hcP0hsNUt7Tsuc6pZGQyaQqFYl3RkZE80WiW8fEcfX091NX1EQ7nWbu2n9mzi7+nTz45TDzeT29vJ6ecUhwVWijAbbd1M29eL+3PWVjp+uu7mTdvLW95y5ZgoKoK/vN78N63NTEpVsGxTRsIBOD+rlaCzYfynz/+Ar/5zSeZFfw5x8ydODqrshwuPiHDeGYNP//DOmKJFl57TBP7TcmzH3/mjqcreCh6GfGedcyoXMzkhomBRHkUjp2TJZPv4ZEn+8iHqzhk3xqi4eIX6qOLw1QmEuzTXk7XQIGTDyrQP5zh3sdHyOZyFAoBprfFmT01zpOrUnT2R2mpjdBQnqSnp5vGxqbd8P+SpN0hnR4kHodkMkc2myaTSVG1VTmpNWvyBIMTS2WkUhP3kcvtOBg99/WX8Ll3XcWRc1busM3SdTB1zkn/UP8laVdMmtRGNLofjz9+BwcemNlp22y2imCwntpaZ8FIenG9/j2f5vuX/o53VD+6+bk3z8xw7X2reP2R0/nOHzawobCWT797y/Vb3wDMmn4qrzmy+DiTz7NgzSjzJk8MLmsr4ITZae5Z1EEkGqe+fufrQITDYdonTyeTyTA2NkahUKAqFqPMmbnaRQajL5KKigp6YzGGR0ep3M7r3UA8HCaChV21e+TzeZLJ9dTWwvh4gVwuDeTo7u6hvr6PUCjP+vX9zJqVJ5OBxx7ro7Kyn0ymi2OPLX5hjY8XuOWWDl772tEJKzWn03muumo173hHP7W1E9934cIAy5f/N7+49UMMDw/zxBNPAPDJAw6galMqMeuDNzA09F1+fO1HaB75Ea85KD1hH2UR+KdX5cjl1nHLHRtIB5o45/gWTtpvhLH0t/jz4kn0T/sYK9c9QBMPM6d94tT+SBAOn5UHBnhy+SCDqQQH71vDodMhkx9gyYohsqkYf3ogT11FhuPm5iiPQr4ASzeMc/OdSY47uI6lHaO01FZTU5Ghf7DPYFR6GQkERgEoFLIkk6NEIhM/Z7q6QkQi/ROee+6ib7ncjk+ga2pqqJ76Gv72zDUcNTu9zeuZLPz372fyX9/76D94BJK0c5lMhlCom3/7t0u55JJf8uY3d+xwwZEnnoAjjjiArq4xZs3as/2U9MoXCAR4w9f/wu+vaOe1bcVzsGAAzmjs5yO3V3LQ7BT7TNs4YZsb/lDFcUftT3GlleI1XLiQBLYd0RkIwKHT0zyyaAPHn7Rra7FEIhHXbdE/xIzuRdTY1sZAJMI6iqNDk0A/0BEIEI5EqAuHKQB5p89rNxge7qayMkMmAyMj44TDOTZu7KaxsY9AIMfGjf3MnJlnaCjPwoVd5POd1NVtZO7cYjLQ2Znh9tuXc8EFE0PRVavGuf76RXzkI9uGor/8ZQ2h0EOce+6HAKisrOTYY4/l2GOP3RyKPquqqoo3v/t/edV7B/jZun/nlofLee4A61AIzj0yz4VHdHLnfY9zwx87yGVznH3gWhrW/it9nYupOfS/uL/ntTy2sozcdma0HjClwKv2Haars4O/PtrFwHCWuZPyBAqjHDw9xbFzc+SyeTq6MgwM55jdVuDcIzLcs6CX7KaVn7J5CIb8UpVeLlKpFPF48aZPLAbp9ChNW015z2QgGoWqqtEJ20UiExOF/PNMg7/yM//DL5edx9d/V89AcfFUCgW468kwV/xgNu/95M9oaGh4cQ5Kkp5jYGAtVVXFWqMXXfQuvvCF6u226+6GL3+5ncsuezUHHJBl6dIn93BPJZWC6poamt/3G1YOFR+vTcJXFkJ+ZA2DGxax9kF4x7vh/26G4SQseGZ/gg2vIr/VNdzs9izrera94QxQEYXM6Cjp9PZfl14sjhh9EcXjcSLl5dSPjzOay5GhuABTezC4OYEeAeKV2xtTKv3jRkeHicWGCIWgtzdNRUWedeu6aW3tJ5fL0ds7wD775OnoGGdsrJf+/o0cffTY5gD08ceHyefXc8YZE5PG3/62l4aGNbztbYXnvB/8+MdHc/HFt1FRUfF39TUej/OGK/6TTOYz/PrGq8gt/QLnHDpI9Dk55GmHFoBu7l7Qzcq+Os46vp3z5vyNx28/j6H4RRx8xrdZsOBO0mt/zaHThil7zvb7tMA+LaN09o9yx5Iy+sfqyLcEuPr3fSSiWRqqCgyPBugaCjF/ThVHz6ng3iXDQB0bBsqZdWDb33VckvaedHqAqioYHMwRDueAUWJbzbrq6gqSy3UxZcqW0eYjI5BI/H3BaCgU4kvf+hkLn3yS//r+5xgf2UguH+KI4y/geze+zSlbknabsbERKiuTmx+feeYZPP74St7xjps555xeDjssy+go/OIXCVaubOUrX7mcRKKMRAKeeaZzL/Zc0ivZ/Fedwo/vejfpRd/kvxfBly+EugooLkddvIH8iyfh//05yFmHBcgUKliyIcyctiyZPKzeCE939DOlpYLZU8upKp84di8cKrB8+VPMmnUA4bDxlXaPQOG588h2bJcblrKxsTGGOzupzWZ57nizFDAQi9HQ2uoftV6Q8fFxhof7yeczQIF8fpy6OkgmM8TjWdau7aS9fYDx8RzDw/1MmVJg4cJhKioG6OjYwPHHb6kneuutXRx0UB+tW5WfymYLfPvbq7joon4aGye+9zPPBFi48JOcf/5/vCiLE+XzeW799Q/pf/TjnHdoF+U7KCHz8FJYuLaaU4+dREtdGbc/naD6gCs57OhTefrJB+h75mccNKmLqh3U5n50GTy+Et58MkS2GrRdKMAdT4bIhyrZONrCua+ezbK+dg6ef+wLPjZJe8bQ0EqqqrL09aVJJocJhTpo32oF+cWLG+nsvJXjj7+N4Kbz7SefhMce6+Ytb+nY3O766w/mkkse29Pdl6SdKhQK9PYupqFhy82dsTG4777HOeGEPn7/+wU8/fQKYrEyzjzzKGbNmlgK6O67p3DccWfv6W5LKhH5fJ5z9o/xs8szVOzgHvE37w4wZ+YMHo++lqOPnU5lvptVG2HfydBcDak0PNURJp0Nc8phtZRtmtVz15JyDjlqLgMD9UyZYl0Q/d12KbAwnXuRxeNxaGmhv6eHcDZLNJulAIxFIgRjMerr6w1F9YL09/eQzQ6TSGQZGsqQzeaBAuvXQyhUoKdnA5MnD5JKZUkmB2htLfDQQ33U1AyQTG7k+OOLYcHYWIE//GENZ545xtaDnDZsSPPb3y7h3e9Obw4QnnXLLQmmTPkjF1zw4oWGwWCQM173dgrnXcFdt/2Cjrs/xFkHrKb2OQOrD5sFh80a5Ok1g9x6T4ITDp9C09iV/OY73+eQM/6LeRdexcqVz/DYo9czp2ElzVvNLssW4OFlcMVpxdo3WwsE4OQDc/z83iS5YIanNtRxyGGHv2jHJ2n3Gh8fp6wsSyYD4XCeoaFBZs/eEoqmUhAI1JNOb5jwmbZxY4hAYGK94kJhx6ueStLeMjjYTXX1xM+rnp4cFRUxBgZCnHPOfM45Z/52t+3sDFJb6ywYSbvPoqef5ph9Q1SU7XhBuLcfVeBDv99I07x1hCpmkeyGc47cqkEFtNZm6Utm+d19fZx9fD3DI1BVX0VVFXR3D5HJZKwhqt3CGqO7QTwep2nyZCra2ii0tBBobaW2vZ2Glhb/kPWCDA8PAoMkEhl6elJUV+doby/Q3g7t7QVGRtYzefIgo6NZRkcHqK7OsXBhF8FgF+XlnRxwQDEsWL8+zV//uozzz58Yit51Vz9LljzFO94xMRQdH4fvfe9gjjuugwMP3D0jKQOBACeeeiGXfHYVSyfdxjX3z6Wzb9t2+02By05OEhx9mp//YREzKhYSePwN/Obqf6OhoY0TLvgcwblf4a9rDmJlV/EgHlsGh8/eNhTd2qsPzrKxZ5BDDz+BaDS6W45R0otvfHyIsjJIpXJEIvnNizA9a8OGIIXCRtLpngmLLaVSEQqF3IS2gYDBqKSXlkwmQzC4ka0vIfr7YXR0iPr6Sh57bMdrFxQKsGBBmFmz9t8DPZVUqv76p19xwf6pnbaJRSBMhpHhLpYuXEffxtX8+vZVLFoxMKFdXQIOmDLOwmWjPNoRZ+68FgBqajIkk8nt7Fl64Ry6uBuVlZVZb0wvqpGRQRoa8qxfn6apqUAqlae3N0MqlWZoqJ+5c0cZHs6QzQ6Qz6fp7OxhYKCLI44Y5dlSoI8+OkQ4vJ7TTtuy33y+wA9+sJqzzuqj7TmDClasgAce+CBvf/sXX5Sp87viiGNezRHHPM3CJx7iDze/kxOnPMz0loltpjXDZc1jdPY9wx/viTFv1hBLbr6HvprLOPnMt3P8OR9meGiIu++9gYXL7uaK03ZeDaS2AhLxkDcvpJedJD09KZ55Zg3B4DgVFVkWLiy+MnUqLFmS5e67fwhsZMkSSKehshLy+Sjx+MRgdGQkt+3uJWkvGhxcy9ZrumUy0NU1QjxeLKPU2VnBww+PcPDBObaelJbJwGOPhaivr6C/v5eWFkeNSto9CoU8u3KZmMoWGB0ZoXvp7Rx2aJZwGB5fGOYXt1bw+tPmMnefGgBmtMKP7x7nvDfMJBYr3vwJhyGTye5k79I/zmBUepkYHx8nGs3R0zPO+vUbWLs2STCYIxAIkM0WmD07z9BQhnx+gL6+YSorB1i3bgMnnJAlGCyOGvjjHzcyf34/TVuVnuruTvPLXy7l7W8fJ/ScQQe33hqnvv7XvOlNr9mzB7vJ/gcezv4HPsTyZYu5+v/eyVFNdzB38sQ2LXVw6ckp+oeXccvDEdondXPbd2+k5YhPcvBhJ3DcGf/M6s4x4KHnfb+KhAujSS8nmUyG3t4eNm7s4LDDshNGwA8O5rnxxmdYv/52PvCBJHV1W17r7YWvfW2UhoaJU75GRvpZsWIx++wzZw8dgSTt2OhokkRi4gip3t4C2ewYra2wdGmKQw8tY2AgyD33jBKP5ygvLzAyEmB8PMS++5bT0BBm4cJlBqOSdpujTjiDP131ZfZtGt5hm0wOlvRF+MVHoTaxJeCc1ZYlmx/kql8tIHjqwcyeVkMwAHU1YaqqtsziGx0NUVnpzB7tHk6ll14m8vk84+NjLFmyjH337aexcZixsT7S6W7y+R7uv7+PtWsH6Ojoo7y8l97edZx0UjEUHRvLc/PNqzjllImh6AMPDPLYY0/xL/8yMRTNZOD735/DYYet4bDD9k4ourUZM+dw2cdvp/rMDq5+/BweXrbtR1dtJVxyUoYjJ6+kf83drLj1Ldz87Teybu1qZs89jIVrdj4SdGAE4pUtO20j6aUhl8sxPDzE2rUr6ehYu00oOj5e4L77+ujouIuPf3xiKApQXw+f/GSWQmGcjRthzZriSNLWVnj88b/wdyxMKUm7RaFQIJXqILbVopRjY5DPjxMK5YhEYHw8T0UFtLdHOPHEag44oIZJk2o46KAaTjihmtbWyKYp+I6ykrT7HHrY4dyxtpL0Tj5qfvwwXHJ2I7WJbV8LB+H956X4xa2LNz9XYMvFaT4Po6MxEontbCy9CBwxKr1MhEIhli1bzuGHp3n44UHa2zOccEJhcy3QoaE8zzzTQ1nZAKFQD4ccUny+o2OcxYvXcP75W6aIFgoFrr9+LSec0M2RR058n44OuOuuf+Vtb/sWweeuvrSXtbVP4rJ//zW9PT1cd/0HmJq9gRP2n/gNXBGDN52QJZ1Zw28eXMet37qH6llvYk1nNYuXLmbV+iSRUJ5cIUBNZZzzTmiguTbMnxfWcNy5r9tLRyZpV6VSY/T3dxMKjdHX18k++2R57pqGixePMjz8NOedN7zNSHgofgbecEM3f/1rmpERKC8vlg3p64rrbvQAAJavSURBVFvLpZd2sG7dWiZNmrzthpK0hwwMdFNZOfEcp68vR2NjjmfL7IXDAcbH2XxjqLw8SHn5xP0UClAo7JlSSJJK1+xjUrz7T/C1V0P8Ocs1/OxR+MUiqFq3hgWLO8nlw7zuxCZOml+1uVRbOAiTa0fp6BylqqqceGUVUPwMW7s2QmPjpD19SCohgb9jVITDJ6S9qLu7m66uv5LPD1FbO0pbG5tD0bVr04yN9bB+fQ+HHJKkqvg9wkMPDRKPb2D/rWru9/dn+NnPlnL55Smeu8bQHXeUUVZ2I8ccc86eOagXaHh4mN9e/zHqB77HaYeMb7dNLge/fzjILQsaeecFrezTUuD6P3Tx1PJBgoE86/qCVNVN4eyL/p0L/umte/gIJP090uk0fX3rqK/P0tmZYuXKlRx7bGqb8POuuwZ48MFb+OAH121T86pQKPDxj6/myCMHOPvsiTWx1q+Hf/u3Bq644jpOOeWM3X9AkrSVTCZDMjlMNpsFNlJTU6CnJ8PYWJ5CIU8+XyAchpGREfbfP09/f5aeniSzZu34Mq2zM0g6fQhTpszYcwciqaTcf//v2Hffs+jtgW9+FqpTMKMchsbgjhVQVw+fvAimNhfbpzPw83uC3L80wdfeN4NwuHgy9sAzMB47hKHAdA476RhCoQRDQ2U0NEymurp2Lx6hXsZ26c6gwaj0MvHEEw8QCDzBY491Ul9fnCI/Ph4kGAxw4IFjrFjRyfHHpwmHi3fWfve7To46amBCwf7HHhti/frlnHnmxD/nXA6uu24fXvvae2hubt3DR/bCjY+P85uffoHImq9y9vzkdkeIAfzkDrjxniCfvCjPoVtdHzy1Gr70hxl8+PM3sd/+B+6ZTkv6u/X0dFJVNczoaI5AIMOCBSs44YRtb4rcfXcfDz74O/7t39Zv89ott/QzOLiKiy/e/mlNfz+8//1Hcs01f3vR+y9J25PJZOjp2UA+P0JVVfEcb3S0QE/POJMn59h69mgmA4sXjzN9eppEAh56KMl++2U2L7I5cb/wyCNVzJ9/iotLStotcrkcP/tZMxdd1Lv5ud//HgKBGdzymzrKOh/iK29nu4szPbYcfvtoPf/xtqkA3PFEgMcHjuXok85n8pTpxOOV1NTU7LEFgPWKtEu/PC+tebKSdmjp0oWMjHRw+OEDxOO9VFT0c/zxY0yZMsy6des4+eT0plEEOW6+eRWnnbYlFC0UCvzsZ+tIJJZtE4p2dsL111/CW96y5GUZigKUlZVx4WWf5qyP9fP7kav42X01jE9cU4V8Hv70KPzkgxNDUYB5U+F7b1vOf33sjaTT6T3XcUl/l3x+nGgUUqk85eUQDkfp6dm2XaEQoLq6is7ObV/73e+6eOMbd3yvt7YWpk1bQ0dHx4vYc0navmw2S2fnGmpqBmlvz1JZCRUVkEyOs88+E0NRgEgE5syJsmRJiFQKDjywgqeeCtPRESC7aeZ9oVAcKfrII1XMm3esoaik3eZXv/o8//RPW0LRfB6GhpqIx+sZGxjlE2/afigKcMgMGBgcZni0WPLtjqereP0lH+LIo06grW0KtbW1hqLaIwxGpZeBpUsXEQ4v4ckn19Ld3cW++/YzeXIvN920ij/8YSXTphVHTK1eneKBB1Zw/vkpnj0HHh7O8r3vPcO5525k1qyJ+7333jCLF/+USy+9jtCOhlm+jITDYc5+w/u48NO93BW4muv+2khyrPjaXxbAmYdDYgeLGUYjcOkxy/jlz67ZU92V9HcoznDJAxAMBsjnYfr0BpZtZzG2aDTMKaccxM03l2/zWllZZoejyp91+ukbuPfeP70Y3ZaknRoeHqKqapT4VucnY2N5otH8hEXlthaJBJg9O86iRTHWro0xa1YlmUw5Dz0U4b77ynjwwVpSqYOZP/8UKisr98yBSCo5AwP91NZ+ZkKt95tuCvH617fz9NMtFEa6qX2ej6Bzjkhz698GWLI+SO2kw5k0yVqi2vNcfEl6GbjnnlsoK+vkbW8rTq968MEkt93WS1NThtmzC9xwQ4Du7iDHH5/mzDO3bPfUUyMsXbqUf/7n/Db7vO66SZx88t1MmjRtzx3IHhIMBjn17EspnPVWPvjO13Fg4g88tDjNV9++8+1OOiDDe39xI2+65B17pqOSdllxxEBx1EBFRZChIWhtjbFyZQWLFyeZPbuweUTCfvuV8/jjGSoq9uEvf3mGU07JbLWf53+vYBDy+dzzN5SkF2hkpI+2tonPDQxkqa3deRWziooANTXVNDfPpL+/m0gkz5w5NU47lbTH/O//nsZHP7rlfGlwEBobJ7NhQ4Bzz53Jo3+8+3n3UVcJf34oxxPD+/OJz39ud3ZX2iGDUeklrlAo0Nu7hA98oBiK/v73/fT19fDRj+bYetH4TAZ+8AO47z445hi46aYNzJ27gfPOm7i/3l749a8v4JJLbnjFT60KBAIcdfxZHBRay10LFxGNjD5PewgFsjttI2nvCQTKyGQyVFQE6esLks3mmT+/jUcfXc8DD4xRX58jkSiQSoXJZqtobj6WjRtj/Od/LuOww4Zobc2zYUOYQiG904D0zjubeN3rTtpzByapZAUC+Qnnc1Ccirpr2WaB8vJyysun7o6uSdIOLVz4CK9//UMTnrvpphiXXVbHz38e58ILY8Upec/j8ZVBKma8lQ9/9NOviBmMenlyKr30Erdy5QrmzUsTDEJvb5bFi3t585tz25xERyLwL/8Cf/hDnu985xlOO20Dc+dObPPggyEeffQHXH75L17xoeizTj/rDfzu8TZmTCqno3vnbQeSUFbRvGc6JunvVllZS39/mEIBGhujbNgAEOTww9tpbW1hYKCG1asr6eqqoalpFvPnn8/cuW/h6KP/mY6OC7n55uMJBA7jt7/d8enP2BgsXz6LWc+tPSJJu8W2CWh5eZCRkZ1vlc+DY1wk7S1/+cs5E8q0LV0Kr3rVdLJZWLu2mUIB9jtsBk+v3vl+7lk9l49c+RlDUe1VfptKL3GdnWuYNas4ivE3v+nlwguL/10oFFixIs3YWJ4pU6JUVYVYunSUefOW8sY3bjsF9Kc/beLoo//KEUfM3qP939sqKyuZtN9ZpOJd/PBPA3zq4h2PCL32rnoufsfH9mDvJP09YrEYuVwDGzf2UF2dpaUlRl9fhkwmTzhcSXNzNaFQOTU1jVRUFFcsadi0Ct2JJxb3kc/nec97zqal5TaOOGLiKm3JJLz3vfvw0Y/+aE8elqQSFghEyWS21IYHqKoKs2pVhvr6/A5Hjg4MBKisbNwznZSkrfzxj9dy2WXrJzx3zz3VXHZZnPvuC3D00TH+/OcB3vavR/PW85ZxzTu7qUlsu5+v/76WU8//AMHnjviR9jCDUeklLhKJMbZpAaG+vnEmTSrwgx9s5MEH+5k3L00iAdddF2Lt2gCXXDLOG984cfvBQbjxxtN5y1t+RdmOqvi/wl148Tu4+ecRFtzxGW59dDWnHbpt3a6HlkZYkz+JAw48cC/0UNKuqqioJBqNkUwOksmMEQzGiMXCJBI1xGKx562tFwwG+frXf8NXv/oRfvSj33PEEesoL8/yxBP1JJPz+PjHv8306fvsoaORVOpqahrp6UnS2rqlHnwoBLW1EdauTTNpUmGbcHRkBPr7E0ybVr+HeyupVOVyOe65569s2LCaxYv/mVNP3fLa7bcHuOCCYkmP3t4EZ58N1147Qjxew6kXXsa7fvInDm9fzhuPHaYiBg8tC/Ozh6Zy/Jnv54I3Xr6XjkjaIlBc5XWX7HJDSS+ekZERbr75Y7z5zd18+cur6etbxPHH93P66RP/JIeG4P3vh49+FGbOLD63YEGQtWu/yllnvXcv9PylJ51O82//7w2Mb7yHS0/sZXpzgQ198H8PTqO87RQ+8YXvOo1DKiHZbJYnn3yS8fFxZs6cuXl0qSTtSX19XYyPd9HQUJgwcrSrK0tfX5aamjyJRIFcDvr7ywgGE7S2TiuZskiS9q4f/vC/eeih6zj55BW0t6dYvRr++lc47jh4wxvg3/+9lba2Vs44AyoqpjNlShlPPgnd3Q309Mzjn/7pHSx47FF+d9MPSY2NMPfAozn/n95KLBbb24emV75dqthtMCq9DPzsZ9/muOPu56qrnmT//Rfy1rduu8o8FGvjvec98P3vw4031nHwwXew776OgHyu4eFhbr7xWjrXLaO+aTLnv+Fyamtr93a3JElSiUomh+npWU8wmCUUypPLBQkEQoTD5UQicfL5LIFAiJqaWgNRSXvMl770b+yzz/e54ILhbV67/nq4774A3/rWwYyMBPjMZ+CDH5xJS0uYzk74yU/qufjiK2lpadkLPZcAg1HplSOdTnPttf/NnXd+i2uv7SK8kyIYV10FAwOH8JGP3EN5efme66QkSZJekEwmQyqVolAoEI1GHVElaa9Zs2YNP/rRkXzqU507bPPe94b4+Mf3o7ExQjIJ3/pWOf/+71N4+ml45JFjueSSd+7BHkvb2KVg1Cq30stANBrl0kv/nUSibKehKMCpp8KkSW8wFJUkSXqZiUQiVFZWUlVVZSgqaa+6+urP8fa37zgUBXjXu3Jce+1GABIJqK9P09mZ5YEHanj96y/bE92UXjCDUellIhKJUF5e8bztioPAd+nGiCRJkiRJ2xgYWE57+87bzJoF69alNj+ePz/LX/4yTl3d4cTj8d3cQ+nFYTAqvYyMj7eQyey8zW23NXDssWfumQ5JkiRJkl5x8vkAz1d5MZ9nQpt8HlaunMnZZ79l93ZOehEZjEovI+ed935uuCGxw9dHR2HRolnst9+8PdgrSZIkSdIryQEHnMZDD4V22ub22+GYY2o2P77//gb++Z8/QjBo1KSXD39bpZeR17zmbBYvfh2///229UMHB+Hd757BRz5y3V7omSRJkiTpleJNb3on3/3uNPL57b+ezcL115dx3nl1AAwMQCq1L42NjXuuk9KLwFXppZeZQqHAj370ZR544KfMnbuORCLLkiU15HIH8L73fYMpU6bu7S5KkiRJkl7m7rnnNn7+87fxqU91UFu75fmeHviP/4jytrdNY/78BN3d8M1vzuB97/sOdXV1e6/D0kS7tPiKwaj0MlUoFFi1ahVjY2NMnjyZysrKvd0lSZIkSdIryLJlS/n+9z9GMrmAeHycrq5x+vtjnHTSZNrayujoqKS8fH8uuugD1G6dnkp7n8GoJEmSJEmSXphCoUA2myUSiVAoFNi4cSOZTIampibKysr2dvek7TEYlSRJkiRJklRydikYdfElSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRiVJkiRJkiSVHINRSZIkSZIkSSXHYFSSJEmSJElSyTEYlSRJkiRJklRyDEYlSZIkSZIklRyDUUmSJEmSJEklx2BUkiRJkiRJUskxGJUkSZIkSZJUcgxGJUmSJEmSJJUcg1FJkiRJkiRJJcdgVJIkSZIkSVLJMRiVJEmSJEmSVHIMRlUyTjzxRN73vvdtfjxt2jS+9rWv7fL211xzDTU1NTtt86lPfYqDDz74H+qfJEmSJEmS9hyDUb1sdHd386//+q9MmTKFsrIyWlpaOO2007j33nv3dtf+YatWreJtb3sb06dPJx6PM2PGDD75yU+STqcntLv11ls56qijqKyspLGxkQsuuIBVq1btnU5LkiRJkiS9AhiM6mXjggsu4LHHHuPaa69lyZIl/OY3v+HEE0+kt7d3b3ftH7Z48WLy+Tzf/e53eeqpp7jqqqv4zne+w5VXXrm5zcqVKzn33HM5+eSTWbBgAbfeeis9PT2cf/75e7HnkiRJkiRJL28Go3pZGBgY4O677+a///u/Oemkk5g6dSpHHHEEH/3oRznnnHO4/PLLOeussyZsk8lkaGpq4oc//OEuvcdXv/pVDjjgACoqKpg8eTLvfOc7SSaT27T71a9+xaxZs4jFYpx22ml0dHTsdL8/+MEPmDt3LrFYjDlz5vA///M/m187/fTTufrqqzn11FPZZ599OOecc/jgBz/ITTfdtLnNI488Qi6X43Of+xwzZszg0EMP5YMf/CALFiwgk8ns0rFJkiRJkiRpIoNRvSwkEgkSiQS/+tWvGB8f3+b1K664gj/+8Y9s2LBh83O33HILo6OjvOENb9il9wgGg3zjG9/gqaee4tprr+X222/nwx/+8IQ2o6OjfP7zn+e6667j3nvvZWBggDe+8Y073OdPfvIT/uM//oPPf/7zLFq0iC984Qt84hOf4Nprr93hNoODg9TV1W1+PH/+fILBIFdffTW5XI7BwUGuv/56Xv3qVxOJRHbp2CRJkiRJkjSRwaheFsLhMNdccw3XXnstNTU1HHvssVx55ZU88cQTABxzzDHMnj2b66+/fvM2V199NRdeeCGJRGKX3uN973sfJ510EtOmTePkk0/mc5/7HDfeeOOENplMhm9961scffTRzJ8/n2uvvZb77ruPBx98cLv7/OQnP8lXvvIVzj//fKZPn87555/P+9//fr773e9ut/2yZcv45je/yT//8z9vfm769On86U9/4sorr6SsrIyamhrWrl27Td8kSZIkSZK06wxG9bJxwQUXsH79en7zm99w+umnc+edd3LooYdyzTXXAMVRo1dffTUAGzdu5A9/+AOXX375Lu//z3/+M6eccgrt7e1UVlZyySWX0Nvby+jo6OY24XCYww8/fPPjOXPmUFNTw6JFi7bZ38jICMuXL+dtb3vb5hGviUSCz33ucyxfvnyb9uvWreP000/nwgsv5O1vf/vm5zs7O3n729/OW9/6Vh566CHuuusuotEor3/96ykUCrt8fJIkSZIkSdrCYFQvK7FYjNe85jV84hOf4L777uPSSy/lk5/8JABvectbWLFiBffffz8//vGPmT59Oscdd9wu7XfVqlWcddZZHHjggfzyl7/kkUce4dvf/jbANivE76pn65N+//vfZ8GCBZv/LVy4kL/97W8T2q5fv56TTjqJY445hu9973sTXvv2t79NdXU1X/ziFznkkEM4/vjj+fGPf8xf/vIXHnjggX+ob5IkSZIkSaUuvLc7IL0Q++23H7/61a8AqK+v57zzzuPqq6/m/vvv57LLLtvl/TzyyCPk83m+8pWvEAwW7xdsb6p6Npvl4Ycf5ogjjgDgmWeeYWBggLlz527Ttrm5mba2NlasWMHFF1+8w/det24dJ510EvPnz+fqq6/e/P7PGh0d3ea5UCgEQD6f3+VjlCRJkiRJ0hYGo3pZ6O3t5cILL+Tyyy/nwAMPpLKykocffpgvfvGLnHvuuZvbXXHFFZx11lnkcjne+ta37vL+Z86cSSaT4Zvf/CZnn3029957L9/5zne2aReJRHj3u9/NN77xDcLhMO9617s46qijNgelz/XpT3+a97znPVRXV3P66aczPj7Oww8/TH9/Px/4wAdYt24dJ554IlOnTuXLX/4y3d3dm7dtaWkB4Mwzz+Sqq67iM5/5DG9605sYHh7myiuvZOrUqRxyyCG7fIySJEmSJEnawmBULwuJRIIjjzySq666iuXLl5PJZJg8eTJvf/vbufLKKze3e/WrX01rayvz5s2jra1tl/d/0EEH8dWvfpX//u//5qMf/SjHH388//mf/8lb3vKWCe3Ky8v5yEc+wkUXXcS6des47rjj+OEPf7jD/V5xxRWUl5fzpS99iQ996ENUVFRwwAEH8L73vQ+A2267jWXLlrFs2TImTZo0Ydtn64eefPLJ/PSnP+WLX/wiX/ziFykvL+foo4/mj3/8I/F4fJePUZIkSZIkSVsE/o7FW1zlRS95yWSS9vZ2rr76as4///y93R1JkiRJkiTteYFdaeSIUb0i5PN5enp6+MpXvkJNTQ3nnHPO3u6SJEmSJEmSXsIMRvWKsGbNGqZPn86kSZO45pprCIf91ZYkSZIkSdKOOZVekiRJkiRJ0ivJLk2lD+7uXkiSJEmSJEnSS43BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJJjMCpJkiRJkiSp5BiMSpIkSZIkSSo5BqOSJEmSJEmSSo7BqCRJkiRJkqSSYzAqSZIkSZIkqeQYjEqSJEmSJEkqOQajkiRJkiRJkkqOwagkSZIkSZKkkmMwKkmSJEmSJKnkGIxKkiRJkiRJKjkGo5IkSZIkSZJKjsGoJEmSJEmSpJIT3tsdkLR7ZDIZhocHyKbHIAAUAsTKq4jHy4lEInu7e5IkSZIkSXtVoFAo7GrbXW4oae8aHR1haLCLmsossU23P/LA6DgMj5bR0NBqOCpJkiRJkl6pArvUyGBUemXJZDL09qylqTZLEEhlC/QPZsjni6/nC5DNRCCYh/z4pq3CVFU3UVtXTzBohQ1JkiRJkvSyZjAqlaK+vm7KowPEwtCfzDE2lqG+FspCxdc39KTJ5jK0NEBkUwaaA/oHQwyPVjJ56kzCYatsSJIkSZKkl61dCkYdGia9wmTTY8TCMJrOkxrP0NawJRTtT2aBDJObtoSiACGgoTpHQ/UgnRs69ka3JUmSJEmS9iiDUemVZtM9kYHBLA01E18aGMjSWLfjTSvLC2TSg2Qymd3WPUmSJEmSpJcC58tKrzSFADmgUMhPGBWaAQLBPOHnGUwei6RYs2YVsVgZlZU1VFVV7c7eSpIkSZIk7RUGo9IrTLy8mtGx8W2raeQL7GxdpVS2wMrVKcLhHA21wwQDMNAVoWN1nMlT51BVVb1b+y1JkiRJkrQnGYxKrzDx8nJ6usvIF8aB/Obnw8EAuez2t0lnCyxdMcqsqXlikS3PV1dkyBQyLFv1BIGpB1Hp6FFJkiRJkvQKYY1R6RUmHA5T39BCIR9lNL3l+QAQjoQYS2+7TceGcfaZNDEUfVYkADOnpljb8cxu67MkSZIkSdKeZjAqvQJFIhFa26fR019GZsugUZoboqzdGCSd2/JcHkins1TEdrK/IEQjo4yOju62PkuSJEmSJO1JTqWXXqEikQgtbdPo3NhBWThNVSJLMBCgvvb/s3ff0ZFUd97/35U6t3IcSZMjE2CGAQYwORmTbAy21zmH9Xof7643/DbvPs/mvF6vvV5HcE6YYDAGG8yQByYCk7NybKlzV3fV74/WSNMz0gSbrM/rnD5IVbdu3eqR+hw++t57Q+w9lCcW8air8SkVfWzr5P3FwwXS6TSGYZDL5XAch1gs9tI/iIiIiIiIiIjIS8Dwff9U255yQxF5dclmsyRGBnALSeJRj1gM3ILH6FiRZNrDK42ydM6Jf8V3Hy4xlLCIhnLEwkVyBYtUJsystkW0dcx9eR5EREREREREROTkjt2SeupGCkZFZo7E8EH80hi1RxV6DoxCb98gyxcUp11bo3fYpas7y6plJZyjPlp8YF9XAM9exKJFy1/KoYuIiIiIiIiInKpTCka1xqjIDBIMN4Hh4B71Z47GaggEQvQMTH2N68GBwxnWnFEZikL5U2ZBW4Fcai/JZPIlG7eIiIiIiIiIyItNwajIDBIOh8GKkExVHm+sj5DJBTjcR0Vo6gO79+fpaPFP+KeWBe05Duzf/lIMWURERERERETkJaHNl0RmmEikiXw6S6FUIDC+6VJd3CSbixF0suw54OL5JSzDp+RDMuWyZN6JV9KIBKFYSJ2wjYiIiIiIiIjIq4kqRkVmmGAwhG/ESGUqf/0jYZtoxGbZgjBnLIxRUw3L50Mo8AoNVERERERERETkJaSKUZEZKBJtJJtOk3XzhJ3ysdqYSdeATVtDEQPw/QBQoL7GoX+oSGv99FWj6RzYwTgA2WyWnTu2kBwbAAwam+awYOFSHMd5yZ9LRERERERERORUaVd6kRlqdKSHkjtMXdybPJYuEXRyhBzIF33yuRThMGzeNsrZK7xpS8y37Qkxb/HlHD64m8H+bZwxL0l9dflDo7vfYufBOEuWX0Rb2+yX5dlEREREREREZEbTrvQiMr1IrAHbCZAuTB6rjloMj5YXHg3aBsm0hWPAnPYIW3eauKXKPjxg50GDSNUi+vo6KWY2cdFZ5VAUyp9CbU0lLj0nwb5dDzM8PPiyPJuIiIiIiIiIyMkoGBWZocpT26vJ5ys/BmIxh1yx/HUkEsT1oKnOYU5blK07HTbtdNhzGJ7fCxufh4Dj09LSwaF9W1m5MDvlvUzg3BVJXnju6Zf2oURERERERERETpGCUZEZLByrxXYCjB2VZ1aFLUYS5arRmrjFYKJ8vLbK5uwVMWpqGrFMg3ntsHY5zJsFB/Y/R2Pt2AnvFbTBLw3juu5L9DQiIiIiIiIiIqdOmy+JzGCO45C1aijlB4HixPFy1WiJkA1Q3oTpiMaaLP1DVUSCoxPHMqkeGqvyx/XfN+iy8bkkpVIJw/QZTQ5jPb2etee8gUBA292LiIiIiIiIyCtHwajIDBcO15AtjZFIFamJlY/FwxY9gxat9SXqawIkUoWJc7EwHHZDwGQwWhPLkc5VBqjbdmboHUhx6blFwsHyMd8vcrj3Ye66YyfX3fh+wuHwy/OQIiIiIiIiIiLH0FR6kRnOcRwMqw4Mh6MnuceiDvkiBGyDdLbybyhVsQJj6cnv58226B6YDDkHhov09qe46vzJUBTAMGB2a4mrzu3k5w98/yV6IhERERERERGRk1MwKiJEY9WYlkMqNXksHp7coT4WDVA4akf6pgYYGp2cCm8bEItF2X6gnIJufD7JBWsmp+YfqzoOEbuHRCLxoj6HiIiIiIiIiMipUjAqIpimiek0YtkOrj95/EjVaFXMYigxedwxIFeorCKd3QquuZIntkXJ54tEQye+57L5Y+zesfXFewgRERERERERkdOgYFREAIhG4/gESR5TNToyamAAGJWbJdVUWeSPqiKtr3WZPWcxK9fcQqEY5GRCAXDd4zdsEhERERERERF5OSgYFREADMPACTYScAIVgWckGqRQhIbaACPJyeMtDTaDw5MfISEbRof3EYvFCATr8I+qPJ1Kd3+Axqb2Kc+NjY3x0IN3c9cdt/Ho+gfI5XK/zqOJiIiIiIiIiBxHu9KLyIRIJMZoPkQ+UyQY9wCoClv0Dhu01EImZ1MbL68dagDpjAV4kx2UegCYM38New53s2j21OuM+j7sOlzLTecuqTjuui53/OBL+IUdrFs5SNUc6B+0+P437qOh9Vze+Ka3YxjGi/7cIiIiIiIiIjLzqGJURCoEQo2Egg7Zo7aoj4TLmy/FYwHyR2Wd8ZhTcW1VNEMul+OM5WexeXcrg4nj+/d9+PlTQZYtvwTTNI867vPN2/6d85Y8yduvGWTOLKiNw5J5Jd5zfR8tkQe4585vvMhPKyIiIiIiIiIzlYJREakQDkdwvRDZ3OTHQ1XYJjFmUBW1GB6drNhsqA8yODp5bW0MhgYO4TgON775Azy0Ic79j9kc7oX+Ydi6C378cyh6cZaecWbFfffs2cWcxl3MmTV1lenqZTnSiWcYGxt7cR9YRERERERERGYkBaMicpxwuIlQKEDqqKU9Q8EAbgkMc3ITJseEZKryY6SQPQhAMBhk1aq1XHhOA1t2BjnQBdEw3HQFzG0dxT9mEdKnH/8J55+VOeG4Ljl7kPW//Mmv+XQiIiIiIiIiIgpGRWQKwVCIQilCoWhNHKuK2oyMGdTXBhg+ahMmy67crT7kjFIqlXdvqqlfRsAxOfesKpYvggUdYBowZ5ZLX19vxXWelyJU2dVxWhtheKj713s4EREREREREREUjIrINCLR8lqjY9nJY6FgObnM5ib3baurCZDJT7ZpqPEYHh4AoLGpld4hi8Y6h8M9k1Pww0Ho736m4n5eyTjpTva5AtjWSdJTEREREREREZFToGBURKYUCARwSzFKJZvS+LGqqM3IqEFVLEBufHOmWNhiMDEZejoWpBL7ADBNk3S+DgPI5CoDzVJuV8X3s+etYc8hixN5akuEs8+98td6LhERERERERERUDAqIicQiTUQCNqMpSaPBQMBQuHKTZhKJbviOov+ia8DkYXlvqLhijYN1SPk85OlpudfeAU/f7qJYokppbOw49AsFi1e+qs+joiIiIiIiIjIBAWjIjItx3EolqqxLAd3fJp7VaxcNWoeNaU9Fg3hepPX1VcXSKXKaWpz62JGUzBnVojeock2bU0+hw/tnPg+FArxxus/wVd/3EzPwGQ734fdB+ErP27lbe/8NIYxGciKiIiIiIiIiPyqFIyKyAlFYrWYlkVyvGrUoDzNvrY6wNBY+Vhtjc3QyOQ10RAMD+4HIBaL0Z8IEw6a9A1OVpaaBiSHNlfca+68Bbz9PX/Jxv3X8d/fhtvvhK/9CDp7YN3511BbW3vS8fq+z+7du3l0/Xq2bduG53knvUZEREREREREZh775E1EZCZzHIcsNQScIVzPxTGhOmYzMFzALdhAEduAbL789RGlQiewsvw1rcA+PAIVbQLGwePuV1VVxXU3vpPHf/YUF6wanDj+2PZ9Jx3ro488wEMPfo2ls7tpbxrj0IEwd/+gjeVnXcdNb3nvr/gOiIiIiIiIiMjrkYJReU1zXRfP8zBNE8dxXunhvG6FIzVkU2Mk00Xq4j4G4AQChAIlsm6RsAMBx+Ho0DMeTuG6Lo7jEK9diuvvo6EuQqGYITD+ydPelGNkZGTKStCS0QJMBqNe4dAJx/iz+35I/4HP86cfHmFytn2W6y7dw0NPfomvfqmPD3z493+t90FEREREREREXj80lV5ek5JjY3Tt38vggb2MHt5H/4G9HNi7m0P799J5YD89Pd0kEgmy2ewrPdTXBcdxMKw6QiGH3Hj2WROzybsWw+M70tfXBUikJ6+pq/YZHOwCoKm5nYEhk1ktAQ71TLapjkPXoY1T3jMQnlv5vTk0ZTuAbDbLxidv493XHx2KTrpsXRov9TMOHTpxuCoiIiIiIiIiM4eCUXnNGR4YINvXRTMZWm2XKsMll04STA0RSPQw2nmA1L4XSG5/hs4tT/L8s08zONB/8o7lhKKxaoolh3S2/LFhALYTwLIDeEDIMRkdm/xIMYFcqrzOqOM4jGWqsYBkKlDRr5t+Ycr7VddX7j5fE0tRKk29Zf1P7/0+11/cfcLx33zFMHff8cUTthERERERERGRmUNT6aWC53lks1lM0yQcDr/SwzlOLpcjnxik1S6XLRY8n8NjOeaGPAYzLtl8nmURH3OiarCI549ycN923Hye1vaOV2zsr3WmaWI6DUTsXjKFPJFAuWp0oBBgaDRPYzX4hgPkJ64JmCP4vo9hGFjBecAIgVAQKEy0qY4MUCwWse3Kj6OW1nkM9EDj+Cz7Wc0+AwMDtLS0HDe2fXs28+bfOPEmS7U1kMv0cODAAf73v/+M0aGNOFaBfDHK3IVX8OGP/wl1dXW/4rsjIiIiIiIiIq81CkZf5UZGRujs7CQejzNnzhyMqeYJnyLXdfF9vzwteryfQqHAof37SI2N0bl3L8WhAaqLOYoYpMJR5q5aw1nnnfdr3ffX4boupVIJt1DAy+cYTYzQbE6uY9mXKdAe8PA8n9FsnqVRH4Bc0ePAaJ6sW64wNEwYybtEq6oJhUIYhvGqXpPU98vPUSyWn/XVMtZoNM5YYoiS6xIJeOW1Rp0gmXQWKFIdD5Av5gmOf7I01BZJJEaora2joXkp6dxG2lsijCST1MbLbTpaS3R3HWD2nIUV96qurua5bTaNteX3IB6B/T27pwxGTdPE88CyTjz+gwe7+Ne/voQ//dghmuonj+8+sIXf/c17+cu/u5e58+b9iu+OiIiIiIiIiLyWKBh9ldq+bRv//ZnPEN++nYXJJCOOw86ODq746Ed5+8c+dkp9HKn+HOrvp+/QAexsGhsoWDbBunrSyRSF3i5mZUfZ0TvI5XaBpqN+IvxUgh2PDXD/4UNcc8utL3o46nkexWKRwnjoWSrkKeULeG6eUsGlVCzieUVKxRJ4HvgenufR7fsYvofpQwmfETxGSj4d0XK/PSmX7rEMiyIeVcHysbwHBzIjbHn0F3TUVeNjgGniGRbYFqZpYds2pmWD5WA6QaxAADsQxAkGCQQCL/kGT67r0t/bQyGbpOgWKLlFHNPHtEw8M0ikqoaGppZXNCQ1DAMn2EjQ6SGVyxELQU3cJpMNkS2kqInbdPcZtDWWg92gDT0De6mtraO2tpZ9zweYP6vAtp0WtfFyaO1YMNS38bhg1DAMUtk4MDJxLDW6E7jouHGdfc41PLbxCS4+Jzft2LduLzLQs4Fv/fPwceuQLpoLn/vjHXzyT27hq9985hX7Q4CIiIiIiIiIvHwUjL5CPM/j4fvu48kf/pDgyAi+YeA2NnLFe96D7Th85W1v458OHeLoyez+wADf+4M/4B927OAP/+3fpu27VCpxaO8ekv195JNjxN0cq20P+0jWU4RfPt/JQgq0WT4/HRzhjU6BmmOq7QwDlhkFvIPb2fbMM6w655zyOHyfdDqNYRhEIpEpQyTf98nn82QyGTKpFPlMGi+bgWIBikVMzyu/ABMP0/cx8bEor00ZMMo/nA5gG2Adewtj/DVuzIMqG0bzJfpSGdbEvYrwK2jCkqhPVd5lNJVkaf3kO+v5UHChUADXg5IPJQxcDAqY+Ab4ZnmkmCamaWJZJhgm/viLiUDVwQiEcAJBAuOBaiAQOGnQVigU6Dywl4ZAkWE3R61Voi4CI5kigxkX30symhpmLDHC/MXLXtFwNBKJMToSoOQWIVSu5rTsEMOjadoafQquDbgT7U2vd+LrfKkZOEzRdYDJ9UItb9+U9yrSzNHBaCl/eMp2F150OX/9J1/kgtX7sKf4VPN9+H+fS/PnHz8+FD0iGoEr1u5m/SO/4OJLrpi6kYiIiIiIiIi8bigYfQWUSiX+9TOfYe1TT/FH+fzEDljuwYPcs307X921ix/19R33j2MAbx8b419vu41N7343q88++7i+Xddl29NPMtfLUV0s0udmWer4FW16cy51JZcm2+dQpoBbKFATmX68Z5gFvvngfYzu2c6O/QfIDvTR5Ln4wLATpn72XFYuWzoRahqUQ04TsMb/G5rozcf3J6eKl48cCSOh3Gs5yPLxj2rBxHcc/TjjX/s+7M/49Lglzq72pw2/qq0SuxMZoo5BbcghaFs4JoSso8d4pGMfOH7dyrwHhWI5RC2Oh6gegGHhmwAmJcskZ5jkTQPLLKe4JcDHoISJj4FvWviWhWE5ZHM54gFIpEvErRIRy+f53iz1wRLzY+AYkClCV6qf7VsyrDz7lVveAMAJ1pNOFRgximTSEDRtssUgJXKEQgE83Imf6+pYllwuRygUIhpfDBymqjpCyc9NBN4t9UnS6TTRaLTiPnZoLrBj8r7m1JtoWZbFb7zvL/inr/1/fPId3VTFJs/l8vClH9YSCedYsejEz3XL1Un+4sv/q2BUREREREREZAZQMPoS6e3tpbenh2AoxMKFCysq/L71n//JG594gpWuW3GNA9R0d3PrwMAJ/2E+MjzMxz/wAf7o9ttZvHQpwWB5vnhybIw9z21jdilHnQXPpnKstH1yxRKDmRxDiRTpsTSlTI46r0Rf0SMJLIqf+FkMA2qLeZ5/9JdcY+eYZ8NE6uWn2bZ7iF/s2s45c9rwbRvfDmA5DsFgiFgoSG0oQHXAxjZfpCDPmOZryrWFQZPjdKdcdg9nCBklakyfzs4Ee7Bor44QDzkUfQMPgxIGGCaGZWAZJrZl4lgWIcuYCFGD5rH3OJLUFo+7b74IBa8conrHhKiYYFKuPq01DZyCyahr0FoNO/ozLKn2CBx1H8vwaY14JN0kzz29ntqqaDl2Ngx8w8QwjfIGSaaFhQGmwWRCXK5wLX9Zjq8Nc/xZTbMcaBsWGJSrYSn3V768fA8MAwOjPMV9bJT6MPSNQGMMwjYYRBkezdFQF2Aokaaxpny76ijs79nDvHkraJ41j/5umN0aoqsXZreW2zTVwo4DW1m6/PyK96+qbinw04nvq6Opic2cjrV02Qre+5HP89XvfZbRvgdpboBkqhyaL179QfYe+K/jfzCOEQqC604/HV9EREREREREXj8UjL7I9u7YwcPf+Q4tfX10jI0x6jh8o7GRhlWreNM734nv+3Q+9hjvOSYUPeLx0VE+6p14d+040LRtG/PPOovngM54nOHmZpLNzbizZlHV1sbs6mqqDYN9RZdaz6cZaJ+irxRTTFOf6rkGhnirnaNtip+YlQEf203TOdjPVS01UMyUM8IsuD6kStADZHyDPCZ5w8DFomRb+KaNbztYgQAR2yYSsHGs8TU/TaMcTBomAdOYeE2Xr/o+U1aK7k3kGE5muLCqNLmcAFD0S2waK5HLB6gLWtQEbapClVO8AUrF8jT7lA/5ElOHqKaJbf4aIapfvkerBwd6oT3GRCjan3Q5nMjjGCUCFuSKkCxZtMWgLhKYfB6g5EGxOF6BO/7yMcbvWu7QMMqhqIWJYYJlmlimgTn+5k3UyvrjXx9VlQvlGlobGMuBY5dDUYDasMPBEZvG6iKpjEljzeTPsZvay/7dFu1zFzIyFqepJsnQqM3s1sn3ITO2FagMRltnLWC4H+pqxr9v9BgcHKSxsZGptLW3839+9x944PuXsm5lklCwPMaHdmQxnSZSaYhFp7wUgC3bDRYvXTt9AxERERERERF53VAw+iLa9fzzbPjCF3j/8DATy3W6Luu6u9nd3883BwY4+5prWNnX92vfyweiwNnA2ckkJJOwZw8ABWA3sMey2BIOk6ypwWtoIN7WxoLmZqoCAXzfx/N9Cp7H2CD0Gl45+fLA8DwM38f0wfDB8z2Clkdb7fTjWebAA8NJ6m2DeDBEQ9BmMJXhnhf2UMykMX2PvGWzqKWZ65fOJWyPv0NeecBeHtJeOXxM+5DBpGiYeJj4lgmmBZaNYdvYtk3ItjANA88w8Yxy+Idh4gI7MuWg0rDL1ZmdyRyX1x0fNu8ZydI5lCZmeLgO7CqZjBkBVs9pZnbt5Fxsy4CwRXm9V/vIu38k4Dx5iFquEj15iBoYfxULUDu+adSBkTy5XJYz6/2KULdQ8tjU2U9NPEYsHMSyTAKWRShgEbYNKotmpx7rES5QdMvLAhQnKlvhyKII419iGCamYWCaBrZpkHYN6uPlZxtfgZVcNkwmn8SyAsBk5WVVrEDMSrBv12byhTiQxLJClKP5sojTjeu6FdXVtbW1vPCCRV1NeUQ1cXiuZ++0wejEMxXriEeTE9/nUvt453v/mNvu+iW/+RuJaa+77Z65/OU///YJ+xYRERERERGR1wcFoy8S3/d5+Bvf4MPDw0wxk5tFxSL9W7bwwNgYywuFafu5sLqaB4aHeecJqkbHAGvasxAAlgPLSyVIpcqvzk68zZvZD+w2DHptm0QoRC4axayuprm6msZwGMdxCNo2IcchattU2TbDpskZx+7MNIW5Zol56RGqs3BnT4KugUE+Ei0SC0622d4zxl909vCRC9cyNxrCOTK724C4Va6GLfOoWN/Tp1wSWYSsB0mvHKAWMHENs1y9aVkETYuSZWI7DlHb4jAWK+uO/xfZ0JuilE3z5rpSRZVpyS/wi/15MoU2ljZXn/SZj3W6IWp+ihDV8A32F0yKQLJQZE1T5RqxAAEL1jZ6PDOQYmGtNVH1my3AaKa8DmoJA7AwTRPHNgkHLIK2edwvvUN5Z/jj+ceMefJrj/K6rMVUefwlH3IliAVgJGFQXxuY2LUeoL7WJ9GXozoQpmA3kit209IUIZ1LER1vM6e1yIEdT1JTVzceyNoYlsNoKgJMhpzJkR3AuqkGPDk+qwU4ODnyfCdnrz2Xb952JY9vuosLVh//O/j1H9ew5KwPUVVVdcK+RUREREREROT1QcHor2F4eJhnf/ELCoODDIyMsLi7e8pQ9IjzikU2bd/OXuCqadpcUlXFBwMB3pbLTfuP81lgLvACsIhysHUqTGABsMD3wXXLr2QSenvppFxlOmDbZAMBApEItbEYLdEo1YEApmHwlAFZ08S1TIqmQcm2wLGwHAs7YNGFw8aYjWfA4f4Bfjt+fIXisgD8YSnFFx/fwDuWdDDmGWSwKJoWhm3h2A6RoEN1MEBDwCFiHz83PmyWX2VHB6huRYDq+hAsQWoEdlCevl+0ytWoIyWX65uPDxwtA66scfnB/k4oupRME8+wMC2LkG0TDVjEgxYRy5x2g6dTZRkQsSACU4aoz6dgcf2Jr2+LeBwayTO3NohxJJSdCDmPvBlluQIkM+U1T4tHhaa2bRK2LYIB87R+lo5UuB5xKA3NtTCWCRII+vT0GsRC5edxTEgVU7TXhNk/bNPTbzJ3ls2OPSbL5pX//cJByKb2sGjOWsr/pkVcP4dbquboYDQ3toOBru34WBimg20HsAJBTCuIbTtYloUTmg08NXGNbQzgui6/9wf/xJf+O8RXfvQzfuPafpobYO8h+NHP4KzzP8HH3v/pU3wHREREREREROS1TsHor2jT44/T98gjXJBMUgXcl0iwvHj85jtHs4Fq26YrECBXLB6zC3qZYRi8Z/Zs3nPgAF8pFMqVh+N84JumidfQwG+2t5MpldiSzdKZSpFMpzFyOapcl3bPYynjgdspah9/USyWX5kMDA4yCOwEeiyLTCCAFQ5TG40yNxplYShEwJxMxo6EvZ+14b3N09+r3oKaTJYDw0lWVYepC/gYxvh7Nx5q5lIwVIL9HmSwyJsmvjU+jd4JUBUKUB+wqXasaQNKx4A6G+om3r3yPR7KwYVN04/PMGBtvEhPcozzW+IYxmTAW3IhnYPuokHGNylg4RoW/vimRyHbojZoUh0wp9wE6nQUgNhJksqWKGwZyBDys+Q9A9+wCNg2sZBDLGhVrB8bssqvssrQNF+EVB7cUjk09bHKU+Zti5BtEnIsnJMEwSW/vJ5qXdRhKJHDLdqUJ+qXGVaOoaSLYzvksmEM0mRzDpCfaFMs9jAwWiBgm4SCFgHLIBBoAjonnyMwQmO8/INS8vO4JXDzUCyV3zPPM/H8yoVEo4FRBgd6qI+6fPTjf0RP59t49qEbOdAF7S3w55+E4OyrGek/hN/QQTQWQ0RERERERERe3xSMTsP3ffL5PL7vEwwGMY8KAA/s3Uvi5z/njbnJNRQtptqTfIp+gd9obuZfOjv5fdclcMz5JPDL+np+Z948/mjrVsKjoywoFhkxTXaHw1y8cCEfmjuXXaUSKdcl5boESiWqikW8UoliscjeYpEn0mlyY2PYqRThbJY616W9VGIpUHMa70PD+ItSCbLZ8mt4mCSwBegyTZKOgxEOUxWN0h6LMVYdpsY8cSr4xpDH091dFEbgZ+kCdxzsI5PN4RsGoWiMa1cuYVl9Le1hhyqb8XDSLc/mLkEpC8Ml2OVBCpO8YVIybUzbJuDYxEJB6gMOtQF7Yrr+ERmg9iSB49wwbO1PsruYIo2FYQewbZvDgyMkUllCAZtz5rawqLoyfi75kE7D4JhBDoecaeOaDp5p45s2tmWVq0TNEgF8gqZP6JjKy9MxsbZnLkcqXyRgW7TWBjFcg6EM5EomJSxs2yIScqgKHv9+AASt8qvs6NDUxS1BJl+e7l/wDUq+BaaBbVqEAyaxgEXABANwvXJ16GjCJpUK4ba4E9P0m+p9BrvSDGeDVEfrgDTRaJijg9HGujRVwRxBxyLrQiIL2JUpdlU0g+f5mKaBZYBlQ6jik8xj7qwO3GJ54yWAuuocjjfESNLGtuDMZR0Ue+DcM8vni0XY3NXNWavr6RnsIRJdiPHrlgSLiIiIiIiIyKuagtFjlEol9u3cSaKri0guhwGkAwHiLS0sOOMMHMfhyZ/8hLceFYoCLI9G2ZRM0lqaeoMbgCwwCvREoyybM4c/6+pieaHA+aUSReBh2+ZAKMRFc+dSCIW4ae5chlyX/kyG+miUxbW1eMEgz0QiWPE4gepq4rW1NMdiFEMhOkI2hmXQny9CPk+p5BLDx/ChL1fgcCLJjv5exp7bRvG5F3AOHaZueJj2XI6lfnnn+lMVB84BzvE8yOfLr0SCHOUfqnssk0TIwY8FidZEmNUQY2l1hJrxpCpslGsJ7z/cxws9/fx/EZc540V6h90xPvfoAIPNjVw7u4VdHoxhkTMtfDtAMOAQCwdpCIfoCAWI2HBk6jU+5bLBAox50FmCUd8gZ5QrOw3bYtQMsM12qIk4NIQdwlNM12f8ORZHfXy/yP0HBhkaS3FBVZH2cHmjqPVbB7nTC/O2c5bQOL47vGVAlQ1V9lEDOcKfrDhNlgzGTAfXClFywhSdEH4giGEFsL0CZrFAppQhW/Qndn2fyrPdWRLJLDVVHu0Bn1wJnj2QwXYc1nTEccwjSw24uF6O5CikiwZFLCzLIujYVIdtQvb0yaxjQvVEgn9MaOpDNgOJIli+QVefhWlBLldkZavNQMKgtb48nT4SBMwCy5os9g6HGUnC7FlhegcTtDSUe2xrgn1dfSycM4uwA2EHFna0kEiWN14CaGnw6OzpJxyN4/smhmFgW+WlAAIBE9uA1pZm+nrL1aAArY2Qzybw7AZqqsEyLAYTQY6EsrYNgwO7gZXYZo7BgQFqamsrNoISERERERERkdeXGR2Muq5LsVjEtm0cx6FUKrH58cfp6Otj0dGbH2WzjIyNsXloiOXr1hEcGTluLcY2x+E+xyFdKhFlag9aFtc3NzMvUE6Zbl64kOeTSZ4YGiJvmixsaWFdVRVmIEApEKAqFKIuHGZhLEaopoaq6mosx8G2ymtDZi2L+qBJEIMi0JMrUGd4zAsEGAw4ZIolMiWPgg9V9VEuXdOM5TiYjoNl21iWhW3b+L5PZ2cnP3/qKQbXrye/aROBffto6O9nUbHInNN4T0PAKmBVyYN0vvzqG6O0E/YCT5oGw0GH/nCQvbbJHD/JP1ZXbjTV4cDfV7v890Afz0UCXNtax0SpKIVyoupCdhR6itDvGSSxKJgWOA7BYJCqUJCmUJBZ4QDzLJiYow/gZaAXcn55uv6IDxnDomCZ+I6NHbJJWUEsx2E0b/Nkd5L6whhvbJkcZ40FNzQUGXGTfOPxbVy6qJWiGcCyHaqjYVpiDkHz+MB1yuC0NFZ+tFx5R/hUCVIlg4DhsGe/h+2YmLZFIGARCtnlNU4DJtv7MoSKWa5qr3z/WqMlBrIlntjvcdH86omlBhwT6kJQd/RCrOQZS8NQwaCAAaZF0HaIh21iJ1im4AjHAMeBKgeOhKZ7k7CgAWwTcqMW1E/WUpfsAhEnTMg26RsKsHRugd3DNi0N5TamAcnkYWDWxDX1tTF27jSoiZcD1voa6D3Qx+yWGJMbQhVxi5BJQ6EIJc+kb9CkffzfrK4GNh3opqmjYWJJgGSmChiYuE9X724O92QIB3289H66R7vwrAits+YQCk218IWIiIiIiIiIvJbNyGA0OTZG1969kErhFIvkgIJtg23T0ddH0xQ7wtf6PosHB3nkBz8gNs2O8Tc3NnJ7by+3ui5H75lTBH5uWeTicVLhMJssC8O2CQYCWI2NnLd0KXnHoRSJYFRVEaupIRIKYYyHoJZlYZkmQcviyAbvLpN1e4ZR3oCpNRRgtFgkUfQwAgahALh2gPbWthOumWgYBrNnz2b27Nlw660Tx33fp7+/n/UbN9L78MNkN27E3r2but5e5uXzLKS8hMCpsIDFwGLPL2+dni3wsQD8bsf013wiVuLWnV1Q8og6DvGATU3ApsaxqHIswobBfAfmHxP0kU9BHpIjcKAEg55B2rAomDam4xAKBKkOh2iJBGkOOrSZMBG8Fl1IASQp+tDXC51FuGbu1GOsdeDy6jxjyRTnz4rh+wW2d/Xy81QeKxihsa4GxwlSEwnSEg3gmOX3NZXNE3AsgsdUJJrHBqdHr7XggleAdAk6XegvwGWzpx5XYxgGc0W6ky5tVSeueqwKQFXgyMZP5erSbB76kgbZkkEJi4BjEw/ZxEL2SdcaLXgQGb9lgABHLzIRi3kUSh4tMdjeFwUKeH5lG8cZYSwDoQAEbDBNg9FUmPIiCGXJZDflrcQmOfbk1HnweSEVpFynXdbTu4eW9oX0J0xMwyRbrOXoYNQqHWLexIZcPpCnUMxz8GCWjjlLFY6KiIiIiIiIvM7MuGB0ZHCQvu3bWZDLVVR9usUiTyeTU4aiR1QDlu+Xc7Mp1FkW72xt5cGREXpyOWp8n4JhkAkGWdnWxrL6elK2TTEQwI/F8GpqqInFMAMBsCzsIy/TJGRZE+M7MiE7b9vkgkHMcBgnHKbWshjs7aG2mCdklKv3GhwbnHI1ZMIO0dTS8itPBzYMg+bmZpqvvRauvbbiXCKR4JmtW+l6+GHSzzyDsWMH1d3dzEmnWQxTbix1tB6gI1QOAqe/P6y1XVYMHqLDgaIPCa9c5bnXg5QPGQyymLiGSdEw8U0LLAvTtrBth6BtEwo4RAIOTc54sFqwqMpZjA4b7CzCsGeSNk2KloPlOISDQWoiIWaFguzB5uKGEz/LGVH4394kYyMjbNhziOWBLA12kZ6iwwY3zKXL57F0Xiu7+3P8773rGe48QKtVoOAbpMJ1vOXqy7h23epT+SfBNCBuw+EcLDvBjvUAC6s9ftZTYMyuI2bkiJku8QBMs3JAhbANYbsyLHU9GE1AqmhS8Cx80yLsODRGLcJHpaVHV5k2RBx6BmGgH4qFcsXsvkKSgG2BEcb1Rqivj5B3MwTHf0xnt7gUQ4sohsOkcymK+STZfA1HB6NuYTLQnE62EOfoYNQo9dBa43MkBDfMRmDXxHnT7zuuj4ANc+tzdB7ez7xFy07+xomIiIiIiIjIa8aMCkZd16Vr507OyOU4dkVF1/OoO0EoekSNZZF0HFLFIlPVYFaZJm+pr+c2x2FRSwtGJEKgpoZodTXBUAjHtsshqF3eiMcxTYKmiUM5fioArmmScRwMx8GMRgmEQkQdZ8rNYBpaWhkdGWEsl8EZH79rmpihCPUv4RqJNTU1nHfxxXDxxRXHM5kM2194gf2/+AVbvvZVars66chkmF30WEp5bVKAEaDpFH76mi0Y9qCDcqDXYJVfkyaDrgpH3swCE3maOx6sDhzZtMmHjG+SN4yJYNUzLQzLpsu22GzaZC2bTU4ALxTCCYeoqorQVBuhvTFOWzxAbdDGMGD/oS6anRH+tO3ocbj4vst3dr5A53CSe3/xS/68YYCFR61NUPITfPknXXx6007e+pabqAs7dERsYvaJQ+NECRaFT/zeOSZUmRna8/sYzDv02nE6Q7X4Tpiw5RJ0M4TJUx3wCZxC6a9jQkMIGphct7Tk50ilYdA1yGFTNC1SBYt03iYSMEnmPA71w5ktR2+Q5JEremztNekdMJjVHGDfAVg0Xj1cFYMnt97F7PnnEK5qoaZhNsH4EqB7YiymlWYg34JfTGP4BWyzQND2CTmT75tv1AL9k+91cbDiecLhtorvA9bI1M9tgW1myeVyqhoVEREREREReR2ZUcHoYG8vLdnscaEolOO1U9mDuiEQYLC2lgdcl+uLxePWGvWBB22b6rY2OlavBtueCEGDpokzHoIemQo/EYKGQliRCIFgkNBphJmO49DQ1ITrupTGN36Kj9/zlRCJRFi9di2r167l5j/4A0ZHR3ny4Yc4ODLCoXCE0MgIo08+SWLTJvYe3Eb5HZveQRfeFDlhk1PmGNBolV+TjoThUwSr4/s5kQdSkPfLgWTCK782+ZD2TVKYzDUNkgGT22wbw7GwAhZ20CYYcKgL2/zTo8/x7SVpGoOVt7EM+Ghrnv/ufgZ/Twfzl8yjJ2Wxx4yQDcUpheKEgw5xigRKOYKeS9zyMQ0oju8AD9CbdNnclaJULD+HZVusaY+DZxN3IO64wDAwTLEAwzkY9cKkw7X0O01YVohgKYORHyXo5ah2vPFNrU7MMqA6CNVBn4nFXyOQTkB3Dg5kYN28crujhWxY2+axZdCmo9klmQpw9EZVQbuXWZEeXLeHVCcUC5UdVEVTNDTPqfhjQaFQIJVNU8gn8dwMRVqBnRPnbStR0UcsPrfi+3gkOe1zxkIFMqmUglERERERERGR15EZFYyODg6yxJ86iAuaJulT6GPQMAjV1jKrVOLOkREWFIss9TxM4JBh8JxlYdbXc/7atTTGYpiU46ICULRt3EAAIxjEiUSwHYfwi1TR6TjOq3IH7erqaq656c2VBz/+cQB+85rLcfc+NO2alUUfnvNsnrZDhN080ZJLtQk146/I9BupvySCBjTb0Fxx9EiCeuTbfDlIzQPjOVtnES5yOC4UPdqHmnJ85qEnufiMecwPl8oX+0nIQioFPQWbsWA1fl0rw81zcEyHXT0bObO2yKP7xyjls1zcUJpY2zPtwtOHXBJ+GNriFfeyDWgKQxNZIAu5bnIpGMhbZKwYuVgjqdgsHCeAm+zHzo8QKBWIWx6xwIkrWY+I2jDgw6LG40PRI0wDrGL5zwTBUIijg9HqeJpiycOxTGpjsLijmmQa4uM7m7XUF9mz/Vma2xYQi1djmiaBQIBAIADUAlDXug54eKLPSChLf6YOvCyGnydSvYBSCazxoLyuOofneZjmND9Yp/KXExERERERERF5zZhRwShMn23YQMhxGMvnqZqmTRYohcMsnjePXYEA88JhIqkUj2bKc7XjgQBV9fXMWbaMcE0NyaPWAw0HAtMHLjPUh/7yb/izD97K37ldx+1+7vvwF3Yrf3DPdzn3DReNH/NJp9MkEgn2JRKMDvST7u0m299DfqCP4tAApcQQ/mgCMzWKmU4SyKYJFrKE3QJRz50IVWtMCL9M/xwP5eC6+SduE7QgO9RHrlAkFKj8tYxZsChcBIYgMQSJ5xh1oadg8pN9Js3VRda2VvYXdeCytiIbBjJs6bE4s/XEZbchGzrsEjAKxVEY3MNIHhKuTT4UoVhTSy5WRzLkU0plKKVy2G6BsFGiOjBZuXq0kTzMaTnxc8+tCZHMZOhoCTOSHKN2PMPtaPXp7htldms55GysjbNnv8GSeeU/bDTWwdCBbYTrRhgeNsh7EcxAPdHqVuLxOIZhUNe0hGIRjhRP11blqGueP1FNPZ8Weg5B+/gY21ugr3+Y1pbjF5VNZoI0N8SPOy4iIiIiIiIir10zKhiNVFczMjg4Xk92vMXhMM+WSqyYYv3QHLApFGLV/PnEHIfWM8+kL5Viz/79BPJ5PMvCam3lvNWriUajU64HKpXOPv98Rv/5C3z4j3+Xd48e4FLbBeCRosPtVXN521//w0QoCuXNoGKxGLFYjPb29tO+n+/7pFIpEokEexIJRgf6yPQcFawOD1IcGYKxEYzkKEZqDDuTJJTPES0ViBv+RKhaa5UrSE9FEQicQtt4KcNTX/5n9sc6WHHeGzh70expf46qHah2PLZnPc4+weZQaxtK/HAwRKzmXNzhTqzsMDVWjrrg9JWcR9QGoTZYBMYgNYY7dpDBHGQIUIrESIWqCc2NkDQN0sM5MkM5Qp5LCI/q8crVk/0aVIUtdvWbLJ4Lz+20qI2XlwJwLOga6qOurpZYECzLJJEMUv5NLEske3GsdhpiPpAG0hQyhxgcMih4USzfoW8Q2saDz9ZGGBgYoLW1nCK3tbWx4enJYHRWE2zs7DkuGM0XoWSGCQZPUPIrIiIiIiIiIq85MyoYbe3oYHdXF7W53JTnHcOgNh5ndz5PwXVpLZUwgGHbphAOM2/OHEqRCGPj64E2BIO0rj613cRlapdfdz0XXnkVP/z61/jd++8F4Owrr+FzH/zQix5EGYZBPB4nHo/T0dEBrDzlaz3PY8uzG3j4gXvJjiVIpnMYoSi1jsPux37BGamDxLwiJbcEbhGjWMIueTieR9Ir8YtBWDVdKTLlCtlsCS6p9biEg4w9fZDvPRBiqHU5V110AYsaj69WHC5AQ+TE4aNhQE2+n94926ifewaNqy4lHInQ299DuvcA3mgPQXeMxmCR2ElWYnBMKBeeFoBhyA2TeQ4GcyZpM4AXC2C2xahqjlFwIb8lSzLvEj/BP2OqAKMpGyjgug5Hr/UaDoxh1l1I/9ggbrafTC7G0cGoWxg+rr+ABY0xH0jhx302PmvQ1lKuMm2ohSf276ClpQXDMLAsi8HRCEd257IsGBzYx9E/FzkXDo9EmD133onfHBERERERERF5zZlRwajjONTOncuenTtZ4PvHTavvAsKBALF4HC8cJuR5+MCSUIixcJjatrZX5Tqer3XBYJB3fvRjvPOjH3ulhzIt0zRZfc55rD7nvOPOjY2N8blPvouP1nRPGVJ6Plz/4x180stMOeUc4IFBuKRu8vsqG95en4PCs+y+61m+aLRROutKzj5jCfV+ltZAibxfnjJ/MtWOx5ziXpq69jK2/2725wJkA3VYjfOoWbiO1rZ2ctks+3sOkR84CKl+qsjSEPYJnGS5gYgNs2MekAMvB4fHGNoNiaJFkChbR4ssbg1SG7Oxp+hr/zDURUK4XoGcG6Ho5SbatTVlGNx1FyWniXDdQgLxZcD6o64eI1uAcGDqsRmGwVi6sso01fcgA/t8XKJYoUZS2WqOBKMAnX2H6Uo4mECuaGE5UebMm0OpVGJwcBDLsojFYvocEBEREREREXkdmFHBKJSrRnuAjXv2UFcsEqZc/5a0LKodBwIBMoEACyORieA0A9iRiMIQmVJVVRVveO+n+Pzt/8IH44OEjtr1Pl2Cz440sPayq/nEo/fx+cX548LRLWPwX4cN/mPJ1BuDLYrCIrrwdn6dh58yebplDYs+8Mc01lTT+9wjlLf3mt5wHlaM/+hWBaAqUAB6IdmLu/UJep80GCFGKd5GoGUBDYs6qK8PMTKcJNHdT25giEgpTa1VpO4UNmWvD0E9JWAMgEIqQ98ApEsWnm0RigSprwkwmLPJ+RZxs8jPH4aFdSG6+2D2+HqpjbVw39Yerlln4nmD7MpU7hpfHc/h115IX6KPYnYAhxRVkRKhoz7VcvnKKtNiro+mqiNjGyOfr1x71bFGqGs5AwDbtsnn8+zduQ3bTFMVLOD6Jr2HgwTCdcyeu1CfCSIiIiIiIiKvYTMuGIVyOFrX1MShPXvoSyYJAAHLImkYVIXDLAgEMCivDZkxTXKhEPX19a/wqOXV7KKrrqZx1iw++7UvYA8cpAqXEYKYsxbwjs98irnz5rHl2Wf58O9+hPnDO1kXypD24OGxIA3VMX741tl0pfJ8Y28v8XyCq+s8wlblPUwDLq/1uDz/DKOfvZl7R4Nsb1hB+/Xnsmr21CvnFjxwSxCe5jfdMaEj5tNBEvwd0LODxEHYk7PJBquhtpnquW10zK2lVPI51JMgcaiPQCFNpFigIeQROcmnSMCEtiiUp8mXgALJASBvEbKr2d6b5spF5XVFNw3azG4tTlxbG0uycWeKtUtjLGirJZODyHg421RXZKCvm4458zHN8lT3dDpNb6KXUm6AAGmKXjUwONFfqVQ5/d60G4G9E98b+R2Mdj9JyYhRMmsYG+llSWsOZ+LfokRrTYaxbIZdO9IsWXbWxGZOIiIiIiIiIvLaYvj+1FVqUzjlhq8lruviuu7EJjepkRH84ngwYxhEqquJju9yLXIqCoUC6XR62inX+/fv54Xnn6f38EGqO7fS1r2FtVF3opLU832e6Bpld2cvS+0M62pOfL9daXg4V0Vm3kouv2QNy9riOCaUfLirK0p9cwdxd4B4aZS2cJGwDVnXJ1f0iAWtaaf3H+F60JMxSBAmE6ymdl4TrfObcF2LUmmM9GCa/FAaK10gbhSpD029S/1UNg9BezM0RMvfb+0dY9Wa1MT5HfsNBnvaWXtWLZbhc6DnbhbNPvI+wc59F1BV00rBbKS6cQm19Y0Tv6u+73PPD/+KG867e6K/+9bXcO1VfzTx/fd++HneduUdE99/97463v6O7wDwfCcsbuWoULTSYMogby+irWPOqT2siIiIiIiIiLxcTinIm/GlTo7jVIRX4XD4FRyNvB4EAgECgWkWvgTmzZvHvHmTm/mUSiW2PLGervu+QWv3NlbHilzYXsOF7TWkCiXu2DtAZmSAS6pc2qeYyr44CoujY3ijj/Hw1x/ni04z9pI1OHNXcP5bbmDZ8vJmQp7n8cT6X7LxoXuo9hLErSKJAoSCQa5cXEtjbOqPA8eE2TGf2WSADHT2MLIX+nM2uXAYu7GGujkNVNfF6eyMU4ruJzNQoJRwCeZL1DoeNdNswDTmToaiAK2RCOlsiuj4r+GcNp/SUI5dB/OsWhAiMepwZOkA04Bkcohl8xqBHgrZHrpecHCtZmqaF1NTU09N/TJgMhiNhPP05ObgFYYImGnCscpQMxZOA5DKQSQwfSgKUB/1eaG3R8GoiIiIiIiIyGvUjA9GRV5plmWx5g2XsuYNl+K6LhsefoCBB7/D7IHtnBmDtyxrAVrYl8hx+74eqvOjXF3nVaxlCuNT7et8LqeXxMF7+dnWB3nssR+z/8x1VC0/lz5CRHs28Im5yYqNkJKFIrdt9Vi2oIM5kSKzIsdP4z9WbRBqg0UgCaNJ3M2H6Ukb5HDI1oSJtlXRdk4c3w+QSp3HWCZDZuAgRqqfaClFQ6g8fuOYytKGuM2OLpNlCz2gvLFS1s8wMlLNlq46UpkokJhony9Ofh2woL3WBTrJJzvp7HbIZUsUi3BktntdPEvTrAVY1mI8zyN+0KBY/PeJ8w01eUqlEsmcRU3l8qPHMQywDBff91VRLiIiIiIiIvIaNOOn0ou8WuVyOTY8cDejD/2ABaN7WRYt/wp6vs/jnaPs7upluZ3h3JoT97MjBY8VquietZQ/vH4eAfv41NMtwVf6Wvnw73+Uvt4Eh144SCDVSTA3RpNdoPFXKKQeyUFP3iYfacZumEfd3OU0t87CNE0SI8OMdO9nz47NXLOkUHHdxr4h1qzO45ZgcARe2GXTVNXI4naDhzbt5Y0X9U20fWxjhAvPumraMQwn0uQy/8Ws5vL3A8NQav4JLS0tAPT29lLY1crsWeXznb0wEHyYcBCa4gXqYid+xh29YZasuFDBqIiIiIiIiMiryyn9j7qCUZHXgFQqxTP33UH60TtYmj7IgvFqxmShxAN7BsgmBrisymXWCXaNL/nw0IhBZ3ULqy9YyZnzGirO/7IvSPP1v0FD40LC4RTR8SnuIyM5ug8cptDfgz+coIYssyL+cRWrJ1PwoDttMmpU4VXPIjxrMfsPbeGchnzFdPoXejL05RMkR6A1Uv4k60vZWCGHmqos684+MNF203aHmtaPYpf6aK5yCRwzJt/32fzcP7H6jMmPr589826WrLiY+tYlRCIRnvyBzQVryudLJXgm8SirVq2hc+8zLGrOMZ2SDzv7ajhj5Tmn90aIiIiIiIiIyEtNa4yKvF7EYjEuvfU9cOt7SIyM8OA938V98h5WeD3cfEZ5qv3eRI7b9/ZQUxjl6nqP4DHT1C0DrqzzgR5GHu3he/cFKM2Zx5VvOIPG6jCra/J89+u3sbCjkVB7Ow1LljBn/izyeY/lq2cxNtYMZIhGPfr7MwzuHyTXPUIon6YlUDxpVWnAhLlxD0hAKQGHX6ApD127TQ5ZNk4kSE1NkBcOZnnDXGhZfPTVRZK5Ij/Y4nHWSgiNL+HaVO8y3P88ocbVJIJtpIf2EvD6aaoq4lhgGAaJsQCQn+gp6HcyJ7qX1MBeDqdCDI2GgGz5PbJgqG8b4fCFlIwYuWKO0DSfkn2jNg1N7af07yciIiIiIiIirz6qGBV5DRsYGGDjj7+B/8xPWc0gzcHyVPtHD4+yr6uX5YEM51SfuI8XUrDBr6Zm+VJyDfN4++JyouqW4EAKBv0ohZpGzKZZrDivndracrmq68KePSaLFg2Rz5foPjBG6vAY3lCWOrPIrGjpuHD2ZNb3wNx26KiZ+vxIBkaqDjC/o/x9sQQH9q9gYXsVXSMWY147TXPOw/NLpAZ3EfT62bzp87zpksREH/c9soJrL7tu4vvvfP8TvOO64Ynvv/nL3+RdH/oc+XyeXds3Mr8xRfSovbR8vxyKJv0mFi46Q9PoRURERERERF59VDEq8nrX2NjINR/5HfjI79Dd2cl9P74Ne+vPWdtqcPHsGsYKJX64p4/8yBCXVbu0TjHV/owYnMEopYNP8fNNG/jahhbOvnAFK+c2sKgaFpEG0tB/gIHvwcasTTpaw1iwlUXn1gLVRKMWi5bXMthcRyBwJdFoFf29vQweeAG3fy+h7ABNTo6GE1SV+j4MuXBRzfRtaiOwc8hkfkd5cybbgpHkKFBFW22JNg6SHjrI4ZE4wfqzaJizjvzGuzl6w6aSl6jo0y1VA5PBaGHkCfZvuxc7Nod5C1fS23OYwuAwAbuE5xkU/AB1DbNor21gZGQEy7KoqqpSQCoiIiIiIiLyGqNgVOR1YlZ7O7N+64+BP2b/3j08/uOvE3rhEa5ebBG3Z7F7JMtt+3qpKyS4qt6fcqr91fUe0M3wI918794g3tx5rDhzEY8eSJHK5jEMsG2H65anuCg4SO4J2PMzgyEjjFtTg916EWeeZ2NZFq1tbbS2tQHlzZHGxsZ46IlvUuMPYowWqfaKzIoyMY5MEapPYZOnVNLh6KnxOTddcT4ahKUtSTx/PZ3bHidfqEyDfdL0jZk0xD0sAzDqgf0T521zmHn1KeB5Rnqex8zHiVXNo7axnUAgQC6XY+/OzQwe3kIs7FIsGezKhalt6GDBwmUKSEVEREREREReIxSMirwOzVuwkHm/93/xfZ89O17glz/+GpHEI9x6VpiA4fPo4QT7e3pZ6WQ5e4qp9nUBeFtdHsZ28Ny9O3AKIVYtaeKyZfVkiznueD7D+lAVF8+N8N2HXyCRzhINBfmNi18gtfu77MiHycbaCLWvpPWMc2hpncXhwz/hsms8oA6AYhF27ywy2tlKIDOEmRqgeFTgOZ181uboYLTo5xnLQdUx1bCmAbPrSxysrvyYq4rlqVtwE/39neRG9lAyWyvOB+zRia9ro1AbTQJbGe7ZxuGxKKNpl7Pn5XEqNnoq0DOaZtvmMVaedZ7CUREREREREZHXAAWjIq9jhmGwaNlyFi37J1zX5fb//nvyGx9hRbXJOztqybolfrCnj8LoIFdUF2kOHt/HijisIEex/xAP7TxMVyTOmSta+NdHd3Noc4ZPLCvQFIGRHNz2YBe73Xr+4b0XEQ3sgf49ZLrvYNeYQcIJ8ujzcWoX1TF3cQ2ZjENN3Y0sWz4PAM/z+PaX/gaYfid4gEQyCExWiVbFXbzmd7LjwFNEvAO015Uwj8ola6rqKZXKGysB1MYy7HnuIdoXrqO1bR4v7DkA3D3RPh7NTHnfuqjP3u4UZy8EZ4rcs7W6SK7QQ29vN62tbSd8BhERERERERF55WnzJZEZwPd9EonD1Nbm8Tyfffu6eOHZ58g9/SRzh/ewJlpkXyLLk/t7aCiMcmW9T+AEGyd9cid87FxY1Xj8uf2j8He7mvifj18xbeXk4SR0FSIUauYQnr2K9mVr6ex6GDf7PK0pmFc79X1Hc3DvjgJvvb6bgFM+dqgHuvvWMnvFm2lsbqfzwAsUhrbQXpckGoDBkQyu/x1ax8faPwj478AJRuhN1bNv0Obq+bfijPf39BaoXXAP9dEMddHJj73hNAwlYVHL9O+L68PWzkbOPu+y6RuJiIiIiIiIyEtNmy+JSFkyOUQsVp5+bpoGCxe209g4l8jNH2d4eDcbn3+Mvvt/xspAlBXhEg8eTPBcVy9XRLOsqarsa0cKFjRPHYoCzKuGC6tGeHxXPxcuaZ6yTUccOsgA26FrO8l93yWUNsgFbJ5Mh+hsjHDO/DAhZ/KakQzcuwvedK7D/Y9AfzcEKP/FJl3YRtjrpNfpoHnpTcxb916GhwY4dOAp4sZhBgaYCEabGmDLCwnOXBKhNjpElZmkqw/mtpfPt7eA13Am0cZGDvfsp5jcT00oydAYNNec+H12DDD9ky8HICIiIiIiIiKvPAWjIq9zuVwWxxmZqIgEyGYhFGolm01RV1ei+fJ1uBeto78/wYOPPMYwv6Suvpb5C4p8f1Mfxa4hrogXaQrCN/vh0xed+J5vW+Dyrp8+T6i+lbkxj/rQidvHg7Ay6AMuxFxKXpJDW6AzZ5PwQ4xYdVTPXs7Vt1zD177wt1zaDtevhCMFqflinof2DEA8x+LmL7FxRwNNS25k2dnXUygU2HXnNzh6in5ibAiYBcCshhiP758MRlsa4d6Nj3HdDW+jY+5SYCm5XI6xLU/QzMDpvPUiIiIiIiIi8iqmYFTkdcx1XQqFbqqqjj4GpVIDtm1RKnVPBKYjIzmqqkwuvO5sgjev4rP/51+pCY9w6wVtQBs7+rL8dHM3+/Kj1J9k9/iwDe3FXvoe+RZPBJpg0UpmNc2m2bdptlLMifnHbF5UyTLLlafzqotACkgxkjjE9//tft66ymP2MVPtgza8cUmJB3aPcaAryJr5g6QTX2HjzgYal9xAvtQAdE60H04mJr42DIPhRAAoAGCaUOr7EXueTkJsGR0LziYUCjF/8Vn0HXiEqvD0FaGuD75xkjdHRERERERERF4VFIyKvI6lUv3U1nrHHItQW1vLyEjXxLl0ukQoVCSdztDSAuBgNbeSckeIjQenS5vDLL1mAVt/vI+0myDqMK2iV67mfNNsD+iFZC87DsHTiRgb2y+i5YK3Yqafo4kEVTmXuVGf6ik2fjpa2IZQ4PhQ9GiXLfC4fdsoK+aHiQZhzexBMqNfJTFSqGhnhSPsTq+B1AvMbsiRzkY4EowCZLM9LGrOA5vp27mF/YVW6jvOJZGL4paO3ZF+0sF+h/a5S078ICIiIiIiIiLyqqBgVOR1KpkcIRar3GF9bMwkFmuiUMjjOMMAeB6k0zny+SwdHZNtb37/m/iXv+rmj5cmKoLAt61p4ls7knxkWWnae9+zH67uqDy2tBaW1qaA+8g+cR+P9hhs9quJrjsD64oz2XLIInMoRo07QouVpCNarhw9YlcCVrSf+JltE2yvSMmbvDYSgJpIoKKdYwywaPn5+P46+noPM5b9VyAxcd51hya+bq72aaabXPLHVBlhHt8e4uxFFrHg5DrOJR+27iuRyIVpj4xQVV1DKHSS9QNERERERERE5BWlYFTkdSSfz+N5Hr7vYduDx60rGgiU19UcG+ukoaF8fGiogOvmaWmpDDpTmShv+8wN/L//uo+LoqO8ocHFMqAYquF7nTVcP3uI1ujxY0jk4avb4X9OsDF72IarOnyuIgHdj7Pvnx9n00CA/tozWXbLZ1hx1ZvYvX83Qzufwe/fSbU7QDJfpKbu5O9BqVTisecKXLxqMgyNBmIVbYJWPwf372DOvKW0tM4mXHMmsG/ivO8njus35MCSlixLWrJ0jRhsHwnhBOOkMkWSqRTzW0p0tAyTzxzm2fXPYEVaOfvcS3CcE5TWioiIiIiIiMgrxvB9/1TbnnJDEXl5pVJjpFIJAoESluVRKHiUShCPQzQK2azP4KCBaXqAh+cVsW2IRj3S6TGqq3NEIpP97dmTp6MjQzAInufx1GN7efRnOzDNJladdwlnrl3HH77/Jq4LbOWmtgyOBSUPftpt8+1EM+/61PXsfXQv6R07aSr1s7Qqz7lNlRWg08mX4PEeeD5fh7v4Gq796J+xZOlStmzZzP5f/BtvWT59pSrAV56Bmy+FJ/fWUFMdZ91Si227h1m+8mnM8ftv3g6LW9/Izt4aauZfx7NPfJ1b1v3jRB/fua+BM8/7LC3RIWqnCH+PeP4w5AoGZy84/uNxMGnyXG8bF19xPaZ5Cg8uIiIiIiIiIi8W4+RNFIyKvOaNjY1QKiWoqSlO7NIO5SnyiQSUSh7ZbJHWVq+igtR1obs7TTyepO6oSsze3iKRSLJiw6ZkEnK5C2lsbDuqf49777qDO778l1Q5GUq2wxW3rOO6m9cwMBDAtm+gsbGNRCLBkw/cydM//nfig/uZ7aQ4v6nErMoizmkdTsJjfTZ7/Ab6ajv461vmURuduti9ZwyeGYQbzi9/PzAGGw404Dg2K5c+TMt4lWzfIJi5S2msC5Fz4bZ79/DRm7830c99j4S49oNZUqkUnXufJljYQ3uDi3PU++v68NjzcOmK6ce+u9ch0HwFc+YtOLWHFREREREREZEXg4JRkdc713UZGemkqak4zXno7MwxezZYx2wYNDhYIJ8foa1t8lc7lSqRSiVpaZk8VihAb+8yZs9eWXF9Pp9j377vsWxZsuJ4T4+Fbd9IY+Os8T6T7Nv3WVatSgPQ25PloR/tZs8D22jM9nNGPMv5zZxwl/ojih482QvbxkKMRps4+5zFXL66CcuE0Rx8awu8743ldUWP1j0EfcUdrD6j/L3nwXNbz2XV4nIivOmFHpYv+SqB8es2bIPVN7jYdjmALZVKdB3aTWZgI7PiI1SFYU8vBCyY3XiC8frw+N42Lr7yzSd/OBERERERERF5sSgYFXm9Gx4eIBJJMN0+PyMjJSzLpaoKXNdnaKhINlueil4opFiyZDJQdV04dGiUBQsqd7Hfu7eVBQsuqjiWz+fYv/97LF06VSh6E42NrQCMjSU4dOi/WLEiW9Fu/36bcPhDNDd3sHP7Czxw+7+T3XIvs+nn/KYic6o4Jb1peLTXZE++ir7qMzjrymtYN3s3S9q849re8cxOBvp8kgnwfMBq4Dd/4yyiYZvBkQzJ7L8zb3zDqN4B2OP+jAsvuhLDqPwsHRtL0L33KXq69rFuiUf4JEuIrt/dyEVXve3UHkhEREREREREXgwKRkVe7/r7u2hqykx7vqurwKxZHul0iZ6ePC0tPvE49Pe71NTkJiokfR927kyxdKlbcf3evTFmz76qYgOhXC7HgQPfZenSVEXbnh4Lx7mJhoZyKDoyMkxv7+dYtix3TJ82VVUfm6goPVqxWGT9Lx7kqW/9E7HeDSyLJLmwBUKnsE2c58Mz/bA1EaTTaqFhaQdvu76FproA37i3n4GuYd51FjTFy+1f6IVvb4tw4xuXsXZlA49t/jvesHayv18+9S6qm1YQn30t8xevOi4g3bjhURbGtlAVPvG41u9u4qKrbj35A4iIiIiIiIjIi0XBqMjr3akEo01NJQ4cyDJ7drmKslDw8P1MxRqiu3dnmDs3X7EGaVeXQ339GwmFJpO/cij6HZYuTVfcp7vbIhCYDEWHhwcYGPhvliwpVLTbvTtAbe3HaWhoPuFz5XI5nnjiTzjnnC5+dNtBeh/cS2t2hHWNRRbVnPDSCUPZcjXp1lSEQEsdn7ymlliocr6+78PfPxxizUXnUcj8F2+6xGVwBBwbHttwJTdcfi75ImzvjhPtuIaFS86aCEiHh4Y4+NxdrJ4z/fufzMELw2dw3gWXndqgRUREREREROTFoGBU5PVuZGSAUChBeJqqxf7+IsPDI7hujlDIwzQhk/GJRKCjAwIB6O7OU1OTqdiVfmTEwDAup6amfuJYORT9NkuXVgaBXV0WweCbaWhoAWBgoIdE4gssWlS57umOHUGam3+T2tqGEz5TOp3m2Wf/mIsvHqw43tlp8MILN7H9gXvhhZ+w1BnkDa0+0ZNMZT9i0wC8kA5g1FaxankDyzvCGIZBKg9/82iEQ4eepsooMCcO+SJs6w1zw3Vv4L23rMUwDApF2N4dI9J+DQuXrsYwDB564A7Wze2edjr9o7tirFr3FqqqTnFtABERERERERF5MSgYFXm9O9nmS7t3DxKJpJg1i4od69Np2LcPWluLmGblrvTZLCQSa2ltnT9xLJfLcvDgd1iy5PhQNBR6C/X15QrQ3t7DZLNfYt68yvE8/3yIjo5PUVVVc8LnSSbH2Lr1j7jwwtGK4/v3mxSLv8OiRWsmjnmexy9++hM2f+cfaBjcyDm1WZbXH9vj1BJ5eKzPYNCKEG6q4X/XD/OFm7MsOGojJd+Hn+60eWh0Ef/wp2+eqBR1S7C9K0a47SpmzVnG44/czVntwzTGJz8isy5sPhClZd465i1cemqDEhEREREREZEXi4JRkZlgbGyUQmGYmpoi9lFrcfb3Z8jnB+jomPpXd2zMY3R0rOJ8eRf7Bcybd/bEsROFouHwzdTVNQHQ07Mf1/0as2dXhqLbtkWZO/e3iMdPXDU5MjLM7t3/H+eeW7l26e7dNrb9h8ybd8YJrx8eHubur32WxPqvs8A7yMWtHlWBE14CwO89CZ+5EVqrpz7/4+dt8guu4u03rq447nqwvTOK3XwZhaLB6OAhTMPFx8QK1LJw6Wrq6+sndrYXERERERERkZeNglGRmSKTSZFKjWAYRWzbp1g06O7uZvnyDFPlcsUiHDx4/A70e/bUsWDBFRPVkdlslkOHpg5FI5G3UltbLrE8fHg3hnE77e2linZbtsRZuPBTRKPRE45/YKCXzs4/YfXqyo2atm+3icX+jI6Ohaf0Phzh+z6bnn2a+z/3ZzT2/pK1NQXOajy+3XAO/nEX/P1bT9QXvOfONr7+ufdhTfGx6nqwoyuC03olc+avYsOTDzHSt4ewnafkgWvUsGzlBSxauvy0nkFEREREREREfmWnFIyqlEnkdSASiRGJxHBdl1KphGVZDA11TRmKAhw8mDouFN2/P8ScOZdUhKKHD588FD10aDuO8y1aWyv727SphqVLf4vwdAugjuvtPczAwJ+zenXlRk1btwZoavprWlo6Tvr8xzIMgzNWnMm+65rY/tgKFqwq8dPnRug7OEydm+HCZo+6EDzZC5ctO1lfUGuO8rOdC5kd72TJrBz2UR+vjgkrOzKMZe7iB1//CW88u0TjrMnzJT/Bxv29rO89xEWXXnvazyIiIiIiIiIiLw0FoyKvI47j4DgO5UrwqYu8Dx/O0tHhVhzr7TVparocZ3xb+mw2S2fnd1i8OHPMtRax2GQoun//ViKR79HcXHmvZ56pY8WKTxIKhU443sOHd5NK/T9Wrqycfr9pU4j29r+hsbHlpM88lZGRIR577F3ccssAD0bhwacs3nJOA5zTgO/77OzK8cDzgzzdN8y1s0sn77CY4tDT/0nkzEsZcNfRYO9iaVtlQPrI8/Dm80vEj3lky4Bz5ud4cvez7N+3iHnzT6/6VUREREREREReGuYrPQARefEZhkGp5HDsShkDAy41NTkCR629OTYGtn0h0WgMgGw2Q2fnt1m06MSh6O7dzxKLHR+KbtjQyKpVnzppKLp///Pkcv+PZcsqQ9Gnn44yd+4//sqh6IEDO3nuuZu4/voBAK68FlgK//QE7BsqvzdL2sLMPWMewx1n84tDJ+8zD7xn7WZ2P/E5tv7iH+npH+OJ7nPY1hnC9WAsA6EAx4WiRzt7QZbnNq7/lZ5JRERERERERF58qhgVeZ2qqmpmx44hMpkCpulRLAKUWHbU1PF8HkZHV9LR0QocCUW/w6JF2Yq+yqHoLdTWNgCwY8eTNDXdXbGbPcCGDa2cddbHJipPp7N790Yc599YtKhy+v3jj1ezcuXfn3Sjpuls3vwQtv2HXHRRZRXo0jNN6lr/mCf2dHHH9l34GFhxl9/7yy/w+b+FVB5iwan7fGI/rF4KkRB8+GqXRGoj33n0efKBB2hbfDkp50qGuzawanZh6g7GOSYYXuJXei4RERERERERefFp8yWR1yHXddm0aT1tbUO0tvqY47Xh6TTs2gUtLdDUBHv3trB48cUAZDJpurq+e1woeuiQTTz+1olQdPv29bS2/pSamsk2ngcbNnSwZs2HThqKbt/+OPH4f9PeXvmR8sgjDaxd+7dEIifeqGk6Dz30VRYu/C86jlmS9IkngjQ1fZEFC1YA5Y2ZvvGNT3L11Z+nuRkOHoC//Dj85xXHV3w+3wP/+gR84XfBOebPSD3DcPczYYzIQtzoSi4+u40Vc0/87D/Z0sB1b/udX+n5REREREREROSUaVd6kZlq8+bHWLCgi3j8+HO+D5s3QzZrUl09n0AgSENDK4ODd7BoUeWu8OVQ9BZqa+sBeP75h+joeJCqowo6i0V49tm5rF37QSzLOuG4tm79Bc3NX6a5ufL4Qw+1cP75f3PS6fdT8X2fe+75Sy655J6KcQHcd18da9d+g8bG8g3z+Txf+9pVvO996zn6VocOwac/EGJxVT3nNA5Q8GweH2gn0r6WuNPDiqpf8ubzK6tbj9jbAw+9EKO+eS4j+RYuvXA582cdH+76Pty5pYM3v/3jp/2MIiIiIiIiInJatCu9yEyUyWSwrOEpQ1GAw4cLDA9naGnxaGrqo1CAbdsMHMdn7lw4UvB56JBDVdUt1NSU58tv3fpT5s9fTyw22ZfrwubNizjnnPdimidesvjZZ+9hzpxv09BQefznP5/NxRf/9UkrTadSKBS4666Pc9NNWzj68lIJ7rxzIdde+zXC4TAAAwN9/OQn5/Cxjx2u6COXg/vuu5hv3/szxsbG2LFjB7Ztc/OaNQSD5fn1mUyGf/nbd7MqcidXra4MSBe0woLWFFv3P8fGwU5SnX185+k4s+cu5fwzWzDGP4r39NnMWXjWaT+jiIiIiIiIiLw0VDEq8jqzd+926uq2UVt7/LmDB/P096dZu7Y0EdgdMTJSriS96CLo7naorr6V6upyJ5s23c3SpU8ynjECR0LRM1i79p0Yx3Z2jKee+j5Llvy4Yvp9qQQPPbSQSy75018pFB0dTfDII+/ihht6K45nMnD//Zdy443/OFHBun37s+zffzFvelPlhlJ9ffDAA7/Ju971Xyd9BoBUKsVt//EhVke+z/lLp/5IfGI77B2s57qLO3hmr0PBamfxkkU8N7CYG25+H7atv0eJiIiIiIiIvMQ0lV5kJtq5cyttbTsqKjuhvA7oL385wqWXFo8LRY84dAi6uy3OOOPtVFXV4vs+mzbdwRlnPFsx9Tyfh23bzuTss289aaD42GO3s2rVTysqWF0XfvnLFVx22R+cdPr9VA4f3su+fR/gkkvSFcf7+2Hz5vdz9dWfmjj20EPfpLr6vaxZU1np+dxzBl1dX+Caaz562vcfGhzkji9+iHW1d7Fi7tRtHthkMFxo4ubLZrG312BHYgVLLvw/LFux5rTvJyIiIiIiIiKnRcGoyEzU1XUIeJq2tsog8NChAvl88rid4I/mefDQQ41cccXb8H2fZ575HqtWbSV41I7t2Szs2LGW1avfcsJx+L7P+vX/yznn/LKi0jSfh8cfX8ull376lKo0j7Vt26P4/u+xalWx4vju3SYjI3/OuefeMHH/7373D7noon+ira2yjwceCNHc/CCrVl142vc/WlfnIe7/6vu5rOMh5jUff97z4O6nTYxQK9e/oYl03uDx3bXY7W9nyepr2fjkLxhNDBGNVXH+JW+itbX11xqPiIiIiIiIiAAKRkVmpmKxyJYtD3D22cmK41u2pJk7N0N19YmvX7++jje84R0888w3OOusHRVrd2YysGvXBZx11nUn7MP3fR5++D+54IKnjwtVN2y4iIsu+tivFIquX/8t5sz5V2bPrvw4evrpALW1n2fRorMAcF2Xr371Bt797vuJRCr7+OY3W7j88g20traf9v2ns2vnczz6rfdy3ZJNNE+xhIFbhB89YdHQ1M7la+swDIONe2H/QJTVyxqprwnxyM5qRorzeNt7PvUrbUIlIiIiIiIiIhMUjIrMVIcO7Saff55FiwoTx557LkNLS/q4zY+O9cgjdQSDOdas2VMRiqZSsH//JaxcefUJr/c8j4cf/kcuumjbcddv2XI1F174vtN+Ht/3+clP/pYLL/zRcWunPvBADatW3U5z8ywARkaGueOOc/ngB/dWtHNd+MpXzuG97/3lxIZML7bNzzzK5js/wFvO3EP18RvTk87Bj55wWLRgNutWlBPqwTF4erdNPF7P3PYafrZ7JR/8xB/9SsGxiIiIiIiIiAAKRkVmtgMHdjM0tIdZs9JUV3uMjnocOJDg/PNLE23SaY+NG9MkEnlMEwoFA9c1efObswQCk32NjcHhw1eyfPllJ7xnsVjk4Yf/hssu28XRS4eOjsL27W9m3bpbT/s5XNflrrt+ixtueKZiTJ4Hd945l6uv/jrRaHlB1T17nue55y7gzW8eq+hjaAjuvvt9vO99X31ZAsdHH7qTg7/4BDef3UM4ePz54STc+XSQtSvnsHJheewlD57eZbCjt4all/4F5194EQAHDx7k7h98kXyyG9+HeMN8brzlw5p2LyIiIiIiIjI9BaMiM53ruvT2dpJKJbBtm87OvZxzThexGHR2FtiwIcGFFxZpaiq39zzYvRu2bYPrroNwuLxbfV/fm1i69MTrcbquyyOP/AVXXHGw4vjQEOzf/w7Wrr3htMefTI7xi1+8h5tu6qw4ns3CffddwI03/tvELu+PPfZjLOsW1q0rVbTdudNg9+5/5frrP33a9/91+L7Pz+76CmMbP8NbzklgT7HHVNcg3L8lwqXnzmV+2+T0+c4h2NQ9jx0DHdTwHLecO0zt+GZa/Qn47pONzF3zbm54y7tfnocREREREREReW1RMCoilXK5HOvX38WSJQM8+2w/N95YZKpN4UdH4ZFH4IILYHj4JhYtOvek/T7xxJ9y2WU9Fcd7e6Gv7/2ceeZVpz3W7u6D7NjxXi6/PFVxfHAQNmx4J2984+9OVH/+8Id/xZo1f8m8eZV9/PKXAaLRe1i79vTv/2IplUrc9e1/xtn/11y/NjNlm93dsH5HnDddNIeW+nJZ7P0by0H1tWun7vebj9aw+NK/4pzz3vBSDV1ERERERETktUrBqIgcr1AocO+932Hdup20tEzf7uGHobr6jaxefdEJ+8tk0jzzzB9z8cWDFce7ugwSiY+zfPnpB3fbtz9NLvcpVq+u3Hl+3z6Dvr4/5vzzbwbKoeNXv3orb3vbHVRVVfbx3e/Wc/75TzF79oLTvv9LIZ/P87e//yYubXuIy1ZN/XG6eR9sPlTDTZd28Ll7Hf70HdP3V/LgH356Fn/8N19+iUYsIiIiIiIi8pp1SsGo+VKPQkReXQKBAJaVOWEoCnDWWTA2NnrCNslkkk2b/uC4UPTgQZN0+tO/Uij6+OPfJxT6zeNC0WefdcjnPz8RiiaTY3zlK6v4wAcqQ9FSCf7nf1Zy3XUHXjWhKEAwGOTdn/pvMvXv4bbH57Fh1/FtzpoP7780wfPPb6OYOkQ2703bn2VCrdPD2NjYtG1EREREREREZHr2Kz0AEXn5mWbppG3CYcjns9OeTyRG2Lnzj7jwwsqp7nv2WFjWH7B48YrTGpPv+9x33z9z3nnfob6+8tzPfx7njDNup7W1A4CDB/fw9NPn8ZGPDFe0Gx2F73//Fj784e9gTbVGwCts4cLFPHz/At52bRE3dxZffeBpzpvbxRmzK9u9YTm8Yfkg9z8xxFixmTdf2opjH//HrupIgVQqRdWx5bIiIiIiIiIiclKqGBWZgUqlIKWTZKODgwa1tc1TnhsY6GXv3t/nvPMqQ9EdO2yCwT9j3rzTC0WLxSI/+tFvcfnlx4eiP/5xB+eee/dEKLphw/0cOnQGt95aGYru2wc///n/5cMf/v6rMhQFMAyDd7zvd/j20ws4NFrDe992CfHZN/Llhxo52H98+2vW+Lx1bS/3PLiFu9f34XnlKfi9Qy7/8d0ufvDTnXz39s8xMDDwMj+JiIiIiIiIyGuf1hgVmYG2b98K3MGyZYVp29x7bz2XXfYJwuFwxfHe3sP09/8Fq1blK45v2xagoeGvaG09pvzxJFKpJA888H5uuukA5lF/qsnn4Z57zuHGGz+L4zgA3HXXP7Ns2R+waFHlx9ETT9jADzn//BtP696vlFKpxJaNG9i28ReY5PH8AKF4M+mdn+WGM7bSWH38NQUXvvuoyUPbQrTVubznUpe2BjjUD994ogO76Sr+/O+++KoNhUVEREREREReRtp8SUSmViqVuPPOr3DZZQeorT3+/PPPBxkdPZ8LLqjczb2zcy/J5F+zbFnl+p+bNoVob/9/NDa2ntY4enu72LbtPVx1VeVapiMj8MQTt3DttX+EYRh4nsfXvvZe3vzmb1JXV9nHHXdUs2rV4yxYcMZp3fvV6tmnHua5ez7EW87aR1Wk8tyn/wfedwWsXnj8dU/tcvjR3lv4h//81sszUBEREREREZFXLwWjIjK9XC7Hgw9+j2i0mzPPTBIOw+CgydattdTWrmTduisYGxvD932qqqo4dGgXxeLfs2hRZSi6YUOUhQv/ltrahtO6/86dGxkb+yTnnFNZtXrggEFn52d4wxvKW7JnMhluv/0iPvjBjYwXjgLgefCVryzi1lufprq65ld6D17NHnnwh+x78CP8xgUjBB3Yuh9+uQ0+dYKi2H+4s5lbfv8xFix49Ww6JSIiIiIiIvIKUDAqIic3PDzM888/TT6fpba2iWXLVvPoo/dx+PAzNDaOYRjQ1WUTjw9y660QCExe+8QTVSxf/vdUVU0x9/sEnnzyThoa/i8LF1Z+rGzebOM4/87y5ecD0N19mIcfXss731m5AGc6Dd/61pv4wAfuxLZfv3vI+b7PT37weVKbf5+NOzP8yTugOjp9+/4EfG7TB/irf/zKyzZGERERERERkVchBaMicnqKxSK33/7PvOEN24+rDD18GO69F97/fggG4ZFH6jj77L8nGj1BUjeF++//d9asuZ3GxsrjDz0UY9Gir9HePg+AzZsfYWjoaq64onIt08OH4fHH/4i3v/3vTvv5XquKxSLvv2kx3/it/Sdt+zs/upp/+9/7X4ZRiYiIiIiIiLxqnVIw+vottRKR0/bgg3dMGYoCdHTATTfB3XdDfX0z55//t4RCoVPuu7yu6e9x7bXrOWY/J+68s5VLL/3GxJT4n/70C3R0/CZXXFH595hnnrFIpW7n7W//jdN+ttcy27apb11MqbSfE+2tVHDBtMLTNxARERERERGRCebJm4jITOD7Pl1dz04Zih7R0gIDAzbnnvt/TysUzWQy3Hnnb/DmN1eGooUC/PCHZ/GmN91BdXUNvu9z220fZ82aT7B8eWUoes89MWKxDVx66cwKRY+4/Nr38dNNwRO2uePpKG+6+WMv04hEREREREREXtsUjIoIACMjIzQ1JU/absUKm76+vlPud2Cgl/Xrb+Tmm/diHvWJMzoK999/Izff/CUcxyGfz/PFL17E29/+PzQ1Vfbx1a/O5vzz97N06epTvu/rzXU33cp3nllEOjf1+bEM3PvCEi6/6o0v78BEREREREREXqMUjIoIAJ7nVQSX07GscttTsWfPNvbuvZlrrhmpOH74MGze/H+44Ya/wDAMBgb6+O53F/Kxjz1G8KiiyFwO/ud/LuVd79pNff3p7Xr/emPbNn/9b3fyia+dwVM7LY4sD+378OjzFr91+0r+9rN3YRintIyKiIiIiIiIyIynNUZFBICamhr6+6NA4oTt9u+v5qyzZp20v2eeuY94/M9Zt64yRN22zcbz/pFLLrkEgOeff5pDhy7jve/NVLTr7YUHH/wUH/3ofyjsGzdv/ny+8O0NfPu2z3P7d36EY+QoeCHOu/QdfPE7Hzmt5Q1EREREREREZjoFoyIClCsSa2vPoLu7i1nT5J6jo2AYc4hEIhXHfd/H8zys8Z2BHnjg86xc+SVaWiqvX78+wpw5X2H27EUA/Pznt1FX9wGuvbYyPN261aSv74u8+90fenEe7nUkEonwoY//Hnz8917poYiIiIiIiIi8phm+75+8VdkpNxSR16ZcLsfXvvZ33HzznuPW+RwdhW99q513v/tPicfjAGzatIH77/8KltWNbZfIZKLkci6//dsHaWysvP6ee5q58MJvUFtbh+/7fPvbn+GSS/6VtrbKdj/7WYjW1l+wcuX5L+GTioiIiIiIiMjr2ClNPVUwKiIVstksd9/9NXK5PSxaNIJhwN691cBsbrjhQ1RVVQHwgx/8L4XCd7jllgSBwOT1e/fC178Ov/d7UF0Nrgt33bWC66//IsFgENd1+epXr+Pd736AYwpP+cY3Wrnyyg20tByTloqIiIiIiIiInDoFoyLyq8vlchw+fBjf92lrayMajU6ce+GFbWzY8Gne977ElNcODcGXvwy33urypS8tZPXqt7J27YVUV1dxxx3n8MEP7qtoXyjAV796Hu9970OEw+GX8rFERERERERE5PXvlIJRrTEqIlMKhUIsWrRoynP33vslPvnJxLTXGkaRxx8/QHe3yY03bsO27+C22xrYtm2Yf/iHyvVEBwfhnns+wEc/+mVtsiQiIiIiIiIiLxsFoyJy2ny/i+kKO0dHS3z607v5n//J0tw8efziiwfJZOC3fxv+9E9h7lzYscNg795/5/3v/+2XZdwiIiIiIiIiIkeYr/QAROS1xzSnX1njP/+zi7/8y8pQ9IhIBP7jP+Bf/gUefjhAOv0A112nUFREREREREREXn6qGBWR05bLxfB9OHbme6nk09OTZP786a+NRiEQMKiufpTVq895aQcqIiIiIiIiIjINVYyKyGk7++ybePLJ4/+uMjhYpKOjdNLrL7zQYWws81IMTURERERERETklCgYFZHTduWVN/Lgg2exf3/lR0ggYJDLnfz6fN4iEAi8RKMTERERERERETk5TaUXkdNm2zZ/+Ief5ctf/hsymc1ccEE34bDHzp31PPtsFb4/fNw0+6M99lgHN9+85uUbsIiIiIiIiIjIMRSMisivJBAI8IlP/BXpdJqNG59mcDDLmjWLKBYf4K67fp+bbpp6qvxzz1k0N19GMBh8mUcsIiIiIiIiIjLJ8P3pd5c+xik3FJGZy/d9/uRP3suZZ97BrbemMc0jx2H9eptvfOMi/uu/fqqp9CIiIiIiIiLyUjnBPNajGikYFZEXm+/7/PjH3+T++z9PONyNZfmMjdVz3nnv5H3v+z/YtorVRUREREREROQlo2BURF55nufh+z6WZb3SQxERERERERGRmUHBqIiIiIiIiIiIiMw4pxSMmi/1KERERERERERERERebRSMioiIiIiIiIiIyIyjYFRERERERERERERmHAWjIiIiIiIiIiIiMuMoGBUREREREREREZEZR8GoiIiIiIiIiIiIzDgKRkVERERERERERGTGUTAqIiIiIiIiIiIiM46CUREREREREREREZlxFIyKiIiIiIiIiIjIjKNgVERERERERERERGYcBaMiIiIiIiIiIiIy4ygYFRERERERERERkRlHwaiIiIiIiIiIiIjMOApGRUREREREREREZMZRMCoiIiIiIiIiIiIzjoJRERERERERERERmXEUjIqIiIiIiIiIiMiMo2BUREREREREREREZhwFoyIiIiIiIiIiIjLjKBgVERERERERERGRGUfBqIiIiIiIiIiIiMw4CkZFRERERERERERkxlEwKiIiIiIiIiIiIjOOglERERERERERERGZcRSMioiIiIiIiIiIyIyjYFRERERERERERERmHAWjIiIiIiIiIiIiMuMoGBUREREREREREZEZR8GoiIiIiIiIiIiIzDgKRkVERERERERERGTGUTAqIiIiIiIiIiIiM46CUREREREREREREZlxFIyKiIiIiIiIiIjIjKNgVERERERERERERGYcBaMiIiIiIiIiIiIy4ygYFRERERERERERkRlHwaiIiIiIiIiIiIjMOApGRUREREREREREZMZRMCoiIiIiIiIiIiIzjoJRERERERERERERmXEUjIqIiIiIiIiIiMiMo2BUREREREREREREZhwFoyIiIiIiIiIiIjLjKBgVERERERERERGRGUfBqIiIiIiIiIiIiMw4CkZFRERERERERERkxlEwKiIiIiIiIiIiIjOOglERERERERERERGZcRSMioiIiIiIiIiIyIyjYFRERERERERERERmHAWjIiIiIiIiIiIiMuMoGBUREREREREREZEZR8GoiIiIiIiIiIiIzDgKRkVERERERERERGTGUTAqIiIiIiIiIiIiM46CUREREREREREREZlxFIyKiIiIiIiIiIjIjKNgVERERERERERERGYcBaMiIiIiIiIiIiIy4ygYFRERERERERERkRlHwaiIiIiIiIiIiIjMOApGRUREREREREREZMZRMCoiIiIiIiIiIiIzjoJRERERERERERERmXEUjIqIiIiIiIiIiMiMo2BUREREREREREREZhwFoyIiIiIiIiIiIjLjKBgVERERERERERGRGUfBqIiIiIiIiIiIiMw4CkZFRERERERERERkxlEwKiIiIiIiIiIiIjOOglERERERERERERGZcRSMioiIiIiIiIiIyIyjYFRERERERERERERmHAWjIiIiIiIiIiIiMuMoGBUREREREREREZEZR8GoiIiIiIiIiIiIzDgKRkVERERERERERGTGUTAqIiIiIiIiIiIiM46CUREREREREREREZlxFIyKiIiIiIiIiIjIjKNgVERERERERERERGYcBaMiIiIiIiIiIiIy4ygYFRERERERERERkRlHwaiIiIiIiIiIiIjMOApGRUREREREREREZMZRMCoiIiIiIiIiIiIzjoJRERERERERERERmXEUjIqIiIiIiIiIiMiMo2BUREREREREREREZhwFoyIiIiIiIiIiIjLjKBgVERERERERERGRGUfBqIiIiIiIiIiIiMw4CkZFRERERERERERkxlEwKiIiIiIiIiIiIjOOglERERERERERERGZcRSMioiIiIiIiIiIyIyjYFRERERERERERERmHAWjIiIiIiIiIiIiMuMoGBUREREREREREZEZR8GoiIiIiIiIiIiIzDgKRkVERERERERERGTGUTAqIiIiIiIiIiIiM46CUREREREREREREZlxFIyKiIiIiIiIiIjIjKNgVERERERERERERGYcBaMiIiIiIiIiIiIy4ygYFRERERERERERkRlHwaiIiIiIiIiIiIjMOApGRUREREREREREZMZRMCoiIiIiIiIiIiIzjoJRERERERERERERmXEUjIqIiIiIiIiIiMiMo2BUREREREREREREZhwFoyIiIiIiIiIiIjLjKBgVERERERERERGRGUfBqIiIiIiIiIiIiMw4CkZFRERERERERERkxlEwKiIi8v+3a4c2AMAwEMRUKfvP3BkKGnI2fhB8CgAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnCKAAAAACQI4wCAAAAADnzsD3frgAAAAAAWORjFAAAAADIEUYBAAAAgBxhFAAAAADIEUYBAAAAgBxhFAAAAADIEUYBAAAAgBxhFAAAAADIEUYBAAAAgBxhFAAAAADIubeu9zyJN6iuAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1360x3625.68 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing grid movies to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/grid_movies\n",
-      "Using window size of 128 pixels\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Generating grid movies: 100%|███████████| 30/30 [00:59<00:00,  2.00s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving dendrogram plot to /Users/milagros/Documents/datajoint-elements/element-moseq/data/outbox/kpms_project_tutorial/2024_03_13-17_33_50/similarity_dendrogram\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "kpms_model.GenerateResults.populate()"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 9a1d43b052c9a2586fc198221f06bb46400c5b6b Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 06:41:59 +0100
Subject: [PATCH 78/90] blackformatting `tutorial`

---
 notebooks/tutorial.ipynb | 145 +++++++++++++++++++++------------------
 1 file changed, 80 insertions(+), 65 deletions(-)

diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index 6193d6f..6feeec8 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -140,7 +140,7 @@
     "from pathlib import Path\n",
     "import numpy as np\n",
     "\n",
-    "from element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir \n",
+    "from element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir\n",
     "from element_interface.utils import find_full_path"
    ]
   },
@@ -952,10 +952,7 @@
     }
    ],
    "source": [
-    "(\n",
-    "    dj.Diagram(kpms_pca)\n",
-    "    + dj.Diagram(kpms_model)\n",
-    ")"
+    "(dj.Diagram(kpms_pca) + dj.Diagram(kpms_model))"
    ]
   },
   {
@@ -2551,10 +2548,14 @@
     "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
     "from element_moseq.readers.kpms_reader import load_kpms_dj_config\n",
     "\n",
-    "kpms_project_output_dir = (kpms_pca.PCATask & pca_task_key).fetch1(\"kpms_project_output_dir\")\n",
-    "kpms_project_output_dir = get_kpms_processed_data_dir()/kpms_project_output_dir\n",
+    "kpms_project_output_dir = (kpms_pca.PCATask & pca_task_key).fetch1(\n",
+    "    \"kpms_project_output_dir\"\n",
+    ")\n",
+    "kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir\n",
     "\n",
-    "kpms_dj_config = load_kpms_dj_config(kpms_project_output_dir.as_posix(), check_if_valid=False, build_indexes=False)\n",
+    "kpms_dj_config = load_kpms_dj_config(\n",
+    "    kpms_project_output_dir.as_posix(), check_if_valid=False, build_indexes=False\n",
+    ")\n",
     "pca = load_pca(kpms_project_output_dir.as_posix())\n",
     "\n",
     "plot_scree(pca, project_dir=kpms_project_output_dir.as_posix())\n",
@@ -2901,11 +2902,11 @@
    ],
    "source": [
     "fps = (kpms_pca.LoadKeypointSet & pca_task_key).fetch1(\"average_frame_rate\")\n",
-    "kappa_min = (12 / fps) * 1000 #ms\n",
-    "kappa_max = 1e4 #ms \n",
+    "kappa_min = (12 / fps) * 1000  # ms\n",
+    "kappa_max = 1e4  # ms\n",
     "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
     "kappa_range = np.round(kappa_range).astype(int)\n",
-    "print(['kappa = {:.2f} ms'.format(x) for x in kappa_range])\n"
+    "print([\"kappa = {:.2f} ms\".format(x) for x in kappa_range])"
    ]
   },
   {
@@ -2985,13 +2986,16 @@
     }
    ],
    "source": [
-    "prefitting_keys = [{\n",
-    "    **pca_task_key,\n",
-    "    'pre_latent_dim': 4,\n",
-    "    'pre_kappa': int(i),\n",
-    "    'pre_num_iterations': 5,\n",
-    "    'pre_fitting_desc': f\"Testing Pre-fitting task {c}\"\n",
-    "} for c, i in enumerate(kappa_range, start=1)]\n",
+    "prefitting_keys = [\n",
+    "    {\n",
+    "        **pca_task_key,\n",
+    "        \"pre_latent_dim\": 4,\n",
+    "        \"pre_kappa\": int(i),\n",
+    "        \"pre_num_iterations\": 5,\n",
+    "        \"pre_fitting_desc\": f\"Testing Pre-fitting task {c}\",\n",
+    "    }\n",
+    "    for c, i in enumerate(kappa_range, start=1)\n",
+    "]\n",
     "\n",
     "prefitting_keys"
    ]
@@ -3429,14 +3433,15 @@
    "outputs": [],
    "source": [
     "# modify kappa to maintain the desired syllable time-scale\n",
-    "full_fitting_key = ({**pca_task_key,\n",
-    "                      'full_latent_dim': 4,\n",
-    "                      'full_kappa': 10000,\n",
-    "                      'full_num_iterations':5,\n",
-    "                      'full_fitting_desc':\"Fitting task with kappa = 10000 ms\"\n",
-    "})\n",
+    "full_fitting_key = {\n",
+    "    **pca_task_key,\n",
+    "    \"full_latent_dim\": 4,\n",
+    "    \"full_kappa\": 10000,\n",
+    "    \"full_num_iterations\": 5,\n",
+    "    \"full_fitting_desc\": \"Fitting task with kappa = 10000 ms\",\n",
+    "}\n",
     "\n",
-    "kpms_model.FullFittingTask.insert1(full_fitting_key, skip_duplicates=True)                                 "
+    "kpms_model.FullFittingTask.insert1(full_fitting_key, skip_duplicates=True)"
    ]
   },
   {
@@ -3452,14 +3457,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "full_fitting_key_2 = ({**pca_task_key,\n",
-    "                      'full_latent_dim': 4,\n",
-    "                      'full_kappa': 5000,\n",
-    "                      'full_num_iterations':5,\n",
-    "                      'full_fitting_desc':\"Fitting task with kappa = 5000 ms\"\n",
-    "})\n",
+    "full_fitting_key_2 = {\n",
+    "    **pca_task_key,\n",
+    "    \"full_latent_dim\": 4,\n",
+    "    \"full_kappa\": 5000,\n",
+    "    \"full_num_iterations\": 5,\n",
+    "    \"full_fitting_desc\": \"Fitting task with kappa = 5000 ms\",\n",
+    "}\n",
     "\n",
-    "kpms_model.FullFittingTask.insert1(full_fitting_key_2, skip_duplicates=True)    "
+    "kpms_model.FullFittingTask.insert1(full_fitting_key_2, skip_duplicates=True)"
    ]
   },
   {
@@ -3795,11 +3801,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 10000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
-    "kpms_model.Model.insert1({\n",
-    "    \"model_name\" : model_name,\n",
-    "    \"latent_dim\" :latent_dim,\n",
-    "    \"kappa\" : kappa}, skip_duplicates=True)"
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 10000\").fetch1(\n",
+    "    \"model_name\", \"full_latent_dim\", \"full_kappa\"\n",
+    ")\n",
+    "kpms_model.Model.insert1(\n",
+    "    {\"model_name\": model_name, \"latent_dim\": latent_dim, \"kappa\": kappa},\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
@@ -3808,11 +3816,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 5000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
-    "kpms_model.Model.insert1({\n",
-    "    \"model_name\" : model_name,\n",
-    "    \"latent_dim\" :latent_dim,\n",
-    "    \"kappa\" : kappa}, skip_duplicates=True)"
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 5000\").fetch1(\n",
+    "    \"model_name\", \"full_latent_dim\", \"full_kappa\"\n",
+    ")\n",
+    "kpms_model.Model.insert1(\n",
+    "    {\"model_name\": model_name, \"latent_dim\": latent_dim, \"kappa\": kappa},\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {
@@ -3987,11 +3997,16 @@
     "\n",
     "checkpoint_paths = []\n",
     "for model_name in model_names:\n",
-    "    checkpoint_paths.append(get_kpms_processed_data_dir()/Path(model_name)/\"checkpoint.h5\")\n",
-    "checkpoint_paths    \n",
+    "    checkpoint_paths.append(\n",
+    "        get_kpms_processed_data_dir() / Path(model_name) / \"checkpoint.h5\"\n",
+    "    )\n",
+    "checkpoint_paths\n",
     "\n",
     "from keypoint_moseq import expected_marginal_likelihoods, plot_eml_scores\n",
-    "eml_scores, eml_std_errs = expected_marginal_likelihoods(checkpoint_paths=checkpoint_paths)\n",
+    "\n",
+    "eml_scores, eml_std_errs = expected_marginal_likelihoods(\n",
+    "    checkpoint_paths=checkpoint_paths\n",
+    ")\n",
     "best_model = model_names[np.argmax(eml_scores)]\n",
     "print(f\"Best model: {best_model}\")\n",
     "\n",
@@ -4060,8 +4075,12 @@
     "kpset_videos_dir = (kpms_pca.KeypointSet & pca_task_key).fetch1(\"kpset_videos_dir\")\n",
     "kpset_videos_dir = find_full_path(get_kpms_root_data_dir(), kpset_videos_dir)\n",
     "root_dir = get_kpms_root_data_dir()[0]\n",
-    "video_extensions = ['.mp4']\n",
-    "video_names = [file.relative_to(root_dir) for file in kpset_videos_dir.rglob('*') if file.suffix.lower() in video_extensions]\n",
+    "video_extensions = [\".mp4\"]\n",
+    "video_names = [\n",
+    "    file.relative_to(root_dir)\n",
+    "    for file in kpset_videos_dir.rglob(\"*\")\n",
+    "    if file.suffix.lower() in video_extensions\n",
+    "]\n",
     "video_names = [str(name) for name in video_names]\n",
     "video_names"
    ]
@@ -4096,13 +4115,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "recording_key = {\n",
-    "    **key, \n",
-    "    \"recording_id\": 1, \n",
-    "    \"format_method\":\"deeplabcut\"\n",
-    "    }\n",
-    "kpms_model.VideoRecording.insert1(recording_key,\n",
-    "                                  skip_duplicates=True)"
+    "recording_key = {**key, \"recording_id\": 1, \"format_method\": \"deeplabcut\"}\n",
+    "kpms_model.VideoRecording.insert1(recording_key, skip_duplicates=True)"
    ]
   },
   {
@@ -4118,12 +4132,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for idx,video_name in enumerate(video_names):\n",
-    "    kpms_model.VideoRecording.File.insert1(dict(\n",
-    "                                **recording_key,\n",
-    "                                file_id = idx,\n",
-    "                                file_path = video_name),\n",
-    "                                skip_duplicates=True)"
+    "for idx, video_name in enumerate(video_names):\n",
+    "    kpms_model.VideoRecording.File.insert1(\n",
+    "        dict(**recording_key, file_id=idx, file_path=video_name), skip_duplicates=True\n",
+    "    )"
    ]
   },
   {
@@ -4322,7 +4334,7 @@
     }
    ],
    "source": [
-    "kpms_model.InferenceTask.heading\n"
+    "kpms_model.InferenceTask.heading"
    ]
   },
   {
@@ -4344,13 +4356,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_model.InferenceTask.insert1({\n",
+    "kpms_model.InferenceTask.insert1(\n",
+    "    {\n",
     "        **recording_key,\n",
-    "        \"model_name\"   : model_name,\n",
+    "        \"model_name\": model_name,\n",
     "        \"inference_output_dir\": \"inference_output\",\n",
     "        \"inference_desc\": \"Inference task for the tutorial\",\n",
-    "        \"num_iterations\": 5, # for tutorial purposes.\n",
-    "}, skip_duplicates=True)"
+    "        \"num_iterations\": 5,  # for tutorial purposes.\n",
+    "    },\n",
+    "    skip_duplicates=True,\n",
+    ")"
    ]
   },
   {

From 5bb5c9f4b4bd3cfb4496b7360848e2f0a99efda2 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 06:42:55 +0100
Subject: [PATCH 79/90] tutorial black

---
 notebooks/tutorial.ipynb | 280 +++++++++++++++++++++++++++------------
 1 file changed, 193 insertions(+), 87 deletions(-)

diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb
index 6feeec8..c5148c1 100644
--- a/notebooks/tutorial.ipynb
+++ b/notebooks/tutorial.ipynb
@@ -140,7 +140,7 @@
     "from pathlib import Path\n",
     "import numpy as np\n",
     "\n",
-    "from element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir\n",
+    "from element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir \n",
     "from element_interface.utils import find_full_path"
    ]
   },
@@ -952,7 +952,10 @@
     }
    ],
    "source": [
-    "(dj.Diagram(kpms_pca) + dj.Diagram(kpms_model))"
+    "(\n",
+    "    dj.Diagram(kpms_pca)\n",
+    "    + dj.Diagram(kpms_model)\n",
+    ")"
    ]
   },
   {
@@ -2548,14 +2551,10 @@
     "from keypoint_moseq import load_pca, plot_scree, plot_pcs\n",
     "from element_moseq.readers.kpms_reader import load_kpms_dj_config\n",
     "\n",
-    "kpms_project_output_dir = (kpms_pca.PCATask & pca_task_key).fetch1(\n",
-    "    \"kpms_project_output_dir\"\n",
-    ")\n",
-    "kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir\n",
+    "kpms_project_output_dir = (kpms_pca.PCATask & pca_task_key).fetch1(\"kpms_project_output_dir\")\n",
+    "kpms_project_output_dir = get_kpms_processed_data_dir()/kpms_project_output_dir\n",
     "\n",
-    "kpms_dj_config = load_kpms_dj_config(\n",
-    "    kpms_project_output_dir.as_posix(), check_if_valid=False, build_indexes=False\n",
-    ")\n",
+    "kpms_dj_config = load_kpms_dj_config(kpms_project_output_dir.as_posix(), check_if_valid=False, build_indexes=False)\n",
     "pca = load_pca(kpms_project_output_dir.as_posix())\n",
     "\n",
     "plot_scree(pca, project_dir=kpms_project_output_dir.as_posix())\n",
@@ -2902,11 +2901,11 @@
    ],
    "source": [
     "fps = (kpms_pca.LoadKeypointSet & pca_task_key).fetch1(\"average_frame_rate\")\n",
-    "kappa_min = (12 / fps) * 1000  # ms\n",
-    "kappa_max = 1e4  # ms\n",
+    "kappa_min = (12 / fps) * 1000 #ms\n",
+    "kappa_max = 1e4 #ms \n",
     "kappa_range = np.logspace(np.log10(kappa_min), np.log10(kappa_max), num=3)\n",
     "kappa_range = np.round(kappa_range).astype(int)\n",
-    "print([\"kappa = {:.2f} ms\".format(x) for x in kappa_range])"
+    "print(['kappa = {:.2f} ms'.format(x) for x in kappa_range])\n"
    ]
   },
   {
@@ -2986,16 +2985,13 @@
     }
    ],
    "source": [
-    "prefitting_keys = [\n",
-    "    {\n",
-    "        **pca_task_key,\n",
-    "        \"pre_latent_dim\": 4,\n",
-    "        \"pre_kappa\": int(i),\n",
-    "        \"pre_num_iterations\": 5,\n",
-    "        \"pre_fitting_desc\": f\"Testing Pre-fitting task {c}\",\n",
-    "    }\n",
-    "    for c, i in enumerate(kappa_range, start=1)\n",
-    "]\n",
+    "prefitting_keys = [{\n",
+    "    **pca_task_key,\n",
+    "    'pre_latent_dim': 4,\n",
+    "    'pre_kappa': int(i),\n",
+    "    'pre_num_iterations': 5,\n",
+    "    'pre_fitting_desc': f\"Testing Pre-fitting task {c}\"\n",
+    "} for c, i in enumerate(kappa_range, start=1)]\n",
     "\n",
     "prefitting_keys"
    ]
@@ -3433,15 +3429,14 @@
    "outputs": [],
    "source": [
     "# modify kappa to maintain the desired syllable time-scale\n",
-    "full_fitting_key = {\n",
-    "    **pca_task_key,\n",
-    "    \"full_latent_dim\": 4,\n",
-    "    \"full_kappa\": 10000,\n",
-    "    \"full_num_iterations\": 5,\n",
-    "    \"full_fitting_desc\": \"Fitting task with kappa = 10000 ms\",\n",
-    "}\n",
+    "full_fitting_key = ({**pca_task_key,\n",
+    "                      'full_latent_dim': 4,\n",
+    "                      'full_kappa': 10000,\n",
+    "                      'full_num_iterations':5,\n",
+    "                      'full_fitting_desc':\"Fitting task with kappa = 10000 ms\"\n",
+    "})\n",
     "\n",
-    "kpms_model.FullFittingTask.insert1(full_fitting_key, skip_duplicates=True)"
+    "kpms_model.FullFittingTask.insert1(full_fitting_key, skip_duplicates=True)                                 "
    ]
   },
   {
@@ -3457,15 +3452,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "full_fitting_key_2 = {\n",
-    "    **pca_task_key,\n",
-    "    \"full_latent_dim\": 4,\n",
-    "    \"full_kappa\": 5000,\n",
-    "    \"full_num_iterations\": 5,\n",
-    "    \"full_fitting_desc\": \"Fitting task with kappa = 5000 ms\",\n",
-    "}\n",
+    "full_fitting_key_2 = ({**pca_task_key,\n",
+    "                      'full_latent_dim': 4,\n",
+    "                      'full_kappa': 5000,\n",
+    "                      'full_num_iterations':5,\n",
+    "                      'full_fitting_desc':\"Fitting task with kappa = 5000 ms\"\n",
+    "})\n",
     "\n",
-    "kpms_model.FullFittingTask.insert1(full_fitting_key_2, skip_duplicates=True)"
+    "kpms_model.FullFittingTask.insert1(full_fitting_key_2, skip_duplicates=True)    "
    ]
   },
   {
@@ -3801,13 +3795,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 10000\").fetch1(\n",
-    "    \"model_name\", \"full_latent_dim\", \"full_kappa\"\n",
-    ")\n",
-    "kpms_model.Model.insert1(\n",
-    "    {\"model_name\": model_name, \"latent_dim\": latent_dim, \"kappa\": kappa},\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 10000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
+    "kpms_model.Model.insert1({\n",
+    "    \"model_name\" : model_name,\n",
+    "    \"latent_dim\" :latent_dim,\n",
+    "    \"kappa\" : kappa}, skip_duplicates=True)"
    ]
   },
   {
@@ -3816,13 +3808,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 5000\").fetch1(\n",
-    "    \"model_name\", \"full_latent_dim\", \"full_kappa\"\n",
-    ")\n",
-    "kpms_model.Model.insert1(\n",
-    "    {\"model_name\": model_name, \"latent_dim\": latent_dim, \"kappa\": kappa},\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "model_name, latent_dim, kappa = (kpms_model.FullFitting & \"full_kappa = 5000\").fetch1(\"model_name\",\"full_latent_dim\",\"full_kappa\")\n",
+    "kpms_model.Model.insert1({\n",
+    "    \"model_name\" : model_name,\n",
+    "    \"latent_dim\" :latent_dim,\n",
+    "    \"kappa\" : kappa}, skip_duplicates=True)"
    ]
   },
   {
@@ -3997,16 +3987,11 @@
     "\n",
     "checkpoint_paths = []\n",
     "for model_name in model_names:\n",
-    "    checkpoint_paths.append(\n",
-    "        get_kpms_processed_data_dir() / Path(model_name) / \"checkpoint.h5\"\n",
-    "    )\n",
-    "checkpoint_paths\n",
+    "    checkpoint_paths.append(get_kpms_processed_data_dir()/Path(model_name)/\"checkpoint.h5\")\n",
+    "checkpoint_paths    \n",
     "\n",
     "from keypoint_moseq import expected_marginal_likelihoods, plot_eml_scores\n",
-    "\n",
-    "eml_scores, eml_std_errs = expected_marginal_likelihoods(\n",
-    "    checkpoint_paths=checkpoint_paths\n",
-    ")\n",
+    "eml_scores, eml_std_errs = expected_marginal_likelihoods(checkpoint_paths=checkpoint_paths)\n",
     "best_model = model_names[np.argmax(eml_scores)]\n",
     "print(f\"Best model: {best_model}\")\n",
     "\n",
@@ -4075,12 +4060,8 @@
     "kpset_videos_dir = (kpms_pca.KeypointSet & pca_task_key).fetch1(\"kpset_videos_dir\")\n",
     "kpset_videos_dir = find_full_path(get_kpms_root_data_dir(), kpset_videos_dir)\n",
     "root_dir = get_kpms_root_data_dir()[0]\n",
-    "video_extensions = [\".mp4\"]\n",
-    "video_names = [\n",
-    "    file.relative_to(root_dir)\n",
-    "    for file in kpset_videos_dir.rglob(\"*\")\n",
-    "    if file.suffix.lower() in video_extensions\n",
-    "]\n",
+    "video_extensions = ['.mp4']\n",
+    "video_names = [file.relative_to(root_dir) for file in kpset_videos_dir.rglob('*') if file.suffix.lower() in video_extensions]\n",
     "video_names = [str(name) for name in video_names]\n",
     "video_names"
    ]
@@ -4115,8 +4096,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "recording_key = {**key, \"recording_id\": 1, \"format_method\": \"deeplabcut\"}\n",
-    "kpms_model.VideoRecording.insert1(recording_key, skip_duplicates=True)"
+    "recording_key = {\n",
+    "    **key, \n",
+    "    \"recording_id\": 1, \n",
+    "    \"format_method\":\"deeplabcut\"\n",
+    "    }\n",
+    "kpms_model.VideoRecording.insert1(recording_key,\n",
+    "                                  skip_duplicates=True)"
    ]
   },
   {
@@ -4132,10 +4118,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for idx, video_name in enumerate(video_names):\n",
-    "    kpms_model.VideoRecording.File.insert1(\n",
-    "        dict(**recording_key, file_id=idx, file_path=video_name), skip_duplicates=True\n",
-    "    )"
+    "for idx,video_name in enumerate(video_names):\n",
+    "    kpms_model.VideoRecording.File.insert1(dict(\n",
+    "                                **recording_key,\n",
+    "                                file_id = idx,\n",
+    "                                file_path = video_name),\n",
+    "                                skip_duplicates=True)"
    ]
   },
   {
@@ -4334,7 +4322,7 @@
     }
    ],
    "source": [
-    "kpms_model.InferenceTask.heading"
+    "kpms_model.InferenceTask.heading\n"
    ]
   },
   {
@@ -4356,16 +4344,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kpms_model.InferenceTask.insert1(\n",
-    "    {\n",
+    "kpms_model.InferenceTask.insert1({\n",
     "        **recording_key,\n",
-    "        \"model_name\": model_name,\n",
+    "        \"model_name\"   : model_name,\n",
     "        \"inference_output_dir\": \"inference_output\",\n",
     "        \"inference_desc\": \"Inference task for the tutorial\",\n",
-    "        \"num_iterations\": 5,  # for tutorial purposes.\n",
-    "    },\n",
-    "    skip_duplicates=True,\n",
-    ")"
+    "        \"num_iterations\": 5, # for tutorial purposes.\n",
+    "}, skip_duplicates=True)"
    ]
   },
   {
@@ -5113,17 +5098,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  42716,\n",
+       "  42765),\n",
+       " ('21_12_10_def6a_1_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  42084,\n",
+       "  42089),\n",
+       " ('21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  18393,\n",
+       "  18432),\n",
+       " ('22_04_26_cage4_0_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  50910,\n",
+       "  50917),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  14109,\n",
+       "  14135),\n",
+       " ('21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  7634,\n",
+       "  7644),\n",
+       " ('21_12_2_def6a_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  67149,\n",
+       "  67155),\n",
+       " ('21_12_10_def6b_3.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  8500,\n",
+       "  8515),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  14281,\n",
+       "  14287),\n",
+       " ('22_04_26_cage4_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  8129,\n",
+       "  8141),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  16238,\n",
+       "  16242),\n",
+       " ('21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  59947,\n",
+       "  59975),\n",
+       " ('22_04_26_cage4_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  24508,\n",
+       "  24519),\n",
+       " ('22_27_04_cage4_mouse2_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  43048,\n",
+       "  43055),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  58227,\n",
+       "  58237),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  17804,\n",
+       "  17858),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  22076,\n",
+       "  22094),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  49088,\n",
+       "  49091),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  14055,\n",
+       "  14061),\n",
+       " ('22_04_26_cage4_1_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  24676,\n",
+       "  24682),\n",
+       " ('21_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  40065,\n",
+       "  40068),\n",
+       " ('21_12_10_def6a_1_1.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  35168,\n",
+       "  35188),\n",
+       " ('22_04_26_cage4_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  49128,\n",
+       "  49141),\n",
+       " ('21_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000',\n",
+       "  2149,\n",
+       "  2153)]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "instance_syllable_0 = (kpms_model.Inference.GridMoviesSampledInstances & \"syllable = 0\").fetch1(\"instances\")\n",
+    "instance_syllable_0"
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "The instance for syllable 0 is specified as a tuple with the video name, start frame and end frame.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following this tutorial, we have:\n",
+    "\n",
+    "- Covered the essential functionality of `element-moseq`\n",
+    "- Acquired the skills to load the keypoint data and insert metadata into the pipeline\n",
+    "- Learned how to fit a PCA, run the AR-HMM fitting and the Keypoint-SLDS fitting\n",
+    "- Executed and ingested results of the motion sequencing analysis with Keypoint-MoSeq\n",
+    "- Visualized and stored the results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Documentation and DataJoint tutorials\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Detailed [documentation on `element-moseq`](https://datajoint.com/docs/elements/element-moseq/0.1/)\n",
+    "- [General `DataJoint-Python` interactive tutorials](https://github.com/datajoint/datajoint-tutorials), covering fundamentals, such as table tiers, query operations, fetch operations, automated computations with the make function, and more.\n",
+    "- [Documentation for `DataJoint-Python`](https://datajoint.com/docs/core/datajoint-python/0.14/)\n"
+   ]
   }
  ],
  "metadata": {

From b0d182ff5ade38fe8f17797c8a101f56d0f2ab05 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 06:44:20 +0100
Subject: [PATCH 80/90] update `kpms_reader.py`

---
 element_moseq/readers/kpms_reader.py | 51 ++++++++++++++++------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py
index 243be32..058fa27 100644
--- a/element_moseq/readers/kpms_reader.py
+++ b/element_moseq/readers/kpms_reader.py
@@ -6,16 +6,16 @@
 logger = logging.getLogger("datajoint")
 
 
-def generate_dj_config(project_dir, **kwargs):
-    """Generate a `dj_config.yml` file with project settings. Default settings
-    will be used unless overriden by a keyword argument.
+def generate_kpms_dj_config(output_dir, **kwargs):
+    """This function mirrors the behavior of the `generate_config` function from the `keypoint_moseq`
+    package. Nonetheless, it produces a duplicate of the initial configuration file, titled
+    `kpms_dj_config.yml`, in the output directory to maintain the integrity of the original file.
+    This replicated file accommodates any customized project settings, with default configurations
+    utilized unless specified differently via keyword arguments.
 
     Args:
-        project_dir (str): Directory containing the `dj_config.yml` that will be generated.
+        output_dir (str): Directory containing the `kpms_dj_config.yml` that will be generated.
         kwargs (dict): Custom project settings.
-
-    Returns:
-        config (dict): configuration settings
     """
 
     def _build_yaml(sections, comments):
@@ -138,42 +138,49 @@ def _update_dict(new, original):
         ("OTHER", other),
     ]
 
-    with open(os.path.join(project_dir, "dj_config.yml"), "w") as f:
+    with open(os.path.join(output_dir, "kpms_dj_config.yml"), "w") as f:
         f.write(_build_yaml(sections, comments))
 
 
-def load_dj_config(output_dir, check_if_valid=True, build_indexes=True):
+def load_kpms_dj_config(output_dir, check_if_valid=True, build_indexes=True):
     """
-    Load a project `dj_config.yml` file from `output_dir`
+    This function mirrors the functionality of the `load_config` function from the `keypoint_moseq`
+    package. Similarly, this function loads the `kpms_dj_config.yml` from the output directory.
 
     Args:
-        project_dir (str): Directory containing the `dj_config.yml` that will be loaded.
+        output_dir (str): Directory containing the `kpms_dj_config.yml` that will be loaded.
         check_if_valid (bool): default=True. Check if the config is valid using :py:func:`keypoint_moseq.io.check_config_validity`
         build_indexes (bool): default=True. Add keys `"anterior_idxs"` and `"posterior_idxs"` to the config. Each maps to a jax array indexing the elements of `config["anterior_bodyparts"]` and `config["posterior_bodyparts"]` by their order in `config["use_bodyparts"]`
 
     Returns:
-        config (dict): configuration settings
+        kpms_dj_config (dict): configuration settings
     """
 
     from keypoint_moseq import check_config_validity
 
-    config_path = os.path.join(output_dir, "dj_config.yml")
+    config_path = os.path.join(output_dir, "kpms_dj_config.yml")
 
     with open(config_path, "r") as f:
-        config = yaml.safe_load(f)
+        kpms_dj_config = yaml.safe_load(f)
 
     if check_if_valid:
-        check_config_validity(config)
+        check_config_validity(kpms_dj_config)
 
     if build_indexes:
-        config["anterior_idxs"] = jnp.array(
-            [config["use_bodyparts"].index(bp) for bp in config["anterior_bodyparts"]]
+        kpms_dj_config["anterior_idxs"] = jnp.array(
+            [
+                kpms_dj_config["use_bodyparts"].index(bp)
+                for bp in kpms_dj_config["anterior_bodyparts"]
+            ]
         )
-        config["posterior_idxs"] = jnp.array(
-            [config["use_bodyparts"].index(bp) for bp in config["posterior_bodyparts"]]
+        kpms_dj_config["posterior_idxs"] = jnp.array(
+            [
+                kpms_dj_config["use_bodyparts"].index(bp)
+                for bp in kpms_dj_config["posterior_bodyparts"]
+            ]
         )
 
-    if not "skeleton" in config or config["skeleton"] is None:
-        config["skeleton"] = []
+    if not "skeleton" in kpms_dj_config or kpms_dj_config["skeleton"] is None:
+        kpms_dj_config["skeleton"] = []
 
-    return config
+    return kpms_dj_config

From e62092224b2388072fc825ef6b59cdf5006c1b71 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 06:53:35 +0100
Subject: [PATCH 81/90] update `kpms_pca.py`

---
 element_moseq/kpms_pca.py | 100 +++++++++++++++++++++++---------------
 1 file changed, 60 insertions(+), 40 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index aa6e262..9cc3411 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -1,13 +1,15 @@
 from datetime import datetime, timezone
 import inspect
+import os
 from pathlib import Path
 from typing import Optional
-import importlib
-import os
+
 import cv2
-import datajoint as dj
 import numpy as np
 
+import datajoint as dj
+import importlib
+
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import generate_kpms_dj_config, load_kpms_dj_config
 
@@ -101,18 +103,18 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 @schema
 class PoseEstimationMethod(dj.Lookup):
-    """Table for storing the pose estimation methods supported by the keypoint loader of keypoint-moseq package.
+    """Table to store the pose estimation methods supported by the keypoint loader of `keypoint-moseq` package.
 
     Attributes:
-        format_method (str): Pose estimation method.
-        pose_estimation_desc (str): Pose estimation method description with the formats supported.
+        format_method (str): Pose estimation method (e.g. deeplabcut, sleap, etc.)
+        pose_estimation_desc (str): Pose estimation method description with the supported formats.
     """
 
     definition = """ 
     # Parameters used to obtain the keypoints data based on a specific pose estimation method.        
-    format_method           : char(15)         # deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap,
+    format_method           : char(15)         # Supported pose estimation method (deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap)
     ---
-    pose_estimation_desc    : varchar(1000)    # Optional. Pose estimation method description
+    pose_estimation_desc    : varchar(1000)    # Optional. Pose estimation method description with the supported formats.
     """
 
     contents = [
@@ -127,7 +129,7 @@ class PoseEstimationMethod(dj.Lookup):
 
 @schema
 class KeypointSet(dj.Manual):
-    """Table for storing the keypoint data and videos directory to train the model.
+    """Table to store the keypoint data and video set directory to train the model.
 
     Attributes:
         kpset_id (int): Unique ID for each keypoint set.
@@ -138,9 +140,9 @@ class KeypointSet(dj.Manual):
     """
 
     definition = """
-    kpset_id                        : int
+    kpset_id                        : int           # Unique ID for each keypoint set   
     ---
-    -> PoseEstimationMethod
+    -> PoseEstimationMethod                         # Unique format method used to obtain the keypoints data
     kpset_config_dir                : varchar(255)  # Path relative to root data directory where the config file is located
     kpset_videos_dir                : varchar(255)  # Path relative to root data directory where the videos and their keypoints are located
     kpset_desc=''                   : varchar(300)  # Optional. User-entered description
@@ -156,7 +158,7 @@ class VideoFile(dj.Part):
 
         definition = """
         -> master
-        video_id                    : int
+        video_id                    : int           # Unique ID for each video
         ---
         video_path                  : varchar(1000) # Filepath of each video, relative to root data directory
         """
@@ -164,7 +166,7 @@ class VideoFile(dj.Part):
 
 @schema
 class Bodyparts(dj.Manual):
-    """Table for storing the bodyparts used in the analysis.
+    """Table to store the body parts to use in the analysis.
 
     Attributes:
         KeypointSet (foreign key)       : Unique ID for each keypoint set.
@@ -176,20 +178,20 @@ class Bodyparts(dj.Manual):
     """
 
     definition = """
-    -> KeypointSet
-    bodyparts_id                : int
+    -> KeypointSet                              # Unique ID for each keypoint set
+    bodyparts_id                : int           # Unique ID for each bodypart
     ---
     bodyparts_desc=''           : varchar(1000) # Optional. User-entered description.
-    anterior_bodyparts          : blob  # List of strings of anterior bodyparts
-    posterior_bodyparts         : blob  # List of strings of posterior bodyparts
-    use_bodyparts               : blob  # List of strings of bodyparts to be used
+    anterior_bodyparts          : blob          # List of strings of anterior bodyparts
+    posterior_bodyparts         : blob          # List of strings of posterior bodyparts
+    use_bodyparts               : blob          # List of strings of bodyparts to be used
     """
 
 
 @schema
 class PCATask(dj.Manual):
     """
-    Staging table for the PCA task and its output directory.
+    Staging table to define the PCA task and its output directory.
 
     Attributes:
         Bodyparts (foreign key)         : Bodyparts Key
@@ -197,9 +199,9 @@ class PCATask(dj.Manual):
     """
 
     definition = """ 
-    -> Bodyparts
+    -> Bodyparts                                        # Unique ID for each Bodyparts key
     ---
-    kpms_project_output_dir=''          : varchar(255) # KPMS's output directory relative to root
+    kpms_project_output_dir=''          : varchar(255)  # KPMS's output directory relative to root
     """
 
 
@@ -217,12 +219,12 @@ class LoadKeypointSet(dj.Imported):
     """
 
     definition = """
-    -> PCATask
+    -> PCATask                          # Unique ID for each PCATask
     ---
-    coordinates             : longblob # Keypoint coordinates
-    confidences             : longblob # Keypoint confidences                 
-    formatted_bodyparts     : longblob # Formatted bodyparts
-    average_frame_rate      : float    # Average frame rate of the trained videos
+    coordinates             : longblob  # Dictionary mapping filenames to keypoint coordinates as ndarrays of shape (n_frames, n_bodyparts, 2[or 3])
+    confidences             : longblob  # Dictionary mapping filenames to `likelihood` scores as ndarrays of shape (n_frames, n_bodyparts)           
+    formatted_bodyparts     : longblob  # List of bodypart names. The order of the names matches the order of the bodyparts in `coordinates` and `confidences`.
+    average_frame_rate      : float     # Average frame rate of the trained videos
     """
 
     def make(self, key):
@@ -235,6 +237,7 @@ def make(self, key):
             key (dict): Primary key from the PCATask table.
 
         Raises:
+            NotImplementedError: `format_method` is only supported for `deeplabcut`. If support required for another format method, reach out to us.
 
         High-Level Logic:
         1. Fetches the bodyparts, output_dir and keypoint method, and keypoint config and videoset directories.
@@ -254,12 +257,14 @@ def make(self, key):
             "use_bodyparts",
         )
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
-        kpms_project_output_dir = get_kpms_processed_data_dir()/ kpms_project_output_dir
+        kpms_project_output_dir = (
+            get_kpms_processed_data_dir() / kpms_project_output_dir
+        )
 
         format_method, kpset_config_dir, kpset_videos_dir = (KeypointSet & key).fetch1(
             "format_method", "kpset_config_dir", "kpset_videos_dir"
         )
-        
+
         file_paths, video_ids = (KeypointSet.VideoFile & key).fetch(
             "video_path", "video_id"
         )
@@ -282,16 +287,26 @@ def make(self, key):
             anterior_bodyparts=anterior_bodyparts,
             posterior_bodyparts=posterior_bodyparts,
             use_bodyparts=use_bodyparts,
-            
         )
         kpms_config.update(**kpms_dj_config_kwargs_dict)
         generate_kpms_dj_config(kpms_project_output_dir.as_posix(), **kpms_config)
 
-        filepath_patterns = [(kpset_videos_dir / (os.path.splitext(os.path.basename(path))[0] + '*')).as_posix() for path in file_paths]
+        filepath_patterns = [
+            (
+                kpset_videos_dir / (os.path.splitext(os.path.basename(path))[0] + "*")
+            ).as_posix()
+            for path in file_paths
+        ]
 
-        coordinates, confidences, formatted_bodyparts = load_keypoints(
-            filepath_pattern=filepath_patterns, format=format_method
-        )
+        if format_method == "deeplabcut":
+            coordinates, confidences, formatted_bodyparts = load_keypoints(
+                filepath_pattern=filepath_patterns, format=format_method
+            )
+        else:
+            raise NotImplementedError(
+                "The currently supported format method is `deeplabcut`. If you require \
+        support for another format method, please reach out to us at `support at datajoint.com`."
+            )
 
         fps_list = []
         for fp, video_id in zip(file_paths, video_ids):
@@ -322,14 +337,14 @@ class PCAFitting(dj.Computed):
     """
 
     definition = """
-    -> LoadKeypointSet
+    -> LoadKeypointSet                   # LoadKeypointSet Key
     ---
     pca_fitting_time=NULL    : datetime  # datetime of the PCA fitting analysis
     """
 
     def make(self, key):
         """
-        Make function to format the keypoint data, fit the PCA model, and store it as a `pca.p` file in the output directory.
+        Make function to format the keypoint data, fit the PCA model, and store it as a `pca.p` file in the KPMS output directory.
         
         Args:
             key (dict): LoadKeypointSet Key
@@ -345,7 +360,9 @@ def make(self, key):
         """
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
-        kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir
+        kpms_project_output_dir = (
+            get_kpms_processed_data_dir() / kpms_project_output_dir
+        )
 
         from keypoint_moseq import format_data, fit_pca, save_pca
 
@@ -369,7 +386,8 @@ def make(self, key):
 @schema
 class LatentDimension(dj.Imported):
     #     """
-    #     Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) necessary for the model fitting.
+    #     Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) \
+    #     necessary for the model fitting.
     #     The analysis aims to select each of the components that explain the 90% of variance (fixed threshold).
 
     #     Attributes:
@@ -380,10 +398,10 @@ class LatentDimension(dj.Imported):
     #     """
 
     definition = """
-    -> PCAFitting
+-> PCAFitting                                   # PCAFitting Key
     ---
     variance_percentage      : float            # Variance threshold. Fixed value to 0.9
-    latent_dimension         : int              # Number of principal components to explain the variance.
+    latent_dimension         : int              # Number of principal components required to explain the specified variance.
     latent_dim_desc          : varchar(1000)    # Automated description of the computation result.
     """
 
@@ -409,7 +427,9 @@ def make(self, key):
         from keypoint_moseq import load_pca
 
         kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
-        kpms_project_output_dir = get_kpms_processed_data_dir() / kpms_project_output_dir
+        kpms_project_output_dir = (
+            get_kpms_processed_data_dir() / kpms_project_output_dir
+        )
 
         pca = load_pca(kpms_project_output_dir.as_posix())
 

From 4a3a2c201d4aa13631aba5c6395e117620b6dd33 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 07:20:21 +0100
Subject: [PATCH 82/90] update `kpms_model.py`

---
 element_moseq/kpms_model.py | 675 +++++++++++++++++++++++-------------
 1 file changed, 426 insertions(+), 249 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index 4d6805c..2e3727d 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -2,28 +2,19 @@
 import inspect
 import os
 from pathlib import Path
-import pickle
 from typing import Optional
-import importlib
+
+from matplotlib import pyplot as plt
 
 import datajoint as dj
+import importlib
+from datajoint import DataJointError
+
+from element_moseq.kpms_pca import LoadKeypointSet, PCATask
 
-from element_moseq.kpms_pca import PCATask, FormattedDataset
-from .readers.kpms_reader import load_dj_config, generate_dj_config
-from keypoint_moseq import (
-    load_pca,
-    init_model,
-    update_hypparams,
-    fit_model,
-    load_checkpoint,
-    reindex_syllables_in_checkpoint,
-    extract_results,
-    save_results_as_csv,
-    generate_trajectory_plots,
-    generate_grid_movies,
-    plot_similarity_dendrogram,
-    apply_model,
-)
+from element_interface.utils import find_full_path
+from .readers.kpms_reader import load_kpms_dj_config, generate_kpms_dj_config
+from keypoint_moseq import update_hypparams, fit_model, load_checkpoint
 
 
 schema = dj.schema()
@@ -118,131 +109,121 @@ def get_kpms_processed_data_dir() -> Optional[str]:
 
 @schema
 class PreFittingTask(dj.Manual):
-    """Table to specify the parameters for pre-fitting the AR-HMM model and to optionally initialize the model.
+    """Table to specify the parameters for the pre-fitting (AR-HMM) of the model.
 
     Attributes:
-        kpms_pca.PCAFitting (foreign key)   : PCA fitting task
-        pre_latent_dim (int)                : Number of latent dimensions for the AR-HMM pre-fitting
-        pre_kappa (int)                     : Kappa value for the AR-HMM pre-fitting
-        pre_num_iterations (int)            : Number of iterations for the AR-HMM pre-fitting
-        pre_fitting_desc(varchar)           : Description of the pre-fitting task
-        model_initialization (Yes or No)    : Whether to initialize a new AR-HMM model and output_dir
-        model_name_initialization (varchar) : Default name of the chosen initialized model to be pre-fitted (optional)
+        kpms_pca.PCAFitting (foreign key)   : PCA fitting task.
+        pre_latent_dim (int)                : Number of latent dimensions to use for the model pre-fitting.
+        pre_kappa (int)                     : Kappa value to use for the model pre-fitting.
+        pre_num_iterations (int)            : Number of Gibbs sampling iterations to run in the model pre-fitting.
+        pre_fitting_desc(varchar)           : User-defined description of the pre-fitting task.
     """
 
     definition = """
-    -> kpms_pca.PCAFitting
-    pre_latent_dim               : int
-    pre_kappa                    : int
-    pre_num_iterations           : int
+    -> kpms_pca.PCAFitting             # PCAFitting Key
+    pre_latent_dim               : int # Number of latent dimensions to use for the model pre-fitting
+    pre_kappa                    : int # Kappa value to use for the model pre-fitting
+    pre_num_iterations           : int # Number of Gibbs sampling iterations to run in the model pre-fitting.
     ---
-    pre_fitting_desc=''      : varchar(1000)
-    model_initialization="Yes"     :enum("Yes","No") # 'Yes' initialize a new AR-HMM model and output_dir, 'No' will directly pre-fit the model
-    model_name_initialization='' : varchar(100) # Optional. Name of the initialized model to be pre-fitted. Only needed if model_initialization = False
+    pre_fitting_desc=''          : varchar(1000) # User-defined description of the pre-fitting task
     """
 
 
 @schema
 class PreFitting(dj.Computed):
-    """Table for storing the pre-fitted AR-HMM model and its duration of generation.
+    """Automated computation to fit a AR-HMM model.
 
     Attributes:
-        PreFittingTask (foreign key)        : Pre-fitting task
-        model_name (varchar)                : Name of the model
-        pre_fitting_duration (time)         : Duration of generation of the full fitting model
+        PreFittingTask (foreign key)        : PreFittingTask Key.
+        model_name (varchar)                : Name of the model as "kpms_project_output_dir/model_name".
+        pre_fitting_duration (time)         : Time duration of the model fitting computation.
     """
 
     definition = """
-    -> PreFittingTask
+    -> PreFittingTask                           # PreFittingTask Key
     ---
-    model_name=''                : varchar(100) # Name of the model
-    pre_fitting_duration=NULL    : time  # Duration of generation of the full fitting model 
+    model_name=''                : varchar(100) # Name of the model as "kpms_project_output_dir/model_name"
+    pre_fitting_duration=NULL    : time         # Time duration of the model fitting computation
     """
 
     def make(self, key):
         """
-        Make function to pre-fit the AR-HMM model and store the model and its duration of generation.
+        Make function to fit the AR-HMM model using the latent trajectory defined by `model['states']['x'].
 
         Args:
-            key (dict) : dictionary with the primary key of the `PreFittingTask` table
+            key (dict) : dictionary with the `PreFittingTask` Key.
 
         Raises:
 
         High-level Logic:
-        1. Fetch the `output_dir` and parameters specified in the `PreFittingTask` table
-        2. Update `dj_config.yml` with the latent dimensions and kappa
-        3. Load the pca, data, and metadata from files
-        4. Initialize the model if `model_initialization` is set to "Yes", else load the model from the specified `model_name_initialization`
-        5. Update the model with the chosen kappa
-        6. Fit the model for `pre_num_iterations` and save the model in the `output_dir`. This will also:
-            - generates a name for the model and a corresponding directory in `output_dir`
-            - saves a checkpoint every 25 iterations from which fitting can be restarted
-            - plots the progress of fitting every 25 iterations, including:
-                -- the distributions of syllable frequencies and durations for the most recent iteration
-                -- the change in median syllable duration across fitting iterations
-                -- a sample of the syllable sequence across iterations in a random window
-        7. Insert the autogenerated `model_name` and `pre_fitting_duration` into the `PreFitting` table
+        1. Fetch the `kpms_project_output_dir` and the model parameters from the `PreFittingTask` table
+        2. Update the `dj_config.yml` with the selected latent dimension and kappa for the AR-HMM fitting.
+        3. Load the pca model
+        4. Fetch `coordinates` and `confidences` scores to format the data for the model initialization. \
+            # Data - contains the data for model fitting. \
+            # Metadata - contains the recordings and start/end frames for the data.
+        5. Initialize the model that create a `model` dict containing states, parameters, hyperparameters, noise prior, and random seed.
+        6. Update the model dict with the selected kappa for the AR-HMM fitting
+        7. Fit the AR-HMM model using the `pre_num_iterations` and create a subdirectory in `kpms_project_output_dir` with the model's latest checkpoint
+        8. Calculate the duration of the model fitting computation and insert it in the `PreFitting` table
         """
 
-        output_dir = (PCATask & key).fetch1("output_dir")
-        model_initialization, pre_latent_dim, pre_kappa, pre_num_iterations = (
-            PreFittingTask & key
-        ).fetch1(
-            "model_initialization", "pre_latent_dim", "pre_kappa", "pre_num_iterations"
+        kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
+        kpms_project_output_dir = (
+            get_kpms_processed_data_dir() / kpms_project_output_dir
         )
 
-        # Update `dj_config.yml` with the latent dimensions and kappa
-        config = load_dj_config(output_dir, check_if_valid=True, build_indexes=True)
-        config.update(dict(latent_dim=int(pre_latent_dim), kappa=int(pre_kappa)))
-        generate_dj_config(output_dir, **config)
+        pre_latent_dim, pre_kappa, pre_num_iterations = (PreFittingTask & key).fetch1(
+            "pre_latent_dim", "pre_kappa", "pre_num_iterations"
+        )
 
-        # Load the pca, data, and metadata
-        pca = load_pca(output_dir)
+        kpms_dj_config = load_kpms_dj_config(
+            kpms_project_output_dir.as_posix(), check_if_valid=True, build_indexes=True
+        )
+        kpms_dj_config.update(
+            dict(latent_dim=int(pre_latent_dim), kappa=int(pre_kappa))
+        )
+        generate_kpms_dj_config(kpms_project_output_dir.as_posix(), **kpms_dj_config)
 
-        data_path = os.path.join(output_dir, "data.pkl")
-        with open(data_path, "rb") as data_file:
-            data = pickle.load(data_file)
+        from keypoint_moseq import load_pca, format_data, init_model, update_hypparams
 
-        metadata_path = os.path.join(output_dir, "metadata.pkl")
-        with open(metadata_path, "rb") as data_file:
-            metadata = pickle.load(data_file)
+        pca = load_pca(kpms_project_output_dir.as_posix())
 
-        if model_initialization == "Yes":
-            model = init_model(data=data, metadata=metadata, pca=pca, **config)
-        else:
-            model_name = (PreFittingTask & key).fetch1("model_name_initialization")
-            model = load_checkpoint(output_dir, model_name)[0]
+        coordinates, confidences = (LoadKeypointSet & key).fetch1(
+            "coordinates", "confidences"
+        )
+        data, metadata = format_data(coordinates, confidences, **kpms_dj_config)
 
-        # update with the chosen kappa
-        model = update_hypparams(model, kappa=int(pre_kappa))
+        model = init_model(data=data, metadata=metadata, pca=pca, **kpms_dj_config)
 
-        start_time = datetime.utcnow()
+        model = update_hypparams(
+            model, kappa=int(pre_kappa), latent_dim=int(pre_latent_dim)
+        )
 
+        start_time = datetime.now()
         model, model_name = fit_model(
-            model,
-            data,
-            metadata,
-            output_dir,
+            model=model,
+            data=data,
+            metadata=metadata,
+            project_dir=kpms_project_output_dir.as_posix(),
             ar_only=True,
             num_iters=pre_num_iterations,
         )
+        end_time = datetime.now()
 
-        model_path = os.path.join(output_dir + "/" + model_name + "/" + "model.pkl")
-        with open(model_path, "wb") as data_file:
-            pickle.dump(model, data_file)
-
-        end_time = datetime.utcnow()
         duration_seconds = (end_time - start_time).total_seconds()
         hours, remainder = divmod(duration_seconds, 3600)
         minutes, seconds = divmod(remainder, 60)
         duration_formatted = "{:02}:{:02}:{:02}".format(
             int(hours), int(minutes), int(seconds)
         )
-
         self.insert1(
             {
                 **key,
-                "model_name": model_name,
+                "model_name": (
+                    kpms_project_output_dir.relative_to(get_kpms_processed_data_dir())
+                    / model_name
+                ).as_posix(),
                 "pre_fitting_duration": duration_formatted,
             }
         )
@@ -250,234 +231,430 @@ def make(self, key):
 
 @schema
 class FullFittingTask(dj.Manual):
-    """This table is used to specify the parameters for fitting the full model.
-    The full model will generally require a lower value of kappa to yield the same target syllable durations.
+    """Table to specify the parameters for the full fitting of the model. The full model will generally require a lower value of kappa to yield the same target syllable durations.
 
     Attributes:
-        kpms_pca.PCAFitting (foreign key)    : PCA fitting task
-        full_latent_dim (int)                : Number of latent dimensions for the full fitting
-        full_kappa (int)                     : Kappa value for the full fitting
-        full_num_iterations (int)            : Number of iterations for the full fitting
-        full_fitting_desc(varchar)           : Description of the full fitting task
-        task_mode ('trigger' or 'load')      : 'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca
-        sort_syllables (bool)                : Whether to sort syllables by frequency (reindexing; Optional)
-        results_as_csv (bool)                : Whether to save results as csv (Optional)
-        visualizations (bool)                : Whether to save visualizations (Optional)
+        kpms_pca.PCAFitting (foreign key)    : PCAFitting Key.
+        full_latent_dim (int)                : Number of latent dimensions to use for the model full fitting.
+        full_kappa (int)                     : Kappa value to use for the model full fitting.
+        full_num_iterations (int)            : Number of Gibbs sampling iterations to run in the model full fitting.
+        full_fitting_desc(varchar)           : User-defined description of the model full fitting task.
 
     """
 
     definition = """
-    -> kpms_pca.PCAFitting
-    full_latent_dim              : int
-    full_kappa                   : int
-    full_num_iterations          : int
+    -> kpms_pca.PCAFitting                          # PCAFitting Key
+    full_latent_dim              : int              # Number of latent dimensions to use for the model full fitting
+    full_kappa                   : int              # Kappa value to use for the model full fitting
+    full_num_iterations          : int              # Number of Gibbs sampling iterations to run in the model full fitting.
     ---
-    full_fitting_desc=''         : varchar(1000)
-    task_mode                    : enum('trigger', 'load') # 'trigger' train a new full model, 'load' use an existing model and apply it to a different keypoint formatted data and pca
-    sort_syllables               : bool # Whether to sort syllables by frequency (reindexing)
-    results_as_csv               : bool # Whether to save results as csv (Optional)
-    visualizations               : bool # Whether to save visualizations (Optional)
+    full_fitting_desc=''         : varchar(1000)    # User-defined description of the model full fitting task   
     """
 
 
 @schema
 class FullFitting(dj.Computed):
-    """This table is used to fit the full model and store the model and its duration of generation.
+    """Automated computation to fit the full model.
 
     Attributes:
-        FullFittingTask (foreign key)        : Full fitting task
-        full_fitting_duration (time)         : Duration of generation of the full fitting model
+        FullFittingTask (foreign key)        : FullFittingTask Key.
+        model_name                           : varchar(100) # Name of the full-fitted model (output_dir/model_name)
+        full_fitting_duration (time)         : Time duration of the full fitting model
     """
 
     definition = """
-    -> FullFittingTask
+    -> FullFittingTask                           # FullFittingTask Key
     ---
-    full_fitting_duration=NULL    : time  # Duration of generation of the full fitting model 
+    model_name                    : varchar(100) # Name of the full-fitted model (output_dir/model_name)
+    full_fitting_duration=NULL    : time         # Time duration of the full fitting model 
     """
 
     def make(self, key):
         """
-        Make function to fit the full model and store the model and its duration of generation.
+            Make function to fit the full (keypoint-SLDS) model
+
+            Args:
+                key (dict): dictionary with the `FullFittingTask` Key.
+
+            Raises:
+
+            High-level Logic:
+            1. Fetch the `kpms_project_output_dir` and the model parameters from the `FullFittingTask` table
+            2. Update the `dj_config.yml` with the selected latent dimension and kappa for the full-fitting.
+            3. Initialize and fit the full model in a new `model_name` directory
+            4. Load the pca and fetch the `coordinates` and `confidences` scores to format the data for the model initialization
+            5. Initialize the model that create a `model` dict containing states, parameters, hyperparameters, noise prior, and random seed.
+            6. Update the model dict with the selected kappa for the AR-HMM fitting
+            7. Fit the AR-HMM model using the `full_num_iterations` and create a subdirectory in `kpms_project_output_dir` with the model's latest checkpoint
+            8. Reindex syllable labels by their frequency in the most recent model snapshot in a checkpoint file. \
+                This function permutes the states and parameters of a saved checkpoint so that syllables are labeled \
+                in order of frequency (i.e. so that 0 is the most frequent, 1 is the second most, and so on).
+            8. Calculate the duration of the model fitting computation and insert it in the `PreFitting` table
+        """
 
-        Args:
-            key (dict): Primary key from the FullFittingTask table.
+        kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir")
+        kpms_project_output_dir = (
+            get_kpms_processed_data_dir() / kpms_project_output_dir
+        )
 
-        Raises:
+        full_latent_dim, full_kappa, full_num_iterations = (
+            FullFittingTask & key
+        ).fetch1("full_latent_dim", "full_kappa", "full_num_iterations")
 
-        High-level Logic:
-        1. Fetch the `output_dir` and parameters specified in the `FullFittingTask` and `PreFitting` tables
-        2. If `task_mode` is 'trigger':
-        - load the initialized model checkpoint
-        - update it with `full_kappa` to maintain the desired syllable time-scale
-        - fit the model for `full_num_iterations` and save the model in the `output_dir`
-        3. If `task_mode` is 'load':
-        - load the chosen model with its most recent checkpoint, and load the corresponding pca object
-        - apply the model to new keypoint data and save the results
-        """
+        kpms_dj_config = load_kpms_dj_config(
+            kpms_project_output_dir.as_posix(), check_if_valid=True, build_indexes=True
+        )
+        kpms_dj_config.update(
+            dict(latent_dim=int(full_latent_dim), kappa=int(full_kappa))
+        )
+        generate_kpms_dj_config(kpms_project_output_dir.as_posix(), **kpms_dj_config)
 
-        output_dir = (PCATask & key).fetch1("output_dir")
-        try:
-            selected_key = (
-                PreFitting
-                & "pre_latent_dim = {}".format(key["full_latent_dim"])
-                & "pre_kappa = {}".format(key["full_kappa"])
-            )
-            model_name, pre_num_iterations = selected_key.fetch1(
-                "model_name", "pre_num_iterations"
-            )
-        except:
-            print("No prefitting model found")
+        from keypoint_moseq import (
+            load_pca,
+            format_data,
+            init_model,
+            reindex_syllables_in_checkpoint,
+        )
 
-        task_mode, full_kappa, full_num_iterations = (FullFittingTask & key).fetch1(
-            "task_mode", "full_kappa", "full_num_iterations"
+        pca = load_pca(kpms_project_output_dir.as_posix())
+        coordinates, confidences = (LoadKeypointSet & key).fetch1(
+            "coordinates", "confidences"
+        )
+        data, metadata = format_data(coordinates, confidences, **kpms_dj_config)
+        model = init_model(data=data, metadata=metadata, pca=pca, **kpms_dj_config)
+        model = update_hypparams(
+            model, kappa=int(full_kappa), latent_dim=int(full_latent_dim)
         )
 
-        if task_mode == "trigger":
-            pre_model, data, metadata, current_iter = load_checkpoint(
-                output_dir, model_name, iteration=pre_num_iterations
-            )
+        start_time = datetime.utcnow()
+        model, model_name = fit_model(
+            model=model,
+            data=data,
+            metadata=metadata,
+            project_dir=kpms_project_output_dir.as_posix(),
+            ar_only=False,
+            num_iters=full_num_iterations,
+        )
+        end_time = datetime.utcnow()
+        duration_seconds = (end_time - start_time).total_seconds()
+        hours, remainder = divmod(duration_seconds, 3600)
+        minutes, seconds = divmod(remainder, 60)
+        duration_formatted = "{:02}:{:02}:{:02}".format(
+            int(hours), int(minutes), int(seconds)
+        )
 
-            model = update_hypparams(pre_model, kappa=int(full_kappa))
+        reindex_syllables_in_checkpoint(
+            kpms_project_output_dir.as_posix(), Path(model_name).parts[-1]
+        )
 
-            start_time = datetime.utcnow()
+        self.insert1(
+            {
+                **key,
+                "model_name": (
+                    kpms_project_output_dir.relative_to(get_kpms_processed_data_dir())
+                    / model_name
+                ).as_posix(),
+                "full_fitting_duration": duration_formatted,
+            }
+        )
 
-            full_model = fit_model(
-                model,
-                data,
-                metadata,
-                output_dir,
-                model_name,
-                ar_only=False,
-                start_iter=current_iter,
-                num_iters=full_num_iterations,
-            )[0]
-
-            model_path = os.path.join(
-                output_dir + "/" + model_name + "/" + "full_model.pkl"
-            )
-            with open(model_path, "wb") as data_file:
-                pickle.dump(full_model, data_file)
 
-            end_time = datetime.utcnow()
-            duration_seconds = (end_time - start_time).total_seconds()
-            hours, remainder = divmod(duration_seconds, 3600)
-            minutes, seconds = divmod(remainder, 60)
-            duration_formatted = "{:02}:{:02}:{:02}".format(
-                int(hours), int(minutes), int(seconds)
-            )
+@schema
+class Model(dj.Manual):
+    """Table to register the models.
 
-            self.insert1({**key, "full_fitting_duration": duration_formatted})
+    Attributes:
+        model_name (varchar)                : Generated model name (output_dir/model_name)
+        latent_dim (int)                    : Number of latent dimensions of the model
+        kappa (int)                         : Kappa value of the model
 
-        else:
-            # load the most recent model checkpoint and pca object
-            model, data, metadata = load_checkpoint(output_dir, model_name)
-            pca = load_pca(output_dir)
+    """
+
+    definition = """
+    model_name              : varchar(64)  # Generated model name (output_dir/model_name)
+    ---
+    latent_dim              : int          # Number of latent dimensions of the model
+    kappa                   : int          # Kappa value of the model
+    """
+
+
+@schema
+class VideoRecording(dj.Manual):
+    """Set of video recordings for the Keypoint-MoSeq inference.
+
+    Attributes:
+        Session (foreign key)               : Session primary key.
+        PoseEstimationMethod (foreign key)  : Pose estimation method.
+        recording_id (int)                  : Unique ID for each recording.
+    """
 
-            # load the keypoints data
-            # data_path = os.path.join(output_dir, 'data.pkl')
-            # with open(data_path, 'rb') as data_file:
-            #     data = pickle.load(data_file)
+    definition = """
+    -> Session                  # Session primary key
+    -> PoseEstimationMethod     # Pose estimation method
+    recording_id: int           # Unique ID for each recording
+    """
 
-            # metadata_path = os.path.join(output_dir, 'metadata.pkl')
-            # with open(metadata_path, 'rb') as data_file:
-            #     metadata = pickle.load(data_file)
+    class File(dj.Part):
+        """File IDs and paths associated with a given `recording_id`.
 
-            results = apply_model(model, pca, data, metadata, output_dir, model_name)
+        Attributes:
+            VideoRecording (foreign key)   : Video recording primary key.
+            file_id(int)                   : Unique ID for each file.
+            file_path (varchar)            : Filepath of each video, relative to root data directory.
+        """
 
-            # TO-DO: Save the results in files/table
+        definition = """
+        -> master               
+        file_id: int             # Unique ID for each file
+        ---
+        file_path: varchar(1000) # Filepath of each video, relative to root data directory.
+        """
 
 
 @schema
-class GenerateResults(dj.Computed):
-    """
-    This table is used to extract the model results from the checkpoint file and save them to `{output_dir}/{model_name}/results.h5`.
-    The results are stored as follows:
-        results.h5
-        ├──recording_name1
-        │  ├──syllable      # syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).
-        │  ├──latent_state  # inferred low-dim pose state (x). Low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.
-        │  ├──centroid      # inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.
-        │  └──heading       # inferred heading (h). The heading of the animal in each frame, as estimated by the model.
+class InferenceTask(dj.Manual):
+    """Table to specify the model, the video set, and the output directory for the inference task
 
     Attributes:
-        FullFitting (foreign key)               : Unique ID for full fitting task
-        grid_movies_sampled_instances(longlbob) : Dictionary mapping syllables to lists of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+        -> VideoRecording                    : Video recording primary key
+        -> Model                             : Model primary key
+        inference_output_dir (varchar)       : Sub-directory where the results will be stored
+        inference_desc (varchar)             : User-defined description of the inference task
+        num_iterations (int)                 : Number of iterations to use for the model inference. If null, the default number internally is 50.
     """
 
     definition = """
-    -> FullFitting
+    -> VideoRecording                              # Video recording primary key
+    -> Model                                       # Model primary key 
     ---
-    grid_movies_sampled_instances : longblob # Dictionary mapping syllables to lists of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+    inference_output_dir=''       : varchar(1000)  # Optional. Sub-directory where the results will be stored
+    inference_desc=''             : varchar(1000)  # Optional. User-defined description of the inference task
+    num_iterations=NULL           : int            # Optional. Number of iterations to use for the model inference. If null, the default number internally is 50.
+    """
+
+
+@schema
+class Inference(dj.Computed):
+    """This table is used to infer the model results from the checkpoint file and save them to `{output_dir}/{model_name}/{inference_output_dir}/results.h5`.
+
+    Attributes:
+        -> InferenceTask                    : InferenceTask primary key
+        inference_duration (time)           : Time duration of the inference computation
     """
 
+    definition = """
+    -> InferenceTask                    # InferenceTask primary key
+    --- 
+    inference_duration=NULL    : time   # Time duration of the inference computation
+    """
+
+    class MotionSequence(dj.Part):
+        """This table is used to store the results of the model inference.
+
+        Attributes:
+            video_name (varchar)                : Name of the video
+            syllable (longblob)                 : Syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).
+            latent_state (longblob)             : Inferred low-dim pose state (x). Low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.
+            centroid (longblob)                 : Inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.
+            heading (longblob)                  : Inferred heading (h). The heading of the animal in each frame, as estimated by the model.
+        """
+
+        definition = """
+        -> master
+        video_name      : varchar(150)    # Name of the video
+        ---
+        syllable        : longblob        # Syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).
+        latent_state    : longblob        # Inferred low-dim pose state (x). Low-dimensional representation of the animal's pose in each frame. These are similar to PCA scores, are modified to reflect the pose dynamics and noise estimates inferred by the model.
+        centroid        : longblob        # Inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.
+        heading         : longblob        # Inferred heading (h). The heading of the animal in each frame, as estimated by the model.
+        """
+
+    class GridMoviesSampledInstances(dj.Part):
+        """This table is used to store the grid movies sampled instances.
+
+        Attributes:
+            syllable (int)                  : Syllable label
+            instances (longblob)            : List of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+        """
+
+        definition = """
+        -> master
+        syllable: int           # Syllable label
+        ---
+        instances: longblob     # List of instances shown in each in grid movie (in row-major order), where each instance is specified as a tuple with the video name, start frame and end frame.
+        """
+
     def make(self, key):
         """
-        Make function to extract the model results from the checkpoint file and save them to `{output_dir}/{model_name}/results.h5`.
+        This function is used to infer the model results from the checkpoint file and save them to `{output_dir}/{model_name}/{inference_output_dir}/results.h5`.
 
         Args:
-            key (dict): Primary key from the FullFitting table.
+            key (dict): Primary key from the InferenceTask table.
 
         Raises:
+            NotImplementedError: If the format method is not `deeplabcut`.
 
         High-level Logic:
-        1. Fetch the `output_dir`, `model_name`, and parameters specified in the `FullFitting` table
-        2. Sort syllables if `sort_syllables` is set to True. This function permutes the states and parameters of a saved checkpoint so that syllables are labeled in order of frequency (i.e. so that 0 is the most frequent, 1 is the second most, and so on).
-        3. Load the most recent model checkpoint
-        4. Extract the model results
-        5. If `results_as_csv` is set to True, save the results as csv
-        6. If `visualizations` is set to True, generate visualizations
-        7. Insert the `grid_movies_sampled_instances` into the `GenerateResults` table
+        1. Fetch the `inference_output_dir` where the results will be stored, and if it is not present, create it.
+        2. Fetch the `model_name` and the `num_iterations` from the `InferenceTask` table
+        3. Load the most recent model checkpoint and the pca model
+        4. Load the new keypoint data as `filepath_patterns` and format the data
+        5. Initialize and apply the model with the new keypoint data
+        6. If the `num_iterations` is set, fit the model with the new keypoint data for `num_iterations` iterations; otherwise, fit the model with the default number of iterations (50)
+        7. Save the results as a CSV file and store the histogram showing the frequency of each syllable
+        8. Generate and save the plots showing the median trajectory of poses associated with each given syllable.
+        9. Generate and save video clips showing examples of each syllable.
+        10. Generate and save the dendrogram representing distances between each syllable's median trajectory.
+        11. Insert the inference duration in the `Inference` table
+        12. Insert the results in the `MotionSequence` and `GridMoviesSampledInstances` tables
         """
 
-        output_dir = (PCATask & key).fetch1("output_dir")
-        try:
-            selected_key = (
-                PreFitting
-                & "pre_latent_dim = {}".format(key["full_latent_dim"])
-                & "pre_kappa = {}".format(key["full_kappa"])
+        from keypoint_moseq import (
+            load_pca,
+            load_keypoints,
+            format_data,
+            apply_model,
+            save_results_as_csv,
+            plot_syllable_frequencies,
+            generate_trajectory_plots,
+            generate_grid_movies,
+            plot_similarity_dendrogram,
+        )
+
+        inference_output_dir, model_name, num_iterations = (InferenceTask & key).fetch1(
+            "inference_output_dir", "model_name", "num_iterations"
+        )
+        inference_output_full_dir = (
+            get_kpms_processed_data_dir() / model_name / inference_output_dir
+        )
+        if not os.path.exists(inference_output_full_dir):
+            os.makedirs(inference_output_full_dir)
+
+        model_full_path = get_kpms_processed_data_dir() / model_name
+        format_method = (VideoRecording & key).fetch1("format_method")
+        file_paths = (VideoRecording.File & key).fetch("file_path")
+
+        pca = load_pca(model_full_path.parent.as_posix())
+        model = load_checkpoint(
+            project_dir=model_full_path.parent, model_name=Path(model_full_path).name
+        )[0]
+
+        filepath_patterns = []
+        for path in file_paths:
+            full_path = find_full_path(get_kpms_root_data_dir(), path)
+            temp = (
+                Path(full_path).parent
+                / (os.path.splitext(os.path.basename(path))[0] + "*")
+            ).as_posix()
+            filepath_patterns.append(temp)
+        kpms_dj_config = load_kpms_dj_config(
+            model_full_path.parent.as_posix(), check_if_valid=True, build_indexes=True
+        )
+
+        if format_method == "deeplabcut":
+            coordinates, confidences, _ = load_keypoints(
+                filepath_pattern=filepath_patterns, format=format_method
             )
-            model_name, pre_num_iterations = selected_key.fetch1(
-                "model_name", "pre_num_iterations"
+        else:
+            raise NotImplementedError(
+                "The currently supported format method is `deeplabcut`. If you require \
+        support for another format method, please reach out to us at `support@datajoint.com`."
             )
-        except:
-            print("No prefitting model found")
-            
-        sort_syllables, results_as_csv, visualizations = (FullFittingTask & key).fetch1(
-            "sort_syllables", "results_as_csv", "visualizations"
-        )
 
-        # sort syllables
-        if sort_syllables:
-            reindex_syllables_in_checkpoint(output_dir, model_name)
+        data, metadata = format_data(coordinates, confidences, **kpms_dj_config)
 
-        model, data, metadata, current_iter = load_checkpoint(
-            output_dir, model_name, iteration=None
-        )
+        if num_iterations:
+            start_time = datetime.utcnow()
+            results = apply_model(
+                model=model,
+                data=data,
+                metadata=metadata,
+                pca=pca,
+                project_dir=model_full_path.parent.as_posix(),
+                model_name=Path(model_full_path).name,
+                results_path=(inference_output_full_dir / "results.h5").as_posix(),
+                return_model=False,
+                num_iters=num_iterations,
+                **kpms_dj_config,
+            )
+            end_time = datetime.utcnow()
+        else:
+            start_time = datetime.utcnow()
+            results = apply_model(
+                model=model,
+                data=data,
+                metadata=metadata,
+                pca=pca,
+                project_dir=model_full_path.parent.as_posix(),
+                model_name=Path(model_full_path).name,
+                results_path=(inference_output_full_dir / "results.h5").as_posix(),
+                return_model=False,
+                **kpms_dj_config,
+            )
+            end_time = datetime.utcnow()
 
-        results = extract_results(model, metadata, output_dir, model_name)
+        duration_seconds = (end_time - start_time).total_seconds()
+        hours, remainder = divmod(duration_seconds, 3600)
+        minutes, seconds = divmod(remainder, 60)
+        duration_formatted = "{:02}:{:02}:{:02}".format(
+            int(hours), int(minutes), int(seconds)
+        )
 
-        if results_as_csv:
-            save_results_as_csv(results, output_dir, model_name)
+        save_results_as_csv(
+            results=results,
+            project_dir=model_full_path.parent.as_posix(),
+            model_name=Path(model_full_path).name,
+            save_dir=(inference_output_full_dir / "results_as_csv").as_posix(),
+        )
 
-        if visualizations:
-            coordinates = (FormattedDataset & key).fetch1("coordinates")
-            config = load_dj_config(output_dir)
+        fig, _ = plot_syllable_frequencies(
+            results=results, path=inference_output_full_dir.as_posix()
+        )
+        fig.savefig(inference_output_full_dir / "syllable_frequencies.png")
+        plt.close(fig)
+
+        generate_trajectory_plots(
+            coordinates=coordinates,
+            results=results,
+            project_dir=model_full_path.parent.as_posix(),
+            model_name=Path(model_name).parts[-1],
+            output_dir=(inference_output_full_dir / "trajectory_plots").as_posix(),
+            **kpms_dj_config,
+        )
 
-            # Generate plots showing the median trajectory of poses associated with each given syllable.
-            generate_trajectory_plots(
-                coordinates, results, output_dir, model_name, **config
-            )
+        sampled_instances = generate_grid_movies(
+            coordinates=coordinates,
+            results=results,
+            project_dir=model_full_path.parent.as_posix(),
+            model_name=Path(model_name).parts[-1],
+            output_dir=(inference_output_full_dir / "grid_movies").as_posix(),
+            **kpms_dj_config,
+        )
 
-            # Generate video clips showing examples of each syllable.
-            grid_movies_sampled_instances = generate_grid_movies(
-                results, output_dir, model_name, coordinates=coordinates, **config
-            )
+        plot_similarity_dendrogram(
+            coordinates=coordinates,
+            results=results,
+            project_dir=model_full_path.parent.as_posix(),
+            model_name=Path(model_name).parts[-1],
+            save_path=(inference_output_full_dir / "similarity_dendogram").as_posix(),
+            **kpms_dj_config,
+        )
 
-            # Plot a dendrogram representing distances between each syllable’s median trajectory.
-            plot_similarity_dendrogram(
-                coordinates, results, output_dir, model_name, **config
+        self.insert1({**key, "inference_duration": duration_formatted})
+
+        for results_idx in results.keys():
+            self.MotionSequence.insert1(
+                {
+                    **key,
+                    "video_name": results_idx,
+                    "syllable": results[results_idx]["syllable"],
+                    "latent_state": results[results_idx]["latent_state"],
+                    "centroid": results[results_idx]["centroid"],
+                    "heading": results[results_idx]["heading"],
+                }
             )
 
-            self.insert1(
-                {**key, "grid_movies_sampled_instances": grid_movies_sampled_instances}
+        for syllable in sampled_instances.keys():
+            self.GridMoviesSampledInstances.insert1(
+                {**key, "syllable": syllable, "instances": sampled_instances[syllable]}
             )

From 2a2d780d9cb6d194cc2c1d3c1d206ce0baac2cfe Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 07:20:38 +0100
Subject: [PATCH 83/90] update `kpms_pca.py`

---
 element_moseq/kpms_pca.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py
index 9cc3411..fa14d5f 100644
--- a/element_moseq/kpms_pca.py
+++ b/element_moseq/kpms_pca.py
@@ -385,20 +385,20 @@ def make(self, key):
 
 @schema
 class LatentDimension(dj.Imported):
-    #     """
-    #     Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) \
-    #     necessary for the model fitting.
-    #     The analysis aims to select each of the components that explain the 90% of variance (fixed threshold).
-
-    #     Attributes:
-    #         PCAFitting (foreign key)           : PCAFitting Key.
-    #         variance_percentage (float)        : Variance threshold. Fixed value to 90%.
-    #         latent_dimension (int)             : Number of principal components required to explain the specified variance.
-    #         latent_dim_desc (varchar)          : Automated description of the computation result.
-    #     """
+    """
+    Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) \
+    necessary for the model fitting.
+    The analysis aims to select each of the components that explain the 90% of variance (fixed threshold).
+
+    Attributes:
+        PCAFitting (foreign key)           : PCAFitting Key.
+        variance_percentage (float)        : Variance threshold. Fixed value to 90%.
+        latent_dimension (int)             : Number of principal components required to explain the specified variance.
+        latent_dim_desc (varchar)          : Automated description of the computation result.
+    """
 
     definition = """
--> PCAFitting                                   # PCAFitting Key
+    -> PCAFitting                                   # PCAFitting Key
     ---
     variance_percentage      : float            # Variance threshold. Fixed value to 0.9
     latent_dimension         : int              # Number of principal components required to explain the specified variance.

From d6b9d93f19f1b62e689d116eec29e4cdbfa6d342 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 07:40:10 +0100
Subject: [PATCH 84/90] update `.devcontainer`

---
 .devcontainer/Dockerfile          | 12 ++++++------
 .devcontainer/devcontainer.json   |  6 +++++-
 .devcontainer/docker-compose.yaml |  3 +++
 3 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 1b55788..745d45a 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -31,20 +31,20 @@ COPY ./ /tmp/element-moseq/
 
 RUN \
     # pipeline dependencies
-    apt-get update && \
-    apt-get install -y gcc ffmpeg graphviz && \
-    pip install --no-cache-dir -e /tmp/element-moseq[elements] && \
+    apt-get install gcc g++ ffmpeg libsm6 libxext6 libgl1 libegl1 -y && \
+    pip install --no-cache-dir -e /tmp/element-moseq[elements,tests] && \
     # clean up
     rm -rf /tmp/element-moseq/ && \
     apt-get clean
 
+# Install Keypoint-MoSeq (CPU version)
+RUN pip install "jax[cpu]==0.3.22" -f https://storage.googleapis.com/jax-releases/jax_releases.html
+
 ENV DJ_HOST fakeservices.datajoint.io
 ENV DJ_USER root
 ENV DJ_PASS simple
 
-#ENV DATA_MOUNTPOINT /workspaces/element-moseq/example_data
-#ENV DLC_ROOT_DATA_DIR $DATA_MOUNTPOINT/inbox
-#ENV DLC_PROCESSED_DATA_DIR $DATA_MOUNTPOINT/outbox
+ENV KPMS_ROOT_DATA_DIR /workspaces/element-moseq/example_data
 ENV DATABASE_PREFIX neuro_
 
 USER vscode
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 922a2fd..5717d05 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -3,7 +3,11 @@
 	"dockerComposeFile": "docker-compose.yaml",
 	"service": "app",
 	"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
-	"onCreateCommand": "pip install -e .",
+	"remoteEnv": {
+		"LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}"
+	},
+	"onCreateCommand": "mkdir -p ${KPMS_ROOT_DATA_DIR} && pip install -e .",
+	"postStartCommand": "docker volume prune -f && s3fs ${DJ_PUBLIC_S3_LOCATION} ${KPMS_ROOT_DATA_DIR} -o nonempty,multipart_size=530,endpoint=us-east-1,url=http://s3.amazonaws.com,public_bucket=1",
 	"hostRequirements": {
 		"cpus": 4,
 		"memory": "8gb",
diff --git a/.devcontainer/docker-compose.yaml b/.devcontainer/docker-compose.yaml
index bd0a1ea..4831410 100644
--- a/.devcontainer/docker-compose.yaml
+++ b/.devcontainer/docker-compose.yaml
@@ -6,8 +6,11 @@ services:
     build:
       context: ..
       dockerfile: ./.devcontainer/Dockerfile
+    # image: datajoint/element_moseq:latest
     extra_hosts:
       - fakeservices.datajoint.io:127.0.0.1
+    environment:
+      - DJ_PUBLIC_S3_LOCATION=djhub.vathes.datapub.elements:/workflow-moseq/v1
     devices:
       - /dev/fuse
     cap_add:

From 73e62b050618b0ca65580dfd0c92db2e8fd68702 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 07:40:21 +0100
Subject: [PATCH 85/90] update `setup.py`

---
 setup.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/setup.py b/setup.py
index 6422765..6e8ecdf 100644
--- a/setup.py
+++ b/setup.py
@@ -9,13 +9,13 @@
 with open(path.join(here, "README.md"), "r") as f:
     long_description = f.read()
 
-# with open(path.join(here, pkg_name, "version.py")) as f:
-#    exec(f.read())
+with open(path.join(here, pkg_name, "version.py")) as f:
+   exec(f.read())
 
 setup(
     name=pkg_name.replace("_", "-"),
-    # version=__version__,  # noqa: F821
-    description="Keypoint Moseq DataJoint Element",
+    version=__version__,  # noqa: F821
+    description="Keypoint-MoSeq DataJoint Element",
     long_description=long_description,
     long_description_content_type="text/markdown",
     author="DataJoint",
@@ -25,17 +25,20 @@
     keywords="neuroscience keypoint-moseq science datajoint",
     packages=find_packages(exclude=["contrib", "docs", "tests*"]),
     scripts=[],
-    install_requires=["datajoint>=0.13.0"],
-    extras_require={
-        "kpms_default": [
-            "panel==1.3.4",
-            "keypoint-moseq",
+    install_requires=[
+        "datajoint>=0.13.0",
+        "ipykernel>=6.0.1",
+        "opencv-python",
+        "element-interface @ git+https://github.com/datajoint/element-interface.git",
+        "keypoint-moseq @ git+https://github.com/dattalab/keypoint-moseq.git"
         ],
+    extras_require={
         "elements": [
-            "element-lab>=0.3.0",
-            "element-animal>=0.1.8",
-            "element-session>=0.1.5",
-            "element-interface>=0.6.0",
+            "element-animal @ git+https://github.com/datajoint/element-animal.git",
+            "element-event @ git+https://github.com/datajoint/element-event.git",
+            "element-lab @ git+https://github.com/datajoint/element-lab.git",
+            "element-session @ git+https://github.com/datajoint/element-session.git",
         ],
+        "tests": ["pytest", "pytest-cov", "shutils"],
     },
 )

From cf224a74569a9213265df29dcb42e7547e073de8 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 08:35:31 +0100
Subject: [PATCH 86/90] update `docs`

---
 docs/.docker/pip_requirements.txt |  2 +-
 docs/docker-compose.yaml          |  2 +-
 docs/mkdocs.yaml                  |  2 +-
 docs/src/citation.md              |  2 +-
 docs/src/concepts.md              | 40 +++++++++++-------------------
 docs/src/partnerships.md          |  6 +----
 docs/src/pipeline.md              | 41 ++++++++++++++++++++++++++-----
 docs/src/roadmap.md               |  8 +-----
 docs/src/tutorials/index.md       |  4 +--
 9 files changed, 58 insertions(+), 49 deletions(-)

diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt
index 3842f28..ae44fb5 100644
--- a/docs/.docker/pip_requirements.txt
+++ b/docs/.docker/pip_requirements.txt
@@ -1,4 +1,4 @@
-mkdocs-material==9.1.17
+mkdocs-material
 mkdocs-redirects
 mkdocstrings
 mkdocstrings-python
diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml
index 5fbd23e..1eb04eb 100644
--- a/docs/docker-compose.yaml
+++ b/docs/docker-compose.yaml
@@ -1,4 +1,4 @@
-# MODE="LIVE|QA|PUSH" PACKAGE=element_calcium_imaging UPSTREAM_REPO=https://github.com/datajoint/element-calcium-imaging.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build
+# MODE="LIVE|QA|PUSH" PACKAGE=element_moseq UPSTREAM_REPO=https://github.com/datajoint/element-moseq.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build
 version: "2.4"
 services:
   docs:
diff --git a/docs/mkdocs.yaml b/docs/mkdocs.yaml
index c187218..f162929 100644
--- a/docs/mkdocs.yaml
+++ b/docs/mkdocs.yaml
@@ -5,7 +5,7 @@ site_url: http://localhost/docs/elements/element-moseq
 repo_url: https://github.com/datajoint/element-moseq
 repo_name: datajoint/element-moseq
 nav:
-  - Element Calcium Imaging: index.md
+  - Element MoSeq: index.md
   - Data Pipeline: pipeline.md
   - Tutorials:
       - tutorials/index.md
diff --git a/docs/src/citation.md b/docs/src/citation.md
index 9471de2..0148b90 100644
--- a/docs/src/citation.md
+++ b/docs/src/citation.md
@@ -10,4 +10,4 @@ If your work uses the following resources, please cite the respective manuscript
   + [RRID:SCR_021894](https://scicrunch.org/resolver/SCR_021894)
 
 + Keypoint-MoSeq
-  + [Manuscripts](https://www.biorxiv.org/content/10.1101/2023.03.16.532307v1)
+  + [Manuscripts](https://www.biorxiv.org/content/10.1101/2023.03.16.532307v2.full.pdf)
diff --git a/docs/src/concepts.md b/docs/src/concepts.md
index 8b0eba0..5d9fc25 100644
--- a/docs/src/concepts.md
+++ b/docs/src/concepts.md
@@ -1,38 +1,28 @@
 # Concepts
 
-## Keypoint-MoSeq: Pose Dynamics for Advanced Motion Sequencing
+## Keypoint-MoSeq: Advanced Motion Sequencing through Pose Dynamics
 
-<!-- In the domain of animal behavior analysis, Keypoint-MoSeq has emerged as a highly efficient alternative to conventional methods. This machine learning software is specifically designed to automatically identify and comprehend behavioral modules or "syllables" in observed animal behavior without requiring human intervention. Its primary objective is to accurately quantify and analyze animal movements and actions from keypoint data extracted from standard video recordings. 
+Keypoint-MoSeq[^1] introduces a novel machine learning platform tailored for identifying behavioral modules or "syllables" from keypoint data extracted from conventional video recordings of animal behavior. This innovative approach addresses the challenge posed by continuous keypoint data, prone to high-frequency jitter, often mistaken for transitions between behavioral states by conventional clustering algorithms. To overcome this hurdle, Keypoint-MoSeq leverages a generative model adept at discerning between keypoint noise and genuine behavior, facilitating precise identification of syllables marked by natural sub-second discontinuities inherent in mouse behavior.
 
-Keypoint-MoSeq utilizes a sophisticated generative model. This model effectively distinguishes genuine behavior patterns from noise within the keypoint data, ensuring precise identification of sub-second behavior transitions. The resulting syllables can establish correlations between neural activity and behavior, a task often daunting for traditional clustering methods.
+While keypoint tracking methods have significantly advanced the quantification of animal movement kinematics, the task of clustering behavioral data into discrete modules remains complex. Such clustering is vital for creating ethograms that delineate the sequential expression of behavioral modules. Existing methods vary in logic and assumptions, yielding diverse descriptions of identical behavior. Motion Sequencing (MoSeq)[^2] stands out as a validated technique for identifying behavioral modules and their temporal sequences using unsupervised machine learning. However, conventional MoSeq is tailored for depth camera data and faces challenges with high-frequency keypoint jitter.
 
-The Keypoint-MoSeq PCA (`kpms_pca`) pipeline includes [x]. -->
+To address the limitations of traditional MoSeq when applied to keypoint data, Keypoint-MoSeq emerges as a promising solution. This new model enables simultaneous inference of keypoint positions and associated behavioral syllables, facilitating the identification of behavioral structure across diverse experimental settings without necessitating specialized hardware. Keypoint-MoSeq excels over alternative clustering methods in accurately delineating behavioral transitions, capturing neural activity correlations, and identifying complex features of solitary and social behavior. Its flexibility and accessibility, with freely available code for academic use[^3], promise widespread adoption and further innovation in behavioral analysis methods.
 
+[^1]: Weinreb, C., Pearl, J., Lin, S., Osman, M. A. M., Zhang, L., Annapragada, S., Conlin, E., Hoffman, R., Makowska, S., Gillis, W. F., Jay, M., Ye, S., Mathis, A., Mathis, M. W., Pereira, T., Linderman, S. W., & Datta, S. R. (2023). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. bioRxiv : the preprint server for biology, 2023.03.16.532307. https://doi.org/10.1101/2023.03.16.532307
 
-Element MoSeq uses the keypoint data extracted from the mentioned pose estimation method and the behavior segmentation with Keypoint-MoSeq software for the management of data and
-its analysis.
+[^2]: Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., ... & Datta, S. R. (2015). Mapping sub-second structure in mouse behavior. Neuron, 88(6), 1121-1135.
 
-## Acquisition tools
+[^3]: www.MoSeq4all.org
 
-### Hardware
+## Element Features
 
-The primary acquisition systems are video cameras that record standard video recordings capturing animal behavior.
+Through our interviews and direct collaborations, we identified the core motifs to construct Element MoSeq.
 
-### Software
+Key features include:
+- Ingestion and storage of input video metadata 
+- Loading and formatting of 2D deeplabcut keypoint tracking data for model training
+- Queue management and initiation of Keypoint-MoSeq analysis across multiple sessions
+- Ingestion of analysis outcomes such as PCA, AR-HMM, and Keypoint-SLDS components
+- Ingestion of analysis outcomes from motion sequencing inference 
 
-- [Keypoint-MoSeq](https://github.com/dattalab/keypoint-moseq)
-- [DeepLabCut]()
 
-<!-- ## Data Export and Publishing
-
-Element MoSeq supports exporting of all data into standard Neurodata Without Borders (NWB) files. This makes it easy to share files with collaborators and
-publish results on [DANDI Archive](https://dandiarchive.org/).
-[NWB](https://www.nwb.org/), as an organization, is dedicated to standardizing data
-formats and maximizing interoperability across tools for neurophysiology.
-
-To use the export functionality with additional related dependencies, install the
-Element with the `nwb` option as follows:
-
-```console
-pip install element-moseq[nwb]
-``` -->
\ No newline at end of file
diff --git a/docs/src/partnerships.md b/docs/src/partnerships.md
index 4143b52..e6c4606 100644
--- a/docs/src/partnerships.md
+++ b/docs/src/partnerships.md
@@ -1,7 +1,3 @@
 # Key partnerships
 
-Several labs have developed DataJoint-based data management and behavior analysis pipelines for advanced motion sequencing. Our team collaborated with several of them during their projects. Additionally, we interviewed these teams to understand their experiment workflow, pipeline design, associated tools, and interfaces.
-
-These teams include:
-
-- 
\ No newline at end of file
+Element MoSeq was developed in collaboration with the [Keypoint-MoSeq developers](https://github.com/dattalab/keypoint-moseq) in Datta's Lab at Harvard Medical School to promote integration and interoperability between Keypoint-MoSeq and the DataJoint Element MoSeq.
diff --git a/docs/src/pipeline.md b/docs/src/pipeline.md
index 4655c0f..23a57c6 100644
--- a/docs/src/pipeline.md
+++ b/docs/src/pipeline.md
@@ -5,16 +5,23 @@ corresponding table in the database.  Within the pipeline, Element MoSeq
 connects to upstream Elements including Lab, Animal, Session, and Event. For more 
 detailed documentation on each table, see the API docs for the respective schemas.
 
-The Element is composed of two main schemas, `kpms_pca` and `kpms_model`.
+The Element is composed of two main schemas, `kpms_pca` and `kpms_model`. The `kpms_pca` schema is designed to handle the analysis and ingestion of PCA model for formatted keypoint tracking. The `kpms_model` schema is designed to handle the analysis and ingestion of Keypoint-MoSeq's motion sequencing on video recordings.
 
 ## Diagrams
 
-- [x].
+### `kpms_pca` module
 
-     ![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline.svg)
+- The `kpms_pca` schema is designed to handle the analysis and ingestion of a PCA model for formatted keypoint tracking.
 
+     ![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline_kpms_pca.svg)
 
-## Table descriptions
+### `kpms_model` module
+
+- The `kpms_model` schema is designed to handle the analysis and ingestion of Keypoint-MoSeq's motion sequencing on video recordings.
+
+     ![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline_kpms_model.svg)
+
+## Table Descriptions
 
 ### `lab` schema
 
@@ -46,10 +53,32 @@ The Element is composed of two main schemas, `kpms_pca` and `kpms_model`.
 
 - For further details see the [kpms_pca schema API docs](https://datajoint.com/docs/elements/element-moseq/latest/api/element_moseq/kpms_pca/)
 
-[x]
+| Table | Description |
+| --- | --- |
+| PoseEstimationMethod | Table to store the pose estimation methods supported by the keypoint loader of `keypoint-moseq` package. |
+| KeypointSet | Table to store the keypoint data and video set directory to train the model.|
+| KeypointSet.VideoFile | IDs and file paths of each video file that will be used to train the model.|
+| Bodyparts | Table to store the body parts to use in the analysis.|
+| PCATask | Staging table to define the PCA task and its output directory. |
+| LoadKeypointSet | Table to create the `kpms_project_output_dir`, and create and update the `config.yml` by creating a new `dj_config.yml`. |
+| PCAFitting | Automated fitting of the PCA model.|
+| LatentDimension | Automated computation to calculate the latent dimension as one of the autoregressive hyperparameters (`ar_hypparams`) necessary for the model fitting. |
+
 
 ### `kpms_model` schema
 
 - For further details see the [kpms_model schema API docs](https://datajoint.com/docs/elements/element-moseq/latest/api/element_moseq/kpms_model/)
 
-[x]
\ No newline at end of file
+| Table | Description |
+| --- | --- |
+| PreFittingTask | Table to specify the parameters for the pre-fitting (AR-HMM) of the model. |
+| PreFitting | Automated computation to fit a AR-HMM model. |
+| FullFittingTask | Table to specify the parameters for the full fitting of the model. The full model will generally require a lower value of kappa to yield the same target syllable durations. |
+| FullFitting | Automated computation to fit the full model. |
+| Model | Table to register the models. |
+| VideoRecording | Set of video recordings for the Keypoint-MoSeq inference. |
+| VideoRecording.File | File IDs and paths associated with a given `recording_id`. |
+| InferenceTask | Table to specify the model, the video set, and the output directory for the inference task. |
+| Inference | This table is used to infer the model results from the checkpoint file and save them to `{output_dir}/{model_name}/{inference_output_dir}/results.h5`. |
+| Inference.MotionSequence | This table is used to store the results of the model inference.|
+| Inference.GridMoviesSampledInstances | This table is used to store the grid movies sampled instances.|
\ No newline at end of file
diff --git a/docs/src/roadmap.md b/docs/src/roadmap.md
index 1da2e32..364d017 100644
--- a/docs/src/roadmap.md
+++ b/docs/src/roadmap.md
@@ -1,9 +1,3 @@
 # Roadmap
 
-Through our interviews and direct collaboration on key projects, we identified
-the common motifs to create Element MoSeq. Major features include:
-
-- [x]
-
-Further development of this Element is community driven. Upon user requests and based on
-guidance from the Scientific Steering Group we will continue adding features to this Element.
+Further development of this Element is community driven. Upon user requests and based on guidance from the Scientific Steering Group we will continue adding features to this Element.
diff --git a/docs/src/tutorials/index.md b/docs/src/tutorials/index.md
index d622431..ed2e647 100644
--- a/docs/src/tutorials/index.md
+++ b/docs/src/tutorials/index.md
@@ -13,6 +13,6 @@
 We recommend users to configure a database specifically for production pipelines.  Instructions for a local installation of the integrated development environment with a database can be found on the [User Guide](https://datajoint.com/docs/elements/user-guide/) page.
 
 
-## POSE ESTIMATION METHOD
+## Pose Estimation Method
 
-+ Behavioral segmentation analysis using keypoint data extracted with DeepLabCut is currently supported for single animal.
++ At present, behavioral segmentation analysis is compatible with keypoint data extracted with DeepLabCut with single-animal datasets.
\ No newline at end of file

From 59bbeb423c71ab029cba07d77ae593f14ffcaa5d Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 08:50:48 +0100
Subject: [PATCH 87/90] update `README`

---
 README.md | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 53ecddf..c270049 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,16 @@
 # DataJoint Element for Motion Sequencing with Keypoint-MoSeq
 
-DataJoint Element for motion sequencing with
-[Keypoint-Moseq](https://dattalab.github.io/moseq2-website/index.html) ([scientific article](https://doi.org/10.1101/2023.03.16.532307)). Serving as an open-source tool, Keypoint-MoSeq utilizes an advanced generative model to capture animal behavior. This machine learning software excels in automatically identifying and interpreting behavioral modules, or "syllables," from keypoint data without requiring manual intervention. The keypoint timeseries data can originate from DeepLabCut, SLEAP, or other pose estimation algorithms.
+DataJoint Element for advanced motion sequencing of animal behavior using [Keypoint-MoSeq](https://dattalab.github.io/moseq2-website/index.html). This Element facilitates Keypoint-MoSeq analysis, employing an advanced generative model to automatically identify behavioral modules or "syllables" from keypoint data extracted from conventional video recordings of animal behavior, eliminating the need for manual intervention.
 
-DataJoint Elements collectively standardize
-and automate data collection and analysis for neuroscience experiments.  Each Element is
-a modular pipeline for data storage and processing with corresponding database
-tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline.
+DataJoint Elements collectively standardize and automate data collection and analysis for neuroscience experiments.  Each Element is a modular pipeline for data storage and processing with corresponding database tables that can be combined with other Elements to assemble a fully functional pipeline. This repository also provides a tutorial environment and notebooks to learn the pipeline.
 
 ## Experiment Flowchart
 
-![pipeline](../images/flowchart.svg)
+![flowchart](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/flowchart.svg)
 
 ## Data Pipeline Diagram
 
-![pipeline](../images/pipeline.svg)
+![pipeline](https://raw.githubusercontent.com/datajoint/element-moseq/main/images/pipeline.svg)
 
 ## Getting Started
 

From 43545d568b332631064349d314c67f711f5920c7 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 09:05:05 +0100
Subject: [PATCH 88/90] update `kpms_model.py`

---
 element_moseq/kpms_model.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/element_moseq/kpms_model.py b/element_moseq/kpms_model.py
index 2e3727d..317394d 100644
--- a/element_moseq/kpms_model.py
+++ b/element_moseq/kpms_model.py
@@ -10,7 +10,7 @@
 import importlib
 from datajoint import DataJointError
 
-from element_moseq.kpms_pca import LoadKeypointSet, PCATask
+from element_moseq.kpms_pca import *
 
 from element_interface.utils import find_full_path
 from .readers.kpms_reader import load_kpms_dj_config, generate_kpms_dj_config
@@ -389,9 +389,9 @@ class VideoRecording(dj.Manual):
     """
 
     definition = """
-    -> Session                  # Session primary key
-    -> PoseEstimationMethod     # Pose estimation method
-    recording_id: int           # Unique ID for each recording
+    -> Session                          # Session primary key
+    -> kpms_pca.PoseEstimationMethod    # Pose estimation method
+    recording_id: int                   # Unique ID for each recording
     """
 
     class File(dj.Part):

From 7294c8016bd09035cfc834e70f4400868905e9e4 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 09:14:57 +0100
Subject: [PATCH 89/90] update `changelog`

---
 CHANGELOG.md | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f98f9d6..628a4a6 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,14 +5,16 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and
 
 ## [0.1.0] - 2024-03-20
 
-+ Add - `docs` 
-+ Add - `kpms_reader` reader
-+ Add - `element_moseq` containing `kpms_pca` and `kpms_model` modules
-+ Add -  `flowchart` and `pipeline` images 
-+ Add - `tutorial.ipynb` consistent with the DataJoint Elements
++ Add - `CHANGELOG` and version for first release
++ Add - DevContainer configuration for GitHub Codespaces
++ Add - Updated documentation in `docs` for schemas and tutorial
++ Add - `kpms_reader` readers
++ Add - `element_moseq` pipeline architecture and design containing `kpms_pca` and `kpms_model` modules 
++ Add - `images` with flowchart and pipeline images 
++ Add - `tutorial.ipynb` consistent across DataJoint Elements that can be launched using GitHub Codespaces
 + Add - `tutorial_pipeline.py` script for notebooks to import and activate schemas
 + Add - spelling, markdown, and pre-commit config files
 + Add - GitHub Actions that call reusable workflows in the `datajoint/.github` repository
-+ Add - `LICENSE`, `CONTRIBUTING`, `CODE_OF_CONDUCT`, `CHANGELOG`
-+ Add - `README` consistent with the DataJoint Elements
-+ Add - `setup.py` with `extras_require` feature 
++ Add - `LICENSE`, `CONTRIBUTING`, `CODE_OF_CONDUCT`
++ Add - `README` consistent across DataJoint Elements
++ Add - `setup.py` with `extras_require` and `tests` features

From 9f22796bf2e91e66e6689c8adee3917b7336e272 Mon Sep 17 00:00:00 2001
From: MilagrosMarin <milmarina02@gmail.com>
Date: Wed, 20 Mar 2024 09:27:33 +0100
Subject: [PATCH 90/90] update env variables in dockerfile

---
 .devcontainer/Dockerfile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 745d45a..78fda48 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -44,7 +44,8 @@ ENV DJ_HOST fakeservices.datajoint.io
 ENV DJ_USER root
 ENV DJ_PASS simple
 
-ENV KPMS_ROOT_DATA_DIR /workspaces/element-moseq/example_data
+ENV KPMS_ROOT_DATA_DIR /workspaces/element-moseq/example_data/inbox
+ENV KPMS_ROOT_OUTPUT_DIR /workspaces/element-moseq/example_data/outbox
 ENV DATABASE_PREFIX neuro_
 
 USER vscode