diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 0000000..78fda48 --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,54 @@ +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 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 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 +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 new file mode 100644 index 0000000..5717d05 --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,30 @@ +{ + "name": "Environment + Data", + "dockerComposeFile": "docker-compose.yaml", + "service": "app", + "workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}", + "remoteEnv": { + "LOCAL_WORKSPACE_FOLDER": "${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", + "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..4831410 --- /dev/null +++ b/.devcontainer/docker-compose.yaml @@ -0,0 +1,25 @@ +version: "3" +services: + app: + cpus: 4 + mem_limit: 8g + 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: + - 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/.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//.} + 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 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/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..628a4a6 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,20 @@ +# 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 - `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` ++ Add - `README` consistent across DataJoint Elements ++ Add - `setup.py` with `extras_require` and `tests` features 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/README.md b/README.md index 8b13789..c270049 100644 --- a/README.md +++ b/README.md @@ -1 +1,75 @@ +# DataJoint Element for Motion Sequencing with Keypoint-MoSeq +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. + +## 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 this repository. + ++ Clone the repository to your computer. + + ```bash + git clone https://github.com//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. 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 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/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/${PACKAGE} +COPY --chown=anaconda:anaconda ./docs/mkdocs.yaml /main/docs/mkdocs.yaml +COPY --chown=anaconda:anaconda ./docs/src /main/docs/src +COPY --chown=anaconda:anaconda ./CHANGELOG.md /main/ \ No newline at end of file diff --git a/docs/.docker/apk_requirements.txt b/docs/.docker/apk_requirements.txt new file mode 100644 index 0000000..0899c29 --- /dev/null +++ b/docs/.docker/apk_requirements.txt @@ -0,0 +1 @@ +git \ No newline at end of file diff --git a/docs/.docker/pip_requirements.txt b/docs/.docker/pip_requirements.txt new file mode 100644 index 0000000..ae44fb5 --- /dev/null +++ b/docs/.docker/pip_requirements.txt @@ -0,0 +1,12 @@ +mkdocs-material +mkdocs-redirects +mkdocstrings +mkdocstrings-python +mike +mdx-truly-sane-lists +mkdocs-gen-files +mkdocs-literate-nav +mkdocs-exclude-search +mkdocs-markdownextradata-plugin +mkdocs-jupyter +mkdocs-section-index \ No newline at end of file diff --git a/docs/docker-compose.yaml b/docs/docker-compose.yaml new file mode 100644 index 0000000..1eb04eb --- /dev/null +++ b/docs/docker-compose.yaml @@ -0,0 +1,54 @@ +# MODE="LIVE|QA|PUSH" PACKAGE=element_moseq UPSTREAM_REPO=https://github.com/datajoint/element-moseq.git HOST_UID=$(id -u) docker compose -f docs/docker-compose.yaml up --build +version: "2.4" +services: + docs: + 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..f162929 --- /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 MoSeq: 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 --- /dev/null +++ b/docs/src/.overrides/.icons/main/company-logo.svg @@ -0,0 +1,11 @@ + + + + + + + + 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 @@ + + + + Asset 3 + + + + + + + + + + + + + + + \ No newline at end of file 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" %} + + +{% block content %} +

🚧 Not Found 👷

+

+ Unfortunately, we could not find what you were looking for. +
+
+ Usually there are two possibilities for this: +
+

+
+Please make sure you are navigating to the correct address. +

+{% endblock %} diff --git a/docs/src/.overrides/assets/images/company-logo-blue.png b/docs/src/.overrides/assets/images/company-logo-blue.png new file mode 100644 index 0000000..d15194b Binary files /dev/null and b/docs/src/.overrides/assets/images/company-logo-blue.png differ 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 %} + \ 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..0148b90 --- /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/10.1101/2023.03.16.532307v2.full.pdf) diff --git a/docs/src/concepts.md b/docs/src/concepts.md new file mode 100644 index 0000000..5d9fc25 --- /dev/null +++ b/docs/src/concepts.md @@ -0,0 +1,28 @@ +# Concepts + +## Keypoint-MoSeq: Advanced Motion Sequencing through Pose Dynamics + +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. + +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. + +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 + +[^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. + +[^3]: www.MoSeq4all.org + +## Element Features + +Through our interviews and direct collaborations, we identified the core motifs to construct Element MoSeq. + +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 + + 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//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..e6c4606 --- /dev/null +++ b/docs/src/partnerships.md @@ -0,0 +1,3 @@ +# Key partnerships + +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 new file mode 100644 index 0000000..23a57c6 --- /dev/null +++ b/docs/src/pipeline.md @@ -0,0 +1,84 @@ +# 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`. 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 + +### `kpms_pca` module + +- 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) + +### `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 + +- 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/) + +| 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/) + +| 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 new file mode 100644 index 0000000..364d017 --- /dev/null +++ b/docs/src/roadmap.md @@ -0,0 +1,3 @@ +# Roadmap + +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..ed2e647 --- /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 + ++ At present, behavioral segmentation analysis is compatible with keypoint data extracted with DeepLabCut with single-animal datasets. \ No newline at end of file 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/kpms_model.py b/element_moseq/kpms_model.py new file mode 100644 index 0000000..317394d --- /dev/null +++ b/element_moseq/kpms_model.py @@ -0,0 +1,660 @@ +from datetime import datetime +import inspect +import os +from pathlib import Path +from typing import Optional + +from matplotlib import pyplot as plt + +import datajoint as dj +import importlib +from datajoint import DataJointError + +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 +from keypoint_moseq import update_hypparams, fit_model, load_checkpoint + + +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 PreFittingTask(dj.Manual): + """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 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 # 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) # User-defined description of the pre-fitting task + """ + + +@schema +class PreFitting(dj.Computed): + """Automated computation to fit a AR-HMM model. + + Attributes: + 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 Key + --- + 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 fit the AR-HMM model using the latent trajectory defined by `model['states']['x']. + + Args: + key (dict) : dictionary with the `PreFittingTask` Key. + + Raises: + + High-level Logic: + 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 + """ + + kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir") + kpms_project_output_dir = ( + get_kpms_processed_data_dir() / kpms_project_output_dir + ) + + pre_latent_dim, pre_kappa, pre_num_iterations = (PreFittingTask & key).fetch1( + "pre_latent_dim", "pre_kappa", "pre_num_iterations" + ) + + 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) + + from keypoint_moseq import load_pca, format_data, init_model, update_hypparams + + 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(pre_kappa), latent_dim=int(pre_latent_dim) + ) + + start_time = datetime.now() + model, model_name = fit_model( + 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() + + 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": ( + kpms_project_output_dir.relative_to(get_kpms_processed_data_dir()) + / model_name + ).as_posix(), + "pre_fitting_duration": duration_formatted, + } + ) + + +@schema +class FullFittingTask(dj.Manual): + """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) : 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 # 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) # User-defined description of the model full fitting task + """ + + +@schema +class FullFitting(dj.Computed): + """Automated computation to fit the full model. + + Attributes: + 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 Key + --- + 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 (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 + """ + + kpms_project_output_dir = (PCATask & key).fetch1("kpms_project_output_dir") + kpms_project_output_dir = ( + get_kpms_processed_data_dir() / kpms_project_output_dir + ) + + full_latent_dim, full_kappa, full_num_iterations = ( + FullFittingTask & key + ).fetch1("full_latent_dim", "full_kappa", "full_num_iterations") + + 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) + + from keypoint_moseq import ( + load_pca, + format_data, + init_model, + reindex_syllables_in_checkpoint, + ) + + 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) + ) + + 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) + ) + + reindex_syllables_in_checkpoint( + kpms_project_output_dir.as_posix(), Path(model_name).parts[-1] + ) + + 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, + } + ) + + +@schema +class Model(dj.Manual): + """Table to register the models. + + 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 + + """ + + 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. + """ + + definition = """ + -> Session # Session primary key + -> kpms_pca.PoseEstimationMethod # Pose estimation method + recording_id: int # Unique ID for each recording + """ + + class File(dj.Part): + """File IDs and paths associated with a given `recording_id`. + + 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. + """ + + 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 InferenceTask(dj.Manual): + """Table to specify the model, the video set, and the output directory for the inference task + + Attributes: + -> 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 = """ + -> VideoRecording # Video recording primary key + -> Model # Model primary key + --- + 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): + """ + 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 InferenceTask table. + + Raises: + NotImplementedError: If the format method is not `deeplabcut`. + + High-level Logic: + 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 + """ + + 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 + ) + 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`." + ) + + data, metadata = format_data(coordinates, confidences, **kpms_dj_config) + + 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() + + 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) + ) + + 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(), + ) + + 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, + ) + + 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, + ) + + 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, + ) + + 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"], + } + ) + + for syllable in sampled_instances.keys(): + self.GridMoviesSampledInstances.insert1( + {**key, "syllable": syllable, "instances": sampled_instances[syllable]} + ) diff --git a/element_moseq/kpms_pca.py b/element_moseq/kpms_pca.py new file mode 100644 index 0000000..fa14d5f --- /dev/null +++ b/element_moseq/kpms_pca.py @@ -0,0 +1,459 @@ +from datetime import datetime, timezone +import inspect +import os +from pathlib import Path +from typing import Optional + +import cv2 +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 + + +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 to store the pose estimation methods supported by the keypoint loader of `keypoint-moseq` package. + + Attributes: + 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) # Supported pose estimation method (deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap) + --- + pose_estimation_desc : varchar(1000) # Optional. Pose estimation method description with the supported formats. + """ + + 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 to store the keypoint data and video set directory to train the model. + + 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_desc (str): Optional. User-entered description. + """ + + definition = """ + kpset_id : int # Unique ID for each keypoint set + --- + -> 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 + """ + + class VideoFile(dj.Part): + """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. + video_path (str): Filepath of each video, relative to root data directory. + """ + + definition = """ + -> master + video_id : int # Unique ID for each video + --- + video_path : varchar(1000) # Filepath of each video, relative to root data directory + """ + + +@schema +class Bodyparts(dj.Manual): + """Table to store the body parts to use in the analysis. + + Attributes: + KeypointSet (foreign key) : Unique ID for each keypoint set. + bodyparts_id (int) : Unique ID for each bodypart. + 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 # 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 + """ + + +@schema +class PCATask(dj.Manual): + """ + Staging table to define the PCA task and its output directory. + + Attributes: + Bodyparts (foreign key) : Bodyparts Key + kpms_project_output_dir (str) : KPMS's output directory relative to root + """ + + definition = """ + -> Bodyparts # Unique ID for each Bodyparts key + --- + kpms_project_output_dir='' : varchar(255) # KPMS's output directory relative to root + """ + + +@schema +class LoadKeypointSet(dj.Imported): + """ + 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 = """ + -> PCATask # 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 : float # Average frame rate of the trained videos + """ + + def make(self, key): + """ + 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: + 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. + 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. 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. + """ + + anterior_bodyparts, posterior_bodyparts, use_bodyparts = ( + Bodyparts & key + ).fetch1( + "anterior_bodyparts", + "posterior_bodyparts", + "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_config = load_config( + kpms_project_output_dir.as_posix(), check_if_valid=True, build_indexes=False + ) + + kpms_dj_config_kwargs_dict = dict( + 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.as_posix(), **kpms_config) + + filepath_patterns = [ + ( + kpset_videos_dir / (os.path.splitext(os.path.basename(path))[0] + "*") + ).as_posix() + for path in file_paths + ] + + 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): + 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): + """Automated fitting of the PCA model. + + Attributes: + LoadKeypointSet (foreign key) : LoadKeypointSet Key. + pca_fitting_time (datetime) : datetime of the PCA fitting analysis. + """ + + definition = """ + -> 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 KPMS output directory. + + Args: + key (dict): LoadKeypointSet Key + + Raises: + + High-Level Logic: + 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. + """ + + 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.as_posix(), check_if_valid=True, build_indexes=True + ) + coordinates, confidences = (LoadKeypointSet & key).fetch1( + "coordinates", "confidences" + ) + data, _ = format_data( + **kpms_default_config, coordinates=coordinates, confidences=confidences + ) + + pca = fit_pca(**data, **kpms_default_config) + save_pca(pca, kpms_project_output_dir.as_posix()) + + creation_datetime = datetime.now(timezone.utc) + self.insert1(dict(**key, pca_fitting_time=creation_datetime)) + + +@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. + """ + + definition = """ + -> 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. + 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 90% variance threshold. + + Args: + key (dict): PCAFitting Key. + + Raises: + + High-Level Logic: + 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") + 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( + pca.explained_variance_ratio_ + ) # explained_variance_ratio_ndarray of shape (n_components,) + + if cs[-1] < variance_threshold: + latent_dimension = len(cs) + variance_percentage = cs[-1] * 100 + latent_dim_desc = ( + f"All components together only explain {cs[-1]*100}% of variance." + ) + else: + 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." + + self.insert1( + dict( + **key, + variance_percentage=variance_percentage, + latent_dimension=latent_dimension, + latent_dim_desc=latent_dim_desc, + ) + ) diff --git a/element_moseq/readers/kpms_reader.py b/element_moseq/readers/kpms_reader.py new file mode 100644 index 0000000..058fa27 --- /dev/null +++ b/element_moseq/readers/kpms_reader.py @@ -0,0 +1,186 @@ +import os +import logging +import yaml +import jax.numpy as jnp + +logger = logging.getLogger("datajoint") + + +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: + output_dir (str): Directory containing the `kpms_dj_config.yml` that will be generated. + kwargs (dict): Custom project 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()} + + 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(output_dir, "kpms_dj_config.yml"), "w") as f: + f.write(_build_yaml(sections, comments)) + + +def load_kpms_dj_config(output_dir, check_if_valid=True, build_indexes=True): + """ + 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: + 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: + kpms_dj_config (dict): configuration settings + """ + + from keypoint_moseq import check_config_validity + + config_path = os.path.join(output_dir, "kpms_dj_config.yml") + + with open(config_path, "r") as f: + kpms_dj_config = yaml.safe_load(f) + + if check_if_valid: + check_config_validity(kpms_dj_config) + + if build_indexes: + kpms_dj_config["anterior_idxs"] = jnp.array( + [ + kpms_dj_config["use_bodyparts"].index(bp) + for bp in kpms_dj_config["anterior_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 kpms_dj_config or kpms_dj_config["skeleton"] is None: + kpms_dj_config["skeleton"] = [] + + return kpms_dj_config 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" diff --git a/images/flowchart.drawio b/images/flowchart.drawio new file mode 100644 index 0000000..c1b9bd3 --- /dev/null +++ b/images/flowchart.drawio @@ -0,0 +1,78 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/images/flowchart.svg b/images/flowchart.svg new file mode 100644 index 0000000..62cb73a --- /dev/null +++ b/images/flowchart.svg @@ -0,0 +1,4 @@ + + + +
Load keypoint data and body parts into pipeline
Load keypoint data and...
Synchronize data modalities & exploratory analysis
Synchronize data...
Visualize



 
Visualize...

 Export & publish

 
 
Export & publish...
Enter metadata
into pipeline
Enter metadata...
  Fit PCA, select latent dimension & kappa, 
and fit the model
Fit PCA, select laten...
 Film new videos & 
load metadata 
Film new videos &...
Pair videos with 
models & run 
inference
Pair videos with...Text is not SVG - cannot display \ No newline at end of file diff --git a/images/pipeline.svg b/images/pipeline.svg new file mode 100644 index 0000000..f3ac42b --- /dev/null +++ b/images/pipeline.svg @@ -0,0 +1,294 @@ + + + + + +kpms_pca.PCAFitting + + +kpms_pca.PCAFitting + + + + + +kpms_model.FullFittingTask + + +kpms_model.FullFittingTask + + + + + +kpms_pca.PCAFitting->kpms_model.FullFittingTask + + + + +kpms_model.PreFittingTask + + +kpms_model.PreFittingTask + + + + + +kpms_pca.PCAFitting->kpms_model.PreFittingTask + + + + +kpms_pca.LatentDimension + + +kpms_pca.LatentDimension + + + + + +kpms_pca.PCAFitting->kpms_pca.LatentDimension + + + + +kpms_model.VideoRecording.File + + +kpms_model.VideoRecording.File + + + + + +kpms_model.PreFitting + + +kpms_model.PreFitting + + + + + +kpms_model.VideoRecording + + +kpms_model.VideoRecording + + + + + +kpms_model.VideoRecording->kpms_model.VideoRecording.File + + + + +kpms_model.InferenceTask + + +kpms_model.InferenceTask + + + + + +kpms_model.VideoRecording->kpms_model.InferenceTask + + + + +kpms_model.FullFitting + + +kpms_model.FullFitting + + + + + +kpms_model.FullFittingTask->kpms_model.FullFitting + + + + +kpms_pca.KeypointSet + + +kpms_pca.KeypointSet + + + + + +kpms_pca.KeypointSet.VideoFile + + +kpms_pca.KeypointSet.VideoFile + + + + + +kpms_pca.KeypointSet->kpms_pca.KeypointSet.VideoFile + + + + +kpms_pca.Bodyparts + + +kpms_pca.Bodyparts + + + + + +kpms_pca.KeypointSet->kpms_pca.Bodyparts + + + + +kpms_model.Model + + +kpms_model.Model + + + + + +kpms_model.Model->kpms_model.InferenceTask + + + + +kpms_model.Inference.GridMoviesSampledInstances + + +kpms_model.Inference.GridMoviesSampledInstances + + + + + +kpms_model.PreFittingTask->kpms_model.PreFitting + + + + +kpms_pca.PCATask + + +kpms_pca.PCATask + + + + + +kpms_pca.Bodyparts->kpms_pca.PCATask + + + + +kpms_model.Inference + + +kpms_model.Inference + + + + + +kpms_model.InferenceTask->kpms_model.Inference + + + + +kpms_pca.LoadKeypointSet + + +kpms_pca.LoadKeypointSet + + + + + +kpms_pca.PCATask->kpms_pca.LoadKeypointSet + + + + +subject.Subject + + +subject.Subject + + + + + +session.Session + + +session.Session + + + + + +subject.Subject->session.Session + + + + +session.Session->kpms_model.VideoRecording + + + + +kpms_pca.PoseEstimationMethod + + +kpms_pca.PoseEstimationMethod + + + + + +kpms_pca.PoseEstimationMethod->kpms_model.VideoRecording + + + + +kpms_pca.PoseEstimationMethod->kpms_pca.KeypointSet + + + + +kpms_pca.LoadKeypointSet->kpms_pca.PCAFitting + + + + +kpms_model.Inference->kpms_model.Inference.GridMoviesSampledInstances + + + + +kpms_model.Inference.MotionSequence + + +kpms_model.Inference.MotionSequence + + + + + +kpms_model.Inference->kpms_model.Inference.MotionSequence + + + + \ No newline at end of file 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 @@ -0,0 +1,172 @@ + + + + + +kpms_model.VideoRecording + + +kpms_model.VideoRecording + + + + + +kpms_model.VideoRecording.File + + +kpms_model.VideoRecording.File + + + + + +kpms_model.VideoRecording->kpms_model.VideoRecording.File + + + + +kpms_model.InferenceTask + + +kpms_model.InferenceTask + + + + + +kpms_model.VideoRecording->kpms_model.InferenceTask + + + + +kpms_model.Model + + +kpms_model.Model + + + + + +kpms_model.Model->kpms_model.InferenceTask + + + + +kpms_model.Inference.GridMoviesSampledInstances + + +kpms_model.Inference.GridMoviesSampledInstances + + + + + +kpms_model.Inference + + +kpms_model.Inference + + + + + +kpms_model.Inference->kpms_model.Inference.GridMoviesSampledInstances + + + + +kpms_model.Inference.MotionSequence + + +kpms_model.Inference.MotionSequence + + + + + +kpms_model.Inference->kpms_model.Inference.MotionSequence + + + + +kpms_model.PreFittingTask + + +kpms_model.PreFittingTask + + + + + +kpms_model.PreFitting + + +kpms_model.PreFitting + + + + + +kpms_model.PreFittingTask->kpms_model.PreFitting + + + + +kpms_model.FullFittingTask + + +kpms_model.FullFittingTask + + + + + +kpms_model.FullFitting + + +kpms_model.FullFitting + + + + + +kpms_model.FullFittingTask->kpms_model.FullFitting + + + + +kpms_model.InferenceTask->kpms_model.Inference + + + + +subject.Subject + + +subject.Subject + + + + + +session.Session + + +session.Session + + + + + +subject.Subject->session.Session + + + + +session.Session->kpms_model.VideoRecording + + + + \ 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 @@ -0,0 +1,112 @@ + + + + + +kpms_pca.PCAFitting + + +kpms_pca.PCAFitting + + + + + +kpms_pca.LatentDimension + + +kpms_pca.LatentDimension + + + + + +kpms_pca.PCAFitting->kpms_pca.LatentDimension + + + + +kpms_pca.LoadKeypointSet + + +kpms_pca.LoadKeypointSet + + + + + +kpms_pca.LoadKeypointSet->kpms_pca.PCAFitting + + + + +kpms_pca.Bodyparts + + +kpms_pca.Bodyparts + + + + + +kpms_pca.PCATask + + +kpms_pca.PCATask + + + + + +kpms_pca.Bodyparts->kpms_pca.PCATask + + + + +kpms_pca.PCATask->kpms_pca.LoadKeypointSet + + + + +kpms_pca.KeypointSet + + +kpms_pca.KeypointSet + + + + + +kpms_pca.KeypointSet->kpms_pca.Bodyparts + + + + +kpms_pca.KeypointSet.VideoFile + + +kpms_pca.KeypointSet.VideoFile + + + + + +kpms_pca.KeypointSet->kpms_pca.KeypointSet.VideoFile + + + + +kpms_pca.PoseEstimationMethod + + +kpms_pca.PoseEstimationMethod + + + + + +kpms_pca.PoseEstimationMethod->kpms_pca.KeypointSet + + + + \ No newline at end of file diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb new file mode 100644 index 0000000..c5148c1 --- /dev/null +++ b/notebooks/tutorial.ipynb @@ -0,0 +1,5257 @@ +{ + "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": "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 keypoint-SLDS 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 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 Keypoint-SLDS Models_\n", + "- _Run the inference_ task and _visualize_ the results\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Setup**\n" + ] + }, + { + "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 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, 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", + "#### 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 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" + ] + }, + { + "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": 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 element_moseq.kpms_pca import get_kpms_root_data_dir, get_kpms_processed_data_dir \n", + "from element_interface.utils import find_full_path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[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" + ] + }, + { + "data": { + "text/plain": [ + "DataJoint connection (connected) root@localhost:3306" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dj.conn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **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": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[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" + ] + }, + { + "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 root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'plotly': 'https://cdn.plot.ly/plotly-2.18.0.min', 'tabulator': 'https://cdn.jsdelivr.net/npm/tabulator-tables@5.5.0/dist/js/tabulator', 'moment': 'https://cdn.jsdelivr.net/npm/luxon/build/global/luxon.min', 'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'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 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in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", + "application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "ce094051-92c7-46b7-8716-4784ac4589f1" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from tutorial_pipeline import lab, subject, session, kpms_pca, kpms_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting\n", + "\n", + "\n", + "kpms_pca.PCAFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet->kpms_pca.PCAFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet\n", + "\n", + "\n", + "kpms_pca.KeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod->kpms_pca.KeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod->kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet->kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.Bodyparts\n", + "\n", + "\n", + "kpms_pca.Bodyparts\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet->kpms_pca.Bodyparts\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask->kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_model.PreFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_model.FullFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LatentDimension\n", + "\n", + "\n", + "kpms_pca.LatentDimension\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_pca.LatentDimension\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCATask\n", + "\n", + "\n", + "kpms_pca.PCATask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.Bodyparts->kpms_pca.PCATask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask->kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCATask->kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask->kpms_model.Inference\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "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": "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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting\n", + "\n", + "\n", + "kpms_pca.PCAFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LoadKeypointSet->kpms_pca.PCAFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet\n", + "\n", + "\n", + "kpms_pca.KeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod->kpms_pca.KeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PoseEstimationMethod->kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet->kpms_pca.KeypointSet.VideoFile\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.Bodyparts\n", + "\n", + "\n", + "kpms_pca.Bodyparts\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.KeypointSet->kpms_pca.Bodyparts\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask->kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_model.PreFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_model.FullFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.LatentDimension\n", + "\n", + "\n", + "kpms_pca.LatentDimension\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCAFitting->kpms_pca.LatentDimension\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCATask\n", + "\n", + "\n", + "kpms_pca.PCATask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.Bodyparts->kpms_pca.PCATask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask->kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_pca.PCATask->kpms_pca.LoadKeypointSet\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask->kpms_model.Inference\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\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": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

\n", + " \n", + "
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subject_nickname

\n", + " \n", + "
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sex

\n", + " \n", + "
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subject_birth_date

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subject_description

\n", + " \n", + "
subject1F2024-01-01test subject
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject1 F 2024-01-01 test subject \n", + " (Total: 1)" + ] + }, + "execution_count": 8, + "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": 9, + "metadata": {}, + "outputs": [], + "source": [ + "subject.Subject.insert1(\n", + " dict(\n", + " subject=\"subject1\",\n", + " sex=\"F\",\n", + " subject_birth_date=\"2024-01-01\",\n", + " subject_description=\"test subject\",\n", + " ),\n", + " skip_duplicates=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create session keys and input them into the `Session` table:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Definition of the dictionary named \"session_keys\"\n", + "session_keys = [\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", + "session.Session.insert(session_keys, skip_duplicates=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirm the inserted data:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

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session_datetime

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subject12024-03-15 14:04:22
subject12024-03-16 14:43:10
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Total: 2

\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject1 2024-03-15 14:\n", + "subject1 2024-03-16 14:\n", + " (Total: 2)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "session.Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a `key` to use throughout the notebook:\n" + ] + }, + { + "cell_type": "code", + "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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Parameters used to obtain the keypoints data based on a specific pose estimation method.\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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format_method

\n", + " deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.\n", + "
\n", + "

pose_estimation_desc

\n", + " Optional. Pose estimation method description\n", + "
anipose`.csv` files generated by anipose analysis
deeplabcut`.csv` and `.h5/.hdf5` files generated by DeepLabcut analysis
facemap`.h5` files generated by Facemap analysis
nwb`.nwb` files with Neurodata Without Borders (NWB) format
sleap`.slp` and `.h5/.hdf5` files generated by SLEAP analysis
sleap-anipose`.h5/.hdf5` files generated by sleap-anipose analysis
\n", + " \n", + "

Total: 6

\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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_pca.PoseEstimationMethod()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert keypoint input metadata into the `KeypointSet` table:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "kpms_pca.KeypointSet.insert1(\n", + " {\n", + " \"kpset_id\": 1,\n", + " \"format_method\": \"deeplabcut\",\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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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format_method

\n", + " deeplabcut, sleap, anipose, sleap-anipose, nwb, facemap.\n", + "
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kpset_config_dir

\n", + " Path relative to root data directory where the config file is located\n", + "
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kpset_videos_dir

\n", + " Path relative to root data directory where the videos and their keypoints are located\n", + "
\n", + "

kpset_desc

\n", + " Optional. User-entered description\n", + "
\n", + " \n", + "

Total: 0

\n", + " " + ], + "text/plain": [ + "*kpset_id format_method kpset_config_d kpset_videos_d kpset_desc \n", + "+----------+ +------------+ +------------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 16, + "metadata": {}, + "outputs": [], + "source": [ + "video_files = [\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_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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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video_id

\n", + " \n", + "
\n", + "

video_path

\n", + " Filepath of each video, relative to root data directory\n", + "
10dlc_project/videos/21_11_8_one_mouse.top.ir.Mp4
11dlc_project/videos/21_12_2_def6a_1.top.ir.mp4
12dlc_project/videos/21_12_2_def6b_2.top.ir.mp4
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Total: 3

\n", + " " + ], + "text/plain": [ + "*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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
\n", + "

bodyparts_id

\n", + " \n", + "
\n", + "

bodyparts_desc

\n", + " Optional. User-entered description.\n", + "
\n", + "

anterior_bodyparts

\n", + " List of strings of anterior bodyparts\n", + "
\n", + "

posterior_bodyparts

\n", + " List of strings of posterior bodyparts\n", + "
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use_bodyparts

\n", + " List of strings of bodyparts to be used\n", + "
11=BLOB==BLOB==BLOB=
\n", + " \n", + "

Total: 1

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id bodyparts_desc anterior_b posterior_ use_bodypa\n", + "+----------+ +------------+ +------------+ +--------+ +--------+ +--------+\n", + "1 1 =BLOB= =BLOB= =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 19, + "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": "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": {}, + "source": [ + "The `PCATask` table serves the purpose of specifying the PCA task.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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kpms_project_output_dir

\n", + " KPMS's output directory relative to root\n", + "
11kpms_project_tutorial
\n", + " \n", + "

Total: 1

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id kpms_project_o\n", + "+----------+ +------------+ +------------+\n", + "1 1 kpms_project_t\n", + " (Total: 1)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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kpms_project_output_dir

\n", + " KPMS's output directory relative to root\n", + "
11kpms_project_tutorial
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Total: 1

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id kpms_project_o\n", + "+----------+ +------------+ +------------+\n", + "1 1 kpms_project_t\n", + " (Total: 1)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_pca.PCATask()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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coordinates

\n", + " Keypoint coordinates\n", + "
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confidences

\n", + " Keypoint confidences\n", + "
\n", + "

formatted_bodyparts

\n", + " Formatted bodyparts\n", + "
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average_frame_rate

\n", + " Average frame rate of the trained videos\n", + "
\n", + " \n", + "

Total: 0

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id coordinate confidence formatted_ average_frame_\n", + "+----------+ +------------+ +--------+ +--------+ +--------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_pca.LoadKeypointSet()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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coordinates

\n", + " Keypoint coordinates\n", + "
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confidences

\n", + " Keypoint confidences\n", + "
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formatted_bodyparts

\n", + " Formatted bodyparts\n", + "
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average_frame_rate

\n", + " Average frame rate of the trained videos\n", + "
11=BLOB==BLOB==BLOB=30.0
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Total: 1

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id coordinate confidence formatted_ average_frame_\n", + "+----------+ +------------+ +--------+ +--------+ +--------+ +------------+\n", + "1 1 =BLOB= =BLOB= =BLOB= 30.0 \n", + " (Total: 1)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 26, + "metadata": {}, + "outputs": [], + "source": [ + "kpms_pca.PCAFitting.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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pca_fitting_time

\n", + " datetime of the PCA fitting analysis\n", + "
112024-03-20 04:59:56
\n", + " \n", + "

Total: 1

\n", + " " + ], + "text/plain": [ + "*kpset_id *bodyparts_id pca_fitting_ti\n", + "+----------+ +------------+ +------------+\n", + "1 1 2024-03-20 04:\n", + " (Total: 1)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 28, + "metadata": {}, + "outputs": [], + "source": [ + "kpms_pca.LatentDimension.populate()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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kpset_id

\n", + " \n", + "
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bodyparts_id

\n", + " \n", + "
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variance_percentage

\n", + " Variance threshold. Fixed value to 0.9\n", + "
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latent_dimension

\n", + " Number of principal components to explain the variance.\n", + "
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latent_dim_desc

\n", + " Automated description of the computation result.\n", + "
1190.04>=90.0% of variance explained by 4 components.
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Total: 1

\n", + " " + ], + "text/plain": [ + "*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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "kpms_model.Model\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Model->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "kpms_model.FullFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.FullFittingTask->kpms_model.FullFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "kpms_model.Inference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.InferenceTask->kpms_model.Inference\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.GridMoviesSampledInstances\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.Inference->kpms_model.Inference.MotionSequence\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "kpms_model.PreFittingTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.PreFittingTask->kpms_model.PreFitting\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "kpms_model.VideoRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.InferenceTask\n", + "\n", + "\n", + "\n", + "\n", + "kpms_model.VideoRecording->kpms_model.VideoRecording.File\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "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", + " \n", + " \n", + " \n", + "
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pre_latent_dim

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pre_num_iterations

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\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", + " \n", + " \n", + " \n", + "
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pre_num_iterations

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1144005Testing Pre-fitting task 1
11420005Testing Pre-fitting task 2
114100005Testing Pre-fitting task 3
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\n", + " " + ], + "text/plain": [ + "*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 will be cast to \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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", 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" + ] + }, + "metadata": {}, + "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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yMDBQ6ziScnV1BZDQsXqDBg1Uht27d08anlkiIiLw9u1bva0vIiKi7IiP7xERESUSFxeHjh074uzZs9i2bRtq1KiRbN62bdti3759ePbsmZR29OhR3L9/H+3bt1fJO3ToUGzZsgXLly9HmzZtNJbXqVMn7Nq1S+0DAE2bNsWuXbtUHj9LjbbxvXnzRuP4v/zyC2QyGSpXrqz1NAHt5hVIuBPr4sWLKg1T9+7dw7Fjx1Ti8/HxUfko71Zq2bIljI2NsXz5cimvEAIrV65EwYIFUbNmTSm9Y8eOePbsmUoD19u3b7F79240aNAABgbanRIpFAr4+Pjg119/VXk74MaNGxEZGakSd7t27RAXF6fytrbPnz9j3bp1qF69epo6J9e0jv78809cvnxZ6/6wNLl79y6ePn2arnEtLS0BQO2tdR06dEBcXBymTZumNk5sbKxafk2Udykl7vMqLCxMY8f7lpaWWpVZpUoVODg4YOXKlSqPVe7fvx9BQUFo1qxZqmVo49OnTxrfHDlt2jQIITK0voiIiHIb3ilFRESUyOjRo7Fnzx60aNEC79+/x6+//qoyPPEbvsaPH49t27ahfv36GD58OCIjIzF37lyUK1cO/v7+Ur5FixZh+fLlqFGjBiwsLNTKbN26NSwtLVGqVCmUKlVKY1zu7u5a3SGVmLbx/fjjjzh9+jQaN26MwoUL4/3799ixYwcuXryIoUOHolixYlpPU9t5BYBBgwZh9erVaNasGcaMGQNjY2MsWLAABQoUwOjRo1OdVqFChTBixAjMnTsXMTExqFq1Kn7//Xf89ddf2LRpk8rjV+PGjcPWrVvRtm1bjBo1CgqFAitXrkRMTAxmzJih9fwBCcurZs2a8PLyQr9+/fD8+XPMnz8fjRo1UmlwqF69Otq3b49x48YhNDQUxYoVw/r16/H48WP88ssvaZpmzZo1UalSJVSpUgUKhQJXrlzB2rVr4eLiIvWzpHTy5EmcPHkSQEJj1sePHzF9+nQAQN26dVG3bl0pb+nSpeHl5YXjx4+nKR4goeNyABg2bBh8fX1haGiITp06wcvLC/3798fMmTNx7do1NGrUCMbGxnjw4AG2bduGxYsXo127dimW3ahRI5iYmKBFixbo378/IiMjsXr1ajg4OKg9Ounp6YkVK1Zg+vTpKFasGBwcHNTuhAIAY2NjzJ49G/7+/vDy8kLnzp3x+vVrLF68GG5ubhg5cmSal4Emr169QqVKldC5c2epPh88eBB//vknGjdujJYtW+pkOkRERLmCICIiIomXl5cAkOwnqVu3bolGjRoJCwsLYWNjI7p27SpevXqlksfPzy/FMh89epRiTADE4MGD0zU/2sR36NAh0bx5c+Hs7CyMjY2FtbW1qFWrlli3bp2Ij49P0/TSOq/Pnj0T7dq1E3K5XFhZWYnmzZuLBw8eaD29uLg4MWPGDOHq6ipMTExEmTJlxK+//qoxb3BwsGjdurWQy+XC3NxcNGjQQFy4cCFN86f0119/iZo1awozMzNhb28vBg8eLMLDw9XyRUdHizFjxghHR0dhamoqqlatKg4cOJDm6X3//feiYsWKQqFQCGNjY1G4cGExcOBAtXUphBCTJk1KdvlPmjRJJS8A4eXller0161bJwCIixcvSmmxsbFi6NChwt7eXshkMrX6sWrVKuHp6SnMzc2FtbW1KFeunPj222/Fy5cvpTyurq6iWbNmGqe5Z88eUb58eWFmZibc3NzE7Nmzxdq1a9W2o1evXolmzZoJa2trlfkJDAwUAERgYKBKuVu2bBGVKlUSpqamws7OTnTt2lU8f/5cJY+fn5+wtLRUi0m5bFPyzz//iG7duolixYoJCwsLYWpqKsqUKSNmzJghvnz5kuK4REREeY1MiEx4FzAREREREREREVEK2KcUERERERERERFlOfYpRURElMO8efMGcXFxyQ43MTGBnZ2dTqcZHR2d6lv47OzsYGJiotPpZqVXr16lONzc3BwKhUKn0wwLC5PecpgcR0dHnU6TiIiIKLvg43tEREQ5jJubG548eZLs8PR2XJ2SgIAAlc7RNQkMDES9evV0Ot2sJJPJUhzu5+eHgIAAnU6zZ8+eWL9+fYp5eKpGREREuRUbpYiIiHKY06dPp3h3ja2trfRmNF0JCQnB7du3U8zj6ekJW1tbnU43Kx05ciTF4c7OzvDw8NDpNO/cuYOXL1+mmMfHx0en0yQiIiLKLtgoRUREREREREREWY4dnRMRERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOTZKERERERERERFRlmOjFBERERERERERZTk2ShERERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOTZKERERERERERFRlmOjFBERERERERERZTk2ShERERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOTZKERERERERERFRlmOjFBERERERERERZTk2ShERERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOTZKERERERERERFRlmOjVC40efJkyGQylTQ3Nzf07Nkz06f9+PFjyGQyBAQESGk9e/aElZVVpk9bSSaTYfLkyVk2PdIdTduuPmnanrMyxnr16qFevXrS9+PHj0Mmk2H79u1ZMv2ePXvCzc0tS6ZF+pH02KDcxo4fP663mJLKquNXWmXHZUVERLonk8kwZMiQVPMFBARAJpPh8ePHmR8UUS6i80YpZWXU9Bk7dqyuJ0eZ6M8//8y2jTvZOTZ9S1oHzczM4OzsDF9fXyxZsgQRERF6jS8qKgqTJ0/OUxdyL1++xOTJk3Ht2jV9h6ImO8eWVySus6dOnVIbLoSAi4sLZDIZmjdvrocI87bly5erNEwTUe518+ZNtGvXDq6urjAzM0PBggXRsGFDLF26VCXfjBkz8Pvvv6d7Onfu3MHkyZPZeEF58ryYKCmjzCp46tSpcHd3V0krW7ZsZk2OUnHv3j0YGKStDfLPP//ETz/9lKbGH1dXV0RHR8PY2DiNEaZNSrFFR0fDyCjTNu0cQ1kHY2Ji8OrVKxw/fhwjRozAggULsGfPHpQvX14vcUVFRWHKlCkAoHIXEAD88MMP2b7xOj0xvnz5ElOmTIGbmxsqVqyo9XiHDh1KY3Rpl1Jsq1evRnx8fKbHQAnMzMzw22+/oXbt2irpJ06cwPPnz2FqaprpMdStWxfR0dEwMTHJ9GnlFMuXL0f+/PnV7tbisiLKXc6cOYP69eujcOHC6Nu3LxwdHfHs2TOcO3cOixcvxtChQ6W8M2bMQLt27dCqVat0TevOnTuYMmUK6tWrxzuSc5Hu3bujU6dOaTpep3ReTJRXZNqVe5MmTVClShWt8n769AkmJiZpbjQh7WX2xUxsbCzi4+NhYmICMzOzTJ1WavQ9/ewiaR0cN24cjh07hubNm+Prr79GUFAQzM3NMzydxOs+o4yMjLJ9g2JWxBgVFQULCwu9X+xmduMyqWratCm2bduGJUuWqGxjv/32Gzw9PfH27dtMj8HAwCBX70OFEPj06ZNO9n25fVkR5TU//vgjFAoFLl68CBsbG5VhoaGh+gmKchRDQ0MYGhrqOwwAwMePH2FpaanvMIi0kuWtQMo+GDZv3owffvgBBQsWhIWFBcLDwwEA58+fR+PGjaFQKGBhYQEvLy+cPn1arZxTp06hatWqMDMzQ9GiRfHzzz+r9fWiqT8YJU39Dr148QK9evVCgQIFYGpqijJlymDt2rUa49+6dSt+/PFHFCpUCGZmZvD29sbDhw/VpnP+/Hk0bdoUtra2sLS0RPny5bF48WIAwLp16yCTyXD16lW18WbMmAFDQ0O8ePEixeWpaTlokrRPjpiYGEyZMgXFixeHmZkZ8uXLh9q1a+Pw4cMAEvqS+emnn6RlpfwA/y3XefPmYdGiRShatChMTU1x586dFJf533//DV9fX1haWsLZ2RlTp06FEEJt2Sa9fTVpmSnFpkxLum6vXr2KJk2aQC6Xw8rKCt7e3jh37pxKHuUjNKdPn8aoUaNgb28PS0tLtG7dGm/evNG8AnKYBg0aYMKECXjy5Al+/fVXKT1p30VKSfsUSmndf/nyBRMnToSnpycUCgUsLS1Rp04dBAYGqoxvb28PAJgyZYq07pTrS1N/TbGxsZg2bZo0LTc3N4wfPx6fP39Wyefm5obmzZvj1KlTqFatGszMzFCkSBFs2LBBq2Xz4cMH9OzZEwqFAjY2NvDz88OHDx/U8mmK8fDhw6hduzZsbGxgZWWFkiVLYvz48QAStuuqVasCAPz9/aV5Vm7P9erVQ9myZXH58mXUrVsXFhYW0rjJrZe4uDiMHz8ejo6OsLS0xNdff41nz56pLQ9N/fAkLjO12DT1KfXx40eMHj0aLi4uMDU1RcmSJTFv3jyVugz81//C77//jrJly0r71AMHDqjFRAk6d+6Md+/eSfthAPjy5Qu2b9+OLl26aBwnPj4eixYtQpkyZWBmZoYCBQqgf//++Oeff1TyCSEwffp0FCpUCBYWFqhfvz5u376tVp6m/fBff/2F9u3bo3DhwjA1NYWLiwtGjhyJ6OholXGV/Qe+ePECrVq1gpWVFezt7TFmzBjExcWlOv/axphcv26a+vJQ7hcOHjyIKlWqwNzcXDpOrlu3Dg0aNICDgwNMTU3h4eGBFStWqJTp5uaG27dv48SJE1L9SFx/NB2ztm3bBk9PT5ibmyN//vzo1q2b2rE8o8uKiHQvODgYZcqUUWuQAgAHBwfpf5lMho8fP2L9+vXSfkF5vH3y5AkGDRqEkiVLwtzcHPny5UP79u1V9ksBAQFo3749AKB+/fpSGYn3Jfv370edOnVgaWkJa2trNGvWTOP+MKnUzu+V7t69i3bt2sHOzg5mZmaoUqUK9uzZo1be7du30aBBA5ibm6NQoUKYPn061q5dq7avTa4/V03nIh8+fMCIESOk84hixYph9uzZKndmJz7fXLVqlXQOWLVqVVy8eFFtOnfv3kWHDh1gb28Pc3NzlCxZEt9//71KHm2u81KT2jmNpuPQpUuX4Ovri/z588Pc3Bzu7u7o1auXNJ8pnRcDwLFjx6RtwcbGBi1btkRQUJDKdJXHxTt37qBLly6wtbVF7dq1dXKtSZQVMu3n/rCwMLVfdfPnzy/9P23aNJiYmGDMmDH4/PkzTExMcOzYMTRp0gSenp6YNGkSDAwMpJPGv/76C9WqVQOQ8Lx3o0aNYG9vj8mTJyM2NhaTJk1CgQIF0h3v69ev8dVXX0kXUvb29ti/fz969+6N8PBwjBgxQiX/rFmzYGBggDFjxiAsLAxz5sxB165dcf78eSnP4cOH0bx5czg5OWH48OFwdHREUFAQ9u3bh+HDh6Ndu3YYPHgwNm3ahEqVKqmUv2nTJtSrVw8FCxZMNuaMLIfJkydj5syZ6NOnD6pVq4bw8HBcunQJV65cQcOGDdG/f3+8fPkShw8fxsaNGzWWsW7dOnz69An9+vWDqakp7Ozskn3UJy4uDo0bN8ZXX32FOXPm4MCBA5g0aRJiY2MxderUVONNTJvYErt9+zbq1KkDuVyOb7/9FsbGxvj5559Rr149nDhxAtWrV1fJP3ToUNja2mLSpEl4/PgxFi1ahCFDhmDLli1pijO76t69O8aPH49Dhw6hb9++6SpD07oPDw/HmjVr0LlzZ/Tt2xcRERH45Zdf4OvriwsXLqBixYqwt7fHihUrMHDgQLRu3Rpt2rQBgBQfJezTpw/Wr1+Pdu3aYfTo0Th//jxmzpyJoKAg7Nq1SyXvw4cP0a5dO/Tu3Rt+fn5Yu3YtevbsCU9PT5QpUybZaQgh0LJlS5w6dQoDBgxA6dKlsWvXLvj5+aW6LG7fvo3mzZujfPnymDp1KkxNTfHw4UOpMb106dKYOnUqJk6ciH79+qFOnToAgJo1a0plvHv3Dk2aNEGnTp3QrVu3VOvwjz/+CJlMhu+++w6hoaFYtGgRfHx8cO3atTTdAaJNbIkJIfD1118jMDAQvXv3RsWKFXHw4EF88803ePHiBRYuXKiS/9SpU9i5cycGDRoEa2trLFmyBG3btsXTp0+RL18+rePMK9zc3FCjRg3873//Q5MmTQAkXJiEhYWhU6dOWLJkido4/fv3R0BAAPz9/TFs2DA8evQIy5Ytw9WrV3H69GnpbreJEydi+vTpaNq0KZo2bYorV66gUaNG+PLlS6pxbdu2DVFRURg4cCDy5cuHCxcuYOnSpXj+/Dm2bdumkjcuLg6+vr6oXr065s2bhyNHjmD+/PkoWrQoBg4cmOJ0MhJjSu7du4fOnTujf//+6Nu3L0qWLAkAWLFiBcqUKYOvv/4aRkZG2Lt3LwYNGoT4+HgMHjwYALBo0SIMHToUVlZW0gVOSvVTuS6qVq2KmTNn4vXr11i8eDFOnz6Nq1evqlzsZmRZEZHuubq64uzZs7h161aKXY5s3LhROn/u168fAKBo0aIAgIsXL+LMmTPo1KkTChUqhMePH2PFihWoV68e7ty5AwsLC9StWxfDhg3DkiVLMH78eJQuXRoApL8bN26En58ffH19MXv2bERFRWHFihWoXbs2rl69muLjfqmd3wMJ5y21atVCwYIFMXbsWFhaWmLr1q1o1aoVduzYgdatWwMAXr16hfr16yM2NlbKt2rVqgzdaRoVFQUvLy+8ePEC/fv3R+HChXHmzBmMGzcOISEhWLRokUr+3377DREREejfvz9kMhnmzJmDNm3a4O+//5aObzdu3ECdOnVgbGyMfv36wc3NDcHBwdi7dy9+/PFHAGm/ztMkPec0oaGh0rXa2LFjYWNjg8ePH2Pnzp0AkOp58ZEjR9CkSRMUKVIEkydPRnR0NJYuXYpatWrhypUrattC+/btUbx4ccyYMQNCiAxfaxJlGaFj69atEwA0foQQIjAwUAAQRYoUEVFRUdJ48fHxonjx4sLX11fEx8dL6VFRUcLd3V00bNhQSmvVqpUwMzMTT548kdLu3LkjDA0NReJZevTokQAg1q1bpxYnADFp0iTpe+/evYWTk5N4+/atSr5OnToJhUIhxaqMv3Tp0uLz589SvsWLFwsA4ubNm0IIIWJjY4W7u7twdXUV//zzj0qZieevc+fOwtnZWcTFxUlpV65cSTbuxLRdDkII4erqKvz8/KTvFSpUEM2aNUux/MGDB6uVI8R/y1Uul4vQ0FCNwxLH7ufnJwCIoUOHSmnx8fGiWbNmwsTERLx580YI8d+yDQwMTLXM5GITQn3dtmrVSpiYmIjg4GAp7eXLl8La2lrUrVtXSlNuuz4+PirraOTIkcLQ0FB8+PBB4/SyG+V8XLx4Mdk8CoVCVKpUSfru5eUlvLy81PL5+fkJV1dX6XtK6z42NlalTgghxD///CMKFCggevXqJaW9efNGbR0pTZo0SWW9Xrt2TQAQffr0Uck3ZswYAUAcO3ZMSnN1dRUAxMmTJ6W00NBQYWpqKkaPHq15Qfzr999/FwDEnDlzVOanTp06atte0hgXLlwoAEjbsSYXL15Mtk57eXkJAGLlypUahyVeL8o6UrBgQREeHi6lb926VQAQixcvltKS1vnkykwptqTrX7mcpk+frpKvXbt2QiaTiYcPH0ppAISJiYlK2vXr1wUAsXTpUrVp5WWJ6+yyZcuEtbW1dMxp3769qF+/vhAiYZ0m3m//9ddfAoDYtGmTSnkHDhxQSQ8NDRUmJiaiWbNmKvu28ePHCwAq24mm/XDiY7XSzJkzhUwmUzn+KPf1U6dOVclbqVIl4enpmeIySEuMSeugknI5Pnr0SEpT7hcOHDigll/TfPn6+ooiRYqopJUpU0bj/jHpsvry5YtwcHAQZcuWFdHR0VK+ffv2CQBi4sSJUlpGlhURZY5Dhw4JQ0NDYWhoKGrUqCG+/fZbcfDgQfHlyxe1vJaWlhqPsZr2K2fPnhUAxIYNG6S0bdu2aTznjYiIEDY2NqJv374q6a9evRIKhUItPSltzu+9vb1FuXLlxKdPn6S0+Ph4UbNmTVG8eHEpbcSIEQKAOH/+vJQWGhoqFAqF2r42ufO6pOci06ZNE5aWluL+/fsq+caOHSsMDQ3F06dPhRD/nW/my5dPvH//Xsq3e/duAUDs3btXSqtbt66wtrZWOR4p50lJ2+u85Gh7TpP0OLRr165Uz8lTOi+uWLGicHBwEO/evVOZroGBgejRo4eUpjwudu7cWa2MjFxrEmWVTHt876effsLhw4dVPon5+fmptLRfu3YNDx48QJcuXfDu3Tu8ffsWb9++xcePH+Ht7Y2TJ08iPj4ecXFxOHjwIFq1aoXChQtL45cuXRq+vr7pilUIgR07dqBFixYQQkjTfvv2LXx9fREWFoYrV66ojOPv76/S34vyDoO///4bQMLjYo8ePcKIESPUbgNO/NhBjx498PLlS5VHnDZt2gRzc3O0bds22ZgzuhxsbGxw+/ZtPHjwINW8yWnbtq10y6k2Er9KVflLxZcvX3DkyJF0x5CauLg4HDp0CK1atUKRIkWkdCcnJ3Tp0gWnTp2SHh1V6tevn8o6qlOnDuLi4vDkyZNMizOrWVlZZegtfJrWvaGhoVQn4uPj8f79e8TGxqJKlSpq9Udbf/75JwBg1KhRKumjR48GAPzxxx8q6R4eHlJdBBJ+gSpZsqRUL1OajpGRkcrdCYaGhiqdmiZHWb93796d7k7BTU1N4e/vr3X+Hj16wNraWvrerl07ODk5Scsrs/z5558wNDTEsGHDVNJHjx4NIQT279+vku7j4yP9egwk/PInl8tTXR95WYcOHRAdHY19+/YhIiIC+/btS/bRvW3btkGhUKBhw4Yqxy1PT09YWVlJx5UjR47gy5cvGDp0qMq+TZtfhgGoHKs/fvyIt2/fombNmhBCaHwkYMCAASrf69Spk+o6z2iMKXF3d9d4XEw8X8q7u728vPD3338jLCwszdO5dOkSQkNDMWjQIJW+ppo1a4ZSpUqp7a+A9C0rIsocDRs2xNmzZ/H111/j+vXrmDNnDnx9fVGwYEGNj7Zpkni/EhMTg3fv3qFYsWKwsbHR6lzo8OHD+PDhAzp37qyyXzc0NET16tVVrhc0Se38/v379zh27Bg6dOiAiIgIqfx3797B19cXDx48kB7n+vPPP/HVV19JT6oACedVXbt21WZRaLRt2zbUqVMHtra2KvPn4+ODuLg4nDx5UiV/x44dYWtrK31Per315s0bnDx5Er169VK5HgL+u95Kz3WeJuk5p1GeI+7btw8xMTGpTiOxkJAQXLt2DT179oSdnZ3KdBs2bKjxnC/pMQVI/7UmUVbKtMf3qlWrlmJH50nfzKfceab0uExYWBg+f/6M6OhoFC9eXG14yZIl03VR9ubNG3z48AGrVq3CqlWrNOZJ2sFh0h2fcoep7McjODgYQOpvHGzYsCGcnJywadMmeHt7Iz4+Hv/73//QsmVLlYtOTTFnZDlMnToVLVu2RIkSJVC2bFk0btwY3bt3T9Mb2ZKuw5QYGBioNAoBQIkSJQAgU1+H++bNG0RFRUmPayRWunRpxMfH49mzZyqPdqW2bnODyMhIlf4R0iq5db9+/XrMnz8fd+/eVTn4pmVbSezJkycwMDBAsWLFVNIdHR1hY2Oj1lCYdN0BCesvtXX35MkTODk5wcrKSiVd03aTVMeOHbFmzRr06dMHY8eOhbe3N9q0aYN27dpp/fKGggULpqlT86T1XiaToVixYpn+auknT57A2dlZbd+kfORAV+sjL7O3t4ePjw9+++03REVFIS4uDu3atdOY98GDBwgLC0u2LiuPW8r1knS7sbe3VznZT87Tp08xceJE7NmzR23dJW28MTMzU2uw1rYOZiTGlCS3/zl9+jQmTZqEs2fPIioqSmVYWFgYFApFmqajnAdN+41SpUrh1KlTKmnpXVZElHmqVq2KnTt34suXL7h+/Tp27dqFhQsXol27drh27Ro8PDxSHD86OhozZ87EunXr8OLFC5X+FrVp7FZeDzVo0EDjcLlcnuL4qZ3fP3z4EEIITJgwARMmTNBYRmhoKAoWLIgnT56odXEBaHdulJwHDx7gxo0byf6ondbrLWWDUErXW+m5ztMkPec0Xl5eaNu2LaZMmYKFCxeiXr16aNWqFbp06ZLqS6hSOqaULl0aBw8eVOvMXNPxLr3XmkRZSW+vuUr6PLLyDoO5c+cm+8p0Kysrtc6NU6KpI1QAap2IKqfdrVu3ZBvFkjbWJPdmhcQHH20YGhqiS5cuWL16NZYvX47Tp0/j5cuX6NatW5rKSau6desiODgYu3fvxqFDh7BmzRosXLgQK1euRJ8+fbQqQxdvL0pM2/WV2XS1brOr58+fIywsTKWhRyaTaZy/5Ja9pnX/66+/omfPnmjVqhW++eYbODg4wNDQEDNnzpQaadMruW0jKX2sO3Nzc5w8eRKBgYH4448/cODAAWzZsgUNGjTAoUOHtHoLi67rEpByfcqqN8Pk9rqUWbp06YK+ffvi1atXaNKkicZOd4GEY5eDgwM2bdqkcXha7mRNTlxcHBo2bIj379/ju+++Q6lSpWBpaYkXL16gZ8+eancHZsW2ldZjhab6FRwcDG9vb5QqVQoLFiyAi4sLTExM8Oeff2LhwoXpvusxLbLLG5qISJ2JiQmqVq2KqlWrokSJEvD398e2bdswadKkFMcbOnQo1q1bhxEjRqBGjRpQKBSQyWTo1KmTVvsVZZ6NGzfC0dFRbXhqb/9N7fxeWf6YMWOSfbIi6Q+BGaHpmqthw4b49ttvNeZX/mCtpIvziPRc52mSnlhkMhm2b9+Oc+fOYe/evTh48CB69eqF+fPn49y5c2o/hmaUpuOdvq41idIi27x7XXk7pFwuh4+PT7L5lG9V0HRb6r1791S+K1vTk75BK+mv+fb29rC2tkZcXFyK004L5fzcunUr1TJ79OiB+fPnY+/evdi/fz/s7e1TfQQvLcshOXZ2dvD394e/vz8iIyNRt25dTJ48WWqU0rYhQBvx8fH4+++/VQ429+/fBwCpkz5t11daYrO3t4eFhYXGZXL37l0YGBjAxcVFq7JyC2Xn8Im3MVtbW423H6flkcXt27ejSJEi2Llzp8r6SXoCl5btytXVFfHx8Xjw4IF0Nw6Q0GHlhw8f4OrqqnVZqU3n6NGjiIyMVDlB0LYuGRgYwNvbG97e3liwYAFmzJiB77//HoGBgfDx8dFpXQKgVu+FEHj48KHKSZWtra3Gtwc+efJE5a7FtK6PI0eOICIiQuXXtbt370rDKeNat26N/v3749y5cym+YKFo0aI4cuQIatWqlWLDpnK9PHjwQGXdv3nzJtW7cm7evIn79+9j/fr16NGjh5Se9JH8jEpLjImPFYkb7NKyv9q7dy8+f/6MPXv2qPz6renRGG3riHIe7t27p3aXw71791g/iHIo5ZMfISEhUlpy+4Xt27fDz88P8+fPl9I+ffqkdjxObnzl9YODg0O6r0lSOr9X7l+NjY1TLd/V1VXr6wxN5xxfvnxRWWZAwvxFRkbq7HpLOT+3bt1KNk9mXOel1VdffYWvvvoKP/74I3777Td07doVmzdvRp8+fZLdFhIfU5K6e/cu8ufPr3KXVErSc61JlJUyrU+ptPL09ETRokUxb948REZGqg1/8+YNgITWXl9fX/z+++94+vSpNDwoKAgHDx5UGUculyN//vxqzycvX75c5buhoSHatm2LHTt2aNypKaedFpUrV4a7uzsWLVqktpNO2qJevnx5lC9fHmvWrMGOHTvQqVOnVH8JScty0OTdu3cq362srFCsWDGVO9GUOzpNF7bpsWzZMul/IQSWLVsGY2NjeHt7A0jY+RoaGqa6vtISm6GhIRo1aoTdu3erPNr0+vVr/Pbbb6hdu3aqt0LnJseOHcO0adPg7u6u0idA0aJFcffuXZVt/fr169Ib5LSh/AUp8fZ9/vx5nD17ViWfhYUFAO22q6ZNmwKA2ttYFixYACChrxZdaNq0KWJjY1VeBx8XF4elS5emOu779+/V0pR3eyrrk67r0oYNG1T6BNu+fTtCQkKkN7YBCev03LlzKm8u27dvH549e6ZSVlpia9q0KeLi4lTqMgAsXLgQMplMZfqUflZWVlixYgUmT56MFi1aJJuvQ4cOiIuLw7Rp09SGxcbGSuvUx8cHxsbGWLp0qUr9TFqvNNFUr4UQWLx4sZZzo520xKi8aEt8rFC+nl1bmuYrLCwM69atU8traWmpVf2oUqUKHBwcsHLlSpVj6f79+xEUFKSz/RURZY7AwECNd70ou8RI/BhVcvsFQ0NDtTKWLl2qdsdQcsdeX19fyOVyzJgxQ2MfRKldk6R2fu/g4IB69erh559/VmswSlp+06ZNce7cOVy4cEFluKa7c4sWLap2/r5q1Sq1+e7QoQPOnj2r8Vrlw4cPiI2NTXH+krK3t0fdunWxdu1alesh4L/9e2Zc52nrn3/+Udsekp4jJnde7OTkhIoVK2L9+vUqw27duoVDhw5J58jaSM+1JlFWyjZbo4GBAdasWYMmTZqgTJky8Pf3R8GCBfHixQsEBgZCLpdj7969AIApU6bgwIEDqFOnDgYNGoTY2FgsXboUZcqUwY0bN1TK7dOnD2bNmoU+ffqgSpUqOHnypHSHTmKzZs1CYGAgqlevjr59+8LDwwPv37/HlStXcOTIEY0XnqnNz4oVK9CiRQtUrFgR/v7+cHJywt27d3H79m21nXGPHj0wZswYAND6dsq0LIekPDw8UK9ePXh6esLOzg6XLl3C9u3bVToj9/T0BAAMGzYMvr6+MDQ0RKdOndKyGCRmZmY4cOAA/Pz8UL16dezfvx9//PEHxo8fLz1iolAo0L59eyxduhQymQxFixbFvn37ND7nnZbYpk+fjsOHD6N27doYNGgQjIyM8PPPP+Pz58+YM2dOuuYnJ9i/fz/u3r2L2NhYvH79GseOHcPhw4fh6uqKPXv2qHTE26tXLyxYsAC+vr7o3bs3QkNDsXLlSpQpU0atI/jkNG/eHDt37kTr1q3RrFkzPHr0CCtXroSHh4dKQ7O5uTk8PDywZcsWlChRAnZ2dihbtqzG/gAqVKgAPz8/rFq1Ch8+fICXlxcuXLiA9evXo1WrVqhfv37GFxSAFi1aoFatWhg7diweP34MDw8P7Ny5U6v+H6ZOnYqTJ0+iWbNmcHV1RWhoKJYvX45ChQqhdu3aABJO1mxsbLBy5UpYW1vD0tIS1atXT3dfW3Z2dqhduzb8/f3x+vVrLFq0CMWKFUPfvn2lPH369MH27dvRuHFjdOjQAcHBwfj1119VOulMa2wtWrRA/fr18f333+Px48eoUKECDh06hN27d2PEiBFqZVP6pdS/opKXlxf69++PmTNn4tq1a2jUqBGMjY3x4MEDbNu2DYsXL0a7du1gb2+PMWPGYObMmWjevDmaNm2Kq1evYv/+/cifP3+K0yhVqhSKFi2KMWPG4MWLF5DL5dixY4fO+z1KS4yNGjVC4cKF0bt3b3zzzTcwNDTE2rVrYW9vr3ZRkpxGjRrBxMQELVq0QP/+/REZGYnVq1fDwcFB7ULN09MTK1aswPTp01GsWDE4ODho7O/F2NgYs2fPhr+/P7y8vNC5c2e8fv0aixcvhpubG0aOHJn+BUREmW7o0KGIiopC69atUapUKXz58gVnzpzBli1b4ObmpvJCEk9PTxw5cgQLFiyAs7Mz3N3dUb16dTRv3hwbN26EQqGAh4cHzp49iyNHjiBfvnwq06pYsSIMDQ0xe/ZshIWFwdTUFA0aNICDgwNWrFiB7t27o3LlyujUqZO0b/vjjz9Qq1YttR+GEtPm/P6nn35C7dq1Ua5cOfTt2xdFihTB69evcfbsWTx//hzXr18HAHz77bfYuHEjGjdujOHDh8PS0hKrVq2Cq6urxuutAQMGoG3btmjYsCGuX7+OgwcPqu2/v/nmG+zZswfNmzdHz5494enpiY8fP+LmzZvYvn07Hj9+nOpxKaklS5agdu3aqFy5Mvr16wd3d3c8fvwYf/zxB65duwZA99d52lq/fj2WL1+O1q1bo2jRooiIiMDq1ashl8ulRqWUzovnzp2LJk2aoEaNGujduzeio6OxdOlSKBQKTJ48OU2xpOdakyjL6Pp1fqm9jl75CuVt27ZpHH716lXRpk0bkS9fPmFqaipcXV1Fhw4dxNGjR1XynThxQnh6egoTExNRpEgRsXLlSo2viY6KihK9e/cWCoVCWFtbiw4dOojQ0FCNr958/fq1GDx4sHBxcRHGxsbC0dFReHt7i1WrVqUav/LVpUlfrXnq1CnRsGFDYW1tLSwtLUX58uU1vg49JCREGBoaihIlSmhcLsnRdjkkfSXr9OnTRbVq1YSNjY0wNzcXpUqVEj/++KPKa29jY2PF0KFDhb29vZDJZFKZynmdO3euWjyaloOfn5+wtLQUwcHBolGjRsLCwkIUKFBATJo0SeX1pEIkvBa1bdu2wsLCQtja2or+/fuLW7duqZWZXGxCaH4t7ZUrV4Svr6+wsrISFhYWon79+uLMmTMqeZLbdjW9Ij07U86H8mNiYiIcHR1Fw4YNxeLFi0V4eLjG8X799VdRpEgRYWJiIipWrCgOHjwo/Pz8hKurq5QnpXUfHx8vZsyYIVxdXYWpqamoVKmS2Ldvn1oZQghx5swZabtNvL40bbsxMTFiypQpwt3dXRgbGwsXFxcxbtw4lVcZC5GwjWt6DbKXl5fG17kn9e7dO9G9e3chl8uFQqEQ3bt3F1evXlXb9pLGePToUdGyZUvh7OwsTExMhLOzs+jcubPa6453794tPDw8hJGRkUqZXl5eokyZMhpjShq7clv83//+J8aNGyccHByEubm5aNasmdqrkIUQYv78+aJgwYLC1NRU1KpVS1y6dEnj8kguNk3rLiIiQowcOVI4OzsLY2NjUbx4cTF37lyVVy8LkVAPBw8erBZT0n0RpX7cVEpuG1+1apXw9PQU5ubmwtraWpQrV058++234uXLl1KeuLg4MWXKFOHk5CTMzc1FvXr1xK1bt9TWh6b93Z07d4SPj4+wsrIS+fPnF3379pVeha1pX5+UpnqtibYxCiHE5cuXRfXq1YWJiYkoXLiwWLBggdqruFNaZkIIsWfPHlG+fHlhZmYm3NzcxOzZs8XatWvVynj16pVo1qyZsLa2FgCk+pPcsWHLli2iUqVKwtTUVNjZ2YmuXbuK58+fq+TJ6LIiIt3bv3+/6NWrlyhVqpSwsrISJiYmolixYmLo0KHi9evXKnnv3r0r6tatK8zNzQUAaR/1zz//CH9/f5E/f35hZWUlfH19xd27dzXux1avXi2KFCkiDA0N1fYlgYGBwtfXVygUCmFmZiaKFi0qevbsKS5dupTiPGhzfi+EEMHBwaJHjx7C0dFRGBsbi4IFC4rmzZuL7du3q+S7ceOG8PLyEmZmZqJgwYJi2rRp4pdfflHbT8bFxYnvvvtO5M+fX1hYWAhfX1/x8OFDjfMdEREhxo0bJ4oVKyZMTExE/vz5Rc2aNcW8efOkOFM639R0nn/r1i3RunVrYWNjI8zMzETJkiXFhAkTVPJoc52XHG3PaZIeh65cuSI6d+4sChcuLExNTYWDg4No3ry52npM7rxYCCGOHDkiatWqJczNzYVcLhctWrQQd+7cURlfeex48+ZNsvOQ3mtNoqwgEyL39Dg7efJkTJkyJUd2ovv27Vs4OTlh4sSJyb4Ng4iIiIiISF8CAgLg7++PR48eSf3CUvbHa03KzrJNn1J5XUBAAOLi4tC9e3d9h0JERERERES5BK81KTvLNn1K5VXHjh3DnTt38OOPP6JVq1b8xYGIiIiIiIgyjNealBOwUUrPpk6dijNnzqBWrVpavemLiIiIiIiIKDW81qScIFf1KUVERERERERERDkD+5QiIiIiIiIiIqIsx0YpIiIiIiIiIiLKcmyUIiIiIiIiIiKiLKeXjs7j4+Px8uVLWFtbQyaT6SMEIhVCCERERMDZ2RkGBrmjrZb1jLIb1jOizMd6RkRERNmBtuckemmUevnyJVxcXPQxaaIUPXv2DIUKFdJ3GDrBekbZFesZUeZjPSMiIqLsILVzEr00SllbWwNICE4ul+sjBCIV4eHhcHFxkbbN3ID1jLIb1jOizJeb69nkZ11gJjfBwEMBUHQKQ9htBRSlNgBtW6Ln2hUaxw1QOGMsemDW2DD4jtsFAIiBiVq+Xz93xQZTP7X0gYcCgOCE/xVj5yNs1mi0HvibNPzYqmbAWGDx+77o6bEVihcHMDZsP8Z5LEb4m4Q8LvXDEFZcgTXLNM/fGwDj7iZJHPrvNA+PBQCcxiycA3AqbAV2KZ4A9cYBpkDrrb8lfP/XV2HV0UPRGl3vAoq9YTj9jQK1MFYadnBX64Rye44Fxo4DAOwa54vWlw4CAJyrBOOlYg+AT1KZYaNnJfxTDlA8DwNMAXwGrvygQOW5YcA34Qi7+1+joY3hS4ghlggzV2Di+rHooJiFWvXCcOW4AgAQDaDsaOCPiQ3QrN2x/+a5K3Cs9VcAgNaKetK03b4PwuPQ0mjg9AcA4HKH2sDBmQg7OgvzqwzE0rhhGG5XGg0B1MJyoF9XtWUcVloB7FJN67A3ABfiqgMAJhtOwvDfVmPDQAWOhc1CwG8DpXy2HV/iny3OsO34Eo9DSyfE5/QbdoV0UZuOm0MQHq8qDZu2LwEAovgradhyVAYATA+7gpcKawARcAv7gm8VX2Hwg5cQnS0Tyj76G/pgDQCgW9yv0vj/2Dn9+98R2L4vg+F2pfE6bCL8sAFfKeYBAE6jHQDAMMwNcYrHiEgU2+SwhAWwCCMRp3gs5VMqfekxgqq44SvFJgCAZ9gHAMCx75tBsTEMyAec/luBvWHDYa9YjDcAroYFYOuunlIZt3oiYXsrPU5Kcz4XjKC1lXGs11eIhgUAoNmlhPWuGBKGsEIJ24XieRg8z53C5aDa6FA6AFs7/FvuwVuAQ1kg9BZQpWxC2qVbWI5aCAUw+WgYXpazgfOLf+Bc5G+0xO8AgBWKUcBcGcL2KoBgYNMLYNDVD0AlGcKGKOA2NQiPr5bGqSqe2IdmGKWYCgAorghD2OKEmIQXYDMzDAAwdpUC4xYmWqC2gGJ2QvzBh4G7YQ3Q5VLCMi5d5TIA4Ny8Bgl5i0JVwqpGeFfA5csHwEGGsQ8mwgZh6GofAG2VehyMu25JC9d+XKWUyjj/BagAwGHIvwldAcW1hGXysq0NAMDypsC/qxaKXmGYH6TA6Llh+K1fa3T5ahc6nAvA1qCeCLur0DgN50bBGtOVhjoXxdKXyecZ6lwUs/AAPcN2ICBoIMZ+9d90roYFqOQ9+FtroBIQ9jEhzx9VGkjbIwAcq5KwnXb56t8dRi8kzMepJDsQAKebKVB2VfJx/9GxgfR/l692wfdcQhkHT7VOfqRU/Fa7NewUCfHWOJoQfxfFzwCAsIDiCZlKAgEeHaRxel7aCgBQ3A9DWIl/69vhMCwe11caphTs/e8/Yc4IVLzEaDyQhoUdLS79v8kbiAnroDJuz0tbpeMlkDD/XQ6tB9qnfk6il0Yp5a3XcrmcJ/GUreSmxwJYzyi7Yj0jyny5sZ6ZyU1gJjeB3AKATA65NQBYAMZymMjNkhnbAqYJI8NYnnDVJDQ0Ssk/y2BmqiHdAoBUtDnkZoDRv+UkJMkBGWAuN4HcAAAsYSY3TfhfuQqM5ZCbAubJRGgK/DsviRgnHgpYIWF8Y7l5QpqRHDBO9P1fRnJLmCvLM5fDKlEZRnJL/Be6KWCWsM+ylBsBlgn/G8it/p1h8d8yUBZvgYRxzBLmzUo5/0nilxnKIYwtITcGTOVmCfmMlLEAhv+WaSE3gtz4v/Fg8W8siWKWmwIyuTXk0YCh/N8reWM5AFPILQEzuSlkcdYw+3cZAeaAifq+WG4OtaseY7kFZHEJgZsbmgDmclgACduS+X9lyOQRgLkcMnkE5NHK5WwBeaTaZBJiNQNk1gnjC/yXSbn+E5axNQABA/kXmCMhvzCy/Dcuc1jCMKG8uMQbhjImC8jkCfOcsHwNoGwRkJax3ABxAOITjW307/KzQsIwZT6pdEvASm4glWIoj0lINwUgkwMGCUPM5KZQbnXGcgskrg7S9mb43/IzkFtBbp6wbmX/rgTlqoSh/L9twFCesI6t5DCRm/+7nv8t1UCe8NfovzRz/Fs1LeWQy2VAuBwGciuYShVWDpjLIDcCYPDv8reWA5D9t11ZApZyQ5jCDNKSlsmleRJySNuTKf7djpQs/ovfCgnbs7IeGcoTloS0W0q0jFS+y/6N00AGM7kpzGACeRp23TK5dZryJx1XKaUyLJCwtUr7ASv8V+//HdHSUkgNbTCUJyxrc3nCMjH8d31ayVW2leRi0cQ0lTwJoVkn1F0reaI9IqT9vsRcDlglqk1yo/+2RyTaTpXbsLnquk3MCkm2iSQs5Il2Oob/HYM0laUtC7mRtKjllsppJCwbaVatEo5JSJQPAGAu/+9/M3nCcSvRvP876r8jGfy7z0q0nSTKaw7ASK56zJRbQuVAZyE3AiwS5jW1c5Lc0dkAERERERERERHlKHq5U4qIiIiI0i4uXuDCo/cIjfgEB2szVHO3g6FB7rkrioiIiPIWvTZKlZ10EAamFng8q5k+wyDK1ZT1LCnWOyKinOXArRBM2XsHIWH/9ffjpDDDpBYeaFzWKYUxiYiIiLIn3ilFlEe5jf0j2WFssCIiyl4O3ArBwF+vJOrpJ8GrsE8Y+OsVrOhWmQ1TRERElOOwTykiIiKibCwuXmDK3jtqDVLAf91RT9l7B3HxmnIQERERZV9slCIiIiLKxi48eq/yyF5SAkBI2CdcePQ+64IiIiIi0gE2ShERERFlY6ERyTdIpScfERERUXbBRikiIiKibMzB2kyn+YiIiIiyCzZKEREREWVj1dzt4KQwgyyZ4TIkvIWvmrtdVoZFRERElGFslCIiIiLKxgwNZJjUwgMA1BqmlN8ntfCAoUFyzVZERERE2RMbpYiIiIiyucZlnbCiW2U4KlQf0XNUmGFFt8poXNZJT5ERERERpZ+RvgMgIiIiotQ1LuuEhh6OuPDoPUIjPsHBOuGRPd4hRURERDkVG6WIiIiIcghDAxlqFM2n7zCIiIiIdIKP7xERERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOXZ0TkRq3Mb+oTH98axmWRwJERERERER5Va8U4qIiIiIiIiIiLIcG6WIiIiIiIiIiCjLsVGKiIgIwKxZsyCTyTBixAh9h0JERERElCewUYqIiPK8ixcv4ueff0b58uX1HQoRERERUZ6Rrkapjx8/6joOItKAdY0o80VGRqJr165YvXo1bG1t9R0OUa5VuXJlyGQyyGQyGBoaYsKECfoOiYiIiPQsXY1SBQoUQK9evXDq1Cldx0NEibCuEWW+wYMHo1mzZvDx8dF3KES5VpMmTXD16lXUrl0bS5cuhbW1NaZPn46TJ0/qOzQiIiLSo3Q1Sv366694//49GjRogBIlSmDWrFl4+fKlrmMjyvNY14gy1+bNm3HlyhXMnDlTq/yfP39GeHi4yoeIUnfo0CHY2dnhr7/+wpAhQxAaGgoA6NOnj54jIyIiIn1KV6NUq1at8Pvvv+PFixcYMGAAfvvtN7i6uqJ58+bYuXMnYmNjdR0nUZ7EukaUeZ49e4bhw4dj06ZNMDMz02qcmTNnQqFQSB8XF5dMjpIo53v//j3i4+NRr149Kc3ExARWVlZ4+vSp/gIjIiIivctQR+f29vYYNWoUbty4gQULFuDIkSNo164dnJ2dMXHiRERFRekqTqI8jXWNSPcuX76M0NBQVK5cGUZGRjAyMsKJEyewZMkSGBkZIS4uTm2ccePGISwsTPo8e/ZMD5ET5SwXL14EABQrVkwl3crKCjExMWr537x5g6CgIOlz7969LImTiIiIsp5RRkZ+/fo11q9fj4CAADx58gTt2rVD79698fz5c8yePRvnzp3DoUOHdBUrUZ7Fukake97e3rh586ZKmr+/P0qVKoXvvvsOhoaGauOYmprC1NQ0q0Ik0rvo6GgIIWBhYQEAePLkCXbt2gUPDw80atQoU6ZZtWpVPHnyJFPKJiIiouwlXY1SO3fuxLp163Dw4EF4eHhg0KBB6NatG2xsbKQ8NWvWROnSpbUqz23sH9L/j2c1S09IRLmSrusaEf3H2toaZcuWVUmztLREvnz51NKJ8qqWLVuiTZs2GDBgAD58+IDq1avD2NgYb9++xYIFCzBw4MBUy6hatSoA4OHDhyrpkZGRMDY2Vst/8eJFvH37ViVftWrVMjgnRERElB2lq1HK398fnTp1wunTp6UTjaScnZ3x/fffZyg4oryOdY2IiPTpypUrWLhwIQBg+/btKFCgAK5evYodO3Zg4sSJWjVK2dnZwcDAACdOnJDSvnz5gsjISBQvXlwtv729Pezt7aXvfKEAERFR7pWuRqmQkBDpNu7kmJubY9KkSekKiogSsK4RJe/BgwcIDAxEaGgo4uPjVYZNnDgxXWUeP35cB5ER5R5RUVGwtrYGkPAGvTZt2sDAwABfffVVmh6xa9SoEQ4cOAAvLy906tQJ48ePBwCsWrUqU+ImIiKinCFdjVLW1tYICQmBg4ODSvq7d+/g4OCgsXNYIko71jUizVavXo2BAwcif/78cHR0hEwmk4bJZLJ0N0oRkapixYrh999/R+vWrXHw4EGMHDkSABAaGgq5XK51Ofv370elSpVw8uRJnDx5EgYGBhg/frzKG/mIiIgo70lXo5QQQmP658+fYWJikqGAiOg/rGtEmk2fPh0//vgjvvvuO32HQpSrTZw4EV26dMHIkSPh7e2NGjVqAEi4a6pSpUppKuvq1auZESIRERHlYGlqlFqyZAmAhF+h16xZAysrK2lYXFwcTp48iVKlSuk2QqI8iHWNKGX//PMP2rdvr+8wiHK9du3aoXbt2ggJCUGFChWkdG9vb7Ru3VqPkREREVFukKZGKWVHl0IIrFy5UuV12SYmJnBzc8PKlSt1GyFRHpRd61riN2UmxTdnUlZq3749Dh06hAEDBug7FKJcz9HREY6OjippfBseERER6UKaGqUePXoEAKhfvz527twJW1vbTAmKKK9jXSNKWbFixTBhwgScO3cO5cqVU3ut/LBhw/QUGVHu8vHjR8yaNQtHjx7V+FKBv//+W0+RERERUW6Qrj6lAgMDdR0HEWnAukak2apVq2BlZYUTJ06ovGYeSHjslY1SRLrRp08fnDhxAt27d4eTk5PKSwWIiIiIMkrrRqlRo0Zh2rRpsLS0xKhRo1LMu2DBggwHRpRXsa4RpU55NyERZa79+/fjjz/+QK1atfQdChEREeVCWjdKXb16FTExMdL/yeEvaEQZw7pGlDbKt1SyThDpnq2tLezs7PQdBhEREeVSWjdKJX6MiI8UEWUe1jUi7WzYsAFz587FgwcPAAAlSpTAN998g+7du+s5MqLcY9q0aZg4cSLWr18PCwsLfYdDREREuUy6+pQiIiLSpwULFmDChAkYMmSI9FjRqVOnMGDAALx9+xYjR47Uc4REucP8+fMRHByMAgUKwM3NTe2lAleuXNFTZERERJQbaN0o1aZNG60L3blzZ7qCISLWNSJtLF26FCtWrECPHj2ktK+//hplypTB5MmT2ShFpCOtWrXSdwhERESUi2ndKKVQKDIzDiL6F+saUepCQkJQs2ZNtfSaNWsiJCREDxER5U6TJk3SdwhERESUi2ndKLVu3brMjIOI/sW6RpS6YsWKYevWrRg/frxK+pYtW1C8eHE9RUWUe12+fBlBQUEAgDJlyqBSpUp6joiIiIhyA/YpRUREOc6UKVPQsWNHnDx5UupT6vTp0zh69Ci2bt2q5+iIco/Q0FB06tQJx48fh42NDQDgw4cPqF+/PjZv3gx7e3v9BkhEREQ5mtaNUpUrV8bRo0dha2uLSpUqpfjqbXZ6SZR+rGtEqWvbti3Onz+PhQsX4vfffwcAlC5dGhcuXOAdHEQ6NHToUEREROD27dsoXbo0AODOnTvw8/PDsGHD8L///U/PERIREVFOpnWjVMuWLWFqagqAnV4SZaacWtfcxv6R7LDHs5plYSSUV3h6euLXX3/VdxhEudqBAwdw5MgRqUEKADw8PPDTTz+hUaNGeoyMiIiIcgOtG6USd3TJTi+JMg/rGpFm4eHhkMvl0v8pUeYjooyJj4+HsbGxWrqxsTHi4+P1EBERERHlJhnqU+rSpUtSp5ceHh7w9PTUSVBEpIp1jQiwtbVFSEgIHBwcYGNjo/HRViEEZDIZ4uLi9BAhUe7ToEEDDB8+HP/73//g7OwMAHjx4gVGjhwJb29vPUdHREREOV26GqWeP3+Ozp074/Tp0yqdXtasWRObN29GoUKF0h2Q8hEgPu5DlLl1jSinOXbsGOzs7AAAgYGBeo6GKG9YtmwZvv76a7i5ucHFxQUA8OzZM5QtW5aPzxIREVGGpatRqk+fPoiJiUFQUBBKliwJALh37x78/f3Rp08fHDhwQKdBEuVVrGtE//Hy8pL+d3d3h4uLi9rdUkIIPHv2LKtDI8q1XFxccOXKFRw5cgR3794FkPBSAR8fHz1HRkRERLlBuhqlTpw4gTNnzkgXyQBQsmRJLF26FHXq1NFZcER5HesakWbu7u7So3yJvX//Hu7u7nx8j0iHZDIZGjZsiIYNG+o7FCIiIspl0tUo5eLigpiYGLX0uLg4qb8BIso41jUizZR9RyUVGRkJMzMzPURElHssWbIE/fr1g5mZGZYsWZJi3mHDhmVRVERERJQbpatRau7cuRg6dCh++uknVKlSBUBCR8zDhw/HvHnzdBogUV7GukakatSoUQAS7tyYMGECLCwspGFxcXE4f/48KlasqKfoiHKHhQsXomvXrjAzM8PChQuTzSeTydgoRURERBmidaOUra2tyq/SHz9+RPXq1WFklFBEbGwsjIyM0KtXL7Rq1UrngRLlFaxrRMm7evUqgIQ7pW7evAkTExNpmImJCSpUqIAxY8boKzyiXOHRo0ca/yciIiLSNa0bpRYtWpSJYRCRUm6sa8q3amrCN21SWijfuufv74/FixdDLpfrOSKi3G3q1KkYM2aMyl2JABAdHY25c+di4sSJeoqMiIiIcgOtG6X8/PwyMw41yotYXrBSXpPVdY0oJ1q3bp2+QyDKE6ZMmYIBAwaoNUpFRUVhypQpbJQiIiKiDJEJIURGCvj06RO+fPmikpbaL9fh4eFQKBRwGbEVBqYWKeZloxRlBeU2GRYWlm3vvEhrXUtLPdMn1vG8Q9f17NKlS9i6dSuePn2qVjd27typVRkzZ87Ezp07cffuXZibm6NmzZqYPXu2yhsvU5IT9h2Ut+h6mzQwMMDr169hb2+vkn7s2DF07NgRb968yfA0UqOcJ/QLA0zSNk/xU2QwmJSOU90RCeOULXpJSro5pprmvEEplJPGNrvZX6Utf3p8txWAZfrG7d50lVraxm39Uh8xndNLTLYi9fUoFqi+AGNHif/+vwlgCq4DADqIqyr5tvzcM/np3ko03REC+CQDzJKJZYT6CzjW7VNPA4CeKxNNY8CfyU5f6ZYYopZWVrYs1fHCTJtCcTkh3lllE2IZaPrfcEVfAYwQKtu6Jkdkmrf/KuJ+qjHoyrNt/63QUx1Szlt7a8amFd79v/8XfE74O2WywKTJqutzfmSo1mVGvsoPAAgrY6CSruirYXsaocV+a5FMu3zppGmb0LQflH1WjeHW0qLJlxn8MMNxXS9mkOLwWmlYJxmlXKcaLdJc99O9zj4lU146VStzItlhF257pb3ApPvFRTLgSziwKvVzkpTXaDI+fvyIIUOGwMHBAZaWlrC1tVX5EJFusK4RabZ582bUrFkTQUFB2LVrF2JiYnD79m0cO3Ys4eJVSydOnMDgwYNx7tw5HD58GDExMWjUqBE+fvyYidETZX+2traws7ODTCZDiRIlYGdnJ30UCgUaNmyIDh1SuSokIiIiSkW63r737bffIjAwECtWrED37t3x008/4cWLF/j5558xa9YsXcdIlGexrhFpNmPGDCxcuBCDBw+GtbU1Fi9eDHd3d/Tv3x9OTk5al3PgwAGV7wEBAXBwcMDly5dRt25dXYdNlGMsWrQIQgj06tULU6ZMUWnsNTExgZubG2rUqKHHCImIiCg3SFej1N69e7FhwwbUq1cP/v7+qFOnDooVKwZXV1ds2rQJXbt21XWcRHkS6xqRZsHBwWjWLOHRTxMTE3z8+BEymQwjR45EgwYNMGXKlHSVGxYWBgCws7PTWaxEOZGyf0N3d3fUrFkTxsbGeo6IiIiIcqN0Pb73/v17FClSBEBCnzbv378HANSuXRsnT57UXXREeRzrGpFmtra2iIiIAAAULFgQt27dAgB8+PABUVFR6SozPj4eI0aMQK1atVC2bFmNeT5//ozw8HCVD1Fu5uXlJTVIffr0ids/ERER6VS6GqWKFCmCR48eAQBKlSqFrVsTepLbu3cvbGxsdBYcUV7HukakWd26dXH48GEAQPv27TF8+HD07dsXnTt3hre3d7rKHDx4MG7duoXNmzcnm2fmzJlQKBTSx8XFJV3TIsopoqKi2LchERERZZp0NUr5+/vj+vWEt1eMHTsWP/30E8zMzDBy5Eh88803Og2QKC9jXSPSbNmyZejUqRMA4Pvvv8eoUaPw+vVrtG3bFr/88kuayxsyZAj27duHwMBAFCpUKNl848aNQ1hYmPR59uxZuueBKCf45ptvcOzYMaxYsQKmpqZYs2YNpkyZAmdnZ2zYsEHf4REREVEOl64+pUaOHCn97+Pjg6CgIFy5cgXFihVD+fLldRYcUV7HukakLjY2Fvv27YOvry+AhFfWjx07Nl1lCSEwdOhQ7Nq1C8ePH4e7u3uK+U1NTWFqappiHqLchH0bEhERUWZKV6NUUm5ubnBzc9NFUUSUgtxY19zG/pHssMezmmVhJJRTGBkZYcCAAQgKCspwWYMHD8Zvv/2G3bt3w9raGq9evQIAKBQKmJubZ7h8opwupb4NBw4cqM/QiIiIKBdI1+N7AHD06FE0b94cRYsWRdGiRdG8eXMcOXJEl7EREVjXiDSpVq0arl27luFyVqxYgbCwMNSrVw9OTk7SZ8uWLRkPkigXYN+GRERElJnS1Si1fPlyNG7cGNbW1hg+fDiGDx8OuVyOpk2b4qefftJ1jER5FusakWaDBg3CqFGjsGzZMpw9exY3btxQ+WhLCKHx07Nnz8wLnigHYd+GRERElJnS9fjejBkzsHDhQgwZMkRKGzZsGGrVqoUZM2Zg8ODBOguQKC9jXSPSTNnJ+bBhw6Q0mUwGIQRkMhni4uL0FRpRrpK0b8O7d+/i8uXL7NuQiIiIdCJdjVIfPnxA48aN1dIbNWqE7777LsNBEVEC1jUizZSPExFR5omJiUHjxo2xcuVKFC9eHADg6uoKV1dXPUdGREREuUW6Ht/7+uuvsWvXLrX03bt3o3nz5hkOiogSsK4Raaa8ME7uQ0QZZ2xsnKbHYYmIiIjSSus7pZYsWSL97+HhgR9//BHHjx9HjRo1AADnzp3D6dOnMXr0aN1HSZSHsK4RpW7Dhg0pDu/Ro0cWRUKUu3Xr1g2//PILZs2ape9QiIiIKBfSulFq4cKFKt9tbW1x584d3LlzR0qzsbHB2rVr8cMPP+guQqI8hnWNKHXDhw9X+R4TE4OoqCiYmJjAwsKCjVJEOhIbG4u1a9fiyJEj8PT0hKWlpcrwBQsW6CkyIiIiyg20bpRi/x1EWYN17T9uY/9IdtjjWc2yMBLKbv755x+1tAcPHmDgwIF8IxiRDt26dQuVK1cGANy/f19lmEwm00dIRERElIukq6PzxIQQADLvxCTxRSkvQikvy+y6RpTTFS9eHLNmzUK3bt1w9+5dfYdDlCsEBgbqOwQiIiLKxdLV0TmQ0J9HuXLlYG5uDnNzc5QvXx4bN27UZWxEBNY1orQwMjLCy5cv9R0GACAuXuBs8DvsvvYCZ4PfIS5e6DskonR7+PAhDh48iOjoaAD//VBCRERElBHpulNqwYIFmDBhAoYMGYJatWoBAE6dOoUBAwbg7du3GDlypE6DJMqrWNeINNuzZ4/KdyEEQkJCsGzZMqmu6NOBWyGYsvcOQsI+SWlOCjNMauGBxmWd9BgZUdq8e/cOHTp0QGBgIGQyGR48eIAiRYqgd+/esLW1xfz58/UdIhEREeVg6WqUWrp0KVasWKHSkezXX3+NMmXKYPLkybxQJtIR1jUizVq1aqXyXSaTwd7eHg0aNND7RfKBWyEY+OsVJL2P5FXYJwz89QpWdKvMhinKMUaOHAljY2M8ffoUpUuXltI7duyIUaNG6b2+ERERUc6WrkapkJAQ1KxZUy29Zs2aCAkJyXBQRJSAdY1Is/j4eH2HoFFcvMCUvXfUGqQAQACQAZiy9w4aejjC0ID9w1H2d+jQIRw8eBCFChVSSS9evDiePHmip6iIiIgot0hXo1SxYsWwdetWjB8/XiV9y5YtKF68uE4C00TZ6Tk7PKe8Ql91jSg7GjVqlNZ59fWa+guP3qs8speUABAS9gkXHr1HjaL5si4wonT6+PEjLCws1NLfv38PU1NTPUREREREuUm6GqWmTJmCjh074uTJk1LfHadPn8bRo0exdetWnQZIlJexriUv8Zs5k2LDde509epVle9XrlxBbGwsSpYsCSDhdfWGhobw9PTUR3gAgNCI5Buk0pOPSN/q1KmDDRs2YNq0aQASHpWNj4/HnDlzUL9+fT1HR0RERDlduhql2rZtiwsXLmDBggX4/fffAQClS5fGhQsXUKlSJV3GR5Snsa4R/Sfxq+kXLFgAa2trrF+/Hra2tgCAf/75B/7+/qhTp46+QoSDtZlO8xHp25w5c+Dt7Y1Lly7hy5cv+Pbbb3H79m28f/8ep0+f1nd4RERElMOluVEqJiYG/fv3x4QJE/Drr79mRkxEBNY1opTMnz8fhw4dkhqkAMDW1hbTp09Ho0aNMHr0aL3EVc3dDk4KM7wK+6SxXykZAEeFGaq522V1aETpUrZsWdy/fx/Lli2DtbU1IiMj0aZNGwwePBhOTuywn4iIiDImzY1SxsbG2LFjByZMmJAZ8WhF02M7fFyHcpvsUNeIsqvw8HC8efNGLf3NmzeIiIjQQ0QJDA1kmNTCAwN/vQIZoNIwpezWfFILD3ZyTjnG06dP4eLigu+//17jsMKFC6dahpubm8ZO0YXQ1HRLREREeYlBekZq1aqV9CgREWUe1jUizVq3bg1/f3/s3LkTz58/x/Pnz7Fjxw707t0bbdq00Wtsjcs6YUW3ynBUqD6i56gww4puldG4LO8uoZzD3d1dYwPwu3fv4O7urnU5MpkMR48elT5nz57VZZhERESUQ6WrT6nixYtj6tSpOH36NDw9PWFpaakyfNiwYToJLi0S3z3Fu6Yot8iOdY0oO1i5ciXGjBmDLl26ICYmBgBgZGSE3r17Y+7cuXqOLqFhqqGHIy48eo/QiE9wsE54ZI93SFFOI4SATKa+3UZGRsLMLG19ozVo0EBXYREREVEuka5GqV9++QU2Nja4fPkyLl++rDJMJpPp/UJZ2UDFxinK6bJ7Xcuu+Ga+3M/CwgLLly/H3LlzERwcDAAoWrSoWsOtPhkayFCjaD59h0GULqNGjQKQcKyZMGECLCwspGFxcXE4f/48KlasqHV5iRu3LCws8Ouvv6J169Ya87558wZv376VvkdGRqZjDoiIiCgnSFej1KNHj6T/lf0BaPoVTd/YOEU5XU6pa0T6YmlpifLly+s7DKJc5+rVqwASjj03b96EiYmJNMzExAQVKlTAmDFjtCqrTp06KFu2LOrXr4+bN29i48aNaNOmDe7du4cSJUqo5a9atarGPqiIiIgo90lXoxSQcAfHwoUL8eDBAwAJjxmNGDECffr00VlwRMS6RkREWS8wMBAA4O/vjyVLlsDa2jrdZW3cuFHl+6hRo1ChQgX4+/vj9OnTavkvXryodqdUtWrV0j19IiIiyr7S1Sg1ceJELFiwAEOHDkWNGjUAAGfPnsXIkSPx9OlTTJ06VadBZhT7m6KcKqfVtZyAj/YREaUu8QsD/Pz8ks23c+fONJddvnx5GBgY4PHjxxqH29vbw97eXvoeHh6e5mkQERFRzpCuRqkVK1Zg9erV6Ny5s5T29ddfo3z58hg6dCgvlIl0hHWNiIj0QaFQZFrZwcHBiI+PR/78+TNtGkRERJQzpKtRKiYmBlWqVFFL9/T0RGxsbIaDIqIErGtERKQP69at01lZjo6OaN++PerWrYuLFy9i4cKFAIDly5frbBpERESUM6WrUap79+5YsWIFFixYoJK+atUqdO3aVSeBZRZNj+7wkR3KrnJyXSMiIgISHr9btmwZli1bBgAwNzfHunXrUKtWLT1HRkRERPqWoY7ODx06hK+++goAcP78eTx9+hQ9evSQXiMMQO1iOjtiQxVlZ7mprhERUd4TFRWl7xCIiIgom0pXo9StW7dQuXJlAAn9AgBA/vz5kT9/fty6dUvKl5NfXa9sqGLjFOlTXqhr2UlynaBzP0BERERERKR76WqUUr4mOC/gXVSkT3mprhHpy08//YS5c+fi1atXqFChApYuXcrXzxMRERERZYF0P76Xl/EuKqK8Jbk7qADuB3K6LVu2YNSoUVi5ciWqV6+ORYsWwdfXF/fu3YODg4O+wyMiIiIiytUM9B0AERGRvixYsAB9+/aFv78/PDw8sHLlSlhYWGDt2rX6Do2IiIiIKNfjnVIZkPjuCd4tQUSUs3z58gWXL1/GuHHjpDQDAwP4+Pjg7NmzGsf5/PkzPn/+LH0PDw/P9DiJiIiIiHIrNkoREWUAH+3Lud6+fYu4uDgUKFBAJb1AgQK4e/euxnFmzpyJKVOmZEV4RERERES5Hh/fIyIi0tK4ceMQFhYmfZ49e6bvkIiIiIiIcizeKUVElEl4F1X2lj9/fhgaGuL169cq6a9fv4ajo6PGcUxNTWFqapoV4RERERER5XpslCIi0gM2WOmfiYkJPD09cfToUbRq1QoAEB8fj6NHj2LIkCH6DY6IiIiIKA9go5SOKC8weTFJRJRzjBo1Cn5+fqhSpQqqVauGRYsW4ePHj/D399d3aEREREREuR4bpXQspbsfEmPjFRGR/nXs2BFv3rzBxIkT8erVK1SsWBEHDhxQ6/yciIiIiIh0j41SesJHd4goOdw/ZK0hQ4bwcT0iIiIiIj3g2/eIiIiIiIiIiCjL8U6pbEjTXRK8O4KIAN5FRUREREREuQcbpXIINlQRUWrYYEVERERERDkJG6VyMDZUEREREREREVFOxUapXEbZUMXGKSJKTNs3gybG/QgREREREWUmNkrlUum5AFXihSgRAXwckIiIiIiIMhcbpUhNRhq0tMGLWaKcLz37ifjPUZkQCRERERER5VRslKIsx76wiIiIiIiIiIiNUpQt8DEhIiIiIiIioryFjVKU7Wn7mBAbr4iIiIiIiIhyDjZKUa6R1j5u2IhFREREREREpD96aZQSQgBgp7ekX+Hh4Wr/K7fN3ID1jLIb5baYG+tZ4v0JkT7l5uMZvqS9noWHp288RCRMMy488r+yPieTNyaFcj6mbbKf0pY9XcKjAMjSN+6X8GjN5aUmndNTEZP6eky0ugAAiUNLWH2R/xalOh8aZus/ibefCAF8lgExydSvGPUZTa5o1WmmvhAjw+M1pKY+XrgAEJkwD58Spyl9CQcihMq2rklEMunxqYynS4m3tdSqllbbZUrjJ1pGUtX/FI6kuwERntyS0SDCRK1sAJr3URFa7MO/yLTLl06atgmN+8Ek8WveVv8VkfHzpdS2uDStk4z6d51q9CWZHV9619lnXexI/xMXnkItikzHekq6X/wik7aN1M5JZEIPZy3Pnz+Hi4tLVk+WKFXPnj1DoUKF9B2GTrCeUXbFekaU+VjPiIiIKDtI7ZxEL41S8fHxePnyJYQQKFy4MJ49ewa5XJ7VYehFeHg4XFxc8sw855T5FUIgIiICzs7OMDAw0Hc4OqGsZ9bW1pDJdNuynhPklG1PX/SxfPJiPctr2yHnV/9yaz27d+8ePDw8stWyzomy4zabU3FZ6g6XpW5wOeoOl6VuaHtOopfH9wwMDFCoUCHpFnO5XJ7nVnZem+ecML8KhULfIeiUsp7ldTlh29OnrF4+ebWe5bXtkPOrX7mxnhUsWBBA9lvWORWXo+5wWeoOl6VucDnqDpdlxmlzTpI7fkIjIiIiIiIiIqIchY1SRERERERERESU5fTaKGVqaopJkybB1NRUn2Fkqbw2z3ltfin74LaXMi6frJHXljPnlzILl7VucDnqDpel7nBZ6gaXo+5wWWYtvXR0TkREREREREREeRsf3yMiIiIiIiIioizHRikiIiIiIiIiIspybJQiIiIiIiIiIqIsp7dGqZ9++glubm4wMzND9erVceHCBX2FonMzZ85E1apVYW1tDQcHB7Rq1Qr37t1TyVOvXj3IZDKVz4ABA/QUccZNnjxZbX5KlSolDf/06RMGDx6MfPnywcrKCm3btsXr16/1GDHlFidPnkSLFi3g7OwMmUyG33//XWW4EAITJ06Ek5MTzM3N4ePjgwcPHugnWD3QZn/E+pk5cvNxLjFttrHcbNasWZDJZBgxYoS+Q8m18kpdyghdHAvfv3+Prl27Qi6Xw8bGBr1790ZkZGQWzoX+6eqY+fTpUzRr1gwWFhZwcHDAN998g9jY2KycFb1bsWIFypcvD7lcDrlcjho1amD//v3ScC7H9NF0zOGy1I4urle5HDOHXhqltmzZglGjRmHSpEm4cuUKKlSoAF9fX4SGhuojHJ07ceIEBg8ejHPnzuHw4cOIiYlBo0aN8PHjR5V8ffv2RUhIiPSZM2eOniLWjTJlyqjMz6lTp6RhI0eOxN69e7Ft2zacOHECL1++RJs2bfQYLeUWHz9+RIUKFfDTTz9pHD5nzhwsWbIEK1euxPnz52FpaQlfX198+vQpiyPVD232R6yfupfbj3OJaXvMy40uXryIn3/+GeXLl9d3KLlWXqpLGaGLY2HXrl1x+/ZtHD58GPv27cPJkyfRr1+/rJqFbEEXx8y4uDg0a9YMX758wZkzZ7B+/XoEBARg4sSJ+pglvSlUqBBmzZqFy5cv49KlS2jQoAFatmyJ27dvA+ByTI/kjjlcltrLyPUql2MmEnpQrVo1MXjwYOl7XFyccHZ2FjNnztRHOJkuNDRUABAnTpyQ0ry8vMTw4cP1F5SOTZo0SVSoUEHjsA8fPghjY2Oxbds2KS0oKEgAEGfPns2iCCkvACB27dolfY+PjxeOjo5i7ty5UtqHDx+Eqamp+N///qeHCPUv6f6I9TNz5LXjXGKajnm5UUREhChevLg4fPhwrjumZyd5uS6lV3qOhXfu3BEAxMWLF6U8+/fvFzKZTLx48SLLYs9u0nPM/PPPP4WBgYF49eqVlGfFihVCLpeLz58/Z+0MZDO2trZizZo1XI7pkNwxh8tSexm9XuVyzDxZfqfUly9fcPnyZfj4+EhpBgYG8PHxwdmzZ7M6nCwRFhYGALCzs1NJ37RpE/Lnz4+yZcti3LhxiIqK0kd4OvPgwQM4OzujSJEi6Nq1K54+fQoAuHz5MmJiYlTWealSpVC4cOFcu84pe3j06BFevXqlsu0pFApUr149z257SfdHrJ+6lxePc4kld8zLbQYPHoxmzZqprGfSrbxel3RFm2Ph2bNnYWNjgypVqkh5fHx8YGBggPPnz2d5zNlFeo6ZZ8+eRbly5VCgQAEpj6+vL8LDw6W7hPKauLg4bN68GR8/fkSNGjW4HNMhuWMOl2XaZOR6lcsx8xhl9QTfvn2LuLg4lZUJAAUKFMDdu3ezOpxMFx8fjxEjRqBWrVooW7aslN6lSxe4urrC2dkZN27cwHfffYd79+5h586deow2/apXr46AgACULFkSISEhmDJlCurUqYNbt27h1atXMDExgY2Njco4BQoUwKtXr/QTMOUJyu1L0/4mL257mvZHrJ+6l9eOc4kld8zLbTZv3owrV67g4sWL+g4lV8vLdUmXtDkWvnr1Cg4ODirDjYyMYGdnl2ePBek9Zr569UrjslYOy0tu3ryJGjVq4NOnT7CyssKuXbvg4eGBa9eucTmmQUrHHG6T2svo9SqXY+bJ8kapvGbw4MG4deuWyvOqAFSe0S9XrhycnJzg7e2N4OBgFC1aNKvDzLAmTZpI/5cvXx7Vq1eHq6srtm7dCnNzcz1GRkRKye2PiHQlL2xjz549w/Dhw3H48GGYmZnpOxwiyiR5YX+W2UqWLIlr164hLCwM27dvh5+fH06cOKHvsHIUHnN0h9er2VeWP76XP39+GBoaqvVk//r1azg6OmZ1OJlqyJAh2LdvHwIDA1GoUKEU81avXh0A8PDhw6wILdPZ2NigRIkSePjwIRwdHfHlyxd8+PBBJU9uXOeUvSi3r7ywv0lNcvsj1k/dy0vHucTScszLyS5fvozQ0FBUrlwZRkZGMDIywokTJ7BkyRIYGRkhLi5O3yHmGnm1LumaNsdCR0dHtc7jY2Nj8f79+zy5rDNyzHR0dNS4rJXD8hITExMUK1YMnp6emDlzJipUqIDFixdzOaZBasecAgUKcFmmU1qvV7kcM0+WN0qZmJjA09MTR48eldLi4+Nx9OhR1KhRI6vDyRRCCAwZMgS7du3CsWPH4O7unuo4165dAwA4OTllcnRZIzIyEsHBwXBycoKnpyeMjY1V1vm9e/fw9OnTXLPOKXtyd3eHo6OjyrYXHh6O8+fP55ltL7X9Eeun7uWF41xi6Tnm5WTe3t64efMmrl27Jn2qVKmCrl274tq1azA0NNR3iLlGXqtLmUWbY2GNGjXw4cMHXL58Wcpz7NgxxMfHSz+c5gW6OGbWqFEDN2/eVGnkO3z4MORyOTw8PLJmRrKp+Ph4fP78mcsxDVI75lSpUoXLMp3Ser3K5ZiJ9NG7+ubNm4WpqakICAgQd+7cEf369RM2NjYqPdnnZAMHDhQKhUIcP35chISESJ+oqCghhBAPHz4UU6dOFZcuXRKPHj0Su3fvFkWKFBF169bVc+TpN3r0aHH8+HHx6NEjcfr0aeHj4yPy588vQkNDhRBCDBgwQBQuXFgcO3ZMXLp0SdSoUUPUqFFDz1FTbhARESGuXr0qrl69KgCIBQsWiKtXr4onT54IIYSYNWuWsLGxEbt37xY3btwQLVu2FO7u7iI6OlrPkWeN1PZHQrB+ZobcfpxLTJttLLfj2/cyT16qSxmhi2Nh48aNRaVKlcT58+fFqVOnRPHixUXnzp31NUt6oYtjZmxsrChbtqxo1KiRuHbtmjhw4ICwt7cX48aN08cs6c3YsWPFiRMnxKNHj8SNGzfE2LFjhUwmE4cOHRJCcDlmRNJjDpeldjJ6vcrlmHn00iglhBBLly4VhQsXFiYmJqJatWri3Llz+gpF5wBo/Kxbt04IIcTTp09F3bp1hZ2dnTA1NRXFihUT33zzjQgLC9Nv4BnQsWNH4eTkJExMTETBggVFx44dxcOHD6Xh0dHRYtCgQcLW1lZYWFiI1q1bi5CQED1GTLlFYGCgxvrm5+cnhEh4FfaECRNEgQIFhKmpqfD29hb37t3Tb9BZKLX9kRCsn5klNx/nEtNmG8vt2CiVufJKXcoIXRwL3717Jzp37iysrKyEXC4X/v7+IiIiQg9zoz+6OmY+fvxYNGnSRJibm4v8+fOL0aNHi5iYmCyeG/3q1auXcHV1FSYmJsLe3l54e3tLDVJCcDlmRNJjDpeldnRxvcrlmDlkQgiRufdiERERERERERERqcryPqWIiIiIiIiIiIjYKEVERERERERERFmOjVJERERERERERJTl2ChFRERERERERERZjo1SRERERERERESU5dgoRUREREREREREWY6NUkRERERERERElOXYKEVERERERERERFmOjVI6FhAQABsbG+n75MmTUbFixTSVIZPJ8Pvvvyc7/PHjx5DJZLh27Vq6Ysxsx48fh0wmw4cPH/QdCmkhJ2+zSWPXBzc3NyxatChLptW9e3fMmDFD+h4VFYW2bdtCLpfnyDp34MABVKxYEfHx8foOhTSoV68eRowYoe8wVKS2ryEiIiKinIWNUkREWkiuAezixYvo169fpk//+vXr+PPPPzFs2DApbf369fjrr79w5swZhISEQKFQZHocutS4cWMYGxtj06ZN+g6FNNi5cyemTZsGIGsbX4HkG8dDQkLQpEmTLIuDiCg36NmzJ1q1aqW36fNHNSJKCRulCAAQFxfHHSvlSV++fMnQ+Pb29rCwsNBRNMlbunQp2rdvDysrKyktODgYpUuXRtmyZeHo6AiZTKY2XkbnL7P17NkTS5Ys0XcYpIGdnR2sra11WmZGt0dHR0eYmprqKBoiopxPJpOl+Jk8eTIWL16MgIAAvcTHH9WIKDVslNJg+/btKFeuHMzNzZEvXz74+Pjg48ePOHnyJIyNjfHq1SuV/CNGjECdOnW0KvvixYto2LAh8ufPD4VCAS8vL1y5ckUtn/LXYHNzcxQpUgTbt29Psdxbt26hSZMmsLKyQoECBdC9e3e8ffs22fzKuz727NkDDw8PmJqa4unTp1rFJ5PJsGbNGrRu3RoWFhYoXrw49uzZk+y0oqKi0KRJE9SqVSvH/RKSU+SFbRZI2G4LFy4MCwsLtG7dGu/evVMZrumXwBEjRqBevXrS93r16mHIkCEYMWIE8ufPD19fXwDAggULUK5cOVhaWsLFxQWDBg1CZGQkgIRHUv39/REWFqZykgeo30Hy9OlTtGzZElZWVpDL5ejQoQNev34tDVfeAbJx40a4ublBoVCgU6dOiIiISHa+4+LisH37drRo0UJlPubPn4+TJ09CJpNJ8+jm5oZp06ahR48ekMvl0l1c3333HUqUKAELCwsUKVIEEyZMQExMjFpca9euReHChWFlZYVBgwYhLi4Oc+bMgaOjIxwcHPDjjz+qxPbhwwf06dMH9vb2kMvlaNCgAa5fvy4Nv379OurXrw9ra2vI5XJ4enri0qVL0vAWLVrg0qVLCA4OTnb+ST+Uj+/Vq1cPT548wciRI6XtX+nUqVOoU6cOzM3N4eLigmHDhuHjx4/S8PRsjwEBAZgyZQquX78uTU95MZX08b2bN2+iQYMG0r6vX79+Ur0F/tsnzJs3D05OTsiXLx8GDx6ssu0TEeVkISEh0mfRokWQy+UqaWPGjIFCodBbdwf8UY2IUsNGqSRCQkLQuXNn9OrVC0FBQTh+/DjatGkDIQTq1q2LIkWKYOPGjVL+mJgYbNq0Cb169dKq/IiICPj5+eHUqVM4d+4cihcvjqZNm6pdkE6YMAFt27bF9evX0bVrV3Tq1AlBQUEay/zw4QMaNGiASpUq4dKlSzhw4ABev36NDh06pBhLVFQUZs+ejTVr1uD27dtwcHDQOr4pU6agQ4cOuHHjBpo2bYquXbvi/fv3GmNr2LAh4uPjcfjwYb33/5Mb5ZVt9vz58+jduzeGDBmCa9euoX79+pg+fbpW85DU+vXrYWJigtOnT2PlypUAAAMDAyxZsgS3b9/G+vXrcezYMXz77bcAgJo1a6qd6I0ZM0at3Pj4eLRs2RLv37/HiRMncPjwYfz999/o2LGjSr7g4GD8/vvv2LdvH/bt24cTJ05g1qxZycZ748YNhIWFoUqVKlLazp070bdvX9SoUQMhISHYuXOnNGzevHmoUKECrl69igkTJgAArK2tERAQgDt37mDx4sVYvXo1Fi5cqBbX/v37ceDAAfzvf//DL7/8gmbNmuH58+c4ceIEZs+ejR9++AHnz5+Xxmnfvj1CQ0Oxf/9+XL58GZUrV4a3t7e0P+jatSsKFSqEixcv4vLlyxg7diyMjY2l8QsXLowCBQrgr7/+Snmlkd7s3LkThQoVwtSpU6XtH0jYXho3boy2bdvixo0b2LJlC06dOoUhQ4aojJ/W7bFjx44YPXo0ypQpI00vaR0CgI8fP8LX1xe2tra4ePEitm3bhiNHjqhNPzAwEMHBwQgMDMT69esREBCgtzsGiIh0zdHRUfooFArIZDKVNCsrK7Uf7erVq4ehQ4dixIgRsLW1RYECBbB69Wp8/PgR/v7+sLa2RrFixbB//36VaaX1B0X+qEZEWhGk4vLlywKAePz4scbhs2fPFqVLl5a+79ixQ1hZWYnIyEghhBDr1q0TCoVCGj5p0iRRoUKFZKcXFxcnrK2txd69e6U0AGLAgAEq+apXry4GDhwohBDi0aNHAoC4evWqEEKIadOmiUaNGqnkf/bsmQAg7t27p3G669atEwDEtWvXko0tpfh++OEH6XtkZKQAIPbv3y+EECIwMFAAEEFBQaJ8+fKibdu24vPnzylOh9Ivr2yznTt3Fk2bNlVJ69ixo0rsfn5+omXLlip5hg8fLry8vKTvXl5eolKlSsnOn9K2bdtEvnz5pO9Jl5OSq6urWLhwoRBCiEOHDglDQ0Px9OlTafjt27cFAHHhwgUhRMLytbCwEOHh4VKeb775RlSvXj3ZWHbt2iUMDQ1FfHx8ivOmjKdVq1apzt/cuXOFp6en9F1TXL6+vsLNzU3ExcVJaSVLlhQzZ84UQgjx119/CblcLj59+qRSdtGiRcXPP/8shBDC2tpaBAQEpBhLpUqVxOTJk1ONmbKWl5eXGD58uBBCdTtX6t27t+jXr59K2l9//SUMDAxEdHS0NF56t0dN+yEAYteuXUIIIVatWiVsbW2lfZkQQvzxxx/CwMBAvHr1SgiRsE9wdXUVsbGxUp727duLjh07phoTEVFOk9y5StLzIy8vL2FtbS2mTZsm7t+/L6ZNmyYMDQ1FkyZNxKpVq8T9+/fFwIEDRb58+cTHjx+FEEL8888/wt7eXowbN04EBQWJK1euiIYNG4r69esnG8+VK1cEAGmfLIQQ7969E3379hU1atQQISEh4t27d0KIhOOFXC4X8+bNEw8fPhQPHz4UQiScM54+fVo8evRI7NmzRxQoUEDMnj1bKm/SpEnCyspKtGvXTty+fVvs2bNHmJiYCF9fXzF06FBx9+5dsXbtWgFAnDt3ThrPx8dHtGjRQly8eFHcv39fjB49WuTLl0+Kp0yZMqJbt24iKChI3L9/X2zdulXtuqlAgQJi3bp12q0cIkoW75RKokKFCvD29ka5cuXQvn17rF69Gv/88480vGfPnnj48CHOnTsHIOExgw4dOsDS0lKr8l+/fo2+ffuiePHiUCgUkMvliIyMxNOnT1Xy1ahRQ+17cnedXL9+HYGBgbCyspI+pUqVAoAUW+9NTExQvnz5dMWXeDxLS0vI5XKEhoaq5GnYsCGKFSuGLVu2wMTEJNk4KGPyyjYbFBSE6tWrpzhNbXl6eqqlHTlyBN7e3ihYsCCsra3RvXt3vHv3DlFRUVqXGxQUBBcXF7i4uEhpHh4esLGxUVkWbm5uKn31ODk5qdWfxKKjo2Fqaqrx9nZNEt9RpbRlyxbUqlVL+tX0hx9+UFuHSeMqUKAAPDw8YGBgoJKmjPX69euIjIxEvnz5VNblo0ePpPU4atQo9OnTBz4+Ppg1a5bG9Wtubp6m5UzZw/Xr1xEQEKCy7n19fREfH49Hjx5J+dK7PaYmKCgIFSpUUNmX1apVC/Hx8bh3756UVqZMGRgaGkrfU6tvRER5QYUKFfDDDz+gePHiGDduHMzMzJA/f37pnG/ixIl49+4dbty4AQBYtmwZKlWqhBkzZqBUqVKoVKkS1q5di8DAQNy/f1/jNJ48eQJDQ0M4ODhIaXZ2drCwsICJiQkcHR1hZ2cnDWvQoAFGjx6NokWLomjRogCAH374ATVr1oSbmxtatGiBMWPGYOvWrSrTiY+Px9q1a+Hh4YEWLVqgfv36uHfvHhYtWoSSJUvC398fJUuWRGBgIICER88vXLiAbdu2oUqVKihevDjmzZsHGxsbqfuJp0+fwsfHB6VKlULx4sXRvn17VKhQQWW6zs7OePLkSQbXBBEZ6TuA7MbQ0BCHDx/GmTNncOjQISxduhTff/89zp8/D3d3dzg4OKBFixZYt24d3N3dsX//fhw/flzr8v38/PDu3TssXrwYrq6uMDU1RY0aNTL03HRkZCRatGiB2bNnqw1zcnJKdjxzc3O1i1xt40v8+A2Q0M9H0o7SmzVrhh07duDOnTsoV65cWmeLtJSXttnUGBgYQAihkqap75ikDXKPHz9G8+bNMXDgQPz444+ws7PDqVOn0Lt3b3z58kXnHZlrU38Sy58/P6KiovDlyxetGniTzt/Zs2fRtWtXTJkyBb6+vlAoFNi8eTPmz5+falwpxRoZGQknJyeN25PyUd3JkyejS5cu+OOPP7B//35MmjQJmzdvRuvWraW879+/h729farzRdlLZGQk+vfvr9J5rVLhwoWl/9O7PepKWusbEVFekPgHZkNDQ+TLl0/lfL1AgQIAoPJDlPIHxaSCg4NRokQJtXRd/ai2ZMkSBAcHIzIyErGxsZDL5Sp5NP2oZmhoqNWPaknjTfqj2saNG+Hj44P27dtLDWVK/FGNSDfYKKWBTCZDrVq1UKtWLUycOBGurq7YtWsXRo0aBQDo06cPOnfujEKFCqFo0aKoVauW1mWfPn0ay5cvR9OmTQEAz5490/gs9rlz59CjRw+V75UqVdJYZuXKlbFjxw64ubnByChjq1Tb+LQxa9YsWFlZwdvbG8ePH4eHh0eGYqPk5YVttnTp0ip9GSmnkZi9vT1u3bqlknbt2jW1i9KkLl++jPj4eMyfP186gUn6K5yJiQni4uJSjfHZs2d49uyZdLfUnTt38OHDhwxt/xUrVpTKUv6fFmfOnIGrqyu+//57KU0Xv+xVrlwZr169gpGREdzc3JLNV6JECZQoUQIjR45E586dsW7dOqlR6tOnTwgODk52W6HsQdP2X7lyZdy5cwfFihVLU1nabI/a1reAgAB8/PhRavg6ffo0DAwMULJkyTTFRESU16T2Q5SyISnxD1Fp/UGRP6oRkTb4+F4S58+fx4wZM3Dp0iU8ffoUO3fuxJs3b1C6dGkpj6+vL+RyOaZPnw5/f/80lV+8eHFs3LgRQUFBOH/+PLp27Qpzc3O1fNu2bcPatWtx//59TJo0CRcuXFDrvFVp8ODBeP/+PTp37oyLFy8iODgYBw8ehL+/f6on9emNT1vz5s1D165d0aBBA9y9ezfd5VDy8so2O2zYMBw4cADz5s3DgwcPsGzZMhw4cEAlT4MGDXDp0iVs2LABDx48wKRJk9QaqTQpVqwYYmJisHTpUvz999/YuHGj1AG6kpubGyIjI3H06FG8fftW4y9jPj4+KFeuHLp27YorV67gwoUL6NGjB7y8vDT++qcte3t7VK5cGadOnUrX+MWLF8fTp0+xefNmBAcHY8mSJdi1a1e641Hy8fFBjRo10KpVKxw6dAiPHz/GmTNn8P333+PSpUuIjo7GkCFDcPz4cTx58gSnT5/GxYsXVbbNc+fOSXffUfbl5uaGkydP4sWLF1Kj9HfffYczZ85ILx948OABdu/enWy9V9Jme3Rzc8OjR49w7do1vH37Fp8/f1Yrp2vXrjAzM4Ofnx9u3bqFwMBADB06FN27d5d+4SciIt2oXLkybt++DTc3NxQrVkzlk1yXEIl/VEuPxD9iKB+z0/WPaknnJX/+/FI+5Q9qhw4dQps2bbBu3TppGH9UI9IdNkolIZfLcfLkSTRt2hQlSpTADz/8gPnz56NJkyZSHgMDA/Ts2RNxcXEqd4Zo45dffsE///yDypUro3v37hg2bJjKc9ZKU6ZMwebNm1G+fHls2LAB//vf/5K908LZ2RmnT59GXFwcGjVqhHLlymHEiBGwsbFRuW1Vl/GlxcKFC9GhQwc0aNAg2WfOKf3yyjb71VdfYfXq1Vi8eDEqVKiAQ4cO4YcfflDJ4+vriwkTJuDbb79F1apVERERodX8VqhQAQsWLMDs2bNRtmxZbNq0CTNnzlTJU7NmTQwYMAAdO3aEvb095syZo1aOTCbD7t27YWtri7p168LHxwdFihTBli1bUo0hNX369MGmTZvSNe7XX3+NkSNHYsiQIahYsSLOnDkjvQUtI2QyGf7880/UrVsX/v7+KFGiBDp16oQnT55It86/e/cOPXr0QIkSJdChQwc0adIEU6ZMkcr43//+h65du+r8EUnSralTp+Lx48coWrSo9Ktw+fLlceLECdy/fx916tRBpUqVMHHiRDg7O6dYljbbY9u2bdG4cWPUr18f9vb2+N///qdWjoWFBQ4ePIj379+jatWqaNeuHby9vbFs2TLdzTgREQFI3w+K/FGNiLSi757Wc6pevXqJFi1a6DsMIq1xm83ZoqKihIuLizhz5oy+Q9GZN2/eCDs7O/H333/rOxQiIqIcLy1v31O+XVVJ01tWkeiNp0IIcf/+fdG6dWthY2MjzM3NRalSpcSIESPU3g6c2PLly8VXX32lkpbc24OTTl+IhDcU58uXT1hZWYmOHTuKhQsXpvrWaE1vY046z+Hh4WLo0KHC2dlZGBsbCxcXF9G1a1fx9OlT8fnzZ9GpUyfh4uIiTExMhLOzsxgyZIj0ZlkhhOjXr5/o379/svNNRNqTCZGkV2BKUVhYGG7evImGDRtiz549aNiwob5DIkoRt9nc4/jx44iIiECLFi30HYpOXLp0CcHBwejYsaO+QyEiIqJMEB0djZIlS2LLli255q6it2/fomTJkrh06RLc3d31HQ5RjsdGqTSqV68eLly4gP79+2PhwoX6DocoVdxmiYiIiEhf+KMaEaWEjVJERERERERERJTl2NE5ERERERERERFlOTZKERERERERERFRlmOjFBERERERERERZTk2ShERERERERERUZZjoxQREREREREREWU5NkoREREREREREVGWY6MUERERERERERFlOTZKERERERERERFRlmOjFBERERERERERZbn/Awkt6wVwrK0LAAAAAElFTkSuQmCC", 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" + ] + }, + "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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", 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\n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "

kpset_id

\n", + " \n", + "
\n", + "

bodyparts_id

\n", + " \n", + "
\n", + "

pre_latent_dim

\n", + " Number of latent dimensions to use for the model pre-fitting\n", + "
\n", + "

pre_kappa

\n", + " Kappa value to use for the model pre-fitting\n", + "
\n", + "

pre_num_iterations

\n", + " Number of iterations to use for the model pre-fitting\n", + "
\n", + "

model_name

\n", + " Name of the model as \"kpms_project_output_dir/model_name\"\n", + "
\n", + "

pre_fitting_duration

\n", + " Time duration of the model fitting computation\n", + "
1144005kpms_project_tutorial/2024_03_20-06_00_080:00:37
11420005kpms_project_tutorial/2024_03_20-06_00_510:00:12
114100005kpms_project_tutorial/2024_03_20-06_01_070:00:10
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\n", + " " + ], + "text/plain": [ + "*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": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 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": [ + "# 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": 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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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11450005Fitting task with kappa = 5000 ms
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\n", + " " + ], + "text/plain": [ + "*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": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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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q06YNTExMsGLFCimvEAKrVq1CkSJFUKdOHSm9S5cuePz4sayB6/Xr19i9ezcaN24MIyPdTolUKhV8fX3x888/y94OuGHDBkRGRmqsd33Jqu0yODgYwcHBGYpJ3YCT/G167du3h7GxMQICAjTuKBJC4M2bN2mWrb5DKen4Hz9+lK3rpHHo0nDq7OyMqlWrYv369bKYb9y4gYMHD6JFixZplqErbXVq5cqVePXqlc79lxEREeUHvFOKiIgoibFjx2LPnj1o3bo13r59i59//lk2POkbviZOnIitW7eiUaNGGDlyJCIjIzFv3jxUqlRJ1nHz4sWLsWLFCnh5ecHS0lKjzHbt2sHKygrlypVDuXLltMbl7u6u0x1SSeka3zfffINTp06hWbNmKF68ON6+fYvt27fjwoULGD58OEqVKqXzNHWdVwD4/PPP8f3336Nly5YYN24cTExMsHDhQhQuXBhjx45Nc1pFixbFqFGjMG/ePMTGxqJmzZrYtWsX/vzzT2zcuFH26NWECROwZcsWdOjQAWPGjIFKpcKqVasQGxuLmTNn6jx/QOLyqlOnDry9vTFw4EA8efIECxYsQNOmTTUaHJYvX453795JbwL87bff8OTJEwCJjXe6PDIIZM12CSS+YRHIWIfnnp6eAICvvvoKXbt2hYmJCVq3bo2SJUtixowZmDBhAh48eIC2bdvCxsYGISEh2LlzJwYOHIhx48alWnadOnVgZ2eH3r17Y8SIEVAoFNiwYYPWx+Y8PT2xefNmjBkzBjVr1pT6CtNm3rx5aN68Oby8vNCvXz/ExMRg2bJlUKlUmDp1arqXQUpcXV3RpUsXVKpUCebm5jh58iQ2bdqEqlWrarxlkIiIKF8TREREJPH29hYAUvwkd+PGDdG0aVNhaWkpbG1tRY8ePcTz589leXr37p1qmSEhIanGBEAMHTo0Q/OjS3wHDx4UrVq1Ei4uLsLExETY2NiIunXrirVr14qEhIR0TS+98/r48WPRsWNHoVQqhbW1tWjVqpW4d++eztOLj48XM2fOFK6ursLU1FRUqFBB/Pzzz1rzBgcHi3bt2gmlUiksLCxE48aNxfnz59M1f2p//vmnqFOnjjA3NxcODg5i6NChIjw8XCOfq6trhtd7UlmxXarjc3V1TXP6gYGBAoDYunWrLH369OmiSJEiwsjISGOetm/fLurVqyesrKyElZWVKFeunBg6dKi4c+eObL4qVKigdZqnTp0Sn3zyibCwsBAuLi7iyy+/FAcOHBAARGBgoJQvMjJSdO/eXdja2goA0vyEhIQIAGLt2rWycg8fPizq1q0rLCwshFKpFK1btxa3bt2S5ZkyZYoAIF69eiVLX7t2rU7rrn///sLDw0PY2NgIExMTUapUKfG///1P6zZCRESUnymEyGBPjURERERERERERBnEPqWIiIiIiIiIiCjbsU8pIiKiXObVq1eIj49PcbipqSns7e31Os2YmJg0O5O2t7eHqampXqebnZ4/f57qcAsLC537gNJVZGQkIiMjU83j4OAg6x+LiIiIKK/g43tERES5jJubGx4+fJjicG9vbxw7dkyv01y3bp1GJ9nJBQYGomHDhnqdbnZSKBSpDu/duzfWrVun12lOnToVAQEBqeYJCQmBm5ubXqdLRERElBOwUYqIiCiXOXXqFGJiYlIcbmdnJ70ZTV9CQ0Nx8+bNVPN4enrCzs5Or9PNTocPH051uIuLCzw8PPQ6zb///ht///13qnnq1asHc3NzvU6XiIiIKCdgoxQREREREREREWU7dnRORERERERERETZjo1SRERERERERESU7dgoRURERERERERE2Y6NUkRERERERERElO3YKEVERERERERERNmOjVJERERERERERJTt2ChFRERERERERETZjo1SRERERERERESU7dgoRURERERERERE2Y6NUkRERERERERElO3YKEVERERERERERNmOjVJERERERERERJTt2ChFRERERERERETZjo1SRERERERERESU7dgoRURERERERERE2Y6NUkRERERERERElO3YKEVERERERERERNmOjVJERERERERERJTt2ChFRERERERERETZjo1SRERERERERESU7dgoRURERERERERE2Y6NUkRERERERERElO3YKEVERERERERERNmOjVJ5yNSpU6FQKGRpbm5u6NOnT5ZP+8GDB1AoFFi3bp2U1qdPH1hbW2f5tNUUCgWmTp2abdOjrKVtezYkbdt4dsbYsGFDNGzYUPp+7NgxKBQKbNu2LVum36dPH7i5uWXLtChnSH78UG9zx44dM1hMyWXXMS69cuKyIiIi/VMoFBg2bFia+datWweFQoEHDx5kfVBEuYzeGqXUFU3bZ/z48fqaDGWDP/74I8c27uTk2HKi5PXS3NwcLi4u8PPzw9KlSxEREWHQ+KKjozF16tR8deH27NkzTJ06FVevXjV0KBpycmz5VdI6fPLkSY3hQggUK1YMCoUCrVq1MkCE+duKFStkDdVElHf99ddf6NixI1xdXWFubo4iRYqgSZMmWLZsmSzfzJkzsWvXrgxP59atW5g6dSobLyhfnidT/lRA3wVOmzYN7u7usrSKFSvqezKkozt37sDIKH1tj3/88Qe+/fbbdDX+uLq6IiYmBiYmJumMMH1Siy0mJgYFCuh9k84T1PUyNjYWz58/x7FjxzBq1CgsXLgQe/bsQeXKlQ0SV3R0NAICAgBAdhcQAHz99dc5vkE7IzE+e/YMAQEBcHNzQ9WqVXUe7+DBg+mMLv1Si+37779HQkJClsdA2pmbm+OXX35BvXr1ZOnHjx/HkydPYGZmluUxNGjQADExMTA1Nc3yaeUWK1asQKFChTTu1uKyIspbTp8+jUaNGqF48eIYMGAAnJyc8PjxY5w9exZLlizB8OHDpbwzZ85Ex44d0bZt2wxN69atWwgICEDDhg15h3Ie0rNnT3Tt2jVdx+vUzpOJ8hK9X8E3b94cNWrU0Cnv+/fvYWpqmu5GE9JdVl+oxMXFISEhAaampjA3N8/SaaXF0NPPyZLXywkTJuDo0aNo1aoVPv30UwQFBcHCwiLT00m6PWRWgQIFcnwjY3bEGB0dDUtLS4Nf3GZ1gzOlrkWLFti6dSuWLl0q2+Z++eUXeHp64vXr11keg5GRUZ7ezwoh8P79e73sC/P6siLKb7755huoVCpcuHABtra2smEvX740TFCUqxgbG8PY2NjQYQAAoqKiYGVlZegwiCTZ1hqk7l9h06ZN+Prrr1GkSBFYWloiPDwcAHDu3Dk0a9YMKpUKlpaW8Pb2xqlTpzTKOXnyJGrWrAlzc3OULFkS3333nUa/Ltr6flHT1u/Q06dP0bdvXxQuXBhmZmaoUKEC1qxZozX+LVu24JtvvkHRokVhbm4OHx8f3L9/X2M6586dQ4sWLWBnZwcrKytUrlwZS5YsAQCsXbsWCoUCV65c0Rhv5syZMDY2xtOnT1NdntqWgzbJ+9uIjY1FQEAASpcuDXNzcxQsWBD16tXDoUOHACT2G/Ptt99Ky0r9Af5brvPnz8fixYtRsmRJmJmZ4datW6ku87///ht+fn6wsrKCi4sLpk2bBiGExrJNfmtq8jJTi02dlnzdXrlyBc2bN4dSqYS1tTV8fHxw9uxZWR714zGnTp3CmDFj4ODgACsrK7Rr1w6vXr3SvgLygMaNG2PSpEl4+PAhfv75Zyk9ed9Fasn7FEpte/j48SMmT54MT09PqFQqWFlZoX79+ggMDJSN7+DgAAAICAiQ1qd6HWrrrykuLg7Tp0+XpuXm5oaJEyfiw4cPsnxubm5o1aoVTp48iVq1asHc3BwlSpTATz/9pNOyeffuHfr06QOVSgVbW1v07t0b796908inLcZDhw6hXr16sLW1hbW1NcqWLYuJEycCSNzWa9asCQDw9/eX5lm9jTds2BAVK1bEpUuX0KBBA1haWkrjprRe4uPjMXHiRDg5OcHKygqffvopHj9+rLE8tPW7k7TMtGLT1qdUVFQUxo4di2LFisHMzAxly5bF/PnzZfUb+K+/hV27dqFixYrSfnb//v0aMZF23bp1w5s3b6R9NQB8/PgR27ZtQ/fu3bWOk5CQgMWLF6NChQowNzdH4cKFMWjQIPzzzz+yfEIIzJgxA0WLFoWlpSUaNWqEmzdvapSnbV/9559/olOnTihevDjMzMxQrFgxjB49GjExMbJx1X0MPn36FG3btoW1tTUcHBwwbtw4xMfHpzn/usaYUj9v2vryUO8nDhw4gBo1asDCwkI6lq5duxaNGzeGo6MjzMzM4OHhgZUrV8rKdHNzw82bN3H8+HGpviStT9qOa1u3boWnpycsLCxQqFAhfPbZZxrH+8wuKyLSv+DgYFSoUEGjQQoAHB0dpf8VCgWioqKwfv16ab+gPv4+fPgQn3/+OcqWLQsLCwsULFgQnTp1ku2X1q1bh06dOgEAGjVqJJWRdF+yb98+1K9fH1ZWVrCxsUHLli217g+TS+saQO327dvo2LEj7O3tYW5ujho1amDPnj0a5d28eRONGzeGhYUFihYtihkzZmDNmjUa+9qU+nzVdm7y7t07jBo1SjqvKFWqFObMmSO7Uzvp+efq1aulc8KaNWviwoULGtO5ffs2OnfuDAcHB1hYWKBs2bL46quvZHl0uRZMS1rnONqOQxcvXoSfnx8KFSoECwsLuLu7o2/fvtJ8pnaeDABHjx6VtgVbW1u0adMGQUFBsumqj4u3bt1C9+7dYWdnh3r16unlepRIX/T+E39YWJjGL7aFChWS/p8+fTpMTU0xbtw4fPjwAaampjh69CiaN28OT09PTJkyBUZGRtIJ4Z9//olatWoBSHyWu2nTpnBwcMDUqVMRFxeHKVOmoHDhwhmO98WLF/jkk0+kiyYHBwfs27cP/fr1Q3h4OEaNGiXLP3v2bBgZGWHcuHEICwvD3Llz0aNHD5w7d07Kc+jQIbRq1QrOzs4YOXIknJycEBQUhL1792LkyJHo2LEjhg4dio0bN6JatWqy8jdu3IiGDRuiSJEiKcacmeUwdepUzJo1C/3790etWrUQHh6Oixcv4vLly2jSpAkGDRqEZ8+e4dChQ9iwYYPWMtauXYv3799j4MCBMDMzg729fYqP9cTHx6NZs2b45JNPMHfuXOzfvx9TpkxBXFwcpk2blma8SekSW1I3b95E/fr1oVQq8eWXX8LExATfffcdGjZsiOPHj6N27dqy/MOHD4ednR2mTJmCBw8eYPHixRg2bBg2b96crjhzk549e2LixIk4ePAgBgwYkKEytG0P4eHh+OGHH9CtWzcMGDAAERER+PHHH+Hn54fz58+jatWqcHBwwMqVKzFkyBC0a9cO7du3B4BUHyXs378/1q9fj44dO2Ls2LE4d+4cZs2ahaCgIOzcuVOW9/79++jYsSP69euH3r17Y82aNejTpw88PT1RoUKFFKchhECbNm1w8uRJDB48GOXLl8fOnTvRu3fvNJfFzZs30apVK1SuXBnTpk2DmZkZ7t+/LzWwly9fHtOmTcPkyZMxcOBA1K9fHwBQp04dqYw3b96gefPm6Nq1Kz777LM06/U333wDhUKB//3vf3j58iUWL14MX19fXL16NV13fOgSW1JCCHz66acIDAxEv379ULVqVRw4cABffPEFnj59ikWLFsnynzx5Ejt27MDnn38OGxsbLF26FB06dMCjR49QsGBBnePMr9zc3ODl5YVff/0VzZs3B5B4YRIWFoauXbti6dKlGuMMGjQI69atg7+/P0aMGIGQkBAsX74cV65cwalTp6S73yZPnowZM2agRYsWaNGiBS5fvoymTZvi48ePaca1detWREdHY8iQIShYsCDOnz+PZcuW4cmTJ9i6dassb3x8PPz8/FC7dm3Mnz8fhw8fxoIFC1CyZEkMGTIk1elkJsbU3LlzB926dcOgQYMwYMAAlC1bFgCwcuVKVKhQAZ9++ikKFCiA3377DZ9//jkSEhIwdOhQAMDixYsxfPhwWFtbSxc4qdVX9bqoWbMmZs2ahRcvXmDJkiU4deoUrly5IrvYzcyyIiL9c3V1xZkzZ3Djxo1UuyXZsGGDdI49cOBAAEDJkiUBABcuXMDp06fRtWtXFC1aFA8ePMDKlSvRsGFD3Lp1C5aWlmjQoAFGjBiBpUuXYuLEiShfvjwASH83bNiA3r17w8/PD3PmzEF0dDRWrlyJevXq4cqVK6k+7pfWNQCQeB5Tt25dFClSBOPHj4eVlRW2bNmCtm3bYvv27WjXrh0A4Pnz52jUqBHi4uKkfKtXr87UnabR0dHw9vbG06dPMWjQIBQvXhynT5/GhAkTEBoaisWLF8vy//LLL4iIiMCgQYOgUCgwd+5ctG/fHn///bd0fLt+/Trq168PExMTDBw4EG5ubggODsZvv/2Gb775BkD6rwW1ycg5zsuXL6XrufHjx8PW1hYPHjzAjh07ACDN8+TDhw+jefPmKFGiBKZOnYqYmBgsW7YMdevWxeXLlzW2hU6dOqF06dKYOXMmhBCZvh4l0iuhJ2vXrhUAtH6EECIwMFAAECVKlBDR0dHSeAkJCaJ06dLCz89PJCQkSOnR0dHC3d1dNGnSREpr27atMDc3Fw8fPpTSbt26JYyNjUXSWQkJCREAxNq1azXiBCCmTJkife/Xr59wdnYWr1+/luXr2rWrUKlUUqzq+MuXLy8+fPgg5VuyZIkAIP766y8hhBBxcXHC3d1duLq6in/++UdWZtL569atm3BxcRHx8fFS2uXLl1OMOyldl4MQQri6uorevXtL36tUqSJatmyZavlDhw7VKEeI/5arUqkUL1++1Dosaey9e/cWAMTw4cOltISEBNGyZUthamoqXr16JYT4b9kGBgamWWZKsQmhuW7btm0rTE1NRXBwsJT27NkzYWNjIxo0aCClqbddX19f2ToaPXq0MDY2Fu/evdM6vdxAPW8XLlxIMY9KpRLVqlWTvnt7ewtvb2+NfL179xaurq7S99S2h7i4OFk9EUKIf/75RxQuXFj07dtXSnv16pXGelObMmWKbF1fvXpVABD9+/eX5Rs3bpwAII4ePSqlubq6CgDixIkTUtrLly+FmZmZGDt2rPYF8a9du3YJAGLu3Lmy+alfv77G9pg8xkWLFgkA0ratzYULF1Ks597e3gKAWLVqldZhSdeLut4UKVJEhIeHS+lbtmwRAMSSJUuktOT7gZTKTC225OtfvZxmzJghy9exY0ehUCjE/fv3pTQAwtTUVJZ27do1AUAsW7ZMY1r0n6R1ePny5cLGxkY6LnXq1Ek0atRICJG4jpPu2//8808BQGzcuFFW3v79+2XpL1++FKampqJly5ay/d/EiRMFANl2o21fnfR4rjZr1iyhUChkxyj18WDatGmyvNWqVROenp6pLoP0xJi8Tqqpl2NISIiUpt5P7N+/XyO/tvny8/MTJUqUkKVVqFBB6/4y+bL6+PGjcHR0FBUrVhQxMTFSvr179woAYvLkyVJaZpYVEWWNgwcPCmNjY2FsbCy8vLzEl19+KQ4cOCA+fvyokdfKykrrMVfbfuXMmTMCgPjpp5+ktK1bt2o9L46IiBC2trZiwIABsvTnz58LlUqlkZ6cLtcAPj4+olKlSuL9+/dSWkJCgqhTp44oXbq0lDZq1CgBQJw7d05Ke/nypVCpVBr72pTO85Kfm0yfPl1YWVmJu3fvyvKNHz9eGBsbi0ePHgkh/jv/LFiwoHj79q2Ub/fu3QKA+O2336S0Bg0aCBsbG9nxSD1ParpeC6ZE13Oc5MehnTt3pnmOntp5ctWqVYWjo6N48+aNbLpGRkaiV69eUpr6uNitWzeNMjJzPUqkT3p/fO/bb7/FoUOHZJ+kevfuLWtFv3r1Ku7du4fu3bvjzZs3eP36NV6/fo2oqCj4+PjgxIkTSEhIQHx8PA4cOIC2bduiePHi0vjly5eHn59fhmIVQmD79u1o3bo1hBDStF+/fg0/Pz+EhYXh8uXLsnH8/f1lfbuo7yb4+++/ASQ+LhYSEoJRo0Zp3OKb9JGCXr164dmzZ7LHmTZu3AgLCwt06NAhxZgzuxxsbW1x8+ZN3Lt3L828KenQoYN0O6kukr4mVf0rxMePH3H48OEMx5CW+Ph4HDx4EG3btkWJEiWkdGdnZ3Tv3h0nT56UHh1VGzhwoGwd1a9fH/Hx8Xj48GGWxZkTWFtbZ+otfNq2B2NjY6meJCQk4O3bt4iLi0ONGjU06pSu/vjjDwDAmDFjZOljx44FAPz++++ydA8PD6l+Aom/OJUtW1aqq6lNp0CBArK7EYyNjWWdmKZEXed3796d4U7BzczM4O/vr3P+Xr16wcbGRvresWNHODs7S8srq/zxxx8wNjbGiBEjZOljx46FEAL79u2Tpfv6+kq/FgOJv/Qplco01wf9p3PnzoiJicHevXsRERGBvXv3pvjo3tatW6FSqdCkSRPZsc3T0xPW1tbSsefw4cP4+PEjhg8fLtv/6fLLMADZ8TwqKgqvX79GnTp1IITQ+kjA4MGDZd/r16+f5jaQ2RhT4+7urvXYmXS+1HeAe3t74++//0ZYWFi6p3Px4kW8fPkSn3/+uayvqZYtW6JcuXIa+y8gY8uKiLJGkyZNcObMGXz66ae4du0a5s6dCz8/PxQpUkTro23aJN2vxMbG4s2bNyhVqhRsbW11Ojc6dOgQ3r17h27dusn268bGxqhdu7bsmkKbtK4B3r59i6NHj6Jz586IiIiQyn/z5g38/Pxw79496XGuP/74A5988on0NAuQeJ7Vo0cPXRaFVlu3bkX9+vVhZ2cnmz9fX1/Ex8fjxIkTsvxdunSBnZ2d9D35NdmrV69w4sQJ9O3bV3bNBPx3TZaRa0FtMnKOoz5n3Lt3L2JjY9OcRlKhoaG4evUq+vTpA3t7e9l0mzRpovUcMPkxBcj49SiRvun98b1atWql2tF58jfzqXeMqT0aExYWhg8fPiAmJgalS5fWGF62bNkMXYC9evUK7969w+rVq7F69WqteZJ3Xph8p6beGar76AgODgaQ9hsHmzRpAmdnZ2zcuBE+Pj5ISEjAr7/+ijZt2sguMLXFnJnlMG3aNLRp0wZlypRBxYoV0axZM/Ts2TNdb19Lvg5TY2RkJGsUAoAyZcoAQJa+6vbVq1eIjo6WHsVIqnz58khISMDjx49lj3GltW7zqsjISFl/COmV0vawfv16LFiwALdv35YdbNOz/ST18OFDGBkZoVSpUrJ0Jycn2NraajQeJl+fQOI6TWt9Pnz4EM7OzrC2tpala9uWkuvSpQt++OEH9O/fH+PHj4ePjw/at2+Pjh076vxChyJFiqSrU/Pk+wKFQoFSpUpl+aukHz58CBcXF439lfoRA32tD/qPg4MDfH198csvvyA6Ohrx8fHo2LGj1rz37t1DWFhYinVbfWxTr6fk25GDg4PsZD8ljx49wuTJk7Fnzx6NdZm88cbc3FyjAVvXOpmZGFOT0v7o1KlTmDJlCs6cOYPo6GjZsLCwMKhUqnRNRz0P2vYj5cqVw8mTJ2VpGV1WRJR1atasiR07duDjx4+4du0adu7ciUWLFqFjx464evUqPDw8Uh0/JiYGs2bNwtq1a/H06VNZ/4u6NHarr5kaN26sdbhSqUx1/LSuAe7fvw8hBCZNmoRJkyZpLePly5coUqQIHj58qNENBqDbuVJK7t27h+vXr6f4w3d6r8nUDUKpXZNl5FpQm4yc43h7e6NDhw4ICAjAokWL0LBhQ7Rt2xbdu3dP80VVqR1TypcvjwMHDmh0Zq7teJfR61Eifcv2V1slf9ZYfTfBvHnzUnw9urW1tUZHxqnR1skpAI0OQtXT/uyzz1JsFEveWJPSWxOSHlh0YWxsjO7du+P777/HihUrcOrUKTx79gyfffZZuspJrwYNGiA4OBi7d+/GwYMH8cMPP2DRokVYtWoV+vfvr1MZ+ngzUVK6rq+spq91m5s8efIEYWFhsoYehUKhdZ5TWh/atoeff/4Zffr0Qdu2bfHFF1/A0dERxsbGmDVrltRwm1EpbS/JGWJ9WlhY4MSJEwgMDMTvv/+O/fv3Y/PmzWjcuDEOHjyo01tX9F2/gNTrWHa9CSY/1q+s0L17dwwYMADPnz9H8+bNtXa6CyQe3xwdHbFx40atw9Nzt2tK4uPj0aRJE7x9+xb/+9//UK5cOVhZWeHp06fo06ePxt2C2bGtpfd4oq2+BQcHw8fHB+XKlcPChQtRrFgxmJqa4o8//sCiRYsyfBdkeuSUNzQRkSZTU1PUrFkTNWvWRJkyZeDv74+tW7diypQpqY43fPhwrF27FqNGjYKXlxdUKhUUCgW6du2q035FnWfDhg1wcnLSGJ7W24DTugZQlz9u3LgUn75I/sNgZmi7LmvSpAm+/PJLrfnVP2qr6eO8IiPXgtpkJBaFQoFt27bh7Nmz+O2333DgwAH07dsXCxYswNmzZzV+HM0sbcc7Q12PEiVn8Petq291VCqV8PX1TTGf+o0J2m45vXPnjuy7uqU8+duykv9y7+DgABsbG8THx6c67fRQz8+NGzfSLLNXr15YsGABfvvtN+zbtw8ODg5pPoKXnuWQEnt7e/j7+8Pf3x+RkZFo0KABpk6dKjVK6XrRr4uEhAT8/fffsgPJ3bt3AUDqgE/X9ZWe2BwcHGBpaal1mdy+fRtGRkYoVqyYTmXlZeoO45Nud3Z2dlpvN07PY4zbtm1DiRIlsGPHDtk6S37Clp5tzdXVFQkJCbh37550Nw6Q2EHlu3fv4OrqqnNZaU3nyJEjiIyMlJ0Q6Fq/jIyM4OPjAx8fHyxcuBAzZ87EV199hcDAQPj6+uq1fgHQ2BcIIXD//n3ZSZSdnZ3Wtwc+fPhQdidjetfH4cOHERERIfs17fbt29Jw0r927dph0KBBOHv2bKovYShZsiQOHz6MunXrptrQqV5P9+7dk20Lr169SvOunL/++gt3797F+vXr0atXLyk9+WP7mZWeGJMeT5I22KVn//Xbb7/hw4cP2LNnj+zXb22PxuhaZ9TzcOfOHY27HO7cucP6QpRLqZ8OCQ0NldJS2i9s27YNvXv3xoIFC6S09+/faxyfUxpffY3h6OiY4euW1K4B1PtXExOTNMt3dXXV+VpE2znIx48fZcsMSJy/yMhIvV2Tqefnxo0bKebJimvB9Prkk0/wySef4JtvvsEvv/yCHj16YNOmTejfv3+K20LSY0pyt2/fRqFChWR3SaUmI9ejRPqm9z6l0svT0xMlS5bE/PnzERkZqTH81atXABJbcv38/LBr1y48evRIGh4UFIQDBw7IxlEqlShUqJDGs8crVqyQfTc2NkaHDh2wfft2rTss9bTTo3r16nB3d8fixYs1dsDJW8srV66MypUr44cffsD27dvRtWvXNH/lSM9y0ObNmzey79bW1ihVqpTsTjT1TkzbRWxGLF++XPpfCIHly5fDxMQEPj4+ABJ3rMbGxmmur/TEZmxsjKZNm2L37t2yx5hevHiBX375BfXq1UvzNue87ujRo5g+fTrc3d1lfQCULFkSt2/flm3/165dk94gpwv1L0ZJt/lz587hzJkzsnyWlpYAdNvWWrRoAQAab19ZuHAhgMS+WfShRYsWiIuLk73+PT4+HsuWLUtz3Ldv32qkqe8AVdcxfdevn376SdYn2LZt2xAaGiq9oQ1IXKdnz56Vvals7969ePz4says9MTWokULxMfHy+o3ACxatAgKhUI2fdIfa2trrFy5ElOnTkXr1q1TzNe5c2fEx8dj+vTpGsPi4uKkdezr6wsTExMsW7ZMVl+T1zNttNVzIQSWLFmi49zoJj0xqi/akh5P1K9n15W2+QoLC8PatWs18lpZWelUX2rUqAFHR0esWrVKdrzdt28fgoKC9Lb/IqKsERgYqPWuF3W3GUkfo0ppv2BsbKxRxrJlyzTuGErpWOzn5welUomZM2dq7YMoreuWtK4BHB0d0bBhQ3z33XcaDUbJy2/RogXOnj2L8+fPy4Zruzu3ZMmSGuf4q1ev1pjvzp0748yZM1qvZ969e4e4uLhU5y85BwcHNGjQAGvWrJFdMwH/7d+z4lpQV//884/G9pD8nDGl82RnZ2dUrVoV69evlw27ceMGDh48KJ0z6yIj16NE+mbwLc7IyAg//PADmjdvjgoVKsDf3x9FihTB06dPERgYCKVSid9++w0AEBAQgP3796N+/fr4/PPPERcXh2XLlqFChQq4fv26rNz+/ftj9uzZ6N+/P2rUqIETJ05Id+gkNXv2bAQGBqJ27doYMGAAPDw88PbtW1y+fBmHDx/WepGZ1vysXLkSrVu3RtWqVeHv7w9nZ2fcvn0bN2/e1NjR9urVC+PGjQMAnW+VTM9ySM7DwwMNGzaEp6cn7O3tcfHiRWzbtk3WGbmnpycAYMSIEfDz84OxsTG6du2ansUgMTc3x/79+9G7d2/Url0b+/btw++//46JEydKj4+oVCp06tQJy5Ytg0KhQMmSJbF3716tz3CnJ7YZM2bg0KFDqFevHj7//HMUKFAA3333HT58+IC5c+dmaH5yq3379uH27duIi4vDixcvcPToURw6dAiurq7Ys2ePrOPdvn37YuHChfDz80O/fv3w8uVLrFq1ChUqVNDoHD4lrVq1wo4dO9CuXTu0bNkSISEhWLVqFTw8PGSNzxYWFvDw8MDmzZtRpkwZ2Nvbo2LFilqf/69SpQp69+6N1atX4927d/D29sb58+exfv16tG3bFo0aNcr8ggLQunVr1K1bF+PHj8eDBw/g4eGBHTt26NTfw7Rp03DixAm0bNkSrq6uePnyJVasWIGiRYuiXr16ABJPzmxtbbFq1SrY2NjAysoKtWvXznBfW/b29qhXrx78/f3x4sULLF68GKVKlcKAAQOkPP3798e2bdvQrFkzdO7cGcHBwfj5559lnXKmN7bWrVujUaNG+Oqrr/DgwQNUqVIFBw8exO7duzFq1CiNskl/UuuDUc3b2xuDBg3CrFmzcPXqVTRt2hQmJia4d+8etm7diiVLlqBjx45wcHDAuHHjMGvWLLRq1QotWrTAlStXsG/fPhQqVCjVaZQrVw4lS5bEuHHj8PTpUyiVSmzfvl3v/R6lJ8amTZuiePHi6NevH7744gsYGxtjzZo1cHBw0LgoSUnTpk1hamqK1q1bY9CgQYiMjMT3338PR0dHjQs1T09PrFy5EjNmzECpUqXg6Oiotb8XExMTzJkzB/7+/vD29ka3bt3w4sULLFmyBG5ubhg9enTGFxARZbnhw4cjOjoa7dq1Q7ly5fDx40ecPn0amzdvhpubm+wFJZ6enjh8+DAWLlwIFxcXuLu7o3bt2mjVqhU2bNgAlUoFDw8PnDlzBocPH0bBggVl06patSqMjY0xZ84chIWFwczMDI0bN4ajoyNWrlyJnj17onr16ujatau0b/v9999Rt25djR+KktLlGuDbb79FvXr1UKlSJQwYMAAlSpTAixcvcObMGTx58gTXrl0DAHz55ZfYsGEDmjVrhpEjR8LKygqrV6+Gq6ur1muywYMHo0OHDmjSpAmuXbuGAwcOaOy/v/jiC+zZswetWrVCnz594OnpiaioKPz111/Ytm0bHjx4kOZxKbmlS5eiXr16qF69OgYOHAh3d3c8ePAAv//+O65evQpA/9eCulq/fj1WrFiBdu3aoWTJkoiIiMD3338PpVIpNSqldp48b948NG/eHF5eXujXrx9iYmKwbNkyqFQqTJ06NV2xZOR6lEiv9PUav7RePa9+PfLWrVu1Dr9y5Ypo3769KFiwoDAzMxOurq6ic+fO4siRI7J8x48fF56ensLU1FSUKFFCrFq1SusroKOjo0W/fv2ESqUSNjY2onPnzuLly5daX6v54sULMXToUFGsWDFhYmIinJychI+Pj1i9enWa8atfS5r8tZknT54UTZo0ETY2NsLKykpUrlxZ66vPQ0NDhbGxsShTpozW5ZISXZdD8tetzpgxQ9SqVUvY2toKCwsLUa5cOfHNN9/IXmkbFxcnhg8fLhwcHIRCoZDKVM/rvHnzNOLRthx69+4trKysRHBwsGjatKmwtLQUhQsXFlOmTJG9elSIxFeedujQQVhaWgo7OzsxaNAgcePGDY0yU4pNCO2vnL18+bLw8/MT1tbWwtLSUjRq1EicPn1alielbVfb689zG/W8qT+mpqbCyclJNGnSRCxZskSEh4drHe/nn38WJUqUEKampqJq1ariwIEDonfv3sLV1VXKk9r2kJCQIGbOnClcXV2FmZmZqFatmti7d69GGUIIcfr0aWlbTroOtW3PsbGxIiAgQLi7uwsTExNRrFgxMWHCBNmri4VI3O61vfbY29tb6+vbk3vz5o3o2bOnUCqVQqVSiZ49e4orV65obI/JYzxy5Iho06aNcHFxEaampsLFxUV069ZN4/XGu3fvFh4eHqJAgQKyMr29vUWFChW0xpQ8dvX2+euvv4oJEyYIR0dHYWFhIVq2bKnx6mMhhFiwYIEoUqSIMDMzE3Xr1hUXL17UujxSik3buouIiBCjR48WLi4uwsTERJQuXVrMmzdP9qplIRLr5tChQzViSr5/Ik1pHVvVUtrmV69eLTw9PYWFhYWwsbERlSpVEl9++aV49uyZlCc+Pl4EBAQIZ2dnYWFhIRo2bChu3LihsX607RNv3bolfH19hbW1tShUqJAYMGCA9CpsbceD5LTVc210jVEIIS5duiRq164tTE1NRfHixcXChQs1XsWd2jITQog9e/aIypUrC3Nzc+Hm5ibmzJkj1qxZo1HG8+fPRcuWLYWNjY0AINWnlI4fmzdvFtWqVRNmZmbC3t5e9OjRQzx58kSWJ7PLioj0b9++faJv376iXLlywtraWpiamopSpUqJ4cOHixcvXsjy3r59WzRo0EBYWFgIANI+6p9//hH+/v6iUKFCwtraWvj5+Ynbt29r3Y99//33okSJEsLY2FhjXxIYGCj8/PyESqUS5ubmomTJkqJPnz7i4sWLqc6DLtcAQggRHBwsevXqJZycnISJiYkoUqSIaNWqldi2bZss3/Xr14W3t7cwNzcXRYoUEdOnTxc//vijxn4yPj5e/O9//xOFChUSlpaWws/PT9y/f1/rfEdERIgJEyaIUqVKCVNTU1GoUCFRp04dMX/+fCnO1M4/tV0L3LhxQ7Rr107Y2toKc3NzUbZsWTFp0iRZHl2uBVOi6zlO8uPQ5cuXRbdu3UTx4sWFmZmZcHR0FK1atdJYjymdJwshxOHDh0XdunWFhYWFUCqVonXr1uLWrVuy8dXHjlevXqU4Dxm9HiXSF4UQub+X2alTpyIgICBXdpj7+vVrODs7Y/LkySm+6YKIiIiIiCgnW7duHfz9/RESEiL1HUs5H69HydAM3qdUfrdu3TrEx8ejZ8+ehg6FiIiIiIiI8hFej5KhGbxPqfzq6NGjuHXrFr755hu0bduWvyYQERERERFRtuD1KOUUbJQykGnTpuH06dOoW7euTm/1IiIiIiIiItIHXo9STpEn+pQiIiIiIiIiIqLchX1KERERERERERFRtmOjFBERERERERERZTs2ShERERERERERUbYzSEfnCQkJePbsGWxsbKBQKAwRAuVjQghERETAxcUFRkb5p12W9Y4MjXWPdY8Mg3WPdY+IiCi76Xr+YZBGqWfPnqFYsWKGmDSR5PHjxyhatKihw8g2rHeUU7DuERkG6x4RERFlt7TOPwzSKGVjYwMgMTilUmmIECgfCw8PR7FixaTtML9gvSNDY91j3SPDyO91D30eAxWUwD0ApVPI/CbJ/1uABX+rMHZeWLqmF7ZYBdXT8cDACVKa3exn+GeXC+zaPpPShhsvBQCMnb8SsAeC+rrJyvHHGjyPdwYAOBmH4uzFxhrTUvlsQ9ikjlDtS4xx5JFZsuGhSBx/DBbhE59rsjyhcIYzQtOcn6T5lgRNQOfy63Aovqksz0j78mmWAwAJYUMAAMviR+D8v+M4FgHwzb8ZLv6Xt903v2DnV92l/wFgvqo7St5OUuByIHx14r+vPgIlbwOznEdiwrQlsumGLwMqPw/Cgz3lMavdSADAhM3/5VEt+ncdByUum7Ajs/F7jcZoefEoMFtzPlSeifkXzFZJaY3CXAAA1T8JAgBMDfpvmHlYHwDAkI7r8HqbFa6jkkaZFVVnUbpGGHDxBmaHfa850WTWo5f0fxdVY0y98g7jS0wBAMxePS3N8cMcVFAlJNm2PwBh9xNjVi04BaypCABY0TcxrVxYeTQOOisv5JMbQI2K8nLn/Dffqsv/lj8DmP1oZJoxKVXrUhz2+cD01UOtVu9G2KJeUC0Lg9+VnTigKg23MGNcu/UJAGCjF/Do36wTjgB+NXbiwMV2siJUd+VxLBiiQrWw8oiBJQCg5S9HpWGPR6pQ7OM26fuQsOt4DmeMUw2RlRGc5P8yYW4phu+PNTg7qjFUJnpYFkDifvADgK8TIwi7XR0AcKOcPJtxmBs+UaWxTf3eBiPr/bdv2VK6T2KZnVXAnf+yvd5mhQH4b/tW1/GXyxO/vwTgmKTYH8P+ra9fLZHK+eHQf8P7j/2vfv/T1AWPr6tQrE8YcBhAX2Dq1ypMLREGfJ2YX1E/CrUdzwEAzqoaATP+u3t28vDxmKaahbAztrjhBVT8t+6rAsPQbssv2Hmy+38TbrkosYywdQAS100l/CUNHoNFssVTfsiDxLJGhCFsqQqo/O+A6//+VX93QsqsAFUxzXUfdkalJTPwS9926B68M3G63ySOF/aNCqpy7/7LVEKBMG8VJi8eLxt3Wu/ZwCeA6kUYwnqqoKp+D2GL5AfOAX3l+9ktJ/tI/3eut042LDb8PXYW+1+a5x8GaZRS30KtVCp5gk4Gk99u5We9o5yCdY/IMPJr3YOpErBQAqYALFLIbJ7kf6N/s1mkr74qjQDALHF66hiUEYClMvGvelLGZon5zRPjsVbKH2kwhjUU8Ykn8MbGEVBaaZuaZeL4BRKnZaY0lw01/XdGrWGkkccUFjCDPL82snzWSpgqLaS4pHlJs5RECcrEeVbE20BdgtII+Pd6HjD7L28BpSX+zY4CSst/5wNQJp20GYB/V2/Mv8PMlWbSeBIFoFDaQGmZOBwAlEm3AWP1uvp3mBVgqSyQuMxNtMyIeWL+pEVI6+/fssxl2U0TyzUBPigVsNJy6WWTOKMArKX8qTGG9X/lA4CNUpo3XbZZpSWAhCT5jJBkuVkDlvJ5tFYaA9bJy7WWtiup3KTbqToOBXSap5SqJQBZfco4y8T1bqSEidISgDWMlMZQWv83ffUiUFoBBZRWmvUu2bK1QOKyMYKxxnClInGaamZKc5jAIsmaU0f1n+T7gaSMYQ2lKQATPZ1DWODf+pMYkbpuJY/PWFZJU2CllO1bYJQYo9IMsjr0QalAgSRlqbe5mH+/RwNIWsWl+pqknKTbifLffYBCaQMUUCYuc1NlYo/Z5v/WDSOlFL7CxhgFpJWqBMz/Ox4mTksJpfW/+xp1JTZRwkRpAVglXe6JA9XryxjW0v4W+HefmzRO9eZvrUz8X122Ol39PbXFbAktdTDZviwJC6WJtG2rt5nEdZy03iugNNU8dihN/o3JTPlvGTYa0zFNnpBk+WgM+1da5x/5p2MBIiIiIiIiIiLKMdgoRURERERERERE2c4gj++pVZxyAEZmlngwu6UhwyDKV1jviIjyjvgEgfMhb/Ey4j0cbcxRy90exkb56zE9IiIiyr0M2ihFRERERBmz/0YoAn67hdCw91Kas8ocU1p7oFlFZwNGRkRERKQbPr5HRERElMvsvxGKIT9fljVIAcDzsPcY8vNl7L+R9tvViIiIiAyNd0oR5VNu43+X/uejfEREuUd8gkDAb7cgtAwTSHypUsBvt9DEw4mP8hEREVGOxkYpIkoRG66IiHKe8yFvNe6QSkoACA17j/Mhb+FVsmD2BUZERESUTnx8j4iIiCgXeRmRcoNURvIRERERGQrvlCIi3hFFRJSLONqY6zUfERERkaHwTikiIiKiXKSWuz2cVeZIqbcoBRLfwlfL3T47wyIiIiJKNzZKEREREeUixkYKTGntAQAaDVPq71Nae7CTcyIiIsrx2ChFRERElMs0q+iMlZ9Vh5NK/oiek8ocKz+rjmYVnQ0UGREREZHu2KcUERERUS7UrKIzmng44XzIW7yMeA9Hm8RH9niHFBEREeUWbJQiIp2oO0NnR+hERDmHsZECXiULGjoMIiIiogzh43tERERERERERJTt2ChFRERERERERETZjo/vEZGM+jE9IiIiIiIioqzEO6WIiIiIiIiIiCjb8U4pIkqXpHdSsdNzIiIiIiIiyijeKUVERERERERERNmOjVJERERERERERJTt2ChFRERERERERETZLkONUlFRUfqOg4h0kJPrntv436UPERGRPlSvXh0KhQIKhQLGxsaYNGmSoUMiIiIiPcpQo1ThwoXRt29fnDx5Ut/xEFEqWPeIiCi/aN68Oa5cuYJ69eph2bJlsLGxwYwZM3DixAlDh0ZERER6kqFGqZ9//hlv375F48aNUaZMGcyePRvPnj3Td2xElAzrHhER5RcHDx6Evb09/vzzTwwbNgwvX74EAPTv39/AkREREZG+ZKhRqm3btti1axeePn2KwYMH45dffoGrqytatWqFHTt2IC4uTt9xEhFY94iIKH94+/YtEhIS0LBhQynN1NQU1tbWePTokeECIyIiIr3KVEfnDg4OGDNmDK5fv46FCxfi8OHD6NixI1xcXDB58mRER0frVA77oSFKH33VvcxiP1KU2504cQKtW7eGi4sLFAoFdu3aJRsuhMDkyZPh7OwMCwsL+Pr64t69e4YJligfuXDhAgCgVKlSsnRra2vExsZqHefVq1cICgqSPnfu3MnyOImIiChzMtUo9eLFC8ydOxceHh4YP348OnbsiCNHjmDBggXYsWMH2rZtq6cwiSgp1j0i/YiKikKVKlXw7bffah0+d+5cLF26FKtWrcK5c+dgZWUFPz8/vH//PpsjJco9YmJiZD+OPHz4EIsXL8bBgwezdLo1a9aEh4eH9KlVq1aWTo+IiIgyr0BGRtqxYwfWrl2LAwcOwMPDA59//jk+++wz2NraSnnq1KmD8uXL6ytOIgLrHpG+NW/eHM2bN9c6TAiBxYsX4+uvv0abNm0AAD/99BMKFy6MXbt2oWvXrtkZKlGu0aZNG7Rv3x6DBw/Gu3fvULt2bZiYmOD169dYuHAhhgwZkmYZNWvWBADcv39flh4ZGQkTExOt41y4cAGvX7+W5WXDFBERUc6WoUYpf39/dO3aFadOnZJOGpJzcXHBV199langiEgut9S9pI/zPZjd0oCREGVcSEgInj9/Dl9fXylNpVKhdu3aOHPmDBuliFJw+fJlLFq0CACwbds2FC5cGFeuXMH27dsxefJknRql7O3tYWRkhOPHj0tpHz9+RGRkJEqXLq11HAcHBzg4OEjfw8PDMzknRERElNUy1CgVGhoKS0vLVPNYWFhgypQpGQqKiLRj3SP6z7179xAYGIiXL18iISFBNmzy5MmZLv/58+cAgMKFC8vSCxcuLA3T5sOHD/jw4YP0nRfGlN9ER0fDxsYGQOIb9Nq3bw8jIyN88sknePjwoc7lNG3aFPv374e3tze6du2KiRMnAgBWr16dJXETERFR9stQo5SNjQ1CQ0Ph6OgoS3/z5g0cHR0RHx+foWDUd1fwzgoi7bKq7hHlNt9//z2GDBmCQoUKwcnJCQqFQhqmUCj00iiVUbNmzUJAQIDBpk9kaKVKlcKuXbvQrl07HDhwAKNHjwYAvHz5EkqlUudy9u3bh2rVquHEiRM4ceIEjIyMMHHiRNkb+YiIiCh3y1CjlBBCa/qHDx9gamqaqYCIKGWse0SJZsyYgW+++Qb/+9//smwaTk5OABJfLODs7Cylv3jxAlWrVk1xvAkTJmDMmDHS9/DwcBQrVizL4iTKaSZPnozu3btj9OjR8PHxgZeXF4DEu6aqVauWrrKuXLmSFSESERFRDpGuRqmlS5cCSPwV+ocffoC1tbU0LD4+HidOnEC5cuX0GyERse4RJfPPP/+gU6dOWToNd3d3ODk54ciRI1IjVHh4OM6dO5dqnzhmZmYwMzPL0tiIcrKOHTuiXr16CA0NRZUqVaR0Hx8ftGvXzoCRERERUU6TrkYpdaeVQgisWrUKxsbG0jBTU1O4ublh1apV+o2QiFj3iJLp1KkTDh48iMGDB2eqnMjISNnbvUJCQnD16lXY29ujePHiGDVqFGbMmIHSpUvD3d0dkyZNgouLC9q2bZvJOSDK25ycnKS7DdX4JjwiIiJKLl2NUiEhIQCARo0aYceOHbCzs8uSoNi3FJFcdtU9otyiVKlSmDRpEs6ePYtKlSppvCJ+xIgROpVz8eJFNGrUSPqufuyud+/eWLduHb788ktERUVh4MCBePfuHerVq4f9+/fD3NxcfzNDlMdERUVh9uzZOHLkiNYXEfz9998GioyIiIhymgz1KRUYGKjvOIhIB6x7RIlWr14Na2trHD9+XPbKeCDxMVddG6UaNmyYYl9t6rKmTZuGadOmZSpeovykf//+OH78OHr27AlnZ2fZiwiIiIiIktK5UWrMmDGYPn06rKysZB24arNw4cJMB0ZEiXJ73VPf+Qjw7kfSH/Xdg0SU8+zbtw+///476tata+hQiIiIKIfTuVHqypUriI2Nlf5PCX8NI9Iv1j2i1KnvdGIdIMoZ7OzsYG9vb+gwiIiIKBfQuVEq6WND2fUIEe+wIDJM3SPKDX766SfMmzcP9+7dAwCUKVMGX3zxBXr27GngyIjyt+nTp2Py5MlYv349LC0tDR0OERER5WAZ6lOKiIjIkBYuXIhJkyZh2LBh0iNCJ0+exODBg/H69WuMHj3awBES5V8LFixAcHAwChcuDDc3N40XEVy+fNlAkREREVFOo3OjVPv27XUudMeOHRkKhog0se4RaVq2bBlWrlyJXr16SWmffvopKlSogKlTp7JRisiA2rZta+gQiIiIKJfQuVFKpVJlZRxElALWPSJNoaGhqFOnjkZ6nTp1EBoaaoCIiEhtypQphg6BiIiIcgmdG6XWrl2blXEQUQpY94g0lSpVClu2bMHEiRNl6Zs3b0bp0qUNFBURJXXp0iUEBQUBACpUqIBq1aoZOCIiIiLKadinFBER5ToBAQHo0qULTpw4IfUpderUKRw5cgRbtmwxcHRE+dvLly/RtWtXHDt2DLa2tgCAd+/eoVGjRti0aRMcHBwMGyARERHlGDo3SlWvXh1HjhyBnZ0dqlWrluqrt9mBJZH+sO4RaerQoQPOnTuHRYsWYdeuXQCA8uXL4/z587wbg8jAhg8fjoiICNy8eRPly5cHANy6dQu9e/fGiBEj8Ouvvxo4QiIiIsopdG6UatOmDczMzACwA0ui7JSX6p7b+N+l/x/MbmnASCgv8PT0xM8//2zoMIgomf379+Pw4cNSgxQAeHh44Ntvv0XTpk0NGBkRERHlNDo3SiXttJIdWBJlH9Y9okTh4eFQKpXS/6lR5yOi7JeQkAATExONdBMTEyQkJBggIiIiIsqpMtWn1MWLF6UOLD08PODp6amXoIgodXmt7qnvoOLdU5QaOzs7hIaGwtHREba2tlofZRVCQKFQID4+3gAREhEANG7cGCNHjsSvv/4KFxcXAMDTp08xevRo+Pj4GDg6IiIiykky1Cj15MkTdOvWDadOnZJ1YFmnTh1s2rQJRYsW1WeMRPSvvFT3kj7Kpy2NDVSU3NGjR2Fvbw8ACAwMNHA0RJSS5cuX49NPP4WbmxuKFSsGAHj8+DEqVqzIR26JiIhIJkONUv3790dsbCyCgoJQtmxZAMCdO3fg7++P/v37Y//+/XoNEuCdFESAYeoeUU7h7e0t/e/u7o5ixYpp3C0lhMDjx4+zOzQiSqJYsWK4fPkyDh8+jNu3bwNIfBGBr6+vgSMjIiKinCZDjVLHjx/H6dOnpYtiAChbtiyWLVuG+vXr6y04IpJj3SNK5O7uLj3Kl9Tbt2/h7u7Ox/eIDEyhUKBJkyZo0qSJoUMhIiKiHCxDjVLFihVDbGysRnp8fLzUdwAR6R/rHlEidd9RyUVGRsLc3NwAERHlb0uXLsXAgQNhbm6OpUuXppp3xIgR2RQVERER5XQZapSaN28ehg8fjm+//RY1atQAkNjx8siRIzF//ny9BkhE/2Hdo/xuzJgxABLvwpg0aRIsLS2lYfHx8Th37hyqVq1qoOiI8q9FixahR48eMDc3x6JFi1LMp1Ao2ChFREREEp0bpezs7GS/SkdFRaF27dooUCCxiLi4OBQoUAB9+/ZF27Zt9R4oUX7Fukf0nytXrgBIvFPqr7/+gqmpqTTM1NQUVapUwbhx4wwVHlG+FRISovV/IiIiotTo3Ci1ePHiLAyDiFLCukf0H/Vb9/z9/bFkyRIolUoDR0REyU2bNg3jxo2T3ckIADExMZg3bx4mT55soMiIiIgop9G5Uap3795ZGYfO+BY+ym9ySt0jyknWrl1r6BCIKAUBAQEYPHiwRqNUdHQ0AgIC2ChFREREEoUQQmSmgPfv3+Pjx4+ytLR+uQ4PD4dKpUKxUVtgZGaZat6UsFGKMkq9/YWFheXquyzSW/f0Ue+yE+t43qPvunfx4kVs2bIFjx490qgLO3bsyHT5+pJX9jmUe2X3NmhkZIQXL17AwcFBln706FF06dIFr169yvIYgP/m+2hYeVgrjdM1bi3FZ4n/DPuf7iPVk39d21UB/3WJp9n2nz1Nc/QTxg10n1YGVRwenOXTMLQpy+UvwQjYlI5LnZNJ/l++L4VMRdIdU2asRRWt6RVERb1Op5Zio17LSyqledCXPquytPhUjR4003AT18Gi7yamOExxQ+Dacs2XxujKIoX00mOTfAnSraxXv1sDABwVp2TpncUV2fctimqpDtenLesTbww437tSlk0jNcnr5PMsrkfanEw7S6qiAfQC0jz/MMpI4VFRURg2bBgcHR1hZWUFOzs72Sc7uI3/Xbpriii/yAl1jygn2LRpE+rUqYOgoCDs3LkTsbGxuHnzJo4ePQqVSmXo8IjyJTs7O9jb20OhUKBMmTKwt7eXPiqVCk2aNEHnzp0NHSYRERHlIBl6+96XX36JwMBArFy5Ej179sS3336Lp0+f4rvvvsPs2bP1HSMR/Yt1jyjRzJkzsWjRIgwdOhQ2NjZYsmQJ3N3dMWjQIDg7Oxs6PKJ8afHixRBCoG/fvggICJA1EJuamsLNzQ1eXl4GjJCIiIhymgw1Sv3222/46aef0LBhQ/j7+6N+/fooVaoUXF1dsXHjRvTo0UPfcRIRWPeI1IKDg9GyZeIjnqampoiKioJCocDo0aPRuHFjBAQEGDhCovxH3Qeiu7s76tSpAxMTEwNHRERERDldhh7fe/v2LUqUKAEgsQ+bt2/fAgDq1auHEydO6C86HfAxPspPclLdIzIkOzs7REREAACKFCmCGzduAADevXuH6OhoQ4ZGlO95e3tLDVLv379HeHi47ENERESklqFGqRIlSiAkJAQAUK5cOWzZsgVA4l0ctra2egsuPdg4RflBTqx72UFdv1nHSa1BgwY4dOgQAKBTp04YOXIkBgwYgG7dusHHx8fA0RHlb9HR0ez/kIiIiHSSoUYpf39/XLt2DQAwfvx4fPvttzA3N8fo0aPxxRdf6DVAIvoP6x5RouXLl6Nr164AgK+++gpjxozBixcv0KFDB/z4448Gjo4of/viiy9w9OhRrFy5EmZmZvjhhx8QEBAAFxcX/PTTT4YOj4iIiHKQDPUpNXr0aOl/X19fBAUF4fLlyyhVqhQqV66st+CISC4/1b2U7opKmv5gdsvsCodykLi4OOzduxd+fn4AEl8/P378eANHRURq7P+QiIiIdJWhRqnk3Nzc4Obmpo+iiCgdWPcoPypQoAAGDx6MoKAgQ4dCRFqk1v/hkCFDDBkaERER5TAZenwPAI4cOYJWrVqhZMmSKFmyJFq1aoXDhw/rMzYi0oJ1jwioVasWrl69mi3Tmjp1KhQKhexTrly5bJk2UW6UX/s/JCIiovTL0J1SK1aswMiRI9GxY0eMHDkSAHD27Fm0aNECixYtwtChQ/UaZHrw0R7Ky3Jy3SPKTp9//jnGjBmDx48fw9PTE1ZWVrLh+n6ctUKFCrLG3wIF9HKjMVGepO7/0NvbG+PHj0fr1q2xfPlyxMbGYuHChYYOj4iIiHKQDJ1Vz5w5E4sWLcKwYcOktBEjRqBu3bqYOXMmL4yJsgjrnhwbofMvdSfnI0aMkNIUCgWEEFAoFIiPj9fr9AoUKAAnJye9lkmUVyXv//D27du4dOlSnuz/kIiIiDInQ4/vvXv3Ds2aNdNIb9q0KcLCwjIdlL7wFfKU1+SWukeU1UJCQjQ+f//9t/RX3+7duwcXFxeUKFECPXr0wKNHj1LM++HDB4SHh8s+RPlFbGwsfHx8cO/ePSnN1dUV7du3Z4MUERERachQo9Snn36KnTt3aqTv3r0brVq1ynRQRKQd6x5RIldX11Q/+lS7dm2sW7cO+/fvx8qVKxESEoL69esjIiJCa/5Zs2ZBpVJJn2LFiuk1HqKczMTEBNevXzd0GERERJRL6Pz43tKlS6X/PTw88M033+DYsWPw8vICkNivzalTpzB27Fj9R0mUj7Hu6YaP8uUvP/30U6rDe/XqpbdpNW/eXPq/cuXKqF27NlxdXbFlyxb069dPI/+ECRMwZswY6Xt4eDgbpihf+eyzz/Djjz9i9uzZhg6FiIiIcjidG6UWLVok+25nZ4dbt27h1q1bUpqtrS3WrFmDr7/+Wn8REuVzrHtEmtQd/avFxsYiOjoapqamsLS01GujVHK2trYoU6YM7t+/r3W4mZkZzMzMsmz6RDldXFwc1qxZg8OHD2t9EQE7OyciIiI1nRul1K/2JaLsxbpHpOmff/7RSLt37x6GDBmCL774IkunHRkZieDgYPTs2TNLp0OUW924cQPVq1cHANy9e1c2TKFQGCIkIiIiyqEy/U5rIQQAnmQQZTfWPSK50qVLY/bs2fjss89w+/ZtvZU7btw4tG7dGq6urnj27BmmTJkCY2NjdOvWTW/TIMpLAgMDDR0CERER5RIZ6ugcSOzPo1KlSrCwsICFhQUqV66MDRs26DM2ItKCdS99+BbO/KVAgQJ49uyZXst88uQJunXrhrJly6Jz584oWLAgzp49CwcHB71OhyivuX//Pg4cOICYmBgA//2YQkRERKSWoTulFi5ciEmTJmHYsGGoW7cuAODkyZMYPHgwXr9+jdGjR+s1SCJKxLpHlGjPnj2y70IIhIaGYvny5VLd0JdNmzbptTyivO7Nmzfo3LkzAgMDoVAocO/ePZQoUQL9+vWDnZ0dFixYYOgQiYiIKIfIUKPUsmXLsHLlSllHsp9++ikqVKiAqVOn5rgL4+R3SfDNXJRb5ba6R5RV2rZtK/uuUCjg4OCAxo0b84KXyMBGjx4NExMTPHr0COXLl5fSu3TpgjFjxrCOEhERkSRDjVKhoaGoU6eORnqdOnUQGhqa6aCymrqRio1TlNvk9rqXXfi4Xt6XkJBg6BCIKAUHDx7EgQMHULRoUVl66dKl8fDhQwNFRURERDlRhhqlSpUqhS1btmDixImy9M2bN6N06dJ6CSw7sHGKcpu8UvcMIWlDFet87jRmzBid8/KV80SGExUVBUtLS430t2/fwszMzAARERERUU6VoUapgIAAdOnSBSdOnJD67jh16hSOHDmCLVu26DVAIvoP655+pHQnFRurcrYrV67Ivl++fBlxcXEoW7YsgMRXzxsbG8PT09MQ4RHRv+rXr4+ffvoJ06dPB5D4eG1CQgLmzp2LRo0aGTg6IiIiykky1CjVoUMHnD9/HgsXLsSuXbsAAOXLl8f58+dRrVo1fcaXLXgHBeUWea3uEaVH0tfML1y4EDY2Nli/fj3s7OwAAP/88w/8/f1Rv359Q4VIRADmzp0LHx8fXLx4ER8/fsSXX36Jmzdv4u3btzh16pShwyMiIqIcJN2NUrGxsRg0aBAmTZqEn3/+OStiIiItWPeI/rNgwQIcPHhQapACADs7O8yYMQNNmzbF2LFjDRgdUf5WsWJF3L17F8uXL4eNjQ0iIyPRvn17DB06FM7OzoYOj4iIiHIQo/SOYGJigu3bt2dFLESUCtY9ov+Eh4fj1atXGumvXr1CRESEASIiIrVHjx5BqVTiq6++wpYtW/DHH39gxowZcHZ2xqNHj3Qqw83NDQqFQuNDREREeUu6G6WAxFdxqx8dymvcxv/ON3dRjpWX615OoK7/3AfkfO3atYO/vz927NiBJ0+e4MmTJ9i+fTv69euH9u3bGzo8onzN3d1da6Pxmzdv4O7urnM5CoUCR44ckT5nzpzRZ5hERESUA2SoT6nSpUtj2rRpOHXqFDw9PWFlZSUbPmLECL0ER0RyrHvZh33N5WyrVq3CuHHj0L17d8TGxgIAChQogH79+mHevHkGjo4ofxNCaL2rKTIyEubm5ukqq3HjxvoKi4iIiHKgDDVK/fjjj7C1tcWlS5dw6dIl2TCFQpEnLoyT3ynBi1LKCfJD3SPShaWlJVasWIF58+YhODgYAFCyZEmNhloiyj5jxowBkHg8mjRpEiwtLaVh8fHxOHfuHKpWrapzeUkbtywtLfHzzz+jXbt2KeZ/9eoVXr9+LX2PjIxM5xwQERFRdstQo1RISIj0vxACAPL8c/7qRio2TpEh5ce6lxPwrqmcy8rKCpUrVzZ0GEQE4MqVKwASj09//fUXTE1NpWGmpqaoUqUKxo0bp1NZ9evXR8WKFdGoUSP89ddf2LBhA9q3b487d+6gTJkyWsepWbMmHj58mPkZISIiomyToUYpIPGOjUWLFuHevXsAEh8rGjVqFPr376+34IhIE+seERHlRIGBgQAAf39/LF26FDY2Nhkua8OGDbLvY8aMQZUqVeDv749Tp05pHefChQsad0rVqlUrwzEQERFR1stQo9TkyZOxcOFCDB8+HF5eXgCAM2fOYPTo0Xj06BGmTZum1yBzEm0dIPPOCcou+bnu5US8g5IyIz5B4HzIW7yMeA9HG3PUcreHsRHvfKTcK+lLBnr37p1ivh07dqS77MqVK8PIyAgPHjxIMY+DgwMcHByk7+Hh4emeDhEREWWvDDVKrVy5Et9//z26desmpX366aeoXLkyhg8fzgtjoizCukeUN+y/EYqA324hNOy9lOasMseU1h5oVtHZgJERZZxKpcqysoODg5GQkIBChQpl2TSIiIgo+2WoUSo2NhY1atTQSPf09ERcXFymgyIi7Vj3DE/b3ZJE6bH/RiiG/HwZIln687D3GPLzZaz8rDobpihXWrt2rd7KcnJyQqdOndCgQQNcuHABixYtAgCsWLFCb9MgIiIiwzPKyEg9e/bEypUrNdJXr16NHj16ZDooItKOdY8od4tPEAj47ZZGgxQAKS3gt1uIT9CWgyj/CA8Px/Lly9G5c2fMmzcPJiYm2LBhA+rWrWvo0IiIiEiPMtXR+cGDB/HJJ58AAM6dO4dHjx6hV69e0iuBAWDhwoWZj5KIJKx7OQ/fzke6Oh/yVvbIXnICQGjYe5wPeQuvkgWzLzCiHCY6OtrQIRAREVE2yFCj1I0bN1C9enUAic/4A0ChQoVQqFAh3LhxQ8qXX15Vz86OKbuw7uV8bKCi1LyMSLlBKiP5iIiIiIhysww1Sqlf+UtE2Yt1jyh3c7Qx12s+IiIiIqLcLMOP75Gm5B0g8y4JIiJKqpa7PZxV5nge9l5rv1IKAE4qc9Ryt8/u0IiIiIiIsh0bpbIQH+sjyt90eZSPj/vlL8ZGCkxp7YEhP1+GApA1TKkfup3S2gPGRnwEl4iIiIjyPjZKZQNtr5DnxSdR/pKeBiruH/K2ZhWdsfKz6gj47Zas03MnlTmmtPZAs4rOBoyOiIiIiCj7sFGKiIgomzWr6IwmHk44H/IWLyPew9Em8ZE93iFFRERERPkJG6WIiIgMwNhIAa+SBQ0dBhERERGRwbBRiogom2l7pDel4XyUj4iIiIiI8io2ShlIShelvAAlopSwsYqIiIiIiPISNkrlMOwUnYiSSuuuquR5ku4v2IhFREREREQ5mZGhA6C0uY3/XacLUyIiIiIiIiKi3IKNUrkIG6eIKC3q/UTyfQX3H5nz7bffws3NDebm5qhduzbOnz9v6JCIiIiIiHI9Pr6XC6kvLPk4DlH+xQam7LN582aMGTMGq1atQu3atbF48WL4+fnhzp07cHR0NHR4RERERES5FhuliIjyEb5kIf0WLlyIAQMGwN/fHwCwatUq/P7771izZg3Gjx9v4OiIiIiIiHIvNkrlYrxjioj0JTOdouflfdHHjx9x6dIlTJgwQUozMjKCr68vzpw5Y8DIiIiIiIhyPzZK5QF8Yx8R6RPf2vef169fIz4+HoULF5alFy5cGLdv39Y6zocPH/Dhwwfpe3h4eJbGSERERESUW7FRKo/iIzpEpA/suyr9Zs2ahYCAAEOHQURERESU47FRKp9JzwUmG7CIKD3y4h1WhQoVgrGxMV68eCFLf/HiBZycnLSOM2HCBIwZM0b6Hh4ejmLFimVpnEREREREuREbpShFbMAiIn1Q70sSPkQbOJL0MzU1haenJ44cOYK2bdsCABISEnDkyBEMGzZM6zhmZmYwMzPLxiiJiIiIiHInNkqRXrABi4iSykuP/Y0ZMwa9e/dGjRo1UKtWLSxevBhRUVHS2/iIiIiIiChj2ChF2S6ti1U2WhFRTtKlSxe8evUKkydPxvPnz1G1alXs379fo/NzIiIiIiJKHzZKUY6TkTss2JBFRFlp2LBhKT6uR0REREREGcNGKcoTdGnIYsMVERERERERUc5hkEYpIQSA3NnpLeVe4eHhsr/q7TC/YL0jQ1Nve/m17qn3PUTZLb8f96LC4zMw9vvEPx/TUW+THV5jACDm32UfHpHm6JHGCbpPK6PSMz+51IfkCdHpmOePshFTyBSZrngyKyalKDK0Xacm6+YrpXnQl/CsnkAqPoS/N9zEdZDqsvkYnqm1ntIWGJ60EsbqVlZEuPr4JI8oVmMG0hquR//uv/Vf13Qln9e0jyL6l9mrRvXaSev8QyEMcIby5MkTvh6bDO7x48coWrSoocPINqx3lFOw7hEZRn6re3///TdKlixp6DCIiIjytbTOPwzSKJWQkIBnz55BCIHixYvj8ePHUCqV2R1GlgkPD0exYsU4XzmUEAIRERFwcXGBkZGRocPJNup6Z2NjA4VCYehw9C6vbJ9pyc3zybqnWfdy8/pMr/wyrzlxPvNr3Xv37h3s7Ozw6NEjqFQqQ4eT6+TEbTm34TLMHC6/zOMyzBwuv8zR9fzDII/vGRkZoWjRotLt5EqlMk+uZM5XzpUfT07V9S6vywvbpy5y63yy7mmXW9dnRuSXec1p85lf6x6QOO85aV3kNjltW86NuAwzh8sv87gMM4fLL+N0Of/IPz+XERERERERERFRjsFGKSIiIiIiIiIiynYGbZQyMzPDlClTYGZmZsgw9I7zRZT98sv2mV/mM7/IT+szv8xrfpnP3IDrInO4/DKPyzBzuPwyj8swc7j8sodBOjonIiIiIiIiIqL8jY/vERERERERERFRtmOjFBERERERERERZTs2ShERERERERERUbYzWKPUt99+Czc3N5ibm6N27do4f/68oULJkFmzZqFmzZqwsbGBo6Mj2rZtizt37sjyNGzYEAqFQvYZPHiwgSLW3dSpUzXiLleunDT8/fv3GDp0KAoWLAhra2t06NABL168MGDElF/oUu/y4vY5e/ZsKBQKjBo1SkrLi/OZH+X2Y2FadKmzeZG2OkvZL6/Xr4w6ceIEWrduDRcXFygUCuzatUs2XAiByZMnw9nZGRYWFvD19cW9e/dked6+fYsePXpAqVTC1tYW/fr1Q2RkZDbOheHo61zk0aNHaNmyJSwtLeHo6IgvvvgCcXFx2TkrBrFy5UpUrlwZSqUSSqUSXl5e2LdvnzScyy59MnqOmJ+XoT6udfPz8ssKBmmU2rx5M8aMGYMpU6bg8uXLqFKlCvz8/PDy5UtDhJMhx48fx9ChQ3H27FkcOnQIsbGxaNq0KaKiomT5BgwYgNDQUOkzd+5cA0WcPhUqVJDFffLkSWnY6NGj8dtvv2Hr1q04fvw4nj17hvbt2xswWsovdKl3eW37vHDhAr777jtUrlxZlp7X5jM/ygvHwrToeqzMS1Kqs5S98kP9yqioqChUqVIF3377rdbhc+fOxdKlS7Fq1SqcO3cOVlZW8PPzw/v376U8PXr0wM2bN3Ho0CHs3bsXJ06cwMCBA7NrFgxKH+ci8fHxaNmyJT5+/IjTp09j/fr1WLduHSZPnmyIWcpWRYsWxezZs3Hp0iVcvHgRjRs3Rps2bXDz5k0AXHbpkdFzRC7DzF3rcvllAWEAtWrVEkOHDpW+x8fHCxcXFzFr1ixDhKMXL1++FADE8ePHpTRvb28xcuRIwwWVQVOmTBFVqlTROuzdu3fCxMREbN26VUoLCgoSAMSZM2eyKUKiRMnrXV7bPiMiIkTp0qXFoUOHZPuTvDaf+VVePBamRduxMi9Jqc5S9suP9SsjAIidO3dK3xMSEoSTk5OYN2+elPbu3TthZmYmfv31VyGEELdu3RIAxIULF6Q8+/btEwqFQjx9+jTbYs8pMnIu8scffwgjIyPx/PlzKc/KlSuFUqkUHz58yN4ZyAHs7OzEDz/8wGWXDpk5R8zvyzCz17r5ffllhWy/U+rjx4+4dOkSfH19pTQjIyP4+vrizJkz2R2O3oSFhQEA7O3tZekbN25EoUKFULFiRUyYMAHR0dGGCC/d7t27BxcXF5QoUQI9evTAo0ePAACXLl1CbGysbP2VK1cOxYsXz9Xrj3Kn5PUur22fQ4cORcuWLWXzA+S9+cyP8uqxMC0pHSvzipTqLGWv/Fq/9CEkJATPnz+XLTuVSoXatWtLy+7MmTOwtbVFjRo1pDy+vr4wMjLCuXPnsj1mQ8vIuciZM2dQqVIlFC5cWMrj5+eH8PBw6Y6h/CA+Ph6bNm1CVFQUvLy8uOzSITPniFyGmbvW5fLTvwLZPcHXr18jPj5ethIBoHDhwrh9+3Z2h6MXCQkJGDVqFOrWrYuKFStK6d27d4erqytcXFxw/fp1/O9//8OdO3ewY8cOA0abttq1a2PdunUoW7YsQkNDERAQgPr16+PGjRt4/vw5TE1NYWtrKxuncOHCeP78uWECpnxJW73LS9vnpk2bcPnyZVy4cEFjWF6az/wqLx4L05LSsTKvSK3OUvbKj/VLX9THEG3LTj3s+fPncHR0lA0vUKAA7O3t890xKKPnIs+fP9e6jNXD8rq//voLXl5eeP/+PaytrbFz5054eHjg6tWrXHY6yOw5Yn5fhpm91s3vyy8rZHujVF40dOhQ3LhxQ/YsKgDZs/WVKlWCs7MzfHx8EBwcjJIlS2Z3mDpr3ry59H/lypVRu3ZtuLq6YsuWLbCwsDBgZET/Sane5QWPHz/GyJEjcejQIZibmxs6HCK9YJ0lorwmL+/XslLZsmVx9epVhIWFYdu2bejduzeOHz9u6LByBR5vMo/XujlPtj++V6hQIRgbG2v0YP/ixQs4OTlldziZNmzYMOzduxeBgYEoWrRoqnlr164NALh//352hKY3tra2KFOmDO7fvw8nJyd8/PgR7969k+XJreuPcqeU6l1e2T4vXbqEly9fonr16ihQoAAKFCiA48ePY+nSpShQoAAKFy6cJ+YzP8trx8K0pOdYmRulVWfj4+MNHWK+kt/qlz6pl09qy87JyUmjw/i4uDi8ffs2Xy3fzJyLODk5aV3G6mF5nampKUqVKgVPT0/MmjULVapUwZIlS7jsdKCPc8T8vgyTS++1Lpef/mV7o5SpqSk8PT1x5MgRKS0hIQFHjhyBl5dXdoeTYUIIDBs2DDt37sTRo0fh7u6e5jhXr14FADg7O2dxdPoVGRmJ4OBgODs7w9PTEyYmJrL1d+fOHTx69ChXrT/KndKqd3ll+/Tx8cFff/2Fq1evSp8aNWqgR48e0v95YT7zs7xyLExLRo6VuVFaddbY2NjQIeYr+aV+ZQV3d3c4OTnJll14eDjOnTsnLTsvLy+8e/cOly5dkvIcPXoUCQkJ0g+weZk+zkW8vLzw119/yRr3Dh06BKVSCQ8Pj+yZkRwkISEBHz584LLTgT7OEfP7Mkwuvde6XH5ZwBC9q2/atEmYmZmJdevWiVu3bomBAwcKW1tbWQ/2Od2QIUOESqUSx44dE6GhodInOjpaCCHE/fv3xbRp08TFixdFSEiI2L17tyhRooRo0KCBgSNP29ixY8WxY8dESEiIOHXqlPD19RWFChUSL1++FEIIMXjwYFG8eHFx9OhRcfHiReHl5SW8vLwMHDXlB2nVOyHy7vaZ/E1eeXU+85O8cCxMiy51Nq/i2/cMKz/Ur4yKiIgQV65cEVeuXBEAxMKFC8WVK1fEw4cPhRBCzJ49W9ja2ordu3eL69evizZt2gh3d3cRExMjldGsWTNRrVo1ce7cOXHy5ElRunRp0a1bN0PNUrbSx7lIXFycqFixomjatKm4evWq2L9/v3BwcBATJkwwxCxlq/Hjx4vjx4+LkJAQcf36dTF+/HihUCjEwYMHhRBcdhmR3nPE/L4MM3utm9+XX1YwSKOUEEIsW7ZMFC9eXJiamopatWqJs2fPGiqUDAGg9bN27VohhBCPHj0SDRo0EPb29sLMzEyUKlVKfPHFFyIsLMywgeugS5cuwtnZWZiamooiRYqILl26iPv370vDY2JixOeffy7s7OyEpaWlaNeunQgNDTVgxJRfpFXvhMi722fyE468Op/5TW4/FqZFlzqbV7FRyvDyev3KqMDAQK31snfv3kIIIRISEsSkSZNE4cKFhZmZmfDx8RF37tyRlfHmzRvRrVs3YW1tLZRKpfD39xcREREGmJvsp69zkQcPHojmzZsLCwsLUahQITF27FgRGxubzXOT/fr27StcXV2FqampcHBwED4+PlKDlBBcdhmRkXPE/LwM9XGtm5+XX1ZQCCFEdtyRRUREREREREREpJbtfUoRERERERERERGxUYqIiIiIiIiIiLIdG6WIiIiIiIiIiCjbsVGKiIiIiIiIiIiyHRuliIiIiIiIiIgo27FRioiIiIiIiIiIsh0bpYiIiIiIiIiIKNuxUYqIiIiIiIiIiLIdG6X0ZN26dbC1tZW+T506FVWrVk1XGQqFArt27Upx+IMHD6BQKHD16tUMxZjVjh07BoVCgXfv3hk6FMqA3LwNJ4/dENzc3LB48eJsmVbPnj0xc+ZM6Xt0dDQ6dOgApVKZK+vg/v37UbVqVSQkJBg6FNJRw4YNMWrUKEOHIZPW/oeIiIiIch42ShERpUNKDWAXLlzAwIEDs3z6165dwx9//IERI0ZIaevXr8eff/6J06dPIzQ0FCqVKsvj0KdmzZrBxMQEGzduNHQopKMdO3Zg+vTpALK3QRZIucE8NDQUzZs3z7Y4iIjygj59+qBt27YGmz5/aCMiNkrlc/Hx8dxpEgH4+PFjpsZ3cHCApaWlnqJJ2bJly9CpUydYW1tLacHBwShfvjwqVqwIJycnKBQKjfEyO39ZrU+fPli6dKmhwyAd2dvbw8bGRq9lZnYbdXJygpmZmZ6iISLK/RQKRaqfqVOnYsmSJVi3bp1B4uMPbUQEsFFKZtu2bahUqRIsLCxQsGBB+Pr6IioqCidOnICJiQmeP38uyz9q1CjUr19fp7IvXLiAJk2aoFChQlCpVPD29sbly5c18ql/6bWwsECJEiWwbdu2VMu9ceMGmjdvDmtraxQuXBg9e/bE69evU8yvvstjz5498PDwgJmZGR49eqRTfAqFAj/88APatWsHS0tLlC5dGnv27ElxWtHR0WjevDnq1q2b637lyK3ywzYMJG7HxYsXh6WlJdq1a4c3b97Ihmv71W/UqFFo2LCh9L1hw4YYNmwYRo0ahUKFCsHPzw8AsHDhQlSqVAlWVlYoVqwYPv/8c0RGRgJIfETV398fYWFhshM6QPNukUePHqFNmzawtraGUqlE586d8eLFC2m4+m6PDRs2wM3NDSqVCl27dkVERESK8x0fH49t27ahdevWsvlYsGABTpw4AYVCIc2jm5sbpk+fjl69ekGpVEp3cf3vf/9DmTJlYGlpiRIlSmDSpEmIjY3ViGvNmjUoXrw4rK2t8fnnnyM+Ph5z586Fk5MTHB0d8c0338hie/fuHfr37w8HBwcolUo0btwY165dk4Zfu3YNjRo1go2NDZRKJTw9PXHx4kVpeOvWrXHx4kUEBwenOP+Uc6gf32vYsCEePnyI0aNHS3VC7eTJk6hfvz4sLCxQrFgxjBgxAlFRUdLwjGyj69atQ0BAAK5duyZNT30xlfzxvb/++guNGzeW9ocDBw6U6jLw335i/vz5cHZ2RsGCBTF06FBZfSAiys1CQ0Olz+LFi6FUKmVp48aNg0qlMlgXCPyhjYgANkpJQkND0a1bN/Tt2xdBQUE4duwY2rdvDyEEGjRogBIlSmDDhg1S/tjYWGzcuBF9+/bVqfyIiAj07t0bJ0+exNmzZ1G6dGm0aNFC4wJ00qRJ6NChA65du4YePXqga9euCAoK0lrmu3fv0LhxY1SrVg0XL17E/v378eLFC3Tu3DnVWKKjozFnzhz88MMPuHnzJhwdHXWOLyAgAJ07d8b169fRokUL9OjRA2/fvtUaW5MmTZCQkIBDhw4ZvL+f/CC/bMPnzp1Dv379MGzYMFy9ehWNGjXCjBkzdJqH5NavXw9TU1OcOnUKq1atAgAYGRlh6dKluHnzJtavX4+jR4/iyy+/BADUqVNH46Ru3LhxGuUmJCSgTZs2ePv2LY4fP45Dhw7h77//RpcuXWT5goODsWvXLuzduxd79+7F8ePHMXv27BTjvX79OsLCwlCjRg0pbceOHRgwYAC8vLwQGhqKHTt2SMPmz5+PKlWq4MqVK5g0aRIAwMbGBuvWrcOtW7ewZMkSfP/991i0aJFGXPv27cP+/fvx66+/4scff0TLli3x5MkTHD9+HHPmzMHXX3+Nc+fOSeN06tQJL1++xL59+3Dp0iVUr14dPj4+0v6hR48eKFq0KC5cuIBLly5h/PjxMDExkcYvXrw4ChcujD///DP1lUY5yo4dO1C0aFFMmzZNqhNA4jbUrFkzdOjQAdevX8fmzZtx8uRJDBs2TDZ+erfRLl26YOzYsahQoYI0veT1CgCioqLg5+cHOzs7XLhwAVu3bsXhw4c1ph8YGIjg4GAEBgZi/fr1WLduncHuGCAi0jcnJyfpo1KpoFAoZGnW1tYaP+Q1bNgQw4cPx6hRo2BnZ4fChQvj+++/R1RUFPz9/WFjY4NSpUph3759smml90dG/tBGRBJBQgghLl26JACIBw8eaB0+Z84cUb58een79u3bhbW1tYiMjBRCCLF27VqhUqmk4VOmTBFVqlRJcXrx8fHCxsZG/Pbbb1IaADF48GBZvtq1a4shQ4YIIYQICQkRAMSVK1eEEEJMnz5dNG3aVJb/8ePHAoC4c+eO1umuXbtWABBXr15NMbbU4vv666+l75GRkQKA2LdvnxBCiMDAQAFABAUFicqVK4sOHTqIDx8+pDod0p/8sg1369ZNtGjRQpbWpUsXWey9e/cWbdq0keUZOXKk8Pb2lr57e3uLatWqpTh/alu3bhUFCxaUvidfTmqurq5i0aJFQgghDh48KIyNjcWjR4+k4Tdv3hQAxPnz54UQicvX0tJShIeHS3m++OILUbt27RRj2blzpzA2NhYJCQmpzps6nrZt26Y5f/PmzROenp7Sd21x+fn5CTc3NxEfHy+llS1bVsyaNUsIIcSff/4plEqleP/+vazskiVLiu+++04IIYSNjY1Yt25dqrFUq1ZNTJ06Nc2YyfC8vb3FyJEjhRDybV+tX79+YuDAgbK0P//8UxgZGYmYmBhpvIxuo9r2TQDEzp07hRBCrF69WtjZ2Un7NyGE+P3334WRkZF4/vy5ECJxP+Hq6iri4uKkPJ06dRJdunRJMyYiotwmpfOX5OdM3t7ewsbGRkyfPl3cvXtXTJ8+XRgbG4vmzZuL1atXi7t374ohQ4aIggULiqioKCGEEP/8849wcHAQEyZMEEFBQeLy5cuiSZMmolGjRinGc/nyZQFA2icLIcSbN2/EgAEDhJeXlwgNDRVv3rwRQiQeL5RKpZg/f764f/++uH//vhAi8Tzy1KlTIiQkROzZs0cULlxYzJkzRypvypQpwtraWnTs2FHcvHlT7NmzR5iamgo/Pz8xfPhwcfv2bbFmzRoBQJw9e1Yaz9fXV7Ru3VpcuHBB3L17V4wdO1YULFhQiqdChQris88+E0FBQeLu3btiy5YtGtdWhQsXFmvXrtVt5RDlc7xT6l9VqlSBj48PKlWqhE6dOuH777/HP//8Iw3v06cP7t+/j7NnzwJIfISgc+fOsLKy0qn8Fy9eYMCAAShdujRUKhWUSiUiIyPx6NEjWT4vLy+N7yndZXLt2jUEBgbC2tpa+pQrVw4AUm2ZNzU1ReXKlTMUX9LxrKysoFQq8fLlS1meJk2aoFSpUti8eTNMTU1TjIP0K79sw0FBQahdu3aq09SVp6enRtrhw4fh4+ODIkWKwMbGBj179sSbN28QHR2tc7lBQUEoVqwYihUrJqV5eHjA1tZWtizc3Nxk/fI4Oztr1KekYmJiYGZmpvVWdm2S3lGltnnzZtStW1f6hfTrr7/WWIfJ4ypcuDA8PDxgZGQkS1PHeu3aNURGRqJgwYKydRkSEiKtxzFjxqB///7w9fXF7Nmzta5fCwuLdC1nyrmuXbuGdevWybYHPz8/JCQkICQkRMqX0W00LUFBQahSpYps/1a3bl0kJCTgzp07UlqFChVgbGwsfU+rDhIR5QdVqlTB119/jdKlS2PChAkwNzdHoUKFpPPAyZMn482bN7h+/ToAYPny5ahWrRpmzpyJcuXKoVq1alizZg0CAwNx9+5drdN4+PAhjI2N4ejoKKXZ29vD0tISpqamcHJygr29vTSscePGGDt2LEqWLImSJUsCAL7++mvUqVMHbm5uaN26NcaNG4ctW7bIppOQkIA1a9bAw8MDrVu3RqNGjXDnzh0sXrwYZcuWhb+/P8qWLYvAwEAAiY+enz9/Hlu3bkWNGjVQunRpzJ8/H7a2tlKXFI8ePYKvry/KlSuH0qVLo1OnTqhSpYpsui4uLnj48GEm1wRR/lDA0AHkFMbGxjh06BBOnz6NgwcPYtmyZfjqq69w7tw5uLu7w9HREa1bt8batWvh7u6Offv24dixYzqX37t3b7x58wZLliyBq6srzMzM4OXllalnoiMjI9G6dWvMmTNHY5izs3OK41lYWGhc1OoaX9LHbYDEPjySd5TesmVLbN++Hbdu3UKlSpXSO1uUQflpG06LkZERhBCyNG39xCRvkHvw4AFatWqFIUOG4JtvvoG9vT1OnjyJfv364ePHj3rvyFyX+pRUoUKFEB0djY8fP+rU4Jt8/s6cOYMePXogICAAfn5+UKlU2LRpExYsWJBmXKnFGhkZCWdnZ63bk/rR3alTp6J79+74/fffsW/fPkyZMgWbNm1Cu3btpLxv376Fg4NDmvNFOV9kZCQGDRok67xWrXjx4tL/Gd1G9SW9dZCIKD9I+iO0sbExChYsKDunL1y4MADIfpxS/8iYXHBwMMqUKaORrq8f2pYuXYrg4GBERkYiLi4OSqVSlkfbD23GxsY6/dCWPN7kP7Rt2LABvr6+6NSpk9RQpsYf2oh0x0apJBQKBerWrYu6deti8uTJcHV1xc6dOzFmzBgAQP/+/dGtWzcULVoUJUuWRN26dXUu+9SpU1ixYgVatGgBAHj8+LHW56zPnj2LXr16yb5Xq1ZNa5nVq1fH9u3b4ebmhgIFMrcqdY1PF7Nnz4a1tTV8fHxw7NgxeHh4ZCo20l1+2IbLly8v68tIPY2kHBwccOPGDVna1atXNS5Ak7t06RISEhKwYMEC6WQl+S9upqamiI+PTzPGx48f4/Hjx9LdUrdu3cK7d+8yVR+qVq0qlaX+Pz1Onz4NV1dXfPXVV1KaPn7Fq169Op4/f44CBQrAzc0txXxlypRBmTJlMHr0aHTr1g1r166VGqXev3+P4ODgFLcVyrm01Ynq1avj1q1bKFWqVLrK0mUb1bUOrlu3DlFRUVLD16lTp2BkZISyZcumKyYiovwmrR+n1A1JSX+cSu+PjPyhjYjU+Pjev86dO4eZM2fi4sWLePToEXbs2IFXr16hfPnyUh4/Pz8olUrMmDED/v7+6Sq/dOnS2LBhA4KCgnDu3Dn06NEDFhYWGvm2bt2KNWvW4O7du5gyZQrOnz+v0TGr2tChQ/H27Vt069YNFy5cQHBwMA4cOAB/f/80T9gzGp+u5s+fjx49eqBx48a4fft2hssh3eWXbXjEiBHYv38/5s+fj3v37mH58uXYv3+/LE/jxo1x8eJF/PTTT7h37x6mTJmi0UilTalSpRAbG4tly5bh77//xoYNG6QO0NXc3NwQGRmJI0eO4PXr11p/BfP19UWlSpXQo0cPXL58GefPn0evXr3g7e2t9Zc+XTk4OKB69eo4efJkhsYvXbo0Hj16hE2bNiE4OBhLly7Fzp07MxyPmq+vL7y8vNC2bVscPHgQDx48wOnTp/HVV1/h4sWLiImJwbBhw3Ds2DE8fPgQp06dwoULF2Tb5tmzZ6W77yh3cXNzw4kTJ/D06VOpofp///sfTp8+Lb2Q4N69e9i9e3eK+wI1XbZRNzc3hISE4OrVq3j9+jU+fPigUU6PHj1gbm6O3r1748aNGwgMDMTw4cPRs2dP6Rd+IiLSj+rVq+PmzZtwc3NDqVKlZJ+UuolI+kNbRiT9EUP9mJ2+f2hLPi+FChWS8ql/ZDt48CDat2+PtWvXSsP4QxtR+rBR6l9KpRInTpxAixYtUKZMGXz99ddYsGABmjdvLuUxMjJCnz59EB8fL7sTRBc//vgj/vnnH1SvXh09e/bEiBEjZM9QqwUEBGDTpk2oXLkyfvrpJ/z6668p3lnh4uKCU6dOIT4+Hk2bNkWlSpUwatQo2Nraym5J1Wd86bFo0SJ07twZjRs3TvF5ctKf/LINf/LJJ/j++++xZMkSVKlSBQcPHsTXX38ty+Pn54dJkybhyy+/RM2aNREREaHT/FapUgULFy7EnDlzULFiRWzcuBGzZs2S5alTpw4GDx6MLl26wMHBAXPnztUoR6FQYPfu3bCzs0ODBg3g6+uLEiVKYPPmzWnGkJb+/ftj48aNGRr3008/xejRozFs2DBUrVoVp0+flt54lhkKhQJ//PEHGjRoAH9/f5QpUwZdu3bFw4cPpdvk37x5g169eqFMmTLo3LkzmjdvjoCAAKmMX3/9FT169ND7I5KU9aZNm4YHDx6gZMmS0q/ClStXxvHjx3H37l3Ur18f1apVw+TJk+Hi4pJqWbpsox06dECzZs3QqFEjODg44Ndff9Uox9LSEgcOHMDbt29Rs2ZNdOzYET4+Pli+fLn+ZpyIiABk7EdG/tBGRBJD97Se2/Tt21e0bt3a0GEQZRi34dwtOjpaFCtWTJw+fdrQoejNq1evhL29vfj7778NHQoREVGelJ6376nfrqqm7S2rSPLGUyGEuHv3rmjXrp2wtbUVFhYWoly5cmLUqFEabwxOasWKFeKTTz6RpaX0RuHk0xci8a3FBQsWFNbW1qJLly5i0aJFab5JWtsbmpPPc3h4uBg+fLhwcXERJiYmolixYqJHjx7i0aNH4sOHD6Jr166iWLFiwtTUVLi4uIhhw4ZJb5YVQoiBAweKQYMGpTjfRCSnECJZb8CkVVhYGP766y80adIEe/bsQZMmTQwdElG6cBvOO44dO4aIiAi0bt3a0KHoxcWLFxEcHIwuXboYOhQiIiLKJjExMShbtiw2b96cZ+4qev36NcqWLYuLFy/C3d3d0OEQ5QpslNJRw4YNcf78eQwaNAiLFi0ydDhE6cZtmIiIiIhyEv7QRkRslCIiIiIiIiIiomzHjs6JiIiIiIiIiCjbsVGKiIiIiIiIiIiyHRuliIiIiIiIiIgo27FRioiIiIiIiIiIsh0bpYiIiIiIiIiIKNuxUYqIiIiIiIiIiLIdG6WIiIiIiIiIiCjbsVGKiIiIiIiIiIiyHRuliIiIiIiIiIgo2/0fevW+1Z/oraYAAAAASUVORK5CYII=", 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", 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" + ] + }, + "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": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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full_kappa

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full_num_iterations

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model_name

\n", + " Name of the full-fitted model (output_dir/model_name)\n", + "
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full_fitting_duration

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\n", + " " + ], + "text/plain": [ + "*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": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 46, + "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)" + ] + }, + { + "cell_type": "code", + "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": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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model_name

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kappa

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\n", + " " + ], + "text/plain": [ + "*model_name latent_dim kappa \n", + "+------------+ +------------+ +-------+\n", + "kpms_project_t 4 5000 \n", + "kpms_project_t 4 10000 \n", + " (Total: 2)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 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": { + "text/plain": [ + "(
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model_names = (kpms_model.FullFitting).fetch(\"model_name\")\n", + "\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", + "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_eml_scores(eml_scores, eml_std_errs, model_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": [ + "Insert the video set to be used for inference into the `VideoRecording` table as well.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['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": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 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": 50, + "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 # " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_model.VideoRecording.heading" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert video files into the `VideoRecording.File` table:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12024-03-15 14:04:22deeplabcut10dlc_project/videos/21_12_10_def6a_3.top.ir.mp4
subject12024-03-15 14:04:22deeplabcut11dlc_project/videos/22_04_26_cage4_1_1.top.ir.mp4
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subject12024-03-15 14:04:22deeplabcut17dlc_project/videos/21_12_10_def6b_3.top.ir.Mp4
subject12024-03-15 14:04:22deeplabcut18dlc_project/videos/22_04_26_cage4_0_2.top.ir.mp4
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subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_20inference_outputInference task for the tutorial5
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\n", + " " + ], + "text/plain": [ + "*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": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "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": 57, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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": [ + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Saving to csv: 100%|████████████████████| 10/10 [00:01<00:00, 6.71it/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_20-06_01_20/inference_output/trajectory_plots\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating trajectory plots: 100%|██████| 42/42 [00:09<00:00, 4.41it/s]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "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_20-06_01_20/inference_output/grid_movies\n", + "Using window size of 144 pixels\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating grid movies: 100%|███████████| 42/42 [01:27<00:00, 2.09s/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_20-06_01_20/inference_output/similarity_dendogram\n" + ] + }, + { + "data": { + "image/png": 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model_name

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inference_duration

\n", + " Time duration of the inference computation\n", + "
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_200:01:44
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Total: 1

\n", + " " + ], + "text/plain": [ + "*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": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_model.Inference()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `MotionSequence` table contains the results for the inference (syllables, latent_state, centroid, and heading):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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video_name

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syllable

\n", + " syllable labels (z). The syllable label assigned to each frame (i.e. the state indexes assigned by the model).\n", + "
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latent_state

\n", + " 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.\n", + "
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centroid

\n", + " inferred centroid (v). The centroid of the animal in each frame, as estimated by the model.\n", + "
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heading

\n", + " inferred heading (h). The heading of the animal in each frame, as estimated by the model.\n", + "
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2021_11_8_one_mouse.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000=BLOB==BLOB==BLOB==BLOB=
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subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2021_12_10_def6a_3.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000=BLOB==BLOB==BLOB==BLOB=
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subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2021_12_2_def6b_2.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000=BLOB==BLOB==BLOB==BLOB=
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subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2022_27_04_cage4_mouse2_0.top.irDLC_resnet50_moseq_exampleAug21shuffle1_500000=BLOB==BLOB==BLOB==BLOB=
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\n", + " " + ], + "text/plain": [ + "*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": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_model.Inference.MotionSequence()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_200=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_201=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_202=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_203=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_204=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_205=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_206=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_207=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_208=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_209=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2010=BLOB=
subject12024-03-15 14:04:22deeplabcut1kpms_project_tutorial/2024_03_20-06_01_2011=BLOB=
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Total: 42

\n", + " " + ], + "text/plain": [ + "*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": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kpms_model.Inference.GridMoviesSampledInstances()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "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": "markdown", + "metadata": {}, + "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": { + "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..efdcb67 --- /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 kpms_pca, kpms_model + +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 element-moseq schemas ----------------------------------- + +kpms_pca.activate(db_prefix + "kpms_pca", linking_module=__name__) +kpms_model.activate(db_prefix + "kpms_model", linking_module=__name__) diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..6e8ecdf --- /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", + "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-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"], + }, +)