Merge branch 'develop' into dallas/nod-83-rigol-ds10000-series-scope

AI_ML/CLASSIFICATION/ACCURACY/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "These dataframes should both be single columns.",
+  "long_description": "These dataframes should both be single columns.",
+  "short_description": "The ACCURACY node takes two dataframes with the true and predicted labels from a classification task, and indicates whether the prediction was correct or not.",
   "parameters": [
     {
       "name": "true_label",

AI_ML/CLASSIFICATION/ONE_HOT_ENCODING/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndata : DataFrame\n    The input dataframe containing the categorical features.",
+  "long_description": "Inputs\n------\ndata : DataFrame\n    The input dataframe containing the categorical features.",
+  "short_description": "The ONE_HOT_ENCODING node creates a one hot encoding from a dataframe containing categorical features.",
   "parameters": [
     {
       "name": "feature_col",

AI_ML/CLASSIFICATION/SUPPORT_VECTOR_MACHINE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "It takes two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data.",
+  "long_description": "It takes two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data.",
+  "short_description": "The SUPPORT_VECTOR_MACHINE node is used to train a support vector machine model for classification tasks.",
   "parameters": [
     {
       "name": "kernel",

AI_ML/CLASSIFICATION/TORCHSCRIPT_CLASSIFIER/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n----------\ninput_image : Image\n    The image to classify.\nclass_names : DataFrame\n    A dataframe containing the class names.",
+  "long_description": "Inputs\n----------\ninput_image : Image\n    The image to classify.\nclass_names : DataFrame\n    A dataframe containing the class names.",
+  "short_description": "Execute a torchscript classifier against an input image.",
   "parameters": [
     {
       "name": "model_path",

AI_ML/CLASSIFICATION/TRAIN_TEST_SPLIT/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The TRAIN_TEST_SPLIT node is used to split the data into test and training according to a size specified before any ML tasks.",
   "parameters": [
     {
       "name": "test_size",

AI_ML/IMAGE_CAPTIONING/NLP_CONNECT_VIT_GPT2/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The NLP_CONNECT_VIT_GPT2 node captions an input image and produces an output string wrapped in a dataframe.",
   "parameters": [],
   "returns": [
     {

AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "For more information about Vision Transformers,\nsee: https://huggingface.co/google/vit-base-patch16-224\n\nFor a complete list of models, see:\nhttps://huggingface.co/models?pipeline_tag=image-classification\n\nFor examples of how revision parameters (such as 'main') is used,\nsee: https://huggingface.co/google/vit-base-patch16-224/commits/main",
+  "long_description": "For more information about Vision Transformers,\nsee: https://huggingface.co/google/vit-base-patch16-224\n\nFor a complete list of models, see:\nhttps://huggingface.co/models?pipeline_tag=image-classification\n\nFor examples of how revision parameters (such as 'main') is used,\nsee: https://huggingface.co/google/vit-base-patch16-224/commits/main",
+  "short_description": "The HUGGING_FACE_PIPELINE node uses a classification pipeline to process and classify an image.",
   "parameters": [
     {
       "name": "default",

AI_ML/LOAD_MODEL/ONNX_MODEL/app.json

@@ -0,0 +1,66 @@
+{
+    "rfInstance": {
+        "nodes": [
+            {
+                "width": 208,
+                "height": 96,
+                "id": "ONNX_MODEL-3bfae6ce-ad2f-4d4d-8342-96d824eb7380",
+                "type": "AI_ML",
+                "data": {
+                    "id": "ONNX_MODEL-3bfae6ce-ad2f-4d4d-8342-96d824eb7380",
+                    "label": "ONNX MODEL",
+                    "func": "ONNX_MODEL",
+                    "type": "AI_ML",
+                    "ctrls": {
+                        "file_path": {
+                            "type": "str",
+                            "default": null,
+                            "desc": "Path to a ONNX model to load and use for prediction.",
+                            "overload": null,
+                            "functionName": "ONNX_MODEL",
+                            "param": "file_path",
+                            "value": ""
+                        }
+                    },
+                    "initCtrls": {},
+                    "inputs": [
+                        {
+                            "name": "default",
+                            "id": "default",
+                            "type": "Vector",
+                            "multiple": false,
+                            "desc": "The input tensor to use for prediction.\nFor now, only a single input tensor is supported.\nNote that the input tensor shape is not checked against the model's input shape."
+                        }
+                    ],
+                    "outputs": [
+                        {
+                            "name": "default",
+                            "id": "default",
+                            "type": "Vector",
+                            "desc": "The predictions made by the ONNX model.\nFor now, only a single output tensor is supported."
+                        }
+                    ],
+                    "path": "AI_ML/LOAD_MODEL/ONNX_MODEL/ONNX_MODEL.py",
+                    "selected": true
+                },
+                "position": {
+                    "x": -171.967500985471,
+                    "y": -191.24623740170063
+                },
+                "selected": true,
+                "positionAbsolute": {
+                    "x": -171.967500985471,
+                    "y": -191.24623740170063
+                },
+                "dragging": true
+            }
+        ],
+        "edges": [],
+        "viewport": {
+            "x": 669.537650867023,
+            "y": 395.1114317341454,
+            "zoom": 0.7467072297113905
+        }
+    },
+    "textNodes": []
+}

AI_ML/LOAD_MODEL/ONNX_MODEL/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "This allows supporting a wide range of deep learning frameworks and hardware platforms.",
+  "long_description": "This allows supporting a wide range of deep learning frameworks and hardware platforms.",
+  "short_description": "ONNX_MODEL loads a serialized ONNX model and uses it to make predictions using ONNX Runtime.",
   "parameters": [
     {
       "name": "file_path",

AI_ML/NLP/COUNT_VECTORIZER/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The COUNT_VECTORIZER node receives a collection (matrix, vector or dataframe) of text documents and converts it to a matrix of token counts.",
   "parameters": [],
   "returns": [
     {

AI_ML/OBJECT_DETECTION/OBJECT_DETECTION/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault : Image",
+  "long_description": "Inputs\n------\ndefault : Image",
+  "short_description": "The OBJECT_DETECTION node detects objects in the input image, and returns an 'image' DataContainer with those objects highlighted.",
   "parameters": [],
   "returns": [
     {

AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type.\n\nThis node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model.\nThis model can be loaded as follows:\n\n    ```python\n    from prophet.serialize import model_from_json\n\n    model = model_from_json(dc_inputs.extra[\"prophet\"])\n    ```",
+  "long_description": "The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type.\n\nThis node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model.\nThis model can be loaded as follows:",
+  "short_description": "The PROPHET_PREDICT node runs a Prophet model on the incoming dataframe.",
   "parameters": [
     {
       "name": "run_forecast",

AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/example.md

@@ -1,7 +1,5 @@
 In this example, the `TIMESERIES` node generates random time series data
 
-<!-- '<table border="1" class="dataframe">  <thead>   <tr style="text-align: right;">     <th></th>     <th>Timestamp</th>     <th>Data</th>  </tr>  </thead>  <tbody>    <tr>      <th>0</th>      <td>2023-01-01</td>      <td>-0.187903</td>    </tr>    <tr>      <th>1</th>      <td>2023-01-02</td>      <td>0.204290</td>    </tr>    <tr>      <th>2</th>      <td>2023-01-03</td>      <td>-0.659945</td>    </tr>  </tbody></table>' -->
-
 This dataframe is then passed to the `PROPHET_PREDICT` node, with the default parameters
 of `run_forecast=True` and `periods=365`. This node trains a `Prophet` model and runs a prediction
 forecast over a 365 period. 
@@ -16,4 +14,4 @@ It returns a DataContainer with the following
 
 Finally, this is passed to 2 nodes, `PROPHET_PLOT` and `PROPHET_COMPONENTS`, wherein
 the forecast and the trend components are plotted in Plotly. Because a forecast was already run,
-the `PROPHET_PLOT` and `PROPHET_COMPONENTS` nodes know to use the already predicted dataframe.
+the `PROPHET_PLOT` and `PROPHET_COMPONENTS` nodes know to use the already predicted dataframe.

AI_ML/REGRESSION/LEAST_SQUARES/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The LEAST_SQUARE node computes the coefficients that minimize the distance between the inputs 'Matrix' or 'OrderedPair' class and the regression.",
   "parameters": [],
   "returns": [
     {

AI_ML/SEGMENTATION/DEEPLAB_V3/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "The input image is expected to be a DataContainer of an 'image' type.\n\nThe output is a DataContainer of an 'image' type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask.",
+  "long_description": "The input image is expected to be a DataContainer of an 'image' type.\n\nThe output is a DataContainer of an 'image' type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask.",
+  "short_description": "The DEEPLAB_V3 node returns a segmentation mask from an input image in a dataframe.",
   "parameters": [],
   "returns": [
     {

AI_ML/TEXT_SUMMARIZATION/BART_LARGE_CNN/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "The \"summary_text\" column contains a summary of the text in the corresponding row of the input dataframe.",
+  "long_description": "The \"summary_text\" column contains a summary of the text in the corresponding row of the input dataframe.",
+  "short_description": "The BART_LARGE_CNN node takes an input dataframe with multiple rows and a single column, and produces a dataframe with a single \"summary_text\" column.",
   "parameters": [],
   "returns": [
     {

EXTRACTORS/DATAFRAME/EXTRACT_COLUMNS/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault : DataFrame|Matrix\n    Input to use as the table for column extraction",
+  "long_description": "Inputs\n------\ndefault : DataFrame|Matrix\n    Input to use as the table for column extraction",
+  "short_description": "The EXTRACT_COLUMNS node takes an input dataframe/matrix and returns a dataframe/matrix with only the specified columns.",
   "parameters": [
     {
       "name": "columns",

EXTRACTORS/DEBUGGING/DATACONTAINER_TYPE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Must use the TEXT_VIEW node to view the text.",
+  "long_description": "Must use the TEXT_VIEW node to view the text.",
+  "short_description": "The DATACONTAINER_TYPE node returns a TextBlob containing the input DataContainer type (e.g. Vector).",
   "parameters": [],
   "returns": [
     {

EXTRACTORS/DEBUGGING/DC_CONTENT_TYPE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Must use the TEXT_VIEW node to view the text.",
+  "long_description": "Must use the TEXT_VIEW node to view the text.",
+  "short_description": "The DC_CONTENT_TYPE node returns a TextBlob containing the type of the input DataContainer's content.",
   "parameters": [],
   "returns": [
     {

EXTRACTORS/DEBUGGING/PRINT_DATACONTAINER/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Must use the TEXT_VIEW node to view the text.",
+  "long_description": "Must use the TEXT_VIEW node to view the text.",
+  "short_description": "The PRINT_DATACONTAINER node returns a TextBlob containing input DataContainer information.",
   "parameters": [],
   "returns": [
     {

EXTRACTORS/FILE/OPEN_IMAGE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault: None",
+  "long_description": "Inputs\n------\ndefault: None",
+  "short_description": "The OPEN_IMAGE node loads an image file from disk and returns an image type DataContainer object.",
   "parameters": [
     {
       "name": "file_path",

EXTRACTORS/FILE/OPEN_PARQUET/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault: None",
+  "long_description": "Inputs\n------\ndefault: None",
+  "short_description": "The OPEN_PARQUET node loads a local file of the .parquet file format. It then returns the file as a pandas.Dataframe type.",
   "parameters": [
     {
       "name": "file_path",

EXTRACTORS/FILE/READ_CSV/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The READ_CSV node reads a .csv file from disk or a URL, and then returns a dataframe.",
   "parameters": [
     {
       "name": "file_path",

EXTRACTORS/FILE/READ_S3/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault: None",
+  "long_description": "Inputs\n------\ndefault: None",
+  "short_description": "The READ_S3 node takes a S3_key name, S3 bucket name, and file name as input, and extracts the file from the specified bucket using the S3_key that was saved.",
   "parameters": [
     {
       "name": "s3_name",

GENERATORS/SAMPLE_DATASETS/PLOTLY_DATASET/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The PLOTLY_DATASET node retrieves a pandas DataFrame from a Plotly built-in dataset using the provided dataset_key parameter and returns it wrapped in a Flojoy DataFrame class.",
   "parameters": [
     {
       "name": "dataset_key",

GENERATORS/SAMPLE_DATASETS/SCIKIT_LEARN_DATASET/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "'sklearn.datasets', using the provided dataset_key parameter, and returns\nit wrapped in a DataContainer.",
+  "long_description": "'sklearn.datasets', using the provided dataset_key parameter, and returns\nit wrapped in a DataContainer.",
+  "short_description": "The SCIKIT_LEARN_DATASET node retrieves a pandas DataFrame from",
   "parameters": [
     {
       "name": "dataset_name",

GENERATORS/SAMPLE_DATASETS/TEXT_DATASET/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "The data is returned as a dataframe with one column containing the text and the other containing the category.",
+  "long_description": "The data is returned as a dataframe with one column containing the text and the other containing the category.",
+  "short_description": "The TEXT_DATASET node loads the 20 newsgroups dataset from scikit-learn.",
   "parameters": [
     {
       "name": "subset",

GENERATORS/SAMPLE_IMAGES/SKIMAGE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Examples can be found here:\nhttps://scikit-image.org/docs/stable/auto_examples/index.html",
+  "long_description": "Examples can be found here:\nhttps://scikit-image.org/docs/stable/auto_examples/index.html",
+  "short_description": "The SKIMAGE node is designed to load example images from 'scikit-image'.",
   "parameters": [
     {
       "name": "img_key",

GENERATORS/SIMULATIONS/BASIC_OSCILLATOR/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "It offers a more straightforward way to generate signals, with sample rate and the time in seconds as parameters, along with all the parameters in the SINE node.",
+  "long_description": "It offers a more straightforward way to generate signals, with sample rate and the time in seconds as parameters, along with all the parameters in the SINE node.",
+  "short_description": "The BASIC_OSCILLATOR node is a combination of the LINSPACE and SINE nodes.",
   "parameters": [
     {
       "name": "sample_rate",

GENERATORS/SIMULATIONS/BASIC_OSCILLATOR/example.md

@@ -1,6 +1,6 @@
 In this example, `BASIC_OSCILLATOR` node generates a sine wave with a frequency of 1Hz and an amplitude of 3 for
-10 seconds with a sample rate of 400Hz. 
+10 seconds with a sample rate of 400Hz.
 
-The same output can be generated with `LINSPACE` and `SINE` nodes combined. 
-`SINE` would have the same parameters, but for `LINSPACE`, the `start` parameter will be 0, `end` is the time, which is 10 in this case, 
-and `step` is the total samples, which is sample_rate * time in this case.
+The same output can be generated with `LINSPACE` and `SINE` nodes combined.
+`SINE` would have the same parameters, but for `LINSPACE`, the `start` parameter will be 0, `end` is the time, which is 10 in this case,
+and `step` is the total samples, which is sample_rate * time in this case

GENERATORS/SIMULATIONS/CONSTANT/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault : OrderedPair|Vector\n    Optional input that defines the size of the output.",
+  "long_description": "Inputs\n------\ndefault : OrderedPair|Vector\n    Optional input that defines the size of the output.",
+  "short_description": "The CONSTANT node generates a single x-y vector of numeric (floating point) constants.",
   "parameters": [
     {
       "name": "dc_type",

GENERATORS/SIMULATIONS/FEEDBACK/FEEDBACK.py

@@ -1,10 +1,11 @@
-from typing import Optional, Any
+from typing import Any, Optional
+
 from flojoy import (
-    flojoy,
     DataContainer,
-    get_job_result,
-    NodeReference,
     JobResultBuilder,
+    NodeReference,
+    flojoy,
+    get_job_result,
 )
 
 

GENERATORS/SIMULATIONS/FEEDBACK/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The FEEDBACK node captures the result of the specified node ID. If the result is not found, it passes the result of the parent node.",
   "parameters": [
     {
       "name": "referred_node",

GENERATORS/SIMULATIONS/LINSPACE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "It uses the 'linspace' numpy function. It is useful for generating an x-axis for the OrderedPair data type.\n\nInputs\n------\ndefault : OrderedPair\n    Optional input in case LINSPACE is used in a loop. Not used.",
+  "long_description": "It uses the 'linspace' numpy function. It is useful for generating an x-axis for the OrderedPair data type.\n\nInputs\n------\ndefault : OrderedPair\n    Optional input in case LINSPACE is used in a loop. Not used.",
+  "short_description": "The LINSPACE node generates data spaced evenly between two points.",
   "parameters": [
     {
       "name": "start",

GENERATORS/SIMULATIONS/MATRIX/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Based on these inputs, it generates a random matrix where the integers inside the matrix are between 0 and 19.",
+  "long_description": "Based on these inputs, it generates a random matrix where the integers inside the matrix are between 0 and 19.",
+  "short_description": "The MATRIX node takes two arguments, 'row' and 'col', as input.",
   "parameters": [
     {
       "name": "row",

GENERATORS/SIMULATIONS/POPULATE/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault : OrderedPair|Vector\n    Input to use as the x-axis for the random samples.",
+  "long_description": "Inputs\n------\ndefault : OrderedPair|Vector\n    Input to use as the x-axis for the random samples.",
+  "short_description": "The POPULATE node generates random numbers, depending on the distribution selected and the input data.",
   "parameters": [
     {
       "name": "distribution",

GENERATORS/SIMULATIONS/RAND/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "Inputs\n------\ndefault : DataContainer\n    unused in this node",
+  "long_description": "Inputs\n------\ndefault : DataContainer\n    unused in this node",
+  "short_description": "The RAND node generates a random number or a list of random numbers, depending on the distribution selected.",
   "parameters": [
     {
       "name": "distribution",

GENERATORS/SIMULATIONS/SCALAR/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": null,
+  "long_description": null,
+  "short_description": "The SCALAR node returns a single Scalar value.",
   "parameters": [
     {
       "name": "value",

GENERATORS/SIMULATIONS/SECOND_ORDER_SYSTEM/docstring.json

@@ -1,5 +1,6 @@
 {
-  "description": "This node is designed to be used in a loop. The data is appended as the loop progresses and written to memory.\n\nInputs\n------\ndefault : Scalar\n    PID node output.",
+  "long_description": "This node is designed to be used in a loop. The data is appended as the loop progresses and written to memory.\n\nInputs\n------\ndefault : Scalar\n    PID node output.",
+  "short_description": "The SECOND_ORDER_SYSTEM has a second order exponential function.",
   "parameters": [
     {
       "name": "d1",