diff --git a/.github/workflows/labgraph_monitor.yml b/.github/workflows/labgraph_monitor.yml new file mode 100644 index 000000000..0f8e40d2b --- /dev/null +++ b/.github/workflows/labgraph_monitor.yml @@ -0,0 +1,22 @@ +on: [push] +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + node-version: [14.x] + steps: + - uses: actions/checkout@v2 + - name: Use Node.js ${{ matrix.node-version }} + uses: actions/setup-node@v1 + with: + node-version: ${{ matrix.node-version }} + - name: Install dependencies + run: | + cd extensions/prototypes/labgraph_monitor + yarn + - name: Run labgraph_monitor tests + run: | + cd extensions/prototypes/labgraph_monitor + yarn test --watchAll=false + yarn build diff --git a/Dockerfile b/Dockerfile index 0048e1d5a..b6e948f97 100644 --- a/Dockerfile +++ b/Dockerfile @@ -4,7 +4,6 @@ FROM quay.io/pypa/manylinux2014_x86_64 # Install devtoolset-9 -RUN yum update -y RUN yum install -y centos-release-scl RUN yum install -y devtoolset-9 RUN echo "source /opt/rh/devtoolset-9/enable" >> /etc/bashrc diff --git a/extensions/graphviz_support/README.md b/extensions/graphviz_support/README.md index 9bacfeae4..c79d81dd6 100644 --- a/extensions/graphviz_support/README.md +++ b/extensions/graphviz_support/README.md @@ -1,6 +1,6 @@ # Graphviz for LabGraph graphs -This extension provides an API to generate a graphviz visualization of the LabGraph topology. +This extension provides an API to generate a [graphviz](https://graphviz.org/) visualization of the LabGraph topology. ## Quick Start @@ -23,22 +23,33 @@ python setup.py install To make sure things are working: -1- Move to the root of the LabGraph directory: -``` -labgraph\extensions\graphviz_support> cd ../.. +1. Move to the root of the LabGraph directory: + +```bash +labgraph/extensions/graphviz_support> cd ../.. labgraph> ``` -2- Run the following test -``` +2. Run the following test + +```bash python -m extensions.graphviz_support.graphviz_support.tests.test_lg_graphviz_api ``` -**The output of the file for this test can be found at:**\ -extensions\graphviz_support\graphviz_support\tests\output + +**The output of the file for this test can be found at:** + +`extensions/graphviz_support/graphviz_support/tests/output/test.svg` + ### Generating a graphviz file To generate a graph visualization just call 'generate_graphviz' function and pass the appropriate parameters -``` -from extensions/graphviz_support/graphviz_support/generate_graphviz/generate_graphviz.py import generate_graphviz.py -generate_graphviz(graph, output_file) +```python +from extensions.graphviz_support.graphviz_support.generate_graphviz.generate_graphviz import generate_graphviz +from extensions.graphviz_support.graphviz_support.tests.demo_graph.demo import Demo + +generate_graphviz(Demo(), "output.png") # you can also use "output.svg" ``` + +**It will produce the following diagram named `output.png` in your current directory:** + +![Graphviz diagram for nodes: RollingAverager, NoiseGenerator, Amplifier, Sink, and Attenuator](https://i.imgur.com/M4yL39x.png) diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/amplifier.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/amplifier.py index 1ac52cd66..9d5a1067e 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/amplifier.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/amplifier.py @@ -12,20 +12,20 @@ class AmplifierConfig(lg.Config): class Amplifier(lg.Node): - INPUT = lg.Topic(RandomMessage) - OUTPUT = lg.Topic(RandomMessage) + AMPLIFIER_INPUT = lg.Topic(RandomMessage) + AMPLIFIER_OUTPUT = lg.Topic(RandomMessage) config: AmplifierConfig def output(self, _in: float) -> float: return self.config.out_in_ratio * _in - @lg.subscriber(INPUT) - @lg.publisher(OUTPUT) + @lg.subscriber(AMPLIFIER_INPUT) + @lg.publisher(AMPLIFIER_OUTPUT) async def amplify(self, message: RandomMessage) -> lg.AsyncPublisher: current_time = time.time() output_data = np.array( [self.output(_in) for _in in message.data] ) - yield self.OUTPUT, RandomMessage( + yield self.AMPLIFIER_OUTPUT, RandomMessage( timestamp=current_time, data=output_data ) diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/attenuator.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/attenuator.py index 55ecdb022..d90a5c6d6 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/attenuator.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/attenuator.py @@ -12,20 +12,20 @@ class AttenuatorConfig(lg.Config): class Attenuator(lg.Node): - INPUT = lg.Topic(RandomMessage) - OUTPUT = lg.Topic(RandomMessage) + ATTENUATOR_INPUT = lg.Topic(RandomMessage) + ATTENUATOR_OUTPUT = lg.Topic(RandomMessage) config: AttenuatorConfig def output(self, _in: float) -> float: return pow(10, (self.config.attenuation / 20)) * _in - @lg.subscriber(INPUT) - @lg.publisher(OUTPUT) + @lg.subscriber(ATTENUATOR_INPUT) + @lg.publisher(ATTENUATOR_OUTPUT) async def attenuate(self, message: RandomMessage) -> lg.AsyncPublisher: current_time = time.time() output_data = np.array( [self.output(_in) for _in in message.data] ) - yield self.OUTPUT, RandomMessage( + yield self.ATTENUATOR_OUTPUT, RandomMessage( timestamp=current_time, data=output_data ) diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py index 27237c942..3dadf96c4 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py @@ -47,12 +47,12 @@ def setup(self) -> None: def connections(self) -> lg.Connections: return ( - (self.NOISE_GENERATOR.OUTPUT, self.ROLLING_AVERAGER.INPUT), - (self.NOISE_GENERATOR.OUTPUT, self.AMPLIFIER.INPUT), - (self.NOISE_GENERATOR.OUTPUT, self.ATTENUATOR.INPUT), - (self.ROLLING_AVERAGER.OUTPUT, self.SINK.INPUT_1), - (self.AMPLIFIER.OUTPUT, self.SINK.INPUT_2), - (self.ATTENUATOR.OUTPUT, self.SINK.INPUT_3), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.ROLLING_AVERAGER.ROLLING_AVERAGER_INPUT), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.AMPLIFIER.AMPLIFIER_INPUT), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.ATTENUATOR.ATTENUATOR_INPUT), + (self.ROLLING_AVERAGER.ROLLING_AVERAGER_OUTPUT, self.SINK.SINK_INPUT_1), + (self.AMPLIFIER.AMPLIFIER_OUTPUT, self.SINK.SINK_INPUT_2), + (self.ATTENUATOR.ATTENUATOR_OUTPUT, self.SINK.SINK_INPUT_3), ) def process_modules(self) -> Tuple[lg.Module, ...]: diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/noise_generator.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/noise_generator.py index f42aef9f1..31f04e052 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/noise_generator.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/noise_generator.py @@ -14,13 +14,13 @@ class NoiseGeneratorConfig(lg.Config): class NoiseGenerator(lg.Node): - OUTPUT = lg.Topic(RandomMessage) + NOISE_GENERATOR_OUTPUT = lg.Topic(RandomMessage) config: NoiseGeneratorConfig - @lg.publisher(OUTPUT) + @lg.publisher(NOISE_GENERATOR_OUTPUT) async def generate_noise(self) -> lg.AsyncPublisher: while True: - yield self.OUTPUT, RandomMessage( + yield self.NOISE_GENERATOR_OUTPUT, RandomMessage( timestamp=time.time(), data=np.random.rand(self.config.num_features) ) diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/rolling_averager.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/rolling_averager.py index 90bc78575..c4d51a463 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/rolling_averager.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/rolling_averager.py @@ -18,14 +18,14 @@ class RollingConfig(lg.Config): class RollingAverager(lg.Node): - INPUT = lg.Topic(RandomMessage) - OUTPUT = lg.Topic(RandomMessage) + ROLLING_AVERAGER_INPUT = lg.Topic(RandomMessage) + ROLLING_AVERAGER_OUTPUT = lg.Topic(RandomMessage) state: RollingState config: RollingConfig - @lg.subscriber(INPUT) - @lg.publisher(OUTPUT) + @lg.subscriber(ROLLING_AVERAGER_INPUT) + @lg.publisher(ROLLING_AVERAGER_OUTPUT) async def average(self, message: RandomMessage) -> lg.AsyncPublisher: current_time = time.time() self.state.messages.append(message) @@ -40,7 +40,7 @@ async def average(self, message: RandomMessage) -> lg.AsyncPublisher: [message.data for message in self.state.messages] ) mean_data = np.mean(all_data, axis=0) - yield self.OUTPUT, RandomMessage( + yield self.ROLLING_AVERAGER_OUTPUT, RandomMessage( timestamp=current_time, data=mean_data ) diff --git a/extensions/graphviz_support/graphviz_support/tests/demo_graph/sink.py b/extensions/graphviz_support/graphviz_support/tests/demo_graph/sink.py index 59c340b6b..3736482ec 100644 --- a/extensions/graphviz_support/graphviz_support/tests/demo_graph/sink.py +++ b/extensions/graphviz_support/graphviz_support/tests/demo_graph/sink.py @@ -14,20 +14,20 @@ class SinkState(lg.State): class Sink(lg.Node): - INPUT_1 = lg.Topic(RandomMessage) - INPUT_2 = lg.Topic(RandomMessage) - INPUT_3 = lg.Topic(RandomMessage) + SINK_INPUT_1 = lg.Topic(RandomMessage) + SINK_INPUT_2 = lg.Topic(RandomMessage) + SINK_INPUT_3 = lg.Topic(RandomMessage) state: SinkState - @lg.subscriber(INPUT_1) + @lg.subscriber(SINK_INPUT_1) def got_message(self, message: RandomMessage) -> None: self.state.data_1 = message.data - @lg.subscriber(INPUT_2) + @lg.subscriber(SINK_INPUT_2) def got_message_2(self, message: RandomMessage) -> None: self.state.data_2 = message.data - @lg.subscriber(INPUT_3) + @lg.subscriber(SINK_INPUT_3) def got_message_3(self, message: RandomMessage) -> None: self.state.data_3 = message.data diff --git a/extensions/graphviz_support/graphviz_support/tests/output/test b/extensions/graphviz_support/graphviz_support/tests/output/test new file mode 100644 index 000000000..b7eb1c3b1 --- /dev/null +++ b/extensions/graphviz_support/graphviz_support/tests/output/test @@ -0,0 +1,17 @@ +digraph Demo { + center=true rankdir=LR + node [fontsize=12 height=1.5 shape=circle width=1.5] + NoiseGenerator + RollingAverager + Amplifier + Attenuator + Sink + Sink + Sink + NoiseGenerator -> RollingAverager + NoiseGenerator -> Amplifier + NoiseGenerator -> Attenuator + RollingAverager -> Sink + Amplifier -> Sink + Attenuator -> Sink +} diff --git a/extensions/graphviz_support/graphviz_support/tests/output/test.svg b/extensions/graphviz_support/graphviz_support/tests/output/test.svg new file mode 100644 index 000000000..daf81fccc --- /dev/null +++ b/extensions/graphviz_support/graphviz_support/tests/output/test.svg @@ -0,0 +1,79 @@ + + + + + + +Demo + + + +NoiseGenerator + +NoiseGenerator + + + +RollingAverager + +RollingAverager + + + +NoiseGenerator->RollingAverager + + + + + +Amplifier + +Amplifier + + + +NoiseGenerator->Amplifier + + + + + +Attenuator + +Attenuator + + + +NoiseGenerator->Attenuator + + + + + +Sink + +Sink + + + +RollingAverager->Sink + + + + + +Amplifier->Sink + + + + + +Attenuator->Sink + + + + + diff --git a/extensions/graphviz_support/graphviz_support/tests/test_lg_graphviz_api.py b/extensions/graphviz_support/graphviz_support/tests/test_lg_graphviz_api.py index ea63d4391..2711b5644 100644 --- a/extensions/graphviz_support/graphviz_support/tests/test_lg_graphviz_api.py +++ b/extensions/graphviz_support/graphviz_support/tests/test_lg_graphviz_api.py @@ -33,47 +33,47 @@ def test_out_edge_node_mapper(self) -> None: self.assertEqual(4, len(out_edge_node_map)) self.assertEqual( 'generate_noise', - out_edge_node_map['NOISE_GENERATOR/OUTPUT'].name + out_edge_node_map['NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT'].name ) self.assertEqual( 'average', - out_edge_node_map['ROLLING_AVERAGER/OUTPUT'].name + out_edge_node_map['ROLLING_AVERAGER/ROLLING_AVERAGER_OUTPUT'].name ) self.assertEqual( 'amplify', - out_edge_node_map['AMPLIFIER/OUTPUT'].name + out_edge_node_map['AMPLIFIER/AMPLIFIER_OUTPUT'].name ) self.assertEqual( 'attenuate', - out_edge_node_map['ATTENUATOR/OUTPUT'].name + out_edge_node_map['ATTENUATOR/ATTENUATOR_OUTPUT'].name ) def test_in_out_edge_mapper(self) -> None: in_out_edge_map = in_out_edge_mapper(self.graph.__streams__.values()) self.assertEqual(6, len(in_out_edge_map)) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['ROLLING_AVERAGER/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['ROLLING_AVERAGER/ROLLING_AVERAGER_INPUT'] ) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['AMPLIFIER/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['AMPLIFIER/AMPLIFIER_INPUT'] ) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['ATTENUATOR/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['ATTENUATOR/ATTENUATOR_INPUT'] ) self.assertEqual( - 'ROLLING_AVERAGER/OUTPUT', - in_out_edge_map['SINK/INPUT_1'] + 'ROLLING_AVERAGER/ROLLING_AVERAGER_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_1'] ) self.assertEqual( - 'AMPLIFIER/OUTPUT', - in_out_edge_map['SINK/INPUT_2'] + 'AMPLIFIER/AMPLIFIER_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_2'] ) self.assertEqual( - 'ATTENUATOR/OUTPUT', - in_out_edge_map['SINK/INPUT_3'] + 'ATTENUATOR/ATTENUATOR_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_3'] ) def test_connect_to_upstream(self) -> None: diff --git a/extensions/prototypes/labgraph_monitor/.eslintignore b/extensions/prototypes/labgraph_monitor/.eslintignore new file mode 100644 index 000000000..c523dda43 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/.eslintignore @@ -0,0 +1,2 @@ +node_modules +public \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/.eslintrc.json b/extensions/prototypes/labgraph_monitor/.eslintrc.json new file mode 100644 index 000000000..fde66f482 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/.eslintrc.json @@ -0,0 +1,18 @@ +{ + "extends": ["react-app", "react-app/jest", "prettier"], + "plugins": ["prettier"], + "rules": { + "prettier/prettier": [ + "error", + { + "endOfLine": "auto" + } + ], + "no-console": [ + "warn", + { + "allow": ["warn", "error"] + } + ] + } +} diff --git a/extensions/prototypes/labgraph_monitor/.gitignore b/extensions/prototypes/labgraph_monitor/.gitignore index a00fddb79..d3ff5fc90 100644 --- a/extensions/prototypes/labgraph_monitor/.gitignore +++ b/extensions/prototypes/labgraph_monitor/.gitignore @@ -4,7 +4,6 @@ /node_modules /.pnp .pnp.js -/public # testing /coverage @@ -22,3 +21,5 @@ npm-debug.log* yarn-debug.log* yarn-error.log* + +package-lock.json \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/.husky/pre-commit b/extensions/prototypes/labgraph_monitor/.husky/pre-commit new file mode 100644 index 000000000..546369c29 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/.husky/pre-commit @@ -0,0 +1,8 @@ +#!/bin/sh +. "$(dirname "$0")/_/husky.sh" +cd ./extensions/prototypes/labgraph_monitor +SRC_PATTERN="extensions/prototypes/labgraph_monitor" +git diff --cached --name-only | if grep --quiet "$SRC_PATTERN" +then + yarn check-code +fi \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/.prettierignore b/extensions/prototypes/labgraph_monitor/.prettierignore new file mode 100644 index 000000000..fc1666b42 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/.prettierignore @@ -0,0 +1,2 @@ +public +node_modules \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/.prettierrc.json b/extensions/prototypes/labgraph_monitor/.prettierrc.json new file mode 100644 index 000000000..00a58d4d7 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/.prettierrc.json @@ -0,0 +1,7 @@ +{ + "trailingComma": "es5", + "tabWidth": 4, + "semi": true, + "singleQuote": true, + "endOfLine": "auto" +} diff --git a/extensions/prototypes/labgraph_monitor/App.css b/extensions/prototypes/labgraph_monitor/App.css deleted file mode 100644 index 40d5810d5..000000000 --- a/extensions/prototypes/labgraph_monitor/App.css +++ /dev/null @@ -1,21 +0,0 @@ -table{ - border: 1px solid black; - text-align: center; - margin-right:10px; - margin-top: 20px; -} - -th, td{ - border: 1px solid black; - text-align: center; -} - -.annotations{ - align-items: center; - margin-right:50px; - margin-top: 15px; -} - -.table-div{ - float:right; -} \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/App.js b/extensions/prototypes/labgraph_monitor/App.js deleted file mode 100644 index 5497b02af..000000000 --- a/extensions/prototypes/labgraph_monitor/App.js +++ /dev/null @@ -1,161 +0,0 @@ -/** - -Copyright (c) Facebook, Inc. and its affiliates. -This source code is licensed under the MIT license found in the -LICENSE file in the root directory of this source tree. -*/ - -import React, { useState, useEffect, useCallback} from 'react'; -import { w3cwebsocket as W3CWebSocket } from "websocket"; -import ReactFlow, { addEdge, MiniMap, Controls, Background, isNode} from 'react-flow-renderer'; -import { dataToConnections, connectionsToNodes, dataToObjects } from './helper'; -import './App.css'; -import dagre from 'dagre'; - -// sample connections array: {'NoiseGenerator': ['RollingAverager'], 'RollingAverager': ['AveragedNoise'], 'AveragedNoise': ['Plot']} - -// dagreGraph helps layout the nodes in the graph -const dagreGraph = new dagre.graphlib.Graph(); -dagreGraph.setDefaultEdgeLabel(() => ({})); - -const nodeWidth = 172; -const nodeHeight = 36; - -// setting up the horizontal and vertical options to change the orientation -const getLayoutedElements = (elements, direction = 'TB') => { - const isHorizontal = direction === 'LR'; - dagreGraph.setGraph({ rankdir: direction }); - - elements.forEach((el) => { - if (isNode(el)) { - dagreGraph.setNode(el.id, { width: nodeWidth, height: nodeHeight }); - } else { - dagreGraph.setEdge(el.source, el.target); - } - }); - - dagre.layout(dagreGraph); - - return elements.map((el) => { - if (isNode(el)) { - const nodeWithPosition = dagreGraph.node(el.id); - el.targetPosition = isHorizontal ? 'left' : 'top'; - el.sourcePosition = isHorizontal ? 'right' : 'bottom'; - el.position = { - x: nodeWithPosition.x - nodeWidth / 2 + Math.random() / 1000, - y: nodeWithPosition.y - nodeHeight / 2, - }; - } - - return el; - }); -}; - -const InteractionGraph = () => { - const [node_name, setName] = useState("None"); - const [connections, setConnections] = useState([]); - const [elements, setElements] = useState([]); - const [nodeObjects, setNodeObjects] = useState([]); - const [type, setType] = useState('None') - - // getting the node that the user is clicking - const onElementClick = (event, element) => { - setName(element.id) - setType(nodeObjects[element.id].type) - console.log("node", node_name) - }; - - // receiving the messages from server - useEffect(() => { - // connecting to the server - const client = new W3CWebSocket('ws://localhost:9000'); - - client.onopen = () => { - console.log('connected'); - client.send(JSON.stringify({ - "api_version": "0.1", - "api_request": { - "request_id": 1, - "start_stream_request": { - "stream_id": "LABGRAPH.MONITOR", - "labgraph.monitor": { - } - } - } - })) - }; - - client.onmessage = (message) => { - const dataFromServer = JSON.parse(message.data); - if (dataFromServer.stream_batch){ - const dataArray = dataFromServer.stream_batch["labgraph.monitor"].samples[0].data; // dataArray is the part of JSON data we are interested in - const connections = dataToConnections(dataArray); // generating the connections array - const elements = connectionsToNodes(connections); // changing the connections array to an elements array having the properties of nodes in reactflow - const layoutedElements = getLayoutedElements(elements); // using dagreGraph and the getLayoutedElements function defined earlier to layout the nodes - const nodeObjects = dataToObjects(dataArray); - setElements(layoutedElements); - setConnections(connections); - setNodeObjects(nodeObjects); - console.log("nodeObjects", nodeObjects); - console.log("connection", connections); - console.log("server message received ", dataArray) - } - }; - - return () => { - client.close(); - } - }, []); - - const onLayout = useCallback( - (direction) => { - const layoutedElements = getLayoutedElements(elements, direction); - setElements(layoutedElements); - }, - [elements] - ); - - return( -
-
-
-
- - -
- - - - - - - - - - - - - -
NodeType
{node_name}{type}
-
- -
- - - - - -
- -
- - - ) -}; - -export default InteractionGraph; \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/README.md b/extensions/prototypes/labgraph_monitor/README.md index 27a20d41c..a5b1566b3 100644 --- a/extensions/prototypes/labgraph_monitor/README.md +++ b/extensions/prototypes/labgraph_monitor/README.md @@ -1,77 +1,95 @@ -# LabGraph Monitor +# Labgraph Monitor -LabGraph Monitor helps visualize the graph built. In order to run the application, you will need to first set it up using React using the below steps: +This extension is an interactive visualization tool to monitor and make real-time changes to LabGraph graph and nodes. -In case you do not have npm installed, follow the below steps to get started: +## Quick Start -Download Node.js from [here](https://nodejs.org/en/download/) +**Prerequisites**: -Once Node is installed, you can verify that node/npm was installed successfully by running the following: +- [Node.js](https://nodejs.org/en/) +- [Yarn](https://classic.yarnpkg.com/lang/en/docs/install) + +Check that node and yarn were properly installed by running the following commands ``` node -v -npm -v ``` -The above should display the version of node/npm that were installed. For example: - ``` -6.14.13 +yarn -v ``` -Now that you have npm installed, you can create a new React application using the terminal. Navigate to the folder where you want the application to be installed and run the below code to create a new React application. We are going to call the application "labgraph_monitor_extension" for the sake of this example but feel free to choose another name. +**Set up the application** + +1. Be sure that you are inside **extensions/prototypes/labgraph_monitor** directory -Note: npx is a package that comes with npm 5.2+ ``` -npx create-react-app labgraph_monitor_extension +cd extensions/prototypes/labgraph_monitor ``` -Next, you will need to install the dagre library, websocket, and ReactFlow that help in layouting the graph. -Run the code below to install them +2. Install dependencies by running **yarn** command + ``` -npm i dagre +yarn ``` +3. Test the application by running the following command + ``` -npm install websocket +yarn test --watchAll=false ``` +4. Run the application + ``` -npm install --save react-flow-renderer +yarn start ``` -Once the application is created, we will need to change and add some files to get the LabGraph Monitor working. +(!) The application will be running on **localhost:3000** by default -First, you can validate that everything is going well so far by running the new sample React app in your browser. Navigate using the terminal to the labgraph_monitor_extension folder and run +## UI Overview -``` -npm start -``` + + +### Mocks: -This should open a sample React app in your localhost and display the React icon. +Mocks are a quick way to get familiar with the user interface. They do not require any connection to the LabGraph Websockets API and provide visualization for most of the existing [LabGraph examples](https://github.com/facebookresearch/labgraph/tree/main/labgraph/examples). -Now that we have the initial application set up, we can start adding the new files. +Mocks also can be useful to experiment with new UI features. -Add the App.js, helper.js and App.css files downloaded from [labgraph repo](https://github.com/facebookresearch/labgraph) found at .\labgraph\extensions\prototypes\labgraph_monitor\src to your labgraph_monitor_extension application. You need to add them in the ./labgraph_monitor_extension/src folder +### Realtime: -You can now navigate to your localhost in browser again to see the changes. As the backend server is not running yet, you will see a blank white page. +This is the core feature of Labgraph Monitor, it provides real-time visualization of the graph by connecting to Labgraph Websockets API. -Note: incase you closed your react app while downloading other libraries, you will need to start it again by navigating to the labgraph_monitor_extension and running: +To use this feature properly few steps are required: + +1. Within **extensions/prototypes/labgraph_monitor** directory, Create a **.env.local** that contains the following information ``` -npm start +REACT_APP_WS_API="ws://127.0.0.1:9000" ``` -With that, LabGraph Monitor is ready! Before you see the graph displayed in your browser, you will need to start the backend server. +(!) LabGraph Websocket server runs on localhost:9000 by default -## Running an example +2. Run Labgraph Websockets server. The following tutorial shows how to run LabGraph Websocket server properly : [tutorial](https://github.com/facebookresearch/labgraph/pull/58/files#diff-247005c77570899ce53f81a83b2a5fe6e7535616cc96564d67378fe7f73dac49) -Inorder to run a sample graph, you can go to the simpleVizGraph.js file found under labgraph_monitor/examples/simpleVizGraph.js and replace the current code in App.js with the simpleVizGraph.js code. Next, you can start the application using +3. Under LabGraph Monitor settings panel click on REALTIME option and click connect. -``` -npm start -``` +### Nodes & Edges + +To see the information related to a specific node or edge, just click on it, the appropriate information will be automatically displayed on the setting panel. + + + + + + + + + + +
NodeEdge
-This code shows the sample graph of the simple_viz.py example found under labgraph/examples of the main repository. It works by having the expected JSON data the server should send using the parser and rendering it through our React application. +**Nodes** : currently when a node is clicked its name will be displayed, However, this feature will be updated in the future to include more information. -You can also try other another example by following the above steps on the mockDataGraph.js found under labgraph_monitor/examples/mockDataGraph.js \ No newline at end of file +**Edges** : currently when an edge is clicked the "message_name", "message_fields" and "fields_datatypes" will be displayed, However, this feature will be updated in the future to include more information (E.g: the field value in realtime). diff --git a/extensions/prototypes/labgraph_monitor/examples/mockDataGraph.js b/extensions/prototypes/labgraph_monitor/examples/mockDataGraph.js deleted file mode 100644 index 0b7244ae4..000000000 --- a/extensions/prototypes/labgraph_monitor/examples/mockDataGraph.js +++ /dev/null @@ -1,477 +0,0 @@ -/** - -Copyright (c) Facebook, Inc. and its affiliates. -This source code is licensed under the MIT license found in the -LICENSE file in the root directory of this source tree. -*/ - -import React, { useState, useEffect, useCallback} from 'react'; -import ReactFlow, { addEdge, MiniMap, Controls, Background, isNode} from 'react-flow-renderer'; -import { dataToConnections, connectionsToNodes, dataToObjects } from './helper'; -import './App.css'; -import dagre from 'dagre'; - -const data = [ - [ - { - "NoiseGenerator": { - "type": "Node", - "config": { - "NoiseGeneratorConfig": { - "sample_rate": "float", - "num_features": "int" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - }, - { - "RollingAverager": { - "type": "Node", - "state": { - "RollingState": { - "messages": "List.RandomMessage" - } - }, - "config": { - "RollingConfig": { - "window": "float" - } - }, - "inputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - } - ], - [ - { - "RollingAverager": { - "type": "Node", - "state": { - "RollingState": { - "messages": "List.RandomMessage" - } - }, - "config": { - "RollingConfig": { - "window": "float" - } - }, - "inputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - }, - { - "AveragedNoise": { - "type": "Group", - "config": { - "AveragedNoiseConfig": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Node2": { - "type": "Group", - "config": { - "Node2Config": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Node4": { - "type": "Group", - "config": { - "Node2Config": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Node5": { - "type": "Group", - "config": { - "Node2Config": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - ], - [ - { - "AveragedNoise": { - "type": "Group", - "config": { - "AveragedNoiseConfig": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Plot": { - "type": "Node", - "state": "PlotState", - "config": "PlotConfig", - "inputs": [ - "RandomMessage" - ], - "outputs": [] - } - } - ], - [ - { - "Node2": { - "type": "Group", - "config": { - "AveragedNoiseConfig": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Node6": { - "type": "Group", - "config": { - "Node2Config": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - { - "Node7": { - "type": "Group", - "config": { - "Node2Config": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise", - "RollingAverager": "Node2", - "RollingAverager": "Node4", - "RollingAverager": "Node5", - "Node2": "Node6", - "Node2": "Node7", - } - } - }, - ] -] - -// dagreGraph helps layout the nodes in the graph -const dagreGraph = new dagre.graphlib.Graph(); -dagreGraph.setDefaultEdgeLabel(() => ({})); - -const nodeWidth = 172; -const nodeHeight = 36; - -// setting up the horizontal and vertical options to change the orientation -const getLayoutedElements = (elements, direction = 'TB') => { - const isHorizontal = direction === 'LR'; - dagreGraph.setGraph({ rankdir: direction }); - - elements.forEach((el) => { - if (isNode(el)) { - dagreGraph.setNode(el.id, { width: nodeWidth, height: nodeHeight }); - } else { - dagreGraph.setEdge(el.source, el.target); - } - }); - - dagre.layout(dagreGraph); - - return elements.map((el) => { - if (isNode(el)) { - const nodeWithPosition = dagreGraph.node(el.id); - el.targetPosition = isHorizontal ? 'left' : 'top'; - el.sourcePosition = isHorizontal ? 'right' : 'bottom'; - el.position = { - x: nodeWithPosition.x - nodeWidth / 2 + Math.random() / 1000, - y: nodeWithPosition.y - nodeHeight / 2, - }; - } - - return el; - }); -}; - -const InteractionGraph = () => { - const [node_name, setName] = useState("None"); - const [connections, setConnections] = useState([]); - const [elements, setElements] = useState([]); - const [nodeObjects, setNodeObjects] = useState([]); - const [type, setType] = useState('None') - - // getting the node that the user is clicking - const onElementClick = (event, element) => { - setName(element.id) - setType(nodeObjects[element.id].type) - console.log("node", node_name) - }; - - // receiving the messages from server - useEffect(() => { - const connections = dataToConnections(data); // generating the connections array - const elements = connectionsToNodes(connections); // changing the connections array to an elements array having the properties of nodes in reactflow - const layoutedElements = getLayoutedElements(elements); // using dagreGraph and the getLayoutedElements function defined earlier to layout the nodes - const nodeObjects = dataToObjects(data); - setElements(layoutedElements); - setConnections(connections); - setNodeObjects(nodeObjects); - console.log("nodeObjects", nodeObjects); - console.log("connection", connections); - console.log("server message received ", data) - - return () => { - } - }, []); - - const onLayout = useCallback( - (direction) => { - const layoutedElements = getLayoutedElements(elements, direction); - setElements(layoutedElements); - }, - [elements] - ); - - return( -
-
-
-
- - -
- - - - - - - - - - - - - -
NodeType
{node_name}{type}
-
- -
- - - - - -
- -
- - - ) -}; - -export default InteractionGraph; \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/examples/simpleVizGraph.js b/extensions/prototypes/labgraph_monitor/examples/simpleVizGraph.js deleted file mode 100644 index f40bb5165..000000000 --- a/extensions/prototypes/labgraph_monitor/examples/simpleVizGraph.js +++ /dev/null @@ -1,285 +0,0 @@ -/** - -Copyright (c) Facebook, Inc. and its affiliates. -This source code is licensed under the MIT license found in the -LICENSE file in the root directory of this source tree. -*/ - -import React, { useState, useEffect, useCallback} from 'react'; -import ReactFlow, { addEdge, MiniMap, Controls, Background, isNode} from 'react-flow-renderer'; -import { dataToConnections, connectionsToNodes, dataToObjects } from './helper'; -import './App.css'; -import dagre from 'dagre'; - -const data = [ - [ - { - "NoiseGenerator": { - "type": "Node", - "config": { - "NoiseGeneratorConfig": { - "sample_rate": "float", - "num_features": "int" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - }, - { - "RollingAverager": { - "type": "Node", - "state": { - "RollingState": { - "messages": "List.RandomMessage" - } - }, - "config": { - "RollingConfig": { - "window": "float" - } - }, - "inputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - } - ], - [ - { - "RollingAverager": { - "type": "Node", - "state": { - "RollingState": { - "messages": "List.RandomMessage" - } - }, - "config": { - "RollingConfig": { - "window": "float" - } - }, - "inputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ] - } - }, - { - "AveragedNoise": { - "type": "Group", - "config": { - "AveragedNoiseConfig": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise" - } - } - } - ], - [ - { - "AveragedNoise": { - "type": "Group", - "config": { - "AveragedNoiseConfig": { - "sample_rate": "float", - "num_features": "int", - "window": "float" - } - }, - "inputs": [], - "outputs": [ - { - "RandomMessage": { - "timestamp": "float", - "data": "np.ndarray" - } - } - ], - "connections": { - "NoiseGenerator": "RollingAverager", - "RollingAverager": "AveragedNoise" - } - } - }, - { - "Plot": { - "type": "Node", - "state": "PlotState", - "config": "PlotConfig", - "inputs": [ - "RandomMessage" - ], - "outputs": [] - } - } - ] -] - -// dagreGraph helps layout the nodes in the graph -const dagreGraph = new dagre.graphlib.Graph(); -dagreGraph.setDefaultEdgeLabel(() => ({})); - -const nodeWidth = 172; -const nodeHeight = 36; - -// setting up the horizontal and vertical options to change the orientation -const getLayoutedElements = (elements, direction = 'TB') => { - const isHorizontal = direction === 'LR'; - dagreGraph.setGraph({ rankdir: direction }); - - elements.forEach((el) => { - if (isNode(el)) { - dagreGraph.setNode(el.id, { width: nodeWidth, height: nodeHeight }); - } else { - dagreGraph.setEdge(el.source, el.target); - } - }); - - dagre.layout(dagreGraph); - - return elements.map((el) => { - if (isNode(el)) { - const nodeWithPosition = dagreGraph.node(el.id); - el.targetPosition = isHorizontal ? 'left' : 'top'; - el.sourcePosition = isHorizontal ? 'right' : 'bottom'; - el.position = { - x: nodeWithPosition.x - nodeWidth / 2 + Math.random() / 1000, - y: nodeWithPosition.y - nodeHeight / 2, - }; - } - - return el; - }); -}; - -const InteractionGraph = () => { - const [node_name, setName] = useState("None"); - const [connections, setConnections] = useState([]); - const [elements, setElements] = useState([]); - const [nodeObjects, setNodeObjects] = useState([]); - const [type, setType] = useState('None') - - // getting the node that the user is clicking - const onElementClick = (event, element) => { - setName(element.id) - setType(nodeObjects[element.id].type) - console.log("node", node_name) - }; - - // receiving the messages from server - useEffect(() => { - const connections = dataToConnections(data); // generating the connections array - const elements = connectionsToNodes(connections); // changing the connections array to an elements array having the properties of nodes in reactflow - const layoutedElements = getLayoutedElements(elements); // using dagreGraph and the getLayoutedElements function defined earlier to layout the nodes - const nodeObjects = dataToObjects(data); - setElements(layoutedElements); - setConnections(connections); - setNodeObjects(nodeObjects); - console.log("nodeObjects", nodeObjects); - console.log("connection", connections); - console.log("server message received ", data) - - return () => { - } - }, []); - - const onLayout = useCallback( - (direction) => { - const layoutedElements = getLayoutedElements(elements, direction); - setElements(layoutedElements); - }, - [elements] - ); - - return( -
-
-
-
- - -
- - - - - - - - - - - - - -
NodeType
{node_name}{type}
-
- -
- - - - - -
- -
- - - ) -}; - -export default InteractionGraph; \ No newline at end of file diff --git a/extensions/prototypes/labgraph_monitor/helper.js b/extensions/prototypes/labgraph_monitor/helper.js deleted file mode 100644 index d2a9d859e..000000000 --- a/extensions/prototypes/labgraph_monitor/helper.js +++ /dev/null @@ -1,104 +0,0 @@ -/** - -Copyright (c) Facebook, Inc. and its affiliates. -This source code is licensed under the MIT license found in the -LICENSE file in the root directory of this source tree. -*/ - -/** - * Converts the data from server to connections array - * @param {Array} data - data received from server through websocket - * @return {Array} An array having arrays of connections - */ - function dataToConnections(data){ - const connections = {} - for (const array of data) { - for (let index = 1; index < array.length; index++){ - const parent = Object.keys(array[0])[0] - const childNode = Object.keys(array[index])[0] - if (connections[parent]){ - connections[parent].push(childNode) - } else{ - connections[parent] = [childNode] - } - } - } - return connections -} - -/** - * Converts the data from server to Objects array of the nodes - * @param {Array} data - data received from server through websocket - * @return {Array} An array having arrays of Objects representation of data from server. - * Every object represents a node and has properties such as inputs, outputs, config.. for that node - */ - function dataToObjects(data){ - const objects = {} - for (const array of data) { - const parent = array[0] - objects[Object.keys(parent)[0]] = parent[Object.keys(parent)[0]] - for (let index = 1; index < array.length; index++){ - const childNode = array[index] - objects[Object.keys(childNode)[0]] = childNode[Object.keys(childNode)[0]] - } - } - return objects -} - -/** - * Converts the data from server to connections array - * @param {Array} connections - connections between nodes generated from server data - * @return {Array} Elements array having Nodes for graph representation. A sample graph Node has id, data and position - */ - function connectionsToNodes(connections){ - const adjacencyListKeys = Object.keys(connections) - console.log("adjacency list keys", adjacencyListKeys) - console.log("connections are" , connections) - // adding all nodes of the graph without duplicates - const allNodes = Object.keys(connections) - for (let [, edgeNodes] of Object.entries(connections)) { - for (let n of edgeNodes) { - if (!allNodes.includes(n)) { - allNodes.push(n) - } - } - } - // generating the elements list having nodes with their properties - const elements = [] - for (let node of allNodes) { - elements.push({ - id: node, - data: { label: node }, - position: 0, - }) - } - - const elements_index = {} - - for (let i = 0; i < elements.length; i++) { - const node = elements[i].id - elements_index[node] = i - } - - // generating the edges between every source and target node - for (let i = 0; i < adjacencyListKeys.length; i++) { - const node = adjacencyListKeys[i] - const edgeNodes = connections[node] - let j = -1 - for (let edgeNode of edgeNodes) { - elements.push({ id: `e-${node}-${edgeNode}`, source: node, target: edgeNode, animated: true },) - j ++ - } - } - - console.log(elements) - - return elements - } - -module.exports = { - dataToConnections, - dataToObjects, - connectionsToNodes, -} - diff --git a/extensions/prototypes/labgraph_monitor/package.json b/extensions/prototypes/labgraph_monitor/package.json new file mode 100644 index 000000000..decb87738 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/package.json @@ -0,0 +1,80 @@ +{ + "name": "labgraph_monitor", + "version": "0.1.0", + "private": true, + "dependencies": { + "@babel/core": "^7.17.2", + "@emotion/react": "^11.7.1", + "@emotion/styled": "^11.6.0", + "@mui/icons-material": "^5.4.1", + "@mui/lab": "^5.0.0-alpha.68", + "@mui/material": "^5.4.1", + "@mui/styles": "^5.4.1", + "@reduxjs/toolkit": "^1.7.2", + "@testing-library/jest-dom": "^5.14.1", + "@testing-library/react": "^12.1.3", + "@testing-library/user-event": "^13.2.1", + "@types/jest": "^27.4.0", + "@types/lodash": "^4.14.178", + "@types/node": "^16.7.13", + "@types/react": "^17.0.20", + "@types/react-dom": "^17.0.9", + "@types/socket.io-client": "^3.0.0", + "@types/websocket": "^1.0.5", + "dagre": "^0.8.5", + "lodash": "^4.17.21", + "react": "^17.0.2", + "react-dom": "^17.0.2", + "react-flow-renderer": "^9.7.4", + "react-redux": "^7.2.6", + "react-scripts": "5.0.0", + "resize-observer-polyfill": "^1.5.1", + "socket.io-client": "^4.4.1", + "typescript": "^4.4.2", + "web-vitals": "^2.1.0", + "websocket": "^1.0.34" + }, + "scripts": { + "start": "react-scripts start", + "build": "react-scripts build", + "test": "react-scripts test", + "eject": "react-scripts eject", + "format": "prettier --write .", + "lint": "eslint ./src --ext .ts --ext .tsx --max-warnings=0", + "check-code": "yarn run lint && yarn run format", + "prepare": "cd ../../.. && husky install extensions/prototypes/labgraph_monitor/.husky" + }, + "eslintConfig": { + "extends": [ + "react-app", + "react-app/jest" + ] + }, + "browserslist": { + "production": [ + ">0.2%", + "not dead", + "not op_mini all" + ], + "development": [ + "last 1 chrome version", + "last 1 firefox version", + "last 1 safari version" + ] + }, + "devDependencies": { + "@types/dagre": "^0.7.47", + "@typescript-eslint/eslint-plugin": "^5.11.0", + "@typescript-eslint/parser": "^5.11.0", + "eslint": "^8.8.0", + "eslint-config-airbnb": "^19.0.4", + "eslint-config-prettier": "^8.3.0", + "eslint-plugin-import": "^2.25.4", + "eslint-plugin-jsx-a11y": "^6.5.1", + "eslint-plugin-prettier": "^4.0.0", + "eslint-plugin-react": "^7.28.0", + "eslint-plugin-react-hooks": "^4.3.0", + "husky": "^7.0.4", + "prettier": "2.5.1" + } +} diff --git a/extensions/prototypes/labgraph_monitor/public/favicon.ico b/extensions/prototypes/labgraph_monitor/public/favicon.ico new file mode 100644 index 000000000..536909711 Binary files /dev/null and b/extensions/prototypes/labgraph_monitor/public/favicon.ico differ diff --git a/extensions/prototypes/labgraph_monitor/public/index.html b/extensions/prototypes/labgraph_monitor/public/index.html new file mode 100644 index 000000000..8b28b36c8 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/public/index.html @@ -0,0 +1,24 @@ + + + + + + + + + + + Labgraph Monitor + + + +
+ + diff --git a/extensions/prototypes/labgraph_monitor/public/manifest.json b/extensions/prototypes/labgraph_monitor/public/manifest.json new file mode 100644 index 000000000..361b4cb67 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/public/manifest.json @@ -0,0 +1,15 @@ +{ + "short_name": "Labgraph Monitor", + "name": "This app is an interactive visualization tool to monitor and make real-time changes to LabGraph graph and nodes.", + "icons": [ + { + "src": "favicon.ico", + "sizes": "64x64 32x32 24x24 16x16", + "type": "image/x-icon" + } + ], + "start_url": ".", + "display": "standalone", + "theme_color": "#000000", + "background_color": "#ffffff" +} diff --git a/extensions/prototypes/labgraph_monitor/public/robots.txt b/extensions/prototypes/labgraph_monitor/public/robots.txt new file mode 100644 index 000000000..eb0536286 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/public/robots.txt @@ -0,0 +1,2 @@ +User-agent: * +Disallow: diff --git a/extensions/prototypes/labgraph_monitor/src/App.tsx b/extensions/prototypes/labgraph_monitor/src/App.tsx new file mode 100644 index 000000000..d431257a3 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/App.tsx @@ -0,0 +1,21 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { Home } from './pages'; + +/** + * The main component of the application + * @returns {JSX.Element} + */ +const App: React.FC = (): JSX.Element => { + return ( +
+ +
+ ); +}; + +export default App; diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/Graph.tsx b/extensions/prototypes/labgraph_monitor/src/components/Graph/Graph.tsx new file mode 100644 index 000000000..5e802d649 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/Graph.tsx @@ -0,0 +1,154 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { useEffect, useState } from 'react'; +import ReactFlow, { + ReactFlowProvider, + Controls, + Background, + Edge, + Node, + isNode, + isEdge, +} from 'react-flow-renderer'; +import { TGraphElement } from './types/TGraphElement'; +import { useUIContext } from '../../contexts'; +import { layoutGraph } from './util/layoutGraph'; +import { RootState } from '../../redux/store'; +import { useSelector, useDispatch } from 'react-redux'; +import { + setPanel, + setTabIndex, + setSelectedNode, + setSelectedEdge, +} from '../../redux/reducers/config/configReducer'; +import WS_STATE from '../../redux/reducers/graph/ws/enums/WS_STATE'; +import { Box } from '@mui/material'; +import { makeStyles } from '@mui/styles'; + +const useStyles = makeStyles({ + node: { + display: 'flex', + justifyContent: 'center', + alignItems: 'center', + width: '150px', + height: '150px', + borderRadius: '50%', + fontWeight: '500', + fill: 'currentColor', + }, + + edge: { + stroke: 'inherit', + }, + + controllers: { + position: 'absolute', + display: 'flex', + justifyContent: 'center', + alignItems: 'center', + bottom: '40px', + left: '40px', + cursor: 'pointer', + }, +}); + +/** + * A component that represents LabGraph computational graph. + * + * @returns {JSX.Element} + */ +const Graph: React.FC = (): JSX.Element => { + const styles = useStyles(); + const { layout } = useUIContext(); + const { mockGraph } = useSelector((state: RootState) => state.mock); + + const { connection, graph: realtimeGraph } = useSelector( + (state: RootState) => state.ws + ); + + const serializedGraph = + connection === WS_STATE.CONNECTED ? realtimeGraph : mockGraph; + + const [graph, setGraph] = useState>( + [] as Array + ); + + const dispatch = useDispatch(); + + useEffect(() => { + const { nodes } = serializedGraph; + const deserializedGraph = [] as Array; + if (!nodes) return; + for (const [name, data] of Object.entries(nodes)) { + deserializedGraph.push({ + id: name, + data: { + label: name, + }, + position: { x: 0, y: 0 }, + targetPosition: layout === 'horizontal' ? 'left' : 'top', + sourcePosition: layout === 'horizontal' ? 'right' : 'bottom', + className: styles.node, + }); + + Object.entries(data.upstreams).forEach(([upstream, messages]) => { + messages.forEach((message) => { + deserializedGraph.push({ + id: `e${upstream}-${name}`, + label: `${message.name}`, + source: upstream, + target: name, + arrowHeadType: 'arrow', + type: 'default', + animated: Object.keys(nodes[upstream].upstreams).length + ? false + : true, + className: styles.edge, + }); + }); + }); + } + setGraph(layoutGraph(deserializedGraph, layout)); + }, [serializedGraph, layout, styles]); + + const handleElementClick = ( + event: React.MouseEvent, + element: Node | Edge + ) => { + if (isNode(element)) { + dispatch(setPanel(true)); + dispatch(setTabIndex('2')); + dispatch(setSelectedNode(element)); + } else if (isEdge(element)) { + dispatch(setPanel(true)); + dispatch(setTabIndex('3')); + dispatch(setSelectedEdge(element)); + } + }; + + return ( + + + + + + + + + ); +}; + +export default Graph; diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/__test__/Graph.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/Graph/__test__/Graph.test.tsx new file mode 100644 index 000000000..ae66bdd07 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/__test__/Graph.test.tsx @@ -0,0 +1,26 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import Graph from '../Graph'; + +const MockGraph = () => { + return ( + + + + ); +}; + +describe('Graph', () => { + it('should render Graph component', async () => { + render(); + const graph = screen.getByTestId('graph'); + expect(graph).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/IEdge.ts b/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/IEdge.ts new file mode 100644 index 000000000..9a6ec67e1 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/IEdge.ts @@ -0,0 +1,19 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +interface IEdge { + id: string; + label?: string; + source: string; + target: string; + arrowHeadType?: 'arrow' | 'arrowclosed'; + type?: 'default' | 'straight' | 'step' | 'smoothstep'; + animated?: boolean; + style?: { [property: string]: string }; + className?: string; +} + +export default IEdge; diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/INode.ts b/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/INode.ts new file mode 100644 index 000000000..1aca15137 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/interfaces/INode.ts @@ -0,0 +1,22 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +interface INode { + id: string; + data?: { + label: string; + }; + position: { + x: number; + y: number; + }; + sourcePosition: 'left' | 'top' | 'right' | 'bottom'; + targetPosition: 'left' | 'top' | 'right' | 'bottom'; + style?: { [property: string]: string }; + className?: string; +} + +export default INode; diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/types/TGraphElement.ts b/extensions/prototypes/labgraph_monitor/src/components/Graph/types/TGraphElement.ts new file mode 100644 index 000000000..75cfa4ccb --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/types/TGraphElement.ts @@ -0,0 +1,9 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import IEdge from '../interfaces/IEdge'; +import INode from '../interfaces/INode'; +export type TGraphElement = INode | IEdge; diff --git a/extensions/prototypes/labgraph_monitor/src/components/Graph/util/layoutGraph.ts b/extensions/prototypes/labgraph_monitor/src/components/Graph/util/layoutGraph.ts new file mode 100644 index 000000000..89d7b21f0 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/Graph/util/layoutGraph.ts @@ -0,0 +1,45 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import dagre from 'dagre'; +import { isNode, isEdge } from 'react-flow-renderer'; +import { TGraphElement } from '../types/TGraphElement'; + +const dagreGraph = new dagre.graphlib.Graph(); +dagreGraph.setDefaultEdgeLabel(() => ({})); + +/** + * A function used to update the graph layout + * + * @param {Array, + layout: string +): Array => { + dagreGraph.setGraph({ rankdir: layout === 'horizontal' ? 'LR' : 'TB' }); + elements.forEach((el: any) => { + if (isNode(el)) { + dagreGraph.setNode(el.id, { width: 400, height: 200 }); + } else if (isEdge(el)) { + dagreGraph.setEdge(el.source, el.target); + } + }); + + dagre.layout(dagreGraph); + + return elements.map((el: any) => { + if (isNode(el)) { + const { x, y } = dagreGraph.node(el.id); + el.position = { x, y }; + } + + return el; + }); +}; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/EdgeSettings.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/EdgeSettings.tsx new file mode 100644 index 000000000..be5c4f749 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/EdgeSettings.tsx @@ -0,0 +1,95 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { + Box, + Paper, + Table, + TableBody, + TableCell, + TableContainer, + TableHead, + TableRow, + Typography, +} from '@mui/material'; +import React from 'react'; +import { RootState } from '../../redux/store'; +import { useSelector } from 'react-redux'; +import WS_STATE from '../../redux/reducers/graph/ws/enums/WS_STATE'; + +interface IMessage { + name: string; + fields: { [fieldName: string]: string }; +} + +/** + * A component that manages the settings of an edge. + * All components related to edge settings should be children of this component. + * + * @returns {JSX.Element} + */ +const Edge: React.FC = (): JSX.Element => { + const { selectedEdge } = useSelector((state: RootState) => state.config); + const { connection, graph: realtimeGraph } = useSelector( + (state: RootState) => state.ws + ); + const { mockGraph } = useSelector((state: RootState) => state.mock); + + const graph = connection === WS_STATE.CONNECTED ? realtimeGraph : mockGraph; + + const messages: IMessage[] = + graph && selectedEdge.target + ? graph['nodes'][selectedEdge.target]['upstreams'][ + selectedEdge.source + ] + : []; + return ( + + + {messages.map((message: IMessage, index) => { + return ( + + + + + {message.name} + + + + {Object.entries(message.fields).map( + ([name, type]) => { + return ( + + + {name} + + + {type} + + + ); + } + )} + +
+ + ); + })} + + {!messages.length && ( + + Click on an edge to see the message information + + )} + + + ); +}; + +export default Edge; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/GraphSettings.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/GraphSettings.tsx new file mode 100644 index 000000000..f09924992 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/GraphSettings.tsx @@ -0,0 +1,187 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { useCallback, useState } from 'react'; +import { TabContext, TabList, TabPanel } from '@mui/lab'; +import { + Box, + Tab, + MenuItem, + InputLabel, + FormControl, + Button, +} from '@mui/material'; +import { makeStyles } from '@mui/styles'; +import Select, { SelectChangeEvent } from '@mui/material/Select'; +import { MOCK } from '../../mocks'; +import { useDispatch, useSelector } from 'react-redux'; +import { RootState } from '../../redux/store'; +import { setMockGraph } from '../../redux/reducers/graph/mock/mockReducer'; +import { setConnection } from '../../redux/reducers/graph/ws/WSReducer'; +import WS_STATE from '../../redux/reducers/graph/ws/enums/WS_STATE'; + +const useStyles = makeStyles({ + root: { + width: '100%', + }, + + tab: { + width: '50%', + }, + tabLabel: { + fontSize: '.85rem', + fontWeight: 400, + }, + + textFieldLabel: { + fontSize: '.85rem', + }, +}); + +/** + * A component that manages the settings of a graph. + * + * @returns {JSX.Element} + */ +const GraphSettings: React.FC = (): JSX.Element => { + const classes = useStyles(); + const { connection } = useSelector((state: RootState) => state.ws); + const [tabIndex, setTabIndex] = useState('1'); + const [mock, setMock] = useState(''); + const dispatch = useDispatch(); + + const toggleTab = (_: React.SyntheticEvent, newValue: string) => { + setTabIndex(newValue); + }; + + const handleMockChange = (event: SelectChangeEvent) => { + dispatch(setMockGraph(event.target.value)); + setMock(event.target.value); + }; + + const handleConnect = (event: React.FormEvent) => { + event.preventDefault(); + dispatch(setConnection(WS_STATE.IS_CONNECTING)); + }; + + const handleDisconnect = (event: React.FormEvent) => { + event.preventDefault(); + dispatch(setConnection(WS_STATE.IS_DISCONNECTING)); + }; + + const renderConnectionButton = useCallback(() => { + switch (connection) { + case WS_STATE.IS_CONNECTING: + return ( + + ); + case WS_STATE.CONNECTED: + return ( + + ); + case WS_STATE.IS_DISCONNECTING: + return ( + + ); + case WS_STATE.DISCONNECTED: + return ( + + ); + } + }, [connection]); + + return ( + + + + + + realtime + + } + value="1" + /> + mock + } + value="2" + /> + + + + +
+ + {renderConnectionButton()} + +
+ + + + + + + Mock + + + + + + + + ); +}; + +export default GraphSettings; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/NodeSettings.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/NodeSettings.tsx new file mode 100644 index 000000000..5c4b8e948 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/NodeSettings.tsx @@ -0,0 +1,36 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import { RootState } from '../../redux/store'; +import { useSelector } from 'react-redux'; +import { Box, Typography } from '@mui/material'; + +/** + * A component that manages the settings of a node. + * All components related to node settings should be children of this component. + * + * @returns {JSX.Element} + */ +const Node: React.FC = (): JSX.Element => { + const { selectedNode } = useSelector((state: RootState) => state.config); + + return ( + + + {selectedNode?.id ? ( + {selectedNode.id} + ) : ( + + Click on a node to see its information + + )} + + + ); +}; + +export default Node; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingPanel.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingPanel.tsx new file mode 100644 index 000000000..2bc3b7a9d --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingPanel.tsx @@ -0,0 +1,139 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { Box, Drawer, CssBaseline, IconButton, Divider } from '@mui/material'; +import { + ChevronRight, + Brightness7, + DarkMode, + SettingsApplicationsRounded, + AlignHorizontalLeftOutlined, + AlignVerticalTopOutlined, +} from '@mui/icons-material'; +import SettingTabs from './SettingTabs'; +import { makeStyles } from '@mui/styles'; +import { useUIContext } from '../../contexts'; +import { RootState } from '../../redux/store'; +import { useSelector, useDispatch } from 'react-redux'; +import { setPanel } from '../../redux/reducers/config/configReducer'; + +const PANEL_WIDTH = 280; + +const useStyles = makeStyles({ + root: { + display: 'flex', + }, + + settingButton: { + position: 'absolute', + top: '10px', + right: '20px', + zIndex: '10', + }, + + settingIcon: { + '&:hover': { + opacity: 0.9, + }, + }, + + settingPanel: { + width: PANEL_WIDTH, + flexShrink: 0, + '& .MuiDrawer-paper': { + width: PANEL_WIDTH, + }, + }, + + themeBar: { + display: 'flex', + width: '100%', + justifyContent: 'space-around', + alignItem: 'center', + padding: '2px 4px 2px 4px', + }, +}); + +/** + * A component that represents the setting panel of the application. + * All components related to UI or Graph settings should be children of this component. + * + * @returns {JSX.Element} + */ +const SettingPanel: React.FC = (): JSX.Element => { + const classes = useStyles(); + const { mode, layout, toggleMode, toggleLayout } = useUIContext(); + const { panelOpen } = useSelector((state: RootState) => state.config); + const dispatch = useDispatch(); + + return ( + + + dispatch(setPanel(true))} + className={classes.settingButton} + data-testid="open-setting-btn" + sx={{ + display: panelOpen ? 'none' : 'block', + position: 'absolute', + '&:hover, &.Mui-focusVisible': { + backgroundColor: 'transparent', + }, + }} + > + + + + + dispatch(setPanel(false))} + data-testid="close-setting-btn" + > + + + + + + + {mode === 'light' ? ( + + ) : ( + + )} + + + {layout === 'horizontal' ? ( + + ) : ( + + )} + + + + + + + ); +}; + +export default SettingPanel; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingTabs.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingTabs.tsx new file mode 100644 index 000000000..1ab14f926 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/SettingTabs.tsx @@ -0,0 +1,66 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { TabContext, TabList, TabPanel } from '@mui/lab'; +import { Box, Tab } from '@mui/material'; +import { makeStyles } from '@mui/styles'; +import NodeSettings from './NodeSettings'; +import EdgeSettings from './EdgeSettings'; +import GraphSettings from './GraphSettings'; +import { RootState } from '../../redux/store'; +import { useSelector, useDispatch } from 'react-redux'; +import { setTabIndex } from '../../redux/reducers/config/configReducer'; + +const useStyles = makeStyles({ + root: { + width: '100%', + }, +}); + +/** + * A component that categorizes settings within the settings panel + * + * @returns {JSX.Element} + */ +const SettingTabs: React.FC = (): JSX.Element => { + const classes = useStyles(); + const { tabIndex } = useSelector((state: RootState) => state.config); + const dispatch = useDispatch(); + return ( + + + + + dispatch(setTabIndex(newValue)) + } + data-testid="tab-list" + > + + + + + + + + + + + + + + + + + ); +}; + +export default SettingTabs; diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/EdgeSettings.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/EdgeSettings.test.tsx new file mode 100644 index 000000000..4cc46044b --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/EdgeSettings.test.tsx @@ -0,0 +1,26 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import EdgeSettings from '../EdgeSettings'; + +const MockEdgeSettings = () => { + return ( + + + + ); +}; + +describe('EdgeSettings', () => { + it('should render EdgeSettings component', async () => { + render(); + const edgeSettings = screen.getByTestId('edge-settings'); + expect(edgeSettings).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/GraphSettings.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/GraphSettings.test.tsx new file mode 100644 index 000000000..b0f9490eb --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/GraphSettings.test.tsx @@ -0,0 +1,26 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import GraphSettings from '../GraphSettings'; + +const MockGraphSettings = () => { + return ( + + + + ); +}; + +describe('GraphSettings', () => { + it('should render GraphSettings component', async () => { + render(); + const graphSettings = screen.getByTestId('graph-settings'); + expect(graphSettings).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/NodeSettings.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/NodeSettings.test.tsx new file mode 100644 index 000000000..f208c438e --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/NodeSettings.test.tsx @@ -0,0 +1,26 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import NodeSettings from '../NodeSettings'; + +const MockNodeSettings = () => { + return ( + + + + ); +}; + +describe('NodeSettings', () => { + it('should render NodeSettings component', async () => { + render(); + const nodeSettings = screen.getByTestId('node-settings'); + expect(nodeSettings).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingPanel.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingPanel.test.tsx new file mode 100644 index 000000000..8b0b6a211 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingPanel.test.tsx @@ -0,0 +1,48 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen, fireEvent } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import SettingPanel from '../SettingPanel'; + +const MockSettingPanel = () => { + return ( + + + + ); +}; + +describe('SettingPanel', () => { + it('should render SettingPanel component', async () => { + render(); + const settingPanel = screen.getByTestId('setting-panel'); + expect(settingPanel).toBeInTheDocument(); + }); + + it('should toggle mode', async () => { + render(); + const toggleModeBtn = screen.getByTestId('toggle-mode-btn'); + fireEvent.click(toggleModeBtn); + const lightModeIcon = screen.getByTestId('light-mode-icon'); + const darkModeIcon = screen.queryByText('dark-mode-icon'); + expect(lightModeIcon).toBeInTheDocument(); + expect(darkModeIcon).toBeNull(); + }); + + it('should toggle layout', async () => { + render(); + const toggleLayoutBtn = screen.getByTestId('toggle-layout-btn'); + fireEvent.click(toggleLayoutBtn); + const horizontalLayoutIcon = screen.getByTestId( + 'horizontal-layout-icon' + ); + const verticalLayoutIcon = screen.queryByText('vertical-layout-icon'); + expect(horizontalLayoutIcon).toBeInTheDocument(); + expect(verticalLayoutIcon).toBeNull(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingTabs.test.tsx b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingTabs.test.tsx new file mode 100644 index 000000000..55ea14df4 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/SettingPanel/__test__/SettingTabs.test.tsx @@ -0,0 +1,26 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import SettingTabs from '../SettingTabs'; + +const MockSettingTabs = () => { + return ( + + + + ); +}; + +describe('SettingTabs', () => { + it('should render SettingTabs component', async () => { + render(); + const settingPanel = screen.getByTestId('setting-tabs'); + expect(settingPanel).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/components/index.ts b/extensions/prototypes/labgraph_monitor/src/components/index.ts new file mode 100644 index 000000000..1aa3e9711 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/components/index.ts @@ -0,0 +1,8 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +export { default as SettingPanel } from './SettingPanel/SettingPanel'; +export { default as Graph } from './Graph/Graph'; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/UIContext.tsx b/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/UIContext.tsx new file mode 100644 index 000000000..cd520f5fe --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/UIContext.tsx @@ -0,0 +1,114 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { + ReactNode, + useState, + useEffect, + createContext, + useContext, + useMemo, +} from 'react'; +import { createTheme, ThemeProvider } from '@mui/material/styles'; +import IUIContext from './interfaces/IUIContext'; + +const UIContext = createContext({} as IUIContext); +export const useUIContext = (): IUIContext => useContext(UIContext); + +/** + * A context component used to share UI related information with different component + * + * @param {ReactNode} props represents the react components wrapped by this context + * @returns {JSX.Element} + */ +const UIContextProvider: React.FC = ({ children }): JSX.Element => { + const localMode = localStorage.getItem('labgraph_monitor_mode') as + | 'dark' + | 'light'; + + const [mode, setMode] = useState<'light' | 'dark'>(() => { + return localMode ? localMode : 'dark'; + }); + const [layout, setLayout] = useState<'horizontal' | 'vertical'>( + 'horizontal' + ); + + useEffect(() => { + return () => { + localStorage.setItem( + 'labgraph_monitor_mode', + mode === 'dark' ? 'light' : 'dark' + ); + }; + }, [mode]); + + const themeObject = useMemo( + () => ({ + toggleMode: () => { + setMode((prevMode) => + prevMode === 'light' ? 'dark' : 'light' + ); + }, + mode, + }), + [mode] + ); + + const layoutObject = useMemo( + () => ({ + toggleLayout: () => { + setLayout((prevLayout) => + prevLayout === 'horizontal' ? 'vertical' : 'horizontal' + ); + }, + layout, + }), + [layout] + ); + + const theme = useMemo( + () => + createTheme({ + palette: { + mode, + ...(mode === 'dark' + ? { + background: { + default: '#111827', + paper: '#1B2533', + }, + text: { + primary: '#F7F7F7', + secondary: '#FFF', + }, + } + : { + background: { + default: '#FEFEFE', + paper: '#FFF', + }, + text: { + primary: '#111827', + }, + }), + }, + }), + [mode] + ); + + return ( + + {children} + + ); +}; + +export default UIContextProvider; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/interfaces/IUIContext.ts b/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/interfaces/IUIContext.ts new file mode 100644 index 000000000..b67d5feda --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/UIContext/interfaces/IUIContext.ts @@ -0,0 +1,14 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +interface IUIContext { + mode: 'light' | 'dark'; + toggleMode: () => void; + layout: 'horizontal' | 'vertical'; + toggleLayout: () => void; +} + +export default IUIContext; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/WSContext.tsx b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/WSContext.tsx new file mode 100644 index 000000000..c640a8ff6 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/WSContext.tsx @@ -0,0 +1,83 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { ReactNode, useRef, createContext, useEffect } from 'react'; +import { w3cwebsocket as W3CWebSocket } from 'websocket'; +import { RootState } from '../../redux/store'; +import { useSelector, useDispatch } from 'react-redux'; +import { + setConnection, + setGraph, +} from '../../redux/reducers/graph/ws/WSReducer'; +import { copyRealtimeGraph } from '../../redux/reducers/graph/mock/mockReducer'; +import WS_STATE from '../../redux/reducers/graph/ws/enums/WS_STATE'; +import startStreamRequest from './json/startStreamRequest.json'; +import endStreamRequest from './json/endStreamRequest.json'; +import _ from 'lodash'; + +const GraphContext = createContext<{}>({}); + +/** + * A context component used to share WebSocket related information with different component. + * All interactions with the LabGraph WebSockets API are happening inside this context. + * + * @param {ReactNode} props represents the react components wrapped by this context + * @returns {JSX.Element} + */ +const WSContextProvider: React.FC = ({ children }): JSX.Element => { + const { connection, graph } = useSelector((state: RootState) => state.ws); + + const clientRef = useRef(null); + const dispatch = useDispatch(); + + useEffect(() => { + if (!process.env.REACT_APP_WS_API) return; + switch (connection) { + case WS_STATE.IS_CONNECTING: + clientRef.current = new W3CWebSocket( + process.env.REACT_APP_WS_API as string + ); + + clientRef.current.onopen = () => { + clientRef.current?.send(JSON.stringify(startStreamRequest)); + dispatch(setConnection(WS_STATE.CONNECTED)); + }; + + clientRef.current.onerror = (err: any) => { + dispatch(setConnection(WS_STATE.DISCONNECTED)); + }; + + break; + + case WS_STATE.CONNECTED: + if (!clientRef.current) return; + clientRef.current.onmessage = (message: any) => { + const data = JSON.parse(message.data); + if (!data['stream_batch']) return; + const { + stream_batch: { + 'labgraph.monitor': { samples }, + }, + } = data; + if (!_.isEqual(samples[0]['data'], graph)) { + dispatch(setGraph(samples[0]['data'])); + } + }; + + break; + + case WS_STATE.IS_DISCONNECTING: + clientRef.current?.send(JSON.stringify(endStreamRequest)); + clientRef.current?.close(); + dispatch(setConnection(WS_STATE.DISCONNECTED)); + dispatch(copyRealtimeGraph(graph)); + } + }, [connection, graph, dispatch]); + + return {children}; +}; + +export default WSContextProvider; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IGraph.ts b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IGraph.ts new file mode 100644 index 000000000..3b53d7c7b --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IGraph.ts @@ -0,0 +1,14 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import INode from './INode'; + +interface IGraph { + name: string; + nodes: INode; +} + +export default IGraph; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/INode.ts b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/INode.ts new file mode 100644 index 000000000..1e4e58895 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/INode.ts @@ -0,0 +1,20 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +interface INode { + [name: string]: { + upstreams: { + [upstream: string]: Array<{ + name: string; + fields: { + [fieldName: string]: string; + }; + }>; + }; + }; +} + +export default INode; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IWSContext.ts b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IWSContext.ts new file mode 100644 index 000000000..75711857e --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/interfaces/IWSContext.ts @@ -0,0 +1,20 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { Dispatch, SetStateAction } from 'react'; +import IGraph from './IGraph'; + +interface IWSContext { + graph: IGraph; + mock: string; + setMock: Dispatch>; + endPoint: string; + setEndPoint: Dispatch>; + isConnected: boolean; + setIsConnected: Dispatch>; +} + +export default IWSContext; diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/endStreamRequest.json b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/endStreamRequest.json new file mode 100644 index 000000000..4bd50588c --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/endStreamRequest.json @@ -0,0 +1,10 @@ +{ + "api_version": "0.1", + "api_request": { + "request_id": 1, + "end_stream_request": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": {} + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/startStreamRequest.json b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/startStreamRequest.json new file mode 100644 index 000000000..0cb6b1d90 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/WSContext/json/startStreamRequest.json @@ -0,0 +1,10 @@ +{ + "api_version": "0.1", + "api_request": { + "request_id": 1, + "start_stream_request": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": {} + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/contexts/index.ts b/extensions/prototypes/labgraph_monitor/src/contexts/index.ts new file mode 100644 index 000000000..e1621c451 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/contexts/index.ts @@ -0,0 +1,9 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +export { default as WSContextProvider } from './WSContext/WSContext'; +export { default as UIContextProvider } from './UIContext/UIContext'; +export { useUIContext } from './UIContext/UIContext'; diff --git a/extensions/prototypes/labgraph_monitor/src/index.tsx b/extensions/prototypes/labgraph_monitor/src/index.tsx new file mode 100644 index 000000000..1e8e641fc --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/index.tsx @@ -0,0 +1,27 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import ReactDOM from 'react-dom'; +import App from './App'; +import { UIContextProvider } from './contexts'; +import reportWebVitals from './reportWebVitals'; +import './statics/css/global.css'; +import { store } from './redux/store'; +import { Provider } from 'react-redux'; + +ReactDOM.render( + + + + + + + , + document.getElementById('root') +); + +reportWebVitals(); diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/enums/MOCK.ts b/extensions/prototypes/labgraph_monitor/src/mocks/enums/MOCK.ts new file mode 100644 index 000000000..2d4a5ca60 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/enums/MOCK.ts @@ -0,0 +1,15 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +enum MOCK { + SIMPLE_VIZ = 'simple_viz', + SIMPLE_VIZ_ZMQ = 'simple_viz_zmq', + SIMPLE_VIZ_FIXED_RATE = 'simple_viz_fixed_rate', + SIMULATION = 'simulation', + DEMO = 'demo', +} + +export default MOCK; diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/index.ts b/extensions/prototypes/labgraph_monitor/src/mocks/index.ts new file mode 100644 index 000000000..8a5fd7bfb --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/index.ts @@ -0,0 +1,8 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +export { selectMock } from './util/selectMock'; +export { default as MOCK } from './enums/MOCK'; diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/json/demo.json b/extensions/prototypes/labgraph_monitor/src/mocks/json/demo.json new file mode 100644 index 000000000..868386e20 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/json/demo.json @@ -0,0 +1,92 @@ +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "name": "Demo", + "nodes": { + "NoiseGenerator": { + "upstreams": {} + }, + "RollingAverager": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "Amplifier": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "Attenuator": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "Sink": { + "upstreams": { + "RollingAverager": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ], + "Amplifier": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ], + "Attenuator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + } + } + }, + "produced_timestamp_s": 1644931422.141309, + "timestamp_s": 1644931422.141309 + } + ], + "batch_num": 54 + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz.json b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz.json new file mode 100644 index 000000000..cb09e6846 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz.json @@ -0,0 +1,48 @@ +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "name": "Demo", + "nodes": { + "Plot": { + "upstreams": { + "RollingAverager": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "NoiseGenerator": { + "upstreams": {} + }, + "RollingAverager": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + } + } + }, + "produced_timestamp_s": 1644930793.478768, + "timestamp_s": 1644930793.478768 + } + ], + "batch_num": 132 + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_fixed_rate.json b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_fixed_rate.json new file mode 100644 index 000000000..efdd93bf8 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_fixed_rate.json @@ -0,0 +1,48 @@ +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "name": "Demo", + "nodes": { + "Plot": { + "upstreams": { + "RollingAverager": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "NoiseGenerator": { + "upstreams": {} + }, + "RollingAverager": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + } + } + }, + "produced_timestamp_s": 1644930955.5364213, + "timestamp_s": 1644930955.5364213 + } + ], + "batch_num": 94 + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_zmq.json b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_zmq.json new file mode 100644 index 000000000..fd54835af --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/json/simple_viz_zmq.json @@ -0,0 +1,60 @@ +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "name": "Demo", + "nodes": { + "Plot": { + "upstreams": { + "RollingAverager": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + }, + "ZMQPollerNode": { + "upstreams": {} + }, + "ZMQDeserializer": { + "upstreams": { + "ZMQPollerNode": [ + { + "name": "ZMQMessage", + "fields": { + "data": "bytes" + } + } + ] + } + }, + "RollingAverager": { + "upstreams": { + "ZMQDeserializer": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": "float", + "data": "ndarray" + } + } + ] + } + } + } + }, + "produced_timestamp_s": 1644931059.3961816, + "timestamp_s": 1644931059.3961816 + } + ], + "batch_num": 132 + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/json/simulation.json b/extensions/prototypes/labgraph_monitor/src/mocks/json/simulation.json new file mode 100644 index 000000000..87856e25f --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/json/simulation.json @@ -0,0 +1,35 @@ +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "name": "Demo", + "nodes": { + "SimulationGenerator": { + "upstreams": {} + }, + "Plot": { + "upstreams": { + "SimulationGenerator": [ + { + "name": "SimulationMessage", + "fields": { + "timestamp": "ndarray", + "daub_data": "ndarray" + } + } + ] + } + } + } + }, + "produced_timestamp_s": 1644931259.3396878, + "timestamp_s": 1644931259.3396878 + } + ], + "batch_num": 24 + } + } +} diff --git a/extensions/prototypes/labgraph_monitor/src/mocks/util/selectMock.ts b/extensions/prototypes/labgraph_monitor/src/mocks/util/selectMock.ts new file mode 100644 index 000000000..cc60057bd --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/mocks/util/selectMock.ts @@ -0,0 +1,37 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import demo from '../json/demo.json'; +import simpleViz from '../json/simple_viz.json'; +import simpleVizZMQ from '../json/simple_viz_zmq.json'; +import simpleVizFixedRate from '../json/simple_viz_fixed_rate.json'; +import simulation from '../json/simulation.json'; +import MOCK from '../enums/MOCK'; + +/** + * A function that picks a mock example based on the argument passed + * + * @param {string} mock the name of the mock example + * @returns a parsed version of the JSON file containing the mock sample. + */ +export const selectMock = (mock: string) => { + switch (mock) { + case MOCK.SIMPLE_VIZ: + return simpleViz; + + case MOCK.SIMULATION: + return simulation; + + case MOCK.SIMPLE_VIZ_ZMQ: + return simpleVizZMQ; + + case MOCK.SIMPLE_VIZ_FIXED_RATE: + return simpleVizFixedRate; + + default: + return demo; + } +}; diff --git a/extensions/prototypes/labgraph_monitor/src/pages/Home/Home.tsx b/extensions/prototypes/labgraph_monitor/src/pages/Home/Home.tsx new file mode 100644 index 000000000..7a9d2ce85 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/pages/Home/Home.tsx @@ -0,0 +1,27 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import { SettingPanel, Graph } from '../../components'; +import { WSContextProvider } from '../../contexts'; + +/** + * A component that represents the Home page of the application + * + * @returns {JSX.Element} + */ +const Home: React.FC = (): JSX.Element => { + return ( + + + + + + + ); +}; + +export default Home; diff --git a/extensions/prototypes/labgraph_monitor/src/pages/Home/__test__/Home.test.tsx b/extensions/prototypes/labgraph_monitor/src/pages/Home/__test__/Home.test.tsx new file mode 100644 index 000000000..3057f9f8a --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/pages/Home/__test__/Home.test.tsx @@ -0,0 +1,28 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { render, screen } from '@testing-library/react'; +import { store } from '../../../redux/store'; +import { Provider } from 'react-redux'; +import Home from '../Home'; + +const MockHome = () => { + return ( + + + + ); +}; + +describe('Home', () => { + it('should render Home component', async () => { + render(); + const settingPanel = screen.getByTestId('setting-panel'); + const graph = screen.getByTestId('graph'); + expect(settingPanel).toBeInTheDocument(); + expect(graph).toBeInTheDocument(); + }); +}); diff --git a/extensions/prototypes/labgraph_monitor/src/pages/index.ts b/extensions/prototypes/labgraph_monitor/src/pages/index.ts new file mode 100644 index 000000000..1d9354179 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/pages/index.ts @@ -0,0 +1,7 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +export { default as Home } from './Home/Home'; diff --git a/extensions/prototypes/labgraph_monitor/src/react-app-env.d.ts b/extensions/prototypes/labgraph_monitor/src/react-app-env.d.ts new file mode 100644 index 000000000..11724e4aa --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/react-app-env.d.ts @@ -0,0 +1,7 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +/// diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/configReducer.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/configReducer.ts new file mode 100644 index 000000000..888d9e01d --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/configReducer.ts @@ -0,0 +1,42 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { createSlice, PayloadAction } from '@reduxjs/toolkit'; +import { Edge, Node } from 'react-flow-renderer'; +import IConfig from './interfaces/IConfig'; + +const initialState: IConfig = { + panelOpen: false, + tabIndex: '1', + selectedNode: {} as Node, + selectedEdge: {} as Edge, +}; + +export const configSlice = createSlice({ + name: 'config', + initialState, + reducers: { + setPanel: (state, action: PayloadAction) => { + state.panelOpen = action.payload; + }, + setTabIndex: (state, action: PayloadAction) => { + state.tabIndex = action.payload; + }, + + setSelectedNode: (state, action: PayloadAction) => { + state.selectedNode = action.payload; + }, + + setSelectedEdge: (state, action: PayloadAction) => { + state.selectedEdge = action.payload; + }, + }, +}); + +export const { setPanel, setTabIndex, setSelectedNode, setSelectedEdge } = + configSlice.actions; + +export default configSlice.reducer; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/interfaces/IConfig.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/interfaces/IConfig.ts new file mode 100644 index 000000000..90a5eeecb --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/config/interfaces/IConfig.ts @@ -0,0 +1,16 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { Edge, Node } from 'react-flow-renderer'; + +interface IConfig { + panelOpen: boolean; + tabIndex: string; + selectedNode: Node; + selectedEdge: Edge; +} + +export default IConfig; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/IGraph.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/IGraph.ts new file mode 100644 index 000000000..3b53d7c7b --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/IGraph.ts @@ -0,0 +1,14 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import INode from './INode'; + +interface IGraph { + name: string; + nodes: INode; +} + +export default IGraph; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/INode.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/INode.ts new file mode 100644 index 000000000..1e4e58895 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/common/interfaces/INode.ts @@ -0,0 +1,20 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +interface INode { + [name: string]: { + upstreams: { + [upstream: string]: Array<{ + name: string; + fields: { + [fieldName: string]: string; + }; + }>; + }; + }; +} + +export default INode; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/interfaces/IMock.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/interfaces/IMock.ts new file mode 100644 index 000000000..6d6df85e9 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/interfaces/IMock.ts @@ -0,0 +1,13 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import IGraph from '../../common/interfaces/IGraph'; + +interface IMock { + mockGraph: IGraph; +} + +export default IMock; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/mockReducer.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/mockReducer.ts new file mode 100644 index 000000000..ffe996def --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/mock/mockReducer.ts @@ -0,0 +1,38 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { createSlice, PayloadAction } from '@reduxjs/toolkit'; +import IGraph from '../common/interfaces/IGraph'; +import IMock from './interfaces/IMock'; +import { selectMock } from '../../../../mocks'; + +const initialState: IMock = { + mockGraph: {} as IGraph, +}; + +export const mockSlice = createSlice({ + name: 'mock', + initialState, + reducers: { + setMockGraph: (state, action: PayloadAction) => { + const { + stream_batch: { + 'labgraph.monitor': { samples }, + }, + } = selectMock(action.payload); + + state.mockGraph = samples[0]['data']; + }, + + copyRealtimeGraph: (state, action: PayloadAction) => { + state.mockGraph = action.payload; + }, + }, +}); + +export const { copyRealtimeGraph, setMockGraph } = mockSlice.actions; + +export default mockSlice.reducer; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/WSReducer.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/WSReducer.ts new file mode 100644 index 000000000..de188ad47 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/WSReducer.ts @@ -0,0 +1,33 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { createSlice, PayloadAction } from '@reduxjs/toolkit'; +import IWS from './interfaces/IWS'; +import IGraph from '../common/interfaces/IGraph'; +import WS_STATE from './enums/WS_STATE'; + +const initialState: IWS = { + connection: WS_STATE.DISCONNECTED, + graph: {} as IGraph, +}; + +const WSSlice = createSlice({ + name: 'ws', + initialState, + reducers: { + setConnection: (state, action: PayloadAction) => { + state.connection = action.payload; + }, + + setGraph: (state, action: PayloadAction) => { + state.graph = action.payload; + }, + }, +}); + +export const { setConnection, setGraph } = WSSlice.actions; + +export default WSSlice.reducer; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/enums/WS_STATE.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/enums/WS_STATE.ts new file mode 100644 index 000000000..39d712ce0 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/enums/WS_STATE.ts @@ -0,0 +1,14 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +enum WS_STATE { + CONNECTED = 'CONNECTED', + IS_CONNECTING = 'IS_CONNECTING', + DISCONNECTED = 'DISCONNECTED', + IS_DISCONNECTING = 'IS_DISCONNECTING', +} + +export default WS_STATE; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/interfaces/IWS.ts b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/interfaces/IWS.ts new file mode 100644 index 000000000..590496de2 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/reducers/graph/ws/interfaces/IWS.ts @@ -0,0 +1,15 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import IGraph from '../../common/interfaces/IGraph'; +import WS_STATE from '../enums/WS_STATE'; + +interface IWS { + connection: WS_STATE; + graph: IGraph; +} + +export default IWS; diff --git a/extensions/prototypes/labgraph_monitor/src/redux/store.ts b/extensions/prototypes/labgraph_monitor/src/redux/store.ts new file mode 100644 index 000000000..3d9d9b920 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/redux/store.ts @@ -0,0 +1,25 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { configureStore } from '@reduxjs/toolkit'; +import configReducer from './reducers/config/configReducer'; +import mockReducer from './reducers/graph/mock/mockReducer'; +import WSReducer from './reducers/graph/ws/WSReducer'; + +export const store = configureStore({ + reducer: { + config: configReducer, + mock: mockReducer, + ws: WSReducer, + }, + middleware: (getDefaultMiddleware) => + getDefaultMiddleware({ + serializableCheck: false, + }), +}); + +export type RootState = ReturnType; +export type AppDispatch = typeof store.dispatch; diff --git a/extensions/prototypes/labgraph_monitor/src/reportWebVitals.ts b/extensions/prototypes/labgraph_monitor/src/reportWebVitals.ts new file mode 100644 index 000000000..03e3f23c4 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/reportWebVitals.ts @@ -0,0 +1,23 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import { ReportHandler } from 'web-vitals'; + +const reportWebVitals = (onPerfEntry?: ReportHandler) => { + if (onPerfEntry && onPerfEntry instanceof Function) { + import('web-vitals').then( + ({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { + getCLS(onPerfEntry); + getFID(onPerfEntry); + getFCP(onPerfEntry); + getLCP(onPerfEntry); + getTTFB(onPerfEntry); + } + ); + } +}; + +export default reportWebVitals; diff --git a/extensions/prototypes/labgraph_monitor/src/setupTests.ts b/extensions/prototypes/labgraph_monitor/src/setupTests.ts new file mode 100644 index 000000000..018b9ca07 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/setupTests.ts @@ -0,0 +1,11 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import '@testing-library/jest-dom'; +import '@testing-library/jest-dom/extend-expect'; +import ResizeObserver from 'resize-observer-polyfill'; + +global.ResizeObserver = ResizeObserver; diff --git a/extensions/prototypes/labgraph_monitor/src/statics/css/global.css b/extensions/prototypes/labgraph_monitor/src/statics/css/global.css new file mode 100644 index 000000000..93f43c534 --- /dev/null +++ b/extensions/prototypes/labgraph_monitor/src/statics/css/global.css @@ -0,0 +1,15 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700;800;900&display=swap'); + +* { + font-family: 'Poppins', sans-serif !important; +} + +.react-flow__edge { + cursor: pointer !important; +} diff --git a/extensions/prototypes/labgraph_monitor/tsconfig.json b/extensions/prototypes/labgraph_monitor/tsconfig.json new file mode 100644 index 000000000..831946b19 --- /dev/null +++ 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Monitor -This extension provides an API to generate a serialized version of the labgraph topology. The serialized graph topology can be used in different applications E.g: server-client communication or to get a simplified overview of the topology in case of complicated graphs. +**This extension provides an API to generate a serialized version of the graph topology.** + +The serialized graph topology can be used in different applications. But it was specifically designed to work with server-client communications, such as working in sync with [LabGraph Monitor Front-End](https://github.com/facebookresearch/labgraph/tree/main/extensions/prototypes/labgraph_monitor) to get a simplified overview of the topology, along with real-time messaging, in case of complicated graphs. -## Quick Start -### Method 1 - building from source code +## Quick Start -**Prerequisites**: +### Prerequisites: - Python3\ Supported python version(s) - _ [Python3.6](https://www.python.org/downloads/) - _ [Python3.8](https://www.python.org/downloads/) (**RECOMMENDED**) -- Make sure to install [labgraph](https://github.com/facebookresearch/labgraph) before proceeding + - [Python3.6](https://www.python.org/downloads/) + - [Python3.8](https://www.python.org/downloads/) (**RECOMMENDED**) +- [LabGraph](https://github.com/facebookresearch/labgraph) -``` +```bash cd labgraph/extensions/yaml_support python setup.py install ``` @@ -25,43 +26,52 @@ To make sure things are working: 1- Move to the root of the LabGraph directory: -``` +```bash labgraph\extensions\yaml_support> cd ../.. labgraph> ``` 2- Run the following tests -``` +```bash python -m extensions.yaml_support.labgraph_monitor.tests.test_lg_monitor_api -``` -``` python -m extensions.yaml_support.labgraph_yaml_parser.tests.test_lg_yaml_api ``` -### API +### LabGraph Monitor API: -#### Labgraph Monitor: +#### Stream Graph Topology Only: -**generate_labgraph_monitor(graph: lg.Graph) -> None** : This function can be used to generate a serialized version of the passed graph instance. The serialized version of the graph will be streamed -to the clients using LabGraph Websockets API. + - **`LabgraphMonitor(graph: lg.Graph)`** : This class serves as a facade for monitor's functions, + such as sending either topology or topology + real-time messages + - **`stream_graph_topology() -> None`**: This function is used to send the generated serialized version of the graph instance via LabGraph WebSockets API -1. Call **generate_labgraph_monitor(graph: lg.Graph) -> None** and pass an instance of the graph as a parameter +The serialized version of the graph will be streamed to the clients using LabGraph's [WebSockets API](https://github.com/facebookresearch/labgraph/blob/main/docs/websockets-api.md). -``` -from extensions.yaml_support.labgraph_monitor.generate_lg_monitor.generate_lg_monitor import ( - generate_labgraph_monitor -) +1. Instantiate a monitor object with **`LabgraphMonitor(graph: lg.Graph)`** to serialize your graph and stream via WebSockets API using its method **`stream_graph_topology() -> None`** + +```python +from extensions.graphviz_support.graphviz_support.tests.demo_graph.demo import Demo + +from extensions.yaml_support.labgraph_monitor.labgraph_monitor import LabgraphMonitor -generate_labgraph_monitor(graph) + +# Initialize a Demo graph +graph = Demo() + +# Initialize a monitor object +monitor = LabgraphMonitor(graph) + +# Run the WebSockets API to send the topology to Front-End +monitor.stream_graph_topology() ``` This will start a websocket server on localhost port 9000 (127.0.0.1:9000) 2. To start receiving data from the server, send the following `StartStreamRequest` to **ws://127.0.0.1:9000** -``` +```bash { "api_version": "0.1", "api_request": { @@ -79,7 +89,7 @@ A serialized representation of the graph should be received each 200ms The graph representation has the following schema: -``` +```python { name: "graph_name", nodes: { @@ -88,7 +98,14 @@ The graph representation has the following schema: "upstream_name":[ { name: "message_name", - type: "message_type", + fields: { + "timestamp": { + "type": "float", + }, + "data": { + "type": "ndarray" + } + } } ] } @@ -97,9 +114,9 @@ The graph representation has the following schema: } ``` -E.g: +An example of a single WebSockets API message -``` +```python { "stream_batch": { "stream_id": "LABGRAPH.MONITOR", @@ -118,8 +135,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ] @@ -131,8 +152,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ] @@ -144,8 +169,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ] @@ -157,8 +186,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ], @@ -166,8 +199,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ], @@ -175,8 +212,12 @@ E.g: { "name": "RandomMessage", "fields": { - "timestamp": "float", - "data": "ndarray" + "timestamp": { + "type": "float" + }, + "data": { + "type": "ndarray" + } } } ] @@ -184,22 +225,1029 @@ E.g: } } }, - "produced_timestamp_s": 1644931422.141309, - "timestamp_s": 1644931422.141309 + "produced_timestamp_s": 1648581339.9652574, + "timestamp_s": 1648581339.965258 } ], - "batch_num": 54 + "batch_num": 31 } } } ``` The above-serialized representation was generated for the following graph: -https://github.com/facebookresearch/labgraph/blob/main/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py +[`extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py`](https://github.com/facebookresearch/labgraph/blob/main/extensions/graphviz_support/graphviz_support/tests/demo_graph/demo.py +) 3. To stop receiving data from the server, send the following `EndStreamRequest` to **ws://127.0.0.1:9000** +```python +{ + "api_version": "0.1", + "api_request": { + "request_id": 1, + "end_stream_request": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + } + } + } +} +``` + +#### Stream Graph Topology AND Real-Time Messages: + +This is used to stream both the graph topology and real-time messages received by nodes. However, it requires certain modifications to your graph outlined below: + +1. Make sure that your graph is compatible with real-time messaging + + - It requires a `set_topology()` method to add attributes to your graph. This function is used by `generate_graph_topology(graph: lg.Graph) -> None` internally + + ```python + def set_topology(self, topology: SerializedGraph, sub_pub_map: Dict[str, str]) -> None: + self._topology = topology + self._sub_pub_match = sub_pub_map + ``` + +- It also requires adding `WS_SERVER_NODE` and `SERIALIZER` nodes to your graph + + ```python + self.WS_SERVER_NODE.configure( + WSAPIServerConfig( + app_id=APP_ID, + ip=WS_SERVER.DEFAULT_IP, + port=ENUMS.WS_SERVER.DEFAULT_PORT, + api_version=ENUMS.WS_SERVER.DEFAULT_API_VERSION, + num_messages=-1, + enums=ENUMS(), + sample_rate=SAMPLE_RATE, + ) + ) + self.SERIALIZER.configure( + SerializerConfig( + data=self._topology, + sub_pub_match=self._sub_pub_match, + sample_rate=SAMPLE_RATE, + stream_name=STREAM.LABGRAPH_MONITOR, + stream_id=STREAM.LABGRAPH_MONITOR_ID, + ) + ) + ``` + + - As well as establishing connections between those nodes. Learn more in the example at [`yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py`](https://github.com/facebookresearch/labgraph/blob/main/extensions/yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py) + + 2. You can then run your graph + + ```python + from extensions.yaml_support.labgraph_monitor.examples.labgraph_monitor_example import Demo + + from extensions.yaml_support.labgraph_monitor.labgraph_monitor import LabgraphMonitor + + # Initialize a Demo graph + graph = Demo() + + # Initialize a monitor object + monitor = LabgraphMonitor(graph) + + # Run the WebSockets API to send the real-time topology to Front-End + monitor.stream_real_time_graph() + ``` + +3. Alternatively, you can simply run the example graph to try it out + + ```bash + labgraph> python extensions/yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py + ``` + +4. To start receiving data from the server, send the following `StartStreamRequest` to **ws://127.0.0.1:9000** + + ```bash + { + "api_version": "0.1", + "api_request": { + "request_id": 1, + "start_stream_request": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + } + } + } + } + ``` + +The graph representation has the following schema: + +```python +{ + name: "graph_name", + nodes: { + "node_name":{ + upstreams:{ + "upstream_name":[ + { + name: "message_name", + fields: { + "timestamp": { + "type": "float", + "content": real-time value + }, + "data": { + "type": "ndarray", + "content" real-time value + } + } + } + ] + } + } + } +} +``` + + +
+ +Click to see a full example of a serialized graph + +```python + +{ + "stream_batch": { + "stream_id": "LABGRAPH.MONITOR", + "labgraph.monitor": { + "samples": [ + { + "data": { + "nodes": { + "WSAPIServerNode": { + "upstreams": { + "Serializer": [ + { + "name": "WSStreamMessage", + "fields": { + "samples": { + "type": "float64" + }, + "stream_name": { + "type": "str" + }, + "stream_id": { + "type": "str" + } + } + } + ] + } + }, + "Serializer": { + "upstreams": { + "NoiseGenerator": [ + { + "name": "RandomMessage", + "fields": { + "timestamp": { + "type": "float", + "content": 1649542864.7681 + }, + "data": { + "type": "ndarray", + "content": [ + 0.6556638018597015, + 0.24539091264180568, + 0.5181571344277014, + 0.15984381323928065, + 0.3701566776459857, + 0.7696829688461163, + 0.5492927884621397, + 0.21755380404615476, + 0.175755558441702, + 0.3505148522415169, + 0.4350243443086518, + 0.8366274099373696, + 0.03302475842524499, + 0.8704738525112928, + 0.027029976451051985, + 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+ +3. To stop receiving data from the server, send the following `EndStreamRequest` to **ws://127.0.0.1:9000** + +```python { "api_version": "0.1", "api_request": { @@ -213,13 +1261,13 @@ https://github.com/facebookresearch/labgraph/blob/main/extensions/graphviz_suppo } ``` -#### Labgraph YAML Parser: +# LabGraph YAML Parser: -**yamlify(python_file: str, yaml_file: str) -> str** : This function can be used to generate a YAMLIFIED version of the passed LabGraph source code(.py). The serialized version will be saved as a YAML file at the specified folder(yml_file) +**`yamlify(python_file: str, yaml_file: str) -> str`** : This function can be used to generate a YAMLIFIED version of the passed LabGraph source code (.py). The serialized version will be saved as a YAML file at the specified folder (yml_file) 1. Call **yamlify(python_file: str, yaml_file: str) -> str** and pass the appropriate parameters -``` +```python from extensions.yaml_support.labgraph_yaml_parser.yamlify import ( yamlify ) @@ -228,3 +1276,88 @@ yamlify(python_file, output_yaml_file) ``` This will generate a YAML file in the specified location + +Example of a YAML file produced for [`simple_viz.py`](https://github.com/facebookresearch/labgraph/blob/main/labgraph/examples/simple_viz.py) example: + +```python +RandomMessage: + type: Message + fields: + timestamp: float + data: np.ndarray + +NoiseGeneratorConfig: + type: Config + fields: + sample_rate: float + num_features: int + +NoiseGenerator: + type: Node + config: NoiseGeneratorConfig + inputs: [] + outputs: + - RandomMessage + +RollingState: + type: State + fields: + messages: List.RandomMessage + +RollingConfig: + type: Config + fields: + window: float + +RollingAverager: + type: Node + state: RollingState + config: RollingConfig + inputs: + - RandomMessage + outputs: + - RandomMessage + +AveragedNoiseConfig: + type: Config + fields: + sample_rate: float + num_features: int + window: float + +AveragedNoise: + type: Group + config: AveragedNoiseConfig + inputs: [] + outputs: + - RandomMessage + connections: + NoiseGenerator: RollingAverager + RollingAverager: AveragedNoise + +PlotState: + type: State + fields: + data: Optional.np.ndarray + +PlotConfig: + type: Config + fields: + refresh_rate: float + num_bars: int + +Plot: + type: Node + state: PlotState + config: PlotConfig + inputs: + - RandomMessage + outputs: [] + +Demo: + type: Graph + inputs: [] + outputs: [] + connections: + AveragedNoise: Plot +``` diff --git a/extensions/yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py b/extensions/yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py new file mode 100644 index 000000000..59bcc7247 --- /dev/null +++ b/extensions/yaml_support/labgraph_monitor/examples/labgraph_monitor_example.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import labgraph as lg +from typing import Dict, Tuple + +# Make the imports work when running from LabGraph root directory +import sys +sys.path.append("./") + +# LabGraph WebSockets Components +from labgraph.websockets.ws_server.ws_api_node_server import ( + WSAPIServerConfig, + WSAPIServerNode, +) + +# LabGraph Monitor Components +from extensions.yaml_support.labgraph_monitor.server.enums.enums import ENUMS +from extensions.yaml_support.labgraph_monitor.aliases.aliases import SerializedGraph +from extensions.yaml_support.labgraph_monitor.server.serializer_node import SerializerConfig, Serializer +from extensions.yaml_support.labgraph_monitor.generate_lg_monitor.generate_lg_monitor import set_graph_topology + +# Graph Components +from extensions.graphviz_support.graphviz_support.tests.demo_graph.noise_generator import NoiseGeneratorConfig, NoiseGenerator +from extensions.graphviz_support.graphviz_support.tests.demo_graph.amplifier import AmplifierConfig, Amplifier +from extensions.graphviz_support.graphviz_support.tests.demo_graph.attenuator import AttenuatorConfig, Attenuator +from extensions.graphviz_support.graphviz_support.tests.demo_graph.rolling_averager import RollingConfig, RollingAverager + +# LabGraph WebSockets Configurations +APP_ID = 'LABGRAPH.MONITOR' +WS_SERVER = ENUMS.WS_SERVER +STREAM = ENUMS.STREAM +DEFAULT_IP = WS_SERVER.DEFAULT_IP +DEFAULT_PORT = WS_SERVER.DEFAULT_PORT +DEFAULT_API_VERSION = WS_SERVER.DEFAULT_API_VERSION +SAMPLE_RATE = 5 + +# Graph Configurations +NUM_FEATURES = 100 +WINDOW = 2.0 +REFRESH_RATE = 2.0 +OUT_IN_RATIO = 1.2 +ATTENUATION = 5.0 + +class Demo(lg.Graph): + # LabGraph WebSockets Component + WS_SERVER_NODE: WSAPIServerNode + + # LabGraph Monitor Component + SERIALIZER: Serializer + + # Graph Components + NOISE_GENERATOR: NoiseGenerator + ROLLING_AVERAGER: RollingAverager + AMPLIFIER: Amplifier + ATTENUATOR: Attenuator + + # Used when running `generate_labgraph_monitor(graph)` + def set_topology(self, topology: SerializedGraph, sub_pub_map: Dict) -> None: + self._topology = topology + self._sub_pub_match = sub_pub_map + + def setup(self) -> None: + self.WS_SERVER_NODE.configure( + WSAPIServerConfig( + app_id=APP_ID, + ip=WS_SERVER.DEFAULT_IP, + port=ENUMS.WS_SERVER.DEFAULT_PORT, + api_version=ENUMS.WS_SERVER.DEFAULT_API_VERSION, + num_messages=-1, + enums=ENUMS(), + sample_rate=SAMPLE_RATE, + ) + ) + self.SERIALIZER.configure( + SerializerConfig( + data=self._topology, + sub_pub_match=self._sub_pub_match, + sample_rate=SAMPLE_RATE, + stream_name=STREAM.LABGRAPH_MONITOR, + stream_id=STREAM.LABGRAPH_MONITOR_ID, + ) + ) + self.NOISE_GENERATOR.configure( + NoiseGeneratorConfig( + sample_rate=float(SAMPLE_RATE), + num_features=NUM_FEATURES, + ) + ) + self.ROLLING_AVERAGER.configure( + RollingConfig( + window=WINDOW, + ) + ) + self.AMPLIFIER.configure( + AmplifierConfig( + out_in_ratio=OUT_IN_RATIO, + ) + ) + self.ATTENUATOR.configure( + AttenuatorConfig( + attenuation=ATTENUATION, + ) + ) + + def connections(self) -> lg.Connections: + return ( + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.ROLLING_AVERAGER.ROLLING_AVERAGER_INPUT), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.AMPLIFIER.AMPLIFIER_INPUT), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.ATTENUATOR.ATTENUATOR_INPUT), + (self.NOISE_GENERATOR.NOISE_GENERATOR_OUTPUT, self.SERIALIZER.SERIALIZER_INPUT_1), + (self.ROLLING_AVERAGER.ROLLING_AVERAGER_OUTPUT, self.SERIALIZER.SERIALIZER_INPUT_2), + (self.AMPLIFIER.AMPLIFIER_OUTPUT, self.SERIALIZER.SERIALIZER_INPUT_3), + (self.ATTENUATOR.ATTENUATOR_OUTPUT, self.SERIALIZER.SERIALIZER_INPUT_4), + (self.SERIALIZER.SERIALIZER_OUTPUT, self.WS_SERVER_NODE.topic), + ) + + def process_modules(self) -> Tuple[lg.Module, ...]: + return ( + self.NOISE_GENERATOR, + self.ROLLING_AVERAGER, + self.AMPLIFIER, + self.ATTENUATOR, + self.SERIALIZER, + self.WS_SERVER_NODE, + ) + +if __name__ == "__main__": + graph = Demo() + set_graph_topology(graph=graph) + + runner = lg.ParallelRunner(graph=graph) + runner.run() \ No newline at end of file diff --git a/extensions/yaml_support/labgraph_monitor/generate_lg_monitor/generate_lg_monitor.py b/extensions/yaml_support/labgraph_monitor/generate_lg_monitor/generate_lg_monitor.py index 7f67f1152..7f50c1835 100644 --- a/extensions/yaml_support/labgraph_monitor/generate_lg_monitor/generate_lg_monitor.py +++ b/extensions/yaml_support/labgraph_monitor/generate_lg_monitor/generate_lg_monitor.py @@ -1,14 +1,12 @@ #!/usr/bin/env python3 -# Copyright 2004-present Facebook. All Rights Reserve +# Copyright 2004-present Facebook. All Rights Reserved. import labgraph as lg from labgraph.graphs.stream import Stream -from typing import List, Dict +from typing import List, Dict, Tuple from ..lg_monitor_node.lg_monitor_node import LabgraphMonitorNode from ..lg_monitor_node.lg_monitor_message import LabgraphMonitorMessage from ..aliases.aliases import SerializedGraph -from ..server.lg_monitor_server import run_server - def identify_upstream_message( in_edge: str, @@ -141,10 +139,7 @@ def connect_to_upstream( return nodes -def serialize_graph( - name: str, - nodes: List[LabgraphMonitorNode] -) -> SerializedGraph: +def serialize_graph(graph: lg.Graph) -> SerializedGraph: """ A function that returns a serialized version of the graph topology. @@ -154,8 +149,17 @@ def serialize_graph( @return: A serialized version of the graph topology """ + # List of graph nodes + nodes: List[LabgraphMonitorNode] = [] + + # Identify graph nodes + nodes = identify_graph_nodes(graph) + + # Connect graph edges + nodes = connect_to_upstream(nodes, graph.__streams__.values()) + serialized_graph: SerializedGraph = { - "name": name, + "name": type(graph).__name__, "nodes": {} } @@ -185,31 +189,91 @@ def serialize_graph( return serialized_graph - -def generate_labgraph_monitor(graph: lg.Graph) -> None: +def sub_pub_grouping_map(graph: lg.Graph) -> Dict[str, str]: """ - A function that serialize the graph topology - and send it using to LabGraphMonitor Front-End - using Labgraph Websocket API + A function that matches subscribers with their publishers + to automate assigning real-time messages to serialized graph @params: graph: An instance of the computational graph + + @return: A dictionary where the key is a publisher grouping + and the value is a dictionary of sets of topic paths subscribed and their groupings """ - # Local variables - nodes: List[LabgraphMonitorNode] = [] + sub_pub_grouping_map: Dict[str, str] = {} + for stream in graph.__streams__.values(): + difference = set(stream.topic_paths).difference(LabgraphMonitorNode.in_edges) + if difference: + upstream_edge = max(difference, key=len) + for edge in stream.topic_paths: + if edge != upstream_edge: + # convert SERIALIZER/SERIALIZER_INPUT_1 to its grouping Serializer + edge_path = "/".join(edge.split("/")[:-1]) + edge_grouping = type(graph.__descendants__[edge_path]).__name__ + + # convert SERIALIZER/SERIALIZER_INPUT_1 to its topic SERIALIZER_INPUT_1 + topic_path = edge.split("/")[-1] + + # convert NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT to its grouping NoiseGenerator + group_path = "/".join(upstream_edge.split("/")[:-1]) + grouping = type(graph.__descendants__[group_path]).__name__ + + if grouping in sub_pub_grouping_map: + sub_pub_grouping_map[grouping]["topics"].add(topic_path) + sub_pub_grouping_map[grouping]["subscribers"].add(edge_grouping) + else: + sub_pub_grouping_map[grouping] = { + "topics": {topic_path}, + "subscribers": {edge_grouping}, + } + + return sub_pub_grouping_map + +def generate_graph_topology(graph: lg.Graph) -> SerializedGraph: + """ + A function that serializes the graph topology + and sends it to LabGraph Monitor Front-End + using WebSockets API - # Identify graph nodes - nodes = identify_graph_nodes(graph) + @params: + graph: An instance of the computational graph + + @return: Serialized topology of the graph + """ + serialized_graph = serialize_graph(graph) - # Connect graph edges - nodes = connect_to_upstream(nodes, graph.__streams__.values()) + return serialized_graph +def set_graph_topology(graph: lg.Graph) -> None: + """ + A function that serializes the graph topology + and applies the information to serve as graph + attribute for LabGraph Monitor Front-End + real-time messaging using WebSockets API + + @params: + graph: An instance of the computational graph + """ # Serialize the graph topology - serialized_graph = serialize_graph( - type(graph).__name__, - nodes - ) - - # Send the serialized graph to Front-End - # using LabGraph Websockets API - run_server(serialized_graph) + serialized_graph = serialize_graph(graph) + + # Match subscribers with their publishers + sub_pub_map = sub_pub_grouping_map(graph) + + # Set graph's topology and real-time messages matching + if hasattr(graph, "set_topology"): + graph.set_topology(serialized_graph, sub_pub_map) + else: + raise AttributeError( + """ + Provided graph is missing `set_topology` method to establish + its topology and possible real-time messsaging + + Please add the following method to your graph + ``` + def set_topology(self, topology: SerializedGraph, sub_pub_map: Dict) -> None: + self._topology = topology + self._sub_pub_match = sub_pub_map + ``` + """ + ) \ No newline at end of file diff --git a/extensions/yaml_support/labgraph_monitor/labgraph_monitor.py b/extensions/yaml_support/labgraph_monitor/labgraph_monitor.py new file mode 100644 index 000000000..2c6078926 --- /dev/null +++ b/extensions/yaml_support/labgraph_monitor/labgraph_monitor.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved, + +import labgraph as lg +from .generate_lg_monitor.generate_lg_monitor import generate_graph_topology, set_graph_topology +from .server.lg_monitor_server import run_topology + +class LabgraphMonitor: + """ + A class that serves as a facade for + LabGraph Monitor's Back-End functions + """ + + def __init__(self, graph: lg.Graph) -> None: + self.graph = graph + + def stream_graph_topology(self) -> None: + """ + Stream graph topology via WebSockets + """ + topology = generate_graph_topology(self.graph) + run_topology(topology) + + def stream_real_time_graph(self) -> None: + """ + Stream graph topology and real-time + messages via WebSockets + """ + set_graph_topology(self.graph) + + runner = lg.ParallelRunner(self.graph) + runner.run() \ No newline at end of file diff --git a/extensions/yaml_support/labgraph_monitor/lg_monitor_node/lg_monitor_message.py b/extensions/yaml_support/labgraph_monitor/lg_monitor_node/lg_monitor_message.py index 1e4916762..c4024647f 100644 --- a/extensions/yaml_support/labgraph_monitor/lg_monitor_node/lg_monitor_message.py +++ b/extensions/yaml_support/labgraph_monitor/lg_monitor_node/lg_monitor_message.py @@ -34,6 +34,6 @@ def serialize(self) -> SerializedMessage: name = annotation[0] type = (annotation[1]).__name__ - serialized_message['fields'][name] = type + serialized_message['fields'][name] = {"type": type} return serialized_message diff --git a/extensions/yaml_support/labgraph_monitor/server/enums/enums.py b/extensions/yaml_support/labgraph_monitor/server/enums/enums.py index 2a39b0966..832756013 100644 --- a/extensions/yaml_support/labgraph_monitor/server/enums/enums.py +++ b/extensions/yaml_support/labgraph_monitor/server/enums/enums.py @@ -2,9 +2,15 @@ # Copyright 2004-present Facebook. All Rights Reserved. from .enums_base import ENUMS as ENUMS_base +class STREAM_LISTS: + supported_stream_list = [ + "labgraph.monitor", + ] + sleep_pause_streams = supported_stream_list class ENUMS: API = ENUMS_base.API WS_SERVER = ENUMS_base.WS_SERVER STREAM = ENUMS_base.STREAM + STREAM_LISTS = STREAM_LISTS \ No newline at end of file diff --git a/extensions/yaml_support/labgraph_monitor/server/lg_monitor_server.py b/extensions/yaml_support/labgraph_monitor/server/lg_monitor_server.py index 552226f14..1f5053b63 100644 --- a/extensions/yaml_support/labgraph_monitor/server/lg_monitor_server.py +++ b/extensions/yaml_support/labgraph_monitor/server/lg_monitor_server.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -# Copyright 2004-present Facebook. All Rights Reserve +# Copyright 2004-present Facebook. All Rights Reserved. import labgraph as lg from typing import Tuple @@ -20,7 +20,7 @@ SAMPLE_RATE = 5 -def run_server(data: SerializedGraph) -> None: +def run_topology(data: SerializedGraph) -> None: """ A function that creates a Websocket server graph. The server graph streams the lagraph topology to the clients @@ -50,7 +50,7 @@ def setup(self) -> None: self.WS_SERVER_NODE.configure(wsapi_server_config) def connections(self) -> lg.Connections: - return ((self.SERIALIZER.TOPIC, self.WS_SERVER_NODE.topic),) + return ((self.SERIALIZER.SERIALIZER_OUTPUT, self.WS_SERVER_NODE.topic),) def process_modules(self) -> Tuple[lg.Module, ...]: return (self.SERIALIZER, self.WS_SERVER_NODE, ) diff --git a/extensions/yaml_support/labgraph_monitor/server/serializer_node.py b/extensions/yaml_support/labgraph_monitor/server/serializer_node.py index 064294ab7..5395bd3e1 100644 --- a/extensions/yaml_support/labgraph_monitor/server/serializer_node.py +++ b/extensions/yaml_support/labgraph_monitor/server/serializer_node.py @@ -1,6 +1,8 @@ #!/usr/bin/env python3 -# Copyright 2004-present Facebook. All Rights Reserve +# Copyright 2004-present Facebook. All Rights Reserved. +from dataclasses import field +from typing import Dict, Optional import labgraph as lg import asyncio from labgraph.websockets.ws_server.ws_server_stream_message import ( @@ -8,30 +10,111 @@ ) from ..aliases.aliases import SerializedGraph +# Make it work with RandomMessage +from ....graphviz_support.graphviz_support.tests.demo_graph.random_message import RandomMessage class SerializerConfig(lg.Config): data: SerializedGraph + sub_pub_match: Optional[Dict] = field(default_factory=dict) sample_rate: int stream_name: str stream_id: str +class DataState(lg.State): + data_1: Optional[Dict] = None + data_2: Optional[Dict] = None + data_3: Optional[Dict] = None + data_4: Optional[Dict] = None class Serializer(lg.Node): """ Convenience node for sending messages to a `WSAPIServerNode`. """ - TOPIC = lg.Topic(WSStreamMessage) + SERIALIZER_OUTPUT = lg.Topic(WSStreamMessage) config: SerializerConfig + state: DataState - @lg.publisher(TOPIC) + SERIALIZER_INPUT_1 = lg.Topic(RandomMessage) + SERIALIZER_INPUT_2 = lg.Topic(RandomMessage) + SERIALIZER_INPUT_3 = lg.Topic(RandomMessage) + SERIALIZER_INPUT_4 = lg.Topic(RandomMessage) + + def get_grouping(self, topic: lg.Topic) -> str: + """ + Matches subscriber topics with grouping that produced information + """ + for key, value in self.config.sub_pub_match.items(): + if topic.name in value["topics"]: + return key + return "" + + @lg.subscriber(SERIALIZER_INPUT_1) + def add_message_1(self, message: RandomMessage) -> None: + grouping = self.get_grouping(self.SERIALIZER_INPUT_1) + self.state.data_1 = { + "grouping": grouping, + "timestamp": message.timestamp, + "numpy": list(message.data), + } + + @lg.subscriber(SERIALIZER_INPUT_2) + def add_message_2(self, message: RandomMessage) -> None: + grouping = self.get_grouping(self.SERIALIZER_INPUT_2) + self.state.data_2 = { + "grouping": grouping, + "timestamp": message.timestamp, + "numpy": list(message.data), + } + + @lg.subscriber(SERIALIZER_INPUT_3) + def add_message_3(self, message: RandomMessage) -> None: + grouping = self.get_grouping(self.SERIALIZER_INPUT_3) + self.state.data_3 = { + "grouping": grouping, + "timestamp": message.timestamp, + "numpy": list(message.data), + } + + @lg.subscriber(SERIALIZER_INPUT_4) + def add_message_4(self, message: RandomMessage) -> None: + grouping = self.get_grouping(self.SERIALIZER_INPUT_4) + self.state.data_4 = { + "grouping": grouping, + "timestamp": message.timestamp, + "numpy": list(message.data), + } + + def output(self, _in: Dict) -> Dict: + """ + Updates serialized message with data according to grouping + + @params: + value of a dictionary that represents individual nodes + """ + for node, value in _in.items(): + for state in self.state.__dict__.values(): + if state["grouping"] in value["upstreams"].keys(): + value["upstreams"][state["grouping"]][0]["fields"]["timestamp"]["content"] = state["timestamp"] + value["upstreams"][state["grouping"]][0]["fields"]["data"]["content"] = state["numpy"] + + return _in + + @lg.publisher(SERIALIZER_OUTPUT) async def source(self) -> lg.AsyncPublisher: - await asyncio.sleep(.01) + await asyncio.sleep(.1) while True: - msg = WSStreamMessage( - samples=self.config.data, + output_data = self.config.data + # check if the monitor is in real-time mode + if self.config.sub_pub_match: + # populate Serialized Graph with real-time data + output_data = { + key: self.output(value) for key, value in self.config.data.items() if key == "nodes" + } + # otherwise, only send the topology + yield self.SERIALIZER_OUTPUT, WSStreamMessage( + samples=output_data, stream_name=self.config.stream_name, stream_id=self.config.stream_id, ) - yield self.TOPIC, msg - await asyncio.sleep(1 / self.config.sample_rate) + await asyncio.sleep(1 / self.config.sample_rate), diff --git a/extensions/yaml_support/labgraph_monitor/tests/test_lg_monitor_api.py b/extensions/yaml_support/labgraph_monitor/tests/test_lg_monitor_api.py index 94870d6f4..304078009 100644 --- a/extensions/yaml_support/labgraph_monitor/tests/test_lg_monitor_api.py +++ b/extensions/yaml_support/labgraph_monitor/tests/test_lg_monitor_api.py @@ -21,7 +21,7 @@ def setUp(self) -> None: def test_identify_upstream_message(self) -> None: upstream_message = identify_upstream_message( - 'ROLLING_AVERAGER/INPUT', + 'ROLLING_AVERAGER/ROLLING_AVERAGER_INPUT', self.graph.__topics__ ) @@ -38,47 +38,47 @@ def test_out_edge_node_mapper(self) -> None: self.assertEqual(4, len(out_edge_node_map)) self.assertEqual( 'generate_noise', - out_edge_node_map['NOISE_GENERATOR/OUTPUT'].name + out_edge_node_map['NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT'].name ) self.assertEqual( 'average', - out_edge_node_map['ROLLING_AVERAGER/OUTPUT'].name + out_edge_node_map['ROLLING_AVERAGER/ROLLING_AVERAGER_OUTPUT'].name ) self.assertEqual( 'amplify', - out_edge_node_map['AMPLIFIER/OUTPUT'].name + out_edge_node_map['AMPLIFIER/AMPLIFIER_OUTPUT'].name ) self.assertEqual( 'attenuate', - out_edge_node_map['ATTENUATOR/OUTPUT'].name + out_edge_node_map['ATTENUATOR/ATTENUATOR_OUTPUT'].name ) def test_in_out_edge_mapper(self) -> None: in_out_edge_map = in_out_edge_mapper(self.graph.__streams__.values()) self.assertEqual(6, len(in_out_edge_map)) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['ROLLING_AVERAGER/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['ROLLING_AVERAGER/ROLLING_AVERAGER_INPUT'] ) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['AMPLIFIER/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['AMPLIFIER/AMPLIFIER_INPUT'] ) self.assertEqual( - 'NOISE_GENERATOR/OUTPUT', - in_out_edge_map['ATTENUATOR/INPUT'] + 'NOISE_GENERATOR/NOISE_GENERATOR_OUTPUT', + in_out_edge_map['ATTENUATOR/ATTENUATOR_INPUT'] ) self.assertEqual( - 'ROLLING_AVERAGER/OUTPUT', - in_out_edge_map['SINK/INPUT_1'] + 'ROLLING_AVERAGER/ROLLING_AVERAGER_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_1'] ) self.assertEqual( - 'AMPLIFIER/OUTPUT', - in_out_edge_map['SINK/INPUT_2'] + 'AMPLIFIER/AMPLIFIER_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_2'] ) self.assertEqual( - 'ATTENUATOR/OUTPUT', - in_out_edge_map['SINK/INPUT_3'] + 'ATTENUATOR/ATTENUATOR_OUTPUT', + in_out_edge_map['SINK/SINK_INPUT_3'] ) def test_connect_to_upstream(self) -> None: @@ -89,9 +89,7 @@ def test_connect_to_upstream(self) -> None: self.assertEqual(expected_node_count, len(nodes)) def test_serialize_graph(self) -> None: - nodes = identify_graph_nodes(self.graph) - nodes = connect_to_upstream(nodes, self.graph.__streams__.values()) - serialized_graph = serialize_graph("Demo", nodes) + serialized_graph = serialize_graph(self.graph) self.assertEqual('Demo', serialized_graph["name"]) self.assertEqual(5, len(serialized_graph["nodes"])) diff --git a/setup_py36.py b/setup_py36.py index dea4091f1..a04188016 100644 --- a/setup_py36.py +++ b/setup_py36.py @@ -32,8 +32,10 @@ install_requires=[ "appdirs==1.4.3", "click==7.0", + "cppy==1.1.0", "dataclasses==0.6", "h5py==2.10.0", + "kiwisolver==1.3.2", "matplotlib==3.1.1", "mypy==0.782", "numpy==1.16.4", @@ -43,8 +45,8 @@ "pytest_mock==2.0.0", "pyzmq==18.1.0", "typeguard==2.5.1", - "typing_extensions>=3.7.4.3", + "typing_extensions==3.7.4.3", "websockets==8.1", "yappi==1.2.5", ], -) \ No newline at end of file +) diff --git a/signal_processing/README.md b/signal_processing/README.md new file mode 100644 index 000000000..ff8403e36 --- /dev/null +++ b/signal_processing/README.md @@ -0,0 +1,19 @@ +# Signal Processing + +## What this folder contains +Files in this directory contain signal processing examples used with LabGraph + +## Folder structure +- filter: Signal processing filters +- messages: LabGraph messages +- nodes: LabGraph nodes for signal processing +- synthetic: Synthetic signal generator and nodes +- synthetic_data: Synthetic data (currently only for continuous fNIRS signals) +- utils: utility files used in signal processing + +## Example use +- python3 -m pytest --pyargs signal_processing.filter +- python3 -m pytest --pyargs signal_processing.nodes + +## Main contributors +Main contributors for this folder are currently Sho Nakagome and Allen Yin. diff --git a/signal_processing/__init__.py b/signal_processing/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/filter/README.md b/signal_processing/filter/README.md new file mode 100644 index 000000000..4fd5c2255 --- /dev/null +++ b/signal_processing/filter/README.md @@ -0,0 +1,5 @@ +Files in this directory relate to filtering in the signal processing sense, +as processes that remove some unwanted components or features from a signal. + +Also include functions and transforms typically associated with signal processing, +e.g. STFT, FFT. diff --git a/signal_processing/filter/__init__.py b/signal_processing/filter/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/filter/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/filter/detrend.py b/signal_processing/filter/detrend.py new file mode 100644 index 000000000..f11a7d516 --- /dev/null +++ b/signal_processing/filter/detrend.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import logging + +import numpy as np +from scipy import signal + + +def _get_non_zero_index(data: np.array): + return data.nonzero()[0] + + +def interpolate_nan(y: np.ndarray): + nans = np.isnan(y) + y1 = y.copy() + y1[nans] = np.interp( + _get_non_zero_index(nans), _get_non_zero_index(~nans), y[~nans] + ) + return y1 + + +def detrend_and_offset(data: np.ndarray, fix_negative: bool = False) -> np.ndarray: + """ + Detrend time series by subtracting from it + a linear fit. Then offset it so the detrended + time series is centered on the temporal mean + of the original data. + + If `fix_negative` is True, then any data points that + become negative after the process will be interpolated. + Use this option when the input is DOT power measurements + that will be logmean'd afterward. + + Input: + - data: np.ndarray, data array of shape [n_time, n_channels] + - fix_negative: bool, if True then after detrend and offset, + any points that are negative will be interpolated to + be positive. This requires the mean of the original + data to be positive. + + Returns: + - output: np.ndarray, same shape as data, contains + the detrended and output signal + """ + detrended = signal.detrend(data, axis=0) + output = detrended + np.mean(data, axis=0, keepdims=True) + all_nan_channels = 0 + + if fix_negative: + if np.any(np.mean(data, axis=0) < 0): + raise ValueError("data mean is negative, cannot fix negative") + # check how many results <=0 + n_le0 = np.sum(output <= 0) + bad_ch = np.unique(np.argwhere(output <= 0)[:, 1]) + logging.info(f"bad_ch are {bad_ch}") + logging.info(f"Found {n_le0} points with values <=0, interpolating") + output[output <= 0] = np.nan + + for ch in bad_ch: + if np.all(np.isnan(output[:, ch])): + logging.warning("Channel all nans, skipping") + all_nan_channels += 1 + continue + output[:, ch] = interpolate_nan(output[:, ch]) + if all_nan_channels > 0: + logging.warning( + f"{all_nan_channels} channels all nans, check channel std and mean!" + ) + return output diff --git a/signal_processing/filter/fir_filters.py b/signal_processing/filter/fir_filters.py new file mode 100644 index 000000000..7bfa61f93 --- /dev/null +++ b/signal_processing/filter/fir_filters.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Tuple, Union + +import numpy as np +from scipy.signal import firwin, lfilter_zi, lfilter + + +def fir_coefficients_states( + cutoff: Union[float, list], fs: float, btype: bool, order: int +) -> Tuple[np.ndarray, np.ndarray]: + """Real-time equivalent FIR filter returning coefficients + + Args: + ---------- + cutoff: float or list + cutoff frequency to be specified + e.g. cutoff = 3 # Hz + when using bandpass or bandstop, pass list + e.g. [0.01, 3] = 0.01 Hz - 3 Hz + + fs: float + sampling frequency + + btype: bool + [default = True (lowpass). False = highpass] + In scipy 1.3.0 or above, the following can be used [TODO] + Options: ‘bandpass’, ‘lowpass’, ‘highpass’, ‘bandstop’ + Equivalent to btype in iir_filter + + order: int + filter order + + Returns: + ---------- + b: np.ndarray + coefficients of length num_taps FIR filter + + z: np.ndarray + the initial state for the filter + + Example of real-time emulation: + ---------- + # Get coefficients and initial state + b, z = fir_coefficients_states(cutoff=cutoff, + fs=fs, + btype='lowpass', + order=order) + # Initialize + filtered = np.zeros(data.size) + # Sample by sample + for i, x in enumerate(data): + filtered[i], z = lfilter(b, 1, [x], zi = z) + """ + + # Calculate the nyquist frequency + nyquist = fs / 2.0 + # Normalize the cutoff frequency using nyquist + normalized_cutoff = np.array(cutoff) / nyquist + # Design coefficients of length numtaps for FIR filter + b = firwin(numtaps=order, cutoff=normalized_cutoff, pass_zero=btype) + # Compute initial steady state of step response + z = lfilter_zi(b, 1) + + return b, z + + +def fir_filter( + data: np.ndarray, + cutoff: float, + fs: float, + btype: bool = True, + order: int = 50, + axis: int = -1, +) -> np.ndarray: + """FIR filtering returning filtered signal, + The filtered output will start at the same initial value as the input signal + by matching initial conditions. + + Note despite matching initial condition through the use of `lfilter_zi`, the + first few samples of the output signal will not be simply a delayed version. + The number of weird samples scale with the filter order. + + Args: + ---------- + data: np.ndarray + data to be filtered + + cutoff: float + cutoff frequency to be specified + e.g. cutoff = 3 # Hz + + fs: float + sampling frequency + + btype: bool + [default = True (lowpass). False = highpass] + In scipy 1.3.0 or above, the following can be used [TODO] + Options: ‘bandpass’, ‘lowpass’, ‘highpass’, ‘bandstop’ + Default: 'lowpass' + Equivalent to btype in iir + + order: int + filter order + + axis: int, dimension along which to apply the filter, same as that for `lfilter` + default is -1 + + Returns: + ---------- + filtered: np.ndarray + filtered data, same shape as data + """ + + # Get coefficients and initial state + b, z = fir_coefficients_states(cutoff=cutoff, fs=fs, btype=btype, order=order) + x_dims = data.shape + + # if multi-dimensional array, have to modify initial condition z + # to fit the dimension of the input data + if len(x_dims) > 1: + z_dims = np.ones(len(x_dims), dtype=int) + z_dims[axis] = -1 + z = z.reshape(z_dims) # convert to row vector + tile_dims = np.array(x_dims) + tile_dims[axis] = 1 + z = np.tile(z, tile_dims) + + filtered, _ = lfilter(b, 1, data, zi=z, axis=axis) + + return filtered + + +def check_symmetry(h: np.ndarray) -> bool: + """ + Given FIR filter coefficients `h`, check if it's symmetric + or antisymmetric + """ + N = len(h) + symmetry = all(np.isclose(h[k], h[N - k - 1]) for k in range(N)) + antisymmetry = all(np.isclose(h[k], -h[N - k - 1]) for k in range(N)) + if symmetry or antisymmetry: + return True + return False + + +def symmetric_fir_group_delay(h: np.ndarray) -> int: + """ + Group delay for symmetric or antisymmetric FIR filters is about + half of the filter length. + + If not symmetric, use `.signal_transforms.filter_group_delay` instead + """ + if not check_symmetry(h): + raise RuntimeError("FIR filter is not symmetric or antisymmetric!") + N = len(h) + if N % 2 == 1: + return (N - 1) // 2 + else: + return N // 2 diff --git a/signal_processing/filter/iir_filters.py b/signal_processing/filter/iir_filters.py new file mode 100644 index 000000000..d0504c364 --- /dev/null +++ b/signal_processing/filter/iir_filters.py @@ -0,0 +1,397 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Tuple, Union + +import numpy as np +from scipy.signal import ( + butter, + filtfilt, + lfilter, + sosfilt, + sosfiltfilt, + sosfilt_zi, +) + +from .signal_transforms import filter_group_delay + + +def butter_coefficients( + cutoff: Union[float, list], fs: float, btype: str, order=4 +) -> Tuple[np.ndarray, np.ndarray]: + """Butterworth filter coefficients + + Args: + ---------- + cutoff: float or list of floats + Cutoff frequency in Hz (e.g. 3 Hz) + fs: float + Sampling frequency in Hz + btype: str + Type of filter + Options: ['high', 'low', 'pass', 'stop'] + 'high': highpass + 'low': lowpass + 'pass': bandpass + 'stop': bandstop + order: int + Order of the filter + + Returns: + ---------- + b, a : ndarray, ndarray + Numerator and denominator polynomials + of the IIR filter + """ + + # calculate the nyquist frequency + nyquist = fs / 2.0 + # normalize + normalized_cutoff = np.asarray(cutoff) / nyquist + # calculate coefficients - analog=False to return a filter defined + # in z-domain, otherwise it's defined in s-domained and transformed + # through bi-linear transformation + b, a = butter(order, normalized_cutoff, btype=btype, analog=False) + return b, a + + +def butterworth_filter( + data: np.ndarray, + cutoff: Union[float, list], + fs: float, + btype: str, + axis: int = -1, + noDelay: bool = True, + order: int = 4, + **kwargs, +) -> np.ndarray: + """Butterworth filter using transfer function (tf) implementation. + For order higher than 4, it's recommended to use `butterworth_sos_filter` + for numerical stability. + + Please see butter_coefficients for details + in other attributes. + + Args: + ---------- + data: np.ndarray, array of data you want to filter. Last dimension + assumed to be time, following conventions of `lfilter` and `filtfilt` + Assumed to be of dimension [n_channels, n_time_samples] + + axis: dimension along which to apply the filter, same as the option + in `lfilter` and `filtfilt`. + If data is [n_time_samples, n_channels], then axis should be 0. + If data is [n_channels, n_time_samples], then axis is 1 or -1 (default). + + noDelay: flag if true, apply filter using `filtfilt`, which is zero-phase delay. + This means the filtered signal is not delayed w.r.t input. + Otherwise, filter is applied using `lfilter`. + + `lfilter` with butterworth should be linear phase in the passband. + `filtfilt` is guaranteed to be zero-phase, and by extension linear phase. + + By default with noDelay=False, `lfilter` output does not start at the + same initial value as the data input. Pass in values for 'zi' if this + is desired (values can be generated with `lfilter_zi`) + + cutoff, fs, btype, order: See `butter_coefficients`. + + **kwargs: Extra keyword args for `lfilter` (zi) or `filtfilt` (padtype, padlen) + + Returns: + -------- + out: np.ndarray, array of the filtered data. Same shape as data. + """ + + b, a = butter_coefficients(cutoff, fs, btype, order) + + if noDelay: + out = filtfilt(b, a, data, axis=axis, **kwargs) + else: + out = lfilter(b, a, data, axis=axis, **kwargs) + return out + + +def butter_sos( + cutoff: Union[float, list], fs: float, btype: str, order: int = 4 +) -> np.ndarray: + """Butterworth filter second-order section coefficients (sos) + + Args: + ---- + cutoff: float or list of floats + Cutoff frequency in Hz (e.g. 3 Hz) + fs: float + Sampling frequency in Hz + btype: str + Type of filter + Options: ['high', 'low', 'pass', 'stop'] + 'high': highpass + 'low': lowpass + 'pass': bandpass + 'stop': bandstop + order: int + Order of the filter + + Returns: + ---------- + sos : ndarray of second-order filter coefficients, must have shape (n_sections, 6). + Each row corresponds to a second-order section, with the first three columns + providing the numerator coefficients and the last three providing the + denominator coefficients. + """ + nyq = fs / 2.0 + normalized_cutoff = np.asarray(cutoff) / nyq + sos = butter(order, normalized_cutoff, btype, analog=False, output="sos") + return sos + + +def butterworth_sos_filter( + data: np.ndarray, + cutoff: Union[float, list], + fs: float, + btype: str, + axis: int = -1, + noDelay: bool = True, + order: int = 4, + **kwargs, +) -> np.ndarray: + """Butterworth filter using second-order sections (sos) implementation. + This is suitable for all frequency and filter order. However, for order + less than 4, use `butterworth_filter` for slightly better performance. + + Please see butter_sos for details in other attributes. + + Args: + ---------- + data: np.ndarray, array of data you want to filter. Last dimension + assumed to be time, following conventions of `lfilter` and `filtfilt` + Assumed to be of dimension [n_channels, n_time_samples] + + axis: dimension along which to apply the filter, same as the option + in `sosfilt` and `sosfiltfilt`. + If data is [n_time_samples, n_channels], then axis should be 0. + If data is [n_channels, n_time_samples], then axis is 1 or -1 (default). + + noDelay: flag if true, apply filter using `sosfiltfilt`, results + in zero-phase delay w.r.t input. Otherwise, filter is applied + using `sosfilt`. + + Using `sosfiltfilt` guarantees zero-phase and linear phase. + Using `sosfilt` with butterworth gives maximum linear-phase in the passband. + + By default with noDelay=False, `sos_filt` output does not start at the + same initial value as the data input. Pass in values for 'zi' if this + is desired (values can be generated with `sosfilt_zi`) + + cutoff, fs, btype, order: See `butter_sos`. + + **kwargs: Extra keyword args for `sosfilt` (zi), `sosfiltfilt` (padtype, padlen), + + Returns: + -------- + out: np.ndarray, array of the filtered data. Same shape as data. + """ + sos = butter_sos(cutoff=cutoff, fs=fs, btype=btype, order=order) + + if noDelay: + # sosfiltfilt + out = sosfiltfilt(sos, data, axis, **kwargs) + return out + else: + # sosfilt, with zi + out = sosfilt(sos, data, axis, **kwargs) + return out + + +def butter_group_delay( + cutoff: np.ndarray, fs: float, btype: str, order: int, N: int = 2048 +) -> Tuple[np.ndarray, np.ndarray]: + """ + Return frequency in Hz (ranging 0 to fs/2), + and group delay in samples + """ + sos_butter = butter_sos(cutoff, fs, btype, order) + freq, group_delay = filter_group_delay(sos_butter, N, fs, sos=True) + return freq, group_delay + + +def exponential_moving_average(x: np.ndarray, y_prev: np.ndarray, N: int) -> np.ndarray: + """EMA: Exponential Moving Average + + From the paper: + "Moving Average Convergence Divergence filter preprocessing for + real-time event-related peak activity onset detection: + application to fNIRS signals" + + Equations: + y_t = EMA(x) = \frac{2}{N+1} x_t + \frac{N-1}{N+1} y_{t-1} + + Arguments: + ---------- + x: raw signal input at time t + y_prev: filtered signal at time t-1 + N: parameter 1 = how many time samples to use for moving average + + Returns: + ---------- + y: filtered signal at time t + """ + + coeff_x = 2 / (N + 1) + coeff_y_prev = (N - 1) / (N + 1) + y = coeff_x * x + coeff_y_prev * y_prev + + return y + + +def moving_average_convergence_divergence( + x: np.ndarray, y_prev: np.ndarray, N_short: int, N_long: int +) -> np.ndarray: + """MACD: Moving Average Convergence/Divergence + + From the paper: + "Moving Average Convergence Divergence filter preprocessing for + real-time event-related peak activity onset detection: + application to fNIRS signals" + + Equations: + MACD(x_t) = EMA_short(x_t) - EMA_long(x_t) + + Arguments: + ---------- + x: raw signal input at time t + y_prev: filtered signal at time t-1 + N_short: parameter 1 = how many time samples to use for moving average + N_long: parameter 2 for long trend + + Returns: + ---------- + y: filtered signal at time t + """ + + ema_short = exponential_moving_average(x=x, y_prev=y_prev, N=N_short) + ema_long = exponential_moving_average(x=x, y_prev=y_prev, N=N_long) + + y = ema_short - ema_long + + return y + + +class CausalButterworth: + """Causal Butterworth Filter + + Can be used for both processing all the samples and + processing one sample (so that it can be combined) + """ + + # second-order sections + sos: np.ndarray # shape is [n_sections, 6], n_sections = ceil(order / 2) + # initial conditions + z: np.ndarray # shape [n_sections, 2, n_ch] + n_channels: int + + def __init__( + self, + first_time_sample: np.ndarray, + cutoff: Union[float, list], + sample_freq: float, + btype: str = "pass", + order: int = 4, + ) -> None: + """Initializing states for the filter + + first_time_sample: + The very first time sample at t = 0 + If one channel, data[0] + If multiple channels, data[0, :] + (given data = [time x channel]) + cutoff: + if 'high' or 'low' pass, float + if 'pass' or 'stop', List[float, float] + sample_freq: + sample frequency of the signal in Hz + btype: + type of filter. + Options: ['high', 'low', 'pass', 'stop'] + order: + filter order. + Note that this is IIR. + Use second-order sections and sosfilt for stability. + Note that the filter delay increases with filter order. + """ + + self.sos = butter_sos(cutoff, sample_freq, btype, order) + zi = sosfilt_zi(self.sos) # shape = [n_sections, 2] + if first_time_sample.ndim == 0: + self.n_channels = 1 + elif first_time_sample.ndim <= 2: + self.n_channels = first_time_sample.reshape((1, -1)).shape[1] + else: + raise RuntimeError("first_time_sample.ndim > 2") + self.z = np.tile(zi[:, :, None], (1, 1, self.n_channels)) + self.z = self.z * first_time_sample # shape = [n_sections, 2, n_ch] + + def process_sample(self, sample: np.ndarray) -> np.ndarray: + """Process sample causally + + This is so that you can combine with other process + per sample. (e.g. Log -> HbO/HbR -> this in one sample) + + sample: np.ndarray, assume to be a vector of shape [n_time, n_channel] + If n_dim is 1, assume to be [1, n_channel] sample + + ============ Ex. usage =========== + # Prepare data + fs = 100 # sampling frequency in Hz + duration_short = 1.0 # signal duration is 1 second + t_short = np.arange(0, duration_short, 1.0 / fs) + test_freq_short = [5, 25, 45] + data_short = np.hstack( + [ + np.cos(2 * np.pi * f * t_short).reshape([-1, 1]) + for f in test_freq_short + ] + ) # data_short.shape = (100, 3) + + # Filtering one channel only + cur_data = data_short[:, 0] + cb = CausalButterworth( + first_time_sample=cur_data[0], + cutoff=7, + sample_freq=fs, + btype='high' + ) + results = [] + # Process each sample causally + for i in range(len(data_short)): + results.append(cb.process_sample(sample=cur_data[i])) + results_np = np.vstack(results) + + ===== Multiple channels case ===== + cb = CausalButterworth( + first_time_sample=data_short[0, :], # all channels + cutoff=7, + sample_freq=fs, + btype='high' + ) + results = [] + # Process each sample causally + for i in range(data_short.shape[0]): + results.append(cb.process_sample(sample=data_short[i, :])) + results_np = np.vstack(results) + """ + sample = np.asarray(sample) + if sample.ndim == 0 and self.n_channels != 1: + raise RuntimeError( + f"Expected {self.n_channels} channels data, got 1 channel data" + ) + # reshape for ndim==0 and ndim==1 + sample = sample.reshape((1, -1)) + + if sample.shape[1] != self.n_channels: + raise RuntimeError( + f"Expected {self.n_channels} channels sample, received {sample.shape[1]} channel sample" + ) + output, self.z = sosfilt(self.sos, sample, axis=0, zi=self.z) + return output diff --git a/signal_processing/filter/signal_transforms.py b/signal_processing/filter/signal_transforms.py new file mode 100644 index 000000000..45e4ce3d4 --- /dev/null +++ b/signal_processing/filter/signal_transforms.py @@ -0,0 +1,206 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Tuple, Optional + +import numpy as np +from scipy.fftpack import fft, fftfreq, fftshift +from scipy.signal import sosfilt, unit_impulse, lfilter + + +def do_fft( + x: np.ndarray, + dt: float, + axis: int = -1, + dB: bool = False, +) -> Tuple[np.ndarray, np.ndarray]: + """Perform vanilla fft given then time-domain signal x and sampling time dt. + + Args: + ----------- + x: np.ndarray, data to perform fft on + + dt: float, sampling time of the signal in seconds + + axis: int, axis along wich to perform fft. Each sample along this axis is + considered to be sampled with dt + + dB: bool, flag to indicate whether the outputs will be returned in dB. + + Returns: + -------- + xf: np.ndarray, gives the two-sided frequency values for the fft. + + xFFT: np.ndarray, same shape as x, giving the magnitude of the fft + coefficients. If `dB`=True, then the result is `20*np.log10(xFFT)` + """ + + x_dims = x.shape + N = x_dims[axis] + xf = fftfreq(N) / dt + x_fft = fft(x, axis=axis) + xFFT = 1.0 / N * np.abs(x_fft) + + # move the values around so frequencies is from negative to positive + xf = fftshift(xf) + xFFT = fftshift(xFFT, axes=(axis,)) + + if dB: + xFFT = 20 * np.log10(xFFT) + + return xf, xFFT + + +def omega_to_f(omega: np.ndarray, fs: float) -> np.ndarray: + """ + Convert normalized frequency in radians/sample to frequency in Hz + """ + return fs * omega / (2 * np.pi) + + +def f_to_omega(f: np.ndarray, fs: float) -> np.ndarray: + """ + Convert frequency in Hz to normalized frequncy in radians/sample + """ + return f / fs * 2 * np.pi + + +def filter_impulse_response( + sos_or_fir_coef: np.ndarray, + N: int = 2048, + fs: float = None, + sos: bool = True, +) -> Tuple[np.ndarray, Optional[np.ndarray]]: + """ + Given a filter specification in second order sections (sos), + return the impulse response. + + Inputs: + - sos_or_fir_coef: np.ndarray, second order section of iir filter or fir_coef of a + FIR filter. + - N: int, number of samples to calculate for the impulse response + - fs: float, sampling rate in Hz. If not None, will return the time in seconds + corresponding to the samples. + - sos: bool. If true, assume `sos_or_fir_coef` is sos, otherwise as fir_coef + + Output: + - response: np.ndarray, impulse response values. By default, the time unit + is in samples. + - time: np.ndarray, if fs is not None, return the time in seconds. + """ + if sos: + response = sosfilt(sos_or_fir_coef, unit_impulse(N)) + else: + response = lfilter(b=sos_or_fir_coef, a=1, x=unit_impulse(N)) + if fs is not None: + time = np.arange(N) / fs + return response, time + else: + return response + + +def filter_freq_response( + sos: np.ndarray, N: int = 2048, fs: float = None +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Given filter specification in second order sections (sos), + return the frequency response by taking FFT of the impulse + response. + + Inputs: + - sos: np.ndarray, second order section of filter + - N: int, number of samples to calculate for the impulse and frequency response + - fs: float, sampling rate in Hz. If not None, will return the frequency in Hz, + otherwise normalized frequency will be returned. + + Output: + - frequency: np.ndarray, frequency of the frequency response. If fs is None, + unit will be in radians/sample (ranging from 0 to np.pi), + otherwise will be in Hz (ranging from 0 to fs / 2). + - magnitude: np.ndarray, magnitude of filter + - phase: np.ndarray, phase repsonse of filter, unit in radians. + """ + impulse_response = filter_impulse_response(sos, N) + + # Get frequency response + omega = (fftfreq(N) * 2 * np.pi)[0 : N // 2] + freq_response = fft(impulse_response)[0 : N // 2] + magnitude = np.abs(freq_response) + phase = np.angle(freq_response) + + if fs is not None: + freq = omega_to_f(omega, fs) + return freq, magnitude, phase + else: + return omega, magnitude, phase + + +def filter_phase_delay( + sos: np.ndarray, N: int = 2048, fs: float = None +) -> Tuple[np.ndarray, np.ndarray]: + """ + Given filter spec in second order sections of an IIR filter, return + phase delay in samples, extracted from the phase response + + Note for FIR filters, phase delay is constant and equal to either + (D/2) or (D-1)/2, where D is the number of coefficients in the filter. + + Inputs: + - sos: np.ndarray, second order section of filter + - N: int, number of samples to calculate for the impulse and frequency response + - fs: float, sampling rate in Hz. If not None, will return the frequency in Hz, + otherwise normalized frequency will be returned. + + Output: + - frequency: np.ndarray, frequency of the frequency response. If fs is None, + unit will be in radians/sample (ranging from 0 to np.pi), + otherwise will be in Hz (ranging from 0 to fs / 2). + - phase_delay: np.ndarray, phase delay of filter as function of frequency, unit + is in samples. + """ + omega, _, phase = filter_freq_response(sos, N) + phase_delay = -phase / omega + if fs is not None: + freq = omega_to_f(omega, fs) + return freq, phase_delay + else: + return omega, phase_delay + + +def filter_group_delay( + sos_or_fir_coef: np.ndarray, + N: int = 2048, + fs: float = None, + sos: bool = True, +) -> Tuple[np.ndarray, np.ndarray]: + """ + Given filter spec in second order sections or (num, den) form, return group delay. + Uses method in [1], which is cited by `scipy.signal.group_delay` but incorrectly implemented. + + Inputs: + - sos_or_fir_coef: np.ndarray, second order section of iir filter or fir_coef of a + FIR filter. + - N: int, number of samples to calculate for the impulse and frequency response + - fs: float, sampling rate in Hz. If not None, will return the frequency in Hz, + otherwise normalized frequency will be returned. + - sos: bool. If true, assume `sos_or_fir_coef` is sos, otherwise as fir_coef + + Output: + - frequency: np.ndarray, frequency of the frequency response. If fs is None, + unit will be in radians/sample (ranging from 0 to np.pi), + otherwise will be in Hz (ranging from 0 to fs / 2). + - group_delay: np.ndarray, group delay of filter as function of frequency, unit + is in samples. + + [1] Richard G. Lyons, "Understanding Digital Signal Processing, 3rd edition", p. 830. + """ + + impulse_response = filter_impulse_response(sos_or_fir_coef, N, sos=sos) + k = np.arange(N) + fft_gd = np.real(fft(k * impulse_response) / fft(impulse_response))[0 : N // 2] + omega = (fftfreq(N) * 2 * np.pi)[0 : N // 2] + if fs is not None: + freq = omega_to_f(omega, fs)[0 : N // 2] + return freq, fft_gd + else: + return omega, fft_gd diff --git a/signal_processing/filter/tests/__init__.py b/signal_processing/filter/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/filter/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/filter/tests/common.py b/signal_processing/filter/tests/common.py new file mode 100644 index 000000000..b904f09a1 --- /dev/null +++ b/signal_processing/filter/tests/common.py @@ -0,0 +1,5 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +# Defaulting to a gower phantom recording to use for tests +SAMPLE_PACKAGE_NAME = "sample" diff --git a/signal_processing/filter/tests/test_detrend.py b/signal_processing/filter/tests/test_detrend.py new file mode 100644 index 000000000..32742aa9a --- /dev/null +++ b/signal_processing/filter/tests/test_detrend.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import unittest + +import numpy as np + +from ..detrend import detrend_and_offset + + +class DetrendTest(unittest.TestCase): + def test_detrend_and_offset_positive_trend(self): + # signal is sine wave of different frequency + t = np.arange(0, 1, 0.01) + signal = np.vstack([np.cos(2 * np.pi * 5 * t), np.cos(2 * np.pi * 10 * t)]).T + + # Add DC offset + signal_with_offset = signal + 1 + + # Add linear trend, positive trend + linear_trend = t.reshape((-1, 1)) + + # composite signal + composite_signal = signal_with_offset + linear_trend + + # No need to fix negative because positive trend + signal_detrend_and_offset = detrend_and_offset( + composite_signal, fix_negative=False + ) + + # close to only 1 decimal place because + # detrend accumulates error due to least-square fit + np.testing.assert_array_almost_equal( + signal_detrend_and_offset, + signal + np.mean(composite_signal, axis=0), + decimal=1, + ) + + def test_detrend_and_offset_negative_trend(self): + # signal is sine wave of different frequency + t = np.arange(0, 1, 0.01) + signal = np.vstack([np.cos(2 * np.pi * 5 * t), np.cos(2 * np.pi * 10 * t)]).T + + # Add DC offset + signal_with_offset = signal + 1 + + # Add linear trend, positive trend + linear_trend = -t.reshape((-1, 1)) + + # composite signal + composite_signal = signal_with_offset + linear_trend + + # No need to fix negative because positive trend + signal_detrend_and_offset = detrend_and_offset( + composite_signal, fix_negative=False + ) + + np.testing.assert_array_almost_equal( + signal_detrend_and_offset, + signal + np.mean(composite_signal, axis=0), + decimal=1, + ) + + # now fix negative values and make sure that's correct + signal_detrend_and_offset_fix_negative = detrend_and_offset( + composite_signal, fix_negative=True + ) + self.assertTrue(np.all(signal_detrend_and_offset_fix_negative >= 0)) diff --git a/signal_processing/filter/tests/test_fir_filters.py b/signal_processing/filter/tests/test_fir_filters.py new file mode 100644 index 000000000..aa0e60278 --- /dev/null +++ b/signal_processing/filter/tests/test_fir_filters.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import unittest + +import mne +import numpy as np +from ..fir_filters import fir_filter + + +class FiniteImpulseResponseFilterTest(unittest.TestCase): + def setUp(self): + + import scipy + + print(scipy.__version__) + + # ===== 1) Preparing data (for lowpass) ===== + # time length + t_len = 50 + # sampling frequency + sample_freq = 10 + x = np.linspace(0, t_len, t_len * sample_freq, endpoint=False) + # making sure this has high power before filtering + signal_4Hz = 2 * np.sin(2 * np.pi * 4 * x) + signal_1Hz = np.sin(2 * np.pi * 1 * x) + combined = signal_4Hz + signal_1Hz + + # before filtering + power, freqs = mne.time_frequency.psd_array_multitaper(combined, sample_freq) + power_log10 = np.log10(power) + ind_of_peak = np.unravel_index( + np.argmax(power_log10, axis=None), power_log10.shape + )[0] + # make sure 4 Hz has higher power + self.assertEqual(freqs[ind_of_peak], 4.0) + + # Things needed for testing later + self.data = combined # data is [n_samples, ] + self.filt_order = 50 + self.sample_freq = sample_freq + + def check_peak_freq(self, data, cutoff, fs, btype, order, axis, peak_freq): + """Helper function to check that the filtered signal has the correct + peak-frequncies. + """ + filtered_data = fir_filter( + data=data, cutoff=cutoff, fs=fs, btype=btype, order=order, axis=axis + ) + + # psd_aray_multitaper requires the inputs to be of dimension: + # [..., n_times] + # axis is suppoed to be n_times + filtered_data_reshape = np.moveaxis(filtered_data, axis, -1) + power, freqs = mne.time_frequency.psd_array_multitaper( + filtered_data_reshape, fs + ) + power_log10 = np.log10(power) + ind_of_peak = np.argmax(power_log10, axis=-1) + self.assertEqual(np.all(freqs[ind_of_peak] == peak_freq), True) + + def test_fir_filter_1D(self): + """Test a simple FIR filter, input is one-dimensional + + 1) Create dummy sine waves with different frequencies + with one sine wave having higher amplitude = power + 2) Initial peak in PSD should show the frequency as dominant power + 3) Filter out using the filter + 4) This time the dominant power should be the other frequency + """ + + # ===== 2) Test lowpass ===== + # run filter lowpass + cutoff = 2 # Hz -- peak should be 1Hz + self.check_peak_freq( + data=self.data, + cutoff=cutoff, + fs=self.sample_freq, + btype=True, + order=self.filt_order, + axis=-1, + peak_freq=np.array([1.0]), + ) + + def test_fir_filter_nD(self): + """Test a simple lowpass FIR filter, input is multi-dimensional""" + cutoff = 2 # Hz + combined = np.vstack((self.data, self.data)) # dim is [2, n_samples] + self.check_peak_freq( + data=combined, + cutoff=cutoff, + fs=self.sample_freq, + btype=True, + order=self.filt_order, + axis=1, + peak_freq=np.array([1.0, 1.0]), + ) + + def test_fir_filter_axis(self): + """Test lowpass FIR filter works even on nx1 or 1xn data + + Creating the correct initial condition shape can be tricky + """ + cutoff = 2 + + # Check column vector works + col_data = self.data.reshape([-1, 1]) + print("test_fir_filter_axis: checking column vector input") + self.check_peak_freq( + data=col_data, + cutoff=cutoff, + fs=self.sample_freq, + btype=True, + order=self.filt_order, + axis=0, + peak_freq=np.array([1.0]), + ) + + # Check row vector works + row_data = self.data.reshape([1, -1]) + print("test_fir_filter_axis: checking row vector input") + self.check_peak_freq( + data=row_data, + cutoff=cutoff, + fs=self.sample_freq, + btype=True, + order=self.filt_order, + axis=1, + peak_freq=np.array([1.0]), + ) + + def test_fir_filter_linearPhase(self): + """Test lowpass FIR filter produces linear phase delay + + 1) Generate sample waveform + 2) Lowpass with different filter order + 3) Check that the different filtered signals are delayed by roughly + order/2 samples. This is done by looking through multiple periods + and find where the peaks are, and compare the sample number. + + Use different signals here to chose nice numbers to check + """ + + fs = 50 + duration = 1.0 + t = np.arange(0, duration, 1.0 / fs) + sin_10Hz = np.sin(2 * np.pi * 10 * t).reshape([-1, 1]) # col vector + cutoff = 20 # 20Hz lowpass, signal shouldn't be affected much + + # signal's peaks + nPeaks = 8 + signal_peaks = [] + for cur_order in [2, 4, 6, 8]: + y_LPF = fir_filter( + sin_10Hz, cutoff=cutoff, fs=fs, btype=True, order=cur_order, axis=0 + ) + y_LPF_peaks = [] + for i in range(nPeaks): + # find peaks -- period every 5 samples + idx_start = i * 5 + idx_end = (i + 1) * 5 + y_LPF_peaks.append(np.argmax(y_LPF[idx_start:idx_end])) + if len(signal_peaks) < nPeaks: + signal_peaks.append(np.argmax(sin_10Hz[idx_start:idx_end])) + + mean_delay = np.mean(np.array(y_LPF_peaks) - np.array(signal_peaks)) + # if mean_delay is negative, that means the first peak of the + # filtered signal is now closer to the second peak of the original + if mean_delay < 0: + print("mean_delay=" + str(mean_delay) + " adjust by period") + mean_delay = 5 + mean_delay + self.assertEqual(np.rint(mean_delay), cur_order / 2) diff --git a/signal_processing/filter/tests/test_iir_filters.py b/signal_processing/filter/tests/test_iir_filters.py new file mode 100644 index 000000000..7c86871b0 --- /dev/null +++ b/signal_processing/filter/tests/test_iir_filters.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import unittest +from functools import partial + +import numpy as np +from ..iir_filters import ( + butterworth_filter, + butterworth_sos_filter, + exponential_moving_average, + moving_average_convergence_divergence, + CausalButterworth, +) +from ..signal_transforms import do_fft +from ...synthetic_data.dummy_generator import dummy_physiological_noise +from scipy.signal import find_peaks + + +# Use FFT to check attenuation of bandpass +# Use peak finding to check for delay +# Use narrow passband to check difference between filtfilt and sosfiltfilt +class InfiniteImpulseResponseFilterTest(unittest.TestCase): + def setUp(self): + # Initializing sampling parameters and signals + + self.fs = 100 # sampling frequency in Hz + self.duration_short = 1.0 # signal duration is 1 second + self.t_short = np.arange(0, self.duration_short, 1.0 / self.fs) + + self.duration_long = 1000.0 + self.t_long = np.arange(0, self.duration_long, 1.0 / self.fs) + + # Nyquist is fs/2 = 50Hz, + # Create cosines of 5, 25, 45 Hz + # Data is of dimension [n_samples, n_channel] + # Nominal filtering data -- short duration + self.test_freq_short = [5, 25, 45] + self.data_short = np.hstack( + [ + np.cos(2 * np.pi * f * self.t_short).reshape([-1, 1]) + for f in self.test_freq_short + ] + ) + + # Use FFT for checking frequency attenuation + xf, xFFT = do_fft(self.data_short, dt=1.0 / self.fs, axis=0, dB=False) + self.freq_idx_short = np.hstack( + [np.argmin(np.abs(xf - i)) for i in self.test_freq_short] + ) + self.mag_short = np.diag(xFFT[self.freq_idx_short, :]) + print(f"magnitude for {self.test_freq_short} is {self.mag_short}") + # Use peak finding to check zero-phase delay + self.peaks_idx_short = [ + find_peaks(self.data_short[:, i], height=0.5)[0] + for i in range(len(self.test_freq_short)) + ] + + # Create cosines of [0.01, 0.05, 0.1] Hz for testing filter stability + # Low frequency, long duration + self.test_freq_long = [0.01, 0.05, 0.1] + self.data_long = np.hstack( + [ + np.cos(2 * np.pi * f * self.t_long).reshape([-1, 1]) + for f in self.test_freq_long + ] + ) + + # Use FFT for checking frequency attenuation + xf, xFFT = do_fft(self.data_long, dt=1.0 / self.fs, axis=0, dB=False) + self.freq_idx_long = np.hstack( + [np.argmin(np.abs(xf - i)) for i in self.test_freq_long] + ) + self.mag_long = np.diag(xFFT[self.freq_idx_long, :]) + + # Use peak finding to check zero-phase delay + self.peaks_idx_long = [ + find_peaks(self.data_long[:, i], height=0.5)[0] + for i in range(len(self.test_freq_long)) + ] + + # Alias for the functions to test + self.do_lfilter = partial(butterworth_filter, noDelay=False) + self.do_filtfilt = partial(butterworth_filter, noDelay=True) + self.do_sosfilt = partial(butterworth_sos_filter, noDelay=False) + self.do_sosfiltfilt = partial(butterworth_sos_filter, noDelay=True) + + self.filter_funcs = [ + (self.do_lfilter, "lfilter"), + (self.do_filtfilt, "filtfilt"), + (self.do_sosfilt, "sosfilt"), + (self.do_sosfiltfilt, "sosfiltfilt"), + ] + + def check_attenuation( + self, filtered_data, ref_val, truth, doPrint=False, checkFor=True + ): + """Helper function to check if the frequencies are attenuated. + + Args: + --- + filterd_data: 2D ndarray of filtered data. Assumed to be + [n_samples, n_channel] + ref_val: str, 'short' or 'long', to indicate which data set to + check against + truth: length n_freq list of booleans indicating whether + each frequency should be attenuated. + doPrint: boolean -- true to print out the Vout/Vin ratio. + checkFor: boolean -- check that the given truth values match or not + """ + + # Use FFT to check frequency of the filtered data + xf, xFFT = do_fft(filtered_data, dt=1.0 / self.fs, axis=0, dB=False) + if ref_val == "short": + mag_new = np.diag(xFFT[self.freq_idx_short, :]) + ratio = mag_new / self.mag_short + elif ref_val == "long": + mag_new = np.diag(xFFT[self.freq_idx_long, :]) + ratio = mag_new / self.mag_long + if doPrint: + print("Vout/Vin: ratio") + + answer = ratio <= np.sqrt(2) / 2 # -3dB attenuation as threshold + self.assertEqual(np.all(answer == truth), checkFor) + + def test_butterworth_filter_shape_and_attenuation(self): + """Test butter worth filter's shape and attenuation + + 1) Filter the signals using different filters: + lowpass, highpass, and bandpass + 2) Check output shape is correct + 3) Check the correct frequencies are attenuated + + To check frequency attenuation, check that the filtered signals' + max value is at least 10% reduced compared from before. This attenuation + is dependent on the cutoff frequency, and the test-frequencies are + spaced far apart to detect this difference. + + This method only works when `linearPhase=False` because `filtfilt` will + try to match the boundary conditions which we don't want. + """ + + print(f"Data shape = {self.data_short.shape}") + # Now applying filter + # 1) highpass (cutoff 7Hz, 4th order, lfilter) + y_HPF = self.do_lfilter( + self.data_short, cutoff=7, fs=self.fs, btype="high", order=4, axis=0 + ) + print(f"tf_filter: Data shape after highpass filter = {y_HPF.shape}") + self.assertEqual(y_HPF.shape, self.data_short.shape) + + # high pass filtering at 7Hz, that means 5Hz are attenuated + # are unaffected + truth = [True, False, False] + self.check_attenuation(y_HPF, "short", truth) + + y_HPF = self.do_sosfilt( + self.data_short, cutoff=7, fs=self.fs, btype="high", order=4, axis=0 + ) + print(f"sos_filter: Data shape after highpass filter = {y_HPF.shape}") + self.assertEqual(y_HPF.shape, self.data_short.shape) + self.check_attenuation(y_HPF, "short", truth) + + # 2) bandpass (cutoff 7 - 40 Hz) + y_BPF = self.do_lfilter( + self.data_short, + cutoff=[7, 40], + fs=self.fs, + btype="pass", + order=4, + axis=0, + noDelay=False, + ) + print(f"tf_filter: Data shape after bandpass filter = {y_BPF.shape}") + self.assertEqual(y_BPF.shape, self.data_short.shape) + + # band pass filtering at 7-40 Hz, that means 5Hz and 45Hz are attenuated + truth = [True, False, True] + self.check_attenuation(y_BPF, "short", truth) + + y_BPF = self.do_sosfilt( + self.data_short, cutoff=[7, 40], fs=self.fs, btype="pass", order=4, axis=0 + ) + print(f"sos_filter: Data shape after bandpass filter = {y_BPF.shape}") + self.assertEqual(y_BPF.shape, self.data_short.shape) + self.check_attenuation(y_BPF, "short", truth) + + # 3) LPF (cutoff 40Hz) + y_LPF = self.do_lfilter( + self.data_short, cutoff=40, fs=self.fs, btype="low", order=4, axis=0 + ) + print(f"tf_filter: Data shape after lowpass filter = {y_LPF.shape}") + self.assertEqual(y_LPF.shape, self.data_short.shape) + + # low pass filtering at 40 Hz, that means 45Hz is attenuated + truth = [False, False, True] + self.check_attenuation(y_LPF, "short", truth) + + y_LPF = self.do_sosfilt( + self.data_short, cutoff=40, fs=self.fs, btype="low", order=4, axis=0 + ) + print(f"sos_filter: Data shape after lowpass filter = {y_LPF.shape}") + self.assertEqual(y_LPF.shape, self.data_short.shape) + self.check_attenuation(y_LPF, "short", truth) + + def test_causal_butterworth(self): + """Causal butterworth filter test""" + # ===== 1) high pass ===== + cb = CausalButterworth( + first_time_sample=self.data_long[0, :], + cutoff=0.03, + sample_freq=self.fs, + btype="high", + ) + results = [] + for i in range(self.data_long.shape[0]): + results.append(cb.process_sample(sample=self.data_long[i, :])) + y_HPF = np.vstack(results) + + # Check dimension + self.assertEqual(np.all(self.data_long), np.all(y_HPF)) + + # Check stability + self.assertTrue(np.sum(np.isnan(y_HPF)) == 0) + + # high pass filtering at 0.03Hz, that means 0.01Hz are attenuated + # are unaffected + truth = [True, False, False] + self.check_attenuation(y_HPF, "long", truth) + + # ===== 2) band pass ===== + cb = CausalButterworth( + first_time_sample=self.data_long[0, :], + cutoff=[0.02, 0.08], + sample_freq=self.fs, + btype="pass", + ) + results = [] + for i in range(self.data_long.shape[0]): + results.append(cb.process_sample(sample=self.data_long[i, :])) + y_BPF = np.vstack(results) + + self.assertEqual(y_BPF.shape, self.data_long.shape) + + # Check stability + self.assertTrue(np.sum(np.isnan(y_BPF)) == 0) + + # band pass filtering at 0.02-0.08 Hz, that means 0.01Hz and 0.1Hz are attenuated + truth = [True, False, True] + self.check_attenuation(y_BPF, "long", truth) + + # ===== 3) low pass ===== + cb = CausalButterworth( + first_time_sample=self.data_long[0, :], + cutoff=0.08, + sample_freq=self.fs, + btype="low", + ) + results = [] + for i in range(self.data_long.shape[0]): + results.append(cb.process_sample(sample=self.data_long[i, :])) + y_LPF = np.vstack(results) + + self.assertEqual(y_LPF.shape, self.data_long.shape) + + # Check stability + self.assertTrue(np.sum(np.isnan(y_LPF)) == 0) + + # low pass filtering at 0.08 Hz, that means 0.1Hz is attenuated + truth = [False, False, True] + self.check_attenuation(y_LPF, "long", truth) + + def test_butterworth_filter_axis(self): + """Make sure when changing axis option, the results are different, + if using the wrong option the results should not be right. + """ + + # Use the bandpass example, but filter channel-wise + y_BPF = self.do_lfilter( + self.data_short, cutoff=[7, 40], fs=self.fs, btype="pass", order=4, axis=1 + ) + print(f"Data shape after bandpass filter(axis=1) = {y_BPF.shape}") + self.assertEqual(y_BPF.shape, self.data_short.shape) + + # band pass filtering at 7-40 Hz, that means 5Hz and 45Hz are attenuated + truth = [True, False, True] + # Make sure the result is not correct + self.check_attenuation(y_BPF, "short", truth, checkFor=False) + + # Do it correctly! + y_BPF = self.do_lfilter( + self.data_short, cutoff=[7, 40], fs=self.fs, btype="pass", order=4, axis=0 + ) + print(f"Data shape after bandpass filter(axis=0) = {y_BPF.shape}") + self.assertEqual(y_BPF.shape, self.data_short.shape) + self.check_attenuation(y_BPF, "short", truth, checkFor=True) + + # Check stability + def test_butterworth_filter_stability(self): + """Test to make sure that sos filters are stable even for high + filter order and narrow passband, while tf-filters are not. + (If the second part of the test break, then that means scipy + has changed..?) + """ + + # Test 1, data_short for testing + # BPF[10, 30], 4th order + # All filters should pass + cutoff = np.array([10, 30]) + filter_order = 4 + for (func, func_name) in self.filter_funcs: + print(f"Order {filter_order}, BPF({cutoff}), {func_name}") + out = func( + data=self.data_short, + cutoff=cutoff, + fs=self.fs, + btype="pass", + axis=0, + order=filter_order, + ) + self.check_attenuation(out, "short", [True, False, True]) + + # Test 2, data_long for testing + # BPF[0.02, 0.08], 4th order + # Only the sos filters should pass + filter_order = 4 + cutoff = np.array([0.02, 0.08]) + for (func, func_name) in self.filter_funcs[0:2]: + print(f"Order {filter_order}, BPF({cutoff}), {func_name}") + out = func( + data=self.data_long, + cutoff=cutoff, + fs=self.fs, + btype="pass", + axis=0, + order=filter_order, + ) + self.check_attenuation(out, "long", [True, False, True], checkFor=False) + + for (func, func_name) in self.filter_funcs[2:]: + print(f"Order {filter_order}, BPF({cutoff}), {func_name}") + out = func( + data=self.data_long, + cutoff=cutoff, + fs=self.fs, + btype="pass", + axis=0, + order=filter_order, + ) + self.check_attenuation(out, "long", [True, False, True], checkFor=True) + + # Check noDelay + def test_butterworth_filter_noDelay(self): + """Test to make sure that the noDelay option is working, + really just means that we are using either `filtfilt` vs. `lfilter`, + or `sosfilt` vs. `sosfiltfilt` here. + + Since the testing signals are cosine, y(0)=1 + + Best way to check this is to see if the filtered signals' peaks + line up in time with that of the original signal's. + """ + + filter_order = 4 + cutoff = np.array([10, 30]) + + # Bandpass on data_short , check the peaks for the 25Hz signal + # For lfilter and sos_filt + for (func, func_name) in [self.filter_funcs[0], self.filter_funcs[2]]: + print(f"Order {filter_order}, BPF({cutoff}), {func_name} peak-check") + out = func( + data=self.data_short, + cutoff=cutoff, + fs=self.fs, + btype="pass", + axis=0, + order=filter_order, + ) + peaks_new, _ = find_peaks(out[:, 1], height=0.5) + # Should have the same number of peaks + self.assertEqual(len(peaks_new), len(self.peaks_idx_short[1])) + # The peaks should be spaced more than at least 1 samples apart + self.assertTrue(np.all(np.abs(peaks_new - self.peaks_idx_short[1]) >= 1)) + + # For filtfilt and sos_filtfilt + for (func, func_name) in [self.filter_funcs[1], self.filter_funcs[3]]: + print(f"Order {filter_order}, BPF({cutoff}), {func_name} peak-check") + out = func( + data=self.data_short, + cutoff=cutoff, + fs=self.fs, + btype="pass", + axis=0, + order=filter_order, + ) + peaks_new, _ = find_peaks(out[:, 1], height=0.5) + # Should have the same number of peaks + self.assertEqual(len(peaks_new), len(self.peaks_idx_short[1])) + # The peaks should not be spaced more than at least 1 samples apart + self.assertTrue(np.all(np.abs(peaks_new - self.peaks_idx_short[1]) < 1)) + + def test_macd_filters(self): + # Defining parameters to generate synthetic data + duration = 200 # seconds + sample_freq = 5 # Hz + + # ===== Create simulated data ===== + cardiac = dummy_physiological_noise( + amplitude=0.3, + sample_rate=sample_freq, + interest_freq=1, + phase=np.pi / 4, + duration=duration, + ) + slow_drift = dummy_physiological_noise( + amplitude=0.1, + sample_rate=sample_freq, + interest_freq=0.01, + phase=0, + duration=duration, + ) + interest_freq = 0.1 + important = dummy_physiological_noise( + amplitude=0.3, + sample_rate=sample_freq, + interest_freq=interest_freq, + phase=0, + duration=duration, + ) + signal = cardiac + slow_drift + important + + # ===== Do filtering (Causal) ===== + macd_results = [0] + ema_on_macd = [0] + for i in range(1, len(signal)): + temp1 = moving_average_convergence_divergence( + x=signal[i], + y_prev=macd_results[i - 1], + N_short=sample_freq * 5, + N_long=sample_freq * 15, + ) + macd_results.append(temp1) + temp2 = exponential_moving_average( + x=macd_results[i], y_prev=ema_on_macd[i - 1], N=10 + ) + ema_on_macd.append(temp2) + ema_on_macd_np = np.array(ema_on_macd) + + # ===== Apply FFT ===== + xf_ema_macd, xFFT_ema_macd = do_fft( + ema_on_macd_np, dt=1.0 / sample_freq, axis=0, dB=False + ) + + # ===== Get the peak, make sure it's the same frequency of interest ===== + peaks_ema_macd = find_peaks(xFFT_ema_macd, distance=duration * sample_freq / 2) + self.assertAlmostEqual(*xf_ema_macd[peaks_ema_macd[0]], interest_freq) diff --git a/signal_processing/filter/tests/test_signal_transforms.py b/signal_processing/filter/tests/test_signal_transforms.py new file mode 100644 index 000000000..2db9734b6 --- /dev/null +++ b/signal_processing/filter/tests/test_signal_transforms.py @@ -0,0 +1,226 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from __future__ import absolute_import, division, print_function, unicode_literals + +import unittest + +import numpy as np +import scipy +from ..fir_filters import symmetric_fir_group_delay +from ..iir_filters import butter_group_delay, butter_sos, CausalButterworth +from ..signal_transforms import ( + do_fft, + filter_freq_response, + filter_group_delay, + filter_impulse_response, + filter_phase_delay, +) + + +class SignalTransformsTest(unittest.TestCase): + def setUp(self): + self.dt = 1 / 100 + self.t = np.arange(0, 10, self.dt) # 100Hz + + # FFT magnitude for the different freq + self.mag_10Hz = 5 + self.mag_15Hz = 2.5 + self.mag_DC = 1 + # [n_samples, ] + self.x_1D = ( + 2 * self.mag_10Hz * np.cos(2 * np.pi * 10 * self.t) + + 2 * self.mag_15Hz * np.cos(2 * np.pi * 15 * self.t) + + self.mag_DC + ) + + # [n_samples, 2] + self.x_2D = np.hstack((self.x_1D.reshape([-1, 1]), self.x_1D.reshape([-1, 1]))) + + # [2, n_samples, 2] + self.x_3D = np.concatenate( + (np.expand_dims(self.x_2D, axis=0), np.expand_dims(self.x_2D, axis=0)), + axis=0, + ) + # second 'page' has no DC + self.x_3D[1, :, :] = self.x_3D[1, :, :] - 1 + + # Basic 1D fft + def test_FFT_1D(self): + xf, xFFT = do_fft(self.x_1D, self.dt) + + # Check 10Hz freq component + self.assertAlmostEqual(xFFT[np.argwhere(xf == 10)][0][0], self.mag_10Hz, 3) + self.assertAlmostEqual(xFFT[np.argwhere(xf == -10)][0][0], self.mag_10Hz, 3) + + # Check 15Hz freq component + self.assertAlmostEqual(xFFT[np.argwhere(xf == 15)][0][0], self.mag_15Hz, 3) + self.assertAlmostEqual(xFFT[np.argwhere(xf == -15)][0][0], self.mag_15Hz, 3) + + # Check DC freq component + self.assertAlmostEqual(xFFT[np.argwhere(xf == 0)][0][0], self.mag_DC, 3) + + # Use dB values + xf, xFFT_dB = do_fft(self.x_1D, self.dt, dB=True) + # Check 10Hz freq component + val = 20 * np.log10(self.mag_10Hz) + self.assertAlmostEqual(xFFT_dB[np.argwhere(xf == 10)][0][0], val, 3) + self.assertAlmostEqual(xFFT_dB[np.argwhere(xf == -10)][0][0], val, 3) + + # Check 15Hz freq component + val = 20 * np.log10(self.mag_15Hz) + self.assertAlmostEqual(xFFT_dB[np.argwhere(xf == 15)][0][0], val, 3) + self.assertAlmostEqual(xFFT_dB[np.argwhere(xf == -15)][0][0], val, 3) + + # Check DC freq component + val = 20 * np.log10(self.mag_DC) + self.assertAlmostEqual(xFFT_dB[np.argwhere(xf == 0)][0][0], val, 3) + + # 2D FFT + def test_FFT_2D(self): + xf, xFFT = do_fft(self.x_2D, self.dt, axis=0) + + # Check 10Hz freq component + self.assertEqual( + np.all(xFFT[np.argwhere(xf == 10)[0][0], :] - self.mag_10Hz < 1e-3), True + ) + self.assertEqual( + np.all(xFFT[np.argwhere(xf == -10)[0][0], :] - self.mag_10Hz < 1e-3), True + ) + + # Check 15Hz freq component + self.assertEqual( + np.all(xFFT[np.argwhere(xf == 15)[0][0], :] - self.mag_15Hz < 1e-3), True + ) + self.assertEqual( + np.all(xFFT[np.argwhere(xf == -15)[0][0], :] - self.mag_15Hz < 1e-3), True + ) + + # Check DC freq component + self.assertEqual( + np.all(xFFT[np.argwhere(xf == 0)[0][0], :] - self.mag_DC < 1e-3), True + ) + + # 3D FFT + def test_FFT_3D(self): + xf, xFFT = do_fft(self.x_3D, self.dt, axis=1) + + # Check 10Hz freq component + for page in range(1): + for f in [10, -10]: + idx = np.argwhere(xf == f)[0][0] + self.assertEqual( + np.all(xFFT[page, idx, :] - self.mag_10Hz < 1e-3), True + ) + + # Check 15Hz freq component + for page in range(1): + for f in [15, -15]: + idx = np.argwhere(xf == f)[0][0] + self.assertEqual( + np.all(xFFT[page, idx, :] - self.mag_15Hz < 1e-3), True + ) + + # Check DC freq component + self.assertEqual( + np.all(xFFT[0, np.argwhere(xf == 0)[0][0], :] - self.mag_DC < 1e-3), True + ) + # Second page has no DC component + self.assertEqual( + np.all(xFFT[1, np.argwhere(xf == 0)[0][0], :] - 0 < 1e-3), True + ) + + def test_filter_impulse_response(self): + """ + Get impulse response of 2048 points, for 5th-order IIR filter + with bandwidth [0.01, 0.1]Hz, sampling frequency of 10Hz + """ + N = 2048 + fs = 10.0 + bandwidth = (0.01, 0.1) + + order = 5 + sos_iir = butter_sos(bandwidth, fs=fs, btype="bandpass", order=order) + iir_impulse_response, time = filter_impulse_response(sos_iir, N, fs, sos=True) + + self.assertTrue( + np.abs(iir_impulse_response[0]) < 1e-6 + ) # impulse response starts at 0 + np.testing.assert_array_almost_equal( + iir_impulse_response, + scipy.signal.sosfilt(sos_iir, scipy.signal.unit_impulse(N)), + ) + np.testing.assert_array_almost_equal(time, np.arange(0, 2048 / fs, 1 / fs)) + + """ + Get impulse response of 2048 points, for a 25-tap FIR filter with + bandwidth [0.01, 0.1]Hz, sampling frequency of 10Hz + """ + n_coefs = 25 + fir_coefs = scipy.signal.firwin(n_coefs, bandwidth, pass_zero=False, fs=fs) + fir_impulse_response, time = filter_impulse_response( + fir_coefs, N, fs, sos=False + ) + self.assertTrue( + fir_impulse_response[-1] == 0 + ) # FIR impulse response is finite! + np.testing.assert_array_almost_equal( + fir_impulse_response, + scipy.signal.lfilter(b=fir_coefs, a=1, x=scipy.signal.unit_impulse(N)), + ) + + def test_filter_freq_response(self): + """ + Get frequency response of a 5th-order IIR filter + with bandwidth [0.01, 0.1]Hz, sampling frequency of 10Hz + """ + N = 2048 + fs = 10.0 + bandwidth = (0.01, 0.1) + order = 5 + sos_iir = butter_sos(bandwidth, fs=fs, btype="bandpass", order=order) + + # check freq response is roughly flat between 0.01 and 0.06Hz + freq, magnitude, phase = filter_freq_response(sos_iir, N, fs) + np.testing.assert_array_almost_equal( + magnitude[(freq >= 0.01) & (freq <= 0.06)], 1, decimal=2 + ) + # check phase response at 0 starts at -pi + # sosfreqz has phase response starting at 0 + self.assertTrue(np.abs(phase[0] - -np.pi) < 1e-3) + + def test_filter_phase_delay_iir(self): + """ + Check phase delay at near center band for 5th order + butterworth bandpass filter, with bandwidth (0.01, 0.1)Hz, + sampling frequency of 10Hz. + + The phase delay at 0.05Hz should be about 33 samples. + """ + N = 2048 + fs = 10.0 + bandwidth = (0.01, 0.1) + test_freq = 0.05 + order = 5 + sos_iir = butter_sos(bandwidth, fs=fs, btype="bandpass", order=order) + freq, phase_delay = filter_phase_delay(sos_iir, N, fs) + self.assertTrue( + np.abs(phase_delay[np.argmin(np.abs(freq - test_freq))] - 33) < 1 + ) + + def test_filter_group_delay_fir(self): + """ + Group delay calculated via impulse response and + symmetric_fir_group_delay should match for + over 98% of the frequencies (mismatch due to numerical + errors) + """ + n_coefs = 25 + bandwidth = (0.01, 0.1) + fs = 10 + fir_coefs = scipy.signal.firwin(n_coefs, bandwidth, pass_zero=False, fs=fs) + symmetric_gd = symmetric_fir_group_delay(fir_coefs) + _, impulse_gd = filter_group_delay(fir_coefs, fs=fs, sos=False) + self.assertTrue( + np.sum(np.abs(impulse_gd - symmetric_gd) < 1) / len(impulse_gd) >= 0.98 + ) diff --git a/signal_processing/messages/generic_signal_sample.py b/signal_processing/messages/generic_signal_sample.py new file mode 100644 index 000000000..9c9cd1f60 --- /dev/null +++ b/signal_processing/messages/generic_signal_sample.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Optional + +import labgraph as lg +import numpy as np + + +class SignalSampleMessage(lg.TimestampedMessage): + # The actual shape of this depends on the configuration + sample: np.ndarray + """ + This field is useful in filtering and signal processing nodes. + - `timestamp` (inherited from lg.TimestampedMessage) represents + time when the message is created, this is useful probably + for labgraph message syncing purposes. + - `sample_timestamp` here represents the absolute timestamp for which + the current message corresponds to (for causal filtering, this is + in the past compared to `__super__.timestamp. + The default value cannot be nan because writing None-type does not + have dtype and cannot be written to hdf5. + """ + sample_timestamp: float = np.nan + + +class BooleanSampleMessage(lg.TimestampedMessage): + sample: np.ndarray + sample_timestamp: float = np.nan diff --git a/signal_processing/nodes/__init__.py b/signal_processing/nodes/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/nodes/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/nodes/faucet/__init__.py b/signal_processing/nodes/faucet/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/nodes/faucet/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/nodes/faucet/iir_filter_node.py b/signal_processing/nodes/faucet/iir_filter_node.py new file mode 100644 index 000000000..6c0dccb11 --- /dev/null +++ b/signal_processing/nodes/faucet/iir_filter_node.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" Node for a IIR filter for causal Faucet """ + +import asyncio +import logging +import time +from dataclasses import field +from typing import List, Optional, Union + +import labgraph as lg +import numpy as np +from ...messages.generic_signal_sample import SignalSampleMessage +from ...filter.iir_filters import CausalButterworth + + +# Define states and configs +class CausalButterFilterNodeConfig(lg.Config): + """Sampling frequency in Hz + + sample_rate: sampling frequency in Hz + highpass_cutoff: highpass cutoff in Hz + highpass_order: order of highpass filter + lowpass_cutoffs: List of lowpass filter cutoff frequencies. + length of this list is the total order of lowpass + + Online estimate of sampling frequency: + + If sample_rate was not given when node is initialized, + the filter node will fill a buffer of length `initial_buffer_len` + messages, and derive the sample_rate from their timestamps. This + assumes uniform sampling. Default is 10 samples, in which case the + output will be non-null at the second sample. + """ + + sample_rate: float = -1.0 + initial_buffer_len: int = 10 + + highpass_cutoff: Optional[float] = 0.01 + highpass_order: Optional[int] = 5 + lowpass_cutoffs: List = field(default_factory=list) + + """ + Option to invert the input sample. + Faucet expects the input to bandpass is -log(power_measurement). + Node outputs log(power_measurements). If bandpass is the first + filtering stage, the default `invert_input=True` would take care + of the inversion. + + If used for something else (e.g. IMU, etc), set this appropriately! + """ + invert_input: bool = True + pass_through: bool = False + + def __post_init__(self): + super().__post_init__() + if self.sample_rate < 0 and self.initial_buffer_len <= 0: + raise ValueError( + "initial_buffer_len needs to be greater than 0 when sample rate unspecified" + ) + + +class DefaultBandpassConfigGenerator: + def getConfig(): + return CausalButterFilterNodeConfig( + sample_rate=-1.0, # estimate sample rate + initial_buffer_len=10, + highpass_cutoff=0.01, + highpass_order=5, + lowpass_cutoffs=[0.1, 0.1, 0.1, 0.1, 0.1], + invert_input=True, + pass_through=False, + ) + + +class CausalButterFilterState(lg.State): + initial_buffer: List[SignalSampleMessage] = field(default_factory=list) + filters: List[CausalButterworth] = field(default_factory=list) + sample_rate: float = -1.0 + + +class CausalButterFilterNode(lg.Node): + """ + If the input SignalSampleMessage has an empty + `sample_timestamp`, then it's assumed this is the + first casual filter of the signal processing chain + and follows directly after a data source. + + Otherwise, the sample rate estimation will be calculated + from the `sample_timestamp` field of the SignalSampleMessage. + + This is because in the second case, the sample rate is best + estimated from the timestamps of the raw messages directly + from the data source. + """ + + # Subscribed topics + INPUT = lg.Topic(SignalSampleMessage) + TERMINATION_TOPIC = lg.Topic(lg.TerminationMessage) + + # Published topics + OUTPUT = lg.Topic(SignalSampleMessage) + + state: CausalButterFilterState + config: CausalButterFilterNodeConfig + + def setup(self) -> None: + self._shutdown = asyncio.Event() + # Have to be assigned here because config cannot be modified in runtime + self.state.sample_rate = self.config.sample_rate + + @lg.subscriber(TERMINATION_TOPIC) + def terminate(self, message: lg.TerminationMessage) -> None: + self._shutdown.set() + raise lg.NormalTermination() + + def _init_filter_class(self, message: SignalSampleMessage) -> bool: + # Returns True if filter class is initialized + + if len(self.state.filters) > 0: + return True + + self.state.initial_buffer.append(message) + if self.state.sample_rate < 0: + if len(self.state.initial_buffer) <= self.config.initial_buffer_len: + return False + if not np.isnan(self.state.initial_buffer[0].sample_timestamp): + timestamps = np.array( + [message.sample_timestamp for message in self.state.initial_buffer] + ) + else: + timestamps = np.array( + [message.timestamp for message in self.state.initial_buffer] + ) + self.state.sample_rate = 1 / np.mean(np.diff(timestamps)) + logging.info(f"estimated sample rate = {self.state.sample_rate}Hz") + + self._init_and_run_filters() + return True + + def _init_and_run_filters(self): + # Initialize filters and run it for all but last sample in initial_buffer + if len(self.state.filters) != 0: + logging.warning( + "_init_filters called when self.state.filters is not empty, skipping!" + ) + return + # shape is [n_channel, ] + # initial state of each cascaded filter is calculated by taking + # the steady-state value of the previous filter in response to + # that previous filter's initial state! + initial_state = self.state.initial_buffer[0].sample + # initialize highpass filter + if ( + self.config.highpass_cutoff is not None + and self.config.highpass_order is not None + ): + self.state.filters.append( + CausalButterworth( + first_time_sample=initial_state, + cutoff=self.config.highpass_cutoff, + sample_freq=self.state.sample_rate, + btype="highpass", + order=self.config.highpass_order, + ) + ) + initial_state = self.state.filters[-1].process_sample(initial_state) + # initialize lowpass filters + if len(self.config.lowpass_cutoffs) > 0: + for cutoff in self.config.lowpass_cutoffs: + self.state.filters.append( + CausalButterworth( + first_time_sample=initial_state, + cutoff=cutoff, + sample_freq=self.state.sample_rate, + btype="lowpass", + order=1, + ) + ) + initial_state = self.state.filters[-1].process_sample(initial_state) + for buffered_message in self.state.initial_buffer[:-1]: + _ = self._process_sample(buffered_message.sample) + + def _process_sample(self, sample: np.ndarray) -> np.ndarray: + # Run process_sample through the entire cascade of filters + output = sample + for filt in self.state.filters: + output = filt.process_sample(output) + return output + + @lg.subscriber(INPUT) + @lg.publisher(OUTPUT) + async def run_filter(self, message: SignalSampleMessage) -> lg.AsyncPublisher: + """ + Assume message.sample is np.ndarray with dimension [n_channels, ] + """ + + if self.config.invert_input: + message = SignalSampleMessage(message.timestamp, -message.sample) + + if self.config.pass_through: + yield self.OUTPUT, message + return + + if not self._init_filter_class(message): + return + + filtered_sample = self._process_sample(message.sample) + """ + Return the filtered sample + filtered_sample now has to be reshaped to (channel, time) + because HDF5 file requires it that way + """ + sample_timestamp = ( + message.timestamp + if np.isnan(message.sample_timestamp) + else message.sample_timestamp + ) + yield self.OUTPUT, SignalSampleMessage( + timestamp=time.time(), + sample_timestamp=sample_timestamp, + sample=filtered_sample.reshape((-1, 1)), + ) diff --git a/signal_processing/nodes/faucet/logmean_node.py b/signal_processing/nodes/faucet/logmean_node.py new file mode 100644 index 000000000..e3fb60b6a --- /dev/null +++ b/signal_processing/nodes/faucet/logmean_node.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" +Node for performing logmean online +""" + +import time +from dataclasses import field +from typing import List, Optional + +import labgraph as lg +import numpy as np +from ...messages.generic_signal_sample import SignalSampleMessage + + +class LogMeanNodeConfig(lg.Config): + # When LogarithmNode is part of a bigger group, setting + # this to True provides an easy way to turn off + # this stage. + pass_through: bool = False + + +class LogMeanNodeState(lg.State): + # initialized to nan + last_nonzero_values: Optional[np.ndarray] = None + + +class LogmeanNode(lg.Node): + """ + The logic here follows closedly with that of `compute_log_all_samples`: + + Instead of taking the -log(x / mean(x)), we directly take the -log of a channel's + incoming signal value (assumed to be in units of Watt). + + If the incoming signal value is 0 or negative, this is assumed to be an anomaly, + either the sensor is bad (even noise has non-zero energy!) or turned off. + + In this case, we impute this value with the last non-zero signal value for this + channel. + + If the first value we receive for a channel is 0, then we return 0 for the + logmean result, and do not update the last nonzero-value for this channel. + """ + + # Subscribed topics + INPUT = lg.Topic(SignalSampleMessage) + + # Published topics + OUTPUT = lg.Topic(SignalSampleMessage) + + state: LogMeanNodeState + config: LogMeanNodeConfig + + @lg.subscriber(INPUT) + @lg.publisher(OUTPUT) + async def get_logmean(self, message: SignalSampleMessage) -> lg.AsyncPublisher: + if self.config.pass_through: + yield self.OUTPUT, message + else: + if self.state.last_nonzero_values is None: + self.state.last_nonzero_values = np.ones(message.sample.shape) * np.nan + + output = np.copy(message.sample) + + # Find 0 or negative channel values + is_less_eq_zero = output <= 0 + output[~is_less_eq_zero] = -np.log10(output[~is_less_eq_zero]) + # update the last_nonzero_values + self.state.last_nonzero_values[~is_less_eq_zero] = message.sample[ + ~is_less_eq_zero + ] + + # missing value imputation, no need to update state for these + can_impute = (~np.isnan(self.state.last_nonzero_values)) & is_less_eq_zero + output[can_impute] = -np.log10(self.state.last_nonzero_values[can_impute]) + + # deal with the non-imputable -- output will be 0, these are just bad channels! + cant_impute = np.isnan(self.state.last_nonzero_values) & is_less_eq_zero + output[cant_impute] = 0 + + yield self.OUTPUT, SignalSampleMessage( + timestamp=time.time(), + sample_timestamp=message.timestamp, + sample=output, + ) diff --git a/signal_processing/nodes/faucet/tests/__init__.py b/signal_processing/nodes/faucet/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/nodes/faucet/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/nodes/faucet/tests/test_logmean_node.py b/signal_processing/nodes/faucet/tests/test_logmean_node.py new file mode 100644 index 000000000..696cef164 --- /dev/null +++ b/signal_processing/nodes/faucet/tests/test_logmean_node.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import logging + +import labgraph as lg +import numpy as np +import pytest +from ..logmean_node import ( + LogmeanNode, + LogMeanNodeConfig, +) +from ....messages.generic_signal_sample import SignalSampleMessage + + +def build_test_signal( + n_time: int = 1000, + n_channels: int = 6, + power_scaler: float = 1e-10, +) -> np.ndarray: + if n_channels < 5: + logging.info("build_test_signal requires at least 5 channels, setting to 5") + n_channels = 5 + + signals = np.random.rand(n_time, n_channels) * power_scaler + # Set first channel to all 0 + signals[:, 0] = 0 + # Set second channel to all negative + signals[:, 1] = -signals[:, 1] + # Set third channel first sample to 0 + signals[0, 2] = 0 + # Set fourth channel first sample to negative + signals[0, 3] = -signals[0, 3] + # Set 10% of samples in signals[1:, 2:] to 0 or negative (5% and 5%) + indices = list(np.ndindex(signals[1:, 2:].shape)) # each index is offset by (1, 2) + np.random.shuffle(indices) # shuffle inplace + for (row, col) in indices[0 : len(indices) // 20]: + signals[row + 1, col + 2] = 0 + for (row, col) in indices[len(indices) // 20 : len(indices) // 10]: + signals[row + 1, col + 2] = -signals[row + 1, col + 2] + return signals + + +def get_correct_logmean_signal(signals: np.ndarray) -> np.ndarray: + output = np.copy(signals) + + for ch in range(signals.shape[1]): + sub_value = np.nan + for i in range(signals.shape[0]): + if signals[i, ch] > 0: + output[i, ch] = -np.log10(signals[i, ch]) + sub_value = signals[i, ch] + + elif signals[i, ch] <= 0: + output[i, ch] = 0 if np.isnan(sub_value) else -np.log10(sub_value) + + return output + + +def test_logmean_node_pass_through() -> None: + test_signals = build_test_signal() + timestamps = np.arange(len(test_signals)) * 0.1 + + test_harness = lg.NodeTestHarness(LogmeanNode) + messages = [ + SignalSampleMessage(timestamp=timestamps[i], sample=test_signals[i, :]) + for i in range(len(test_signals)) + ] + with test_harness.get_node(config=LogMeanNodeConfig(pass_through=True)) as node: + for i in range(0, len(messages)): + result = lg.run_async(node.get_logmean, args=(messages[i],)) + assert result is not None + np.testing.assert_array_equal(result[0][1].sample, test_signals[i, :]) + + +def test_logmean_node() -> None: + test_signals = build_test_signal() + correct_signals = get_correct_logmean_signal(test_signals) + timestamps = np.arange(len(test_signals)) * 0.1 + + test_harness = lg.NodeTestHarness(LogmeanNode) + messages = [ + SignalSampleMessage(timestamp=timestamps[i], sample=test_signals[i, :]) + for i in range(len(test_signals)) + ] + with test_harness.get_node(config=LogMeanNodeConfig(pass_through=False)) as node: + for i in range(0, len(messages)): + result = lg.run_async(node.get_logmean, args=(messages[i],)) + assert result is not None + # almost because log imprecision + np.testing.assert_array_almost_equal( + result[0][1].sample, correct_signals[i, :] + ) diff --git a/signal_processing/nodes/function_to_node.py b/signal_processing/nodes/function_to_node.py new file mode 100644 index 000000000..964c0a986 --- /dev/null +++ b/signal_processing/nodes/function_to_node.py @@ -0,0 +1,625 @@ +# Copyright 2004-present Facebook. All Rights Reserved. + +import asyncio +import inspect +from dataclasses import asdict, astuple, dataclass, field +from functools import wraps +from hashlib import sha1 +from inspect import Parameter, signature +from typing import Any, Callable, Dict, List, Optional, Set, Tuple + +import labgraph as lg +from toolz import curry +from ..utils.doc.docfill import fill_in_docstring + + +# The documentation for this function is very long; to make the function itself +# easier to read, the documention is moved here. +_argument_doc = fill_in_docstring( + { + "f": """f: Callable[[Any], Any] + The function to convert to a node. + """, + "base_cls": """base_cls: lg.Message = lg.Message + The class to inherit from for the ArgumentMessage and ReturnMessage. + All parameters inherited from this base class are passed through + directly to the output topic of the function node without alteration. + """, + "keyword_only_in_message": """keyword_only_in_message: bool = False, + Whether to include keyword only arguments in the message type. If + false, keyword only arguments can be passed only through the + `FunctionConfig` object; if true, the keyword arguments become fields + in the ArgumentMessage type. + """, + "default_keyword_in_message": """default_keyword_in_message: bool = False, + Whether to include arguments that have a default value in the + ArgumentMessage type. + """, + "deconstruct_dataclass_return_type": """deconstruct_dataclass_return_type: bool = True, + If a function returns a dataclass, setting this to true will make the + fields of the dataclass fields in the ReturnMessage class. + """, + "single_return_param_name": """single_return_param_name: str = "sample", + What to name the output message field parameter. If + `deconstruct_dataclass_return_type` is set to True, this parameter has + no effect. + """, + "argument_topic_name": """argument_topic_name: str = "INPUT", + Name of the input/argument topic in the FunctionNode. + """, + "return_topic_name": """return_topic_name: str = "OUTPUT", + Name of the output/return topic in the FunctionNode. + """, + "sleep_time": """sleep_time: float = 0.01, + If the function is a source (has no argument), how long should the + process wait before running the function again. + """, + "error": """error: Callable[[str], None] = lambda x: None, + The function to use to collect errors with. Multiple errors can be + captured during a function call. + """, + } +) + +############################################################################### +# Types # +############################################################################### + + +@dataclass +class FunctionToNode: + ArgumentMessage: Optional[lg.Message] + ReturnMessage: Optional[lg.Message] + FunctionNode: lg.Node + + def __iter__(self): + return iter(astuple(self)) + + +class FunctionConfig(lg.Config): + kwargs: Dict[str, Any] = field(default_factory=dict) + + +class _BlankMesage(lg.Message): + pass + + +############################################################################### +# Helper functions to determine function parameters # +############################################################################### + + +def is_keyword(arg): + return ( + arg.kind is Parameter.POSITIONAL_OR_KEYWORD + or arg.kind is Parameter.KEYWORD_ONLY + ) + + +def is_keyword_only(arg): + return arg.kind is Parameter.KEYWORD_ONLY + + +def is_nondefault_keyword(arg): + return is_keyword(arg) and arg.default is Parameter.empty + + +def is_default_keyword(arg): + return is_keyword(arg) and arg.default is not Parameter.empty + + +def positionally_passed(arg): + return (arg.kind is Parameter.POSITIONAL_ONLY) or ( + arg.kind is Parameter.POSITIONAL_OR_KEYWORD and arg.default is Parameter.empty + ) + + +def call_with_error(error_type): + """Collects a bunch of errors and returns them all once. + + Decorator that collects the errors in the decorated function so that the + user can see everything they need to fix at once. All errors are thrown + with the same error type. + + The decorated must have an `error` keyword parameter. The `error` parameter + is then ignored if the end user passes in that argument. + + Parameters + ---------- + error_type: type + The type of error to throw. For example, `ValueError`. + + Returns + ------- + Callable[Callable[[Any], Any], Callable[[Any], Any]] + Returns a decorator + + Example + ------- + >>> @call_with_error(ValueError) + >>> def func(a: int, b: int, error: Callable[[str], None]) -> int: + ... if a < 0: + ... error("a must be zero or greater") + ... if b < 0: + ... error("b must be zero or greater") + ... return a + b + + >>> func(-1, 0) + ValueError("a must be zero or greater") + + >>> func(0, -1) + ValueError("b must be zero or greater") + + >>> func(-1, -1) + ValueError("a must be zero or greater\nb must be zero or greater") + """ + + def _call_with_error(f): + @curry + def error(log, msg): + log.append(msg) + + @wraps(f) + def wrapped(*args, **kwargs): + log = [] + result = f(*args, error=error(log), **kwargs) + if len(log) > 0: + raise error_type("\n".join(log)) + return result + + return wrapped + + return _call_with_error + + +############################################################################### +# Determine function argument and return types # +############################################################################### + + +@_argument_doc +def parse_argument_annotations( + argument_annotations: Tuple[Parameter, ...], + function_name: str, + keyword_only_in_message: bool, + default_keyword_in_message: bool, + error: Callable[[str], None], +) -> Optional[Dict[str, type]]: + """Parse which argument_annotations should be placed into a Labgraph Message. + + The primary purpose of this function is to collect which parameters should + be passed to the final lg.Node through a lg.Message and which should be + passed through either default values or FunctionConfig. + + Parameters + ---------- + argument_annotations: Tuple[Parameter, ...] + The argument_annotations to a function, as Parameter objects. + function_name: str + The name of the function the argument_annotations came from. + {keyword_only_in_message} + {default_keyword_in_message} + {error} + + Returns + ------- + Optional[Dict[str, type]] + If no argument_annotations are provided, then None is returned. Else a + dictionary of argument names and types that are to go into a Labgraph + Message is returned. + """ + # Error handling components + var_err_msg = ( + f"Function `{function_name}` takes in a variable {{var_type}} parameter " + f"named `{{arg_name}}`. Functions with variable {{var_type}} parameters " + f"cannot be converted to Labgraph nodes using `function_to_node`." + ) + + # Functions with no argument_annotations are sources of data (type `() -> A`). + if len(argument_annotations) == 0: + return None + + argument_types = {} + for arg in argument_annotations: + # We do not support variable argument_annotations since we can't determine how + # many fields to make in the return Labgraph message. + if arg.kind is Parameter.VAR_POSITIONAL: + error(var_err_msg.format(var_type="argument", arg_name=arg.name)) + + if arg.kind is Parameter.VAR_KEYWORD: + error(var_err_msg.format(var_type="keyword argument", arg_name=arg.name)) + + if arg.annotation is Parameter.empty: + error( + f"Function `{function_name}` argument with name `{arg.name}` does not " + f"have a type annotation. All parameters require a type annotation to " + f"convert the function to a Labgraph node using `function_to_node`." + ) + + if positionally_passed(arg): + argument_types[arg.name] = arg.annotation + + elif default_keyword_in_message and is_default_keyword(arg): + # Python prevents us from having functions where one parameter is + # POSITIONAL_OR_KEYWORD with a default and another parameter that + # is KEYWORD_ONLY without a default. This means we can assume that + # default keyword arguments must be after non-default arguments. + # i.e the following is illegal. + # def f(a, b: bool = True, *, c:str) + argument_types[arg.name] = arg.annotation + + elif keyword_only_in_message and is_keyword_only(arg): + # We could potentially want default keywords to not be in the final + # message type and pass those in by default or through the + # `FunctionConfig` `kwargs` parameter. + if not default_keyword_in_message and is_default_keyword(arg): + pass + else: + argument_types[arg.name] = arg.annotation + + return argument_types + + +@_argument_doc +def parse_return_annotation( + return_annotation: type, + deconstruct_dataclass_return_type: bool, + single_return_param_name: str, +) -> Tuple[Optional[Dict[str, type]], bool]: + """Parse return annotation into something Labgraph can use. + + This function is primarily for unpacking return types that have an + __annotations__ property. We use this property to move the contents of the + return type directly into the final return lg.Message fields for + convenience and efficiency. + + Parameters + ---------- + return_annotation: type + The return type of a function + {deconstruct_dataclass_return_type} + {single_return_param_name} + + Returns + ------- + Tuple[Optional[Dict[str, type]], bool] + If no return type is provided, then (None, False) is returned. Else a + dictionary of the potentially deconstructed return names and types that + are to go into a Labgraph Message is returned, along with a bool of + whether the deconstruction occurred. + """ + # Base case of a sink function. + return_types = None + unpack_return = False + + # We have a function that is something besides a sink. + if return_annotation is not Parameter.empty: + + # We have a sink function (a function that returns nothing) + if return_annotation is None: + return_types = None + unpack_return = False + + # If the return type has an __annotations__ field we can attempt to + # unpack the results into the message directly. + elif deconstruct_dataclass_return_type and hasattr( + return_annotation, "__annotations__" + ): + return_types = { + name: typ for name, typ in return_annotation.__annotations__.items() + } + unpack_return = True + + # If the return type is anything else, do nothing with it but store it + # in the specified name. + else: + return_types = {single_return_param_name: return_annotation} + unpack_return = False + + return return_types, unpack_return + + +@call_with_error(ValueError) +def parse_function_annotations( + f: Callable[..., Any], + keyword_only_in_message: bool = False, + default_keyword_in_message: bool = False, + deconstruct_dataclass_return_type: bool = True, + single_return_param_name: str = "sample", + error: Callable[[str], None] = lambda x: None, +) -> Tuple[Optional[Dict[str, type]], Optional[Dict[str, type]], bool]: + """Convert function annotations into the fields to be turned into the + Labgraph Message argument and return types. + + Parameters + ---------- + {f} + {keyword_only_in_message} + {default_keyword_in_message} + {deconstruct_dataclass_return_type} + {single_return_param_name} + {error} + + Returns + ------- + Tuple[Optional[Dict[str, type]], Optional[Dict[str, type]], bool] + Dictionaries containing the argument types (if they exist), the return + types (if they exist), and a flag on if the return type was + deconstructed into its constituent pieces. + """ + + sig = signature(f) + name = f.__name__ + + argument_annotations = tuple(p for _, p in sig.parameters.items()) + return_annotation = sig.return_annotation + + # Returned parameters. Setting them all to the base case of no result. + argument_types = parse_argument_annotations( + argument_annotations, + name, + keyword_only_in_message, + default_keyword_in_message, + error, + ) + return_types, unpack_return = parse_return_annotation( + return_annotation, deconstruct_dataclass_return_type, single_return_param_name + ) + + if argument_types is None and return_types is None: + error( + f"Function `{name}` does not take or return any values. The function must " + f"either take a value, return a value, or take and return a value to be " + f"converted to a Labgraph node." + ) + + return (argument_types, return_types, unpack_return) + + +############################################################################### +# Message to dict (and reverse) conversions # +############################################################################### + + +def extract_message_fields(message: lg.Message, fields: List[str]) -> Dict[str, Any]: + return {field: getattr(message, field) for field in fields} + + +def remove_dict_keys(d: Dict[str, Any], fields: Set[str]) -> Dict[str, Any]: + return {name: value for name, value in d.items() if name not in fields} + + +def generate_message_class( + function_name: str, + message_class_name: str, + types: Optional[Dict[str, type]], + base_cls: type, +) -> type: + + # We do not generate a Message class when there are no types associated + # with either the arguments or return of a function. + if types is None: + return None + + # The `hash` function is not stable; it can return different results + # for the same input in different processes or on different platforms. + type_hash = sha1(repr(types.items()).encode("ascii")).hexdigest() + + # We put the hash in the message class name in order to prevent name + # clashes in cthulhu. + return type( + f"{message_class_name}_{function_name}_{type_hash}", + (base_cls,), + {"__annotations__": dict(types)}, + ) + + +############################################################################### +# Convert functions into classes # +############################################################################### + + +class _BlankMessage(lg.Message): + """This class is for passing someting to `call` in the source case. It + removes some branching logic that would reduce the readability of the + `call` function.""" + + pass + + +@_argument_doc +def function_to_node( + f: Callable[[Any], Any], + base_cls: lg.Message = lg.Message, + keyword_only_in_message: bool = False, + default_keyword_in_message: bool = False, + deconstruct_dataclass_return_type: bool = True, + single_return_param_name: str = "sample", + argument_topic_name: str = "INPUT", + return_topic_name: str = "OUTPUT", + sleep_time: float = 0.01, +) -> FunctionToNode: + """Convert a function to a labgraph node that operates on every message. + + For example, to lifting the following function: + + ``` + def f(a: int, b: int = 2) -> int: + return a * b * 4 + + ArgumentMessage, ReturnMessage, FunctionNode = function_to_node(f) + + # or + function_node = function_to_node(f) + ArgumentMessage = function_node.ArgumentMessage + ReturnMessage = function_node.ReturnMessage + FunctionNode = function_node.FunctionNode + ``` + + is the same as the following code + + ``` + def f(a: int, b: int = 2) -> int: + return a * b * 4 + + class ArgumentMessage(lg.Message): + sample: int + + class ReturnMessage(lg.Message): + sample: int + + class FunctionNode(lg.Node): + INPUT = lg.Topic(ArgumentMessage) + OUTPUT = lg.Topic(ReturnMessage) + + # keyword parameters can be passed by this config object, which takes a + # "kwargs" field with a dictionary value. + config: FunctionConfig + + @lg.subscriber(INPUT) + @lg.publisher(OUTPUT) + async def run(self, message: ArgumentMessage) -> lg.AsyncPublisher: + to_value = f(message.sample, **self.config.kwargs) + yield self.OUTPUT, ReturnMessage(sample=to_value) + ``` + + Note that if you would like to use the node in its own process (through the + `process_modules` method in a class), you MUST assign the results of this + function to the module namespace. This allows the returned classes to be + called from each process. + + Parameters + ---------- + {f} + {base_cls} + {keyword_only_in_message} + {default_keyword_in_message} + {deconstruct_dataclass_return_type} + {single_return_param_name} + {argument_topic_name} + {return_topic_name} + {sleep_time} + + Returns + ------- + FunctionToNode + Returns a type for the input Message, output Message, configuration + (keyword arguments) and Node that runs the function. + """ + function_name = f.__name__ + + argument_types, return_types, unpack_return = parse_function_annotations( + f, + keyword_only_in_message=keyword_only_in_message, + default_keyword_in_message=default_keyword_in_message, + deconstruct_dataclass_return_type=deconstruct_dataclass_return_type, + single_return_param_name=single_return_param_name, + ) + + # Generate message classes and topics ############################ + + base_cls_fields = base_cls.__message_fields__ + + ArgumentMessage = generate_message_class( + function_name, "ArgumentMessage", argument_types, base_cls + ) + ReturnMessage = generate_message_class( + function_name, "ReturnMessage", return_types, base_cls + ) + + topics = {} + + if argument_types is not None: + argument_topic = lg.Topic(ArgumentMessage) + topics[argument_topic_name] = argument_topic + + if return_types is not None: + return_topic = lg.Topic(ReturnMessage) + topics[return_topic_name] = return_topic + + # Generic method to call the provided function ################### + + def call(message, kwargs): + # Any fields from the base message class is passed directly through to + # the output message. For example, if the base class is + # `lg.TimestampedMessage`, then the field `timestamp` is passed through + # to the output message directly. + pass_through_fields = extract_message_fields(message, base_cls_fields) + + # If we have default keyword allowed but not keyword arguments, we run into + # a case where the order of argument types skips over an argument. This means + # that positional only arguments are not called correctly. Since we are not + # using Python 3.8 for a while, we are going to call everything by keyword + # instead. + message_fields = remove_dict_keys(message.asdict(), base_cls_fields) + if len(kwargs) > 0: + function_result = f(**{**message_fields, **kwargs}) + else: + function_result = f(**message_fields) + + if unpack_return: + result_dict = asdict(function_result) + else: + result_dict = {single_return_param_name: function_result} + + if ReturnMessage is not None: + return ReturnMessage(**{**pass_through_fields, **result_dict}) + + # Generate node function linked to topics for source, ############ + # sink, and pipe-through function cases ############ + + if argument_types is None and return_types is not None: + + # Case where function takes in nothing and produces some output (a + # source function). + @lg.publisher(return_topic) + async def run(self) -> lg.AsyncPublisher: + # We need a loop here of some sort, otherwise the node will only + # run once. + while True: + output_msg = call(_BlankMessage(), self.config.kwargs) + yield getattr(self, return_topic_name), output_msg + await asyncio.sleep(sleep_time) + + if argument_types is not None and return_types is None: + + # Case where function takes an input and produces no output (a sink + # function). + @lg.subscriber(argument_topic) + async def run(self, message: ArgumentMessage): # noqa: F811 + call(message, self.config.kwargs) + + if argument_types is not None and return_types is not None: + + # Case where a function both takes an input and produces an output (the + # pipe-through case) + @lg.subscriber(argument_topic) + @lg.publisher(return_topic) + async def run( # noqa: F811 + self, message: ArgumentMessage + ) -> lg.AsyncPublisher: + output_msg = call(message, self.config.kwargs) + yield getattr(self, return_topic_name), output_msg + + # Generate node to call function from prior pieces ############### + + # We provide a __module__ name for the case when the function is called in + # the top-level module namespace. This allows the node generated from this + # class to be used as a member of `process_modules` method in a graph. + caller_frame = inspect.stack()[1] + caller_module_name = inspect.getmodule(caller_frame[0]).__name__ + + FunctionNode = type( + f"FunctionNode_{function_name}", + (lg.Node,), + { + **topics, + **{ + "__annotations__": {"config": FunctionConfig}, + "__module__": caller_module_name, + "run": run, + }, + }, + ) + + return FunctionToNode(ArgumentMessage, ReturnMessage, FunctionNode) diff --git a/signal_processing/nodes/tests/__init__.py b/signal_processing/nodes/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/nodes/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/nodes/tests/test_function_to_node.py b/signal_processing/nodes/tests/test_function_to_node.py new file mode 100644 index 000000000..0e996ec5c --- /dev/null +++ b/signal_processing/nodes/tests/test_function_to_node.py @@ -0,0 +1,650 @@ +# Copyright 2004-present Facebook. All Rights Reserved. + +from dataclasses import dataclass +from textwrap import dedent +from typing import Callable, Optional, Tuple + +import labgraph as lg +import pytest +from ..function_to_node import ( + FunctionConfig, + function_to_node, + parse_function_annotations, +) + + +############################################################################### +# Test parse_function_annotations bad function error cases # +############################################################################### + + +def test_var_args_raises() -> None: + def f(a: int, *args): + pass + + with pytest.raises(ValueError): + parse_function_annotations(f) + + +def test_var_kwargs_raises() -> None: + def f(a: int, **kwargs): + pass + + with pytest.raises(ValueError): + parse_function_annotations(f) + + +def test_no_function_annotation_raises() -> None: + def f(a): + pass + + with pytest.raises(ValueError): + parse_function_annotations(f) + + +def test_empty_function_raises() -> None: + def f(): + pass + + with pytest.raises(ValueError): + parse_function_annotations(f) + + +def test_multiple_errors_returned_at_once() -> None: + def f(a, *args, **kwargs): + pass + + err_msg = dedent( + """ + Function `f` argument with name `a` does not have a type annotation. All parameters require a type annotation to convert the function to a Labgraph node using `function_to_node`. + Function `f` takes in a variable argument parameter named `args`. Functions with variable argument parameters cannot be converted to Labgraph nodes using `function_to_node`. + Function `f` argument with name `args` does not have a type annotation. All parameters require a type annotation to convert the function to a Labgraph node using `function_to_node`. + Function `f` takes in a variable keyword argument parameter named `kwargs`. Functions with variable keyword argument parameters cannot be converted to Labgraph nodes using `function_to_node`. + Function `f` argument with name `kwargs` does not have a type annotation. All parameters require a type annotation to convert the function to a Labgraph node using `function_to_node`.""" + ).strip() + + with pytest.raises(ValueError) as excinfo: + parse_function_annotations(f) + assert err_msg == str(excinfo.value) + + +############################################################################### +# Test parse_function_annotations gives correct output # +############################################################################### + + +def test_no_input_or_output() -> None: + def f(): + pass + + with pytest.raises(ValueError): + parse_function_annotations(f) + + +def test_source() -> None: + def f() -> str: + pass + + assert parse_function_annotations(f) == (None, {"sample": str}, False) + + +def test_sink() -> None: + def f(a: int) -> None: + pass + + assert parse_function_annotations(f) == ({"a": int}, None, False) + + +def test_arity_1() -> None: + def f(a: int) -> str: + pass + + assert parse_function_annotations(f) == ({"a": int}, {"sample": str}, False) + + +def test_arity_2() -> None: + def f(a: int, b: bool) -> str: + pass + + assert parse_function_annotations(f) == ( + {"a": int, "b": bool}, + {"sample": str}, + False, + ) + + +def test_keyword_only_in_message() -> None: + def f(a: int, *, b: bool) -> str: + pass + + assert parse_function_annotations(f, keyword_only_in_message=False) == ( + {"a": int}, + {"sample": str}, + False, + ) + assert parse_function_annotations(f, keyword_only_in_message=True) == ( + {"a": int, "b": bool}, + {"sample": str}, + False, + ) + + +def test_default_keyword_in_message() -> None: + def f(a: int, b: bool = True) -> str: + pass + + assert parse_function_annotations(f, default_keyword_in_message=False) == ( + {"a": int}, + {"sample": str}, + False, + ) + assert parse_function_annotations(f, default_keyword_in_message=True) == ( + {"a": int, "b": bool}, + {"sample": str}, + False, + ) + + +def test_default_keyword_in_message_and_keyword_only_in_message() -> None: + def f(a: int, *, b: bool, c: float = 0.0) -> str: + pass + + assert parse_function_annotations( + f, default_keyword_in_message=False, keyword_only_in_message=False + ) == ({"a": int}, {"sample": str}, False) + assert parse_function_annotations( + f, default_keyword_in_message=False, keyword_only_in_message=True + ) == ({"a": int, "b": bool}, {"sample": str}, False) + assert parse_function_annotations( + f, default_keyword_in_message=True, keyword_only_in_message=False + ) == ({"a": int, "c": float}, {"sample": str}, False) + assert parse_function_annotations( + f, default_keyword_in_message=True, keyword_only_in_message=True + ) == ({"a": int, "b": bool, "c": float}, {"sample": str}, False) + + +def test_deconstruct_dataclass_return_type() -> None: + @dataclass + class A: + field1: int + field2: str + + def f(a: int) -> A: + pass + + assert parse_function_annotations(f, deconstruct_dataclass_return_type=False) == ( + {"a": int}, + {"sample": A}, + False, + ) + assert parse_function_annotations(f, deconstruct_dataclass_return_type=True) == ( + {"a": int}, + {"field1": int, "field2": str}, + True, + ) + + +def test_single_return_param_name() -> None: + def f(a: int) -> str: + pass + + assert parse_function_annotations( + f, single_return_param_name="zhu_li_do_the_thing" + ) == ({"a": int}, {"zhu_li_do_the_thing": str}, False) + + +############################################################################### +# Test function_to_node messages match function # +############################################################################### + + +def test_no_input_message() -> None: + def f() -> str: + pass + + (ArgumentMessage, ReturnMessage, _) = function_to_node(f) + + assert ArgumentMessage is None + + # Can we make an instance of the output message? + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="hello") + + +def test_no_output_message() -> None: + def f(a: int): + pass + + (ArgumentMessage, ReturnMessage, _) = function_to_node(f) + + # Can we make an instance of the input message? + assert ArgumentMessage.__annotations__ == {"a": int} + ArgumentMessage(a=0) + + assert ReturnMessage is None + + +def test_message_arity_1() -> None: + def f(a: int) -> str: + pass + + (ArgumentMessage, ReturnMessage, _) = function_to_node(f) + + assert ArgumentMessage.__annotations__ == {"a": int} + ArgumentMessage(a=0) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="hello") + + +def test_message_keyword_only_in_message() -> None: + def f(a: int, *, b: bool) -> str: + pass + + # False case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, keyword_only_in_message=False + ) + assert ArgumentMessage.__annotations__ == {"a": int} + ArgumentMessage(a=1) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + # True case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, keyword_only_in_message=True + ) + assert ArgumentMessage.__annotations__ == {"a": int, "b": bool} + ArgumentMessage(a=1, b=False) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + +def test_message_default_keyword_in_message() -> None: + def f(a: int, b: bool = True) -> str: + pass + + # False case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=False + ) + assert ArgumentMessage.__annotations__ == {"a": int} + ArgumentMessage(a=1) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + # True case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=True + ) + assert ArgumentMessage.__annotations__ == {"a": int, "b": bool} + ArgumentMessage(a=1, b=False) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + +def test_message_default_keyword_in_message_and_keyword_only_in_message() -> None: + def f(a: int, *, b: bool, c: float = 0.0) -> str: + pass + + # False, False case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=False, keyword_only_in_message=False + ) + assert ArgumentMessage.__annotations__ == {"a": int} + ArgumentMessage(a=1) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + # False, True case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=False, keyword_only_in_message=True + ) + assert ArgumentMessage.__annotations__ == {"a": int, "b": bool} + ArgumentMessage(a=1, b=False) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + # True, False case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=True, keyword_only_in_message=False + ) + assert ArgumentMessage.__annotations__ == {"a": int, "c": float} + ArgumentMessage(a=1, c=0.0) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + # True, True case + (ArgumentMessage, ReturnMessage, _) = function_to_node( + f, default_keyword_in_message=True, keyword_only_in_message=True + ) + assert ArgumentMessage.__annotations__ == {"a": int, "b": bool, "c": float} + ArgumentMessage(a=1, b=False, c=0.0) + + assert ReturnMessage.__annotations__ == {"sample": str} + ReturnMessage(sample="Hello") + + +# If this is included inside of the function it cannot be pickled. +@dataclass +class A: + field1: int + field2: str + + +def test_output_message_unpacking() -> None: + def f(a: int) -> A: + pass + + (_, ReturnMessage, _) = function_to_node(f, deconstruct_dataclass_return_type=False) + assert ReturnMessage.__annotations__ == {"sample": A} + ReturnMessage(sample=A(1, "Hello")) + + (_, ReturnMessage, _) = function_to_node(f, deconstruct_dataclass_return_type=True) + assert ReturnMessage.__annotations__ == {"field1": int, "field2": str} + ReturnMessage(field1=1, field2="Hello") + + +def test_message_single_return_param_name() -> None: + def f(a: int) -> str: + pass + + (_, ReturnMessage, _) = function_to_node( + f, single_return_param_name="zhu_li_do_the_thing" + ) + + assert ReturnMessage.__annotations__ == {"zhu_li_do_the_thing": str} + ReturnMessage(zhu_li_do_the_thing="alright_varrick") + + +def test_topic_names() -> None: + def f(a: int) -> str: + pass + + input_topic_name = "hello" + output_topic_name = "goodbye" + + (ArgumentMessage, ReturnMessage, FunctionNode) = function_to_node( + f, argument_topic_name=input_topic_name, return_topic_name=output_topic_name + ) + + input_topic = getattr(FunctionNode, input_topic_name) + isinstance(input_topic, lg.Topic) + assert input_topic.message_type is ArgumentMessage + + output_topic = getattr(FunctionNode, output_topic_name) + isinstance(output_topic, lg.Topic) + assert output_topic.message_type is ReturnMessage + + +############################################################################### +# Test that FunctionNode has correct properties # +############################################################################### + + +def test_function_node_has_required_entities() -> None: + def f(a: int) -> str: + pass + + (ArgumentMessage, ReturnMessage, FunctionNode) = function_to_node(f) + + input_topic = FunctionNode.INPUT + isinstance(input_topic, lg.Topic) + assert input_topic.message_type is ArgumentMessage + + output_topic = FunctionNode.OUTPUT + isinstance(output_topic, lg.Topic) + assert output_topic.message_type is ReturnMessage + + assert FunctionNode.__module__ == __name__ + + assert callable(FunctionNode.run) + + assert FunctionNode.__annotations__ == {"config": FunctionConfig} + + +############################################################################### +# Test function_to_node(f).run(x) == f(x) # +############################################################################### + + +# We generate a class here to not rely on external changes. Additionally we +# layer the classes to demonstrate that the layers of fields are passed through +# correctly. +class SampledIndexMessage(lg.Message): + sample_index: int + + +class SampledFlavorMessage(SampledIndexMessage): + sample_flavor: str + + +def test_function_returns_same_after_conversion() -> None: + """Besides testing that the conversion doesn't change the function, it also + tests that a base class that is not `lg.Message` has its parameters passed + through.""" + + def f(a: int, *, b: int) -> int: + return a + b + + # Get the autogenerated node and associated classes + ArgumentMessage, ReturnMessage, FunctionNode = function_to_node( + f, base_cls=SampledFlavorMessage + ) + + test_harness = lg.NodeTestHarness(FunctionNode) + + # Check that the output from the node matches the output from the function. + a = 2 + b = 3 + flavor = "bubblegum" + + with test_harness.get_node(config=FunctionConfig(kwargs={"b": b})) as node: + msg = ArgumentMessage(sample_index=0, sample_flavor=flavor, a=a) + node_to_value_msg = lg.run_async(node.run, args=[msg])[0][1] + + assert isinstance(node_to_value_msg, ReturnMessage) + assert node_to_value_msg.sample == f(a, b=b) + assert node_to_value_msg.sample_index == 0 + assert node_to_value_msg.sample_flavor == flavor + + +def test_function_config_overrides_message(): + def f(a: int, *, b: int) -> int: + return a + b + + # Get the autogenerated node and associated classes + ArgumentMessage, ReturnMessage, FunctionNode = function_to_node( + f, keyword_only_in_message=True + ) + + test_harness = lg.NodeTestHarness(FunctionNode) + + # Check that the output from the node matches the output from the function. + a = 2 + b_message = 3 + b_config = 4 + + with test_harness.get_node(config=FunctionConfig(kwargs={"b": b_config})) as node: + msg = ArgumentMessage(a=a, b=b_message) + node_to_value_msg = lg.run_async(node.run, args=[msg])[0][1] + + assert isinstance(node_to_value_msg, ReturnMessage) + assert node_to_value_msg.sample == f(a, b=b_config) + + +############################################################################### +# Run in a graph # +############################################################################### + +# Source function ############################################################# + + +def generate_source_function() -> Callable[[], int]: + counter = 0 + + def source() -> int: + nonlocal counter + counter += 1 + return counter + + return source + + +_, SourceReturnMessage, SourceFunctionNode = function_to_node( + generate_source_function(), sleep_time=0.03 +) + + +class SourceConsumer(lg.Node): + INPUT = lg.Topic(SourceReturnMessage) + + last_count: Optional[int] + + def setup(self): + self.last_count = None + + @lg.subscriber(INPUT) + async def run(self, message: SourceReturnMessage): + if self.last_count is None: + self.last_count = message.sample - 1 + assert (message.sample - self.last_count) == 1 + + self.last_count = message.sample + if message.sample > 10: + raise lg.NormalTermination() + + +class SourceGraph(lg.Graph): + FUNCTION: SourceFunctionNode + CONSUMER: SourceConsumer + + def connections(self) -> lg.Connections: + return ((self.FUNCTION.OUTPUT, self.CONSUMER.INPUT),) + + def process_modules(self) -> Tuple[lg.Module, ...]: + return (self.FUNCTION, self.CONSUMER) + + +@pytest.mark.skip(reason="Currently complains of hanging source node") +def test_source_graph() -> None: + g = SourceGraph() + runner = lg.ParallelRunner(graph=g) + runner.run() + + +# Sink function ############################################################### + + +def sink(a: int): + pass + + +SinkArgumentMessage, _, SinkFunctionNode = function_to_node(sink) + + +class SinkGenerator(lg.Node): + OUTPUT = lg.Topic(SinkArgumentMessage) + + counter: int + + def setup(self): + self.counter = 0 + + @lg.publisher(OUTPUT) + async def run(self) -> lg.AsyncPublisher: + while True: + self.counter += 1 + if self.counter > 10: + raise lg.NormalTermination() + + yield self.OUTPUT, SinkArgumentMessage(a=self.counter) + + +class SinkGraph(lg.Graph): + GENERATOR: SinkGenerator + FUNCTION: SinkFunctionNode + + def connections(self) -> lg.Connections: + return ((self.GENERATOR.OUTPUT, self.FUNCTION.INPUT),) + + def process_modules(self) -> Tuple[lg.Module, ...]: + return (self.GENERATOR, self.FUNCTION) + + +def test_sink_graph() -> None: + g = SinkGraph() + runner = lg.ParallelRunner(graph=g) + runner.run() + + +# Function that takes and returns a value ##################################### + + +def input_output_function(a: int) -> int: + return a + 4 + + +( + InputOutputArgumentMessage, + InputOutputReturnMessage, + InputOutputFunctionNode, +) = function_to_node(input_output_function) + + +class InputOutputGenerator(lg.Node): + OUTPUT = lg.Topic(InputOutputArgumentMessage) + + counter: int + + def setup(self): + self.counter = 0 + + @lg.publisher(OUTPUT) + async def run(self) -> lg.AsyncPublisher: + while self.counter < 10: + self.counter += 1 + yield self.OUTPUT, InputOutputArgumentMessage(a=self.counter) + raise lg.NormalTermination() + + +class InputOutputConsumer(lg.Node): + INPUT = lg.Topic(InputOutputReturnMessage) + + count: int + + def setup(self): + self.count = 0 + + @lg.subscriber(INPUT) + async def run(self, message: InputOutputReturnMessage): + self.count += 1 + assert message.sample == input_output_function(self.count) + + +class InputOutputGraph(lg.Graph): + GENERATOR: InputOutputGenerator + FUNCTION: InputOutputFunctionNode + CONSUMER: InputOutputConsumer + + def connections(self) -> lg.Connections: + return ( + (self.GENERATOR.OUTPUT, self.FUNCTION.INPUT), + (self.FUNCTION.OUTPUT, self.CONSUMER.INPUT), + ) + + def process_modules(self) -> Tuple[lg.Module, ...]: + return (self.GENERATOR, self.FUNCTION, self.CONSUMER) + + +def test_input_output_graph() -> None: + g = InputOutputGraph() + runner = lg.ParallelRunner(graph=g) + runner.run() diff --git a/signal_processing/nodes/tests/test_window_trigger.py b/signal_processing/nodes/tests/test_window_trigger.py new file mode 100644 index 000000000..dc4312b22 --- /dev/null +++ b/signal_processing/nodes/tests/test_window_trigger.py @@ -0,0 +1,144 @@ +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import List, Optional + +import labgraph as lg +from ..window_trigger import ( + make_window_trigger_node, + WindowTriggerConfig, +) + + +############################################################################### +# Types used across tests # +############################################################################### + + +class SampleMessage(lg.TimestampedMessage): + sample: int + + def __eq__(self, other: "SampleMessage"): + return (self.timestamp == other.timestamp) and (self.sample == other.sample) + + +############################################################################### +# Parameters used across tests # +############################################################################### + + +def trigger_on_index(index: int) -> bool: + return index % 2 == 0 + + +def trigger(messages: List[SampleMessage]) -> bool: + return trigger_on_index(messages[0].sample) + + +############################################################################### +# Test returned window node matches expected state machine # +############################################################################### + + +class TestMakeWindowTriggerNode: + + StreamState = Optional[List[lg.TimestampedMessage]] + + length = 2 + messages_to_send = 10 + + def predict_state(self, timestamp: float, index: int) -> StreamState: + if trigger_on_index(index - self.length + 1): + return [ + SampleMessage(timestamp=timestamp - k, sample=index - k) + for k in range(self.length) + ][::-1] + + return None + + def convert_run_async_message_to_stream_state(self, window_message) -> StreamState: + if len(window_message) == 0: + return None + + return window_message[0][1].sample + + def test_function_returns_same_after_lift(self) -> None: + WindowTriggerMessage, WindowTriggerNode = make_window_trigger_node( + SampleMessage, trigger + ) + + test_harness = lg.NodeTestHarness(WindowTriggerNode) + config = WindowTriggerConfig(self.length) + + with test_harness.get_node(config=config) as node: + for k in range(self.messages_to_send): + msg = SampleMessage(timestamp=float(k), sample=k) + + predicted_state = self.predict_state(msg.timestamp, k) + state = self.convert_run_async_message_to_stream_state( + lg.run_async(node.run, args=[msg]) + ) + + assert predicted_state == state + + +############################################################################### +# Run in a graph # +############################################################################### + +# # Needs to be at a top level in order to be run through an actual graph +length = 2 +messages_to_send = 10 + +WindowTriggerMessage, WindowTriggerNode = make_window_trigger_node( + SampleMessage, trigger +) + + +class Generator(lg.Node): + OUTPUT = lg.Topic(SampleMessage) + + counter: int + + def setup(self): + self.counter = 0 + + @lg.publisher(OUTPUT) + async def run(self) -> lg.AsyncPublisher: + while self.counter < messages_to_send: + self.counter += 1 + yield self.OUTPUT, SampleMessage( + timestamp=float(self.counter), sample=self.counter + ) + raise lg.NormalTermination() + + +class Consumer(lg.Node): + INPUT = lg.Topic(WindowTriggerMessage) + + @lg.subscriber(INPUT) + async def run(self, messages: WindowTriggerMessage): + assert trigger(messages) + + +class Graph(lg.Graph): + GENERATOR: Generator + WINDOW_TRIGGER: WindowTriggerNode + CONSUMER: Consumer + + config: WindowTriggerConfig + + def setup(self) -> None: + self.WINDOW_TRIGGER.configure(self.config) + + def connections(self) -> lg.Connections: + return ( + (self.GENERATOR.OUTPUT, self.WINDOW_TRIGGER.INPUT), + (self.WINDOW_TRIGGER.OUTPUT, self.CONSUMER.INPUT), + ) + + +def test_in_graph() -> None: + g = Graph() + g.configure(WindowTriggerConfig(2)) + runner = lg.ParallelRunner(graph=g) + runner.run() diff --git a/signal_processing/nodes/window_trigger.py b/signal_processing/nodes/window_trigger.py new file mode 100644 index 000000000..111b0ed62 --- /dev/null +++ b/signal_processing/nodes/window_trigger.py @@ -0,0 +1,158 @@ +# Copyright 2004-present Facebook. All Rights Reserved. + +from collections import deque +from dataclasses import astuple, dataclass +from typing import Callable, List, Optional, Type + +import labgraph as lg + + +TriggerWindowPredicate = Callable[[List[lg.TimestampedMessage]], bool] + + +@dataclass +class WindowTriggerMessageAndNode: + message: lg.TimestampedMessage + node: lg.Node + + def __iter__(self): + return iter(astuple(self)) + + +class WindowTriggerConfig(lg.Config): + """Config for the node class produced by `make_window_trigger_node()` + + Members + ---------- + length: int + How many messages should be in a window. + """ + + length: int + + +@dataclass +class _WindowEmitter: + """ + A stateful object that keeps track of `n` samples from a stream, and + returns the `n` samples if some trigger predicate returns true for the + current set of samples. + """ + + # Window lengths are in number of messages, since the time stamps for the + # messages can vary and lead to uneven window sizes. + length: int + trigger_window_predicate: TriggerWindowPredicate + + def __post_init__(self): + self._window = deque(maxlen=self.length) + + def __update_window(self, message: lg.TimestampedMessage): + self._window.append(message) + + @property + def __window_is_full(self): + return len(self._window) >= self.length + + def __call__( + self, message: lg.TimestampedMessage + ) -> Optional[List[lg.TimestampedMessage]]: + self.__update_window(message) + if self.__window_is_full: + if self.trigger_window_predicate(self._window): + return list(self._window) + return None + + +def make_window_trigger_message(message_type: Type[lg.TimestampedMessage]): + class WindowTriggerMessage(lg.TimestampedMessage): + sample: List[message_type] + + return WindowTriggerMessage + + +def make_window_trigger_node( + message_type: Type[lg.TimestampedMessage], + trigger_window_predicate: TriggerWindowPredicate, +) -> WindowTriggerMessageAndNode: + """Generates a node that windows from a stream if some trigger fires. + + Parameters + ---------- + message_type: type + The type of the message for the stream to window/buffer + trigger_window_predicate: TriggerWindowPredicate + Whether to send out the current window to a stream or not. + + Returns + ------- + WindowTriggerMessageAndNode + A dataclass with a `message` field for the type of the output message + from the node, and `node` field that holds the window triggering node. + + Example + ------- + + Say you are streaming simple messages and want to capture every 10 samples + where the first sample is zero. First, define the messages that are going + to be streamed. + + >>> import labgraph as lg + >>> class SampleMessage(lg.TimestampedMessage): + ... field: int + + Now define the number of samples that are in a window + + >>> n_samples = 10 + + Along with a trigger function, which determines when a list of those + messages is a window that you want. We specified that we wanted the buffer + to have the `SampleMessage` field be zero as the first message in the + window, which can be presented as the following function. + + >>> def trigger(messages: List[SampleMessage]) -> bool: + ... return messages[0].field == 0 + >>> + + Finally, pass the window length and trigger function to the + `make_window_trigger_node` function to generate a Labgraph node that will + buffer a stream and return windows into that stream that cause the trigger + function to return True. + + >>> message_and_node = make_window_trigger_node(n_samples, trigger) + + Now just unpack the result as you would like. If you want to use in a + graph, you will likely need to assign the results of + `make_window_trigger_node` to the top level of a module for Labgraph to be + able to pick up the classes/types correctly. + + >>> WindowTriggerNode = message_and_node.node + >>> WindowTriggerMessage = message_and_node.message + """ + + WindowTriggerMessage = make_window_trigger_message(message_type) + + class WindowTriggerNode(lg.Node): + INPUT = lg.Topic(message_type) + OUTPUT = lg.Topic(WindowTriggerMessage) + + window_emitter: _WindowEmitter + + config: WindowTriggerConfig + + def setup(self) -> None: + self.window_emitter = _WindowEmitter( + self.config.length, trigger_window_predicate + ) + + @lg.subscriber(INPUT) + @lg.publisher(OUTPUT) + async def run(self, message: message_type) -> lg.AsyncPublisher: + optional_window = self.window_emitter(message) + + if optional_window is not None: + yield self.OUTPUT, WindowTriggerMessage( + timestamp=message.timestamp, sample=optional_window + ) + + return WindowTriggerMessageAndNode(WindowTriggerMessage, WindowTriggerNode) diff --git a/signal_processing/synthetic/__init__.py b/signal_processing/synthetic/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic/generators/__init__.py b/signal_processing/synthetic/generators/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic/generators/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic/generators/noise_generator.py b/signal_processing/synthetic/generators/noise_generator.py new file mode 100644 index 000000000..affa90ccc --- /dev/null +++ b/signal_processing/synthetic/generators/noise_generator.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from dataclasses import dataclass + +import numpy as np +from labgraph.simulations import ( + FunctionChannelConfig, + FunctionGenerator, + FunctionGeneratorMessage, +) + + +@dataclass +class NoiseChannelConfig(FunctionChannelConfig): + """ + Configuration variables describing the samples produced by this generator. + Noise is generated + Args: + - amplitudes: The amplitude of the noise + - offsets: the offset of the noise + - sample_rate: the sample rate. + """ + + amplitudes: np.ndarray + offsets: np.ndarray + sample_rate: float + + def __post_init__(self) -> None: + assert self.shape == self.amplitudes.shape == self.offsets.shape + + +class NoiseGenerator(FunctionGenerator): + """ + Generator which produces a uniformly distributed random noise on each channel. + Ammplitude and offset of the noise is controlled by the configuration. + """ + + def __init__(self, config: NoiseChannelConfig) -> None: + """Passing the config in __init__ seems easier than separately calling + set_config on the object afterwards. Also means I can avoid the + check for a valid config in the next_sample function + """ + super().__init__() + self.set_channel_config(config) + self._time = 0.0 + + def next_sample(self) -> np.ndarray: + sample = ( + self.channel_config.amplitudes * np.random.random(self.channel_config.shape) + + self.channel_config.offsets + ) + sample_message = FunctionGeneratorMessage(self._time, sample) + # Update time + self._time += 1 / self.channel_config.sample_rate + return sample_message diff --git a/signal_processing/synthetic/generators/sine_wave_generator.py b/signal_processing/synthetic/generators/sine_wave_generator.py new file mode 100644 index 000000000..66b55aefc --- /dev/null +++ b/signal_processing/synthetic/generators/sine_wave_generator.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from dataclasses import dataclass + +import numpy as np +from labgraph.simulations import ( + FunctionChannelConfig, + FunctionGenerator, + FunctionGeneratorMessage, +) + + +@dataclass +class SineWaveChannelConfig(FunctionChannelConfig): + """ + Configuration variables describing the samples produced by this generator. + + Args: + - amplitudes: The amplitude of the sinusoid. + - frequencies: The frequency of the sinusoid. + - phase_shifts: The phase shift for this sinusoid, in rads. + - midlines: The midline of the sinusoid. + - sample_rate: the sample rate. + """ + + amplitudes: np.ndarray + frequencies: np.ndarray + phase_shifts: np.ndarray + midlines: np.ndarray + sample_rate: float + + def __post_init__(self) -> None: + assert ( + self.amplitudes.shape + == self.frequencies.shape + == self.phase_shifts.shape + == self.midlines.shape + ) + assert self.sample_rate > 0.0 + + +class SineWaveGenerator(FunctionGenerator): + """ + Generator which produces a continuous sinusoid on each channel at the amplitude, + frequency, phase shift, and midline specified by each channel's + SineWaveChannelConfig object in this generator's config. + """ + + def __init__(self, config: SineWaveChannelConfig) -> None: + """Passing the config in __init__ seems easier than separately calling + set_config on the object afterwards. Also means I can avoid the + check for a valid config in the next_sample function + """ + super().__init__() + self.set_channel_config(config) + # This is time, which starts at zero + self._time = np.zeros(self.channel_config.shape) + + def next_sample(self) -> FunctionGeneratorMessage: + # Calculate output of the set of sine waves + angles = self.channel_config.frequencies * 2 * np.pi * self._time + angles = angles + self.channel_config.phase_shifts + sample = ( + self.channel_config.amplitudes * np.sin(angles) + + self.channel_config.midlines + ) + sample_message = FunctionGeneratorMessage(self._time.flatten()[0], sample) + # Update time + self._time += 1 / self.channel_config.sample_rate + return sample_message diff --git a/signal_processing/synthetic/generators/tests/__init__.py b/signal_processing/synthetic/generators/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic/generators/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic/generators/tests/test_noise_generator.py b/signal_processing/synthetic/generators/tests/test_noise_generator.py new file mode 100644 index 000000000..e49086126 --- /dev/null +++ b/signal_processing/synthetic/generators/tests/test_noise_generator.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import numpy as np + +from ..noise_generator import NoiseChannelConfig, NoiseGenerator + + +def test_generate_noise() -> None: + """ + Tests that the samples generated from each channel matches the parameters + specified in their configuration. Since this is noise, only check the bounds + """ + + num_samples = 10 + + # Test configurations + shape = (2,) + amplitudes = np.array([2.0, 5.0]) + offsets = np.array([1.0, -2.5]) + sample_rate = 1 + + config = NoiseChannelConfig(shape, amplitudes, offsets, sample_rate) + + # The generator + generator = NoiseGenerator(config) + + max_values = np.array([amplitudes * np.ones(shape) + offsets] * num_samples) + min_values = np.array([offsets] * num_samples) + # Generate expected values + values = np.array([generator.next_sample().data for i in range(num_samples)]) + assert values[0].shape == shape + np.testing.assert_array_less(values, max_values) + # Note: small delta subtraction is because the generator produces samples on + # the half open intervals [offsets, amplitudes+offsets) + np.testing.assert_array_less(min_values - 0.00001, values) diff --git a/signal_processing/synthetic/generators/tests/test_sine_wave_generator.py b/signal_processing/synthetic/generators/tests/test_sine_wave_generator.py new file mode 100644 index 000000000..a7666dbb1 --- /dev/null +++ b/signal_processing/synthetic/generators/tests/test_sine_wave_generator.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import numpy as np + +from ..sine_wave_generator import SineWaveChannelConfig, SineWaveGenerator + + +def test_generate_sinusoid() -> None: + """ + Tests that the samples generated from each channel matches the parameters + specified in their configuration. + """ + + test_clock_frequency = 100 # hz + test_duration = 300 # sec + + # test configurations + shape = (2,) + amplitudes = np.array([5.0, 3.0]) + frequencies = np.array([5, 10]) + phase_shifts = np.array([1.0, 5.0]) + midlines = np.array([3.0, -2.5]) + sample_rate = test_clock_frequency + + config = SineWaveChannelConfig( + shape, amplitudes, frequencies, phase_shifts, midlines, sample_rate + ) + + # The generator + generator = SineWaveGenerator(config) + + # Generate expected values + t_s = np.arange(0, test_duration, 1 / test_clock_frequency) + angles = np.expand_dims(frequencies, 1) * np.expand_dims(2 * np.pi * t_s, 0) + angles = angles + np.expand_dims(phase_shifts, 1) + expected = np.expand_dims(amplitudes, 1) * np.sin(angles) + np.expand_dims( + midlines, 1 + ) + + values = [generator.next_sample().data for t in t_s] + values = np.array(values).T + np.testing.assert_almost_equal(values, expected) + + +if __name__ == "__main__": + test_generate_sinusoid() diff --git a/signal_processing/synthetic/nodes/__init__.py b/signal_processing/synthetic/nodes/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic/nodes/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic/nodes/mixer_one_input_node.py b/signal_processing/synthetic/nodes/mixer_one_input_node.py new file mode 100644 index 000000000..2df93ecbe --- /dev/null +++ b/signal_processing/synthetic/nodes/mixer_one_input_node.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" Simple 1 input mixer node. This takes the input of M channels, and produces + an output of N channels, using simple matrix mulitplication. +""" + +import labgraph as lg +import numpy as np +from ...messages.generic_signal_sample import SignalSampleMessage + + +class MixerOneInputConfig(lg.Config): + # This is an NxM matrix (for M inputs, N outputs) + weights: np.ndarray + + +class MixerOneInputNode(lg.Node): + + IN_SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + OUT_SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + + @lg.subscriber(IN_SAMPLE_TOPIC) + @lg.publisher(OUT_SAMPLE_TOPIC) + async def mix_samples(self, in_sample: SignalSampleMessage) -> lg.AsyncPublisher: + if in_sample.sample.shape[0] != self.config.weights.shape[1]: + raise lg.util.LabgraphError("Mismatching input dimensions") + out_sample = SignalSampleMessage( + timestamp=in_sample.timestamp, + sample=np.dot(self.config.weights, in_sample.sample), + ) + yield self.OUT_SAMPLE_TOPIC, out_sample diff --git a/signal_processing/synthetic/nodes/mixer_two_input_node.py b/signal_processing/synthetic/nodes/mixer_two_input_node.py new file mode 100644 index 000000000..aa10b40ff --- /dev/null +++ b/signal_processing/synthetic/nodes/mixer_two_input_node.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" Simple 2 input mixer node. This takes two inputs of potentially different sizes, + weights them and then sums them to form an output. +""" +import asyncio +import queue + +import labgraph as lg +import numpy as np +from ...messages.generic_signal_sample import SignalSampleMessage + + +SHORT_SLEEP_SECS = 0.001 + + +class MixerTwoInputConfig(lg.Config): + # These are NxL and NxR matrices (for L,R inputs, same dimension N outputs) + left_weights: np.ndarray + right_weights: np.ndarray + + +class MixerTwoInputNode(lg.Node): + + IN_LEFT_SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + IN_RIGHT_SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + OUT_SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + + def setup(self) -> None: + # Check the weights are correctly dimensioned to be summed on output + if self.config.left_weights.shape[0] != self.config.right_weights.shape[0]: + raise ValueError("Mismatch in left-right output dimensions") + self._left_in = queue.Queue() + self._right_in = queue.Queue() + self._shutdown = asyncio.Event() + + def cleanup(self) -> None: + self._shutdown.set() + + @lg.subscriber(IN_LEFT_SAMPLE_TOPIC) + def left_input(self, in_sample: SignalSampleMessage) -> None: + # Check in the input dimensions against left weights + if in_sample.sample.shape[0] != self.config.left_weights.shape[1]: + raise ValueError("Mismatch in left input dimension") + self._left_in.put(in_sample) + + @lg.subscriber(IN_RIGHT_SAMPLE_TOPIC) + def right_input(self, in_sample: SignalSampleMessage) -> None: + # Check in the input dimensions against right weights + if in_sample.sample.shape[0] != self.config.right_weights.shape[1]: + raise ValueError("Mismatch in right input dimension") + self._right_in.put(in_sample) + + @lg.publisher(OUT_SAMPLE_TOPIC) + async def mix_samples(self) -> lg.AsyncPublisher: + while not self._shutdown.is_set(): + while self._left_in.empty() or self._right_in.empty(): + await asyncio.sleep(SHORT_SLEEP_SECS) + left = self._left_in.get() + right = self._right_in.get() + mixed_output = np.dot(self.config.left_weights, left.sample) + np.dot( + self.config.right_weights, right.sample + ) + # I am just using the left timetamp for this, since I don't + # know what else makes sense! + out_sample = SignalSampleMessage( + timestamp=left.timestamp, sample=mixed_output + ) + yield self.OUT_SAMPLE_TOPIC, out_sample diff --git a/signal_processing/synthetic/nodes/signal_capture_node.py b/signal_processing/synthetic/nodes/signal_capture_node.py new file mode 100644 index 000000000..8bf14db4f --- /dev/null +++ b/signal_processing/synthetic/nodes/signal_capture_node.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" This is a simple node to capture SignalSampleMessage's useful for testing. + NOTE: This needs to run in the same process as the test for this to work, + since you need to be able to directly access memory from the capture node +""" + +import asyncio +from typing import List + +import labgraph as lg +from ...messages.generic_signal_sample import SignalSampleMessage + + +class SignalCaptureConfig(lg.Config): + num_capture: int + + +class SignalCaptureNode(lg.Node): + """Node that captures a number of messages, and then terminates""" + + SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + + def setup(self): + self._shutdown = asyncio.Event() + self._samples: List[SignalSampleMessage] = [] + + @lg.subscriber(SAMPLE_TOPIC) + def sample_sink(self, message: SignalSampleMessage) -> None: + if not self._shutdown.is_set(): + self._samples.append(message.sample) + if len(self._samples) >= self.config.num_capture: + self._shutdown.set() + raise lg.NormalTermination() + + @property + def samples(self): + return self._samples diff --git a/signal_processing/synthetic/nodes/signal_generator_node.py b/signal_processing/synthetic/nodes/signal_generator_node.py new file mode 100644 index 000000000..5a8d97443 --- /dev/null +++ b/signal_processing/synthetic/nodes/signal_generator_node.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" Node for a generic signal generator, eg SineWaveGenerator """ + +import asyncio + +import labgraph as lg +from ...messages.generic_signal_sample import SignalSampleMessage +from labgraph.simulations import FunctionGeneratorNode + + +# This assume we would not be delivering samples at close to 1KHz +PUBLISHER_SLEEP_SECS = 0.001 + + +class SignalGeneratorNode(FunctionGeneratorNode): + + SAMPLE_TOPIC = lg.Topic(SignalSampleMessage) + + def setup(self): + self._shutdown = asyncio.Event() + + def cleanup(self): + self._shutdown.set() + + @lg.publisher(SAMPLE_TOPIC) + async def publish_samples(self) -> lg.AsyncPublisher: + while not self._shutdown.is_set(): + sample_message = self._generator.next_sample() + yield self.SAMPLE_TOPIC, SignalSampleMessage( + timestamp=sample_message.timestamp, sample=sample_message.data + ) + await asyncio.sleep(PUBLISHER_SLEEP_SECS) diff --git a/signal_processing/synthetic/nodes/tests/__init__.py b/signal_processing/synthetic/nodes/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic/nodes/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic/nodes/tests/test_mixer_one_input_node.py b/signal_processing/synthetic/nodes/tests/test_mixer_one_input_node.py new file mode 100644 index 000000000..dcefa6f49 --- /dev/null +++ b/signal_processing/synthetic/nodes/tests/test_mixer_one_input_node.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import labgraph as lg +import numpy as np +import pytest +from ...generators.sine_wave_generator import ( + SineWaveChannelConfig, + SineWaveGenerator, +) + +from ..mixer_one_input_node import MixerOneInputConfig, MixerOneInputNode +from ..signal_capture_node import SignalCaptureConfig, SignalCaptureNode +from ..signal_generator_node import SignalGeneratorNode + + +class MyGraphConfig(lg.Config): + sine_wave_channel_config: SineWaveChannelConfig + mixer_config: MixerOneInputConfig + capture_config: SignalCaptureConfig + + +class MyGraph(lg.Graph): + + sample_source: SignalGeneratorNode + mixer_node: MixerOneInputNode + capture_node: SignalCaptureNode + + def setup(self) -> None: + self.capture_node.configure(self.config.capture_config) + self.sample_source.set_generator( + SineWaveGenerator(self.config.sine_wave_channel_config) + ) + self.mixer_node.configure(self.config.mixer_config) + + def connections(self) -> lg.Connections: + return ( + (self.mixer_node.IN_SAMPLE_TOPIC, self.sample_source.SAMPLE_TOPIC), + (self.capture_node.SAMPLE_TOPIC, self.mixer_node.OUT_SAMPLE_TOPIC), + ) + + +def test_mixer_one_input_node() -> None: + """ + Tests that node mixes correctly, uses sine wave as input + """ + + sample_rate = 1 # Hz + test_duration = 10 # sec + + # Test configurations + shape = (2,) + amplitudes = np.array([5.0, 3.0]) + frequencies = np.array([5, 10]) + phase_shifts = np.array([1.0, 5.0]) + midlines = np.array([3.0, -2.5]) + + # Generate expected values + t_s = np.arange(0, test_duration, 1 / sample_rate) + angles = np.expand_dims(frequencies, 1) * np.expand_dims(2 * np.pi * t_s, 0) + angles = angles + np.expand_dims(phase_shifts, 1) + expected = np.expand_dims(amplitudes, 1) * np.sin(angles) + np.expand_dims( + midlines, 1 + ) + + # Create the graph + generator_config = SineWaveChannelConfig( + shape, amplitudes, frequencies, phase_shifts, midlines, sample_rate + ) + capture_config = SignalCaptureConfig(int(test_duration / sample_rate)) + mixer_weights = np.identity(2) + mixer_config = MixerOneInputConfig(mixer_weights) + my_graph_config = MyGraphConfig(generator_config, mixer_config, capture_config) + graph = MyGraph() + graph.configure(my_graph_config) + + runner = lg.LocalRunner(module=graph) + runner.run() + received = np.array(graph.capture_node.samples).T + np.testing.assert_almost_equal(received, expected) diff --git a/signal_processing/synthetic/nodes/tests/test_mixer_two_input_node.py b/signal_processing/synthetic/nodes/tests/test_mixer_two_input_node.py new file mode 100644 index 000000000..66ba34778 --- /dev/null +++ b/signal_processing/synthetic/nodes/tests/test_mixer_two_input_node.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +""" Simple test for a two input mixer """ + +import labgraph as lg +import numpy as np +import pytest +from ...generators.sine_wave_generator import ( + SineWaveChannelConfig, + SineWaveGenerator, +) + +from ..mixer_two_input_node import MixerTwoInputConfig, MixerTwoInputNode +from ..signal_capture_node import SignalCaptureConfig, SignalCaptureNode +from ..signal_generator_node import SignalGeneratorNode + + +class MyGraphConfig(lg.Config): + sine_wave_channel_config: SineWaveChannelConfig + mixer_config: MixerTwoInputConfig + capture_config: SignalCaptureConfig + + +class MyGraph(lg.Graph): + + sample_source: SignalGeneratorNode + mixer_node: MixerTwoInputNode + capture_node: SignalCaptureNode + + def setup(self) -> None: + self.capture_node.configure(self.config.capture_config) + self.sample_source.set_generator( + SineWaveGenerator(self.config.sine_wave_channel_config) + ) + self.mixer_node.configure(self.config.mixer_config) + + def connections(self) -> lg.Connections: + return ( + # Note connecting both sides of the mixer to the same input for this test + (self.mixer_node.IN_LEFT_SAMPLE_TOPIC, self.sample_source.SAMPLE_TOPIC), + (self.mixer_node.IN_RIGHT_SAMPLE_TOPIC, self.sample_source.SAMPLE_TOPIC), + (self.capture_node.SAMPLE_TOPIC, self.mixer_node.OUT_SAMPLE_TOPIC), + ) + + +def test_mixertwo_input_node() -> None: + """ + Tests that node mixes correctly, uses sine wave as input + """ + + sample_rate = 1 # Hz + test_duration = 10 # sec + + # Test configurations + shape = (2,) + amplitudes = np.array([5.0, 3.0]) + frequencies = np.array([5, 10]) + phase_shifts = np.array([1.0, 5.0]) + midlines = np.array([3.0, -2.5]) + + # Generate expected values + t_s = np.arange(0, test_duration, 1 / sample_rate) + angles = np.expand_dims(frequencies, 1) * np.expand_dims(2 * np.pi * t_s, 0) + angles = angles + np.expand_dims(phase_shifts, 1) + expected = np.expand_dims(amplitudes, 1) * np.sin(angles) + np.expand_dims( + midlines, 1 + ) + + # Create the graph + generator_config = SineWaveChannelConfig( + shape, amplitudes, frequencies, phase_shifts, midlines, sample_rate + ) + capture_config = SignalCaptureConfig(int(test_duration / sample_rate)) + mixer_left_weights = 0.5 * np.identity(2) + mixer_right_weights = 0.5 * np.identity(2) + mixer_config = MixerTwoInputConfig(mixer_left_weights, mixer_right_weights) + my_graph_config = MyGraphConfig(generator_config, mixer_config, capture_config) + graph = MyGraph() + graph.configure(my_graph_config) + + runner = lg.LocalRunner(module=graph) + runner.run() + received = np.array(graph.capture_node.samples).T + np.testing.assert_almost_equal(received, expected) diff --git a/signal_processing/synthetic/nodes/tests/test_sine_wave_generator_node.py b/signal_processing/synthetic/nodes/tests/test_sine_wave_generator_node.py new file mode 100644 index 000000000..50e2b6c52 --- /dev/null +++ b/signal_processing/synthetic/nodes/tests/test_sine_wave_generator_node.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import labgraph as lg +import numpy as np +from ...generators.sine_wave_generator import ( + SineWaveChannelConfig, + SineWaveGenerator, +) + +from ..signal_capture_node import SignalCaptureConfig, SignalCaptureNode +from ..signal_generator_node import SignalGeneratorNode + + +SHORT_SLEEP_SECS = 0.01 + + +class MyGraphConfig(lg.Config): + capture_config: SignalCaptureConfig + sine_wave_channel_config: SineWaveChannelConfig + + +class MyGraph(lg.Graph): + # Note any nodes must be defined here, since connections are checked + # before on creation of the class, i.e., before setup is called + capture_node: SignalCaptureNode + sample_source: SignalGeneratorNode + + def setup(self): + self.capture_node.configure(self.config.capture_config) + self.sample_source.set_generator( + SineWaveGenerator(self.config.sine_wave_channel_config) + ) + + def connections(self) -> lg.Connections: + return ((self.capture_node.SAMPLE_TOPIC, self.sample_source.SAMPLE_TOPIC),) + + +def test_sine_wave_generator_node() -> None: + """ + Tests that node configures itself correctly and returns samples from the generator. + """ + + sample_rate = 1 # Hz + test_duration = 10 # sec + + # Test configurations + shape = (2,) + amplitudes = np.array([5.0, 3.0]) + frequencies = np.array([5, 10]) + phase_shifts = np.array([1.0, 5.0]) + midlines = np.array([3.0, -2.5]) + + # Generate expected values + t_s = np.arange(0, test_duration, 1 / sample_rate) + angles = np.expand_dims(frequencies, 1) * np.expand_dims(2 * np.pi * t_s, 0) + angles = angles + np.expand_dims(phase_shifts, 1) + expected = np.expand_dims(amplitudes, 1) * np.sin(angles) + np.expand_dims( + midlines, 1 + ) + + # Create the graph + generator_config = SineWaveChannelConfig( + shape, amplitudes, frequencies, phase_shifts, midlines, sample_rate + ) + capture_config = SignalCaptureConfig(int(test_duration / sample_rate)) + my_graph_config = MyGraphConfig(capture_config, generator_config) + + graph = MyGraph() + graph.configure(my_graph_config) + + runner = lg.LocalRunner(module=graph) + runner.run() + received = np.array(graph.capture_node.samples).T + np.testing.assert_almost_equal(received, expected) diff --git a/signal_processing/synthetic_data/README.md b/signal_processing/synthetic_data/README.md new file mode 100644 index 000000000..41af08ab8 --- /dev/null +++ b/signal_processing/synthetic_data/README.md @@ -0,0 +1,13 @@ +# Synthetic data + +## What this folder contains +Files in this directory relate to generation and testing of synthetic data + +## Folder structure +- dummy_generator.py: functions to generate dummy physiological signals +- README: this file +- tests: folder that contains all the unit tests + - test_dummy_generator.py + +## Example use +- Check tests/test_dummy_generator.py diff --git a/signal_processing/synthetic_data/__init__.py b/signal_processing/synthetic_data/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic_data/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic_data/additive_synth_dataset.py b/signal_processing/synthetic_data/additive_synth_dataset.py new file mode 100644 index 000000000..9d69692e7 --- /dev/null +++ b/signal_processing/synthetic_data/additive_synth_dataset.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import List, Optional, Tuple, Union + +import numpy as np +from colorednoise import powerlaw_psd_gaussian +from .dummy_generator import ( + dummy_physiological_noise, + motion_noise, + simulated_hemodynamics, +) +from torch.utils.data import Dataset + + +class AdditiveSynthDataset(Dataset): + """ + Generates a synthetic finger-tapping fNIRS dataset using the same + additive noise model as: + Scarpa, F., Brigadoi, S., Cutini, S., Scatturin, P., Zorzi, M., + Dell’Acqua, R., & Sparacino, G. (2013). A reference-channel based + methodology to improve estimation of event-related hemodynamic + response from fNIRS measurements. + NeuroImage, 72, 106–119. https://doi.org/10.1016/j.neuroimage.2013.01.021 + + Default parameters are as best as we could match that paper, but the paper + was ambiguous in many ways, so no guarantees. + """ + + def __init__( + self, + n_trials: int, + trial_length: float, + channel_dists: np.ndarray, + class_proportions: Optional[List[float]] = None, + sample_rate: int = 5, + ): + self.n_trials = n_trials + self.trial_length = trial_length + self.channel_dists = channel_dists + self.n_channels = len(channel_dists) + self.sample_rate = sample_rate + self.trial_samples = int(trial_length * sample_rate) + + if class_proportions is None: + class_proportions = [1 / 3] * 3 + + self.labels = np.random.choice( + np.arange(len(class_proportions)), + size=n_trials, + replace=True, + p=class_proportions, + ) + + self.X = np.zeros((n_trials, self.n_channels, self.trial_samples)) + + hrf_scales = self.hrf_scale_from_channel_dists(channel_dists) + + for trial in range(n_trials): + for channel, hrf_scale in enumerate(hrf_scales): + # HACK: fragile: assume label 0 means no HRF, + # label 1 means left and left is first half of channels, + # label 2 means right and right is 2nd half of channels, + # otherwise 0 + if ( + self.labels[trial] == 0 + or (self.labels[trial] == 1 and channel > (self.n_channels // 2)) + or (self.labels[trial] == 2 and channel <= (self.n_channels // 2)) + ): + hrf_scale = 0 + + self.X[trial, channel, :] = self.generate_synth_data(hrf_scale) + + def hrf_scale_from_channel_dists(self, channel_dists: np.ndarray) -> np.ndarray: + """ + hrf weight (k in the paper) is a function of channel dists, + if we follow the original paper + there's 2 cutoffs and k is 0 for surface/close dists, 0.5 for "medium" + dists, and 1.0 for "long"/deep dists + they don'ts ay what "medium" dists are but elsewhere in the paper + they say distances above 1.5cm but below 3cm are too + short to be standard channel but aren't reference channels, so we assume + those are "medium". + """ + + hrf_scale = np.zeros_like(channel_dists) + hrf_scale[channel_dists > 15] = 0.5 + # distances 3cm or above are standard channels + hrf_scale[channel_dists >= 30] = 1.0 + return hrf_scale + + def __len__(self) -> int: + return self.n_trials + + def __getitem__(self, index: Union[int, slice]) -> Tuple[np.ndarray, np.ndarray]: + return self.X[index, ...], self.labels[index] + + def sim_physio_noise( + self, + frequencies_mean: Optional[List[float]] = None, + frequencies_sd: Optional[List[float]] = None, + amplitudes_mean: Optional[List[float]] = None, + amplitudes_sd: Optional[List[float]] = None, + ) -> np.ndarray: + """ + Physiological noise + Per paper table 1, there are 5 components: + very low freq, low freq, vasomotor, respiratory, cardiac (given in + defaults) + """ + if frequencies_mean is None: + frequencies_mean = [0.002, 0.01, 0.07, 0.2, 1.1] + if frequencies_sd is None: + frequencies_sd = [0.0001, 0.001, 0.04, 0.03, 0.1] + if amplitudes_mean is None: + amplitudes_mean = [700, 700, 400, 200, 400] + if amplitudes_sd is None: + amplitudes_sd = [100, 100, 10, 10, 10] + + physio_noises = [ + dummy_physiological_noise( + amplitude=np.random.normal(a_mean, a_sd), + sample_rate=self.sample_rate, + interest_freq=np.random.normal(f_mean, f_sd), + phase=np.random.normal() * 2 * np.pi, + duration=self.trial_length, + ) + for f_mean, f_sd, a_mean, a_sd in zip( + frequencies_mean, frequencies_sd, amplitudes_mean, amplitudes_sd + ) + ] + + return np.sum(np.stack(physio_noises), axis=0) + + def generate_measurement_noise( + self, + exponent: float = 0, + amplitude: float = 400, + sd: float = 180, + fmin: float = 1, + ) -> np.ndarray: + """ + white noise in original paper, amplitude in the paper is 400 ± 180, + TODO: mean and sd doesn't really make sense for non-white noise + """ + eta = powerlaw_psd_gaussian( + exponent=exponent, + size=self.trial_length * self.sample_rate, + fmin=fmin, + ) + + return eta / np.mean(eta) * sd + amplitude + + def generate_hrf( + self, hrf_scale: float = 1, amplitude: float = 420.0 + ) -> np.ndarray: + """ + From the paper, + "In order to simulate the HR due to two different + stimuli and with shapes, amplitudes and latencies in + agreement with previous findings regarding finger + tapping tasks, two utrue profiles were generated + by properly tuning the parameters in Eq. (2), + allowing small variations in peak amplitude and + latency between a trial and another. For HbO, this led to a + first HR profile with a peak amplitude of 420±20 nM and a peak + latency equal to 5.0±.2 s, while the second HR profile had a + peak amplitude of 360±20 nM and a peak latency equal to 5.5±.2 s." + So basically we have no idea what the parameters are. + + hrf_scale here is `k` in the paper. Eventually we may want very close + source/detector pairs to have hrf_scale=0, saturating to hrf_scale=1. + """ + + u_true = hrf_scale * simulated_hemodynamics( + amplitude=amplitude, + sample_rate=self.sample_rate, + duration=self.trial_length, + ) + return u_true + + def generate_motion_noise(self) -> np.ndarray: + """ + No idea what motion params are? + """ + return motion_noise( + motion_amplitude=500, + motion_duration_mean=0.5, + sample_rate=self.sample_rate, + sample_duration=self.trial_length, + ) + + def generate_synth_data(self, hrf_scale: float = 1) -> np.ndarray: + """ + y(t) = hrf_scale * u_true(t) + \\phi_sim(t) + \\eta(t) + r(t), where: + - hrf_scale is a scaling coefficient based on depth + (in the original paper this is 0 for ref channels, + 1 for signal channels, and 0.5 for some other "standard channels") + - u_true(t) is the true HRF, made by simulated_hemodynamics() + - \\phi_sim(t) is the physiological noise, made from sim_physio_noise() + - \\eta(t) is "random" or white noise + - r(t) is motion noise + """ + + phi_sim = self.sim_physio_noise() + u_true = self.generate_hrf(hrf_scale) + measurement_noise = self.generate_measurement_noise() + motion_noise = self.generate_motion_noise() + return u_true + phi_sim + measurement_noise + motion_noise diff --git a/signal_processing/synthetic_data/additive_synth_dataset_variant.py b/signal_processing/synthetic_data/additive_synth_dataset_variant.py new file mode 100644 index 000000000..d5c82bf87 --- /dev/null +++ b/signal_processing/synthetic_data/additive_synth_dataset_variant.py @@ -0,0 +1,295 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import random +from typing import List, Optional, Tuple, Union + +import numpy as np +from colorednoise import powerlaw_psd_gaussian +from ..utils.doc.docfill import fill_in_docstring +from .dummy_generator import ( + dummy_physiological_noise, + simulated_hemodynamics, +) +from scipy import signal + +############################################################################### +# Documentation for Synthesized Data # +############################################################################### +_common_docs = fill_in_docstring( + { + "n_trials": """channel_data : int + Number of trials in the experiment including all conditions. + """, + "trial_length": """trial_length : float + Length of each trial in seconds + """, + "class_labels": """class_labels: List[str] + class_labels is a list of strings defining the labels or conditions + """, + "channel_dists": """channel_dists : np.ndarray + SD distance array for all channels + """, + "hrf_scale_for_cond": """ hrf_scale_for_cond : np.ndarray [n_channels, n_conds] + hrf_scale_for_condition to scale the data based on condition. + n_channels is the number of channels + n_conds is the the number of conditions or class_labels + details. + """, + "sample_rate": """ sample_rate : float + Sampling frequency of acquisition in Hz + """, + } +) + + +@_common_docs +class AdditiveSynthDatasetVariant(object): + """ + based on the combining the ideas of 2 papers + 1. Scholkmann F, Spichtig S, Muehlemann T, Wolf M. + How to detect and reduce movement artifacts in near-infrared imaging + using moving standard deviation and spline interpolation. + Physiol Meas. 2010 May;31(5):649-62. + doi: 10.1088/0967-3334/31/5/004. Epub 2010 Mar 22. PMID: 20308772. (Default is this one) + + 2. Scarpa, F., Brigadoi, S., Cutini, S., Scatturin, P., Zorzi, M., + Dell’Acqua, R., & Sparacino, G. (2013). A reference-channel based + methodology to improve estimation of event-related hemodynamic + response from fNIRS measurements. + NeuroImage, 72, 106–119. https://doi.org/10.1016/j.neuroimage.2013.01.021 + + """ + + def __init__( + self, + n_trials: int, + trial_length: float, + channel_dists: np.ndarray, + class_labels: List[str], + hrf_scale_for_cond: np.ndarray, + sample_rate: float = 10.0, + ): + self.n_trials = n_trials + self.trial_length = trial_length + self.channel_dists = channel_dists + self.n_channels = len(channel_dists) + self.sample_rate = sample_rate + self.trial_samples = int(trial_length * sample_rate) + self.labels = class_labels + + self.X = np.zeros((n_trials, self.n_channels, self.trial_samples)) + + hrf_scales_from_distance = hrf_scale_from_channel_dists(channel_dists) + + for trial in range(n_trials): + for label in range(len(class_labels)): + for channel, hrf_scale_from_distance in enumerate( + hrf_scales_from_distance + ): + hrf_scale_final = ( + hrf_scale_from_distance * hrf_scale_for_cond[channel, label] + ) + + self.X[trial, channel, :] = generate_synth_data( + hrf_scale_final, self.sample_rate, self.trial_length + ) + + def __len__(self) -> int: + return self.n_trials + + def __getitem__(self, index: Union[int, slice]) -> Tuple[np.ndarray, np.ndarray]: + return self.X[index, ...], self.labels[index] + + +def hrf_scale_from_channel_dists(channel_dists: np.ndarray) -> np.ndarray: + """ + Assuming hrf_scales would attenuate linearly with SD distance + """ + + hrf_scale = np.zeros_like(channel_dists) + hrf_scale[np.array(channel_dists) < 10] = 0.5 + hrf_scale[np.array(channel_dists) >= 10] = 1.00 + hrf_scale[np.array(channel_dists) >= 20] = 0.50 + hrf_scale[np.array(channel_dists) >= 30] = 0.33 + hrf_scale[np.array(channel_dists) >= 40] = 0.25 + hrf_scale[np.array(channel_dists) >= 50] = 0.20 + hrf_scale[np.array(channel_dists) >= 60] = 0.16 + return hrf_scale + + +def sim_physio_noise( + sample_rate: float, + trial_length: float, + frequencies_mean: Optional[List[float]] = None, + frequencies_sd: Optional[List[float]] = None, + amplitudes_mean: Optional[List[float]] = None, + amplitudes_sd: Optional[List[float]] = None, +) -> np.ndarray: + """ + Sample_rate in Hz and trial length in seconds + from the paper Scholkmann et al, 2010 + The amplitude and frequency values of each sine wave were defined according to the mean frequencies of real NIRI signals + + very high frequency oscillation (heart rate, f = 1 Hz, µ = 0.6) + high frequency oscillation (respiration, f = 0.25 Hz, µ = 0.2) + low frequency oscillation (f = 0.1 Hz, µ = 0.9) + very low frequency oscillation (f = 0.04 Hz, µ = 1). + + """ + if frequencies_mean is None: + frequencies_mean = [0.002, 0.03, 0.1, 0.25, 1.1] + if frequencies_sd is None: + frequencies_sd = [0.0001, 0.01, 0.03, 0.05, 0.2] + if amplitudes_mean is None: + amplitudes_mean = [0.7, 0.9, 0.9, 0.2, 0.6] + if amplitudes_sd is None: + amplitudes_sd = [0.01, 0.01, 0.1, 0.1, 0.1] + + physio_noises = [ + dummy_physiological_noise( + amplitude=np.random.normal(a_mean, a_sd), + sample_rate=sample_rate, + interest_freq=np.random.normal(f_mean, f_sd), + phase=np.random.normal() * 2 * np.pi, + duration=trial_length, + ) + for f_mean, f_sd, a_mean, a_sd in zip( + frequencies_mean, frequencies_sd, amplitudes_mean, amplitudes_sd + ) + ] + + return np.sum(np.stack(physio_noises), axis=0) + + +def generate_measurement_noise(sample_rate: float, trial_length: float) -> np.ndarray: + + exponent = 1 + amplitude = 0.4 + sd = 0.018 + fmin = 0 + + eta = powerlaw_psd_gaussian( + exponent=exponent, size=int(trial_length * sample_rate), fmin=fmin + ) + + return eta / np.mean(eta) * sd + amplitude + + +def generate_hrf( + hrf_scale: float, sample_rate: float, trial_length: float +) -> np.ndarray: + """ + From the paper, + "In order to simulate the HR due to two different + stimuli and with shapes, amplitudes and latencies in + agreement with previous findings regarding finger + tapping tasks, two utrue profiles were generated + by properly tuning the parameters in Eq. (2), + allowing small variations in peak amplitude and + latency between a trial and another. For HbO, this led to a + first HR profile with a peak amplitude of 420±20 nM and a peak + latency equal to 5.0±.2 s, while the second HR profile had a + peak amplitude of 360±20 nM and a peak latency equal to 5.5±.2 s." + So basically we have no idea what the parameters are. + + hrf_scale here is `k` in the paper. Eventually we may want very close + source/detector pairs to have hrf_scale=0, saturating to hrf_scale=1 + on pairs. + """ + amplitude = 1 + + u_true = hrf_scale * simulated_hemodynamics( + amplitude=amplitude, sample_rate=sample_rate, duration=trial_length + ) + return u_true + + +def generate_motion_noise(sample_rate: float, trial_length: float) -> np.ndarray: + """ + The better simulation of motion artifacts in Scholkmann et al, 2010 than Scarpa et al, 2013 + motivated to reproduce a variant of this paper + Here Motion = Base Shift + pulse + temporary LF oscillation + Spike (spike added; not in original paper) + + """ + # Pulse + """ + Simulating a pulse with values between [-4 4] and in the entire duration + """ + duration = int(trial_length * sample_rate) + pulse = np.zeros(duration) + + pulse[random.randint(1, int(len(pulse) / 3))] = random.randint(-4, 4) + + # Shift + """ + Simulating a base shift with values between [-4 4] and in the 1/4th of entire duration + """ + shift = np.zeros(duration) + shift[ + random.randint(1, len(shift) // 4) : random.randint( + len(shift) // 4, len(shift) // 2 + ) + ] = random.randint(-4, 4) + + # temporary LF oscillation + """ + Not exactly clear what motion it corresponds to probably due to slouching or slow head movement + Using a 10s Gaussian wave to define it + """ + LFO = np.zeros(duration) + start = len(LFO) // 4 + end = 3 * len(LFO) // 4 + LFO[start:end] = signal.gaussian(len(LFO) // 2, std=len(LFO) // 20) + + # spikes + """ + Using a spikes 0f 2s duration, similar to head motion or body motion + """ + spike = np.zeros(duration) + + loc, scale = random.randint(0, 1), random.randint(0, 1) + s = np.random.laplace(loc, scale, 20) + start = random.randint(1, len(spike) - 20) + end = start + 20 + spike[start:end] = s + + """ + It returns motion for each trial. It is highly improbable that all types of motion are present within one trial. + So returning a random combination of motions (including no motion for a trial) + e.g. one trial can have no motion or no_motion+spikes + """ + no_motion = np.zeros(duration) + motions = [no_motion, pulse, shift, LFO, spike] + motion = sum(motions[: random.randint(0, 5)]) + if isinstance(motion, list) and len(motion) == 0: + return no_motion + elif np.array(motion).size == 1: + return no_motion + else: + return np.array(motion) + + +def generate_synth_data( + hrf_scale: float, sample_rate: float, trial_length: float +) -> np.ndarray: + """ + y(t) = hrf_scale * u_true(t) + \\phi_sim(t) + \\eta(t) + r(t), where: + - u_true(t) is the true HRF, made by simulated_hemodynamics() + - \\phi_sim(t) is the physiological noise, made from sim_physio_noise() + - \\eta(t) is "random" or white noise + - r(t) is motion noise + """ + sample_rate = sample_rate + trial_length = trial_length + hrf_scale = hrf_scale + + phi_sim = sim_physio_noise(sample_rate, trial_length) + + u_true = generate_hrf(hrf_scale, sample_rate, trial_length) + + measurement_noise = generate_measurement_noise(sample_rate, trial_length) + + motion_noise = generate_motion_noise(sample_rate, trial_length) + + return u_true + phi_sim + measurement_noise + motion_noise diff --git a/signal_processing/synthetic_data/dummy_generator.py b/signal_processing/synthetic_data/dummy_generator.py new file mode 100644 index 000000000..478796f19 --- /dev/null +++ b/signal_processing/synthetic_data/dummy_generator.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import logging + +import numpy as np +import scipy.stats + +# colored noise related +from numpy import sqrt, newaxis +from numpy import sum as npsum +from numpy.fft import irfft, rfftfreq +from numpy.random import normal +from scipy.stats import truncnorm + + +def _make_time_index(duration: float, sample_rate: float) -> np.ndarray: + """makes indices for a time series of given duration (in s) + and sample rate (in hz) + """ + if duration < 1 / sample_rate: + raise RuntimeError( + f"sample rate {sample_rate}\ + too slow for duration {duration}!" + ) + return np.arange(0, duration, 1 / sample_rate) + + +def gamma_shape_rate(x: np.ndarray, shape: float, rate: float) -> np.ndarray: + """ + scipy.stats.gamma is parameterized by shape and scale=1/rate, + we flip this for convenience + """ + scale = 1 / rate + return scipy.stats.gamma.pdf(x, a=shape, scale=scale) + + +def double_gamma_hrf( + t: float, + A: float, + alpha1: float = 6.0, + alpha2: float = 16.0, + beta1: float = 1, + beta2: float = 1, + c: float = 1 / 6, +) -> np.ndarray: + """ + HRF as linear combination of two gamma distributions + Default parameters match: + https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3318970/#FD7 + """ + return A * ( + gamma_shape_rate(t, alpha1, beta1) - c * gamma_shape_rate(t, alpha2, beta2) + ) + + +def simulated_hemodynamics( + amplitude: float, duration: float, sample_rate: float, pad_len: int = 0 +) -> np.ndarray: + """Generate simulated hemodynamics + + Generate simulated hemodynamics using double-gamma HRF over + a particular window and sample rate + + Attributes: + ---------- + pad_len: int + how many samples to shift the HRF + [Default = 0] + e.g. pad_len = 50 + shift the HRF start by 50 samples + """ + ts = _make_time_index(duration, sample_rate) + # canonical HRF is in seconds + hrf = double_gamma_hrf(ts, amplitude) + # shift the initiation point + # only left padding to shift with constant 0 + if int(duration * sample_rate) == 0: + logging.warning( + "Your int(duration * sample_rate) is zero. \ + Returned array will be empty." + ) + if pad_len > int(duration * sample_rate): + logging.warning( + "Your pad_len is larger than the whole data. \ + Returned array will be all zeros." + ) + hrf = np.pad(hrf, (pad_len, 0), "constant")[: int(duration * sample_rate)] + + return hrf + + +def dummy_physiological_noise( + amplitude: float, + sample_rate: float, + interest_freq: float, + phase: float, + duration: float, +) -> np.ndarray: + """Generate dummy physiological noise + + Attributes: + ---------- + amplitude: float + controls the amplitude of the dummy signal + sample_rate: float + sampling frequency + interest_freq: float + which frequency to generate the signal on + e.g. sample_rate = 5 and interest_freq = 1 + means the sampling frequency is 5 Hz + but the dominant power exists as 1 Hz + phase: float + phase of the signal + duration: float + duration of signal + + Returns: + ---------- + dummy_noise: np.ndarray + generated dummy signal + + References: + ---------- + Nguyen et. al. 2018: "Adaptive filtering of + physiological noises in fNIRS data" + Scarpa et. al. 2013: "A reference-channel based methodology + to improve estimation of event-related + hemodynamic response from fNIRS measurements" + Equation (4) + """ + + ts = _make_time_index(duration, sample_rate) + + dummy_noise = amplitude * np.sin(2 * np.pi * interest_freq * ts + phase) + + return dummy_noise + + +def motion_noise( + motion_amplitude: float, + motion_duration_mean: float, + sample_rate: float, + sample_duration: float, +) -> np.ndarray: + """ + Best guess of motor drift from Scarpa et al. 2013 + Attributes: + ---------- + motion_amplitude: float + slope of the motion drift + motion_duration_mean: float + mean duration of motion + sample_rate: float + sampling frequency + duration: float + duration of signal + + Returns: + ---------- + dummy_noise: np.ndarray + generated dummy signal + Notes: + -------- + from Scarpa et al 2013: + "In order to simulate artifacts (e.g., due to movements of the participant or + shifts of a source or a detector) short non-cyclic abrupt drifts were added in + 6 out of30 par- ticipants (it is the typical fraction noticed in our + experiment), at a random temporal position and with random amplitude, + and is repre- sented by the noise term r(t), different channel by channel. + + this is super vague, but just so we have an implementation: + - let's pretend "non cyclic abrupt drift" is a linear trend with some slope + - let's pretend "random temporal position" is interval based on + uniform start and truncated normal duration (truncated at 0) + with parametric mean and constant coeff of variation=1" + + References: + ---------- + Scarpa et. al. 2013: "A reference-channel based methodology + to improve estimation of event-related + hemodynamic response from fNIRS measurements" + p.109 + + """ + times = _make_time_index(sample_duration, sample_rate) + motion_start = np.random.randint(low=0, high=len(times)) + + # duration with constant coeff of variation + motion_duration = truncnorm.rvs( + loc=motion_duration_mean * sample_rate, + scale=motion_duration_mean * sample_rate, + a=0, + b=np.inf, + ) + + motion_times = _make_time_index(motion_duration, sample_rate) + + # in case we picked a motion duration greater than the window length, + # it's still valid, but we truncate + motion_times = motion_times[ + : min(motion_start + len(motion_times), len(times) - motion_start) + ] + + noise = motion_times * motion_amplitude + out = np.zeros_like(times) + out[motion_start : (motion_start + len(motion_times))] = noise + return out diff --git a/signal_processing/synthetic_data/tests/__init__.py b/signal_processing/synthetic_data/tests/__init__.py new file mode 100644 index 000000000..860ac27c6 --- /dev/null +++ b/signal_processing/synthetic_data/tests/__init__.py @@ -0,0 +1,2 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. diff --git a/signal_processing/synthetic_data/tests/test_additive_synth_dataset_variant.py b/signal_processing/synthetic_data/tests/test_additive_synth_dataset_variant.py new file mode 100644 index 000000000..7d064ef45 --- /dev/null +++ b/signal_processing/synthetic_data/tests/test_additive_synth_dataset_variant.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import unittest + +import numpy as np +from ..additive_synth_dataset_variant import ( + AdditiveSynthDatasetVariant, + generate_synth_data, + hrf_scale_from_channel_dists, +) + + +sample_rate = 10 +trial_length = 26 +hrf_scale = 1 + + +class AdditiveSyntheticDatasetVariantTest(unittest.TestCase): + @unittest.skip("only for manual local testing") + def test_generate_synth_data_one_trial(self): + + trial = generate_synth_data(hrf_scale, sample_rate, trial_length) + self.assertEqual(len(trial), trial_length * sample_rate) + + def test_generate_synth_data_multiple_channels(self): + class_labels = ["0", "1", "2"] + n_channels = 9 + hrf_scale_for_cond = np.zeros((n_channels, len(np.unique(class_labels)))) + hrf_scale_for_cond[0:2, 0] = 0.8 # first 10 channels responsive to cond1 + hrf_scale_for_cond[3:5, 1] = 0.7 # second 10 channels responsive to cond2 + hrf_scale_for_cond[6:8, 2] = 0 # second 10 channels responsive to cond3 + n_trials = 10 + trial_length = 26 + channel_dists = [10, 10, 10, 20, 20, 20, 30, 30, 30] + sample_rate = 10 + data = AdditiveSynthDatasetVariant( + n_trials, + trial_length, + channel_dists, + class_labels, + hrf_scale_for_cond, + sample_rate, + ) + + X = np.zeros( + (data.n_trials, data.n_channels, int(trial_length * sample_rate)) + ) # output data + + hrf_scales_from_distance = hrf_scale_from_channel_dists(channel_dists) + + for trial in range(n_trials): + for label in range(len(class_labels)): + for channel, hrf_scale_from_distance in enumerate( + hrf_scales_from_distance + ): + hrf_scale_final = ( + hrf_scale_from_distance * hrf_scale_for_cond[channel, label] + ) + X[trial, channel, :] = generate_synth_data( + hrf_scale_final, data.sample_rate, data.trial_length + ) + + # check dimensions for returned data, should be similar to output data (X) + self.assertEqual(X.shape, (10, 9, 260)) diff --git a/signal_processing/synthetic_data/tests/test_dummy_generator.py b/signal_processing/synthetic_data/tests/test_dummy_generator.py new file mode 100644 index 000000000..2a42991c7 --- /dev/null +++ b/signal_processing/synthetic_data/tests/test_dummy_generator.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import unittest + +import numpy as np +from colorednoise import powerlaw_psd_gaussian +from ..dummy_generator import ( + _make_time_index, + dummy_physiological_noise, + motion_noise, + simulated_hemodynamics, +) +from mne.time_frequency import psd_array_multitaper +from scipy.signal import savgol_filter + + +class DummyGeneratorTest(unittest.TestCase): + def setUp(self): + np.random.seed(42) + + def test_time_index(self): + + with self.assertRaises(RuntimeError): + _make_time_index(0.001, 100) + + time_index = _make_time_index(1, 1000) + + self.assertTrue(np.allclose(time_index, np.arange(0, 1, 0.001))) + + time_index = _make_time_index(5, 50) + self.assertEqual(time_index.shape[0], 50 * 5) + + def test_consistent_behavior(self): + # duration and sample_rate should work the same way in all the generators + + sample_rate = 50 + duration = 1 + n_samples = duration * sample_rate + sim_Hb = simulated_hemodynamics( + amplitude=1, sample_rate=sample_rate, duration=duration + ) + + physio_noise = dummy_physiological_noise( + amplitude=4e-7, + sample_rate=sample_rate, + interest_freq=1, + phase=np.pi / 4, + duration=duration, + ) + + measurement_noise = powerlaw_psd_gaussian(exponent=1.0, size=n_samples) + + self.assertEqual(sim_Hb.shape, physio_noise.shape) + self.assertEqual(sim_Hb.shape, measurement_noise.shape) + + def test_sim_hemodynamics(self): + duration = 30 + sample_rate = 50 + + # Create simulated hemodynamics + + sim_Hb1 = simulated_hemodynamics( + amplitude=1, sample_rate=sample_rate, duration=duration + ) + + sim_Hb2 = simulated_hemodynamics( + amplitude=1, sample_rate=sample_rate * 2, duration=duration + ) + + # check that peak is basically 5s + ts1 = _make_time_index(duration, sample_rate) + ts2 = _make_time_index(duration, sample_rate * 2) + self.assertAlmostEqual(np.abs(ts1[np.argmax(sim_Hb1)] - 5.0), 0) + self.assertAlmostEqual(np.abs(ts2[np.argmax(sim_Hb2)] - 5.0), 0) + + def test_physiological_noise(self): + duration = 1 + sample_rate = 1000 + interest_freq = 1 + + sim_Hb = simulated_hemodynamics( + amplitude=1, sample_rate=sample_rate, duration=duration + ) + # Simulated cardiac noise + cardiac_wave = dummy_physiological_noise( + amplitude=4e-7, + sample_rate=sample_rate, + interest_freq=interest_freq, + phase=np.pi / 4, + duration=duration, + ) + # Check dimensions + self.assertEqual(sim_Hb.shape, cardiac_wave.shape) + + # Calculate the PSD and make sure the peak is in 1 Hz + # calculate multitaper PSD + psds, freqs = psd_array_multitaper(cardiac_wave, sample_rate, n_jobs=12) + power_log10 = np.log10(psds) + ind_of_peak = np.unravel_index( + np.argmax(power_log10, axis=None), power_log10.shape + )[0] + self.assertAlmostEqual(freqs[ind_of_peak], interest_freq) + + cardiac_wave_double_freq = dummy_physiological_noise( + amplitude=4e-7, + sample_rate=sample_rate * 2, + interest_freq=interest_freq, + phase=np.pi / 4, + duration=duration, + ) + + self.assertTrue(np.allclose(cardiac_wave, cardiac_wave_double_freq[::2])) + + def test_pink_noise(self): + beta = 1 + sample_rate = 500 + duration = 2 + n_samples = duration * sample_rate + y = powerlaw_psd_gaussian(exponent=beta, size=n_samples) + # calculate the PSD + psds, freqs = psd_array_multitaper(y, sample_rate, n_jobs=12) + + # after enough smoothing on PSD, + # pink noise should follow smooth 1/f curve + # thus low freq power > high freq power + psds_smooth = savgol_filter(psds, 101, 3) + low_freq_power = np.median(psds_smooth[:10]) + med_freq_power = np.median(psds_smooth[80:110]) + high_freq_power = np.median(psds_smooth[400:]) + self.assertGreaterEqual(low_freq_power, med_freq_power) + self.assertGreaterEqual(med_freq_power, high_freq_power) + + # make sure behavior is same if we change sampling frequency + # TODO: make this a slightly more robust test + y_double_freq = powerlaw_psd_gaussian(exponent=beta, size=2 * n_samples) + psds, freqs = psd_array_multitaper(y_double_freq, sample_rate, n_jobs=12) + + psds_smooth = savgol_filter(psds, 101, 3) + low_freq_power = np.median(psds_smooth[:10]) + med_freq_power = np.median(psds_smooth[80:110]) + high_freq_power = np.median(psds_smooth[400:]) + self.assertGreaterEqual(low_freq_power, med_freq_power) + self.assertGreaterEqual(med_freq_power, high_freq_power) + + def test_motion_noise(self): + duration = 20 + sample_rate = 5000 + + # this conveniently checks that we don't throw if duration + # exceeds window ize + noise = motion_noise( + motion_amplitude=3, + motion_duration_mean=0.5, + sample_rate=sample_rate, + sample_duration=duration, + ) + + # the nonzero (noise) part should have slope \approx motion_amplitude + # but this is not a tight test since we get a random chunk + nonzero = noise[noise > 0] + slope = (nonzero[-1] - nonzero[0]) / len(nonzero) * sample_rate + self.assertAlmostEqual(slope, 3.0, places=2) + + sample_rate = 150 + noise = motion_noise( + motion_amplitude=1.75, + motion_duration_mean=0.5, + sample_rate=sample_rate, + sample_duration=duration, + ) + + nonzero = noise[noise > 0] + slope = (nonzero[-1] - nonzero[0]) / len(nonzero) * sample_rate + self.assertAlmostEqual(slope, 1.75, places=2) diff --git a/signal_processing/utils/data_view/_data_view_common_docs.py b/signal_processing/utils/data_view/_data_view_common_docs.py new file mode 100644 index 000000000..525f653f8 --- /dev/null +++ b/signal_processing/utils/data_view/_data_view_common_docs.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from ..doc.docfill import fill_in_docstring + +__all__ = ["data_view_common_docs"] + +data_view_common_docs = fill_in_docstring( + { + "size": """size: int + The size of each chunk + """, + "step": """step: int + How much to move by when taking a chunk. + """, + "ragged": """ragged: bool, optional, default False + If true, the last chunk could be smaller than the specified size. If + false, the last chunk that would have a number of element fewer than size + is discarded. + """, + } +) diff --git a/signal_processing/utils/data_view/array_utils.py b/signal_processing/utils/data_view/array_utils.py new file mode 100644 index 000000000..a32ecd98c --- /dev/null +++ b/signal_processing/utils/data_view/array_utils.py @@ -0,0 +1,667 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +import multiprocessing +from enum import Enum, auto +from itertools import count, product +from numbers import Number +from typing import ( + Any, + Callable, + Iterable, + Iterator, + Optional, + Union, + List, + Tuple, + Dict, + TypeVar, +) + +import numpy as np +from skimage.util import view_as_windows + +from ._data_view_common_docs import data_view_common_docs + +""" +This file holds simple utility functions for operations on numpy arrays. +""" + +A = TypeVar("A") +Index = Union[int, List, Tuple, slice, np.ndarray] +Indexer = Union[Index, Tuple[Index, ...]] + + +def is_index(value) -> bool: + return type(value) in (int, list, tuple, slice, np.ndarray) + + +def is_indexer(value) -> bool: + if type(value) is tuple: + return all(is_index(v) for v in value) + else: + return is_index(value) + + +def check_index(index: Index) -> "Index": + if not is_index(index): + raise IndexError( + "Index '{}' is not one of the valid index types: " + "[int, list, tuple, slice, np.ndarray]".format(index) + ) + else: + return index + + +def check_indexer(indexer: Indexer, ndim: int = 2) -> "Indexer": + if not is_indexer(indexer): + raise IndexError( + "Indexer '{}' is not one of the valid indexer types: " + "[Index, Tuple[Index, ...]]".format(indexer) + ) + if type(indexer) is tuple: + length = len(indexer) + else: + indexer, length = (indexer,), 1 + if not length == ndim and ndim is not None: + raise IndexError( + "Expected indexer of length '{}' but got indexer of length '{}'.".format( + ndim, len(indexer) + ) + ) + + return indexer + + +def is_one_axis_array(x: np.ndarray) -> bool: + """Evaluate whether the input array has multiple elements only along one axis + + Example: + + a.shape = (0,) -> True + a.shape = (5,) -> True + a.shape = (1,) -> True + a.shape = (5,1) -> True + a.shape = (1,5) -> True + a.shape = (5,2) -> False + a.shape = (1,1,5) -> True + a.shape = (2,1,5) -> False + a.shape = (1,1,1,1) -> True + + Args: + ---- + x: np.ndarray, input array to evaluate + + Returns: + ---- + Returns true if input array has multiple elements only along one axis + """ + + x_dims = x.shape + # case is when x is one dimensional + if len(x_dims) == 1: + return True + # case when x has only one element, dimension doesn't matter then + if x.size == 1: + return True + + # otherwise, x_dims should have none-1's at all except for one value + return np.sum(np.array(x_dims) > 1) == 1 + + +def d1_to_col(array: np.ndarray) -> np.ndarray: + """ + If 1D array, convert to column. + Otherwise return itself + """ + if len(array.shape) < 2: + return array.reshape((-1, 1)) + else: + # print("array not 1D, do nothing") + return array + + +def d1_to_row(array: np.ndarray) -> np.ndarray: + """ + If 1D array, convert to row. + Otherwise return itself + """ + if len(array.shape) < 2: + return array.reshape((1, -1)) + else: + # print("array not 1D, do nothing") + return array + + +def scalar_to_array(value) -> np.ndarray: + if np.isscalar(value): + return np.atleast_1d(value) + else: + return value + + +def scalar_to_slice_index(value: Index) -> "Index": + value = check_index(value) + if type(value) is int: + return slice(value, value + 1, 1) + else: + return value + + +def scalars_to_slice_indexer(value: Indexer, ndim: int = 2) -> "Indexer": + if not type(value) is tuple: + indexer = scalar_to_slice_index(check_indexer(value)) + else: + indexer = tuple(scalar_to_slice_index(v) for v in check_indexer(value)) + + return check_indexer(indexer, ndim=ndim) + + +def raise_1d_to_2d(array: np.ndarray) -> np.ndarray: + # Raise 1D array to 2D array with singleton dimension on right + assert array.ndim == 1 or array.ndim == 2 + if array.ndim == 1: + array = array[:, None] + + return array + + +def diagflat(v: np.ndarray, k: int = 0) -> np.ndarray: + """Wraps "np.diagflat" for >= 2D inputs.""" + # Transform flat array to diagonal matrix + v = ( + np.diagflat(v, k=k) + if v.ndim == 1 + else v[..., None] * np.eye(v.shape[-1], k=k)[(v.ndim - 1) * (None,)] + ) + + return v + + +def diagonal(v: np.ndarray, k: int = 0) -> np.ndarray: + """Wraps "np.diagonal" for >= 2D inputs.""" + # Transform diagonal matrix to flat array + v = np.diagonal(v, offset=k, axis1=-2, axis2=-1) + + return v + + +def upcast_ndim(x: np.ndarray, ndim: int) -> np.ndarray: + """Upcast array to dimensionality. + + NumPy matrix operations typically allow array broadcasting such that dimensions between two arrays can either + match or equal 1. + + Additionally, dimensions on the left may be missing from the left on one of two arrays, which would otherwise + satisfy the aforementioned constraints. In this case, the lower-rank array is treated as if it had singleton + dimensions to its left, which is equivalent to "upcasting". This allows for matrix operations on arrays of differing + ranks but otherwise compatible shapes. + + For more info, see https://numpy.org/doc/stable/user/basics.broadcasting.html. + + For example: np.ones((8, 4, 5, 1)) * np.ones((4, 1, 10)) -> np.ones((8, 4, 5, 10)) + + Args: + ---- + x: np.ndarray, input array to upcast + ndim: int, dimensionality to upcast to + + Returns: + ---- + Returns upcasted input array + """ + # Upcast array to dimensionality + shape = (ndim - x.ndim) * (1,) + x.shape + if not x.shape == shape: + x = x.reshape(shape) + + return x + + +def upcast_tile(x: np.ndarray, shape: Tuple) -> np.ndarray: + """Upcasts and tiles array to be match given shape. + + For NumPy operations that don't support flexible array broadcasting, it may also be necessary to ensure both arrays + have identical shapes. Singleton dimensions can simply be tiled to match the shape of the other array. + + For example: {np.ones((4, 5, 1)), (4, 1, 10)} -> np.ones((4, 5, 10)) + + + Args: + ---- + x: np.ndarray, input array to upcast and tile + + Returns: + ---- + Returns upcasted and tile input array + """ + # Upcast array then tile singleton dimensions + x = upcast_ndim(x, len(shape)) + if not x.shape == shape: + assert not any(not xs == s and not xs == 1 for xs, s in zip(x.shape, shape)) + x = np.tile(x, [1 if xs == s else s for xs, s in zip(x.shape, shape)]) + + return x + + +class SliceMaker(object): + """ + Utility to enable using slice syntax in functions + """ + + def __getitem__(self, item): + return item + + +""" +Object that can be used to generate slice object via +numpy style slicing notation. + +Ex. + ``` + >>> array = np.ones((5)) + >>> new_array = array[make_slice[0:3]] + >>> new_array + array([1., 1., 1.]) + ``` +Use this for the `select_x` methods in `TimeSeries` classes +""" +make_slice = SliceMaker() + + +""" +Function that act as masks that operate on numpy arrays, +convenience functions to serve as partial functions +""" + + +def equal_mask(x: np.ndarray, target: float) -> np.ndarray: + return x == target + + +def less_mask(x: np.ndarray, target: float) -> np.ndarray: + return x < target + + +def greater_mask(x: np.ndarray, target: float) -> np.ndarray: + return x > target + + +def le_mask(x: np.ndarray, target: float) -> np.ndarray: + return x <= target + + +def ge_mask(x: np.ndarray, target: float) -> np.ndarray: + return x >= target + + +class IntervalTypes(Enum): + CLOSED_OPEN = auto() # [a, b) + OPEN_CLOSED = auto() # (a, b] + CLOSED_CLOSED = auto() # [a, b] + OPEN_OPEN = auto() # (a, b) + + +def closed_open_range_mask( + x: np.ndarray, end_points: Tuple[float, float] +) -> np.ndarray: + return (x >= end_points[0]) & (x < end_points[1]) + + +def open_closed_range_mask( + x: np.ndarray, end_points: Tuple[float, float] +) -> np.ndarray: + return (x > end_points[0]) & (x <= end_points[1]) + + +def closed_closed_range_mask( + x: np.ndarray, end_points: Tuple[float, float] +) -> np.ndarray: + return (x >= end_points[0]) & (x <= end_points[1]) + + +def open_open_range_mask(x: np.ndarray, end_points: Tuple[float, float]) -> np.ndarray: + return (x > end_points[0]) & (x < end_points[1]) + + +@data_view_common_docs +def chunk( + arr: A, + size: int, + step: int = 1, + ragged: bool = False, + slice_function: Optional[Callable[[A, int, int], A]] = None, + len_function: Optional[Callable[[A], int]] = None, +) -> Iterator[A]: + """Chunk an array into `size` segments every `step` elements. + + Parameters + ---------- + arr: A + Any object on which the provided slice_function and len_function + operate on. + {size} + {step} + {ragged} + slice_function: Optional[Callable[[A, int, int], A]] + The function that can be used to slice the input array `arr` into + segments. If no slice function is specified, `__slice__` is called. + len_function: Optional[Callable[[A], int]] + The function that can be used to get the length of the input array + `arr`. If no length function is specified, `__len__` is called. + + Examples + -------- + >>> import numpy as np + >>> list(chunk(np.arange(0, 10), 4, 2)) + [array([0, 1, 2, 3]), + array([2, 3, 4, 5]), + array([4, 5, 6, 7]), + array([6, 7, 8, 9])] + + See Also + -------- + skimage.view_as_windows: Rolling window view of the input n-dimensional array + """ + # These default functions work for 1D numpy arrays + # as well as lists and tuples. + if slice_function is None: + + def slice_function(x, start, stop): + return x[start:stop] + + if len_function is None: + + def len_function(x): + return len(x) + + arr_len = len_function(arr) + + def compare_value(k): + return k if ragged else k + size - 1 + + for k in count(start=0, step=step): + if compare_value(k) >= arr_len: + break + else: + yield slice_function(arr, k, k + size) + + +# I stole these functions from https://stackoverflow.com/a/45555516 + + +def _unpacking_apply_along_axis( + all_args: Tuple[Callable, int, np.ndarray, Tuple, Dict] +) -> np.ndarray: + """ + Like numpy.apply_along_axis(), but with arguments in a tuple + instead. + + This function is useful with multiprocessing.Pool().map(): (1) + map() only handles functions that take a single argument, and (2) + this function can generally be imported from a module, as required + by map(). + """ + (func1d, axis, arr, args, kwargs) = all_args + return np.apply_along_axis(func1d, axis, arr, *args, **kwargs) + + +def parallel_apply_along_axis( + func1d: Callable, + axis: int, + arr: np.ndarray, + *args: Tuple, + pool: Optional["multiprocessing.Pool"] = None, + **kwargs: Dict +) -> np.ndarray: + """ + Like numpy.apply_along_axis(), but takes advantage of multiple + cores. + """ + # Effective axis where apply_along_axis() will be applied by each + # worker (any non-zero axis number would work, so as to allow the use + # of `np.array_split()`, which is only done on axis 0): + effective_axis = 1 if axis == 0 else axis + if effective_axis != axis: + arr = arr.swapaxes(axis, effective_axis) + + # Chunks for the mapping (only a few chunks): + chunks = [ + (func1d, effective_axis, sub_arr, args, kwargs) + for sub_arr in np.array_split(arr, multiprocessing.cpu_count()) + ] + + if pool is None: + pool = multiprocessing.Pool() + close_pool = True + else: + close_pool = False + + individual_results = pool.map(_unpacking_apply_along_axis, chunks) + + # Freeing the workers: + if close_pool: + pool.close() + pool.join() + + return np.concatenate(individual_results) + + +def grouped(iterable: Iterable[Any], n: int) -> Iterable[Iterable[Any]]: + "s -> (s0,s1,s2,...sn-1), (sn,sn+1,sn+2,...s2n-1), (s2n,s2n+1,s2n+2,...s3n-1), ..." + return zip(*[iter(iterable)] * n) + + +def nan_like(target: Union[Number, np.ndarray]) -> Union[Number, np.ndarray]: + if isinstance(target, Number): + return np.nan + elif isinstance(target, np.ndarray): + out = np.empty(target.shape) + out[:] = np.nan + return out + else: + raise ValueError("Target is not supported") + + +def window_inplace( + x: np.ndarray, window: Union[int, Tuple[int, int]], causal: bool = False +) -> np.ndarray: + """Wrapper around "skimage.util.view_as_windows" to support arbitray, causal or acausal window shifts. + + The window should either be an integer sample size or a tuple with inclusive left and right bounds relative to + the current sample (considered as 0). The latter allows for arbitrarily time-shifted and asymmetric windows. + + Windowing with a for loop and copy is slow and memory-hungry. For an arbitrary array with window W, the size + of the array in memory increases by a factor of W. For large arrays, this can easily fill up RAM. Instead of + copying, one can instead take a strided view into the array that simply repeats the indices to simulated the + windowed array in place without reallocating memory. + + Args: + ---- + x: np.ndarray, input array to window + window: int or tuple pair of ints, window size or inclusive window bounds relative to current sample + causal: bool, when using an integer window whether to distribute the samples causally or not + + Returns: + ---- + Returns windowed input array + """ + # Window in-place (without copying) using memory-efficient stride tricks + if type(window) is int: + window = ( + (-((window - 1) // 2), (window - 1) // 2 + (window - 1) % 2) + if not causal + else (-window + 1, 0) + ) + assert window[0] <= window[1] + x = np.pad( + x, + ((-min(window[0], 0), max(window[1] - 1, 0) + 1),) + (x.ndim - 1) * ((0, 0),), + mode="constant", + ) + w = view_as_windows(x, (window[1] - window[0] + 1,) + x.shape[1:])[ + None if window[0] < 0 else window[0] : None if window[1] > 0 else window[1] - 1 + ] + if x.ndim > 1: + w = w.squeeze(axis=tuple(range(1, x.ndim))) + w = w.transpose((0,) + tuple(range(2, w.ndim)) + (1,)) + + return w + + +def mix_array( + x: np.ndarray, + nrepeat: Optional[int] = None, + axis: Tuple[int, int] = (0, -1), + mixing: Optional[np.ndarray] = None, +) -> np.ndarray: + """Multiplies ND array by an independent 2D mixing matrix. + + Consider the simplest block-diagonal (mixing) matrix, the identity matrix, for example: + [[1, 0, 0, 0, 0, 0], + [0, 1, 0, 0, 0, 0], + [0, 0, 1, 0, 0, 0], + [0, 0, 0, 1, 0, 0], + [0, 0, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 1]] + This constitutes no effective mixing or scaling. + + An arbitrary block-diagonal matrix contains sub-matrices (blocks) >= 1 x 1 placed along the diagonal as so: + [[A, 0, 0]] + [[0, B, 0]] + [[0, 0, c]] + + In practice, the blocks can be of varying (but always rectangular) shapes. + [[6, 4, 0, 0, 0, 0], + [2, 1, 0, 0, 0, 0], + [0, 0, 1, 1, 4, 0], + [0, 0, 3, 8, 5, 0], + [0, 0, 6, 7, 1, 0], + [0, 0, 0, 0, 0, 1]] + + However, unlike "scipy.linalg.block_diag," the mixing matrix need not be a block-digonal matrix, and can instead + mixing the array with arbitray mixing patterns. + + Args: + ---- + x: np.ndarray, input array to mix + nrepeat: int, number of times to effectively repeat array when using the default identity mixing matrix + axis: tuple, pair of axes to multiply mixing matrix with, with the latter being repeated 2nd dimension of mixing + matrix # of times + mixing: np.ndarray, mixing matrix to multiply input array with + + Returns: + ---- + Returns mixed input array + """ + # Parse axes + assert x.ndim >= 2 + assert len(axis) == 2 + axis = [ax % x.ndim for ax in axis] + if axis[0] == axis[1]: + x = x[(axis[1] + 1) * (slice(None, None, None),) + (None,)] + if nrepeat is None: + nrepeat = x.shape[axis[0]] + assert nrepeat == x.shape[axis[0]] or x.shape[axis[0]] == 1 or nrepeat == 1 + + # Apply mixing matrix + x = x[(axis[1] + 1) * (slice(None, None, None),) + (None,)] + if mixing is None: + mixing = np.eye(nrepeat) + else: + assert mixing.ndim == 2 + mixing = mixing[ + (min(axis) + int(axis[1] < axis[0])) * (None,) + + (slice(None, None, None),) + + (abs(axis[1] - axis[0]) - int(axis[1] < axis[0])) * (None,) + + (slice(None, None, None),) + ] + x = x * mixing + x = x.reshape(x.shape[: axis[1]] + (-1,) + x.shape[axis[1] + 2 :]) + + return x + + +def upcast_apply( + func: Callable[[Any], Any], + *args: Tuple, + narr: Optional[int] = None, + sub_ndim: int = 2, + **kwargs: Dict +) -> Optional[Any]: + """ + Allows normally *D-only (typically linear algebra) functions to support ND inputs. Many, but not all, NumPy and + SciPy functions support this natively for functions that normally operate on 2D inputs. + + Args: + ---- + func: callable, function to call on *D slices of upcasted input arrays + *args: variable arguments, arrays to provide function followed by any positional arguments + narr: int, number of positional arguments to consider to be arrays (otherwise inferred) + sub_ndim: int, dimensionality of array slices to provide function + **kwargs: variable named arguments, named keyword arguments to provide function + + Returns: + ---- + Returns mixed input array + """ + # Parse arrays + if narr is None: + arr = () + for i, arg in enumerate(args): + if isinstance(arg, np.ndarray): + arr += (arg,) + else: + args = args[i:] + break + else: + arr, args = args[:narr], args[narr:] + ndim = max(a.ndim for a in arr) + assert ndim >= sub_ndim + + # Cast inputs as needed then apply function + if ndim == 2: + out = func(*(arr + args), **kwargs) + else: + arr = tuple(upcast_ndim(a, ndim) for a in arr) + assert not any( + not all((s[0] == ss for ss in s or s[0] == 1 or ss == 1) for ss in s) + for s in zip(*[a.shape[:-sub_ndim] for a in arr]) + ) + samples = tuple( + max(sample) for sample in zip(*[a.shape[:-sub_ndim] for a in arr]) + ) + out = [ + func( + *[ + a[tuple(index % s for index, s, in zip(indices, a.shape))] + for a in arr + ], + **kwargs + ) + for indices in product(*[range(sample) for sample in samples]) + ] + if not isinstance(out[0], tuple): + out = np.array(out) + else: + for ( + i, + o, + ) in enumerate(zip(*out)): + if not o[0].shape: + out[i] = np.reshape(o, samples) + elif len(o[0]): + out[i] = np.reshape(o, samples + o[0].shape) + else: + shape, slc = zip( + *[ + (s, slice(None, None, None)) + if s + else (1, slice(0, 0, None)) + for s in o[0].shape + ] + ) + slc = len(samples) * (slice(None, None, None),) + slc + out[i] = np.empty(samples + shape)[slc] + + return out diff --git a/signal_processing/utils/doc/docfill.py b/signal_processing/utils/doc/docfill.py new file mode 100644 index 000000000..a06ead75e --- /dev/null +++ b/signal_processing/utils/doc/docfill.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +"""Filling out a docstring with identifier placeholders. + +The docstring filling decorator and associated functions are based off +scipy._lib.doccer, which has been moved to an internal location in scipy. +""" + +from typing import Any, Callable, Dict + +__all__ = ["fill_in_docstring"] + +AnyFunction = Callable[[Any], Any] + + +def fill_in_docstring(keywords: Dict[str, str]) -> Callable[[AnyFunction], AnyFunction]: + """Replace identifiers like `{ident}` in a docstring. + + Parameters + ---------- + keywords : Dict[str, str] + A mapping from identifiers to strings. + + Returns + ------- + decorator + A decorator that will replace f-string like identifiers in a docstring. + + Example + ------- + >>> common_doc = fill_in_docstring({'ident': 'This identifier '}) + >>> + >>> @common_doc + ... def f(x): + ... '''{ident} is common between functions!''' + ... pass + >>> + >>> f.__doc__ == "This identifier is common between functions!" + True + """ + + def _docfill(f): + f.__doc__ = f.__doc__.format( + **{ + identifier: description.rstrip() + for identifier, description in keywords.items() + } + ) + return f + + return _docfill diff --git a/signal_processing/utils/types/channel_info.py b/signal_processing/utils/types/channel_info.py new file mode 100644 index 000000000..5316577f7 --- /dev/null +++ b/signal_processing/utils/types/channel_info.py @@ -0,0 +1,298 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Any, Dict, List, Optional, Union + +import numpy as np + +from ..data_view.array_utils import is_one_axis_array + + +class ChannelInfo(object): + """ + This is a parent class to represent different sensor + modality's channel information. + + A `ChannelInfo` object can combine with a `TimeSeries` to form + `LabeledTimeSeires`, where each column of the `TimeSeries`' + `channel_data` can be described by each "row" of the + `ChannelInfo`. + + The individual "columns" of attributes can be accessed by + `my_channelinfo.myattr`. + + Slicing by different channels (or "rows") is supported, e.g. + `my_channelinfo[0:2]` will return a new `ChannelInfo` object + with the information about the first two channels only. + + All properties/attributes are readonly. + + Note that for each specific sensor modality, `ChannelInfo` should + be subclassed (e.g. `DOTChannelInfo`). The child class should + provide and instantiate the attributes in the constructor, and + provide its generation from `HardwareMetadata`, if applicable. + """ + + info_type: str + n_channels: int + + def __init__(self, info_type: str, n_channels: int = 0) -> None: + """ + Args: + --- + info_type: str, to describe the ChannelInfo + n_channels: int. Number of channels + """ + self.info_type = info_type + self.n_channels = n_channels + + def __repr__(self) -> str: + info_array = self.info_array + if len(info_array) == 0: + return f"{self.info_type}: Empty" + header = "|".join(info_array.dtype.names) + return f"{self.info_type}\n{header}\n{info_array}" + + @property + def info_array(self) -> np.recarray: + # Return the recarray formed from the ndarray attributes + # Preserving dtype + columns = [] + header = "" + for k, v in self.__dict__.items(): + if type(v) != np.ndarray: + continue + header += k + ", " + columns.append(v) + return np.core.records.fromarrays(columns, names=header) + + # All properties are readonly! And I don't want to make and decorate + # a new method for each property + def __setattr__(self, name: str, value: any) -> None: + if name in self.__dict__: + raise AttributeError( + f"Forbidden to set attributes for {self.__class__.__name__}" + ) + else: + self.__dict__[name] = value + + def __delattr__(self, name: str) -> None: + raise AttributeError( + f"Forbidden to delete attributes from {self.__class__.__name__}" + ) + + def __eq__(self, other: "ChannelInfo") -> bool: + same = True + for index, (value, other_value) in enumerate( + zip(self.__dict__.values(), other.__dict__.values()) + ): + if index < 2: + # ignore baseclass init arguments + continue + same &= np.array_equal(value, other_value) + + return same + + def __hash__(self) -> int: + hash_values = [] + for _, value in sorted(vars(self).items()): + if isinstance(value, np.ndarray): + hash_values.append(value.data.tobytes()) + else: + hash_values.append(value) + return hash(tuple(hash_values)) + + def __getitem__(self, key: Union[List, slice, np.ndarray, int]) -> Any: + func_args = [] + # Class attribute definition order is preserved iin __dict__ + # per PEP0520 (https://www.python.org/dev/peps/pep-0520/) + for index, value in enumerate(self.__dict__.values()): + if index < 2: + # ignore baseclass init arguments + continue + if type(value) is np.ndarray: + func_args.append(np.atleast_1d(value[key])) + else: + func_args.append(value) + return self.__class__(*func_args) + + def check_and_assign( + self, var_name: str, value: np.ndarray, var_dtype: np.dtype + ) -> None: + if value.dtype != var_dtype: + raise TypeError(f"{var_name}.dtype != {var_dtype}") + setattr(self, var_name, value) + + def __len__(self): + # len(channel_info) returns number of channels + return self.n_channels + + +class DOTChannelInfo(ChannelInfo): + # Inherited properties + # info_type: str + + # Properties + detector_idx: np.ndarray # int + source_idx: np.ndarray # int + distance: np.ndarray # float + source_x: np.ndarray # int + source_y: np.ndarray # int + source_z: np.ndarray # int + detector_x: np.ndarray # float + detector_y: np.ndarray # float + detector_z: np.ndarray # float + carrier_freq: np.ndarray # float + carrier_freq_idx: np.ndarray # int + wavelength: np.ndarray # float + subdevice_idx: np.ndarray # int + + # The entries here is {wavelength_idx:wavelength} + # such as {0 : 830.0, 1 : 685.0} + wavelength_table: Dict[int, float] + + # Include device name which is useful for any dependent processing + device_name: str + + def __init__( + self, + detector_idx: np.ndarray, + source_idx: np.ndarray, + distance: np.ndarray, + source_x: np.ndarray, + source_y: np.ndarray, + source_z: np.ndarray, + detector_x: np.ndarray, + detector_y: np.ndarray, + detector_z: np.ndarray, + carrier_freq: np.ndarray, + carrier_freq_idx: np.ndarray, + wavelength: np.ndarray, + subdevice_idx: np.ndarray, + wavelength_table: Dict[int, float], + device_name: Optional[str] = None, + ): + # args checking + # check all arguments are the same length + args = list(locals().values()) + args = [x for x in args if type(x) == np.ndarray] + + # make sure they are all 1D arrays + if not all(is_one_axis_array(x) for x in args): + raise ValueError("The inputs need to have all elements on the same axis") + # make sure they are the same length + args_len = np.array([x.size for x in args if type(x) == np.ndarray]) + if not np.all(args_len == args_len[0]): + raise ValueError("The inputs need to all have the same length!") + super().__init__("Flattened DOT", args_len[0]) + + # assign them to things + self.check_and_assign("detector_idx", detector_idx, "int") + self.check_and_assign("source_idx", source_idx, "int") + self.check_and_assign("distance", distance, "float") + self.check_and_assign("source_x", source_x, "float") + self.check_and_assign("source_y", source_y, "float") + self.check_and_assign("source_z", source_z, "float") + self.check_and_assign("detector_x", detector_x, "float") + self.check_and_assign("detector_y", detector_y, "float") + self.check_and_assign("detector_z", detector_z, "float") + self.check_and_assign("carrier_freq", carrier_freq, "float") + self.check_and_assign("carrier_freq_idx", carrier_freq_idx, "int") + self.check_and_assign("wavelength", wavelength, "float") + self.check_and_assign("subdevice_idx", subdevice_idx, "int") + self.wavelength_table = wavelength_table + self.device_name = device_name + + def __repr__(self): + return ( + f"Device={self.device_name}\n" + + super().__repr__() + + f"\nwavelength_table: {self.wavelength_table}" + ) + + +class Metric2ChannelInfo(ChannelInfo): + # Essentially the same as DOTChannelInfo, but now + # labels whether a channel corresponds to Metric1 or not + + # Inherited property + # info_type: str + + # Propertys + detector_idx: np.ndarray # int + source_idx: np.ndarray # int + distance: np.ndarray # float + source_x: np.ndarray # int + source_y: np.ndarray # int + source_z: np.ndarray # int + detector_x: np.ndarray # float + detector_y: np.ndarray # float + detector_z: np.ndarray # float + is_Metric1: np.ndarray # bool -- True if Metric1, False if HbR + subdevice_idx: np.ndarray # int + + # Include device name which is useful for any dependent processing + device_name: str + + def __init__( + self, + detector_idx: np.ndarray, + source_idx: np.ndarray, + distance: np.ndarray, + source_x: np.ndarray, + source_y: np.ndarray, + source_z: np.ndarray, + detector_x: np.ndarray, + detector_y: np.ndarray, + detector_z: np.ndarray, + is_Metric1: np.ndarray, + subdevice_idx: np.ndarray, + device_name: Optional[str] = None, + ): + # args checking + # check all arguments are the same length + args = list(locals().values()) + args = [x for x in args if type(x) == np.ndarray] + + # make sure they are all 1D arrays + if not all(is_one_axis_array(x) for x in args): + raise ValueError("The inputs need to have all elements on the same axis") + # make sure they are the same length + args_len = np.array([x.size for x in args if type(x) == np.ndarray]) + if not np.all(args_len == args_len[0]): + raise ValueError("The inputs need to all have the same length!") + super().__init__("Flattened Metric2 data", args_len[0]) + + # assign them to things + self.check_and_assign("detector_idx", detector_idx, "int") + self.check_and_assign("source_idx", source_idx, "int") + self.check_and_assign("distance", distance, "float") + self.check_and_assign("source_x", source_x, "float") + self.check_and_assign("source_y", source_y, "float") + self.check_and_assign("source_z", source_z, "float") + self.check_and_assign("detector_x", detector_x, "float") + self.check_and_assign("detector_y", detector_y, "float") + self.check_and_assign("detector_z", detector_z, "float") + self.check_and_assign("is_Metric1", is_Metric1, "bool") + self.check_and_assign("subdevice_idx", subdevice_idx, "int") + self.device_name = device_name + + def __repr__(self): + return f"Device={self.device_name}" + super().__repr__() + + +class StringChannelInfo(ChannelInfo): + """ + This is to generate a generic ChannelInfo class that specify column names of a + a time series, To be added + """ + + column_names: Union[List[str], np.array] + + def __init__(self, column_names: Union[List[str], np.array]) -> None: + if type(column_names) is list: + column_names = np.array(column_names, dtype="U32") + if not is_one_axis_array(column_names): + raise ValueError("column_names must be 1D") + super().__init__("column_names", len(column_names)) + self.check_and_assign("column_names", column_names, "U32") diff --git a/signal_processing/utils/types/time_series.py b/signal_processing/utils/types/time_series.py new file mode 100644 index 000000000..9ab42c6c5 --- /dev/null +++ b/signal_processing/utils/types/time_series.py @@ -0,0 +1,711 @@ +#!/usr/bin/env python3 +# Copyright 2004-present Facebook. All Rights Reserved. + +from typing import Callable, Union, List, Tuple + +import h5py +import numpy as np + +from ..data_view.array_utils import Index, scalars_to_slice_indexer +from .channel_info import ChannelInfo, StringChannelInfo + + +def is_valid_timestamps(timestamps: np.ndarray) -> bool: + try: + length = len(timestamps) + except TypeError: + length = 0 + if length == 1: + return True + if not timestamps.ndim == 1: + return False + return np.all(np.diff(timestamps) > 0) + + +def check_timestamps_and_data(timestamps: np.ndarray, data: np.ndarray) -> bool: + if not is_valid_timestamps(timestamps): + print("timestamps needs to be 1D, flat and monotonically increasing") + return False + + if data.ndim != 2: + print("data needs to be a 2D array") + return False + + if len(timestamps) != len(data): + print("timestamps and data need the same number of samples") + return False + + return True + + +def match_data_shape_to_timestamps( + timestamps: np.ndarray, data: np.ndarray +) -> np.ndarray: + """ + Assume `timestamps` come from a column vector, and `data` come from + a [time, channels] array. + + Sliced `data` can lose dimension information -- restore it here to match + the time points + """ + if not is_valid_timestamps(timestamps): + print("timestamps need to be 1D, monotonically increasing") + + time_points = len(timestamps) + if data.size < time_points: + raise ValueError("Impossible to match data with timestamps") + if len(data) == time_points: + return data + return data.reshape((time_points, -1)) + + +class HardwareMetadata(object): + """ + Represents the hardware specification for a given recording. + """ + pass + + +class TimeSeries(object): + """ + TimeSeries represents time series data (duh), with core properties + `TimeSeries.timestamps` and `TimeSeries.channel_data`. + + A useful model is to think about this object as a 2D array, with + rows increasing in time. The first column is the `timestamps`, and the + other columns are different channels. This representation can be accessed + through the `TimeSeries.time_series` property/method. + + `TimeSeries` enforces the invariant that time must increase monotonically, + and the timestamps and channel_data must have the same number of time points. + Setting either `timestamps` or `channel_data` property that violates this + will result in errors. + + `TimeSeries` supports setting and selecting data for given time + points (`select_time`), channels (`select_channels`), or combination of + both (`select_channel_data_at_time`), with syntax similar to + numpy array basic indexing. + (https://numpy.org/doc/stable/user/basics.indexing.html#basics-indexing) + + Slicing syntax can be achieved with the help of `array_utils.SliceMaker` class. + + Specifically, `select_time` can be thought of as selecting rows, + `select_channels` can be thought of as seleting columns of the `channel_data`, + therefore 1D numpy array indexing applies. + + Functions can be applied to `channel_data` (`transform_channel_data`), the returned + type (`TimeSeries` or numpy array) will differ depending on the type of transformation. + """ + + _timestamps: np.ndarray + _channel_data: np.ndarray + + def __init__(self, timestamps: np.ndarray, channel_data: np.ndarray) -> None: + """ + timestamps: 1D array with length equal to time + channel_data: 2D array of shape [time, channels] + """ + # In case we have a single number here + timestamps = np.atleast_1d(timestamps) + channel_data = np.atleast_1d(channel_data) + if check_timestamps_and_data(timestamps, channel_data): + self._timestamps = timestamps + self._channel_data = channel_data + else: + raise ValueError("timestamps and channel_data not compatible") + + def __repr__(self): + return "Time | Channels...\n" + str(self.time_series) + + def __len__(self): + return self.shape[0] + + def __eq__(self, other: "TimeSeries") -> bool: + return np.array_equal(self.timestamps, other.timestamps) and np.array_equal( + self.channel_data, other.channel_data + ) + + def __getitem__(self, indexer: Tuple[Index, Index]) -> "TimeSeries": + """ + Return new TimeSeries with given time and channel selection. Syntax is the same + as numpy array indexing for a 2D array of shape [n_times, n_channels] except + that scalar indexing returns a TimeSeries with singleton dimensions to preserve + data type. + + The `timestamps` and `channel_data` attributes of the returned + object are views of the original TimeSeries' attrributes. Therefore + modifications directly to the attributes via the property-getter can result + in unwanted side-effects (in which case make a deepcopy before proceeding!) + + Ex: + ``` + >>> ts = TimeSeries(np.arange(4), np.arange(8).reshape((4, -1))) + >>> ts.channel_data + array([[0, 1], + [2, 3], + [4, 5], + [6, 7]]) + >>> ts2 = ts[0:3, :] + >>> ts2.channel_data + array([[0, 1], + [2, 3], + [4, 5]]) + >>> # scalar casts to singleton + >>> ts2 = ts[0, :] + >>> ts2.channel_data + array([[0, 1]]) + >>> # will throw an error... + >>> ts.channel_data[0:2, :] = 1 + >>> ts.channel_data + array([[1, 1], + [1, 1], + [4, 5], + [6, 7]]) + >>> ts2.channel_data + array([[1, 1], + [1, 1], + [4, 5]]) + # ts2's channel_data has also changed due to shared reference + ``` + """ + time_indexer, channel_indexer = scalars_to_slice_indexer(indexer) + return self.__class__( + self.timestamps[time_indexer], + self.channel_data[time_indexer, channel_indexer], + ) + + @property + def shape(self): + return self._channel_data.shape + + @classmethod + def from_array(cls, array: np.ndarray) -> "TimeSeries": + """ + Assume first column is time, all other is data + """ + assert len(array.shape) == 2 and array.shape[1] >= 2 + return cls(array[:, 0], array[:, 1:]) + + @classmethod + def load(cls, filepath: str) -> "TimeSeries": + with h5py.File(filepath, "r") as f: + timestamps = f.get("timestamps").value + channel_data = f.get("channel_data").value + return cls(timestamps, channel_data) + + def save(self, filepath: str) -> None: + with h5py.File(filepath, "w") as f: + f.create_dataset("timestamps", data=self._timestamps) + f.create_dataset("channel_data", data=self._channel_data) + + @property + def timestamps(self) -> np.ndarray: + return self._timestamps + + @timestamps.setter + def timestamps(self, new_timestamps: np.ndarray) -> None: + """ + Set the timestamps to new_timestamps, must be same length as before + """ + if not is_valid_timestamps(new_timestamps): + raise ValueError( + "new_timestamps is either not 1D or not monotonically increasing" + ) + elif len(self._timestamps) != len(new_timestamps): + raise ValueError("new_timestamps has the incorrect shape!") + self._timestamps = new_timestamps + + @property + def channel_data(self) -> np.ndarray: + return self._channel_data + + @channel_data.setter + def channel_data(self, value: np.ndarray) -> None: + """ + Set the channel_data, must be the same length as before + """ + if len(value.shape) != 2: + raise ValueError("value needs to be 2-dimensional") + elif len(value) != len(self._channel_data): + raise ValueError("value has wrong number of time points") + self._channel_data = value + + @property + def sample_rate(self) -> float: + return 1.0 / np.mean(np.diff(self._timestamps)) + + @property + def time_series(self) -> np.ndarray: + return np.hstack((self.timestamps.reshape((-1, 1)), self._channel_data)) + + def select_channels(self, channel_selection: Index) -> "TimeSeries": + """ + Return new TimeSeries with the selected channels only + + Args: + --- + `channel_selection` : Expression used to do basic indexing of + a numpy array in a single dimension. + + - List, Tuple, np.ndarray type inputs can be integer and boolean + masks. + - Slicing operation (e.g. array[a:b] syntax) can be achieved through + + ``` + from data_view.array_utils import SliceMaker + make_slice = SliceMaker() + # select all but the last channel + time_series.select_channels(make_slice[0:-2]) + ``` + + Returns: + --- + A new `TimeSeries` objects with the selected channels. + """ + return self[:, channel_selection] + + def select_time(self, time_selection: Index) -> "TimeSeries": + """ + Return new TimeSeries with the selected time points only + + Args: + --- + `time_selection` : Expression used to do basic indexing of + a numpy array in a single dimension. + + - List, Tuple, np.ndarray type inputs can be integer and boolean + masks. + - Slicing operation (e.g. array[a:b] syntax) can be achieved through + + ``` + from data_view.array_utils import SliceMaker + make_slice = SliceMaker() + # select all but last time point + time_series.select_time(make_slice[0:-2]) + ``` + + Returns: + --- + A new `TimeSeries` objects with the selected channels. + """ + return self[time_selection, :] + + def select_channel_data_at_time( + self, + time_selection: Index, + channel_selection: Index, + ) -> "TimeSeries": + """ + Return new TimeSeries with the selected time points and + selected channels only + + Args: + --- + - `time_selection`, `channel_selection` : + Expression used to do basic indexing of a numpy array in a single dimension. + + - List, Tuple, np.ndarray type inputs should be integers. If using boolean + masking, can run into errors: + + "IndexError: shape mismatch: indexing arrays could not be broadcast + together with shapes..." + + This is due to underlying numpy array indexing behavior, + see https://stackoverflow.com/a/30176382 + + - Slicing operation (e.g. array[a:b] syntax) can be achieved through + `SliceMaker` similar to `select_time()` and `select_channels`. + + Example: + ``` + from data_view.array_utils import SliceMaker + make_slice = SliceMaker() + # select all but last time point + time_series.select_channel_data_at_time( + make_slice[0:5], # select first 6 time points + [0, 1, -1], # select first 2 and last channel + ) + """ + return self[time_selection, channel_selection] + + def transform_channel_data( + self, func: Callable[[np.ndarray], np.ndarray] + ) -> "TimeSeries": + """ + Apply `func` on `channel_data`. + + If the transformed `channel_data` is 2D and has the same number + of rows as before, a new `TimeSeries` with the transformed + data and original `timestamps` is returned. + + This means if the `func` does not change the number of time points, + a new `TimeSeries` is returned. + + Otherwise, the transformed `channel_data` is returned as an np.ndarray + + Ex. + ``` + new_ts = ts.transform_channel_data(partial(scipy.stats.zscore, axis=0)) + ``` + returns a TimeSeries with the channels zscored in time. + + ``` + new_ts = ts.transform_channel_data(partial(np.mean, axis=1, keepdims=True)) + ``` + returns a TimeSeries with channel_data equal to the mean of `ts`'s channels. + + ``` + new_ts = ts.transform_channel_data(lambda x: np.vstack((x, x))) + ``` + returns an np.ndarray equal to `np.vstack((ts.channel_data, ts.channel_data))` + + ``` + new_ts = ts.transform_channel_data(lambda x: np.hstack((x, x))) + ``` + returns a TimeSeries with channel_data equal to + `np.hstack((ts.channel_data, ts.channel_data))` + + """ + + out = func(self._channel_data) + if len(out.shape) < 2 or len(out) != len(self._timestamps): + return out + else: + return self.__class__(self._timestamps, out) + + +class LabeledTimeSeries(TimeSeries): + """ + Class to bundle TimeSeries with ChannelInfo object that + gives information about each channel. + + The attributes of the ChannelInfo can be referred to with + the same name from a LabeledTimeSeries. + + Ex. + + `lts` is a LabeledTimeSeries with the `channel_info` object: + + `lts = LabeledTimeSeries(timestamps, channel_data, channel_info)` + + The `detector_idx` property of `channel_info` can be referred to + by `lts.detector_idx`. + """ + + # Addition to TimeSeries base class + _channel_info: ChannelInfo + + """ + Methods inherited and not overridden from TimeSeries: + - sample_rate + """ + + def __init__( + self, + timestamps: np.ndarray, + channel_data: np.ndarray, + channel_info: ChannelInfo, + ) -> None: + # In case we have single number as timestamps and channel_data + timestamps = np.atleast_1d(timestamps) + channel_data = np.atleast_1d(channel_data) + + # Check channel_info' shape and channel_data match, + # and channel_info is right type + if not issubclass(type(channel_info), ChannelInfo): + raise TypeError("channel_info must be a ChannelInfo class") + if len(channel_data.shape) != 2: + raise ValueError("channel_data needs to have dimension [time, channels]") + if len(channel_info) != channel_data.shape[1]: + raise ValueError( + "channel_data should have num of cols equal to length of ChannelInfo" + ) + if not check_timestamps_and_data(timestamps, channel_data): + raise ValueError("timestamps and channel_data are not compatible") + + self._channel_info = channel_info + self._timestamps = timestamps + self._channel_data = channel_data + + # __getstate__ and __setstate__ need to be set in order to pickle LTS + # objects. Otherwise pickle attempts to call __getattr__ and runs into an + # infinite loop. # more information can be found here: + # https://stackoverflow.com/questions/50888391/pickle-of-object-with-getattr-method-in-python-returns-typeerror-object-no/50888571#50888571 + def __getstate__(self): + return vars(self) + + def __setstate__(self, state): + vars(self).update(state) + + # Auto-generate properties from the ChannelInfo + # `__getattr__` is only called if python can't find + # the attribute via other means + def __getattr__(self, name): + """ + Ex. `my_labeled_time_series.channel_info.channel_attribute` + is the same as `my_labeled_time_series.channel_attribute` + """ + return getattr(self._channel_info, name) + + def __repr__(self): + return ( + "LabeledTimeSeries (Time | Channels...)\n" + + str(self.time_series) + + "\nLabels\n" + + str(self._channel_info) + ) + + def __eq__(self, other: "LabeledTimeSeries") -> bool: + return super().__eq__(self, other) and self.channel_info == other.channel_info + + def __getitem__(self, indexer: Tuple[Index, Index]) -> "TimeSeries": + """ + Return new LabeledTimeSeries with given time and channel selection. Syntax + is the same as numpy array indexing for a 2D array of shape [n_times, n_channels] + except that scalar indexing returns a LabeledTimeSeries with singleton dimensions + to preserve data type. + + The `timestamps` and `channel_data` attributes of the returned + object are views of the original LabeledTimeSeries' attrributes. Therefore + modifications directly to the attributes via the property-getter can result + in unwanted side-effects (in which case make a deepcopy before proceeding!) + + Ex: + ``` + >>> ts = LabeledTimeSeries(np.arange(4), np.arange(8).reshape((4, -1)), channel_info) + >>> ts.channel_data + array([[0, 1], + [2, 3], + [4, 5], + [6, 7]]) + >>> ts2 = ts[0:3, :] + >>> ts2.channel_data + array([[0, 1], + [2, 3], + [4, 5]]) + >>> # scalar casts to singleton + >>> ts2 = ts[0, :] + >>> ts2.channel_data + array([[0, 1]]) + >>> # will throw an error... + >>> ts.channel_data[0:2, :] = 1 + >>> ts.channel_data + array([[1, 1], + [1, 1], + [4, 5], + [6, 7]]) + >>> ts2.channel_data + array([[1, 1], + [1, 1], + [4, 5]]) + # ts2's channel_data has also changed due to shared reference + ``` + """ + time_indexer, channel_indexer = scalars_to_slice_indexer(indexer) + return self.__class__( + self.timestamps[time_indexer], + self.channel_data[time_indexer, channel_indexer], + self.channel_info[channel_indexer], + ) + + @classmethod + def from_array( + cls, array: np.ndarray, channel_info: ChannelInfo + ) -> "LabeledTimeSeries": + """ + Assume first column is time, all other is data + """ + assert len(array.shape) == 2 and array.shape[1] >= 2 + return cls(array[:, 0], array[:, 1:], channel_info) + + @classmethod + def from_hardware( + cls, + timestamps: np.ndarray, + channel_data: np.ndarray, + hardware: HardwareMetadata, + channel_info: ChannelInfo, + ) -> "LabeledTimeSeries": + # Assume the ChannelInfo subclass implements `from_hardware` method + channel_info = channel_info.from_hardware(hardware) + return cls(timestamps, channel_data, channel_info) + + # Override load/save -- ChannelInfo has no load/save so.. + @classmethod + def load(cls, filepath: str) -> "LabeledTimeSeries": + raise NotImplementedError("LabeledTimeSeries.load is not implemented") + + def save(self, filepath: str) -> None: + raise NotImplementedError("LabeledTimeSeries.save is not implemented") + + @property + def channel_data(self) -> None: + return self._channel_data + + @channel_data.setter + def channel_data(self, value: np.ndarray) -> None: + """ + Set the channel_data, must be the same shape as before to be + consistent with channel_info + """ + if len(value.shape) != 2: + raise ValueError("value needs to be 2-dimensional") + elif value.shape != self._channel_data.shape: + raise ValueError( + "value needs to be the same shape as the original channel-data" + ) + self._channel_data = value + + @property + def channel_info(self): + return self._channel_info + + def select_channels(self, channel_selection: Index) -> "LabeledTimeSeries": + """ + Return new LabeledTimeSeries with selected channels, + timestamps is unchanged in the output. + + Args: + --- + - channel_selection: See `TimeSeries.select_channels` + """ + return self[:, channel_selection] + + def select_time(self, time_selection: Index) -> "LabeledTimeSeries": + """ + Return new LabeledTimeSeries with selected time points. + Both timestamps and channel_data will be different in the output + + Args: + --- + - time_selection: See `TimeSeries.select_time`. + """ + return self[time_selection, :] + + def select_channel_data_at_time( + self, + time_selection: Index, + channel_selection: Index, + ) -> "LabeledTimeSeries": + """ + Return new LabeledTimeSeries with selected channels, at the selected + time points. + Both timestamps and channel_data will be different in the output + + Args: See `TimeSeries.select_channel_data_at_time`. + """ + return self[time_selection, channel_selection] + + def transform_channel_data( + self, func: Callable[[np.ndarray], np.ndarray] + ) -> Union["LabeledTimeSeries", TimeSeries, np.ndarray]: + """ + Apply `func` on `channel_data`. + + The output's type depends on whether the supplied `func` changes the number + of rows (time points) and columns (channels) of `channel_data`. + + + | preserve channels | change channels + -------------|--------------------------|-------------------------- + preserve time| `LabeledTimeSeries` with | TimeSeries with new + | new channel_data, same | channel_data, same + | timestamps, same | timestamps, no + | channel_info | channel_info + -------------|---------------------------------------------------- + change time | new data as np.ndarray | new data as np.ndarray + + Ex. + ``` + new_lts = lts.transform_channel_data(partial(scipy.stats.zscore, axis=0)) + ``` + returns a LabeledTimeSeries with the channels zscored in time. + + ``` + new_lts = lts.transform_channel_data(partial(np.mean, axis=1)) + ``` + returns a TimeSeries with channel_data equal to the mean of `lts`'s channels. + The channel_info are lost in the process. + + ``` + new_lts = lts.transform_channel_data(lambda x: np.vstack((x, x))) + ``` + returns an np.ndarray equal to `np.vstack((ts.channel_data, ts.channel_data))` + + ``` + # ts.channel_data has shape (5,5) + new_lts = lts.transform_channel_data(lambda x: x[0:2, 0:2]) + ``` + returns an np.ndarray equal to `lts.channel_data[0:2, 0:2]` + """ + + out = func(self._channel_data) + if len(out.shape) < 2 or len(out) != len(self._timestamps): + # changes time + return out + elif out.shape[1] != len(self._channel_info): + # changes channel + return TimeSeries(self._timestamps, out) + else: + # preserve time and channel + return self.__class__(self._timestamps, out, self._channel_info) + + +class StringLabeledTimeSeries(LabeledTimeSeries): + """ + Class to handle simple labeling, e.g., just add the column names + """ + + # Addition to TimeSeries base class + # _timestamps: np.ndarray + # _channel_data: np.ndarray + _channel_info: StringChannelInfo + + """ + Methods inherited from LabeledTimeSeries: + - sample_rate + """ + + def __init__( + self, + timestamps: np.ndarray, + channel_data: np.ndarray, + channel_info: StringChannelInfo, + ) -> None: + timestamps = np.atleast_1d(timestamps) + channel_data = np.atleast_1d(channel_data) + # Check channel_info' shape and channel_data match, + # and channel_info is right type + if not issubclass(type(channel_info), ChannelInfo): + raise TypeError("channel_info must be a ChannelInfo class") + if len(channel_data.shape) != 2: + raise ValueError("channel_data needs to have dimension [time, channels]") + if len(channel_info) != channel_data.shape[1]: + raise ValueError( + "channel_data should have num of cols equal to length of ChannelInfo" + ) + if not check_timestamps_and_data(timestamps, channel_data): + raise ValueError("timestamps and channel_data are not compatible") + + self._channel_info = channel_info + self._timestamps = timestamps + self._channel_data = channel_data + + def from_hardware(self): + """ + Forget this inherited class + """ + print("not implemented, ignoring it") + pass + + def select_channels_by_set( + self, channel_set: Union[List[str], np.array] + ) -> "StringLabeledTimeSeries": + """ + An easier wrapper for select_channels, example usage + `time_series2 = time_series1.select_channels_by_set(['LEFT_INDEX'])` + Inputs: + channel_set, a list of channel names, or an np.array of strings + Output: StringLabeledTimeSeries + """ + return self[:, np.in1d(self.channel_info.column_names, channel_set)]