An easy-to-use platform for EEG experimentation in the classroom
Updated utils to typescript
authored by
jdpigeon
and committed by
Teon L Brooks
8b461a8d
aef07315
+33
-277
-114
app/utils/pyodide/cell.js
-114
app/utils/pyodide/cell.js
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/* eslint-disable */
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// File we be replaced by pyodide update
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import * as path from 'path';
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import { readFileSync } from 'fs';
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import { RESOURCE_PATH } from '../../constants/constants';
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export const imports = () =>
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[
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'from mne import Epochs, find_events, set_eeg_reference, read_epochs, concatenate_epochs',
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'from time import time, strftime, gmtime',
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'import os',
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'from collections import OrderedDict',
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'from glob import glob',
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'from mne import create_info, concatenate_raws',
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'from mne.io import RawArray',
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'from mne.io import RawArray',
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'from mne.channels import read_montage',
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'import pandas as pd',
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'import numpy as np',
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'import seaborn as sns',
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'from matplotlib import pyplot as plt',
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"plt.style.use('fivethirtyeight')",
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].join('\n');
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export const utils = () =>
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readFileSync(path.join(RESOURCE_PATH, '/utils/jupyter/utils.py'), 'utf8');
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export const loadCSV = (filePathArray: Array<string>) =>
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[
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`files = [${filePathArray.map((filePath) => formatFilePath(filePath))}]`,
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`replace_ch_names = None`,
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`raw = load_data(files, replace_ch_names)`,
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].join('\n');
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export const loadCleanedEpochs = (filePathArray: Array<string>) =>
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[
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`files = [${filePathArray.map((filePath) => formatFilePath(filePath))}]`,
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`clean_epochs = concatenate_epochs([read_epochs(file) for file in files])`,
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`conditions = OrderedDict({key: [value] for (key, value) in clean_epochs.event_id.items()})`,
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].join('\n');
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// NOTE: this command includes a ';' to prevent returning data
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export const filterIIR = (lowCutoff: number, highCutoff: number) =>
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`raw.filter(${lowCutoff}, ${highCutoff}, method='iir');`;
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export const plotPSD = () =>
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[`%matplotlib inline`, `raw.plot_psd(fmin=1, fmax=30)`].join('\n');
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export const epochEvents = (
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eventIDs: {
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[key: string]: number;
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},
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tmin: number,
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tmax: number,
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reject: Array<string> | string = 'None'
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) => {
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const IDs = Object.keys(eventIDs)
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.filter((k) => k !== '')
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.reduce((res, key) => ((res[key] = eventIDs[key]), res), {});
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const command = [
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`event_id = ${JSON.stringify(IDs)}`,
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`tmin=${tmin}`,
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`tmax=${tmax}`,
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`baseline= (tmin, tmax)`,
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`picks = None`,
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`reject = ${reject}`,
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'events = find_events(raw)',
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`raw_epochs = Epochs(raw, events=events, event_id=event_id,
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tmin=tmin, tmax=tmax, baseline=baseline, reject=reject, preload=True,
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verbose=False, picks=picks)`,
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`conditions = OrderedDict({key: [value] for (key, value) in raw_epochs.event_id.items()})`,
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].join('\n');
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return command;
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};
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export const requestEpochsInfo = (variableName: string) =>
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`get_epochs_info(${variableName})`;
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export const requestChannelInfo = () =>
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`[ch for ch in clean_epochs.ch_names if ch != 'Marker']`;
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export const cleanEpochsPlot = () =>
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[
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`%matplotlib`,
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`raw_epochs.plot(scalings='auto', n_epochs=6, title="Clean Data", events=None)`,
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].join('\n');
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export const plotTopoMap = () =>
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[`%matplotlib inline`, `plot_topo(clean_epochs, conditions)`].join('\n');
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export const plotERP = (channelIndex: number | string) =>
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[
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`%matplotlib inline`,
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`X, y = plot_conditions(clean_epochs, ch_ind=${channelIndex}, conditions=conditions`,
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`X, y = plot_conditions(clean_epochs, ch_ind=${channelIndex}, conditions=conditions,
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ci=97.5, n_boot=1000, title='', diff_waveform=None)`,
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].join('\n');
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export const saveEpochs = (workspaceDir: string, subject: string) =>
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`raw_epochs.save(${formatFilePath(
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path.join(
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workspaceDir,
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'Data',
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subject,
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'EEG',
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`${subject}-cleaned-epo.fif`
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)
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)})`;
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// -------------------------------------------
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// Helper methods
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const formatFilePath = (filePath: string) =>
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`"${filePath.replace(/\\/g, '/')}"`;
-39
app/utils/pyodide/functions.js
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app/utils/pyodide/functions.js
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// -------------------------------------------
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// Helper & utility functions
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export const parseSingleQuoteJSON = (string: string) =>
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JSON.parse(string.replace(/'/g, '"'));
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export const debugParseMessage = (msg: Record<string, any>) => {
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let content = '';
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switch (msg.channel) {
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case 'iopub':
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if (msg.content.execution_state) {
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content = JSON.stringify(msg.content);
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}
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if (msg.content.code) {
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content = msg.content.code.slice(0, 300).concat('...');
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}
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if (msg.content.text) {
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content = msg.content.text;
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}
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if (msg.content.data) {
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content = JSON.stringify(Object.keys(msg.content.data));
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}
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break;
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case 'shell':
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content = JSON.stringify(msg.content);
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break;
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default:
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content = JSON.stringify(msg);
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}
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return `${msg.channel} ${content}`;
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};
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export const formatFilePath = (filePath: string) =>
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`"${filePath.replace(/\\/g, '/')}"`;
+8
app/utils/pyodide/functions.ts
+8
app/utils/pyodide/functions.ts
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app/utils/pyodide/index.js
app/utils/pyodide/index.ts
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app/utils/pyodide/index.js
app/utils/pyodide/index.ts
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import { readFileSync } from 'fs';
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import { formatFilePath } from './functions';
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declare const pyodideWorker: Worker;
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// ---------------------------------
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// This file contains the JS functions that allow the app to access python-wasm through pyodide
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// These functions wrap the python strings defined in the
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});
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export const loadCSV = async (csvArray: Array<any>) => {
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// TODO: Pass attached variable name as parameter to load_data
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// @ts-expect-error
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window.csvArray = csvArray;
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// TODO: Pass attached variable name as parameter to load_data
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await pyodideWorker.postMessage({ data: `raw = load_data()` });
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};
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// ---------------------------
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// MNE-Related Data Processing
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// export const loadCleanedEpochs = (epocsArray: Array<any>) =>
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// [
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// `clean_epochs = concatenate_epochs([read_epochs(file) for file in files])`,
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// `conditions = OrderedDict({key: [value] for (key, value) in clean_epochs.event_id.items()})`
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// ].join("\n");
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export const loadCleanedEpochs = async (epochsArray: string[]) => {
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await pyodideWorker.postMessage({
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data: [
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`clean_epochs = concatenate_epochs([read_epochs(file) for file in ${epochsArray}])`,
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`conditions = OrderedDict({key: [value] for (key, value) in clean_epochs.event_id.items()})`,
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].join('\n'),
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});
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};
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// NOTE: this command includes a ';' to prevent returning data
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export const filterIIR = async (lowCutoff: number, highCutoff: number) =>
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});
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export const epochEvents = async (
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eventIDs: { [string]: number },
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eventIDs: { [k: string]: number },
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tmin: number,
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tmax: number,
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reject?: Array<string> | string = 'None'
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reject?: string[] | 'None'
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) =>
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pyodideWorker.postMessage({
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data: [
···
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export const cleanEpochsPlot = async () => {
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// TODO: Figure out how to get image results from pyodide
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pyodideWorker.postMessage({
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await pyodideWorker.postMessage({
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data: `raw_epochs.plot(scalings='auto', n_epochs=6, title="Clean Data", events=None)`,
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});
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};
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export const plotPSD = async () => {
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// TODO: Figure out how to get image results from pyodide
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pyodideWorker.postMessage({ data: `raw.plot_psd(fmin=1, fmax=30)` });
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return pyodideWorker.postMessage({ data: `raw.plot_psd(fmin=1, fmax=30)` });
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};
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export const plotTopoMap = async () => {
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// TODO: Figure out how to get image results from pyodide
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pyodideWorker.postMessage({ data: `plot_topo(clean_epochs, conditions)` });
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return pyodideWorker.postMessage({
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data: `plot_topo(clean_epochs, conditions)`,
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});
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};
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export const plotERP = (channelIndex: number) =>
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`X, y = plot_conditions(clean_epochs, ch_ind=${channelIndex}, conditions=conditions,
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ci=97.5, n_boot=1000, title='', diff_waveform=None)`;
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export const plotERP = async (channelIndex: number) => {
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return pyodideWorker.postMessage({
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data: `X, y = plot_conditions(clean_epochs, ch_ind=${channelIndex}, conditions=conditions,
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ci=97.5, n_boot=1000, title='', diff_waveform=None)`,
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});
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};
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export const saveEpochs = (workspaceDir: string, subject: string) =>
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pyodideWorker.postMessage({
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app/utils/pyodide/pipes.js
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app/utils/pyodide/pipes.js
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import { pipe } from 'rxjs';
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import { map, pluck, filter, take, mergeMap } from 'rxjs/operators';
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import { executeRequest } from '@nteract/messaging';
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import { PyodideActions } from '../../actions';
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import { RECEIVE_EXECUTE_REPLY } from '../../epics/pyodideEpics';
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// Refactor this so command can be calculated either up stream or inside pipe
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export const execute = (command, state$) =>
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pipe(
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map(() => state$.value.pyodide.mainChannel.next(executeRequest(command)))
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);
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export const awaitOkMessage = (action$) =>
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pipe(
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mergeMap(() =>
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action$
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.ofType(PyodideActions.ReceiveExecuteReply.type)
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.pipe(
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pluck('payload'),
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filter <
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any >
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((msg) => msg.channel === 'shell' && msg.content.status === 'ok'),
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take(1)
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)
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)
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);
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app/utils/pyodide/statements.json
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app/utils/pyodide/statements.json
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// DEPRECATED
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// import * as path from "path";
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// import { readFileSync } from "fs";
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// // The output of the functions contained in this file are python commands encoded as strings
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// // that would be run in a notebook environment in order to perform the experimental analyses underlying BrainWaves
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// export const utils = () =>
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// readFileSync(path.join(__dirname, "/utils/pyodide/utils.py"), "utf8");
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// // export const loadCSV = (filePathArray: Array<string>) =>
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// // [
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// // `files = [${filePathArray.map(filePath => formatFilePath(filePath))}]`,
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// // `replace_ch_names = None`,
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// // `raw = load_data(files, replace_ch_names)`
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// // ].join("\n");
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// // export const loadCleanedEpochs = (filePathArray: Array<string>) =>
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// // [
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// // `files = [${filePathArray.map(filePath => formatFilePath(filePath))}]`,
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// // `clean_epochs = concatenate_epochs([read_epochs(file) for file in files])`,
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// // `conditions = OrderedDict({key: [value] for (key, value) in clean_epochs.event_id.items()})`
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// // ].join("\n");
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// // NOTE: this command includes a ';' to prevent returning data
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// export const filterIIR = (lowCutoff: number, highCutoff: number) =>
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// `raw.filter(${lowCutoff}, ${highCutoff}, method='iir');`;
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// export const plotPSD = () =>
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// [`%matplotlib inline`, `raw.plot_psd(fmin=1, fmax=30)`].join("\n");
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// export const epochEvents = (
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// eventIDs: { [string]: number },
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// tmin: number,
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// tmax: number,
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// reject?: Array<string> | string = "None"
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// ) =>
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// [
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// `event_id = ${JSON.stringify(eventIDs)}`,
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// `tmin=${tmin}`,
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// `tmax=${tmax}`,
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// `baseline= (tmin, tmax)`,
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// `picks = None`,
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// `reject = ${reject}`,
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// "events = find_events(raw)",
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// `raw_epochs = Epochs(raw, events=events, event_id=event_id,
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// tmin=tmin, tmax=tmax, baseline=baseline, reject=reject, preload=True,
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// verbose=False, picks=picks)`,
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// `conditions = OrderedDict({key: [value] for (key, value) in raw_epochs.event_id.items()})`
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// ].join("\n");
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// export const requestEpochsInfo = (variableName: string) =>
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// `get_epochs_info(${variableName})`;
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// export const requestChannelInfo = () =>
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// `[ch for ch in clean_epochs.ch_names if ch != 'Marker']`;
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// export const cleanEpochsPlot = () =>
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// [
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// `%matplotlib`,
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// `raw_epochs.plot(scalings='auto', n_epochs=6, title="Clean Data", events=None)`
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// ].join("\n");
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// export const plotTopoMap = () =>
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// [`%matplotlib inline`, `plot_topo(clean_epochs, conditions)`].join("\n");
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// export const plotERP = (channelIndex: number) =>
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// [
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// `%matplotlib inline`,
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// `X, y = plot_conditions(clean_epochs, ch_ind=${channelIndex}, conditions=conditions,
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// ci=97.5, n_boot=1000, title='', diff_waveform=None)`
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// ].join("\n");
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// export const saveEpochs = (workspaceDir: string, subject: string) =>
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// `raw_epochs.save(${formatFilePath(
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// path.join(
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// workspaceDir,
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// "Data",
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// subject,
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// "EEG",
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// `${subject}-cleaned-epo.fif`
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// )
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// )})`;