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Merge pull request #72 from the-virtual-brain/WID-69
WID-69. Add support for EDF files
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,202 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "bae5e88c-1828-4349-b67a-01082e41aaf7", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## EDF TimeSeries Widget" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "866da714-9a4c-4242-b469-8643ba5a8573", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "---" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a28b8eb4-4709-4056-9e6e-4530a3735011", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### This notebook is dedicated to showcasing the TimeSeries widget using data from an EDF file" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "id": "56d5c4b6-7804-46d6-931f-fcc5593977d1", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%matplotlib widget" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "id": "28420169-2274-4e0e-a73d-c1ab54e6da9c", | ||
| "metadata": { | ||
| "scrolled": true, | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "17-04-2024 05:02:37 - DEBUG - tvbwidgets - Package is not fully installed\n", | ||
| "17-04-2024 05:02:37 - DEBUG - tvbwidgets - Version read from the internal package.json file\n", | ||
| "17-04-2024 05:02:37 - INFO - tvbwidgets - Version: 2.0.0\n", | ||
| "Using matplotlib as 2D backend.\n", | ||
| " INFO Cannot import syncrypto library.\n", | ||
| "17-04-2024 05:02:49 - INFO - tvbwidgets.core.pse.parameters - ImportError: Dask dependency is not included, so this functionality won't be available\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "from tvbwidgets.api import plot_timeseries\n", | ||
| "from tvbwidgets.readers import read_edf_file" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "59594729-3afd-4537-87c2-7413dcb66606", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Reading the data" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "id": "94687d23-0222-4e9f-887e-e97169737304", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "Extracting EDF parameters from C:\\Work\\TVB\\tvb-widgets\\tvbwidgets\\tests\\data\\test_file.edf...\n", | ||
| "EDF file detected\n", | ||
| "Setting channel info structure...\n", | ||
| "Creating raw.info structure...\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "edf_file_path = '../tvbwidgets/tests/data/test_file.edf' # replace path with your actual EDF file path\n", | ||
| "data, freq, index = read_edf_file(edf_file_path)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "id": "12da93b3-f96f-44cf-8555-52eee8aaecfd", | ||
| "metadata": { | ||
| "tags": [] | ||
| }, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "(92000, 32)\n", | ||
| "400.0\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "print(data.shape)\n", | ||
| "print(freq)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "94d8b0ec-654f-47a7-a0c8-ce3b28651e22", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### TS Viewer" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "id": "cac83fe3-b4fe-486a-8955-70203dda1004", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "backend = 'plotly' # change to 'matplotlib' to see the other TS widget" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "id": "20c3eae2-49dd-4d01-af9a-06a0dba15888", | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "17-04-2024 05:02:49 - INFO - tvbwidgets.ui.ts.plotly_ts_widget - TimeSeries Widget with Plotly initialized\n", | ||
| "Creating RawArray with float64 data, n_channels=32, n_times=92000\n", | ||
| " Range : 0 ... 91999 = 0.000 ... 229.998 secs\n", | ||
| "Ready.\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "application/vnd.jupyter.widget-view+json": { | ||
| "model_id": "28d8ffb85be94f28b59e85b521e4aeeb", | ||
| "version_major": 2, | ||
| "version_minor": 0 | ||
| }, | ||
| "text/plain": [ | ||
| "TimeSeriesWidgetPlotly(children=(HBox(children=(Output(),)), VBox(children=(Dropdown(description='Colormap:', …" | ||
| ] | ||
| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "tsw = plot_timeseries(data=data, sample_freq=freq, ch_idx=index, backend=backend)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "83da35ea-9c05-4f4b-90e8-93ee7002554c", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.18" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,24 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # "TheVirtualBrain - Widgets" package | ||
| # | ||
| # (c) 2022-2024, TVB Widgets Team | ||
| # | ||
|
|
||
| import mne | ||
| import numpy as np | ||
|
|
||
|
|
||
| def read_edf_file(filepath): | ||
| # type: (str) -> (numpy.ndarray, float, int) | ||
| """ | ||
| Reads an EDF file located at filepath and returns the data array, the sample frequency and the channel index, | ||
| all necessary for the `api.plot_timeseries` function | ||
| """ | ||
| raw = mne.io.read_raw_edf(filepath) | ||
| data, _ = raw[:] | ||
| data = np.transpose(data) | ||
| ch_idx = len(data.shape) - 1 | ||
| sample_freq = raw.info['sfreq'] | ||
|
|
||
| return data, sample_freq, ch_idx |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,31 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # "TheVirtualBrain - Widgets" package | ||
| # | ||
| # (c) 2022-2024, TVB Widgets Team | ||
| # | ||
|
|
||
| import numpy as np | ||
| import os | ||
| import tvbwidgets.readers as readers | ||
|
|
||
| TEST_DATA = os.path.join(os.path.dirname(__file__), 'data') | ||
|
|
||
|
|
||
| def test_read_edf_file(): | ||
| edf_test_file = 'test_file.edf' | ||
| edf_path = os.path.join(TEST_DATA, edf_test_file) | ||
|
|
||
| data, freq, idx = readers.read_edf_file(edf_path) | ||
| assert data is not None | ||
| assert freq is not None | ||
| assert idx is not None | ||
|
|
||
| assert isinstance(data, np.ndarray) | ||
| assert data.shape == (92000, 32) | ||
|
|
||
| assert isinstance(freq, float) | ||
| assert freq == 400 | ||
|
|
||
| assert isinstance(idx, int) | ||
| assert idx == 1 |
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