diff --git a/.buildinfo b/.buildinfo index cce61473..3ed22fc2 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 75670e2e8c59814fc976e9ef5fbe74ea +config: 0b0845f06aa0ea4305158e51e2e10944 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip index d0c43a60..61d2c7e6 100644 Binary files a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip and b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip differ diff --git a/_downloads/18e4b69d6ddc9302425e645c1fec16c3/04r__cueing_group_analysis_winter2019.py b/_downloads/18e4b69d6ddc9302425e645c1fec16c3/04r__cueing_group_analysis_winter2019.py index 8c2fb9cc..8e0c1e93 100644 --- a/_downloads/18e4b69d6ddc9302425e645c1fec16c3/04r__cueing_group_analysis_winter2019.py +++ b/_downloads/18e4b69d6ddc9302425e645c1fec16c3/04r__cueing_group_analysis_winter2019.py @@ -22,7 +22,7 @@ from mne.time_frequency import tfr_morlet # EEG-Noteooks functions -from eegnb.analysis.utils import load_data,plot_conditions +from eegnb.analysis.utils import load_data from eegnb.datasets import fetch_dataset # sphinx_gallery_thumbnail_number = 1 @@ -136,14 +136,14 @@ # Left Cue tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) - tfr = tfr.apply_baseline([-1,-.5],mode='mean') + tfr = tfr.apply_baseline((-1,-.5),mode='mean') power_Ipsi_TP9 = tfr.data[0,:,:] power_Contra_TP10 = tfr.data[1,:,:] # Right Cue tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) - tfr = tfr.apply_baseline([-1,-.5],mode='mean') + tfr = tfr.apply_baseline((-1,-.5),mode='mean') power_Contra_TP9 = tfr.data[0,:,:] power_Ipsi_TP10 = tfr.data[1,:,:] diff --git a/_downloads/1f56a155b1a68a196eeb8aea2e0b7517/02r__cueing_group_analysis.ipynb b/_downloads/1f56a155b1a68a196eeb8aea2e0b7517/02r__cueing_group_analysis.ipynb index 0fa210b9..3326933d 100644 --- a/_downloads/1f56a155b1a68a196eeb8aea2e0b7517/02r__cueing_group_analysis.ipynb +++ b/_downloads/1f56a155b1a68a196eeb8aea2e0b7517/02r__cueing_group_analysis.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -80,7 +69,7 @@ }, "outputs": [], "source": [ - "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n 202, 203, 204, 205, 207, 208, 209, 210, 211, \n 301, 302, 303, 304, 305, 306, 307, 308, 309]\n\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies = np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0 \n \n \n \nfor sub in subs:\n print(sub)\n \n sub_count += 1\n\n \n if (sub_count in really_bad_subs):\n rej_thresh_uV = 90\n elif (sub_count in bad_subs):\n rej_thresh_uV = 90\n else:\n rej_thresh_uV = 90\n\n rej_thresh = rej_thresh_uV*1e-6\n \n \n # Load both sessions\n raw = load_data(sub,1, # subject, session\n experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n data_dir = eegnb_data_path)\n \n raw.append(\n load_data(sub,2, # subject, session\n experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n data_dir = eegnb_data_path))\n \n\n # Filter Raw Data\n raw.filter(1,30, method='iir')\n\n #Select Events\n events = find_events(raw)\n event_id = {'LeftCue': 1, 'RightCue': 2}\n epochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 3])\n print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n # Compute morlet wavelet\n\n # Left Cue\n tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n #tfr.plot(picks=[0], mode='logratio', \n # title='TP9 - Ipsi');\n #tfr.plot(picks=[3], mode='logratio', \n # title='TP10 - Contra');\n power_Ipsi_TP9 = tfr.data[0,:,:]\n power_Contra_TP10 = tfr.data[1,:,:]\n\n # Right Cue\n tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n #tfr.plot(picks=[0], mode='logratio', \n # title='TP9 - Contra');\n #tfr.plot(picks=[3], mode='logratio', \n # title='TP10 - Ipsi');\n power_Contra_TP9 = tfr.data[0,:,:]\n power_Ipsi_TP10 = tfr.data[1,:,:]\n\n # Plot Differences\n #%matplotlib inline\n times = epochs.times\n power_Avg_Ipsi = (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n\n\n #find max to make color range\n plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])\n plot_diff_max = np.max(np.abs(power_Avg_Diff))\n\n \n \n #Ipsi\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Ipsi,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title('Power Average Ipsilateral to Cue')\n cb = fig.colorbar(im)\n cb.set_label('Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n\n #TP10\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Contra,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title(str(sub) + ' - Power Average Contra to Cue')\n cb = fig.colorbar(im)\n cb.set_label('Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n\n #difference between conditions\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Diff,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title('Power Difference Ipsi-Contra')\n cb = fig.colorbar(im)\n cb.set_label('Ipsi-Contra Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n \n \n \n \n #output data into array\n Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n ) \n Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n \n Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1))\n \n \n diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n \n ERSP_diff_out.append(power_Avg_Diff)\n ERSP_Ipsi_out.append(power_Avg_Ipsi)\n ERSP_Contra_out.append(power_Avg_Contra)\n\n\n \nprint(np.shape(ERSP_diff_out))\nprint(np.shape(Contra_spectra_out))\n\nprint(diff_out)" + "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n 202, 203, 204, 205, 207, 208, 209, 210, 211, \n 301, 302, 303, 304, 305, 306, 307, 308, 309]\n\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies = np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0 \n \n \n \nfor sub in subs:\n print(sub)\n \n sub_count += 1\n\n \n if (sub_count in really_bad_subs):\n rej_thresh_uV = 90\n elif (sub_count in bad_subs):\n rej_thresh_uV = 90\n else:\n rej_thresh_uV = 90\n\n rej_thresh = rej_thresh_uV*1e-6\n \n \n # Load both sessions\n raw = load_data(sub,1, # subject, session\n experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n data_dir = eegnb_data_path)\n \n raw.append(\n load_data(sub,2, # subject, session\n experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n data_dir = eegnb_data_path))\n \n\n # Filter Raw Data\n raw.filter(1,30, method='iir')\n\n #Select Events\n events = find_events(raw)\n event_id = {'LeftCue': 1, 'RightCue': 2}\n epochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 3])\n print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n # Compute morlet wavelet\n\n # Left Cue\n tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n #tfr.plot(picks=[0], mode='logratio', \n # title='TP9 - Ipsi');\n #tfr.plot(picks=[3], mode='logratio', \n # title='TP10 - Contra');\n power_Ipsi_TP9 = tfr.data[0,:,:]\n power_Contra_TP10 = tfr.data[1,:,:]\n\n # Right Cue\n tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n #tfr.plot(picks=[0], mode='logratio', \n # title='TP9 - Contra');\n #tfr.plot(picks=[3], mode='logratio', \n # title='TP10 - Ipsi');\n power_Contra_TP9 = tfr.data[0,:,:]\n power_Ipsi_TP10 = tfr.data[1,:,:]\n\n # Plot Differences\n #%matplotlib inline\n times = epochs.times\n power_Avg_Ipsi = (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n\n\n #find max to make color range\n plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])\n plot_diff_max = np.max(np.abs(power_Avg_Diff))\n\n \n \n #Ipsi\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Ipsi,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title('Power Average Ipsilateral to Cue')\n cb = fig.colorbar(im)\n cb.set_label('Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n\n #TP10\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Contra,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title(str(sub) + ' - Power Average Contra to Cue')\n cb = fig.colorbar(im)\n cb.set_label('Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n\n #difference between conditions\n fig, ax = plt.subplots(1)\n im = plt.imshow(power_Avg_Diff,\n extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)\n plt.xlabel('Time (sec)')\n plt.ylabel('Frequency (Hz)')\n plt.title('Power Difference Ipsi-Contra')\n cb = fig.colorbar(im)\n cb.set_label('Ipsi-Contra Power')\n # Create a Rectangle patch\n rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n # Add the patch to the Axes\n ax.add_patch(rect)\n \n \n \n \n #output data into array\n Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n ) \n Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n \n Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1))\n \n \n diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n \n ERSP_diff_out.append(power_Avg_Diff)\n ERSP_Ipsi_out.append(power_Avg_Ipsi)\n ERSP_Contra_out.append(power_Avg_Contra)\n\n\n \nprint(np.shape(ERSP_diff_out))\nprint(np.shape(Contra_spectra_out))\n\nprint(diff_out)" ] }, { @@ -172,7 +161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/2cbf7eec12f4415419df2cf85cbe5c5b/visual_p300_python.zip b/_downloads/2cbf7eec12f4415419df2cf85cbe5c5b/visual_p300_python.zip index f0576d7e..0f1a8209 100644 Binary files a/_downloads/2cbf7eec12f4415419df2cf85cbe5c5b/visual_p300_python.zip and b/_downloads/2cbf7eec12f4415419df2cf85cbe5c5b/visual_p300_python.zip differ diff --git a/_downloads/2f1a411d3414306e436c6540c86910c5/00x__n170_run_experiment.ipynb b/_downloads/2f1a411d3414306e436c6540c86910c5/00x__n170_run_experiment.ipynb index b3f8b2ad..4ccbaaf4 100644 --- a/_downloads/2f1a411d3414306e436c6540c86910c5/00x__n170_run_experiment.ipynb +++ b/_downloads/2f1a411d3414306e436c6540c86910c5/00x__n170_run_experiment.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,25 +22,7 @@ }, "outputs": [], "source": [ - "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments.visual_n170 import n170\n\n# Define some variables\nboard_name = \"muse\"\nexperiment = \"visual_n170\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initiate EEG device\n\nStart EEG device\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "eeg_device = EEG(device=board_name)\n\n# Create save file name\nsave_fn = generate_save_fn(board_name, experiment, subject_id, session_nb)\nprint(save_fn)" + "from eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments import VisualN170\n\n# Define some variables\nboard_name = \"muse2\" # board name\nexperiment_name = \"visual_n170\" # experiment name\nsubject_id = 0 # test subject id\nsession_nb = 0 # session number\nrecord_duration = 120 # recording duration\n\n# generate save path\nsave_fn = generate_save_fn(board_name, experiment_name, subject_id, session_nb)\n\n# create device object\neeg_device = EEG(device=board_name)\n\n# Experiment type\nexperiment = VisualN170(duration=record_duration, eeg=eeg_device, save_fn=save_fn)" ] }, { @@ -69,7 +40,7 @@ }, "outputs": [], "source": [ - "n170.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn)" + "experiment.run()\n\n# Saved csv location\nprint(\"Recording saved in\", experiment.save_fn)" ] } ], @@ -89,7 +60,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/2fc210914b93a8aaaaa1a6667585ad74/visual_cueing_jupyter.zip b/_downloads/2fc210914b93a8aaaaa1a6667585ad74/visual_cueing_jupyter.zip index ca27b83d..9dc938ee 100644 Binary files a/_downloads/2fc210914b93a8aaaaa1a6667585ad74/visual_cueing_jupyter.zip and b/_downloads/2fc210914b93a8aaaaa1a6667585ad74/visual_cueing_jupyter.zip differ diff --git a/_downloads/335ce423c80436163e4a75b13e3dba64/00x__ssvep_run_experiment.py b/_downloads/335ce423c80436163e4a75b13e3dba64/00x__ssvep_run_experiment.py index 1741760e..dfb8c046 100644 --- a/_downloads/335ce423c80436163e4a75b13e3dba64/00x__ssvep_run_experiment.py +++ b/_downloads/335ce423c80436163e4a75b13e3dba64/00x__ssvep_run_experiment.py @@ -15,10 +15,10 @@ import os from eegnb import generate_save_fn from eegnb.devices.eeg import EEG -from eegnb.experiments.visual_ssvep import ssvep +from eegnb.experiments import VisualSSVEP # Define some variables -board_name = "muse" +board_name = "muse2" experiment = "visual_ssvep" subject_id = 0 session_nb = 0 @@ -39,4 +39,5 @@ # Run experiment # --------------------- # -ssvep.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn) +ssvep = VisualSSVEP(duration=record_duration, eeg=eeg_device, save_fn=save_fn) +ssvep.run() \ No newline at end of file diff --git a/_downloads/482813616f7e52f19737a9e9e4714600/01r__ssvep_viz.ipynb b/_downloads/482813616f7e52f19737a9e9e4714600/01r__ssvep_viz.ipynb index bb88a75c..d197e645 100644 --- a/_downloads/482813616f7e52f19737a9e9e4714600/01r__ssvep_viz.ipynb +++ b/_downloads/482813616f7e52f19737a9e9e4714600/01r__ssvep_viz.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -26,7 +15,7 @@ }, "outputs": [], "source": [ - "# Some standard pythonic imports\nimport os, numpy as np, pandas as pd\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\n\n# MNE functions\nfrom mne import Epochs,find_events\nfrom mne.time_frequency import psd_welch,tfr_morlet\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" + "# Some standard pythonic imports\nimport os, numpy as np, pandas as pd\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\n\n# MNE functions\nfrom mne import Epochs,find_events\nfrom mne.time_frequency import tfr_morlet\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" ] }, { @@ -44,7 +33,7 @@ }, "outputs": [], "source": [ - "eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data') \nssvep_data_path = os.path.join(eegnb_data_path, 'visual-SSVEP', 'eegnb_examples')\n\n# If dataset hasn't been downloaded yet, download it \nif not os.path.isdir(ssvep_data_path):\n fetch_dataset(data_dir=eegnb_data_path, experiment='visual-SSVEP', site='eegnb_examples'); \n\n\nsubject = 1\nsession = 1\nraw = load_data(subject, session, \n experiment='visual-SSVEP', site='eegnb_examples', device_name='muse2016',\n data_dir = eegnb_data_path,\n replace_ch_names={'Right AUX': 'POz'})" + "eegnb_data_path = os.path.join(os.path.expanduser('~/'),'.eegnb', 'data') \nssvep_data_path = os.path.join(eegnb_data_path, 'visual-SSVEP', 'eegnb_examples')\n\n# If dataset hasn't been downloaded yet, download it \nif not os.path.isdir(ssvep_data_path):\n fetch_dataset(data_dir=eegnb_data_path, experiment='visual-SSVEP', site='eegnb_examples'); \n\n\nsubject = 1\nsession = 1\nraw = load_data(subject, session, \n experiment='visual-SSVEP', site='eegnb_examples', device_name='muse2016',\n data_dir = eegnb_data_path,\n replace_ch_names={'Right AUX': 'POz'})\nraw.set_channel_types({'POz': 'eeg'})" ] }, { @@ -98,7 +87,7 @@ }, "outputs": [], "source": [ - "# Next, we can compare the PSD of epochs specifically during 20hz and 30hz stimulus presentation\n\nf, axs = plt.subplots(2, 1, figsize=(10, 10))\npsd1, freq1 = psd_welch(epochs['30 Hz'], n_fft=1028, n_per_seg=256 * 3, picks='all')\npsd2, freq2 = psd_welch(epochs['20 Hz'], n_fft=1028, n_per_seg=256 * 3, picks='all')\npsd1 = 10 * np.log10(psd1)\npsd2 = 10 * np.log10(psd2)\n\npsd1_mean = psd1.mean(0)\npsd1_std = psd1.mean(0)\n\npsd2_mean = psd2.mean(0)\npsd2_std = psd2.mean(0)\n\naxs[0].plot(freq1, psd1_mean[[0, 3], :].mean(0), color='b', label='30 Hz')\naxs[0].plot(freq2, psd2_mean[[0, 3], :].mean(0), color='r', label='20 Hz')\n\naxs[1].plot(freq1, psd1_mean[4, :], color='b', label='30 Hz')\naxs[1].plot(freq2, psd2_mean[4, :], color='r', label='20 Hz')\n\naxs[0].set_title('TP9 and TP10')\naxs[1].set_title('POz')\n\naxs[0].set_ylabel('Power Spectral Density (dB)')\naxs[1].set_ylabel('Power Spectral Density (dB)')\n\naxs[0].set_xlim((2, 50))\naxs[1].set_xlim((2, 50))\n\naxs[1].set_xlabel('Frequency (Hz)')\n\naxs[0].legend()\naxs[1].legend()\n\nplt.show();\n\n# With this visualization we can clearly see distinct peaks at 30hz and 20hz in the PSD, corresponding to the frequency of the visual stimulation. The peaks are much larger at the POz electrode, but still visible at TP9 and TP10" + "# Next, we can compare the PSD of epochs specifically during 20hz and 30hz stimulus presentation\n\nf, axs = plt.subplots(2, 1, figsize=(10, 10))\n\nwelch_params=dict(method='welch',\n n_fft=1028,\n n_per_seg=256 * 3,\n picks='all')\n\npsd1, freq1 = epochs['30 Hz'].compute_psd(**welch_params).get_data(return_freqs=True)\npsd2, freq2 = epochs['20 Hz'].compute_psd(**welch_params).get_data(return_freqs=True)\npsd1 = 10 * np.log10(psd1)\npsd2 = 10 * np.log10(psd2)\n\npsd1_mean = psd1.mean(0)\npsd1_std = psd1.mean(0)\n\npsd2_mean = psd2.mean(0)\npsd2_std = psd2.mean(0)\n\naxs[0].plot(freq1, psd1_mean[[0, 3], :].mean(0), color='b', label='30 Hz')\naxs[0].plot(freq2, psd2_mean[[0, 3], :].mean(0), color='r', label='20 Hz')\n\naxs[1].plot(freq1, psd1_mean[4, :], color='b', label='30 Hz')\naxs[1].plot(freq2, psd2_mean[4, :], color='r', label='20 Hz')\n\naxs[0].set_title('TP9 and TP10')\naxs[1].set_title('POz')\n\naxs[0].set_ylabel('Power Spectral Density (dB)')\naxs[1].set_ylabel('Power Spectral Density (dB)')\n\naxs[0].set_xlim((2, 50))\naxs[1].set_xlim((2, 50))\n\naxs[1].set_xlabel('Frequency (Hz)')\n\naxs[0].legend()\naxs[1].legend()\n\nplt.show();\n\n# With this visualization we can clearly see distinct peaks at 30hz and 20hz in the PSD, corresponding to the frequency of the visual stimulation. The peaks are much larger at the POz electrode, but still visible at TP9 and TP10" ] }, { @@ -116,7 +105,7 @@ }, "outputs": [], "source": [ - "# We can also look for SSVEPs in the spectrogram, which uses color to represent the power of frequencies in the EEG signal over time\n\nfrequencies = np.logspace(1, 1.75, 60)\ntfr, itc = tfr_morlet(epochs['30 Hz'], freqs=frequencies,picks='all',\n n_cycles=15, return_itc=True)\ntfr.plot(picks=[4], baseline=(-0.5, -0.1), mode='logratio', \n title='POz - 30 Hz stim');\n\ntfr, itc = tfr_morlet(epochs['20 Hz'], freqs=frequencies,picks='all',\n n_cycles=15, return_itc=True)\ntfr.plot(picks=[4], baseline=(-0.5, -0.1), mode='logratio', \n title='POz - 20 Hz stim');\n\nplt.tight_layout()\n\n# Once again we can see clear SSVEPs at 30hz and 20hz" + "# We can also look for SSVEPs in the spectrogram, which uses color to represent the power of frequencies in the EEG signal over time\n\nfrequencies = np.logspace(1, 1.75, 60)\ntfr, itc = tfr_morlet(epochs['30 Hz'], freqs=frequencies,picks='all',\n n_cycles=15, return_itc=True)\ntfr.plot(picks=[4], baseline=(-0.5, -0.1), mode='logratio', \n title='POz - 30 Hz stim');\n\ntfr, itc = tfr_morlet(epochs['20 Hz'], freqs=frequencies,picks='all',\n n_cycles=15, return_itc=True)\ntfr.plot(picks=[4], baseline=(-0.5, -0.1), mode='logratio', \n title='POz - 20 Hz stim');\n\n# Set Layout engine to tight to fix error with using colorbar layout error\nplt.figure().set_layout_engine('tight');\nplt.tight_layout()\n\n# Once again we can see clear SSVEPs at 30hz and 20hz" ] } ], @@ -136,7 +125,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/4c0d23639fbd212d64cd06010552da22/visual_n170_python.zip b/_downloads/4c0d23639fbd212d64cd06010552da22/visual_n170_python.zip index 6f67e453..7417a01c 100644 Binary files a/_downloads/4c0d23639fbd212d64cd06010552da22/visual_n170_python.zip and b/_downloads/4c0d23639fbd212d64cd06010552da22/visual_n170_python.zip differ diff --git a/_downloads/4cd288a0df5274e2be1a468e1967a571/04r__cueing_group_analysis_winter2019.ipynb b/_downloads/4cd288a0df5274e2be1a468e1967a571/04r__cueing_group_analysis_winter2019.ipynb index c39b3737..822f31bd 100644 --- a/_downloads/4cd288a0df5274e2be1a468e1967a571/04r__cueing_group_analysis_winter2019.ipynb +++ b/_downloads/4cd288a0df5274e2be1a468e1967a571/04r__cueing_group_analysis_winter2019.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "# Standard Pythonic imports\nimport os,sys,glob,numpy as np, pandas as pd\nimport scipy\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as patches\n\n# MNE functions\nfrom mne import Epochs, find_events, concatenate_raws\nfrom mne.time_frequency import tfr_morlet\n\n# EEG-Noteooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" + "# Standard Pythonic imports\nimport os,sys,glob,numpy as np, pandas as pd\nimport scipy\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as patches\n\n# MNE functions\nfrom mne import Epochs, find_events, concatenate_raws\nfrom mne.time_frequency import tfr_morlet\n\n# EEG-Noteooks functions\nfrom eegnb.analysis.utils import load_data\nfrom eegnb.datasets import fetch_dataset" ] }, { @@ -69,7 +58,7 @@ }, "outputs": [], "source": [ - "subs = [1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n 1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n 1301, 1302, 1313, \n 1401, 1402, 1403, 1404, 1405, 1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n#\n# Both\n# subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n# 202, 203, 204, 205, 207, 208, 209, 210, 211, \n# 301, 302, 303, 304, 305, 306, 307, 308, 309,\n# 1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n# 1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n# 1301, 1302, 1313, \n# 1401, 1402, 1403, 1404, 1405, 1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n#\n#\n# placeholders to add to for each subject\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies = np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0 \n \n \n \nfor sub in subs:\n print(sub)\n \n sub_count += 1\n\n \n if (sub_count in really_bad_subs):\n rej_thresh_uV = 90\n elif (sub_count in bad_subs):\n rej_thresh_uV = 90\n else:\n rej_thresh_uV = 90\n\n rej_thresh = rej_thresh_uV*1e-6\n \n \n # Load both sessions\n raw = load_data(sub,1, # subject, session\n experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n data_dir = eegnb_data_path)\n \n raw.append(\n load_data(sub,2, # subject, session\n experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n data_dir = eegnb_data_path))\n \n\n # Filter Raw Data\n raw.filter(1,30, method='iir')\n\n #Select Events\n events = find_events(raw)\n event_id = {'LeftCue': 1, 'RightCue': 2}\n epochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 3])\n print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n # Compute morlet wavelet\n # Left Cue\n tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n power_Ipsi_TP9 = tfr.data[0,:,:]\n power_Contra_TP10 = tfr.data[1,:,:]\n\n # Right Cue\n tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline([-1,-.5],mode='mean')\n power_Contra_TP9 = tfr.data[0,:,:]\n power_Ipsi_TP10 = tfr.data[1,:,:]\n\n # Compute averages Differences\n power_Avg_Ipsi = (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n \n #output data into array\n times = epochs.times\n Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n ) \n Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n \n Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1))\n \n \n diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n #save the spectrograms to average over after\n ERSP_diff_out.append(power_Avg_Diff)\n ERSP_Ipsi_out.append(power_Avg_Ipsi)\n ERSP_Contra_out.append(power_Avg_Contra)" + "subs = [1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n 1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n 1301, 1302, 1313, \n 1401, 1402, 1403, 1404, 1405, 1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n#\n# Both\n# subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n# 202, 203, 204, 205, 207, 208, 209, 210, 211, \n# 301, 302, 303, 304, 305, 306, 307, 308, 309,\n# 1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n# 1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n# 1301, 1302, 1313, \n# 1401, 1402, 1403, 1404, 1405, 1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n#\n#\n# placeholders to add to for each subject\ndiff_out = []\nIpsi_out = []\nContra_out = []\nIpsi_spectra_out = []\nContra_spectra_out = []\ndiff_spectra_out = []\nERSP_diff_out = []\nERSP_Ipsi_out = []\nERSP_Contra_out = []\n\nfrequencies = np.linspace(6, 30, 100, endpoint=True)\nwave_cycles = 6\n\n# time frequency window for analysis\nf_low = 7 # Hz\nf_high = 10\nf_diff = f_high-f_low\n \nt_low = 0 # s\nt_high = 1\nt_diff = t_high-t_low\n\nbad_subs= [6, 7, 13, 26]\nreally_bad_subs = [11, 12, 19]\nsub_count = 0 \n \n \n \nfor sub in subs:\n print(sub)\n \n sub_count += 1\n\n \n if (sub_count in really_bad_subs):\n rej_thresh_uV = 90\n elif (sub_count in bad_subs):\n rej_thresh_uV = 90\n else:\n rej_thresh_uV = 90\n\n rej_thresh = rej_thresh_uV*1e-6\n \n \n # Load both sessions\n raw = load_data(sub,1, # subject, session\n experiment='visual-cueing',site='kylemathlab_dev',device_name='muse2016',\n data_dir = eegnb_data_path)\n \n raw.append(\n load_data(sub,2, # subject, session\n experiment='visual-cueing', site='kylemathlab_dev', device_name='muse2016',\n data_dir = eegnb_data_path))\n \n\n # Filter Raw Data\n raw.filter(1,30, method='iir')\n\n #Select Events\n events = find_events(raw)\n event_id = {'LeftCue': 1, 'RightCue': 2}\n epochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 3])\n print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n\n # Compute morlet wavelet\n # Left Cue\n tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n power_Ipsi_TP9 = tfr.data[0,:,:]\n power_Contra_TP10 = tfr.data[1,:,:]\n\n # Right Cue\n tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\n tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n power_Contra_TP9 = tfr.data[0,:,:]\n power_Ipsi_TP10 = tfr.data[1,:,:]\n\n # Compute averages Differences\n power_Avg_Ipsi = (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n \n #output data into array\n times = epochs.times\n Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n ) \n Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n \n Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1))\n \n \n diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n np.argmax(frequencies>f_high)-1,\n np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n )\n )\n diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n np.argmax(times>t_high)-1 ],1\n )\n )\n \n #save the spectrograms to average over after\n ERSP_diff_out.append(power_Avg_Diff)\n ERSP_Ipsi_out.append(power_Avg_Ipsi)\n ERSP_Contra_out.append(power_Avg_Contra)" ] }, { @@ -219,7 +208,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/5ea8fb0b660bbd8665ebbc30a424eb42/visual_gonogo_jupyter.zip b/_downloads/5ea8fb0b660bbd8665ebbc30a424eb42/visual_gonogo_jupyter.zip deleted file mode 100644 index 15cb0ecb..00000000 Binary files a/_downloads/5ea8fb0b660bbd8665ebbc30a424eb42/visual_gonogo_jupyter.zip and /dev/null differ diff --git a/_downloads/67001aab4aa80bdc4a44406add4d085e/visual_cueing_python.zip b/_downloads/67001aab4aa80bdc4a44406add4d085e/visual_cueing_python.zip index 38aa69d9..33e73a87 100644 Binary files a/_downloads/67001aab4aa80bdc4a44406add4d085e/visual_cueing_python.zip and b/_downloads/67001aab4aa80bdc4a44406add4d085e/visual_cueing_python.zip differ diff --git a/_downloads/679acb9da15a2bd0f1cf78d5dea6995b/00x__n170_run_experiment.py b/_downloads/679acb9da15a2bd0f1cf78d5dea6995b/00x__n170_run_experiment.py index 2258aa08..51b6d2c6 100644 --- a/_downloads/679acb9da15a2bd0f1cf78d5dea6995b/00x__n170_run_experiment.py +++ b/_downloads/679acb9da15a2bd0f1cf78d5dea6995b/00x__n170_run_experiment.py @@ -12,31 +12,31 @@ # --------------------- # # Imports -import os from eegnb import generate_save_fn from eegnb.devices.eeg import EEG -from eegnb.experiments.visual_n170 import n170 +from eegnb.experiments import VisualN170 # Define some variables -board_name = "muse" -experiment = "visual_n170" -subject_id = 0 -session_nb = 0 -record_duration = 120 - -################################################################################################### -# Initiate EEG device -# --------------------- -# -# Start EEG device +board_name = "muse2" # board name +experiment_name = "visual_n170" # experiment name +subject_id = 0 # test subject id +session_nb = 0 # session number +record_duration = 120 # recording duration + +# generate save path +save_fn = generate_save_fn(board_name, experiment_name, subject_id, session_nb) + +# create device object eeg_device = EEG(device=board_name) -# Create save file name -save_fn = generate_save_fn(board_name, experiment, subject_id, session_nb) -print(save_fn) +# Experiment type +experiment = VisualN170(duration=record_duration, eeg=eeg_device, save_fn=save_fn) ################################################################################################### # Run experiment # --------------------- -# -n170.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn) +# +experiment.run() + +# Saved csv location +print("Recording saved in", experiment.save_fn) diff --git a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index a57c29cb..7f94d2d8 100644 Binary files a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip and b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip differ diff --git a/_downloads/872178a6cf309a4de1aa7d759d171a7b/02r__n170_decoding.ipynb b/_downloads/872178a6cf309a4de1aa7d759d171a7b/02r__n170_decoding.ipynb index f4daf2ea..cf64d3c5 100644 --- a/_downloads/872178a6cf309a4de1aa7d759d171a7b/02r__n170_decoding.ipynb +++ b/_downloads/872178a6cf309a4de1aa7d759d171a7b/02r__n170_decoding.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -143,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/89f5ff349033ec3c3d48bcfb2c3c3de2/01r__ssvep_viz.py b/_downloads/89f5ff349033ec3c3d48bcfb2c3c3de2/01r__ssvep_viz.py index 91d9ad50..22da3b6a 100644 --- a/_downloads/89f5ff349033ec3c3d48bcfb2c3c3de2/01r__ssvep_viz.py +++ b/_downloads/89f5ff349033ec3c3d48bcfb2c3c3de2/01r__ssvep_viz.py @@ -26,7 +26,7 @@ # MNE functions from mne import Epochs,find_events -from mne.time_frequency import psd_welch,tfr_morlet +from mne.time_frequency import tfr_morlet # EEG-Notebooks functions from eegnb.analysis.utils import load_data,plot_conditions @@ -59,6 +59,7 @@ experiment='visual-SSVEP', site='eegnb_examples', device_name='muse2016', data_dir = eegnb_data_path, replace_ch_names={'Right AUX': 'POz'}) +raw.set_channel_types({'POz': 'eeg'}) ################################################################################################### # Visualize the power spectrum @@ -87,8 +88,14 @@ # Next, we can compare the PSD of epochs specifically during 20hz and 30hz stimulus presentation f, axs = plt.subplots(2, 1, figsize=(10, 10)) -psd1, freq1 = psd_welch(epochs['30 Hz'], n_fft=1028, n_per_seg=256 * 3, picks='all') -psd2, freq2 = psd_welch(epochs['20 Hz'], n_fft=1028, n_per_seg=256 * 3, picks='all') + +welch_params=dict(method='welch', + n_fft=1028, + n_per_seg=256 * 3, + picks='all') + +psd1, freq1 = epochs['30 Hz'].compute_psd(**welch_params).get_data(return_freqs=True) +psd2, freq2 = epochs['20 Hz'].compute_psd(**welch_params).get_data(return_freqs=True) psd1 = 10 * np.log10(psd1) psd2 = 10 * np.log10(psd2) @@ -139,6 +146,8 @@ tfr.plot(picks=[4], baseline=(-0.5, -0.1), mode='logratio', title='POz - 20 Hz stim'); +# Set Layout engine to tight to fix error with using colorbar layout error +plt.figure().set_layout_engine('tight'); plt.tight_layout() # Once again we can see clear SSVEPs at 30hz and 20hz diff --git a/_downloads/8d83acdbf2986ad81d4d9e035ff593ef/01r__n170_viz.ipynb b/_downloads/8d83acdbf2986ad81d4d9e035ff593ef/01r__n170_viz.ipynb index 47acd03f..00fdb815 100644 --- a/_downloads/8d83acdbf2986ad81d4d9e035ff593ef/01r__n170_viz.ipynb +++ b/_downloads/8d83acdbf2986ad81d4d9e035ff593ef/01r__n170_viz.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "# Some standard pythonic imports\nimport os\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# MNE functions\nfrom mne import Epochs,find_events\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" + "# Some standard pythonic imports\nimport os\nfrom matplotlib import pyplot as plt \nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# MNE functions\nfrom mne import Epochs,find_events\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" ] }, { @@ -123,7 +112,7 @@ }, "outputs": [], "source": [ - "conditions = OrderedDict()\nconditions['House'] = [1]\nconditions['Face'] = [2]\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=None, #(1, 2))\n channel_order=[1,0,2,3]) # reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes\n\n# Manually adjust the ylims\nfor i in [0,2]: ax[i].set_ylim([-0.5,0.5])\nfor i in [1,3]: ax[i].set_ylim([-1.5,2.5])" + "conditions = OrderedDict()\n#conditions['House'] = [1]\n#conditions['Face'] = [2]\nconditions['House'] = ['House']\nconditions['Face'] = ['Face']\ndiffwav = ('Face', 'House')\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=diffwav,\n channel_order=[1,0,2,3]) \n# reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes\n\n# Manually adjust the ylims\nfor i in [0,2]: ax[i].set_ylim([-0.5e6,0.5e6])\nfor i in [1,3]: ax[i].set_ylim([-1.5e6,2.5e6])\nplt.tight_layout()" ] } ], @@ -143,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/a74331ba25f1e4b93e2ecf2b3efd4a13/02r__ssvep_decoding.ipynb b/_downloads/a74331ba25f1e4b93e2ecf2b3efd4a13/02r__ssvep_decoding.ipynb index ae8385cb..e5c4128b 100644 --- a/_downloads/a74331ba25f1e4b93e2ecf2b3efd4a13/02r__ssvep_decoding.ipynb +++ b/_downloads/a74331ba25f1e4b93e2ecf2b3efd4a13/02r__ssvep_decoding.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -125,7 +114,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/b1193b749e4caeff4dbfdf0c20ae6801/visual_ssvep_jupyter.zip b/_downloads/b1193b749e4caeff4dbfdf0c20ae6801/visual_ssvep_jupyter.zip index fef1d03d..329fe4c0 100644 Binary files a/_downloads/b1193b749e4caeff4dbfdf0c20ae6801/visual_ssvep_jupyter.zip and b/_downloads/b1193b749e4caeff4dbfdf0c20ae6801/visual_ssvep_jupyter.zip differ diff --git a/_downloads/b1c28450479cda89a20f021d24809e55/visual_n170_jupyter.zip b/_downloads/b1c28450479cda89a20f021d24809e55/visual_n170_jupyter.zip index b8bdedcb..b7f9a84a 100644 Binary files a/_downloads/b1c28450479cda89a20f021d24809e55/visual_n170_jupyter.zip and b/_downloads/b1c28450479cda89a20f021d24809e55/visual_n170_jupyter.zip differ diff --git a/_downloads/b2ef39fb5bc5abfa985af6266c497c15/01r__p300_viz.ipynb b/_downloads/b2ef39fb5bc5abfa985af6266c497c15/01r__p300_viz.ipynb index 451b208c..5339b78f 100644 --- a/_downloads/b2ef39fb5bc5abfa985af6266c497c15/01r__p300_viz.ipynb +++ b/_downloads/b2ef39fb5bc5abfa985af6266c497c15/01r__p300_viz.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "# Some standard pythonic imports\nimport os\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# MNE functions\nfrom mne import Epochs,find_events\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" + "# Some standard pythonic imports\nimport os\nfrom matplotlib import pyplot as plt\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# MNE functions\nfrom mne import Epochs,find_events\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" ] }, { @@ -105,7 +94,7 @@ }, "outputs": [], "source": [ - "# Create an array containing the timestamps and type of each stimulus (i.e. face or house)\nevents = find_events(raw)\nevent_id = {'Non-Target': 1, 'Target': 2}\nepochs = Epochs(raw, events=events, event_id=event_id,\n tmin=-0.1, tmax=0.8, baseline=None, \n reject={'eeg': 100e-6}, preload=True, \n verbose=False, picks=[0,1,2,3])\n\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n\nepochs" + "# Create an array containing the timestamps and type of each stimulus (i.e. face or house)\nevents = find_events(raw)\nevent_id = {'non-target': 1, 'target': 2}\nepochs = Epochs(raw, events=events, event_id=event_id,\n tmin=-0.1, tmax=0.8, baseline=None, reject={'eeg': 100e-6}, preload=True, \n verbose=False, picks=[0,1,2,3])\n\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)" ] }, { @@ -123,7 +112,7 @@ }, "outputs": [], "source": [ - "conditions = OrderedDict()\nconditions['Non-target'] = [1]\nconditions['Target'] = [2]\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=(1, 2))" + "conditions = OrderedDict()\nconditions['non-target'] = ['non-target']\nconditions['target'] = ['target']\ndiffwav = [\"non-target\", \"target\"]\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n channel_order=[1,0,2,3],ylim=[-2E6,2.5E6],\n diff_waveform = diffwav)\n\n# Manually adjust the ylims\nfor i in [0,2]: ax[i].set_ylim([-0.5e6,0.5e6])\nfor i in [1,3]: ax[i].set_ylim([-1.5e6,2.5e6])\n\nplt.tight_layout()" ] } ], @@ -143,7 +132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/b6763bc95c5e2ede1c4ce186cc0c606a/00x__p300_run_experiment.ipynb b/_downloads/b6763bc95c5e2ede1c4ce186cc0c606a/00x__p300_run_experiment.ipynb index 45c048c9..d2c71277 100644 --- a/_downloads/b6763bc95c5e2ede1c4ce186cc0c606a/00x__p300_run_experiment.ipynb +++ b/_downloads/b6763bc95c5e2ede1c4ce186cc0c606a/00x__p300_run_experiment.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments.visual_p300 import p300\n\n# Define some variables\nboard_name = \"muse\"\nexperiment = \"visual_p300\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120" + "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments import VisualP300\n\n# Define some variables\nboard_name = \"muse2\"\nexperiment = \"visual_p300\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120" ] }, { @@ -58,7 +47,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Run experiment\n--------------------- \n\n\n" + "Run experiment\n--------------------- \n\nCreate Experiment Object\n\n" ] }, { @@ -69,7 +58,7 @@ }, "outputs": [], "source": [ - "p300.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn)" + "p300 = VisualP300(duration=record_duration, eeg=eeg_device, save_fn=save_fn)\np300.run()" ] } ], @@ -89,7 +78,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/b6fdaf0dd3351d46675abacd38607890/00x__p300_run_experiment.py b/_downloads/b6fdaf0dd3351d46675abacd38607890/00x__p300_run_experiment.py index 8edcdbfd..754f5340 100644 --- a/_downloads/b6fdaf0dd3351d46675abacd38607890/00x__p300_run_experiment.py +++ b/_downloads/b6fdaf0dd3351d46675abacd38607890/00x__p300_run_experiment.py @@ -15,10 +15,10 @@ import os from eegnb import generate_save_fn from eegnb.devices.eeg import EEG -from eegnb.experiments.visual_p300 import p300 +from eegnb.experiments import VisualP300 # Define some variables -board_name = "muse" +board_name = "muse2" experiment = "visual_p300" subject_id = 0 session_nb = 0 @@ -38,5 +38,7 @@ ################################################################################################### # Run experiment # --------------------- -# -p300.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn) +# +# Create Experiment Object +p300 = VisualP300(duration=record_duration, eeg=eeg_device, save_fn=save_fn) +p300.run() diff --git a/_downloads/bffc7389ff14934d31aa05a911f58bf0/01r__cueing_singlesub_analysis.ipynb b/_downloads/bffc7389ff14934d31aa05a911f58bf0/01r__cueing_singlesub_analysis.ipynb index 8beefb4e..1dc876ed 100644 --- a/_downloads/bffc7389ff14934d31aa05a911f58bf0/01r__cueing_singlesub_analysis.ipynb +++ b/_downloads/bffc7389ff14934d31aa05a911f58bf0/01r__cueing_singlesub_analysis.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "# Some standard pythonic imports\nimport os,sys,glob,numpy as np,pandas as pd\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as patches\n\n# MNE functions\nfrom mne import Epochs,find_events, concatenate_raws\nfrom mne.time_frequency import tfr_morlet\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" + "# Some standard pythonic imports\nimport os,numpy as np#,sys,glob,pandas as pd\nfrom collections import OrderedDict\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as patches\n\n# MNE functions\nfrom mne import Epochs,find_events#, concatenate_raws\nfrom mne.time_frequency import tfr_morlet\n\n# EEG-Notebooks functions\nfrom eegnb.analysis.utils import load_data,plot_conditions\nfrom eegnb.datasets import fetch_dataset" ] }, { @@ -87,7 +76,7 @@ }, "outputs": [], "source": [ - "raw.plot_psd();\n\n# Should see the electrical noise at 60 Hz, and maybe a peak at the red and blue channels between 7-14 Hz (Alpha)" + "raw.compute_psd().plot();\n\n# Should see the electrical noise at 60 Hz, and maybe a peak at the red and blue channels between 7-14 Hz (Alpha)" ] }, { @@ -105,7 +94,7 @@ }, "outputs": [], "source": [ - "raw.filter(1,30, method='iir')\nraw.plot_psd(fmin=1, fmax=30);" + "raw.filter(1,30, method='iir');\nraw.compute_psd(fmin=1, fmax=30).plot();" ] }, { @@ -123,7 +112,7 @@ }, "outputs": [], "source": [ - "raw.filter(1,30, method='iir')\nevents = find_events(raw)\nevent_id = {'LeftCue': 1, 'RightCue': 2}\n\nrej_thresh_uV = 150\nrej_thresh = rej_thresh_uV*1e-6\n\nepochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 1, 2, 3])\n\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n\nconditions = OrderedDict()\nconditions['LeftCue'] = [1]\nconditions['RightCue'] = [2]\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=(1, 2), ylim=(-20,20))" + "raw.filter(1,30, method='iir')\nevents = find_events(raw)\nevent_id = {'LeftCue': 1, 'RightCue': 2}\n\nrej_thresh_uV = 150\nrej_thresh = rej_thresh_uV*1e-6\n\nepochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-1, tmax=2, baseline=(-1, 0), \n reject={'eeg':rej_thresh}, preload=True,\n verbose=False, picks=[0, 1, 2, 3])\n\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n\nconditions = OrderedDict()\n#conditions['LeftCue'] = [1]\n#conditions['RightCue'] = [2]\nconditions['LeftCue'] = ['LeftCue']\nconditions['RightCue'] = ['RightCue']\ndiffwave = ('LeftCue', 'RightCue')\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=diffwave, ylim=(-20,20))" ] }, { @@ -141,7 +130,7 @@ }, "outputs": [], "source": [ - "frequencies = np.linspace(6, 30, 100, endpoint=True)\n\nwave_cycles = 6\n\n# Compute morlet wavelet\n\n# Left Cue\ntfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\ntfr = tfr.apply_baseline([-1,-.5],mode='mean')\ntfr.plot(picks=[0], mode='logratio', \n title='TP9 - Ipsi');\ntfr.plot(picks=[1], mode='logratio', \n title='TP10 - Contra');\npower_Ipsi_TP9 = tfr.data[0,:,:]\npower_Contra_TP10 = tfr.data[1,:,:]\n\n# Right Cue\ntfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\ntfr = tfr.apply_baseline([-1,-.5],mode='mean')\ntfr.plot(picks=[0], mode='logratio', \n title='TP9 - Contra');\ntfr.plot(picks=[1], mode='logratio', \n title='TP10 - Ipsi');\npower_Contra_TP9 = tfr.data[0,:,:]\npower_Ipsi_TP10 = tfr.data[1,:,:]" + "frequencies = np.linspace(6, 30, 100, endpoint=True)\n\nwave_cycles = 6\n\n# Compute morlet wavelet\n\n# Left Cue\ntfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\ntfr = tfr.apply_baseline((-1,-.5),mode='mean')\ntfr.plot(picks=[0], mode='logratio', \n title='TP9 - Ipsi');\ntfr.plot(picks=[1], mode='logratio', \n title='TP10 - Contra');\npower_Ipsi_TP9 = tfr.data[0,:,:]\npower_Contra_TP10 = tfr.data[1,:,:]\n\n# Right Cue\ntfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n n_cycles=wave_cycles, return_itc=True)\ntfr = tfr.apply_baseline((-1,-.5),mode='mean')\ntfr.plot(picks=[0], mode='logratio', \n title='TP9 - Contra');\ntfr.plot(picks=[1], mode='logratio', \n title='TP10 - Ipsi');\npower_Contra_TP9 = tfr.data[0,:,:]\npower_Ipsi_TP10 = tfr.data[1,:,:]" ] }, { @@ -177,7 +166,7 @@ }, "outputs": [], "source": [ - "events = find_events(raw)\nevent_id = {'InvalidTarget_Left': 11, 'InvalidTarget_Right': 12,\n 'ValidTarget_Left': 21,'ValidTarget_Right': 11}\n\nepochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-.2, tmax=1, baseline=(-.2, 0), \n reject={'eeg':.0001}, preload=True,\n verbose=False, picks=[0, 1, 2, 3])\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n\nconditions = OrderedDict()\nconditions['ValidTarget'] = [21,22]\nconditions['InvalidTarget'] = [11,12]\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=(1, 2), ylim=(-20,20))" + "events = find_events(raw)\nevent_id = {'InvalidTarget_Left': 11, 'InvalidTarget_Right': 12,\n 'ValidTarget_Left': 21,'ValidTarget_Right': 11}\n\nepochs = Epochs(raw, events=events, event_id=event_id, \n tmin=-.2, tmax=1, baseline=(-.2, 0), \n reject={'eeg':.0001}, preload=True,\n verbose=False, picks=[0, 1, 2, 3])\nprint('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n\nconditions = OrderedDict()\nconditions['ValidTarget'] = ['ValidTarget_Left', 'ValidTarget_Right']\nconditions['InvalidTarget'] = ['InvalidTarget_Left', 'InvalidTarget_Right']\ndiffwave = ('ValidTarget', 'InvalidTarget')\n\nfig, ax = plot_conditions(epochs, conditions=conditions, \n ci=97.5, n_boot=1000, title='',\n diff_waveform=diffwave, ylim=(-20,20))" ] } ], @@ -197,7 +186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/c8168bec3b40b12111ac87ce4dfeac13/01r__cueing_singlesub_analysis.py b/_downloads/c8168bec3b40b12111ac87ce4dfeac13/01r__cueing_singlesub_analysis.py index fcee0f83..b3b4cf69 100644 --- a/_downloads/c8168bec3b40b12111ac87ce4dfeac13/01r__cueing_singlesub_analysis.py +++ b/_downloads/c8168bec3b40b12111ac87ce4dfeac13/01r__cueing_singlesub_analysis.py @@ -14,7 +14,7 @@ # # Some standard pythonic imports -import os,sys,glob,numpy as np,pandas as pd +import os,numpy as np#,sys,glob,pandas as pd from collections import OrderedDict import warnings warnings.filterwarnings('ignore') @@ -22,7 +22,7 @@ import matplotlib.patches as patches # MNE functions -from mne import Epochs,find_events, concatenate_raws +from mne import Epochs,find_events#, concatenate_raws from mne.time_frequency import tfr_morlet # EEG-Notebooks functions @@ -73,7 +73,7 @@ # One way to analyze the SSVEP is to plot the power spectral density, or PSD. SSVEPs should appear as peaks in power for certain frequencies. We expect clear peaks in the spectral domain at the stimulation frequencies of 30 and 20 Hz. # -raw.plot_psd(); +raw.compute_psd().plot(); # Should see the electrical noise at 60 Hz, and maybe a peak at the red and blue channels between 7-14 Hz (Alpha) @@ -84,8 +84,8 @@ # Most ERP components are composed of lower frequency fluctuations in the EEG signal. Thus, we can filter out all frequencies between 1 and 30 hz in order to increase our ability to detect them. # -raw.filter(1,30, method='iir') -raw.plot_psd(fmin=1, fmax=30); +raw.filter(1,30, method='iir'); +raw.compute_psd(fmin=1, fmax=30).plot(); ################################################################################################### # Epoching @@ -109,12 +109,15 @@ print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100) conditions = OrderedDict() -conditions['LeftCue'] = [1] -conditions['RightCue'] = [2] +#conditions['LeftCue'] = [1] +#conditions['RightCue'] = [2] +conditions['LeftCue'] = ['LeftCue'] +conditions['RightCue'] = ['RightCue'] +diffwave = ('LeftCue', 'RightCue') fig, ax = plot_conditions(epochs, conditions=conditions, ci=97.5, n_boot=1000, title='', - diff_waveform=(1, 2), ylim=(-20,20)) + diff_waveform=diffwave, ylim=(-20,20)) ################################################################################################### # Spectrogram @@ -132,7 +135,7 @@ # Left Cue tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) -tfr = tfr.apply_baseline([-1,-.5],mode='mean') +tfr = tfr.apply_baseline((-1,-.5),mode='mean') tfr.plot(picks=[0], mode='logratio', title='TP9 - Ipsi'); tfr.plot(picks=[1], mode='logratio', @@ -143,7 +146,7 @@ # Right Cue tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) -tfr = tfr.apply_baseline([-1,-.5],mode='mean') +tfr = tfr.apply_baseline((-1,-.5),mode='mean') tfr.plot(picks=[0], mode='logratio', title='TP9 - Contra'); tfr.plot(picks=[1], mode='logratio', @@ -242,10 +245,11 @@ print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100) conditions = OrderedDict() -conditions['ValidTarget'] = [21,22] -conditions['InvalidTarget'] = [11,12] +conditions['ValidTarget'] = ['ValidTarget_Left', 'ValidTarget_Right'] +conditions['InvalidTarget'] = ['InvalidTarget_Left', 'InvalidTarget_Right'] +diffwave = ('ValidTarget', 'InvalidTarget') fig, ax = plot_conditions(epochs, conditions=conditions, ci=97.5, n_boot=1000, title='', - diff_waveform=(1, 2), ylim=(-20,20)) + diff_waveform=diffwave, ylim=(-20,20)) diff --git a/_downloads/c9435ee669e38dd54bad678a26f2c8c1/00x__ssvep_run_experiment.ipynb b/_downloads/c9435ee669e38dd54bad678a26f2c8c1/00x__ssvep_run_experiment.ipynb index 0be78638..faf9ed5e 100644 --- a/_downloads/c9435ee669e38dd54bad678a26f2c8c1/00x__ssvep_run_experiment.ipynb +++ b/_downloads/c9435ee669e38dd54bad678a26f2c8c1/00x__ssvep_run_experiment.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -33,7 +22,7 @@ }, "outputs": [], "source": [ - "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments.visual_ssvep import ssvep\n\n# Define some variables\nboard_name = \"muse\"\nexperiment = \"visual_ssvep\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120" + "import os\nfrom eegnb import generate_save_fn\nfrom eegnb.devices.eeg import EEG\nfrom eegnb.experiments import VisualSSVEP\n\n# Define some variables\nboard_name = \"muse2\"\nexperiment = \"visual_ssvep\"\nsubject_id = 0\nsession_nb = 0\nrecord_duration = 120" ] }, { @@ -69,7 +58,7 @@ }, "outputs": [], "source": [ - "ssvep.present(duration=record_duration, eeg=eeg_device, save_fn=save_fn)" + "ssvep = VisualSSVEP(duration=record_duration, eeg=eeg_device, save_fn=save_fn)\nssvep.run()" ] } ], @@ -89,7 +78,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/d57fff739827ca575034e03b51d68a54/02r__cueing_group_analysis.py b/_downloads/d57fff739827ca575034e03b51d68a54/02r__cueing_group_analysis.py index 9053412a..535d8db3 100644 --- a/_downloads/d57fff739827ca575034e03b51d68a54/02r__cueing_group_analysis.py +++ b/_downloads/d57fff739827ca575034e03b51d68a54/02r__cueing_group_analysis.py @@ -123,7 +123,7 @@ # Left Cue tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) - tfr = tfr.apply_baseline([-1,-.5],mode='mean') + tfr = tfr.apply_baseline((-1,-.5),mode='mean') #tfr.plot(picks=[0], mode='logratio', # title='TP9 - Ipsi'); #tfr.plot(picks=[3], mode='logratio', @@ -134,7 +134,7 @@ # Right Cue tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, n_cycles=wave_cycles, return_itc=True) - tfr = tfr.apply_baseline([-1,-.5],mode='mean') + tfr = tfr.apply_baseline((-1,-.5),mode='mean') #tfr.plot(picks=[0], mode='logratio', # title='TP9 - Contra'); #tfr.plot(picks=[3], mode='logratio', diff --git a/_downloads/d8a61e599ef859175e65b658c0800a68/visual_ssvep_python.zip b/_downloads/d8a61e599ef859175e65b658c0800a68/visual_ssvep_python.zip index 259fa313..0636338d 100644 Binary files a/_downloads/d8a61e599ef859175e65b658c0800a68/visual_ssvep_python.zip and b/_downloads/d8a61e599ef859175e65b658c0800a68/visual_ssvep_python.zip differ diff --git a/_downloads/d8e5dee04f613448d49984a260dabc0b/visual_p300_jupyter.zip b/_downloads/d8e5dee04f613448d49984a260dabc0b/visual_p300_jupyter.zip index 0e5821be..2a292f83 100644 Binary files a/_downloads/d8e5dee04f613448d49984a260dabc0b/visual_p300_jupyter.zip and b/_downloads/d8e5dee04f613448d49984a260dabc0b/visual_p300_jupyter.zip differ diff --git a/_downloads/db47b930ce10faf6f74a13d608925887/02r__p300_decoding.ipynb b/_downloads/db47b930ce10faf6f74a13d608925887/02r__p300_decoding.ipynb index 4b075870..aba3738a 100644 --- a/_downloads/db47b930ce10faf6f74a13d608925887/02r__p300_decoding.ipynb +++ b/_downloads/db47b930ce10faf6f74a13d608925887/02r__p300_decoding.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -125,7 +114,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.18" } }, "nbformat": 4, diff --git a/_downloads/db7df1055da643ad96640e6377001dbf/01r__n170_viz.py b/_downloads/db7df1055da643ad96640e6377001dbf/01r__n170_viz.py index 9aa48f7d..ca4a3c02 100644 --- a/_downloads/db7df1055da643ad96640e6377001dbf/01r__n170_viz.py +++ b/_downloads/db7df1055da643ad96640e6377001dbf/01r__n170_viz.py @@ -24,6 +24,7 @@ # Some standard pythonic imports import os +from matplotlib import pyplot as plt from collections import OrderedDict import warnings warnings.filterwarnings('ignore') @@ -96,16 +97,20 @@ # ---------------------------- conditions = OrderedDict() -conditions['House'] = [1] -conditions['Face'] = [2] +#conditions['House'] = [1] +#conditions['Face'] = [2] +conditions['House'] = ['House'] +conditions['Face'] = ['Face'] +diffwav = ('Face', 'House') fig, ax = plot_conditions(epochs, conditions=conditions, ci=97.5, n_boot=1000, title='', - diff_waveform=None, #(1, 2)) - channel_order=[1,0,2,3]) # reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes + diff_waveform=diffwav, + channel_order=[1,0,2,3]) +# reordering of epochs.ch_names according to [[0,2],[1,3]] of subplot axes # Manually adjust the ylims -for i in [0,2]: ax[i].set_ylim([-0.5,0.5]) -for i in [1,3]: ax[i].set_ylim([-1.5,2.5]) - +for i in [0,2]: ax[i].set_ylim([-0.5e6,0.5e6]) +for i in [1,3]: ax[i].set_ylim([-1.5e6,2.5e6]) +plt.tight_layout() diff --git a/_downloads/e500d6e9072380c7b8d43c4a40d8e4f1/01r__p300_viz.py b/_downloads/e500d6e9072380c7b8d43c4a40d8e4f1/01r__p300_viz.py index 67d2bc5e..2b0f3ac8 100644 --- a/_downloads/e500d6e9072380c7b8d43c4a40d8e4f1/01r__p300_viz.py +++ b/_downloads/e500d6e9072380c7b8d43c4a40d8e4f1/01r__p300_viz.py @@ -21,6 +21,7 @@ # Some standard pythonic imports import os +from matplotlib import pyplot as plt from collections import OrderedDict import warnings warnings.filterwarnings('ignore') @@ -80,26 +81,30 @@ # Create an array containing the timestamps and type of each stimulus (i.e. face or house) events = find_events(raw) -event_id = {'Non-Target': 1, 'Target': 2} +event_id = {'non-target': 1, 'target': 2} epochs = Epochs(raw, events=events, event_id=event_id, - tmin=-0.1, tmax=0.8, baseline=None, - reject={'eeg': 100e-6}, preload=True, + tmin=-0.1, tmax=0.8, baseline=None, reject={'eeg': 100e-6}, preload=True, verbose=False, picks=[0,1,2,3]) print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100) -epochs - - ################################################################################################### # Epoch average # ---------------------------- conditions = OrderedDict() -conditions['Non-target'] = [1] -conditions['Target'] = [2] +conditions['non-target'] = ['non-target'] +conditions['target'] = ['target'] +diffwav = ["non-target", "target"] fig, ax = plot_conditions(epochs, conditions=conditions, ci=97.5, n_boot=1000, title='', - diff_waveform=(1, 2)) + channel_order=[1,0,2,3],ylim=[-2E6,2.5E6], + diff_waveform = diffwav) + +# Manually adjust the ylims +for i in [0,2]: ax[i].set_ylim([-0.5e6,0.5e6]) +for i in [1,3]: ax[i].set_ylim([-1.5e6,2.5e6]) + +plt.tight_layout() diff --git a/_downloads/fd5112e79635b428992c2be1eb2fdd3a/03r__cueing_behaviour_analysis_winter2019.ipynb b/_downloads/fd5112e79635b428992c2be1eb2fdd3a/03r__cueing_behaviour_analysis_winter2019.ipynb index 8ac6e106..5ef5f9fe 100644 --- a/_downloads/fd5112e79635b428992c2be1eb2fdd3a/03r__cueing_behaviour_analysis_winter2019.ipynb +++ b/_downloads/fd5112e79635b428992c2be1eb2fdd3a/03r__cueing_behaviour_analysis_winter2019.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -197,7 +186,7 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+jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css index bf18350b..cfc60b86 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -222,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 450px; + min-width: 360px; max-width: 800px; } @@ -237,16 +237,6 @@ a.headerlink { visibility: hidden; } -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -335,12 +325,16 @@ p.sidebar-title { font-weight: bold; } +nav.contents, +aside.topic, div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, div.topic { border: 1px solid #ccc; padding: 7px; @@ -379,6 +373,8 @@ div.body p.centered { div.sidebar > :last-child, aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -386,6 +382,8 @@ div.admonition > :last-child { div.sidebar::after, aside.sidebar::after, +nav.contents::after, +aside.topic::after, div.topic::after, div.admonition::after, blockquote::after { @@ -428,10 +426,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -615,19 +609,26 @@ ul.simple p { margin-bottom: 0; } -dl.footnote > dt, -dl.citation > dt { +aside.footnote > span, +div.citation > span { float: left; - margin-right: 0.5em; } - -dl.footnote > dd, -dl.citation > dd { +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { margin-bottom: 0em; } - -dl.footnote > dd:after, -dl.citation > dd:after { +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { content: ""; clear: both; } @@ -644,10 +645,6 @@ dl.field-list > dt { padding-right: 5px; } -dl.field-list > dt:after { - content: ":"; -} - dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -673,6 +670,16 @@ dd { margin-left: 30px; } +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + dl > dd:last-child, dl > dd:last-child > :last-child { margin-bottom: 0; @@ -741,6 +748,14 @@ abbr, acronym { cursor: help; } +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + /* -- code displays --------------------------------------------------------- */ pre { diff --git a/_static/copybutton.css b/_static/copybutton.css index 40eafe5f..f1916ec7 100644 --- a/_static/copybutton.css +++ b/_static/copybutton.css @@ -35,7 +35,8 @@ div.highlight { position: relative; } -.highlight:hover button.copybtn { +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { opacity: 1; } diff --git a/_static/copybutton.js b/_static/copybutton.js index 26e42afe..d06852f6 100644 --- a/_static/copybutton.js +++ b/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', + 'copy_to_clipboard': 'Copier dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -102,18 +102,25 @@ const clearSelection = () => { } } -// Changes tooltip text for two seconds, then changes it back +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) el.classList.add('success') - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) - setTimeout(() => el.classList.remove('success'), 2000) + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) } // Changes the copy button icon for two seconds, then changes it back const temporarilyChangeIcon = (el) => { el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, 2000) + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) } const addCopyButtonToCodeCells = () => { @@ -125,7 +132,8 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) @@ -141,10 +149,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; @@ -199,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '$ ', false, true, true, true, '', '') + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '$ ', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js index b9168c55..dbe1aaad 100644 --- a/_static/copybutton_funcs.js +++ b/_static/copybutton_funcs.js @@ -2,10 +2,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. 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b/_static/doctools.js @@ -2,357 +2,155 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return result; }; -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - this.initOnKeyListeners(); +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('
') - .appendTo($('#searchbox')); + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists } }, - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - var url = new URL(window.location); - url.searchParams.delete('highlight'); - window.history.replaceState({}, '', url); + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, - /** + /** * helper function to focus on search bar */ - focusSearchBar : function() { - $('input[name=q]').first().focus(); + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * make the url absolute + * Initialise the domain index toggle buttons */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - initOnKeyListeners: function() { + initOnKeyListeners: () => { // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - return; - - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON') { - if (event.altKey || event.ctrlKey || event.metaKey) - return; - - if (!event.shiftKey) { - switch (event.key) { - case 'ArrowLeft': - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) - break; - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - break; - case 'ArrowRight': - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) - break; - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - break; - case 'Escape': - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - break; - Documentation.hideSearchWords(); - return false; - } - } - - // some keyboard layouts may need Shift to get / + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { switch (event.key) { - case '/': - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - break; - Documentation.focusSearchBar(); - return false; + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); +_ready(Documentation.init); diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.5.1.js deleted file mode 100644 index 50937333..00000000 --- a/_static/jquery-3.5.1.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML