From 525b9b97084c80424effbaba3c603e64cb690a02 Mon Sep 17 00:00:00 2001 From: Alex Date: Sun, 2 May 2021 00:39:38 -0700 Subject: [PATCH] changed core feature to use change in voltage, maintenance cleanup --- docs/.buildinfo | 2 +- .../auto_examples_python.zip | Bin 19347 -> 20217 bytes .../plot_find_pd_events.py | 27 +- .../plot_recover_events.py | 47 +- .../plot_find_audio_events.ipynb | 108 + .../plot_find_pd_events.ipynb | 16 +- .../plot_recover_events.ipynb | 26 +- .../plot_find_pd_on_and_off.ipynb | 10 +- .../auto_examples_jupyter.zip | Bin 26517 -> 27609 bytes .../plot_find_audio_events.py | 111 + .../plot_find_pd_on_and_off.py | 22 +- .../sphx_glr_plot_find_audio_events_001.png | Bin 0 -> 38073 bytes .../sphx_glr_plot_find_audio_events_thumb.png | Bin 0 -> 28031 bytes .../sphx_glr_plot_find_pd_events_001.png | Bin 39794 -> 41283 bytes .../sphx_glr_plot_find_pd_events_002.png | Bin 184692 -> 202553 bytes .../sphx_glr_plot_find_pd_events_003.png | Bin 83787 -> 84118 bytes 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{docsrc/_build/html/_downloads/238cd1236e2081ff7294485992514de3 => docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385}/auto_examples_python.zip (84%) create mode 100644 docs/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb rename {docsrc/_build/html/_downloads/4e9f78ce2ee5ecb258e35211a260857e => docs/_downloads/6f1e7a639e0699d6164445b55e6c116d}/auto_examples_jupyter.zip (60%) create mode 100644 docs/_downloads/81406c451e497331ddbbb284a4d8eb5b/plot_find_audio_events.py create mode 100644 docs/_images/sphx_glr_plot_find_audio_events_001.png create mode 100644 docs/_images/sphx_glr_plot_find_audio_events_thumb.png create mode 100644 docs/_sources/auto_examples/plot_find_audio_events.rst.txt create mode 100644 docs/_static/binder_badge_logo.svg create mode 100644 docs/_static/gallery-rendered-html.css create mode 100644 docs/auto_examples/plot_find_audio_events.html create mode 100644 docs/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip create mode 100644 docs/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip create mode 100644 docs/dev/_static/binder_badge_logo.svg create mode 100644 docs/dev/_static/gallery-rendered-html.css create mode 100644 docsrc/_build/html/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip create mode 100644 docsrc/_build/html/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip create mode 100644 docsrc/_build/html/_static/binder_badge_logo.svg create mode 100644 docsrc/_build/html/_static/gallery-rendered-html.css diff --git a/docs/.buildinfo b/docs/.buildinfo index ac3f58c..f014d34 100644 --- a/docs/.buildinfo +++ b/docs/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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That data is saved in the same folder # as the raw file (in this case, a temperary directory generated by # :func:`_TempDir`). The data can be used directly, or it can be accessed via # :func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine # data structure (BIDS) standardized format before using it. -pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5) +pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5) ############################################################################### # Add events relative to the photodiode events: @@ -116,9 +119,9 @@ # Note: if more than one photodiode event is used, the parser can be # used for each event separately using the keyword `add_event=True`. -pd_parser.add_pd_relative_events( - fname, behf, - relative_event_cols=['go_time', 'response_time'], +pd_parser.add_relative_events( + raw, beh, + relative_event_keys=['go_time', 'response_time'], relative_event_names=['Go Cue', 'Response']) diff --git a/docs/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py b/docs/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py index ee99b21..6e9d46d 100644 --- a/docs/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py +++ b/docs/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py @@ -27,7 +27,7 @@ from mne.utils import _TempDir import pd_parser -from pd_parser.parse_pd import _to_tsv +from pd_parser.parse_pd import _load_data import matplotlib.pyplot as plt @@ -38,7 +38,7 @@ n_events = 300 # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds n_secs_on = np.random.random(n_events) * 0.5 + 0.25 -raw, beh_df, events, _ = \ +raw, beh, events, _ = \ pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on, prop_corrupted=0.0) sfreq = np.round(raw.info['sfreq']).astype(int) @@ -51,16 +51,19 @@ axes[0].set_ylabel('voltage') for j, i in enumerate(events[corrupted_indices, 0]): if j == 0: - raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount + raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount elif j == 1: raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount else: - raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount + raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount axes[j].plot(np.linspace(-1, 2, 3 * sfreq), raw._data[0, i - sfreq: i + sfreq * 2]) axes[j].set_xlabel('time (s)') +# make figure nicer +fig.tight_layout() + # make fake electrophysiology data info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'], ['seeg'] * 3) @@ -71,15 +74,13 @@ raw.info['dig'] = None raw.info['line_freq'] = 60 -# save to disk as required by ``pd-parser`` -fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') -raw.save(fname) # add some offsets to the behavior so it's a bit more realistic offsets = np.random.randn(n_events) * 0.001 -beh_df['time'] = np.array(beh_df['time']) + offsets -behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv') -_to_tsv(behf, beh_df) +beh['time'] = np.array(beh['time']) + offsets +# save to disk as required by ``pd-parser``, raw needs to have a filename +fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') +raw.save(fname) ############################################################################### # Find the photodiode events relative to the behavioral timing of interest: @@ -92,8 +93,8 @@ # your own input depending on whether you want to keep the events or not. with mock.patch('builtins.input', return_value='y'): - pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf, - pd_ch_names=['pd'], beh_col='time', recover=True) + pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh, + pd_ch_names=['pd'], beh_key='time', recover=True) ############################################################################### # Find cessations of the photodiode deflections @@ -108,3 +109,25 @@ # following the instructions. pd_parser.add_pd_off_events(fname, off_event_name='Stim Off') + +############################################################################### +# Check the results: +# +# Finally, we'll check that the recovered events and the original events match. + +annot = _load_data(fname)[0] +raw.set_annotations(annot) +events2, event_id = mne.events_from_annotations(raw) +on_events = events2[events2[:, 2] == event_id['Stim On']] +print(f'Original: {events[corrupted_indices, 0]}\n' + f'Recovered: {on_events[corrupted_indices, 0]}') + +''' +# uncomment when using interactively, this section doesn't work +# for the non-interactive documentation +off_events = events2[events2[:, 2] == event_id['Stim Off']] +original_off = events[corrupted_indices, 0] + \ + np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int) +print(f'Original off: {original_off}\n' + f'Recovered off: {on_events[corrupted_indices, 0]}') +''' diff --git a/docs/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb b/docs/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb new file mode 100644 index 0000000..ecfa221 --- /dev/null +++ b/docs/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Use Audio to Align Video Data\nIn this example, we use ``pd-parser`` to find audio events using the same\nalgorithm for matching with time-stamps and rejecting misaligned\naudio, but applied using the onset of an audio deflection instead of detecting\nphotodiode events based on their square wave shape.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Alex Rockhill \n#\n# License: BSD (3-clause)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load in a video with audio:\n\nIn this example, we'll use audio and instead of aligning electrophysiology\ndata, we'll align a video. This example data is from a task where movements\nare played on a monitor for the participant to mirror and the video recording\nis synchronized by playing a pre-recorded clap. This clap sound, or a similar\nsound, is recommended for synchronizing audio because the onset is clear and\nallows good precision in synchronizing events.\n\nNote that the commands that require ffmpeg are pre-computed and commented\nout because ffmpeg must be installed to use them and it is not required by\n``pd-parser``.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\nimport os.path as op\nimport numpy as np\nfrom scipy.io import wavfile\nfrom subprocess import call\n# from subprocess import run, PIPE, STDOUT\n# import datetime\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data # , _read_tsv\n\n# get the data\nout_dir = _TempDir()\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n 'master/pd_parser/tests/data/test_video.mp4 '\n '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n 'master/pd_parser/tests/data/test_video.wav '\n '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n 'master/pd_parser/tests/data/test_video_beh.tsv '\n '-o ' + op.join(out_dir, 'test_video_beh.tsv')],\n shell=True, env=os.environ)\n\n# navigate to the example video\nvideo_fname = op.join(out_dir, 'test_video.mp4')\n\naudio_fname = video_fname.replace('mp4', 'wav') # pre-computed\n# extract audio (requires ffmpeg)\n# run(['ffmpeg', '-i', video_fname, audio_fname])\n\nfs, data = wavfile.read(audio_fname)\ndata = data.mean(axis=1) # stereo audio but only need one source\ninfo = mne.create_info(['audio'], fs, ['stim'])\nraw = mne.io.RawArray(data[np.newaxis], info)\n\n# find audio-visual time offset\noffset = 0 # pre-computed value for this video\n'''\nresult = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time',\n '-v', '0', '-of', 'compact=p=1:nk=0', video_fname],\n stdout=PIPE, stderr=STDOUT)\noutput = result.stdout.decode('utf-8').split('\\n')\noffset = float(output[0].strip('stream|codec_type=video|start_time')) - \\\n float(output[1].strip('stream|codec_type=audio|start_time'))\n'''\n\n# save to disk as required by ``pd-parser``, raw needs a filename\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n\n# navigate to corresponding behavior\nbehf = op.join(out_dir, 'test_video_beh.tsv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the parser:\n\nNow we'll call the main function to automatically parse the audio events.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',\n audio_ch_names=['audio'], zscore=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the results:\n\nFinally, we'll load the events and use them to crop the video although it\nrequires ffmpeg so it is commented out.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "annot = _load_data(fname)[0]\nprint('Here are the event times: ', annot.onset)\n\n# Crop the videos with ffmpeg\n'''\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size): # skip the first video\n action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n str(datetime.timedelta(\n seconds=annot.onset[i] - action_time - offset)),\n '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n beh['movement'][i], beh['action_type'][i]))])\n'''" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb b/docs/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb index 9d75ab1..0c9f31f 100644 --- a/docs/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb +++ b/docs/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Find Photodiode Events\n\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign them to behavior. Then, we save the data to BIDS format.\n" + "\n# Find Photodiode Events\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign them to behavior. Then, we save the data to BIDS format.\n" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv\n\nout_dir = _TempDir()\nprint(f'After running this example, you can find the data here: {out_dir}')\n\n# simulate photodiode data\nn_events = 300\nprop_corrupted = 0.01\nraw, beh_df, events, corrupted_indices = \\\n pd_parser.simulate_pd_data(n_events=n_events,\n prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)" + "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _read_raw, _to_tsv\n\nout_dir = _TempDir()\nprint(f'After running this example, you can find the data here: {out_dir}')\n\n# simulate photodiode data\nn_events = 300\nprop_corrupted = 0.01\nraw, beh, events, corrupted_indices = \\\n pd_parser.simulate_pd_data(n_events=n_events,\n prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n\n# roundtrip so that raw is properly loaded from disk and has a filename\nraw = _read_raw(fname)" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "np.random.seed(12)\n# add some noise to make it harder to align, use just over\n# the exclusion of 0.03 to make some events excluded\noffsets = np.random.random(n_events) * 0.035 - 0.0125\n# in this example, the fixation would always be 700 ms\n# after which point a cue would appear which is the \"go time\"\ngo_time = np.repeat(0.7, n_events)\n# let's make the response time between 0.5 and 1.5 seconds uniform random\nresponse_time = list(go_time + np.random.random(n_events) + 1.5)\nfor i in [10, 129, 232, 288]:\n response_time[i] = 'n/a' # make some no responses\n# put in dictionary to be converted to tsv file\nbeh_df['fix_onset_time'] = beh_df['time'] + offsets\nbeh_df['go_time'] = go_time\nbeh_df['response_time'] = response_time\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n# save behavior file out\n_to_tsv(behf, beh_df)" + "np.random.seed(12)\n# add some noise to make it harder to align, use just over\n# the exclusion of 0.03 to make some events excluded\noffsets = np.random.random(n_events) * 0.035 - 0.0125\n# in this example, the fixation would always be 700 ms\n# after which point a cue would appear which is the \"go time\"\ngo_time = np.repeat(0.7, n_events)\n# let's make the response time between 0.5 and 1.5 seconds uniform random\nresponse_time = list(go_time + np.random.random(n_events) + 1.5)\nfor i in [10, 129, 232, 288]:\n response_time[i] = 'n/a' # make some no responses\n# put in dictionary to be converted to tsv file\nbeh['fix_onset_time'] = beh['time'] + offsets\nbeh['go_time'] = go_time\nbeh['response_time'] = response_time\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n# save behavior file out\n_to_tsv(behf, beh)" ] }, { @@ -80,14 +80,14 @@ }, "outputs": [], "source": [ - "pd_parser.find_pd_params(fname, pd_ch_names=['pd'])" + "pd_parser.find_pd_params(raw, pd_ch_names=['pd'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Find the photodiode events relative to the behavioral timing of interest:\n\nThis function will use the default parameters or the parameters you\nfound from :func:`pd_parser.find_pd_parameters` to find and align the\nphotodiode events, excluding events that were off because the commuter\nhung up on computation, for instance. That data is saved in the same folder\nas the raw file (in this case, a temperary directory generated by\n:func:`_TempDir`). The data can be used directly, or it can be accessed via\n:func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine\ndata structure (BIDS) standardized format before using it.\n\n" + "Find the photodiode events relative to the behavioral timing of interest:\n\nThis function will use the default parameters or the parameters you\nfound from :func:`pd_parser.find_pd_parameters` to find and align the\nphotodiode events, excluding events that were off because the computer\nhung up on computation, for instance. That data is saved in the same folder\nas the raw file (in this case, a temperary directory generated by\n:func:`_TempDir`). The data can be used directly, or it can be accessed via\n:func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine\ndata structure (BIDS) standardized format before using it.\n\n" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)" + "pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)" ] }, { @@ -116,7 +116,7 @@ }, "outputs": [], "source": [ - "pd_parser.add_pd_relative_events(\n fname, behf,\n relative_event_cols=['go_time', 'response_time'],\n relative_event_names=['Go Cue', 'Response'])" + "pd_parser.add_relative_events(\n raw, beh,\n relative_event_keys=['go_time', 'response_time'],\n relative_event_names=['Go Cue', 'Response'])" ] }, { @@ -154,7 +154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb b/docs/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb index 5bce2fe..a451b96 100644 --- a/docs/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb +++ b/docs/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Manually Recover Events Not Found by the Algorithm\n\nIn this example, we use ``pd-parser`` to find photodiode events that\nhave corrupted pre-event baselines, photodiode plateaus or post-event\nbaselines but not corrupted onsets or offsets.\nNote that it might be a good idea not to recover these events\nas there might be noise in the data around this time.\n" + "\n# Manually Recover Events Not Found by the Algorithm\nIn this example, we use ``pd-parser`` to find photodiode events that\nhave corrupted pre-event baselines, photodiode plateaus or post-event\nbaselines but not corrupted onsets or offsets.\nNote that it might be a good idea not to recover these events\nas there might be noise in the data around this time.\n" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "import os.path as op\nimport numpy as np\nimport mock\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv\n\nimport matplotlib.pyplot as plt\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.25 to 0.75 seconds\nn_secs_on = np.random.random(n_events) * 0.5 + 0.25\nraw, beh_df, events, _ = \\\n pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n prop_corrupted=0.0)\nsfreq = np.round(raw.info['sfreq']).astype(int)\n\n# corrupt some events\ncorrupted_indices = [8, 144, 234]\namount = raw._data.max()\nfig, axes = plt.subplots(1, len(corrupted_indices), figsize=(8, 4))\nfig.suptitle('Corrupted Events')\naxes[0].set_ylabel('voltage')\nfor j, i in enumerate(events[corrupted_indices, 0]):\n if j == 0:\n raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount\n elif j == 1:\n raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount\n else:\n raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount\n axes[j].plot(np.linspace(-1, 2, 3 * sfreq),\n raw._data[0, i - sfreq: i + sfreq * 2])\n axes[j].set_xlabel('time (s)')\n\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.001\nbeh_df['time'] = np.array(beh_df['time']) + offsets\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n_to_tsv(behf, beh_df)" + "import os.path as op\nimport numpy as np\nimport mock\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data\n\nimport matplotlib.pyplot as plt\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.25 to 0.75 seconds\nn_secs_on = np.random.random(n_events) * 0.5 + 0.25\nraw, beh, events, _ = \\\n pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n prop_corrupted=0.0)\nsfreq = np.round(raw.info['sfreq']).astype(int)\n\n# corrupt some events\ncorrupted_indices = [8, 144, 234]\namount = raw._data.max()\nfig, axes = plt.subplots(1, len(corrupted_indices), figsize=(8, 4))\nfig.suptitle('Corrupted Events')\naxes[0].set_ylabel('voltage')\nfor j, i in enumerate(events[corrupted_indices, 0]):\n if j == 0:\n raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount\n elif j == 1:\n raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount\n else:\n raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount\n axes[j].plot(np.linspace(-1, 2, 3 * sfreq),\n raw._data[0, i - sfreq: i + sfreq * 2])\n axes[j].set_xlabel('time (s)')\n\n\n# make figure nicer\nfig.tight_layout()\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.001\nbeh['time'] = np.array(beh['time']) + offsets\n\n# save to disk as required by ``pd-parser``, raw needs to have a filename\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "with mock.patch('builtins.input', return_value='y'):\n pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,\n pd_ch_names=['pd'], beh_col='time', recover=True)" + "with mock.patch('builtins.input', return_value='y'):\n pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,\n pd_ch_names=['pd'], beh_key='time', recover=True)" ] }, { @@ -82,6 +82,24 @@ "source": [ "pd_parser.add_pd_off_events(fname, off_event_name='Stim Off')" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the results:\n\nFinally, we'll check that the recovered events and the original events match.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "annot = _load_data(fname)[0]\nraw.set_annotations(annot)\nevents2, event_id = mne.events_from_annotations(raw)\non_events = events2[events2[:, 2] == event_id['Stim On']]\nprint(f'Original: {events[corrupted_indices, 0]}\\n'\n f'Recovered: {on_events[corrupted_indices, 0]}')\n\n'''\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] + \\\n np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\\n'\n f'Recovered off: {on_events[corrupted_indices, 0]}')\n'''" + ] } ], "metadata": { @@ -100,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb b/docs/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb index 855ee20..c3c4431 100644 --- a/docs/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb +++ b/docs/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# Find Photodiode On and Off Events\n\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign both the onset of the deflection and the cessation to\nto behavior.\n" + "\n# Find Photodiode On and Off Events\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign both the onset of the deflection and the cessation to\nto behavior.\n" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv, _load_pd_data\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.5 to 1 second\nn_secs_on = np.random.random(n_events) * 0.5 + 0.5\nprop_corrupted = 0.01\nraw, beh_df, events, corrupted_indices = \\\n pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.01\nbeh_df['time'] = np.array(beh_df['time']) + offsets\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n_to_tsv(behf, beh_df)" + "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.5 to 1 second\nn_secs_on = np.random.random(n_events) * 0.5 + 0.5\nprop_corrupted = 0.01\nraw, beh, events, corrupted_indices = \\\n pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.01\nbeh['time'] = np.array(beh['time']) + offsets\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,\n pd_ch_names=['pd'], beh_col='time',\n max_len=1.5) # none are on longer than 1.5 seconds" + "pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,\n pd_ch_names=['pd'], beh_key='time',\n max_len=1.5) # none are on longer than 1.5 seconds" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "annot, pd_ch_names, beh_df = _load_pd_data(fname)\nraw.set_annotations(annot)\nevents, event_id = mne.events_from_annotations(raw)\non_events = events[events[:, 2] == event_id['Stim On']]\noff_events = events[events[:, 2] == event_id['Stim Off']]\n\nrecovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']\nnot_corrupted = [s != 'n/a' for s in beh_df['pd_sample']]\nground_truth_not_corrupted = n_secs_on[not_corrupted]\n\nfig, ax = plt.subplots()\nax.scatter(ground_truth_not_corrupted, recovered,\n s=1, color=cm.rainbow(np.linspace(0, 1, len(recovered))))\nax.set_title('Photodiode offset eventfidelity of recovery')\nax.set_xlabel('ground truth duration (s)')\nax.set_ylabel('recovered duration (s)')\n\nprint('Mean difference in the recovered from simulated length is {:.3f} '\n 'milliseconds'.format(\n 1000 * abs(ground_truth_not_corrupted - recovered).mean()))" + "annot, pd_ch_names, beh = _load_data(fname)\nraw.set_annotations(annot)\nevents, event_id = mne.events_from_annotations(raw)\non_events = events[events[:, 2] == event_id['Stim On']]\noff_events = events[events[:, 2] == event_id['Stim Off']]\n\nrecovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']\nnot_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']]\nground_truth_not_corrupted = n_secs_on[not_corrupted]\n\nfig, ax = plt.subplots()\nax.scatter(ground_truth_not_corrupted, recovered,\n s=1, color=cm.rainbow(np.linspace(0, 1, len(recovered))))\nax.set_title('Photodiode offset eventfidelity of recovery')\nax.set_xlabel('ground truth duration (s)')\nax.set_ylabel('recovered duration (s)')\n\nprint('Mean difference in the recovered from simulated length is {:.3f} '\n 'milliseconds'.format(\n 1000 * abs(ground_truth_not_corrupted - recovered).mean()))" ] } ], @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docsrc/_build/html/_downloads/4e9f78ce2ee5ecb258e35211a260857e/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip similarity index 60% rename from docsrc/_build/html/_downloads/4e9f78ce2ee5ecb258e35211a260857e/auto_examples_jupyter.zip rename to docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index b21ea5d99aafc27fbf9151a54f05e110867512fb..a33f3c1059b65d3c5e5dc8601a0a5a4718897f3b 100644 GIT binary patch delta 1599 zcmaJ>O>7%Q6!zNrBh(4OO`JNxnUKIbX==wwOp+#1>sD1N0!o@TQHh-_i#_XIV$Yb} zwL?^ef~dr)sM64i7DYl)rE)@`IdFjj;_pb2ilR!WLMo^iKtU97LhxoCgN;=7Fq)nD z=FPnC``)|wwRCG+^3Tn9YWBLs_p@hf{*BD9zpVIOu9f%wMTu=i!u%u2U&p?!f4v!@ z9yEXy6RT;(wu(H}fNOZvdrM+J^z^k2AX8mK1mm=YRHPYMOw=4^-rgs9UGwi!)6{cV z*9H)b$|_#xjV*skel&%YEQN4s)mB5AIMA0ETxKEPQY!$(cKNoDINrxz_MP-6WSbJK zP-0I&`SiZGYS>bPm)#leX5Qdk{zuT`emHgz84%ynO#A|hL{M~)J=p!~qr<5G z-?b5e*^gqCYfrHEx~~O|Ou@v67%6NfiDqQNwvo|oC9AH}f*s0#;9ID7)=sL zfyKxCK1k2VVR^X^LQ+kGBrSHkClahA*rrHVi@2KT%}_+Z^xbCmx@0 z*9hX-U^>9gPqcdk^tX#vp38yq;6-l+M0CalKO|gxdGh^G3W^8e;e2@dt(yrHDgp@(A+fO@r7WO`hEi zdk~#r@1V}$Mlwm{Btg!n$@Assz&<64fSlcs`;r7uGT?epfuv|oh>q5DOi;lxNCrV{ zY^o{SDB|q;0KDI@kcHu&5|pOcA~L|Kxheu^)N~RglpLry2e?#0#8t(qBmq4XCjp-Y zx$dS?ky3zNI8xtSW=R1!z{Go_AZ8M>LWtzUCM}S3h$!$F3v8T7QPV8sZJbsNk_N9> z%itt-sS6$ZFG0c(f`nbZ#0B@`sthgh<=&7vZTDcGwNxB>&^dwMxVB&GDO3*=%jXY>i;Hsgjw&e{KAWiF>vD!}yHE z8;{=hlLQ|B~$%O7nl5EESBmUc0>HJ6?@m;2GS*BOaK z6m>C?;E+LfBNRb56^Dep2qEw;j4(!Bi2eZu-2`1oot$P4!MCV#TpC5@;@PJ18zHUc5<`|?3v7c4NlGI*jRP73ULUD3Q4)5l#nKXphYGT+2Z$(uq+tpholnV)<7X7= z0zQ1rrNiS|CBEyb=kpZ5SrJd(PCfripz`5_-#a(7cs*CoaXE6cYmNaRR&kFnE1Y7( z)lHpbq(=Zu>AZJ@to42cc)*dQ@}pSeQ2)m(5TkRrNWPuPUU6v)XHe70#y~eyLiWyW z^ZQO#hUq<)zI+;2nK({GTHVooBaLMDe2&NOJZsFmd2d328Rh>`o3dKA?x!fLJH0NEOMDa}Pu zTObZ;OQC33j$}{>WoU)63Kx;6`g5IbJx~bzKOPx=bJb}oEn1FiV2`oTJ-H>}x@jvc rv6WhDEXk>| +# +# License: BSD (3-clause) + +############################################################################### +# Load in a video with audio: +# +# In this example, we'll use audio and instead of aligning electrophysiology +# data, we'll align a video. This example data is from a task where movements +# are played on a monitor for the participant to mirror and the video recording +# is synchronized by playing a pre-recorded clap. This clap sound, or a similar +# sound, is recommended for synchronizing audio because the onset is clear and +# allows good precision in synchronizing events. +# +# Note that the commands that require ffmpeg are pre-computed and commented +# out because ffmpeg must be installed to use them and it is not required by +# ``pd-parser``. +import os +import os.path as op +import numpy as np +from scipy.io import wavfile +from subprocess import call +# from subprocess import run, PIPE, STDOUT +# import datetime + +import mne +from mne.utils import _TempDir + +import pd_parser +from pd_parser.parse_pd import _load_data # , _read_tsv + +# get the data +out_dir = _TempDir() +call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/' + 'master/pd_parser/tests/data/test_video.mp4 ' + '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ) +call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/' + 'master/pd_parser/tests/data/test_video.wav ' + '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ) +call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/' + 'master/pd_parser/tests/data/test_video_beh.tsv ' + '-o ' + op.join(out_dir, 'test_video_beh.tsv')], + shell=True, env=os.environ) + +# navigate to the example video +video_fname = op.join(out_dir, 'test_video.mp4') + +audio_fname = video_fname.replace('mp4', 'wav') # pre-computed +# extract audio (requires ffmpeg) +# run(['ffmpeg', '-i', video_fname, audio_fname]) + +fs, data = wavfile.read(audio_fname) +data = data.mean(axis=1) # stereo audio but only need one source +info = mne.create_info(['audio'], fs, ['stim']) +raw = mne.io.RawArray(data[np.newaxis], info) + +# find audio-visual time offset +offset = 0 # pre-computed value for this video +''' +result = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time', + '-v', '0', '-of', 'compact=p=1:nk=0', video_fname], + stdout=PIPE, stderr=STDOUT) +output = result.stdout.decode('utf-8').split('\n') +offset = float(output[0].strip('stream|codec_type=video|start_time')) - \ + float(output[1].strip('stream|codec_type=audio|start_time')) +''' + +# save to disk as required by ``pd-parser``, raw needs a filename +fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') +raw.save(fname) + +# navigate to corresponding behavior +behf = op.join(out_dir, 'test_video_beh.tsv') + +############################################################################### +# Run the parser: +# +# Now we'll call the main function to automatically parse the audio events. +pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time', + audio_ch_names=['audio'], zscore=10) + +############################################################################### +# Load the results: +# +# Finally, we'll load the events and use them to crop the video although it +# requires ffmpeg so it is commented out. +annot = _load_data(fname)[0] +print('Here are the event times: ', annot.onset) + +# Crop the videos with ffmpeg +''' +beh = _read_tsv(behf) +for i in range(annot.onset.size): # skip the first video + action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000 + run(['ffmpeg', '-i', f'{video_fname}', '-ss', + str(datetime.timedelta( + seconds=annot.onset[i] - action_time - offset)), + '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)), + op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format( + beh['movement'][i], beh['action_type'][i]))]) +''' diff --git a/docs/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py b/docs/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py index decedfb..df6d077 100644 --- a/docs/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py +++ b/docs/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py @@ -24,7 +24,7 @@ from mne.utils import _TempDir import pd_parser -from pd_parser.parse_pd import _to_tsv, _load_pd_data +from pd_parser.parse_pd import _load_data import matplotlib.pyplot as plt import matplotlib.cm as cm @@ -37,7 +37,7 @@ # let's make our photodiode events on random uniform from 0.5 to 1 second n_secs_on = np.random.random(n_events) * 0.5 + 0.5 prop_corrupted = 0.01 -raw, beh_df, events, corrupted_indices = \ +raw, beh, events, corrupted_indices = \ pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on, prop_corrupted=prop_corrupted) @@ -51,15 +51,13 @@ raw.info['dig'] = None raw.info['line_freq'] = 60 -# save to disk as required by ``pd-parser`` -fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') -raw.save(fname) # add some offsets to the behavior so it's a bit more realistic offsets = np.random.randn(n_events) * 0.01 -beh_df['time'] = np.array(beh_df['time']) + offsets -behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv') -_to_tsv(behf, beh_df) +beh['time'] = np.array(beh['time']) + offsets +# save to disk as required by ``pd-parser`` +fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') +raw.save(fname) ############################################################################### # Find the photodiode events relative to the behavioral timing of interest: @@ -69,8 +67,8 @@ # One percent of the 300 events (3) were corrupted as shown in the plots and # some were too far off from large offsets that we're going to exclude them. -pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf, - pd_ch_names=['pd'], beh_col='time', +pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh, + pd_ch_names=['pd'], beh_key='time', max_len=1.5) # none are on longer than 1.5 seconds ############################################################################### @@ -93,14 +91,14 @@ # the lengths are recovered within 1 millisecond. Note that the colors are # arbitrary and are only used to increase contrast and ease of visualization. -annot, pd_ch_names, beh_df = _load_pd_data(fname) +annot, pd_ch_names, beh = _load_data(fname) raw.set_annotations(annot) events, event_id = mne.events_from_annotations(raw) on_events = events[events[:, 2] == event_id['Stim On']] off_events = events[events[:, 2] == event_id['Stim Off']] recovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq'] -not_corrupted = [s != 'n/a' for s in beh_df['pd_sample']] +not_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']] ground_truth_not_corrupted = n_secs_on[not_corrupted] fig, ax = plt.subplots() diff --git a/docs/_images/sphx_glr_plot_find_audio_events_001.png b/docs/_images/sphx_glr_plot_find_audio_events_001.png new file mode 100644 index 0000000000000000000000000000000000000000..98213f7f9194b49c53943445d21b48c080491e61 GIT binary patch literal 38073 zcmdSBWn5KV)IGWnM@mvrx?80Yq`{yJ1W`e{QIM3*gCgCbB5eUm3P=eYN=ibdJ4L0D 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All modules for which code is available

© Copyright 2020, Alex Rockhill.
- Created using Sphinx 3.2.1.
+ Created using Sphinx 3.3.1.

diff --git a/docs/_modules/parse_pd.html b/docs/_modules/parse_pd.html index f0f84c5..01f0bb1 100644 --- a/docs/_modules/parse_pd.html +++ b/docs/_modules/parse_pd.html @@ -4,12 +4,13 @@ - parse_pd — pd-parser 0.3 documentation - + parse_pd — pd-parser v0.4 documentation + + @@ -39,7 +40,7 @@ - pd-parser 0.3 + pd-parser v0.4 @@ -157,154 +158,242 @@

Source code for parse_pd

             fid.write('\t'.join([str(val[i]) for val in df.values()]) + '\n')
 
 
-def _read_raw(fname, preload=True, verbose=True):
-    _, ext = op.splitext(fname)
-    """Read raw data into an mne.io.Raw object."""
-    if verbose:
-        print('Reading in {}'.format(fname))
-    if ext == '.fif':
-        raw = mne.io.read_raw_fif(fname, preload=preload)
-    elif ext == '.edf':
-        raw = mne.io.read_raw_edf(fname, preload=preload)
-    elif ext == '.bdf':
-        raw = mne.io.read_raw_bdf(fname, preload=preload)
-    elif ext == '.vhdr':
-        raw = mne.io.read_raw_brainvision(fname, preload=preload)
-    elif ext == '.set':
-        raw = mne.io.read_raw_eeglab(fname, preload=preload)
+def _read_raw(raw, preload=True, verbose=True):
+    """Read raw object from file if it's not already loaded."""
+    if isinstance(raw, mne.io.BaseRaw):
+        if preload:
+            raw.load_data()
+        elif preload is not None:
+            if raw.preload:
+                raise ValueError('`raw` object cannot be preloaded')
+        if raw.filenames[0] is None:
+            raise ValueError('`raw` object must be loaded from disk')
     else:
-        raise ValueError('Extension {} not recognized, options are'
-                         'fif, edf, bdf, vhdr (brainvision) and set '
-                         '(eeglab)'.format(ext))
+        _, ext = op.splitext(raw)
+        """Read raw data into an mne.io.Raw object."""
+        if verbose:
+            print('Reading in {}'.format(raw))
+        if ext == '.fif':
+            raw = mne.io.read_raw_fif(raw, preload=preload)
+        elif ext == '.edf':
+            raw = mne.io.read_raw_edf(raw, preload=preload)
+        elif ext == '.bdf':
+            raw = mne.io.read_raw_bdf(raw, preload=preload)
+        elif ext == '.vhdr':
+            raw = mne.io.read_raw_brainvision(raw, preload=preload)
+        elif ext == '.set':
+            raw = mne.io.read_raw_eeglab(raw, preload=preload)
+        else:
+            raise ValueError('Extension {} not recognized, options are'
+                             'fif, edf, bdf, vhdr (brainvision), set '
+                             '(eeglab)'.format(ext))
     return raw
 
 
-def _load_beh_df(behf, beh_col):
+def _load_beh(beh, beh_key):
     """Load the behavioral data frame and check columns."""
-    df = _read_tsv(behf)
-    if beh_col not in df:
-        raise ValueError(f'beh_col {beh_col} not in the columns of '
-                         f'behf {behf}. Please check that the correct '
-                         'column is provided')
-    return df[beh_col], df
+    if not isinstance(beh, dict):
+        beh = _read_tsv(beh)
+    if beh_key not in beh:
+        raise ValueError(f'`beh_key` {beh_key} not in the columns of '
+                         f'the `beh` behavior dictionary. Please check '
+                         'that the correct column is provided')
+    return beh[beh_key], beh
 
 
-def _get_pd_channel_data(raw, pd_ch_names):
+def _get_channel_data(raw, ch_names):
     """Get the time-series data from the channel names."""
-    if any([ch not in raw.ch_names for ch in pd_ch_names]):
-        raise ValueError(f'Not all pd_ch_names, {pd_ch_names}, '
+    if any([ch not in raw.ch_names for ch in ch_names]):
+        raise ValueError(f'Not all pd_ch_names, {ch_names}, '
                          'in raw channel names')
-    if len(pd_ch_names) == 2:
-        pd = raw._data[raw.ch_names.index(pd_ch_names[0])]
-        pd -= raw._data[raw.ch_names.index(pd_ch_names[1])]
+    if len(ch_names) == 2:
+        ch_data = raw._data[raw.ch_names.index(ch_names[0])]
+        ch_data -= raw._data[raw.ch_names.index(ch_names[1])]
     else:
-        pd = raw._data[raw.ch_names.index(pd_ch_names[0])]
-    pd -= np.median(pd)
-    return pd
+        ch_data = raw._data[raw.ch_names.index(ch_names[0])]
+    ch_data -= np.median(ch_data)
+    return ch_data
 
 
-def _get_pd_data(raw, pd_ch_names):
+def _get_data(raw, ch_names):
     """Get the names of the photodiode channels from the user."""
     # if pd_ch_names provided
-    if pd_ch_names is not None:
-        if any([ch not in raw.ch_names for ch in pd_ch_names]):
-            raise ValueError(f'Not all pd_ch_names, {pd_ch_names}, '
+    if ch_names is not None:
+        if any([ch not in raw.ch_names for ch in ch_names]):
+            raise ValueError(f'Not all channel names, {ch_names}, '
                              'in raw channel names')
     else:  # if no pd_ch_names provided
-        pd_ch_names = input('Enter photodiode channel names separated by a '
-                            'comma or type "plot" to plot the data first:\t')
-        if pd_ch_names.lower() == 'plot':
+        ch_names = input('Enter channel names separated by a '
+                         'comma or type "plot" to plot the data first:\t')
+        if ch_names.lower() == 'plot':
             raw.plot()
-        n_chs = 0 if pd_ch_names == 'plot' else len(pd_ch_names.split(','))
+        n_chs = 0 if ch_names == 'plot' else len(ch_names.split(','))
         while n_chs not in (1, 2) or not all([ch.strip() in raw.ch_names for
-                                              ch in pd_ch_names.split(',')]):
-            pd_ch_names = input('Enter photodiode channel names '
-                                'separated by a comma:\t')
-            for ch in pd_ch_names.split(','):
+                                              ch in ch_names.split(',')]):
+            ch_names = input('Enter channel names separated by a comma:\t')
+            for ch in ch_names.split(','):
                 if not ch.strip() in raw.ch_names:
                     print(f'{ch.strip()} not in raw channel names')
-            n_chs = len(pd_ch_names.split(','))
+            n_chs = len(ch_names.split(','))
             if n_chs > 2:
                 print(f'{n_chs} is too many names, enter 1 name '
                       'for common referenced photodiode data or '
                       '2 names for bipolar reference')
-        pd_ch_names = [ch.strip() for ch in pd_ch_names.split(',')]
+        ch_names = [ch.strip() for ch in ch_names.split(',')]
     # get pd data using channel names
-    pd = _get_pd_channel_data(raw, pd_ch_names)
-    return pd, pd_ch_names
+    ch_data = _get_channel_data(raw, ch_names)
+    return ch_data, ch_names
 
 
-def _check_if_pd_event(pd, i, max_len_i, zscore, min_i, baseline_i):
+def _check_if_pd_event(pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i):
     """Take one stretch of data and determine if there is an event there.
 
     Move in chunks twice as long as your longest photodiode signal
     with the first 0.25 as baseline to test whether the signal goes
     above/below baseline and back below/above.
-    The event onset must have a minimum length `min_i`.
+    The event onset must be of maximum length `max_flip_i`.
     """
-    b = pd[i - baseline_i:i]
+    b = pd_diff[i - baseline_i:i]
     # use twice the length so event can be centered
-    s = (pd[i:i + 2 * max_len_i] - np.median(b)) / np.std(b)
+    s = (pd_diff[i:i + 2 * max_len_i] - np.median(b)) / np.std(b)
     binned_s = np.digitize(s, [-np.inf, -zscore, zscore, np.inf]) - 2
-    for direction, binary_s in {'up': binned_s, 'down': s < -binned_s}.items():
+    for direction, binary_s in {'up': binned_s, 'down': -binned_s}.items():
         onset = np.where(binary_s[:max_len_i] == 1)[0]
-        if onset.size > min_i:  # must be long enough
+        # must be flip on but can't flip back and forth
+        if onset.size > 0 and onset.size < max_flip_i and \
+                all(binary_s[onset[0]:onset[-1]] == 1):
             e = onset[0]
             if all(binary_s[:e] == 0):  # must start off:
                 # must have an offset and no more events
-                offset = np.where(binary_s[e:] < 1)[0]
-                if offset.size > 0:
+                offset = np.where(binary_s[e:] == -1)[0]
+                if offset.size > 0 and offset.size < max_flip_i and \
+                        all(binary_s[e + offset[0]:e + offset[-1]] == -1):
                     oe = offset[0]
-                    if oe <= max_len_i and \
-                            all(binary_s[e + oe:e + max_len_i] == 0):
-                        return direction, i + e, i + e + oe
+                    eo = all(binary_s[e + oe + max_flip_i:e + max_len_i] == 0)
+                    if oe <= max_len_i and eo:  # must end off
+                        return direction, i + e + 1, i + e + oe + 2
     return None, None, None
 
 
-def _find_pd_candidates(pd, max_len_i, baseline_i, zscore, min_i,
+def _find_pd_candidates(pd, max_len_i, baseline_i, zscore, max_flip_i,
                         verbose=True):
     """Find all points in the signal that look like a square wave."""
     if verbose:
         print('Finding photodiode events')
     pd_candidates = dict(up=set(), down=set())
-    for i in tqdm(range(baseline_i, len(pd) - max_len_i - baseline_i,
+    pd_diff = np.diff(pd)
+    for i in tqdm(range(baseline_i, len(pd_diff) - max_len_i - baseline_i,
                         baseline_i // 2)):
         direction, onset, _ = _check_if_pd_event(
-            pd, i, max_len_i, zscore, min_i, baseline_i)
+            pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i)
         # no rounding errors
         if onset is not None and all(
-            [onset + j not in this_pd_cs for j in range(-min_i, min_i + 1)
+            [onset + j not in this_pd_cs for j in
+             range(-max_flip_i, max_flip_i + 1)
              for this_pd_cs in pd_candidates.values()]):
             pd_candidates[direction].add(onset)
     pd_direction = 'down' if \
         len(pd_candidates['down']) > len(pd_candidates['up']) else 'up'
     pd_candidates = pd_candidates[pd_direction]
+    if len(pd_candidates) == 0:
+        raise ValueError('No photodiode candidates found, please raise an'
+                         'issue with code to reproduce the error on GitHub')
     if verbose:
         print(f'{len(pd_candidates)} {pd_direction}-deflection photodiode '
               'candidate events found')
-    sorted_pds = np.array(sorted(pd_candidates))
-    return pd_candidates, sorted_pds
+    return np.array(sorted(pd_candidates))
+
+
+def _get_audio_zscore(audio, fs):
+    import matplotlib as mpl
+    mpl.rcParams['toolbar'] = 'None'
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots(figsize=(6, 6))
+    fig.subplots_adjust(top=0.75, left=0.15)
 
+    def scale(event):
+        amount = 0.95 if event.key in ('left', 'down') else 1.25
+        if event.key in ('up', 'down'):
+            ymin, ymax = ax.get_ylim()
+            # ymin < 0 and ymax > 0 because median subtracted
+            ymin *= amount
+            ymax *= amount
+            ax.set_ylim([ymin, ymax])
+        elif event.key in ('left', 'right'):
+            xmin, xmax = ax.get_xlim()
+            # ymin < 0 and ymax > 0 because median subtracted
+            xmin /= amount
+            xmax *= amount
+            if xmin < xmax:
+                ax.set_xlim([xmin, xmax])
+        fig.canvas.draw()
 
-def _pd_event_dist(b_event, pd_candidates, max_index, exclude_shift_i):
+    ax.set_title(
+        'Use the left/right keys to scale time on the x axis '
+        '\nand use the up/down keys to zoom the yaxis in and out'
+        '\nfind a y value that includes all the events'
+        '\nit is recommended to choose the lowest value that is above baseline'
+        '\nclose the window when finished')
+    ax.set_xlabel('time (s)')
+    ax.set_ylabel('zscore')
+    ax.plot(audio, color='b')
+    xmin = max([0, audio.size // 2 - 10 * fs])
+    xmax = min([audio.size, audio.size // 2 + 10 * fs])
+    ax.set_xlim(xmin, xmax)
+    ax.set_xticks(np.linspace(0, audio.size, 5))
+    ax.set_xticklabels(np.round(np.linspace(0, audio.size / fs, 5), 2))
+    ax.set_ylim(audio.min() * 0.9, audio.max() * 1.25)
+    fig.canvas.mpl_connect('key_press_event', scale)
+    fig.show()
+
+    # get user input of zscore
+    zscore = None
+    while zscore is None:
+        zscore = input('What zscore should be used? ')
+        if not zscore or any([not d.isdigit() and d != '.' for d in zscore]):
+            print('A positive number input is required for zscore')
+            zscore = None
+        else:
+            zscore = float(zscore)
+    return zscore
+
+
+def _find_audio_candidates(audio, sfreq, max_len_i, zscore, verbose=True):
+    if verbose:
+        print('Finding points where the audio is above `zscore` threshold...')
+    audio = abs((audio - np.median(audio)) / audio.std())
+    if zscore is None:
+        zscore = _get_audio_zscore(audio, sfreq)
+    candidates = np.where(audio > zscore)[0]
+    delete_indices = list()
+    for i, candidate in enumerate(candidates):
+        if any(audio[candidate - max_len_i: candidate] > zscore):
+            delete_indices.append(i)
+    candidates = np.delete(candidates, delete_indices)
+    if verbose:
+        print(f'{len(candidates)} audio candidate events found')
+    return candidates
+
+
+def _event_dist(b_event, candidates_set, max_index, exclude_shift_i):
     """Find the shortest distance from the behavioral event to a pd event."""
     j = 0
     max_index += 2 * exclude_shift_i
     b_event = np.round(b_event).astype(int)
     while b_event + j < max_index and b_event - j > 0 and j < exclude_shift_i:
-        if b_event - j in pd_candidates:
+        if b_event - j in candidates_set:
             return j
-        if b_event + j in pd_candidates:
+        if b_event + j in candidates_set:
             return -j
         j += 1
     return exclude_shift_i
 
 
-def _check_alignment(beh_events, pd_candidates, max_index, max_i):
+def _check_alignment(beh_events, candidates_set, max_index, max_i):
     """Check the alignment, account for misalignment accumulation."""
     errors = np.zeros(beh_events.size)
     for i, b_event in enumerate(beh_events):
-        j = _pd_event_dist(b_event, pd_candidates, max_index, max_i)
+        j = _event_dist(b_event, candidates_set, max_index, max_i)
         if abs(j) < max_i:
             beh_events -= j
             errors[i] = j
@@ -313,32 +402,31 @@ 
Source code for parse_pd
     return errors
 
 
-def _find_best_alignment(beh_events, sorted_pds, max_i, sfreq,
-                         verbose=True):
+def _find_best_alignment(beh_events, candidates, max_i, sfreq, verbose=True):
     """Find the beh event that causes the best alignment when used to start."""
     beh_diffs = beh_events[1:] - beh_events[:-1]
-    pd_candidates = set(sorted_pds)
-    pd_dists = np.ones((sorted_pds.size - 1, sorted_pds.size - 1)) * np.inf
-    for i in range(1, sorted_pds.size):
-        for j in range(sorted_pds.size - 1):
+    candidates_set = set(candidates)
+    dists = np.ones((candidates.size - 1, candidates.size - 1)) * np.inf
+    for i in range(1, candidates.size):
+        for j in range(candidates.size - 1):
             if i > j:
-                pd_dists[j, i - 1] = abs(sorted_pds[i] - sorted_pds[j])
+                dists[j, i - 1] = abs(candidates[i] - candidates[j])
     # check best alignments
     min_error = best_alignment = best_errors = None
     if verbose:
-        print('Checking best behavior-photodiode difference alignments')
+        print('Checking best alignments')
     for i, beh_diff in enumerate(tqdm(beh_diffs)):
-        best_offset = np.argmin(np.min(abs(pd_dists - beh_diff), axis=1))
-        bevs = beh_events.copy() - beh_events[i] + sorted_pds[best_offset]
-        beh_errors = _check_alignment(bevs, pd_candidates,
-                                      sorted_pds[-1], max_i)
+        best_offset = np.argmin(np.min(abs(dists - beh_diff), axis=1))
+        bevs = beh_events.copy() - beh_events[i] + candidates[best_offset]
+        beh_errors = _check_alignment(bevs, candidates_set, candidates[-1],
+                                      max_i)
         error_metric = np.median(abs(beh_errors))
         if min_error is None or error_metric < min_error:
-            best_alignment = sorted_pds[best_offset] - beh_events[i]
+            best_alignment = candidates[best_offset] - beh_events[i]
             min_error = error_metric
             best_errors = beh_errors
     if verbose:
-        print('Best alignment with the photodiode shifted {:.0f} ms '
+        print('Best alignment with the events shifted {:.0f} ms '
               'relative to the first behavior event\nerrors: min {:.0f}, '
               'q1 {:.0f}, med {:.0f}, q3 {:.0f}, max {:.0f}'.format(
                   (beh_events[0] + best_alignment) / sfreq, min(best_errors),
@@ -347,52 +435,55 @@ 
Source code for parse_pd
     return best_alignment
 
 
-def _recover_event(pd, b_event, exclude_shift_i, zscore):
+def _recover_event(ch_data, b_event, exclude_shift_i, zscore):
     """Recover with a corrupted baseline or plateau but not on/offset."""
     b_event_i = np.round(b_event).astype(int)
-    section = pd[b_event_i - exclude_shift_i:
-                 b_event_i + exclude_shift_i].copy()
-    baseline = pd[b_event_i - 2 * exclude_shift_i: b_event_i - exclude_shift_i]
+    section = np.diff(ch_data[b_event_i - exclude_shift_i:
+                              b_event_i + exclude_shift_i])
+    baseline = np.diff(ch_data[b_event_i - 2 * exclude_shift_i:
+                               b_event_i - exclude_shift_i])
     section = (section - np.median(baseline)) / baseline.std()
-    event = np.where(abs(section) > zscore)[0]
-    return b_event_i - exclude_shift_i + event[0]if event.size > 0 else None
+    event = np.where(abs(section) > zscore)[0] + 1
+    return b_event_i - exclude_shift_i + event[0] if event.size > 0 else None
 
 
-def _exclude_ambiguous_events(beh_events, sorted_pds, best_alignment, pd,
-                              exclude_shift_i, max_len_i, resync_i, sfreq,
-                              zscore, recover, verbose=True):
+def _exclude_ambiguous_events(beh_events, candidates, best_alignment,
+                              ch_data, exclude_shift_i, resync_i, max_len,
+                              sfreq, zscore, recover, verbose=True):
     """Exclude all events that are outside the given shift compared to beh."""
-    if verbose:
+    if verbose or recover:
         import matplotlib.pyplot as plt
-        pd_section_data = dict(b_event=list(), title=list())
+        section_data = dict(b_event=list(), title=list(), section=list())
         print('Excluding events that have zero close events or more than '
               'one photodiode event within `max_len` time')
-    sfreq_i = np.round(sfreq).astype(int)
+    max_len_i = np.round(sfreq * max_len).astype(int)
     events = dict()
     errors = dict()
-    pd_candidates = set(sorted_pds)
-    max_index = max(sorted_pds)
+    candidates_set = set(candidates)
+    max_index = max(candidates)
     for i in sorted(beh_events.keys()):
         b_event = beh_events[i] + best_alignment
-        j = _pd_event_dist(b_event, pd_candidates, max_index,
-                           exclude_shift_i=np.inf)
+        j = _event_dist(b_event, candidates_set, max_index,
+                        exclude_shift_i=np.inf)
         if abs(j) < exclude_shift_i:
             best_alignment -= j
             events[i] = np.round(b_event - j).astype(int)
             errors[i] = j
-            pd_events = np.logical_and(sorted_pds < (events[i] + max_len_i),
-                                       sorted_pds > (events[i] - max_len_i))
-            if sum(pd_events) > 1:
+            these_events = np.logical_and(candidates < (events[i] + max_len_i),
+                                          candidates > (events[i] - max_len_i))
+            if sum(these_events) > 1:
                 events.pop(i)
                 errors.pop(i)
                 if verbose:
-                    print(f'Excluding event {i}, {sum(pd_events)} events')
-                    pd_section_data['b_event'].append(b_event)
-                    pd_section_data['title'].append(
-                        f'{sum(pd_events)} events found '
+                    print(f'Excluding event {i}, {sum(these_events)} events')
+                    section_data['b_event'].append(b_event)
+                    section_data['title'].append(
+                        f'{sum(these_events)} events found '
                         f'for\nbeh event {i}, excluding')
+                    section_data['section'].append(ch_data[
+                        int(b_event - max_len_i): int(b_event + max_len_i)])
         else:
-            if verbose:
+            if verbose or recover:
                 # if off by nearly exclude_shift_i, still use for adjusting
                 if abs(j) < resync_i:
                     best_alignment -= j
@@ -402,15 +493,18 @@ 
Source code for parse_pd
                     text = f'Excluding event {i},\noff by {j_ms} ms'
                 else:  # if off by more than max_len, just say missing
                     if recover:
-                        event = _recover_event(pd, b_event, exclude_shift_i,
-                                               zscore)
+                        event = _recover_event(ch_data, b_event,
+                                               exclude_shift_i, zscore)
                         if event is None:
                             text = f'No event found to recover\nfor event {i}'
                         else:
                             fig, ax = plt.subplots()
-                            section = \
-                                pd[event - 2 * sfreq_i: event + 2 * sfreq_i]
-                            ax.plot(np.linspace(-2, 2, 4 * sfreq_i), section)
+                            section_size = np.round(1.5 * max_len * sfreq
+                                                    ).astype(int)
+                            section = ch_data[event - section_size:
+                                              event + section_size]
+                            ax.plot(np.linspace(-1.5 * max_len, 1.5 * max_len,
+                                                section.size), section)
                             ax.plot([0, 0], [section.min(), section.max()])
                             ax.set_title(f'Corrupted Event {i}')
                             ax.set_xlabel('time (s)')
@@ -424,10 +518,12 @@ 
Source code for parse_pd
                     else:
                         text = f'Excluding event {i},\nno event found'
                 print(text.replace('\n', ' '))
-                pd_section_data['b_event'].append(b_event)
-                pd_section_data['title'].append(text)
+                section_data['b_event'].append(b_event)
+                section_data['title'].append(text)
+                section_data['section'].append(ch_data[
+                    int(b_event - max_len_i): int(b_event + max_len_i)])
     if verbose:
-        n_events_ex = len(pd_section_data['b_event'])
+        n_events_ex = len(section_data['b_event'])
         if n_events_ex:  # only plot if some events were excluded
             nrows = int(n_events_ex**0.5)
             ncols = int(np.ceil(n_events_ex / nrows))
@@ -441,13 +537,12 @@ 
Source code for parse_pd
                 axes = axes.flatten()
             for ax in axes[n_events_ex:]:
                 ax.axis('off')  # turn off all unused axes
-            for b_event, title, ax in zip(pd_section_data['b_event'],
-                                          pd_section_data['title'],
-                                          axes[:n_events_ex]):
-                pd_section = pd[int(b_event - max_len_i):
-                                int(b_event + max_len_i)]
-                ax.plot(np.linspace(-1, 1, pd_section.size), pd_section)
-                ax.plot([0, 0], [pd_section.min(), pd_section.max()],
+            for b_event, title, section, ax in zip(section_data['b_event'],
+                                                   section_data['title'],
+                                                   section_data['section'],
+                                                   axes[:n_events_ex]):
+                ax.plot(np.linspace(-1, 1, section.size), section)
+                ax.plot([0, 0], [section.min(), section.max()],
                         color='r')
                 ax.set_title(title, fontsize=12)
                 ax.set_ylabel('voltage')
@@ -461,69 +556,136 @@ 
Source code for parse_pd
         ax.plot(trials, [errors[t] / sfreq * 1000 for t in trials])
         ax.set_ylabel('Difference (ms)')
         ax.set_xlabel('Trial')
-        ax.set_title('Photodiode Events Compared to Behavior Events')
+        ax.set_title('Synchronization Events Compared to Behavior Events')
         fig.show()
     return events
 
 
-def _save_pd_data(fname, raw, events, event_id, pd_ch_names, beh_df=None,
-                  add_events=False, overwrite=False):
+def _save_data(raw, events, event_id, ch_names, beh=None,
+               add_events=False, overwrite=False):
     """Save the events determined from the photodiode."""
+    fname = raw.filenames[0]
     basename = op.splitext(op.basename(fname))[0]
-    pd_data_dir = op.join(op.dirname(fname), basename + '_pd_data')
-    if not op.isdir(pd_data_dir):
-        os.makedirs(pd_data_dir)
-    behf = op.join(pd_data_dir, basename + '_beh_df.tsv')
-    if beh_df is None:
+    out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
+    if not op.isdir(out_dir):
+        os.makedirs(out_dir)
+    behf = op.join(out_dir, basename + '_beh_df.tsv')
+    if beh is None:
         if op.isfile(behf) and overwrite:
             os.remove(behf)
     else:
-        if 'pd_sample' in beh_df and not add_events and not overwrite:
+        if 'pd_parser_sample' in beh and not add_events and not overwrite:
             raise ValueError(
-                'The column name `pd_sample` is not allowed in the behavior '
-                'tsv file (it\'s reserved for internal use. Please rename '
-                'that column to continue.')
+                'The key (column name) `pd_parser_sample` is not allowed '
+                'in the behavior tsv file (it\'s reserved for internal use. '
+                'Please rename that key (column) to continue.')
         if not add_events:
-            beh_df['pd_sample'] = \
+            beh['pd_parser_sample'] = \
                 [events[i] if i in events else 'n/a' for i in
-                 range(len(beh_df[list(beh_df.keys())[0]]))]
-            _to_tsv(behf, beh_df)
+                 range(len(beh[list(beh.keys())[0]]))]
+            _to_tsv(behf, beh)
     onsets = np.array([events[i] for i in sorted(events.keys())])
     annot = mne.Annotations(onset=raw.times[onsets],
                             duration=np.repeat(0.1, len(onsets)),
                             description=np.repeat(event_id,
                                                   len(onsets)))
     if add_events:
-        annot_orig, pd_ch_names_orig, _ = _load_pd_data(fname)
+        annot_orig, ch_names_orig, _ = _load_data(raw)
         annot += annot_orig
-        pd_ch_names += [ch for ch in pd_ch_names if ch not in pd_ch_names]
-    annot.save(op.join(pd_data_dir, basename + '_pd_annot.fif'))
-    with open(op.join(pd_data_dir, basename + 'pd_ch_names.tsv'), 'w') as fid:
-        fid.write('\t'.join(pd_ch_names))
+        ch_names += [ch for ch in ch_names_orig if ch not in ch_names]
+        overwrite = True
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=overwrite)
+    with open(op.join(out_dir, basename + '_ch_names.tsv'), 'w') as fid:
+        fid.write('\t'.join(ch_names))
+    return annot
 
 
-def _load_pd_data(fname):
+def _load_data(raw):
     """Load previously saved photodiode data--annot and pd channel names."""
+    raw = _read_raw(raw, preload=None)
+    fname = raw.filenames[0]
     basename = op.splitext(op.basename(fname))[0]
-    pd_data_dir = op.join(op.dirname(fname), basename + '_pd_data')
-    annot_fname = op.join(pd_data_dir, basename + '_pd_annot.fif')
-    pd_channels_fname = op.join(pd_data_dir, basename + 'pd_ch_names.tsv')
-    behf = op.join(pd_data_dir, basename + '_beh_df.tsv')
-    if not op.isfile(annot_fname) or not op.isfile(pd_channels_fname):
-        raise ValueError(f'Photodiode data not found in {pd_data_dir}, '
+    out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
+    annot_fname = op.join(out_dir, basename + '_annot.fif')
+    channels_fname = op.join(out_dir, basename + '_ch_names.tsv')
+    behf = op.join(out_dir, basename + '_beh_df.tsv')
+    if not op.isfile(annot_fname) or not op.isfile(channels_fname):
+        raise ValueError(f'pd-parser data not found in {out_dir}, '
                          f'specifically, {annot_fname} and '
-                         f'{pd_channels_fname}. Either `parse_pd` was '
-                         f'not run, or it failed or {pd_data_dir} '
+                         f'{channels_fname}. Either `parse_pd` was '
+                         f'not run, or it failed or {out_dir} '
                          'may have been moved or deleted. Rerun '
-                         '`parse_pd` and optionally `add_pd_relative_events` '
+                         '`parse_pd` and optionally `add_relative_events` '
                          'to fix this')
-    with open(pd_channels_fname, 'r') as fid:
-        pd_ch_names = fid.readline().rstrip().split('\t')
+    with open(channels_fname, 'r') as fid:
+        ch_names = fid.readline().rstrip().split('\t')
     beh_df = _read_tsv(behf) if op.isfile(behf) else None
-    return mne.read_annotations(annot_fname), pd_ch_names, beh_df
+    return mne.read_annotations(annot_fname), ch_names, beh_df
+
+
+def _check_overwrite(raw, add_events, overwrite):
+    """Check if the ``pd-parser`` data directory already exists."""
+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    if op.isdir(op.join(op.dirname(
+        raw.filenames[0]), basename + '_pd_parser_data')) and \
+            not overwrite and not add_events:
+        raise ValueError('Photodiode data directory already exists and '
+                         'overwrite=False, set overwrite=True to overwrite')
 
 
-
[docs]def find_pd_params(fname, pd_ch_names=None, verbose=True):
+def _recover_off_events(recovered, events, off_events, pd, max_len, sfreq):
+    import matplotlib.pyplot as plt
+    recover_data = dict(event=None)
+
+    def align_keypress(event):
+        if event.key in ('enter', 'e'):
+            if event.key == 'enter':
+                recover_data['event'] = recover_data['line'].get_xdata()[0]
+            else:
+                recover_data['event'] = None
+            plt.close(fig)
+        elif event.key in ('left', 'right'):
+            xmin, xmax = ax.get_xlim()
+            linex = recover_data['line'].get_xdata()[0]
+            linex += 1 if event.key == 'right' else -1
+            if linex >= xmin and linex <= xmax:
+                recover_data['line'].set_xdata([linex, linex])
+                fig.canvas.draw()
+        elif event.key in ('-', '+', '='):
+            xmin, xmax = ax.get_xlim()
+            linex = recover_data['line'].get_xdata()[0]
+            xmin = np.round(max([0, xmin - (linex - xmin)]) if event.key == '-'
+                            else np.mean([linex, xmin])).astype(int)
+            xmax = np.round(xmax + (xmax - linex) if event.key == '-' else
+                            np.mean([linex, xmax])).astype(int)
+            ax.set_xlim([xmin, xmax])
+            ax.set_ylim([section[xmin: xmax].min(), section[xmin: xmax].max()])
+            fig.canvas.draw()
+
+    for event_idx in recovered:
+        i = events[event_idx] - int(max_len * sfreq) // 4  # show beginning
+        section = pd[i: i + np.round(max_len * sfreq).astype(int)]
+        fig, ax = plt.subplots(figsize=(6, 6))
+        fig.subplots_adjust(top=0.75)
+        ax.set_title('Use the left/right keys to find the event offset\n'
+                     '+/- to scale the x axis\npress enter when finished\n'
+                     'or `e` to exclude the event')
+        ax.set_xlabel('time (s)')
+        ax.set_ylabel('voltage')
+        ax.plot(section, color='b')
+        ax.set_xticks([0, section.size])
+        ax.set_xticklabels([0, max_len])
+        recover_data['line'] = ax.plot(
+            [section.size - 1, section.size - 1],
+            [section.min(), section.max()], color='k')[0]
+        fig.canvas.mpl_connect('key_press_event', align_keypress)
+        plt.show(block=True)
+        if recover_data['event'] is not None:
+            off_events[event_idx] = recover_data['event'] + i
+    return off_events
+
+
+
[docs]def find_pd_params(raw, pd_ch_names=None, verbose=True):
     """Plot the data so the user can determine the right parameters.
 
     The user can adjust window size to determine max_len and horizontal
@@ -531,8 +693,9 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     pd_ch_names : list
         Names of the channel(s) containing the photodiode data.
         One channel is to be given for a common reference and
@@ -547,8 +710,8 @@ 
Source code for parse_pd
     import matplotlib as mpl
     mpl.rcParams['toolbar'] = 'None'
     import matplotlib.pyplot as plt
-    raw = _read_raw(fname, verbose=verbose)
-    pd, _ = _get_pd_data(raw, pd_ch_names)
+    raw = _read_raw(raw, verbose=verbose)
+    pd, _ = _get_data(raw, pd_ch_names)
     fig, ax = plt.subplots(figsize=(6, 6))
     fig.subplots_adjust(top=0.75, left=0.15)
     plot_data = dict()
@@ -616,8 +779,8 @@ 
Source code for parse_pd
                 color='r')[0]
             ax.set_title(
                 'Scale\nUse the up/down arrows to set the horizontal line \n'
-                'at a level where the plateau of all the photodiode events \n'
-                'is above the green line nothing is below the red line\n'
+                'half way up the photodiode onset event with the baseline \n'
+                'in the middle of the y-axis\n'
                 'Use +/- to scale the time axis to see more events\n'
                 'press enter when finished')
             fig.canvas.draw()
@@ -675,11 +838,11 @@ 
Source code for parse_pd
     fig.show()

 
 
-
[docs]def parse_pd(fname, pd_event_name='Fixation', behf=None,
-             beh_col='fix_onset_time', pd_ch_names=None,
-             exclude_shift=0.03, resync=0.075,
-             max_len=1., zscore=100, min_i=10, baseline=0.25,
-             add_events=False, recover=False, overwrite=False, verbose=True):
+
[docs]def parse_pd(raw, pd_event_name='Fixation', beh=None,
+             beh_key='fix_onset_time', pd_ch_names=None,
+             exclude_shift=0.03, resync=0.075, max_len=1., zscore=10,
+             max_flip_i=40, baseline=0.25, add_events=False, recover=False,
+             overwrite=False, verbose=True):
     """Parse photodiode events.
 
     Parses photodiode events from a likely very corrupted channel
@@ -690,14 +853,16 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     pd_event_name: str
         The name of the event corresponding to the photodiode.
-    behf : str
-        The filepath to a tsv file with the behavioral timing.
-    beh_col : str
-        The column of the behf tsv that corresponds to the events.
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    beh_key: str
+        The key (column name) of the beh dictionary that corresponds
+        to the events.
     pd_ch_names : list
         Names of the channel(s) containing the photodiode data.
         One channel is to be given for a common reference and
@@ -706,7 +871,7 @@ 
Source code for parse_pd
     exclude_shift: float
         How many seconds different than expected from the behavior events
         to exclude that event. Use `find_pd_params` to determine if unsure.
-    resync : float
+    resync: float
         The number of seconds to difference allowed to still use a photodiode
         event to resynchronize with time-stamped events. Events with
         differences between `resync` and `exclude_shift` will still be
@@ -724,34 +889,33 @@ 
Source code for parse_pd
         How large of a z-score difference to use to threshold photodiode
         events. Note, the must be large enough that any overshoot when
         returning to threshold is less than zscore compared to baseline.
-    min_i: int
-        The minimum number of samples the photodiode event must be on for.
-        This should not be changed unless the event is shorter.
+    max_flip_i: int
+        The maximum number of samples the photodiode event can take to
+        transition. This shouldn't usually need to be changed unless
+        the transition takes longer.
     baseline: float
         How much relative to the max_len to use to idenify the time before
         the photodiode event. This should not be changed most likely
         unless there is a specific reason/issue.
-    add_events : bool
+    add_events: bool
         Whether to add the events found from the current call of `parse_pd`
         to a events found previously (e.g. first parse with
         `pd_event_name='Fixation'` and then parse with
         `pd_event_name='Response'`.
-        Note: `pd_parser.add_pd_relative_events` will be relative to the
+        Note: `pd_parser.add_relative_events` will be relative to the
         first event added.
-    recover : bool
+    recover: bool
         Whether to recover corrupted events manually.
-    verbose : bool
+    verbose: bool
         Whether to display or supress text output on the progress
         of the function.
-    overwrite : bool
+    overwrite: bool
         Whether to overwrite existing data if it exists.
 
     Returns
     -------
-    events: DataFrame
-        A DataFrame that has a column for to the (zero)
-        indexed behavioral events and another column corresponding
-        to the time stamp of the eeg file.
+    annot: mne.Annotations
+        The annotations with the added events.
 
     """
     if resync < exclude_shift:
@@ -759,62 +923,174 @@ 
Source code for parse_pd
                          f'than `resync` ({resync})')
     if baseline <= 0 or baseline > 1:
         raise ValueError(f'baseline must be between 0 and 1, got {baseline}')
-    # check if already parsed
-    basename = op.splitext(op.basename(fname))[0]
-    if op.isdir(op.join(op.dirname(fname), basename + '_pd_data')
-                ) and not overwrite and not add_events:
-        raise ValueError('Photodiode data directory already exists and '
-                         'overwrite=False, set overwrite=True to overwrite')
     # load raw data file with the photodiode data
-    raw = _read_raw(fname, verbose=verbose)
+    raw = _read_raw(raw, verbose=verbose)
+    # check if already parsed
+    _check_overwrite(raw, add_events, overwrite)
     # transform behavior events to sample time
     max_len_i = np.round(raw.info['sfreq'] * max_len).astype(int)
-    exclude_shift_i = np.round(raw.info['sfreq'] * exclude_shift).astype(int)
+    exclude_shift_i = np.round(raw.info['sfreq'] * exclude_shift
+                               ).astype(int)
     baseline_i = np.round(max_len_i * baseline).astype(int)
     resync_i = np.round(raw.info['sfreq'] * resync).astype(int)
-    # use keyword argument if given, otherwise get the user to enter pd names
-    # and get data
-    pd, pd_ch_names = _get_pd_data(raw, pd_ch_names)
-    pd_candidates, sorted_pds = _find_pd_candidates(
+    # use keyword argument if given, otherwise get the user
+    # to enter pd names and get data
+    pd, pd_ch_names = _get_data(raw, pd_ch_names)
+    candidates = _find_pd_candidates(
         pd=pd, max_len_i=max_len_i, baseline_i=baseline_i, zscore=zscore,
-        min_i=min_i, verbose=verbose)
+        max_flip_i=max_flip_i, verbose=verbose)
+    # load behavioral data with which to validate event timing
+    if beh is None:
+        if verbose:
+            print('No behavioral tsv file was provided so the photodiode '
+                  'events will be returned without validation by task '
+                  'timing')
+        events = {i: pd_event for i, pd_event in enumerate(candidates)}
+        _save_data(raw=raw, events=events, event_id=pd_event_name,
+                   ch_names=pd_ch_names, overwrite=overwrite)
+        return
+    # if behavior is given use it to synchronize and exclude events
+    beh_events, beh = _load_beh(beh=beh, beh_key=beh_key)
+    beh_events = {i: beh_ev * raw.info['sfreq'] for i, beh_ev in
+                  enumerate(beh_events) if beh_ev != 'n/a'}
+    beh_events_sorted = np.array(list(beh_events.values()))
+    best_alignment = _find_best_alignment(
+        beh_events=beh_events_sorted, candidates=candidates,
+        max_i=resync_i, sfreq=raw.info['sfreq'], verbose=verbose)
+    events = _exclude_ambiguous_events(
+        beh_events=beh_events, candidates=candidates,
+        best_alignment=best_alignment, ch_data=pd,
+        exclude_shift_i=exclude_shift_i, resync_i=resync_i,
+        max_len=max_len, sfreq=raw.info['sfreq'],
+        zscore=zscore, recover=recover, verbose=verbose)
+    annot = _save_data(raw=raw, events=events, event_id=pd_event_name,
+                       ch_names=pd_ch_names, beh=beh,
+                       add_events=add_events, overwrite=overwrite)
+    return annot

+
+
+
[docs]def parse_audio(raw, audio_event_name='Tone', beh=None,
+                beh_key='tone_onset_time', audio_ch_names=None,
+                exclude_shift=0.03, resync=0.075, max_len=0.25,
+                zscore=None, add_events=False, recover=False,
+                overwrite=False, verbose=True):
+    """Parse audio events.
+
+    Parses photodiode events from a likely very corrupted channel
+    using behavioral data to sync events to determine which
+    behavioral events don't have a match and are thus corrupted
+    and should be excluded (while ignoring events that look like
+    photodiode events but don't match behavior)
+
+    Parameters
+    ----------
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
+    audio_event_name: str
+        The name of the event corresponding to the audio.
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    beh_key: str
+        The key (column name) of the beh dictionary that corresponds
+        to the events.
+    audio_ch_names: list
+        Names of the channel(s) containing the audio data.
+        One channel is to be given for a common reference and
+        two for a bipolar reference. If no channels are provided,
+        the data will be plotted and the user will provide them.
+    exclude_shift: float
+        How many seconds different than expected from the behavior events
+        to exclude that event.
+    resync: float
+        The number of seconds to difference allowed to still use an audio event
+        event to resynchronize with time-stamped events. See
+        :func:`pd_parser.parse_pd` for more information.
+    max_len: float
+        The longest audio event can be.
+    zscore: float
+        How large of a z-score difference to use to threshold the correlation
+        of the audio with the sound. If None is passed a plot will be shown to
+        pick a reasonable zscore. 25 is a typical value that works.
+    add_events: bool
+        Whether to add the events found from the current call of `parse_pd`
+        to a events found previously (e.g. first parse with
+        `pd_event_name='Fixation'` and then parse with
+        `pd_event_name='Response'`.
+        Note: `pd_parser.add_relative_events` will be relative to the
+        first event added.
+    recover: bool
+        Whether to recover corrupted events manually.
+    verbose: bool
+        Whether to display or supress text output on the progress
+        of the function.
+    overwrite: bool
+        Whether to overwrite existing data if it exists.
+
+    Returns
+    -------
+    annot: mne.Annotations
+        The annotations with the added events.
+
+    """
+    if resync < exclude_shift:
+        raise ValueError(f'`exclude_shift` ({exclude_shift}) cannot be longer '
+                         f'than `resync` ({resync})')
+    # load raw data file with the photodiode data
+    raw = _read_raw(raw, verbose=verbose)
+    # check if already parsed
+    _check_overwrite(raw, add_events, overwrite)
+    # transform behavior events to sample time
+    exclude_shift_i = np.round(raw.info['sfreq'] * exclude_shift
+                               ).astype(int)
+    max_len_i = np.round(raw.info['sfreq'] * max_len).astype(int)
+    resync_i = np.round(raw.info['sfreq'] * resync).astype(int)
+    # use keyword argument if given, otherwise get the user
+    # to enter pd names and get data
+    audio, audio_ch_names = _get_data(raw, audio_ch_names)
+    candidates = _find_audio_candidates(
+        audio=audio, sfreq=raw.info['sfreq'], max_len_i=max_len_i,
+        zscore=zscore, verbose=verbose)
     # load behavioral data with which to validate event timing
-    if behf is None:
+    if beh is None:
         if verbose:
             print('No behavioral tsv file was provided so the photodiode '
                   'events will be returned without validation by task '
                   'timing')
-        events = {i: pd_event for i, pd_event in enumerate(sorted_pds)}
-        _save_pd_data(fname, raw=raw, events=events, event_id=pd_event_name,
-                      pd_ch_names=pd_ch_names, overwrite=overwrite)
+        events = {i: audio_event for i, audio_event in enumerate(candidates)}
+        _save_data(raw=raw, events=events, event_id=audio_event_name,
+                   ch_names=audio_ch_names, overwrite=overwrite)
         return
     # if behavior is given use it to synchronize and exclude events
-    beh_events, beh_df = _load_beh_df(behf=behf, beh_col=beh_col)
+    beh_events, beh = _load_beh(beh=beh, beh_key=beh_key)
     beh_events = {i: beh_ev * raw.info['sfreq'] for i, beh_ev in
                   enumerate(beh_events) if beh_ev != 'n/a'}
     beh_events_sorted = np.array(list(beh_events.values()))
     best_alignment = _find_best_alignment(
-        beh_events=beh_events_sorted, sorted_pds=sorted_pds,
+        beh_events=beh_events_sorted, candidates=candidates,
         max_i=resync_i, sfreq=raw.info['sfreq'], verbose=verbose)
     events = _exclude_ambiguous_events(
-        beh_events=beh_events, sorted_pds=sorted_pds,
-        best_alignment=best_alignment, pd=pd, exclude_shift_i=exclude_shift_i,
-        max_len_i=max_len_i, resync_i=resync_i, sfreq=raw.info['sfreq'],
+        beh_events=beh_events, candidates=candidates,
+        best_alignment=best_alignment, ch_data=audio,
+        exclude_shift_i=exclude_shift_i, resync_i=resync_i,
+        max_len=max_len, sfreq=raw.info['sfreq'],
         zscore=zscore, recover=recover, verbose=verbose)
-    _save_pd_data(fname, raw=raw, events=events, event_id=pd_event_name,
-                  pd_ch_names=pd_ch_names, beh_df=beh_df,
-                  add_events=add_events, overwrite=overwrite)

+    annot = _save_data(raw=raw, events=events, event_id=audio_event_name,
+                       ch_names=audio_ch_names, beh=beh,
+                       add_events=add_events, overwrite=overwrite)
+    return annot

 
 
-
[docs]def add_pd_off_events(fname, off_event_name='Stim Off', max_len=1.,
-                      zscore=100, min_i=10, baseline=0.25,
+
[docs]def add_pd_off_events(raw, off_event_name='Stim Off', max_len=1.,
+                      zscore=10, max_flip_i=40, baseline=0.25,
                       verbose=True, overwrite=False):
     """Add events for when the photodiode deflection returns to baseline.
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     off_event : str
         If None, no event will be assigned to cessation of the photodiode
         deflection. If a string is provided, an event of that name will
@@ -824,8 +1100,9 @@ 
Source code for parse_pd
     zscore: float
         How large of a z-score difference to use to threshold photodiode
         events.
-    min_i: int
-        The minimum number of samples the photodiode event must be on for.
+    max_flip_i: int
+        The maximum number of samples the photodiode event may take to
+        transition.
     baseline: float
         How much relative to max_len to use to idenify the time before
         the photodiode event.
@@ -835,121 +1112,96 @@ 
Source code for parse_pd
     overwrite : bool
         Whether to overwrite existing data if it exists.
 
-    """
-    recover_data = dict(event=None)
-    recovered_events = list()
-
-    def align_keypress(event):
-        if event.key in ('enter', 'e'):
-            if event.key == 'enter':
-                recover_data['event'] = recover_data['line'].get_xdata()[0]
-            else:
-                recover_data['event'] = None
-            plt.close(fig)
-        elif event.key in ('left', 'right'):
-            xmin, xmax = ax.get_xlim()
-            linex = recover_data['line'].get_xdata()[0]
-            linex += 1 if event.key == 'right' else -1
-            if linex >= xmin and linex <= xmax:
-                recover_data['line'].set_xdata([linex, linex])
-                fig.canvas.draw()
-        elif event.key in ('-', '+', '='):
-            xmin, xmax = ax.get_xlim()
-            linex = recover_data['line'].get_xdata()[0]
-            xmin = np.round(max([0, xmin - (linex - xmin)]) if event.key == '-'
-                            else np.mean([linex, xmin])).astype(int)
-            xmax = np.round(xmax + (xmax - linex) if event.key == '-' else
-                            np.mean([linex, xmax])).astype(int)
-            ax.set_xlim([xmin, xmax])
-            ax.set_ylim([section[xmin: xmax].min(), section[xmin: xmax].max()])
-            fig.canvas.draw()
+    Returns
+    -------
+    annot: mne.Annotations
+        The annotations with the added events.
 
-    raw = _read_raw(fname, verbose=verbose)
-    annot, pd_ch_names, beh_df = _load_pd_data(fname)
-    pd = _get_pd_channel_data(raw, pd_ch_names)
+    .. note::
+       The same parameters must be used for :func:`pd_parser.parse_pd`.
+    """
+    raw = _read_raw(raw, verbose=verbose)
+    annot, pd_ch_names, beh = _load_data(raw)
+    pd = _get_channel_data(raw, pd_ch_names)
+    pd_diff = np.diff(pd)
     max_len_i = np.round(raw.info['sfreq'] * max_len).astype(int)
     baseline_i = np.round(max_len_i * baseline).astype(int)
-    events = {i: samp for i, samp in enumerate(beh_df['pd_sample'])
+    events = {samp: i for i, samp in enumerate(beh['pd_parser_sample'])
               if samp != 'n/a'}
     off_events = {'up': dict(), 'down': dict()}
-    for event_idx, i in events.items():
-        i -= baseline_i  # move back one baseline avoid ramp up
-        direction, pd_start, pd_end = _check_if_pd_event(
-            pd, i, max_len_i, zscore, min_i, baseline_i)
-        if pd_start is None:  # event added manually, get offset manually
-            import matplotlib.pyplot as plt
-            sfreq_i = np.round(raw.info['sfreq']).astype(int)
-            section = pd[i: i + np.round(max_len * sfreq_i).astype(int)]
-            fig, ax = plt.subplots(figsize=(6, 6))
-            fig.subplots_adjust(top=0.75)
-            ax.set_title('Use the left/right keys to find the event offset\n'
-                         '+/- to scale the x axis\npress enter when finished\n'
-                         'or `e` to exclude the event')
-            ax.set_xlabel('time (s)')
-            ax.set_ylabel('voltage')
-            ax.plot(section, color='b')
-            ax.set_xticks([0, section.size])
-            ax.set_xticklabels([0, max_len])
-            recover_data['line'] = ax.plot(
-                [section.size - 1, section.size - 1],
-                [section.min(), section.max()], color='k')[0]
-            fig.canvas.mpl_connect('key_press_event', align_keypress)
-            plt.show()
-            if recover_data['event'] is not None:
-                recovered_events.append(recover_data['event'] + i)
-        else:
-            assert abs(pd_start - events[event_idx]) < min_i
-            off_events[direction][event_idx] = pd_end
+    for i in tqdm(range(baseline_i, len(pd_diff) - max_len_i - baseline_i,
+                        baseline_i // 2)):
+        direction, onset, offset = _check_if_pd_event(
+            pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i)
+        if onset in events:
+            off_events[direction][events[onset]] = offset
+
     pd_direction = 'down' if \
         len(off_events['down']) > len(off_events['up']) else 'up'
     off_events = off_events[pd_direction]
-    onsets = np.array(list(off_events.values()) + recovered_events)
+    recovered = [event_idx for event_idx in events.values()
+                 if event_idx not in off_events.keys()]
+    if recovered:  # some events found manually, recover
+        events = {i: samp for i, samp in enumerate(beh['pd_parser_sample'])
+                  if samp != 'n/a'}  # invert lookup for sample
+        off_events = _recover_off_events(recovered, events, off_events,
+                                         pd, max_len, raw.info['sfreq'])
+    onsets = np.array(list(off_events.values()))
     annot += mne.Annotations(
         onset=raw.times[onsets], duration=np.repeat(0.1, onsets.size),
         description=np.repeat(off_event_name, onsets.size))
     # save modified data
-    basename = op.splitext(op.basename(fname))[0]
-    pd_data_dir = op.join(op.dirname(fname), basename + '_pd_data')
-    annot.save(op.join(pd_data_dir, basename + '_pd_annot.fif'))

+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    out_dir = op.join(op.dirname(raw.filenames[0]),
+                      basename + '_pd_parser_data')
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=True)
+    return annot

 
 
-
[docs]def add_pd_relative_events(fname, behf, relative_event_cols,
-                           relative_event_names=None,
-                           overwrite=False, verbose=True):
+
[docs]def add_relative_events(raw, beh, relative_event_keys,
+                        relative_event_names=None,
+                        overwrite=False, verbose=True):
     """Add events relative to those determined from the photodiode.
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
-    behf : str
-        The filepath to a tsv file with the behavioral timing
-    relative_event_cols : list
-        The names of the columns where time data is stored
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    relative_event_keys : list
+        The names of the keys where time data is stored
         relative to the photodiode event
     relative_event_names : list
-        The names of the events in `relative_event_cols`.
-    verbose : bool
+        The names of the events in `relative_event_keys`.
+    verbose: bool
         Whether to display or supress text output on the progress
         of the function.
-    overwrite : bool
+    overwrite: bool
         Whether to overwrite existing data if it exists.
 
+    Returns
+    -------
+    annot: mne.Annotations
+        The annotations with the added events.
+
     """
     if relative_event_names is None:
         if verbose:
-            print('Using relative event cols {} as relative event '
-                  'names'.format(', '.join(relative_event_cols)))
-        relative_event_names = relative_event_cols
-    if len(relative_event_cols) != len(relative_event_names):
-        raise ValueError('Mismatched length of relative event behavior '
-                         f'file column names, {len(relative_event_cols)} and '
-                         f'names of the events {len(relative_event_names)}')
-    raw = _read_raw(fname, verbose=verbose)
+            print('Using relative event keys {} as relative event '
+                  'names'.format(', '.join(relative_event_keys)))
+        relative_event_names = relative_event_keys
+    if len(relative_event_keys) != len(relative_event_names):
+        raise ValueError(
+            'Mismatched length of relative event behavior '
+            f'file keys (column names), {len(relative_event_keys)} and '
+            f'names of the events {len(relative_event_names)}')
+    raw = _read_raw(raw, verbose=verbose)
     relative_events = \
-        {name: _load_beh_df(behf, rel_event)[0]
-         for name, rel_event in zip(relative_event_names, relative_event_cols)}
-    annot, _, beh_df = _load_pd_data(fname)
+        {name: _load_beh(beh, rel_event)[0]
+         for name, rel_event in zip(relative_event_names, relative_event_keys)}
+    annot, _, beh = _load_data(raw)
     for event_name in relative_event_names:
         if event_name in annot.description:
             if overwrite:
@@ -959,7 +1211,7 @@ 
Source code for parse_pd
                 raise ValueError(f'Event name {event_name} already exists in '
                                  'saved events and `overwrite=False`, use '
                                  '`overwrite=True` to overwrite')
-    events = {i: samp for i, samp in enumerate(beh_df['pd_sample'])
+    events = {i: samp for i, samp in enumerate(beh['pd_parser_sample'])
               if samp != 'n/a'}
     for name, beh_events in relative_events.items():
         onsets = np.array([events[i] + (beh_events[i] * raw.info['sfreq'])
@@ -969,13 +1221,14 @@ 
Source code for parse_pd
                                  duration=np.repeat(0.1, onsets.size),
                                  description=np.repeat(name, onsets.size))
     # save modified data
-    basename = op.splitext(op.basename(fname))[0]
-    pd_data_dir = op.join(op.dirname(fname), basename + '_pd_data')
-    annot.save(op.join(pd_data_dir, basename + '_pd_annot.fif'))

+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    out_dir = op.join(op.dirname(raw.filenames[0]),
+                      basename + '_pd_parser_data')
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=True)
+    return annot

 
 
-
[docs]def add_pd_events_to_raw(fname, out_fname=None, drop_pd_channels=True,
-                         verbose=True, overwrite=False):
+
[docs]def add_events_to_raw(raw, drop_pd_channels=True, verbose=True):
     """Save out a new raw file with photodiode events.
 
     Note: this function is not recommended, rather just skip it and
@@ -986,61 +1239,49 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
-    out_fname : str
-        The filepath to save the modified raw data to.
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     drop_pd_channels : bool
         Whether to drop the channel(s) the photodiode data was on.
-    verbose : bool
+    verbose: bool
         Whether to display or supress text output on the progress
         of the function.
-    overwrite : bool
-        Whether to overwrite existing data if it exists.
 
     Returns
     -------
-    out_fname : str
-        The filepath to save the modified raw data to.
+    raw : mne.io.Raw
+        The modified raw object with events.
 
     """
-    raw = _read_raw(fname, verbose=verbose)
-    if out_fname is None:
-        _, ext = op.splitext(fname)
-        out_fname = fname.replace(ext, 'pd_raw.fif')
-    if op.isfile(out_fname) and not overwrite:
-        raise ValueError(f'out_fname {out_fname} exists, and overwrite=False, '
-                         'set overwrite=True to overwrite')
-    annot, pd_ch_names, _ = _load_pd_data(fname)
+    raw = _read_raw(raw, verbose=verbose)
+    annot, pd_ch_names, _ = _load_data(raw)
     raw.set_annotations(annot)
     if drop_pd_channels:
         raw.drop_channels([ch for ch in pd_ch_names if ch in raw.ch_names])
-    if op.splitext(out_fname)[-1] != '.fif':
-        raise ValueError('Only saving as fif is supported, got '
-                         f'{op.splitext(out_fname)}')
-    raw.save(out_fname, overwrite=overwrite)
-    return out_fname

+    return raw

 
 
-
[docs]def pd_parser_save_to_bids(bids_dir, fname, sub, task, ses=None, run=None,
+
[docs]def pd_parser_save_to_bids(bids_dir, raw, sub, task, ses=None, run=None,
                            data_type=None, eogs=None, ecgs=None, emgs=None,
                            verbose=True, overwrite=False):
     """Convert data to BIDS format with events found from the photodiode.
 
     Parameters
     ----------
-    bids_dir : str
+    bids_dir: str
         The subject directory in the bids directory where the data
         should be saved.
-    fname : str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
-    sub : str
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
+    sub: str
         The name of the subject.
-    task : str
+    task: str
         The name of the task.
-    ses : str
+    ses: str
         The name of the session (optional).
-    run : str
+    run: str
         The name of the run (optional).
     data_type: str
         The type of the channels containing data, i.e. 'eeg' or 'seeg'.
@@ -1050,17 +1291,20 @@ 
Source code for parse_pd
         The channels recording heart electrophysiology.
     emgs: list | None
         The channels recording muscle electrophysiology.
-    verbose : bool
+    beh: None | str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+        If None, the stored data is used.
+    verbose: bool
         Whether to display or supress text output on the progress
         of the function.
-    overwrite : bool
+    overwrite: bool
         Whether to overwrite existing data if it exists.
 
     """
     import mne_bids
     if not op.isdir(bids_dir):
         os.makedirs(bids_dir)
-    raw = _read_raw(fname, preload=False, verbose=verbose)
+    raw = _read_raw(raw, preload=False, verbose=verbose)
     aux_chs = list()
     for name, ch_list in zip(['eog', 'ecg', 'emg'], [eogs, ecgs, emgs]):
         if ch_list is not None:
@@ -1069,33 +1313,24 @@ 
Source code for parse_pd
     if data_type is not None:
         raw.set_channel_types({ch: data_type for ch in raw.ch_names if
                                ch not in aux_chs})
-    annot, pd_channels, beh_df = _load_pd_data(fname)
+    annot, pd_channels, beh = _load_data(raw)
     raw.set_annotations(annot)
     events, event_id = mne.events_from_annotations(raw)
-    raw.set_channel_types({ch: 'stim' for ch in pd_channels
-                           if ch in raw.ch_names})
+    # raw.set_channel_types({ch: 'stim' for ch in pd_channels
+    #                        if ch in raw.ch_names})
     bids_path = mne_bids.BIDSPath(subject=sub, session=ses, task=task,
                                   run=run, root=bids_dir)
-    mne_bids.write_raw_bids(raw, bids_path, events_data=events,
-                            event_id=event_id, verbose=verbose,
+    mne_bids.write_raw_bids(raw, bids_path, verbose=verbose,
                             overwrite=overwrite)
-    '''
-    beh_path = bids_path.copy().update(datatype='beh', extension='tsv')
-    if not op.isdir(op.dirname(beh_path.fpath)):
-        os.makedirs(op.dirname(beh_path.fpath))
-    if beh_df is not None:
-        _to_tsv(beh_path.fpath, beh_df)
-    '''
-    bids_beh_dir = \
-        op.join(bids_dir, f'sub-{sub}', f'ses-{ses}' if ses else '', 'beh')
-    if not op.isdir(bids_beh_dir):
-        os.makedirs(bids_beh_dir)
-    if beh_df is not None:
-        _to_tsv(op.join(bids_beh_dir, bids_path.basename + '_beh.tsv'), beh_df)

+    beh_path = bids_path.copy().update(datatype='beh')
+    if not op.isdir(op.dirname(beh_path.fpath)):
+        os.makedirs(op.dirname(beh_path.fpath))
+    if beh is not None:
+        _to_tsv(str(beh_path.fpath) + '_beh.tsv', beh)

 
 
 
[docs]def simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300., iti=6.,
-                     iti_jitter=1.5, drift=0.1, prop_corrupted=0.1,
+                     iti_jitter=1.5, rc_decay=0.0001, prop_corrupted=0.1,
                      sfreq=1000., seed=11, show=False):
     """Simulate photodiode data.
 
@@ -1105,34 +1340,34 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    n_events : float
+    n_events: float
         The number of events to simulate.
-    n_secs_on : float | np.array
+    n_secs_on: float | np.array
         The number of seconds each event is on. If a float is provided, the
         time is the same for each event. If an array is provided, it must be
         the length of the number of events, and it determines the length of
         each event respectively.
-    amp : float
+    amp: float
         The amplitude of the photodiode in standard deviations above baseline.
-    iti : float
+    iti: float
         The interval in between events.
-    iti_jitter : float
+    iti_jitter: float
         The jitter displacing the events from exactly `iti` distance
         away from each other.
-    drift : float
-        The factor controlling how much the photodiode changes value
+    rc_decay: float
+        The factor controlling how much the photodiode decays back to baseline
         over time with no external simulus (0. == perfect square wave).
-    sfreq : float
+    sfreq: float
         The sampling frequency of the data.
-    show : bool
+    show: bool
         Whether to plot the data.
 
     Returns
     -------
-    raw : mne.io.Raw
+    raw: mne.io.Raw
         The raw object containing the photodiode data
-    beh_df : dict
-        A dictionary with columns:
+    beh: dict
+        A dictionary with keys (columns names):
 
             `trial` : int
                 The index of the event.
@@ -1141,18 +1376,18 @@ 
Source code for parse_pd
                 The time that both the corrupted and uncorrupted events
                 occurred in seconds.
 
-    events : np.array
+    events: np.array
         The uncorrupted events where the first column is the time stamp,
         the second column is unused (zero) and the third column is the
         event identifier.
-    corrupted_indices : np.array
+    corrupted_indices: np.array
         The indices of the events which were corrupted in the simulation.
     """
     if isinstance(n_secs_on, list) and len(n_secs_on) != n_events:
         raise ValueError('If a list of `n_secs_on` is provided, it must '
                          f'match the number of events, {n_events}, got '
                          f'{len(n_secs_on)}')
-    assert drift > 0 and iti > 0 and iti_jitter > 0
+    assert rc_decay >= 0 and iti > 0 and iti_jitter > 0
     # n_secs on as list is okay, just make an array
     if isinstance(n_secs_on, list):
         n_secs_on = np.array(n_secs_on)
@@ -1177,19 +1412,30 @@ 
Source code for parse_pd
         (np.random.random(n_events) * iti_jitter + iti) * sfreq)).astype(int)
     events[:, 2] = 1
     # make pink noise
-    n_points = events[:, 0].max() + int(2 * iti * sfreq)
+    n_secs_on_mean = n_secs_on if isinstance(n_secs_on, (float, int)) else \
+        np.array(n_secs_on).max()
+    n_points = events[:, 0].max() + int(4 * n_secs_on_mean * sfreq)
     n_points += n_points % 2  # must be even
     x = np.random.randn(n_points // 2) + np.random.randn(n_points // 2) * 1j
     x /= np.sqrt(np.arange(1, x.size + 1))
     pd_data = np.fft.irfft(x).real
     pd_data /= pd_data.std()
     # add photodiode square waves to pink noise
-    for event, n_on in zip(events[:, 0], n_samp_on):
-        drift_array = np.linspace(0, 1, n_on)
-        pd_data[event: event + n_on] += \
-            amp - drift * amp * drift_array
-        pd_data[event + n_on: event + 2 * n_on] += \
-            drift * amp * drift_array - amp * drift
+    flip_data = np.zeros((pd_data.shape))
+    for i in range(n_events):
+        event = events[i, 0]
+        n_on = n_samp_on[i]
+        next_event = events[i + 1, 0] - n_on if i < n_events - 1 else \
+            pd_data.size - n_on
+        flip_data[event] += amp
+        for i in range(1, n_on):  # on decay
+            flip_data[event + i] += flip_data[event + i - 1] * (1 - rc_decay)
+        flip_data[event + n_on - 1] -= amp * ((1 - rc_decay)**(n_on / 2))
+        max_i = min([next_event - event, int(i / rc_decay), int(10 * sfreq)])
+        for i in range(max_i):
+            flip_data[event + n_on + i] += \
+                flip_data[event + n_on + i - 1] * (1 - rc_decay)
+    pd_data += flip_data
     # corrupt some events
     n_events_corrupted = np.round(n_events * prop_corrupted).astype(int)
     corrupted_indices = np.random.choice(range(n_events), n_events_corrupted,
@@ -1202,10 +1448,10 @@ 
Source code for parse_pd
         ts_cor = int(len(samp_range) * np.random.random() * 0.02 + 0.005)
         for ts in np.random.choice(samp_range, ts_cor, replace=False):
             # disrupt 1 / 5 of on time, 5 times amplitude
-            pd_data[ts - n_on // 10: ts + n_on // 10] = \
-                np.random.random() * 5 * amp - amp
-    beh_df = dict(trial=np.arange(n_events),
-                  time=events[:, 0].astype(float) / sfreq)
+            pd_data[ts - n_on // 10: ts + n_on // 10] += \
+                (np.random.random() - 0.5) * 5 * amp - amp
+    beh = dict(trial=np.arange(n_events),
+               time=events[:, 0].astype(float) / sfreq)
     events = np.delete(events, corrupted_indices, axis=0)
     # plot if show
     if show:
@@ -1219,7 +1465,7 @@ 
Source code for parse_pd
     # create mne.io.Raw object
     info = mne.create_info(['pd'], sfreq, ['stim'])
     raw = mne.io.RawArray(pd_data[np.newaxis], info)
-    return raw, beh_df, events, corrupted_indices

+    return raw, beh, events, corrupted_indices

 

 
     

@@ -1234,7 +1480,7 @@ 
Source code for parse_pd
     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   

 
diff --git a/docs/_sources/auto_examples/index.rst.txt b/docs/_sources/auto_examples/index.rst.txt
index d68f197..1b83684 100644
--- a/docs/_sources/auto_examples/index.rst.txt
+++ b/docs/_sources/auto_examples/index.rst.txt
@@ -24,6 +24,7 @@ Here we show some exemplary use cases for pd-parser.
 .. only:: html
 
  .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_events_thumb.png
+     :alt: Find Photodiode Events
 
      :ref:`sphx_glr_auto_examples_plot_find_pd_events.py`
 
@@ -39,13 +40,14 @@ Here we show some exemplary use cases for pd-parser.
 
 .. raw:: html
 
-    

+    

 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_recover_events_thumb.png
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_on_and_off_thumb.png
+     :alt: Find Photodiode On and Off Events
 
-     :ref:`sphx_glr_auto_examples_plot_recover_events.py`
+     :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py`
 
 .. raw:: html
 
@@ -55,17 +57,18 @@ Here we show some exemplary use cases for pd-parser.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_recover_events
+   /auto_examples/plot_find_pd_on_and_off
 
 .. raw:: html
 
-    

+    

 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_on_and_off_thumb.png
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_audio_events_thumb.png
+     :alt: Use Audio to Align Video Data
 
-     :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py`
+     :ref:`sphx_glr_auto_examples_plot_find_audio_events.py`
 
 .. raw:: html
 
@@ -75,7 +78,28 @@ Here we show some exemplary use cases for pd-parser.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_find_pd_on_and_off
+   /auto_examples/plot_find_audio_events
+
+.. raw:: html
+
+    

+
+.. only:: html
+
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_recover_events_thumb.png
+     :alt: Manually Recover Events Not Found by the Algorithm
+
+     :ref:`sphx_glr_auto_examples_plot_recover_events.py`
+
+.. raw:: html
+
+    

+
+
+.. toctree::
+   :hidden:
+
+   /auto_examples/plot_recover_events
 .. raw:: html
 
     

@@ -90,13 +114,13 @@ Here we show some exemplary use cases for pd-parser.
 
   .. container:: sphx-glr-download sphx-glr-download-python
 
-    :download:`Download all examples in Python source code: auto_examples_python.zip `
+    :download:`Download all examples in Python source code: auto_examples_python.zip `
 
 
 
   .. container:: sphx-glr-download sphx-glr-download-jupyter
 
-    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
 
 
 .. only:: html
diff --git a/docs/_sources/auto_examples/plot_find_audio_events.rst.txt b/docs/_sources/auto_examples/plot_find_audio_events.rst.txt
new file mode 100644
index 0000000..2613ea2
--- /dev/null
+++ b/docs/_sources/auto_examples/plot_find_audio_events.rst.txt
@@ -0,0 +1,266 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/plot_find_audio_events.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_plot_find_audio_events.py:
+
+
+=============================
+Use Audio to Align Video Data
+=============================
+In this example, we use ``pd-parser`` to find audio events using the same
+algorithm for matching with time-stamps and rejecting misaligned
+audio, but applied using the onset of an audio deflection instead of detecting
+photodiode events based on their square wave shape.
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-15
+
+.. code-block:: default
+
+
+    # Authors: Alex Rockhill 
+    #
+    # License: BSD (3-clause)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 16-28
+
+Load in a video with audio:
+
+In this example, we'll use audio and instead of aligning electrophysiology
+data, we'll align a video. This example data is from a task where movements
+are played on a monitor for the participant to mirror and the video recording
+is synchronized by playing a pre-recorded clap. This clap sound, or a similar
+sound, is recommended for synchronizing audio because the onset is clear and
+allows good precision in synchronizing events.
+
+Note that the commands that require ffmpeg are pre-computed and commented
+out because ffmpeg must be installed to use them and it is not required by
+``pd-parser``.
+
+.. GENERATED FROM PYTHON SOURCE LINES 28-85
+
+.. code-block:: default
+
+    import os
+    import os.path as op
+    import numpy as np
+    from scipy.io import wavfile
+    from subprocess import call
+    # from subprocess import run, PIPE, STDOUT
+    # import datetime
+
+    import mne
+    from mne.utils import _TempDir
+
+    import pd_parser
+    from pd_parser.parse_pd import _load_data  # , _read_tsv
+
+    # get the data
+    out_dir = _TempDir()
+    call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+          'master/pd_parser/tests/data/test_video.mp4 '
+          '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ)
+    call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+          'master/pd_parser/tests/data/test_video.wav '
+          '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ)
+    call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+          'master/pd_parser/tests/data/test_video_beh.tsv '
+          '-o ' + op.join(out_dir, 'test_video_beh.tsv')],
+         shell=True, env=os.environ)
+
+    # navigate to the example video
+    video_fname = op.join(out_dir, 'test_video.mp4')
+
+    audio_fname = video_fname.replace('mp4', 'wav')  # pre-computed
+    # extract audio (requires ffmpeg)
+    # run(['ffmpeg', '-i', video_fname, audio_fname])
+
+    fs, data = wavfile.read(audio_fname)
+    data = data.mean(axis=1)  # stereo audio but only need one source
+    info = mne.create_info(['audio'], fs, ['stim'])
+    raw = mne.io.RawArray(data[np.newaxis], info)
+
+    # find audio-visual time offset
+    offset = 0  # pre-computed value for this video
+    '''
+    result = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time',
+                  '-v', '0', '-of', 'compact=p=1:nk=0', video_fname],
+                 stdout=PIPE, stderr=STDOUT)
+    output = result.stdout.decode('utf-8').split('\n')
+    offset = float(output[0].strip('stream|codec_type=video|start_time')) - \
+        float(output[1].strip('stream|codec_type=audio|start_time'))
+    '''
+
+    # save to disk as required by ``pd-parser``, raw needs a filename
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
+
+    # navigate to corresponding behavior
+    behf = op.join(out_dir, 'test_video_beh.tsv')
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Creating RawArray with float64 data, n_channels=1, n_times=16464896
+        Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+    Ready.
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    [done]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 86-89
+
+Run the parser:
+
+Now we'll call the main function to automatically parse the audio events.
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-92
+
+.. code-block:: default
+
+    pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',
+                          audio_ch_names=['audio'], zscore=10)
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_find_audio_events_001.png
+    :alt: Synchronization Events Compared to Behavior Events
+    :class: sphx-glr-single-img
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+    Isotrak not found
+        Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+    Ready.
+    Reading 0 ... 16464895  =      0.000 ...   343.019 secs...
+    Finding points where the audio is above `zscore` threshold...
+    17 audio candidate events found
+    Checking best alignments
+
      0%|          | 0/14 [00:00
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-97
+
+Load the results:
+
+Finally, we'll load the events and use them to crop the video although it
+requires ffmpeg so it is commented out.
+
+.. GENERATED FROM PYTHON SOURCE LINES 97-112
+
+.. code-block:: default
+
+    annot = _load_data(fname)[0]
+    print('Here are the event times: ', annot.onset)
+
+    # Crop the videos with ffmpeg
+    '''
+    beh = _read_tsv(behf)
+    for i in range(annot.onset.size):  # skip the first video
+        action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000
+        run(['ffmpeg', '-i', f'{video_fname}', '-ss',
+             str(datetime.timedelta(
+                 seconds=annot.onset[i] - action_time - offset)),
+             '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),
+             op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(
+                 beh['movement'][i], beh['action_type'][i]))])
+    '''
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+    Isotrak not found
+        Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+    Ready.
+    Here are the event times:  [ 19.05112457  39.9129982   61.88574982  83.54243469 104.41456604
+     126.07720947 147.5539856  168.61270142 189.57843018 211.35673523
+     250.20858765 271.68209839 292.14001465 313.30532837 333.78097534]
+
+    "\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh['movement'][i], beh['action_type'][i]))])\n"
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  28.131 seconds)
+
+
+.. _sphx_glr_download_auto_examples_plot_find_audio_events.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: plot_find_audio_events.py `
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: plot_find_audio_events.ipynb `
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery `_
diff --git a/docs/_sources/auto_examples/plot_find_pd_events.rst.txt b/docs/_sources/auto_examples/plot_find_pd_events.rst.txt
index caa3f98..c5eca4c 100644
--- a/docs/_sources/auto_examples/plot_find_pd_events.rst.txt
+++ b/docs/_sources/auto_examples/plot_find_pd_events.rst.txt
@@ -1,12 +1,21 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/plot_find_pd_events.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
 .. only:: html
 
     .. note::
         :class: sphx-glr-download-link-note
 
-        Click :ref:`here `     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
 
-    .. _sphx_glr_auto_examples_plot_find_pd_events.py:
+.. _sphx_glr_auto_examples_plot_find_pd_events.py:
 
 
 ======================
@@ -15,6 +24,7 @@ Find Photodiode Events
 In this example, we use ``pd-parser`` to find photodiode events and
 align them to behavior. Then, we save the data to BIDS format.
 
+.. GENERATED FROM PYTHON SOURCE LINES 8-13
 
 .. code-block:: default
 
@@ -30,12 +40,15 @@ align them to behavior. Then, we save the data to BIDS format.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 14-19
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw object` so that we can save out some random
 data with a photodiode event channel in it in `fif` format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 19-54
 
 .. code-block:: default
 
@@ -46,7 +59,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv
+    from pd_parser.parse_pd import _read_raw, _to_tsv
 
     out_dir = _TempDir()
     print(f'After running this example, you can find the data here: {out_dir}')
@@ -54,7 +67,7 @@ electrophysiology data format).
     # simulate photodiode data
     n_events = 300
     prop_corrupted = 0.01
-    raw, beh_df, events, corrupted_indices = \
+    raw, beh, events, corrupted_indices = \
         pd_parser.simulate_pd_data(n_events=n_events,
                                    prop_corrupted=prop_corrupted)
 
@@ -71,6 +84,9 @@ electrophysiology data format).
     fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
     raw.save(fname)
 
+    # roundtrip so that raw is properly loaded from disk and has a filename
+    raw = _read_raw(fname)
+
 
 
 
@@ -81,18 +97,27 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6
+    Creating RawArray with float64 data, n_channels=1, n_times=2036106
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
+    Ready.
+    Creating RawArray with float64 data, n_channels=3, n_times=2036106
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    [done]
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
+    Isotrak not found
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif [done]
+    Reading 0 ... 2036105  =      0.000 ...  2036.105 secs...
+
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 55-63
 
 Make behavior data:
 
@@ -103,6 +128,7 @@ might look in an experimental setting.
 Let's make a task where there is a fixation stimulus, then a go cue,
 and a then response as an example.
 
+.. GENERATED FROM PYTHON SOURCE LINES 63-83
 
 .. code-block:: default
 
@@ -119,12 +145,13 @@ and a then response as an example.
     for i in [10, 129, 232, 288]:
         response_time[i] = 'n/a'  # make some no responses
     # put in dictionary to be converted to tsv file
-    beh_df['fix_onset_time'] = beh_df['time'] + offsets
-    beh_df['go_time'] = go_time
-    beh_df['response_time'] = response_time
+    beh['fix_onset_time'] = beh['time'] + offsets
+    beh['go_time'] = go_time
+    beh['response_time'] = response_time
     behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
     # save behavior file out
-    _to_tsv(behf, beh_df)
+    _to_tsv(behf, beh)
+
 
 
 
@@ -132,6 +159,7 @@ and a then response as an example.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 84-91
 
 Use the interactive graphical user interface (GUI) to find parameters:
 
@@ -141,53 +169,43 @@ into a console running python (ipython recommended), you can see how to
 interact with the photodiode data to pick reasonable parameters by
 following the instructions.
 
+.. GENERATED FROM PYTHON SOURCE LINES 91-94
 
 .. code-block:: default
 
 
-    pd_parser.find_pd_params(fname, pd_ch_names=['pd'])
+    pd_parser.find_pd_params(raw, pd_ch_names=['pd'])
 
 
 
 
 .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_001.png
+    :alt: Align Use the left/right keys to find an uncorrupted photodiode event and align the onset to the center of the window use +/- to zoom the yaxis in and out (up/down to translate) press enter when finished
     :class: sphx-glr-single-img
 
 
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
-    Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-    Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-    /Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:581: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      fig.show()
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 95-105
 
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters or the parameters you
 found from :func:`pd_parser.find_pd_parameters` to find and align the
-photodiode events, excluding events that were off because the commuter
+photodiode events, excluding events that were off because the computer
 hung up on computation, for instance. That data is saved in the same folder
 as the raw file (in this case, a temperary directory generated by
 :func:`_TempDir`). The data can be used directly, or it can be accessed via
 :func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine
 data structure (BIDS) standardized format before using it.
 
+.. GENERATED FROM PYTHON SOURCE LINES 105-108
 
 .. code-block:: default
 
 
-    pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)
+    pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)
 
 
 
@@ -198,12 +216,14 @@ data structure (BIDS) standardized format before using it.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_002.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Excluding event 5, off by 32 ms, Excluding event 7, off by -30 ms, Excluding event 8, off by 32 ms, Excluding event 14, off by -30 ms, Excluding event 37, off by 83 ms, Excluding event 76, off by -30 ms, Excluding event 96, off by 31 ms, Excluding event 98, off by -32 ms, Excluding event 180, no event found, Excluding event 262, no event found, Excluding event 279, off by 31 ms, Excluding event 289, off by -30 ms
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_003.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -212,40 +232,32 @@ data structure (BIDS) standardized format before using it.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
-    Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-    Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
     Finding photodiode events
-
      0%|          | 0/10920 [00:00`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
 
-    .. _sphx_glr_auto_examples_plot_find_pd_on_and_off.py:
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_plot_find_pd_on_and_off.py:
 
 
 =================================
@@ -16,6 +25,7 @@ In this example, we use ``pd-parser`` to find photodiode events and
 align both the onset of the deflection and the cessation to
 to behavior.
 
+.. GENERATED FROM PYTHON SOURCE LINES 9-14
 
 .. code-block:: default
 
@@ -31,12 +41,15 @@ to behavior.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 15-20
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw` object so that we can save out some random
 data with a photodiode event channel in it in fif format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 20-62
 
 .. code-block:: default
 
@@ -47,7 +60,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv, _load_pd_data
+    from pd_parser.parse_pd import _load_data
 
     import matplotlib.pyplot as plt
     import matplotlib.cm as cm
@@ -60,7 +73,7 @@ electrophysiology data format).
     # let's make our photodiode events on random uniform from 0.5 to 1 second
     n_secs_on = np.random.random(n_events) * 0.5 + 0.5
     prop_corrupted = 0.01
-    raw, beh_df, events, corrupted_indices = \
+    raw, beh, events, corrupted_indices = \
         pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                    prop_corrupted=prop_corrupted)
 
@@ -74,15 +87,13 @@ electrophysiology data format).
     raw.info['dig'] = None
     raw.info['line_freq'] = 60
 
-    # save to disk as required by ``pd-parser``
-    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-    raw.save(fname)
     # add some offsets to the behavior so it's a bit more realistic
     offsets = np.random.randn(n_events) * 0.01
-    beh_df['time'] = np.array(beh_df['time']) + offsets
-    behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-    _to_tsv(behf, beh_df)
+    beh['time'] = np.array(beh['time']) + offsets
 
+    # save to disk as required by ``pd-parser``
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
 
 
 
@@ -94,18 +105,21 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=1, n_times=2036104
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=3, n_times=2036104
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif [done]
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    [done]
 
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 63-69
+
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters to find and align the
@@ -113,12 +127,13 @@ photodiode events, excluding events that were off.
 One percent of the 300 events (3) were corrupted as shown in the plots and
 some were too far off from large offsets that we're going to exclude them.
 
+.. GENERATED FROM PYTHON SOURCE LINES 69-74
 
 .. code-block:: default
 
 
-    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                       pd_ch_names=['pd'], beh_col='time',
+    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                       pd_ch_names=['pd'], beh_key='time',
                        max_len=1.5)  # none are on longer than 1.5 seconds
 
 
@@ -130,12 +145,14 @@ some were too far off from large offsets that we're going to exclude them.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_on_and_off_001.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Excluding event 44, off by -37 ms, Excluding event 56, off by -37 ms, Excluding event 71, off by 31 ms, Excluding event 154, off by 37 ms, Excluding event 209, off by 30 ms, Excluding event 238, no event found, Excluding event 266, off by -33 ms, Excluding event 274, no event found, Excluding event 281, off by -43 ms
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_on_and_off_002.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -144,43 +161,41 @@ some were too far off from large offsets that we're going to exclude them.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+    Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
     Finding photodiode events
-
      0%|          | 0/10920 [00:00`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
 
-    .. _sphx_glr_auto_examples_plot_recover_events.py:
+.. _sphx_glr_auto_examples_plot_recover_events.py:
 
 
 ==================================================
@@ -18,6 +27,7 @@ baselines but not corrupted onsets or offsets.
 Note that it might be a good idea not to recover these events
 as there might be noise in the data around this time.
 
+.. GENERATED FROM PYTHON SOURCE LINES 11-16
 
 .. code-block:: default
 
@@ -33,12 +43,15 @@ as there might be noise in the data around this time.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 17-22
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw` object so that we can save out some random
 data with a photodiode event channel in it in fif format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 22-85
 
 .. code-block:: default
 
@@ -50,7 +63,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv
+    from pd_parser.parse_pd import _load_data
 
     import matplotlib.pyplot as plt
 
@@ -61,7 +74,7 @@ electrophysiology data format).
     n_events = 300
     # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds
     n_secs_on = np.random.random(n_events) * 0.5 + 0.25
-    raw, beh_df, events, _ = \
+    raw, beh, events, _ = \
         pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                    prop_corrupted=0.0)
     sfreq = np.round(raw.info['sfreq']).astype(int)
@@ -74,16 +87,19 @@ electrophysiology data format).
     axes[0].set_ylabel('voltage')
     for j, i in enumerate(events[corrupted_indices, 0]):
         if j == 0:
-            raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount
+            raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount
         elif j == 1:
             raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount
         else:
-            raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount
+            raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount
         axes[j].plot(np.linspace(-1, 2, 3 * sfreq),
                      raw._data[0, i - sfreq: i + sfreq * 2])
         axes[j].set_xlabel('time (s)')
 
 
+    # make figure nicer
+    fig.tight_layout()
+
     # make fake electrophysiology data
     info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
                            ['seeg'] * 3)
@@ -94,20 +110,19 @@ electrophysiology data format).
     raw.info['dig'] = None
     raw.info['line_freq'] = 60
 
-    # save to disk as required by ``pd-parser``
-    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-    raw.save(fname)
     # add some offsets to the behavior so it's a bit more realistic
     offsets = np.random.randn(n_events) * 0.001
-    beh_df['time'] = np.array(beh_df['time']) + offsets
-    behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-    _to_tsv(behf, beh_df)
+    beh['time'] = np.array(beh['time']) + offsets
 
+    # save to disk as required by ``pd-parser``, raw needs to have a filename
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
 
 
 
 
 .. image:: /auto_examples/images/sphx_glr_plot_recover_events_001.png
+    :alt: Corrupted Events
     :class: sphx-glr-single-img
 
 
@@ -117,18 +132,21 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=1, n_times=2035104
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=3, n_times=2035104
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif [done]
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    [done]
 
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 86-94
+
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters to find and align the
@@ -138,13 +156,14 @@ Note that the mock function mocks user input so when you run the example,
 you want to delete that line and unindent the next line, and then provide
 your own input depending on whether you want to keep the events or not.
 
+.. GENERATED FROM PYTHON SOURCE LINES 94-99
 
 .. code-block:: default
 
 
     with mock.patch('builtins.input', return_value='y'):
-        pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                           pd_ch_names=['pd'], beh_col='time', recover=True)
+        pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                           pd_ch_names=['pd'], beh_key='time', recover=True)
 
 
 
@@ -155,27 +174,32 @@ your own input depending on whether you want to keep the events or not.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_002.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 8
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_003.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 144
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_004.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 234
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_005.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Event 8 recovered (not excluded), Event 144 recovered (not excluded), Event 234 recovered (not excluded)
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_006.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -184,33 +208,29 @@ your own input depending on whether you want to keep the events or not.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+    Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
     Finding photodiode events
-
      0%|          | 0/16341 [00:00
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 114-117
+
+Check the results:
+
+Finally, we'll check that the recovered events and the original events match.
+
+.. GENERATED FROM PYTHON SOURCE LINES 117-134
+
+.. code-block:: default
+
+
+    annot = _load_data(fname)[0]
+    raw.set_annotations(annot)
+    events2, event_id = mne.events_from_annotations(raw)
+    on_events = events2[events2[:, 2] == event_id['Stim On']]
+    print(f'Original: {events[corrupted_indices, 0]}\n'
+          f'Recovered: {on_events[corrupted_indices, 0]}')
+
+    '''
+    # uncomment when using interactively, this section doesn't work
+    # for the non-interactive documentation
+    off_events = events2[events2[:, 2] == event_id['Stim Off']]
+    original_off = events[corrupted_indices, 0] + \
+      np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)
+    print(f'Original off: {original_off}\n'
+          f'Recovered off: {on_events[corrupted_indices, 0]}')
+    '''
+
+
 
 
 .. rst-class:: sphx-glr-script-out
@@ -255,22 +330,23 @@ following the instructions.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-    /Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:803: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      plt.show()
+    Used Annotations descriptions: ['Stim Off', 'Stim On']
+    Original: [  65602  989660 1595483]
+    Recovered: [  65602  989660 1595483]
 
+    "\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] +   np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\n'\n      f'Recovered off: {on_events[corrupted_indices, 0]}')\n"
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.919 seconds)
+   **Total running time of the script:** ( 0 minutes  30.425 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_recover_events.py:
diff --git a/docs/_sources/auto_examples/sg_execution_times.rst.txt b/docs/_sources/auto_examples/sg_execution_times.rst.txt
index 45af171..142f961 100644
--- a/docs/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/docs/_sources/auto_examples/sg_execution_times.rst.txt
@@ -5,12 +5,14 @@
 
 Computation times
 =================
-**00:46.944** total execution time for **auto_examples** files:
+**00:58.556** total execution time for **auto_examples** files:
 
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_find_pd_events.py` (``plot_find_pd_events.py``)         | 00:20.175 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_recover_events.py` (``plot_recover_events.py``)         | 00:30.425 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py` (``plot_find_pd_on_and_off.py``) | 00:14.849 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_find_audio_events.py` (``plot_find_audio_events.py``)   | 00:28.131 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_recover_events.py` (``plot_recover_events.py``)         | 00:11.919 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_find_pd_events.py` (``plot_find_pd_events.py``)         | 00:00.000 | 0.0 MB |
++-------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py` (``plot_find_pd_on_and_off.py``) | 00:00.000 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/_sources/generated/cli.rst.txt b/docs/_sources/generated/cli.rst.txt
index 0f41452..25523c0 100644
--- a/docs/_sources/generated/cli.rst.txt
+++ b/docs/_sources/generated/cli.rst.txt
@@ -14,18 +14,18 @@ Here we list the pd_parser tools that you can use from the command line.
 
 
 
-.. _gen_add_pd_events_to_raw:
+.. _gen_add_events_to_raw:
 
-add_pd_events_to_raw
-====================
+add_events_to_raw
+=================
 
 .. rst-class:: callout
 
-usage: ``add_pd_events_to_raw fname [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]``
+usage: ``add_events_to_raw raw [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
+raw
 	The electrophysiology filepath
 
 
@@ -59,12 +59,12 @@ add_pd_off_events
 
 .. rst-class:: callout
 
-usage: ``add_pd_off_events fname [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]``
+usage: ``add_pd_off_events raw [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -82,7 +82,7 @@ optional arguments
 --zscore ZSCORE		The same zscore as used for `parse_pd`.
 
 
---min_i MIN_I		The same min_i as used for `parse_pd`.
+--max_flip_i MAX_FLIP_I		The same max_flip_i as used for `parse_pd`.
 
 
 --baseline BASELINE		The same baseline as used for `parse_pd`.
@@ -99,19 +99,19 @@ optional arguments
 
 
 
-.. _gen_add_pd_relative_events:
+.. _gen_add_relative_events:
 
-add_pd_relative_events
-======================
+add_relative_events
+===================
 
 .. rst-class:: callout
 
-usage: ``add_pd_relative_events fname [-h] [--behf BEHF] [--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS] [--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES] [--verbose VERBOSE] [-o]``
+usage: ``add_relative_events raw [-h] [--beh BEH] [--relative_event_keys [RELATIVE_EVENT_KEYS ...]] [--relative_event_names [RELATIVE_EVENT_NAMES ...]] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -120,13 +120,13 @@ optional arguments
 -h, --help		show this help message and exit
 
 
---behf BEHF		The behavioral tsv filepath
+--beh BEH		The behavioral tsv filepath
 
 
---relative_event_cols LIST_OF_RELATIVE_EVENT_COLS		A behavioral column in the tsv file that has the time relative to the photodiode events on the same trial as in the `beh_col` event.
+--relative_event_keys [RELATIVE_EVENT_KEYS ...]		A behavioral key (column) in the tsv file that has the time relative to the photodiode events on the same trial as in the `beh_key` event.
 
 
---relative_event_names LIST_OF_RELATIVE_EVENT_NAMES		The name of the corresponding `relative_event_cols` events
+--relative_event_names [RELATIVE_EVENT_NAMES ...]		The name of the corresponding `relative_event_keys` events
 
 
 --verbose VERBOSE		Set verbose output to True or False.
@@ -147,12 +147,12 @@ find_pd_params
 
 .. rst-class:: callout
 
-usage: ``find_pd_params fname [-h] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--verbose VERBOSE]``
+usage: ``find_pd_params raw [-h] [--pd_ch_names [PD_CH_NAMES ...]] [--verbose VERBOSE]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -161,7 +161,7 @@ optional arguments
 -h, --help		show this help message and exit
 
 
---pd_ch_names LIST_OF_PD_CH_NAMES		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
+--pd_ch_names [PD_CH_NAMES ...]		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
 
 
 --verbose VERBOSE		Set verbose output to True or False.
@@ -172,6 +172,68 @@ optional arguments
 
 
 
+.. _gen_parse_audio:
+
+parse_audio
+===========
+
+.. rst-class:: callout
+
+usage: ``parse_audio raw [-h] [--audio_event_name AUDIO_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--audio_ch_names [AUDIO_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
+
+positional arguments
+--------------------
+raw
+	TThe electrophysiology raw object or filepath
+
+
+
+optional arguments
+------------------
+-h, --help		show this help message and exit
+
+
+--audio_event_name AUDIO_EVENT_NAME		The name of the audio event
+
+
+--beh BEH		The behavioral dictionary or tsv filepath
+
+
+--beh_key BEH_KEY		The name of the behavioral key (column) corresponding to the audio event timing
+
+
+--audio_ch_names [AUDIO_CH_NAMES ...]		The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced
+
+
+--exclude_shift EXCLUDE_SHIFT		How many seconds off to exclude an audio-behavioral event difference
+
+
+--resync RESYNC		How large of a difference to use to resynchronize events. See `pd_parser.parse_pd` for more information
+
+
+--max_len MAX_LEN		The length of the longest audio event
+
+
+--zscore ZSCORE		How many standard deviations larger than the baseline the correlation of the audio is. If None, zscore is found interactively.
+
+
+--add_events		Whether to run the parser a second time to add more events from deflections corresponding to multiple events on the same channel
+
+
+--recover		Whether to recover corrupted events manually.
+
+
+--verbose VERBOSE		Set verbose output to True or False.
+
+
+-o, --overwrite		Pass this flag to overwrite an existing file
+
+
+
+
+
+
+
 .. _gen_parse_pd:
 
 parse_pd
@@ -179,12 +241,12 @@ parse_pd
 
 .. rst-class:: callout
 
-usage: ``parse_pd fname [-h] [--pd_event_name PD_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
+usage: ``parse_pd raw [-h] [--pd_event_name PD_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--pd_ch_names [PD_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -196,13 +258,13 @@ optional arguments
 --pd_event_name PD_EVENT_NAME		The name of the photodiode event
 
 
---behf BEHF		The behavioral tsv filepath
+--beh BEH		The behavioral dictionary or tsv filepath
 
 
---beh_col BEH_COL		The name of the behavioral column corresponding to the photodiode event timing
+--beh_key BEH_KEY		The name of the behavioral key (column) corresponding to the photodiode event timing
 
 
---pd_ch_names LIST_OF_PD_CH_NAMES		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
+--pd_ch_names [PD_CH_NAMES ...]		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
 
 
 --exclude_shift EXCLUDE_SHIFT		How many seconds off to exclude a photodiode- behavioral event difference
@@ -217,7 +279,7 @@ optional arguments
 --zscore ZSCORE		How many standard deviations larger than the baseline the photodiode event is. Decrease if too many events are being found and increase if too few. Use `find_pd_params` to determine if unsure.
 
 
---min_i MIN_I		The minimum number of samples to qualify as a pd event. Increase for fewer false-positives, decrease if your photodiode is on for fewer samples.
+--max_flip_i MAX_FLIP_I		The maximum number of samples the photodiode event takes to transition. Increase if the transitions are not being found, decrease for fewer false positives.
 
 
 --baseline BASELINE		How much relative to the max_lento use to idenify the time before the photodiode event. Probably don't change but increasing will reduce false-positives and decreasing will reduce false-negatives.
@@ -247,14 +309,14 @@ pd_parser_save_to_bids
 
 .. rst-class:: callout
 
-usage: ``pd_parser_save_to_bids bids_dir fname sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs LIST_OF_EOGS] [--ecgs LIST_OF_ECGS] [--emgs LIST_OF_EMGS] [--verbose VERBOSE] [-o]``
+usage: ``pd_parser_save_to_bids bids_dir raw sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs [EOGS ...]] [--ecgs [ECGS ...]] [--emgs [EMGS ...]] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
 bids_dir
 	Filepath of the BIDS directory to save to
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 sub
 	The subject identifier
 task
@@ -276,13 +338,13 @@ optional arguments
 --data_type DATA_TYPE		The type of data if not set correctly already (ieeg is often set as eeg for instance)
 
 
---eogs LIST_OF_EOGS		The eogs if not set correctly already
+--eogs [EOGS ...]		The eogs if not set correctly already
 
 
---ecgs LIST_OF_ECGS		The ecgs if not set correctly already
+--ecgs [ECGS ...]		The ecgs if not set correctly already
 
 
---emgs LIST_OF_EMGS		The emgs if not set correctly already
+--emgs [EMGS ...]		The emgs if not set correctly already
 
 
 --verbose VERBOSE		Set verbose output to True or False.
diff --git a/docs/_static/basic.css b/docs/_static/basic.css
index 24bc73e..24a49f0 100644
--- a/docs/_static/basic.css
+++ b/docs/_static/basic.css
@@ -764,6 +764,7 @@ div.code-block-caption code {
 }
 
 table.highlighttable td.linenos,
+span.linenos,
 div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
     user-select: none;
 }
diff --git a/docs/_static/binder_badge_logo.svg b/docs/_static/binder_badge_logo.svg
new file mode 100644
index 0000000..327f6b6
--- /dev/null
+++ b/docs/_static/binder_badge_logo.svg
@@ -0,0 +1 @@
+ launchlaunchbinderbinder 
\ No newline at end of file
diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js
index daccd20..7d88f80 100644
--- a/docs/_static/doctools.js
+++ b/docs/_static/doctools.js
@@ -285,9 +285,10 @@ var Documentation = {
   initOnKeyListeners: function() {
     $(document).keydown(function(event) {
       var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
+      // don't navigate when in search box, textarea, dropdown or button
       if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
         switch (event.keyCode) {
           case 37: // left
             var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js
index a29ec21..7ef5966 100644
--- a/docs/_static/documentation_options.js
+++ b/docs/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.3',
+    VERSION: 'v0.4',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/_static/gallery-rendered-html.css b/docs/_static/gallery-rendered-html.css
new file mode 100644
index 0000000..cea2197
--- /dev/null
+++ b/docs/_static/gallery-rendered-html.css
@@ -0,0 +1,209 @@
+/* Adapted from notebook/static/style/style.min.css */
+
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
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+  font-weight: bold;
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+}
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+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
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+}
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+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
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+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: #fff;
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+.rendered_html code {
+  background-color: #eff0f1;
+}
+.rendered_html p code {
+  padding: 1px 5px;
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+.rendered_html pre code {
+  background-color: #fff;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: #000;
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/docs/_static/gallery.css b/docs/_static/gallery.css
index aa897ff..848774f 100644
--- a/docs/_static/gallery.css
+++ b/docs/_static/gallery.css
@@ -175,6 +175,15 @@ ul.sphx-glr-horizontal img {
   height: auto;
 }
 
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation{
+  display: block;
+}
+
 p.sphx-glr-signature a.reference.external {
   -moz-border-radius: 5px;
   -webkit-border-radius: 5px;
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index b0ec841..e5cf736 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -1,5 +1,10 @@
+pre { line-height: 125%; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
+.highlight { background: #f8f8f8; }
 .highlight .c { color: #8f5902; font-style: italic } /* Comment */
 .highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
 .highlight .g { color: #000000 } /* Generic */
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
index 970d0d9..261ecaa 100644
--- a/docs/_static/searchtools.js
+++ b/docs/_static/searchtools.js
@@ -59,10 +59,10 @@ var Search = {
   _pulse_status : -1,
 
   htmlToText : function(htmlString) {
-      var htmlElement = document.createElement('span');
-      htmlElement.innerHTML = htmlString;
-      $(htmlElement).find('.headerlink').remove();
-      docContent = $(htmlElement).find('[role=main]')[0];
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
       if(docContent === undefined) {
           console.warn("Content block not found. Sphinx search tries to obtain it " +
                        "via '[role=main]'. Could you check your theme or template.");
diff --git a/docs/api.html b/docs/api.html
index 1d95a69..cfeed30 100644
--- a/docs/api.html
+++ b/docs/api.html
@@ -4,12 +4,13 @@
   
     
     
-    API Documentation — pd-parser 0.3 documentation
-    
+    API Documentation — pd-parser v0.4 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser 0.3
+          pd-parser v0.4
         
       

 
@@ -105,8 +106,8 @@ 
pd-parser
-

-add_pd_events_to_raw(fname, out_fname=None, drop_pd_channels=True, verbose=True, overwrite=False)[source]¶

+

+add_events_to_raw(raw, drop_pd_channels=True, verbose=True)[source]¶

 
Save out a new raw file with photodiode events.

 
Note: this function is not recommended, rather just skip it and
 use save_to_bids which doesn’t modify the underlying raw data
@@ -116,22 +117,19 @@ 
pd-parser
 
Parameters

 

-
fname: str
The filepath to the electrophysiology file (meg/eeg/ieeg).

-

-
out_fnamestr
The filepath to save the modified raw data to.

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
drop_pd_channelsbool
Whether to drop the channel(s) the photodiode data was on.

 

-
verbosebool
Whether to display or supress text output on the progress
+
verbose: bool
Whether to display or supress text output on the progress
 of the function.

 

-
overwritebool
Whether to overwrite existing data if it exists.

-

 

 

 
Returns

 

-
out_fnamestr
The filepath to save the modified raw data to.

+
rawmne.io.Raw
The modified raw object with events.

 

 

 

@@ -140,12 +138,13 @@ 
pd-parser
 

-add_pd_off_events(fname, off_event_name='Stim Off', max_len=1.0, zscore=100, min_i=10, baseline=0.25, verbose=True, overwrite=False)[source]¶

+add_pd_off_events(raw, off_event_name='Stim Off', max_len=1.0, zscore=10, max_flip_i=40, baseline=0.25, verbose=True, overwrite=False)[source]¶

Add events for when the photodiode deflection returns to baseline.

-
+
Parameters
-
fname: str

The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

off_eventstr

If None, no event will be assigned to cessation of the photodiode deflection. If a string is provided, an event of that name will @@ -156,7 +155,8 @@

pd-parserReturns +
+
annot: mne.Annotations

The annotations with the added events.

+
+
+
+

Note

+

The same parameters must be used for pd_parser.parse_pd().

+
+

-
-add_pd_relative_events(fname, behf, relative_event_cols, relative_event_names=None, overwrite=False, verbose=True)[source]¶
+
+add_relative_events(raw, beh, relative_event_keys, relative_event_names=None, overwrite=False, verbose=True)[source]¶

Add events relative to those determined from the photodiode.

Parameters
-
fname: str

The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

-
behfstr

The filepath to a tsv file with the behavioral timing

+
beh: str | dict

The dictionary or filepath to a tsv file with the behavioral timing.

-
relative_event_colslist

The names of the columns where time data is stored +

relative_event_keyslist

The names of the keys where time data is stored relative to the photodiode event

-
relative_event_nameslist

The names of the events in relative_event_cols.

+
relative_event_nameslist

The names of the events in relative_event_keys.

-
verbosebool

Whether to display or supress text output on the progress +

verbose: bool

Whether to display or supress text output on the progress of the function.

-
overwritebool

Whether to overwrite existing data if it exists.

+
overwrite: bool

Whether to overwrite existing data if it exists.

+
+
+
+
Returns
+
+
annot: mne.Annotations

The annotations with the added events.

@@ -199,14 +216,15 @@

pd-parser
-find_pd_params(fname, pd_ch_names=None, verbose=True)[source]¶
+find_pd_params(raw, pd_ch_names=None, verbose=True)[source]¶

Plot the data so the user can determine the right parameters.

The user can adjust window size to determine max_len and horizontal line height to determine zscore.

Parameters
-
fname: str

The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

pd_ch_nameslist

Names of the channel(s) containing the photodiode data. One channel is to be given for a common reference and @@ -221,9 +239,74 @@

pd-parser +
+parse_audio(raw, audio_event_name='Tone', beh=None, beh_key='tone_onset_time', audio_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=0.25, zscore=None, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶
+

Parse audio events.

+

Parses photodiode events from a likely very corrupted channel +using behavioral data to sync events to determine which +behavioral events don’t have a match and are thus corrupted +and should be excluded (while ignoring events that look like +photodiode events but don’t match behavior)

+
+
Parameters
+
+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

+
+
audio_event_name: str

The name of the event corresponding to the audio.

+
+
beh: str | dict

The dictionary or filepath to a tsv file with the behavioral timing.

+
+
beh_key: str

The key (column name) of the beh dictionary that corresponds +to the events.

+
+
audio_ch_names: list

Names of the channel(s) containing the audio data. +One channel is to be given for a common reference and +two for a bipolar reference. If no channels are provided, +the data will be plotted and the user will provide them.

+
+
exclude_shift: float

How many seconds different than expected from the behavior events +to exclude that event.

+
+
resync: float

The number of seconds to difference allowed to still use an audio event +event to resynchronize with time-stamped events. See +pd_parser.parse_pd() for more information.

+
+
max_len: float

The longest audio event can be.

+
+
zscore: float

How large of a z-score difference to use to threshold the correlation +of the audio with the sound. If None is passed a plot will be shown to +pick a reasonable zscore. 25 is a typical value that works.

+
+
add_events: bool

Whether to add the events found from the current call of parse_pd +to a events found previously (e.g. first parse with +pd_event_name=’Fixation’ and then parse with +pd_event_name=’Response’. +Note: pd_parser.add_relative_events will be relative to the +first event added.

+
+
recover: bool

Whether to recover corrupted events manually.

+
+
verbose: bool

Whether to display or supress text output on the progress +of the function.

+
+
overwrite: bool

Whether to overwrite existing data if it exists.

+
+
+
+
Returns
+
+
annot: mne.Annotations

The annotations with the added events.

+
+
+
+
+

+
-parse_pd(fname, pd_event_name='Fixation', behf=None, beh_col='fix_onset_time', pd_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=1.0, zscore=100, min_i=10, baseline=0.25, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶
+parse_pd(raw, pd_event_name='Fixation', beh=None, beh_key='fix_onset_time', pd_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=1.0, zscore=10, max_flip_i=40, baseline=0.25, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶

Parse photodiode events.

Parses photodiode events from a likely very corrupted channel using behavioral data to sync events to determine which @@ -233,13 +316,15 @@

pd-parser
Parameters
-
fname: str

The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

pd_event_name: str

The name of the event corresponding to the photodiode.

-
behfstr

The filepath to a tsv file with the behavioral timing.

+
beh: str | dict

The dictionary or filepath to a tsv file with the behavioral timing.

-
beh_colstr

The column of the behf tsv that corresponds to the events.

+
beh_key: str

The key (column name) of the beh dictionary that corresponds +to the events.

pd_ch_nameslist

Names of the channel(s) containing the photodiode data. One channel is to be given for a common reference and @@ -249,7 +334,7 @@

pd-parserfloat

The number of seconds to difference allowed to still use a photodiode +

resync: float

The number of seconds to difference allowed to still use a photodiode event to resynchronize with time-stamped events. Events with differences between resync and exclude_shift will still be used for alignment but will be excluded from the events. When @@ -267,34 +352,33 @@

pd-parserbool

Whether to add the events found from the current call of parse_pd +

add_events: bool

Whether to add the events found from the current call of parse_pd to a events found previously (e.g. first parse with pd_event_name=’Fixation’ and then parse with pd_event_name=’Response’. -Note: pd_parser.add_pd_relative_events will be relative to the +Note: pd_parser.add_relative_events will be relative to the first event added.

-
recoverbool

Whether to recover corrupted events manually.

+
recover: bool

Whether to recover corrupted events manually.

-
verbosebool

Whether to display or supress text output on the progress +

verbose: bool

Whether to display or supress text output on the progress of the function.

-
overwritebool

Whether to overwrite existing data if it exists.

+
overwrite: bool

Whether to overwrite existing data if it exists.

Returns
-
events: DataFrame

A DataFrame that has a column for to the (zero) -indexed behavioral events and another column corresponding -to the time stamp of the eeg file.

+
annot: mne.Annotations

The annotations with the added events.

@@ -303,23 +387,24 @@

pd-parser
-pd_parser_save_to_bids(bids_dir, fname, sub, task, ses=None, run=None, data_type=None, eogs=None, ecgs=None, emgs=None, verbose=True, overwrite=False)[source]¶
+pd_parser_save_to_bids(bids_dir, raw, sub, task, ses=None, run=None, data_type=None, eogs=None, ecgs=None, emgs=None, verbose=True, overwrite=False)[source]¶

Convert data to BIDS format with events found from the photodiode.

Parameters
-
bids_dirstr

The subject directory in the bids directory where the data +

bids_dir: str

The subject directory in the bids directory where the data should be saved.

-
fnamestr

The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object

The object or filepath of the time-series data file +(e.g. meg, eeg, ieeg).

-
substr

The name of the subject.

+
sub: str

The name of the subject.

-
taskstr

The name of the task.

+
task: str

The name of the task.

-
sesstr

The name of the session (optional).

+
ses: str

The name of the session (optional).

-
runstr

The name of the run (optional).

+
run: str

The name of the run (optional).

data_type: str

The type of the channels containing data, i.e. ‘eeg’ or ‘seeg’.

@@ -329,10 +414,13 @@

pd-parserbool

Whether to display or supress text output on the progress +

beh: None | str | dict

The dictionary or filepath to a tsv file with the behavioral timing. +If None, the stored data is used.

+
+
verbose: bool

Whether to display or supress text output on the progress of the function.

-
overwritebool

Whether to overwrite existing data if it exists.

+
overwrite: bool

Whether to overwrite existing data if it exists.

@@ -341,7 +429,7 @@

pd-parser
-simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300.0, iti=6.0, iti_jitter=1.5, drift=0.1, prop_corrupted=0.1, sfreq=1000.0, seed=11, show=False)[source]¶
+simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300.0, iti=6.0, iti_jitter=1.5, rc_decay=0.0001, prop_corrupted=0.1, sfreq=1000.0, seed=11, show=False)[source]¶

Simulate photodiode data.

Simulate data that is a square wave with a linear change in deflection drift amount towards zero that then over shoots and drifts back as @@ -349,34 +437,34 @@

pd-parser
Parameters
-
n_eventsfloat

The number of events to simulate.

+
n_events: float

The number of events to simulate.

-
n_secs_onfloat | np.array

The number of seconds each event is on. If a float is provided, the +

n_secs_on: float | np.array

The number of seconds each event is on. If a float is provided, the time is the same for each event. If an array is provided, it must be the length of the number of events, and it determines the length of each event respectively.

-
ampfloat

The amplitude of the photodiode in standard deviations above baseline.

+
amp: float

The amplitude of the photodiode in standard deviations above baseline.

-
itifloat

The interval in between events.

+
iti: float

The interval in between events.

-
iti_jitterfloat

The jitter displacing the events from exactly iti distance +

iti_jitter: float

The jitter displacing the events from exactly iti distance away from each other.

-
driftfloat

The factor controlling how much the photodiode changes value +

rc_decay: float

The factor controlling how much the photodiode decays back to baseline over time with no external simulus (0. == perfect square wave).

-
sfreqfloat

The sampling frequency of the data.

+
sfreq: float

The sampling frequency of the data.

-
showbool

Whether to plot the data.

+
show: bool

Whether to plot the data.

Returns
-
rawmne.io.Raw

The raw object containing the photodiode data

+
raw: mne.io.Raw

The raw object containing the photodiode data

-
beh_dfdict

A dictionary with columns:

+
beh: dict

A dictionary with keys (columns names):

@@ -414,7 +502,7 @@

pd-parserSphinx 3.2.1.
+ Created using Sphinx 3.3.1.

diff --git a/docs/auto_examples/index.html b/docs/auto_examples/index.html index 4de1e66..89b7917 100644 --- a/docs/auto_examples/index.html +++ b/docs/auto_examples/index.html @@ -4,12 +4,13 @@ - Examples Gallery — pd-parser 0.3 documentation - + Examples Gallery — pd-parser v0.4 documentation + + @@ -39,7 +40,7 @@ - pd-parser 0.3 + pd-parser v0.4 @@ -99,29 +100,35 @@

Examples Gallery¶

Here we show some exemplary use cases for pd-parser.

-
-../_images/sphx_glr_plot_recover_events_thumb.png -

Manually Recover Events Not Found by the Algorithm¶

+
+
+
+
-
diff --git a/docs/auto_examples/plot_find_audio_events.html b/docs/auto_examples/plot_find_audio_events.html new file mode 100644 index 0000000..6fcfed8 --- /dev/null +++ b/docs/auto_examples/plot_find_audio_events.html @@ -0,0 +1,285 @@ + + + + + + + Use Audio to Align Video Data — pd-parser v0.4 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+

Note

+

Click here +to download the full example code

+
+
+

Use Audio to Align Video Data¶

+

In this example, we use pd-parser to find audio events using the same +algorithm for matching with time-stamps and rejecting misaligned +audio, but applied using the onset of an audio deflection instead of detecting +photodiode events based on their square wave shape.

+
# Authors: Alex Rockhill <aprockhill@mailbox.org>
+#
+# License: BSD (3-clause)
+
+
+

Load in a video with audio:

+

In this example, we’ll use audio and instead of aligning electrophysiology +data, we’ll align a video. This example data is from a task where movements +are played on a monitor for the participant to mirror and the video recording +is synchronized by playing a pre-recorded clap. This clap sound, or a similar +sound, is recommended for synchronizing audio because the onset is clear and +allows good precision in synchronizing events.

+

Note that the commands that require ffmpeg are pre-computed and commented +out because ffmpeg must be installed to use them and it is not required by +pd-parser.

+
import os
+import os.path as op
+import numpy as np
+from scipy.io import wavfile
+from subprocess import call
+# from subprocess import run, PIPE, STDOUT
+# import datetime
+
+import mne
+from mne.utils import _TempDir
+
+import pd_parser
+from pd_parser.parse_pd import _load_data  # , _read_tsv
+
+# get the data
+out_dir = _TempDir()
+call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+      'master/pd_parser/tests/data/test_video.mp4 '
+      '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ)
+call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+      'master/pd_parser/tests/data/test_video.wav '
+      '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ)
+call(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'
+      'master/pd_parser/tests/data/test_video_beh.tsv '
+      '-o ' + op.join(out_dir, 'test_video_beh.tsv')],
+     shell=True, env=os.environ)
+
+# navigate to the example video
+video_fname = op.join(out_dir, 'test_video.mp4')
+
+audio_fname = video_fname.replace('mp4', 'wav')  # pre-computed
+# extract audio (requires ffmpeg)
+# run(['ffmpeg', '-i', video_fname, audio_fname])
+
+fs, data = wavfile.read(audio_fname)
+data = data.mean(axis=1)  # stereo audio but only need one source
+info = mne.create_info(['audio'], fs, ['stim'])
+raw = mne.io.RawArray(data[np.newaxis], info)
+
+# find audio-visual time offset
+offset = 0  # pre-computed value for this video
+'''
+result = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time',
+              '-v', '0', '-of', 'compact=p=1:nk=0', video_fname],
+             stdout=PIPE, stderr=STDOUT)
+output = result.stdout.decode('utf-8').split('\n')
+offset = float(output[0].strip('stream|codec_type=video|start_time')) - \
+    float(output[1].strip('stream|codec_type=audio|start_time'))
+'''
+
+# save to disk as required by ``pd-parser``, raw needs a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
+
+# navigate to corresponding behavior
+behf = op.join(out_dir, 'test_video_beh.tsv')
+
+
+

Out:

+
Creating RawArray with float64 data, n_channels=1, n_times=16464896
+    Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+Ready.
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+[done]
+
+
+

Run the parser:

+

Now we’ll call the main function to automatically parse the audio events.

+
pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',
+                      audio_ch_names=['audio'], zscore=10)
+
+
+Synchronization Events Compared to Behavior Events +

Out:

+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+Ready.
+Reading 0 ... 16464895  =      0.000 ...   343.019 secs...
+Finding points where the audio is above `zscore` threshold...
+17 audio candidate events found
+Checking best alignments
+
+  0%|          | 0/14 [00:00<?, ?it/s]
+ 21%|##1       | 3/14 [00:00<00:00, 23.71it/s]
+ 71%|#######1  | 10/14 [00:00<00:00, 29.34it/s]
+100%|##########| 14/14 [00:00<00:00, 31.55it/s]
+100%|##########| 14/14 [00:00<00:00, 41.45it/s]
+Best alignment with the events shifted 19 ms relative to the first behavior event
+errors: min -517, q1 -388, med -35, q3 246, max 485
+Excluding events that have zero close events or more than one photodiode event within `max_len` time
+
+<Annotations | 15 segments: Tone (15)>
+
+
+

Load the results:

+

Finally, we’ll load the events and use them to crop the video although it +requires ffmpeg so it is commented out.

+
annot = _load_data(fname)[0]
+print('Here are the event times: ', annot.onset)
+
+# Crop the videos with ffmpeg
+'''
+beh = _read_tsv(behf)
+for i in range(annot.onset.size):  # skip the first video
+    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000
+    run(['ffmpeg', '-i', f'{video_fname}', '-ss',
+         str(datetime.timedelta(
+             seconds=annot.onset[i] - action_time - offset)),
+         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),
+         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(
+             beh['movement'][i], beh['action_type'][i]))])
+'''
+
+
+

Out:

+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+Ready.
+Here are the event times:  [ 19.05112457  39.9129982   61.88574982  83.54243469 104.41456604
+ 126.07720947 147.5539856  168.61270142 189.57843018 211.35673523
+ 250.20858765 271.68209839 292.14001465 313.30532837 333.78097534]
+
+"\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh['movement'][i], beh['action_type'][i]))])\n"
+
+
+

Total running time of the script: ( 0 minutes 28.131 seconds)

+ +

Gallery generated by Sphinx-Gallery

+
+ + +
+ +
+
+
+
+

+ Back to top + +

+

+ © Copyright 2020, Alex Rockhill.
+ Created using Sphinx 3.3.1.
+

+
+
+ + \ No newline at end of file diff --git a/docs/auto_examples/plot_find_pd_events.html b/docs/auto_examples/plot_find_pd_events.html index 0dce6a1..2582e4d 100644 --- a/docs/auto_examples/plot_find_pd_events.html +++ b/docs/auto_examples/plot_find_pd_events.html @@ -4,12 +4,13 @@ - Find Photodiode Events — pd-parser 0.3 documentation - + Find Photodiode Events — pd-parser v0.4 documentation + + @@ -39,7 +40,7 @@ - pd-parser 0.3 + pd-parser v0.4
@@ -97,7 +98,8 @@

Note

-

Click here to download the full example code

+

Click here +to download the full example code

Find Photodiode Events¶

@@ -119,7 +121,7 @@ from mne.utils import _TempDir import pd_parser -from pd_parser.parse_pd import _to_tsv +from pd_parser.parse_pd import _read_raw, _to_tsv out_dir = _TempDir() print(f'After running this example, you can find the data here: {out_dir}') @@ -127,7 +129,7 @@ # simulate photodiode data n_events = 300 prop_corrupted = 0.01 -raw, beh_df, events, corrupted_indices = \ +raw, beh, events, corrupted_indices = \ pd_parser.simulate_pd_data(n_events=n_events, prop_corrupted=prop_corrupted) @@ -143,18 +145,28 @@ fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif') raw.save(fname) + +# roundtrip so that raw is properly loaded from disk and has a filename +raw = _read_raw(fname)

Out:

-
After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06
-Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6
+Creating RawArray with float64 data, n_channels=1, n_times=2036106
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2036106
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+[done]
+Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
+Ready.
+Reading 0 ... 2036105  =      0.000 ...  2036.105 secs...
 

 

 
Make behavior data:

@@ -176,12 +188,12 @@
 for i in [10, 129, 232, 288]:
     response_time[i] = 'n/a'  # make some no responses
 # put in dictionary to be converted to tsv file
-beh_df['fix_onset_time'] = beh_df['time'] + offsets
-beh_df['go_time'] = go_time
-beh_df['response_time'] = response_time
+beh['fix_onset_time'] = beh['time'] + offsets
+beh['go_time'] = go_time
+beh['response_time'] = response_time
 behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
 # save behavior file out
-_to_tsv(behf, beh_df)
+_to_tsv(behf, beh)

Use the interactive graphical user interface (GUI) to find parameters:

@@ -190,169 +202,183 @@ into a console running python (ipython recommended), you can see how to interact with the photodiode data to pick reasonable parameters by following the instructions.

-
pd_parser.find_pd_params(fname, pd_ch_names=['pd'])
-
-
-../_images/sphx_glr_plot_find_pd_events_001.png -

Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:581: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
+
pd_parser.find_pd_params(raw, pd_ch_names=['pd'])
 

 

+Align Use the left/right keys to find an uncorrupted photodiode event and align the onset to the center of the window use +/- to zoom the yaxis in and out (up/down to translate) press enter when finished
 
Find the photodiode events relative to the behavioral timing of interest:

 
This function will use the default parameters or the parameters you
 found from pd_parser.find_pd_parameters() to find and align the
-photodiode events, excluding events that were off because the commuter
+photodiode events, excluding events that were off because the computer
 hung up on computation, for instance. That data is saved in the same folder
 as the raw file (in this case, a temperary directory generated by
 _TempDir()). The data can be used directly, or it can be accessed via
 pd_parser.pd_parser_save_to_bids() to store it in the brain imagine
 data structure (BIDS) standardized format before using it.

-
pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)
+
pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)
 

 

 
    
-
  • ../_images/sphx_glr_plot_find_pd_events_002.png
+
  • Excluded Events, Excluding event 5, off by 32 ms, Excluding event 7, off by -30 ms, Excluding event 8, off by 32 ms, Excluding event 14, off by -30 ms, Excluding event 37, off by 83 ms, Excluding event 76, off by -30 ms, Excluding event 96, off by 31 ms, Excluding event 98, off by -32 ms, Excluding event 180, no event found, Excluding event 262, no event found, Excluding event 279, off by 31 ms, Excluding event 289, off by -30 ms
 
    • 
-
  • ../_images/sphx_glr_plot_find_pd_events_003.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-Finding photodiode events
-
-  0%|          | 0/10920 [00:00<?, ?it/s]
-  2%|2         | 227/10920 [00:00<00:04, 2243.73it/s]
-  3%|3         | 366/10920 [00:00<00:05, 1891.78it/s]
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-  7%|6         | 713/10920 [00:00<00:05, 1702.17it/s]
-  9%|8         | 978/10920 [00:00<00:05, 1906.31it/s]
- 12%|#2        | 1332/10920 [00:00<00:04, 2202.26it/s]
- 15%|#5        | 1644/10920 [00:00<00:03, 2415.02it/s]
- 19%|#8        | 2055/10920 [00:00<00:03, 2755.64it/s]
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- 60%|#####9    | 6542/10920 [00:02<00:01, 4159.48it/s]
- 64%|######4   | 7003/10920 [00:02<00:00, 4284.31it/s]
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- 76%|#######6  | 8312/10920 [00:02<00:00, 4123.64it/s]
- 80%|########  | 8749/10920 [00:02<00:00, 4194.54it/s]
- 84%|########4 | 9208/10920 [00:02<00:00, 4304.71it/s]
- 88%|########8 | 9643/10920 [00:02<00:00, 4261.88it/s]
- 92%|#########2| 10073/10920 [00:02<00:00, 4219.85it/s]
- 96%|#########6| 10498/10920 [00:03<00:00, 4200.05it/s]
-100%|##########| 10920/10920 [00:03<00:00, 4145.68it/s]
-100%|##########| 10920/10920 [00:03<00:00, 3459.96it/s]
+
Finding photodiode events
+
+  0%|          | 0/10877 [00:00<?, ?it/s]
+  1%|          | 69/10877 [00:00<00:16, 649.30it/s]
+  2%|1         | 165/10877 [00:00<00:14, 718.70it/s]
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+  6%|6         | 703/10877 [00:00<00:08, 1148.39it/s]
+  9%|8         | 978/10877 [00:00<00:07, 1391.48it/s]
+ 11%|#         | 1174/10877 [00:00<00:08, 1201.91it/s]
+ 13%|#2        | 1381/10877 [00:00<00:06, 1374.79it/s]
+ 15%|#5        | 1684/10877 [00:00<00:05, 1643.86it/s]
+ 18%|#7        | 1933/10877 [00:01<00:04, 1817.09it/s]
+ 20%|#9        | 2156/10877 [00:01<00:04, 1909.39it/s]
+ 23%|##2       | 2473/10877 [00:01<00:03, 2166.35it/s]
+ 26%|##5       | 2786/10877 [00:01<00:03, 2386.76it/s]
+ 29%|##8       | 3145/10877 [00:01<00:02, 2653.38it/s]
+ 32%|###2      | 3491/10877 [00:01<00:02, 2851.89it/s]
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+ 38%|###7      | 4104/10877 [00:01<00:02, 2829.41it/s]
+ 40%|####      | 4405/10877 [00:01<00:02, 2864.32it/s]
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+ 60%|######    | 6577/10877 [00:02<00:01, 2645.67it/s]
+ 63%|######3   | 6855/10877 [00:02<00:01, 2642.69it/s]
+ 66%|######5   | 7146/10877 [00:02<00:01, 2716.35it/s]
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+ 71%|#######1  | 7755/10877 [00:03<00:01, 2706.56it/s]
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+ 80%|########  | 8708/10877 [00:03<00:00, 2981.52it/s]
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+ 98%|#########8| 10670/10877 [00:04<00:00, 2828.38it/s]
+100%|##########| 10877/10877 [00:04<00:00, 2642.46it/s]
 298 up-deflection photodiode candidate events found
-Checking best behavior-photodiode difference alignments
+Checking best alignments
 
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-Best alignment with the photodiode shifted 12 ms relative to the first behavior event
+  1%|1         | 4/299 [00:00<00:10, 26.84it/s]
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+ 46%|####6     | 139/299 [00:04<00:07, 20.55it/s]
+ 47%|####7     | 142/299 [00:05<00:07, 22.25it/s]
+ 48%|####8     | 145/299 [00:05<00:08, 17.22it/s]
+ 49%|####9     | 148/299 [00:05<00:09, 16.66it/s]
+ 50%|#####     | 150/299 [00:05<00:08, 16.59it/s]
+ 51%|#####     | 152/299 [00:05<00:09, 16.22it/s]
+ 52%|#####1    | 154/299 [00:05<00:09, 14.72it/s]
+ 53%|#####2    | 157/299 [00:06<00:08, 16.42it/s]
+ 54%|#####3    | 160/299 [00:06<00:07, 18.09it/s]
+ 55%|#####4    | 163/299 [00:06<00:06, 19.96it/s]
+ 56%|#####5    | 166/299 [00:06<00:06, 21.11it/s]
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+ 58%|#####7    | 172/299 [00:06<00:06, 20.26it/s]
+ 59%|#####8    | 176/299 [00:06<00:05, 22.28it/s]
+ 60%|#####9    | 179/299 [00:07<00:05, 23.28it/s]
+ 61%|######    | 182/299 [00:07<00:05, 21.57it/s]
+ 62%|######1   | 185/299 [00:07<00:05, 20.13it/s]
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+ 71%|#######   | 212/299 [00:08<00:05, 17.13it/s]
+ 72%|#######1  | 215/299 [00:08<00:04, 18.77it/s]
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+ 81%|########  | 241/299 [00:10<00:05, 10.27it/s]
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+ 92%|#########1| 274/299 [00:13<00:03,  8.22it/s]
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+100%|##########| 299/299 [00:14<00:00, 20.48it/s]
+Best alignment with the events shifted 12 ms relative to the first behavior event
 errors: min -32, q1 -10, med 0, q3 10, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
 Excluding event 5, off by 32 ms
 Excluding event 7, off by -30 ms
 Excluding event 8, off by 32 ms
-Excluding event 9, no event found
 Excluding event 14, off by -30 ms
-Excluding event 27, no event found
-Excluding event 76, off by -31 ms
+Excluding event 37, off by 83 ms
+Excluding event 76, off by -30 ms
 Excluding event 96, off by 31 ms
 Excluding event 98, off by -32 ms
-Excluding event 235, no event found
+Excluding event 180, no event found
+Excluding event 262, no event found
 Excluding event 279, off by 31 ms
 Excluding event 289, off by -30 ms
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:364: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:371: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 

 

 
Add events relative to the photodiode events:

@@ -366,19 +392,14 @@
 pd-parser in order to add the events.
 Note: if more than one photodiode event is used, the parser can be
 used for each event separately using the keyword add_event=True.

-
pd_parser.add_pd_relative_events(
-    fname, behf,
-    relative_event_cols=['go_time', 'response_time'],
+
pd_parser.add_relative_events(
+    raw, beh,
+    relative_event_keys=['go_time', 'response_time'],
     relative_event_names=['Go Cue', 'Response'])
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+
Overwriting existing file.
 

 

 
Save data to BIDS format:

@@ -392,21 +413,18 @@
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
 Used Annotations descriptions: ['Fixation', 'Go Cue', 'Response']
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:981: RuntimeWarning: The unit for channel(s) pd has changed from V to NA.
-  raw.set_channel_types({ch: 'stim' for ch in pd_channels
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/sub-1_task-mytask_raw.fif...
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Creating folder: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/ieeg
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/README'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/README'...
 
 References
 ----------
@@ -415,12 +433,12 @@
 Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7
 
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/participants.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/participants.tsv'...
 
 participant_id  age     sex     hand
 sub-1   n/a     n/a     n/a
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/participants.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/participants.json'...
 
 {
     "participant_id": {
@@ -447,16 +465,16 @@
     }
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/ieeg/sub-1_task-mytask_events.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_events.tsv'...
 
 onset   duration        trial_type      value   sample
-12.27   0.0     Fixation        1       12270
-12.97   0.0     Go Cue  2       12970
-14.996  0.0     Response        3       14996
-18.299  0.0     Fixation        1       18299
-18.999  0.0     Go Cue  2       18999
+12.27   0.09999942779541016     Fixation        1       12270
+12.97   0.09999942779541016     Go Cue  2       12970
+14.996  0.09999942779541016     Response        3       14996
+18.299  0.10000038146972656     Fixation        1       18299
+18.999  0.10000038146972656     Go Cue  2       18999
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/dataset_description.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/dataset_description.json'...
 
 {
     "Name": " ",
@@ -467,7 +485,7 @@
     ]
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/ieeg/sub-1_task-mytask_ieeg.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_ieeg.json'...
 
 {
     "TaskName": "mytask",
@@ -475,7 +493,7 @@
     "PowerLineFrequency": 60.0,
     "SamplingFrequency": 1000.0,
     "SoftwareFilters": "n/a",
-    "RecordingDuration": 2044.105,
+    "RecordingDuration": 2036.105,
     "RecordingType": "continuous",
     "iEEGReference": "n/a",
     "ECOGChannelCount": 0,
@@ -488,24 +506,28 @@
     "TriggerChannelCount": 1
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/ieeg/sub-1_task-mytask_channels.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_channels.tsv'...
 
 name    type    units   low_cutoff      high_cutoff     description     sampling_frequency      status  status_description
-pd      TRIG    n/a     0.0     500.0   Trigger 1000.0  good    n/a
+pd      TRIG    V       0.0     500.0   Trigger 1000.0  good    n/a
 ch1     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
 ch2     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
 ch3     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
-/Users/alexrockhill/software/mne-bids/mne_bids/write.py:1115: RuntimeWarning: Converting data files to BrainVision format
+/Users/alexrockhill/projects/mne-bids/mne_bids/write.py:1352: RuntimeWarning: Converting data files to BrainVision format
   warn('Converting data files to BrainVision format')
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/sub-1_scans.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/sub-1_scans.tsv'...
 
 filename        acq_time
 ieeg/sub-1_task-mytask_ieeg.vhdr        n/a
-Wrote /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_87musw06/bids_dir/sub-1/sub-1_scans.tsv entry with ieeg/sub-1_task-mytask_ieeg.vhdr.
+Wrote /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/sub-1_scans.tsv entry with ieeg/sub-1_task-mytask_ieeg.vhdr.
+/Users/alexrockhill/projects/mne-bids/mne_bids/path.py:473: RuntimeWarning: Could not locate a data file of a supported format. This is likely a problem with your BIDS dataset. Please run the BIDS validator on your data. (root=/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir, basename=sub-1_task-mytask). []
+  warn(msg)
+/Users/alexrockhill/projects/mne-bids/mne_bids/path.py:473: RuntimeWarning: Could not locate a data file of a supported format. This is likely a problem with your BIDS dataset. Please run the BIDS validator on your data. (root=/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir, basename=sub-1_task-mytask). []
+  warn(msg)
 

 

-
Total running time of the script: ( 0 minutes  20.175 seconds)

+
Total running time of the script: ( 0 minutes  35.983 seconds)

 
 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Find Photodiode On and Off Events¶

@@ -120,7 +122,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv, _load_pd_data
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
@@ -133,7 +135,7 @@
 # let's make our photodiode events on random uniform from 0.5 to 1 second
 n_secs_on = np.random.random(n_events) * 0.5 + 0.5
 prop_corrupted = 0.01
-raw, beh_df, events, corrupted_indices = \
+raw, beh, events, corrupted_indices = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=prop_corrupted)
 
@@ -147,25 +149,25 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
+# add some offsets to the behavior so it's a bit more realistic
+offsets = np.random.randn(n_events) * 0.01
+beh['time'] = np.array(beh['time']) + offsets
+
 # save to disk as required by ``pd-parser``
 fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
 raw.save(fname)
-# add some offsets to the behavior so it's a bit more realistic
-offsets = np.random.randn(n_events) * 0.01
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
 

 

 
Out:

-
Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
Creating RawArray with float64 data, n_channels=1, n_times=2036104
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2036104
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+[done]
 

 

 
Find the photodiode events relative to the behavioral timing of interest:

@@ -173,137 +175,152 @@
 photodiode events, excluding events that were off.
 One percent of the 300 events (3) were corrupted as shown in the plots and
 some were too far off from large offsets that we’re going to exclude them.

-
pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                   pd_ch_names=['pd'], beh_col='time',
+
pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                   pd_ch_names=['pd'], beh_key='time',
                    max_len=1.5)  # none are on longer than 1.5 seconds
 

 

 
    
-
  • ../_images/sphx_glr_plot_find_pd_on_and_off_001.png
+
  • Excluded Events, Excluding event 44, off by -37 ms, Excluding event 56, off by -37 ms, Excluding event 71, off by 31 ms, Excluding event 154, off by 37 ms, Excluding event 209, off by 30 ms, Excluding event 238, no event found, Excluding event 266, off by -33 ms, Excluding event 274, no event found, Excluding event 281, off by -43 ms
 
    • 
-
  • ../_images/sphx_glr_plot_find_pd_on_and_off_002.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
 Finding photodiode events
 
-  0%|          | 0/10920 [00:00<?, ?it/s]
-  4%|4         | 457/10920 [00:00<00:02, 4561.92it/s]
-  8%|8         | 886/10920 [00:00<00:02, 4475.90it/s]
- 12%|#2        | 1343/10920 [00:00<00:02, 4503.53it/s]
- 16%|#6        | 1801/10920 [00:00<00:02, 4523.75it/s]
- 21%|##        | 2252/10920 [00:00<00:01, 4517.47it/s]
- 25%|##4       | 2686/10920 [00:00<00:01, 4460.66it/s]
- 29%|##8       | 3134/10920 [00:00<00:01, 4464.16it/s]
- 33%|###2      | 3592/10920 [00:00<00:01, 4495.65it/s]
- 37%|###7      | 4048/10920 [00:00<00:01, 4512.65it/s]
- 41%|####1     | 4497/10920 [00:01<00:01, 4505.05it/s]
- 45%|####5     | 4950/10920 [00:01<00:01, 4509.86it/s]
- 50%|####9     | 5406/10920 [00:01<00:01, 4523.52it/s]
- 54%|#####3    | 5853/10920 [00:01<00:01, 4498.01it/s]
- 58%|#####7    | 6311/10920 [00:01<00:01, 4520.17it/s]
- 62%|######1   | 6761/10920 [00:01<00:00, 4472.11it/s]
- 66%|######5   | 7207/10920 [00:01<00:00, 4463.14it/s]
- 70%|#######   | 7659/10920 [00:01<00:00, 4479.21it/s]
- 74%|#######4  | 8110/10920 [00:01<00:00, 4485.76it/s]
- 78%|#######8  | 8565/10920 [00:01<00:00, 4503.08it/s]
- 83%|########2 | 9019/10920 [00:02<00:00, 4511.82it/s]
- 87%|########6 | 9470/10920 [00:02<00:00, 4506.24it/s]
- 91%|######### | 9927/10920 [00:02<00:00, 4524.42it/s]
- 95%|#########5| 10388/10920 [00:02<00:00, 4546.94it/s]
- 99%|#########9| 10843/10920 [00:02<00:00, 4528.10it/s]
-100%|##########| 10920/10920 [00:02<00:00, 4499.69it/s]
-297 up-deflection photodiode candidate events found
-Checking best behavior-photodiode difference alignments
+  0%|          | 0/10877 [00:00<?, ?it/s]
+  4%|3         | 414/10877 [00:00<00:02, 4136.78it/s]
+  8%|7         | 844/10877 [00:00<00:02, 4183.20it/s]
+ 11%|#1        | 1218/10877 [00:00<00:02, 4039.49it/s]
+ 15%|#5        | 1632/10877 [00:00<00:02, 4066.14it/s]
+ 19%|#8        | 2045/10877 [00:00<00:02, 4084.06it/s]
+ 22%|##1       | 2385/10877 [00:00<00:02, 3228.78it/s]
+ 25%|##4       | 2688/10877 [00:00<00:02, 3055.11it/s]
+ 28%|##7       | 3002/10877 [00:00<00:02, 3078.74it/s]
+ 30%|###       | 3302/10877 [00:01<00:02, 2589.07it/s]
+ 33%|###2      | 3569/10877 [00:01<00:03, 2353.49it/s]
+ 35%|###5      | 3830/10877 [00:01<00:02, 2423.16it/s]
+ 38%|###7      | 4080/10877 [00:01<00:02, 2371.87it/s]
+ 40%|####      | 4353/10877 [00:01<00:02, 2468.45it/s]
+ 42%|####2     | 4621/10877 [00:01<00:02, 2527.19it/s]
+ 45%|####5     | 4907/10877 [00:01<00:02, 2617.72it/s]
+ 48%|####7     | 5182/10877 [00:01<00:02, 2653.46it/s]
+ 50%|#####     | 5450/10877 [00:01<00:02, 2626.52it/s]
+ 53%|#####2    | 5715/10877 [00:01<00:02, 2503.10it/s]
+ 55%|#####4    | 5968/10877 [00:02<00:01, 2483.09it/s]
+ 57%|#####7    | 6219/10877 [00:02<00:01, 2389.86it/s]
+ 59%|#####9    | 6460/10877 [00:02<00:01, 2377.93it/s]
+ 62%|######1   | 6700/10877 [00:02<00:01, 2364.13it/s]
+ 64%|######3   | 6938/10877 [00:02<00:01, 2356.37it/s]
+ 66%|######5   | 7175/10877 [00:02<00:01, 2294.88it/s]
+ 68%|######8   | 7413/10877 [00:02<00:01, 2315.23it/s]
+ 71%|#######1  | 7724/10877 [00:02<00:01, 2506.99it/s]
+ 75%|#######4  | 8129/10877 [00:02<00:00, 2829.84it/s]
+ 78%|#######7  | 8480/10877 [00:03<00:00, 3003.57it/s]
+ 81%|########1 | 8842/10877 [00:03<00:00, 3163.89it/s]
+ 85%|########4 | 9197/10877 [00:03<00:00, 3269.09it/s]
+ 88%|########7 | 9555/10877 [00:03<00:00, 3356.07it/s]
+ 91%|#########1| 9922/10877 [00:03<00:00, 3442.88it/s]
+ 94%|#########4| 10273/10877 [00:03<00:00, 3453.66it/s]
+ 98%|#########7| 10630/10877 [00:03<00:00, 3483.89it/s]
+100%|##########| 10877/10877 [00:03<00:00, 2930.45it/s]
+298 up-deflection photodiode candidate events found
+Checking best alignments
 
   0%|          | 0/299 [00:00<?, ?it/s]
-  1%|1         | 4/299 [00:00<00:07, 38.07it/s]
-  4%|4         | 12/299 [00:00<00:06, 42.67it/s]
-  6%|6         | 18/299 [00:00<00:06, 45.18it/s]
-  8%|7         | 23/299 [00:00<00:06, 45.01it/s]
- 10%|#         | 30/299 [00:00<00:05, 46.99it/s]
- 12%|#1        | 35/299 [00:00<00:05, 47.37it/s]
- 13%|#3        | 40/299 [00:00<00:06, 42.00it/s]
- 15%|#5        | 45/299 [00:01<00:06, 39.32it/s]
- 16%|#6        | 49/299 [00:01<00:06, 37.63it/s]
- 18%|#8        | 55/299 [00:01<00:05, 41.42it/s]
- 20%|##        | 60/299 [00:01<00:06, 39.23it/s]
- 22%|##1       | 65/299 [00:01<00:06, 36.23it/s]
- 23%|##3       | 69/299 [00:01<00:07, 32.19it/s]
- 24%|##4       | 73/299 [00:01<00:07, 31.95it/s]
- 26%|##5       | 77/299 [00:01<00:07, 31.33it/s]
- 27%|##7       | 81/299 [00:02<00:06, 32.22it/s]
- 28%|##8       | 85/299 [00:02<00:06, 32.39it/s]
- 30%|###       | 90/299 [00:02<00:06, 34.14it/s]
- 32%|###1      | 95/299 [00:02<00:05, 34.99it/s]
- 33%|###3      | 99/299 [00:02<00:05, 33.93it/s]
- 35%|###4      | 104/299 [00:02<00:05, 37.02it/s]
- 36%|###6      | 108/299 [00:02<00:05, 34.34it/s]
- 37%|###7      | 112/299 [00:02<00:05, 32.54it/s]
- 39%|###8      | 116/299 [00:03<00:05, 33.94it/s]
- 40%|####      | 120/299 [00:03<00:05, 33.89it/s]
- 41%|####1     | 124/299 [00:03<00:05, 34.71it/s]
- 43%|####2     | 128/299 [00:03<00:05, 33.20it/s]
- 44%|####4     | 132/299 [00:03<00:04, 34.83it/s]
- 45%|####5     | 136/299 [00:03<00:05, 32.00it/s]
- 47%|####6     | 140/299 [00:03<00:04, 32.22it/s]
- 48%|####8     | 144/299 [00:03<00:04, 31.24it/s]
- 50%|####9     | 149/299 [00:04<00:04, 33.38it/s]
- 51%|#####1    | 153/299 [00:04<00:04, 34.85it/s]
- 53%|#####2    | 157/299 [00:04<00:04, 34.40it/s]
- 54%|#####3    | 161/299 [00:04<00:04, 33.63it/s]
- 56%|#####5    | 166/299 [00:04<00:03, 35.78it/s]
- 57%|#####6    | 170/299 [00:04<00:03, 36.67it/s]
- 58%|#####8    | 174/299 [00:04<00:03, 34.07it/s]
- 60%|#####9    | 178/299 [00:04<00:03, 32.49it/s]
- 61%|######    | 182/299 [00:05<00:03, 33.70it/s]
- 63%|######2   | 187/299 [00:05<00:03, 37.11it/s]
- 64%|######3   | 191/299 [00:05<00:03, 35.49it/s]
- 65%|######5   | 195/299 [00:05<00:02, 35.39it/s]
- 67%|######6   | 200/299 [00:05<00:02, 37.48it/s]
- 68%|######8   | 204/299 [00:05<00:02, 38.07it/s]
- 70%|#######   | 210/299 [00:05<00:02, 40.74it/s]
- 72%|#######1  | 215/299 [00:05<00:02, 39.79it/s]
- 74%|#######3  | 220/299 [00:05<00:02, 37.05it/s]
- 75%|#######4  | 224/299 [00:06<00:01, 37.51it/s]
- 77%|#######6  | 229/299 [00:06<00:01, 38.32it/s]
- 78%|#######8  | 234/299 [00:06<00:01, 40.12it/s]
- 80%|#######9  | 239/299 [00:06<00:01, 38.08it/s]
- 81%|########1 | 243/299 [00:06<00:01, 37.81it/s]
- 83%|########2 | 248/299 [00:06<00:01, 38.87it/s]
- 84%|########4 | 252/299 [00:06<00:01, 38.50it/s]
- 87%|########6 | 259/299 [00:06<00:00, 42.67it/s]
- 88%|########8 | 264/299 [00:07<00:00, 43.50it/s]
- 90%|########9 | 269/299 [00:07<00:00, 41.69it/s]
- 92%|#########1| 274/299 [00:07<00:00, 39.94it/s]
- 93%|#########3| 279/299 [00:07<00:00, 40.90it/s]
- 95%|#########5| 285/299 [00:07<00:00, 43.46it/s]
- 97%|#########6| 290/299 [00:07<00:00, 44.46it/s]
- 99%|#########8| 295/299 [00:07<00:00, 45.88it/s]
-100%|##########| 299/299 [00:07<00:00, 38.24it/s]
-Best alignment with the photodiode shifted 12 ms relative to the first behavior event
-errors: min -42, q1 -8, med -1, q3 9, max 75
+  1%|1         | 3/299 [00:00<00:10, 27.25it/s]
+  3%|2         | 8/299 [00:00<00:09, 31.46it/s]
+  4%|4         | 12/299 [00:00<00:08, 32.75it/s]
+  5%|5         | 16/299 [00:00<00:08, 31.84it/s]
+  7%|7         | 21/299 [00:00<00:08, 32.77it/s]
+  8%|8         | 25/299 [00:00<00:07, 34.41it/s]
+ 10%|#         | 30/299 [00:00<00:07, 37.40it/s]
+ 11%|#1        | 34/299 [00:00<00:07, 35.41it/s]
+ 13%|#2        | 38/299 [00:01<00:07, 36.55it/s]
+ 14%|#4        | 42/299 [00:01<00:07, 36.15it/s]
+ 15%|#5        | 46/299 [00:01<00:07, 35.86it/s]
+ 17%|#6        | 50/299 [00:01<00:08, 28.35it/s]
+ 18%|#8        | 54/299 [00:01<00:09, 24.93it/s]
+ 19%|#9        | 57/299 [00:01<00:09, 25.16it/s]
+ 20%|##        | 61/299 [00:01<00:08, 27.37it/s]
+ 22%|##1       | 65/299 [00:02<00:08, 29.00it/s]
+ 23%|##3       | 69/299 [00:02<00:08, 27.81it/s]
+ 24%|##4       | 72/299 [00:02<00:08, 27.26it/s]
+ 26%|##5       | 77/299 [00:02<00:07, 30.97it/s]
+ 27%|##7       | 81/299 [00:02<00:06, 31.90it/s]
+ 28%|##8       | 85/299 [00:02<00:06, 30.74it/s]
+ 30%|##9       | 89/299 [00:02<00:06, 30.44it/s]
+ 31%|###1      | 93/299 [00:02<00:06, 31.96it/s]
+ 32%|###2      | 97/299 [00:03<00:06, 31.05it/s]
+ 34%|###3      | 101/299 [00:03<00:06, 31.48it/s]
+ 35%|###5      | 105/299 [00:03<00:05, 33.10it/s]
+ 36%|###6      | 109/299 [00:03<00:05, 33.78it/s]
+ 38%|###7      | 113/299 [00:03<00:05, 34.67it/s]
+ 39%|###9      | 117/299 [00:03<00:05, 31.13it/s]
+ 40%|####      | 121/299 [00:03<00:05, 32.61it/s]
+ 42%|####1     | 125/299 [00:03<00:05, 31.88it/s]
+ 43%|####3     | 129/299 [00:04<00:05, 31.86it/s]
+ 44%|####4     | 133/299 [00:04<00:05, 31.35it/s]
+ 46%|####5     | 137/299 [00:04<00:05, 29.66it/s]
+ 47%|####7     | 141/299 [00:04<00:06, 25.89it/s]
+ 48%|####8     | 144/299 [00:04<00:06, 25.18it/s]
+ 49%|####9     | 147/299 [00:04<00:06, 23.72it/s]
+ 50%|#####     | 150/299 [00:04<00:06, 22.96it/s]
+ 51%|#####1    | 153/299 [00:05<00:06, 24.30it/s]
+ 52%|#####2    | 156/299 [00:05<00:06, 23.32it/s]
+ 53%|#####3    | 159/299 [00:05<00:05, 24.47it/s]
+ 55%|#####4    | 163/299 [00:05<00:05, 26.77it/s]
+ 56%|#####5    | 166/299 [00:05<00:05, 26.54it/s]
+ 57%|#####6    | 170/299 [00:05<00:04, 29.31it/s]
+ 58%|#####8    | 174/299 [00:05<00:04, 29.33it/s]
+ 60%|#####9    | 178/299 [00:05<00:04, 29.90it/s]
+ 61%|######    | 182/299 [00:06<00:03, 29.79it/s]
+ 62%|######2   | 186/299 [00:06<00:03, 31.99it/s]
+ 64%|######3   | 190/299 [00:06<00:03, 31.79it/s]
+ 65%|######4   | 194/299 [00:06<00:03, 32.83it/s]
+ 66%|######6   | 198/299 [00:06<00:03, 33.55it/s]
+ 68%|######7   | 202/299 [00:06<00:02, 33.26it/s]
+ 69%|######8   | 206/299 [00:06<00:02, 33.03it/s]
+ 70%|#######   | 210/299 [00:06<00:02, 33.99it/s]
+ 72%|#######1  | 214/299 [00:06<00:02, 33.86it/s]
+ 73%|#######2  | 218/299 [00:07<00:02, 34.17it/s]
+ 74%|#######4  | 222/299 [00:07<00:02, 35.37it/s]
+ 76%|#######5  | 226/299 [00:07<00:02, 30.78it/s]
+ 77%|#######6  | 230/299 [00:07<00:02, 30.84it/s]
+ 78%|#######8  | 234/299 [00:07<00:02, 30.78it/s]
+ 80%|#######9  | 239/299 [00:07<00:01, 33.63it/s]
+ 82%|########1 | 244/299 [00:07<00:01, 34.36it/s]
+ 83%|########3 | 249/299 [00:07<00:01, 36.94it/s]
+ 85%|########4 | 253/299 [00:08<00:01, 32.70it/s]
+ 86%|########5 | 257/299 [00:08<00:01, 33.91it/s]
+ 88%|########7 | 262/299 [00:08<00:01, 36.00it/s]
+ 90%|########9 | 269/299 [00:08<00:00, 39.16it/s]
+ 92%|#########1| 274/299 [00:08<00:00, 40.53it/s]
+ 93%|#########3| 279/299 [00:08<00:00, 39.07it/s]
+ 95%|#########4| 284/299 [00:08<00:00, 36.52it/s]
+ 96%|#########6| 288/299 [00:09<00:00, 36.44it/s]
+ 98%|#########7| 292/299 [00:09<00:00, 35.86it/s]
+100%|##########| 299/299 [00:09<00:00, 41.15it/s]
+100%|##########| 299/299 [00:09<00:00, 32.39it/s]
+Best alignment with the events shifted 12 ms relative to the first behavior event
+errors: min -43, q1 -9, med 1, q3 9, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
-Excluding event 9, no event found
-Excluding event 27, no event found
-Excluding event 37, off by 35 ms
-Excluding event 115, off by -34 ms
-Excluding event 116, off by 32 ms
-Excluding event 143, off by -31 ms
-Excluding event 153, off by -40 ms
-Excluding event 154, off by 40 ms
-Excluding event 167, off by -42 ms
-Excluding event 235, no event found
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:364: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:371: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
+Excluding event 44, off by -37 ms
+Excluding event 56, off by -37 ms
+Excluding event 71, off by 31 ms
+Excluding event 154, off by 37 ms
+Excluding event 209, off by 30 ms
+Excluding event 238, no event found
+Excluding event 266, off by -33 ms
+Excluding event 274, no event found
+Excluding event 281, off by -43 ms
 

 

 
Find cessations of the photodiode deflections

@@ -313,12 +330,13 @@
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_lgnan_rl/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
+Overwriting existing file.
 

 

 
Check recovered event lengths and compare to the simulation ground truth

@@ -330,14 +348,14 @@
 correspondance) is not shown as it would occlude the plotted data; the
 the lengths are recovered within 1 millisecond. Note that the colors are
 arbitrary and are only used to increase contrast and ease of visualization.

-
annot, pd_ch_names, beh_df = _load_pd_data(fname)
+
annot, pd_ch_names, beh = _load_data(fname)
 raw.set_annotations(annot)
 events, event_id = mne.events_from_annotations(raw)
 on_events = events[events[:, 2] == event_id['Stim On']]
 off_events = events[events[:, 2] == event_id['Stim Off']]
 
 recovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']
-not_corrupted = [s != 'n/a' for s in beh_df['pd_sample']]
+not_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']]
 ground_truth_not_corrupted = n_secs_on[not_corrupted]
 
 fig, ax = plt.subplots()
@@ -352,13 +370,18 @@
           1000 * abs(ground_truth_not_corrupted - recovered).mean()))
 

 

-../_images/sphx_glr_plot_find_pd_on_and_off_003.png
+Photodiode offset eventfidelity of recovery
 
Out:

-
Used Annotations descriptions: ['Stim Off', 'Stim On']
-Mean difference in the recovered from simulated length is 0.254 milliseconds
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
+Ready.
+Used Annotations descriptions: ['Stim Off', 'Stim On']
+Mean difference in the recovered from simulated length is 0.252 milliseconds
 

 

-
Total running time of the script: ( 0 minutes  14.849 seconds)

+
Total running time of the script: ( 0 minutes  22.081 seconds)

 
 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Manually Recover Events Not Found by the Algorithm¶

@@ -123,7 +125,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 
@@ -134,7 +136,7 @@
 n_events = 300
 # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds
 n_secs_on = np.random.random(n_events) * 0.5 + 0.25
-raw, beh_df, events, _ = \
+raw, beh, events, _ = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=0.0)
 sfreq = np.round(raw.info['sfreq']).astype(int)
@@ -147,16 +149,19 @@
 axes[0].set_ylabel('voltage')
 for j, i in enumerate(events[corrupted_indices, 0]):
     if j == 0:
-        raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount
+        raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount
     elif j == 1:
         raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount
     else:
-        raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount
+        raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount
     axes[j].plot(np.linspace(-1, 2, 3 * sfreq),
                  raw._data[0, i - sfreq: i + sfreq * 2])
     axes[j].set_xlabel('time (s)')
 
 
+# make figure nicer
+fig.tight_layout()
+
 # make fake electrophysiology data
 info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
                        ['seeg'] * 3)
@@ -167,26 +172,26 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
-# save to disk as required by ``pd-parser``
-fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-raw.save(fname)
 # add some offsets to the behavior so it's a bit more realistic
 offsets = np.random.randn(n_events) * 0.001
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
+beh['time'] = np.array(beh['time']) + offsets
+
+# save to disk as required by ``pd-parser``, raw needs to have a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
 

 

-../_images/sphx_glr_plot_recover_events_001.png
+Corrupted Events
 
Out:

-
Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
Creating RawArray with float64 data, n_channels=1, n_times=2035104
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2035104
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+[done]
 

 

 
Find the photodiode events relative to the behavioral timing of interest:

@@ -196,116 +201,142 @@
 you want to delete that line and unindent the next line, and then provide
 your own input depending on whether you want to keep the events or not.

 
with mock.patch('builtins.input', return_value='y'):
-    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                       pd_ch_names=['pd'], beh_col='time', recover=True)
+    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                       pd_ch_names=['pd'], beh_key='time', recover=True)
 

 

 
    
-
  • ../_images/sphx_glr_plot_recover_events_002.png
+
  • Corrupted Event 8
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_003.png
+
  • Corrupted Event 144
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_004.png
+
  • Corrupted Event 234
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_005.png
+
  • Excluded Events, Event 8 recovered (not excluded), Event 144 recovered (not excluded), Event 234 recovered (not excluded)
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_006.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
 Finding photodiode events
 
-  0%|          | 0/16341 [00:00<?, ?it/s]
-  3%|3         | 506/16341 [00:00<00:03, 5055.65it/s]
-  6%|6         | 1010/16341 [00:00<00:03, 5048.99it/s]
-  9%|9         | 1511/16341 [00:00<00:02, 5035.45it/s]
- 12%|#2        | 2015/16341 [00:00<00:02, 5036.36it/s]
- 15%|#5        | 2512/16341 [00:00<00:02, 5015.83it/s]
- 18%|#8        | 3004/16341 [00:00<00:02, 4984.75it/s]
- 21%|##1       | 3502/16341 [00:00<00:02, 4980.67it/s]
- 24%|##4       | 3998/16341 [00:00<00:02, 4973.78it/s]
- 27%|##7       | 4467/16341 [00:00<00:02, 4700.85it/s]
- 30%|###       | 4920/16341 [00:01<00:02, 3868.46it/s]
- 33%|###2      | 5320/16341 [00:01<00:03, 3555.36it/s]
- 35%|###4      | 5697/16341 [00:01<00:02, 3613.89it/s]
- 38%|###7      | 6184/16341 [00:01<00:02, 3915.75it/s]
- 41%|####      | 6656/16341 [00:01<00:02, 4126.70it/s]
- 43%|####3     | 7082/16341 [00:01<00:02, 3889.05it/s]
- 46%|####5     | 7483/16341 [00:01<00:02, 3772.03it/s]
- 49%|####8     | 7971/16341 [00:01<00:02, 4046.26it/s]
- 52%|#####1    | 8432/16341 [00:01<00:01, 4198.62it/s]
- 54%|#####4    | 8904/16341 [00:02<00:01, 4340.43it/s]
- 57%|#####7    | 9377/16341 [00:02<00:01, 4447.82it/s]
- 60%|######    | 9856/16341 [00:02<00:01, 4544.15it/s]
- 63%|######3   | 10353/16341 [00:02<00:01, 4663.55it/s]
- 66%|######6   | 10841/16341 [00:02<00:01, 4724.56it/s]
- 69%|######9   | 11334/16341 [00:02<00:01, 4782.70it/s]
- 72%|#######2  | 11818/16341 [00:02<00:00, 4796.63it/s]
- 75%|#######5  | 12300/16341 [00:02<00:00, 4723.65it/s]
- 78%|#######8  | 12774/16341 [00:02<00:00, 4690.08it/s]
- 81%|########1 | 13245/16341 [00:02<00:00, 4664.40it/s]
- 84%|########4 | 13736/16341 [00:03<00:00, 4733.57it/s]
- 87%|########7 | 14218/16341 [00:03<00:00, 4757.68it/s]
- 90%|######### | 14717/16341 [00:03<00:00, 4824.06it/s]
- 93%|#########3| 15219/16341 [00:03<00:00, 4878.70it/s]
- 96%|#########6| 15718/16341 [00:03<00:00, 4908.96it/s]
- 99%|#########9| 16231/16341 [00:03<00:00, 4972.48it/s]
-100%|##########| 16341/16341 [00:03<00:00, 4557.76it/s]
+  0%|          | 0/16269 [00:00<?, ?it/s]
+  1%|1         | 195/16269 [00:00<00:12, 1308.17it/s]
+  2%|1         | 252/16269 [00:00<00:17, 939.51it/s]
+  2%|2         | 348/16269 [00:00<00:16, 944.93it/s]
+  3%|2         | 437/16269 [00:00<00:17, 927.32it/s]
+  3%|3         | 506/16269 [00:00<00:40, 392.53it/s]
+  4%|3         | 633/16269 [00:00<00:31, 495.05it/s]
+  5%|4         | 803/16269 [00:01<00:24, 628.37it/s]
+  6%|5         | 959/16269 [00:01<00:20, 760.26it/s]
+  7%|6         | 1095/16269 [00:01<00:17, 876.17it/s]
+  8%|7         | 1232/16269 [00:01<00:15, 977.36it/s]
+  8%|8         | 1359/16269 [00:01<00:15, 956.21it/s]
+  9%|9         | 1480/16269 [00:01<00:14, 1019.82it/s]
+ 10%|9         | 1598/16269 [00:01<00:23, 628.15it/s]
+ 10%|#         | 1690/16269 [00:02<00:21, 663.30it/s]
+ 11%|#1        | 1790/16269 [00:02<00:20, 712.42it/s]
+ 12%|#1        | 1877/16269 [00:02<00:20, 686.15it/s]
+ 12%|#2        | 1972/16269 [00:02<00:19, 748.01it/s]
+ 13%|#2        | 2059/16269 [00:02<00:24, 585.50it/s]
+ 13%|#3        | 2142/16269 [00:02<00:22, 634.30it/s]
+ 14%|#3        | 2235/16269 [00:02<00:20, 699.19it/s]
+ 14%|#4        | 2349/16269 [00:02<00:17, 790.72it/s]
+ 15%|#5        | 2500/16269 [00:03<00:14, 922.18it/s]
+ 16%|#6        | 2624/16269 [00:03<00:13, 998.67it/s]
+ 19%|#8        | 3017/16269 [00:03<00:10, 1286.51it/s]
+ 21%|##        | 3395/16269 [00:03<00:08, 1603.83it/s]
+ 23%|##3       | 3764/16269 [00:03<00:06, 1930.65it/s]
+ 26%|##5       | 4177/16269 [00:03<00:05, 2297.44it/s]
+ 28%|##8       | 4576/16269 [00:03<00:04, 2631.37it/s]
+ 31%|###       | 4966/16269 [00:03<00:03, 2914.54it/s]
+ 33%|###2      | 5364/16269 [00:03<00:03, 3167.88it/s]
+ 35%|###5      | 5764/16269 [00:03<00:03, 3376.61it/s]
+ 38%|###7      | 6174/16269 [00:04<00:02, 3564.50it/s]
+ 40%|####      | 6566/16269 [00:04<00:02, 3663.05it/s]
+ 43%|####2     | 6956/16269 [00:04<00:02, 3689.23it/s]
+ 45%|####5     | 7361/16269 [00:04<00:02, 3787.27it/s]
+ 48%|####7     | 7777/16269 [00:04<00:02, 3889.25it/s]
+ 50%|#####     | 8175/16269 [00:04<00:02, 3300.76it/s]
+ 52%|#####2    | 8527/16269 [00:04<00:02, 3263.58it/s]
+ 55%|#####4    | 8938/16269 [00:04<00:02, 3475.52it/s]
+ 57%|#####7    | 9301/16269 [00:04<00:02, 3457.96it/s]
+ 59%|#####9    | 9657/16269 [00:05<00:01, 3351.07it/s]
+ 62%|######1   | 10036/16269 [00:05<00:01, 3469.69it/s]
+ 64%|######3   | 10390/16269 [00:05<00:01, 3226.85it/s]
+ 66%|######5   | 10721/16269 [00:05<00:01, 3171.38it/s]
+ 68%|######7   | 11044/16269 [00:05<00:01, 3055.13it/s]
+ 70%|######9   | 11355/16269 [00:05<00:01, 2914.14it/s]
+ 72%|#######1  | 11653/16269 [00:05<00:01, 2932.68it/s]
+ 73%|#######3  | 11950/16269 [00:05<00:01, 2922.49it/s]
+ 76%|#######5  | 12295/16269 [00:05<00:01, 3062.97it/s]
+ 78%|#######7  | 12659/16269 [00:06<00:01, 3214.20it/s]
+ 80%|########  | 13024/16269 [00:06<00:00, 3333.50it/s]
+ 82%|########2 | 13380/16269 [00:06<00:00, 3397.66it/s]
+ 84%|########4 | 13735/16269 [00:06<00:00, 3441.52it/s]
+ 87%|########6 | 14095/16269 [00:06<00:00, 3485.80it/s]
+ 89%|########8 | 14446/16269 [00:06<00:00, 3221.75it/s]
+ 91%|#########1| 14826/16269 [00:06<00:00, 3375.55it/s]
+ 93%|#########3| 15170/16269 [00:06<00:00, 3250.19it/s]
+ 95%|#########5| 15500/16269 [00:06<00:00, 3231.71it/s]
+ 98%|#########7| 15900/16269 [00:06<00:00, 3428.39it/s]
+100%|##########| 16269/16269 [00:07<00:00, 2295.44it/s]
 297 up-deflection photodiode candidate events found
-Checking best behavior-photodiode difference alignments
+Checking best alignments
 
   0%|          | 0/299 [00:00<?, ?it/s]
-  3%|2         | 8/299 [00:00<00:03, 76.29it/s]
-  7%|6         | 20/299 [00:00<00:03, 84.72it/s]
- 12%|#1        | 35/299 [00:00<00:02, 94.18it/s]
- 14%|#4        | 43/299 [00:00<00:03, 80.78it/s]
- 17%|#7        | 51/299 [00:00<00:03, 76.82it/s]
- 20%|##        | 60/299 [00:00<00:03, 77.27it/s]
- 23%|##3       | 69/299 [00:00<00:03, 76.05it/s]
- 26%|##5       | 77/299 [00:00<00:03, 72.45it/s]
- 28%|##8       | 85/299 [00:01<00:03, 65.14it/s]
- 31%|###       | 92/299 [00:01<00:03, 59.45it/s]
- 33%|###3      | 99/299 [00:01<00:03, 53.12it/s]
- 35%|###5      | 105/299 [00:01<00:03, 51.41it/s]
- 39%|###9      | 118/299 [00:01<00:02, 62.65it/s]
- 43%|####3     | 129/299 [00:01<00:02, 69.39it/s]
- 46%|####6     | 138/299 [00:01<00:02, 70.59it/s]
- 49%|####8     | 146/299 [00:01<00:02, 72.93it/s]
- 52%|#####1    | 154/299 [00:02<00:02, 71.91it/s]
- 55%|#####4    | 163/299 [00:02<00:01, 75.87it/s]
- 57%|#####7    | 171/299 [00:02<00:02, 62.83it/s]
- 60%|#####9    | 178/299 [00:02<00:01, 62.24it/s]
- 62%|######1   | 185/299 [00:02<00:01, 62.46it/s]
- 64%|######4   | 192/299 [00:02<00:01, 63.16it/s]
- 69%|######8   | 205/299 [00:02<00:01, 72.73it/s]
- 72%|#######1  | 215/299 [00:02<00:01, 75.36it/s]
- 75%|#######4  | 224/299 [00:03<00:01, 66.04it/s]
- 78%|#######7  | 232/299 [00:03<00:01, 60.81it/s]
- 80%|#######9  | 239/299 [00:03<00:01, 57.69it/s]
- 84%|########3 | 250/299 [00:03<00:00, 64.18it/s]
- 87%|########6 | 260/299 [00:03<00:00, 71.65it/s]
- 90%|########9 | 269/299 [00:03<00:00, 75.68it/s]
- 93%|#########2| 278/299 [00:03<00:00, 70.87it/s]
- 97%|#########6| 290/299 [00:03<00:00, 78.94it/s]
-100%|##########| 299/299 [00:04<00:00, 73.44it/s]
-Best alignment with the photodiode shifted 12 ms relative to the first behavior event
-errors: min -5, q1 -1, med 0, q3 1, max 75
+  1%|1         | 4/299 [00:00<00:07, 39.55it/s]
+  4%|4         | 12/299 [00:00<00:06, 46.09it/s]
+  7%|7         | 21/299 [00:00<00:05, 53.55it/s]
+  9%|9         | 28/299 [00:00<00:04, 57.40it/s]
+ 11%|#1        | 34/299 [00:00<00:05, 51.57it/s]
+ 13%|#3        | 40/299 [00:00<00:05, 47.03it/s]
+ 16%|#6        | 49/299 [00:00<00:04, 54.29it/s]
+ 19%|#8        | 56/299 [00:00<00:04, 53.69it/s]
+ 21%|##1       | 63/299 [00:01<00:04, 57.02it/s]
+ 24%|##3       | 71/299 [00:01<00:04, 56.20it/s]
+ 26%|##5       | 77/299 [00:01<00:04, 49.62it/s]
+ 28%|##8       | 84/299 [00:01<00:04, 47.14it/s]
+ 30%|###       | 90/299 [00:01<00:04, 46.09it/s]
+ 32%|###2      | 96/299 [00:01<00:04, 45.97it/s]
+ 34%|###4      | 103/299 [00:01<00:03, 50.48it/s]
+ 36%|###6      | 109/299 [00:02<00:03, 51.33it/s]
+ 38%|###8      | 115/299 [00:02<00:03, 46.85it/s]
+ 41%|####      | 122/299 [00:02<00:03, 51.70it/s]
+ 43%|####3     | 129/299 [00:02<00:03, 53.75it/s]
+ 46%|####5     | 137/299 [00:02<00:02, 58.95it/s]
+ 48%|####8     | 145/299 [00:02<00:02, 63.28it/s]
+ 52%|#####1    | 154/299 [00:02<00:02, 66.86it/s]
+ 54%|#####3    | 161/299 [00:02<00:02, 64.83it/s]
+ 56%|#####6    | 168/299 [00:03<00:02, 54.15it/s]
+ 59%|#####8    | 176/299 [00:03<00:02, 59.75it/s]
+ 61%|######1   | 183/299 [00:03<00:02, 55.77it/s]
+ 63%|######3   | 189/299 [00:03<00:02, 54.86it/s]
+ 65%|######5   | 195/299 [00:03<00:01, 54.33it/s]
+ 68%|######7   | 203/299 [00:03<00:01, 56.75it/s]
+ 72%|#######1  | 215/299 [00:03<00:01, 65.78it/s]
+ 75%|#######4  | 223/299 [00:03<00:01, 57.31it/s]
+ 77%|#######6  | 230/299 [00:04<00:01, 54.46it/s]
+ 80%|#######9  | 238/299 [00:04<00:01, 59.22it/s]
+ 84%|########3 | 251/299 [00:04<00:00, 70.74it/s]
+ 88%|########8 | 264/299 [00:04<00:00, 77.23it/s]
+ 91%|#########1| 273/299 [00:04<00:00, 74.95it/s]
+ 94%|#########4| 282/299 [00:04<00:00, 65.29it/s]
+ 97%|#########6| 290/299 [00:04<00:00, 66.42it/s]
+100%|#########9| 298/299 [00:04<00:00, 69.04it/s]
+100%|##########| 299/299 [00:04<00:00, 60.74it/s]
+Best alignment with the events shifted 12 ms relative to the first behavior event
+errors: min -3, q1 -1, med 0, q3 1, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:324: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 Event 8 recovered (not excluded)
 Event 144 recovered (not excluded)
 Event 234 recovered (not excluded)
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:364: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:371: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 

 

 
Find cessations of the photodiode deflections

@@ -320,25 +351,100 @@
 

 

 
    
-
  • ../_images/sphx_glr_plot_recover_events_007.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_008.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_009.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:803: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  plt.show()
+Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
+
+  0%|          | 0/16269 [00:00<?, ?it/s]
+  2%|1         | 307/16269 [00:00<00:05, 3062.11it/s]
+  5%|4         | 756/16269 [00:00<00:04, 3383.39it/s]
+  6%|5         | 975/16269 [00:00<00:05, 2900.93it/s]
+  8%|8         | 1327/16269 [00:00<00:04, 3054.18it/s]
+ 10%|#         | 1634/16269 [00:00<00:04, 3057.32it/s]
+ 12%|#1        | 1900/16269 [00:00<00:05, 2722.11it/s]
+ 14%|#4        | 2311/16269 [00:00<00:04, 3027.41it/s]
+ 16%|#6        | 2673/16269 [00:00<00:04, 3183.26it/s]
+ 19%|#8        | 3033/16269 [00:00<00:04, 3296.18it/s]
+ 21%|##        | 3413/16269 [00:01<00:03, 3432.29it/s]
+ 23%|##3       | 3758/16269 [00:01<00:03, 3418.20it/s]
+ 26%|##5       | 4215/16269 [00:01<00:03, 3697.33it/s]
+ 29%|##9       | 4740/16269 [00:01<00:02, 4057.14it/s]
+ 32%|###1      | 5162/16269 [00:01<00:02, 3912.52it/s]
+ 35%|###4      | 5661/16269 [00:01<00:02, 4182.27it/s]
+ 38%|###7      | 6171/16269 [00:01<00:02, 4420.30it/s]
+ 41%|####1     | 6680/16269 [00:01<00:02, 4601.02it/s]
+ 44%|####4     | 7182/16269 [00:01<00:01, 4718.29it/s]
+ 47%|####7     | 7715/16269 [00:01<00:01, 4886.12it/s]
+ 50%|#####     | 8212/16269 [00:02<00:01, 4834.88it/s]
+ 53%|#####3    | 8701/16269 [00:02<00:01, 4488.88it/s]
+ 56%|#####6    | 9159/16269 [00:02<00:01, 4051.54it/s]
+ 59%|#####8    | 9579/16269 [00:02<00:01, 3634.93it/s]
+ 61%|######1   | 9961/16269 [00:02<00:01, 3493.90it/s]
+ 63%|######3   | 10325/16269 [00:02<00:01, 3354.56it/s]
+ 66%|######5   | 10672/16269 [00:02<00:01, 3231.41it/s]
+ 68%|######8   | 11084/16269 [00:02<00:01, 3453.96it/s]
+ 71%|#######   | 11499/16269 [00:03<00:01, 3636.67it/s]
+ 74%|#######3  | 11973/16269 [00:03<00:01, 3908.05it/s]
+ 77%|#######6  | 12447/16269 [00:03<00:00, 4123.81it/s]
+ 79%|#######9  | 12924/16269 [00:03<00:00, 4293.92it/s]
+ 82%|########2 | 13364/16269 [00:03<00:00, 4077.49it/s]
+ 85%|########5 | 13833/16269 [00:03<00:00, 4242.77it/s]
+ 88%|########7 | 14266/16269 [00:03<00:00, 4261.11it/s]
+ 91%|######### | 14747/16269 [00:03<00:00, 4410.52it/s]
+ 94%|#########3| 15227/16269 [00:03<00:00, 4514.88it/s]
+ 96%|#########6| 15683/16269 [00:03<00:00, 4404.48it/s]
+100%|#########9| 16208/16269 [00:04<00:00, 4627.82it/s]
+100%|##########| 16269/16269 [00:04<00:00, 4010.28it/s]
+Overwriting existing file.
+
+<Annotations | 597 segments: Stim Off (297), Stim On (300)>
+

+

+
Check the results:

+
Finally, we’ll check that the recovered events and the original events match.

+
annot = _load_data(fname)[0]
+raw.set_annotations(annot)
+events2, event_id = mne.events_from_annotations(raw)
+on_events = events2[events2[:, 2] == event_id['Stim On']]
+print(f'Original: {events[corrupted_indices, 0]}\n'
+      f'Recovered: {on_events[corrupted_indices, 0]}')
+
+'''
+# uncomment when using interactively, this section doesn't work
+# for the non-interactive documentation
+off_events = events2[events2[:, 2] == event_id['Stim Off']]
+original_off = events[corrupted_indices, 0] + \
+  np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)
+print(f'Original off: {original_off}\n'
+      f'Recovered off: {on_events[corrupted_indices, 0]}')
+'''
+

+

+
Out:

+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
+Ready.
+Used Annotations descriptions: ['Stim Off', 'Stim On']
+Original: [  65602  989660 1595483]
+Recovered: [  65602  989660 1595483]
+
+"\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] +   np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\n'\n      f'Recovered off: {on_events[corrupted_indices, 0]}')\n"
 

 

-
Total running time of the script: ( 0 minutes  11.919 seconds)

+
Total running time of the script: ( 0 minutes  30.425 seconds)

 
 
@@ -97,7 +98,7 @@
       
   

 
Computation times¶

-
00:46.944 total execution time for auto_examples files:

+
00:58.556 total execution time for auto_examples files:

 @@ -105,16 +106,20 @@
 
-
-
+
+
-
-
+
+
-
-
+
+
+
+
+
+
@@ -134,7 +139,7 @@
     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

diff --git a/docs/dev/.buildinfo b/docs/dev/.buildinfo
index ff2c552..c0d9ad2 100644
--- a/docs/dev/.buildinfo
+++ b/docs/dev/.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: 316c94ff3ba7c8a8172702245a8fce8d
+config: 42af046dcf9b125d8a353f788d0bfc30
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/docs/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
new file mode 100644
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diff --git a/docs/dev/_downloads/332eafcd55124069bc52ae95a623ffe2/plot_find_pd_events.py b/docs/dev/_downloads/332eafcd55124069bc52ae95a623ffe2/plot_find_pd_events.py
index b3f953c..6fd662f 100644
--- a/docs/dev/_downloads/332eafcd55124069bc52ae95a623ffe2/plot_find_pd_events.py
+++ b/docs/dev/_downloads/332eafcd55124069bc52ae95a623ffe2/plot_find_pd_events.py
@@ -23,7 +23,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv
+from pd_parser.parse_pd import _read_raw, _to_tsv
 
 out_dir = _TempDir()
 print(f'After running this example, you can find the data here: {out_dir}')
@@ -31,7 +31,7 @@
 # simulate photodiode data
 n_events = 300
 prop_corrupted = 0.01
-raw, beh_df, events, corrupted_indices = \
+raw, beh, events, corrupted_indices = \
     pd_parser.simulate_pd_data(n_events=n_events,
                                prop_corrupted=prop_corrupted)
 
@@ -48,6 +48,9 @@
 fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
 raw.save(fname)
 
+# roundtrip so that raw is properly loaded from disk and has a filename
+raw = _read_raw(fname)
+
 ###############################################################################
 # Make behavior data:
 #
@@ -70,12 +73,12 @@
 for i in [10, 129, 232, 288]:
     response_time[i] = 'n/a'  # make some no responses
 # put in dictionary to be converted to tsv file
-beh_df['fix_onset_time'] = beh_df['time'] + offsets
-beh_df['go_time'] = go_time
-beh_df['response_time'] = response_time
+beh['fix_onset_time'] = beh['time'] + offsets
+beh['go_time'] = go_time
+beh['response_time'] = response_time
 behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
 # save behavior file out
-_to_tsv(behf, beh_df)
+_to_tsv(behf, beh)
 
 ###############################################################################
 # Use the interactive graphical user interface (GUI) to find parameters:
@@ -86,21 +89,21 @@
 # interact with the photodiode data to pick reasonable parameters by
 # following the instructions.
 
-pd_parser.find_pd_params(fname, pd_ch_names=['pd'])
+pd_parser.find_pd_params(raw, pd_ch_names=['pd'])
 
 ###############################################################################
 # Find the photodiode events relative to the behavioral timing of interest:
 #
 # This function will use the default parameters or the parameters you
 # found from :func:`pd_parser.find_pd_parameters` to find and align the
-# photodiode events, excluding events that were off because the commuter
+# photodiode events, excluding events that were off because the computer
 # hung up on computation, for instance. That data is saved in the same folder
 # as the raw file (in this case, a temperary directory generated by
 # :func:`_TempDir`). The data can be used directly, or it can be accessed via
 # :func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine
 # data structure (BIDS) standardized format before using it.
 
-pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)
+pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)
 
 ###############################################################################
 # Add events relative to the photodiode events:
@@ -117,8 +120,8 @@
 # used for each event separately using the keyword `add_event=True`.
 
 pd_parser.add_relative_events(
-    fname, behf,
-    relative_event_cols=['go_time', 'response_time'],
+    raw, beh,
+    relative_event_keys=['go_time', 'response_time'],
     relative_event_names=['Go Cue', 'Response'])
 
 
diff --git a/docs/dev/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py b/docs/dev/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py
index ee99b21..6e9d46d 100644
--- a/docs/dev/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py
+++ b/docs/dev/_downloads/3b9b6f3cd8645b6f7e8cdb8a4aee6517/plot_recover_events.py
@@ -27,7 +27,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 
@@ -38,7 +38,7 @@
 n_events = 300
 # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds
 n_secs_on = np.random.random(n_events) * 0.5 + 0.25
-raw, beh_df, events, _ = \
+raw, beh, events, _ = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=0.0)
 sfreq = np.round(raw.info['sfreq']).astype(int)
@@ -51,16 +51,19 @@
 axes[0].set_ylabel('voltage')
 for j, i in enumerate(events[corrupted_indices, 0]):
     if j == 0:
-        raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount
+        raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount
     elif j == 1:
         raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount
     else:
-        raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount
+        raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount
     axes[j].plot(np.linspace(-1, 2, 3 * sfreq),
                  raw._data[0, i - sfreq: i + sfreq * 2])
     axes[j].set_xlabel('time (s)')
 
 
+# make figure nicer
+fig.tight_layout()
+
 # make fake electrophysiology data
 info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
                        ['seeg'] * 3)
@@ -71,15 +74,13 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
-# save to disk as required by ``pd-parser``
-fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-raw.save(fname)
 # add some offsets to the behavior so it's a bit more realistic
 offsets = np.random.randn(n_events) * 0.001
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
+beh['time'] = np.array(beh['time']) + offsets
 
+# save to disk as required by ``pd-parser``, raw needs to have a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
 
 ###############################################################################
 # Find the photodiode events relative to the behavioral timing of interest:
@@ -92,8 +93,8 @@
 # your own input depending on whether you want to keep the events or not.
 
 with mock.patch('builtins.input', return_value='y'):
-    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                       pd_ch_names=['pd'], beh_col='time', recover=True)
+    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                       pd_ch_names=['pd'], beh_key='time', recover=True)
 
 ###############################################################################
 # Find cessations of the photodiode deflections
@@ -108,3 +109,25 @@
 # following the instructions.
 
 pd_parser.add_pd_off_events(fname, off_event_name='Stim Off')
+
+###############################################################################
+# Check the results:
+#
+# Finally, we'll check that the recovered events and the original events match.
+
+annot = _load_data(fname)[0]
+raw.set_annotations(annot)
+events2, event_id = mne.events_from_annotations(raw)
+on_events = events2[events2[:, 2] == event_id['Stim On']]
+print(f'Original: {events[corrupted_indices, 0]}\n'
+      f'Recovered: {on_events[corrupted_indices, 0]}')
+
+'''
+# uncomment when using interactively, this section doesn't work
+# for the non-interactive documentation
+off_events = events2[events2[:, 2] == event_id['Stim Off']]
+original_off = events[corrupted_indices, 0] + \
+  np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)
+print(f'Original off: {original_off}\n'
+      f'Recovered off: {on_events[corrupted_indices, 0]}')
+'''
diff --git a/docs/dev/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb b/docs/dev/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb
index f3a70a1..ecfa221 100644
--- a/docs/dev/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb
+++ b/docs/dev/_downloads/45db2a75d199d40435fefaab0063e78d/plot_find_audio_events.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Use Audio to Align Video Data\n\nIn this example, we use ``pd-parser`` to find audio events using the same\nalgorithm for matching with time-stamps and rejecting misaligned\naudio, but applied using the onset of an audio deflection instead of detecting\nphotodiode events based on their square wave shape.\n"
+        "\n# Use Audio to Align Video Data\nIn this example, we use ``pd-parser`` to find audio events using the same\nalgorithm for matching with time-stamps and rejecting misaligned\naudio, but applied using the onset of an audio deflection instead of detecting\nphotodiode events based on their square wave shape.\n"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\nimport os.path as op\nimport numpy as np\nfrom scipy.io import wavfile\nfrom subprocess import call\n# from subprocess import run , PIPE, STDOUT\n# import datetime\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data\n\n# get the data\nout_dir = _TempDir()\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video.mp4 '\n      '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video.wav '\n      '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video_beh.tsv '\n      '-o ' + op.join(out_dir, 'test_video_beh.tsv')],\n     shell=True, env=os.environ)\n\n# navigate to the example video\nvideo_fname = op.join(out_dir, 'test_video.mp4')\n\naudio_fname = video_fname.replace('mp4', 'wav')  # pre-computed\n# extract audio (requires ffmpeg)\n# run(['ffmpeg', '-i', video_fname, audio_fname])\n\nfs, data = wavfile.read(audio_fname)\ndata = data.mean(axis=1)  # stereo audio but only need one source\ninfo = mne.create_info(['audio'], fs, ['stim'])\nraw = mne.io.RawArray(data[np.newaxis], info)\n\n# save out data file\nfname = op.join(out_dir, 'test_video-raw.fif')\nraw.save(fname, overwrite=True)\n\n# find audio-visual time offset\noffset = 0  # pre-computed value for this video\n'''\nresult = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time',\n              '-v', '0', '-of', 'compact=p=1:nk=0', video_fname],\n             stdout=PIPE, stderr=STDOUT)\noutput = result.stdout.decode('utf-8').split('\\n')\noffset = float(output[0].strip('stream|codec_type=video|start_time')) - \\\n    float(output[1].strip('stream|codec_type=audio|start_time'))\n'''\n\n# navigate to corresponding behavior\nbehf = op.join(out_dir, 'test_video_beh.tsv')"
+        "import os\nimport os.path as op\nimport numpy as np\nfrom scipy.io import wavfile\nfrom subprocess import call\n# from subprocess import run, PIPE, STDOUT\n# import datetime\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data  # , _read_tsv\n\n# get the data\nout_dir = _TempDir()\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video.mp4 '\n      '-o ' + op.join(out_dir, 'test_video.mp4')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video.wav '\n      '-o ' + op.join(out_dir, 'test_video.wav')], shell=True, env=os.environ)\ncall(['curl -L https://raw.githubusercontent.com/alexrockhill/pd-parser/'\n      'master/pd_parser/tests/data/test_video_beh.tsv '\n      '-o ' + op.join(out_dir, 'test_video_beh.tsv')],\n     shell=True, env=os.environ)\n\n# navigate to the example video\nvideo_fname = op.join(out_dir, 'test_video.mp4')\n\naudio_fname = video_fname.replace('mp4', 'wav')  # pre-computed\n# extract audio (requires ffmpeg)\n# run(['ffmpeg', '-i', video_fname, audio_fname])\n\nfs, data = wavfile.read(audio_fname)\ndata = data.mean(axis=1)  # stereo audio but only need one source\ninfo = mne.create_info(['audio'], fs, ['stim'])\nraw = mne.io.RawArray(data[np.newaxis], info)\n\n# find audio-visual time offset\noffset = 0  # pre-computed value for this video\n'''\nresult = run(['ffprobe', '-show_entries', 'stream=codec_type,start_time',\n              '-v', '0', '-of', 'compact=p=1:nk=0', video_fname],\n             stdout=PIPE, stderr=STDOUT)\noutput = result.stdout.decode('utf-8').split('\\n')\noffset = float(output[0].strip('stream|codec_type=video|start_time')) - \\\n    float(output[1].strip('stream|codec_type=audio|start_time'))\n'''\n\n# save to disk as required by ``pd-parser``, raw needs a filename\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n\n# navigate to corresponding behavior\nbehf = op.join(out_dir, 'test_video_beh.tsv')"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "pd_parser.parse_audio(fname, behf=behf, beh_col='tone_onset_time',\n                      audio_ch_names=['audio'], zscore=10)"
+        "pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',\n                      audio_ch_names=['audio'], zscore=10)"
       ]
     },
     {
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "annot, _, beh_df = _load_data(fname)\nprint('Here are the event times: ', annot.onset)\n\n# Crop the videos with ffmpeg\n'''\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh_df['movement'][i], beh_df['action_type'][i]))])\n'''"
+        "annot = _load_data(fname)[0]\nprint('Here are the event times: ', annot.onset)\n\n# Crop the videos with ffmpeg\n'''\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh['movement'][i], beh['action_type'][i]))])\n'''"
       ]
     }
   ],
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.9.1"
     }
   },
   "nbformat": 4,
diff --git a/docs/dev/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb b/docs/dev/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb
index 5a05fb1..0c9f31f 100644
--- a/docs/dev/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb
+++ b/docs/dev/_downloads/48a5e5aeddd3e241d0dc38ccbd94a450/plot_find_pd_events.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Find Photodiode Events\n\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign them to behavior. Then, we save the data to BIDS format.\n"
+        "\n# Find Photodiode Events\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign them to behavior. Then, we save the data to BIDS format.\n"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv\n\nout_dir = _TempDir()\nprint(f'After running this example, you can find the data here: {out_dir}')\n\n# simulate photodiode data\nn_events = 300\nprop_corrupted = 0.01\nraw, beh_df, events, corrupted_indices = \\\n    pd_parser.simulate_pd_data(n_events=n_events,\n                               prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)"
+        "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _read_raw, _to_tsv\n\nout_dir = _TempDir()\nprint(f'After running this example, you can find the data here: {out_dir}')\n\n# simulate photodiode data\nn_events = 300\nprop_corrupted = 0.01\nraw, beh, events, corrupted_indices = \\\n    pd_parser.simulate_pd_data(n_events=n_events,\n                               prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n\n# roundtrip so that raw is properly loaded from disk and has a filename\nraw = _read_raw(fname)"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "np.random.seed(12)\n# add some noise to make it harder to align, use just over\n# the exclusion of 0.03 to make some events excluded\noffsets = np.random.random(n_events) * 0.035 - 0.0125\n# in this example, the fixation would always be 700 ms\n# after which point a cue would appear which is the \"go time\"\ngo_time = np.repeat(0.7, n_events)\n# let's make the response time between 0.5 and 1.5 seconds uniform random\nresponse_time = list(go_time + np.random.random(n_events) + 1.5)\nfor i in [10, 129, 232, 288]:\n    response_time[i] = 'n/a'  # make some no responses\n# put in dictionary to be converted to tsv file\nbeh_df['fix_onset_time'] = beh_df['time'] + offsets\nbeh_df['go_time'] = go_time\nbeh_df['response_time'] = response_time\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n# save behavior file out\n_to_tsv(behf, beh_df)"
+        "np.random.seed(12)\n# add some noise to make it harder to align, use just over\n# the exclusion of 0.03 to make some events excluded\noffsets = np.random.random(n_events) * 0.035 - 0.0125\n# in this example, the fixation would always be 700 ms\n# after which point a cue would appear which is the \"go time\"\ngo_time = np.repeat(0.7, n_events)\n# let's make the response time between 0.5 and 1.5 seconds uniform random\nresponse_time = list(go_time + np.random.random(n_events) + 1.5)\nfor i in [10, 129, 232, 288]:\n    response_time[i] = 'n/a'  # make some no responses\n# put in dictionary to be converted to tsv file\nbeh['fix_onset_time'] = beh['time'] + offsets\nbeh['go_time'] = go_time\nbeh['response_time'] = response_time\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n# save behavior file out\n_to_tsv(behf, beh)"
       ]
     },
     {
@@ -80,14 +80,14 @@
       },
       "outputs": [],
       "source": [
-        "pd_parser.find_pd_params(fname, pd_ch_names=['pd'])"
+        "pd_parser.find_pd_params(raw, pd_ch_names=['pd'])"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Find the photodiode events relative to the behavioral timing of interest:\n\nThis function will use the default parameters or the parameters you\nfound from :func:`pd_parser.find_pd_parameters` to find and align the\nphotodiode events, excluding events that were off because the commuter\nhung up on computation, for instance. That data is saved in the same folder\nas the raw file (in this case, a temperary directory generated by\n:func:`_TempDir`). The data can be used directly, or it can be accessed via\n:func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine\ndata structure (BIDS) standardized format before using it.\n\n"
+        "Find the photodiode events relative to the behavioral timing of interest:\n\nThis function will use the default parameters or the parameters you\nfound from :func:`pd_parser.find_pd_parameters` to find and align the\nphotodiode events, excluding events that were off because the computer\nhung up on computation, for instance. That data is saved in the same folder\nas the raw file (in this case, a temperary directory generated by\n:func:`_TempDir`). The data can be used directly, or it can be accessed via\n:func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine\ndata structure (BIDS) standardized format before using it.\n\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)"
+        "pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "pd_parser.add_relative_events(\n    fname, behf,\n    relative_event_cols=['go_time', 'response_time'],\n    relative_event_names=['Go Cue', 'Response'])"
+        "pd_parser.add_relative_events(\n    raw, beh,\n    relative_event_keys=['go_time', 'response_time'],\n    relative_event_names=['Go Cue', 'Response'])"
       ]
     },
     {
@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.9.1"
     }
   },
   "nbformat": 4,
diff --git a/docs/dev/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb b/docs/dev/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb
index 5bce2fe..a451b96 100644
--- a/docs/dev/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb
+++ b/docs/dev/_downloads/5258f1bb54b38679ec8488158187b98c/plot_recover_events.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Manually Recover Events Not Found by the Algorithm\n\nIn this example, we use ``pd-parser`` to find photodiode events that\nhave corrupted pre-event baselines, photodiode plateaus or post-event\nbaselines but not corrupted onsets or offsets.\nNote that it might be a good idea not to recover these events\nas there might be noise in the data around this time.\n"
+        "\n# Manually Recover Events Not Found by the Algorithm\nIn this example, we use ``pd-parser`` to find photodiode events that\nhave corrupted pre-event baselines, photodiode plateaus or post-event\nbaselines but not corrupted onsets or offsets.\nNote that it might be a good idea not to recover these events\nas there might be noise in the data around this time.\n"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import os.path as op\nimport numpy as np\nimport mock\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv\n\nimport matplotlib.pyplot as plt\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.25 to 0.75 seconds\nn_secs_on = np.random.random(n_events) * 0.5 + 0.25\nraw, beh_df, events, _ = \\\n    pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n                               prop_corrupted=0.0)\nsfreq = np.round(raw.info['sfreq']).astype(int)\n\n# corrupt some events\ncorrupted_indices = [8, 144, 234]\namount = raw._data.max()\nfig, axes = plt.subplots(1, len(corrupted_indices), figsize=(8, 4))\nfig.suptitle('Corrupted Events')\naxes[0].set_ylabel('voltage')\nfor j, i in enumerate(events[corrupted_indices, 0]):\n    if j == 0:\n        raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount\n    elif j == 1:\n        raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount\n    else:\n        raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount\n    axes[j].plot(np.linspace(-1, 2, 3 * sfreq),\n                 raw._data[0, i - sfreq: i + sfreq * 2])\n    axes[j].set_xlabel('time (s)')\n\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.001\nbeh_df['time'] = np.array(beh_df['time']) + offsets\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n_to_tsv(behf, beh_df)"
+        "import os.path as op\nimport numpy as np\nimport mock\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data\n\nimport matplotlib.pyplot as plt\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.25 to 0.75 seconds\nn_secs_on = np.random.random(n_events) * 0.5 + 0.25\nraw, beh, events, _ = \\\n    pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n                               prop_corrupted=0.0)\nsfreq = np.round(raw.info['sfreq']).astype(int)\n\n# corrupt some events\ncorrupted_indices = [8, 144, 234]\namount = raw._data.max()\nfig, axes = plt.subplots(1, len(corrupted_indices), figsize=(8, 4))\nfig.suptitle('Corrupted Events')\naxes[0].set_ylabel('voltage')\nfor j, i in enumerate(events[corrupted_indices, 0]):\n    if j == 0:\n        raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount\n    elif j == 1:\n        raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount\n    else:\n        raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount\n    axes[j].plot(np.linspace(-1, 2, 3 * sfreq),\n                 raw._data[0, i - sfreq: i + sfreq * 2])\n    axes[j].set_xlabel('time (s)')\n\n\n# make figure nicer\nfig.tight_layout()\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.001\nbeh['time'] = np.array(beh['time']) + offsets\n\n# save to disk as required by ``pd-parser``, raw needs to have a filename\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "with mock.patch('builtins.input', return_value='y'):\n    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,\n                       pd_ch_names=['pd'], beh_col='time', recover=True)"
+        "with mock.patch('builtins.input', return_value='y'):\n    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,\n                       pd_ch_names=['pd'], beh_key='time', recover=True)"
       ]
     },
     {
@@ -82,6 +82,24 @@
       "source": [
         "pd_parser.add_pd_off_events(fname, off_event_name='Stim Off')"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Check the results:\n\nFinally, we'll check that the recovered events and the original events match.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "annot = _load_data(fname)[0]\nraw.set_annotations(annot)\nevents2, event_id = mne.events_from_annotations(raw)\non_events = events2[events2[:, 2] == event_id['Stim On']]\nprint(f'Original: {events[corrupted_indices, 0]}\\n'\n      f'Recovered: {on_events[corrupted_indices, 0]}')\n\n'''\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] + \\\n  np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\\n'\n      f'Recovered off: {on_events[corrupted_indices, 0]}')\n'''"
+      ]
     }
   ],
   "metadata": {
@@ -100,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.9.1"
     }
   },
   "nbformat": 4,
diff --git a/docs/dev/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb b/docs/dev/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb
index dcb64e1..c3c4431 100644
--- a/docs/dev/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb
+++ b/docs/dev/_downloads/5a16fa2b3158185b4d69c20afbbe9c0d/plot_find_pd_on_and_off.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Find Photodiode On and Off Events\n\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign both the onset of the deflection and the cessation to\nto behavior.\n"
+        "\n# Find Photodiode On and Off Events\nIn this example, we use ``pd-parser`` to find photodiode events and\nalign both the onset of the deflection and the cessation to\nto behavior.\n"
       ]
     },
     {
@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _to_tsv, _load_data\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.5 to 1 second\nn_secs_on = np.random.random(n_events) * 0.5 + 0.5\nprop_corrupted = 0.01\nraw, beh_df, events, corrupted_indices = \\\n    pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n                               prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.01\nbeh_df['time'] = np.array(beh_df['time']) + offsets\nbehf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')\n_to_tsv(behf, beh_df)"
+        "import os.path as op\nimport numpy as np\n\nimport mne\nfrom mne.utils import _TempDir\n\nimport pd_parser\nfrom pd_parser.parse_pd import _load_data\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\n\nout_dir = _TempDir()\n\n# simulate photodiode data\nnp.random.seed(29)\nn_events = 300\n# let's make our photodiode events on random uniform from 0.5 to 1 second\nn_secs_on = np.random.random(n_events) * 0.5 + 0.5\nprop_corrupted = 0.01\nraw, beh, events, corrupted_indices = \\\n    pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,\n                               prop_corrupted=prop_corrupted)\n\n# make fake electrophysiology data\ninfo = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],\n                       ['seeg'] * 3)\nraw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)\nraw2.info['lowpass'] = raw.info['lowpass']  # these must match to combine\nraw.add_channels([raw2])\n# bids needs these data fields\nraw.info['dig'] = None\nraw.info['line_freq'] = 60\n\n# add some offsets to the behavior so it's a bit more realistic\noffsets = np.random.randn(n_events) * 0.01\nbeh['time'] = np.array(beh['time']) + offsets\n\n# save to disk as required by ``pd-parser``\nfname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')\nraw.save(fname)"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,\n                   pd_ch_names=['pd'], beh_col='time',\n                   max_len=1.5)  # none are on longer than 1.5 seconds"
+        "pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,\n                   pd_ch_names=['pd'], beh_key='time',\n                   max_len=1.5)  # none are on longer than 1.5 seconds"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "annot, pd_ch_names, beh_df = _load_data(fname)\nraw.set_annotations(annot)\nevents, event_id = mne.events_from_annotations(raw)\non_events = events[events[:, 2] == event_id['Stim On']]\noff_events = events[events[:, 2] == event_id['Stim Off']]\n\nrecovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']\nnot_corrupted = [s != 'n/a' for s in beh_df['pd_parser_sample']]\nground_truth_not_corrupted = n_secs_on[not_corrupted]\n\nfig, ax = plt.subplots()\nax.scatter(ground_truth_not_corrupted, recovered,\n           s=1, color=cm.rainbow(np.linspace(0, 1, len(recovered))))\nax.set_title('Photodiode offset eventfidelity of recovery')\nax.set_xlabel('ground truth duration (s)')\nax.set_ylabel('recovered duration (s)')\n\nprint('Mean difference in the recovered from simulated length is {:.3f} '\n      'milliseconds'.format(\n          1000 * abs(ground_truth_not_corrupted - recovered).mean()))"
+        "annot, pd_ch_names, beh = _load_data(fname)\nraw.set_annotations(annot)\nevents, event_id = mne.events_from_annotations(raw)\non_events = events[events[:, 2] == event_id['Stim On']]\noff_events = events[events[:, 2] == event_id['Stim Off']]\n\nrecovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']\nnot_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']]\nground_truth_not_corrupted = n_secs_on[not_corrupted]\n\nfig, ax = plt.subplots()\nax.scatter(ground_truth_not_corrupted, recovered,\n           s=1, color=cm.rainbow(np.linspace(0, 1, len(recovered))))\nax.set_title('Photodiode offset eventfidelity of recovery')\nax.set_xlabel('ground truth duration (s)')\nax.set_ylabel('recovered duration (s)')\n\nprint('Mean difference in the recovered from simulated length is {:.3f} '\n      'milliseconds'.format(\n          1000 * abs(ground_truth_not_corrupted - recovered).mean()))"
       ]
     }
   ],
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.6"
+      "version": "3.9.1"
     }
   },
   "nbformat": 4,
diff --git a/docs/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
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diff --git a/docs/dev/_downloads/81406c451e497331ddbbb284a4d8eb5b/plot_find_audio_events.py b/docs/dev/_downloads/81406c451e497331ddbbb284a4d8eb5b/plot_find_audio_events.py
index 83ac872..1e45545 100644
--- a/docs/dev/_downloads/81406c451e497331ddbbb284a4d8eb5b/plot_find_audio_events.py
+++ b/docs/dev/_downloads/81406c451e497331ddbbb284a4d8eb5b/plot_find_audio_events.py
@@ -30,14 +30,14 @@
 import numpy as np
 from scipy.io import wavfile
 from subprocess import call
-# from subprocess import run , PIPE, STDOUT
+# from subprocess import run, PIPE, STDOUT
 # import datetime
 
 import mne
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _load_data
+from pd_parser.parse_pd import _load_data  # , _read_tsv
 
 # get the data
 out_dir = _TempDir()
@@ -64,10 +64,6 @@
 info = mne.create_info(['audio'], fs, ['stim'])
 raw = mne.io.RawArray(data[np.newaxis], info)
 
-# save out data file
-fname = op.join(out_dir, 'test_video-raw.fif')
-raw.save(fname, overwrite=True)
-
 # find audio-visual time offset
 offset = 0  # pre-computed value for this video
 '''
@@ -79,6 +75,10 @@
     float(output[1].strip('stream|codec_type=audio|start_time'))
 '''
 
+# save to disk as required by ``pd-parser``, raw needs a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
+
 # navigate to corresponding behavior
 behf = op.join(out_dir, 'test_video_beh.tsv')
 
@@ -86,7 +86,7 @@
 # Run the parser:
 #
 # Now we'll call the main function to automatically parse the audio events.
-pd_parser.parse_audio(fname, behf=behf, beh_col='tone_onset_time',
+pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',
                       audio_ch_names=['audio'], zscore=10)
 
 ###############################################################################
@@ -94,17 +94,18 @@
 #
 # Finally, we'll load the events and use them to crop the video although it
 # requires ffmpeg so it is commented out.
-annot, _, beh_df = _load_data(fname)
+annot = _load_data(fname)[0]
 print('Here are the event times: ', annot.onset)
 
 # Crop the videos with ffmpeg
 '''
+beh = _read_tsv(behf)
 for i in range(annot.onset.size):  # skip the first video
-    action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000
+    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000
     run(['ffmpeg', '-i', f'{video_fname}', '-ss',
          str(datetime.timedelta(
              seconds=annot.onset[i] - action_time - offset)),
          '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),
          op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(
-             beh_df['movement'][i], beh_df['action_type'][i]))])
+             beh['movement'][i], beh['action_type'][i]))])
 '''
diff --git a/docs/dev/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py b/docs/dev/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py
index 540dd1b..df6d077 100644
--- a/docs/dev/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py
+++ b/docs/dev/_downloads/fb5c5b7987665badd299d9a46518fdc1/plot_find_pd_on_and_off.py
@@ -24,7 +24,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv, _load_data
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
@@ -37,7 +37,7 @@
 # let's make our photodiode events on random uniform from 0.5 to 1 second
 n_secs_on = np.random.random(n_events) * 0.5 + 0.5
 prop_corrupted = 0.01
-raw, beh_df, events, corrupted_indices = \
+raw, beh, events, corrupted_indices = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=prop_corrupted)
 
@@ -51,15 +51,13 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
-# save to disk as required by ``pd-parser``
-fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-raw.save(fname)
 # add some offsets to the behavior so it's a bit more realistic
 offsets = np.random.randn(n_events) * 0.01
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
+beh['time'] = np.array(beh['time']) + offsets
 
+# save to disk as required by ``pd-parser``
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
 
 ###############################################################################
 # Find the photodiode events relative to the behavioral timing of interest:
@@ -69,8 +67,8 @@
 # One percent of the 300 events (3) were corrupted as shown in the plots and
 # some were too far off from large offsets that we're going to exclude them.
 
-pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                   pd_ch_names=['pd'], beh_col='time',
+pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                   pd_ch_names=['pd'], beh_key='time',
                    max_len=1.5)  # none are on longer than 1.5 seconds
 
 ###############################################################################
@@ -93,14 +91,14 @@
 # the lengths are recovered within 1 millisecond. Note that the colors are
 # arbitrary and are only used to increase contrast and ease of visualization.
 
-annot, pd_ch_names, beh_df = _load_data(fname)
+annot, pd_ch_names, beh = _load_data(fname)
 raw.set_annotations(annot)
 events, event_id = mne.events_from_annotations(raw)
 on_events = events[events[:, 2] == event_id['Stim On']]
 off_events = events[events[:, 2] == event_id['Stim Off']]
 
 recovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']
-not_corrupted = [s != 'n/a' for s in beh_df['pd_parser_sample']]
+not_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']]
 ground_truth_not_corrupted = n_secs_on[not_corrupted]
 
 fig, ax = plt.subplots()
diff --git a/docs/dev/_images/sphx_glr_plot_find_audio_events_001.png b/docs/dev/_images/sphx_glr_plot_find_audio_events_001.png
index a92dfa2f2c92b64dcb482854ffaa96c3c7b03de4..98213f7f9194b49c53943445d21b48c080491e61 100644
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diff --git a/docs/dev/_modules/index.html b/docs/dev/_modules/index.html
index 62b9de1..395c111 100644
--- a/docs/dev/_modules/index.html
+++ b/docs/dev/_modules/index.html
@@ -4,12 +4,13 @@
   
     
     
-    Overview: module code — pd-parser v0.4dev0 documentation
-    
+    Overview: module code — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -107,7 +108,7 @@ 
All modules for which code is available

     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/dev/_modules/parse_pd.html b/docs/dev/_modules/parse_pd.html
index f0f6c46..4340f61 100644
--- a/docs/dev/_modules/parse_pd.html
+++ b/docs/dev/_modules/parse_pd.html
@@ -4,12 +4,13 @@
   
     
     
-    parse_pd — pd-parser v0.4dev0 documentation
-    
+    parse_pd — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -157,36 +158,47 @@ 
Source code for parse_pd
             fid.write('\t'.join([str(val[i]) for val in df.values()]) + '\n')
 
 
-def _read_raw(fname, preload=True, verbose=True):
-    _, ext = op.splitext(fname)
-    """Read raw data into an mne.io.Raw object."""
-    if verbose:
-        print('Reading in {}'.format(fname))
-    if ext == '.fif':
-        raw = mne.io.read_raw_fif(fname, preload=preload)
-    elif ext == '.edf':
-        raw = mne.io.read_raw_edf(fname, preload=preload)
-    elif ext == '.bdf':
-        raw = mne.io.read_raw_bdf(fname, preload=preload)
-    elif ext == '.vhdr':
-        raw = mne.io.read_raw_brainvision(fname, preload=preload)
-    elif ext == '.set':
-        raw = mne.io.read_raw_eeglab(fname, preload=preload)
+def _read_raw(raw, preload=True, verbose=True):
+    """Read raw object from file if it's not already loaded."""
+    if isinstance(raw, mne.io.BaseRaw):
+        if preload:
+            raw.load_data()
+        elif preload is not None:
+            if raw.preload:
+                raise ValueError('`raw` object cannot be preloaded')
+        if raw.filenames[0] is None:
+            raise ValueError('`raw` object must be loaded from disk')
     else:
-        raise ValueError('Extension {} not recognized, options are'
-                         'fif, edf, bdf, vhdr (brainvision), set '
-                         '(eeglab)'.format(ext))
+        _, ext = op.splitext(raw)
+        """Read raw data into an mne.io.Raw object."""
+        if verbose:
+            print('Reading in {}'.format(raw))
+        if ext == '.fif':
+            raw = mne.io.read_raw_fif(raw, preload=preload)
+        elif ext == '.edf':
+            raw = mne.io.read_raw_edf(raw, preload=preload)
+        elif ext == '.bdf':
+            raw = mne.io.read_raw_bdf(raw, preload=preload)
+        elif ext == '.vhdr':
+            raw = mne.io.read_raw_brainvision(raw, preload=preload)
+        elif ext == '.set':
+            raw = mne.io.read_raw_eeglab(raw, preload=preload)
+        else:
+            raise ValueError('Extension {} not recognized, options are'
+                             'fif, edf, bdf, vhdr (brainvision), set '
+                             '(eeglab)'.format(ext))
     return raw
 
 
-def _load_beh_df(behf, beh_col):
+def _load_beh(beh, beh_key):
     """Load the behavioral data frame and check columns."""
-    df = _read_tsv(behf)
-    if beh_col not in df:
-        raise ValueError(f'beh_col {beh_col} not in the columns of '
-                         f'behf {behf}. Please check that the correct '
-                         'column is provided')
-    return df[beh_col], df
+    if not isinstance(beh, dict):
+        beh = _read_tsv(beh)
+    if beh_key not in beh:
+        raise ValueError(f'`beh_key` {beh_key} not in the columns of '
+                         f'the `beh` behavior dictionary. Please check '
+                         'that the correct column is provided')
+    return beh[beh_key], beh
 
 
 def _get_channel_data(raw, ch_names):
@@ -233,51 +245,59 @@ 
Source code for parse_pd
     return ch_data, ch_names
 
 
-def _check_if_pd_event(pd, i, max_len_i, zscore, min_i, baseline_i):
+def _check_if_pd_event(pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i):
     """Take one stretch of data and determine if there is an event there.
 
     Move in chunks twice as long as your longest photodiode signal
     with the first 0.25 as baseline to test whether the signal goes
     above/below baseline and back below/above.
-    The event onset must have a minimum length `min_i`.
+    The event onset must be of maximum length `max_flip_i`.
     """
-    b = pd[i - baseline_i:i]
+    b = pd_diff[i - baseline_i:i]
     # use twice the length so event can be centered
-    s = (pd[i:i + 2 * max_len_i] - np.median(b)) / np.std(b)
+    s = (pd_diff[i:i + 2 * max_len_i] - np.median(b)) / np.std(b)
     binned_s = np.digitize(s, [-np.inf, -zscore, zscore, np.inf]) - 2
-    for direction, binary_s in {'up': binned_s, 'down': s < -binned_s}.items():
+    for direction, binary_s in {'up': binned_s, 'down': -binned_s}.items():
         onset = np.where(binary_s[:max_len_i] == 1)[0]
-        if onset.size > min_i:  # must be long enough
+        # must be flip on but can't flip back and forth
+        if onset.size > 0 and onset.size < max_flip_i and \
+                all(binary_s[onset[0]:onset[-1]] == 1):
             e = onset[0]
             if all(binary_s[:e] == 0):  # must start off:
                 # must have an offset and no more events
-                offset = np.where(binary_s[e:] < 1)[0]
-                if offset.size > 0:
+                offset = np.where(binary_s[e:] == -1)[0]
+                if offset.size > 0 and offset.size < max_flip_i and \
+                        all(binary_s[e + offset[0]:e + offset[-1]] == -1):
                     oe = offset[0]
-                    if oe <= max_len_i and \
-                            all(binary_s[e + oe:e + max_len_i] == 0):
-                        return direction, i + e, i + e + oe
+                    eo = all(binary_s[e + oe + max_flip_i:e + max_len_i] == 0)
+                    if oe <= max_len_i and eo:  # must end off
+                        return direction, i + e + 1, i + e + oe + 2
     return None, None, None
 
 
-def _find_pd_candidates(pd, max_len_i, baseline_i, zscore, min_i,
+def _find_pd_candidates(pd, max_len_i, baseline_i, zscore, max_flip_i,
                         verbose=True):
     """Find all points in the signal that look like a square wave."""
     if verbose:
         print('Finding photodiode events')
     pd_candidates = dict(up=set(), down=set())
-    for i in tqdm(range(baseline_i, len(pd) - max_len_i - baseline_i,
+    pd_diff = np.diff(pd)
+    for i in tqdm(range(baseline_i, len(pd_diff) - max_len_i - baseline_i,
                         baseline_i // 2)):
         direction, onset, _ = _check_if_pd_event(
-            pd, i, max_len_i, zscore, min_i, baseline_i)
+            pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i)
         # no rounding errors
         if onset is not None and all(
-            [onset + j not in this_pd_cs for j in range(-min_i, min_i + 1)
+            [onset + j not in this_pd_cs for j in
+             range(-max_flip_i, max_flip_i + 1)
              for this_pd_cs in pd_candidates.values()]):
             pd_candidates[direction].add(onset)
     pd_direction = 'down' if \
         len(pd_candidates['down']) > len(pd_candidates['up']) else 'up'
     pd_candidates = pd_candidates[pd_direction]
+    if len(pd_candidates) == 0:
+        raise ValueError('No photodiode candidates found, please raise an'
+                         'issue with code to reproduce the error on GitHub')
     if verbose:
         print(f'{len(pd_candidates)} {pd_direction}-deflection photodiode '
               'candidate events found')
@@ -418,17 +438,17 @@ 
Source code for parse_pd
 def _recover_event(ch_data, b_event, exclude_shift_i, zscore):
     """Recover with a corrupted baseline or plateau but not on/offset."""
     b_event_i = np.round(b_event).astype(int)
-    section = ch_data[b_event_i - exclude_shift_i:
-                      b_event_i + exclude_shift_i].copy()
-    baseline = ch_data[b_event_i - 2 * exclude_shift_i:
-                       b_event_i - exclude_shift_i]
+    section = np.diff(ch_data[b_event_i - exclude_shift_i:
+                              b_event_i + exclude_shift_i])
+    baseline = np.diff(ch_data[b_event_i - 2 * exclude_shift_i:
+                               b_event_i - exclude_shift_i])
     section = (section - np.median(baseline)) / baseline.std()
-    event = np.where(abs(section) > zscore)[0]
+    event = np.where(abs(section) > zscore)[0] + 1
     return b_event_i - exclude_shift_i + event[0] if event.size > 0 else None
 
 
 def _exclude_ambiguous_events(beh_events, candidates, best_alignment,
-                              ch_data, exclude_shift_i, max_len_i, resync_i,
+                              ch_data, exclude_shift_i, resync_i, max_len,
                               sfreq, zscore, recover, verbose=True):
     """Exclude all events that are outside the given shift compared to beh."""
     if verbose or recover:
@@ -436,7 +456,7 @@ 
Source code for parse_pd
         section_data = dict(b_event=list(), title=list(), section=list())
         print('Excluding events that have zero close events or more than '
               'one photodiode event within `max_len` time')
-    sfreq_i = np.round(sfreq).astype(int)
+    max_len_i = np.round(sfreq * max_len).astype(int)
     events = dict()
     errors = dict()
     candidates_set = set(candidates)
@@ -479,9 +499,12 @@ 
Source code for parse_pd
                             text = f'No event found to recover\nfor event {i}'
                         else:
                             fig, ax = plt.subplots()
-                            section = ch_data[event - 2 * sfreq_i:
-                                              event + 2 * sfreq_i]
-                            ax.plot(np.linspace(-2, 2, 4 * sfreq_i), section)
+                            section_size = np.round(1.5 * max_len * sfreq
+                                                    ).astype(int)
+                            section = ch_data[event - section_size:
+                                              event + section_size]
+                            ax.plot(np.linspace(-1.5 * max_len, 1.5 * max_len,
+                                                section.size), section)
                             ax.plot([0, 0], [section.min(), section.max()])
                             ax.set_title(f'Corrupted Event {i}')
                             ax.set_xlabel('time (s)')
@@ -538,44 +561,49 @@ 
Source code for parse_pd
     return events
 
 
-def _save_data(fname, raw, events, event_id, ch_names, beh_df=None,
+def _save_data(raw, events, event_id, ch_names, beh=None,
                add_events=False, overwrite=False):
     """Save the events determined from the photodiode."""
+    fname = raw.filenames[0]
     basename = op.splitext(op.basename(fname))[0]
     out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
     if not op.isdir(out_dir):
         os.makedirs(out_dir)
     behf = op.join(out_dir, basename + '_beh_df.tsv')
-    if beh_df is None:
+    if beh is None:
         if op.isfile(behf) and overwrite:
             os.remove(behf)
     else:
-        if 'pd_parser_sample' in beh_df and not add_events and not overwrite:
+        if 'pd_parser_sample' in beh and not add_events and not overwrite:
             raise ValueError(
-                'The column name `pd_parser_sample` is not allowed in the '
-                'behavior tsv file (it\'s reserved for internal use. Please '
-                'rename that column to continue.')
+                'The key (column name) `pd_parser_sample` is not allowed '
+                'in the behavior tsv file (it\'s reserved for internal use. '
+                'Please rename that key (column) to continue.')
         if not add_events:
-            beh_df['pd_parser_sample'] = \
+            beh['pd_parser_sample'] = \
                 [events[i] if i in events else 'n/a' for i in
-                 range(len(beh_df[list(beh_df.keys())[0]]))]
-            _to_tsv(behf, beh_df)
+                 range(len(beh[list(beh.keys())[0]]))]
+            _to_tsv(behf, beh)
     onsets = np.array([events[i] for i in sorted(events.keys())])
     annot = mne.Annotations(onset=raw.times[onsets],
                             duration=np.repeat(0.1, len(onsets)),
                             description=np.repeat(event_id,
                                                   len(onsets)))
     if add_events:
-        annot_orig, ch_names_orig, _ = _load_data(fname)
+        annot_orig, ch_names_orig, _ = _load_data(raw)
         annot += annot_orig
         ch_names += [ch for ch in ch_names_orig if ch not in ch_names]
-    annot.save(op.join(out_dir, basename + '_annot.fif'))
+        overwrite = True
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=overwrite)
     with open(op.join(out_dir, basename + '_ch_names.tsv'), 'w') as fid:
         fid.write('\t'.join(ch_names))
+    return annot
 
 
-def _load_data(fname):
+def _load_data(raw):
     """Load previously saved photodiode data--annot and pd channel names."""
+    raw = _read_raw(raw, preload=None)
+    fname = raw.filenames[0]
     basename = op.splitext(op.basename(fname))[0]
     out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
     annot_fname = op.join(out_dir, basename + '_annot.fif')
@@ -595,7 +623,69 @@ 
Source code for parse_pd
     return mne.read_annotations(annot_fname), ch_names, beh_df
 
 
-
[docs]def find_pd_params(fname, pd_ch_names=None, verbose=True):
+def _check_overwrite(raw, add_events, overwrite):
+    """Check if the ``pd-parser`` data directory already exists."""
+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    if op.isdir(op.join(op.dirname(
+        raw.filenames[0]), basename + '_pd_parser_data')) and \
+            not overwrite and not add_events:
+        raise ValueError('Photodiode data directory already exists and '
+                         'overwrite=False, set overwrite=True to overwrite')
+
+
+def _recover_off_events(recovered, events, off_events, pd, max_len, sfreq):
+    import matplotlib.pyplot as plt
+    recover_data = dict(event=None)
+
+    def align_keypress(event):
+        if event.key in ('enter', 'e'):
+            if event.key == 'enter':
+                recover_data['event'] = recover_data['line'].get_xdata()[0]
+            else:
+                recover_data['event'] = None
+            plt.close(fig)
+        elif event.key in ('left', 'right'):
+            xmin, xmax = ax.get_xlim()
+            linex = recover_data['line'].get_xdata()[0]
+            linex += 1 if event.key == 'right' else -1
+            if linex >= xmin and linex <= xmax:
+                recover_data['line'].set_xdata([linex, linex])
+                fig.canvas.draw()
+        elif event.key in ('-', '+', '='):
+            xmin, xmax = ax.get_xlim()
+            linex = recover_data['line'].get_xdata()[0]
+            xmin = np.round(max([0, xmin - (linex - xmin)]) if event.key == '-'
+                            else np.mean([linex, xmin])).astype(int)
+            xmax = np.round(xmax + (xmax - linex) if event.key == '-' else
+                            np.mean([linex, xmax])).astype(int)
+            ax.set_xlim([xmin, xmax])
+            ax.set_ylim([section[xmin: xmax].min(), section[xmin: xmax].max()])
+            fig.canvas.draw()
+
+    for event_idx in recovered:
+        i = events[event_idx] - int(max_len * sfreq) // 4  # show beginning
+        section = pd[i: i + np.round(max_len * sfreq).astype(int)]
+        fig, ax = plt.subplots(figsize=(6, 6))
+        fig.subplots_adjust(top=0.75)
+        ax.set_title('Use the left/right keys to find the event offset\n'
+                     '+/- to scale the x axis\npress enter when finished\n'
+                     'or `e` to exclude the event')
+        ax.set_xlabel('time (s)')
+        ax.set_ylabel('voltage')
+        ax.plot(section, color='b')
+        ax.set_xticks([0, section.size])
+        ax.set_xticklabels([0, max_len])
+        recover_data['line'] = ax.plot(
+            [section.size - 1, section.size - 1],
+            [section.min(), section.max()], color='k')[0]
+        fig.canvas.mpl_connect('key_press_event', align_keypress)
+        plt.show(block=True)
+        if recover_data['event'] is not None:
+            off_events[event_idx] = recover_data['event'] + i
+    return off_events
+
+
+
[docs]def find_pd_params(raw, pd_ch_names=None, verbose=True):
     """Plot the data so the user can determine the right parameters.
 
     The user can adjust window size to determine max_len and horizontal
@@ -603,8 +693,9 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     pd_ch_names : list
         Names of the channel(s) containing the photodiode data.
         One channel is to be given for a common reference and
@@ -619,7 +710,7 @@ 
Source code for parse_pd
     import matplotlib as mpl
     mpl.rcParams['toolbar'] = 'None'
     import matplotlib.pyplot as plt
-    raw = _read_raw(fname, verbose=verbose)
+    raw = _read_raw(raw, verbose=verbose)
     pd, _ = _get_data(raw, pd_ch_names)
     fig, ax = plt.subplots(figsize=(6, 6))
     fig.subplots_adjust(top=0.75, left=0.15)
@@ -688,8 +779,8 @@ 
Source code for parse_pd
                 color='r')[0]
             ax.set_title(
                 'Scale\nUse the up/down arrows to set the horizontal line \n'
-                'at a level where the plateau of all the photodiode events \n'
-                'is above the green line nothing is below the red line\n'
+                'half way up the photodiode onset event with the baseline \n'
+                'in the middle of the y-axis\n'
                 'Use +/- to scale the time axis to see more events\n'
                 'press enter when finished')
             fig.canvas.draw()
@@ -747,10 +838,10 @@ 
Source code for parse_pd
     fig.show()

 
 
-
[docs]def parse_pd(fname, pd_event_name='Fixation', behf=None,
-             beh_col='fix_onset_time', pd_ch_names=None,
-             exclude_shift=0.03, resync=0.075, max_len=1., zscore=100,
-             min_i=10, baseline=0.25, add_events=False, recover=False,
+
[docs]def parse_pd(raw, pd_event_name='Fixation', beh=None,
+             beh_key='fix_onset_time', pd_ch_names=None,
+             exclude_shift=0.03, resync=0.075, max_len=1., zscore=10,
+             max_flip_i=40, baseline=0.25, add_events=False, recover=False,
              overwrite=False, verbose=True):
     """Parse photodiode events.
 
@@ -762,14 +853,16 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the time-series data file (e.g. meg, eeg, ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     pd_event_name: str
         The name of the event corresponding to the photodiode.
-    behf: str
-        The filepath to a tsv file with the behavioral timing.
-    beh_col: str
-        The column of the behf tsv that corresponds to the events.
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    beh_key: str
+        The key (column name) of the beh dictionary that corresponds
+        to the events.
     pd_ch_names : list
         Names of the channel(s) containing the photodiode data.
         One channel is to be given for a common reference and
@@ -796,9 +889,10 @@ 
Source code for parse_pd
         How large of a z-score difference to use to threshold photodiode
         events. Note, the must be large enough that any overshoot when
         returning to threshold is less than zscore compared to baseline.
-    min_i: int
-        The minimum number of samples the photodiode event must be on for.
-        This should not be changed unless the event is shorter.
+    max_flip_i: int
+        The maximum number of samples the photodiode event can take to
+        transition. This shouldn't usually need to be changed unless
+        the transition takes longer.
     baseline: float
         How much relative to the max_len to use to idenify the time before
         the photodiode event. This should not be changed most likely
@@ -820,10 +914,8 @@ 
Source code for parse_pd
 
     Returns
     -------
-    events: DataFrame
-        A DataFrame that has a column for to the (zero)
-        indexed behavioral events and another column corresponding
-        to the time stamp of the eeg file.
+    annot: mne.Annotations
+        The annotations with the added events.
 
     """
     if resync < exclude_shift:
@@ -831,14 +923,10 @@ 
Source code for parse_pd
                          f'than `resync` ({resync})')
     if baseline <= 0 or baseline > 1:
         raise ValueError(f'baseline must be between 0 and 1, got {baseline}')
-    # check if already parsed
-    basename = op.splitext(op.basename(fname))[0]
-    if op.isdir(op.join(op.dirname(fname), basename + '_pd_parser_data')
-                ) and not overwrite and not add_events:
-        raise ValueError('Photodiode data directory already exists and '
-                         'overwrite=False, set overwrite=True to overwrite')
     # load raw data file with the photodiode data
-    raw = _read_raw(fname, verbose=verbose)
+    raw = _read_raw(raw, verbose=verbose)
+    # check if already parsed
+    _check_overwrite(raw, add_events, overwrite)
     # transform behavior events to sample time
     max_len_i = np.round(raw.info['sfreq'] * max_len).astype(int)
     exclude_shift_i = np.round(raw.info['sfreq'] * exclude_shift
@@ -850,19 +938,19 @@ 
Source code for parse_pd
     pd, pd_ch_names = _get_data(raw, pd_ch_names)
     candidates = _find_pd_candidates(
         pd=pd, max_len_i=max_len_i, baseline_i=baseline_i, zscore=zscore,
-        min_i=min_i, verbose=verbose)
+        max_flip_i=max_flip_i, verbose=verbose)
     # load behavioral data with which to validate event timing
-    if behf is None:
+    if beh is None:
         if verbose:
             print('No behavioral tsv file was provided so the photodiode '
                   'events will be returned without validation by task '
                   'timing')
         events = {i: pd_event for i, pd_event in enumerate(candidates)}
-        _save_data(fname, raw=raw, events=events, event_id=pd_event_name,
+        _save_data(raw=raw, events=events, event_id=pd_event_name,
                    ch_names=pd_ch_names, overwrite=overwrite)
         return
     # if behavior is given use it to synchronize and exclude events
-    beh_events, beh_df = _load_beh_df(behf=behf, beh_col=beh_col)
+    beh_events, beh = _load_beh(beh=beh, beh_key=beh_key)
     beh_events = {i: beh_ev * raw.info['sfreq'] for i, beh_ev in
                   enumerate(beh_events) if beh_ev != 'n/a'}
     beh_events_sorted = np.array(list(beh_events.values()))
@@ -872,16 +960,17 @@ 
Source code for parse_pd
     events = _exclude_ambiguous_events(
         beh_events=beh_events, candidates=candidates,
         best_alignment=best_alignment, ch_data=pd,
-        exclude_shift_i=exclude_shift_i, max_len_i=max_len_i,
-        resync_i=resync_i, sfreq=raw.info['sfreq'],
+        exclude_shift_i=exclude_shift_i, resync_i=resync_i,
+        max_len=max_len, sfreq=raw.info['sfreq'],
         zscore=zscore, recover=recover, verbose=verbose)
-    _save_data(fname, raw=raw, events=events, event_id=pd_event_name,
-               ch_names=pd_ch_names, beh_df=beh_df,
-               add_events=add_events, overwrite=overwrite)

+    annot = _save_data(raw=raw, events=events, event_id=pd_event_name,
+                       ch_names=pd_ch_names, beh=beh,
+                       add_events=add_events, overwrite=overwrite)
+    return annot

 
 
-
[docs]def parse_audio(fname, audio_event_name='Tone', behf=None,
-                beh_col='tone_onset_time', audio_ch_names=None,
+
[docs]def parse_audio(raw, audio_event_name='Tone', beh=None,
+                beh_key='tone_onset_time', audio_ch_names=None,
                 exclude_shift=0.03, resync=0.075, max_len=0.25,
                 zscore=None, add_events=False, recover=False,
                 overwrite=False, verbose=True):
@@ -895,17 +984,16 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the time-series data file (e.g. meg, eeg, ieeg).
-    sound_fname: str
-        The filepath to the .wav file that the computer played to
-        match the events to.
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     audio_event_name: str
         The name of the event corresponding to the audio.
-    behf: str
-        The filepath to a tsv file with the behavioral timing.
-    beh_col: str
-        The column of the behf tsv that corresponds to the events.
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    beh_key: str
+        The key (column name) of the beh dictionary that corresponds
+        to the events.
     audio_ch_names: list
         Names of the channel(s) containing the audio data.
         One channel is to be given for a common reference and
@@ -941,23 +1029,17 @@ 
Source code for parse_pd
 
     Returns
     -------
-    events: DataFrame
-        A DataFrame that has a column for to the (zero)
-        indexed behavioral events and another column corresponding
-        to the time stamp of the eeg file.
+    annot: mne.Annotations
+        The annotations with the added events.
 
     """
     if resync < exclude_shift:
         raise ValueError(f'`exclude_shift` ({exclude_shift}) cannot be longer '
                          f'than `resync` ({resync})')
-    # check if already parsed
-    basename = op.splitext(op.basename(fname))[0]
-    if op.isdir(op.join(op.dirname(fname), basename + '_pd_parser_data')
-                ) and not overwrite and not add_events:
-        raise ValueError('Photodiode data directory already exists and '
-                         'overwrite=False, set overwrite=True to overwrite')
     # load raw data file with the photodiode data
-    raw = _read_raw(fname, verbose=verbose)
+    raw = _read_raw(raw, verbose=verbose)
+    # check if already parsed
+    _check_overwrite(raw, add_events, overwrite)
     # transform behavior events to sample time
     exclude_shift_i = np.round(raw.info['sfreq'] * exclude_shift
                                ).astype(int)
@@ -970,17 +1052,17 @@ 
Source code for parse_pd
         audio=audio, sfreq=raw.info['sfreq'], max_len_i=max_len_i,
         zscore=zscore, verbose=verbose)
     # load behavioral data with which to validate event timing
-    if behf is None:
+    if beh is None:
         if verbose:
             print('No behavioral tsv file was provided so the photodiode '
                   'events will be returned without validation by task '
                   'timing')
         events = {i: audio_event for i, audio_event in enumerate(candidates)}
-        _save_data(fname, raw=raw, events=events, event_id=audio_event_name,
+        _save_data(raw=raw, events=events, event_id=audio_event_name,
                    ch_names=audio_ch_names, overwrite=overwrite)
         return
     # if behavior is given use it to synchronize and exclude events
-    beh_events, beh_df = _load_beh_df(behf=behf, beh_col=beh_col)
+    beh_events, beh = _load_beh(beh=beh, beh_key=beh_key)
     beh_events = {i: beh_ev * raw.info['sfreq'] for i, beh_ev in
                   enumerate(beh_events) if beh_ev != 'n/a'}
     beh_events_sorted = np.array(list(beh_events.values()))
@@ -990,23 +1072,25 @@ 
Source code for parse_pd
     events = _exclude_ambiguous_events(
         beh_events=beh_events, candidates=candidates,
         best_alignment=best_alignment, ch_data=audio,
-        exclude_shift_i=exclude_shift_i, max_len_i=max_len_i,
-        resync_i=resync_i, sfreq=raw.info['sfreq'],
+        exclude_shift_i=exclude_shift_i, resync_i=resync_i,
+        max_len=max_len, sfreq=raw.info['sfreq'],
         zscore=zscore, recover=recover, verbose=verbose)
-    _save_data(fname, raw=raw, events=events, event_id=audio_event_name,
-               ch_names=audio_ch_names, beh_df=beh_df,
-               add_events=add_events, overwrite=overwrite)

+    annot = _save_data(raw=raw, events=events, event_id=audio_event_name,
+                       ch_names=audio_ch_names, beh=beh,
+                       add_events=add_events, overwrite=overwrite)
+    return annot

 
 
-
[docs]def add_pd_off_events(fname, off_event_name='Stim Off', max_len=1.,
-                      zscore=100, min_i=10, baseline=0.25,
+
[docs]def add_pd_off_events(raw, off_event_name='Stim Off', max_len=1.,
+                      zscore=10, max_flip_i=40, baseline=0.25,
                       verbose=True, overwrite=False):
     """Add events for when the photodiode deflection returns to baseline.
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     off_event : str
         If None, no event will be assigned to cessation of the photodiode
         deflection. If a string is provided, an event of that name will
@@ -1016,8 +1100,9 @@ 
Source code for parse_pd
     zscore: float
         How large of a z-score difference to use to threshold photodiode
         events.
-    min_i: int
-        The minimum number of samples the photodiode event must be on for.
+    max_flip_i: int
+        The maximum number of samples the photodiode event may take to
+        transition.
     baseline: float
         How much relative to max_len to use to idenify the time before
         the photodiode event.
@@ -1027,121 +1112,96 @@ 
Source code for parse_pd
     overwrite : bool
         Whether to overwrite existing data if it exists.
 
-    """
-    recover_data = dict(event=None)
-    recovered_events = list()
-
-    def align_keypress(event):
-        if event.key in ('enter', 'e'):
-            if event.key == 'enter':
-                recover_data['event'] = recover_data['line'].get_xdata()[0]
-            else:
-                recover_data['event'] = None
-            plt.close(fig)
-        elif event.key in ('left', 'right'):
-            xmin, xmax = ax.get_xlim()
-            linex = recover_data['line'].get_xdata()[0]
-            linex += 1 if event.key == 'right' else -1
-            if linex >= xmin and linex <= xmax:
-                recover_data['line'].set_xdata([linex, linex])
-                fig.canvas.draw()
-        elif event.key in ('-', '+', '='):
-            xmin, xmax = ax.get_xlim()
-            linex = recover_data['line'].get_xdata()[0]
-            xmin = np.round(max([0, xmin - (linex - xmin)]) if event.key == '-'
-                            else np.mean([linex, xmin])).astype(int)
-            xmax = np.round(xmax + (xmax - linex) if event.key == '-' else
-                            np.mean([linex, xmax])).astype(int)
-            ax.set_xlim([xmin, xmax])
-            ax.set_ylim([section[xmin: xmax].min(), section[xmin: xmax].max()])
-            fig.canvas.draw()
+    Returns
+    -------
+    annot: mne.Annotations
+        The annotations with the added events.
 
-    raw = _read_raw(fname, verbose=verbose)
-    annot, pd_ch_names, beh_df = _load_data(fname)
+    .. note::
+       The same parameters must be used for :func:`pd_parser.parse_pd`.
+    """
+    raw = _read_raw(raw, verbose=verbose)
+    annot, pd_ch_names, beh = _load_data(raw)
     pd = _get_channel_data(raw, pd_ch_names)
+    pd_diff = np.diff(pd)
     max_len_i = np.round(raw.info['sfreq'] * max_len).astype(int)
     baseline_i = np.round(max_len_i * baseline).astype(int)
-    events = {i: samp for i, samp in enumerate(beh_df['pd_parser_sample'])
+    events = {samp: i for i, samp in enumerate(beh['pd_parser_sample'])
               if samp != 'n/a'}
     off_events = {'up': dict(), 'down': dict()}
-    for event_idx, i in events.items():
-        i -= baseline_i  # move back one baseline avoid ramp up
-        direction, pd_start, pd_end = _check_if_pd_event(
-            pd, i, max_len_i, zscore, min_i, baseline_i)
-        if pd_start is None:  # event added manually, get offset manually
-            import matplotlib.pyplot as plt
-            sfreq_i = np.round(raw.info['sfreq']).astype(int)
-            section = pd[i: i + np.round(max_len * sfreq_i).astype(int)]
-            fig, ax = plt.subplots(figsize=(6, 6))
-            fig.subplots_adjust(top=0.75)
-            ax.set_title('Use the left/right keys to find the event offset\n'
-                         '+/- to scale the x axis\npress enter when finished\n'
-                         'or `e` to exclude the event')
-            ax.set_xlabel('time (s)')
-            ax.set_ylabel('voltage')
-            ax.plot(section, color='b')
-            ax.set_xticks([0, section.size])
-            ax.set_xticklabels([0, max_len])
-            recover_data['line'] = ax.plot(
-                [section.size - 1, section.size - 1],
-                [section.min(), section.max()], color='k')[0]
-            fig.canvas.mpl_connect('key_press_event', align_keypress)
-            plt.show()
-            if recover_data['event'] is not None:
-                recovered_events.append(recover_data['event'] + i)
-        else:
-            assert abs(pd_start - events[event_idx]) < min_i
-            off_events[direction][event_idx] = pd_end
+    for i in tqdm(range(baseline_i, len(pd_diff) - max_len_i - baseline_i,
+                        baseline_i // 2)):
+        direction, onset, offset = _check_if_pd_event(
+            pd_diff, i, max_len_i, zscore, max_flip_i, baseline_i)
+        if onset in events:
+            off_events[direction][events[onset]] = offset
+
     pd_direction = 'down' if \
         len(off_events['down']) > len(off_events['up']) else 'up'
     off_events = off_events[pd_direction]
-    onsets = np.array(list(off_events.values()) + recovered_events)
+    recovered = [event_idx for event_idx in events.values()
+                 if event_idx not in off_events.keys()]
+    if recovered:  # some events found manually, recover
+        events = {i: samp for i, samp in enumerate(beh['pd_parser_sample'])
+                  if samp != 'n/a'}  # invert lookup for sample
+        off_events = _recover_off_events(recovered, events, off_events,
+                                         pd, max_len, raw.info['sfreq'])
+    onsets = np.array(list(off_events.values()))
     annot += mne.Annotations(
         onset=raw.times[onsets], duration=np.repeat(0.1, onsets.size),
         description=np.repeat(off_event_name, onsets.size))
     # save modified data
-    basename = op.splitext(op.basename(fname))[0]
-    out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
-    annot.save(op.join(out_dir, basename + '_annot.fif'))

+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    out_dir = op.join(op.dirname(raw.filenames[0]),
+                      basename + '_pd_parser_data')
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=True)
+    return annot

 
 
-
[docs]def add_relative_events(fname, behf, relative_event_cols,
+
[docs]def add_relative_events(raw, beh, relative_event_keys,
                         relative_event_names=None,
                         overwrite=False, verbose=True):
     """Add events relative to those determined from the photodiode.
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
-    behf : str
-        The filepath to a tsv file with the behavioral timing
-    relative_event_cols : list
-        The names of the columns where time data is stored
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
+    beh: str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+    relative_event_keys : list
+        The names of the keys where time data is stored
         relative to the photodiode event
     relative_event_names : list
-        The names of the events in `relative_event_cols`.
+        The names of the events in `relative_event_keys`.
     verbose: bool
         Whether to display or supress text output on the progress
         of the function.
     overwrite: bool
         Whether to overwrite existing data if it exists.
 
+    Returns
+    -------
+    annot: mne.Annotations
+        The annotations with the added events.
+
     """
     if relative_event_names is None:
         if verbose:
-            print('Using relative event cols {} as relative event '
-                  'names'.format(', '.join(relative_event_cols)))
-        relative_event_names = relative_event_cols
-    if len(relative_event_cols) != len(relative_event_names):
-        raise ValueError('Mismatched length of relative event behavior '
-                         f'file column names, {len(relative_event_cols)} and '
-                         f'names of the events {len(relative_event_names)}')
-    raw = _read_raw(fname, verbose=verbose)
+            print('Using relative event keys {} as relative event '
+                  'names'.format(', '.join(relative_event_keys)))
+        relative_event_names = relative_event_keys
+    if len(relative_event_keys) != len(relative_event_names):
+        raise ValueError(
+            'Mismatched length of relative event behavior '
+            f'file keys (column names), {len(relative_event_keys)} and '
+            f'names of the events {len(relative_event_names)}')
+    raw = _read_raw(raw, verbose=verbose)
     relative_events = \
-        {name: _load_beh_df(behf, rel_event)[0]
-         for name, rel_event in zip(relative_event_names, relative_event_cols)}
-    annot, _, beh_df = _load_data(fname)
+        {name: _load_beh(beh, rel_event)[0]
+         for name, rel_event in zip(relative_event_names, relative_event_keys)}
+    annot, _, beh = _load_data(raw)
     for event_name in relative_event_names:
         if event_name in annot.description:
             if overwrite:
@@ -1151,7 +1211,7 @@ 
Source code for parse_pd
                 raise ValueError(f'Event name {event_name} already exists in '
                                  'saved events and `overwrite=False`, use '
                                  '`overwrite=True` to overwrite')
-    events = {i: samp for i, samp in enumerate(beh_df['pd_parser_sample'])
+    events = {i: samp for i, samp in enumerate(beh['pd_parser_sample'])
               if samp != 'n/a'}
     for name, beh_events in relative_events.items():
         onsets = np.array([events[i] + (beh_events[i] * raw.info['sfreq'])
@@ -1161,13 +1221,14 @@ 
Source code for parse_pd
                                  duration=np.repeat(0.1, onsets.size),
                                  description=np.repeat(name, onsets.size))
     # save modified data
-    basename = op.splitext(op.basename(fname))[0]
-    out_dir = op.join(op.dirname(fname), basename + '_pd_parser_data')
-    annot.save(op.join(out_dir, basename + '_annot.fif'))

+    basename = op.splitext(op.basename(raw.filenames[0]))[0]
+    out_dir = op.join(op.dirname(raw.filenames[0]),
+                      basename + '_pd_parser_data')
+    annot.save(op.join(out_dir, basename + '_annot.fif'), overwrite=True)
+    return annot

 
 
-
[docs]def add_events_to_raw(fname, out_fname=None, drop_pd_channels=True,
-                      verbose=True, overwrite=False):
+
[docs]def add_events_to_raw(raw, drop_pd_channels=True, verbose=True):
     """Save out a new raw file with photodiode events.
 
     Note: this function is not recommended, rather just skip it and
@@ -1178,43 +1239,30 @@ 
Source code for parse_pd
 
     Parameters
     ----------
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
-    out_fname : str
-        The filepath to save the modified raw data to.
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     drop_pd_channels : bool
         Whether to drop the channel(s) the photodiode data was on.
     verbose: bool
         Whether to display or supress text output on the progress
         of the function.
-    overwrite: bool
-        Whether to overwrite existing data if it exists.
 
     Returns
     -------
-    out_fname : str
-        The filepath to save the modified raw data to.
+    raw : mne.io.Raw
+        The modified raw object with events.
 
     """
-    raw = _read_raw(fname, verbose=verbose)
-    if out_fname is None:
-        _, ext = op.splitext(fname)
-        out_fname = fname.replace(ext, 'pd_raw.fif')
-    if op.isfile(out_fname) and not overwrite:
-        raise ValueError(f'out_fname {out_fname} exists, and overwrite=False, '
-                         'set overwrite=True to overwrite')
-    annot, pd_ch_names, _ = _load_data(fname)
+    raw = _read_raw(raw, verbose=verbose)
+    annot, pd_ch_names, _ = _load_data(raw)
     raw.set_annotations(annot)
     if drop_pd_channels:
         raw.drop_channels([ch for ch in pd_ch_names if ch in raw.ch_names])
-    if op.splitext(out_fname)[-1] != '.fif':
-        raise ValueError('Only saving as fif is supported, got '
-                         f'{op.splitext(out_fname)}')
-    raw.save(out_fname, overwrite=overwrite)
-    return out_fname

+    return raw

 
 
-
[docs]def pd_parser_save_to_bids(bids_dir, fname, sub, task, ses=None, run=None,
+
[docs]def pd_parser_save_to_bids(bids_dir, raw, sub, task, ses=None, run=None,
                            data_type=None, eogs=None, ecgs=None, emgs=None,
                            verbose=True, overwrite=False):
     """Convert data to BIDS format with events found from the photodiode.
@@ -1224,8 +1272,9 @@ 
Source code for parse_pd
     bids_dir: str
         The subject directory in the bids directory where the data
         should be saved.
-    fname: str
-        The filepath to the electrophysiology file (meg/eeg/ieeg).
+    raw: str | mne Raw object
+        The object or filepath of the time-series data file
+        (e.g. meg, eeg, ieeg).
     sub: str
         The name of the subject.
     task: str
@@ -1242,6 +1291,9 @@ 
Source code for parse_pd
         The channels recording heart electrophysiology.
     emgs: list | None
         The channels recording muscle electrophysiology.
+    beh: None | str | dict
+        The dictionary or filepath to a tsv file with the behavioral timing.
+        If None, the stored data is used.
     verbose: bool
         Whether to display or supress text output on the progress
         of the function.
@@ -1252,7 +1304,7 @@ 
Source code for parse_pd
     import mne_bids
     if not op.isdir(bids_dir):
         os.makedirs(bids_dir)
-    raw = _read_raw(fname, preload=False, verbose=verbose)
+    raw = _read_raw(raw, preload=False, verbose=verbose)
     aux_chs = list()
     for name, ch_list in zip(['eog', 'ecg', 'emg'], [eogs, ecgs, emgs]):
         if ch_list is not None:
@@ -1261,33 +1313,24 @@ 
Source code for parse_pd
     if data_type is not None:
         raw.set_channel_types({ch: data_type for ch in raw.ch_names if
                                ch not in aux_chs})
-    annot, pd_channels, beh_df = _load_data(fname)
+    annot, pd_channels, beh = _load_data(raw)
     raw.set_annotations(annot)
     events, event_id = mne.events_from_annotations(raw)
-    raw.set_channel_types({ch: 'stim' for ch in pd_channels
-                           if ch in raw.ch_names})
+    # raw.set_channel_types({ch: 'stim' for ch in pd_channels
+    #                        if ch in raw.ch_names})
     bids_path = mne_bids.BIDSPath(subject=sub, session=ses, task=task,
                                   run=run, root=bids_dir)
-    mne_bids.write_raw_bids(raw, bids_path, events_data=events,
-                            event_id=event_id, verbose=verbose,
+    mne_bids.write_raw_bids(raw, bids_path, verbose=verbose,
                             overwrite=overwrite)
-    '''
-    beh_path = bids_path.copy().update(datatype='beh', extension='tsv')
-    if not op.isdir(op.dirname(beh_path.fpath)):
-        os.makedirs(op.dirname(beh_path.fpath))
-    if beh_df is not None:
-        _to_tsv(beh_path.fpath, beh_df)
-    '''
-    bids_beh_dir = \
-        op.join(bids_dir, f'sub-{sub}', f'ses-{ses}' if ses else '', 'beh')
-    if not op.isdir(bids_beh_dir):
-        os.makedirs(bids_beh_dir)
-    if beh_df is not None:
-        _to_tsv(op.join(bids_beh_dir, bids_path.basename + '_beh.tsv'), beh_df)

+    beh_path = bids_path.copy().update(datatype='beh')
+    if not op.isdir(op.dirname(beh_path.fpath)):
+        os.makedirs(op.dirname(beh_path.fpath))
+    if beh is not None:
+        _to_tsv(str(beh_path.fpath) + '_beh.tsv', beh)

 
 
 
[docs]def simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300., iti=6.,
-                     iti_jitter=1.5, drift=0.1, prop_corrupted=0.1,
+                     iti_jitter=1.5, rc_decay=0.0001, prop_corrupted=0.1,
                      sfreq=1000., seed=11, show=False):
     """Simulate photodiode data.
 
@@ -1311,8 +1354,8 @@ 
Source code for parse_pd
     iti_jitter: float
         The jitter displacing the events from exactly `iti` distance
         away from each other.
-    drift: float
-        The factor controlling how much the photodiode changes value
+    rc_decay: float
+        The factor controlling how much the photodiode decays back to baseline
         over time with no external simulus (0. == perfect square wave).
     sfreq: float
         The sampling frequency of the data.
@@ -1323,8 +1366,8 @@ 
Source code for parse_pd
     -------
     raw: mne.io.Raw
         The raw object containing the photodiode data
-    beh_df: dict
-        A dictionary with columns:
+    beh: dict
+        A dictionary with keys (columns names):
 
             `trial` : int
                 The index of the event.
@@ -1344,7 +1387,7 @@ 
Source code for parse_pd
         raise ValueError('If a list of `n_secs_on` is provided, it must '
                          f'match the number of events, {n_events}, got '
                          f'{len(n_secs_on)}')
-    assert drift > 0 and iti > 0 and iti_jitter > 0
+    assert rc_decay >= 0 and iti > 0 and iti_jitter > 0
     # n_secs on as list is okay, just make an array
     if isinstance(n_secs_on, list):
         n_secs_on = np.array(n_secs_on)
@@ -1369,19 +1412,30 @@ 
Source code for parse_pd
         (np.random.random(n_events) * iti_jitter + iti) * sfreq)).astype(int)
     events[:, 2] = 1
     # make pink noise
-    n_points = events[:, 0].max() + int(2 * iti * sfreq)
+    n_secs_on_mean = n_secs_on if isinstance(n_secs_on, (float, int)) else \
+        np.array(n_secs_on).max()
+    n_points = events[:, 0].max() + int(4 * n_secs_on_mean * sfreq)
     n_points += n_points % 2  # must be even
     x = np.random.randn(n_points // 2) + np.random.randn(n_points // 2) * 1j
     x /= np.sqrt(np.arange(1, x.size + 1))
     pd_data = np.fft.irfft(x).real
     pd_data /= pd_data.std()
     # add photodiode square waves to pink noise
-    for event, n_on in zip(events[:, 0], n_samp_on):
-        drift_array = np.linspace(0, 1, n_on)
-        pd_data[event: event + n_on] += \
-            amp - drift * amp * drift_array
-        pd_data[event + n_on: event + 2 * n_on] += \
-            drift * amp * drift_array - amp * drift
+    flip_data = np.zeros((pd_data.shape))
+    for i in range(n_events):
+        event = events[i, 0]
+        n_on = n_samp_on[i]
+        next_event = events[i + 1, 0] - n_on if i < n_events - 1 else \
+            pd_data.size - n_on
+        flip_data[event] += amp
+        for i in range(1, n_on):  # on decay
+            flip_data[event + i] += flip_data[event + i - 1] * (1 - rc_decay)
+        flip_data[event + n_on - 1] -= amp * ((1 - rc_decay)**(n_on / 2))
+        max_i = min([next_event - event, int(i / rc_decay), int(10 * sfreq)])
+        for i in range(max_i):
+            flip_data[event + n_on + i] += \
+                flip_data[event + n_on + i - 1] * (1 - rc_decay)
+    pd_data += flip_data
     # corrupt some events
     n_events_corrupted = np.round(n_events * prop_corrupted).astype(int)
     corrupted_indices = np.random.choice(range(n_events), n_events_corrupted,
@@ -1394,10 +1448,10 @@ 
Source code for parse_pd
         ts_cor = int(len(samp_range) * np.random.random() * 0.02 + 0.005)
         for ts in np.random.choice(samp_range, ts_cor, replace=False):
             # disrupt 1 / 5 of on time, 5 times amplitude
-            pd_data[ts - n_on // 10: ts + n_on // 10] = \
-                np.random.random() * 5 * amp - amp
-    beh_df = dict(trial=np.arange(n_events),
-                  time=events[:, 0].astype(float) / sfreq)
+            pd_data[ts - n_on // 10: ts + n_on // 10] += \
+                (np.random.random() - 0.5) * 5 * amp - amp
+    beh = dict(trial=np.arange(n_events),
+               time=events[:, 0].astype(float) / sfreq)
     events = np.delete(events, corrupted_indices, axis=0)
     # plot if show
     if show:
@@ -1411,7 +1465,7 @@ 
Source code for parse_pd
     # create mne.io.Raw object
     info = mne.create_info(['pd'], sfreq, ['stim'])
     raw = mne.io.RawArray(pd_data[np.newaxis], info)
-    return raw, beh_df, events, corrupted_indices

+    return raw, beh, events, corrupted_indices

 

 
     

@@ -1426,7 +1480,7 @@ 
Source code for parse_pd
     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   

 
diff --git a/docs/dev/_sources/auto_examples/index.rst.txt b/docs/dev/_sources/auto_examples/index.rst.txt
index 323a1c5..1b83684 100644
--- a/docs/dev/_sources/auto_examples/index.rst.txt
+++ b/docs/dev/_sources/auto_examples/index.rst.txt
@@ -24,6 +24,7 @@ Here we show some exemplary use cases for pd-parser.
 .. only:: html
 
  .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_events_thumb.png
+     :alt: Find Photodiode Events
 
      :ref:`sphx_glr_auto_examples_plot_find_pd_events.py`
 
@@ -39,13 +40,14 @@ Here we show some exemplary use cases for pd-parser.
 
 .. raw:: html
 
-    

+    

 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_recover_events_thumb.png
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_on_and_off_thumb.png
+     :alt: Find Photodiode On and Off Events
 
-     :ref:`sphx_glr_auto_examples_plot_recover_events.py`
+     :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py`
 
 .. raw:: html
 
@@ -55,17 +57,18 @@ Here we show some exemplary use cases for pd-parser.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_recover_events
+   /auto_examples/plot_find_pd_on_and_off
 
 .. raw:: html
 
-    

+    

 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_pd_on_and_off_thumb.png
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_audio_events_thumb.png
+     :alt: Use Audio to Align Video Data
 
-     :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py`
+     :ref:`sphx_glr_auto_examples_plot_find_audio_events.py`
 
 .. raw:: html
 
@@ -75,17 +78,18 @@ Here we show some exemplary use cases for pd-parser.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_find_pd_on_and_off
+   /auto_examples/plot_find_audio_events
 
 .. raw:: html
 
-    

+    

 
 .. only:: html
 
- .. figure:: /auto_examples/images/thumb/sphx_glr_plot_find_audio_events_thumb.png
+ .. figure:: /auto_examples/images/thumb/sphx_glr_plot_recover_events_thumb.png
+     :alt: Manually Recover Events Not Found by the Algorithm
 
-     :ref:`sphx_glr_auto_examples_plot_find_audio_events.py`
+     :ref:`sphx_glr_auto_examples_plot_recover_events.py`
 
 .. raw:: html
 
@@ -95,7 +99,7 @@ Here we show some exemplary use cases for pd-parser.
 .. toctree::
    :hidden:
 
-   /auto_examples/plot_find_audio_events
+   /auto_examples/plot_recover_events
 .. raw:: html
 
     

@@ -110,13 +114,13 @@ Here we show some exemplary use cases for pd-parser.
 
   .. container:: sphx-glr-download sphx-glr-download-python
 
-    :download:`Download all examples in Python source code: auto_examples_python.zip `
+    :download:`Download all examples in Python source code: auto_examples_python.zip `
 
 
 
   .. container:: sphx-glr-download sphx-glr-download-jupyter
 
-    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip `
 
 
 .. only:: html
diff --git a/docs/dev/_sources/auto_examples/plot_find_audio_events.rst.txt b/docs/dev/_sources/auto_examples/plot_find_audio_events.rst.txt
index 47f20a8..2613ea2 100644
--- a/docs/dev/_sources/auto_examples/plot_find_audio_events.rst.txt
+++ b/docs/dev/_sources/auto_examples/plot_find_audio_events.rst.txt
@@ -1,12 +1,21 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/plot_find_audio_events.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
 .. only:: html
 
     .. note::
         :class: sphx-glr-download-link-note
 
-        Click :ref:`here `     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
 
-    .. _sphx_glr_auto_examples_plot_find_audio_events.py:
+.. _sphx_glr_auto_examples_plot_find_audio_events.py:
 
 
 =============================
@@ -17,6 +26,7 @@ algorithm for matching with time-stamps and rejecting misaligned
 audio, but applied using the onset of an audio deflection instead of detecting
 photodiode events based on their square wave shape.
 
+.. GENERATED FROM PYTHON SOURCE LINES 10-15
 
 .. code-block:: default
 
@@ -32,6 +42,8 @@ photodiode events based on their square wave shape.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 16-28
+
 Load in a video with audio:
 
 In this example, we'll use audio and instead of aligning electrophysiology
@@ -45,6 +57,7 @@ Note that the commands that require ffmpeg are pre-computed and commented
 out because ffmpeg must be installed to use them and it is not required by
 ``pd-parser``.
 
+.. GENERATED FROM PYTHON SOURCE LINES 28-85
 
 .. code-block:: default
 
@@ -53,14 +66,14 @@ out because ffmpeg must be installed to use them and it is not required by
     import numpy as np
     from scipy.io import wavfile
     from subprocess import call
-    # from subprocess import run , PIPE, STDOUT
+    # from subprocess import run, PIPE, STDOUT
     # import datetime
 
     import mne
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _load_data
+    from pd_parser.parse_pd import _load_data  # , _read_tsv
 
     # get the data
     out_dir = _TempDir()
@@ -87,10 +100,6 @@ out because ffmpeg must be installed to use them and it is not required by
     info = mne.create_info(['audio'], fs, ['stim'])
     raw = mne.io.RawArray(data[np.newaxis], info)
 
-    # save out data file
-    fname = op.join(out_dir, 'test_video-raw.fif')
-    raw.save(fname, overwrite=True)
-
     # find audio-visual time offset
     offset = 0  # pre-computed value for this video
     '''
@@ -102,6 +111,10 @@ out because ffmpeg must be installed to use them and it is not required by
         float(output[1].strip('stream|codec_type=audio|start_time'))
     '''
 
+    # save to disk as required by ``pd-parser``, raw needs a filename
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
+
     # navigate to corresponding behavior
     behf = op.join(out_dir, 'test_video_beh.tsv')
 
@@ -118,26 +131,31 @@ out because ffmpeg must be installed to use them and it is not required by
     Creating RawArray with float64 data, n_channels=1, n_times=16464896
         Range : 0 ... 16464895 =      0.000 ...   343.019 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif [done]
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    [done]
 
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 86-89
+
 Run the parser:
 
 Now we'll call the main function to automatically parse the audio events.
 
+.. GENERATED FROM PYTHON SOURCE LINES 89-92
 
 .. code-block:: default
 
-    pd_parser.parse_audio(fname, behf=behf, beh_col='tone_onset_time',
+    pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',
                           audio_ch_names=['audio'], zscore=10)
 
 
 
 
 .. image:: /auto_examples/images/sphx_glr_plot_find_audio_events_001.png
+    :alt: Synchronization Events Compared to Behavior Events
     :class: sphx-glr-single-img
 
 
@@ -147,8 +165,8 @@ Now we'll call the main function to automatically parse the audio events.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
     Isotrak not found
         Range : 0 ... 16464895 =      0.000 ...   343.019 secs
     Ready.
@@ -156,37 +174,40 @@ Now we'll call the main function to automatically parse the audio events.
     Finding points where the audio is above `zscore` threshold...
     17 audio candidate events found
     Checking best alignments
-
      0%|          | 0/14 [00:00
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 93-97
 
 Load the results:
 
 Finally, we'll load the events and use them to crop the video although it
 requires ffmpeg so it is commented out.
 
+.. GENERATED FROM PYTHON SOURCE LINES 97-112
 
 .. code-block:: default
 
-    annot, _, beh_df = _load_data(fname)
+    annot = _load_data(fname)[0]
     print('Here are the event times: ', annot.onset)
 
     # Crop the videos with ffmpeg
     '''
+    beh = _read_tsv(behf)
     for i in range(annot.onset.size):  # skip the first video
-        action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000
+        action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000
         run(['ffmpeg', '-i', f'{video_fname}', '-ss',
              str(datetime.timedelta(
                  seconds=annot.onset[i] - action_time - offset)),
              '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),
              op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(
-                 beh_df['movement'][i], beh_df['action_type'][i]))])
+                 beh['movement'][i], beh['action_type'][i]))])
     '''
 
 
@@ -198,18 +219,23 @@ requires ffmpeg so it is commented out.
 
  .. code-block:: none
 
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+    Isotrak not found
+        Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+    Ready.
     Here are the event times:  [ 19.05112457  39.9129982   61.88574982  83.54243469 104.41456604
      126.07720947 147.5539856  168.61270142 189.57843018 211.35673523
      250.20858765 271.68209839 292.14001465 313.30532837 333.78097534]
 
-    "\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh_df['movement'][i], beh_df['action_type'][i]))])\n"
+    "\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh['movement'][i], beh['action_type'][i]))])\n"
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  32.427 seconds)
+   **Total running time of the script:** ( 0 minutes  28.131 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_find_audio_events.py:
diff --git a/docs/dev/_sources/auto_examples/plot_find_pd_events.rst.txt b/docs/dev/_sources/auto_examples/plot_find_pd_events.rst.txt
index 9a3eef5..c5eca4c 100644
--- a/docs/dev/_sources/auto_examples/plot_find_pd_events.rst.txt
+++ b/docs/dev/_sources/auto_examples/plot_find_pd_events.rst.txt
@@ -1,12 +1,21 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "auto_examples/plot_find_pd_events.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
 .. only:: html
 
     .. note::
         :class: sphx-glr-download-link-note
 
-        Click :ref:`here `     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
 
-    .. _sphx_glr_auto_examples_plot_find_pd_events.py:
+.. _sphx_glr_auto_examples_plot_find_pd_events.py:
 
 
 ======================
@@ -15,6 +24,7 @@ Find Photodiode Events
 In this example, we use ``pd-parser`` to find photodiode events and
 align them to behavior. Then, we save the data to BIDS format.
 
+.. GENERATED FROM PYTHON SOURCE LINES 8-13
 
 .. code-block:: default
 
@@ -30,12 +40,15 @@ align them to behavior. Then, we save the data to BIDS format.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 14-19
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw object` so that we can save out some random
 data with a photodiode event channel in it in `fif` format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 19-54
 
 .. code-block:: default
 
@@ -46,7 +59,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv
+    from pd_parser.parse_pd import _read_raw, _to_tsv
 
     out_dir = _TempDir()
     print(f'After running this example, you can find the data here: {out_dir}')
@@ -54,7 +67,7 @@ electrophysiology data format).
     # simulate photodiode data
     n_events = 300
     prop_corrupted = 0.01
-    raw, beh_df, events, corrupted_indices = \
+    raw, beh, events, corrupted_indices = \
         pd_parser.simulate_pd_data(n_events=n_events,
                                    prop_corrupted=prop_corrupted)
 
@@ -71,6 +84,9 @@ electrophysiology data format).
     fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
     raw.save(fname)
 
+    # roundtrip so that raw is properly loaded from disk and has a filename
+    raw = _read_raw(fname)
+
 
 
 
@@ -81,18 +97,27 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6
+    Creating RawArray with float64 data, n_channels=1, n_times=2036106
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
+    Ready.
+    Creating RawArray with float64 data, n_channels=3, n_times=2036106
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    [done]
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
+    Isotrak not found
+        Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif [done]
+    Reading 0 ... 2036105  =      0.000 ...  2036.105 secs...
+
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 55-63
 
 Make behavior data:
 
@@ -103,6 +128,7 @@ might look in an experimental setting.
 Let's make a task where there is a fixation stimulus, then a go cue,
 and a then response as an example.
 
+.. GENERATED FROM PYTHON SOURCE LINES 63-83
 
 .. code-block:: default
 
@@ -119,12 +145,13 @@ and a then response as an example.
     for i in [10, 129, 232, 288]:
         response_time[i] = 'n/a'  # make some no responses
     # put in dictionary to be converted to tsv file
-    beh_df['fix_onset_time'] = beh_df['time'] + offsets
-    beh_df['go_time'] = go_time
-    beh_df['response_time'] = response_time
+    beh['fix_onset_time'] = beh['time'] + offsets
+    beh['go_time'] = go_time
+    beh['response_time'] = response_time
     behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
     # save behavior file out
-    _to_tsv(behf, beh_df)
+    _to_tsv(behf, beh)
+
 
 
 
@@ -132,6 +159,7 @@ and a then response as an example.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 84-91
 
 Use the interactive graphical user interface (GUI) to find parameters:
 
@@ -141,53 +169,43 @@ into a console running python (ipython recommended), you can see how to
 interact with the photodiode data to pick reasonable parameters by
 following the instructions.
 
+.. GENERATED FROM PYTHON SOURCE LINES 91-94
 
 .. code-block:: default
 
 
-    pd_parser.find_pd_params(fname, pd_ch_names=['pd'])
+    pd_parser.find_pd_params(raw, pd_ch_names=['pd'])
 
 
 
 
 .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_001.png
+    :alt: Align Use the left/right keys to find an uncorrupted photodiode event and align the onset to the center of the window use +/- to zoom the yaxis in and out (up/down to translate) press enter when finished
     :class: sphx-glr-single-img
 
 
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
-    Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-    Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-    /Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:653: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      fig.show()
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 95-105
 
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters or the parameters you
 found from :func:`pd_parser.find_pd_parameters` to find and align the
-photodiode events, excluding events that were off because the commuter
+photodiode events, excluding events that were off because the computer
 hung up on computation, for instance. That data is saved in the same folder
 as the raw file (in this case, a temperary directory generated by
 :func:`_TempDir`). The data can be used directly, or it can be accessed via
 :func:`pd_parser.pd_parser_save_to_bids` to store it in the brain imagine
 data structure (BIDS) standardized format before using it.
 
+.. GENERATED FROM PYTHON SOURCE LINES 105-108
 
 .. code-block:: default
 
 
-    pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)
+    pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)
 
 
 
@@ -198,12 +216,14 @@ data structure (BIDS) standardized format before using it.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_002.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Excluding event 5, off by 32 ms, Excluding event 7, off by -30 ms, Excluding event 8, off by 32 ms, Excluding event 14, off by -30 ms, Excluding event 37, off by 83 ms, Excluding event 76, off by -30 ms, Excluding event 96, off by 31 ms, Excluding event 98, off by -32 ms, Excluding event 180, no event found, Excluding event 262, no event found, Excluding event 279, off by 31 ms, Excluding event 289, off by -30 ms
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_events_003.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -212,40 +232,32 @@ data structure (BIDS) standardized format before using it.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
-    Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-    Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
     Finding photodiode events
-
      0%|          | 0/10920 [00:00`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
 
-    .. _sphx_glr_auto_examples_plot_find_pd_on_and_off.py:
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_auto_examples_plot_find_pd_on_and_off.py:
 
 
 =================================
@@ -16,6 +25,7 @@ In this example, we use ``pd-parser`` to find photodiode events and
 align both the onset of the deflection and the cessation to
 to behavior.
 
+.. GENERATED FROM PYTHON SOURCE LINES 9-14
 
 .. code-block:: default
 
@@ -31,12 +41,15 @@ to behavior.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 15-20
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw` object so that we can save out some random
 data with a photodiode event channel in it in fif format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 20-62
 
 .. code-block:: default
 
@@ -47,7 +60,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv, _load_data
+    from pd_parser.parse_pd import _load_data
 
     import matplotlib.pyplot as plt
     import matplotlib.cm as cm
@@ -60,7 +73,7 @@ electrophysiology data format).
     # let's make our photodiode events on random uniform from 0.5 to 1 second
     n_secs_on = np.random.random(n_events) * 0.5 + 0.5
     prop_corrupted = 0.01
-    raw, beh_df, events, corrupted_indices = \
+    raw, beh, events, corrupted_indices = \
         pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                    prop_corrupted=prop_corrupted)
 
@@ -74,15 +87,13 @@ electrophysiology data format).
     raw.info['dig'] = None
     raw.info['line_freq'] = 60
 
-    # save to disk as required by ``pd-parser``
-    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-    raw.save(fname)
     # add some offsets to the behavior so it's a bit more realistic
     offsets = np.random.randn(n_events) * 0.01
-    beh_df['time'] = np.array(beh_df['time']) + offsets
-    behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-    _to_tsv(behf, beh_df)
+    beh['time'] = np.array(beh['time']) + offsets
 
+    # save to disk as required by ``pd-parser``
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
 
 
 
@@ -94,18 +105,21 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=1, n_times=2036104
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=3, n_times=2036104
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif [done]
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    [done]
 
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 63-69
+
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters to find and align the
@@ -113,12 +127,13 @@ photodiode events, excluding events that were off.
 One percent of the 300 events (3) were corrupted as shown in the plots and
 some were too far off from large offsets that we're going to exclude them.
 
+.. GENERATED FROM PYTHON SOURCE LINES 69-74
 
 .. code-block:: default
 
 
-    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                       pd_ch_names=['pd'], beh_col='time',
+    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                       pd_ch_names=['pd'], beh_key='time',
                        max_len=1.5)  # none are on longer than 1.5 seconds
 
 
@@ -130,12 +145,14 @@ some were too far off from large offsets that we're going to exclude them.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_on_and_off_001.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Excluding event 44, off by -37 ms, Excluding event 56, off by -37 ms, Excluding event 71, off by 31 ms, Excluding event 154, off by 37 ms, Excluding event 209, off by 30 ms, Excluding event 238, no event found, Excluding event 266, off by -33 ms, Excluding event 274, no event found, Excluding event 281, off by -43 ms
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_find_pd_on_and_off_002.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -144,43 +161,41 @@ some were too far off from large offsets that we're going to exclude them.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+    Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
     Finding photodiode events
-
      0%|          | 0/10920 [00:00`     to download the full example code
-    .. rst-class:: sphx-glr-example-title
+        Click :ref:`here `
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
 
-    .. _sphx_glr_auto_examples_plot_recover_events.py:
+.. _sphx_glr_auto_examples_plot_recover_events.py:
 
 
 ==================================================
@@ -18,6 +27,7 @@ baselines but not corrupted onsets or offsets.
 Note that it might be a good idea not to recover these events
 as there might be noise in the data around this time.
 
+.. GENERATED FROM PYTHON SOURCE LINES 11-16
 
 .. code-block:: default
 
@@ -33,12 +43,15 @@ as there might be noise in the data around this time.
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 17-22
+
 Simulate data and use it to make a raw object:
 
 We'll make an `mne.io.Raw` object so that we can save out some random
 data with a photodiode event channel in it in fif format (a commonly used
 electrophysiology data format).
 
+.. GENERATED FROM PYTHON SOURCE LINES 22-85
 
 .. code-block:: default
 
@@ -50,7 +63,7 @@ electrophysiology data format).
     from mne.utils import _TempDir
 
     import pd_parser
-    from pd_parser.parse_pd import _to_tsv
+    from pd_parser.parse_pd import _load_data
 
     import matplotlib.pyplot as plt
 
@@ -61,7 +74,7 @@ electrophysiology data format).
     n_events = 300
     # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds
     n_secs_on = np.random.random(n_events) * 0.5 + 0.25
-    raw, beh_df, events, _ = \
+    raw, beh, events, _ = \
         pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                    prop_corrupted=0.0)
     sfreq = np.round(raw.info['sfreq']).astype(int)
@@ -74,16 +87,19 @@ electrophysiology data format).
     axes[0].set_ylabel('voltage')
     for j, i in enumerate(events[corrupted_indices, 0]):
         if j == 0:
-            raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount
+            raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount
         elif j == 1:
             raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount
         else:
-            raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount
+            raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount
         axes[j].plot(np.linspace(-1, 2, 3 * sfreq),
                      raw._data[0, i - sfreq: i + sfreq * 2])
         axes[j].set_xlabel('time (s)')
 
 
+    # make figure nicer
+    fig.tight_layout()
+
     # make fake electrophysiology data
     info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
                            ['seeg'] * 3)
@@ -94,20 +110,19 @@ electrophysiology data format).
     raw.info['dig'] = None
     raw.info['line_freq'] = 60
 
-    # save to disk as required by ``pd-parser``
-    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-    raw.save(fname)
     # add some offsets to the behavior so it's a bit more realistic
     offsets = np.random.randn(n_events) * 0.001
-    beh_df['time'] = np.array(beh_df['time']) + offsets
-    behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-    _to_tsv(behf, beh_df)
+    beh['time'] = np.array(beh['time']) + offsets
 
+    # save to disk as required by ``pd-parser``, raw needs to have a filename
+    fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+    raw.save(fname)
 
 
 
 
 .. image:: /auto_examples/images/sphx_glr_plot_recover_events_001.png
+    :alt: Corrupted Events
     :class: sphx-glr-single-img
 
 
@@ -117,18 +132,21 @@ electrophysiology data format).
 
  .. code-block:: none
 
-    Creating RawArray with float64 data, n_channels=1, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=1, n_times=2035104
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Creating RawArray with float64 data, n_channels=3, n_times=2044106
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Creating RawArray with float64 data, n_channels=3, n_times=2035104
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif [done]
+    Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    [done]
 
 
 
 
+.. GENERATED FROM PYTHON SOURCE LINES 86-94
+
 Find the photodiode events relative to the behavioral timing of interest:
 
 This function will use the default parameters to find and align the
@@ -138,13 +156,14 @@ Note that the mock function mocks user input so when you run the example,
 you want to delete that line and unindent the next line, and then provide
 your own input depending on whether you want to keep the events or not.
 
+.. GENERATED FROM PYTHON SOURCE LINES 94-99
 
 .. code-block:: default
 
 
     with mock.patch('builtins.input', return_value='y'):
-        pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                           pd_ch_names=['pd'], beh_col='time', recover=True)
+        pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                           pd_ch_names=['pd'], beh_key='time', recover=True)
 
 
 
@@ -155,27 +174,32 @@ your own input depending on whether you want to keep the events or not.
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_002.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 8
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_003.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 144
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_004.png
-            :class: sphx-glr-multi-img
+          :alt: Corrupted Event 234
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_005.png
-            :class: sphx-glr-multi-img
+          :alt: Excluded Events, Event 8 recovered (not excluded), Event 144 recovered (not excluded), Event 234 recovered (not excluded)
+          :class: sphx-glr-multi-img
 
     *
 
       .. image:: /auto_examples/images/sphx_glr_plot_recover_events_006.png
-            :class: sphx-glr-multi-img
+          :alt: Synchronization Events Compared to Behavior Events
+          :class: sphx-glr-multi-img
 
 
 .. rst-class:: sphx-glr-script-out
@@ -184,33 +208,29 @@ your own input depending on whether you want to keep the events or not.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+    Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
     Finding photodiode events
-
      0%|          | 0/16341 [00:00
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 114-117
+
+Check the results:
+
+Finally, we'll check that the recovered events and the original events match.
+
+.. GENERATED FROM PYTHON SOURCE LINES 117-134
+
+.. code-block:: default
+
+
+    annot = _load_data(fname)[0]
+    raw.set_annotations(annot)
+    events2, event_id = mne.events_from_annotations(raw)
+    on_events = events2[events2[:, 2] == event_id['Stim On']]
+    print(f'Original: {events[corrupted_indices, 0]}\n'
+          f'Recovered: {on_events[corrupted_indices, 0]}')
+
+    '''
+    # uncomment when using interactively, this section doesn't work
+    # for the non-interactive documentation
+    off_events = events2[events2[:, 2] == event_id['Stim Off']]
+    original_off = events[corrupted_indices, 0] + \
+      np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)
+    print(f'Original off: {original_off}\n'
+          f'Recovered off: {on_events[corrupted_indices, 0]}')
+    '''
+
+
 
 
 .. rst-class:: sphx-glr-script-out
@@ -255,22 +330,23 @@ following the instructions.
 
  .. code-block:: none
 
-    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+    Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+    Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
     Isotrak not found
-        Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+        Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
     Ready.
-    Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-    /Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:803: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-      plt.show()
+    Used Annotations descriptions: ['Stim Off', 'Stim On']
+    Original: [  65602  989660 1595483]
+    Recovered: [  65602  989660 1595483]
 
+    "\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] +   np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\n'\n      f'Recovered off: {on_events[corrupted_indices, 0]}')\n"
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  11.919 seconds)
+   **Total running time of the script:** ( 0 minutes  30.425 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_recover_events.py:
diff --git a/docs/dev/_sources/auto_examples/sg_execution_times.rst.txt b/docs/dev/_sources/auto_examples/sg_execution_times.rst.txt
index 183b008..142f961 100644
--- a/docs/dev/_sources/auto_examples/sg_execution_times.rst.txt
+++ b/docs/dev/_sources/auto_examples/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**00:32.427** total execution time for **auto_examples** files:
+**00:58.556** total execution time for **auto_examples** files:
 
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_find_audio_events.py` (``plot_find_audio_events.py``)   | 00:32.427 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_recover_events.py` (``plot_recover_events.py``)         | 00:30.425 | 0.0 MB |
++-------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_plot_find_audio_events.py` (``plot_find_audio_events.py``)   | 00:28.131 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_find_pd_events.py` (``plot_find_pd_events.py``)         | 00:00.000 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_auto_examples_plot_find_pd_on_and_off.py` (``plot_find_pd_on_and_off.py``) | 00:00.000 | 0.0 MB |
 +-------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_recover_events.py` (``plot_recover_events.py``)         | 00:00.000 | 0.0 MB |
-+-------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/dev/_sources/generated/cli.rst.txt b/docs/dev/_sources/generated/cli.rst.txt
index 2ab14ba..25523c0 100644
--- a/docs/dev/_sources/generated/cli.rst.txt
+++ b/docs/dev/_sources/generated/cli.rst.txt
@@ -21,11 +21,11 @@ add_events_to_raw
 
 .. rst-class:: callout
 
-usage: ``add_events_to_raw fname [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]``
+usage: ``add_events_to_raw raw [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
+raw
 	The electrophysiology filepath
 
 
@@ -59,12 +59,12 @@ add_pd_off_events
 
 .. rst-class:: callout
 
-usage: ``add_pd_off_events fname [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]``
+usage: ``add_pd_off_events raw [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -82,7 +82,7 @@ optional arguments
 --zscore ZSCORE		The same zscore as used for `parse_pd`.
 
 
---min_i MIN_I		The same min_i as used for `parse_pd`.
+--max_flip_i MAX_FLIP_I		The same max_flip_i as used for `parse_pd`.
 
 
 --baseline BASELINE		The same baseline as used for `parse_pd`.
@@ -106,12 +106,12 @@ add_relative_events
 
 .. rst-class:: callout
 
-usage: ``add_relative_events fname [-h] [--behf BEHF] [--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS] [--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES] [--verbose VERBOSE] [-o]``
+usage: ``add_relative_events raw [-h] [--beh BEH] [--relative_event_keys [RELATIVE_EVENT_KEYS ...]] [--relative_event_names [RELATIVE_EVENT_NAMES ...]] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -120,13 +120,13 @@ optional arguments
 -h, --help		show this help message and exit
 
 
---behf BEHF		The behavioral tsv filepath
+--beh BEH		The behavioral tsv filepath
 
 
---relative_event_cols LIST_OF_RELATIVE_EVENT_COLS		A behavioral column in the tsv file that has the time relative to the photodiode events on the same trial as in the `beh_col` event.
+--relative_event_keys [RELATIVE_EVENT_KEYS ...]		A behavioral key (column) in the tsv file that has the time relative to the photodiode events on the same trial as in the `beh_key` event.
 
 
---relative_event_names LIST_OF_RELATIVE_EVENT_NAMES		The name of the corresponding `relative_event_cols` events
+--relative_event_names [RELATIVE_EVENT_NAMES ...]		The name of the corresponding `relative_event_keys` events
 
 
 --verbose VERBOSE		Set verbose output to True or False.
@@ -147,12 +147,12 @@ find_pd_params
 
 .. rst-class:: callout
 
-usage: ``find_pd_params fname [-h] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--verbose VERBOSE]``
+usage: ``find_pd_params raw [-h] [--pd_ch_names [PD_CH_NAMES ...]] [--verbose VERBOSE]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -161,7 +161,7 @@ optional arguments
 -h, --help		show this help message and exit
 
 
---pd_ch_names LIST_OF_PD_CH_NAMES		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
+--pd_ch_names [PD_CH_NAMES ...]		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
 
 
 --verbose VERBOSE		Set verbose output to True or False.
@@ -179,12 +179,12 @@ parse_audio
 
 .. rst-class:: callout
 
-usage: ``parse_audio fname [-h] [--audio_event_name AUDIO_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--audio_ch_names LIST_OF_AUDIO_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
+usage: ``parse_audio raw [-h] [--audio_event_name AUDIO_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--audio_ch_names [AUDIO_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	TThe electrophysiology raw object or filepath
 
 
 
@@ -196,13 +196,13 @@ optional arguments
 --audio_event_name AUDIO_EVENT_NAME		The name of the audio event
 
 
---behf BEHF		The behavioral tsv filepath
+--beh BEH		The behavioral dictionary or tsv filepath
 
 
---beh_col BEH_COL		The name of the behavioral column corresponding to the audio event timing
+--beh_key BEH_KEY		The name of the behavioral key (column) corresponding to the audio event timing
 
 
---audio_ch_names LIST_OF_AUDIO_CH_NAMES		The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced
+--audio_ch_names [AUDIO_CH_NAMES ...]		The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced
 
 
 --exclude_shift EXCLUDE_SHIFT		How many seconds off to exclude an audio-behavioral event difference
@@ -241,12 +241,12 @@ parse_pd
 
 .. rst-class:: callout
 
-usage: ``parse_pd fname [-h] [--pd_event_name PD_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
+usage: ``parse_pd raw [-h] [--pd_event_name PD_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--pd_ch_names [PD_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 
 
 
@@ -258,13 +258,13 @@ optional arguments
 --pd_event_name PD_EVENT_NAME		The name of the photodiode event
 
 
---behf BEHF		The behavioral tsv filepath
+--beh BEH		The behavioral dictionary or tsv filepath
 
 
---beh_col BEH_COL		The name of the behavioral column corresponding to the photodiode event timing
+--beh_key BEH_KEY		The name of the behavioral key (column) corresponding to the photodiode event timing
 
 
---pd_ch_names LIST_OF_PD_CH_NAMES		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
+--pd_ch_names [PD_CH_NAMES ...]		The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick
 
 
 --exclude_shift EXCLUDE_SHIFT		How many seconds off to exclude a photodiode- behavioral event difference
@@ -279,7 +279,7 @@ optional arguments
 --zscore ZSCORE		How many standard deviations larger than the baseline the photodiode event is. Decrease if too many events are being found and increase if too few. Use `find_pd_params` to determine if unsure.
 
 
---min_i MIN_I		The minimum number of samples to qualify as a pd event. Increase for fewer false-positives, decrease if your photodiode is on for fewer samples.
+--max_flip_i MAX_FLIP_I		The maximum number of samples the photodiode event takes to transition. Increase if the transitions are not being found, decrease for fewer false positives.
 
 
 --baseline BASELINE		How much relative to the max_lento use to idenify the time before the photodiode event. Probably don't change but increasing will reduce false-positives and decreasing will reduce false-negatives.
@@ -309,14 +309,14 @@ pd_parser_save_to_bids
 
 .. rst-class:: callout
 
-usage: ``pd_parser_save_to_bids bids_dir fname sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs LIST_OF_EOGS] [--ecgs LIST_OF_ECGS] [--emgs LIST_OF_EMGS] [--verbose VERBOSE] [-o]``
+usage: ``pd_parser_save_to_bids bids_dir raw sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs [EOGS ...]] [--ecgs [ECGS ...]] [--emgs [EMGS ...]] [--verbose VERBOSE] [-o]``
 
 positional arguments
 --------------------
 bids_dir
 	Filepath of the BIDS directory to save to
-fname
-	The electrophysiology filepath
+raw
+	The electrophysiology raw object or filepath
 sub
 	The subject identifier
 task
@@ -338,13 +338,13 @@ optional arguments
 --data_type DATA_TYPE		The type of data if not set correctly already (ieeg is often set as eeg for instance)
 
 
---eogs LIST_OF_EOGS		The eogs if not set correctly already
+--eogs [EOGS ...]		The eogs if not set correctly already
 
 
---ecgs LIST_OF_ECGS		The ecgs if not set correctly already
+--ecgs [ECGS ...]		The ecgs if not set correctly already
 
 
---emgs LIST_OF_EMGS		The emgs if not set correctly already
+--emgs [EMGS ...]		The emgs if not set correctly already
 
 
 --verbose VERBOSE		Set verbose output to True or False.
diff --git a/docs/dev/_static/basic.css b/docs/dev/_static/basic.css
index 24bc73e..24a49f0 100644
--- a/docs/dev/_static/basic.css
+++ b/docs/dev/_static/basic.css
@@ -764,6 +764,7 @@ div.code-block-caption code {
 }
 
 table.highlighttable td.linenos,
+span.linenos,
 div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
     user-select: none;
 }
diff --git a/docs/dev/_static/binder_badge_logo.svg b/docs/dev/_static/binder_badge_logo.svg
new file mode 100644
index 0000000..327f6b6
--- /dev/null
+++ b/docs/dev/_static/binder_badge_logo.svg
@@ -0,0 +1 @@
+ launchlaunchbinderbinder 
\ No newline at end of file
diff --git a/docs/dev/_static/doctools.js b/docs/dev/_static/doctools.js
index daccd20..7d88f80 100644
--- a/docs/dev/_static/doctools.js
+++ b/docs/dev/_static/doctools.js
@@ -285,9 +285,10 @@ var Documentation = {
   initOnKeyListeners: function() {
     $(document).keydown(function(event) {
       var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
+      // don't navigate when in search box, textarea, dropdown or button
       if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
         switch (event.keyCode) {
           case 37: // left
             var prevHref = $('link[rel="prev"]').prop('href');
diff --git a/docs/dev/_static/documentation_options.js b/docs/dev/_static/documentation_options.js
index c0349c9..fd8c696 100644
--- a/docs/dev/_static/documentation_options.js
+++ b/docs/dev/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: 'v0.4dev0',
+    VERSION: 'v0.5dev0',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/dev/_static/gallery-rendered-html.css b/docs/dev/_static/gallery-rendered-html.css
new file mode 100644
index 0000000..cea2197
--- /dev/null
+++ b/docs/dev/_static/gallery-rendered-html.css
@@ -0,0 +1,209 @@
+/* Adapted from notebook/static/style/style.min.css */
+
+.rendered_html {
+  color: #000;
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: black;
+  background-color: black;
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: #fff;
+}
+.rendered_html code {
+  background-color: #eff0f1;
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: #fff;
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: #000;
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/docs/dev/_static/gallery.css b/docs/dev/_static/gallery.css
index aa897ff..848774f 100644
--- a/docs/dev/_static/gallery.css
+++ b/docs/dev/_static/gallery.css
@@ -175,6 +175,15 @@ ul.sphx-glr-horizontal img {
   height: auto;
 }
 
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation{
+  display: block;
+}
+
 p.sphx-glr-signature a.reference.external {
   -moz-border-radius: 5px;
   -webkit-border-radius: 5px;
diff --git a/docs/dev/_static/pygments.css b/docs/dev/_static/pygments.css
index b0ec841..e5cf736 100644
--- a/docs/dev/_static/pygments.css
+++ b/docs/dev/_static/pygments.css
@@ -1,5 +1,10 @@
+pre { line-height: 125%; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
+.highlight { background: #f8f8f8; }
 .highlight .c { color: #8f5902; font-style: italic } /* Comment */
 .highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
 .highlight .g { color: #000000 } /* Generic */
diff --git a/docs/dev/_static/searchtools.js b/docs/dev/_static/searchtools.js
index 970d0d9..261ecaa 100644
--- a/docs/dev/_static/searchtools.js
+++ b/docs/dev/_static/searchtools.js
@@ -59,10 +59,10 @@ var Search = {
   _pulse_status : -1,
 
   htmlToText : function(htmlString) {
-      var htmlElement = document.createElement('span');
-      htmlElement.innerHTML = htmlString;
-      $(htmlElement).find('.headerlink').remove();
-      docContent = $(htmlElement).find('[role=main]')[0];
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
       if(docContent === undefined) {
           console.warn("Content block not found. Sphinx search tries to obtain it " +
                        "via '[role=main]'. Could you check your theme or template.");
diff --git a/docs/dev/api.html b/docs/dev/api.html
index 17c2c90..a609f5f 100644
--- a/docs/dev/api.html
+++ b/docs/dev/api.html
@@ -4,12 +4,13 @@
   
     
     
-    API Documentation — pd-parser v0.4dev0 documentation
-    
+    API Documentation — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       

 
@@ -106,7 +107,7 @@ 
pd-parser
 

-add_events_to_raw(fname, out_fname=None, drop_pd_channels=True, verbose=True, overwrite=False)[source]¶

+add_events_to_raw(raw, drop_pd_channels=True, verbose=True)[source]¶
 
Save out a new raw file with photodiode events.

 
Note: this function is not recommended, rather just skip it and
 use save_to_bids which doesn’t modify the underlying raw data
@@ -116,22 +117,19 @@ 
pd-parser
 
Parameters

 

-
fname: str
The filepath to the electrophysiology file (meg/eeg/ieeg).

-

-
out_fnamestr
The filepath to save the modified raw data to.

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
drop_pd_channelsbool
Whether to drop the channel(s) the photodiode data was on.

 

 
verbose: bool
Whether to display or supress text output on the progress
 of the function.

 

-
overwrite: bool
Whether to overwrite existing data if it exists.

-

 

 

 
Returns

 

-
out_fnamestr
The filepath to save the modified raw data to.

+
rawmne.io.Raw
The modified raw object with events.

 

 

 

@@ -140,12 +138,13 @@ 
pd-parser
 

-add_pd_off_events(fname, off_event_name='Stim Off', max_len=1.0, zscore=100, min_i=10, baseline=0.25, verbose=True, overwrite=False)[source]¶

+add_pd_off_events(raw, off_event_name='Stim Off', max_len=1.0, zscore=10, max_flip_i=40, baseline=0.25, verbose=True, overwrite=False)[source]¶
 
Add events for when the photodiode deflection returns to baseline.

-

+

 
Parameters

 

-
fname: str
The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
off_eventstr
If None, no event will be assigned to cessation of the photodiode
 deflection. If a string is provided, an event of that name will
@@ -156,7 +155,8 @@ 
pd-parserReturns
+

+
annot: mne.Annotations
The annotations with the added events.

+

+

+

+
Note

+
The same parameters must be used for pd_parser.parse_pd().

+

+

 

 

 
 

 

-add_relative_events(fname, behf, relative_event_cols, relative_event_names=None, overwrite=False, verbose=True)[source]¶

+add_relative_events(raw, beh, relative_event_keys, relative_event_names=None, overwrite=False, verbose=True)[source]¶
 
Add events relative to those determined from the photodiode.

 

 
Parameters

 

-
fname: str
The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

-
behfstr
The filepath to a tsv file with the behavioral timing

+
beh: str | dict
The dictionary or filepath to a tsv file with the behavioral timing.

 

-
relative_event_colslist
The names of the columns where time data is stored
+
relative_event_keyslist
The names of the keys where time data is stored
 relative to the photodiode event

 

-
relative_event_nameslist
The names of the events in relative_event_cols.

+
relative_event_nameslist
The names of the events in relative_event_keys.

 

 
verbose: bool
Whether to display or supress text output on the progress
 of the function.

@@ -194,19 +205,26 @@ 
pd-parserReturns
+

+
annot: mne.Annotations
The annotations with the added events.

+

+

+

 

 

 
 

 

-find_pd_params(fname, pd_ch_names=None, verbose=True)[source]¶

+find_pd_params(raw, pd_ch_names=None, verbose=True)[source]¶
 
Plot the data so the user can determine the right parameters.

 
The user can adjust window size to determine max_len and horizontal
 line height to determine zscore.

 

 
Parameters

 

-
fname: str
The filepath to the electrophysiology file (meg/eeg/ieeg).

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
pd_ch_nameslist
Names of the channel(s) containing the photodiode data.
 One channel is to be given for a common reference and
@@ -223,7 +241,7 @@ 
pd-parser
 

-parse_audio(fname, audio_event_name='Tone', behf=None, beh_col='tone_onset_time', audio_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=0.25, zscore=None, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶

+parse_audio(raw, audio_event_name='Tone', beh=None, beh_key='tone_onset_time', audio_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=0.25, zscore=None, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶
 
Parse audio events.

 
Parses photodiode events from a likely very corrupted channel
 using behavioral data to sync events to determine which
@@ -233,16 +251,15 @@ 
pd-parser
 
Parameters

 

-
fname: str
The filepath to the time-series data file (e.g. meg, eeg, ieeg).

-

-
sound_fname: str
The filepath to the .wav file that the computer played to
-match the events to.

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
audio_event_name: str
The name of the event corresponding to the audio.

 

-
behf: str
The filepath to a tsv file with the behavioral timing.

+
beh: str | dict
The dictionary or filepath to a tsv file with the behavioral timing.

 

-
beh_col: str
The column of the behf tsv that corresponds to the events.

+
beh_key: str
The key (column name) of the beh dictionary that corresponds
+to the events.

 

 
audio_ch_names: list
Names of the channel(s) containing the audio data.
 One channel is to be given for a common reference and
@@ -280,9 +297,7 @@ 
pd-parserReturns
 

-
events: DataFrame
A DataFrame that has a column for to the (zero)
-indexed behavioral events and another column corresponding
-to the time stamp of the eeg file.

+
annot: mne.Annotations
The annotations with the added events.

 

 

 

@@ -291,7 +306,7 @@ 
pd-parser
 

-parse_pd(fname, pd_event_name='Fixation', behf=None, beh_col='fix_onset_time', pd_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=1.0, zscore=100, min_i=10, baseline=0.25, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶

+parse_pd(raw, pd_event_name='Fixation', beh=None, beh_key='fix_onset_time', pd_ch_names=None, exclude_shift=0.03, resync=0.075, max_len=1.0, zscore=10, max_flip_i=40, baseline=0.25, add_events=False, recover=False, overwrite=False, verbose=True)[source]¶
 
Parse photodiode events.

 
Parses photodiode events from a likely very corrupted channel
 using behavioral data to sync events to determine which
@@ -301,13 +316,15 @@ 
pd-parser
 
Parameters

 

-
fname: str
The filepath to the time-series data file (e.g. meg, eeg, ieeg).

+
raw: str | mne Raw object
The object or filepath of the time-series data file
+(e.g. meg, eeg, ieeg).

 

 
pd_event_name: str
The name of the event corresponding to the photodiode.

 

-
behf: str
The filepath to a tsv file with the behavioral timing.

+
beh: str | dict
The dictionary or filepath to a tsv file with the behavioral timing.

 

-
beh_col: str
The column of the behf tsv that corresponds to the events.

+
beh_key: str
The key (column name) of the beh dictionary that corresponds
+to the events.

 

 
pd_ch_nameslist
Names of the channel(s) containing the photodiode data.
 One channel is to be given for a common reference and
@@ -335,8 +352,9 @@ 
pd-parserReturns
 

-
events: DataFrame
A DataFrame that has a column for to the (zero)
-indexed behavioral events and another column corresponding
-to the time stamp of the eeg file.

+
annot: mne.Annotations
The annotations with the added events.

 

 

 

@@ -371,7 +387,7 @@ 
pd-parser
 

-pd_parser_save_to_bids(bids_dir, fname, sub, task, ses=None, run=None, data_type=None, eogs=None, ecgs=None, emgs=None, verbose=True, overwrite=False)[source]¶

+pd_parser_save_to_bids(bids_dir, raw, sub, task, ses=None, run=None, data_type=None, eogs=None, ecgs=None, emgs=None, verbose=True, overwrite=False)[source]¶
 
Convert data to BIDS format with events found from the photodiode.

 

 
Parameters

@@ -379,7 +395,8 @@ 
pd-parser
 

-simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300.0, iti=6.0, iti_jitter=1.5, drift=0.1, prop_corrupted=0.1, sfreq=1000.0, seed=11, show=False)[source]¶

+simulate_pd_data(n_events=10, n_secs_on=1.0, amp=300.0, iti=6.0, iti_jitter=1.5, rc_decay=0.0001, prop_corrupted=0.1, sfreq=1000.0, seed=11, show=False)[source]¶
 
Simulate photodiode data.

 
Simulate data that is a square wave with a linear change in deflection
 drift amount towards zero that then over shoots and drifts back as
@@ -431,7 +451,7 @@ 
pd-parser

 
raw: mne.io.Raw
The raw object containing the photodiode data

 

-
beh_df: dict
A dictionary with columns:

+
beh: dict
A dictionary with keys (columns names):

 

 
 
diff --git a/docs/dev/auto_examples/index.html b/docs/dev/auto_examples/index.html
index 789df0d..7d0067f 100644
--- a/docs/dev/auto_examples/index.html
+++ b/docs/dev/auto_examples/index.html
@@ -4,12 +4,13 @@
   
     
     
-    Examples Gallery — pd-parser v0.4dev0 documentation
-    
+    Examples Gallery — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       

 
@@ -99,35 +100,35 @@
 
Examples Gallery¶

 
Here we show some exemplary use cases for pd-parser.

 

 

 

-

-../_images/sphx_glr_plot_recover_events_thumb.png
-
Manually Recover Events Not Found by the Algorithm¶

+

 

 

-

-../_images/sphx_glr_plot_find_pd_on_and_off_thumb.png
-
Find Photodiode On and Off Events¶

+

 

 

-

 
diff --git a/docs/dev/auto_examples/plot_find_audio_events.html b/docs/dev/auto_examples/plot_find_audio_events.html
index 83bf014..34f1859 100644
--- a/docs/dev/auto_examples/plot_find_audio_events.html
+++ b/docs/dev/auto_examples/plot_find_audio_events.html
@@ -4,12 +4,13 @@
   
     
     
-    Use Audio to Align Video Data — pd-parser v0.4dev0 documentation
-    
+    Use Audio to Align Video Data — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       

 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Use Audio to Align Video Data¶

@@ -125,14 +127,14 @@
 import numpy as np
 from scipy.io import wavfile
 from subprocess import call
-# from subprocess import run , PIPE, STDOUT
+# from subprocess import run, PIPE, STDOUT
 # import datetime
 
 import mne
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _load_data
+from pd_parser.parse_pd import _load_data  # , _read_tsv
 
 # get the data
 out_dir = _TempDir()
@@ -159,10 +161,6 @@
 info = mne.create_info(['audio'], fs, ['stim'])
 raw = mne.io.RawArray(data[np.newaxis], info)
 
-# save out data file
-fname = op.join(out_dir, 'test_video-raw.fif')
-raw.save(fname, overwrite=True)
-
 # find audio-visual time offset
 offset = 0  # pre-computed value for this video
 '''
@@ -174,6 +172,10 @@
     float(output[1].strip('stream|codec_type=audio|start_time'))
 '''
 
+# save to disk as required by ``pd-parser``, raw needs a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
+
 # navigate to corresponding behavior
 behf = op.join(out_dir, 'test_video_beh.tsv')
 

@@ -182,20 +184,21 @@
 
Creating RawArray with float64 data, n_channels=1, n_times=16464896
     Range : 0 ... 16464895 =      0.000 ...   343.019 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+[done]
 

 

 
Run the parser:

 
Now we’ll call the main function to automatically parse the audio events.

-
pd_parser.parse_audio(fname, behf=behf, beh_col='tone_onset_time',
+
pd_parser.parse_audio(fname, beh=behf, beh_key='tone_onset_time',
                       audio_ch_names=['audio'], zscore=10)
 

 

-../_images/sphx_glr_plot_find_audio_events_001.png
+Synchronization Events Compared to Behavior Events
 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_n3u0s07t/test_video-raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
 Isotrak not found
     Range : 0 ... 16464895 =      0.000 ...   343.019 secs
 Ready.
@@ -205,44 +208,51 @@
 Checking best alignments
 
   0%|          | 0/14 [00:00<?, ?it/s]
- 43%|####2     | 6/14 [00:00<00:00, 43.38it/s]
- 86%|########5 | 12/14 [00:00<00:00, 45.70it/s]
-100%|##########| 14/14 [00:00<00:00, 51.68it/s]
+ 21%|##1       | 3/14 [00:00<00:00, 23.71it/s]
+ 71%|#######1  | 10/14 [00:00<00:00, 29.34it/s]
+100%|##########| 14/14 [00:00<00:00, 31.55it/s]
+100%|##########| 14/14 [00:00<00:00, 41.45it/s]
 Best alignment with the events shifted 19 ms relative to the first behavior event
 errors: min -517, q1 -388, med -35, q3 246, max 485
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:443: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
+
+<Annotations | 15 segments: Tone (15)>
 

 

 
Load the results:

 
Finally, we’ll load the events and use them to crop the video although it
 requires ffmpeg so it is commented out.

-
annot, _, beh_df = _load_data(fname)
+
annot = _load_data(fname)[0]
 print('Here are the event times: ', annot.onset)
 
 # Crop the videos with ffmpeg
 '''
+beh = _read_tsv(behf)
 for i in range(annot.onset.size):  # skip the first video
-    action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000
+    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000
     run(['ffmpeg', '-i', f'{video_fname}', '-ss',
          str(datetime.timedelta(
              seconds=annot.onset[i] - action_time - offset)),
          '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),
          op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(
-             beh_df['movement'][i], beh_df['action_type'][i]))])
+             beh['movement'][i], beh['action_type'][i]))])
 '''
 

 

 
Out:

-
Here are the event times:  [ 19.05112457  39.9129982   61.88574982  83.54243469 104.41456604
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_xku3l1yp/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 16464895 =      0.000 ...   343.019 secs
+Ready.
+Here are the event times:  [ 19.05112457  39.9129982   61.88574982  83.54243469 104.41456604
  126.07720947 147.5539856  168.61270142 189.57843018 211.35673523
  250.20858765 271.68209839 292.14001465 313.30532837 333.78097534]
 
-"\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh_df['tone_onset'][i] - beh_df['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh_df['movement'][i], beh_df['action_type'][i]))])\n"
+"\nbeh = _read_tsv(behf)\nfor i in range(annot.onset.size):  # skip the first video\n    action_time = (beh['tone_onset'][i] - beh['action_onset'][i]) / 1000\n    run(['ffmpeg', '-i', f'{video_fname}', '-ss',\n         str(datetime.timedelta(\n             seconds=annot.onset[i] - action_time - offset)),\n         '-to', str(datetime.timedelta(seconds=annot.onset[i] - offset)),\n         op.join(out_dir, 'movement-{}+action_type-{}.mp4'.format(\n             beh['movement'][i], beh['action_type'][i]))])\n"
 

 

-
Total running time of the script: ( 0 minutes  32.427 seconds)

+
Total running time of the script: ( 0 minutes  28.131 seconds)

 
 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Find Photodiode Events¶

@@ -119,7 +121,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv
+from pd_parser.parse_pd import _read_raw, _to_tsv
 
 out_dir = _TempDir()
 print(f'After running this example, you can find the data here: {out_dir}')
@@ -127,7 +129,7 @@
 # simulate photodiode data
 n_events = 300
 prop_corrupted = 0.01
-raw, beh_df, events, corrupted_indices = \
+raw, beh, events, corrupted_indices = \
     pd_parser.simulate_pd_data(n_events=n_events,
                                prop_corrupted=prop_corrupted)
 
@@ -143,18 +145,28 @@
 
 fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
 raw.save(fname)
+
+# roundtrip so that raw is properly loaded from disk and has a filename
+raw = _read_raw(fname)
 

 

 
Out:

-
After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh
-Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
After running this example, you can find the data here: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6
+Creating RawArray with float64 data, n_channels=1, n_times=2036106
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2036106
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+[done]
+Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
+Ready.
+Reading 0 ... 2036105  =      0.000 ...  2036.105 secs...
 

 

 
Make behavior data:

@@ -176,12 +188,12 @@
 for i in [10, 129, 232, 288]:
     response_time[i] = 'n/a'  # make some no responses
 # put in dictionary to be converted to tsv file
-beh_df['fix_onset_time'] = beh_df['time'] + offsets
-beh_df['go_time'] = go_time
-beh_df['response_time'] = response_time
+beh['fix_onset_time'] = beh['time'] + offsets
+beh['go_time'] = go_time
+beh['response_time'] = response_time
 behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
 # save behavior file out
-_to_tsv(behf, beh_df)
+_to_tsv(behf, beh)
 

 

 
Use the interactive graphical user interface (GUI) to find parameters:

@@ -190,166 +202,183 @@
 into a console running python (ipython recommended), you can see how to
 interact with the photodiode data to pick reasonable parameters by
 following the instructions.

-
pd_parser.find_pd_params(fname, pd_ch_names=['pd'])
-

-

-../_images/sphx_glr_plot_find_pd_events_001.png
-
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:653: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
+
pd_parser.find_pd_params(raw, pd_ch_names=['pd'])
 

 

+Align Use the left/right keys to find an uncorrupted photodiode event and align the onset to the center of the window use +/- to zoom the yaxis in and out (up/down to translate) press enter when finished
 
Find the photodiode events relative to the behavioral timing of interest:

 
This function will use the default parameters or the parameters you
 found from pd_parser.find_pd_parameters() to find and align the
-photodiode events, excluding events that were off because the commuter
+photodiode events, excluding events that were off because the computer
 hung up on computation, for instance. That data is saved in the same folder
 as the raw file (in this case, a temperary directory generated by
 _TempDir()). The data can be used directly, or it can be accessed via
 pd_parser.pd_parser_save_to_bids() to store it in the brain imagine
 data structure (BIDS) standardized format before using it.

-
pd_parser.parse_pd(fname, behf=behf, pd_ch_names=['pd'], max_len=1.5)
+
pd_parser.parse_pd(raw, beh=beh, pd_ch_names=['pd'], max_len=1.5)
 

 

 
    
-
  • ../_images/sphx_glr_plot_find_pd_events_002.png
+
  • Excluded Events, Excluding event 5, off by 32 ms, Excluding event 7, off by -30 ms, Excluding event 8, off by 32 ms, Excluding event 14, off by -30 ms, Excluding event 37, off by 83 ms, Excluding event 76, off by -30 ms, Excluding event 96, off by 31 ms, Excluding event 98, off by -32 ms, Excluding event 180, no event found, Excluding event 262, no event found, Excluding event 279, off by 31 ms, Excluding event 289, off by -30 ms
 
    • 
-
  • ../_images/sphx_glr_plot_find_pd_events_003.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-Finding photodiode events
-
-  0%|          | 0/10920 [00:00<?, ?it/s]
-  4%|4         | 443/10920 [00:00<00:02, 4421.91it/s]
-  8%|8         | 878/10920 [00:00<00:02, 4397.77it/s]
- 11%|#1        | 1230/10920 [00:00<00:02, 4090.03it/s]
- 15%|#5        | 1641/10920 [00:00<00:02, 4092.92it/s]
- 19%|#8        | 2060/10920 [00:00<00:02, 4118.76it/s]
- 23%|##2       | 2467/10920 [00:00<00:02, 4101.69it/s]
- 26%|##6       | 2883/10920 [00:00<00:01, 4117.61it/s]
- 30%|###       | 3297/10920 [00:00<00:01, 4122.54it/s]
- 34%|###3      | 3685/10920 [00:00<00:01, 3905.76it/s]
- 37%|###7      | 4084/10920 [00:01<00:01, 3929.32it/s]
- 41%|####      | 4467/10920 [00:01<00:01, 3803.37it/s]
- 45%|####4     | 4885/10920 [00:01<00:01, 3908.89it/s]
- 49%|####8     | 5298/10920 [00:01<00:01, 3969.09it/s]
- 52%|#####2    | 5710/10920 [00:01<00:01, 4012.05it/s]
- 56%|#####6    | 6151/10920 [00:01<00:01, 4122.87it/s]
- 60%|######    | 6583/10920 [00:01<00:01, 4177.55it/s]
- 64%|######4   | 7029/10920 [00:01<00:00, 4255.95it/s]
- 68%|######8   | 7476/10920 [00:01<00:00, 4317.92it/s]
- 73%|#######2  | 7919/10920 [00:01<00:00, 4350.12it/s]
- 77%|#######6  | 8363/10920 [00:02<00:00, 4376.34it/s]
- 81%|########  | 8806/10920 [00:02<00:00, 4392.28it/s]
- 85%|########4 | 9246/10920 [00:02<00:00, 4391.86it/s]
- 89%|########8 | 9686/10920 [00:02<00:00, 4340.85it/s]
- 93%|#########2| 10121/10920 [00:02<00:00, 4304.13it/s]
- 97%|#########6| 10552/10920 [00:02<00:00, 4230.34it/s]
-100%|##########| 10920/10920 [00:02<00:00, 4050.83it/s]
+
Finding photodiode events
+
+  0%|          | 0/10877 [00:00<?, ?it/s]
+  1%|          | 69/10877 [00:00<00:16, 649.30it/s]
+  2%|1         | 165/10877 [00:00<00:14, 718.70it/s]
+  4%|3         | 394/10877 [00:00<00:11, 904.85it/s]
+  6%|6         | 703/10877 [00:00<00:08, 1148.39it/s]
+  9%|8         | 978/10877 [00:00<00:07, 1391.48it/s]
+ 11%|#         | 1174/10877 [00:00<00:08, 1201.91it/s]
+ 13%|#2        | 1381/10877 [00:00<00:06, 1374.79it/s]
+ 15%|#5        | 1684/10877 [00:00<00:05, 1643.86it/s]
+ 18%|#7        | 1933/10877 [00:01<00:04, 1817.09it/s]
+ 20%|#9        | 2156/10877 [00:01<00:04, 1909.39it/s]
+ 23%|##2       | 2473/10877 [00:01<00:03, 2166.35it/s]
+ 26%|##5       | 2786/10877 [00:01<00:03, 2386.76it/s]
+ 29%|##8       | 3145/10877 [00:01<00:02, 2653.38it/s]
+ 32%|###2      | 3491/10877 [00:01<00:02, 2851.89it/s]
+ 35%|###4      | 3802/10877 [00:01<00:02, 2842.30it/s]
+ 38%|###7      | 4104/10877 [00:01<00:02, 2829.41it/s]
+ 40%|####      | 4405/10877 [00:01<00:02, 2864.32it/s]
+ 43%|####3     | 4725/10877 [00:01<00:02, 2935.77it/s]
+ 46%|####6     | 5026/10877 [00:02<00:02, 2911.96it/s]
+ 49%|####8     | 5322/10877 [00:02<00:02, 2683.13it/s]
+ 51%|#####1    | 5598/10877 [00:02<00:01, 2686.89it/s]
+ 54%|#####4    | 5905/10877 [00:02<00:01, 2790.33it/s]
+ 58%|#####7    | 6269/10877 [00:02<00:01, 2999.62it/s]
+ 60%|######    | 6577/10877 [00:02<00:01, 2645.67it/s]
+ 63%|######3   | 6855/10877 [00:02<00:01, 2642.69it/s]
+ 66%|######5   | 7146/10877 [00:02<00:01, 2716.35it/s]
+ 69%|######8   | 7464/10877 [00:02<00:01, 2839.09it/s]
+ 71%|#######1  | 7755/10877 [00:03<00:01, 2706.56it/s]
+ 74%|#######3  | 8032/10877 [00:03<00:01, 2647.28it/s]
+ 77%|#######6  | 8359/10877 [00:03<00:00, 2806.89it/s]
+ 80%|########  | 8708/10877 [00:03<00:00, 2981.52it/s]
+ 83%|########3 | 9074/10877 [00:03<00:00, 3155.23it/s]
+ 86%|########6 | 9403/10877 [00:03<00:00, 3194.29it/s]
+ 89%|########9 | 9728/10877 [00:03<00:00, 2986.80it/s]
+ 93%|#########2| 10063/10877 [00:03<00:00, 3085.53it/s]
+ 95%|#########5| 10377/10877 [00:03<00:00, 2788.57it/s]
+ 98%|#########8| 10670/10877 [00:04<00:00, 2828.38it/s]
+100%|##########| 10877/10877 [00:04<00:00, 2642.46it/s]
 298 up-deflection photodiode candidate events found
 Checking best alignments
 
   0%|          | 0/299 [00:00<?, ?it/s]
-  1%|1         | 3/299 [00:00<00:12, 23.73it/s]
-  2%|1         | 5/299 [00:00<00:13, 21.94it/s]
-  3%|3         | 9/299 [00:00<00:11, 24.18it/s]
-  4%|3         | 11/299 [00:00<00:14, 20.57it/s]
-  5%|4         | 14/299 [00:00<00:12, 22.47it/s]
-  7%|6         | 20/299 [00:00<00:10, 26.57it/s]
-  8%|7         | 23/299 [00:00<00:10, 25.67it/s]
-  9%|9         | 28/299 [00:00<00:09, 28.98it/s]
- 11%|#1        | 34/299 [00:01<00:07, 33.69it/s]
- 13%|#3        | 39/299 [00:01<00:07, 36.59it/s]
- 15%|#4        | 44/299 [00:01<00:07, 35.37it/s]
- 16%|#6        | 49/299 [00:01<00:06, 36.11it/s]
- 18%|#8        | 54/299 [00:01<00:06, 37.36it/s]
- 20%|#9        | 59/299 [00:01<00:06, 39.46it/s]
- 21%|##1       | 64/299 [00:01<00:05, 39.82it/s]
- 23%|##3       | 69/299 [00:01<00:06, 38.26it/s]
- 25%|##4       | 74/299 [00:02<00:05, 38.04it/s]
- 26%|##6       | 78/299 [00:02<00:05, 37.88it/s]
- 28%|##7       | 83/299 [00:02<00:05, 38.26it/s]
- 29%|##9       | 87/299 [00:02<00:05, 37.28it/s]
- 31%|###1      | 93/299 [00:02<00:05, 40.06it/s]
- 33%|###2      | 98/299 [00:02<00:04, 40.70it/s]
- 34%|###4      | 103/299 [00:02<00:04, 42.55it/s]
- 36%|###6      | 108/299 [00:02<00:04, 39.54it/s]
- 38%|###7      | 113/299 [00:03<00:04, 40.77it/s]
- 39%|###9      | 118/299 [00:03<00:04, 37.43it/s]
- 41%|####      | 122/299 [00:03<00:05, 32.69it/s]
- 42%|####2     | 126/299 [00:03<00:05, 32.26it/s]
- 43%|####3     | 130/299 [00:03<00:05, 33.39it/s]
- 45%|####4     | 134/299 [00:03<00:04, 33.86it/s]
- 46%|####6     | 138/299 [00:03<00:04, 35.17it/s]
- 47%|####7     | 142/299 [00:03<00:04, 34.19it/s]
- 49%|####8     | 146/299 [00:04<00:04, 35.59it/s]
- 50%|#####     | 150/299 [00:04<00:04, 35.38it/s]
- 52%|#####1    | 154/299 [00:04<00:04, 35.18it/s]
- 53%|#####2    | 158/299 [00:04<00:04, 33.52it/s]
- 55%|#####4    | 163/299 [00:04<00:03, 35.43it/s]
- 56%|#####5    | 167/299 [00:04<00:03, 34.78it/s]
- 57%|#####7    | 171/299 [00:04<00:04, 31.93it/s]
- 59%|#####8    | 175/299 [00:04<00:03, 33.14it/s]
- 60%|#####9    | 179/299 [00:05<00:03, 32.13it/s]
- 61%|######1   | 183/299 [00:05<00:03, 32.36it/s]
- 63%|######2   | 187/299 [00:05<00:03, 29.63it/s]
- 64%|######3   | 191/299 [00:05<00:03, 30.71it/s]
- 65%|######5   | 195/299 [00:05<00:03, 30.44it/s]
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+  1%|1         | 4/299 [00:00<00:10, 26.84it/s]
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+ 92%|#########1| 274/299 [00:13<00:03,  8.22it/s]
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+100%|##########| 299/299 [00:14<00:00, 20.48it/s]
 Best alignment with the events shifted 12 ms relative to the first behavior event
 errors: min -32, q1 -10, med 0, q3 10, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
 Excluding event 5, off by 32 ms
 Excluding event 7, off by -30 ms
 Excluding event 8, off by 32 ms
-Excluding event 9, no event found
 Excluding event 14, off by -30 ms
-Excluding event 27, no event found
-Excluding event 76, off by -31 ms
+Excluding event 37, off by 83 ms
+Excluding event 76, off by -30 ms
 Excluding event 96, off by 31 ms
 Excluding event 98, off by -32 ms
-Excluding event 235, no event found
+Excluding event 180, no event found
+Excluding event 262, no event found
 Excluding event 279, off by 31 ms
 Excluding event 289, off by -30 ms
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:436: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:443: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 

 

 
Add events relative to the photodiode events:

@@ -364,18 +393,13 @@
 Note: if more than one photodiode event is used, the parser can be
 used for each event separately using the keyword add_event=True.

 
pd_parser.add_relative_events(
-    fname, behf,
-    relative_event_cols=['go_time', 'response_time'],
+    raw, beh,
+    relative_event_keys=['go_time', 'response_time'],
     relative_event_names=['Go Cue', 'Response'])
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
-Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
-Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+
Overwriting existing file.
 

 

 
Save data to BIDS format:

@@ -389,21 +413,18 @@
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
 Used Annotations descriptions: ['Fixation', 'Go Cue', 'Response']
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:1173: RuntimeWarning: The unit for channel(s) pd has changed from V to NA.
-  raw.set_channel_types({ch: 'stim' for ch in pd_channels
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/sub-1_task-mytask_raw.fif...
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036105 =      0.000 ...  2036.105 secs
 Ready.
-Creating folder: /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/ieeg
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/README'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/README'...
 
 References
 ----------
@@ -412,12 +433,12 @@
 Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7
 
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/participants.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/participants.tsv'...
 
 participant_id  age     sex     hand
 sub-1   n/a     n/a     n/a
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/participants.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/participants.json'...
 
 {
     "participant_id": {
@@ -444,16 +465,16 @@
     }
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/ieeg/sub-1_task-mytask_events.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_events.tsv'...
 
 onset   duration        trial_type      value   sample
-12.27   0.0     Fixation        1       12270
-12.97   0.0     Go Cue  2       12970
-14.996  0.0     Response        3       14996
-18.299  0.0     Fixation        1       18299
-18.999  0.0     Go Cue  2       18999
+12.27   0.09999942779541016     Fixation        1       12270
+12.97   0.09999942779541016     Go Cue  2       12970
+14.996  0.09999942779541016     Response        3       14996
+18.299  0.10000038146972656     Fixation        1       18299
+18.999  0.10000038146972656     Go Cue  2       18999
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/dataset_description.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/dataset_description.json'...
 
 {
     "Name": " ",
@@ -464,7 +485,7 @@
     ]
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/ieeg/sub-1_task-mytask_ieeg.json'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_ieeg.json'...
 
 {
     "TaskName": "mytask",
@@ -472,7 +493,7 @@
     "PowerLineFrequency": 60.0,
     "SamplingFrequency": 1000.0,
     "SoftwareFilters": "n/a",
-    "RecordingDuration": 2044.105,
+    "RecordingDuration": 2036.105,
     "RecordingType": "continuous",
     "iEEGReference": "n/a",
     "ECOGChannelCount": 0,
@@ -485,24 +506,28 @@
     "TriggerChannelCount": 1
 }
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/ieeg/sub-1_task-mytask_channels.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/ieeg/sub-1_task-mytask_channels.tsv'...
 
 name    type    units   low_cutoff      high_cutoff     description     sampling_frequency      status  status_description
-pd      TRIG    n/a     0.0     500.0   Trigger 1000.0  good    n/a
+pd      TRIG    V       0.0     500.0   Trigger 1000.0  good    n/a
 ch1     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
 ch2     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
 ch3     SEEG    V       0.0     500.0   StereoEEG       1000.0  good    n/a
-/Users/alexrockhill/software/mne-bids/mne_bids/write.py:1115: RuntimeWarning: Converting data files to BrainVision format
+/Users/alexrockhill/projects/mne-bids/mne_bids/write.py:1352: RuntimeWarning: Converting data files to BrainVision format
   warn('Converting data files to BrainVision format')
 
-Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/sub-1_scans.tsv'...
+Writing '/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/sub-1_scans.tsv'...
 
 filename        acq_time
 ieeg/sub-1_task-mytask_ieeg.vhdr        n/a
-Wrote /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zczb3reh/bids_dir/sub-1/sub-1_scans.tsv entry with ieeg/sub-1_task-mytask_ieeg.vhdr.
+Wrote /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir/sub-1/sub-1_scans.tsv entry with ieeg/sub-1_task-mytask_ieeg.vhdr.
+/Users/alexrockhill/projects/mne-bids/mne_bids/path.py:473: RuntimeWarning: Could not locate a data file of a supported format. This is likely a problem with your BIDS dataset. Please run the BIDS validator on your data. (root=/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir, basename=sub-1_task-mytask). []
+  warn(msg)
+/Users/alexrockhill/projects/mne-bids/mne_bids/path.py:473: RuntimeWarning: Could not locate a data file of a supported format. This is likely a problem with your BIDS dataset. Please run the BIDS validator on your data. (root=/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_iwg6vys6/bids_dir, basename=sub-1_task-mytask). []
+  warn(msg)
 

 

-
Total running time of the script: ( 0 minutes  17.287 seconds)

+
Total running time of the script: ( 0 minutes  35.983 seconds)

 
 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Find Photodiode On and Off Events¶

@@ -120,7 +122,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv, _load_data
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm
@@ -133,7 +135,7 @@
 # let's make our photodiode events on random uniform from 0.5 to 1 second
 n_secs_on = np.random.random(n_events) * 0.5 + 0.5
 prop_corrupted = 0.01
-raw, beh_df, events, corrupted_indices = \
+raw, beh, events, corrupted_indices = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=prop_corrupted)
 
@@ -147,25 +149,25 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
+# add some offsets to the behavior so it's a bit more realistic
+offsets = np.random.randn(n_events) * 0.01
+beh['time'] = np.array(beh['time']) + offsets
+
 # save to disk as required by ``pd-parser``
 fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
 raw.save(fname)
-# add some offsets to the behavior so it's a bit more realistic
-offsets = np.random.randn(n_events) * 0.01
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
 

 

 
Out:

-
Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
Creating RawArray with float64 data, n_channels=1, n_times=2036104
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2036104
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+[done]
 

 

 
Find the photodiode events relative to the behavioral timing of interest:

@@ -173,161 +175,152 @@
 photodiode events, excluding events that were off.
 One percent of the 300 events (3) were corrupted as shown in the plots and
 some were too far off from large offsets that we’re going to exclude them.

-
pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                   pd_ch_names=['pd'], beh_col='time',
+
pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                   pd_ch_names=['pd'], beh_key='time',
                    max_len=1.5)  # none are on longer than 1.5 seconds
 

 

 
    
-
  • ../_images/sphx_glr_plot_find_pd_on_and_off_001.png
+
  • Excluded Events, Excluding event 44, off by -37 ms, Excluding event 56, off by -37 ms, Excluding event 71, off by 31 ms, Excluding event 154, off by 37 ms, Excluding event 209, off by 30 ms, Excluding event 238, no event found, Excluding event 266, off by -33 ms, Excluding event 274, no event found, Excluding event 281, off by -43 ms
 
    • 
-
  • ../_images/sphx_glr_plot_find_pd_on_and_off_002.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
 Finding photodiode events
 
-  0%|          | 0/10920 [00:00<?, ?it/s]
-  4%|3         | 412/10920 [00:00<00:02, 4116.55it/s]
-  8%|7         | 864/10920 [00:00<00:02, 4227.76it/s]
- 12%|#1        | 1280/10920 [00:00<00:02, 4204.46it/s]
- 16%|#5        | 1705/10920 [00:00<00:02, 4217.32it/s]
- 19%|#9        | 2119/10920 [00:00<00:02, 4191.88it/s]
- 23%|##3       | 2547/10920 [00:00<00:01, 4216.94it/s]
- 27%|##7       | 2994/10920 [00:00<00:01, 4287.64it/s]
- 31%|###1      | 3390/10920 [00:00<00:01, 4140.87it/s]
- 35%|###4      | 3782/10920 [00:00<00:01, 3658.51it/s]
- 38%|###7      | 4143/10920 [00:01<00:02, 2924.37it/s]
- 41%|####      | 4456/10920 [00:01<00:02, 2901.65it/s]
- 45%|####4     | 4874/10920 [00:01<00:01, 3193.10it/s]
- 48%|####8     | 5268/10920 [00:01<00:01, 3384.73it/s]
- 51%|#####1    | 5623/10920 [00:01<00:01, 2970.88it/s]
- 54%|#####4    | 5941/10920 [00:01<00:02, 1835.99it/s]
- 57%|#####6    | 6193/10920 [00:02<00:02, 1978.02it/s]
- 59%|#####8    | 6442/10920 [00:02<00:02, 1929.53it/s]
- 61%|######1   | 6671/10920 [00:02<00:04, 1048.29it/s]
- 63%|######2   | 6866/10920 [00:02<00:03, 1216.18it/s]
- 65%|######4   | 7047/10920 [00:02<00:02, 1335.07it/s]
- 66%|######6   | 7255/10920 [00:02<00:02, 1494.31it/s]
- 68%|######8   | 7443/10920 [00:03<00:03, 901.69it/s]
- 69%|######9   | 7589/10920 [00:03<00:05, 569.18it/s]
- 71%|#######   | 7700/10920 [00:04<00:06, 488.69it/s]
- 71%|#######1  | 7789/10920 [00:04<00:06, 486.23it/s]
- 72%|#######2  | 7866/10920 [00:04<00:06, 479.85it/s]
- 73%|#######2  | 7934/10920 [00:04<00:06, 454.42it/s]
- 73%|#######3  | 7994/10920 [00:04<00:07, 409.29it/s]
- 76%|#######5  | 8285/10920 [00:04<00:04, 551.43it/s]
- 79%|#######8  | 8576/10920 [00:04<00:03, 728.52it/s]
- 81%|########1 | 8871/10920 [00:05<00:02, 941.10it/s]
- 83%|########3 | 9081/10920 [00:05<00:01, 1026.84it/s]
- 86%|########5 | 9366/10920 [00:05<00:01, 1270.43it/s]
- 88%|########7 | 9599/10920 [00:05<00:00, 1469.96it/s]
- 90%|########9 | 9825/10920 [00:05<00:00, 1620.09it/s]
- 92%|#########1| 10044/10920 [00:05<00:00, 1329.52it/s]
- 94%|#########3| 10225/10920 [00:05<00:00, 1252.42it/s]
- 96%|#########5| 10453/10920 [00:06<00:00, 1447.46it/s]
- 98%|#########7| 10660/10920 [00:06<00:00, 1590.67it/s]
-100%|#########9| 10895/10920 [00:06<00:00, 1760.87it/s]
-100%|##########| 10920/10920 [00:06<00:00, 1720.77it/s]
-297 up-deflection photodiode candidate events found
+  0%|          | 0/10877 [00:00<?, ?it/s]
+  4%|3         | 414/10877 [00:00<00:02, 4136.78it/s]
+  8%|7         | 844/10877 [00:00<00:02, 4183.20it/s]
+ 11%|#1        | 1218/10877 [00:00<00:02, 4039.49it/s]
+ 15%|#5        | 1632/10877 [00:00<00:02, 4066.14it/s]
+ 19%|#8        | 2045/10877 [00:00<00:02, 4084.06it/s]
+ 22%|##1       | 2385/10877 [00:00<00:02, 3228.78it/s]
+ 25%|##4       | 2688/10877 [00:00<00:02, 3055.11it/s]
+ 28%|##7       | 3002/10877 [00:00<00:02, 3078.74it/s]
+ 30%|###       | 3302/10877 [00:01<00:02, 2589.07it/s]
+ 33%|###2      | 3569/10877 [00:01<00:03, 2353.49it/s]
+ 35%|###5      | 3830/10877 [00:01<00:02, 2423.16it/s]
+ 38%|###7      | 4080/10877 [00:01<00:02, 2371.87it/s]
+ 40%|####      | 4353/10877 [00:01<00:02, 2468.45it/s]
+ 42%|####2     | 4621/10877 [00:01<00:02, 2527.19it/s]
+ 45%|####5     | 4907/10877 [00:01<00:02, 2617.72it/s]
+ 48%|####7     | 5182/10877 [00:01<00:02, 2653.46it/s]
+ 50%|#####     | 5450/10877 [00:01<00:02, 2626.52it/s]
+ 53%|#####2    | 5715/10877 [00:01<00:02, 2503.10it/s]
+ 55%|#####4    | 5968/10877 [00:02<00:01, 2483.09it/s]
+ 57%|#####7    | 6219/10877 [00:02<00:01, 2389.86it/s]
+ 59%|#####9    | 6460/10877 [00:02<00:01, 2377.93it/s]
+ 62%|######1   | 6700/10877 [00:02<00:01, 2364.13it/s]
+ 64%|######3   | 6938/10877 [00:02<00:01, 2356.37it/s]
+ 66%|######5   | 7175/10877 [00:02<00:01, 2294.88it/s]
+ 68%|######8   | 7413/10877 [00:02<00:01, 2315.23it/s]
+ 71%|#######1  | 7724/10877 [00:02<00:01, 2506.99it/s]
+ 75%|#######4  | 8129/10877 [00:02<00:00, 2829.84it/s]
+ 78%|#######7  | 8480/10877 [00:03<00:00, 3003.57it/s]
+ 81%|########1 | 8842/10877 [00:03<00:00, 3163.89it/s]
+ 85%|########4 | 9197/10877 [00:03<00:00, 3269.09it/s]
+ 88%|########7 | 9555/10877 [00:03<00:00, 3356.07it/s]
+ 91%|#########1| 9922/10877 [00:03<00:00, 3442.88it/s]
+ 94%|#########4| 10273/10877 [00:03<00:00, 3453.66it/s]
+ 98%|#########7| 10630/10877 [00:03<00:00, 3483.89it/s]
+100%|##########| 10877/10877 [00:03<00:00, 2930.45it/s]
+298 up-deflection photodiode candidate events found
 Checking best alignments
 
   0%|          | 0/299 [00:00<?, ?it/s]
-  0%|          | 1/299 [00:00<01:51,  2.66it/s]
-  1%|1         | 3/299 [00:00<01:23,  3.56it/s]
-  2%|2         | 6/299 [00:00<01:01,  4.80it/s]
-  3%|3         | 9/299 [00:00<00:45,  6.38it/s]
-  4%|3         | 11/299 [00:00<00:42,  6.77it/s]
-  4%|4         | 13/299 [00:01<00:42,  6.68it/s]
-  5%|5         | 15/299 [00:01<00:36,  7.79it/s]
-  6%|6         | 18/299 [00:01<00:29,  9.50it/s]
-  7%|7         | 21/299 [00:01<00:23, 11.66it/s]
-  9%|8         | 26/299 [00:01<00:18, 14.76it/s]
- 10%|#         | 31/299 [00:01<00:14, 18.40it/s]
- 12%|#2        | 36/299 [00:02<00:11, 22.26it/s]
- 13%|#3        | 40/299 [00:02<00:11, 23.12it/s]
- 15%|#4        | 44/299 [00:02<00:12, 21.12it/s]
- 16%|#5        | 47/299 [00:02<00:11, 22.86it/s]
- 17%|#6        | 50/299 [00:02<00:11, 21.69it/s]
- 18%|#8        | 54/299 [00:02<00:09, 24.80it/s]
- 20%|#9        | 59/299 [00:02<00:08, 28.00it/s]
- 21%|##1       | 63/299 [00:03<00:08, 29.10it/s]
- 22%|##2       | 67/299 [00:03<00:08, 26.10it/s]
- 23%|##3       | 70/299 [00:03<00:11, 19.79it/s]
- 24%|##4       | 73/299 [00:03<00:12, 17.98it/s]
- 25%|##5       | 76/299 [00:03<00:12, 17.72it/s]
- 26%|##6       | 79/299 [00:04<00:11, 19.16it/s]
- 27%|##7       | 82/299 [00:04<00:10, 20.76it/s]
- 29%|##8       | 86/299 [00:04<00:08, 23.84it/s]
- 30%|###       | 90/299 [00:04<00:07, 26.67it/s]
- 32%|###1      | 95/299 [00:04<00:07, 29.11it/s]
- 33%|###3      | 99/299 [00:04<00:06, 29.52it/s]
- 35%|###4      | 104/299 [00:04<00:06, 31.84it/s]
- 36%|###6      | 108/299 [00:04<00:06, 30.58it/s]
- 37%|###7      | 112/299 [00:05<00:06, 30.55it/s]
- 39%|###8      | 116/299 [00:05<00:05, 32.13it/s]
- 40%|####      | 120/299 [00:05<00:05, 32.06it/s]
- 41%|####1     | 124/299 [00:05<00:05, 33.31it/s]
- 43%|####2     | 128/299 [00:05<00:05, 30.32it/s]
- 44%|####4     | 132/299 [00:05<00:05, 32.56it/s]
- 45%|####5     | 136/299 [00:05<00:05, 31.27it/s]
- 47%|####6     | 140/299 [00:05<00:05, 31.70it/s]
- 48%|####8     | 144/299 [00:06<00:04, 31.63it/s]
- 50%|####9     | 149/299 [00:06<00:04, 34.14it/s]
- 51%|#####1    | 153/299 [00:06<00:04, 35.52it/s]
- 53%|#####2    | 157/299 [00:06<00:04, 33.64it/s]
- 54%|#####3    | 161/299 [00:06<00:04, 33.54it/s]
- 56%|#####5    | 166/299 [00:06<00:03, 36.03it/s]
- 57%|#####7    | 171/299 [00:06<00:03, 36.32it/s]
- 59%|#####8    | 175/299 [00:06<00:03, 34.78it/s]
- 60%|#####9    | 179/299 [00:06<00:03, 32.88it/s]
- 62%|######1   | 184/299 [00:07<00:03, 34.74it/s]
- 63%|######3   | 189/299 [00:07<00:02, 36.86it/s]
- 65%|######4   | 193/299 [00:07<00:02, 35.83it/s]
- 66%|######5   | 197/299 [00:07<00:02, 36.66it/s]
- 67%|######7   | 201/299 [00:07<00:02, 37.31it/s]
- 69%|######8   | 206/299 [00:07<00:02, 39.67it/s]
- 71%|#######   | 212/299 [00:07<00:02, 41.55it/s]
- 73%|#######2  | 217/299 [00:07<00:02, 37.66it/s]
- 74%|#######3  | 221/299 [00:08<00:02, 36.31it/s]
- 76%|#######5  | 227/299 [00:08<00:01, 39.59it/s]
- 78%|#######7  | 232/299 [00:08<00:01, 38.11it/s]
- 79%|#######9  | 237/299 [00:08<00:01, 38.03it/s]
- 81%|########  | 241/299 [00:08<00:01, 36.40it/s]
- 82%|########1 | 245/299 [00:08<00:01, 31.32it/s]
- 84%|########3 | 250/299 [00:08<00:01, 33.28it/s]
- 86%|########5 | 256/299 [00:09<00:01, 37.46it/s]
- 87%|########7 | 261/299 [00:09<00:00, 38.96it/s]
- 89%|########8 | 266/299 [00:09<00:00, 38.85it/s]
- 91%|######### | 272/299 [00:09<00:00, 41.91it/s]
- 93%|#########2| 277/299 [00:09<00:00, 42.35it/s]
- 94%|#########4| 282/299 [00:09<00:00, 41.82it/s]
- 97%|#########6| 290/299 [00:09<00:00, 45.25it/s]
- 99%|#########8| 295/299 [00:09<00:00, 42.81it/s]
-100%|##########| 299/299 [00:10<00:00, 29.68it/s]
+  1%|1         | 3/299 [00:00<00:10, 27.25it/s]
+  3%|2         | 8/299 [00:00<00:09, 31.46it/s]
+  4%|4         | 12/299 [00:00<00:08, 32.75it/s]
+  5%|5         | 16/299 [00:00<00:08, 31.84it/s]
+  7%|7         | 21/299 [00:00<00:08, 32.77it/s]
+  8%|8         | 25/299 [00:00<00:07, 34.41it/s]
+ 10%|#         | 30/299 [00:00<00:07, 37.40it/s]
+ 11%|#1        | 34/299 [00:00<00:07, 35.41it/s]
+ 13%|#2        | 38/299 [00:01<00:07, 36.55it/s]
+ 14%|#4        | 42/299 [00:01<00:07, 36.15it/s]
+ 15%|#5        | 46/299 [00:01<00:07, 35.86it/s]
+ 17%|#6        | 50/299 [00:01<00:08, 28.35it/s]
+ 18%|#8        | 54/299 [00:01<00:09, 24.93it/s]
+ 19%|#9        | 57/299 [00:01<00:09, 25.16it/s]
+ 20%|##        | 61/299 [00:01<00:08, 27.37it/s]
+ 22%|##1       | 65/299 [00:02<00:08, 29.00it/s]
+ 23%|##3       | 69/299 [00:02<00:08, 27.81it/s]
+ 24%|##4       | 72/299 [00:02<00:08, 27.26it/s]
+ 26%|##5       | 77/299 [00:02<00:07, 30.97it/s]
+ 27%|##7       | 81/299 [00:02<00:06, 31.90it/s]
+ 28%|##8       | 85/299 [00:02<00:06, 30.74it/s]
+ 30%|##9       | 89/299 [00:02<00:06, 30.44it/s]
+ 31%|###1      | 93/299 [00:02<00:06, 31.96it/s]
+ 32%|###2      | 97/299 [00:03<00:06, 31.05it/s]
+ 34%|###3      | 101/299 [00:03<00:06, 31.48it/s]
+ 35%|###5      | 105/299 [00:03<00:05, 33.10it/s]
+ 36%|###6      | 109/299 [00:03<00:05, 33.78it/s]
+ 38%|###7      | 113/299 [00:03<00:05, 34.67it/s]
+ 39%|###9      | 117/299 [00:03<00:05, 31.13it/s]
+ 40%|####      | 121/299 [00:03<00:05, 32.61it/s]
+ 42%|####1     | 125/299 [00:03<00:05, 31.88it/s]
+ 43%|####3     | 129/299 [00:04<00:05, 31.86it/s]
+ 44%|####4     | 133/299 [00:04<00:05, 31.35it/s]
+ 46%|####5     | 137/299 [00:04<00:05, 29.66it/s]
+ 47%|####7     | 141/299 [00:04<00:06, 25.89it/s]
+ 48%|####8     | 144/299 [00:04<00:06, 25.18it/s]
+ 49%|####9     | 147/299 [00:04<00:06, 23.72it/s]
+ 50%|#####     | 150/299 [00:04<00:06, 22.96it/s]
+ 51%|#####1    | 153/299 [00:05<00:06, 24.30it/s]
+ 52%|#####2    | 156/299 [00:05<00:06, 23.32it/s]
+ 53%|#####3    | 159/299 [00:05<00:05, 24.47it/s]
+ 55%|#####4    | 163/299 [00:05<00:05, 26.77it/s]
+ 56%|#####5    | 166/299 [00:05<00:05, 26.54it/s]
+ 57%|#####6    | 170/299 [00:05<00:04, 29.31it/s]
+ 58%|#####8    | 174/299 [00:05<00:04, 29.33it/s]
+ 60%|#####9    | 178/299 [00:05<00:04, 29.90it/s]
+ 61%|######    | 182/299 [00:06<00:03, 29.79it/s]
+ 62%|######2   | 186/299 [00:06<00:03, 31.99it/s]
+ 64%|######3   | 190/299 [00:06<00:03, 31.79it/s]
+ 65%|######4   | 194/299 [00:06<00:03, 32.83it/s]
+ 66%|######6   | 198/299 [00:06<00:03, 33.55it/s]
+ 68%|######7   | 202/299 [00:06<00:02, 33.26it/s]
+ 69%|######8   | 206/299 [00:06<00:02, 33.03it/s]
+ 70%|#######   | 210/299 [00:06<00:02, 33.99it/s]
+ 72%|#######1  | 214/299 [00:06<00:02, 33.86it/s]
+ 73%|#######2  | 218/299 [00:07<00:02, 34.17it/s]
+ 74%|#######4  | 222/299 [00:07<00:02, 35.37it/s]
+ 76%|#######5  | 226/299 [00:07<00:02, 30.78it/s]
+ 77%|#######6  | 230/299 [00:07<00:02, 30.84it/s]
+ 78%|#######8  | 234/299 [00:07<00:02, 30.78it/s]
+ 80%|#######9  | 239/299 [00:07<00:01, 33.63it/s]
+ 82%|########1 | 244/299 [00:07<00:01, 34.36it/s]
+ 83%|########3 | 249/299 [00:07<00:01, 36.94it/s]
+ 85%|########4 | 253/299 [00:08<00:01, 32.70it/s]
+ 86%|########5 | 257/299 [00:08<00:01, 33.91it/s]
+ 88%|########7 | 262/299 [00:08<00:01, 36.00it/s]
+ 90%|########9 | 269/299 [00:08<00:00, 39.16it/s]
+ 92%|#########1| 274/299 [00:08<00:00, 40.53it/s]
+ 93%|#########3| 279/299 [00:08<00:00, 39.07it/s]
+ 95%|#########4| 284/299 [00:08<00:00, 36.52it/s]
+ 96%|#########6| 288/299 [00:09<00:00, 36.44it/s]
+ 98%|#########7| 292/299 [00:09<00:00, 35.86it/s]
+100%|##########| 299/299 [00:09<00:00, 41.15it/s]
+100%|##########| 299/299 [00:09<00:00, 32.39it/s]
 Best alignment with the events shifted 12 ms relative to the first behavior event
-errors: min -42, q1 -8, med -1, q3 9, max 75
+errors: min -43, q1 -9, med 1, q3 9, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
-Excluding event 9, no event found
-Excluding event 27, no event found
-Excluding event 37, off by 35 ms
-Excluding event 115, off by -34 ms
-Excluding event 116, off by 32 ms
-Excluding event 143, off by -31 ms
-Excluding event 153, off by -40 ms
-Excluding event 154, off by 40 ms
-Excluding event 167, off by -42 ms
-Excluding event 235, no event found
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:436: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:443: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
+Excluding event 44, off by -37 ms
+Excluding event 56, off by -37 ms
+Excluding event 71, off by 31 ms
+Excluding event 154, off by 37 ms
+Excluding event 209, off by 30 ms
+Excluding event 238, no event found
+Excluding event 266, off by -33 ms
+Excluding event 274, no event found
+Excluding event 281, off by -43 ms
 

 

 
Find cessations of the photodiode deflections

@@ -337,12 +330,13 @@
 

 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_4igi_0_w/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2036103  =      0.000 ...  2036.103 secs...
+Overwriting existing file.
 

 

 
Check recovered event lengths and compare to the simulation ground truth

@@ -354,14 +348,14 @@
 correspondance) is not shown as it would occlude the plotted data; the
 the lengths are recovered within 1 millisecond. Note that the colors are
 arbitrary and are only used to increase contrast and ease of visualization.

-
annot, pd_ch_names, beh_df = _load_data(fname)
+
annot, pd_ch_names, beh = _load_data(fname)
 raw.set_annotations(annot)
 events, event_id = mne.events_from_annotations(raw)
 on_events = events[events[:, 2] == event_id['Stim On']]
 off_events = events[events[:, 2] == event_id['Stim Off']]
 
 recovered = (off_events[:, 0] - on_events[:, 0]) / raw.info['sfreq']
-not_corrupted = [s != 'n/a' for s in beh_df['pd_parser_sample']]
+not_corrupted = [s != 'n/a' for s in beh['pd_parser_sample']]
 ground_truth_not_corrupted = n_secs_on[not_corrupted]
 
 fig, ax = plt.subplots()
@@ -376,13 +370,18 @@
           1000 * abs(ground_truth_not_corrupted - recovered).mean()))
 

 

-../_images/sphx_glr_plot_find_pd_on_and_off_003.png
+Photodiode offset eventfidelity of recovery
 
Out:

-
Used Annotations descriptions: ['Stim Off', 'Stim On']
-Mean difference in the recovered from simulated length is 0.254 milliseconds
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_9tocoov8/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2036103 =      0.000 ...  2036.103 secs
+Ready.
+Used Annotations descriptions: ['Stim Off', 'Stim On']
+Mean difference in the recovered from simulated length is 0.252 milliseconds
 

 

-
Total running time of the script: ( 0 minutes  22.514 seconds)

+
Total running time of the script: ( 0 minutes  22.081 seconds)

 
 
@@ -97,7 +98,8 @@
       
   

 
Note

-
Click here     to download the full example code

+
Click here
+to download the full example code

 

 

 
Manually Recover Events Not Found by the Algorithm¶

@@ -123,7 +125,7 @@
 from mne.utils import _TempDir
 
 import pd_parser
-from pd_parser.parse_pd import _to_tsv
+from pd_parser.parse_pd import _load_data
 
 import matplotlib.pyplot as plt
 
@@ -134,7 +136,7 @@
 n_events = 300
 # let's make our photodiode events on random uniform from 0.25 to 0.75 seconds
 n_secs_on = np.random.random(n_events) * 0.5 + 0.25
-raw, beh_df, events, _ = \
+raw, beh, events, _ = \
     pd_parser.simulate_pd_data(n_events=n_events, n_secs_on=n_secs_on,
                                prop_corrupted=0.0)
 sfreq = np.round(raw.info['sfreq']).astype(int)
@@ -147,16 +149,19 @@
 axes[0].set_ylabel('voltage')
 for j, i in enumerate(events[corrupted_indices, 0]):
     if j == 0:
-        raw._data[0, i - sfreq // 3: i - sfreq // 4] = -amount
+        raw._data[0, i - sfreq // 5: i - sfreq // 10] = -amount
     elif j == 1:
         raw._data[0, i + sfreq // 4: i + sfreq // 3] = -amount
     else:
-        raw._data[0, i + 2 * sfreq // 3: i + 4 * sfreq // 4] = amount
+        raw._data[0, i + 3 * sfreq // 4: i + 5 * sfreq // 6] = amount
     axes[j].plot(np.linspace(-1, 2, 3 * sfreq),
                  raw._data[0, i - sfreq: i + sfreq * 2])
     axes[j].set_xlabel('time (s)')
 
 
+# make figure nicer
+fig.tight_layout()
+
 # make fake electrophysiology data
 info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
                        ['seeg'] * 3)
@@ -167,26 +172,26 @@
 raw.info['dig'] = None
 raw.info['line_freq'] = 60
 
-# save to disk as required by ``pd-parser``
-fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
-raw.save(fname)
 # add some offsets to the behavior so it's a bit more realistic
 offsets = np.random.randn(n_events) * 0.001
-beh_df['time'] = np.array(beh_df['time']) + offsets
-behf = op.join(out_dir, 'sub-1_task-mytask_beh.tsv')
-_to_tsv(behf, beh_df)
+beh['time'] = np.array(beh['time']) + offsets
+
+# save to disk as required by ``pd-parser``, raw needs to have a filename
+fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
+raw.save(fname)
 

 

-../_images/sphx_glr_plot_recover_events_001.png
+Corrupted Events
 
Out:

-
Creating RawArray with float64 data, n_channels=1, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+
Creating RawArray with float64 data, n_channels=1, n_times=2035104
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Creating RawArray with float64 data, n_channels=3, n_times=2044106
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+Creating RawArray with float64 data, n_channels=3, n_times=2035104
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Writing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Closing /private/var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif [done]
+Writing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Closing /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+[done]
 

 

 
Find the photodiode events relative to the behavioral timing of interest:

@@ -196,116 +201,142 @@
 you want to delete that line and unindent the next line, and then provide
 your own input depending on whether you want to keep the events or not.

 
with mock.patch('builtins.input', return_value='y'):
-    pd_parser.parse_pd(fname, pd_event_name='Stim On', behf=behf,
-                       pd_ch_names=['pd'], beh_col='time', recover=True)
+    pd_parser.parse_pd(fname, pd_event_name='Stim On', beh=beh,
+                       pd_ch_names=['pd'], beh_key='time', recover=True)
 

 

 
    
-
  • ../_images/sphx_glr_plot_recover_events_002.png
+
  • Corrupted Event 8
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_003.png
+
  • Corrupted Event 144
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_004.png
+
  • Corrupted Event 234
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_005.png
+
  • Excluded Events, Event 8 recovered (not excluded), Event 144 recovered (not excluded), Event 234 recovered (not excluded)
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_006.png
+
  • Synchronization Events Compared to Behavior Events
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
+Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
 Finding photodiode events
 
-  0%|          | 0/16341 [00:00<?, ?it/s]
-  3%|3         | 506/16341 [00:00<00:03, 5055.65it/s]
-  6%|6         | 1010/16341 [00:00<00:03, 5048.99it/s]
-  9%|9         | 1511/16341 [00:00<00:02, 5035.45it/s]
- 12%|#2        | 2015/16341 [00:00<00:02, 5036.36it/s]
- 15%|#5        | 2512/16341 [00:00<00:02, 5015.83it/s]
- 18%|#8        | 3004/16341 [00:00<00:02, 4984.75it/s]
- 21%|##1       | 3502/16341 [00:00<00:02, 4980.67it/s]
- 24%|##4       | 3998/16341 [00:00<00:02, 4973.78it/s]
- 27%|##7       | 4467/16341 [00:00<00:02, 4700.85it/s]
- 30%|###       | 4920/16341 [00:01<00:02, 3868.46it/s]
- 33%|###2      | 5320/16341 [00:01<00:03, 3555.36it/s]
- 35%|###4      | 5697/16341 [00:01<00:02, 3613.89it/s]
- 38%|###7      | 6184/16341 [00:01<00:02, 3915.75it/s]
- 41%|####      | 6656/16341 [00:01<00:02, 4126.70it/s]
- 43%|####3     | 7082/16341 [00:01<00:02, 3889.05it/s]
- 46%|####5     | 7483/16341 [00:01<00:02, 3772.03it/s]
- 49%|####8     | 7971/16341 [00:01<00:02, 4046.26it/s]
- 52%|#####1    | 8432/16341 [00:01<00:01, 4198.62it/s]
- 54%|#####4    | 8904/16341 [00:02<00:01, 4340.43it/s]
- 57%|#####7    | 9377/16341 [00:02<00:01, 4447.82it/s]
- 60%|######    | 9856/16341 [00:02<00:01, 4544.15it/s]
- 63%|######3   | 10353/16341 [00:02<00:01, 4663.55it/s]
- 66%|######6   | 10841/16341 [00:02<00:01, 4724.56it/s]
- 69%|######9   | 11334/16341 [00:02<00:01, 4782.70it/s]
- 72%|#######2  | 11818/16341 [00:02<00:00, 4796.63it/s]
- 75%|#######5  | 12300/16341 [00:02<00:00, 4723.65it/s]
- 78%|#######8  | 12774/16341 [00:02<00:00, 4690.08it/s]
- 81%|########1 | 13245/16341 [00:02<00:00, 4664.40it/s]
- 84%|########4 | 13736/16341 [00:03<00:00, 4733.57it/s]
- 87%|########7 | 14218/16341 [00:03<00:00, 4757.68it/s]
- 90%|######### | 14717/16341 [00:03<00:00, 4824.06it/s]
- 93%|#########3| 15219/16341 [00:03<00:00, 4878.70it/s]
- 96%|#########6| 15718/16341 [00:03<00:00, 4908.96it/s]
- 99%|#########9| 16231/16341 [00:03<00:00, 4972.48it/s]
-100%|##########| 16341/16341 [00:03<00:00, 4557.76it/s]
+  0%|          | 0/16269 [00:00<?, ?it/s]
+  1%|1         | 195/16269 [00:00<00:12, 1308.17it/s]
+  2%|1         | 252/16269 [00:00<00:17, 939.51it/s]
+  2%|2         | 348/16269 [00:00<00:16, 944.93it/s]
+  3%|2         | 437/16269 [00:00<00:17, 927.32it/s]
+  3%|3         | 506/16269 [00:00<00:40, 392.53it/s]
+  4%|3         | 633/16269 [00:00<00:31, 495.05it/s]
+  5%|4         | 803/16269 [00:01<00:24, 628.37it/s]
+  6%|5         | 959/16269 [00:01<00:20, 760.26it/s]
+  7%|6         | 1095/16269 [00:01<00:17, 876.17it/s]
+  8%|7         | 1232/16269 [00:01<00:15, 977.36it/s]
+  8%|8         | 1359/16269 [00:01<00:15, 956.21it/s]
+  9%|9         | 1480/16269 [00:01<00:14, 1019.82it/s]
+ 10%|9         | 1598/16269 [00:01<00:23, 628.15it/s]
+ 10%|#         | 1690/16269 [00:02<00:21, 663.30it/s]
+ 11%|#1        | 1790/16269 [00:02<00:20, 712.42it/s]
+ 12%|#1        | 1877/16269 [00:02<00:20, 686.15it/s]
+ 12%|#2        | 1972/16269 [00:02<00:19, 748.01it/s]
+ 13%|#2        | 2059/16269 [00:02<00:24, 585.50it/s]
+ 13%|#3        | 2142/16269 [00:02<00:22, 634.30it/s]
+ 14%|#3        | 2235/16269 [00:02<00:20, 699.19it/s]
+ 14%|#4        | 2349/16269 [00:02<00:17, 790.72it/s]
+ 15%|#5        | 2500/16269 [00:03<00:14, 922.18it/s]
+ 16%|#6        | 2624/16269 [00:03<00:13, 998.67it/s]
+ 19%|#8        | 3017/16269 [00:03<00:10, 1286.51it/s]
+ 21%|##        | 3395/16269 [00:03<00:08, 1603.83it/s]
+ 23%|##3       | 3764/16269 [00:03<00:06, 1930.65it/s]
+ 26%|##5       | 4177/16269 [00:03<00:05, 2297.44it/s]
+ 28%|##8       | 4576/16269 [00:03<00:04, 2631.37it/s]
+ 31%|###       | 4966/16269 [00:03<00:03, 2914.54it/s]
+ 33%|###2      | 5364/16269 [00:03<00:03, 3167.88it/s]
+ 35%|###5      | 5764/16269 [00:03<00:03, 3376.61it/s]
+ 38%|###7      | 6174/16269 [00:04<00:02, 3564.50it/s]
+ 40%|####      | 6566/16269 [00:04<00:02, 3663.05it/s]
+ 43%|####2     | 6956/16269 [00:04<00:02, 3689.23it/s]
+ 45%|####5     | 7361/16269 [00:04<00:02, 3787.27it/s]
+ 48%|####7     | 7777/16269 [00:04<00:02, 3889.25it/s]
+ 50%|#####     | 8175/16269 [00:04<00:02, 3300.76it/s]
+ 52%|#####2    | 8527/16269 [00:04<00:02, 3263.58it/s]
+ 55%|#####4    | 8938/16269 [00:04<00:02, 3475.52it/s]
+ 57%|#####7    | 9301/16269 [00:04<00:02, 3457.96it/s]
+ 59%|#####9    | 9657/16269 [00:05<00:01, 3351.07it/s]
+ 62%|######1   | 10036/16269 [00:05<00:01, 3469.69it/s]
+ 64%|######3   | 10390/16269 [00:05<00:01, 3226.85it/s]
+ 66%|######5   | 10721/16269 [00:05<00:01, 3171.38it/s]
+ 68%|######7   | 11044/16269 [00:05<00:01, 3055.13it/s]
+ 70%|######9   | 11355/16269 [00:05<00:01, 2914.14it/s]
+ 72%|#######1  | 11653/16269 [00:05<00:01, 2932.68it/s]
+ 73%|#######3  | 11950/16269 [00:05<00:01, 2922.49it/s]
+ 76%|#######5  | 12295/16269 [00:05<00:01, 3062.97it/s]
+ 78%|#######7  | 12659/16269 [00:06<00:01, 3214.20it/s]
+ 80%|########  | 13024/16269 [00:06<00:00, 3333.50it/s]
+ 82%|########2 | 13380/16269 [00:06<00:00, 3397.66it/s]
+ 84%|########4 | 13735/16269 [00:06<00:00, 3441.52it/s]
+ 87%|########6 | 14095/16269 [00:06<00:00, 3485.80it/s]
+ 89%|########8 | 14446/16269 [00:06<00:00, 3221.75it/s]
+ 91%|#########1| 14826/16269 [00:06<00:00, 3375.55it/s]
+ 93%|#########3| 15170/16269 [00:06<00:00, 3250.19it/s]
+ 95%|#########5| 15500/16269 [00:06<00:00, 3231.71it/s]
+ 98%|#########7| 15900/16269 [00:06<00:00, 3428.39it/s]
+100%|##########| 16269/16269 [00:07<00:00, 2295.44it/s]
 297 up-deflection photodiode candidate events found
-Checking best behavior-photodiode difference alignments
+Checking best alignments
 
   0%|          | 0/299 [00:00<?, ?it/s]
-  3%|2         | 8/299 [00:00<00:03, 76.29it/s]
-  7%|6         | 20/299 [00:00<00:03, 84.72it/s]
- 12%|#1        | 35/299 [00:00<00:02, 94.18it/s]
- 14%|#4        | 43/299 [00:00<00:03, 80.78it/s]
- 17%|#7        | 51/299 [00:00<00:03, 76.82it/s]
- 20%|##        | 60/299 [00:00<00:03, 77.27it/s]
- 23%|##3       | 69/299 [00:00<00:03, 76.05it/s]
- 26%|##5       | 77/299 [00:00<00:03, 72.45it/s]
- 28%|##8       | 85/299 [00:01<00:03, 65.14it/s]
- 31%|###       | 92/299 [00:01<00:03, 59.45it/s]
- 33%|###3      | 99/299 [00:01<00:03, 53.12it/s]
- 35%|###5      | 105/299 [00:01<00:03, 51.41it/s]
- 39%|###9      | 118/299 [00:01<00:02, 62.65it/s]
- 43%|####3     | 129/299 [00:01<00:02, 69.39it/s]
- 46%|####6     | 138/299 [00:01<00:02, 70.59it/s]
- 49%|####8     | 146/299 [00:01<00:02, 72.93it/s]
- 52%|#####1    | 154/299 [00:02<00:02, 71.91it/s]
- 55%|#####4    | 163/299 [00:02<00:01, 75.87it/s]
- 57%|#####7    | 171/299 [00:02<00:02, 62.83it/s]
- 60%|#####9    | 178/299 [00:02<00:01, 62.24it/s]
- 62%|######1   | 185/299 [00:02<00:01, 62.46it/s]
- 64%|######4   | 192/299 [00:02<00:01, 63.16it/s]
- 69%|######8   | 205/299 [00:02<00:01, 72.73it/s]
- 72%|#######1  | 215/299 [00:02<00:01, 75.36it/s]
- 75%|#######4  | 224/299 [00:03<00:01, 66.04it/s]
- 78%|#######7  | 232/299 [00:03<00:01, 60.81it/s]
- 80%|#######9  | 239/299 [00:03<00:01, 57.69it/s]
- 84%|########3 | 250/299 [00:03<00:00, 64.18it/s]
- 87%|########6 | 260/299 [00:03<00:00, 71.65it/s]
- 90%|########9 | 269/299 [00:03<00:00, 75.68it/s]
- 93%|#########2| 278/299 [00:03<00:00, 70.87it/s]
- 97%|#########6| 290/299 [00:03<00:00, 78.94it/s]
-100%|##########| 299/299 [00:04<00:00, 73.44it/s]
-Best alignment with the photodiode shifted 12 ms relative to the first behavior event
-errors: min -5, q1 -1, med 0, q3 1, max 75
+  1%|1         | 4/299 [00:00<00:07, 39.55it/s]
+  4%|4         | 12/299 [00:00<00:06, 46.09it/s]
+  7%|7         | 21/299 [00:00<00:05, 53.55it/s]
+  9%|9         | 28/299 [00:00<00:04, 57.40it/s]
+ 11%|#1        | 34/299 [00:00<00:05, 51.57it/s]
+ 13%|#3        | 40/299 [00:00<00:05, 47.03it/s]
+ 16%|#6        | 49/299 [00:00<00:04, 54.29it/s]
+ 19%|#8        | 56/299 [00:00<00:04, 53.69it/s]
+ 21%|##1       | 63/299 [00:01<00:04, 57.02it/s]
+ 24%|##3       | 71/299 [00:01<00:04, 56.20it/s]
+ 26%|##5       | 77/299 [00:01<00:04, 49.62it/s]
+ 28%|##8       | 84/299 [00:01<00:04, 47.14it/s]
+ 30%|###       | 90/299 [00:01<00:04, 46.09it/s]
+ 32%|###2      | 96/299 [00:01<00:04, 45.97it/s]
+ 34%|###4      | 103/299 [00:01<00:03, 50.48it/s]
+ 36%|###6      | 109/299 [00:02<00:03, 51.33it/s]
+ 38%|###8      | 115/299 [00:02<00:03, 46.85it/s]
+ 41%|####      | 122/299 [00:02<00:03, 51.70it/s]
+ 43%|####3     | 129/299 [00:02<00:03, 53.75it/s]
+ 46%|####5     | 137/299 [00:02<00:02, 58.95it/s]
+ 48%|####8     | 145/299 [00:02<00:02, 63.28it/s]
+ 52%|#####1    | 154/299 [00:02<00:02, 66.86it/s]
+ 54%|#####3    | 161/299 [00:02<00:02, 64.83it/s]
+ 56%|#####6    | 168/299 [00:03<00:02, 54.15it/s]
+ 59%|#####8    | 176/299 [00:03<00:02, 59.75it/s]
+ 61%|######1   | 183/299 [00:03<00:02, 55.77it/s]
+ 63%|######3   | 189/299 [00:03<00:02, 54.86it/s]
+ 65%|######5   | 195/299 [00:03<00:01, 54.33it/s]
+ 68%|######7   | 203/299 [00:03<00:01, 56.75it/s]
+ 72%|#######1  | 215/299 [00:03<00:01, 65.78it/s]
+ 75%|#######4  | 223/299 [00:03<00:01, 57.31it/s]
+ 77%|#######6  | 230/299 [00:04<00:01, 54.46it/s]
+ 80%|#######9  | 238/299 [00:04<00:01, 59.22it/s]
+ 84%|########3 | 251/299 [00:04<00:00, 70.74it/s]
+ 88%|########8 | 264/299 [00:04<00:00, 77.23it/s]
+ 91%|#########1| 273/299 [00:04<00:00, 74.95it/s]
+ 94%|#########4| 282/299 [00:04<00:00, 65.29it/s]
+ 97%|#########6| 290/299 [00:04<00:00, 66.42it/s]
+100%|#########9| 298/299 [00:04<00:00, 69.04it/s]
+100%|##########| 299/299 [00:04<00:00, 60.74it/s]
+Best alignment with the events shifted 12 ms relative to the first behavior event
+errors: min -3, q1 -1, med 0, q3 1, max 75
 Excluding events that have zero close events or more than one photodiode event within `max_len` time
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:324: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 Event 8 recovered (not excluded)
 Event 144 recovered (not excluded)
 Event 234 recovered (not excluded)
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:364: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:371: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  fig.show()
 

 

 
Find cessations of the photodiode deflections

@@ -320,25 +351,100 @@
 

 

 
    
-
  • ../_images/sphx_glr_plot_recover_events_007.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_008.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
-
  • ../_images/sphx_glr_plot_recover_events_009.png
+
  • Use the left/right keys to find the event offset +/- to scale the x axis press enter when finished or `e` to exclude the event
 
    • 
 

 
Out:

-
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif
-Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_zhxg4cq6/sub-1_task-mytask_raw.fif...
+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
 Isotrak not found
-    Range : 0 ... 2044105 =      0.000 ...  2044.105 secs
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
 Ready.
-Reading 0 ... 2044105  =      0.000 ...  2044.105 secs...
-/Users/alexrockhill/projects/pd-parser/pd_parser/parse_pd.py:803: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
-  plt.show()
+Reading 0 ... 2035103  =      0.000 ...  2035.103 secs...
+
+  0%|          | 0/16269 [00:00<?, ?it/s]
+  2%|1         | 307/16269 [00:00<00:05, 3062.11it/s]
+  5%|4         | 756/16269 [00:00<00:04, 3383.39it/s]
+  6%|5         | 975/16269 [00:00<00:05, 2900.93it/s]
+  8%|8         | 1327/16269 [00:00<00:04, 3054.18it/s]
+ 10%|#         | 1634/16269 [00:00<00:04, 3057.32it/s]
+ 12%|#1        | 1900/16269 [00:00<00:05, 2722.11it/s]
+ 14%|#4        | 2311/16269 [00:00<00:04, 3027.41it/s]
+ 16%|#6        | 2673/16269 [00:00<00:04, 3183.26it/s]
+ 19%|#8        | 3033/16269 [00:00<00:04, 3296.18it/s]
+ 21%|##        | 3413/16269 [00:01<00:03, 3432.29it/s]
+ 23%|##3       | 3758/16269 [00:01<00:03, 3418.20it/s]
+ 26%|##5       | 4215/16269 [00:01<00:03, 3697.33it/s]
+ 29%|##9       | 4740/16269 [00:01<00:02, 4057.14it/s]
+ 32%|###1      | 5162/16269 [00:01<00:02, 3912.52it/s]
+ 35%|###4      | 5661/16269 [00:01<00:02, 4182.27it/s]
+ 38%|###7      | 6171/16269 [00:01<00:02, 4420.30it/s]
+ 41%|####1     | 6680/16269 [00:01<00:02, 4601.02it/s]
+ 44%|####4     | 7182/16269 [00:01<00:01, 4718.29it/s]
+ 47%|####7     | 7715/16269 [00:01<00:01, 4886.12it/s]
+ 50%|#####     | 8212/16269 [00:02<00:01, 4834.88it/s]
+ 53%|#####3    | 8701/16269 [00:02<00:01, 4488.88it/s]
+ 56%|#####6    | 9159/16269 [00:02<00:01, 4051.54it/s]
+ 59%|#####8    | 9579/16269 [00:02<00:01, 3634.93it/s]
+ 61%|######1   | 9961/16269 [00:02<00:01, 3493.90it/s]
+ 63%|######3   | 10325/16269 [00:02<00:01, 3354.56it/s]
+ 66%|######5   | 10672/16269 [00:02<00:01, 3231.41it/s]
+ 68%|######8   | 11084/16269 [00:02<00:01, 3453.96it/s]
+ 71%|#######   | 11499/16269 [00:03<00:01, 3636.67it/s]
+ 74%|#######3  | 11973/16269 [00:03<00:01, 3908.05it/s]
+ 77%|#######6  | 12447/16269 [00:03<00:00, 4123.81it/s]
+ 79%|#######9  | 12924/16269 [00:03<00:00, 4293.92it/s]
+ 82%|########2 | 13364/16269 [00:03<00:00, 4077.49it/s]
+ 85%|########5 | 13833/16269 [00:03<00:00, 4242.77it/s]
+ 88%|########7 | 14266/16269 [00:03<00:00, 4261.11it/s]
+ 91%|######### | 14747/16269 [00:03<00:00, 4410.52it/s]
+ 94%|#########3| 15227/16269 [00:03<00:00, 4514.88it/s]
+ 96%|#########6| 15683/16269 [00:03<00:00, 4404.48it/s]
+100%|#########9| 16208/16269 [00:04<00:00, 4627.82it/s]
+100%|##########| 16269/16269 [00:04<00:00, 4010.28it/s]
+Overwriting existing file.
+
+<Annotations | 597 segments: Stim Off (297), Stim On (300)>
+

+

+
Check the results:

+
Finally, we’ll check that the recovered events and the original events match.

+
annot = _load_data(fname)[0]
+raw.set_annotations(annot)
+events2, event_id = mne.events_from_annotations(raw)
+on_events = events2[events2[:, 2] == event_id['Stim On']]
+print(f'Original: {events[corrupted_indices, 0]}\n'
+      f'Recovered: {on_events[corrupted_indices, 0]}')
+
+'''
+# uncomment when using interactively, this section doesn't work
+# for the non-interactive documentation
+off_events = events2[events2[:, 2] == event_id['Stim Off']]
+original_off = events[corrupted_indices, 0] + \
+  np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)
+print(f'Original off: {original_off}\n'
+      f'Recovered off: {on_events[corrupted_indices, 0]}')
+'''
+

+

+
Out:

+
Reading in /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif
+Opening raw data file /var/folders/s4/y1vlkn8d70jfw7s8s03m9p540000gn/T/tmp_mne_tempdir_de9nwvs_/sub-1_task-mytask_raw.fif...
+Isotrak not found
+    Range : 0 ... 2035103 =      0.000 ...  2035.103 secs
+Ready.
+Used Annotations descriptions: ['Stim Off', 'Stim On']
+Original: [  65602  989660 1595483]
+Recovered: [  65602  989660 1595483]
+
+"\n# uncomment when using interactively, this section doesn't work\n# for the non-interactive documentation\noff_events = events2[events2[:, 2] == event_id['Stim Off']]\noriginal_off = events[corrupted_indices, 0] +   np.round(n_secs_on[corrupted_indices] * raw.info['sfreq']).astype(int)\nprint(f'Original off: {original_off}\n'\n      f'Recovered off: {on_events[corrupted_indices, 0]}')\n"
 

 

-
Total running time of the script: ( 0 minutes  11.919 seconds)

+
Total running time of the script: ( 0 minutes  30.425 seconds)

 
 
@@ -97,7 +98,7 @@
       
   

 
Computation times¶

-
00:32.427 total execution time for auto_examples files:

+
00:58.556 total execution time for auto_examples files:

 
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zXU0QoXnM{*3XcGc8EDW+VYhO9kr`Ie(=1Gj*}mF&?H;r=8

 
 

 
 
Find Photodiode Events (plot_find_pd_events.py)
00:20.175
Manually Recover Events Not Found by the Algorithm (plot_recover_events.py)
00:30.425

 
0.0 MB

 
Find Photodiode On and Off Events (plot_find_pd_on_and_off.py)
00:14.849
Use Audio to Align Video Data (plot_find_audio_events.py)
00:28.131

 
0.0 MB

 
Manually Recover Events Not Found by the Algorithm (plot_recover_events.py)
00:11.919
Find Photodiode Events (plot_find_pd_events.py)
00:00.000
0.0 MB
Find Photodiode On and Off Events (plot_find_pd_on_and_off.py)
00:00.000

 
0.0 MB

 

 
     
   
 
@@ -105,19 +106,19 @@
 
-
-
+
+
-
-
+
+
-
+
-
+
@@ -138,7 +139,7 @@
     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/dev/generated/cli.html b/docs/dev/generated/cli.html
index f963501..046edf9 100644
--- a/docs/dev/generated/cli.html
+++ b/docs/dev/generated/cli.html
@@ -4,12 +4,13 @@
   
     
     
-    pd_parser Command Line Interface (CLI) — pd-parser v0.4dev0 documentation
-    
+    pd_parser Command Line Interface (CLI) — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -112,11 +113,11 @@
 
 

 
add_events_to_raw¶

-
usage: add_events_to_raw fname [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]

+
usage: add_events_to_raw raw [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology filepath

 

 

 

@@ -143,11 +144,11 @@ 
optional arguments
 
add_pd_off_events¶

-
usage: add_pd_off_events fname [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]

+
usage: add_pd_off_events raw [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -166,8 +167,8 @@ 
optional arguments--zscore ZSCORE
 
The same zscore as used for parse_pd.

 

-
--min_i MIN_I

-
The same min_i as used for parse_pd.

+
--max_flip_i MAX_FLIP_I

+
The same max_flip_i as used for parse_pd.

 

 
--baseline BASELINE

 
The same baseline as used for parse_pd.

@@ -183,11 +184,11 @@ 
optional arguments
 
add_relative_events¶

-
usage: add_relative_events fname [-h] [--behf BEHF] [--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS] [--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES] [--verbose VERBOSE] [-o]

+
usage: add_relative_events raw [-h] [--beh BEH] [--relative_event_keys [RELATIVE_EVENT_KEYS ...]] [--relative_event_names [RELATIVE_EVENT_NAMES ...]] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -197,15 +198,13 @@ 
optional arguments-h, --help
 
show this help message and exit

 

-
--behf BEHF

+
--beh BEH

 
The behavioral tsv filepath

 

-
--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS

-
A behavioral column in the tsv file that has the time relative to the photodiode events on the same trial as in the beh_col event.

-

-
--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES

-
The name of the corresponding relative_event_cols events

-

+
+
–relative_event_keys [RELATIVE_EVENT_KEYS …]         A behavioral key (column) in the tsv file that has the time relative to the photodiode events on the same trial as in the beh_key event.

+
–relative_event_names [RELATIVE_EVENT_NAMES …]               The name of the corresponding relative_event_keys events

+

 
--verbose VERBOSE

 
Set verbose output to True or False.

 

@@ -217,11 +216,11 @@ 
optional arguments
 
find_pd_params¶

-
usage: find_pd_params fname [-h] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--verbose VERBOSE]

+
usage: find_pd_params raw [-h] [--pd_ch_names [PD_CH_NAMES ...]] [--verbose VERBOSE]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -231,9 +230,9 @@ 
optional arguments-h, --help
 
show this help message and exit

 

-
--pd_ch_names LIST_OF_PD_CH_NAMES

-
The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

-

+

+
–pd_ch_names [PD_CH_NAMES …]         The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+

 
--verbose VERBOSE

 
Set verbose output to True or False.

 

@@ -242,11 +241,11 @@ 
optional arguments
 
parse_audio¶

-
usage: parse_audio fname [-h] [--audio_event_name AUDIO_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--audio_ch_names LIST_OF_AUDIO_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

+
usage: parse_audio raw [-h] [--audio_event_name AUDIO_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--audio_ch_names [AUDIO_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
TThe electrophysiology raw object or filepath

 

 

 

@@ -259,15 +258,15 @@ 
optional arguments--audio_event_name AUDIO_EVENT_NAME
 
The name of the audio event

 

-
--behf BEHF

-
The behavioral tsv filepath

-

-
--beh_col BEH_COL

-
The name of the behavioral column corresponding to the audio event timing

+
--beh BEH

+
The behavioral dictionary or tsv filepath

 

-
--audio_ch_names LIST_OF_AUDIO_CH_NAMES

-
The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced

+
--beh_key BEH_KEY

+
The name of the behavioral key (column) corresponding to the audio event timing

 

+

+
–audio_ch_names [AUDIO_CH_NAMES …]           The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced

+

 
--exclude_shift EXCLUDE_SHIFT

 
How many seconds off to exclude an audio-behavioral event difference

 

@@ -297,11 +296,11 @@ 
optional arguments
 
parse_pd¶

-
usage: parse_pd fname [-h] [--pd_event_name PD_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

+
usage: parse_pd raw [-h] [--pd_event_name PD_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--pd_ch_names [PD_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -314,15 +313,15 @@ 
optional arguments--pd_event_name PD_EVENT_NAME
 
The name of the photodiode event

 

-
--behf BEHF

-
The behavioral tsv filepath

-

-
--beh_col BEH_COL

-
The name of the behavioral column corresponding to the photodiode event timing

+
--beh BEH

+
The behavioral dictionary or tsv filepath

 

-
--pd_ch_names LIST_OF_PD_CH_NAMES

-
The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+
--beh_key BEH_KEY

+
The name of the behavioral key (column) corresponding to the photodiode event timing

 

+

+
–pd_ch_names [PD_CH_NAMES …]         The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+

 
--exclude_shift EXCLUDE_SHIFT

 
How many seconds off to exclude a photodiode- behavioral event difference

 

@@ -335,8 +334,8 @@ 
optional arguments--zscore ZSCORE
 
How many standard deviations larger than the baseline the photodiode event is. Decrease if too many events are being found and increase if too few. Use find_pd_params to determine if unsure.

 

-
--min_i MIN_I

-
The minimum number of samples to qualify as a pd event. Increase for fewer false-positives, decrease if your photodiode is on for fewer samples.

+
--max_flip_i MAX_FLIP_I

+
The maximum number of samples the photodiode event takes to transition. Increase if the transitions are not being found, decrease for fewer false positives.

 

 
--baseline BASELINE

 
How much relative to the max_lento use to idenify the time before the photodiode event. Probably don’t change but increasing will reduce false-positives and decreasing will reduce false-negatives.

@@ -358,13 +357,13 @@ 
optional arguments
 
pd_parser_save_to_bids¶

-
usage: pd_parser_save_to_bids bids_dir fname sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs LIST_OF_EOGS] [--ecgs LIST_OF_ECGS] [--emgs LIST_OF_EMGS] [--verbose VERBOSE] [-o]

+
usage: pd_parser_save_to_bids bids_dir raw sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs [EOGS ...]] [--ecgs [ECGS ...]] [--emgs [EMGS ...]] [--verbose VERBOSE] [-o]

 
 
diff --git a/docs/dev/genindex.html b/docs/dev/genindex.html
index 3ede21e..58af558 100644
--- a/docs/dev/genindex.html
+++ b/docs/dev/genindex.html
@@ -4,12 +4,13 @@
   
     
     
-    Index — pd-parser v0.4dev0 documentation
-    
+    Index — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       

 
@@ -180,7 +181,7 @@ 
S

     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/dev/index.html b/docs/dev/index.html
index acafcd8..93a0728 100644
--- a/docs/dev/index.html
+++ b/docs/dev/index.html
@@ -4,12 +4,13 @@
   
     
     
-    pd-parser — pd-parser v0.4dev0 documentation
-    
+    pd-parser — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -198,7 +199,7 @@ 
ContributingSphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/dev/py-modindex.html b/docs/dev/py-modindex.html
index 8786976..818ee3e 100644
--- a/docs/dev/py-modindex.html
+++ b/docs/dev/py-modindex.html
@@ -4,12 +4,13 @@
   
     
     
-    Python Module Index — pd-parser v0.4dev0 documentation
-    
+    Python Module Index — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -46,7 +47,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -129,7 +130,7 @@ 
Python Module Index

     

     

         © Copyright 2020, Alex Rockhill.

-      Created using Sphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/dev/search.html b/docs/dev/search.html
index 18917c0..1bab0bc 100644
--- a/docs/dev/search.html
+++ b/docs/dev/search.html
@@ -4,12 +4,13 @@
   
     
     
-    Search — pd-parser v0.4dev0 documentation
-    
+    Search — pd-parser v0.5dev0 documentation
     
+    
     
     
     
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\ No newline at end of file
diff --git a/docs/dev/whats_new.html b/docs/dev/whats_new.html
index f82fa35..9363198 100644
--- a/docs/dev/whats_new.html
+++ b/docs/dev/whats_new.html
@@ -4,12 +4,13 @@
   
     
     
-    What’s new? — pd-parser v0.4dev0 documentation
-    
+    What’s new? — pd-parser v0.5dev0 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser v0.4dev0
+          pd-parser v0.5dev0
         
       
 
@@ -157,7 +158,7 @@ 
AuthorsSphinx 3.2.1.

+      Created using Sphinx 3.3.1.

     

   
 
diff --git a/docs/generated/cli.html b/docs/generated/cli.html
index dc4dbde..a4c6cdd 100644
--- a/docs/generated/cli.html
+++ b/docs/generated/cli.html
@@ -4,12 +4,13 @@
   
     
     
-    pd_parser Command Line Interface (CLI) — pd-parser 0.3 documentation
-    
+    pd_parser Command Line Interface (CLI) — pd-parser v0.4 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser 0.3
+          pd-parser v0.4
         
       
 
@@ -101,21 +102,22 @@
 
-

-
add_pd_events_to_raw¶

-
usage: add_pd_events_to_raw fname [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]

+

+
add_events_to_raw¶

+
usage: add_events_to_raw raw [-h] [--out_fname OUT_FNAME] [--drop_pd_channels DROP_PD_CHANNELS] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology filepath

 

 

 

@@ -141,12 +143,12 @@ 
optional arguments
-
add_pd_off_events¶

-
usage: add_pd_off_events fname [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]

+
add_pd_off_events¶

+
usage: add_pd_off_events raw [-h] [--off_event_name OFF_EVENT_NAME] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -165,8 +167,8 @@ 
optional arguments--zscore ZSCORE
 
The same zscore as used for parse_pd.

 

-
--min_i MIN_I

-
The same min_i as used for parse_pd.

+
--max_flip_i MAX_FLIP_I

+
The same max_flip_i as used for parse_pd.

 

 
--baseline BASELINE

 
The same baseline as used for parse_pd.

@@ -180,13 +182,13 @@ 
optional arguments
-
add_pd_relative_events¶

-
usage: add_pd_relative_events fname [-h] [--behf BEHF] [--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS] [--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES] [--verbose VERBOSE] [-o]

+

+
add_relative_events¶

+
usage: add_relative_events raw [-h] [--beh BEH] [--relative_event_keys [RELATIVE_EVENT_KEYS ...]] [--relative_event_names [RELATIVE_EVENT_NAMES ...]] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -196,15 +198,13 @@ 
optional arguments-h, --help
 
show this help message and exit

 

-
--behf BEHF

+
--beh BEH

 
The behavioral tsv filepath

 

-
--relative_event_cols LIST_OF_RELATIVE_EVENT_COLS

-
A behavioral column in the tsv file that has the time relative to the photodiode events on the same trial as in the beh_col event.

-

-
--relative_event_names LIST_OF_RELATIVE_EVENT_NAMES

-
The name of the corresponding relative_event_cols events

-

+
+
–relative_event_keys [RELATIVE_EVENT_KEYS …]         A behavioral key (column) in the tsv file that has the time relative to the photodiode events on the same trial as in the beh_key event.

+
–relative_event_names [RELATIVE_EVENT_NAMES …]               The name of the corresponding relative_event_keys events

+

 
--verbose VERBOSE

 
Set verbose output to True or False.

 

@@ -215,12 +215,12 @@ 
optional arguments
-
find_pd_params¶

-
usage: find_pd_params fname [-h] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--verbose VERBOSE]

+
find_pd_params¶

+
usage: find_pd_params raw [-h] [--pd_ch_names [PD_CH_NAMES ...]] [--verbose VERBOSE]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 

 

@@ -230,22 +230,22 @@ 
optional arguments-h, --help
 
show this help message and exit

 

-
--pd_ch_names LIST_OF_PD_CH_NAMES

-
The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

-

+

+
–pd_ch_names [PD_CH_NAMES …]         The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+

 
--verbose VERBOSE

 
Set verbose output to True or False.

 

 

 

 

-

-
parse_pd¶

-
usage: parse_pd fname [-h] [--pd_event_name PD_EVENT_NAME] [--behf BEHF] [--beh_col BEH_COL] [--pd_ch_names LIST_OF_PD_CH_NAMES] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--min_i MIN_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

+

+
parse_audio¶

+
usage: parse_audio raw [-h] [--audio_event_name AUDIO_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--audio_ch_names [AUDIO_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

 

 
positional arguments¶

 

-
fname
The electrophysiology filepath

+
raw
TThe electrophysiology raw object or filepath

 

 

 

@@ -255,18 +255,73 @@ 
optional arguments-h, --help
 
show this help message and exit

 

+
--audio_event_name AUDIO_EVENT_NAME

+
The name of the audio event

+

+
--beh BEH

+
The behavioral dictionary or tsv filepath

+

+
--beh_key BEH_KEY

+
The name of the behavioral key (column) corresponding to the audio event timing

+

+
+
–audio_ch_names [AUDIO_CH_NAMES …]           The name(s) of the channels with the audio data. Note that they will be if thereare two channels they will be bipolar referenced

+

+
--exclude_shift EXCLUDE_SHIFT

+
How many seconds off to exclude an audio-behavioral event difference

+

+
--resync RESYNC

+
How large of a difference to use to resynchronize events. See pd_parser.parse_pd for more information

+

+
--max_len MAX_LEN

+
The length of the longest audio event

+

+
--zscore ZSCORE

+
How many standard deviations larger than the baseline the correlation of the audio is. If None, zscore is found interactively.

+

+
--add_events

+
Whether to run the parser a second time to add more events from deflections corresponding to multiple events on the same channel

+

+
--recover

+
Whether to recover corrupted events manually.

+

+
--verbose VERBOSE

+
Set verbose output to True or False.

+

+
-o, --overwrite

+
Pass this flag to overwrite an existing file

+

+

+

+

+

+
parse_pd¶

+
usage: parse_pd raw [-h] [--pd_event_name PD_EVENT_NAME] [--beh BEH] [--beh_key BEH_KEY] [--pd_ch_names [PD_CH_NAMES ...]] [--exclude_shift EXCLUDE_SHIFT] [--resync RESYNC] [--max_len MAX_LEN] [--zscore ZSCORE] [--max_flip_i MAX_FLIP_I] [--baseline BASELINE] [--add_events] [--recover] [--verbose VERBOSE] [-o]

+

+
positional arguments¶

+

+
raw
The electrophysiology raw object or filepath

+

+

+

+

+
optional arguments¶

+

+
-h, --help

+
show this help message and exit

+

 
--pd_event_name PD_EVENT_NAME

 
The name of the photodiode event

 

-
--behf BEHF

-
The behavioral tsv filepath

-

-
--beh_col BEH_COL

-
The name of the behavioral column corresponding to the photodiode event timing

+
--beh BEH

+
The behavioral dictionary or tsv filepath

 

-
--pd_ch_names LIST_OF_PD_CH_NAMES

-
The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+
--beh_key BEH_KEY

+
The name of the behavioral key (column) corresponding to the photodiode event timing

 

+

+
–pd_ch_names [PD_CH_NAMES …]         The name(s) of the channels with the photodiode data. Can be one channel for common referenced recording or two for a bipolar recording. If not provided, the data will be plotted for the user to pick

+

 
--exclude_shift EXCLUDE_SHIFT

 
How many seconds off to exclude a photodiode- behavioral event difference

 

@@ -279,8 +334,8 @@ 
optional arguments--zscore ZSCORE
 
How many standard deviations larger than the baseline the photodiode event is. Decrease if too many events are being found and increase if too few. Use find_pd_params to determine if unsure.

 

-
--min_i MIN_I

-
The minimum number of samples to qualify as a pd event. Increase for fewer false-positives, decrease if your photodiode is on for fewer samples.

+
--max_flip_i MAX_FLIP_I

+
The maximum number of samples the photodiode event takes to transition. Increase if the transitions are not being found, decrease for fewer false positives.

 

 
--baseline BASELINE

 
How much relative to the max_lento use to idenify the time before the photodiode event. Probably don’t change but increasing will reduce false-positives and decreasing will reduce false-negatives.

@@ -301,14 +356,14 @@ 
optional arguments
-
pd_parser_save_to_bids¶

-
usage: pd_parser_save_to_bids bids_dir fname sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs LIST_OF_EOGS] [--ecgs LIST_OF_ECGS] [--emgs LIST_OF_EMGS] [--verbose VERBOSE] [-o]

-

-
positional arguments¶

+
pd_parser_save_to_bids¶

+
usage: pd_parser_save_to_bids bids_dir raw sub task [-h] [--ses SES] [--run RUN] [--data_type DATA_TYPE] [--eogs [EOGS ...]] [--ecgs [ECGS ...]] [--emgs [EMGS ...]] [--verbose VERBOSE] [-o]

+

+
positional arguments¶

 

 
bids_dir
Filepath of the BIDS directory to save to

 

-
fname
The electrophysiology filepath

+
raw
The electrophysiology raw object or filepath

 

 
sub
The subject identifier

 

@@ -316,8 +371,8 @@ 
positional arguments
-
optional arguments¶

+
 
diff --git a/docs/genindex.html b/docs/genindex.html
index bad9d63..052312e 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -4,12 +4,13 @@
   
     
     
-    Index — pd-parser 0.3 documentation
-    
+    Index — pd-parser v0.4 documentation
     
+    
     
     
     
+    
     
     
     
@@ -39,7 +40,7 @@
           
         
         
-          pd-parser 0.3
+          pd-parser v0.4
         
       

 
@@ -105,13 +106,13 @@ 
Index

 
A

 

 
 

 
 
Use Audio to Align Video Data (plot_find_audio_events.py)
00:32.427
Manually Recover Events Not Found by the Algorithm (plot_recover_events.py)
00:30.425

 
0.0 MB

 
Find Photodiode Events (plot_find_pd_events.py)
00:00.000
Use Audio to Align Video Data (plot_find_audio_events.py)
00:28.131

 
0.0 MB

 
Find Photodiode On and Off Events (plot_find_pd_on_and_off.py)
Find Photodiode Events (plot_find_pd_events.py)

 
00:00.000

 
0.0 MB

 
Manually Recover Events Not Found by the Algorithm (plot_recover_events.py)
Find Photodiode On and Off Events (plot_find_pd_on_and_off.py)

 
00:00.000

 
0.0 MB

 

   
   
 

@@ -140,6 +141,8 @@ 
M

 
P