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update doocs for compatibility
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alexrockhill committed Sep 19, 2022
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2 changes: 1 addition & 1 deletion docs/.buildinfo
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# 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: d4be34b5e5af3c7ec95e48f6e606b708
config: 33848b7c133faf0de63c67542fddb2e9
tags: 645f666f9bcd5a90fca523b33c5a78b7
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info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
['seeg'] * 3)
raw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)
raw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine
raw.add_channels([raw2])
# bids needs these data fields
raw.info['dig'] = None
raw.info['line_freq'] = 60
raw.info['line_freq'] = 60 # for bids

fname = op.join(out_dir, 'sub-1_task-mytask_raw.fif')
raw.save(fname)
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Expand Up @@ -68,11 +68,8 @@
info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
['seeg'] * 3)
raw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)
raw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine
raw.add_channels([raw2])
# bids needs these data fields
raw.info['dig'] = None
raw.info['line_freq'] = 60
raw.info['line_freq'] = 60 # for bids

# add some offsets to the behavior so it's a bit more realistic
offsets = np.random.randn(n_events) * 0.001
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Expand Up @@ -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 _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)"
"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)\nraw.add_channels([raw2])\nraw.info['line_freq'] = 60 # for bids\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)"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.5"
}
},
"nbformat": 4,
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Expand Up @@ -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 _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)"
"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)\nraw.add_channels([raw2])\nraw.info['line_freq'] = 60 # for bids\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)"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.5"
}
},
"nbformat": 4,
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Expand Up @@ -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 _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)"
"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)\nraw.add_channels([raw2])\nraw.info['line_freq'] = 60 # for bids\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)"
]
},
{
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.5"
}
},
"nbformat": 4,
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info = mne.create_info(['ch1', 'ch2', 'ch3'], raw.info['sfreq'],
['seeg'] * 3)
raw2 = mne.io.RawArray(np.random.random((3, raw.times.size)) * 1e-6, info)
raw2.info['lowpass'] = raw.info['lowpass'] # these must match to combine
raw.add_channels([raw2])
# bids needs these data fields
raw.info['dig'] = None
raw.info['line_freq'] = 60
raw.info['line_freq'] = 60 # for bids

# add some offsets to the behavior so it's a bit more realistic
offsets = np.random.randn(n_events) * 0.01
Expand Down
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32 changes: 16 additions & 16 deletions docs/_modules/index.html
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<!DOCTYPE html>

<html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Overview: module code &#8212; pd-parser v0.7 documentation</title>
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/bootstrap-sphinx.css" type="text/css" />
<link rel="stylesheet" type="text/css" href="../_static/gallery.css" />
<link rel="stylesheet" type="text/css" href="../_static/gallery-binder.css" />
<link rel="stylesheet" type="text/css" href="../_static/gallery-dataframe.css" />
<link rel="stylesheet" type="text/css" href="../_static/gallery-rendered-html.css" />
<script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
<title>Overview: module code &#8212; pd-parser v0.8 documentation</title>
<link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
<script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
<script src="../_static/jquery.js"></script>
<script src="../_static/underscore.js"></script>
<script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
<script src="../_static/doctools.js"></script>
<script src="../_static/language_data.js"></script>
<link rel="shortcut icon" href="../_static/favicon.png"/>
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<meta charset='utf-8'>
<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
<meta name="apple-mobile-web-app-capable" content="yes">
<script type="text/javascript" src="../_static/js/jquery-1.11.0.min.js "></script>
<script type="text/javascript" src="../_static/js/jquery-fix.js "></script>
<script type="text/javascript" src="../_static/bootstrap-3.3.7/js/bootstrap.min.js "></script>
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<script type="text/javascript" src="../_static/js/jquery-fix.js"></script>
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<script type="text/javascript" src="../_static/bootstrap-sphinx.js"></script>

</head><body>

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<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="../index.html">
pd-parser v0.7</a>
pd-parser v0.8</a>
<span class="navbar-text navbar-version pull-left"><b></b></span>
</div>

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</p>
<p>
&copy; Copyright 2020, Alex Rockhill.<br/>
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.3.1.<br/>
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 5.1.1.<br/>
</p>
</div>
</footer>
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