CircleCI update of dev docs (68903).

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: ff4f6a21e2cfdd9a6b55becbf8a6e76a
+config: 5837a174cc5fa29a55182e5862a1229b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/21ced0ae825210e623b8a6f8ca8464b0/20_event_arrays.py

@@ -5,8 +5,8 @@
 Working with events
 ===================
 
-This tutorial describes event representation and how event arrays are used to
-subselect data.
+This tutorial describes event representation and how event arrays are used to subselect
+data.
 
 As usual we'll start by importing the modules we need, loading some
 :ref:`example data <sample-dataset>`, and cropping the :class:`~mne.io.Raw`

dev/_downloads/2d6a273bc3ded0e873d80d802edc2b7e/70_report.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\n# Getting started with mne.Report\n\n:class:`mne.Report` is a way to create interactive HTML summaries of your data.\nThese reports can show many different visualizations for one or multiple\nparticipants. A common use case is creating diagnostic summaries to check data\nquality at different stages in the processing pipeline. The report can show\nthings like plots of data before and after each preprocessing step, epoch\nrejection statistics, MRI slices with overlaid BEM shells, all the way up to\nplots of estimated cortical activity.\n\nCompared to a Jupyter notebook, :class:`mne.Report` is easier to deploy, as the\nHTML pages it generates are self-contained and do not require a running Python\nenvironment. However, it is less flexible as you can't change code and re-run\nsomething directly within the browser. This tutorial covers the basics of\nbuilding a report. As usual, we will start by importing the modules and data we need:\n"
+        "\n\n# Getting started with mne.Report\n\n:class:`mne.Report` is a way to create interactive HTML summaries of your data.\nThese reports can show many different visualizations for one or multiple participants.\nA common use case is creating diagnostic summaries to check data\nquality at different stages in the processing pipeline. The report can show\nthings like plots of data before and after each preprocessing step, epoch\nrejection statistics, MRI slices with overlaid BEM shells, all the way up to\nplots of estimated cortical activity.\n\nCompared to a Jupyter notebook, :class:`mne.Report` is easier to deploy, as the\nHTML pages it generates are self-contained and do not require a running Python\nenvironment. However, it is less flexible as you can't change code and re-run\nsomething directly within the browser. This tutorial covers the basics of\nbuilding a report. As usual, we will start by importing the modules and data we need:\n"
       ]
     },
     {

dev/_downloads/437c08be932c64819097c787b767439e/read_impedances.ipynb

@@ -0,0 +1,68 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Getting impedances from raw files\n\nMany EEG systems provide impedance measurements for each channel within their file\nformat. MNE does not parse this information and does not store it in the\n:class:`~mne.io.Raw` object. However, it is possible to extract this information from\nthe raw data and store it in a separate data structure.\n\n## ANT Neuro\n\nThe ``.cnt`` file format from ANT Neuro stores impedance information in the form of\ntriggers. The function :func:`mne.io.read_raw_ant` reads this information and marks the\ntime-segment during which an impedance measurement was performed as\n:class:`~mne.Annotations` with the description set in the argument\n``impedance_annotation``. However, it doesn't extract the impedance values themselves.\nTo do so, use the function ``antio.parser.read_triggers``.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: The MNE-Python contributors.\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors.\n\nfrom antio import read_cnt\nfrom antio.parser import read_triggers\nfrom matplotlib import pyplot as plt\n\nfrom mne.datasets import testing\nfrom mne.io import read_raw_ant\nfrom mne.viz import plot_topomap\n\nfname = testing.data_path() / \"antio\" / \"CA_208\" / \"test_CA_208.cnt\"\ncnt = read_cnt(fname)\n_, _, _, impedances, _ = read_triggers(cnt)\n\nraw = read_raw_ant(fname, eog=r\"EOG\")\nimpedances = [{ch: imp[k] for k, ch in enumerate(raw.ch_names)} for imp in impedances]\nprint(impedances[0])  # impedances measurement at the beginning of the recording"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that the impedance measurement contains all channels, including the bipolar ones.\nWe can visualize the impedances on a topographic map; below we show a topography of\nimpedances before and after the recording for the EEG channels only.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "raw.pick(\"eeg\").set_montage(\"standard_1020\")\nimpedances = [{ch: imp[ch] for ch in raw.ch_names} for imp in impedances]\n\nf, ax = plt.subplots(1, 2, layout=\"constrained\", figsize=(10, 5))\nf.suptitle(\"Impedances (kOhm)\")\nimpedance = list(impedances[0].values())\nplot_topomap(\n    impedance,\n    raw.info,\n    vlim=(0, 50),\n    axes=ax[0],\n    show=False,\n    names=[f\"{elt:.1f}\" for elt in impedance],\n)\nax[0].set_title(\"Impedances at the beginning of the recording\")\nimpedance = list(impedances[0].values())\nplot_topomap(\n    impedance,\n    raw.info,\n    vlim=(0, 50),\n    axes=ax[1],\n    show=False,\n    names=[f\"{elt:.1f}\" for elt in impedance],\n)\nax[1].set_title(\"Impedances at the end of the recording\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this very short test file, the impedances are stable over time.\n\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": "3.12.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

dev/_downloads/9552276573be20bde95d1b4bc52b4768/20_event_arrays.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\n# Working with events\n\nThis tutorial describes event representation and how event arrays are used to\nsubselect data.\n\nAs usual we'll start by importing the modules we need, loading some\n`example data <sample-dataset>`, and cropping the :class:`~mne.io.Raw`\nobject to just 60 seconds before loading it into RAM to save memory:\n"
+        "\n\n# Working with events\n\nThis tutorial describes event representation and how event arrays are used to subselect\ndata.\n\nAs usual we'll start by importing the modules we need, loading some\n`example data <sample-dataset>`, and cropping the :class:`~mne.io.Raw`\nobject to just 60 seconds before loading it into RAM to save memory:\n"
       ]
     },
     {

dev/_downloads/998fd5313bceaa9591d37ed3918d07e4/70_report.py

@@ -6,8 +6,8 @@
 ===============================
 
 :class:`mne.Report` is a way to create interactive HTML summaries of your data.
-These reports can show many different visualizations for one or multiple
-participants. A common use case is creating diagnostic summaries to check data
+These reports can show many different visualizations for one or multiple participants.
+A common use case is creating diagnostic summaries to check data
 quality at different stages in the processing pipeline. The report can show
 things like plots of data before and after each preprocessing step, epoch
 rejection statistics, MRI slices with overlaid BEM shells, all the way up to

dev/_downloads/c8e0a5c54898db31ae8adfd86e797885/read_impedances.py

@@ -0,0 +1,77 @@
+"""
+.. _ex-io-impedances:
+
+=================================
+Getting impedances from raw files
+=================================
+
+Many EEG systems provide impedance measurements for each channel within their file
+format. MNE does not parse this information and does not store it in the
+:class:`~mne.io.Raw` object. However, it is possible to extract this information from
+the raw data and store it in a separate data structure.
+
+ANT Neuro
+---------
+
+The ``.cnt`` file format from ANT Neuro stores impedance information in the form of
+triggers. The function :func:`mne.io.read_raw_ant` reads this information and marks the
+time-segment during which an impedance measurement was performed as
+:class:`~mne.Annotations` with the description set in the argument
+``impedance_annotation``. However, it doesn't extract the impedance values themselves.
+To do so, use the function ``antio.parser.read_triggers``.
+"""
+
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+from antio import read_cnt
+from antio.parser import read_triggers
+from matplotlib import pyplot as plt
+
+from mne.datasets import testing
+from mne.io import read_raw_ant
+from mne.viz import plot_topomap
+
+fname = testing.data_path() / "antio" / "CA_208" / "test_CA_208.cnt"
+cnt = read_cnt(fname)
+_, _, _, impedances, _ = read_triggers(cnt)
+
+raw = read_raw_ant(fname, eog=r"EOG")
+impedances = [{ch: imp[k] for k, ch in enumerate(raw.ch_names)} for imp in impedances]
+print(impedances[0])  # impedances measurement at the beginning of the recording
+
+# %%
+# Note that the impedance measurement contains all channels, including the bipolar ones.
+# We can visualize the impedances on a topographic map; below we show a topography of
+# impedances before and after the recording for the EEG channels only.
+
+raw.pick("eeg").set_montage("standard_1020")
+impedances = [{ch: imp[ch] for ch in raw.ch_names} for imp in impedances]
+
+f, ax = plt.subplots(1, 2, layout="constrained", figsize=(10, 5))
+f.suptitle("Impedances (kOhm)")
+impedance = list(impedances[0].values())
+plot_topomap(
+    impedance,
+    raw.info,
+    vlim=(0, 50),
+    axes=ax[0],
+    show=False,
+    names=[f"{elt:.1f}" for elt in impedance],
+)
+ax[0].set_title("Impedances at the beginning of the recording")
+impedance = list(impedances[0].values())
+plot_topomap(
+    impedance,
+    raw.info,
+    vlim=(0, 50),
+    axes=ax[1],
+    show=False,
+    names=[f"{elt:.1f}" for elt in impedance],
+)
+ax[1].set_title("Impedances at the end of the recording")
+plt.show()
+
+# %%
+# In this very short test file, the impedances are stable over time.

dev/_downloads/e6b944a6353320d1697a70002905a99a/70_reading_eyetracking_data.py

@@ -179,7 +179,7 @@
 # position for each sample in the file. MNE will read in these data if they are
 # present in the file, but will label their channel types as ``'misc'``.
 #
-#  .. warning:: Eyelink's EDF2ASC API allows for modification of the data
+# .. warning:: Eyelink's EDF2ASC API allows for modification of the data
 #              and format that is converted to ASCII. However, MNE-Python
 #              assumes a specific structure, which the default parameters of
 #              EDF2ASC follow. ASCII files should be tab-deliminted, and both

dev/_downloads/ecce6733463df02d318eed5ad3ab1675/70_reading_eyetracking_data.ipynb

@@ -101,7 +101,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Pupil Size Data\nPupil size is measured by the EyeLink system at up to 500 samples per second.\nIt may be reported as pupil *area*, or pupil *diameter* (i.e. the diameter\nof a circle/ellipse model fit to the pupil area).\nWhich of these datatypes you get is specified by your recording- and/or your\nEDF2ASC settings. The pupil size data is not calibrated and reported in\narbitrary units. Typical pupil *area* data range between 800 to 2000 units,\nwith a precision of 1 unit, while pupil *diameter* data range between\n1800-3000 units.\n\n### Velocity, resolution, and head position data\nEyelink files can produce data on saccadic velocity, resolution, and head\nposition for each sample in the file. MNE will read in these data if they are\npresent in the file, but will label their channel types as ``'misc'``.\n\n .. warning:: Eyelink's EDF2ASC API allows for modification of the data\n             and format that is converted to ASCII. However, MNE-Python\n             assumes a specific structure, which the default parameters of\n             EDF2ASC follow. ASCII files should be tab-deliminted, and both\n             Samples and Events should be output. If the data were recorded\n             at 2000Hz, timestamps should be floating point numbers. Manual\n             modification of ASCII conversion via EDF2ASC is not recommended.\n\n"
+        "### Pupil Size Data\nPupil size is measured by the EyeLink system at up to 500 samples per second.\nIt may be reported as pupil *area*, or pupil *diameter* (i.e. the diameter\nof a circle/ellipse model fit to the pupil area).\nWhich of these datatypes you get is specified by your recording- and/or your\nEDF2ASC settings. The pupil size data is not calibrated and reported in\narbitrary units. Typical pupil *area* data range between 800 to 2000 units,\nwith a precision of 1 unit, while pupil *diameter* data range between\n1800-3000 units.\n\n### Velocity, resolution, and head position data\nEyelink files can produce data on saccadic velocity, resolution, and head\nposition for each sample in the file. MNE will read in these data if they are\npresent in the file, but will label their channel types as ``'misc'``.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Eyelink's EDF2ASC API allows for modification of the data\n             and format that is converted to ASCII. However, MNE-Python\n             assumes a specific structure, which the default parameters of\n             EDF2ASC follow. ASCII files should be tab-deliminted, and both\n             Samples and Events should be output. If the data were recorded\n             at 2000Hz, timestamps should be floating point numbers. Manual\n             modification of ASCII conversion via EDF2ASC is not recommended.</p></div>\n\n"
       ]
     }
   ],