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MRG: No warning in example (mne-tools#5742)
* FIX: No warning in example * FIX: Links
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‎doc/conf.py‎

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@@ -339,6 +339,8 @@ def reset_warnings(gallery_conf, fname):
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warnings.filterwarnings(
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'always', '.*converting a masked element to nan.*') # matplotlib?
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# allow these warnings, but don't show them
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warnings.filterwarnings(
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'ignore', '.*OpenSSL\\.rand is deprecated.*')
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warnings.filterwarnings('ignore', '.*is currently using agg.*')
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warnings.filterwarnings( # SciPy-related warning (maybe 1.2.0 will fix it)
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'ignore', '.*the matrix subclass is not the recommended.*')

‎doc/tutorials/philosophy.rst‎

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@@ -9,16 +9,16 @@ One of the first things you might be wondering about is how to get your
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data into mne. Assuming that you have unprocessed data, you will probably
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be happy with at least one of these readers:
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* :func:`read_raw_fif <mne.io.read_raw_fif>`
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* :func:`read_raw_kit <mne.io.read_raw_kit>`
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* :func:`read_raw_bti <mne.io.read_raw_bti>`
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* :func:`read_raw_ctf <mne.io.read_raw_ctf>`
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* :func:`read_raw_brainvision <mne.io.read_raw_brainvision>`
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* :func:`read_raw_cnt <mne.io.read_raw_cnt>`
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* :func:`read_raw_edf <mne.io.read_raw_edf>`
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* :func:`read_raw_eeglab <mne.io.read_raw_eeglab>`
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* :func:`read_raw_egi <mne.io.read_raw_egi>`
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* :func:`read_raw_nicolet <mne.io.read_raw_nicolet>`
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* :func:`~mne.io.read_raw_fif`
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* :func:`~mne.io.read_raw_kit`
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* :func:`~mne.io.read_raw_bti`
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* :func:`~mne.io.read_raw_ctf`
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* :func:`~mne.io.read_raw_brainvision`
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* :func:`~mne.io.read_raw_cnt`
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* :func:`~mne.io.read_raw_edf`
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* :func:`~mne.io.read_raw_eeglab`
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* :func:`~mne.io.read_raw_egi`
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* :func:`~mne.io.read_raw_nicolet`
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They all have in common to return an :class:`mne.io.Raw`-like object.
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See :ref:`ch_convert`.
@@ -87,7 +87,7 @@ Besides ``.ch_names`` another important attribute is ``.info``. It contains
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the channel information and some details about the processing history.
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This is especially relevant if your data cannot be read using the io
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functions listed above. You then need to learn how to create an info.
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See :ref:`tut_info_objects`.
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See :ref:`sphx_glr_auto_tutorials_plot_info.py`.
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4. MNE is modular
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-----------------

‎tutorials/plot_creating_data_structures.py‎

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@@ -14,11 +14,11 @@
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###############################################################################
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# ------------------------------------------------------
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# Creating :class:`Info <mne.Info>` objects
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# Creating :class:`~mne.Info` objects
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# ------------------------------------------------------
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#
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# .. note:: for full documentation on the `Info` object, see
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# :ref:`tut_info_objects`. See also
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# .. note:: for full documentation on the :class:`~mne.Info` object, see
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# :ref:`sphx_glr_auto_tutorials_plot_info.py`. See also
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# :ref:`sphx_glr_auto_examples_io_plot_objects_from_arrays.py`.
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#
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# Normally, :class:`mne.Info` objects are created by the various
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# - The `ch_names` field should be consistent with the `name` field
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# of the channel information contained in `chs`.
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#
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# ---------------------------------------------
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# Creating :class:`Raw <mne.io.Raw>` objects
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# ---------------------------------------------
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# -------------------------------------
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# Creating :class:`~mne.io.Raw` objects
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# -------------------------------------
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#
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# To create a :class:`mne.io.Raw` object from scratch, you can use the
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# :class:`mne.io.RawArray` class, which implements raw data that is backed by a
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print(custom_raw)
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###############################################################################
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# ---------------------------------------------
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# Creating :class:`Epochs <mne.Epochs>` objects
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# ---------------------------------------------
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# -------------------------------------
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# Creating :class:`~mne.Epochs` objects
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# -------------------------------------
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#
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# To create an :class:`mne.Epochs` object from scratch, you can use the
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# :class:`mne.EpochsArray` class, which uses a numpy array directly without
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_ = custom_epochs['smiling'].average().plot(time_unit='s')
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###############################################################################
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# ---------------------------------------------
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# Creating :class:`Evoked <mne.Evoked>` Objects
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# ---------------------------------------------
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# -------------------------------------
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# Creating :class:`~mne.Evoked` Objects
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# -------------------------------------
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# If you already have data that is collapsed across trials, you may also
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# directly create an evoked array. Its constructor accepts an array of
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# `shape(n_chans, n_times)` in addition to some bookkeeping parameters.

‎tutorials/plot_epoching_and_averaging.py‎

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###############################################################################
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# In MNE, `epochs` refers to a collection of `single trials` or short segments
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# of time locked raw data. If you haven't already, you might want to check out
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# :ref:`tut_epochs_objects`. In this tutorial we take a deeper look into
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# :ref:`sphx_glr_auto_tutorials_plot_object_epochs.py`.
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# In this tutorial we take a deeper look into
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# construction of epochs and averaging the epoch data to evoked instances.
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# First let's read in the raw sample data.
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data_path = mne.datasets.sample.data_path()
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# you might ask. They're different because ``picks`` is simply a list of
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# channel indices and as the epochs were constructed, also a new info structure
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# is created where the channel indices run from 0 to ``epochs.info['nchan']``.
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# See :ref:`tut_info_objects` for more information.
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# See :ref:`sphx_glr_auto_tutorials_plot_info.py` for more information.
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picks = mne.pick_types(epochs.info, meg=True, eog=True)
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evoked_left = epochs['Auditory/Left'].average(picks=picks)
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evoked_right = epochs['Auditory/Right'].average(picks=picks)

‎tutorials/plot_info.py‎

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"""
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.. _tut_info_objects:
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The :class:`~mne.Info` data structure
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=====================================
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The :class:`Info <mne.Info>` data structure
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===========================================
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The :class:`Info <mne.Info>` data object is typically created
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The :class:`~mne.Info` data object is typically created
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when data is imported into MNE-Python and contains details such as:
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- date, subject information, and other recording details

‎tutorials/plot_object_raw.py‎

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# -*- coding: utf-8 -*-
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"""
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.. _tut_raw_objects:
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The :class:`~mne.io.Raw` data structure: continuous data
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========================================================
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The :class:`Raw <mne.io.Raw>` data structure: continuous data
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=============================================================
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Continuous data is stored in objects of type :class:`Raw <mne.io.Raw>`.
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Continuous data is stored in objects of type :class:`~mne.io.Raw`.
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The core data structure is simply a 2D numpy array (channels × samples)
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(in memory or loaded on demand) combined with an
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:class:`Info <mne.Info>` object (`.info` attribute)
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(see :ref:`tut_info_objects`).
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:class:`~mne.Info` object (`.info` attribute)
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(see :ref:`sphx_glr_auto_tutorials_plot_info.py`).
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The most common way to load continuous data is from a .fif file. For more
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information on :ref:`loading data from other formats <ch_convert>`, or
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# variable directly but rely on indexing syntax detailed just below.
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###############################################################################
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# Information about the channels contained in the :class:`Raw <mne.io.Raw>`
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# object is contained in the :class:`Info <mne.Info>` attribute.
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# Information about the channels contained in the :class:`~mne.io.Raw`
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# object is contained in the :class:`~mne.Info` attribute.
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# This is essentially a dictionary with a number of relevant fields (see
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# :ref:`tut_info_objects`).
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# :ref:`sphx_glr_auto_tutorials_plot_info.py`).
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###############################################################################
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# Indexing data
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# -------------
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#
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# To access the data stored within :class:`Raw <mne.io.Raw>` objects,
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# it is possible to index the :class:`Raw <mne.io.Raw>` object.
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# To access the data stored within :class:`~mne.io.Raw` objects,
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# it is possible to index the :class:`~mne.io.Raw` object.
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#
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# Indexing a :class:`Raw <mne.io.Raw>` object will return two arrays: an array
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# Indexing a :class:`~mne.io.Raw` object will return two arrays: an array
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# of times, as well as the data representing those timepoints. This works
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# even if the data is not preloaded, in which case the data will be read from
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# disk when indexing. The syntax is as follows:
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print('Number of channels reduced from', nchan, 'to', raw.info['nchan'])
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###############################################################################
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# --------------------------------------------------
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# Concatenating :class:`Raw <mne.io.Raw>` objects
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# --------------------------------------------------
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# ------------------------------------------
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# Concatenating :class:`~mne.io.Raw` objects
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# ------------------------------------------
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#
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# :class:`Raw <mne.io.Raw>` objects can be concatenated in time by using the
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# :func:`append <mne.io.Raw.append>` function. For this to work, they must
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# have the same number of channels and their :class:`Info
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# <mne.Info>` structures should be compatible.
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# :class:`~mne.io.Raw` objects can be concatenated in time by using the
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# :func:`~mne.io.Raw.append` function. For this to work, they must
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# have the same number of channels and their :class:`~mne.Info`
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# structures should be compatible.
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# Create multiple :class:`Raw <mne.io.RawFIF>` objects
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# Create multiple :class:`~mne.io.Raw` objects
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raw1 = raw.copy().crop(0, 10)
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raw2 = raw.copy().crop(10, 20)
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raw3 = raw.copy().crop(20, 40)

‎tutorials/plot_point_spread.py‎

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"""
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.. _point_spread:
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Corrupt known signal with point spread
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======================================
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fwd['info']['bads'] = []
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inv_op = read_inverse_operator(fname_inv)
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raw = mne.io.RawFIF(op.join(data_path, 'MEG', 'sample',
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'sample_audvis_raw.fif'))
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raw = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
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'sample_audvis_raw.fif'))
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raw.set_eeg_reference(projection=True)
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events = mne.find_events(raw)
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event_id = {'Auditory/Left': 1, 'Auditory/Right': 2}
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epochs = mne.Epochs(raw, events, event_id, baseline=(None, 0), preload=True)

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