"""
.. _tut-sensor-locations:

=============================
Working with sensor locations
=============================

This tutorial describes how to read and plot sensor locations, and how
MNE-Python handles physical locations of sensors.
As usual we'll start by importing the modules we need:
"""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

# %%

from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np

import mne

# %%
# About montages and layouts
# ^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# `Montages <mne.channels.DigMontage>` contain sensor positions in 3D (x, y, z
# in meters), which can be assigned to existing EEG/MEG data. By specifying the
# locations of sensors relative to the brain,
# `Montages <mne.channels.DigMontage>` play an important role in computing the
# forward solution and inverse estimates.
#
# In contrast, `Layouts <mne.channels.Layout>` are *idealized* 2D
# representations of sensor positions. They are primarily used for arranging
# individual sensor subplots in a topoplot or for showing the *approximate*
# relative arrangement of sensors as seen from above.
#
# .. note::
#
#    If you're working with EEG data exclusively, you'll want to use
#    `Montages <mne.channels.DigMontage>`, not layouts. Idealized montages
#    (e.g., those provided by the manufacturer, or the ones shipping with
#    MNE-Python mentioned below) are typically referred to as
#    :term:`template montages <template montage>`.
#
# Working with built-in montages
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# .. admonition:: Computing sensor locations
#     :class: sidebar note
#
#     If you are interested in how standard (idealized) EEG sensor positions
#     are computed on a spherical head model, make sure to check out the
#     `eeg_positions`_ repository.
#
# The 3D coordinates of MEG sensors are included in the raw recordings from MEG
# systems. They are automatically stored in the ``info`` attribute of the
# `~mne.io.Raw` object upon loading. EEG electrode locations are much more
# variable because of differences in head shape. Idealized montages
# (":term:`template montages <template montage>`") for many EEG systems are
# included in MNE-Python, and you can get an overview of them by using
# :func:`mne.channels.get_builtin_montages`:

builtin_montages = mne.channels.get_builtin_montages(descriptions=True)
for montage_name, montage_description in builtin_montages:
    print(f"{montage_name}: {montage_description}")

# %%
# These built-in EEG montages can be loaded with
# `mne.channels.make_standard_montage`:

easycap_montage = mne.channels.make_standard_montage("easycap-M1")
print(easycap_montage)

# %%
# `Montage <mne.channels.DigMontage>` objects have a
# `~mne.channels.DigMontage.plot` method for visualizing the sensor locations
# in 2D or 3D:

easycap_montage.plot()  # 2D
fig = easycap_montage.plot(kind="3d", show=False)  # 3D
fig = fig.gca().view_init(azim=70, elev=15)  # set view angle for tutorial

# %%
# Once loaded, a montage can be applied to data with the
# `~mne.io.Raw.set_montage` method, for example
# `raw.set_montage() <mne.io.Raw.set_montage>`,
# `epochs.set_montage() <mne.Epochs.set_montage>`, or
# `evoked.set_montage() <mne.Evoked.set_montage>`. This will only work with
# data whose EEG channel names correspond to those in the montage.
# (Therefore, we're loading some EEG data below, and not the usual MNE "sample"
# dataset.)
#
# You can then visualize the sensor locations via the
# :meth:`~mne.io.Raw.plot_sensors` method.
#
# It is also possible to skip the manual montage loading step by passing the
# montage name directly to the :meth:`~mne.io.Raw.set_montage` method.

ssvep_folder = mne.datasets.ssvep.data_path()
ssvep_data_raw_path = (
    ssvep_folder / "sub-02" / "ses-01" / "eeg" / "sub-02_ses-01_task-ssvep_eeg.vhdr"
)
ssvep_raw = mne.io.read_raw_brainvision(ssvep_data_raw_path, verbose=False)

# Use the preloaded montage
ssvep_raw.set_montage(easycap_montage)
fig = ssvep_raw.plot_sensors(show_names=True)

# Apply a template montage directly, without preloading
ssvep_raw.set_montage("easycap-M1")
fig = ssvep_raw.plot_sensors(show_names=True)

# %%
# .. note::
#
#    You may have noticed that the figures created via
#    :meth:`~mne.io.Raw.plot_sensors` contain fewer sensors than the result of
#    `easycap_montage.plot() <mne.channels.DigMontage.plot>`. This is because
#    the montage contains *all channels defined for that EEG system*; but not
#    all recordings will necessarily use all possible channels. Thus when
#    applying a montage to an actual EEG dataset, information about sensors
#    that are not actually present in the data is removed.
#
# Plotting 2D sensor locations like EEGLAB
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# .. admonition:: The ``sphere`` keyword is available in many places!
#     :class: sidebar hint
#
#     All MNE plotting functions for EEG topographies and sensor locations
#     support the ``sphere`` keyword argument, and therefore allow for
#     adjustment of the way the sensors are projected onto the head circle.
#
# In MNE-Python, by default the head center is calculated using
# :term:`fiducial points <fiducial>`. This means that
# the head circle represents the head circumference **at the nasion and ear
# level,** and not where it is commonly measured in the 10–20 EEG system
# (i.e., above the nasion at T4/T8, T3/T7, Oz, and Fpz).
#
# If you prefer to draw the head circle using 10–20 conventions (which are also
# used by EEGLAB), you can pass ``sphere='eeglab'``:

fig = ssvep_raw.plot_sensors(show_names=True, sphere="eeglab")

# %%
# Because the data we're using here doesn't contain an Fpz channel, its
# putative location was approximated automatically.
#
# .. _control-chan-projection:
#
# Manually controlling 2D channel projection
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Channel positions in 2D space are obtained by projecting their actual 3D
# positions onto a sphere, then projecting the sphere onto a plane.
# By default, a sphere with origin at ``(0, 0, 0)`` (x, y, z coordinates) and
# radius of ``0.095`` meters (9.5 cm) is used. You can use a different sphere
# radius by passing a single value as the  ``sphere`` argument in any function
# that plots channels in 2D (like `~mne.channels.DigMontage.plot` that we use
# here, but also for example `mne.viz.plot_topomap`):

fig1 = easycap_montage.plot()  # default radius of 0.095
fig2 = easycap_montage.plot(sphere=0.07)

# %%
# To change not only the radius, but also the sphere origin, pass a
# ``(x, y, z, radius)`` tuple as the ``sphere`` argument:

fig = easycap_montage.plot(sphere=(0.03, 0.02, 0.01, 0.075))

# %%
# .. _reading-dig-montages:
#
# Reading sensor digitization files
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# In the sample data, the sensor positions are already available in the
# ``info`` attribute of the `~mne.io.Raw` object (see the documentation of the
# reading functions and :meth:`~mne.io.Raw.set_montage` for details on how that
# works). Therefore, we can plot sensor locations directly from the
# `~mne.io.Raw` object using :meth:`~mne.io.Raw.plot_sensors`, which provides
# similar functionality to `montage.plot() <mne.channels.DigMontage.plot>`. In
# addition, :meth:`~mne.io.Raw.plot_sensors` supports channel selection by
# type, color-coding channels in various ways (by default, channels listed in
# ``raw.info['bads']`` will be plotted in red), and drawing in an existing
# Matplotlib ``Axes`` object (so the channel positions can easily be added as a
# subplot in a multi-panel figure):

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_path = sample_data_folder / "MEG" / "sample" / "sample_audvis_raw.fif"
sample_raw = mne.io.read_raw_fif(sample_data_raw_path, preload=False, verbose=False)

# sphinx_gallery_thumbnail_number = 9
fig = plt.figure()
ax2d = fig.add_subplot(121)
ax3d = fig.add_subplot(122, projection="3d")
sample_raw.plot_sensors(ch_type="eeg", axes=ax2d)
sample_raw.plot_sensors(ch_type="eeg", axes=ax3d, kind="3d")
ax3d.view_init(azim=70, elev=15)

# %%
# The previous 2D topomap reveals irregularities in the EEG sensor positions in
# the :ref:`sample dataset <sample-dataset>` — this is because the sensor
# positions in that dataset are digitizations of actual sensor positions on the
# head rather than idealized sensor positions based on a spherical head model.
# Depending on the digitization device (e.g., a Polhemus Fastrak digitizer),
# you need to use different montage reading functions (see :ref:`dig-formats`).
# The resulting `montage <mne.channels.DigMontage>` can then be added to
# `~mne.io.Raw` objects by passing it as an argument to the
# :meth:`~mne.io.Raw.set_montage` method (just as we did before with the name
# of the predefined ``'standard_1020'`` montage). Once loaded, locations can be
# plotted with the :meth:`~mne.channels.DigMontage.plot` method and saved with
# the :meth:`~mne.channels.DigMontage.save` method of the
# `montage <mne.channels.DigMontage>` object.
#
# .. note::
#
#     When setting a montage with :meth:`~mne.io.Raw.set_montage`, the
#     measurement info is updated in two places (both ``chs`` and ``dig``
#     entries are updated) – see :ref:`tut-info-class` for more details. Note
#     that ``dig`` may contain HPI, fiducial, or head shape points in addition
#     to electrode locations.
#
#
# Visualizing sensors in 3D surface renderings
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# It is also possible to render an image of an MEG sensor helmet using 3D
# surface rendering instead of matplotlib. This works by calling
# :func:`mne.viz.plot_alignment`:

fig = mne.viz.plot_alignment(
    sample_raw.info,
    dig=False,
    eeg=False,
    surfaces=[],
    meg=["helmet", "sensors"],
    coord_frame="meg",
)
mne.viz.set_3d_view(fig, azimuth=50, elevation=90, distance=0.5)

# %%
# Note that :func:`~mne.viz.plot_alignment` requires an `~mne.Info` object, and
# can also render MRI surfaces of the scalp, skull, and brain (by passing a
# dict with keys like ``'head'``, ``'outer_skull'`` or ``'brain'`` to the
# ``surfaces`` parameter). This makes the function useful for
# :ref:`assessing coordinate frame transformations <tut-source-alignment>`.
# For examples of various uses of :func:`~mne.viz.plot_alignment`, see
# :ref:`plot_montage`, :ref:`ex-eeg-on-scalp`, and :ref:`ex-plot-meg-sensors`.
#
#
# Working with layout files
# ^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Similar to montages, many layout files are included with MNE-Python. They are
# stored in the :file:`mne/channels/data/layouts` folder:

layout_dir = Path(mne.__file__).parent / "channels" / "data" / "layouts"
layouts = sorted(path.name for path in layout_dir.iterdir())
print("\nBUILT-IN LAYOUTS\n================")
print("\n".join(layouts))

# %%
# To load a layout file, use the `mne.channels.read_layout` function.
# You can then visualize the layout using its
# `~mne.channels.Layout.plot` method:

biosemi_layout = mne.channels.read_layout("biosemi")

# %%
# Similar to the ``picks`` argument for selecting channels from `~mne.io.Raw`
# objects, the :meth:`~mne.channels.Layout.plot` method of
# `~mne.channels.Layout` objects also has a ``picks`` argument. However,
# because layouts only contain information about sensor name and location (not
# sensor type), the :meth:`~mne.channels.Layout.plot` method only supports
# picking channels by index (not by name or by type). In the following example,
# we find the desired indices using :func:`numpy.where`; selection by name or
# type is possible with :func:`mne.pick_channels` or :func:`mne.pick_types`.

midline = np.where([name.endswith("z") for name in biosemi_layout.names])[0]
biosemi_layout.plot(picks=midline)

# %%
# If you have a `~mne.io.Raw` object that contains sensor positions, you can
# create a `~mne.channels.Layout` object with either
# :func:`mne.channels.make_eeg_layout` or :func:`mne.channels.find_layout`.

layout_from_raw = mne.channels.make_eeg_layout(sample_raw.info)
# same result as mne.channels.find_layout(raw.info, ch_type='eeg')
layout_from_raw.plot()

# %%
# .. note::
#
#     There is no corresponding ``make_meg_layout()`` function because sensor
#     locations are fixed in an MEG system (unlike in EEG, where sensor caps
#     deform to fit snugly on a specific head). Therefore, MEG layouts are
#     consistent (constant) for a given system and you can simply load them
#     with :func:`mne.channels.read_layout` or use
#     :func:`mne.channels.find_layout` with
#     the ``ch_type`` parameter (as previously demonstrated for EEG).
#
# All `~mne.channels.Layout` objects have a `~mne.channels.Layout.save` method
# that writes layouts to disk as either :file:`.lout` or :file:`.lay` formats
# (inferred from the file extension contained in the ``fname`` argument). The
# choice between :file:`.lout` and :file:`.lay` format only matters if you need
# to load the layout file in some other application (MNE-Python can read both
# formats).
#
#
# .. LINKS
#
# .. _`eeg_positions`: https://github.com/sappelhoff/eeg_positions