"""
.. _ex-electrode-pos-2d:

===================================================
How to convert 3D electrode positions to a 2D image
===================================================

Sometimes we want to convert a 3D representation of electrodes into a 2D
image. For example, if we are using electrocorticography it is common to create
scatterplots on top of a brain, with each point representing an electrode.

In this example, we'll show two ways of doing this in MNE-Python. First,
if we have the 3D locations of each electrode then we can use PyVista to
take a snapshot of a view of the brain. If we do not have these 3D locations,
and only have a 2D image of the electrodes on the brain, we can use the
:class:`mne.viz.ClickableImage` class to choose our own electrode positions
on the image.
"""
# Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#          Alex Rockhill        <aprockhill@mailbox.org>
#
# License: BSD-3-Clause

# %%
from os.path import dirname
from pathlib import Path

import numpy as np
from matplotlib import pyplot as plt

import mne
from mne.io.fiff.raw import read_raw_fif
from mne.viz import ClickableImage  # noqa: F401
from mne.viz import plot_alignment, set_3d_view, snapshot_brain_montage

misc_path = mne.datasets.misc.data_path()
subjects_dir = misc_path / "ecog"
ecog_data_fname = subjects_dir / "sample_ecog_ieeg.fif"

# We've already clicked and exported
layout_path = Path(dirname(mne.__file__)) / "data" / "image"
layout_name = "custom_layout.lout"

# %%
# Load data
# ---------
#
# First we will load a sample ECoG dataset which we'll use for generating
# a 2D snapshot.

raw = read_raw_fif(ecog_data_fname)
raw.pick_channels([f"G{i}" for i in range(1, 257)])  # pick just one grid

# Since we loaded in the ecog data from FIF, the coordinates
# are in 'head' space, but we actually want them in 'mri' space.
# So we will apply the head->mri transform that was used when
# generating the dataset (the estimated head->mri transform).
montage = raw.get_montage()
trans = mne.coreg.estimate_head_mri_t("sample_ecog", subjects_dir)
montage.apply_trans(trans)

# %%
# Project 3D electrodes to a 2D snapshot
# --------------------------------------
#
# Because we have the 3D location of each electrode, we can use the
# :func:`mne.viz.snapshot_brain_montage` function to return a 2D image along
# with the electrode positions on that image. We use this in conjunction with
# :func:`mne.viz.plot_alignment`, which visualizes electrode positions.

fig = plot_alignment(
    raw.info,
    trans=trans,
    subject="sample_ecog",
    subjects_dir=subjects_dir,
    surfaces=dict(pial=0.9),
)
set_3d_view(figure=fig, azimuth=20, elevation=80)
xy, im = snapshot_brain_montage(fig, montage)

# Convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in raw.ch_names])

# Compute beta power to visualize
raw.load_data()
beta_power = raw.filter(20, 30).apply_hilbert(envelope=True).get_data()
beta_power = beta_power.max(axis=1)  # take maximum over time

# This allows us to use matplotlib to create arbitrary 2d scatterplots
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
cmap = ax.scatter(*xy_pts.T, c=beta_power, s=100, cmap="coolwarm")
cbar = fig2.colorbar(cmap)
cbar.ax.set_ylabel("Beta Power")
ax.set_axis_off()

# fig2.savefig('./brain.png', bbox_inches='tight')  # For ClickableImage

# %%
# Manually creating 2D electrode positions
# ----------------------------------------
#
# If we don't have the 3D electrode positions then we can still create a
# 2D representation of the electrodes. Assuming that you can see the electrodes
# on the 2D image, we can use :class:`mne.viz.ClickableImage` to open the image
# interactively. You can click points on the image and the x/y coordinate will
# be stored.
#
# We'll open an image file, then use ClickableImage to
# return 2D locations of mouse clicks (or load a file already created).
# Then, we'll return these xy positions as a layout for use with plotting topo
# maps.


# This code opens the image so you can click on it. Commented out
# because we've stored the clicks as a layout file already.

# # The click coordinates are stored as a list of tuples
# im = plt.imread('./brain.png')
# click = ClickableImage(im)
# click.plot_clicks()

# # Generate a layout from our clicks and normalize by the image
# print('Generating and saving layout...')
# lt = click.to_layout()
# lt.save(layout_path / layout_name)  # save if we want

# # We've already got the layout, load it
lt = mne.channels.read_layout(layout_path / layout_name, scale=False)
x = lt.pos[:, 0] * float(im.shape[1])
y = (1 - lt.pos[:, 1]) * float(im.shape[0])  # Flip the y-position
fig, ax = plt.subplots()
ax.imshow(im)
ax.scatter(x, y, s=80, color="r")
fig.tight_layout()
ax.set_axis_off()

60bdec9d64f4e916009833dbd46f8d2efca4f2ff
<mask token>

<mask token>
raw.pick_channels([f'G{i}' for i in range(1, 257)])
<mask token>
montage.apply_trans(trans)
<mask token>
set_3d_view(figure=fig, azimuth=20, elevation=80)
<mask token>
raw.load_data()
<mask token>
ax.imshow(im)
<mask token>
cbar.ax.set_ylabel('Beta Power')
ax.set_axis_off()
<mask token>
ax.imshow(im)
ax.scatter(x, y, s=80, color='r')
fig.tight_layout()
ax.set_axis_off()

<mask token>
misc_path = mne.datasets.misc.data_path()
subjects_dir = misc_path / 'ecog'
ecog_data_fname = subjects_dir / 'sample_ecog_ieeg.fif'
layout_path = Path(dirname(mne.__file__)) / 'data' / 'image'
layout_name = 'custom_layout.lout'
raw = read_raw_fif(ecog_data_fname)
raw.pick_channels([f'G{i}' for i in range(1, 257)])
montage = raw.get_montage()
trans = mne.coreg.estimate_head_mri_t('sample_ecog', subjects_dir)
montage.apply_trans(trans)
fig = plot_alignment(raw.info, trans=trans, subject='sample_ecog',
    subjects_dir=subjects_dir, surfaces=dict(pial=0.9))
set_3d_view(figure=fig, azimuth=20, elevation=80)
xy, im = snapshot_brain_montage(fig, montage)
xy_pts = np.vstack([xy[ch] for ch in raw.ch_names])
raw.load_data()
beta_power = raw.filter(20, 30).apply_hilbert(envelope=True).get_data()
beta_power = beta_power.max(axis=1)
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
cmap = ax.scatter(*xy_pts.T, c=beta_power, s=100, cmap='coolwarm')
cbar = fig2.colorbar(cmap)
cbar.ax.set_ylabel('Beta Power')
ax.set_axis_off()
lt = mne.channels.read_layout(layout_path / layout_name, scale=False)
x = lt.pos[:, 0] * float(im.shape[1])
y = (1 - lt.pos[:, 1]) * float(im.shape[0])
fig, ax = plt.subplots()
ax.imshow(im)
ax.scatter(x, y, s=80, color='r')
fig.tight_layout()
ax.set_axis_off()

<mask token>
from os.path import dirname
from pathlib import Path
import numpy as np
from matplotlib import pyplot as plt
import mne
from mne.io.fiff.raw import read_raw_fif
from mne.viz import ClickableImage
from mne.viz import plot_alignment, set_3d_view, snapshot_brain_montage
misc_path = mne.datasets.misc.data_path()
subjects_dir = misc_path / 'ecog'
ecog_data_fname = subjects_dir / 'sample_ecog_ieeg.fif'
layout_path = Path(dirname(mne.__file__)) / 'data' / 'image'
layout_name = 'custom_layout.lout'
raw = read_raw_fif(ecog_data_fname)
raw.pick_channels([f'G{i}' for i in range(1, 257)])
montage = raw.get_montage()
trans = mne.coreg.estimate_head_mri_t('sample_ecog', subjects_dir)
montage.apply_trans(trans)
fig = plot_alignment(raw.info, trans=trans, subject='sample_ecog',
    subjects_dir=subjects_dir, surfaces=dict(pial=0.9))
set_3d_view(figure=fig, azimuth=20, elevation=80)
xy, im = snapshot_brain_montage(fig, montage)
xy_pts = np.vstack([xy[ch] for ch in raw.ch_names])
raw.load_data()
beta_power = raw.filter(20, 30).apply_hilbert(envelope=True).get_data()
beta_power = beta_power.max(axis=1)
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
cmap = ax.scatter(*xy_pts.T, c=beta_power, s=100, cmap='coolwarm')
cbar = fig2.colorbar(cmap)
cbar.ax.set_ylabel('Beta Power')
ax.set_axis_off()
lt = mne.channels.read_layout(layout_path / layout_name, scale=False)
x = lt.pos[:, 0] * float(im.shape[1])
y = (1 - lt.pos[:, 1]) * float(im.shape[0])
fig, ax = plt.subplots()
ax.imshow(im)
ax.scatter(x, y, s=80, color='r')
fig.tight_layout()
ax.set_axis_off()

"""
.. _ex-electrode-pos-2d:

===================================================
How to convert 3D electrode positions to a 2D image
===================================================

Sometimes we want to convert a 3D representation of electrodes into a 2D
image. For example, if we are using electrocorticography it is common to create
scatterplots on top of a brain, with each point representing an electrode.

In this example, we'll show two ways of doing this in MNE-Python. First,
if we have the 3D locations of each electrode then we can use PyVista to
take a snapshot of a view of the brain. If we do not have these 3D locations,
and only have a 2D image of the electrodes on the brain, we can use the
:class:`mne.viz.ClickableImage` class to choose our own electrode positions
on the image.
"""
# Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#          Alex Rockhill        <aprockhill@mailbox.org>
#
# License: BSD-3-Clause

# %%
from os.path import dirname
from pathlib import Path

import numpy as np
from matplotlib import pyplot as plt

import mne
from mne.io.fiff.raw import read_raw_fif
from mne.viz import ClickableImage  # noqa: F401
from mne.viz import plot_alignment, set_3d_view, snapshot_brain_montage

misc_path = mne.datasets.misc.data_path()
subjects_dir = misc_path / "ecog"
ecog_data_fname = subjects_dir / "sample_ecog_ieeg.fif"

# We've already clicked and exported
layout_path = Path(dirname(mne.__file__)) / "data" / "image"
layout_name = "custom_layout.lout"

# %%
# Load data
# ---------
#
# First we will load a sample ECoG dataset which we'll use for generating
# a 2D snapshot.

raw = read_raw_fif(ecog_data_fname)
raw.pick_channels([f"G{i}" for i in range(1, 257)])  # pick just one grid

# Since we loaded in the ecog data from FIF, the coordinates
# are in 'head' space, but we actually want them in 'mri' space.
# So we will apply the head->mri transform that was used when
# generating the dataset (the estimated head->mri transform).
montage = raw.get_montage()
trans = mne.coreg.estimate_head_mri_t("sample_ecog", subjects_dir)
montage.apply_trans(trans)

# %%
# Project 3D electrodes to a 2D snapshot
# --------------------------------------
#
# Because we have the 3D location of each electrode, we can use the
# :func:`mne.viz.snapshot_brain_montage` function to return a 2D image along
# with the electrode positions on that image. We use this in conjunction with
# :func:`mne.viz.plot_alignment`, which visualizes electrode positions.

fig = plot_alignment(
    raw.info,
    trans=trans,
    subject="sample_ecog",
    subjects_dir=subjects_dir,
    surfaces=dict(pial=0.9),
)
set_3d_view(figure=fig, azimuth=20, elevation=80)
xy, im = snapshot_brain_montage(fig, montage)

# Convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in raw.ch_names])

# Compute beta power to visualize
raw.load_data()
beta_power = raw.filter(20, 30).apply_hilbert(envelope=True).get_data()
beta_power = beta_power.max(axis=1)  # take maximum over time

# This allows us to use matplotlib to create arbitrary 2d scatterplots
fig2, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
cmap = ax.scatter(*xy_pts.T, c=beta_power, s=100, cmap="coolwarm")
cbar = fig2.colorbar(cmap)
cbar.ax.set_ylabel("Beta Power")
ax.set_axis_off()

# fig2.savefig('./brain.png', bbox_inches='tight')  # For ClickableImage

# %%
# Manually creating 2D electrode positions
# ----------------------------------------
#
# If we don't have the 3D electrode positions then we can still create a
# 2D representation of the electrodes. Assuming that you can see the electrodes
# on the 2D image, we can use :class:`mne.viz.ClickableImage` to open the image
# interactively. You can click points on the image and the x/y coordinate will
# be stored.
#
# We'll open an image file, then use ClickableImage to
# return 2D locations of mouse clicks (or load a file already created).
# Then, we'll return these xy positions as a layout for use with plotting topo
# maps.


# This code opens the image so you can click on it. Commented out
# because we've stored the clicks as a layout file already.

# # The click coordinates are stored as a list of tuples
# im = plt.imread('./brain.png')
# click = ClickableImage(im)
# click.plot_clicks()

# # Generate a layout from our clicks and normalize by the image
# print('Generating and saving layout...')
# lt = click.to_layout()
# lt.save(layout_path / layout_name)  # save if we want

# # We've already got the layout, load it
lt = mne.channels.read_layout(layout_path / layout_name, scale=False)
x = lt.pos[:, 0] * float(im.shape[1])
y = (1 - lt.pos[:, 1]) * float(im.shape[0])  # Flip the y-position
fig, ax = plt.subplots()
ax.imshow(im)
ax.scatter(x, y, s=80, color="r")
fig.tight_layout()
ax.set_axis_off()
