def test_setup_vmin_vmax_warns():
"""Test that _setup_vmin_vmax warns properly."""
expected_msg = r'\(min=0.0, max=1\) range.*minimum of data is -1'
with pytest.warns(UserWarning, match=expected_msg):
_setup_vmin_vmax(data=[-1, 0], vmin=None, vmax=None, norm=True)
def _plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True,
res=64, axes=None, names=None, show_names=False, mask=None,
mask_params=None, outlines='head',
contours=6, image_interp='bilinear', show=True,
head_pos=None, onselect=None, extrapolate='box', border=0):
import matplotlib.pyplot as plt
from matplotlib.widgets import RectangleSelector
data = np.asarray(data)
if isinstance(pos, Info): # infer pos from Info object
picks = _pick_data_channels(pos) # pick only data channels
pos = pick_info(pos, picks)
# check if there is only 1 channel type, and n_chans matches the data
ch_type = {channel_type(pos, idx)
for idx, _ in enumerate(pos["chs"])}
info_help = ("Pick Info with e.g. mne.pick_info and "
"mne.io.pick.channel_indices_by_type.")
if len(ch_type) > 1:
raise ValueError("Multiple channel types in Info structure. "
+ info_help)
elif len(pos["chs"]) != data.shape[0]:
raise ValueError("Number of channels in the Info object and "
"the data array does not match. " + info_help)
else:
ch_type = ch_type.pop()
if any(type_ in ch_type for type_ in ('planar', 'grad')):
# deal with grad pairs
from mne.channels.layout import (_merge_grad_data, find_layout,
_pair_grad_sensors)
picks, pos = _pair_grad_sensors(pos, find_layout(pos))
data = _merge_grad_data(data[picks]).reshape(-1)
else:
picks = list(range(data.shape[0]))
pos = _find_topomap_coords(pos, picks=picks)
if data.ndim > 1:
raise ValueError("Data needs to be array of shape (n_sensors,); got "
"shape %s." % str(data.shape))
# Give a helpful error message for common mistakes regarding the position
# matrix.
pos_help = ("Electrode positions should be specified as a 2D array with "
"shape (n_channels, 2). Each row in this matrix contains the "
"(x, y) position of an electrode.")
if pos.ndim != 2:
error = ("{ndim}D array supplied as electrode positions, where a 2D "
"array was expected").format(ndim=pos.ndim)
raise ValueError(error + " " + pos_help)
elif pos.shape[1] == 3:
error = ("The supplied electrode positions matrix contains 3 columns. "
"Are you trying to specify XYZ coordinates? Perhaps the "
"mne.channels.create_eeg_layout function is useful for you.")
raise ValueError(error + " " + pos_help)
# No error is raised in case of pos.shape[1] == 4. In this case, it is
# assumed the position matrix contains both (x, y) and (width, height)
# values, such as Layout.pos.
elif pos.shape[1] == 1 or pos.shape[1] > 4:
raise ValueError(pos_help)
if len(data) != len(pos):
raise ValueError("Data and pos need to be of same length. Got data of "
"length %s, pos of length %s" % (len(data), len(pos)))
norm = min(data) >= 0
vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm)
if cmap is None:
cmap = 'Reds' if norm else 'RdBu_r'
pos, outlines = _check_outlines(pos, outlines, head_pos)
assert isinstance(outlines, dict)
ax = axes if axes else plt.gca()
_prepare_topomap(pos, ax)
_use_default_outlines = any(k.startswith('head') for k in outlines)
if _use_default_outlines:
# prepare masking
_autoshrink(outlines, pos, res)
mask_params = _handle_default('mask_params', mask_params)
# find mask limits
xlim = np.inf, -np.inf,
ylim = np.inf, -np.inf,
mask_ = np.c_[outlines['mask_pos']]
xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]),
np.max(np.r_[xlim[1], mask_[:, 0]]))
ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]),
np.max(np.r_[ylim[1], mask_[:, 1]]))
# interpolate the data, we multiply clip radius by 1.06 so that pixelated
# edges of the interpolated image would appear under the mask
head_radius = (None if extrapolate == 'local' else
outlines['clip_radius'][0] * 1.06)
xi = np.linspace(xmin, xmax, res)
yi = np.linspace(ymin, ymax, res)
Xi, Yi = np.meshgrid(xi, yi)
interp = _GridData(pos, extrapolate, head_radius, border).set_values(data)
Zi = interp.set_locations(Xi, Yi)()
# plot outline
patch_ = None
if 'patch' in outlines:
patch_ = outlines['patch']
patch_ = patch_() if callable(patch_) else patch_
patch_.set_clip_on(False)
ax.add_patch(patch_)
ax.set_transform(ax.transAxes)
ax.set_clip_path(patch_)
if _use_default_outlines:
from matplotlib import patches
patch_ = patches.Ellipse((0, 0),
2 * outlines['clip_radius'][0],
2 * outlines['clip_radius'][1],
clip_on=True,
transform=ax.transData)
# plot interpolated map
im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower',
aspect='equal', extent=(xmin, xmax, ymin, ymax),
interpolation=image_interp)
# This tackles an incomprehensible matplotlib bug if no contours are
# drawn. To avoid rescalings, we will always draw contours.
# But if no contours are desired we only draw one and make it invisible.
linewidth = mask_params['markeredgewidth']
no_contours = False
if isinstance(contours, (np.ndarray, list)):
pass # contours precomputed
elif contours == 0:
contours, no_contours = 1, True
if (Zi == Zi[0, 0]).all():
cont = None # can't make contours for constant-valued functions
else:
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore')
cont = ax.contour(Xi, Yi, Zi, contours, colors='k',
linewidths=linewidth / 2.)
if no_contours and cont is not None:
for col in cont.collections:
col.set_visible(False)
if patch_ is not None:
im.set_clip_path(patch_)
if cont is not None:
for col in cont.collections:
col.set_clip_path(patch_)
pos_x, pos_y = pos.T
if sensors is not False and mask is None:
_plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax)
elif sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
idx = np.where(~mask)[0]
_plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax)
elif not sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
if isinstance(outlines, dict):
_draw_outlines(ax, outlines)
if show_names:
if names is None:
raise ValueError("To show names, a list of names must be provided"
" (see `names` keyword).")
if show_names is True:
def _show_names(x):
return x
else:
_show_names = show_names
show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0]
for ii, (p, ch_id) in enumerate(zip(pos, names)):
if ii not in show_idx:
continue
ch_id = _show_names(ch_id)
ax.text(p[0], p[1], ch_id, horizontalalignment='center',
verticalalignment='center', size='x-small')
if onselect is not None:
ax.RS = RectangleSelector(ax, onselect=onselect)
plt_show(show)
return im, cont, interp, patch_
def plot_topomap(data, pos, vmin=None, vmax=None, cmap=None, sensors=True,
res=64, axes=None, names=None, show_names=False, mask=None,
mask_params=None, outlines='head', image_mask=None,
contours=6, image_interp='bilinear', show=True,
head_pos=None, onselect=None, axis=None):
''' see the docstring for mne.viz.plot_topomap,
which i've simply modified to return more objects '''
from matplotlib.widgets import RectangleSelector
from mne.io.pick import (channel_type, pick_info, _pick_data_channels)
from mne.utils import warn
from mne.viz.utils import (_setup_vmin_vmax, plt_show)
from mne.defaults import _handle_default
from mne.channels.layout import _find_topomap_coords
from mne.io.meas_info import Info
from mne.viz.topomap import _check_outlines, _prepare_topomap, _griddata, _make_image_mask, _plot_sensors, \
_draw_outlines
data = np.asarray(data)
if isinstance(pos, Info): # infer pos from Info object
picks = _pick_data_channels(pos) # pick only data channels
pos = pick_info(pos, picks)
# check if there is only 1 channel type, and n_chans matches the data
ch_type = set(channel_type(pos, idx)
for idx, _ in enumerate(pos["chs"]))
info_help = ("Pick Info with e.g. mne.pick_info and "
"mne.channels.channel_indices_by_type.")
if len(ch_type) > 1:
raise ValueError("Multiple channel types in Info structure. " +
info_help)
elif len(pos["chs"]) != data.shape[0]:
raise ValueError("Number of channels in the Info object and "
"the data array does not match. " + info_help)
else:
ch_type = ch_type.pop()
if any(type_ in ch_type for type_ in ('planar', 'grad')):
# deal with grad pairs
from ..channels.layout import (_merge_grad_data, find_layout,
_pair_grad_sensors)
picks, pos = _pair_grad_sensors(pos, find_layout(pos))
data = _merge_grad_data(data[picks]).reshape(-1)
else:
picks = list(range(data.shape[0]))
pos = _find_topomap_coords(pos, picks=picks)
if data.ndim > 1:
raise ValueError("Data needs to be array of shape (n_sensors,); got "
"shape %s." % str(data.shape))
# Give a helpful error message for common mistakes regarding the position
# matrix.
pos_help = ("Electrode positions should be specified as a 2D array with "
"shape (n_channels, 2). Each row in this matrix contains the "
"(x, y) position of an electrode.")
if pos.ndim != 2:
error = ("{ndim}D array supplied as electrode positions, where a 2D "
"array was expected").format(ndim=pos.ndim)
raise ValueError(error + " " + pos_help)
elif pos.shape[1] == 3:
error = ("The supplied electrode positions matrix contains 3 columns. "
"Are you trying to specify XYZ coordinates? Perhaps the "
"mne.channels.create_eeg_layout function is useful for you.")
raise ValueError(error + " " + pos_help)
# No error is raised in case of pos.shape[1] == 4. In this case, it is
# assumed the position matrix contains both (x, y) and (width, height)
# values, such as Layout.pos.
elif pos.shape[1] == 1 or pos.shape[1] > 4:
raise ValueError(pos_help)
if len(data) != len(pos):
raise ValueError("Data and pos need to be of same length. Got data of "
"length %s, pos of length %s" % (len(data), len(pos)))
norm = min(data) >= 0
vmin, vmax = _setup_vmin_vmax(data, vmin, vmax, norm)
if cmap is None:
cmap = 'Reds' if norm else 'RdBu_r'
pos, outlines = _check_outlines(pos, outlines, head_pos)
if axis is not None:
axes = axis
warn('axis parameter is deprecated and will be removed in 0.13. '
'Use axes instead.', DeprecationWarning)
ax = axes if axes else plt.gca()
pos_x, pos_y = _prepare_topomap(pos, ax)
if outlines is None:
xmin, xmax = pos_x.min(), pos_x.max()
ymin, ymax = pos_y.min(), pos_y.max()
else:
xlim = np.inf, -np.inf,
ylim = np.inf, -np.inf,
mask_ = np.c_[outlines['mask_pos']]
xmin, xmax = (np.min(np.r_[xlim[0], mask_[:, 0]]),
np.max(np.r_[xlim[1], mask_[:, 0]]))
ymin, ymax = (np.min(np.r_[ylim[0], mask_[:, 1]]),
np.max(np.r_[ylim[1], mask_[:, 1]]))
# interpolate data
xi = np.linspace(xmin, xmax, res)
yi = np.linspace(ymin, ymax, res)
Xi, Yi = np.meshgrid(xi, yi)
Zi = _griddata(pos_x, pos_y, data, Xi, Yi)
if outlines is None:
_is_default_outlines = False
elif isinstance(outlines, dict):
_is_default_outlines = any(k.startswith('head') for k in outlines)
if _is_default_outlines and image_mask is None:
# prepare masking
image_mask, pos = _make_image_mask(outlines, pos, res)
mask_params = _handle_default('mask_params', mask_params)
# plot outline
linewidth = mask_params['markeredgewidth']
patch = None
if 'patch' in outlines:
patch = outlines['patch']
patch_ = patch() if callable(patch) else patch
patch_.set_clip_on(False)
ax.add_patch(patch_)
ax.set_transform(ax.transAxes)
ax.set_clip_path(patch_)
# plot map and countour
im = ax.imshow(Zi, cmap=cmap, vmin=vmin, vmax=vmax, origin='lower',
aspect='equal', extent=(xmin, xmax, ymin, ymax),
interpolation=image_interp)
# This tackles an incomprehensible matplotlib bug if no contours are
# drawn. To avoid rescalings, we will always draw contours.
# But if no contours are desired we only draw one and make it invisible .
no_contours = False
if contours in (False, None):
contours, no_contours = 1, True
cont = ax.contour(Xi, Yi, Zi, contours, colors='k',
linewidths=linewidth)
if no_contours is True:
for col in cont.collections:
col.set_visible(False)
if _is_default_outlines:
from matplotlib import patches
patch_ = patches.Ellipse((0, 0),
2 * outlines['clip_radius'][0],
2 * outlines['clip_radius'][1],
clip_on=True,
transform=ax.transData)
if _is_default_outlines or patch is not None:
im.set_clip_path(patch_)
if cont is not None:
for col in cont.collections:
col.set_clip_path(patch_)
if sensors is not False and mask is None:
_plot_sensors(pos_x, pos_y, sensors=sensors, ax=ax)
elif sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
idx = np.where(~mask)[0]
_plot_sensors(pos_x[idx], pos_y[idx], sensors=sensors, ax=ax)
elif not sensors and mask is not None:
idx = np.where(mask)[0]
ax.plot(pos_x[idx], pos_y[idx], **mask_params)
if isinstance(outlines, dict):
_draw_outlines(ax, outlines)
if show_names:
if names is None:
raise ValueError("To show names, a list of names must be provided"
" (see `names` keyword).")
if show_names is True:
def _show_names(x):
return x
else:
_show_names = show_names
show_idx = np.arange(len(names)) if mask is None else np.where(mask)[0]
for ii, (p, ch_id) in enumerate(zip(pos, names)):
if ii not in show_idx:
continue
ch_id = _show_names(ch_id)
ax.text(p[0], p[1], ch_id, horizontalalignment='center',
verticalalignment='center', size='x-small')
plt.subplots_adjust(top=.95)
if onselect is not None:
ax.RS = RectangleSelector(ax, onselect=onselect)
plt_show(show)
return ax, im, cont, pos_x, pos_y
