def test_plot_topomap_bads_grad():
"""Test plotting topomap with bad gradiometer channels (gh-8802)."""
import matplotlib.pyplot as plt
data = np.random.RandomState(0).randn(203)
info = read_info(evoked_fname)
info['bads'] = ['MEG 2242']
picks = pick_types(info, meg='grad')
info = pick_info(info, picks)
assert len(info['chs']) == 203
plot_topomap(data, info, res=8)
plt.close('all')
def test_plot_topomap_bads():
"""Test plotting topomap with bad channels (gh-7213)."""
import matplotlib.pyplot as plt
data = np.random.RandomState(0).randn(3, 1000)
raw = RawArray(data, create_info(3, 1000., 'eeg'))
ch_pos_dict = {name: pos for name, pos in zip(raw.ch_names, np.eye(3))}
raw.info.set_montage(make_dig_montage(ch_pos_dict, coord_frame='head'))
for count in range(3):
raw.info['bads'] = raw.ch_names[:count]
raw.info._check_consistency()
plot_topomap(data[:, 0], raw.info)
plt.close('all')
def test_plot_topomap_cnorm():
"""Test colormap normalization."""
if check_version("matplotlib", "3.2.0"):
from matplotlib.colors import TwoSlopeNorm
else:
from matplotlib.colors import DivergingNorm as TwoSlopeNorm
from matplotlib.colors import PowerNorm
rng = np.random.default_rng(42)
v = rng.uniform(low=-1, high=2.5, size=64)
v[:3] = [-1, 0, 2.5]
montage = make_standard_montage("biosemi64")
info = create_info(montage.ch_names, 256, "eeg").set_montage("biosemi64")
cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=2.5)
# pass only cnorm, no vmin/vmax
plot_topomap(v, info, cnorm=cnorm)
# pass cnorm and vmin
msg = "vmin=-1.* is implicitly defined by cnorm, ignoring vmin=-10.*"
with pytest.warns(RuntimeWarning, match=msg):
plot_topomap(v, info, vmin=-10, cnorm=cnorm)
# pass cnorm and vmax
msg = "vmax=2.5 is implicitly defined by cnorm, ignoring vmax=10.*"
with pytest.warns(RuntimeWarning, match=msg):
plot_topomap(v, info, vmax=10, cnorm=cnorm)
# try another subclass of mpl.colors.Normalize
plot_topomap(v, info, cnorm=PowerNorm(0.5))
def __init__(self, values, info, side=None, **kwargs):
from mne.viz.topomap import plot_topomap
self.info = info
self.values = values
has_axis = 'axis' in kwargs.keys()
if has_axis:
self.axis = kwargs['axis']
plt.sca(self.axis)
if side is not None and side == 'right':
info, kwargs = _construct_topo_side(info, kwargs)
# plot using mne's topomap
im, lines = plot_topomap(values, info, **kwargs)
self.fig = im.figure
if not has_axis:
self.axis = im.axes
self.img = im
self.lines = lines
self.marks = list()
# get channel objects and channel positions from topo
self.chans, self.chan_pos = _extract_topo_channels(im.axes)
def PlotICACom_mne(W,
title=None,
ch_names=[
'AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8',
'T8', 'FC6', 'F4', 'F8', 'AF4'
]):
from mne.channels import read_montage
from mne.viz.topomap import plot_topomap
#ch_names = ['AF3','F7','F3','FC5','T7','P7','O1','O2','P8','T8','FC6','F4','F8','AF4']
montage = read_montage('standard_1020', ch_names)
epos = montage.get_pos2d()
ch = montage.ch_names
eOrder = [ch_names.index(c) for c in ch]
nch = len(ch_names)
mask = np.ones(nch).astype(int)
fig, ax = plt.subplots(2, nch // 2, figsize=(15, 5))
i, j = 0, 0
for k in range(14):
#e=np.random.randn(14)
e = W[:, k]
plot_topomap(e[eOrder],
epos,
axes=ax[i, j],
show=False,
cmap='jet',
mask=mask)
for kk in range(len(eOrder)):
ax[i, j].text(epos[kk, 0] / 3.99,
epos[kk, 1] / 3,
ch_names[eOrder[kk]],
fontsize=6)
if title is None:
ax[i, j].set_title(str(k))
else:
ax[i, j].set_title(str(title[k]))
j += 1
if j == 7:
i += 1
j = 0
#plt.axis('off')
plt.subplots_adjust(hspace=0.0, wspace=0.0)
plt.show()
def test_plot_topomap():
"""Test topomap plotting."""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 8
fast_test = {"res": res, "contours": 0, "sensors": False}
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad',
times=[0, 0.1],
butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
assert_raises(ValueError, plt_topomap, ch_type='mag')
assert_raises(TypeError, plt_topomap, head_pos='foo')
assert_raises(KeyError, plt_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, plt_topomap, head_pos=dict(center=0))
assert_raises(ValueError, plt_topomap, times=[-100]) # bad time
assert_raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100])
plt_topomap = partial(evoked.plot_topomap, **fast_test)
plt_topomap(0.1, layout=layout, scalings=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad',
times=[0],
mask=mask,
show_names=proc_names,
**fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times,
ch_type='grad',
mask=mask,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError, plt_topomap, times, ch_type='eeg', average=-1e3)
assert_raises(ValueError, plt_topomap, times, ch_type='eeg', average='x')
p = plt_topomap(times,
ch_type='grad',
image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [
x for x in p.get_children() if isinstance(x, matplotlib.axes.Subplot)
][0]
assert_true(
all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test)
# fail with multiple channel types
assert_raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
plt_topomap(times, ch_type='mag', layout=None)
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
fig1 = evoked.plot_topomap('interactive',
'mag',
proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert_true(np.max(fig1.axes[0].images[0]._A) != data_max)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos,
'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked,
times=[0., 0.1],
ch_type='eeg',
cmap=('Reds', True),
title='title',
**fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked,
times,
ch_type='eeg',
outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
with warnings.catch_warnings(record=True): # rightmost column
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0])
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter("always")
res = 16
evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type="grad", times=[0, 0.1], butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close("all")
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type="mag")
assert_raises(TypeError, ev_bad.plot_topomap, head_pos="foo")
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo="bar"))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close("all")
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close("all")
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close("all")
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type="mag", outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type="eeg", res=res, scale=1)
evoked.plot_topomap(times, ch_type="grad", mask=mask, res=res)
evoked.plot_topomap(times, ch_type="planar1", res=res)
evoked.plot_topomap(times, ch_type="planar2", res=res)
evoked.plot_topomap(times, ch_type="grad", mask=mask, res=res, show_names=True, mask_params={"marker": "x"})
plt.close("all")
assert_raises(ValueError, evoked.plot_topomap, times, ch_type="eeg", res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type="eeg", res=res, average="hahahahah")
p = evoked.plot_topomap(
times, ch_type="grad", res=res, show_names=lambda x: x.replace("MEG", ""), image_interp="bilinear"
)
subplot = [x for x in p.get_children() if isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all("MEG" not in x.get_text() for x in subplot.get_children() if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type="eeg", res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type="eeg", title="Custom", res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], "Custom")
plt.close("all")
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter("always")
evoked.plot_topomap(times, ch_type="mag", layout=None, res=res)
assert_raises(
RuntimeError, plot_evoked_topomap, evoked, 0.1, "mag", proj="interactive"
) # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, "Left Auditory", baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter("always")
evoked.plot_topomap(0.1, "mag", proj="interactive", res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(0.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter("always")
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp["desc"].lower().find("eeg") < 0]
plot_projs_topomap(projs, res=res)
plt.close("all")
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close("all")
for ch in evoked.info["chs"]:
if ch["coil_type"] == FIFF.FIFFV_COIL_EEG:
ch["loc"].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info["dig"][85]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, "head")
assert_true("head" in outlines.keys())
assert_true("nose" in outlines.keys())
assert_true("ear_left" in outlines.keys())
assert_true("ear_right" in outlines.keys())
assert_true("autoshrink" in outlines.keys())
assert_true(outlines["autoshrink"])
assert_true("clip_radius" in outlines.keys())
assert_array_equal(outlines["clip_radius"], 0.5)
pos, outlines = _check_outlines(pos, "skirt")
assert_true("head" in outlines.keys())
assert_true("nose" in outlines.keys())
assert_true("ear_left" in outlines.keys())
assert_true("ear_right" in outlines.keys())
assert_true("autoshrink" in outlines.keys())
assert_true(not outlines["autoshrink"])
assert_true("clip_radius" in outlines.keys())
assert_array_equal(outlines["clip_radius"], 0.625)
pos, outlines = _check_outlines(pos, "skirt", head_pos={"scale": [1.2, 1.2]})
assert_array_equal(outlines["clip_radius"], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type="eeg", outlines="skirt")
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type="eeg", outlines=outlines)
plt.close("all")
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687), radius=0.46, clip_on=True, transform=plt.gca().transAxes)
outlines["patch"] = patch
plot_evoked_topomap(evoked, times, ch_type="eeg", outlines=outlines)
# Remove digitization points. Now topomap should fail
evoked.info["dig"] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked, times, ch_type="eeg")
plt.close("all")
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close("all")
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
with warnings.catch_warnings(record=True): # rightmost column
evoked.plot_topomap(times="peaks", axes=axes)
plt.close("all")
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
def test_plot_topomap_basic(monkeypatch):
"""Test basics of topomap plotting."""
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
pytest.raises(ValueError, plt_topomap, ch_type='mag')
pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time
pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms')
# extrapolation to the edges of the convex hull or the head circle
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='local')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head',
outlines='skirt')
# extrapolation options when < 4 channels:
temp_data = np.random.random(3)
picks = channel_indices_by_type(evoked.info)['mag'][:3]
info_sel = pick_info(evoked.info, picks)
plot_topomap(temp_data, info_sel, extrapolate='local', res=res)
plot_topomap(temp_data, info_sel, extrapolate='head', res=res)
# make sure extrapolation works for 3 channels with border='mean'
# (if extra points are placed incorrectly some of them have only
# other extra points as neighbours and border='mean' fails)
plot_topomap(temp_data,
info_sel,
extrapolate='local',
border='mean',
res=res)
# border=0 and border='mean':
# ---------------------------
ch_pos = np.array(
sum(([[0, 0, r], [r, 0, 0], [-r, 0, 0], [0, -r, 0], [0, r, 0]]
for r in np.linspace(0.2, 1.0, 5)), []))
rng = np.random.RandomState(23)
data = np.full(len(ch_pos), 5) + rng.randn(len(ch_pos))
info = create_info(len(ch_pos), 250, 'eeg')
ch_pos_dict = {name: pos for name, pos in zip(info['ch_names'], ch_pos)}
dig = make_dig_montage(ch_pos_dict, coord_frame='head')
info.set_montage(dig)
# border=0
ax, _ = plot_topomap(data, info, extrapolate='head', border=0, sphere=1)
img_data = ax.get_array().data
assert np.abs(img_data[31, 31] - data[0]) < 0.12
assert np.abs(img_data[0, 0]) < 1.5
# border='mean'
ax, _ = plot_topomap(data,
info,
extrapolate='head',
border='mean',
sphere=1)
img_data = ax.get_array().data
assert np.abs(img_data[31, 31] - data[0]) < 0.12
assert img_data[0, 0] > 5
# error when not numeric or str:
error_msg = 'border must be an instance of numeric or str'
with pytest.raises(TypeError, match=error_msg):
plot_topomap(data, info, extrapolate='head', border=[1, 2, 3])
# error when str is not 'mean':
error_msg = "The only allowed value is 'mean', but got 'fancy' instead."
with pytest.raises(ValueError, match=error_msg):
plot_topomap(data, info, extrapolate='head', border='fancy')
# test channel placement when only 'grad' are picked:
# ---------------------------------------------------
info_grad = evoked.copy().pick('grad').info
n_grads = len(info_grad['ch_names'])
data = np.random.randn(n_grads)
img, _ = plot_topomap(data, info_grad)
# check that channels are scattered around x == 0
pos = img.axes.collections[-1].get_offsets()
prop_channels_on_the_right = (pos[:, 0] > 0).mean()
assert prop_channels_on_the_right < 0.6
# other:
# ------
plt_topomap = partial(evoked.plot_topomap, **fast_test)
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad',
times=[0],
mask=mask,
show_names=proc_names,
**fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times,
ch_type='grad',
mask=mask,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
with pytest.raises(ValueError, match='number of seconds; got -'):
plt_topomap(times, ch_type='eeg', average=-1e3)
with pytest.raises(TypeError, match='number of seconds; got type'):
plt_topomap(times, ch_type='eeg', average='x')
p = plt_topomap(times,
ch_type='grad',
image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))]
assert len(subplot) >= 1, [type(x) for x in p.get_children()]
subplot = subplot[0]
have_all = all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text))
assert have_all
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test_noscale)
# fail with multiple channel types
pytest.raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
plt_topomap(times, ch_type='mag')
# projs have already been applied
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive',
time_unit='s')
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
fig1 = evoked.plot_topomap('interactive',
'mag',
proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert (np.max(fig1.axes[0].images[0]._A) != data_max)
with monkeypatch.context() as m: # speed it up by not actually plotting
m.setattr(topomap, '_plot_topomap', lambda *args, **kwargs:
(None, None, None))
with pytest.warns(RuntimeWarning, match='More than 25 topomaps plots'):
plot_evoked_topomap(evoked, [0.1] * 26, colorbar=False)
pytest.raises(ValueError,
plot_evoked_topomap,
evoked, [-3e12, 15e6],
time_unit='s')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
eeg_picks = pick_types(evoked.info, meg=False, eeg=True)
pos, outlines = _get_pos_outlines(evoked.info, eeg_picks, 0.1)
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked,
times=[0., 0.1],
ch_type='eeg',
cmap=('Reds', True),
title='title',
**fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked,
times,
ch_type='eeg',
outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg',
time_unit='s')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
pytest.raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
pytest.raises(ValueError, plot_topomap, data, pos_1d)
pytest.raises(ValueError, plot_topomap, data, pos_3d)
pytest.raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
pytest.raises(ValueError, plot_topomap, data, pos_x)
pytest.raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0], time_unit='ms')
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
def test_plot_topomap():
"""Test topomap plotting."""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad', times=[0, 0.1],
butterfly=False, time_unit='s')
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
pytest.raises(ValueError, plt_topomap, ch_type='mag')
pytest.raises(TypeError, plt_topomap, head_pos='foo')
pytest.raises(KeyError, plt_topomap, head_pos=dict(foo='bar'))
pytest.raises(ValueError, plt_topomap, head_pos=dict(center=0))
pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time
pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1], ch_type='eeg', scalings=1, res=res,
contours=[-100, 0, 100], time_unit='ms')
plt_topomap = partial(evoked.plot_topomap, **fast_test)
plt_topomap(0.1, layout=layout, scalings=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad', times=[0], mask=mask,
show_names=proc_names, **fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times, ch_type='grad', mask=mask, show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average=-1e3)
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average='x')
p = plt_topomap(times, ch_type='grad', image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))]
assert len(subplot) >= 1, [type(x) for x in p.get_children()]
subplot = subplot[0]
assert (all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test_noscale)
# fail with multiple channel types
pytest.raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
plt_topomap(times, ch_type='mag', layout=None)
# projs have already been applied
pytest.raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive', time_unit='s')
# change to no-proj mode
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0), proj=False)
fig1 = evoked.plot_topomap('interactive', 'mag', proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert (np.max(fig1.axes[0].images[0]._A) != data_max)
pytest.raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50), time_unit='s')
pytest.raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6],
time_unit='s')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert ('head' in outlines.keys())
assert ('nose' in outlines.keys())
assert ('ear_left' in outlines.keys())
assert ('ear_right' in outlines.keys())
assert ('autoshrink' in outlines.keys())
assert (outlines['autoshrink'])
assert ('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert ('head' in outlines.keys())
assert ('nose' in outlines.keys())
assert ('ear_left' in outlines.keys())
assert ('ear_right' in outlines.keys())
assert ('autoshrink' in outlines.keys())
assert (not outlines['autoshrink'])
assert ('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos, 'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked, times=[0., 0.1], ch_type='eeg',
cmap=('Reds', True), title='title', **fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687), radius=.46,
clip_on=True, transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked, times, ch_type='eeg', outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
pytest.raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg', time_unit='s')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
pytest.raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
pytest.raises(ValueError, plot_topomap, data, pos_1d)
pytest.raises(ValueError, plot_topomap, data, pos_3d)
pytest.raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
pytest.raises(ValueError, plot_topomap, data, pos_x)
pytest.raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0], time_unit='ms')
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
# Test animation
_, anim = evoked.animate_topomap(ch_type='grad',
times=[0, 0.1],
butterfly=False)
anim._func(1) # _animate has to be tested separately on 'Agg' backend.
plt.close('all')
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close('all')
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times,
ch_type='grad',
mask=mask,
res=res,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError,
evoked.plot_topomap,
times,
ch_type='eeg',
res=res,
average=-1000)
assert_raises(ValueError,
evoked.plot_topomap,
times,
ch_type='eeg',
res=res,
average='hahahahah')
p = evoked.plot_topomap(times,
ch_type='grad',
res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [
x for x in p.get_children() if isinstance(x, matplotlib.axes.Subplot)
][0]
assert_true(
all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info)
# fail with multiple channel types
assert_raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos,
'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt')
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines)
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked, times, ch_type='eeg', outlines=outlines)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
with warnings.catch_warnings(record=True): # rightmost column
evoked.plot_topomap(times='peaks', axes=axes)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
def test_plot_topomap():
"""Test topomap plotting
"""
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# evoked
warnings.simplefilter('always')
res = 16
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0))
ev_bad = evoked.pick_types(meg=False, eeg=True, copy=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
ev_bad.plot_topomap(times=ev_bad.times[:2] - 1e-6) # auto, should plot EEG
assert_raises(ValueError, ev_bad.plot_topomap, ch_type='mag')
assert_raises(TypeError, ev_bad.plot_topomap, head_pos='foo')
assert_raises(KeyError, ev_bad.plot_topomap, head_pos=dict(foo='bar'))
assert_raises(ValueError, ev_bad.plot_topomap, head_pos=dict(center=0))
assert_raises(ValueError, ev_bad.plot_topomap, times=[-100]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
assert_raises(ValueError, ev_bad.plot_topomap, times=[[0]]) # bad time
evoked.plot_topomap(0.1, layout=layout, scale=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
evoked.plot_topomap(axes=axes, colorbar=False)
plt.close('all')
evoked.plot_topomap(times=[-0.1, 0.2])
plt.close('all')
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
evoked.plot_topomap(ch_type='mag', outlines=None)
times = [0.1]
evoked.plot_topomap(times, ch_type='eeg', res=res, scale=1)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res)
evoked.plot_topomap(times, ch_type='planar1', res=res)
evoked.plot_topomap(times, ch_type='planar2', res=res)
evoked.plot_topomap(times, ch_type='grad', mask=mask, res=res,
show_names=True, mask_params={'marker': 'x'})
plt.close('all')
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average=-1000)
assert_raises(ValueError, evoked.plot_topomap, times, ch_type='eeg',
res=res, average='hahahahah')
p = evoked.plot_topomap(times, ch_type='grad', res=res,
show_names=lambda x: x.replace('MEG', ''),
image_interp='bilinear')
subplot = [x for x in p.get_children() if
isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = evoked.plot_topomap(times, ch_type='eeg', res=res, average=0.01)
assert_equal(len(get_texts(p)), 0)
p = evoked.plot_topomap(times, ch_type='eeg', title='Custom', res=res)
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
with warnings.catch_warnings(record=True): # can't show
warnings.simplefilter('always')
evoked.plot_topomap(times, ch_type='mag', layout=None, res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
# change to no-proj mode
evoked = read_evokeds(evoked_fname, 'Left Auditory',
baseline=(None, 0), proj=False)
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
evoked.plot_topomap(0.1, 'mag', proj='interactive', res=res)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
with warnings.catch_warnings(record=True): # file conventions
warnings.simplefilter('always')
projs = read_proj(ecg_fname)
projs = [pp for pp in projs if pp['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs, res=res)
plt.close('all')
ax = plt.subplot(111)
plot_projs_topomap([projs[0]], res=res, axes=ax) # test axes param
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
pos = make_eeg_layout(evoked.info).pos[:, :2]
pos, outlines = _check_outlines(pos, 'head')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.5)
pos, outlines = _check_outlines(pos, 'skirt')
assert_true('head' in outlines.keys())
assert_true('nose' in outlines.keys())
assert_true('ear_left' in outlines.keys())
assert_true('ear_right' in outlines.keys())
assert_true('autoshrink' in outlines.keys())
assert_true(not outlines['autoshrink'])
assert_true('clip_radius' in outlines.keys())
assert_array_equal(outlines['clip_radius'], 0.625)
pos, outlines = _check_outlines(pos, 'skirt',
head_pos={'scale': [1.2, 1.2]})
assert_array_equal(outlines['clip_radius'], 0.75)
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt')
# Pass custom outlines without patch
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines)
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687), radius=.46,
clip_on=True, transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked, times, ch_type='eeg', outlines=outlines)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
assert_raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
assert_raises(ValueError, plot_topomap, data, pos_1d)
assert_raises(ValueError, plot_topomap, data, pos_3d)
assert_raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
assert_raises(ValueError, plot_topomap, data, pos_x)
assert_raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# plot first pattern
pattern = csp.patterns_[0, :]
pattern -= pattern.mean()
ix = np.argmax(abs(pattern))
# the parttern is sign invariant.
# invert it for display purpose
if pattern[ix] > 0:
sign = 1.0
else:
sign = -1.0
fig, ax_topo = plt.subplots(1, 1, figsize=(12, 4))
title = 'Spatial Pattern'
fig.suptitle(title, fontsize=14)
img, _ = plot_topomap(sign * pattern, epos, axis=ax_topo, show=False)
divider = make_axes_locatable(ax_topo)
# add axes for colorbar
ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)
plt.colorbar(img, cax=ax_colorbar)
# plot spectrum
fix = (f > 7) & (f < 35)
ax_spectrum = divider.append_axes('right', size='300%', pad=1.2)
ax_spectrum.plot(f[fix],
np.log(po[y == 1][:, fix].mean(axis=0).T),
'-r',
lw=2)
ax_spectrum.plot(f[fix],
np.log(po[y == -1][:, fix].mean(axis=0).T),
'-b',
# plot first pattern
pattern = csp.patterns_[0,:]
pattern -= pattern.mean()
ix = np.argmax(abs(pattern))
# the parttern is sign invariant.
# invert it for display purpose
if pattern[ix]>0:
sign = 1.0
else:
sign = -1.0
fig, ax_topo = plt.subplots(1, 1, figsize=(12, 4))
title = 'Spatial Pattern'
fig.suptitle(title, fontsize=14)
img, _ = plot_topomap(sign*pattern,epos,axis=ax_topo,show=False)
divider = make_axes_locatable(ax_topo)
# add axes for colorbar
ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)
plt.colorbar(img, cax=ax_colorbar)
# plot spectrum
fix = (f>7) & (f<35)
ax_spectrum = divider.append_axes('right', size='300%', pad=1.2)
ax_spectrum.plot(f[fix],np.log(po[y==1][:,fix].mean(axis=0).T),'-r',lw=2)
ax_spectrum.plot(f[fix],np.log(po[y==-1][:,fix].mean(axis=0).T),'-b',lw=2)
ax_spectrum.set_xlabel('Frequency (Hz)')
ax_spectrum.set_ylabel('Power (dB)')
plt.grid()
plt.legend(['during','after'])
plt.title('Subject %d' % subject)
def test_plot_topomap_basic():
"""Test basics of topomap plotting."""
evoked = read_evokeds(evoked_fname, 'Left Auditory', baseline=(None, 0))
res = 8
fast_test = dict(res=res, contours=0, sensors=False, time_unit='s')
fast_test_noscale = dict(res=res, contours=0, sensors=False)
ev_bad = evoked.copy().pick_types(meg=False, eeg=True)
ev_bad.pick_channels(ev_bad.ch_names[:2])
plt_topomap = partial(ev_bad.plot_topomap, **fast_test)
plt_topomap(times=ev_bad.times[:2] - 1e-6) # auto, plots EEG
pytest.raises(ValueError, plt_topomap, ch_type='mag')
with pytest.deprecated_call(match='head_pos'):
plt_topomap(head_pos='foo')
pytest.raises(ValueError, plt_topomap, times=[-100]) # bad time
pytest.raises(ValueError, plt_topomap, times=[[0]]) # bad time
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms')
# extrapolation to the edges of the convex hull or the head circle
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='local')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head')
evoked.plot_topomap([0.1],
ch_type='eeg',
scalings=1,
res=res,
contours=[-100, 0, 100],
time_unit='ms',
extrapolate='head',
outlines='skirt')
# extrapolation options when < 4 channels:
temp_data = np.random.random(3)
picks = channel_indices_by_type(evoked.info)['mag'][:3]
info_sel = pick_info(evoked.info, picks)
plot_topomap(temp_data, info_sel, extrapolate='local', res=res)
plot_topomap(temp_data, info_sel, extrapolate='head', res=res)
plt_topomap = partial(evoked.plot_topomap, **fast_test)
with pytest.deprecated_call(match='layout'):
plt_topomap(0.1, layout=layout, scalings=dict(mag=0.1))
plt.close('all')
axes = [plt.subplot(221), plt.subplot(222)]
plt_topomap(axes=axes, colorbar=False)
plt.close('all')
plt_topomap(times=[-0.1, 0.2])
plt.close('all')
evoked_grad = evoked.copy().crop(0, 0).pick_types(meg='grad')
mask = np.zeros((204, 1), bool)
mask[[0, 3, 5, 6]] = True
names = []
def proc_names(x):
names.append(x)
return x[4:]
evoked_grad.plot_topomap(ch_type='grad',
times=[0],
mask=mask,
show_names=proc_names,
**fast_test)
assert_equal(sorted(names),
['MEG 011x', 'MEG 012x', 'MEG 013x', 'MEG 014x'])
mask = np.zeros_like(evoked.data, dtype=bool)
mask[[1, 5], :] = True
plt_topomap(ch_type='mag', outlines=None)
times = [0.1]
plt_topomap(times, ch_type='grad', mask=mask)
plt_topomap(times, ch_type='planar1')
plt_topomap(times, ch_type='planar2')
plt_topomap(times,
ch_type='grad',
mask=mask,
show_names=True,
mask_params={'marker': 'x'})
plt.close('all')
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average=-1e3)
pytest.raises(ValueError, plt_topomap, times, ch_type='eeg', average='x')
p = plt_topomap(times,
ch_type='grad',
image_interp='bilinear',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if 'Subplot' in str(type(x))]
assert len(subplot) >= 1, [type(x) for x in p.get_children()]
subplot = subplot[0]
have_all = all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text))
assert have_all
# Plot array
for ch_type in ('mag', 'grad'):
evoked_ = evoked.copy().pick_types(eeg=False, meg=ch_type)
plot_topomap(evoked_.data[:, 0], evoked_.info, **fast_test_noscale)
# fail with multiple channel types
pytest.raises(ValueError, plot_topomap, evoked.data[0, :], evoked.info)
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plt_topomap(times, ch_type='eeg', average=0.01)
assert_equal(len(get_texts(p)), 0)
p = plt_topomap(times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
plt.close('all')
# delaunay triangulation warning
plt_topomap(times, ch_type='mag', layout=None)
# projs have already been applied
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive',
time_unit='s')
# change to no-proj mode
evoked = read_evokeds(evoked_fname,
'Left Auditory',
baseline=(None, 0),
proj=False)
fig1 = evoked.plot_topomap('interactive',
'mag',
proj='interactive',
**fast_test)
_fake_click(fig1, fig1.axes[1], (0.5, 0.5)) # click slider
data_max = np.max(fig1.axes[0].images[0]._A)
fig2 = plt.gcf()
_fake_click(fig2, fig2.axes[0], (0.075, 0.775)) # toggle projector
# make sure projector gets toggled
assert (np.max(fig1.axes[0].images[0]._A) != data_max)
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
np.repeat(.1, 50),
time_unit='s')
pytest.raises(ValueError,
plot_evoked_topomap,
evoked, [-3e12, 15e6],
time_unit='s')
for ch in evoked.info['chs']:
if ch['coil_type'] == FIFF.FIFFV_COIL_EEG:
ch['loc'].fill(0)
# Remove extra digitization point, so EEG digitization points
# correspond with the EEG electrodes
del evoked.info['dig'][85]
# Plot skirt
evoked.plot_topomap(times, ch_type='eeg', outlines='skirt', **fast_test)
# Pass custom outlines without patch
eeg_picks = pick_types(evoked.info, meg=False, eeg=True)
pos, outlines = _get_pos_outlines(evoked.info, eeg_picks, 0.1)
evoked.plot_topomap(times, ch_type='eeg', outlines=outlines, **fast_test)
plt.close('all')
# Test interactive cmap
fig = plot_evoked_topomap(evoked,
times=[0., 0.1],
ch_type='eeg',
cmap=('Reds', True),
title='title',
**fast_test)
fig.canvas.key_press_event('up')
fig.canvas.key_press_event(' ')
fig.canvas.key_press_event('down')
cbar = fig.get_axes()[0].CB # Fake dragging with mouse.
ax = cbar.cbar.ax
_fake_click(fig, ax, (0.1, 0.1))
_fake_click(fig, ax, (0.1, 0.2), kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
_fake_click(fig, ax, (0.1, 0.1), button=3)
_fake_click(fig, ax, (0.1, 0.2), button=3, kind='motion')
_fake_click(fig, ax, (0.1, 0.3), kind='release')
fig.canvas.scroll_event(0.5, 0.5, -0.5) # scroll down
fig.canvas.scroll_event(0.5, 0.5, 0.5) # scroll up
plt.close('all')
# Pass custom outlines with patch callable
def patch():
return Circle((0.5, 0.4687),
radius=.46,
clip_on=True,
transform=plt.gca().transAxes)
outlines['patch'] = patch
plot_evoked_topomap(evoked,
times,
ch_type='eeg',
outlines=outlines,
**fast_test)
# Remove digitization points. Now topomap should fail
evoked.info['dig'] = None
pytest.raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg',
time_unit='s')
plt.close('all')
# Error for missing names
n_channels = len(pos)
data = np.ones(n_channels)
pytest.raises(ValueError, plot_topomap, data, pos, show_names=True)
# Test error messages for invalid pos parameter
pos_1d = np.zeros(n_channels)
pos_3d = np.zeros((n_channels, 2, 2))
pytest.raises(ValueError, plot_topomap, data, pos_1d)
pytest.raises(ValueError, plot_topomap, data, pos_3d)
pytest.raises(ValueError, plot_topomap, data, pos[:3, :])
pos_x = pos[:, :1]
pos_xyz = np.c_[pos, np.zeros(n_channels)[:, np.newaxis]]
pytest.raises(ValueError, plot_topomap, data, pos_x)
pytest.raises(ValueError, plot_topomap, data, pos_xyz)
# An #channels x 4 matrix should work though. In this case (x, y, width,
# height) is assumed.
pos_xywh = np.c_[pos, np.zeros((n_channels, 2))]
plot_topomap(data, pos_xywh)
plt.close('all')
# Test peak finder
axes = [plt.subplot(131), plt.subplot(132)]
evoked.plot_topomap(times='peaks', axes=axes, **fast_test)
plt.close('all')
evoked.data = np.zeros(evoked.data.shape)
evoked.data[50][1] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[1])
evoked.data[80][100] = 1
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 100]])
evoked.data[2][95] = 2
assert_array_equal(_find_peaks(evoked, 10), evoked.times[[1, 95]])
assert_array_equal(_find_peaks(evoked, 1), evoked.times[95])
# Test excluding bads channels
evoked_grad.info['bads'] += [evoked_grad.info['ch_names'][0]]
orig_bads = evoked_grad.info['bads']
evoked_grad.plot_topomap(ch_type='grad', times=[0], time_unit='ms')
assert_array_equal(evoked_grad.info['bads'], orig_bads)
plt.close('all')
