/
misc.py
1099 lines (957 loc) · 38.7 KB
/
misc.py
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# -*- coding: utf-8 -*-
"""Functions to make simple plots with M/EEG data."""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Cathy Nangini <cnangini@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
#
# License: Simplified BSD
import copy
from glob import glob
from itertools import cycle
import os.path as op
import warnings
from distutils.version import LooseVersion
from collections import defaultdict
import numpy as np
from scipy import linalg
from ..defaults import DEFAULTS
from ..surface import read_surface
from ..io.proj import make_projector
from ..io.pick import _DATA_CH_TYPES_SPLIT, pick_types
from ..source_space import read_source_spaces, SourceSpaces
from ..utils import (logger, verbose, get_subjects_dir, warn, _check_option,
_mask_to_onsets_offsets)
from ..io.pick import _picks_by_type
from ..filter import estimate_ringing_samples
from ..fixes import get_sosfiltfilt
from .utils import tight_layout, _get_color_list, _prepare_trellis, plt_show
@verbose
def plot_cov(cov, info, exclude=[], colorbar=True, proj=False, show_svd=True,
show=True, verbose=None):
"""Plot Covariance data.
Parameters
----------
cov : instance of Covariance
The covariance matrix.
info: dict
Measurement info.
exclude : list of string | str
List of channels to exclude. If empty do not exclude any channel.
If 'bads', exclude info['bads'].
colorbar : bool
Show colorbar or not.
proj : bool
Apply projections or not.
show_svd : bool
Plot also singular values of the noise covariance for each sensor
type. We show square roots ie. standard deviations.
show : bool
Show figure if True.
%(verbose)s
Returns
-------
fig_cov : instance of matplotlib.figure.Figure
The covariance plot.
fig_svd : instance of matplotlib.figure.Figure | None
The SVD spectra plot of the covariance.
"""
if exclude == 'bads':
exclude = info['bads']
picks_list = \
_picks_by_type(info, meg_combined=False, ref_meg=False,
exclude=exclude)
picks_by_type = dict(picks_list)
ch_names = [n for n in cov.ch_names if n not in exclude]
ch_idx = [cov.ch_names.index(n) for n in ch_names]
info_ch_names = info['ch_names']
idx_by_type = defaultdict(list)
for ch_type, sel in picks_by_type.items():
idx_by_type[ch_type] = [ch_names.index(info_ch_names[c])
for c in sel if info_ch_names[c] in ch_names]
idx_names = [(idx_by_type[key],
'%s covariance' % DEFAULTS['titles'][key],
DEFAULTS['units'][key],
DEFAULTS['scalings'][key])
for key in _DATA_CH_TYPES_SPLIT
if len(idx_by_type[key]) > 0]
C = cov.data[ch_idx][:, ch_idx]
if proj:
projs = copy.deepcopy(info['projs'])
# Activate the projection items
for p in projs:
p['active'] = True
P, ncomp, _ = make_projector(projs, ch_names)
if ncomp > 0:
logger.info(' Created an SSP operator (subspace dimension'
' = %d)' % ncomp)
C = np.dot(P, np.dot(C, P.T))
else:
logger.info(' The projection vectors do not apply to these '
'channels.')
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
fig_cov, axes = plt.subplots(1, len(idx_names), squeeze=False,
figsize=(3.8 * len(idx_names), 3.7))
for k, (idx, name, _, _) in enumerate(idx_names):
vlim = np.max(np.abs(C[idx][:, idx]))
im = axes[0, k].imshow(C[idx][:, idx], interpolation="nearest",
norm=Normalize(vmin=-vlim, vmax=vlim),
cmap='RdBu_r')
axes[0, k].set(title=name)
if colorbar:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(axes[0, k])
cax = divider.append_axes("right", size="5.5%", pad=0.05)
plt.colorbar(im, cax=cax, format='%.0e')
fig_cov.subplots_adjust(0.04, 0.0, 0.98, 0.94, 0.2, 0.26)
tight_layout(fig=fig_cov)
fig_svd = None
if show_svd:
fig_svd, axes = plt.subplots(1, len(idx_names), squeeze=False,
figsize=(3.8 * len(idx_names), 3.7))
for k, (idx, name, unit, scaling) in enumerate(idx_names):
s = linalg.svd(C[idx][:, idx], compute_uv=False)
# Protect against true zero singular values
s[s <= 0] = 1e-10 * s[s > 0].min()
s = np.sqrt(s) * scaling
axes[0, k].plot(s)
axes[0, k].set(ylabel=u'Noise σ (%s)' % unit, yscale='log',
xlabel='Eigenvalue index', title=name)
tight_layout(fig=fig_svd)
plt_show(show)
return fig_cov, fig_svd
def plot_source_spectrogram(stcs, freq_bins, tmin=None, tmax=None,
source_index=None, colorbar=False, show=True):
"""Plot source power in time-freqency grid.
Parameters
----------
stcs : list of SourceEstimate
Source power for consecutive time windows, one SourceEstimate object
should be provided for each frequency bin.
freq_bins : list of tuples of float
Start and end points of frequency bins of interest.
tmin : float
Minimum time instant to show.
tmax : float
Maximum time instant to show.
source_index : int | None
Index of source for which the spectrogram will be plotted. If None,
the source with the largest activation will be selected.
colorbar : bool
If true, a colorbar will be added to the plot.
show : bool
Show figure if True.
"""
import matplotlib.pyplot as plt
# Input checks
if len(stcs) == 0:
raise ValueError('cannot plot spectrogram if len(stcs) == 0')
stc = stcs[0]
if tmin is not None and tmin < stc.times[0]:
raise ValueError('tmin cannot be smaller than the first time point '
'provided in stcs')
if tmax is not None and tmax > stc.times[-1] + stc.tstep:
raise ValueError('tmax cannot be larger than the sum of the last time '
'point and the time step, which are provided in stcs')
# Preparing time-frequency cell boundaries for plotting
if tmin is None:
tmin = stc.times[0]
if tmax is None:
tmax = stc.times[-1] + stc.tstep
time_bounds = np.arange(tmin, tmax + stc.tstep, stc.tstep)
freq_bounds = sorted(set(np.ravel(freq_bins)))
freq_ticks = copy.deepcopy(freq_bounds)
# Reject time points that will not be plotted and gather results
source_power = []
for stc in stcs:
stc = stc.copy() # copy since crop modifies inplace
stc.crop(tmin, tmax - stc.tstep)
source_power.append(stc.data)
source_power = np.array(source_power)
# Finding the source with maximum source power
if source_index is None:
source_index = np.unravel_index(source_power.argmax(),
source_power.shape)[1]
# If there is a gap in the frequency bins record its locations so that it
# can be covered with a gray horizontal bar
gap_bounds = []
for i in range(len(freq_bins) - 1):
lower_bound = freq_bins[i][1]
upper_bound = freq_bins[i + 1][0]
if lower_bound != upper_bound:
freq_bounds.remove(lower_bound)
gap_bounds.append((lower_bound, upper_bound))
# Preparing time-frequency grid for plotting
time_grid, freq_grid = np.meshgrid(time_bounds, freq_bounds)
# Plotting the results
fig = plt.figure(figsize=(9, 6))
plt.pcolor(time_grid, freq_grid, source_power[:, source_index, :],
cmap='Reds')
ax = plt.gca()
ax.set(title='Source power', xlabel='Time (s)', ylabel='Frequency (Hz)')
time_tick_labels = [str(np.round(t, 2)) for t in time_bounds]
n_skip = 1 + len(time_bounds) // 10
for i in range(len(time_bounds)):
if i % n_skip != 0:
time_tick_labels[i] = ''
ax.set_xticks(time_bounds)
ax.set_xticklabels(time_tick_labels)
plt.xlim(time_bounds[0], time_bounds[-1])
plt.yscale('log')
ax.set_yticks(freq_ticks)
ax.set_yticklabels([np.round(freq, 2) for freq in freq_ticks])
plt.ylim(freq_bounds[0], freq_bounds[-1])
plt.grid(True, ls='-')
if colorbar:
plt.colorbar()
tight_layout(fig=fig)
# Covering frequency gaps with horizontal bars
for lower_bound, upper_bound in gap_bounds:
plt.barh(lower_bound, time_bounds[-1] - time_bounds[0], upper_bound -
lower_bound, time_bounds[0], color='#666666')
plt_show(show)
return fig
def _plot_mri_contours(mri_fname, surfaces, src, orientation='coronal',
slices=None, show=True):
"""Plot BEM contours on anatomical slices."""
import matplotlib.pyplot as plt
import nibabel as nib
# plot axes (x, y, z) as data axes (0, 1, 2)
if orientation == 'coronal':
x, y, z = 0, 1, 2
elif orientation == 'axial':
x, y, z = 2, 0, 1
elif orientation == 'sagittal':
x, y, z = 2, 1, 0
else:
raise ValueError("Orientation must be 'coronal', 'axial' or "
"'sagittal'. Got %s." % orientation)
# Load the T1 data
nim = nib.load(mri_fname)
data = nim.get_data()
try:
affine = nim.affine
except AttributeError: # older nibabel
affine = nim.get_affine()
n_sag, n_axi, n_cor = data.shape
orientation_name2axis = dict(sagittal=0, axial=1, coronal=2)
orientation_axis = orientation_name2axis[orientation]
if slices is None:
n_slices = data.shape[orientation_axis]
slices = np.linspace(0, n_slices, 12, endpoint=False).astype(np.int)
# create of list of surfaces
surfs = list()
trans = linalg.inv(affine)
# XXX : next line is a hack don't ask why
trans[:3, -1] = [n_sag // 2, n_axi // 2, n_cor // 2]
for file_name, color in surfaces:
surf = dict()
surf['rr'], surf['tris'] = read_surface(file_name)
# move back surface to MRI coordinate system
surf['rr'] = nib.affines.apply_affine(trans, surf['rr'])
surfs.append((surf, color))
src_points = list()
if isinstance(src, SourceSpaces):
for src_ in src:
points = src_['rr'][src_['inuse'].astype(bool)] * 1e3
src_points.append(nib.affines.apply_affine(trans, points))
elif src is not None:
raise TypeError("src needs to be None or SourceSpaces instance, not "
"%s" % repr(src))
fig, axs = _prepare_trellis(len(slices), 4)
for ax, sl in zip(axs, slices):
# adjust the orientations for good view
if orientation == 'coronal':
dat = data[:, :, sl].transpose()
elif orientation == 'axial':
dat = data[:, sl, :]
elif orientation == 'sagittal':
dat = data[sl, :, :]
# First plot the anatomical data
ax.imshow(dat, cmap=plt.cm.gray)
ax.set_autoscale_on(False)
ax.axis('off')
# and then plot the contours on top
for surf, color in surfs:
with warnings.catch_warnings(record=True): # ignore contour warn
warnings.simplefilter('ignore')
ax.tricontour(surf['rr'][:, x], surf['rr'][:, y],
surf['tris'], surf['rr'][:, z],
levels=[sl], colors=color, linewidths=1.0,
zorder=1)
for sources in src_points:
in_slice = np.logical_and(sources[:, z] > sl - 0.5,
sources[:, z] < sl + 0.5)
ax.scatter(sources[in_slice, x], sources[in_slice, y], marker='.',
color='#FF00FF', s=1, zorder=2)
plt.subplots_adjust(left=0., bottom=0., right=1., top=1., wspace=0.,
hspace=0.)
plt_show(show)
return fig
def plot_bem(subject=None, subjects_dir=None, orientation='coronal',
slices=None, brain_surfaces=None, src=None, show=True):
"""Plot BEM contours on anatomical slices.
Parameters
----------
subject : str
Subject name.
subjects_dir : str | None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
orientation : str
'coronal' or 'axial' or 'sagittal'.
slices : list of int
Slice indices.
brain_surfaces : None | str | list of str
One or more brain surface to plot (optional). Entries should correspond
to files in the subject's ``surf`` directory (e.g. ``"white"``).
src : None | SourceSpaces | str
SourceSpaces instance or path to a source space to plot individual
sources as scatter-plot. Only sources lying in the shown slices will be
visible, sources that lie between visible slices are not shown. Path
can be absolute or relative to the subject's ``bem`` folder.
show : bool
Show figure if True.
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure.
See Also
--------
mne.viz.plot_alignment
"""
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
# Get the MRI filename
mri_fname = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
if not op.isfile(mri_fname):
raise IOError('MRI file "%s" does not exist' % mri_fname)
# Get the BEM surface filenames
bem_path = op.join(subjects_dir, subject, 'bem')
if not op.isdir(bem_path):
raise IOError('Subject bem directory "%s" does not exist' % bem_path)
surfaces = []
for surf_name, color in (('*inner_skull', '#FF0000'),
('*outer_skull', '#FFFF00'),
('*outer_skin', '#FFAA80')):
surf_fname = glob(op.join(bem_path, surf_name + '.surf'))
if len(surf_fname) > 0:
surf_fname = surf_fname[0]
logger.info("Using surface: %s" % surf_fname)
surfaces.append((surf_fname, color))
if brain_surfaces is not None:
if isinstance(brain_surfaces, str):
brain_surfaces = (brain_surfaces,)
for surf_name in brain_surfaces:
for hemi in ('lh', 'rh'):
surf_fname = op.join(subjects_dir, subject, 'surf',
hemi + '.' + surf_name)
if op.exists(surf_fname):
surfaces.append((surf_fname, '#00DD00'))
else:
raise IOError("Surface %s does not exist." % surf_fname)
if isinstance(src, str):
if not op.exists(src):
src_ = op.join(subjects_dir, subject, 'bem', src)
if op.exists(src_):
src = src_
else:
raise IOError("%s does not exist" % src)
src = read_source_spaces(src)
elif src is not None and not isinstance(src, SourceSpaces):
raise TypeError("src needs to be None, str or SourceSpaces instance, "
"not %s" % repr(src))
if len(surfaces) == 0:
raise IOError('No surface files found. Surface files must end with '
'inner_skull.surf, outer_skull.surf or outer_skin.surf')
# Plot the contours
return _plot_mri_contours(mri_fname, surfaces, src, orientation, slices,
show)
def plot_events(events, sfreq=None, first_samp=0, color=None, event_id=None,
axes=None, equal_spacing=True, show=True):
"""Plot events to get a visual display of the paradigm.
Parameters
----------
events : array, shape (n_events, 3)
The events.
sfreq : float | None
The sample frequency. If None, data will be displayed in samples (not
seconds).
first_samp : int
The index of the first sample. Recordings made on Neuromag systems
number samples relative to the system start (not relative to the
beginning of the recording). In such cases the ``raw.first_samp``
attribute can be passed here. Default is 0.
color : dict | None
Dictionary of event_id integers as keys and colors as values. If None,
colors are automatically drawn from a default list (cycled through if
number of events longer than list of default colors). Color can be any
valid :doc:`matplotlib color <tutorials/colors/colors>`.
event_id : dict | None
Dictionary of event labels (e.g. 'aud_l') as keys and their associated
event_id values. Labels are used to plot a legend. If None, no legend
is drawn.
axes : instance of Axes
The subplot handle.
equal_spacing : bool
Use equal spacing between events in y-axis.
show : bool
Show figure if True.
Returns
-------
fig : matplotlib.figure.Figure
The figure object containing the plot.
Notes
-----
.. versionadded:: 0.9.0
"""
if sfreq is None:
sfreq = 1.0
xlabel = 'Samples'
else:
xlabel = 'Time (s)'
events = np.asarray(events)
unique_events = np.unique(events[:, 2])
if event_id is not None:
# get labels and unique event ids from event_id dict,
# sorted by value
event_id_rev = {v: k for k, v in event_id.items()}
conditions, unique_events_id = zip(*sorted(event_id.items(),
key=lambda x: x[1]))
for this_event in unique_events_id:
if this_event not in unique_events:
raise ValueError('%s from event_id is not present in events.'
% this_event)
for this_event in unique_events:
if this_event not in unique_events_id:
warn('event %s missing from event_id will be ignored'
% this_event)
else:
unique_events_id = unique_events
color = _handle_event_colors(unique_events, color, unique_events_id)
import matplotlib.pyplot as plt
fig = None
if axes is None:
fig = plt.figure()
ax = axes if axes else plt.gca()
unique_events_id = np.array(unique_events_id)
min_event = np.min(unique_events_id)
max_event = np.max(unique_events_id)
for idx, ev in enumerate(unique_events_id):
ev_mask = events[:, 2] == ev
kwargs = {}
if event_id is not None:
event_label = '{} ({})'.format(event_id_rev[ev], np.sum(ev_mask))
kwargs['label'] = event_label
if ev in color:
kwargs['color'] = color[ev]
if equal_spacing:
ax.plot((events[ev_mask, 0] - first_samp) / sfreq,
(idx + 1) * np.ones(ev_mask.sum()), '.', **kwargs)
else:
ax.plot((events[ev_mask, 0] - first_samp) / sfreq,
events[ev_mask, 2], '.', **kwargs)
if equal_spacing:
ax.set_ylim(0, unique_events_id.size + 1)
ax.set_yticks(1 + np.arange(unique_events_id.size))
ax.set_yticklabels(unique_events_id)
else:
ax.set_ylim([min_event - 1, max_event + 1])
ax.set(xlabel=xlabel, ylabel='Events id')
ax.grid(True)
fig = fig if fig is not None else plt.gcf()
if event_id is not None:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.canvas.draw()
plt_show(show)
return fig
def _get_presser(fig):
"""Get our press callback."""
import matplotlib
callbacks = fig.canvas.callbacks.callbacks['button_press_event']
func = None
for key, val in callbacks.items():
if LooseVersion(matplotlib.__version__) >= '3':
func = val()
else:
func = val.func
if func.__class__.__name__ == 'partial':
break
else:
func = None
assert func is not None
return func
def plot_dipole_amplitudes(dipoles, colors=None, show=True):
"""Plot the amplitude traces of a set of dipoles.
Parameters
----------
dipoles : list of instance of Dipole
The dipoles whose amplitudes should be shown.
colors: list of color | None
Color to plot with each dipole. If None default colors are used.
show : bool
Show figure if True.
Returns
-------
fig : matplotlib.figure.Figure
The figure object containing the plot.
Notes
-----
.. versionadded:: 0.9.0
"""
import matplotlib.pyplot as plt
if colors is None:
colors = cycle(_get_color_list())
fig, ax = plt.subplots(1, 1)
xlim = [np.inf, -np.inf]
for dip, color in zip(dipoles, colors):
ax.plot(dip.times, dip.amplitude * 1e9, color=color, linewidth=1.5)
xlim[0] = min(xlim[0], dip.times[0])
xlim[1] = max(xlim[1], dip.times[-1])
ax.set(xlim=xlim, xlabel='Time (s)', ylabel='Amplitude (nAm)')
if show:
fig.show(warn=False)
return fig
def adjust_axes(axes, remove_spines=('top', 'right'), grid=True):
"""Adjust some properties of axes.
Parameters
----------
axes : list
List of axes to process.
remove_spines : list of str
Which axis spines to remove.
grid : bool
Turn grid on (True) or off (False).
"""
axes = [axes] if not isinstance(axes, (list, tuple, np.ndarray)) else axes
for ax in axes:
if grid:
ax.grid(zorder=0)
for key in remove_spines:
ax.spines[key].set_visible(False)
def _filter_ticks(lims, fscale):
"""Create approximately spaced ticks between lims."""
if fscale == 'linear':
return None, None # let matplotlib handle it
lims = np.array(lims)
ticks = list()
if lims[1] > 20 * lims[0]:
base = np.array([1, 2, 4])
else:
base = np.arange(1, 11)
for exp in range(int(np.floor(np.log10(lims[0]))),
int(np.floor(np.log10(lims[1]))) + 1):
ticks += (base * (10 ** exp)).tolist()
ticks = np.array(ticks)
ticks = ticks[(ticks >= lims[0]) & (ticks <= lims[1])]
ticklabels = [('%g' if t < 1 else '%d') % t for t in ticks]
return ticks, ticklabels
def _get_flim(flim, fscale, freq, sfreq=None):
"""Get reasonable frequency limits."""
if flim is None:
if freq is None:
flim = [0.1 if fscale == 'log' else 0., sfreq / 2.]
else:
if fscale == 'linear':
flim = [freq[0]]
else:
flim = [freq[0] if freq[0] > 0 else 0.1 * freq[1]]
flim += [freq[-1]]
if fscale == 'log':
if flim[0] <= 0:
raise ValueError('flim[0] must be positive, got %s' % flim[0])
elif flim[0] < 0:
raise ValueError('flim[0] must be non-negative, got %s' % flim[0])
return flim
def _check_fscale(fscale):
"""Check for valid fscale."""
if not isinstance(fscale, str) or fscale not in ('log', 'linear'):
raise ValueError('fscale must be "log" or "linear", got %s'
% (fscale,))
_DEFAULT_ALIM = (-80, 10)
def plot_filter(h, sfreq, freq=None, gain=None, title=None, color='#1f77b4',
flim=None, fscale='log', alim=_DEFAULT_ALIM, show=True,
compensate=False):
"""Plot properties of a filter.
Parameters
----------
h : dict or ndarray
An IIR dict or 1D ndarray of coefficients (for FIR filter).
sfreq : float
Sample rate of the data (Hz).
freq : array-like or None
The ideal response frequencies to plot (must be in ascending order).
If None (default), do not plot the ideal response.
gain : array-like or None
The ideal response gains to plot.
If None (default), do not plot the ideal response.
title : str | None
The title to use. If None (default), deteremine the title based
on the type of the system.
color : color object
The color to use (default '#1f77b4').
flim : tuple or None
If not None, the x-axis frequency limits (Hz) to use.
If None, freq will be used. If None (default) and freq is None,
``(0.1, sfreq / 2.)`` will be used.
fscale : str
Frequency scaling to use, can be "log" (default) or "linear".
alim : tuple
The y-axis amplitude limits (dB) to use (default: (-60, 10)).
show : bool
Show figure if True (default).
compensate : bool
If True, compensate for the filter delay (phase will not be shown).
- For linear-phase FIR filters, this visualizes the filter coefficients
assuming that the output will be shifted by ``N // 2``.
- For IIR filters, this changes the filter coefficient display
by filtering backward and forward, and the frequency response
by squaring it.
.. versionadded:: 0.18
Returns
-------
fig : matplotlib.figure.Figure
The figure containing the plots.
See Also
--------
mne.filter.create_filter
plot_ideal_filter
Notes
-----
.. versionadded:: 0.14
"""
from scipy.signal import freqz, group_delay, lfilter, filtfilt, sosfilt
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter, NullFormatter
sosfiltfilt = get_sosfiltfilt()
sfreq = float(sfreq)
_check_option('fscale', fscale, ['log', 'linear'])
flim = _get_flim(flim, fscale, freq, sfreq)
if fscale == 'log':
omega = np.logspace(np.log10(flim[0]), np.log10(flim[1]), 1000)
else:
omega = np.linspace(flim[0], flim[1], 1000)
omega /= sfreq / (2 * np.pi)
if isinstance(h, dict): # IIR h.ndim == 2: # second-order sections
if 'sos' in h:
H = np.ones(len(omega), np.complex128)
gd = np.zeros(len(omega))
for section in h['sos']:
this_H = freqz(section[:3], section[3:], omega)[1]
H *= this_H
if compensate:
H *= this_H.conj() # time reversal is freq conj
else:
# Assume the forward-backward delay zeros out, which it
# mostly should
with warnings.catch_warnings(record=True): # singular GD
warnings.simplefilter('ignore')
gd += group_delay((section[:3], section[3:]), omega)[1]
n = estimate_ringing_samples(h['sos'])
delta = np.zeros(n)
delta[0] = 1
if compensate:
delta = np.pad(delta, [(n - 1, 0)], 'constant')
func = sosfiltfilt
gd += (len(delta) - 1) // 2
else:
func = sosfilt
h = func(h['sos'], delta)
else:
H = freqz(h['b'], h['a'], omega)[1]
if compensate:
H *= H.conj()
with warnings.catch_warnings(record=True): # singular GD
warnings.simplefilter('ignore')
gd = group_delay((h['b'], h['a']), omega)[1]
if compensate:
gd += group_delay(h['b'].conj(), h['a'].conj(), omega)[1]
n = estimate_ringing_samples((h['b'], h['a']))
delta = np.zeros(n)
delta[0] = 1
if compensate:
delta = np.pad(delta, [(n - 1, 0)], 'constant')
func = filtfilt
else:
func = lfilter
h = func(h['b'], h['a'], delta)
if title is None:
title = 'SOS (IIR) filter'
if compensate:
title += ' (forward-backward)'
else:
H = freqz(h, worN=omega)[1]
with warnings.catch_warnings(record=True): # singular GD
warnings.simplefilter('ignore')
gd = group_delay((h, [1.]), omega)[1]
title = 'FIR filter' if title is None else title
if compensate:
title += ' (delay-compensated)'
# eventually axes could be a parameter
fig, (ax_time, ax_freq, ax_delay) = plt.subplots(3)
t = np.arange(len(h))
if compensate:
n_shift = (len(h) - 1) // 2
t -= n_shift
assert t[0] == -t[-1]
gd -= n_shift
t = t / sfreq
gd = gd / sfreq
f = omega * sfreq / (2 * np.pi)
ax_time.plot(t, h, color=color)
ax_time.set(xlim=t[[0, -1]], xlabel='Time (s)',
ylabel='Amplitude', title=title)
mag = 10 * np.log10(np.maximum((H * H.conj()).real, 1e-20))
sl = slice(0 if fscale == 'linear' else 1, None, None)
# Magnitude
ax_freq.plot(f[sl], mag[sl], color=color, linewidth=2, zorder=4)
if freq is not None and gain is not None:
plot_ideal_filter(freq, gain, ax_freq, fscale=fscale, show=False)
ax_freq.set(ylabel='Magnitude (dB)', xlabel='', xscale=fscale)
# Delay
ax_delay.plot(f[sl], gd[sl], color=color, linewidth=2, zorder=4)
# shade nulled regions
for start, stop in zip(*_mask_to_onsets_offsets(mag <= -39.9)):
ax_delay.axvspan(f[start], f[stop - 1], facecolor='k', alpha=0.05,
zorder=5)
ax_delay.set(xlim=flim, ylabel='Group delay (s)', xlabel='Frequency (Hz)',
xscale=fscale)
xticks, xticklabels = _filter_ticks(flim, fscale)
dlim = np.abs(t).max() / 2.
dlim = [-dlim, dlim]
for ax, ylim, ylabel in ((ax_freq, alim, 'Amplitude (dB)'),
(ax_delay, dlim, 'Delay (s)')):
if xticks is not None:
ax.set(xticks=xticks)
ax.set(xticklabels=xticklabels)
ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
ax.xaxis.set_minor_formatter(NullFormatter())
ax.set(xlim=flim, ylim=ylim, xlabel='Frequency (Hz)', ylabel=ylabel)
adjust_axes([ax_time, ax_freq, ax_delay])
tight_layout()
plt_show(show)
return fig
def plot_ideal_filter(freq, gain, axes=None, title='', flim=None, fscale='log',
alim=_DEFAULT_ALIM, color='r', alpha=0.5, linestyle='--',
show=True):
"""Plot an ideal filter response.
Parameters
----------
freq : array-like
The ideal response frequencies to plot (must be in ascending order).
gain : array-like or None
The ideal response gains to plot.
axes : instance of Axes | None
The subplot handle. With None (default), axes are created.
title : str
The title to use, (default: '').
flim : tuple or None
If not None, the x-axis frequency limits (Hz) to use.
If None (default), freq used.
fscale : str
Frequency scaling to use, can be "log" (default) or "linear".
alim : tuple
If not None (default), the y-axis limits (dB) to use.
color : color object
The color to use (default: 'r').
alpha : float
The alpha to use (default: 0.5).
linestyle : str
The line style to use (default: '--').
show : bool
Show figure if True (default).
Returns
-------
fig : instance of matplotlib.figure.Figure
The figure.
See Also
--------
plot_filter
Notes
-----
.. versionadded:: 0.14
Examples
--------
Plot a simple ideal band-pass filter::
>>> from mne.viz import plot_ideal_filter
>>> freq = [0, 1, 40, 50]
>>> gain = [0, 1, 1, 0]
>>> plot_ideal_filter(freq, gain, flim=(0.1, 100)) #doctest: +ELLIPSIS
<...Figure...>
"""
import matplotlib.pyplot as plt
my_freq, my_gain = list(), list()
if freq[0] != 0:
raise ValueError('freq should start with DC (zero) and end with '
'Nyquist, but got %s for DC' % (freq[0],))
freq = np.array(freq)
# deal with semilogx problems @ x=0
_check_option('fscale', fscale, ['log', 'linear'])
if fscale == 'log':
freq[0] = 0.1 * freq[1] if flim is None else min(flim[0], freq[1])
flim = _get_flim(flim, fscale, freq)
transitions = list()
for ii in range(len(freq)):
if ii < len(freq) - 1 and gain[ii] != gain[ii + 1]:
transitions += [[freq[ii], freq[ii + 1]]]
my_freq += np.linspace(freq[ii], freq[ii + 1], 20,
endpoint=False).tolist()
my_gain += np.linspace(gain[ii], gain[ii + 1], 20,
endpoint=False).tolist()
else:
my_freq.append(freq[ii])
my_gain.append(gain[ii])
my_gain = 10 * np.log10(np.maximum(my_gain, 10 ** (alim[0] / 10.)))
if axes is None:
axes = plt.subplots(1)[1]
for transition in transitions:
axes.axvspan(*transition, color=color, alpha=0.1)
axes.plot(my_freq, my_gain, color=color, linestyle=linestyle, alpha=0.5,
linewidth=4, zorder=3)
xticks, xticklabels = _filter_ticks(flim, fscale)
axes.set(ylim=alim, xlabel='Frequency (Hz)', ylabel='Amplitude (dB)',
xscale=fscale)
if xticks is not None:
axes.set(xticks=xticks)
axes.set(xticklabels=xticklabels)
axes.set(xlim=flim)
if title:
axes.set(title=title)
adjust_axes(axes)
tight_layout()
plt_show(show)
return axes.figure
def _handle_event_colors(unique_events, color, unique_events_id):
"""Handle event colors."""
if color is None:
if len(unique_events) > len(_get_color_list()):
warn('More events than colors available. You should pass a list '
'of unique colors.')
colors = cycle(_get_color_list())
color = dict()
for this_event, this_color in zip(sorted(unique_events_id), colors):
color[this_event] = this_color
else:
for this_event in color:
if this_event not in unique_events_id:
raise ValueError('Event ID %s is in the color dict but is not '
'present in events or event_id.' % this_event)
for this_event in unique_events_id:
if this_event not in color:
warn('Color is not available for event %d. Default colors '
'will be used.' % this_event)
return color
def plot_csd(csd, info=None, mode='csd', colorbar=True, cmap=None,
n_cols=None, show=True):
"""Plot CSD matrices.
A sub-plot is created for each frequency. If an info object is passed to
the function, different channel types are plotted in different figures.
Parameters
----------
csd : instance of CrossSpectralDensity
The CSD matrix to plot.
info: instance of Info | None
To split the figure by channel-type, provide the measurement info.
By default, the CSD matrix is plotted as a whole.
mode : 'csd' | 'coh'
Whether to plot the cross-spectral density ('csd', the default), or
the coherence ('coh') between the channels.
colorbar : bool
Whether to show a colorbar. Defaults to ``True``.
cmap : str | None
The matplotlib colormap to use. Defaults to None, which means the
colormap will default to matplotlib's default.
n_cols : int | None
CSD matrices are plotted in a grid. This parameter controls how
many matrix to plot side by side before starting a new row. By
default, a number will be chosen to make the grid as square as
possible.
show : bool