def test_api():
"""Test LCMV/DICS API equivalence."""
lcmv_names = _get_args(make_lcmv)
dics_names = _get_args(make_dics)
dics_names[dics_names.index('csd')] = 'data_cov'
dics_names[dics_names.index('noise_csd')] = 'noise_cov'
dics_names.pop(dics_names.index('real_filter')) # not a thing for LCMV
assert lcmv_names == dics_names
def matplotlib_config():
"""Configure matplotlib for viz tests."""
import matplotlib
# "force" should not really be necessary but should not hurt
kwargs = dict()
if 'warn' in _get_args(matplotlib.use):
kwargs['warn'] = False
matplotlib.use('agg', force=True, **kwargs) # don't pop up windows
import matplotlib.pyplot as plt
assert plt.get_backend() == 'agg'
# overwrite some params that can horribly slow down tests that
# users might have changed locally (but should not otherwise affect
# functionality)
plt.ioff()
plt.rcParams['figure.dpi'] = 100
try:
from traits.etsconfig.api import ETSConfig
except Exception:
pass
else:
ETSConfig.toolkit = 'qt4'
try:
with warnings.catch_warnings(record=True): # traits
from mayavi import mlab
except Exception:
pass
else:
mlab.options.backend = 'test'
def matplotlib_config():
"""Configure matplotlib for viz tests."""
import matplotlib
# "force" should not really be necessary but should not hurt
kwargs = dict()
if 'warn' in _get_args(matplotlib.use):
kwargs['warn'] = False
matplotlib.use('agg', force=True, **kwargs) # don't pop up windows
import matplotlib.pyplot as plt
assert plt.get_backend() == 'agg'
# overwrite some params that can horribly slow down tests that
# users might have changed locally (but should not otherwise affect
# functionality)
plt.ioff()
plt.rcParams['figure.dpi'] = 100
try:
from traits.etsconfig.api import ETSConfig
except Exception:
pass
else:
ETSConfig.toolkit = 'qt4'
try:
with warnings.catch_warnings(record=True): # traits
from mayavi import mlab
except Exception:
pass
else:
mlab.options.backend = 'test'
def check_parameters_match(func, doc=None):
"""Helper to check docstring, returns list of incorrect results"""
from numpydoc import docscrape
incorrect = []
name_ = get_name(func)
if not name_.startswith('mne.') or name_.startswith('mne.externals'):
return incorrect
if inspect.isdatadescriptor(func):
return incorrect
args = _get_args(func)
# drop self
if len(args) > 0 and args[0] == 'self':
args = args[1:]
if doc is None:
with warnings.catch_warnings(record=True) as w:
doc = docscrape.FunctionDoc(func)
if len(w):
raise RuntimeError('Error for %s:\n%s' % (name_, w[0]))
# check set
param_names = [name for name, _, _ in doc['Parameters']]
# clean up some docscrape output:
param_names = [name.split(':')[0].strip('` ') for name in param_names]
param_names = [name for name in param_names if '*' not in name]
if len(param_names) != len(args):
bad = str(sorted(list(set(param_names) - set(args)) +
list(set(args) - set(param_names))))
if not any(d in name_ for d in _docstring_ignores) and \
'deprecation_wrapped' not in func.__code__.co_name:
incorrect += [name_ + ' arg mismatch: ' + bad]
else:
for n1, n2 in zip(param_names, args):
if n1 != n2:
incorrect += [name_ + ' ' + n1 + ' != ' + n2]
return incorrect
def check_parameters_match(func, doc=None):
"""Helper to check docstring, returns list of incorrect results"""
incorrect = []
name_ = get_name(func)
if not name_.startswith("mne.") or name_.startswith("mne.externals"):
return incorrect
if inspect.isdatadescriptor(func):
return incorrect
args = _get_args(func)
# drop self
if len(args) > 0 and args[0] == "self":
args = args[1:]
if doc is None:
with warnings.catch_warnings(record=True) as w:
doc = docscrape.FunctionDoc(func)
if len(w):
raise RuntimeError("Error for %s:\n%s" % (name_, w[0]))
# check set
param_names = [name for name, _, _ in doc["Parameters"]]
# clean up some docscrape output:
param_names = [name.split(":")[0].strip("` ") for name in param_names]
param_names = [name for name in param_names if "*" not in name]
if len(param_names) != len(args):
bad = str(sorted(list(set(param_names) - set(args)) + list(set(args) - set(param_names))))
if not any(d in name_ for d in _docstring_ignores) and "deprecation_wrapped" not in func.__code__.co_name:
incorrect += [name_ + " arg mismatch: " + bad]
else:
for n1, n2 in zip(param_names, args):
if n1 != n2:
incorrect += [name_ + " " + n1 + " != " + n2]
return incorrect
def check_parameters_match(func, doc=None):
"""Helper to check docstring, returns list of incorrect results"""
from numpydoc import docscrape
incorrect = []
name_ = get_name(func)
if not name_.startswith('mne.') or name_.startswith('mne.externals'):
return incorrect
if inspect.isdatadescriptor(func):
return incorrect
args = _get_args(func)
# drop self
if len(args) > 0 and args[0] == 'self':
args = args[1:]
if doc is None:
with warnings.catch_warnings(record=True) as w:
doc = docscrape.FunctionDoc(func)
if len(w):
raise RuntimeError('Error for %s:\n%s' % (name_, w[0]))
# check set
param_names = [name for name, _, _ in doc['Parameters']]
# clean up some docscrape output:
param_names = [name.split(':')[0].strip('` ') for name in param_names]
param_names = [name for name in param_names if '*' not in name]
if len(param_names) != len(args):
bad = str(sorted(list(set(param_names) - set(args)) +
list(set(args) - set(param_names))))
if not any(d in name_ for d in _docstring_ignores) and \
'deprecation_wrapped' not in func.__code__.co_name:
incorrect += [name_ + ' arg mismatch: ' + bad]
else:
for n1, n2 in zip(param_names, args):
if n1 != n2:
incorrect += [name_ + ' ' + n1 + ' != ' + n2]
return incorrect
def check_parameters_match(func, doc=None, cls=None):
"""Check docstring, return list of incorrect results."""
from numpydoc import docscrape
incorrect = []
name_ = get_name(func, cls=cls)
if not name_.startswith('mne.') or name_.startswith('mne.externals'):
return incorrect
if inspect.isdatadescriptor(func):
return incorrect
args = _get_args(func)
# drop self
if len(args) > 0 and args[0] == 'self':
args = args[1:]
if doc is None:
with pytest.warns(None) as w:
try:
doc = docscrape.FunctionDoc(func)
except Exception as exp:
incorrect += [name_ + ' parsing error: ' + str(exp)]
return incorrect
if len(w):
raise RuntimeError('Error for %s:\n%s' % (name_, w[0]))
# check set
parameters = doc['Parameters']
# clean up some docscrape output:
parameters = [[p[0].split(':')[0].strip('` '), p[2]] for p in parameters]
parameters = [p for p in parameters if '*' not in p[0]]
param_names = [p[0] for p in parameters]
if len(param_names) != len(args):
bad = str(
sorted(
list(set(param_names) - set(args)) +
list(set(args) - set(param_names))))
if not any(re.match(d, name_) for d in docstring_ignores) and \
'deprecation_wrapped' not in func.__code__.co_name:
incorrect += [name_ + ' arg mismatch: ' + bad]
else:
for n1, n2 in zip(param_names, args):
if n1 != n2:
incorrect += [name_ + ' ' + n1 + ' != ' + n2]
for param_name, desc in parameters:
desc = '\n'.join(desc)
full_name = name_ + '::' + param_name
if full_name in docstring_length_ignores:
assert len(desc) > char_limit # assert it actually needs to be
elif len(desc) > char_limit:
incorrect += [
'%s too long (%d > %d chars)' %
(full_name, len(desc), char_limit)
]
return incorrect
def test_regularize_cov():
"""Test cov regularization."""
raw = read_raw_fif(raw_fname)
raw.info['bads'].append(raw.ch_names[0]) # test with bad channels
noise_cov = read_cov(cov_fname)
# Regularize noise cov
reg_noise_cov = regularize(noise_cov, raw.info,
mag=0.1, grad=0.1, eeg=0.1, proj=True,
exclude='bads')
assert noise_cov['dim'] == reg_noise_cov['dim']
assert noise_cov['data'].shape == reg_noise_cov['data'].shape
assert np.mean(noise_cov['data'] < reg_noise_cov['data']) < 0.08
# make sure all args are represented
assert set(_DATA_CH_TYPES_SPLIT) - set(_get_args(regularize)) == set()
def test_regularize_cov():
"""Test cov regularization."""
raw = read_raw_fif(raw_fname)
raw.info['bads'].append(raw.ch_names[0]) # test with bad channels
noise_cov = read_cov(cov_fname)
# Regularize noise cov
reg_noise_cov = regularize(noise_cov, raw.info,
mag=0.1, grad=0.1, eeg=0.1, proj=True,
exclude='bads', rank='full')
assert noise_cov['dim'] == reg_noise_cov['dim']
assert noise_cov['data'].shape == reg_noise_cov['data'].shape
assert np.mean(noise_cov['data'] < reg_noise_cov['data']) < 0.08
# make sure all args are represented
assert set(_DATA_CH_TYPES_SPLIT) - set(_get_args(regularize)) == set()
def check_parameters_match(func, doc=None, cls=None):
"""Check docstring, return list of incorrect results."""
from numpydoc import docscrape
incorrect = []
name_ = get_name(func, cls=cls)
if not name_.startswith('mne.') or name_.startswith('mne.externals'):
return incorrect
if inspect.isdatadescriptor(func):
return incorrect
args = _get_args(func)
# drop self
if len(args) > 0 and args[0] == 'self':
args = args[1:]
if doc is None:
with pytest.warns(None) as w:
try:
doc = docscrape.FunctionDoc(func)
except Exception as exp:
incorrect += [name_ + ' parsing error: ' + str(exp)]
return incorrect
if len(w):
raise RuntimeError('Error for %s:\n%s' % (name_, w[0]))
# check set
parameters = doc['Parameters']
# clean up some docscrape output:
parameters = [[p[0].split(':')[0].strip('` '), p[2]]
for p in parameters]
parameters = [p for p in parameters if '*' not in p[0]]
param_names = [p[0] for p in parameters]
if len(param_names) != len(args):
bad = str(sorted(list(set(param_names) - set(args)) +
list(set(args) - set(param_names))))
if not any(re.match(d, name_) for d in docstring_ignores) and \
'deprecation_wrapped' not in func.__code__.co_name:
incorrect += [name_ + ' arg mismatch: ' + bad]
else:
for n1, n2 in zip(param_names, args):
if n1 != n2:
incorrect += [name_ + ' ' + n1 + ' != ' + n2]
for param_name, desc in parameters:
desc = '\n'.join(desc)
full_name = name_ + '::' + param_name
if full_name in docstring_length_ignores:
assert len(desc) > char_limit # assert it actually needs to be
elif len(desc) > char_limit:
incorrect += ['%s too long (%d > %d chars)'
% (full_name, len(desc), char_limit)]
return incorrect
def matplotlib_config():
"""Configure matplotlib for viz tests."""
import matplotlib
# "force" should not really be necessary but should not hurt
kwargs = dict()
if 'warn' in _get_args(matplotlib.use):
kwargs['warn'] = False
matplotlib.use('agg', force=True, **kwargs) # don't pop up windows
import matplotlib.pyplot as plt
assert plt.get_backend() == 'agg'
# overwrite some params that can horribly slow down tests that
# users might have changed locally (but should not otherwise affect
# functionality)
plt.ioff()
plt.rcParams['figure.dpi'] = 100
def verbose(function):
"""Verbose decorator to allow functions to override log-level.
Parameters
----------
function : callable
Function to be decorated by setting the verbosity level.
Returns
-------
dec : callable
The decorated function.
"""
arg_names = _get_args(function)
def wrapper(*args, **kwargs):
default_level = verbose_level = None
if len(arg_names) > 0 and arg_names[0] == 'self':
default_level = getattr(args[0], 'verbose', None)
if 'verbose' in kwargs:
verbose_level = kwargs.pop('verbose')
else:
try:
verbose_level = args[arg_names.index('verbose')]
except (IndexError, ValueError):
pass
# This ensures that object.method(verbose=None) will use object.verbose
if verbose_level is None:
verbose_level = default_level
if verbose_level is not None:
# set it back if we get an exception
with use_log_level(verbose_level):
return function(*args, **kwargs)
return function(*args, **kwargs)
return FunctionMaker.create(
function, 'return decfunc(%(signature)s)',
dict(decfunc=wrapper), __wrapped__=function,
__qualname__=function.__qualname__)
def _get_epo_kwargs():
from mne.fixes import _get_args
epo_kwargs = dict(verbose=False)
if 'overwrite' in _get_args(Epochs.save):
epo_kwargs['overwrite'] = True
return epo_kwargs
def test_sourcemorph_consistency():
"""Test SourceMorph class consistency."""
assert _get_args(SourceMorph.__init__)[1:] == \
mne.morph._SOURCE_MORPH_ATTRIBUTES
def test_sourcemorph_consistency():
"""Test SourceMorph class consistency."""
assert _get_args(SourceMorph.__init__)[1:] == \
mne.morph._SOURCE_MORPH_ATTRIBUTES
def test_search_light():
"""Test SlidingEstimator."""
from sklearn.linear_model import Ridge, LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import roc_auc_score, make_scorer
with pytest.warns(None): # NumPy module import
from sklearn.ensemble import BaggingClassifier
from sklearn.base import is_classifier
logreg = LogisticRegression(solver='liblinear',
multi_class='ovr',
random_state=0)
X, y = make_data()
n_epochs, _, n_time = X.shape
# init
pytest.raises(ValueError, SlidingEstimator, 'foo')
sl = SlidingEstimator(Ridge())
assert (not is_classifier(sl))
sl = SlidingEstimator(LogisticRegression(solver='liblinear'))
assert (is_classifier(sl))
# fit
assert_equal(sl.__repr__()[:18], '<SlidingEstimator(')
sl.fit(X, y)
assert_equal(sl.__repr__()[-28:], ', fitted with 10 estimators>')
pytest.raises(ValueError, sl.fit, X[1:], y)
pytest.raises(ValueError, sl.fit, X[:, :, 0], y)
sl.fit(X, y, sample_weight=np.ones_like(y))
# transforms
pytest.raises(ValueError, sl.predict, X[:, :, :2])
y_pred = sl.predict(X)
assert (y_pred.dtype == int)
assert_array_equal(y_pred.shape, [n_epochs, n_time])
y_proba = sl.predict_proba(X)
assert (y_proba.dtype == float)
assert_array_equal(y_proba.shape, [n_epochs, n_time, 2])
# score
score = sl.score(X, y)
assert_array_equal(score.shape, [n_time])
assert (np.sum(np.abs(score)) != 0)
assert (score.dtype == float)
sl = SlidingEstimator(logreg)
assert_equal(sl.scoring, None)
# Scoring method
for scoring in ['foo', 999]:
sl = SlidingEstimator(logreg, scoring=scoring)
sl.fit(X, y)
pytest.raises((ValueError, TypeError), sl.score, X, y)
# Check sklearn's roc_auc fix: scikit-learn/scikit-learn#6874
# -- 3 class problem
sl = SlidingEstimator(logreg, scoring='roc_auc')
y = np.arange(len(X)) % 3
sl.fit(X, y)
with pytest.raises(ValueError, match='for two-class'):
sl.score(X, y)
# But check that valid ones should work with new enough sklearn
if 'multi_class' in _get_args(roc_auc_score):
scoring = make_scorer(roc_auc_score,
needs_proba=True,
multi_class='ovo')
sl = SlidingEstimator(logreg, scoring=scoring)
sl.fit(X, y)
sl.score(X, y) # smoke test
# -- 2 class problem not in [0, 1]
y = np.arange(len(X)) % 2 + 1
sl.fit(X, y)
score = sl.score(X, y)
assert_array_equal(score, [
roc_auc_score(y - 1, _y_pred - 1)
for _y_pred in sl.decision_function(X).T
])
y = np.arange(len(X)) % 2
# Cannot pass a metric as a scoring parameter
sl1 = SlidingEstimator(logreg, scoring=roc_auc_score)
sl1.fit(X, y)
pytest.raises(ValueError, sl1.score, X, y)
# Now use string as scoring
sl1 = SlidingEstimator(logreg, scoring='roc_auc')
sl1.fit(X, y)
rng = np.random.RandomState(0)
X = rng.randn(*X.shape) # randomize X to avoid AUCs in [0, 1]
score_sl = sl1.score(X, y)
assert_array_equal(score_sl.shape, [n_time])
assert (score_sl.dtype == float)
# Check that scoring was applied adequately
scoring = make_scorer(roc_auc_score, needs_threshold=True)
score_manual = [
scoring(est, x, y)
for est, x in zip(sl1.estimators_, X.transpose(2, 0, 1))
]
assert_array_equal(score_manual, score_sl)
# n_jobs
sl = SlidingEstimator(logreg, n_jobs=1, scoring='roc_auc')
score_1job = sl.fit(X, y).score(X, y)
sl.n_jobs = 2
score_njobs = sl.fit(X, y).score(X, y)
assert_array_equal(score_1job, score_njobs)
sl.predict(X)
# n_jobs > n_estimators
sl.fit(X[..., [0]], y)
sl.predict(X[..., [0]])
# pipeline
class _LogRegTransformer(LogisticRegression):
# XXX needs transformer in pipeline to get first proba only
def __init__(self):
super(_LogRegTransformer, self).__init__()
self.multi_class = 'ovr'
self.random_state = 0
self.solver = 'liblinear'
def transform(self, X):
return super(_LogRegTransformer, self).predict_proba(X)[..., 1]
pipe = make_pipeline(SlidingEstimator(_LogRegTransformer()), logreg)
pipe.fit(X, y)
pipe.predict(X)
# n-dimensional feature space
X = np.random.rand(10, 3, 4, 2)
y = np.arange(10) % 2
y_preds = list()
for n_jobs in [1, 2]:
pipe = SlidingEstimator(make_pipeline(Vectorizer(), logreg),
n_jobs=n_jobs)
y_preds.append(pipe.fit(X, y).predict(X))
features_shape = pipe.estimators_[0].steps[0][1].features_shape_
assert_array_equal(features_shape, [3, 4])
assert_array_equal(y_preds[0], y_preds[1])
# Bagging classifiers
X = np.random.rand(10, 3, 4)
for n_jobs in (1, 2):
pipe = SlidingEstimator(BaggingClassifier(None, 2), n_jobs=n_jobs)
pipe.fit(X, y)
pipe.score(X, y)
assert (isinstance(pipe.estimators_[0], BaggingClassifier))
def read_ica(fname):
"""Restore ICA solution from fif file.
Parameters
----------
fname : str
Absolute path to fif file containing ICA matrices.
The file name should end with -ica.fif or -ica.fif.gz.
Returns
-------
ica : instance of ICA
The ICA estimator.
"""
# TODO: do I actually need this?
from mne.utils import logger, check_fname
from mne.io.open import fiff_open
from mne.io.meas_info import read_meas_info
from mne.io.tree import dir_tree_find
from mne.io.constants import FIFF
from mne.io.tag import read_tag
from mne.preprocessing.ica import _deserialize
from mne import Covariance
from scipy import linalg
from mne.fixes import _get_args
check_fname(fname, 'ICA', ('-ica.fif', '-ica.fif.gz'))
logger.info('Reading %s ...' % fname)
fid, tree, _ = fiff_open(fname)
try:
# we used to store bads that weren't part of the info...
info, meas = read_meas_info(fid, tree, clean_bads=True)
except ValueError:
logger.info('Could not find the measurement info. \n'
'Functionality requiring the info won\'t be'
' available.')
info = None
ica_data = dir_tree_find(tree, FIFF.FIFFB_MNE_ICA)
if len(ica_data) == 0:
ica_data = dir_tree_find(tree, 123) # Constant 123 Used before v 0.11
if len(ica_data) == 0:
fid.close()
raise ValueError('Could not find ICA data')
my_ica_data = ica_data[0]
for d in my_ica_data['directory']:
kind = d.kind
pos = d.pos
if kind == FIFF.FIFF_MNE_ICA_INTERFACE_PARAMS:
tag = read_tag(fid, pos)
ica_init = tag.data
elif kind == FIFF.FIFF_MNE_ROW_NAMES:
tag = read_tag(fid, pos)
ch_names = tag.data
elif kind == FIFF.FIFF_MNE_ICA_WHITENER:
tag = read_tag(fid, pos)
pre_whitener = tag.data
elif kind == FIFF.FIFF_MNE_ICA_PCA_COMPONENTS:
tag = read_tag(fid, pos)
pca_components = tag.data
elif kind == FIFF.FIFF_MNE_ICA_PCA_EXPLAINED_VAR:
tag = read_tag(fid, pos)
pca_explained_variance = tag.data
elif kind == FIFF.FIFF_MNE_ICA_PCA_MEAN:
tag = read_tag(fid, pos)
pca_mean = tag.data
elif kind == FIFF.FIFF_MNE_ICA_MATRIX:
tag = read_tag(fid, pos)
unmixing_matrix = tag.data
elif kind == FIFF.FIFF_MNE_ICA_BADS:
tag = read_tag(fid, pos)
exclude = tag.data
elif kind == FIFF.FIFF_MNE_ICA_MISC_PARAMS:
tag = read_tag(fid, pos)
ica_misc = tag.data
fid.close()
ica_init, ica_misc = [_deserialize(k) for k in (ica_init, ica_misc)]
current_fit = ica_init.pop('current_fit')
if ica_init['noise_cov'] == Covariance.__name__:
logger.info('Reading whitener drawn from noise covariance ...')
logger.info('Now restoring ICA solution ...')
# make sure dtypes are np.float64 to satisfy fast_dot
def f(x):
return x.astype(np.float64)
ica_init = dict(
(k, v) for k, v in ica_init.items() if k in _get_args(ICA.__init__))
ica = ICA(**ica_init)
ica.current_fit = current_fit
ica.ch_names = ch_names.split(':')
ica._pre_whitener = f(pre_whitener)
ica.pca_mean_ = f(pca_mean)
ica.pca_components_ = f(pca_components)
ica.n_components_ = unmixing_matrix.shape[0]
ica._update_ica_names()
ica.pca_explained_variance_ = f(pca_explained_variance)
ica.unmixing_matrix_ = f(unmixing_matrix)
ica.mixing_matrix_ = linalg.pinv(ica.unmixing_matrix_)
ica.exclude = [] if exclude is None else list(exclude)
ica.info = info
if 'n_samples_' in ica_misc:
ica.n_samples_ = ica_misc['n_samples_']
if 'labels_' in ica_misc:
labels_ = ica_misc['labels_']
if labels_ is not None:
ica.labels_ = labels_
if 'method' in ica_misc:
ica.method = ica_misc['method']
logger.info('Ready.')
return ica
def plot_3d_montage(info,
view_map,
*,
src_det_names='auto',
ch_names='numbered',
subject='fsaverage',
trans='fsaverage',
surface='pial',
subjects_dir=None,
verbose=None):
"""
Plot a 3D sensor montage.
Parameters
----------
info : instance of Info
Measurement info.
view_map : dict
Dict of view (key) to channel-pair-numbers (value) to use when
plotting. Note that, because these get plotted as 1-based channel
*numbers*, the values should be 1-based rather than 0-based.
The keys are of the form:
``'{side}-{view}'``
For views like ``'left-lat'`` or ``'right-frontal'`` where the side
matters.
``'{view}'``
For views like ``'caudal'`` that are along the midline.
See :meth:`mne.viz.Brain.show_view` for ``view`` options, and the
Examples section below for usage examples.
src_det_names : None | dict | str
Source and detector names to use. "auto" (default) will see if the
channel locations correspond to standard 10-20 locations and will
use those if they do (otherwise will act like None). None will use
S1, S2, ..., D1, D2, ..., etc. Can also be an explicit dict mapping,
for example::
src_det_names=dict(S1='Fz', D1='FCz', ...)
ch_names : str | dict | None
If ``'numbered'`` (default), use ``['1', '2', ...]`` for the channel
names, or ``None`` to use ``['S1_D2', 'S2_D1', ...]``. Can also be a
dict to provide a mapping from the ``'S1_D2'``-style names (keys) to
other names, e.g., ``defaultdict(lambda: '')`` will prevent showing
the names altogether.
.. versionadded:: 0.3
subject : str
The subject.
trans : str | Transform
The subjects head<->MRI transform.
surface : str
The FreeSurfer surface name (e.g., 'pial', 'white').
subjects_dir : str
The subjects directory.
%(verbose)s
Returns
-------
figure : matplotlib.figure.Figure
The matplotlib figimage.
Examples
--------
For a Hitachi system with two sets of 12 source-detector arrangements,
one on each side of the head, showing 1-12 on the left and 13-24 on the
right can be accomplished using the following ``view_map``::
>>> view_map = {
... 'left-lat': np.arange(1, 13),
... 'right-lat': np.arange(13, 25),
... }
NIRx typically involves more complicated arrangements. See
:ref:`the 3D tutorial <tut-fnirs-vis-brain-plot-3d-montage>` for
an advanced example that incorporates the ``'caudal'`` view as well.
""" # noqa: E501
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
_validate_type(info, Info, 'info')
_validate_type(view_map, dict, 'views')
_validate_type(src_det_names, (None, dict, str), 'src_det_names')
_validate_type(ch_names, (dict, str, None), 'ch_names')
info = pick_info(info, pick_types(info, fnirs=True, exclude=())[::2])
if isinstance(ch_names, str):
_check_option('ch_names', ch_names, ('numbered', ), extra='when str')
ch_names = {
name.split()[0]: str(ni)
for ni, name in enumerate(info['ch_names'], 1)
}
info['bads'] = []
if isinstance(src_det_names, str):
_check_option('src_det_names',
src_det_names, ('auto', ),
extra='when str')
# Decide if we can map to 10-20 locations
names, pos = zip(
*transform_to_head(make_standard_montage(
'standard_1020')).get_positions()['ch_pos'].items())
pos = np.array(pos, float)
locs = dict()
bad = False
for ch in info['chs']:
name = ch['ch_name']
s_name, d_name = name.split()[0].split('_')
for name, loc in [(s_name, ch['loc'][3:6]),
(d_name, ch['loc'][6:9])]:
if name in locs:
continue
# see if it's close enough
idx = np.where(cdist(loc[np.newaxis], pos)[0] < 1e-3)[0]
if len(idx) < 1:
bad = True
break
# Some are duplicated (e.g., T7+T3) but we can rely on the
# first one being the canonical one
locs[name] = names[idx[0]]
if bad:
break
if bad:
src_det_names = None
logger.info('Could not automatically map source/detector names to '
'10-20 locations.')
else:
src_det_names = locs
logger.info('Source-detector names automatically mapped to 10-20 '
'locations')
head_mri_t = _get_trans(trans, 'head', 'mri')[0]
del trans
views = list()
for key, num in view_map.items():
_validate_type(key, str, f'view_map key {repr(key)}')
_validate_type(num, np.ndarray, f'view_map[{repr(key)}]')
if '-' in key:
hemi, v = key.split('-', maxsplit=1)
hemi = dict(left='lh', right='rh')[hemi]
views.append((hemi, v, num))
else:
views.append(('lh', key, num))
del view_map
size = (400 * len(views), 400)
brain = Brain(subject,
'both',
surface,
views=['lat'] * len(views),
size=size,
background='w',
units='m',
view_layout='horizontal',
subjects_dir=subjects_dir)
with _safe_brain_close(brain):
brain.add_head(dense=False, alpha=0.1)
brain.add_sensors(info,
trans=head_mri_t,
fnirs=['channels', 'pairs', 'sources', 'detectors'])
add_text_kwargs = dict()
if 'render' in _get_args(brain.plotter.add_text):
add_text_kwargs['render'] = False
for col, view in enumerate(views):
plotted = set()
brain.show_view(view[1],
hemi=view[0],
focalpoint=(0, -0.02, 0.02),
distance=0.4,
row=0,
col=col)
brain.plotter.subplot(0, col)
vp = brain.plotter.renderer
for ci in view[2]: # figure out what we need to plot
this_ch = info['chs'][ci - 1]
ch_name = this_ch['ch_name'].split()[0]
s_name, d_name = ch_name.split('_')
needed = [
(ch_names, 'ch_names', ch_name, this_ch['loc'][:3], 12,
'Centered'),
(src_det_names, 'src_det_names', s_name,
this_ch['loc'][3:6], 8, 'Bottom'),
(src_det_names, 'src_det_names', d_name,
this_ch['loc'][6:9], 8, 'Bottom'),
]
for lookup, lname, name, ch_pos, font_size, va in needed:
if name in plotted:
continue
plotted.add(name)
orig_name = name
if lookup is not None:
name = lookup[name]
_validate_type(name, str, f'{lname}[{repr(orig_name)}]')
ch_pos = apply_trans(head_mri_t, ch_pos)
vp.SetWorldPoint(np.r_[ch_pos, 1.])
vp.WorldToDisplay()
ch_pos = (np.array(vp.GetDisplayPoint()[:2]) -
np.array(vp.GetOrigin()))
actor = brain.plotter.add_text(name,
ch_pos,
font_size=font_size,
color=(0., 0., 0.),
**add_text_kwargs)
prop = actor.GetTextProperty()
getattr(prop, f'SetVerticalJustificationTo{va}')()
prop.SetJustificationToCentered()
actor.SetTextProperty(prop)
prop.SetBold(True)
img = brain.screenshot()
return plt.figimage(img, resize=True).figure
