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proj.py
846 lines (706 loc) · 29.2 KB
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proj.py
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Matti Hämäläinen <msh@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Teon Brooks <teon.brooks@gmail.com>
#
# License: BSD (3-clause)
from copy import deepcopy
from itertools import count
from math import sqrt
import numpy as np
from scipy import linalg
from .tree import dir_tree_find
from .tag import find_tag
from .constants import FIFF
from .pick import pick_types
from .write import (write_int, write_float, write_string, write_name_list,
write_float_matrix, end_block, start_block)
from ..utils import logger, verbose, warn, fill_doc
class Projection(dict):
"""Projection vector.
A basic class to proj a meaningful print for projection vectors.
"""
def __repr__(self): # noqa: D105
s = "%s" % self['desc']
s += ", active : %s" % self['active']
s += ", n_channels : %s" % self['data']['ncol']
return "<Projection | %s>" % s
# speed up info copy by taking advantage of mutability
def __deepcopy__(self, memodict):
"""Make a deepcopy."""
cls = self.__class__
result = cls.__new__(cls)
for k, v in self.items():
if k == 'data':
v = v.copy()
v['data'] = v['data'].copy()
result[k] = v
else:
result[k] = v # kind, active, desc, explained_var immutable
return result
@fill_doc
def plot_topomap(self, info, cmap=None, sensors=True,
colorbar=False, res=64, size=1, show=True,
outlines='head', contours=6, image_interp='bilinear',
axes=None, vlim=(None, None), layout=None,
sphere=None, border=0):
"""Plot topographic maps of SSP projections.
Parameters
----------
info : instance of Info | None
The measurement information to use to determine the layout. If both
``info`` and ``layout`` are provided, the layout will take
precedence.
%(proj_topomap_kwargs)s
%(topomap_sphere_auto)s
%(topomap_border)s
Returns
-------
fig : instance of Figure
Figure distributing one image per channel across sensor topography.
Notes
-----
.. versionadded:: 0.15.0
""" # noqa: E501
from ..viz.topomap import plot_projs_topomap
return plot_projs_topomap(self, info, cmap, sensors, colorbar,
res, size, show, outlines,
contours, image_interp, axes, vlim, layout,
sphere=sphere, border=border)
class ProjMixin(object):
"""Mixin class for Raw, Evoked, Epochs.
Notes
-----
This mixin adds a proj attribute as a property to data containers.
It is True if at least one proj is present and all of them are active.
The projs might not be applied yet if data are not preloaded. In
this case it's the _projector attribute that does the job.
If a private _data attribute is present then the projs applied
to it are the ones marked as active.
A proj parameter passed in constructor of raw or epochs calls
apply_proj and hence after the .proj attribute is True.
As soon as you've applied the projs it will stay active in the
remaining pipeline.
The suggested pipeline is proj=True in epochs (it's cheaper than for raw).
When you use delayed SSP in Epochs, projs are applied when you call
get_data() method. They are not applied to the evoked._data unless you call
apply_proj(). The reason is that you want to reject with projs although
it's not stored in proj mode.
"""
@property
def proj(self):
"""Whether or not projections are active."""
return (len(self.info['projs']) > 0 and
all(p['active'] for p in self.info['projs']))
@verbose
def add_proj(self, projs, remove_existing=False, verbose=None):
"""Add SSP projection vectors.
Parameters
----------
projs : list
List with projection vectors.
remove_existing : bool
Remove the projection vectors currently in the file.
%(verbose_meth)s
Returns
-------
self : instance of Raw | Epochs | Evoked
The data container.
"""
if isinstance(projs, Projection):
projs = [projs]
if (not isinstance(projs, list) and
not all(isinstance(p, Projection) for p in projs)):
raise ValueError('Only projs can be added. You supplied '
'something else.')
# mark proj as inactive, as they have not been applied
projs = deactivate_proj(projs, copy=True, verbose=self.verbose)
if remove_existing:
# we cannot remove the proj if they are active
if any(p['active'] for p in self.info['projs']):
raise ValueError('Cannot remove projectors that have '
'already been applied')
self.info['projs'] = projs
else:
self.info['projs'].extend(projs)
# We don't want to add projectors that are activated again.
self.info['projs'] = _uniquify_projs(self.info['projs'],
check_active=False, sort=False)
return self
def apply_proj(self):
"""Apply the signal space projection (SSP) operators to the data.
Returns
-------
self : instance of Raw | Epochs | Evoked
The instance.
Notes
-----
Once the projectors have been applied, they can no longer be
removed. It is usually not recommended to apply the projectors at
too early stages, as they are applied automatically later on
(e.g. when computing inverse solutions).
Hint: using the copy method individual projection vectors
can be tested without affecting the original data.
With evoked data, consider the following example::
projs_a = mne.read_proj('proj_a.fif')
projs_b = mne.read_proj('proj_b.fif')
# add the first, copy, apply and see ...
evoked.add_proj(a).copy().apply_proj().plot()
# add the second, copy, apply and see ...
evoked.add_proj(b).copy().apply_proj().plot()
# drop the first and see again
evoked.copy().del_proj(0).apply_proj().plot()
evoked.apply_proj() # finally keep both
"""
from ..epochs import BaseEpochs
from ..evoked import Evoked
from .base import BaseRaw
if self.info['projs'] is None or len(self.info['projs']) == 0:
logger.info('No projector specified for this dataset. '
'Please consider the method self.add_proj.')
return self
# Exit delayed mode if you apply proj
if isinstance(self, BaseEpochs) and self._do_delayed_proj:
logger.info('Leaving delayed SSP mode.')
self._do_delayed_proj = False
if all(p['active'] for p in self.info['projs']):
logger.info('Projections have already been applied. '
'Setting proj attribute to True.')
return self
_projector, info = setup_proj(deepcopy(self.info), add_eeg_ref=False,
activate=True, verbose=self.verbose)
# let's not raise a RuntimeError here, otherwise interactive plotting
if _projector is None: # won't be fun.
logger.info('The projections don\'t apply to these data.'
' Doing nothing.')
return self
self._projector, self.info = _projector, info
if isinstance(self, (BaseRaw, Evoked)):
if self.preload:
self._data = np.dot(self._projector, self._data)
else: # BaseEpochs
if self.preload:
for ii, e in enumerate(self._data):
self._data[ii] = self._project_epoch(e)
else:
self.load_data() # will automatically apply
logger.info('SSP projectors applied...')
return self
def del_proj(self, idx='all'):
"""Remove SSP projection vector.
.. note:: The projection vector can only be removed if it is inactive
(has not been applied to the data).
Parameters
----------
idx : int | list of int | str
Index of the projector to remove. Can also be "all" (default)
to remove all projectors.
Returns
-------
self : instance of Raw | Epochs | Evoked
The instance.
"""
if isinstance(idx, str) and idx == 'all':
idx = list(range(len(self.info['projs'])))
idx = np.atleast_1d(np.array(idx, int)).ravel()
if any(self.info['projs'][ii]['active'] for ii in idx):
raise ValueError('Cannot remove projectors that have already '
'been applied')
keep = np.ones(len(self.info['projs']))
keep[idx] = False # works with negative indexing and does checks
self.info['projs'] = [p for p, k in zip(self.info['projs'], keep) if k]
return self
@fill_doc
def plot_projs_topomap(self, ch_type=None, layout=None, cmap=None,
sensors=True, colorbar=False, res=64, size=1,
show=True, outlines='head', contours=6,
image_interp='bilinear', axes=None,
vlim=(None, None), sphere=None, extrapolate='box',
border=0):
"""Plot SSP vector.
Parameters
----------
ch_type : 'mag' | 'grad' | 'planar1' | 'planar2' | 'eeg' | None | list
The channel type to plot. For 'grad', the gradiometers are collec-
ted in pairs and the RMS for each pair is plotted. If None
(default), it will return all channel types present. If a list of
ch_types is provided, it will return multiple figures.
layout : object
Deprecated, do not use.
%(proj_topomap_kwargs)s
%(topomap_sphere_auto)s
%(topomap_extrapolate)s
.. versionadded:: 0.20
%(topomap_border)s
Returns
-------
fig : instance of Figure
Figure distributing one image per channel across sensor topography.
"""
if self.info['projs'] is not None or len(self.info['projs']) != 0:
from ..viz.topomap import plot_projs_topomap
fig = plot_projs_topomap(self.info['projs'], self.info, cmap=cmap,
sensors=sensors, colorbar=colorbar,
res=res, size=size, show=show,
outlines=outlines, contours=contours,
image_interp=image_interp, axes=axes,
vlim=vlim, sphere=sphere,
extrapolate=extrapolate, border=border)
else:
raise ValueError("Info is missing projs. Nothing to plot.")
return fig
def _proj_equal(a, b, check_active=True):
"""Test if two projectors are equal."""
equal = ((a['active'] == b['active'] or not check_active) and
a['kind'] == b['kind'] and
a['desc'] == b['desc'] and
a['data']['col_names'] == b['data']['col_names'] and
a['data']['row_names'] == b['data']['row_names'] and
a['data']['ncol'] == b['data']['ncol'] and
a['data']['nrow'] == b['data']['nrow'] and
np.all(a['data']['data'] == b['data']['data']))
return equal
@verbose
def _read_proj(fid, node, verbose=None):
"""Read spatial projections from a FIF file.
Parameters
----------
fid : file
The file descriptor of the open file.
node : tree node
The node of the tree where to look.
%(verbose)s
Returns
-------
projs : list of Projection
The list of projections.
"""
projs = list()
# Locate the projection data
nodes = dir_tree_find(node, FIFF.FIFFB_PROJ)
if len(nodes) == 0:
return projs
tag = find_tag(fid, nodes[0], FIFF.FIFF_NCHAN)
if tag is not None:
global_nchan = int(tag.data)
items = dir_tree_find(nodes[0], FIFF.FIFFB_PROJ_ITEM)
for item in items:
# Find all desired tags in one item
tag = find_tag(fid, item, FIFF.FIFF_NCHAN)
if tag is not None:
nchan = int(tag.data)
else:
nchan = global_nchan
tag = find_tag(fid, item, FIFF.FIFF_DESCRIPTION)
if tag is not None:
desc = tag.data
else:
tag = find_tag(fid, item, FIFF.FIFF_NAME)
if tag is not None:
desc = tag.data
else:
raise ValueError('Projection item description missing')
# XXX : is this useful ?
# tag = find_tag(fid, item, FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST)
# if tag is not None:
# namelist = tag.data
# else:
# raise ValueError('Projection item channel list missing')
tag = find_tag(fid, item, FIFF.FIFF_PROJ_ITEM_KIND)
if tag is not None:
kind = int(tag.data)
else:
raise ValueError('Projection item kind missing')
tag = find_tag(fid, item, FIFF.FIFF_PROJ_ITEM_NVEC)
if tag is not None:
nvec = int(tag.data)
else:
raise ValueError('Number of projection vectors not specified')
tag = find_tag(fid, item, FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST)
if tag is not None:
names = tag.data.split(':')
else:
raise ValueError('Projection item channel list missing')
tag = find_tag(fid, item, FIFF.FIFF_PROJ_ITEM_VECTORS)
if tag is not None:
data = tag.data
else:
raise ValueError('Projection item data missing')
tag = find_tag(fid, item, FIFF.FIFF_MNE_PROJ_ITEM_ACTIVE)
if tag is not None:
active = bool(tag.data)
else:
active = False
tag = find_tag(fid, item, FIFF.FIFF_MNE_ICA_PCA_EXPLAINED_VAR)
if tag is not None:
explained_var = tag.data
else:
explained_var = None
# handle the case when data is transposed for some reason
if data.shape[0] == len(names) and data.shape[1] == nvec:
data = data.T
if data.shape[1] != len(names):
raise ValueError('Number of channel names does not match the '
'size of data matrix')
# Use exactly the same fields in data as in a named matrix
one = Projection(kind=kind, active=active, desc=desc,
data=dict(nrow=nvec, ncol=nchan, row_names=None,
col_names=names, data=data),
explained_var=explained_var)
projs.append(one)
if len(projs) > 0:
logger.info(' Read a total of %d projection items:' % len(projs))
for k in range(len(projs)):
if projs[k]['active']:
misc = 'active'
else:
misc = ' idle'
logger.info(' %s (%d x %d) %s'
% (projs[k]['desc'], projs[k]['data']['nrow'],
projs[k]['data']['ncol'], misc))
return projs
###############################################################################
# Write
def _write_proj(fid, projs):
"""Write a projection operator to a file.
Parameters
----------
fid : file
The file descriptor of the open file.
projs : dict
The projection operator.
"""
if len(projs) == 0:
return
start_block(fid, FIFF.FIFFB_PROJ)
for proj in projs:
start_block(fid, FIFF.FIFFB_PROJ_ITEM)
write_int(fid, FIFF.FIFF_NCHAN, proj['data']['ncol'])
write_name_list(fid, FIFF.FIFF_PROJ_ITEM_CH_NAME_LIST,
proj['data']['col_names'])
write_string(fid, FIFF.FIFF_NAME, proj['desc'])
write_int(fid, FIFF.FIFF_PROJ_ITEM_KIND, proj['kind'])
if proj['kind'] == FIFF.FIFFV_PROJ_ITEM_FIELD:
write_float(fid, FIFF.FIFF_PROJ_ITEM_TIME, 0.0)
write_int(fid, FIFF.FIFF_PROJ_ITEM_NVEC, proj['data']['nrow'])
write_int(fid, FIFF.FIFF_MNE_PROJ_ITEM_ACTIVE, proj['active'])
write_float_matrix(fid, FIFF.FIFF_PROJ_ITEM_VECTORS,
proj['data']['data'])
if proj['explained_var'] is not None:
write_float(fid, FIFF.FIFF_MNE_ICA_PCA_EXPLAINED_VAR,
proj['explained_var'])
end_block(fid, FIFF.FIFFB_PROJ_ITEM)
end_block(fid, FIFF.FIFFB_PROJ)
###############################################################################
# Utils
def _check_projs(projs, copy=True):
"""Check that projs is a list of Projection."""
if not isinstance(projs, (list, tuple)):
raise TypeError('projs must be a list or tuple, got %s'
% (type(projs),))
for pi, p in enumerate(projs):
if not isinstance(p, Projection):
raise TypeError('All entries in projs list must be Projection '
'instances, but projs[%d] is type %s'
% (pi, type(p)))
return deepcopy(projs) if copy else projs
def make_projector(projs, ch_names, bads=(), include_active=True):
"""Create an SSP operator from SSP projection vectors.
Parameters
----------
projs : list
List of projection vectors.
ch_names : list of str
List of channels to include in the projection matrix.
bads : list of str
Some bad channels to exclude. If bad channels were marked
in the raw file when projs were calculated using mne-python,
they should not need to be included here as they will
have been automatically omitted from the projectors.
include_active : bool
Also include projectors that are already active.
Returns
-------
proj : array of shape [n_channels, n_channels]
The projection operator to apply to the data.
nproj : int
How many items in the projector.
U : array
The orthogonal basis of the projection vectors.
"""
return _make_projector(projs, ch_names, bads, include_active)
def _make_projector(projs, ch_names, bads=(), include_active=True,
inplace=False):
"""Subselect projs based on ch_names and bads.
Use inplace=True mode to modify ``projs`` inplace so that no
warning will be raised next time projectors are constructed with
the given inputs. If inplace=True, no meaningful data are returned.
"""
nchan = len(ch_names)
if nchan == 0:
raise ValueError('No channel names specified')
default_return = (np.eye(nchan, nchan), 0, np.empty((nchan, 0)))
# Check trivial cases first
if projs is None:
return default_return
nvec = 0
nproj = 0
for p in projs:
if not p['active'] or include_active:
nproj += 1
nvec += p['data']['nrow']
if nproj == 0:
return default_return
# Pick the appropriate entries
vecs = np.zeros((nchan, nvec))
nvec = 0
nonzero = 0
bads = set(bads)
for k, p in enumerate(projs):
if not p['active'] or include_active:
if (len(p['data']['col_names']) !=
len(np.unique(p['data']['col_names']))):
raise ValueError('Channel name list in projection item %d'
' contains duplicate items' % k)
# Get the two selection vectors to pick correct elements from
# the projection vectors omitting bad channels
sel = []
vecsel = []
p_set = set(p['data']['col_names']) # faster membership access
for c, name in enumerate(ch_names):
if name not in bads and name in p_set:
sel.append(c)
vecsel.append(p['data']['col_names'].index(name))
# If there is something to pick, pickit
nrow = p['data']['nrow']
this_vecs = vecs[:, nvec:nvec + nrow]
if len(sel) > 0:
this_vecs[sel] = p['data']['data'][:, vecsel].T
# Rescale for better detection of small singular values
for v in range(p['data']['nrow']):
psize = sqrt(np.sum(this_vecs[:, v] * this_vecs[:, v]))
if psize > 0:
orig_n = p['data']['data'].any(axis=0).sum()
# Average ref still works if channels are removed
if len(vecsel) < 0.9 * orig_n and not inplace and \
(p['kind'] != FIFF.FIFFV_PROJ_ITEM_EEG_AVREF or
len(vecsel) == 1):
warn('Projection vector "%s" has magnitude %0.2f '
'(should be unity), applying projector with '
'%s/%s of the original channels available may '
'be dangerous, consider recomputing and adding '
'projection vectors for channels that are '
'eventually used. If this is intentional, '
'consider using info.normalize_proj()'
% (p['desc'], psize, len(vecsel), orig_n))
this_vecs[:, v] /= psize
nonzero += 1
# If doing "inplace" mode, "fix" the projectors to only operate
# on this subset of channels.
if inplace:
p['data']['data'] = this_vecs[sel].T
p['data']['col_names'] = [p['data']['col_names'][ii]
for ii in vecsel]
nvec += p['data']['nrow']
# Check whether all of the vectors are exactly zero
if nonzero == 0 or inplace:
return default_return
# Reorthogonalize the vectors
U, S, V = linalg.svd(vecs[:, :nvec], full_matrices=False)
# Throw away the linearly dependent guys
nproj = np.sum((S / S[0]) > 1e-2)
U = U[:, :nproj]
# Here is the celebrated result
proj = np.eye(nchan, nchan) - np.dot(U, U.T)
if nproj >= nchan: # e.g., 3 channels and 3 projectors
raise RuntimeError('Application of %d projectors for %d channels '
'will yield no components.' % (nproj, nchan))
return proj, nproj, U
def _normalize_proj(info):
"""Normalize proj after subselection to avoid warnings.
This is really only useful for tests, and might not be needed
eventually if we change or improve our handling of projectors
with picks.
"""
# Here we do info.get b/c info can actually be a noise cov
_make_projector(info['projs'], info.get('ch_names', info.get('names')),
info['bads'], include_active=True, inplace=True)
def make_projector_info(info, include_active=True):
"""Make an SSP operator using the measurement info.
Calls make_projector on good channels.
Parameters
----------
info : dict
Measurement info.
include_active : bool
Also include projectors that are already active.
Returns
-------
proj : array of shape [n_channels, n_channels]
The projection operator to apply to the data.
nproj : int
How many items in the projector.
"""
proj, nproj, _ = make_projector(info['projs'], info['ch_names'],
info['bads'], include_active)
return proj, nproj
@verbose
def activate_proj(projs, copy=True, verbose=None):
"""Set all projections to active.
Useful before passing them to make_projector.
Parameters
----------
projs : list
The projectors.
copy : bool
Modify projs in place or operate on a copy.
%(verbose)s
Returns
-------
projs : list
The projectors.
"""
if copy:
projs = deepcopy(projs)
# Activate the projection items
for proj in projs:
proj['active'] = True
logger.info('%d projection items activated' % len(projs))
return projs
@verbose
def deactivate_proj(projs, copy=True, verbose=None):
"""Set all projections to inactive.
Useful before saving raw data without projectors applied.
Parameters
----------
projs : list
The projectors.
copy : bool
Modify projs in place or operate on a copy.
%(verbose)s
Returns
-------
projs : list
The projectors.
"""
if copy:
projs = deepcopy(projs)
# Deactivate the projection items
for proj in projs:
proj['active'] = False
logger.info('%d projection items deactivated' % len(projs))
return projs
@verbose
def make_eeg_average_ref_proj(info, activate=True, verbose=None):
"""Create an EEG average reference SSP projection vector.
Parameters
----------
info : dict
Measurement info.
activate : bool
If True projections are activated.
%(verbose)s
Returns
-------
eeg_proj: instance of Projection
The SSP/PCA projector.
"""
if info.get('custom_ref_applied', False):
raise RuntimeError('A custom reference has been applied to the '
'data earlier. Please use the '
'mne.io.set_eeg_reference function to move from '
'one EEG reference to another.')
logger.info("Adding average EEG reference projection.")
eeg_sel = pick_types(info, meg=False, eeg=True, ref_meg=False,
exclude='bads')
ch_names = info['ch_names']
eeg_names = [ch_names[k] for k in eeg_sel]
n_eeg = len(eeg_sel)
if n_eeg == 0:
raise ValueError('Cannot create EEG average reference projector '
'(no EEG data found)')
vec = np.ones((1, n_eeg))
vec /= n_eeg
explained_var = None
eeg_proj_data = dict(col_names=eeg_names, row_names=None,
data=vec, nrow=1, ncol=n_eeg)
eeg_proj = Projection(active=activate, data=eeg_proj_data,
desc='Average EEG reference',
kind=FIFF.FIFFV_PROJ_ITEM_EEG_AVREF,
explained_var=explained_var)
return eeg_proj
def _has_eeg_average_ref_proj(projs, check_active=False):
"""Determine if a list of projectors has an average EEG ref.
Optionally, set check_active=True to additionally check if the CAR
has already been applied.
"""
for proj in projs:
if (proj['desc'] == 'Average EEG reference' or
proj['kind'] == FIFF.FIFFV_PROJ_ITEM_EEG_AVREF):
if not check_active or proj['active']:
return True
return False
def _needs_eeg_average_ref_proj(info):
"""Determine if the EEG needs an averge EEG reference.
This returns True if no custom reference has been applied and no average
reference projection is present in the list of projections.
"""
eeg_sel = pick_types(info, meg=False, eeg=True, ref_meg=False,
exclude='bads')
return (len(eeg_sel) > 0 and
not info['custom_ref_applied'] and
not _has_eeg_average_ref_proj(info['projs']))
@verbose
def setup_proj(info, add_eeg_ref=True, activate=True, verbose=None):
"""Set up projection for Raw and Epochs.
Parameters
----------
info : dict
The measurement info.
add_eeg_ref : bool
If True, an EEG average reference will be added (unless one
already exists).
activate : bool
If True projections are activated.
%(verbose)s
Returns
-------
projector : array of shape [n_channels, n_channels]
The projection operator to apply to the data.
info : dict
The modified measurement info (Warning: info is modified inplace).
"""
# Add EEG ref reference proj if necessary
if add_eeg_ref and _needs_eeg_average_ref_proj(info):
eeg_proj = make_eeg_average_ref_proj(info, activate=activate)
info['projs'].append(eeg_proj)
# Create the projector
projector, nproj = make_projector_info(info)
if nproj == 0:
if verbose:
logger.info('The projection vectors do not apply to these '
'channels')
projector = None
else:
logger.info('Created an SSP operator (subspace dimension = %d)'
% nproj)
# The projection items have been activated
if activate:
info['projs'] = activate_proj(info['projs'], copy=False)
return projector, info
def _uniquify_projs(projs, check_active=True, sort=True):
"""Make unique projs."""
final_projs = []
for proj in projs: # flatten
if not any(_proj_equal(p, proj, check_active) for p in final_projs):
final_projs.append(proj)
my_count = count(len(final_projs))
def sorter(x):
"""Sort in a nice way."""
digits = [s for s in x['desc'] if s.isdigit()]
if digits:
sort_idx = int(digits[-1])
else:
sort_idx = next(my_count)
return (sort_idx, x['desc'])
return sorted(final_projs, key=sorter) if sort else final_projs