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source_estimate.py
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4061 lines (3581 loc) · 133 KB
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import contextlib
import copy
import os.path as op
from types import GeneratorType
import numpy as np
from scipy import sparse
from scipy.spatial.distance import cdist, pdist
from ._fiff.constants import FIFF
from ._fiff.meas_info import Info
from ._fiff.pick import _picks_to_idx, pick_types
from ._freesurfer import _get_atlas_values, _get_mri_info_data, read_freesurfer_lut
from .baseline import rescale
from .cov import Covariance
from .evoked import _get_peak
from .filter import FilterMixin, _check_fun, resample
from .fixes import _eye_array, _reshape_view, _safe_svd
from .parallel import parallel_func
from .source_space._source_space import (
SourceSpaces,
_check_volume_labels,
_ensure_src,
_ensure_src_subject,
_get_morph_src_reordering,
_get_src_nn,
get_decimated_surfaces,
)
from .surface import _get_ico_surface, _project_onto_surface, mesh_edges, read_surface
from .transforms import _get_trans, apply_trans
from .utils import (
TimeMixin,
_build_data_frame,
_check_fname,
_check_option,
_check_pandas_index_arguments,
_check_pandas_installed,
_check_preload,
_check_src_normal,
_check_stc_units,
_check_subject,
_check_time_format,
_convert_times,
_ensure_int,
_import_h5io_funcs,
_import_nibabel,
_path_like,
_pl,
_time_mask,
_validate_type,
copy_function_doc_to_method_doc,
fill_doc,
get_subjects_dir,
logger,
object_size,
sizeof_fmt,
verbose,
warn,
)
from .viz import (
plot_source_estimates,
plot_vector_source_estimates,
plot_volume_source_estimates,
)
def _read_stc(filename):
"""Aux Function."""
with open(filename, "rb") as fid:
buf = fid.read()
stc = dict()
offset = 0
num_bytes = 4
# read tmin in ms
stc["tmin"] = (
float(np.frombuffer(buf, dtype=">f4", count=1, offset=offset).item()) / 1000.0
)
offset += num_bytes
# read sampling rate in ms
stc["tstep"] = (
float(np.frombuffer(buf, dtype=">f4", count=1, offset=offset).item()) / 1000.0
)
offset += num_bytes
# read number of vertices/sources
vertices_n = int(np.frombuffer(buf, dtype=">u4", count=1, offset=offset).item())
offset += num_bytes
# read the source vector
stc["vertices"] = np.frombuffer(buf, dtype=">u4", count=vertices_n, offset=offset)
offset += num_bytes * vertices_n
# read the number of timepts
data_n = int(np.frombuffer(buf, dtype=">u4", count=1, offset=offset).item())
offset += num_bytes
if (
vertices_n
and ( # vertices_n can be 0 (empty stc)
(len(buf) // 4 - 4 - vertices_n) % (data_n * vertices_n)
)
!= 0
):
raise ValueError("incorrect stc file size")
# read the data matrix
stc["data"] = np.frombuffer(
buf, dtype=">f4", count=vertices_n * data_n, offset=offset
)
stc["data"] = stc["data"].reshape([data_n, vertices_n]).T
return stc
def _write_stc(filename, tmin, tstep, vertices, data):
"""Write an STC file.
Parameters
----------
filename : path-like
The name of the STC file.
tmin : float
The first time point of the data in seconds.
tstep : float
Time between frames in seconds.
vertices : array of integers
Vertex indices (0 based).
data : 2D array
The data matrix (nvert * ntime).
"""
with open(filename, "wb") as fid:
# write start time in ms
fid.write(np.array(1000 * tmin, dtype=">f4").tobytes())
# write sampling rate in ms
fid.write(np.array(1000 * tstep, dtype=">f4").tobytes())
# write number of vertices
fid.write(np.array(vertices.shape[0], dtype=">u4").tobytes())
# write the vertex indices
fid.write(np.array(vertices, dtype=">u4").tobytes())
# write the number of timepts
fid.write(np.array(data.shape[1], dtype=">u4").tobytes())
# write the data
fid.write(np.array(data.T, dtype=">f4").tobytes())
def _read_3(fid):
"""Read 3 byte integer from file."""
data = np.fromfile(fid, dtype=np.uint8, count=3).astype(np.int32)
out = np.left_shift(data[0], 16) + np.left_shift(data[1], 8) + data[2]
return out
def _read_w(filename):
"""Read a w file.
w files contain activations or source reconstructions for a single time
point.
Parameters
----------
filename : path-like
The name of the w file.
Returns
-------
data: dict
The w structure. It has the following keys:
vertices vertex indices (0 based)
data The data matrix (nvert long)
"""
with open(filename, "rb", buffering=0) as fid: # buffering=0 for np bug
# skip first 2 bytes
fid.read(2)
# read number of vertices/sources (3 byte integer)
vertices_n = int(_read_3(fid))
vertices = np.zeros((vertices_n), dtype=np.int32)
data = np.zeros((vertices_n), dtype=np.float32)
# read the vertices and data
for i in range(vertices_n):
vertices[i] = _read_3(fid)
data[i] = np.fromfile(fid, dtype=">f4", count=1).item()
w = dict()
w["vertices"] = vertices
w["data"] = data
return w
def _write_3(fid, val):
"""Write 3 byte integer to file."""
f_bytes = np.zeros((3), dtype=np.uint8)
f_bytes[0] = (val >> 16) & 255
f_bytes[1] = (val >> 8) & 255
f_bytes[2] = val & 255
fid.write(f_bytes.tobytes())
def _write_w(filename, vertices, data):
"""Write a w file.
w files contain activations or source reconstructions for a single time
point.
Parameters
----------
filename: path-like
The name of the w file.
vertices: array of int
Vertex indices (0 based).
data: 1D array
The data array (nvert).
"""
assert len(vertices) == len(data)
with open(filename, "wb") as fid:
# write 2 zero bytes
fid.write(np.zeros((2), dtype=np.uint8).tobytes())
# write number of vertices/sources (3 byte integer)
vertices_n = len(vertices)
_write_3(fid, vertices_n)
# write the vertices and data
for i in range(vertices_n):
_write_3(fid, vertices[i])
# XXX: without float() endianness is wrong, not sure why
fid.write(np.array(float(data[i]), dtype=">f4").tobytes())
def read_source_estimate(fname, subject=None):
"""Read a source estimate object.
Parameters
----------
fname : path-like
Path to (a) source-estimate file(s).
subject : str | None
Name of the subject the source estimate(s) is (are) from.
It is good practice to set this attribute to avoid combining
incompatible labels and SourceEstimates (e.g., ones from other
subjects). Note that due to file specification limitations, the
subject name isn't saved to or loaded from files written to disk.
Returns
-------
stc : SourceEstimate | VectorSourceEstimate | VolSourceEstimate | MixedSourceEstimate
The source estimate object loaded from file.
Notes
-----
- for volume source estimates, ``fname`` should provide the path to a
single file named ``'*-vl.stc``` or ``'*-vol.stc'``
- for surface source estimates, ``fname`` should either provide the
path to the file corresponding to a single hemisphere (``'*-lh.stc'``,
``'*-rh.stc'``) or only specify the asterisk part in these patterns. In
any case, the function expects files for both hemisphere with names
following this pattern.
- for vector surface source estimates, only HDF5 files are supported.
- for mixed source estimates, only HDF5 files are supported.
- for single time point ``.w`` files, ``fname`` should follow the same
pattern as for surface estimates, except that files are named
``'*-lh.w'`` and ``'*-rh.w'``.
""" # noqa: E501
fname_arg = fname
# expand `~` without checking whether the file actually exists – we'll
# take care of that later, as it's complicated by the different suffixes
# STC files can have
fname = str(_check_fname(fname=fname, overwrite="read", must_exist=False))
# make sure corresponding file(s) can be found
ftype = None
if op.exists(fname):
if fname.endswith(("-vl.stc", "-vol.stc", "-vl.w", "-vol.w")):
ftype = "volume"
elif fname.endswith(".stc"):
ftype = "surface"
if fname.endswith(("-lh.stc", "-rh.stc")):
fname = fname[:-7]
else:
err = (
f"Invalid .stc filename: {fname!r}; needs to end with "
"hemisphere tag ('...-lh.stc' or '...-rh.stc')"
)
raise OSError(err)
elif fname.endswith(".w"):
ftype = "w"
if fname.endswith(("-lh.w", "-rh.w")):
fname = fname[:-5]
else:
err = (
f"Invalid .w filename: {fname!r}; needs to end with "
"hemisphere tag ('...-lh.w' or '...-rh.w')"
)
raise OSError(err)
elif fname.endswith(".h5"):
ftype = "h5"
fname = fname[:-3]
else:
raise RuntimeError(f"Unknown extension for file {fname_arg}")
if ftype != "volume":
stc_exist = [op.exists(f) for f in [fname + "-rh.stc", fname + "-lh.stc"]]
w_exist = [op.exists(f) for f in [fname + "-rh.w", fname + "-lh.w"]]
if all(stc_exist) and ftype != "w":
ftype = "surface"
elif all(w_exist):
ftype = "w"
elif op.exists(fname + ".h5"):
ftype = "h5"
elif op.exists(fname + "-stc.h5"):
ftype = "h5"
fname += "-stc"
elif any(stc_exist) or any(w_exist):
raise OSError(f"Hemisphere missing for {fname_arg!r}")
else:
raise OSError(f"SourceEstimate File(s) not found for: {fname_arg!r}")
# read the files
if ftype == "volume": # volume source space
if fname.endswith(".stc"):
kwargs = _read_stc(fname)
elif fname.endswith(".w"):
kwargs = _read_w(fname)
kwargs["data"] = kwargs["data"][:, np.newaxis]
kwargs["tmin"] = 0.0
kwargs["tstep"] = 0.0
else:
raise OSError("Volume source estimate must end with .stc or .w")
kwargs["vertices"] = [kwargs["vertices"]]
elif ftype == "surface": # stc file with surface source spaces
lh = _read_stc(fname + "-lh.stc")
rh = _read_stc(fname + "-rh.stc")
assert lh["tmin"] == rh["tmin"]
assert lh["tstep"] == rh["tstep"]
kwargs = lh.copy()
kwargs["data"] = np.r_[lh["data"], rh["data"]]
kwargs["vertices"] = [lh["vertices"], rh["vertices"]]
elif ftype == "w": # w file with surface source spaces
lh = _read_w(fname + "-lh.w")
rh = _read_w(fname + "-rh.w")
kwargs = lh.copy()
kwargs["data"] = np.atleast_2d(np.r_[lh["data"], rh["data"]]).T
kwargs["vertices"] = [lh["vertices"], rh["vertices"]]
# w files only have a single time point
kwargs["tmin"] = 0.0
kwargs["tstep"] = 1.0
ftype = "surface"
elif ftype == "h5":
read_hdf5, _ = _import_h5io_funcs()
kwargs = read_hdf5(fname + ".h5", title="mnepython")
ftype = kwargs.pop("src_type", "surface")
if isinstance(kwargs["vertices"], np.ndarray):
kwargs["vertices"] = [kwargs["vertices"]]
if ftype != "volume":
# Make sure the vertices are ordered
vertices = kwargs["vertices"]
if any(np.any(np.diff(v.astype(int)) <= 0) for v in vertices):
sidx = [np.argsort(verts) for verts in vertices]
vertices = [verts[idx] for verts, idx in zip(vertices, sidx)]
data = kwargs["data"][np.r_[sidx[0], len(sidx[0]) + sidx[1]]]
kwargs["vertices"] = vertices
kwargs["data"] = data
if "subject" not in kwargs:
kwargs["subject"] = subject
if subject is not None and subject != kwargs["subject"]:
raise RuntimeError(
f'provided subject name "{subject}" does not match '
f'subject name from the file "{kwargs["subject"]}'
)
if ftype in ("volume", "discrete"):
klass = VolVectorSourceEstimate
elif ftype == "mixed":
klass = MixedVectorSourceEstimate
else:
assert ftype == "surface"
klass = VectorSourceEstimate
if kwargs["data"].ndim < 3:
klass = klass._scalar_class
return klass(**kwargs)
def _get_src_type(src, vertices, warn_text=None):
src_type = None
if src is None:
if warn_text is None:
warn("src should not be None for a robust guess of stc type.")
else:
warn(warn_text)
if isinstance(vertices, list) and len(vertices) == 2:
src_type = "surface"
elif (
isinstance(vertices, np.ndarray)
or isinstance(vertices, list)
and len(vertices) == 1
):
src_type = "volume"
elif isinstance(vertices, list) and len(vertices) > 2:
src_type = "mixed"
else:
src_type = src.kind
assert src_type in ("surface", "volume", "mixed", "discrete")
return src_type
def _make_stc(
data,
vertices,
src_type=None,
tmin=None,
tstep=None,
subject=None,
vector=False,
source_nn=None,
warn_text=None,
):
"""Generate a surface, vector-surface, volume or mixed source estimate."""
def guess_src_type():
return _get_src_type(src=None, vertices=vertices, warn_text=warn_text)
src_type = guess_src_type() if src_type is None else src_type
if vector and src_type == "surface" and source_nn is None:
raise RuntimeError("No source vectors supplied.")
# infer Klass from src_type
if src_type == "surface":
Klass = VectorSourceEstimate if vector else SourceEstimate
elif src_type in ("volume", "discrete"):
Klass = VolVectorSourceEstimate if vector else VolSourceEstimate
elif src_type == "mixed":
Klass = MixedVectorSourceEstimate if vector else MixedSourceEstimate
else:
raise ValueError(
"vertices has to be either a list with one or more arrays or an array"
)
# Rotate back for vector source estimates
if vector:
n_vertices = sum(len(v) for v in vertices)
assert data.shape[0] in (n_vertices, n_vertices * 3)
if len(data) == n_vertices:
assert src_type == "surface" # should only be possible for this
assert source_nn.shape == (n_vertices, 3)
data = data[:, np.newaxis] * source_nn[:, :, np.newaxis]
else:
data = data.reshape((-1, 3, data.shape[-1]))
assert source_nn.shape in ((n_vertices, 3, 3), (n_vertices * 3, 3))
# This will be an identity transform for volumes, but let's keep
# the code simple and general and just do the matrix mult
data = np.matmul(
np.transpose(source_nn.reshape(n_vertices, 3, 3), axes=[0, 2, 1]), data
)
return Klass(data=data, vertices=vertices, tmin=tmin, tstep=tstep, subject=subject)
def _verify_source_estimate_compat(a, b):
"""Make sure two SourceEstimates are compatible for arith. operations."""
compat = False
if type(a) is not type(b):
raise ValueError(f"Cannot combine {type(a)} and {type(b)}.")
if len(a.vertices) == len(b.vertices):
if all(np.array_equal(av, vv) for av, vv in zip(a.vertices, b.vertices)):
compat = True
if not compat:
raise ValueError(
"Cannot combine source estimates that do not have "
"the same vertices. Consider using stc.expand()."
)
if a.subject != b.subject:
raise ValueError(
"source estimates do not have the same subject "
f"names, {repr(a.subject)} and {repr(b.subject)}"
)
class _BaseSourceEstimate(TimeMixin, FilterMixin):
_data_ndim = 2
@verbose
def __init__(self, data, vertices, tmin, tstep, subject=None, verbose=None):
assert hasattr(self, "_data_ndim"), self.__class__.__name__
assert hasattr(self, "_src_type"), self.__class__.__name__
assert hasattr(self, "_src_count"), self.__class__.__name__
kernel, sens_data = None, None
if isinstance(data, tuple):
if len(data) != 2:
raise ValueError("If data is a tuple it has to be length 2")
kernel, sens_data = data
data = None
if kernel.shape[1] != sens_data.shape[0]:
raise ValueError(
f"kernel ({kernel.shape}) and sens_data ({sens_data.shape}) "
"have invalid dimensions"
)
if sens_data.ndim != 2:
raise ValueError(
"The sensor data must have 2 dimensions, got {sens_data.ndim}"
)
_validate_type(vertices, list, "vertices")
if self._src_count is not None:
if len(vertices) != self._src_count:
raise ValueError(
f"vertices must be a list with {self._src_count} entries, "
f"got {len(vertices)}."
)
vertices = [np.array(v, np.int64) for v in vertices] # makes copy
if any(np.any(np.diff(v) <= 0) for v in vertices):
raise ValueError("Vertices must be ordered in increasing order.")
n_src = sum([len(v) for v in vertices])
# safeguard the user against doing something silly
if data is not None:
if data.ndim not in (self._data_ndim, self._data_ndim - 1):
raise ValueError(
f"Data (shape {data.shape}) must have {self._data_ndim} "
f"dimensions for {self.__class__.__name__}"
)
if data.shape[0] != n_src:
raise ValueError(
f"Number of vertices ({n_src}) and stc.data.shape[0] "
f"({data.shape[0]}) must match"
)
if self._data_ndim == 3:
if data.shape[1] != 3:
raise ValueError(
"Data for VectorSourceEstimate must have "
f"shape[1] == 3, got shape {data.shape}"
)
if data.ndim == self._data_ndim - 1: # allow upbroadcasting
data = data[..., np.newaxis]
self._data = data
self._tmin = tmin
self._tstep = tstep
self.vertices = vertices
self._kernel = kernel
self._sens_data = sens_data
self._kernel_removed = False
self._times = None
self._update_times()
self.subject = _check_subject(None, subject, raise_error=False)
def __repr__(self): # noqa: D105
s = f"{sum(len(v) for v in self.vertices)} vertices"
if self.subject is not None:
s += f", subject : {self.subject}"
s += ", tmin : %s (ms)" % (1e3 * self.tmin)
s += ", tmax : %s (ms)" % (1e3 * self.times[-1])
s += ", tstep : %s (ms)" % (1e3 * self.tstep)
s += f", data shape : {self.shape}"
sz = sum(object_size(x) for x in (self.vertices + [self.data]))
s += f", ~{sizeof_fmt(sz)}"
return f"<{type(self).__name__} | {s}>"
@fill_doc
def get_peak(
self, tmin=None, tmax=None, mode="abs", vert_as_index=False, time_as_index=False
):
"""Get location and latency of peak amplitude.
Parameters
----------
%(get_peak_parameters)s
Returns
-------
pos : int
The vertex exhibiting the maximum response, either ID or index.
latency : float
The latency in seconds.
"""
stc = self.magnitude() if self._data_ndim == 3 else self
if self._n_vertices == 0:
raise RuntimeError("Cannot find peaks with no vertices")
vert_idx, time_idx, _ = _get_peak(stc.data, self.times, tmin, tmax, mode)
if not vert_as_index:
vert_idx = np.concatenate(self.vertices)[vert_idx]
if not time_as_index:
time_idx = self.times[time_idx]
return vert_idx, time_idx
@verbose
def extract_label_time_course(
self, labels, src, mode="auto", allow_empty=False, verbose=None
):
"""Extract label time courses for lists of labels.
This function will extract one time course for each label. The way the
time courses are extracted depends on the mode parameter.
Parameters
----------
%(labels_eltc)s
%(src_eltc)s
%(mode_eltc)s
%(allow_empty_eltc)s
%(verbose)s
Returns
-------
%(label_tc_el_returns)s
See Also
--------
extract_label_time_course : Extract time courses for multiple STCs.
Notes
-----
%(eltc_mode_notes)s
"""
return extract_label_time_course(
self,
labels,
src,
mode=mode,
return_generator=False,
allow_empty=allow_empty,
verbose=verbose,
)
@verbose
def apply_function(
self, fun, picks=None, dtype=None, n_jobs=None, verbose=None, **kwargs
):
"""Apply a function to a subset of vertices.
%(applyfun_summary_stc)s
Parameters
----------
%(fun_applyfun_stc)s
%(picks_all)s
%(dtype_applyfun)s
%(n_jobs)s Ignored if ``vertice_wise=False`` as the workload
is split across vertices.
%(verbose)s
%(kwargs_fun)s
Returns
-------
self : instance of SourceEstimate
The SourceEstimate object with transformed data.
"""
_check_preload(self, "source_estimate.apply_function")
picks = _picks_to_idx(len(self._data), picks, exclude=(), with_ref_meg=False)
if not callable(fun):
raise ValueError("fun needs to be a function")
data_in = self._data
if dtype is not None and dtype != self._data.dtype:
self._data = self._data.astype(dtype)
# check the dimension of the source estimate data
_check_option("source_estimate.ndim", self._data.ndim, [2, 3])
parallel, p_fun, n_jobs = parallel_func(_check_fun, n_jobs)
if n_jobs == 1:
# modify data inplace to save memory
for idx in picks:
self._data[idx, :] = _check_fun(fun, data_in[idx, :], **kwargs)
else:
# use parallel function
data_picks_new = parallel(
p_fun(fun, data_in[p, :], **kwargs) for p in picks
)
for pp, p in enumerate(picks):
self._data[p, :] = data_picks_new[pp]
return self
@verbose
def apply_baseline(self, baseline=(None, 0), *, verbose=None):
"""Baseline correct source estimate data.
Parameters
----------
%(baseline_stc)s
Defaults to ``(None, 0)``, i.e. beginning of the data until
time point zero.
%(verbose)s
Returns
-------
stc : instance of SourceEstimate
The baseline-corrected source estimate object.
Notes
-----
Baseline correction can be done multiple times.
"""
self.data = rescale(self.data, self.times, baseline, copy=False)
return self
@verbose
def save(self, fname, ftype="h5", *, overwrite=False, verbose=None):
"""Save the full source estimate to an HDF5 file.
Parameters
----------
fname : path-like
The file name to write the source estimate to, should end in
``'-stc.h5'``.
ftype : str
File format to use. Currently, the only allowed values is ``"h5"``.
%(overwrite)s
.. versionadded:: 1.0
%(verbose)s
"""
fname = _check_fname(fname=fname, overwrite=True) # check below
if ftype != "h5":
raise ValueError(
f"{self.__class__.__name__} objects can only be written as HDF5 files."
)
_, write_hdf5 = _import_h5io_funcs()
if fname.suffix != ".h5":
fname = fname.with_name(f"{fname.name}-stc.h5")
fname = _check_fname(fname=fname, overwrite=overwrite)
write_hdf5(
fname,
dict(
vertices=self.vertices,
data=self.data,
tmin=self.tmin,
tstep=self.tstep,
subject=self.subject,
src_type=self._src_type,
),
title="mnepython",
overwrite=True,
)
@copy_function_doc_to_method_doc(plot_source_estimates)
def plot(
self,
subject=None,
surface="inflated",
hemi="lh",
colormap="auto",
time_label="auto",
smoothing_steps=10,
transparent=True,
alpha=1.0,
time_viewer="auto",
*,
subjects_dir=None,
figure=None,
views="auto",
colorbar=True,
clim="auto",
cortex="classic",
size=800,
background="black",
foreground=None,
initial_time=None,
time_unit="s",
backend="auto",
spacing="oct6",
title=None,
show_traces="auto",
src=None,
volume_options=1.0,
view_layout="vertical",
add_data_kwargs=None,
brain_kwargs=None,
verbose=None,
):
brain = plot_source_estimates(
self,
subject,
surface=surface,
hemi=hemi,
colormap=colormap,
time_label=time_label,
smoothing_steps=smoothing_steps,
transparent=transparent,
alpha=alpha,
time_viewer=time_viewer,
subjects_dir=subjects_dir,
figure=figure,
views=views,
colorbar=colorbar,
clim=clim,
cortex=cortex,
size=size,
background=background,
foreground=foreground,
initial_time=initial_time,
time_unit=time_unit,
backend=backend,
spacing=spacing,
title=title,
show_traces=show_traces,
src=src,
volume_options=volume_options,
view_layout=view_layout,
add_data_kwargs=add_data_kwargs,
brain_kwargs=brain_kwargs,
verbose=verbose,
)
return brain
@property
def sfreq(self):
"""Sample rate of the data."""
return 1.0 / self.tstep
@property
def _n_vertices(self):
return sum(len(v) for v in self.vertices)
def _remove_kernel_sens_data_(self):
"""Remove kernel and sensor space data and compute self._data."""
if self._kernel is not None or self._sens_data is not None:
self._kernel_removed = True
self._data = np.dot(self._kernel, self._sens_data)
self._kernel = None
self._sens_data = None
@fill_doc
def crop(self, tmin=None, tmax=None, include_tmax=True):
"""Restrict SourceEstimate to a time interval.
Parameters
----------
tmin : float | None
The first time point in seconds. If None the first present is used.
tmax : float | None
The last time point in seconds. If None the last present is used.
%(include_tmax)s
Returns
-------
stc : instance of SourceEstimate
The cropped source estimate.
"""
mask = _time_mask(
self.times, tmin, tmax, sfreq=self.sfreq, include_tmax=include_tmax
)
self.tmin = self.times[np.where(mask)[0][0]]
if self._kernel is not None and self._sens_data is not None:
self._sens_data = self._sens_data[..., mask]
else:
self.data = self.data[..., mask]
return self # return self for chaining methods
@verbose
def resample(
self,
sfreq,
*,
npad=100,
method="fft",
window="auto",
pad="auto",
n_jobs=None,
verbose=None,
):
"""Resample data.
If appropriate, an anti-aliasing filter is applied before resampling.
See :ref:`resampling-and-decimating` for more information.
Parameters
----------
sfreq : float
New sample rate to use.
npad : int | str
Amount to pad the start and end of the data.
Can also be "auto" to use a padding that will result in
a power-of-two size (can be much faster).
%(method_resample)s
.. versionadded:: 1.7
%(window_resample)s
.. versionadded:: 1.7
%(pad_resample_auto)s
.. versionadded:: 1.7
%(n_jobs)s
%(verbose)s
Returns
-------
stc : instance of SourceEstimate
The resampled source estimate.
Notes
-----
For some data, it may be more accurate to use npad=0 to reduce
artifacts. This is dataset dependent -- check your data!
Note that the sample rate of the original data is inferred from tstep.
"""
from .filter import _check_resamp_noop
o_sfreq = 1.0 / self.tstep
if _check_resamp_noop(sfreq, o_sfreq):
return self
# resampling in sensor instead of source space gives a somewhat
# different result, so we don't allow it
self._remove_kernel_sens_data_()
data = self.data
if data.dtype == np.float32:
data = data.astype(np.float64)
self.data = resample(
data, sfreq, o_sfreq, npad=npad, window=window, n_jobs=n_jobs, method=method
)
# adjust indirectly affected variables
self.tstep = 1.0 / sfreq
return self
@property
def data(self):
"""Numpy array of source estimate data."""
if self._data is None:
# compute the solution the first time the data is accessed and
# remove the kernel and sensor data
self._remove_kernel_sens_data_()
return self._data
@data.setter
def data(self, value):
value = np.asarray(value)
if self._data is not None and value.ndim != self._data.ndim:
raise ValueError(f"Data array should have {self._data.ndim} dimensions.")
n_verts = sum(len(v) for v in self.vertices)
if value.shape[0] != n_verts:
raise ValueError(
"The first dimension of the data array must match the number of "
f"vertices ({value.shape[0]} != {n_verts})."
)
self._data = value
self._update_times()
@property
def shape(self):
"""Shape of the data."""
if self._data is not None:
return self._data.shape
return (self._kernel.shape[0], self._sens_data.shape[1])
@property
def tmin(self):
"""The first timestamp."""
return self._tmin
@tmin.setter
def tmin(self, value):
self._tmin = float(value)
self._update_times()
@property
def tstep(self):
"""The change in time between two consecutive samples (1 / sfreq)."""
return self._tstep
@tstep.setter
def tstep(self, value):
if value <= 0:
raise ValueError(".tstep must be greater than 0.")
self._tstep = float(value)
self._update_times()
@property
def times(self):
"""A timestamp for each sample."""
return self._times
@times.setter
def times(self, value):
raise ValueError(
"You cannot write to the .times attribute directly. "