mne.MixedSourceEstimate¶
-
class
mne.
MixedSourceEstimate
(data, vertices=None, tmin=None, tstep=None, subject=None, verbose=None)[source]¶ Container for mixed surface and volume source estimates.
- Parameters
- data
array
of shape (n_dipoles, n_times) |tuple
, shape (2,) The data in source space. The data can either be a single array or a tuple with two arrays: “kernel” shape (n_vertices, n_sensors) and “sens_data” shape (n_sensors, n_times). In this case, the source space data corresponds to “numpy.dot(kernel, sens_data)”.
- vertices
list
ofarray
Vertex numbers corresponding to the data.
- tminscalar
Time point of the first sample in data.
- tstepscalar
Time step between successive samples in data.
- subject
str
|None
The subject name. While not necessary, it is safer to set the subject parameter to avoid analysis errors.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more).
- data
See also
SourceEstimate
A container for surface source estimates.
VectorSourceEstimate
A container for vector source estimates.
VolSourceEstimate
A container for volume source estimates.
VolVectorSourceEstimate
A container for Volume vector source estimates.
Notes
New in version 0.9.0.
Attributes
A timestamp for each sample.
Numpy array of source estimate data.
Shape of the data.
subject
(str | None) The subject name.
vertices
(list of array) The indices of the dipoles in each source space.
Methods
__add__
(self, a)Add source estimates.
__div__
(self, a)Divide source estimates.
__hash__
(self, /)Return hash(self).
__mul__
(self, a)Multiply source estimates.
__neg__
(self)Negate the source estimate.
__sub__
(self, a)Subtract source estimates.
bin
(self, width[, tstart, tstop, func])Return a source estimate object with data summarized over time bins.
copy
(self)Return copy of source estimate instance.
crop
(self[, tmin, tmax])Restrict SourceEstimate to a time interval.
mean
(self)Make a summary stc file with mean over time points.
plot_surface
(self, src[, subject, surface, …])Plot surface source estimates with PySurfer.
resample
(self, sfreq[, npad, window, …])Resample data.
save
(self, fname[, ftype, verbose])Save the full source estimate to an HDF5 file.
sqrt
(self)Take the square root.
sum
(self)Make a summary stc file with sum over time points.
time_as_index
(self, times[, use_rounding])Convert time to indices.
to_data_frame
(self[, picks, index, …])Export data in tabular structure as a pandas DataFrame.
transform
(self, func[, idx, tmin, tmax, copy])Apply linear transform.
transform_data
(self, func[, idx, tmin_idx, …])Get data after a linear (time) transform has been applied.
-
__hash__
(self, /)¶ Return hash(self).
-
bin
(self, width, tstart=None, tstop=None, func=<function mean at 0x7f870cd47050>)[source]¶ Return a source estimate object with data summarized over time bins.
Time bins of
width
seconds. This method is intended for visualization only. No filter is applied to the data before binning, making the method inappropriate as a tool for downsampling data.- Parameters
- widthscalar
Width of the individual bins in seconds.
- tstartscalar |
None
Time point where the first bin starts. The default is the first time point of the stc.
- tstopscalar |
None
Last possible time point contained in a bin (if the last bin would be shorter than width it is dropped). The default is the last time point of the stc.
- func
callable()
Function that is applied to summarize the data. Needs to accept a numpy.array as first input and an
axis
keyword argument.
- Returns
- stc
SourceEstimate
|VectorSourceEstimate
The binned source estimate.
- stc
-
property
data
¶ Numpy array of source estimate data.
-
mean
(self)[source]¶ Make a summary stc file with mean over time points.
- Returns
- stc
SourceEstimate
|VectorSourceEstimate
The modified stc.
- stc
-
plot_surface
(self, src, subject=None, surface='inflated', hemi='lh', colormap='auto', time_label='time=%02.f ms', smoothing_steps=10, transparent=None, alpha=1.0, time_viewer=False, config_opts=None, subjects_dir=None, figure=None, views='lat', colorbar=True, clim='auto')[source]¶ Plot surface source estimates with PySurfer.
Note: PySurfer currently needs the SUBJECTS_DIR environment variable, which will automatically be set by this function. Plotting multiple SourceEstimates with different values for subjects_dir will cause PySurfer to use the wrong FreeSurfer surfaces when using methods of the returned Brain object. It is therefore recommended to set the SUBJECTS_DIR environment variable or always use the same value for subjects_dir (within the same Python session).
- Parameters
- src
SourceSpaces
The source spaces to plot.
- subject
str
|None
The subject name corresponding to FreeSurfer environment variable SUBJECT. If None stc.subject will be used. If that is None, the environment will be used.
- surface
str
The type of surface (inflated, white etc.).
- hemi
str
, ‘lh’ | ‘rh’ | ‘split’ | ‘both’ The hemisphere to display. Using ‘both’ or ‘split’ requires PySurfer version 0.4 or above.
- colormap
str
|np.ndarray
offloat
, shape(n_colors, 3 | 4) Name of colormap to use. See plot_source_estimates.
- time_label
str
How to print info about the time instant visualized.
- smoothing_steps
int
The amount of smoothing.
- transparentbool |
None
If True, use a linear transparency between fmin and fmid. None will choose automatically based on colormap type.
- alpha
float
Alpha value to apply globally to the overlay.
- time_viewerbool
Display time viewer GUI.
- config_opts
dict
Keyword arguments for Brain initialization. See pysurfer.viz.Brain.
- subjects_dir
str
The path to the FreeSurfer subjects reconstructions. It corresponds to FreeSurfer environment variable SUBJECTS_DIR.
- figureinstance of
mayavi.mlab.Figure
|None
If None, the last figure will be cleaned and a new figure will be created.
- views
str
|list
View to use. See
surfer.Brain
.- colorbarbool
If True, display colorbar on scene.
- clim
str
|dict
Colorbar properties specification. See plot_source_estimates.
- src
- Returns
- braininstance of
surfer.Brain
A instance of
surfer.Brain
from PySurfer.
- braininstance of
-
resample
(self, sfreq, npad='auto', window='boxcar', n_jobs=1, verbose=None)[source]¶ Resample data.
- 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).
- window
str
ortuple
Window to use in resampling. See scipy.signal.resample.
- n_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). Defaults to self.verbose.
- sfreq
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.
-
save
(self, fname, ftype='h5', verbose=None)[source]¶ Save the full source estimate to an HDF5 file.
- Parameters
- fname
str
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”.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). Defaults to self.verbose.
- fname
-
property
sfreq
¶ Sample rate of the data.
-
property
shape
¶ Shape of the data.
-
sqrt
(self)[source]¶ Take the square root.
- Returns
- stcinstance of
SourceEstimate
A copy of the SourceEstimate with sqrt(data).
- stcinstance of
-
sum
(self)[source]¶ Make a summary stc file with sum over time points.
- Returns
- stc
SourceEstimate
|VectorSourceEstimate
The modified stc.
- stc
-
property
times
¶ A timestamp for each sample.
-
property
tmin
¶ The first timestamp.
-
to_data_frame
(self, picks=None, index=None, scaling_time=1000.0, scalings=None, copy=True, start=None, stop=None, long_format=False)[source]¶ Export data in tabular structure as a pandas DataFrame.
Columns and indices will depend on the object being converted. Generally this will include as much relevant information as possible for the data type being converted. This makes it easy to convert data for use in packages that utilize dataframes, such as statsmodels or seaborn.
- Parameters
- picks
str
|list
|slice
|None
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']
) will pick channels of those types, channel name strings (e.g.,['MEG0111', 'MEG2623']
will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels.- index
tuple
ofstr
|None
Column to be used as index for the data. Valid string options are ‘epoch’, ‘time’ and ‘condition’. If None, all three info columns will be included in the table as categorial data.
- scaling_time
float
Scaling to be applied to time units.
- scalings
dict
|None
Scaling to be applied to the channels picked. If None, defaults to
scalings=dict(eeg=1e6, grad=1e13, mag=1e15, misc=1.0)
.- copybool
If true, data will be copied. Else data may be modified in place.
- start
int
|None
If it is a Raw object, this defines a starting index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal.
- stop
int
|None
If it is a Raw object, this defines a stop index for creating the dataframe from a slice. The times will be interpolated from the index and the sampling rate of the signal.
- long_formatbool
If True, the dataframe is returned in long format where each row is one observation of the signal at a unique coordinate of channels, time points, epochs and conditions. The number of factors depends on the data container. For convenience, a ch_type column is added when using this option that will facilitate subsetting the resulting dataframe. Defaults to False.
- picks
- Returns
- dfinstance of
pandas.DataFrame
A dataframe suitable for usage with other statistical/plotting/analysis packages. Column/Index values will depend on the object type being converted, but should be human-readable.
- dfinstance of
-
transform
(self, func, idx=None, tmin=None, tmax=None, copy=False)[source]¶ Apply linear transform.
The transform is applied to each source time course independently.
- Parameters
- func
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is the input data. The first two dimensions of the transformed data should be (i) vertices and (ii) time. See Notes for details.- idx
array
|None
Indices of source time courses for which to compute transform. If None, all time courses are used.
- tmin
float
|int
|None
First time point to include (ms). If None, self.tmin is used.
- tmax
float
|int
|None
Last time point to include (ms). If None, self.tmax is used.
- copybool
If True, return a new instance of SourceEstimate instead of modifying the input inplace.
- func
- Returns
- stcs
SourceEstimate
|VectorSourceEstimate
|list
The transformed stc or, in the case of transforms which yield N-dimensional output (where N > 2), a list of stcs. For a list, copy must be True.
- stcs
Notes
Transforms which yield 3D output (e.g. time-frequency transforms) are valid, so long as the first two dimensions are vertices and time. In this case, the copy parameter must be True and a list of SourceEstimates, rather than a single instance of SourceEstimate, will be returned, one for each index of the 3rd dimension of the transformed data. In the case of transforms yielding 2D output (e.g. filtering), the user has the option of modifying the input inplace (copy = False) or returning a new instance of SourceEstimate (copy = True) with the transformed data.
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
-
transform_data
(self, func, idx=None, tmin_idx=None, tmax_idx=None)[source]¶ Get data after a linear (time) transform has been applied.
The transform is applied to each source time course independently.
- Parameters
- func
callable()
The transform to be applied, including parameters (see, e.g.,
functools.partial()
). The first parameter of the function is the input data. The first return value is the transformed data, remaining outputs are ignored. The first dimension of the transformed data has to be the same as the first dimension of the input data.- idx
array
|None
Indicices of source time courses for which to compute transform. If None, all time courses are used.
- tmin_idx
int
|None
Index of first time point to include. If None, the index of the first time point is used.
- tmax_idx
int
|None
Index of the first time point not to include. If None, time points up to (and including) the last time point are included.
- func
- Returns
- data_t
ndarray
The transformed data.
- data_t
Notes
Applying transforms can be significantly faster if the SourceEstimate object was created using “(kernel, sens_data)”, for the “data” parameter as the transform is applied in sensor space. Inverse methods, e.g., “apply_inverse_epochs”, or “apply_lcmv_epochs” do this automatically (if possible).
-
property
tstep
¶ The change in time between two consecutive samples (1 / sfreq).