mne_nirs.statistics.ContrastResults#
- class mne_nirs.statistics.ContrastResults(info, data, design)[source]#
Class containing GLM contrast results.
- Parameters:
- infomne.Info
Info.
- datadict
Dictionary.
- designdataframe
Design matrix.
- Returns:
- glm_estResultsGLM,
Result class.
- Attributes:
ch_names
Return the channel names.
compensation_grade
The current gradient compensation grade.
- data
Methods
copy
()Return a copy of the GLM results.
get_channel_types
([picks, unique, only_data_chs])Get a list of channel type for each channel.
plot_topo
([figsize, sphere])Plot topomap GLM contrast data.
save
(fname[, overwrite])Save GLM results to disk.
scatter
([conditions, exclude_no_interest, ...])Scatter plot of the GLM results.
to_dataframe
([order])Return a tidy dataframe representing the GLM results.
- __contains__(ch_type)#
Check channel type membership.
- Parameters:
- ch_typestr
Channel type to check for. Can be e.g.
'meg'
,'eeg'
,'stim'
, etc.
- Returns:
- inbool
Whether or not the instance contains the given channel type.
Examples
Channel type membership can be tested as:
>>> 'meg' in inst True >>> 'seeg' in inst False
- __hash__ = None#
- property ch_names#
Return the channel names.
- Returns:
- namesarray
The channel names.
- property compensation_grade#
The current gradient compensation grade.
- copy()#
Return a copy of the GLM results.
- Returns:
- instinstance of ResultsGLM
A copy of the object.
- get_channel_types(picks=None, unique=False, only_data_chs=False)#
Get a list of channel type for each channel.
- Parameters:
- picksstr | array-like | 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. Note that channels ininfo['bads']
will be included if their names or indices are explicitly provided.- uniquebool
Whether to return only unique channel types. Default is
False
.- only_data_chsbool
Whether to ignore non-data channels. Default is
False
.
- Returns:
- channel_typeslist
The channel types.
- plot_topo(figsize=(12, 7), sphere=None)[source]#
Plot topomap GLM contrast data.
- Parameters:
- figsizenumbers
TODO: Remove this, how does MNE usually deal with this.
- spherenumbers
As specified in MNE.
- Returns:
- figfigure
Figure of each design matrix component for hbo (top row) and hbr (bottom row).
- save(fname, overwrite=False)#
Save GLM results to disk.
- Parameters:
- fnamestr
The filename to use to write the HDF5 data. Should end in
'glm.h5'
.- overwritebool
If True (default False), overwrite the destination file if it exists.
- scatter(conditions=[], exclude_no_interest=True, axes=None, no_interest=None)#
Scatter plot of the GLM results.
- Parameters:
- conditionslist
List of condition names to plot. By default plots all regressors of interest.
- exclude_no_interestbool
Exclude regressors of no interest from the figure.
- axesAxes
Optional axes on which to plot the data.
- no_interestlist
List of regressors that are of no interest. If none are specified then conditions starting with [“drift”, “constant”, “short”, “Short”] will be excluded.
- Returns:
- pltmatplotlib.Figure
Scatter plot.
- to_dataframe(order=None)#
Return a tidy dataframe representing the GLM results.
- Parameters:
- orderlist
Order in which the rows should be returned by channel name.
- Returns:
- tidypandas.DataFrame
Dataframe containing GLM results.