trftools.pipeline.TRFExperiment

class trftools.pipeline.TRFExperiment(root, **state)

Pipeline for TRF analysis (see also: eelbrain.pipeline.Pipeline)

Setup attributes

stim_var: str = 'stimulus'

Event variable that identifies stimulus files for loading predictors

stim_var is specified as event dataset column name. For example, with:

stim_var = 'stimulus'

and the following event dataset:

>>> print(alice.load_events())
#    i_start   trigger   stimulus  time     SOA      subject   duration
-----------------------------------------------------------------------
0    1863      1         stim_1    3.726    57.618   S01       58.541
1    30672     5         stim_2    61.344   60.898   S01       61.845
...

The model term gammatone-8 will use the predictor files stim_1~gammatone-8 for the first event, and stim_2~gammatone-8 for the second.

Initialization

Parameters:

root (Path | str)

Methods

add_predictor(ds, code[, filter_x, y])

Add predictor variable to a Dataset

add_predictors(ds, model[, filter_x, y])

Add all predictor variables in a given model to a Dataset

copy(temp[, dst_root, inclusive, confirm, ...])

Copy files to a different root folder

expand_template(temp[, keep])

Expand all constant variables in a template

find_keys(temp[, root])

Find all terminal field names that are relevant for a template.

fix_events(ds)

Modify event order or timing

format(string[, vmatch])

Format a string (i.e., replace any '{xxx}' fields with their values)

get(temp[, fmatch, vmatch, match, mkdir, make])

Retrieve a formatted template

get_field_values(field[, exclude])

Find values for a field taking into account exclusion

glob(temp[, inclusive])

Find all files matching a certain pattern

inv_str([ori, snr, method, depth, pick_normal])

Construct inv string from settings; see set_inv()

invalidate(regressor[, backup])

Remove cache and result files when input data becomes invalid

iter([fields, exclude, values, progress_bar])

Cycle the experiment's state through all values on the given fields

iter_range([start, stop, field])

Iterate through a range on a field with ordered values.

iter_temp(temp[, exclude, values])

Iterate through all paths conforming to a template given in temp.

label_events(ds)

Add event labels to events loaded from raw files

label_groups(subject, groups)

Generate Factor for group membership

label_subjects(ds)

Label the subjects in ds

load_annot(**state)

Load a parcellation (from an annot file)

load_bad_channels(**kwargs)

Load bad channels

load_cov(**kwargs)

Load the covariance matrix

load_edf(**kwargs)

Load the edf file ("edf-file" template)

load_epochs([subjects, baseline, ndvar, ...])

Load a Dataset with epochs for a given epoch definition

load_epochs_stc([subjects, baseline, ...])

Load a Dataset with stcs for single epochs

load_epochs_stf([subjects, baseline, mask, ...])

Load frequency space single trial data

load_events([subject, add_bads, data_raw])

Load events from a raw file.

load_evoked([subjects, baseline, ndvar, ...])

Load a Dataset with condition average responses for each subject.

load_evoked_stc([subjects, baseline, ...])

Load evoked source estimates.

load_evoked_stf([subjects, baseline, mask, ...])

Load frequency space evoked data

load_fwd([surf_ori, ndvar, mask])

Load the forward solution

load_ica(**state)

Load the mne-python ICA object

load_induced_stc([subjects, frequencies, ...])

Morlet wavelet induced power and phase in source space.

load_inv([fiff, ndvar, mask])

Load the inverse operator

load_label(label, **kwargs)

Retrieve a label as mne Label object

load_model_test(x[, tstart, tstop, basis, ...])

Test comparing model fit between two models

load_neighbor_correlation([subjects, epoch, ...])

Load sensor neighbor correlation

load_predictor(code[, tstep, n_samples, ...])

Load predictor NDVar

load_predictors(stim, model[, tstep, ...])

Multiple predictors corresponding to model in a list

load_psf([mask])

Load inverse point spread function

load_raw([add_bads, preload, ndvar, ...])

Load a raw file as mne Raw object.

load_raw_stc([mask, morph, ndvar, ...])

Load a raw file as mne Raw object.

load_selected_events([subjects, reject, ...])

Load events and return a subset based on epoch and rejection

load_source_morph(**state)

Load the morph matrix from mrisubject to common_brain

load_src([add_geom, ndvar])

Load the current source space

load_test(test[, tstart, tstop, pmin, parc, ...])

Create and load spatio-temporal cluster test results

load_trf(x[, tstart, tstop, basis, error, ...])

TRF estimated with boosting

load_trf_test(x[, tstart, tstop, basis, ...])

Load TRF test result

load_trfs(subject, x[, tstart, tstop, ...])

Load TRFs for the group in a Dataset (see .load_trf())

make_annot(**state)

Make sure the annot files for both hemispheres exist

make_bad_channels([bad_chs, redo])

Write the bad channel definition file for a raw file

make_bad_channels_auto([flat, redo])

Automatically detect bad channels

make_bad_channels_neighbor_correlation(r[, ...])

Iteratively exclude bad channels based on low average neighbor-correlation

make_copy(temp, field, src, dst[, overwrite])

Make a copy of a file to a new path by substituting one field value

make_cov()

Make a noise covariance (cov) file

make_epoch_selection([samplingrate, data, ...])

Open gui.select_epochs() for manual epoch selection

make_fwd()

Make the forward model

make_ica(**state)

Compute ICA decomposition for a pipeline.RawICA preprocessing step

make_ica_selection([epoch, samplingrate, ...])

Select ICA components to remove through a GUI

make_link(temp, field, src, dst[, redo])

Make a hard link

make_model_test_report(x[, tstart, tstop, ...])

Generate report for model comparison

make_mov_ga_dspm([subjects, baseline, ...])

Make a grand average movie from dSPM values (requires PySurfer 0.6)

make_mov_ttest([subjects, model, c1, c0, p, ...])

Make a t-test movie (requires PySurfer 0.6)

make_mrat_evoked(**kwargs)

Produce the sensor data fiff files needed for MRAT sensor analysis

make_mrat_stcs(**kwargs)

Produce the STC files needed for the MRAT analysis tool

make_plot_annot([surf, redo])

Create a figure for the contents of an annotation file

make_plot_label(label[, surf, redo])

make_plots_labels([surf, redo])

make_predictor(code[, tstep, n_samples, ...])

make_raw(**kwargs)

Make a raw file

make_report(test[, parc, mask, pmin, ...])

Create an HTML report on spatio-temporal clusters

make_report_coreg([file_name])

Create HTML report with plots of the MEG/MRI coregistration

make_report_rois(test[, parc, pmin, tstart, ...])

Create an HTML report on ROI time courses

make_src(**state)

Make the source space

merge_bad_channels()

Merge bad channel definitions for different tasks

move(temp[, dst_root, inclusive, confirm, ...])

Move files to a different root folder

next([field])

Change field to the next value

plot_annot([parc, surf, views, hemi, ...])

Plot the annot file on which the current parcellation is based

plot_brain([common_brain])

Plot the brain model

plot_coregistration([surfaces, meg, dig, ...])

Plot the coregistration (Head shape and MEG helmet)

plot_evoked([subjects, data, separate, ...])

Plot evoked sensor data

plot_label(label[, surf, views, w])

Plot a label

plot_raw([decim, xlim, add_bads, subtract_mean])

Plot raw sensor data

plot_whitened_gfp([s_start, s_stop, run])

Plot the GFP of the whitened evoked to evaluate the the covariance matrix

prune_models([prune_tests])

Remove internal models that have no corresponding files

remove_model(model)

Remove a named model and delete all associated files

rename(old, new[, exclude])

Rename all files corresponding to a pattern (or template)

rename_field(temp, field, old, new[, exclude])

Change the value of one field in paths corresponding to a template

reset()

Reset all field values to the state at initialization

rm(temp[, inclusive, confirm])

Remove all files corresponding to a template

run_mne_analyze([modal])

Run mne_analyze

run_mne_browse_raw([modal])

Run mne_analyze

set([subject, match, allow_asterisk])

Set variable values.

set_inv([ori, snr, method, depth, pick_normal])

Set the type of inverse solution used for source estimation

show_bad_channels([tasks])

List bad channels

show_cached_trfs([model, term, raw_names, ...])

List cached TRFs and how much space they take

show_contamination([threshold, separate, ...])

Table of data exceeding threshold in epochs

show_fields([str_out])

Generate a table for all iterable fields and ther values.

show_file_status(temp[, col, row, count, ...])

Compile a table about the existence of files

show_file_status_mult(files, fields[, ...])

Compile a table about the existence of multiple files

show_in_finder(temp, **kwargs)

Reveal the file corresponding to the temp template in the Finder.

show_input_tree()

Print a tree of the files needed as input

show_localization_test(x, terms[, ...])

Test for localization difference of two terms in a model

show_model_code(model)

show_model_terms(x[, cv])

Table showing terms in a model or comparison

show_model_test(x[, brain_view, axw, surf, ...])

Document section for one or several model tests

show_models([term, stim, rand, model, sort, ...])

List models that contain a term that matches term

show_predictors([pattern])

List predictors that have been added to the TRFExperiment

show_raw_info(**state)

Display the selected pipeline for raw processing

show_reg_params([asds])

Show the covariance matrix regularization parameters

show_rej_info([flagp, asds, bads])

Information about artifact rejection

show_state([temp, empty, hide])

List all top-level fields and their values

show_subjects([raw, mri, mrisubject, ...])

Create a Dataset with subject information

show_tree([root, fields])

Print a tree of the filehierarchy implicit in the templates

show_trf_test(x[, xlim, times, brain_view, ...])

Show mass-univariate test of TRFs

trf_job(x[, priority])

Create a batch-job for computing TRFs