pyhrf.paradigm.
Paradigm
(stimOnsets, sessionDurations=None, stimDurations=None)¶delete_condition
(cond)¶from_csv
(csvFile, delim=None)¶Create a Paradigm object from a CSV file which columns are: session, task name, stimulation onset, stimulation duration, [amplitude]
from_session_dict
(d, sessionDurations=None)¶from_spm_mat
(spm_mat_file)¶TODO: handle session durations
get_info
(long=True)¶get_joined_and_rastered
(dt)¶get_joined_durations
()¶For each condition, join stimulus durations of all sessions.
get_joined_durations_dim
()¶For each condition, join stimulus durations of all sessions.
get_joined_onsets
()¶For each condition, join onsets of all sessions.
get_joined_onsets_dim
()¶For each condition, join onsets of all sessions.
get_nb_trials
()¶get_rastered
(dt, tMax=None)¶Return binary sequences of stimulus arrivals. Each stimulus event is approximated to the closest time point on the time grid defined by dt. eg return
{ 'cond1' : [np.array([ 0 0 0 1 0 0 1 1 1 0 1]),
np.array([ 0 1 1 1 0 0 1 0 1 0 0])] },
'cond2' : [np.array([ 0 0 0 1 0 0 1 1 1 0 0]),
np.array([ 1 1 0 1 0 1 0 0 0 0 0])] },
Parameters: |
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get_stimulus_names
()¶get_t_max
()¶join_sessions
()¶save_csv
(csvFile)¶save_spm_mat_for_1st_level_glm
(mat_file, session=0)¶to_nipy_paradigm
()¶pyhrf.paradigm.
check_stim_durations
(stim_onsets, stimDurations)¶If no durations specified (stimDurations is None or empty np.array) then assume spiked stimuli: return a sequence of zeros with same shape as onsets sequence. Check that durations have same shape as onsets.
pyhrf.paradigm.
contrasts_to_spm_vec
(condition_list, contrasts)¶pyhrf.paradigm.
extend_sampled_events
(sampled_events, sampled_durations)¶Add events to encode stimulus duration
pyhrf.paradigm.
merge_onsets
(onsets, new_condition, criterion=None, durations=None, discard=None)¶Convention for definition of onsets or durations.
OrderedDict({
'condition_name': [ <array of timings for sess1>,
<array of timings for sess2>,
...]
}
pyhrf.paradigm.
restarize_events
(events, durations, dt, t_max)¶build a binary sequence of events. Each event start is approximated to the nearest time point on the time grid defined by dt and t_max.