pyhrf.paradigm module¶

class pyhrf.paradigm.Paradigm(stimOnsets, sessionDurations=None, stimDurations=None)¶

delete_condition(cond)¶

classmethod from_csv(csvFile, delim=None)¶

Create a Paradigm object from a CSV file which columns are: session, task name, stimulation onset, stimulation duration, [amplitude]

classmethod from_session_dict(d, sessionDurations=None)¶

classmethod 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:	dt (float) – temporal resolution of the target grid tMax (float) – total duration of the paradigm. If None, then use the session lengths

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.