expyriment.design.Trial

class expyriment.design.Trial

A class implementing an experimental trial.

__init__(): Create a Trial.

add_stimulus(stimulus)

Add a stimulus to the trial.

Parameters:

stimulusexpyriment stimulus: stimulus to add (expyriment.stimuli.* object)

Notes

This will add references to stimuli, not copies!

clear_factors(): Clear all factors.

clear_stimuli(): Clear the stimuli.

compare(trial)

Compares this trial with another trail and returns True if all factors associated with both trials are identical. Added stimuli will be ignored for the comparison.

Parameters:

traildesign.Trial

Returns:

identical: boolean

copy(): Return a copy of the trial.

property factor_dict: The dictionary with all factors of the trial.

property factor_names: Getter for factors names.

property factors_as_text: Return all factor names and values as csv string line

find_stimulus(id)

Find the positions of a stimulus.

Parameters:

idint: stimulus id to look for

Returns:

posint: positions as a list or None if not in stimuli list

get_factor(name, return_none_if_not_defined=False)

Get a factor of the trial.

Parameters:

namestr: factor name
return_none_if_not_definedboolean (default: False): suppresses exception and returns None if factor is not defined.

Returns:

factor_valstr or numeric

has_factor(name)

Checks if a factor is defined.

Parameters:

namestr: factor name (str)

Returns:

boolboolean

property id: Getter for id.

order_stimuli(order)

Order the stimuli.

Parameters:

orderlist: list with the new order of positions

preload_stimuli()

Preload all stimuli in trial.

Returns:

timeint: time it took to execute this method in ms

remove_stimulus(position)

Remove stimulus from trial.

Parameters:

positionint: position of the stimulus

set_factor(name, value)

Set a factor for the trial.

Parameters:

namestr: factor name
valuestr or numeric: factor value

shuffle_stimuli(max_repetitions=None, n_segments=None)

Shuffle all stimuli.

Parameters:

max_repetitionsint, optional: see documentation of randomise.shuffle_list, default = None
n_segmentsint, optional: see documentation of randomise.shuffle_list, default = None

Returns:

successbool: randomisation was successful and fulfilled the specified constrains (see max_repetitions)