expyriment.design.Trial¶

class expyriment.design.Trial[source]¶

A class implementing an experimental trial.

Methods

__init__()[source]¶: Create a Trial.

add_stimulus(stimulus)[source]¶

Add a stimulus to the trial.

Parameters:

stimulus : expyriment stimulus

stimulus to add (expyriment.stimuli.* object)

Notes

This will add references to stimuli, not copies!

clear_factors()[source]¶: Clear all factors.

clear_stimuli()[source]¶: Clear the stimuli.

compare(trial)[source]¶

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.

Returns:	identical: boolean :

copy()[source]¶: Return a copy of the trial.

factor_dict¶: The dictionary with all factors of the trial.

factor_names¶: Getter for factors names.

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

find_stimulus(id)[source]¶

Find the positions of a stimulus.

Parameters:

id : int

stimulus id to look for

Returns:

pos : int

positions as a list or None if not in stimuli list

get_factor(name)[source]¶

Get a factor of the trial.

Parameters:

name : str

factor name

Returns:

factor_val : str or numeric

id¶: Getter for id.

order_stimuli(order)[source]¶

Order the stimuli.

Parameters:

order : list

list with the new order of positions

preload_stimuli()[source]¶

Preload all stimuli in trial.

Returns:

time : int

time it took to execute this method in ms

remove_stimulus(position)[source]¶

Remove stimulus from trial.

Parameters:

position : int

position of the stimulus

set_factor(name, value)[source]¶

Set a factor for the trial.

Parameters:

name : str

factor name

value : str or numeric

factor value

shuffle_stimuli(max_repetitions=None, n_segments=None)[source]¶

Shuffle all stimuli.

Parameters:

max_repetitions : int, optional

see documentation of randomize.shuffle_list, default = None

n_segments : int, optional

see documentation of randomize.shuffle_list, default = None

Returns:

success : bool

randomization was successful and fulfilled the specified constrains (see max_repetitions)