Tutorials¶

Video Tutorial¶

Run in MATLAB¶

Requirement: MATLAB R2016b+

Step 1 - Initialize screen¶

>> stimulus.open

Step 2 - Generate stimulus conditions and queue trials¶

Stimulus trials are generated and queued by the scripts in the +stimulus/+conf directory. You need to know which configuration script needs to be run.

For example, to prepare the grate stimulus, run

>> stimulus.conf.grate

While the stimulus is loaded, you will see a sequence of dots . and asterisks *, which respectively indicate whether the conditions are computed anew or are loaded from the database. Some stimuli take a long time to compute and you might like to run the configuration before you begin the experiment. On subsequent runs, the computed stimuli will be loaded from the database and will not take as long.

Step 3 - Run the stimulus¶

The stimulus must be run for a specific scan in the experiment.Scan table. Table experiment.Scan contains an example entry that can be used for testing. Its primary key is struct('animal_id', 0, 'session', 0, 'scan_idx', 0). During the experiment, the correct scan identification must be provided.

The following command will run the queued stimulus trials for the example scan.

>> stimulus.run(struct('animal_id', 0, 'session', 0, 'scan_idx', 0))

Step 4 - Interrupt and resume the stimulus¶

While the stimulus is playing, you can interrupt with Ctrl+c. The stimulus program will handle this event, cancel the ongoing trial, and clear the screen.

To resume the stimulus, repeat the stimulus.run call above. Or to queue a new set of trials, run the configuration script again.

Step 5 - Exit¶

>> stimulus.close

Run in Python¶

The stimulus configuration and playback are written and executed in MATLAB. However, the control software can be written in Python to reproduce the steps above.

Step 1 - Configure¶

Configure the MATLAB Engine API for Python as described in the MathWorks documentation.

Step 2 - Import packages¶

import matlab.engine as eng
mat = eng.start_matlab()

Step 3 - Initialize screen¶

mat.stimulus.open(nargout=0)            

Step 4 - Initialize conditions and queue trials¶

mat.stimulus.conf.grate(nargout=0)  

Step 5 - Run the stimulus for a specific scan¶

f = mat.stimulus.run(dict(animal_id=0, session=0, scan_idx=0), nargout=0, async=True)

Step 6 - Interrupt and resume stimulus¶

f.cancel()
f = mat.stimulus.run(dict(animal_id=0, session=0, scan_idx=0), nargout=0, async=True)

Step 7 - Exit¶

f.done()  # True if stimulus is done
f.result()  # Waits until the stimulus is done
f.stimulus.close(nargout=0)  # Close the stimulus screen 

Example queries¶

If the language is unspecified below, the queries run in both MATLAB and Python.

All scans with any visual stimuli¶

1	`visualScans = experiment.Scan() & stimulus.Trial()`

All scans with the `Monet` stimulus¶

1	`monetScans = experiment.Scan() & (stimulus.Trial() * stimulus.Monet())`

or

monetScans = experiment.Scan() & (stimulus.Trial() * stimulus.Condition() & 'stimulus_type="stimulus.Monet"')

All unique conditions shown during a given scan¶

## python
session_key = dict(session=7302)
scan_conditions = stimulus.Condition() & (stimulus.Trial() & session_key)

% matlab
sessionKey = struct('session', 7302);
scanConditions = stimulus.Condition & (stimulus.Trial & sessionKey);