Tutorial 3: Analysing data in Python

You could simply output your data as tab- or comma-separated text files and analyse the data in some spreadsheet package. But the matplotlib library in Python also allows for very neat and simple creation of publication-quality plots.

This script shows you how to use a couple of functions from PsychoPy to open some data files (psychopy.gui.fileOpenDlg()) and create a psychometric function out of some staircase data (psychopy.data.functionFromStaircase()).

Matplotlib is then used to plot the data.

Note

Matplotlib and pylab. Matplotlib is a python library that has similar command syntax to most of the plotting functions in Matlab(tm). In can be imported in different ways; the import pylab line at the beginning of the script is the way to import matploblib as well as a variety of other scientific tools (that aren’t strictly to do with plotting per se).

#This analysis script takes one or more staircase datafiles as input
#from a GUI. It then plots the staircases on top of each other on 
#the left and a combined psychometric function from the same data
#on the right

from psychopy import data, gui, core
from psychopy.tools.filetools import fromFile
import pylab

#Open a dialog box to select files from
files = gui.fileOpenDlg('.')
if not files:
    core.quit()

#get the data from all the files
allIntensities, allResponses = [],[]
for thisFileName in files:
    thisDat = fromFile(thisFileName)
    allIntensities.append( thisDat.intensities )
    allResponses.append( thisDat.data )
    
#plot each staircase
pylab.subplot(121)
colors = 'brgkcmbrgkcm'
lines, names = [],[]
for fileN, thisStair in enumerate(allIntensities):
    #lines.extend(pylab.plot(thisStair))
    #names = files[fileN]
    pylab.plot(thisStair, label=files[fileN])
#pylab.legend()

#get combined data
combinedInten, combinedResp, combinedN = \
             data.functionFromStaircase(allIntensities, allResponses, 5)
#fit curve - in this case using a Weibull function
fit = data.FitFunction('weibullTAFC',combinedInten, combinedResp, \
guess=[0.2, 0.5])
smoothInt = pylab.arange(min(combinedInten), max(combinedInten), 0.001)
smoothResp = fit.eval(smoothInt)
thresh = fit.inverse(0.8)
print thresh

#plot curve
pylab.subplot(122)
pylab.plot(smoothInt, smoothResp, '-')
pylab.plot([thresh, thresh],[0,0.8],'--'); pylab.plot([0, thresh],\
[0.8,0.8],'--')
pylab.title('threshold = %0.3f' %(thresh))
#plot points
pylab.plot(combinedInten, combinedResp, 'o')
pylab.ylim([0,1])

pylab.show()