STYLE: revised coder demos

psychopy/demos/coder/experiment control/JND_staircase_analysis.py

@@ -1,56 +1,59 @@
 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
+from __future__ import division
 
-# 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.
+"""
+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.
 
-# The combined plot uses every unique X value form the staircase, and alters the
-# size of the points according to how many trials were run at that level
+The combined plot uses every unique X value form the staircase, and alters the
+size of the points according to how many trials were run at that level
+"""
 
-from __future__ import division
 from psychopy import data, gui, core
 from psychopy.tools.filetools import fromFile
 import pylab, scipy
 import os
 
 # set to 0.5 for Yes/No (or PSE). Set to 0.8 for a 2AFC threshold
 threshVal = 0.5
+
 # set to zero for Yes/No (or PSE). Set to 0.5 for 2AFC
 expectedMin = 0.0
 
 files = gui.fileOpenDlg('.')
-if not files: 
+if not files:
     core.quit()
 
 # get the data from all the files
 allIntensities, allResponses = [], []
-for thisFileName in files: 
+for thisFileName in files:
     thisDat = fromFile(thisFileName)
     assert isinstance(thisDat, data.StairHandler)
-    allIntensities.append( thisDat.intensities )
-    allResponses.append( thisDat.data )
-dataFolder = os.path.split(thisFileName)[0]  # just the path, excluding file name
+    allIntensities.append(thisDat.intensities)
+    allResponses.append(thisDat.data)
+dataFolder = os.path.split(thisFileName)[0]  # just the path, not file name
 
 # plot each staircase in left hand panel
 pylab.subplot(121)
 colors = 'brgkcmbrgkcm'
 lines, names = [], []
-for fileN, thisStair in enumerate(allIntensities): 
-    # lines.extend(pylab.plot(thisStair))  # uncomment these lines to get a legend for files
+for fileN, thisStair in enumerate(allIntensities):
+    # lines.extend(pylab.plot(thisStair))  # uncomment for a legend for files
     # names = files[fileN]
     pylab.plot(thisStair, label=files[fileN])
 # pylab.legend()
 
 # get combined data
-combinedInten, combinedResp, combinedN = \
-             data.functionFromStaircase(allIntensities, allResponses, bins='unique')
-combinedN = pylab.array(combinedN)  # convert to array so we can do maths with them
+i, r, n = data.functionFromStaircase(allIntensities, allResponses, bins='unique')
+combinedInten, combinedResp, combinedN = i, r, n
+combinedN = pylab.array(combinedN)  # convert to array so we can do maths
 
 # fit curve
-fit = data.FitWeibull(combinedInten, combinedResp, expectedMin=expectedMin, 
-    sems = 1.0/combinedN)
+fit = data.FitWeibull(combinedInten, combinedResp, expectedMin=expectedMin,
+    sems=1.0 / combinedN)
 smoothInt = pylab.arange(min(combinedInten), max(combinedInten), 0.001)
 smoothResp = fit.eval(smoothInt)
 thresh = fit.inverse(threshVal)
@@ -65,8 +68,8 @@
 
 # plot points
 pointSizes = pylab.array(combinedN) * 5  # 5 pixels per trial at each point
-points = pylab.scatter(combinedInten, combinedResp, s=pointSizes, 
-    edgecolors=(0, 0, 0), facecolor=(1, 1, 1), linewidths=1, 
+points = pylab.scatter(combinedInten, combinedResp, s=pointSizes,
+    edgecolors=(0, 0, 0), facecolor=(1, 1, 1), linewidths=1,
     zorder=10,  # make sure the points plot on top of the line
     )
 
@@ -77,6 +80,7 @@
 pylab.savefig(outputFile)
 print('saved figure to: ' + outputFile)
 pylab.show()
+
 core.quit()
 
-# The contents of this file are in the public domain.
+# The contents of this file are in the public domain.

psychopy/demos/coder/experiment control/JND_staircase_exp.py

@@ -3,7 +3,8 @@
 from __future__ import division
 
 """
-measure your JND in orientation using a staircase method"""
+Measure your JND in orientation using a staircase method
+"""
 
 from psychopy import core, visual, gui, data, event
 from psychopy.tools.filetools import fromFile, toFile
@@ -17,9 +18,9 @@
 
 # present a dialogue to change params
 dlg = gui.DlgFromDict(expInfo, title='simple JND Exp', fixed=['date'])
-if dlg.OK: 
+if dlg.OK:
     toFile('lastParams.pickle', expInfo)  # save params to file for next time
-else: 
+else:
     core.quit()  # the user hit cancel so exit
 
 # make a text file to save data
@@ -34,15 +35,18 @@
 foil = visual.GratingStim(win, sf=1, size=4, mask='gauss', ori=expInfo['refOrientation'])
 target = visual.GratingStim(win, sf=1,  size=4, mask='gauss', ori=expInfo['refOrientation'])
 fixation = visual.GratingStim(win, color='black', tex=None, mask='circle', size=0.2)
-message1 = visual.TextStim(win, pos=[0, + 3], text='Hit a key when ready.')
-message2 = visual.TextStim(win, pos=[0, -3], text="Then press left or right to identify the %.1fdegree probe." %(expInfo['refOrientation']))
+message1 = visual.TextStim(win, pos=[0, + 3],
+    text='Hit a key when ready.')
+message2 = visual.TextStim(win, pos=[0, -3],
+    text="Then press left or right to identify the %.1fdegree probe." % expInfo['refOrientation'])
 
 # create the staircase handler
-staircase = data.StairHandler(startVal = 20.0, 
-                          stepType = 'lin', stepSizes=[8, 4, 4, 2, 2, 1, 1],  # reduce step size every two reversals
-                          minVal=0, maxVal=90, 
-                          nUp=1, nDown=3,  # will home in on the 80% threshold
-                          nTrials=50)
+staircase = data.StairHandler(startVal=20.0,
+    stepType='lin',
+    stepSizes=[8, 4, 4, 2, 2, 1, 1],  # reduce step size every two reversals
+    minVal=0, maxVal=90,
+    nUp=1, nDown=3,  # will home in on the 80% threshold
+    nTrials=50)
 
 # display instructions and wait
 message1.draw()
@@ -54,7 +58,7 @@
 
 for thisIncrement in staircase:  # will step through the staircase
     # set location of stimuli
-    targetSide= round(numpy.random.random()) * 2-1  # will be either + 1(right) or -1(left)
+    targetSide = round(numpy.random.random()) * 2 - 1  # +1 = right, -1 = left
     foil.setPos([-5 * targetSide, 0])
     target.setPos([5 * targetSide, 0])  # in other location
 
@@ -74,33 +78,34 @@
     win.flip()
 
     # get response
-    thisResp=None
-    while thisResp is None: 
-        allKeys=event.waitKeys()
-        for thisKey in allKeys: 
-            if (thisKey == 'left' and targetSide == -1) or (thisKey == 'right' and targetSide == 1):
+    thisResp = None
+    while thisResp is None:
+        allKeys = event.waitKeys()
+        for thisKey in allKeys:
+            if ((thisKey == 'left' and targetSide == -1) or
+                (thisKey == 'right' and targetSide == 1)):
                 thisResp = 1  # correct
-            elif (thisKey == 'right' and targetSide == -1) or (thisKey == 'left' and targetSide == 1):
+            elif ((thisKey == 'right' and targetSide == -1) or
+                (thisKey == 'left' and targetSide == 1)):
                 thisResp = 0  # incorrect
             elif thisKey in ['q', 'escape']:
                 core.quit()  # abort experiment
         event.clearEvents('mouse')  # only really needed for pygame windows
 
     # add the data to the staircase so it can calculate the next level
     staircase.addResponse(thisResp)
-    dataFile.write('%i	%.3f	%i\n' %(targetSide, thisIncrement, thisResp))
+    dataFile.write('%i	%.3f	%i\n' % (targetSide, thisIncrement, thisResp))
 
 # staircase has ended
 dataFile.close()
-staircase.saveAsPickle(fileName)  # special python binary file to save all the info
+staircase.saveAsPickle(fileName)  # special python data file to save all the info
 
 # give some output to user
 print('reversals:')
 print(staircase.reversalIntensities)
-print('mean of final 6 reversals = %.3f' %(numpy.average(staircase.reversalIntensities[-6:])))
-
+print('mean of final 6 reversals = %.3f' % numpy.average(staircase.reversalIntensities[-6:]))
 
 win.close()
 core.quit()
 
-# The contents of this file are in the public domain.
+# The contents of this file are in the public domain.

psychopy/demos/coder/experiment control/TrialHandler.py

@@ -2,18 +2,21 @@
 # -*- coding: utf-8 -*-
 from __future__ import division
 
+"""
+Demo of TrialHandler
+"""
+
 from random import random
 from psychopy import data
 
 # create your list of stimuli
 # NB as of version 1.62 you could simply import an excel spreadsheet with this
 # using data.importTrialTypes('someFile.xlsx')
 stimList = []
-for ori in range(90, 180, 30): 
-    for sf in [0.5, 1.0, 2.0]: 
-        stimList.append(
-            {'sf':sf, 'ori':ori}  # this is a python 'dictionary'
-            )
+for ori in range(90, 180, 30):
+    for sf in [0.5, 1.0, 2.0]:
+        # append a python 'dictionary' to the list
+        stimList.append({'sf':sf, 'ori':ori})
 
 # organise them with the trial handler
 trials = data.TrialHandler(stimList, 10, extraInfo= {'participant':"Nobody", 'session':1})
@@ -24,27 +27,27 @@
 nDone = 0
 for thisTrial in trials:  # handler can act like a for loop
     # simulate some data
-    thisReactionTime = random() + float(thisTrial['sf'])/2.0
+    thisReactionTime = random() + float(thisTrial['sf']) / 2.0
     thisChoice = round(random())
     trials.data.add('RT', thisReactionTime)  # add the data to our set
     trials.data.add('choice', thisChoice)
     nDone += 1  # just for a quick reference
 
-    print('trial %i had position %s in the list (sf=%.1f)' \
-          %(nDone, trials.thisIndex, thisTrial['sf']))
+    msg = 'trial %i had position %s in the list (sf=%.1f)'
+    print(msg % (nDone, trials.thisIndex, thisTrial['sf']))
 
 # after the experiment
 print('\n')
 trials.printAsText(stimOut=['sf', 'ori'],  # write summary data to screen
                   dataOut=['RT_mean', 'RT_std', 'choice_raw'])
 trials.saveAsText(fileName='testData',  # also write summary data to a text file
-                  stimOut=['sf', 'ori'], 
+                  stimOut=['sf', 'ori'],
                   dataOut=['RT_mean', 'RT_std', 'choice_raw'])
 trials.saveAsExcel(fileName='testData',  # ...or an xlsx file (which supports sheets)
-                  sheetName = 'rawData', 
-                  stimOut=['sf', 'ori'], 
+                  sheetName = 'rawData',
+                  stimOut=['sf', 'ori'],
                   dataOut=['RT_mean', 'RT_std', 'choice_raw'])
 trials.saveAsPickle(fileName = 'testData')  # this saves a copy of the whole object
 df = trials.saveAsWideText("testDataWide.txt")  # wide is useful for analysis with R or SPSS. Also returns dataframe df
 
-# The contents of this file are in the public domain.
+# The contents of this file are in the public domain.

psychopy/demos/coder/experiment control/autoDraw_autoLog.py

@@ -10,38 +10,42 @@
 stim.setAutoDraw(False) is called. By default a logging message of
 level EXP will be created when the setAutoDraw is called.
 
-This can be turned off for each call with stim with stim.setAutoDraw(True, autoLog=False)
+This can be turned off for each call with stim.setAutoDraw(True, autoLog=False)
 """
 
 win = visual.Window([800, 800])
-stim1 = visual.GratingStim(win, pos=[-0.5, -0.5], name='stim1')  # name is used in log entries
+
+# a stim's name is used in log entries
+stim1 = visual.GratingStim(win, pos=[-0.5, -0.5], name='stim1')
 stim2 = visual.TextStim(win, pos=[0.5, 0.5], text='stim2', name='textStim')
-fixation = visual.GratingStim(win, mask='gauss', tex=None, size=0.02, 
-    name='fixation', autoLog=False)  # no need to log the fixation piont info
 
-fixation.setAutoDraw(True)  # no need to log fixation info
+# no need to log the fixation point info, use autoLog=False
+fixation = visual.GratingStim(win, mask='gauss', tex=None, size=0.02,
+    name='fixation', autoLog=False)
+
+fixation.setAutoDraw(True)
 stim1.setAutoDraw(True)
 stim2.setAutoDraw(True)
 # both on
 for frameN in range(20):  # run 20 frames like this
     win.flip()
 
 stim2.setAutoDraw(False)
-# only stim1 (and fixation)
+# will draw only stim1 (and fixation)
 for frameN in range(20):  # run 20 frames like this
     win.flip()
 
 stim1.setAutoDraw(False)
 stim2.setAutoDraw(True)
-# only stim2 (and fixation)
+# will draw only stim2 (and fixation)
 for frameN in range(20):  # run 20 frames like this
     win.flip()
 
-for stim in [stim1, stim2, fixation]: 
+for stim in [stim1, stim2, fixation]:
     stim.setAutoDraw(False)
 win.flip()  # will cause the 'off' log messages to be sent
 
 win.close()
 core.quit()
 
-# The contents of this file are in the public domain.
+# The contents of this file are in the public domain.

psychopy/demos/coder/experiment control/experimentHandler.py

@@ -2,52 +2,57 @@
 # -*- coding: utf-8 -*-
 from __future__ import division
 
+"""
+Demo of class data.ExperimentHandler
+"""
+
 from psychopy import data, logging
 from numpy import random
 logging.console.setLevel(logging.DEBUG)
 
-exp = data.ExperimentHandler(name='testExp', 
-                version='0.1', 
-                extraInfo={'participant':'jwp', 'ori':45}, 
-                runtimeInfo=None, 
-                originPath=None, 
-                savePickle=True, 
-                saveWideText=True, 
+exp = data.ExperimentHandler(name='testExp',
+                version='0.1',
+                extraInfo={'participant':'jwp', 'ori':45},
+                runtimeInfo=None,
+                originPath=None,
+                savePickle=True,
+                saveWideText=True,
                 dataFileName='testExp')
 
 # a first loop (like training?)
-conds = data.createFactorialTrialList({'faceExpression':['happy', 'sad'], 'presTime':[0.2, 0.3]})
-training = data.TrialHandler(trialList=conds, nReps=3, name='train', 
-                 method='random', 
-                 seed=100)  # this will set the global seed - so fixed for whole exp
+conds = data.createFactorialTrialList(
+    {'faceExpression':['happy', 'sad'], 'presTime':[0.2, 0.3]})
+training = data.TrialHandler(trialList=conds, nReps=3, name='train',
+                 method='random',
+                 seed=100)  # this will set the global seed - for the whole exp
 exp.addLoop(training)
 # run those trials
-for trial in training: 
+for trial in training:
     training.addData('training.rt', random.random() * 0.5 + 0.5)
-    if random.random() > 0.5: 
+    if random.random() > 0.5:
         training.addData('training.key', 'left')
-    else: 
+    else:
         training.addData('training.key', 'right')
     exp.nextEntry()
 
 # then run 3 repeats of a staircase
-outerLoop = data.TrialHandler(trialList=[], nReps=3, name='stairBlock', 
+outerLoop = data.TrialHandler(trialList=[], nReps=3, name='stairBlock',
                  method='random')
 exp.addLoop(outerLoop)
 for thisRep in outerLoop:  # the outer loop doesn't save any data
     staircase = data.StairHandler(startVal=10, name='staircase', nTrials=5)
     exp.addLoop(staircase)
-    for thisTrial in staircase: 
+    for thisTrial in staircase:
         id=random.random()
-        if random.random() > 0.5: 
+        if random.random() > 0.5:
             staircase.addData(1)
-        else: 
+        else:
             staircase.addData(0)
         exp.addData('id', id)
         exp.nextEntry()
-for e in exp.entries: 
+for e in exp.entries:
     print(e)
 print("Done. 'exp' experimentHandler will now (end of script) save data to testExp.csv")
-print(" and also to testExp.psydat, which is 'exp' pickled")
+print(" and also to testExp.psydat, which is a pickled version of `exp`")
 
-# The contents of this file are in the public domain.
+# The contents of this file are in the public domain.