#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import copy
import numpy as np
import pandas as pd
from psychopy import logging
from psychopy.tools.filetools import (openOutputFile, genDelimiter,
genFilenameFromDelimiter)
from .utils import importConditions
from .base import _BaseTrialHandler, DataHandler
class TrialType(dict):
"""This is just like a dict, except that you can access keys with obj.key
"""
def __getattribute__(self, name):
try: # to get attr from dict in normal way (passing self)
return dict.__getattribute__(self, name)
except AttributeError:
try:
return self[name]
except KeyError:
msg = "TrialType has no attribute (or key) \'%s\'"
raise AttributeError(msg % name)
[docs]class TrialHandler(_BaseTrialHandler):
"""Class to handle trial sequencing and data storage.
Calls to .next() will fetch the next trial object given to this handler,
according to the method specified (random, sequential, fullRandom).
Calls will raise a StopIteration error if trials have finished.
See demo_trialHandler.py
The psydat file format is literally just a pickled copy of the
TrialHandler object that saved it. You can open it with::
from psychopy.tools.filetools import fromFile
dat = fromFile(path)
Then you'll find that `dat` has the following attributes that
"""
def __init__(self,
trialList,
nReps,
method='random',
dataTypes=None,
extraInfo=None,
seed=None,
originPath=None,
name='',
autoLog=True):
"""
:Parameters:
trialList: a simple list (or flat array) of dictionaries
specifying conditions. This can be imported from an
excel/csv file using :func:`~psychopy.data.importConditions`
nReps: number of repeats for all conditions
method: *'random',* 'sequential', or 'fullRandom'
'sequential' obviously presents the conditions in the order
they appear in the list. 'random' will result in a shuffle
of the conditions on each repeat, but all conditions
occur once before the second repeat etc. 'fullRandom'
fully randomises the trials across repeats as well,
which means you could potentially run all trials of
one condition before any trial of another.
dataTypes: (optional) list of names for data storage.
e.g. ['corr','rt','resp']. If not provided then these
will be created as needed during calls to
:func:`~psychopy.data.TrialHandler.addData`
extraInfo: A dictionary
This will be stored alongside the data and usually
describes the experiment and subject ID, date etc.
seed: an integer
If provided then this fixes the random number generator to
use the same pattern of trials, by seeding its startpoint
originPath: a string describing the location of the
script / experiment file path. The psydat file format will
store a copy of the experiment if possible. If
`originPath==None` is provided here then the TrialHandler
will still store a copy of the script where it was
created. If `OriginPath==-1` then nothing will be stored.
:Attributes (after creation):
.data - a dictionary (or more strictly, a `DataHandler` sub-
class of a dictionary) of numpy arrays, one for each data
type stored
.trialList - the original list of dicts, specifying the conditions
.thisIndex - the index of the current trial in the original
conditions list
.nTotal - the total number of trials that will be run
.nRemaining - the total number of trials remaining
.thisN - total trials completed so far
.thisRepN - which repeat you are currently on
.thisTrialN - which trial number *within* that repeat
.thisTrial - a dictionary giving the parameters of the current
trial
.finished - True/False for have we finished yet
.extraInfo - the dictionary of extra info as given at beginning
.origin - the contents of the script or builder experiment that
created the handler
"""
self.name = name
self.autoLog = autoLog
if trialList in (None, []): # user wants an empty trialList
# which corresponds to a list with a single empty entry
self.trialList = [None]
# user has hopefully specified a filename
elif isinstance(trialList, str) and os.path.isfile(trialList):
# import conditions from that file
self.trialList = importConditions(trialList)
else:
self.trialList = trialList
# convert any entry in the TrialList into a TrialType object (with
# obj.key or obj[key] access)
for n, entry in enumerate(self.trialList):
if type(entry) == dict:
self.trialList[n] = TrialType(entry)
self.nReps = int(nReps)
self.nTotal = self.nReps * len(self.trialList)
self.nRemaining = self.nTotal # subtract 1 each trial
self.method = method
self.thisRepN = 0 # records which repetition or pass we are on
self.thisTrialN = -1 # records trial number within this repetition
self.thisN = -1
self.thisIndex = 0 # index of current trial in the conditions list
self.thisTrial = []
self.finished = False
self.extraInfo = extraInfo
self.seed = seed
# create dataHandler
self.data = DataHandler(trials=self)
if dataTypes != None:
self.data.addDataType(dataTypes)
self.data.addDataType('ran')
self.data['ran'].mask = False # this is a bool; all entries are valid
self.data.addDataType('order')
# generate stimulus sequence
if self.method in ['random', 'sequential', 'fullRandom']:
self.sequenceIndices = self._createSequence()
else:
self.sequenceIndices = []
self.originPath, self.origin = self.getOriginPathAndFile(originPath)
self._exp = None # the experiment handler that owns me!
def __iter__(self):
return self
def __repr__(self):
"""prints a more verbose version of self as string
"""
return self.__str__(verbose=True)
def __str__(self, verbose=False):
"""string representation of the object
"""
strRepres = 'psychopy.data.{}(\n'.format(self.__class__.__name__)
attribs = dir(self)
# data first, then all others
try:
data = self.data
except Exception:
data = None
if data:
strRepres += str('\tdata=')
strRepres += str(data) + '\n'
method_string = "<class 'method'>"
for thisAttrib in attribs:
# can handle each attribute differently
if method_string in str(type(getattr(self, thisAttrib))):
# this is a method
continue
elif thisAttrib[0] == '_':
# the attrib is private
continue
elif thisAttrib == 'data':
# we handled this first
continue
elif len(str(getattr(self, thisAttrib))) > 20 and not verbose:
# just give type of LONG public attribute
strRepres += str('\t' + thisAttrib + '=')
strRepres += str(type(getattr(self, thisAttrib))) + '\n'
else:
# give the complete contents of attribute
strRepres += str('\t' + thisAttrib + '=')
strRepres += str(getattr(self, thisAttrib)) + '\n'
strRepres += ')'
return strRepres
[docs] def _createSequence(self):
"""Pre-generates the sequence of trial presentations
(for non-adaptive methods). This is called automatically when
the TrialHandler is initialised so doesn't need an explicit call
from the user.
The returned sequence has form indices[stimN][repN]
Example: sequential with 6 trialtypes (rows), 5 reps (cols), returns::
[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]
[5 5 5 5 5]]
These 30 trials will be returned by .next() in the order:
0, 1, 2, 3, 4, 5, 0, 1, 2, ... ... 3, 4, 5
To add a new type of sequence (as of v1.65.02):
- add the sequence generation code here
- adjust "if self.method in [ ...]:" in both __init__ and .next()
- adjust allowedVals in experiment.py -> shows up in DlgLoopProperties
Note that users can make any sequence whatsoever outside of PsychoPy,
and specify sequential order; any order is possible this way.
"""
# create indices for a single rep
indices = np.asarray(self._makeIndices(self.trialList), dtype=int)
rng = np.random.default_rng(seed=self.seed)
if self.method == 'random':
sequenceIndices = []
for thisRep in range(self.nReps):
thisRepSeq = rng.permutation(indices.flat).tolist()
sequenceIndices.append(thisRepSeq)
sequenceIndices = np.transpose(sequenceIndices)
elif self.method == 'sequential':
sequenceIndices = np.repeat(indices, self.nReps, 1)
elif self.method == 'fullRandom':
# indices*nReps, flatten, shuffle, unflatten; only use seed once
sequential = np.repeat(indices, self.nReps, 1) # = sequential
randomFlat = rng.permutation(sequential.flat)
sequenceIndices = np.reshape(
randomFlat, (len(indices), self.nReps))
if self.autoLog:
msg = 'Created sequence: %s, trialTypes=%d, nReps=%i, seed=%s'
vals = (self.method, len(indices), self.nReps, str(self.seed))
logging.exp(msg % vals)
return sequenceIndices
[docs] def _makeIndices(self, inputArray):
"""
Creates an array of tuples the same shape as the input array
where each tuple contains the indices to itself in the array.
Useful for shuffling and then using as a reference.
"""
# make sure its an array of objects (can be strings etc)
inputArray = np.asarray(inputArray, 'O')
# get some simple variables for later
dims = inputArray.shape
dimsProd = np.product(dims)
dimsN = len(dims)
dimsList = list(range(dimsN))
listOfLists = []
# this creates space for an array of any objects
arrayOfTuples = np.ones(dimsProd, 'O')
# for each dimension create list of its indices (using modulo)
for thisDim in dimsList:
prevDimsProd = np.product(dims[:thisDim])
# NB this means modulus in python
thisDimVals = np.arange(dimsProd) / prevDimsProd % dims[thisDim]
listOfLists.append(thisDimVals)
# convert to array
indexArr = np.asarray(listOfLists)
for n in range(dimsProd):
arrayOfTuples[n] = tuple((indexArr[:, n]))
return (np.reshape(arrayOfTuples, dims)).tolist()
def __next__(self):
"""Advances to next trial and returns it.
Updates attributes; thisTrial, thisTrialN and thisIndex
If the trials have ended this method will raise a StopIteration error.
This can be handled with code such as::
trials = data.TrialHandler(.......)
for eachTrial in trials: # automatically stops when done
# do stuff
or::
trials = data.TrialHandler(.......)
while True: # ie forever
try:
thisTrial = trials.next()
except StopIteration: # we got a StopIteration error
break #break out of the forever loop
# do stuff here for the trial
"""
# update pointer for next trials
self.thisTrialN += 1 # number of trial this pass
self.thisN += 1 # number of trial in total
self.nRemaining -= 1
if self.thisTrialN == len(self.trialList):
# start a new repetition
self.thisTrialN = 0
self.thisRepN += 1
if self.thisRepN >= self.nReps:
# all reps complete
self.thisTrial = []
self.finished = True
if self.finished == True:
self._terminate()
# fetch the trial info
if self.method in ('random', 'sequential', 'fullRandom'):
self.thisIndex = self.sequenceIndices[
self.thisTrialN][self.thisRepN]
self.thisTrial = self.trialList[self.thisIndex]
self.data.add('ran', 1)
self.data.add('order', self.thisN)
if self.autoLog:
msg = 'New trial (rep=%i, index=%i): %s'
vals = (self.thisRepN, self.thisTrialN, self.thisTrial)
logging.exp(msg % vals, obj=self.thisTrial)
return self.thisTrial
next = __next__ # allows user to call without a loop `val = trials.next()`
[docs] def getCurrentTrial(self):
"""Returns the condition for the current trial, without
advancing the trials.
"""
return self.trialList[self.thisIndex]
[docs] def getFutureTrial(self, n=1):
"""Returns the condition for n trials into the future,
without advancing the trials. A negative n returns a previous (past)
trial. Returns 'None' if attempting to go beyond the last trial.
"""
# check that we don't go out of bounds for either positive or negative
if n > self.nRemaining or self.thisN + n < 0:
return None
seqs = np.array(self.sequenceIndices).transpose().flat
condIndex = seqs[self.thisN + n]
return self.trialList[condIndex]
[docs] def getEarlierTrial(self, n=-1):
"""Returns the condition information from n trials previously.
Useful for comparisons in n-back tasks. Returns 'None' if trying
to access a trial prior to the first.
"""
# treat positive offset values as equivalent to negative ones:
return self.getFutureTrial(-abs(n))
[docs] def _createOutputArray(self, stimOut, dataOut, delim=None,
matrixOnly=False):
"""Does the leg-work for saveAsText and saveAsExcel.
Combines stimOut with ._parseDataOutput()
"""
if (stimOut == [] and
len(self.trialList) and
hasattr(self.trialList[0], 'keys')):
stimOut = list(self.trialList[0].keys())
# these get added somewhere (by DataHandler?)
if 'n' in stimOut:
stimOut.remove('n')
if 'float' in stimOut:
stimOut.remove('float')
lines = []
# parse the dataout section of the output
dataOut, dataAnal, dataHead = self._createOutputArrayData(dataOut)
if not matrixOnly:
thisLine = []
lines.append(thisLine)
# write a header line
for heading in list(stimOut) + dataHead:
if heading == 'ran_sum':
heading = 'n'
elif heading == 'order_raw':
heading = 'order'
thisLine.append(heading)
# loop through stimuli, writing data
for stimN in range(len(self.trialList)):
thisLine = []
lines.append(thisLine)
# first the params for this stim (from self.trialList)
for heading in stimOut:
thisLine.append(self.trialList[stimN][heading])
# then the data for this stim (from self.data)
for thisDataOut in dataOut:
# make a string version of the data and then format it
tmpData = dataAnal[thisDataOut][stimN]
if hasattr(tmpData, 'tolist'): # is a numpy array
strVersion = str(tmpData.tolist())
# for numeric data replace None with a blank cell
if tmpData.dtype.kind not in ['SaUV']:
strVersion = strVersion.replace('None', '')
elif tmpData in [None, 'None']:
strVersion = ''
else:
strVersion = str(tmpData)
if strVersion == '()':
# 'no data' in masked array should show as "--"
strVersion = "--"
# handle list of values (e.g. rt_raw )
if (len(strVersion) and
strVersion[0] in '[(' and
strVersion[-1] in '])'):
strVersion = strVersion[1:-1] # skip first and last chars
# handle lists of lists (e.g. raw of multiple key presses)
if (len(strVersion) and
strVersion[0] in '[(' and
strVersion[-1] in '])'):
tup = eval(strVersion) # convert back to a tuple
for entry in tup:
# contents of each entry is a list or tuple so keep in
# quotes to avoid probs with delim
thisLine.append(str(entry))
else:
thisLine.extend(strVersion.split(','))
# add self.extraInfo
if (self.extraInfo != None) and not matrixOnly:
lines.append([])
# give a single line of space and then a heading
lines.append(['extraInfo'])
for key, value in list(self.extraInfo.items()):
lines.append([key, value])
return lines
[docs] def _createOutputArrayData(self, dataOut):
"""This just creates the dataOut part of the output matrix.
It is called by _createOutputArray() which creates the header
line and adds the stimOut columns
"""
dataHead = [] # will store list of data headers
dataAnal = dict([]) # will store data that has been analyzed
if type(dataOut) == str:
# don't do list convert or we get a list of letters
dataOut = [dataOut]
elif type(dataOut) != list:
dataOut = list(dataOut)
# expand any 'all' dataTypes to be full list of available dataTypes
allDataTypes = list(self.data.keys())
# treat these separately later
allDataTypes.remove('ran')
# ready to go through standard data types
dataOutNew = []
for thisDataOut in dataOut:
if thisDataOut == 'n':
# n is really just the sum of the ran trials
dataOutNew.append('ran_sum')
continue # no need to do more with this one
# then break into dataType and analysis
dataType, analType = thisDataOut.rsplit('_', 1)
if dataType == 'all':
dataOutNew.extend(
[key + "_" + analType for key in allDataTypes])
if 'order_mean' in dataOutNew:
dataOutNew.remove('order_mean')
if 'order_std' in dataOutNew:
dataOutNew.remove('order_std')
else:
dataOutNew.append(thisDataOut)
dataOut = dataOutNew
# sort so all datatypes come together, rather than all analtypes
dataOut.sort()
# do the various analyses, keeping track of fails (e.g. mean of a
# string)
dataOutInvalid = []
# add back special data types (n and order)
if 'ran_sum' in dataOut:
# move n to the first column
dataOut.remove('ran_sum')
dataOut.insert(0, 'ran_sum')
if 'order_raw' in dataOut:
# move order_raw to the second column
dataOut.remove('order_raw')
dataOut.append('order_raw')
# do the necessary analysis on the data
for thisDataOutN, thisDataOut in enumerate(dataOut):
dataType, analType = thisDataOut.rsplit('_', 1)
if not dataType in self.data:
# that analysis can't be done
dataOutInvalid.append(thisDataOut)
continue
thisData = self.data[dataType]
# set the header
dataHead.append(dataType + '_' + analType)
# analyse thisData using numpy module
if analType in dir(np):
try:
# will fail if we try to take mean of a string for example
if analType == 'std':
thisAnal = np.std(thisData, axis=1, ddof=0)
# normalise by N-1 instead. This should work by
# setting ddof=1 but doesn't as of 08/2010 (because
# of using a masked array?)
N = thisData.shape[1]
if N == 1:
thisAnal *= 0 # prevent a divide-by-zero error
else:
sqrt = np.sqrt
thisAnal = thisAnal * sqrt(N) / sqrt(N - 1)
else:
thisAnal = eval("np.%s(thisData,1)" % analType)
except Exception:
# that analysis doesn't work
dataHead.remove(dataType + '_' + analType)
dataOutInvalid.append(thisDataOut)
continue # to next analysis
elif analType == 'raw':
thisAnal = thisData
else:
raise AttributeError('You can only use analyses from numpy')
# add extra cols to header if necess
if len(thisAnal.shape) > 1:
for n in range(thisAnal.shape[1] - 1):
dataHead.append("")
dataAnal[thisDataOut] = thisAnal
# remove invalid analyses (e.g. average of a string)
for invalidAnal in dataOutInvalid:
dataOut.remove(invalidAnal)
return dataOut, dataAnal, dataHead
[docs] def saveAsWideText(self, fileName,
delim=None,
matrixOnly=False,
appendFile=True,
encoding='utf-8-sig',
fileCollisionMethod='rename'):
"""Write a text file with the session, stimulus, and data values
from each trial in chronological order. Also, return a
pandas DataFrame containing same information as the file.
That is, unlike 'saveAsText' and 'saveAsExcel':
- each row comprises information from only a single trial.
- no summarizing is done (such as collapsing to produce mean and
standard deviation values across trials).
This 'wide' format, as expected by R for creating dataframes, and
various other analysis programs, means that some information must
be repeated on every row.
In particular, if the trialHandler's 'extraInfo' exists, then each
entry in there occurs in every row. In builder, this will include
any entries in the 'Experiment info' field of the
'Experiment settings' dialog. In Coder, this information can be
set using something like::
myTrialHandler.extraInfo = {'SubjID': 'Joan Smith',
'Group': 'Control'}
:Parameters:
fileName:
if extension is not specified, '.csv' will be appended
if the delimiter is ',', else '.tsv' will be appended.
Can include path info.
delim:
allows the user to use a delimiter other than the default
tab ("," is popular with file extension ".csv")
matrixOnly:
outputs the data with no header row.
appendFile:
will add this output to the end of the specified file if
it already exists.
fileCollisionMethod:
Collision method passed to
:func:`~psychopy.tools.fileerrortools.handleFileCollision`
encoding:
The encoding to use when saving a the file.
Defaults to `utf-8-sig`.
"""
if self.thisTrialN < 1 and self.thisRepN < 1:
# if both are < 1 we haven't started
logging.info('TrialHandler.saveAsWideText called but no '
'trials completed. Nothing saved')
return -1
# set default delimiter if none given
if delim is None:
delim = genDelimiter(fileName)
# create the file or send to stdout
fileName = genFilenameFromDelimiter(fileName, delim)
f = openOutputFile(fileName, append=appendFile,
fileCollisionMethod=fileCollisionMethod,
encoding=encoding)
# collect parameter names related to the stimuli:
if self.trialList[0]:
header = list(self.trialList[0].keys())
else:
header = []
# and then add parameter names related to data (e.g. RT)
header.extend(self.data.dataTypes)
# get the extra 'wide' parameter names into the header line:
header.insert(0, "TrialNumber")
# this is wide format, so we want fixed information
# (e.g. subject ID, date, etc) repeated every line if it exists:
if self.extraInfo is not None:
for key in self.extraInfo:
header.insert(0, key)
df = pd.DataFrame(columns=header)
# loop through each trial, gathering the actual values:
dataOut = []
trialCount = 0
# total number of trials = number of trialtypes * number of
# repetitions:
repsPerType = {}
entriesList = []
for rep in range(self.nReps):
for trialN in range(len(self.trialList)):
# find out what trial type was on this trial
trialTypeIndex = self.sequenceIndices[trialN, rep]
# determine which repeat it is for this trial
if trialTypeIndex not in repsPerType:
repsPerType[trialTypeIndex] = 0
else:
repsPerType[trialTypeIndex] += 1
# what repeat are we on for this trial type?
trep = repsPerType[trialTypeIndex]
# create a dictionary representing each trial:
nextEntry = {}
# add a trial number so the original order of the data can
# always be recovered if sorted during analysis:
trialCount += 1
# now collect the value from each trial of vars in header:
for prmName in header:
# the header includes both trial and data variables, so
# need to check before accessing:
tti = trialTypeIndex
if self.trialList[tti] and prmName in self.trialList[tti]:
nextEntry[prmName] = self.trialList[tti][prmName]
elif prmName in self.data:
nextEntry[prmName] = self.data[prmName][tti][trep]
elif self.extraInfo != None and prmName in self.extraInfo:
nextEntry[prmName] = self.extraInfo[prmName]
else:
# allow a null value if this parameter wasn't
# explicitly stored on this trial:
if prmName == "TrialNumber":
nextEntry[prmName] = trialCount
else:
nextEntry[prmName] = ''
# store this trial's data
dataOut.append(nextEntry)
# df = df.append(nextEntry, ignore_index=True)
entriesList.append(nextEntry)
df = pd.concat([df, pd.DataFrame(entriesList)])
if not matrixOnly:
# write the header row:
nextLine = ''
for prmName in header:
nextLine = nextLine + prmName + delim
# remove the final orphaned tab character
f.write(nextLine[:-1] + '\n')
# write the data matrix:
for trial in dataOut:
nextLine = ''
for prmName in header:
nextLine = nextLine + str(trial[prmName]) + delim
# remove the final orphaned tab character
nextLine = nextLine[:-1]
f.write(nextLine + '\n')
if f != sys.stdout:
f.close()
logging.info('saved wide-format data to %s' % f.name)
# Converts numbers to numeric, such as float64, boolean to bool.
# Otherwise they all are "object" type, i.e. strings
# df = df.convert_objects()
return df
[docs] def saveAsJson(self,
fileName=None,
encoding='utf-8',
fileCollisionMethod='rename'):
raise NotImplementedError('Not implemented for TrialHandler.')
[docs] def addData(self, thisType, value, position=None):
"""Add data for the current trial
"""
self.data.add(thisType, value, position=None)
if self.getExp() != None: # update the experiment handler too
self.getExp().addData(thisType, value)
[docs]class TrialHandler2(_BaseTrialHandler):
"""Class to handle trial sequencing and data storage.
Calls to .next() will fetch the next trial object given to this handler,
according to the method specified (random, sequential, fullRandom).
Calls will raise a StopIteration error if trials have finished.
See demo_trialHandler.py
The psydat file format is literally just a pickled copy of the
TrialHandler object that saved it. You can open it with::
from psychopy.tools.filetools import fromFile
dat = fromFile(path)
Then you'll find that `dat` has the following attributes that
"""
def __init__(self,
trialList,
nReps,
method='random',
dataTypes=None,
extraInfo=None,
seed=None,
originPath=None,
name='',
autoLog=True):
"""
:Parameters:
trialList: filename or a simple list (or flat array) of
dictionaries specifying conditions
nReps: number of repeats for all conditions
method: *'random',* 'sequential', or 'fullRandom'
'sequential' obviously presents the conditions in the order
they appear in the list. 'random' will result in a shuffle
of the conditions on each repeat, but all conditions occur
once before the second repeat etc. 'fullRandom' fully
randomises the trials across repeats as well, which means
you could potentially run all trials of one condition
before any trial of another.
dataTypes: (optional) list of names for data storage.
e.g. ['corr','rt','resp']. If not provided then these
will be created as needed during calls to
:func:`~psychopy.data.TrialHandler.addData`
extraInfo: A dictionary
This will be stored alongside the data and usually describes
the experiment and subject ID, date etc.
seed: an integer
If provided then this fixes the random number generator to
use the same pattern of trials, by seeding its startpoint.
originPath: a string describing the location of the script /
experiment file path. The psydat file format will store a
copy of the experiment if possible. If `originPath==None`
is provided here then the TrialHandler will still store a
copy of the script where it was
created. If `OriginPath==-1` then nothing will be stored.
:Attributes (after creation):
.data - a dictionary of numpy arrays, one for each data type
stored
.trialList - the original list of dicts, specifying the conditions
.thisIndex - the index of the current trial in the original
conditions list
.nTotal - the total number of trials that will be run
.nRemaining - the total number of trials remaining
.thisN - total trials completed so far
.thisRepN - which repeat you are currently on
.thisTrialN - which trial number *within* that repeat
.thisTrial - a dictionary giving the parameters of the current
trial
.finished - True/False for have we finished yet
.extraInfo - the dictionary of extra info as given at beginning
.origin - the contents of the script or builder experiment that
created the handler
"""
self.name = name
self.autoLog = autoLog
if trialList in [None, [None], []]: # user wants an empty trialList
# which corresponds to a list with a single empty entry
self.trialList = [None]
self.columns = []
# user has hopefully specified a filename
elif isinstance(trialList, str) and os.path.isfile(trialList):
# import conditions from that file
self.trialList, self.columns = importConditions(
trialList,
returnFieldNames=True)
else:
self.trialList = trialList
self.columns = list(trialList[0].keys())
# convert any entry in the TrialList into a TrialType object (with
# obj.key or obj[key] access)
for n, entry in enumerate(self.trialList):
if type(entry) == dict:
self.trialList[n] = TrialType(entry)
self.nReps = int(nReps)
self.nTotal = self.nReps * len(self.trialList)
self.nRemaining = self.nTotal # subtract 1 each trial
self.remainingIndices = []
self.prevIndices = []
self.method = method
self.thisRepN = 0 # records which repetition or pass we are on
self.thisTrialN = -1 # records trial number within this repetition
self.thisN = -1
self.thisIndex = None # index of current trial in the conditions list
self.thisTrial = {}
self.finished = False
self.extraInfo = extraInfo
self.seed = seed
self._rng = np.random.default_rng(seed=seed)
self._trialAborted = False
# store a list of dicts, convert to pandas DataFrame on access
self._data = []
self.originPath, self.origin = self.getOriginPathAndFile(originPath)
self._exp = None # the experiment handler that owns me!
def __iter__(self):
return self
def __repr__(self):
"""prints a more verbose version of self as string
"""
return self.__str__(verbose=True)
def __str__(self, verbose=False):
"""string representation of the object
"""
strRepres = 'psychopy.data.{}(\n'.format(self.__class__.__name__)
attribs = dir(self)
# data first, then all others
try:
data = self.data
except Exception:
strRepres += '\t(no data)\n'
else:
strRepres += str('\tdata=')
strRepres += str(data) + '\n'
method_string = "<class 'method'>"
for thisAttrib in attribs:
# can handle each attribute differently
if method_string in str(type(getattr(self, thisAttrib))):
# this is a method
continue
elif thisAttrib[0] == '_':
# the attrib is private
continue
elif thisAttrib == 'data':
# we handled this first
continue
elif (len(str(getattr(self, thisAttrib))) > 20 and
not verbose):
# just give type of LONG public attribute
strRepres += str('\t' + thisAttrib + '=')
strRepres += str(type(getattr(self, thisAttrib))) + '\n'
else:
# give the complete contents of attribute
strRepres += str('\t' + thisAttrib + '=')
strRepres += str(getattr(self, thisAttrib)) + '\n'
strRepres += ')'
return strRepres
def __eq__(self, other):
# We want to ignore the RNG object when doing the comparison.
self_copy = copy.deepcopy(self)
other_copy = copy.deepcopy(other)
del self_copy._rng, other_copy._rng
result = super(TrialHandler2, self_copy).__eq__(other_copy)
return result
@property
def data(self):
"""Returns a pandas DataFrame of the trial data so far
Read only attribute - you can't directly modify TrialHandler.data
Note that data are stored internally as a list of dictionaries,
one per trial. These are converted to a DataFrame on access.
"""
return pd.DataFrame(self._data)
def __next__(self):
"""Advances to next trial and returns it.
Updates attributes; thisTrial, thisTrialN and thisIndex
If the trials have ended this method will raise a StopIteration error.
This can be handled with code such as::
trials = data.TrialHandler(.......)
for eachTrial in trials: # automatically stops when done
# do stuff
or::
trials = data.TrialHandler(.......)
while True: # ie forever
try:
thisTrial = trials.next()
except StopIteration: # we got a StopIteration error
break # break out of the forever loop
# do stuff here for the trial
"""
# update pointer for next trials
self.thisTrialN += 1 # number of trial this pass
self.thisN += 1 # number of trial in total
self.nRemaining -= 1
if self.thisIndex is not None:
self.prevIndices.append(self.thisIndex)
# thisRepN has exceeded nReps
if self.remainingIndices == []:
# we've just started, or just starting a new repeat
sequence = list(range(len(self.trialList)))
if (self.method == 'fullRandom' and
self.thisN < (self.nReps * len(self.trialList))):
# we've only just started on a fullRandom sequence
sequence *= self.nReps
# NB permutation *returns* a shuffled array
self.remainingIndices = list(self._rng.permutation(sequence))
elif (self.method in ('sequential', 'random') and
self.thisRepN < self.nReps):
# start a new repetition
self.thisTrialN = 0
self.thisRepN += 1
if self.method == 'random':
self._rng.shuffle(sequence) # shuffle (is in-place)
self.remainingIndices = list(sequence)
else:
# we've finished
self.finished = True
self._terminate() # raises Stop (code won't go beyond here)
# fetch the trial info
if len(self.trialList) == 0:
self.thisIndex = 0
self.thisTrial = {}
else:
self.thisIndex = self.remainingIndices.pop(0)
# if None then use empty dict
thisTrial = self.trialList[self.thisIndex] or {}
self.thisTrial = copy.copy(thisTrial)
# for fullRandom check how many times this has come up before
if self.method == 'fullRandom':
self.thisRepN = self.prevIndices.count(self.thisIndex)
# update data structure with new info
self._data.append(self.thisTrial) # update the data list of dicts
self.addData('thisN', self.thisN)
self.addData('thisTrialN', self.thisTrialN)
self.addData('thisRepN', self.thisRepN)
if self.autoLog:
msg = 'New trial (rep=%i, index=%i): %s'
vals = (self.thisRepN, self.thisTrialN, self.thisTrial)
logging.exp(msg % vals, obj=self.thisTrial)
# if the trial was aborted, reset the flag
self._trialAborted = False
return self.thisTrial
next = __next__ # allows user to call without a loop `val = trials.next()`
@property
def trialAborted(self):
"""`True` if the trial has been aborted an should end.
This flag is reset to `False` on the next call to `next()`.
"""
return self._trialAborted
[docs] def abortCurrentTrial(self, action='random'):
"""Abort the current trial.
Calling this during an experiment replace this trial. The condition
related to the aborted trial will be replaced elsewhere in the session
depending on the `method` in use for sampling conditions.
Parameters
----------
action : str
Action to take with the aborted trial. Can be either of `'random'`,
or `'append'`. The default action is `'random'`.
Notes
-----
* When using `action='random'`, the RNG state for the trial handler is
not used.
"""
# check if value for parameter `action` is valid
if not isinstance(action, str): # type checks for params
raise TypeError(
"Parameter `action` specified incorrect type, must be `str`.")
if action not in ('random', 'append'):
raise ValueError(
"Value for parameter `action` must be either 'random' or "
"'append'.")
# use the appropriate action for the current sampling method
if action == 'random': # insert trial into random index
# use numpy RNG to sample a new index
newIndex = np.random.randint(0, len(self.remainingIndices))
self.remainingIndices.insert(newIndex, self.thisIndex)
elif action == 'append': # insert at end of trial block
self.remainingIndices.append(self.thisIndex)
# flag that the trial has been aborted, user can take approriate action
self._trialAborted = True
[docs] def getFutureTrial(self, n=1):
"""Returns the condition for n trials into the future, without
advancing the trials. Returns 'None' if attempting to go beyond
the last trial.
"""
# check that we don't go out of bounds for either positive or negative
# offsets:
if n > self.nRemaining or self.thisN + n < 0:
return None
seqs = np.array(self.sequenceIndices).transpose().flat
condIndex = seqs[self.thisN + n]
return self.trialList[condIndex]
[docs] def getEarlierTrial(self, n=-1):
"""Returns the condition information from n trials previously.
Useful for comparisons in n-back tasks. Returns 'None' if trying
to access a trial prior to the first.
"""
# treat positive offset values as equivalent to negative ones:
if n > 0:
n = n * -1
return self.getFutureTrial(n)
[docs] def saveAsWideText(self, fileName,
delim=None,
matrixOnly=False,
appendFile=True,
encoding='utf-8-sig',
fileCollisionMethod='rename'):
"""Write a text file with the session, stimulus, and data values
from each trial in chronological order. Also, return a
pandas DataFrame containing same information as the file.
That is, unlike 'saveAsText' and 'saveAsExcel':
- each row comprises information from only a single trial.
- no summarising is done (such as collapsing to produce mean and
standard deviation values across trials).
This 'wide' format, as expected by R for creating dataframes, and
various other analysis programs, means that some information must
be repeated on every row.
In particular, if the trialHandler's 'extraInfo' exists, then each
entry in there occurs in every row. In builder, this will include
any entries in the 'Experiment info' field of the
'Experiment settings' dialog. In Coder, this information can be set
using something like::
myTrialHandler.extraInfo = {'SubjID': 'Joan Smith',
'Group': 'Control'}
:Parameters:
fileName:
if extension is not specified, '.csv' will be appended if
the delimiter is ',', else '.tsv' will be appended.
Can include path info.
delim:
allows the user to use a delimiter other than the default
tab ("," is popular with file extension ".csv")
matrixOnly:
outputs the data with no header row.
appendFile:
will add this output to the end of the specified file if
it already exists.
fileCollisionMethod:
Collision method passed to
:func:`~psychopy.tools.fileerrortools.handleFileCollision`
encoding:
The encoding to use when saving a the file.
Defaults to `utf-8-sig`.
"""
if self.thisTrialN < 1 and self.thisRepN < 1:
# if both are < 1 we haven't started
logging.info('TrialHandler.saveAsWideText called but no '
'trials completed. Nothing saved')
return -1
# set default delimiter if none given
if delim is None:
delim = genDelimiter(fileName)
# create the file or send to stdout
fileName = genFilenameFromDelimiter(fileName, delim)
with openOutputFile(fileName=fileName, append=appendFile,
fileCollisionMethod=fileCollisionMethod,
encoding=encoding) as f:
csvData = self.data.to_csv(sep=delim,
encoding=encoding,
columns=self.columns, # sets the order
header=(not matrixOnly),
index=False)
f.write(csvData)
if (fileName is not None) and (fileName != 'stdout'):
logging.info('saved wide-format data to %s' % f.name)
[docs] def saveAsJson(self,
fileName=None,
encoding='utf-8',
fileCollisionMethod='rename'):
"""
Serialize the object to the JSON format.
Parameters
----------
fileName: string, or None
the name of the file to create or append. Can include a relative or
absolute path. If `None`, will not write to a file, but return an
in-memory JSON object.
encoding : string, optional
The encoding to use when writing the file.
fileCollisionMethod : string
Collision method passed to
:func:`~psychopy.tools.fileerrortools.handleFileCollision`. Can be
either of `'rename'`, `'overwrite'`, or `'fail'`.
Notes
-----
Currently, a copy of the object is created, and the copy's .origin
attribute is set to an empty string before serializing
because loading the created JSON file would sometimes fail otherwise.
The RNG self._rng cannot be serialized as-is, so we store its state in
self._rng_state so we can restore it when loading.
"""
self_copy = copy.deepcopy(self)
self_copy._rng_state = self_copy._rng.bit_generator.state
del self_copy._rng
r = (super(TrialHandler2, self_copy)
.saveAsJson(fileName=fileName,
encoding=encoding,
fileCollisionMethod=fileCollisionMethod))
if fileName is None:
return r
[docs] def addData(self, thisType, value):
"""Add a piece of data to the current trial
"""
# store in the columns list to help ordering later
if thisType not in self.columns:
self.columns.append(thisType)
# save the actual value in a data dict
self.thisTrial[thisType] = value
if self.getExp() is not None:
# update the experiment handler too
self.getExp().addData(thisType, value)
[docs]class TrialHandlerExt(TrialHandler):
"""A class for handling trial sequences in a *non-counterbalanced design*
(i.e. *oddball paradigms*). Its functions are a superset of the
class TrialHandler, and as such, can also be used for normal trial
handling.
TrialHandlerExt has the same function names for data storage facilities.
To use non-counterbalanced designs, all TrialType dict entries in the
trial list must have a key called "weight". For example, if you want
trial types A, B, C, and D to have 10, 5, 3, and 2 repetitions per
block, then the trialList can look like:
[{Name:'A', ..., weight:10},
{Name:'B', ..., weight:5},
{Name:'C', ..., weight:3},
{Name:'D', ..., weight:2}]
For experimenters using an excel or csv file for trial list, a column
called weight is appropriate for this purpose.
Calls to .next() will fetch the next trial object given to this handler,
according to the method specified (random, sequential, fullRandom).
Calls will raise a StopIteration error when all trials are exhausted.
*Authored by Suddha Sourav at BPN, Uni Hamburg - heavily borrowing
from the TrialHandler class*
"""
def __init__(self,
trialList,
nReps,
method='random',
dataTypes=None,
extraInfo=None,
seed=None,
originPath=None,
name='',
autoLog=True):
"""
:Parameters:
trialList: a simple list (or flat array) of dictionaries
specifying conditions. This can be imported from an
excel / csv file using :func:`~psychopy.data.importConditions`
For non-counterbalanced designs, each dict entry in
trialList must have a key called weight!
nReps: number of repeats for all conditions. When using a
non-counterbalanced design, nReps is analogous to the number
of blocks.
method: *'random',* 'sequential', or 'fullRandom'
When the weights are not specified:
'sequential' presents the conditions in the order they appear
in the list. 'random' will result in a shuffle of the
conditions on each repeat, but all conditions occur once
before the second repeat etc. 'fullRandom' fully randomises
the trials across repeats as well, which means you could
potentially run all trials of one condition before any trial
of another.
In the presence of weights:
'sequential' presents each trial type the number of times
specified by its weight, before moving on to the next type.
'random' randomizes the presentation order within block.
'fulLRandom' shuffles trial order across weights an nRep,
that is, a full shuffling.
dataTypes: (optional) list of names for data storage. e.g.
['corr','rt','resp']. If not provided then these will be
created as needed during calls to
:func:`~psychopy.data.TrialHandler.addData`
extraInfo: A dictionary
This will be stored alongside the data and usually describes
the experiment and subject ID, date etc.
seed: an integer
If provided then this fixes the random number generator
to use the same pattern
of trials, by seeding its startpoint
originPath: a string describing the location of the script /
experiment file path. The psydat file format will store a
copy of the experiment if possible. If `originPath==None`
is provided here then the TrialHandler will still store a
copy of the script where it was created. If `OriginPath==-1`
then nothing will be stored.
:Attributes (after creation):
.data - a dictionary of numpy arrays, one for each data type
stored
.trialList - the original list of dicts, specifying the conditions
.thisIndex - the index of the current trial in the original
conditions list
.nTotal - the total number of trials that will be run
.nRemaining - the total number of trials remaining
.thisN - total trials completed so far
.thisRepN - which repeat you are currently on
.thisTrialN - which trial number *within* that repeat
.thisTrial - a dictionary giving the parameters of the current
trial
.finished - True/False for have we finished yet
.extraInfo - the dictionary of extra info as given at beginning
.origin - the contents of the script or builder experiment that
created the handler
.trialWeights - None if all weights are not specified. If all
weights are specified, then a list containing the weights
of the trial types.
"""
self.name = name
self.autoLog = autoLog
if trialList in (None, []):
# user wants an empty trialList
# which corresponds to a list with a single empty entry
self.trialList = [None]
# user has hopefully specified a filename
elif isinstance(trialList, str) and os.path.isfile(trialList):
# import conditions from that file
self.trialList = importConditions(trialList)
else:
self.trialList = trialList
# convert any entry in the TrialList into a TrialType object (with
# obj.key or obj[key] access)
for n, entry in enumerate(self.trialList):
if type(entry) == dict:
self.trialList[n] = TrialType(entry)
self.nReps = nReps
# Add Su
if not trialList or not all('weight' in d for d in trialList):
self.trialWeights = None
self.nTotal = self.nReps * len(self.trialList)
else:
self.trialWeights = [d['weight'] for d in trialList]
self.nTotal = self.nReps * sum(self.trialWeights)
self.nRemaining = self.nTotal # subtract 1 each trial
self.method = method
self.thisRepN = 0 # records which repetition or pass we are on
self.thisTrialN = -1 # records trial number within this repetition
self.thisN = -1
self.thisIndex = 0 # index of current trial in the conditions list
self.thisTrial = []
self.finished = False
self.extraInfo = extraInfo
self.seed = seed
# create dataHandler
if self.trialWeights is None:
self.data = DataHandler(trials=self)
else:
self.data = DataHandler(trials=self,
dataShape=[sum(self.trialWeights), nReps])
if dataTypes is not None:
self.data.addDataType(dataTypes)
self.data.addDataType('ran')
self.data['ran'].mask = False # bool - all entries are valid
self.data.addDataType('order')
# generate stimulus sequence
if self.method in ('random', 'sequential', 'fullRandom'):
self.sequenceIndices = self._createSequence()
else:
self.sequenceIndices = []
self.originPath, self.origin = self.getOriginPathAndFile(originPath)
self._exp = None # the experiment handler that owns me!
[docs] def _createSequence(self):
"""Pre-generates the sequence of trial presentations (for
non-adaptive methods). This is called automatically when the
TrialHandler is initialised so doesn't need an explicit call
from the user.
The returned sequence has form indices[stimN][repN]
Example: sequential with 6 trialtypes (rows), 5 reps (cols), returns::
[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]
[5 5 5 5 5]]
These 30 trials will be returned by .next() in the order:
0, 1, 2, 3, 4, 5, 0, 1, 2, ... ... 3, 4, 5
Example: random, with 3 trialtypes, where the weights of
conditions 0,1, and 2 are 3,2, and 1 respectively,
and a rep value of 5, might return::
[[0 1 2 0 1]
[1 0 1 1 1]
[0 2 0 0 0]
[0 0 0 1 0]
[2 0 1 0 2]
[1 1 0 2 0]]
These 30 trials will be returned by .next() in the order:
0, 1, 0, 0, 2, 1, 1, 0, 2, 0, 0, 1, ...
... 0, 2, 0 *stopIteration*
To add a new type of sequence (as of v1.65.02):
- add the sequence generation code here
- adjust "if self.method in [ ...]:" in both __init__ and .next()
- adjust allowedVals in experiment.py -> shows up in DlgLoopProperties
Note that users can make any sequence whatsoever outside of PsychoPy,
and specify sequential order; any order is possible this way.
"""
# create indices for a single rep
indices = np.asarray(self._makeIndices(self.trialList), dtype=int)
repeat = np.repeat
reshape = np.reshape
rng = np.random.default_rng(seed=self.seed)
if self.method == 'random':
seqIndices = []
if self.trialWeights is None:
thisRepSeq = indices.flat # take a fresh copy
else:
thisRepSeq = repeat(indices, self.trialWeights)
for thisRep in range(self.nReps):
seqIndices.append(rng.permutation(thisRepSeq))
seqIndices = np.transpose(seqIndices)
elif self.method == 'sequential':
if self.trialWeights is None:
seqIndices = repeat(indices, self.nReps, 1)
else:
_base = repeat(indices, self.trialWeights, 0)
seqIndices = repeat(_base, self.nReps, 1)
elif self.method == 'fullRandom':
if self.trialWeights is None:
# indices * nReps, flatten, shuffle, unflatten;
# only use seed once
sequential = np.repeat(indices, self.nReps, 1) # = sequential
randomFlat = rng.permutation(sequential.flat)
seqIndices = np.reshape(
randomFlat, (len(indices), self.nReps))
else:
_base = repeat(indices, self.trialWeights, 0)
sequential = repeat(_base, self.nReps, 1)
randomFlat = rng.permutation(sequential.flat)
seqIndices = reshape(randomFlat,
(sum(self.trialWeights), self.nReps))
if self.autoLog:
# Change
msg = 'Created sequence: %s, trialTypes=%d, nReps=%d, seed=%s'
vals = (self.method, len(indices), self.nReps, str(self.seed))
logging.exp(msg % vals)
return seqIndices
def __next__(self):
"""Advances to next trial and returns it.
Updates attributes; thisTrial, thisTrialN and thisIndex
If the trials have ended this method will raise a StopIteration error.
This can be handled with code such as::
trials = data.TrialHandler(.......)
for eachTrial in trials: # automatically stops when done
# do stuff
or::
trials = data.TrialHandler(.......)
while True: # ie forever
try:
thisTrial = trials.next()
except StopIteration: # we got a StopIteration error
break # break out of the forever loop
# do stuff here for the trial
"""
# update pointer for next trials
self.thisTrialN += 1 # number of trial this pass
self.thisN += 1 # number of trial in total
self.nRemaining -= 1
if self.trialWeights is None:
if self.thisTrialN == len(self.trialList):
# start a new repetition
self.thisTrialN = 0
self.thisRepN += 1
else:
if self.thisTrialN == sum(self.trialWeights):
# start a new repetition
self.thisTrialN = 0
self.thisRepN += 1
if self.thisRepN >= self.nReps:
# all reps complete
self.thisTrial = []
self.finished = True
if self.finished == True:
self._terminate()
# fetch the trial info
if self.method in ('random', 'sequential', 'fullRandom'):
if self.trialWeights is None:
idx = self.sequenceIndices[self.thisTrialN]
self.thisIndex = idx[self.thisRepN]
self.thisTrial = self.trialList[self.thisIndex]
self.data.add('ran', 1)
self.data.add('order', self.thisN)
else:
idx = self.sequenceIndices[self.thisTrialN]
self.thisIndex = idx[self.thisRepN]
self.thisTrial = self.trialList[self.thisIndex]
self.data.add('ran', 1,
position=self.getNextTrialPosInDataHandler())
# The last call already adds a ran to this trial, so get the
# current pos now
self.data.add('order', self.thisN,
position=self.getCurrentTrialPosInDataHandler())
if self.autoLog:
msg = 'New trial (rep=%i, index=%i): %s'
vals = (self.thisRepN, self.thisTrialN, self.thisTrial)
logging.exp(msg % vals, obj=self.thisTrial)
return self.thisTrial
next = __next__ # allows user to call without a loop `val = trials.next()`
[docs] def getCurrentTrialPosInDataHandler(self):
# if there's no trial weights, then the current position is simply
# [trialIndex, nRepetition]
if self.trialWeights is None:
repN = sum(self['ran'][self.trials.thisIndex]) - 1
position = [self.trials.thisIndex, repN]
else:
# if there are trial weights, the situation is slightly more
# involved, because the same index can be repeated for a number
# of times. If we had a sequential array, then the rows in
# DataHandler for that trialIndex would be from
# sum(trialWeights[begin:trialIndex]) to
# sum(trialWeights[begin:trialIndex+1]).
# if we haven't begun the experiment yet, then the last row
# of the first column is used as the current position,
# emulating what TrialHandler does. The following two lines
# also prevents calculating garbage position values in case
# the first row has a null weight
if self.thisN < 0:
return [0, -1]
firstRowIndex = sum(self.trialWeights[:self.thisIndex])
lastRowIndex = sum(self.trialWeights[:self.thisIndex + 1])
# get the number of the trial presented by summing in ran for the
# rows above and all columns
# BF-Sourav-29032021: numpy returns float, so cast to int
nThisTrialPresented = int(round(np.sum(
self.data['ran'][firstRowIndex:lastRowIndex, :])))
_tw = self.trialWeights[self.thisIndex]
dataRowThisTrial = firstRowIndex + (nThisTrialPresented - 1) % _tw
dataColThisTrial = int((nThisTrialPresented - 1) // _tw)
position = [dataRowThisTrial, dataColThisTrial]
return position
[docs] def getNextTrialPosInDataHandler(self):
# if there's no trial weights, then the current position is
# simply [trialIndex, nRepetition]
if self.trialWeights is None:
repN = sum(self['ran'][self.trials.thisIndex])
position = [self.trials.thisIndex, repN]
else:
# if there are trial weights, the situation is slightly more
# involved, because the same index can be repeated for a
# number of times. If we had a sequential array, then the
# rows in DataHandler for that trialIndex would
# be from sum(trialWeights[begin:trialIndex]) to
# sum(trialWeights[begin:trialIndex+1]).
firstRowIndex = sum(self.trialWeights[:self.thisIndex])
lastRowIndex = sum(self.trialWeights[:self.thisIndex + 1])
# get the number of the trial presented by summing in ran for the
# rows above and all columns
# BF-Sourav-29032021: numpy returns float, so cast to int
nThisTrialPresented = int(round(np.sum(
self.data['ran'][firstRowIndex:lastRowIndex, :])))
_tw = self.trialWeights[self.thisIndex]
dataRowThisTrial = firstRowIndex + nThisTrialPresented % _tw
dataColThisTrial = int(nThisTrialPresented // _tw)
position = [dataRowThisTrial, dataColThisTrial]
return position
[docs] def addData(self, thisType, value, position=None):
"""Add data for the current trial
"""
if self.trialWeights is None:
pos = None
else:
pos = self.getCurrentTrialPosInDataHandler()
self.data.add(thisType, value, position=pos)
# change this!
if self.getExp() is not None:
# update the experiment handler too:
self.getExp().addData(thisType, value)
[docs] def _createOutputArrayData(self, dataOut):
"""This just creates the dataOut part of the output matrix.
It is called by _createOutputArray() which creates the header
line and adds the stimOut columns
"""
if self.trialWeights is not None:
# remember to use other array instead of self.data
_vals = np.arange(len(self.trialList))
idx_data = np.repeat(_vals, self.trialWeights)
# list of data headers
dataHead = []
# will store data that has been analyzed
dataAnal = dict([])
if type(dataOut) == str:
# don't do list convert or we get a list of letters
dataOut = [dataOut]
elif type(dataOut) != list:
dataOut = list(dataOut)
# expand any 'all' dataTypes to the full list of available dataTypes
allDataTypes = list(self.data.keys())
# treat these separately later
allDataTypes.remove('ran')
# ready to go through standard data types
dataOutNew = []
for thisDataOut in dataOut:
if thisDataOut == 'n':
# n is really just the sum of the ran trials
dataOutNew.append('ran_sum')
continue # no need to do more with this one
# then break into dataType and analysis
dataType, analType = thisDataOut.rsplit('_', 1)
if dataType == 'all':
keyType = [key + "_" + analType for key in allDataTypes]
dataOutNew.extend(keyType)
if 'order_mean' in dataOutNew:
dataOutNew.remove('order_mean')
if 'order_std' in dataOutNew:
dataOutNew.remove('order_std')
else:
dataOutNew.append(thisDataOut)
dataOut = dataOutNew
# sort so that all datatypes come together, rather than all analtypes
dataOut.sort()
# do the various analyses, keeping track of fails (e.g. mean of a
# string)
dataOutInvalid = []
# add back special data types (n and order)
if 'ran_sum' in dataOut:
# move n to the first column
dataOut.remove('ran_sum')
dataOut.insert(0, 'ran_sum')
if 'order_raw' in dataOut:
# move order_raw to the second column
dataOut.remove('order_raw')
dataOut.append('order_raw')
# do the necessary analysis on the data
for thisDataOutN, thisDataOut in enumerate(dataOut):
dataType, analType = thisDataOut.rsplit('_', 1)
if not dataType in self.data:
# that analysis can't be done
dataOutInvalid.append(thisDataOut)
continue
if self.trialWeights is None:
thisData = self.data[dataType]
else:
# BF_202302210_trialHandlerExt_save_nonnumeric_excel
# Allow saving non-numeric data to the excel format
# Previous case: masked arrays for numeric data
if self.data.isNumeric[dataType]:
resizedData = np.ma.masked_array(
np.zeros((len(self.trialList),
max(self.trialWeights) * self.nReps)),
np.ones((len(self.trialList),
max(self.trialWeights) * self.nReps),
dtype=bool))
for curTrialIndex in range(len(self.trialList)):
thisDataChunk = self.data[dataType][
idx_data == curTrialIndex, :]
padWidth = (max(self.trialWeights) * self.nReps -
np.prod(thisDataChunk.shape))
thisDataChunkRowPadded = np.pad(
thisDataChunk.transpose().flatten().data,
(0, padWidth), mode='constant',
constant_values=(0, 0))
thisDataChunkRowPaddedMask = np.pad(
thisDataChunk.transpose().flatten().mask,
(0, padWidth), mode='constant',
constant_values=(0, True))
thisDataChunkRow = np.ma.masked_array(
thisDataChunkRowPadded,
mask=thisDataChunkRowPaddedMask)
resizedData[curTrialIndex, :] = thisDataChunkRow
# For non-numeric data, Psychopy uses typical object arrays in-
# stead of masked arrays. Adjust accordingly, filling with '--'
# instead of masks
else:
resizedData = np.array(np.zeros((len(self.trialList),
max(self.trialWeights) *
self.nReps)), dtype='O')
for curTrialIndex in range(len(self.trialList)):
thisDataChunk = self.data[dataType][
idx_data == curTrialIndex, :]
padWidth = (max(self.trialWeights) * self.nReps -
np.prod(thisDataChunk.shape))
thisDataChunkRowPadded = np.pad(
thisDataChunk.transpose().flatten().data,
(0, padWidth), mode='constant',
constant_values=('--', '--'))
resizedData[curTrialIndex, :] = thisDataChunkRowPadded
thisData = resizedData
# set the header
dataHead.append(dataType + '_' + analType)
# analyse thisData using numpy module
if analType in dir(np):
try:
# this will fail if we try to take mean of a string
if analType == 'std':
thisAnal = np.std(thisData, axis=1, ddof=0)
# normalise by N-1 instead. This should work by
# setting ddof=1 but doesn't as of 08/2010
# (because of using a masked array?)
N = thisData.shape[1]
if N == 1:
thisAnal *= 0 # prevent a divide-by-zero error
else:
sqrt = np.sqrt
thisAnal = thisAnal * sqrt(N) / sqrt(N - 1)
else:
thisAnal = eval("np.%s(thisData,1)" % analType)
except Exception:
# that analysis doesn't work
dataHead.remove(dataType + '_' + analType)
dataOutInvalid.append(thisDataOut)
continue # to next analysis
elif analType == 'raw':
thisAnal = thisData
else:
raise AttributeError('You can only use analyses from numpy')
# add extra cols to header if necess
if len(thisAnal.shape) > 1:
for n in range(thisAnal.shape[1] - 1):
dataHead.append("")
dataAnal[thisDataOut] = thisAnal
# remove invalid analyses (e.g. average of a string)
for invalidAnal in dataOutInvalid:
dataOut.remove(invalidAnal)
return dataOut, dataAnal, dataHead
[docs] def saveAsWideText(self,
fileName,
delim='\t',
matrixOnly=False,
appendFile=True,
encoding='utf-8-sig',
fileCollisionMethod='rename'):
"""Write a text file with the session, stimulus, and data values
from each trial in chronological order.
That is, unlike 'saveAsText' and 'saveAsExcel':
- each row comprises information from only a single trial.
- no summarizing is done (such as collapsing to produce mean and
standard deviation values across trials).
This 'wide' format, as expected by R for creating dataframes, and
various other analysis programs, means that some information must
be repeated on every row.
In particular, if the trialHandler's 'extraInfo' exists, then each
entry in there occurs in every row. In builder, this will include
any entries in the 'Experiment info' field of the
'Experiment settings' dialog. In Coder, this information can be set
using something like::
myTrialHandler.extraInfo = {'SubjID':'Joan Smith',
'Group':'Control'}
:Parameters:
fileName:
if extension is not specified, '.csv' will be appended if
the delimiter is ',', else '.txt' will be appended.
Can include path info.
delim:
allows the user to use a delimiter other than the default
tab ("," is popular with file extension ".csv")
matrixOnly:
outputs the data with no header row.
appendFile:
will add this output to the end of the specified file if
it already exists.
fileCollisionMethod:
Collision method passed to
:func:`~psychopy.tools.fileerrortools.handleFileCollision`
encoding:
The encoding to use when saving a the file.
Defaults to `utf-8-sig`.
"""
if self.thisTrialN < 1 and self.thisRepN < 1:
# if both are < 1 we haven't started
logging.info('TrialHandler.saveAsWideText called but no trials'
' completed. Nothing saved')
return -1
# set default delimiter if none given
if delim is None:
delim = genDelimiter(fileName)
# create the file or send to stdout
fileName = genFilenameFromDelimiter(fileName, delim)
f = openOutputFile(fileName=fileName, append=appendFile,
fileCollisionMethod=fileCollisionMethod,
encoding=encoding)
# collect parameter names related to the stimuli:
if self.trialList[0]:
header = list(self.trialList[0].keys())
else:
header = []
# and then add parameter names related to data (e.g. RT)
header.extend(self.data.dataTypes)
# loop through each trial, gathering the actual values:
dataOut = []
trialCount = 0
# total number of trials = number of trialtypes * number of
# repetitions:
repsPerType = {}
for rep in range(self.nReps):
if self.trialWeights is None:
nRows = len(self.trialList)
else:
nRows = sum(self.trialWeights)
for trialN in range(nRows):
# find out what trial type was on this trial
trialTypeIndex = self.sequenceIndices[trialN, rep]
# determine which repeat it is for this trial
if trialTypeIndex not in repsPerType:
repsPerType[trialTypeIndex] = 0
else:
repsPerType[trialTypeIndex] += 1
# create a dictionary representing each trial:
# this is wide format, so we want fixed information (e.g.
# subject ID, date, etc) repeated every line if it exists:
if self.extraInfo != None:
nextEntry = self.extraInfo.copy()
else:
nextEntry = {}
# add a trial number so the original order of the data can
# always be recovered if sorted during analysis:
trialCount += 1
nextEntry["TrialNumber"] = trialCount
# what repeat are we on for this trial type?
trep = repsPerType[trialTypeIndex]
# collect the value from each trial of the vars in the header:
tti = trialTypeIndex
for prmName in header:
# the header includes both trial and data variables, so
# need to check before accessing:
if self.trialList[tti] and prmName in self.trialList[tti]:
nextEntry[prmName] = self.trialList[tti][prmName]
elif prmName in self.data:
if self.trialWeights is None:
nextEntry[prmName] = self.data[prmName][tti][trep]
else:
firstRowIndex = sum(self.trialWeights[:tti])
_tw = self.trialWeights[tti]
row = firstRowIndex + rep % _tw
col = int(rep // _tw)
nextEntry[prmName] = self.data[prmName][row][col]
else:
# allow a null value if this parameter wasn't
# explicitly stored on this trial:
nextEntry[prmName] = ''
# store this trial's data
dataOut.append(nextEntry)
# get the extra 'wide' parameter names into the header line:
header.insert(0, "TrialNumber")
if self.extraInfo is not None:
for key in self.extraInfo:
header.insert(0, key)
# write a header row:
if not matrixOnly:
f.write(delim.join(header) + '\n')
# write the data matrix:
for trial in dataOut:
line = delim.join([str(trial[prm]) for prm in header])
f.write(line + '\n')
if (fileName is not None) and (fileName != 'stdout'):
f.close()
logging.info('saved wide-format data to %s' % f.name)
[docs] def saveAsJson(self,
fileName=None,
encoding='utf-8',
fileCollisionMethod='rename'):
raise NotImplementedError('Not implemented for TrialHandlerExt.')
