def analyzeStimulus(stimulusName):
data = getData.getRawData(dataset, stimulusName)
trials = []
trialNum = len(data[0]) / numberOfFramesPerTrial
for x in range(trialNum):
trials.append(
map(
lambda neuron: neuron[x * numberOfFramesPerTrial:
(x + 1) * numberOfFramesPerTrial], data))
for t in range(len(trials)):
analyzeTrial(stimulusName, trials[t], t)
Config.read(rawDataDirectory + 'analysis.config')
number_of_shuffles = int(Config.get("analysis","number_of_shuffles_for_correlation_analysis"))
def listdir_nohidden(path):
for f in os.listdir(path):
if not f.startswith('.'):
if not f.endswith('p'):
yield f
#get number of neurons
originalFiles=map(lambda x: x.rpartition('.')[0],listdir_nohidden(rawDataDirectory + 'stimuliData'))
originalData=getData.getRawData(dataset, originalFiles[0])
number_of_neurons=len(originalData)
stimuliNames = map(lambda x: x.rpartition('.')[0], listdir_nohidden(outputDirectory + 'mergedExportingN'))
#generates all of the combinations of two different neurons
neuronCombinations=list(itertools.combinations(range(number_of_neurons),2))
def getOutputData(location):
f = open(location+'.txt', 'r')
data=[]
for line in f:
data.append(line)
#gets directory of raw data and where output should go
rawDataDirectory = '../datasets/' + dataset + '/'
outputDirectory = '../analyzedDatasets/' + dataset + '/'
def listdir_nohidden(path):
for f in os.listdir(path):
if not f.startswith('.'):
if not f.endswith('p'):
yield f
#CAN USE THE RAW DATA FOR RAW CORRELATION TO DISTANCE IN ANOTHER ANALYSIS
stimuliNames = map(lambda x: x.rpartition('.')[0],
listdir_nohidden(rawDataDirectory + 'stimuliData'))
rawData = getData.getRawData(dataset, stimuliNames[0])
#get number of neurons
number_of_neurons = len(rawData)
def getOutputData(location):
f = open(outputDirectory + 'exportingN/' + location + '.txt', 'r')
data = []
for line in f:
data.append(line)
data = map(lambda x: map(lambda y: float(y.strip()),
x.strip().split(',')), data)
return data
rawDataDirectory = '../datasets/' + dataset + '/'
outputDirectory = '../analyzedDatasets/' + dataset + '/'
Config = ConfigParser.ConfigParser()
Config.read(rawDataDirectory + 'analysis.config')
def listdir_nohidden(path):
for f in os.listdir(path):
if not f.startswith('.'):
if not f.endswith('p'):
yield f
analysisOptions = Config.options("analysis")
#generate list of settings
analysisSettings = map(
lambda option: option + ":" + Config.get('analysis', option),
analysisOptions)
#add number of neurons
files = map(lambda x: x.rpartition('.')[0],
listdir_nohidden(rawDataDirectory + 'stimuliData'))
data = getData.getRawData(dataset, files[0])
analysisSettings.append('number_of_neurons:' + str(len(data)))
#write file
f = open(outputDirectory + "mathematicaConfig.txt", "w")
for option in analysisSettings:
f.write(option + '\n')
f.close()