def getBoutDur(mice, eventType, behType, trials):
"""
DEPRICATED: Needs major revision!
"""
print "DEPRICATED: Needs major revision!"
# Load the data
dataList = loadData(mice)
# Load the events
fileList = loopMice(mice, behType)
eventList = getBeh(mice, fileList['Behaviour'], behType)
durationData = pd.DataFrame()
for mus, sess in mice:
# Find the events
boutDur = np.array([])
startList = eventList[mus][eventType[0]].dropna().values
endList = eventList[mus][eventType[1]].dropna().values
for i in range(trials[0] - 1, trials[1]):
ind, start = find_nearest(dataList[mus].index.values, startList[i])
ind, end = find_nearest(dataList[mus].index.values, endList[i])
boutDur = np.append(boutDur, end - start)
durationData[mus] = boutDur
durationData.set_index(np.arange(trials[0], trials[1] + 1), inplace=True)
return durationData
import matplotlib.patches as mpatches
from imagingIO import loopMice
from trials import markTrials
from analysis import dffCalc, filtData, smoothData, normData
# Start running the analysis
mice = [(8404, 6), (8857, 3), (8864, 1)]
behType = 'FR1'
fs = 0.05
base = -5.0
duration = 30
trials=[1,12]
# Locate the files
fileList = loopMice(mice, behType)
# Load the events
eventList = getBeh(mice, fileList['Behaviour'], behType)
# Load the data
dataList = loadData(mice, behType)
# dFF and plot again
filtList = filtData(mice, dffCalc(mice, dataList, lowest=False), cutoff=5.0, order=6)
dFFList = smoothData(mice, filtList, window=4)
eventType = 'Eat_Start'
eventsData = markTrials(mice, dataList, base=base, duration=duration, eventType=eventType, behType=behType, trials=trials, baselining=True)
sem=eventsData.pivot_table(index=['Event', 'Cell'], columns='New_Time').sem()
time = np.arange(base,duration,fs)
print len(sem), len(time)