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
def markTrials(mice,
dataList,
base,
duration,
eventType,
behType,
trials,
baselining=False):
"""
"""
# Load the events
fileList = loopMice(mice, behType)
eventList = getBeh(mice, fileList['Behaviour'], behType)
eventsData = pd.DataFrame()
for mus, sess in mice:
# Find the events
eventTimes = np.array([])
df = eventList[mus][eventType].dropna().reset_index(drop=True)
for i, event in enumerate(df):
ind, nearest = find_nearest(dataList[mus].index.values, event)
eventTimes = np.append(eventTimes, nearest)
fs = dataList[mus].index[1] - dataList[mus].index[0]
newTime = np.arange(base, 10000, fs)
for col in dataList[mus].columns:
if not col == 'Time (s)':
data = dataList[mus][col]
for i, event in enumerate(eventTimes[trials[0] - 1:trials[1]]):
slicedData = pd.DataFrame()
if baselining:
basedFF = data.loc[event + base + 0.0001:event +
0.0001].mean()
elif not baselining:
basedFF = 0
dataSlice = data.loc[event + base + 0.0001:event +
duration + 0.0001].values - basedFF
slicedData['Fluoro'] = dataSlice
slicedData['Cell'] = col
slicedData['Event'] = i + 1
slicedData['New_Time'] = newTime[0:len(dataSlice)]
#slicedData['New_Time'] = np.round(np.linspace(base,((len(slicedData['Fluoro'])-1)*fs)+base,len(slicedData['Fluoro'])), 2)
##print np.linspace(base,((len(slicedData['Fluoro'])-1)*fs)+base,len(slicedData['Fluoro']))
##print np.round(np.linspace(base,((len(slicedData['Fluoro'])-1)*fs)+base,len(slicedData['Fluoro'])), 2)
#np.arange(+base,duration,fs)
eventsData = eventsData.append(slicedData)
print "\n", len(mice), " mice were loaded."
if behType == 'FR1':
for mus, sess in mice:
print "Mouse number", mus, " had ", eventList[mus][
'Right_Count'].max(), " total rewards."
return eventsData
def markBouts(mice,
dataList,
eventType,
behType,
trials,
base,
dff=True,
baseline=False):
"""
DEPRICATED: Use getBouts instead.
"""
print "DEPRICATED: Use getBouts instead."
fileList = loopMice(mice, behType)
eventList = getBeh(mice, fileList['Behaviour'], behType)
eventsData = pd.DataFrame()
for mus, sess in mice:
# Find the events
eventTimes1 = np.array([])
eventTimes2 = np.array([])
startTimes = eventList[mus][eventType[0]].dropna().reset_index(
drop=True)
endTimes = eventList[mus][eventType[1]].dropna().reset_index(drop=True)
for i, event in enumerate(startTimes):
ind1, nearest1 = find_nearest(dataList[mus].index.values,
startTimes.loc[i])
ind2, nearest2 = find_nearest(dataList[mus].index.values,
endTimes.loc[i])
eventTimes1 = np.append(eventTimes1, nearest1)
eventTimes2 = np.append(eventTimes2, nearest2)
fs = dataList[mus].index[1] - dataList[mus].index[0]
for col in dataList[mus].columns:
if not col == 'Time (s)':
data = dataList[mus][col]
for i, event in enumerate(eventTimes1[trials[0] -
1:trials[1]]):
start = eventTimes1[i]
end = eventTimes2[i]
slicedData = pd.DataFrame()
if baseline:
basedFF = data.loc[start + base:start + 0.0001].mean()
elif not baseline:
basedFF = 0.0
slicedData['Fluoro'] = data.loc[start + base:end +
0.0001].values - basedFF
slicedData['Cell'] = col
slicedData['Event'] = i + 1
if len(data.loc[start + base:end + 0.0001].values -
basedFF) == len(
np.arange(base, end - start + 0.0001, fs)):
slicedData['New_Time'] = np.arange(
base, end - start + 0.0001, fs)
if len(data.loc[start + base:end + 0.0001].values -
basedFF) < len(
np.arange(base, end - start + 0.0001, fs)):
slicedData['New_Time'] = np.arange(
base, end - start, fs)
eventsData = eventsData.append(slicedData)
print "\n", len(mice), " mice were loaded."
if behType == 'FR1':
for mus, sess in mice:
print "Mouse number", mus, " had ", eventList[mus][
'Right_Count'].max(), " total rewards."
if baseline:
print "\nBaseline was set", baseline, "sec before the event."
elif not baseline:
print "\nTraces were not baselined."
return eventsData
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)