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 getBouts_(mice,
dataList,
eventStart,
eventEnd,
baseEvent,
base,
behType,
trials,
baselining=False):
"""
Use this one for varying baseline length.
"""
eventsData = pd.DataFrame()
for mus, sess in mice:
for i in range(trials[0], trials[1] + 1):
ind1, nearest1 = find_nearest(dataList[mus].index.values,
eventStart[mus].loc[i])
ind2, nearest2 = find_nearest(dataList[mus].index.values,
eventEnd[mus].loc[i])
ind3, nearest3 = find_nearest(dataList[mus].index.values,
baseEvent[mus].loc[i])
ind4, nearest4 = find_nearest(-dataList[mus].index.values,
base[mus].loc[i])
eventStart[mus].loc[i] = nearest1
eventEnd[mus].loc[i] = nearest2
baseEvent[mus].loc[i] = nearest3
base[mus].loc[i] = nearest4
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 j in range(trials[0], trials[1] + 1):
startT = eventStart[mus].loc[j]
endT = eventEnd[mus].loc[j]
baseT = baseEvent[mus].loc[j]
slicedData = pd.DataFrame()
newTime = np.arange(base[mus].loc[j], 10000, fs)
if baselining:
if base[mus].loc[j] < 0:
basedFF = data.loc[baseT + base[mus].loc[j]:baseT +
0.0001].mean()
elif base[mus].loc[j] > 0:
basedFF = data.loc[baseT:baseT + base[mus].loc[j] +
0.0001].mean()
elif not baselining:
basedFF = 0.0
if base[mus].loc[j] < 0:
slicePre = data.loc[baseT + base[mus].
loc[j]:baseT].values - basedFF
slicePost = data.loc[startT:endT].values - basedFF
dataSlice = np.concatenate((slicePre, slicePost),
axis=0)
slicedData['Fluoro'] = dataSlice
slicedData['New_Time'] = newTime[0:len(dataSlice)]
#slicedData['New_Time'] = np.linspace(base,((len(dataSlice)-1)*fs)+base,len(dataSlice))
elif base[mus].loc[j] > 0:
print "Oh, no!"
dataSlice = data.loc[startT + base[mus].loc[j]:endT +
0.0001].values - basedFF
slicedData['Fluoro'] = dataSlice
slicedData['New_Time'] = np.linspace(
base,
((len(dataSlice) - 1) * fs) + base[mus].loc[j],
len(dataSlice))
slicedData['Cell'] = col
slicedData['Event'] = j
eventsData = eventsData.append(slicedData)
print "\n", len(mice), " mice were loaded."
if baselining:
print "\nBaseline was set", base.mean().mean(), "sec before the event."
elif not baselining:
print "\nTraces were not baselined."
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