def getSingleEventTable(self, event_name):
"""
Returns pandas table containing all found events for given event name'
* Primary mouse' meta data
* Event start time
* Event end time
* Event duration
Args:
event_name (str): Event name e. g. Rearing
Returns:
DataFrame
"""
data = defaultdict(list)
for animal in self.getAnimalList():
with mute_prints():
eventTimeLine = EventTimeLine(self.conn,
event_name,
idA=animal.baseId,
minFrame=self.detectionStartFrame,
maxFrame=self.detectionEndFrame)
for e in eventTimeLine.getEventList():
data["RFID"] .append(f"{animal.name}_{animal.RFID}")
data["name"] .append(f"{animal.name}")
data["genotype"] .append(f"{animal.genotype}")
data["event_name"] .append(event_name)
data["start_sec"] .append(e.startFrame / oneSecond)
data["end_sec"] .append(e.endFrame / oneSecond)
data["duration"] .append(e.duration() / oneSecond)
df = pd.DataFrame(data)
df.insert(2, "time", pd.to_timedelta(df["start_sec"], unit="s"))
return df.sort_values("time").reset_index(drop=True)
def getDyadicGenotypeGroupedEventTable(self, event_list):
"""
Returns pandas table containing all found events for given event name
and the involved mice' genotype
* Primary / secondary mice genotype
* Event start time
* Event end time
* Event duration
Args:computeEventFeatures
event_name (str): Event name e. g. Rearing
Returns:
DataFrame
"""
data = defaultdict(list)
animal_list = self.getAnimalList()
for a in animal_list:
if a.genotype is None:
print("Genotype for all mice needs to be set... (aborting)")
for event_name in tqdm(event_list, desc="Event..."):
for A in animal_list:
for B in animal_list:
if A.baseId == B.baseId:
continue
with mute_prints():
eventTimeLine = EventTimeLine(
self.conn,
event_name,
idA=A.baseId,
idB=B.baseId,
minFrame=self.detectionStartFrame,
maxFrame=self.detectionEndFrame,
)
event_list = eventTimeLine.getEventList()
for e in event_list:
data["genotype_primary"].append(f"{A.genotype}")
data["genotype_secondary"].append(f"{B.genotype}")
data["event_name"].append(event_name)
data["start_sec"].append(e.startFrame / oneSecond)
data["end_sec"].append(e.endFrame / oneSecond)
data["duration"].append(e.duration() / oneSecond)
df = pd.DataFrame(data)
if len(df) == 0:
return df
df.insert(2, "time", pd.to_timedelta(df["start_sec"], unit="s"))
return df.sort_values("time").reset_index(drop=True)
def plotNight(self, show=True, saveFile=None):
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 4))
nightTimeLine = EventTimeLine(self.conn, "night", None, None, None,
None)
for nightEvent in nightTimeLine.eventList:
ax.axvspan(nightEvent.startFrame,
nightEvent.endFrame,
alpha=0.1,
color='black')
ax.text((nightEvent.startFrame + nightEvent.endFrame) / 2,
0.5,
"dark phase",
fontsize=8,
ha='center')
''' set x axis '''
formatter = ticker.FuncFormatter(self.frameToTimeTicker)
ax.xaxis.set_major_formatter(formatter)
ax.tick_params(labelsize=6)
ax.xaxis.set_major_locator(ticker.MultipleLocator(30 * 60 * 60 * 12))
ax.xaxis.set_minor_locator(ticker.MultipleLocator(30 * 60 * 60))
if saveFile != None:
print("Saving figure : " + saveFile)
fig.savefig(saveFile, dpi=100)
if (show):
plt.show()
plt.close()
def EventTimeLineCached(connection,
file,
eventName,
idA=None,
idB=None,
idC=None,
idD=None,
minFrame=None,
maxFrame=None,
loadEventIndependently=False):
global eventCacheDico_
if eventCacheEnable_ == True:
#print ("Cache debug:", connection, file, eventName, "ids:" , idA, idB, idC, idD, "frames:", minFrame, maxFrame , loadEventWithoutOverlapCheck )
if (file, eventName, idA, idB, idC, idD, minFrame, maxFrame,
loadEventIndependently) in eventCacheDico_:
eventTimeLine = eventCacheDico_[file, eventName, idA, idB, idC,
idD, minFrame, maxFrame,
loadEventIndependently]
print(eventName, " Id(", idA, ",", idB, ",", idC,
",", idD, ") Loaded from cache (",
len(eventTimeLine.eventList), " records. )")
return eventTimeLine
eventTimeLine = EventTimeLine(
connection,
eventName,
idA=idA,
idB=idB,
idC=idC,
idD=idD,
minFrame=minFrame,
maxFrame=maxFrame,
loadEventIndependently=loadEventIndependently)
if eventCacheEnable_ == True:
print("Caching eventTimeLine")
eventCacheDico_[file, eventName, idA, idB, idC, idD, minFrame,
maxFrame, loadEventIndependently] = eventTimeLine
else:
print("Loading event from base (Cache event disabled)")
return eventTimeLine
def plotSensorData(self,
sensor="TEMPERATURE",
show=True,
saveFile=None,
minValue=0,
autoNight=False):
"""
plots data for temperature
"""
print("plotting sensor data. ", sensor)
cursor = self.conn.cursor()
# Check the number of row available in base
query = "SELECT FRAMENUMBER," + sensor + " FROM FRAME"
try:
cursor.execute(query)
except:
print("plot sensor data: can't access data for ", sensor)
return
rows = cursor.fetchall()
cursor.close()
frameNumberList = []
sensorValueList = []
for row in rows:
sensorValue = row[1]
if (sensorValue > minValue):
if sensorValue != None and minValue != None:
frameNumberList.append(row[0])
sensorValueList.append(sensorValue)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(24, 8))
''' set x axis '''
formatter = ticker.FuncFormatter(self.frameToTimeTicker)
ax.xaxis.set_major_formatter(formatter)
ax.tick_params(labelsize=20)
ax.xaxis.set_major_locator(ticker.MultipleLocator(30 * 60 * 60 * 12))
ax.xaxis.set_minor_locator(ticker.MultipleLocator(30 * 60 * 60))
ax.plot(frameNumberList,
sensorValueList,
color="black",
linestyle='-',
linewidth=1,
label=sensor)
ax.legend(loc=1)
# plot night
nightTimeLine = EventTimeLine(self.conn, "night", None, None, None,
None)
mean = 0
std = 0
if len(sensorValueList) > 1:
mean = np.mean(sensorValueList)
std = np.std(sensorValueList)
for nightEvent in nightTimeLine.eventList:
ax.axvspan(nightEvent.startFrame,
nightEvent.endFrame,
alpha=0.1,
color='black',
ymin=0,
ymax=1)
ax.text((nightEvent.startFrame + nightEvent.endFrame) / 2,
mean + std,
"dark phase",
fontsize=20,
ha='center')
autoNightList = []
if autoNight:
# compute mean value:
plt.axhline(y=mean, color='r', linestyle='--')
ax.text(0,
mean,
"auto night threshold",
fontsize=20,
ha='left',
va='bottom')
inNight = False
start = None
end = None
for i in range(len(frameNumberList)):
frame = frameNumberList[i]
value = sensorValueList[i]
if value < mean: # night
if inNight:
continue
else:
inNight = True
start = frame
else:
if inNight:
end = frame
inNight = False
if end - start > 300:
autoNightList.append((start, end))
else:
print(
"Skipping very short night phase. (less than 10 seconds)"
)
start = None
end = None
for autoNight in autoNightList:
ax.axvspan(autoNight[0],
autoNight[1],
alpha=0.1,
color='red',
ymin=0.2,
ymax=0.8)
ax.text((autoNight[0] + autoNight[1]) / 2,
mean,
"auto dark phase",
fontsize=20,
ha='center',
color='red')
plt.xlabel('time')
if sensor == "TEMPERATURE":
plt.ylabel('Temperature (C)')
plt.ylim(17, 28)
if sensor == "SOUND":
plt.ylabel('Sound level (indice)')
if sensor == "HUMIDITY":
plt.ylabel('Humidity %')
plt.ylim(0, 100)
if sensor == "LIGHTVISIBLE":
plt.ylabel('Visible light (indice)')
if sensor == "LIGHTVISIBLEANDIR":
plt.ylabel('Visible and infrared light (indice)')
if saveFile != None:
print("Saving figure : " + saveFile)
fig.savefig(saveFile, dpi=100)
'''
print("TEST 1")
import os
os.startfile( saveFile, 'open')
print( "TEST 2")
'''
if (show):
plt.show()
plt.close()
if autoNight:
return autoNightList
for file in files:
# connect to database
connection = sqlite3.connect(file)
# create an animalPool, which basically contains your animals
animalPool = AnimalPool()
# load infos about the animals
animalPool.loadAnimals(connection)
# load all detection (positions) of all animals for the first hour
animalPool.loadDetection(start=0, end=oneHour)
eventTimeLine = EventTimeLine(connection,
"Oral-genital Contact",
idA=1,
idB=2,
minFrame=0,
maxFrame=oneHour)
print("Event list for label ", eventTimeLine.eventNameWithId)
print("for animal 1:", animalPool.getAnimalDictionnary()[1].RFID)
print("for animal 2:", animalPool.getAnimalDictionnary()[2].RFID)
print("Number of events:", len(eventTimeLine.getEventList()))
print("start frame", "end frame", "duration(in frame)")
for event in eventTimeLine.eventList:
print(event.startFrame, event.endFrame, event.duration())
if __name__ == '__main__':
#ask the user for database to process
files = getFilesToProcess()
for file in files:
# connect to database
connection = sqlite3.connect( file )
# create an animalPool, which basically contains your animals
animalPool = AnimalPool()
# load infos about the animals
animalPool.loadAnimals( connection )
# load all detection (positions) of all animals for the first hour
animalPool.loadDetection( start = 0, end = oneHour )
eventTimeLineList = []
for a in animalPool.getAnimalDictionnary():
for b in animalPool.getAnimalDictionnary():
eventTimeLine = EventTimeLine( connection, "Oral-genital Contact", idA = a, idB = b, minFrame = 0, maxFrame = oneHour )
eventTimeLineList.append( eventTimeLine )
plotMultipleTimeLine( eventTimeLineList )
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import *
from lmtanalysis.Event import EventTimeLine
if __name__ == '__main__':
#ask the user for database to process
files = getFilesToProcess()
for file in files:
# connect to database
connection = sqlite3.connect(file)
# create an animalPool, which basically contains your animals
animalPool = AnimalPool()
# load infos about the animals
animalPool.loadAnimals(connection)
# load all detection (positions) of all animals for the first hour
animalPool.loadDetection(start=0, end=oneHour)
eventTimeLine = EventTimeLine(connection,
"Oral-genital Contact",
minFrame=0,
maxFrame=oneHour)
eventTimeLine.plotTimeLine()
min = start*oneHour
max = (start+1)*oneHour
for animal in animalPool.getAnimalList():
bt = animal.getBodyThreshold( tmin= min, tmax = max )
bs = animal.getMedianBodyHeight( tmin= min , tmax = max )
print ( "min:\t" , str(min), "\tmax:\t", str(max), "\tAnimal:\t" , str(animal.baseId), "\tBT:\t " , str(bt) , "\tBS:\t" , str(bs) )
quit()
'''
eventTimeLineList = []
naiveMoveTimeLine = EventTimeLine(connection,
"Stop",
idA=1,
minFrame=minT,
maxFrame=maxT,
inverseEvent=True)
print("MOVE")
print("Naive move Total time: ",
str(naiveMoveTimeLine.getTotalLength()))
naiveMoveTimeLine.plotTimeLine()
detectionMoveTimeLine = EventTimeLine(connection,
"Detection",
idA=1,
minFrame=minT,
maxFrame=maxT)
eventTimeLineList.append(detectionMoveTimeLine)
eventTimeLineList.append(naiveMoveTimeLine)
files = getFilesToProcess()
for file in files:
# connect to database
connection = sqlite3.connect(file)
# create an animalPool, which basically contains your animals
animalPool = AnimalPool()
# load infos about the animals
animalPool.loadAnimals(connection)
# load all detection (positions) of all animals for the first hour
animalPool.loadDetection(start=0, end=oneHour)
eventTimeLine1 = EventTimeLine(connection,
"Oral-genital Contact",
idA=1,
idB=2,
minFrame=0,
maxFrame=oneHour)
eventTimeLine2 = EventTimeLine(connection,
"Oral-genital Contact",
idA=2,
idB=1,
minFrame=0,
maxFrame=oneHour)
plotMultipleTimeLine([eventTimeLine1, eventTimeLine2])
