def process(file, dataframe, expListToCheck, expListToCheckForPause,
expListToCheckForRfid):
print(file)
#open the database file
connection = sqlite3.connect(file)
c = connection.cursor()
pool = AnimalPool()
pool.loadAnimals(connection)
if len(pool.animalDictionnary.keys()) > 1:
print('More than one animal in this experiment. Please check.')
expListToCheck.append(file)
elif len(pool.animalDictionnary.keys()) == 1:
query = "SELECT PAUSED FROM FRAME WHERE PAUSED = '1'"
c.execute(query)
rows = c.fetchall()
print('existing paused frames: ', rows)
if len(rows) == 0:
# get the RFID of the tracked animal in the database
rfidDatabase = pool.animalDictionnary[1].RFID
print('RFID database: ', rfidDatabase)
#attribute the corresponding frame number to be changed
count = 0
for rfidTable in dataframe['RFID']:
#print('RFID table: ', rfidTable)
if rfidTable in rfidDatabase:
print('RFID: ', rfidTable, rfidDatabase)
frameToPause = dataframe[dataframe['RFID'] ==
rfidTable]['paused_frame'].item()
print('paused frame: ', frameToPause)
count = 1
if count == 0:
print('This RFID is not referenced in the table.')
print(file, rfidDatabase)
expListToCheckForRfid.append(file)
else:
#modify the PAUSED status in the database for the corresponding framenumber
query = "UPDATE FRAME SET PAUSED = '1' WHERE FRAMENUMBER = {}".format(
frameToPause)
c.execute(query)
else:
print('Paused frames already exist; please check')
expListToCheckForPause.append(file)
connection.commit()
c.close()
connection.close()
def set_genotype(files):
if files is None:
print("No files selected (aborting)")
return
for file in files:
print("\n\nFile: ", file)
print("-" * 80)
if not os.path.exists(file):
print(" ! File does not exist... (skipping)")
continue
with mute_prints():
connection = sqlite3.connect(file)
pool = AnimalPool()
pool.loadAnimals(connection)
print(
f"Setting genotype of {len(pool.getAnimalList())} mice, press [Enter] to keep existing one):"
)
for animal in sorted(pool.getAnimalList(), key=lambda a: a.name):
genotype = input(
f" * {animal.name}_{animal.RFID} [{animal.genotype}]: ")
genotype = genotype.strip()
if len(genotype) > 0:
print(
f" - setting {animal.name}_{animal.RFID} to '{genotype}'"
)
animal.setGenotype(genotype)
else:
print(f" - keeping genotype {animal.genotype}")
print("Genotype saved in database.")
print("-" * 120)
def process(file):
print("**********************")
print(file)
print("**********************")
connection = sqlite3.connect(file)
animalPool = AnimalPool()
animalPool.loadAnimals(connection)
# show the mask of animals at frame 300
animalPool.showMask(300)
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",
idA=1,
idB=2,
minFrame=0,
maxFrame=oneHour)
print("Event list for label ", eventTimeLine.eventNameWithId)
import sqlite3
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour
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)
# filter detection by animalSpeed (speed is in centimeters per second)
animalPool.filterDetectionByInstantSpeed(30, 70)
# plot and show trajectory
animalPool.plotTrajectory(
title="Trajectories filtered by speed (between 30 to 70 cm/s) ")
import sqlite3
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour
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)
# filter detection by animalSpeed (speed is in centimeters per second)
animalPool.filterDetectionByInstantSpeed(0, 2)
# plot and show trajectory
animalPool.plotTrajectory(
title="Trajectories filtered by speed (max 2) ")
@author: Fab
'''
import sqlite3
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour
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, lightLoad=True)
# plot and show trajectory
animalPool.plotTrajectory()
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour, oneMinute
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=10 * oneMinute)
# filter detection by area (in cm from the top left of the cage)
animalPool.filterDetectionByArea(0, 30, 25, 50)
# loop over all animals in this database
for animal in animalPool.getAnimalList():
animal.plotTrajectory3D()
import sqlite3
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour, oneMinute
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=10 * oneMinute)
# filter detection by area (in cm from the top left of the cage)
animalPool.filterDetectionByArea(0, 30, 25, 50)
# plot and show trajectory
animalPool.plotTrajectory(title="Trajectories filtered by area")
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import *
from lmtanalysis.Event import EventTimeLine, plotMultipleTimeLine
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.Animal import AnimalPool
from lmtanalysis.Measure import *
from lmtanalysis.Event import EventTimeLine, plotMultipleTimeLine
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 )
animalPool.loadDetection( 0, 3*24*oneHour )
min = 0
max = 24*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) )
min = 24*oneHour
max = 2*24*oneHour
for animal in animalPool.getAnimalList():
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()
if __name__ == '__main__':
#ask the user for database to process
files = getFilesToProcess()
minT = 6 * oneHour
#maxT = 3*oneDay
maxT = (6 + 1) * oneHour
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)
animalPool.loadDetection(0, 1 * oneHour)
'''
min = 0
max = 24*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) )
print("---")
for start in range( 0,24 ):
print("**********")
from lmtanalysis.FileUtil import getFilesToProcess
from lmtanalysis.Animal import AnimalPool
from lmtanalysis.Measure import oneHour, oneMinute
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=10 * oneMinute)
# filter detection by area (in cm from the top left of the cage)
animalPool.filterDetectionByArea(0, 30, 25, 50)
# loop over all animals in this database
for animal in animalPool.getAnimalList():
# print RFID of animal
print("Animal : ", animal.RFID)
