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) ")
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")
@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()
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)
# distance traveled by animal (in cm):
print("Distance traveled in area: (in centimeter): ",
animal.getDistance())
animalPool.plotTrajectory(title="Trajectories filtered by area",
scatter=True)