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Case study

Benjamin Gallois

Benjamin Gallois

FastTrack creator
Copyright (C) FastTrack.
Permission is granted to copy, distribute and/or modify this document.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Case study #1#

Objective: track multiple D. Melanogaster and determine the time spent in the upper area of the experimental apparatus. Dataset: http://cloud.ljp.upmc.fr/datasets/TD2/#DRO_004

Import the data#

import fastanalysis as fa
import seaborn as sns
import numpy as np
import matplotlib as mpl
plt.style.use("fivethirtyeight")
import warnings
warnings.filterwarnings('ignore')
data = fa.Load("tracking.txt")

Explore the tracking data#

# Distribution of vertical positions for all the objects
p0 = sns.histplot(data=data.getDataframe(), x="yBody", kde=True);
p0.set_xlabel("Vertical position");

png

# Distribution of vertical positions for each individual
p1 = sns.displot(data=data.getDataframe(), x="yBody", hue="id", kind="kde");
p1.ax.set_xlabel("Vertical position");

png

p2 = sns.boxplot(data=data.getDataframe(), x="id", y="yBody");
p2.set_xlabel("Id");
p2.set_ylabel("Vertical position");

png

Compute the preference#

# For each individual computes a preference index
pi = []
for i in range(data.getObjectNumber()):
dat = data.getObjects(i)
dat.loc[:, "diffTime"] = dat.imageNumber.diff().values
up = dat[dat.yBody > 100]
down = dat[dat.yBody <= 100]
pi.append((up.diffTime.sum() - down.diffTime.sum())/(up.diffTime.sum() + down.diffTime.sum()))
p3 = sns.boxplot(y=pi)
p3.set_ylim(-1, 1)
p3.set_ylabel("Preference index");
p3.set_title("Objects preference to the upper side");

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p4 = sns.kdeplot(data=data.getDataframe(), x=np.random.normal(size=data.getDataframe().values.shape[0]), y="yBody", fill=True);
p4.set_xlabel("Horizontal position randomized");
p4.set_ylabel("Vertical position");
p4.set_title("Distribution of presence");

png

pi = []
for i in range(data.getObjectNumber()):
dat = data.getObjects(i)
dat.loc[:, "diffTime"] = dat.imageNumber.diff().values
pref = []
for l, __ in enumerate(dat.yBody.values):
up = dat[0:l][dat[0:l].yBody.values > 100]
down = dat[0:l][dat[0:l].yBody.values <= 100]
pref.append((up.diffTime.sum() - down.diffTime.sum())/(up.diffTime.sum() + down.diffTime.sum()))
pi.append(pref)
for i, j in enumerate(pi):
p5= sns.lineplot(x=np.arange(len(j)), y=j, label=str(i))
p5.set_xlabel("Time (images)");
p5.set_ylabel("Preference index");
p5.set_title("Preference index function of time");
p5.legend(title="id", bbox_to_anchor=(1.05, 1));

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