import fastanalysis as faimport seaborn as snsimport numpy as npimport matplotlib as mplplt.style.use("fivethirtyeight")import warningswarnings.filterwarnings('ignore')

data = fa.Load("tracking.txt")

# Distribution of vertical positions for all the objectsp0 = sns.histplot(data=data.getDataframe(), x="yBody", kde=True);p0.set_xlabel("Vertical position");

# Distribution of vertical positions for each individualp1 = sns.displot(data=data.getDataframe(), x="yBody", hue="id", kind="kde");p1.ax.set_xlabel("Vertical position");

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

# For each individual computes a preference indexpi = []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");

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");

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));

import sys!{sys.executable} -m pip install fastanalysis

import fastanalysis as fa

data = fa.Load("tracking.txt")

print("Total objects: ", data.getObjectNumber())

print("Keys: ", data.getKeys())

data.getObjects(0)

data.getObjects([0, 2, 4])

data.getFrames(100)

data.getFrames([100, 102])

data.getObjectsInFrames(ids=[0,2,4], indexes=[100,102])

data.isObjectsInFrame(ids=[0,2,4], index=100)

import matplotlib.pyplot as plt%matplotlib inline
plotObj = fa.Plot(data)plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=True);

plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=False, subplots=False);

plotObj.velocityDistribution(ids=[0, 1], key="Body", pooled=True);

import seaborn as sns
data_object_0 = data.getObjects(0)sns.jointplot(data=data_object_0, x="xBody", y="yBody");

# All objects pooledsns.kdeplot(data=data.getObjects(list(range(data.getObjectNumber()))), x="xBody", y="yBody", shade=True);

tmp = data.getObjects([0,2,4])tmp["displacement"] = (tmp["xBody"]**2 + tmp["yBody"]**2)**0.5sns.violinplot(data=tmp, x="id", y="displacement");

sns.boxplot(data=tmp, x="id", y="displacement");

sns.boxenplot(data=tmp, x="id", y="displacement");

import pandas as pdimport numpy as npimport matplotlib.pyplot as plt

# Import the datadata = pd.read_csv("tracking.txt", sep='\t')data

# Count the number of detected objectobjectNumber = len(set(data["id"].values))objectNumber

# Count the number of imageimageNumber = np.max(data["imageNumber"]) + 1imageNumber

# Plot the number of objects detected by frameobjectByFrame = np.zeros(imageNumber)for i in range(imageNumber):    objectByFrame[i] = data[data["imageNumber"] == i].shape[0]    plt.scatter(range(imageNumber), objectByFrame)

# Plot the trajectory of the first object and its orientationdataObject0 = data[data["id"] == 0]distance = np.sqrt(np.diff(dataObject0["xBody"].values)**2 + np.diff(dataObject0["yBody"].values)**2)framerate = 50time = np.diff(dataObject0["imageNumber"].values)/frameratevelocity = distance/time
fig, ax = plt.subplots(1, 2)fig.subplots_adjust(right = 2)
ax[0] = plt.subplot(121)plot = ax[0].scatter(dataObject0["xBody"][0:-1], dataObject0["yBody"][0:-1], c = velocity, s = 1)ax[0].set_xlabel("x-position")ax[0].set_ylabel("y-position")bar = fig.colorbar(plot)bar.set_label("Velocity")
ax[1] = plt.subplot(122, projection='polar')ax[1].scatter(range(dataObject0["tBody"].shape[0]), dataObject0["tBody"], s = 0.4)ax[1].set_title("Object direction")