day3_exercise3.py

import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns

#Create config for mathplotlib rc params
rc = {'lines.linewidth': 2, 'axes.labelsize': 18, 'axes.titlesize': 18}
sns.set(rc=rc)

#Load text
xa_high = np.loadtxt('data/xa_high_food.csv', comments = '#')
xa_low = np.loadtxt('data/xa_low_food.csv', comments = '#')

#Empirical cumulitive distribution function
def ecdf(data):
    '''
    Compute x, y values for an empirical distribution function
    '''

    x = np.sort(data)
    y = np.arange(1, 1+len(x)) / len(x)

    return x, y

x_high, y_high = ecdf(xa_high)
x_low, y_low = ecdf(xa_low)
plt.close()
plt.plot(x_high, y_high, marker='.', linestyle='none', markersize=20, alpha=0.5)
plt.plot(x_low, y_low, marker='.', linestyle='none', markersize=20, alpha=0.5)
plt.margins(0.03)
plt.xlabel('Cross-sectional area (µm)')
plt.ylabel('eCDF')
plt.legend(('high food', 'low food'), loc='lower right')
plt.show()