lesson23_24_ex.py

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

import seaborn as sns
rc = {'lines.linewidth' : 2, 'axes.labelsize' : 18,
        'axes.titlesize' : 18}
sns.set(rc=rc)


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

x_high, y_high = ecdf(xa_high)
x_low, y_low = ecdf(xa_low)

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.xlabel('Cross-sectional area ($\mu$m)')
plt.ylabel('eCDF')
plt.legend(('High Food', 'Low Food'), loc='lower right')
plt.show()