effectsize.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

trials = 200

x = np.random.randn(trials, 9, 16)

effect_size = np.linspace(0, 1, 9)
sample_size = np.linspace(10, 1000, 16)

for trial in range(trials):
	for es_i,es_v in enumerate(effect_size):
		for ss_i,ss_v in enumerate(sample_size):
			patients = np.random.randn(ss_v)+es_v
			controls = np.random.randn(ss_v)
			x[trial, es_i, ss_i] = (np.mean(patients)-np.mean(controls))

plt.figure(figsize=(16,9))
plt.imshow(np.std(x, axis=0), interpolation='nearest', cmap='afmhot')
plt.xlabel('Sample size')
plt.ylabel('Effect size')
plt.gca().invert_yaxis()
plt.xticks(range(len(sample_size)), sample_size.astype(int))
plt.yticks(range(len(effect_size)), effect_size)
plt.title(r'$\phi$')
plt.colorbar()
plt.tight_layout()
plt.show()