import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
np.random.seed(0) # Set seed so plots look the same.
def histogram_demo(ax):
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(10000)
num_bins = 50
# The histogram of the data.
_, bins, _ = ax.hist(x, num_bins, normed=1, label='data')
# Add a 'best fit' line.
y = mlab.normpdf(bins, mu, sigma)
ax.plot(bins, y, '-s', label='best fit')
ax.legend()
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
def bar_plot(ax):
categories = ['a', 'b', 'c', 'd', 'e']
x = np.arange(len(categories))
n_modes = 3
width = 0.25
for i in range(n_modes):
y = np.random.randint(1, 25, size=len(categories))
x_offset = i * width
ax.bar(x+x_offset, y, width, color=plt.rcParams['axes.color_cycle'][i])
ax.set_xticks(x+width)
ax.set_xticklabels(categories)
fig, axes = plt.subplots(nrows=2)
histogram_demo(axes[0])
bar_plot(axes[1])
fig.tight_layout()
plt.show()
