matplotlib/examples/animation/bayes_update.py at master · feisuo/matplotlib · GitHub

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# update a distribution based on new data.

import numpy as np

import matplotlib.pyplot as plt

import scipy.stats as ss

from matplotlib.animation import FuncAnimation

class UpdateDist(object):

def __init__(self, ax, prob=0.5):

self.success = 0

self.prob = prob

self.line, = ax.plot([], [], 'k-')

self.x = np.linspace(0, 1, 200)

self.ax = ax

# Set up plot parameters

self.ax.set_xlim(0, 1)

self.ax.set_ylim(0, 15)

self.ax.grid(True)

# This vertical line represents the theoretical value, to

# which the plotted distribution should converge.

self.ax.axvline(prob, linestyle='--', color='black')

def init(self):

self.success = 0

self.line.set_data([], [])

return self.line,

def __call__(self, i):

# This way the plot can continuously run and we just keep

# watching new realizations of the process

if i == 0:

return self.init()

# Choose success based on exceed a threshold with a uniform pick

if np.random.rand(1,) < self.prob:

self.success += 1

y = ss.beta.pdf(self.x, self.success + 1, (i - self.success) + 1)

self.line.set_data(self.x, y)

return self.line,

fig, ax = plt.subplots()

ud = UpdateDist(ax, prob=0.7)

anim = FuncAnimation(fig, ud, frames=np.arange(100), init_func=ud.init,

interval=100, blit=True)

plt.show()

matplotlib/examples/animation/bayes_update.py at master · feisuo/matplotlib · GitHub

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