def plot_iteration(self, index, t):
""" Plots an iteration (a probability distribution).
The iteration has to exist.
index: index of the distribution of which the iteration should be plotted
t: iteration time
"""
import matplotlib.pyplot as plt
iteration = self.get_iteration(index, t)
mkm.pyplot_bar(iteration)
plt.title("Probability distribution after %d steps" % (t))
plt.xlabel("Markov chain state space")
plt.ylabel("Probabiliy")
plt.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
plt.xlim(0, self.n)
plt.ylim(0, 1.1 * numpy.max(iteration))
plt.show()
def frame(i):
fig = plt.figure(figsize=(19.20, 10.80), dpi=100)
# time of closest iteration
t = self.closest_iteration_time(index,i*frametime)
iteration = self.get_iteration(index,t)
mkm.pyplot_bar(iteration)
plt.title("Probability distribution after %d steps" % (t))
plt.xlabel("Markov chain state space")
plt.ylabel("Probabiliy")
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
plt.xlim(0, self.n)
plt.ylim(0, 1.1*numpy.max(iteration))
return fig
def frame(i):
fig = plt.figure(figsize=(19.20, 10.80), dpi=100)
# time of closest iteration
t = self.closest_iteration_time(index, i * frametime)
iteration = self.get_iteration(index, t)
mkm.pyplot_bar(iteration)
plt.title("Probability distribution after %d steps" % (t))
plt.xlabel("Markov chain state space")
plt.ylabel("Probabiliy")
plt.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
plt.xlim(0, self.n)
plt.ylim(0, 1.1 * numpy.max(iteration))
return fig
def plot_iteration(self,index,t):
""" Plots an iteration (a probability distribution).
The iteration has to exist.
index: index of the distribution of which the iteration should be plotted
t: iteration time
"""
import matplotlib.pyplot as plt
iteration = self.get_iteration(index,t)
mkm.pyplot_bar(iteration)
plt.title("Probability distribution after %d steps" % (t))
plt.xlabel("Markov chain state space")
plt.ylabel("Probabiliy")
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
plt.xlim(0, self.n)
plt.ylim(0, 1.1*numpy.max(iteration))
plt.show()
