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()