import numpy as np
import matplotlib.pyplot as plt
from plotstyle import *

# normal distribution:
x = np.arange(-3.0, 5.0, 0.01)
g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
x1=0.0
x2=1.0

fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
ax.set_xlabel('x')
ax.set_ylabel('Probability density p(x)')
ax.set_ylim(0.0, 0.46)
ax.set_yticks(np.arange(0.0, 0.45, 0.1))
ax.annotate('Gaussian',
            xy=(-1.0, 0.28), xycoords='data',
            xytext=(-2.5, 0.35), textcoords='data', ha='left',
            arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
            connectionstyle="angle3,angleA=10,angleB=110"))
ax.annotate('$P(0<x<1) = \int_0^1 p(x) \, dx$',
            xy=(0.5, 0.24), xycoords='data',
            xytext=(1.2, 0.4), textcoords='data', ha='left',
            arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
            connectionstyle="angle3,angleA=10,angleB=80"))
ax.fill_between(x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], **fsBs)
ax.plot(x,g, **lsA)
fig.savefig('pdfprobabilities.pdf')