content: #!/usr/bin/env python
  
  """
  Make plots showing how to calculate the p-value
  """
  
  import matplotlib.pyplot as pl
  from scipy.stats import norm
  from scipy.special import erf
  import numpy as np
  
  mu = 0. # the mean, mu
  sigma = 1. # standard deviation
  
  x = np.linspace(-4, 4, 1000) # x
  
  # set plot to render labels using latex
  pl.rc('text', usetex=True)
  pl.rc('font', family='serif')
  pl.rc('font', size=14)
  fig = pl.figure(figsize=(7,4), dpi=100)
  
  # value of x for calculating p-value
  Z = 1.233
  
  y = norm.pdf(x, mu, sigma)
  
  # plot pdfs
  pl.plot(x, y, 'r')
  pl.plot([-Z, -Z], [0., np.max(y)], 'k--')
  pl.plot([Z, Z], [0., np.max(y)], 'k--')
  
  pl.fill_between(x, np.zeros(len(x)), y, where=x<=-Z, facecolor='green', interpolate=True, alpha=0.6)
  pl.fill_between(x, np.zeros(len(x)), y, where=x>=Z, facecolor='green', interpolate=True, alpha=0.6)
  
  pvalue = 1.-erf(Z/np.sqrt(2.))
  
  ax = pl.gca()
  ax.set_xlabel('$Z$', fontsize=14)
  ax.set_ylabel('$p(Z)$', fontsize=14)
  ax.set_xlim(-4, 4)
  ax.grid(True)
  
  ax.text(Z+0.1, 0.3, '$Z_{\\textrm{obs}} = 1.233$', fontsize=16)
  ax.text(-3.6, 0.31, '$p$-value$= %.2f$' % pvalue, fontsize=18,
          bbox={'facecolor': 'none', 'pad':12, 'ec': 'r'})
  
  fig.subplots_adjust(bottom=0.15)
  
  pl.savefig('../pvalue.pdf')
  pl.show()
  
  