text: """
  Flux Errors
  -----------
  Figure 3.5.
  
  An example of Gaussian flux errors becoming non-Gaussian magnitude errors.
  The dotted line shows the location of the mean flux; note that this is not
  coincident with the peak of the magnitude distribution.
  """
  # Author: Jake VanderPlas
  # License: BSD
  #   The figure produced by this code is published in the textbook
  #   "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
  #   For more information, see http://astroML.github.com
  #   To report a bug or issue, use the following forum:
  #    https://groups.google.com/forum/#!forum/astroml-general
  import numpy as np
  from matplotlib import pyplot as plt
  from scipy.stats import norm
  
  #----------------------------------------------------------------------
  # This function adjusts matplotlib settings for a uniform feel in the textbook.
  # Note that with usetex=True, fonts are rendered with LaTeX.  This may
  # result in an error if LaTeX is not installed on your system.  In that case,
  # you can set usetex to False.
  if "setup_text_plots" not in globals():
      from astroML.plotting import setup_text_plots
  setup_text_plots(fontsize=8, usetex=True)
  
  #------------------------------------------------------------
  # Create our data
  
  # generate 10000 normally distributed points
  np.random.seed(1)
  dist = norm(1, 0.25)
  flux = dist.rvs(10000)
  flux_fit = np.linspace(0.001, 2, 1000)
  pdf_flux_fit = dist.pdf(flux_fit)
  
  # transform this distribution into magnitude space
  mag = -2.5 * np.log10(flux)
  mag_fit = -2.5 * np.log10(flux_fit)
  pdf_mag_fit = pdf_flux_fit.copy()
  pdf_mag_fit[1:] /= abs(mag_fit[1:] - mag_fit[:-1])
  pdf_mag_fit /= np.dot(pdf_mag_fit[1:], abs(mag_fit[1:] - mag_fit[:-1]))
  
  #------------------------------------------------------------
  # Plot the result
  fig = plt.figure(figsize=(5, 2.5))
  fig.subplots_adjust(bottom=0.17, top=0.9,
                      left=0.12, right=0.95, wspace=0.3)
  
  # first plot the flux distribution
  ax = fig.add_subplot(121)
  ax.hist(flux, bins=np.linspace(0, 2, 50),
          histtype='stepfilled', fc='gray', alpha=0.5, density=True)
  ax.plot(flux_fit, pdf_flux_fit, '-k')
  ax.plot([1, 1], [0, 2], ':k', lw=1)
  ax.set_xlim(-0.1, 2.1)
  ax.set_ylim(0, 1.8)
  
  ax.set_xlabel(r'${\rm flux}$')
  ax.set_ylabel(r'$p({\rm flux})$')
  ax.yaxis.set_major_locator(plt.MultipleLocator(0.4))
  ax.text(0.04, 0.95, r'${\rm 20\%\ flux\ error}$',
          ha='left', va='top', transform=ax.transAxes)
  
  # next plot the magnitude distribution
  ax = fig.add_subplot(122)
  ax.hist(mag, bins=np.linspace(-1, 2, 50),
          histtype='stepfilled', fc='gray', alpha=0.5, density=True)
  ax.plot(mag_fit, pdf_mag_fit, '-k')
  ax.plot([0, 0], [0, 2], ':k', lw=1)
  ax.set_xlim(-1.1, 1.1)
  ax.set_ylim(0, 1.8)
  ax.yaxis.set_major_locator(plt.MultipleLocator(0.4))
  ax.text(0.04, 0.95, r'${\rm mag} = -2.5\log_{10}({\rm flux})$',
          ha='left', va='top', transform=ax.transAxes)
  
  ax.set_xlabel(r'${\rm mag}$')
  ax.set_ylabel(r'$p({\rm mag})$')
  
  plt.show()
  