old_contents: import numpy as np
  import os
  
  import matplotlib as mpl
  mpl.use('PS')
  import matplotlib.pyplot as plt
  
  import chippr
  from chippr import utils as u
  from chippr import plot_utils as pu
  
  def plot_true_histogram(true_samps, n_bins=50, plot_loc='', plot_name='true_hist.png'):
      """
      Plots a histogram of true input values
  
      Parameters
      ----------
      true_samps: numpy.ndarray, float
          vector of true values of scalar input
      n_bins: int, optional
          number of histogram bins in which to place input values
      plot_loc: string, optional
          location in which to store plot
      plot_name: string, optional
          filename for plot
      """
      pu.set_up_plot()
      f = plt.figure(figsize=(5, 5))
      sps = f.add_subplot(1, 1, 1)
      sps.hist(true_samps, bins=n_bins, normed=1)
      sps.set_xlabel(r'$z_{true}$')
      sps.set_ylabel(r'$n(z_{true})$')
      f.savefig(os.path.join(plot_loc, plot_name))
  
  def plot_obs_scatter(true_samps, obs_samps, plot_loc='', plot_name='obs_scatter.png'):
      """
      Plots a scatterplot of true and observed redshift values
  
      Parameters
      ----------
      true_samps: numpy.ndarray, float
          vector of true values of scalar input
      obs_samps: numpy.ndarray, float
          vector of observed values of scalar input
      plot_loc: string, optional
          location in which to store plot
      plot_name: string, optional
          filename for plot
      """
      pu.set_up_plot()
      f = plt.figure(figsize=(5, 5))
      sps = f.add_subplot(1, 1, 1)
      sps.scatter(true_samps, obs_samps)
      sps.set_xlabel(r'$z_{true}$')
      sps.set_ylabel(r'$z_{obs}$')
      f.savefig(os.path.join(plot_loc, plot_name))
  
new_contents: import numpy as np
  import os
  
  import matplotlib as mpl
  mpl.use('PS')
  import matplotlib.pyplot as plt
  
  import chippr
  from chippr import utils as u
  from chippr import plot_utils as pu
  
  def plot_true_histogram(true_samps, n_bins=50, plot_loc='', plot_name='true_hist.png'):
      """
      Plots a histogram of true input values
  
      Parameters
      ----------
      true_samps: numpy.ndarray, float
          vector of true values of scalar input
      n_bins: int, optional
          number of histogram bins in which to place input values
      plot_loc: string, optional
          location in which to store plot
      plot_name: string, optional
          filename for plot
      """
      pu.set_up_plot()
      f = plt.figure(figsize=(5, 5))
      sps = f.add_subplot(1, 1, 1)
      sps.hist(true_samps, bins=n_bins, normed=1, color='k')
      sps.set_xlabel(r'$z_{true}$')
      sps.set_ylabel(r'$n(z_{true})$')
      f.savefig(os.path.join(plot_loc, plot_name))
  
  def plot_obs_scatter(true_samps, obs_samps, plot_loc='', plot_name='obs_scatter.png'):
      """
      Plots a scatterplot of true and observed redshift values
  
      Parameters
      ----------
      true_samps: numpy.ndarray, float
          vector of true values of scalar input
      obs_samps: numpy.ndarray, float
          vector of observed values of scalar input
      plot_loc: string, optional
          location in which to store plot
      plot_name: string, optional
          filename for plot
      """
      pu.set_up_plot()
      f = plt.figure(figsize=(5, 5))
      sps = f.add_subplot(1, 1, 1)
      sps.scatter(true_samps, obs_samps, 'k.')
      sps.set_xlabel(r'$z_{true}$')
      sps.set_ylabel(r'$z_{obs}$')
      f.savefig(os.path.join(plot_loc, plot_name))
  
subject: make plots slightly prettier