old_contents: import sys
  
  import numpy as np
  from sklearn.neighbors.kde import KernelDensity
  import matplotlib
  matplotlib.use("cairo")
  from matplotlib import pyplot as plt
  
  def estimate_distribution(samples, h=0.1, n_points=100):
  	kde = KernelDensity(bandwidth=h)
  	min_xs = min(samples)
  	max_xs = max(samples)
  	samples = samples[:, np.newaxis]
  	kde.fit(samples)
  	xs = np.linspace(min_xs, max_xs, n_points)
  	ys = kde.score_samples(xs[:, np.newaxis])
  	print xs.shape, ys.shape
  	return xs, ys
  
  def plot_log_probs(plot_flname, subset):
  	plt.hold(True)
  	for loci in xrange(subset.shape[1]):
  		samples = subset[:, loci]
  		xs, ys = estimate_distribution(samples)
  		plt.plot(xs, ys)
  	plt.xlabel("Log Prob", fontsize=16)
  	plt.ylabel("Frequency", fontsize=16)
  	plt.savefig(plot_flname, DPI=200)
  
  
  if __name__ == "__main__":
  	npy_flname = sys.argv[1]
  	start = int(sys.argv[2])
  	end = int(sys.argv[3])
  	plot_flname = sys.argv[4]
  
  	phi_values = np.load(npy_flname)
  	subset = phi_values[:, start:end]
  
  	plot_log_probs(plot_flname, subset)
  
  
  
  
  
  
new_contents: import sys
  
  import numpy as np
  from sklearn.neighbors.kde import KernelDensity
  import matplotlib
  matplotlib.use("cairo")
  from matplotlib import pyplot as plt
  
  def estimate_distribution(samples, h=0.1, n_points=100):
  	kde = KernelDensity(bandwidth=h)
  	min_xs = min(samples)
  	max_xs = max(samples)
  	samples = samples[:, np.newaxis]
  	kde.fit(samples)
  	xs = np.linspace(min_xs, max_xs, n_points)
  	ys = np.exp(kde.score_samples(xs[:, np.newaxis]))
  	print xs.shape, ys.shape
  	return xs, ys
  
  def plot_log_probs(plot_flname, subset):
  	plt.hold(True)
  	for loci in xrange(subset.shape[1]):
  		samples = subset[:, loci]
  		xs, ys = estimate_distribution(samples)
  		plt.plot(xs, ys)
  	plt.xlabel("Log Prob", fontsize=16)
  	plt.ylabel("Frequency", fontsize=16)
  	plt.savefig(plot_flname, DPI=200)
  
  
  if __name__ == "__main__":
  	npy_flname = sys.argv[1]
  	start = int(sys.argv[2])
  	end = int(sys.argv[3])
  	plot_flname = sys.argv[4]
  
  	phi_values = np.load(npy_flname)
  	subset = phi_values[:, start:end]
  
  	plot_log_probs(plot_flname, subset)
  
  
  
  
  
  
subject: Fix call to score