class_docstr: A truncated positive exponential continuous random variable.
  
  The probability distribution is::
  
     p(x) ~ exp(k * x)   between a and b
          = 0            otherwise
  
  The arguments are (a, b, k)
  
  %(before_notes)s
  
  %(example)s
class_docstr_tok: ["A","truncated","positive","exponential","continuous","random","variable",".","The","probability","distribution","is",":",":","p","(","x",")","~","exp","(","k","*","x",")","between","a","and","b","=","0","otherwise","The","arguments","are","(","a",",","b",",","k",")","%","(","before_notes",")","s","%","(","example",")","s"]
code: def _pdf(self, x, a, b, k):
      pdf = self._const * np.exp(k * x)
      pdf[(x < a) | (x > b)] = 0
      return pdf
  
file_path: astroML/astroML/stats/random.py
imports: import numpy
  from scipy.stats.distributions import rv_continuous
label_desc: Use image node_id 2 for calling the trunc_exp_gen obj's underlying member method code with example usage: obj._pdf(x, a, b, k) and returns: pdf