Source Code for Module pype.vectorops

  1  # -*- Mode: Python; tab-width: 4; py-indent-offset: 4; -*- 
  2   
  3  """Simple vector operations 
  4   
  5  Implements a bunch of common but useful vector operations using 
  6  Numeric (if possible). If Numeric's not available, tries to fall 
  7  back to (slower) raw-python. 
  8   
  9  Author -- James A. Mazer (mazerj@gmail.com) 
 10   
 11  """ 
 12   
 13  import numpy as np 
 14   
 15  # take these direction from numpy! 
 16  mean = np.mean 
 17  std = np.std 
 18  diff = np.diff 
 19   
 20 -def find(boolvec): 
 21          """Find indices of all TRUE elements in boolvec. 
 22   
 23          You can use like this:: 
 24   
 25            take(x, find(greater(x, 0))) 
 26   
 27          to select all elements of x greater than zero.. 
 28   
 29          """ 
 30          return np.compress(boolvec, np.arange(len(boolvec))) 
 31   
 32 -def sem(v, sig=None): 
 33          """Compute standard error of the mean of vector. 
 34   
 35          """ 
 36          if len(v) > 1: 
 37                  if sig is None: 
 38                          sig = std(v) 
 39                  return sig/(float(len(v))**0.5) 
 40          else: 
 41                  return 0.0 
 42   
 43 -def zeros(v): 
 44          """Count number of zero entries in vector. 
 45   
 46          """ 
 47          if not isinstance(v, np.ndarray): 
 48                  v = np.array(v, np.float) 
 49          return len(v)-len(np.nonzero(v)) 
 50   
 51 -def smooth_boxcar(v, kn=1): 
 52          """Smooth vector using a boxcar (square) filter (ie, running 
 53          average), where kn=1 is a 3pt average, kn=2 is a 5pt average etc.. 
 54   
 55          """ 
 56          if not isinstance(v, np.ndarray): 
 57                  v = np.array(v, np.float) 
 58          n = len(v) 
 59          vout = np.zeros(v.shape) 
 60          for ix in range(0, n): 
 61                  a = ix - kn 
 62                  if a < 0: 
 63                          a = 0 
 64                  b = ix + kn 
 65                  if b > n: 
 66                          b = n 
 67                  vout[ix] = mean(v[a:b]) 
 68          return vout 
 69   
 70 -def decimate(v, n): 
 71          """Simple decimatation (ie, downsampling) vector by n. No effort 
 72          to be smart about this -- integer decimation only! 
 73   
 74          """ 
 75          if not isinstance(v, np.ndarray): 
 76                  v = np.array(v, np.float) 
 77          return np.take(v, range(0,len(v),n)) 
 78   
 79 -def sparseness(v): 
 80          """Compute (Tove & Rolls) sparseness of vector. 
 81   
 82          """ 
 83          if not isinstance(v, np.ndarray): 
 84                  v = np.array(v, np.float) 
 85          n = float(len(v)); 
 86          if np.sum(v) == 0: 
 87                  return 0 
 88          else: 
 89                  a = ((np.sum(v) / n) ** 2.0) / (np.sum((v**2.0) / n)) 
 90                  return ((1.0 - a) / (1.0 - (1.0 / n))) 
 91   
 92 -def nanround(n, digits=0): 
 93      if n is not None: 
 94          n = round(n, digits) 
 95      return n 
 96   
 97  if __name__=='__main__' : 
 98          pass 
 99