Source Code for Module dimer.ops

 1  ''' 
 2  common operations on numpy arrays 
 3  @author: odenas 
 4  ''' 
 5   
 6  import numpy as np 
 7   
 8 -def fit(x, axis=None, nans=0.0): 
 9      """scale values of x in the interval [-1, 1] 
10       
11      @param x: ndarray 
12      @param axis: passed to numpy.max and numpy.min 
13      @param nans: if nan values are produced replace them with this 
14      @return: scaled values of x""" 
15       
16       
17      M = x.max(axis = axis) 
18      m = x.min(axis = axis) 
19      mid_p = (M + m) / 2 
20      rng = (M - m) 
21       
22      fitx = 2 * (x - mid_p) / rng 
23      fitx[ np.isnan(fitx) ] = nans 
24   
25      #if not (np.max(fitx) == 1 and np.min(fitx) == -1): 
26      #    import pdb 
27      #    pdb.set_trace() 
28       
29      return fitx 
30   
31 -def standardize(x, axis=None, nans=0.0): 
32      """transform each component of flattened X examples to 0 mean and 1 std 
33      So the values of track t at position i are 0 mean and 1 std 
34   
35      x: a pandas data panel of the form <anchors> X <tracks> X <genome position> 
36      return: (the shifted input, 
37      the mean for each input component, the sd of each input component) 
38      the latter 2 are arrays of shape(<tracks>, <genome position>) 
39      """ 
40   
41   
42      m = x.mean( axis = axis ) 
43      v = x.std( axis = axis ) 
44   
45      normX = ( (x - m) / v ) 
46      normX[ np.isnan(normX) ] = nans 
47       
48      return normX, m, v 
49