dispersiveXanes/dispersiveXanes_utils.py

from __future__ import print_function,division
import mcutils as mc
import joblib
import numpy as np

# /--------\
# |        |
# | UTILS  |
# |        |
# \--------/


def rebin1D(a,shape):
  n0 = a.shape[0]//shape
  sh = shape,n0
  return a[:n0*shape].reshape(sh).mean(1)

def calcFOM(p1,p2,ratio,threshold=0.1):
  idx = ( p1>p1.max()*threshold )# & (p2>p2.max()/10)
  ratio = ratio[idx]
  return ratio.std()/np.abs(ratio.mean())
  
def getCenterOfMass(img,x=None,axis=0,threshold=0.05):
  img = img.copy()
  if img.ndim == 1: img=img[np.newaxis,:]; axis=1
  img[img<img.max()*threshold] = 0
  if axis == 1: img=img.T
  p   = img.mean(1)
  if x is None: x   = np.arange(img.shape[0])
  return np.sum(x*p)/np.sum(p)


def maskLowIntensity(p1,p2,threshold=0.03,squeeze=True):
  if p1.ndim == 1:
    p1 = p1[np.newaxis,:]
    p2 = p2[np.newaxis,:]
  p1 = np.ma.asarray( p1.copy() )
  p2 = np.ma.asarray( p2.copy() )
  if threshold is not None:
    m1 = np.nanmax(p1,axis=1); m2 = np.nanmax(p2,axis=1)
    # find where each spectrum is smaller than threshold*max_for_that_shot; they will be masked out
    idx1 = p1 < (m1[:,np.newaxis]*threshold)
    #idx2 = p2 < (m2[:,np.newaxis]*threshold)
    idx = idx1# & idx2
    p1.mask = idx
    p2.mask = idx
  if squeeze:
    p1 = np.squeeze(p1);
    p2 = np.squeeze(p2)
  p1.fill_value=np.nan
  p2.fill_value=np.nan
  return p1,p2

def ratioOfAverage(p1,p2,threshold=0.03):
  """ 
   p1 and p2 are the energy spectrum. if 2D the first index has to be the shot number
   calculate median ratio taking into account only regions where p1 and p2 are > 5% of the max """
  p1,p2 = maskLowIntensity(p1,p2,threshold=threshold,squeeze=False)
  # using masked array because some pixel will have zero shots contributing
  av1 = np.ma.average(p1,axis=0,weights=p1)
  av2 = np.ma.average(p2,axis=0,weights=p2)
  return av2/av1

def medianRatio(p1,p2,threshold=0.03):
  """ 
   p1 and p2 are the energy spectrum. if 2D the first index has to be the shot number
   calculate median ratio taking into account only regions where p1 and p2 are > 5% of the max """
  p1,p2 = maskLowIntensity(p1,p2,threshold=threshold,squeeze=False)
  ratio = p2/p1
  return np.ma.average(ratio,axis=0,weights=p1)