mcutils/xray/dataReduction.py

from __future__ import print_function,division

import logging 
log = logging.getLogger(__name__)


import numpy as np
np.seterr(all='ignore')
from . import utils
from . import storage
import os

def subtractReferences(i,idx_ref, useRatio = False):
  """ given data in i (first index is shot num) and the indeces of the 
      references (idx_ref, array of integers) it interpolates the closest
      reference data for each shot and subtracts it (or divides it, depending
      on useRatio = [True|False]; 
      Note: it works in place (i.e. it modifies i) """
  iref=np.empty_like(i)
  idx_ref = np.squeeze(idx_ref)
  idx_ref = np.atleast_1d(idx_ref)
  if idx_ref.shape[0] == 1:
    return i-i[idx_ref]
  # references before first ref are "first ref"
  iref[:idx_ref[0]] = i[idx_ref[0]]
  # references after last ref are "last ref"
  iref[idx_ref[-1]:] = i[idx_ref[-1]]
  _ref = 0
  for _i in range(idx_ref[0],idx_ref[-1]):
    idx_ref_before = idx_ref[_ref]
    idx_ref_after  = idx_ref[_ref+1]
    ref_before = i[idx_ref_before]
    ref_after  = i[idx_ref_after]
    weight_before = float(_i-idx_ref_before)/(idx_ref_after-idx_ref_before)
    weight_after  = 1-weight_before
    if weight_after == 1:
      iref[_i] = ref_before
    elif weight_before == 1:
      iref[_i] = ref_after
    else:
      # normal reference for an on chi, the weighted average
      iref[_i]      = weight_before*ref_before + weight_after*ref_after
    if _i>=idx_ref_after: _ref += 1
    log.debug("SubtractRederence For image %d : %d-%d"%(_i,idx_ref_before,idx_ref_after))
  if useRatio:
    i /= iref
  else:
    i -= iref
  return i

def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
    funcForAveraging=np.nanmean):
  """ given scanpoints in 'scan' and corresponding data in 'data'
      average all data corresponding the exactly the same scanpoint.
      If the values in scan are coming from a readback, rounding might be
      necessary.
      No normalization is done inside this function
      if isRef is provided must be a boolean array of the same shape as 'scan'
         is there is at least one scanpoint marked as True, the data are 
         subtracted/divided by the interpolated reference
      if lpower is provided the data is divided by it (how it is done depends
         if one uses the ratio or not
      funcForAveraging: is usually np.nanmean or np.nanmedian. it can be any 
         function that support axis=0 as keyword argument
"""
  data = data.astype(np.float)
  avData = np.nanmedian( data , axis = 0 )

  if isRef is None: isRef = np.zeros( data.shape[0], dtype=bool )
  assert data.shape[0] == isRef.shape[0]

  # subtract reference only is there is at least one
  if isRef.sum()>0:
    # create a copy (subtractReferences works in place)
    diff = subtractReferences(data.copy(),np.argwhere(isRef), useRatio=useRatio)
    avNeg = funcForAveraging(data[isRef],axis=0)
  else:
    diff = data
    avNeg = np.zeros_like(avData)

  # normalize signal for laser intensity if provided
  if lpower is not None:
    lpower = utils.reshapeToBroadcast(lpower,data)
    if useRatio is False:
      diff /= lpower
    else:
      diff = (data-1)/lpower+1

  scan_pos = np.unique(scan)
  shape_out = [len(scan_pos),] + list(diff.shape[1:])
  ret       = np.empty(shape_out)
  err       = np.empty(shape_out)
  data_abs  = np.empty(shape_out)
  diffsInScanPoint = []
  chi2_0 = []
  for i,t in enumerate(scan_pos):
    shot_idx = (scan == t)

    # select data for the scan point
    diff_for_scan = diff[shot_idx]
    diffsInScanPoint.append( diff_for_scan )

    # calculate average
    ret[i] = funcForAveraging(diff_for_scan,axis=0)
    data_abs[i] = funcForAveraging(data[shot_idx],axis=0)

    # calculate std
    noise = np.nanstd(diff[shot_idx], axis = 0)

    # calculate chi2 of different repetitions
    chi2 = np.power( (diff_for_scan - ret[i])/noise,2)
    # sum over all axis but first
    for _ in range(diff_for_scan.ndim-1):
      chi2 = np.nansum( chi2, axis=-1 )

    # store chi2_0
    chi2_0.append( chi2/ret[i].size )

    # store error of mean
    err[i] = noise/np.sqrt(shot_idx.sum())
  ret = dict(scan=scan_pos,data=ret,dataUnmasked=ret.copy(),err=err,
        errUnmasked=err.copy(),chi2_0=chi2_0,diffsInScanPoint=diffsInScanPoint,
        dataAbsAvNeg = avNeg, dataAsAbs=ret+avNeg,
        dataAbsAvAll=avData,dataAbsAvScanPoint=data_abs,dataAbs=data.copy())
  ret = storage.DataStorage(ret)
  return ret


def calcTimeResolvedSignal(scan,data,reference="min",monitor=None,q=None,
    saveTxt=True,folder="./",**kw):
  """
    reference: can be 'min', 'max', a float|integer or an array of booleans
    q        : is needed if monitor is a tuple|list
    monitor  : normalization vector (if it is interpreted a list it is
               interpreted as q-range normalization)
    saveTxt  : will save txt outputfiles (diff_av_*) in folder
    other keywords are passed to averageScanPoints
  """
  if reference == "min":
    isRef = (scan == scan.min())
  elif reference == "max":
    isRef = (scan == scan.max())
  elif isinstance(reference,(float,int)):
    isRef = (scan == reference)
  else:
    isRef = reference
  # normalize if needed
  if monitor is not None:
    if isinstance(monitor,(tuple,list)):
      assert q is not None
      assert data.ndim == 2
      idx = (q>= monitor[0]) & (q<= monitor[1])
      monitor = np.nanmedian(data[:,idx],axis=1)
    monitor = utils.reshapeToBroadcast(monitor,data)
    data = data/monitor
  ret = averageScanPoints(scan,data,isRef=isRef,**kw)
  if q is not None: ret["q"] = q
  return ret

def errorMask(data,threshold=5):
  """ Q-by-Q mask !
      Very simple but effective mask for zinger like noise
      The noise is in general lower when using nanmean instead than
      nanmedian but nanmean does not mask out 'spikes'.
      This mask mitigate this effect by using nanmedian for the q points
      that have an higher than usual error (proxy for spikes ...)
      tested with mi1245/dec2016/tiox/tiox1/run3
  """
  assert data.data.ndim == 2
  idx_mask = []
  for iscan in range(len(data.diffsInScanPoint)):
    temp = data.diffsInScanPoint[iscan]
    # sqrt(len(temp)) = sqrt(numOfDiffs); it is needed to estimate error of single Diff
    idx = np.abs(temp-np.median(temp,axis=0)) > threshold*data.err[iscan]*np.sqrt(len(temp))
    idx_mask.append( idx )

    log.debug("errorMask mask, scanpoint: %s, fraction of q points filtered out (average) %.4e [max %.4e])"%\
              (data.scan[iscan],idx.sum()/idx.size,max(np.sum(idx,axis=1)/idx.shape[1])) ) 
  if "masks" not in data: data['masks'] = dict()
  data['masks']['error'] = idx_mask
  return data

def chi2Mask(data,threshold=2):
  """ 
      The noise is in general lower when using nanmean instead than
      nanmedian but nanmean does not mask out 'spikes'.
      This mask mitigate this effect by using nanmedian for the q points
      that have an higher than usual error (proxy for spikes ...)
      tested with mi1245/dec2016/tiox/tiox1/run3
  """
  idx_mask = []
  for iscan in range(len(data.diffsInScanPoint)):
    idx = data.chi2_0[iscan] > threshold
    # expand along other axis (q ...)
    idx = utils.reshapeToBroadcast(idx,data.diffsInScanPoint[iscan])
    idx_mask.append(idx)
    log.debug("Chi2 mask, scanpoint: %s, curves filtereout out %d/%d (%.2f%%)"%\
              (data.scan[iscan],idx.sum(),len(idx),idx.sum()/len(idx)*100) )
  if "masks" not in data: data['masks'] = dict()
  data['masks']['chi2'] = idx_mask
  return data

def applyMasks(data,which='all',funcForAveraging=np.nanmean):
  # don't do anything if no mask is defined
  if 'masks' not in data: return data
  if which == 'all': which = list(data['masks'].keys())
  totmask = []
  for iscan in range(len(data.diffsInScanPoint)):
    mask = data['masks'][which[0]][iscan]
    for w in which[1:]:
      mask = np.logical_and(mask,data['masks'][w][iscan])
    mask = np.squeeze(mask)
    totmask.append(mask); # store for later
    # check is a q-by-q mask
    if mask.shape == data.diffsInScanPoint[iscan].shape:
      temp = np.ma.MaskedArray(data=data.diffsInScanPoint[iscan],mask=mask)
      data.data[iscan] = funcForAveraging( temp,axis=0 )
    else:
      data.data[iscan] = funcForAveraging( data.diffsInScanPoint[iscan][~mask],axis=0)
  data['mask'] = totmask
  return data

  
def saveTxt(folder,data,delayToStr=True,basename='auto',info="",**kw):
  """ data must be a DataStorage instance """
  # folder ends usually with sample/run so use the last two subfolders
  if basename == 'auto':
    basename = "_".join(folder.rstrip("/").split("/")[-2:]) + "_"
  q = data.q if "q" in data else np.arange(data.data.shape[-1])
  # save one file with all average diffs
  fname = "%s/%sdiff_av_matrix.txt" % (folder,basename)
  utils.saveTxt(fname,q,data.data,headerv=data.scan,**kw)
  # save error bars in the matrix form
  fname = "%s/%sdiff_av_matrix_err.txt" % (folder,basename)
  utils.saveTxt(fname,q,data.err,headerv=data.scan,**kw)

  for iscan,scan in enumerate(data.scan):
    scan = utils.timeToStr(scan) if delayToStr else "%+10.5e" % scan

    # try retreiving info on chi2
    try:
      chi2_0  = data.chi2_0[iscan]
      info_delay = [ "# rep_num : chi2_0 , discarded by chi2masking ?", ]
      for irep,value in enumerate(chi2_0):
        info_delay.append( "# %d : %.3f" % (irep,value))
        if 'chi2' in data.masks: info_delay[-1] += " %s"%str(data.masks['chi2'][iscan][irep])
      info_delay = "\n".join(info_delay)
      if info != '': info_delay = "%s\n%s" % (info,info_delay)
    except AttributeError:
      info_delay = info

    # save one file per timedelay with average diff (and err)
    fname = "%s/%sdiff_av_%s.txt" % (folder,basename,scan)
    if 'mask' in data:
      tosave = np.vstack( (data.data[iscan],data.err[iscan],
               data.dataUnmasked[iscan],data.errUnmasked[iscan] ) ) 
      columns = 'q diffmask errmask diffnomask errnomask'.split()
    else: 
      tosave = np.vstack( (data.data[iscan],data.err[iscan] ) )
      columns = 'q diff err'.split()
    utils.saveTxt(fname,q,tosave,info=info_delay,columns=columns)

    # save one file per timedelay with all diffs for given delay
    fname = "%s/%sdiffs_%s.txt" % (folder,basename,scan)
    utils.saveTxt(fname,q,data.diffsInScanPoint[iscan],info=info_delay,**kw)


def read_diff_av(folder,plot2D=False,save=None):
  print("Never tested !!!")
  basename = folder+"/"+"diff_av*"
  files = glob.glob(basename)
  files.sort()
  if len(files) == 0:
    print("No file found (basename %s)" % basename)
    return None
  temp   = [os.path.basename(f[:-4]) for f in files]
  delays = [f.split("_")[-1] for f in temp ]
  diffav = collections.OrderedDict()
  diffs  = collections.OrderedDict()
  for d,f in zip(delays,files):
    data = np.loadtxt(f)
    diffav[d]=data[:,1]
    diffs[d] = np.loadtxt(folder+"/diffs_%s.dat"%d)[:,1:]
    q     =data[:,0]
  t = np.asarray( [mc.strToTime(ti) for ti in delays] )
  if plot2D:
    idx = t>0
    i = np.asarray( diffav.values() )
    plt.pcolor(np.log10(t[idx]),q,i[idx].T)
    plt.xlabel(r"$\log_{10}(t)$")
    plt.ylabel(r"q ($\AA^{-1}$)")
  it=np.asarray(diffav.values())
  if save:
    tosave = np.vstack( (q,it) )
    header = np.hstack( (len(it),t) )
    tosave = np.vstack( (header,tosave.T)  )
    np.savetxt(folder + "/all_diffs_av_matrix.txt",tosave)
  return q,it,diffs,t