mcutils/xray/azav.py

from __future__ import print_function,division

import logging
log = logging.getLogger(__name__)


import numpy as np
np.seterr(all='ignore')
import os
import collections
import glob
import pathlib
from . import storage
from . import utils
import re
import fabio
import pyFAI

try:
  import matplotlib.pyplot as plt
except ImportError:
  log.warn("Can't import matplotlib !")

def _read(fname):
  """ read data from file using fabio """
  f = fabio.open(fname)
  data = f.data
  del f; # close file
  return data

def read(fnames):
  """ read data from file(s) using fabio """
  if isinstance(fnames,str):
    data = _read(fnames)
  else:
    # read one image to know img size
    temp = _read(fnames[0])
    shape = [len(fnames),]+list(temp.shape)
    data  = np.empty(shape)
    data[0] = temp
    for i in range(1,len(fnames)): data[i] = _read(fnames[i])
  return data
    
def ai_as_dict(ai):
  """ ai is a pyFAI azimuthal intagrator"""
  methods = dir(ai)
  methods = [m for m in methods if m.find("get_") == 0]
  names   = [m[4:] for m in methods]
  values  = [getattr(ai,m)() for m in methods]
  ret = dict( zip(names,values) )
  ret["detector"] = ai.detector.get_name()
  return ret

def ai_as_str(ai):
  """ ai is a pyFAI azimuthal intagrator"""
  s=[ "# Detector        : %s" % ai.detector.name,
      "# Pixel      [um] : %.2fx%.2f" % (ai.pixel1*1e6,ai.pixel2*1e6),
      "# Distance   [mm] : %.3f" % (ai.dist*1e3),
      "# Center     [mm] : %.3f,%.3f" % (ai.poni1*1e3,ai.poni2*1e3),
      "# Center     [px] : %.3f,%.3f" % (ai.poni1/ai.pixel1,ai.poni2/ai.pixel2),
      "# Wavelength [A]  : %.5f" % (ai.wavelength*1e10),
      "# rot[1,2,3] [rad]: %.3f,%.3f,%.3f" % (ai.rot1,ai.rot2,ai.rot3) ]
  return "\n".join(s)

def do1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
    """ ai is a pyFAI azimuthal intagrator 
              it can be defined with pyFAI.load(ponifile)
        mask: True are points to be masked out """
    # force float to be sure of type casting for img
    if isinstance(dark,int): dark = float(dark);
    if imgs.ndim == 2: imgs = (imgs,)
    out_i = np.empty( ( len(imgs), npt_radial) )
    out_s = np.empty( ( len(imgs), npt_radial) )
    for _i,img in enumerate(imgs):
      q,i, sig = ai.integrate1d(img-dark, npt_radial, mask= mask, safe = safe,\
                 unit="q_A^-1", method = method, error_model = "poisson",
                 polarization_factor = polCorr)
      out_i[_i] = i
      out_s[_i] = sig
    return q,np.squeeze(out_i),np.squeeze(out_s)

def do2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr',safe=True,dark=10., polCorr = 1):
    """ ai is a pyFAI azimuthal intagrator 
              it can be defined with pyFAI.load(ponifile)
        mask: True are points to be masked out """
    # force float to be sure of type casting for img
    if isinstance(dark,int): dark = float(dark);
    if imgs.ndim == 2: imgs = (imgs,)
    out = np.empty( ( len(imgs), npt_azim,npt_radial) )
    for _i,img in enumerate(imgs):
      i2d,q,azTheta = ai.integrate2d(img-dark, npt_radial, npt_azim=npt_azim,
                      mask= mask, safe = safe,unit="q_A^-1", method = method,
                      polarization_factor = polCorr )
      out[_i] = i2d
    return q,azTheta,np.squeeze(out)

def getAI(poni,folder=None):
  """ get AzimuthalIntegrator instance:
      → if poni is a dictionary use it to define one
      → if poni is a string look, it is used as filename to read.
        in this case if folder is given it is used (together with all its 
        subfolder) as search path (along with ./ and home folder)
  """
  if isinstance(poni,pyFAI.azimuthalIntegrator.AzimuthalIntegrator):
    ai = poni
  elif isinstance(poni,dict):
    ai = pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**poni)
  elif isinstance(poni,str):
    # look is file exists in cwd
    if os.path.isfilee(poni):
      ai = pyFAI.load(poni)
    # if file does not exist look for one with that name around
    else:
      # build search paths
      folders = []
      if folder is not None:
        temp = os.path.abspath(folder)
        path = pathlib.Path(temp)
        folders = [ str(path), ]
        for p in path.parents: folders.append(str(p))
      folders.append( "./" )
      folders.append( os.path.expanduser("~/") )
      # look for file
      for path in folders:
        fname = path + "/" + poni
        if os.path.isfile(fname):
          log.info("Found poni file %s",fname)
          break
        else:
          log.debug("Could not poni file %s",fname)
    ai = pyFAI.load(fname)
  return ai

g_mask_str = re.compile("(\w)\s*(<|>)\s*(\d+)")

def interpretMask(mask,shape=None):
  """
    if mask is an existing filename, returns it
    if mask is a string like [x|y] [<|>] int;
      for example y>500 will dis-regard out for y>500
  """
  maskout = None
  if isinstance(mask,str) and os.path.isfile(mask):
    maskout = read(mask).astype(np.bool)
  elif isinstance(mask,str) and not os.path.isfile(mask):
    err_msg = ValueError("The string '%s' could not be interpreted as simple\
              mask; it should be something like x>10"%mask)
    assert shape is not None
    # interpret string
    maskout = np.zeros(shape,dtype=bool)
    match = g_mask_str.match(mask)
    if match is None: raise err_msg
    (axis,sign,lim) = match.groups()
    if axis not in ("x","y"): raise err_msg
    if sign not in (">","<"): raise err_msg
    lim = int(lim)
    idx = slice(lim,None) if sign == ">" else slice(None,lim)
    if axis == 'y':
      maskout[idx,:] = True
    else:
      maskout[:,idx] = True
  elif isinstance(mask,np.ndarray):
    maskout = mask.astype(np.bool)
  elif mask is None:
    assert shape is not None
    maskout = np.zeros(shape,dtype=bool)
  else:
    maskout = None
    raise ValueError("Could not interpret %s as mask input"%mask)

  if shape is not None and maskout.shape != shape:
    raise ValueError("The mask shape %s does not match the shape given as\
      argument %s"%(maskout.shape,shape))
  return maskout


def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
             saveChi=True,poni='pyfai.poni',storageFile='auto',diagnostic=None):
  """ calc 1D curves from files in folder, returning a dictionary of stuff
      nQ    : number of Q-points (equispaced)
      force : if True, redo from beginning even if previous data are found
              if False, do only new files
      mask  : can be a filename or an array of booleans; pixels that are True
              are dis-regarded
      saveChi: self-explanatory
      poni  : could be:
              → an AzimuthalIntegrator instance
              → a filename that will be look for in
                 1 'folder' first
                 2 in ../folder
                 3 in ../../folder
                 ....
                 n-1 in pwd
                 n   in homefolder
              → a dictionary (use to bootstrap an AzimuthalIntegrator using 
                AzimuthalIntegrator(**poni)
 """
  if storageFile == 'auto': storageFile = folder + "/" + "pyfai_1d.h5"

  if os.path.isfile(storageFile) and not force:
    saved = storage.DataStorage(storageFile)
  else:
    saved = None

  # which poni file to use:
  ai = getAI(poni,folder)

  files = utils.getFiles(folder,files)
  if saved is not None:
    files = [f for f in files if utils.getBasename(f) not in saved["files"]]

  if len(files) > 0:
    shape = read(files[0]).shape
    mask = interpretMask(mask,shape)
      
    data   = np.empty( (len(files),nQ) )
    err    = np.empty( (len(files),nQ) )
    for ifname,fname in enumerate(files):
      img = read(fname)
      q,i,e = do1d(ai,img,mask=mask,npt_radial=nQ)
      data[ifname] = i
      err[ifname]  = e
      if saveChi:
        chi_fname = utils.removeExt(fname) + ".chi"
        utils.saveTxt(chi_fname,q,i,e,info=ai_as_str(ai),overwrite=True)

    files = [ utils.getBasename(f) for f in files ]
    files = np.asarray(files)
    if saved is not None:
      files = np.concatenate( (saved["files"] ,files ) )
      data  = np.concatenate( (saved["data"]  ,data  ) )
      err   = np.concatenate( (saved["err"]   ,err   ) )
    ret = dict(q=q,folder=folder,files=files,data=data,err=err,
          pyfai=ai_as_dict(ai),pyfai_info=ai_as_str(ai),mask=mask)

    # add info from diagnostic if provided
    if diagnostic is not None:
      for k in diagnostic:
        ret[k] = np.asarray( [diagnostic[k][f] for f in ret['files']] )
    ret = storage.DataStorage(ret)
    if storageFile is not None: ret.save(storageFile)
  else:
    ret = saved
  return ret


def _calc_R(x,y, xc, yc):
  """ calculate the distance of each 2D points from the center (xc, yc) """
  return np.sqrt((x-xc)**2 + (y-yc)**2)

def _chi2(c, x, y):
  """ calculate the algebraic distance between the data points and the mean
      circle centered at c=(xc, yc) """
  Ri = _calc_R(x, y, *c)
  return Ri - Ri.mean()

def leastsq_circle(x,y):
  from scipy import optimize
  # coordinates of the barycenter
  center_estimate = np.nanmean(x), np.nanmean(y)
  center, ier = optimize.leastsq(_chi2, center_estimate, args=(x,y))
  xc, yc = center
  Ri       = _calc_R(x, y, *center)
  R        = Ri.mean()
  residu   = np.sum((Ri - R)**2)
  return xc, yc, R

def find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,center=None,reference=None,**kwargs):
  """ center is the initial centr (can be None)
      reference is a reference position to be plot in 2D plots """
  plt.ion()
  kw = dict( pixel1 = psize, pixel2 = psize, dist = dist,wavelength=wavelength )
  kw.update(kwargs)
  ai =  pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**kw)
  fig_img,ax_img = plt.subplots(1,1)
  fig_pyfai,ax_pyfai = plt.subplots(1,1)
  fig_pyfai = plt.figure(2)
  temp= ax_img.imshow(img)
  plt.sca(ax_img); # set figure to use for mouse interaction
  temp.set_clim( *np.percentile(img,(2,95) ) )
  ans = ""
  print("Enter 'end' when done")
  while ans != "end":
    if center is None:
      print("Click on beam center:")
      plt.sca(ax_img); # set figure to use for mouse interaction
      center = plt.ginput()[0]
    print("Selected center:",center)
    ai.set_poni1(center[0]*psize)
    ai.set_poni2(center[1]*psize)
    q,az,i = do2d(ai,img)
    mesh = ax_pyfai.pcolormesh(q,az,i)
    mesh.set_clim( *np.percentile(i,(2,95) ) )
    ax_pyfai.set_title(str(center))
    if reference is not None: ax_pyfai.axvline(reference)
    plt.pause(0.01)
    plt.draw()
    ans=input("Enter to continue with clinking or enter xc,yc values ")
    if ans == '':
      center = None
    else:
      try:
        center = list(map(float,ans.split(",")))
      except Exception as e:
        center = None
    if center == []: center = None
  print("Final values: (in pixels) %.3f %.3f"%(center[0],center[1]))
  return ai

def average(fileOrFolder,delays=slice(None),scale=1,norm=None,returnAll=False,plot=False,
  showTrend=False):
  data = storage.DataStorage(fileOrFolder)
  if isinstance(delays,slice):
    idx = np.arange(data.delays.shape[0])[delays]
  elif isinstance(delays,(int,float)):
    idx = data.delays == float(delays)
  else:
    idx = data.delays < 0
  if idx.sum() == 0:
    print("No data with the current filter")
    return None
  i   = data.data[idx]
  q   = data.q
  if isinstance(norm,(tuple,list)):
    idx  = ( q>norm[0] ) & (q<norm[1])
    norm = np.nanmean(i[:,idx],axis=1)
    i = i/norm[:,np.newaxis]
  if isinstance(norm,np.ndarray):
    i = i/norm[:,np.newaxis]
  title = "%s %s" % (fileOrFolder,str(delays))
  utils.plotdata(q,i*scale,showTrend=showTrend,plot=plot,title=title)
  if returnAll:
    return q,i.mean(axis=0)*scale,i
  else:
    return q,i.mean(axis=0)*scale

#### Utilities for chi files ####
def chiRead(fname,scale=1):
  q,i = np.loadtxt(fname,unpack=True,usecols=(0,1))
  return q,i*scale

def chiPlot(fname,useTheta=False,E=12.4):
  q,i = chiRead(fname)
  lam = 12.4/E
  theta = 2*180/3.14*np.arcsin(q*lam/4/3.14)
  x = theta if useTheta else q
  plt.plot(x,i,label=fname)



def chiAverage(folder,basename="",scale=1,norm=None,returnAll=False,plot=False,showTrend=False,clim='auto'):
  files = glob.glob("%s/%s*chi"%(folder,basename))
  files.sort()
  print(files)
  if len(files) == 0:
    print("No file found (basename %s)" % basename)
    return None
  q,_ = chiRead(files[0])
  i   = np.asarray( [ chiRead(f)[1] for f in files ] )
  if isinstance(norm,(tuple,list)):
    idx  = ( q>norm[0] ) & (q<norm[1])
    norm = np.nanmean(i[:,idx],axis=1)
    i = i/norm[:,np.newaxis]
  if isinstance(norm,np.ndarray):
    i = i/norm[:,np.newaxis]
  title = "%s %s" % (folder,basename)
  plotdata(q,i,plot=plot,showTrend=showTrend,title=title,clim=clim)
  if (showTrend and plot): plt.subplot(1,2,1)
  if showTrend:
    plt.pcolormesh(np.arange(i.shape[0]),q,i.T)
    plt.xlabel("image number, 0 being older")
    plt.ylabel(r"q ($\AA^{-1}$)")
  if (showTrend and plot): plt.subplot(1,2,2)
  if plot:
    plt.plot(q,i.mean(axis=0)*scale)
  if (plot or showTrend):
    plt.title(folder+"/"+basename)
  if returnAll:
    return q,i.mean(axis=0)*scale,i
  else:
    return q,i.mean(axis=0)*scale