325 lines
10 KiB
Python
325 lines
10 KiB
Python
from __future__ import print_function,division
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import logging as log
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log.basicConfig(level=log.INFO)
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import numpy as np
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np.seterr(all='ignore')
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import os
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import collections
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import glob
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import pathlib
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from . import storage as storage
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import pyFAI
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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log.warn("Can't import matplotlib !")
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def removeExt(fname):
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""" special remove extension meant to work with compressed files.edf and .edf.gz files """
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if fname[-3:] == ".gz": fname = fname[-3:]
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return os.path.splitext(fname)[0]
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def getBasename(fname):
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return removeExt(os.path.basename(fname))
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def pyFAIread(fname):
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""" read data from file using fabio """
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import fabio
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f = fabio.open(fname)
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data = f.data
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del f
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return data
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def pyFAI_dict(ai):
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""" ai is a pyFAI azimuthal intagrator"""
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methods = dir(ai)
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methods = [m for m in methods if m.find("get_") == 0]
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names = [m[4:] for m in methods]
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values = [getattr(ai,m)() for m in methods]
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ret = dict( zip(names,values) )
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ret["detector"] = ai.detector.get_name()
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return ret
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def pyFAI1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
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""" ai is a pyFAI azimuthal intagrator
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it can be defined with pyFAI.load(ponifile)
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mask: True are points to be masked out """
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# force float to be sure of type casting for img
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if isinstance(dark,int): dark = float(dark);
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if imgs.ndim == 2: imgs = (imgs,)
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out_i = np.empty( ( len(imgs), npt_radial) )
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out_s = np.empty( ( len(imgs), npt_radial) )
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for _i,img in enumerate(imgs):
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q,i, sig = ai.integrate1d(img-dark, npt_radial, mask= mask, safe = safe,\
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unit="q_A^-1", method = method, error_model = "poisson",
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polarization_factor = polCorr)
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out_i[_i] = i
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out_s[_i] = sig
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return q,np.squeeze(out_i),np.squeeze(out_s)
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def pyFAI2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr',safe=True,dark=10., polCorr = 1):
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""" ai is a pyFAI azimuthal intagrator
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it can be defined with pyFAI.load(ponifile)
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mask: True are points to be masked out """
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# force float to be sure of type casting for img
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if isinstance(dark,int): dark = float(dark);
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if imgs.ndim == 2: imgs = (imgs,)
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out = np.empty( ( len(imgs), npt_azim,npt_radial) )
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for _i,img in enumerate(imgs):
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i2d,q,azTheta = ai.integrate2d(img-dark, npt_radial, npt_azim=npt_azim,
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mask= mask, safe = safe,unit="q_A^-1", method = method,
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polarization_factor = polCorr )
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out[_i] = i2d
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return q,azTheta,np.squeeze(out)
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def pyFAI_saveChi(fname,q,i,e=None,ai=None,overwrite=False):
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if os.path.exists(fname) and not overwrite:
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log.warn("File %s exists, returning",fname)
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return
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if ai is not None:
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if not isinstance(ai,dict): ai = pyFAI_dict(ai)
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header = [ "# %s : %s" %(k,v) for (k,v) in zip(ai.keys(),ai.values()) ]
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header = "\n".join(header)[1:]; # skip first #, will be added by np
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else:
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header = ""
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x = np.stack( (q,i,e) ) if e is not None else np.stack( (q,i) )
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np.savetxt(fname,x.T,fmt="%+10.5e",header=header)
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class pyFAI_storage(dict):
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""" Storage for pyfai integrated info """
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def __init__(self,fileOrDict):
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if isinstance(fileOrDict,dict):
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self.filename = None
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d = fileOrDict
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else:
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assert isinstance(fileOrDict,str)
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self.filename = fileOrDict
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d = storage.read(fileOrDict)
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# allow accessing with .data, .delays, etc.
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for k,v in d.items(): setattr(self,k,v)
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# allow accessing as proper dict
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self.update( **dict(d) )
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def __setitem__(self, key, value):
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setattr(self,key,value)
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super().__setitem__(key, value)
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def __delitem__(self, key):
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delattr(self,key)
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super().__delitem__(key)
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def save(self,fname=None):
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if fname is None: fname = self.filename
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assert fname is not None
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storage.save(fname,dict(self))
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#def asdict(self): return dict(self)
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def readNpzFile(h5File):
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if os.path.isdir(h5File): h5File = "%s/pyfai_1d.h5" % h5File
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return pyFAI_storage(h5File)
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def _getAI(poni,folder):
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if isinstance(poni,pyFAI.azimuthalIntegrator.AzimuthalIntegrator):
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ai = poni
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elif isinstance(poni,dict):
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ai = pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**poni)
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else:
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if poni == 'auto':
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temp = os.path.abspath(folder)
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path = pathlib.Path(temp)
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folders = [ str(path), ]
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for p in path.parents: folders.append(str(p))
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folders.append( "./" )
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folders.append( os.path.expanduser("~/") )
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for path in folders:
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poni = path + "/" + "pyfai.poni"
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if os.path.exists(poni):
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log.info("Found pyfai.poni in %s",path)
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break
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else:
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log.debug("Could not find pyfai.poni in %s",path)
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ai = pyFAI.load(poni)
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return ai
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def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
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saveChi=True,poni='auto',h5File='auto',diagnostic=None):
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""" calc 1D curves from files in folder, returning a dictionary of stuff
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nQ : number of Q-points (equispaced)
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force : if True, redo from beginning even if previous data are found
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if False, do only new files
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mask : can be a filename or an array of booleans; pixels that are True
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are dis-regarded
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saveChi: self-explanatory
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poni : could be:
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→ an AzimuthalIntegrator instance
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→ a filename
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→ a dictionary (use to bootstrap an AzimuthalIntegrator using
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AzimuthalIntegrator(**poni)
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→ the string 'auto' that will look for the file 'pyfai.poni' in:
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1 'folder' first
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2 in ../folder
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3 in ../../folder
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....
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n-1 in pwd
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n in homefolder
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"""
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if h5File == 'auto': h5File = folder + "/" + "pyfai_1d.h5"
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if os.path.exists(h5File) and not force:
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print("Loading")
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saved = readNpzFile(h5File)
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print("done")
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else:
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saved = None
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# which poni file to use:
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ai = _getAI(poni,folder)
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files = glob.glob("%s/%s"%(folder,files))
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files.sort()
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if saved is not None:
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files = [f for f in files if getBasename(f) not in saved["files"]]
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if len(files) > 0:
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# work out mask to use
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if isinstance(mask,np.ndarray):
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mask = mask.astype(bool)
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elif mask is not None:
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mask = pyFAIread(mask).astype(bool)
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data = np.empty( (len(files),nQ),dtype=np.float32 )
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err = np.empty( (len(files),nQ),dtype=np.float32 )
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for ifname,fname in enumerate(files):
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img = pyFAIread(fname)
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q,i,e = pyFAI1d(ai,img,mask=mask,npt_radial=nQ)
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data[ifname] = i
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err[ifname] = e
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if saveChi:
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chi_fname = removeExt(fname) + ".chi"
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pyFAI_saveChi(chi_fname,q,i,e,ai=ai,overwrite=True)
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files = [ getBasename(f) for f in files ]
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files = np.asarray(files)
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if saved is not None:
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files = np.concatenate( (saved["files"] ,files ) )
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data = np.concatenate( (saved["data"] ,data ) )
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err = np.concatenate( (saved["err"] ,err ) )
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ret = dict(q=q,folder=folder,files=files,data=data,err=err,
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pyfai=pyFAI_dict(ai),mask=mask)
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# add info from diagnostic if provided
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if diagnostic is not None:
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for k in diagnostic:
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ret[k] = np.asarray( [diagnostic[k][f] for f in ret['files']] )
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if h5File is not None: np.savez(h5File,**ret)
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else:
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ret = saved
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return pyFAI_storage(ret)
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def _calc_R(x,y, xc, yc):
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""" calculate the distance of each 2D points from the center (xc, yc) """
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return np.sqrt((x-xc)**2 + (y-yc)**2)
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def _chi2(c, x, y):
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""" calculate the algebraic distance between the data points and the mean
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circle centered at c=(xc, yc) """
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Ri = _calc_R(x, y, *c)
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return Ri - Ri.mean()
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def leastsq_circle(x,y):
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from scipy import optimize
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# coordinates of the barycenter
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center_estimate = np.nanmean(x), np.nanmean(y)
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center, ier = optimize.leastsq(_chi2, center_estimate, args=(x,y))
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xc, yc = center
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Ri = _calc_R(x, y, *center)
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R = Ri.mean()
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residu = np.sum((Ri - R)**2)
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return xc, yc, R
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def pyFAI_find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
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plt.ion()
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kw = dict( pixel1 = psize, pixel2 = psize, dist = dist,wavelength=wavelength )
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kw.update(kwargs)
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ai = pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**kw)
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fig_img,ax_img = plt.subplots(1,1)
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fig_pyfai,ax_pyfai = plt.subplots(1,1)
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fig_pyfai = plt.figure(2)
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ax_img.imshow(img)
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plt.sca(ax_img); # set figure to use for mouse interaction
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ans = ""
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print("Enter 'end' when done")
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while ans != "end":
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if ans == "":
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print("Click on beam center:")
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plt.sca(ax_img); # set figure to use for mouse interaction
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xc,yc = plt.ginput()[0]
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else:
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xc,yc = map(float,ans.split(","))
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print("Selected center:",xc,yc)
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ai.set_poni1(xc*psize)
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ai.set_poni2(yc*psize)
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q,az,i = pyFAI2d(ai,img)
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ax_pyfai.pcolormesh(q,az,i)
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ax_pyfai.set_title(str( (xc,yc) ))
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plt.pause(0.01)
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plt.draw()
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ans=input("Enter to continue with clinking or enter xc,yc values")
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print("Final values: (in pixels) %.3f %.3f"%(xc,yc))
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return ai
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#### Utilities for chi files ####
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def chiRead(fname,scale=1):
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q,i = np.loadtxt(fname,unpack=True,usecols=(0,1))
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return q,i*scale
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def chiPlot(fname,useTheta=False,E=12.4):
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q,i = chiRead(fname)
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lam = 12.4/E
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theta = 2*180/3.14*np.arcsin(q*lam/4/3.14)
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if useTheta:
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x = theta
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else:
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x = q
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plt.plot(x,i,label=fname)
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def chiAverage(folder,basename="",scale=1,returnAll=False,plot=False,showTrend=False,norm=None):
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files = glob.glob("%s/%s*chi"%(folder,basename))
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files.sort()
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print(files)
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if len(files) == 0:
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print("No file found (basename %s)" % basename)
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return None
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q,_ = chiRead(files[0])
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i = np.asarray( [ chiRead(f)[1] for f in files ] )
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if norm is not None:
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idx = ( q>norm[0] ) & (q<norm[1])
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norm = np.nanmean(i[:,idx],axis=1)
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i = i/norm[:,np.newaxis]
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if (showTrend and plot): plt.subplot(1,2,1)
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if showTrend:
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plt.pcolormesh(np.arange(i.shape[0]),q,i.T)
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plt.xlabel("image number, 0 being older")
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plt.ylabel(r"q ($\AA^{-1}$)")
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if (showTrend and plot): plt.subplot(1,2,2)
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if plot:
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plt.plot(q,i.mean(axis=0)*scale)
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if (plot or showTrend):
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plt.title(folder+"/"+basename)
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if returnAll:
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return q,i.mean(axis=0)*scale,i
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else:
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return q,i.mean(axis=0)*scale
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