361 lines
13 KiB
Python
361 lines
13 KiB
Python
import os
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import sys
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import numpy as np
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np.warnings.simplefilter('ignore')
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import time
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import matplotlib.pyplot as plt
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import h5py
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import collections
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import re
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from x3py import x3py
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import dispersiveXanes_alignment as alignment
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import mcutils as mc
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cmap = plt.cm.viridis if hasattr(plt.cm,"viridis") else plt.cm.gray
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kw_2dplot = dict(
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interpolation = "none",
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aspect = "auto",
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cmap = cmap
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)
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g_exp = "mecl3616"
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g_exp = "xppl3716"
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g_bml = g_exp[:3]
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x3py.config.updateBeamline(g_bml)
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g_folder_init = g_exp+"_init_pars/"
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g_folder_out = g_exp+"_output/"
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g_folder_data = "/reg/d/psdm/"+g_bml+"/"+ g_exp +"/hdf5/"
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import socket
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hostname = socket.gethostname()
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if hostname == "x1":
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g_folder_data = "/home/marco/temp"
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if hostname == "apcluster0":
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g_folder_data = "/data/marcoc/singleShotXanes/"+ g_exp +"/hdf5/"
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# set defaults based on experiment
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if g_bml == "xpp":
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g_roi_height = 200
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g_swapx = False
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g_swapy = False
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else:
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g_roi_height = 100
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g_swapx = True
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g_swapy = False
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print("Working on experiment",g_exp,"(beamline %s)"%g_bml)
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print(" folder data →",g_folder_data)
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print(" folder init_pars →",g_folder_init)
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print(" folder outout →",g_folder_out)
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#g_folder = "/reg/d/psdm/xpp/xppl3716/ftc/hdf5/"
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def readDataset(fnameOrRun=7,
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force=False,
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doBkgSub=False):
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if isinstance(fnameOrRun,str) and (fnameOrRun[-3:]=="npz"):
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d = x3py.toolsVarious.DropObject()
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temp = np.load(fnameOrRun)
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spec1 = temp["spec1"]
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spec2 = temp["spec2"]
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nS = spec1.shape[0]
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d.spec1 = x3py.toolsDetectors.wrapArray("spec1",spec1,time=np.arange(nS))
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d.spec2 = x3py.toolsDetectors.wrapArray("spec2",spec2,time=np.arange(nS))
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else:
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if isinstance(fnameOrRun,int):
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fnameOrRun=g_folder_data+"/"+g_exp+"-r%04d.h5" % fnameOrRun
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d = x3py.Dataset(fnameOrRun,detectors=["opal0","opal1","fee_spec","opal2","ebeam"])
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if g_bml == "xpp":
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d.spec1 = d.opal0
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d.spec2 = d.opal1
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else:
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d.spec1 = d.fee_spec
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d.spec2 = d.opal2
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if not hasattr(d,"scan"):
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d.scan = x3py.toolsVarious.DropObject()
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d.scan.scanmotor0_values = [0,]
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return d
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def getCenter(img,axis=0,threshold=0.05):
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img = img.copy()
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img[img<img.max()*threshold] = 0
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if axis == 1: img=img.T
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p = img.mean(1)
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x = np.arange(img.shape[0])
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return int(np.sum(x*p)/np.sum(p))
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def showShots(im1,im2):
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nS = im1.shape[0]
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fig,ax = plt.subplots(2,nS,sharex=True,sharey=True)
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if im1.ndim == 3:
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for a,i1,i2 in zip(ax.T,im1,im2):
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a[0].imshow(i1.T,**kw_2dplot)
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a[1].imshow(i2.T,**kw_2dplot)
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else:
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for a,p1,p2 in zip(ax.T,im1,im2):
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a[0].plot(p1)
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a[1].plot(p2)
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def sliceToIndices(shot_slice,nShots):
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return list(range(*shot_slice.indices(nShots)))
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class AnalyzeRun(object):
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def __init__(self,run,initAlign="auto",swapx=g_swapx,swapy=g_swapy):
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""" swapx → swap x axis of first spectrometer
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swapy → swap y axis of first spectrometer
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initAlign: could be:
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1. None if you want default transformation parameters
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2. a dict if you want to overwrite certain parameters of the default ones
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3. an integer (to look for xppl3716_init_pars/run????_transform.npy)
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4. a file name (that has been previosly saved with r.saveTransform(fname)
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"""
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self.data = readDataset(run)
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self.scanpos = self.data.scan.scanmotor0_values
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self.nCalib = self.data.spec1.nCalib
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self.nShotsPerCalib = self.data.spec1.lens
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if isinstance(run,str):
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run = int( re.search("\d{3,4}",run).group() )
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self.run = run
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self.results = collections.OrderedDict()
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self.swap = (swapx,swapy)
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#self.clearCache()
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d = self.data
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self.spec1 = d.spec1 ; # spec1 is the one that is moved
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self.spec2 = d.spec2 ;
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self.E = alignment.defaultE
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try:
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self.loadTransform(initAlign)
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except (AttributeError,FileNotFoundError):
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if initAlign is None:
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print("Set to default transform")
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self.initAlign = self.setDefaultTransform()
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else:
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self.initAlign = initAlign
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def getShots(self,shots=0,calib=None,bkgSub="line",roi=g_roi_height):
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if shots == "all":
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if calib != None:
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shots = slice(0,self.nShotsPerCalib[calib])
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else:
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shots = slice(0,self.data.spec1.nShots)
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# read data
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im1 = self.spec1.getShots(shots,calib=calib)
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im2 = self.spec2.getShots(shots,calib=calib)
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# subtractBkg bkg
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im1 = alignment.subtractBkg(im1,bkg_type=bkgSub)
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im2 = alignment.subtractBkg(im2,bkg_type=bkgSub)
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# rebin and swap im1 if necessary
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if im1.shape[-1] != 1024:
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im1 = mc.rebin(im1, (im1.shape[0],im1.shape[1],1024) )
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if self.swap[0]: im1 = im1[:,:,::-1]
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if self.swap[1]: im1 = im1[:,::-1,:]
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if roi is None:
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pass
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elif isinstance(roi,slice):
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im1 = im1[:,roi,:]
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im2 = im2[:,roi,:]
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elif isinstance(roi,int):
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if not hasattr(self,"roi1"): self.roi1 = alignment.findRoi(im1[0],roi)
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if not hasattr(self,"roi2"): self.roi2 = alignment.findRoi(im2[0],roi)
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im1 = im1[:,self.roi1,:]; im2 = im2[:,self.roi2,:]
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return im1,im2
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def guiAlign(self,shot=0,save="auto"):
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im1,im2 = self.getShot(shot)
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gui = alignment.GuiAlignment(im1[0],im2[0])
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input("Enter to start")
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gui.start()
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if save == "auto":
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fname = g_folder_init+"/run%04d_gui_align.npy" % self.run
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else:
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fname = save
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self.initAlign = gui.transform
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gui.save(fname)
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def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
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if initpars is None: initpars= self.initAlign
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if (im1 is None) or (im2 is None):
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im1,im2 = self.getShots(shot,calib=calib); im1=im1[0]; im2=im2[0]
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r = alignment.doShot(im1,im2,initpars,doFit=doFit,show=showInit)
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im1 = r.im1
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im2 = r.im2
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self.initAlign = r.final_pars
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if show:
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if savePlot == "auto":
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if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
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savePlot = g_folder_out+"/run%04d_calib%s_shot%04d_fit.png" % (self.run,calib,shot)
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alignment.plotShot(im1,im2,res=r,save=savePlot)
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if save: self.saveTransform()
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return r
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def doShots(self,shots=slice(0,50),calib=None,initpars=None,doFit=False,returnBestTransform=False,nSaveImg=5,nInChunks=250):
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"""
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shots : slice to define shots to read, use 'all' for all shots in calibcycle
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nSaveImg : save saveImg images in memory (self.results), use 'all' for all
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useful for decreasing memory footprint
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"""
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if initpars is None: initpars= self.initAlign
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if shots == "all": shots = list(range(self.nShotsPerCalib[calib]))
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if isinstance(shots,slice):
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nmax = self.nShotsPerCalib[calib] if calib is not None else self.data.spec1.nShots
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shots = sliceToIndices(shots,nmax)
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chunks = x3py.toolsVarious.chunk(shots,nInChunks)
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ret_chunks = []
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for chunk in chunks:
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s1,s2 = self.getShots(chunk,calib=calib)
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ret_chunk = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
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returnBestTransform=False,nSaveImg=nSaveImg)
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ret_chunks.append(ret_chunk)
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if nSaveImg != 'all' and len(chunk)<nSaveImg: nSaveImg = 0
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ret = alignment.unravel_results(ret_chunks)
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bestT,fom = alignment.getBestTransform(ret)
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if doFit: self.initAlign = bestT
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# keep it for later !
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self.results[calib] = ret
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if returnBestTransform:
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return ret,bestT
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else:
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return ret
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def analyzeScan(self,initpars=None,nShotsPerCalib="all",calibs="all",calibsToFit="all",nImagesToFit=0,nSaveImg=5):
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""" nImagesToFit: number of images to Fit per calibcycle, (int or "all")
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calibs to fit could be 'all','even','odd'
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"""
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if initpars is None: initpars= self.initAlign
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if calibs == "all": calibs=list(range(self.nCalib))
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if isinstance(calibs,slice): calibs=list(range(self.nCalib))[calibs]
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nC = len(calibs)
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for ic,calib in enumerate(calibs):
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shots = list(range(self.nShotsPerCalib[calib]))
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if nShotsPerCalib != "all": shots = shots[:nShotsPerCalib]
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if nImagesToFit == "all":
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nToFit = self.nShotsPerCalib[calib]
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else:
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nToFit = nImagesToFit
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if calibsToFit == 'even' and (calib%2==1): nToFit=0
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if calibsToFit == 'odd' and (calib%2==0): nToFit=0
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print("Calib %d, tofit %d"%(calib,nToFit))
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ret = None
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if nToFit > 0:
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ret,bestTransf = self.doShots(shots=shots[:nToFit],calib=calib,doFit=True,\
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initpars=initpars,nSaveImg=nSaveImg,returnBestTransform=True);
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initpars = bestTransf; self.initAlign=bestTransf
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if nToFit < len(shots):
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ret2 = self.doShots(shots[nToFit:],calib=calib,initpars=initpars,
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doFit=False,nSaveImg=0)
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if ret is None:
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ret = ret2
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else:
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ret = alignment.unravel_results( (ret,ret2) )
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#print("Memory available 3",x3py.toolsOS.memAvailable())
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self.results[calib] = ret
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#print("Memory available 4",x3py.toolsOS.memAvailable())
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print("Calib cycle %d/%d -> %.3f (best FOM: %.2f)" % (ic,nC,self.scanpos[ic],np.nanmin(ret.fom)))
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return [self.results[c] for c in calibs]
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def save(self,fname="auto",overwrite=False):
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if len(self.results) == 0:
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print("self.results are empty, returning without saving")
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return
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if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
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if fname == "auto":
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fname = g_folder_out+"/run%04d_analysis.npz" % self.run
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if os.path.exists(fname) and not overwrite:
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print("File %s exists, **NOT** saving, use overwrite=True is you want ..."%fname)
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return
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h = dict()
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h["roi1"] = (self.roi1.start,self.roi1.stop)
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h["roi2"] = (self.roi2.start,self.roi2.stop)
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if hasattr(self.data.scan,"scanmotor0"):
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h["scanmot0"] = self.data.scan.scanmotor0
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else:
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h["scanmot0"] = 'notascan'
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h["scanpos0"] = self.data.scan.scanmotor0_values
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if hasattr(self.data.scan,"scanmotor1"):
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h["scanmot1"] = self.data.scan.scanmotor1
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h["scanpos1"] = self.data.scan.scanmotor1_values
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h["results"] = self.results
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h["E"] = self.E
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np.savez(fname,**h)
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#h["transform"] = self.initAlign
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def load(self,fname="auto"):
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if fname == "auto": fname = g_folder_out+"/run%04d_analysis.npz" % self.run
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temp = np.load(fname)
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self.results = temp["results"].item()
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temp.close()
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def _auto_transform_name(self,run=None,calib=None):
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if run is None: run = self.run
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fname = g_folder_init+"/run%04d_transform" % run
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if calib is not None:
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fname = fname + "_c%03d" % calib
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return fname + ".npy"
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def saveTransform(self,fname="auto",calib=None,transform=None):
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if transform is None: transform = self.initAlign
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if fname == "auto": fname = self._auto_transform_name(calib=calib)
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print("Saving roi and transformation parameter to %s"%fname)
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alignment.saveAlignment(fname,self.initAlign,self.roi1,self.roi2,self.swap)
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def loadTransform(self,fname="auto", calib=None):
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if isinstance(fname,dict): raise FileNotFoundError
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if fname == "auto": fname = self._auto_transform_name(calib=calib)
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if isinstance(fname,int):
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fname = g_folder_init+"/run%04d_transform.npy" % fname
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if not os.path.exists(fname): print("Asked to read %s, but it does not exist"%fname)
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temp = np.load(fname).item()
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self.initAlign = temp["transform"]
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self.roi1 = temp["roi1"]
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self.roi2 = temp["roi2"]
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if 'swap' in temp: self.swap=temp['swap']
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print("init transform and ROIs from %s"%fname)
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def clearCache(self):
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del self.roi1
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del self.roi2
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alignment.clearCache(); # nedded for multiprocessing can leave bad parameters in the cache
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def setDefaultTransform( self ):
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#dict( scalex=0.65,rotation=0.0,transx=90, iblur1=4.3,fix_iblur1=False )
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t = alignment.g_fit_default_kw
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self.initAlign = t
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return t
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def quick_mec(run,ref=236,divideByRef=False,returnRes=False):
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""" useful to analyze the runs around 140 (done with the focusing """
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ref_run = 236
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h=h5py.File("mecl3616_output/run%04d_analysis.h5" %ref,"r")
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ref = np.nanmean(h["calibNone"]["ratio"][...],axis=0)
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r = AnalyzeRun(run,initAlign=ref,swapx=True,swapy=False)
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res=r.doShots(slice(5),doFit=False)
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ret = res["ratio"]/ref if divideByRef else res["ratio"]
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if returnRes:
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return ret,res
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else:
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return ret
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def quickAndDirty(run,nShots=300,returnAll=True,doFit=False):
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""" useful to analyze the runs around 140 (done with the focusing """
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r = AnalyzeRun(run,swap=True,initAlign=g_folder_init+"/run0144_transform.npy")
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res=r.doShots(slice(nShots),doFit=doFit)
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o = alignment.unravel_results(res)
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ref = np.nanmedian(o["ratio"][:40],0)
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sam = np.nanmedian(o["ratio"][50:],0)
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if returnAll:
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return sam/ref,o["ratio"]/ref
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else:
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return sam/ref
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