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parameterCorrection.py
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parameterCorrection.py
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from toolsLog import logbook
from ixppy import tools,wrapFunc,dataset
import pylab as plt
import numpy as np
from scipy import linalg,io
import copy
from toolsExternalWrapped import nansum
import datetime,os
def corrNonlinGetpars(expar,Imat,order=3,exparWP=0,Iwp=None):
if Iwp is not None:
Imat -= Iwp
pol = np.vander(expar-exparWP,order+1)
if Iwp is not None:
np.delete(pol,-1,1)
scale = np.sqrt((pol*pol).sum(axis=0))
pol /= scale
comps,resid,rnk,singv = linalg.lstsq(pol,Imat)
comps = (comps.T/scale).T
if Iwp is None:
Iwp = np.array(np.matrix(pol)*np.matrix(comps))
return comps,exparWP,Iwp
#def corrNonlin(expar,Iuncorr,comps,exparWP,Iwp,):
#pol = np.vander(expar-exparWP,order+1)
#
#if Iwp is not None:
#np.delete(pol,-1,1)
def getCorrectionFunc(dmat=None,i=None,ic=None,order=5,sc=None,search_dc_limits=None, removeDependence=True):
"""
Create nonlinear correction function from a calibration dataset consiting of:
i array of intensity values (floats) of the calibration
dmat ND array of the reference patterns corresponding to values of i,
The first dimension corresponds to the calibration intensity
values and has the same length as i.
ic A working point around which a polynomial correction will be
developed for each pixel.
order the polynomial order up to which the correction will be
deveoped.
sc optional: calibration image at ic. default is the image at ic
search_dc_limits absolute limits around ic which are used to determine the
calibration value of ic as linear approximation of a short interval.
optional, can sometimes help to avoid strong deviations of the
polynomial approximatiuon from the real measured points.
Returns corrFunc(D,i), a function that takes an ND array input for correction
(1st dimension corresponds to the different intensity values)
as well as the intensity array i.
"""
if search_dc_limits is not None:
search_dc_limits = iterfy(search_dc_limits)
if len(search_dc_limits)==1:
msk = (i>i-np.abs(search_dc_limits)) & (i<i+np.abs(search_dc_limits))
elif len(search_dc_limits)==2:
msk = (i>i-np.min(search_dc_limits)) & (i<i+np.max(search_dc_limits))
p0 = tools.polyFit(i[msk],dmat[msk,...],2)
dc = tools.polyVal(p0,i0_wp)
pc = tools.polyFit(i-ic,Imat-dc,order,removeOrders=[0])
pcprime = tools.polyDer(pc)
c = lambda(i): polyVal(pc,i-ic) + dc
else:
pc = polyFit(i-ic,dmat,order,removeOrders=[])
pcprime = polyDer(pc)
c = lambda(i): polyVal(pc,i-ic)
dc = c(ic)
c_prime = lambda(i): polyVal(pcprime,i-ic)
cprimeic = c_prime(ic)
if sc is None:
sc = c(ic)
def corrFunc(D,i):
i = np.asarray(i).ravel()
return (sc.swapaxes(0,-1)/ic*i).swapaxes(0,-1) + cprimeic/c_prime(i)* sc/dc * (D-c(i))
def remDepFunc(D,i):
i = np.asarray(i).ravel()
return D-c(i)+dc
if removeDependence:
corrFunc = remDepFunc
if wrapit:
def corrFuncTransposed(D,i,normalize=False,fillValue=np.nan):
cr = corrFunc(D.swapaxes(0,-1),i).swapaxes(0,-1)
if normalize:
cr/=i
cr[:,~np.logical_and(i>np.min(i0),i<np.max(i0))] *= fillValue
return cr
corrFuncWrapped = wrapFunc(corrFuncTransposed,transposeStack=True)
def corrFuncWrap(D,i,normalize=False,fillValue=np.nan):
Df = D*i.filter([np.min(i0),np.max(i0)]).ones()
return corrFuncWrapped(Df,i=i,normalize=normalize,fillValue=fillValue)
return corrFuncWrap
else:
return corrFunc
return corrFunc
def getCorrectionFunc(order=5,Imat=None,p=None,pc=None,fraclims_dc=[.9,1.1],wrapit=True):
"""
Getting nonlinear correction factors form a calibration dataset consiting of:
i0 array of intensity/parameter values the calibration has been made for
Imat 2D array of the corresponding reference patterns, in each row
there is one ravelled array of each intensity bin in i0.
i0_wp a working point around which a correction polynomial will be
developed for each pixel.
order the polynomial order up to which will be deveoped.
fraclims_dc relative factor for the i0,Imat data limits which are used to
determine the working point location.
Returns corrFunc(i,D), a function that takes a flat array of intensity/
parameter values as well as a Matrix D of flattened patterns
the correction is to be applied on (rows in D are again corresponding
to each intensity in i).
"""
if pc is None:
pc = np.mean(p)
msk = tools.filtvec(p,pc*np.asarray(fraclims_dc))
p0 = tools.polyFit(p[msk],Imat[msk,...],2)
dc = tools.polyVal(p0,pc)
comps = tools.polyFit(p-pc,Imat-dc,order,removeOrders=[0])
compsder = tools.polyDer(comps)
c = lambda(i): tools.polyVal(comps,i-np.asarray(tools.iterfy(pc)))+dc
c_prime = lambda(i): tools.polyVal(compsder,i-np.asarray(tools.iterfy(pc)))
t = lambda(i): (c_prime(pc).T * (i-pc)).T + dc
cprimeic = c_prime(pc)
dcorr_const = -cprimeic*pc + c(pc) - t(0)
def corrFunc(D,i):
i = i.ravel()
return cprimeic * ( i + ((D-c(i))/c_prime(i)).swapaxes(0,-1) ).swapaxes(0,-1)
if wrapit:
def corrFuncTransposed(D,i=None,normalize=False,fillValue=np.nan):
if i is None:
i = np.apply_over_axes(np.nansum,D,range(np.ndim(D)-1)).ravel()
cr = corrFunc(D.swapaxes(0,-1),i).swapaxes(0,-1)
if normalize:
cr/=i
cr[:,~np.logical_and(i>np.min(i0),i<np.max(i0))] *= fillValue
return cr
#else:
#return corrFunc(D.swapaxes(0,-1),i).swapaxes(0,-1)
corrFuncWrapped = wrapFunc(corrFuncTransposed,transposeStack=True)
def corrFuncWrap(D,i=None,normalize=False,fillValue=np.nan):
if i is not None:
Df = D*i.filter([np.min(i0),np.max(i0)]).ones()
else:
Df = D
return corrFuncWrapped(Df,i=i,normalize=normalize,fillValue=fillValue)
return corrFuncWrap
else:
return corrFunc
#return corrFunc
class CorrPar(object):
def __init__(self,data=None,Iref=None,Imat=None,fina=None):
self.data = data
self.Iref = Iref
self.refDataFilter = 1
if fina is not None:
assert fina[-7:]=='.ixp.h5', "File name has to be of extension ... .ixp.h5"
self.dataset = dataset(fina)
if 'corrNonLin_I0' in self.dataset.__dict__:
self.I0 = self.dataset['corrNonLin_I0']
else:
self.I0 = None
if 'corrNonLin_Imat' in self.dataset.__dict__:
self.Imat = self.dataset['corrNonLin_Imat']
else:
self.Imat = None
else:
self.dataset = None
def getRefdataMask(self,*args):
flt = 1
if 'step' in args:
flt *= (self.data.ones()*self.data.scan[0]).filter().ones()
self.refDataFilter = flt
def _getRefdata(self):
return self.refDataFilter*self.data
refData = property(_getRefdata)
def getRefIntensity(self,imagemask=None):
self.Iref = self.refDataFilter * nansum(self.data)
fina = 'tmp_getRefIntensity_' + \
datetime.datetime.now().isoformat() + '.ixp.h5'
logbook(fina)
self.Iref.setFile(fina)
self.Iref.evaluate()
self.Iref = self.Iref.get_memdata()[0]
os.remove(fina)
def getI0Imat(self,bins=None,evaluate=False):
digi = (self.refDataFilter*self.Iref).digitize(bins=bins)
self.I0 = digi.scan.bincenters
self.Imat = digi.ones()*self.data
if evaluate:
fina = 'tmp_getImat_' \
+ datetime.datetime.now().isoformat() + '.ixp.h5'
logbook(fina)
self.Imat.setFile(fina)
self.Imat.evaluate()
self.Imat = np.asarray(self.Imat.mean())
os.remove(fina)
else:
self.Imat = np.asarray(self.Imat.mean())
if self.dataset is not None:
self.dataset['corrNonLin_Imat'] = self.Imat
self.dataset['corrNonLin_I0'] = self.I0
self.dataset.save()
def getCorrFunc(self,order=5,i0_wp=None,fraclims_dc=[.9,1.1], wrapit=True):
if not ((self.Imat is None) and (self.I0 is None)):
self.correct = getCorrectionFunc(order=order,Imat=self.Imat,i0=self.I0,i0_wp=i0_wp,fraclims_dc=fraclims_dc, wrapit=wrapit)
return self.correct
def testCorrfunc(self,order=5,ic=None):
fig = tools.nfigure('test_correction_func_order_%d'%order)
plt.clf()
fig,ax = plt.subplots(1,2,num=fig.number)
plt.axes(ax[0])
it = (self.Imat.T/self.I0).T
tools.imagesc(np.arange(np.shape(self.Imat)[1]),self.I0,(it/np.mean(it,0))-1)
tools.clim_std(2)
plt.colorbar()
plt.draw()
cf = self.getCorrFunc(order=order,i0_wp=ic,wrapit=False)
Icorr = cf(self.Imat,self.I0)
plt.axes(ax[1])
it = (Icorr.T/self.I0).T
tools.imagesc((it/np.mean(it,0))-1)
tools.clim_std(2)
plt.colorbar()