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
def getCorr(order=5,i0=None,Imat=None,i0_wp=1e6,fraclims_dc=[.9,1.1]):
""" Getting nonlinear correction factors form a calibration dataset consiting of:
i0 array of intensities 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
"""
#i0,Imat = getData()
msk = tools.filtvec(i0,i0_wp*np.asarray(fraclims_dc))
p0 = tools.polyFit(i0[msk],Imat[msk,:],2)
dc = tools.polyVal(p0,i0_wp)
comps = tools.polyFit(i0-i0_wp,Imat-dc,order,removeOrders=[0])
compsder = tools.polyDer(comps)
c = lambda(i): tools.polyVal(comps,i-np.asarray(tools.iterfy(i0_wp)))+dc
c_prime = lambda(i): tools.polyVal(compsder,i-np.asarray(tools.iterfy(i0_wp)))
t = lambda(i): (c_prime(i0_wp).T * (i-i0_wp)).T + dc
cprimeic = c_prime(i0_wp)
dcorr_const = -cprimeic*i0_wp + c(i0_wp) - t(0)
def dcorr(i,D):
return (i*cprimeic.T + dcorr_const.T + ((D-c(i))*cprimeic/c_prime(i)).T).T
#return (i*cprimeic.T + dcorr_const.T ).T
return dcorr,comps,t
tools.nfigure('testplot')
plt.clf()
plt.subplot(1,2,1)
Imean = (Imat.T/i0).T
tools.imagesc(np.asarray([ti / np.mean(Imean[-10:,:],0) for ti in Imean]))
tools.clim_std(6)
cl = plt.gci().get_clim()
plt.colorbar()
plt.set_cmap(plt.cm.RdBu_r)
plt.subplot(1,2,2)
cmps = copy.copy(comps)
cmps[-2,:] = 0
cc = lambda(i): tools.polyVal(cmps,i-np.asarray(tools.iterfy(i0_wp)))
Ir = Imat-c(i0)+t(i0)-t(0)
Ir = dcorr(i0,Imat)
#Ir = ((Imat-cc(i0)).T/i0).T
#tools.imagesc(Ir)
Ir = (Ir.T/i0).T
tools.imagesc(np.asarray([ti / np.mean(Ir[-10:,:],0) for ti in Ir]))
plt.clim(cl)
plt.colorbar()
plt.set_cmap(plt.cm.RdBu_r)
plt.draw()
tools.nfigure('testplot_components')
plt.clf()
ah = None
for n,comp in enumerate(comps):
if ah is None:
ah = plt.subplot(len(comps),1,n+1)
else:
plt.subplot(len(comps),1,n+1,sharex=ah)
plt.plot(comp)
lims = np.percentile(comp,[1,99])
plt.ylim(lims)
return c,c_prime
