def _getOneWire(self,points,i,rmax=1000):
inp = 0
while not inp=='q':
print inp
rmin = float(inp)
tools.nfigure('wire radius selector')
pl.clf()
self._plotStripe(points,i,rmin,rmax)
pl.draw()
inp = raw_input('Enter radius change, q to finish: ')
if inp is not 'q': width=int(inp)
return width
def histOverview(data,clearFig=True):
Nax = len(data)
msk = maskEdge()
unb = createMaskUnbonded(1)[0]
fig = nfigure('Cspad histogram overview')
if clearFig: plt.clf()
binvec = np.arange(np.round(np.min(data.ravel())),np.round(np.max(data.ravel())),1)
ah = []
for n,tdat in enumerate(data):
hist = getCommonModeFromHist(tdat)
#tdat = commonModeCorrectTile(tdat)[0]
unbpx = tdat[unb]
tdat = tdat[~msk]
if len(ah)==0:
ah.append(plt.subplot(8,4,n+1))
else:
ah.append(plt.subplot(8,4,n+1,sharex=ah[0]))
h = np.histogram(tdat,bins=binvec)
lh = plt.step(binvec[:-1],h[0],where='pre')
lh = lh[0]
ub = np.median(unbpx)
plt.axvline(ub,color=lh.get_color())
plt.axvline(np.median(tdat),linestyle='--',color=lh.get_color())
plt.axvline(hist,color="red")
print "Tile %02d, unbonded %-.2f, hist %-.2f" % (n,ub,hist)
plt.text(.5,.8,str(n),horizontalalignment='center',transform=ah[-1].transAxes)
#plt.title(str(n))
print "Continous BLUE line: unbounded pixels"
print "Continous RED line: from histogram"
plt.xlim(-50,50)
fig.subplots_adjust(hspace=0)
def getCommonModeFromHist(im,gainAv=30,searchRadiusFrac=.4,debug=False):
im = im.ravel()
bins = np.arange(-2*gainAv,3*gainAv)
hst,dum = np.histogram(im.ravel(),bins)
bins = bins[:-1]
rad = np.round(gainAv*searchRadiusFrac)
idx = filtvec(bins,[-rad,rad])
pk = bins[idx][hst[idx].argmax()]
idx = filtvec(bins,[pk-rad,pk+rad])
bins = bins[idx]
hst = hst[idx]
bg = np.sum(bins*hst)/np.sum(hst)
if debug:
nfigure('debug common mode hist correction')
plt.clf()
plt.plot(bins,hst)
plt.waitforbuttonpress()
return bg
def getNoiseMap(Istack,lims_perc=None,lims=None):
noise = noiseMap(Istack)
#np.shape(noise)
if lims_perc is not None:
lims = np.percentile(noise,lims_perc)
tools.nfigure('Selected noise limits')
pl.clf()
tools.histSmart(noise.ravel()[~np.isnan(noise.ravel())],fac=200)
pp = plt.axhspan(*lims)
plt.gca().add_patch(pp)
plt.draw()
if lims==None:
tools.nfigure('Find noise limits')
pl.clf()
tools.histSmart(noise.ravel()[~np.isnan(noise.ravel())],fac=200)
#pl.gca().set_xscale('log')
pl.draw()
print "Select noise limits"
lims = tools.getSpanCoordinates()
return ~tools.filtvec(noise,lims),noise
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()
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
def makeplots(i0,Imat,mask,ind=[27, 29, 37, 67],order=6):
Imatref = Imat[np.ix_([27, 29, 37, 67])]
i0ref = i0[np.ix_([27, 29, 37, 67])]
dcorr,comps,t = getCorr(order,i0,Imat,i0ref[0])
patt = cspad.CspadPattern(Nx=300,Ny=300)
if 1:
Dcorr = dcorr(i0ref,Imatref)
DcorrNorm = (Dcorr.T/i0ref).T
DcorrNorm = rearrangeData(DcorrNorm,mask)
ImatrefNorm = (Imatref.T/i0ref).T
ImatrefNorm = rearrangeData(ImatrefNorm,mask)
fig = tools.nfigure('figure2')
#fig.set_size_inches(3.5,3)
plt.clf()
ah = None
for n in range(3):
if ah is None:
ah = plt.subplot(2,3,n+1)
tah = ah
else:
tah = plt.subplot(2,3,n+1,sharex=ah,sharey=ah)
patt.imageShow((ImatrefNorm[1+n]/ImatrefNorm[0]-1)*100)
plt.set_cmap(plt.cm.RdBu_r)
plt.clim([-5,5])
plt.axis('equal')
plt.axis([60000,13e4,6e4,13e4])
plt.setp(tah.get_xticklabels(), visible=False)
plt.setp(tah.get_yticklabels(), visible=False)
plt.text(6.4e4,11.8e4,['(a)','(b)','(c)'][n],fontsize=20,bbox={'facecolor':'white', 'alpha':1, 'pad':10})
tah = plt.subplot(2,3,n+4,sharex=ah,sharey=ah)
pp = patt.bin((DcorrNorm[1+n]/DcorrNorm[0]-1)*100)
im = tools.imagesc(patt.xVec,patt.yVec,pp)
plt.set_cmap(plt.cm.RdBu_r)
plt.clim([-5,5])
plt.axis('equal')
plt.axis([60000,13e4,6e4,13e4])
plt.setp(tah.get_xticklabels(), visible=False)
plt.setp(tah.get_yticklabels(), visible=False)
plt.text(6.4e4,11.8e4,['(d)','(e)','(f)'][n],fontsize=20,bbox={'facecolor':'white', 'alpha':1, 'pad':10})
fig.subplots_adjust(left=.05,top=.95,bottom=.05,right=0.85,hspace=.05,wspace=.05)
cbar_ax = fig.add_axes([0.88, 0.05, 0.03, 0.9])
fig.colorbar(im, cax=cbar_ax,label='Percent')
# FIGURE 4
fig = tools.nfigure('figure4')
#fig.set_size_inches(3.5,1.5)
plt.clf()
ah = None
count=1
toplot = [t(0),comps[-3],comps[-4]]
for n,comp in enumerate(toplot):
if ah is None:
ah = plt.subplot(1,3,count)
tah = ah
else:
tah = plt.subplot(1,3,count,sharex=ah,sharey=ah)
timg = rearrangeData([comp],mask)
patt.imageShow(timg)
plt.set_cmap(plt.cm.RdBu_r)
#plt.clim([-5,5])
plt.axis('equal')
plt.axis([60000,13e4,6e4,13e4])
plt.setp(tah.get_xticklabels(), visible=False)
plt.setp(tah.get_yticklabels(), visible=False)
plt.text(6.4e4,11.8e4,['$t(0)$','$g=2$','$g=3$','d','e','f','g','h','i','j','k'][n],fontsize=20,bbox={'facecolor':'white', 'alpha':1, 'pad':10})
count += 1
fig.subplots_adjust(left=.05,top=.95,bottom=.05,right=0.95,hspace=.05,wspace=.05)