def _plotStripe(self,points,i,rmin,rmax):
ax = self.xpx
ay = self.ypx
#find closest points to find limits
m = np.diff(points[:,1])/np.diff(points[:,0])
c = points[0,1] - points[0,0]*m
m_ = -1/m
dist_perp = (ay-m*ax-c)/np.sqrt(m**2+1)
dist_par = (ay-m_*ax)/np.sqrt(m_**2+1)
distparrange = dist_par[np.abs(dist_perp)<=rmax]
distparrange = [np.min(distparrange),np.max(distparrange)]
i[np.abs(dist_perp)<rmin] = np.nan
tperpvec = np.arange(-rmax,rmax,110.*np.sqrt(2))
tparvec = np.arange(distparrange[0],distparrange[1],110.*np.sqrt(2))
perpind = np.digitize(dist_perp.ravel(),tperpvec)
parind = np.digitize(dist_par.ravel(),tparvec)
binning = np.ravel_multi_index((perpind,parind),(len(tperpvec)+1,len(tparvec)+1))
numperbin = np.bincount(binning)
wireROI = np.bincount(binning, weights=np.asfarray(i.ravel()))
wireROI = wireROI/numperbin
P = np.zeros((len(tperpvec)+1) * (len(tparvec)+1))
P[:len(wireROI)] = wireROI
P = np.reshape(P,(len(tperpvec)+1,len(tparvec)+1))
pl.clf()
tools.imagesc(tparvec[:-1]+np.mean(np.diff(tparvec)),
tperpvec[:-1]+np.mean(np.diff(tperpvec)),
P[1:-1,1:-1])
tools.clim_std()
pl.axis('normal')
pl.draw()
def _refinePoints(self,points,i,refinerad=5000):
#refine points
newpoints = []
reffig = pl.figure()
for point in points:
xind = (point[0]-refinerad <= self.xVec) & (self.xVec <= point[0]+refinerad)
yind = (point[1]-refinerad <= self.yVec) & (self.yVec <= point[1]+refinerad)
pl.clf()
tools.imagesc(self.xVec[xind],self.yVec[yind],
i[np.min(yind.nonzero()):np.max(yind.nonzero())+1,np.min(xind.nonzero()):np.max(xind.nonzero())+1])
pl.plot(point[0],point[1],'go')
tools.clim_std()
pl.draw()
newpoints.append(pl.ginput(1)[0])
points = np.array(newpoints)
return points
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 createWireMask(self,data=None):
if data==None:
import ixppy
d = ixppy.dataset('/reg/g/xpp/data/example_data/cspad/liquid_scattering/hdf5/xpp43512-r0004.h5',['cspad'])
data = d.cspad.rdStepData(0,range(10))
i = np.mean(data,axis=0)
ib = self.bin(i)
tools.imagesc(self.xVec,self.yVec,ib)
tools.clim_std()
pl.draw()
print "Roughly select wire end pairs, press middle mouse button when finished."
wires = []
tpos = np.array(pl.ginput(2))
pointno = 0
while not tpos==[]:
tpos = np.array(tpos)
pl.plot(tpos[:,0],tpos[:,1],'go-')
for pno in range(2):pl.text(tpos[pno,0],tpos[pno,1],str(pointno),color='r')
wires.append(tpos)
pointno += 1
tpos = pl.ginput(2)
refwires = []
for pos in wires:
refwires.append(self._refinePoints(pos,ib))
wirewidths = []
print refwires
for refwire in refwires:
trad = self._getOneWire(refwire,i,rmax=1000)
wirewidths.append(trad)
masked = np.zeros(np.shape(i),dtype=bool)
ax = self.xpx
ay = self.ypx
for refwire,wirewidth in zip(refwires,wirewidths):
points = refwire
m = np.diff(points[:,1])/np.diff(points[:,0])
c = points[0,1] - points[0,0]*m
m_ = -1/m
dist_perp = (ay-m*ax-c)/np.sqrt(m**2+1)
dist_par = (ay-m_*ax)/np.sqrt(m_**2+1)
masked[np.abs(dist_perp)<wirewidth] = True
np.save('cspad_last_pixel_mask.npy',masked)
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