def saveto(self):
if self.currentdataset is None or self.currentparam is None:
self.logger('Error: No integrated dataset present! Integrate or load something first!',"ERROR")
return
try:
B1python.write1dsasdict(self.currentdataset,self.savefilename.get())
self.logger('Successfully saved file %s' % self.savefilename.get(),'Info')
except:
self.logger('Error saving file %s'%self.savefilename.get(),'Error')
def adjustmask(self):
fsn=self.getfsn()
data,dataerr,param=B1python.read2dintfile(fsn,dirs=self.getinputdirs())
if len(data)!=1 and len(dataerr)!=1 and len(param)!=1:
self.logger("Could not find files for FSN %d"%fsn,priority="error")
return
if self.maskmatrix is None:
self.maskmatrix=np.zeros(data[0].shape,dtype=np.uint8)
try:
self.maskmatrix=B1python.makemask(self.maskmatrix,data[0])
except:
self.logger("Mask editing was interrupted by user!",'WARNING')
def loadfrom(self):
data=B1python.readintfile(self.savefilename.get())
if len(data)==0:
self.logger('Error: Cannot load intensity from file %s' % self.savefilename.get(),'ERROR')
self.currentdataset=data
self.currentparam=self.savefilename.get()
self.setflag('intdata',True)
def createmask(self):
fsn=self.getfsn()
data,dataerr,param=B1python.read2dintfile(fsn,dirs=self.getinputdirs())
if len(data)!=1 and len(dataerr)!=1 and len(param)!=1:
self.logger("Could not find files for FSN %d"%fsn,priority="error")
return
self.maskmatrix=np.ones(data.shape,dtype=np.uint8)
self.logger('Created an empty %d x %d mask'%data[0].shape)
self.setflag('mask',True)
def replot(self):
self.setflag('busy',True)
fsn=self.getfsn()
data,dataerr,param=B1python.read2dintfile(fsn,dirs=self.getinputdirs())
self.setflag('busy',False)
if len(data)!=1 and len(dataerr)!=1 and len(param)!=1:
self.logger("Could not find files for FSN %d"%fsn,priority="error")
return
else:
self.logger('Files for FSN %d loaded successfully'%fsn,priority='INFO')
mask=self.maskmatrix
self.setflag('busy',True)
if self.getplottype()=='2D':
try:
self.figure.show()
except:
self.figure=pylab.figure(self.figurenum)
pylab.figure(self.figurenum)
self.figure.clf()
B1python.plot2dmatrix(data[0],header=param[0],showqscale=True,mask=self.maskmatrix)
pylab.xlabel(u'q (1/%c)'%197)
pylab.ylabel(u'q (1/%c)'%197)
pylab.colorbar()
elif self.getplottype()=='1D':
if mask is None:
mask=np.ones(data[0].shape,dtype=np.uint8)
if not (self.qminmanual.get() and self.qmaxmanual.get() and self.Nqmanual.get()):
qmin,qmax,Nq=B1python.qrangefrommask(mask, param[0]['EnergyCalibrated'],param[0]['Dist'],param[0]['PixelSize'],param[0]['BeamPosX'],param[0]['BeamPosY'])
self.logger('Auto-determined integration bounds: %.8f < q < %.8f, optimal number of bins is %d' %(qmin,qmax,Nq))
if self.qminmanual.get():
qmin=float(self.qmin.get())
else:
self.qmin['state']='normal'
self.qmin.delete(0,Tkinter.END)
self.qmin.insert(0,'%.8f' % qmin)
self.qmin['state']='disabled'
if self.qmaxmanual.get():
qmax=float(self.qmax.get())
else:
self.qmax['state']='normal'
self.qmax.delete(0,Tkinter.END)
self.qmax.insert(0,'%.8f' % qmax)
self.qmax['state']='disabled'
if self.Nqmanual.get():
Nq=long(self.Nq.get())
else:
self.Nq['state']='normal'
self.Nq.delete(0,Tkinter.END)
self.Nq.insert(0,'%.8f' % Nq)
self.Nq['state']='disabled'
phi0=None
dphi=None
if self.phi0manual.get():
phi0=float(self.phi0.get())*np.pi/180.0
if self.dphimanual.get():
dphi=float(self.dphi.get())*np.pi/180.0
qrange=np.linspace(qmin,qmax,Nq)
self.logger('Integration starting...',"INFO")
t0=time.time()
q,I,E,A,maskout=B1python.radintC(data[0],dataerr[0],param[0]['EnergyCalibrated'],param[0]['Dist'],param[0]['PixelSize'],param[0]['BeamPosX'],param[0]['BeamPosY'],1-mask,qrange,phi0=phi0,dphi=dphi,returnmask=True,returnavgq=True)
maskout=1-maskout
self.currentdataset=B1python.SASDict(q,I,E,Area=A)
self.currentdataset.sanitize()
self.currentparam=param[0]
self.setflag('intdata',True)
self.logger('Integration finished in %.2f seconds'%(time.time()-t0),"INFO")
try:
self.figure.show()
except:
self.figure=pylab.figure(self.figurenum)
pylab.figure(self.figurenum)
B1python.plotintegrated(data[0],q,I,E,A,qrange,maskout,param[0],mode='radial')
elif self.setplottype()=='Azimuthal':
self.logger('Azimuthal integration not yet supported!','WARNING')
self.setflag('busy',False)
pylab.draw()
pylab.gcf().show()
del data
del dataerr
del param
def fit(self):
if self.currentdataset is None or self.currentparam is None:
self.logger('Error: No integrated dataset present! Integrate or load something first!',"ERROR")
return
B1python.basicfittinggui(B1python.SASDict(**(self.currentdataset)))
