if hasattr(material,'space_group'):
peaks = from_unitcell(x.mean(axis=0),counts.mean(axis=0),material.unit_cell,
material.space_group,qmin,qmax,name=material.name,
ymin_on_ymax=ymin_on_ymax)
else:
peaks=[]
for mat in material:
peaks.extend(from_unitcell(x.mean(axis=0),counts.mean(axis=0),mat.unit_cell,
mat.space_group,qmin,qmax,name=mat.name,
ymin_on_ymax=ymin_on_ymax))
print 'fitting %i peaks'%len(peaks)
print [p.name for p in peaks]
fc = FitterCollection()
fc.create_many(['element_%i'%i for i in range(counts.shape[0])],x,peaks,**kwargs)
fc.backup()
fc(counts,reestimate=False)
yield fc.lastfit_condensed,fc.lastresult_condensed,info
for counts,info in source:
fc.restore()
fc(counts,reestimate=reestimate)
yield fc.lastfit_condensed,fc.lastresult_condensed,info
def calcstrain(source,phi,Q0=None,allow_shear=True):
if len(phi) > 2:
take = np.ones(len(phi)).astype(bool)
take[5] = take[8] = False
def test_FitterCollection(self):
#create one fitter to add
fitter1 = ChunkFitter(self.x,makepeaks(self.x,self.y))
#create the collection with one fitter
fc = FitterCollection(detector1=fitter1)
#now make a second and add it to the collection
fitter2 = ChunkFitter(self.x,makepeaks(self.x,self.y))
fc.add('detector2',fitter2)
#make a set of fitters and add all at once
fitters = [('detector%i'%i,ChunkFitter(self.x,makepeaks(self.x,self.y))) for i in range(2,6)]
fc.add_many(fitters)
#check create works
fc.create('detector6',self.x,makepeaks(self.x,self.y))
#check create fails if we send 2d data
self.assertRaises(FitterError,fc.create,'detector6',np.tile(self.x,(3,1)),makepeaks(self.x,self.y))
#check create many works with ideal data
names = ['detector%i'%i for i in range(7,10)]
xs = np.tile(self.x,(len(names),1))
fc.create_many(names,xs,makepeaks(self.x,self.y))
#check we raise an error if we add with mismatching names and data shape
xs = np.tile(self.x,(2,1)) #fake 2D data set only 2 wide (should be 1 or 4)
names = ['detector%i'%i for i in range(4)]
self.assertRaises(FitterError,fc.create_many,names,xs,makepeaks(self.x,self.y))
detnames = ['detector1', 'detector2', 'detector3', 'detector4', 'detector5',
'detector6','detector7','detector8','detector9']
parnames = ['centre','width','area','eta','asymmetry','background']
peaknames = ['LaB6_110', 'LaB6_111', 'LaB6_200', 'LaB6_210',
'LaB6_211', 'LaB6_220', 'LaB6_300', 'LaB6_310']
self.assertListEqual(fc.keys(),detnames)
bck = copy.deepcopy(fc)
# #make sure refine fails if we send y data of wrong shape
# ys = np.tile(self.y,(4,1))
# self.assertRaises(FitterError,fc.refine,ys)
#this checks that refine with 2D data works
ys = np.tile(self.y,(9,1))
fc.refine(ys)
result = fc.lastresult #check results by accessing attributes
yfit = fc.lastfit
self.assertEqual(len(yfit),9)
self.assertListEqual(yfit.keys(),detnames)
self.assertTupleEqual(yfit['detector1'].shape,(4096,))
self.assertEqual(len(result),9)
self.assertListEqual(result.keys(),detnames)
self.assertListEqual(result['detector1'].keys(),peaknames)
self.assertListEqual(sorted(result['detector1']['LaB6_110'].keys()),sorted(parnames))
cens = [result['detector1'][peaknames[i]]['centre'].nominal_value for i in range(6)]
self.assertAlmostEquals(cens[0],2.1391,4)
self.assertAlmostEquals(cens[1],2.6195,4)
self.assertAlmostEquals(cens[2],3.0249,4)
self.assertAlmostEquals(cens[3],3.3815,4)
self.assertAlmostEquals(cens[4],3.7053,4)
self.assertAlmostEquals(cens[5],4.2773,4)
fc = copy.deepcopy(bck)
#check ok with 9 columns instead of rows
yfit,result = fc.refine(ys.transpose()) #check results from returns
self.assertEqual(len(yfit),9)
self.assertListEqual(yfit.keys(),detnames)
self.assertTupleEqual(yfit['detector1'].shape,(4096,))
self.assertEqual(len(result),9)
self.assertListEqual(result.keys(),detnames)
self.assertListEqual(result['detector1'].keys(),peaknames)
self.assertListEqual(sorted(result['detector1']['LaB6_110'].keys()),sorted(parnames))
cens = [result['detector1'][peaknames[i]]['centre'].nominal_value for i in range(6)]
self.assertAlmostEquals(cens[0],2.1391,4)
self.assertAlmostEquals(cens[1],2.6195,4)
self.assertAlmostEquals(cens[2],3.0249,4)
self.assertAlmostEquals(cens[3],3.3815,4)
self.assertAlmostEquals(cens[4],3.7053,4)
self.assertAlmostEquals(cens[5],4.2773,4)
fc = copy.deepcopy(bck)
#check ok with dictionary
yd ={detnames[i] : ys[i] for i in range(len(ys))}
yfit,result = fc.refine(yd) #check results from returns
self.assertEqual(len(yfit),9)
self.assertListEqual(yfit.keys(),detnames)
self.assertTupleEqual(yfit['detector1'].shape,(4096,))
self.assertEqual(len(result),9)
self.assertListEqual(result.keys(),detnames)
self.assertListEqual(result['detector1'].keys(),peaknames)
self.assertListEqual(sorted(result['detector1']['LaB6_110'].keys()),sorted(parnames))
cens = [result['detector1'][peaknames[i]]['centre'].nominal_value for i in range(6)]
self.assertAlmostEquals(cens[0],2.1391,4)
self.assertAlmostEquals(cens[1],2.6195,4)
self.assertAlmostEquals(cens[2],3.0249,4)
self.assertAlmostEquals(cens[3],3.3815,4)
self.assertAlmostEquals(cens[4],3.7053,4)
self.assertAlmostEquals(cens[5],4.2773,4)
