Пример #1
0
    def test_calibrate_no_search(self):
        #from uncertainties import unumpy 
        
        x,y = self.edxdata['LaB6']
       
        searcher = WaveletPeakSearcher(1.2,25,1.15,critical_sep=2,noiselevel=20,min_ridge_length=9,
                                       min_child_length=8,ridge_skip=0)
    
        searcher.set_data(x,y)
        pks = searcher.identify_peaks()

        cf = ChunkFitter(x,pks,shape='g')
        cf.refine(y)

        candidates = np.array([peak.centre for peak in cf.peaks])
         
        #uncertainty = unumpy.std_devs(candidates)
        #uncertainty[uncertainty==0] = 1
        #candidates = unumpy.nominal_values(candidates)

        unit_cell = [4.15695,4.15695,4.15695,90,90,90]
        space_group = 'pm-3m'          
        c = match_fit(candidates,unit_cell,space_group)
        
        self.assertLess(abs(c.values[0]),1e-9)
        self.assertAlmostEqual(c.values[1], 0.001942,5)
        self.assertLess(abs(c.values[2]),0.005)
Пример #2
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 def test_calibration_fakedata(self):
     c0,c1 = 2,0.0019
     b = unitcell.get_unique_peaks([4.04,4.04,4.04,90,90,90],'fm-3m',0,6)[0]
     a = -c0 + b*(1/c1) 
     a = np.hstack((a[0:1],[1500],a[1:2],[1900],a[2:]))
     
     cal = match_fit(a,[4.04,4.04,4.04,90,90,90],'fm-3m',qmax=7)
     cal = cal.values
     self.assertAlmostEqual(cal[0],0,3)
     self.assertAlmostEqual(cal[1],c1,4)
     self.assertAlmostEqual(cal[2],c1*c0,4)  
Пример #3
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cal = calibration.search_match_fit(x,y,unit_cell,space_group,**settings)

#the rest is all plotting
sob = cal.search[0] #calibration objects may have data from several spectra
f,axs = plt.subplots(2,sharex=True)
ax1,ax2 = axs
ax1.plot(sob.x,sob.y,'b')
ax1.plot(sob.x,sob.pfit,'r')
ax1.plot(sob.centres,sob.heights, 'ro',markerfacecolor='none')
ax1.set_yscale('log')

mob = cal.match[0] #calibration objects may have data from several spectra
ax2.plot(mob.candidates.filtered,mob.known.filtered,'ko')
ax2.plot(cal.xfit,cal.yfit,'r--')
ax2.set_xbound(0,mob.candidates.filtered.max())

#Now test the method without using peak search
#i.e. some candidates found by some other method preferred by the user
cands = [775.8, 1098.3, 1345.4, 1553.8, 1737.1, 1903.6, 2197.5,  2330.9]
cal = calibration.match_fit(cands,unit_cell,space_group)

#again - most code is plotting
mob = cal.match[0]
f,ax = plt.subplots(1)
ax.plot(mob.candidates.filtered,mob.known.filtered,'ko')
ax.plot(cal.xfit,cal.yfit,'r--')
ax.set_xbound(0,mob.candidates.filtered.max())

plt.show()
#