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)
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)
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()
#