def from_unitcell(x,y,unit_cell,space_group,qmin,qmax,**kwargs):
'''Creates a series of estimated a peaks from the unit cell
This will only work reliably if the spectrum is composed of fairly well
separated peaks and is well calibrated. The peaks are provided with hkl
attributes and a new name
Parameters
==========
x : array_like
independent variable of spectrum e.g. q or energy
y : array_like
dependent variable of spectrum e.g. counts
unit_cell : list of floats
unit cell of the material in form [a,b,c,alpha,beta,gamma]
space_group : str or int
Hermann–Mauguin notation of the space group
qmin : float
minimum q value for the first reflection
qmax : float
maximum q value for the last reflection
Returns
=======
peaks : list, Peak
A list of peaks with approximately correct parameters
Other Parameters
================
kwargs : dict
additional keywords passed into estimate_peak
'''
qs,hkls = unitcell.get_unique_peaks(unit_cell,space_group,qmin,qmax)
return from_hkls(x,y,unit_cell,hkls,**kwargs)
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)
def identify(peaks,unit_cell,space_group,name='phase',qmax=10):
'''Finds abd labels peaks that correspond to the provided parameters
This will only work if the spectrum is well calibrated. The peaks
are provided with hkl attributes and a new name
Parameters
==========
peaks : sequence
any sequence of Peak instances
unit_cell : list of floats
unit cell of the material in form [a,b,c,alpha,beta,gamma]
space_group : str or int
Hermann–Mauguin notation of the space group
name : string
new name to give peaks that are match
qmax : float
maximum q value for the last reflection
Returns
=======
subset : list, Peak
A list of those peaks that matched the unit cell
'''
from edxrd import calibration
from edxrd.tools import unitcell
cens = np.asarray([peak.centre.nominal_value for peak in peaks])
known,hkls = unitcell.get_unique_peaks(unit_cell,space_group,0,qmax)
known = np.asarray(known)
cancond,nwncond = calibration.find_neighbours(cens,known)
#known = known[nwncond]
#hkls = hkls[nwncond]
#matched = calibration.match(cens,known)
#peaks = [peak for i,peak in enumerate(peaks) if cancond[i] == True]
subset=[]
j=0
for i,peak in enumerate(peaks):
if cancond[i] == True:
peak.name = name+'_'+''.join([str(int(n)) for n in hkls[j]])
j+=1
subset.append(peak)
return subset
def test_matchbest(self):
b = unitcell.get_unique_peaks([4.04,4.04,4.04,90,90,90],'fm-3m',0,6)[0]
a = 1 + b*(1/0.0019)
a = np.hstack((a[0:1],[1500],a[1:2],[1900],a[2:]))
cal = match(a,b)
ma = cal.candidates.matched.tolist()
self.assertAlmostEqual(ma[0],1418.769,3)
self.assertAlmostEqual(ma[1],1638.099,3)
self.assertAlmostEqual(ma[2],2316.207,3)
self.assertAlmostEqual(ma[3],2715.821,3)
self.assertAlmostEqual(ma[4],2836.538,3)
mb = cal.known.matched.tolist()
self.assertAlmostEqual(mb[0],2.694,3)
self.assertAlmostEqual(mb[1],3.11,3)
self.assertAlmostEqual(mb[2],4.399,3)
self.assertAlmostEqual(mb[3],5.158,3)
self.assertAlmostEqual(mb[4],5.388,3)