# -*- coding: utf-8 -*-
#GSASII cell indexing program: variation on that of A. Coehlo
# includes cell refinement from peak positions (not zero as yet)
########### SVN repository information ###################
# $Date: 2024-03-30 08:47:38 -0500 (Sat, 30 Mar 2024) $
# $Author: vondreele $
# $Revision: 5774 $
# $URL: https://subversion.xray.aps.anl.gov/pyGSAS/trunk/GSASIIindex.py $
# $Id: GSASIIindex.py 5774 2024-03-30 13:47:38Z vondreele $
########### SVN repository information ###################
'''
Classes and routines defined in :mod:`GSASIIindex` follow.
'''
from __future__ import division, print_function
import math
import time
import numpy as np
import GSASIIpath
GSASIIpath.SetVersionNumber("$Revision: 5774 $")
import GSASIIlattice as G2lat
import GSASIIpwd as G2pwd
import GSASIIspc as G2spc
import GSASIImath as G2mth
import scipy.optimize as so
# trig functions in degrees
sind = lambda x: math.sin(x*math.pi/180.)
asind = lambda x: 180.*math.asin(x)/math.pi
tand = lambda x: math.tan(x*math.pi/180.)
atand = lambda x: 180.*math.atan(x)/math.pi
atan2d = lambda y,x: 180.*math.atan2(y,x)/math.pi
cosd = lambda x: math.cos(x*math.pi/180.)
acosd = lambda x: 180.*math.acos(x)/math.pi
rdsq2d = lambda x,p: round(1.0/math.sqrt(x),p)
#numpy versions
npsind = lambda x: np.sin(x*np.pi/180.)
npasind = lambda x: 180.*np.arcsin(x)/math.pi
npcosd = lambda x: np.cos(x*math.pi/180.)
nptand = lambda x: np.tan(x*math.pi/180.)
npatand = lambda x: 180.*np.arctan(x)/np.pi
npatan2d = lambda y,x: 180.*np.arctan2(y,x)/np.pi
try: # fails on doc build
rpd = np.pi/180.
except TypeError:
pass
[docs]
def scaleAbyV(A,V):
'needs a doc string'
v = G2lat.calc_V(A)
scale = math.exp(math.log(v/V)/3.)**2
for i in range(6):
A[i] *= scale
[docs]
def ranaxis(dmin,dmax):
'needs a doc string'
import random as rand
return rand.random()*(dmax-dmin)+dmin
[docs]
def ran2axis(k,N):
'needs a doc string'
import random as rand
T = 1.5+0.49*k/N
# B = 0.99-0.49*k/N
# B = 0.99-0.049*k/N
B = 0.99-0.149*k/N
R = (T-B)*rand.random()+B
return R
#def ranNaxis(k,N):
# import random as rand
# T = 1.0+1.0*k/N
# B = 1.0-1.0*k/N
# R = (T-B)*rand.random()+B
# return R
[docs]
def ranAbyV(Bravais,dmin,dmax,V):
'needs a doc string'
cell = [0,0,0,0,0,0]
bad = True
while bad:
bad = False
cell = rancell(Bravais,dmin,dmax)
G,g = G2lat.cell2Gmat(cell)
A = G2lat.Gmat2A(G)
if G2lat.calc_rVsq(A) < 1:
scaleAbyV(A,V)
cell = G2lat.A2cell(A)
for i in range(3):
bad |= cell[i] < dmin
return A
[docs]
def ranAbyR(Bravais,A,k,N,ranFunc):
'needs a doc string'
R = ranFunc(k,N)
if Bravais in [0,1,2]: #cubic - not used
A[0] = A[1] = A[2] = A[0]*R
A[3] = A[4] = A[5] = 0.
elif Bravais in [3,4]: #hexagonal/trigonal
A[0] = A[1] = A[3] = A[0]*R
A[2] *= R
A[4] = A[5] = 0.
elif Bravais in [5,6]: #tetragonal
A[0] = A[1] = A[0]*R
A[2] *= R
A[3] = A[4] = A[5] = 0.
elif Bravais in [7,8,9,10,11,12]: #orthorhombic
A[0] *= R
A[1] *= R
A[2] *= R
A[3] = A[4] = A[5] = 0.
elif Bravais in [13,14,15,16]: #monoclinic
A[0] *= R
A[1] *= R
A[2] *= R
A[4] *= R
A[3] = A[5] = 0.
else: #triclinic
A[0] *= R
A[1] *= R
A[2] *= R
A[3] *= R
A[4] *= R
A[5] *= R
return A
[docs]
def rancell(Bravais,dmin,dmax):
'needs a doc string'
if Bravais in [0,1,2]: #cubic
a = b = c = ranaxis(dmin,dmax)
alp = bet = gam = 90
elif Bravais in [3,4]: #hexagonal/trigonal
a = b = ranaxis(dmin,dmax)
c = ranaxis(dmin,dmax)
alp = bet = 90
gam = 120
elif Bravais in [5,6]: #tetragonal
a = b = ranaxis(dmin,dmax)
c = ranaxis(dmin,dmax)
alp = bet = gam = 90
elif Bravais in [7,8,9,10,11,12]: #orthorhombic - F,I,P - a<b<c convention
abc = [ranaxis(dmin,dmax),ranaxis(dmin,dmax),ranaxis(dmin,dmax)]
if Bravais in [7,8,12]:
abc.sort()
a = abc[0]
b = abc[1]
c = abc[2]
alp = bet = gam = 90
elif Bravais in [13,14,15,16]: #monoclinic - C,P - a<c convention
ac = [ranaxis(dmin,dmax),ranaxis(dmin,dmax)]
if Bravais in [13,14]:
ac.sort()
a = ac[0]
b = ranaxis(dmin,dmax)
c = ac[1]
alp = gam = 90
bet = ranaxis(90.,140.)
else: #triclinic - a<b<c convention
abc = [ranaxis(dmin,dmax),ranaxis(dmin,dmax),ranaxis(dmin,dmax)]
abc.sort()
a = abc[0]
b = abc[1]
c = abc[2]
r = 0.5*b/c
alp = ranaxis(acosd(r),acosd(-r))
r = 0.5*a/c
bet = ranaxis(acosd(r),acosd(-r))
r = 0.5*a/b
gam = ranaxis(acosd(r),acosd(-r))
return [a,b,c,alp,bet,gam]
[docs]
def calc_M20(peaks,HKL,ifX20=True):
'needs a doc string'
diff = 0
X20 = 0
for Nobs20,peak in enumerate(peaks):
if peak[3]:
Qobs = 1.0/peak[7]**2
Qcalc = 1.0/peak[8]**2
diff += abs(Qobs-Qcalc)
elif peak[2]:
X20 += 1
if Nobs20 == 19:
d20 = peak[7]
break
else:
d20 = peak[7]
Nobs20 = len(peaks)
for N20,hkl in enumerate(HKL):
if hkl[3] < d20:
break
Q20 = 1.0/d20**2
if diff:
M20 = Q20/(2.0*diff)
else:
M20 = 0
if ifX20:
M20 /= (1.+X20)
return M20,X20
[docs]
def calc_M20SS(peaks,HKL):
'needs a doc string'
diff = 0
X20 = 0
for Nobs20,peak in enumerate(peaks):
if peak[3]:
Qobs = 1.0/peak[8]**2
Qcalc = 1.0/peak[9]**2
diff += abs(Qobs-Qcalc)
elif peak[2]:
X20 += 1
if Nobs20 == 19:
d20 = peak[8]
break
else:
d20 = peak[8]
Nobs20 = len(peaks)
for N20,hkl in enumerate(HKL):
if hkl[4] < d20:
break
Q20 = 1.0/d20**2
if diff:
M20 = Q20/(2.0*diff)
else:
M20 = 0
M20 /= (1.+X20)
return M20,X20
[docs]
def sortM20(cells):
'needs a doc string'
#cells is M20,X20,Bravais,a,b,c,alp,bet,gam
#sort highest M20 1st
T = []
for i,M in enumerate(cells):
T.append((M[0],i))
D = dict(zip(T,cells))
T.sort()
T.reverse()
X = []
for key in T:
X.append(D[key])
return X
def sortCells(cells,col):
#cells is M20,X20,Bravais,a,b,c,alp,bet,gam,volume
#sort smallest a,b,c,alpha,beta,gamma or volume 1st
T = []
for i,M in enumerate(cells):
T.append((M[col],i))
D = dict(zip(T,cells))
T.sort()
X = []
for key in T:
X.append(D[key])
return X
def findMV(peaks,controls,ssopt,Inst,dlg):
def Val2Vec(vec,Vref,values):
Vec = []
i = 0
for j,r in enumerate(Vref):
if r:
if values.size > 1:
Vec.append(max(-1.,min(1.0,values[i])))
else:
Vec.append(max(0.0,min(1.0,values)))
i += 1
else:
Vec.append(vec[j])
return np.array(Vec,dtype=float)
def ZSSfunc(values,peaks,dmin,Inst,SGData,SSGData,vec,Vref,maxH,A,wave,Z,dlg=None):
Vec = Val2Vec(vec,Vref,values)
HKL = G2pwd.getHKLMpeak(dmin,Inst,SGData,SSGData,Vec,maxH,A)
Peaks = np.array(IndexSSPeaks(peaks,HKL)[1]).T
Qo = 1./Peaks[-2]**2
Qc = G2lat.calc_rDsqZSS(Peaks[4:8],A,Vec,Z,Peaks[0],wave)
chi = np.sum((Qo-Qc)**2)
if dlg:
dlg.Pulse()
return chi
def TSSfunc(values,peaks,dmin,Inst,SGData,SSGData,vec,Vref,maxH,A,difC,Z,dlg=None):
Vec = Val2Vec(vec,Vref,values)
HKL = G2pwd.getHKLMpeak(dmin,Inst,SGData,SSGData,Vec,maxH,A)
Peaks = np.array(IndexSSPeaks(peaks,HKL)[1]).T
Qo = 1./Peaks[-2]**2
Qc = G2lat.calc_rDsqTSS(Peaks[4:8],A,Vec,Z,Peaks[0],difC)
chi = np.sum((Qo-Qc)**2)
if dlg:
dlg.Pulse()
return chi
if 'T' in Inst['Type'][0]:
difC = Inst['difC'][1]
else:
wave = G2mth.getWave(Inst)
SGData = G2spc.SpcGroup(controls[13])[1]
SSGData = G2spc.SSpcGroup(SGData,ssopt['ssSymb'])[1]
A = G2lat.cell2A(controls[6:12])
Z = controls[1]
Vref = [True if x in ssopt['ssSymb'] else False for x in ['a','b','g']]
values = []
ranges = []
dT = 0.01 #seems to be a good choice
for v,r in zip(ssopt['ModVec'],Vref):
if r:
ranges += [slice(dT,1.-dT,dT),] #NB: unique part for (00g) & (a0g); (abg)?
values += [v,]
dmin = getDmin(peaks)-0.005
if 'T' in Inst['Type'][0]:
result = so.brute(TSSfunc,ranges,finish=so.fmin_cg,full_output=True,
args=(peaks,dmin,Inst,SGData,SSGData,ssopt['ModVec'],Vref,1,A,difC,Z,dlg))
else:
result = so.brute(ZSSfunc,ranges,finish=so.fmin_cg,full_output=True,
args=(peaks,dmin,Inst,SGData,SSGData,ssopt['ModVec'],Vref,1,A,wave,Z,dlg))
return Val2Vec(ssopt['ModVec'],Vref,result[0]),result
[docs]
def IndexPeaks(peaks,HKL):
'needs a doc string'
import bisect
N = len(HKL)
if N == 0: return False,peaks
hklds = list(np.array(HKL).T[3])+[1000.0,0.0,]
hklds.sort() # ascending sort - upper bound at end
hklmax = [0,0,0]
for ipk,peak in enumerate(peaks):
peak[4:7] = [0,0,0] #clear old indexing
peak[8] = 0.
if peak[2]:
i = bisect.bisect_right(hklds,peak[7]) # find peak position in hkl list
dm = peak[-2]-hklds[i-1] # peak to neighbor hkls in list
dp = hklds[i]-peak[-2]
pos = N-i # reverse the order
if dp > dm: pos += 1 # closer to upper than lower
if pos >= N:
break
hkl = HKL[pos] # put in hkl
if hkl[-1] >= 0: # peak already assigned - test if this one better
opeak = peaks[int(hkl[-1])] #hkl[-1] needs to be int here
dold = abs(opeak[-2]-hkl[3])
dnew = min(dm,dp)
if dold > dnew: # new better - zero out old
opeak[4:7] = [0,0,0]
opeak[8] = 0.
else: # old better - do nothing
continue
hkl[-1] = ipk
peak[4:7] = hkl[:3]
peak[8] = hkl[3] # fill in d-calc
for peak in peaks:
peak[3] = False
if peak[2]:
if peak[-1] > 0.:
for j in range(3):
if abs(peak[j+4]) > hklmax[j]: hklmax[j] = abs(peak[j+4])
peak[3] = True
if hklmax[0]*hklmax[1]*hklmax[2] > 0:
return True,peaks
else:
return False,peaks #nothing indexed!
[docs]
def IndexSSPeaks(peaks,HKL):
'needs a doc string'
import bisect
N = len(HKL)
Peaks = np.copy(peaks)
if N == 0: return False,Peaks
if len(peaks[0]) == 9: #add m column if missing
Peaks = np.insert(Peaks,7,np.zeros_like(Peaks.T[0]),axis=1)
# for peak in Peaks:
# peak.insert(7,0)
hklds = list(np.array(HKL).T[4])+[1000.0,0.0,]
hklds.sort() # ascending sort - upper bound at end
hklmax = [0,0,0,0]
for ipk,peak in enumerate(Peaks):
peak[4:8] = [0,0,0,0] #clear old indexing
peak[9] = 0.
if peak[2]: #Use
i = bisect.bisect_right(hklds,peak[8]) # find peak position in hkl list
dm = peak[8]-hklds[i-1] # peak to neighbor hkls in list
dp = hklds[i]-peak[8]
pos = N-i # reverse the order
if dp > dm: pos += 1 # closer to upper than lower
if pos >= N:
# print ('%.4f %d'%(pos,N))
break
hkl = HKL[pos] # put in hkl
if hkl[-1] >= 0: # peak already assigned - test if this one better
opeak = Peaks[int(hkl[-1])]
dold = abs(opeak[-2]-hkl[4])
dnew = min(dm,dp)
if dold > dnew: # new better - zero out old
opeak[4:8] = [0,0,0,0]
opeak[9] = 0.
else: # old better - do nothing
continue
hkl[-1] = ipk
peak[4:8] = hkl[:4]
peak[9] = hkl[4] # fill in d-calc
for peak in Peaks:
peak[3] = False
if peak[2]:
if peak[-1] > 0.:
for j in range(4):
if abs(peak[j+4]) > hklmax[j]: hklmax[j] = abs(peak[j+4])
peak[3] = True
if hklmax[0]*hklmax[1]*hklmax[2]*hklmax[3] > 0:
return True,Peaks
else:
return False,Peaks #nothing indexed!
[docs]
def Values2A(ibrav,values):
'needs a doc string'
if ibrav in [0,1,2]:
return [values[0],values[0],values[0],0,0,0]
elif ibrav in [3,4]:
return [values[0],values[0],values[1],values[0],0,0]
elif ibrav in [5,6]:
return [values[0],values[0],values[1],0,0,0]
elif ibrav in [7,8,9,10,11,12]:
return [values[0],values[1],values[2],0,0,0]
elif ibrav in [13,14,15,16]:
return [values[0],values[1],values[2],0,values[3],0]
else:
return list(values[:6])
[docs]
def A2values(ibrav,A):
'needs a doc string'
if ibrav in [0,1,2]:
return [A[0],]
elif ibrav in [3,4,5,6]:
return [A[0],A[2]]
elif ibrav in [7,8,9,10,11,12]:
return [A[0],A[1],A[2]]
elif ibrav in [13,14,15,16]:
return [A[0],A[1],A[2],A[4]]
else:
return A
def Values2Vec(ibrav,vec,Vref,val):
if ibrav in [3,4,5,6]:
Nskip = 2
elif ibrav in [7,8,9,10,11,12]:
Nskip = 3
elif ibrav in [13,14,15,16]:
Nskip = 4
else:
Nskip = 6
Vec = []
i = 0
for j,r in enumerate(Vref):
if r:
Vec.append(val[i+Nskip])
i += 1
else:
Vec.append(vec[j])
return np.array(Vec)
[docs]
def FitHKL(ibrav,peaks,A,Pwr):
'needs a doc string'
def errFit(values,ibrav,d,H,Pwr):
A = Values2A(ibrav,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsq(H,A)
return (Qo-Qc)*d**Pwr
def dervFit(values,ibrav,d,H,Pwr):
if ibrav in [0,1,2]:
derv = [H[0]*H[0]+H[1]*H[1]+H[2]*H[2],]
elif ibrav in [3,4,]:
derv = [H[0]*H[0]+H[1]*H[1]+H[0]*H[1],H[2]*H[2]]
elif ibrav in [5,6]:
derv = [H[0]*H[0]+H[1]*H[1],H[2]*H[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2]]
elif ibrav in [13,14,15,16]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[2]]
else:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[1],H[0]*H[2],H[1]*H[2]]
derv = -np.array(derv)
return (derv*d**Pwr).T
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
result = so.leastsq(errFit,values,Dfun=dervFit,full_output=True,ftol=0.000001,
args=(ibrav,Peaks[7],Peaks[4:7],Pwr))
A = Values2A(ibrav,result[0])
return True,np.sum(errFit(result[0],ibrav,Peaks[7],Peaks[4:7],Pwr)**2),A,result
def errFitZ(values,ibrav,d,H,tth,wave,Z,Zref):
Zero = Z
if Zref:
Zero = values[-1]
A = Values2A(ibrav,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsqZ(H,A,Zero,tth,wave)
return (Qo-Qc)
def dervFitZ(values,ibrav,d,H,tth,wave,Z,Zref):
if ibrav in [0,1,2]:
derv = [H[0]*H[0]+H[1]*H[1]+H[2]*H[2],]
elif ibrav in [3,4,]:
derv = [H[0]*H[0]+H[1]*H[1]+H[0]*H[1],H[2]*H[2]]
elif ibrav in [5,6]:
derv = [H[0]*H[0]+H[1]*H[1],H[2]*H[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2]]
elif ibrav in [13,14,15,16]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[2]]
else:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[1],H[0]*H[2],H[1]*H[2]]
if Zref:
derv.append(npsind(tth)*2.0*rpd/wave**2)
derv = -np.array(derv)
return derv.T
[docs]
def FitHKLZ(wave,ibrav,peaks,A,Z,Zref):
'needs a doc string'
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
if Zref:
values.append(Z)
#TODO: try Hessian refinement here - might improve things
# result = G2mth.HessianLSQ(errFitZ,values,Hess=peakHess, #would need "peakHess" routine; returns vec & Hess
# args=(ibrav,Peaks[7],Peaks[4:7],Peaks[0],wave,Z,Zref]),ftol=.01,maxcyc=10)
result = so.leastsq(errFitZ,values,Dfun=dervFitZ,full_output=True,ftol=0.0001,
args=(ibrav,Peaks[7],Peaks[4:7],Peaks[0],wave,Z,Zref))
A = Values2A(ibrav,result[0][:6])
if Zref:
Z = result[0][-1]
chisq = np.sum(errFitZ(result[0],ibrav,Peaks[7],Peaks[4:7],Peaks[0],wave,Z,Zref)**2)
return True,chisq,A,Z,result
def errFitZSS(values,ibrav,d,H,tth,wave,vec,Vref,Z,Zref):
Zero = Z
if Zref:
Zero = values[-1]
A = Values2A(ibrav,values)
Vec = Values2Vec(ibrav,vec,Vref,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsqZSS(H,A,Vec,Zero,tth,wave)
return (Qo-Qc)
def dervFitZSS(values,ibrav,d,H,tth,wave,vec,Vref,Z,Zref):
A = Values2A(ibrav,values)
Vec = Values2Vec(ibrav,vec,Vref,values)
HM = H[:3]+(H[3][:,np.newaxis]*Vec).T
if ibrav in [3,4,]:
derv = [HM[0]*HM[0]+HM[1]*HM[1]+HM[0]*HM[1],HM[2]*HM[2]]
elif ibrav in [5,6]:
derv = [HM[0]*HM[0]+HM[1]*HM[1],HM[2]*HM[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2]]
elif ibrav in [13,14,15,16]:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2],HM[0]*HM[2]]
else:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2],HM[0]*HM[1],HM[0]*HM[2],HM[1]*HM[2]]
if Vref[0]:
derv.append(2.*A[0]*HM[0]*H[3]+A[3]*HM[1]*H[3]+A[4]*HM[2]*H[3])
if Vref[1]:
derv.append(2.*A[1]*HM[1]*H[3]+A[3]*HM[0]*H[3]+A[5]*HM[2]*H[3])
if Vref[2]:
derv.append(2.*A[2]*HM[2]*H[3]+A[4]*HM[1]*H[3]+A[5]*HM[0]*H[3])
if Zref:
derv.append(npsind(tth)*2.0*rpd/wave**2)
derv = -np.array(derv)
return derv.T
[docs]
def FitHKLZSS(wave,ibrav,peaks,A,V,Vref,Z,Zref):
'needs a doc string'
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
for v,r in zip(V,Vref):
if r:
values.append(v)
if Zref:
values.append(Z)
result = so.leastsq(errFitZSS,values,Dfun=dervFitZSS,full_output=True,ftol=1.e-6,
args=(ibrav,Peaks[8],Peaks[4:8],Peaks[0],wave,V,Vref,Z,Zref))
A = Values2A(ibrav,result[0])
Vec = Values2Vec(ibrav,V,Vref,result[0])
if Zref:
Z = result[0][-1]
chisq = np.sum(errFitZSS(result[0],ibrav,Peaks[8],Peaks[4:8],Peaks[0],wave,Vec,Vref,Z,Zref)**2)
return True,chisq,A,Vec,Z,result
def errFitT(values,ibrav,d,H,tof,difC,Z,Zref):
Zero = Z
if Zref:
Zero = values[-1]
A = Values2A(ibrav,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsqT(H,A,Zero,tof,difC)
return (Qo-Qc)
def dervFitT(values,ibrav,d,H,tof,difC,Z,Zref):
if ibrav in [0,1,2]:
derv = [H[0]*H[0]+H[1]*H[1]+H[2]*H[2],]
elif ibrav in [3,4,]:
derv = [H[0]*H[0]+H[1]*H[1]+H[0]*H[1],H[2]*H[2]]
elif ibrav in [5,6]:
derv = [H[0]*H[0]+H[1]*H[1],H[2]*H[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2]]
elif ibrav in [13,14,15,16]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[2]]
else:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[1],H[0]*H[2],H[1]*H[2]]
if Zref:
derv.append(np.ones_like(d)/difC)
derv = np.array(derv)
return derv.T
[docs]
def FitHKLT(difC,ibrav,peaks,A,Z,Zref):
'needs a doc string'
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
if Zref:
values.append(Z)
result = so.leastsq(errFitT,values,Dfun=dervFitT,full_output=True,ftol=0.0001,
args=(ibrav,Peaks[7],Peaks[4:7],Peaks[0],difC,Z,Zref))
A = Values2A(ibrav,result[0])
if Zref:
Z = result[0][-1]
chisq = np.sum(errFitT(result[0],ibrav,Peaks[7],Peaks[4:7],Peaks[0],difC,Z,Zref)**2)
return True,chisq,A,Z,result
[docs]
def FitHKLE(tth,ibrav,peaks,A):
'needs a doc string'
def errFitE(values,ibrav,d,H,keV,tth):
A = Values2A(ibrav,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsq(H,A)
return (Qo-Qc)
def dervFitE(values,ibrav,d,H,keV,tth):
if ibrav in [0,1,2]:
derv = [H[0]*H[0]+H[1]*H[1]+H[2]*H[2],]
elif ibrav in [3,4,]:
derv = [H[0]*H[0]+H[1]*H[1]+H[0]*H[1],H[2]*H[2]]
elif ibrav in [5,6]:
derv = [H[0]*H[0]+H[1]*H[1],H[2]*H[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2]]
elif ibrav in [13,14,15,16]:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[2]]
else:
derv = [H[0]*H[0],H[1]*H[1],H[2]*H[2],H[0]*H[1],H[0]*H[2],H[1]*H[2]]
derv = np.array(derv)
return derv.T
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
result = so.leastsq(errFitE,values,Dfun=dervFitE,full_output=True,ftol=0.0001,
args=(ibrav,Peaks[7],Peaks[4:7],Peaks[0],tth))
A = Values2A(ibrav,result[0])
chisq = np.sum(errFitE(result[0],ibrav,Peaks[7],Peaks[4:7],Peaks[0],tth)**2)
return True,chisq,A,result
def errFitTSS(values,ibrav,d,H,tof,difC,vec,Vref,Z,Zref):
Zero = Z
if Zref:
Zero = values[-1]
A = Values2A(ibrav,values)
Vec = Values2Vec(ibrav,vec,Vref,values)
Qo = 1./d**2
Qc = G2lat.calc_rDsqTSS(H,A,Vec,Zero,tof,difC)
return (Qo-Qc)
def dervFitTSS(values,ibrav,d,H,tof,difC,vec,Vref,Z,Zref):
A = Values2A(ibrav,values)
Vec = Values2Vec(ibrav,vec,Vref,values)
HM = H[:3]+(H[3][:,np.newaxis]*Vec).T
if ibrav in [3,4,]:
derv = [HM[0]*HM[0]+HM[1]*HM[1]+HM[0]*HM[1],HM[2]*HM[2]]
elif ibrav in [5,6]:
derv = [HM[0]*HM[0]+HM[1]*HM[1],HM[2]*HM[2]]
elif ibrav in [7,8,9,10,11,12]:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2]]
elif ibrav in [13,14,15,16]:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2],HM[0]*HM[2]]
else:
derv = [HM[0]*HM[0],HM[1]*HM[1],HM[2]*HM[2],HM[0]*HM[1],HM[0]*HM[2],HM[1]*HM[2]]
if Vref[0]:
derv.append(2.*A[0]*HM[0]*H[3]+A[3]*HM[1]*H[3]+A[4]*HM[2]*H[3])
if Vref[1]:
derv.append(2.*A[1]*HM[1]*H[3]+A[3]*HM[0]*H[3]+A[5]*HM[2]*H[3])
if Vref[2]:
derv.append(2.*A[2]*HM[2]*H[3]+A[4]*HM[1]*H[3]+A[5]*HM[0]*H[3])
if Zref:
derv.append(np.ones_like(d)/difC)
derv = np.array(derv)
return derv.T
[docs]
def FitHKLTSS(difC,ibrav,peaks,A,V,Vref,Z,Zref):
'needs a doc string'
Peaks = np.array(peaks).T
values = A2values(ibrav,A)
for v,r in zip(V,Vref):
if r:
values.append(v)
if Zref:
values.append(Z)
print(values)
result = so.leastsq(errFitTSS,values,Dfun=dervFitTSS,full_output=True,ftol=0.0001,
args=(ibrav,Peaks[8],Peaks[4:8],Peaks[0],difC,V,Vref,Z,Zref))
print(result)
A = Values2A(ibrav,result[0])
Vec = Values2Vec(ibrav,V,Vref,result[0])
if Zref:
Z = result[0][-1]
chisq = np.sum(errFitTSS(result[0],ibrav,Peaks[8],Peaks[4:8],Peaks[0],difC,V,Vref,Z,Zref)**2)
return True,chisq,A,Vec,Z,result
[docs]
def rotOrthoA(A):
'needs a doc string'
return [A[1],A[2],A[0],0,0,0]
[docs]
def swapMonoA(A):
'needs a doc string'
return [A[2],A[1],A[0],0,A[4],0]
[docs]
def oddPeak(indx,peaks):
'needs a doc string'
noOdd = True
for peak in peaks:
H = peak[4:7]
if H[indx] % 2:
noOdd = False
return noOdd
[docs]
def halfCell(ibrav,A,peaks):
'needs a doc string'
if ibrav in [0,1,2]:
if oddPeak(0,peaks):
A[0] *= 2
A[1] = A[2] = A[0]
elif ibrav in [3,4,5,6]:
if oddPeak(0,peaks):
A[0] *= 2
A[1] = A[0]
if oddPeak(2,peaks):
A[2] *=2
else:
if oddPeak(0,peaks):
A[0] *=2
if oddPeak(1,peaks):
A[1] *=2
if oddPeak(2,peaks):
A[2] *=2
return A
[docs]
def getDmin(peaks):
'needs a doc string'
return peaks[-1][-2]
[docs]
def getDmax(peaks):
'needs a doc string'
return peaks[0][-2]
[docs]
def refinePeaksZ(peaks,wave,ibrav,A,Zero,ZeroRef):
'needs a doc string'
dmin = getDmin(peaks)
OK,smin,Aref,Z,result = FitHKLZ(wave,ibrav,peaks,A,Zero,ZeroRef)
Peaks = np.array(peaks).T
H = Peaks[4:7]
Peaks[8] = 1./np.sqrt(G2lat.calc_rDsqZ(H,Aref,Z,Peaks[0],wave))
peaks = Peaks.T
HKL = G2lat.GenHBravais(dmin,ibrav,A)
M20,X20 = calc_M20(peaks,HKL)
return len(HKL),M20,X20,Aref,Z
[docs]
def refinePeaksT(peaks,difC,ibrav,A,Zero,ZeroRef):
'needs a doc string'
dmin = getDmin(peaks)
OK,smin,Aref,Z,result = FitHKLT(difC,ibrav,peaks,A,Zero,ZeroRef)
Peaks = np.array(peaks).T
H = Peaks[4:7]
Peaks[8] = 1./np.sqrt(G2lat.calc_rDsqT(H,Aref,Z,Peaks[0],difC))
peaks = Peaks.T
HKL = G2lat.GenHBravais(dmin,ibrav,A)
M20,X20 = calc_M20(peaks,HKL)
return len(HKL),M20,X20,Aref,Z
[docs]
def refinePeaksE(peaks,tth,ibrav,A):
'needs a doc string'
dmin = getDmin(peaks)
OK,smin,Aref,result = FitHKLE(tth,ibrav,peaks,A)
Peaks = np.array(peaks).T
H = Peaks[4:7]
Peaks[8] = 1./np.sqrt(G2lat.calc_rDsq(H,Aref))
peaks = Peaks.T
HKL = G2lat.GenHBravais(dmin,ibrav,A)
M20,X20 = calc_M20(peaks,HKL)
return len(HKL),M20,X20,Aref
[docs]
def refinePeaksZSS(peaks,wave,Inst,SGData,SSGData,maxH,ibrav,A,vec,vecRef,Zero,ZeroRef):
'needs a doc string'
dmin = getDmin(peaks)
OK,smin,Aref,Vref,Z,result = FitHKLZSS(wave,ibrav,peaks,A,vec,vecRef,Zero,ZeroRef)
Peaks = np.array(peaks).T
H = Peaks[4:8]
Peaks[9] = 1./np.sqrt(G2lat.calc_rDsqZSS(H,Aref,Vref,Z,Peaks[0],wave)) #H,A,vec,Z,tth,lam
peaks = Peaks.T
HKL = G2pwd.getHKLMpeak(dmin,Inst,SGData,SSGData,Vref,maxH,Aref)
M20,X20 = calc_M20SS(peaks,HKL)
return len(HKL),M20,X20,Aref,Vref,Z
[docs]
def refinePeaksTSS(peaks,difC,Inst,SGData,SSGData,maxH,ibrav,A,vec,vecRef,Zero,ZeroRef):
'needs a doc string'
dmin = getDmin(peaks)
OK,smin,Aref,Vref,Z,result = FitHKLTSS(difC,ibrav,peaks,A,vec,vecRef,Zero,ZeroRef)
Peaks = np.array(peaks).T
H = Peaks[4:8]
Peaks[9] = 1./np.sqrt(G2lat.calc_rDsqTSS(H,Aref,Vref,Z,Peaks[0],difC))
peaks = Peaks.T
HKL = G2pwd.getHKLMpeak(dmin,Inst,SGData,SSGData,Vref,maxH,Aref)
HKL = G2lat.GenHBravais(dmin,ibrav,A)
M20,X20 = calc_M20SS(peaks,HKL)
return len(HKL),M20,X20,Aref,Vref,Z
[docs]
def refinePeaks(peaks,ibrav,A,ifX20=True,cctbx_args=None):
'needs a doc string'
dmin = getDmin(peaks)
smin = 1.0e10
pwr = 8
maxTries = 10
OK = False
tries = 0
HKL = G2lat.GenHBravais(dmin,ibrav,A,cctbx_args)
while len(HKL) > 2 and IndexPeaks(peaks,HKL)[0]:
Pwr = pwr - (tries % 2)
HKL = []
tries += 1
osmin = smin
oldA = A[:]
Vold = G2lat.calc_V(oldA)
OK,smin,A,result = FitHKL(ibrav,peaks,A,Pwr)
Vnew = G2lat.calc_V(A)
if Vnew > 2.0*Vold or Vnew < 2.:
A = ranAbyR(ibrav,oldA,tries+1,maxTries,ran2axis)
OK = False
continue
try:
HKL = G2lat.GenHBravais(dmin,ibrav,A,cctbx_args)
except FloatingPointError:
A = oldA
OK = False
break
if len(HKL) == 0: break #absurd cell obtained!
rat = (osmin-smin)/smin
if abs(rat) < 1.0e-5 or not OK: break
if tries > maxTries: break
if OK:
OK,smin,A,result = FitHKL(ibrav,peaks,A,2)
Peaks = np.array(peaks).T
H = Peaks[4:7]
try:
Peaks[8] = 1./np.sqrt(G2lat.calc_rDsq(H,A))
peaks = Peaks.T
except FloatingPointError:
A = oldA
M20,X20 = calc_M20(peaks,HKL,ifX20)
return len(HKL),M20,X20,A
[docs]
def findBestCell(dlg,ncMax,A,Ntries,ibrav,peaks,V1,ifX20=True,cctbx_args=None):
'needs a doc string'
# dlg & ncMax are used for wx progress bar
# A != 0 find the best A near input A,
# A = 0 for random cell, volume normalized to V1;
# returns number of generated hkls, M20, X20 & A for best found
mHKL = [3,3,3, 5,5, 5,5, 7,7,7,7,7,7, 9,9,9,9, 10]
dmin = getDmin(peaks)-0.05
amin = 2.5
amax = 5.*getDmax(peaks)
Asave = []
GoOn = True
Skip = False
if A:
HKL = G2lat.GenHBravais(dmin,ibrav,A[:])
if len(HKL) > mHKL[ibrav]:
peaks = IndexPeaks(peaks,HKL)[1]
Asave.append([calc_M20(peaks,HKL,ifX20),A[:]])
tries = 0
while tries < Ntries and GoOn:
if A:
Abeg = ranAbyR(ibrav,A,tries+1,Ntries,ran2axis)
if ibrav > 12: #monoclinic & triclinic
Abeg = ranAbyR(ibrav,A,tries/10+1,Ntries,ran2axis)
else:
Abeg = ranAbyV(ibrav,amin,amax,V1)
HKL = G2lat.GenHBravais(dmin,ibrav,Abeg)
Nc = len(HKL)
if Nc >= ncMax:
GoOn = False
else:
if dlg:
dlg.Raise()
GoOn = dlg.Update(100*Nc//ncMax)[0]
if Skip or not GoOn:
GoOn = False
break
if IndexPeaks(peaks,HKL)[0] and len(HKL) > mHKL[ibrav]:
Lhkl,M20,X20,Aref = refinePeaks(peaks,ibrav,Abeg,ifX20,cctbx_args=cctbx_args)
Asave.append([calc_M20(peaks,HKL,ifX20),Aref[:]])
if ibrav in [9,10,11]: #A,B,or C-centered orthorhombic
for i in range(2):
Abeg = rotOrthoA(Abeg[:])
Lhkl,M20,X20,Aref = refinePeaks(peaks,ibrav,Abeg,ifX20,cctbx_args=cctbx_args)
HKL = G2lat.GenHBravais(dmin,ibrav,Aref)
peaks = IndexPeaks(peaks,HKL)[1]
Asave.append([calc_M20(peaks,HKL,ifX20),Aref[:]])
# elif ibrav == 15: #C-centered monoclinic
# Abeg = swapMonoA(Abeg[:])
# Lhkl,M20,X20,Aref = refinePeaks(peaks,ibrav,Abeg,ifX20,cctbx_args=cctbx_args)
# HKL = G2lat.GenHBravais(dmin,ibrav,Aref)
# peaks = IndexPeaks(peaks,HKL)[1]
# Asave.append([calc_M20(peaks,HKL,ifX20),Aref[:]])
else:
break
Nc = len(HKL)
tries += 1
X = sortM20(Asave)
if X:
Lhkl,M20,X20,A = refinePeaks(peaks,ibrav,X[0][1],ifX20,cctbx_args=cctbx_args)
return GoOn,Skip,Lhkl,M20,X20,A
else:
return GoOn,Skip,0,0,0,0
[docs]
def monoCellReduce(ibrav,A):
'needs a doc string'
a,b,c,alp,bet,gam = G2lat.A2cell(A)
if bet < 90.:
bet = 180.-bet #always want obtuse beta
A = G2lat.cell2A([a,b,c,90.,bet,90.])
G,g = G2lat.A2Gmat(A)
if ibrav in [14,]: #A-monoclinic - OK?
u = [0,0,-1]
v = [1,0,2]
anew = math.sqrt(np.dot(np.dot(v,g),v))
if anew < a:
cang = np.dot(np.dot(u,g),v)/(anew*c)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([anew,b,c,90,beta,90])
elif ibrav in [15,]: #C-monoclinic - OK?
u = [-1,0,0]
v = [1,0,1]
cnew = math.sqrt(np.dot(np.dot(v,g),v))
if cnew < c:
cang = np.dot(np.dot(u,g),v)/(a*cnew)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([a,b,cnew,90,beta,90])
elif ibrav in [13,]: #I-monoclinic - checked OK
uc = [0,0,-1]
vc = [1,0,2]
ua = [-1,0,0]
va = [2,0,1]
anew = math.sqrt(np.dot(np.dot(va,g),va))
cnew = math.sqrt(np.dot(np.dot(vc,g),vc))
if cnew < c:
cang = np.dot(np.dot(uc,g),vc)/(a*cnew)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([a,b,cnew,90,beta,90])
if anew < a:
cang = np.dot(np.dot(ua,g),va)/(c*anew)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([anew,b,c,90,beta,90])
else: #P
ua = [0,0,-1]
uc = [-1,0,0]
va = [1,0,1]
vc = [1,0,1]
anew = math.sqrt(np.dot(np.dot(va,g),va))
cnew = math.sqrt(np.dot(np.dot(vc,g),vc))
if anew < a:
cang = np.dot(np.dot(ua,g),va)/(anew*c)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([anew,b,c,90,beta,90])
if cnew < c:
cang = np.dot(np.dot(uc,g),vc)/(a*cnew)
beta = acosd(-abs(cang))
if beta < 90.: beta = 180.-beta #always want obtuse beta
A = G2lat.cell2A([a,b,cnew,90,beta,90])
return A
[docs]
def DoIndexPeaks(peaks,controls,bravais,dlg,ifX20=True,
timeout=None,M20_min=2.0,X20_max=None,return_Nc=False,
cctbx_args=None):
'needs a doc string'
delt = 0.005 #lowest d-spacing cushion - can be fixed?
amin = 2.5
amax = 5.0*getDmax(peaks)
dmin = getDmin(peaks)-delt
bravaisNames = ['Cubic-F','Cubic-I','Cubic-P','Trigonal-R','Trigonal/Hexagonal-P',
'Tetragonal-I','Tetragonal-P','Orthorhombic-F','Orthorhombic-I','Orthorhombic-A',
'Orthorhombic-B','Orthorhombic-C',
'Orthorhombic-P','Monoclinic-I','Monoclinic-A','Monoclinic-C','Monoclinic-P','Triclinic']
tries = ['1st','2nd','3rd','4th','5th','6th','7th','8th','9th','10th']
N1s = [1,1,1, 5,5, 5,5, 50,50,50,50,50,50, 100,100,100,100, 200]
N2s = [1,1,1, 2,2, 2,2, 2,2,2,2,2,2, 2,2,2,2, 4]
Nm = [1,1,1, 1,1, 1,1, 1,1,1,1,1,1, 2,2,2,2, 4]
notUse = 0
for peak in peaks:
if not peak[2]:
notUse += 1
Nobs = len(peaks)-notUse
zero,ncno = controls[1:3]
ncMax = Nobs*ncno
print ("%s %8.3f %8.3f" % ('lattice parameter range = ',amin,amax))
print ("%s %.4f %s %d %s %d" % ('Zero =',zero,'Nc/No max =',ncno,' Max Nc =',ncno*Nobs))
cells = []
lastcell = np.zeros(7)
for ibrav in range(len(bravaisNames)):
begin = time.time()
if bravais[ibrav]:
print ('cell search for ',bravaisNames[ibrav])
print (' M20 X20 Nc a b c alpha beta gamma volume V-test')
V1 = controls[3]
bestM20 = 0
topM20 = 0
cycle = 0
while cycle < 5:
if dlg:
dlg.Raise()
dlg.Update(0,newmsg=tries[cycle]+" cell search for "+bravaisNames[ibrav])
try:
GoOn = True
while GoOn: #Loop over increment of volume
N2 = 0
while N2 < N2s[ibrav]: #Table 2 step (iii)
if timeout and time.time() - begin > timeout:
GoOn = False
break
if ibrav > 2:
if not N2:
A = []
GoOn,Skip,Nc,M20,X20,A = findBestCell(dlg,ncMax,A,Nm[ibrav]*N1s[ibrav],ibrav,peaks,V1,ifX20,cctbx_args=cctbx_args)
if Skip:
break
if A:
GoOn,Skip,Nc,M20,X20,A = findBestCell(dlg,ncMax,A[:],N1s[ibrav],ibrav,peaks,0,ifX20,cctbx_args=cctbx_args)
else:
GoOn,Skip,Nc,M20,X20,A = findBestCell(dlg,ncMax,0,Nm[ibrav]*N1s[ibrav],ibrav,peaks,V1,ifX20,cctbx_args=cctbx_args)
if Skip:
break
elif Nc >= ncMax:
GoOn = False
break
elif 3*Nc < Nobs:
N2 = 10
break
else:
if not GoOn:
break
if 1.e6 > M20 > 1.0: #exclude nonsense
bestM20 = max(bestM20,M20)
A = halfCell(ibrav,A[:],peaks)
if ibrav in [13,14,15,16]:
A = monoCellReduce(ibrav,A[:])
HKL = G2lat.GenHBravais(dmin,ibrav,A)
peaks = IndexPeaks(peaks,HKL)[1]
a,b,c,alp,bet,gam = G2lat.A2cell(A)
V = G2lat.calc_V(A)
if (
(M20 >= M20_min) and
(X20_max is None or X20 <= X20_max)
):
cell = [M20,X20,ibrav,a,b,c,alp,bet,gam,V,False,False]
if return_Nc: cell.append(Nc)
newcell = np.array(cell[3:10])
if not np.allclose(newcell,lastcell):
print ("%10.3f %3d %3d %10.5f %10.5f %10.5f %10.3f %10.3f %10.3f %10.2f %10.2f %s"
%(M20,X20,Nc,a,b,c,alp,bet,gam,V,V1,bravaisNames[ibrav]))
cells.append(cell)
lastcell = np.array(cell[3:10])
if not GoOn:
break
N2 += 1
if Skip:
cycle = 10
GoOn = False
break
if ibrav < 13:
V1 *= 1.1
elif ibrav in range(13,18):
V1 *= 1.025
if not GoOn:
if bestM20 > topM20:
topM20 = bestM20
if cells:
V1 = cells[0][9]
else:
V1 = controls[3]
ncMax += Nobs
cycle += 1
print ('Restart search, new Max Nc = %d'%ncMax)
else:
cycle = 10
finally:
pass
# dlg.Destroy()
print ('%s%s%s%s'%('finished cell search for ',bravaisNames[ibrav], \
', elapsed time = ',G2lat.sec2HMS(time.time()-begin)))
if cells:
return True,dmin,cells
else:
return False,0,[]
NeedTestData = True
[docs]
def TestData():
'needs a doc string'
global NeedTestData
NeedTestData = False
global TestData
TestData = [14, [7.,8.70,10.86,90.,102.95,90.], [7.76006,8.706215,10.865679,90.,102.947,90.],3,
[[2.176562137832974, 761.60902227696033, True, True, 0, 0, 1, 10.591300714328161, 10.589436],
[3.0477561489789498, 4087.2956049071572, True, True, 1, 0, 0, 7.564238997554908, 7.562777],
[3.3254921120068524, 1707.0253890991009, True, True, 1, 0, -1, 6.932650301411212, 6.932718],
[3.428121546163426, 2777.5082170150563, True, True, 0, 1, 1, 6.725163158013632, 6.725106],
[4.0379791325512118, 1598.4321673135987, True, True, 1, 1, 0, 5.709789097440156, 5.70946],
[4.2511182350743937, 473.10955149057577, True, True, 1, 1, -1, 5.423637972781876, 5.42333],
[4.354684330373451, 569.88528280256071, True, True, 0, 0, 2, 5.2947091882172534, 5.294718],
[4.723324574319177, 342.73882372499997, True, True, 1, 0, -2, 4.881681587039431, 4.881592],
[4.9014773581253994, 5886.3516356615492, True, True, 1, 1, 1, 4.704350709093183, 4.70413],
[5.0970774474587275, 3459.7541692903033, True, True, 0, 1, 2, 4.523933797797693, 4.523829],
[5.2971997607389518, 1290.0229964239879, True, True, 0, 2, 0, 4.353139557169142, 4.353108],
[5.4161306205553847, 1472.5726977257755, True, True, 1, 1, -2, 4.257619398422479, 4.257944],
[5.7277364698554161, 1577.8791668322888, True, True, 0, 2, 1, 4.026169751907777, 4.026193],
[5.8500213058834163, 420.74210142657131, True, True, 1, 0, 2, 3.9420803081518443, 3.942219],
[6.0986764166731708, 163.02160537058708, True, True, 2, 0, 0, 3.7814965150452537, 3.781389],
[6.1126665157702753, 943.25461245706833, True, True, 1, 2, 0, 3.772849962062199, 3.772764],
[6.2559260555056957, 250.55355015505376, True, True, 1, 2, -1, 3.6865353266375283, 3.686602],
[6.4226243128279892, 5388.5560141098349, True, True, 1, 1, 2, 3.5909481979190283, 3.591214],
[6.5346132446561134, 1951.6070344509026, True, True, 0, 0, 3, 3.5294722429440584, 3.529812],
[6.5586952135236443, 259.65938178131034, True, True, 2, 1, -1, 3.516526936765838, 3.516784],
[6.6509216222783722, 93.265376597376573, True, True, 2, 1, 0, 3.4678179073694952, 3.468369],
[6.7152737044107722, 289.39386813803162, True, True, 1, 2, 1, 3.4346235125812807, 3.434648],
[6.8594130457361899, 603.54959764648322, True, True, 0, 2, 2, 3.362534044860622, 3.362553],
[7.0511627728884454, 126.43246447656593, True, True, 0, 1, 3, 3.2712038721790675, 3.271181],
[7.077700845503319, 125.49742760019636, True, True, 1, 1, -3, 3.2589538988480626, 3.259037],
[7.099393757363675, 416.55444885434633, True, True, 1, 2, -2, 3.2490085228959193, 3.248951],
[7.1623933278642742, 369.27397110921817, True, True, 2, 1, -2, 3.2204673608202383, 3.220487],
[7.4121734953058924, 482.84120827021826, True, True, 2, 1, 1, 3.1120858221599876, 3.112308]]
]
global TestData2
TestData2 = [14, [0.15336547830008007, 0.017345499139401827, 0.008122368657493792, 0, 0.02893538955687591, 0], 3,
[[2.176562137832974, 761.6090222769603, True, True, 0, 0, 1, 10.591300714328161, 11.095801],
[3.0477561489789498, 4087.295604907157, True, True, 0, 1, 0, 7.564238997554908, 7.592881],
[3.3254921120068524, 1707.025389099101, True, False, 0, 0, 0, 6.932650301411212, 0.0],
[3.428121546163426, 2777.5082170150563, True, True, 0, 1, 1, 6.725163158013632, 6.266192],
[4.037979132551212, 1598.4321673135987, True, False, 0, 0, 0, 5.709789097440156, 0.0],
[4.251118235074394, 473.10955149057577, True, True, 0, 0, 2, 5.423637972781876, 5.5479],
[4.354684330373451, 569.8852828025607, True, True, 0, 0, 2, 5.2947091882172534, 5.199754],
[4.723324574319177, 342.738823725, True, False, 0, 0, 0, 4.881681587039431, 0.0],
[4.901477358125399, 5886.351635661549, True, False, 0, 0, 0, 4.704350709093183, 0.0],
[5.0970774474587275, 3459.7541692903033, True, True, 0, 1, 2, 4.523933797797693, 4.479534],
[5.297199760738952, 1290.022996423988, True, True, 0, 1, 0, 4.353139557169142, 4.345087],
[5.416130620555385, 1472.5726977257755, True, False, 0, 0, 0, 4.257619398422479, 0.0],
[5.727736469855416, 1577.8791668322888, True, False, 0, 0, 0, 4.026169751907777, 0.0],
[5.850021305883416, 420.7421014265713, True, False, 0, 0, 0, 3.9420803081518443, 0.0],
[6.098676416673171, 163.02160537058708, True, True, 0, 2, 0, 3.7814965150452537, 3.796441],
[6.112666515770275, 943.2546124570683, True, False, 0, 0, 0, 3.772849962062199, 0.0],
[6.255926055505696, 250.55355015505376, True, True, 0, 0, 3, 3.6865353266375283, 3.6986],
[6.422624312827989, 5388.556014109835, True, True, 0, 2, 1, 3.5909481979190283, 3.592005],
[6.534613244656113, 191.6070344509026, True, True, 1, 0, -1, 3.5294722429440584, 3.546166],
[6.558695213523644, 259.65938178131034, True, True, 0, 0, 3, 3.516526936765838, 3.495428],
[6.650921622278372, 93.26537659737657, True, True, 0, 0, 3, 3.4678179073694952, 3.466503],
[6.715273704410772, 289.3938681380316, True, False, 0, 0, 0, 3.4346235125812807, 0.0],
[6.85941304573619, 603.5495976464832, True, True, 0, 1, 3, 3.362534044860622, 3.32509],
[7.051162772888445, 126.43246447656593, True, True, 0, 1, 2, 3.2712038721790675, 3.352121],
[7.077700845503319, 125.49742760019636, True, False, 0, 0, 0, 3.2589538988480626, 0.0],
[7.099393757363675, 416.55444885434633, True, False, 0, 0, 0, 3.2490085228959193, 0.0],
[7.162393327864274, 369.27397110921817, True, False, 0, 0, 0, 3.2204673608202383, 0.0],
[7.412173495305892, 482.84120827021826, True, True, 0, 2, 2, 3.112085822159976, 3.133096]]
]
#
def test0():
if NeedTestData: TestData()
ibrav,cell,bestcell,Pwr,peaks = TestData
print ('best cell:',bestcell)
print ('old cell:',cell)
Peaks = np.array(peaks)
HKL = Peaks[4:7]
print (calc_M20(peaks,HKL))
A = G2lat.cell2A(cell)
OK,smin,A,result = FitHKL(ibrav,peaks,A,Pwr)
print ('new cell:',G2lat.A2cell(A))
print ('x:',result[0])
print ('cov_x:',result[1])
print ('infodict:')
for item in result[2]:
print (item,result[2][item])
print ('msg:',result[3])
print ('ier:',result[4])
result = refinePeaks(peaks,ibrav,A)
N,M20,X20,A = result
print ('refinePeaks:',N,M20,X20,G2lat.A2cell(A))
print ('compare bestcell:',bestcell)
#
def test1():
if NeedTestData: TestData()
ibrav,A,Pwr,peaks = TestData2
print ('bad cell:',G2lat.A2cell(A))
print ('FitHKL')
OK,smin,A,result = FitHKL(ibrav,peaks,A,Pwr)
result = refinePeaks(peaks,ibrav,A)
N,M20,X20,A = result
print ('refinePeaks:',N,M20,X20,A)
# Peaks = np.array(peaks)
# HKL = Peaks[4:7]
# print calc_M20(peaks,HKL)
# OK,smin,A,result = FitHKL(ibrav,peaks,A,Pwr)
# print 'new cell:',G2lat.A2cell(A)
# print 'x:',result[0]
# print 'cov_x:',result[1]
# print 'infodict:'
# for item in result[2]:
# print item,result[2][item]
# print 'msg:',result[3]
# print 'ier:',result[4]
#
if __name__ == '__main__':
test0()
test1()
# test2()
# test3()
# test4()
# test5()
# test6()
# test7()
# test8()
print ("OK")