#/usr/bin/env python
# -*- coding: utf-8 -*-
########### SVN repository information ###################
# $Date: 2024-01-30 14:19:34 -0600 (Tue, 30 Jan 2024) $
# $Author: toby $
# $Revision: 5721 $
# $URL: https://subversion.xray.aps.anl.gov/pyGSAS/trunk/GSASIIpwd.py $
# $Id: GSASIIpwd.py 5721 2024-01-30 20:19:34Z toby $
########### SVN repository information ###################
'''
Classes and routines defined in :mod:`GSASIIpwd` follow.
'''
from __future__ import division, print_function
import sys
import math
import time
import os
import os.path
import subprocess as subp
import datetime as dt
import copy
import numpy as np
import numpy.linalg as nl
import numpy.ma as ma
import random as rand
import numpy.fft as fft
import scipy.interpolate as si
import scipy.stats as st
import scipy.optimize as so
import scipy.special as sp
import scipy.signal as signal
import GSASIIpath
filversion = "$Revision: 5721 $"
GSASIIpath.SetVersionNumber("$Revision: 5721 $")
import GSASIIlattice as G2lat
import GSASIIspc as G2spc
import GSASIIElem as G2elem
import GSASIImath as G2mth
try:
import pypowder as pyd
except ImportError:
print ('pypowder is not available - profile calcs. not allowed')
try:
import pydiffax as pyx
except ImportError:
print ('pydiffax is not available for this platform')
import GSASIIfiles as G2fil
# trig functions in degrees
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.)
npacosd = lambda x: 180.*np.arccos(x)/math.pi
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
npT2stl = lambda tth, wave: 2.0*npsind(tth/2.0)/wave #=d*
npT2q = lambda tth,wave: 2.0*np.pi*npT2stl(tth,wave) #=2pi*d*
npq2T = lambda Q,wave: 2.0*npasind(0.25*Q*wave/np.pi)
ateln2 = 8.0*math.log(2.0)
sateln2 = np.sqrt(ateln2)
nxs = np.newaxis
is_exe = lambda fpath: os.path.isfile(fpath) and os.access(fpath, os.X_OK)
#### Powder utilities ################################################################################
[docs]
def PhaseWtSum(G2frame,histo):
'''
Calculate sum of phase mass*phase fraction for PWDR data (exclude magnetic phases)
:param G2frame: GSASII main frame structure
:param str histo: histogram name
:returns: sum(scale*mass) for phases in histo
'''
Histograms,Phases = G2frame.GetUsedHistogramsAndPhasesfromTree()
wtSum = 0.0
for phase in Phases:
if Phases[phase]['General']['Type'] != 'magnetic':
if histo in Phases[phase]['Histograms']:
if not Phases[phase]['Histograms'][histo]['Use']: continue
mass = Phases[phase]['General']['Mass']
phFr = Phases[phase]['Histograms'][histo]['Scale'][0]
wtSum += mass*phFr
return wtSum
#### GSASII pwdr & pdf calculation routines ################################################################################
[docs]
def Transmission(Geometry,Abs,Diam):
'''
Calculate sample transmission
:param str Geometry: one of 'Cylinder','Bragg-Brentano','Tilting flat plate in transmission','Fixed flat plate'
:param float Abs: absorption coeff in cm-1
:param float Diam: sample thickness/diameter in mm
'''
if 'Cylinder' in Geometry: #Lobanov & Alte da Veiga for 2-theta = 0; beam fully illuminates sample
MuR = Abs*Diam/20.0
if MuR <= 3.0:
T0 = 16/(3.*math.pi)
T1 = -0.045780
T2 = -0.02489
T3 = 0.003045
T = -T0*MuR-T1*MuR**2-T2*MuR**3-T3*MuR**4
if T < -20.:
return 2.06e-9
else:
return math.exp(T)
else:
T1 = 1.433902
T2 = 0.013869+0.337894
T3 = 1.933433+1.163198
T4 = 0.044365-0.04259
T = (T1-T4)/(1.0+T2*(MuR-3.0))**T3+T4
return T/100.
elif 'plate' in Geometry:
MuR = Abs*Diam/10.
return math.exp(-MuR)
elif 'Bragg' in Geometry:
return 0.0
[docs]
def SurfaceRough(SRA,SRB,Tth):
''' Suortti (J. Appl. Cryst, 5,325-331, 1972) surface roughness correction
:param float SRA: Suortti surface roughness parameter
:param float SRB: Suortti surface roughness parameter
:param float Tth: 2-theta(deg) - can be numpy array
'''
sth = npsind(Tth/2.)
T1 = np.exp(-SRB/sth)
T2 = SRA+(1.-SRA)*np.exp(-SRB)
return (SRA+(1.-SRA)*T1)/T2
[docs]
def SurfaceRoughDerv(SRA,SRB,Tth):
''' Suortti surface roughness correction derivatives
:param float SRA: Suortti surface roughness parameter (dimensionless)
:param float SRB: Suortti surface roughness parameter (dimensionless)
:param float Tth: 2-theta(deg) - can be numpy array
:return list: [dydSRA,dydSRB] derivatives to be used for intensity derivative
'''
sth = npsind(Tth/2.)
T1 = np.exp(-SRB/sth)
T2 = SRA+(1.-SRA)*np.exp(-SRB)
Trans = (SRA+(1.-SRA)*T1)/T2
dydSRA = ((1.-T1)*T2-(1.-np.exp(-SRB))*Trans)/T2**2
dydSRB = ((SRA-1.)*T1*T2/sth-Trans*(SRA-T2))/T2**2
return [dydSRA,dydSRB]
[docs]
def Absorb(Geometry,MuR,Tth,Phi=0,Psi=0):
'''Calculate sample absorption
:param str Geometry: one of 'Cylinder','Bragg-Brentano','Tilting Flat Plate in transmission','Fixed flat plate'
:param float MuR: absorption coeff * sample thickness/2 or radius
:param Tth: 2-theta scattering angle - can be numpy array
:param float Phi: flat plate tilt angle - future
:param float Psi: flat plate tilt axis - future
'''
def muRunder3(MuR,Sth2):
T0 = 16.0/(3.*np.pi)
T1 = (25.99978-0.01911*Sth2**0.25)*np.exp(-0.024551*Sth2)+ \
0.109561*np.sqrt(Sth2)-26.04556
T2 = -0.02489-0.39499*Sth2+1.219077*Sth2**1.5- \
1.31268*Sth2**2+0.871081*Sth2**2.5-0.2327*Sth2**3
T3 = 0.003045+0.018167*Sth2-0.03305*Sth2**2
Trns = -T0*MuR-T1*MuR**2-T2*MuR**3-T3*MuR**4
return np.exp(Trns)
def muRover3(MuR,Sth2):
T1 = 1.433902+11.07504*Sth2-8.77629*Sth2*Sth2+ \
10.02088*Sth2**3-3.36778*Sth2**4
T2 = (0.013869-0.01249*Sth2)*np.exp(3.27094*Sth2)+ \
(0.337894+13.77317*Sth2)/(1.0+11.53544*Sth2)**1.555039
T3 = 1.933433/(1.0+23.12967*Sth2)**1.686715- \
0.13576*np.sqrt(Sth2)+1.163198
T4 = 0.044365-0.04259/(1.0+0.41051*Sth2)**148.4202
Trns = (T1-T4)/(1.0+T2*(MuR-3.0))**T3+T4
return Trns/100.
Sth2 = npsind(Tth/2.0)**2
if 'Cylinder' in Geometry: #Lobanov & Alte da Veiga for 2-theta = 0; beam fully illuminates sample
if 'array' in str(type(MuR)):
MuRSTh2 = np.vstack((MuR,Sth2))
AbsCr = np.where(MuRSTh2[0]<=3.0,muRunder3(MuRSTh2[0],MuRSTh2[1]),muRover3(MuRSTh2[0],MuRSTh2[1]))
return AbsCr
else:
if MuR <= 3.0:
return muRunder3(MuR,Sth2)
else:
return muRover3(MuR,Sth2)
elif 'Bragg' in Geometry:
return 1.0
elif 'Fixed' in Geometry: #assumes sample plane is perpendicular to incident beam
# and only defined for 2theta < 90
MuT = 2.*MuR
T1 = np.exp(-MuT)
T2 = np.exp(-MuT/npcosd(Tth))
Tb = MuT-MuT/npcosd(Tth)
return (T2-T1)/Tb
elif 'Tilting' in Geometry: #assumes symmetric tilt so sample plane is parallel to diffraction vector
MuT = 2.*MuR
cth = npcosd(Tth/2.0)
return np.exp(-MuT/cth)/cth
[docs]
def AbsorbDerv(Geometry,MuR,Tth,Phi=0,Psi=0):
'needs a doc string'
dA = 0.001
AbsP = Absorb(Geometry,MuR+dA,Tth,Phi,Psi)
if MuR:
AbsM = Absorb(Geometry,MuR-dA,Tth,Phi,Psi)
return (AbsP-AbsM)/(2.0*dA)
else:
return (AbsP-1.)/dA
[docs]
def Polarization(Pola,Tth,Azm=0.0):
""" Calculate angle dependent x-ray polarization correction (not scaled correctly!)
:param Pola: polarization coefficient e.g 1.0 fully polarized, 0.5 unpolarized
:param Azm: azimuthal angle e.g. 0.0 in plane of polarization - can be numpy array
:param Tth: 2-theta scattering angle - can be numpy array
which (if either) of these is "right"?
:return: (pola, dpdPola) - both 2-d arrays
* pola = ((1-Pola)*npcosd(Azm)**2+Pola*npsind(Azm)**2)*npcosd(Tth)**2+ \
(1-Pola)*npsind(Azm)**2+Pola*npcosd(Azm)**2
* dpdPola: derivative needed for least squares
"""
cazm = npcosd(Azm)**2
sazm = npsind(Azm)**2
pola = ((1.0-Pola)*cazm+Pola*sazm)*npcosd(Tth)**2+(1.0-Pola)*sazm+Pola*cazm
dpdPola = -npsind(Tth)**2*(sazm-cazm)
return pola,dpdPola
[docs]
def Oblique(ObCoeff,Tth):
'currently assumes detector is normal to beam'
if ObCoeff:
K = (1.-ObCoeff)/(1.0-np.exp(np.log(ObCoeff)/npcosd(Tth)))
return K
else:
return 1.0
[docs]
def Ruland(RulCoff,wave,Q,Compton):
'needs a doc string'
C = 2.9978e8
D = 1.5e-3
hmc = 0.024262734687 #Compton wavelength in A
sinth2 = (Q*wave/(4.0*np.pi))**2
dlam = (wave**2)*Compton*Q/C
dlam_c = 2.0*hmc*sinth2-D*wave**2
return 1.0/((1.0+dlam/RulCoff)*(1.0+(np.pi*dlam_c/(dlam+RulCoff))**2))
def KleinNishina(wave,Q):
hmc = 0.024262734687 #Compton wavelength in A
TTh = npq2T(Q,wave)
P = 1./(1.+(1.-npcosd(TTh)*(hmc/wave)))
KN = (P**3-(P*npsind(TTh))**2+P)/(1.+npcosd(TTh)**2)
return KN
[docs]
def LorchWeight(Q):
'needs a doc string'
return np.sin(np.pi*(Q[-1]-Q)/(2.0*Q[-1]))
[docs]
def GetAsfMean(ElList,Sthl2):
'''Calculate various scattering factor terms for PDF calcs
:param dict ElList: element dictionary contains scattering factor coefficients, etc.
:param np.array Sthl2: numpy array of sin theta/lambda squared values
:returns: mean(f^2), mean(f)^2, mean(compton)
'''
sumNoAtoms = 0.0
FF = np.zeros_like(Sthl2)
FF2 = np.zeros_like(Sthl2)
CF = np.zeros_like(Sthl2)
for El in ElList:
sumNoAtoms += ElList[El]['FormulaNo']
for El in ElList:
el = ElList[El]
ff2 = (G2elem.ScatFac(el,Sthl2)+el['fp'])**2+el['fpp']**2
cf = G2elem.ComptonFac(el,Sthl2)
FF += np.sqrt(ff2)*el['FormulaNo']/sumNoAtoms
FF2 += ff2*el['FormulaNo']/sumNoAtoms
CF += cf*el['FormulaNo']/sumNoAtoms
return FF2,FF**2,CF
[docs]
def GetNumDensity(ElList,Vol):
'needs a doc string'
sumNoAtoms = 0.0
for El in ElList:
sumNoAtoms += ElList[El]['FormulaNo']
return sumNoAtoms/Vol
[docs]
def CalcPDF(data,inst,limits,xydata):
'''Computes I(Q), S(Q) & G(r) from Sample, Bkg, etc. diffraction patterns loaded into
dict xydata; results are placed in xydata.
Calculation parameters are found in dicts data and inst and list limits.
The return value is at present an empty list.
'''
auxPlot = []
if 'T' in inst['Type'][0]:
Ibeg = 0
Ifin = len(xydata['Sample'][1][0])
else:
Ibeg = np.searchsorted(xydata['Sample'][1][0],limits[0])
Ifin = np.searchsorted(xydata['Sample'][1][0],limits[1])+1
#subtract backgrounds - if any & use PWDR limits
IofQ = copy.deepcopy(xydata['Sample'])
IofQ[1] = np.array([I[Ibeg:Ifin] for I in IofQ[1]])
if data['Sample Bkg.']['Name']:
try: # fails if background differs in number of points
IofQ[1][1] += xydata['Sample Bkg.'][1][1][Ibeg:Ifin]*data['Sample Bkg.']['Mult']
except ValueError:
print("Interpolating Sample background since points don't match")
interpF = si.interp1d(xydata['Sample Bkg.'][1][0],xydata['Sample Bkg.'][1][1],
fill_value='extrapolate')
IofQ[1][1] += interpF(IofQ[1][0]) * data['Sample Bkg.']['Mult']
if data['Container']['Name']:
xycontainer = xydata['Container'][1][1]*data['Container']['Mult']
if data['Container Bkg.']['Name']:
try:
xycontainer += xydata['Container Bkg.'][1][1][Ibeg:Ifin]*data['Container Bkg.']['Mult']
except ValueError:
print('Number of points do not agree between Container and Container Bkg.')
return
try: # fails if background differs in number of points
IofQ[1][1] += xycontainer[Ibeg:Ifin]
except ValueError:
print("Interpolating Container background since points don't match")
interpF = si.interp1d(xydata['Container'][1][0],xycontainer,fill_value='extrapolate')
IofQ[1][1] += interpF(IofQ[1][0])
data['IofQmin'] = IofQ[1][1][-1]
IofQ[1][1] -= data.get('Flat Bkg',0.)
#get element data & absorption coeff.
ElList = data['ElList']
Tth = IofQ[1][0] #2-theta or TOF!
if 'X' in inst['Type'][0]:
Abs = G2lat.CellAbsorption(ElList,data['Form Vol'])
#Apply angle dependent corrections
MuR = Abs*data['Diam']/20.0
IofQ[1][1] /= Absorb(data['Geometry'],MuR,Tth)
IofQ[1][1] /= Polarization(inst['Polariz.'][1],Tth,Azm=inst['Azimuth'][1])[0]
if data['DetType'] == 'Area detector':
IofQ[1][1] *= Oblique(data['ObliqCoeff'],Tth)
elif 'T' in inst['Type'][0]: #neutron TOF normalized data - needs wavelength dependent absorption
wave = 2.*G2lat.TOF2dsp(inst,IofQ[1][0])*npsind(inst['2-theta'][1]/2.)
Els = ElList.keys()
Isotope = {El:'Nat. abund.' for El in Els}
GD = {'AtomTypes':ElList,'Isotope':Isotope}
BLtables = G2elem.GetBLtable(GD)
FP,FPP = G2elem.BlenResTOF(Els,BLtables,wave)
Abs = np.zeros(len(wave))
for iel,El in enumerate(Els):
BL = BLtables[El][1]
SA = BL['SA']*wave/1.798197+4.0*np.pi*FPP[iel]**2 #+BL['SL'][1]?
SA *= ElList[El]['FormulaNo']/data['Form Vol']
Abs += SA
MuR = Abs*data['Diam']/2.
IofQ[1][1] /= Absorb(data['Geometry'],MuR,inst['2-theta'][1]*np.ones(len(wave)))
# improves look of F(Q) but no impact on G(R)
# bBut,aBut = signal.butter(8,.5,"lowpass")
# IofQ[1][1] = signal.filtfilt(bBut,aBut,IofQ[1][1])
XY = IofQ[1]
#convert to Q
nQpoints = 5000
if 'C' in inst['Type'][0]:
wave = G2mth.getWave(inst)
minQ = npT2q(Tth[0],wave)
maxQ = npT2q(Tth[-1],wave)
Qpoints = np.linspace(0.,maxQ,nQpoints,endpoint=True)
dq = Qpoints[1]-Qpoints[0]
XY[0] = npT2q(XY[0],wave)
Qdata = si.griddata(XY[0],XY[1],Qpoints,method='linear',fill_value=XY[1][0]) #interpolate I(Q)
elif 'T' in inst['Type'][0]:
difC = inst['difC'][1]
minQ = 2.*np.pi*difC/Tth[-1]
maxQ = 2.*np.pi*difC/Tth[0]
Qpoints = np.linspace(0.,maxQ,nQpoints,endpoint=True)
dq = Qpoints[1]-Qpoints[0]
XY[0] = 2.*np.pi*difC/XY[0]
Qdata = si.griddata(XY[0],XY[1],Qpoints,method='linear',fill_value=XY[1][-1]) #interpolate I(Q)
Qdata -= np.min(Qdata)*data['BackRatio']
qLimits = data['QScaleLim']
maxQ = np.searchsorted(Qpoints,min(Qpoints[-1],qLimits[1]))+1
minQ = np.searchsorted(Qpoints,min(qLimits[0],0.90*Qpoints[-1]))
qLimits = [Qpoints[minQ],Qpoints[maxQ-1]]
newdata = []
if len(IofQ) < 3:
xydata['IofQ'] = [IofQ[0],[Qpoints,Qdata],'']
else:
xydata['IofQ'] = [IofQ[0],[Qpoints,Qdata],IofQ[2]]
for item in xydata['IofQ'][1]:
newdata.append(item[:maxQ])
xydata['IofQ'][1] = newdata
xydata['SofQ'] = copy.deepcopy(xydata['IofQ'])
if 'XC' in inst['Type'][0]:
FFSq,SqFF,CF = GetAsfMean(ElList,(xydata['SofQ'][1][0]/(4.0*np.pi))**2) #these are <f^2>,<f>^2,Cf
else: #TOF
CF = np.zeros(len(xydata['SofQ'][1][0]))
FFSq = np.ones(len(xydata['SofQ'][1][0]))
SqFF = np.ones(len(xydata['SofQ'][1][0]))
Q = xydata['SofQ'][1][0]
# auxPlot.append([Q,np.copy(CF),'CF-unCorr'])
if 'XC' in inst['Type'][0]:
# CF *= KleinNishina(wave,Q)
ruland = Ruland(data['Ruland'],wave,Q,CF)
# auxPlot.append([Q,ruland,'Ruland'])
CF *= ruland
# auxPlot.append([Q,CF,'CF-Corr'])
scale = np.sum((FFSq+CF)[minQ:maxQ])/np.sum(xydata['SofQ'][1][1][minQ:maxQ])
xydata['SofQ'][1][1] *= scale
if 'XC' in inst['Type'][0]:
xydata['SofQ'][1][1] -= CF
xydata['SofQ'][1][1] = xydata['SofQ'][1][1]/SqFF
scale = len(xydata['SofQ'][1][1][minQ:maxQ])/np.sum(xydata['SofQ'][1][1][minQ:maxQ])
xydata['SofQ'][1][1] *= scale
xydata['FofQ'] = copy.deepcopy(xydata['SofQ'])
xydata['FofQ'][1][1] = xydata['FofQ'][1][0]*(xydata['SofQ'][1][1]-1.0)
if data['Lorch']:
xydata['FofQ'][1][1] *= LorchWeight(Q)
xydata['GofR'] = copy.deepcopy(xydata['FofQ'])
xydata['gofr'] = copy.deepcopy(xydata['FofQ'])
nR = len(xydata['GofR'][1][1])
Rmax = GSASIIpath.GetConfigValue('PDF_Rmax',100.)
mul = int(round(2.*np.pi*nR/(Rmax*qLimits[1])))
# mul = int(round(2.*np.pi*nR/(data.get('Rmax',100.)*qLimits[1])))
R = 2.*np.pi*np.linspace(0,nR,nR,endpoint=True)/(mul*qLimits[1])
xydata['GofR'][1][0] = R
xydata['gofr'][1][0] = R
GR = -(2./np.pi)*dq*np.imag(fft.fft(xydata['FofQ'][1][1],mul*nR)[:nR])*data.get('GR Scale',1.0)
# GR = -dq*np.imag(fft.fft(xydata['FofQ'][1][1],mul*nR)[:nR])*data.get('GR Scale',1.0)
xydata['GofR'][1][1] = GR
numbDen = 0.
if 'ElList' in data:
numbDen = GetNumDensity(data['ElList'],data['Form Vol'])
gr = GR/(4.*np.pi*numbDen*R)+1.
# gr = GR/(np.pi*R) ##mising numberdensity
xydata['gofr'][1][1] = gr
if data.get('noRing',True):
Rmin = data['Rmin']
xydata['gofr'][1][1] = np.where(R<Rmin,-4.*numbDen,xydata['gofr'][1][1])
xydata['GofR'][1][1] = np.where(R<Rmin,-4.*R*np.pi*numbDen,xydata['GofR'][1][1])
return auxPlot
def PDFPeakFit(peaks,data):
rs2pi = 1./np.sqrt(2*np.pi)
def MakeParms(peaks):
varyList = []
parmDict = {'slope':peaks['Background'][1][1]}
if peaks['Background'][2]:
varyList.append('slope')
for i,peak in enumerate(peaks['Peaks']):
parmDict['PDFpos;'+str(i)] = peak[0]
parmDict['PDFmag;'+str(i)] = peak[1]
parmDict['PDFsig;'+str(i)] = peak[2]
if 'P' in peak[3]:
varyList.append('PDFpos;'+str(i))
if 'M' in peak[3]:
varyList.append('PDFmag;'+str(i))
if 'S' in peak[3]:
varyList.append('PDFsig;'+str(i))
return parmDict,varyList
def SetParms(peaks,parmDict,varyList):
if 'slope' in varyList:
peaks['Background'][1][1] = parmDict['slope']
for i,peak in enumerate(peaks['Peaks']):
if 'PDFpos;'+str(i) in varyList:
peak[0] = parmDict['PDFpos;'+str(i)]
if 'PDFmag;'+str(i) in varyList:
peak[1] = parmDict['PDFmag;'+str(i)]
if 'PDFsig;'+str(i) in varyList:
peak[2] = parmDict['PDFsig;'+str(i)]
def CalcPDFpeaks(parmdict,Xdata):
Z = parmDict['slope']*Xdata
ipeak = 0
while True:
try:
pos = parmdict['PDFpos;'+str(ipeak)]
mag = parmdict['PDFmag;'+str(ipeak)]
wid = parmdict['PDFsig;'+str(ipeak)]
wid2 = 2.*wid**2
Z += mag*rs2pi*np.exp(-(Xdata-pos)**2/wid2)/wid
ipeak += 1
except KeyError: #no more peaks to process
return Z
def errPDFProfile(values,xdata,ydata,parmdict,varylist):
parmdict.update(zip(varylist,values))
M = CalcPDFpeaks(parmdict,xdata)-ydata
return M
newpeaks = copy.copy(peaks)
iBeg = np.searchsorted(data[1][0],newpeaks['Limits'][0])
iFin = np.searchsorted(data[1][0],newpeaks['Limits'][1])+1
X = data[1][0][iBeg:iFin]
Y = data[1][1][iBeg:iFin]
parmDict,varyList = MakeParms(peaks)
if not len(varyList):
G2fil.G2Print (' Nothing varied')
return newpeaks,None,None,None,None,None
Rvals = {}
values = np.array(Dict2Values(parmDict, varyList))
result = so.leastsq(errPDFProfile,values,full_output=True,ftol=0.0001,
args=(X,Y,parmDict,varyList))
chisq = np.sum(result[2]['fvec']**2)
Values2Dict(parmDict, varyList, result[0])
SetParms(peaks,parmDict,varyList)
Rvals['Rwp'] = np.sqrt(chisq/np.sum(Y**2))*100. #to %
chisq = np.sum(result[2]['fvec']**2)/(len(X)-len(values)) #reduced chi^2 = M/(Nobs-Nvar)
sigList = list(np.sqrt(chisq*np.diag(result[1])))
Z = CalcPDFpeaks(parmDict,X)
newpeaks['calc'] = [X,Z]
return newpeaks,result[0],varyList,sigList,parmDict,Rvals
def MakeRDF(RDFcontrols,background,inst,pwddata):
auxPlot = []
if 'C' in inst['Type'][0] or 'B' in inst['Type'][0]:
Tth = pwddata[0]
wave = G2mth.getWave(inst)
minQ = npT2q(Tth[0],wave)
maxQ = npT2q(Tth[-1],wave)
powQ = npT2q(Tth,wave)
elif 'T' in inst['Type'][0]:
TOF = pwddata[0]
difC = inst['difC'][1]
minQ = 2.*np.pi*difC/TOF[-1]
maxQ = 2.*np.pi*difC/TOF[0]
powQ = 2.*np.pi*difC/TOF
piDQ = np.pi/(maxQ-minQ)
Qpoints = np.linspace(minQ,maxQ,len(pwddata[0]),endpoint=True)
if RDFcontrols['UseObsCalc'] == 'obs-calc':
Qdata = si.griddata(powQ,pwddata[1]-pwddata[3],Qpoints,method=RDFcontrols['Smooth'],fill_value=0.)
elif RDFcontrols['UseObsCalc'] == 'obs-back':
Qdata = si.griddata(powQ,pwddata[1]-pwddata[4],Qpoints,method=RDFcontrols['Smooth'],fill_value=pwddata[1][0])
elif RDFcontrols['UseObsCalc'] == 'calc-back':
Qdata = si.griddata(powQ,pwddata[3]-pwddata[4],Qpoints,method=RDFcontrols['Smooth'],fill_value=pwddata[1][0])
elif RDFcontrols['UseObsCalc'] == 'auto-back':
auto = autoBkgCalc(background[1],pwddata[1])
Qdata = si.griddata(powQ,auto-pwddata[4],Qpoints,method=RDFcontrols['Smooth'],fill_value=0.)
Qdata *= np.sin((Qpoints-minQ)*piDQ)/piDQ
Qdata *= 0.5*np.sqrt(Qpoints) #Qbin normalization
dq = Qpoints[1]-Qpoints[0]
nR = len(Qdata)
R = 0.5*np.pi*np.linspace(0,nR,nR)/(4.*maxQ)
iFin = np.searchsorted(R,RDFcontrols['maxR'])+1
bBut,aBut = signal.butter(4,0.01)
Qsmooth = signal.filtfilt(bBut,aBut,Qdata)
# auxPlot.append([Qpoints,Qdata,'interpolate:'+RDFcontrols['Smooth']])
# auxPlot.append([Qpoints,Qsmooth,'interpolate:'+RDFcontrols['Smooth']])
DofR = dq*np.imag(fft.fft(Qsmooth,16*nR)[:nR])
auxPlot.append([R[:iFin],DofR[:iFin],'D(R) for '+RDFcontrols['UseObsCalc']])
return auxPlot
# PDF optimization =============================================================
def OptimizePDF(data,xydata,limits,inst,showFit=True,maxCycles=25):
import scipy.optimize as opt
numbDen = GetNumDensity(data['ElList'],data['Form Vol'])
Min,Init,Done = SetupPDFEval(data,xydata,limits,inst,numbDen)
xstart = Init()
bakMul = data['Sample Bkg.']['Mult']
if showFit:
rms = Min(xstart)
G2fil.G2Print(' Optimizing corrections to improve G(r) at low r')
if data['Sample Bkg.'].get('Refine',False):
# data['Flat Bkg'] = 0.
G2fil.G2Print(' start: Ruland={:.3f}, Sample Bkg mult={:.3f} (RMS:{:.4f})'.format(
data['Ruland'],data['Sample Bkg.']['Mult'],rms))
else:
G2fil.G2Print(' start: Flat Bkg={:.1f}, BackRatio={:.3f}, Ruland={:.3f} (RMS:{:.4f})'.format(
data['Flat Bkg'],data['BackRatio'],data['Ruland'],rms))
if data['Sample Bkg.'].get('Refine',False):
res = opt.minimize(Min,xstart,bounds=([0.01,1.],[1.2*bakMul,0.8*bakMul]),
method='L-BFGS-B',options={'maxiter':maxCycles},tol=0.001)
else:
res = opt.minimize(Min,xstart,bounds=([0.,None],[0,1],[0.01,1.]),
method='L-BFGS-B',options={'maxiter':maxCycles},tol=0.001)
Done(res['x'])
if showFit:
if res['success']:
msg = 'Converged'
else:
msg = 'Not Converged'
if data['Sample Bkg.'].get('Refine',False):
G2fil.G2Print(' end: Ruland={:.3f}, Sample Bkg mult={:.3f} (RMS:{:.4f}) *** {} ***\n'.format(
data['Ruland'],data['Sample Bkg.']['Mult'],res['fun'],msg))
else:
G2fil.G2Print(' end: Flat Bkg={:.1f}, BackRatio={:.3f}, Ruland={:.3f} RMS:{:.4f}) *** {} ***\n'.format(
data['Flat Bkg'],data['BackRatio'],data['Ruland'],res['fun'],msg))
return res
def SetupPDFEval(data,xydata,limits,inst,numbDen):
Data = copy.deepcopy(data)
BkgMax = 1.
def EvalLowPDF(arg):
'''Objective routine -- evaluates the RMS deviations in G(r)
from -4(pi)*#density*r for for r<Rmin
arguments are ['Flat Bkg','BackRatio','Ruland'] scaled so that
the min & max values are between 0 and 1.
'''
if Data['Sample Bkg.'].get('Refine',False):
R,S = arg
Data['Sample Bkg.']['Mult'] = S
else:
F,B,R = arg
Data['Flat Bkg'] = BkgMax*(2.*F-1.)
Data['BackRatio'] = B
Data['Ruland'] = R
CalcPDF(Data,inst,limits,xydata)
# test low r computation
g = xydata['GofR'][1][1]
r = xydata['GofR'][1][0]
g0 = g[r < Data['Rmin']] + 4*np.pi*r[r < Data['Rmin']]*numbDen
M = sum(g0**2)/len(g0)
return M
def GetCurrentVals():
'''Get the current ['Flat Bkg','BackRatio','Ruland'] with scaling
'''
if data['Sample Bkg.'].get('Refine',False):
return [max(data['Ruland'],.05),data['Sample']['Mult']]
try:
F = 0.5+0.5*data['Flat Bkg']/BkgMax
except:
F = 0
return [F,data['BackRatio'],max(data['Ruland'],.05)]
def SetFinalVals(arg):
'''Set the 'Flat Bkg', 'BackRatio' & 'Ruland' values from the
scaled, refined values and plot corrected region of G(r)
'''
if data['Sample Bkg.'].get('Refine',False):
R,S = arg
data['Sample Bkg.']['Mult'] = S
else:
F,B,R = arg
data['Flat Bkg'] = BkgMax*(2.*F-1.)
data['BackRatio'] = B
data['Ruland'] = R
CalcPDF(data,inst,limits,xydata)
EvalLowPDF(GetCurrentVals())
BkgMax = max(xydata['IofQ'][1][1])/50.
return EvalLowPDF,GetCurrentVals,SetFinalVals
#### GSASII convolution peak fitting routines: Finger, Cox & Jephcoat model
[docs]
def factorize(num):
''' Provide prime number factors for integer num
:returns: dictionary of prime factors (keys) & power for each (data)
'''
factors = {}
orig = num
# we take advantage of the fact that (i +1)**2 = i**2 + 2*i +1
i, sqi = 2, 4
while sqi <= num:
while not num%i:
num /= i
factors[i] = factors.get(i, 0) + 1
sqi += 2*i + 1
i += 1
if num != 1 and num != orig:
factors[num] = factors.get(num, 0) + 1
if factors:
return factors
else:
return {num:1} #a prime number!
[docs]
def makeFFTsizeList(nmin=1,nmax=1023,thresh=15):
''' Provide list of optimal data sizes for FFT calculations
:param int nmin: minimum data size >= 1
:param int nmax: maximum data size > nmin
:param int thresh: maximum prime factor allowed
:Returns: list of data sizes where the maximum prime factor is < thresh
'''
plist = []
nmin = max(1,nmin)
nmax = max(nmin+1,nmax)
for p in range(nmin,nmax):
if max(list(factorize(p).keys())) < thresh:
plist.append(p)
return plist
np.seterr(divide='ignore')
# Normal distribution
# loc = mu, scale = std
_norm_pdf_C = 1./math.sqrt(2*math.pi)
[docs]
class norm_gen(st.rv_continuous):
'''
Normal distribution
The location (loc) keyword specifies the mean.
The scale (scale) keyword specifies the standard deviation.
normal.pdf(x) = exp(-x**2/2)/sqrt(2*pi)
'''
[docs]
def pdf(self,x,*args,**kwds):
loc,scale=kwds['loc'],kwds['scale']
x = (x-loc)/scale
return np.exp(-x**2/2.0) * _norm_pdf_C / scale
norm = norm_gen(name='norm')
## Cauchy
# median = loc
[docs]
class cauchy_gen(st.rv_continuous):
'''
Cauchy distribution
cauchy.pdf(x) = 1/(pi*(1+x**2))
This is the t distribution with one degree of freedom.
'''
[docs]
def pdf(self,x,*args,**kwds):
loc,scale=kwds['loc'],kwds['scale']
x = (x-loc)/scale
return 1.0/np.pi/(1.0+x*x) / scale
cauchy = cauchy_gen(name='cauchy')
[docs]
class fcjde_gen(st.rv_continuous):
"""
Finger-Cox-Jephcoat D(2phi,2th) function for S/L = H/L
Ref: J. Appl. Cryst. (1994) 27, 892-900.
:param x: array -1 to 1
:param t: 2-theta position of peak
:param s: sum(S/L,H/L); S: sample height, H: detector opening,
L: sample to detector opening distance
:param dx: 2-theta step size in deg
:returns: for fcj.pdf
* T = x*dx+t
* s = S/L+H/L
* if x < 0::
fcj.pdf = [1/sqrt({cos(T)**2/cos(t)**2}-1) - 1/s]/|cos(T)|
* if x >= 0: fcj.pdf = 0
"""
def _pdf(self,x,t,s,dx):
T = dx*x+t
ax2 = abs(npcosd(T))
ax = ax2**2
bx = npcosd(t)**2
bx = np.where(ax>bx,bx,ax)
fx = np.where(ax>bx,(np.sqrt(bx/(ax-bx))-1./s)/ax2,0.0)
fx = np.where(fx > 0.,fx,0.0)
return fx
[docs]
def pdf(self,x,*args,**kwds):
loc=kwds['loc']
return self._pdf(x-loc,*args)
fcjde = fcjde_gen(name='fcjde',shapes='t,s,dx')
[docs]
def getFCJVoigt(pos,intens,sig,gam,shl,xdata):
'''Compute the Finger-Cox-Jepcoat modified Voigt function for a
CW powder peak by direct convolution. This version is not used.
'''
DX = xdata[1]-xdata[0]
widths,fmin,fmax = getWidthsCW(pos,sig,gam,shl)
x = np.linspace(pos-fmin,pos+fmin,256)
dx = x[1]-x[0]
Norm = norm.pdf(x,loc=pos,scale=widths[0])
Cauchy = cauchy.pdf(x,loc=pos,scale=widths[1])
arg = [pos,shl/57.2958,dx,]
FCJ = fcjde.pdf(x,*arg,loc=pos)
if len(np.nonzero(FCJ)[0])>5:
z = np.column_stack([Norm,Cauchy,FCJ]).T
Z = fft.fft(z)
Df = fft.ifft(Z.prod(axis=0)).real
else:
z = np.column_stack([Norm,Cauchy]).T
Z = fft.fft(z)
Df = fft.fftshift(fft.ifft(Z.prod(axis=0))).real
Df /= np.sum(Df)
Df = si.interp1d(x,Df,bounds_error=False,fill_value=0.0)
return intens*Df(xdata)*DX/dx
#### GSASII peak fitting routine: Finger, Cox & Jephcoat model
[docs]
def getWidthsCW(pos,sig,gam,shl):
'''Compute the peak widths used for computing the range of a peak
for constant wavelength data. On low-angle side, 50 FWHM are used,
on high-angle side 75 are used, high angle side extended for axial divergence
(for peaks above 90 deg, these are reversed.)
:param pos: peak position; 2-theta in degrees
:param sig: Gaussian peak variance in centideg^2
:param gam: Lorentzian peak width in centidegrees
:param shl: axial divergence parameter (S+H)/L
:returns: widths; [Gaussian sigma, Lorentzian gamma] in degrees, and
low angle, high angle ends of peak; 20 FWHM & 50 FWHM from position
reversed for 2-theta > 90 deg.
'''
widths = [np.sqrt(sig)/100.,gam/100.]
fwhm = 2.355*widths[0]+widths[1]
fmin = 50.*(fwhm+shl*abs(npcosd(pos)))
fmax = 75.0*fwhm
if pos > 90:
fmin,fmax = [fmax,fmin]
return widths,fmin,fmax
[docs]
def getWidthsED(pos,sig,gam):
'''Compute the peak widths used for computing the range of a peak
for energy dispersive data. On low-energy side, 20 FWHM are used,
on high-energy side 20 are used
:param pos: peak position; energy in keV (not used)
:param sig: Gaussian peak variance in keV^2
:param gam: Lorentzian peak width in keV
:returns: widths; [Gaussian sigma, Lorentzian gamma] in keV, and
low angle, high angle ends of peak; 5 FWHM & 5 FWHM from position
'''
widths = [np.sqrt(sig),gam]
fwhm = 2.355*widths[0]+widths[1]
fmin = 5.*fwhm
fmax = 5.*fwhm
return widths,fmin,fmax
[docs]
def getWidthsTOF(pos,alp,bet,sig,gam):
'''Compute the peak widths used for computing the range of a peak
for constant wavelength data. 50 FWHM are used on both sides each
extended by exponential coeff.
param pos: peak position; TOF in musec (not used)
param alp,bet: TOF peak exponential rise & decay parameters
param sig: Gaussian peak variance in musec^2
param gam: Lorentzian peak width in musec
returns: widths; [Gaussian sigma, Lornetzian gamma] in musec
returns: low TOF, high TOF ends of peak; 50FWHM from position
'''
widths = [np.sqrt(sig),gam]
fwhm = 2.355*widths[0]+2.*widths[1]
fmin = 50.*fwhm*(1.+1./alp)
fmax = 50.*fwhm*(1.+1./bet)
return widths,fmin,fmax
[docs]
def getFWHM(pos,Inst,N=1):
'''Compute total FWHM from Thompson, Cox & Hastings (1987) , J. Appl. Cryst. 20, 79-83
via getgamFW(g,s).
:param pos: float peak position in deg 2-theta or tof in musec
:param Inst: dict instrument parameters
:param N: int Inst index (0 for input, 1 for fitted)
:returns float: total FWHM of pseudoVoigt in deg or musec
'''
sig = lambda Th,U,V,W: np.sqrt(max(0.001,U*tand(Th)**2+V*tand(Th)+W))
sigED = lambda E,A,B,C: np.sqrt(max(0.001,A*E**2+B*E+C))
sigTOF = lambda dsp,S0,S1,S2,Sq: np.sqrt(S0+S1*dsp**2+S2*dsp**4+Sq*dsp)
gam = lambda Th,X,Y,Z: Z+X/cosd(Th)+Y*tand(Th)
gamED = lambda E,X,Y,Z: max(0.001,X*E**2+Y*E+Z)
gamTOF = lambda dsp,X,Y,Z: Z+X*dsp+Y*dsp**2
alpTOF = lambda dsp,alp: alp/dsp
betTOF = lambda dsp,bet0,bet1,betq: bet0+bet1/dsp**4+betq/dsp**2
alpPinkX = lambda pos,alp0,alp1: alp0+alp1*nptand(pos/2.)
betPinkX = lambda pos,bet0,bet1: bet0+bet1*nptand(pos/2.)
alpPinkN = lambda pos,alp0,alp1: alp0+alp1*npsind(pos/2.)
betPinkN = lambda pos,bet0,bet1: bet0+bet1*npsind(pos/2.)
if 'LF' in Inst['Type'][0]:
return 3
elif 'T' in Inst['Type'][0]:
dsp = pos/Inst['difC'][N]
alp = alpTOF(dsp,Inst['alpha'][N])
bet = betTOF(dsp,Inst['beta-0'][1],Inst['beta-1'][N],Inst['beta-q'][N])
s = sigTOF(dsp,Inst['sig-0'][N],Inst['sig-1'][N],Inst['sig-2'][N],Inst['sig-q'][N])
g = gamTOF(dsp,Inst['X'][N],Inst['Y'][N],Inst['Z'][N])
return getgamFW(g,s)+np.log(2.0)*(alp+bet)/(alp*bet)
elif 'C' in Inst['Type'][0]:
s = sig(pos/2.,Inst['U'][N],Inst['V'][N],Inst['W'][N])
g = gam(pos/2.,Inst['X'][N],Inst['Y'][N],Inst['Z'][N])
return getgamFW(g,s)/100. #returns FWHM in deg
elif 'E' in Inst['Type'][0]:
s = sigED(pos,Inst['A'][N],Inst['B'][N],Inst['C'][N])
g = gamED(pos,Inst['X'][N],Inst['Y'][N],Inst['Z'][N])
return getgamFW(g,s)
else: #'B'
if 'X' in Inst['Type'][0]:
alp = alpPinkX(pos,Inst['alpha-0'][N],Inst['alpha-1'][N])
bet = betPinkX(pos,Inst['beta-0'][N],Inst['beta-1'][N])
else:
alp = alpPinkN(pos,Inst['alpha-0'][N],Inst['alpha-1'][N])
bet = betPinkN(pos,Inst['beta-0'][N],Inst['beta-1'][N])
s = sig(pos/2.,Inst['U'][N],Inst['V'][N],Inst['W'][N])
g = gam(pos/2.,Inst['X'][N],Inst['Y'][N],Inst['Z'][N])
return getgamFW(g,s)/100.+np.log(2.0)*(alp+bet)/(alp*bet) #returns FWHM in deg
[docs]
def getgamFW(g,s):
'''Compute total FWHM from Thompson, Cox & Hastings (1987), J. Appl. Cryst. 20, 79-83
lambda fxn needs FWHM for both Gaussian & Lorentzian components
:param g: float Lorentzian gamma = FWHM(L)
:param s: float Gaussian sig
:returns float: total FWHM of pseudoVoigt
'''
gamFW = lambda s,g: np.exp(np.log(s**5+2.69269*s**4*g+2.42843*s**3*g**2+4.47163*s**2*g**3+0.07842*s*g**4+g**5)/5.)
return gamFW(2.35482*s,g) #sqrt(8ln2)*sig = FWHM(G)
[docs]
def getBackground(pfx,parmDict,bakType,dataType,xdata,fixback=None):
'''Computes the background based on parameters that may be taken from
a gpx file or the data tree.
:param str pfx: histogram prefix (:h:)
:param dict parmDict: Refinement parameter values
:param str bakType: defines background function to be used. Should be
one of these: 'chebyschev', 'cosine', 'chebyschev-1',
'Q^2 power series', 'Q^-2 power series', 'lin interpolate',
'inv interpolate', 'log interpolate'
:param str dataType: Code to indicate histogram type (PXC, PNC, PNT,...)
:param MaskedArray xdata: independent variable, 2theta (deg*100) or
TOF (microsec?)
:param numpy.array fixback: Array of fixed background points (length
matching xdata) or None
:returns: yb,sumBK where yp is an array of background values (length
matching xdata) and sumBK is a list with three values. The sumBK[0] is
the sum of all yb values, sumBK[1] is the sum of Debye background terms
and sumBK[2] is the sum of background peaks.
'''
if 'T' in dataType:
q = 2.*np.pi*parmDict[pfx+'difC']/xdata
elif 'E' in dataType:
const = 4.*np.pi*npsind(parmDict[pfx+'2-theta']/2.0)
q = const*xdata
else:
wave = parmDict.get(pfx+'Lam',parmDict.get(pfx+'Lam1',1.0))
q = npT2q(xdata,wave)
yb = np.zeros_like(xdata)
nBak = 0
sumBk = [0.,0.,0]
while True:
key = pfx+'Back;'+str(nBak)
if key in parmDict:
nBak += 1
else:
break
#empirical functions
if bakType in ['chebyschev','cosine','chebyschev-1']:
dt = xdata[-1]-xdata[0]
for iBak in range(nBak):
key = pfx+'Back;'+str(iBak)
if bakType == 'chebyschev':
ybi = parmDict[key]*(-1.+2.*(xdata-xdata[0])/dt)**iBak
elif bakType == 'chebyschev-1':
xpos = -1.+2.*(xdata-xdata[0])/dt
ybi = parmDict[key]*np.cos(iBak*np.arccos(xpos))
elif bakType == 'cosine':
ybi = parmDict[key]*npcosd(180.*xdata*iBak/xdata[-1])
yb += ybi
sumBk[0] = np.sum(yb)
elif bakType in ['Q^2 power series','Q^-2 power series']:
QT = 1.
yb += np.ones_like(yb)*parmDict[pfx+'Back;0']
for iBak in range(nBak-1):
key = pfx+'Back;'+str(iBak+1)
if '-2' in bakType:
QT *= (iBak+1)*q**-2
else:
QT *= q**2/(iBak+1)
yb += QT*parmDict[key]
sumBk[0] = np.sum(yb)
elif bakType in ['lin interpolate','inv interpolate','log interpolate',]:
if nBak == 1:
yb = np.ones_like(xdata)*parmDict[pfx+'Back;0']
elif nBak == 2:
dX = xdata[-1]-xdata[0]
T2 = (xdata-xdata[0])/dX
T1 = 1.0-T2
yb = parmDict[pfx+'Back;0']*T1+parmDict[pfx+'Back;1']*T2
else:
xnomask = ma.getdata(xdata)
xmin,xmax = xnomask[0],xnomask[-1]
if bakType == 'lin interpolate':
bakPos = np.linspace(xmin,xmax,nBak,True)
elif bakType == 'inv interpolate':
bakPos = 1./np.linspace(1./xmax,1./xmin,nBak,True)
elif bakType == 'log interpolate':
bakPos = np.exp(np.linspace(np.log(xmin),np.log(xmax),nBak,True))
bakPos[0] = xmin
bakPos[-1] = xmax
bakVals = np.zeros(nBak)
for i in range(nBak):
bakVals[i] = parmDict[pfx+'Back;'+str(i)]
bakInt = si.interp1d(bakPos,bakVals,'linear')
yb = bakInt(ma.getdata(xdata))
sumBk[0] = np.sum(yb)
#Debye function
if pfx+'difC' in parmDict or 'E' in dataType:
ff = 1.
else:
try:
wave = parmDict[pfx+'Lam']
except KeyError:
wave = parmDict[pfx+'Lam1']
SQ = (q/(4.*np.pi))**2
FF = G2elem.GetFormFactorCoeff('Si')[0]
ff = np.array(G2elem.ScatFac(FF,SQ)[0])**2
iD = 0
while True:
try:
dbA = parmDict[pfx+'DebyeA;'+str(iD)]
dbR = parmDict[pfx+'DebyeR;'+str(iD)]
dbU = parmDict[pfx+'DebyeU;'+str(iD)]
ybi = ff*dbA*np.sin(q*dbR)*np.exp(-dbU*q**2)/(q*dbR)
yb += ybi
sumBk[1] += np.sum(ybi)
iD += 1
except KeyError:
break
#peaks
iD = 0
while True:
try:
pkP = parmDict[pfx+'BkPkpos;'+str(iD)]
pkI = max(parmDict[pfx+'BkPkint;'+str(iD)],0.1)
pkS = max(parmDict[pfx+'BkPksig;'+str(iD)],0.01)
pkG = max(parmDict[pfx+'BkPkgam;'+str(iD)],0.1)
if 'C' in dataType:
Wd,fmin,fmax = getWidthsCW(pkP,pkS,pkG,.002)
elif 'E' in dataType:
Wd,fmin,fmax = getWidthsED(pkP,pkS)
else: #'T'OF
Wd,fmin,fmax = getWidthsTOF(pkP,1.,1.,pkS,pkG)
iBeg = np.searchsorted(xdata,pkP-fmin)
iFin = np.searchsorted(xdata,pkP+fmax)
lenX = len(xdata)
if not iBeg:
iFin = np.searchsorted(xdata,pkP+fmax)
elif iBeg == lenX:
iFin = iBeg
else:
iFin = np.searchsorted(xdata,pkP+fmax)
if 'C' in dataType:
ybi = pkI*getFCJVoigt3(pkP,pkS,pkG,0.002,xdata[iBeg:iFin])[0]
elif 'T' in dataType:
ybi = pkI*getEpsVoigt(pkP,1.,1.,pkS,pkG,xdata[iBeg:iFin])[0]
elif 'B' in dataType:
ybi = pkI*getEpsVoigt(pkP,1.,1.,pkS/100.,pkG/1.e4,xdata[iBeg:iFin])[0]
elif 'E' in dataType:
ybi = pkI*getPsVoigt(pkP,pkS*10.**4,pkG*100.,xdata[iBeg:iFin])[0]
else:
raise Exception('dataType of {:} should not happen!'.format(dataType))
yb[iBeg:iFin] += ybi
sumBk[2] += np.sum(ybi)
iD += 1
except KeyError:
break
except ValueError:
G2fil.G2Print ('**** WARNING - backround peak '+str(iD)+' sigma is negative; fix & try again ****')
break
if fixback is not None:
yb += parmDict[pfx+'BF mult']*fixback
sumBk[0] = sum(yb)
return yb,sumBk
[docs]
def getBackgroundDerv(hfx,parmDict,bakType,dataType,xdata,fixback=None):
'''Computes the derivatives of the background
Parameters passed to this may be pulled from gpx file or data tree.
See :func:`getBackground` for parameter definitions.
:returns: dydb,dyddb,dydpk,dydfb where the first three are 2-D arrays
of derivatives with respect to the background terms, the Debye terms and
the background peak terms vs. the points in the diffracton pattern. The
final 1D array is the derivative with respect to the fixed-background
multiplier (= the fixed background values).
'''
if 'T' in dataType:
q = 2.*np.pi*parmDict[hfx+'difC']/xdata
elif 'E' in dataType:
const = 4.*np.pi*npsind(parmDict[hfx+'2-theta']/2.0)
q = const*xdata
else:
wave = parmDict.get(hfx+'Lam',parmDict.get(hfx+'Lam1',1.0))
q = 2.*np.pi*npsind(xdata/2.)/wave
nBak = 0
while True:
key = hfx+'Back;'+str(nBak)
if key in parmDict:
nBak += 1
else:
break
dydb = np.zeros(shape=(nBak,len(xdata)))
dyddb = np.zeros(shape=(3*parmDict[hfx+'nDebye'],len(xdata)))
dydpk = np.zeros(shape=(4*parmDict[hfx+'nPeaks'],len(xdata)))
dydfb = []
if bakType in ['chebyschev','cosine','chebyschev-1']:
dt = xdata[-1]-xdata[0]
for iBak in range(nBak):
if bakType == 'chebyschev':
dydb[iBak] = (-1.+2.*(xdata-xdata[0])/dt)**iBak
elif bakType == 'chebyschev-1':
xpos = -1.+2.*(xdata-xdata[0])/dt
dydb[iBak] = np.cos(iBak*np.arccos(xpos))
elif bakType == 'cosine':
dydb[iBak] = npcosd(180.*xdata*iBak/xdata[-1])
elif bakType in ['Q^2 power series','Q^-2 power series']:
QT = 1.
dydb[0] = np.ones_like(xdata)
for iBak in range(nBak-1):
if '-2' in bakType:
QT *= (iBak+1)*q**-2
else:
QT *= q**2/(iBak+1)
dydb[iBak+1] = QT
elif bakType in ['lin interpolate','inv interpolate','log interpolate',]:
if nBak == 1:
dydb[0] = np.ones_like(xdata)
elif nBak == 2:
dX = xdata[-1]-xdata[0]
T2 = (xdata-xdata[0])/dX
T1 = 1.0-T2
dydb = [T1,T2]
else:
xnomask = ma.getdata(xdata)
xmin,xmax = xnomask[0],xnomask[-1]
if bakType == 'lin interpolate':
bakPos = np.linspace(xmin,xmax,nBak,True)
elif bakType == 'inv interpolate':
bakPos = 1./np.linspace(1./xmax,1./xmin,nBak,True)
elif bakType == 'log interpolate':
bakPos = np.exp(np.linspace(np.log(xmin),np.log(xmax),nBak,True))
bakPos[0] = xmin
bakPos[-1] = xmax
for i,pos in enumerate(bakPos):
if i == 0:
dydb[0] = np.where(xdata<bakPos[1],(bakPos[1]-xdata)/(bakPos[1]-bakPos[0]),0.)
elif i == len(bakPos)-1:
dydb[i] = np.where(xdata>bakPos[-2],(bakPos[-1]-xdata)/(bakPos[-1]-bakPos[-2]),0.)
else:
dydb[i] = np.where(xdata>bakPos[i],
np.where(xdata<bakPos[i+1],(bakPos[i+1]-xdata)/(bakPos[i+1]-bakPos[i]),0.),
np.where(xdata>bakPos[i-1],(xdata-bakPos[i-1])/(bakPos[i]-bakPos[i-1]),0.))
if hfx+'difC' in parmDict:
ff = 1.
else:
wave = parmDict.get(hfx+'Lam',parmDict.get(hfx+'Lam1',1.0))
q = npT2q(xdata,wave)
SQ = (q/(4*np.pi))**2
FF = G2elem.GetFormFactorCoeff('Si')[0]
ff = np.array(G2elem.ScatFac(FF,SQ)[0])*np.pi**2 #needs pi^2~10. for cw data (why?)
iD = 0
while True:
try:
if hfx+'difC' in parmDict:
q = 2*np.pi*parmDict[hfx+'difC']/xdata
dbA = parmDict[hfx+'DebyeA;'+str(iD)]
dbR = parmDict[hfx+'DebyeR;'+str(iD)]
dbU = parmDict[hfx+'DebyeU;'+str(iD)]
sqr = np.sin(q*dbR)/(q*dbR)
cqr = np.cos(q*dbR)
temp = np.exp(-dbU*q**2)
dyddb[3*iD] = ff*sqr*temp
dyddb[3*iD+1] = ff*dbA*temp*(cqr-sqr)/(dbR)
dyddb[3*iD+2] = -ff*dbA*sqr*temp*q**2
iD += 1
except KeyError:
break
iD = 0
while True:
try:
pkP = parmDict[hfx+'BkPkpos;'+str(iD)]
pkI = max(parmDict[hfx+'BkPkint;'+str(iD)],0.1)
pkS = max(parmDict[hfx+'BkPksig;'+str(iD)],0.01)
pkG = max(parmDict[hfx+'BkPkgam;'+str(iD)],0.1)
if 'C' in dataType:
Wd,fmin,fmax = getWidthsCW(pkP,pkS,pkG,.002)
elif 'E' in dataType:
Wd,fmin,fmax = getWidthsED(pkP,pkS)
else: #'T' or 'B'
Wd,fmin,fmax = getWidthsTOF(pkP,1.,1.,pkS,pkG)
iBeg = np.searchsorted(xdata,pkP-fmin)
iFin = np.searchsorted(xdata,pkP+fmax)
lenX = len(xdata)
if not iBeg:
iFin = np.searchsorted(xdata,pkP+fmax)
elif iBeg == lenX:
iFin = iBeg
else:
iFin = np.searchsorted(xdata,pkP+fmax)
if 'C' in dataType:
Df,dFdp,dFds,dFdg,x = getdFCJVoigt3(pkP,pkS,pkG,.002,xdata[iBeg:iFin])
elif 'E' in dataType:
Df,dFdp,dFds,dFdg = getdPsVoigt(pkP,pkS*10.**4,pkG*100.,xdata[iBeg:iFin])
else: #'T'OF
Df,dFdp,x,x,dFds,dFdg = getdEpsVoigt(pkP,1.,1.,pkS,pkG,xdata[iBeg:iFin])
dydpk[4*iD][iBeg:iFin] += pkI*dFdp
dydpk[4*iD+1][iBeg:iFin] += Df
dydpk[4*iD+2][iBeg:iFin] += pkI*dFds
dydpk[4*iD+3][iBeg:iFin] += pkI*dFdg
iD += 1
except KeyError:
break
except ValueError:
G2fil.G2Print ('**** WARNING - backround peak '+str(iD)+' sigma is negative; fix & try again ****')
break
# fixed background from file
if fixback is not None:
dydfb = fixback
return dydb,dyddb,dydpk,dydfb
#### Using old gsas fortran routines for powder peak shapes & derivatives
[docs]
def getFCJVoigt3(pos,sig,gam,shl,xdata):
'''Compute the Finger-Cox-Jepcoat modified Pseudo-Voigt function for a
CW powder peak in external Fortran routine
param pos: peak position in degrees
param sig: Gaussian variance in centideg^2
param gam: Lorentzian width in centideg
param shl: axial divergence parameter (S+H)/L
param xdata: array; profile points for peak to be calculated; bounded by 20FWHM to 50FWHM (or vv)
returns: array: calculated peak function at each xdata
returns: integral of peak; nominally = 1.0
'''
if len(xdata):
cw = np.diff(xdata)
cw = np.append(cw,cw[-1])
Df = pyd.pypsvfcj(len(xdata),xdata-pos,pos,sig,gam,shl)
return Df,np.sum(100.*Df*cw)
else:
return 0.,1.
[docs]
def getdFCJVoigt3(pos,sig,gam,shl,xdata):
'''Compute analytic derivatives the Finger-Cox-Jepcoat modified Pseudo-Voigt
function for a CW powder peak
param pos: peak position in degrees
param sig: Gaussian variance in centideg^2
param gam: Lorentzian width in centideg
param shl: axial divergence parameter (S+H)/L
param xdata: array; profile points for peak to be calculated; bounded by 20FWHM to 50FWHM (or vv)
returns: arrays: function and derivatives of pos, sig, gam, & shl
'''
Df,dFdp,dFds,dFdg,dFdsh = pyd.pydpsvfcj(len(xdata),xdata-pos,pos,sig,gam,shl)
return Df,dFdp,dFds,dFdg,dFdsh
[docs]
def getPsVoigt(pos,sig,gam,xdata):
'''Compute the simple Pseudo-Voigt function for a
small angle Bragg peak in external Fortran routine
param pos: peak position in degrees
param sig: Gaussian variance in centideg^2
param gam: Lorentzian width in centideg
param xdata: array; profile points for peak to be calculated
returns: array: calculated peak function at each xdata
returns: integral of peak; nominally = 1.0
'''
cw = np.diff(xdata)
cw = np.append(cw,cw[-1])
Df = pyd.pypsvoigt(len(xdata),xdata-pos,sig,gam)
return Df,np.sum(100.*Df*cw)
[docs]
def getdPsVoigt(pos,sig,gam,xdata):
'''Compute the simple Pseudo-Voigt function derivatives for a
small angle Bragg peak peak in external Fortran routine
param pos: peak position in degrees
param sig: Gaussian variance in centideg^2
param gam: Lorentzian width in centideg
param xdata: array; profile points for peak to be calculated
returns: arrays: function and derivatives of pos, sig & gam
NB: the pos derivative has the opposite sign compared to that in other profile functions
'''
Df,dFdp,dFds,dFdg = pyd.pydpsvoigt(len(xdata),xdata-pos,sig,gam)
return Df,dFdp,dFds,dFdg
[docs]
def getEpsVoigt(pos,alp,bet,sig,gam,xdata):
'''Compute the double exponential Pseudo-Voigt convolution function for a
neutron TOF & CW pink peak in external Fortran routine
'''
cw = np.diff(xdata)
cw = np.append(cw,cw[-1])
Df = pyd.pyepsvoigt(len(xdata),xdata-pos,alp,bet,sig,gam)
return Df,np.sum(Df*cw)
[docs]
def getdEpsVoigt(pos,alp,bet,sig,gam,xdata):
'''Compute the double exponential Pseudo-Voigt convolution function derivatives for a
neutron TOF & CW pink peak in external Fortran routine
'''
Df,dFdp,dFda,dFdb,dFds,dFdg = pyd.pydepsvoigt(len(xdata),xdata-pos,alp,bet,sig,gam)
return Df,dFdp,dFda,dFdb,dFds,dFdg
[docs]
def ellipseSize(H,Sij,GB):
'''Implements r=1/sqrt(sum((1/S)*(q.v)^2) per note from Alexander Brady
'''
HX = np.inner(H.T,GB)
lenHX = np.sqrt(np.sum(HX**2))
Esize,Rsize = nl.eigh(G2lat.U6toUij(Sij))
R = np.inner(HX/lenHX,Rsize)**2*Esize #want column length for hkl in crystal
lenR = 1./np.sqrt(np.sum(R))
return lenR
[docs]
def ellipseSizeDerv(H,Sij,GB):
'''Implements r=1/sqrt(sum((1/S)*(q.v)^2) derivative per note from Alexander Brady
'''
lenR = ellipseSize(H,Sij,GB)
delt = 0.001
dRdS = np.zeros(6)
for i in range(6):
Sij[i] -= delt
lenM = ellipseSize(H,Sij,GB)
Sij[i] += 2.*delt
lenP = ellipseSize(H,Sij,GB)
Sij[i] -= delt
dRdS[i] = (lenP-lenM)/(2.*delt)
return lenR,dRdS
def getMustrain(HKL,G,SGData,muStrData):
if muStrData[0] == 'isotropic':
return np.ones(HKL.shape[1])*muStrData[1][0]
elif muStrData[0] == 'uniaxial':
H = np.array(HKL)
P = np.array(muStrData[3])
cosP,sinP = np.array([G2lat.CosSinAngle(h,P,G) for h in H.T]).T
Si = muStrData[1][0]
Sa = muStrData[1][1]
return Si*Sa/(np.sqrt((Si*cosP)**2+(Sa*sinP)**2))
else: #generalized - P.W. Stephens model
H = np.array(HKL)
rdsq = np.array([G2lat.calc_rDsq2(h,G) for h in H.T])
Strms = np.array(G2spc.MustrainCoeff(H,SGData))
Sum = np.sum(np.array(muStrData[4])[:,nxs]*Strms,axis=0)
return np.sqrt(Sum)/rdsq
def getCrSize(HKL,G,GB,sizeData):
if sizeData[0] == 'isotropic':
return np.ones(HKL.shape[1])*sizeData[1][0]
elif sizeData[0] == 'uniaxial':
H = np.array(HKL)
P = np.array(sizeData[3])
cosP,sinP = np.array([G2lat.CosSinAngle(h,P,G) for h in H.T]).T
Si = sizeData[1][0]
Sa = sizeData[1][1]
return Si*Sa/(np.sqrt((Si*cosP)**2+(Sa*sinP)**2))
else:
Sij =[sizeData[4][i] for i in range(6)]
H = np.array(HKL)
return 1./np.array([ellipseSize(h,Sij,GB) for h in H.T])**2
[docs]
def getHKLpeak(dmin,SGData,A,Inst=None,nodup=False):
'''
Generates allowed by symmetry reflections with d >= dmin
NB: GenHKLf & checkMagextc return True for extinct reflections
:param dmin: minimum d-spacing
:param SGData: space group data obtained from SpcGroup
:param A: lattice parameter terms A1-A6
:param Inst: instrument parameter info
:returns: HKLs: np.array hkl, etc for allowed reflections
'''
HKL = G2lat.GenHLaue(dmin,SGData,A)
HKLs = []
ds = []
for h,k,l,d in HKL:
ext = G2spc.GenHKLf([h,k,l],SGData)[0]
if ext and 'MagSpGrp' in SGData:
ext = G2spc.checkMagextc([h,k,l],SGData)
if not ext:
if nodup and int(10000*d) in ds:
continue
ds.append(int(10000*d))
if Inst == None:
HKLs.append([h,k,l,d,0,-1])
else:
HKLs.append([h,k,l,d,G2lat.Dsp2pos(Inst,d),-1])
return np.array(HKLs)
[docs]
def getHKLMpeak(dmin,Inst,SGData,SSGData,Vec,maxH,A):
'needs a doc string'
HKLs = []
vec = np.array(Vec)
vstar = np.sqrt(G2lat.calc_rDsq(vec,A)) #find extra needed for -n SS reflections
dvec = 1./(maxH*vstar+1./dmin)
HKL = G2lat.GenHLaue(dvec,SGData,A)
SSdH = [vec*h for h in range(-maxH,maxH+1)]
SSdH = dict(zip(range(-maxH,maxH+1),SSdH))
ifMag = False
if 'MagSpGrp' in SGData:
ifMag = True
for h,k,l,d in HKL:
ext = G2spc.GenHKLf([h,k,l],SGData)[0]
if not ext and d >= dmin:
HKLs.append([h,k,l,0,d,G2lat.Dsp2pos(Inst,d),-1])
for dH in SSdH:
if dH:
DH = SSdH[dH]
H = [h+DH[0],k+DH[1],l+DH[2]]
d = float(1/np.sqrt(G2lat.calc_rDsq(H,A)))
if d >= dmin:
HKLM = np.array([h,k,l,dH])
if G2spc.checkSSextc(HKLM,SSGData) or ifMag:
HKLs.append([h,k,l,dH,d,G2lat.Dsp2pos(Inst,d),-1])
return G2lat.sortHKLd(HKLs,True,True,True)
peakInstPrmMode = True
'''Determines the mode used for peak fitting. When peakInstPrmMode=True peak
width parameters are computed from the instrument parameters (UVW,... or
alpha,... etc) unless the individual parameter is refined. This allows the
instrument parameters to be refined. When peakInstPrmMode=False, the instrument
parameters are not used and cannot be refined.
The default is peakFitMode=True. This is changed only in
:func:`setPeakInstPrmMode`, which is called from :mod:`GSASIIscriptable`
or GSASIIphsGUI.OnSetPeakWidMode ('Gen unvaried widths' menu item).
'''
[docs]
def setPeakInstPrmMode(normal=True):
'''Determines the mode used for peak fitting. If normal=True (default)
peak width parameters are computed from the instrument parameters
unless the individual parameter is refined. If normal=False,
peak widths are used as supplied for each peak.
Note that normal=True unless this routine is called. Also,
instrument parameters can only be refined with normal=True.
:param bool normal: setting to apply to global variable
:data:`peakInstPrmMode`
'''
global peakInstPrmMode
peakInstPrmMode = normal
[docs]
def getPeakProfile(dataType,parmDict,xdata,fixback,varyList,bakType):
'''Computes the profiles from multiple peaks for individual peak fitting
for powder patterns.
NB: not used for Rietveld refinement
'''
yb = getBackground('',parmDict,bakType,dataType,xdata,fixback)[0]
yc = np.zeros_like(yb)
if 'LF' in dataType:
if 'Lam1' in parmDict.keys():
lam = parmDict['Lam1']
lam2 = parmDict['Lam2']
Ka2 = True
lamRatio = 360*(lam2-lam)/(np.pi*lam)
kRatio = parmDict['I(L2)/I(L1)']
else:
lam = parmDict['Lam']
Ka2 = False
shol = 0
# loop over peaks
iPeak = -1
try:
ncells = parmDict['ncell']
clat = parmDict['clat']
except KeyError: # no Laue info must be bkg fit
print('Laue Fit: no params, assuming bkg fit')
return yb
while True:
iPeak += 1
try:
#Qcen = 2 * np.pi * lam * (iPeak+1) / parmDict['clat']
l = parmDict['l'+str(iPeak)]
pos = 360 * np.arcsin(0.5 * lam * l / parmDict['clat']) / np.pi
parmDict['pos'+str(iPeak)] = pos
#tth = (pos-parmDict['Zero'])
intens = parmDict['int'+str(iPeak)]
dampM = parmDict['dampM'+str(iPeak)]
dampP = parmDict['dampP'+str(iPeak)]
sig = parmDict['sig'+str(iPeak)]
gam = parmDict['gam'+str(iPeak)]
fmin = parmDict.get('fitRange',8.0) # width for peak computation: defaults to 8 deg.
fmin = min(0.9*abs(xdata[-1] - xdata[0]),fmin) # unless the data range is smaller
fitPowerM = parmDict.get('fitPowerM',2.0)
fitPowerP = parmDict.get('fitPowerP',2.0)
iBeg = np.searchsorted(xdata,pos-fmin/2)
iFin = np.searchsorted(xdata,pos+fmin/2)
if not iBeg+iFin: # skip peak below low limit
continue
elif not iBeg-iFin: # got peak above high limit (peaks sorted, so we can stop)
break
LaueFringePeakCalc(xdata,yc,lam,pos,intens,sig,gam,shol,ncells,clat,dampM,dampP,fmin,fitPowerM,fitPowerP,plot=False)
if Ka2:
pos2 = pos+lamRatio*tand(pos/2.0) # + 360/pi * Dlam/lam * tan(th)
iBeg = np.searchsorted(xdata,pos2-fmin)
iFin = np.searchsorted(xdata,pos2+fmin)
if iBeg-iFin:
LaueFringePeakCalc(xdata,yc,lam2,pos2,intens*kRatio,sig,gam,shol,ncells,clat,dampM,dampP,fmin,fitPowerM,fitPowerP)
except KeyError: #no more peaks to process
return yb+yc
elif 'C' in dataType:
shl = max(parmDict['SH/L'],0.002)
Ka2 = False
if 'Lam1' in parmDict.keys():
Ka2 = True
lamRatio = 360*(parmDict['Lam2']-parmDict['Lam1'])/(np.pi*parmDict['Lam1'])
kRatio = parmDict['I(L2)/I(L1)']
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tth = (pos-parmDict['Zero'])
intens = parmDict['int'+str(iPeak)]
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
else:
sig = G2mth.getCWsig(parmDict,tth)
sig = max(sig,0.001) #avoid neg sigma^2
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
else:
gam = G2mth.getCWgam(parmDict,tth)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsCW(pos,sig,gam,shl)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = np.searchsorted(xdata,pos+fmin)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
return yb+yc
fp = getFCJVoigt3(pos,sig,gam,shl,xdata[iBeg:iFin])[0]
yc[iBeg:iFin] += intens*fp
if Ka2:
pos2 = pos+lamRatio*tand(pos/2.0) # + 360/pi * Dlam/lam * tan(th)
iBeg = np.searchsorted(xdata,pos2-fmin)
iFin = np.searchsorted(xdata,pos2+fmin)
if iBeg-iFin:
fp2 = getFCJVoigt3(pos2,sig,gam,shl,xdata[iBeg:iFin])[0]
yc[iBeg:iFin] += intens*kRatio*fp2
iPeak += 1
except KeyError: #no more peaks to process
return yb+yc
elif 'E' in dataType:
iPeak = 0
dsp = 1.0 #for now - fix later
while True:
try:
pos = parmDict['pos'+str(iPeak)]
intens = parmDict['int'+str(iPeak)]
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
else:
sig = G2mth.getEDsig(parmDict,pos)
sig = max(sig,0.001) #avoid neg sigma^2
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
else:
gam = G2mth.getEDgam(parmDict,pos)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsED(pos,sig,gam)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = max(iBeg+3,np.searchsorted(xdata,pos+fmin))
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
return yb+yc
yc[iBeg:iFin] += intens*getPsVoigt(pos,sig*10.**4,gam*100.,xdata[iBeg:iFin])[0]
iPeak += 1
except KeyError: #no more peaks to process
return yb+yc
elif 'B' in dataType:
iPeak = 0
dsp = 1.0 #for now - fix later
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tth = (pos-parmDict['Zero'])
intens = parmDict['int'+str(iPeak)]
alpName = 'alp'+str(iPeak)
if alpName in varyList or not peakInstPrmMode:
alp = parmDict[alpName]
else:
if 'X' in dataType:
alp = G2mth.getPinkXalpha(parmDict,tth)
else:
alp = G2mth.getPinkNalpha(parmDict,tth)
alp = max(0.1,alp)
betName = 'bet'+str(iPeak)
if betName in varyList or not peakInstPrmMode:
bet = parmDict[betName]
else:
if 'X' in dataType:
bet = G2mth.getPinkXbeta(parmDict,tth)
else:
bet = G2mth.getPinkNbeta(parmDict,tth)
bet = max(0.1,bet)
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
else:
sig = G2mth.getCWsig(parmDict,tth)
sig = max(sig,0.001) #avoid neg sigma^2
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
else:
gam = G2mth.getCWgam(parmDict,tth)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsTOF(pos,alp,bet,sig,gam)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = np.searchsorted(xdata,pos+fmin)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
return yb+yc
yc[iBeg:iFin] += intens*getEpsVoigt(pos,alp,bet,sig/1.e4,gam/100.,xdata[iBeg:iFin])[0]
iPeak += 1
except KeyError: #no more peaks to process
return yb+yc
else:
Pdabc = parmDict['Pdabc']
difC = parmDict['difC']
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tof = pos-parmDict['Zero']
dsp = tof/difC
intens = parmDict['int'+str(iPeak)]
alpName = 'alp'+str(iPeak)
if alpName in varyList or not peakInstPrmMode:
alp = parmDict[alpName]
else:
if len(Pdabc):
alp = np.interp(dsp,Pdabc[0],Pdabc[1])
else:
alp = G2mth.getTOFalpha(parmDict,dsp)
alp = max(0.1,alp)
betName = 'bet'+str(iPeak)
if betName in varyList or not peakInstPrmMode:
bet = parmDict[betName]
else:
if len(Pdabc):
bet = np.interp(dsp,Pdabc[0],Pdabc[2])
else:
bet = G2mth.getTOFbeta(parmDict,dsp)
bet = max(0.0001,bet)
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
else:
sig = G2mth.getTOFsig(parmDict,dsp)
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
else:
gam = G2mth.getTOFgamma(parmDict,dsp)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsTOF(pos,alp,bet,sig,gam)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = np.searchsorted(xdata,pos+fmax)
lenX = len(xdata)
if not iBeg:
iFin = np.searchsorted(xdata,pos+fmax)
elif iBeg == lenX:
iFin = iBeg
else:
iFin = np.searchsorted(xdata,pos+fmax)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
return yb+yc
yc[iBeg:iFin] += intens*getEpsVoigt(pos,alp,bet,sig,gam,xdata[iBeg:iFin])[0]
iPeak += 1
except KeyError: #no more peaks to process
return yb+yc
[docs]
def getPeakProfileDerv(dataType,parmDict,xdata,fixback,varyList,bakType):
'''Computes the profile derivatives for a powder pattern for single peak fitting
return: np.array([dMdx1,dMdx2,...]) in same order as varylist = backVary,insVary,peakVary order
NB: not used for Rietveld refinement
'''
dMdv = np.zeros(shape=(len(varyList),len(xdata)))
dMdb,dMddb,dMdpk,dMdfb = getBackgroundDerv('',parmDict,bakType,dataType,xdata,fixback)
if 'Back;0' in varyList: #background derivs are in front if present
dMdv[0:len(dMdb)] = dMdb
names = ['DebyeA','DebyeR','DebyeU']
for name in varyList:
if 'Debye' in name:
parm,Id = name.split(';')
ip = names.index(parm)
dMdv[varyList.index(name)] = dMddb[3*int(Id)+ip]
names = ['BkPkpos','BkPkint','BkPksig','BkPkgam']
for name in varyList:
if 'BkPk' in name:
parm,Id = name.split(';')
ip = names.index(parm)
dMdv[varyList.index(name)] = dMdpk[4*int(Id)+ip]
if 'LF' in dataType:
for i,name in enumerate(varyList):
if not np.all(dMdv[i] == 0): continue
deltaParmDict = parmDict.copy()
delta = max(parmDict[name]/1e5,0.001)
deltaParmDict[name] += delta
#print('num. deriv for',name,'val',deltaParmDict[name],'delta',delta)
intArrP = getPeakProfile(dataType,deltaParmDict,xdata,fixback,varyList,bakType)
deltaParmDict[name] -= 2*delta
intArrM = getPeakProfile(dataType,deltaParmDict,xdata,fixback,varyList,bakType)
dMdv[i] = 0.5 * (intArrP - intArrM) / delta
return dMdv
if 'C' in dataType:
shl = max(parmDict['SH/L'],0.002)
Ka2 = False
if 'Lam1' in parmDict.keys():
Ka2 = True
lamRatio = 360*(parmDict['Lam2']-parmDict['Lam1'])/(np.pi*parmDict['Lam1'])
kRatio = parmDict['I(L2)/I(L1)']
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tth = (pos-parmDict['Zero'])
intens = parmDict['int'+str(iPeak)]
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
dsdU = dsdV = dsdW = 0
else:
sig = G2mth.getCWsig(parmDict,tth)
dsdU,dsdV,dsdW = G2mth.getCWsigDeriv(tth)
sig = max(sig,0.001) #avoid neg sigma
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
dgdX = dgdY = dgdZ = 0
else:
gam = G2mth.getCWgam(parmDict,tth)
dgdX,dgdY,dgdZ = G2mth.getCWgamDeriv(tth)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsCW(pos,sig,gam,shl)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = max(iBeg+3,np.searchsorted(xdata,pos+fmin))
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
break
dMdpk = np.zeros(shape=(6,len(xdata)))
dMdipk = getdFCJVoigt3(pos,sig,gam,shl,xdata[iBeg:iFin])
for i in range(1,5):
dMdpk[i][iBeg:iFin] += intens*dMdipk[i]
dMdpk[0][iBeg:iFin] += dMdipk[0]
dervDict = {'int':dMdpk[0],'pos':dMdpk[1],'sig':dMdpk[2],'gam':dMdpk[3],'shl':dMdpk[4]}
if Ka2:
pos2 = pos+lamRatio*tand(pos/2.0) # + 360/pi * Dlam/lam * tan(th)
iBeg = np.searchsorted(xdata,pos2-fmin)
iFin = np.searchsorted(xdata,pos2+fmin)
if iBeg-iFin:
dMdipk2 = getdFCJVoigt3(pos2,sig,gam,shl,xdata[iBeg:iFin])
for i in range(1,5):
dMdpk[i][iBeg:iFin] += intens*kRatio*dMdipk2[i]
dMdpk[0][iBeg:iFin] += kRatio*dMdipk2[0]
dMdpk[5][iBeg:iFin] += dMdipk2[0]
dervDict = {'int':dMdpk[0],'pos':dMdpk[1],'sig':dMdpk[2],'gam':dMdpk[3],'shl':dMdpk[4],'L1/L2':dMdpk[5]*intens}
for parmName in ['pos','int','sig','gam']:
try:
idx = varyList.index(parmName+str(iPeak))
dMdv[idx] = dervDict[parmName]
except ValueError:
pass
if 'U' in varyList:
dMdv[varyList.index('U')] += dsdU*dervDict['sig']
if 'V' in varyList:
dMdv[varyList.index('V')] += dsdV*dervDict['sig']
if 'W' in varyList:
dMdv[varyList.index('W')] += dsdW*dervDict['sig']
if 'X' in varyList:
dMdv[varyList.index('X')] += dgdX*dervDict['gam']
if 'Y' in varyList:
dMdv[varyList.index('Y')] += dgdY*dervDict['gam']
if 'Z' in varyList:
dMdv[varyList.index('Z')] += dgdZ*dervDict['gam']
if 'SH/L' in varyList:
dMdv[varyList.index('SH/L')] += dervDict['shl'] #problem here
if 'I(L2)/I(L1)' in varyList:
dMdv[varyList.index('I(L2)/I(L1)')] += dervDict['L1/L2']
iPeak += 1
except KeyError: #no more peaks to process
break
elif 'E' in dataType:
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
intens = parmDict['int'+str(iPeak)]
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
dsdA = dsdB = dsdC = 0
else:
sig = G2mth.getEDsig(parmDict,pos)
dsdA,dsdB,dsdC = G2mth.getEDsigDeriv(parmDict,pos)
sig = max(sig,0.001) #avoid neg sigma
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
dgdX = dgdY = dgdZ = 0
else:
gam = G2mth.getEDgam(parmDict,pos)
dgdX,dgdY,dgdZ = G2mth.getEDgamDeriv(parmDict,pos)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsED(pos,sig,gam)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = np.searchsorted(xdata,pos+fmin)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
break
dMdpk = np.zeros(shape=(4,len(xdata)))
dMdipk = getdPsVoigt(pos,sig*10.**4,gam*100.,xdata[iBeg:iFin])
dMdpk[0][iBeg:iFin] += dMdipk[0]
for i in range(1,4):
dMdpk[i][iBeg:iFin] += intens*dMdipk[i]
dervDict = {'int':dMdpk[0],'pos':-dMdpk[1],'sig':dMdpk[2]*10**4,'gam':dMdpk[3]*100.}
for parmName in ['pos','int','sig','gam']:
try:
idx = varyList.index(parmName+str(iPeak))
dMdv[idx] = dervDict[parmName]
except ValueError:
pass
if 'A' in varyList:
dMdv[varyList.index('A')] += dsdA*dervDict['sig']
if 'B' in varyList:
dMdv[varyList.index('B')] += dsdB*dervDict['sig']
if 'C' in varyList:
dMdv[varyList.index('C')] += dsdC*dervDict['sig']
if 'X' in varyList:
dMdv[varyList.index('X')] += dgdX*dervDict['gam']
if 'Y' in varyList:
dMdv[varyList.index('Y')] += dgdY*dervDict['gam']
if 'Z' in varyList:
dMdv[varyList.index('Z')] += dgdZ*dervDict['gam']
iPeak += 1
except KeyError: #no more peaks to process
break
elif 'B' in dataType:
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tth = (pos-parmDict['Zero'])
intens = parmDict['int'+str(iPeak)]
alpName = 'alp'+str(iPeak)
if alpName in varyList or not peakInstPrmMode:
alp = parmDict[alpName]
dada0 = dada1 = 0.0
else:
if 'X' in dataType:
alp = G2mth.getPinkXalpha(parmDict,tth)
dada0,dada1 = G2mth.getPinkXalphaDeriv(tth)
else:
alp = G2mth.getPinkNalpha(parmDict,tth)
dada0,dada1 = G2mth.getPinkNalphaDeriv(tth)
alp = max(0.0001,alp)
betName = 'bet'+str(iPeak)
if betName in varyList or not peakInstPrmMode:
bet = parmDict[betName]
dbdb0 = dbdb1 = 0.0
else:
if 'X' in dataType:
bet = G2mth.getPinkXbeta(parmDict,tth)
dbdb0,dbdb1 = G2mth.getPinkXbetaDeriv(tth)
else:
bet = G2mth.getPinkNbeta(parmDict,tth)
dbdb0,dbdb1 = G2mth.getPinkNbetaDeriv(tth)
bet = max(0.0001,bet)
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
dsdU = dsdV = dsdW = 0
else:
sig = G2mth.getCWsig(parmDict,tth)
dsdU,dsdV,dsdW = G2mth.getCWsigDeriv(tth)
sig = max(sig,0.001) #avoid neg sigma
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
dgdX = dgdY = dgdZ = 0
else:
gam = G2mth.getCWgam(parmDict,tth)
dgdX,dgdY,dgdZ = G2mth.getCWgamDeriv(tth)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsTOF(pos,alp,bet,sig/1.e4,gam/100.)
iBeg = np.searchsorted(xdata,pos-fmin)
iFin = np.searchsorted(xdata,pos+fmin)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
break
dMdpk = np.zeros(shape=(7,len(xdata)))
dMdipk = getdEpsVoigt(pos,alp,bet,sig/1.e4,gam/100.,xdata[iBeg:iFin])
for i in range(1,6):
dMdpk[i][iBeg:iFin] += intens*dMdipk[i]
dMdpk[0][iBeg:iFin] += dMdipk[0]
dervDict = {'int':dMdpk[0],'pos':dMdpk[1],'alp':dMdpk[2],'bet':dMdpk[3],'sig':dMdpk[4]/1.e4,'gam':dMdpk[5]/100.}
for parmName in ['pos','int','alp','bet','sig','gam']:
try:
idx = varyList.index(parmName+str(iPeak))
dMdv[idx] = dervDict[parmName]
except ValueError:
pass
if 'U' in varyList:
dMdv[varyList.index('U')] += dsdU*dervDict['sig']
if 'V' in varyList:
dMdv[varyList.index('V')] += dsdV*dervDict['sig']
if 'W' in varyList:
dMdv[varyList.index('W')] += dsdW*dervDict['sig']
if 'X' in varyList:
dMdv[varyList.index('X')] += dgdX*dervDict['gam']
if 'Y' in varyList:
dMdv[varyList.index('Y')] += dgdY*dervDict['gam']
if 'Z' in varyList:
dMdv[varyList.index('Z')] += dgdZ*dervDict['gam']
if 'alpha-0' in varyList:
dMdv[varyList.index('alpha-0')] += dada0*dervDict['alp']
if 'alpha-1' in varyList:
dMdv[varyList.index('alpha-1')] += dada1*dervDict['alp']
if 'beta-0' in varyList:
dMdv[varyList.index('beta-0')] += dbdb0*dervDict['bet']
if 'beta-1' in varyList:
dMdv[varyList.index('beta-1')] += dbdb1*dervDict['bet']
iPeak += 1
except KeyError: #no more peaks to process
break
else:
Pdabc = parmDict['Pdabc']
difC = parmDict['difC']
iPeak = 0
while True:
try:
pos = parmDict['pos'+str(iPeak)]
tof = pos-parmDict['Zero']
dsp = tof/difC
intens = parmDict['int'+str(iPeak)]
alpName = 'alp'+str(iPeak)
if alpName in varyList or not peakInstPrmMode:
alp = parmDict[alpName]
else:
if len(Pdabc):
alp = np.interp(dsp,Pdabc[0],Pdabc[1])
dada0 = 0
else:
alp = G2mth.getTOFalpha(parmDict,dsp)
dada0 = G2mth.getTOFalphaDeriv(dsp)
betName = 'bet'+str(iPeak)
if betName in varyList or not peakInstPrmMode:
bet = parmDict[betName]
else:
if len(Pdabc):
bet = np.interp(dsp,Pdabc[0],Pdabc[2])
dbdb0 = dbdb1 = dbdb2 = 0
else:
bet = G2mth.getTOFbeta(parmDict,dsp)
dbdb0,dbdb1,dbdb2 = G2mth.getTOFbetaDeriv(dsp)
sigName = 'sig'+str(iPeak)
if sigName in varyList or not peakInstPrmMode:
sig = parmDict[sigName]
dsds0 = dsds1 = dsds2 = dsds3 = 0
else:
sig = G2mth.getTOFsig(parmDict,dsp)
dsds0,dsds1,dsds2,dsds3 = G2mth.getTOFsigDeriv(dsp)
gamName = 'gam'+str(iPeak)
if gamName in varyList or not peakInstPrmMode:
gam = parmDict[gamName]
dsdX = dsdY = dsdZ = 0
else:
gam = G2mth.getTOFgamma(parmDict,dsp)
dsdX,dsdY,dsdZ = G2mth.getTOFgammaDeriv(dsp)
gam = max(gam,0.001) #avoid neg gamma
Wd,fmin,fmax = getWidthsTOF(pos,alp,bet,sig,gam)
iBeg = np.searchsorted(xdata,pos-fmin)
lenX = len(xdata)
if not iBeg:
iFin = np.searchsorted(xdata,pos+fmax)
elif iBeg == lenX:
iFin = iBeg
else:
iFin = np.searchsorted(xdata,pos+fmax)
if not iBeg+iFin: #peak below low limit
iPeak += 1
continue
elif not iBeg-iFin: #peak above high limit
break
dMdpk = np.zeros(shape=(7,len(xdata)))
dMdipk = getdEpsVoigt(pos,alp,bet,sig,gam,xdata[iBeg:iFin])
for i in range(1,6):
dMdpk[i][iBeg:iFin] += intens*dMdipk[i]
dMdpk[0][iBeg:iFin] += dMdipk[0]
dervDict = {'int':dMdpk[0],'pos':dMdpk[1],'alp':dMdpk[2],'bet':dMdpk[3],'sig':dMdpk[4],'gam':dMdpk[5]}
for parmName in ['pos','int','alp','bet','sig','gam']:
try:
idx = varyList.index(parmName+str(iPeak))
dMdv[idx] = dervDict[parmName]
except ValueError:
pass
if 'alpha' in varyList:
dMdv[varyList.index('alpha')] += dada0*dervDict['alp']
if 'beta-0' in varyList:
dMdv[varyList.index('beta-0')] += dbdb0*dervDict['bet']
if 'beta-1' in varyList:
dMdv[varyList.index('beta-1')] += dbdb1*dervDict['bet']
if 'beta-q' in varyList:
dMdv[varyList.index('beta-q')] += dbdb2*dervDict['bet']
if 'sig-0' in varyList:
dMdv[varyList.index('sig-0')] += dsds0*dervDict['sig']
if 'sig-1' in varyList:
dMdv[varyList.index('sig-1')] += dsds1*dervDict['sig']
if 'sig-2' in varyList:
dMdv[varyList.index('sig-2')] += dsds2*dervDict['sig']
if 'sig-q' in varyList:
dMdv[varyList.index('sig-q')] += dsds3*dervDict['sig']
if 'X' in varyList:
dMdv[varyList.index('X')] += dsdX*dervDict['gam']
if 'Y' in varyList:
dMdv[varyList.index('Y')] += dsdY*dervDict['gam']
if 'Z' in varyList:
dMdv[varyList.index('Z')] += dsdZ*dervDict['gam']
iPeak += 1
except KeyError: #no more peaks to process
break
if 'BF mult' in varyList:
dMdv[varyList.index('BF mult')] = fixback
return dMdv
[docs]
def Dict2Values(parmdict, varylist):
'''Use before call to leastsq to setup list of values for the parameters
in parmdict, as selected by key in varylist'''
return [parmdict[key] for key in varylist]
[docs]
def Values2Dict(parmdict, varylist, values):
''' Use after call to leastsq to update the parameter dictionary with
values corresponding to keys in varylist'''
parmdict.update(zip(varylist,values))
[docs]
def SetBackgroundParms(Background):
'Loads background parameters into dicts/lists to create varylist & parmdict'
if len(Background) == 1: # fix up old backgrounds
Background.append({'nDebye':0,'debyeTerms':[]})
bakType,bakFlag = Background[0][:2]
backVals = Background[0][3:]
backNames = ['Back;'+str(i) for i in range(len(backVals))]
Debye = Background[1] #also has background peaks stuff
backDict = dict(zip(backNames,backVals))
backVary = []
if bakFlag:
backVary = backNames
backDict['nDebye'] = Debye['nDebye']
debyeDict = {}
debyeList = []
for i in range(Debye['nDebye']):
debyeNames = ['DebyeA;'+str(i),'DebyeR;'+str(i),'DebyeU;'+str(i)]
debyeDict.update(dict(zip(debyeNames,Debye['debyeTerms'][i][::2])))
debyeList += zip(debyeNames,Debye['debyeTerms'][i][1::2])
debyeVary = []
for item in debyeList:
if item[1]:
debyeVary.append(item[0])
backDict.update(debyeDict)
backVary += debyeVary
backDict['nPeaks'] = Debye['nPeaks']
peaksDict = {}
peaksList = []
for i in range(Debye['nPeaks']):
peaksNames = ['BkPkpos;'+str(i),'BkPkint;'+str(i),'BkPksig;'+str(i),'BkPkgam;'+str(i)]
peaksDict.update(dict(zip(peaksNames,Debye['peaksList'][i][::2])))
peaksList += zip(peaksNames,Debye['peaksList'][i][1::2])
peaksVary = []
for item in peaksList:
if item[1]:
peaksVary.append(item[0])
backDict.update(peaksDict)
backVary += peaksVary
if 'background PWDR' in Background[1]:
backDict['Back File'] = Background[1]['background PWDR'][0]
backDict['BF mult'] = Background[1]['background PWDR'][1]
if len(Background[1]['background PWDR']) > 2:
if Background[1]['background PWDR'][2]:
backVary += ['BF mult',]
return bakType,backDict,backVary
[docs]
def autoBkgCalc(bkgdict,ydata):
'''Compute the autobackground using the selected pybaselines function
:param dict bkgdict: background parameters
:param np.array ydata: array of Y values
:returns: points for background intensity at each Y position
'''
import pybaselines.whittaker
lamb = int(10**bkgdict['autoPrms']['logLam'])
if bkgdict['autoPrms']['opt'] == 0:
func = pybaselines.whittaker.arpls
else:
func = pybaselines.whittaker.iarpls
return func(ydata, lam=lamb, max_iter=10)[0]
def DoCalibInst(IndexPeaks,Inst):
def SetInstParms():
dataType = Inst['Type'][0]
insVary = []
insNames = []
insVals = []
for parm in Inst:
insNames.append(parm)
insVals.append(Inst[parm][1])
if parm in ['Lam','difC','difA','difB','Zero','2-theta','XE','YE','ZE']:
if Inst[parm][2]:
insVary.append(parm)
instDict = dict(zip(insNames,insVals))
return dataType,instDict,insVary
def GetInstParms(parmDict,Inst,varyList):
for name in Inst:
Inst[name][1] = parmDict[name]
def InstPrint(Inst,sigDict):
print ('Instrument Parameters:')
if 'C' in Inst['Type'][0] or 'B' in Inst['Type'][0]:
ptfmt = "%12.6f"
else:
ptfmt = "%12.3f"
ptlbls = 'names :'
ptstr = 'values:'
sigstr = 'esds :'
for parm in Inst:
if parm in ['Lam','difC','difA','difB','Zero','2-theta','XE','YE','ZE']:
ptlbls += "%s" % (parm.center(12))
ptstr += ptfmt % (Inst[parm][1])
if parm in sigDict:
sigstr += ptfmt % (sigDict[parm])
else:
sigstr += 12*' '
print (ptlbls)
print (ptstr)
print (sigstr)
def errPeakPos(values,peakDsp,peakPos,peakWt,dataType,parmDict,varyList):
parmDict.update(zip(varyList,values))
return np.sqrt(peakWt)*(G2lat.getPeakPos(dataType,parmDict,peakDsp)-peakPos)
peakPos = []
peakDsp = []
peakWt = []
for peak,sig in zip(IndexPeaks[0],IndexPeaks[1]):
if peak[2] and peak[3] and sig > 0.:
peakPos.append(peak[0])
peakDsp.append(peak[-1]) #d-calc
# peakWt.append(peak[-1]**2/sig**2) #weight by d**2
peakWt.append(1./(sig*peak[-1])) #
peakPos = np.array(peakPos)
peakDsp = np.array(peakDsp)
peakWt = np.array(peakWt)
dataType,insDict,insVary = SetInstParms()
parmDict = {}
parmDict.update(insDict)
varyList = insVary
if not len(varyList):
G2fil.G2Print ('**** ERROR - nothing to refine! ****')
return False
while True:
begin = time.time()
values = np.array(Dict2Values(parmDict, varyList))
result = so.leastsq(errPeakPos,values,full_output=True,ftol=0.000001,
args=(peakDsp,peakPos,peakWt,dataType,parmDict,varyList))
ncyc = int(result[2]['nfev']/2)
runtime = time.time()-begin
chisq = np.sum(result[2]['fvec']**2)
Values2Dict(parmDict, varyList, result[0])
GOF = chisq/(len(peakPos)-len(varyList)) #reduced chi^2
G2fil.G2Print ('Number of function calls: %d Number of observations: %d Number of parameters: %d'%(result[2]['nfev'],len(peakPos),len(varyList)))
G2fil.G2Print ('calib time = %8.3fs, %8.3fs/cycle'%(runtime,runtime/ncyc))
G2fil.G2Print ('chi**2 = %12.6g, reduced chi**2 = %6.2f'%(chisq,GOF))
try:
sig = np.sqrt(np.diag(result[1])*GOF)
if np.any(np.isnan(sig)):
G2fil.G2Print ('*** Least squares aborted - some invalid esds possible ***')
break #refinement succeeded - finish up!
except ValueError: #result[1] is None on singular matrix
G2fil.G2Print ('**** Refinement failed - singular matrix ****')
sigDict = dict(zip(varyList,sig))
GetInstParms(parmDict,Inst,varyList)
InstPrint(Inst,sigDict)
return True
[docs]
def DoPeakFit(FitPgm,Peaks,Background,Limits,Inst,Inst2,data,fixback=None,prevVaryList=[],
oneCycle=False,controls=None,wtFactor=1.0,dlg=None,noFit=False):
'''Called to perform a peak fit, refining the selected items in the peak
table as well as selected items in the background.
:param str FitPgm: type of fit to perform. At present this is ignored.
:param list Peaks: a list of peaks. Each peak entry is a list with paired values:
The number of pairs depends on the data type (see :func:`getHeaderInfo`).
For CW data there are
four values each followed by a refine flag where the values are: position, intensity,
sigma (Gaussian width) and gamma (Lorentzian width). From the Histogram/"Peak List"
tree entry, dict item "peaks". For some types of fits, overall parameters are placed
in a dict entry.
:param list Background: describes the background. List with two items.
Item 0 specifies a background model and coefficients. Item 1 is a dict.
From the Histogram/Background tree entry.
:param list Limits: min and max x-value to use
:param dict Inst: Instrument parameters
:param dict Inst2: more Instrument parameters
:param numpy.array data: a 5xn array. data[0] is the x-values,
data[1] is the y-values, data[2] are weight values, data[3], [4] and [5] are
calc, background and difference intensities, respectively.
:param array fixback: fixed background array; same size as data[0-5]
:param list prevVaryList: Used in sequential refinements to override the
variable list. Defaults as an empty list.
:param bool oneCycle: True if only one cycle of fitting should be performed
:param dict controls: a dict specifying two values, Ftol = controls['min dM/M']
and derivType = controls['deriv type']. If None default values are used.
:param float wtFactor: weight multiplier; = 1.0 by default
:param wx.Dialog dlg: A dialog box that is updated with progress from the fit.
Defaults to None, which means no updates are done.
:param bool noFit: When noFit is True, a refinement is not performed. Default
is False.
'''
def GetBackgroundParms(parmList,Background):
iBak = 0
while True:
try:
bakName = 'Back;'+str(iBak)
Background[0][iBak+3] = parmList[bakName]
iBak += 1
except KeyError:
break
iDb = 0
while True:
names = ['DebyeA;','DebyeR;','DebyeU;']
try:
for i,name in enumerate(names):
val = parmList[name+str(iDb)]
Background[1]['debyeTerms'][iDb][2*i] = val
iDb += 1
except KeyError:
break
iDb = 0
while True:
names = ['BkPkpos;','BkPkint;','BkPksig;','BkPkgam;']
try:
for i,name in enumerate(names):
val = parmList[name+str(iDb)]
Background[1]['peaksList'][iDb][2*i] = val
iDb += 1
except KeyError:
break
if 'BF mult' in parmList:
Background[1]['background PWDR'][1] = parmList['BF mult']
def BackgroundPrint(Background,sigDict):
print ('Background coefficients for '+Background[0][0]+' function')
ptfmt = "%12.5f"
ptstr = 'value: '
sigstr = 'esd : '
for i,back in enumerate(Background[0][3:]):
ptstr += ptfmt % (back)
if Background[0][1]:
prm = 'Back;'+str(i)
if prm in sigDict:
sigstr += ptfmt % (sigDict[prm])
else:
sigstr += " "*12
if len(ptstr) > 75:
print (ptstr)
if Background[0][1]: print (sigstr)
ptstr = 'value: '
sigstr = 'esd : '
if len(ptstr) > 8:
print (ptstr)
if Background[0][1]: print (sigstr)
if Background[1]['nDebye']:
parms = ['DebyeA;','DebyeR;','DebyeU;']
print ('Debye diffuse scattering coefficients')
ptfmt = "%12.5f"
print (' term DebyeA esd DebyeR esd DebyeU esd')
for term in range(Background[1]['nDebye']):
line = ' term %d'%(term)
for ip,name in enumerate(parms):
line += ptfmt%(Background[1]['debyeTerms'][term][2*ip])
if name+str(term) in sigDict:
line += ptfmt%(sigDict[name+str(term)])
else:
line += " "*12
print (line)
if Background[1]['nPeaks']:
print ('Coefficients for Background Peaks')
ptfmt = "%15.3f"
for j,pl in enumerate(Background[1]['peaksList']):
names = 'peak %3d:'%(j+1)
ptstr = 'values :'
sigstr = 'esds :'
for i,lbl in enumerate(['BkPkpos','BkPkint','BkPksig','BkPkgam']):
val = pl[2*i]
prm = lbl+";"+str(j)
names += '%15s'%(prm)
ptstr += ptfmt%(val)
if prm in sigDict:
sigstr += ptfmt%(sigDict[prm])
else:
sigstr += " "*15
print (names)
print (ptstr)
print (sigstr)
if 'BF mult' in sigDict:
print('Background file mult: %.3f(%d)'%(Background[1]['background PWDR'][1],int(1000*sigDict['BF mult'])))
def SetInstParms(Inst):
dataType = Inst['Type'][0]
insVary = []
insNames = []
insVals = []
for parm in Inst:
insNames.append(parm)
insVals.append(Inst[parm][1])
if parm in ['U','V','W','X','Y','Z','SH/L','I(L2)/I(L1)','alpha','A','B','C',
'beta-0','beta-1','beta-q','sig-0','sig-1','sig-2','sig-q','alpha-0','alpha-1'] and Inst[parm][2]:
insVary.append(parm)
instDict = dict(zip(insNames,insVals))
if 'SH/L' in instDict:
instDict['SH/L'] = max(instDict['SH/L'],0.002)
return dataType,instDict,insVary
def GetPkInstParms(parmDict,Inst,varyList):
for name in Inst:
Inst[name][1] = parmDict[name]
iPeak = 0
while True:
try:
sigName = 'sig'+str(iPeak)
pos = parmDict['pos'+str(iPeak)]
if sigName not in varyList and peakInstPrmMode:
if 'T' in Inst['Type'][0]:
dsp = G2lat.Pos2dsp(Inst,pos)
parmDict[sigName] = G2mth.getTOFsig(parmDict,dsp)
if 'E' in Inst['Type'][0]:
parmDict[sigName] = G2mth.getEDsig(parmDict,pos)
else:
parmDict[sigName] = G2mth.getCWsig(parmDict,pos)
gamName = 'gam'+str(iPeak)
if gamName not in varyList and peakInstPrmMode:
if 'T' in Inst['Type'][0]:
dsp = G2lat.Pos2dsp(Inst,pos)
parmDict[gamName] = G2mth.getTOFgamma(parmDict,dsp)
if 'E' in Inst['Type'][0]:
parmDict[gamName] = G2mth.getEDgam(parmDict,pos)
else:
parmDict[gamName] = G2mth.getCWgam(parmDict,pos)
iPeak += 1
except KeyError:
break
def InstPrint(Inst,sigDict):
print ('Instrument Parameters:')
ptfmt = "%12.6f"
ptlbls = 'names :'
ptstr = 'values:'
sigstr = 'esds :'
for parm in Inst:
if parm in ['U','V','W','X','Y','Z','SH/L','I(L2)/I(L1)','alpha','A','B','C',
'beta-0','beta-1','beta-q','sig-0','sig-1','sig-2','sig-q','alpha-0','alpha-1']:
ptlbls += "%s" % (parm.center(12))
ptstr += ptfmt % (Inst[parm][1])
if parm in sigDict:
sigstr += ptfmt % (sigDict[parm])
else:
sigstr += 12*' '
print (ptlbls)
print (ptstr)
print (sigstr)
def SetPeaksParms(dataType,Peaks):
'''Set the contents of peakDict from list Peaks
'''
peakDict = {}
peakVary = []
names,_,_ = getHeaderInfo(dataType)
if 'LF' in dataType:
off = 2
names = names[:-2] # drop 00l & 2theta from header
else:
off = 0
for i,peak in enumerate(Peaks):
if type(peak) is dict:
peakDict.update(peak)
continue
if 'LF' in dataType: peakDict['l'+str(i)] = peak[12]
for j,name in enumerate(names):
parName = name+str(i)
peakDict[parName] = peak[off+2*j]
if peak[off+2*j+1]:
peakVary.append(parName)
return peakDict,peakVary
def GetPeaksParms(Inst,parmDict,Peaks,varyList):
'''Put values into the Peaks list from the refinement results from inside
the parmDict array
'''
names,_,_ = getHeaderInfo(Inst['Type'][0])
off = 0
if 'LF' in Inst['Type'][0]:
off = 2
if 'clat' in varyList:
Peaks[-1]['clat'] = parmDict['clat']
names = names[:-1] # drop 2nd 2theta value
for i,peak in enumerate(Peaks):
if type(peak) is dict: continue
parmDict['ttheta'+str(i)] = peak[-1]
for i,peak in enumerate(Peaks):
if type(peak) is dict:
continue
for j in range(len(names)):
parName = names[j]+str(i)
if parName in varyList or not peakInstPrmMode:
peak[2*j+off] = parmDict[parName]
if 'pos'+str(i) not in parmDict: continue
pos = parmDict['pos'+str(i)]
if 'LF' in Inst['Type'][0]:
peak[0] = pos
peak[-1] = pos
if 'difC' in Inst:
dsp = pos/Inst['difC'][1]
for j in range(len(names)):
parName = names[j]+str(i)
if peak[2*j+off + 1] or not peakInstPrmMode: continue
if 'alp' in parName:
if 'T' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getTOFalpha(parmDict,dsp)
else: #'B'
if 'X' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getPinkXalpha(parmDict,pos)
else:
peak[2*j+off] = G2mth.getPinkNalpha(parmDict,pos)
elif 'bet' in parName:
if 'T' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getTOFbeta(parmDict,dsp)
else: #'B'
if 'X' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getPinkXbeta(parmDict,pos)
else:
peak[2*j+off] = G2mth.getPinkNbeta(parmDict,pos)
elif 'sig' in parName:
if 'T' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getTOFsig(parmDict,dsp)
elif 'E' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getEDsig(parmDict,pos)
else: #'C' & 'B'
peak[2*j+off] = G2mth.getCWsig(parmDict,pos)
elif 'gam' in parName:
if 'T' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getTOFgamma(parmDict,dsp)
elif 'E' in Inst['Type'][0]:
peak[2*j+off] = G2mth.getEDgam(parmDict,pos)
else: #'C' & 'B'
peak[2*j+off] = G2mth.getCWgam(parmDict,pos)
def PeaksPrint(dataType,parmDict,sigDict,varyList,ptsperFW):
if 'clat' in varyList:
print('c = {:.6f} esd {:.6f}'.format(parmDict['clat'],sigDict['clat']))
print ('Peak coefficients:')
names,fmt,_ = getHeaderInfo(dataType)
head = 13*' '
for name in names:
if name == 'l':
head += name
elif name == 'ttheta':
head += name.center(8)
elif name in ['alp','bet']:
head += name.center(8)+'esd'.center(8)
else:
head += name.center(10)+'esd'.center(10)
head += 'bins'.center(12)
print (head)
ptfmt = dict(zip(names,fmt))
for i,peak in enumerate(Peaks):
if type(peak) is dict:
continue
ptstr = ':'
for j in range(len(names)):
name = names[j]
parName = name+str(i)
if parName not in parmDict: continue
ptstr += ptfmt[name] % (parmDict[parName])
if name == 'l' or name == 'ttheta':
continue
if parName in varyList:
ptstr += ptfmt[name] % (sigDict[parName])
else:
if name in ['alp','bet']:
ptstr += 8*' '
else:
ptstr += 10*' '
ptstr += '%8.1f'%(ptsperFW[i])
print ('%s'%(('Peak'+str(i+1)).center(8)),ptstr)
def devPeakProfile(values,xdata,ydata,fixback, weights,dataType,parmdict,varylist,bakType,dlg):
'''Computes a matrix where each row is the derivative of the calc-obs
values (see :func:`errPeakProfile`) with respect to each parameter
in backVary,insVary,peakVary. Used for peak fitting.
'''
parmdict.update(zip(varylist,values))
return np.sqrt(weights)*getPeakProfileDerv(dataType,parmdict,xdata,fixback,varylist,bakType)
def errPeakProfile(values,xdata,ydata,fixback,weights,dataType,parmdict,varylist,bakType,dlg):
'''Computes a vector with the weighted calc-obs values differences
for peak fitting
'''
parmdict.update(zip(varylist,values))
M = np.sqrt(weights)*(getPeakProfile(dataType,parmdict,xdata,fixback,varylist,bakType)-ydata)
Rwp = min(100.,np.sqrt(np.sum(M**2)/np.sum(weights*ydata**2))*100.)
if dlg:
dlg.Raise()
GoOn = dlg.Update(int(Rwp),newmsg='%s%8.3f%s'%('Peak fit Rwp =',Rwp,'%'))[0]
if not GoOn:
return -M #abort!!
return M
#---- beginning of DoPeakFit ---------------------------------------------
if controls:
Ftol = controls['min dM/M']
else:
Ftol = 0.0001
if oneCycle:
Ftol = 1.0
x,y,w,yc,yb,yd = data #these are numpy arrays - remove masks!
if fixback is None:
fixback = np.zeros_like(y)
yc.fill(0.) #set calcd ones to zero
yb.fill(0.)
yd.fill(0.)
xBeg = np.searchsorted(x,Limits[0])
xFin = np.searchsorted(x,Limits[1])+1
# find out what is varied
bakType,bakDict,bakVary = SetBackgroundParms(Background)
dataType,insDict,insVary = SetInstParms(Inst)
peakDict,peakVary = SetPeaksParms(Inst['Type'][0],Peaks)
parmDict = {}
parmDict.update(bakDict)
parmDict.update(insDict)
parmDict.update(peakDict)
parmDict['Pdabc'] = [] #dummy Pdabc
parmDict.update(Inst2) #put in real one if there
if prevVaryList:
varyList = prevVaryList[:]
else:
varyList = bakVary+insVary+peakVary
if 'LF' in Inst['Type'][0] and Peaks:
if Peaks[-1].get('clat-ref'): varyList += ['clat']
fullvaryList = varyList[:]
if not peakInstPrmMode:
for v in ('U','V','W','X','Y','Z','alpha','alpha-0','alpha-1','A','B','C',
'beta-0','beta-1','beta-q','sig-0','sig-1','sig-2','sig-q',):
if v in varyList:
raise Exception('Instrumental profile terms cannot be varied '+
'after setPeakInstPrmMode(False) is used')
if 'LF' in Inst['Type'][0]:
warn = []
for v in ('U','V','W','X','Y','Z','alpha','alpha-0','alpha-1',
'beta-0','beta-1','beta-q','sig-0','sig-1','sig-2','sig-q',):
if v in varyList:
warn.append(v)
del varyList[varyList.index(v)]
if warn:
print('Instrumental profile terms cannot be varied '+
'in Laue Fringe fits:',warn)
while not noFit:
begin = time.time()
values = np.array(Dict2Values(parmDict, varyList))
Rvals = {}
badVary = []
try:
result = so.leastsq(errPeakProfile,values,Dfun=devPeakProfile,full_output=True,ftol=Ftol,col_deriv=True,
args=(x[xBeg:xFin],y[xBeg:xFin],fixback[xBeg:xFin],wtFactor*w[xBeg:xFin],dataType,parmDict,varyList,bakType,dlg))
except Exception as msg:
if GSASIIpath.GetConfigValue('debug'):
print('peak fit failure\n',msg)
import traceback
print (traceback.format_exc())
else:
print('peak fit failure')
return
ncyc = int(result[2]['nfev']/2)
runtime = time.time()-begin
chisq = np.sum(result[2]['fvec']**2)
Values2Dict(parmDict, varyList, result[0])
Rvals['Rwp'] = np.sqrt(chisq/np.sum(wtFactor*w[xBeg:xFin]*y[xBeg:xFin]**2))*100. #to %
Rvals['GOF'] = chisq/(xFin-xBeg-len(varyList)) #reduced chi^2
G2fil.G2Print ('Number of function calls: %d Number of observations: %d Number of parameters: %d'%(result[2]['nfev'],xFin-xBeg,len(varyList)))
if ncyc:
G2fil.G2Print ('fitpeak time = %8.3fs, %8.3fs/cycle'%(runtime,runtime/ncyc))
G2fil.G2Print ('Rwp = %7.2f%%, chi**2 = %12.6g, reduced chi**2 = %6.2f'%(Rvals['Rwp'],chisq,Rvals['GOF']))
sig = [0]*len(varyList)
if len(varyList) == 0: break # if nothing was refined
try:
sig = np.sqrt(np.diag(result[1])*Rvals['GOF'])
if np.any(np.isnan(sig)):
G2fil.G2Print ('*** Least squares aborted - some invalid esds possible ***')
break #refinement succeeded - finish up!
except ValueError: #result[1] is None on singular matrix
G2fil.G2Print ('**** Refinement failed - singular matrix ****')
Ipvt = result[2]['ipvt']
for i,ipvt in enumerate(Ipvt):
if not np.sum(result[2]['fjac'],axis=1)[i]:
G2fil.G2Print ('Removing parameter: '+varyList[ipvt-1])
badVary.append(varyList[ipvt-1])
del(varyList[ipvt-1])
break
else: # nothing removed
break
if dlg: dlg.Destroy()
yb[xBeg:xFin] = getBackground('',parmDict,bakType,dataType,x[xBeg:xFin],fixback[xBeg:xFin])[0]
yc[xBeg:xFin] = getPeakProfile(dataType,parmDict,x[xBeg:xFin],fixback[xBeg:xFin],varyList,bakType)
yd[xBeg:xFin] = y[xBeg:xFin]-yc[xBeg:xFin]
if noFit:
GetPeaksParms(Inst,parmDict,Peaks,varyList)
return
sigDict = dict(zip(varyList,sig))
GetBackgroundParms(parmDict,Background)
if bakVary: BackgroundPrint(Background,sigDict)
GetPkInstParms(parmDict,Inst,varyList)
if insVary: InstPrint(Inst,sigDict)
GetPeaksParms(Inst,parmDict,Peaks,varyList)
binsperFWHM = []
for peak in Peaks:
if type(peak) is dict:
continue
FWHM = getFWHM(peak[0],Inst)
try:
xpk = x.searchsorted(peak[0])
cw = x[xpk]-x[xpk-1]
binsperFWHM.append(FWHM/cw)
except IndexError:
binsperFWHM.append(0.)
if peakVary: PeaksPrint(dataType,parmDict,sigDict,varyList,binsperFWHM)
if len(binsperFWHM):
if min(binsperFWHM) < 1.:
G2fil.G2Print ('*** Warning: calculated peak widths are too narrow to refine profile coefficients ***')
if 'T' in Inst['Type'][0]:
G2fil.G2Print (' Manually increase sig-0, 1, or 2 in Instrument Parameters')
else:
G2fil.G2Print (' Manually increase W in Instrument Parameters')
elif min(binsperFWHM) < 4.:
G2fil.G2Print ('*** Warning: data binning yields too few data points across peak FWHM for reliable Rietveld refinement ***')
G2fil.G2Print ('*** recommended is 6-10; you have %.2f ***'%(min(binsperFWHM)))
return sigDict,result,sig,Rvals,varyList,parmDict,fullvaryList,badVary
[docs]
def calcIncident(Iparm,xdata):
'needs a doc string'
def IfunAdv(Iparm,xdata):
Itype = Iparm['Itype']
Icoef = Iparm['Icoeff']
DYI = np.ones((12,xdata.shape[0]))
YI = np.ones_like(xdata)*Icoef[0]
x = xdata/1000. #expressions are in ms
if Itype == 'Exponential':
for i in [1,3,5,7,9]:
Eterm = np.exp(-Icoef[i+1]*x**((i+1)/2))
YI += Icoef[i]*Eterm
DYI[i] *= Eterm
DYI[i+1] *= -Icoef[i]*Eterm*x**((i+1)/2)
elif 'Maxwell'in Itype:
Eterm = np.exp(-Icoef[2]/x**2)
DYI[1] = Eterm/x**5
DYI[2] = -Icoef[1]*DYI[1]/x**2
YI += (Icoef[1]*Eterm/x**5)
if 'Exponential' in Itype:
for i in range(3,11,2):
Eterm = np.exp(-Icoef[i+1]*x**((i+1)/2))
YI += Icoef[i]*Eterm
DYI[i] *= Eterm
DYI[i+1] *= -Icoef[i]*Eterm*x**((i+1)/2)
else: #Chebyschev
T = (2./x)-1.
Ccof = np.ones((12,xdata.shape[0]))
Ccof[1] = T
for i in range(2,12):
Ccof[i] = 2*T*Ccof[i-1]-Ccof[i-2]
for i in range(1,10):
YI += Ccof[i]*Icoef[i+2]
DYI[i+2] =Ccof[i]
return YI,DYI
Iesd = np.array(Iparm['Iesd'])
Icovar = Iparm['Icovar']
YI,DYI = IfunAdv(Iparm,xdata)
YI = np.where(YI>0,YI,1.)
WYI = np.zeros_like(xdata)
vcov = np.zeros((12,12))
k = 0
for i in range(12):
for j in range(i,12):
vcov[i][j] = Icovar[k]*Iesd[i]*Iesd[j]
vcov[j][i] = Icovar[k]*Iesd[i]*Iesd[j]
k += 1
M = np.inner(vcov,DYI.T)
WYI = np.sum(M*DYI,axis=0)
WYI = np.where(WYI>0.,WYI,0.)
return YI,WYI
#### RMCutilities ################################################################################
def MakeInst(PWDdata,Name,Size,Mustrain,useSamBrd):
inst = PWDdata['Instrument Parameters'][0]
sample = PWDdata['Sample Parameters']
Xsb = 0.
Ysb = 0.
if 'T' in inst['Type'][1]:
difC = inst['difC'][1]
if useSamBrd[0]:
if 'ellipsoidal' not in Size[0]: #take the isotropic term only
Xsb = 1.e-4*difC/Size[1][0]
if useSamBrd[1]:
if 'generalized' not in Mustrain[0]: #take the isotropic term only
Ysb = 1.e-6*difC*Mustrain[1][0]
prms = ['Bank',
'difC','difA','Zero','2-theta','difB',
'alpha','beta-0','beta-1','beta-q',
'sig-0','sig-1','sig-2','sig-q',
'Z','X','Y']
fname = Name+'.inst'
fl = open(fname,'w')
fl.write('1\n')
fl.write('%d\n'%int(inst[prms[0]][1]))
fl.write('%19.11f%19.11f%19.11f%19.11f%19.11f\n'%(inst[prms[1]][1],inst[prms[2]][1],inst[prms[3]][1],inst[prms[4]][1],inst[prms[5]][1],))
fl.write('%12.6e%14.6e%14.6e%14.6e\n'%(inst[prms[6]][1],inst[prms[7]][1],inst[prms[8]][1],inst[prms[9]][1]))
fl.write('%12.6e%14.6e%14.6e%14.6e\n'%(inst[prms[10]][1],inst[prms[11]][1],inst[prms[12]][1],inst[prms[13]][1]))
fl.write('%12.6e%14.6e%14.6e%14.6e%14.6e\n'%(inst[prms[14]][1],inst[prms[15]][1]+Ysb,inst[prms[16]][1]+Xsb,0.0,0.0))
fl.write('%12.6e\n\n\n'%(sample['Absorption'][0]))
fl.close()
else:
if useSamBrd[0]:
wave = G2mth.getWave(inst)
if 'ellipsoidal' not in Size[0]: #take the isotropic term only
Xsb = 1.8*wave/(np.pi*Size[1][0])
if useSamBrd[1]:
if 'generalized' not in Mustrain[0]: #take the isotropic term only
Ysb = 0.0180*Mustrain[1][0]/np.pi
prms = ['Bank',
'Lam','Zero','Polariz.',
'U','V','W',
'X','Y']
fname = Name+'.inst'
fl = open(fname,'w')
fl.write('1\n')
fl.write('%d\n'%int(inst[prms[0]][1]))
fl.write('%10.5f%10.5f%10.4f%10d\n'%(inst[prms[1]][1],100.*inst[prms[2]][1],inst[prms[3]][1],0))
fl.write('%10.3f%10.3f%10.3f\n'%(inst[prms[4]][1],inst[prms[5]][1],inst[prms[6]][1]))
fl.write('%10.3f%10.3f%10.3f\n'%(inst[prms[7]][1]+Xsb,inst[prms[8]][1]+Ysb,0.0))
fl.write('%10.3f%10.3f%10.3f\n'%(0.0,0.0,0.0))
fl.write('%12.6e\n\n\n'%(sample['Absorption'][0]))
fl.close()
return fname
def MakeBack(PWDdata,Name):
Back = PWDdata['Background'][0]
inst = PWDdata['Instrument Parameters'][0]
if 'chebyschev-1' != Back[0]:
return None
Nback = Back[2]
BackVals = Back[3:]
fname = Name+'.back'
fl = open(fname,'w')
fl.write('%10d\n'%Nback)
for val in BackVals:
if 'T' in inst['Type'][1]:
fl.write('%12.6g\n'%(float(val)))
else:
fl.write('%12.6g\n'%val)
fl.close()
return fname
def findDup(Atoms):
Dup = []
Fracs = []
for iat1,at1 in enumerate(Atoms):
if any([at1[0] in dup for dup in Dup]):
continue
else:
Dup.append([at1[0],])
Fracs.append([at1[6],])
for iat2,at2 in enumerate(Atoms[(iat1+1):]):
if np.sum((np.array(at1[3:6])-np.array(at2[3:6]))**2) < 0.00001:
Dup[-1] += [at2[0],]
Fracs[-1]+= [at2[6],]
return Dup,Fracs
def MakeRMC6f(PWDdata,Name,Phase,RMCPdict):
Meta = RMCPdict['metadata']
Atseq = RMCPdict['atSeq']
Supercell = RMCPdict['SuperCell']
generalData = Phase['General']
Dups,Fracs = findDup(Phase['Atoms'])
Sfracs = [np.cumsum(fracs) for fracs in Fracs]
ifSfracs = any([np.any(sfracs-1.) for sfracs in Sfracs])
Sample = PWDdata['Sample Parameters']
Meta['temperature'] = Sample['Temperature']
Meta['pressure'] = Sample['Pressure']
Cell = generalData['Cell'][1:7]
Trans = np.eye(3)*np.array(Supercell)
newPhase = copy.deepcopy(Phase)
newPhase['General']['SGData'] = G2spc.SpcGroup('P 1')[1]
newPhase['General']['Cell'][1:] = G2lat.TransformCell(Cell,Trans)
GB = G2lat.cell2Gmat( newPhase['General']['Cell'][1:7])[0]
RMCPdict['Rmax'] = np.min(np.sqrt(np.array([1./G2lat.calc_rDsq2(H,GB) for H in [[1,0,0],[0,1,0],[0,0,1]]])))/2.
newPhase,Atcodes = G2lat.TransformPhase(Phase,newPhase,Trans,np.zeros(3),np.zeros(3),ifMag=False,Force=True)
Natm = np.core.defchararray.count(np.array(Atcodes),'+') #no. atoms in original unit cell
Natm = np.count_nonzero(Natm-1)
Atoms = newPhase['Atoms']
reset = False
if ifSfracs:
Natm = np.core.defchararray.count(np.array(Atcodes),'+') #no. atoms in original unit cell
Natm = np.count_nonzero(Natm-1)
Satoms = []
for i in range(len(Atoms)//Natm):
ind = i*Natm
Satoms.append(G2mth.sortArray(G2mth.sortArray(G2mth.sortArray(Atoms[ind:ind+Natm],5),4),3))
Natoms = []
for satoms in Satoms:
for idup,dup in enumerate(Dups):
ldup = len(dup)
natm = len(satoms)
i = 0
while i < natm:
if satoms[i][0] in dup:
atoms = satoms[i:i+ldup]
try:
atom = atoms[np.searchsorted(Sfracs[idup],rand.random())]
Natoms.append(atom)
except IndexError: #what about vacancies?
if 'Va' not in Atseq:
reset = True
Atseq.append('Va')
RMCPdict['aTypes']['Va'] = 0.0
atom = atoms[0]
atom[1] = 'Va'
Natoms.append(atom)
i += ldup
else:
i += 1
else:
Natoms = Atoms
NAtype = np.zeros(len(Atseq))
for atom in Natoms:
NAtype[Atseq.index(atom[1])] += 1
NAstr = ['%6d'%i for i in NAtype]
Cell = newPhase['General']['Cell'][1:7]
if os.path.exists(Name+'.his6f'):
os.remove(Name+'.his6f')
if os.path.exists(Name+'.neigh'):
os.remove(Name+'.neigh')
fname = Name+'.rmc6f'
fl = open(fname,'w')
fl.write('(Version 6f format configuration file)\n')
for item in Meta:
fl.write('%-20s%s\n'%('Metadata '+item+':',Meta[item]))
fl.write('Atom types present: %s\n'%' '.join(Atseq))
fl.write('Number of each atom type: %s\n'%''.join(NAstr))
fl.write('Number of atoms: %d\n'%len(Natoms))
fl.write('%-35s%4d%4d%4d\n'%('Supercell dimensions:',Supercell[0],Supercell[1],Supercell[2]))
fl.write('Cell (Ang/deg): %12.6f%12.6f%12.6f%12.6f%12.6f%12.6f\n'%(
Cell[0],Cell[1],Cell[2],Cell[3],Cell[4],Cell[5]))
A,B = G2lat.cell2AB(Cell,True)
fl.write('Lattice vectors (Ang):\n')
for i in [0,1,2]:
fl.write('%12.6f%12.6f%12.6f\n'%(A[i,0],A[i,1],A[i,2]))
fl.write('Atoms (fractional coordinates):\n')
nat = 0
for atm in Atseq:
for iat,atom in enumerate(Natoms):
if atom[1] == atm:
nat += 1
atcode = Atcodes[iat].split(':')
cell = [0,0,0]
if '+' in atcode[1]:
cell = eval(atcode[1].split('+')[1])
fl.write('%6d%4s [%s]%19.15f%19.15f%19.15f%6d%4d%4d%4d\n'%(
nat,atom[1].strip(),atcode[0],atom[3],atom[4],atom[5],(iat)%Natm+1,cell[0],cell[1],cell[2]))
fl.close()
return fname,reset
def MakeBragg(PWDdata,Name,Phase):
generalData = Phase['General']
Vol = generalData['Cell'][7]
Data = PWDdata['Data']
Inst = PWDdata['Instrument Parameters'][0]
Bank = int(Inst['Bank'][1])
Sample = PWDdata['Sample Parameters']
Scale = Sample['Scale'][0]
Limits = PWDdata['Limits'][1]
Ibeg = np.searchsorted(Data[0],Limits[0])
Ifin = np.searchsorted(Data[0],Limits[1])+1
fname = Name+'.bragg'
fl = open(fname,'w')
fl.write('%12d%6d%15.7f%15.4f\n'%(Ifin-Ibeg-2,Bank,Scale,Vol))
if 'T' in Inst['Type'][0]:
fl.write('%12s%12s\n'%(' TOF,ms',' I(obs)'))
for i in range(Ibeg,Ifin-1):
fl.write('%12.8f%12.6f\n'%(Data[0][i]/1000.,Data[1][i]))
else:
fl.write('%12s%12s\n'%(' 2-theta, deg',' I(obs)'))
for i in range(Ibeg,Ifin-1):
fl.write('%11.6f%15.2f\n'%(Data[0][i],Data[1][i]))
fl.close()
return fname
def MakeRMCPdat(PWDdata,Name,Phase,RMCPdict):
Meta = RMCPdict['metadata']
Times = RMCPdict['runTimes']
Atseq = RMCPdict['atSeq']
Natoms = RMCPdict['NoAtoms']
sumatms = np.sum(np.array([Natoms[iatm] for iatm in Natoms]))
Isotope = RMCPdict['Isotope']
Isotopes = RMCPdict['Isotopes']
Atypes = RMCPdict['aTypes']
if 'Va' in Atypes:
Isotope['Va'] = 'Nat. Abund.'
Isotopes['Va'] = {'Nat. Abund.':{'SL':[0.0,0.0]}}
atPairs = RMCPdict['Pairs']
Files = RMCPdict['files']
BraggWt = RMCPdict['histogram'][1]
inst = PWDdata['Instrument Parameters'][0]
try:
pName = Phase['General']['Name']
refList = PWDdata['Reflection Lists'][Name]['RefList']
except TypeError:
return 'Error - missing reflection list; you must do Refine first'
dMin = refList[-1][4]
gsasType = 'xray2'
if 'T' in inst['Type'][1]:
gsasType = 'gsas3'
elif 'X' in inst['Type'][1]:
XFF = G2elem.GetFFtable(Atseq)
Xfl = open(Name+'.xray','w')
for atm in Atseq:
fa = XFF[atm]['fa']
fb = XFF[atm]['fb']
fc = XFF[atm]['fc']
Xfl.write('%2s %8.4f%8.4f%8.4f%8.4f%8.4f%8.4f%8.4f%8.4f%8.4f\n'%(
atm.upper(),fa[0],fb[0],fa[1],fb[1],fa[2],fb[2],fa[3],fb[3],fc))
Xfl.close()
lenA = len(Atseq)
Pairs = []
Ncoeff = []
Nblen = [Isotopes[at][Isotope[at]]['SL'][0] for at in Atypes]
for pair in [[' %s-%s'%(Atseq[i],Atseq[j]) for j in range(i,lenA)] for i in range(lenA)]:
Pairs += pair
for pair in Pairs:
pair = pair.replace(' ','')
at1,at2 = pair.split('-')
if at1 == 'Va' or at2 == 'Va':
ncoef = 0.0
else:
ncoef = Isotopes[at1][Isotope[at1]]['SL'][0]*Natoms[at1]/sumatms
ncoef *= Isotopes[at2][Isotope[at2]]['SL'][0]*Natoms[at2]/sumatms
if at1 != at2:
ncoef *= 2.
Ncoeff += [ncoef,]
pairMin = [atPairs[pair] if pair in atPairs else [0.0,0.,0.] for pair in Pairs ]
maxMoves = [Atypes[atm] if atm in Atypes else 0.0 for atm in Atseq ]
fname = Name+'.dat'
fl = open(fname,'w')
fl.write(' %% Hand edit the following as needed\n')
fl.write('TITLE :: '+Name+'\n')
fl.write('MATERIAL :: '+Meta['material']+'\n')
fl.write('PHASE :: '+Meta['phase']+'\n')
fl.write('TEMPERATURE :: '+str(Meta['temperature'])+'\n')
fl.write('INVESTIGATOR :: '+Meta['owner']+'\n')
if RMCPdict.get('useGPU',False):
fl.write('GPU_ACCELERATOR :: 0\n')
minHD = ' '.join(['%6.3f'%dist[0] for dist in pairMin])
minD = ' '.join(['%6.3f'%dist[1] for dist in pairMin])
maxD = ' '.join(['%6.3f'%dist[2] for dist in pairMin])
fl.write('MINIMUM_DISTANCES :: %s Angstrom\n'%minHD)
maxMv = ' '.join(['%6.3f'%mov for mov in maxMoves])
fl.write('MAXIMUM_MOVES :: %s Angstrom\n'%maxMv)
fl.write('R_SPACING :: 0.0200 Angstrom\n')
fl.write('PRINT_PERIOD :: 100\n')
fl.write('TIME_LIMIT :: %.2f MINUTES\n'%Times[0])
fl.write('SAVE_PERIOD :: %.2f MINUTES\n'%Times[1])
fl.write('\n')
fl.write('ATOMS :: '+' '.join(Atseq)+'\n')
fl.write('\n')
fl.write('FLAGS ::\n')
fl.write(' > NO_MOVEOUT\n')
fl.write(' > NO_SAVE_CONFIGURATIONS\n')
fl.write(' > NO_RESOLUTION_CONVOLUTION\n')
fl.write('\n')
fl.write('INPUT_CONFIGURATION_FORMAT :: rmc6f\n')
fl.write('SAVE_CONFIGURATION_FORMAT :: rmc6f\n')
fl.write('IGNORE_HISTORY_FILE ::\n')
fl.write('\n')
if 'T' in inst['Type'][1]:
fl.write('NEUTRON_COEFFICIENTS :: '+''.join(['%9.5f'%coeff for coeff in Ncoeff])+'\n')
fl.write('DISTANCE_WINDOW ::\n')
fl.write(' > MNDIST :: %s\n'%minD)
fl.write(' > MXDIST :: %s\n'%maxD)
if len(RMCPdict['Potentials']['Stretch']) or len(RMCPdict['Potentials']['Stretch']):
fl.write('\n')
fl.write('POTENTIALS ::\n')
fl.write(' > TEMPERATURE :: %.1f K\n'%RMCPdict['Potentials']['Pot. Temp.'])
fl.write(' > PLOT :: pixels=400, colour=red, zangle=90, zrotation=45 deg\n')
if len(RMCPdict['Potentials']['Stretch']):
fl.write(' > STRETCH_SEARCH :: %.1f%%\n'%RMCPdict['Potentials']['Stretch search'])
for bond in RMCPdict['Potentials']['Stretch']:
fl.write(' > STRETCH :: %s %s %.2f eV %.2f Ang\n'%(bond[0],bond[1],bond[3],bond[2]))
if len(RMCPdict['Potentials']['Angles']):
fl.write(' > ANGLE_SEARCH :: %.1f%%\n'%RMCPdict['Potentials']['Angle search'])
for angle in RMCPdict['Potentials']['Angles']:
fl.write(' > ANGLE :: %s %s %s %.2f eV %.2f deg %.2f %.2f Ang\n'%
(angle[1],angle[0],angle[2],angle[6],angle[3],angle[4],angle[5]))
if RMCPdict['useBVS']:
fl.write('BVS ::\n')
fl.write(' > ATOM :: '+' '.join(Atseq)+'\n')
fl.write(' > WEIGHTS :: %s\n'%' '.join(['%6.3f'%RMCPdict['BVS'][bvs][2] for bvs in RMCPdict['BVS']]))
oxid = []
for val in RMCPdict['Oxid']:
if len(val) == 3:
oxid.append(val[0][1:])
else:
oxid.append(val[0][2:])
fl.write(' > OXID :: %s\n'%' '.join(oxid))
fl.write(' > RIJ :: %s\n'%' '.join(['%6.3f'%RMCPdict['BVS'][bvs][0] for bvs in RMCPdict['BVS']]))
fl.write(' > BVAL :: %s\n'%' '.join(['%6.3f'%RMCPdict['BVS'][bvs][1] for bvs in RMCPdict['BVS']]))
fl.write(' > CUTOFF :: %s\n'%' '.join(['%6.3f'%RMCPdict['BVS'][bvs][2] for bvs in RMCPdict['BVS']]))
fl.write(' > SAVE :: 100000\n')
fl.write(' > UPDATE :: 100000\n')
if len(RMCPdict['Swaps']):
fl.write('\n')
fl.write('SWAP_MULTI ::\n')
for swap in RMCPdict['Swaps']:
try:
at1 = Atseq.index(swap[0])
at2 = Atseq.index(swap[1])
except ValueError:
break
fl.write(' > SWAP_ATOMS :: %d %d %.2f\n'%(at1,at2,swap[2]))
if len(RMCPdict['FxCN']):
fl.write('FIXED_COORDINATION_CONSTRAINTS :: %d\n'%len(RMCPdict['FxCN']))
for ifx,fxcn in enumerate(RMCPdict['FxCN']):
try:
at1 = Atseq.index(fxcn[0])
at2 = Atseq.index(fxcn[1])
except ValueError:
break
fl.write(' > CSTR%d :: %d %d %.2f %.2f %.2f %.2f %.6f\n'%(ifx+1,at1+1,at2+1,fxcn[2],fxcn[3],fxcn[4],fxcn[5],fxcn[6]))
if len(RMCPdict['AveCN']):
fl.write('AVERAGE_COORDINATION_CONSTRAINTS :: %d\n'%len(RMCPdict['AveCN']))
for iav,avcn in enumerate(RMCPdict['AveCN']):
try:
at1 = Atseq.index(avcn[0])
at2 = Atseq.index(avcn[1])
except ValueError:
break
fl.write(' > CAVSTR%d :: %d %d %.2f %.2f %.2f %.6f\n'%(iav+1,at1+1,at2+1,avcn[2],avcn[3],avcn[4],avcn[5]))
for File in Files:
if Files[File][0] and Files[File][0] != 'Select':
if 'Xray' in File and 'F(Q)' in File:
fqdata = open(Files[File][0],'r')
lines = int(fqdata.readline()[:-1])
fqdata.close()
fl.write('\n')
fl.write('%s ::\n'%File.split(';')[0].upper().replace(' ','_'))
fl.write(' > FILENAME :: %s\n'%Files[File][0])
fl.write(' > DATA_TYPE :: %s\n'%Files[File][2])
fl.write(' > FIT_TYPE :: %s\n'%Files[File][2])
if 'Xray' not in File:
fl.write(' > START_POINT :: 1\n')
fl.write(' > END_POINT :: 3000\n')
fl.write(' > WEIGHT :: %.4f\n'%Files[File][1])
fl.write(' > CONSTANT_OFFSET 0.000\n')
fl.write(' > NO_FITTED_OFFSET\n')
if RMCPdict['FitScale']:
fl.write(' > FITTED_SCALE\n')
else:
fl.write(' > NO_FITTED_SCALE\n')
if Files[File][3] !='RMC':
fl.write(' > %s\n'%Files[File][3])
if 'reciprocal' in File:
fl.write(' > CONVOLVE ::\n')
if 'Xray' in File:
fl.write(' > RECIPROCAL_SPACE_FIT :: 1 %d 1\n'%lines)
fl.write(' > RECIPROCAL_SPACE_PARAMETERS :: 1 %d %.4f\n'%(lines,Files[File][1]))
fl.write(' > REAL_SPACE_FIT :: 1 %d 1\n'%(3*lines//2))
fl.write(' > REAL_SPACE_PARAMETERS :: 1 %d %.4f\n'%(3*lines//2,1./Files[File][1]))
fl.write('\n')
fl.write('BRAGG ::\n')
fl.write(' > BRAGG_SHAPE :: %s\n'%gsasType)
fl.write(' > RECALCUATE\n')
fl.write(' > DMIN :: %.2f\n'%(dMin-0.02))
fl.write(' > WEIGHT :: %10.3f\n'%BraggWt)
if 'T' in inst['Type'][1]:
fl.write(' > SCATTERING LENGTH :: '+''.join(['%8.4f'%blen for blen in Nblen])+'\n')
fl.write('\n')
fl.write('END ::\n')
fl.close()
return fname
# def FindBonds(Phase,RMCPdict):
# generalData = Phase['General']
# cx,ct,cs,cia = generalData['AtomPtrs']
# atomData = Phase['Atoms']
# Res = 'RMC'
# if 'macro' in generalData['Type']:
# Res = atomData[0][ct-3]
# AtDict = {atom[ct-1]:atom[ct] for atom in atomData}
# Pairs = RMCPdict['Pairs'] #dict!
# BondList = []
# notNames = []
# for FrstName in AtDict:
# nbrs = G2mth.FindAllNeighbors(Phase,FrstName,list(AtDict.keys()),notName=notNames,Short=True)[0]
# Atyp1 = AtDict[FrstName]
# if 'Va' in Atyp1:
# continue
# for nbr in nbrs:
# Atyp2 = AtDict[nbr[0]]
# if 'Va' in Atyp2:
# continue
# try:
# bndData = Pairs[' %s-%s'%(Atyp1,Atyp2)][1:]
# except KeyError:
# bndData = Pairs[' %s-%s'%(Atyp2,Atyp1)][1:]
# if any(bndData):
# if bndData[0] <= nbr[1] <= bndData[1]:
# bondStr = str((FrstName,nbr[0])+tuple(bndData))+',\n'
# revbondStr = str((nbr[0],FrstName)+tuple(bndData))+',\n'
# if bondStr not in BondList and revbondStr not in BondList:
# BondList.append(bondStr)
# notNames.append(FrstName)
# return Res,BondList
# def FindAngles(Phase,RMCPdict):
# generalData = Phase['General']
# Cell = generalData['Cell'][1:7]
# Amat = G2lat.cell2AB(Cell)[0]
# cx,ct,cs,cia = generalData['AtomPtrs']
# atomData = Phase['Atoms']
# AtLookup = G2mth.FillAtomLookUp(atomData,cia+8)
# AtDict = {atom[ct-1]:atom[ct] for atom in atomData}
# Angles = RMCPdict['Angles']
# AngDict = {'%s-%s-%s'%(angle[0],angle[1],angle[2]):angle[3:] for angle in Angles}
# AngleList = []
# for MidName in AtDict:
# nbrs,nbrIds = G2mth.FindAllNeighbors(Phase,MidName,list(AtDict.keys()),Short=True)
# if len(nbrs) < 2: #need 2 neighbors to make an angle
# continue
# Atyp2 = AtDict[MidName]
# for i,nbr1 in enumerate(nbrs):
# Atyp1 = AtDict[nbr1[0]]
# for j,nbr3 in enumerate(nbrs[i+1:]):
# Atyp3 = AtDict[nbr3[0]]
# IdList = [nbrIds[1][i],nbrIds[0],nbrIds[1][i+j+1]]
# try:
# angData = AngDict['%s-%s-%s'%(Atyp1,Atyp2,Atyp3)]
# except KeyError:
# try:
# angData = AngDict['%s-%s-%s'%(Atyp3,Atyp2,Atyp1)]
# except KeyError:
# continue
# XYZ = np.array(G2mth.GetAtomItemsById(atomData,AtLookup,IdList,cx,numItems=3))
# calAngle = G2mth.getRestAngle(XYZ,Amat)
# if angData[0] <= calAngle <= angData[1]:
# angStr = str((MidName,nbr1[0],nbr3[0])+tuple(angData))+',\n'
# revangStr = str((MidName,nbr3[0],nbr1[0])+tuple(angData))+',\n'
# if angStr not in AngleList and revangStr not in AngleList:
# AngleList.append(angStr)
# return AngleList
# def GetSqConvolution(XY,d):
# n = XY.shape[1]
# snew = np.zeros(n)
# dq = np.zeros(n)
# sold = XY[1]
# q = XY[0]
# dq[1:] = np.diff(q)
# dq[0] = dq[1]
# for j in range(n):
# for i in range(n):
# b = abs(q[i]-q[j])
# t = q[i]+q[j]
# if j == i:
# snew[j] += q[i]*sold[i]*(d-np.sin(t*d)/t)*dq[i]
# else:
# snew[j] += q[i]*sold[i]*(np.sin(b*d)/b-np.sin(t*d)/t)*dq[i]
# snew[j] /= np.pi*q[j]
# snew[0] = snew[1]
# return snew
# def GetMaxSphere(pdbName):
# try:
# pFil = open(pdbName,'r')
# except FileNotFoundError:
# return None
# while True:
# line = pFil.readline()
# if 'Boundary' in line:
# line = line.split()[3:]
# G = np.array([float(item) for item in line])
# G = np.reshape(G,(3,3))**2
# G = nl.inv(G)
# pFil.close()
# break
# dspaces = [0.5/np.sqrt(G2lat.calc_rDsq2(H,G)) for H in np.eye(3)]
# return min(dspaces)
[docs]
def findfullrmc():
'''Find where fullrmc is installed. Tries the following:
1. Returns the Config var 'fullrmc_exec', if defined. If an executable
is found at that location it is assumed to run and supply
fullrmc 5.0+
2. The path is checked for a fullrmc image as named by Bachir
:returns: the full path to a python executable that is assumed to
have fullrmc installed or None, if it was not found.
'''
fullrmc_exe = GSASIIpath.GetConfigValue('fullrmc_exec')
if fullrmc_exe is not None and is_exe(fullrmc_exe):
return fullrmc_exe
pathlist = os.environ["PATH"].split(os.pathsep)
for p in (GSASIIpath.path2GSAS2,GSASIIpath.binaryPath,os.getcwd(),
os.path.split(sys.executable)[0]):
if p not in pathlist: pathlist.append(p)
import glob
for p in pathlist:
if sys.platform == "win32":
lookfor = "fullrmc5*.exe"
else:
lookfor = "fullrmc5*64bit"
fl = glob.glob(os.path.join(p,lookfor))
if len(fl) > 0:
fullrmc_exe = os.path.abspath(sorted(fl)[0])
if GSASIIpath.GetConfigValue('debug'):
print('fullrmc found as',fullrmc_exe)
return fullrmc_exe
[docs]
def fullrmcDownload():
'''Downloads the fullrmc executable from Bachir's site to the current
GSAS-II binary directory.
Does some error checking.
'''
import os
import requests
import platform
if platform.architecture()[0] != '64bit':
return "fullrmc is only available for 64 bit machines. This is 32 bit"
setXbit = True
if sys.platform == "darwin":
URL = "https://github.com/bachiraoun/fullrmc/raw/master/standalones/fullrmc500_3p8p6_macOS-10p16-x86_64-i386-64bit"
elif sys.platform == "win32":
setXbit = False
URL = "https://github.com/bachiraoun/fullrmc/raw/master/standalones/fullrmc500_3p8p10_Windows-10-10p0p19041-SP0.exe"
else:
if 'aarch' in platform.machine() or 'arm' in platform.machine():
return "Sorry, fullrmc is only available for Intel-compatible machines."
URL = "https://github.com/bachiraoun/fullrmc/raw/master/standalones/fullrmc500_3p8p5_Linux-4p19p121-linuxkit-x86_64-with-glibc2p29"
GSASIIpath.SetBinaryPath()
fil = os.path.join(GSASIIpath.binaryPath,os.path.split(URL)[1])
print('Starting installation of fullrmc\nDownloading from',
'https://github.com/bachiraoun/fullrmc/tree/master/standalones',
'\nCreating '+fil,
'\nThis may take a while...')
open(fil, "wb").write(requests.get(URL).content)
print('...Download completed')
if setXbit:
import stat
os.chmod(fil, os.stat(fil).st_mode | stat.S_IEXEC)
return ''
[docs]
def findPDFfit():
'''Find if PDFfit2 is installed (may be local to GSAS-II). Does the following:
:returns: two items: (1) the full path to a python executable or None, if
it was not found and (2) path(s) to the PDFfit2 location(s) as a list.
'''
if GSASIIpath.GetConfigValue('pdffit2_exec') is not None and is_exe(
GSASIIpath.GetConfigValue('pdffit2_exec')):
return GSASIIpath.GetConfigValue('pdffit2_exec'),None
pdffitloc = os.path.join(GSASIIpath.path2GSAS2,'PDFfit2')
if not os.path.exists(pdffitloc):
print('PDFfit2 not found in GSAS-II \n\t(expected in '+pdffitloc+')')
return None,[]
if pdffitloc not in sys.path: sys.path.append(pdffitloc)
try:
from diffpy.pdffit2 import PdfFit
import diffpy
import inspect
pdffitloc = [os.path.dirname(os.path.dirname(inspect.getfile(diffpy)))]
# is this the original version of diffpy (w/pdffit2.py)
try:
from diffpy.pdffit2 import pdffit2
except ImportError:
# or the GSAS-II version w/o; for this we need to find the binary's location
try:
import pdffit2 # added for GSAS-II to relocate binary file
except ImportError:
print('\nError: pdffit2 failed to load with this python\n')
return None,[]
except ModuleNotFoundError:
print('\nGSAS-II does not have a PDFfit2 module compatible\nwith this Python interpreter\n')
return None,[]
pdffitloc += [os.path.dirname(inspect.getfile(pdffit2))]
return sys.executable,pdffitloc
except Exception as msg:
print('Error importing PDFfit2:\n',msg)
return None,[]
[docs]
def GetPDFfitAtomVar(Phase,RMCPdict):
''' Find dict of independent "@n" variables for PDFfit in atom constraints
'''
General = Phase['General']
Atoms = Phase['Atoms']
cx,ct,cs,cia = General['AtomPtrs']
AtomVar = RMCPdict['AtomVar']
varnames = []
for iat,atom in enumerate(RMCPdict['AtomConstr']):
for it,item in enumerate(atom):
if it > 1 and item:
itms = item.split('@')
for itm in itms[1:]:
itnum = itm[:2]
varname = '@%s'%itnum
varnames.append(varname)
if it < 6:
if varname not in AtomVar:
AtomVar[varname] = 0.0 #put ISODISTORT mode displ here?
else:
for i in range(3):
if varname not in AtomVar:
AtomVar[varname] = Atoms[iat][cia+i+2]
varnames = set(varnames)
for name in list(AtomVar.keys()): #clear out unused parameters
if name not in varnames:
del AtomVar[name]
[docs]
def MakePDFfitAtomsFile(Phase,RMCPdict):
'''Make the PDFfit atoms file
'''
General = Phase['General']
if General['SGData']['SpGrp'] != 'P 1':
return 'Space group symmetry must be lowered to P 1 for PDFfit'
fName = General['Name']+'-PDFfit.stru'
fName = fName.replace(' ','_')
if 'sequential' in RMCPdict['refinement']:
fName = 'Sequential_PDFfit.stru'
fatm = open(fName,'w')
fatm.write('title structure of '+General['Name']+'\n')
fatm.write('format pdffit\n')
fatm.write('scale 1.000000\n') #fixed
sharp = '%10.6f,%10.6f,%10.6f,%10.6f\n'%(RMCPdict['delta2'][0],RMCPdict['delta1'][0],RMCPdict['sratio'][0],RMCPdict['rcut'])
fatm.write('sharp '+sharp)
shape = ''
if RMCPdict['shape'] == 'sphere' and RMCPdict['spdiameter'][0] > 0.:
shape = ' sphere, %10.6f\n'%RMCPdict['spdiameter'][0]
elif RMCPdict['stepcut'] > 0.:
shape = 'stepcut, %10.6f\n'%RMCPdict['stepcut']
if shape:
fatm.write('shape '+shape)
fatm.write('spcgr %s\n'%RMCPdict['SGData']['SpGrp'].replace(' ',''))
cell = General['Cell'][1:7]
fatm.write('cell %10.6f,%10.6f,%10.6f,%10.6f,%10.6f,%10.6f\n'%(
cell[0],cell[1],cell[2],cell[3],cell[4],cell[5]))
fatm.write('dcell '+5*' 0.000000,'+' 0.000000\n')
Atoms = Phase['Atoms']
fatm.write('ncell %8d,%8d,%8d,%10d\n'%(1,1,1,len(Atoms)))
fatm.write('atoms\n')
cx,ct,cs,cia = General['AtomPtrs']
for atom in Atoms:
fatm.write('%4s%18.8f%18.8f%18.8f%13.4f\n'%(atom[ct][:2].ljust(2),atom[cx],atom[cx+1],atom[cx+2],atom[cx+3]))
fatm.write(' '+'%18.8f%18.8f%18.8f%13.4f\n'%(0.,0.,0.,0.))
fatm.write(' '+'%18.8f%18.8f%18.8f\n'%(atom[cia+2],atom[cia+3],atom[cia+4]))
fatm.write(' '+'%18.8f%18.8f%18.8f\n'%(0.,0.,0.,))
fatm.write(' '+'%18.8f%18.8f%18.8f\n'%(atom[cia+5],atom[cia+6],atom[cia+7]))
fatm.write(' '+'%18.8f%18.8f%18.8f\n'%(0.,0.,0.))
fatm.close()
[docs]
def MakePDFfitRunFile(Phase,RMCPdict):
'''Make the PDFfit python run file
'''
def GetCellConstr(SGData):
if SGData['SGLaue'] in ['m3', 'm3m']:
return [1,1,1,0,0,0]
elif SGData['SGLaue'] in ['3','3m1','31m','6/m','6/mmm','4/m','4/mmm']:
return [1,1,2,0,0,0]
elif SGData['SGLaue'] in ['3R','3mR']:
return [1,1,1,2,2,2]
elif SGData['SGLaue'] == 'mmm':
return [1,2,3,0,0,0]
elif SGData['SGLaue'] == '2/m':
if SGData['SGUniq'] == 'a':
return [1,2,3,4,0,0]
elif SGData['SGUniq'] == 'b':
return [1,2,3,0,4,0]
elif SGData['SGUniq'] == 'c':
return [1,2,3,0,0,4]
else:
return [1,2,3,4,5,6]
General = Phase['General']
Cell = General['Cell'][1:7]
rundata = '''#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys,os
datadir = r'{:}'
pathWrap = lambda f: os.path.join(datadir,f)
'''.format(os.path.abspath(os.getcwd()))
PDFfit_exe,PDFfit_path = findPDFfit() # returns python loc and path(s) for pdffit
if not PDFfit_exe:
print('PDFfit2 is not found. Creating .sh file without paths.')
if PDFfit_path:
for p in PDFfit_path:
rundata += "sys.path.append(r'{:}')\n".format(p)
rundata += 'from diffpy.pdffit2 import PdfFit\n'
rundata += 'pf = PdfFit()\n'
Nd = 0
Np = 0
parms = {}
parmNames = {}
if 'sequential' in RMCPdict['refinement']:
Np = 3
rundata += '#sequential data here\n'
else:
for fil in RMCPdict['files']:
filNam = RMCPdict['files'][fil][0]
if 'Select' in filNam:
continue
if 'Neutron' in fil:
Nd += 1
dType = 'Ndata'
else:
Nd += 1
dType = 'Xdata'
rundata += "pf.read_data(pathWrap(r'%s'), '%s', 30.0, %.4f)\n"%(filNam,dType[0],RMCPdict[dType]['qdamp'][0])
rundata += 'pf.setdata(%d)\n'%Nd
rundata += 'pf.pdfrange(%d, %6.2f, %6.2f)\n'%(Nd,RMCPdict[dType]['Fitrange'][0],RMCPdict[dType]['Fitrange'][1])
for item in ['dscale','qdamp','qbroad']:
if RMCPdict[dType][item][1]:
Np += 1
rundata += 'pf.constrain(pf.%s(),"@%d")\n'%(item,Np)
parms[Np] = RMCPdict[dType][item][0]
parmNames[Np] = item
fName = General['Name']+'-PDFfit.stru'
fName = fName.replace(' ','_')
if 'sequential' in RMCPdict['refinement']:
fName = 'Sequential_PDFfit.stru'
Np = 9
rundata += "pf.read_struct(pathWrap(r'{:}'))\n".format(fName)
for item in ['delta1','delta2','sratio']:
if RMCPdict[item][1]:
Np += 1
rundata += 'pf.constrain(pf.%s,"@%d")\n'%(item,Np)
parms[Np] = RMCPdict[item][0]
parmNames[Np] = item
if 'sphere' in RMCPdict['shape'] and RMCPdict['spdiameter'][1]:
Np += 1
rundata += 'pf.constrain(pf.spdiameter,"@%d")\n'%Np
parms[Np] = RMCPdict['spdiameter'][0]
parmNames[Np] = 'spdiameter'
if RMCPdict['cellref']:
cellconst = GetCellConstr(RMCPdict['SGData'])
used = []
cellNames = ['a','b','c','alpha','beta','gamma']
for ic in range(6):
if cellconst[ic]:
rundata += 'pf.constrain(pf.lat(%d), "@%d")\n'%(ic+1,Np+cellconst[ic])
if cellconst[ic] not in used:
parms[Np+cellconst[ic]] = Cell[ic]
parmNames[Np+cellconst[ic]] = cellNames[ic]
used.append(cellconst[ic])
#Atom constraints here -------------------------------------------------------
AtomVar = RMCPdict['AtomVar']
used = []
for iat,atom in enumerate(RMCPdict['AtomConstr']):
for it,item in enumerate(atom):
names = ['pf.x(%d)'%(iat+1),'pf.y(%d)'%(iat+1),'pf.z(%d)'%(iat+1),'pf.occ(%d)'%(iat+1)]
if it > 1 and item:
itms = item.split('@')
once = False
for itm in itms[1:]:
try:
itnum = int(itm[:2])
except ValueError:
print(' *** ERROR - invalid string in atom constraint %s ***'%(item))
return None
if it < 6:
if not once:
rundata += 'pf.constrain(%s,"%s")\n'%(names[it-2],item)
once = True
if itnum not in used:
parms[itnum] = AtomVar['@%d'%itnum]
parmNames[itnum] = names[it-2].split('.')[1]
used.append(itnum)
else:
uijs = ['pf.u11(%d)'%(iat+1),'pf.u22(%d)'%(iat+1),'pf.u33(%d)'%(iat+1)]
for i in range(3):
rundata += 'pf.constrain(%s,"%s")\n'%(uijs[i],item)
if itnum not in used:
parms[itnum] = AtomVar['@%d'%itnum]
parmNames[itnum] = uijs[i].split('.')[1]
used.append(itnum)
if 'sequential' in RMCPdict['refinement']:
rundata += '#parameters here\n'
RMCPdict['Parms'] = parms #{'n':val,...}
RMCPdict['ParmNames'] = parmNames #{'n':name,...}
else:
# set parameter values
for iprm in parms:
rundata += 'pf.setpar(%d,%.6f)\n'%(iprm,parms[iprm])
# Save results ---------------------------------------------------------------
rundata += 'pf.refine()\n'
if 'sequential' in RMCPdict['refinement']:
fName = 'Sequential_PDFfit'
rfile = open('Seq_PDFfit_template.py','w')
rundata += 'pf.save_pdf(1, pathWrap("%s"))\n'%(fName+'.fgr')
else:
fName = General['Name'].replace(' ','_')+'-PDFfit'
rfile = open(fName+'.py','w')
Nd = 0
for file in RMCPdict['files']:
if 'Select' in RMCPdict['files'][file][0]: #skip unselected
continue
Nd += 1
rundata += 'pf.save_pdf(%d, pathWrap("%s"))\n'%(Nd,fName+file[0]+'.fgr')
rundata += 'pf.save_struct(1, pathWrap("%s"))\n'%(fName+'.rstr')
rundata += 'pf.save_res(pathWrap("%s"))\n'%(fName+'.res')
rfile.writelines(rundata)
rfile.close()
return fName+'.py'
[docs]
def GetSeqCell(SGData,parmDict):
''' For use in processing PDFfit sequential results
'''
try:
if SGData['SGLaue'] in ['m3', 'm3m']:
cell = [parmDict['11'][0],parmDict['11'][0],parmDict['11'][0],90.,90.,90.]
elif SGData['SGLaue'] in ['3','3m1','31m','6/m','6/mmm','4/m','4/mmm']:
cell = [parmDict['11'][0],parmDict['11'][0],parmDict['12'][0],90.,90.,90.]
elif SGData['SGLaue'] in ['3R','3mR']:
cell = [parmDict['11'][0],parmDict['11'][0],parmDict['11'][0],
parmDict['12'][0],parmDict['12'][0],parmDict['12'][0]]
elif SGData['SGLaue'] == 'mmm':
cell = [parmDict['11'][0],parmDict['12'][0],parmDict['13'][0],90.,90.,90.]
elif SGData['SGLaue'] == '2/m':
if SGData['SGUniq'] == 'a':
cell = [parmDict['11'][0],parmDict['12'][0],parmDict['13'][0],parmDict['14'][0],90.,90.]
elif SGData['SGUniq'] == 'b':
cell = [parmDict['11'][0],parmDict['12'][0],parmDict['13'][0],90.,parmDict['14'][0],90.]
elif SGData['SGUniq'] == 'c':
cell = [parmDict['11'][0],parmDict['12'][0],parmDict['13'][0],90.,90.,parmDict['14'][0]]
else:
cell = [parmDict['11'][0],parmDict['12'][0],parmDict['13'][0],
parmDict['14'][0],parmDict['15'][0],parmDict['16'][0]]
return G2lat.cell2A(cell)
except KeyError:
return None
[docs]
def UpdatePDFfit(Phase,RMCPdict):
''' Updates various PDFfit parameters held in GSAS-II
'''
General = Phase['General']
if RMCPdict['refinement'] == 'normal':
fName = General['Name']+'-PDFfit.rstr'
try:
rstr = open(fName.replace(' ','_'),'r')
except FileNotFoundError:
return [fName,'Not found - PDFfit failed']
lines = rstr.readlines()
rstr.close()
header = [line[:-1].split(' ',1) for line in lines[:7]]
resdict = dict(header)
for item in ['sharp','cell']:
resdict[item] = [float(val) for val in resdict[item].split(',')]
General['Cell'][1:7] = resdict['cell']
for inam,name in enumerate(['delta2','delta1','sratio']):
RMCPdict[name][0] = float(resdict['sharp'][inam])
if 'shape' in resdict:
if 'sphere' in resdict['shape']:
RMCPdict['spdiameter'][0] = float(resdict['shape'].split()[-1])
else:
RMCPdict['stepcut'][0] = float(resdict['shape'][-1])
cx,ct,cs,ci = G2mth.getAtomPtrs(Phase)
Atoms = Phase['Atoms']
atmBeg = 0
for line in lines:
atmBeg += 1
if 'atoms' in line:
break
for atom in Atoms:
atstr = lines[atmBeg][:-1].split()
Uiistr = lines[atmBeg+2][:-1].split()
Uijstr = lines[atmBeg+4][:-1].split()
atom[cx:cx+4] = [float(atstr[1]),float(atstr[2]),float(atstr[3]),float(atstr[4])]
atom[ci] = 'A'
atom[ci+2:ci+5] = [float(Uiistr[0]),float(Uiistr[1]),float(Uiistr[2])]
atom[ci+5:ci+8] = [float(Uijstr[0]),float(Uijstr[1]),float(Uijstr[2])]
atmBeg += 6
fName = General['Name']+'-PDFfit.res'
else:
fName = 'Sequential_PDFfit.res'
try:
res = open(fName.replace(' ','_'),'r')
except FileNotFoundError:
return [fName,'Not found - PDFfit failed']
lines = res.readlines()
res.close()
Ibeg = False
resline = ''
XNdata = {'Xdata':RMCPdict['Xdata'],'Ndata':RMCPdict['Ndata']}
for line in lines:
if 'Radiation' in line and 'X-Rays' in line:
dkey = 'Xdata'
if 'Radiation' in line and'Neutrons' in line:
dkey = 'Ndata'
if 'Qdamp' in line and '(' in line:
XNdata[dkey]['qdamp'][0] = float(line.split()[4])
if 'Qbroad' in line and '(' in line:
XNdata[dkey]['qbroad'][0] = float(line.split()[4])
if 'Scale' in line and '(' in line:
XNdata[dkey]['dscale'][0] = float(line.split()[3])
for iline,line in enumerate(lines):
if 'Refinement parameters' in line:
Ibeg = True
continue
if Ibeg:
if '---------' in line:
break
resline += line[:-1]
for iline,line in enumerate(lines):
if 'Rw - ' in line:
if 'nan' in line:
Rwp = 100.0
else:
Rwp = float(line.split(':')[1])
results = resline.replace('(','').split(')')[:-1]
results = ['@'+result.lstrip() for result in results]
results = [item.split() for item in results]
RMCPdict['Parms'] = dict([[item[0][1:-1],float(item[1])] for item in results]) #{'n':val,...}
if RMCPdict['refinement'] == 'normal':
fName = General['Name']+'-PDFfit.py'
py = open(fName.replace(' ','_'),'r')
pylines = py.readlines()
py.close()
py = open(fName.replace(' ','_'),'w')
newpy = []
for pyline in pylines:
if 'setpar' in pyline:
parm = pyline.split('(')[1].split(',')[0]
newpy.append('pf.setpar(%s,%.5f)\n'%(parm,RMCPdict['Parms'][parm]))
else:
newpy.append(pyline)
py.writelines(newpy)
py.close()
RMCPdict.update(XNdata)
results = dict([[item[0][:-1],float(item[1])] for item in results if item[0][:-1] in RMCPdict['AtomVar']])
RMCPdict['AtomVar'].update(results)
return None
else: #sequential
newParms = dict([[item[0][1:-1],[float(item[1]),float(item[2])]] for item in results]) #{'n':[val,esd],...}
return newParms,Rwp
[docs]
def MakefullrmcSupercell(Phase,RMCPdict):
'''Create a fullrmc supercell from GSAS-II
:param dict Phase: phase information from data tree
:param dict RMCPdict: fullrmc parameters from GUI
:param list grpDict: a list of lists where the inner list
contains the atom numbers contained in each group. e.g.
[[0,1,2,3,4],[5,6],[4,6]] creates three groups with
atoms 0-4 in the first
atoms 5 & 6 in the second and
atoms 4 & 6 in the third. Note that it is fine that
atom 4 appears in two groups.
'''
#for i in (0,1): grpDict[i].append(1) # debug: 1st & 2nd atoms in 2nd group
cell = Phase['General']['Cell'][1:7]
A,B = G2lat.cell2AB(cell)
cx,ct,cs,cia = Phase['General']['AtomPtrs']
SGData = Phase['General']['SGData']
atomlist = []
for i,atom in enumerate(Phase['Atoms']):
el = ''.join([i for i in atom[ct] if i.isalpha()])
grps = [j for j,g in enumerate(RMCPdict.get('Groups',[])) if i in g]
atomlist.append((el, atom[ct-1], grps))
# create a list of coordinates with symmetry & unit cell translation duplicates
coordlist = []
cellnum = -1
for a in range(int(0.5-RMCPdict['SuperCell'][0]/2),int(1+RMCPdict['SuperCell'][0]/2)):
for b in range(int(0.5-RMCPdict['SuperCell'][1]/2),int(1+RMCPdict['SuperCell'][1]/2)):
for c in range(int(0.5-RMCPdict['SuperCell'][2]/2),int(1+RMCPdict['SuperCell'][2]/2)):
cellnum += 1
for i,atom in enumerate(Phase['Atoms']):
for item in G2spc.GenAtom(atom[cx:cx+3],SGData,Move=False):
# if i == 0: print(item[0]+[a,b,c])
xyzOrth = np.inner(A,item[0]+[a,b,c])
#coordlist.append((i,list(xyzOrth),cellnum,list(item[0]+[a,b,c])))
coordlist.append((item[1],cellnum,i,list(xyzOrth)))
return atomlist,coordlist
[docs]
def MakefullrmcRun(pName,Phase,RMCPdict):
'''Creates a script to run fullrmc. Returns the name of the file that was
created.
'''
BondList = {}
for k in RMCPdict['Pairs']:
if RMCPdict['Pairs'][k][1]+RMCPdict['Pairs'][k][2]>0:
BondList[k] = (RMCPdict['Pairs'][k][1],RMCPdict['Pairs'][k][2])
AngleList = []
for angle in RMCPdict['Angles']:
if angle[3] == angle[4] or angle[5] >= angle[6] or angle[6] <= 0:
continue
for i in (0,1,2):
angle[i] = angle[i].strip()
AngleList.append(angle)
# rmin = RMCPdict['min Contact']
cell = Phase['General']['Cell'][1:7]
SymOpList = G2spc.AllOps(Phase['General']['SGData'])[0]
cx,ct,cs,cia = Phase['General']['AtomPtrs']
atomsList = []
for atom in Phase['Atoms']:
el = ''.join([i for i in atom[ct] if i.isalpha()])
atomsList.append([el] + atom[cx:cx+4])
projDir,projName = os.path.split(os.path.abspath(pName))
scrname = pName+'-fullrmc.py'
restart = '%s_restart.pdb'%pName
Files = RMCPdict['files']
rundata = ''
rundata += '## fullrmc %s file ##\n## OK to edit this by hand ##\n'%scrname
rundata += '# created in '+__file__+" v"+filversion.split()[1]
rundata += dt.datetime.strftime(dt.datetime.now()," at %Y-%m-%dT%H:%M\n")
rundata += '''
# fullrmc imports (all that are potentially useful)
import os,glob
import time
import pickle
import types
import copy
import numpy as np
import matplotlib as mpl
import fullrmc
from pdbparser import pdbparser
from pdbparser.Utilities.Database import __ATOM__
from fullrmc.Core import Collection
from fullrmc.Engine import Engine
import fullrmc.Constraints.PairDistributionConstraints as fPDF
from fullrmc.Constraints.StructureFactorConstraints import ReducedStructureFactorConstraint, StructureFactorConstraint
from fullrmc.Constraints.RadialDistributionConstraints import RadialDistributionConstraint
from fullrmc.Constraints.StructureFactorConstraints import NormalizedStructureFactorConstraint
from fullrmc.Constraints.DistanceConstraints import DistanceConstraint
from fullrmc.Constraints.BondConstraints import BondConstraint
from fullrmc.Constraints.AngleConstraints import BondsAngleConstraint
from fullrmc.Constraints.DihedralAngleConstraints import DihedralAngleConstraint
from fullrmc.Generators.Swaps import SwapPositionsGenerator
from fullrmc.Core.MoveGenerator import MoveGeneratorCollector
from fullrmc.Generators.Translations import TranslationGenerator
from fullrmc.Generators.Rotations import RotationGenerator
# utility routines
def writeHeader(ENGINE,statFP):
"""header for stats file"""
statFP.write('generated-steps, total-error, ')
for c in ENGINE.constraints:
statFP.write(c.constraintName)
statFP.write(', ')
statFP.write('\\n')
statFP.flush()
def writeCurrentStatus(ENGINE,statFP,plotF):
"""line in stats file & current constraint plots"""
statFP.write(str(ENGINE.generated))
statFP.write(', ')
statFP.write(str(ENGINE.totalStandardError))
statFP.write(', ')
for c in ENGINE.constraints:
statFP.write(str(c.standardError))
statFP.write(', ')
statFP.write('\\n')
statFP.flush()
mpl.use('agg')
fp = open(plotF,'wb')
for c in ENGINE.constraints:
p = c.plot(show=False)
p[0].canvas.draw()
image = p[0].canvas.buffer_rgba()
pickle.dump(c.constraintName,fp)
pickle.dump(np.array(image),fp)
fp.close()
def calcRmax(ENGINE):
"""from Bachir, works for non-orthorhombic cells"""
a,b,c = ENGINE.basisVectors
lens = []
ts = np.linalg.norm(np.cross(a,b))/2
lens.extend( [ts/np.linalg.norm(a), ts/np.linalg.norm(b)] )
ts = np.linalg.norm(np.cross(b,c))/2
lens.extend( [ts/np.linalg.norm(b), ts/np.linalg.norm(c)] )
ts = np.linalg.norm(np.cross(a,c))/2
lens.extend( [ts/np.linalg.norm(a), ts/np.linalg.norm(c)] )
return min(lens)
'''
if RMCPdict.get('Groups',[]): rundata += '''
def makepdb(atoms, coords, bbox=None):
"""creates a supercell directly from atom info"""
# used when ENGINE.build_crystal_set_pdb is not called
prevcell = None
rec = copy.copy(__ATOM__)
rec['residue_name'] = 'MOL'
records = []
seqNum = 0
segId = '0'
groups = {}
for symcell in set([(sym,cell) for sym,cell,atm,xyz in coords]):
seqNum += 1
if seqNum == 9999:
seqNum = 1
segId = str(int(segId) + 1)
for i,(sym,cell,atm,(x,y,z)) in enumerate(coords):
if (sym,cell) != symcell: continue
rec = copy.copy(rec)
for grp in atoms[atm][2]:
if (sym,cell) not in groups:
groups[(sym,cell)] = {}
if grp not in groups[(sym,cell)]:
groups[(sym,cell)][grp] = [len(records)]
else:
groups[(sym,cell)][grp].append(len(records))
rec['coordinates_x'] = x
rec['coordinates_y'] = y
rec['coordinates_z'] = z
rec['element_symbol'] = atoms[atm][0]
rec['atom_name'] = atoms[atm][1]
rec['sequence_number'] = seqNum
rec['segment_identifier'] = segId
records.append(rec)
# create pdb
pdb = pdbparser()
pdb.records = records
if groups:
return pdb,[groups[j][i] for j in groups for i in groups[j]]
else:
return pdb,[]
'''
rundata += '''
### When True, erases an existing engine to provide a fresh start
FRESH_START = {:}
dirName = "{:}"
prefix = "{:}"
project = prefix + "-fullrmc"
time0 = time.time()
'''.format(RMCPdict['ReStart'][0],projDir,projName)
rundata += '# setup structure\n'
rundata += 'cell = ' + str(cell) + '\n'
rundata += 'supercell = ' + str(RMCPdict['SuperCell']) + '\n'
rundata += '\n# define structure info\n'
if RMCPdict.get('Groups',[]):
# compute bounding box coordinates
bbox = []
A,B = G2lat.cell2AB(cell)
for i in range(3):
for val in int(0.5-RMCPdict['SuperCell'][i]/2),int(1+RMCPdict['SuperCell'][0]/2):
fpos = [0,0,0]
fpos[i] = val
bbox.append(np.inner(A,fpos))
rundata += 'bboxlist = [ # orthogonal coordinate for supercell corners\n'
for i in bbox:
rundata += ' '+str(list(i))+',\n'
rundata += ' ] # bboxlist\n\n'
atomlist,coordlist = MakefullrmcSupercell(Phase,RMCPdict)
rundata += 'atomlist = [ # [element, label, grouplist]\n'
for i in atomlist:
rundata += ' '+str(i)+',\n'
rundata += ' ] # atomlist\n\n'
rundata += 'coordlist = [ # (sym#, cell#, atom#, [ortho coords],)\n'
for i in coordlist:
rundata += ' '+str(i)+',\n'
rundata += ' ] # coordlist\n'
else:
rundata += "SymOpList = "+str([i.lower() for i in SymOpList]) + '\n'
rundata += 'atomList = ' + str(atomsList).replace('],','],\n ') + '\n'
rundata += '\n# initialize engine\n'
rundata += '''
engineFileName = os.path.join(dirName, project + '.rmc')
projectStats = os.path.join(dirName, project + '.stats')
projectPlots = os.path.join(dirName, project + '.plots')
projectXYZ = os.path.join(dirName, project + '.atoms')
pdbFile = os.path.join(dirName, project + '_restart.pdb')
# check Engine exists if so (and not FRESH_START) load it otherwise build it
ENGINE = Engine(path=None)
if not ENGINE.is_engine(engineFileName) or FRESH_START:
ENGINE = Engine(path=engineFileName, freshStart=True)
'''
if RMCPdict.get('Groups',[]):
rundata += '''
# create structure from GSAS-II constructed supercell
bbox = (np.array(bboxlist[1::2])-np.array(bboxlist[0::2])).flatten()
pdb,grouplist = makepdb(atomlist,coordlist,bbox)
ENGINE.set_pdb(pdb)
ENGINE.set_boundary_conditions(bbox)
if grouplist: ENGINE.set_groups(grouplist)
'''
if RMCPdict.get('GroupMode',0) == 0: # 'Rotate & Translate'
rundata += '''
for g in ENGINE.groups:
TMG = TranslationGenerator(amplitude=0.2) # create translation generator
if len(g) > 1: # create rotation generator for groups with more than 1 atom
RMG = RotationGenerator(amplitude=2)
MG = MoveGeneratorCollector(collection=[TMG,RMG],randomize=True)
else:
MG = MoveGeneratorCollector(collection=[TMG],randomize=True)
g.set_move_generator( MG )
'''
elif RMCPdict.get('GroupMode',0) == 1: # 'Rotate only'
rundata += '''
for g in ENGINE.groups:
if len(g) > 1: # create rotation generator for groups with more than 1 atom
RMG = RotationGenerator(amplitude=2)
g.set_move_generator( RMG )
'''
else: # 'Translate only'
rundata += ' # translate only set by default'
else:
rundata += '''
# create structure, let fullrmc construct supercell
ENGINE.build_crystal_set_pdb(symOps = SymOpList,
atoms = atomList,
unitcellBC = cell,
supercell = supercell)
ENGINE.set_groups_as_atoms()
'''
rundata += ' rho0 = len(ENGINE.allNames)/ENGINE.volume\n'
rundata += '\n # "Constraints" (includes experimental data) setup\n'
# settings that require a new Engine
for File in Files:
filDat = RMCPdict['files'][File]
if not os.path.exists(filDat[0]): continue
sfwt = 'neutronCohb'
if 'Xray' in File:
sfwt = 'atomicNumber'
if 'G(r)' in File:
rundata += ' GR = np.loadtxt(os.path.join(dirName,"%s")).T\n'%filDat[0]
if filDat[3] == 0:
#rundata += ''' # read and xform G(r) as defined in RMCProfile
# see eq. 44 in Keen, J. Appl. Cryst. (2001) 34 172-177\n'''
#rundata += ' GR[1] *= 4 * np.pi * GR[0] * rho0 / sumCiBi2\n'
#rundata += ' GofR = fPDF.PairDistributionConstraint(experimentalData=GR.T, weighting="%s")\n'%sfwt
rundata += ' # G(r) as defined in RMCProfile\n'
rundata += ' GofR = RadialDistributionConstraint(experimentalData=GR.T, weighting="%s")\n'%sfwt
elif filDat[3] == 1:
rundata += ' # This is G(r) as defined in PDFFIT\n'
rundata += ' GofR = fPDF.PairDistributionConstraint(experimentalData=GR.T, weighting="%s")\n'%sfwt
elif filDat[3] == 2:
rundata += ' # This is g(r)\n'
rundata += ' GofR = fPDF.PairCorrelationConstraint(experimentalData=GR.T, weighting="%s")\n'%sfwt
else:
raise ValueError('Invalid G(r) type: '+str(filDat[3]))
rundata += ' ENGINE.add_constraints([GofR])\n'
rundata += ' GofR.set_limits((None, calcRmax(ENGINE)))\n'
if RMCPdict['addThermalBroadening']:
rundata += " GofR.set_thermal_corrections({'defaultFactor': 0.001})\n"
rundata += " GofR.thermalCorrections['factors'] = {\n"
RMCPdict['addThermalBroadening']
for atm1 in RMCPdict['aTypes']:
for atm2 in RMCPdict['aTypes']:
rundata += " ('{}', '{}'): {},\n".format(
atm1,atm2,
(RMCPdict['ThermalU'].get(atm1,0.005)+ RMCPdict['ThermalU'].get(atm2,0.005))/2)
rundata += ' }\n'
elif '(Q)' in File:
rundata += ' SOQ = np.loadtxt(os.path.join(dirName,"%s")).T\n'%filDat[0]
if filDat[3] == 0:
rundata += ' # F(Q) as defined in RMCProfile\n'
#rundata += ' SOQ[1] *= 1 / sumCiBi2\n'
if filDat[4]:
rundata += ' SOQ[1] = Collection.sinc_convolution(q=SOQ[0],sq=SOQ[1],rmax=calcRmax(ENGINE))\n'
rundata += ' SofQ = NormalizedStructureFactorConstraint(experimentalData=SOQ.T, weighting="%s")\n'%sfwt
elif filDat[3] == 1:
rundata += ' # S(Q) as defined in PDFFIT\n'
rundata += ' SOQ[1] -= 1\n'
if filDat[4]:
rundata += ' SOQ[1] = Collection.sinc_convolution(q=SOQ[0],sq=SOQ[1],rmax=calcRmax(ENGINE))\n'
rundata += ' SofQ = ReducedStructureFactorConstraint(experimentalData=SOQ.T, weighting="%s")\n'%sfwt
else:
raise ValueError('Invalid S(Q) type: '+str(filDat[3]))
rundata += ' ENGINE.add_constraints([SofQ])\n'
else:
print('What is this?')
minDists = ''
if BondList and RMCPdict.get('useBondConstraints',True):
rundata += ''' B_CONSTRAINT = BondConstraint()
ENGINE.add_constraints(B_CONSTRAINT)
B_CONSTRAINT.create_supercell_bonds(bondsDefinition=[
'''
for pair in BondList:
e1,e2 = pair.split('-')
d1,d2 = BondList[pair]
if d1 == 0: continue
if d2 == 0:
minDists += '("element","{}","{}",{}),'.format(e1.strip(),e2.strip(),d1)
else:
rundata += ' ("element","{}","{}",{},{}),\n'.format(
e1.strip(),e2.strip(),d1,d2)
rundata += ' ])\n'
rundata += ' D_CONSTRAINT = DistanceConstraint(defaultLowerDistance={})\n'.format(RMCPdict['min Contact'])
if minDists:
rundata += " D_CONSTRAINT.set_pairs_definition( {'inter':[" + minDists + "]})\n"
rundata += ' ENGINE.add_constraints(D_CONSTRAINT)\n'
if AngleList:
rundata += ''' A_CONSTRAINT = BondsAngleConstraint()
ENGINE.add_constraints(A_CONSTRAINT)
A_CONSTRAINT.create_supercell_angles(anglesDefinition=[
'''
for item in AngleList:
rundata += (' '+
'("element","{1}","{0}","{2}",{5},{6},{5},{6},{3},{4}),\n'.format(*item))
rundata += ' ])\n'
rundata += '''
for f in glob.glob(os.path.join(dirName,prefix+"_*.log")): os.remove(f)
ENGINE.save()
else:
ENGINE = ENGINE.load(path=engineFileName)
ENGINE.set_log_file(os.path.join(dirName,prefix))
'''
if RMCPdict['Swaps']:
rundata += '\n#set up for site swaps\n'
rundata += 'aN = ENGINE.allNames\n'
rundata += 'SwapGen = {}\n'
for swap in RMCPdict['Swaps']:
rundata += 'SwapA = [[idx] for idx in range(len(aN)) if aN[idx]=="%s"]\n'%swap[0]
rundata += 'SwapB = [[idx] for idx in range(len(aN)) if aN[idx]=="%s"]\n'%swap[1]
rundata += 'SwapGen["%s-%s"] = [SwapPositionsGenerator(swapList=SwapA),SwapPositionsGenerator(swapList=SwapB),%.2f]\n'%(swap[0],swap[1],swap[2])
rundata += ' for swaps in SwapGen:\n'
rundata += ' AB = swaps.split("-")\n'
rundata += ' ENGINE.set_groups_as_atoms()\n'
rundata += ' for g in ENGINE.groups:\n'
rundata += ' if aN[g.indexes[0]]==AB[0]:\n'
rundata += ' g.set_move_generator(SwapGen[swaps][0])\n'
rundata += ' elif aN[g.indexes[0]]==AB[1]:\n'
rundata += ' g.set_move_generator(SwapGen[swaps][1])\n'
rundata += ' sProb = SwapGen[swaps][2]\n'
rundata += '''for c in ENGINE.constraints:
if hasattr(c, '_ExperimentalConstraint__adjustScaleFactor'):
def _constraint_copy_needs_lut(self, *args, **kwargs):
result = fPDF.PairDistributionConstraint._constraint_copy_needs_lut(self, *args, **kwargs)
result['_ExperimentalConstraint__adjustScaleFactor'] = '_ExperimentalConstraint__adjustScaleFactor'
return result
c._constraint_copy_needs_lut = types.MethodType(_constraint_copy_needs_lut, c)
if c.__class__.__name__ in ('ReducedStructureFactorConstraint', 'StructureFactorConstraint'):
def _constraint_copy_needs_lut(self, *args, **kwargs):
result = fPDF.PairDistributionConstraint._constraint_copy_needs_lut(self, *args, **kwargs)
result.pop('_PairDistributionConstraint__histogramSize', None)
result.pop('_PairDistributionConstraint__shellVolumes', None)
result.pop('_PairDistributionConstraint__shellCenters', None)
result.pop('_PairDistributionConstraint__windowArray', None)
result.pop('_PairDistributionConstraint__experimentalDistances', None)
result.pop('_PairDistributionConstraint__experimentalPDF', None)
result.pop('_PairDistributionConstraint__minimumDistance', None)
result.pop('_PairDistributionConstraint__maximumDistance', None)
result.pop('_PairDistributionConstraint__distanceBin', None)
result.pop('_shapeFuncParams', None)
result.pop('_shapeArray', None)
result['_StructureFactorConstraint__Gr2SqMatrix'] = '_StructureFactorConstraint__Gr2SqMatrix'
result['_StructureFactorConstraint__histogramSize'] = '_StructureFactorConstraint__histogramSize'
result['_StructureFactorConstraint__shellVolumes'] = '_StructureFactorConstraint__shellVolumes'
result['_StructureFactorConstraint__shellCenters'] = '_StructureFactorConstraint__shellCenters'
result['_StructureFactorConstraint__windowArray'] = '_StructureFactorConstraint__windowArray'
result['_StructureFactorConstraint__experimentalQValues'] = '_StructureFactorConstraint__experimentalQValues'
result['_StructureFactorConstraint__experimentalSF'] = '_StructureFactorConstraint__experimentalSF'
result['_StructureFactorConstraint__minimumDistance'] = '_StructureFactorConstraint__minimumDistance'
result['_StructureFactorConstraint__maximumDistance'] = '_StructureFactorConstraint__maximumDistance'
result['_StructureFactorConstraint__distanceBin'] = '_StructureFactorConstraint__distanceBin'
return result
c._constraint_copy_needs_lut = types.MethodType(_constraint_copy_needs_lut, c)
'''
# rundata += '\n# set weights -- do this now so values can be changed without a restart\n'
# rundata += 'wtDict = {}\n'
# for File in Files:
# filDat = RMCPdict['files'][File]
# if not os.path.exists(filDat[0]): continue
# if 'Xray' in File:
# sfwt = 'atomicNumber'
# else:
# sfwt = 'neutronCohb'
# if 'G(r)' in File:
# typ = 'Pair'
# elif '(Q)' in File:
# typ = 'Struct'
# rundata += 'wtDict["{}-{}"] = {}\n'.format(typ,sfwt,filDat[1])
rundata += '\n# set PDF fitting range\n'
rundata += 'for c in ENGINE.constraints: # loop over predefined constraints\n'
rundata += ' if type(c) is fPDF.PairDistributionConstraint:\n'
# rundata += ' c.set_variance_squared(1./wtDict["Pair-"+c.weighting])\n'
rundata += ' c.set_limits((None,calcRmax(ENGINE)))\n'
if RMCPdict['FitScale']:
rundata += ' c.set_adjust_scale_factor((10, 0.01, 100.))\n'
# rundata += ' c.set_variance_squared(1./wtDict["Struct-"+c.weighting])\n'
if RMCPdict['FitScale']:
rundata += ' elif type(c) is ReducedStructureFactorConstraint:\n'
rundata += ' c.set_adjust_scale_factor((10, 0.01, 100.))\n'
# torsions difficult to implement, must be internal to cell & named with
# fullrmc atom names
# if len(RMCPdict['Torsions']): # Torsions currently commented out in GUI
# rundata += 'for c in ENGINE.constraints: # look for Dihedral Angle Constraints\n'
# rundata += ' if type(c) is DihedralAngleConstraint:\n'
# rundata += ' c.set_variance_squared(%f)\n'%RMCPdict['Torsion Weight']
# rundata += ' c.create_angles_by_definition(anglesDefinition={"%s":[\n'%Res
# for torsion in RMCPdict['Torsions']:
# rundata += ' %s\n'%str(tuple(torsion))
# rundata += ' ]})\n'
rundata += '''
if FRESH_START:
# initialize engine with one step to get starting config energetics
ENGINE.run(restartPdb=pdbFile,numberOfSteps=1, saveFrequency=1)
statFP = open(projectStats,'w')
writeHeader(ENGINE,statFP)
writeCurrentStatus(ENGINE,statFP,projectPlots)
else:
statFP = open(projectStats,'a')
# setup runs for fullrmc
'''
rundata += 'steps = {}\n'.format(RMCPdict['Steps/cycle'])
rundata += 'for _ in range({}):\n'.format(RMCPdict['Cycles'])
rundata += ' expected = ENGINE.generated+steps\n'
rundata += ' ENGINE.run(restartPdb=pdbFile,numberOfSteps=steps, saveFrequency=steps)\n'
rundata += ' writeCurrentStatus(ENGINE,statFP,projectPlots)\n'
rundata += ' if ENGINE.generated != expected: break # run was stopped'
rundata += '''
statFP.close()
fp = open(projectXYZ,'w') # save final atom positions
fp.write('cell: {} {} {} {} {} {}\\n')
fp.write('supercell: {} {} {}\\n')
'''.format(*cell,*RMCPdict['SuperCell'])
rundata += '''# loop over atoms
for n,e,(x,y,z) in zip(ENGINE.allNames,
ENGINE.allElements,ENGINE.realCoordinates):
fp.write('{} {} {:.5f} {:.5f} {:.5f}\\n'.format(n,e,x,y,z))
fp.close()
print("ENGINE run time %.2f s"%(time.time()-time0))
'''
rfile = open(scrname,'w')
rfile.writelines(rundata)
rfile.close()
return scrname
def GetRMCBonds(general,RMCPdict,Atoms,bondList):
bondDist = []
Cell = general['Cell'][1:7]
Supercell = RMCPdict['SuperCell']
Trans = np.eye(3)*np.array(Supercell)
Cell = G2lat.TransformCell(Cell,Trans)[:6]
Amat,Bmat = G2lat.cell2AB(Cell)
indices = (-1,0,1)
Units = np.array([[h,k,l] for h in indices for k in indices for l in indices])
for bonds in bondList:
Oxyz = np.array(Atoms[bonds[0]][1:])
Txyz = np.array([Atoms[tgt-1][1:] for tgt in bonds[1]])
Dx = np.array([Txyz-Oxyz+unit for unit in Units])
Dx = np.sqrt(np.sum(np.inner(Dx,Amat)**2,axis=2))
for dx in Dx.T:
bondDist.append(np.min(dx))
return np.array(bondDist)
def GetRMCAngles(general,RMCPdict,Atoms,angleList):
bondAngles = []
Cell = general['Cell'][1:7]
Supercell = RMCPdict['SuperCell']
Trans = np.eye(3)*np.array(Supercell)
Cell = G2lat.TransformCell(Cell,Trans)[:6]
Amat,Bmat = G2lat.cell2AB(Cell)
indices = (-1,0,1)
Units = np.array([[h,k,l] for h in indices for k in indices for l in indices])
for angle in angleList:
Oxyz = np.array(Atoms[angle[0]][1:])
TAxyz = np.array([Atoms[tgt-1][1:] for tgt in angle[1].T[0]])
TBxyz = np.array([Atoms[tgt-1][1:] for tgt in angle[1].T[1]])
DAxV = np.inner(np.array([TAxyz-Oxyz+unit for unit in Units]),Amat)
DAx = np.sqrt(np.sum(DAxV**2,axis=2))
DBxV = np.inner(np.array([TBxyz-Oxyz+unit for unit in Units]),Amat)
DBx = np.sqrt(np.sum(DBxV**2,axis=2))
iDAx = np.argmin(DAx,axis=0)
iDBx = np.argmin(DBx,axis=0)
for i,[iA,iB] in enumerate(zip(iDAx,iDBx)):
DAv = DAxV[iA,i]/DAx[iA,i]
DBv = DBxV[iB,i]/DBx[iB,i]
bondAngles.append(npacosd(np.sum(DAv*DBv)))
return np.array(bondAngles)
[docs]
def ISO2PDFfit(Phase):
''' Creates new phase structure to be used for PDFfit from an ISODISTORT mode displacement phase.
It builds the distortion mode parameters to be used as PDFfit variables for atom displacements from
the original parent positions as transformed to the child cell wiht symmetry defined from ISODISTORT.
:param Phase: dict GSAS-II Phase structure; must contain ISODISTORT dict. NB: not accessed otherwise
:returns: dict: GSAS-II Phase structure; will contain ['RMC']['PDFfit'] dict
'''
Trans = np.eye(3)
Uvec = np.zeros(3)
Vvec = np.zeros(3)
Phase = copy.deepcopy(Phase)
Atoms = Phase['Atoms']
parentXYZ = Phase['ISODISTORT']['G2parentCoords'] #starting point for mode displacements
cx,ct,cs,cia = Phase['General']['AtomPtrs']
for iat,atom in enumerate(Atoms):
atom[cx:cx+3] = parentXYZ[iat]
SGData = copy.deepcopy(Phase['General']['SGData'])
SGOps = SGData['SGOps']
newPhase = copy.deepcopy(Phase)
newPhase['ranId'] = rand.randint(0,sys.maxsize)
newPhase['General']['Name'] += '_PDFfit'
newPhase['General']['SGData'] = G2spc.SpcGroup('P 1')[1] #this is for filled unit cell
newPhase,atCodes = G2lat.TransformPhase(Phase,newPhase,Trans,Uvec,Vvec,False)
newPhase['Histograms'] = {}
newPhase['Drawing'] = []
Atoms = newPhase['Atoms']
RMCPdict = newPhase['RMC']['PDFfit']
ISOdict = newPhase['ISODISTORT']
RMCPdict['AtomConstr'] = []
RMCPdict['SGData'] = copy.deepcopy(SGData) #this is from the ISODISTORT child; defines PDFfit constraints
Norms = ISOdict['NormList']
ModeMatrix = ISOdict['Mode2VarMatrix']
RMCPdict['AtomVar'] = {'@%d'%(itm+21):val for itm,val in enumerate(ISOdict['modeDispl'])}
for iatm,[atom,atcode] in enumerate(zip(Atoms,atCodes)):
pid,opid = [int(item) for item in atcode.split(':')]
atmConstr = [atom[ct-1],atom[ct],'','','','','',atcode]
for ip,pname in enumerate(['%s_d%s'%(atom[ct-1],x) for x in ['x','y','z']]):
try:
conStr = ''
if Atoms[iatm][cx+ip]:
conStr += '%.5f'%Atoms[iatm][cx+ip]
pid = ISOdict['IsoVarList'].index(pname)
consVec = ModeMatrix[pid]
for ic,citm in enumerate(consVec): #NB: this assumes orthorhombic or lower symmetry
if opid < 0:
citm *= -SGOps[100-opid%100-1][0][ip][ip] #remove centering, if any
else:
citm *= SGOps[opid%100-1][0][ip][ip]
if citm > 0.:
conStr += '+%.5f*@%d'%(citm*Norms[ic],ic+21)
elif citm < 0.:
conStr += '%.5f*@%d'%(citm*Norms[ic],ic+21)
atmConstr[ip+2] = conStr
except ValueError:
atmConstr[ip+2] = ''
RMCPdict['AtomConstr'].append(atmConstr)
return newPhase
def GetAtmDispList(ISOdata):
atmDispList = []
MT = ISOdata['Mode2VarMatrix'].T
DispList = ISOdata['IsoVarList']
N = len(DispList)
for I in range(N):
vary = []
for i in range(N):
if MT[I,i]:
vary.append(DispList[i])
atmDispList.append(vary)
return atmDispList
#### Reflectometry calculations ################################################################################
def REFDRefine(Profile,ProfDict,Inst,Limits,Substances,data):
G2fil.G2Print ('fit REFD data by '+data['Minimizer']+' using %.2f%% data resolution'%(data['Resolution'][0]))
class RandomDisplacementBounds(object):
"""random displacement with bounds"""
def __init__(self, xmin, xmax, stepsize=0.5):
self.xmin = xmin
self.xmax = xmax
self.stepsize = stepsize
def __call__(self, x):
"""take a random step but ensure the new position is within the bounds"""
while True:
# this could be done in a much more clever way, but it will work for example purposes
steps = self.xmax-self.xmin
xnew = x + np.random.uniform(-self.stepsize*steps, self.stepsize*steps, np.shape(x))
if np.all(xnew < self.xmax) and np.all(xnew > self.xmin):
break
return xnew
def GetModelParms():
parmDict = {}
varyList = []
values = []
bounds = []
parmDict['dQ type'] = data['dQ type']
parmDict['Res'] = data['Resolution'][0]/(100.*sateln2) #% FWHM-->decimal sig
for parm in ['Scale','FltBack']:
parmDict[parm] = data[parm][0]
if data[parm][1]:
varyList.append(parm)
values.append(data[parm][0])
bounds.append(Bounds[parm])
parmDict['Layer Seq'] = np.array(['0',]+data['Layer Seq'].split()+[str(len(data['Layers'])-1),],dtype=int)
parmDict['nLayers'] = len(parmDict['Layer Seq'])
for ilay,layer in enumerate(data['Layers']):
name = layer['Name']
cid = str(ilay)+';'
parmDict[cid+'Name'] = name
for parm in ['Thick','Rough','DenMul','Mag SLD','iDenMul']:
parmDict[cid+parm] = layer.get(parm,[0.,False])[0]
if layer.get(parm,[0.,False])[1]:
varyList.append(cid+parm)
value = layer[parm][0]
values.append(value)
if value:
bound = [value*Bfac,value/Bfac]
else:
bound = [0.,10.]
bounds.append(bound)
if name not in ['vacuum','unit scatter']:
parmDict[cid+'rho'] = Substances[name]['Scatt density']
parmDict[cid+'irho'] = Substances[name].get('XImag density',0.)
return parmDict,varyList,values,bounds
def SetModelParms():
line = ' Refined parameters: Histogram scale: %.4g'%(parmDict['Scale'])
if 'Scale' in varyList:
data['Scale'][0] = parmDict['Scale']
line += ' esd: %.4g'%(sigDict['Scale'])
G2fil.G2Print (line)
line = ' Flat background: %15.4g'%(parmDict['FltBack'])
if 'FltBack' in varyList:
data['FltBack'][0] = parmDict['FltBack']
line += ' esd: %15.3g'%(sigDict['FltBack'])
G2fil.G2Print (line)
for ilay,layer in enumerate(data['Layers']):
name = layer['Name']
G2fil.G2Print (' Parameters for layer: %d %s'%(ilay,name))
cid = str(ilay)+';'
line = ' '
line2 = ' Scattering density: Real %.5g'%(Substances[name]['Scatt density']*parmDict[cid+'DenMul'])
line2 += ' Imag %.5g'%(Substances[name].get('XImag density',0.)*parmDict[cid+'DenMul'])
for parm in ['Thick','Rough','DenMul','Mag SLD','iDenMul']:
if parm in layer:
if parm == 'Rough':
layer[parm][0] = abs(parmDict[cid+parm]) #make positive
else:
layer[parm][0] = parmDict[cid+parm]
line += ' %s: %.3f'%(parm,layer[parm][0])
if cid+parm in varyList:
line += ' esd: %.3g'%(sigDict[cid+parm])
G2fil.G2Print (line)
G2fil.G2Print (line2)
def calcREFD(values,Q,Io,wt,Qsig,parmDict,varyList):
parmDict.update(zip(varyList,values))
M = np.sqrt(wt)*(getREFD(Q,Qsig,parmDict)-Io)
return M
def sumREFD(values,Q,Io,wt,Qsig,parmDict,varyList):
parmDict.update(zip(varyList,values))
M = np.sqrt(wt)*(getREFD(Q,Qsig,parmDict)-Io)
return np.sum(M**2)
def getREFD(Q,Qsig,parmDict):
Ic = np.ones_like(Q)*parmDict['FltBack']
Scale = parmDict['Scale']
Nlayers = parmDict['nLayers']
Res = parmDict['Res']
depth = np.zeros(Nlayers)
rho = np.zeros(Nlayers)
irho = np.zeros(Nlayers)
sigma = np.zeros(Nlayers)
for ilay,lay in enumerate(parmDict['Layer Seq']):
cid = str(lay)+';'
depth[ilay] = parmDict[cid+'Thick']
sigma[ilay] = parmDict[cid+'Rough']
if parmDict[cid+'Name'] == u'unit scatter':
rho[ilay] = parmDict[cid+'DenMul']
irho[ilay] = parmDict[cid+'iDenMul']
elif 'vacuum' != parmDict[cid+'Name']:
rho[ilay] = parmDict[cid+'rho']*parmDict[cid+'DenMul']
irho[ilay] = parmDict[cid+'irho']*parmDict[cid+'DenMul']
if cid+'Mag SLD' in parmDict:
rho[ilay] += parmDict[cid+'Mag SLD']
if parmDict['dQ type'] == 'None':
AB = abeles(0.5*Q,depth,rho,irho,sigma[1:]) #Q --> k, offset roughness for abeles
elif 'const' in parmDict['dQ type']:
AB = SmearAbeles(0.5*Q,Q*Res,depth,rho,irho,sigma[1:])
else: #dQ/Q in data
AB = SmearAbeles(0.5*Q,Qsig,depth,rho,irho,sigma[1:])
Ic += AB*Scale
return Ic
def estimateT0(takestep):
Mmax = -1.e-10
Mmin = 1.e10
for i in range(100):
x0 = takestep(values)
M = sumREFD(x0,Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList)
Mmin = min(M,Mmin)
MMax = max(M,Mmax)
return 1.5*(MMax-Mmin)
Q,Io,wt,Ic,Ib,Qsig = Profile[:6]
if data.get('2% weight'):
wt = 1./(0.02*Io)**2
Qmin = Limits[1][0]
Qmax = Limits[1][1]
wtFactor = ProfDict['wtFactor']
Bfac = data['Toler']
Ibeg = np.searchsorted(Q,Qmin)
Ifin = np.searchsorted(Q,Qmax)+1 #include last point
Ic[:] = 0
Bounds = {'Scale':[data['Scale'][0]*Bfac,data['Scale'][0]/Bfac],'FltBack':[0.,1.e-6],
'DenMul':[0.,1.],'Thick':[1.,500.],'Rough':[0.,10.],'Mag SLD':[-10.,10.],'iDenMul':[-1.,1.]}
parmDict,varyList,values,bounds = GetModelParms()
Msg = 'Failed to converge'
if varyList:
if data['Minimizer'] == 'LMLS':
result = so.leastsq(calcREFD,values,full_output=True,epsfcn=1.e-8,ftol=1.e-6,
args=(Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList))
parmDict.update(zip(varyList,result[0]))
chisq = np.sum(result[2]['fvec']**2)
ncalc = result[2]['nfev']
covM = result[1]
newVals = result[0]
elif data['Minimizer'] == 'Basin Hopping':
xyrng = np.array(bounds).T
take_step = RandomDisplacementBounds(xyrng[0], xyrng[1])
T0 = estimateT0(take_step)
G2fil.G2Print (' Estimated temperature: %.3g'%(T0))
result = so.basinhopping(sumREFD,values,take_step=take_step,disp=True,T=T0,stepsize=Bfac,
interval=20,niter=200,minimizer_kwargs={'method':'L-BFGS-B','bounds':bounds,
'args':(Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList)})
chisq = result.fun
ncalc = result.nfev
newVals = result.x
covM = []
elif data['Minimizer'] == 'MC/SA Anneal':
xyrng = np.array(bounds).T
result = G2mth.anneal(sumREFD, values,
args=(Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList),
schedule='log', full_output=True,maxeval=None, maxaccept=None, maxiter=10,dwell=1000,
boltzmann=10.0, feps=1e-6,lower=xyrng[0], upper=xyrng[1], slope=0.9,ranStart=True,
ranRange=0.20,autoRan=False,dlg=None)
newVals = result[0]
parmDict.update(zip(varyList,newVals))
chisq = result[1]
ncalc = result[3]
covM = []
G2fil.G2Print (' MC/SA final temperature: %.4g'%(result[2]))
elif data['Minimizer'] == 'L-BFGS-B':
result = so.minimize(sumREFD,values,method='L-BFGS-B',bounds=bounds, #ftol=Ftol,
args=(Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList))
parmDict.update(zip(varyList,result['x']))
chisq = result.fun
ncalc = result.nfev
newVals = result.x
covM = []
else: #nothing varied
M = calcREFD(values,Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict,varyList)
chisq = np.sum(M**2)
ncalc = 0
covM = []
sig = []
sigDict = {}
result = []
Rvals = {}
Rvals['Rwp'] = np.sqrt(chisq/np.sum(wt[Ibeg:Ifin]*Io[Ibeg:Ifin]**2))*100. #to %
Rvals['GOF'] = chisq/(Ifin-Ibeg-len(varyList)) #reduced chi^2
Ic[Ibeg:Ifin] = getREFD(Q[Ibeg:Ifin],Qsig[Ibeg:Ifin],parmDict)
Ib[Ibeg:Ifin] = parmDict['FltBack']
try:
if not len(varyList):
Msg += ' - nothing refined'
raise ValueError
Nans = np.isnan(newVals)
if np.any(Nans):
name = varyList[Nans.nonzero(True)[0]]
Msg += ' Nan result for '+name+'!'
raise ValueError
Negs = np.less_equal(newVals,0.)
if np.any(Negs):
indx = Negs.nonzero()
name = varyList[indx[0][0]]
if name != 'FltBack' and name.split(';')[1] in ['Thick',]:
Msg += ' negative coefficient for '+name+'!'
raise ValueError
if len(covM):
sig = np.sqrt(np.diag(covM)*Rvals['GOF'])
covMatrix = covM*Rvals['GOF']
else:
sig = np.zeros(len(varyList))
covMatrix = []
sigDict = dict(zip(varyList,sig))
G2fil.G2Print (' Results of reflectometry data modelling fit:')
G2fil.G2Print ('Number of function calls: %d Number of observations: %d Number of parameters: %d'%(ncalc,Ifin-Ibeg,len(varyList)))
G2fil.G2Print ('Rwp = %7.2f%%, chi**2 = %12.6g, reduced chi**2 = %6.2f'%(Rvals['Rwp'],chisq,Rvals['GOF']))
SetModelParms()
return True,result,varyList,sig,Rvals,covMatrix,parmDict,''
except (ValueError,TypeError): #when bad LS refinement; covM missing or with nans
G2fil.G2Print (Msg)
return False,0,0,0,0,0,0,Msg
def makeSLDprofile(data,Substances):
sq2 = np.sqrt(2.)
laySeq = ['0',]+data['Layer Seq'].split()+[str(len(data['Layers'])-1),]
Nlayers = len(laySeq)
laySeq = np.array(laySeq,dtype=int)
interfaces = np.zeros(Nlayers)
rho = np.zeros(Nlayers)
sigma = np.zeros(Nlayers)
name = data['Layers'][0]['Name']
thick = 0.
for ilay,lay in enumerate(laySeq):
layer = data['Layers'][lay]
name = layer['Name']
if 'Thick' in layer:
thick += layer['Thick'][0]
interfaces[ilay] = layer['Thick'][0]+interfaces[ilay-1]
if 'Rough' in layer:
sigma[ilay] = max(0.001,layer['Rough'][0])
if name != 'vacuum':
if name == 'unit scatter':
rho[ilay] = np.sqrt(layer['DenMul'][0]**2+layer['iDenMul'][0]**2)
else:
rrho = Substances[name]['Scatt density']
irho = Substances[name]['XImag density']
rho[ilay] = np.sqrt(rrho**2+irho**2)*layer['DenMul'][0]
if 'Mag SLD' in layer:
rho[ilay] += layer['Mag SLD'][0]
name = data['Layers'][-1]['Name']
x = np.linspace(-0.15*thick,1.15*thick,1000,endpoint=True)
xr = np.flipud(x)
interfaces[-1] = x[-1]
y = np.ones_like(x)*rho[0]
iBeg = 0
for ilayer in range(Nlayers-1):
delt = rho[ilayer+1]-rho[ilayer]
iPos = np.searchsorted(x,interfaces[ilayer])
y[iBeg:] += (delt/2.)*sp.erfc((interfaces[ilayer]-x[iBeg:])/(sq2*sigma[ilayer+1]))
iBeg = iPos
return x,xr,y
def REFDModelFxn(Profile,Inst,Limits,Substances,data):
Q,Io,wt,Ic,Ib,Qsig = Profile[:6]
Qmin = Limits[1][0]
Qmax = Limits[1][1]
iBeg = np.searchsorted(Q,Qmin)
iFin = np.searchsorted(Q,Qmax)+1 #include last point
Ib[:] = data['FltBack'][0]
Ic[:] = 0
Scale = data['Scale'][0]
if data['Layer Seq'] == []:
return
laySeq = ['0',]+data['Layer Seq'].split()+[str(len(data['Layers'])-1),]
Nlayers = len(laySeq)
depth = np.zeros(Nlayers)
rho = np.zeros(Nlayers)
irho = np.zeros(Nlayers)
sigma = np.zeros(Nlayers)
for ilay,lay in enumerate(np.array(laySeq,dtype=int)):
layer = data['Layers'][lay]
name = layer['Name']
if 'Thick' in layer: #skips first & last layers
depth[ilay] = layer['Thick'][0]
if 'Rough' in layer: #skips first layer
sigma[ilay] = layer['Rough'][0]
if 'unit scatter' == name:
rho[ilay] = layer['DenMul'][0]
irho[ilay] = layer['iDenMul'][0]
else:
rho[ilay] = Substances[name]['Scatt density']*layer['DenMul'][0]
irho[ilay] = Substances[name].get('XImag density',0.)*layer['DenMul'][0]
if 'Mag SLD' in layer:
rho[ilay] += layer['Mag SLD'][0]
if data['dQ type'] == 'None':
AB = abeles(0.5*Q[iBeg:iFin],depth,rho,irho,sigma[1:]) #Q --> k, offset roughness for abeles
elif 'const' in data['dQ type']:
res = data['Resolution'][0]/(100.*sateln2)
AB = SmearAbeles(0.5*Q[iBeg:iFin],res*Q[iBeg:iFin],depth,rho,irho,sigma[1:])
else: #dQ/Q in data
AB = SmearAbeles(0.5*Q[iBeg:iFin],Qsig[iBeg:iFin],depth,rho,irho,sigma[1:])
Ic[iBeg:iFin] = AB*Scale+Ib[iBeg:iFin]
[docs]
def abeles(kz, depth, rho, irho=0, sigma=0):
"""
Optical matrix form of the reflectivity calculation.
O.S. Heavens, Optical Properties of Thin Solid Films
Reflectometry as a function of kz for a set of slabs.
:param kz: float[n] (1/Ang). Scattering vector, :math:`2\\pi\\sin(\\theta)/\\lambda`.
This is :math:`\\tfrac12 Q_z`.
:param depth: float[m] (Ang).
thickness of each layer. The thickness of the incident medium
and substrate are ignored.
:param rho: float[n,k] (1e-6/Ang^2)
Real scattering length density for each layer for each kz
:param irho: float[n,k] (1e-6/Ang^2)
Imaginary scattering length density for each layer for each kz
Note: absorption cross section mu = 2 irho/lambda for neutrons
:param sigma: float[m-1] (Ang)
interfacial roughness. This is the roughness between a layer
and the previous layer. The sigma array should have m-1 entries.
Slabs are ordered with the surface SLD at index 0 and substrate at
index -1, or reversed if kz < 0.
"""
def calc(kz, depth, rho, irho, sigma):
if len(kz) == 0: return kz
# Complex index of refraction is relative to the incident medium.
# We can get the same effect using kz_rel^2 = kz^2 + 4*pi*rho_o
# in place of kz^2, and ignoring rho_o
kz_sq = kz**2 + 4e-6*np.pi*rho[:,0]
k = kz
# According to Heavens, the initial matrix should be [ 1 F; F 1],
# which we do by setting B=I and M0 to [1 F; F 1]. An extra matrix
# multiply versus some coding convenience.
B11 = 1
B22 = 1
B21 = 0
B12 = 0
for i in range(0, len(depth)-1):
k_next = np.sqrt(kz_sq - 4e-6*np.pi*(rho[:,i+1] + 1j*irho[:,i+1]))
F = (k - k_next) / (k + k_next)
F *= np.exp(-2*k*k_next*sigma[i]**2)
#print "==== layer",i
#print "kz:", kz
#print "k:", k
#print "k_next:",k_next
#print "F:",F
#print "rho:",rho[:,i+1]
#print "irho:",irho[:,i+1]
#print "d:",depth[i],"sigma:",sigma[i]
M11 = np.exp(1j*k*depth[i]) if i>0 else 1
M22 = np.exp(-1j*k*depth[i]) if i>0 else 1
M21 = F*M11
M12 = F*M22
C1 = B11*M11 + B21*M12
C2 = B11*M21 + B21*M22
B11 = C1
B21 = C2
C1 = B12*M11 + B22*M12
C2 = B12*M21 + B22*M22
B12 = C1
B22 = C2
k = k_next
r = B12/B11
return np.absolute(r)**2
if np.isscalar(kz): kz = np.asarray([kz], 'd')
m = len(depth)
# Make everything into arrays
depth = np.asarray(depth,'d')
rho = np.asarray(rho,'d')
irho = irho*np.ones_like(rho) if np.isscalar(irho) else np.asarray(irho,'d')
sigma = sigma*np.ones(m-1,'d') if np.isscalar(sigma) else np.asarray(sigma,'d')
# Repeat rho,irho columns as needed
if len(rho.shape) == 1:
rho = rho[None,:]
irho = irho[None,:]
return calc(kz, depth, rho, irho, sigma)
def SmearAbeles(kz,dq, depth, rho, irho=0, sigma=0):
y = abeles(kz, depth, rho, irho, sigma)
s = dq/2.
y += 0.1354*(abeles(kz+2*s, depth, rho, irho, sigma)+abeles(kz-2*s, depth, rho, irho, sigma))
y += 0.24935*(abeles(kz-5*s/3., depth, rho, irho, sigma)+abeles(kz+5*s/3., depth, rho, irho, sigma))
y += 0.4111*(abeles(kz-4*s/3., depth, rho, irho, sigma)+abeles(kz+4*s/3., depth, rho, irho, sigma))
y += 0.60653*(abeles(kz-s, depth, rho, irho, sigma) +abeles(kz+s, depth, rho, irho, sigma))
y += 0.80074*(abeles(kz-2*s/3., depth, rho, irho, sigma)+abeles(kz-2*s/3., depth, rho, irho, sigma))
y += 0.94596*(abeles(kz-s/3., depth, rho, irho, sigma)+abeles(kz-s/3., depth, rho, irho, sigma))
y *= 0.137023
return y
def makeRefdFFT(Limits,Profile):
G2fil.G2Print ('make fft')
Q,Io = Profile[:2]
Qmin = Limits[1][0]
Qmax = Limits[1][1]
iBeg = np.searchsorted(Q,Qmin)
iFin = np.searchsorted(Q,Qmax)+1 #include last point
Qf = np.linspace(0.,Q[iFin-1],5000)
QI = si.interp1d(Q[iBeg:iFin],Io[iBeg:iFin],bounds_error=False,fill_value=0.0)
If = QI(Qf)*Qf**4
R = np.linspace(0.,5000.,5000)
F = fft.rfft(If)
return R,F
#### Stacking fault simulation codes ################################################################################
def GetStackParms(Layers):
Parms = []
#cell parms
if Layers['Laue'] in ['-3','-3m','4/m','4/mmm','6/m','6/mmm']:
Parms.append('cellA')
Parms.append('cellC')
else:
Parms.append('cellA')
Parms.append('cellB')
Parms.append('cellC')
if Layers['Laue'] != 'mmm':
Parms.append('cellG')
#Transition parms
for iY in range(len(Layers['Layers'])):
for iX in range(len(Layers['Layers'])):
Parms.append('TransP;%d;%d'%(iY,iX))
Parms.append('TransX;%d;%d'%(iY,iX))
Parms.append('TransY;%d;%d'%(iY,iX))
Parms.append('TransZ;%d;%d'%(iY,iX))
return Parms
[docs]
def StackSim(Layers,ctrls,scale=0.,background={},limits=[],inst={},profile=[]):
'''Simulate powder or selected area diffraction pattern from stacking faults using DIFFaX
:param dict Layers: dict with following items
::
{'Laue':'-1','Cell':[False,1.,1.,1.,90.,90.,90,1.],
'Width':[[10.,10.],[False,False]],'Toler':0.01,'AtInfo':{},
'Layers':[],'Stacking':[],'Transitions':[]}
:param str ctrls: controls string to be written on DIFFaX controls.dif file
:param float scale: scale factor
:param dict background: background parameters
:param list limits: min/max 2-theta to be calculated
:param dict inst: instrument parameters dictionary
:param list profile: powder pattern data
Note that parameters all updated in place
'''
import atmdata
path = sys.path
for name in path:
if 'bin' in name:
DIFFaX = name+'/DIFFaX.exe'
G2fil.G2Print (' Execute '+DIFFaX)
break
# make form factor file that DIFFaX wants - atom types are GSASII style
sf = open('data.sfc','w')
sf.write('GSASII special form factor file for DIFFaX\n\n')
atTypes = list(Layers['AtInfo'].keys())
if 'H' not in atTypes:
atTypes.insert(0,'H')
for atType in atTypes:
if atType == 'H':
blen = -.3741
else:
blen = Layers['AtInfo'][atType]['Isotopes']['Nat. Abund.']['SL'][0]
Adat = atmdata.XrayFF[atType]
text = '%4s'%(atType.ljust(4))
for i in range(4):
text += '%11.6f%11.6f'%(Adat['fa'][i],Adat['fb'][i])
text += '%11.6f%11.6f'%(Adat['fc'],blen)
text += '%3d\n'%(Adat['Z'])
sf.write(text)
sf.close()
#make DIFFaX control.dif file - future use GUI to set some of these flags
cf = open('control.dif','w')
if ctrls == '0\n0\n3\n' or ctrls == '0\n1\n3\n':
x0 = profile[0]
iBeg = np.searchsorted(x0,limits[0])
iFin = np.searchsorted(x0,limits[1])+1
if iFin-iBeg > 20000:
iFin = iBeg+20000
Dx = (x0[iFin]-x0[iBeg])/(iFin-iBeg)
cf.write('GSASII-DIFFaX.dat\n'+ctrls)
cf.write('%.6f %.6f %.6f\n1\n1\nend\n'%(x0[iBeg],x0[iFin],Dx))
else:
cf.write('GSASII-DIFFaX.dat\n'+ctrls)
inst = {'Type':['XSC','XSC',]}
cf.close()
#make DIFFaX data file
df = open('GSASII-DIFFaX.dat','w')
df.write('INSTRUMENTAL\n')
if 'X' in inst['Type'][0]:
df.write('X-RAY\n')
elif 'N' in inst['Type'][0]:
df.write('NEUTRON\n')
if ctrls == '0\n0\n3\n' or ctrls == '0\n1\n3\n':
df.write('%.4f\n'%(G2mth.getMeanWave(inst)))
U = ateln2*inst['U'][1]/10000.
V = ateln2*inst['V'][1]/10000.
W = ateln2*inst['W'][1]/10000.
HWHM = U*nptand(x0[iBeg:iFin]/2.)**2+V*nptand(x0[iBeg:iFin]/2.)+W
HW = np.sqrt(np.mean(HWHM))
# df.write('PSEUDO-VOIGT 0.015 -0.0036 0.009 0.605 TRIM\n')
if 'Mean' in Layers['selInst']:
df.write('GAUSSIAN %.6f TRIM\n'%(HW)) #fast option - might not really matter
elif 'Gaussian' in Layers['selInst']:
df.write('GAUSSIAN %.6f %.6f %.6f TRIM\n'%(U,V,W)) #slow - make a GUI option?
else:
df.write('None\n')
else:
df.write('0.10\nNone\n')
df.write('STRUCTURAL\n')
a,b,c = Layers['Cell'][1:4]
gam = Layers['Cell'][6]
df.write('%.4f %.4f %.4f %.3f\n'%(a,b,c,gam))
laue = Layers['Laue']
if laue == '2/m(ab)':
laue = '2/m(1)'
elif laue == '2/m(c)':
laue = '2/m(2)'
if 'unknown' in Layers['Laue']:
df.write('%s %.3f\n'%(laue,Layers['Toler']))
else:
df.write('%s\n'%(laue))
df.write('%d\n'%(len(Layers['Layers'])))
if Layers['Width'][0][0] < 1. or Layers['Width'][0][1] < 1.:
df.write('%.1f %.1f\n'%(Layers['Width'][0][0]*10000.,Layers['Width'][0][0]*10000.)) #mum to A
layerNames = []
for layer in Layers['Layers']:
layerNames.append(layer['Name'])
for il,layer in enumerate(Layers['Layers']):
if layer['SameAs']:
df.write('LAYER %d = %d\n'%(il+1,layerNames.index(layer['SameAs'])+1))
continue
df.write('LAYER %d\n'%(il+1))
if '-1' in layer['Symm']:
df.write('CENTROSYMMETRIC\n')
else:
df.write('NONE\n')
for ia,atom in enumerate(layer['Atoms']):
[name,atype,x,y,z,frac,Uiso] = atom
if '-1' in layer['Symm'] and [x,y,z] == [0.,0.,0.]:
frac /= 2.
df.write('%4s %3d %.5f %.5f %.5f %.4f %.2f\n'%(atype.ljust(6),ia,x,y,z,78.9568*Uiso,frac))
df.write('STACKING\n')
df.write('%s\n'%(Layers['Stacking'][0]))
if 'recursive' in Layers['Stacking'][0]:
df.write('%s\n'%Layers['Stacking'][1])
else:
if 'list' in Layers['Stacking'][1]:
Slen = len(Layers['Stacking'][2])
iB = 0
iF = 0
while True:
iF += 68
if iF >= Slen:
break
iF = min(iF,Slen)
df.write('%s\n'%(Layers['Stacking'][2][iB:iF]))
iB = iF
else:
df.write('%s\n'%Layers['Stacking'][1])
df.write('TRANSITIONS\n')
for iY in range(len(Layers['Layers'])):
sumPx = 0.
for iX in range(len(Layers['Layers'])):
p,dx,dy,dz = Layers['Transitions'][iY][iX][:4]
p = round(p,3)
df.write('%.3f %.5f %.5f %.5f\n'%(p,dx,dy,dz))
sumPx += p
if sumPx != 1.0: #this has to be picky since DIFFaX is.
G2fil.G2Print ('ERROR - Layer probabilities sum to %.3f DIFFaX will insist it = 1.0'%sumPx)
df.close()
os.remove('data.sfc')
os.remove('control.dif')
os.remove('GSASII-DIFFaX.dat')
return
df.close()
time0 = time.time()
try:
subp.call(DIFFaX)
except OSError:
G2fil.G2Print('DIFFax.exe is not available for this platform',mode='warn')
G2fil.G2Print (' DIFFaX time = %.2fs'%(time.time()-time0))
if os.path.exists('GSASII-DIFFaX.spc'):
Xpat = np.loadtxt('GSASII-DIFFaX.spc').T
iFin = iBeg+Xpat.shape[1]
bakType,backDict,backVary = SetBackgroundParms(background)
backDict['Lam1'] = G2mth.getWave(inst)
profile[4][iBeg:iFin] = getBackground('',backDict,bakType,inst['Type'][0],profile[0][iBeg:iFin])[0]
profile[3][iBeg:iFin] = Xpat[-1]*scale+profile[4][iBeg:iFin]
if not np.any(profile[1]): #fill dummy data x,y,w,yc,yb,yd
rv = st.poisson(profile[3][iBeg:iFin])
profile[1][iBeg:iFin] = rv.rvs()
Z = np.ones_like(profile[3][iBeg:iFin])
Z[1::2] *= -1
profile[1][iBeg:iFin] = profile[3][iBeg:iFin]+np.abs(profile[1][iBeg:iFin]-profile[3][iBeg:iFin])*Z
profile[2][iBeg:iFin] = np.where(profile[1][iBeg:iFin]>0.,1./profile[1][iBeg:iFin],1.0)
profile[5][iBeg:iFin] = profile[1][iBeg:iFin]-profile[3][iBeg:iFin]
#cleanup files..
os.remove('GSASII-DIFFaX.spc')
elif os.path.exists('GSASII-DIFFaX.sadp'):
Sadp = np.fromfile('GSASII-DIFFaX.sadp','>u2')
Sadp = np.reshape(Sadp,(256,-1))
Layers['Sadp']['Img'] = Sadp
os.remove('GSASII-DIFFaX.sadp')
os.remove('data.sfc')
os.remove('control.dif')
os.remove('GSASII-DIFFaX.dat')
def SetPWDRscan(inst,limits,profile):
wave = G2mth.getMeanWave(inst)
x0 = profile[0]
iBeg = np.searchsorted(x0,limits[0])
iFin = np.searchsorted(x0,limits[1])
if iFin-iBeg > 20000:
iFin = iBeg+20000
Dx = (x0[iFin]-x0[iBeg])/(iFin-iBeg)
pyx.pygetinst(wave,x0[iBeg],x0[iFin],Dx)
return iFin-iBeg
def SetStackingSF(Layers,debug):
# Load scattering factors into DIFFaX arrays
import atmdata
atTypes = Layers['AtInfo'].keys()
aTypes = []
for atype in atTypes:
aTypes.append('%4s'%(atype.ljust(4)))
SFdat = []
for atType in atTypes:
Adat = atmdata.XrayFF[atType]
SF = np.zeros(9)
SF[:8:2] = Adat['fa']
SF[1:8:2] = Adat['fb']
SF[8] = Adat['fc']
SFdat.append(SF)
SFdat = np.array(SFdat)
pyx.pyloadscf(len(atTypes),aTypes,SFdat.T,debug)
def SetStackingClay(Layers,Type):
# Controls
rand.seed()
ranSeed = rand.randint(1,2**16-1)
try:
laueId = ['-1','2/m(ab)','2/m(c)','mmm','-3','-3m','4/m','4/mmm',
'6/m','6/mmm'].index(Layers['Laue'])+1
except ValueError: #for 'unknown'
laueId = -1
if 'SADP' in Type:
planeId = ['h0l','0kl','hhl','h-hl'].index(Layers['Sadp']['Plane'])+1
lmax = int(Layers['Sadp']['Lmax'])
else:
planeId = 0
lmax = 0
# Sequences
StkType = ['recursive','explicit'].index(Layers['Stacking'][0])
try:
StkParm = ['infinite','random','list'].index(Layers['Stacking'][1])
except ValueError:
StkParm = -1
if StkParm == 2: #list
StkSeq = [int(val) for val in Layers['Stacking'][2].split()]
Nstk = len(StkSeq)
else:
Nstk = 1
StkSeq = [0,]
if StkParm == -1:
StkParm = int(Layers['Stacking'][1])
Wdth = Layers['Width'][0]
mult = 1
controls = [laueId,planeId,lmax,mult,StkType,StkParm,ranSeed]
LaueSym = Layers['Laue'].ljust(12)
pyx.pygetclay(controls,LaueSym,Wdth,Nstk,StkSeq)
return laueId,controls
def SetCellAtoms(Layers):
Cell = Layers['Cell'][1:4]+Layers['Cell'][6:7]
# atoms in layers
atTypes = list(Layers['AtInfo'].keys())
AtomXOU = []
AtomTp = []
LayerSymm = []
LayerNum = []
layerNames = []
Natm = 0
Nuniq = 0
for layer in Layers['Layers']:
layerNames.append(layer['Name'])
for il,layer in enumerate(Layers['Layers']):
if layer['SameAs']:
LayerNum.append(layerNames.index(layer['SameAs'])+1)
continue
else:
LayerNum.append(il+1)
Nuniq += 1
if '-1' in layer['Symm']:
LayerSymm.append(1)
else:
LayerSymm.append(0)
for ia,atom in enumerate(layer['Atoms']):
[name,atype,x,y,z,frac,Uiso] = atom
Natm += 1
AtomTp.append('%4s'%(atype.ljust(4)))
Ta = atTypes.index(atype)+1
AtomXOU.append([float(Nuniq),float(ia+1),float(Ta),x,y,z,frac,Uiso*78.9568])
AtomXOU = np.array(AtomXOU)
Nlayers = len(layerNames)
pyx.pycellayer(Cell,Natm,AtomTp,AtomXOU.T,Nuniq,LayerSymm,Nlayers,LayerNum)
return Nlayers
def SetStackingTrans(Layers,Nlayers):
# Transitions
TransX = []
TransP = []
for Ytrans in Layers['Transitions']:
TransP.append([trans[0] for trans in Ytrans]) #get just the numbers
TransX.append([trans[1:4] for trans in Ytrans]) #get just the numbers
TransP = np.array(TransP,dtype='float').T
TransX = np.array(TransX,dtype='float')
# GSASIIpath.IPyBreak()
pyx.pygettrans(Nlayers,TransP,TransX)
def CalcStackingPWDR(Layers,scale,background,limits,inst,profile,debug):
# Scattering factors
SetStackingSF(Layers,debug)
# Controls & sequences
laueId,controls = SetStackingClay(Layers,'PWDR')
# cell & atoms
Nlayers = SetCellAtoms(Layers)
Volume = Layers['Cell'][7]
# Transitions
SetStackingTrans(Layers,Nlayers)
# PWDR scan
Nsteps = SetPWDRscan(inst,limits,profile)
# result as Spec
x0 = profile[0]
profile[3] = np.zeros(len(profile[0]))
profile[4] = np.zeros(len(profile[0]))
profile[5] = np.zeros(len(profile[0]))
iBeg = np.searchsorted(x0,limits[0])
iFin = np.searchsorted(x0,limits[1])+1
if iFin-iBeg > 20000:
iFin = iBeg+20000
Nspec = 20001
spec = np.zeros(Nspec,dtype='double')
time0 = time.time()
pyx.pygetspc(controls,Nspec,spec)
G2fil.G2Print (' GETSPC time = %.2fs'%(time.time()-time0))
time0 = time.time()
U = ateln2*inst['U'][1]/10000.
V = ateln2*inst['V'][1]/10000.
W = ateln2*inst['W'][1]/10000.
HWHM = U*nptand(x0[iBeg:iFin]/2.)**2+V*nptand(x0[iBeg:iFin]/2.)+W
HW = np.sqrt(np.mean(HWHM))
BrdSpec = np.zeros(Nsteps)
if 'Mean' in Layers['selInst']:
pyx.pyprofile(U,V,W,HW,1,Nsteps,BrdSpec)
elif 'Gaussian' in Layers['selInst']:
pyx.pyprofile(U,V,W,HW,4,Nsteps,BrdSpec)
else:
BrdSpec = spec[:Nsteps]
BrdSpec /= Volume
iFin = iBeg+Nsteps
bakType,backDict,backVary = SetBackgroundParms(background)
backDict['Lam1'] = G2mth.getWave(inst)
profile[4][iBeg:iFin] = getBackground('',backDict,bakType,inst['Type'][0],profile[0][iBeg:iFin])[0]
profile[3][iBeg:iFin] = BrdSpec*scale+profile[4][iBeg:iFin]
if not np.any(profile[1]): #fill dummy data x,y,w,yc,yb,yd
try:
rv = st.poisson(profile[3][iBeg:iFin])
profile[1][iBeg:iFin] = rv.rvs()
except ValueError:
profile[1][iBeg:iFin] = profile[3][iBeg:iFin]
Z = np.ones_like(profile[3][iBeg:iFin])
Z[1::2] *= -1
profile[1][iBeg:iFin] = profile[3][iBeg:iFin]+np.abs(profile[1][iBeg:iFin]-profile[3][iBeg:iFin])*Z
profile[2][iBeg:iFin] = np.where(profile[1][iBeg:iFin]>0.,1./profile[1][iBeg:iFin],1.0)
profile[5][iBeg:iFin] = profile[1][iBeg:iFin]-profile[3][iBeg:iFin]
G2fil.G2Print (' Broadening time = %.2fs'%(time.time()-time0))
def CalcStackingSADP(Layers,debug):
# Scattering factors
SetStackingSF(Layers,debug)
# Controls & sequences
laueId,controls = SetStackingClay(Layers,'SADP')
# cell & atoms
Nlayers = SetCellAtoms(Layers)
# Transitions
SetStackingTrans(Layers,Nlayers)
# result as Sadp
Nspec = 20001
spec = np.zeros(Nspec,dtype='double')
time0 = time.time()
hkLim,Incr,Nblk = pyx.pygetsadp(controls,Nspec,spec)
Sapd = np.zeros((256,256))
iB = 0
for i in range(hkLim):
iF = iB+Nblk
p1 = 127+int(i*Incr)
p2 = 128-int(i*Incr)
if Nblk == 128:
if i:
Sapd[128:,p1] = spec[iB:iF]
Sapd[:128,p1] = spec[iF:iB:-1]
Sapd[128:,p2] = spec[iB:iF]
Sapd[:128,p2] = spec[iF:iB:-1]
else:
if i:
Sapd[:,p1] = spec[iB:iF]
Sapd[:,p2] = spec[iB:iF]
iB += Nblk
Layers['Sadp']['Img'] = Sapd
G2fil.G2Print (' GETSAD time = %.2fs'%(time.time()-time0))
#### Maximum Entropy Method - Dysnomia ###############################################################################
[docs]
def makePRFfile(data,MEMtype):
''' makes Dysnomia .prf control file from Dysnomia GUI controls
:param dict data: GSAS-II phase data
:param int MEMtype: 1 for neutron data with negative scattering lengths
0 otherwise
:returns str: name of Dysnomia control file
'''
generalData = data['General']
pName = generalData['Name'].replace(' ','_')
DysData = data['Dysnomia']
prfName = pName+'.prf'
prf = open(prfName,'w')
prf.write('$PREFERENCES\n')
prf.write(pName+'.mem\n') #or .fos?
prf.write(pName+'.out\n')
prf.write(pName+'.pgrid\n')
prf.write(pName+'.fba\n')
prf.write(pName+'_eps.raw\n')
prf.write('%d\n'%MEMtype)
if DysData['DenStart'] == 'uniform':
prf.write('0\n')
else:
prf.write('1\n')
if DysData['Optimize'] == 'ZSPA':
prf.write('0\n')
else:
prf.write('1\n')
prf.write('1\n')
if DysData['Lagrange'][0] == 'user':
prf.write('0\n')
else:
prf.write('1\n')
prf.write('%.4f %d\n'%(DysData['Lagrange'][1],DysData['wt pwr']))
prf.write('%.3f\n'%DysData['Lagrange'][2])
prf.write('%.2f\n'%DysData['E_factor'])
prf.write('1\n')
prf.write('0\n')
prf.write('%d\n'%DysData['Ncyc'])
prf.write('1\n')
prf.write('1 0 0 0 0 0 0 0\n')
if DysData['prior'] == 'uniform':
prf.write('0\n')
else:
prf.write('1\n')
prf.close()
return prfName
[docs]
def makeMEMfile(data,reflData,MEMtype,DYSNOMIA):
''' make Dysnomia .mem file of reflection data, etc.
:param dict data: GSAS-II phase data
:param list reflData: GSAS-II reflection data
:param int MEMtype: 1 for neutron data with negative scattering lengths
0 otherwise
:param str DYSNOMIA: path to dysnomia.exe
'''
DysData = data['Dysnomia']
generalData = data['General']
cell = generalData['Cell'][1:7]
A = G2lat.cell2A(cell)
SGData = generalData['SGData']
pName = generalData['Name'].replace(' ','_')
memName = pName+'.mem'
Map = generalData['Map']
Type = Map['Type']
UseList = Map['RefList']
mem = open(memName,'w')
mem.write('%s\n'%(generalData['Name']+' from '+UseList[0]))
a,b,c,alp,bet,gam = cell
mem.write('%10.5f%10.5f%10.5f%10.5f%10.5f%10.5f\n'%(a,b,c,alp,bet,gam))
mem.write(' 0.0000000 0.0000000 -1 0 0 0 P\n') #dummy PO stuff
SGSym = generalData['SGData']['SpGrp']
try:
SGId = G2spc.spgbyNum.index(SGSym)
except ValueError:
return False
org = 1
if SGSym in G2spc.spg2origins:
org = 2
mapsize = Map['rho'].shape
sumZ = 0.
sumpos = 0.
sumneg = 0.
mem.write('%5d%5d%5d%5d%5d\n'%(SGId,org,mapsize[0],mapsize[1],mapsize[2]))
for atm in generalData['NoAtoms']:
Nat = generalData['NoAtoms'][atm]
AtInfo = G2elem.GetAtomInfo(atm)
sumZ += Nat*AtInfo['Z']
isotope = generalData['Isotope'][atm]
blen = generalData['Isotopes'][atm][isotope]['SL'][0]
if blen < 0.:
sumneg += blen*Nat
else:
sumpos += blen*Nat
if 'X' in Type:
mem.write('%10.2f 0.001\n'%sumZ)
elif 'N' in Type and MEMtype:
mem.write('%10.3f%10.3f 0.001\n'%(sumpos,sumneg))
else:
mem.write('%10.3f 0.001\n'%sumpos)
dmin = DysData['MEMdmin']
TOFlam = 2.0*dmin*npsind(80.0)
refSet = G2lat.GenHLaue(dmin,SGData,A) #list of h,k,l,d
refDict = {'%d %d %d'%(ref[0],ref[1],ref[2]):ref for ref in refSet}
refs = []
prevpos = 0.
for ref in reflData:
if ref[3] < 0:
continue
if 'T' in Type:
h,k,l,mult,dsp,pos,sig,gam,Fobs,Fcalc,phase,x,x,x,x,prfo = ref[:16]
s = np.sqrt(max(sig,0.0001)) #var -> sig in deg
FWHM = getgamFW(gam,s)
if dsp < dmin:
continue
theta = npasind(TOFlam/(2.*dsp))
FWHM *= nptand(theta)/pos
pos = 2.*theta
else:
h,k,l,mult,dsp,pos,sig,gam,Fobs,Fcalc,phase,x,prfo = ref[:13]
g = gam/100. #centideg -> deg
s = np.sqrt(max(sig,0.0001))/100. #var -> sig in deg
FWHM = getgamFW(g,s)
delt = pos-prevpos
refs.append([h,k,l,mult,pos,FWHM,Fobs,phase,delt])
prevpos = pos
ovlp = DysData['overlap']
refs1 = []
refs2 = []
nref2 = 0
iref = 0
Nref = len(refs)
start = False
while iref < Nref-1:
if refs[iref+1][-1] < ovlp*refs[iref][5]:
if refs[iref][-1] > ovlp*refs[iref][5]:
refs2.append([])
start = True
if nref2 == len(refs2):
refs2.append([])
refs2[nref2].append(refs[iref])
else:
if start:
refs2[nref2].append(refs[iref])
start = False
nref2 += 1
else:
refs1.append(refs[iref])
iref += 1
if start:
refs2[nref2].append(refs[iref])
else:
refs1.append(refs[iref])
mem.write('%5d\n'%len(refs1))
for ref in refs1:
h,k,l = ref[:3]
hkl = '%d %d %d'%(h,k,l)
if hkl in refDict:
del refDict[hkl]
Fobs = np.sqrt(ref[6])
mem.write('%5d%5d%5d%10.3f%10.3f%10.3f\n'%(h,k,l,Fobs*npcosd(ref[7]),Fobs*npsind(ref[7]),max(0.01*Fobs,0.1)))
while True and nref2:
if not len(refs2[-1]):
del refs2[-1]
else:
break
mem.write('%5d\n'%len(refs2))
for iref2,ref2 in enumerate(refs2):
mem.write('#%5d\n'%iref2)
mem.write('%5d\n'%len(ref2))
Gsum = 0.
Msum = 0
for ref in ref2:
Gsum += ref[6]*ref[3]
Msum += ref[3]
G = np.sqrt(Gsum/Msum)
h,k,l = ref2[0][:3]
hkl = '%d %d %d'%(h,k,l)
if hkl in refDict:
del refDict[hkl]
mem.write('%5d%5d%5d%10.3f%10.3f%5d\n'%(h,k,l,G,max(0.01*G,0.1),ref2[0][3]))
for ref in ref2[1:]:
h,k,l,m = ref[:4]
mem.write('%5d%5d%5d%5d\n'%(h,k,l,m))
hkl = '%d %d %d'%(h,k,l)
if hkl in refDict:
del refDict[hkl]
if len(refDict):
mem.write('%d\n'%len(refDict))
for hkl in list(refDict.keys()):
h,k,l = refDict[hkl][:3]
mem.write('%5d%5d%5d\n'%(h,k,l))
else:
mem.write('0\n')
mem.close()
return True
[docs]
def MEMupdateReflData(prfName,data,reflData):
''' Update reflection data with new Fosq, phase result from Dysnomia
:param str prfName: phase.mem file name
:param list reflData: GSAS-II reflection data
'''
generalData = data['General']
Map = generalData['Map']
Type = Map['Type']
cell = generalData['Cell'][1:7]
A = G2lat.cell2A(cell)
reflDict = {}
newRefs = []
for iref,ref in enumerate(reflData):
if ref[3] > 0:
newRefs.append(ref)
reflDict[hash('%5d%5d%5d'%(ref[0],ref[1],ref[2]))] = iref
fbaName = os.path.splitext(prfName)[0]+'.fba'
if os.path.isfile(fbaName):
fba = open(fbaName,'r')
else:
return False
fba.readline()
Nref = int(fba.readline()[:-1])
fbalines = fba.readlines()
fba.close()
for line in fbalines[:Nref]:
info = line.split()
h = int(info[0])
k = int(info[1])
l = int(info[2])
FoR = float(info[3])
FoI = float(info[4])
Fosq = FoR**2+FoI**2
phase = npatan2d(FoI,FoR)
try:
refId = reflDict[hash('%5d%5d%5d'%(h,k,l))]
except KeyError: #added reflections at end skipped
d = float(1/np.sqrt(G2lat.calc_rDsq([h,k,l],A)))
if 'T' in Type:
newRefs.append([h,k,l,-1,d,0.,0.01,1.0,Fosq,Fosq,phase,1.0,1.0,1.0,1.0,1.0,1.0,1.0])
else:
newRefs.append([h,k,l,-1,d,0.,0.01,1.0,Fosq,Fosq,phase,1.0,1.0,1.0,1.0])
continue
newRefs[refId][8] = Fosq
newRefs[refId][10] = phase
newRefs = np.array(newRefs)
return True,newRefs
#===Laue Fringe code ===================================================================
import NIST_profile as FP
class profileObj(FP.FP_profile):
def conv_Lauefringe(self):
"""Compute the FT of the Laue Fringe function"""
me=self.get_function_name() #the name of this convolver,as a string
wave = self.param_dicts['conv_global']['dominant_wavelength']*1.e10 # in A
pos = np.rad2deg(self.param_dicts["conv_global"]["twotheta0"]) # peak position as 2theta in deg
posQ = np.pi * 4 * np.sin(self.param_dicts["conv_global"]["twotheta0"]/2) / wave # peak position as Q
ttwid = self.twotheta_window_fullwidth_deg
ncell = self.param_dicts[me]['Ncells']
co2 = self.param_dicts[me]['clat'] / 2.
dampM = self.param_dicts[me]['dampM']
dampP = self.param_dicts[me]['dampP']
fpowM = self.param_dicts[me]['fitPowerM']
fpowP = self.param_dicts[me]['fitPowerP']
ttlist = np.linspace(pos-ttwid/2,pos+ttwid/2,len(self._epsb2))
Qs = np.pi * 4 * np.sin(np.deg2rad(ttlist/2)) / wave
w = np.exp(-1*10**((dampM) * np.abs(Qs - posQ)**fpowM))
w2 = np.exp(-1*10**((dampP) * np.abs(Qs - posQ)**fpowP))
w[len(w)//2:] = w2[len(w)//2:]
weqdiv = w * np.sin(Qs * ncell * co2)**2 / (np.sin(Qs * co2)**2)
weqdiv[:np.searchsorted(Qs,posQ - np.pi/self.param_dicts[me]['clat'])] = 0 # isolate central peak, if needed
weqdiv[np.searchsorted(Qs,posQ + np.pi/self.param_dicts[me]['clat']):] = 0
conv = FP.best_rfft(weqdiv)
conv[1::2] *= -1 #flip center
return conv
def conv_Lorentzian(self):
"""Compute the FT of a Lorentz function where gamma is the FWHM"""
ttwid = self.twotheta_window_fullwidth_deg
me=self.get_function_name() #the name of this convolver,as a string
g2gam = self.param_dicts[me]['g2gam'] # gsas-ii gamma in centidegrees
gamma = g2gam/100 # deg
ttlist = np.linspace(-ttwid/2,ttwid/2,len(self._epsb2))
eqdiv = (0.5 * gamma / np.pi) / (gamma**2/4. + ttlist**2)
conv = FP.best_rfft(eqdiv)
conv[1::2] *= -1 #flip center
return conv
def conv_Gaussian(self):
"""Compute the FT of a Gaussian where sigma**2 is the variance"""
ttwid = self.twotheta_window_fullwidth_deg
me=self.get_function_name() #the name of this convolver,as a string
g2sig2 = self.param_dicts[me]['g2sig2'] # gsas-ii sigma**2 in centidegr**2
sigma = math.sqrt(g2sig2)/100.
ttlist = np.linspace(-ttwid/2,ttwid/2,len(self._epsb2))
eqdiv = np.exp(-0.5*ttlist**2/sigma**2) / math.sqrt(2*np.pi*sigma**2)
conv = FP.best_rfft(eqdiv)
conv[1::2] *= -1 #flip center
return conv
[docs]
def LaueFringePeakCalc(ttArr,intArr,lam,peakpos,intens,sigma2,gamma,shol,ncells,clat,dampM,dampP,calcwid,fitPowerM=2,fitPowerP=2,plot=False):
'''Compute the peakshape for a Laue Fringe peak convoluted with a Gaussian, Lorentzian &
an axial divergence asymmetry correction.
:param np.array ttArr: Array of two-theta values (in degrees)
:param np.array intArr: Array of intensity values (peaks are added to this)
:param float lam: wavelength in Angstrom
:param float peakpos: peak position in two-theta (deg.)
:param float intens: intensity factor for peak
:param float sigma2: Gaussian variance (in centidegrees**2) **
:param float gamma: Lorenzian FWHM (in centidegrees) **
:param float shol: FCJ (S + H)/L where S=sample-half height, H=slit half-height, L=radius **
:param float ncells: number of unit cells in specular direction **
:param float clat: c lattice parameter **
:param float dampM:
:param float dampP:
:param float calcwid: two-theta (deg.) width for cutoff of peak computation.
Defaults to 5
:param float fitPowerM: exponent used for damping fall-off on minus side of peak
:param float fitPowerP: exponent used for damping fall-off on plus side of peak
:param bool plot: for debugging, shows contributions to peak
** If term is <= zero, item is removed from convolution
'''
# def LaueFringePeakPlot(ttArr,intArr):
# import matplotlib.pyplot as plt
# refColors = ['xkcd:blue','xkcd:red','xkcd:green','xkcd:cyan','xkcd:magenta','xkcd:black',
# 'xkcd:pink','xkcd:brown','xkcd:teal','xkcd:orange','xkcd:grey','xkcd:violet',]
# fig, ax = plt.subplots()
# ax.set(title='Peak convolution functions @ 2theta={:.3f}'.format(peakpos),
# xlabel=r'$\Delta 2\theta, deg$',
# ylabel=r'Intensity (arbitrary)')
# ax.set_yscale("log",nonpositive='mask')
# ttmin = ttmax = 0
# for i,conv in enumerate(convList):
# f = NISTpk.convolver_funcs[conv]()
# if f is None: continue
# FFT = FP.best_irfft(f)
# if f[1].real > 0: FFT = np.roll(FFT,int(len(FFT)/2.))
# FFT /= FFT.max()
# if i == 0:
# tt = np.linspace(-NISTpk.twotheta_window_fullwidth_deg/2,
# NISTpk.twotheta_window_fullwidth_deg/2,len(FFT))
# ttmin = min(ttmin,tt[np.argmax(FFT>.005)])
# ttmax = max(ttmax,tt[::-1][np.argmax(FFT[::-1]>.005)])
# color = refColors[i%len(refColors)]
# ax.plot(tt,FFT,color,label=conv[5:])
# color = refColors[(i+1)%len(refColors)]
# ax.plot(ttArr-peakpos,intArr/max(intArr),color,label='Convolution')
# ax.set_xlim((ttmin,ttmax))
# ax.legend(loc='best')
# plt.show()
# hardcoded constants
diffRadius = 220 # diffractometer radius in mm; needed for axial divergence, etc, but should not matter
axial_factor = 1.5 # fudge factor to bring sh/l broadening to ~ agree with FPA
equatorial_divergence_deg = 0.5 # not sure exactly what this impacts
NISTparms = {
"": {
'equatorial_divergence_deg' : equatorial_divergence_deg,
'dominant_wavelength' : 1.e-10 * lam,
'diffractometer_radius' : 1e-3* diffRadius, # diffractometer radius in m
'oversampling' : 8,
},
"emission": {
'emiss_wavelengths' : 1.e-10 * np.array([lam]),
'emiss_intensities' : np.array([1.]),
'emiss_gauss_widths' : 1.e-10 * 1.e-3 * np.array([0.001]),
'emiss_lor_widths' : 1.e-10 * 1.e-3 * np.array([0.001]),
'crystallite_size_gauss' : 1.e-9 * 1e6,
'crystallite_size_lor' : 1.e-9 * 1e6
},
"axial": {
'axDiv':"full",
'slit_length_source' : 1e-3 * diffRadius * shol * axial_factor,
'slit_length_target' : 1e-3 * diffRadius * shol * 1.00001 * axial_factor, # != 'slit_length_source'
'length_sample' : 1e-3 * diffRadius * shol * axial_factor,
'n_integral_points' : 10,
'angI_deg' : 2.5,
'angD_deg': 2.5,
},
'Gaussian': {'g2sig2': sigma2},
'Lorentzian': {'g2gam': gamma},
'Lauefringe': {'Ncells': ncells, 'clat':clat, 'dampM': dampM,
'dampP': dampP, 'fitPowerM':fitPowerM, 'fitPowerP':fitPowerP},
}
NISTpk=profileObj(anglemode="twotheta",
output_gaussian_smoother_bins_sigma=1.0,
oversampling=NISTparms.get('oversampling',10))
NISTpk.debug_cache=False
for key in NISTparms: #set parameters for each convolver
if key:
NISTpk.set_parameters(convolver=key,**NISTparms[key])
else:
NISTpk.set_parameters(**NISTparms[key])
# find closest point to peak location (which may be outside limits of the array)
center_bin_idx=min(ttArr.searchsorted(peakpos),len(ttArr)-1)
step = (ttArr[-1]-ttArr[0])/(len(ttArr)-1)
NISTpk.set_optimized_window(twotheta_exact_bin_spacing_deg=step,
twotheta_window_center_deg=ttArr[center_bin_idx],
twotheta_approx_window_fullwidth_deg=calcwid,
)
NISTpk.set_parameters(twotheta0_deg=peakpos)
convList = ['conv_emission']
if ncells: convList += ['conv_Lauefringe']
if sigma2 > 0: convList += ['conv_Gaussian']
if gamma > 0: convList += ['conv_Lorentzian']
if shol > 0: convList += ['conv_axial']
# global deriv
# if deriv:
# peakObj = NISTpk.compute_line_profile(convolver_names=convList,compute_derivative=True)
# else:
# peakObj = NISTpk.compute_line_profile(convolver_names=convList)
peakObj = NISTpk.compute_line_profile(convolver_names=convList)
pkPts = len(peakObj.peak)
pkMax = peakObj.peak.max()
startInd = center_bin_idx-(pkPts//2)
istart = None
pstart = None
iend = None
pend = None
# adjust data range if peak calc begins below data range or ends above data range
# but range of peak calc should not extend past both ends of ttArr
if startInd < 0:
iend = startInd+pkPts
pstart = -startInd
elif startInd > len(intArr):
return
# elif startInd+pkPts >= len(intArr):
elif startInd+pkPts > len(intArr):
offset = pkPts - len( intArr[startInd:] )
istart = startInd
iend = startInd+pkPts-offset
pend = -offset
else:
istart = startInd
iend = startInd+pkPts
intArr[istart:iend] += intens * peakObj.peak[pstart:pend]/pkMax
# if plot:
# LaueFringePeakPlot(ttArr[istart:iend], (intens * peakObj.peak[pstart:pend]/pkMax))
[docs]
def LaueSatellite(peakpos,wave,c,ncell,j=[-4,-3,-2,-1,0,1,2,3,4]):
'''Returns the locations of the Laue satellite positions relative
to the peak position
:param float peakpos: the peak position in degrees 2theta
:param float ncell: Laue fringe parameter, number of unit cells in layer
:param list j: the satellite order, where j=-1 is the first satellite
on the lower 2theta side and j=1 is the first satellite on the high
2theta side. j=0 gives the peak position
'''
Qpos = 4 * np.pi * np.sin(peakpos * np.pi / 360) / wave
dQvals = (2 * np.array(j) + np.sign(j)) * np.pi / (c * ncell)
return np.arcsin((Qpos+dQvals)*wave/(4*np.pi)) * (360 / np.pi)
#### testing data
NeedTestData = True
[docs]
def TestData():
'needs a doc string'
# global NeedTestData
global bakType
bakType = 'chebyschev'
global xdata
xdata = np.linspace(4.0,40.0,36000)
global parmDict0
parmDict0 = {
'pos0':5.6964,'int0':8835.8,'sig0':1.0,'gam0':1.0,
'pos1':11.4074,'int1':3922.3,'sig1':1.0,'gam1':1.0,
'pos2':20.6426,'int2':1573.7,'sig2':1.0,'gam2':1.0,
'pos3':26.9568,'int3':925.1,'sig3':1.0,'gam3':1.0,
'U':1.163,'V':-0.605,'W':0.093,'X':0.0,'Y':2.183,'Z':0.0,'SH/L':0.002,
'Back0':5.384,'Back1':-0.015,'Back2':.004,
}
global parmDict1
parmDict1 = {
'pos0':13.4924,'int0':48697.6,'sig0':1.0,'gam0':1.0,
'pos1':23.4360,'int1':43685.5,'sig1':1.0,'gam1':1.0,
'pos2':27.1152,'int2':123712.6,'sig2':1.0,'gam2':1.0,
'pos3':33.7196,'int3':65349.4,'sig3':1.0,'gam3':1.0,
'pos4':36.1119,'int4':115829.8,'sig4':1.0,'gam4':1.0,
'pos5':39.0122,'int5':6916.9,'sig5':1.0,'gam5':1.0,
'U':22.75,'V':-17.596,'W':10.594,'X':1.577,'Y':5.778,'Z':0.0,'SH/L':0.002,
'Back0':36.897,'Back1':-0.508,'Back2':.006,
'Lam1':1.540500,'Lam2':1.544300,'I(L2)/I(L1)':0.5,
}
global parmDict2
parmDict2 = {
'pos0':5.7,'int0':1000.0,'sig0':0.5,'gam0':0.5,
'U':2.,'V':-2.,'W':5.,'X':0.5,'Y':0.5,'Z':0.0,'SH/L':0.02,
'Back0':5.,'Back1':-0.02,'Back2':.004,
# 'Lam1':1.540500,'Lam2':1.544300,'I(L2)/I(L1)':0.5,
}
global varyList
varyList = []
def test0():
if NeedTestData: TestData()
gplot = plotter.add('FCJ-Voigt, 11BM').gca()
gplot.plot(xdata,getBackground('',parmDict0,bakType,'PXC',xdata)[0])
gplot.plot(xdata,getPeakProfile(parmDict0,xdata,np.zeros_like(xdata),varyList,bakType))
fplot = plotter.add('FCJ-Voigt, Ka1+2').gca()
fplot.plot(xdata,getBackground('',parmDict1,bakType,'PXC',xdata)[0])
fplot.plot(xdata,getPeakProfile(parmDict1,xdata,np.zeros_like(xdata),varyList,bakType))
def test1():
if NeedTestData: TestData()
time0 = time.time()
for i in range(100):
getPeakProfile(parmDict1,xdata,np.zeros_like(xdata),varyList,bakType)
G2fil.G2Print ('100+6*Ka1-2 peaks=1200 peaks %.2f'%time.time()-time0)
def test2(name,delt):
if NeedTestData: TestData()
varyList = [name,]
xdata = np.linspace(5.6,5.8,400)
hplot = plotter.add('derivatives test for '+name).gca()
hplot.plot(xdata,getPeakProfileDerv(parmDict2,xdata,np.zeros_like(xdata),varyList,bakType)[0])
y0 = getPeakProfile(parmDict2,xdata,np.zeros_like(xdata),varyList,bakType)
parmDict2[name] += delt
y1 = getPeakProfile(parmDict2,xdata,np.zeros_like(xdata),varyList,bakType)
hplot.plot(xdata,(y1-y0)/delt,'r+')
def test3(name,delt):
if NeedTestData: TestData()
names = ['pos','sig','gam','shl']
idx = names.index(name)
myDict = {'pos':parmDict2['pos0'],'sig':parmDict2['sig0'],'gam':parmDict2['gam0'],'shl':parmDict2['SH/L']}
xdata = np.linspace(5.6,5.8,800)
dx = xdata[1]-xdata[0]
hplot = plotter.add('derivatives test for '+name).gca()
hplot.plot(xdata,100.*dx*getdFCJVoigt3(myDict['pos'],myDict['sig'],myDict['gam'],myDict['shl'],xdata)[idx+1])
y0 = getFCJVoigt3(myDict['pos'],myDict['sig'],myDict['gam'],myDict['shl'],xdata)[0]
myDict[name] += delt
y1 = getFCJVoigt3(myDict['pos'],myDict['sig'],myDict['gam'],myDict['shl'],xdata)[0]
hplot.plot(xdata,(y1-y0)/delt,'r+')
if __name__ == '__main__':
import GSASIItestplot as plot
global plotter
plotter = plot.PlotNotebook()
# test0()
# for name in ['int0','pos0','sig0','gam0','U','V','W','X','Y','Z','SH/L','I(L2)/I(L1)']:
for name,shft in [['int0',0.1],['pos0',0.0001],['sig0',0.01],['gam0',0.00001],
['U',0.1],['V',0.01],['W',0.01],['X',0.0001],['Y',0.0001],['Z',0.0001],['SH/L',0.00005]]:
test2(name,shft)
for name,shft in [['pos',0.0001],['sig',0.01],['gam',0.0001],['shl',0.00005]]:
test3(name,shft)
G2fil.G2Print ("OK")
plotter.StartEventLoop()
# GSASIIpath.SetBinaryPath(True,False)
# print('found',findfullrmc())