Source code for GSASIIpwd

#/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 getHeaderInfo(dataType): '''Provide parameter name, label name and formatting information for the contents of the Peak Table and where used in DoPeakFit ''' names = ['pos','int'] lnames = ['position','intensity'] if 'LF' in dataType: names = ['int','sig','gam','dampM','dampP','l','ttheta'] lnames = ['intensity','sigma','gamma','damping\nminus', 'damping\nplus','00l', #'2theta ' '2\u03B8' ] fmt = ["%10.2f","%10.3f","%10.3f","%10.3f","%10.3f","%4.0f","%8.3f"] elif 'C' in dataType: names += ['sig','gam'] lnames += ['sigma','gamma'] fmt = ["%10.5f","%10.1f","%10.3f","%10.3f"] elif 'T' in dataType: names += ['alp','bet','sig','gam'] lnames += ['alpha','beta','sigma','gamma'] fmt = ["%10.2f","%10.4f","%8.3f","%8.5f","%10.3f","%10.3f"] elif 'E' in dataType: names += ['sig','gam'] lnames += ['sigma','gamma'] fmt = ["%10.5f","%10.1f","%8.3f","%10.3f"] else: # 'B' names += ['alp','bet','sig','gam'] lnames += ['alpha','beta','sigma','gamma'] fmt = ["%10.5f","%10.1f","%8.2f","%8.4f","%10.3f","%10.3f"] return names, fmt, lnames
[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())