\(\renewcommand\AA{\text{Å}}\)

14. GSASIIpwd: Powder calculations

14.1. Summary/Contents

Routines for powder pattern computations, includes peak fitting, creation of PDF fitting scripts, interfaces to DIFFaX & Dysnomia.

14.2. GSASIIpwd Classes and routines

Classes and routines defined in GSASIIpwd follow.

GSASIIpwd.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

GSASIIpwd.AbsorbDerv(Geometry, MuR, Tth, Phi=0, Psi=0)

needs a doc string

GSASIIpwd.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.

GSASIIpwd.Dict2Values(parmdict, varylist)

Use before call to leastsq to setup list of values for the parameters in parmdict, as selected by key in varylist

GSASIIpwd.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.

Parameters:

• FitPgm (str) – type of fit to perform. At present this is ignored.

• Peaks (list) – a list of peaks. Each peak entry is a list with paired values: The number of pairs depends on the data type (see 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.

• Background (list) – 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.

• Limits (list) – min and max x-value to use

• Inst (dict) – Instrument parameters

• Inst2 (dict) – more Instrument parameters

• data (numpy.array) – 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.

• fixback (array) – fixed background array; same size as data[0-5]

• prevVaryList (list) – Used in sequential refinements to override the variable list. Defaults as an empty list.

• oneCycle (bool) – True if only one cycle of fitting should be performed

• controls (dict) – a dict specifying two values, Ftol = controls[‘min dM/M’] and derivType = controls[‘deriv type’]. If None default values are used.

• wtFactor (float) – weight multiplier; = 1.0 by default

• dlg (wx.Dialog) – A dialog box that is updated with progress from the fit. Defaults to None, which means no updates are done.

• noFit (bool) – When noFit is True, a refinement is not performed. Default is False.

GSASIIpwd.GetAsfMean(ElList, Sthl2)

Calculate various scattering factor terms for PDF calcs

Parameters:

• ElList (dict) – element dictionary contains scattering factor coefficients, etc.

• Sthl2 (np.array) – numpy array of sin theta/lambda squared values

Returns:

mean(f^2), mean(f)^2, mean(compton)

GSASIIpwd.GetNumDensity(ElList, Vol)

needs a doc string

GSASIIpwd.GetPDFfitAtomVar(Phase, RMCPdict)

Find dict of independent “@n” variables for PDFfit in atom constraints

GSASIIpwd.GetSeqCell(SGData, parmDict)

For use in processing PDFfit sequential results

GSASIIpwd.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.

Parameters:

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

GSASIIpwd.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.

Parameters:

• ttArr (np.array) – Array of two-theta values (in degrees)

• intArr (np.array) – Array of intensity values (peaks are added to this)

• lam (float) – wavelength in Angstrom

• peakpos (float) – peak position in two-theta (deg.)

• intens (float) – intensity factor for peak

• sigma2 (float) – Gaussian variance (in centidegrees**2) **

• gamma (float) – Lorenzian FWHM (in centidegrees) **

• shol (float) – FCJ (S + H)/L where S=sample-half height, H=slit half-height, L=radius **

• ncells (float) – number of unit cells in specular direction **

• clat (float) – c lattice parameter **

• dampM (float)

• dampP (float)

• calcwid (float) – two-theta (deg.) width for cutoff of peak computation. Defaults to 5

• fitPowerM (float) – exponent used for damping fall-off on minus side of peak

• fitPowerP (float) – exponent used for damping fall-off on plus side of peak

• plot (bool) – for debugging, shows contributions to peak

** If term is <= zero, item is removed from convolution

GSASIIpwd.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

Parameters:

• peakpos (float) – the peak position in degrees 2theta

• ncell (float) – Laue fringe parameter, number of unit cells in layer

• j (list) – 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

GSASIIpwd.LorchWeight(Q)

needs a doc string

GSASIIpwd.MEMupdateReflData(prfName, data, reflData)

Update reflection data with new Fosq, phase result from Dysnomia

Parameters:

• prfName (str) – phase.mem file name

• reflData (list) – GSAS-II reflection data

GSASIIpwd.MakePDFfitAtomsFile(Phase, RMCPdict)

Make the PDFfit atoms file

GSASIIpwd.MakePDFfitRunFile(Phase, RMCPdict)

Make the PDFfit python run file

GSASIIpwd.MakefullrmcRun(pName, Phase, RMCPdict)

Creates a script to run fullrmc. Returns the name of the file that was created.

GSASIIpwd.MakefullrmcSupercell(Phase, RMCPdict)

Create a fullrmc supercell from GSAS-II

Parameters:

• Phase (dict) – phase information from data tree

• RMCPdict (dict) – fullrmc parameters from GUI

• grpDict (list) – 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.

GSASIIpwd.Oblique(ObCoeff, Tth)

currently assumes detector is normal to beam

GSASIIpwd.PhaseWtSum(G2frame, histo)

Calculate sum of phase mass*phase fraction for PWDR data (exclude magnetic phases)

Parameters:

• G2frame – GSASII main frame structure

• histo (str) – histogram name

Returns:

sum(scale*mass) for phases in histo

GSASIIpwd.Polarization(Pola, Tth, Azm=0.0)

Calculate angle dependent x-ray polarization correction (not scaled correctly!)

Parameters:

• Pola – polarization coefficient e.g 1.0 fully polarized, 0.5 unpolarized

• Azm – azimuthal angle e.g. 0.0 in plane of polarization - can be numpy array

• Tth – 2-theta scattering angle - can be numpy array which (if either) of these is “right”?

Returns:

(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

GSASIIpwd.Ruland(RulCoff, wave, Q, Compton)

needs a doc string

GSASIIpwd.SetBackgroundParms(Background)

Loads background parameters into dicts/lists to create varylist & parmdict

GSASIIpwd.StackSim(Layers, ctrls, scale=0.0, background={}, limits=[], inst={}, profile=[])

Simulate powder or selected area diffraction pattern from stacking faults using DIFFaX

Parameters:

• Layers (dict) –

  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':[]}
  

• ctrls (str) – controls string to be written on DIFFaX controls.dif file

• scale (float) – scale factor

• background (dict) – background parameters

• limits (list) – min/max 2-theta to be calculated

• inst (dict) – instrument parameters dictionary

• profile (list) – powder pattern data

Note that parameters all updated in place

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.TestData()

needs a doc string

GSASIIpwd.Transmission(Geometry, Abs, Diam)

Calculate sample transmission

Parameters:

• Geometry (str) – one of ‘Cylinder’,’Bragg-Brentano’,’Tilting flat plate in transmission’,’Fixed flat plate’

• Abs (float) – absorption coeff in cm-1

• Diam (float) – sample thickness/diameter in mm

GSASIIpwd.UpdatePDFfit(Phase, RMCPdict)

Updates various PDFfit parameters held in GSAS-II

GSASIIpwd.Values2Dict(parmdict, varylist, values)

Use after call to leastsq to update the parameter dictionary with values corresponding to keys in varylist

GSASIIpwd.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.

Parameters:

• kz – float[n] (1/Ang). Scattering vector, \(2\pi\sin(\theta)/\lambda\). This is \(\tfrac12 Q_z\).

• depth – float[m] (Ang). thickness of each layer. The thickness of the incident medium and substrate are ignored.

• rho – float[n,k] (1e-6/Ang^2) Real scattering length density for each layer for each kz

• 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

• 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.

GSASIIpwd.autoBkgCalc(bkgdict, ydata)

Compute the autobackground using the selected pybaselines function

Parameters:

• bkgdict (dict) – background parameters

• ydata (np.array) – array of Y values

Returns:

points for background intensity at each Y position

GSASIIpwd.calcIncident(Iparm, xdata)

needs a doc string

class GSASIIpwd.cauchy_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, seed=None)

Cauchy distribution

cauchy.pdf(x) = 1/(pi*(1+x**2))

This is the t distribution with one degree of freedom.

pdf(x, *args, **kwds)

Probability density function at x of the given RV.

14. Parameters

xarray_like

quantiles

arg1, arg2, arg3,…array_like

The shape parameter(s) for the distribution (see docstring of the instance object for more information)

locarray_like, optional

location parameter (default=0)

scalearray_like, optional

scale parameter (default=1)

14. Returns

pdfndarray

Probability density function evaluated at x

GSASIIpwd.ellipseSize(H, Sij, GB)

Implements r=1/sqrt(sum((1/S)*(q.v)^2) per note from Alexander Brady

GSASIIpwd.ellipseSizeDerv(H, Sij, GB)

Implements r=1/sqrt(sum((1/S)*(q.v)^2) derivative per note from Alexander Brady

GSASIIpwd.factorize(num)

Provide prime number factors for integer num :returns: dictionary of prime factors (keys) & power for each (data)

class GSASIIpwd.fcjde_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, seed=None)

Finger-Cox-Jephcoat D(2phi,2th) function for S/L = H/L Ref: J. Appl. Cryst. (1994) 27, 892-900.

Parameters:

• x – array -1 to 1

• t – 2-theta position of peak

• s – sum(S/L,H/L); S: sample height, H: detector opening, L: sample to detector opening distance

• 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

pdf(x, *args, **kwds)

Probability density function at x of the given RV.

14. Parameters

xarray_like

quantiles

arg1, arg2, arg3,…array_like

The shape parameter(s) for the distribution (see docstring of the instance object for more information)

locarray_like, optional

location parameter (default=0)

scalearray_like, optional

scale parameter (default=1)

14. Returns

pdfndarray

Probability density function evaluated at x

GSASIIpwd.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.

GSASIIpwd.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.

GSASIIpwd.fullrmcDownload()

Downloads the fullrmc executable from Bachir’s site to the current GSAS-II binary directory.

Does some error checking.

GSASIIpwd.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.

Parameters:

• pfx (str) – histogram prefix (:h:)

• parmDict (dict) – Refinement parameter values

• bakType (str) – 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’

• dataType (str) – Code to indicate histogram type (PXC, PNC, PNT,…)

• xdata (MaskedArray) – independent variable, 2theta (deg*100) or TOF (microsec?)

• fixback (numpy.array) – 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.

GSASIIpwd.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 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).

GSASIIpwd.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

GSASIIpwd.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.

GSASIIpwd.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

GSASIIpwd.getFWHM(pos, Inst, N=1)

Compute total FWHM from Thompson, Cox & Hastings (1987) , J. Appl. Cryst. 20, 79-83 via getgamFW(g,s).

Parameters:

• pos – float peak position in deg 2-theta or tof in musec

• Inst – dict instrument parameters

• N – int Inst index (0 for input, 1 for fitted)

Returns float:

total FWHM of pseudoVoigt in deg or musec

GSASIIpwd.getHKLMpeak(dmin, Inst, SGData, SSGData, Vec, maxH, A)

needs a doc string

GSASIIpwd.getHKLpeak(dmin, SGData, A, Inst=None, nodup=False)

Generates allowed by symmetry reflections with d >= dmin NB: GenHKLf & checkMagextc return True for extinct reflections

Parameters:

• dmin – minimum d-spacing

• SGData – space group data obtained from SpcGroup

• A – lattice parameter terms A1-A6

• Inst – instrument parameter info

Returns:

HKLs: np.array hkl, etc for allowed reflections

GSASIIpwd.getHeaderInfo(dataType)

Provide parameter name, label name and formatting information for the contents of the Peak Table and where used in DoPeakFit

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.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.)

Parameters:

• pos – peak position; 2-theta in degrees

• sig – Gaussian peak variance in centideg^2

• gam – Lorentzian peak width in centidegrees

• 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.

GSASIIpwd.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

Parameters:

• pos – peak position; energy in keV (not used)

• sig – Gaussian peak variance in keV^2

• 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

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.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

GSASIIpwd.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

Parameters:

• g – float Lorentzian gamma = FWHM(L)

• s – float Gaussian sig

Returns float:

total FWHM of pseudoVoigt

GSASIIpwd.makeFFTsizeList(nmin=1, nmax=1023, thresh=15)

Provide list of optimal data sizes for FFT calculations

Parameters:

• nmin (int) – minimum data size >= 1

• nmax (int) – maximum data size > nmin

• thresh (int) – maximum prime factor allowed

Returns:

list of data sizes where the maximum prime factor is < thresh

GSASIIpwd.makeMEMfile(data, reflData, MEMtype, DYSNOMIA)

make Dysnomia .mem file of reflection data, etc.

Parameters:

• data (dict) – GSAS-II phase data

• reflData (list) – GSAS-II reflection data

• MEMtype (int) – 1 for neutron data with negative scattering lengths 0 otherwise

• DYSNOMIA (str) – path to dysnomia.exe

GSASIIpwd.makePRFfile(data, MEMtype)

makes Dysnomia .prf control file from Dysnomia GUI controls

Parameters:

• data (dict) – GSAS-II phase data

• MEMtype (int) – 1 for neutron data with negative scattering lengths 0 otherwise

Returns str:

name of Dysnomia control file

class GSASIIpwd.norm_gen(momtype=1, a=None, b=None, xtol=1e-14, badvalue=None, name=None, longname=None, shapes=None, seed=None)

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)

pdf(x, *args, **kwds)

Probability density function at x of the given RV.

14. Parameters

xarray_like

quantiles

arg1, arg2, arg3,…array_like

The shape parameter(s) for the distribution (see docstring of the instance object for more information)

locarray_like, optional

location parameter (default=0)

scalearray_like, optional

scale parameter (default=1)

14. Returns

pdfndarray

Probability density function evaluated at x

GSASIIpwd.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 setPeakInstPrmMode(), which is called from GSASIIscriptable or GSASIIphsGUI.OnSetPeakWidMode (‘Gen unvaried widths’ menu item).

GSASIIpwd.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.

Parameters:

normal (bool) – setting to apply to global variable peakInstPrmMode