Changeset 1301

Timestamp:

Apr 25, 2014 12:51:35 PM (9 years ago)

Author:

vondreele

Message:

 

File:

• trunk/GSASIIsasd.py (modified) (2 diffs)

trunk/GSASIIsasd.py

@@ +1 @@
+<<<<<<< .mine
+#/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+*GSASII small angle calculation module*
+==================================
+
+'''
+########### SVN repository information ###################
+# $Date$
+# $Author$
+# $Revision$
+# $URL$
+# $Id$
+########### SVN repository information ###################
+import os
+import sys
+import math
+import time
+
+import numpy as np
+import scipy as sp
+import numpy.linalg as nl
+from numpy.fft import ifft, fft, fftshift
+import scipy.special as scsp
+import scipy.interpolate as si
+import scipy.stats as st
+import scipy.optimize as so
+#import pdb
+
+import GSASIIpath
+GSASIIpath.SetVersionNumber("$Revision$")
+import GSASIIlattice as G2lat
+import GSASIIspc as G2spc
+import GSASIIElem as G2elem
+import GSASIIgrid as G2gd
+import GSASIIIO as G2IO
+import GSASIImath as G2mth
+import GSASIIpwd as G2pwd
+
+# trig functions in degrees
+sind = lambda x: math.sin(x*math.pi/180.)
+asind = lambda x: 180.*math.asin(x)/math.pi
+tand = lambda x: math.tan(x*math.pi/180.)
+atand = lambda x: 180.*math.atan(x)/math.pi
+atan2d = lambda y,x: 180.*math.atan2(y,x)/math.pi
+cosd = lambda x: math.cos(x*math.pi/180.)
+acosd = lambda x: 180.*math.acos(x)/math.pi
+rdsq2d = lambda x,p: round(1.0/math.sqrt(x),p)
+#numpy versions
+npsind = lambda x: np.sin(x*np.pi/180.)
+npasind = lambda x: 180.*np.arcsin(x)/math.pi
+npcosd = lambda x: np.cos(x*math.pi/180.)
+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
+npT2q = lambda tth,wave: 2.0*np.pi*npT2stl(tth,wave)
+    
+###############################################################################
+#### Particle form factors
+###############################################################################
+
+def SphereFF(Q,R,args=()):
+    ''' Compute hard sphere form factor - can use numpy arrays
+    param float Q: Q value array (usually in A-1)
+    param float R: sphere radius (Usually in A - must match Q-1 units)
+    param array args: ignored
+    returns float: form factors as array as needed
+    '''
+    QR = Q[:,np.newaxis]*R
+    return (3./(QR**3))*(np.sin(QR)-(QR*np.cos(QR)))
+    
+def SpheroidFF(Q,R,args):
+    ''' Compute form factor of cylindrically symmetric ellipsoid (spheroid) 
+    - can use numpy arrays for R & AR; will return corresponding numpy array
+    param float Q : Q value array (usually in A-1)
+    param float R: radius along 2 axes of spheroid
+    param array args: [float AR]: aspect ratio so 3rd axis = R*AR
+    returns float: form factors as array as needed
+    '''
+    NP = 50
+    AR = args[0]
+    if 0.99 < AR < 1.01:
+        return SphereFF(Q,R,0)
+    else:
+        cth = np.linspace(0,1.,NP)
+        Rct = R[:,np.newaxis]*np.sqrt(1.+(AR**2-1.)*cth**2)
+        return np.sqrt(np.sum(SphereFF(Q[:,np.newaxis],Rct,0)**2,axis=2)/NP)
+            
+def CylinderFF(Q,R,args):
+    ''' Compute form factor for cylinders - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float L]: cylinder length (A)
+    returns float: form factor
+    '''
+    L = args[0]
+    NP = 200
+    alp = np.linspace(0,np.pi/2.,NP)
+    QL = Q[:,np.newaxis]*L
+    LBessArg = 0.5*QL[:,:,np.newaxis]**np.cos(alp)
+    LBess = np.where(LBessArg<1.e-6,1.,np.sin(LBessArg)/LBessArg)
+    QR = Q[:,np.newaxis]*R
+    SBessArg = QR[:,:,np.newaxis]*np.sin(alp)
+    SBess = np.where(SBessArg<1.e-6,0.5,scsp.jv(1,SBessArg)/SBessArg)
+    LSBess = LBess*SBess
+    return np.sqrt(2.*np.pi*np.sum(np.sin(alp)*LSBess**2,axis=2)/NP)
+    
+def CylinderDFF(Q,L,args):
+    ''' Compute form factor for cylinders - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float L: cylinder half length (A)
+    param array args: [float R]: cylinder radius (A)
+    returns float: form factor
+    '''
+    R = args[0]
+    return CylinderFF(Q,R,args=[2.*L])    
+    
+def CylinderARFF(Q,R,args): 
+    ''' Compute form factor for cylinders - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float AR]: cylinder aspect ratio = L/D = L/2R
+    returns float: form factor
+    '''
+    AR = args[0]
+    return CylinderFF(Q,R,args=[2.*R*AR])    
+    
+def UniSphereFF(Q,R,args=0):
+    ''' Compute form factor for unified sphere - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: ignored
+    returns float: form factor
+    '''
+    Rg = np.sqrt(3./5.)*R
+    B = np.pi*1.62/(Rg**4)    #are we missing *np.pi? 1.62 = 6*(3/5)**2/(4/3) sense?
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg/np.sqrt(6)))**3
+    return np.sqrt(np.exp((-Q[:,np.newaxis]**2*Rg**2)/3.)+(B/QstV**4))
+    
+def UniRodFF(Q,R,args):
+    ''' Compute form factor for unified rod - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float R]: cylinder radius (A)
+    returns float: form factor
+    '''
+    L = args[0]
+    Rg2 = np.sqrt(R**2/2+L**2/12)
+    B2 = np.pi/L
+    Rg1 = np.sqrt(3.)*R/2.
+    G1 = (2./3.)*R/L
+    B1 = 4.*(L+R)/(R**3*L**2)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg2/np.sqrt(6)))**3
+    FF = np.exp(-Q[:,np.newaxis]**2*Rg2**2/3.)+(B2/QstV)*np.exp(-Rg1**2*Q[:,np.newaxis]**2/3.)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg1/np.sqrt(6)))**3
+    FF += G1*np.exp(-Q[:,np.newaxis]**2*Rg1**2/3.)+(B1/QstV**4)
+    return np.sqrt(FF)
+    
+def UniRodARFF(Q,R,args):
+    ''' Compute form factor for unified rod of fixed aspect ratio - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float AR]: cylinder aspect ratio = L/D = L/2R
+    returns float: form factor
+    '''
+    AR = args[0]
+    return UniRodFF(Q,R,args=[2.*AR*R,])
+    
+def UniDiskFF(Q,R,args):
+    ''' Compute form factor for unified disk - can use numpy arrays
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float T]: disk thickness (A)
+    returns float: form factor
+    '''
+    T = args[0]
+    Rg2 = np.sqrt(R**2/2.+T**2/12.)
+    B2 = 2./R**2
+    Rg1 = np.sqrt(3.)*T/2.
+    RgC2 = 1.1*Rg1
+    G1 = (2./3.)*(T/R)**2
+    B1 = 4.*(T+R)/(R**3*T**2)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg2/np.sqrt(6)))**3
+    FF = np.exp(-Q[:,np.newaxis]**2*Rg2**2/3.)+(B2/QstV**2)*np.exp(-RgC2**2*Q[:,np.newaxis]**2/3.)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg1/np.sqrt(6)))**3
+    FF += G1*np.exp(-Q[:,np.newaxis]**2*Rg1**2/3.)+(B1/QstV**4)
+    return np.sqrt(FF)
+    
+def UniTubeFF(Q,R,args):
+    ''' Compute form factor for unified tube - can use numpy arrays
+    assumes that core of tube is same as the matrix/solvent so contrast
+    is from tube wall vs matrix
+    param float Q: Q value array (A-1)
+    param float R: cylinder radius (A)
+    param array args: [float L,T]: tube length & wall thickness(A)
+    returns float: form factor
+    '''
+    L,T = args[:2]
+    Ri = R-T
+    DR2 = R**2-Ri**2
+    Vt = np.pi*DR2*L
+    Rg3 = np.sqrt(DR2/2.+L**2/12.)
+    B1 = 4.*np.pi**2*(DR2+L*(R+Ri))/Vt**2
+    B2 = np.pi**2*T/Vt
+    B3 = np.pi/L
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*Rg3/np.sqrt(6)))**3
+    FF = np.exp(-Q[:,np.newaxis]**2*Rg3**2/3.)+(B3/QstV)*np.exp(-Q[:,np.newaxis]**2*R**2/3.)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*R/np.sqrt(6)))**3
+    FF += (B2/QstV**2)*np.exp(-Q[:,np.newaxis]**2*T**2/3.)
+    QstV = Q[:,np.newaxis]/(scsp.erf(Q[:,np.newaxis]*T/np.sqrt(6)))**3
+    FF += B1/QstV**4
+    return np.sqrt(FF)
+
+###############################################################################
+#### Particle volumes
+###############################################################################
+
+def SphereVol(R,args=()):
+    ''' Compute volume of sphere
+    - numpy array friendly
+    param float R: sphere radius
+    param array args: ignored
+    returns float: volume
+    '''
+    return (4./3.)*np.pi*R**3
+
+def SpheroidVol(R,args):
+    ''' Compute volume of cylindrically symmetric ellipsoid (spheroid) 
+    - numpy array friendly
+    param float R: radius along 2 axes of spheroid
+    param array args: [float AR]: aspect ratio so radius of 3rd axis = R*AR
+    returns float: volume
+    '''
+    AR = args[0]
+    return AR*SphereVol(R)
+    
+def CylinderVol(R,args):
+    ''' Compute cylinder volume for radius & length
+    - numpy array friendly
+    param float R: diameter (A)
+    param array args: [float L]: length (A)
+    returns float:volume (A^3)
+    '''
+    L = args[0]
+    return np.pi*L*R**2
+    
+def CylinderDVol(L,args):
+    ''' Compute cylinder volume for length & diameter
+    - numpy array friendly
+    param float: L half length (A)
+    param array args: [float D]: diameter (A)
+    returns float:volume (A^3)
+    '''
+    D = args[0]
+    return CylinderVol(D/2.,[2.*L,])
+    
+def CylinderARVol(R,args):
+    ''' Compute cylinder volume for radius & aspect ratio = L/D
+    - numpy array friendly
+    param float: R radius (A)
+    param array args: [float AR]: =L/D=L/2R aspect ratio
+    returns float:volume
+    '''
+    AR = args[0]
+    return CylinderVol(R,[2.*R*AR,])
+    
+def UniSphereVol(R,args=()):
+    ''' Compute volume of sphere
+    - numpy array friendly
+    param float R: sphere radius
+    param array args: ignored
+    returns float: volume
+    '''
+    return SphereVol(R)
+    
+def UniRodVol(R,args):
+    ''' Compute cylinder volume for radius & length
+    - numpy array friendly
+    param float R: diameter (A)
+    param array args: [float L]: length (A)
+    returns float:volume (A^3)
+    '''
+    L = args[0]
+    return CylinderVol(R,[L,])
+    
+def UniRodARVol(R,args):
+    ''' Compute rod volume for radius & aspect ratio
+    - numpy array friendly
+    param float R: diameter (A)
+    param array args: [float AR]: =L/D=L/2R aspect ratio
+    returns float:volume (A^3)
+    '''
+    AR = args[0]
+    return CylinderARVol(R,[AR,])
+    
+def UniDiskVol(R,args):
+    ''' Compute disk volume for radius & thickness
+    - numpy array friendly
+    param float R: diameter (A)
+    param array args: [float T]: thickness
+    returns float:volume (A^3)
+    '''
+    T = args[0]
+    return CylinderVol(R,[T,])
+    
+def UniTubeVol(R,args):
+    ''' Compute tube volume for radius, length & wall thickness
+    - numpy array friendly
+    param float R: diameter (A)
+    param array args: [float L,T]: tube length & wall thickness(A)
+    returns float: volume (A^3) of tube wall
+    '''
+    L,T = args[:2]
+    return CylinderVol(R,[L,])-CylinderVol(R-T,[L,])
+    
+################################################################################
+#### Distribution functions & their cumulative fxns
+################################################################################
+
+def LogNormalDist(x,pos,args):
+    ''' Standard LogNormal distribution - numpy friendly on x axis
+    ref: http://www.itl.nist.gov/div898/handbook/index.htm 1.3.6.6.9
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (m)
+    param float shape: shape - (sigma of log(LogNormal))
+    returns float: LogNormal distribution
+    '''
+    scale,shape = args
+    return np.exp(-np.log((x-pos)/scale)**2/(2.*shape**2))/(np.sqrt(2.*np.pi)*(x-pos)*shape)
+    
+def GaussDist(x,pos,args):
+    ''' Standard Normal distribution - numpy friendly on x axis
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (sigma)
+    param float shape: not used
+    returns float: Normal distribution
+    '''
+    scale = args[0]
+    return (1./(scale*np.sqrt(2.*np.pi)))*np.exp(-(x-pos)**2/(2.*scale**2))
+    
+def LSWDist(x,pos,args=[]):
+    ''' Lifshitz-Slyozov-Wagner Ostwald ripening distribution - numpy friendly on x axis
+    ref: 
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: not used
+    param float shape: not used
+    returns float: LSW distribution    
+    '''
+    redX = x/pos
+    result = (81.*2**(-5/3.))*(redX**2*np.exp(-redX/(1.5-redX)))/((1.5-redX)**(11/3.)*(3.-redX)**(7/3.))
+    
+    return np.nan_to_num(result/pos)
+    
+def SchulzZimmDist(x,pos,args):
+    ''' Schulz-Zimm macromolecule distribution - numpy friendly on x axis
+    ref: http://goldbook.iupac.org/S05502.html
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (sigma)
+    param float shape: not used
+    returns float: Schulz-Zimm distribution
+    '''
+    scale = args[0]
+    b = (2.*pos/scale)**2
+    a = b/pos
+    if b<70:    #why bother?
+        return (a**(b+1.))*x**b*np.exp(-a*x)/scsp.gamma(b+1.)
+    else:
+        return np.exp((b+1.)*np.log(a)-scsp.gammaln(b+1.)+b*np.log(x)-(a*x))
+           
+def LogNormalCume(x,pos,args):
+    ''' Standard LogNormal cumulative distribution - numpy friendly on x axis
+    ref: http://www.itl.nist.gov/div898/handbook/index.htm 1.3.6.6.9
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (sigma)
+    param float shape: shape parameter
+    returns float: LogNormal cumulative distribution
+    '''
+    scale,shape = args
+    lredX = np.log((x-pos)/scale)
+    return (scsp.erf((lredX/shape)/np.sqrt(2.))+1.)/2.
+    
+def GaussCume(x,pos,args):
+    ''' Standard Normal cumulative distribution - numpy friendly on x axis
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (sigma)
+    param float shape: not used
+    returns float: Normal cumulative distribution
+    '''
+    scale = args[0]
+    redX = (x-pos)/scale
+    return (scsp.erf(redX/np.sqrt(2.))+1.)/2.
+    
+def LSWCume(x,pos,args=[]):
+    ''' Lifshitz-Slyozov-Wagner Ostwald ripening cumulative distribution - numpy friendly on x axis
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: not used
+    param float shape: not used
+    returns float: LSW cumulative distribution
+    '''
+    nP = 500
+    xMin,xMax = [0.,2.*pos]
+    X = np.linspace(xMin,xMax,nP,True)
+    fxn = LSWDist(X,pos)
+    mat = np.outer(np.ones(nP),fxn)
+    cume = np.sum(np.tril(mat),axis=1)/np.sum(fxn)
+    return np.interp(x,X,cume,0,1)
+    
+def SchulzZimmCume(x,pos,args):
+    ''' Schulz-Zimm cumulative distribution - numpy friendly on x axis
+    param float x: independent axis (can be numpy array)
+    param float pos: location of distribution
+    param float scale: width of distribution (sigma)
+    param float shape: not used
+    returns float: Normal distribution
+    '''
+    scale = args[0]
+    nP = 500
+    xMin = np.max([0.,pos-4.*scale])
+    xMax = np.min([pos+4.*scale,1.e5])
+    X = np.linspace(xMin,xMax,nP,True)
+    fxn = LSWDist(X,pos)
+    mat = np.outer(np.ones(nP),fxn)
+    cume = np.sum(np.tril(mat),axis=1)/np.sum(fxn)
+    return np.interp(x,X,cume,0,1)
+    
+    return []
+    
+          
+################################################################################
+##### SB-MaxEnt
+################################################################################
+
+def G_matrix(q,r,contrast,FFfxn,Volfxn,args=()):
+    '''Calculates the response matrix :math:`G(Q,r)` 
+    
+    :param float q: :math:`Q`
+    :param float r: :math:`r`
+    :param float contrast: :math:`|\\Delta\\rho|^2`, the scattering contrast
+    :param function FFfxn: form factor function FF(q,r,args)
+    :param function Volfxn: volume function Vol(r,args)
+    :returns float: G(Q,r)
+    '''
+    FF = FFfxn(q,r,args)
+    Vol = Volfxn(r,args)
+    return 1.e-4*(contrast*Vol*FF**2).T     #10^-20 vs 10^-24
+    
+'''
+sbmaxent
+
+Entropy maximization routine as described in the article
+J Skilling and RK Bryan; MNRAS 211 (1984) 111 - 124.
+("MNRAS": "Monthly Notices of the Royal Astronomical Society")
+
+:license: Copyright (c) 2013, UChicago Argonne, LLC
+:license: This file is distributed subject to a Software License Agreement found
+     in the file LICENSE that is included with this distribution. 
+
+References:
+
+1. J Skilling and RK Bryan; MON NOT R ASTR SOC 211 (1984) 111 - 124.
+2. JA Potton, GJ Daniell, and BD Rainford; Proc. Workshop
+   Neutron Scattering Data Analysis, Rutherford
+   Appleton Laboratory, UK, 1986; ed. MW Johnson,
+   IOP Conference Series 81 (1986) 81 - 86, Institute
+   of Physics, Bristol, UK.
+3. ID Culverwell and GP Clarke; Ibid. 87 - 96.
+4. JA Potton, GK Daniell, & BD Rainford,
+   J APPL CRYST 21 (1988) 663 - 668.
+5. JA Potton, GJ Daniell, & BD Rainford,
+   J APPL CRYST 21 (1988) 891 - 897.
+
+'''
+
+class MaxEntException(Exception): 
+    '''Any exception from this module'''
+    pass
+
+def MaxEnt_SB(datum, sigma, G, base, IterMax, image_to_data=None, data_to_image=None, report=False):
+    '''
+    do the complete Maximum Entropy algorithm of Skilling and Bryan
+    
+    :param float datum[]:
+    :param float sigma[]:
+    :param float[][] G: transformation matrix
+    :param float base[]:
+    :param int IterMax:
+    :param obj image_to_data: opus function (defaults to opus)
+    :param obj data_to_image: tropus function (defaults to tropus)
+    
+    :returns float[]: :math:`f(r) dr`
+    '''
+        
+    TEST_LIMIT        = 0.05                    # for convergence
+    CHI_SQR_LIMIT     = 0.01                    # maximum difference in ChiSqr for a solution
+    SEARCH_DIRECTIONS = 3                       # <10.  This code requires value = 3
+    RESET_STRAYS      = 1                       # was 0.001, correction of stray negative values
+    DISTANCE_LIMIT_FACTOR = 0.1                 # limitation on df to constrain runaways
+    
+    MAX_MOVE_LOOPS    = 500                     # for no solution in routine: move, 
+    MOVE_PASSES       = 0.001                   # convergence test in routine: move
+
+    def tropus (data, G):
+        '''
+        tropus: transform data-space -> solution-space:  [G] * data
+        
+        default definition, caller can use this definition or provide an alternative
+        
+        :param float[M] data: observations, ndarray of shape (M)
+        :param float[M][N] G: transformation matrix, ndarray of shape (M,N)
+        :returns float[N]: calculated image, ndarray of shape (N)
+        '''
+        return G.dot(data)
+
+    def opus (image, G):
+        '''
+        opus: transform solution-space -> data-space:  [G]^tr * image
+        
+        default definition, caller can use this definition or provide an alternative
+        
+        :param float[N] image: solution, ndarray of shape (N)
+        :param float[M][N] G: transformation matrix, ndarray of shape (M,N)
+        :returns float[M]: calculated data, ndarray of shape (M)
+        '''
+        return np.dot(G.T,image)    #G.transpose().dot(image)
+
+    def Dist(s2, beta):
+        '''measure the distance of this possible solution'''
+        w = 0
+        n = beta.shape[0]
+        for k in range(n):
+            z = -sum(s2[k] * beta)
+            w += beta[k] * z
+        return w
+    
+    def ChiNow(ax, c1, c2, s1, s2):
+        '''
+        ChiNow
+        
+        :returns tuple: (ChiNow computation of ``w``, beta)
+        '''
+        
+        bx = 1 - ax
+        a =   bx * c2  -  ax * s2
+        b = -(bx * c1  -  ax * s1)
+    
+        beta = ChoSol(a, b)
+        w = 1.0
+        for k in range(SEARCH_DIRECTIONS):
+            w += beta[k] * (c1[k] + 0.5*sum(c2[k] * beta))
+        return w, beta
+    
+    def ChoSol(a, b):
+        '''
+        ChoSol: ? chop the solution vectors ?
+        
+        :returns: new vector beta
+        '''
+        n = b.shape[0]
+        fl = np.zeros((n,n))
+        bl = np.zeros_like(b)
+        
+        #print_arr("ChoSol: a", a)
+        #print_vec("ChoSol: b", b)
+    
+        if (a[0][0] <= 0):
+            msg = "ChoSol: a[0][0] = " 
+            msg += str(a[0][0])
+            msg += '  Value must be positive'
+            raise MaxEntException(msg)
+    
+        # first, compute fl from a
+        # note fl is a lower triangular matrix
+        fl[0][0] = math.sqrt (a[0][0])
+        for i in (1, 2):
+            fl[i][0] = a[i][0] / fl[0][0]
+            for j in range(1, i+1):
+                z = 0.0
+                for k in range(j):
+                    z += fl[i][k] * fl[j][k]
+                    #print "ChoSol: %d %d %d  z = %lg" % ( i, j, k, z)
+                z = a[i][j] - z
+                if j == i:
+                    y = math.sqrt(z)
+                else:
+                    y = z / fl[j][j]
+                fl[i][j] = y
+        #print_arr("ChoSol: fl", fl)
+    
+        # next, compute bl from fl and b
+        bl[0] = b[0] / fl[0][0]
+        for i in (1, 2):
+            z = 0.0
+            for k in range(i):
+                z += fl[i][k] * bl[k]
+                #print "\t", i, k, z
+            bl[i] = (b[i] - z) / fl[i][i]
+        #print_vec("ChoSol: bl", bl)
+    
+        # last, compute beta from bl and fl
+        beta = np.empty((n))
+        beta[-1] = bl[-1] / fl[-1][-1]
+        for i in (1, 0):
+            z = 0.0
+            for k in range(i+1, n):
+                z += fl[k][i] * beta[k]
+                #print "\t\t", i, k, 'z=', z
+            beta[i] = (bl[i] - z) / fl[i][i]
+        #print_vec("ChoSol: beta", beta)
+    
+        return beta
+
+    def MaxEntMove(fSum, blank, chisq, chizer, c1, c2, s1, s2):
+        '''
+        move beta one step closer towards the solution
+        '''
+        a_lower, a_upper = 0., 1.          # bracket  "a"
+        cmin, beta = ChiNow (a_lower, c1, c2, s1, s2)
+        #print "MaxEntMove: cmin = %g" % cmin
+        if cmin*chisq > chizer:
+            ctarg = (1.0 + cmin)/2
+        else:
+            ctarg = chizer/chisq
+        f_lower = cmin - ctarg
+        c_upper, beta = ChiNow (a_upper, c1, c2, s1, s2)
+        f_upper = c_upper - ctarg
+    
+        fx = 2*MOVE_PASSES      # just to start off
+        loop = 1
+        while abs(fx) >= MOVE_PASSES and loop <= MAX_MOVE_LOOPS:
+            a_new = (a_lower + a_upper) * 0.5           # search by bisection
+            c_new, beta = ChiNow (a_new, c1, c2, s1, s2)
+            fx = c_new - ctarg
+            # tighten the search range for the next pass
+            if f_lower*fx > 0:
+                a_lower, f_lower = a_new, fx
+            if f_upper*fx > 0:
+                a_upper, f_upper = a_new, fx
+            loop += 1
+    
+        if abs(fx) >= MOVE_PASSES or loop > MAX_MOVE_LOOPS:
+            msg = "MaxEntMove: Loop counter = " 
+            msg += str(MAX_MOVE_LOOPS)
+            msg += '  No convergence in alpha chop'
+            raise MaxEntException(msg)
+    
+        w = Dist (s2, beta);
+        m = SEARCH_DIRECTIONS
+        if (w > DISTANCE_LIMIT_FACTOR*fSum/blank):        # invoke the distance penalty, SB eq. 17
+            for k in range(m):
+                beta[k] *= math.sqrt (fSum/(blank*w))
+        chtarg = ctarg * chisq
+        return w, chtarg, loop, a_new, fx, beta
+        
+#MaxEnt_SB starts here    
+
+    if image_to_data == None:
+        image_to_data = opus
+    if data_to_image == None:
+        data_to_image = tropus
+    n   = len(base)
+    npt = len(datum)
+
+    # Note that the order of subscripts for
+    # "xi" and "eta" has been reversed from
+    # the convention used in the FORTRAN version
+    # to enable parts of them to be passed as
+    # as vectors to "image_to_data" and "data_to_image".
+    xi      = np.zeros((SEARCH_DIRECTIONS, n))
+    eta     = np.zeros((SEARCH_DIRECTIONS, npt))
+    beta    = np.zeros((SEARCH_DIRECTIONS))
+    # s1      = np.zeros((SEARCH_DIRECTIONS))
+    # c1      = np.zeros((SEARCH_DIRECTIONS))
+    s2      = np.zeros((SEARCH_DIRECTIONS, SEARCH_DIRECTIONS))
+    c2      = np.zeros((SEARCH_DIRECTIONS, SEARCH_DIRECTIONS))
+
+    # TODO: replace blank (scalar) with base (vector)
+    blank = sum(base) / len(base)   # use the average value of base
+
+    chizer, chtarg = npt*1.0, npt*1.0
+    f = base * 1.0                              # starting distribution is base
+
+    fSum  = sum(f)                              # find the sum of the f-vector
+    z = (datum - image_to_data (f, G)) / sigma  # standardized residuals, SB eq. 3
+    chisq = sum(z*z)                            # Chi^2, SB eq. 4
+
+    for iter in range(IterMax):
+        ox = -2 * z / sigma                        # gradient of Chi^2
+
+        cgrad = data_to_image (ox, G)              # cgrad[i] = del(C)/del(f[i]), SB eq. 8
+        sgrad = -np.log(f/base) / (blank*math.exp (1.0))  # sgrad[i] = del(S)/del(f[i])
+        snorm = math.sqrt(sum(f * sgrad*sgrad))    # entropy term, SB eq. 22
+        cnorm = math.sqrt(sum(f * cgrad*cgrad))    # ChiSqr term, SB eq. 22
+        tnorm = sum(f * sgrad * cgrad)             # norm for gradient term TEST 
+
+        a = 1.0
+        b = 1.0 / cnorm
+        if iter == 0:
+            test = 0.0     # mismatch between entropy and ChiSquared gradients
+        else:
+            test = math.sqrt( ( 1.0 - tnorm/(snorm*cnorm) )/2 ) # SB eq. 37?
+            a = 0.5 / (snorm * test)
+            b *= 0.5 / test
+        xi[0] = f * cgrad / cnorm
+        xi[1] = f * (a * sgrad - b * cgrad)
+
+        eta[0] = image_to_data (xi[0], G);          # image --> data
+        eta[1] = image_to_data (xi[1], G);          # image --> data
+        ox = eta[1] / (sigma * sigma)
+        xi[2] = data_to_image (ox, G);              # data --> image
+        a = 1.0 / math.sqrt(sum(f * xi[2]*xi[2]))
+        xi[2] = f * xi[2] * a
+        eta[2] = image_to_data (xi[2], G)           # image --> data
+        
+#         print_arr("MaxEnt: eta.transpose()", eta.transpose())
+#         print_arr("MaxEnt: xi.transpose()", xi.transpose())
+
+        # prepare the search directions for the conjugate gradient technique
+        c1 = xi.dot(cgrad) / chisq                          # C_mu, SB eq. 24
+        s1 = xi.dot(sgrad)                          # S_mu, SB eq. 24
+#         print_vec("MaxEnt: c1", c1)
+#         print_vec("MaxEnt: s1", s1)
+
+        for k in range(SEARCH_DIRECTIONS):
+            for l in range(k+1):
+                c2[k][l] = 2 * sum(eta[k] * eta[l] / sigma/sigma) / chisq
+                s2[k][l] = -sum(xi[k] * xi[l] / f) / blank
+#         print_arr("MaxEnt: c2", c2)
+#         print_arr("MaxEnt: s2", s2)
+
+        # reflect across the body diagonal
+        for k, l in ((0,1), (0,2), (1,2)):
+            c2[k][l] = c2[l][k]                     #  M_(mu,nu)
+            s2[k][l] = s2[l][k]                     #  g_(mu,nu)
+ 
+        beta[0] = -0.5 * c1[0] / c2[0][0]
+        beta[1] = 0.0
+        beta[2] = 0.0
+        if (iter > 0):
+            w, chtarg, loop, a_new, fx, beta = MaxEntMove(fSum, blank, chisq, chizer, c1, c2, s1, s2)
+
+        f_old = f.copy()    # preserve the last image
+        f += xi.transpose().dot(beta)   # move the image towards the solution, SB eq. 25
+        
+        # As mentioned at the top of p.119,
+        # need to protect against stray negative values.
+        # In this case, set them to RESET_STRAYS * base[i]
+        #f = f.clip(RESET_STRAYS * blank, f.max())
+        for i in range(n):
+            if f[i] <= 0.0:
+                f[i] = RESET_STRAYS * base[i]
+        df = f - f_old
+        fSum = sum(f)
+        fChange = sum(df)
+
+        # calculate the normalized entropy
+        S = sum((f/fSum) * np.log(f/fSum))      # normalized entropy, S&B eq. 1
+        z = (datum - image_to_data (f, G)) / sigma  # standardized residuals
+        chisq = sum(z*z)                            # report this ChiSq
+
+        if report:
+            print " MaxEnt trial/max: %3d/%3d" % ((iter+1), IterMax)
+            print " Residual: %5.2lf%% Entropy: %8lg" % (100*test, S)
+            if iter > 0:
+                value = 100*( math.sqrt(chisq/chtarg)-1)
+            else:
+                value = 0
+      #      print " %12.5lg %10.4lf" % ( math.sqrt(chtarg/npt), value )
+            print " Function sum: %.6lg Change from last: %.2lf%%\n" % (fSum,100*fChange/fSum)
+
+        # See if we have finished our task.
+        # do the hardest test first
+        if (abs(chisq/chizer-1.0) < CHI_SQR_LIMIT) and  (test < TEST_LIMIT):
+            print ' Convergence achieved.'
+            return chisq,f,image_to_data(f, G)     # solution FOUND returns here
+    print ' No convergence! Try increasing Error multiplier.'
+    return chisq,f,image_to_data(f, G)       # no solution after IterMax iterations
+
+    
+###############################################################################
+#### IPG/TNNLS Routines
+###############################################################################
+
+def IPG(datum,sigma,G,Bins,Dbins,IterMax,Qvec=[],approach=0.8,Power=-1,report=False):
+    ''' An implementation of the Interior-Point Gradient method of 
+    Michael Merritt & Yin Zhang, Technical Report TR04-08, Dept. of Comp. and 
+    Appl. Math., Rice Univ., Houston, Texas 77005, U.S.A. found on the web at
+    http://www.caam.rice.edu/caam/trs/2004/TR04-08.pdf
+    Problem addressed: Total Non-Negative Least Squares (TNNLS)
+    :param float datum[]:
+    :param float sigma[]:
+    :param float[][] G: transformation matrix
+    :param int IterMax:
+    :param float Qvec: data positions for Power = 0-4
+    :param float approach: 0.8 default fitting parameter
+    :param int Power: 0-4 for Q^Power weighting, -1 to use input sigma
+    
+    '''
+    if Power < 0: 
+        GmatE = G/sigma[:np.newaxis]
+        IntE = datum/sigma
+        pwr = 0
+        QvecP = np.ones_like(datum)
+    else:
+        GmatE = G[:]
+        IntE = datum[:]
+        pwr = Power
+        QvecP = Qvec**pwr
+    Amat = GmatE*QvecP[:np.newaxis]
+    AAmat = np.inner(Amat,Amat)
+    Bvec = IntE*QvecP
+    Xw = np.ones_like(Bins)*1.e-32
+    calc = np.dot(G.T,Xw)
+    nIter = 0
+    err = 10.
+    while (nIter<IterMax) and (err > 1.):
+        #Step 1 in M&Z paper:
+        Qk = np.inner(AAmat,Xw)-np.inner(Amat,Bvec)
+        Dk = Xw/np.inner(AAmat,Xw)
+        Pk = -Dk*Qk
+        #Step 2 in M&Z paper:
+        alpSt = -np.inner(Pk,Qk)/np.inner(Pk,np.inner(AAmat,Pk))
+        alpWv = -Xw/Pk
+        AkSt = [np.where(Pk[i]<0,np.min((approach*alpWv[i],alpSt)),alpSt) for i in range(Pk.shape[0])]
+        #Step 3 in M&Z paper:
+        shift = AkSt*Pk
+        Xw += shift
+        #done IPG; now results
+        nIter += 1
+        calc = np.dot(G.T,Xw)
+        chisq = np.sum(((datum-calc)/sigma)**2)
+        err = chisq/len(datum)
+        if report:
+            print ' Iteration: %d, chisq: %.3g, sum(shift^2): %.3g'%(nIter,chisq,np.sum(shift**2))
+    return chisq,Xw,calc
+
+###############################################################################
+#### SASD Utilities
+###############################################################################
+
+def SetScale(Data,refData):
+    Profile,Limits,Sample = Data
+    refProfile,refLimits,refSample = refData
+    x,y = Profile[:2]
+    rx,ry = refProfile[:2]
+    Beg = np.max([rx[0],x[0],Limits[1][0],refLimits[1][0]])
+    Fin = np.min([rx[-1],x[-1],Limits[1][1],refLimits[1][1]])
+    iBeg = np.searchsorted(x,Beg)
+    iFin = np.searchsorted(x,Fin)
+    sum = np.sum(y[iBeg:iFin])
+    refsum = np.sum(np.interp(x[iBeg:iFin],rx,ry,0,0))
+    Sample['Scale'][0] = refSample['Scale'][0]*refsum/sum
+    
+def Bestimate(G,Rg,P):
+    return (G*P/Rg**P)*np.exp(scsp.gammaln(P/2))
+    
+###############################################################################
+#### Size distribution
+###############################################################################
+
+def SizeDistribution(Profile,ProfDict,Limits,Substances,Sample,data):
+    shapes = {'Spheroid':[SpheroidFF,SpheroidVol],'Cylinder':[CylinderDFF,CylinderDVol],
+        'Cylinder AR':[CylinderARFF,CylinderARVol],'Unified sphere':[UniSphereFF,UniSphereVol],
+        'Unified rod':[UniRodFF,UniRodVol],'Unified rod AR':[UniRodARFF,UniRodARVol],
+        'Unified disk':[UniDiskFF,UniDiskVol],'Sphere':[SphereFF,SphereVol],
+        'Cylinder diam':[CylinderDFF,CylinderDVol]}
+    Shape = data['Size']['Shape'][0]
+    Parms = data['Size']['Shape'][1:]
+    if data['Size']['logBins']:
+        Bins = np.logspace(np.log10(data['Size']['MinDiam']),np.log10(data['Size']['MaxDiam']),
+            data['Size']['Nbins']+1,True)/2.        #make radii
+    else:
+        Bins = np.linspace(data['Size']['MinDiam'],data['Size']['MaxDiam'],
+            data['Size']['Nbins']+1,True)/2.        #make radii
+    Dbins = np.diff(Bins)
+    Bins = Bins[:-1]+Dbins/2.
+    Contrast = Sample['Contrast'][1]
+    Scale = Sample['Scale'][0]
+    Sky = 10**data['Size']['MaxEnt']['Sky']
+    BinsBack = np.ones_like(Bins)*Sky*Scale/Contrast
+    Back = data['Back']
+    Q,Io,wt,Ic,Ib = Profile[:5]
+    Qmin = Limits[1][0]
+    Qmax = Limits[1][1]
+    wtFactor = ProfDict['wtFactor']
+    Ibeg = np.searchsorted(Q,Qmin)
+    Ifin = np.searchsorted(Q,Qmax)
+    BinMag = np.zeros_like(Bins)
+    Ic[:] = 0.
+    Gmat = G_matrix(Q[Ibeg:Ifin],Bins,Contrast,shapes[Shape][0],shapes[Shape][1],args=Parms)
+    if 'MaxEnt' == data['Size']['Method']:
+        chisq,BinMag,Ic[Ibeg:Ifin] = MaxEnt_SB(Scale*Io[Ibeg:Ifin]-Back[0],
+            Scale/np.sqrt(wtFactor*wt[Ibeg:Ifin]),Gmat,BinsBack,
+            data['Size']['MaxEnt']['Niter'],report=True)
+    elif 'IPG' == data['Size']['Method']:
+        chisq,BinMag,Ic[Ibeg:Ifin] = IPG(Scale*Io[Ibeg:Ifin]-Back[0],Scale/np.sqrt(wtFactor*wt[Ibeg:Ifin]),
+            Gmat,Bins,Dbins,data['Size']['IPG']['Niter'],Q[Ibeg:Ifin],approach=0.8,
+            Power=data['Size']['IPG']['Power'],report=True)
+    Ib[:] = Back[0]
+    Ic[Ibeg:Ifin] += Back[0]
+    print ' Final chi^2: %.3f'%(chisq)
+    Vols = shapes[Shape][1](Bins,Parms)
+    data['Size']['Distribution'] = [Bins,Dbins,BinMag/(2.*Dbins)]
+        
+################################################################################
+#### Modelling
+################################################################################
+
+def ModelFit(Profile,ProfDict,Limits,Substances,Sample,Model):
+    shapes = {'Spheroid':[SpheroidFF,SpheroidVol],'Cylinder':[CylinderDFF,CylinderDVol],
+        'Cylinder AR':[CylinderARFF,CylinderARVol],'Unified sphere':[UniSphereFF,UniSphereVol],
+        'Unified rod':[UniRodFF,UniRodVol],'Unified rod AR':[UniRodARFF,UniRodARVol],
+        'Unified disk':[UniDiskFF,UniDiskVol],'Sphere':[SphereFF,SphereVol],
+        'Unified tube':[UniTubeFF,UniTubeVol],'Cylinder diam':[CylinderDFF,CylinderDVol]}
+
+    def GetModelParms():
+        parmOrder = ['Volume','Radius','Mean','StdDev','MinSize','G','Rg','B','P','Cutoff',
+            'PkInt','PkPos','PkSig','PkGam',]
+        parmDict = {'Scale':Sample['Scale'][0]}
+        varyList = []
+        values = []
+        levelTypes = []
+        Back = Model['Back']
+        if Back[1]:
+            varyList += ['Back',]
+            values.append(Back[0])
+        parmDict['Back'] = Back[0]
+        partData = Model['Particle']
+        parmDict['Matrix density'] = Substances['Substances'][partData['Matrix']['Name']].get('XAnom density',0.0)
+        for i,level in enumerate(partData['Levels']):
+            cid = str(i)+':'
+            controls = level['Controls']
+            Type = controls['DistType']
+            levelTypes.append(Type)
+            if Type in ['LogNormal','Gaussian','LSW','Schulz-Zimm','Monodisperse']:
+                if Type not in ['Monodosperse',]:
+                    parmDict[cid+'NumPoints'] = controls['NumPoints']
+                    parmDict[cid+'Cutoff'] = controls['Cutoff']
+                parmDict[cid+'FormFact'] = shapes[controls['FormFact']][0]
+                parmDict[cid+'FFVolume'] = shapes[controls['FormFact']][1]
+                parmDict[cid+'XAnom density'] = Substances['Substances'][controls['Material']].get('XAnom density',0.0)
+                for item in ['Aspect ratio','Length','Thickness','Diameter',]:
+                    if item in controls['FFargs']:
+                        parmDict[cid+item] = controls['FFargs'][item][0]
+                        if controls['FFargs'][item][1]:
+                            varyList.append(cid+item)
+                            values.append(controls['FFargs'][item][0])
+            distDict = controls['DistType']
+            for item in parmOrder:
+                if item in level[distDict]:
+                    parmDict[cid+item] = level[distDict][item][0]
+                    if level[distDict][item][1]:
+                        values.append(level[distDict][item][0])
+                        varyList.append(cid+item)
+        return levelTypes,parmDict,varyList,values
+        
+    def SetModelParms():
+        print ' Refined parameters: Histogram scale: %.4g'%(parmDict['Scale'])
+        if 'Back' in varyList:
+            Model['Back'][0] = parmDict['Back']
+            print '  %15s %15.4f esd: %15.4g'%('Background:',parmDict['Back'],sigDict['Back'])
+        partData = Model['Particle']
+        for i,level in enumerate(partData['Levels']):
+            Type = level['Controls']['DistType']
+            print ' Component %d: Type: %s:'%(i,Type)
+            cid = str(i)+':'
+            controls = level['Controls']
+            Type = controls['DistType']
+            if Type in ['LogNormal','Gaussian','LSW','Schulz-Zimm','Monodisperse']:
+                for item in ['Aspect ratio','Length','Thickness','Diameter',]:
+                    if cid+item in varyList:
+                        controls['FFargs'][item][0] = parmDict[cid+item]
+                        print ' %15s: %15.4g esd: %15.4g'%(cid+item,parmDict[cid+item],sigDict[cid+item])
+            distDict = controls['DistType']
+            for item in level[distDict]:
+                if cid+item in varyList:
+                    level[distDict][item][0] = parmDict[cid+item]
+                    print ' %15s: %15.4g esd: %15.4g'%(cid+item,parmDict[cid+item],sigDict[cid+item])
+                    
+    def calcSASD(values,Q,Io,wt,Ifb,levelTypes,parmDict,varyList):
+        parmDict.update(zip(varyList,values))
+        M = np.sqrt(wt)*(getSASD(Q,levelTypes,parmDict)+Ifb-parmDict['Scale']*Io)
+        return M
+        
+    def getSASD(Q,levelTypes,parmDict):
+        Ic = np.zeros_like(Q)
+        rhoMat = parmDict['Matrix density']
+        for i,Type in enumerate(levelTypes):
+            cid = str(i)+':'
+            if Type in ['LogNormal','Gaussian','LSW','Schulz-Zimm']:
+                FFfxn = parmDict[cid+'FormFact']
+                Volfxn = parmDict[cid+'FFVolume']
+                FFargs = []
+                for item in [cid+'Aspect ratio',cid+'Length',cid+'Thickness',cid+'Diameter',]:
+                    if item in parmDict: 
+                        FFargs.append(parmDict[item])
+                distDict = {}
+                for item in [cid+'Volume',cid+'Mean',cid+'StdDev',cid+'MinSize',]:
+                    if item in parmDict:
+                        distDict[item.split(':')[1]] = parmDict[item]
+                rho = parmDict[cid+'XAnom density']
+                contrast = rho**2-rhoMat**2
+                rBins,dBins,dist = MakeDiamDist(Type,parmDict[cid+'NumPoints'],parmDict[cid+'Cutoff'],distDict)
+                Gmat = G_matrix(Q,rBins,contrast,FFfxn,Volfxn,FFargs).T
+                dist *= parmDict[cid+'Volume']
+                Ic += np.dot(Gmat,dist)
+            elif 'Unified' in Type:
+                Rg,G,B,P,Rgco = parmDict[cid+'Rg'],parmDict[cid+'G'],parmDict[cid+'B'], \
+                    parmDict[cid+'P'],parmDict[cid+'Cutoff']
+                Qstar = Q/(scsp.erf(Q*Rg/np.sqrt(6)))**3
+                Guin = G*np.exp(-(Q*Rg)**2/3)
+                Porod = (B/Qstar**P)*np.exp(-(Q*Rgco)**2/3)
+                Ic += Guin+Porod
+            elif 'Porod' in Type:
+                B,P,Rgco = parmDict[cid+'B'],parmDict[cid+'P'],parmDict[cid+'Cutoff']
+                Porod = (B/Q**P)*np.exp(-(Q*Rgco)**2/3)
+                Ic += Porod
+            elif 'Mono' in Type:
+                FFfxn = parmDict[cid+'FormFact']
+                Volfxn = parmDict[cid+'FFVolume']
+                FFargs = []
+                for item in [cid+'Aspect ratio',cid+'Length',cid+'Thickness',cid+'Diameter',]:
+                    if item in parmDict: 
+                        FFargs.append(parmDict[item])
+                rho = parmDict[cid+'XAnom density']
+                contrast = rho**2-rhoMat**2
+                R = parmDict[cid+'Radius']
+                Gmat = G_matrix(Q,R,contrast,FFfxn,Volfxn,FFargs)             
+                Ic += Gmat[0]*parmDict[cid+'Volume']
+            elif 'Bragg' in distFxn:
+                Ic += parmDict[cid+'PkInt']*G2pwd.getPsVoigt(parmDict[cid+'PkPos'],
+                    parmDict[cid+'PkSig'],parmDict[cid+'PkGam'],Q)
+        Ic += parmDict['Back']  #/parmDict['Scale']
+        return Ic
+        
+    Q,Io,wt,Ic,Ib,Ifb = Profile[:6]
+    Qmin = Limits[1][0]
+    Qmax = Limits[1][1]
+    wtFactor = ProfDict['wtFactor']
+    Ibeg = np.searchsorted(Q,Qmin)
+    Ifin = np.searchsorted(Q,Qmax)
+    Ic[:] = 0
+    levelTypes,parmDict,varyList,values = GetModelParms()
+    if varyList:
+        result = so.leastsq(calcSASD,values,full_output=True,epsfcn=1.e-8,   #ftol=Ftol,
+            args=(Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Ifb[Ibeg:Ifin],levelTypes,parmDict,varyList))
+        ncyc = int(result[2]['nfev']/2)
+        parmDict.update(zip(varyList,result[0]))
+        chisq = np.sum(result[2]['fvec']**2)
+        ncalc = result[2]['nfev']
+        covM = result[1]
+    else:   #nothing varied
+        M = calcSASD(values,Q[Ibeg:Ifin],Io[Ibeg:Ifin],wtFactor*wt[Ibeg:Ifin],Ifb[Ibeg:Ifin],levelTypes,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] = getSASD(Q[Ibeg:Ifin],levelTypes,parmDict)
+    try:
+        if len(covM):
+            sig = np.sqrt(np.diag(covM)*Rvals['GOF'])
+            sigDict = dict(zip(varyList,sig))
+        print ' Results of small angle data modelling fit:'
+        print 'Number of function calls:',ncalc,' Number of observations: ',Ifin-Ibeg,' Number of parameters: ',len(varyList)
+        print 'Rwp = %7.2f%%, chi**2 = %12.6g, reduced chi**2 = %6.2f'%(Rvals['Rwp'],chisq,Rvals['GOF'])
+        SetModelParms()
+        covMatrix = covM*Rvals['GOF']
+        return True,result,varyList,sig,Rvals,covMatrix
+    except ValueError:
+        return False,0,0,0,0,0
+    
+def ModelFxn(Profile,ProfDict,Limits,Substances,Sample,sasdData):
+    shapes = {'Spheroid':[SpheroidFF,SpheroidVol],'Cylinder':[CylinderDFF,CylinderDVol],
+        'Cylinder AR':[CylinderARFF,CylinderARVol],'Unified sphere':[UniSphereFF,UniSphereVol],
+        'Unified rod':[UniRodFF,UniRodVol],'Unified rod AR':[UniRodARFF,UniRodARVol],
+        'Unified disk':[UniDiskFF,UniDiskVol],'Sphere':[SphereFF,SphereVol],
+        'Unified tube':[UniTubeFF,UniTubeVol],'Cylinder diam':[CylinderDFF,CylinderDVol]}
+#    pdb.set_trace()
+    partData = sasdData['Particle']
+    rhoMat = Substances['Substances'][partData['Matrix']['Name']].get('XAnom density',0.0)
+    matFrac = partData['Matrix']['VolFrac'] #[value,flag]        
+    Scale = Sample['Scale']     #[value,flag]
+    Back = sasdData['Back']
+    Q,Io,wt,Ic,Ib,Ifb = Profile[:6]
+    Qmin = Limits[1][0]
+    Qmax = Limits[1][1]
+    wtFactor = ProfDict['wtFactor']
+    Ibeg = np.searchsorted(Q,Qmin)
+    Ifin = np.searchsorted(Q,Qmax)
+    Ib[:] = Back[0]
+    Ic[:] = 0
+    Rbins = []
+    Dist = []
+    for level in partData['Levels']:
+        controls = level['Controls']
+        distFxn = controls['DistType']
+        if distFxn in ['LogNormal','Gaussian','LSW','Schulz-Zimm']:
+            FFfxn = shapes[controls['FormFact']][0]
+            Volfxn = shapes[controls['FormFact']][1]
+            FFargs = []
+            for item in ['Aspect ratio','Length','Thickness','Diameter',]:
+                if item in controls['FFargs']: 
+                    FFargs.append(controls['FFargs'][item][0])
+            rho = Substances['Substances'][level['Controls']['Material']].get('XAnom density',0.0)
+            contrast = rho**2-rhoMat**2
+            parmDict = level[controls['DistType']]
+            distDict = {}
+            for item in parmDict:
+                distDict[item] = parmDict[item][0]
+            rBins,dBins,dist = MakeDiamDist(controls['DistType'],controls['NumPoints'],controls['Cutoff'],distDict)
+            Gmat = G_matrix(Q[Ibeg:Ifin],rBins,contrast,FFfxn,Volfxn,FFargs).T
+            dist *= level[distFxn]['Volume'][0]
+            Ic[Ibeg:Ifin] += np.dot(Gmat,dist)
+            Rbins.append(rBins)
+            Dist.append(dist/(4.*dBins))
+        elif 'Unified' in distFxn:
+            parmDict = level[controls['DistType']]
+            Rg,G,B,P,Rgco = parmDict['Rg'][0],parmDict['G'][0],parmDict['B'][0],    \
+                parmDict['P'][0],parmDict['Cutoff'][0]
+            Qstar = Q[Ibeg:Ifin]/(scsp.erf(Q[Ibeg:Ifin]*Rg/np.sqrt(6)))**3
+            Guin = G*np.exp(-(Q[Ibeg:Ifin]*Rg)**2/3)
+            Porod = (B/Qstar**P)*np.exp(-(Q[Ibeg:Ifin]*Rgco)**2/3)
+            Ic[Ibeg:Ifin] += Guin+Porod
+            Rbins.append([])
+            Dist.append([])
+        elif 'Porod' in distFxn:
+            parmDict = level[controls['DistType']]
+            B,P,Rgco = parmDict['B'][0],parmDict['P'][0],parmDict['Cutoff'][0]
+            Porod = (B/Q[Ibeg:Ifin]**P)*np.exp(-(Q[Ibeg:Ifin]*Rgco)**2/3)
+            Ic[Ibeg:Ifin] += Porod
+            Rbins.append([])
+            Dist.append([])
+        elif 'Mono' in distFxn:
+            FFfxn = shapes[controls['FormFact']][0]
+            Volfxn = shapes[controls['FormFact']][1]
+            FFargs = []
+            for item in ['Aspect ratio','Length','Thickness','Diameter',]:
+                if item in controls['FFargs']: 
+                    FFargs.append(controls['FFargs'][item][0])
+            rho = Substances['Substances'][level['Controls']['Material']].get('XAnom density',0.0)
+            contrast = rho**2-rhoMat**2
+            R = level[controls['DistType']]['Radius'][0]
+            Gmat = G_matrix(Q[Ibeg:Ifin],R,contrast,FFfxn,Volfxn,FFargs)             
+            Ic[Ibeg:Ifin] += Gmat[0]*level[distFxn]['Volume'][0]
+            Rbins.append([])
+            Dist.append([])
+        elif 'Bragg' in distFxn:
+            parmDict = level[controls['DistType']]
+            Ic[Ibeg:Ifin] += parmDict['PkInt'][0]*G2pwd.getPsVoigt(parmDict['PkPos'][0],
+                parmDict['PkSig'][0],parmDict['PkGam'][0],Q[Ibeg:Ifin])
+            Rbins.append([])
+            Dist.append([])
+    Ic[Ibeg:Ifin] += Back[0]
+    sasdData['Size Calc'] = [Rbins,Dist]
+    
+def MakeDiamDist(DistName,nPoints,cutoff,distDict):
+    
+    if 'LogNormal' in DistName:
+        distFxn = 'LogNormalDist'
+        cumeFxn = 'LogNormalCume'
+        pos = distDict['MinSize']
+        args = [distDict['Mean'],distDict['StdDev']]
+        step = 4.*np.sqrt(np.exp(distDict['StdDev']**2)*(np.exp(distDict['StdDev']**2)-1.))
+        mode = distDict['MinSize']+distDict['Mean']/np.exp(distDict['StdDev']**2)
+        minX = 1. #pos
+        maxX = 1.e4 #np.min([mode+nPoints*step*40,1.e5])
+    elif 'Gauss' in DistName:
+        distFxn = 'GaussDist'
+        cumeFxn = 'GaussCume'
+        pos = distDict['Mean']
+        args = [distDict['StdDev']]
+        step = 0.02*distDict['StdDev']
+        mode = distDict['Mean']
+        minX = np.max([mode-4.*distDict['StdDev'],1.])
+        maxX = np.min([mode+4.*distDict['StdDev'],1.e5])
+    elif 'LSW' in DistName:
+        distFxn = 'LSWDist'
+        cumeFxn = 'LSWCume'
+        pos = distDict['Mean']
+        args = []
+        minX,maxX = [0.,2.*pos]
+    elif 'Schulz' in DistName:
+        distFxn = 'SchulzZimmDist'
+        cumeFxn = 'SchulzZimmCume'
+        pos = distDict['Mean']
+        args = [distDict['StdDev']]
+        minX = np.max([1.,pos-4.*distDict['StdDev']])
+        maxX = np.min([pos+4.*distDict['StdDev'],1.e5])
+    nP = 500
+    Diam = np.logspace(0.,5.,nP,True)
+    TCW = eval(cumeFxn+'(Diam,pos,args)')
+    CumeTgts = np.linspace(cutoff,(1.-cutoff),nPoints+1,True)
+    rBins = np.interp(CumeTgts,TCW,Diam,0,0)
+    dBins = np.diff(rBins)
+    rBins = rBins[:-1]+dBins/2.
+    return rBins,dBins,eval(distFxn+'(rBins,pos,args)')
+
+################################################################################
+#### MaxEnt testing stuff
+################################################################################
+
+def print_vec(text, a):
+    '''print the contents of a vector to the console'''
+    n = a.shape[0]
+    print "%s[ = (" % text,
+    for i in range(n):
+        s = " %g, " % a[i]
+        print s,
+    print ")"
+
+def print_arr(text, a):
+    '''print the contents of an array to the console'''
+    n, m = a.shape
+    print "%s[][] = (" % text
+    for i in range(n):
+        print " (",
+        for j in range(m):
+            print " %g, " % a[i][j],
+        print "),"
+    print ")"
+
+def test_MaxEnt_SB(report=True):
+    def readTextData(filename):
+        '''return q, I, dI from a 3-column text file'''
+        if not os.path.exists(filename):
+            raise Exception("file not found: " + filename)
+        buf = [line.split() for line in open(filename, 'r').readlines()]
+        M = len(buf)
+        buf = zip(*buf)         # transpose rows and columns
+        q  = np.array(buf[0], dtype=np.float64)
+        I  = np.array(buf[1], dtype=np.float64)
+        dI = np.array(buf[2], dtype=np.float64)
+        return q, I, dI
+    print "MaxEnt_SB: "
+    test_data_file = os.path.join( 'testinp', 'test.sas')
+    rhosq = 100     # scattering contrast, 10^20 1/cm^-4
+    bkg   = 0.1     #   I = I - bkg
+    dMin, dMax, nRadii = 25, 9000, 40
+    defaultDistLevel = 1.0e-6
+    IterMax = 40
+    errFac = 1.05
+    
+    r    = np.logspace(math.log10(dMin), math.log10(dMax), nRadii)/2
+    dr   = r * (r[1]/r[0] - 1)          # step size
+    f_dr = np.ndarray((nRadii)) * 0  # volume fraction histogram
+    b    = np.ndarray((nRadii)) * 0 + defaultDistLevel  # MaxEnt "sky background"
+    
+    qVec, I, dI = readTextData(test_data_file)
+    G = G_matrix(qVec,r,rhosq,SphereFF,SphereVol,args=())
+    
+    chisq,f_dr,Ic = MaxEnt_SB(I - bkg, dI*errFac, b, IterMax, G, report=report)
+    if f_dr is None:
+        print "no solution"
+        return
+    
+    print "solution reached"
+    for a,b,c in zip(r.tolist(), dr.tolist(), f_dr.tolist()):
+        print '%10.4f %10.4f %12.4g'%(a,b,c)
+
+def tests():
+    test_MaxEnt_SB(report=True)
+
+if __name__ == '__main__':
+    tests()
+
+=======
 #/usr/bin/env python
 # -*- coding: utf-8 -*-
@@ -1265 +2545 @@
     tests()
 
+>>>>>>> .r1299