Changeset 01c8d9e in sasmodels

Timestamp:

Aug 7, 2018 12:32:18 PM (6 years ago)

Author:

Suczewski <ges3@…>

Branches:

master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

c11d09f

Parents:

707cbdb

Message:

beta approximation, first pass

Files:

• explore/beta/sasfit_compare.py (modified) (8 diffs)
• sasmodels/core.py (modified) (4 diffs)
• sasmodels/data.py (modified) (2 diffs)
• sasmodels/generate.py (modified) (9 diffs)
• sasmodels/kernel_iq.c (modified) (8 diffs)
• sasmodels/kernelcl.py (modified) (5 diffs)
• sasmodels/kerneldll.py (modified) (5 diffs)
• sasmodels/modelinfo.py (modified) (7 diffs)
• sasmodels/models/ellipsoid.c (modified) (2 diffs)
• sasmodels/models/lib/sphere_form.c (modified) (1 diff)
• sasmodels/models/sphere.c (added)
• sasmodels/models/sphere.py (modified) (1 diff)
• sasmodels/product.py (modified) (9 diffs)

explore/beta/sasfit_compare.py

@@ -3 +3 @@
 import sys,os
 BETA_DIR = os.path.dirname(os.path.realpath(__file__))
-SASMODELS_DIR = os.path.dirname(os.path.dirname(BETA_DIR))
+#SASMODELS_DIR = os.path.dirname(os.path.dirname(BETA_DIR))
+SASMODELS_DIR = r"C:\Source\sasmodels"
 sys.path.insert(0, SASMODELS_DIR)
 import os
+from collections import namedtuple
+
 from matplotlib import pyplot as plt
 import numpy as np
@@ -14 +17 @@
 from scipy.special import gammaln  # type: ignore
 
-# THE FOLLOWING 7 BOOLEANS TAILOR WHICH GRAPHS ARE PRINTED WHEN THE PROGRAM RUNS
-#RICHARD, YOU WILL WANT SASVIEW, SASFIT, SCHULZ, ELLIPSOID, AND SPHERE ALL TRUE.
-ELLIPSOID = False
-SPHERE = True
-
-GAUSSIAN = True
-SCHULZ = False
-
-SASVIEW=True
-SASFIT=True
-YUN = True
-
-def data_file(name):
-    return os.path.join(BETA_DIR + '\\data_files', name)
+Theory = namedtuple('Theory', 'Q F1 F2 P S I Seff Ibeta')
+Theory.__new__.__defaults__ = (None,) * len(Theory._fields)
 
 #Used to calculate F(q) for the cylinder, sphere, ellipsoid models
@@ -47 +38 @@
     with np.errstate(all='ignore'):
         # GSL bessel_j1 taylor expansion
-        index = (x < 0.25)  
+        index = (x < 0.25)
         y = x[index]**2
         c1 = -1.0/10.0
@@ -71 +62 @@
     calculator = DirectModel(data, model,cutoff=0)
     calculator.pars = pars.copy()
-    calculator.pars.setdefault('background', 1e-3)
+    calculator.pars.setdefault('background', 0)
     return calculator
 
 #gives the hardsphere structure factor that sasview uses
-def hardsphere_simple(q, radius_effective, volfraction): 
-    CUTOFFHS=0.05 
+def _hardsphere_simple(q, radius_effective, volfraction):
+    CUTOFFHS=0.05
     if fabs(radius_effective) < 1.E-12:
         HARDSPH=1.0
@@ -96 +87 @@
         FF = 8.0*A +6.0*B + 4.0*G + ( -0.8*A -B/1.5 -0.5*G +(A/35. +0.0125*B +0.02*G)*X2)*X2
         HARDSPH= 1./(1. + volfraction*FF )
-        return HARDSPH   
+        return HARDSPH
     X4=X2*X2
     S, C = sin(X), cos(X)
-    FF=  (( G*( (4.*X2 -24.)*X*S -(X4 -12.*X2 +24.)*C +24. )/X2 + B*(2.*X*S -(X2-2.)*C -2.) )/X + A*(S-X*C))/X ;
-    HARDSPH= 1./(1. + 24.*volfraction*FF/X2 );
+    FF=  (( G*( (4.*X2 -24.)*X*S -(X4 -12.*X2 +24.)*C +24. )/X2 + B*(2.*X*S -(X2-2.)*C -2.) )/X + A*(S-X*C))/X
+    HARDSPH= 1./(1. + 24.*volfraction*FF/X2 )
     return HARDSPH
+
+def hardsphere_simple(q, radius_effective, volfraction):
+    SQ = [_hardsphere_simple(qk, radius_effective, volfraction) for qk in q]
+    return np.array(SQ)
 
 #Used in gaussian quadrature for polydispersity
 #returns values and the probability of those values based on gaussian distribution
-def gaussian_distribution(center, sigma,lb,ub):
-    #3 standard deviations covers approx. 99.7% 
+N_GAUSS = 35
+NSIGMA_GAUSS = 3
+def gaussian_distribution(center, sigma, lb, ub):
+    #3 standard deviations covers approx. 99.7%
     if sigma != 0:
-        nsigmas=3
-        x = np.linspace(center-sigma*nsigmas, center+sigma*nsigmas, num=35)
+        nsigmas = NSIGMA_GAUSS
+        x = np.linspace(center-sigma*nsigmas, center+sigma*nsigmas, num=N_GAUSS)
         x= x[(x >= lb) & (x <= ub)]
         px = np.exp((x-center)**2 / (-2.0 * sigma * sigma))
@@ -116 +113 @@
         return np.array([center]), np.array([1])
 
+N_SCHULZ = 80
+NSIGMA_SCHULZ = 8
 def schulz_distribution(center, sigma, lb, ub):
     if sigma != 0:
-        nsigmas=8
-        x = np.linspace(center-sigma*nsigmas, center+sigma*nsigmas, num=80)
+        nsigmas = NSIGMA_SCHULZ
+        x = np.linspace(center-sigma*nsigmas, center+sigma*nsigmas, num=N_SCHULZ)
         x= x[(x >= lb) & (x <= ub)]
         R = x/center
@@ -160 +159 @@
 #SQ is monodisperse approach for structure factor
 #SQ_EFF is the effective structure factor from beta approx
-def ellipsoid_Theta(q, radius_polar, radius_equatorial, sld, sld_solvent,volfraction=0,effective_radius=0):
+def ellipsoid_theta(q, radius_polar, radius_equatorial, sld, sld_solvent,
+                    volfraction=0, radius_effective=None):
     #creates values z and corresponding probabilities w from legendre-gauss quadrature
+    volume = ellipsoid_volume(radius_polar, radius_equatorial)
     z, w = leggauss(76)
     F1 = np.zeros_like(q)
     F2 = np.zeros_like(q)
     #use a u subsition(u=cos) and then u=(z+1)/2 to change integration from
-    #0->2pi with respect to alpha to -1->1 with respect to z, allowing us to use 
+    #0->2pi with respect to alpha to -1->1 with respect to z, allowing us to use
     #legendre-gauss quadrature
     for k, qk in enumerate(q):
         r = sqrt(radius_equatorial**2*(1-((z+1)/2)**2)+radius_polar**2*((z+1)/2)**2)
-        F2i = ((sld-sld_solvent)*ellipsoid_volume(radius_polar,radius_equatorial)*sas_3j1x_x(qk*r))**2
-        F2[k] = np.sum(w*F2i)
-        F1i = (sld-sld_solvent)*ellipsoid_volume(radius_polar,radius_equatorial)*sas_3j1x_x(qk*r)
-        F1[k] = np.sum(w*F1i)
+        form = (sld-sld_solvent)*volume*sas_3j1x_x(qk*r)
+        F2[k] = np.sum(w*form**2)
+        F1[k] = np.sum(w*form)
     #the 1/2 comes from the change of variables mentioned above
     F2 = F2/2.0
     F1 = F1/2.0
-    if effective_radius == 0:
-        effective_radius = ER_ellipsoid(radius_polar,radius_equatorial)
-    else:
-        effective_radius = effective_radius
-    SQ = np.array([hardsphere_simple(qk, effective_radius, volfraction) for qk in q])   
-    SQ_EFF = 1 + F1**2/F2*(SQ - 1) 
-    IQM = 1e-4/ellipsoid_volume(radius_polar,radius_equatorial)*F2
+    if radius_effective is None:
+        radius_effective = ER_ellipsoid(radius_polar,radius_equatorial)
+    SQ = hardsphere_simple(q, radius_effective, volfraction)
+    SQ_EFF = 1 + F1**2/F2*(SQ - 1)
+    IQM = 1e-4*F2/volume
     IQSM = volfraction*IQM*SQ
     IQBM = volfraction*IQM*SQ_EFF
-    return F1, F2, IQM, IQSM, IQBM, SQ, SQ_EFF
-
-#IQD is I(q) polydispursed, IQSD is I(q)S(q) polydispursed, etc. 
+    return Theory(Q=q, F1=F1, F2=F2, P=IQM, S=SQ, I=IQSM, Seff=SQ_EFF, Ibeta=IQBM)
+
+#IQD is I(q) polydispursed, IQSD is I(q)S(q) polydispursed, etc.
 #IQBD HAS NOT BEEN CROSS CHECKED AT ALL
-def ellipsoid_pe(q, Rp, Re, sld, sld_solvent, volfraction=0,effective_radius=0,radius_polar_pd=0.1,radius_equatorial_pd=0.1,distribution='gaussian'):
-    if distribution == 'gaussian':
-        Rp_val, Rp_prob = gaussian_distribution(Rp, radius_polar_pd*Rp, 0, inf)
-        Re_val, Re_prob = gaussian_distribution(Re, radius_equatorial_pd*Re, 0, inf)
-    elif distribution == 'schulz':
-        Rp_val, Rp_prob = schulz_distribution(Rp, radius_polar_pd*Rp, 0, inf)
-        Re_val, Re_prob = schulz_distribution(Re, radius_equatorial_pd*Re, 0, inf)
-    Normalization = 0
-    total_weight = 0
-    PQ = np.zeros_like(q)
-    F12,F21 = np.zeros_like(q), np.zeros_like(q)
+def ellipsoid_pe(q, radius_polar, radius_equatorial, sld, sld_solvent,
+                 radius_polar_pd=0.1, radius_equatorial_pd=0.1,
+                 radius_polar_pd_type='gaussian',
+                 radius_equatorial_pd_type='gaussian',
+                 volfraction=0, radius_effective=None,
+                 background=0, scale=1,
+                 norm='sasview'):
+    if norm not in ['sasview', 'sasfit', 'yun']:
+        raise TypeError("unknown norm "+norm)
+    if radius_polar_pd_type == 'gaussian':
+        Rp_val, Rp_prob = gaussian_distribution(radius_polar, radius_polar_pd*radius_polar, 0, inf)
+    elif radius_polar_pd_type == 'schulz':
+        Rp_val, Rp_prob = schulz_distribution(radius_polar, radius_polar_pd*radius_polar, 0, inf)
+    if radius_equatorial_pd_type == 'gaussian':
+        Re_val, Re_prob = gaussian_distribution(radius_equatorial, radius_equatorial_pd*radius_equatorial, 0, inf)
+    elif radius_equatorial_pd_type == 'schulz':
+        Re_val, Re_prob = schulz_distribution(radius_equatorial, radius_equatorial_pd*radius_equatorial, 0, inf)
+    total_weight = total_volume = 0
     radius_eff = 0
+    F1, F2 = np.zeros_like(q), np.zeros_like(q)
     for k, Rpk in enumerate(Rp_val):
         for i, Rei in enumerate(Re_val):
-            F1i, F2i, PQi, IQSM, IQBM, SQ, SQ_EFF = ellipsoid_Theta(q,Rpk,Rei,sld,sld_solvent)
-            radius_eff += Rp_prob[k]*Re_prob[i]*ER_ellipsoid(Rpk,Rei)
-            total_weight +=  Rp_prob[k]*Re_prob[i]
-            Normalization += Rp_prob[k]*Re_prob[i]*ellipsoid_volume(Rpk, Rei)
-            PQ += PQi*Rp_prob[k]*Re_prob[i]*ellipsoid_volume(Rpk, Rei)
-            F12 += F1i*Rp_prob[k]*Re_prob[i]*ellipsoid_volume(Rpk, Rei)
-            F21 += F2i*Rp_prob[k]*Re_prob[i]*ellipsoid_volume(Rpk, Rei)
-    F12 = (F12/Normalization)**2
-    F21 = F21/Normalization
-    if effective_radius == 0:
-        effective_radius = radius_eff/total_weight
-    else:
-        effective_radius = effective_radius
-    SQ = np.array([hardsphere_simple(qk, effective_radius, volfraction) for qk in q])   
-    SQ_EFF = 1 + F12/F21*(SQ - 1)     
-    IQD = PQ/Normalization
-    IQSD = volfraction*IQD*SQ
-    IQBD = volfraction*IQD*SQ_EFF
-    return IQD, IQSD, IQBD, SQ, SQ_EFF
+            theory = ellipsoid_theta(q,Rpk,Rei,sld,sld_solvent)
+            volume = ellipsoid_volume(Rpk, Rei)
+            weight = Rp_prob[k]*Re_prob[i]
+            total_weight += weight
+            total_volume += weight*volume
+            F1 += theory.F1*weight
+            F2 += theory.F2*weight
+            radius_eff += weight*ER_ellipsoid(Rpk,Rei)
+    F1 /= total_weight
+    F2 /= total_weight
+    average_volume = total_volume/total_weight
+    if radius_effective is None:
+        radius_effective = radius_eff/total_weight
+    print("this is the effective radius for pure python",radius_effective)
+    if norm == 'sasfit':
+        IQD = F2
+    elif norm == 'sasview':
+        # Note: internally, sasview uses F2/total_volume because:
+        #   average_volume = total_volume/total_weight
+        #   F2/total_weight / average_volume
+        #     = F2/total_weight / total_volume/total_weight
+        #     = F2/total_volume
+        
+        IQD = F2/average_volume*1e-4*volfraction
+        F1 *= 1e-2  # Yun is using sld in 1/A^2, not 1e-6/A^2
+        F2 *= 1e-4
+    elif norm == 'yun':
+        F1 *= 1e-6  # Yun is using sld in 1/A^2, not 1e-6/A^2
+        F2 *= 1e-12
+        IQD = F2/average_volume*1e8*volfraction
+    SQ = hardsphere_simple(q, radius_effective, volfraction)
+    beta = F1**2/F2
+    SQ_EFF = 1 + beta*(SQ - 1)
+    IQSD = IQD*SQ
+    IQBD = IQD*SQ_EFF
+    print("\n\n\n\n\n this is F1" + str(F1))
+
+    print("\n\n\n\n\n this is F2" + str(F2))
+    print("\n\n\n\n\n this is SQ" + str(SQ))   
+    return Theory(Q=q, F1=F1, F2=F2, P=IQD, S=SQ, I=IQSD, Seff=SQ_EFF, Ibeta=IQBD)
 
 #polydispersity for sphere
-def sphere_r(q,radius,sld,sld_solvent,volfraction=0,radius_pd=0.1,distribution='gaussian',norm_type='volfraction'):
-    if distribution == 'gaussian':    
+def sphere_r(q,radius,sld,sld_solvent,
+             radius_pd=0.1, radius_pd_type='gaussian',
+             volfraction=0, radius_effective=None,
+             background=0, scale=1,
+             norm='sasview'):
+    if norm not in ['sasview', 'sasfit', 'yun']:
+        raise TypeError("unknown norm "+norm)
+    if radius_pd_type == 'gaussian':
         radius_val, radius_prob = gaussian_distribution(radius, radius_pd*radius, 0, inf)
-    elif distribution == 'schulz':
+    elif radius_pd_type == 'schulz':
         radius_val, radius_prob = schulz_distribution(radius, radius_pd*radius, 0, inf)
-    Normalization=0
+    total_weight = total_volume = 0
+    F1 = np.zeros_like(q)
     F2 = np.zeros_like(q)
-    F1 = np.zeros_like(q)
-    if norm_type == 'numdensity':
-        for k, rk in enumerate(radius_val):
-            volume = 4./3.*pi*rk**3
-            Normalization += radius_prob[k]*volume
-            F2i = ((sld-sld_solvent)*volume*sas_3j1x_x(q*rk))**2/volume
-            F1i = (sld-sld_solvent)*volume*sas_3j1x_x(q*rk)/volume
-            
-            F2 += radius_prob[k]*volume*F2i
-            F1 += radius_prob[k]*volume*F1i
-        F21 = F2/Normalization
-        F12 = (F1/Normalization)**2
-        SQ = np.array([hardsphere_simple(qk, radius, volfraction) for qk in q])
-        SQ_EFF = 1 + F12/F21*(SQ - 1) 
-        IQD = 1e-4*F21
-        IQSD = volfraction*IQD*SQ
-        IQBD = volfraction*IQD*SQ_EFF
-    elif norm_type == 'volfraction':
-        for k, rk in enumerate(radius_val):
-            volume = 4./3.*pi*rk**3
-            Normalization += radius_prob[k]
-            F2i = ((sld-sld_solvent)*volume*sas_3j1x_x(q*rk))**2
-            F1i = (sld-sld_solvent)*volume*sas_3j1x_x(q*rk)
-            F2 += radius_prob[k]*F2i
-            F1 += radius_prob[k]*F1i
-    
-        F21 = F2/Normalization
-        F12 = (F1/Normalization)**2
-        SQ = np.array([hardsphere_simple(qk, radius, volfraction) for qk in q])
-        SQ_EFF = 1 + F12/F21*(SQ - 1) 
-        IQD = 1e-4/(4./3.*pi*radius**3)*F21
-        IQSD = volfraction*IQD*SQ
-        IQBD = volfraction*IQD*SQ_EFF
-    return IQD, IQSD, IQBD, SQ, SQ_EFF
+    for k, rk in enumerate(radius_val):
+        volume = 4./3.*pi*rk**3
+        total_weight += radius_prob[k]
+        total_volume += radius_prob[k]*volume
+        form = (sld-sld_solvent)*volume*sas_3j1x_x(q*rk)
+        F2 += radius_prob[k]*form**2
+        F1 += radius_prob[k]*form
+    F1 /= total_weight
+    F2 /= total_weight
+    average_volume = total_volume/total_weight
+
+    if radius_effective is None:
+        radius_effective = radius
+    average_volume = total_volume/total_weight
+    if norm == 'sasfit':
+        IQD = F2
+    elif norm == 'sasview':
+        IQD = F2/average_volume*1e-4*volfraction
+    elif norm == 'yun':
+        F1 *= 1e-6  # Yun is using sld in 1/A^2, not 1e-6/A^2
+        F2 *= 1e-12
+        IQD = F2/average_volume*1e8*volfraction
+    SQ = hardsphere_simple(q, radius_effective, volfraction)
+    beta = F1**2/F2
+    SQ_EFF = 1 + beta*(SQ - 1)
+    IQSD = IQD*SQ
+    IQBD = IQD*SQ_EFF
+    return Theory(Q=q, F1=F1, F2=F2, P=IQD, S=SQ, I=IQSD, Seff=SQ_EFF, Ibeta=IQBD)
 
 ###############################################################################
@@ -276 +304 @@
 ###############################################################################
 ###############################################################################
-# SASVIEW    
-if (ELLIPSOID == True) & (GAUSSIAN == True) & (SASVIEW == True):
+
+def popn(d, keys):
+    """
+    Splits a dict into two, with any key of *d* which is in *keys* removed
+    from *d* and added to *b*. Returns *b*.
+    """
+    b = {}
+    for k in keys:
+        try:
+            b[k] = d.pop(k)
+        except KeyError:
+            pass
+    return b
+
+def sasmodels_theory(q, Pname, **pars):
+    """
+    Call sasmodels to compute the model with and without a hard sphere
+    structure factor.
+    """
+    #mono_pars = {k: (0 if k.endswith('_pd') else v) for k, v in pars.items()}
+    Ppars = pars.copy()
+    Spars = popn(Ppars, ['radius_effective', 'volfraction'])
+    Ipars = pars.copy()
+
+    # Autofill npts and nsigmas for the given pd type
+    for k, v in pars.items():
+        if k.endswith("_pd_type"):
+            if v == "gaussian":
+                n, nsigmas = N_GAUSS, NSIGMA_GAUSS
+            elif v == "schulz":
+                n, nsigmas = N_SCHULZ, NSIGMA_SCHULZ
+            Ppars.setdefault(k.replace("_pd_type", "_pd_n"), n)
+            Ppars.setdefault(k.replace("_pd_type", "_pd_nsigma"), nsigmas)
+            Ipars.setdefault(k.replace("_pd_type", "_pd_n"), n)
+            Ipars.setdefault(k.replace("_pd_type", "_pd_nsigma"), nsigmas)
+
+    
+    #Ppars['scale'] = Spars.get('volfraction', 1)
+    P = build_model(Pname, q)
+    S = build_model("hardsphere", q)
+    I = build_model(Pname+"@hardsphere", q)
+    Pq = P(**Ppars)*pars.get('volfraction', 1)
+    Sq = S(**Spars)
+    Iq = I(**Ipars)
+    #Iq = Pq*Sq*pars.get('volfraction', 1)
+    #Sq = Iq/Pq
+    #Iq = None#= Sq = None
+    r=I._kernel.results
+    return Theory(Q=q, F1=None, F2=None, P=Pq, S=None, I=None, Seff=r[1], Ibeta=Iq)
+
+def compare(title, target, actual, fields='F1 F2 P S I Seff Ibeta'):
+    """
+    Plot fields in common between target and actual, along with relative error.
+    """
+    available = [s for s in fields.split()
+                 if getattr(target, s) is not None and getattr(actual, s) is not None]
+    rows = len(available)
+    for row, field in enumerate(available):
+        Q = target.Q
+        I1, I2 = getattr(target, field), getattr(actual, field)
+        plt.subplot(rows, 2, 2*row+1)
+        plt.loglog(Q, abs(I1), label="target "+field)
+        plt.loglog(Q, abs(I2), label="value "+field)
+        #plt.legend(loc="upper left", bbox_to_anchor=(1,1))
+        plt.legend(loc='lower left')
+        plt.subplot(rows, 2, 2*row+2)
+        plt.semilogx(Q, I2/I1 - 1, label="relative error")
+        #plt.semilogx(Q, I1/I2 - 1, label="relative error")
+    plt.tight_layout()
+    plt.suptitle(title)
+    plt.show()
+
+def data_file(name):
+    return os.path.join(BETA_DIR, 'data_files', name)
+
+def load_sasfit(path):
+    data = np.loadtxt(path, dtype=str, delimiter=';').T
+    data = np.vstack((map(float, v) for v in data[0:2]))
+    return data
+
+COMPARISON = {}  # Type: Dict[(str,str,str)] -> Callable[(), None]
+
+def compare_sasview_sphere(pd_type='schulz'):
+    q = np.logspace(-5, 0, 250)
+    model = 'sphere'
+    pars = dict(
+        radius=20,sld=4,sld_solvent=1,
+        background=0,
+        radius_pd=.1, radius_pd_type=pd_type,
+        volfraction=0.15,
+        #radius_effective=12.59921049894873,  # equivalent average sphere radius
+        )
+    target = sasmodels_theory(q, model, **pars)
+    actual = sphere_r(q, norm='sasview', **pars)
+    title = " ".join(("sasmodels", model, pd_type))
+    compare(title, target, actual)
+COMPARISON[('sasview','sphere','gaussian')] = lambda: compare_sasview_sphere(pd_type='gaussian')
+COMPARISON[('sasview','sphere','schulz')] = lambda: compare_sasview_sphere(pd_type='schulz')
+
+def compare_sasview_ellipsoid(pd_type='gaussian'):
     q = np.logspace(-5, 0, 50)
-    volfraction = 0.2
-    model = build_model("ellipsoid",q)
-    Sq = model(radius_polar=20,radius_equatorial=400,sld=4,sld_solvent=1,\
-               background=0,radius_polar_pd=.1,radius_polar_pd_n=35,radius_equatorial_pd=.1,radius_equatorial_pd_n=35)
-    IQD, IQSD, IQBD, SQ, SQ_EFF = ellipsoid_pe(q,20,400,4,1,volfraction)
-    Sq2 = model(radius_polar=20,radius_equatorial=10,sld=2,sld_solvent=1,background=0)
-    IQM = ellipsoid_Theta(q,20,10,2,1)[2]
-    model2 = build_model("ellipsoid@hardsphere", q)
-    Sq3 = model2(radius_polar=20,radius_equatorial=400,sld=4,sld_solvent=1,background=0,radius_polar_pd=.1,\
-                 radius_polar_pd_n=35,radius_equatorial_pd=.1,radius_equatorial_pd_n=35)
-    Sqp = model(radius_polar=20,radius_equatorial=10,sld=4,sld_solvent=1,\
-                   background=0,radius_polar_pd=0.1,radius_polar_pd_n=35,radius_equatorial_pd=0,radius_equatorial_pd_n=35)
-    IQDp = ellipsoid_pe(q,20,10,4,1,radius_polar_pd=0.1,radius_equatorial_pd=0)[0]  
-    Sqp2 = model(radius_polar=20,radius_equatorial=10,sld=4,sld_solvent=1,\
-                   background=0,radius_polar_pd=0,radius_polar_pd_n=35,radius_equatorial_pd=0.1,radius_equatorial_pd_n=35)
-    IQDe = ellipsoid_pe(q,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.1)[0]
-    print('\n\n\n\n ELLIPSOID COMPARISON WITH SASVIEW WITHOUT V')
-    plt.subplot(323)
-    plt.loglog(q, Sq,'r')
-    plt.loglog(q,IQD,'b')
-    plt.title('IQD')
-    plt.subplot(324)
-    plt.semilogx(q,Sq/IQD-1)
-    plt.subplot(321)
-    plt.title('IQM')
-    plt.loglog(q, Sq2,'r')
-    plt.loglog(q,IQM,'b')
-    plt.subplot(322)
-    plt.semilogx(q,IQM/Sq2-1)
-    plt.subplot(325)
-    plt.title('IQSD')
-    plt.loglog(q, Sq3, '-b')
-    plt.loglog(q, IQSD, '-r')
-    plt.subplot(326)
-    plt.semilogx(q, Sq3/IQSD-1, 'b')
-    plt.tight_layout()
-    plt.show()
-    plt.subplot(221)
-    plt.loglog(q,IQDp,'r')
-    plt.loglog(q,Sqp,'b')
-    plt.title('IQD Polar')
-    plt.subplot(222)
-    plt.plot(q,IQDp/Sqp-1)
-    plt.subplot(223)
-    plt.loglog(q,IQDe,'r')
-    plt.loglog(q,Sqp2,'b')
-    plt.title('IQD Equatorial')
-    plt.subplot(224)
-    plt.plot(q,IQDe/Sqp2-1)
-    plt.tight_layout()
-    plt.show()
-#comparing monodisperse Beta approximation to version without
-    q=np.linspace(1e-5,1)
-    F1, F2, IQM, IQSM, IQBM, SQ, SQ_EFF = ellipsoid_Theta(q,20,10,2,1,0.15,12.59921049894873)
-    plt.subplot(321)
-    plt.loglog(q,IQBM,'g',label='IQBM')
-    plt.loglog(q,IQSM,'r',label= 'IQSM')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.subplot(323)
-    plt.plot(q,IQBM,'g',label='IQBM')
-    plt.plot(q,IQSM,'r',label= 'IQSM')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.subplot(325)
-    plt.plot(q,IQBM/IQSM,'r',label= 'IQBM/IQSM')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.tight_layout()
-    plt.show()
-#comparing polydispersed Beta approximation to version without
-    IQD, IQSD, IQBD, SQ, SQ_EFF = ellipsoid_pe(q, 20,10, 2,1, 0.15,12.59921049894873)
-    plt.subplot(321)
-    plt.loglog(q, SQ)
-    plt.loglog(q, SQ_EFF,label='SQ, SQ_EFF')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.subplot(323)
-    plt.loglog(q,IQBD)
-    plt.loglog(q,IQSD,label='IQBD,IQSD')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.subplot(325)
-    plt.plot(q,IQBD)
-    plt.plot(q,IQSD,label='IQBD,IQSD')
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.tight_layout()
-    plt.show()
-
-# YUN
-if (ELLIPSOID == True) & (GAUSSIAN == True) & (YUN == True):
-    #Yun's data for Beta Approximation
-    data = np.loadtxt(data_file('yun_ellipsoid.dat'),skiprows=2).T   
-    print('\n\n ELLIPSOID COMPARISON WITH YUN WITHOUT V')
-    Q=data[0]
-    F1,F2,IQM,IQSM,IQBM,SQ,SQ_EFF = ellipsoid_Theta(Q,20,10,2,1,0.15,12.59921049894873)
-    plt.subplot(211)
-    plt.loglog(Q,0.15*data[3]*data[5]*ellipsoid_volume(20,10)*1e-4)
-    plt.loglog(Q,IQSM,'-g',label='IQSM')
-    plt.loglog(data[0], data[3]*ellipsoid_volume(20,10)*1e-4,'-k')
-    plt.loglog(Q,IQM,'r',label = 'IQM')
-    plt.ylim(1e-5,4)
-    plt.legend(loc="upper left", bbox_to_anchor=(1,1))
-    plt.show()
-    plt.subplot(221)
-    plt.loglog(data[0],SQ)
-    plt.loglog(data[0],data[5])
-    plt.title('SQ')
-    plt.subplot(222)
-    plt.semilogx(data[0],SQ/data[5]-1)
-    plt.subplot(223)
-    plt.title('SQ_EFF')
-    plt.loglog(data[0],SQ_EFF)
-    plt.loglog(data[0],data[6])
-    plt.subplot(224)
-    plt.semilogx(data[0],(SQ_EFF/data[6])-1,label='Sq effective ratio')
-    plt.tight_layout()
-    plt.show()
-
-#SASFIT
-if ELLIPSOID and GAUSSIAN and SASFIT:
-    print('\n\n ELLIPSOID COMPARISON WITH SASFIT WITHOUT V')
-    #Rp=20,Re=10,eta_core=4,eta_solv=1
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQM.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0)[0],'b')
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata),'g')
-    plt.title('IQM')
-    plt.subplot(322)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0)[0]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.1,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 10% 0.15')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.1,radius_equatorial_pd=0)[3]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq2.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.3,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 30% .15')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.3,radius_equatorial_pd=0)[3]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq3.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.6,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 60% .15')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.6,radius_equatorial_pd=0)[3]-1)
-    plt.tight_layout()
-    plt.show() 
-    print('========================================================================================')
-    print('========================================================================================')
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq4.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.1,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 10% .3')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.1,radius_equatorial_pd=0)[3]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq5.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.3,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 30% .3')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.3,radius_equatorial_pd=0)[3]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq6.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.6,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 60% .3')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.6,radius_equatorial_pd=0)[3]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq7.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.1,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 10% .6')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.1,radius_equatorial_pd=0)[3]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq8.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.3,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 30% .6')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.3,radius_equatorial_pd=0)[3]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sq9.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.6,radius_equatorial_pd=0)[3])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ poly 60% .6')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.6,radius_equatorial_pd=0)[3]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-#checks beta structre factor
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.1,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 10% 0.15')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.1,radius_equatorial_pd=0)[4]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff2.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.3,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 30% .15')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.3,radius_equatorial_pd=0)[4]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.15
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff3.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.6,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 60% .15')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,.15,radius_polar_pd=0.6,radius_equatorial_pd=0)[4]-1)
-    plt.tight_layout()
-    plt.show() 
-    print('========================================================================================')
-    print('========================================================================================')
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff4.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.1,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 10% .3')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.1,radius_equatorial_pd=0)[4]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff5.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.3,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 30% .3')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.3,radius_equatorial_pd=0)[4]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.3
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff6.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.6,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 60% .3')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.3,radius_polar_pd=0.6,radius_equatorial_pd=0)[4]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff7.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(321)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.1,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 10% .6')
-    plt.subplot(322)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.1,radius_equatorial_pd=0)[4]-1)
-#N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff8.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(323)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.3,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 30% .6')
-    plt.subplot(324)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.3,radius_equatorial_pd=0)[4]-1)
-#N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.6
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_ellipsoid_sqeff9.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(325)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.6,radius_equatorial_pd=0)[4])
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.title('SQ_EFF poly 60% .6')
-    plt.subplot(326)
-    plt.plot(sasqdata,np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,0.6,radius_polar_pd=0.6,radius_equatorial_pd=0)[4]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-#checks polydispersion for single parameter and varying pd with sasfit
-#N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.1,radius_equatorial_pd=0)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD polar 10%')
-    plt.subplot(222)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.1,radius_equatorial_pd=0)[0]-1)
-#N=1,s=1,X0=10,distr equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD2.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(223)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.1)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD equatorial 10%')
-    plt.subplot(224)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.1)[0]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-    #N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD3.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.3,radius_equatorial_pd=0)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD polar 30%')
-    plt.subplot(222)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.3,radius_equatorial_pd=0)[0]-1)
-#N=1,s=3,X0=10,distr equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD4.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(223)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.3)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD equatorial 30%')
-    plt.subplot(224)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.3)[0]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-    #N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD5.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.6,radius_equatorial_pd=0)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD polar 60%')
-    plt.subplot(222)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0.6,radius_equatorial_pd=0)[0]-1)
-#N=1,s=6,X0=10,distr equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
-    sasdata = np.loadtxt(data_file('sasfit_ellipsoid_IQD6.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(223)
-    plt.loglog(sasqdata, ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.6)[0])
-    plt.loglog(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata))
-    plt.title('IQD equatorial 60%')
-    plt.subplot(224)
-    plt.plot(sasqdata,1e-4/ellipsoid_volume(20,10)*np.array(sasvaluedata)/ellipsoid_pe(sasqdata,20,10,4,1,radius_polar_pd=0,radius_equatorial_pd=0.6)[0]-1)
-    plt.tight_layout()
-    plt.show()
-    print('========================================================================================')
-    print('========================================================================================')
-
-# TEST FOR RICHARD
+    model = 'ellipsoid'
+    pars = dict(
+        radius_polar=20,radius_equatorial=400,sld=4,sld_solvent=1,
+        background=0,
+        radius_polar_pd=.1, radius_polar_pd_type=pd_type,
+        radius_equatorial_pd=.1, radius_equatorial_pd_type=pd_type,
+        volfraction=0.15,
+        radius_effective=270.7543927018,
+        #radius_effective=12.59921049894873,
+        )
+    target = sasmodels_theory(q, model, beta_mode=1, **pars)
+    actual = ellipsoid_pe(q, norm='sasview', **pars)
+    print(actual)
+    title = " ".join(("sasmodels", model, pd_type))
+    compare(title, target, actual)
+COMPARISON[('sasview','ellipsoid','gaussian')] = lambda: compare_sasview_ellipsoid(pd_type='gaussian')
+COMPARISON[('sasview','ellipsoid','schulz')] = lambda: compare_sasview_ellipsoid(pd_type='schulz')
+
+def compare_yun_ellipsoid_mono():
+    pars = {
+        'radius_polar': 20, 'radius_polar_pd': 0, 'radius_polar_pd_type': 'gaussian',
+        'radius_equatorial': 10, 'radius_equatorial_pd': 0, 'radius_equatorial_pd_type': 'gaussian',
+        'sld': 2, 'sld_solvent': 1,
+        'volfraction': 0.15,
+        # Yun uses radius for same volume sphere for effective radius
+        # whereas sasview uses the average curvature.
+        'radius_effective': 12.59921049894873,
+    }
+
+    data = np.loadtxt(data_file('yun_ellipsoid.dat'),skiprows=2).T
+    Q = data[0]
+    F1 = data[1]
+    P = data[3]*pars['volfraction']
+    S = data[5]
+    Seff = data[6]
+    target = Theory(Q=Q, F1=F1, P=P, S=S, Seff=Seff)
+    actual = ellipsoid_pe(Q, norm='yun', **pars)
+    title = " ".join(("yun", "ellipsoid", "no pd"))
+    #compare(title, target, actual, fields="P S I Seff Ibeta")
+    compare(title, target, actual)
+COMPARISON[('yun','ellipsoid','gaussian')] = compare_yun_ellipsoid_mono
+COMPARISON[('yun','ellipsoid','schulz')] = compare_yun_ellipsoid_mono
+
+def compare_yun_sphere_gauss():
+    # Note: yun uses gauss limits from R0/10 to R0 + 5 sigma steps sigma/100
+    # With pd = 0.1, that's 14 sigma and 1400 points.
+    pars = {
+        'radius': 20, 'radius_pd': 0.1, 'radius_pd_type': 'gaussian',
+        'sld': 6, 'sld_solvent': 0,
+        'volfraction': 0.1,
+    }
+
+    data = np.loadtxt(data_file('testPolydisperseGaussianSphere.dat'),skiprows=2).T
+    Q = data[0]
+    F1 = data[1]
+    P = data[3]
+    S = data[5]
+    Seff = data[6]
+    target = Theory(Q=Q, F1=F1, P=P, S=S, Seff=Seff)
+    actual = sphere_r(Q, norm='yun', **pars)
+    title = " ".join(("yun", "sphere", "10% dispersion 10% Vf"))
+    compare(title, target, actual)
+    data = np.loadtxt(data_file('testPolydisperseGaussianSphere2.dat'),skiprows=2).T
+    pars.update(radius_pd=0.15)
+    Q = data[0]
+    F1 = data[1]
+    P = data[3]
+    S = data[5]
+    Seff = data[6]
+    target = Theory(Q=Q, F1=F1, P=P, S=S, Seff=Seff)
+    actual = sphere_r(Q, norm='yun', **pars)
+    title = " ".join(("yun", "sphere", "15% dispersion 10% Vf"))
+    compare(title, target, actual)
+COMPARISON[('yun','sphere','gaussian')] = compare_yun_sphere_gauss
+
+
+def compare_sasfit_sphere_gauss():
+    #N=1,s=2,X0=20,distr radius R=20,eta_core=4,eta_solv=1,.3
+    pars = {
+        'radius': 20, 'radius_pd': 0.1, 'radius_pd_type': 'gaussian',
+        'sld': 4, 'sld_solvent': 1,
+        'volfraction': 0.3,
+    }
+
+    Q, IQ = load_sasfit(data_file('sasfit_sphere_IQD.txt'))
+    Q, IQSD = load_sasfit(data_file('sasfit_sphere_IQSD.txt'))
+    Q, IQBD = load_sasfit(data_file('sasfit_sphere_IQBD.txt'))
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_sphere_sq.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_sphere_sqeff.txt'))
+    target = Theory(Q=Q, F1=None, F2=None, P=IQ, S=SQ, I=IQSD, Seff=SQ_EFF, Ibeta=IQBD)
+    actual = sphere_r(Q, norm="sasfit", **pars)
+    title = " ".join(("sasfit", "sphere", "pd=10% gaussian"))
+    compare(title, target, actual)
+    #compare(title, target, actual, fields="P")
+COMPARISON[('sasfit','sphere','gaussian')] = compare_sasfit_sphere_gauss
+
+def compare_sasfit_sphere_schulz():
     #radius=20,sld=4,sld_solvent=1,volfraction=0.3,radius_pd=0.1
     #We have scaled the output from sasfit by 1e-4*volume*volfraction
     #0.10050378152592121
-if (SPHERE == True) & (SCHULZ == True) & (SASVIEW == True) & (SASFIT == True):
-    print('                   *****SPHERE MODEL*****')
-    sasdata = np.loadtxt(data_file('richard_test.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    model=build_model('sphere',sasqdata)
-    Iq = model(radius=20,radius_pd=0.1,radius_pd_n=80,sld=4,sld_solvent=1,background=0,radius_pd_type='schulz',radius_pd_nsigma=8)
-    model2=build_model('sphere@hardsphere',sasqdata)
-    IQS = model2(radius=20,radius_pd=0.1,radius_pd_n=80,sld=4,sld_solvent=1,background=0,radius_pd_type='schulz',radius_pd_nsigma=8,volfraction=0.3)
-    
-    sasvaluedata=map(float,sasdata[1])
-    sasdata2 = np.loadtxt(data_file('richard_test2.txt'),dtype=str,delimiter=';').T
-    sasqdata2=map(float,sasdata2[0])
-    sasvaluedata2=map(float,sasdata2[1])
-    sasdata3 = np.loadtxt(data_file('richard_test3.txt'),dtype=str,delimiter=';').T
-    sasqdata3=map(float,sasdata3[0])
-    sasvaluedata3=map(float,sasdata3[1])
-    IQD, IQSD, IQBD, SQ, SQ_EFF = sphere_r(np.array(sasqdata),20,4,1,.3,0.1,distribution='schulz')
-    plt.grid(True)
-    plt.title('IQD(blue) vs. SASVIEW(red) vs. SASFIT(green)')
-    plt.loglog(sasqdata,Iq,'b')
-    plt.loglog(sasqdata,IQD,'r')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20**3)*np.array(sasvaluedata),'g')
-    plt.show()
-    plt.grid(True)
-    plt.title('IQSD(blue) vs. SASVIEW(red) vs. SASFIT(green)')
-    plt.loglog(sasqdata,IQSD,'b')
-    plt.loglog(sasqdata,IQS,'r')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20**3)*0.3*np.array(sasvaluedata2),'g')
-    plt.show()
-    plt.grid(True)
-    plt.title('IQBD(blue) vs. SASFIT(green)')
-    plt.loglog(sasqdata,IQBD,'b')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20**3)*0.3*np.array(sasvaluedata3),'g')
-    plt.show()
-
-# TEST FOR RICHARD
-if (ELLIPSOID == True) & (SCHULZ == True) & (SASVIEW == True) & (SASFIT == True):   
+    pars = {
+        'radius': 20, 'radius_pd': 0.1, 'radius_pd_type': 'schulz',
+        'sld': 4, 'sld_solvent': 1,
+        'volfraction': 0.3,
+    }
+
+    Q, IQ = load_sasfit(data_file('richard_test.txt'))
+    Q, IQSD = load_sasfit(data_file('richard_test2.txt'))
+    Q, IQBD = load_sasfit(data_file('richard_test3.txt'))
+    target = Theory(Q=Q, F1=None, F2=None, P=IQ, S=None, I=IQSD, Seff=None, Ibeta=IQBD)
+    actual = sphere_r(Q, norm="sasfit", **pars)
+    title = " ".join(("sasfit", "sphere", "pd=10% schulz"))
+    compare(title, target, actual)
+COMPARISON[('sasfit','sphere','schulz')] = compare_sasfit_sphere_schulz
+
+def compare_sasfit_ellipsoid_schulz():
     #polarradius=20, equatorialradius=10, sld=4,sld_solvent=1,volfraction=0.3,radius_polar_pd=0.1
-         #Effective radius =13.1353356684
-        #We have scaled the output from sasfit by 1e-4*volume*volfraction
-        #0.10050378152592121
-    print('                   *****ELLIPSOID MODEL*****')
-    sasdata = np.loadtxt(data_file('richard_test4.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    model=build_model('ellipsoid',sasqdata)
-    Iq = model(radius_polar=20,radius_polar_pd=0.1,radius_polar_pd_n=80, radius_equatorial=10, radius_equatorial_pd=0, sld=4,sld_solvent=1,background=0,radius_polar_pd_type='schulz',radius_polar_pd_nsigma=8)
-    model2=build_model('ellipsoid@hardsphere',sasqdata)
-    IQS =  model2(radius_polar=20,radius_equatorial=10,sld=4,sld_solvent=1,background=0,radius_polar_pd=.1,\
-                     radius_polar_pd_n=80,radius_equatorial_pd=0,radius_polar_pd_type='schulz',radius_polar_pd_nsigma=8,volfraction=0.3)
-    sasvaluedata=map(float,sasdata[1])
-    sasdata2 = np.loadtxt(data_file('richard_test5.txt'),dtype=str,delimiter=';').T
-    sasqdata2=map(float,sasdata2[0])
-    sasvaluedata2=map(float,sasdata2[1])
-    sasdata3 = np.loadtxt(data_file('richard_test6.txt'),dtype=str,delimiter=';').T
-    sasqdata3=map(float,sasdata3[0])
-    sasvaluedata3=map(float,sasdata3[1])
-    IQD, IQSD, IQBD, SQ, SQ_EFF = ellipsoid_pe(np.array(sasqdata),20,10,4,1,.3,radius_polar_pd=0.1,radius_equatorial_pd=0,distribution='schulz')    
-    plt.grid(True)
-    plt.title('IQD(blue) vs. SASVIEW(red) vs. SASFIT(green)')
-    plt.loglog(sasqdata,Iq,'b')
-    plt.loglog(sasqdata,IQD,'r')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20*10**2)*np.array(sasvaluedata),'g')
-    plt.show()
-    plt.grid(True)
-    plt.title('IQSD(blue) vs. SASVIEW(red) vs. SASFIT(green)')
-    plt.loglog(sasqdata,IQSD,'b')
-    plt.loglog(sasqdata,IQS,'r')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20*10**2)*0.3*np.array(sasvaluedata2),'g')
-    plt.show()
-    plt.grid(True)
-    plt.title('IQBD(blue) vs. SASFIT(green)')
-    plt.loglog(sasqdata,IQBD,'b')
-    plt.loglog(sasqdata,1e-4/(4./3.*pi*20*10**2)*0.3*np.array(sasvaluedata3),'g')
-    plt.show()    
-    
-# SASVIEW GAUSS
-if (SPHERE == True) & (GAUSSIAN == True) & (SASVIEW == True):
-    q = np.logspace(-5, 0, 200)
-    IQD, IQSD, IQBD, SQ, SQ_EFF = sphere_r(q,20,4,1,.3)
-    model = build_model("sphere", q)
-    Sq = model(radius=20,radius_pd=0.1,radius_pd_n=35,sld=4,sld_solvent=1,background=0)
-    model2 = build_model("sphere@hardsphere", q)
-    S=build_model("hardsphere",q)(radius_effective=20,volfraction=.3,background=0)
-    Sq2 = model2(radius=20,radius_pd=0.1,radius_pd_n=35,sld=4,sld_solvent=1,background=0,volfraction=.3)
-    print('\n\n SPHERE COMPARISON WITH SASVIEW WITHOUT V')
-    plt.subplot(221)
-    plt.title('IQD')
-    plt.loglog(q, IQD, '-b')
-    plt.loglog(q, Sq, '-r')
-    plt.subplot(222)
-    plt.semilogx(q, Sq/IQD-1, '-g')
-    plt.tight_layout()
-    plt.show()
-    plt.subplot(221)
-    plt.title('SQ')
-    plt.plot(q, SQ, '-r')
-    plt.plot(q,S,'-k')
-    plt.subplot(222)
-    plt.plot(SQ/S-1)
-    plt.tight_layout()
-    plt.show()
-    plt.subplot(221)
-    plt.title('IQSD')
-    plt.loglog(q, IQSD, '-b')
-    plt.loglog(q, Sq2, '-r',label='IQSD')
-    plt.subplot(222)
-    plt.semilogx(q, Sq2/IQSD-1, '-g',label='IQSD ratio')
-    plt.tight_layout()
-    plt.show()
-    IQD, IQSD, IQBD, SQ, SQ_EFF = sphere_r(q,20,4,1,0.15)
-    plt.subplot(211)
-    plt.title('SQ vs SQ_EFF')
-    plt.plot(q, SQ)
-    plt.plot(q, SQ_EFF)
-    plt.tight_layout()
-    plt.show() 
-    plt.subplot(221)
-    plt.title('IQSD vs IQBD')
-    plt.loglog(q,IQBD)
-    plt.loglog(q,IQSD,label='IQBD,IQSD')
-    plt.subplot(222)
-    plt.plot(q,IQBD)
-    plt.plot(q,IQSD,)
-    plt.tight_layout()
-    plt.show() 
-
-# SASFIT GAUSS
-if (SPHERE == True) & (GAUSSIAN == True) & (SASFIT == True):
-    print('\n\n SPHERE COMPARISON WITH SASFIT WITHOUT V')
-#N=1,s=2,X0=20,distr radius R=20,eta_core=4,eta_solv=1,.3
-    sasdata = np.loadtxt(data_file('sasfit_sphere_IQD.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    IQD, IQSD, IQBD, SQ, SQ_EFF = sphere_r(np.array(sasqdata),20,4,1,.3)
-    plt.subplot(221)
-    plt.title('IQD')
-    plt.loglog(sasqdata,IQD )
-    plt.loglog(sasqdata,1e-4/(4./3*pi*20**3)*np.array(sasvaluedata))
-    plt.subplot(222)
-    plt.semilogx(sasqdata,1e-4/(4./3*pi*20**3)*np.array(sasvaluedata)/IQD-1)
-    plt.tight_layout()
-    plt.show()
-    sasdata = np.loadtxt(data_file('sasfit_sphere_IQSD.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.title('IQSD')
-    plt.loglog(sasqdata, IQSD)
-    plt.loglog(sasqdata,1e-4/(4./3*pi*20**3)*0.3*np.array(sasvaluedata))
-    plt.subplot(222)
-    plt.semilogx(sasqdata,1e-4/(4./3*pi*20**3)*0.3*np.array(sasvaluedata)/IQSD-1)
-    plt.tight_layout()
-    plt.show()
-    sasdata = np.loadtxt(data_file('sasfit_sphere_IQBD.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.title('IQBD')
-    plt.loglog(sasqdata,IQBD)
-    plt.loglog(sasqdata,1e-4/(4./3*pi*20**3)*0.3*np.array(sasvaluedata))
-    plt.subplot(222)
-    plt.semilogx(sasqdata,1e-4/(4./3*pi*20**3)*0.3*np.array(sasvaluedata)/IQBD-1)
-    plt.tight_layout()
-    plt.show()
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_sphere_sq.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.title('SQ')
-    plt.loglog(sasqdata, SQ)
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.subplot(222)
-    plt.semilogx(sasqdata,np.array(sasvaluedata)/SQ-1)
-    plt.tight_layout()
-    plt.show()
-    sasdata = np.loadtxt(data_file('sasfit_polydisperse_sphere_sqeff.txt'),dtype=str,delimiter=';').T
-    sasqdata=map(float,sasdata[0])
-    sasvaluedata=map(float,sasdata[1])
-    plt.subplot(221)
-    plt.title('SQ_EFF')
-    plt.loglog(sasqdata,SQ_EFF)
-    plt.loglog(sasqdata,np.array(sasvaluedata))
-    plt.subplot(222)
-    plt.semilogx(sasqdata,np.array(sasvaluedata)/SQ_EFF-1)
-    plt.tight_layout()
-    plt.show()
+    #Effective radius =13.1353356684
+    #We have scaled the output from sasfit by 1e-4*volume*volfraction
+    #0.10050378152592121
+    pars = {
+        'radius_polar': 20, 'radius_polar_pd': 0.1, 'radius_polar_pd_type': 'schulz',
+        'radius_equatorial': 10, 'radius_equatorial_pd': 0., 'radius_equatorial_pd_type': 'schulz',
+        'sld': 4, 'sld_solvent': 1,
+        'volfraction': 0.3, 'radius_effective': 13.1353356684,
+    }
+
+    Q, IQ = load_sasfit(data_file('richard_test4.txt'))
+    Q, IQSD = load_sasfit(data_file('richard_test5.txt'))
+    Q, IQBD = load_sasfit(data_file('richard_test6.txt'))
+    target = Theory(Q=Q, F1=None, F2=None, P=IQ, S=None, I=IQSD, Seff=None, Ibeta=IQBD)
+    actual = ellipsoid_pe(Q, norm="sasfit", **pars)
+    title = " ".join(("sasfit", "ellipsoid", "pd=10% schulz"))
+    compare(title, target, actual)
+COMPARISON[('sasfit','ellipsoid','schulz')] = compare_sasfit_ellipsoid_schulz
+
+
+def compare_sasfit_ellipsoid_gaussian():
+    pars = {
+        'radius_polar': 20, 'radius_polar_pd': 0, 'radius_polar_pd_type': 'gaussian',
+        'radius_equatorial': 10, 'radius_equatorial_pd': 0, 'radius_equatorial_pd_type': 'gaussian',
+        'sld': 4, 'sld_solvent': 1,
+        'volfraction': 0, 'radius_effective': None,
+    }
+
+    #Rp=20,Re=10,eta_core=4,eta_solv=1
+    Q, PQ0 = load_sasfit(data_file('sasfit_ellipsoid_IQM.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.0, radius_equatorial_pd=0, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ0)
+    compare("sasfit ellipsoid no poly", target, actual); plt.show()
+
+    #N=1,s=2,X0=20,distr 10% polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Rp10 = load_sasfit(data_file('sasfit_ellipsoid_IQD.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.1, radius_equatorial_pd=0.0, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Rp10)
+    compare("sasfit ellipsoid P(Q) 10% Rp", target, actual); plt.show()
+    #N=1,s=1,X0=10,distr 10% equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Re10 = load_sasfit(data_file('sasfit_ellipsoid_IQD2.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.0, radius_equatorial_pd=0.1, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Re10)
+    compare("sasfit ellipsoid P(Q) 10% Re", target, actual); plt.show()
+    #N=1,s=6,X0=20,distr 30% polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Rp30 = load_sasfit(data_file('sasfit_ellipsoid_IQD3.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.3, radius_equatorial_pd=0.0, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Rp30)
+    compare("sasfit ellipsoid P(Q) 30% Rp", target, actual); plt.show()
+    #N=1,s=3,X0=10,distr 30% equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Re30 = load_sasfit(data_file('sasfit_ellipsoid_IQD4.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.0, radius_equatorial_pd=0.3, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Re30)
+    compare("sasfit ellipsoid P(Q) 30% Re", target, actual); plt.show()
+    #N=1,s=12,X0=20,distr 60% polar Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Rp60 = load_sasfit(data_file('sasfit_ellipsoid_IQD5.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.6, radius_equatorial_pd=0.0, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Rp60)
+    compare("sasfit ellipsoid P(Q) 60% Rp", target, actual); plt.show()
+    #N=1,s=6,X0=10,distr 60% equatorial Rp=20,Re=10,eta_core=4,eta_solv=1, no structure poly
+    Q, PQ_Re60 = load_sasfit(data_file('sasfit_ellipsoid_IQD6.txt'))
+    pars.update(volfraction=0, radius_polar_pd=0.0, radius_equatorial_pd=0.6, radius_effective=None)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, P=PQ_Re60)
+    compare("sasfit ellipsoid P(Q) 60% Re", target, actual); plt.show()
+
+    #N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.15
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff.txt'))
+    pars.update(volfraction=0.15, radius_polar_pd=0.1, radius_equatorial_pd=0, radius_effective=13.1354236254)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 10% Rp 15% Vf", target, actual); plt.show()
+    #N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.15
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq2.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff2.txt'))
+    pars.update(volfraction=0.15, radius_polar_pd=0.3, radius_equatorial_pd=0, radius_effective=13.0901197149)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 30% Rp 15% Vf", target, actual); plt.show()
+    #N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.15
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq3.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff3.txt'))
+    pars.update(volfraction=0.15, radius_polar_pd=0.6, radius_equatorial_pd=0, radius_effective=13.336060917)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 60% Rp 15% Vf", target, actual); plt.show()
+
+    #N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.3
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq4.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff4.txt'))
+    pars.update(volfraction=0.3, radius_polar_pd=0.1, radius_equatorial_pd=0, radius_effective=13.1354236254)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 10% Rp 30% Vf", target, actual); plt.show()
+    #N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.3
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq5.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff5.txt'))
+    pars.update(volfraction=0.3, radius_polar_pd=0.3, radius_equatorial_pd=0, radius_effective=13.0901197149)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 30% Rp 30% Vf", target, actual); plt.show()
+    #N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.3
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq6.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff6.txt'))
+    pars.update(volfraction=0.3, radius_polar_pd=0.6, radius_equatorial_pd=0, radius_effective=13.336060917)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 60% Rp 30% Vf", target, actual); plt.show()
+
+    #N=1,s=2,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.1354236254,.6
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq7.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff7.txt'))
+    pars.update(volfraction=0.6, radius_polar_pd=0.1, radius_equatorial_pd=0, radius_effective=13.1354236254)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 10% Rp 60% Vf", target, actual); plt.show()
+    #N=1,s=6,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.0901197149,.6
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq8.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff8.txt'))
+    pars.update(volfraction=0.6, radius_polar_pd=0.3, radius_equatorial_pd=0, radius_effective=13.0901197149)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 30% Rp 60% Vf", target, actual); plt.show()
+    #N=1,s=12,X0=20,distr polar Rp=20,Re=10,eta_core=4,eta_solv=1, hardsphere ,13.336060917,.6
+    Q, SQ = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sq9.txt'))
+    Q, SQ_EFF = load_sasfit(data_file('sasfit_polydisperse_ellipsoid_sqeff9.txt'))
+    pars.update(volfraction=0.6, radius_polar_pd=0.6, radius_equatorial_pd=0, radius_effective=13.336060917)
+    actual = ellipsoid_pe(Q, norm='sasfit', **pars)
+    target = Theory(Q=Q, S=SQ, Seff=SQ_EFF)
+    compare("sasfit ellipsoid P(Q) 60% Rp 60% Vf", target, actual); plt.show()
+COMPARISON[('sasfit','ellipsoid','gaussian')] = compare_sasfit_ellipsoid_gaussian
+
+def main():
+    key = tuple(sys.argv[1:])
+    if key not in COMPARISON:
+        print("usage: sasfit_compare.py [sasview|sasfit|yun] [sphere|ellipsoid] [gaussian|schulz]")
+        return
+    comparison = COMPARISON[key]
+    comparison()
+
+if __name__ == "__main__":
+    main()

sasmodels/core.py

@@ -140 +140 @@
     used with functions in generate to build the docs or extract model info.
     """
+
     if "+" in model_string:
         parts = [load_model_info(part)
@@ -205 +206 @@
 
     numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform)
-
     source = generate.make_source(model_info)
     if platform == "dll":
@@ -265 +265 @@
     # Assign default platform, overriding ocl with dll if OpenCL is unavailable
     # If opencl=False OpenCL is switched off
-
     if platform is None:
         platform = "ocl"
@@ -291 +290 @@
     else:
         numpy_dtype = np.dtype(dtype)
-
     # Make sure that the type is supported by opencl, otherwise use dll
     if platform == "ocl":

sasmodels/data.py

@@ -329 +329 @@
     else:
         dq = resolution * q
-
     data = Data1D(q, Iq, dx=dq, dy=dIq)
     data.filename = "fake data"
@@ -523 +522 @@
                   else 'log')
         plt.xscale(xscale)
+        
         plt.xlabel("$q$/A$^{-1}$")
         plt.yscale(yscale)

sasmodels/generate.py

@@ -672 +672 @@
 
 # type in IQXY pattern could be single, float, double, long double, ...
-_IQXY_PATTERN = re.compile(r"(^|\s)double\s+I(?P<mode>q(ab?c|xy))\s*[(]",
+_IQXY_PATTERN = re.compile(r"(^|\s)double\s+I(?P<mode>q(ac|abc|xy))\s*[(]",
                            flags=re.MULTILINE)
 def find_xy_mode(source):
@@ -701 +701 @@
     return 'qa'
 
+# type in IQXY pattern could be single, float, double, long double, ...
+_FQ_PATTERN = re.compile(r"(^|\s)void\s+Fq[(]", flags=re.MULTILINE)
+def has_Fq(source):
+    for code in source:
+        m = _FQ_PATTERN.search(code)
+        if m is not None:
+            return True
+    return False
 
 def _add_source(source, code, path, lineno=1):
@@ -730 +738 @@
     # dispersion.  Need to be careful that necessary parameters are available
     # for computing volume even if we allow non-disperse volume parameters.
-
     partable = model_info.parameters
-
     # Load templates and user code
     kernel_header = load_template('kernel_header.c')
     kernel_code = load_template('kernel_iq.c')
     user_code = [(f, open(f).read()) for f in model_sources(model_info)]
-
     # Build initial sources
     source = []
@@ -743 +748 @@
     for path, code in user_code:
         _add_source(source, code, path)
-
     if model_info.c_code:
         _add_source(source, model_info.c_code, model_info.filename,
@@ -789 +793 @@
     source.append("\\\n".join(p.as_definition()
                               for p in partable.kernel_parameters))
-
     # Define the function calls
     if partable.form_volume_parameters:
@@ -800 +803 @@
         call_volume = "#define CALL_VOLUME(v) 1.0"
     source.append(call_volume)
-
     model_refs = _call_pars("_v.", partable.iq_parameters)
-    pars = ",".join(["_q"] + model_refs)
-    call_iq = "#define CALL_IQ(_q, _v) Iq(%s)" % pars
+    #create varaible BETA to turn on and off beta approximation
+    BETA = has_Fq(source)
+    if not BETA:
+        pars = ",".join(["_q"] + model_refs)
+        call_iq = "#define CALL_IQ(_q, _v) Iq(%s)" % pars
+    else:
+        pars = ",".join(["_q", "&_F1", "&_F2",] + model_refs)
+        call_iq = "#define CALL_IQ(_q, _F1, _F2, _v) Fq(%s)" % pars
     if xy_mode == 'qabc':
         pars = ",".join(["_qa", "_qb", "_qc"] + model_refs)
@@ -831 +839 @@
     magpars = [k-2 for k, p in enumerate(partable.call_parameters)
                if p.type == 'sld']
-
     # Fill in definitions for numbers of parameters
+    source.append("#define BETA %d" %(1 if BETA else 0))
     source.append("#define MAX_PD %s"%partable.max_pd)
     source.append("#define NUM_PARS %d"%partable.npars)
@@ -839 +847 @@
     source.append("#define MAGNETIC_PARS %s"%",".join(str(k) for k in magpars))
     source.append("#define PROJECTION %d"%PROJECTION)
-
     # TODO: allow mixed python/opencl kernels?
-
     ocl = _kernels(kernel_code, call_iq, call_iqxy, clear_iqxy, model_info.name)
     dll = _kernels(kernel_code, call_iq, call_iqxy, clear_iqxy, model_info.name)
+
     result = {
         'dll': '\n'.join(source+dll[0]+dll[1]+dll[2]),
         'opencl': '\n'.join(source+ocl[0]+ocl[1]+ocl[2]),
     }
-
+    #print(result['dll'])
     return result
 
@@ -1068 +1075 @@
         model_info = make_model_info(kernel_module)
         source = make_source(model_info)
-        print(source['dll'])
+        #print(source['dll'])
 
 

sasmodels/kernel_iq.c

@@ -36 +36 @@
 //  PROJECTION : equirectangular=1, sinusoidal=2
 //      see explore/jitter.py for definitions.
+
 
 #ifndef _PAR_BLOCK_ // protected block so we can include this code twice.
@@ -270 +271 @@
 #endif // _QABC_SECTION
 
-
 // ==================== KERNEL CODE ========================
-
 kernel
 void KERNEL_NAME(
@@ -339 +338 @@
   // The code differs slightly between opencl and dll since opencl is only
   // seeing one q value (stored in the variable "this_result") while the dll
-  // version must loop over all q.
+  // version must loop over all q
+  #if BETA
+  double *beta_result = &(result[nq+1]); // = result + nq + 1
+  double weight_norm = (pd_start == 0 ? 0.0 : result[2*nq+1]);
+  #endif
   #ifdef USE_OPENCL
     double pd_norm = (pd_start == 0 ? 0.0 : result[nq]);
     double this_result = (pd_start == 0 ? 0.0 : result[q_index]);
+    #if BETA
+      double this_beta_result = (pd_start == 0 ? 0.0 : result[nq + q_index];
   #else // !USE_OPENCL
-    double pd_norm = (pd_start == 0 ? 0.0 : result[nq]);
+    double pd_norm = (pd_start == 0 ? 0.0 : result[nq]); 
     if (pd_start == 0) {
       #ifdef USE_OPENMP
@@ -350 +355 @@
       #endif
       for (int q_index=0; q_index < nq; q_index++) result[q_index] = 0.0;
+      #if BETA
+      for (int q_index=0; q_index < nq; q_index++) beta_result[q_index] = 0.0;
+      #endif
     }
     //if (q_index==0) printf("start %d %g %g\n", pd_start, pd_norm, result[0]);
 #endif // !USE_OPENCL
+
+
+
 
 
@@ -442 +453 @@
 // inner loop and defines the macros that use them.
 
+
 #if defined(CALL_IQ)
   // unoriented 1D
   double qk;
-  #define FETCH_Q() do { qk = q[q_index]; } while (0)
-  #define BUILD_ROTATION() do {} while(0)
-  #define APPLY_ROTATION() do {} while(0)
-  #define CALL_KERNEL() CALL_IQ(qk, local_values.table)
+  #if BETA == 0
+    #define FETCH_Q() do { qk = q[q_index]; } while (0)
+    #define BUILD_ROTATION() do {} while(0)
+    #define APPLY_ROTATION() do {} while(0)
+    #define CALL_KERNEL() CALL_IQ(qk,local_values.table)
+
+  // unoriented 1D Beta
+  #elif BETA == 1
+    double F1, F2;
+    #define FETCH_Q() do { qk = q[q_index]; } while (0)
+    #define BUILD_ROTATION() do {} while(0)
+    #define APPLY_ROTATION() do {} while(0)
+    #define CALL_KERNEL() CALL_IQ(qk,F1,F2,local_values.table)
+  #endif
 
 #elif defined(CALL_IQ_A)
@@ -647 +669 @@
       pd_norm += weight * CALL_VOLUME(local_values.table);
       BUILD_ROTATION();
-
+#if BETA
+    if (weight > cutoff) {
+      weight_norm += weight;}
+#endif
 #ifndef USE_OPENCL
       // DLL needs to explicitly loop over the q values.
@@ -693 +718 @@
           }
         #else  // !MAGNETIC
-          const double scattering = CALL_KERNEL();
+          #if defined(CALL_IQ) && BETA == 1
+            CALL_KERNEL();
+            const double scatteringF1 = F1;
+            const double scatteringF2 = F2;
+          #else
+            const double scattering = CALL_KERNEL();
+          #endif
         #endif // !MAGNETIC
 //printf("q_index:%d %g %g %g %g\n", q_index, scattering, weight0);
 
         #ifdef USE_OPENCL
-          this_result += weight * scattering;
+          #if defined(CALL_IQ)&& BETA == 1
+             this_result += weight * scatteringF2;
+             this_beta_result += weight * scatteringF1;
+            #else
+              this_result += weight * scattering;
+          #endif
         #else // !USE_OPENCL
-          result[q_index] += weight * scattering;
+          #if defined(CALL_IQ)&& BETA == 1
+            result[q_index] += weight * scatteringF2;
+            beta_result[q_index] += weight * scatteringF1;
+            #endif
+            #else
+            result[q_index] += weight * scattering;
+          #endif
         #endif // !USE_OPENCL
       }
     }
   }
-
 // close nested loops
 ++step;
@@ -728 +769 @@
   result[q_index] = this_result;
   if (q_index == 0) result[nq] = pd_norm;
+  #if BETA
+  beta_result[q_index] = this_beta_result;
+  #endif
+  if (q_index == 0) result[nq] = pd_norm;
+
 //if (q_index == 0) printf("res: %g/%g\n", result[0], pd_norm);
 #else // !USE_OPENCL
   result[nq] = pd_norm;
+  #if BETA
+  result[2*nq+1] = weight_norm;
+  #endif
 //printf("res: %g/%g\n", result[0], pd_norm);
 #endif // !USE_OPENCL

sasmodels/kernelcl.py

@@ -420 +420 @@
         if self.program is None:
             compile_program = environment().compile_program
+            with open('model.c','w') as fid:
+                print(self.source['opencl'], file=fid)
             timestamp = generate.ocl_timestamp(self.info)
             self.program = compile_program(
@@ -539 +541 @@
         self.dim = '2d' if q_input.is_2d else '1d'
         # plus three for the normalization values
-        self.result = np.empty(q_input.nq+1, dtype)
+        self.result = np.empty(2*q_input.nq+2,dtype)
 
         # Inputs and outputs for each kernel call
@@ -555 +557 @@
                      else np.float16 if dtype == generate.F16
                      else np.float32)  # will never get here, so use np.float32
-
-    def __call__(self, call_details, values, cutoff, magnetic):
+    __call__= Iq
+
+    def Iq(self, call_details, values, cutoff, magnetic):
         # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray
         context = self.queue.context
@@ -572 +575 @@
         ]
         #print("Calling OpenCL")
-        #call_details.show(values)
-        # Call kernel and retrieve results
+        call_details.show(values)
+        #Call kernel and retrieve results
         wait_for = None
         last_nap = time.clock()
@@ -604 +607 @@
         return scale*self.result[:self.q_input.nq] + background
         # return self.result[:self.q_input.nq]
+     #NEEDS TO BE FINISHED FOR OPENCL
+     def beta():
+         return 0
 
     def release(self):

sasmodels/kerneldll.py

@@ -258 +258 @@
         # Note: if there is a syntax error then compile raises an error
         # and the source file will not be deleted.
-        os.unlink(filename)
-        #print("saving compiled file in %r"%filename)
+        #os.unlink(filename)
+        print("saving compiled file in %r"%filename)
     return dll
 
@@ -371 +371 @@
     def __init__(self, kernel, model_info, q_input):
         # type: (Callable[[], np.ndarray], ModelInfo, PyInput) -> None
+        #,model_info,q_input)
         self.kernel = kernel
         self.info = model_info
@@ -376 +377 @@
         self.dtype = q_input.dtype
         self.dim = '2d' if q_input.is_2d else '1d'
-        self.result = np.empty(q_input.nq+1, q_input.dtype)
+        self.result = np.empty(2*q_input.nq+2, q_input.dtype)
         self.real = (np.float32 if self.q_input.dtype == generate.F32
                      else np.float64 if self.q_input.dtype == generate.F64
                      else np.float128)
 
-    def __call__(self, call_details, values, cutoff, magnetic):
+    def Iq(self, call_details, values, cutoff, magnetic):
         # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray
-
+        self._call_kernel(call_details, values, cutoff, magnetic)
+        #print("returned",self.q_input.q, self.result)
+        pd_norm = self.result[self.q_input.nq]
+        scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
+        background = values[1]
+        #print("scale",scale,background)
+        return scale*self.result[:self.q_input.nq] + background
+    __call__ = Iq
+
+    def beta(self, call_details, values, cutoff, magnetic):
+        # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray
+        self._call_kernel(call_details, values, cutoff, magnetic)
+        w_norm = self.result[2*self.q_input.nq + 1]
+        pd_norm = self.result[self.q_input.nq]
+        if w_norm == 0.:
+            w_norm = 1.
+        F2 = self.result[:self.q_input.nq]/w_norm
+        F1 = self.result[self.q_input.nq+1:2*self.q_input.nq+1]/w_norm
+        volume_avg = pd_norm/w_norm
+        return F1, F2, volume_avg
+
+    def _call_kernel(self, call_details, values, cutoff, magnetic):
         kernel = self.kernel[1 if magnetic else 0]
         args = [
@@ -390 +412 @@
             None, # pd_stop pd_stride[MAX_PD]
             call_details.buffer.ctypes.data, # problem
-            values.ctypes.data,  #pars
-            self.q_input.q.ctypes.data, #q
+            values.ctypes.data,  # pars
+            self.q_input.q.ctypes.data, # q
             self.result.ctypes.data,   # results
             self.real(cutoff), # cutoff
         ]
-        #print("Calling DLL")
+        #print(self.beta_result.ctypes.data)
+#        print("Calling DLL line 397")
+#        print("values", values)
         #call_details.show(values)
         step = 100
@@ -403 +427 @@
             kernel(*args) # type: ignore
 
-        #print("returned",self.q_input.q, self.result)
-        pd_norm = self.result[self.q_input.nq]
-        scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
-        background = values[1]
-        #print("scale",scale,background)
-        return scale*self.result[:self.q_input.nq] + background
-
     def release(self):
         # type: () -> None

sasmodels/modelinfo.py

@@ -163 +163 @@
     parameter.length = length
     parameter.length_control = control
-
     return parameter
 
@@ -418 +417 @@
     # scale and background are implicit parameters
     COMMON = [Parameter(*p) for p in COMMON_PARAMETERS]
-
     def __init__(self, parameters):
         # type: (List[Parameter]) -> None
         self.kernel_parameters = parameters
         self._set_vector_lengths()
-
         self.npars = sum(p.length for p in self.kernel_parameters)
         self.nmagnetic = sum(p.length for p in self.kernel_parameters
@@ -430 +427 @@
         if self.nmagnetic:
             self.nvalues += 3 + 3*self.nmagnetic
-
         self.call_parameters = self._get_call_parameters()
         self.defaults = self._get_defaults()
@@ -444 +440 @@
         self.form_volume_parameters = [p for p in self.kernel_parameters
                                        if p.type == 'volume']
-
         # Theta offset
         offset = 0
@@ -466 +461 @@
         self.magnetism_index = [k for k, p in enumerate(self.call_parameters)
                                 if p.id.startswith('M0:')]
-
         self.pd_1d = set(p.name for p in self.call_parameters
                          if p.polydisperse and p.type not in ('orientation', 'magnetic'))
@@ -770 +764 @@
         # Custom sum/multi models
         return kernel_module.model_info
+
     info = ModelInfo()
     #print("make parameter table", kernel_module.parameters)
@@ -822 +817 @@
     info.lineno = {}
     _find_source_lines(info, kernel_module)
-
     return info
 

sasmodels/models/ellipsoid.c

@@ -20 +20 @@
     //     i(h) = int_0^1 Phi^2(h a sqrt(1 + u^2(v^2-1)) du
     const double v_square_minus_one = square(radius_polar/radius_equatorial) - 1.0;
-
+  
     // translate a point in [-1,1] to a point in [0, 1]
     // const double u = GAUSS_Z[i]*(upper-lower)/2 + (upper+lower)/2;
@@ -36 +36 @@
     const double s = (sld - sld_solvent) * form_volume(radius_polar, radius_equatorial);
     return 1.0e-4 * s * s * form;
+} 
+
+static void
+Fq(double q,
+    double *F1,
+    double *F2,
+    double sld,
+    double sld_solvent,
+    double radius_polar,
+    double radius_equatorial)
+{
+    // Using ratio v = Rp/Re, we can implement the form given in Guinier (1955)
+    //     i(h) = int_0^pi/2 Phi^2(h a sqrt(cos^2 + v^2 sin^2) cos dT
+    //          = int_0^pi/2 Phi^2(h a sqrt((1-sin^2) + v^2 sin^2) cos dT
+    //          = int_0^pi/2 Phi^2(h a sqrt(1 + sin^2(v^2-1)) cos dT
+    // u-substitution of
+    //     u = sin, du = cos dT
+    //     i(h) = int_0^1 Phi^2(h a sqrt(1 + u^2(v^2-1)) du
+    const double v_square_minus_one = square(radius_polar/radius_equatorial) - 1.0;
+    // translate a point in [-1,1] to a point in [0, 1]
+    // const double u = GAUSS_Z[i]*(upper-lower)/2 + (upper+lower)/2;
+    const double zm = 0.5;
+    const double zb = 0.5;
+    double total_F2 = 0.0;
+    double total_F1 = 0.0;
+    for (int i=0;i<GAUSS_N;i++) {
+        const double u = GAUSS_Z[i]*zm + zb;
+        const double r = radius_equatorial*sqrt(1.0 + u*u*v_square_minus_one);
+        const double f = sas_3j1x_x(q*r);
+        total_F2 += GAUSS_W[i] * f * f;
+        total_F1 += GAUSS_W[i] * f;
+    }
+    // translate dx in [-1,1] to dx in [lower,upper]
+    const double form_squared_avg = total_F2*zm;
+    const double form_avg = total_F1*zm;
+    const double s = (sld - sld_solvent) * form_volume(radius_polar, radius_equatorial);
+    *F2 = 1e-4 * s * s * form_squared_avg;
+    *F1 = 1e-2 * s * form_avg;
 }
+
+
+
+
+
 
 static double

sasmodels/models/lib/sphere_form.c

@@ -13 +13 @@
     return 1.0e-4*square(contrast * fq);
 }
+

sasmodels/models/sphere.py

@@ -67 +67 @@
              ]
 
-source = ["lib/sas_3j1x_x.c", "lib/sphere_form.c"]
+source = ["lib/sas_3j1x_x.c", "lib/sphere_form.c", "sphere.c"]
 
-# No volume normalization despite having a volume parameter
-# This should perhaps be volume normalized?
-form_volume = """
-    return sphere_volume(radius);
-    """
-
-Iq = """
-    return sphere_form(q, radius, sld, sld_solvent);
-    """
 
 def ER(radius):

sasmodels/product.py

@@ -16 +16 @@
 import numpy as np  # type: ignore
 
-from .modelinfo import ParameterTable, ModelInfo
+from .modelinfo import ParameterTable, ModelInfo, Parameter
 from .kernel import KernelModel, Kernel
 from .details import make_details, dispersion_mesh
@@ -74 +74 @@
     translate_name = dict((old.id, new.id) for old, new
                           in zip(s_pars.kernel_parameters[1:], s_list))
-    combined_pars = p_pars.kernel_parameters + s_list
+    beta_parameter = Parameter("beta_mode", "", 0, [["P*S"],["P*(1+beta*(S-1))"], "", "Structure factor dispersion calculation mode"])
+    combined_pars = p_pars.kernel_parameters + s_list + [beta_parameter]
     parameters = ParameterTable(combined_pars)
     parameters.max_pd = p_pars.max_pd + s_pars.max_pd
@@ -151 +152 @@
         #: Structure factor modelling interaction between particles.
         self.S = S
+        
         #: Model precision. This is not really relevant, since it is the
         #: individual P and S models that control the effective dtype,
@@ -168 +170 @@
         # in opencl; or both in opencl, but one in single precision and the
         # other in double precision).
+        
         p_kernel = self.P.make_kernel(q_vectors)
         s_kernel = self.S.make_kernel(q_vectors)
@@ -193 +196 @@
         # type: (CallDetails, np.ndarray, float, bool) -> np.ndarray
         p_info, s_info = self.info.composition[1]
-
         # if there are magnetic parameters, they will only be on the
         # form factor P, not the structure factor S.
@@ -205 +207 @@
         nweights = call_details.num_weights
         weights = values[nvalues:nvalues + 2*nweights]
-
         # Construct the calling parameters for P.
         p_npars = p_info.parameters.npars
@@ -218 +219 @@
         p_values.append([0.]*spacer)
         p_values = np.hstack(p_values).astype(self.p_kernel.dtype)
-
         # Call ER and VR for P since these are needed for S.
         p_er, p_vr = calc_er_vr(p_info, p_details, p_values)
         s_vr = (volfrac/p_vr if p_vr != 0. else volfrac)
         #print("volfrac:%g p_er:%g p_vr:%g s_vr:%g"%(volfrac,p_er,p_vr,s_vr))
-
         # Construct the calling parameters for S.
         # The  effective radius is not in the combined parameter list, so
@@ -253 +252 @@
         s_values.append([0.]*spacer)
         s_values = np.hstack(s_values).astype(self.s_kernel.dtype)
-
+        # beta mode is the first parameter after the structure factor pars
+        beta_index = 2+p_npars+s_npars
+        beta_mode = values[beta_index]
         # Call the kernels
-        p_result = self.p_kernel(p_details, p_values, cutoff, magnetic)
-        s_result = self.s_kernel(s_details, s_values, cutoff, False)
-
+        if beta_mode: # beta:
+            F1, F2, volume_avg = self.p_kernel.beta(p_details, p_values, cutoff, magnetic)
+        else:
+            p_result = self.p_kernel.Iq(p_details, p_values, cutoff, magnetic)
+        s_result = self.s_kernel.Iq(s_details, s_values, cutoff, False)
         #print("p_npars",p_npars,s_npars,p_er,s_vr,values[2+p_npars+1:2+p_npars+s_npars])
         #call_details.show(values)
@@ -265 +268 @@
         #s_details.show(s_values)
         #print("=>", s_result)
-
-        # remember the parts for plotting later
-        self.results = [p_result, s_result]
-
         #import pylab as plt
         #plt.subplot(211); plt.loglog(self.p_kernel.q_input.q, p_result, '-')
         #plt.subplot(212); plt.loglog(self.s_kernel.q_input.q, s_result, '-')
         #plt.figure()
-
-        return values[0]*(p_result*s_result) + values[1]
+        if beta_mode:#beta
+            beta_factor = F1**2/F2
+            Sq_eff = 1+beta_factor*(s_result - 1)
+            self.results = [F2, Sq_eff,F1,s_result]
+            final_result = volfrac*values[0]*(F2 + (F1**2)*(s_result - 1))/volume_avg+values[1]
+            #final_result =  volfrac * values[0] * F2 * Sq_eff / volume_avg + values[1]
+        else:
+            # remember the parts for plotting later
+            self.results = [p_result, s_result]
+            final_result = values[0]*(p_result*s_result) + values[1]
+        return final_result
 
     def release(self):