Changes in / [12076cf:8221f1c] in sasmodels

Files:

• example/sesansfit.py (modified) (4 diffs)
• sasmodels/bumps_model.py (modified) (1 diff)
• sasmodels/direct_model.py (modified) (3 diffs)
• sasmodels/kernelpy.py (modified) (3 diffs)
• sasmodels/models/broad_peak.py (modified) (2 diffs)

example/sesansfit.py

@@ -4 +4 @@
 from sasmodels import bumps_model as sas
 kernel = sas.load_model('sphere', dtype='single')
+#kernel = sas.load_model('triaxial_ellipsoid', dtype='single')
 
 
@@ -11 +12 @@
     loader=Loader()
     data=loader.load('testsasview1.ses')
+    data.x /=10
     
 #    data = load_sesans('mydatfile.pz')
@@ -20 +22 @@
     err_data = np.ones_like(SElength)*0.03
 
+    class Sample:
+        zacceptance = 0.1 # [A^-1]
+        thickness = 0.2 # [cm]
+        
     class SESANSData1D:
         #q_zmax = 0.23 # [A^-1]
-        zacceptance = 0.1 # [A^-1]
-        lam = 2e-10 # [m]
-        thickness = 0.2 # [cm]
+        lam = 0.2 # [nm]
         x = SElength
         y = data
         dy = err_data
-    data = SesansData()
-print dir(data)
+        sample = Sample()
+    data = SESANSData1D()
 
 radius = 1000
 data.Rmax = 3*radius # [A]
 
+##  Sphere parameters
+
 phi = Parameter(0.1, name="phi")
 model = sas.BumpsModel(data, kernel,
     scale=phi*(1-phi), sld=7.0, solvent_sld=1.0, radius=radius)
-phi.pmp(10)
-model.radius.pmp(40)
-model.sld.pm(2)
-model.background.range(0,5)
-
+phi.range(0.001,0.90)
+#model.radius.pmp(40)
+model.radius.range(100,10000)
+#model.sld.pmp(5)
+#model.background
+#model.radius_pd=0
+#model.radius_pd_n=0
 
 if False: # have sans data
@@ -49 +57 @@
     problem = FitProblem(model)
 
+
+### Tri-Axial Ellipsoid
+#
+#phi = Parameter(0.1, name='phi')
+#model = sas.BumpsModel(data, kernel,
+#    scale=phi*(1-phi), sld=7.0, solvent_sld=1.0, radius=radius)
+#phi.range(0.001,0.90)
+##model.radius.pmp(40)
+#model.radius.range(100,10000)
+##model.sld.pmp(5)
+##model.background
+##model.radius_pd=0
+##model.radius_pd_n=0
+#
+#if False: # have sans data
+#    sansmodel = sas.BumpsModel(sans_data, kernel, **model.parameters())
+#    problem = FitProblem([model, sansmodel])
+#else:
+#    problem = FitProblem(model)

sasmodels/bumps_model.py

@@ -354 +354 @@
             #self._theory[:] = self._fn.eval(pars, pd_pars)
             if self.data_type == 'sesans':
-                P = sesans.hankel(self.data.x, self.data.lam,
+                P = sesans.hankel(self.data.x, self.data.lam*1e-9,
                                   self.data.sample.thickness/10, self._fn_inputs[0],
                                   self._theory)

sasmodels/direct_model.py

@@ -46 +46 @@
     limits = kernel.info['limits']
     disperser = pars.get(name+'_pd_type', 'gaussian')
-    value = pars.get(name)
+    value = pars.get(name, kernel.info['defaults'][name])
     npts = pars.get(name+'_pd_n', 0)
     width = pars.get(name+'_pd', 0.0)
@@ -67 +67 @@
         q_vectors = [np.ascontiguousarray(q,dtype=dtype) for q in q_vectors]
         self.kernel = make_kernel(self.model, q_vectors)
-    def __call__(self, pars):
+    def __call__(self, **pars):
         return call_kernel(self.kernel, pars)
 
@@ -90 +90 @@
                 for pair in sys.argv[3:]
                 for k,v in [pair.split('=')])
-    Iq = model(pars)
+    Iq = model(**pars)
     print Iq[0]
 

sasmodels/kernelpy.py

@@ -87 +87 @@
         self.pd_pars = pd_pars
 
-    def __call__(self, fixed, pd, cutoff):
+    def __call__(self, fixed, pd, cutoff=1e-5):
         #print "fixed",fixed
         #print "pd", pd
@@ -144 +144 @@
     ##########################################################
 
-    # weight vector, to be populated by polydispersity loops
     weight = np.empty(len(pd), 'd')
-
-    # identify which pd parameters are volume parameters
-    vol_weight_index = np.array([(index in vol_index) for index in pd_index])
-
-    # Sort parameters in decreasing order of pd length
-    Npd = np.array([len(pdi[0]) for pdi in pd], 'i')
-    order = np.argsort(Npd)[::-1]
-    stride = np.cumprod(Npd[order])
-    pd = [pd[index] for index in order]
-    pd_index = pd_index[order]
-    vol_weight_index = vol_weight_index[order]
-
-    fast_value = pd[0][0]
-    fast_weight = pd[0][1]
+    if weight.size > 0:
+        # weight vector, to be populated by polydispersity loops
+
+        # identify which pd parameters are volume parameters
+        vol_weight_index = np.array([(index in vol_index) for index in pd_index])
+
+        # Sort parameters in decreasing order of pd length
+        Npd = np.array([len(pdi[0]) for pdi in pd], 'i')
+        order = np.argsort(Npd)[::-1]
+        stride = np.cumprod(Npd[order])
+        pd = [pd[index] for index in order]
+        pd_index = pd_index[order]
+        vol_weight_index = vol_weight_index[order]
+
+        fast_value = pd[0][0]
+        fast_weight = pd[0][1]
+    else:
+        stride = np.array([1])
+        vol_weight_index = slice(None, None)
 
     ret = np.zeros_like(args[0])
@@ -169 +173 @@
         fast_index = k%stride[0]
         if fast_index == 0:  # bottom loop complete ... check all other loops
-            for i,index, in enumerate(k%stride):
-                args[pd_index[i]] = pd[i][0][index]
-                weight[i] = pd[i][1][index]
+            if weight.size > 0:
+                for i,index, in enumerate(k%stride):
+                    args[pd_index[i]] = pd[i][0][index]
+                    weight[i] = pd[i][1][index]
         else:
             args[pd_index[0]] = fast_value[fast_index]

sasmodels/models/broad_peak.py

@@ -51 +51 @@
 
 import numpy as np
-from numpy import pi, inf, sin, cos, sqrt, exp, log
+from numpy import pi, inf, sin, cos, sqrt, exp, log, fabs
 
 name = "broad_peak"
@@ -92 +92 @@
 def Iq(q, porod_scale, porod_exp, lorentz_scale, lorentz_length, peak_pos, lorentz_exp):
     inten = porod_scale/pow(q,porod_exp) + lorentz_scale/(1.0 \
-        + pow((math.fabs(q-peak_pos)*lorentz_length),lorentz_exp))
+        + pow((fabs(q-peak_pos)*lorentz_length),lorentz_exp))
     return inten