def testGenerator(self):
# Test generator output
from sans.models.SphereModel import SphereModel
model = SphereModel()
gen = sas.SASGenerator("sphere", model)
for pname in model.params:
defval = model.getParam(pname)
par = gen.get(pname)
self.assertEquals(defval, par.getValue())
# Test setting values
par.setValue(1.0)
self.assertEquals(1.0, par.getValue())
self.assertEquals(1.0, model.getParam(pname))
par.setValue(defval)
self.assertEquals(defval, par.getValue())
self.assertEquals(defval, model.getParam(pname))
r = numpy.arange(1, 10, 0.1, dtype = float)
y = gen(r)
refy = model.evalDistribution(r)
diff = y - refy
res = numpy.dot(diff, diff)
self.assertAlmostEqual(0, res)
return
def testSphere(self):
radius = 25
# Calculate sphere cf from SphereModel
from sans.models.SphereModel import SphereModel
model = SphereModel()
model.setParam("radius", radius)
ff = cf.SASCF("sphere", model)
r = numpy.arange(1, 60, 0.1, dtype = float)
fr1 = ff(r)
# Calculate sphere cf analytically
fr2 = cf.sphericalCF(r, 2*radius)
diff = fr1 - fr2
res = numpy.dot(diff, diff)
res /= numpy.dot(fr2, fr2)
self.assertAlmostEqual(0, res, 4)
return
def testGenerator(self):
# Test generator output
from sans.models.SphereModel import SphereModel
model = SphereModel()
gen = sas.SASGenerator("sphere", model)
for pname in model.params:
defval = model.getParam(pname)
par = gen.get(pname)
self.assertEquals(defval, par.getValue())
# Test setting values
par.setValue(1.0)
self.assertEquals(1.0, par.getValue())
self.assertEquals(1.0, model.getParam(pname))
par.setValue(defval)
self.assertEquals(defval, par.getValue())
self.assertEquals(defval, model.getParam(pname))
r = numpy.arange(1, 10, 0.1, dtype=float)
y = gen(r)
refy = model.evalDistribution(r)
diff = y - refy
res = numpy.dot(diff, diff)
self.assertAlmostEqual(0, res)
return
#! /usr/bin/env python
import sys
import numpy
import time
import cplugin
#import pdb; pdb.set_trace()
q = numpy.arange(1000)
disp = {'npts':35, 'width':1, 'nsigmas':3}
from sans.models.SphereModel import SphereModel
from sans.models.dispersion_models import GaussianDispersion as GdOld
oldsm = SphereModel()
#oldsm.set_dispersion('radius', GdOld())
#oldsm.dispersion['radius']['npts'] = disp['npts']
#oldsm.dispersion['radius']['width'] = disp['width']
start=time.time()
print "calc old"; sys.stdout.flush()
Iq=oldsm.evalDistribution(q)
#Iq=oldsm.run(q)
dt = time.time()-start
print "oldsm",dt,Iq[len(q)/2],len(q),disp['npts']
sys.stdout.flush()
#NewSphere = cplugin.cplugin('SampleModel.dll')
NewSphere = cplugin.cplugin('SampleModel.so')
newsm = NewSphere
newsm.params = oldsm.params
#newsm.set_dispersion('radius', cplugin.GaussianDispersion(**disp))
#import pdb; pdb.set_trace()
q = numpy.arange(1000, dtype='d')
disp = {'npts': 10, 'width': 0.1666666667, 'nsigmas': 2}
sphere = {
'scale': 1.0,
'radius': 60.0,
'sldSph': 2.0,
'sldSolv': 1.0,
'background': 0.0
}
from sans.models.SphereModel import SphereModel
from sans.models.dispersion_models import GaussianDispersion
oldsm = SphereModel()
qlist3 = numpy.asarray([0.001, 0.002])
print "run", oldsm.run(0.001), oldsm.run(0.002)
print "runXY", oldsm.runXY(0.001), oldsm.runXY(0.002)
print "evalDist", oldsm.evalDistribution(qlist3)
for k, v in sphere.items():
oldsm.setParam(k, v)
gdisp = GaussianDispersion()
oldsm.set_dispersion('radius', gdisp)
for k, v in disp.items():
oldsm.dispersion['radius'][k] = v
print "G run", oldsm.run(0.001), oldsm.run(0.002)
print "G runXY", oldsm.runXY(0.001), oldsm.runXY(0.002)
import sys
import os
import numpy
import time
import cplugin
#import pdb; pdb.set_trace()
q = numpy.arange(1000,dtype='d')
disp = {'npts':10, 'width':0.1666666667, 'nsigmas':2}
sphere = {'scale':1.0, 'radius':60.0, 'sldSph': 2.0, 'sldSolv': 1.0, 'background': 0.0}
from sans.models.SphereModel import SphereModel
from sans.models.dispersion_models import GaussianDispersion
oldsm = SphereModel()
qlist3 = numpy.asarray([0.001, 0.002])
print "run", oldsm.run(0.001), oldsm.run(0.002)
print "runXY", oldsm.runXY(0.001), oldsm.runXY(0.002)
print "evalDist", oldsm.evalDistribution(qlist3)
for k,v in sphere.items(): oldsm.setParam(k,v)
gdisp = GaussianDispersion()
oldsm.set_dispersion('radius', gdisp)
for k,v in disp.items(): oldsm.dispersion['radius'][k] = v
print "G run", oldsm.run(0.001), oldsm.run(0.002)
print "G runXY", oldsm.runXY(0.001), oldsm.runXY(0.002)
print "G evalDist", oldsm.evalDistribution(qlist3)
