Source code for pySAXS.models.PolyGauss

from model import Model
from pySAXS.LS.LSsca import Qlogspace
from pySAXS.LS.LSsca import getV
from pySAXS.LS.LSsca import P1
import numpy

[docs]class PolyGauss(Model): ''' class monoSphere from LSSca for Spheres poly-Gauss Model by OT 10/06/2009 '''
[docs] def PolyGauss_anaFunction(self,q,par): """ q array of q (A-1) par[0] Mean radius(A) par[1] FWHM (A) par[2] concentration of sphere (cm-3) par[3] scattering length density of sphere (cm-2) par[4] scattering length density of outside (cm-2) """ a=par[0] s=par[1]/(2.*a*(numpy.log(2.))**0.5 ) t1=q*a*s t2=2.*q*a t3=q*a f1=1.+ (1.+0.5*s**2.)*((t3)**2.) - (t2)*( 1.+(t1**2.) )*numpy.sin(t2)*numpy.exp(-t1**2.) - ( 1.+(1.5*(s**2.)-1.)*(t3**2.) + (t1**4.) )*numpy.cos(t2)*numpy.exp(-(t1**2.)) f2=4.5*(t3**(-6.))*(1.+7.5*s**2.+(45./4.)*s**4.+(15./8.)*s**6.)**(-1) normfactor=((4.*(a**3.)*numpy.pi/3.)**2.)*(1.+7.5*(s**2.)+(45./4.)*(s**4.)+(15./8.)*(s**6.)) return normfactor*1e-48*par[2]*((par[3]-par[4])**2.)*f1*f2
''' parameters definition Model(1,PolyGauss_ana, Qlogspace(1e-4,1.,500.) ([250.,10.,1.5e14,2e11,1e10]), ("Mean (A)", "Polydispersity ", "number density", "scattering length density of sphere (cm-2)", "scattering length density of medium (cm-2)" ),("%f","%f","%1.3e","%1.3e","%1.3e") ,(True,True,False,False,False)), from LSsca ''' IntensityFunc=PolyGauss_anaFunction #function N=0 q=Qlogspace(1e-4,1.,500.) #q range(x scale) Arg=[250.,10.,1.5e14,2e11,1e10] #list of defaults parameters Format=["%f","%f","%1.3e","%1.3e","%1.3e"] #list of c format istofit=[True,True,True,True,True] #list of boolean for fitting name="Spheres poly-Gauss" #name of the model Doc=["Mean (A)",\ "Polydispersity ",\ "number density",\ "scattering length density of sphere (cm-2)",\ "scattering length density of medium (cm-2)"] #list of description for parameters
if __name__=="__main__": ''' test code ''' modl=PolyGauss() #plot the model import Gnuplot gp=Gnuplot.Gnuplot() gp("set logscale xy") c=Gnuplot.Data(modl.q,modl.getIntensity(),with_='points') gp.plot(c) raw_input("enter") #plot and fit the noisy model yn=modl.getNoisy(0.4) cn=Gnuplot.Data(modl.q,yn,with_='points') res=modl.fit(yn) cf=Gnuplot.Data(modl.q,modl.IntensityFunc(modl.q,res),with_='lines') gp.plot(c,cn,cf) raw_input("enter") #plot and fit the noisy model with fitBounds bounds=modl.getBoundsFromParam() #[250.0,2e11,1e10,1.5e15] res2=modl.fitBounds(yn,bounds) print res2 raw_input("enter")