Source code for pySAXS.models.SphereGaussAnaDC

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

[docs]class SphereGaussAnaDC(Model): ''' Spheres polydisperses distribution semi-Gaussienne analytique by DC : 18/06/2009 '''
[docs] def SphereGauss_ana_DC(self,q,par): """ q array of q (A-1) par[0] Mean radius(A) par[1] Gaussian standard deviation (A) par[2] concentration of spheres (cm-3) par[3] scattering length density of spheres (cm-2) par[4] scattering length density of outside (cm-2) """ R = par[0] s = par[1] n = par[2] rho1 = par[3] rho2 = par[4] t1 = q*R t2 = q*s prefactor = 1e-48*8.*numpy.pi**2.*n*(rho1-rho2)**2./q**6. fcos = ((1+2.*t2**2.)**2.-t1**2.-t2**2.)*numpy.cos(2.*t1) fsin = 2.*t1*(1.+2.*t2**2.)*numpy.sin(2.*t1) f = 1.+t1**2.+t2**2.-numpy.exp(-2.*t2**2)*(fcos+fsin) return prefactor*f
''' parameters definition Model(2,PolyGauss_ana_DC,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)), ''' IntensityFunc=SphereGauss_ana_DC #function N=0 q=Qlogspace(1e-4,1.,500.) #q range(x scale) Arg=[250.,10.,1.5e14,2e11,1e10] #list of parameters Format=["%f","%f","%1.3e","%1.3e","%1.3e"] #list of c format istofit=[True,True,False,False,False] #list of boolean for fitting name="Spheres : Semi-Gaussian distribution" #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 Description="Spheres : Semi-Gaussian distribution, analytical" # description of model Author="David Carriere" #name of Author
if __name__=="__main__": ''' test code ''' modl=SphereGaussAnaDC() #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(0.8) 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")