Source code for pySAXS.models.PCube

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

[docs]class Cube(Model): ''' Cubes and parallelepiped by OS : 03/11/2011 '''
[docs] def PCube(self,q,par): """ q array of q (A-1) par[0] side length 1 (in 1/q) par[1] SLD particle (cm-2) par[2] SLD medium (cm-2) par[3] number density (cm-3) """ a = par[0] rho1 = par[1] rho2 = par[2] n=par[3] prefactor = 1e-48*n*(rho1-rho2)**2 f = a**6*Ppara(q,a,a,a) return prefactor*f
IntensityFunc=PCube #function N=0 q=Qlogspace(3e-4,1.,500.) #q range(x scale) Arg=[30.,9.8e11,9.8e10,1e10] #list of parameters Format=["%f","%1.3e","%1.3e","%1.3e"] #list of c format istofit=[True,False,False,False] #list of boolean for fitting name="Cube" #name of the model Doc=["side length 1 ",\ "scattering length density of particle (cm-2)",\ "scattering length density of medium (cm-2)",\ "Number density (cm-3)"] #list of description for parameters Description="Cubes " # description of model Author="Olivier Spalla" #name of Author WarningForCalculationTime=True
if __name__=="__main__": ''' test code ''' modl=Cube() #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")