Source code for pySAXS.models.Triplet_Sphere

from model import Model
from pySAXS.LS.LSsca import Qlogspace
from pySAXS.LS.LSsca import getV
from pySAXS.LS.LSsca import Multiplet
from pySAXS.LS.LSsca import Triplet_Multiplet
import numpy

[docs]class Triplet_Sphere(Model): ''' class monoSphere from LSSca by OS 19/11/2011 '''
[docs] def Triplet_Function(self,q,par): """ q array of q (A-1) par[0] radius of the sphere (in 1/q) par[1] Distance between centers (must be higher than 2R) par[2] scattering length density of sphere (cm-2) par[3] scattering length density of outside (cm-2) par[4] concentration of sphere (cm-3) """ rho=[par[3]-par[2],par[3]-par[2],par[3]-par[2]] R=[par[0],par[0],par[0]] if len(par)!=5: sys.stderr.write("This function requires a list of 5 parameters") return -1. else: return par[4]*Multiplet(q,Triplet_Multiplet(par[1]/2.),rho,R) #sys.stderr.write(str(par[0])) #return P1(q,par[0])
''' parameters definition Model(0,MonoSphere,Qlogspace(1e-4,1.,500.),([250.0,2e11,1e10,1.5e15]), ("radius (A)","scattering length density of sphere (cm-2)","scattering length density of outside (cm-2)","number concentration (cm-3)") ,("%f","%1.3e","%1.3e","%1.3e"),(True,True,False,False)), from LSsca ''' IntensityFunc=Triplet_Function #function q=Qlogspace(1e-4,1.,500.) #q range(x scale) Arg=[30.0,70.0,9.8e11,9.8e10,1.e10] #list of defaults parameters Format=["%f","%f","%1.3e","%1.3e","%1.3e"] #list of c format istofit=[True,True,True,False,False] #list of boolean for fitting name="Multi: Triplet of identical spheres" #name of the model Description="Triplet of identical spheres" Author="O. Spalla" Doc=["Radius (1/q)",\ "Distance between centers (1/q): must be larger than diameter",\ "scattering length density of sphere (cm-2)",\ "scattering length density of medium (cm-2)",\ "number concentration (cm-3)"] #list of description for parameters
if __name__=="__main__": ''' test code ''' modl=Triplet_Sphere() #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")