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")