def calc_Rdist(self, R, Rsig, dist, N):
totalR = np.sum(R[:-1])
fdist = eval(dist + '.' + dist +
'(x=0.001, pos=totalR, wid=self.Rsig)')
rmin, rmax = find_minmax(fdist, totalR, Rsig)
dr = np.linspace(rmin, rmax, N)
fdist.x = dr
rdist = fdist.y()
sumdist = np.sum(rdist)
rdist = rdist / sumdist
self.output_params['Distribution'] = {'x': dr, 'y': rdist}
return dr, rdist, totalR
def y(self):
rho=self.rhoc-self.rhosol
self.output_params={}
if self.Rsig<1e-3:
return self.norm*rho**2*(np.sin(self.x*self.R)-self.x*self.R*np.cos(self.x*self.R))**2/self.x**6+self.bkg
else:
if self.dist=='Gaussian':
gau=Gaussian.Gaussian(x=0.001,pos=self.R,wid=self.Rsig)
rmin,rmax=find_minmax(gau,self.R,self.Rsig)
r=np.linspace(rmin,rmax,self.N)
gau.x=r
dist=gau.y()
sumdist=np.sum(dist)
self.output_params['Distribution']={'x':r,'y':dist/sumdist}
if type(self.x)==np.ndarray:
ffactor=[]
for x in self.x:
f=np.sum((np.sin(x*r)-x*r*np.cos(x*r))**2*dist/x**6)
ffactor.append(f/sumdist)
return self.norm*rho**2*np.array(ffactor)+self.bkg
else:
return self.norm*rho**2*np.sum((np.sin(self.x*r)-self.x*r*np.cos(self.x*r))**2*dist/self.x**6)/sumdist+self.bkg
elif self.dist=='LogNormal':
lgn=LogNormal.LogNormal(x=0.001,pos=self.R,wid=self.Rsig)
rmin,rmax=find_minmax(lgn,self.R,self.Rsig)
r=np.linspace(rmin,rmax,self.N)
lgn.x=r
dist=lgn.y()
sumdist=np.sum(dist)
self.output_params['Distribution']={'x':r,'y':dist/sumdist}
if type(self.x)==np.ndarray:
ffactor=[]
for x in self.x:
f=np.sum((np.sin(x*r)-x*r*np.cos(x*r))**2*dist/x**6)
ffactor.append(f/sumdist)
return self.norm*rho**2*np.array(ffactor)+self.bkg
else:
return self.norm*rho**2*np.sum((np.sin(self.x*r)-self.x*r*np.cos(self.x*r))**2*dist/self.x**6)/sumdist+self.bkg
else:
return np.ones_like(x)
def calc_Rdist(self, R, Rsig, dist, Np):
if Rsig > 0.001:
fdist = eval(dist + '.' + dist + '(x=0.001, pos=R, wid=Rsig)')
rmin, rmax = find_minmax(fdist, R, Rsig)
dr = np.linspace(rmin, rmax, Np)
fdist.x = dr
rdist = fdist.y()
sumdist = np.sum(rdist)
rdist = rdist / sumdist
self.output_params['Distribution'] = {'x': dr, 'y': rdist}
return dr, rdist
else:
return [R], [1.0]
def y(self):
self.output_params={}
R=[self.params['__R__%03d'%i] for i in range(len(self.__mpar__['R']))]
rho=[self.params['__rho__%03d'%i] for i in range(len(self.__mpar__['rho']))]
if self.Rsig<0.001:
return self.norm*self.csphere(R,rho)+self.bkg
else:
if self.dist=='Gaussian':
gau=Gaussian.Gaussian(x=0.0,pos=R[0],wid=self.Rsig)
rmin,rmax=find_minmax(gau,pos=R[0],wid=self.Rsig)
dr=np.linspace(rmin,rmax,self.N)
gau.x=dr
dist=gau.y()
sumdist=np.sum(dist)
self.output_params['Distribution']={'x':dr,'y':dist/sumdist}
res=np.zeros_like(self.x)
for i in range(len(dr)):
r=R+dr[i]-R[0]
res=res+dist[i]*self.csphere(r,rho)
return self.norm*res/sumdist+self.bkg
elif self.dist=='LogNormal':
lgn=LogNormal.LogNormal(x=0.0,pos=R[0],wid=self.Rsig)
rmin,rmax=find_minmax(lgn,pos=R[0],wid=self.Rsig)
dr=np.linspace(rmin,rmax,self.N)
lgn.x=dr
dist=lgn.y()
sumdist=np.sum(dist)
self.output_params['Distribution']={'x':dr,'y':dist/sumdist}
res=np.zeros_like(self.x)
for i in range(len(dr)):
r=R+dr[i]-R[0]
res=res+dist[i]*self.csphere(r,rho)
return self.norm*res/sumdist+self.bkg
else:
return np.ones_like(self.x)
def calc_Rdist(self, R, Rsig, dist, N):
R = np.array(R)
totalR = np.sum(R[:-1])
if Rsig > 0.001:
fdist = eval(dist + '.' + dist + '(x=0.001, pos=totalR, wid=Rsig)')
rmin, rmax = find_minmax(fdist, totalR, Rsig)
dr = np.linspace(rmin, rmax, N)
fdist.x = dr
rdist = fdist.y()
sumdist = np.sum(rdist)
rdist = rdist / sumdist
self.output_params['Distribution'] = {'x': dr, 'y': rdist}
return dr, rdist, totalR
else:
self.output_params['Distribution'] = {'x': [totalR], 'y': [1.0]}
return [totalR], [1.0], totalR
def calc_mesh(self, R=[1.0], Rsig=[0.0], Np=100):
"""
Computes a multi-dimensional meshgrid of radii (R) of interfaces with a finite widths (Rsig>0.001) of distribution
:param R:
:param Rsig:
:return:
"""
r1 = 'np.meshgrid('
for (i, r) in enumerate(R):
if Rsig[i] > 0.001:
lgn = eval(self.dist + '.' + self.dist +
'(x=0.001, pos=r, wid=Rsig[i])')
rmin, rmax = find_minmax(lgn, r, Rsig[i])
r1 = r1 + 'np.linspace(%.10f,%.10f,%d),' % (rmin, rmax, Np)
else:
r1 = r1 + '[%.10f],' % r
r1 = r1[:-1] + ',sparse=True)'
return (eval(r1))