def doFUNCTION(self,wl_min=10.0,wl_max=20.0,function='linear'):
"""
Replace all opacities by an extrapolation below a cut-off wavelength.
@keyword wl_min: lower boundary interpolation range and the cutoff
wavelength for the extrapolation
(default: 10.0)
@type wl_min: float
@keyword wl_max: upper boundary interpolation range
(default: 20.0)
@type wl_max: float
@keyword function: type of function used for the inter/extrapolation
See Interpol.pEval for function types
(default: 'linear')
@type function: string
"""
#raise TypeError("WARNING! The CustumOpa().doFUNCTION method is obsolete! New version NYI.")
#- Select relevant inputlines (not saving the scattering matrices)
self.opacity_file = True
inputsel = [line for line in self.input_data if len(line) == 4]
inputsel = array([[float(f) for f in line] for line in inputsel])
wl = inputsel[:,0]
function = function.lower()
#- Select the extrapolation and interpolation regions.
wl_low = wl[wl < wl_min]
wlinter = wl[(wl >= wl_min) * (wl <= wl_max)]
abs_inter = inputsel[:,2][(wl >= wl_min) * (wl <= wl_max)]
sca_inter = inputsel[:,3][(wl >= wl_min) * (wl <= wl_max)]
#- Fit the interpolation region and extrapolate this to lower wavelength
abs_p0 = [abs_inter[0],wl[0],1,1]
sca_p0 = [sca_inter[0],wl[0],1,1]
abs_low = Interpol.fitFunction(x_in=wlinter,y_in=abs_inter,\
x_out=wl_low,func=function,\
initial=abs_p0,show=1)
abs_low = array([a > 0 and a or 0 for a in abs_low])
sca_low = Interpol.fitFunction(x_in=wlinter,y_in=sca_inter,\
x_out=wl_low,func=function,\
initial=sca_p0,show=1)
sca_low = array([s > 0 and s or 0 for s in sca_low])
ext_low = abs_low + sca_low
#- Set the output data
self.output_data = [array([w,el,al,sl])
for w,el,al,sl in zip(wl_low,ext_low,\
abs_low,sca_low)]
self.output_data.extend(inputsel[wl >= wl_min])
def doFUNCTION(self,wl_min=10.0,wl_max=20.0,function='linear'):
"""
Replace all opacities by an extrapolation below a cut-off wavelength.
@keyword wl_min: lower boundary interpolation range and the cutoff
wavelength for the extrapolation
(default: 10.0)
@type wl_min: float
@keyword wl_max: upper boundary interpolation range
(default: 20.0)
@type wl_max: float
@keyword function: type of function used for the inter/extrapolation
See Interpol.pEval for function types
(default: 'linear')
@type function: string
"""
#raise TypeError("WARNING! The CustumOpa().doFUNCTION method is obsolete! New version NYI.")
#- Select relevant inputlines (not saving the scattering matrices)
self.opacity_file = True
inputsel = [line for line in self.input_data if len(line) == 4]
inputsel = array([[float(f) for f in line] for line in inputsel])
wl = inputsel[:,0]
function = function.lower()
#- Select the extrapolation and interpolation regions.
wl_low = wl[wl < wl_min]
wlinter = wl[(wl >= wl_min) * (wl <= wl_max)]
abs_inter = inputsel[:,2][(wl >= wl_min) * (wl <= wl_max)]
sca_inter = inputsel[:,3][(wl >= wl_min) * (wl <= wl_max)]
#- Fit the interpolation region and extrapolate this to lower wavelength
abs_p0 = [abs_inter[0],wl[0],1,1]
sca_p0 = [sca_inter[0],wl[0],1,1]
abs_low = Interpol.fitFunction(x_in=wlinter,y_in=abs_inter,\
x_out=wl_low,func=function,\
initial=abs_p0,show=1)
abs_low = array([a > 0 and a or 0 for a in abs_low])
sca_low = Interpol.fitFunction(x_in=wlinter,y_in=sca_inter,\
x_out=wl_low,func=function,\
initial=sca_p0,show=1)
sca_low = array([s > 0 and s or 0 for s in sca_low])
ext_low = abs_low + sca_low
#- Set the output data
self.output_data = [array([w,el,al,sl])
for w,el,al,sl in zip(wl_low,ext_low,\
abs_low,sca_low)]
self.output_data.extend(inputsel[wl >= wl_min])
def divideContinuum(self,w,f,dtype,frindex):
'''
Divide flux by the continuum flux in a dust feature.
@param w: The wavelength grid
@type w: list/array
@param f: The flux grid
@type f: list/array
@param dtype: data type (only 'model','sws' for now)
@type dtype: string
@param frindex: The index in the franges list for this entry.
@type frindex: int
@keyword plot: Show a plot of the continuum division
@type plot: bool
'''
fr1,fr2 = self.franges[frindex][0],self.franges[frindex][1]
fr3,fr4 = self.franges[frindex][2],self.franges[frindex][3]
w_in = list(w[(w>fr1) * (w<fr2)]) + list(w[(w>fr3) * (w<fr4)])
f_in = list(f[(w>fr1) * (w<fr2)]) + list(f[(w>fr3) * (w<fr4)])
w_cont = w[(w>self.frmin*0.9)*(w<self.frmax*1.1)]
f_ori = f[(w>self.frmin*0.9)*(w<self.frmax*1.1)]
f_cont = Interpol.fitFunction(x_in=w_in,y_in=f_in,x_out=w_cont,\
func=self.func[frindex])
self.cont_division[dtype] = dict()
self.cont_division[dtype]['w_feat'] = w_cont
self.cont_division[dtype]['f_feat'] = f_ori
self.cont_division[dtype]['w_fitsel_feat'] = w_in
self.cont_division[dtype]['f_fitsel_feat'] = f_in
self.cont_division[dtype]['f_interp'] = f_cont
self.cont_division[dtype]['f_division'] = f_ori/f_cont
if self.plot:
x = [w_cont,w_cont]
y = [f_ori,f_cont]
Plotting2.plotCols(x=x,y=y,xmin=self.frmin*0.9,xmax=self.frmax*1.1,\
ylogscale=0,xlogscale=0)
def divideContinuum(self, w, f, dtype, frindex):
'''
Divide flux by the continuum flux in a dust feature.
@param w: The wavelength grid
@type w: list/array
@param f: The flux grid
@type f: list/array
@param dtype: data type (only 'model','sws' for now)
@type dtype: string
@param frindex: The index in the franges list for this entry.
@type frindex: int
@keyword plot: Show a plot of the continuum division
@type plot: bool
'''
fr1, fr2 = self.franges[frindex][0], self.franges[frindex][1]
fr3, fr4 = self.franges[frindex][2], self.franges[frindex][3]
w_in = list(w[(w > fr1) * (w < fr2)]) + list(w[(w > fr3) * (w < fr4)])
f_in = list(f[(w > fr1) * (w < fr2)]) + list(f[(w > fr3) * (w < fr4)])
w_cont = w[(w > self.frmin * 0.9) * (w < self.frmax * 1.1)]
f_ori = f[(w > self.frmin * 0.9) * (w < self.frmax * 1.1)]
f_cont = Interpol.fitFunction(x_in=w_in,y_in=f_in,x_out=w_cont,\
func=self.func[frindex])
self.cont_division[dtype] = dict()
self.cont_division[dtype]['w_feat'] = w_cont
self.cont_division[dtype]['f_feat'] = f_ori
self.cont_division[dtype]['w_fitsel_feat'] = w_in
self.cont_division[dtype]['f_fitsel_feat'] = f_in
self.cont_division[dtype]['f_interp'] = f_cont
self.cont_division[dtype]['f_division'] = f_ori / f_cont
if self.plot:
x = [w_cont, w_cont]
y = [f_ori, f_cont]
Plotting2.plotCols(x=x,y=y,xmin=self.frmin*0.9,xmax=self.frmax*1.1,\
ylogscale=0,xlogscale=0)
