from __future__ import absolute_import, division, print_function
from builtins import (bytes, str, open, super, range,
zip, round, input, int, pow, object, map, zip)
__author__ = "Andrea Tramacere"
from .model_parameters import ModelParameterArray, ModelParameter
from .spectral_shapes import SED
from .data_loader import ObsData
import numpy as np
__all__=['Model']
[docs]class Model(object):
def __init__(self,name='no-name',nu_size=100):
self.model_type=None
self.name=name
self.SED = SED(name=self.name)
self.parameters = ModelParameterArray()
self._scale='lin-lin'
self.nu_size=nu_size
self.nu_min=None
self.nu_max=None
[docs] def eval(self,fill_SED=True,nu=None,get_model=False,loglog=False,plot=None,label=None):
if nu is None:
print("--->", self.nu_min,self.nu_max,self.nu_size)
x1=np.log10(self.nu_min)
x2=np.log10(self.nu_max)
lin_nu=np.logspace(x1,x2,self.nu_size)
model=np.zeros(lin_nu.size)
#print "x1",x1
else:
if np.shape(nu)==():
nu=np.array([nu])
if loglog==False:
log_nu=np.log10(nu)
lin_nu=nu
model=self.lin_func(lin_nu)
else:
log_nu=nu
lin_nu=np.power(10.,log_nu)
log_model= self.log_func(log_nu)
model=np.power(10.,log_model)
if fill_SED==True:
#print "base model", self.model_type,self.nu_min,self.nu_max
#print lin_nu[0]
self.SED.fill(nu=lin_nu,nuFnu=model)
#print model[0]
if plot is not None:
if label is None:
label= self.name
self.PlotModel(plot, clean=True, label=self.name)
if get_model==True:
if loglog==False:
return model
else:
return log_model
else:
return None
[docs] def PlotModel(self,Plot,clean=False,autoscale=False,label=None):
#print "PlotModel base model"
if Plot is not None:
if clean==True:
Plot.clean_model_lines()
if label is None:
label= self.name
Plot.add_model_plot(self.SED,autoscale=autoscale,line_style='-',label=label)
#print self.SED.nu
else:
print ("the plot window is not defined")
[docs] def set_nu_grid(self,nu_min=None,nu_max=None,nu_size=None):
if nu_size is not None:
self.nu_size=nu_size
if nu_min is not None:
self.nu_min=nu_min
if nu_max is not None:
self.nu_max=nu_max
[docs] def lin_func(self,nu):
pass
[docs] def log_func(self,log_nu):
pass
[docs] def get_residuals(self, data, log_log=False,filter_UL=True):
if isinstance(data,ObsData):
data=data.data
model = self.eval(nu=data['nu_data'], fill_SED=False, get_model=True, loglog=False)
# print "loglog",loglog
if filter_UL ==True:
msk=data['UL']==False
else:
msk=np.ones(data.size,dt=True)
residuals = (data['nuFnu_data'] - model) / data['dnuFnu_data']
nu_residuals=data['nu_data']
if log_log == False:
return nu_residuals[msk], residuals[msk]
else:
return np.log10(nu_residuals[msk]), residuals[msk]
