Models



In [1]:

    
from __future__ import print_function

import numpy as np
from matplotlib import pyplot as plt
import sncosmo

%matplotlib inline

Initializing a model



In [2]:

    
model = sncosmo.Model(source='salt2')



In [3]:

    
# a model is initialized with some set of parameters
model.param_names









    Out[3]:





['z', 't0', 'x0', 'x1', 'c']



In [4]:

    
model.parameters









    Out[4]:





array([ 0.,  0.,  1.,  0.,  0.])



In [5]:

    
# printing a model will display some information and the current parameters
print(model)









    



<Model at 0x7fb6d0b2ad68>
source:
  class      : SALT2Source
  name       : 'salt2'
  version    : 2.4
  phases     : [-20, .., 50] days
  wavelengths: [2000, .., 9200] Angstroms
parameters:
  z  = 0.0
  t0 = 0.0
  x0 = 1.0
  x1 = 0.0
  c  = 0.0

Setting parameters



In [6]:

    
# setting and getting parameters by name
model.set(x1=1.0)



In [7]:

    
# set multiple parameters at once
model.set(z=0.5, x0=1.e-6, x1=0.5, c=0.2)



In [8]:

    
# this is equivalent:
params = {'z': 0.5, 'x0': 1.e-6, 'x1': 0.5, 'c': 0.2}
model.set(**params)



In [9]:

    
# get parameters by name
model.get('x1')









    Out[9]:





0.5

Spectrum

The most basic thing you can do with a model is ask what its spectrum looks like at a given time.



In [10]:

    
# get individual times and wavelengths
model.flux(0., 4000.)









    Out[10]:





6.0842380131068079e-21



In [11]:

    
# arrays or lists work as you'd expect:
model.flux(0., [3000., 4000.])









    Out[11]:





array([ -5.74626771e-41,   6.08423801e-21])



In [12]:

    
# time introduces a second dimension
model.flux(time=[0., 1.], wave=[3000., 4000.])









    Out[12]:





array([[ -5.74626771e-41,   6.08423801e-21],
       [ -4.79736731e-41,   5.51567414e-21]])



In [13]:

    
# plot the spectrum
wave = np.linspace(3000., 9200., 1000)
flux = model.flux(0., wave)

plt.plot(wave, flux)
plt.ylim(ymin=0.)
plt.xlim(2000, 9200);



In [14]:

    
# light curve at wavelength = 4000 angstroms
time = np.linspace(-20., 50., 71)
flux = model.flux(time, 8000.)
flux.shape









    Out[14]:





(71, 1)



In [15]:

    
plt.plot(time, flux[:,0]);

Bandpasses



In [16]:

    
# there are a lot of built-in bandpasses
band = sncosmo.get_bandpass('sdssg')

(see https://sncosmo.readthedocs.io/en/latest/bandpass-list.html)



In [17]:

    
wave = np.linspace(3000., 6000., 1000)
plt.plot(wave, band(wave));



In [18]:

    
# create a bandpass
band = sncosmo.Bandpass([4000., 5000.], [1., 1.], name='tophatg')



In [19]:

    
wave = np.linspace(3000., 6000., 1000)
plt.plot(wave, band(wave));

Synthetic Photometry



In [20]:

    
model.bandmag('sdssr', 'ab', [0., 10., 20.])









    Out[20]:





array([ 25.15592119,  25.4711469 ,  26.13593855])



In [21]:

    
# equivalent to:
band = sncosmo.get_bandpass('sdssr')
magsys = sncosmo.get_magsystem('ab')
model.bandmag(band, magsys, [0., 10., 20.])









    Out[21]:





array([ 25.15592119,  25.4711469 ,  26.13593855])



In [22]:

    
# in flux units rather than magnitudes
model.bandflux('sdssr', [0., 10., 20.]) # physical flux in photons/s/cm^2









    Out[22]:





array([  4.27478014e-05,   3.19759533e-05,   1.73343212e-05])



In [23]:

    
# more useful scaling:
model.bandflux('sdssr', [0., 10., 20.], zp=20., zpsys='ab') # flux scaled to desired zp









    Out[23]:





array([ 0.00866227,  0.0064795 ,  0.00351257])

Aside: data locations



In [24]:

    
!more $HOME/.astropy/config/sncosmo.cfg









    



## Directory containing SFD (1998) dust maps, with names:
## 'SFD_dust_4096_ngp.fits' and 'SFD_dust_4096_sgp.fits'
## Example: sfd98_dir = /home/user/data/sfd98
# sfd98_dir = None

## Directory where sncosmo will store and read downloaded data resources.
## If None, ASTROPY_CACHE_DIR/sncosmo will be used.
## Example: data_dir = /home/user/data/sncosmo
data_dir = /home/kyle/data/sncosmo



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