In [1]:

    

from __future__ import print_function

import matplotlib.pyplot as plt
import numpy as np

import pysynphot as S
print(S.__version__)


    

    




/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')






    




0.9.8.6

In [2]:

    

%matplotlib inline


    

In [3]:

    

spectrum          = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Specs/0443.51873.152_rebin.txt'
jplus_good_filter = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/JPLUS_SDSS_filters/rSDSS_2cols.txt'
jplus_bad_filter  = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/JPLUS_SDSS_filters/iSDSS_2cols.txt'


    

In [4]:

    

S.setref(area=4400)


    

In [5]:

    

sdss_spectrum = S.FileSpectrum(spectrum)
jplus_good_bandpass = S.FileBandpass(jplus_good_filter)
jplus_bad_bandpass = S.FileBandpass(jplus_bad_filter)


    

In [6]:

    

# For scaling of throughput so all would fit in same plot
fmax = sdss_spectrum.flux.max()

# Check bandpass coverage
plt.plot(sdss_spectrum.wave, sdss_spectrum.flux,
         label='spectrum', alpha=0.5, color='blue')
plt.plot(jplus_good_bandpass.wave, jplus_good_bandpass.throughput * fmax,
         label='SDSS r', color='green')
plt.plot(jplus_bad_bandpass.wave, jplus_bad_bandpass.throughput * fmax,
         label='SDSS i', color='red')
plt.xlabel('Wavelength (Angstrom)')
plt.ylabel('Flux or throughput')
plt.legend(loc='upper left')


    

    
Out[6]:





<matplotlib.legend.Legend at 0xaad635cc>






    

In [7]:

    

binset = np.arange(3000, 11000)


    

In [8]:

    

photometry_good = S.Observation(
    sdss_spectrum, jplus_good_bandpass,
    binset=binset)  # extrap is unnecessary for this case
photometry_good_flam = photometry_good.effstim('flam')
lambda_eff_good = photometry_good.efflam()
print(photometry_good_flam, lambda_eff_good)


    

    




4.93492437612e-16 6333.9045939

In [9]:

    

photometry_bad = S.Observation(
    sdss_spectrum, jplus_bad_bandpass,
    binset=binset)  # extrap is unnecessary for this case
photometry_bad_flam = photometry_bad.effstim('flam')
lambda_eff_bad = photometry_bad.efflam()
print(photometry_bad_flam, lambda_eff_bad)


    

    




3.85719286331e-16 7516.30368089

In [10]:

    

i = np.where(sdss_spectrum.flux > 0)
sp2 = S.ArraySpectrum(
    wave=sdss_spectrum.wave[i], flux=sdss_spectrum.flux[i],
    waveunits=sdss_spectrum.waveunits,
    fluxunits=sdss_spectrum.fluxunits)
obs2 = S.Observation(
    sp2, jplus_bad_bandpass, binset=binset,
    force='extrap')  # extrap needed now
phot_obs2 = obs2.effstim('flam')
lamb_obs2 = obs2.efflam()
print(phot_obs2, lamb_obs2)


    

    




3.8571928694e-16 7516.30368248

In [11]:

    

plt.plot(sdss_spectrum.wave, sdss_spectrum.flux, alpha=0.5)
plt.plot(lambda_eff_good, photometry_good_flam, 'o', color='black', markersize=10, label='good')
plt.plot(lamb_obs2, phot_obs2, '*', color='red', markersize=10, label='badfixed')
plt.xlabel('Wavelength (Angstrom)')
plt.ylabel('Flux (FLAM)')
plt.legend(loc='upper left')


    

    
Out[11]:





<matplotlib.legend.Legend at 0xaa474acc>