In [1]:

    

import numpy as np
import os
import pysynphot as s
import pandas as pd


    

    




/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:44: UserWarning: PYSYN_CDBS is undefined; functionality will be SEVERELY crippled.
  warnings.warn("PYSYN_CDBS is undefined; functionality will be SEVERELY "
/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:119: UserWarning: Extinction files should be moved to $PYSYN_CDBS/extinction for compatibility with future versions of pysynphot.
  warnings.warn('Extinction files should be moved to '
/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:155: UserWarning: Extinction files not found in grid/extinction
  warnings.warn('Extinction files not found in %s' % (extdir,))
/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/*_tmg.fits file
  warnings.warn("PYSYN_CDBS is undefined; cannot find %s file" % template)
/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/*_tmc.fits file
  warnings.warn("PYSYN_CDBS is undefined; cannot find %s file" % template)
/usr/local/lib/python2.7/dist-packages/pysynphot/locations.py:133: UserWarning: PYSYN_CDBS is undefined; cannot find mtab/*_tmt.fits file
  warnings.warn("PYSYN_CDBS is undefined; cannot find %s file" % template)

In [2]:

    

jpas_filters_path  = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/JPAS_filters'
jplus_filters_path = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/JPLUS_SDSS_filters'
test_specs         = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Specs'
results_path       = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Results'
jpas_filters_list  = np.loadtxt('/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/jpas_filters_list.txt', dtype=str)
jplus_filters_list = np.loadtxt('/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Filters/jplus_filters_list.txt', dtype=str)
specs_list         = np.loadtxt('/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/specslist2.txt', dtype=str)


    

In [3]:

    

# Constants --------------------------------------------------------------------------------------------------------
c = 2.99792458E18                  # Light Speed in Angstrom s^-1


    

In [4]:

    

# Setting the T80 M1 effective area in cm^2 ------------------------------------------------------------------------
s.setref(area=4400)


    

In [5]:

    

# # JPAS -------------------------------------------------------------------------------------------------------------
all_jpas = []
for each_spectrum in specs_list:
    # Transforming the specs from f_lambda to f_nu -----------------------------------------------------------------
    wavelength = np.loadtxt(os.path.join(test_specs, each_spectrum), usecols=[0])
    f_lambda   = np.loadtxt(os.path.join(test_specs, each_spectrum), usecols=[1])
    # Simulating the photometry for each J-PAS band ----------------------------------------------------------------
    filter_name = []
    photometry  = []
    for jpas_filters in jpas_filters_list:
        filter_name_i          = jpas_filters.split('.')[0]
        filter_name.append(filter_name_i)
        jpas_filter_bandpass = s.FileBandpass(os.path.join(jpas_filters_path, jpas_filters))        
#         print jpas_filter_bandpass.throughput
        sdss_spectrum        = s.FileSpectrum(os.path.join(test_specs, each_spectrum))
        photometry_i         = s.Observation(sdss_spectrum, jpas_filter_bandpass, binset=np.arange(3000, 11000), 
                                             force='extrap')
        photometry.append(photometry_i.effstim('abmag'))
        print photometry_i.efflam()

    filter_name       = np.array(filter_name)
    photometry        = np.array(photometry)
    photometry_fnu    = 10**(photometry + 48.60)/(-2.5)
    galaxy_simulation_abmag = np.vstack((filter_name, photometry))
    galaxy_simulation_abmag = pd.DataFrame(galaxy_simulation_abmag)
    galaxy_simulation_abmag.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPAS_abmag.csv'), 
                                   sep=',', header=None, index=False)
    galaxy_simulation_fnu = np.vstack((filter_name, photometry_fnu))
    galaxy_simulation_fnu = pd.DataFrame(galaxy_simulation_fnu)
    galaxy_simulation_fnu.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPAS_fnu.csv'), 
                                 sep=',', header=None, index=False)

 # JPLUS ------------------------------------------------------------------------------------------------------------
for each_spectrum in specs_list:
    # Transforming the specs from f_lambda to f_nu -----------------------------------------------------------------
    wavelength = np.loadtxt(os.path.join(test_specs, each_spectrum), usecols=[0])
    f_lambda   = np.loadtxt(os.path.join(test_specs, each_spectrum), usecols=[1])
    # Simulating the photometry for each J-PAS band ----------------------------------------------------------------
    filter_name2 = []
    photometry2  = []
    for jplus_filters in jplus_filters_list:
        filter_name2_i          = jplus_filters.split('.')[0]
        filter_name2.append(filter_name2_i)
        jplus_filter_bandpass = s.FileBandpass(os.path.join(jplus_filters_path, jplus_filters))        
#         print jplus_filter_bandpass.throughput
        sdss_spectrum        = s.FileSpectrum(os.path.join(test_specs, each_spectrum))
        photometry2_i       = s.Observation(sdss_spectrum, jplus_filter_bandpass, binset=np.arange(3000, 11000), 
                                             force='extrap')
        print photometry_i.efflam()
        photometry2.append(photometry_i.effstim('abmag'))

    filter_name2    = np.array(filter_name2)
    photometry2     = np.array(photometry2)
    photometry2_fnu = 10**(photometry2 + 48.60)/(-2.5)
    galaxy_simulation_abmag = np.vstack((filter_name2, photometry2))
    galaxy_simulation_abmag = pd.DataFrame(galaxy_simulation_abmag)
    galaxy_simulation_abmag.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPLUS_abmag.csv'), 
                                   sep=',', header=None, index=False)
    galaxy_simulation_fnu = np.vstack((filter_name2, photometry2_fnu))
    galaxy_simulation_fnu = pd.DataFrame(galaxy_simulation_fnu)
    galaxy_simulation_fnu.to_csv(os.path.join(results_path, os.path.split(each_spectrum)[-1][0:14]+'_JPLUS_fnu.csv'), 
                                 sep=',', header=None, index=False)


    

    




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In [ ]: