pyphot - A tool for computing photometry from spectra¶

Some examples are provided in this notebook

Full documentation available at http://mfouesneau.github.io/docs/pyphot/

In [1]:

%matplotlib inline

import pylab as plt
import numpy as np

import sys
sys.path.append('../')
from pyphot import astropy as pyphot

Quick Start¶

Quick start example to access the library and it's content

In [2]:

# get the internal default library of passbands filters
lib = pyphot.get_library()
print("Library contains: ", len(lib), " filters")
# find all filter names that relates to IRAC
# and print some info
f = lib.find('irac')
for name in f:
    lib[name].info(show_zeropoints=True)

Library contains:  256  filters
Filter object information:
    name:                 SPITZER_IRAC_36
    detector type:        photon
    wavelength units:     AA
    central wavelength:   35634.293911 Angstrom
    pivot wavelength:     35569.270678 Angstrom
    effective wavelength: 35134.324451 Angstrom
    photon wavelength:    35263.778583 Angstrom
    minimum wavelength:   31310.000000 Angstrom
    maximum wavelength:   39740.000000 Angstrom
    norm:                 3181.966405
    effective width:      6848.829972 Angstrom
    fullwidth half-max:   7430.000000 Angstrom
    definition contains 505 points

    Zeropoints
        Vega: 27.948397 mag,
              6.6166982851778816e-12 erg / (Angstrom cm2 s),
              279.2354479161161 Jy
              5.511736607732767 ph / (Angstrom cm2 s)
          AB: 25.163323 mag,
              8.603413213872212e-11 erg / (Angstrom cm2 s),
              3630.7805477009956 Jy
          ST: 21.100000 mag,
              3.6307805477010028e-09 erg / (Angstrom cm2 s),
              153224.8545764173 Jy
        
Filter object information:
    name:                 SPITZER_IRAC_45
    detector type:        photon
    wavelength units:     AA
    central wavelength:   45110.141614 Angstrom
    pivot wavelength:     45020.219955 Angstrom
    effective wavelength: 44425.747085 Angstrom
    photon wavelength:    44603.204646 Angstrom
    minimum wavelength:   39250.000000 Angstrom
    maximum wavelength:   50640.000000 Angstrom
    norm:                 4664.680820
    effective width:      8714.143135 Angstrom
    fullwidth half-max:   10110.000000 Angstrom
    definition contains 417 points

    Zeropoints
        Vega: 28.933083 mag,
              2.6715713304827696e-12 erg / (Angstrom cm2 s),
              180.61811118349118 Jy
              3.246736588322029 ph / (Angstrom cm2 s)
          AB: 25.674986 mag,
              5.370385702161592e-11 erg / (Angstrom cm2 s),
              3630.780547701007 Jy
          ST: 21.100000 mag,
              3.6307805477010028e-09 erg / (Angstrom cm2 s),
              245467.79536259372 Jy
        
Filter object information:
    name:                 SPITZER_IRAC_58
    detector type:        photon
    wavelength units:     AA
    central wavelength:   57593.367607 Angstrom
    pivot wavelength:     57450.413675 Angstrom
    effective wavelength: 56473.407964 Angstrom
    photon wavelength:    56760.560047 Angstrom
    minimum wavelength:   49340.000000 Angstrom
    maximum wavelength:   65060.000000 Angstrom
    norm:                 2116.064825
    effective width:      12454.766480 Angstrom
    fullwidth half-max:   14060.000000 Angstrom
    definition contains 370 points

    Zeropoints
        Vega: 29.955073 mag,
              1.0422472235401564e-12 erg / (Angstrom cm2 s),
              114.74568537901237 Jy
              0.5133774402352529 ph / (Angstrom cm2 s)
          AB: 26.204414 mag,
              3.2978764583832674e-11 erg / (Angstrom cm2 s),
              3630.7805477010043 Jy
          ST: 21.100000 mag,
              3.6307805477010028e-09 erg / (Angstrom cm2 s),
              399728.9635290507 Jy
        
Filter object information:
    name:                 SPITZER_IRAC_80
    detector type:        photon
    wavelength units:     AA
    central wavelength:   79594.931057 Angstrom
    pivot wavelength:     79157.670512 Angstrom
    effective wavelength: 76171.441325 Angstrom
    photon wavelength:    77030.477773 Angstrom
    minimum wavelength:   63550.000000 Angstrom
    maximum wavelength:   96210.000000 Angstrom
    norm:                 8138.005260
    effective width:      25631.512630 Angstrom
    fullwidth half-max:   28760.000000 Angstrom
    definition contains 421 points

    Zeropoints
        Vega: 31.291951 mag,
              3.042422601167823e-13 erg / (Angstrom cm2 s),
              63.58941738453659 Jy
              0.38702836200357943 ph / (Angstrom cm2 s)
          AB: 26.900413 mag,
              1.7371394883847807e-11 erg / (Angstrom cm2 s),
              3630.7805477010024 Jy
          ST: 21.100000 mag,
              3.6307805477010028e-09 erg / (Angstrom cm2 s),
              758866.3704732973 Jy

Suppose one has a calibrated spectrum and wants to compute the vega magnitude throug the HST WFC3 F110W passband,

In [3]:

# convert to magnitudes
import numpy as np

# We'll use Vega spectrum as example
from pyphot.astropy import Vega
vega = Vega()
f = lib['HST_WFC3_F110W']
# compute the integrated flux through the filter f
# note that it work on many spectra at once
fluxes = f.get_flux(vega.wavelength, vega.flux, axis=-1)
# Note that fluxes is now with units of erg/s/cm2/AA
# pyphot gives Vega in flam and can convert between flux density units. 
fluxes, vega.wavelength, vega.flux

Out[3]:

(<Quantity 4.08225856e-10 erg / (Angstrom cm2 s)>,
 <Quantity [9.00452026e+02, 9.01354004e+02, 9.02257996e+02, ...,
            2.99353200e+06, 2.99653275e+06, 2.99953700e+06] Angstrom>,
 <Quantity [1.23800003e-17, 1.67599994e-17, 1.78000003e-17, ...,
            1.40099994e-19, 1.38700004e-19, 1.26499994e-19] flam>)

In [4]:

# convert to vega magnitudes
mags = -2.5 * np.log10(fluxes.value) - f.Vega_zero_mag
print("Vega magnitude of Vega in {0:s} is : {1:f} mag".format(f.name, mags))
mags = -2.5 * np.log10(fluxes.value) - f.AB_zero_mag
print("AB magnitude of Vega in {0:s} is : {1:f} mag".format(f.name, mags))
mags = -2.5 * np.log10(fluxes.value) - f.ST_zero_mag
print("ST magnitude of Vega in {0:s} is : {1:f} mag".format(f.name, mags))

Vega magnitude of Vega in HST_WFC3_F110W is : 0.000000 mag
AB magnitude of Vega in HST_WFC3_F110W is : 0.751950 mag
ST magnitude of Vega in HST_WFC3_F110W is : 2.372749 mag

Provided Filter library¶

This section shows the content of the provided library with respective properties of the passband filters. The code to generate the table is also provided in the documentation.

In [5]:

# define header and table format (as csv)
hdr = ("name", "detector type", "wavelength units",
       "central wavelength", "pivot wavelength", "effective wavelength",
       "Vega mag", "Vega flux", "Vega Jy",
       "AB mag", "AB flux", "AB Jy",
       "ST mag", "ST flux", "ST Jy")
fmt = "{0:s},{1:s},{2:s},{3:.3f},{4:.3f},{5:.3f},{6:.5f},{7:.5g},{8:.5g},{9:.5f},{10:.5g},{11:.5g},{12:.5f},{13:.5g},{14:.5g}\n"

l = pyphot.get_library()

with open('table.csv', 'w') as output:
    output.write(','.join(hdr) + '\n')

    for k in sorted(l.content):
        fk = l[k]
        rec = (fk.name, fk.dtype, fk.wavelength_unit,
               fk.cl.value, fk.lpivot.value, fk.leff.value,
               fk.Vega_zero_mag, fk.Vega_zero_flux.value, fk.Vega_zero_Jy.value,
               fk.AB_zero_mag, fk.AB_zero_flux.value, fk.AB_zero_Jy.value,
               fk.ST_zero_mag, fk.ST_zero_flux.value, fk.ST_zero_Jy.value)
        output.write(fmt.format(*rec)) 

Table description

name: the identification name of the filter in the library.
detector type: energy or photon counter.
wavelength units: filter defined with these units and all wavelength properties: central wavelength, pivot wavelength, and effective wavelength.
mag: magnitude in Vega, AB or ST system (w.r.t. the detector type)
flux: flux in $erg/s/cm^2/AA $ in the X system
Jy: flux in $Jy$ (Jansky) in the X system

In [6]:

import pandas as pd
df = pd.read_csv('./table.csv')
df.head()

Out[6]:

	name	detector type	wavelength units	central wavelength	pivot wavelength	effective wavelength	Vega mag	Vega flux	Vega Jy	AB mag	AB flux	AB Jy	ST mag	ST flux	ST Jy
0	2MASS_H	photon	AA	16513.665	16494.947	16386.037	24.86219	1.135300e-10	1030.40	23.49470	4.000500e-10	3630.8	21.1	3.630800e-09	32952.0
1	2MASS_J	photon	AA	12407.217	12389.684	12282.394	23.76727	3.112400e-10	1593.60	22.87325	7.090900e-10	3630.8	21.1	3.630800e-09	18591.0
2	2MASS_Ks	photon	AA	21655.839	21638.144	21521.419	25.92164	4.279000e-11	668.29	24.08405	2.324800e-10	3630.8	21.1	3.630800e-09	56705.0
3	CFHT_CFH12K_CFH7406	photon	AA	4888.664	4888.514	4891.329	20.91453	4.307100e-09	3433.40	20.85383	4.554800e-09	3630.8	21.1	3.630800e-09	2894.2
4	CFHT_CFH12K_CFH7504	photon	AA	5037.756	5037.593	5036.966	20.83347	4.641000e-09	3928.60	20.91906	4.289200e-09	3630.8	21.1	3.630800e-09	3073.4

Extention to Lick indices¶

We also include functions to compute lick indices and provide a series of commonly use ones.

The Lick system of spectral line indices is one of the most commonly used methods of determining ages and metallicities of unresolved (integrated light) stellar populations.

In [7]:

# convert to magnitudes
import numpy as np
from pyphot.astropy import UnitLickLibrary as LickLibrary
from pyphot.astropy import Vega

vega = Vega()
# using the internal collection of indices
lib = LickLibrary()
f = lib['CN_1']
# work on many spectra at once
index = f.get(vega.wavelength, vega.flux, axis=-1)
print("The index of Vega in {0:s} is {1:f} {2:s}".format(f.name, index, f.index_unit))

The index of Vega in CN_1 is -0.281614 mag

Similarly, we show the content of the provided library with respective properties of the passband filters. The table below is also part of the documentation.

In [8]:

# define header and table format (as csv)
hdr = ("name", "wavelength units", "index units", "min", "max" "min blue", "max blue", "min red", "max red")
fmt = "{0:s},{1:s},{2:s},{3:.3f},{4:.3f},{5:.3f},{6:.5f},{7:.3f},{8:.3f}\n"

l = pyphot.UnitLickLibrary()

with open('licks_table.csv', 'w') as output:
    output.write(','.join(hdr) + '\n')

    for k in sorted(l.content):
        fk = l[k]
        # wavelength have units
        band = fk.band.value
        blue = fk.blue.value
        red = fk.red.value
        rec = (fk.name, fk.wavelength_unit, fk.index_unit, band[0], band[1],
               blue[0], blue[1], red[0], red[1])
        output.write(fmt.format(*rec))

In [9]:

import pandas as pd
df = pd.read_csv('./licks_table.csv', index_col=False)
df.head()

Out[9]:

	name	wavelength units	index units	min	maxmin blue	max blue	min red	max red
0	CN_1	AA	mag	4142.125	4177.125	4080.125	4117.625	4244.125
1	CN_2	AA	mag	4142.125	4177.125	4083.875	4096.375	4244.125
2	Ca1_LB13	AA	ew	8484.000	8513.000	8474.000	8484.000	8563.000
3	Ca2_LB13	AA	ew	8522.000	8562.000	8474.000	8484.000	8563.000
4	Ca3_LB13	AA	ew	8642.000	8682.000	8619.000	8642.000	8700.000