Flux Unit Conversions with synphot and stsynphot#
Learning Goals#
By the end of this tutorial, you will:
Perform conversions between various systems of flux and magnitude using the
synphotandstsynphotpackages.Extrapolate an output flux at a different wavelength than the input flux, by using a spectrum defined using the same packages.
Provide a framework to adapt a more personalized and streamlined conversion process, if desired.
Table of Contents#
Introduction
1. Imports
2. Input and output setup
2.1 Units
2.2 Bandpasses
2.3 Choosing a spectrum
2.4 User settings
3. Set up the conversion
4. Perform the conversion and create a plot
5. Examples
5.1 Flux in Jy to AB mag with a flat spectrum in \(F_\nu\)
5.2 Flux in flam to Flux in flam along a blackbody
5.3 Flux in fnu to flux in photnu, any spectrum (same wavelength)
5.4 mag to mag from an HST bandpass to a Johnson bandpass, flat spectrum in \(F_\lambda\)
6. Conclusions
Additional Resources
About the Notebook
Citations
Introduction#
This notebook is based on the prior “HST Photometric Conversion Tool” that returns unit conversions between various flux units and magnitude systems. It is not intended to replace more detailed functionality such as that provided by the Exposure Time Calculator (ETC). Rather, it is intended to provide a simple, quick result for flux unit conversions.
stsynphot requires access to data distributed by the Calibration Data Reference System (CRDS) in order to operate. Both packages look for an environment variable called PYSYN_CDBS to find the directory containing these data.
Users can obtain these data files from the CDRS. Information on how to obtain the most up-to-date reference files (and what they contain) can be found here. An example of how to download these files using curl and set up this environment variable is presented in the imports section below.
For detailed instructions on how to install and set up these packages, see the synphot and stsynphot documentation.
1. Imports#
This notebook assumes you have created the virtual environment in WFC3 notebooks’ installation instructions.
We import:
os for setting environment variables
tarfile for extracting a .tar archive
numpy for handling array functions
matplotlib.pyplot for plotting data
synphot and stsynphot for evaluating synthetic photometry
astropy.units and synphot.units for handling units
Additionally, we will need to set the PYSYN_CDBS environment variable before importing stsynphot. We will also create a Vega spectrum using synphot’s inbuilt from_vega() method, as the latter package will supercede this method’s functionality and require a downloaded copy of the latest Vega spectrum to be provided.
import os
import tarfile
import numpy as np
import matplotlib.pyplot as plt
from synphot import SourceSpectrum
from synphot.models import BlackBody1D, PowerLawFlux1D
from synphot.units import convert_flux
from astropy import units as u
from synphot import units as su
%matplotlib inline
vegaspec = SourceSpectrum.from_vega()
This section obtains the WFC3 throughput component tables for use with stsynphot. This step only needs to be done once. If these reference files have already been downloaded, this section can be skipped.
cmd_input = 'curl -O https://archive.stsci.edu/hlsps/reference-atlases/hlsp_reference-atlases_hst_multi_everything_multi_v11_sed.tar'
os.system(cmd_input)
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 796M 0 224k 0 0 337k 0 0:40:16 --:--:-- 0:40:16 337k
3 796M 3 24.6M 0 0 14.8M 0 0:00:53 0:00:01 0:00:52 14.8M
6 796M 6 48.8M 0 0 18.2M 0 0:00:43 0:00:02 0:00:41 18.2M
8 796M 8 64.5M 0 0 17.6M 0 0:00:45 0:00:03 0:00:42 17.6M
9 796M 9 75.7M 0 0 16.2M 0 0:00:49 0:00:04 0:00:45 16.2M
11 796M 11 87.9M 0 0 15.4M 0 0:00:51 0:00:05 0:00:46 17.4M
12 796M 12 100M 0 0 15.0M 0 0:00:52 0:00:06 0:00:46 15.1M
14 796M 14 112M 0 0 14.6M 0 0:00:54 0:00:07 0:00:47 12.8M
15 796M 15 125M 0 0 14.4M 0 0:00:55 0:00:08 0:00:47 12.1M
17 796M 17 138M 0 0 14.3M 0 0:00:55 0:00:09 0:00:46 12.5M
18 796M 18 150M 0 0 14.1M 0 0:00:56 0:00:10 0:00:46 12.5M
20 796M 20 160M 0 0 13.7M 0 0:00:57 0:00:11 0:00:46 12.0M
21 796M 21 171M 0 0 13.5M 0 0:00:58 0:00:12 0:00:46 11.8M
22 796M 22 182M 0 0 13.3M 0 0:00:59 0:00:13 0:00:46 11.4M
24 796M 24 195M 0 0 13.2M 0 0:00:59 0:00:14 0:00:45 11.3M
26 796M 26 207M 0 0 13.2M 0 0:01:00 0:00:15 0:00:45 11.4M
27 796M 27 217M 0 0 13.0M 0 0:01:00 0:00:16 0:00:44 11.3M
28 796M 28 227M 0 0 12.8M 0 0:01:01 0:00:17 0:00:44 11.1M
29 796M 29 237M 0 0 12.7M 0 0:01:02 0:00:18 0:00:44 10.9M
30 796M 30 245M 0 0 12.4M 0 0:01:03 0:00:19 0:00:44 10.0M
31 796M 31 251M 0 0 12.1M 0 0:01:05 0:00:20 0:00:45 9032k
32 796M 32 256M 0 0 11.8M 0 0:01:07 0:00:21 0:00:46 8036k
32 796M 32 261M 0 0 11.5M 0 0:01:08 0:00:22 0:00:46 7056k
33 796M 33 266M 0 0 11.2M 0 0:01:10 0:00:23 0:00:47 5861k
33 796M 33 270M 0 0 10.9M 0 0:01:12 0:00:24 0:00:48 5126k
34 796M 34 274M 0 0 10.6M 0 0:01:14 0:00:25 0:00:49 4663k
34 796M 34 278M 0 0 10.4M 0 0:01:16 0:00:26 0:00:50 4462k
35 796M 35 282M 0 0 10.2M 0 0:01:17 0:00:27 0:00:50 4331k
35 796M 35 286M 0 0 9.9M 0 0:01:19 0:00:28 0:00:51 4153k
36 796M 36 290M 0 0 9.7M 0 0:01:21 0:00:29 0:00:52 4106k
36 796M 36 294M 0 0 9826k 0 0:01:22 0:00:30 0:00:52 4064k
37 796M 37 298M 0 0 9653k 0 0:01:24 0:00:31 0:00:53 4064k
38 796M 38 302M 0 0 9500k 0 0:01:25 0:00:32 0:00:53 4092k
38 796M 38 307M 0 0 9346k 0 0:01:27 0:00:33 0:00:54 4258k
39 796M 39 311M 0 0 9204k 0 0:01:28 0:00:34 0:00:54 4374k
39 796M 39 315M 0 0 9071k 0 0:01:29 0:00:35 0:00:54 4450k
40 796M 40 319M 0 0 8934k 0 0:01:31 0:00:36 0:00:55 4363k
40 796M 40 323M 0 0 8795k 0 0:01:32 0:00:37 0:00:55 4208k
41 796M 41 327M 0 0 8661k 0 0:01:34 0:00:38 0:00:56 4121k
41 796M 41 330M 0 0 8541k 0 0:01:35 0:00:39 0:00:56 3967k
41 796M 41 334M 0 0 8420k 0 0:01:36 0:00:40 0:00:56 3770k
42 796M 42 337M 0 0 8307k 0 0:01:38 0:00:41 0:00:57 3708k
42 796M 42 341M 0 0 8198k 0 0:01:39 0:00:42 0:00:57 3695k
43 796M 43 345M 0 0 8098k 0 0:01:40 0:00:43 0:00:57 3671k
43 796M 43 348M 0 0 8000k 0 0:01:41 0:00:44 0:00:57 3696k
44 796M 44 352M 0 0 7910k 0 0:01:43 0:00:45 0:00:58 3767k
44 796M 44 356M 0 0 7827k 0 0:01:44 0:00:46 0:00:58 3850k
45 796M 45 360M 0 0 7756k 0 0:01:45 0:00:47 0:00:58 3981k
45 796M 45 364M 0 0 7671k 0 0:01:46 0:00:48 0:00:58 3940k
46 796M 46 368M 0 0 7592k 0 0:01:47 0:00:49 0:00:58 3960k
46 796M 46 371M 0 0 7512k 0 0:01:48 0:00:50 0:00:58 3901k
47 796M 47 374M 0 0 7427k 0 0:01:49 0:00:51 0:00:58 3680k
47 796M 47 377M 0 0 7335k 0 0:01:51 0:00:52 0:00:59 3324k
47 796M 47 379M 0 0 7240k 0 0:01:52 0:00:53 0:00:59 3051k
47 796M 47 381M 0 0 7148k 0 0:01:54 0:00:54 0:01:00 2716k
48 796M 48 383M 0 0 7060k 0 0:01:55 0:00:55 0:01:00 2444k
48 796M 48 386M 0 0 6976k 0 0:01:56 0:00:56 0:01:00 2325k
48 796M 48 388M 0 0 6897k 0 0:01:58 0:00:57 0:01:01 2304k
49 796M 49 390M 0 0 6824k 0 0:01:59 0:00:58 0:01:01 2370k
49 796M 49 393M 0 0 6756k 0 0:02:00 0:00:59 0:01:01 2480k
49 796M 49 396M 0 0 6690k 0 0:02:01 0:01:00 0:01:01 2592k
50 796M 50 399M 0 0 6633k 0 0:02:02 0:01:01 0:01:01 2744k
50 796M 50 402M 0 0 6583k 0 0:02:03 0:01:02 0:01:01 2942k
51 796M 51 407M 0 0 6546k 0 0:02:04 0:01:03 0:01:01 3287k
51 796M 51 411M 0 0 6519k 0 0:02:05 0:01:04 0:01:01 3718k
52 796M 52 416M 0 0 6489k 0 0:02:05 0:01:05 0:01:00 4039k
52 796M 52 419M 0 0 6446k 0 0:02:06 0:01:06 0:01:00 4141k
53 796M 53 423M 0 0 6406k 0 0:02:07 0:01:07 0:01:00 4212k
53 796M 53 427M 0 0 6371k 0 0:02:07 0:01:08 0:00:59 4164k
54 796M 54 431M 0 0 6340k 0 0:02:08 0:01:09 0:00:59 4001k
54 796M 54 435M 0 0 6304k 0 0:02:09 0:01:10 0:00:59 3872k
54 796M 54 437M 0 0 6258k 0 0:02:10 0:01:11 0:00:59 3741k
55 796M 55 440M 0 0 6207k 0 0:02:11 0:01:12 0:00:59 3491k
55 796M 55 443M 0 0 6161k 0 0:02:12 0:01:13 0:00:59 3248k
56 796M 56 446M 0 0 6119k 0 0:02:13 0:01:14 0:00:59 3032k
56 796M 56 448M 0 0 6076k 0 0:02:14 0:01:15 0:00:59 2853k
56 796M 56 451M 0 0 6035k 0 0:02:15 0:01:16 0:00:59 2863k
57 796M 57 454M 0 0 6000k 0 0:02:15 0:01:17 0:00:58 2979k
57 796M 57 458M 0 0 5964k 0 0:02:16 0:01:18 0:00:58 3070k
57 796M 57 461M 0 0 5934k 0 0:02:17 0:01:19 0:00:58 3171k
58 796M 58 465M 0 0 5906k 0 0:02:18 0:01:20 0:00:58 3338k
58 796M 58 469M 0 0 5885k 0 0:02:18 0:01:21 0:00:57 3582k
59 796M 59 474M 0 0 5877k 0 0:02:18 0:01:22 0:00:56 3972k
60 796M 60 480M 0 0 5883k 0 0:02:18 0:01:23 0:00:55 4591k
61 796M 61 487M 0 0 5902k 0 0:02:18 0:01:24 0:00:54 5388k
62 796M 62 497M 0 0 5947k 0 0:02:17 0:01:25 0:00:52 6597k
63 796M 63 509M 0 0 6017k 0 0:02:15 0:01:26 0:00:49 8170k
65 796M 65 523M 0 0 6110k 0 0:02:13 0:01:27 0:00:46 9980k
67 796M 67 541M 0 0 6247k 0 0:02:10 0:01:28 0:00:42 12.0M
70 796M 70 562M 0 0 6420k 0 0:02:06 0:01:29 0:00:37 14.8M
73 796M 73 586M 0 0 6624k 0 0:02:03 0:01:30 0:00:33 17.7M
76 796M 76 607M 0 0 6783k 0 0:02:00 0:01:31 0:00:29 19.5M
78 796M 78 627M 0 0 6932k 0 0:01:57 0:01:32 0:00:25 20.8M
80 796M 80 643M 0 0 7039k 0 0:01:55 0:01:33 0:00:22 20.6M
83 796M 83 661M 0 0 7158k 0 0:01:53 0:01:34 0:00:19 19.9M
85 796M 85 679M 0 0 7279k 0 0:01:52 0:01:35 0:00:17 18.7M
87 796M 87 698M 0 0 7399k 0 0:01:50 0:01:36 0:00:14 18.2M
90 796M 90 717M 0 0 7523k 0 0:01:48 0:01:37 0:00:11 17.9M
91 796M 91 729M 0 0 7571k 0 0:01:47 0:01:38 0:00:09 17.1M
92 796M 92 739M 0 0 7601k 0 0:01:47 0:01:39 0:00:08 15.6M
94 796M 94 751M 0 0 7640k 0 0:01:46 0:01:40 0:00:06 14.2M
95 796M 95 762M 0 0 7682k 0 0:01:46 0:01:41 0:00:05 12.8M
96 796M 96 771M 0 0 7699k 0 0:01:45 0:01:42 0:00:03 10.8M
98 796M 98 780M 0 0 7713k 0 0:01:45 0:01:43 0:00:02 10.2M
98 796M 98 787M 0 0 7708k 0 0:01:45 0:01:44 0:00:01 9836k
99 796M 99 795M 0 0 7706k 0 0:01:45 0:01:45 --:--:-- 9040k
100 796M 100 796M 0 0 7707k 0 0:01:45 0:01:45 --:--:-- 8321k
0
Once the downloaded is complete, extract the file and set the environment variable PYSYN_CDBS to the path of the trds subdirectory. The next cell will do this for you, as long as the .tar file downloaded above has not been moved.
tar_archive = 'hlsp_reference-atlases_hst_multi_everything_multi_v11_sed.tar'
extract_to = 'hlsp_reference-atlases_hst_multi_everything_multi_v11_sed'
with tarfile.open(tar_archive, 'r') as tar:
tar.extractall(path=extract_to)
os.environ['PYSYN_CDBS'] = 'hlsp_reference-atlases_hst_multi_everything_multi_v11_sed/grp/redcat/trds/'
Now, after having set up PYSYN_CDBS, we import stsynphot. A warning regarding the Vega spectrum is expected here.
import stsynphot as stsyn
WARNING: Failed to load Vega spectrum from hlsp_reference-atlases_hst_multi_everything_multi_v11_sed/grp/redcat/trds//calspec/alpha_lyr_stis_010.fits; Functionality involving Vega will be cripped: FileNotFoundError(2, 'No such file or directory') [stsynphot.spectrum]
2. Input and output setup#
2.1 Units#
The conversion framework below will accept any astropy or synphot unit with dimensions of spectral flux density (\(F_\lambda\) or \(F_\nu\)) or photon flux density. Flux units with any of the following dimensions will be supported by the tool:
[power] [area] [wavelength]\(^{-1}\)
[power] [area] [frequency]\(^{-1}\)
photons [area] [wavelength]\(^{-1}\)
photons [area] [frequency]\(^{-1}\)
Alternatively, a magnitude system may be specified as the unit for the input or the output in the same way that a flux density would be. The tables below lists flux units which are defined by name in astropy and synphot, and the magnitude systems supported by the tool.
Unit |
Definition |
astropy/synphot attribute |
|---|---|---|
Jansky |
$\(10^{-26} \text{ W} \text{ m}^{-2} \text{ Hz}^{-1}\)$ |
|
fnu |
$\(\text{erg} \text{ s}^{-1} \text{ cm}^{-2} \text{ Hz}^{-1}\)$ |
|
flam |
$\(\text{erg} \text{ s}^{-1} \text{ cm}^{-2} \text{ Å}^{-1}\)$ |
|
photnu |
$\(\text{photons} \text{ s}^{-1} \text{ cm}^{-2} \text{ Hz}^{-1}\)$ |
|
photlam |
$\(\text{photons} \text{ s}^{-1} \text{ cm}^{-2} \text{ Å}^{-1}\)$ |
|
Mag System |
astropy/synphot attribute |
|---|---|
ABmag |
|
STmag |
|
vegamag |
|
For more information on accepted units in synphot, refer to the documentation here.
2.2 Bandpasses#
When selecting a magnitude as an input or output, the tool will need a bandpass to be defined, which is done with a string of observation mode, or obsmode, keywords. The pivot wavelength for that bandpass will then serve as the characteristic wavelength to be used for the conversion.
For HST bandpasses, stsynphot accounts for the telescope’s optics by combining throughput information along the entire optical path. As an example, 'wfc3, uvis2, f475w, mjd#59367' tells stsynphot to retrieve the latest throughput tables for the UVIS2 detector on WFC3, through the F475W filter for the Modified Julian Date 59367 (June 1, 2021). The option to specify a Julian date is provided for instruments which show changes in sensitivity over time. If no date is specified, stsynphot will use the reference epoch for each instrument as default.
As the required and optional obsmode keywords vary from instrument to instrument, it would be impractical to list the available options here in their entirety. Please refer to the full list of available obsmode keywords for details on how to specify HST bandpasses.
For non-HST filter systems, the only required keywords are the filter system’s name and that of the desired filter within that system (e.g. 'johnson, v'). A list of the non-HST filter systems accepted by stsynphot is given here:
System |
Bands |
|---|---|
cousins |
r, i |
galex |
nuv, fuv |
johnson |
u, b, v, r, i, j, k |
landolt |
u, b, v, r, i |
sdss |
u, g, r, i, z, |
stromgren |
u, v, b, y |
2.3 Choosing a spectrum#
You’ll also need to define a spectrum, which the tool will use to extrapolate your input flux to an output at a different wavelength.
The embedded code below shows how to generate or load various useful spectra. You can simply copy one of them into the cell below and modify as appropriate, or create your own. For more information, see the Source Spectrum documentation.
Some notes:
For evaluation and plotting, these models default to outputting flux in photlam, however, the output unit may be specified with the
flux_unitkeyword argument.synphot.models.BlackBody1Doutputs a function according to Planck’s law, which means that the output unit carries an implicit “per unit solid angle,” in steradians. For a normalized blackbody, you can useBlackBodyNorm1D, whose output is normalized to a 1 solar radius star at a distance of 1 kpc, or multiply your source spectrum by some solid angle of your choosing.synphot.models.PowerLawFlux1Duses the definition \(f(x) = A (\frac{x}{x_0})^{-\alpha}\), where \(A\) is input flux (flux_in), and \(x_0\) is the input wavelength (wavelength_in). Note the negative sign in front of the power law index \(\alpha\). The model can generate curves with \(x\) as either frequency or wavelength, but the example here assumes that wavelength will be used. The y-axis unit will be taken from \(A\).A wide array of reference spectra are available for download from spectral atlases located here.
Example spectrum definitions:
# Blackbody
bb_temp = 5800 * u.K
model = BlackBody1D(bb_temp)
spectrum = SourceSpectrum(model)
# Power law
pl_index = 0
model = PowerLawFlux1D(amplitude=flux_in, x_0=wavelength_in, alpha=pl_index)
spectrum = SourceSpectrum(model)
# Load from a FITS table
spectrum = SourceSpectrum.from_file('/path/to/your/spectrum.fits')
The notebook has been set up to perform an extrapolation from \(V = 0.0\) in the Vega system to the R-band in the same system, using the Vega spectrum defined in the imports cell. Further examples of input and output settings are available at the bottom of this notebook.
2.4 User settings#
First, we define our conversion input settings:
value_in: numerical value of input flux/mag (float)unit_in: unit or mag system for input valuewaveband_in: input’s wavelength (float) for flux, or bandpass obsmode (string) for magnitudes. The use of “waveband” here is only for the purpose of variable naming, since this can be either a wavelength or a bandpass.wavelength_unit: wavelength unit (used for plots, so needs to be specified even when using a bandpass)
value_in = 0.0
unit_in = su.VEGAMAG
waveband_in = 'johnson, v'
wavelength_unit = u.nm
Next, we define our conversion output settings:
unit_out: unit or mag system for the outputwaveband_out: wavelength (float) or bandpass obsmode (string) to find output for; if a wavelength, the unit specified in the cell above bywavelength_unitwill be used.
unit_out = su.VEGAMAG
waveband_out = 'cousins, r'
Finally, we define our spectrum to use (copy/paste from above, or define your own).
spectrum = vegaspec
3. Set up the conversion#
Now, we need to run a few cells that will set up the conversion. First, we check whether the input and output are a flux or a magnitude, and set some variables appropriately. Then, we scale the chosen spectrum such that it passes through the defined input so the extrapolation to the output wavelength will give an accurate result.
Note that the default plotting unit is set to the input unit for conversions made from flux units, and photlam for conversions from magnitudes. This may be altered, if desired, by changing the plot_unit variable definitions in the cell below.
Note: No inputs required for the cells below. All the inputs were assigned in Section 2.4.
First, let’s combine input value and unit as a quantity, then list the systems that will work for the tool.
quantity_in = value_in * unit_in
mag_systems = [u.STmag, u.ABmag, su.VEGAMAG]
flux_systems = ['spectral flux density', 'spectral flux density wav',
'photon flux density', 'photon flux density wav']
Next, let’s get inputs set up for following steps. Here are the three possible outcomes:
If the input is a magnitude,
define bandpass with input obsmode string
pivot wavelength of input bandpass
set
flux_into equivalent flux in photlam at pivot wavelength (Vega spectrum needed if using VEGAMAG)set plotting unit and flux for later
If the input is a flux,
combine wavelength for flux and unit as the input wavelength
set plotting unit and flux for later
If anything else, print
unit_in not a flux density unit or magnitude system
if unit_in in mag_systems:
mag_in = quantity_in
bandpass_in = stsyn.band(waveband_in)
wavelength_in = bandpass_in.pivot().to(wavelength_unit)
flux_in = convert_flux(wavelength_in, quantity_in, su.PHOTLAM,
vegaspec=vegaspec)
plot_unit = su.PHOTLAM
plot_flux_in = convert_flux(wavelength_in, flux_in, plot_unit)
elif unit_in.physical_type in flux_systems:
flux_in = quantity_in
wavelength_in = waveband_in * wavelength_unit
plot_unit = unit_in
plot_flux_in = convert_flux(wavelength_in, flux_in, plot_unit)
else:
print('unit_in not a flux density unit or magnitude system')
We perform a similar setup for outputs:
If the output is a magnitude,
use output wavelength to calculate output bandpass
output bandpass’s pivot wavelength
If the output is a flux,
combine wavelength for flux and unit as output wavelength
If anything else, print
unit_out not a flux density unit or magnitude system
if unit_out in mag_systems:
bandpass_out = stsyn.band(waveband_out)
wavelength_out = bandpass_out.pivot().to(wavelength_unit)
elif unit_out.physical_type in flux_systems:
wavelength_out = waveband_out * wavelength_unit
else:
print('unit_out not a flux density unit or magnitude system')
Finally, we convert the flux, scale, and multiply by our spectrum. The default evaluation unit is set to photlam.
scale = convert_flux(wavelength_in, flux_in, su.PHOTLAM) / \
spectrum(wavelength_in)
scaled_spectrum = spectrum * scale
4. Perform the conversion and create a plot#
We can now find our output by using the convert_flux function from synphot. We will first scale the spectrum defined above such that its value at the input wavelength is that of the input flux. Then, we convert the scaled spectrum’s value at the output wavelength to the selected output units, as well as the unit that will be used for plotting.
if unit_out in mag_systems:
flux_out = convert_flux(wavelength_out, scaled_spectrum(wavelength_out), unit_out,
vegaspec=vegaspec)
plot_flux_out = convert_flux(
wavelength_out, scaled_spectrum(wavelength_out), plot_unit)
else:
flux_out = convert_flux(
wavelength_out, scaled_spectrum(wavelength_out), unit_out)
plot_flux_out = convert_flux(
wavelength_out, scaled_spectrum(wavelength_out), plot_unit)
value_out = flux_out.value
Print the input and output values.
print(f'Input: {value_in:.4f} {unit_in:s} at {wavelength_in.value:.1f} {wavelength_in.unit:s}\n')
print(f'Input: {value_out:.4f} {unit_out:s} at {wavelength_out.value:.1f} {wavelength_out.unit:s}\n')
# print(f'Output: {float(value_out):.4} {} at {:.1f} {}'.format(, str(unit_out), wavelength_out.value, str(wavelength_out.unit)))
Input: 0.0000 mag(VEGA) at 547.9 nm
Input: -0.0000 mag(VEGA) at 647.3 nm
While not strictly necessary for performing the conversion, plotting the selected spectrum with the input and output points can be a useful check to see if the spectrum looks like what you’re expecting.
Let’s define a set of wavelengths and minimum/maximum bounds for the plot.
wavelength_settings = [wl.value for wl in [wavelength_in, wavelength_out]]
short = min(wavelength_settings)
long = max(wavelength_settings)
if short == long: # In case wavelength_in == wavelength_out
diff = 10.
else:
diff = long - short
right = long + diff
left = max([short - diff, 0.1])
wavelength_space = np.linspace(left, right, 10000) * wavelength_unit
We will also set the colors for the in/out markers.
if wavelength_in.value > wavelength_out.value:
c_in = 'r'
c_out = 'b'
else:
c_in = 'b'
c_out = 'r'
Plot the spectrum, input, and output. If the input was a magnitude, then plot_unit (see Section 3) will be used. In addition, if the input and output wavelength are the same, then print the input and output.
plt.figure()
# Plot spectrum
plt.plot(wavelength_space, scaled_spectrum(wavelength_space,
flux_unit=plot_unit), c='k', label='Source Spectrum')
# Plot input
plt.plot(wavelength_in.value, plot_flux_in.value,
marker='o', color=c_in, ls='none',
label='Input: {:.4} {} at {:.1f} {}'.format(
float(value_in), str(unit_in), wavelength_in.value, str(wavelength_in.unit)))
# Plot output
plt.plot(wavelength_out.value, plot_flux_out.value,
marker='s', color=c_out, ls='none',
label='Output: {:.4} {} at {:.1f} {}'.format(
float(value_out), str(unit_out), wavelength_out.value, str(wavelength_out.unit)))
# Set heights for dotted lines to markers as % of plot range
bottom, top = plt.ylim()
yrange = top - bottom
inheight = (plot_flux_in.value - bottom) / yrange
outheight = (plot_flux_out.value - bottom) / yrange
# Plot dotted lines to markers
plt.axvline(wavelength_in.to(wavelength_unit).value,
ymax=inheight, ls=':', c=c_in)
plt.axvline(wavelength_out.to(wavelength_unit).value,
ymax=outheight, ls=':', c=c_out)
# Miscellaneous
plt.ylabel('Flux ({})'.format(str(plot_unit)))
plt.xlabel('Wavelength ({})'.format(str(wavelength_unit)))
plt.legend(fontsize='small')
plt.tight_layout()
5. Examples#
Here, we provide some examples to illustrate a few of the many conversions which are possible. If desired, you may run an example cell to set the inputs for the tool, then run through the notebook (without running the cells in Section 2.4) to see the results. Each cell will define an input, output, and spectrum.
5.1. Flux in Jy to AB mag with a flat spectrum in \(F_\nu\)#
Run cell then go to Section 3 to convert flux and plot.
# Input: 3631 Jy at 550. nm
value_in = 3631.
unit_in = u.Jy
waveband_in = 550.
wavelength_unit = u.nm
# Output: Johnson V mag (AB)
unit_out = u.ABmag
waveband_out = 'Johnson, V'
# Spectrum: Flat power law in F_nu
pl_index = 0
model = PowerLawFlux1D(amplitude=flux_in, x_0=wavelength_in, alpha=pl_index)
spectrum = SourceSpectrum(model)
5.2. Flux in flam to Flux in flam along a blackbody#
Run cell then go to Section 3 to convert flux and plot.
# Input: 1.234e-8 flam at 500. nm
value_in = 1.234e-8
unit_in = su.FLAM
waveband_in = 500.
wavelength_unit = u.nm
# Output: flam at 800. nm
unit_out = su.FLAM
waveband_out = 800.
# Spectrum: 5800 K blackbody
bb_temp = 5800 * u.K
model = BlackBody1D(bb_temp)
spec = SourceSpectrum(model)
5.3. Flux in fnu to flux in photnu, any spectrum (same wavelength)#
Run cell then go to Section 3 to convert flux and plot. Note the spectrum is irrelevant since conversion is at the same wavelength.
# Input: 1.234e-21 fnu at 686. nm
value_in = 1.234e-21
unit_in = su.FNU
waveband_in = 686.
wavelength_unit = u.nm
# Output: photnu at 686. nm
unit_out = su.PHOTNU
waveband_out = 686.
# Spectrum: 5800 K blackbody
bb_temp = 5800 * u.K
model = BlackBody1D(bb_temp)
spectrum = SourceSpectrum(model)
5.4. mag to mag from an HST bandpass to a Johnson bandpass, flat spectrum in \(F_\lambda\)#
Run cell then go to Section 3 to convert flux and plot. Note we run convert_flux for the power law amplitude to ensure the spectrum is flat in \(F_\lambda\) rather than in photlam.
# Input: STmag = 12.240, F606W filter on WFC3 UVIS 2
value_in = 12.240
unit_in = u.STmag
waveband_in = 'wfc3, uvis2, f606w, mjd#59367'
wavelength_unit = u.nm
# Output: Johnson V mag (AB)
unit_out = u.STmag
waveband_out = 'Johnson, V'
# Spectrum: Flat power law in F_lambda
pl_index = 0
model = PowerLawFlux1D(amplitude=convert_flux(
606 * u.nm, 1., su.FLAM), x_0=606 * u.nm, alpha=pl_index)
spec = SourceSpectrum(model)
model = PowerLawFlux1D(amplitude=flux_in, x_0=wavelength_in, alpha=pl_index)
6. Conclusions#
Thank you for walking through this notebook. Now using WFC3 data, you should be more familiar with:
Performing conversions between various systems of flux and magnitude using the
synphotandstsynphotpackages.Extrapolating an output flux at a different wavelength than the input flux.
Adapting a more personalized and streamlined conversion process.
Congratulations, you have completed the notebook!#
Additional Resources#
Below are some additional resources that may be helpful. Please send any questions through the HST Helpdesk.
-
see sections 9.5.2 for reference to this notebook
About this Notebook#
Authors: Aidan Pidgeon, Joel Green; WFC3 Instrument Team
Updated on: 2021-09-13
Citations#
If you use numpy, astropy, synphot, or stsynphot for published research, please cite the
authors. Follow these links for more information about citing the libraries below:
Top of Page
