.. _astropy_synphot: ****************************** Synthetic Photometry (synphot) ****************************** Introduction ============ **synphot** simulates photometric data and spectra, observed or otherwise. You can incorporate your own filters, spectra, and data. You can also use a pre-defined standard star (Vega), bandpass, or extinction law. Furthermore, it allows you to: * Construct complicated composite spectra using different models. * Simulate observations. * Compute photometric properties such as count rate, effective wavelength, and effective stimulus. * Manipulate a spectrum; e.g., applying redshift or normalize it to a given flux value in a given bandpass. * Sample a spectrum at given wavelengths. * Plot a quick-view of a spectrum. * Perform repetitive operations such as simulating the observations of multiple type of sources through multiple bandpasses. This package covers the general functionalities not related to any particular observatory. If you use HST, you might also be interested in **stsynphot** (https://github.com/spacetelescope/stsynphot_refactor), which covers synthetic photometry for the telescope(s). If you use **synphot** in your work, please see `CITATION `_ for details on how to cite it in your publications. If you have questions or concerns regarding the software, please open an issue at https://github.com/spacetelescope/synphot_refactor/issues (if not already reported) or contact `STScI Help Desk `_. .. _synphot-installation-setup: Installation and Setup ====================== **synphot** works for Python 3.9 or later only. It requires the following packages: * numpy * astropy * scipy * matplotlib (optional for plotting) * specutils (optional) You can install **synphot** using one of the following ways: * From the ``conda-forge`` channel:: conda install synphot -c conda-forge * From standalone release:: pip install synphot * From source (example shown is for the ``dev`` version):: git clone https://github.com/spacetelescope/synphot_refactor.git cd synphot_refactor pip install -e . To use the pre-defined standard star, extinction laws, and bandpasses, it is recommended for non-internal STScI users to download the necessary data files to a local directory so you can avoid connecting directly to STScI HTTP service, which is slower and might not be available all the time. To download the files via HTTP, create a local directory where you plan to store the data files (e.g., ``/my/local/dir/trds``) and run the following: >>> from synphot.utils import download_data >>> file_list = download_data('/my/local/dir/trds') # doctest: +SKIP With ``astropy``, you can generate a ``$HOME/.astropy/config/synphot.cfg`` file like this (otherwise, you can manually create one from :ref:`synphot_config_file`): >>> from astropy.config import generate_config >>> generate_config(pkgname='synphot') Then, you can modify it to your needs; Uncomment and replace every instance of file prefix with ``/my/local/dir/trds`` so that ``synphot`` knows where to look for these files. On the contrary, if you wish to rely solely on Astropy caching mechanism, you can use ``download_data(None)``, but make sure that you do *not* modify your default ``$HOME/.astropy/config/synphot.cfg`` file. Otherwise, ``synphot`` will try to use what is in ``synphot.cfg`` instead. .. note:: **synphot** data files are a minimal subset of those required by **stsynphot**. If you plan to use the latter anyway, please also read the instructions in its documentation. If you have your own version of the data files that you wish to use, you can modify ``synphot.cfg`` to point to your own copies without having to download using the function above. However, before you do so, make sure that your own file(s) can be read in successfully with :func:`~synphot.specio.read_fits_spec`. For example, if you want to use your own Johnson *V* throughput file, you can modify this line in your ``$HOME/.astropy/config/synphot.cfg`` file:: johnson_v_file = /my/other/dir/my_johnson_v.fits Alternately, you can also take advantage of :ref:`astropy:astropy_config` to manage **synphot** data files. This example below overwrites the Johnson *V* throughput file setting for the entire Python session (this supersedes what is set in ``synphot.cfg`` above):: >>> from synphot.config import conf >>> conf.johnson_v_file = '/my/local/dir/trds/comp/nonhst/johnson_v_004_syn.fits' >>> print(conf.johnson_v_file) /my/local/dir/trds/comp/nonhst/johnson_v_004_syn.fits Using the configuration system, you can also temporarily use a different Johnson *V* throughput file:: >>> with conf.set_temp('johnson_v_file', '/my/other/dir/my_johnson_v.fits'): ... print(conf.johnson_v_file) /my/other/dir/my_johnson_v.fits >>> print(conf.johnson_v_file) /my/local/dir/trds/comp/nonhst/johnson_v_004_syn.fits .. testsetup:: >>> conf.reset('johnson_v_file') .. _synphot_getting_started: Getting Started =============== This section only contains minimal examples showing how to use this package. For detailed documentation, see :ref:`synphot_using`. In the examples below, you will notice that most models are from `synphot.models`, not ``astropy.modeling.models``, because the models in ``synphot`` have extra things like ``sampleset`` that are not (yet) available in Astropy. Despite this, some models like `~astropy.modeling.functional_models.Const1D` does not need the extra things to work, so they can be used directly. When in doubt, see if a model is in `synphot.models` first before using Astropy's. :: >>> from astropy import units as u >>> from synphot import units, SourceSpectrum >>> from synphot.models import BlackBodyNorm1D, GaussianFlux1D Create a Gaussian absorption line with the given amplitude centered at 4000 Angstrom with a sigma of 20 Angstrom:: >>> g_abs = SourceSpectrum(GaussianFlux1D, amplitude=1*u.mJy, ... mean=4000, stddev=20) Create a Gaussian emission line with the given total flux centered at 3000 Angstrom with FWHM of 100 Angstrom:: >>> g_em = SourceSpectrum(GaussianFlux1D, ... total_flux=3.5e-13*u.erg/(u.cm**2 * u.s), ... mean=3000, fwhm=100) Create a blackbody source spectrum with a temperature of 6000 K:: >>> bb = SourceSpectrum(BlackBodyNorm1D, temperature=6000) Combine the above components to create a source spectrum that is twice the original blackbody flux with the Gaussian emission and absorption lines:: >>> sp = 2 * bb + g_em - g_abs Plot the spectrum, zooming in on the line features:: >>> sp.plot(left=1, right=7000) # doctest: +SKIP .. plot:: from astropy import units as u from synphot import units, SourceSpectrum from synphot.models import BlackBodyNorm1D, GaussianFlux1D g_abs = SourceSpectrum(GaussianFlux1D, amplitude=1*u.mJy, mean=4000, stddev=20) g_em = SourceSpectrum(GaussianFlux1D, total_flux=3.5e-13*u.erg/(u.cm**2 * u.s), mean=3000, fwhm=100) bb = SourceSpectrum(BlackBodyNorm1D, temperature=6000) sp = 2 * bb + g_em - g_abs sp.plot(left=1, right=7000) Sample the spectrum at 0.3 micron:: >>> sp(0.3 * u.micron) # doctest: +FLOAT_CMP Or sample the same thing but in a different flux unit:: >>> sp(0.3 * u.micron, flux_unit=units.FLAM) # doctest: +FLOAT_CMP Sample the spectrum at its native wavelength set:: >>> sp(sp.waveset) # doctest: +FLOAT_CMP +ELLIPSIS Models that built the spectrum:: >>> print(sp) # doctest: +ELLIPSIS SourceSpectrum at z=0.0 Model: CompoundModel... Inputs: ('x',) Outputs: ('y',) Model set size: 1 Expression: ([0] | [1]) + [2] - [3] Components: [0]: [1]: [2]: [3]: Parameters: ... Redshift the source spectrum by :math:`z = 0.2`:: >>> sp.z = 0.2 Create a box-shaped bandpass centered at 4000 Angstrom with a width of 2000 Angstrom:: >>> from synphot import SpectralElement >>> from synphot.models import Box1D >>> bp = SpectralElement(Box1D, amplitude=1, x_0=4000, width=2000) Normalize the source spectrum to 1 Jy in a given box bandpass and integrate it:: >>> sp_rn = sp.normalize(1 * u.Jy, band=bp) >>> sp_rn.integrate() # doctest: +FLOAT_CMP Create an observation by passing the redshifted and normalized source spectrum through the box bandpass:: >>> from synphot import Observation >>> obs = Observation(sp_rn, bp) Calculate the count rate of the observation above for an 2-meter telescope: >>> import numpy as np >>> area = np.pi * (1 * u.m) ** 2 >>> area # doctest: +FLOAT_CMP >>> obs.countrate(area=area) # doctest: +FLOAT_CMP .. _synphot_using: Using **synphot** ================= .. toctree:: :maxdepth: 1 synphot/overview synphot/config synphot/from_pysyn_iraf synphot/bandpass synphot/spectrum synphot/observation synphot/formulae synphot/units synphot/filter_par synphot/tutorials .. _synphot_history: A Brief History =============== A brief history: First, there was STSDAS SYNPHOT (IRAF). Then, there was ASTROLIB PYSYNPHOT (:ref:`Lim et al. 2015 `), which aimed at replacing STSDAS SYNPHOT using Python. In order to take advantage of :ref:`astropy:astropy-modeling` and :ref:`astropy:astropy-units` and to repurpose the functionality for a wider audience (other than HST users), it was refactored again and separated into **synphot** and **stsynphot** (see :ref:`astropy_synphot`). .. _synphot_api: API === .. automodapi:: synphot.binning :no-inheritance-diagram: Also imports this C-extension to local namespace: .. toctree:: :maxdepth: 1 synphot/c_ext .. automodapi:: synphot.blackbody :no-inheritance-diagram: .. automodapi:: synphot.config :no-inheritance-diagram: .. automodapi:: synphot.exceptions .. automodapi:: synphot.filter_parameterization .. automodapi:: synphot.models .. automodapi:: synphot.observation .. automodapi:: synphot.reddening .. automodapi:: synphot.specio :no-inheritance-diagram: .. automodapi:: synphot.spectrum :no-inheritance-diagram: .. automodapi:: synphot.thermal .. automodapi:: synphot.units :no-inheritance-diagram: .. automodapi:: synphot.utils :no-inheritance-diagram: .. _synphot_biblio: References ========== .. _synphot-ref-extinction-calzetti2000: Calzetti, D., Armus, L., Bohlin, R. C., Kinney, A. L., Koornneef, J., & Storchi-Bergmann, T. 2000, ApJ, 533, 682 .. _synphot-ref-extinction-cardelli1989: Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245 .. _synphot-ref-extinction-gordon2003: Gordon, K. D., Clayton, G. C., Misselt, K. A., Landolt, A. U., & Wolff, M. J. 2003, ApJ, 594, 279 .. _synphot-ref-horne1988: Horne, K. 1988, in New Directions in Spectophotometry: A Meeting Held in Las Vegas, NV, March 28-30, Application of Synthetic Photometry Techniques to Space Telescope Calibration, ed. A. G. Davis Philip, D. S. Hayes, & S. J. Adelman (Schenectady, NY: L. Davis Press), 145 .. _synphot-ref-koornneef1986: Koornneef, J., Bohlin, R., Buser, R., Horne, K., & Turnshek, D. 1986, Highlights Astron., 7, 833 .. _synphot-ref-laidler2008: Laidler, V., et al. 2008, Synphot Data User's Guide, Version 1.2 (Baltimore, MD: STScI) .. _synphot-ref-lim2015: Lim, P. L., Diaz, R. I., & Laidler, V. 2015, PySynphot User's Guide (Baltimore, MD: STScI), https://pysynphot.readthedocs.io/en/latest/ .. _synphot-ref-madau1995: Madau, P., et al. 1995, ApJ, 441, 18 .. _synphot-ref-oke1974: Oke, J. B., 1974, ApJS, 27, 21 .. _synphot-ref-rybicki1979: Rybicki, G. B., & Lightman, A. P. 1979, Radiative Processes in Astrophysics (New York, NY: Wiley) .. _synphot-ref-schneider1983: Schneider, D. P., Gunn, J. E., & Hoessel J. G. 1983, ApJ, 264, 337