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Merge pull request #257 from pllim/tynt-poc
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Initial port of tynt to filter_parameterization subpackage
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pllim authored May 24, 2022
2 parents 5662d6f + 7fc2381 commit 2a382d7
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3 changes: 3 additions & 0 deletions CHANGES.rst
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1.2.0 (unreleased)
==================

- New ``filter_parameterization`` subpackage to handle filter parameterization,
adapted from ``tynt`` package written by Brett Morris. [#257]

- Dropped support for Python 3.6 and 3.7. Minimum supported Python
version is now 3.8. [#330]

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3 changes: 3 additions & 0 deletions docs/index.rst
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Expand Up @@ -287,6 +287,7 @@ Using **synphot**
synphot/observation
synphot/formulae
synphot/units
synphot/filter_par
synphot/tutorials

.. _synphot_history:
Expand Down Expand Up @@ -325,6 +326,8 @@ Also imports this C-extension to local namespace:

.. automodapi:: synphot.exceptions

.. automodapi:: synphot.filter_parameterization

.. automodapi:: synphot.models

.. automodapi:: synphot.observation
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124 changes: 124 additions & 0 deletions docs/synphot/filter_par.rst
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.. _synphot_par_filters:

Parameterized Filters
=====================

.. note::

The algorithm for parameterized filters here was originally developed by
Brett Morris for the `tynt <https://github.com/bmorris3/tynt/>`_ package.

Filter responses can be approximated using Fast Fourier Transform (FFT).
If a filter is approximated this way, one only needs to store its FFT
parameters instead of all the sampled data points. This reduces the
storage size and increases performance, at the cost of reduced accuracy.
If you decide to use the parameterization functions provided here,
it is up to you to decide whether the results are good enough for your
use cases or not.

.. _filter_fft_generation:

Generating FFT
--------------

.. testsetup::

>>> import os
>>> from astropy.utils.data import get_pkg_data_filename
>>> filename = get_pkg_data_filename(
... os.path.join('data', 'hst_acs_hrc_f555w.fits'),
... package='synphot.tests')

You could parameterize a given filter using
:func:`~synphot.filter_parameterization.filter_to_fft` as follows.
By default, 10 FFT parameters are returned as complex numbers::

>>> from synphot import SpectralElement
>>> from synphot.filter_parameterization import filter_to_fft
>>> filename = 'hst_acs_hrc_f555w.fits' # doctest: +SKIP
>>> bp = SpectralElement.from_file(filename)
>>> n_lambda, lambda_0, delta_lambda, tr_max, fft_pars = filter_to_fft(bp)
>>> n_lambda # Number of elements in wavelengths
10000
>>> lambda_0 # Starting value of wavelengths # doctest: +FLOAT_CMP
<Quantity 3479.999 Angstrom>
>>> delta_lambda # Median wavelength separation # doctest: +FLOAT_CMP
<Quantity 0.66748047 Angstrom>
>>> tr_max # Peak value of throughput # doctest: +FLOAT_CMP
<Quantity 0.241445>
>>> fft_pars # FFT parameters # doctest: +SKIP
[(407.5180314841658+7.494005416219807e-16j),
(-78.52240189503877-376.53990235136575j),
(-294.86589196496584+127.25464850352665j),
(130.20273803287864+190.84263652863257j),
(96.62299079012317-91.70087676328245j),
(-32.572468348727654-34.227696019221035j),
(-8.051741476066471-21.354793540998294j),
(-51.708676896903725+6.883836090870033j),
(13.08719675518801+54.48177212720124j),
(38.635087381362396-13.02803811279449j)]

.. TODO: Only skipping the fft_pars comparison above because output is very
different for NUMPY_LT_1_17. Unskip it and replace with +FLOAT_CMP when
Numpy minversion is 1.17.
It is up to you to decide how to store this data, though storing it in a
table format is recommended. In fact, if you have many filters to parameterize,
:func:`~synphot.filter_parameterization.filters_to_fft_table`
will store the results in a table for you::

>>> from synphot.filter_parameterization import filters_to_fft_table
>>> mapping = {'HST/ACS/HRC/F555W': (bp, None)}
>>> filter_pars_table = filters_to_fft_table(mapping)
>>> filter_pars_table # doctest: +SKIP
<Table length=1>
filter n_lambda ... fft_9
...
str17 int... ... complex128
----------------- -------- ... ---------------------------------------
HST/ACS/HRC/F555W 10000 ... (38.635087381362396-13.02803811279449j)
>>> filter_pars_table.write('my_filter_pars.fits') # doctest: +SKIP

.. TODO: Only skipping the filter_pars_table comparison above because output
is slightly different for NUMPY_LT_1_17. Unskip it and replace with
+FLOAT_CMP +ELLIPSIS when Numpy minversion is 1.17.
.. _filter_fft_construction:

Reconstructing Filter from FFT
------------------------------

Once you have a parameterized filter (see :ref:`filter_fft_generation`),
you can reconstruct it for use using
:func:`~synphot.filter_parameterization.filter_from_fft`.
Following from the example above::

>>> from synphot.filter_parameterization import filter_from_fft
>>> reconstructed_bp = filter_from_fft(
... n_lambda, lambda_0, delta_lambda, tr_max, fft_pars)

For this particular example using HST ACS/HRC F555W filter, perhaps 10
parameters are not quite sufficient. Therefore, caution needs to be exercised
if you opt to parameterize your filters using this method.

.. plot::

import os
import matplotlib.pyplot as plt
from astropy.utils.data import get_pkg_data_filename
from synphot import SpectralElement
from synphot.filter_parameterization import filter_to_fft, filter_from_fft
filename = get_pkg_data_filename(
os.path.join('data', 'hst_acs_hrc_f555w.fits'),
package='synphot.tests')
bp = SpectralElement.from_file(filename)
fit_result = filter_to_fft(bp)
reconstructed_bp = filter_from_fft(*fit_result)
w = bp.waveset
plt.plot(w, bp(w), 'b-', label='Original')
plt.plot(w, reconstructed_bp(w), 'r--', label='Reconstructed')
plt.xlim(3500, 8000)
plt.xlabel('Wavelength (Angstrom)')
plt.ylabel('Throughput')
plt.title('HST ACS/HRC F555W')
plt.legend(loc='upper right', numpoints=1)
29 changes: 29 additions & 0 deletions licenses/TYNT_LICENSE.txt
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BSD 3-Clause License

Copyright (c) 2019, Brett M. Morris
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
1 change: 1 addition & 0 deletions setup.cfg
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Expand Up @@ -61,6 +61,7 @@ docs =
[options.package_data]
synphot.tests = data/*
synphot.filter_parameterization.tests = data/*
[flake8]
# Ignoring these for now:
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6 changes: 4 additions & 2 deletions synphot/compat.py
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"""Module to handle backward-compatibility."""

import astropy
import numpy
from astropy.utils.introspection import minversion

try:
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else:
HAS_DUST_EXTINCTION = True


__all__ = ['ASTROPY_LT_5_0', 'ASTROPY_LT_4_3', 'ASTROPY_LT_4_1',
'ASTROPY_LT_4_0', 'HAS_SPECUTILS', 'HAS_DUST_EXTINCTION']
'ASTROPY_LT_4_0', 'NUMPY_LT_1_17', 'HAS_SPECUTILS',
'HAS_DUST_EXTINCTION']

ASTROPY_LT_5_0 = not minversion(astropy, '4.99') # astropy<5 but includes 5.0.dev # noqa
ASTROPY_LT_4_3 = not minversion(astropy, '4.3')
ASTROPY_LT_4_1 = not minversion(astropy, '4.1')
ASTROPY_LT_4_0 = not minversion(astropy, '4.0')
NUMPY_LT_1_17 = not minversion(numpy, '1.17')
9 changes: 9 additions & 0 deletions synphot/filter_parameterization/__init__.py
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"""This subpackage handles filter parameterization.
The algorithms in this subpackage were originally developed by
Brett Morris as part of the `tynt <https://github.com/bmorris3/tynt>`_
package.
"""

from .filter_fft import * # noqa
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