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Merge pull request #257 from pllim/tynt-poc
Initial port of tynt to filter_parameterization subpackage
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.. _synphot_par_filters: | ||
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Parameterized Filters | ||
===================== | ||
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.. note:: | ||
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The algorithm for parameterized filters here was originally developed by | ||
Brett Morris for the `tynt <https://github.com/bmorris3/tynt/>`_ package. | ||
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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. | ||
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.. _filter_fft_generation: | ||
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Generating FFT | ||
-------------- | ||
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.. testsetup:: | ||
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>>> 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') | ||
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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:: | ||
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>>> 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)] | ||
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.. 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:: | ||
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>>> 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 | ||
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.. 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: | ||
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Reconstructing Filter from FFT | ||
------------------------------ | ||
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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:: | ||
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>>> from synphot.filter_parameterization import filter_from_fft | ||
>>> reconstructed_bp = filter_from_fft( | ||
... n_lambda, lambda_0, delta_lambda, tr_max, fft_pars) | ||
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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. | ||
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.. plot:: | ||
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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) |
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BSD 3-Clause License | ||
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Copyright (c) 2019, Brett M. Morris | ||
All rights reserved. | ||
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Redistribution and use in source and binary forms, with or without | ||
modification, are permitted provided that the following conditions are met: | ||
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1. Redistributions of source code must retain the above copyright notice, this | ||
list of conditions and the following disclaimer. | ||
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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. | ||
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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. | ||
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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. |
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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. | ||
""" | ||
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from .filter_fft import * # noqa |
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