https://github.com/astro-informatics/darkmappy
Scalable mapping of the dark universe
https://github.com/astro-informatics/darkmappy
bayesian dark-matter harmonic-analysis inverse-problems signal-processing
Last synced: 5 months ago
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Scalable mapping of the dark universe
- Host: GitHub
- URL: https://github.com/astro-informatics/darkmappy
- Owner: astro-informatics
- License: gpl-3.0
- Created: 2021-12-20T08:59:38.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-03-02T16:09:53.000Z (over 3 years ago)
- Last Synced: 2025-01-16T15:09:16.660Z (5 months ago)
- Topics: bayesian, dark-matter, harmonic-analysis, inverse-problems, signal-processing
- Language: Python
- Homepage: https://astro-informatics.github.io/DarkMappy/
- Size: 24.1 MB
- Stars: 2
- Watchers: 3
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
- License: LICENSE.txt
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README
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|logo| DarkMappy: mapping the dark universe
=================================================================================================================
.. |logo| raw:: html
``darkmappy`` is a lightweight python package which implements the hybrid Bayesian dark-matter reconstruction techniques
outlined on the plane in `Price et al. 2019 `_, and on the celestial sphere in `Price et al. 2021 `_. For comparison (and as initilaisiation for our iterations) the spherical Kaiser-Squires estimator of the convergence is implemented (see `Wallis et al. 2021 `_). These techniques are based on *maximum a posteriori* estimation which, by construction, support principled uncertainty quantification, see `Pereyra 2016 `_. Further examples of such uncertainty quantification techniques developed for the weak lensing setting can be found in related articles `Price et al. 2019a `_ and `Price et al. 2019b `_.
INSTALLATION
============
``darkmappy`` can be installed through PyPi by running
.. code-block:: bash
pip install darkmappy
or alternatively from source by running the following
.. code-block:: bash
git clone https://github.com/astro-informatics/DarkMappy.git
cd DarkMappy
bash build_darkmappy.sh
following which the test suite can be executed by running
.. code-block:: bash
pytest --black darkmappy/tests
BASIC USAGE
===========
For planar reconstructions across the flat-sky the estimator can be run by the following, note that images must be square.
.. code-block:: python
import numpy as np
import darkmappy.estimators as dm
# LOAD YOUR DATA
data = np.load()
ngal = np.load()
mask = np.load()
# BUILD THE ESTIMATOR
dm_estimator = dm.DarkMappyPlane(
n = n, # Dimension of image
data = data, # Observed shear field
mask = mask, # Observational mask
ngal = ngal, # Map of number density of observations per pixel
wav = [], # see https://tinyurl.com/mrxeat3t
levels = level, # Wavelet levels
supersample = supersample) # Super-resolution factor (typically <~2)
# RUN THE ESTIMATOR
convergence, diagnostics = dm_estimator.run_estimator()
For spherical reconstructions across the full-sky the estimator can be run by the following, note images must be of dimension L by 2L-1, see `McEwen & Wiaux 2011 `_.
.. code-block:: python
import numpy as np
import darkmappy.estimators as dm
# LOAD YOUR DATA
data = np.load()
ngal = np.load()
mask = np.load()
# BUILD THE ESTIMATOR
dm_estimator = dm.DarkMapperSphere(
L = L, # Angular Bandlimit
N = N, # Azimuthal Bandlimit (wavelet directionality)
data = data, # Observational shear data
mask = mask, # Observation mask
ngal = ngal) # Map of number density of observations per pixel
# RUN THE ESTIMATOR
convergence, diagnostics = dm_estimator.run_estimator()
CONTRIBUTORS
============
`Matthew A. Price `_, `Jason D. McEwen `_ & Contributors
ATTRIBUTION
===========
A BibTeX entry for ``darkmappy`` is:
.. code-block::
@article{price:2021:spherical,
title = {Sparse Bayesian mass-mapping with uncertainties: Full sky observations on the celestial sphere},
author = {M.~A.~Price and J.~D.~McEwen and L.~Pratley and T.~D.~Kitching},
journal = {Monthly Notices of the Royal Astronomical Society},
year = 2021,
month = jan,
volume = {500},
number = {4},
pages = {5436-5452},
doi = {10.1093/mnras/staa3563},
publisher = {Oxford University Press}
}
.. code-block::
@article{price:2021:hypothesis,
title = {Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure},
author = {M.~A.~Price and J.~D.~McEwen and X.~Cai and T.~D.~Kitching and C.~G.~R.~Wallis and {LSST Dark Energy Science Collaboration}},
journal = {Monthly Notices of the Royal Astronomical Society},
year = 2021,
month = jul,
volume = {506},
number = {3},
pages = {3678--3690},
doi = {10.1093/mnras/stab1983},
publisher = {Oxford University Press}
}
If, at any point, the direction inverse functionality (i.e. spherical Kaiser-Squires) please cite
.. code-block::
@article{wallis:2021:massmappy,
title = {Mapping dark matter on the celestial sphere with weak gravitational lensing},
author = {C.~G.~R.~Wallis and M.~A.~Price and J.~D.~McEwen and T.~D.~Kitching and B.~Leistedt and A.~Plouviez},
journal = {Monthly Notices of the Royal Astronomical Society},
year = 2021,
month = Nov,
volume = {509},
number = {3},
pages = {4480-4497},
doi = {10.1093/mnras/stab3235},
publisher = {Oxford University Press}
}
Finally, if uncertainty quantification techniques which rely on the approximate level-set threshold (derived by `Pereyra 2016 `_) are performed please consider citing relating articles appropriately.
LICENSE
=======
``darkmappy`` is released under the GPL-3 license (see `LICENSE.txt `_).
.. code-block::
DarkMappy
Copyright (C) 2022 Matthew A. Price, Jason D. McEwen & contributors
This program is released under the GPL-3 license (see LICENSE.txt).
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.