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darkmappy-0.1.0
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مشاهده بیشترتوضیحات
Scalable hybrid Bayesian dark-matter reconstruction algorithms
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | darkmappy-0.1.0 |
| نام | darkmappy |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Matthew A. Price, Jason D. McEwen & Contributors |
| ایمیل نویسنده | m.price.17@ucl.ac.uk |
| آدرس صفحه اصلی | https://github.com/astro-informatics/DarkMappy |
| آدرس اینترنتی | https://pypi.org/project/darkmappy/ |
| مجوز | GNU General Public License v3 (GPLv3) |
DarkMappy: mapping the dark universe
=================================================================================================================
.. image:: https://img.shields.io/badge/GitHub-DarkMappy-brightgreen.svg?style=flat
:target: https://github.com/astro-informatics/DarkMappy
.. image:: https://github.com/astro-informatics/DarkMappy/actions/workflows/python.yml/badge.svg?branch=main
:target: https://github.com/astro-informatics/DarkMappy/actions/workflows/python.yml
.. image:: https://readthedocs.org/projects/ansicolortags/badge/?version=latest
:target: https://astro-informatics.github.io/DarkMappy
.. image:: https://codecov.io/gh/astro-informatics/DarkMappy/branch/main/graph/badge.svg?token=A5ogGPslpU
:target: https://codecov.io/gh/astro-informatics/DarkMappy
.. image:: https://img.shields.io/badge/License-GPL-blue.svg
:target: http://perso.crans.org/besson/LICENSE.html
.. image:: http://img.shields.io/badge/arXiv-2004.07855-orange.svg?style=flat
:target: https://arxiv.org/abs/2004.07855
.. image:: http://img.shields.io/badge/arXiv-1812.04014-orange.svg?style=flat
:target: https://arxiv.org/abs/1812.04014
``darkmappy`` is a lightweight python package which implements the hybrid Bayesian dark-matter reconstruction techniques
outlined on the plane in `Price et al. 2019 <https://academic.oup.com/mnras/article-abstract/506/3/3678/6319513>`_, and on the celestial sphere in `Price et al. 2021 <https://academic.oup.com/mnras/article/500/4/5436/5986632>`_. For comparison (and as initilaisiation for our iterations) the spherical Kaiser-Squires estimator of the convergence is implemented (see `Wallis et al. 2021 <https://academic.oup.com/mnras/article-abstract/509/3/4480/6424933>`_). These techniques are based on *maximum a posteriori* estimation which, by construction, support principled uncertainty quantification, see `Pereyra 2016 <https://epubs.siam.org/doi/10.1137/16M1071249>`_. Further examples of such uncertainty quantification techniques developed for the weak lensing setting can be found in related articles `Price et al. 2019a <https://academic.oup.com/mnras/article/489/3/3236/5554769>`_ and `Price et al. 2019b <https://academic.oup.com/mnras/article/492/1/394/5672642>`_.
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(<path_to_shear_data>)
ngal = np.load(<path_to_ngal_per_pixel_map>)
mask = np.load(<path_to_observation_mask>)
# 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 = [<select_wavelets>], # 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 <https://ieeexplore.ieee.org/document/6006544>`_.
.. code-block:: python
import numpy as np
import darkmappy.estimators as dm
# LOAD YOUR DATA
data = np.load(<path_to_shear_data>)
ngal = np.load(<path_to_ngal_per_pixel_map>)
mask = np.load(<path_to_observation_mask>)
# 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 <https://cosmomatt.github.io>`_, `Jason D. McEwen <http://www.jasonmcewen.org>`_ & 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 <https://epubs.siam.org/doi/10.1137/16M1071249>`_) are performed please consider citing relating articles appropriately.
LICENSE
=======
``darkmappy`` is released under the GPL-3 license (see `LICENSE.txt <https://github.com/astro-informatics/DarkMappy/blob/main/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.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | colorlog |
| - | pyyaml |
| - | pys2let |
| - | pyssht |
| - | optimusprimal |
نحوه نصب
نصب پکیج whl darkmappy-0.1.0:
pip install darkmappy-0.1.0.whl
نصب پکیج tar.gz darkmappy-0.1.0:
pip install darkmappy-0.1.0.tar.gz