D\ :sup:3\ PO

D3PO project homepage: <http://www.mpa-garching.mpg.de/ift/d3po/>_

Summary

Description ...........

The D3PO algorithm addresses the inference problem of D\enoising, D\econvolving, and D\ecomposing P\hoton O\bservations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed.

In order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory <http://www.mpa-garching.mpg.de/ift/> based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. Since the derivation of the solution is not dependent on the underlying position space, the implementation of the D3PO algorithm uses the NIFTY <http://www.mpa-garching.mpg.de/ift/nifty/> package to ensure applicability to various spatial grids and at any resolution.

Parts of this summary are taken from [1]_ without marking them explicitly as quotations.

Installation

Requirements ............

• Python <http://www.python.org/>_ (v2.7.x)

  • NumPy <http://www.numpy.org/> and SciPy <http://www.scipy.org/>
  • matplotlib <http://matplotlib.org/>_
  • multiprocessing <http://docs.python.org/2/library/multiprocessing.html>_ (standard library)

• NIFTY <https://github.com/information-field-theory/nifty>_ (v1.0.6) - Numerical Information Field Theory

Download ........

The latest release is tagged v1.0.1 and is available as a source package at <https://github.com/information-field-theory/d3po/tags>_. The current version can be obtained by cloning the repository::

git clone git://github.com/information-field-theory/d3po.git

Installation ............

• D3PO can be installed using PyPI <https://pypi.python.org/pypi>_ and pip by running the following command::

  pip install ift_d3po

  Alternatively, a private or user specific installation can be done by::

  pip install --user ift_d3po

• D3PO can be installed using Distutils by running the following command::

  cd d3po
  python setup.py install

  Alternatively, a private or user specific installation can be done by::

  python setup.py install --user
  python setup.py install --install-lib=/SOMEWHERE

First Steps ...........

To get started, you can browse through the how-to guide <http://www.mpa-garching.mpg.de/ift/d3po/HOWTO.html>_ or simply run the demonstration::

    >>> run -m d3po.demo

Acknowledgement

Please, acknowledge the use of D3PO in your publication(s) by using a phrase such as the following:

*"Some of the results in this publication have been derived using the D3PO
algorithm [Selig et al., 2014]."*

References ..........

.. [1] Selig et. al., "Denoising, Deconvolving, and Decomposing Photon Observations", accepted by Astronomy & Astrophysics, A&A, vol. 574, id. A74 <http://dx.doi.org/10.1051/0004-6361/201323006>, 2014; arXiv:1311.1888 <http://www.arxiv.org/abs/1311.1888>

Release Notes

The D3PO module is licensed under the GPLv3 <http://www.gnu.org/licenses/gpl.html>_ and is distributed without any warranty.

D3PO project homepage: <http://www.mpa-garching.mpg.de/ift/d3po/>_