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        "author": "Julien Peloton",
        "author_email": "j.peloton@sussex.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": "=============================\ns4cmb (public version)\n=============================\n\n.. image:: https://travis-ci.org/JulienPeloton/s4cmb.svg?branch=master\n    :target: https://travis-ci.org/JulienPeloton/s4cmb\n\n.. image:: https://coveralls.io/repos/github/JulienPeloton/s4cmb/badge.svg\n    :target: https://coveralls.io/github/JulienPeloton/s4cmb\n\n.. raw:: html\n\n    <img src=\"https://github.com/JulienPeloton/s4cmb/blob/master/s4cmb/data/intro.png\" height=\"400px\">\n\n.. contents:: **Table of Contents**\n\nThe package\n===============\nSystematics For Cosmic Microwave Background (s4cmb), is a package to\nstudy instrumental systematic effects in the context of current and future\nCosmic Microwave Background experiments. Currently accessible:\n\n* Electrical crosstalk in the multiplexed readout.\n* Relative gain-calibration uncertainty between the two detectors in a focal plane pixel.\n* Time drift of the gains between two consecutive calibration measurements.\n* Differential pointing between the two detectors in a pixel.\n* ... more to come!\n\nRequirements\n===============\nThe pipeline is mainly written in python and it has the following dependencies (see requirements.txt):\n\n* numpy, matplotlib\n* astropy, ephem, pyslalib, healpy (astro libs)\n* f2py (interfacing with python)\n\nWhile we use python 2.7, we try to make it compatible with python 3.5 and 3.6.\nIf you are using python 3.x and you encounter an error, please open an issue or a\npull request so that we fix it asap.\n\nSome parts of the pipeline are written in Fortran which is interfaced with\npython using f2py. The compilation is done usually when you install the\npackage (see setup.py), but we also provide a Makefile for more\ncustomized compilations (see the Makefile in s4cmb).\n\nInstallation\n===============\n\nThe type of installation depends on what you want to do with the code:\njust using it or also developing it?\n\n**I just want to use the code:**\n\nYou can easily install the package using pip\n\n::\n\n    pip install s4cmb\n\n**In addition to use the code, I want to be a developer:**\n\nThe best is to fork the repo from this github repository to your account and clone it to your machine.\nOnce you have the repo cloned on your machine, use the makefile to compile the source\n\n::\n\n    cd /path/to/s4cmb\n    pip install -r requirements.txt\n    make\n\nDo not forget to update your PYTHONPATH. Just add in your bashrc:\n\n::\n\n    s4cmbPATH=/path/to/the/s4cmb\n    export PYTHONPATH=$PYTHONPATH:$s4cmbPATH\n\nThen run the test suite and the coverage:\n\n::\n\n    ./coverage_and_test.sh\n\nIt should print the actual coverage of the test suite, and exit with no errors.\n\nInstallation and usage at NERSC\n===============\n\nAgain, you can easily install the package using pip\n\n::\n\n    pip install s4cmb --user\n\nAlternatively, if you want to do dev at NERSC and do a manual installation, it's better to keep most of your packages under Anaconda.\nI recommend to have a look first at the `NERSC page <https://www.nersc.gov/users/data-analytics/data-analytics-2/python/anaconda-python/>`_ describing how to use it.\n\nThe installation of s4cmb can be done in few steps:\n\n* Clone the repo somewhere in your $HOME\n* Install dependencies (see requirements.txt) using Anaconda\n* Compile the source (using make in /path/s4cmb)\n\nWorking with Docker\n===============\nAlternatively if you do not want install the package on your computer,\nwe provide a docker image for s4cmb with always the latest version. Install\ndocker on your computer, and pull the image:\n\n::\n\n    docker pull julienpeloton/s4cmb:latest\n\nThen create a new container and run an interactive session by just running\n\n::\n\n    docker run -i -t julienpeloton/s4cmb:latest bash\n\nQuick examples\n===============\nWe provide a quick end-to-end example for using the package:\n\n::\n\n    python examples/test/simple_app.py -inifile examples/inifiles/simple_parameters.py -tag test\n\nYou can also run it on many processors, using MPI (you will need the package mpi4py):\n\n::\n\n    mpirun -n <nproc> python examples/test/simple_app.py -inifile examples/inifiles/simple_parameters.py -tag test_MPI\n\nwhere nproc should not be greater than the number of scans to run.\nNote that for NERSC users, we also provide a quick submission script for jobs on Cori (see examples/nersc_cori.batch).\n\ns4cmb bootcamp\n===============\n\nYou can find a bootcamp in two parts (notebooks + examples) at `s4cmb-resources <https://github.com/JulienPeloton/s4cmb-resources>`_.\nThe goal of this bootcamp is to describe the basic parts of the API, and provide ready-to-use examples (for use on laptop and supercomputer).\n\n\nTODO\n===============\n\n* Add WHWP demodulation module.\n* Add correlated noise simulator (and update mapmaking weights).\n\nMain developers\n===============\n* Julien Peloton (j.peloton at sussex.ac.uk)\n* Giulio Fabbian (gfabbian at ias.u-psud.fr)\n\nThanks to\n===============\n* @ngoecknerwald: original author for a large part of the scanning strategy module.\n* @giuspugl, @dpole, @joydidier, and all `contributors <https://github.com/JulienPeloton/s4cmb/graphs/contributors>`_ for all valuable comments, tests, and feedbacks!\n\nSupport\n===============\n\n.. raw:: html\n\n    <img src=\"https://github.com/JulienPeloton/s4cmb/blob/master/s4cmb/data/LOGO-ERC.jpg\" height=\"200px\">\n",
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