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        "author": "Rohin Kumar Y",
        "author_email": "yrohinkumar@gmail.com",
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        "description": "correlcalc\n==========\n\nA Python package to calculate 2-point correlation function(2pCF) from\ngalaxy redshift surveys for any generic model of Cosmology or geometry.\n\nSummary\n-------\n\ncorrelcalc calculates two-point correlation function (2pCF) of\ngalaxies/quasars using redshift surveys. It can be used for any assumed\ngeometry or Cosmology model. Using BallTree algorithms to reduce the\ncomputational effort for large datasets, it is faster than brute-force\nmethods. It takes redshift (z), Right Ascension (RA) and Declination\n(DEC) data of galaxies and random catalogs given by redshift survey as\ninputs. If random catalog is not provided, it generates one of desired\nsize based on the input redshift distribution and a mangle polygon file\nin .ply format describing the survey geometry. It also calculates\nanisotropic 2pCF. Optionally it makes healpix maps of the survey\nproviding visualization.\n\nInstallation\n------------\n\nTo install this package type \"``pip install correlcalc``\" in your\nterminal. If this method doesn't work\n\nTo install the package Download this git repositry and in terminal enter\nthe folder that contains setup.py and type \"``pip install .``\" or\n\"``python setup.py install``\"\n\nIf you do not have root permission, you can install by adding\n\"``--user``\" at the end of above commands\n\nIf you have an older version installed already you can upgrade by\n\"``pip install correlcalc --upgrade``\" command\n\nA note on Dependencies:\n~~~~~~~~~~~~~~~~~~~~~~~\n\nAll the required dependencies such as sklearn, cython, scipy, numpy etc.\nshould get automatically installed if installed through pip. In case, if\nsome of the dependencies do not automatically get installed. The list of\ndependencies can be seen in the setup.py file to manually install them.\nIn case of any problems feel free to raise an issue. \"healpix\\_util\"\npackage from http://github.com/esheldon/healpix\\_util is not available\non pip. So it needs to be manually installed following the commands to\ninstall from git repositry in the above section\n\nTheory\n------\n\nThe algorithm and formulae used are presented in the paper entitled *A\n\\`Generic' Recipe for Quick Computation of Two-point Correlation\nfunction*\n\nIt is available on arXiv:1710.01723 at https://arxiv.org/abs/1710.01723.\n\nPlease cite the same if you use this package or the 'recipe' presented\nherein\n\nUsage\n-----\n\nCalculation of 2pCF\n~~~~~~~~~~~~~~~~~~~\n\nUsage of the package is given in jupyter notebook \"Using correlcalc\nexample.nb\" and in ``main.py``\n\nAll the methods in correlcalc can be imported using the following\ncommand\n\n``from correlcalc import *``\n\nWe first need to define bins (in :math:`c/H_0` units) to calculate 2pCF.\nFor e.g. to calculate correlation between 0-180Mpc in steps of 6Mpc, we\nsay\n\n``bins=np.arange(0.002,0.06,0.002)``\n\nTo calculate 2pCF using input data file (both ascii and fits files are\nsupported), use ``tpcf`` method as follows\n\n``correl, poserr=tpcf('/path/to/datfile.dat',bins, randfile='/path/to/randomfile.dat', weights='eq')``\n\nIf random file is not available or not provided, we can generate random\ncatalog by providing the mangle mask file in ``.ply`` format along with\nspecifying the size of the catalog in multiples of size of data catalog\n(default 2x size). To do this\n\n``correl, poserr=tpcf('/path/to/datfile.dat', bins, maskfile='/path/to/maskfile.ply', weights=True, randfact=3)``\n\nThis returns ``correl`` and ``poserr`` ``numpy`` arrays corresponding to\nTwo-point correlation and Poisson error\n\nKeyword Arguments\n~~~~~~~~~~~~~~~~~\n\nThe following keyword arguments can be included as needed\n\nData file (Mandatory)\n^^^^^^^^^^^^^^^^^^^^^\n\nData file of galaxy/quasar redshift survey must be passed as the first\nargument to both ``tpcf`` and ``atpcf`` methods.\n\n**Supported filetypes**: ascii text files with columns, csv files or\nfits files are all supported. Most files provided by SDSS Value added\ncatalogs should be directly usable.\n\n**To contain**: Any type of file provided must at least have columns\nnamed **Z** (redshift), **RA** (Right Ascension), **DEC** (Declination).\nThese column names can be in any case.\n\nIf one intends to use ``weights=True`` option (must to obtain accurate\nresults) the data file must also contain radial weights with column\ntitle **radial\\_weight** or **WEIGHT\\_SYSTOT**\n\nbins (Mandatory)\n^^^^^^^^^^^^^^^^\n\nA numpy array with ascending values in :math:`c/H_0` units must be\nprovided as the second argument to both ``tpcf`` and ``atpcf`` methods.\nIn case of ``atpcf`` it automatically creates 2D bins as\n``bins2d=(bins,bins)`` from provided 1D ``bins``\n\n``randfile=`` Path to random file (semi-Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf not provided, ``maskfile=`` argument must be given ``.ply`` file.\n\n**Supported filetypes**: ascii text files with columns, csv files or\nfits files are all supported. Most files provided by SDSS Value added\ncatalogs should be directly usable.\n\n**To contain**: Any type of file provided must at least have columns\nnamed **Z** (redshift), **RA** (Right Ascension), **DEC** (Declination).\nThese column names can be in any case.\n\nIf one intends to use ``weights=True`` option (must to obtain accurate\nresults) the data file must also contain radial weights with column\ntitle **radial\\_weight** or **WEIGHT\\_SYSTOT**\n\n**Beta Testing:** Beta support for other column titles for weights is\nadded.\n\nAlso added is calculation of weights from n(z) during random catalog\ngeneration.\n\n``mask=`` Path to mangle polygon file (semi-Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf not provided, ``randfile=`` argument must be provided.\n\n**Supported filetypes**: ``.ply`` file containing Mangle polygons\ndescribing survey geometry in the standard format. Most files provided\nby SDSS Value added catalogs should be directly usable.\n\n``randfact=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nSize of the random catalog in integer multiples of size of data catalog\nif random catalog file is not provided. Default value is ``2``\n\n``weights=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^\n\nIt is highly recommended to use weights argument by providing\n``weights=True`` or ``weights='eq'`` to obtain accurate two-point\ncorrelation calculations. This picks up radial weights in the prescribed\nformat (with column title **radial\\_weight** or **WEIGHT\\_SYSTOT** )\nfrom the data and random files provided.\n\n``weights=``\\ eq'\\ ``sets equal weights and hence adds *+1* - This implementation is parallelized and is faster than``\\ weights=False\\`\nimplementation on most machines\n\nIf ``weights=False``, by default *+1* will be added for each\ngalaxy/random pair found within the bin instead of adding total weight.\nFor more details on weights and references, see\nhttp://www.sdss3.org/dr9/tutorials/lss\\_galaxy.php\n\n``geometry='flat'`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**Available options**:\n\n``'flat'``\\ (default) - for flat geometry of the Universe\n\n``'open'`` - for Open Universe models like Milne\n\n``'close'`` - for Closed Universe\n\n**Customization**\n\nFormulae for calculation of distances between two points (Z1, RA1, DEC1)\nand (Z2, RA2, DEC2) is taken from *T. Matsubara, Correlation function in\ndeep redshift space as a cosmological probe, The Astrophysical Journal\n615 (2) (2004) 573*. Using the formulae in this paper, distances squares\n(to reduce additional computational time distance squares are calculated\nto avoid using expensive ``sqrt`` function every time) are computed in\nthe ``metrics.pyx`` file for all the above mentioned geometries.\n``Cython`` is chosen for implementation to obtain faster results in\nbuilding ``BallTree``\\ s calculating ``cdist`` and to reduce ``query``\ntime.\n\nOne can customize metric definitions as per one's need by editing this\nfile. Also **K** (curvature parameter) in the formulae given in this\nreference need to be manually changed in the ``metrics.pyx`` for closed\nand open cases as per the model. After changing this compile it using\n``python metricsetup.py build_ext --inplace``\n\n``cosmology='lcdm'`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nUsed to calculate co-moving distances from redshifts.\n\n**Available options**:\n\n``'lcdm'`` (default)- for Lambda CDM model\n\n``'lc'`` - for :math:`R_h=ct` and linear coasting models\n\n**To add**: ``wcdm`` and other popular cosmology models soon\n\n``estimator=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**Available options**:\n\n``'dp'`` - Davis - Peebles estimator (default - fastest)\n\n``'ls'``- Landy - Szalay estimator\n\n``'ph'`` - Peebles- Hauser estimator\n\n``'hew'`` - Hewitt estimator\n\n``'h'`` - Hamilton estimator\n\nFor more details on estimator formulae see\nhttps://arxiv.org/pdf/1211.6211.pdf\n\nCalculation of Anisotropic (3D) 2pCF\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nUsage of the package is given in jupyter notebook \"Using correlcalc\nexample-anisotropic.nb\" and in ``main.py``\n\nAll the methods in correlcalc can be imported using the following\ncommand\n\n``from correlcalc import *``\n\nWe first need to define bins (in :math:`c/H_0` units) to calculate 2pCF.\nFor e.g. to calculate correlation between 0-180Mpc in steps of 6Mpc, we\nsay\n\n``bins=np.arange(0.002,0.06,0.002)``\n\nTo calculate anisotropic 2pCF using input data file (both ascii and fits\nfiles are supported), use ``atpcf`` method as follows\n\n``correl3d, poserr=atpcf('/path/to/datfile.dat',binspar, binsper, randfile='/path/to/randomfile.dat', vtype='sigpi', weights=True)``\n\nIf random file is not available or not provided, we can generate random\ncatalog by providing the mangle mask file in ``.ply`` format along with\nspecifying the size of the catalog in multiples of size of data catalog\n(default 2x size). To do this\n\n``correl3d, poserr=atpcf('/path/to/datfile.dat', binspar, binsper, maskfile='/path/to/maskfile.ply', vtype='smu', weights='eq', randfact=3)``\n\nThis returns ``correl3d`` and ``poserr`` ``numpy`` arrays corresponding\nto anisotropic Two-point correlation and Poisson error\n\nKeyword Arguments\n~~~~~~~~~~~~~~~~~\n\nThe following keyword arguments can be included as needed\n\nData file (Mandatory)\n^^^^^^^^^^^^^^^^^^^^^\n\nData file of galaxy/quasar redshift survey must be passed as the first\nargument to both ``tpcf`` and ``atpcf`` methods.\n\n**Supported filetypes**: ascii text files with columns, csv files or\nfits files are all supported. Most files provided by SDSS Value added\ncatalogs should be directly usable.\n\n**To contain**: Any type of file provided must at least have columns\nnamed **Z** (redshift), **RA** (Right Ascension), **DEC** (Declination).\nThese column names can be in any case.\n\nIf one intends to use ``weights=True`` option (must to obtain accurate\nresults) the data file must also contain radial weights with column\ntitle **radial\\_weight** or **WEIGHT\\_SYSTOT**\n\nbinspar (Mandatory)\n^^^^^^^^^^^^^^^^^^^\n\nA numpy array with ascending values in :math:`c/H_0` units (for\ndistances) or :math:`\\delta z` as per choice of ``'vtype'`` must be\nprovided as the second argument to ``atpcf`` method.\n\nbinsper (Mandatory)\n^^^^^^^^^^^^^^^^^^^\n\nA numpy array with ascending values in :math:`c/H_0` units (for\ndistances), :math:`z\\delta \\theta` or :math:`\\mu = \\cos \\alpha` must be\nprovided as the third argument to ``atpcf`` method.\n\n``randfile=`` Path to random file (semi-Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf not provided, ``maskfile=`` argument must be given ``.ply`` file.\n\n**Supported filetypes**: ascii text files with columns, csv files or\nfits files are all supported. Most files provided by SDSS Value added\ncatalogs should be directly usable.\n\n**To contain**: Any type of file provided must at least have columns\nnamed **Z** (redshift), **RA** (Right Ascension), **DEC** (Declination).\nThese column names can be in any case.\n\nIf one intends to use ``weights=True`` option the data file must also\ncontain radial weights with column title **radial\\_weight** or\n**WEIGHT\\_SYSTOT**\n\n**Beta Testing:** Beta support for other column titles for weights is\nadded.\n\nAlso added is calculation of weights from n(z) during random catalog\ngeneration.\n\n``mask=`` Path to mangle polygon file (semi-Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf not provided, ``randfile=`` argument must be provided.\n\n**Supported filetypes**: ``.ply`` file containing Mangle polygons\ndescribing survey geometry in the standard format. Most files provided\nby SDSS Value added catalogs should be directly usable.\n\n``randfact=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nSize of the random catalog in integer multiples of size of data catalog\nif random catalog file is not provided. Default value is ``2``\n\n``weights=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^\n\nIt is highly recommended to use weights argument by providing\n``weights=True`` or ``weights='eq'`` to obtain accurate two-point\ncorrelation calculations. This picks up radial weights in the prescribed\nformat (with column title **radial\\_weight** or **WEIGHT\\_SYSTOT** )\nfrom the data and random files provided.\n\n``weights=``\\ eq'\\ ``sets equal weights and hence adds *+1* - This implementation is parallelized and is faster than``\\ weights=False\\`\nimplementation on most machines\n\nIf ``weights=False``, by default *+1* will be added for each\ngalaxy/random pair found within the bin instead of adding total weight.\nFor more details on weights and references, see\nhttp://www.sdss3.org/dr9/tutorials/lss\\_galaxy.php\n\nMetrics in parallel and perpendicular directions\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nCalculates anisotropic 2pCF for the following cases.\n\n``vtype=``\n^^^^^^^^^^\n\nValuation method\n\n**Available options**:\n\n``'smu'`` (default)- Calculates 2pCF in s - mu\n\n``'sigpi'`` - Calculates 2pCF using parallel and perpendicular distances\n\n``'ap'`` calculates 2pCF for small :math:`\\Delta \\theta` and\n:math:`z \\Delta\\theta` . But results can be converted to any cosmology\nmodel of choice (ref: https://arxiv.org/pdf/1312.0003.pdf)\n\n**Customization**\n\nFormulae for calculation of distances in parallel and perpendicular\ndirections is taken from https://arxiv.org/pdf/1312.0003.pdf. Using the\nformulae in this paper, :math:`\\Delta z` and :math:`z \\Delta \\theta` are\ncomputed in the ``metrics.pyx`` file for the above mentioned. ``Cython``\nis chosen for implementation to obtain faster results in building\n``BallTree``\\ s calculating ``cdist`` and to reduce ``query`` time.\n\nOne can customize metric definitions as per one's need by editing the\n``metrics.pyx`` file. After changing this compile it using\n``python metricsetup.py build_ext --inplace``\n\n**To add:**\n\nDirect calculation of distances in LOS and perpendicular to the LOS to\nbe added to support standard model Cosmology and other popular models.\nFor now, one needs to manually convert the angular bins to physical\ndistances to get the approximate results\n\n``cosmology='lcdm'`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nUsed to calculate co-moving distances from redshifts.\n\n**Available options**:\n\n``'lcdm'`` (default)- for Lambda CDM model\n\n``'lc'`` - for :math:`R_h=ct` and linear coasting models\n\n**To add**: ``wcdm`` and other popular cosmology models soon\n\n``geometry='flat'`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nUsed to calculate co-moving distances between a pair of objects\n\n**Available options**:\n\n``'flat'`` (default)- for Lambda CDM model\n\n``'open'``\n\n``'close'``\n\n``estimator=`` (Optional)\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\n**Available options**:\n\n``'dp'`` - Davis - Peebles estimator (default - fastest)\n\n``'ls'``- Landy - Szalay estimator\n\n``'ph'`` - Peebles- Hauser estimator\n\n``'hew'`` - Hewitt estimator\n\n``'h'`` - Hamilton estimator\n\nFor more details on estimator formulae see\nhttps://arxiv.org/pdf/1211.6211.pdf\n",
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