{
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        "author": "Danilo Horta",
        "author_email": "horta@ebi.ac.uk",
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        "classifiers": [
            "Development Status :: 5 - Production/Stable",
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            "License :: OSI Approved :: MIT License",
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        "description": "# pandas-plink\n\n[![Travis](https://img.shields.io/travis/com/limix/pandas-plink.svg?style=flat-square&label=linux%20%2F%20macos%20build)](https://travis-ci.com/limix/pandas-plink) [![AppVeyor](https://img.shields.io/appveyor/ci/Horta/pandas-plink.svg?style=flat-square&label=windows%20build)](https://ci.appveyor.com/project/Horta/pandas-plink) [![Documentation](https://img.shields.io/readthedocs/pandas-plink.svg?style=flat-square&version=stable)](https://pandas-plink.readthedocs.io/)\n\nPandas-plink is a Python package for reading [PLINK binary file format](https://www.cog-genomics.org/plink2/formats) and (since version 2.0.0) PLINK and GCTA realized relationship matrices.\nThe file reading is taken place via [lazy loading](https://en.wikipedia.org/wiki/Lazy_loading), meaning that it saves up memory by actually reading only the genotypes that are actually accessed by the user.\n\nNotable changes can be found at the [CHANGELOG.md](CHANGELOG.md).\n\n## Install\n\nIt be installed using [pip](https://pypi.python.org/pypi/pip):\n\n```bash\npip install pandas-plink\n```\n\nAlternatively it can be intalled via [conda](http://conda.pydata.org/docs/index.html):\n\n```bash\nconda install -c conda-forge pandas-plink\n```\n\n## Usage\n\nIt is as simple as\n\n```python\n>>> from pandas_plink import read_plink1_bin\n>>> G = read_plink1_bin(\"chr11.bed\", \"chr11.bim\", \"chr11.fam\", verbose=False)\n>>> print(G)\n<xarray.DataArray 'genotype' (sample: 14, variant: 779)>\ndask.array<shape=(14, 779), dtype=float64, chunksize=(14, 779)>\nCoordinates:\n  * sample   (sample) object 'B001' 'B002' 'B003' ... 'B012' 'B013' 'B014'\n  * variant  (variant) object '11_316849996' '11_316874359' ... '11_345698259'\n    father   (sample) <U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'\n    fid      (sample) <U4 'B001' 'B002' 'B003' 'B004' ... 'B012' 'B013' 'B014'\n    gender   (sample) <U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'\n    i        (sample) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n    iid      (sample) <U4 'B001' 'B002' 'B003' 'B004' ... 'B012' 'B013' 'B014'\n    mother   (sample) <U1 '0' '0' '0' '0' '0' '0' ... '0' '0' '0' '0' '0' '0'\n    trait    (sample) <U2 '-9' '-9' '-9' '-9' '-9' ... '-9' '-9' '-9' '-9' '-9'\n    a0       (variant) <U1 'C' 'G' 'G' 'C' 'C' 'T' ... 'T' 'A' 'C' 'A' 'A' 'T'\n    a1       (variant) <U1 'T' 'C' 'C' 'T' 'T' 'A' ... 'C' 'G' 'T' 'G' 'C' 'C'\n    chrom    (variant) <U2 '11' '11' '11' '11' '11' ... '11' '11' '11' '11' '11'\n    cm       (variant) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n    pos      (variant) int64 157439 181802 248969 ... 28937375 28961091 29005702\n    snp      (variant) <U9 '316849996' '316874359' ... '345653648' '345698259'\n>>> print(G.sel(sample=\"B003\", variant=\"11_316874359\").values)\n0.0\n>>> print(G.a0.sel(variant=\"11_316874359\").values)\nG\n>>> print(G.sel(sample=\"B003\", variant=\"11_316941526\").values)\n2.0\n>>> print(G.a1.sel(variant=\"11_316941526\").values)\nC\n```\nPortions of the genotype will be read as the user access them.\n\nCovariance matrices can also be read very easily.\nExample:\n\n```python\n>>> from pandas_plink import read_rel\n>>> K = read_rel(\"plink2.rel.bin\")\n>>> print(K)\n<xarray.DataArray (sample_0: 10, sample_1: 10)>\narray([[ 0.885782,  0.233846, -0.186339, -0.009789, -0.138897,  0.287779,\n         0.269977, -0.231279, -0.095472, -0.213979],\n       [ 0.233846,  1.077493, -0.452858,  0.192877, -0.186027,  0.171027,\n         0.406056, -0.013149, -0.131477, -0.134314],\n       [-0.186339, -0.452858,  1.183312, -0.040948, -0.146034, -0.204510,\n        -0.314808, -0.042503,  0.296828, -0.011661],\n       [-0.009789,  0.192877, -0.040948,  0.895360, -0.068605,  0.012023,\n         0.057827, -0.192152, -0.089094,  0.174269],\n       [-0.138897, -0.186027, -0.146034, -0.068605,  1.183237,  0.085104,\n        -0.032974,  0.103608,  0.215769,  0.166648],\n       [ 0.287779,  0.171027, -0.204510,  0.012023,  0.085104,  0.956921,\n         0.065427, -0.043752, -0.091492, -0.227673],\n       [ 0.269977,  0.406056, -0.314808,  0.057827, -0.032974,  0.065427,\n         0.714746, -0.101254, -0.088171, -0.063964],\n       [-0.231279, -0.013149, -0.042503, -0.192152,  0.103608, -0.043752,\n        -0.101254,  1.423033, -0.298255, -0.074334],\n       [-0.095472, -0.131477,  0.296828, -0.089094,  0.215769, -0.091492,\n        -0.088171, -0.298255,  0.910274, -0.024663],\n       [-0.213979, -0.134314, -0.011661,  0.174269,  0.166648, -0.227673,\n        -0.063964, -0.074334, -0.024663,  0.914586]])\nCoordinates:\n  * sample_0  (sample_0) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'\n  * sample_1  (sample_1) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'\n    fid       (sample_1) object 'HG00419' 'HG00650' ... 'NA20508' 'NA20753'\n    iid       (sample_1) object 'HG00419' 'HG00650' ... 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