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        "author": "Henry Zhang",
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            "License :: OSI Approved :: MIT License",
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        "description": "# pyBedGraph\nA Python package for fast operations on 1-dimensional genomic signal tracks.\n\n## Features\n- Finds the mean, approx. mean, max, min, coverage, or standard deviation for a given interval in a bedGraph file\n- Partly written in Cython for speed improvements\n- Can look up exact statistics of 1 million regions in ~0.26 second on a conventional laptop\n- An approximate mean for 10,000 regions can be computed in ~0.0012 second w/ error rate of less than 5 percent\n\n## Drawbacks\n- Uses memory to load files\n    - 16 bytes per line in bedGraph file\n    - 4 bytes per basePair in every chromosome loaded\n- Loading the bedGraph file can take up to a minute or two\n- Only works with sorted bedgraph files\n\n## Installation\n\nDependency requirements:\n- Numpy v1.16.4\n- Cython v0.29.13\n- pyBigWig v0.3.16 (for benchmark)\n\nWith pip:\n```bash\npip3 install pyBedGraph\n```\n\n## Usage\n\n### Download the test files here:\nhttps://thejacksonlaboratory.ent.box.com/s/3jglutwf3d54pnomnp33ivo7a9546vhe\n\n### Create the object:\n```python\nfrom pyBedGraph import BedGraph\nfrom logging.config import fileConfig\n\nfileConfig('default.conf')\n\n# arg1 - chromosome sizes file\n# arg2 - bedgraph file\n# arg3 - (optional) chromosome_name\n# Just load chromosome 'chr1' (uses less memory and takes less time)\nbedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph', 'chr1')\n\n# Load the whole bedGraph file\nbedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph')\n\n# Option to not ignore missing basePairs when calculating statistics\n# Used the exact same way but produces slightly different results\ninclusive_bedGraph = BedGraph('myChrom.sizes', 'random_test.bedGraph', ignore_missing_bp=False)\n```\n\n### Choose and load a chromosome to search for:\n```python\nbedGraph.load_chrom_data('chr1')\ninclusive_bedGraph.load_chrom_data('chr1')\n```\n### Load bins for finding mean:\nFor approx_mean:\n1. Smaller bin size -> more accurate but slower\n2. Larger bin size -> less accurate but faster\n```python\nbedGraph.load_chrom_bins('chr1', 3)\ninclusive_bedGraph.load_chrom_bins('chr1', 3)\n```\n### Choose a specific statistic to search for:\n  - `'mean'`\n  - `'approx_mean'` - an approximate mean is faster than exact mean, with < 5% error rate\n  - `'max'`\n  - `'min'`\n  - `'coverage'`\n  - `'std'` - (population standard deviation)\n\n### Search from a list of intervals:\n```python\nimport numpy as np\n\n# Option 1\ntest_intervals = [\n    ['chr1', 24, 26],\n    ['chr1', 12, 15],\n    ['chr1', 8, 12],\n    ['chr1', 9, 10],\n    ['chr1', 0, 5]\n]\nvalues = bedGraph.stats(intervals=test_intervals)\n\n# Option 2\nstart_list = np.array([24, 12, 8, 9, 0], dtype=np.int32)\nend_list = np.array([26, 15, 12, 10, 5], dtype=np.int32)\nchrom_name = 'chr1'\n\n# arg1 - (optional) stat (default is 'mean')\n# arg2 - intervals\n# arg3 - start_list\n# arg4 - end_list\n# arg5 - chrom_name\n# must have either intervals or start_list, end_list, chrom_name\n# returns a numpy array of values\nresult = bedGraph.stats(start_list=start_list, end_list=end_list, chrom_name=chrom_name)\n\n# [-1.    0.9   0.1  -1.    0.82]\nprint(result)\n```\n\n### Search from a file:\n```python\n# arg1 - interval file\n# arg2 - (optional) output_to_file (default is True and outputs to 'chr1_out.txt'\n# arg3 - (optional) stat (default is 'mean')\n# returns a dictionary; keys are chromosome names, values are numpy arrays\nresult = bedGraph.stats_from_file('test_intervals.txt', output_to_file=False, stat='mean')\n\n# {'chr1': array([-1.  ,  0.9 ,  0.1 , -1.  ,  0.82])}\nprint(result)\n```\n\n### Sample Tests (from included test files):\n```python\n# [-1.    0.9   0.1  -1.    0.82]\nbedGraph.stats('mean', test_intervals)\n\n# [-1.          0.9        -1.         -1.          0.76666667]\nbedGraph.stats('approx_mean', test_intervals)\n\n# [0.         0.33333333 0.25       0.         1.        ]\nbedGraph.stats('coverage', test_intervals)\n\n# [-1.   0.9  0.1 -1.   0.7]\nbedGraph.stats('min', test_intervals)\n\n# [-1.   0.9  0.1 -1.   0.9]\nbedGraph.stats('max', test_intervals)\n\n# [-1.          0.          0.         -1.          0.09797959]\nbedGraph.stats('std', test_intervals)\n```\n\n```python\n# [0.    0.3   0.025 0.    0.82 ]\ninclusive_bedGraph.stats('mean', test_intervals)\n\n# [0.         0.3        0.00833333 0.         0.7       ]\ninclusive_bedGraph.stats('approx_mean', test_intervals)\n\n# [0.         0.33333333 0.25       0.         1.        ]\ninclusive_bedGraph.stats('coverage', test_intervals)\n\n# [0.  0.  0.1 0.  0.7]\ninclusive_bedGraph.stats('min', test_intervals)\n\n# [0.  0.9 0.1 0.  0.9]\ninclusive_bedGraph.stats('max', test_intervals)\n\n# [0.         0.42426407 0.04330127 0.         0.09797959]\ninclusive_bedGraph.stats('std', test_intervals)\n```\n\n## Benchmark:\nActual values are found from the `stats` function in pyBigWig with the `exact` argument being `True`. The error for exact stats will be ~1e-8 due to rounding error of conversion of bigWig and bedGraph files.\n\nAlternatively, one can make actual values be pyBedGraph's exact statistics. \n```python\nfrom pyBedGraph import Benchmark, BedGraph\n\nbedGraph = BedGraph('mm10.chrom.sizes', 'ENCFF376VCU.bedGraph', 'chr1')\nbedGraph.load_chrom_data('chr1')\nbedGraph.load_chrom_bins('chr1', 100)\n\n# arg1 - BedGraph object\n# arg2 - bigwig file\nbench = Benchmark(bedGraph, 'ENCFF376VCU.bigWig')\n\n# arg1 - num_tests\n# arg2 - interval_size\n# arg3 - chrom_nam\n# arg4 - bin_size\n# arg5 - stats (optional) (Default is all stats)\n# arg6 - just_runtime (optional) (Default is False)\n# arg6 - bench_pyBigWig_approx (optional) (Default is True)\n# arg6 - make_pyBigWig_baseline (optional) (Default is True)\nresult = bench.benchmark(10000, 500, 'chr1', 100, stats=['mean'])\n\nfor key in result:\n    print(key, result[key])\n# formatted\n# mean {'run_time': 0.002971172332763672, 'error': {'percent_error': 1.1133849453411403e-08, 'ms_error': 1.1558877957200436e-15, 'abs_error': 5.565259658128112e-09, 'not_included': 0}}\n# pyBigWig_mean {'approx_run_time': 0.570319652557373, 'exact_run_time': 0.5670754909515381, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n\n\n# Test all statistics\nresult = bench.benchmark(10000, 500, 'chr1', 100)\n\n# mean {'run_time': 0.0033969879150390625, 'error': {'percent_error': 1.1133849453411403e-08, 'ms_error': 1.1558877957200436e-15, 'abs_error': 5.565259658128112e-09, 'not_included': 0}}\n# pyBigWig_mean {'approx_run_time': 1.4938299655914307, 'exact_run_time': 1.4855470657348633, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n# approx_mean {'run_time': 0.0029401779174804688, 'error': {'percent_error': 0.05871362950772767, 'ms_error': 0.0007750126193535608, 'abs_error': 0.017845196959357015, 'not_included': 107}}\n# max {'run_time': 0.003038167953491211, 'error': {'percent_error': 2.1245231544977356e-08, 'ms_error': 9.128975974031677e-13, 'abs_error': 6.218157096711807e-08, 'not_included': 0}}\n# pyBigWig_max {'approx_run_time': 1.4961540699005127, 'exact_run_time': 1.5022919178009033, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n# min {'run_time': 0.002947092056274414, 'error': {'percent_error': 2.3296755440892273e-10, 'ms_error': 9.931400247350677e-19, 'abs_error': 7.883071898306948e-11, 'not_included': 0}}\n# pyBigWig_min {'approx_run_time': 1.4919359683990479, 'exact_run_time': 1.4932668209075928, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n# coverage {'run_time': 0.002975940704345703, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n# pyBigWig_coverage {'approx_run_time': 1.4844129085540771, 'exact_run_time': 1.5427591800689697, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n# std {'run_time': 0.010123968124389648, 'error': {'percent_error': 0.0008802452423860437, 'ms_error': 3.5123006260771487e-07, 'abs_error': 0.0004987475752671237, 'not_included': 0}}\n# pyBigWig_std {'approx_run_time': 1.5250320434570312, 'exact_run_time': 1.4730277061462402, 'error': {'percent_error': 0.0, 'ms_error': 0.0, 'abs_error': 0.0, 'not_included': 0}}\n```\n\n## Reference \n[pyBedGraph: a Python package for fast operations on 1-dimensional genomic signal tracks](https://www.biorxiv.org/content/10.1101/709683v1), Zhang et al., bioRxiv, 2019\n\n## Bug reports\nTo report bugs, contact Henry (henry.zhang@jax.org) and Minji (minji.kim@jax.org) or visit the [Issues](https://github.com/TheJacksonLaboratory/pyBedGraph/issues) page.",
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