Metadata-Version: 1.0
Name: cyvcf2
Version: 0.5.0
Summary: fast vcf parsing with cython + htslib
Home-page: https://github.com/brentp/cyvcf2/
Author: Brent Pedersen
Author-email: bpederse@gmail.com
License: MIT
Description: cyvcf2
        ======
        
        Fast python **(2 and 3)** parsing of VCF and BCF including region-queries.
        
        [![Build Status](https://travis-ci.org/brentp/cyvcf2.svg?branch=master)](https://travis-ci.org/brentp/cyvcf2)
        
        cyvcf2 is a cython wrapper around [htslib](https://github.com/samtools/htslib) built for fast parsing of [Variant Call Format](https://en.m.wikipedia.org/wiki/Variant_Call_Format) (VCF) files.
        It is targetted toward our use-case in [gemini](http://gemini.rtfd.org) but should also be of general utility.
        
        On a file with 189 samples that takes [cyvcf](https://github.com/arq5x/cyvcf) **21 seconds** to parse and extract all sample information, it takes `cyvcf2` **1.4 seconds**.
        
        Attributes like `variant.gt_ref_depths` return a numpy array directly so they are immediately ready for downstream use.
        **note** that the array is backed by the underlying C data, so, once `variant` goes out of scope. The array will contain nonsense.
        To persist a copy, use: `cpy = np.array(variant.gt_ref_depths)` instead of just `arr = variant.gt_ref_depths`.
        
        Example
        =======
        
        The example below shows much of the use of cyvcf2.
        
        ```Python
        from cyvcf2 import VCF
        
        for variant in VCF('some.vcf.gz'): # or VCF('some.bcf')
        
        	variant.gt_types # numpy array
        	variant.gt_ref_depths, variant.gt_alt_depths # numpy arrays
        	variant.gt_phases, variant.gt_quals # numpy arrays
        	variant.gt_bases # numpy array
        	variant.CHROM, variant.start, variant.end, variant.ID, \
        				variant.REF, variant.ALT, variant.FILTER, variant.QUAL
        	variant.INFO.get('DP') # int
        	variant.INFO.get('FS') # float
        	variant.INFO.get('AC') # float
            a = variant.gt_phred_ll_homref # numpy array
            b = variant.gt_phred_ll_het # numpy array
            c = variant.gt_phred_ll_homalt # numpy array
        
        	str(variant)
        	# Get a numpy array of the depth per sample:
            dp = variant.format('DP', int)
            # or of any other format field:
            sb = variant.format('SB', float)
            assert sb.shape == (n_samples, 4) # 4-values per
        
        # to do a region-query:
        
        vcf = VCF('some.vcf.gz')
        for v in vcf('11:435345-556565'):
            if v.INFO["AF"] > 0.1: continue
            print(str(v))
        
        ```
        
        Installation
        ============
        
        ```
        pip install cyvcf2
        ```
        
        Testing
        =======
        
        Tests can be run with:
        
        ```
        python setup.py test
        ```
        
        See Also
        ========
        
        Pysam also [has a cython wrapper to htslib](https://github.com/pysam-developers/pysam/blob/master/pysam/cbcf.pyx) and one block of code here is taken directly from that library. But, the optimizations that we want for gemini are very specific so we have chosen to create a separate project.
        
Platform: UNKNOWN
