Skip to main content

Load numpy arrays from a VCF (variant call file).

Project description

Load numpy arrays from a VCF (variant call file).

Installation

Installation requires numpy and cython:

$ pip install vcfnp

…or:

$ git clone --recursive git://github.com/alimanfoo/vcfnp.git
$ cd vcfnp
$ python setup.py build_ext --inplace

Usage

import sys
import vcfnp
import numpy as np
import matplotlib.pyplot as plt

filename = '/path/to/my.vcf'

# load data from fixed fields (except INFO)
v = vcfnp.variants(filename).view(np.recarray)

# print some simple variant metrics
print 'found %s variants (%s SNPs)' % (v.size, np.count_nonzero(v.is_snp))
print 'QUAL mean (std): %s (%s)' % (np.mean(v.QUAL), np.std(v.QUAL))

# load data from INFO field
i = vcfnp.info(filename).view(np.recarray)

# plot a histogram of variant depth
fig = plt.figure(1)
ax = fig.add_subplot(111)
ax.hist(i.DP)
ax.set_title('DP histogram')
ax.set_xlabel('DP')
plt.show()

# load data from sample columns
c = vcfnp.calldata(filename).view(np.recarray)
c = vcfnp.view2d(c)

# print some simple genotype metrics
count_phased = np.count_nonzero(c.is_phased)
count_variant = np.count_nonzero(np.any(c.genotype > 0, axis=2))
count_missing = np.count_nonzero(~c.is_called)
print 'calls (phased, variant, missing): %s (%s, %s, %s)' % (c.flatten().size, count_phased, count_variant, count_missing)

# plot a histogram of genotype quality
fig = plt.figure(2)
ax = fig.add_subplot(111)
ax.hist(c.GQ.flatten())
ax.set_title('GQ histogram')
ax.set_xlabel('GQ')
plt.show()

Acknowledgments

Based on Erik Garrison’s vcflib.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vcfnp-0.7.tar.gz (391.0 kB view details)

Uploaded Source

File details

Details for the file vcfnp-0.7.tar.gz.

File metadata

  • Download URL: vcfnp-0.7.tar.gz
  • Upload date:
  • Size: 391.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for vcfnp-0.7.tar.gz
Algorithm Hash digest
SHA256 8246f0c2ce8fbb11b61458526d6617500535ccc44113e0960bad298823ad1780
MD5 f1fbeab84bbbf3e865d3a0109b8f7151
BLAKE2b-256 4bc536d47e6dfcd2af1c95ca175e313694b6d3009d903e8b8f1d09cf10887c57

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page