semvec/pypi-packages huggingface dataset:
paga-business-client 0.0.5
Paga Business CLient
License: MIT
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*

========
Overview
========

Paga Business CLient

* Free software: MIT license

Installation
============

::

    pip install paga_business_client

You can also install the in-development version with::

    pip install git+ssh://git@github.com/pagadevcomm/pagadevcomm/paga_business_library.git@master

Documentation
=============


To use the project:

.. code-block:: python

    import paga_business_client
    paga_business_client.longest()


Development
===========

To run the all tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox


Changelog
=========

0.0.1 (2020-04-08)
------------------

* First release on PyPI.
ld-estimator 1.1.2
Package for estimating linkage disequilibrium
License: MIT
Requires Python: >3.6.0

## ld_estimator: A package to estimate linkage disequilibrium
Computes linkage disequilibrium in python. This uses the maximum-likelihood of
[Excoffier & Slatkin](https://doi.org/10.1093/oxfordjournals.molbev.a040269),
just converted from [HaploView](https://github.com/jazzywhit/Haploview) to c++
, with python bindings. It's not too slow, can calculate ~1500 pairs per
second from the HapMap Phase 3 VCFs.

### Installation
The simplest way to install ld_estimator is through pip:
```sh
pip install ld_estimator
```

### Usage
Use ld_estimator within a python environment
```python
from ld_estimator import pairwise_ld

var1 = [(0, 0), (0, 0), (0, 1), (1, 0), (1, 1), (0, 1), (0, 0), (0, 0), (1, 1)]
var2 = [(0, 0), (0, 0), (0, 1), (1, 0), (1, 1), (0, 1), (0, 0), (1, 1), (1, 1)]
is_haploid = [False, False, False, False, False, False, False, True, True, True]
ld = pairwise_ld(var1, var2, is_haploid)
print(ld.dprime)
print(ld.r_squared)

# or calculate LD for all pairs of variants in a region in a VCF:
from ld_estimator import region_ld
vcf_path = 'PATH_TO_VCF'
ld = region_ld(vcf_path, chrom, start, end)

# or calculate LD to a site within a region in a VCF. This defaults to checking
# variants within a 100 kb window of the specified site.
from ld_estimator import site_ld
vcf_path = 'PATH_TO_VCF'
ld = site_ld(vcf_path, chrom, pos, window=200000)

# can pass in multiple positions in the same region at once
ld = site_ld(vcf_path, chrom, [pos2, pos2, pos3], window=200000)

# both region_ld() and site_ld() can take a list of sample IDs to subset the
# samples used for calculating LD. For example:
ld = site_ld(vcf_path, chrom, pos, subset=['sample1', 'sample2'])

# if the variant is on a sex chromosome, you'll have to pass in a list of sample
# sexes (matching order of the subset IDs if present, otherwise the VCF samples)
ld = site_ld(vcf_path, 'X', 20000000, sexes=['male', 'female'])
```