Commit 8059fff8 authored by Andreas Tille's avatar Andreas Tille
Browse files

New upstream version 0.9.5

parent 3f7b27a9

.travis.yml

+42 −19
Original line number Diff line number Diff line 
language: c

sudo: false



os:

    - osx

    - linux

env:

    # Earliest supported versions

    - CONDA_PY=2.7 PANDAS=0.18.1 PYSAM=0.10.0

    - CONDA_PY=3.5 PANDAS=0.18.1 PYSAM=0.10.0

    # The latest versions, whatever they are

    - CONDA_PY=3.6 PANDAS='*' PYSAM='*'



install:

    - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install md5sha1sum; fi

    - source devtools/travis-ci/install_miniconda.sh



before_script:

    - conda create -q -y --name testenv python=$CONDA_PY

    - conda create -yq --name testenv python=$CONDA_PY

    - source activate testenv

    - conda build -c bioconda --python $CONDA_PY ./devtools/conda-recipe/  # Build the conda recipe for the latest dev

    - conda install -c bioconda --use-local -yq cnvkit  # Install the locally built package and its requirements

    - conda install -c bioconda -yq pandas==$PANDAS pysam==$PYSAM  # Install the versions desired for testing

    - conda remove cnvkit -y -q  # Remove the conda cnvkit package but keep the dependencies installed

    - python setup.py install  # Install directly from source

    - if [[ "$CONDA_PY" == "2.7" ]]; then conda install -yq futures>=3.0; fi

    - if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then conda install -yq -c bioconda atlas; fi

    # Install the versions desired for testing

    - conda install -yq -c bioconda

        cython

        future

        matplotlib

        numpy

        pyfaidx

        pysam

        reportlab

        scipy

        pandas==$PANDAS

        pysam==$PYSAM

    # R linking is broken on OS X - try recompilation instead of conda there

    - if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then

        conda install -yq -c bioconda bioconductor-dnacopy r-cghflasso;

      fi

    - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then

        conda install -yq -c bioconda r-base;

        Rscript -e "source('http://bioconductor.org/biocLite.R'); biocLite(c('DNAcopy', 'cghFLasso'))";

      fi

    # hmmlearn 0.2 isn't packaged for conda yet

    - pip install -q scikit-learn hmmlearn

    # Install CNVkit in-place from source

    - pip install -e .

    - cd test/

    # For codecov.io

    - pip install codecov
@@ -21,18 +52,10 @@ script: 
    - coverage run test_io.py

    - coverage run -a test_genome.py

    - coverage run -a test_cnvlib.py

    - coverage run -a test_r.py

    #- coverage run -a test_r.py

    # OS X can't install cghFLasso package?

    - if [[ "$TRAVIS_OS_NAME" != "osx" ]]; then coverage run -a test_r.py; fi



after_success:

    - coverage report

    - codecov
 


os:

  # - osx

  - linux



env:

  - CONDA_PY=2.7 PANDAS=0.18.1 PYSAM=0.10.0

  - CONDA_PY=2.7 PANDAS=0.22.0 PYSAM=0.13.0

  - CONDA_PY=3.5 PANDAS=0.18.1 PYSAM=0.10.0

  - CONDA_PY=3.5 PANDAS=0.22.0 PYSAM=0.13.0

LICENSE

+2 −1
Original line number Diff line number Diff line 
Copyright (c) 2013-2017 Eric Talevich

Copyright (c) 2013-2018 Eric Talevich

Copyright (c) 2013-2018 University of California



Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

README.rst

+11 −2
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@@ -78,11 +78,20 @@ packages. 


This approach is preferred on Mac OS X, and is a solid choice on Linux, too.



To download and install CNVkit and its Python dependencies::

To download and install CNVkit and its Python dependencies in a clean

environment::



    # Configure the sources where conda will find packages

    conda config --add channels defaults

    conda config --add channels conda-forge

    conda config --add channels bioconda

    # Install CNVkit in a new environment named "cnvkit"

    conda create -n cnvkit cnvkit

    # Activate the environment with CNVkit installed:

    source activate cnvkit



Or, in an existing environment::



    conda install cnvkit
@@ -143,7 +152,7 @@ Then install the rest of CNVkit's dependencies:: 


Alternatively, you can use `Homebrew <http://brew.sh/>`_ to install an

up-to-date Python (e.g. ``brew install python``) and as many of the Python

packages as possible (primarily NumPy, SciPy, matplotlib and pandas).

packages as possible (primarily NumPy and SciPy; ideally matplotlib and pandas).

Then, proceed with pip::



    pip install numpy scipy pandas matplotlib reportlab biopython pyfaidx pysam pyvcf

cnvlib/_version.py

+1 −1
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__version__ = "0.9.3"
 
__version__ = "0.9.5"

cnvlib/autobin.py

+15 −3
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@@ -9,6 +9,7 @@ import pandas as pd 
from skgenome import tabio, GenomicArray as GA



from . import coverage, samutil

from .antitarget import compare_chrom_names

from .descriptives import weighted_median
@@ -47,6 +48,14 @@ def do_autobin(bam_fname, method, targets=None, access=None, 
        ((target depth, target avg. bin size),

         (antitarget depth, antitarget avg. bin size))

    """

    if method in ('amplicon', 'hybrid'):

        if targets is None:

            raise ValueError("Target regions are required for method %r "

                             "but were not provided." % method)

        if not len(targets):

            raise ValueError("Target regions are required for method %r "

                             "but were not provided." % method)



    # Closes over bp_per_bin

    def depth2binsize(depth, min_size, max_size):

        if depth:
@@ -95,7 +104,9 @@ def hybrid(rc_table, read_len, bam_fname, targets, access=None): 
    """Hybrid capture sequencing."""

    # Identify off-target regions

    if access is None:

        access = idxstats2ga(rc_table)

        access = idxstats2ga(rc_table, bam_fname)

        # Verify BAM chromosome names match those in target BED

        compare_chrom_names(access, targets)

    antitargets = access.subtract(targets)

    # Only examine chromosomes present in all 2-3 input datasets

    rc_table, targets, antitargets = shared_chroms(rc_table, targets,
@@ -126,9 +137,10 @@ def average_depth(rc_table, read_length): 
    return weighted_median(mean_depths, rc_table.length)





def idxstats2ga(table):

def idxstats2ga(table, bam_fname):

    return GA(table.assign(start=0, end=table.length)

              .loc[:, ('chromosome', 'start', 'end')])

              .loc[:, ('chromosome', 'start', 'end')],

              meta_dict={'filename': bam_fname})





def sample_region_cov(bam_fname, regions, max_num=100):