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Quickstart
==========
Installation
------------
Install :py:mod:`cooler` from `PyPI <https://pypi.org/project/cooler>`_ using :command:`pip`.
::
$ pip install cooler
Requirements:
- Python 2.7 or 3.4 and higher
- libhdf5
- Python packages :py:mod:`numpy`, :py:mod:`scipy`, :py:mod:`pandas`, :py:mod:`h5py`.
We highly recommend using the conda package manager to install scientific packages like these. To get :command:`conda`, you can download either the full `Anaconda <https://www.continuum.io/downloads>`_ Python distribution which comes with lots of data science software or the minimal `Miniconda <http://conda.pydata.org/miniconda.html>`_ distribution which is just the standalone package manager plus Python. In the latter case, you can install the packages as follows:
::
$ conda install numpy scipy pandas h5py
If you are using conda, you can alternatively install cooler from the `bioconda channel <https://bioconda.github.io>`_.
::
$ conda install -c conda-forge -c bioconda cooler
Command line interface
----------------------
See:
- Jupyter Notebook `CLI walkthrough <https://github.com/open2c/cooler-binder/blob/master/cooler_cli.ipynb>`_.
- The `CLI Reference <http://cooler.readthedocs.io/en/latest/cli.html>`_ for more information.
The :py:mod:`cooler` package includes command line tools for creating, querying and manipulating cooler files.
::
$ cooler cload pairs hg19.chrom.sizes:10000 $PAIRS_FILE out.10000.cool
$ cooler balance -p 10 out.10000.cool
$ cooler dump -b -t pixels --header --join -r chr3:10M-12M -r2 chr17 out.10000.cool | head
Output:
::
chrom1 start1 end1 chrom2 start2 end2 count balanced
chr3 10000000 10010000 chr17 0 10000 1 0.810766
chr3 10000000 10010000 chr17 520000 530000 1 1.2055
chr3 10000000 10010000 chr17 640000 650000 1 0.587372
chr3 10000000 10010000 chr17 900000 910000 1 1.02558
chr3 10000000 10010000 chr17 1030000 1040000 1 0.718195
chr3 10000000 10010000 chr17 1320000 1330000 1 0.803212
chr3 10000000 10010000 chr17 1500000 1510000 1 0.925146
chr3 10000000 10010000 chr17 1750000 1760000 1 0.950326
chr3 10000000 10010000 chr17 1800000 1810000 1 0.745982
Python API
----------
See:
- Jupyter Notebook `API walkthrough <https://github.com/open2c/cooler-binder/blob/master/cooler_api.ipynb>`_.
- The :ref:`api-reference` for more information.
The :py:mod:`cooler` library provides a thin wrapper over the excellent NumPy-aware `h5py <http://docs.h5py.org/en/latest/>`_ Python interface to HDF5. It supports creation of cooler files and the following types of **range queries** on the data:
- Tabular selections are retrieved as Pandas DataFrames and Series.
- Matrix selections are retrieved as NumPy arrays, DataFrames, or SciPy sparse matrices.
- Metadata is retrieved as a json-serializable Python dictionary.
- Range queries can be supplied using either integer bin indexes or genomic coordinate intervals.
::
>>> import cooler
>>> import matplotlib.pyplot as plt
>>> c = cooler.Cooler('bigDataset.cool')
>>> resolution = c.binsize
>>> mat = c.matrix(balance=True).fetch('chr5:10,000,000-15,000,000')
>>> plt.matshow(np.log10(mat), cmap='YlOrRd')
::
>>> import multiprocessing as mp
>>> import h5py
>>> pool = mp.Pool(8)
>>> c = cooler.Cooler('bigDataset.cool')
>>> weights, stats = cooler.balance_cooler(c, map=pool.map, ignore_diags=3, min_nnz=10)
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