Metadata-Version: 2.1
Name: hictkpy
Version: 0.0.2
Summary: Blazing fast toolkit to work with .hic and .cool files
Author: Roberto Rossini
Author-email: roberros@uio.no
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: msvc-runtime; os_name == "nt"
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: importlib-metadata; python_version < "3.8"
Provides-Extra: test
Requires-Dist: pytest>=6.0; extra == "test"

<!--
Copyright (C) 2023 Roberto Rossini <roberros@uio.no>

SPDX-License-Identifier: MIT
-->

# hictkpy

[![License](https://img.shields.io/badge/license-MIT-green)](./LICENSE)
[![Download from Bioconda](https://img.shields.io/conda/vn/bioconda/hictkpy?label=bioconda&logo=Anaconda)](https://anaconda.org/bioconda/hictkpy)

<!-- [![Zenodo DOI]()]() -->
---

Python bindings for hictk, a blazing fast toolkit to work with .hic and .cool files.

## Installing hictkpy

hictkpy can be installed in various ways.

### PIP

`pip install hictkpy`

### Conda (bioconda)

`conda install -c conda-forge -c bioconda hictkpy`

### From source

`pip install 'git+https://github.com/paulsengroup/hictkpy.git@main'`

On Windows you will have to manually install some of hictk dependencies, namely hdf5 (with zlib support) and libdeflate.

## Using hictkpy

```python3
import hictkpy

path_to_clr = "file.mcool"  # "file.hic"

clr = hictkpy.File(path_to_clr, 100_000)
sel = clr.fetch("chr1")

df = sel.to_df()     # Get interactions as a pd.DataFrame
m1 = sel.to_numpy()  # Get interactions as a numpy matrix
m2 = sel.to_coo()    # Get interactions as a scipy.sparse.coo_matrix

# Loop over interactions
for bin1_id, bin2_id, count in clr.fetch("chr1"):
  print(bin1_id, ...)

# Loop over interactions
for chrom1, start1, end1, chrom2, start2, end2, count in clr.fetch("chr1", join=True):
  print(chrom1, ...)

# Fetch interactions using UCSC queries
clr.fetch("chr1:0-10,000,000").to_df()
clr.fetch("chr1:0-10,000,000", "chr2:100,000,000-105,000,000").to_df()

# Fetch interactions using BED queries
clr.fetch("chr1\t0\t10000000", query_type="BED").to_df()

# Fetch balanced interactions
clr.fetch("chr1", normalization="weight").to_df()
clr.fetch("chr1", normalization="VC").to_df()

# Sum interactions overlapping query
clr.fetch("chr1").sum()

# Count non-zero entries
clr.fetch("chr1").nnz()
```

MIT License

Copyright (c) 2023 Roberto Rossini

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
