{% set name = "hatchet" %}
{% set version = "0.4.7" %}

package:
  name: '{{ name|lower }}'
  version: '{{ version }}'

source:
  - url: https://github.com/raphael-group/{{ name }}/archive/v{{ version }}.zip
    sha256: 33fc295c3f388c26485647e15ac2453842deb02e549d40d487f905c29b69a5b9

  - url: https://packages.gurobi.com/9.0/gurobi9.0.2_linux64.tar.gz  # [not osx]
    sha256: 6527581aef11c3e425c52993d108861ef53ec13971d9931861bf9c88d9d111f3  # [not osx]
    folder: gurobi902  # [not osx]

  - url: https://packages.gurobi.com/9.0/gurobi9.0.2_mac64.pkg  # [osx]
    sha256: 1d58586f9a33ac7035f51f6a04707248218f70bddbec78cb83f11c986ac652cd  # [osx]

build:
  number: 0
  skip: True  # [py < 37]

requirements:
  build:  
    - {{ compiler('cxx') }}
    - cmake>=3.9
    - make
  host:
    - python
    - setuptools
  run:
    - python
    - bcftools
    - biopython
    - matplotlib-base
    - pandas
    - psutil
    - pyomo
    - pysam
    - requests
    - samtools
    - seaborn 
    - scikit-learn
    - scipy 

test:
  imports:
    - hatchet 

about:
  home: https://github.com/raphael-group/hatchet
  license: BSD-3
  summary: "Holistic Allele-specific Tumor Copy-number Heterogeneity"
  description: |
    HATCHet is an algorithm to infer allele and clone-specific copy-number
    aberrations (CNAs), clone proportions, and whole-genome duplications
    (WGD) for several tumor clones jointly from multiple bulk-tumor samples
    of the same patient or from a single bulk-tumor sample.
