{% set name = "tinyarray" %}
{% set version = "1.1.0" %}

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

source:
  git_url: https://gitlab.kwant-project.org/kwant/tinyarray.git
  git_tag: v{{ version }}

build:
  skip: true  # [win]
  number: 0
  script: python setup.py install --single-version-externally-managed --record record.txt

requirements:
  build:
    - python
    - setuptools
    - nose
    - toolchain
  run:
    - python
    - nose

test:
  imports:
    - tinyarray

about:
  home: http://git.kwant-project.org/tinyarray/about/
  license: BSD License
  license_file: LICENSE.rst
  summary: 'Arrays of numbers for Python, optimized for small sizes'
  description: |
    Tinyarrays are similar to NumPy arrays, but optimized for small sizes. 
    Common operations on very small arrays are to 3-7 times faster than 
    with NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times 
    less memory is used to store them. Tinyarrays are useful if you need 
    many small arrays of numbers, and cannot combine them into a few 
    large ones. (The resulting code is still much slower than C, but it 
    may now be fast enough.)
  doc_url: http://git.kwant-project.org/tinyarray/about/

extra:
  recipe-maintainers:
    - basnijholt

{% set name = "tinyarray" %}
{% set version = "1.1.0" %}

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

source:
  git_url: https://gitlab.kwant-project.org/kwant/tinyarray.git
  git_tag: v{{ version }}

build:
  skip: true  # [win]
  number: 0
  script: python setup.py install --single-version-externally-managed --record record.txt

requirements:
  build:
    - python
    - setuptools
    - nose
    - toolchain
  run:
    - python
    - nose

test:
  imports:
    - tinyarray

about:
  home: http://git.kwant-project.org/tinyarray/about/
  license: BSD 2-Clause
  license_file: LICENSE.rst
  summary: 'Arrays of numbers for Python, optimized for small sizes'
  description: |
    Tinyarrays are similar to NumPy arrays, but optimized for small sizes. 
    Common operations on very small arrays are to 3-7 times faster than 
    with NumPy (with NumPy 1.6 it used to be up to 35 times), and 3 times 
    less memory is used to store them. Tinyarrays are useful if you need 
    many small arrays of numbers, and cannot combine them into a few 
    large ones. (The resulting code is still much slower than C, but it 
    may now be fast enough.)
  doc_url: http://git.kwant-project.org/tinyarray/about/

extra:
  recipe-maintainers:
    - basnijholt

Change BSD to BSD 2-Clause
Change BSD to BSD 2-Clause