{% set label = "'full'" %}
{% set tests = "['pywt']" %}
{% set name = "PyWavelets" %}
{% set version = "1.5.0" %}

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

source:
  url: https://pypi.io/packages/source/{{ name[0]|lower }}/{{ name|lower }}/{{ name|lower }}-{{ version }}.tar.gz
  sha256: d9e25c7cabef7ccd53f5fead26ab22152fe4cb937bad7411b5d506e2b5de38f6

build:
  number: 0
  skip: True  # [py<39]
  script: {{ PYTHON }} -m pip install . --no-deps --no-build-isolation --ignore-installed --no-cache-dir -vv

requirements:
  build:
    - {{ compiler('c') }}
  host:
    - python
    - cython >=0.29.35
    - numpy {{ numpy }}
    - pip
    - meson-python
    - wheel
  run:
    - python
    - {{ pin_compatible('numpy') }}

test:
  imports:
    - pywt
  requires:
    - pip
    - pytest
  commands:
    - pip check
    - python -c "import sys; import pywt; sys.exit(not pywt.test(verbose=2, label={{ label }}, tests={{ tests }}, extra_argv=['--durations=50']))"

about:
  home: https://github.com/PyWavelets/pywt
  license: MIT
  license_family: MIT
  license_file:
    - LICENSE
    - LICENSES_bundled.txt
  summary: Discrete Wavelet Transforms in Python
  description: |
    PyWavelets is a free Open Source library for wavelet transforms in Python.
    Wavelets are mathematical basis functions that are localized in both time and frequency.
    Wavelet transforms are time-frequency transforms employing wavelets. They are similar to
    Fourier transforms, the difference being that Fourier transforms are localized only in
    frequency instead of in time and frequency.
  doc_url: https://pywavelets.readthedocs.io
  dev_url: https://github.com/PyWavelets/pywt

extra:
  recipe-maintainers:
    - grlee77
    - jakirkham
    - ocefpaf