# This file created by conda-build 3.27.0
# meta.yaml template originally from:
# C:\Users\dev-admin\perseverance-python-buildout\aggregate\numexpr-feedstock\recipe, last modified Wed Nov  8 19:27:30 2023
# ------------------------------------------------

package:
  name: numexpr
  version: 2.8.7
source:
  sha256: 596eeb3bbfebc912f4b6eaaf842b61ba722cebdb8bc42dfefa657d3a74953849
  url: https://pypi.io/packages/source/n/numexpr/numexpr-2.8.7.tar.gz
build:
  number: '0'
  script: C:\\Users\\dev-admin\\perseverance-python-buildout\\croot\\numexpr_1699503421264\\_h_env\\python.exe
    -m pip install . --no-deps --no-build-isolation -vv
  string: py312h96b7d27_0
requirements:
  build:
    - vs2017_win-64 19.16.27032.1 hb4161e2_3
    - vswhere 2.8.4 haa95532_0
  host:
    - blas 1.0 mkl
    - bzip2 1.0.8 he774522_0
    - ca-certificates 2023.08.22 haa95532_0
    - expat 2.5.0 hd77b12b_0
    - intel-openmp 2023.1.0 h59b6b97_46319
    - libffi 3.4.4 hd77b12b_0
    - mkl 2023.1.0 h6b88ed4_46357
    - mkl-devel 2023.1.0 h670ff1c_46357
    - mkl-include 2023.1.0 haa95532_46357
    - mkl-service 2.4.0 py312h2bbff1b_1
    - numpy 1.26.0 py312hfd52020_0
    - numpy-base 1.26.0 py312h4dde369_0
    - openssl 3.0.12 h2bbff1b_0
    - pip 23.3 py312haa95532_0
    - python 3.12.0 h1d929f7_0
    - setuptools 68.0.0 py312haa95532_0
    - sqlite 3.41.2 h2bbff1b_0
    - tbb 2021.8.0 h59b6b97_0
    - tk 8.6.12 h2bbff1b_0
    - tzdata 2023c h04d1e81_0
    - vc 14.2 h21ff451_1
    - vs2015_runtime 14.27.29016 h5e58377_2
    - wheel 0.41.2 py312haa95532_0
    - xz 5.4.2 h8cc25b3_0
    - zlib 1.2.13 h8cc25b3_0
  run:
    - blas 1.0 mkl
    - mkl >=2023.1.0,<2024.0a0
    - mkl-service >=2.3.0,<3.0a0
    - numpy >=1.26.0,<2.0a0
    - python >=3.12,<3.13.0a0
    - vc >=14.1,<15.0a0
    - vs2015_runtime >=14.16.27012,<15.0a0
test:
  commands:
    - pip check
  requires:
    - pip
    - pytest
about:
  description: 'Numexpr is a fast numerical expression evaluator for NumPy. With it,

    expressions that operate on arrays (like "3*a+4*b") are accelerated and use

    less memory than doing the same calculation in Python.

    '
  dev_url: https://github.com/pydata/numexpr
  doc_url: https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide
  home: https://github.com/pydata/numexpr
  license: MIT
  license_family: MIT
  license_file: LICENSE.txt
  summary: Fast numerical expression evaluator for NumPy
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - djkirkham
    - msarahan
    - ocefpaf
    - scopatz