# This file created by conda-build 24.5.1
# meta.yaml template originally from:
# C:\b\abs_ad6i6tq3u1\clone\recipe, last modified Tue Jul 16 22:36:29 2024
# ------------------------------------------------

package:
  name: pymc
  version: 5.16.1
source:
  sha256: 00eff0ccc7c06029aab2c580524f101e922315d3937a82d116b842065a120847
  url: https://github.com/pymc-devs/pymc/archive/v5.16.1.tar.gz
build:
  number: '0'
  script:
    - python -m pip install . -vv --no-deps --no-build-isolation
  string: py312h6d93a49_0
requirements:
  build:
    - intel-fortran_win-64 2022.1.0 h950cae3_2
    - vs2019_win-64 19.29.30154 h96f319f_4
    - vswhere 2.8.4 haa95532_0
  host:
    - bzip2 1.0.8 h2bbff1b_6
    - ca-certificates 2024.7.2 haa95532_0
    - expat 2.6.2 hd77b12b_0
    - icc_rt 2022.1.0 h6049295_2
    - libffi 3.4.4 hd77b12b_1
    - openssl 3.0.14 h827c3e9_0
    - pip 24.0 py312haa95532_0
    - python 3.12.4 h14ffc60_1
    - setuptools 69.5.1 py312haa95532_0
    - sqlite 3.45.3 h2bbff1b_0
    - tk 8.6.14 h0416ee5_0
    - tzdata 2024a h04d1e81_0
    - vc 14.2 h2eaa2aa_4
    - vs2015_runtime 14.29.30133 h43f2093_4
    - wheel 0.43.0 py312haa95532_0
    - xz 5.4.6 h8cc25b3_1
    - zlib 1.2.13 h8cc25b3_1
  run:
    - arviz >=0.13.0
    - cachetools >=4.2.1
    - cloudpickle
    - icc_rt >=2022.1.0
    - numpy >=1.15.0
    - pandas >=0.24.0
    - pytensor >=2.23,<2.24
    - python >=3.12,<3.13.0a0
    - rich >=13.7.1
    - scipy >=1.4.1,<1.13
    - threadpoolctl >=3.1.0,<4.0.0
    - typing-extensions >=3.7.4
    - vc >=14.2,<15.0a0
    - vs2015_runtime >=14.29.30133,<15.0a0
test:
  commands:
    - pip check
    - pytest -v tests/model/test_core.py
  imports:
    - pymc
    - pymc.backends
    - pymc.distributions
    - pymc.gp
    - pymc.ode
    - pymc.step_methods
    - pymc.tuning
    - pymc.variational
  requires:
    - pip
    - pytest
  source_files:
    - tests
about:
  description: 'PyMC is a python module that implements Bayesian statistical models
    and fitting algorithms, including Markov chain

    Monte Carlo. Its flexibility and extensibility make it applicable to a large suite
    of problems. Along with core

    sampling functionality, PyMC includes methods for summarizing output, plotting,
    goodness-of-fit and convergence

    diagnostics.

    '
  dev_url: https://github.com/pymc-devs/pymc
  doc_url: https://www.pymc.io/projects/docs/en/stable/api.html
  home: https://www.pymc.io/welcome.html
  license: Apache-2.0 AND MIT
  license_family: OTHER
  license_file: LICENSE
  summary: 'PyMC: Bayesian Stochastic Modelling in Python'
extra:
  copy_test_source_files: true
  final: true
  flow_run_id: adbd7991-d9c3-4c9d-a08a-2f18094247ce
  recipe-maintainers:
    - fonnesbeck
    - ocefpaf
  remote_url: git@github.com:AnacondaRecipes/pymc-feedstock.git
  sha: 11c832aed12b7a72c06602408aaa31fc03c5d747
