meta.yaml

{% set version = "4.4.1" %}

package:
  name: pcraster
  version: {{ version }}


source:
  url: http://pcraster.geo.uu.nl/pcraster/packages/src/pcraster-{{ version }}.tar.bz2
  sha256: 99edb3ada4e960fbb0d39b6cb4dee08b37983aa1412ab23ef74f0c4172fb80a7


build:
  number: 10

  ignore_run_exports_from:
    - libboost-devel


requirements:
  build:
    - python                                 # [build_platform != target_platform]
    - cross-python_{{ target_platform }}     # [build_platform != target_platform]
    - numpy                                  # [build_platform != target_platform]
    - {{ compiler('c') }}
    - {{ stdlib("c") }}
    - {{ compiler('cxx') }}
    - cmake
    - ninja
    - make                                  # [win]

  host:
    - xorg-libxfixes                        # [linux]
    - libgl-devel                           # [linux]
    - libboost-devel
    - xerces-c
    - qt-main
    - ncurses                               # [not win]
    - libgdal
    - python
    - numpy
    - vs2015_runtime                        # [win]
    - libglu                                # [linux]

  run:
    - python
    - libgdal
    - ncurses                               # [not win]
    - vs2015_runtime                        # [win]
    - libglu                                # [linux]


test:
  imports:
    - pcraster
  commands:
    - asc2map
    - col2map
    - legend
    - map2asc
    - map2col
    - mapattr
    - oldcalc
    - pcrcalc
    - resample
    - table


about:
  home: http://www.pcraster.eu
  license: GPL-3.0-only
  license_file: LICENSE
  summary: Environmental modelling software.
  description: |
    PCRaster is a collection of tools and software libraries tailored to the
    construction of spatio-temporal environmental models. Application domains
    are amongst others hydrology (rainfall-runoff, global water balance,
    groundwater (with Modflow)), ecology, or land use change.
    PCRaster includes a rich set of spatial operations for manipulating and
    analysing raster maps. A Python framework supports Monte Carlo simulations
    and data assimilation (Ensemble Kalman Filter and Particle Filter). The
    Aguila tool allows for the interactive visualisation of stochastic spatio-
    temporal data.
  doc_url: https://pcraster.geo.uu.nl/pcraster/latest/documentation/index.html
  dev_url: https://github.com/pcraster/pcraster


extra:
  recipe-maintainers:
    - OliverSchmitz
    - kordejong