{% set version="2.6.1" %}

package:
    name: numexpr
    version: {{ version }}

source:
    fn: numexpr-{{ version }}.tar.gz
    url: https://github.com/pydata/numexpr/archive/v{{ version }}.tar.gz
    sha256: e92c83d066fa8da63864d69b5f218287cc31437ae844db77390f2183123aab22

build:
    number: 0
    script: python setup.py install --single-version-externally-managed --record=record.txt

requirements:
    build:
        - python
        - setuptools
        - numpy x.x
    run:
        - python
        - numpy x.x

test:
    requires:
        - setuptools
    imports:
        - numexpr
        - numexpr.interpreter

about:
    home: https://github.com/pydata/numexpr
    license: MIT
    license_file: LICENSE.txt
    summary: 'Fast numerical expression evaluator for NumPy.'
    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.
    doc_url: https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide
    dev_url: https://github.com/pydata/numexpr

{% set version="2.6.2" %}

package:
    name: numexpr
    version: {{ version }}

source:
    fn: numexpr-{{ version }}.tar.gz
    url: https://github.com/pydata/numexpr/archive/v{{ version }}.tar.gz
    sha256: 8c57759bbd64fb81d985e54772191b71e8b63d58843d64933009521d2dddfd2f

build:
    number: 0
    script: python setup.py install --single-version-externally-managed --record=record.txt

requirements:
    build:
        - python
        - setuptools
        - numpy x.x
    run:
        - python
        - numpy x.x

test:
    requires:
        - setuptools
    imports:
        - numexpr
        - numexpr.interpreter

about:
    home: https://github.com/pydata/numexpr
    license: MIT
    license_file: LICENSE.txt
    summary: 'Fast numerical expression evaluator for NumPy.'
    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.
    doc_url: https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide
    dev_url: https://github.com/pydata/numexpr

Update numexpr recipe to version 2.6.2
Update numexpr recipe to version 2.6.2
