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meta.yaml
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meta.yaml
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{% set name = "verde" %}
{% set version = "1.8.1" %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
sha256: e97a55ceecb1a4bbc6ad09232738cce95b6c93d82d514b9f64dea14ded505ec3
build:
noarch: python
number: 0
script: {{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-cache-dir -vvv
requirements:
host:
- python >=3.9
- pip
- setuptools_scm
run:
- python >=3.9
- numpy >=1.23
- scipy >=1.8
- pandas >=1.4
- xarray >=2022.03
- scikit-learn >=1.0
- pooch >=1.2
- dask >=2022.01.0
test:
requires:
- pytest
- pyproj
imports:
- verde
commands:
- pytest --pyargs verde.tests.test_minimal
about:
home: http://github.com/fatiando/verde
license: BSD-3-Clause
license_family: BSD
license_file: LICENSE.txt
summary: Processing and gridding spatial data, machine-learning style
description: |
Verde is a Python library for processing spatial data (topography,
point clouds, bathymetry, geophysics surveys, etc) and interpolating
them on a 2D surface (i.e., gridding) with a hint of machine learning.
Our core interpolation methods are inspired by machine-learning. As
such, Verde implements an interface that is similar to the popular
scikit-learn library. We also provide other analysis methods that are
often used in combination with gridding, like trend removal,
blocked/windowed operations, cross-validation, and more!
doc_url: https://www.fatiando.org/verde
dev_url: https://github.com/fatiando/verde
extra:
recipe-maintainers:
- leouieda