-
-
Notifications
You must be signed in to change notification settings - Fork 1
/
meta.yaml
61 lines (54 loc) · 1.66 KB
/
meta.yaml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
{% set name = "verde" %}
{% set version = "1.8.0" %}
package:
name: {{ name|lower }}
version: {{ version }}
source:
url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
sha256: 274d52f459e5e6f696bacab49b4630692825151c1b8a7be672bd89a95b48d0c2
build:
noarch: python
number: 0
script: {{ PYTHON }} -m pip install . --no-deps --ignore-installed --no-cache-dir -vvv
requirements:
host:
- python >=3.7
- pip
- setuptools_scm
run:
- python >=3.7
- numpy >=1.19
- scipy >=1.5
- pandas >=1.1
- xarray >=0.16
- scikit-learn >=0.24
- pooch >=1.2
- dask >=2021.05.0
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