climatePy 0.4.33

A Python package for getting point and gridded climate data by AOI

climatePy

Description

A Python 📦 for getting point and gridded climate data by AOI. climatePy is the Python version of the climateR R package, providing all of the same functionality but in Python.

As its stated in the climateR README: climatePy simplifies the steps needed to get climate data into Python. At its core it provides three main things:

1. A climate catalog of over 100,000k datasets from over 2,000 data providers/archives. See (params())

2. A general toolkit for accessing remote and local gridded data files bounded by space, time, and variable constraints (dap(), dap_crop(), read_dap_file())

3. A set of shortcuts that implement these methods for a core set of selected catalog elements

Links

• climatePy (Lynker intel)
• climatePy PyPI
• climateR/climatePy slideshow

Table of Contents

• Installation
• Usage
• Credits
• License
• How to Contribute

Installation

climatePy can be downloaded from PyPI via pip like so:

pip install climatePy

Note: climatePy is still in development

Usage

Loading climate catalog

import climatePy
import geopandas as gpd
import matplotlib.pyplot as plt

# load climate catalog
catalog = climatePy.params()

# load example AOI data
AOI = gpd.read_file('src/data/san_luis_obispo_county.gpkg')

Using climatepy_filter():

The climatepy_filter() is one of the core functions of climatePy and is used to do the first round of filtering on the base climate catalog.

Here we filter down our climate catalog to TerraClim precipitation data for San Luis Obispo County, CA.

# collect raw meta data
raw = climatePy.climatepy_filter(
        id        = "terraclim", 
        AOI       = AOI, 
        varname   = "ppt"
        )

id	asset	varname
gridmet	agg_terraclimate_ppt_1958_CurrentYear_GLOBE	ppt

AOI

Getting climate data in AOI

Now lets use the getTerraClim() function from climatePy to get precipitation data for January 1st, 2018 in San Luis Obispo County, CA.

# collect raw meta data
prcp = climatePy.getTerraClim(
    AOI       = AOI,
    varname   = "ppt",
    startDate = "2018-01-01",
    endDate   = "2018-01-01"
    )

Get data within a date range

We can also get data within a date range. we'll use getTerraClim() to get monthly precipitation data for all of 2018 in San Luis Obispo County, CA.

# collect raw meta data
prcp = climatePy.getTerraClim(
    AOI       = AOI,
    varname   = "ppt",
    startDate = "2018-01-01",
    endDate   = "2018-12-01"
    )

Data from known bounding coordinates

climatePy offers support for shapely bounding boxes. Here we are requesting wind velocity data for the four corners region of the USA by bounding coordinates.

from shapely.geometry import box

bbox = box(-112, 34, -105, 39)

bbox = gpd.GeoDataFrame(geometry=[bbox], crs ='EPSG:4326')

vs = climatePy.getGridMET(
       AOI       = bbox, 
       varname   = "vs",
       startDate = "2018-09-01"
       )

Credits

Credit to Mike J Johnson and the other contributors to the original climateR package listed below:

• Max Joseph
• Eric R. Scott
• James Tsakalos

License

MIT License

Copyright (c) 2023 Angus Watters, Mike J. Johnson

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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How to Contribute

If you would like to contribute, submit a PR and we will get to as soon as we can! If you have any issues please open an issue on GitHub. For any questions, feel free to ask @anguswg-ucsb or @mikejohnson51, or simply create an issue on GitHub.