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https://github.com/chilipp/latlon-utils
Retrieve WorldClim climate and other information for lat-lon grid cells
https://github.com/chilipp/latlon-utils
Last synced: 20 days ago
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Retrieve WorldClim climate and other information for lat-lon grid cells
- Host: GitHub
- URL: https://github.com/chilipp/latlon-utils
- Owner: Chilipp
- License: gpl-3.0
- Created: 2019-03-02T11:31:13.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-29T17:18:34.000Z (6 months ago)
- Last Synced: 2024-10-15T09:13:15.177Z (about 1 month ago)
- Language: Python
- Size: 49.8 KB
- Stars: 6
- Watchers: 2
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
Retrieve WorldClim climate and other information for lat-lon grid cells
=======================================================================
|travis| |codecov| |version| |github| |supported-versions| |supported-implementations|
This repository contains utility functions to retrieve information for given
latitudes and longitude.
Installation
------------
We strongly recommend to use conda_ for the installation. First, install the
necessary dependencies, namely netCDF4_, shapely_ and pandas_::
conda install netcdf4 shapely pandas
To enable the download of the WorldClim data, you will also need rasterio_ and
xarray_ (you can skip this if you already downloaded the data) via::
conda install rasterio xarray
Then install ``latlon_utils`` via::
pip install latlon_utils
or from the source directory via::
pip install .
Usage
-----
So far, the package has two functions: the `get_climate` and `get_country`
function. Use these like
Get the country for 50 degrees north and 10 degrees east::
>>> from latlon_utils import get_country
>>> get_country(50, 10)
'Germany'
Get the climate for 50 degrees north and 10 degrees east::
>>> from latlon_utils import get_climate
# limit the number of columns printed by pandas
>>> import pandas; pandas.options.display.max_columns = 5
>>> get_climate(50, 10)
tavg jan 0.044739
feb 0.974976
mar 4.705505
apr 8.232239
mai 13.150024
jun 16.012268
jul 17.958984
aug 17.828735
sep 13.779480
oct 8.787476
nov 4.039001
dec 1.430237
djf 0.816650
mam 8.695923
jja 17.266663
son 8.868652
ann 8.911972
prec jan 48.000000
feb 42.000000
mar 44.000000
apr 44.000000
mai 56.000000
jun 68.000000
jul 65.000000
aug 52.000000
sep 47.000000
oct 52.000000
nov 52.000000
dec 59.000000
djf 49.666667
mam 48.000000
jja 61.666667
son 50.333333
ann 52.416667
Name: (50, 10), dtype: float64
>>> get_climate(50, 10)['tavg', 'djf']
0.816650390625
>>> get_climate([10, 11], [50, 51])
tavg ... prec
jan feb ... son ann
lat lon ...
10 50 21.810730 22.687988 ... 10.666667 6.833333
11 51 24.617249 24.678040 ... 7.666667 3.750000
[2 rows x 34 columns]
Data download
-------------
This package is built upon freely available datasets but does not contain any
data. This data is downloaded on request (see the next section). In particular,
the `get_climate` method uses the data from [WorldClim2.1]_, the `get_country`
function uses the `datasets/geo-countries`_ repository.
Download directory
******************
To download and process the necessary datasets, run::
python -m latlon_utils.download
(see ``python -m latlon_utils.download --help`` for available options).
We download the GeoTIFF files from WorldClim_ and transform them to netCDF
datasets. The default directory to store the data is in
``$HOME/.local/share/latlon_utils``, where ``$HOME`` stands for the users home
directory. If you want to use a different directory, set the ``LATLONDATA``
variable, e.g.::
export LATLONDATA=$HOME/my_data
python download.py $LATLONDATA
The ``LATLONDATA`` environment variable is necessary to ensure that the python
package finds the data later again.
WorldClim resolutions
*********************
The default resolution that we use is ``10m``. However, you can also specify
other resolutions in the python functions or via the ``LATLONRES`` environment
variable. To use, for example the 5 minutes resolutions, simply run::
export LATLONRES='5m'
References
----------
.. [WorldClim2.1] Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. http://worldclim.org/
.. _WorldClim: https://worldclim.org/
.. _datasets/geo-countries: https://github.com/datasets/geo-countries
.. _xarray: http://xarray.pydata.org/en/stable/
.. _rasterio: https://rasterio.readthedocs.io/en/stable/
.. _netCDF4: https://github.com/Unidata/netcdf4-python
.. _pandas: https://pandas.pydata.org/
.. _conda: https://conda.io/projects/conda/en/latest/
.. _shapely: https://shapely.readthedocs.io/en/latest/
.. |travis| image:: https://travis-ci.org/Chilipp/latlon-utils.svg?branch=master
:alt: Travis
:target: https://travis-ci.org/Chilipp/latlon-utils
.. |codecov| image:: https://codecov.io/gh/Chilipp/latlon-utils/branch/master/graph/badge.svg
:alt: Coverage
:target: https://codecov.io/gh/Chilipp/latlon-utils
.. |version| image:: https://img.shields.io/pypi/v/latlon-utils.svg?style=flat
:alt: PyPI Package latest release
:target: https://pypi.python.org/pypi/latlon-utils
.. |supported-versions| image:: https://img.shields.io/pypi/pyversions/latlon-utils.svg?style=flat
:alt: Supported versions
:target: https://pypi.python.org/pypi/latlon-utils
.. |supported-implementations| image:: https://img.shields.io/pypi/implementation/latlon-utils.svg?style=flat
:alt: Supported implementations
:target: https://pypi.python.org/pypi/latlon-utils
.. |github| image:: https://img.shields.io/github/release/Chilipp/latlon-utils.svg
:target: https://github.com/Chilipp/latlon-utils/releases/latest
:alt: Latest github release