GPM-API

Provides an easy-to-use python interface to download, read, process and visualize most of the products of the Global Precipitation Measurement Mission (GPM) data archive.
https://github.com/ghiggi/gpm_api

Category: Hydrosphere
Sub Category: Ocean and Hydrology Data Access

Keywords

analysis-ready-data earth-observation eumetsat gpm jaxa nasa noaa open-data passive-microwave precipitation python radar rainfall reflectivity remote-sensing reproducible-research satellite-data snowfall trmm xarray

Keywords from Contributors

archiving transforms measur observation projection conversion optimize mock data-profiling region

Last synced: about 12 hours ago
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Repository metadata

Global Precipitation Measurement Mission (GPM) python package to download and analyze data with xarray

Host: GitHub
URL: https://github.com/ghiggi/gpm_api
Owner: ghiggi
License: mit
Created: 2020-08-11T06:30:57.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2025-04-07T21:29:09.000Z (19 days ago)
Last Synced: 2025-04-10T06:39:25.018Z (17 days ago)
Topics: analysis-ready-data, earth-observation, eumetsat, gpm, jaxa, nasa, noaa, open-data, passive-microwave, precipitation, python, radar, rainfall, reflectivity, remote-sensing, reproducible-research, satellite-data, snowfall, trmm, xarray
Language: Jupyter Notebook
Homepage: https://gpm-api.readthedocs.io
Size: 130 MB
Stars: 64
Watchers: 1
Forks: 7
Open Issues: 3
Releases: 19
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Citation: CITATION.bib
- Authors: AUTHORS.md

README.md


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Slack | Documentation

🚀 Quick start

GPM-API provides an easy-to-use python interface to download, read, process and visualize most
of the products of the Global Precipitation Measurement Mission (GPM) data archive.

The list of available products can be retrieved using:

import gpm

gpm.available_products(product_types="RS")  # research products
gpm.available_products(product_types="NRT")  # near-real-time products

Before starting using GPM-API, we highly suggest to save into a configuration file:

your credentials to access the NASA Precipitation Processing System (PPS) servers
the directory on the local disk where to save the GPM dataset of interest.

To facilitate the creation of the configuration file, you can run the following script:

import gpm

username_pps = "<your PPS username>"  # likely your mail
password_pps = "<your PPS password>"  # likely your mail
base_dir = "<path/to/directory/GPM"  # path to the directory where to download the data
gpm.define_configs(
    username_pps=username_pps, password_pps=password_pps, base_dir=base_dir
)

# You can check that the config file has been correctly created with:
configs = gpm.read_configs()
print(configs)

📥 Download GPM data

Now you can either start to download GPM data within python:

import gpm
import datetime

product = "2A-DPR"
product_type = "RS"
version = 7

start_time = datetime.datetime(2020, 7, 22, 0, 1, 11)
end_time = datetime.datetime(2020, 7, 22, 0, 23, 5)

gpm.download(
    product=product,
    product_type=product_type,
    version=version,
    n_threads=2,
    start_time=start_time,
    end_time=end_time,
)

or from the terminal using i.e. download_daily_gpm_data <product> <year> <month> <day>:

download_daily_gpm_data 2A-DPR 2022 7 22

💫 Open GPM files into xarray

A GPM granule can be opened in python using:

import gpm

ds = gpm.open_granule_dataset(<path_to_granule>)
# or
dt = gpm.open_granule_datatree(<path_to_granule>)

while multiple granules over a specific time period can be opened using:

import gpm
import datetime

product = "2A-DPR"
product_type = "RS"
version = 7

start_time = datetime.datetime(2020,7, 22, 0, 1, 11)
end_time = datetime.datetime(2020,7, 22, 0, 23, 5)
ds = gpm.open_dataset(product=product,
                      product_type=product_type,
                      version=version
                      start_time=start_time,
                      end_time=end_time)

📖 Explore the GPM-API documentation

To discover all GPM-API download, manipulation, analysis and plotting features,
please read the software documentation available at https://gpm-api.readthedocs.io/en/latest/.

If you are new to GPM-API, we recommend starting with the following pages:

All GPM-API tutorials are available as Jupyter Notebooks in the tutorial directory.

🛠️ Installation

conda

GPM-API can be installed via conda on Linux, Mac, and Windows.
Install the package by typing the following command in the terminal:

conda install gpm-api

In case conda-forge is not set up for your system yet, see the easy to follow instructions on conda-forge.

pip

GPM-API can be installed also via pip on Linux, Mac, and Windows.
On Windows you can install WinPython to get Python and pip running.
Prior installation of GPM-API, try to install to cartopy>=0.21.0 package to ensure there are not GEOS library version incompatibilities.
If you can't solve the problems and install cartopy with pip, you should install at least cartopy with conda using conda install cartopy>=0.21.0.

Then, install the GPM-API package by typing the following command in the terminal:

pip install gpm-api

To install the latest development version via pip, see the documentation.

💭 Feedback and Contributing Guidelines

If you aim to contribute your data or discuss the future development of GPM-API,
we highly suggest to join the GPM-API Slack Workspace

Feel free to also open a GitHub Issue or a GitHub Discussion specific to your questions or ideas.

Citation

If you are using GPM-API in your publication please cite our Zenodo repository:

Ghiggi Gionata. ghiggi/gpm_api. Zenodo.

If you want to cite a specific software version, have a look at the Zenodo site.

License

The content of this repository is released under the terms of the MIT license.

Citation (CITATION.bib)

@Article{gmd-XX-XXXX-2024,
AUTHOR = {Ghiggi, G. and ....},
TITLE = {\texttt{GPM-API} v1.0: An API to access and process the Global Precipitation Measurement data archive},
JOURNAL = {Geoscientific Model Development},
VOLUME = {XX},
YEAR = {XX},
NUMBER = {XX},
PAGES = {XXXX--XXXX},
URL = {https://gmd.copernicus.org/articles/XX/XXXX/2024/},
DOI = {10.5194/gmd-XX-XXXX-XXXX}
}

Owner metadata

Name: Gionata Ghiggi
Login: ghiggi
Email:
Kind: user
Description:
Website:
Location:
Twitter:
Company:
Icon url: https://avatars.githubusercontent.com/u/19285200?v=4
Repositories: 22
Last ynced at: 2023-04-28T13:00:26.474Z
Profile URL: https://github.com/ghiggi

GitHub Events

Total

Create event: 6
Release event: 3
Issues event: 6
Watch event: 8
Delete event: 2
Issue comment event: 12
Push event: 91
Pull request event: 11
Fork event: 2

Last Year

Create event: 6
Release event: 3
Issues event: 6
Watch event: 8
Delete event: 2
Issue comment event: 12
Push event: 91
Pull request event: 11
Fork event: 2

Committers metadata

Last synced: 7 days ago

Total Commits: 618
Total Committers: 6
Avg Commits per committer: 103.0
Development Distribution Score (DDS): 0.314

Commits in past year: 39
Committers in past year: 2
Avg Commits per committer in past year: 19.5
Development Distribution Score (DDS) in past year: 0.051

Name	Email	Commits
ghiggi	g**i@g**m	424
Son Pham-Ba	s**a@e**h	140
Evan Thomas	e**s@e**h	41
dependabot[bot]	4****]	6
pre-commit-ci[bot]	6****]	4
Randy J. Chase	r**2@g**m	3

Committer domains:

epfl.ch: 2

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 14
Total pull requests: 65
Average time to close issues: about 2 months
Average time to close pull requests: 22 days
Total issue authors: 10
Total pull request authors: 7
Average comments per issue: 1.43
Average comments per pull request: 1.83
Merged pull request: 52
Bot issues: 0
Bot pull requests: 17

Past year issues: 7
Past year pull requests: 16
Past year average time to close issues: about 2 months
Past year average time to close pull requests: about 2 months
Past year issue authors: 5
Past year pull request authors: 4
Past year average comments per issue: 2.0
Past year average comments per pull request: 1.63
Past year merged pull request: 10
Past year bot issues: 0
Past year bot pull requests: 7

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/ghiggi/gpm_api

Top Issue Authors

ghiggi (2)
kmuehlbauer (2)
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lllliso (1)
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naseefo (1)
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edsml-ds423 (1)

Top Pull Request Authors

sphamba (24)
ghiggi (20)
pre-commit-ci[bot] (11)
dependabot[bot] (6)
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Top Issue Labels

enhancement (4)
bug (2)
documentation (1)

Top Pull Request Labels

dependencies (6)
documentation (1)

Package metadata

Total packages: 1
Total downloads:
- pypi: 801 last-month
Total dependent packages: 0
Total dependent repositories: 0
Total versions: 19
Total maintainers: 1

pypi.org: gpm-api

Python Package for the Global Precipitation Measurement (GPM) Mission Data Archive

Homepage:
Documentation: https://gpm-api.readthedocs.io/
Licenses: MIT License Copyright (c) 2023 Gionata Ghiggi 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. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.3.4 (published 20 days ago)
Last Synced: 2025-04-25T14:01:42.090Z (1 day ago)
Versions: 19
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 801 Last month
Rankings:
- Dependent packages count: 6.633%
- Average: 23.298%
- Stargazers count: 25.456%
- Forks count: 30.492%
- Dependent repos count: 30.611%
Maintainers (1)
- ghiggi

Dependencies

pyproject.toml pypi

cartopy >=0.20.0
dask *
h5py *
netcdf4 *
pyresample *
tqdm *
trollsift *
wget *
xarray *

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actions/checkout v3 composite
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pre-commit/action v3.0.0 composite

.github/workflows/release_to_pypi.yml actions

actions/checkout v2 composite
actions/create-release v1 composite
actions/setup-python v2 composite
actions/upload-release-asset v1 composite
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actions/checkout v3 composite
codecov/codecov-action v3 composite
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ci/environment.yml pypi

pycolorbar *
ximage *

setup.py pypi

Score: 12.683560696485536

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