# Welcome to pymapee
[](https://pypi.python.org/pypi/pymapee)
[](https://anaconda.org/conda-forge/pymapee)
[](https://pepy.tech/project/pymapee)
[](https://pymapee.org)
[](https://github.com/tuyenhavan/pymapee/actions?query=workflow%3Abuild)
[](https://opensource.org/licenses/MIT)
**A Simple Python package to pre-processing satellite-based data using Google Earth Engine.**
`pymapee` is a simple Python package that aims to provide common functionalities to pre-process or calculate vegetation drought indices using Google Earth Engine. Currently, this package supports cloud masking (MODIS, Landsat, Sentinel-2), composite (monthly), and calculation of vegetation anomaly index (VAI) and vegetation condition index (VCI). It also supports to download an image collection (e.g., time-series NDVI or LST) or an image. This package is under active development, and changes are expected over time.
- GitHub repo: [https://github.com/tuyenhavan/pymapee](https://github.com/tuyenhavan/pymapee)
- Free software: MIT license
---
## Features
- Masking cloud-related pixels (e.g., MODIS, Landsat, and Sentinel-2)
- Making monthly composite
- Calculating monthly vegetation anomaly index (VAI) and vegetation condition index (VCI).
- Scaling data
- Downloading an image or image collection (e.g., time-series NDVI)
Examples are provided [here](https://github.com/tuyenhavan/pymapee/tree/main/examples), and it will be regularly updated.
---
## Installation
Install `pymapee` using pip
`pip install pymapee`
or install from Github to get the latest update.
`pip install git+https://github.com/tuyenhavan/pymapee.git`
---
## Credits
This package was created with [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the [giswqs/pypackage](https://github.com/giswqs/pypackage) project template.
Raw data
{
"_id": null,
"home_page": "https://github.com/tuyenhavan/pymapee",
"name": "pymapee",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "pymapee",
"author": "Tuyen Ha",
"author_email": "tuyenmassey@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/3c/e2/11d24d9dbbcf77ff1a6e508cb37c70e91651bc841032bbb6cb1e05d3b45a/pymapee-0.0.4.tar.gz",
"platform": null,
"description": "# Welcome to pymapee\n\n[](https://pypi.python.org/pypi/pymapee)\n[](https://anaconda.org/conda-forge/pymapee)\n[](https://pepy.tech/project/pymapee)\n[](https://pymapee.org)\n[](https://github.com/tuyenhavan/pymapee/actions?query=workflow%3Abuild)\n[](https://opensource.org/licenses/MIT)\n\n\n**A Simple Python package to pre-processing satellite-based data using Google Earth Engine.**\n\n`pymapee` is a simple Python package that aims to provide common functionalities to pre-process or calculate vegetation drought indices using Google Earth Engine. Currently, this package supports cloud masking (MODIS, Landsat, Sentinel-2), composite (monthly), and calculation of vegetation anomaly index (VAI) and vegetation condition index (VCI). It also supports to download an image collection (e.g., time-series NDVI or LST) or an image. This package is under active development, and changes are expected over time.\n\n- GitHub repo: [https://github.com/tuyenhavan/pymapee](https://github.com/tuyenhavan/pymapee)\n- Free software: MIT license\n---\n## Features\n\n- Masking cloud-related pixels (e.g., MODIS, Landsat, and Sentinel-2)\n- Making monthly composite\n- Calculating monthly vegetation anomaly index (VAI) and vegetation condition index (VCI).\n- Scaling data\n- Downloading an image or image collection (e.g., time-series NDVI)\n\nExamples are provided [here](https://github.com/tuyenhavan/pymapee/tree/main/examples), and it will be regularly updated.\n\n---\n## Installation\nInstall `pymapee` using pip \n\n`pip install pymapee`\n\nor install from Github to get the latest update.\n\n`pip install git+https://github.com/tuyenhavan/pymapee.git`\n\n---\n## Credits\n\nThis package was created with [Cookiecutter](https://github.com/cookiecutter/cookiecutter) and the [giswqs/pypackage](https://github.com/giswqs/pypackage) project template.",
"bugtrack_url": null,
"license": "MIT license",
"summary": "A Simple Python package to pre-processing satellite data using Google Earth Engine.",
"version": "0.0.4",
"split_keywords": [
"pymapee"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3ce211d24d9dbbcf77ff1a6e508cb37c70e91651bc841032bbb6cb1e05d3b45a",
"md5": "6767909085cfc587ec3eb2f7a6248f1c",
"sha256": "e6c6fae44c84baf6defc4310ed9c1836c494799d07314ce414992a4d7d5c3d52"
},
"downloads": -1,
"filename": "pymapee-0.0.4.tar.gz",
"has_sig": false,
"md5_digest": "6767909085cfc587ec3eb2f7a6248f1c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 18465,
"upload_time": "2023-04-07T14:42:42",
"upload_time_iso_8601": "2023-04-07T14:42:42.771636Z",
"url": "https://files.pythonhosted.org/packages/3c/e2/11d24d9dbbcf77ff1a6e508cb37c70e91651bc841032bbb6cb1e05d3b45a/pymapee-0.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-04-07 14:42:42",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "tuyenhavan",
"github_project": "pymapee",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "earthengine-api",
"specs": [
[
">=",
"0.1.316"
]
]
}
],
"lcname": "pymapee"
}
JSON
