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setup.py
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setup.py
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import codecs
import os
from setuptools import find_packages, setup
from stemflow.version import __version__
here = os.path.abspath(os.path.dirname(__file__))
with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
long_description = "\n" + fh.read()
requirements = open(os.path.join(here, "requirements.txt"), "r", encoding="utf-8").read().strip().split("\n")
requirements = [i for i in requirements if not i.startswith("#")]
DESCRIPTION = "A package for Adaptive Spatio-Temporal Exploratory Model (AdaSTEM) in python"
LONG_DESCRIPTION = '**Stemflow** is a toolkit for Adaptive Spatio-Temporal Exploratory Model (AdaSTEM [1,2]) in python. A typical usage is daily abundance estimation using eBird citizen science data. It leverages the "adjacency" information of surrounding target values in space and time to predict the classes/continuous values of target spatial-temporal points. In the demo, we use a two-step hurdle model as "base model", with XGBoostClassifier for occurrence modeling and XGBoostRegressor for abundance modeling.'
# Setting up
setup(
name="stemflow",
version=__version__,
author="Yangkang Chen",
author_email="[email protected]",
url="https://github.com/chenyangkang/stemflow",
description=DESCRIPTION,
long_description_content_type="text/markdown",
long_description=long_description,
packages=find_packages(),
package_dir={"stemflow": "stemflow"},
python_requires=">=3.8.0",
install_requires=requirements,
platforms="any",
keywords=[
"python",
"spatial-temporal model",
"ebird",
"citizen science",
"spatial temporal exploratory model",
"STEM",
"AdaSTEM",
"abundance",
"phenology",
],
classifiers=[
"Development Status :: 5 - Production/Stable",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Operating System :: Unix",
"Operating System :: MacOS :: MacOS X",
"Operating System :: Microsoft :: Windows",
],
)