%global _empty_manifest_terminate_build 0 Name: python-forestatrisk Version: 1.1 Release: 1 Summary: Modelling and forecasting deforestation in the tropics License: GPLv3 URL: https://github.com/ghislainv/forestatrisk Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f4/29/1e37ca27b519478874436856fa8cfb8eaa104d42d1cde0575781844bcb3c/forestatrisk-1.1.tar.gz Requires: python3-earthengine-api Requires: python3-gdal Requires: python3-numpy Requires: python3-matplotlib Requires: python3-pandas Requires: python3-patsy Requires: python3-pywdpa Requires: python3-sklearn Requires: python3-jupyter Requires: python3-dotenv Requires: python3-geopandas Requires: python3-descartes Requires: python3-folium %description The ``forestatrisk`` Python package can be used to **model** the tropical deforestation spatially, **predict** the spatial risk of deforestation, and **forecast** the future forest cover in the tropics. It provides functions to estimate the spatial probability of deforestation as a function of various spatial explanatory variables. Spatial explanatory variables can be derived from topography (altitude, slope, and aspect), accessibility (distance to roads, towns, and forest edge), deforestation history (distance to previous deforestation), or land conservation status (eg. protected area) for example. %package -n python3-forestatrisk Summary: Modelling and forecasting deforestation in the tropics Provides: python-forestatrisk BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-forestatrisk The ``forestatrisk`` Python package can be used to **model** the tropical deforestation spatially, **predict** the spatial risk of deforestation, and **forecast** the future forest cover in the tropics. It provides functions to estimate the spatial probability of deforestation as a function of various spatial explanatory variables. Spatial explanatory variables can be derived from topography (altitude, slope, and aspect), accessibility (distance to roads, towns, and forest edge), deforestation history (distance to previous deforestation), or land conservation status (eg. protected area) for example. %package help Summary: Development documents and examples for forestatrisk Provides: python3-forestatrisk-doc %description help The ``forestatrisk`` Python package can be used to **model** the tropical deforestation spatially, **predict** the spatial risk of deforestation, and **forecast** the future forest cover in the tropics. It provides functions to estimate the spatial probability of deforestation as a function of various spatial explanatory variables. Spatial explanatory variables can be derived from topography (altitude, slope, and aspect), accessibility (distance to roads, towns, and forest edge), deforestation history (distance to previous deforestation), or land conservation status (eg. protected area) for example. %prep %autosetup -n forestatrisk-1.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-forestatrisk -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 15 2023 Python_Bot - 1.1-1 - Package Spec generated