automatic import of python-idf-analysisopeneuler20.03

3 files changed, 88 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/idf-analysis-0.1.15.tar.gz
diff --git a/python-idf-analysis.spec b/python-idf-analysis.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-idf-analysis
+Version:	0.1.15
+Release:	1
+Summary:	heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)
+License:	MIT
+URL:		https://pypi.org/project/idf-analysis/
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/b8/3b/14620590064f8ccb9287f0c4f7f3ae2d0ed7c894c34be413e79ae89b8d39/idf-analysis-0.1.15.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-tqdm
+Requires:	python3-matplotlib
+Requires:	python3-tzlocal
+Requires:	python3-pytz
+Requires:	python3-scipy
+Requires:	python3-PyYAML
+Requires:	python3-pyarrow
+Requires:	python3-sphinx
+Requires:	python3-nbsphinx
+Requires:	python3-recommonmark
+Requires:	python3-pydata-sphinx-theme
+Requires:	python3-ipython
+
+%description
+> Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through "Coordinated Heavy Rainfall Regionalisation Analyses" (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed. In the DWA-A 531 worksheet, the main features of the ATVA 121 worksheet published in 1985 and of the identical DVWK-R 124 booklet of the DVWK Rules for Water Management "Heavy Rain Evaluation after Return Time and Duration" are retained. The aim of the revision is to take account of current developments without, however, calling into question the standardisation of the procedure for statistical heavy rain analyses which was intended at the time.
+
+%package -n python3-idf-analysis
+Summary:	heavy rain as a function of the duration and the return period acc. to DWA-A 531 (2012)
+Provides:	python-idf-analysis
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-idf-analysis
+> Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through "Coordinated Heavy Rainfall Regionalisation Analyses" (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed. In the DWA-A 531 worksheet, the main features of the ATVA 121 worksheet published in 1985 and of the identical DVWK-R 124 booklet of the DVWK Rules for Water Management "Heavy Rain Evaluation after Return Time and Duration" are retained. The aim of the revision is to take account of current developments without, however, calling into question the standardisation of the procedure for statistical heavy rain analyses which was intended at the time.
+
+%package help
+Summary:	Development documents and examples for idf-analysis
+Provides:	python3-idf-analysis-doc
+%description help
+> Heavy rainfall data are among the most important planning parameters in water management and hydraulic engineering practice. In urban areas, for example, they are required as initial parameters for the design of rainwater drainage systems and in watercourses for the dimensioning of hydraulic structures. The accuracy of the target values of the corresponding calculation methods and models depends crucially on their accuracy. Their overestimation can lead to considerable additional costs in the structural implementation, their underestimation to an unacceptable, excessive residual risk of failure during the operation of water management and hydraulic engineering facilities. Despite the area-wide availability of heavy rainfall data through "Coordinated Heavy Rainfall Regionalisation Analyses" (KOSTRA), there is still a need for local station analyses, e.g. to evaluate the now extended data series, to evaluate recent developments or to classify local peculiarities in comparison to the KOSTRA data. However, this is only possible without restrictions if the methodological approach recommended in the worksheet is followed. In the DWA-A 531 worksheet, the main features of the ATVA 121 worksheet published in 1985 and of the identical DVWK-R 124 booklet of the DVWK Rules for Water Management "Heavy Rain Evaluation after Return Time and Duration" are retained. The aim of the revision is to take account of current developments without, however, calling into question the standardisation of the procedure for statistical heavy rain analyses which was intended at the time.
+
+%prep
+%autosetup -n idf-analysis-0.1.15
+
+%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-idf-analysis -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.15-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..a253972
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+9daad66978017dead6ac45d83e4b711f  idf-analysis-0.1.15.tar.gz