1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
|
%global _empty_manifest_terminate_build 0
Name: python-ipfn
Version: 1.4.4
Release: 1
Summary: Iterative Proportional Fitting with N dimensions, for python
License: MIT
URL: https://github.com/Dirguis/ipfn.git
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/82/acf73d6e8b3877e3b0ace42aad730bd8ac3b7320a70963092cdc6cc85ec9/ipfn-1.4.4.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
%description
Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
The algorithm exists in 2 versions:
* numpy version, which the fastest by far
* pandas version, which is much slower but easier to use than the numpy version
The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
* pip install ipfn
* pip install git+http://github.com/dirguis/ipfn@master
For more information and examples, please visit:
* `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
* `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
%package -n python3-ipfn
Summary: Iterative Proportional Fitting with N dimensions, for python
Provides: python-ipfn
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ipfn
Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
The algorithm exists in 2 versions:
* numpy version, which the fastest by far
* pandas version, which is much slower but easier to use than the numpy version
The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
* pip install ipfn
* pip install git+http://github.com/dirguis/ipfn@master
For more information and examples, please visit:
* `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
* `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
%package help
Summary: Development documents and examples for ipfn
Provides: python3-ipfn-doc
%description help
Iterative proportional fitting is an algorithm used is many different fields such as economics or social sciences, to alter results in such a way that aggregates along one or several dimensions match known marginals (or aggregates along these same dimensions).
The algorithm exists in 2 versions:
* numpy version, which the fastest by far
* pandas version, which is much slower but easier to use than the numpy version
The algorithm recognizes the input variable type and and uses the appropriate version to solve the problem. To install the package:
* pip install ipfn
* pip install git+http://github.com/dirguis/ipfn@master
For more information and examples, please visit:
* `wikipedia page on ipf <https://en.wikipedia.org/wiki/Iterative_proportional_fitting>`_
* `slides explaining the methodology and links to specific examples <http://www.demog.berkeley.edu/~eddieh/IPFDescription/AKDOLWDIPFTWOD.pdf>`_
%prep
%autosetup -n ipfn-1.4.4
%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-ipfn -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.4-1
- Package Spec generated
|
