%global _empty_manifest_terminate_build 0
Name:		python-cache-decorator
Version:	2.1.14
Release:	1
Summary:	a simple decorator to cache the results of computationally heavy functions
License:	MIT
URL:		https://github.com/zommiommy/cache_decorator
Source0:	https://mirrors.aliyun.com/pypi/web/packages/82/97/a3dd4feacebc2206c8cb37c800d908ab6d801d215fa7639b4ed782bbeebe/cache_decorator-2.1.14.tar.gz
BuildArch:	noarch


%description
|pip| |downloads|
A simple decorator to cache the results of computationally heavy functions.
The package automatically serialize and deserialize depending on the format of the save path.
By default it supports ``.json .json.gz .json.bz .json.lzma`` and ``.pkl .pkl.gz .pkl.bz .pkl.lzma .pkl.zip`` 
but other extensions can be used if the following packages are installed:
numpy: ``.npy .npz``
pandas: ``.csv .csv.gz .csv.bz2 .csv.zip .csv.xz``
Also there is an optimized format for numerical dataframes:
pandas: ``.embedding .embedding.gz .embedding.bz2 .embedding.xz``
This creates an optionally compressed tar archive with pickles of the index and 
columns and a ``.npy`` of the values.
    import time
    import numpy as np
    import pandas as pd
    from cache_decorator import Cache
    @Cache(
        cache_path={
            "info": "/tmp/{function_name}/{_hash}.json.xz",
            "data": "/tmp/{function_name}/{_hash}.csv.gz",
        },
        validity_duration="24d",
        args_to_ignore=("verbose",),
        enable_cache_arg_name="enable_cache",
    )
    def function_to_cache(seed: int, verbose: bool = True):
        np.random.seed(seed)
        if verbose:
            print(f"using seed {seed}")
        return {
            "info": {"timestamp": time.time(), "seed": seed,},
            "data": pd.DataFrame(
                np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD")
            ),
        }

%package -n python3-cache-decorator
Summary:	a simple decorator to cache the results of computationally heavy functions
Provides:	python-cache-decorator
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-cache-decorator
|pip| |downloads|
A simple decorator to cache the results of computationally heavy functions.
The package automatically serialize and deserialize depending on the format of the save path.
By default it supports ``.json .json.gz .json.bz .json.lzma`` and ``.pkl .pkl.gz .pkl.bz .pkl.lzma .pkl.zip`` 
but other extensions can be used if the following packages are installed:
numpy: ``.npy .npz``
pandas: ``.csv .csv.gz .csv.bz2 .csv.zip .csv.xz``
Also there is an optimized format for numerical dataframes:
pandas: ``.embedding .embedding.gz .embedding.bz2 .embedding.xz``
This creates an optionally compressed tar archive with pickles of the index and 
columns and a ``.npy`` of the values.
    import time
    import numpy as np
    import pandas as pd
    from cache_decorator import Cache
    @Cache(
        cache_path={
            "info": "/tmp/{function_name}/{_hash}.json.xz",
            "data": "/tmp/{function_name}/{_hash}.csv.gz",
        },
        validity_duration="24d",
        args_to_ignore=("verbose",),
        enable_cache_arg_name="enable_cache",
    )
    def function_to_cache(seed: int, verbose: bool = True):
        np.random.seed(seed)
        if verbose:
            print(f"using seed {seed}")
        return {
            "info": {"timestamp": time.time(), "seed": seed,},
            "data": pd.DataFrame(
                np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD")
            ),
        }

%package help
Summary:	Development documents and examples for cache-decorator
Provides:	python3-cache-decorator-doc
%description help
|pip| |downloads|
A simple decorator to cache the results of computationally heavy functions.
The package automatically serialize and deserialize depending on the format of the save path.
By default it supports ``.json .json.gz .json.bz .json.lzma`` and ``.pkl .pkl.gz .pkl.bz .pkl.lzma .pkl.zip`` 
but other extensions can be used if the following packages are installed:
numpy: ``.npy .npz``
pandas: ``.csv .csv.gz .csv.bz2 .csv.zip .csv.xz``
Also there is an optimized format for numerical dataframes:
pandas: ``.embedding .embedding.gz .embedding.bz2 .embedding.xz``
This creates an optionally compressed tar archive with pickles of the index and 
columns and a ``.npy`` of the values.
    import time
    import numpy as np
    import pandas as pd
    from cache_decorator import Cache
    @Cache(
        cache_path={
            "info": "/tmp/{function_name}/{_hash}.json.xz",
            "data": "/tmp/{function_name}/{_hash}.csv.gz",
        },
        validity_duration="24d",
        args_to_ignore=("verbose",),
        enable_cache_arg_name="enable_cache",
    )
    def function_to_cache(seed: int, verbose: bool = True):
        np.random.seed(seed)
        if verbose:
            print(f"using seed {seed}")
        return {
            "info": {"timestamp": time.time(), "seed": seed,},
            "data": pd.DataFrame(
                np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD")
            ),
        }

%prep
%autosetup -n cache_decorator-2.1.14

%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-cache-decorator -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2.1.14-1
- Package Spec generated