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@@ -0,0 +1 @@ +/cache_decorator-2.1.14.tar.gz diff --git a/python-cache-decorator.spec b/python-cache-decorator.spec new file mode 100644 index 0000000..3c24491 --- /dev/null +++ b/python-cache-decorator.spec @@ -0,0 +1,171 @@ +%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 @@ -0,0 +1 @@ +eaa96907168945db685ea72501083972 cache_decorator-2.1.14.tar.gz |