%global _empty_manifest_terminate_build 0 Name: python-jupyter-cache Version: 0.5.0 Release: 1 Summary: A defined interface for working with a cache of jupyter notebooks. License: MIT URL: https://github.com/executablebooks/jupyter-cache Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b3/07/feded9f29b7ae087e5b49b6f93f74c59f444300c2b226801e8417ae83a17/jupyter-cache-0.5.0.tar.gz BuildArch: noarch Requires: python3-attrs Requires: python3-click Requires: python3-importlib-metadata Requires: python3-nbclient Requires: python3-nbformat Requires: python3-pyyaml Requires: python3-sqlalchemy Requires: python3-tabulate Requires: python3-click-log Requires: python3-pre-commit Requires: python3-nbdime Requires: python3-jupytext Requires: python3-myst-nb Requires: python3-sphinx-book-theme Requires: python3-sphinx-copybutton Requires: python3-nbdime Requires: python3-coverage Requires: python3-ipykernel Requires: python3-jupytext Requires: python3-matplotlib Requires: python3-nbformat Requires: python3-numpy Requires: python3-pandas Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-pytest-regressions Requires: python3-sympy %description # jupyter-cache [![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![Code style: black][black-badge]][black-link] [![PyPI][pypi-badge]][pypi-link] A defined interface for working with a cache of jupyter notebooks. ## Why use jupyter-cache? If you have a number of notebooks whose execution outputs you want to ensure are kept up to date, without having to re-execute them every time (particularly for long running code, or text-based formats that do not store the outputs). The notebooks must have deterministic execution outputs: - You use the same environment to run them (e.g. the same installed packages) - They run no non-deterministic code (e.g. random numbers) - They do not depend on external resources (e.g. files or network connections) that change over time For example, it is utilised by [jupyter-book](https://jupyterbook.org/content/execute.html#caching-the-notebook-execution), to allow for fast document re-builds. ## Install ```bash pip install jupyter-cache ``` For development: ```bash git clone https://github.com/executablebooks/jupyter-cache cd jupyter-cache git checkout develop pip install -e .[cli,code_style,testing] ``` See the documentation for usage. ## Development Some desired requirements (not yet all implemented): - Persistent - Separates out "edits to content" from "edits to code cells". Cell rearranges and code cell changes should require a re-execution. Content changes should not. - Allow parallel access to notebooks (for execution) - Store execution statistics/reports - Store external assets: Notebooks being executed often require external assets: importing scripts/data/etc. These are prepared by the users. - Store execution artefacts: created during execution - A transparent and robust cache invalidation: imagine the user updating an external dependency or a Python module, or checking out a different git branch. ## Contributing jupyter-cache follows the [Executable Book Contribution Guide](https://executablebooks.org/en/latest/contributing.html). We'd love your help! ### Code Style Code style is tested using [flake8](http://flake8.pycqa.org), with the configuration set in `.flake8`, and code formatted with [black](https://github.com/ambv/black). Installing with `jupyter-cache[code_style]` makes the [pre-commit](https://pre-commit.com/) package available, which will ensure this style is met before commits are submitted, by reformatting the code and testing for lint errors. It can be setup by: ```shell >> cd jupyter-cache >> pre-commit install ``` Optionally you can run `black` and `flake8` separately: ```shell >> black . >> flake8 . ``` Editors like VS Code also have automatic code reformat utilities, which can adhere to this standard. [github-ci]: https://github.com/executablebooks/jupyter-cache/workflows/continuous-integration/badge.svg?branch=master [github-link]: https://github.com/executablebooks/jupyter-cache [codecov-badge]: https://codecov.io/gh/executablebooks/jupyter-cache/branch/master/graph/badge.svg [codecov-link]: https://codecov.io/gh/executablebooks/jupyter-cache [rtd-badge]: https://readthedocs.org/projects/jupyter-cache/badge/?version=latest [rtd-link]: https://jupyter-cache.readthedocs.io/en/latest/?badge=latest [black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg [pypi-badge]: https://img.shields.io/pypi/v/jupyter-cache.svg [pypi-link]: https://pypi.org/project/jupyter-cache [black-link]: https://github.com/ambv/black %package -n python3-jupyter-cache Summary: A defined interface for working with a cache of jupyter notebooks. Provides: python-jupyter-cache BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-jupyter-cache # jupyter-cache [![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![Code style: black][black-badge]][black-link] [![PyPI][pypi-badge]][pypi-link] A defined interface for working with a cache of jupyter notebooks. ## Why use jupyter-cache? If you have a number of notebooks whose execution outputs you want to ensure are kept up to date, without having to re-execute them every time (particularly for long running code, or text-based formats that do not store the outputs). The notebooks must have deterministic execution outputs: - You use the same environment to run them (e.g. the same installed packages) - They run no non-deterministic code (e.g. random numbers) - They do not depend on external resources (e.g. files or network connections) that change over time For example, it is utilised by [jupyter-book](https://jupyterbook.org/content/execute.html#caching-the-notebook-execution), to allow for fast document re-builds. ## Install ```bash pip install jupyter-cache ``` For development: ```bash git clone https://github.com/executablebooks/jupyter-cache cd jupyter-cache git checkout develop pip install -e .[cli,code_style,testing] ``` See the documentation for usage. ## Development Some desired requirements (not yet all implemented): - Persistent - Separates out "edits to content" from "edits to code cells". Cell rearranges and code cell changes should require a re-execution. Content changes should not. - Allow parallel access to notebooks (for execution) - Store execution statistics/reports - Store external assets: Notebooks being executed often require external assets: importing scripts/data/etc. These are prepared by the users. - Store execution artefacts: created during execution - A transparent and robust cache invalidation: imagine the user updating an external dependency or a Python module, or checking out a different git branch. ## Contributing jupyter-cache follows the [Executable Book Contribution Guide](https://executablebooks.org/en/latest/contributing.html). We'd love your help! ### Code Style Code style is tested using [flake8](http://flake8.pycqa.org), with the configuration set in `.flake8`, and code formatted with [black](https://github.com/ambv/black). Installing with `jupyter-cache[code_style]` makes the [pre-commit](https://pre-commit.com/) package available, which will ensure this style is met before commits are submitted, by reformatting the code and testing for lint errors. It can be setup by: ```shell >> cd jupyter-cache >> pre-commit install ``` Optionally you can run `black` and `flake8` separately: ```shell >> black . >> flake8 . ``` Editors like VS Code also have automatic code reformat utilities, which can adhere to this standard. [github-ci]: https://github.com/executablebooks/jupyter-cache/workflows/continuous-integration/badge.svg?branch=master [github-link]: https://github.com/executablebooks/jupyter-cache [codecov-badge]: https://codecov.io/gh/executablebooks/jupyter-cache/branch/master/graph/badge.svg [codecov-link]: https://codecov.io/gh/executablebooks/jupyter-cache [rtd-badge]: https://readthedocs.org/projects/jupyter-cache/badge/?version=latest [rtd-link]: https://jupyter-cache.readthedocs.io/en/latest/?badge=latest [black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg [pypi-badge]: https://img.shields.io/pypi/v/jupyter-cache.svg [pypi-link]: https://pypi.org/project/jupyter-cache [black-link]: https://github.com/ambv/black %package help Summary: Development documents and examples for jupyter-cache Provides: python3-jupyter-cache-doc %description help # jupyter-cache [![Github-CI][github-ci]][github-link] [![Coverage Status][codecov-badge]][codecov-link] [![Documentation Status][rtd-badge]][rtd-link] [![Code style: black][black-badge]][black-link] [![PyPI][pypi-badge]][pypi-link] A defined interface for working with a cache of jupyter notebooks. ## Why use jupyter-cache? If you have a number of notebooks whose execution outputs you want to ensure are kept up to date, without having to re-execute them every time (particularly for long running code, or text-based formats that do not store the outputs). The notebooks must have deterministic execution outputs: - You use the same environment to run them (e.g. the same installed packages) - They run no non-deterministic code (e.g. random numbers) - They do not depend on external resources (e.g. files or network connections) that change over time For example, it is utilised by [jupyter-book](https://jupyterbook.org/content/execute.html#caching-the-notebook-execution), to allow for fast document re-builds. ## Install ```bash pip install jupyter-cache ``` For development: ```bash git clone https://github.com/executablebooks/jupyter-cache cd jupyter-cache git checkout develop pip install -e .[cli,code_style,testing] ``` See the documentation for usage. ## Development Some desired requirements (not yet all implemented): - Persistent - Separates out "edits to content" from "edits to code cells". Cell rearranges and code cell changes should require a re-execution. Content changes should not. - Allow parallel access to notebooks (for execution) - Store execution statistics/reports - Store external assets: Notebooks being executed often require external assets: importing scripts/data/etc. These are prepared by the users. - Store execution artefacts: created during execution - A transparent and robust cache invalidation: imagine the user updating an external dependency or a Python module, or checking out a different git branch. ## Contributing jupyter-cache follows the [Executable Book Contribution Guide](https://executablebooks.org/en/latest/contributing.html). We'd love your help! ### Code Style Code style is tested using [flake8](http://flake8.pycqa.org), with the configuration set in `.flake8`, and code formatted with [black](https://github.com/ambv/black). Installing with `jupyter-cache[code_style]` makes the [pre-commit](https://pre-commit.com/) package available, which will ensure this style is met before commits are submitted, by reformatting the code and testing for lint errors. It can be setup by: ```shell >> cd jupyter-cache >> pre-commit install ``` Optionally you can run `black` and `flake8` separately: ```shell >> black . >> flake8 . ``` Editors like VS Code also have automatic code reformat utilities, which can adhere to this standard. [github-ci]: https://github.com/executablebooks/jupyter-cache/workflows/continuous-integration/badge.svg?branch=master [github-link]: https://github.com/executablebooks/jupyter-cache [codecov-badge]: https://codecov.io/gh/executablebooks/jupyter-cache/branch/master/graph/badge.svg [codecov-link]: https://codecov.io/gh/executablebooks/jupyter-cache [rtd-badge]: https://readthedocs.org/projects/jupyter-cache/badge/?version=latest [rtd-link]: https://jupyter-cache.readthedocs.io/en/latest/?badge=latest [black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg [pypi-badge]: https://img.shields.io/pypi/v/jupyter-cache.svg [pypi-link]: https://pypi.org/project/jupyter-cache [black-link]: https://github.com/ambv/black %prep %autosetup -n jupyter-cache-0.5.0 %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-jupyter-cache -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.5.0-1 - Package Spec generated