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|
%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 <Python_Bot@openeuler.org> - 0.5.0-1
- Package Spec generated
|
