%global _empty_manifest_terminate_build 0
Name: python-lumen
Version: 0.5.1
Release: 1
Summary: A monitoring solution built on Panel.
License: BSD
URL: https://github.com/holoviz/lumen
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c4/f8/a5dc48aa924fc2858d413a7927a3d3dc22984c7387665905e615826266ec/lumen-0.5.1.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-bokeh
Requires: python3-param
Requires: python3-panel
Requires: python3-pandas
Requires: python3-hvplot
Requires: python3-holoviews
Requires: python3-packaging
Requires: python3-intake
Requires: python3-jinja2
Requires: python3-codecov
Requires: python3-fastparquet
Requires: python3-flake8
Requires: python3-intake
Requires: python3-intake-sql
Requires: python3-msgpack-python
Requires: python3-nbsite
Requires: python3-pre-commit
Requires: python3-pydata-sphinx-theme
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-sphinx-copybutton
Requires: python3-sphinx-design
Requires: python3-toolz
Requires: python3-pydata-sphinx-theme
Requires: python3-nbsite
Requires: python3-sphinx-design
Requires: python3-sphinx-copybutton
Requires: python3-pytest
Requires: python3-flake8
Requires: python3-intake
Requires: python3-intake-sql
Requires: python3-fastparquet
Requires: python3-msgpack-python
Requires: python3-toolz
Requires: python3-pytest-cov
Requires: python3-codecov
Requires: python3-pre-commit
%description
# Lumen
*Illuminate your data*
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/lumen/actions/workflows/test.yml)
| Coverage | [](https://codecov.io/gh/holoviz/lumen) |
| Latest dev release | [](https://github.com/holoviz/lumen/tags) [](https://pyviz-dev.github.io/lumen/) |
| Latest release | [](https://github.com/holoviz/lumen/releases) [](https://pypi.python.org/pypi/lumen) [](https://anaconda.org/pyviz/lumen) [](https://anaconda.org/conda-forge/lumen) [](https://anaconda.org/anaconda/lumen) |
| Docs | [](https://github.com/holoviz/lumen/tree/gh-pages) [](https://lumen.holoviz.org) |
| Support | [](https://discourse.holoviz.org/) |
## Why Lumen?
The Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification. The power of Lumen comes from the ability to leverage the powerful data intake, data processing and data visualization libraries available in the PyData ecosystem.
- **Data Intake**: A flexible system for declaring data sources with strong integration with [Intake](https://intake.readthedocs.io/en/latest/), allows Lumen to query data from a wide range of sources including many file formats such as CSV or Parquet but also SQL and many others.
- **Data Proccessing**: Internally Lumen stores data as DataFrame objects, allowing users to leverage familiar APIs for filtering and transforming data using [Pandas](https://pandas.pydata.org/) while also providing the ability to scale these transformations out to a cluster thanks to [Dask](https://dask.org/).
- **Data Visualization**: Since Lumen is built on [Panel](https://panel.holoviz.org) all the most popular plotting libraries and many other components such as powerful datagrids and BI indicators are supported.
The core strengths of Lumen include:
- **Flexibility**: The design of Lumen allows flexibly combining data intake, data processing and data visualization into a simple declarative pipeline.
- **Extensibility**: Every part of Lumen is designed to be extended letting you define custom Source, Filter, Transform and View components.
- **Scalability**: Lumen is designed with performance in mind and supports scalable Dask DataFrames out of the box, letting you scale to datasets larger than memory or even scale out to a cluster.
- **Security**: Lumen ships with a wide range of OAuth providers out of the box, making it a breeze to add authentication to your applications.
## Examples
## Getting started
Lumen works with Python 3 and above on Linux, Windows, or Mac. The recommended way to install Lumen is using the [`conda`](https://conda.pydata.org/docs/) command provided by [Anaconda](https://docs.continuum.io/anaconda/install) or [`Miniconda`](https://conda.pydata.org/miniconda.html):
conda install -c pyviz lumen
or using PyPI:
pip install lumen
Once installed you will be able to start a Lumen server by running:
lumen serve dashboard.yaml --show
This will open a browser serving the application or dashboard declared by your yaml file in a browser window. During development it is very helpful to use the `--autoreload` flag, which will automatically refresh and update the application in your browser window, whenever you make an edit to the dashboard yaml specification. In this way you can quickly iterate on your dashboard.
Try it out! Click on one of the examples below, copy the yaml specification and launch your first Lumen application.
%package -n python3-lumen
Summary: A monitoring solution built on Panel.
Provides: python-lumen
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-lumen
# Lumen
*Illuminate your data*
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/lumen/actions/workflows/test.yml)
| Coverage | [](https://codecov.io/gh/holoviz/lumen) |
| Latest dev release | [](https://github.com/holoviz/lumen/tags) [](https://pyviz-dev.github.io/lumen/) |
| Latest release | [](https://github.com/holoviz/lumen/releases) [](https://pypi.python.org/pypi/lumen) [](https://anaconda.org/pyviz/lumen) [](https://anaconda.org/conda-forge/lumen) [](https://anaconda.org/anaconda/lumen) |
| Docs | [](https://github.com/holoviz/lumen/tree/gh-pages) [](https://lumen.holoviz.org) |
| Support | [](https://discourse.holoviz.org/) |
## Why Lumen?
The Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification. The power of Lumen comes from the ability to leverage the powerful data intake, data processing and data visualization libraries available in the PyData ecosystem.
- **Data Intake**: A flexible system for declaring data sources with strong integration with [Intake](https://intake.readthedocs.io/en/latest/), allows Lumen to query data from a wide range of sources including many file formats such as CSV or Parquet but also SQL and many others.
- **Data Proccessing**: Internally Lumen stores data as DataFrame objects, allowing users to leverage familiar APIs for filtering and transforming data using [Pandas](https://pandas.pydata.org/) while also providing the ability to scale these transformations out to a cluster thanks to [Dask](https://dask.org/).
- **Data Visualization**: Since Lumen is built on [Panel](https://panel.holoviz.org) all the most popular plotting libraries and many other components such as powerful datagrids and BI indicators are supported.
The core strengths of Lumen include:
- **Flexibility**: The design of Lumen allows flexibly combining data intake, data processing and data visualization into a simple declarative pipeline.
- **Extensibility**: Every part of Lumen is designed to be extended letting you define custom Source, Filter, Transform and View components.
- **Scalability**: Lumen is designed with performance in mind and supports scalable Dask DataFrames out of the box, letting you scale to datasets larger than memory or even scale out to a cluster.
- **Security**: Lumen ships with a wide range of OAuth providers out of the box, making it a breeze to add authentication to your applications.
## Examples
## Getting started
Lumen works with Python 3 and above on Linux, Windows, or Mac. The recommended way to install Lumen is using the [`conda`](https://conda.pydata.org/docs/) command provided by [Anaconda](https://docs.continuum.io/anaconda/install) or [`Miniconda`](https://conda.pydata.org/miniconda.html):
conda install -c pyviz lumen
or using PyPI:
pip install lumen
Once installed you will be able to start a Lumen server by running:
lumen serve dashboard.yaml --show
This will open a browser serving the application or dashboard declared by your yaml file in a browser window. During development it is very helpful to use the `--autoreload` flag, which will automatically refresh and update the application in your browser window, whenever you make an edit to the dashboard yaml specification. In this way you can quickly iterate on your dashboard.
Try it out! Click on one of the examples below, copy the yaml specification and launch your first Lumen application.
%package help
Summary: Development documents and examples for lumen
Provides: python3-lumen-doc
%description help
# Lumen
*Illuminate your data*
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/lumen/actions/workflows/test.yml)
| Coverage | [](https://codecov.io/gh/holoviz/lumen) |
| Latest dev release | [](https://github.com/holoviz/lumen/tags) [](https://pyviz-dev.github.io/lumen/) |
| Latest release | [](https://github.com/holoviz/lumen/releases) [](https://pypi.python.org/pypi/lumen) [](https://anaconda.org/pyviz/lumen) [](https://anaconda.org/conda-forge/lumen) [](https://anaconda.org/anaconda/lumen) |
| Docs | [](https://github.com/holoviz/lumen/tree/gh-pages) [](https://lumen.holoviz.org) |
| Support | [](https://discourse.holoviz.org/) |
## Why Lumen?
The Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification. The power of Lumen comes from the ability to leverage the powerful data intake, data processing and data visualization libraries available in the PyData ecosystem.
- **Data Intake**: A flexible system for declaring data sources with strong integration with [Intake](https://intake.readthedocs.io/en/latest/), allows Lumen to query data from a wide range of sources including many file formats such as CSV or Parquet but also SQL and many others.
- **Data Proccessing**: Internally Lumen stores data as DataFrame objects, allowing users to leverage familiar APIs for filtering and transforming data using [Pandas](https://pandas.pydata.org/) while also providing the ability to scale these transformations out to a cluster thanks to [Dask](https://dask.org/).
- **Data Visualization**: Since Lumen is built on [Panel](https://panel.holoviz.org) all the most popular plotting libraries and many other components such as powerful datagrids and BI indicators are supported.
The core strengths of Lumen include:
- **Flexibility**: The design of Lumen allows flexibly combining data intake, data processing and data visualization into a simple declarative pipeline.
- **Extensibility**: Every part of Lumen is designed to be extended letting you define custom Source, Filter, Transform and View components.
- **Scalability**: Lumen is designed with performance in mind and supports scalable Dask DataFrames out of the box, letting you scale to datasets larger than memory or even scale out to a cluster.
- **Security**: Lumen ships with a wide range of OAuth providers out of the box, making it a breeze to add authentication to your applications.
## Examples
## Getting started
Lumen works with Python 3 and above on Linux, Windows, or Mac. The recommended way to install Lumen is using the [`conda`](https://conda.pydata.org/docs/) command provided by [Anaconda](https://docs.continuum.io/anaconda/install) or [`Miniconda`](https://conda.pydata.org/miniconda.html):
conda install -c pyviz lumen
or using PyPI:
pip install lumen
Once installed you will be able to start a Lumen server by running:
lumen serve dashboard.yaml --show
This will open a browser serving the application or dashboard declared by your yaml file in a browser window. During development it is very helpful to use the `--autoreload` flag, which will automatically refresh and update the application in your browser window, whenever you make an edit to the dashboard yaml specification. In this way you can quickly iterate on your dashboard.
Try it out! Click on one of the examples below, copy the yaml specification and launch your first Lumen application.
%prep
%autosetup -n lumen-0.5.1
%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-lumen -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot - 0.5.1-1
- Package Spec generated
