# Lumen
*Illuminate your data*
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| Support | [![Discourse](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.holoviz.org)](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
<table>
<tr>
<td><a href="https://lumen.holoviz.org/gallery/bikes.html"><b>London Bike Points</b><br><img src="https://raw.githubusercontent.com/holoviz/lumen/master/docs/_static/bikes.png" /></a></td>
<td><a href="https://lumen.holoviz.org/gallery/nyc_taxi.html"><b>NYC Taxi</b><br><img src="https://raw.githubusercontent.com/holoviz/lumen/master/docs/_static/nyc_taxi.png" /></a></td>
</tr>
<tr>
<td><a href="https://lumen.holoviz.org/gallery/penguins.html"><b>Palmer Penguins</b><br><img src="https://raw.githubusercontent.com/holoviz/lumen/master/docs/_static/penguins.png" /></a></td>
<td><a href="https://lumen.holoviz.org/gallery/precip.html"><b>Precipitation</b><br><img src="https://raw.githubusercontent.com/holoviz/lumen/master/docs/_static/precip.png" /></a></td>
</tr>
<tr>
<td><a href="https://lumen.holoviz.org/gallery/seattle.html"><b>Seattle Weather</b><br><img src="https://raw.githubusercontent.com/holoviz/lumen/master/docs/_static/seattle.png" /></a></td>
</tr>
</table>
## 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.
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lumen-0.5.0a7.tar.gz (67个子文件)
lumen-0.5.0a7
MANIFEST.in 283B
PKG-INFO 7KB
pyproject.toml 97B
LICENSE 1KB
setup.cfg 295B
examples
bikes
dashboard.yaml 2KB
nyc_taxi
dashboard.yaml 2KB
precip
dashboard.yaml 1KB
data
SRLCC_a1b_Precip_PCM-NCAR.csv 55KB
SRLCC_b1_Precip_PCM-NCAR.csv 55KB
SRLCC_a2_Precip_MIROC3.2(medres).csv 55KB
SRLCC_a1b_Precip_MIROC3.2(medres).csv 55KB
SRLCC_a2_Precip_PCM-NCAR.csv 55KB
SRLCC_b1_Precip_ECHAM5-MPI.csv 55KB
SRLCC_a1b_Precip_ECHAM5-MPI.csv 55KB
SRLCC_b1_Precip_MIROC3.2(medres).csv 55KB
SRLCC_a2_Precip_ECHAM5-MPI.csv 55KB
penguins
dashboard.yaml 2KB
seattle
dashboard.yaml 2KB
setup.py 3KB
lumen.egg-info
PKG-INFO 7KB
requires.txt 313B
SOURCES.txt 2KB
entry_points.txt 102B
top_level.txt 6B
dependency_links.txt 1B
README.md 6KB
lumen
.version 68B
tests
sample_dashboard
views.py 128B
dashboard.yml 163B
test_target.py 2KB
test_schema.py 847B
conftest.py 612B
test_config.py 291B
views
__init__.py 0B
test_base.py 122B
__init__.py 0B
transforms
__init__.py 0B
test_base.py 152B
filters
test_base.py 134B
sources
test_intake.py 702B
test_derived.py 5KB
catalog.yml 206B
__init__.py 0B
test_base.py 135B
test.csv 128B
test_dashboard.py 433B
util.py 7KB
dashboard.py 18KB
state.py 5KB
views
__init__.py 27B
base.py 21KB
__init__.py 352B
transforms
__init__.py 27B
base.py 8KB
schema.py 8KB
target.py 21KB
command.py 3KB
filters
__init__.py 27B
base.py 9KB
config.py 2KB
rest.py 6KB
sources
intake.py 3KB
ae5.py 7KB
__init__.py 27B
prometheus.py 8KB
base.py 32KB
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