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exactlyallan committed Jul 7, 2023
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API Reference
=============

The two main components to cuxfilter are `DataFrame` for connecting the dashboard to a cuDF backed dataframe, and `Dashboard` for setting dashboard options.

.. currentmodule:: cuxfilter.dataframe

DataFrame
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html_logo = "_static/rapids_logo.png"
html_static_path = ["_static"]

# Removes sidebar
htmlhelp_basename = "cuxfilterdoc"

html_sidebars = {
"user_guide/index": []
}

html_theme_options = {
"external_links": [],
"icon_links": [],
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18 changes: 5 additions & 13 deletions docs/source/index.rst
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Welcome to cuxfilter's documentation!
Welcome to cuxfilter's documentation
=====================================

cuxfilter acts as a connector library, which provides the connections between different visualization libraries and a GPU dataframe without much hassle. This also allows the user to use charts from different libraries in a single dashboard, while also providing the interaction.
cuxfilter enables GPU accelerated cross-filtering dashboards from notebooks, in just a few lines of Python code. Well integrated with the HoloViz ecosystem, it also incorporates other outstanding visualization libraries. RAPIDS cuxfilter is ideal for multi-chart exploratory data analysis and dashboard prototyping within the RAPIDS ecosystem.

.. toctree::
:maxdepth: 2
:caption: Contents:


./installation.rst
./dataframe.rst
./deployment.rst
./10_minutes_to_cuxfilter.ipynb
./Dask-cudf-support.rst
./charts/charts.rst
./layouts/Layouts.ipynb
./themes/Themes.ipynb
./examples/examples.rst
user_guide/index
api_reference/dataframe


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
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Charts
======

cuxfilter is able to use a wide range of chart types:

* Bokeh: line and bar
* Datashader: high density line, stacked line, scatter, geospatial scatter, heatmap, and graph
* DeckGL: webGL based geospatial 2D and 3D choropleth
* Widgets: sliders, dropdowns, and cards
* Custom: data table

.. toctree::
:maxdepth: 1
:caption: Contents:

bokeh_charts.rst
datashader_charts.rst
deckgl_charts.rst
widgets.rst
custom_charts.rst
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Panel Widgets
Widgets
=============

.. currentmodule:: cuxfilter.charts
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Examples
========
Example Dashboards
==================

.. toctree::
:maxdepth: 1
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User Guide
==========

The user guide is intended as an overview of cuxfilter's capabilities as well as a showcase for examples. The best place to start is `10 Minutes to Cuxfilter`. For more advanced topics, see `Dask-cudf-support` for multi-GPU capabilities and `deployment` for exporting a dashboard for standalone usage outside of a notebook.

.. toctree::
:maxdepth: 2

installation
10_minutes_to_cuxfilter
charts/index
layouts/Layouts
themes/Themes
examples/index
Dask-cudf-support
deployment
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Conda
-----
For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS `Get Started Page <https://rapids.ai/start.html#rapids-release-selector>`_.
For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS `Install Page <https://docs.rapids.ai/install>`_.

cuxfilter conda example installation:

.. code-block:: bash
conda install -c rapidsai -c conda-forge -c nvidia \
cuxfilter=23.02 python=3.10 cudatoolkit=11.8
cuxfilter=23.6 python=3.10 cudatoolkit=11.8
Docker container
----------------
For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS `Get Started Page <https://rapids.ai/start.html#rapids-release-selector>`_.
For the most customized way of installing RAPIDS and cuxfilter, visit the selector on the RAPIDS `Install Page <https://docs.rapids.ai/install>`_.

cuxfilter docker example installation:

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Build/Install from Source
-------------------------

See `build instructions <https://github.com/rapidsai/cuxfilter/blob/branch-23.02/CONTRIBUTING.md#setting-up-your-build-environment>`_.
See `build instructions <https://github.com/rapidsai/cuxfilter/blob/main/CONTRIBUTING.md#setting-up-your-build-environment>` on our GitHub.



Troubleshooting
---------------

1. Install jupyterlab dependencies
Install jupyterlab dependencies
**********************************

.. code-block:: bash
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jupyter labextension install @pyviz/jupyterlab_pyviz
jupyter labextension install @bokeh/jupyter_bokeh
2.running the server
********************

.. code-block:: bash
#enter ip address without http://
#current port is the port at which jupyterlab is running
d.app(notebook_url='ip.addr:current_port')
# OR for a separate web app
d.show(notebook_url='ip.addr:current_port')
3. libxcomposite.so.1 not found error
libxcomposite.so.1 not found error
*************************************

If using **await d.preview()** throws a **libxcomposite.so.1 not found error**, execute the following commands:
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apt-get update
apt-get install libxcomposite1 libxcursor1 libxdamage1 libxfixes3 libxi6 libxrandr2 libxtst6 libcups2 libxss1 libasound2 libpangocairo-1.0-0 libpango-1.0-0 libatk1.0-0 libgtk-3-0 libgdk-pixbuf2.0-0
Download datasets
-----------------

1. Auto download datasets
1. Automatically download datasets
*************************

The notebooks inside `python/notebooks` already have a check function which verifies whether the example dataset is downloaded, and downloads it if it's not.
The notebooks inside `python/notebooks` already have a check function which verifies whether the example dataset is downloaded, and downloads if not present.

2. Download manually
********************
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