BabylonJS widget
Project description
TileDB-PyBabylonJS
The TileDB-PyBabylonJS library is a geospatial data visualization Python library that interactively visualizes TileDB arrays with Babylon.js in a Jupyter notebook widget.
Installation
This project is available from PyPI and can be installed with pip:
You can install using pip:`
pip install pybabylonjs
If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:
jupyter nbextension enable --py [--sys-prefix|--user|--system] pybabylonjs
Development Installation
Create a dev environment:
conda create -n pybabylonjs-dev -c conda-forge nodejs yarn python jupyterlab
conda activate pybabylonjs-dev
Fork or clone the repo. Install the Python package. This will also build the TS package.
pip install -e ".[test, examples]"
When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For jupyter lab, this is done by the command:
jupyter labextension install @jupyter-widgets/jupyterlab-manager
yarn run build
jupyter labextension install .
For a classic notebook, you need to run:
jupyter nbextension install --sys-prefix --symlink --overwrite --py pybabylonjs
jupyter nbextension enable --sys-prefix --py pybabylonjs
Note that the --symlink flag doesn't work on Windows, so you will here have to run
the install command every time that you rebuild your extension. For certain installations
you might also need another flag instead of --sys-prefix, but we won't cover the meaning
of those flags here.
How to see your changes
Typescript:
If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the widget.
# Watch the source directory in one terminal, automatically rebuilding when needed
yarn run watch
# Run JupyterLab in another terminal
jupyter lab
After a change wait for the build to finish and then refresh your browser and the changes should take effect.
Python:
If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
Usage
Currently two data visualizations are supported for LiDAR point clouds:
- 3D point cloud visualization
- 3D MBRS visualization
Full examples can be found it the example notebooks [here]](https://github.com/TileDB-Inc/TileDB-PyBabylonJS/tree/main/examples).
3D point cloud visualization
To create this visualization load a slice of the data and create a dictionary with the coordinates of the points and the RGB values:
from pybabylonjs import Show as show
with tiledb.open("autzen1") as arr:
df = pd.DataFrame(arr[636800:637800, 851000:853000, 406.14:615.26])
data = {
'X': df['X'],
'Y': df['Y'],
'Z': df['Z'],
'Red': df['Red'] / 255.0,
'Green': df['Green'] / 255.0,
'Blue': df['Blue'] / 255.0
}
Visualize the 3D point cloud with pybabylonjs.Show.from_dict() by specifying data and the style to use. Optional parameters are the width and height of the frame, the scaling factor z_scale of the z-axis and the wheel precision wheel_precision:
show.from_dict(data=data,
style = 'pointcloud',
width = 800,
height = 600,
z_scale = .3,
wheel_precision = 50)
This creates an interactive visualization in a notebook widget of which the below is a screenshot:
3D MBRS visualization
This visualization is created directly from a sparse array by specifying the array, the style as mbrs and optional height and width parameters:
show.from_array(array='autzen',
style='mbrs',
width=800,
height=600,
z_scale = 0.5)
Which creates the below interactive visualization in a notebook widget of which the below is a screenshot:
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