Visualization Improvements

[carschno]

Visualization Improvements

Goals:

• Make interaction smooth even for large datasets (in a notebook)
• Allow for filtering dynamically on various metadata such as time periods, provenance, genre etc.
• Integration with Weaviate database might be a nice-to-have (#32).

See issues related to visualization: #30, #24, #22, #21 .

Available Libraries

Jupyter Scatter is designed for Jupyter Notebooks:

Explore datasets with millions of data points with ease in Jupyter Notebook, Lab, and Google Colab.

The DeepScatter visualization is designed for

interactive visualization of extremely large datasets, in browser

DataMapPlot :

Creating beautiful plots of data maps. This provides basic tools for generating presentation or publication worthy static plots of labelled data maps. All you need to do is label clusters of points in the data map and DataMapPlot will take care of the rest. There are a number of options for tweaking the results, but the aim is to have something good-looking straight out-of-the-box.

Altair:

Vega-Altair is a declarative visualization library for Python. Its simple, friendly and consistent API, built on top of the powerful Vega-Lite grammar, empowers you to spend less time writing code and more time exploring your data.

A related option would be adding an option for using the Atlas service, which offers a free trial for up to 250k data points (see pricing).

[carschno]

This should resolve the visualization task in #71.

[carschno]

Also fix histogram for larger datasets.