apps bounded pages

AGILab ships a handful of prebuilt Streamlit bundles that you can expose from the Explore catalogue or the home dashboard. Each bundle is a standalone Streamlit project with its own pyproject.toml/.venv so the launcher spins it up in an isolated interpreter. Add the module name to the [pages].view_module list in app_settings.toml (or use Explore → Configure) and the page becomes available to every user of the project.

view_autoencoder_latenspace

Interactive dimensionality reduction playground powered by an autoencoder.

  • Loads the dataframe exported from Execute and lets you pick the features to embed into latent space.

  • Trains a configurable autoencoder (Keras) on the fly and visualises the latent space with barycentric projections using barviz.

  • Supports both categorical and continuous colouring, including automatic data normalisation and training/test splits.

view_barycentric

Barycentric simplex visualisation for high-dimensional metrics.

  • Uses the same barviz primitives as the autoencoder view but expects a set of aggregated KPI columns that should sum to one.

  • Provides interactive controls to choose the variables plotted on the simplex and to re-centre the projection around a particular point.

  • Handy for comparing relative contributions (for example cluster weights or class probabilities) across the exported dataset.

view_maps

2D cartography dashboard focused on geolocated telemetry.

  • Discovers datasets under ${AGILAB_EXPORT_ABS} and lets you point at the CSV/parquet file to render.

  • Requires latitude/longitude columns; optional value column drives the colour scale and can be toggled between discrete and continuous modes.

  • Offers deterministic down-sampling, colour palette selection and mapbox basemap overlays to keep the view responsive with large datasets.

view_maps_3d

Deck.gl powered 3D cartography with optional beam overlays.

  • Plots the selected dataset on a terrain surface (elevation tiles + satellite imagery) and supports multiple CSV sources (e.g. telemetry and beam footprints).

  • Lets you colour by discrete categories, adjust extrusion heights and switch between plotly based scatter maps and 3D mesh layers.

  • Generates random palettes when needed so each category stands out against the terrain background.

view_maps_network

Network topology explorer that synchronises geographic and graph views.

  • Reads link definitions (satcom_link, optical_link, legacy_link, …) directly from the dataset and builds the associated edge list.

  • Colours each link type consistently across the map, the 3D deck.gl layer and the accompanying plotly/networkx charts.

  • Helpful to debug dynamic routing: filter by active links, inspect node trajectories and replay snapshots side by side.