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First of all this PR demonstrates how to implement _lazy visualization_: - one needs to write/enhance Enso visualization libraries - this PR adds two optional parameters (`bounds` and `limit`) to `process_to_json_text` function. - the `process_to_json_text` can be tested by standard Enso test harness which this PR also does - then one has to modify JavaScript on the IDE side to construct `setPreprocessor` expression using the optional parameters The idea of _scatter plot lazy visualization_ is to limit the amount of points the IDE requests. Initially the limit is set to `limit=1024`. The `Scatter_Plot.enso` then processes the data and selects/generates the `limit` subset. Right now it includes `min`, `max` in both `x`, `y` axis plus randomly chosen points up to the `limit`.  The D3 visualization widget is capable of _zooming in_. When that happens the JavaScript widget composes new expression with `bounds` set to the newly visible area. By calling `setPreprocessor` the engine recomputes the visualization data, filters out any data outside of the `bounds` and selects another `limit` points from the new data. The IDE visualization then updates itself to display these more detailed data. Users can zoom-in to see the smallest detail where the number of points gets bellow `limit` or they can select _Fit all_ to see all the data without any `bounds`. # Important Notes Randomly selecting `limit` samples from the dataset may be misleading. Probably implementing _k-means clustering_ (where `k=limit`) would generate more representative approximation. |
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| .. | ||
| bin | ||
| engine/THIRD-PARTY | ||
| launcher | ||
| lib/Standard | ||
| project-manager/THIRD-PARTY | ||
| enso.bundle.template | ||
| launcher-manifest.yaml | ||
| manifest.template.yaml | ||
