
Field | Type | Notes |
|---|---|---|
title | &str | Names the chart for the screen, accessibility tree, and test output. |
x_axis / y_axis | Axis::category() | Names the matrix columns and rows. |
visual_map | VisualMap | Maps numeric intensity to color. |
series | HeatmapSeries | Stores x index, y index, and value triples. |
width / height | f32 | Optional fixed size; omit them when the chart should flex inside Fission layout. |
use fission::charts::{Axis, Chart, HeatmapSeries, VisualMap};
let chart = Chart::new()
.title("Heatmap")
.x_axis(Axis::category(vec!["12a", "1a", "2a"]))
.y_axis(Axis::category(vec!["Sat", "Fri", "Thu"]))
.visual_map(VisualMap::new().min(0.0).max(10.0))
.series(vec![HeatmapSeries::new("Load").data(vec![(0, 0, 5.0), (1, 1, 4.0), (2, 2, 9.0)]).into()]);
use fission::prelude::*;
use fission::charts::{Axis, Chart, HeatmapSeries, VisualMap};
pub struct CartesianHeatmapChart;
impl From<CartesianHeatmapChart> for Widget {
fn from(_: CartesianHeatmapChart) -> Widget {
Chart::new()
.title("Heatmap")
.x_axis(Axis::category(vec!["12a", "1a", "2a"]))
.y_axis(Axis::category(vec!["Sat", "Fri", "Thu"]))
.visual_map(VisualMap::new().min(0.0).max(10.0))
.series(vec![HeatmapSeries::new("Load").data(vec![(0, 0, 5.0), (1, 1, 4.0), (2, 2, 9.0)]).into()])
.into()
}
}
Area | What to decide | How to verify |
|---|---|---|
Data shape | Keep source rows in typed Rust structs, then map them into the series type shown in the example. | Unit test the mapping separately from rendering. |
Options | Choose axes, legends, labels, animation, and interaction based on the user's task. | Add a screenshot test when changing visual behavior. |
Accessibility | Provide a clear title and adjacent summary text for important trends or outliers. | Inspect the generated semantics and make sure the chart is understandable without color alone. |
Failure handling | Render an empty, loading, or error state before constructing the chart if data is unavailable. | Test empty data, partial data, and failed fetches. |
Performance | Prefer summarized or windowed data for very large datasets; keep full raw history in the data layer. | Profile frame time and interaction latency with representative data volumes. |