算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
visualization-and-charts【设计与创意】Use geemap charting, colormap, legend, cartoee static map, Plotly, pydeck, kepler.gl, and MapLibre visualization workflows.
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Use this sub-skill when a task needs geemap visual outputs: bqplot charts from local or Earth Engine data, matplotlib colorbars and palettes, built-in dataset legends, publication-style static cartopy/cartoee maps, or optional Plotly, pydeck, kepler.gl, and MapLibre visualization backends.
For package setup and Earth Engine authentication, use the root setup reference: installation and auth. For cross-cutting package failures, use root troubleshooting first, then this sub-skill's visualization troubleshooting.
Use this sub-skill for:
geemap.chart: DataTable, Chart, array_values, feature charts, image/region charts, histograms, and time series charts.geemap.colormaps, top-level geemap.create_colorbar, and geemap.legends: palettes, colorbars, built-in legends, and legend table conversion.geemap.cartoee: cartopy-based static maps with Earth Engine layers, colorbars, gridlines, north arrows, scale bars, legends, and savefig.geemap.plot and geemap.plotlymap: Plotly Express charts, Plotly map tiles, heatmaps, GeoDataFrames, and static Plotly export.geemap.deck, geemap.kepler, and geemap.maplibregl: optional visualization backends and style/layer validation.Route elsewhere when the primary task is:
geemap.chart.Chart or geemap.chart.* wrappers;geemap.plot.* for Plotly Express;geemap.cartoee;plotlymap, deck, kepler, or maplibregl.min/max scalar values, opacity range, colorbar orientation, legend label/color lengths, and chart reducer/scale/region requirements.