stackstac.show#

stackstac.show(arr, center=None, zoom=None, range=None, cmap=None, checkerboard=True, interpolation='linear', **map_kwargs)#

Quickly create an ipyleaflet.Map displaying a DataArray.

As you pan around the map, the part of the array that’s in view is computed on the fly by dask. This requires using a dask distributed cluster.

Parameters:

  • arr (DataArray) –

    DataArray to visualize. Must have x and y, and optionally band dims, and the epsg coordinate set.

    arr must have 1-3 bands. Single-band data can be colormapped; multi-band data will be displayed as RGB. For 2-band arrays, the first band will be duplicated into the third band’s spot, then shown as RGB.

  • center – Centerpoint for the map, in (lat, lon) order. If None (default), the map will automatically be centered on the array.

  • zoom – Initial zoom level for the map. If None (default), a zoom level to fit the array on a reasonably-sized map is picked.

  • range (Optional[Tuple[float, float]]) –

    Min and max values in arr which will become black (0) and white (255) in the visualization.

    If None (default), it will automatically use the 2nd/98th percentile values of the entire array (unless it’s a boolean array; then we just use 0-1). For large arrays, this can be very slow and expensive, and slow down tile rendering a lot, so passing an explicit range is usually a good idea.

  • cmap (Union[str, Colormap, None]) –

    Colormap to use for single-band data. Can be a matplotlib colormap name as a string, or a Colormap object for custom colormapping.

    If None (default), the default matplotlib colormap (usually viridis) will automatically be used for 1-band data. Setting a colormap for multi-band data is an error.

  • checkerboard (bool) –

    Whether to show a checkerboard pattern for missing data (default), or leave it fully transparent.

    Note that only NaN is considered a missing value; any custom fill value should be converted to NaN before visualizing.

  • interpolation (Literal[‘linear’, ‘nearest’]) – Interpolation method to use while reprojecting: "linear" or "nearest" (default "linear"). Use "linear" for continuous data, such as imagery, SAR, DEMs, weather data, etc. Use "nearest" for discrete/categorical data, such as classification maps.

Returns:

The new map showing this array.

Return type:

ipyleaflet.Map