H.datalens.HeatmapLayer. Options
Type Definition Summary
HeatmapLayer
.Type Definition Description
HeatmapLayer
is similar to RasterLayer
. The initial step of rendering is to split the tile data by rows, where each row represents a bucket. By default this step is processed with H.datalens.HeatmapLayer.defaultDataToRows
. This behavior can be changed by defining the dataToRows
callback. To collect the rows for a tile including buffer, the rows must be translated to H.datalens.HeatmapLayer.TilePoint
. This translation must be specified with the rowToTilePoint
callback. Other options define the blending options for the heat map.Property Details
dataToRows : { function( H.datalens.Service.Data , H.datalens.QueryTileProvider.X , H.datalens.QueryTileProvider.Y , H.datalens.QueryTileProvider.Zoom ) : Array.< H.datalens.HeatmapLayer.Row > } [optional]
rowToTilePoint : { function( H.datalens.HeatmapLayer.Row , H.datalens.QueryTileProvider.X , H.datalens.QueryTileProvider.Y ) : H.datalens.HeatmapLayer.TilePoint }
H.datalens.HeatmapLayer.TilePoint
. This callback is called for each row returned from dataToRows
. bandwidth : { H.datalens.HeatmapLayer ~Bandwidth | H.datalens.HeatmapLayer ~BandwidthStop | Array.< H.datalens.HeatmapLayer ~BandwidthStop> | H.datalens.HeatmapLayer ~BandwidthCallback } [optional]
3 * bandwidth
, a multiple (default 3) of bandwidth. valueRange : { function( H.datalens.QueryTileProvider.Zoom ) : Array.<Number> } [optional]
countRange : { function( H.datalens.QueryTileProvider.Zoom ) : Array.<Number> } [optional]
colorScale : { function( number ) : string } [optional]
[0, 1]
. alphaScale : { function( number ) : number } [optional]
[0, 1]
and the range [0, 1]
. aggregation : { H.datalens.HeatmapLayer.Aggregation } [optional]
SUM
or AVERAGE
, if the aggregation type is AVERAGE
, then an averaged heat map is rendered. inputScale : { H.datalens.HeatmapLayer.InputScale } [optional]
TilePoint
value. Note: If the value is not in a linear scale, then the aggregation in the source query must be defined with respect to the scale type. For example, before applying the average aggregation function in a query, the value must be transformed to the linear scale, this guarantees correct linear averaging of values.