Thanks! I've used it when the data is itself binned frequency data, which is one way to reduce data volume (e.g. binning 2 weeks of minutely time series data into (hour, percentile) buckets and plotting them as weighted points).
When the number of the bins in the data is not an exact integer multiple of the number of bins in the histogram, adjacent data bins can get mapped to the same histogram bin, resulting in e.g. 2x the data volume in some rows/columns of the histogram.
When a bit of loss of fidelity is acceptable the two solutions I've used are to render the histogram at an exact factor of the number of data bins then set the canvas dimensions to the desired size (relying on the browser to downsample the resulting image), or to use `max` as the reduceOp and render the histogram directly at the intended size.
When the number of the bins in the data is not an exact integer multiple of the number of bins in the histogram, adjacent data bins can get mapped to the same histogram bin, resulting in e.g. 2x the data volume in some rows/columns of the histogram.
When a bit of loss of fidelity is acceptable the two solutions I've used are to render the histogram at an exact factor of the number of data bins then set the canvas dimensions to the desired size (relying on the browser to downsample the resulting image), or to use `max` as the reduceOp and render the histogram directly at the intended size.