heatmap | A GitHub-inspired graph for visualising activity | Data Visualization library

If you are displaying this in a single regular monitor (Full HD) that has a 1920 x 1080 resolution you won't probably be able to display every single point.

This is because the resolution is actually the number of pixels you can represent - so your max, considering you are using the whole screen, would be 1920 pixels x 1080 pixels (~2M pixels/dots).

So, in order to represent the amount of pixels you want, in a single regular monitor you have to go with the options below:

• Group the items;
• Adding a panel with scrolling bars;
• Reduce the data set.

After you decide on your strategy, you can use the D3 javascript library to plot it.

Here you can find a good post about a strategy to group the items and them plot them using D3: https://int21.io/post/50-million-points/index.html

Here: http://jsfiddle.net/BlackLabel/e9Lp3xvu/ I have reproduced the issue without Angular.

The problem is caused by the default value of tickPixelInterval for y-axis (72). As a solution you can reduce the value.

You can compute the maximum absolute value in your array, then use it to set the clims argument. c.f. http://docs.juliaplots.org/latest/generated/attributes_subplot/

I'm not that experienced with Vaadin Chart, but these are the questions that I can comment on:

(1) With 40x40 items you go over the threshold of 1000 in which the Chart switches into "turbo" mode for performance reasons. This seems to not be compatible with the heatmap series. You can disable turbo mode by setting plotOptions.setTurboThreshold(0);

(2) Unfortunately the ColorAxis doesn't support this, it only has an API for min and max color. Definitely a valid use-case though, and it seems to be supported by the Highcharts library that the Vaadin Chart uses under the hood. You should consider opening a feature request for this in the Github repo.

(3) This seems to be a documentation issue. The methods are available in later Vaadin platform versions, but not in 14.6.

(5) In theory not, but in practice there will be a huge performance hit in the browser due to the excessive amount of DOM elements (quick test of 100x100 froze the browser for 10s). I'm afraid the component isn't really made for such extreme use-cases. In this case it might be better to utilize a low-level JS drawing library using the canvas, or draw an image on the server-side and display that in the browser. Maybe you can also consider modifying your use-case so that you only display one slice of your data and allow the user to switch between slices.

Well, the clustermap clusters the values according to similarity. This changes the order of the rows and the columns.

You could create a regular clustermap, and in a second step apply the mask:

To activate or deactivate a grid you can add show_grid=True or show_grid=False to opts.HeatMap(...).

But in your example there is no grid activated, so you can't deactivate the grid lines. The white lines you can see are coming through the background color (which is defined by default as white).

You could change the background adding bgcolor ='#ABABAB' to opts.HeatMap(...), which makes a figure like

.

But sometimes you have to apply the changes you want to make directly in the bokeh figure object because not all the possibilities are added to holoviews keyword arguments. If you have to do this, you can follow this introduction.

Extend your example with the following to add an alpha value to the background as an example:

According to the documentation on adding traces to subplots, the add_trace and append_trace methods only take accept plotly graph_objects. Therefore, your code block:

Just fill the titles_count with 1 first, since they denote 1 count per row.

A quick solution is to add scale_y_discrete and with the limits argument, you can order your y axis with your source variable: