ColorBar | color selection bar - 颜色选择条

Restating the problem - My understanding given the different comments and your updates is the following:

• someone other than you is in possession of data, which as it happens is 2D data, i.e. an Nx2 matrix;
• using the covariance matrix, they are effectively saying something about the joint distribution of these two dimensions, specifically about the variance;
• if they assume a Gaussian distribution, as is implied by your comment regarding 68%, 95% and 99.7% for 1sigma, 2sigma and 3sigma, they can draw ellipses which represent the 2D-normal distribution: these are in fact some of the contour lines associated with the 3D "bell" surface;
• you have obtained the contour lines in a graph and are trying to obtain the covariance matrix (not the original data...);
• you are concerned about the complexity of having to extract the information from each ellipsis.

Partial answer:

• It is impossible to recover the original data, I hope you are already aware of that, but in case you are not let's just note that the covariance matrix is a summary statistic of the data, much like the average, and although it says something about the data many different datasets could happen to have the same summary statistic (the same way many different sets of numbers can give you an average of 10).
• It is possible to somewhat recover the covariance matrix, i.e. the 3 numbers a, b and c in the matrix [a,b;b,c], though the error in doing so will likely be large because of how imprecise the pixel representation is. Essentially, you will be looking for the dimensions of the two axes, for the variances, as well as the angle of one of the axes, for the covariance.
• Unless I am mistaken, under the Gaussian assumption above, you only need to measure this for one of the three ellipses, and then factor by whatever number of sigmas that contour represents. Here you might want to either use the best-defined ellipse, or attempt to use the largest one, which will provide the maximum precision for your measurements (cf. pixelization).
• Also, the problem of finding the axes and angle for the ellipse need not be as complex as what it seems like in your first trials: instead of trying to find the contour of the ellipses, find the bounding rectangle.
• In order to further simplify this process, if your images are color-coded the way you show, then a filter on blue pixels might be enough in terms of image processing. Then simply take the minimum and maximum (x,y) coordinates in order to obtain the bounding rectangle.
• Once the bounding rectangle is obtained, find the equation to your ellipse (that's a question for a math group, but you could start here for example).

Happy filtering!

You can reduce your palette by slicing: For example

Thanks to @JohanC, I've been able to fix my issues: Using scatter1=ax[1].scatter(longitude,latitude,cmap='RdBu',s=20) and fig.colorbar(scatter1, ax=ax[1], pad=0) have help ensuring the colorbar was not overflowing below the graph. This is what I get when I'm inserting these line in my code :

Then, by working on the code by my self, I realized that in order to have the dots in different colors depending on the temperature, I could 'simply' remove the line cmap = plt.cm.RdBu and, change in the scatter function (scatter1=) the value to temp: scatter1=ax[1].scatter(longitude,latitude,c=temp,s=20)

Thus, I get exactly what I wanted :

You can grab the subplot with the colorbar via g.ax_cbar. Then you can change its position, title, spines and tick lengths:

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:

You're not using R in z; try

Not a perfect solution but maybe it fits your needs:

1. To show the endpoints add show.limits=TRUE as I already suggested in my comment.
2. To get rid of the intermediate labels I make use of a custom labeller function. This function is called two times by the scale. Once for the default intermediate breaks (which in almost(!!) all cases is a vector of length > 2) and once for the limits (which is a vector of length 2). Hence I check for the length of the passed vector and keep only the labels for the "limits". But keep in mind that this is only a kind of heuristic which may fail in extreme special cases.

I edited your code a bit according to this documentation and it did the trick:

Simply add pad to the label :