detect high values from numpy array

Question

Im working on a detecting alogrythm for detecting storm cells on a radar imagery. I have radar data in 2d numpy arrays that we plot on a basemap. We got azymuth and rangebins data that we put in a polargrid with lat/lon coordinates.

The values in our numpy array are based on dBZ height in range from zero till maximum 80.

Here a printout of our numpy array named data:

[[-31.5 -31.5  16.5 ..., -31.5 -31.5 -31.5]
[-31.5 -31.5 -31.5 ..., -31.5 -31.5 -31.5]
[-31.5 -31.5 -31.5 ..., -31.5 -31.5 -31.5]
...,
[-31.5 -31.5 -31.5 ..., -31.5 -31.5 -31.5]
[-31.5 -31.5 -31.5 ..., -31.5 -31.5 -31.5]
[-31.5  11.5 -31.5 ..., -31.5 -31.5 -31.5]]

While -31.5 stands for null or hidden values. We only need the positive values. Even decimals make no sense.

So what do we want to do:

Detect the clusters of high values, and make them a red square around that cell. I have tried something with a image mask but i got stuck there. Even i dont know if an image mask is a good solution for this issue.

Here is my code to process the data.

gain             = 0.5                                  
offset           = -31.5

az = np.arange(0.,360.,360./scan["scan_number_azim"])
r  = np.arange(scan["scan_start_azim"], (scan["scan_start_azim"] + scan["scan_number_range"] * rscale), rscale)
data = gain * raw["scan2/scan_Z_data"] + offset

So the count of the detections would fluctuate very often. Maybe i need something like DBscan also ?

Can someone help me up with this ?

Answer 1

If you can use scipy, I think something like this will work:

import scipy.ndimage

mask = data > 20
labels, num_labels = scipy.ndimage.label(mask)
custers = scipy.ndimage.find_objects(labels)

And clusters will now be a list of tuples of slices, and you can get the starting and ending rows and columns of your rectangles as:

for row_slice, col_slice in clusters:
    start_row = row_slice.start
    end_row = row_slice.stop
    start_col = col_slice.start
    end_col = col_slice.stop
    # draw your rectangle