Implement classification of results for the heatmap

[metemaddar]

Goal of this issue

We classify the results into 5 classes + 1 one class for 0 values.
This means we apply the classes:

0,1,2,3,4,5 to the heatmap results.

These classes can be created using different classifications. Currently, the idea is to implement equal interval and quintiles.

Resources

• numpy
• Code Academy

Deliverables

Branch to derive

[metemaddar]

Summarizing the discussion

We have 5 particles + zeros. So first we need to exclude the zeros from values. Then, calculate the quintiles. And after extracting the data by quntiles, we can add zeros as the zero class. Something like this:

array(where x > 0).quntile([0.2, 0.4, 0.6, 0.8]) ----> 4 quantiles (1|2|3|4|5) + 0s

[metemaddar]

This also can help us (Partitioning array)
Here also we need to extract the zeros first, And then do the partitioning.

[metemaddar]

Implemented with the following results:

quantile for 10000 elements is: 7 ms
count 0: 845
count 1: 1831
count 2: 1831
count 3: 1831
count 4: 1831
count 5: 1831

Example of output:
[4 3 2 3 5 5 2 2 5 3 4 1 2 3 1 4 1 1 5 4]

[p4b-bro]

This task/issue closed on Tue Jun 06 2023 ✅