Create a continuous heatmap Most heatmap tutorials look at discrete data, where each cell has a well-defined boundary and a single value. How do you create a heatmap of continuous data, where individual points may be very close together without actually being identical?
Example: I have a set of 1000 map coordinates, all in the range (-8192, -8192) to (8192, 8192). They might look like this:
745.2218627929688,808.1373901367188
741.4943237304688,775.325927734375
757.3713989257812,806.7517700195312
-300.1320495605469,1425.9727783203125
-1845.8055419921875,2091.180419921875
-289.85308837890625,1429.4222412109375
-306.8935241699219,1434.9525146484375
-287.78253173828125,1439.9078369140625

The locations will tend to be clustered, and the clusters are the most interesting, but outliers are still of some value. What I'd like would be to show colours on an image that indicate this density.
It's highly likely that all I need is the right term to search for, but &quot;heatmap&quot; keeps finding discrete data tutorials only :)
What about imshow? Check these different interpolation methods which will allow you to define the &quot;discreteness&quot;.
Example with quadric interpolation:
import matplotlib.pyplot as plt
import numpy as np

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])


fig, ax = plt.subplots()
im = ax.imshow(harvest,interpolation=&quot;quadric&quot;)

Output image: