Perform sliding window analysis on count data in a 2d array

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Perform sliding window analysis on count data in a 2d array

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Last edited Jul 10, 2023

Created on Jul 10, 2023

This example visualizes the spatial distribution of spot counts and total intensity in a 2D array using sliding-window binning. It shows two heatmaps side-by-side, one for spot count and one for total intensity, with the y-axis inverted to match image orientation, plus a joint regression plot revealing the correlation between the two metrics. The code uses pandas for data loading and quantile-based thresholding, then bins the filtered data into 10x10 pixel windows. Seaborn’s `heatmap` and `jointplot` render the visualizations, while `matplotlib.pyplot` handles the subplot layout and axis inversion.

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