High-resolution spectral images and digital elevation models are widely available. With this quantity of data, it is imperative to develop fast algorithms to extract information. We present a Python library that implements a set of algorithms for aggregating data within sliding windows. The algorithms have O(log(n)) time complexity and maintain the original image resolution. They are vectorized and written with NumPy to create fast code with C-like performance. The library offers several analysis procedures, architected such that additional procedures utilizing sliding windows can easily be added. Slope, aspect, and curvature analyses exist for digital elevation models. Fractal dimensions and correlation analyses are also present to be used on a range of different images. The software architecture of the library is outlined and motivated. It includes visualized comparisons of analyses and unit testing. Testing procedures are implemented using analytical results from Wolfram Mathematica combined with brute-force algorithms.