Features & Design¶
A Python package for performing adjacent correlation analysis on image data.
The input are images 1 and image 2, in the form of Numpy arrays of the same size. The method is designed to reveal regularities by comparing these images through correlations.
The adjacent correlation analysis is performed by calculating and visualizing the adjacency-induced correlation in the phase space. The adjacent correlation map is a spatially-resolved representation of the correlation between the two images.
The methods are designed to represent the data using correlations, which can be used to perform visualization and interactive data explorations.
Adjacent Correlation Analysis¶
The adjacent correlation analysis is a method to derive correlation vectors, which can be plotted on top of the density map representing the Probably Density Function (PDF) of the two images data.
The adjacent correlation analysis applied to data from MHD turbulence simulation. The output consists of a correlation vector field overlaid on the density map (density PDF). The correlation degree is the normalized length of the vector, and the both the length and the orientation of the vector can be seen in the adjacent correlation plot.
The adjacent correlation analysis applied to the Lorentz system. The vectors derived using the adjacent correlation analysis reflects a projected view of the vector field in the phase space on the x-y plane.
Adjacent Correlation Map¶
The adjacent correlation map is a method to provide maps of the correlation between the two images. It contains a correlation angle map, a map of the correlation degree, and a correlation coefficient map.
The adjacent correlation map applied to temperature and precipitation data. The output consists of a correlation angle map, a map of the correlation degree, and a correlation coefficient map (available as the program output). The correlation angle map shows the direction of the correlation in the phase space, while the correlation degree map shows the strength of the correlation. Different colors represent different ways temperature T,x and log(percipation) are correlated.