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Analytical corner plot for Gaussian distributions

Project description


This code produces a corner plot for analytical multi-dimensional Gaussian distribution, using covariance matrix and mean matrix. It also allows us to plot another distribution, with reduced dimensionality, on top. I.e. a distribution for a case where we fixed one variable.

See for examples.


Option 1:

  • pip install normal_corner

Option 2:

  • python install in cloned directory


The main component is function normal_corner inside a normal_corner package. Below is a description of inputs and outputs.


  • Matplotlib figure object with a corner plot

Main input:

  • covm : covariance matrix, numpy array, NxN.
  • mean : mean matrix, numpy array, 1xN.
  • varlabels : labels for plotting, 1xN, list of str in LaTex format, between ($$).

Input for a second distribution on top:

  • fixedvarindex : index of variable that we do not use (fix), int, starting from 0. If not None, covm2 and mean2 must not be None.
  • fixedvarvalue : value of fixed variable, float. Leave None not to plot fixed value.
  • newcov and newmean : new covariance and mean matrices, same format, as above.

Optional input:

  • scale_factor : scale factor for plotting area, float, in sigma.
  • diagnostic : an option to print out some diagnostic messages, bool.
  • color : color for a main Normal distribution, str.
  • color2 : color for a secondary Normal distribution, with reduced dimensionality, str.

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