Analytical corner plot for Gaussian distributions
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 demo.py for examples.
pip install normal_corner
python setup.py installin 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
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.
newmean: new covariance and mean matrices, same format, as above.
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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