ASCL Code Record
[ascl:1907.004]
pyGTC: Parameter covariance plots
pyGTC creates giant triangle confusogram (GTC) plots. Triangle plots display the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar analysis. The recovered parameter constraints are displayed on a grid in which the diagonal shows the one-dimensional posteriors (and, optionally, priors) and the lower-left triangle shows the pairwise projections. Such plots are useful for seeing the parameter covariances along with the priors when fitting a model to data.
- Code site:
- https://github.com/SebastianBocquet/pygtc
- Used in:
- https://ui.adsabs.harvard.edu/abs/2017ApJ...837..124F
- Described in:
- https://ui.adsabs.harvard.edu/abs/2016JOSS....1...46B
- Bibcode:
- 2019ascl.soft07004B
Preferred citation method:
Views: 3242
