GAMBIT plotting tools

A collection of plotting tools developed for use with GAMBIT and GAMBIT-light (see gambitbsm.org).

Installation

Simply install using pip:

pip install git+https://github.com/GambitBSM/gambit_plotting_tools.git

Examples

1D profile likelihood plots

• Make a single 1D profile likelihood plot from the data in the hdf5 file example_data/results_run1.hdf5

  python example_1D_profile_like_hdf5.py
  

  This should give a plot looking like this:

  

  This example also illustrates how to add the small GAMBIT logo to the bottom-right corner of a plot.

2D profile likelihood plots

• Make a single 2D profile likelihood plot from the data in the hdf5 file example_data/results_run1.hdf5

  python example_2D_profile_like_hdf5.py
  

  This should give a plot looking like this:

  

  This example also illustrates how to add a contour drawn from a set of stored coordinates, e.g. from some digitised plot.

• Make multiple 2D profile likelihood plots in one go, combining the data from the hdf5 files example_data/results_run1.hdf5 and example_data/results_run2.hdf5

  python example_2D_profile_like_hdf5_multiple.py
  

• Make a single 2D profile likelihood plot from the data in the ascii file example_data/samples.dat

  python example_2D_profile_like_ascii.py
  

1D posterior plots

• Make a single 1D posterior plot from the data in the hdf5 file example_data/results_multinest.hdf5

  python example_1D_posterior_hdf5.py
  

  This should give a plot looking like this:

  

2D posterior plots

• Make a single 2D posterior plot from the data in the hdf5 file example_data/results_multinest.hdf5

  python example_2D_posterior_hdf5.py
  

  This should give a plot looking like this:

  

Utility scripts

There are also some commands for other common tasks besides plotting

Print confidence level table

print_confidence_level_table

Print dataset names

• Read the hdf5 file example_data/results_run1.hdf5 and print all the dataset names, ignoring datasets whose name begins with "metadata" or ends with "_isvalid"

print_dataset_names example_data/results_run1.hdf5 --ignore-startswith "metadata" --ignore-endswith "_isvalid"

This should give an output looking like this:

data/#NormalDist_parameters @NormalDist::primary_parameters::mu
data/#NormalDist_parameters @NormalDist::primary_parameters::sigma
data/#normaldist_loglike @ExampleBit_A::lnL_gaussian
data/LogLike
data/MPIrank
data/ModifiedLogLike
data/Runtime(ms) interloop
data/Runtime(ms) intraloop
data/Runtime(ms) totalloop
data/Runtime(ns) for #NormalDist_parameters @NormalDist::primary_parameters
data/Runtime(ns) for #normaldist_loglike @ExampleBit_A::lnL_gaussian
data/Suspicious Point Code
data/pointID
data/scanID
data/unitCubeParameters[0]
data/unitCubeParameters[1]

Get parameters of the highest-likelihood points

• Read the data group in the hdf5 file example_data/results_run1.hdf5 and print the input parameters for the 3 highest-likelihood points

print_high_loglike_points example_data/results_run1.hdf5 data 3

This should give the following output:

File:  example_data/results_run1.hdf5
Group: data

The 3 highest log-likelihood point(s):

LogLike: -2.53130026064170e+01
Parameters:
  NormalDist:
    mu: 2.08331783688065e+01
    sigma: 2.15049587260387e+00


LogLike: -2.53130028409013e+01
Parameters:
  NormalDist:
    mu: 2.08336541456091e+01
    sigma: 2.15096678343953e+00


LogLike: -2.53130031660756e+01
Parameters:
  NormalDist:
    mu: 2.08344819535121e+01
    sigma: 2.14981940988637e+00