numcmctools 1.0.0

MCMC plotting framework for official results from neutrino oscillation experiments

NuMCMCTools

This package is intended as a lightweight tool to assist in analysis using public releases of MCMC chains from neutrino oscillation analyses. The following experiments have released data compatible with this software:

• Experiment 1
• Experiment 2

Installation

To install numcmctools with PyPI, run the following command:

pip install numcmctools

To install directly from source, first clone numcmctools with git:

git clone https://github.com/NuMCMCTools/NuMCMCTools.git

enter the new NuMCMCTool directory and simply run:

pip install .

The pacakge does not need to be installed to use it, you can simply git clone the package and add your own macros that include the numcmcmtools folder.

File Format

The expected input format for this code is a ROOT file containing at least one TTree. The tree can have any name. Inside the tree, there will be at least six branches called:

• DeltaCP, in radians, across any $2\pi$ interval
• Theta13, in radians, [0, $\pi$/2]
• Theta23, in radians, [0, $\pi$/2]
• Theta12, in radians, [0, $\pi$/2]
• Deltam2_32, in $\text{eV}^2$
• Deltam2_21, in $\text{eV}^2$

which correspond to the standard parameters of neutrino mixing; the first four are the PMNS angles, and the last two are the mass splittings. For more information on the parameterization see the PDG review on neutrino mixing.

Each parameter has some prior set by the original analyzers. The format of this information is a TList containing a TNamed for each branch, which specifies the name of the branch and its prior.

The priors are specified as either Uniform or Gaussian(mean, sigma). There is then a further specification as to which variable the functional form applies to. For example, a specification of

Theta23 Uniform:sin^2(Theta23)

indicates that the prior for Theta23 is uniform in $\sin^2\theta_{23}$.

The file additionally contains a citation to the original analysis that produced the chain.

There may be additional information contained in the file; please see the data release from the particular analysis for more detail.

In this code, the class mcmcsamples is responsible for loading in the required information from the file. The constructor takes the filepath for the input file and the name of the oscillation parameters tree, and an optional argument for additional variables to be loaded.

An example tree (examples/testchaindata.root) with the required features is provided for testing the functionality of this software.

A very basic script for loading the example tree is examples/load_mcmc.py and can be run as follows:

 python -m examples.load_mcmc

Posterior Density Functions and Contours

After the tree is loaded, an instance of plotstack can be created, which is initialized with an mcmcsamples instance. Individual plots can be added to the plot stack. One and two dimensional plots are currently supported.

A plot is added to the plot stack with the add_plot function, which takes as arguments an array of the name(s) of the variables to be plotted as strings, any change in priors to be applied (see section below for details), the bins and axis ranges for the plots, as defined in numpy.histogram or numpy.histogram2d, and finally whether to treat the two mass orderings separately (True) or together (False, default).

Any number of plots can be added to the stack. When all plots are created, they can be filled by calling fill_plots, which has two optional arguments. The first causes only the first n_steps to be filled from the tree; this is useful in testing the plot stack for very large input trees; the second restricts the number of steps read from the tree to be batchsize at any given time, which is helpful for memory management. Above a batch size of approximately 100000, there is no performance impact on the time it takes to read large trees and fill plots; this value is therefore set at the default. The number of plots to be filled does have an impact on the run time of the code.

After the histograms are filled, they are normalized to create probability density functions, such that individual bins are normalized to the width/area of the bin and the integral is normalized to 1.

Credible intervals can be created by calling make_intervals on either the plot stack or individual plots within the stack. The function takes an array of credible interval levels.

PDFs and intervals can be drawn with draw_plot and draw_interval respectively, and can take an Figure argument from matplotlib. When dividing a figure into subfigures, use the subfigures command, rather than subplots, as subplots is used internally.

The plot stack can also be used to automatically draw all of the plots in the stack, choosing the optimal dimensions for the subfigure array.

An example of the plotting features is in examples/simpleplots.py and can be run as follows:

 python -m examples.simpleplots

The output of this example should look as follows

Derived Variables

Variables derived from the six required parameters—for example, $\sin^2\theta_{23}$-can be defined and used for plotting. Each new variable must be given a unique name and defined as a function which uses the required parameters as input variables, spelled exactly as they are defined above.

The new variable is then attached to an instance of the mcmcsamples class using the add_variable function, which takes as arguments the name of the new variable and the function defining it.

An example of this feature is in examples/custom_variables.py and can be run as follows:

 python -m examples.custom_variables

The output of this example should have two canvases which look as follows

Changing Priors

Priors for parameters can be changed automatically when making plots. A list of new priors is passed when adding a new plot to the plotstack. This feature currently only works in 1D and with Uniform or Gaussian priors in a limited set of transformations of variables. The list of currently available transformations is:

x
sin(x)
sin^2(x)
cos(x)
cos^2(x)
2x
sin(2x)
sin^2(2x)
cos(2x)
cos^2(2x)
exp(-ix)

For example, an input chain that has a prior set to be uniform in DeltaCP can have the following passed to the input chain to make plots with a prior uniform in $\sin\delta_{CP}$:

["Uniform:sin(DeltaCP)"]

Examples of this in practice are in the custom_variables example.

Plotting

Plots can be drawn directly from the plotstack (shown in the custom_variables example), where the arrangement of pdfs or intervals is done algorithmically. For more control, the figure and its subdivisions can be arranged by the user and each plot drawn individually. An example of the latter is shown in both the simpleplots example, and in the plot_triangle example, which creates a standard triangle (corner) plot with the mass ordering separated. This example can be run as follows:

  python -m examples.plot_triangle -f examples/testchaindata.root -c mcmc    

or

  plot_triangle -f examples/testchaindata.root -c mcmc    

if installed throgh pip, which produces the output below.