Matrix of Perturbation Correlation Plots

This example demonstrates how to generate a matrix plot of perturbation plots for a suite of applications and experiments. This method is detailed in the technical manual, and requires randomly sampling nuclear data libraries to generate a scatter plot. It is recommended that the user refers to Perturbation Correlation Plots for more information on the perturbation correlation plots that compose the matrix plots shown herein before continuing with this example.

Interactive Matrix Plots

Interactive matrix plots wrap several Plotly plots in an html grid that’s served by a Dash app that allows users to interact with each of the individual plots. This is useful for comparing multiple applications and experiments in a single figure. Specifically, it is useful for viewing all combinations of a given set of SDF files in a single figure

Note that by default the the correlation plots in the off-diagonal cells are tsunami_ip_utils.viz.scatter_plot.InteractiveScatterLegend plots, which are fully interactive, and dynamically update the calculated summary statistics and regression when points are excluded with the legend. However, this interactivity requires clientiside python to run, and so is not represented in the documentation. The off-diagonal plot types can be changed, and doing so is documented in Changing Plot Defaults.

from paths import EXAMPLES

application_files = [ EXAMPLES / 'data' / 'example_sdfs' / 'u235-dummy' / f'sphere_model_{i}.sdf' for i in range(1,4) ]
experiment_files = application_files

Generate the Points Array

First, an array of points must be generated for each application-experiment pair (these are used to construct the perturbation plots).

from tsunami_ip_utils.perturbations import generate_points
from pathlib import Path

base_library = EXAMPLES / 'data' / 'dummy_56_v7.1'
perturbation_factors = Path("~/codes/SCALE-6.3.1/data/perturb/56n.v7.1")
num_perturbations = 100

points = generate_points(application_files, experiment_files, base_library, perturbation_factors, num_perturbations)

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Generate the Plot Objects

Now, the tsunami_ip_utils.viz.viz.matrix_plot() function needs a numpy array of plot objects to generate the matrix plot. We can generate this array using the tsunami_ip_utils.viz.plot_utils.generate_plot_objects_array_from_perturbations() function. This function takes a dictionary of contributions and the name of the integral index, and returns a numpy array of plot objects that can be passed to the tsunami_ip_utils.viz.viz.matrix_plot() function.

from tsunami_ip_utils.viz.plot_utils import generate_plot_objects_array_from_perturbations

plot_objects = generate_plot_objects_array_from_perturbations(points)

Create the Matrix Plot

Finally, we can create the matrix plot using the tsunami_ip_utils.viz.viz.matrix_plot() function. This function takes the numpy array of plot objects generated in the previous step, and generates a matrix plot of perturbation correlation plots.

from tsunami_ip_utils.viz.viz import matrix_plot

labels = {
    'applications': [ file.name for file in application_files ],
    'experiments': [ file.name for file in experiment_files ]
}
fig = matrix_plot(plot_objects, plot_type='interactive', labels=labels)
fig.show()

- Zoom Out + Zoom In

The plot can be saved to an image by using the tsunami_ip_utils.viz.matrix_plot.InteractiveMatrixPlot.to_image() method

fig = matrix_plot(plot_objects, plot_type='interactive', labels=labels)
fig.to_image( EXAMPLES / '_static' / 'perturbation_matrix.png' )

# sphinx_gallery_thumbnail_path = '../../examples/_static/perturbation_matrix.png'

- Zoom Out + Zoom In