SALib.plotting package

Submodules module, ax=None)[source]

Create bar chart of results.


Si_df (*) –


* ax

Return type

matplotlib axes object


>>> from import plot as barplot
>>> from SALib.test_functions import Ishigami
>>> # See README for example problem specification
>>> X = saltelli.sample(problem, 512)
>>> Y = Ishigami.evaluate(X)
>>> Si = sobol.analyze(problem, Y, print_to_console=False)
>>> total, first, second = Si.to_df()
>>> barplot(total)

SALib.plotting.hdmr module

Created on Dec 20, 2019

@author: @sahin-abdullah

This submodule produces two diffent figures: (1) emulator vs simulator, (2) regression lines of first order component functions


SALib.plotting.morris module

Created on 29 Jun 2015

@author: @willu47

This module provides the basic infrastructure for plotting charts for the Method of Morris results

The procedures should build upon and return an axes instance:

import matplotlib.pyplot as plt
Si = morris.analyze(problem, param_values, Y, conf_level=0.95,
                    print_to_console=False, num_levels=10)

# set plot style etc.
fig, ax = plt.subplots(1)
p = SALib.plotting.morris.horizontal_bar_plot(ax, Si, {'marker':'x'})
SALib.plotting.morris.covariance_plot(ax, Si, opts=None, unit='')[source]

Plots mu* against sigma or the 95% confidence interval

SALib.plotting.morris.horizontal_bar_plot(ax, Si, opts=None, sortby='mu_star', unit='')[source]

Updates a matplotlib axes instance with a horizontal bar plot of mu_star, with error bars representing mu_star_conf.

SALib.plotting.morris.sample_histograms(fig, input_sample, problem, opts=None)[source]

Plots a set of subplots of histograms of the input sample

Module contents