SALib.plotting package

Submodules

SALib.plotting.bar module

SALib.plotting.bar.plot(Si_df, ax=None)[source]

Create bar chart of results

Parameters

Si_df (*) –

Returns

* ax

Return type

matplotlib axes object

Examples

>>> from SALib.plotting.bar import plot as barplot
>>> from SALib.test_functions import Ishigami
>>>
>>> X = saltelli.sample(problem, 1000)
>>> Y = Ishigami.evaluate(X)
>>> Si = sobol.analyze(problem, Y, print_to_console=False)
>>> Si_df = Si.to_df()
>>> barplot(Si_df)

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.plot as plt
Si = morris.analyze(problem, param_values, Y, conf_level=0.95,
                    print_to_console=False, num_levels=10)
p = morris.horizontal_bar_plot(Si)
# set plot style etc.

fig, ax = plt.subplots(1, 1)
my_plotter(ax, data1, data2, {'marker':'x'})

p.show()
SALib.plotting.morris.covariance_plot(ax, Si, param_dict, unit='')[source]

Plots mu* against sigma or the 95% confidence interval

SALib.plotting.morris.horizontal_bar_plot(ax, Si, param_dict, 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, param_dict)[source]

Plots a set of subplots of histograms of the input sample

Module contents