SALib.plotting package#
Submodules#
SALib.plotting.bar module#
- SALib.plotting.bar.plot(Si_df, ax=None)[source]#
Create bar chart of results.
Examples
>>> from SALib.plotting.bar 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)
- Parameters:
Si_df (*) –
- Returns:
* ax
- Return type:
matplotlib axes object
SALib.plotting.hdmr module#
Created on Dec 20, 2019
@author: @sahin-abdullah
This submodule produces two different figures: (1) emulator vs simulator, (2) regression lines of first order component functions
SALib.plotting.heatmap module#
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'})
p.show()
- SALib.plotting.morris.covariance_plot(ax, Si, opts=None, unit='')[source]#
Plots mu* against sigma or the 95% confidence interval