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