'''
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()
'''
import numpy as np
def _sort_Si(Si, key, sortby='mu_star'):
return np.array([Si[key][x] for x in np.argsort(Si[sortby])])
def _sort_Si_by_index(Si, key, index):
return np.array([Si[key][x] for x in index])
[docs]def horizontal_bar_plot(ax, Si, param_dict, sortby='mu_star', unit=''):
'''Updates a matplotlib axes instance with a horizontal bar plot
of mu_star, with error bars representing mu_star_conf
'''
assert sortby in ['mu_star', 'mu_star_conf', 'sigma', 'mu']
# Sort all the plotted elements by mu_star (or optionally another
# metric)
names_sorted = _sort_Si(Si, 'names', sortby)
mu_star_sorted = _sort_Si(Si, 'mu_star', sortby)
mu_star_conf_sorted = _sort_Si(Si, 'mu_star_conf', sortby)
# Plot horizontal barchart
y_pos = np.arange(len(mu_star_sorted))
plot_names = names_sorted
out = ax.barh(y_pos,
mu_star_sorted,
xerr=mu_star_conf_sorted,
align='center',
ecolor='black',
**param_dict)
ax.set_yticks(y_pos)
ax.set_yticklabels(plot_names)
ax.set_xlabel(r'$\mu^\star$' + unit)
ax.set_ylim(min(y_pos)-1, max(y_pos)+1)
return out
[docs]def covariance_plot(ax, Si, param_dict, unit=""):
'''Plots mu* against sigma or the 95% confidence interval
'''
if Si['sigma'] is not None:
# sigma is not present if using morris groups
y = Si['sigma']
out = ax.scatter(Si['mu_star'], y, c=u'k', marker=u'o',
**param_dict)
ax.set_ylabel(r'$\sigma$')
ax.set_xlim(0,)
ax.set_ylim(0,)
x_axis_bounds = np.array(ax.get_xlim())
line1, = ax.plot(x_axis_bounds, x_axis_bounds, 'k-')
line2, = ax.plot(x_axis_bounds, 0.5 * x_axis_bounds, 'k--')
line3, = ax.plot(x_axis_bounds, 0.1 * x_axis_bounds, 'k-.')
ax.legend((line1, line2, line3), (r'$\sigma / \mu^{\star} = 1.0$',
r'$\sigma / \mu^{\star} = 0.5$',
r'$\sigma / \mu^{\star} = 0.1$'),
loc='best')
else:
y = Si['mu_star_conf']
out = ax.scatter(Si['mu_star'], y, c=u'k', marker=u'o',
**param_dict)
ax.set_ylabel(r'$95\% CI$')
ax.set_xlabel(r'$\mu^\star$ ' + unit)
ax.set_ylim(0-(0.01 * np.array(ax.get_ylim()[1])), )
return out
[docs]def sample_histograms(fig, input_sample, problem, param_dict):
'''Plots a set of subplots of histograms of the input sample
'''
num_vars = problem['num_vars']
names = problem['names']
framing = 101 + (num_vars * 10)
# Find number of levels
num_levels = len(set(input_sample[:, 1]))
out = []
for variable in range(num_vars):
ax = fig.add_subplot(framing + variable)
out.append(ax.hist(input_sample[:, variable],
bins=num_levels,
density=False,
label=None,
**param_dict))
ax.set_title('%s' % (names[variable]))
ax.tick_params(axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge off)
if variable > 0:
ax.tick_params(axis='y', # changes apply to the y-axis
which='both', # both major and minor ticks affected
labelleft='off') # labels along the left edge off)
return out
if __name__ == '__main__':
pass