# Copyright (c) 2013,Vienna University of Technology,
# Department of Geodesy and Geoinformation
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'''
Module for visualizing the test results
Created on Tue Nov 25 13:44:56 2014
@author: Christoph.Paulik@geo.tuwien.ac.at
'''
try:
import seaborn as sns
seaborn_installed = True
except ImportError:
seaborn_installed = False
pass
import matplotlib.pyplot as plt
[docs]def plot_boxplots(*args, **kwargs):
"""
plots means and confidence intervals
of given TestResults objects
Parameters
----------
*args: TestResults instances
any Number of TestResults instances that should be plotted
side by side
conf_level: int, optional
confidence level to use for the computed confidence intervals
**kwargs: varied
all other keyword arguments will be passed on to the
plt.subplots function
Returns
-------
fig: matplotlib.Figure
ax1: matplotlib.axes
"""
if 'conf_level' in kwargs:
conf_level = kwargs.pop('conf_level')
else:
conf_level = 95
measurements = []
conf_intervals = []
means = []
names = []
for res in args:
conf = res.confidence_int(conf_level=conf_level)
measurements.append(res._measurements)
conf_intervals.append([conf[0], conf[2]])
means.append(res.mean)
names.append(res.name)
fig, ax1 = plt.subplots(**kwargs)
if seaborn_installed:
ax = sns.boxplot(measurements, conf_intervals=conf_intervals, notch=True,
usermedians=means)
else:
ax = plt.boxplot(measurements, conf_intervals=conf_intervals, notch=True,
usermedians=means)
xtickNames = plt.setp(ax1, xticklabels=names)
plt.setp(xtickNames)
plt.title('Boxplots with notches at confidence level %d %%.' % conf_level)
plt.ylabel('Time [s]')
return fig, ax1