def test_histograms_with_all():
df = results.get_histograms(r,
include_attrs=True,
include_itervars=True,
include_runattrs=True,
include_param_assignments=True,
include_config_entries=True)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "histograms_with_all.csv"),
"content mismatch")
from omnetpp.scave import results, chart params = chart.get_configured_properties() # This expression selects the results filter_expression = params["filter"] # The data is returned as a Pandas DataFrame df = results.get_histograms(filter_expression, include_attrs=True, include_itervars=True) # You can perform any transformations on the data here print(df) # Finally, the results are plotted chart.plot_histograms(df) chart.copy_properties()
from omnetpp.scave import results, chart, utils, plot
import matplotlib.pyplot as plt
import pandas as pd
props = chart.get_properties()
utils.preconfigure_plot(props)
stats = results.get_statistics(props["filter"],
include_attrs=True,
include_runattrs=True,
include_itervars=True)
hists = results.get_histograms(props["filter"],
include_attrs=True,
include_runattrs=True,
include_itervars=True)
df = pd.concat([stats, hists], sort=False)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
title, legend = utils.extract_label_columns(df)
df.sort_values(by=[l for i, l in legend], axis='index', inplace=True)
ax = plt.gca()
# This is how much of the standard deviation will give the 25th and 75th
# percentiles, assuming normal distribution.
# >>> math.sqrt(2) * scipy.special.erfinv(0.5)
def test_histograms_with_attrs():
df = results.get_histograms(r, include_attrs=True)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "histograms_with_attrs.csv"),
"content mismatch")
def test_histograms():
df = results.get_histograms(r)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "histograms.csv"), "content mismatch")