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")