def get_best_performing_dataframe_separately_packetreceived():
"""Selects the runs corresponding to the most received packets,
and returns them in three separate dataframes for the three configurations"""
# filter expressions for packetReceived:count for the three configurations
filter_expression_bmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticBMac" AND module =~ "*.server.app[0]" AND name =~ packetReceived:count"""
filter_expression_xmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticXMac" AND module =~ "*.server.app[0]" AND name =~ packetReceived:count"""
filter_expression_lmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticLMac" AND module =~ "*.server.app[0]" AND name =~ packetReceived:count"""
# dataframes for packetReceived:count
df_b = results.get_scalars(filter_expression_bmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
df_x = results.get_scalars(filter_expression_xmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
df_l = results.get_scalars(filter_expression_lmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
# average repetitions
df_b = df_b.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
if debug:
print("\ndf_b after averaging:\n-------------------", df_b)
df_x = df_x.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
if debug:
print("\ndf_x after averaging:\n-------------------", df_x)
df_l = df_l.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
if debug:
print("\ndf_l after averaging:\n-------------------", df_l)
# get index of the most received packets
df_b_max_index = df_b[['value']].idxmax()
df_x_max_index = df_x[['value']].idxmax()
df_l_max_index = df_l[['value']].idxmax()
# create dataframe containing only the run with the most received packets
df_b = df_b.iloc[df_b_max_index]
df_x = df_x.iloc[df_x_max_index]
df_l = df_l.iloc[df_l_max_index]
if debug:
print(
"----\nget_best_performing_dataframe_separately_packetreceived()\n----"
)
print("returning ", df_b, "\n", df_x, "\n", df_l, "\n")
return df_b, df_x, df_l
def importing_5():
df = results.get_scalars("name =~ rxBytes:sum OR name =~ txBytes:sum",
include_runattrs=True)
print(chart.extract_label_columns(df))
df = pd.pivot_table(df, columns=["name", "replication"], index=["module"])
print(df)
chart.plot_scalars(df)
def get_power_for_each_module_dataframes():
"""Returns the per-module residualEnergyCapacity values for the three configurations in three separate dataframes."""
filter_expression = """* AND name =~ residualEnergyCapacity:last"""
filter_expression_bmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticBMac" AND """ + filter_expression
filter_expression_xmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticXMac" AND """ + filter_expression
filter_expression_lmac = """type =~ scalar AND isfield =~ false AND runattr:configname =~ "StatisticLMac" AND """ + filter_expression
df_b = results.get_scalars(filter_expression_bmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
df_x = results.get_scalars(filter_expression_xmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
df_l = results.get_scalars(filter_expression_lmac,
include_fields=False,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
# average repetitions
df_b = df_b.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
# print("\ndf_b after averaging:\n-------------------",df_b)
df_x = df_x.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
# print("\ndf_x after averaging:\n-------------------",df_x)
df_l = df_l.groupby(
['name', 'configname', 'module', 'iterationvars', 'inifile'],
as_index=False).mean()
# print("\ndf_l after averaging:\n-------------------",df_l)
print("get_power_dataframes\nreturning:")
print("df_b:\n", df_b)
print("df_x:\n", df_x)
print("df_l:\n", df_l)
return df_b, df_x, df_l
def test_scalars_with_all():
df = results.get_scalars(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, "scalars_with_all.csv"),
"content mismatch")
def chart_function_bars():
df = results.get_scalars("name =~ rxBytes:sum OR name =~ txBytes:sum",
include_runattrs=True)
names = utils.get_names_for_title(df, dict())
df = pd.pivot_table(df, index="name", columns="module", values='value')
utils.preconfigure_plot(dict())
utils.plot_bars(df, dict(), names)
utils.postconfigure_plot(dict())
def get_data(filter):
sc = results.get_scalars(filter, include_attrs=True)
iv = results.get_itervars(filter)
ra = results.get_run_attrs(filter)
df = pd.concat([sc, iv, ra])
#print(df)
df["value"] = pd.to_numeric(df["value"], errors="ignore")
df = pd.pivot_table(df, columns="name", index="runID", dropna=False, aggfunc=aggfunc)
#print(df)
return df
def get_data(filter):
try:
sc = results.get_scalars(filter, include_attrs=True)
iv = results.get_itervars(filter)
ra = results.get_runattrs(filter)
except ValueError as e:
raise chart.ChartScriptError("Error while querying results: " + str(e))
df = pd.concat([sc, iv, ra])
df["value"] = pd.to_numeric(df["value"], errors="ignore")
df = pd.pivot_table(df, columns="name", index="runID", dropna=False, aggfunc=aggfunc)
return df
filter_expression = params["filter"]
groups = params["groups"]
bars = params["bars"]
if not groups:
groups = "module,experiment"
if not bars:
bars = "name,measurement"
# TODO: make sure no column is present in both lists
# The data is returned as a Pandas DataFrame
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True,
include_runattrs=True)
print(df)
# You can perform any transformations on the data here
df = pd.pivot_table(df,
values="value",
index=groups.split(","),
columns=bars.split(","))
# You can perform any transformations on the data here
# Finally, the results are plotted
chart.plot_scalars(df)
chart.copy_properties()
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from omnetpp.scave import results, chart
params = chart.get_configured_properties()
if not (params["x_attr"] and params["y_attr"]):
raise Exception("Please select axis attributes in the properties dialog!")
df = results.get_scalars(params["scalar_filter"],
include_attrs=True,
include_itervars=True,
include_runattrs=True)
df[params["x_attr"]] = pd.to_numeric(df[params["x_attr"]])
df[params["y_attr"]] = pd.to_numeric(df[params["y_attr"]])
#print(df)
title_col, legend_cols = chart.extract_label_columns(df)
print(legend_cols)
title = str(list(df[title_col])[0]) if title_col else None
df = pd.pivot_table(df,
columns=[params["x_attr"]],
index=params["y_attr"],
def test_scalars_with_config_entries():
df = results.get_scalars(r, include_config_entries=True)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "scalars_with_config_entries.csv"),
"content mismatch")
def test_scalars():
df = results.get_scalars(r)
_assert_sequential_index(df)
_assert(sanitize_and_compare_csv(df, "scalars.csv"), "content mismatch")
from omnetpp.scave import results, chart params = chart.get_properties() # This expression selects the results (you might be able to logically simplify it) filter_expression = params["filter"] # The data is returned as a Pandas DataFrame df = results.get_scalars(filter_expression) # You can perform any transformations on the data here # Finally, the results are plotted chart.plot_scalars(df)