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:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
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
from omnetpp.scave import results, chart, utils, plot
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
# query vector data into a data frame
try:
df = results.get_histograms(filter_expression,
include_attrs=True,
include_itervars=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
# plot
utils.plot_histograms(df, props)
utils.postconfigure_plot(props)
utils.export_image_if_needed(props)
utils.export_data_if_needed(df, props)
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)
coeff = 0.6744897501960817
boxes = [(r.min, r.mean - r.stddev * coeff, r.mean, r.mean + r.stddev * coeff,
r.max) for r in df.itertuples(index=False) if r.count > 0]
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
iso_itervars = utils.split(props["iso_itervar"])
# query data into a data frame
try:
df = results.get_scalars(filter_expression,
include_runattrs=True,
include_attrs=True,
include_itervars=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
if not xaxis_itervar and not iso_itervars:
print(
"The 'Itervar for X Axis' and 'Itervar for iso line' options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, iso_itervar = utils.pick_two_columns(df)
iso_itervars = [iso_itervar] if iso_itervar else []
print("X axis: " + xaxis_itervar + " iso lines: " + ",".join(iso_itervars))
if xaxis_itervar:
def assert_columns_exist(df, cols):
for c in cols:
if c not in df:
plot.set_warning("Expected column missing from DataFrame: " + c)
exit(1)
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
iso_itervars = utils.split(props["iso_itervar"])
# query data into a data frame
try:
df = results.get_scalars(filter_expression,
include_runattrs=True,
include_attrs=True,
include_itervars=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
if not xaxis_itervar and not iso_itervars:
print(
"The 'Itervar for X Axis' and 'Itervar for iso line' options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, iso_itervar = utils.pick_two_columns(df)
iso_itervars = [iso_itervar] if iso_itervar else []
print("X axis: " + xaxis_itervar + " iso lines: " + ",".join(iso_itervars))
if xaxis_itervar:
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
iso_itervar = props["iso_itervar"]
# query data into a data frame
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True)
if not xaxis_itervar and not iso_itervar:
print(
"The X Axis and Iso Line options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, iso_itervar = utils.pick_two_columns(df)
if not iso_itervar or not iso_itervar:
plot.set_warning(
"Please set the X Axis and Iso Lines options in the dialog!")
exit(1)
df[xaxis_itervar] = pd.to_numeric(df[xaxis_itervar], errors="ignore")
df[iso_itervar] = pd.to_numeric(df[iso_itervar], errors="ignore")
if df.empty:
plot.set_warning("Select scalars for the Y axis in the Properties dialog")
exit(1)
unique_names = df["name"].unique()
if len(unique_names) != 1:
plot.set_warning("Scalar names should be unique")
exit(1)
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
# query scalar data into dataframe
try:
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True,
include_runattrs=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
groups = props["groups"].split(",")
series = props["series"].split(",")
if not groups[0] and not series[0]:
print(
"The Groups and Series options were not set in the dialog, inferring them from the data."
)
g, s = ("module", "name") if len(df) == 1 else utils.pick_two_columns(df)
groups, series = [g], [s]
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
# query scalar data into dataframe
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True,
include_runattrs=True)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
groups = props["groups"].split(",")
series = props["series"].split(",")
if not groups[0] and not series[0]:
print(
"The Groups and Series options were not set in the dialog, inferring them from the data."
)
g, s = ("module", "name") if len(df) == 1 else utils.pick_two_columns(df)
if not g or not s:
plot.set_warning(
"Please set the Groups and Series options in the dialog!")
exit(1)
groups, series = [g], [s]
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
iso_itervar = props["iso_itervar"]
# query data into a data frame
df = results.get_scalars(filter_expression,
include_runattrs=True,
include_attrs=True,
include_itervars=True)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
if not xaxis_itervar and not iso_itervar:
print(
"The X Axis and Iso Line options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, iso_itervar = utils.pick_two_columns(df)
if not iso_itervar or not iso_itervar:
plot.set_warning(
"Please set the X Axis and Iso Lines options in the dialog!")
exit(1)
utils.assert_columns_exist(df, [xaxis_itervar, iso_itervar])
df[xaxis_itervar] = pd.to_numeric(df[xaxis_itervar], errors="ignore")
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
yaxis_itervar = props["yaxis_itervar"]
# query data into a data frame
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
if not xaxis_itervar and not yaxis_itervar:
print(
"The X Axis and Y Axis options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, yaxis_itervar = utils.pick_two_columns(df)
if not xaxis_itervar or not yaxis_itervar:
plot.set_warning(
"Please set the X Axis and Y Axis options in the dialog!")
exit(1)
utils.assert_columns_exist(df, [xaxis_itervar, yaxis_itervar])
df[xaxis_itervar] = pd.to_numeric(df[xaxis_itervar], errors="ignore")
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
# query scalar data into dataframe
try:
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True,
include_runattrs=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
# determine "groups" and "series" for pivoting, and check them
groups = utils.split(props["groups"])
series = utils.split(props["series"])
if not groups and not series:
print(
"The Groups and Series options were not set in the dialog, inferring them from the data."
)
g, s = ("module", "name") if len(df) == 1 else utils.pick_two_columns(df)
# get chart properties
props = chart.get_properties()
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
yaxis_itervar = props["yaxis_itervar"]
# query data into a data frame
try:
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True)
except ValueError as e:
plot.set_warning("Error while querying results: " + str(e))
exit(1)
if df.empty:
plot.set_warning("The result filter returned no data.")
exit(1)
if not xaxis_itervar and not yaxis_itervar:
print(
"The X Axis and Y Axis options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, yaxis_itervar = utils.pick_two_columns(df)
if not xaxis_itervar or not yaxis_itervar:
plot.set_warning(
"Please set both the X Axis and Y Axis options in the dialog - or neither, for automatic selection!"
)
filter_expression = props["filter"]
xaxis_itervar = props["xaxis_itervar"]
yaxis_itervar = props["yaxis_itervar"]
# query data into a data frame
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True)
if not xaxis_itervar and not yaxis_itervar:
print(
"The X Axis and Y Axis options were not set in the dialog, inferring them from the data.."
)
xaxis_itervar, yaxis_itervar = utils.pick_two_columns(df)
if not xaxis_itervar or not yaxis_itervar:
plot.set_warning(
"Please set the X Axis and Y Axis options in the dialog!")
exit(1)
df[xaxis_itervar] = pd.to_numeric(df[xaxis_itervar], errors="ignore")
df[yaxis_itervar] = pd.to_numeric(df[yaxis_itervar], errors="ignore")
title_col, legend_cols = utils.extract_label_columns(df)
title = str(list(df[title_col])[0]) if title_col else None
unique_names = df["name"].unique()
if len(unique_names) != 1:
plot.set_warning("Scalar names should be unique")
exit(1)
# query scalar data into dataframe
df = results.get_scalars(filter_expression,
include_attrs=True,
include_itervars=True,
include_runattrs=True)
groups = props["groups"].split(",")
series = props["series"].split(",")
if not groups[0] and not series[0]:
print(
"The Groups and Series options were not set in the dialog, inferring them from the data."
)
g, s = utils.pick_two_columns(df)
if not g or not s:
plot.set_warning(
"Please set the Groups and Series options in the dialog!")
exit(1)
groups, series = [g], [s]
for c in groups + series:
df[c] = pd.to_numeric(df[c], errors="ignore")
df.sort_values(by=groups + series, axis='index', inplace=True)
names = utils.get_names_for_title(df, props)
df = pd.pivot_table(df, index=groups, columns=series, values='value')
utils.plot_bars(df, props, names)
utils.postconfigure_plot(props)
