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())
utils.preconfigure_plot(props)
# collect parameters for query
filter_expression = props["filter"]
include_fields = props["include_fields"] == "true"
# query scalar data into dataframe
try:
df = results.get_scalars(filter_expression,
include_fields=include_fields,
include_attrs=True,
include_runattrs=True,
include_itervars=True)
except results.ResultQueryError as e:
raise chart.ChartScriptError("Error while querying results: " + str(e))
if df.empty:
raise chart.ChartScriptError("The result filter returned no data.")
groups, series = utils.select_groups_series(df, props)
confidence_level = utils.get_confidence_level(props)
valuedf, errorsdf, metadf = utils.pivot_for_barchart(df, groups, series,
confidence_level)
utils.plot_bars(valuedf, errorsdf, metadf, props)
utils.postconfigure_plot(props)
utils.export_image_if_needed(props)
utils.export_data_if_needed(df, props)
utils.assert_columns_exist(df, groups + series,
"No such iteration variable or run attribute")
for c in groups + series:
df[c] = pd.to_numeric(df[c], errors="ignore")
# names for title, confidence level
names = ", ".join(utils.get_names_for_title(df, props))
confidence_level_str = props[
"confidence_level"] if "confidence_level" in props else "none"
# pivoting and plotting
df.sort_values(by=groups + series, axis='index', inplace=True)
if confidence_level_str == "none":
df = pd.pivot_table(df, index=groups, columns=series, values='value')
utils.plot_bars(df, props, names)
else:
confidence_level = float(confidence_level_str[:-1]) / 100
def conf_intv(values):
return utils.confidence_interval(confidence_level, values)
df = pd.pivot_table(df,
index=groups,
columns=series,
values='value',
aggfunc=[np.mean, conf_intv],
dropna=False)
utils.plot_bars(df["mean"], props, names, df["conf_intv"])
utils.postconfigure_plot(props)
utils.assert_columns_exist(df, groups + series,
"No such iteration variable or run attribute")
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)
confidence_level_str = props[
"confidence_level"] if "confidence_level" in props else "none"
if confidence_level_str == "none":
df = pd.pivot_table(df, index=groups, columns=series, values='value')
utils.plot_bars(df, props, names)
else:
confidence_level = float(confidence_level_str[:-1]) / 100
conf_int = lambda values: utils.confidence_interval(
confidence_level, values) if len(values) > 1 else math.nan
df = pd.pivot_table(df,
index=groups,
columns=series,
values='value',
aggfunc=[np.mean, conf_int],
dropna=False)
utils.plot_bars(df["mean"], props, names, df["<lambda>"])
utils.postconfigure_plot(props)
utils.export_image_if_needed(props)
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]
utils.assert_columns_exist(df, groups + series)
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)
utils.export_image_if_needed(props)
utils.export_data_if_needed(df, props)
