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]
utils.customized_box_plot(boxes, ax)
utils.set_plot_title(utils.make_chart_title(df, title, legend))
utils.assert_columns_exist(
df, [xaxis_itervar],
"The iteration variable for the X axis could not be found")
utils.assert_columns_exist(
df, [yaxis_itervar],
"The iteration variable for the Y axis could not be found")
if xaxis_itervar == yaxis_itervar:
raise chart.ChartScriptError(
"The itervar for the X and Y axes are the same: " + xaxis_itervar)
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:
raise chart.ChartScriptError("Selected scalars must share the same name.")
scalar_name = unique_names[0]
df = pd.pivot_table(df,
columns=[xaxis_itervar],
index=yaxis_itervar,
values="value",
dropna=False)
errors_df = None
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
pivoted = pd.pivot_table(df,
values="value",
columns=iso_itervars,
index=xaxis_itervar if xaxis_itervar else "name",
aggfunc=[np.mean, conf_int],
dropna=False)
df = pivoted["mean"]
errors_df = pivoted["<lambda>"]
legend_cols, _ = utils.extract_label_columns(df)
p = plot if chart.is_native_chart() else plt
try:
xs = pd.to_numeric(df.index.values)
p.xlabel(xaxis_itervar)
except:
xs = np.zeros_like(df.index.values)
p.ylabel(scalar_names)
for c in df.columns:
style = utils._make_line_args(props, c, df)
if len(xs) < 2 and style["marker"] == ' ':
style["marker"] = '.'
utils.assert_columns_exist(
df, [xaxis_itervar],
"The iteration variable for the X axis could not be found")
utils.assert_columns_exist(
df, [yaxis_itervar],
"The iteration variable for the Y axis could not be found")
if xaxis_itervar == yaxis_itervar:
raise chart.ChartScriptError(
"The itervar for the X and Y axes are the same: " + xaxis_itervar)
df[xaxis_itervar] = pd.to_numeric(df[xaxis_itervar], errors="ignore")
df[yaxis_itervar] = pd.to_numeric(df[yaxis_itervar], errors="ignore")
title_cols, legend_cols = utils.extract_label_columns(df, props)
title = utils.make_chart_title(df, title_cols)
unique_names = df["name"].unique()
if len(unique_names) != 1:
raise chart.ChartScriptError("Selected scalars must share the same name.")
scalar_name = unique_names[0]
df = pd.pivot_table(df,
columns=[xaxis_itervar],
index=yaxis_itervar,
values="value",
dropna=False)