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)
max_y = max(q4_end, max_y)
mid_y = (min_y + max_y) / 2
# The y axis is rescaled to fit the new box plot completely with 10%
# of the maximum value at both ends
axes.set_ylim(
[mid_y - (mid_y - min_y) * 1.25, mid_y + (max_y - mid_y) * 1.25])
# If redraw is set to true, the canvas is updated.
if redraw:
axes.figure.canvas.draw()
return box_plot
title, legend = chart.extract_label_columns(df)
df.sort_values(by=[l for i, l in legend], axis='index', inplace=True)
# 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
customized_box_plot([(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],
plt.gca())
if 'title' in df:
plt.title(df[title][0])
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"],
values="value",
dropna=False)
ax = plt.gcf().add_subplot(111, projection='3d')
print(df)
X, Y = np.meshgrid(df.columns, df.index)