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)