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How to use _get_adjusted_bar method in autotest

Best Python code snippet using autotest_python

graphing_utils.py

Source:graphing_utils.py

...231        # markers in the legend.232        for line in legend.get_lines():233            line.set_marker(_LEGEND_MARKER_TYPE)234    return (figure, area_data)235def _get_adjusted_bar(x, bar_width, series_index, num_plots):236    """\237    Adjust the list 'x' to take the multiple series into account. Each series238    should be shifted such that the middle series lies at the appropriate x-axis239    tick with the other bars around it.  For example, if we had four series240    (i.e. four bars per x value), we want to shift the left edges of the bars as241    such:242    Bar 1: -2 * width243    Bar 2: -width244    Bar 3: none245    Bar 4: width246    """247    adjust = (-0.5 * num_plots - 1 + series_index) * bar_width248    return [x_val + adjust for x_val in x]249# TODO(showard): merge much of this function with _create_line by extracting and250# parameterizing methods251def _create_bar(plots, labels, plot_info):252    """\253    Given all the data for the metrics, create a line plot.254    plots: list of dicts containing the plot data.255            x: list of x-values for the plot256            y: list of corresponding y-values257            errors: errors for each data point, or None if no error information258                    available259            label: plot title260    labels: list of x-tick labels261    plot_info: a MetricsPlot262    """263    area_data = []264    bars = []265    figure, height = _create_figure(_SINGLE_PLOT_HEIGHT)266    # Set up the plot267    subplot = figure.add_subplot(1, 1, 1)268    subplot.set_xticks(range(0, len(labels)))269    subplot.set_xlim(-1, len(labels))270    subplot.set_xticklabels(labels, rotation=90, size=_BAR_XTICK_LABELS_SIZE)271    # draw a bold line at y=0, making it easier to tell if bars are dipping272    # below the axis or not.273    subplot.axhline(linewidth=2, color='black')274    # width here is the width for each bar in the plot. Matplotlib default is275    # 0.8.276    width = 0.8 / len(plots)277    # Plot the data278    for plot_index, (plot, color) in enumerate(zip(plots, _colors(len(plots)))):279        # Invert the y-axis if needed280        if plot['label'] in plot_info.inverted_series:281            plot['y'] = [-y for y in plot['y']]282        adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,283                                       len(plots))284        bar_data = subplot.bar(adjusted_x, plot['y'],285                               width=width, yerr=plot['errors'],286                               facecolor=color,287                               label=plot['label'])288        bars.append(bar_data[0])289    # Construct the information for the drilldowns.290    # See comment in _create_line for why we need a separate loop to do this.291    for plot_index, plot in enumerate(plots):292        adjusted_x = _get_adjusted_bar(plot['x'], width, plot_index + 1,293                                       len(plots))294        # Let matplotlib plot the data, so that we can get the data-to-image295        # coordinate transforms296        line = subplot.plot(adjusted_x, plot['y'], linestyle='None')[0]297        label = plot['label']298        upper_left_coords = line.get_transform().transform(zip(adjusted_x,299                                                               plot['y']))300        bottom_right_coords = line.get_transform().transform(301            [(x + width, 0) for x in adjusted_x])302        # Get the drilldown query303        drill = plot_info.query_dict['__' + label + '__']304        # Set the title attributes305        x_labels = [labels[x] for x in plot['x']]306        titles = ['%s - %s: %f' % (plot['label'], label, y)...

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