How to use _get_adjusted_bar method in autotest

Best Python code snippet using autotest_python Github


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...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 =, 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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