How to use _normalize_to_series method in autotest

Best Python code snippet using autotest_python

graphing_utils.py

Source:graphing_utils.py

...397    for index, plot in enumerate(plots):398        if plot['label'] == label:399            return index400    raise ValueError('no plot labeled "%s" found' % label)401def _normalize_to_series(plots, base_series):402    base_series_index = _find_plot_by_label(plots, base_series)403    base_plot = plots[base_series_index]404    base_xs = base_plot['x']405    base_values = base_plot['y']406    base_errors = base_plot['errors']407    del plots[base_series_index]408    for plot in plots:409        old_xs, old_values, old_errors = plot['x'], plot['y'], plot['errors']410        new_xs, new_values, new_errors = [], [], []411        new_base_values, new_base_errors = [], []412        # Select only points in the to-be-normalized data that have a413        # corresponding baseline value414        for index, x_value in enumerate(old_xs):415            try:416                base_index = base_xs.index(x_value)417            except ValueError:418                continue419            new_xs.append(x_value)420            new_values.append(old_values[index])421            new_base_values.append(base_values[base_index])422            if old_errors:423                new_errors.append(old_errors[index])424                new_base_errors.append(base_errors[base_index])425        if not new_xs:426            raise NoDataError('No normalizable data for series ' +427                              plot['label'])428        plot['x'] = new_xs429        plot['y'] = new_values430        if old_errors:431            plot['errors'] = new_errors432        plot['y'], plot['errors'] = _normalize(plot['y'], plot['errors'],433                                               new_base_values,434                                               new_base_errors)435def _create_metrics_plot_helper(plot_info, extra_text=None):436    """437    Create a metrics plot of the given plot data.438    plot_info: a MetricsPlot object.439    extra_text: text to show at the uppper-left of the graph440    TODO(showard): move some/all of this logic into methods on MetricsPlot441    """442    query = plot_info.query_dict['__main__']443    cursor = readonly_connection.cursor()444    cursor.execute(query)445    if not cursor.rowcount:446        raise NoDataError('query did not return any data')447    rows = cursor.fetchall()448    # "transpose" rows, so columns[0] is all the values from the first column,449    # etc.450    columns = zip(*rows)451    plots = []452    labels = [str(label) for label in columns[0]]453    needs_resort = (cursor.description[0][0] == 'kernel')454    # Collect all the data for the plot455    col = 1456    while col < len(cursor.description):457        y = columns[col]458        label = cursor.description[col][0]459        col += 1460        if (col < len(cursor.description) and461            'errors-' + label == cursor.description[col][0]):462            errors = columns[col]463            col += 1464        else:465            errors = None466        if needs_resort:467            y = _resort(labels, y)468            if errors:469                errors = _resort(labels, errors)470        x = [index for index, value in enumerate(y) if value is not None]471        if not x:472            raise NoDataError('No data for series ' + label)473        y = [y[i] for i in x]474        if errors:475            errors = [errors[i] for i in x]476        plots.append({477            'label': label,478            'x': x,479            'y': y,480            'errors': errors481        })482    if needs_resort:483        labels = _resort(labels, labels)484    # Normalize the data if necessary485    normalize_to = plot_info.normalize_to486    if normalize_to == 'first' or normalize_to.startswith('x__'):487        if normalize_to != 'first':488            baseline = normalize_to[3:]489            try:490                baseline_index = labels.index(baseline)491            except ValueError:492                raise ValidationError({493                    'Normalize' : 'Invalid baseline %s' % baseline494                    })495        for plot in plots:496            if normalize_to == 'first':497                plot_index = 0498            else:499                try:500                    plot_index = plot['x'].index(baseline_index)501                # if the value is not found, then we cannot normalize502                except ValueError:503                    raise ValidationError({504                        'Normalize' : ('%s does not have a value for %s'505                                       % (plot['label'], normalize_to[3:]))506                        })507            base_values = [plot['y'][plot_index]] * len(plot['y'])508            if plot['errors']:509                base_errors = [plot['errors'][plot_index]] * len(plot['errors'])510            plot['y'], plot['errors'] = _normalize(plot['y'], plot['errors'],511                                                   base_values,512                                                   None or base_errors)513    elif normalize_to.startswith('series__'):514        base_series = normalize_to[8:]515        _normalize_to_series(plots, base_series)516    # Call the appropriate function to draw the line or bar plot517    if plot_info.is_line:518        figure, area_data = _create_line(plots, labels, plot_info)519    else:520        figure, area_data = _create_bar(plots, labels, plot_info)521    # TODO(showard): extract these magic numbers to named constants522    if extra_text:523        text_y = .95 - .0075 * len(plots)524        figure.text(.1, text_y, extra_text, size='xx-small')525    return (figure, area_data)526def create_metrics_plot(query_dict, plot_type, inverted_series, normalize_to,527                        drilldown_callback, extra_text=None):528    plot_info = MetricsPlot(query_dict, plot_type, inverted_series,529                            normalize_to, drilldown_callback)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.