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How to use _create_figure method in autotest
Best Python code snippet using autotest_python
plotter.py
Source:plotter.py 
...21 for plot_name, figure_ in self.plots.items():22 target_file_name = f'{plot_name}.{self.file_extension}'23 target_file_path = f'{self.target_directory}/{target_file_name}'24 figure_.savefig(target_file_path, dpi=150)25 def _create_figure(self) -> tuple[Figure, Axes]:26 figure_ = figure(figsize=self.figure_size)27 figure_.set_tight_layout('True')28 axis = figure_.add_subplot()29 return figure_, axis30 @abstractmethod31 def create_plots(self):32 pass33class LinePlotter(Plotter, ABC):34 def _create_figure(self) -> tuple[Figure, Axes]:35 figure_, axis = super()._create_figure()36 axis.xaxis.set_major_locator(MaxNLocator(integer=True))37 return figure_, axis38 @abstractmethod39 def create_plots(self):40 pass41class ClientLearningPlotter(LinePlotter):42 def __init__(self, metrics_collector: MetricsCollector):43 super().__init__(metrics_collector)44 self.target_directory = f'{generated_data_path.plots}/learning_plots'45 def create_plots(self):46 for client_id, client_metrics in self.metrics_collector.clients_metrics.items():47 client_accuracy_plot_name = f'client_{client_id}_accuracy_plot'48 client_loss_plot_name = f'client_{client_id}_loss_plot'49 self.plots[client_accuracy_plot_name] = self.__create_client_accuracy_plot(client_id, client_metrics)50 self.plots[client_loss_plot_name] = self.__create_client_loss_plot(client_id, client_metrics)51 all_clients_accuracy_plot_name = f'clients_accuracy_comparison_plot'52 all_clients_loss_plot_name = f'clients_loss_comparison_plot'53 self.plots[all_clients_accuracy_plot_name] = self.__create_all_clients_accuracy_comparison()54 self.plots[all_clients_loss_plot_name] = self.__create_all_clients_loss_comparison()55 def __create_client_accuracy_plot(self, client_id: int, client_metrics: ClientMetrics) -> Figure:56 figure_, axis = self._create_figure()57 accuracy_line = axis.plot(client_metrics.iterations, client_metrics.accuracy,58 label='Funkcja dokÅadnoÅci uczenia')59 val_accuracy_line = axis.plot(client_metrics.iterations, client_metrics.val_accuracy,60 label='Funkcja dokÅadnoÅci walidacji')61 axis.set_title(f'Wykres dokÅadnoÅci (ang. accuracy) procesu uczenia modelu dla klienta \n'62 f'o identyfikatorze "{client_id}"')63 axis.set_xlabel('Iteracje')64 axis.set_ylabel('DokÅadnoÅÄ modelu')65 axis.grid(True)66 axis.legend(handles=[*accuracy_line, *val_accuracy_line], loc='upper left')67 return figure_68 def __create_client_loss_plot(self, client_id: int, client_metrics: ClientMetrics) -> Figure:69 figure_, axis = self._create_figure()70 loss_line = axis.plot(client_metrics.iterations, client_metrics.loss, label='Funkcja strat uczenia')71 val_loss_line = axis.plot(client_metrics.iterations, client_metrics.val_loss, label='Funkcja strat walidacji')72 axis.set_title(f'Wykres strat (ang. loss) procesu uczenia modelu dla klienta \n'73 f'o identyfikatorze "{client_id}"')74 axis.set_xlabel('Iteracje')75 axis.set_ylabel('Wartosc funkcji strat')76 axis.grid(True)77 axis.legend(handles=[*loss_line, *val_loss_line], loc='upper right')78 return figure_79 def __create_all_clients_accuracy_comparison(self):80 figure_, axis = self._create_figure()81 accuracy_lines = []82 for client_id, client_metrics in self.metrics_collector.clients_metrics.items():83 accuracy_lines.append(axis.plot(client_metrics.iterations, client_metrics.val_accuracy,84 label=f'Model klienta o id "{client_id}"'))85 axis.set_title(f'Wykres dokÅadnoÅci (ang. accuracy) procesu uczenia modelu dla \nwszystkich klientów.')86 axis.set_xlabel('Iteracje')87 axis.set_ylabel('DokÅadnoÅÄ modelu')88 axis.grid(True)89 axis.legend(handles=[line[0] for line in accuracy_lines], loc='upper left')90 return figure_91 def __create_all_clients_loss_comparison(self):92 figure_, axis = self._create_figure()93 loss_lines = []94 for client_id, client_metrics in self.metrics_collector.clients_metrics.items():95 loss_lines.append(axis.plot(client_metrics.iterations, client_metrics.val_loss,96 label=f'Model klienta o id "{client_id}"'))97 axis.set_title(f'Wykres strat (ang. loss) procesu uczenia modelu dla \nwszystkich klientów.')98 axis.set_xlabel('Iteracje')99 axis.set_ylabel('Wartosc funkcji strat')100 axis.grid(True)101 axis.legend(handles=[line[0] for line in loss_lines], loc='upper right')102 return figure_103class TraditionalParticipantLearningPlotter(LinePlotter):104 def __init__(self, metrics_collector: MetricsCollector):105 super().__init__(metrics_collector)106 self.target_directory = f'{generated_data_path.plots}/learning_plots'107 def create_plots(self):108 metrics = self.metrics_collector.traditional_participant_training_metrics109 client_accuracy_plot_name = f'{metrics.full_name}_accuracy_plot'110 client_loss_plot_name = f'client_{metrics.full_name}_loss_plot'111 self.plots[client_accuracy_plot_name] = self._create_accuracy_plot(metrics)112 self.plots[client_loss_plot_name] = self._create_loss_plot(metrics)113 def _create_accuracy_plot(self, metrics: TraditionalParticipantTrainingMetrics) -> Figure:114 figure_, axis = self._create_figure()115 accuracy_line = axis.plot(metrics.iterations, metrics.accuracy, label='Funkcja dokÅadnoÅci uczenia')116 val_accuracy_line = axis.plot(metrics.iterations, metrics.val_accuracy, label='Funkcja dokÅadnoÅci walidacji')117 axis.set_title(f'Wykres dokÅadnoÅci (ang. accuracy) procesu uczenia')118 axis.set_xlabel('Iteracje')119 axis.set_ylabel('DokÅadnoÅÄ modelu')120 axis.grid(True)121 axis.legend(handles=[*accuracy_line, *val_accuracy_line], loc='upper left')122 return figure_123 def _create_loss_plot(self, metrics: TraditionalParticipantTrainingMetrics) -> Figure:124 figure_, axis = self._create_figure()125 loss_line = axis.plot(metrics.iterations, metrics.loss, label='Funkcja strat uczenia')126 val_loss_line = axis.plot(metrics.iterations, metrics.val_loss, label='Funkcja strat walidacji')127 axis.set_title(f'Wykres strat (ang. loss) procesu uczenia')128 axis.set_xlabel('Iteracje')129 axis.set_ylabel('Wartosc funkcji strat')130 axis.grid(True)131 axis.legend(handles=[*loss_line, *val_loss_line], loc='upper right')132 return figure_133class ServerTestingPlotter(LinePlotter):134 def __init__(self, metrics_collector: MetricsCollector):135 super(ServerTestingPlotter, self).__init__(metrics_collector)136 self.server_metrics = self.metrics_collector.server_metrics137 self.target_directory = f'{generated_data_path.plots}/learning_plots'138 def create_plots(self):139 figure_, axis = self._create_figure()140 accuracy_line = axis.plot(self.server_metrics.iterations, self.server_metrics.accuracy,141 label='Funkcja dokÅadnoÅci')142 loss_line = axis.plot(self.server_metrics.iterations, self.server_metrics.loss, label='Funkcja strat')143 axis.set_title(f'Wykres dokÅadnoÅci (ang. accuracy) oraz strat (ang. loss) \ndla modelu globalnego')144 axis.set_xlabel('Iteracje')145 axis.set_ylabel('WartoÅÄ')146 axis.grid(True)147 axis.legend(handles=[*accuracy_line, *loss_line], loc='upper left')148 self.plots['server_accuracy_and_loss'] = figure_149class ConfusionMatrixMaker(Plotter):150 def __init__(self, metrics_collector: MetricsCollector):151 super(ConfusionMatrixMaker, self).__init__(metrics_collector)152 self.target_directory = f'{generated_data_path.plots}/confusion_matrixes'153 self.font_scale = 2.2154 self.figure_size = (6, 6)155 self.font_size = 22156 def create_plots(self):157 for participant, predictions in self.metrics_collector.predictions.items():158 classes = participant.dataset_used_for_predictions.classes159 number_of_classes = len(classes)160 matrix = confusion_matrix(predictions.max_label, predictions.predicted_max_label)161 self.figure_size = 2 * (2 * number_of_classes,)162 figure_, axis = self._create_figure()163 box_labels = self.__get_box_labels(matrix, number_of_classes)164 heat_map = heatmap(matrix, cmap='Blues', linecolor='black', linewidths=1, xticklabels=classes,165 yticklabels=classes, annot=box_labels, fmt='', cbar=False,166 annot_kws={"size": self.font_size},)167 heat_map.set_xticklabels(labels=heat_map.get_xticklabels(), fontsize=self.font_size)168 heat_map.set_yticklabels(labels=heat_map.get_yticklabels(), fontsize=self.font_size)169 if isinstance(participant, Server):170 axis.set_title(f'Macierz pomyÅek dla modelu globalnego')171 elif isinstance(participant, Client):172 axis.set_title(f'Macierz pomyÅek dla klienta o identyfikatorze "{participant.id}"')173 else:174 axis.set_title(f'Macierz pomyÅek')175 axis.title.set_fontsize(self.font_size)176 axis.set_xlabel('Klasa prawdziwa', fontsize=self.font_size)...
tay_plots.py
Source:tay_plots.py
...65 else:66 y_vals = [_number_from_token(n, value_index) for n in tokens[1:]]67 c, f, l = _format_and_label(label, radius, all_lines, dataset)68 plt.plot(x_vals, y_vals, f, color=c, label=l)69def _create_figure(out_filename, in_filenames, value_index, value_label, plot_see_radii, plot_structures, ylim, all_lines):70 fig = plt.figure(figsize=(7, 3.2))71 ax = plt.axes()72 plt.xlabel("Depth correction")73 plt.ylabel(value_label)74 dataset = 075 for in_filename in in_filenames:76 _create_plots_from_file(in_filename,77 out_filename=out_filename,78 value_index=value_index,79 value_label=value_label,80 plot_see_radii=plot_see_radii,81 plot_structures=plot_structures,82 all_lines=all_lines,83 dataset=dataset)84 dataset += 185 if ylim is not None:86 plt.ylim([0, ylim])87 plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))88 plt.legend(bbox_to_anchor=(1, 1), loc="upper left");89 plt.tight_layout()90 plt.show()91 # fig.savefig('%s.png' % out_filename)92# _create_figure('plot1', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [0], ['CpuSimple', 'CpuGrid'], None, False)93# _create_figure('plot2', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [2], ['CpuSimple', 'CpuGrid'], None, True)94# _create_figure('plot3', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [0, 1, 2], ['CpuTree'], 300, False)95# _create_figure('plot4', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [0, 1, 2], ['CpuTree', 'CpuGrid'], 100, False)96# _create_figure('plot5', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [0, 1, 2], ['CpuGrid', 'GpuSimple (direct)'], 100, False)97# _create_figure('plot6', ['plot_uniform_runtimes'], 0, 'Milliseconds per step', [0, 1, 2], ['GpuSimple (direct)', 'GpuSimple (indirect)'], None, False)98# _create_figure('plot7', ['plot_uniform_telemetry'], 3, 'Narrow / broad phase ratio (%)', [0, 1, 2], ['CpuSimple', 'CpuTree', 'CpuGrid'], None, False)99# _create_figure('plot8', ['plot_uniform_telemetry'], 0, 'Thread unbalancing (%)', [0, 1, 2], ['CpuSimple', 'CpuTree', 'CpuGrid'], None, False)100# _create_figure('plot9', ['plot_uniform_runtimes', 'plot_clump_runtimes'], 0, 'Milliseconds per step', [0], ['CpuGrid', 'CpuTree'], None, False)...
make_graphs.py
Source:make_graphs.py
...3import matplotlib.pyplot as plt4def _load_df():5 df = pd.read_csv("internet_speeds_dataset.csv", index_col="Date")6 return df7def _create_figure(df, col_name, fig_num, title=None):8 plt.figure(fig_num)9 if title is not None:10 plt.title(title)11 else:12 plt.title(f"{col_name} over Time")13 plt.ylabel(col_name)14 df[col_name].plot()15 plt.xticks(rotation=90)16 plt.show(block=False)17def draw_graphs():18 df = _load_df()19 print(df.columns)20 _create_figure(df, "Download (Mb/s)", 1)21 _create_figure(df, "Upload (Mb/s)", 2)22 plt.show(block=True)23if __name__ == "__main__":...
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