How to use handle_plot method in autotest

Best Python code snippet using autotest_python

make_plots.py

Source:make_plots.py

...58        dataset_to_disease_abv(x)59        for x in df['dge']60    ]61    return df62def handle_plot(bp, xloc, yloc, legend_title, use_ncol=False):63    bp.set_xlabel("")64    ylabel = bp.get_ylabel()65    bp.set_ylabel("AUC-ROC" if ylabel == 'auc' else "AUC-PR")66    handles, labels = bp.get_legend_handles_labels()67    bp.legend(68        handles,69        labels,70        ncol=len(labels) if use_ncol else 1,71        loc=(xloc, yloc),72        title=legend_title73    )74    bp.set_xticklabels(75        bp.get_xticklabels(),76        rotation=3077    )78def main():79    """"""80    thiner = 0.581    subfolder = 'part1'82    results = get_dataframe(os.path.join(results_base_folder, subfolder))83    results['eval'] = [change_labels[x] for x in results['eval']]84    results['sort2'] = [85        0 if x == 'STRING' else 2 if x == 'BEL' else 186        for x in results['eval']87    ]88    results = results.sort_values(by=['sort1', 'sort2', 'dge_relabel'])89    # Prepare results90    for metric in ['auc', 'aps']:91        for key, datasets in DGE_DATASETS.items():92            bp = sns.boxplot(93                data=results[results['dge'].isin(datasets)],94                x='dge_relabel',95                y=metric,96                hue='eval',97                width=thiner if key == 'nonad' else 198            )99            handle_plot(bp, 0.1, 0.45, 'Network')100            fig = bp.get_figure()101            fig.tight_layout()102            fig.savefig(os.path.join(results_base_folder, f'compare1_{metric}_{key}.png'))  # AUC non-AD and AD103            plt.close()104    subfolder = 'part3'  # Weighting105    results = get_dataframe(os.path.join(results_base_folder, subfolder))106    results['eval'] = [107        'Yes' if x == 'weighted' else 'No'108        for x in results['eval']109    ]110    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])111    # Prepare results112    for metric in ['auc', 'aps']:113        bp = sns.boxplot(114            data=results,115            x='dge_relabel',116            y=metric,117            hue='eval',118            width=thiner119        )120        handle_plot(bp, 0.05, 0.3, 'Weighting')121        fig = bp.get_figure()122        fig.tight_layout()123        fig.subplots_adjust(left=0.07)124        fig.set_size_inches(12.8, 4.8)125        fig.savefig(os.path.join(results_base_folder, f'compare4_{metric}.png'))  # AUC non-AD and AD126        plt.close()127    subfolder = 'part4'  # g2v128    results = get_dataframe(os.path.join(results_base_folder, subfolder))129    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])130    # Prepare results131    for metric in ['auc', 'aps']:132        for analyzed_param in set(results['param']):133            colors = sns.color_palette()134            # swap blue to the default g2v param135            # dim = 128, nw = 10, wl = 80, ws = 5136            if analyzed_param in ['Dimension', 'Walk Length']:137                (colors[0], colors[2]) = (colors[2], colors[0])138            elif analyzed_param in ['Num Walks', 'Window Size']:139                (colors[0], colors[1]) = (colors[1], colors[0])140            bp = sns.boxplot(141                data=results[results['param'] == analyzed_param],142                x='dge_relabel',143                y=metric,144                hue='eval',145                palette=colors146            )147            handle_plot(bp, 0.01, 0.05, analyzed_param, use_ncol=True)148            fig = bp.get_figure()149            fig.tight_layout()150            fig.subplots_adjust(left=0.07)151            fig.set_size_inches(12.8, 4.8)152            param = ''.join(analyzed_param.split(' '))153            fig.savefig(os.path.join(results_base_folder, f'compare5_{metric}_{param}.png'))  # AUC non-AD and AD154            plt.close()155    subfolder = 'part6'156    results = get_dataframe(os.path.join(results_base_folder, subfolder))157    results['eval'] = [158        'Yes' if x == 'phewas' else 'No'159        for x in results['eval']160    ]161    results['dge_relabel'] = [change_labels[x] for x in results['dge']]162    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])163    # Prepare results164    for metric in ['auc', 'aps']:165        bp = sns.boxplot(166            data=results,167            x='dge_relabel',168            y=metric,169            hue='eval',170            width=thiner171        )172        handle_plot(bp, 0.1, 0.3, 'Using PheWAS')173        fig = bp.get_figure()174        fig.tight_layout()175        fig.subplots_adjust(left=0.07)176        fig.set_size_inches(12.8, 4.8)177        fig.savefig(os.path.join(results_base_folder, f'compare2_{metric}.png'))  # AUC non-AD and AD178        plt.close()179    subfolder = 'part2'180    results = get_dataframe(os.path.join(results_base_folder, subfolder))181    results['sort2'] = [182        0 if x == 'cv' else 1 if x == 'nested_cv' else 2183        for x in results['eval']184    ]185    results['eval'] = [186        'Logistic regression' if x == 'cv' else 'Nested logistic regression' if x == 'nested_cv' else 'Biased SVM'187        for x in results['eval']188    ]189    results['dge_relabel'] = [change_labels[x] for x in results['dge']]190    results = results.sort_values(by=['sort1', 'dge_relabel', 'sort2'])191    # Prepare results192    for metric in ['auc', 'aps']:193        bp = sns.boxplot(194            data=results,195            x='dge_relabel',196            y=metric,197            hue='eval',198            width=thiner + 0.1199        )200        handle_plot(bp, 0.01, 0.01, 'Classification')201        fig = bp.get_figure()202        fig.tight_layout()203        fig.subplots_adjust(left=0.07)204        fig.set_size_inches(12.8, 4.8)205        fig.savefig(os.path.join(results_base_folder, f'compare3_{metric}.png'))  # AUC non-AD and AD206        plt.close()207    subfolder = 'link_prediction'  # Link prediction208    results = get_dataframe(os.path.join(results_base_folder, subfolder))209    results['eval'] = [210        'Yes' if x else 'No'211        for x in results['eval']212    ]213    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])214    # Prepare results215    for metric in ['auc', 'aps']:216        bp = sns.boxplot(217            data=results,218            x='dge_relabel',219            y=metric,220            hue='eval',221            width=thiner222        )223        handle_plot(bp, 0.05, 0.3, 'Use DGE')224        fig = bp.get_figure()225        fig.tight_layout()226        fig.subplots_adjust(left=0.07)227        fig.set_size_inches(12.8, 4.8)228        fig.savefig(os.path.join(results_base_folder, f'compare7_{metric}.png'))  # AUC non-AD and AD229        plt.close()230    subfolder = 'link_prediction'  # Link prediction vs OpenTargets231    results = get_dataframe(os.path.join(results_base_folder, subfolder))232    ot_results = pd.read_csv(os.path.join(results_base_folder, 'ot_target_prediction.tsv'), sep='\t')233    ot_results['dge_relabel'] = [change_labels[x] for x in ot_results['dge']]234    ot_results['eval'] = 'OpenTargets'235    ot_results['tr'] = 0236    ot_results['sort1'] = [237        dataset_to_disease_abv(x)238        for x in ot_results['dge']239    ]240    results = results[results['eval']]241    results['eval'] = 'Link Prediction'242    results = results.append(ot_results, ignore_index=False, sort=True)243    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])244    # Prepare results245    for metric in ['auc', 'aps']:246        bp = sns.boxplot(247            data=results,248            x='dge_relabel',249            y=metric,250            hue='eval',251            width=thiner252        )253        handle_plot(bp, 0.005, 0.4, 'Method')254        fig = bp.get_figure()255        fig.tight_layout()256        fig.subplots_adjust(left=0.07)257        fig.set_size_inches(12.8, 4.8)258        fig.savefig(os.path.join(results_base_folder, f'compare7_vs_ot_{metric}.png'))  # AUC non-AD and AD259        plt.close()260    subfolder = 'link_prediction2'  # Link 5fcv261    results = get_dataframe(os.path.join(results_base_folder, subfolder))262    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])263    relabel_param = {264        'd': 'Dimension',265        'wl': 'Walk Length',266        'nw': 'Number of Walks',267        'ws': 'Window size'268    }269    results['param'] = [270        relabel_param[x]271        for x in results['param']272    ]273    # Prepare results274    for metric in ['auc', 'aps']:275        for analyzed_param in set(results['param']):276            default_color = sns.color_palette()[0]277            colors = sns.color_palette("ch:2.5,-.2,dark=.3")278            # Assign blue to the default g2v param279            # dim = 128, nw = 10, wl = 80, ws = 5280            if analyzed_param in ['Dimension', 'Walk Length']:281                colors[2] = default_color282            elif analyzed_param in ['Number of Walks', 'Window size']:283                colors[1] = default_color284            bp = sns.boxplot(285                data=results[results['param'] == analyzed_param],286                x='dge_relabel',287                y=metric,288                hue='eval',289                palette=colors290            )291            handle_plot(bp, 0.01, 0.05, analyzed_param, use_ncol=True)292            fig = bp.get_figure()293            fig.tight_layout()294            fig.subplots_adjust(left=0.07)295            fig.set_size_inches(12.8, 4.8)296            param = ''.join(analyzed_param.split(' '))297            fig.savefig(os.path.join(results_base_folder, f'compare7g2v_{metric}_{param}.png'))  # AUC non-AD and AD298            plt.close()299    subfolder = 'link_prediction3'  # Link prediction vs OpenTargets300    sns.color_palette("muted")301    results = get_dataframe(os.path.join(results_base_folder, subfolder))302    results['dge_relabel'] = [change_labels[x] for x in results['dge']]303    results['eval'] = [304        'Yes' if x else 'No'305        for x306        in results['eval']307    ]308    results = results.sort_values(by=['sort1', 'dge_relabel', 'eval'])309    # Prepare results310    for metric in ['auc', 'aps']:311        bp = sns.boxplot(312            data=results,313            x='dge_relabel',314            y=metric,315            hue='eval',316            width=thiner317        )318        handle_plot(bp, 0.005, 0.4, 'Use DGE')319        fig = bp.get_figure()320        fig.tight_layout()321        fig.subplots_adjust(left=0.07)322        fig.set_size_inches(12.8, 4.8)323        fig.savefig(os.path.join(results_base_folder, f'compare7cv_{metric}.png'))  # AUC non-AD and AD324        plt.close()325if __name__ == '__main__':...

sps.py

Source:sps.py

...10#common11def handle_connection(request_str : str):12    req = json.loads(request_str)13    if req.get("req_type") == "plot&data":14        handle_plot(req)15#plot16def handle_plot(request):17    fig = plt.figure(1)18    plt.plot(19        request.get("plot_spe").get("data"),20        figure = fig21    )22    plt.draw()23    plt.pause(0.001)24## plot utility25#simple plot26def plot(data,title='',x_axis_label='',y_axis_label=''):27    dataOut = {28        "req_type" : "plot&data",29        "plot_spe" : {30            "data" : data,...

plotter.py

Source:plotter.py

...18    #plt.legend(loc=0)19    20    plt.draw()21    22def handle_plot(req):23    t = time.time()24    plt.clf()25    ax = plt.subplot(111)26    plt.subplots_adjust(top=0.6)27    for plot_data in req.plots:28        plot(plot_data)29    labels = [line.get_label() for line in ax.lines]30    plt.figlegend(ax.lines, labels, 'upper right')31    plt.savefig(location + str(t) + '.png')32    33    return PlotResponse()34def plot_server():35    rospy.init_node('plotter')36    s = rospy.Service('plot', Plot, handle_plot)...

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