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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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