plot_congestion_scatter(["TempAvg"], ["SpatAvg"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) plot_congestion_scatter(["TempDist"], ["SpatDist"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) plot_congestion_scatter(["TLCar"], ["TLHGV"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) ################## ### Histograms ### ################## # Plot histogram of roadworks over time / months plt.figure(figsize=set_size(418, 1.8)) plt.style.use('seaborn') plt.rcParams.update(tex_fonts) plt.title( 'Histogram of roadwork per month, with at least one adjacent congestion' ) plt.ylabel('Count') plt.xlabel('Month of 2019') sns.countplot(x='Month', data=arbis_matched, palette='Spectral', order=months) if save_plot: plt.savefig(plot_path + file_prefix + '_hist_month.pdf') if not show_plot: plt.close()
plt.savefig(plot_path + file_prefix + '_hist_highway.pdf') if not show_plot: plt.close() if show_plot: plt.show() else: plt.close() ############## ### Counts ### ############## # Multi plots scale = 1.0 (width, height) = set_size(418, scale) fig, axs = plt.subplots(4, 1, figsize=(width, 3.5 * height)) plt.style.use('seaborn') plt.rcParams.update(tex_fonts) sns.countplot(ax=axs[0], x='Kat', data=baysis_selected, palette='Spectral') sns.countplot(ax=axs[1], x='Typ', data=baysis_selected, palette='Spectral') sns.countplot(ax=axs[2], x='Betei', data=baysis_selected, palette='Spectral') atr = 'UArt' concat = pd.concat( [baysis_selected[atr + '1'], baysis_selected[atr + '2']], keys=[atr]) sns.countplot(ax=axs[3], x=atr, data=concat, palette='Spectral') if save_plot:
plot_congestion_scatter(["TempExMax"], ["SpatExMax"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) plot_congestion_scatter(["TempDist"], ["SpatDist"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) plot_congestion_scatter(["TimeLossCar"], ["TimeLossHGV"], arbis_matched, plot_path, file_prefix, save_plot, show_plot) locators = [ "temporalGlobalLoc", "spatialGlobalLoc", "temporalInternalLoc", "spatialInternalLoc" ] for atr in locators: plt.figure(figsize=set_size(418, 0.8)) plt.style.use('seaborn') plt.rcParams.update(tex_fonts) plt.title('Distribution of ' + atr) plt.ylabel('Count') arbis_matched.plot.scatter(x='TempExMax', y='SpatExMax', c=atr, colormap='viridis') plt.xlabel(atr) if save_plot: plt.savefig(plot_path + file_prefix + '_scatter_E_' + atr + '.pdf') if not show_plot: plt.close() if show_plot: plt.show()