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()
