def get_plot_avg_accidents_by_weekdays(start_datetime=None, end_datetime=None):
data = d.get_traffic_accident_by_date(
get_start_datetime(start_datetime),
get_end_datetime(end_datetime))['overallStartTime']
data = data.map(lambda p: p.date())
avg_accident_per_weekdays = data.map(lambda p: p.weekday()).value_counts(
).sort_index() / pd.Series(
data.unique()).map(lambda p: p.weekday()).value_counts().sort_index()
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
plt.title("Priemerný počet nehôd podľa dňa v týždni", fontsize=30, pad=20)
plt.subplots_adjust(left=0.07, right=0.99)
g = sns.barplot(x=avg_accident_per_weekdays.index,
y=avg_accident_per_weekdays.values,
palette=get_min_max_colors(
avg_accident_per_weekdays.values))
#g.set_xlabel("Deň v týždni",fontsize=30, labelpad=15)
g.set_ylabel("Priemerný počet nehôd", fontsize=30, labelpad=20)
g.set_xticklabels(get_weekday_xtickslabels(
avg_accident_per_weekdays.index),
rotation=45,
fontsize=30)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_county_choropleth(start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)['countyId']
county_names = d.get_county()
data = data.value_counts()
data.index = data.index.map(lambda p: county_names.loc[p]['name'])
zeroes = pd.Series(data=0, index=county_names.name)
data = data + zeroes
data = data.fillna(0)
data = data.sort_values()
df = pd.DataFrame(dict(county=data.index, count=data.values))
geojson_file = os.path.join(os.path.dirname(__file__), 'regions_epsg_4326.geojson.txt')
with open(geojson_file, encoding='utf-8') as file:
geo_counties = json.loads(file.read())
fig = px.choropleth(data_frame=df, geojson=geo_counties, featureidkey='properties.NM4', locations='county', color='count',
color_continuous_scale='tealrose',
range_color=(0, df['count'].mean()*2),
projection='sinusoidal',
labels={'count':'Počet nehôd'},
hover_data={'county':False},
hover_name=df['county'])
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(
margin={"r":0,"t":0,"l":0,"b":0},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
geo=dict(bgcolor= 'rgba(0,0,0,0)'),
coloraxis_showscale=False,
)
return plots.encode_plot(fig, output)
def get_plot_total_accident_trend(start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['overallStartTime']
df = prepare_data_for_trend_plot(data, start_datetime, end_datetime)
fig = get_plot_accident_trend(df)
return encode_plot(fig, output)
def get_total_accidents_for_road(road_number,
start_datetime,
end_datetime,
start_km=0,
end_km=999999999):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[(data.roadPosition >= start_km)
& (data.roadPosition <= end_km)]
data = data.loc[data.roadNumber == road_number]['overallStartTime']
return len(data.index)
def get_plot_accident_trend_in_district(district_id,
start_datetime,
end_datetime,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.districtId == district_id]['overallStartTime']
df = prepare_data_for_trend_plot(data, start_datetime, end_datetime)
fig = get_plot_accident_trend(df)
return encode_plot(fig, output)
def get_plot_total_accidents_ratio_by_roads(start_datetime,
end_datetime,
max_records,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['roadNumber']
data = data.value_counts()
road = d.get_road()
road = road.loc[road.direction == 1]
data = pd.merge(data, road, left_index=True, right_on='number')
if len(data.index) == 0:
return encode_plot(get_empty_plot(), output)
data.rename(columns={'roadNumber': 'count'}, inplace=True)
data['shape_length'] = data['shape_length'] / 1000
data['ratio'] = data['count'] / data['shape_length']
data = data.sort_values(by='ratio').tail(max_records)
fig = px.bar(
data,
x='number',
y='ratio',
labels={
'ratio': 'Počet nehôd na 1km',
'shape_length': 'Dĺžka v km',
'count': 'Počet nehôd',
'number': 'Cesta'
},
hover_data={
'shape_length': True,
'count': True
},
)
fig.update_layout(
xaxis=dict(
title_text='Cesta',
titlefont=dict(size=20),
type='category',
),
yaxis=dict(
title_text='Počet nehôd na 1km',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_plot_accident_trend_on_road(road_number,
start_datetime,
end_datetime,
start_km=0,
end_km=999999999,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.roadNumber == road_number]
data = data.loc[(data.roadPosition >= start_km)
& (data.roadPosition <= end_km)]
data = data['overallStartTime']
df = prepare_data_for_trend_plot(data, start_datetime, end_datetime)
fig = get_plot_accident_trend(df)
return encode_plot(fig, output)
def get_plot_total_accidents_by_roads(start_datetime,
end_datetime,
max_records,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['roadNumber']
road = d.get_road()
road = road.loc[road.direction == 1]
data = pd.merge(data, road, left_on='roadNumber', right_on='number')
if len(data.index) == 0:
return encode_plot(get_empty_plot(), output)
data = data['roadNumber'].value_counts()
data = data.sort_values().tail(max_records)
df = pd.DataFrame(dict(road=data.index, count=data.values))
fig = px.bar(
df,
x='road',
y='count',
labels={
'count': 'Počet nehôd',
'road': 'Cesta'
},
)
fig.update_layout(
xaxis=dict(
title_text='Cesta',
titlefont=dict(size=20),
type='category',
),
yaxis=dict(
title_text='Celkový počet nehôd',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_plot_total_accidents_by_city(start_datetime,
end_datetime,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['cityId']
city_names = d.get_city()
data = data.value_counts()
data.index = data.index.map(lambda p: city_names.loc[p]['name']
if p in city_names.index else 'NA')
zeroes = pd.Series(data=0, index=city_names.name)
data = data + zeroes
data = data.fillna(0)
data = data.sort_values().tail(50)
df = pd.DataFrame(dict(city=data.index, count=data.values))
fig = px.bar(
df,
x='city',
y='count',
labels={
'count': 'Počet nehôd',
'city': 'Obec'
},
)
fig.update_layout(
xaxis=dict(
title_text='Obec',
titlefont=dict(size=20),
),
height=700,
yaxis=dict(
title_text='Celkový počet nehôd',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_plot_avg_accidents_by_weekdays(start_datetime,
end_datetime,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['overallStartTime']
data = data.map(lambda p: p.date())
data = data.map(lambda p: p.weekday()).value_counts().sort_index(
) / pd.Series(
data.unique()).map(lambda p: p.weekday()).value_counts().sort_index()
df = pd.DataFrame(dict(weekday=data.index, avg_count=data.values))
fig = px.bar(
df,
x='weekday',
y='avg_count',
labels={
'weekday': 'Deň v týždni',
'avg_count': 'Priemerný počet nehôd'
},
)
fig.update_layout(
xaxis=dict(
tickmode='array',
tickvals=df['weekday'],
ticktext=get_weekday_xtickslabels(df['weekday']),
title_text='Deň v týždni',
titlefont=dict(size=20),
),
height=500,
yaxis=dict(
title_text='Priemerný počet nehôd',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_plot_total_accidents_by_county(start_datetime,
end_datetime,
output='json'):
acc = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['countyId']
acc = acc.value_counts()
data = d.get_county()
data['count'] = 0
data['count'] += acc
data['count'] = data['count'].fillna(0)
data.sort_values(by='count', inplace=True)
fig = px.bar(
data,
x='name',
y='count',
custom_data=[data.index],
labels={
'count': 'Počet nehôd',
'name': 'Kraj'
},
)
fig.update_layout(
xaxis=dict(
title_text='Kraj',
titlefont=dict(size=20),
),
height=600,
yaxis=dict(
title_text='Celkový počet nehôd',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_plot_accident_by_time_in_day(start_datetime,
end_datetime,
output='json'):
data = d.get_traffic_accident_by_date(start_datetime,
end_datetime)['overallStartTime']
total_count = data.size
data = data.map(lambda p: p.time().hour).value_counts().sort_index()
data = data * 100 / total_count
df = pd.DataFrame(dict(hour=data.index, pct=data.values))
fig = px.bar(
df,
x='hour',
y='pct',
labels={
'hour': 'Hodina počas dňa',
'pct': 'Percento nehôd'
},
hover_data={'pct': ':.2f'},
)
fig.update_layout(
xaxis=dict(
tickmode='array',
tickvals=df['hour'],
title_text='Hodina počas dňa',
titlefont=dict(size=20),
),
height=500,
yaxis=dict(
title_text='Percento nehôd',
gridcolor='rgb(140,140,140)',
titlefont=dict(size=20),
),
dragmode=False,
margin={
"r": 0,
"t": 0,
"l": 0,
"b": 0
},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
font=dict(size=14, ),
)
return encode_plot(fig, output)
def get_map_with_most_frequent_accidents_for_road(road_number, max_number_accidents_returned, start_datetime, end_datetime, start_km=0, end_km=999999999, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.roadNumber == road_number]
data = data.loc[(data.roadPosition >= start_km) & (data.roadPosition <= end_km)]
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 8)
if data.size > 0:
shape = d.get_road_shape(road_number)
if shape is not None:
shape_list = parse_shape_string(shape)
shape_list = filter_shape(shape_list, start_km, end_km)
for s in shape_list:
fig.add_trace(go.Scattermapbox(
mode = 'lines',
line=dict(width=4, color="#006699"),
showlegend=False,
lon = s[1],
lat = s[0],
hoverinfo='skip'))
return plots.encode_plot(fig, output)
def get_plot_accidents_by_county(start_datetime=None, end_datetime=None):
data = d.get_traffic_accident_by_date(
get_start_datetime(start_datetime),
get_end_datetime(end_datetime))['countyId']
data = data.value_counts().sort_values()
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
plt.title("Absolútny počet nehôd podľa kraju", fontsize=30, pad=20)
plt.subplots_adjust(top=0.93, bottom=0.18, left=0.07, right=0.99)
g = sns.barplot(x=get_county_xticklabels(data.index),
y=data.values,
palette=get_min_max_colors(data.values))
g.set_ylabel("Absolútny počet nehôd", fontsize=30, labelpad=20)
g.set_xticklabels(get_county_xticklabels(data.index),
rotation=45,
fontsize=20)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_plot_accident_by_time_in_day(start_datetime=None, end_datetime=None):
data = d.get_traffic_accident_by_date(
get_start_datetime(start_datetime),
get_end_datetime(end_datetime))['overallStartTime']
total_count = data.size
data = data.map(lambda p: p.time().hour).value_counts().sort_index()
data = data * 100 / total_count
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
plt.title("Percento nehôd podľa hodiny v dni", fontsize=30, pad=20)
plt.subplots_adjust(left=0.07, right=0.99)
g = sns.barplot(x=data.index,
y=data.values,
palette=get_min_max_colors(data.values))
g.set_xlabel("Hodina počas dňa", fontsize=30, labelpad=15)
g.set_ylabel("Percento nehôd", fontsize=30, labelpad=20)
g.tick_params(axis='x', labelsize=25)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_plot_total_accidents_by_days(start_datetime=None, end_datetime=None):
data = d.get_traffic_accident_by_date(
get_start_datetime(start_datetime),
get_end_datetime(end_datetime))['overallStartTime']
days = data.map(lambda p: p.date()).value_counts().sort_index()
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
plt.title("Histogram nehôd - počet nehôd za deň", fontsize=30, pad=20)
plt.subplots_adjust(left=0.07, right=0.99)
g = sns.barplot(x=days.index,
y=days.values,
palette=get_min_max_colors(days.values))
#g.set_xlabel("Deň",fontsize=30, labelpad=15)
g.set_ylabel("Počet nehôd", fontsize=30, labelpad=20)
g.set_xticklabels(get_date_xtickslabels(days.index),
rotation=90,
fontsize=20)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_plot_accident_trend_in_district(district_id,
start_datetime=None,
end_datetime=None):
data = d.get_traffic_accident_by_date(get_start_datetime(start_datetime),
get_end_datetime(end_datetime))
data = data.loc[data.districtId == district_id]['overallStartTime']
data = data.map(lambda p: p.date())
data = data.value_counts().sort_index()
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
plt.title("Vývoj nehôd v čase pre okres " +
d.get_district().loc[district_id]['name'],
fontsize=30,
pad=20)
plt.subplots_adjust(left=0.07, right=0.99)
g = sns.lineplot(x=data.index, y=data.values)
#g.set_xlabel("Deň",fontsize=30, labelpad=15)
g.set_ylabel("Počet nehôd", fontsize=30, labelpad=20)
#g.set_xticklabels(get_date_xtickslabels(data.index), rotation=90, fontsize=20)
g.tick_params(axis='x', labelsize=25, rotation=45)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_plot_accidents_by_district(start_datetime=None, end_datetime=None):
data = d.get_traffic_accident_by_date(
get_start_datetime(start_datetime),
get_end_datetime(end_datetime))['districtId']
data = data.value_counts()
zeroes = pd.Series(index=d.get_district().index)
zeroes.values[:] = 0
zeroes += data
data = zeroes.fillna(0).sort_values()
sns.set_style("whitegrid")
fig = plt.figure(figsize=(25, 15))
fig.tight_layout()
plt.title("Absolútny počet nehôd podľa okresu", fontsize=30, pad=20)
plt.subplots_adjust(top=0.93, bottom=0.2, left=0.07, right=0.99)
g = sns.barplot(x=get_district_xticklabels(data.index),
y=data.values,
palette=get_min_max_colors(data.values))
g.set_ylabel("Absolútny počet nehôd", fontsize=30, labelpad=20)
g.set_xticklabels(get_district_xticklabels(data.index),
rotation=90,
fontsize=13)
g.tick_params(axis='y', labelsize=25)
return encode_plot(fig)
def get_min_date():
retval = d.get_traffic_accident_by_date()['overallStartTime'].min()
retval = retval.replace(hour=0, minute=0, second=0, microsecond=0)
return retval
def get_map_with_most_frequent_accidents_for_country(max_number_accidents_returned, start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 6, center = {'lat':48.663863, 'lon':19.502998})
return plots.encode_plot(fig, output)
def get_map_with_most_frequent_accidents_for_district(district_id, max_number_accidents_returned, start_datetime, end_datetime, output='json'):
data = d.get_traffic_accident_by_date(start_datetime, end_datetime)
data = data.loc[data.districtId == district_id]
fig = get_map_with_most_frequent_accidents(max_number_accidents_returned, data, 9.5)
return plots.encode_plot(fig, output)
