def _refresh_emission_cards(self):
card = self.get_card('parking-emission-reductions')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='kt (CO2e.)/a',
fill=True,
smoothing=True,
)
with override_variable(
'residential_parking_fee_share_of_cars_impacted', 0):
df0 = predict_cars_emissions()
df1 = predict_cars_emissions()
df1['Emissions'] -= df0['Emissions']
self.yearly_emissions_impact = df1['Emissions'].iloc[-1]
df_forecast = df1[df1.Forecast]
fig.add_series(df=df_forecast,
column_name='Emissions',
trace_name=_('Emissions'))
card.set_figure(fig)
card = self.get_card('car-emission-factor')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='g/km',
)
fig.add_series(df=df1,
column_name='EmissionFactor',
trace_name=_('Emission factor'))
card.set_figure(fig)
def render(self):
self._calc_emissions()
pötkylä = dbc.Col([
html.H6(
_('Emission reductions according to scenario') + ' %s–%s' %
(self.last_historical_year, self.target_year)),
self._render_emissions_bar()
],
md=6)
emissions_summary = self._render_value_summary(
self.scenario_emissions, self.target_emissions,
_('Total net emissions'), _('kt/year'), True)
emissions_summary = dbc.Col(emissions_summary, md=3)
if self.value is not None:
summary = self._render_value_summary(self.value, self.goal,
self.label, self.unit,
self.below_goal_good)
summary = dbc.Col(summary, md=3)
else:
summary = dbc.Col(md=3)
return dbc.Alert([dbc.Row([pötkylä, summary, emissions_summary])],
className="page-summary fixed-bottom")
def _make_navbar(self):
if flask.has_request_context():
custom_setting_count = len(
[k for k in session.keys() if not k.startswith('_')])
else:
custom_setting_count = 0
badge_el = None
if custom_setting_count:
badge_el = dbc.Badge(f'{custom_setting_count} ',
className='badge-danger')
els = [
dbc.DropdownMenu(
[
dbc.DropdownMenuItem(_('Population'), href='/vaesto'),
dbc.DropdownMenuItem(_('Buildings'), href='/rakennukset')
],
nav=True,
in_navbar=True,
label=_("Defaults"),
# id="dropdown-nav"
),
dbc.NavItem(
dbc.NavLink(href='/omat-asetukset',
children=[
_("Own settings"),
badge_el,
])),
dbc.NavItem(html.Span(html.I(className='language-icon')),
className='nav-link pr-0'),
dbc.DropdownMenu(
[
dbc.DropdownMenuItem(
"Suomi", href='/language/fi', external_link=True),
dbc.DropdownMenuItem(
"English", href='/language/en', external_link=True)
],
nav=True,
in_navbar=True,
label=get_active_locale().upper(),
right=True,
),
]
return dbc.NavbarSimple(brand=get_variable('site_name_%s' %
get_active_locale()),
brand_href="/",
color="primary",
dark=True,
fluid=True,
children=els)
def make_emission_nav(current_page):
df = predict_emissions()
target_year = get_variable('target_year')
ts = df.sort_index().drop(columns='Forecast', level=0).loc[target_year]
items = []
current_sector = current_page.emission_sector if current_page and current_page.emission_sector else None
# Sort sectors based on the target year emissions
sector_emissions = ts.sum(level=0).sort_values(ascending=False)
def render_sector(s, sector_path, level):
sector_emissions = s.sum(level=0).sort_values(ascending=False)
for subsector_name, emissions in sector_emissions.iteritems():
if not subsector_name:
continue
subsector_path = tuple([*sector_path, subsector_name])
next_metadata = SECTORS
for sp in subsector_path:
metadata = next_metadata[sp]
next_metadata = metadata.get('subsectors', {})
if current_sector == subsector_path:
active = True
else:
active = False
page = get_page_for_emission_sector(*subsector_path)
name = metadata.get('name_%s' % get_active_locale())
if name is None:
name = metadata['name']
item = _make_nav_item(name, emissions, level, page, active=active)
items.append(item)
ss = s[subsector_name]
if isinstance(ss, pd.Series):
render_sector(ss, subsector_path, level + 1)
render_sector(ts, tuple(), 0)
items.append(
_make_nav_item(_('Total'), sector_emissions.sum(), 0, None, bold=True))
return html.Div([
html.H6(_('Emissions in year %(year)d', year=target_year)),
dbc.ListGroup(children=items)
])
def get_content(self):
lang = get_active_locale()
main_sector_name = self.emission_sector[0]
main_sector_metadata = SECTORS[main_sector_name]
cols = []
edf = predict_emissions().dropna(axis=1, how='all')
forecast = edf.pop('Forecast')
edf = edf[main_sector_name]
subsectors = main_sector_metadata['subsectors']
colors = generate_color_scale(main_sector_metadata['color'],
len(subsectors))
graph = PredictionFigure(sector_name=main_sector_name,
unit_name='kt',
title=_('Total emissions'),
smoothing=True,
fill=True,
stacked=True,
legend=True,
legend_x=0.8)
for idx, (sector_name,
sector_metadata) in enumerate(subsectors.items()):
df = pd.DataFrame(edf[sector_name])
df['Forecast'] = forecast
fig = self.make_sector_fig(df, sector_name, sector_metadata,
colors[idx])
sector_page = get_page_for_emission_sector(main_sector_name,
sector_name)
card = GraphCard(id='emissions-%s-%s' %
(main_sector_name, sector_name),
graph=dict(figure=fig),
link_to_page=sector_page)
cols.append(dbc.Col(card.render(), md=6))
# Add the summed sector to the all emissions graph
df = df.drop(columns=['Forecast'])
s = df.sum(axis=1)
s.name = 'Emissions'
df = pd.DataFrame(s)
df['Forecast'] = forecast
graph.add_series(df=df,
trace_name=sector_metadata.get(
'name_%s' % lang, sector_metadata['name']),
column_name='Emissions',
historical_color=colors[idx])
self.total_emissions = edf.iloc[-1].sum()
card = GraphCard(id='%s-emissions-total' % main_sector_name,
graph=dict(figure=graph.get_figure()))
return html.Div([
dbc.Row(dbc.Col(card.render())),
dbc.Row(cols),
])
def make_sector_fig(self, df, name, metadata, base_color):
lang = get_active_locale()
fig = PredictionFigure(
sector_name=name,
unit_name='kt',
title=metadata.get('name_%s' % lang, metadata['name']),
smoothing=True,
# allow_nonconsecutive_years=True,
fill=True,
stacked=True,
)
if len(df.columns) == 2:
fig.add_series(df=df,
trace_name=_('Emissions'),
column_name='',
historical_color=base_color)
else:
fig.legend = True
fig.legend_x = 0.8
column_names = list(df.columns)
column_names.remove('Forecast')
colors = generate_color_scale(base_color, len(column_names))
for idx, col_name in enumerate(column_names):
subsector = metadata['subsectors'][col_name]
fig.add_series(df=df,
trace_name=subsector.get(
'name_%s' % lang, metadata['name']),
column_name=col_name,
historical_color=colors[idx])
return fig.get_figure()
def _render_value_summary(self, value, goal, label, unit, below_goal_good):
classes = []
if goal is not None:
if (value <= goal and below_goal_good) or \
(value >= goal and not below_goal_good):
classes.append('page-summary__total--good')
else:
classes.append('page-summary__total--bad')
target_el = html.Div([
_("goal") + ' %.0f' % goal,
html.Span(" %s" % unit, className="unit")
],
className="page-summary__target")
else:
target_el = None
classes.append('page-summary__total')
summary = [
html.H6(f'{label} ({self.target_year})'),
html.Div([
html.Div([
"%.0f" % value,
html.Span(" %s" % unit, className="unit")
],
className=' '.join(classes)),
target_el,
],
className="page-summary__totals"),
]
return summary
def generate_buildings_forecast_graph():
df = generate_building_floor_area_forecast()
cdf = pd.DataFrame(index=df.index)
for name, attrs in BUILDING_USES.items():
cdf[name] = df[attrs['types']].sum(axis=1) / 1000000
# Sort columns based on the amounts in the last measured year
last_year = cdf.loc[cdf.index.max()]
columns = list(last_year.sort_values(ascending=False).index.values)
traces = []
lang = get_active_locale()
for name in columns:
attrs = BUILDING_USES[name]
val = df[attrs['types']].sum(axis=1) / 1000000
label = name
if lang == 'en':
label = BUILDINGS_EN[name]
trace = go.Bar(x=df.index,
y=val,
name=label,
marker=dict(color=attrs['color']),
hoverinfo='name',
hovertemplate='%{x}: %{y} Mkem²')
traces.append(trace)
last_hist_year = df[~df.Forecast].index.max()
forecast_divider = dict(type='line',
x0=last_hist_year + 0.5,
x1=last_hist_year + 0.5,
xref='x',
y0=0,
y1=1,
yref='paper',
line=dict(dash='dot', color='grey'))
layout = make_layout(
barmode='stack',
yaxis=dict(title='1 000 000 kem²', ),
xaxis=dict(title=_('Year')),
title=_('Floor area by building purpose'),
shapes=[forecast_divider],
showlegend=True,
autosize=True,
)
return dict(data=traces, layout=layout)
def _refresh_trip_cards(self):
df = predict_passenger_kms()
card = self.get_card('number-of-passenger-kms')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='km',
)
fig.add_series(df,
column_name='KmsPerResident',
trace_name=_('passenger kms (p.c.)'))
card.set_figure(fig)
df.pop('KmsPerResident')
total = df.sum(axis=1)
fc = df.pop('Forecast')
df = df.div(total, axis=0)
df *= 100
# df['Other'] += df.pop('Taxi')
df['Rail'] = df.pop('Metro') + df.pop('Tram') + df.pop('Train')
ccard = self.get_card('modal-share-car')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
)
fig.add_series(df=df,
forecast=fc,
column_name='Car',
trace_name='Henkilöautot')
ccard.set_figure(fig)
df.pop('Car')
mcard = self.get_card('modal-shares-rest')
color_scale = len(df.columns)
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
legend=True,
color_scale=color_scale,
)
last_hist_year = fc[~fc].index.max()
columns = list(
df.loc[last_hist_year].sort_values(ascending=False).index)
for idx, col in enumerate(columns):
name = MODE_TRANSLATIONS.get(col)
if name is not None:
name = name.get(get_active_locale())
if name is None:
name = col
fig.add_series(df=df,
forecast=fc,
column_name=col,
trace_name=name,
color_idx=idx)
mcard.set_figure(fig)
def _refresh_parking_fee_cards(self):
pcard = self.get_card('residential-parking-fee')
self.set_variable('residential_parking_fee_increase',
pcard.get_slider_value())
icard = self.get_card('cars-affected-by-residential-parking-fee')
self.set_variable('residential_parking_fee_share_of_cars_impacted',
icard.get_slider_value())
cd = CardDescription()
df = predict_parking_fee_impact()
last_hist_year = df[~df.Forecast].index.max()
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('€/year'),
smoothing=False,
# y_min=df['Fees'].min(),
y_max=df.loc[last_hist_year]['ResidentialFees'] *
(1 + MAX_PARKING_FEE_INCREASE_PERC / 100))
fig.add_series(df=df,
column_name='ResidentialFees',
trace_name='Asukaspysäköinnin hinta')
pcard.set_figure(fig)
pcard.set_description(
cd.render("""
Oletuksena on Wienin tutkimus maksullisen pysäköinnin vaikutuksesta, jonka mukaan pysäköintimaksun
kaksinkertaistaminen vähentää pysäköintiä ja siten autoliikennettä siihen kohdistuvilla alueilla
noin 8,8 %. Asukaspysäköintimaksujen korotus alkoi vuonna 2015 ja vuosimaksu nousee
120 eurosta 360 euroon vuoteen 2021 mennessä. Asukaspysäköintitunnusten määrä on vuosina 2014–2019
vähentynyt noin 600 kpl vaikka asukasluku on kasvanut kantakaupungissa."""
))
"""{municipality_locative} lyhytaikainen pysäköinti on vähentynyt vuoden 2017 pysäköintimaksujen korotuksen
jälkeen noin 10%."""
fig = PredictionFigure(sector_name='Transportation',
unit_name='%',
smoothing=False,
y_max=100)
df['ResidentialShareOfCarsImpacted'] *= 100
cd.set_values(
residential_share_target=df['ResidentialShareOfCarsImpacted'].
iloc[-1],
residential_share=df[~df.Forecast]
['ResidentialShareOfCarsImpacted'].iloc[-1],
)
fig.add_series(df=df,
column_name='ResidentialShareOfCarsImpacted',
trace_name='Osuus')
icard.set_figure(fig)
icard.set_description(
cd.render("""
Tarkastelussa on mukana vain keskustan asukaspysäköintivyöhykkeet.
Asukaspysäköintilupia on myönnetty nyt n. {residential_share} % {municipality_genitive}
liikennekäytössä olevalle henkilöautokannalle.
Skenaariossa vuonna {target_year} asukaspysäköinnin piirissä on {residential_share_target} %
auton omistajista.
"""))
layout = make_layout(
barmode='stack',
yaxis=dict(title='1 000 000 kem²', ),
xaxis=dict(title=_('Year')),
title=_('Floor area by building purpose'),
shapes=[forecast_divider],
showlegend=True,
autosize=True,
)
return dict(data=traces, layout=layout)
def render_page():
fig = generate_buildings_forecast_graph()
ret = dbc.Row([
dbc.Col([GraphCard(id='buildings', graph=dict(figure=fig)).render()],
md=8)
])
return ret
page = Page(id='rakennukset',
path='/rakennukset',
name=_('Buildings'),
content=render_page)
if __name__ == '__main__':
generate_buildings_forecast_graph()
def render_page():
language = get_active_locale()
cols = []
edf = predict_emissions().dropna(axis=1, how='all')
forecast = edf.pop('Forecast')
graph = PredictionFigure(sector_name=None,
unit_name='kt',
title=_('Total emissions'),
smoothing=True,
fill=True,
stacked=True,
legend=True,
legend_x=0.8)
for sector_name, sector_metadata in SECTORS.items():
df = pd.DataFrame(edf[sector_name])
df['Forecast'] = forecast
fig = make_sector_fig(df, sector_name, sector_metadata)
sector_page = get_page_for_emission_sector(sector_name, None)
card = GraphCard(id='emissions-%s' % sector_name,
graph=dict(figure=fig),
link_to_page=sector_page)
cols.append(dbc.Col(card.render(), md=6))
# Add the summed sector to the all emissions graph
df = df.drop(columns=['Forecast'])
s = df.sum(axis=1)
s.name = 'Emissions'
df = pd.DataFrame(s)
df['Forecast'] = forecast
name = sector_metadata.get('name_%s' % language,
sector_metadata['name'])
graph.add_series(df=df,
trace_name=name,
column_name='Emissions',
historical_color=sector_metadata['color'])
target_year = get_variable('target_year')
ref_year = get_variable('ghg_reductions_reference_year')
perc_off = get_variable('ghg_reductions_percentage_in_target_year')
last_hist_year = edf.loc[~forecast].index.max()
last_hist = edf.loc[last_hist_year].sum()
end_emissions = edf.loc[target_year].sum()
ref_emissions = edf.loc[ref_year].sum()
target_emissions = ref_emissions * (1 - perc_off / 100)
target_year_emissions = edf.loc[target_year].sum()
sticky = StickyBar(
label=_('Total emission reductions'),
goal=last_hist - target_emissions,
value=last_hist - end_emissions,
unit='kt',
below_goal_good=False,
)
card = GraphCard(id='emissions-total',
graph=dict(figure=graph.get_figure()))
return html.Div(
[dbc.Row(dbc.Col(card.render())),
dbc.Row(cols),
sticky.render()])
class CarFleetPage(Page):
id = 'car-fleet'
path = '/car-fleet'
emission_sector = ('Transportation', )
name = _('Car fleet')
def make_cards(self):
from .modal_share import ModalSharePage
self.add_graph_card(
id='ev-parking-fee-discount',
title='Sähköautolle myönnetty pysäköintietuus',
title_i18n=dict(en='Parking fee discount given to EVs'),
slider=dict(
min=0,
max=1000,
step=50,
value=get_variable('parking_subsidy_for_evs'),
marks={x: '%d €' % x
for x in range(0, 1001, 100)},
),
)
self.add_graph_card(
id='share-of-ev-charging-stations-built',
title='Rakennettu osuus tarvittavista julkisista latausasemista',
title_i18n=dict(
en='Share of needed public EV charging stations built'),
slider=dict(
min=0,
max=100,
step=10,
value=get_variable(
'share_of_ev_charging_station_demand_built'),
marks={x: '%d %%' % x
for x in range(0, 101, 10)},
),
)
self.add_graph_card(
id='newly-registered-evs',
title='Ensirekisteröityjen autojen osuudet käyttövoiman mukaan',
title_i18n=dict(
en='Share of newly registered cars by engine type'),
)
self.add_graph_card(
id='yearly-fleet-turnover',
title='Autokannan vuosittainen uudistuminen',
title_i18n=dict(en='Yearly turnover of car fleet'),
)
self.add_graph_card(
id='cars-per-resident',
title='Liikennekäytössä olevat henkilöautot asukasta kohti',
title_i18n=dict(
en='Cars registered for transportation per resident'),
)
self.add_graph_card(
id='car-fleet',
title='Liikennekäytössä olevat henkilöautot',
title_i18n=dict(en='Cars registered for transportation'),
)
self.add_graph_card(
id='mileage',
title='%s ajetut henkilöautokilometrit' %
get_variable('municipality_locative'),
title_i18n=dict(en='%s ajetut henkilöautokilometrit' %
get_variable('municipality_name')),
link_to_page=ModalSharePage,
)
self.add_graph_card(
id='emission-factor',
title='Henkilöautojen päästökerroin',
title_i18n=dict(en='Emission factor of cars'),
)
self.add_graph_card(
id='emission-impact',
title='Toimenpiteiden päästövaikutukset',
title_i18n=dict(en='Emission impact of actions'),
)
def get_content(self):
grid = ConnectedCardGrid()
grid.make_new_row()
c1a = self.get_card('ev-parking-fee-discount')
c1b = self.get_card('share-of-ev-charging-stations-built')
c1c = self.get_card('cars-per-resident')
grid.add_card(c1a)
grid.add_card(c1b)
grid.add_card(c1c)
grid.make_new_row()
c2a = self.get_card('newly-registered-evs')
c2b = self.get_card('yearly-fleet-turnover')
grid.add_card(c2a)
grid.add_card(c2b)
c1a.connect_to(c2a)
c1b.connect_to(c2a)
c1c.connect_to(c2b)
grid.make_new_row()
c3 = self.get_card('car-fleet')
grid.add_card(c3)
c2a.connect_to(c3)
c2b.connect_to(c3)
grid.make_new_row()
c4a = self.get_card('emission-factor')
c4b = self.get_card('mileage')
grid.add_card(c4a)
grid.add_card(c4b)
c3.connect_to(c4a)
grid.make_new_row()
c5 = self.get_card('emission-impact')
grid.add_card(c5)
c4a.connect_to(c5)
c4b.connect_to(c5)
return grid.render()
def get_summary_vars(self):
return dict(label=_('Emission reductions'),
value=self.yearly_emissions_impact,
unit='kt/a')
def refresh_graph_cards(self):
card = self.get_card('ev-parking-fee-discount')
self.set_variable('parking_subsidy_for_evs', card.get_slider_value())
df = predict_parking_fee_impact()
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('€/year'),
y_max=1000,
)
fig.add_series(df=df,
column_name='ParkingSubsidyForEVs',
trace_name=_('Yearly subsidy'))
card.set_figure(fig)
cd = CardDescription()
cd.set_values(
parking_subsidy_for_evs=get_variable('parking_subsidy_for_evs'))
card.set_description(
cd.render("""
Skenaariossa täyssähköautoille myönnetään pysäköintietuuksia
jatkossa yhteensä {parking_subsidy_for_evs} €/vuosi.
"""))
card.set_description(cd.render("""
In this scenario, BEVs will be given extra parking subsidies
in total {parking_subsidy_for_evs} € per year.
"""),
lang='en')
card = self.get_card('share-of-ev-charging-stations-built')
self.set_variable('share_of_ev_charging_station_demand_built',
card.get_slider_value())
df = predict_ev_charging_station_demand()
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('charging stations'),
color_scale=2,
)
fig.add_series(df=df,
column_name='Built',
trace_name=_('Built'),
color_idx=0)
fig.add_series(df=df,
column_name='Demand',
trace_name=_('Demand'),
color_idx=1)
card.set_figure(fig)
cd.set_values(
stations_per_bev=int(
get_variable('number_of_charging_stations_per_bev') * 100),
share_stations=get_variable(
'share_of_ev_charging_station_demand_built'),
built=df.iloc[-1].Built,
demand=df.iloc[-1].Demand,
)
card.set_description(
cd.render("""
Täyssähköautoihin siirtyminen on nopeinta, kun julkisia latauspisteitä
rakennetaan {stations_per_bev:noround} kpl jokaista 100 sähköautoa kohti. Skenaariossa
tästä määrästä rakennetaan {share_stations} %. Vuonna {target_year} olisi
tarve {demand} latauspisteelle ja rakennettuna on {built} latauspistettä.
"""))
card.set_description(cd.render("""
Moving to BEVs will happen the fastest when public charging stations are being
built so that there will be {stations_per_bev:noround} stations for each
100 electric vehicles. In this scenario, the city will build {share_stations} %.
In the year {target_year} there would be demand for {demand} stations and
there will be {built} stations built.
"""),
lang='en')
card = self.get_card('yearly-fleet-turnover')
df = predict_cars_in_use()
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
smoothing=True,
)
df['YearlyTurnover'] *= 100
fig.add_series(df=df,
column_name='YearlyTurnover',
trace_name=_('Turnover'))
card.set_figure(fig)
cd.set_values(mean_yearly_turnover=df[~df.Forecast]
['YearlyTurnover'].tail(8).mean())
card.set_description(
cd.render("""
Viime vuosina {municipality_locative} henkilöautokannasta uusittiin noin
{mean_yearly_turnover} % vuodessa. Laskentamallissa oletataan, että
uusiutumisnopeus ei juuri muutu.
"""))
card = self.get_card('cars-per-resident')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('cars/1,000 res.'),
smoothing=True,
)
df['CarsPerResident'] *= 1000
fig.add_series(df=df,
column_name='CarsPerResident',
trace_name=_('Cars'))
card.set_figure(fig)
cd.set_values(
cars_in_use=df[~df.Forecast]['NumberOfCars'].iloc[-1],
cars_in_use_year=df[~df.Forecast].index.max(),
)
card.set_description(
cd.render("""
Laskentamallissa tarkastellaan ainoastaan liikennekäyttöön rekisteröityjä
henkilöautoja. Vuonna {cars_in_use_year} {municipality_locative} oli liikennekäytössä
{cars_in_use} henkilöautoa.
"""))
card = self.get_card('newly-registered-evs')
df = predict_newly_registered_cars()
fc = df.pop('Forecast')
total = df.sum(axis=1)
df = df.div(total, axis=0)
df *= 100
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
smoothing=True,
color_scale=4,
legend=True,
)
for engine_type in ('BEV', 'PHEV', 'gasoline', 'diesel', 'other'):
et = ENGINE_TYPES[engine_type]
fig.add_series(df=df,
forecast=fc,
column_name=engine_type,
trace_name=et['name'],
historical_color=et['color'])
card.set_figure(fig)
cd.set_values(
bev_share=df[~fc]['BEV'].iloc[-1],
phev_share=df[~fc]['PHEV'].iloc[-1],
hist_year=df[~fc].index.max(),
)
card.set_description(
cd.render("""
Vuonna {hist_year} {municipality_genitive} ensirekisteröidyistä
henkilöautoista {bev_share} % oli täyssähköautoja (BEV) ja
{phev_share} % ladattavia hybridiautoja (PHEV).
"""))
card = self.get_card('car-fleet')
df = predict_cars_in_use_by_engine_type()
df = df.sum(axis=1, level='EngineType')
df['Forecast'] = True
fc = df.pop('Forecast')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('cars'),
fill=True,
stacked=True,
legend=True,
)
for engine_type in ('BEV', 'PHEV', 'gasoline', 'diesel', 'other'):
fig.add_series(df=df,
forecast=fc,
column_name=engine_type,
trace_name=_(engine_type),
forecast_color=ENGINE_TYPE_COLORS[engine_type])
card.set_figure(fig)
df = predict_cars_emissions()
with override_variable('share_of_ev_charging_station_demand_built', 0):
with override_variable('parking_subsidy_for_evs', 0):
df0 = predict_cars_emissions()
card = self.get_card('emission-factor')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='g/km',
color_scale=2,
legend=True,
legend_x=0.6,
)
fig.add_series(df=df,
column_name='EmissionFactor',
trace_name=_('Emission factor with actions'),
color_idx=0)
fig.add_series(df=df0,
column_name='EmissionFactor',
trace_name=_('Emission factor without actions'),
color_idx=1)
card.set_figure(fig)
df.Mileage /= 1000000
card = self.get_card('mileage')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('M km'),
fill=True,
)
fig.add_series(
df=df,
column_name='Mileage',
trace_name=_('Vehicle mileage'),
)
card.set_figure(fig)
card = self.get_card('emission-impact')
df['Emissions'] -= df0['Emissions']
df = df[df.Forecast]
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('kt/a'),
fill=True,
)
fig.add_series(df=df,
column_name='Emissions',
trace_name=_('Reductions'))
self.net_emissions_impact = df['Emissions'].sum()
self.yearly_emissions_impact = df['Emissions'].iloc[-1]
card.set_figure(fig)
def refresh_graph_cards(self):
card = self.get_card('ev-parking-fee-discount')
self.set_variable('parking_subsidy_for_evs', card.get_slider_value())
df = predict_parking_fee_impact()
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('€/year'),
y_max=1000,
)
fig.add_series(df=df,
column_name='ParkingSubsidyForEVs',
trace_name=_('Yearly subsidy'))
card.set_figure(fig)
cd = CardDescription()
cd.set_values(
parking_subsidy_for_evs=get_variable('parking_subsidy_for_evs'))
card.set_description(
cd.render("""
Skenaariossa täyssähköautoille myönnetään pysäköintietuuksia
jatkossa yhteensä {parking_subsidy_for_evs} €/vuosi.
"""))
card.set_description(cd.render("""
In this scenario, BEVs will be given extra parking subsidies
in total {parking_subsidy_for_evs} € per year.
"""),
lang='en')
card = self.get_card('share-of-ev-charging-stations-built')
self.set_variable('share_of_ev_charging_station_demand_built',
card.get_slider_value())
df = predict_ev_charging_station_demand()
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('charging stations'),
color_scale=2,
)
fig.add_series(df=df,
column_name='Built',
trace_name=_('Built'),
color_idx=0)
fig.add_series(df=df,
column_name='Demand',
trace_name=_('Demand'),
color_idx=1)
card.set_figure(fig)
cd.set_values(
stations_per_bev=int(
get_variable('number_of_charging_stations_per_bev') * 100),
share_stations=get_variable(
'share_of_ev_charging_station_demand_built'),
built=df.iloc[-1].Built,
demand=df.iloc[-1].Demand,
)
card.set_description(
cd.render("""
Täyssähköautoihin siirtyminen on nopeinta, kun julkisia latauspisteitä
rakennetaan {stations_per_bev:noround} kpl jokaista 100 sähköautoa kohti. Skenaariossa
tästä määrästä rakennetaan {share_stations} %. Vuonna {target_year} olisi
tarve {demand} latauspisteelle ja rakennettuna on {built} latauspistettä.
"""))
card.set_description(cd.render("""
Moving to BEVs will happen the fastest when public charging stations are being
built so that there will be {stations_per_bev:noround} stations for each
100 electric vehicles. In this scenario, the city will build {share_stations} %.
In the year {target_year} there would be demand for {demand} stations and
there will be {built} stations built.
"""),
lang='en')
card = self.get_card('yearly-fleet-turnover')
df = predict_cars_in_use()
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
smoothing=True,
)
df['YearlyTurnover'] *= 100
fig.add_series(df=df,
column_name='YearlyTurnover',
trace_name=_('Turnover'))
card.set_figure(fig)
cd.set_values(mean_yearly_turnover=df[~df.Forecast]
['YearlyTurnover'].tail(8).mean())
card.set_description(
cd.render("""
Viime vuosina {municipality_locative} henkilöautokannasta uusittiin noin
{mean_yearly_turnover} % vuodessa. Laskentamallissa oletataan, että
uusiutumisnopeus ei juuri muutu.
"""))
card = self.get_card('cars-per-resident')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('cars/1,000 res.'),
smoothing=True,
)
df['CarsPerResident'] *= 1000
fig.add_series(df=df,
column_name='CarsPerResident',
trace_name=_('Cars'))
card.set_figure(fig)
cd.set_values(
cars_in_use=df[~df.Forecast]['NumberOfCars'].iloc[-1],
cars_in_use_year=df[~df.Forecast].index.max(),
)
card.set_description(
cd.render("""
Laskentamallissa tarkastellaan ainoastaan liikennekäyttöön rekisteröityjä
henkilöautoja. Vuonna {cars_in_use_year} {municipality_locative} oli liikennekäytössä
{cars_in_use} henkilöautoa.
"""))
card = self.get_card('newly-registered-evs')
df = predict_newly_registered_cars()
fc = df.pop('Forecast')
total = df.sum(axis=1)
df = df.div(total, axis=0)
df *= 100
fig = PredictionFigure(
sector_name='Transportation',
unit_name='%',
smoothing=True,
color_scale=4,
legend=True,
)
for engine_type in ('BEV', 'PHEV', 'gasoline', 'diesel', 'other'):
et = ENGINE_TYPES[engine_type]
fig.add_series(df=df,
forecast=fc,
column_name=engine_type,
trace_name=et['name'],
historical_color=et['color'])
card.set_figure(fig)
cd.set_values(
bev_share=df[~fc]['BEV'].iloc[-1],
phev_share=df[~fc]['PHEV'].iloc[-1],
hist_year=df[~fc].index.max(),
)
card.set_description(
cd.render("""
Vuonna {hist_year} {municipality_genitive} ensirekisteröidyistä
henkilöautoista {bev_share} % oli täyssähköautoja (BEV) ja
{phev_share} % ladattavia hybridiautoja (PHEV).
"""))
card = self.get_card('car-fleet')
df = predict_cars_in_use_by_engine_type()
df = df.sum(axis=1, level='EngineType')
df['Forecast'] = True
fc = df.pop('Forecast')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('cars'),
fill=True,
stacked=True,
legend=True,
)
for engine_type in ('BEV', 'PHEV', 'gasoline', 'diesel', 'other'):
fig.add_series(df=df,
forecast=fc,
column_name=engine_type,
trace_name=_(engine_type),
forecast_color=ENGINE_TYPE_COLORS[engine_type])
card.set_figure(fig)
df = predict_cars_emissions()
with override_variable('share_of_ev_charging_station_demand_built', 0):
with override_variable('parking_subsidy_for_evs', 0):
df0 = predict_cars_emissions()
card = self.get_card('emission-factor')
fig = PredictionFigure(
sector_name='Transportation',
unit_name='g/km',
color_scale=2,
legend=True,
legend_x=0.6,
)
fig.add_series(df=df,
column_name='EmissionFactor',
trace_name=_('Emission factor with actions'),
color_idx=0)
fig.add_series(df=df0,
column_name='EmissionFactor',
trace_name=_('Emission factor without actions'),
color_idx=1)
card.set_figure(fig)
df.Mileage /= 1000000
card = self.get_card('mileage')
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('M km'),
fill=True,
)
fig.add_series(
df=df,
column_name='Mileage',
trace_name=_('Vehicle mileage'),
)
card.set_figure(fig)
card = self.get_card('emission-impact')
df['Emissions'] -= df0['Emissions']
df = df[df.Forecast]
fig = PredictionFigure(
sector_name='Transportation',
unit_name=_('kt/a'),
fill=True,
)
fig.add_series(df=df,
column_name='Emissions',
trace_name=_('Reductions'))
self.net_emissions_impact = df['Emissions'].sum()
self.yearly_emissions_impact = df['Emissions'].iloc[-1]
card.set_figure(fig)
class TransportationPage(MainSectorPage):
id = 'transportation'
path = '/liikenne'
emission_sector = ('Transportation', )
emission_name = _('Transportation emissions')
is_main_page = True
def get_summary_vars(self):
return dict(label=_('Emission reductions'),
value=self.yearly_emissions_impact,
unit='kt/a')
ref_emissions = edf.loc[ref_year].sum()
target_emissions = ref_emissions * (1 - perc_off / 100)
target_year_emissions = edf.loc[target_year].sum()
sticky = StickyBar(
label=_('Total emission reductions'),
goal=last_hist - target_emissions,
value=last_hist - end_emissions,
unit='kt',
below_goal_good=False,
)
card = GraphCard(id='emissions-total',
graph=dict(figure=graph.get_figure()))
return html.Div(
[dbc.Row(dbc.Col(card.render())),
dbc.Row(cols),
sticky.render()])
page = Page(
id='emissions',
name=_('Total GHG emissions'),
content=render_page,
path='/',
)
if __name__ == '__main__':
render_page()