def render_page():
cols = []
edf = predict_emissions().dropna(axis=1, how='all')
forecast = edf.pop('Forecast')
graph = PredictionFigure(sector_name=None,
unit_name='kt',
title='Päästöt yhteensä',
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
graph.add_series(df=df,
trace_name=sector_metadata['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='Päästövähennykset yhteensä',
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()])
def population_callback(value):
set_variable('population_forecast_correction', value)
pop_df = get_adjusted_population_forecast()
target_year = get_variable('target_year')
pop_in_target_year = pop_df.loc[target_year].Population
last_hist = pop_df[~pop_df.Forecast].iloc[-1]
fig = generate_population_forecast_graph(pop_df)
cd = CardDescription()
cd.set_values(
pop_in_target_year=pop_in_target_year,
pop_adj=get_variable('population_forecast_correction'),
pop_diff=(1 - last_hist.Population / pop_in_target_year) * 100,
)
cd.set_variables(last_year=last_hist.name)
pop_desc = cd.render("""
{municipality_genitive} väkiluku vuonna {target_year} on {pop_in_target_year}.
Muutos viralliseen väestöennusteeseen on {pop_adj:noround} %.
Väkiluvun muutos vuoteen {last_year} verrattuna on {pop_diff} %.
""")
bar = StickyBar(
label="Väkiluku %s" % get_variable('municipality_locative'),
value=pop_in_target_year,
unit='asukasta',
)
# return fig, pop_in_target_year.round()
return [fig, dbc.Col(pop_desc), bar.render()]
def population_callback(value):
set_variable('population_forecast_correction', value)
pop_df = predict_population()
target_year = get_variable('target_year')
pop_in_target_year = pop_df.loc[target_year].Population
last_hist = pop_df[~pop_df.Forecast].iloc[-1]
fig = generate_population_forecast_graph(pop_df)
cd = CardDescription()
cd.set_values(
pop_in_target_year=pop_in_target_year,
pop_adj=get_variable('population_forecast_correction'),
pop_diff=(1 - last_hist.Population / pop_in_target_year) * 100,
)
cd.set_variables(last_year=last_hist.name)
pop_desc = cd.render("""
The population of {municipality} in the year {target_year} will be {pop_in_target_year}.
The difference compared to the official population forecast is {pop_adj:noround} %.
Population change compared to {last_year} is {pop_diff} %.
""")
# pop_desc = cd.render("""
# {municipality_genitive} väkiluku vuonna {target_year} on {pop_in_target_year}.
# Muutos viralliseen väestöennusteeseen on {pop_adj:noround} %.
# Väkiluvun muutos vuoteen {last_year} verrattuna on {pop_diff} %.
#""")
bar = StickyBar(
label=gettext('Population in %(municipality)s') %
dict(municipality=get_variable('municipality_name')),
value=pop_in_target_year,
unit=gettext('residents'),
)
# return fig, pop_in_target_year.round()
return [fig, dbc.Col(pop_desc), bar.render()]
def render_page():
cols = []
edf = predict_emissions().set_index('Year')
forecast = edf.groupby('Year')['Forecast'].first()
graph = PredictionGraph(sector_name=None,
unit_name='kt',
title='Päästöt yhteensä',
smoothing=True,
fill=True,
stacked=True)
for sector_name, sector_metadata in SECTORS.items():
df = edf.loc[edf.Sector1 == sector_name,
['Sector2', 'Forecast', 'Emissions']]
df = df.pivot(columns='Sector2', values='Emissions')
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
graph.add_series(df=df,
trace_name=sector_metadata['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')
ref_emissions = edf.loc[ref_year].Emissions.sum()
target_emissions = ref_emissions * (1 - perc_off / 100)
target_year_emissions = edf.loc[target_year].Emissions.sum()
sticky = StickyBar(
label='Päästöt yhteensä',
goal=target_emissions,
value=target_year_emissions,
unit='kt',
below_goal_good=True,
)
card = GraphCard(id='emissions-total',
graph=dict(figure=graph.get_figure()))
return html.Div(
[dbc.Row(cols),
dbc.Row(dbc.Col(card.render())),
sticky.render()])
def make_bottom_bar(df):
last_emissions = df.iloc[-1].loc['Emissions']
bar = StickyBar(
label="Henkilöautojen päästöt",
value=last_emissions,
# goal=CARS_GOAL,
unit='kt (CO₂e.) / a',
current_page=page,
)
return bar.render()
def make_bottom_bar(df):
s = df['District heat consumption emissions']
last_emissions = s.iloc[-1]
target_emissions = DISTRICT_HEATING_GOAL
bar = StickyBar(label="Kaukolämmön kulutuksen päästöt",
value=last_emissions,
goal=target_emissions,
unit='kt (CO₂e.)',
current_page=page)
return bar.render()
def make_bottom_bar(df):
s = df['NetEmissions']
last_emissions = s.iloc[-1]
target_emissions = DISTRICT_HEATING_GOAL
bar = StickyBar(
label=_('District heating emissions'),
value=last_emissions,
# goal=target_emissions,
unit='kt (CO₂e.)',
current_page=page)
return bar.render()
def district_heating_callback(*args):
# set_variable('bio_emission_factor', bio_emission_factor)
args = list(args)
weights = [args.pop(0) for m in production_methods]
total_weights = sum(weights)
if total_weights == 0:
shares = [1 / len(weights)] * len(weights)
else:
shares = [val / total_weights for val in weights]
shares = [int(x * 100) for x in shares]
# Make sure the sum is 100
diff = 100 - sum(shares)
shares[0] += diff
for method, share in zip(production_methods, shares):
method.set_ratio(share)
vals = [args.pop(0) for o in method.get_extra_inputs()]
if vals:
method.set_extra_input_values(vals)
production_stats, production_source = calc_district_heating_unit_emissions_forecast(
)
ef_fig = generate_district_heating_emission_factor_graph(production_stats)
fig = generate_district_heating_forecast_graph(production_stats)
table = generate_district_heating_forecast_table(production_stats)
last_row = production_stats.iloc[-1]
sticky = StickyBar(
label='Kaukolämmöntuotannon päästökerroin',
value=last_row['Emission factor'],
unit='g/kWh',
current_page=page,
)
method_outputs = []
for method in production_methods:
method_outputs += method.get_output_values(production_stats,
production_source)
return [ef_fig, fig, table, sticky.render(), *method_outputs]
def solar_power_callback(existing_building_perc, new_building_perc):
# First see what the maximum solar production capacity is to set the
# Y axis maximum.
set_variable('solar_power_existing_buildings_percentage', 100)
set_variable('solar_power_new_buildings_percentage', 100)
kwp_max = predict_solar_power_production()
# Then predict with the given percentages.
set_variable('solar_power_existing_buildings_percentage',
existing_building_perc)
set_variable('solar_power_new_buildings_percentage', new_building_perc)
kwp_df = predict_solar_power_production()
ymax = kwp_max.SolarPowerExisting.iloc[-1]
fig_old = generate_solar_power_graph(kwp_df, "Vanhan",
"SolarPowerExisting", ymax, True)
# ymax = kwp_max.SolarPowerNew.iloc[-1]
fig_new = generate_solar_power_graph(kwp_df, "Uuden", "SolarPowerNew",
ymax, False)
fig_tot, ekwpa, nkwpa = generate_solar_power_stacked(kwp_df)
graph = PredictionFigure(sector_name='ElectricityConsumption',
unit_name='kt',
title='Aurinkopaneelien päästövaikutukset',
fill=True)
ef_df = predict_electricity_emission_factor()
kwp_df['NetEmissions'] = -kwp_df['SolarProduction'] * ef_df[
'EmissionFactor'] / 1000
graph.add_series(df=kwp_df,
column_name='NetEmissions',
trace_name='Päästövaikutukset')
fig_emissions = graph.get_figure()
s = kwp_df.SolarProduction
cd = CardDescription()
city_owned = get_variable('building_area_owned_by_org') / 100
cd.set_values(existing_building_perc=existing_building_perc,
org_existing_building_kwp=1000 * ekwpa * city_owned,
others_existing_building_kwp=1000 * ekwpa * (1 - city_owned))
existing_kwpa = cd.render("""
Kun aurinkopaneeleita rakennetaan {existing_building_perc:noround} % kaikesta vanhan
rakennuskannan kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleita
{org_existing_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_existing_building_kwp} kWp aurinkopaneeleita vuodessa.
""")
cd.set_values(new_building_perc=new_building_perc,
org_new_building_kwp=1000 * nkwpa * city_owned,
others_new_building_kwp=1000 * nkwpa * (1 - city_owned))
new_kwpa = cd.render("""
Kun uuteen rakennuskantaan rakennetaan aurinkopaneeleja {new_building_perc:noround} %
kaikesta kattopotentiaalista, {org_genitive} tulee rakentaa aurinkopaneeleja
{org_new_building_kwp} kWp vuodessa skenaarion toteutumiseksi. Muiden kuin {org_genitive}
tulee rakentaa {others_new_building_kwp} kWp aurinkopaneeleita vuodessa.
""")
forecast = s.iloc[-1] * get_variable('yearly_pv_energy_production_kwh_wp')
sticky = StickyBar(
label='Aurinkosähkön tuotanto',
value=forecast,
unit='GWh',
current_page=page,
)
return [
fig_old, fig_new, fig_tot, fig_emissions, existing_kwpa, new_kwpa,
sticky.render()
]
def _make_summary_bar(self):
bar = StickyBar(current_page=self, **self.get_summary_vars())
return bar.render()
def electricity_consumption_callback(value):
set_variable('electricity_consumption_per_capita_adjustment', value / 10)
df = predict_electricity_consumption_emissions()
graph = PredictionFigure(sector_name='ElectricityConsumption',
title=_('Electricity consumption per resident'),
unit_name='kWh/as.')
graph.add_series(df=df,
trace_name=_('Consumption/res.'),
column_name='ElectricityConsumptionPerCapita')
per_capita_fig = graph.get_figure()
graph = PredictionFigure(
sector_name='ElectricityConsumption',
title=_('Electricity consumption'),
unit_name='GWh',
fill=True,
)
graph.add_series(df=df,
trace_name=_('Electricity consumption'),
column_name='NetConsumption')
consumption_fig = graph.get_figure()
graph = PredictionFigure(
sector_name='ElectricityConsumption',
title=_('Electricity production emission factor'),
unit_name='g/kWh',
smoothing=True,
)
graph.add_series(df=df,
trace_name=_('Emission factor'),
column_name='EmissionFactor')
factor_fig = graph.get_figure()
graph = PredictionFigure(
sector_name='ElectricityConsumption',
title=_('Electricity consumption emissions'),
unit_name='kt',
smoothing=True,
fill=True,
)
graph.add_series(df=df, trace_name='Päästöt', column_name='NetEmissions')
emission_fig = graph.get_figure()
graph = PredictionFigure(
sector_name='ElectricityConsumption',
title=_('Local PV production'),
unit_name='GWh',
smoothing=True,
fill=True,
)
graph.add_series(df=df,
trace_name='Tuotanto',
column_name='SolarProduction')
solar_fig = graph.get_figure()
first_forecast = df[df.Forecast].iloc[0]
last_forecast = df[df.Forecast].iloc[-1]
last_history = df[~df.Forecast].iloc[-1]
last_history_year = df[~df.Forecast].index.max()
cd = CardDescription()
cd.set_values(
per_resident_adj=get_variable(
'electricity_consumption_per_capita_adjustment'),
per_resident_change=(
(last_forecast.ElectricityConsumptionPerCapita /
last_history.ElectricityConsumptionPerCapita) - 1) * 100,
last_history_year=last_history.name,
solar_production_target=last_forecast.SolarProduction,
solar_production_perc=(last_forecast.SolarProduction * 100) /
last_forecast.ElectricityConsumption,
solar_production_hist=last_history.SolarProduction,
)
per_resident_desc = cd.render("""
Skenaariossa asukaskohtainen sähkönkulutus pienenee {per_resident_adj:noround} % vuodessa.
Vuonna {target_year} asukaskohtainen kulutus on muuttunut {per_resident_change} % nykyhetkestä.
""")
solar_desc = cd.render("""
Vuonna {target_year} {municipality_locative} sijaitsevilla aurinkopaneeleilla
tuotetaan {solar_production_target} GWh, joka on {solar_production_perc} %
kaikesta vuoden {target_year} kulutussähköstä.
""")
bar = StickyBar(label=_('Electicity consumption emissions'),
value=last_forecast.NetEmissions,
unit='kt',
current_page=page)
return [
per_capita_fig,
dbc.Col(per_resident_desc, style=dict(minHeight='6rem')), solar_fig,
dbc.Col(solar_desc, style=dict(minHeight='6rem')), consumption_fig,
factor_fig, emission_fig,
bar.render()
]
def solar_power_callback(existing_building_perc, new_building_perc):
# First see what the maximum solar production capacity is to set the
# Y axis maximum.
set_variable('solar_power_existing_buildings_percentage', 100)
set_variable('solar_power_new_buildings_percentage', 100)
kwp_max = predict_solar_power_production()
# Then predict with the given percentages.
set_variable('solar_power_existing_buildings_percentage',
existing_building_perc)
set_variable('solar_power_new_buildings_percentage', new_building_perc)
kwp_df = predict_solar_power_production()
ymax = kwp_max.SolarPowerExisting.iloc[-1]
fig_old = generate_solar_power_graph(kwp_df, "Vanhan",
"SolarPowerExisting", ymax, True)
ymax = kwp_max.SolarPowerNew.iloc[-1]
fig_new = generate_solar_power_graph(kwp_df, "Uuden", "SolarPowerNew",
ymax, False)
fig_tot, ekwpa, nkwpa = generate_solar_power_stacked(kwp_df)
ef_df = predict_electricity_emission_factor()
kwp_df['EmissionReductions'] = ef_df['EmissionFactor'] * kwp_df[
'SolarPowerAll'] / 1000
graph = PredictionGraph(
sector_name='ElectricityConsumption',
unit_name='kt',
title='Aurinkopaneelien päästövähennykset',
)
graph.add_series(kwp_df,
trace_name='Päästövähennykset',
column_name='EmissionReductions')
fig_emissions = graph.get_figure()
s = kwp_df.SolarPowerAll
forecast = s.iloc[-1] * get_variable('yearly_pv_energy_production_kwh_wp')
existing_kwpa = html.Div([
html.Span("Kun aurinkopaneeleita rakennetaan "),
html.Span('%d %%' % existing_building_perc,
className='summary-card__value'),
html.Span(
" kaikesta vanhan rakennuskannan kattopotentiaalista, Helsingin kaupunkikonsernin tulee rakentaa aurinkopaneeleja "
),
html.Span("%d kWp" % (1000 * ekwpa * CITY_OWNED / 100),
className='summary-card__value'),
html.Span(
" vuodessa skenaarion toteutumiseksi. Muiden kuin Helsingin kaupungin tulee rakentaa "
),
html.Span("%d kWp" % (1000 * ekwpa * (100 - CITY_OWNED) / 100),
className='summary-card__value'),
html.Span(" vuodessa."),
])
new_kwpa = html.Div([
html.Span("Kun uuteen rakennuskantaan rakennetaan aurinkopaneeleja "),
html.Span('%d %%' % new_building_perc,
className='summary-card__value'),
html.Span(
" kaikesta kattopotentiaalista, Helsingin kaupunkikonsernin tulee rakentaa aurinkopaneeleja "
),
html.Span("%d kWp" % (1000 * nkwpa * CITY_OWNED / 100),
className='summary-card__value'),
html.Span(
" vuodessa skenaarion toteutumiseksi. Muiden kuin Helsingin kaupungin tulee rakentaa "
),
html.Span("%d kWp" % (1000 * nkwpa * (100 - CITY_OWNED) / 100),
className='summary-card__value'),
html.Span(" vuodessa."),
])
sticky = StickyBar(
label='Aurinkosähkön tuotanto',
value=forecast,
goal=SOLAR_POWER_GOAL,
unit='GWh',
current_page=page,
below_goal_good=False,
)
return [
fig_old, fig_new, fig_tot, fig_emissions, existing_kwpa, new_kwpa,
sticky.render()
]