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 generate_solar_power_stacked(df):
pv_kwh_wp = get_variable('yearly_pv_energy_production_kwh_wp')
df.SolarPowerNew = df.SolarPowerNew * pv_kwh_wp
df.SolarPowerExisting = df.SolarPowerExisting * pv_kwh_wp
df.loc[~df.Forecast, 'SolarPowerNew'] = np.nan
graph = PredictionFigure(sector_name='ElectricityConsumption',
unit_name='GWh',
title='Aurinkopaneelien sähköntuotanto',
stacked=True,
fill=True,
color_scale=2)
graph.add_series(df=df,
trace_name='Vanhat rakennukset',
column_name='SolarPowerExisting',
color_idx=0)
graph.add_series(df=df,
trace_name='Uudet rakennukset',
column_name='SolarPowerNew',
color_idx=1)
forecast_df = df[df.Forecast]
hist_df = df[~df.Forecast]
years_left = forecast_df.index.max() - hist_df.index.max()
ekwpa = (forecast_df.SolarPowerExisting.iloc[-1] -
hist_df.SolarPowerExisting.iloc[-1]) / years_left
nkwpa = forecast_df.SolarPowerNew.iloc[-1] / years_left
return graph.get_figure(), ekwpa / pv_kwh_wp, nkwpa / pv_kwh_wp
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(df, name, metadata):
fig = PredictionFigure(
sector_name=name,
unit_name='kt',
title=metadata['name'],
smoothing=True,
# allow_nonconsecutive_years=True,
fill=True,
stacked=True,
)
if len(df.columns) == 2:
fig.add_series(df=df, trace_name='Päästöt', column_name='')
else:
fig.legend = True
fig.legend_x = 0.8
column_names = list(df.columns)
column_names.remove('Forecast')
colors = generate_color_scale(metadata['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['name'],
column_name=col_name,
historical_color=colors[idx])
return fig.get_figure()
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 draw_district_heat_consumption_emissions(df):
graph = PredictionFigure(
sector_name='BuildingHeating',
unit_name='kt',
title='Kaukolämmön kulutuksen päästöt',
smoothing=True,
allow_nonconsecutive_years=True,
fill=True,
)
graph.add_series(df=df, column_name='NetEmissions', trace_name='Päästöt')
return graph.get_figure()
def draw_new_building_unit_heat_factor_graph(df):
graph = PredictionFigure(
sector_name='BuildingHeating',
unit_name='kWh/k-m²',
title=_('Future building stock heat efficiency'),
color_scale=2,
)
graph.add_series(df=df,
column_name='NewBuildingHeatUsePerNetArea',
trace_name=_('Heat efficiency'),
color_idx=1)
return graph.get_figure()
def draw_new_building_unit_heat_factor_graph(df):
graph = PredictionFigure(
sector_name='BuildingHeating',
unit_name='kWh/k-m²',
title='Uuden rakennuskannan ominaislämmönkulutus',
color_scale=2,
)
graph.add_series(df=df,
column_name='NewBuildingHeatUsePerNetArea',
trace_name='Ominaislämmönkulutus',
color_idx=1)
return graph.get_figure()
def generate_district_heating_emission_factor_graph(df):
graph = PredictionFigure(
sector_name='BuildingHeating',
unit_name='g/kWh',
title='Kaukolämmön päästökerroin',
smoothing=True,
allow_nonconsecutive_years=True,
)
graph.add_series(
df=df,
column_name='Emission factor',
trace_name='Päästökerroin',
)
return graph.get_figure()
def generate_district_heating_forecast_graph(df):
graph = PredictionFigure(
sector_name='BuildingHeating',
unit_name='kt/vuosi',
title='Kaukolämmön kulutuksen päästöt',
smoothing=True,
allow_nonconsecutive_years=True,
fill=True,
)
graph.add_series(
df=df,
column_name='District heat consumption emissions',
trace_name='Päästöt',
)
return graph.get_figure()
def generate_solar_power_graph(df, label, col, ymax, is_existing):
graph = PredictionFigure(
sector_name='ElectricityConsumption',
unit_name='MWp',
title='%s rakennuskannan aurinkopaneelien piikkiteho' % label,
y_max=ymax,
color_scale=2)
if is_existing:
color_idx = 0
else:
color_idx = 1
graph.add_series(df=df,
trace_name='Piikkiteho',
column_name=col,
color_idx=color_idx)
return graph.get_figure()
def draw_bev_chart(df):
engines = {'electric', 'gasoline', 'diesel'}
df = df.dropna()[[*engines, 'Forecast']].copy()
graph = PredictionFigure(
sector_name='Transportation',
unit_name='%',
title='Sähköautojen ajosuoriteosuus',
legend=True,
legend_x=0.6,
y_max=100,
)
for col in engines:
et = ENGINE_TYPES[col]
df[col] *= 100
graph.add_series(
df=df, column_name=col, trace_name=et['name'], historical_color=et['color']
)
return graph.get_figure()
def draw_heat_consumption(df):
df.loc[~df.Forecast, 'NewBuildingHeatUse'] = np.nan
graph = PredictionFigure(
title=_('District heat net consumption'),
unit_name='GWh',
sector_name='BuildingHeating',
smoothing=True,
stacked=True,
fill=True,
color_scale=2,
)
graph.add_series(
df=df,
column_name='ExistingBuildingHeatUse',
trace_name=_('Existing buildings'),
color_idx=0,
)
graph.add_series(df=df,
column_name='NewBuildingHeatUse',
trace_name=_('Future buildings'),
color_idx=1)
return graph.get_figure()
def draw_heat_consumption(df):
df.loc[~df.Forecast, 'NewBuildingHeatUse'] = np.nan
graph = PredictionFigure(
title='Kaukolämmön kokonaiskulutus',
unit_name='GWh',
sector_name='BuildingHeating',
smoothing=True,
stacked=True,
fill=True,
color_scale=2,
)
graph.add_series(
df=df,
column_name='ExistingBuildingHeatUse',
trace_name='Vanhat rakennukset',
color_idx=0,
)
graph.add_series(df=df,
column_name='NewBuildingHeatUse',
trace_name='Uudet rakennukset',
color_idx=1)
return graph.get_figure()
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 cars_callback(bev_percentage, mileage_adj):
set_variable('cars_bev_percentage', bev_percentage)
set_variable('cars_mileage_per_resident_adjustment', mileage_adj)
df = predict_cars_emissions()
df['Mileage'] /= 1000000
bev_chart = draw_bev_chart(df)
"""
graph = PredictionFigure(
sector_name='Transportation',
unit_name='%',
title='Biopolttoaineiden osuus myydyissä polttoaineissa',
)
graph.add_series(
df=df, column_name='electric', trace_name='Bion osuus'
)
biofuel_chart = graph.get_figure()
"""
graph = PredictionFigure(
sector_name='Transportation',
unit_name='km/as.',
title='Ajokilometrit asukasta kohti',
)
graph.add_series(
df=df, column_name='PerResident', trace_name='Suorite/as.',
)
per_resident_chart = graph.get_figure()
# Total mileage
graph = PredictionFigure(
sector_name='Transportation',
unit_name='milj. km',
title='%s ajetut henkilöautokilometrit' % get_variable('municipality_locative'),
fill=True,
)
graph.add_series(
df=df, column_name='Mileage', trace_name='Ajosuorite',
)
mileage_chart = graph.get_figure()
# Total emissions
graph = PredictionFigure(
sector_name='Transportation',
unit_name='g/km',
title='Henkilöautojen päästökerroin',
)
graph.add_series(
df=df, column_name='EmissionFactor', trace_name='Päästökerroin',
)
emission_factor_chart = graph.get_figure()
# Total emissions
graph = PredictionFigure(
sector_name='Transportation',
unit_name='kt (CO₂e.)',
title='Henkilöautoilun päästöt',
fill=True,
)
graph.add_series(
df=df, column_name='Emissions', trace_name='Päästöt',
)
emissions_chart = graph.get_figure()
cd = CardDescription()
first_forecast = df[df.Forecast].iloc[0]
last_forecast = df[df.Forecast].iloc[-1]
last_history = df[~df.Forecast].iloc[-1]
cd.set_values(
bev_percentage=get_variable('cars_bev_percentage'),
bev_mileage=last_forecast.Mileage * last_forecast.electric,
per_resident_adjustment=get_variable('cars_mileage_per_resident_adjustment'),
target_population=last_forecast.Population,
)
bev_desc = cd.render("""
Skenaariossa polttomoottorihenkilöautot korvautuvat sähköautoilla siten,
että vuonna {target_year} {municipality_genitive} kaduilla ja teillä
ajetaan sähköautoilla {bev_percentage:noround} % kaikista ajokilometreistä
({bev_mileage} milj. km).
""")
pr_desc = cd.render("""
Vuonna {target_year} {municipality_locative} asuu {target_population} ihmistä.
Skenaariossa ajokilometrit asukasta kohti muuttuvat vuoteen {target_year} mennessä
{per_resident_adjustment:noround} %.
""")
sticky = make_bottom_bar(df)
return [
bev_chart, dbc.Col(bev_desc), emission_factor_chart,
per_resident_chart, dbc.Col(pr_desc), mileage_chart,
emissions_chart, sticky
]
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 district_heating_consumption_callback(existing_building_perc,
new_building_perc):
set_variable('district_heating_existing_building_efficiency_change',
existing_building_perc / 10)
set_variable('district_heating_new_building_efficiency_change',
new_building_perc / 10)
df = predict_district_heat_consumption()
geo_df = predict_geothermal_production()
geo_df = geo_df[geo_df.Forecast]
df['GeoEnergyProductionExisting'] = geo_df['GeoEnergyProductionExisting']
df['GeoEnergyProductionNew'] = geo_df['GeoEnergyProductionNew']
df['ExistingBuildingHeatUse'] -= df['GeoEnergyProductionExisting'].fillna(
0)
df['NewBuildingHeatUse'] -= df['GeoEnergyProductionNew'].fillna(0)
first_forecast = df[df.Forecast].iloc[0]
last_forecast = df[df.Forecast].iloc[-1]
last_history = df[~df.Forecast].iloc[-1]
cd = CardDescription()
org_owned = get_variable('building_area_owned_by_org') / 100
geo_production = last_forecast.GeoEnergyProductionExisting + last_forecast.GeoEnergyProductionNew
cd.set_values(
existing_renovation=get_variable(
'district_heating_existing_building_efficiency_change'),
new_improvement=get_variable(
'district_heating_new_building_efficiency_change'),
existing_next_year_reduction=(1 -
(first_forecast.ExistingBuildingHeatUse -
last_history.ExistingBuildingHeatUse)) *
org_owned,
new_area=last_forecast.NewBuildingNetArea,
existing_area=last_forecast.ExistingBuildingNetArea,
geo_production=geo_production,
geo_percentage=(geo_production / last_forecast.TotalHeatConsumption) *
100,
)
existing_desc = cd.render("""
Skenaarion mukaan nykyistä rakennuskantaa remontoidaan siten, että rakennuksien
energiatehokkuus paranee {existing_renovation:noround} % vuodessa. Ensi vuonna {org_genitive}
omistamien rakennuksien lämmönkulutuksen pitää pudota {existing_next_year_reduction} GWh.
""")
new_desc = cd.render("""
Uuden rakennuskannan energiatehokkuus paranee skenaariossa {new_improvement:noround} % vuodessa.
Vuonna {target_year} nykyistä rakennuskantaa on {existing_area} milj. kem² ja uutta rakennuskantaa
{new_area} milj. kem².
""")
geo_desc = cd.render("""
Vanhassa ja uudessa rakennuskannassa korvataan kaukolämpöä paikallisesti tuotetulla
maalämmöllä siten, että vuonna {target_year} kaukolämpöä korvataan {geo_production} GWh
({geo_percentage} % sen vuoden kaukolämmönkulutuksesta).
""")
fig1 = draw_existing_building_unit_heat_factor_graph(df)
fig2 = draw_new_building_unit_heat_factor_graph(df)
geo_fig = PredictionFigure(
sector_name='BuildingHeating',
unit_name='GWh',
title='Maalämmöllä korvattu kaukolämpö',
fill=True,
)
geo_fig.add_series(
df=geo_df,
column_name='GeoEnergyProduction',
trace_name='Maalämpötuotanto',
)
fig3 = draw_heat_consumption(df)
df = predict_district_heating_emissions()
unit_emissions_card = make_unit_emissions_card(df)
fig4 = draw_district_heat_consumption_emissions(df)
sticky = make_bottom_bar(df)
return [
fig1,
dbc.Col(existing_desc, style=dict(minHeight='8rem')), fig2,
dbc.Col(new_desc, style=dict(minHeight='8rem')),
geo_fig.get_figure(),
dbc.Col(geo_desc, style=dict(minHeight='8rem')), fig3,
unit_emissions_card, fig4, sticky
]