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 make_intervention_shapes(df):
Y_START = -0.3
shapes = []
annotations = []
cur_y = Y_START
ivs = get_variable('interventions')
icu_units = get_variable('icu_units')
hospital_beds = get_variable('hospital_beds')
out = []
for iv in ivs:
name = iv[0]
# Disable limit mass gatherings for now
if name == 'limit-mass-gatherings':
continue
if name == 'test-all-with-symptoms':
name = 'testing-mode'
strength = 0.3
elif name == 'test-only-severe-symptoms':
name = 'testing-mode'
strength = 0.2
elif name == 'test-with-contact-tracing':
name = 'testing-mode'
strength = 0.3 + (iv[2] / 100) * 0.7
elif name == 'limit-mobility':
strength = iv[2] / 100
elif name == 'limit-mass-gatherings':
if iv[2] >= 500:
strength = 0.1
elif iv[2] >= 100:
strength = 0.3
elif iv[2] >= 50:
strength = 0.5
elif iv[2] >= 10:
strength = 0.8
elif iv[2] >= 2:
strength = 1.0
elif name == 'import-infections':
strength = 0.5
else:
continue
out.append((name, iv[1], strength))
ivs = sorted(out, key=lambda x: (INTERVENTION_TYPES[x[0]]['order'], x[0], x[1]))
bar_count = 0
for name, group in groupby(ivs, lambda x: x[0]):
ranges = [InterventionRange(date.fromisoformat(x[1]), x[2]) for x in group]
s, a = _draw_one_intervention(df.index, ranges, name, cur_y)
shapes += s
annotations += a
cur_y -= IV_Y_MARGIN
bar_count += 1
return shapes, annotations, bar_count
def make_cards(self):
self.add_graph_card(
id='existing-buildings',
slider=dict(
min=0,
max=90,
step=5,
value=get_variable('solar_power_existing_buildings_percentage'),
marks={x: '%d %%' % x for x in range(0, 90 + 1, 10)},
),
title='Vanhan rakennuskannan aurinkopaneelien piikkiteho',
title_i18n=dict(en='Peak power of PV panels on existing building stock')
)
self.add_graph_card(
id='new-buildings',
slider=dict(
min=20,
max=100,
step=5,
value=get_variable('solar_power_new_buildings_percentage'),
marks={x: '%d %%' % x for x in range(20, 100 + 1, 10)},
),
title='Uuden rakennuskannan aurinkopaneelien piikkiteho',
title_i18n=dict(en='Peak power of PV panels on future building stock')
)
self.add_graph_card(
id='production',
title='Aurinkopaneelien sähköntuotanto',
title_i18n=dict(en='PV panel energy production')
)
self.add_graph_card(
id='emission-impact',
title='Aurinkopaneelien päästövaikutukset',
title_i18n=dict(en='Emission impact of PV energy production')
)
def resolve_validation_metrics(query, info):
df = get_detected_cases()
sim_start = date.fromisoformat(get_variable('start_date'))
sim_end = sim_start + timedelta(days=get_variable('simulation_days'))
df = df[df.index < sim_end]
df['detected'] = df['all_detected'].diff()
df['detected'] = df['detected'].rolling(window=14).mean().round().astype('Int64').replace({np.nan: None})
dates = df.index.astype(str).values
metrics = []
for col in df.columns:
m = get_metric(col)
if not m:
raise Exception('no metric found for %s' % col)
int_values = df[col].to_numpy()
metrics.append(
Metric(
type=m.id,
label=m.label,
description=m.description,
unit=m.unit,
color=m.color,
is_integer=m.is_integer,
is_simulated=False,
int_values=int_values
)
)
return DailyMetrics(dates=dates, metrics=metrics)
def make_cards(self):
from .modal_share import ModalSharePage
from .car_fleet import CarFleetPage
self.add_graph_card(
id='bev-percentage',
title='Sähköautojen ajosuoriteosuus',
title_i18n=dict(en='BEV mileage share'),
link_to_page=CarFleetPage,
)
self.add_graph_card(
id='mileage-per-resident',
title='Ajokilometrit asukasta kohti',
title_i18n=dict(en='Car mileage per resident'),
link_to_page=ModalSharePage,
)
self.add_graph_card(
id='total-mileage',
title='%s ajetut henkilöautokilometrit' % get_variable('municipality_locative'),
title_i18n=dict(en='Car mileage driven in %s' % get_variable('municipality_name')),
)
self.add_graph_card(
id='emission-factor',
title='Henkilöautojen päästökerroin',
title_i18n=dict(en='Emission factor of cars'),
)
self.add_graph_card(
id='emissions',
title='Henkilöautoilun päästöt',
title_i18n=dict(en='Emissions from car transport'),
)
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 get_active_interventions(variables=None):
if variables:
scenarios = variables['scenarios']
active_scenario = variables['active_scenario']
interventions = variables['interventions']
else:
scenarios = get_variable('scenarios')
active_scenario = get_variable('active_scenario')
interventions = get_variable('interventions')
out = []
for idx, iv in enumerate(interventions):
obj = iv_tuple_to_obj(iv)
obj.id = str(idx)
out.append(obj)
mobility_ivs = generate_mobility_ivs(variable_store=variables)
for iv in mobility_ivs:
out.append(iv_tuple_to_obj(iv))
vaccinate_ivs = generate_vaccination_ivs(variable_store=variables)
for iv in vaccinate_ivs:
out.append(iv_tuple_to_obj(iv))
if active_scenario:
for s in scenarios:
if s['id'] == active_scenario:
break
else:
raise Exception('Invalid active scenario: %s' % active_scenario)
added_ivs = s.get('add_interventions', [])
for iv in added_ivs:
out.append(iv_tuple_to_obj(iv))
return out
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 get_active_interventions(variables=None):
if variables:
scenarios = variables['scenarios']
active_scenario = variables['active_scenario']
interventions = variables['interventions']
else:
scenarios = get_variable('scenarios')
active_scenario = get_variable('active_scenario')
interventions = get_variable('interventions')
out = []
for iv in interventions:
out.append(iv_tuple_to_obj(iv))
if active_scenario:
for s in scenarios:
if s['id'] == active_scenario:
break
else:
raise Exception('Invalid active scenario: %s' % active_scenario)
added_ivs = s.get('add_interventions', [])
for iv in added_ivs:
out.append(iv_tuple_to_obj(iv))
return out
def _get_default_context(self):
return dict(
org_genitive=get_variable('org_genitive'),
org_nominative=get_variable('org_nominative'),
municipality_genitive=get_variable('municipality_genitive'),
municipality_locative=get_variable('municipality_locative'),
target_year=get_variable('target_year'),
)
def generate_page():
grid = ConnectedCardGrid()
bev_perc_card = GraphCard(
id='cars-bev-percentage',
slider=dict(
min=0,
max=100,
step=5,
value=get_variable('cars_bev_percentage'),
marks={x: '%d %%' % x for x in range(0, 100 + 1, 10)},
),
)
per_resident_card = GraphCard(
id='cars-mileage-per-resident',
slider=dict(
min=-60,
max=20,
step=5,
value=get_variable('cars_mileage_per_resident_adjustment'),
marks={x: '%d %%' % (x) for x in range(-60, 20 + 1, 10)},
),
)
mileage_card = GraphCard(
id='cars-total-mileage',
)
emission_factor_card = GraphCard(
id='cars-emission-factor',
)
emissions_card = GraphCard(
id='cars-emissions',
)
"""
biofuel_card = GraphCard(
id='cars-biofuel-percentage',
)
"""
grid.make_new_row()
grid.add_card(bev_perc_card)
#grid.add_card(biofuel_card)
grid.add_card(per_resident_card)
grid.make_new_row()
grid.add_card(emission_factor_card)
grid.add_card(mileage_card)
grid.make_new_row()
grid.add_card(emissions_card)
bev_perc_card.connect_to(emission_factor_card)
#biofuel_card.connect_to(emission_factor_card)
emission_factor_card.connect_to(emissions_card)
per_resident_card.connect_to(mileage_card)
mileage_card.connect_to(emissions_card)
return html.Div([
grid.render(),
html.Div(id='cars-sticky-page-summary-container')
])
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 generate_page():
grid = ConnectedCardGrid()
existing_card = GraphCard(
id='district-heating-existing-building-unit-heat-factor',
slider=dict(
min=-60,
max=20,
step=5,
value=get_variable(
'district_heating_existing_building_efficiency_change') * 10,
marks={x: '%.1f %%' % (x / 10)
for x in range(-60, 20 + 1, 10)},
))
new_card = GraphCard(
id='district-heating-new-building-unit-heat-factor',
slider=dict(
min=-60,
max=20,
step=5,
value=get_variable(
'district_heating_new_building_efficiency_change') * 10,
marks={x: '%.1f %%' % (x / 10)
for x in range(-60, 20 + 1, 10)},
),
)
geo_card = GraphCard(id='district-heating-geothermal-production',
link_to_page=('BuildingHeating', 'GeothermalHeating'))
row = grid.make_new_row()
row.add_card(existing_card)
row.add_card(new_card)
row.add_card(geo_card)
consumption_card = GraphCard(
id='district-heating-consumption',
extra_content=html.Div(id='district-heating-unit-emissions-card'))
existing_card.connect_to(consumption_card)
new_card.connect_to(consumption_card)
geo_card.connect_to(consumption_card)
row = grid.make_new_row()
row.add_card(consumption_card)
emissions_card = GraphCard(id='district-heating-consumption-emissions')
consumption_card.connect_to(emissions_card)
row = grid.make_new_row()
row.add_card(emissions_card)
return html.Div([
grid.render(),
html.Div(id='district-heating-sticky-page-summary-container')
])
def resolve_area(query, info):
name = get_variable('area_name')
name_long = get_variable('area_name_long')
s = get_age_grouped_population()
total = s.sum()
age_groups = [dict(label=x, count=y) for x, y in zip(s.index, s)]
return dict(
name=name,
name_long=name_long,
total_population=total,
age_groups=age_groups,
)
def get_population_forecast():
correction_perc = get_variable('population_forecast_correction')
target_year = get_variable('target_year')
df = population_forecast[population_forecast.index <= target_year].copy()
forecast = df.loc[df.Forecast]
n_years = forecast.index.max() - forecast.index.min()
base = (1 + (correction_perc / 100))**(1 / n_years)
multipliers = [base**year for year in range(n_years + 1)]
m_series = pd.Series(multipliers, index=forecast.index)
df.loc[df.Forecast, 'Population'] *= m_series
df.Population = df.Population.astype(int)
return df
def interventions_callback(ts, reset_clicks, add_intervention_clicks, rows,
new_date, new_id, new_val):
ctx = dash.callback_context
is_reset = False
changed = False
if ctx.triggered:
c_id = ctx.triggered[0]['prop_id'].split('.')[0]
if reset_clicks is not None and c_id == 'interventions-reset-defaults':
reset_variable('interventions')
is_reset = True
if add_intervention_clicks is not None and c_id == 'new-intervention-add':
d = date.fromisoformat(new_date)
sstart = date.fromisoformat(get_variable('start_date'))
if d < sstart or d > sstart + timedelta(
days=get_variable('simulation_days')):
raise dash.exceptions.PreventUpdate()
if new_id in ('test-all-with-symptoms', ):
new_val = None
elif new_id == ('test-with-contact-tracing',
'test-only-severe-symptoms'):
if new_val > 100 or new_val < 0:
raise dash.exceptions.PreventUpdate()
elif new_id in ('limit-mobility', ):
if new_val > 100 or new_val < 0:
raise dash.exceptions.PreventUpdate()
elif new_id in ('limit-mass-gatherings', 'import-infections',
'build-new-icu-units', 'build-new-hospital-beds'):
if new_val < 0:
raise dash.exceptions.PreventUpdate()
else:
raise dash.exceptions.PreventUpdate()
changed = True
rows.append(dict(name=new_id, date=d.isoformat(), value=new_val))
if c_id == 'interventions-table':
changed = True
if not is_reset and changed:
ivs = []
for row in sorted(rows, key=lambda x: x['date']):
val = row['value']
if isinstance(val, str):
val = int(val)
ivs.append([row['name'], row['date'], val])
set_variable('interventions', ivs)
rows = interventions_to_rows()
return rows
def population_callback(value):
set_variable('population_forecast_correction', value)
pop_df = get_population_forecast()
pop_in_target_year = pop_df.loc[[get_variable('target_year')]].Population
fig = generate_population_forecast_graph(pop_df)
return fig, pop_in_target_year.round()
def render(self):
if (self.value <= self.goal and self.below_goal_good) or \
(self.value >= self.goal and not self.below_goal_good):
sticky_class = 'page-summary__total--good'
else:
sticky_class = 'page-summary__total--bad'
target_year = get_variable('target_year')
summary = dbc.Col([
html.H6(f'{self.label} ({target_year})'),
html.Div([
html.Div([
"%.0f" % self.value,
html.Span(" %s" % self.unit, className="unit")
],
className="page-summary__total " + sticky_class),
html.Div([
"tavoite %.0f" % self.goal,
html.Span(" %s" % self.unit, className="unit")
],
className="page-summary__target")
],
className="page-summary__totals"),
],
md=6)
pötkylä = dbc.Col(
[html.H6('Päästövähennykset'),
self._render_emissions_bar()], md=6)
return dbc.Alert([dbc.Row([pötkylä, summary])],
className="page-summary fixed-bottom")
def render_page():
grid = ConnectedCardGrid()
per_capita_card = GraphCard(
id='electricity-consumption-per-capita',
slider=dict(
min=-50,
max=20,
step=5,
value=get_variable(
'electricity_consumption_per_capita_adjustment'),
marks={x: '%d %%' % (x / 10)
for x in range(-50, 20 + 1, 10)},
))
grid.make_new_row()
grid.add_card(per_capita_card)
consumption_card = GraphCard(id='electricity-consumption')
emission_factor_card = GraphCard(
id='electricity-consumption-emission-factor')
per_capita_card.connect_to(consumption_card)
grid.make_new_row()
grid.add_card(consumption_card)
grid.add_card(emission_factor_card)
emission_card = GraphCard(id='electricity-consumption-emissions')
consumption_card.connect_to(emission_card)
emission_factor_card.connect_to(emission_card)
grid.make_new_row()
grid.add_card(emission_card)
return grid.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 _calculate_cache_key(hash_data):
funcs = hash_data['funcs']
variables = hash_data['variables']
var_data = json.dumps({x: get_variable(x) for x in variables}, sort_keys=True)
func_hash = 0
for func in funcs:
func_hash ^= hash(func)
return '%s:%s' % (hash(var_data), func_hash)
def generate_page():
grid = ConnectedCardGrid()
existing_card = GraphCard(
id='solar-power-existing-buildings',
slider=dict(
min=0,
max=90,
step=5,
value=get_variable('solar_power_existing_buildings_percentage'),
marks={x: '%d %%' % x
for x in range(0, 90 + 1, 10)},
),
extra_content=html.Div(id='solar-power-existing-kwpa'),
)
new_card = GraphCard(
id='solar-power-new-buildings',
slider=dict(
min=20,
max=100,
step=5,
value=get_variable('solar_power_new_buildings_percentage'),
marks={x: '%d %%' % x
for x in range(20, 100 + 1, 10)},
),
extra_content=html.Div(id='solar-power-new-kwpa'),
)
grid.make_new_row()
grid.add_card(existing_card)
grid.add_card(new_card)
production_card = GraphCard(id='solar-power-production')
existing_card.connect_to(production_card)
new_card.connect_to(production_card)
grid.make_new_row()
grid.add_card(production_card)
emission_card = GraphCard(id='solar-power-emission-reductions')
production_card.connect_to(emission_card)
grid.make_new_row()
grid.add_card(emission_card)
return html.Div([
grid.render(),
html.Div(id='solar-power-sticky-page-summary-container')
])
def _calc_emissions(self):
df = predict_emissions()
forecast = df.pop('Forecast')
self.emissions_df = df
df = df.sum(axis=1)
self.last_historical_year = df.loc[~forecast].index.max()
self.target_year = get_variable('target_year')
ref_year = get_variable('ghg_reductions_reference_year')
perc = get_variable('ghg_reductions_percentage_in_target_year')
ref_emissions = df.loc[ref_year]
last_emissions = df.loc[self.last_historical_year]
target_emissions = ref_emissions * (1 - perc / 100)
self.target_emissions = target_emissions
self.needed_reductions = last_emissions - target_emissions
self.scenario_emissions = df.loc[self.target_year]
self.scenario_reductions = last_emissions - self.scenario_emissions
def mutate(root, info, event_id):
iv_id = int(event_id)
iv_list = list(get_variable('interventions'))
if iv_id >= len(iv_list):
raise GraphQLError('invalid intervention ID')
del iv_list[iv_id]
set_variable('interventions', iv_list)
return dict(ok=True)
def _calculate_cache_key(func, hash_data, var_store):
funcs = hash_data['funcs']
variables = hash_data['variables']
var_data = json.dumps({x: get_variable(x, var_store=var_store) for x in variables}, sort_keys=True)
func_hash = _hash_funcs(funcs)
func_name = '.'.join((func.__module__, func.__name__))
return '%s:%s:%s' % (func_name, hashlib.md5(var_data.encode()).hexdigest(), func_hash)
def calculate_district_heating_unit_emissions(fuel_use_df, production_df):
emissionless_bio = get_variable('bio_is_emissionless')
fuel_co2 = fuel_classification[['code', 'co2e_emission_factor',
'is_bio']].set_index('code')
df = fuel_use_df.merge(fuel_co2,
how='left',
left_on='StatfiFuelCode',
right_index=True)
df.co2e_emission_factor = df.co2e_emission_factor.astype('pint[t/TJ]')
df.Value = df.Value.astype('pint[GWh]')
df['Emissions'] = (df.Value *
df.co2e_emission_factor).pint.to('tonne').pint.m
#df = production_df
#print(df.loc[df.index == 2017])
#print(df.loc[df.index == 2018])
if emissionless_bio:
df.loc[df.is_bio == True, 'Emissions'] = 0 # noqa
emissions = df.groupby('Year')['Emissions'].sum()
emissions.name = 'Emissions'
df = production_df
heat_production = df[HEAT_DEMAND_COL]
heat_production.name = 'Heat production'
chp_electricity_production = df[CHP_ELECTRICITY_PRODUCTION_COL]
chp_electricity_production.name = 'Electricity production'
# Determine the CHP alternate production energy consumptions according to the efficiency method
electricity_production_alternate = chp_electricity_production / 0.39
heat_production_alternate = heat_production / 0.90
total = electricity_production_alternate + heat_production_alternate
heat_share = heat_production_alternate / total
heat_share.name = 'Fuel share of heat production in CHP'
heat_demand = df[HEAT_DEMAND_COL]
heat_demand.name = 'Heat demand'
heat_pump_prod = production_df[HEAT_PUMP_COL]
heat_pump_prod.name = 'Production with heat pumps'
df = pd.concat([
heat_demand, chp_electricity_production, heat_share, heat_pump_prod,
emissions
],
axis=1)
df['Unit emissions'] = df.Emissions * heat_share / heat_demand
df['Emissions'] /= 1000
df['District heat consumption emissions'] = heat_demand * df[
'Unit emissions'] / 1000
df['Forecast'] = production_df['Forecast']
return df
def render_page():
slider = dict(
min=-20,
max=20,
step=5,
value=get_variable('population_forecast_correction'),
marks={x: '%d %%' % x for x in range(-20, 20 + 1, 5)},
)
card = GraphCard(id='population', slider=slider).render()
return dbc.Row(dbc.Col(card, md=6))
def disease_params_data_callback(ts, reset_clicks, rows):
ctx = dash.callback_context
if ctx.triggered:
c_id = ctx.triggered[0]['prop_id'].split('.')[0]
if reset_clicks is not None and c_id == 'disease-params-reset-defaults':
for row in rows:
reset_variable(row['id'])
row['value'] = get_variable(row['id'])
for row in rows:
if not isinstance(row['value'], (int, float)):
row['value'] = get_variable(row['id'])
if row['value'] < 0:
row['value'] = 0
elif row['value'] > 100:
row['value'] = 100
set_variable(row['id'], float(row['value']))
return rows
def get_district_heating_unit_emissions_forecast():
operator = get_variable('district_heating_operator')
target_ratios = get_variable('district_heating_target_production_ratios')
target_year = get_variable('target_year')
target_demand_change = get_variable(
'district_heating_target_demand_change')
assert sum(target_ratios.values()) == 100
df = dh_fuel_df
fuel_df = df[df.Operator == operator].drop(
columns=['Operator', 'OperatorName'])
df = dh_production_df
df = df[df.Operator == operator].drop(columns=['Operator', 'OperatorName'])
df = df.set_index('Year').drop(columns='Unit').pivot(columns='Quantity',
values='Value')
# Fill in the missing columns from previous years
df[HEAT_PUMP_COL] = df[HEAT_PUMP_COL].fillna(0)
df[FUEL_NET_PRODUCTION_COL] = df[TOTAL_PRODUCTION_COL] - df['Osto'] - df[
HEAT_PUMP_COL]
production_df = df
heat_pump_share = target_ratios.get('Lämpöpumput') / 100
production_forecast = generate_production_forecast(
production_df, target_year, target_demand_change / 100,
heat_pump_share)
fuel_use_forecast = generate_fuel_use_forecast(fuel_df,
production_forecast,
target_year, target_ratios)
production_df['Forecast'] = False
production_forecast['Forecast'] = True
production_df = pd.concat([production_df, production_forecast],
sort=False).sort_index()
fuel_df = pd.concat([fuel_df, fuel_use_forecast],
sort=False).set_index('Year').sort_index()
df = calculate_district_heating_unit_emissions(fuel_df, production_df)
return df
def resolve_scenarios(query, info):
scenarios = get_variable('scenarios')
active_scenario = get_variable('active_scenario')
out = []
customized_variables = list(get_session_variables().keys())
if 'active_scenario' in customized_variables:
customized_variables.remove('active_scenario')
if len(customized_variables):
customized = True
else:
customized = False
for s in scenarios:
if s['id'] == active_scenario and not customized:
active = True
else:
active = False
out.append(Scenario(
id=s['id'], label=s['label'], description=s['description'], active=active
))
return out