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 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 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_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)
item = _make_nav_item(metadata['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('Yhteensä', sector_emissions.sum(), 0, None, bold=True))
return html.Div([
html.H6('Päästöt vuonna %s' % target_year),
dbc.ListGroup(children=items)
])
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 _render_emissions_bar(self):
df = predict_emissions()
last_historical_year = df.loc[~df.Forecast].Year.max()
start_s = df[df.Year == last_historical_year].groupby(
'Sector1')['Emissions'].sum()
target_s = df[df.Year == df.Year.max()].groupby(
'Sector1')['Emissions'].sum()
reductions = (start_s - target_s).sort_values(ascending=False)
traces = []
page = self.current_page
for sector_name, emissions in reductions.iteritems():
sector_metadata = SECTORS[sector_name]
if page is not None and page.emission_sector is not None and \
page.emission_sector[0] == sector_name:
active = True
else:
active = False
bar = dict(type='bar',
x=[emissions],
name=sector_metadata['name'],
orientation='h',
hovertemplate='%{x: .0f}',
marker=dict(color=GHG_MAIN_SECTOR_COLORS[sector_name]))
if active:
bar['marker'].update(dict(
line_color='#888',
line_width=4,
))
traces.append(bar)
sum_reductions = reductions.sum()
fig = go.Figure(data=traces,
layout=go.Layout(
xaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
domain=[0.15, 1],
autorange=False,
range=[0, sum_reductions],
),
yaxis=dict(
showgrid=False,
showline=False,
showticklabels=False,
zeroline=False,
),
margin=dict(l=0, r=0, b=0, t=0),
barmode='stack',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
showlegend=False,
width=None,
autosize=True,
clickmode='none',
dragmode=False,
))
graph = dcc.Graph(
config={
'displayModeBar': False,
'responsive': True,
},
style={
'height': 60,
'width': '100%'
},
figure=fig,
)
return graph
def make_emission_nav(current_page):
df = predict_emissions()
target_year = get_variable('target_year')
df = df[df.Year == df.Year.max()]
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 = df.groupby('Sector1').Emissions.sum().sort_values(
ascending=False)
for sector_name, emissions in sector_emissions.iteritems():
sector_metadata = SECTORS[sector_name]
sdf = df.loc[df.Sector1 == sector_name]
page = get_page_for_emission_sector(sector_name, None)
subsectors = sdf.Sector2.unique()
if current_sector and sector_name == current_sector[0] and len(
subsectors) <= 1:
active = True
else:
active = False
item = _make_nav_item(sector_metadata['name'],
emissions,
False,
page,
active=active)
items.append(item)
if len(subsectors) <= 1:
continue
sdf = sdf.sort_values('Sector2',
ascending=True).set_index('Sector2').Emissions
for subsector_name, emissions in sdf.iteritems():
if current_sector and sector_name == current_sector[
0] and subsector_name == current_sector[1]:
active = True
else:
active = False
page = get_page_for_emission_sector(sector_name, subsector_name)
item = _make_nav_item(
sector_metadata['subsectors'][subsector_name]['name'],
emissions,
True,
page,
active=active)
items.append(item)
sum_emissions = sector_emissions.sum()
items.append(
_make_nav_item('Yhteensä', sum_emissions, False, None, bold=True))
return html.Div([
html.H6('Päästöt vuonna %s' % target_year),
dbc.ListGroup(children=items)
])