def aggregate_gross_floor_area(self, yr_e, buildings):
chart_data = []
y_display_map = {
0: '0-99k',
100000: '100-199k',
200000: '200k-299k',
300000: '300k-399k',
400000: '400-499k',
500000: '500-599k',
600000: '600-699k',
700000: '700-799k',
800000: '800-899k',
900000: '900-999k',
1000000: 'over 1,000k',
}
max_bin = max(y_display_map)
# Group buildings in this year_ending group into ranges
grouped_ranges = defaultdict(list)
for b in buildings:
area = b['y']
# make sure anything greater than the biggest bin gets put in
# the biggest bin
range_bin = min(max_bin, round_down_hundred_thousand(area))
grouped_ranges[range_bin].append(b)
# Now iterate over range groups to make each chart item
for range_floor, buildings_in_range in grouped_ranges.items():
chart_data.append({
'x': median([b['x'] for b in buildings_in_range]),
'y': y_display_map[range_floor],
'yr_e': yr_e
})
return chart_data
def aggregate_use_description(self, yr_e, buildings):
# Group buildings in this year_ending group into uses
chart_data = []
grouped_uses = defaultdict(list)
for b in buildings:
grouped_uses[str(b['y']).lower()].append(b)
# Now iterate over use groups to make each chart item
for use, buildings_in_uses in grouped_uses.items():
chart_data.append({
'x': median([b['x'] for b in buildings_in_uses]),
'y': use.capitalize(),
'yr_e': yr_e
})
return chart_data
def aggregate_use_description(self, yr_e, buildings):
# Group buildings in this year_ending group into uses
chart_data = []
grouped_uses = defaultdict(list)
for b in buildings:
grouped_uses[str(b['y']).lower()].append(b)
# Now iterate over use groups to make each chart item
for use, buildings_in_uses in grouped_uses.items():
chart_data.append({
'x': median([b['x'] for b in buildings_in_uses]),
'y': use.capitalize(),
'yr_e': yr_e
})
return chart_data
def aggregate_year_built(self, yr_e, buildings):
# Group buildings in this year_ending group into decades
chart_data = []
grouped_decades = defaultdict(list)
for b in buildings:
grouped_decades['%s0' % str(b['y'])[:-1]].append(b)
# Now iterate over decade groups to make each chart item
for decade, buildings_in_decade in grouped_decades.items():
chart_data.append({
'x': median(
[b['x'] for b in buildings_in_decade]
),
'y': '%s-%s' % (decade, '%s9' % str(decade)[:-1]), # 1990-1999
'yr_e': yr_e
})
return chart_data
def aggregate_year_built(self, yr_e, buildings):
# Group buildings in this year_ending group into decades
chart_data = []
grouped_decades = defaultdict(list)
for b in buildings:
grouped_decades['%s0' % str(b['y'])[:-1]].append(b)
# Now iterate over decade groups to make each chart item
for decade, buildings_in_decade in grouped_decades.items():
chart_data.append({
'x':
median([b['x'] for b in buildings_in_decade]),
'y':
'%s-%s' % (decade, '%s9' % str(decade)[:-1]), # 1990-1999
'yr_e':
yr_e
})
return chart_data
def aggregate_gross_floor_area(self, yr_e, buildings):
chart_data = []
y_display_map = {
0: '0-99k',
100000: '100-199k',
200000: '200k-299k',
300000: '300k-399k',
400000: '400-499k',
500000: '500-599k',
600000: '600-699k',
700000: '700-799k',
800000: '800-899k',
900000: '900-999k',
1000000: 'over 1,000k',
}
max_bin = max(y_display_map.keys())
# Group buildings in this year_ending group into ranges
grouped_ranges = defaultdict(list)
for b in buildings:
area = b['y']
# make sure anything greater than the biggest bin gets put in
# the biggest bin
range_bin = min(max_bin, round_down_hundred_thousand(area))
grouped_ranges[range_bin].append(b)
# Now iterate over range groups to make each chart item
for range_floor, buildings_in_range in grouped_ranges.items():
chart_data.append({
'x': median(
[b['x'] for b in buildings_in_range]
),
'y': y_display_map[range_floor],
'yr_e': yr_e
})
return chart_data
