return contents
def bikable_zone(unit):
if 'original_value' not in unit or unit.get('original_value') is None:
return False
return unit.get('original_value') > 1.5 and unit.get('bike_val') < 25
renderer = DefaultRenderer(center=home,
zoom=12,
opacity=0.5,
label=make_label,
color_scale=custom_color_scale)
h_map = HeatMap(home,
geo_json,
filename='bike_vs_ubahn',
square_size=800,
num_threads=100,
load_intermediate_results=True)
h_map.generate(calc_time)
h_map.normalize(normalize_log2_scale)
h_map.generate_polygon(bikable_zone,
color='#0012b3',
opacity=0.6,
weight=5,
dash_array=[1, 6])
h_map.render(
renderer,
before_saving=lambda r, _: r.add_circle(home, color='#0012b3', radius=20))
# The extractor gets the data
toilet_extr = ToiletExtractor(
'./data/oeffentlichetoilettenmuenchen2016-06-28.csv', 'latitude',
'longitude')
# The renderer deals with visual aspects of the map
renderer = DefaultRenderer(center=marienplatz,
zoom=12,
opacity=0.35,
color_scale=custom_color_scale,
tiles='cartodbdark_matter')
# Create a new heatmap
h_map = HeatMap(None,
geo_json,
filename='toilet',
square_size=500,
num_threads=100,
load_intermediate_results=True)
h_map.generate(toilet_extr.get_value)
h_map.normalize()
# Generate the polygon areas within 1km of a public toilet
h_map.generate_polygon(lambda v: v.get('lin_value', 999) < 1.0,
dash_array=[5, 5],
color='#0ef',
opacity=0.5,
weight=2)
# Render and save
h_map.render(renderer, before_saving=toilet_extr.add_markers)
with open('./geo/muenchen.json', 'r') as fp:
geo_json = json.load(fp)
def calc_time(pt1, pt2):
""" Calculate the time to get somewhere by UBahn """
t_ubahn = mvg.average_time_between(pt1, pt2, datetime.now())
if not t_ubahn:
return None
value = int(t_ubahn / 60.0)
# Since the value will be normalized, we save it also on a separate key to show it on the label
return {'value': value, 'time': value}
renderer = DefaultRenderer(center=home,
zoom=12,
color_scale=color,
label=label)
h_map = HeatMap(home,
geo_json,
square_size=250,
filename='mvg',
load_intermediate_results=True)
h_map.generate(calc_time)
# Normalize on a log2 scale
h_map.normalize(lambda v, _1, _2: log2(v))
# Then again on a 0 to 1 range
h_map.normalize()
h_map.render(renderer)