def plot_metric(ax: Axes, metric: PlotMetric,
gdf: GeoDataFrame) -> Axes: # type: ignore
# nicely format the colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
gdf.plot( # type: ignore
metric.metric,
ax=ax,
legend=True,
cmap=metric.cmap,
vmin=metric.vmin,
vmax=metric.vmax,
cax=cax)
ax.set_title(f'{metric.metric.upper()}')
return ax
def _plot_single_gdf(ax: Axes,
gdf: GeoDataFrame,
column_to_plot: str,
title: Optional[str] = None,
cmap: Optional[str] = 'viridis',
vmin: Optional[float] = None,
vmax: Optional[float] = None) -> Axes:
# nicely format the colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
gdf.plot(column_to_plot,
ax=ax,
legend=True,
cmap=cmap,
vmin=vmin,
vmax=vmax,
cax=cax)
ax.set_title(title)
return ax
def simplify_and_mapping(data_source):
if data_source == 'lwm':
tau = 2
buildings = extract_footprint_from_prism('../data/lwm-prism.gml')
else: # Only for Manhattan, New York
tau = 0.00003
buildings = ox.footprints_from_place(
'{}, Manhattan, New York City'.format(data_source))
douglas_peucker_buildings = GeoDataFrame()
simplified_buildings = GeoDataFrame()
sum_haus = [0.0, 0.0]
total_points = [0, 0]
tolerance = tau * 3 / 5
def comparison(footprint, i):
new_footprint = prism.simplify(footprint,
tau=tau,
epsilon=math.pi / 30)
if new_footprint is not None:
simplified_buildings.loc[i, 'geometry'] = new_footprint
haus = footprint.hausdorff_distance(new_footprint)
sum_haus[1] += haus
total_points[1] += len(new_footprint.exterior.coords)
dp_footprint = footprint.simplify(tolerance)
douglas_peucker_buildings.loc[i, 'geometry'] = dp_footprint
haus = footprint.hausdorff_distance(dp_footprint)
sum_haus[0] += haus
total_points[0] += len(dp_footprint.exterior.coords)
count = 0
for geom in buildings['geometry']:
if geom.geom_type == 'Polygon':
comparison(geom, count)
count += 1
if geom.geom_type == 'MultiPolygon':
for poly in geom:
comparison(poly, count)
count += 1
print("Average Hausdorff Distance (Douglas Peucker):", sum_haus[0] / count)
print("Average Hausdorff Distance (Indoor Simplification):",
sum_haus[1] / count)
print("Total Number of Points (Douglas Peucker):", total_points[0])
print("Total Number of Points (Indoor Simplification):", total_points[1])
cell_text = [[tolerance, tau], [sum_haus[0] / count, sum_haus[1] / count],
[total_points[0], total_points[1]]]
# mapping
minx, miny, maxx, maxy = buildings.total_bounds
map_scale = 50
width = maxx - minx
height = maxy - miny
ratio = width / height
mbr = (ratio * map_scale, map_scale)
fig, ax = plt.subplots(figsize=mbr)
buildings.plot(ax=ax,
facecolor='green',
edgecolor='grey',
linewidth=0.2,
alpha=0.1)
douglas_peucker_buildings.plot(ax=ax, facecolor='blue', alpha=0.1)
simplified_buildings.plot(ax=ax, facecolor='red', alpha=0.1)
ax.table(cellText=cell_text,
rowLabels=[
"Distance Tolerance", "Average Hausdorff Distance",
"Total Number of Points"
],
colLabels=["Douglas Peucker", "Indoor Simplification"],
colWidths=[0.05 / ratio, 0.05 / ratio],
loc='lower right')
legend_elements = [
Patch(facecolor='green',
edgecolor='grey',
linewidth=0.2,
alpha=0.1,
label='Original'),
Patch(facecolor='blue', alpha=0.1, label='Douglas Peucker'),
Patch(facecolor='red', alpha=0.1, label='Indoor Simplification')
]
ax.legend(handles=legend_elements,
loc='upper right',
title='Simplification Method',
fontsize=map_scale,
title_fontsize=map_scale)
plt.tight_layout()
plt.savefig('../examples/{}.pdf'.format(data_source), format='pdf')
postcode_dao = PostcodeDAO(session)
way_dao = WayDAO(session)
# Representación de las calles con GeoSeries
# Extraemos todas las calles
ways = []
for way in way_dao.getAll():
ways.append(to_shape(way.geom))
wgs = GeoSeries(ways)
base = wgs.plot(color="blue")
qfilter = {'ad_type': 'RENT', 'asset_type': 'GARAGE'}
prices = session.execute(
'SELECT postcode, avg_price FROM inmosig_average_prices WHERE ad_type = :ad_type AND '
'asset_type = :asset_type', qfilter)
gdf = GeoDataFrame(columns=['geometry', 'price', 'postcode'])
for price in prices.fetchall():
postcode = postcode_dao.search_by_postcode(price[0])
if postcode is not None:
gdf = gdf.append(
{
'geometry': to_shape(postcode.geom),
'price': float(price[1]),
'postcode': price[0]
},
ignore_index=True)
gdf.head()
gdf.plot(ax=base, column='price', cmap='OrRd', scheme="quantiles", legend=True)
plt.show()