def make_clusters(points_by_type, lat_min, lat_max, lon_min, lon_max,
grid_size):
def average_rating(points):
assert len(points) > 0
ratings = [point.rating for point in points]
return sum(ratings) / float(len(ratings))
all_clusters = []
for type, points in points_by_type.iteritems():
centers, groups = kmeans(lat_min, lat_max, lon_min, lon_max,
points, grid_size)
clusters = []
for i, points in enumerate(groups):
if len(points) > 0:
clusters.append({
'center':
centers[i],
'points':
points,
'type':
type,
'averageRating':
average_rating(points),
})
all_clusters.extend(clusters)
return all_clusters
def make_clusters(points_by_type, lat_min, lat_max,
lon_min, lon_max, grid_size):
def average_rating(points):
assert len(points) > 0
ratings = [point.rating for point in points]
return sum(ratings) / float(len(ratings))
all_clusters = []
for type, points in points_by_type.iteritems():
centers, groups = kmeans(lat_min, lat_max,
lon_min, lon_max,
points, grid_size)
clusters = []
for i, points in enumerate(groups):
if len(points) > 0:
clusters.append({
'center': centers[i],
'points': points,
'type': type,
'averageRating': average_rating(points),
})
all_clusters.extend(clusters)
return all_clusters
def getClusters(catalog_tool, filters):
# the objects are searched for in the tile limits (to get the same clusters every time)
grid_size = 16 # geopoints' and clusters' density on map / also depends on map frame size
# unpack map limits
if filters:
lat_min = float(filters[0]['geo_latitude']['query'][0])
lat_max = float(filters[0]['geo_latitude']['query'][1])
lon_min = float(filters[0]['geo_longitude']['query'][0])
lon_max = float(filters[0]['geo_longitude']['query'][1])
else: # this should not happen
return [], []
tlat_min, tlat_max, tlon_min, tlon_max = clusters.get_discretized_limits(
lat_min, lat_max, lon_min, lon_max, grid_size)
catalog = catalog_tool._catalog
# getting the inner indexes for lat and lon
lat_index = catalog.getIndex('geo_latitude')._index
lon_index = catalog.getIndex('geo_longitude')._index
# adjust to cover results outside frame, but very close to margins
# trying to fix cluster flickering near margins
# applying the lat and lon indexes to get the rids
rs = None
lat_set, lat_dict = _apply_index_with_range_dict_results(
lat_index, Decimal(str(tlat_min)), Decimal(str(tlat_max)))
w, rs = weightedIntersection(rs, lat_set)
lon_set, lon_dict = _apply_index_with_range_dict_results(
lon_index, Decimal(str(tlon_min)), Decimal(str(tlon_max)))
w, rs = weightedIntersection(rs, lon_set)
rs_final = None
# OR the filters and apply the index for each one
for f in filters:
rs_f = rs
#adjust geo limits in filters to be consistent with discretized tile limits
f['geo_longitude']['query'] = (Decimal(str(tlon_min)),
Decimal(str(tlon_max)))
f['geo_latitude']['query'] = (Decimal(str(tlat_min)),
Decimal(str(tlat_max)))
#this code is from the search function in the catalog implementation in Zope
for i in catalog.indexes.keys():
index = catalog.getIndex(i)
_apply_index = getattr(index, "_apply_index", None)
if _apply_index is None:
continue
r = _apply_index(f)
if r is not None:
r, u = r
w, rs_f = weightedIntersection(rs_f, r)
w, rs_final = weightedUnion(rs_f, rs_final)
r_list = list(rs_final)
# transform objects to points
points = []
for i in range(len(r_list)):
points.append(
clusters.Point(i, float(lat_dict[r_list[i]]),
float(lon_dict[r_list[i]])))
centers, groups = clusters.kmeans(tlat_min, tlat_max, tlon_min, tlon_max,
points, grid_size)
# transform group points to rids
for i in range(len(groups)):
groups[i] = map(lambda p: r_list[p.id], groups[i])
return centers, groups
def getClusters(catalog_tool, filters):
# the objects are searched for in the tile limits (to get the same clusters every time)
grid_size = 12 # geopoints' and clusters' density on map / also depends on map frame size
# unpack map limits
if filters:
lat_min = float(filters[0]['geo_latitude']['query'][0])
lat_max = float(filters[0]['geo_latitude']['query'][1])
lon_min = float(filters[0]['geo_longitude']['query'][0])
lon_max = float(filters[0]['geo_longitude']['query'][1])
else: # this should not happen
return [], []
tlat_min, tlat_max, tlon_min, tlon_max = clusters.get_discretized_limits(lat_min, lat_max, lon_min, lon_max, grid_size)
catalog = catalog_tool._catalog
# getting the inner indexes for lat and lon
lat_index = catalog.getIndex('geo_latitude')._index
lon_index = catalog.getIndex('geo_longitude')._index
# adjust to cover results outside frame, but very close to margins
# trying to fix cluster flickering near margins
# applying the lat and lon indexes to get the rids
rs = None
lat_set, lat_dict = _apply_index_with_range_dict_results(lat_index, Decimal(str(tlat_min)), Decimal(str(tlat_max)))
w, rs = weightedIntersection(rs, lat_set)
lon_set, lon_dict = _apply_index_with_range_dict_results(lon_index, Decimal(str(tlon_min)), Decimal(str(tlon_max)))
w, rs = weightedIntersection(rs, lon_set)
rs_final = None
# OR the filters and apply the index for each one
for f in filters:
rs_f = rs
#adjust geo limits in filters to be consistent with discretized tile limits
f['geo_longitude']['query'] = (Decimal(str(tlon_min)), Decimal(str(tlon_max)))
f['geo_latitude']['query'] = (Decimal(str(tlat_min)), Decimal(str(tlat_max)))
#this code is from the search function in the catalog implementation in Zope
for i in catalog.indexes.keys():
index = catalog.getIndex(i)
_apply_index = getattr(index, "_apply_index", None)
if _apply_index is None:
continue
r = _apply_index(f)
if r is not None:
r, u = r
w, rs_f = weightedIntersection(rs_f, r)
w, rs_final = weightedUnion(rs_f, rs_final)
r_list = list(rs_final)
# transform objects to points
points = []
for i in range(len(r_list)):
points.append(clusters.Point(i, float(lat_dict[r_list[i]]), float(lon_dict[r_list[i]])))
centers, groups = clusters.kmeans(tlat_min, tlat_max, tlon_min, tlon_max, points, grid_size)
# transform group points to rids
for i in range(len(groups)):
groups[i] = map(lambda p: r_list[p.id], groups[i])
return centers, groups
