def test_margin(self):
assert (get_discretized_limits(-90., 90., -180., 180., 10)
== get_discretized_limits(-89., 89., -179., 179., 10))
def xrjs_getClusters(self, lat_min, lat_max, lon_min, lon_max):
""" """
def make_points(r_list, lan_dict, lon_dict, type_dict, rating_dict):
points = []
for i, rid in enumerate(r_list):
point = Point(i, float(lat_dict[rid]), float(lon_dict[rid]))
point.type = type_dict[rid]
point.rating = rating_dict[rid]
points.append(point)
return points
def make_points_by_type(points):
points_by_type = {}
for point in points:
if not point.type in points_by_type:
points_by_type[point.type] = []
points_by_type[point.type].append(point)
return points_by_type
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 point_data(cluster, r_list):
def cluster_tooltip(cluster, r_list):
cluster_rids = [
r_list[point.id] for point in cluster['points']
]
pins = [
getObjectFromCatalog(catalog, rid) for rid in cluster_rids
]
return self.cluster_index(pins=pins)
num_points = len(cluster['points'])
if num_points == 1:
point = cluster['points'][0]
pin = getObjectFromCatalog(catalog, r_list[point.id])
icon_name = 'mk_single_rating_%s_%d' % (pin.type, pin.rating)
return {
'id': pin.id,
'icon_name': icon_name,
'lat': pin.latitude,
'lon': pin.longitude,
'display_tooltip': False,
'tooltip': '',
'label': ''
}
else:
rating = int(cluster['averageRating'])
display_tooltip = (lat_max - lat_min) < 1.
if display_tooltip:
tooltip = cluster_tooltip(cluster, r_list)
else:
tooltip = ''
icon_name = 'mk_rating_%s_%d_%d' % (cluster['type'], rating,
num_points)
return {
'id': '',
'icon_name': icon_name,
'lat': cluster['center'].lat,
'lon': cluster['center'].lon,
'display_tooltip': display_tooltip,
'tooltip': tooltip,
'label': 'cluster',
'num_points': num_points
}
tc_start = time()
lat_min, lat_max = float(lat_min), float(lat_max)
lon_min, lon_max = float(lon_min), float(lon_max)
grid_size = 7
tlat_min, tlat_max, tlon_min, tlon_max = get_discretized_limits(
lat_min, lat_max, lon_min, lon_max, grid_size)
catalog = self.context.catalog
tc_start_apply_idxs = time()
type_dict = get_index_dict('type', catalog)
lat_dict = get_index_dict('latitude', catalog, tlat_min, tlat_max)
lon_dict = get_index_dict('longitude', catalog, tlon_min, tlon_max)
rating_dict = get_index_dict('rating', catalog)
tc_end_apply_idxs = time()
print 'apply indexes', tc_end_apply_idxs - tc_start_apply_idxs
map_filters = {
'latitude': {
'query': (tlat_min, tlat_max),
'range': 'min:max'
},
'longitude': {
'query': (tlon_min, tlon_max),
'range': 'min:max'
}
}
rids = filter_rids(catalog, map_filters)
if rids is None:
r_list = []
else:
r_list = list(set(rids))
points = make_points(r_list, lat_dict, lon_dict, type_dict,
rating_dict)
points_by_type = make_points_by_type(points)
clusters = make_clusters(points_by_type, tlat_min, tlat_max, tlon_min,
tlon_max, grid_size)
points = [point_data(cluster, r_list) for cluster in clusters]
tc_end = time()
print 'clusters', tc_end - tc_start
return json.dumps({'points': points})
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
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 xrjs_getClusters(self, lat_min, lat_max, lon_min, lon_max):
""" """
def make_points(r_list, lan_dict, lon_dict, type_dict, rating_dict):
points = []
for i, rid in enumerate(r_list):
point = Point(i, float(lat_dict[rid]), float(lon_dict[rid]))
point.type = type_dict[rid]
point.rating = rating_dict[rid]
points.append(point)
return points
def make_points_by_type(points):
points_by_type = {}
for point in points:
if not point.type in points_by_type:
points_by_type[point.type] = []
points_by_type[point.type].append(point)
return points_by_type
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 point_data(cluster, r_list):
def cluster_tooltip(cluster, r_list):
cluster_rids = [r_list[point.id] for point in cluster['points']]
pins = [getObjectFromCatalog(catalog, rid) for rid in cluster_rids]
return self.cluster_index(pins=pins)
num_points = len(cluster['points'])
if num_points == 1:
point = cluster['points'][0]
pin = getObjectFromCatalog(catalog, r_list[point.id])
icon_name = 'mk_single_rating_%s_%d' % (pin.type, pin.rating)
return {'id': pin.id,
'icon_name': icon_name,
'lat': pin.latitude,
'lon': pin.longitude,
'display_tooltip': False,
'tooltip': '',
'label': ''}
else:
rating = int(cluster['averageRating'])
display_tooltip = (lat_max - lat_min) < 1.
if display_tooltip:
tooltip = cluster_tooltip(cluster, r_list)
else:
tooltip = ''
icon_name = 'mk_rating_%s_%d_%d' % (cluster['type'],
rating,
num_points)
return {'id': '',
'icon_name': icon_name,
'lat': cluster['center'].lat,
'lon': cluster['center'].lon,
'display_tooltip': display_tooltip,
'tooltip': tooltip,
'label': 'cluster',
'num_points': num_points}
tc_start = time()
lat_min, lat_max = float(lat_min), float(lat_max)
lon_min, lon_max = float(lon_min), float(lon_max)
grid_size = 7
tlat_min, tlat_max, tlon_min, tlon_max = get_discretized_limits(
lat_min, lat_max, lon_min, lon_max, grid_size)
catalog = self.context.catalog
tc_start_apply_idxs = time()
type_dict = get_index_dict('type', catalog)
lat_dict = get_index_dict('latitude', catalog, tlat_min, tlat_max)
lon_dict = get_index_dict('longitude', catalog, tlon_min, tlon_max)
rating_dict = get_index_dict('rating', catalog)
tc_end_apply_idxs = time()
print 'apply indexes', tc_end_apply_idxs - tc_start_apply_idxs
map_filters = {'latitude': {'query': (tlat_min, tlat_max),
'range': 'min:max'},
'longitude': {'query': (tlon_min, tlon_max),
'range': 'min:max'}}
rids = filter_rids(catalog, map_filters)
if rids is None:
r_list = []
else:
r_list = list(set(rids))
points = make_points(r_list, lat_dict, lon_dict, type_dict, rating_dict)
points_by_type = make_points_by_type(points)
clusters = make_clusters(points_by_type, tlat_min, tlat_max,
tlon_min, tlon_max, grid_size)
points = [point_data(cluster, r_list) for cluster in clusters]
tc_end = time()
print 'clusters', tc_end - tc_start
return json.dumps({'points': points})
