def search_index(gps_location, accuracy, index):
longitude, latitude = gps_location
if accuracy:
accuracy = abs(accuracy)
dlat = accuracy / METERS_PER_DEGREE
latitude_rad = degrees_to_radians(latitude)
dlon = accuracy / (METERS_PER_DEGREE * math.cos(latitude_rad))
query_bbox = (longitude - dlon, latitude - dlat, longitude + dlon,
latitude + dlat)
for registrar_dict in index.intersection(query_bbox, objects="raw"):
polygon = registrar_dict["geometry"]
square = shapely.geometry.Polygon([
(longitude - dlon, latitude - dlat),
(longitude - dlon, latitude + dlat),
(longitude + dlon, latitude + dlat),
(longitude + dlon, latitude - dlat),
])
if polygon.intersects(square):
yield registrar_dict
else:
query_bbox = (longitude, latitude, longitude, latitude)
for registrar_dict in index.intersection(query_bbox, objects="raw"):
polygon = registrar_dict["geometry"]
point = shapely.geometry.Point(*gps_location)
if polygon.contains(point):
yield registrar_dict
def find_shape_in_index(shape: BaseGeometry, index):
# (minx, miny, maxx, maxy)
bounds = shape.bounds
lon = []
lat = []
weights = []
for result in index.intersection(bounds, objects="raw"):
point = result['point']
rect = result['bbox']
poly = Polygon([(r[0], r[1]) for r in rect])
inters = shape.intersection(poly)
# Intersection with bounds might not intersect with detailed poly
if inters:
weights.append(inters.area)
lon.append(point[0])
lat.append(point[1])
lon = np.array(lon)
lat = np.array(lat)
weights = np.array(weights)
weights = weights / np.sum(weights)
return GridLookupResults(lon, lat, weights)
def already_present(p):
# FIXME: cette recherche vas être quadratique si les intersections
# sont importantes, comme à Apatou en Guyane:
center = p.centroid.coords[0]
for i in index.intersection(center):
if p.almost_equals(polygons[i], PARCEL_LIMIT_ALMOST_EQUALS_DECIMAL):
return True
return False
def find_location(coordinates):
coordinates = map(float, coordinates)
point = Point(coordinates)
for candidate in rtree.intersection(coordinates):
polygon = polygons[candidate]
if point.within(polygon):
return locations[candidate]
raise LookupError()
def already_present(p):
# FIXME: cette recherche vas être quadratique si les intersections sont importantes,
# comme à Apatou en Guyane:
center = shapely.geometry.box(*(p.bounds)).centroid.coords[0]
for i in index.intersection(center):
if p.almost_equals(polygones[i], LIMITE_ALMOST_EQUALS_DECIMAL):
return True
return False
def search_index(index, validTupl, validated_image_filename):
nearHits = list(index.nearest(validTupl, objects=True))
nearHitsId = [str(item.id) + " -- " + item.object for item in nearHits]
for h in nearHitsId:
print("R-tree near for: " + validated_image_filename + ' matches ' + h)
intrHits = list(index.intersection(validTupl, objects=True))
intrHitsId = [str(item.id) + " -- " + item.object for item in intrHits]
for h in intrHitsId:
print("R-tree intersect for: " + validated_image_filename +
' matches ' + h)
def getEntitiesNearPoint(index, slack, tweet_lat, tweet_lng):
try:
#note these are for rochester - go to http://www.csgnetwork.com/degreelenllavcalc.html to calculate for a different latitude
dX = slack * 0.000012297
dY = slack * 0.000009001
lng_low = tweet_lng - dX
lng_high = tweet_lng + dX
lat_low = tweet_lat - dY
lat_high = tweet_lat + dY
intersect = index.intersection(
(tweet_lng - dX, tweet_lat - dY, tweet_lng + dX, tweet_lat + dY),
objects=True)
return [n.object for n in intersect]
except KeyError:
return []
def getEntitiesNearPoint(index, slack, tweet_lat, tweet_lng):
try:
#note these are for rochester - go to http://www.csgnetwork.com/degreelenllavcalc.html to calculate for a different latitude
dX = slack * 0.000012297
dY = slack * 0.000009001
lng_low = tweet_lng - dX
lng_high = tweet_lng + dX
lat_low = tweet_lat - dY
lat_high = tweet_lat + dY
intersect = index.intersection((tweet_lng - dX, tweet_lat - dY, tweet_lng + dX, tweet_lat + dY), objects = True)
return [n.object for n in intersect]
except KeyError:
return []
