def filter_building(way, b, polygons):
near_way_points = nearest_points(polygons[b['id']], way)
b_near_way = near_way_points[0]
buff = b_near_way.buffer(b_near_way.distance(near_way_points[1]) + 0.00001,
mitre_limit=1.0)
buff = shapely.affinity.scale(buff, 1.0, 0.75)
n_intersect = buff.boundary.intersection(way)
n_intersect = [n_intersect] if isinstance(n_intersect,
Point) else list(n_intersect)
n_intersect = MultiPoint(n_intersect)
filtered = True
if n_intersect:
nearest = nearest_points(polygons[b['id']], n_intersect)
mp = MultiPoint(list(n_intersect) + [nearest[0]])
try:
mp = Polygon(mp)
filtered = any(
filter(
lambda (bid, p): bid != b['id'] and p.boundary.intersects(
mp.boundary), polygons.items()))
except ValueError:
pass
return filtered
def line_slice(startPt, stopPt, line):
coords = list(line.coords)
multi_line = MultiPoint(line.coords)
if line.type == 'LineString':
startVertex = nearest_points(multi_line,
startPt['geometry'])[0].coords[0]
stopVertex = nearest_points(multi_line,
stopPt['geometry'])[0].coords[0]
start_indx = coords.index(startVertex)
stop_indx = coords.index(stopVertex)
ends = []
if start_indx <= stop_indx:
ends = [start_indx, stop_indx]
else:
ends = [stop_indx, start_indx]
clipCoords = coords[ends[0]:ends[1] + 1]
if (len(clipCoords) == 1):
clipCoords.append(clipCoords[0])
return LineString(clipCoords)
def find_cut_distance(current_string: LineString, pair, frequency: int,
offset_distance: float, side: str):
neighbor_string = LineString(
[[pair.node_from.utm.north, pair.node_from.utm.east],
[pair.node_to.utm.north, pair.node_to.utm.east]])
offsetted_neighbor_string = neighbor_string.parallel_offset(
frequency * offset_distance, side='left', resolution=10)
# Project neighbor string onto current string.
if side == 'back':
projected_point = Point(
nearest_points(
current_string,
Point(list(offsetted_neighbor_string.coords)[-1]))[0])
else:
projected_point = Point(
nearest_points(
current_string,
Point(list(offsetted_neighbor_string.coords)[0]))[0])
# Create a line from the side of the current string to the projected points.
if side == 'back':
line = LineString(
[Point(list(current_string.coords)[0]), projected_point])
else:
line = LineString(
[Point(list(current_string.coords)[-1]), projected_point])
return line.length
def get_h_line(
pts0: MultiPoint,
pts1: MultiPoint,
) -> LineString:
if isinstance(pts1, Point):
pts1 = MultiPoint([pts1])
if isinstance(pts0, Point):
pts0 = MultiPoint([pts0])
eps = 1e-6
hdist = pts0.hausdorff_distance(pts1)
ls = []
for p0 in pts0:
d = p0.distance(pts1)
if abs(d - hdist) < eps:
#return LineString(nearest_points(p0,pts1))
ls.append(LineString(nearest_points(p0, pts1)))
for p1 in pts1:
d = p1.distance(pts0)
if abs(d - hdist) < eps:
#return LineString(nearest_points(p0,pts1))
ls.append(LineString(nearest_points(p1, pts0)))
return MultiLineString(ls)
def step(s, action):
"""Apply action, return new state, reward, done, empty info dict"""
throttle, turn = action
prev_dist = s.distance_to_centerline()
s.move_car(throttle, turn)
is_display_alive = s.draw()
s.camera_view = s.display.read_screen()
# increment measurements
s.time += 1
s.driving_dist += np.sqrt(s.car.dx**2+s.car.dy**2)
cur_point = Point(s.car.x, s.car.y)
if not hasattr(s, 'prev_track_point'):
s.prev_track_point = nearest_points(s.track_center, Point(s.car.x, s.car.y))[0]
cur_track_point = nearest_points(s.track_center, cur_point)[0]
delta_track_dist = 0.0
if s.is_on_track():
angle = s.get_angle(cur_track_point.x, cur_track_point.y,
s.prev_track_point.x, s.prev_track_point.y)
if (True):#angle > 0):
delta_track_dist = cur_track_point.distance(s.prev_track_point)
s.track_dist += delta_track_dist
s.prev_track_point = cur_track_point
# finalize other values
#reward = delta_track_dist/s.car.max_v
#reward = (abs(prev_dist) - abs(s.distance_to_centerline()))/70
reward = 1.0 if s.is_on_track() else 0.0
state = [s.camera_view.astype(np.float32)/255, np.array([s.time/100])] # true system includes camera, gyroscope,and accelerometer
done = ((not is_display_alive) or (abs(s.distance_to_centerline()) > 80)) #implement your own logic on when to be done
return state, reward, done, {}
def step(s, action):
"""Apply action, return new state, reward, done, empty info dict"""
s.throttle, s.steering_angle = action
prev_dist = s.get_distance_to_center()
s.move_car(s.throttle, s.steering_angle)
is_display_alive = s.draw()
s.camera_view = s.display.read_screen()
# increment measurements
s.time += 1
s.driving_dist += np.sqrt(s.car.dx**2+s.car.dy**2)
cur_point = Point(s.car.x, s.car.y)
if not hasattr(s, 'prev_track_point'):
s.prev_track_point = nearest_points(s.track_center, Point(s.car.x, s.car.y))[0]
cur_track_point = nearest_points(s.track_center, cur_point)[0]
delta_track_dist = 0.0
if s.is_on_track():
delta_track_dist = cur_track_point.distance(s.prev_track_point)
s.track_dist += delta_track_dist
s.prev_track_point = cur_track_point
params = s.get_params()
reward = s._reward_func(params)
state = s.get_state()
done = ((not is_display_alive) or (abs(s.get_distance_to_center()) > 80))
return state, reward, done, {}
def moveBoundaryPoints(self):
""" move boundary weights/points on the geometry boundary """
self.timer.startTimer("moveBoundaryPoints")
inner = Polygon(self.geometry[1])
outer = Polygon(self.geometry[0])
movement = np.zeros((np.shape(self.boundaryIdx)[0], 2))
weightsBoundary = tf.Variable(tf.gather(self.weights,
self.boundaryIdx),
dtype=self.dataType).numpy()
for idx in range(0, np.shape(self.boundaryIdx)[0]):
point = Point(weightsBoundary[idx, 0], weightsBoundary[idx, 1])
pOuter, p = nearest_points(outer.boundary, point)
pInner, p = nearest_points(inner.boundary, point)
if (point.distance(pInner) > point.distance(pOuter)):
movement[idx, 0] = pOuter.x
movement[idx, 1] = pOuter.y
else:
movement[idx, 0] = pInner.x
movement[idx, 1] = pInner.y
print(np.shape(self.boundaryIdx))
print(np.shape(self.boundaryIdx))
tf.compat.v1.scatter_update(self.weights, self.boundaryIdx, movement)
self.timer.stopTimer("moveBoundaryPoints")
def _extend_boundaries(baselines, bin_bl_map):
# find baseline blob boundaries
labelled = label(bin_bl_map)
boundaries = []
for x in regionprops(labelled):
try:
b = boundary_tracing(x)
if len(b) > 3:
boundaries.append(geom.Polygon(b).simplify(0.01).buffer(0))
except Exception as e:
logger.warning(f'Boundary tracing failed in baseline elongation: {e}')
continue
# extend lines to polygon boundary
for bl in baselines:
ls = geom.LineString(bl)
try:
boundary_pol = next(filter(lambda x: x.contains(ls), boundaries))
except Exception:
continue
# 'left' side
if boundary_pol.contains(geom.Point(bl[0])):
l_point = boundary_pol.boundary.intersection(geom.LineString([(bl[0][0]-10*(bl[1][0]-bl[0][0]), bl[0][1]-10*(bl[1][1]-bl[0][1])), bl[0]]))
if l_point.type != 'Point':
bl[0] = np.array(nearest_points(geom.Point(bl[0]), boundary_pol)[1], 'int').tolist()
else:
bl[0] = np.array(l_point, 'int').tolist()
# 'right' side
if boundary_pol.contains(geom.Point(bl[-1])):
r_point = boundary_pol.boundary.intersection(geom.LineString([(bl[-1][0]-10*(bl[-2][0]-bl[-1][0]), bl[-1][1]-10*(bl[-2][1]-bl[-1][1])), bl[-1]]))
if r_point.type != 'Point':
bl[-1] = np.array(nearest_points(geom.Point(bl[-1]), boundary_pol)[1], 'int').tolist()
else:
bl[-1] = np.array(r_point, 'int').tolist()
return baselines
def is_tangential(l1, l2, acceptance_rate=0.3, acc_radius=-1.0, angle=0.95):
step_size = 0.05
tangential_score = 0
nb_probes = 0
# go through l1 and compare tangents with nearest point on l2
t = 0.0
while t < 1.0:
p = l1.interpolate(t, normalized=True)
p_prim = l1.interpolate(t+step_size, normalized=True)
p_coords = np.array(p)
p_prim_coords = np.array(p_prim)
t += step_size
p, q = nearest_points(p, l2)
p_prim, q_prim = nearest_points(p_prim, l2)
q_coords = np.array(q)
q_prim_coords = np.array(q_prim)
if np.all(np.isclose(p,p_prim)) or np.all(np.isclose(q,q_prim)):
continue
t1 = p_prim_coords - p_coords
t2 = q_prim_coords - q_coords
cos_alpha = np.dot(t1, t2)/(np.linalg.norm(t1)*np.linalg.norm(t2))
if cos_alpha > angle:
tangential_score += 1
nb_probes += 1
if nb_probes > 1:
return float(tangential_score)/float(nb_probes) >= acceptance_rate
else:
return False
def metro_dist(from_pt, to_pts, **kwargs):
"""
Finds distance from from_pt to nearest metro station or to_pts.
from_pt : row of e.g. municipality with PROJECTED .geometry field
to_pts (eg metro) : data frame of target points with PROJECTED .geometry field
if additional key COMUNAS is included, distance will be taken from the centroid
of comunas matching the correct municipality index.
returns: distance (METERS, if both dataframes projected projected)
and INDEX (second) of to_pt
"""
# throw error if incorrect type, for whatever reason
assert (type(from_pt.geometry)
in [Point,
Polygon]), "from_pt must contain point or polygon geometry"
to_pts_geom = to_pts.geometry.tolist()
# find nearest point to a given point
if "comunas" in kwargs.keys(): # weighted center of comunas
center = muni_center(from_pt["sort_ind"] + 1, kwargs["comunas"])
r = nearest_points(center, MultiPoint(to_pts_geom))
elif type(from_pt.geometry) == Polygon:
r = nearest_points(from_pt.geometry.centroid, MultiPoint(to_pts_geom))
elif type(from_pt.geometry) == Point:
r = nearest_points(from_pt.geometry, MultiPoint(to_pts_geom))
return r[0].distance(r[1]), to_pts_geom.index(r[1])
def extend_FaultLines(GRDECL_Data,
line,
OldFaults,
startfrom='StartPoint or EndPoint'):
#extend a line from its start point or end point
#the end point must on the boundary
debug = 0
if (startfrom == 'StartPoint'):
p1, p2 = line[0], line[1]
if (startfrom == 'EndPoint'):
p1, p2 = line[-1], line[-2]
if (abs(p1[0] - p2[0]) < 1e-10):
if (debug): print("Line along Y direction")
if (p1[1] - p2[1] < 0): #Y- direction
NewEndPoint = (p1[0], 0)
NextPoint = (p1[0], p1[1] - 0.00001)
if (p1[1] - p2[1] > 0): #Y- direction
NewEndPoint = (p1[0], GRDECL_Data.NY)
NextPoint = (p1[0], p1[1] + 0.00001)
if (abs(p1[1] - p2[1]) < 1e-10):
if (debug): print("Line along X direction")
if (p1[0] - p2[0] < 0): #X- direction
NewEndPoint = (0, p1[1])
NextPoint = (p1[0] - 0.00001, p1[1])
if (p1[0] - p2[0] > 0): #X+ direction
NewEndPoint = (GRDECL_Data.NX, p1[1])
NextPoint = (p1[0] + 0.00001, p1[1])
#Check is hit old fault line and upadte the new endpoint
if (debug): print('P2P1', (p2, p1), 'ExtendSeg', NextPoint, NewEndPoint)
ExtendedSegment = LineString([NextPoint, NewEndPoint])
OldFaults = MultiLineString(OldFaults)
objects = ExtendedSegment.intersection(OldFaults)
if (objects.is_empty == False): #We have hit point
#print('HitGeometry',objects,objects.geom_type)
if (objects.geom_type in ['LineString', 'Point']):
pts = nearest_points(Point(p1), objects)
pts = Shapely2List_MultiPoint(pts)[1]
#print('NearestPoint',pts)
#pts=sorted(list(objects.coords))[0]
elif (objects.geom_type
in ['MultiLineString', 'MultiPoint', 'GeometryCollection']):
pts = nearest_points(Point(p1), objects)
pts = Shapely2List_MultiPoint(pts)[1]
#print('NearestPts',pts)
#pts=Shapely2List_MultiLineString(objects)
#pts=sorted([j for i in pts for j in i])[0]
else:
print('Unkonwn shapely type', objects.geom_type, objects)
pts = (int(pts[0]), int(pts[1]))
if (debug): print('HitPoints', pts)
NewEndPoint = pts
#NewLine=sorted(line+[NewEndPoint])
return [NewEndPoint]
def directionality_error(nodes, polygon):
angles = []
for i in range(len(nodes)):
#edge as vector
v1 = nodes[i - 1] - nodes[i]
#get vector of two closest boundary points to each node
v2 = nearest_points(polygon,Point(nodes[i][0], nodes[i][1]))[0] -\
nearest_points(polygon,Point(nodes[i-1][0], nodes[i-1][1]))[0]
angles.append((angle(v1, v2) % (0.5 * np.pi)))
return 1 - (np.mean(angles) / (0.5 * np.pi))
def passLength(candi,data_probes,data_links):
candi2 = [candi[0]]
for i in range(len(candi)-1):
if len(candi2[i][1]) == 0:
candi2.append(candi[i+1]) # skip the samples that already has no candidate.
continue
probe1 = data_probes.loc[candi2[i][0]]
probe2 = data_probes.loc[candi[i+1][0]]
if (probe1['sampleID'] != probe2['sampleID']):
candi2.append(candi[i+1])
continue
p1_speed = probe1['speed']
p2_speed = probe2['speed']
avg_speed = (p1_speed+p2_speed)/2
time = 5 * (candi[i][0]-candi[i+1][0])
length = avg_speed * time
p1_point = Point(probe1['easting'],probe1['northing'])
p2_point = Point(probe2['easting'],probe2['northing'])
candi1 = []
for j in range (len(candi[i+1][1])):
p1_link = data_links.loc[candi[i][1][0]]
p1_utms = p1_link["utms"][2:-2].replace("), (", "|").replace(", ", " ").replace("|", ", ")
p1_line = wkt.loads("LINESTRING (" + p1_utms + ")")
p1_nearest = nearest_points(p1_line, p1_point)[0]
p2_link = data_links.loc[candi[i+1][1][j]]
p2_utms = p2_link["utms"][2:-2].replace("), (", "|").replace(", ", " ").replace("|", ", ")
p2_line = wkt.loads("LINESTRING (" + p2_utms + ")")
p2_nearest = nearest_points(p2_line, p2_point)[0]
distance = p1_nearest.distance(p2_nearest)
p1_lim = max(p1_link['toRefSpeedLimit'],p1_link['fromRefSpeedLimit'])
p2_lim = max(p2_link['toRefSpeedLimit'],p2_link['fromRefSpeedLimit'])
thrsh = (max(p1_lim,p2_lim)/3.6*time)*1.1
if length <= (thrsh+distance):
candi1.append(candi[i+1][1][j])
if len(candi1) > 0:
each = []
each.append(candi[i+1][0])
each.append(candi1)
candi2.append(each)
else:
each = []
each.append(candi[i+1][0])
each.append([])
candi2.append(each)
return candi2
def latlon_to_reach(lat: float, lon: float) -> tuple:
# determine the region that the point is in
region = latlon_to_region(lat, lon)
# switch the point because the csv's are lat/lon, backwards from what shapely expects (lon then lat)
point = Point(float(lat), float(lon))
# open the region csv
df = pd.read_pickle(
f'/app/GSP_API/geometry/{region}-comid_lat_lon_z.pickle')
points_df = df.loc[:, "Lat":"Lon"].apply(Point, axis=1)
# determine which point is closest
multi_pt = MultiPoint(points_df.tolist())
nearest_pt = nearest_points(point, multi_pt)
reach_id = int(points_df[points_df == nearest_pt[1]].index[0])
distance = nearest_pt[0].distance(nearest_pt[1])
# if the nearest stream if more than .1 degrees away, you probably didn't find the right stream
if distance > 0.11:
raise ValueError(
'This lat/lon pair is too far from a delineated stream. Try again or use the web interface.'
)
else:
return reach_id, region, distance
def simple_line_cut(linestring, point1, dist_thres, tol, trace=True):
from shapely.ops import nearest_points
from shapely.ops import split
from shapely.geometry import MultiPoint, LineString
#Find nearest points on line:
np1 = nearest_points(linestring, point1)
# Return error message if points are too far away from line:
dist1 = np1[0].distance(np1[1])
if dist1 > dist_thres:
if trace:
print(
'Point1 is', dist1,
'units apart from line which is more than the specified distance threshold of',
dist_thres)
return None
#Do split:
split_1 = split(linestring, np1[0].buffer(tol))
if len(list(split_1)) == 1:
if trace:
print('Splitting at mapped point 1 did not work')
return None
return list(split_1)
def nearest_land_pos(self, point: Point, extend_dist: int = 50) -> Point:
"""Returns the nearest point inside a land exclusion zone from point
`extend_dist` determines how far inside the zone the point should be placed"""
if self.is_on_land(point):
return point
point = geometry.Point(point.x, point.y)
nearest_points = []
if not self.landmap:
raise RuntimeError("Landmap not initialized")
for inclusion_zone in self.landmap[0]:
nearest_pair = ops.nearest_points(point, inclusion_zone)
nearest_points.append(nearest_pair[1])
min_distance = point.distance(
nearest_points[0]) # type: geometry.Point
nearest_point = nearest_points[0] # type: geometry.Point
for pt in nearest_points[1:]:
distance = point.distance(pt)
if distance < min_distance:
min_distance = distance
nearest_point = pt
assert isinstance(nearest_point, geometry.Point)
point = Point(point.x, point.y)
nearest_point = Point(nearest_point.x, nearest_point.y)
new_point = point.point_from_heading(
point.heading_between_point(nearest_point),
point.distance_to_point(nearest_point) + extend_dist)
return new_point
def find_nearest_point_on_line(line_pts, pts):
if type(line_pts) is sg.LineString:
sg_line = line_pts
else:
sg_line = sg.asLineString(line_pts)
if pts.ndim == 1:
num_pts = 1
else:
num_pts = np.shape(pts)[0]
if num_pts > 1:
min_dist = 1000
for i_pt in range(num_pts):
test_pt = sg.asPoint(pts[i_pt, :])
test_dist = test_pt.distance(sg_line)
if test_dist < min_dist:
min_dist = test_dist
closest_pt = test_pt
sg_point = closest_pt
else:
sg_point = sg.asPoint(pts)
try:
near_pts = so.nearest_points(sg_line, sg_point)
except:
pass
nndist = near_pts[0].distance(near_pts[1])
nn_pt = near_pts[0]
return nndist, nn_pt
def get_nearest_date(x, selCables):
selCables = selCables.loc[selCables['RFS'] <= x['d2_l1_year_perf_indicators']]
if selCables.shape[0] > 0:
xx = selCables.loc[selCables.geometry == nearest_points(x['geometry'], selCables.unary_union)[1]]
return(xx.sort_values(['RFS'])['RFS'].iloc[0])
else:
return(-1)
def getdestloc_simple(p, w_gdf, u, distvar): #for students, work -> eductation
nearestpoint = nearest_points(p, u)[1] #get nearest point
mindist = p.distance(nearestpoint) #find the distance to the nearest point
#print(mindist) degree
sim_dist = gen_lnorm(distvar, "")
sim_distdeg = sim_dist / 110.8
if sim_distdeg < mindist: # if the distance is shorter than the distance to the nearest points. take the nearest point
sim_distdeg = mindist
des_p = nearestpoint
num_points = 0
print(f'use nearest point as simulated distance is too short.')
# else calculate a buffer and select points.
else:
pbuf = p.buffer(
sim_distdeg) # distance to degree`maybe better to project.
pbuf.crs = {'init': 'epsg:4326', 'no_defs': True}
worklocset = w_gdf[w_gdf.within(pbuf)]
num_points = len(worklocset)
print(f'sample from {num_points} points')
workloc = worklocset.sample(n=1, replace=True, random_state=1)
des_p = workloc.iloc[0][
"geometry"] #get point out of the geopandas dataframe
return (p, des_p, num_points)
def GetCountyByCoord(lat, lon):
point = Point(lon, lat)
for town in towns_polygons:
for town_polygon in towns_polygons[town]:
if len(town_polygon) == 1:
polygon = Polygon(town_polygon[0])
else:
polygon = Polygon(town_polygon)
if polygon.contains(point):
return town
logger.warning(
f'Vague point detected! Start further algorithm to check its town ')
for town in towns_polygons:
for town_polygon in towns_polygons[town]:
if len(town_polygon) == 1:
polygon = Polygon(town_polygon[0])
else:
polygon = Polygon(town_polygon)
p1, _ = nearest_points(polygon, point)
if great_circle_distance_on_earth(
point, p1) < 5: # distance to shore 5km?
logger.info(f'Success detect its town as {town}!')
return town
logger.warning(f'Failed to detect town!')
return '不在台灣啦!'
def find_separation_point(lines):
points = []
threshold = 0.2
for i in range(len(lines) - 1):
ref_line = LineString(lines[i])
line = lines[i + 1]
closest_points = sorted(
[(distance(p1, (p2.x, p2.y)), p1, (p2.x, p2.y))
for p1, p2 in [(p, nearest_points(ref_line, Point(p))[0])
for p in line]],
key=lambda x: x[0])
for j, (dist, p1, p2) in enumerate(closest_points):
if dist > threshold or j == len(closest_points) - 1:
points.append(((p1[0] + p2[0]) / 2, (p1[1] + p2[1]) / 2))
break
return (np.average([p[0]
for p in points]), np.average([p[1] for p in points]))
def trvdir(p,candi,probe_point,data_links,data_probes):
probe = data_probes.loc[p]
candi2 = []
for i in range(len(candi)):
link = data_links.loc[candi[i]]
utms = link["utms"][2:-2].replace("), (", "|").replace(", ", " ").replace("|", ", ")
line = wkt.loads("LINESTRING (" + utms + ")")
nearest = nearest_points(line, probe_point)[0]
#link_distance = nearest.distance(probe_point)
ref_node = Point(line.coords[0][0], line.coords[0][1])
# ref_distance = ref_node.distance(probe_point)
pro_link_dis = nearest.distance(ref_node)
# Update dataframe attributes
data_probes.loc[p, "linkPVID"] = link["linkPVID"]
#data_probes.loc[p, "distFromLink"] = link_distance
#data_probes.loc[p, "distFromRef"] = ref_distance
data_probes.loc[p,"distBetRP"] = pro_link_dis
# Calculate the orientation relative to the ref node
radians = math.atan2(ref_node.y - probe_point.y, ref_node.x - probe_point.x)
ref_node_direction = int(math.degrees(radians))
if ref_node_direction <= 0:
ref_node_direction = ref_node_direction + 360
if abs(ref_node_direction - probe["heading"]) <= 90:
data_probes.loc[p, "direction"] = 'T'
else:
data_probes.loc[p, "direction"] = 'F'
s_direc = data_probes.loc[p, "direction"]
l_direc = link["directionOfTravel"]
if ~((l_direc != 'B') & (s_direc != l_direc)): # only keep the segment candidates whose directionOfTravel is conformed with the sample's current travel direction.
candi2.append(candi[i])
return candi2
def chunk_worker(left_spec, right_spec, pairs_slice):
# recover the SharedMemory bloc
left_shm = SharedMemory(left_spec['name'])
right_shm = SharedMemory(right_spec['name'])
# Create the np.recarray from the buffer of the shared memory
left_arr = np.recarray(shape=left_spec['shape'],
dtype=left_spec['dtype'],
buf=left_shm.buf)
right_arr = np.recarray(shape=right_spec['shape'],
dtype=right_spec['dtype'],
buf=right_shm.buf)
#print (left_arr[0][2], right_arr[0][2])
results = []
for idx_pair in pairs_slice:
geom1 = wkt.loads(left_arr[idx_pair[0]][2])
geom2 = wkt.loads(right_arr[idx_pair[1]][2])
pt1, pt2 = ops.nearest_points(geom1, geom3)
dist, a1, a2 = V_inv((pt1.y, pt1.x), (pt2.y, pt2.x))
dist = dist * 1000 #m
results.append((idx_pair[0], idx_pair[1], dist))
return results
def getNearestWay(query, project, inputCoord):
"""
@param query:
@param project:
@param inputCoord:
@return:
"""
lineStringDict = {}
for i in query.features:
# Transform from WGS 1984 to the projection, this avoids getting degrees in results
shape = transform(project, LineString(i["geometry"]["coordinates"]))
# Calculate nearest point between the input coordinate and the way geom
nearestPt = nearest_points(inputCoord, shape)[1]
# Get the distance to the nearest point from the input coordinate
distToPt = nearestPt.distance(inputCoord)
# Build dict with results, OSM IDs are the top level keys
lineStringDict[i["id"]] = {"name": i["properties"]["name"], "shape": shape, "nearestPt": nearestPt,
"distToPt": distToPt}
lowestVal = ["", 99999]
# Get nearest way name by looping over IDs in dict
for i in lineStringDict.keys():
if lineStringDict[i]["distToPt"] < lowestVal[1]:
lowestVal[0] = lineStringDict[i]["name"]
lowestVal[1] = lineStringDict[i]["distToPt"]
return lowestVal
def check_double_points(gdf, radius=0.001, id_column='id'):
"""
:param gdf:
:param radius:
:param id_column:
:return:
"""
l_ids = []
count = 0
for r, c in gdf.iterrows():
point = c['geometry']
gdf_other = gdf.drop([r])
other_points = cascaded_union(gdf_other['geometry'])
# x1 = nearest_points(point, other_points)[0]
x2 = nearest_points(point, other_points)[1]
if point.distance(x2) <= radius:
l_ids.append(c[id_column])
print('Node ', c[id_column], ' has a near neighbour!', 'Distance ',
point.distance(x2))
count += 1
print('')
print('Number of duplicated points: ', count)
return l_ids
def point_at_intersect(gdf_cross, gdf_line):
gdf_cross = check_gdf(gdf_cross)
gdf_line = check_gdf(gdf_line)
df_points = pd.DataFrame([],
columns=[gdf_line.index],
index=gdf_cross.index,
dtype='object')
for line, linerow in gdf_line.iterrows():
geolist = []
for index, row in gdf_cross.iterrows():
interpoint = row.geometry.intersection(linerow.geometry)
if interpoint.geom_type == 'Point':
geolist.append(interpoint)
else:
nearest_geoms = nearest_points(Point(row['midX'], row['midY']),
interpoint)
geolist.append(nearest_geoms[1])
# df_points[line]=gpd.GeoSeries([row[1].geometry.intersection(linerow.geometry) for row in gdf_cross.iterrows()])
workpath = os.getenv(
'surfdrive'
) + "\\Documents\\DATA\\Netherlands\\main channel\\main_channelbedlevel\\bed_level_data\\"
gpd.GeoDataFrame(gdf_cross.rkm, geometry=geolist).to_file(
os.path.join(workpath, 'worktmp', 'RL_' + str(line) + '.shp'))
df_points[line] = gpd.GeoSeries(geolist, index=gdf_cross.index)
return df_points
def query_k_nearest_road_segments(edge_idx, point, k):
"""
query k-nearest road segments of a given point
:param edge_idx: the road segments r-tree index
:param point: the given point
:param k: the number of segments needed to query
:return: k candidates as a pandas DataFrame
"""
candidates = pd.DataFrame(columns=('distance', 'from', 'to', 'proj_point',
'road'))
hits = edge_idx.nearest((point.x, point.y, point.x, point.y),
k,
objects=True)
for item in hits:
results = nearest_points(point, item.object['geometry'])
d = point.distance(results[1])
s = pd.Series({
'distance': d,
'from': item.object['from'],
'to': item.object['to'],
'proj_point': results[1],
'road': item.object
})
candidates = candidates.append(s, ignore_index=True)
# candidates['observation prob'] = candidates.apply(lambda row: normal_distribution())
candidates.sort_values(by='distance', axis=0, inplace=True)
return candidates
def get_distance_water(gdf, gdf_water):
"""
Function to calculate distance to nearest water body
Input:
gdf: Geodataframe (Ideally a grid network of the city)
gdf_water : Water mask geodataframe
Returns:
Geodataframe with column 'dist_to_water' added to it
"""
print("Populating distance to water!!")
gdf_land = gdf.copy()
gdf_water.to_crs(epsg=out_proj, inplace=True)
gdf_land.to_crs(epsg=out_proj, inplace=True)
water_dist = []
dest_water = MultiPoint([i.centroid for i in gdf_water.geometry])
for i in gdf_land.index:
if i % 1000 == 0:
print("{0} of {1} rows processed" .format(i, len(gdf_land)))
temp_cent = gdf_land.geometry[i].centroid
nearest_geoms = nearest_points(temp_cent, dest_water)
water_dist.append(nearest_geoms[0].distance(nearest_geoms[1]))
gdf['dist_to_water'] = water_dist
return gdf
def get_nearest_row_id(id_done, pt_ref, df_route):
if (id_done < 0):
id_done = 0
id_until = int(round(id_done + (len(df_route) * 0.1)))
if id_until < 1:
id_until = 1
id_until = len(df_route) if id_until > len(df_route) else id_until
df_route_slice = df_route[id_done:id_until]
'''
print('id_done', id_done)
print (pt_ref)
display(df_route.head())
display (df_route_slice.head())
'''
'''
print('pts3')
print (pts3)
print ("pt_ref")
print (pt_ref)
'''
pts3 = df_route_slice.geometry.unary_union
nearest = df_route_slice.geometry == nearest_points(pt_ref, pts3)[1]
list_ids = df_route_slice[nearest].id.values
list_ids.sort()
for i in list_ids:
if i >= id_done:
return i
return -1
def test_nearest(self):
first, second = nearest_points(
Point(0, 0).buffer(1.0), Point(3, 0).buffer(1.0))
self.assertAlmostEqual(first.x, 1.0, 7)
self.assertAlmostEqual(second.x, 2.0, 7)
self.assertAlmostEqual(first.y, 0.0, 7)
self.assertAlmostEqual(second.y, 0.0, 7)
def get_nearest_station(poi=Point(restaurants_points[0])):
stations_mp = MultiPoint(stations_points)
testpoint = Point(nearest_points(poi, stations_mp)[0])
soi = restaurants.Station[(restaurants.Lat == testpoint.y)
& (restaurants.Long == testpoint.x)]
soi = list(soi)[0]
return (soi)
