def fish_distance(point1,point2):
"""
Returns the shortest distance around land (see the Land model) between the two points. Returns the distance in miles and
the geos linestring that represents the path.
NOTE: I'm assuming that the native units of the points and line is meters. This is true for the MLPA project but may
not be true for other processes.
"""
# This is the straight line between the two points
line = geos.LineString(point1,point2)
# See if the straight line crosses land
if line_crosses_land(line):
# The straight line cut across land so we have to do it the hard way.
G = PickledGraph.objects.all()[0].graph
G = add_points_to_graph([point1,point2],G)
# G.add_nodes_from([point1,point2])
# G = add_ocean_edges_for_node(G,get_node_from_point(G,point1))
# G = add_ocean_edges_for_node(G,get_node_from_point(G,point2))
# Replace the straight line with the shortest path around land
line = geos.LineString( nx.dijkstra_path(G,get_node_from_point(G,point1),get_node_from_point(G,point2)) )
line.srid = settings.GEOMETRY_DB_SRID
# Figure out the distance of the line (straight or otherwise) in miles
distance = length_in_display_units(line)
return distance, line
def distance_row_list(from_pnt, to_list):
"""
NOTE: This method assumes that the projection units are meters.
"""
result = []
for point in to_list:
result.append(length_in_display_units(point.distance(from_pnt)))
return result
def distance_row_dict(from_dict, to_dict):
"""
from_dict will be a dict with a point as the key and a label as the value.
to_dict will be of the same format with multiple entries.
will return a dictionary with points as keys and a dictionary as values.
NOTE: This method assumes that the projection units are meters.
"""
from_pnt = from_dict.keys()[0]
for s_pnt in SpacingPoint.objects.all():
to_dict.update({s_pnt.geometry:s_pnt.name})
result = {}
for point, pnt_label in to_dict.iteritems():
result[point] = {
'label': pnt_label,
'distance': length_in_display_units(point.distance(from_pnt)),
'sort': point.y
}
return result
def distance_row_dict(from_dict, to_dict):
"""
from_dict will be a dict with a point as the key and a label as the value.
to_dict will be of the same format with multiple entries.
will return a dictionary with points as keys and a dictionary as values.
NOTE: This method assumes that the projection units are meters.
"""
from_pnt = from_dict.keys()[0]
for s_pnt in SpacingPoint.objects.all():
to_dict.update({s_pnt.geometry:s_pnt.name})
result = {}
for point, pnt_label in to_dict.iteritems():
result[point] = {
'label': pnt_label,
'distance': length_in_display_units(point.distance(from_pnt)),
'sort': point.y
}
return result
def add_ocean_edges_complete(graph, verbose=False):
if verbose:
cnt = 1
import time
t0 = time.time()
print "Starting at %s to add edges for %i nodes." % (time.asctime(time.localtime(t0)), graph.number_of_nodes() )
edge_possibilities = graph.number_of_nodes() * (graph.number_of_nodes() -1)
print "We'll have to look at somewhere around %i edge possibilities." % ( edge_possibilities )
print "Node: ",
for node in graph.nodes_iter():
if verbose:
print str(cnt) + ' ',
cnt += 1
for n in graph.nodes_iter():
if node <> n:
line = geos.LineString(node,n)
if not line_crosses_land(line):
graph.add_edge(node,n,{'weight': length_in_display_units(node.distance(n))})
if verbose:
print "It took %i minutes to load %i edges." % ((time.time() - t0)/60, graph.number_of_edges() )
return graph
def fish_distance_from_edges(geom1,geom2):
"""
"""
# Straight line between geoms
line = shortest_line(geom1,geom2)
# See if the line crosses land
if line_crosses_land(line):
# Get shortest centroid to centroid fish_distance line
c_distance, c_line = fish_distance(geom1.centroid,geom2.centroid)
# Replace the first point in the fish path with the point on geom1
# that lies closest to the second point on the path.
#print c_line[1]
c_line[0] = closest_point(geom1, geos.Point( c_line.coords[1], srid=geom1.srid ) ).coords
# Do the same for the last point in the path
c_line[c_line.num_points - 1] = closest_point(geom2, geos.Point( c_line.coords[c_line.num_points - 2], srid=geom2.srid ) ).coords
line = c_line
# Adjust the distance
distance = length_in_display_units(line)
return distance, line
def distance_row_list(from_pnt, to_list, straight_line=False, with_geom=False):
"""
NOTE: This method assumes that the projection units are meters. This should be changed. Check out
lingcod.unit_converter.models. It's pretty easy to introspect the geometry for the srid and figure
out the native units from that. I'd fix it but this is my last week here before taking a big trip
on a small boat.
"""
result = []
for point in to_list:
point_pair_dict = {}
if straight_line:
point_pair_dict.update( {'distance': length_in_display_units(point.distance(from_pnt)) } )
if with_geom:
line = geos.LineString(point,from_pnt)
else:
distance, line = fish_distance_from_edges(from_pnt,point)
point_pair_dict.update( {'distance': distance} )
if with_geom:
point_pair_dict.update( {'geometry': line} )
result.append(point_pair_dict)
return result
def add_ocean_edges_for_node(graph, node):
for n in graph:
line = geos.LineString(node,n)
if not line_crosses_land(line):
graph.add_edge(node,n,{'weight': length_in_display_units(node.distance(n))})
return graph