def select_geoms_by_geometry(geoms, geometry):
"""Build a STRtree from geoms and returns intersection with geometry
:param geoms: list of geometries you want to search from
:param geometry: intersection geometry
:return: list of geoms intersecting with geometry
"""
if shapely is None:
raise_import_exception('shapely')
if type(geometry) in (bytes, str):
if isinstance(geometry, bytes):
geometry = geometry.decode('utf-8')
# assume wkt, try to load
geometry = loads(geometry)
tree = STRtree(geoms)
# STRtree checks intersection based on bbox of the geometry only:
# https://github.com/Toblerity/Shapely/issues/558
intersected_geoms = tree.query(geometry)
# reverse loop because we pop elements based on index
for i, intersected_geom in zip(reversed(range(len(intersected_geoms))),
reversed(intersected_geoms)):
if not intersected_geom.intersects(geometry):
intersected_geoms.pop(i)
return intersected_geoms
def tree_query(tree, queried): # tree: point, queried: polygon
case_list = []
centroid_tree = STRtree(tree.geometry.representative_point())
for index, row in queried.iterrows():
if centroid_tree.query(Polygon(row['geometry'])) == []:
case_list.append([])
else:
point_input = []
point = centroid_tree.query(Polygon(row['geometry']))
for i in range(len(point)):
if Polygon(row['geometry']).intersects(point[i]) == True:
point_input.append(point[i])
case_list.append(point_input)
tree['X'] = tree['geometry'].representative_point().x
tree['Y'] = tree['geometry'].representative_point().y
queried['Case'] = None # Initialized the attribute column
for index, points in enumerate(case_list):
tempID = []
for point in points:
tempRow = tree.query('X == @point.x and Y == @point.y')
tempID.append(tempRow['PID'].values)
if tempID:
queried.loc[index, 'Case'] = tempID
return queried
def test_query(self):
points = [Point(i, i) for i in range(10)]
tree = STRtree(points)
results = tree.query(Point(2,2).buffer(0.99))
self.assertEqual(len(results), 1)
results = tree.query(Point(2,2).buffer(1.0))
self.assertEqual(len(results), 3)
def find_sample_points_with_data(case_dir, sample_name, spacing, wind_directions, field='U', write=False):
base_points = np.loadtxt(case_dir / "sample_points.txt")
base_points = base_points[:, :2]
buffered_base_points = [(p, Point(p).buffer(spacing * 0.45)) for p in base_points]
filename = f"{sample_name}_{field}.xyz"
kept_sample_points = []
for w in wind_directions:
# case_data_points = np.loadtxt(case_dir/str(w)/filename)
case_data_points = orient_sample_date(case_dir / str(w), sample_name, field)
# case_data_points_2D = [Point(p) for p in case_data_points[:,:2]]
case_data_points_2D = MultiPoint(case_data_points[:, :2])
rtree = STRtree(case_data_points_2D)
kept_sample_index = []
for idx, (p, pt) in enumerate(buffered_base_points):
res = rtree.query(pt)
if len(res) == 0:
continue
else:
kept_sample_points.append(p)
kept_sample_index.append(idx)
for i in kept_sample_index[::-1]:
buffered_base_points.pop(i)
kept_sample_points = np.array(kept_sample_points)
if write:
np.savetxt(case_dir / f"{sample_name}_sample_points.txt", kept_sample_points)
return kept_sample_points
def test_query_items(geoms, items, query_geom, expected):
"""Store enumeration idx"""
tree = STRtree(geoms, items)
results = tree.query_items(query_geom)
expected = [items[idx]
for idx in expected] if items is not None else expected
assert sorted(results) == sorted(expected)
def test_query():
points = [Point(i, i) for i in range(10)]
tree = STRtree(points)
results = tree.query(Point(2, 2).buffer(0.99))
assert len(results) == 1
results = tree.query(Point(2, 2).buffer(1.0))
assert len(results) == 3
def test_query(self):
points = [Point(i, i) for i in range(10)]
tree = STRtree(points)
results = tree.query(Point(2, 2).buffer(0.99))
self.assertEqual(len(results), 1)
results = tree.query(Point(2, 2).buffer(1.0))
self.assertEqual(len(results), 3)
def test_query():
points = [Point(i, i) for i in range(10)]
with pytest.warns(ShapelyDeprecationWarning):
tree = STRtree(points)
results = tree.query(Point(2, 2).buffer(0.99))
assert len(results) == 1
results = tree.query(Point(2, 2).buffer(1.0))
assert len(results) == 3
def _occult_layer(
layers: Dict[int, LineCollection],
tolerance: float,
keep_occulted: bool = False
) -> Tuple[Dict[int, LineCollection], LineCollection]:
"""
Perform occlusion on all provided layers. Optionally returns occulted lines
in a separate LineCollection.
Args:
layers: dictionary of LineCollections to perform occlusion on, keyed by layer ID
tolerance: Max distance between start and end point to consider a path closed
keep_occulted: if True, save removed lines in removed_lines LineCollection.
Otherwise, removed_lines is an empty LineCollection.
Returns:
a tuple with two items:
- new_lines, a dictionary of LineCollections for each layer ID received
- removed_lines, a LineCollection of removed lines
"""
removed_lines = LineCollection()
new_lines = {l_id: LineCollection() for l_id in layers}
line_arr = []
line_arr_lines = []
for l_id, lines in layers.items():
line_arr.extend([[l_id, line] for line in lines.as_mls().geoms])
line_arr_lines.extend([line for line in lines.as_mls().geoms])
# Build R-tree from previous geometries
tree = STRtree(line_arr_lines)
index_by_id = dict((id(pt), i) for i, pt in enumerate(line_arr_lines))
for i, (l_id, line) in enumerate(line_arr):
coords = np.array(line.coords)
if not (len(coords) > 3
and math.hypot(coords[-1, 0] - coords[0, 0],
coords[-1, 1] - coords[0, 1]) < tolerance):
continue
p = Polygon(coords)
geom_idx = [index_by_id[id(g)] for g in tree.query(p)]
geom_idx = [idx for idx in geom_idx if idx < i]
for gi in geom_idx:
# Aggregate removed lines
if keep_occulted:
rl = p.intersection(line_arr_lines[gi])
add_to_linecollection(removed_lines, rl)
# Update previous geometries
line_arr[gi][1] = line_arr[gi][1].difference(p)
for (l_id, line) in line_arr:
add_to_linecollection(new_lines[l_id], line)
return new_lines, removed_lines
def __init__(self, polygons, stoppers):
self._polygons = polygons
self._stop = STRtree(stoppers)
# we sometimes touch text regions which prevent merging, that's
# why we erode a bit here to allow merging to allow touching
# text regions.
# -20 derived from tests on SNP2436020X-19100601-0-0-0-0.09.
self._erosion = 20
def split_points_into_chunks(self, MP, boxes):
tree = STRtree(MP)
pt_chunks = []
for b in boxes:
query_geom = b
pts = [o.wkt for o in tree.query(query_geom)]
pt_chunks.append(pts)
return pt_chunks
def table_polygons(self, kernel=(6, 5)):
# kernel = (6, 5) fixes missed left bottom " in SNP2436020X-19100601-0-0-0-0.13
# and fixes non-contiguous table regions in SNP2436020X-19100601-1-0-0-0.03
# and avoids spilling over v sep in SNP2436020X-19000601-0-0-0-0.09
#mini_regions = self._table_regions_at_iterations((3, 3), (1,))[0]
micro_regions, macro_regions = self._table_regions_at_iterations(
kernel, (2, 5))
# don't do more than 5 iterations here, otherwise tables will merge over text in
# SNP2436020X-19100601-0-0-0-0.14
background0 = self.text_stopper(kernel=(7, 3), iterations=1)
# iterations=1 tuned via SNP2436020X-19100601-0-0-0-0.13.
# kernel, see SNP2436020X-19200601-1-0-0-0.03.
labels, num = skimage.morphology.label(macro_regions.astype(
numpy.bool),
return_num=True)
from .utils.geometry import contours, Simplifier, convex_hull, convex_contours
simplify = Simplifier(simplify=0)
# detect border cases like bottom table in 2436020X_1925-02-27_70_98_006.
tree = STRtree(
[s.path for s in self.segments if s.dominant_label == Label.V])
hulls = []
#tables = numpy.zeros(self._annotations.shape, dtype=numpy.bool)
for i in range(1, num + 1):
added = numpy.logical_and(labels == i, micro_regions)
if not added.any():
continue
added = skimage.morphology.convex_hull_image(added)
added = numpy.logical_and(added, numpy.logical_not(background0))
hull = convex_hull(simplify(contours(added)))
segment_by = None
for s in tree.query(hull):
if hull.buffer(-5).contains(
shapely.geometry.Point(*s.centroid.coords)):
segment_by = s
break
if segment_by:
added = numpy.logical_and(labels == i, micro_regions)
added = numpy.logical_and(added,
numpy.logical_not(background0))
for c in convex_contours(added):
hulls.append(c)
else:
hulls.append(hull)
return _merge_convex_all(hulls)
def _intersects(shape, polygons):
tree = STRtree(polygons)
indices = dict((id(polygon), index) for index, polygon in
enumerate(polygons))
mask = numpy.zeros(len(polygons), dtype=bool)
polygonsToCheck = tree.query(shape)
indicesInShape = [indices[id(polygon)] for polygon in polygonsToCheck if
shape.intersects(polygon)]
mask[indicesInShape] = True
return mask
def _contains(shapes, points):
tree = STRtree(points)
indices = dict((id(point), index) for index, point in enumerate(points))
mask = numpy.zeros(len(points), dtype=bool)
for shape in shapes:
pointsToCheck = tree.query(shape)
indicesInShape = [indices[id(point)] for point in pointsToCheck if
(shape.contains(point) or shape.intersects(point))]
mask[indicesInShape] = True
return mask
def str_tree(geometries):
"""Add ids to geometries and create a STR tree for spatial indexing.
Use this for all spatial operations!
"""
for i in geometries.index:
geometries[i].id = i
try:
tree = STRtree(geometries)
except AttributeError:
tree = STRtree(geometries)
return tree
def test_insert_empty_geometry(self):
"""
Passing nothing but empty geometries results in an empty strtree.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
geoms = [empty]
tree = STRtree(geoms)
assert (tree._n_geoms == 0)
query = Polygon([(0, 0), (1, 1), (2, 0), (0, 0)])
results = tree.query(query)
def __init__(self, shape, center, angle, radius, holes, board, hole_offset, edge_offset):
#self.optimiser = optimiser
self.offset = center
self.angle = angle
self.shape = shape.buffer(hole_offset)
self.board = Polygon(board).buffer(hole_offset).buffer(-edge_offset)
self.shape_buffer = shape.buffer(radius/2)
self.current_max = float('-inf')
self.fail_counter = 0
self.tree = STRtree(holes)
def get_results(filename):
print(filename)
_dir, _, _file = filename.rpartition('/')
_image, _, _ext = _file.rpartition('.')
_annots, _, _im_ext = _image.rpartition('.')
files.append(_image)
# print _image
# grab the train and test annotations
trains = np.load(os.path.join(args.train_dir, _annots + _ + _ext)).item()
tests = np.load(filename)
#print filename, maps_dir+_image, args.train_dir+_annots+_+_ext
# data structure for saving the IoU values
IoU_vals = np.zeros((len(tests), len(trains.keys())))
# save the train anots
train_polys = []
for i in trains.keys():
# print(trains[i]['vertices'])
train_polys.append(Polygon(trains[i]['vertices']))
pass
s = STRtree(train_polys)
# save the test annots
test_polys = []
for i in range(len(tests)):
poly = tests[i]
poly = poly.tolist()
# poly.append(poly[0])
test_poly = Polygon(poly)
if not test_poly.is_valid:
continue
try:
results = s.query(test_poly)
for j in range(len(results)):
_id = train_polys.index(results[j])
_intersection = train_polys[_id].intersection(test_poly).area
_union = train_polys[_id].union(test_poly).area
IoU_vals[i, _id] = _intersection / _union
test_polys.append(test_poly)
except Exception:
continue
# do the linear sum assignment
_row, _col = lsa(1 - IoU_vals)
assignment_matrix = IoU_vals[_row, _col]
# compute the numbers
TP = (assignment_matrix >= IoU_thresh).sum()
FP = (assignment_matrix < IoU_thresh).sum()
FN = len(trains.keys()) - TP
return [TP, FP, FN]
def test_insert_empty_geometry(self):
"""
Passing nothing but empty geometries results in an empty strtree.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
geoms = [empty]
tree = STRtree(geoms)
assert(tree._n_geoms == 0)
query = Polygon([(0,0),(1,1),(2,0),(0,0)])
results = tree.query(query)
def test_nearest(self):
tree = STRtree([
Polygon([(1,0),(2,0),(2,1),(1,1)]),
Polygon([(0,2),(1,2),(1,3),(0,3)]),
Point(0,0.5)])
result = tree.nearest(Point(0,0))
self.assertEqual(result, Point(0,0.5))
result = tree.nearest(Point(0,4))
self.assertEqual(result, Polygon([(0,2),(1,2),(1,3),(0,3)]))
result = tree.nearest(Polygon([(-0.5,-0.5),(0.5,-0.5),(0.5,0.5),(-0.5,0.5)]))
self.assertEqual(result, Point(0,0.5))
def __init__(self, state_polygon, population_polygons): self.finished_districts = 0 speedups.enable() self.state_poly = state_polygon self.population_polygons = population_polygons self.total_pop = 0 self.county_population_list = [] # keep track of each counties population self.county_polygon_list = [] # keep track of each counties polygon for i in population_polygons: self.county_polygon_list.append(i["poly"]) self.county_population_list.append(i["population"]) self.county_tree = STRtree(self.county_polygon_list) # STR trees for faster intersection detection
def process_travel_edges(graph, fill_stitch_graph, shape, travel_edges):
"""Weight the interior edges and pre-calculate intersection with fill stitch rows."""
# Set the weight equal to 5x the edge length, to encourage travel()
# to avoid them.
weight_edges_by_length(graph, 5)
segments = get_segments(fill_stitch_graph)
# The shapely documentation is pretty unclear on this. An STRtree
# allows for building a set of shapes and then efficiently testing
# the set for intersection. This allows us to do blazing-fast
# queries of which line segments overlap each underpath edge.
strtree = STRtree(segments)
# This makes the distance calculations below a bit faster. We're
# not looking for high precision anyway.
outline = shape.boundary.simplify(0.5 * PIXELS_PER_MM,
preserve_topology=False)
for ls in travel_edges:
# In most cases, ls will be a simple line segment. If we're
# unlucky, in rare cases we can get a tiny little extra squiggle
# at the end that can be ignored.
points = [InkstitchPoint(*coord) for coord in ls.coords]
p1, p2 = points[0], points[-1]
edge = (p1.as_tuple(), p2.as_tuple(), 'travel')
for segment in strtree.query(ls):
# It seems like the STRTree only gives an approximate answer of
# segments that _might_ intersect ls. Refining the result is
# necessary but the STRTree still saves us a ton of time.
if segment.crosses(ls):
start, end = segment.coords
fill_stitch_graph[start][end]['segment'][
'underpath_edges'].append(edge)
# The weight of a travel edge is the length of the line segment.
weight = p1.distance(p2)
# Give a bonus to edges that are far from the outline of the shape.
# This includes the outer outline and the outlines of the holes.
# The result is that travel stitching will tend to hug the center
# of the shape.
weight /= ls.distance(outline) + 0.1
graph.add_edge(*edge, weight=weight)
# without this, we sometimes get exceptions like this:
# Exception AttributeError: "'NoneType' object has no attribute 'GEOSSTRtree_destroy'" in
# <bound method STRtree.__del__ of <shapely.strtree.STRtree instance at 0x0D2BFD50>> ignored
del strtree
def get_features_inside_shape(geojson_data, border_shape):
optimized_shapes = katana(border_shape, SPLIT_SIZE)
tree = STRtree(optimized_shapes)
inside_shape = []
for feature in geojson_data['features']:
shape = geometry.shape(feature['geometry'])
for segment in tree.query(shape):
if segment.contains(shape):
inside_shape.append(feature)
geojson_data['features'] = inside_shape
return geojson_data
def test_insert_empty_geometry():
"""
Passing nothing but empty geometries results in an empty strtree.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
geoms = [empty]
with pytest.warns(ShapelyDeprecationWarning):
tree = STRtree(geoms)
assert tree._n_geoms == 0
query = Polygon([(0, 0), (1, 1), (2, 0), (0, 0)])
results = tree.query(query)
assert len(results) == 0
def test_query_empty_geometry():
"""
Empty geometries should be filtered out.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
point = Point(1, 0.5)
geoms = [empty, point]
tree = STRtree(geoms)
query = Polygon([(0, 0), (1, 1), (2, 0), (0, 0)])
results = tree.query(query)
assert len(results) == 1
assert results[0] == point
def find_borders(polygons):
str_tree = STRtree(polygons)
for polygon in polygons:
potentials = str_tree.query(polygon)
for potential in potentials:
if potential is polygon:
continue
intersect = potential.intersection(polygon)
# if not intersect.is_empty and hasattr(intersect, 'exterior'):
if not intersect.is_empty:
yield intersect.convex_hull
def filter_out_of_campus_points(self, whole_campus_x, whole_campus_y):
"""pre processing function to remove all the points that are not within campus.
Args:
whole_campus_x (np.array): x "utm" coordinate in integer (east axis)
whole_campus_y (np.array): y "utm" coordinate in integer (north axis)
Returns:
filtered_x, filtered_y: x, y array with out of campus points removed.
"""
if configure.getboolean("Configure", "debug"):
print("filtering points that are not within campus...")
# open the campus boundary geojson file and read it as a polygon
file = open(configure.get("Constants", "boundary_geojson_file_path"))
df = geopandas.read_file(file)
# translate utm to geographic
lat = (whole_campus_x / 100.0) + self.min_east * 100
lon = (whole_campus_y / 100.0) + self.min_north * 100
raw_geo = utm.to_latlon(lat, lon, 10, "U")
# map 2d array to shapely points
stacked = np.vstack((raw_geo[1], raw_geo[0])).T
s = GeoSeries(map(Point, stacked))
# query the points for faster processing.
# according to https://stackoverflow.com/questions/62280398/checking-if-a-point-is-contained-in-a-polygon-multipolygon-for-many-points
tree = STRtree(s)
# we have to do a if check after query, according to the post
res = [
o for o in tree.query(df.geometry[0]) if df.geometry[0].contains(o)
]
# translate from geographic to utm
filtered_stacked = np.zeros((len(res), 2))
count = 0
for point in res:
filtered_stacked[count, :] = np.asarray(point.xy).reshape(1, 2)
count += 1
raw_utm = utm.from_latlon(filtered_stacked[:, 1], filtered_stacked[:,
0])
filtered_x = (raw_utm[0] - self.min_east * 100) * 100
filtered_y = (raw_utm[1] - self.min_north * 100) * 100
filtered_x = filtered_x.astype(int)
filtered_y = filtered_y.astype(int)
if configure.getboolean("Configure", "debug"):
print("filtering completed.")
return filtered_x, filtered_y
def detector_counts_time(gdf_d, gdf_s, buff, source, mu=[0], polys=None):
'''
This function tallies the total counts each detector sees at each point
in time. It does so by finding all detectors with the same timestep as
the source, and then finding all detectors within range.
Parameters
----------
gdf_d: GeoDataFrame
The geodataframe containing the information for the mobile and
or stationary detectors.
gdf_s: GeoDataFrame
The geodataframe containing the information for the mobile and
or stationary sources.
buff: float
The maximum detection distance
source: float
The strength of the mobile source
Returns
-------
gdf_d: GeoDataFrame
The geodataframe containing the information for the mobile and
or stationary detectors updated with counts.
'''
for index_s in range(len(gdf_s)):
cols = ['detID', 'sourceID', 'counts', 'dist', 'dist2']
interaction_array = []
spoint = gdf_s.at[index_s, 'geometry']
time = gdf_s.at[index_s, 'time']
gdf_t=gdf_d[gdf_d['time'] == time]
geometries = np.append(gdf_t.geometry.values, gdf_s.geometry.values)
tree=STRtree(geometries)
buff1 = buff/111180
d_list = tree.query(spoint.buffer(buff1))
for d in d_list:
if d in gdf_t['geometry']:
index = gdf_t[gdf_t['geometry']==d].index
for index_d in index:
if polys is None:
dist = spoint.distance(d)*111180
val = tally_counts(dist, mu[0], source)
gdf_d.at[index_d, 'count'] += val
gdf_s.at[index_s, 'count'] += val
interaction_array.append([index_d, index_s, val, dist, 0.0])
else:
dist = attenu_lengths(gdf_d, gdf_s, index_d, index_s, polys)
val = tally_counts_buildings(dist, mu, source)
gdf_d.at[index_d, 'count'] += val
gdf_s.at[index_s, 'count'] += val
interaction_array.append([index_d, index_s, val, dist[0], dist[1]])
pd1 = pd.DataFrame(interaction_array, columns=cols)
return pd1
def find_adjacent_polygons(in_polygon,
polygon_list,
buffer_size=None,
Rtree=None):
# find adjacent polygons
# in_polygon is the center polygon
# polygon_list is a polygon list without in_polygon
if buffer_size is not None:
center_poly = in_polygon.buffer(buffer_size)
else:
center_poly = in_polygon
if len(polygon_list) < 1:
return [], []
# https://shapely.readthedocs.io/en/stable/manual.html#str-packed-r-tree
if Rtree is None:
tree = STRtree(polygon_list)
else:
tree = Rtree
# query: Returns a list of all geometries in the strtree whose extents intersect the extent of geom.
# This means that a subsequent search through the returned subset using the desired binary predicate
# (eg. intersects, crosses, contains, overlaps) may be necessary to further filter the results according
# to their specific spatial relationships.
# https://www.geeksforgeeks.org/introduction-to-r-tree/
# R-trees are faster than Quad-trees for Nearest Neighbour queries while for window queries, Quad-trees are faster than R-trees
# quicker than check one by one
# adjacent_polygons = [item for item in tree.query(center_poly) if item.intersection(center_poly) ]
# t0= time.time()
adjacent_polygons = [
item for item in tree.query(center_poly)
if item.intersects(center_poly)
]
adjacent_poly_idx = [
polygon_list.index(item) for item in adjacent_polygons
]
# print('cost %f seconds'%(time.time() - t0))
# adjacent_polygons = []
# adjacent_poly_idx = []
# for idx, poly in enumerate(polygon_list):
# if is_two_polygons_connected(poly, center_poly):
# adjacent_polygons.append(poly)
# adjacent_poly_idx.append(idx)
# print(datetime.now(), 'find %d adjacent polygons' % len(adjacent_polygons))
return adjacent_polygons, adjacent_poly_idx
def test_query_empty_geometry(self):
"""
Empty geometries should be filtered out.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
point = Point(1, 0.5)
geoms = [empty, point]
tree = STRtree(geoms)
assert (tree._n_geoms == 1)
query = Polygon([(0, 0), (1, 1), (2, 0), (0, 0)])
results = tree.query(query)
self.assertEqual(len(results), 1)
self.assertEqual(results[0], point)
def get_STRtrees(network_file, talus_file):
network = fiona.open(network_file, 'r')
talus = fiona.open(talus_file, 'r')
print("building tree for network of size", len(network))
nlines = [shape(l['geometry']) for l in network]
ntree = STRtree(nlines)
print("tree built --------------------------")
network.close()
print(f"building tree for {len(talus)} talus")
tlines = [shape(t['geometry']) for t in talus]
ttree = STRtree(tlines)
print("tree built --------------------------")
talus.close()
return ntree, ttree
def test_query_empty_geometry(self):
"""
Empty geometries should be filtered out.
The query segfaults if the empty geometry was actually inserted.
"""
empty = Polygon()
point = Point(1, 0.5)
geoms = [empty, point]
tree = STRtree(geoms)
assert(tree._n_geoms == 1)
query = Polygon([(0,0),(1,1),(2,0),(0,0)])
results = tree.query(query)
self.assertEqual(len(results), 1)
self.assertEqual(results[0], point)
def intersect(mglist, shp, shptype="POLYGON", length_area_greater_than=0.0):
intersect_dict = {}
s = STRtree(mglist)
result = s.query(shp)
isectshp = []
cellids = []
vertices = []
areas = []
lengths = []
for i, r in enumerate(result):
intersect = shp.intersection(r)
if shptype == "POLYGON":
if intersect.area > length_area_greater_than:
#cellids.append(mglist.index(r)) # this is extremely slow
cellids.append(r.index) # vincent post
isectshp.append(intersect)
areas.append(intersect.area)
vertices.append(intersect.__geo_interface__["coordinates"])
elif shptype == "POLYLINE":
try:
isect_iter = iter(intersect)
except TypeError:
isect_iter = [intersect]
for isect in isect_iter:
if isect.length > length_area_greater_than:
#cellids.append(mglist.index(r)) # this is extremely slow
cellids.append(r.index) # vincent post
isectshp.append(isect)
lengths.append(isect.length)
vertices.append(isect.__geo_interface__["coordinates"])
else:
raise NotImplementedError(
"shptype '{}' is not supported!".format(shptype))
intersect_dict["intersect"] = isectshp
intersect_dict["cellids"] = cellids
intersect_dict["vertices"] = vertices
if shptype == "POLYGON":
intersect_dict["areas"] = areas
elif shptype == "POLYLINE":
intersect_dict["lengths"] = lengths
return intersect_dict
def _process(self, element, key=None):
# Compute view port
x0, x1 = self.p.x_range or element.range(0)
y0, y1 = self.p.y_range or element.range(1)
bounds = bounds_to_poly((x0, y0, x1, y1))
# Initialize or lookup cache with STRTree
if element._plot_id in self._cache:
cache = self._cache[element._plot_id]
domain, tree, geom_dicts, geom_cache, area_cache = cache
else:
if isinstance(element, Polygons):
geom_dicts = polygons_to_geom_dicts(element)
elif isinstance(element, Path):
geom_dicts = path_to_geom_dicts(element)
geoms = [g['geometry'] for g in geom_dicts]
tree = STRtree(geoms)
domain = bounds
geom_cache, area_cache = {}, {}
self._cache.clear()
cache = (domain, tree, geom_dicts, geom_cache, area_cache)
self._cache[element._plot_id] = cache
area = bounds.area
current_zoom = compute_zoom_level(bounds, domain, self.p.zoom_levels)
tol = np.sqrt(bounds.area) * self.p.tolerance_factor
# Query RTree, then cull and simplify polygons
new_geoms, gdict = [], {}
for g in tree.query(bounds):
garea = area_cache.get(id(g))
if garea is None:
is_poly = 'Polygon' in g.geom_type
garea = g.area if is_poly else bounds_to_poly(g.bounds).area
area_cache[id(g)] = garea
# Skip if geometry area is below display threshold or
# does not intersect with viewport
if ((self.p.display_threshold is not None and
(garea/area) < self.p.display_threshold)
or not g.intersects(bounds)):
continue
# Try to look up geometry in cache by zoom level
cache_id = (id(g), current_zoom)
if cache_id in geom_cache and not self.p.clip:
geom_dict = geom_cache[cache_id]
else:
if element.vdims:
gidx = find_geom(tree._geoms, g)
gdict = geom_dicts[gidx]
g = g.simplify(tol, self.p.preserve_topology)
if not g:
continue # Skip if geometry empty
geom_dict = dict(gdict, geometry=g)
if self.p.cache:
geom_cache[cache_id] = geom_dict
if self.p.clip:
geom_dict = dict(geom_dict, geometry=g.intersection(bounds))
new_geoms.append(geom_dict)
return element.clone(new_geoms)
