def test_shapely_intersection():
"""Testing Shapely: Test against prepared geometry intersection bug #603"""
# http://trac.osgeo.org/geos/ticket/603
from shapely.geometry import MultiPolygon, box
from shapely.prepared import prep
from shapely import wkt
assert MultiPolygon([box(0, 0, 1, 10), box(40, 0, 41, 10)]).intersects(box(20, 0, 21, 10)) == False
assert prep(MultiPolygon([box(0, 0, 1, 10), box(40, 0, 41, 10)])).intersects(box(20, 0, 21, 10)) == False
# tile_grid(3857, origin='nw').tile_bbox((536, 339, 10))
tile = box(939258.2035682462, 6731350.458905761, 978393.9620502564, 6770486.217387771)
tile = box(978393.9620502554, 6770486.217387772, 1017529.7205322656, 6809621.975869782)
# "{"type":"FeatureCollection","features":[{"type":"Feature","properties":{"type":"box_control"},"geometry":{"type":"Polygon","coordinates":[[[1449611.9686912997,6878109.5532215],[1449611.9686912997,6909907.3569881],[1476517.8026477,6909907.3569881],[1476517.8026477,6878109.5532215],[1449611.9686912997,6878109.5532215]]]}},{"type":"Feature","properties":{"type":"box_control"},"geometry":{"type":"Polygon","coordinates":[[[909049.30465869,6435386.285393901],[909049.30465869,6457400.14954],[943293.0933304401,6457400.14954],[943293.0933304401,6435386.285393901],[909049.30465869,6435386.285393901]]]}}]}"
coverage = wkt.loads(
"MULTIPOLYGON ("
"((1449611.9686912996694446 6878109.5532214995473623, "
"1449611.9686912996694446 6909907.3569881003350019, "
"1476517.8026477000676095 6909907.3569881003350019, "
"1476517.8026477000676095 6878109.5532214995473623, "
"1449611.9686912996694446 6878109.5532214995473623)), "
"((909049.3046586900018156 6435386.2853939011693001, "
"909049.3046586900018156 6457400.1495399996638298, "
"943293.0933304401114583 6457400.1495399996638298, "
"943293.0933304401114583 6435386.2853939011693001, "
"909049.3046586900018156 6435386.2853939011693001)))"
)
assert prep(coverage).contains(tile) == False
assert prep(coverage).intersects(tile) == False
def __init__(self,
regions_file=REGIONS_FILE,
buffer_file=REGIONS_BUFFER_FILE):
self._buffered_shapes = {}
self._prepared_shapes = {}
self._shapes = {}
self._tree_ids = {}
self._radii = {}
with util.gzip_open(regions_file, 'r') as fd:
regions_data = simplejson.load(fd)
genc_regions = frozenset([rec.alpha2 for rec in genc.REGIONS])
for feature in regions_data['features']:
code = feature['properties']['alpha2']
if code in genc_regions:
shape = geometry.shape(feature['geometry'])
self._shapes[code] = shape
self._prepared_shapes[code] = prepared.prep(shape)
self._radii[code] = feature['properties']['radius']
with util.gzip_open(buffer_file, 'r') as fd:
buffer_data = simplejson.load(fd)
i = 0
envelopes = []
for feature in buffer_data['features']:
code = feature['properties']['alpha2']
if code in genc_regions:
shape = geometry.shape(feature['geometry'])
self._buffered_shapes[code] = prepared.prep(shape)
# Collect rtree index entries, and maintain a separate id to
# code mapping. We don't use index object support as it
# requires un/pickling the object entries on each lookup.
if isinstance(shape, geometry.base.BaseMultipartGeometry):
# Index bounding box of individual polygons instead of
# the multipolygon, to avoid issues with regions crossing
# the -180.0/+180.0 longitude boundary.
for geom in shape.geoms:
envelopes.append((i, geom.envelope.bounds, None))
self._tree_ids[i] = code
i += 1
else:
envelopes.append((i, shape.envelope.bounds, None))
self._tree_ids[i] = code
i += 1
props = index.Property()
props.fill_factor = 0.9
props.leaf_capacity = 20
self._tree = index.Index(envelopes,
interleaved=True, properties=props)
for envelope in envelopes:
self._tree.insert(*envelope)
self._valid_regions = frozenset(self._shapes.keys())
def __init__(self, json_file=JSON_FILE):
self._buffered_shapes = {}
self._prepared_shapes = {}
self._shapes = {}
self._tree_ids = {}
self._radii = {}
with util.gzip_open(json_file, 'r') as fd:
data = simplejson.load(fd)
genc_regions = frozenset([rec.alpha2 for rec in genc.REGIONS])
for feature in data['features']:
code = feature['properties']['alpha2']
if code in genc_regions:
shape = geometry.shape(feature['geometry'])
self._shapes[code] = shape
self._prepared_shapes[code] = prepared.prep(shape)
self._radii[code] = feature['properties']['radius']
i = 0
envelopes = []
for code, shape in self._shapes.items():
# Build up region buffers, to create shapes that include all of
# the coastal areas and boundaries of the regions and anywhere
# a cell signal could still be recorded. The value is in decimal
# degrees (1.0 == ~100km) but calculations don't take projection
# / WSG84 into account.
# After buffering remove any parts that crosses the -180.0/+180.0
# longitude boundary to the east or west.
buffered = (shape.buffer(0.5)
.difference(DATELINE_EAST)
.difference(DATELINE_WEST))
self._buffered_shapes[code] = prepared.prep(buffered)
# Collect rtree index entries, and maintain a separate id to
# code mapping. We don't use index object support as it
# requires un/pickling the object entries on each lookup.
if isinstance(buffered, geometry.base.BaseMultipartGeometry):
# Index bounding box of individual polygons instead of
# the multipolygon, to avoid issues with regions crossing
# the -180.0/+180.0 longitude boundary.
for geom in buffered.geoms:
envelopes.append((i, geom.envelope.bounds, None))
self._tree_ids[i] = code
i += 1
else:
envelopes.append((i, buffered.envelope.bounds, None))
self._tree_ids[i] = code
i += 1
props = index.Property()
props.fill_factor = 0.9
props.leaf_capacity = 20
self._tree = index.Index(envelopes, interleaved=True, properties=props)
self._valid_regions = frozenset(self._shapes.keys())
def test_prepare_already_prepared():
polygon = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
prepared = prep(polygon)
# attempt to prepare an already prepared geometry with `prep`
result = prep(prepared)
assert isinstance(result, PreparedGeometry)
assert result.context is polygon
# attempt to prepare an already prepared geometry with `PreparedGeometry`
result = PreparedGeometry(prepared)
assert isinstance(result, PreparedGeometry)
assert result.context is polygon
def _rings_to_multi_polygon(self, rings, is_ccw):
exterior_rings = []
interior_rings = []
for ring in rings:
if ring.is_ccw != is_ccw:
interior_rings.append(ring)
else:
exterior_rings.append(ring)
polygon_bits = []
# Turn all the exterior rings into polygon definitions,
# "slurping up" any interior rings they contain.
for exterior_ring in exterior_rings:
polygon = sgeom.Polygon(exterior_ring)
prep_polygon = prep(polygon)
holes = []
for interior_ring in interior_rings[:]:
if prep_polygon.contains(interior_ring):
holes.append(interior_ring)
interior_rings.remove(interior_ring)
polygon_bits.append((exterior_ring.coords, [ring.coords for ring in holes]))
# Any left over "interior" rings need "inverting" with respect
# to the boundary.
if interior_rings:
boundary_poly = self.domain
x3, y3, x4, y4 = boundary_poly.bounds
bx = (x4 - x3) * 0.1
by = (y4 - y3) * 0.1
x3 -= bx
y3 -= by
x4 += bx
y4 += by
for ring in interior_rings:
polygon = sgeom.Polygon(ring)
if polygon.is_valid:
x1, y1, x2, y2 = polygon.bounds
bx = (x2 - x1) * 0.1
by = (y2 - y1) * 0.1
x1 -= bx
y1 -= by
x2 += bx
y2 += by
box = sgeom.box(min(x1, x3), min(y1, y3), max(x2, x4), max(y2, y4))
# Invert the polygon
polygon = box.difference(polygon)
# Intersect the inverted polygon with the boundary
polygon = boundary_poly.intersection(polygon)
if not polygon.is_empty:
polygon_bits.append(polygon)
if polygon_bits:
multi_poly = sgeom.MultiPolygon(polygon_bits)
else:
multi_poly = sgeom.MultiPolygon()
return multi_poly
def _set_mapdata(self):
self.df_map = pd.DataFrame({
'poly': [Polygon(points) for points in self.map.seattle],
'name': [hood[self.section_name] for hood in self.map.seattle_info]
})
self.hood_polygons = prep(MultiPolygon(list(self.df_map['poly'].values)))
def cover_geometry(tilescheme, geom, zoom):
"""Covers the provided geometry with tiles.
Args:
tilescheme: The tile scheme to use. This needs to implement
the public protocal of the schemes defined within
tiletanic.
geom: The geometry we would like to cover. This should be a
shapely geometry.
zoom: The zoom level of the tiles to cover geom with.
Yields:
An iterator of Tile objects ((x, y, z) named tuples) that
cover the input geometry.
"""
# Only shapely geometries allowed.
if not isinstance(geom, geometry.base.BaseGeometry):
raise ValueError("Input 'geom' is not a known shapely geometry type")
if geom.is_empty:
return
# Generate the covering.
prep_geom = prepared.prep(geom)
if isinstance(geom, (geometry.Polygon, geometry.MultiPolygon)):
for tile in _cover_polygonal(tilescheme, Tile(0, 0, 0), prep_geom, geom, zoom):
yield tile
else:
for tile in _cover_geometry(tilescheme, Tile(0, 0, 0), prep_geom, geom, zoom):
yield tile
def iter_intersects(self, shapely_geom, geom_mapping, keep_touches=True):
"""
Return an interator for the unique identifiers of the geometries intersecting the target geometry.
:param :class:`shapely.geometry.Geometry` shapely_geom: The geometry to use for subsetting. It is the ``bounds``
attribute fo the geometry that is actually tested.
:param dict geom_mapping: The collection of geometries to do the full intersects test on. The keys of the
dictionary correspond to the integer unique identifiers. The values are Shapely geometries.
:param bool keep_touches: If ``True``, return the unique identifiers of geometries only touching the subset
geometry.
:returns: Generator yield integer unique identifiers.
:rtype: int
"""
# Create the geometry iterator. If it is a multi-geometry, we want to iterator over those individually.
try:
itr = iter(shapely_geom)
except TypeError:
# likely not a multi-geometry
itr = [shapely_geom]
for shapely_geom_sub in itr:
# Return the initial identifiers that intersect with the bounding box using the retree internal method.
ids = self._get_intersection_rtree_(shapely_geom_sub)
# Prepare the geometry for faster operations.
prepared = prep(shapely_geom_sub)
_intersects = prepared.intersects
_touches = shapely_geom_sub.touches
for ii in ids:
geom = geom_mapping[ii]
if _intersects(geom):
if keep_touches == False:
if _touches(geom) == False:
yield (ii)
else:
yield (ii)
def _tiles_inside_geom(tilescheme, tiles, geom):
"""Filters out tiles do not contain the geometry geom
Consider the nine adjacent tiles:
-------------
| 1 | 2 | 3 |
-------------
| 4 | 5 | 6 |
-------------
| 7 | 8 | 9 |
-------------
if the AOI is 5, _tiles_inside_geom will only return 5. Note that
tiletanic.tilecover.cover_geometry returns all 9 tiles
Args:
tilescheme: The tile scheme to use.
tiles: list iterable collection of tiles
geom: Shapely Geometry area of interest
"""
prep_geom = prepared.prep(geom)
for t in tiles:
coords = tilescheme.bbox(t)
tile_geom = geometry.Polygon(((coords.xmin, coords.ymin),
(coords.xmax, coords.ymin),
(coords.xmax, coords.ymax),
(coords.xmin, coords.ymax),
(coords.xmin, coords.ymin)))
# Touches: The Geometries have at least one boundary point in
# common, but no interior points
if not prep_geom.touches(tile_geom):
yield t
def _get_level_geometries(level):
buildings = level.buildings.all()
buildings_geom = cascaded_union([building.geometry for building in buildings])
spaces = {space.pk: space for space in level.spaces.all()}
holes_geom = []
for space in spaces.values():
if space.outside:
space.geometry = space.geometry.difference(buildings_geom)
columns = [column.geometry for column in space.columns.all()]
if columns:
columns_geom = cascaded_union([column.geometry for column in space.columns.all()])
space.geometry = space.geometry.difference(columns_geom)
holes = [hole.geometry for hole in space.holes.all()]
if holes:
space_holes_geom = cascaded_union(holes)
holes_geom.append(space_holes_geom.intersection(space.geometry))
space.geometry = space.geometry.difference(space_holes_geom)
for building in buildings:
building.original_geometry = building.geometry
if holes_geom:
holes_geom = cascaded_union(holes_geom)
holes_geom_prep = prepared.prep(holes_geom)
for obj in buildings:
if holes_geom_prep.intersects(obj.geometry):
obj.geometry = obj.geometry.difference(holes_geom)
results = []
results.extend(buildings)
for door in level.doors.all():
results.append(door)
results.extend(spaces.values())
return results
def clip(coll,igeom):
'''Do an intersects + intersection and set weights based on geometry
areas.
coll :: OcgCollection
igeom :: Shapely Polygon or MultiPolygon
returns
OcgCollection'''
## logic for convenience. just return the provided collection if a NoneType
## is passed for the 'igeom' arugment
if igeom is not None:
## take advange of shapely speedups
prep_igeom = prepared.prep(igeom)
## the weight array
weights = np.empty(coll.gid.shape,dtype=float)
weights = np.ma.array(weights,mask=coll.gid.mask)
## do the spatial operation
for idx,geom in iter_array(coll.geom_masked,
return_value=True):
# import ipdb;ipdb.set_trace()
if keep(prep_igeom,igeom,geom):
# import ipdb;ipdb.set_trace()
new_geom = igeom.intersection(geom)
weights[idx] = new_geom.area
coll.geom[idx] = new_geom
## set maximum weight to one
coll.weights = weights/weights.max()
return(coll)
def intersects(self,polygon):
## do the initial grid subset
grid = self.grid.subset(polygon=polygon)
## a prepared polygon
prep_polygon = prepared.prep(polygon)
## the fill arrays
geom = np.ones(grid.shape,dtype=object)
geom = np.ma.array(geom,mask=True)
geom_mask = geom.mask
try:
row = grid.row.value
col = grid.column.value
for ii,jj in product(range(row.shape[0]),range(col.shape[0])):
pt = Point(col[jj],row[ii])
geom[ii,jj] = pt
if prep_polygon.intersects(pt):
geom_mask[ii,jj] = False
else:
geom_mask[ii,jj] = True
## NcGridMatrixDimension correction
except AttributeError:
_row = grid.row
_col = grid.column
for ii,jj in iter_array(_row):
pt = Point(_col[ii,jj],_row[ii,jj])
geom[ii,jj] = pt
if prep_polygon.intersects(pt):
geom_mask[ii,jj] = False
else:
geom_mask[ii,jj] = True
ret = self.__class__(grid=grid,geom=geom,uid=grid.uid)
return(ret)
def select_values(self,igeom=None,clip=False):
## if an intersection geometry is passed, use it to calculate the weights
## but do not modify the geometry. this weight is used to select values
## to keep for set statistics.
## this is the case of no intersection geometry. basically, requesting
## the entire dataset.
if clip and igeom is None:
mask = np.zeros(self.value_shape)
elif clip and igeom is not None:
prep_igeom = prepared.prep(igeom)
for ii,geom in enumerate(self.geometry):
if keep(prep_igeom,igeom,geom):
new_geom = igeom.intersection(geom)
weight = new_geom.area/geom.area
assert(weight != 0.0) #tdk
self.weight[ii] = weight
## it has now been clipped
clip = False
if not clip:
## loop through the weights determining which values to maintain based
## on weights.
idx = []
for ii,weight in enumerate(self.weight):
if weight > 0.5:
idx.append(ii)
elif weight == 0.5:
warn('0.5 weight encountered. Removing it.')
## select the data and store in special variable for use by set statistics
mask = np.ones(self.value.shape)
mask[:,:,idx] = 0
self.value_set = np.ma.masked_array(self.value,mask=mask)
def CreatePointObjects(BasemapTemplate, MapData, ComplaintData):
''' Here we create a community geometry object from the combined community polygons.
We've done this to speed up membership checking. We perform
the check by creating a mutipolygon from map_points, then filtering using
the contains() method, which returns all points that are contains within
community_poygons. The results is a pandas series returning NYCPoints, which we'll use to make out maps.
'''
# Create Point objects (in a pandas Series) in map coordinates from our DataFrame longitude and latitude values.
# Note that the zip function here aggregates the long/lat data for each observation in our compliant data.
# The purpose of this code is to convert our latitude and longitude into Basemap cartesian map coordinates
MapPoints = pd.Series([Point(BasemapTemplate(MapX, MapY)) for MapX, MapY in zip(ComplaintData['Longitude'], ComplaintData['Latitude'])])
# Creates a MultiPoint object from the list of lat/long values from above.
ComplaintPoints = MultiPoint(list(MapPoints.values))
# Use the prep method to give this MultiPolygon object (created from the earlier DataFrame of complaint locations), so that it
# has the 'contains' method, which we'll use in the next step.
CommunityPolygon = prep(MultiPolygon(list(MapData['poly'].values)))
# calculate points that fall within the community boundaries. Here NYCPoints
# is a list containing Point objects of all the complaint points contained in
# the community polygons.
NYCPoints = filter(CommunityPolygon.contains, ComplaintPoints)
return (NYCPoints)
def initialize_grid(self, geom):
"""
"""
bounds = geom.bounds
(minx, miny, maxx, maxy) = bounds
(minx, miny, maxx, maxy) = (
round(np.floor(minx * self.psi)) / self.psi,
round(np.floor(miny * self.psi)) / self.psi,
round(np.ceil(maxx * self.psi)) / self.psi,
round(np.ceil(maxy * self.psi)) / self.psi)
clean_bounds = (minx, miny, maxx, maxy)
top_left_lon = minx
top_left_lat = maxy
affine = Affine(self.pixel_size, 0, top_left_lon,
0, -self.pixel_size, top_left_lat)
# base_rasterize, base_bounds = self.rasterize_geom(geom)
# self.shape = base_rasterize.shape
nrows = int(np.ceil( (maxy - miny) / self.pixel_size ))
ncols = int(np.ceil( (maxx - minx) / self.pixel_size ))
self.shape = (nrows, ncols)
self.bounds = clean_bounds
self.affine = affine
self.topleft = (top_left_lon, top_left_lat)
self.grid_box = prep(box(*self.bounds))
def update(self, transect):
"""
Gather the data near the transect. Depth convert if necessary.
Returns nothing. Side effect: sets attributes.
Args:
transect (LineString): A shapely LineString object.
"""
Notice.info("Updating " + self.__class__.__name__)
b = self.settings.get('fine_buffer') or self.settings['buffer']
prepared = prep(transect.buffer(b))
for horizon, points in self.lookup.items():
l = len(points)
points = filter(prepared.contains, points)
print horizon, len(points), "of", l, "points"
data, coords = [], []
for p in points:
coords.append(transect.project(p))
if self.domain.lower() in ['depth', 'd', 'z']:
"Depth converting horizon"
zpt = self.velocity.time2depthpt(p.z/1000, coords[-1])
data.append(zpt)
else:
data.append(p.z)
self.data[horizon] = np.array(data)
self.coords[horizon] = np.array(coords)
def associate_nodes(fargs):
""" Find nodes which are within the hull """
cluster, cluster_hull, nodes = fargs
# There is no hull for this community, it's been deleted.
# Orphan all nodes.
if cluster_hull is None:
log.info("Missing hull, orphaning all nodes in cluster %i",
cluster)
output = [topotools.NodeInfo(
orphan.id, orphan.lat, orphan.lon, -1)
for orphan in nodes]
return len(output), output
characteristic_size = math.sqrt(cluster_hull.area)
allowed_distance = characteristic_size * args.buffer
buffered = prep(cluster_hull.buffer(allowed_distance))
output = []
num_orphans = 0
for node in nodes:
# check if it is an interior node
point = Point((node.lon, node.lat))
orphan = not buffered.contains(point)
if orphan:
num_orphans += 1
output.append(topotools.NodeInfo(
node.id, node.lat, node.lon,
-1 if orphan else node.clust))
return num_orphans, output
def run_tests(bbox, service_url, status_path=None):
with fiona.open("ne_10m_land.geojson", "r") as source:
n = source.next()
land_polygon = prep(shape(n["geometry"]))
features = []
session = requests.Session()
for lon in range(bbox[0], bbox[2]):
for lat in range(bbox[1], bbox[3]):
test_coords = []
for x_offset in range(1, 4):
for y_offset in range(1, 4):
test_coords.append((lon + x_offset / 4.0, lat + y_offset / 4.0))
point_on_land = False
for coord in test_coords:
point = Point(coord[0], coord[1])
if land_polygon.contains(point):
point_on_land = True
break
if not point_on_land:
logging.debug("No points on land, %f,%f" % (lon, lat))
continue
hgt_filename = "%s%02i%s%03i.hgt" % ("N" if lat > 0 else "S", abs(lat), "W" if lon < 0 else "E", abs(lon))
elevation = test(test_coords, service_url, session=session)
logging.debug("%i, %i response:%i" % (lon, lat, elevation))
color = SUCCESS_COLOR
if elevation == -9999:
logging.info("fail %i,%i" % (lon, lat))
color = ERROR_COLOR
elif elevation == 0:
logging.info("maybe fail %i,%i" % (lon, lat))
color = POSSIBLE_ERROR_COLOR
status_feature = {
"type": "Feature",
"properties": {
"result": elevation,
"hgt": hgt_filename,
"points": ";".join([",".join([str(f) for f in c]) for c in test_coords]),
"fill-opacity": 0.5,
"fill": color,
"stroke": "#000000",
"stroke-width": 1,
},
"geometry": {
"type": "Polygon",
"coordinates": [[[lon, lat], [lon, lat + 1], [lon + 1, lat + 1], [lon + 1, lat], [lon, lat]]],
},
}
features.append(status_feature)
if status_path is not None and len(features) % 100 == 0:
write_feature_collection(features, path=status_path)
if status_path is not None:
write_feature_collection(features, path=status_path)
def load_polygons(cls, filename):
feature_collection = json.load(open(filename))
polygons = []
for feature in feature_collection['features']:
poly_type = feature['geometry']['type']
if poly_type == 'Polygon':
poly = Polygon(feature['geometry']['coordinates'][0])
polygons.append((feature['properties'], prep(poly)))
elif poly_type == 'MultiPolygon':
polys = []
for coords in feature['geometry']['coordinates']:
poly = Polygon(coords[0])
polys.append(poly)
polygons.append((feature['properties'], prep(MultiPolygon(polys))))
return polygons
def load_polygons(cls, filename):
f = open(filename)
polygons = {}
cls.i = 0
for line in f:
feature = json.loads(line.rstrip())
polygons[cls.i] = prep(cls.polygon_from_geojson(feature))
cls.i += 1
return polygons
def __init__(self, field, filter_value, negation):
self.field = field
self.filter_value = filter_value
self.prepared = prep(self.filter_value)
self.negation = negation
if negation:
self.evaluate = lambda x: not(self._evaluate(x))
else:
self.evaluate = self._evaluate
def load_polygons(cls, filename):
f = open(filename)
polygons = []
for line in f:
feature = json.loads(line.rstrip())
poly_type = feature['geometry']['type']
if poly_type == 'Polygon':
poly = Polygon(feature['geometry']['coordinates'][0])
polygons.append((feature['properties'], prep(poly)))
elif poly_type == 'MultiPolygon':
polys = []
for coords in feature['geometry']['coordinates']:
poly = Polygon(coords[0])
polys.append(poly)
polygons.append((feature['properties'], prep(MultiPolygon(polys))))
return polygons
def intersect(self, **kwargs):
"""
Intersect a Line or Point Collection and the Shoreline
Returns the point of intersection along the coastline
Should also return a linestring buffer around the interseciton point
so we can calculate the direction to bounce a particle.
"""
ls = None
if "linestring" in kwargs:
ls = kwargs.pop('linestring')
spoint = Point(ls.coords[0])
epoint = Point(ls.coords[-1])
elif "start_point" and "end_point" in kwargs:
spoint = kwargs.pop('start_point')
epoint = kwargs.pop('end_point')
ls = LineString(list(spoint.coords) + list(epoint.coords))
else:
raise TypeError( "must provide a LineString geometry object or (2) Point geometry objects" )
inter = False
# If the current point lies outside of our current shapefile index,
# re-query the shapefile in a buffer around this point
if self._spatial_query_object and not ls.within(self._spatial_query_object):
self.index(point=spoint)
for element in self._geoms:
prepped_element = prep(element)
# Test if starting on land
if prepped_element.contains(spoint):
raise Exception('Starting point on land')
inter = ls.intersection(element)
if inter:
# Return the first point in the linestring, and the linestring that it hit
if isinstance(inter, MultiLineString):
inter = inter.geoms[0]
inter = Point(inter.coords[0])
smaller_int = inter.buffer(self._spatialbuffer)
shorelines = element.exterior.intersection(smaller_int)
if isinstance(shorelines, LineString):
shorelines = [shorelines]
else:
shorelines = list(shorelines)
for shore_segment in shorelines:
# Once we find the linestring in the Polygon that was
# intersected, break out and return
if ls.touches(shore_segment):
break
return {'point': Point(inter.x, inter.y, 0), 'feature': shore_segment or None}
return None
def get_polygon_cached(self, i):
poly = self.polygons.get(i, None)
if poly is None:
data = json.loads(self.polygons_db.Get(self.polygon_key(i)))
poly = prep(self.polygon_from_geojson(data))
self.polygons[i] = poly
self.cache_misses += 1
else:
self.cache_hits += 1
return poly
def tiles_from_geom(self, geometry, zoom):
"""
Return all tiles intersecting with input geometry.
- geometry: shapely geometry
- zoom: zoom level
"""
try:
assert geometry.is_valid
except AssertionError:
try:
clean = geometry.buffer(0.0)
assert clean.is_valid
assert clean.area > 0
geometry = clean
except AssertionError:
raise IOError(
str(
"invalid geometry could not be fixed: '%s'" %
explain_validity(geometry)
)
)
if geometry.almost_equals(geometry.envelope, ROUND):
for tile in self.tiles_from_bbox(geometry, zoom):
yield tile
elif geometry.geom_type == "Point":
lon, lat = list(geometry.coords)[0]
tilelon = self.left
tilelat = self.top
tile_x_size = self.tile_x_size(zoom)
tile_y_size = self.tile_y_size(zoom)
col = -1
row = -1
while tilelon < lon:
tilelon += tile_x_size
col += 1
while tilelat > lat:
tilelat -= tile_y_size
row += 1
yield self.tile(zoom, row, col)
elif geometry.geom_type in (
"LineString", "MultiLineString", "Polygon", "MultiPolygon",
"MultiPoint", "GeometryCollection"
):
prepared_geometry = prep(
clip_geometry_to_srs_bounds(geometry, self)
)
for tile in self.tiles_from_bbox(geometry, zoom):
if prepared_geometry.intersects(tile.bbox()):
yield tile
elif geometry.is_empty:
pass
else:
raise ValueError("ERROR: no valid geometry: %s" % geometry.type)
def country_finder(geo_coord, bbox_hits, polygon_df, m):
from shapely.geometry import Point
from shapely.prepared import prep
inpoint = Point(m(geo_coord.lon,geo_coord.lat))
region=polygon_df[polygon_df['region_name'].isin(bbox_hits)]
pgon_list = [idx for idx, pgon in enumerate([prep(pgon) for pgon in region['poly']])\
if pgon.contains(inpoint)]
return region.iloc[pgon_list]
def revmatches():
for row,shp in izip(subdict["refdata"].reader.iterRecords(), subdict["refdata"].reader.iterShapes()):
#if dict(zip(self.fields, row))["SOV0NAME"] != "Russia": continue
#print "...",row[1] #row[4],row[14]
print str(row)[:100]
prepped = prep(asShape(shp)) # prepares geometry for many intersection tests (maybe only useful if is polygon and other is points, but not sure)
bbox = [shp.points[0][0],shp.points[0][1],shp.points[0][0],shp.points[0][1]] if shp.shapeType == pyshp.POINT else shp.bbox
ilist = self.spindex.intersection(bbox)
for i in ilist:
othershp = self.reader.shape(i)
if prepped.intersects(asShape(othershp)):
yield row,i
def _construct_polygon_map(self, polygon_generator, closest=False):
"""Turn a (tz, polygon) generator, into our internal mapping."""
self.timezoneNamesToPolygons = collections.defaultdict(list)
self.unprepTimezoneNamesToPolygons = collections.defaultdict(list)
for (tzname, raw_poly) in polygon_generator:
poly = Polygon(tzwhere._raw_poly_to_poly(raw_poly))
self.timezoneNamesToPolygons[tzname].append(
prep(poly))
if closest:
self.unprepTimezoneNamesToPolygons[tzname].append(
poly)
def _construct_shapely_map(self, polygon_generator, forceTZ):
"""Turn a (tz, polygon) generator, into our internal shapely mapping."""
self.timezoneNamesToPolygons = collections.defaultdict(list)
self.unprepTimezoneNamesToPolygons = collections.defaultdict(list)
for (tzname, poly) in polygon_generator:
poly = Polygon(poly)
self.timezoneNamesToPolygons[tzname].append(
prep(poly))
if forceTZ:
self.unprepTimezoneNamesToPolygons[tzname].append(
poly)
def SummarizeByCommunity(MapData, NYCPoints):
"""Create some additional columns, containing the number of points in each NYC community, and the density
per square meter and square kilometer community. Normalizing allows us to compare communities."""
MapData['count'] = MapData['poly'].map(lambda x: int(len(filter(prep(x).contains, NYCPoints))))
MapData['density_m'] = MapData['count'] / MapData['area_m']
MapData['density_km'] = MapData['count'] / MapData['area_km']
# it's easier to work with NaN values when classifying, so replace zeros with NaNs when appropriate.
MapData.replace(to_replace={'density_m': {0: np.nan}, 'density_km': {0: np.nan}}, inplace=True)
return MapData
def __init__(self):
# filterfn = '/home/behry/workspace/eew/reports/data/california_filter_revised.txt'
filterfn = '/home/behry/workspace/eew/reports/data/SoCalfilter.txt'
lon, lat = np.loadtxt(filterfn, unpack=True)
self.p = pyproj.Proj(proj="aea", lat_0='0.00', lon_0='-120',
lat_1='34.00', lat_2='40.50',
ellps='GRS80', x_0=0.000, y_0=-4000000.000,
units='m', datum='NAD83')
px, py = self.p(lon, lat)
self.points = np.vstack((px, py)).T
self.polygon = Polygon(zip(px, py))
self.prepared_polygon = prep(self.polygon)
self.lat1 = 31
self.lat2 = 43
self.lon1 = -126
self.lon2 = -114
def poly_count(self, poly_list, points):
"""
counts how many agents in each polygon
in:
1D vector of points from agents2state()/get_state(sensor=location),
out:
counts of agents in each polygon in poly_list
"""
counts = []
points = np.array([points[::2], points[1::2]]).T
points = MultiPoint(points)
for poly in self.poly_list:
poly = prep(poly)
counts.append(int(len(list(filter(poly.contains, points)))))
return counts
def is_occupied(self, poly, auto_register=False):
# prepare for multiple checking
prepared = prep(poly)
# filter hits
hits = list(filter(prepared.intersects, self.occupied))
# if we had no hits the area is not occupied
if len(hits) == 0:
if auto_register:
# register box as occupied
self.occupied.append(poly.buffer(20, resolution=1))
return False
# space is occupied
return True
def clip(self, polygon):
## perform an intersects operation first
vd = self.intersects(polygon)
## prepare the geometry for intersection
prep_igeom = prepared.prep(polygon)
## loop for the intersection
geom = vd._geom
for ii, jj in iter_array(geom):
ref = geom[ii, jj]
if not prep_igeom.contains(ref):
new_geom = polygon.intersection(ref)
geom[ii, jj] = new_geom
ret = self.__class__(grid=vd.grid, geom=geom, uid=vd.uid)
return (ret)
def get_country(lat, lon, geo_data):
"""
Determine which country a point lies in
"""
countries = {}
for feature in geo_data["features"]:
geom = feature["geometry"]
country = feature["properties"]["ADMIN"]
countries[country] = prep(shape(geom))
point = Point(lon, lat)
for country, geom in countries.items():
if geom.contains(point):
return country
return "unknown"
def tzNameAt(self, latitude, longitude, forceTZ=False):
'''
Let's you lookup for a given latitude and longitude the appropriate
timezone.
@latitude: latitude
@longitude: longitude
@forceTZ: If forceTZ is true and you can't find a valid timezone return
the closest timezone you can find instead. Only works if the point has
the same integer value for its degree than the timezeone
'''
if forceTZ:
assert self.forceTZ, 'You need to initialize tzwhere with forceTZ'
latTzOptions = self.timezoneLatitudeShortcuts[str(
(math.floor(latitude / self.SHORTCUT_DEGREES_LATITUDE) *
self.SHORTCUT_DEGREES_LATITUDE))]
latSet = set(latTzOptions.keys())
lngTzOptions = self.timezoneLongitudeShortcuts[str(
(math.floor(longitude / self.SHORTCUT_DEGREES_LONGITUDE) *
self.SHORTCUT_DEGREES_LONGITUDE))]
lngSet = set(lngTzOptions.keys())
possibleTimezones = lngSet.intersection(latSet)
queryPoint = geometry.Point(longitude, latitude)
if possibleTimezones:
for tzname in possibleTimezones:
if isinstance(self.timezoneNamesToPolygons[tzname],
COLLECTION_TYPE):
self.timezoneNamesToPolygons[tzname] = list(
map(
lambda p: prepared.prep(
geometry.Polygon(p[0], p[1])),
self.timezoneNamesToPolygons[tzname]))
polyIndices = set(latTzOptions[tzname]).intersection(
set(lngTzOptions[tzname]))
for polyIndex in polyIndices:
poly = self.timezoneNamesToPolygons[tzname][polyIndex]
if poly.contains_properly(queryPoint):
return tzname
if forceTZ:
return self.__forceTZ__(possibleTimezones, latTzOptions,
lngTzOptions, queryPoint)
def _process_osm_trees_gardens(
city_blocks: Set[CityBlock], parks: List[shg.Polygon],
fg_elev: utilities.FGElev) -> Dict[e.TreeType, List[Tree]]:
suburban_trees = list()
town_trees = list()
urban_trees = list()
for city_block in city_blocks:
tree_type = e.map_tree_type_from_settlement_type_garden(
city_block.settlement_type)
if city_block.type_ is e.BuildingZoneType.special_processing:
continue
prep_geom = prep(city_block.geometry)
my_random_points = _generate_random_tree_points_in_polygon(
city_block.geometry, prep_geom,
parameters.C2P_TREES_DIST_BETWEEN_TREES_GARDEN,
parameters.C2P_TREES_SKIP_RATE_TREES_GARDEN)
for point in my_random_points:
exclude = False
for park in parks: # need to check for parks
if point.within(park):
exclude = True
break
if exclude:
continue
if not _test_point_in_building(point, {city_block}, 2.0):
elev = fg_elev.probe_elev((point.x, point.y), False)
my_tree = Tree(
op.get_next_pseudo_osm_id(op.OSMFeatureType.generic_node),
point.x, point.y, elev)
if tree_type is e.TreeType.suburban:
suburban_trees.append(my_tree)
elif tree_type is e.TreeType.town:
town_trees.append(my_tree)
else:
urban_trees.append(my_tree)
garden_trees = dict()
if suburban_trees:
garden_trees[e.TreeType.suburban] = suburban_trees
if town_trees:
garden_trees[e.TreeType.town] = town_trees
if urban_trees:
garden_trees[e.TreeType.urban] = urban_trees
logging.info('Number of trees added in gardens: %i',
len(suburban_trees) + len(town_trees) + len(urban_trees))
return garden_trees
def maxMinDistance(poly, dd):
allLines = []
allLines.append(poly.exterior)
postLayer = dd.layers[DesignDict.POST]
portLayer = dd.layers[DesignDict.PORTS]
possiblePosts = postLayer.index.intersection(poly.bounds)
possiblePorts = portLayer.index.intersection(poly.bounds)
candidatePosts = []
prepPoly = prep(poly)
construct = Polygon(poly)
for objind in possiblePosts:
obj = dd.objects[objind]
if prepPoly.contains_properly(obj.shape):
candidatePosts.append(obj.shape)
for objind in possiblePorts:
obj = dd.objects[objind]
if prepPoly.contains_properly(obj.shape):
candidatePosts.append(obj.shape)
logger.debug(str(len(candidatePosts)) + " candidate support posts")
for post in candidatePosts:
## subtract the post from the polygon interior
construct = construct.difference(post)
for interior in construct.interiors:
candidatePosts.append(Polygon(interior))
allLines.append(interior)
allLines = MultiLineString(allLines)
currentPoint = construct.centroid
if construct.contains(currentPoint):
currentDist = allLines.distance(currentPoint)
else:
currentDist = None
triangles, pconstruct = prepPolygon(construct)
points = nRandomTriangleSamples(pconstruct, triangles)
for p in points:
dist = allLines.distance(p)
if dist > currentDist:
currentPoint = p
currentDist = dist
return (currentPoint, currentDist)
def fndshapeindices(shapevertices=None):
'''Define the gridpoints that lie within the 'shape' or on its boundary.
These points will be included in the sum of emissivities.
Kwargs:
shapevertices (list) : list of vertex coordinates for trial shape ("shape").
Returns:
pCoords (list): coordinates of grid points within or on boundary of shape.
'''
polygon = Polygon(shapevertices)
prepared_polygon = prep(polygon)
bounds = polygon.bounds
ll = bounds[:2]
ur = bounds[2:]
xs = []
ys = []
if (ur[0] - ll[0] < 1) or (ur[1] - ll[1] < 1):
return None
xx = range(int(np.ceil(ll[0])), int(np.floor(ur[0])) + 1, 1)
yy = range(int(np.ceil(ll[1])), int(np.floor(ur[1])) + 1, 1)
listpoints = list(itertools.product(xx, yy))
points = MultiPoint(listpoints)
try:
xvals, yvals = zip(*listpoints)
except:
print('ll', ll, 'ur', ur)
print('vertices', shapevertices)
print('xx', xx, 'yy', yy)
for ii in range(len(xvals)):
if prepared_polygon.intersects(points[ii]):
xs.append(xvals[ii])
ys.append(yvals[ii])
##plot to check
#x,y = polygon.exterior.xy
#plt.plot(x,y)
#plt.show()
pCoords = [xs], [ys]
return pCoords
def _split_polygon_with_line(poly, splitter):
"""Split a Polygon with a LineString"""
assert(isinstance(poly, Polygon))
assert(isinstance(splitter, LineString))
union = poly.boundary.union(splitter)
# greatly improves split performance for big geometries with many
# holes (the following contains checks) with minimal overhead
# for common cases
poly = prep(poly)
# some polygonized geometries may be holes, we do not want them
# that's why we test if the original polygon (poly) contains
# an inner point of polygonized geometry (pg)
return [pg for pg in polygonize(union) if poly.contains(pg.representative_point())]
def get_watershed(dataframe,
shapefile=None,
location_id=None,
lat=None,
lon=None,
singluar_removal=False):
'''
add a column with watershed information
Args:
dataframe: pandas dataframe
shapefile (string, dir to an shp file): shape information of watersheds
location_id (string): the name of column of location names/ids
lat (string): the name of the column of latitude information
lon (string): the name of the column of longitude information
singular_removal: boolean if true the isolated locations wille be removed.
'''
shapefile = WATERSHED_PATH if shapefile is None else shapefile
location_id = 'SITENUMBER' if location_id is None else location_id
lat = 'LATITUDE' if lat is None else lat
lon = 'LONGITUDE' if lon is None else lon
lats_and_lons = dataframe.groupby(location_id).first()[[lat, lon
]].reset_index()
shapes = []
names = []
for i in fiona.open(shapefile):
names.append(i['properties']['NAME'])
shapes.append(prep(shape(i['geometry'])))
dict_ = {}
for idx, row in lats_and_lons.iterrows():
sitenumber, lat, lon = row.values
point = Point([lon, lat])
for watershed_name, shape_ in zip(names, shapes):
if shape_.contains(point):
dict_[sitenumber] = watershed_name
break
dataframe['WATERSHED'] = dataframe[location_id].map(dict_)
if singluar_removal:
number_of_obs = dataframe.groupby(['WATERSHED']).count()[key]
singular_huc_areas = number_of_obs[number_of_obs <= 1].index
dataframe = dataframe[~dataframe.WATERSHED.isin(singular_huc_areas)]
return dataframe
def get_neighbors_within_distance(
self, agent, distance, center=False, relation="intersects"
):
"""Return a list of agents within `distance` of `agent`.
Distance is measured as a buffer around the agent's shape,
set center=True to calculate distance from center.
"""
if center:
shape = agent.shape.center().buffer(distance)
else:
shape = agent.shape.buffer(distance)
possible_neighbors = self._get_rtree_intersections(shape)
prepared_shape = prep(shape)
for other_agent in possible_neighbors:
if getattr(prepared_shape, relation)(other_agent.shape):
yield other_agent
def _get_ocean_shapefile():
""" Load the ocean basins shapefile. """
global _OCEAN_SHAPE
if _OCEAN_SHAPE is None:
try:
ocean_shp_fn = pkg_resources.resource_filename(
"marc_analysis", "data/ne_110m_ocean.shp")
_OCEAN_SHAPE = geopandas.read_file(ocean_shp_fn)
_OCEAN_SHAPE = MultiPolygon(_OCEAN_SHAPE.geometry.values.tolist())
_OCEAN_SHAPE = prep(_OCEAN_SHAPE)
except OSError:
warnings.warn("Unable to locate oceans shapefile; ocean"
" point mask is not available")
return _OCEAN_SHAPE
def makeMouseBites(self, cuts, diameter, spacing, offset=fromMm(0.25)):
"""
Take a list of cuts and perform mouse bites.
"""
bloatedSubstrate = prep(self.boardSubstrate.substrates.buffer(fromMm(0.01)))
for cut in cuts:
cut = cut.simplify(fromMm(0.001)) # Remove self-intersecting geometry
offsetCut = cut.parallel_offset(offset, "left")
length = offsetCut.length
count = int(length / spacing) + 1
for i in range(count):
if count == 1:
hole = offsetCut.interpolate(0.5, normalized=True)
else:
hole = offsetCut.interpolate( i * length / (count - 1) )
if bloatedSubstrate.intersects(hole.buffer(0.8 * diameter / 2)):
self.addNPTHole(wxPoint(hole.x, hole.y), diameter)
def create_grid(site_shape: BaseGeometry,
center: Point,
grid_angle: float,
intrarow_spacing: float,
interrow_spacing: float,
row_phase_offset: float,
max_sites: int = None,
) -> [Point]:
"""
Get a list of coordinates placed along a grid inside a site boundary
:param site_shape: Polygon
:param center: where to center the grid
:param grid_angle: in degrees where 0 is north, increasing clockwise
:param intrarow_spacing: distance between turbines along same row
:param interrow_spacing: distance between rows
:param row_phase_offset: offset of turbines along row from one row to the next
:param max_sites: max number of turbines
:return: list of coordinates
"""
grid_lines: [LineString] = make_grid_lines(
site_shape,
center,
grid_angle,
interrow_spacing
)
phase_offset: float = row_phase_offset * intrarow_spacing
prepared_site = prep(site_shape)
grid_positions: [Point] = []
for row_number, grid_line in enumerate(grid_lines):
length = grid_line.length
# generate points along that line with the right phase offset
x: float = (phase_offset * row_number) % intrarow_spacing
while x <= length:
position = grid_line.interpolate(x)
if prepared_site.contains(position):
if max_sites and len(grid_positions) >= max_sites:
break
grid_positions.append(position)
x += intrarow_spacing
if max_sites and len(grid_positions) >= max_sites:
break
return grid_positions
def remove_duplicates(layer):
indices = []
for i in range(0, len(layer)):
obj_prep = prep(layer[i])
for j in range(i + 1, len(layer)):
obj_comp = layer[j]
if j in indices:
continue
if obj_prep.contains(obj_comp):
indices.append(j)
for i in sorted(indices, reverse=True):
del layer[i]
return len(indices)
def clip(self,igeom):
## logic for convenience. just return the provided collection if a NoneType
## is passed for the 'igeom' arugment
if igeom is not None:
## take advange of shapely speedups
prep_igeom = prepared.prep(igeom)
## the weight array
weights = np.zeros(self.spatial.shape,dtype=float)
weights = np.ma.array(weights,mask=self.spatial._value_mask)
## do the spatial operation
for idx,geom in iter_array(self.spatial.value,return_value=True):
if not prep_igeom.contains(geom):
# if keep(prep_igeom,igeom,geom):
new_geom = igeom.intersection(geom)
weights[idx] = new_geom.area
self.spatial._value[idx] = new_geom
## set maximum weight to one
self.spatial.weights = weights/np.ma.max(weights)
def is_land(x, y, res="110m"):
if 'prep' not in globals():
raise ImportError("cartopy is needed to design ocean-only source.")
assert (res in ["10m", "50m",
"110m"]), "Resolution must be 10m, 50m, 110 m"
land_shp_fname = shpreader.natural_earth(resolution=res,
category='physical',
name='land')
land_geom = unary_union(list(
shpreader.Reader(land_shp_fname).geometries()))
land = prep(land_geom)
is_land = np.zeros(len(x))
for i in range(len(x)):
is_land[i] = land.contains(sgeom.Point(x[i], y[i]))
return is_land
def find_pages_for_polygon(self, polygon):
"""Find pages within polygon using repeated Geosearch
The Geosearch API has no polygon support; only point. To get around
this, I first find a collection of circles that together tile the
polygon, then for each circle I call the geosearch API.
"""
# When set to radius=10000, I got an error from the wikipedia API
points, radii = geom.find_circles_that_tile_polygon(
polygon, radius=10000)
titles = set()
for point, radius in zip(points, radii):
radius = math.ceil(radius)
res = self.find_page_titles_around_point(point=point, radius=radius)
titles.update(res)
# Get wikipedia page metadata for each of the titles I've found above
# Occasionally you get a disambiguation error, because the search is by
# title?
# Note, since I've already found titles that I know exist, I should set
# auto_suggest=False
pages = []
for title in titles:
try:
pages.append(wikipedia.page(title, auto_suggest=False))
except wikipedia.WikipediaException:
pass
# Make sure that all returned articles are within the original polygon
# [::-1] because returned as lat, lon. Point requires lon, lat
# "To test one polygon containment against a large batch of points, one
# should first use the prepared.prep() function"
prepared_polygon = prep(polygon)
new_pages = []
for page in pages:
try:
if prepared_polygon.contains(Point(page.coordinates[::-1])):
new_pages.append(page)
# Occasionally the page won't have a coordinates attribute
except KeyError:
pass
return new_pages
def add_boundary_perimeters(graph, geometries):
"""Add shared perimeter between nodes and the total geometry boundary.
:param graph: NetworkX graph
:param df: Geodataframe containing geometry information.
:return: The updated graph.
"""
prepared_boundary = prep(unary_union(geometries).boundary)
boundary_nodes = geometries.boundary.apply(prepared_boundary.intersects)
for node in graph:
graph.nodes[node]["boundary_node"] = bool(boundary_nodes[node])
if boundary_nodes[node]:
total_perimeter = geometries[node].boundary.length
shared_perimeter = sum(neighbor_data["shared_perim"]
for neighbor_data in graph[node].values())
boundary_perimeter = total_perimeter - shared_perimeter
graph.nodes[node]["boundary_perim"] = boundary_perimeter
def is_in_grid(self, shp):
"""Check if arbitrary polygon is within grid bounding box.
Args:
shp (shape):
Returns:
Bool whether shp is in grid box.
Depends on self.grid_box (shapely prep type) being defined
in environment.
"""
if not hasattr(shp, 'geom_type'):
raise Exception("CoreMSR [is_in_grid] : invalid shp given")
if not isinstance(self.grid_box, type(prep(Point(0, 0)))):
raise Exception("CoreMSR [is_in_grid] : invalid prep_adm0 "
"found")
return self.grid_box.contains(shp)
def edge_intersects_other_restricted_areas(
self, line: LineString, restricted_area_poly: Polygon) -> bool:
"""
check if a linestring intersects with other as restricted marked polygons which are different from the given restricted_Area_poly
:param line: the linestring which shall be checked
:param restricted_area_poly: the polygon which shall be excluded
:return: true if the linstring don't intesect other restricted polygon and touches the given polygon
"""
other_restricted_areas = self._get_other_restricted_area_polys(
restricted_area_poly)
for other_restricted_area in other_restricted_areas:
other_areas = self._get_other_restricted_area_polys(
other_restricted_area)
other_areas.remove(restricted_area_poly)
if prep(other_restricted_area).touches(
line) and not self._is_restricted_line(line, other_areas):
return False
return self._is_restricted_line(line, other_restricted_areas)
def revmatches():
for row, shp in izip(
subdict["refdata"].reader.iterRecords(),
subdict["refdata"].reader.iterShapes()):
#if dict(zip(self.fields, row))["SOV0NAME"] != "Russia": continue
#print "...",row[1] #row[4],row[14]
print str(row)[:100]
prepped = prep(
asShape(shp)
) # prepares geometry for many intersection tests (maybe only useful if is polygon and other is points, but not sure)
bbox = [
shp.points[0][0], shp.points[0][1],
shp.points[0][0], shp.points[0][1]
] if shp.shapeType == pyshp.POINT else shp.bbox
ilist = self.spindex.intersection(bbox)
for i in ilist:
othershp = self.reader.shape(i)
if prepped.intersects(asShape(othershp)):
yield row, i
def filter(self, intersects):
"""Filter results that intersect a given GeoFeature or Vector.
"""
try:
crs = self.crs
vector = intersects.geometry if isinstance(intersects, GeoFeature) else intersects
prepared_shape = prep(vector.get_shape(crs))
hits = []
for feature in self:
target_shape = feature.geometry.get_shape(crs)
if prepared_shape.overlaps(target_shape) or prepared_shape.intersects(target_shape):
hits.append(feature)
except IndexError:
hits = []
return FeatureCollection(hits)
def prepare_earth_geometry(geometry_resolution: str = "50m"):
"""
Preparations necessary for determining whether a point is over land or water.
This code may need to download a ZIP containing Earth geometry data the first time it runs.
Code borrowed from https://stackoverflow.com/a/48062502
:param geometry_resolution: The resolution of the NaturalEarth shapereader to use. Valid values are '10m', '50m'
or '110m'. Default '50m'.
:return: The PreparedGeometry object that can be used for point-land checking.
:raises ValueError: If geometry_resolution is not '10m', '50m', or '110m'.
"""
if geometry_resolution not in ["10m", "50m", "110m"]:
raise ValueError("Argument 'geometry_resolution' must be either '10m', '50m', or '110m'.")
print("-- Preparing Earth geometry...")
land_shp_fname = shpreader.natural_earth(resolution=geometry_resolution, category='physical', name='land')
land_geom = unary_union(list(shpreader.Reader(land_shp_fname).geometries()))
land = prep(land_geom)
print("-- Earth geometry prepared.")
return land
def surround_with_holes(geometry, hole_spacing, hole_radius, padding, max_distance):
"""
Surrounds the given geometry with holes, which are arranged in a square lattice around the structure.
This can be used for generating vortex traps like presented in https://doi.org/10.1103/PhysRevApplied.11.064053
:param geometry: The geometry around which the holes are generated
:param hole_spacing: Spacing between the holes
:param hole_radius: Radius of the holes
:param padding: Padding around the geometry
:param max_distance: Maximum distance of a hole from the geometry
:return: Shapely object, which describes the holes
"""
geometry = geometric_union(geometry if isinstance(geometry, (tuple, list)) else (geometry,))
buffer_around_waveguide = geometry.buffer(max_distance)
area_for_holes = prep(buffer_around_waveguide.difference(geometry.buffer(hole_radius + padding)))
area = buffer_around_waveguide.bounds
points = (Point(x, y) for x in np.arange(area[0], area[2], hole_spacing) for y in
np.arange(area[1], area[3], hole_spacing))
return MultiPolygon([point.buffer(hole_radius) for point in points if area_for_holes.contains(point)])
def prepare(feat):
"""Prepare geojson feature for further processing in `geopip._shapely.p_in_polygon()`
Parameters:
feat: Dict[str, Any] Geojson feature (only Polygon and MultiPolygon will
be processed).
Returns:
List[Dict[str, Any]] Prepared shapes for `geopip._shapely.p_in_polygon()`
"""
if feat['geometry']['type'] in ('Polygon', 'MultiPolygon'):
shp = shape(feat['geometry'])
return [{
'shape': prep(shp),
'properties': feat['properties'],
'bounds': shp.bounds,
}]
else:
return []
def minDistInLayer(rule, dd, layerID, thresh):
layer = dd.layers[layerID]
objs = layer.objs
for id1, o1 in objs.items():
bbox = o1.shape.buffer(thresh).bounds
candidates = layer.index.intersection(bbox)
prepared = None
for cind in candidates:
o2 = objs[cind]
if o1.id < o2.id:
if prepared == None:
prepared = prep(o1.shape)
contained = prepared.contains(o2.shape) or \
o2.shape.contains(o1.shape)
if not contained and checkThresh(o1, o2, thresh):
(p1, p2) = NearLineSegment.featureToFeature(o1, o2)
wit = Witness(Witness.LINE_SEGMENT, (p1, p2))
dd.violations.add(Violation.ofRule(rule, [o1, o2], [wit]))
def geometryvariable_get_mask_from_intersects(
gvar,
geometry,
use_spatial_index=env.USE_SPATIAL_INDEX,
keep_touches=False,
original_mask=None):
# Create the fill array and reference the mask. This is the output geometry value array.
if original_mask is None:
original_mask = gvar.get_mask(create=True)
fill = original_mask.copy()
fill.fill(True)
ref_fill_mask = fill.reshape(-1)
# Track global indices because spatial operations only occur on non-masked values.
global_index = np.arange(original_mask.size)
global_index = np.ma.array(global_index, mask=original_mask).compressed()
# Select the geometry targets. If an original mask is provided, use this. It may be modified to limit the search
# area for intersects operations. Useful for speeding up grid subsetting operations.
geometry_target = np.ma.array(gvar.get_value(),
mask=original_mask).compressed()
if use_spatial_index:
si = gvar.get_spatial_index(target=geometry_target)
# Return the indices of the geometries intersecting the target geometry, and update the mask accordingly.
for idx in si.iter_intersects(geometry,
geometry_target,
keep_touches=keep_touches):
ref_fill_mask[global_index[idx]] = False
else:
# Prepare the polygon for faster spatial operations.
prepared = prep(geometry)
# We are not keeping touches at this point. Remember the mask is an inverse.
for idx, geom in iter_array(geometry_target, return_value=True):
bool_value = False
if prepared.intersects(geom):
if not keep_touches and geometry.touches(geom):
bool_value = True
else:
bool_value = True
ref_fill_mask[global_index[idx]] = bool_value
return fill
def Filter(options):
'''
Rounding
http://gis.stackexchange.com/questions/8650/how-to-measure-the-accuracy-of-latitude-and-longitude
'''
startTime = time.time()
inputData = np.loadtxt(options.inputFileName,
delimiter=',',
usecols=(0, 1, 2))
outputData = open(options.outputFileName, 'w')
lon = inputData[:, 0]
lat = inputData[:, 1]
height = inputData[:, 2]
if options.polyFilter != False:
poly = np.loadtxt(options.polyFilter, delimiter=',', usecols=(1, 2))
poly = Polygon(poly)
poly = prep(poly)
minHeight = float(options.minHeight)
maxHeight = float(options.maxHeight)
numPoints = inputData.shape[0]
for i in range(0, numPoints):
pointLon, pointLat, pointHeight = lon[i], lat[i], height[i]
if minHeight < pointHeight < maxHeight:
if options.polyFilter != False:
point = Point(pointLon, pointLat)
if poly.contains(point):
outputData.write(
str(round(pointLon, 8)) + ',' +
str(round(pointLat, 8)) + ',' +
str(round(pointHeight, 4)) + '\n')
else:
outputData.write(
str(round(pointLon, 8)) + ',' + str(round(pointLat, 8)) +
',' + str(round(pointHeight, 4)) + '\n')
outputData.close()
#Use np.savetxt('test.out', x, delimiter=',')
print(int(time.time() - startTime), ' seconds runtime')
