def testDispatcherClassifierThreeRule(self):
# create polygons to test
box1 = box(0, 0, 100, 100)
box2 = box(0, 0, 10, 10)
poly = Polygon([(0, 0), (0, 1000), (50, 1250), (1000, 1000), (1000, 0), (0, 0)])
dispatcher = RuleBasedDispatcher([QuadrilaterRule(), NotQuadrilaterRule()])
dispatcher_classifier = DispatcherClassifier(dispatcher, [AreaClassifier(500), AreaClassifier(500)])
# simple dispatch test
cls, probability, dispatch, _ = dispatcher_classifier.dispatch_classify(None, box1)
self.assertEqual(1, cls)
self.assertEqual(1.0, probability)
self.assertEqual(0, dispatch)
# batch dispatch test
classes, probas, dispatches, _ = dispatcher_classifier.dispatch_classify_batch(None, [box1, box2, poly])
self.assertEqual(1, classes[0])
self.assertEqual(0, classes[1])
self.assertEqual(1, classes[2])
self.assertEqual(1.0, probas[0])
self.assertEqual(1.0, probas[1])
self.assertEqual(1.0, probas[2])
self.assertEqual(0, dispatches[0])
self.assertEqual(0, dispatches[1])
self.assertEqual(1, dispatches[2])
def test_update_crs_to_cartesian(self):
"""Test a spherical to cartesian CRS update."""
bbox = box(-170., 40., 150., 80.)
original_bounds = deepcopy(bbox.bounds)
geom = GeometryVariable(name='geom', value=[bbox], dimensions='geom', crs=Spherical())
other_crs = Cartesian()
geom.update_crs(other_crs)
actual = geom.get_value()[0].bounds
desired = (-0.7544065067354889, -0.13302222155948895, -0.15038373318043535, 0.38302222155948895)
self.assertNumpyAllClose(np.array(actual), np.array(desired))
self.assertIsInstance(geom.crs, Cartesian)
other_crs = Spherical()
geom.update_crs(other_crs)
self.assertEqual(geom.crs, Spherical())
actual = geom.get_value()[0].bounds
self.assertNumpyAllClose(np.array(original_bounds), np.array(actual))
# Test data may not be wrapped.
bbox = box(0, 40, 270, 80)
geom = GeometryVariable(name='geom', value=[bbox], dimensions='geom', crs=Spherical())
other_crs = Cartesian()
with self.assertRaises(ValueError):
geom.update_crs(other_crs)
def build_grid():
#grid_boundaries=(-185,15,-65,70) # upright edge is plus delta (lower 48 states)
grid={(i,j):{}
for i in range((grid_boundaries[2]-grid_boundaries[0])/delta)
for j in range((grid_boundaries[3]-grid_boundaries[1])/delta) }
with fiona.open(options.shape_file_path) as fc:
print >>sys.stderr, fc.driver,"###",fc.schema,"###", len(fc),"###",fc.crs
print >> sys.stderr,fc.schema
print >>sys.stderr, "Number of records:", len(fc)
print >>sys.stderr, "Bounds of all records:", fc.bounds
print >>sys.stderr, "Bounds applied:",grid_boundaries
print >> sys.stderr,"######## indexing shapes to grid ########"
print >> sys.stderr,"shapes complete:"
c=0
for feature in fc:
c+=1
GEOID=str(feature['properties']['GEOID'])
NAME=feature['properties']['NAME']
INTPTLON=float(feature['properties']['INTPTLON'])
INTPTLAT=float(feature['properties']['INTPTLAT'])
shp=shape(feature['geometry']) # list of coordinates of geometric shape
bb=box(*shp.bounds) #box(minx,miny,maxx,maxy)) creates one boxlike shape to rule them all
for i,j in grid:
grid_box=box(i*delta+grid_boundaries[0]
,j*delta+grid_boundaries[1]
,(i+1)*delta+grid_boundaries[0]
,(j+1)*delta+grid_boundaries[1] )
if grid_box.intersects(bb): #http://toblerity.org/shapely/manual.html#object.intersects
grid[(i,j)][bb]=(shp,GEOID,NAME,INTPTLON,INTPTLAT) # (county shape, countyID)
if c%100==0:
print >> sys.stderr, c
return grid
def set_spatial_ranking(geometry):
"""Given that we have a spatial query in ogc:Filter we check the type of geometry
and set the ranking variables"""
if util.ranking_enabled:
if geometry.type in ['Polygon', 'Envelope']:
util.ranking_pass = True
util.ranking_query_geometry = geometry.wkt
elif geometry.type in ['LineString', 'Point']:
from shapely.geometry.base import BaseGeometry
from shapely.geometry import box
from shapely.wkt import loads,dumps
ls = loads(geometry.wkt)
b = ls.bounds
if geometry.type == 'LineString':
tmp_box = box(b[0],b[1],b[2],b[3])
tmp_wkt = dumps(tmp_box)
if tmp_box.area > 0:
util.ranking_pass = True
util.ranking_query_geometry = tmp_wkt
elif geometry.type == 'Point':
tmp_box = box((float(b[0])-1.0),(float(b[1])-1.0),(float(b[2])+1.0),(float(b[3])+1.0))
tmp_wkt = dumps(tmp_box)
util.ranking_pass = True
util.ranking_query_geometry = tmp_wkt
def _get_reprojected_features(
input_file=None,
dst_bounds=None,
dst_crs=None,
validity_check=None
):
assert isinstance(input_file, str)
assert isinstance(dst_bounds, tuple)
assert isinstance(dst_crs, CRS)
assert isinstance(validity_check, bool)
with fiona.open(input_file, 'r') as vector:
vector_crs = CRS(vector.crs)
# Reproject tile bounding box to source file CRS for filter:
if vector_crs == dst_crs:
dst_bbox = box(*dst_bounds)
else:
dst_bbox = reproject_geometry(
box(*dst_bounds),
src_crs=dst_crs,
dst_crs=vector_crs,
validity_check=True
)
for feature in vector.filter(bbox=dst_bbox.bounds):
feature_geom = shape(feature['geometry'])
if not feature_geom.is_valid:
try:
feature_geom = feature_geom.buffer(0)
assert feature_geom.is_valid
warnings.warn(
"fixed invalid vector input geometry"
)
except AssertionError:
warnings.warn(
"irreparable geometry found in vector input file"
)
continue
geom = clean_geometry_type(
feature_geom.intersection(dst_bbox),
feature_geom.geom_type
)
if geom:
# Reproject each feature to tile CRS
if vector_crs == dst_crs and validity_check:
assert geom.is_valid
else:
try:
geom = reproject_geometry(
geom,
src_crs=vector_crs,
dst_crs=dst_crs,
validity_check=validity_check
)
except ValueError:
warnings.warn("feature reprojection failed")
yield {
'properties': feature['properties'],
'geometry': mapping(geom)
}
def render_pad(self, layer_query, drill, **options):
DRU = self.get_DRU()
if self._layer_matches(layer_query, "Holes"):
hole = shapes.Point(self.get_x(), self.get_y()).buffer(drill/2)
return hole;
radius = (drill/2);
radius = radius + scaleAndBound(radius, DRU.rvPadTop, DRU.rlMinPadTop, DRU.rlMaxPadTop)
if layer_query is not None and self.get_file().get_layer(layer_query).get_number() > 16 and not self._layer_matches(layer_query, "tStop") and not self._layer_matches(layer_query,"bStop"):
return shapes.LineString()
if self.get_shape() == "square":
shape = shapes.box(self.get_x() - radius ,
self.get_y() - radius,
self.get_x() + radius,
self.get_y() + radius)
elif self.get_shape() == "round" or self.get_shape() is None:
shape = shapes.point.Point(self.get_x(), self.get_y()).buffer(radius)
elif self.get_shape() == "octagon":
shape = shapes.box(self.get_x() - radius,
self.get_y() - radius,
self.get_x() + radius,
self.get_y() + radius)
shape = shape.intersection(affinity.rotate(shape, 45))
elif self.get_shape() == "long":
shape = shapely.ops.unary_union([shapes.point.Point(self.get_x() + DRU.psElongationLong/100.0 * radius,
self.get_y()).buffer(radius),
shapes.point.Point(self.get_x() - DRU.psElongationLong/100.0 * radius,
self.get_y()).buffer(radius),
shapes.box(self.get_x() - DRU.psElongationLong/100.0 * radius,
self.get_y() - radius,
self.get_x() + DRU.psElongationLong/100.0 * radius,
self.get_y() + radius)])
elif self.get_shape() == "offset":
shape = shapely.ops.unary_union([shapes.point.Point(self.get_x() + DRU.psElongationOffset/100.0 * radius * 2,
self.get_y()).buffer(radius),
shapes.point.Point(self.get_x(),
self.get_y()).buffer(radius),
shapes.box(self.get_x(),
self.get_y() - radius,
self.get_x() + DRU.psElongationLong/100.0 * radius * 2,
self.get_y() + radius)
])
else:
raise Swoop.SwoopError("Unknown pad shape: '{}'".format(self.get_shape()))
if shape is not None:
if self._layer_matches(layer_query,"tStop") or self._layer_matches(layer_query, "bStop"):
shape = shape.buffer(computeStopMaskExtra(radius, DRU))
if options and "fail_on_missing" in options and options["fail_on_missing"] and shape is None:
raise NotImplemented("Geometry for pad shape '{}' is not implemented yet.".format(self.get_shape()))
elif shape is None:
shape = shapes.LineString()
return shape
def __init__(self, element):
self._root = element
OM_NS = ns.get_versioned_namespace('om','1.0')
GML_NS = ns.get_versioned_namespace('gml','3.1.1')
SWE_NS = ns.get_versioned_namespace('swe','1.0')
XLINK_NS = ns.get_namespace("xlink")
self.name = testXMLValue(self._root.find(nsp("name", GML_NS)))
self.description = testXMLValue(self._root.find(nsp("description", GML_NS)))
self.observedProperties = []
for op in self._root.findall(nsp('observedProperty', OM_NS)):
self.observedProperties.append(testXMLAttribute(op,nsp('href', XLINK_NS)))
# BBOX
try:
envelope = self._root.find(nsp('boundedBy/Envelope', GML_NS))
lower_left_corner = testXMLValue(envelope.find(nsp('lowerCorner', GML_NS))).split(" ")
upper_right_corner = testXMLValue(envelope.find(nsp('upperCorner', GML_NS))).split(" ")
self.bbox_srs = Crs(testXMLAttribute(envelope,'srsName'))
# Always keep the BBOX as minx, miny, maxx, maxy
if self.bbox_srs.axisorder == "yx":
self.bbox = box(float(lower_left_corner[1]), float(lower_left_corner[0]), float(upper_right_corner[1]), float(upper_right_corner[0]))
else:
self.bbox = box(float(lower_left_corner[0]), float(lower_left_corner[1]), float(upper_right_corner[0]), float(upper_right_corner[1]))
except Exception:
self.bbox = None
self.bbox_srs = None
# Time range
fp = nsp('samplingTime', OM_NS)
mp = nsp('TimePeriod', GML_NS)
lp = nsp('beginPosition', GML_NS)
begin_position_element = self._root.find('%s/%s/%s' % (fp, mp, lp))
self.begin_position = extract_time(begin_position_element)
ep = nsp('endPosition', GML_NS)
end_position_element = self._root.find('%s/%s/%s' % (fp, mp, ep))
self.end_position = extract_time(end_position_element)
feature_of_interest_element = self._root.find(nsp("featureOfInterest", OM_NS))
self.feature_type = testXMLValue(feature_of_interest_element.find(nsp("FeatureCollection/metaDataProperty/name", GML_NS)))
# Now the fields change depending on the FeatureType
result_element = self._root.find(nsp("result", OM_NS))
#TODO: This should be implemented as a Factory
self.feature = None
if self.feature_type == 'timeSeries':
self.feature = SweTimeSeries(feature_of_interest=feature_of_interest_element, result=result_element, GML_NS=GML_NS, OM_NS=OM_NS, SWE_NS=SWE_NS)
elif self.feature_type == 'trajectoryProfile':
self.feature = SweTrajectoryProfile(feature_of_interest=feature_of_interest_element, result=result_element, GML_NS=GML_NS, OM_NS=OM_NS, SWE_NS=SWE_NS)
elif self.feature_type == 'timeSeriesProfile':
self.feature = SweTimeseriesProfile(feature_of_interest=feature_of_interest_element, result=result_element, GML_NS=GML_NS, OM_NS=OM_NS, SWE_NS=SWE_NS)
def setUp(self):
self.f1 = FeatureTest(1, influence="notall")
self.f2 = FeatureTest(10, influence="notall")
self.f3 = FeatureTestReplace(100, influence="notall")
self.f4 = FeatureTestAddition(1000, influence="notall")
self.f1.shape = geom.box(0.0, 0.0, 1.0, 1.0)
self.f2.shape = geom.box(0.5, 0.5, 1.5, 1.5)
self.f3.shape = geom.box(0.75, 0, 0.8, 2)
self.f4.shape = geom.box(0.75, 0, 0.8, 2)
def fixture_polygon_with_hole(self):
outer_box = box(2.0, 10.0, 4.0, 20.0)
inner_box = box(2.5, 10.5, 3.5, 15.5)
outer_coords = list(outer_box.exterior.coords)
inner_coords = [list(inner_box.exterior.coords)]
with_interior = Polygon(outer_coords, holes=inner_coords)
return with_interior
def load_polys_and_clip(shpfile, wl, el, nl, sl, dateline=False, lev=1):
"""
Loop through polygons in shapefile, if they overlap with user
specified rectangle, load the polygon and clip
Note that GSHHS polygons are -180-->180, shorelines that cross the 180 line are
clipped into west and east polygons, so there is some special casing to handle
clip rectangles that cross the dateline
Change lev to 2 if loading lakes
These *may not* be able to be used to clip trajectories from GAn in Arc?
"""
bound_box = (wl, sl, el, nl)
clip_box = sgeo.box(*bound_box)
if dateline:
bound_box = (-180, sl, 180, nl)
bna_polys = []
with fiona.open(shpfile) as source:
for s in source.filter(bbox=bound_box):
geo = s['geometry']['coordinates']
for c in geo:
poly = sgeo.Polygon(c)
if dateline:
poly_bnds = poly.bounds
if poly_bnds[0] > -10 and poly_bnds[2] <= 180:
clip_box = sgeo.box(wl, sl, 180, nl)
else:
clip_box = sgeo.box(-180, sl, el, nl)
clipped_polys = poly.intersection(clip_box)
if type(clipped_polys) is sgeo.multipolygon.MultiPolygon:
for clipped_poly in clipped_polys:
try:
bna_polys.append(clipped_poly.exterior.xy)
except AttributeError:
pass
elif type(clipped_polys) is sgeo.polygon.Polygon:
try:
bna_polys.append(clipped_polys.exterior.xy)
except AttributeError:
pass
if len(bna_polys) < 1 and lev == 1: # all water
bna_polys.append(clip_box.exterior.xy)
levels = [2]
else: # define levels and add lakes if specified
levels = [lev for x in range(len(bna_polys))]
if lev == 1:
bna_polys.insert(0, ([wl, wl, el, el], [sl, nl, nl, sl]))
levels.insert(0, 0)
return bna_polys, levels
def test(self):
gs = self.fixture_grid_chunker()
desired_dst_grid_sum = gs.dst_grid.parent['data'].get_value().sum()
desired_dst_grid_sum = MPI_COMM.gather(desired_dst_grid_sum)
if vm.rank == 0:
desired_sum = np.sum(desired_dst_grid_sum)
desired = [{'y': slice(0, 180, None), 'x': slice(0, 240, None)},
{'y': slice(0, 180, None), 'x': slice(240, 480, None)},
{'y': slice(0, 180, None), 'x': slice(480, 720, None)},
{'y': slice(180, 360, None), 'x': slice(0, 240, None)},
{'y': slice(180, 360, None), 'x': slice(240, 480, None)},
{'y': slice(180, 360, None), 'x': slice(480, 720, None)}]
actual = list(gs.iter_dst_grid_slices())
self.assertEqual(actual, desired)
gs.write_chunks()
if vm.rank == 0:
rank_sums = []
for ctr in range(1, gs.nchunks_dst[0] * gs.nchunks_dst[1] + 1):
src_path = gs.create_full_path_from_template('src_template', index=ctr)
dst_path = gs.create_full_path_from_template('dst_template', index=ctr)
src_field = RequestDataset(src_path).get()
dst_field = RequestDataset(dst_path).get()
src_envelope_global = box(*src_field.grid.extent_global)
dst_envelope_global = box(*dst_field.grid.extent_global)
self.assertTrue(does_contain(src_envelope_global, dst_envelope_global))
actual = get_variable_names(src_field.data_variables)
self.assertIn('data', actual)
actual = get_variable_names(dst_field.data_variables)
self.assertIn('data', actual)
actual_data_sum = dst_field['data'].get_value().sum()
actual_data_sum = MPI_COMM.gather(actual_data_sum)
if MPI_RANK == 0:
actual_data_sum = np.sum(actual_data_sum)
rank_sums.append(actual_data_sum)
if vm.rank == 0:
self.assertAlmostEqual(desired_sum, np.sum(rank_sums))
index_path = gs.create_full_path_from_template('index_file')
self.assertTrue(os.path.exists(index_path))
vm.barrier()
index_path = gs.create_full_path_from_template('index_file')
index_field = RequestDataset(index_path).get()
self.assertTrue(len(list(index_field.keys())) > 2)
def __init__(self, *args, **kwargs):
super(TestClassesNodes, self).__init__(*args, **kwargs)
self.f1 = FeatureTest(1)
self.f2 = FeatureTest(10)
self.f2.shape = geom.box(1.0, 0.0, 2.0, 1.0)
self.f3 = FeatureTest(100)
self.f3.shape = geom.box(1.0, 0.0, 3.0, 1.0)
self.n1 = BlendNode([self.f1])
self.n2 = BlendNode([self.f2])
self.n3 = BlendNode([self.f3])
def testRuleBasedDispatcher(self):
# prepare data for test
box1 = box(0, 0, 100, 100)
box2 = box(0, 0, 10, 10)
dispatcher = RuleBasedDispatcher([CatchAllRule()], ["catchall"])
self.assertEqual(dispatcher.dispatch(None, box1), "catchall")
dispatch_batch = dispatcher.dispatch_batch(None, [box1, box2])
assert_array_equal(dispatch_batch, ["catchall", "catchall"])
labels, dispatch_map = dispatcher.dispatch_map(None, [box1, box2])
assert_array_equal(labels, dispatch_batch)
assert_array_equal(dispatch_map, [0, 0])
def _generate_points(self, polygon: Polygon, n) -> list:
"""Generates sample points within a given geometry
:param shapely.geometry.Polygon polygon: the polygon to create points in
:param int n: Number of points to generate in polygon
:return: A list with point in the polygon
:rtype: list[Point]
"""
if n <= 0:
return []
if polygon.area <= 0:
return []
bbox = polygon.envelope
"""(minx, miny, maxx, maxy) bbox"""
if (polygon.area * self.t) < bbox.area:
if (bbox.bounds[2] - bbox.bounds[0]) > (bbox.bounds[3] - bbox.bounds[1]):
bbox_1 = box(*self._bbox_left(bbox.bounds))
bbox_2 = box(*self._bbox_right(bbox.bounds))
else:
bbox_1 = box(*self._bbox_bottom(bbox.bounds))
bbox_2 = box(*self._bbox_top(bbox.bounds))
p1 = shape(polygon)
p1 = p1.difference(bbox_1)
p2 = shape(polygon)
p2 = p2.difference(bbox_2)
del bbox_1, bbox_2
# k = bisect.bisect_left(u, p1.area / polygon.area)
k = int(round(n * (p1.area / polygon.area)))
v = self._generate_points(p1, k) + self._generate_points(p2, n - k)
del polygon, p1, p2
else:
v = []
max_iterations = self.t * n + 5 * math.sqrt(self.t * n)
v_length = len(v)
while v_length < n and max_iterations > 0:
max_iterations -= 1
v.append(self._random_point_in_polygon(polygon))
v_length = len(v)
if len(v) < n:
raise Exception('Too many iterations')
self.logging.debug('Generated %s points', n)
del bbox
return v
def test_multiple_interiors(self):
exterior = numpy.array(sgeom.box(0, 0, 12, 12).exterior.coords)
interiors = [
numpy.array(sgeom.box(1, 1, 2, 2, ccw=False).exterior.coords),
numpy.array(sgeom.box(1, 8, 2, 9, ccw=False).exterior.coords),
]
poly = sgeom.Polygon(exterior, interiors)
target = ccrs.PlateCarree()
source = ccrs.Geodetic()
assert len(list(target.project_geometry(poly, source))) == 1
def testCustomDispatcher(self):
# prepare data for test
box1 = box(0, 0, 500, 500)
box2 = box(0, 0, 10, 10)
box3 = box(0, 0, 1000, 1000)
dispatcher = CustomDispatcher()
self.assertEqual(dispatcher.dispatch(None, box1), "BIG")
dispatch_batch = dispatcher.dispatch_batch(None, [box1, box2, box3])
assert_array_equal(dispatch_batch, ["BIG", "SMALL", "BIG"])
labels, dispatch_map = dispatcher.dispatch_map(None, [box1, box2, box3])
assert_array_equal(labels, dispatch_batch)
assert_array_equal(dispatch_map, [1, 0, 1])
def setUp(self):
self.background = FeatureTest(100, influence="notall", val_influence=1)
self.background.shape = geom.box(5, 5, 45, 45)
self.up = FeatureTest(150, influence="notall", val_influence=1)
self.up.shape = geom.box(10, 10, 30, 30)
self.rep = FeatureTestReplace(0, influence="notall", val_influence=1)
self.rep.shape = geom.box(10, 10, 30, 30)
self.forest = Vegetation(geom.box(5, 5, 45, 45), model=AbstractModel(), tree_number=50)
self.env = Environment([self.rep, self.up, self.background, self.forest])
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 week_data(date, bounds):
"""
Query Transfer data for a week, optionally spatially filtered.
Returns a GeoJSON FeatureCollection.
"""
try:
# week should be in ISO YYYY-MM-DD format
week_start = datetime.datetime.strptime(date, '%Y-%m-%d').date()
except ValueError as e:
r = jsonify({"error": str(e)})
r.status_code = 400
return r
if bounds:
# Optionally, filter the results spatially
# west,south,east,north in degrees (latitude/longitude)
try:
m = re.match(r'((-?\d+(?:\.\d+)?),){3}(-?\d+(\.\d+)?)$', bounds)
if not m:
raise ValueError("Bounds should be longitudes/latitudes in west,south,east,north order")
w,s,e,n = map(float, bounds.split(','))
if w < -180 or w > 180 or e < -180 or e > 180:
raise ValueError("Bounds should be longitudes/latitudes in west,south,east,north order")
elif s < -90 or s > 90 or n < -90 or n > 90 or s > n:
raise ValueError("Bounds should be longitudes/latitudes in west,south,east,north order")
if e < w:
bounds = MultiPolygon([box(w, s, 180, n), box(-180, s, e, n)])
else:
bounds = MultiPolygon([box(w, s, e, n)])
except ValueError as e:
r = jsonify({"error": str(e)})
r.status_code = 400
return r
# Filter the transfers - the DB query happens here
query = Transfer.query.filter_by(week_start=week_start)
if bounds:
query = query.filter(Transfer.location.ST_Intersects(from_shape(bounds, 4326)))
features = []
for transfer in query:
features.append(transfer.as_geojson())
# Format the response as a GeoJSON FeatureCollection
return jsonify({
"type": "FeatureCollection",
"features": features,
})
def test_conflicts(self):
conflict = box(0, 0, 20, 20)
self.map.conflict_union(conflict, margin=0)
#: correct calculation of conflict density
self.assertEqual(self.map.conflict_density(10, 10, radius=10), 1)
self.assertEqual(self.map.conflict_density(50, 50, radius=10), 0)
self.assertAlmostEqual(self.map.conflict_density(10, 20, radius=10),
0.5, places=PLACES)
#: test positioning
new = box(10, 10, 50, 30)
self.assertEqual(self.map.find_free_position(new, step=1, number=10),
(10, 20))
self.assertIsNone(self.map.find_free_position(new, step=1, number=5))
new = box(30, 30, 50, 30)
self.assertEqual(self.map.find_free_position(new, number=0), (30, 30))
def test_polygon_interiors():
ax = plt.subplot(211, projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_global()
pth = Path([[0, -45], [60, -45], [60, 45], [0, 45], [0, 45],
[10, -20], [10, 20], [40, 20], [40, -20], [10, 20]],
[1, 2, 2, 2, 79, 1, 2, 2, 2, 79])
patches_native = []
patches = []
for geos in cpatch.path_to_geos(pth):
for pth in cpatch.geos_to_path(geos):
patches.append(mpatches.PathPatch(pth))
# buffer by 10 degrees (leaves a small hole in the middle)
geos_buffered = geos.buffer(10)
for pth in cpatch.geos_to_path(geos_buffered):
patches_native.append(mpatches.PathPatch(pth))
# Set high zorder to ensure the polygons are drawn on top of coastlines.
collection = PatchCollection(patches_native, facecolor='red', alpha=0.4,
transform=ax.projection, zorder=10)
ax.add_collection(collection)
collection = PatchCollection(patches, facecolor='yellow', alpha=0.4,
transform=ccrs.Geodetic(), zorder=10)
ax.add_collection(collection)
# test multiple interior polygons
ax = plt.subplot(212, projection=ccrs.PlateCarree(),
xlim=[-5, 15], ylim=[-5, 15])
ax.coastlines()
exterior = np.array(sgeom.box(0, 0, 12, 12).exterior.coords)
interiors = [np.array(sgeom.box(1, 1, 2, 2, ccw=False).exterior.coords),
np.array(sgeom.box(1, 8, 2, 9, ccw=False).exterior.coords)]
poly = sgeom.Polygon(exterior, interiors)
patches = []
for pth in cpatch.geos_to_path(poly):
patches.append(mpatches.PathPatch(pth))
collection = PatchCollection(patches, facecolor='yellow', alpha=0.4,
transform=ccrs.Geodetic(), zorder=10)
ax.add_collection(collection)
def test_init(self):
ge = GeometrySplitter(self.fixture_polygon_with_hole)
self.assertIsInstance(ge.geometry, Polygon)
# Test a geometry with no holes.
with self.assertRaises(NoInteriorsError):
GeometrySplitter(box(1, 2, 3, 4))
def test_iter_intersects(self):
desired = {False: [(0, False), (1, False), (2, False), (3, True), (4, False)],
True: [(0, False), (1, True), (2, False), (3, True), (4, False)]}
def the_geometry_iterator():
x = [1, 2, 3, 4, 5]
y = [6, 7, 8, 9, 10]
mask = [False, False, True, False, False, False]
for idx in range(len(x)):
if mask[idx]:
yld = None
else:
yld = Point(x[idx], y[idx])
yield idx, yld
subset_geometry = box(2.0, 7.0, 4.5, 9.5)
for keep_touches in [False, True]:
gp = GeometryProcessor(the_geometry_iterator(), subset_geometry, keep_touches=keep_touches)
actual = list(gp.iter_intersects())
for idx_actual in range(len(actual)):
self.assertEqual(actual[idx_actual][0:2], desired[keep_touches][idx_actual])
if idx_actual == 2:
self.assertIsNone(actual[idx_actual][2])
else:
self.assertIsInstance(actual[idx_actual][2], Point)
# Test the object may only be used once.
with self.assertRaises(ValueError):
list(gp.iter_intersects())
def test_split(self):
to_test = [self.fixture_polygon_with_hole,
MultiPolygon([self.fixture_polygon_with_hole, box(200, 100, 300, 400)])]
desired_counts = {0: 4, 1: 5}
for ctr, t in enumerate(to_test):
ge = GeometrySplitter(t)
split = ge.split()
self.assertEqual(len(split), desired_counts[ctr])
self.assertEqual(split.area, t.area)
actual_bounds = [g.bounds for g in split]
actual_areas = [g.area for g in split]
desired_bounds = [(2.0, 10.0, 3.0, 13.0), (3.0, 10.0, 4.0, 13.0),
(3.0, 13.0, 4.0, 20.0), (2.0, 13.0, 3.0, 20.0)]
desired_areas = [1.75, 1.75, 5.75, 5.75]
if ctr == 1:
desired_bounds.append((200.0, 100.0, 300.0, 400.0))
desired_areas.append(30000.0)
self.assertEqual(actual_bounds, desired_bounds)
self.assertEqual(actual_areas, desired_areas)
def create(self, user_token, layer):
from gbi_server.model import User
user = User.by_authproxy_token(user_token)
if not user:
raise InvalidUserToken()
result = db.session.query(WMTS, WMTS.view_coverage.transform(3857).wkt()).filter_by(name=layer).first()
if result:
wmts, view_coverage = result
if wmts and wmts.is_public:
return wkt.loads(view_coverage)
if user.is_customer:
couch_url = self.couchdb_url
couchdb = CouchDBBox(couch_url, '%s_%s' % (SystemConfig.AREA_BOX_NAME, user.id))
geom = couchdb.layer_extent(self.geometry_layer)
return optimize_geometry(geom) if geom else None
elif user.is_service_provider:
couch_url = self.couchdb_url
couchdb = CouchDBBox(couch_url, '%s_%s' % (SystemConfig.AREA_BOX_NAME, user.id))
geom = couchdb.layer_extent()
return optimize_geometry(geom) if geom else None
elif user.is_admin or user.is_consultant:
# permit access to everything
return box(-20037508.3428, -20037508.3428, 20037508.3428, 20037508.3428)
return None
def test_o_and_m_get_observation(self):
data = open(resource_file(os.path.join('ioos_swe', 'OM-GetObservation.xml')), "rb").read()
d = IoosGetObservation(data)
assert d.ioos_version == "1.0"
assert len(d.observations) == 1
ts = d.observations[0]
assert ts.description.replace("\n", "").replace(" ", "").replace(" -", " -") == "Observations at point station urn:ioos:station:wmo:41001, 150 NM East of Cape HATTERAS. Observations at point station urn:ioos:station:wmo:41002, S HATTERAS - 250 NM East of Charleston, SC"
assert ts.begin_position == datetime(2009, 5, 23, 0, tzinfo=pytz.utc)
assert ts.end_position == datetime(2009, 5, 23, 2, tzinfo=pytz.utc)
assert sorted(ts.procedures) == sorted(["urn:ioos:station:wmo:41001", "urn:ioos:station:wmo:41002"])
assert sorted(ts.observedProperties) == sorted([ "http://mmisw.org/ont/cf/parameter/air_temperature",
"http://mmisw.org/ont/cf/parameter/sea_water_temperature",
"http://mmisw.org/ont/cf/parameter/wind_direction",
"http://mmisw.org/ont/cf/parameter/wind_speed",
"http://mmisw.org/ont/ioos/parameter/dissolved_oxygen"
])
assert ts.feature_type == "timeSeries"
assert ts.bbox_srs.getcode() == "EPSG:4326"
assert ts.bbox.equals(box(-75.42, 32.38, -72.73, 34.7))
assert ts.location["urn:ioos:station:wmo:41001"].equals(Point(-72.73, 34.7))
assert ts.location["urn:ioos:station:wmo:41002"].equals(Point(-75.415, 32.382))
def _rings_to_multi_polygon(self, rings):
exterior_rings = []
interior_rings = []
for ring in rings:
if ring.is_ccw:
interior_rings.append(ring)
else:
exterior_rings.append(ring)
polygon_bits = []
# Turn all the exterior rings into polygon definitions,
# "slurping up" and interior rings they contain.
for exterior_ring in exterior_rings:
polygon = sgeom.Polygon(exterior_ring)
holes = []
for interior_ring in interior_rings[:]:
if 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 = sgeom.Polygon(self.boundary)
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)
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 test_google_wts():
gt = cimgt.GoogleTiles()
ll_target_domain = sgeom.box(-15, 50, 0, 60)
multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
with assert_raises(AssertionError):
list(gt.find_images(target_domain, -1))
assert_equal(tuple(gt.find_images(target_domain, 0)),
((0, 0, 0),))
assert_equal(tuple(gt.find_images(target_domain, 2)),
((1, 1, 2), (2, 1, 2)))
assert_equal(list(gt.subtiles((0, 0, 0))),
[(0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
assert_equal(list(gt.subtiles((1, 0, 1))),
[(2, 0, 2), (2, 1, 2), (3, 0, 2), (3, 1, 2)])
with assert_raises(AssertionError):
gt.tileextent((0, 1, 0))
assert_arr_almost(gt.tileextent((0, 0, 0)), KNOWN_EXTENTS[(0, 0, 0)])
assert_arr_almost(gt.tileextent((2, 0, 2)), KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(gt.tileextent((0, 2, 2)), KNOWN_EXTENTS[(0, 2, 2)])
assert_arr_almost(gt.tileextent((2, 2, 2)), KNOWN_EXTENTS[(2, 2, 2)])
assert_arr_almost(gt.tileextent((8, 9, 4)), KNOWN_EXTENTS[(8, 9, 4)])
def us_grid(resolution=.5, sparse=True):
resolution = .5
bounds = USA.bounds
# Grid boundaries are determined by nearest degree.
min_long = np.floor(bounds[0])
min_lat = np.floor(bounds[1])
max_long = np.ceil(bounds[2])
max_lat = np.ceil(bounds[3])
# Division should be close to an integer.
# Add one to number of points to include the end
# This is robust only to resolutions that "evenly" divide the range.
nPointsLong = np.around((max_long - min_long) / resolution) + 1
nPointsLat = np.around((max_lat - min_lat ) / resolution) + 1
long_points = np.linspace(min_long, max_long, nPointsLong)
lat_points = np.linspace(min_lat, max_lat, nPointsLat )
outline = contiguous_outline2('../tiger/cb_2013_us_nation_20m.shp')
for i, (xi, yi) in enumerate(product(range(len(long_points)-1),
range(len(lat_points)-1))):
cell = box(long_points[xi], lat_points[yi],
long_points[xi+1], lat_points[yi+1])
if sparse:
# Add cell only if it intersects contiguous USA
if cell.intersects(outline):
yield cell
else:
yield cell
def test_unwrap(self):
geom = box(195, -40, 225, -30)
gvar = GeometryVariable(name='geoms', value=geom, crs=Spherical(), dimensions='geoms')
gvar.wrap()
self.assertEqual(gvar.get_value()[0].bounds, (-165.0, -40.0, -135.0, -30.0))
gvar.unwrap()
self.assertEqual(gvar.get_value()[0].bounds, (195.0, -40.0, 225.0, -30.0))
def tile_generator(self,
src,
tile_bounds,
tile_bounds_crs=None,
geom_type='Polygon',
split_multi_geoms=True,
min_partial_perc=0.0,
obj_id_col=None):
"""Generate `src` vector data tiles bounded by `tile_bounds`.
Arguments
---------
src : `str` or :class:`geopandas.GeoDataFrame`
The source vector data to tile. Must either be a path to a GeoJSON
or a :class:`geopandas.GeoDataFrame`.
tile_bounds : list
A :class:`list` made up of ``[left, top, right, bottom] `` sublists
(this can be extracted from
:class:`solaris.tile.raster_tile.RasterTiler` after tiling imagery)
tile_bounds_crs : int, optional
The EPSG code for the CRS that the tile bounds are in. If not
provided, it's assumed that the CRS is the same as in `src`. This
argument must be provided if the bound coordinates and `src` are
not in the same CRS, otherwise tiling will not occur correctly.
geom_type : str, optional (default: "Polygon")
The type of geometries contained within `src`. Defaults to
``"Polygon"``, can also be ``"LineString"``.
split_multi_geoms : bool, optional (default: True)
Should multi-polygons or multi-linestrings generated by clipping
a geometry into discontinuous pieces be separated? Defaults to yes
(``True``).
min_partial_perc : float, optional (default: 0.0)
The minimum percentage of a :class:`shapely.geometry.Polygon` 's
area or :class:`shapely.geometry.LineString` 's length that must
be retained within a tile's bounds to be included in the output.
Defaults to ``0.0``, meaning that the contained portion of a
clipped geometry will be included, no matter how small.
obj_id_col : str, optional (default: None)
If ``split_multi_geoms=True``, the name of a column that specifies
a unique identifier for each geometry (e.g. the ``"BuildingId"``
column in many SpaceNet datasets.) See
:func:`solaris.utils.geo.split_multi_geometries` for more.
Yields
------
tile_gdf : :class:`geopandas.GeoDataFrame`
A tile geodataframe.
tb : list
A list with ``[left, top, right, bottom] `` coordinates for the
boundaries contained by `tile_gdf`.
"""
self.src = _check_gdf_load(src)
if self.verbose:
print("Num tiles:", len(tile_bounds))
self.src_crs = _check_crs(self.src.crs)
# check if the tile bounds and vector are in the same crs
if tile_bounds_crs is not None:
tile_bounds_crs = _check_crs(tile_bounds_crs)
else:
tile_bounds_crs = self.src_crs
if self.src_crs != tile_bounds_crs:
reproject_bounds = True # used to transform tb for clip_gdf()
else:
reproject_bounds = False
self.proj_unit = get_projection_unit(self.src_crs)
if getattr(self, 'dest_crs', None) is None:
self.dest_crs = self.src_crs
for i, tb in enumerate(tile_bounds):
if self.super_verbose:
print("\n", i, "/", len(tile_bounds))
if reproject_bounds:
tile_gdf = clip_gdf(self.src,
reproject_geometry(box(*tb),
tile_bounds_crs,
self.src_crs),
min_partial_perc,
geom_type,
verbose=self.super_verbose)
else:
tile_gdf = clip_gdf(self.src,
tb,
min_partial_perc,
geom_type,
verbose=self.super_verbose)
if self.src_crs != self.dest_crs:
tile_gdf = tile_gdf.to_crs(crs=self.dest_crs.to_wkt())
if split_multi_geoms:
split_multi_geometries(tile_gdf, obj_id_col=obj_id_col)
yield tile_gdf, tb
def create(self, size=100000, iter_size=50000, use_timer=True):
print "\n** build_packages *******************************************"
print "----"
self.package_size = size
timer_a = timeit.default_timer() # Initialize timer
# Containers for edges represented as GeoJSON and as shapely objects:
container_edges_geojson = []
container_edges_geom_2d = []
# Statistics per package:
package_scale_low = np.inf
package_scale_high = 0
package_count_edges = 0
package_size_bytes = 0
package_id = 1
package_num_coords = 0
for edges in self.dataset.get_generator(iter_size=iter_size):
if use_timer:
print "----"
print "Processing new fetch from generator.."
for edge in edges:
# Add edge representations to containers for package:
edge_geojson = {
"type": "Feature",
"geometry": {
"type":
"LineString",
"coordinates":
as_python_linestring(sg_loads(edge["geom"].wkt))
},
"properties": {
"edge_id": edge["edge_id"],
"scale_low": edge["scale_low"],
"scale_high": edge["scale_high"],
}
}
package_num_coords += len(edge["geom"].coords)
edge_bytes = len(bytes(edge_geojson))
if package_size_bytes + edge_bytes > size:
if package_id % 500 == 0 and use_timer:
print "----"
print "Created", package_id, "packages.."
# print "Package: ", package_id
# print "# edges: ", package_count_edges
# print "# bytes: ", package_size_bytes
# print "-------- "
# Update info:
self.number_of_coors_per_package.append(package_num_coords)
self.number_of_edges_per_package.append(
package_count_edges)
self.height_per_package.append(package_scale_high -
package_scale_low)
if package_count_edges < self.package_least_edges:
self.package_least_edges = package_count_edges
if package_count_edges > self.package_most_edges:
self.package_most_edges = package_count_edges
self.package_average_edges += package_count_edges
# Create package:
package_geom_2d = box(
*MultiLineString(container_edges_geom_2d).bounds)
package = self._create_package(package_id, package_geom_2d,
package_scale_low,
package_scale_high,
container_edges_geojson)
self._packages.append(package)
# Empty containers:
container_edges_geojson = []
container_edges_geom_2d = []
# Reset statistics:
package_scale_low = np.inf
package_scale_high = 0
package_size_bytes = 0
package_count_edges = 0
package_id += 1
package_num_coords = 0
container_edges_geojson.append(edge_geojson)
container_edges_geom_2d.append(edge["geom"])
# Keep track of package statistics:
if edge["scale_low"] < package_scale_low:
package_scale_low = edge["scale_low"]
if edge["scale_high"] > package_scale_high:
package_scale_high = edge["scale_high"]
package_count_edges += 1
package_size_bytes += edge_bytes
# Create last package:
if package_count_edges != 0:
# print "Package: ", package_id
# print "# edges: ", package_count_edges
# print "# bytes: ", package_size_bytes
# print "-------- "
# Update info:
self.number_of_coors_per_package.append(package_num_coords)
self.number_of_edges_per_package.append(package_count_edges)
self.height_per_package.append(package_scale_high -
package_scale_low)
if package_count_edges < self.package_least_edges:
self.package_least_edges = package_count_edges
if package_count_edges > self.package_most_edges:
self.package_most_edges = package_count_edges
self.package_average_edges += package_count_edges
# Create last package:
package_geom_2d = box(
*MultiLineString(container_edges_geom_2d).bounds)
package = self._create_package(package_id, package_geom_2d,
package_scale_low,
package_scale_high,
container_edges_geojson)
self._packages.append(package)
self.count = package_id
self.package_average_edges = self.package_average_edges / self.count
timer_b = timeit.default_timer()
if use_timer:
print "----"
print str(package_id), 'packages created in:', timer_b - timer_a, \
'seconds'
if self.db == "corine":
packages = self._packages
self._packages = [] # Remove packages from constructor for memory
return packages
else:
# Calculate total overlap:
print "----"
print "Starting total overlap calculation between packages.."
timer_overlap_a = timeit.default_timer()
self.total_overlap = sum([
(pair[0]["geom"].intersection(pair[1]["geom"]).area *
overlap_1d(pair[0]["scale_low"], pair[0]["scale_high"],
pair[1]["scale_low"], pair[1]["scale_high"]))
for pair in combinations(self._packages, 2)
])
timer_overlap_b = timeit.default_timer()
print 'Overlap between packages calculated in:', \
timer_overlap_b - timer_overlap_a, 'seconds'
return self._packages
def clip_gdf(gdf,
tile_bounds,
min_partial_perc=0.0,
geom_type="Polygon",
use_sindex=True,
verbose=False):
"""Clip GDF to a provided polygon.
Clips objects within `gdf` to the region defined by
`poly_to_cut`. Also adds several columns to the output::
`origarea`
The original area of the polygons (only used if `geom_type` ==
``"Polygon"``).
`origlen`
The original length of the objects (only used if `geom_type` ==
``"LineString"``).
`partialDec`
The fraction of the object that remains after clipping
(fraction of area for Polygons, fraction of length for
LineStrings.) Can filter based on this by using `min_partial_perc`.
`truncated`
Boolean indicator of whether or not an object was clipped.
Arguments
---------
gdf : :py:class:`geopandas.GeoDataFrame`
A :py:class:`geopandas.GeoDataFrame` of polygons to clip.
tile_bounds : `list` or :class:`shapely.geometry.Polygon`
The geometry to clip objects in `gdf` to. This can either be a
``[left, top, right, bottom] `` bounds list or a
:class:`shapely.geometry.Polygon` object defining the area to keep.
min_partial_perc : float, optional
The minimum fraction of an object in `gdf` that must be
preserved. Defaults to 0.0 (include any object if any part remains
following clipping).
geom_type : str, optional
Type of objects in `gdf`. Can be one of
``["Polygon", "LineString"]`` . Defaults to ``"Polygon"`` .
use_sindex : bool, optional
Use the `gdf` sindex be used for searching. Improves efficiency
but requires `libspatialindex <http://libspatialindex.github.io/>`__ .
verbose : bool, optional
Switch to print relevant values.
Returns
-------
cut_gdf : :py:class:`geopandas.GeoDataFrame`
`gdf` with all contained objects clipped to `poly_to_cut` .
See notes above for details on additional clipping columns added.
"""
gdf['geometry'] = gdf.buffer(0)
if isinstance(tile_bounds, tuple):
tb = box(*tile_bounds)
elif isinstance(tile_bounds, list):
tb = box(*tile_bounds)
elif isinstance(tile_bounds, Polygon):
tb = tile_bounds
if use_sindex and (geom_type == "Polygon"):
gdf = search_gdf_polygon(gdf, tb)
# if geom_type == "LineString":
if 'origarea' in gdf.columns:
pass
else:
if "geom_type" == "LineString":
gdf['origarea'] = 0
else:
gdf['origarea'] = gdf.area
if 'origlen' in gdf.columns:
pass
else:
if "geom_type" == "LineString":
gdf['origlen'] = gdf.length
else:
gdf['origlen'] = 0
# TODO must implement different case for lines and for spatialIndex
# (Assume RTree is already performed)
cut_gdf = gdf.copy()
cut_gdf.geometry = gdf.intersection(tb)
if geom_type == 'Polygon':
cut_gdf['partialDec'] = cut_gdf.area / cut_gdf['origarea']
cut_gdf = cut_gdf.loc[cut_gdf['partialDec'] > min_partial_perc, :]
cut_gdf['truncated'] = (cut_gdf['partialDec'] != 1.0).astype(int)
else:
# assume linestrings
# remove null
cut_gdf = cut_gdf[cut_gdf['geometry'].notnull()]
cut_gdf['partialDec'] = 1
cut_gdf['truncated'] = 0
# cut_gdf = cut_gdf[cut_gdf.geom_type != "GeometryCollection"]
if len(cut_gdf) > 0 and verbose:
print("clip_gdf() - gdf.iloc[0]:", gdf.iloc[0])
print("clip_gdf() - tb:", tb)
print("clip_gdf() - gdf_cut:", cut_gdf)
# TODO: IMPLEMENT TRUNCATION MEASUREMENT FOR LINESTRINGS
return cut_gdf
def of_rectangle(min_x: int, min_y: int, max_x: int,
max_y: int) -> BoundingBox:
return BoundingBox(box(min_x, min_y, max_x, max_y))
def main():
args = parse_args()
# load the csv file into pandas for cleanup
print('Loading...')
df = pd.read_csv(args.input_file)
# filter down to area of interest records
print('Finding AoI...')
geohashes_aoi = set()
if args.coverage_file is not None:
# loading coverage polygon from geo json file
coverage_geojson = json.load(open(args.coverage_file))
# generate geohashes covered by the AoI
geohashes_aoi = helpers.geohashes_from_geojson_poly(
coverage_geojson, precision=args.geohash_level)
# filter down to country of interest records
elif args.country_iso is not None:
df = df.loc[df["iso3"] == args.country_iso]
# extract x, y locations and crop of interest
df = df[(["x", "y"] + args.crop_columns)]
df = df.reset_index()
# loop over the x, y which are the cell centroids, and generate a bounding box based on
# the cell size (taken from the associated geotiff resolution)
print('Converting points to bounds...')
centroids = zip(df["x"], df["y"])
bounds = [
geometry.box(
c[0] - CELL_SIZE_X / 2,
c[1] - CELL_SIZE_Y / 2,
c[0] + CELL_SIZE_X / 2,
c[1] + CELL_SIZE_Y / 2,
) for c in tqdm(centroids)
]
# loop through the bounds we've created and intersect each with the intended geohash grid
print('Converting bounds to geohashes...')
geohashes = [
polygon_geohasher.polygon_to_geohashes(b,
precision=args.geohash_level,
inner=False)
for b in tqdm(bounds)
]
# flatten gh set for each cell preserving index - no clean way to do this in pandas
flattened_gh = []
print('Clipping geohashes to AoI...')
for idx, gh_set in tqdm(enumerate(geohashes)):
for gh in gh_set:
if (len(geohashes_aoi) > 0
and gh in geohashes_aoi) or len(geohashes_aoi) is 0:
bounds_str = helpers.geohash_to_array_str(gh)
flattened_gh.append((idx, gh, bounds_str))
# store as a dataframe with any geohashes that were part of 2 cells reduced to 1
# a better implementation of this would take the value of both cells into account and
# compute a final adjusted value for the given geohash
print('Genering output csv...')
geohash_df = pd.DataFrame(flattened_gh,
columns=["cell", "geohash", "bounds"])
geohash_df = geohash_df.drop_duplicates(subset="geohash", keep="first")
geohash_df = geohash_df.set_index("cell")
joined = pd.merge(df, geohash_df, left_index=True, right_index=True)
joined = joined.drop(columns=["x", "y", "index"])
joined.to_csv(args.output_file, index=False)
def plot_topomap(events=None):
from cartopy.feature import NaturalEarthFeature as NEF
from matplotlib.colors import LinearSegmentedColormap
import shapely.geometry as sgeom
from util.imaging import EA_EURO, GEO, add_scale, de_border, add_ticklabels, plot_elevation
mlf = [(12.410, 50.261), (12.485, 50.183), (12.523, 50.127),
(12.517, 50.125), (12.538, 50.131), (12.534, 50.130),
(12.543, 50.109), (12.547, 50.083), (12.547, 50.074),
(12.545, 50.066), (12.546, 50.057), (12.576, 50.034),
(12.594, 50.016), (12.632, 50.004), (12.665, 49.980)]
fig = plt.figure()
# ax = fig.add_axes([0, 0, 1, 1], projection=EA_EURO)
ax = fig.add_subplot(111, projection=EA_EURO)
extent = [12.05, 12.85, 50, 50.45]
ax.set_extent(extent, crs=GEO)
# Create an inset GeoAxes showing the location of the Solomon Islands.
box = ax.get_position().bounds
subax = fig.add_axes(
[box[0] + box[2] - 0.23, box[1] + box[3] - 0.3, 0.28, 0.28],
projection=EA_EURO)
subax.set_extent([8, 16, 47, 55], GEO)
subax.add_feature(NEF('physical', 'land', '10m'),
facecolor='0.7',
alpha=0.5,
rasterized=True) #facecolor='sandybrown'
subax.add_feature(NEF('physical', 'coastline', '10m'),
facecolor='none',
edgecolor='k',
linewidth=0.5,
rasterized=True)
subax.add_feature(NEF('cultural', 'admin_0_boundary_lines_land', '10m'),
facecolor='none',
edgecolor='k',
linewidth=0.5,
rasterized=True)
subax.add_geometries(
[sgeom.box(extent[0], extent[2], extent[1], extent[3])],
GEO,
facecolor='none',
edgecolor='k',
linewidth=1,
alpha=0.5)
lonticks = [12.2, 12.4, 12.6, 12.8]
latticks = [50, 50.1, 50.2, 50.3, 50.4]
add_ticklabels(ax, lonticks, latticks)
ax.tick_params(axis='both', which='major', labelsize=8)
plot_elevation(ax,
shading=False,
cmap=LinearSegmentedColormap.from_list(
'littlegray', ['white', '0.5']),
azimuth=315,
altitude=60,
rasterized=True)
add_scale(ax, 10, (12.15, 50.02))
de_border(ax, edgecolor='0.5', rasterized=True)
eqs, sta = load_webnet(stations=True)
if eqs is not None:
ax.scatter(eqs.lon.values,
eqs.lat.values,
4,
'#9bd7ff',
alpha=0.4,
marker='.',
transform=GEO,
rasterized=True)
if events is not None:
_, _, lon, lat, _, mag, *_ = zip(*events2lists(events))
ax.scatter(lon, lat, 4, 'C0', marker='o', transform=GEO)
ax.plot(*list(zip(*mlf)), color='0.5', transform=GEO)
ax.annotate('MLF', (12.505, 50.145),
None,
GEO._as_mpl_transform(ax),
size='x-small',
zorder=10,
rotation=290)
used_stations = 'NKC LBC VAC KVC STC POC SKC KRC ZHC'.split()
sta = sta[sta.station.isin(used_stations)]
ax.scatter(sta.lon.values,
sta.lat.values,
100,
marker='^',
color='none',
edgecolors='k',
transform=GEO,
zorder=10)
for idx, s in sta.iterrows():
xy = (2, 2) if s.station not in ('KOPD', 'KAC') else (-10, 5)
ax.annotate(s.station, (s.lon, s.lat),
xy,
GEO._as_mpl_transform(ax),
'offset points',
size='x-small',
zorder=10)
x0, y0 = EA_EURO.transform_point(LATLON0[1], LATLON0[0], GEO)
ax.add_geometries([sgeom.box(x0 - 2500, y0 - 2300, x0 + 2500, y0 + 2700)],
EA_EURO,
facecolor='none',
edgecolor='C1',
linewidth=2,
alpha=0.8,
zorder=11)
fig.savefig('figs/topomap.pdf',
bbox_inches='tight',
pad_inches=0.1,
dpi=300)
plt.show()
return sta
def __init__(self,
lulc_raster_filepath,
biophysical_table_filepath,
cc_method,
ref_et_raster_filepaths,
t_refs=None,
uhi_maxs=None,
t_raster_filepaths=None,
station_t_filepath=None,
station_locations_filepath=None,
dates=None,
align_rasters=True,
workspace_dir=None,
extra_ucm_args=None,
num_workers=None):
"""
Pythonic and open source interface to the InVEST urban cooling model.
A set of additional utility methods serve to compute temperature maps
and data frames.
Parameters
----------
lulc_raster_filepath : str
Path to the raster of land use/land cover (LULC) file
biophysical_table_filepath : str
Path to the biophysical table CSV file
cc_method : str
Cooling capacity calculation method. Can be either 'factors' or
'intensity'
ref_et_raster_filepaths : str or list-like
Path to the reference evapotranspiration raster, or sequence of
strings with a path to the reference evapotranspiration raster
t_refs : numeric or list-like, optional
Reference air temperature. If not provided, it will be set as the
minimum observed temperature (raster or station measurements, for
each respective date if calibrating for multiple dates).
uhi_maxs : numeric or list-like, optional
Magnitude of the UHI effect. If not provided, it will be set as the
difference between the maximum and minimum observed temperature
(raster or station measurements, for each respective date if
calibrating for multiple dates).
t_raster_filepaths : str or list-like, optional
Path to the observed temperature raster, or sequence of strings
with a path to the observed temperature rasters. The raster must
be aligned to the LULC raster. Required if calibrating against
temperature map(s).
station_t_filepath : str, optional
Path to a table of air temperature measurements where each column
corresponds to a monitoring station and each row to a datetime.
Required if calibrating against station measurements. Ignored if
providing `t_raster_filepaths`.
station_locations_filepath : str, optional
Path to a table with the locations of each monitoring station,
where the first column features the station labels (that match the
columns of the table of air temperature measurements), and there
are (at least) a column labelled 'x' and a column labelled 'y'
that correspod to the locations of each station (in the same CRS
as the other rasters). Required if calibrating against station
measurements. Ignored if providing `t_raster_filepaths`.
dates : str or datetime-like or list-like, optional
Date or list of dates that correspond to each of the observed
temperature raster provided in `t_raster_filepaths`. Ignored if
`station_t_filepath` is provided.
align_rasters : bool, default True
Whether the rasters should be aligned before passing them as
arguments of the InVEST urban cooling model. Since the model
already aligns the LULC and reference evapotranspiration rasters,
this argument is only useful to align the temperature rasters, and
is therefore ignored if calibrating against station measurements.
workspace_dir : str, optional
Path to the folder where the model outputs will be written. If not
provided, a temporary directory will be used.
extra_ucm_args : dict-like, optional
Other keyword arguments to be passed to the `execute` method of
the urban cooling model.
num_workers : int, optional
Number of workers so that the simulations of each iteration can be
executed at scale. Only useful if calibrating for multiple dates.
If not provided, it will be set automatically depending on the
number of dates and available number of processors in the CPU.
"""
if workspace_dir is None:
# TODO: how do we ensure that this is removed?
workspace_dir = tempfile.mkdtemp()
# TODO: log to warn that we are using a temporary directory
# self.workspace_dir = workspace_dir
# self.base_args.update()
# evapotranspiration rasters for each date
if isinstance(ref_et_raster_filepaths, str):
ref_et_raster_filepaths = [ref_et_raster_filepaths]
# get the raster metadata from lulc (used to predict air temperature
# rasters)
with rio.open(lulc_raster_filepath) as src:
meta = src.meta.copy()
data_mask = src.dataset_mask().astype(bool)
# calibration approaches
if t_raster_filepaths is not None:
# calibrate against a map
if isinstance(t_raster_filepaths, str):
t_raster_filepaths = [t_raster_filepaths]
if align_rasters:
# a list is needed for the `_align_rasters` method
if isinstance(ref_et_raster_filepaths, tuple):
ref_et_raster_filepaths = list(ref_et_raster_filepaths)
if isinstance(t_raster_filepaths, tuple):
t_raster_filepaths = list(t_raster_filepaths)
# align the rasters to the LULC raster and dump them to new
# paths in the workspace directory
dst_lulc_raster_filepath = path.join(workspace_dir, 'lulc.tif')
dst_ref_et_raster_filepaths = [
path.join(workspace_dir, f'ref-et_{i}.tif')
for i in range(len(t_raster_filepaths))
]
dst_t_raster_filepaths = [
path.join(workspace_dir, f't_{i}.tif')
for i in range(len(t_raster_filepaths))
]
# the call below returns the same `dst_lulc_raster_filepath`
# `dst_ref_et_raster_filepaths` and `dst_t_raster_filepaths`
# passed as args
(meta, data_mask, lulc_raster_filepath,
ref_et_raster_filepaths, t_raster_filepaths) = _align_rasters(
lulc_raster_filepath, ref_et_raster_filepaths,
t_raster_filepaths, dst_lulc_raster_filepath,
dst_ref_et_raster_filepaths, dst_t_raster_filepaths)
# observed values array, Tref and UHImax
if t_refs is None:
if uhi_maxs is None:
obs_arrs, t_refs, uhi_maxs = _preprocess_t_rasters(
t_raster_filepaths)
else:
obs_arrs, t_refs, _ = _preprocess_t_rasters(
t_raster_filepaths)
else:
if uhi_maxs is None:
obs_arrs, _, uhi_maxs = _preprocess_t_rasters(
t_raster_filepaths)
else:
obs_arrs, _, __ = _preprocess_t_rasters(t_raster_filepaths)
# need to replace nodata with `nan` so that `dropna` works below
# the `_preprocess_t_rasters` method already uses `np.where` to
# that end, however the `data_mask` used here might be different
# (i.e., the intersection of the data regions of all rasters)
obs_arr = np.concatenate([
np.where(data_mask, _obs_arr, np.nan) for _obs_arr in obs_arrs
])
# attributes to index the samples
if isinstance(dates, str):
dates = [dates]
sample_name = 'pixel'
# the sample index/keys here will select all the pixels of the
# rasters, indexed by their flat-array position - this is rather
# silly but this way the attributes work in the same way when
# calibrating against observed temperature rasters or station
# measurements
# sample_keys = np.flatnonzero(data_mask)
# sample_index = np.arange(data_mask.sum())
sample_index = np.arange(data_mask.size)
sample_keys = np.arange(data_mask.size)
elif station_t_filepath is not None:
station_location_df = pd.read_csv(station_locations_filepath,
index_col=0)
station_t_df = pd.read_csv(station_t_filepath,
index_col=0)[station_location_df.index]
station_t_df.index = pd.to_datetime(station_t_df.index)
# observed values array, Tref and UHImax
if t_refs is None:
t_refs = station_t_df.min(axis=1)
if uhi_maxs is None:
uhi_maxs = station_t_df.max(axis=1) - t_refs
obs_arr = station_t_df.values # .flatten()
# attributes to index the samples
dates = station_t_df.index
sample_name = 'station'
sample_index = station_t_df.columns
sample_keys = np.ravel_multi_index(
transform.rowcol(meta['transform'], station_location_df['x'],
station_location_df['y']),
(meta['height'], meta['width']))
else:
# this is useful in this same method (see below)
dates = None
sample_name = None
sample_index = None
sample_keys = None
obs_arr = None
# create a dummy geojson with the bounding box extent for the area of
# interest - this is completely ignored during the calibration
aoi_vector_filepath = path.join(workspace_dir, 'dummy_aoi.geojson')
with rio.open(lulc_raster_filepath) as src:
# geom = geometry.box(*src.bounds)
with fiona.open(aoi_vector_filepath,
'w',
driver='GeoJSON',
crs=src.crs,
schema={
'geometry': 'Polygon',
'properties': {
'id': 'int'
}
}) as c:
c.write({
'geometry': geometry.mapping(geometry.box(*src.bounds)),
'properties': {
'id': 1
},
})
# store the attributes to index the samples
self.meta = meta
self.data_mask = data_mask
self.dates = dates
self.sample_name = sample_name
self.sample_index = sample_index
self.sample_keys = sample_keys
# store reference temperatures and UHI magnitudes as class attributes
if not _is_sequence(t_refs):
t_refs = [t_refs]
if not _is_sequence(uhi_maxs):
uhi_maxs = [uhi_maxs]
self.t_refs = t_refs
self.uhi_maxs = uhi_maxs
# flat observation array to compute the calibration metric
if obs_arr is not None:
self.obs_arr = obs_arr.flatten()
self.obs_mask = ~np.isnan(self.obs_arr)
self.obs_arr = self.obs_arr[self.obs_mask]
# model parameters: prepare the dict here so that all the paths/
# parameters have been properly set above
self.base_args = {
'lulc_raster_path': lulc_raster_filepath,
'biophysical_table_path': biophysical_table_filepath,
'aoi_vector_path': aoi_vector_filepath,
'cc_method': cc_method,
'workspace_dir': workspace_dir,
}
# if model_params is None:
# model_params = DEFAULT_MODEL_PARAMS
self.base_args.update(**settings.DEFAULT_UCM_PARAMS)
if extra_ucm_args is None:
extra_ucm_args = settings.DEFAULT_EXTRA_UCM_ARGS
if 'do_valuation' not in extra_ucm_args:
extra_ucm_args['do_valuation'] = settings.DEFAULT_EXTRA_UCM_ARGS[
'do_valuation']
self.base_args.update(**extra_ucm_args)
# also store the paths to the evapotranspiration rasters
self.ref_et_raster_filepaths = ref_et_raster_filepaths
# number of workers to perform each calibration iteration at scale
if num_workers is None:
num_workers = min(len(self.ref_et_raster_filepaths),
os.cpu_count())
self.num_workers = num_workers
driver='ESRI Shapefile',
crs='epsg:3006',
schema=covSchema) as dest:
for picFolder, metaFolder in zip(picFolders, metaFolders):
files = [
os.path.splitext(os.path.basename(x))[0]
for x in glob.glob(picFolder + '/*.tif')
]
for file in files:
data = gdal.Open(picFolder + file + '.tif', gdal.GA_ReadOnly)
geoTransform = data.GetGeoTransform()
minx = geoTransform[0]
maxy = geoTransform[3]
maxx = minx + geoTransform[1] * data.RasterXSize
miny = maxy + geoTransform[5] * data.RasterYSize
geom = box(minx, miny, maxx, maxy)
img_id = file[0:11]
img_year = file[12:]
yeard[picFolder + file + '.tif'] = int(img_year)
metapath = metaFolder + img_id + '_flygbild_' + img_year + '.json'
metapath_orto = metaFolder + img_id + '_ortofoto_' + img_year + '.json'
if os.path.isfile(metapath) and os.path.isfile(metapath_orto):
with open(metapath) as metafile:
metadata = json.load(metafile)
try:
img_date = metadata['features'][0]['properties'][
'tidpunkt'][0:10]
img_camera = metadata['features'][0]['properties'][
'kamera']
dest.write({
'geometry': mapping(geom),
def mosaic_bulk(meta_path, s3_list_path, min_scale, max_scale, min_year,
max_year, woodland_tint, allow_orthophoto, bounds, minzoom,
maxzoom, quadkey_zoom, sort_preference, closest_to_year,
filter_only):
"""Create MosaicJSON from CSV of bulk metadata
"""
if (sort_preference == 'closest-to-year') and (not closest_to_year):
msg = 'closest-to-year parameter required when sort-preference is closest-to-year'
raise ValueError(msg)
df = pd.read_csv(meta_path, low_memory=False)
# Rename column names to lower case and snake case
df = df.rename(columns=lambda col: col.lower().replace(' ', '_'))
# Keep only historical maps
# Newer maps are only in GeoPDF, and not in GeoTIFF, let alone COG
df = df[df['series'] == 'HTMC']
# Create year column as Imprint Year if it exists, otherwise Date On Map
df['year'] = df['imprint_year'].fillna(df['date_on_map'])
# Apply filters
if min_scale:
df = df[df['scale'] >= min_scale]
if max_scale:
df = df[df['scale'] <= max_scale]
if min_year:
df = df[df['year'] >= min_year]
if max_year:
df = df[df['year'] <= max_year]
if woodland_tint is not None:
if woodland_tint:
df = df[df['woodland_tint'] == 'Y']
else:
df = df[df['woodland_tint'] == 'N']
if not allow_orthophoto:
df = df[df['orthophoto'].isna()]
# Create s3 GeoTIFF paths from metadata
df['s3_tif'] = construct_s3_tif_url(df['download_product_s3'])
if s3_list_path:
# Load list of GeoTIFF files
s3_files_df = load_s3_list(s3_list_path)
# Keep only files that exist as GeoTIFF
df = filter_cog_exists(df, s3_files_df)
df['geometry'] = df.apply(construct_geometry, axis=1)
gdf = gpd.GeoDataFrame(df)
# Filter within provided bounding box
if bounds:
bounds = box(*map(float, bounds.split(',')))
gdf = gdf[gdf.geometry.intersects(bounds)]
if not maxzoom:
maxzoom = gdf.apply(
lambda row: get_maxzoom(row['scale'], row['scanner_resolution']),
axis=1)
# Take 75th percentile of maxzoom series
maxzoom = int(round(maxzoom.describe()['75%']))
if not minzoom:
minzoom = maxzoom - 5
# Columns to keep for creating MosaicJSON
cols = ['scale', 'year', 's3_tif', 'geometry', 'cell_id']
if sort_preference == 'newest':
sort_by = ['year', 'scale']
sort_ascending = [False, True]
elif sort_preference == 'oldest':
sort_by = ['year', 'scale']
sort_ascending = [True, True]
elif sort_preference == 'closest-to-year':
gdf['reference_year'] = (closest_to_year - gdf['year']).abs()
sort_by = ['reference_year', 'scale']
sort_ascending = [True, True]
cols.remove('year')
cols.append('reference_year')
if filter_only:
for row in gdf[cols].iterfeatures():
print(json.dumps(row, separators=(',', ':')))
return
# Convert to features
features = gdf[cols].__geo_interface__['features']
mosaic = MosaicJSON.from_features(features,
minzoom=minzoom,
maxzoom=maxzoom,
quadkey_zoom=quadkey_zoom,
asset_filter=asset_filter,
accessor=path_accessor,
sort_by=sort_by,
sort_ascending=sort_ascending)
print(json.dumps(mosaic.dict(), separators=(',', ':')))
def load_pa_townships(gdb_path, layer, datastore):
dml = """
INSERT INTO landgrid.pa_township (MSLINK, COUNTY, MUNICIPAL_, MUNICIPAL1,
FIPS_MUN_C, FED_AID_UR, FIPS_COUNT,
FIPS_AREA_, FIPS_NAME, FIPS_SQ_MI,
FIPS_MUN_P, FED_ID_NUM, CLASS_OF_M,
Shape_Length, Shape_Area, County_Name,
County_TOWNSHIP, State_Name, geobounds,
shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
%s, %s, ST_GeomFromText(%s, 4326));
"""
try:
with fiona.open(gdb_path, layer=layer) as src:
datastore.execute_dml('TRUNCATE TABLE landgrid.pa_township;')
# restart
total = len(src)
logger.info(f"Starting load of {layer}: {total} sections...")
# logger.info(f"Schema: {src.schema}")
for i, rec in enumerate(src):
poly = shape(rec['geometry'])
if not poly.is_valid:
logger.warning(
f"Cleaning {rec['properties']['MUNICIPAL1']}...")
clean = poly.buffer(0.0)
assert clean.is_valid, 'Invalid Polygon!'
assert clean.geom_type == 'Polygon' or clean.geom_type == 'MultiPolygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml,
(
rec['properties']['MSLINK'],
rec['properties']['COUNTY'],
rec['properties']['MUNICIPAL_'],
rec['properties']['MUNICIPAL1'],
rec['properties']['FIPS_MUN_C'],
rec['properties']['FED_AID_UR'],
rec['properties']['FIPS_COUNT'],
rec['properties']['FIPS_AREA_'],
rec['properties']['FIPS_NAME'],
rec['properties']['FIPS_SQ_MI'],
rec['properties']['FIPS_MUN_P'],
rec['properties']['FED_ID_NUM'],
rec['properties']['CLASS_OF_M'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'],
rec['properties']['County_Name'],
rec['properties']['County_TOWNSHIP'],
'Pennsylvania', # TMP
g_json,
poly.wkt))
if i % 5000 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total}: {rec['properties']['MUNICIPAL1']}"
)
datastore.batch_commit()
logger.info(f"Completed {layer} load.")
except Exception as e:
# datastore.rollback()
logger.exception("Error processing PA Townships.", e)
raise e
def to_polygon(x):
assert len(x) in [4, 8]
if len(x) == 4:
return box(x[0], x[1], x[0] + x[2], x[1] + x[3])
elif len(x) == 8:
return Polygon([(x[2 * i], x[2 * i + 1]) for i in range(4)])
def load_wv_districts(gdb_path, layer, datastore):
dml = """
INSERT INTO landgrid.wv_district (WV_ID, DNAME, DNUMBER, CNAME, CNUMBER,
Area_sqm, lat, long, Shape_Length,
Shape_Area, County_District,
State_Name, geobounds, shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
ST_GeomFromText(%s, 4326));
"""
try:
with fiona.open(gdb_path, layer=layer) as src:
datastore.execute_dml('TRUNCATE TABLE landgrid.wv_district;')
# restart
total = len(src)
logger.info(f"Starting load of {layer}: {total} sections...")
# logger.info(f"Schema: {src.schema}")
for i, rec in enumerate(src):
poly = shape(rec['geometry'])
if not poly.is_valid:
logger.warning(f"Cleaning {rec['properties']['DNAME']}...")
clean = poly.buffer(0.0)
assert clean.is_valid, 'Invalid Polygon!'
assert clean.geom_type == 'Polygon' or clean.geom_type == 'MultiPolygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml,
(
rec['properties']['WV_ID'],
rec['properties']['DNAME'],
rec['properties']['DNUMBER'],
rec['properties']['CNAME'],
rec['properties']['CNUMBER'],
rec['properties']['Area_sqm'],
rec['properties']['lat'],
rec['properties']['long'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'],
rec['properties']['County_District'],
'West Virginia', # TMP
g_json,
poly.wkt))
if i % 5000 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total}: {rec['properties']['DNAME']}"
)
datastore.batch_commit()
logger.info(f"Completed {layer} load.")
except Exception as e:
# datastore.rollback()
logger.exception("Error processing WV Districts.", e)
raise e
def load_tx_abstracts(gdb_path, layer, datastore):
dml = """
INSERT INTO landgrid.tx_abstract (PERIMETER, FIPS, CountyName,
Shape_Length, Shape_Area,
AbstractNumber, AbstractName,
Block, Township, Section,
AbstractNameALT, FormNumber,
ControlNumber, State_Name,
geobounds, shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
%s, ST_GeomFromText(%s, 4326));
"""
try:
with fiona.open(gdb_path, layer=layer) as src:
datastore.execute_dml('TRUNCATE TABLE landgrid.tx_abstract;')
# restart
total = len(src)
logger.info(f"Starting load of {layer}: {total} abstracts...")
# logger.info(f"Schema: {src.schema}")
for i, rec in enumerate(src):
poly = shape(rec['geometry'])
if not poly.is_valid:
logger.warning(
f"Cleaning {rec['properties']['AbstractName']}...")
clean = poly.buffer(0.0)
assert clean.is_valid, 'Invalid Polygon!'
assert clean.geom_type == 'Polygon' or clean.geom_type == 'MultiPolygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml,
(
rec['properties']['PERIMETER'],
rec['properties']['FIPS'],
rec['properties']['CountyName'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'],
rec['properties']['AbstractNumber'],
rec['properties']['AbstractName'],
rec['properties']['Block'],
rec['properties']['Township'],
rec['properties']['Section'],
rec['properties']['AbstractNameALT'],
rec['properties']['FormNumber'],
rec['properties']['ControlNumber'],
'Texas', # TMP
g_json,
poly.wkt))
if i % 5000 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total}: {rec['properties']['AbstractName']}"
)
datastore.batch_commit()
logger.info(f"Completed {layer} load.")
except Exception as e:
# datastore.rollback()
logger.exception("Error processing Texas Abstracts.", e)
raise e
def load_counties(gdb_path, layer, datastore):
dml = """
INSERT INTO landgrid.county_us (County_Name, State_Name, CountyID,
StateID, FIPS_State, FIPS_County,
API_State, API_County, LAT, LON,
Shape_Length, Shape_Area, geobounds,
shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
ST_GeomFromText(%s, 4326));
"""
with fiona.open(gdb_path, layer=layer) as src:
try:
# logger.info(f"Schema: {src.schema}")
datastore.execute_dml('TRUNCATE TABLE landgrid.county_us;')
# restart
total = len(src)
logger.info(f"Starting load of {total} counties...")
for i, rec in enumerate(src):
poly = shape(rec['geometry'])
if not poly.is_valid:
logger.warning(
f"Cleaning {rec['properties']['County_Name']}...")
clean = poly.buffer(0.0)
assert clean.is_valid, f"Invalid County {rec['properties']['County_Name']}!"
assert clean.geom_type == 'MultiPolygon' or clean.geom_type == 'Polygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml, (rec['properties']['County_Name'],
rec['properties']['State_Name'],
rec['properties']['CountyID'],
rec['properties']['StateID'],
rec['properties']['FIPS_State'],
rec['properties']['FIPS_County'],
rec['properties']['API_State'],
rec['properties']['API_County'],
rec['properties']['LAT'], rec['properties']['LON'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'], g_json, poly.wkt))
if i % 500 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total}: {rec['properties']['County_Name']}"
)
datastore.batch_commit()
# cursor.close()
logger.info("Completed county load.")
except Exception as e:
# datastore.rollback()
logger.exception("Error processing Counties", e)
raise e
self._img = img
self._mean_radiosity = self._compute_mean_radiosity()
return self
if __name__ == "__main__":
from shapely.geometry import mapping
import fiona
from collections import OrderedDict
import pprint
x0, y0, x1, y1 = 0, 0, 10800, 5400
kernel = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], np.float32)
tile = Tile('data/NE1_50M_SR_W/NE1_50M_SR_W.tif', box(x0, y0, x1, y1))
# tile.filter2D(kernel)
# Result in ./res.png
tmp = np.moveaxis(tile.img, 0, -1)
print(tmp.shape)
cv.imwrite("res.png", cv.cvtColor(tmp, cv.COLOR_RGB2BGR))
# ----------------- Test de Polygon
print(tile.bounding_polygon)
# ----------------- Test de Fiona
schema = {
'geometry': 'Polygon',
'properties': OrderedDict([('id', 'int')])
}
async def check_wms(source, session: ClientSession):
"""
Check WMS source
Parameters
----------
source : dict
Source dictionary
session : ClientSession
aiohttp ClientSession object
Returns
-------
list:
Good messages
list:
Warning messages
list:
Error Messages
"""
error_msgs = []
warning_msgs = []
info_msgs = []
wms_url = source["properties"]["url"]
headers = get_http_headers(source)
params = ["{proj}", "{bbox}", "{width}", "{height}"]
missingparams = [p for p in params if p not in wms_url]
if len(missingparams) > 0:
error_msgs.append(
"The following values are missing in the URL: {}".format(
",".join(missingparams)))
wms_args = {}
u = urlparse(wms_url)
url_parts = list(u)
for k, v in parse_qsl(u.query, keep_blank_values=True):
wms_args[k.lower()] = v
def validate_wms_getmap_url():
"""
Layers and styles can contain whitespaces. Ignore them here. They are checked against GetCapabilities later.
"""
url_parts_without_layers = "&".join([
"{}={}".format(key, value) for key, value in wms_args.items()
if key not in {"layers", "styles"}
])
parts = url_parts.copy()
parts[4] = url_parts_without_layers
url = urlunparse(parts).replace("{", "").replace("}", "")
return validators.url(url)
if not validate_wms_getmap_url():
error_msgs.append("URL validation error: {}".format(wms_url))
# Check mandatory WMS GetMap parameters (Table 8, Section 7.3.2, WMS 1.3.0 specification)
missing_request_parameters = set()
is_esri = "request" not in wms_args
if is_esri:
required_parameters = [
"f", "bbox", "size", "imageSR", "bboxSR", "format"
]
else:
required_parameters = [
"version",
"request",
"layers",
"bbox",
"width",
"height",
"format",
]
for request_parameter in required_parameters:
if request_parameter.lower() not in wms_args:
missing_request_parameters.add(request_parameter)
# Nothing more to do for esri rest api
if is_esri:
return info_msgs, warning_msgs, error_msgs
if "version" in wms_args and wms_args["version"] == "1.3.0":
if "crs" not in wms_args:
missing_request_parameters.add("crs")
if "srs" in wms_args:
error_msgs.append(
"WMS {} urls should not contain SRS parameter.".format(
wms_args["version"]))
elif "version" in wms_args and not wms_args["version"] == "1.3.0":
if "srs" not in wms_args:
missing_request_parameters.add("srs")
if "crs" in wms_args:
error_msgs.append(
"WMS {} urls should not contain CRS parameter.".format(
wms_args["version"]))
if len(missing_request_parameters) > 0:
missing_request_parameters_str = ",".join(missing_request_parameters)
error_msgs.append("Parameter '{}' is missing in url.".format(
missing_request_parameters_str))
return info_msgs, warning_msgs, error_msgs
# Styles is mandatory according to the WMS specification, but some WMS servers seems not to care
if "styles" not in wms_args:
warning_msgs.append(
"Parameter 'styles' is missing in url. 'STYLES=' can be used to request default style."
)
def get_getcapabilitie_url(wms_version=None):
get_capabilities_args = {
"service": "WMS",
"request": "GetCapabilities"
}
if wms_version is not None:
get_capabilities_args["version"] = wms_version
# Drop all wms getmap parameters, keep all extra arguments (e.g. such as map or key)
for key in wms_args:
if key not in {
"version",
"request",
"layers",
"bbox",
"width",
"height",
"format",
"crs",
"srs",
"styles",
"transparent",
"dpi",
"map_resolution",
"format_options",
}:
get_capabilities_args[key] = wms_args[key]
url_parts[4] = urlencode(list(get_capabilities_args.items()))
return urlunparse(url_parts)
# We first send a service=WMS&request=GetCapabilities request to server
# According to the WMS Specification Section 6.2 Version numbering and negotiation, the server should return
# the GetCapabilities XML with the highest version the server supports.
# If this fails, it is tried to explicitly specify a WMS version
exceptions = []
wms = None
for wmsversion in [None, "1.3.0", "1.1.1", "1.1.0", "1.0.0"]:
if wmsversion is None:
wmsversion_str = "-"
else:
wmsversion_str = wmsversion
try:
wms_getcapabilites_url = get_getcapabilitie_url(wmsversion)
resp = await get_url(wms_getcapabilites_url,
session,
with_text=True,
headers=headers)
if resp.exception is not None:
exceptions.append("WMS {}: {}".format(wmsversion,
resp.exception))
continue
xml = resp.text
if isinstance(xml, bytes):
# Parse xml encoding to decode
try:
xml_ignored = xml.decode(errors="ignore")
str_encoding = re.search('encoding="(.*?)"',
xml_ignored).group(1)
xml = xml.decode(encoding=str_encoding)
except Exception as e:
raise RuntimeError("Could not parse encoding: {}".format(
str(e)))
wms = parse_wms(xml)
if wms is not None:
break
except Exception as e:
exceptions.append("WMS {}: Error: {}".format(
wmsversion_str, str(e)))
continue
if wms is None:
for msg in exceptions:
error_msgs.append(msg)
return info_msgs, warning_msgs, error_msgs
for access_constraint in wms["AccessConstraints"]:
info_msgs.append("WMS AccessConstraints: {}".format(access_constraint))
for fee in wms["Fees"]:
info_msgs.append("WMS Fees: {}".format(fee))
if source["geometry"] is None:
geom = None
else:
geom = shape(source["geometry"])
# Check layers
if "layers" in wms_args:
layer_arg = wms_args["layers"]
not_found_layers = []
layers = layer_arg.split(",")
for layer_name in layer_arg.split(","):
if layer_name not in wms["layers"]:
for wms_layer in wms["layers"]:
if layer_name.lower() == wms_layer.lower():
warning_msgs.append(
"Layer '{}' is advertised by WMS server as '{}'".
format(layer_name, wms_layer))
not_found_layers.append(layer_name)
if len(not_found_layers) > 0:
error_msgs.append(
"Layers '{}' not advertised by WMS GetCapabilities request. "
"In rare cases WMS server do not advertise layers.".format(
",".join(not_found_layers)))
# Check source geometry against layer bounding box
# Regardless of its projection, each layer should advertise an approximated bounding box in lon/lat.
# See WMS 1.3.0 Specification Section 7.2.4.6.6 EX_GeographicBoundingBox
if geom is not None and geom.is_valid:
max_outside = 0.0
for layer_name in layers:
if layer_name in wms["layers"]:
bbox = wms["layers"][layer_name]["BBOX"]
geom_bbox = box(*bbox)
geom_outside_bbox = geom.difference(geom_bbox)
area_outside_bbox = geom_outside_bbox.area / geom.area * 100.0
max_outside = max(max_outside, area_outside_bbox)
if max_outside > 100.0:
error_msgs.append("{}% of geometry is outside of the layers "
"bounding box.".format(
round(area_outside_bbox, 2)))
elif max_outside > 15.0:
warning_msgs.append("{}% of geometry is outside of the layers "
"bounding box.".format(
round(area_outside_bbox, 2)))
# Check styles
if "styles" in wms_args:
style = wms_args["styles"]
# default style needs not to be advertised by the server
if not (style == "default" or style == ""
or style == "," * len(layers)):
styles = wms_args["styles"].split(",")
if not len(styles) == len(layers):
error_msgs.append(
"Not the same number of styles and layers.")
else:
for layer_name, style in zip(layers, styles):
if (len(style) > 0 and not style == "default"
and layer_name in wms["layers"] and style
not in wms["layers"][layer_name]["Styles"]):
error_msgs.append(
"Layer '{}' does not support style '{}'".
format(layer_name, style))
# Check CRS
crs_should_included_if_available = {"EPSG:4326", "EPSG:3857", "CRS:84"}
if "available_projections" not in source["properties"]:
error_msgs.append(
"source is missing 'available_projections' element.")
else:
for layer_name in layer_arg.split(","):
if layer_name in wms["layers"]:
not_supported_crs = set()
for crs in source["properties"]["available_projections"]:
# WMS sync bot checks if these projections are supported despite not advertised
if crs in {"EPSG:4326", "EPSG:3857"}:
continue
if crs.upper() not in wms["layers"][layer_name]["CRS"]:
not_supported_crs.add(crs)
if len(not_supported_crs) > 0:
supported_crs_str = ",".join(
wms["layers"][layer_name]["CRS"])
not_supported_crs_str = ",".join(not_supported_crs)
warning_msgs.append(
"Layer '{}': CRS '{}' not in: {}".format(
layer_name, not_supported_crs_str,
supported_crs_str))
supported_but_not_included = set()
for crs in crs_should_included_if_available:
if (crs not in source["properties"]
["available_projections"]
and crs in wms["layers"][layer_name]["CRS"]):
supported_but_not_included.add(crs)
if len(supported_but_not_included) > 0:
supported_but_not_included_str = ",".join(
supported_but_not_included)
warning_msgs.append(
"Layer '{}': CRS '{}' not included in available_projections but "
"supported by server.".format(
layer_name, supported_but_not_included_str))
if wms_args["version"] < wms["version"]:
warning_msgs.append(
"Query requests WMS version '{}', server supports '{}'".format(
wms_args["version"], wms["version"]))
# Check formats
imagery_format = wms_args["format"]
imagery_formats_str = "', '".join(wms["formats"])
if imagery_format not in wms["formats"]:
error_msgs.append("Format '{}' not in '{}'.".format(
imagery_format, imagery_formats_str))
if ("category" in source["properties"]
and "photo" in source["properties"]["category"]):
if "jpeg" not in imagery_format and "jpeg" in imagery_formats_str:
warning_msgs.append(
"Server supports JPEG, but '{}' is used. "
"JPEG is typically preferred for photo sources, but might not be always "
"the best choice. "
"(Server supports: '{}')".format(imagery_format,
imagery_formats_str))
# elif 'category' in source['properties'] and 'map' in source['properties']['category']:
# if 'png' not in imagery_format and 'png' in imagery_formats_str:
# warning_msgs.append("Server supports PNG, but '{}' is used. "
# "PNG is typically preferred for map sources, but might not be always "
# "the best choice. "
# "(Server supports: '{}')".format(imagery_format, imagery_formats_str))
return info_msgs, warning_msgs, error_msgs
def main():
# Define the two coordinate systems with different ellipses.
wgs84 = ccrs.PlateCarree(globe=ccrs.Globe(datum='WGS84', ellipse='WGS84'))
sphere = ccrs.PlateCarree(
globe=ccrs.Globe(datum='WGS84', ellipse='sphere'))
# Define the coordinate system of the data we have from Natural Earth and
# acquire the 1:10m physical coastline shapefile.
geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))
dataset = cfeature.NaturalEarthFeature(category='physical',
name='coastline',
scale='10m')
# Create a Stamen map tiler instance, and use its CRS for the GeoAxes.
tiler = Stamen('terrain-background')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=tiler.crs)
ax.set_title('The effect of incorrectly referencing the Solomon Islands')
# Pick the area of interest. In our case, roughly the Solomon Islands, and
# get hold of the coastlines for that area.
extent = [155, 163, -11.5, -6]
ax.set_extent(extent, geodetic)
geoms = list(dataset.intersecting_geometries(extent))
# Add the Stamen aerial imagery at zoom level 7.
ax.add_image(tiler, 7)
# Transform the geodetic coordinates of the coastlines into the two
# projections of differing ellipses.
wgs84_geoms = [
geom_transform(transform_fn_factory(wgs84, geodetic), geom)
for geom in geoms
]
sphere_geoms = [
geom_transform(transform_fn_factory(sphere, geodetic), geom)
for geom in geoms
]
# Using these differently referenced geometries, assume that they are
# both referenced to WGS84.
ax.add_geometries(wgs84_geoms, wgs84, edgecolor='white', facecolor='none')
ax.add_geometries(sphere_geoms, wgs84, edgecolor='gray', facecolor='none')
# Create a legend for the coastlines.
legend_artists = [
Line([0], [0], color=color, linewidth=3) for color in ('white', 'gray')
]
legend_texts = ['Correct ellipse\n(WGS84)', 'Incorrect ellipse\n(sphere)']
legend = ax.legend(legend_artists,
legend_texts,
fancybox=True,
loc='lower left',
framealpha=0.75)
legend.legendPatch.set_facecolor('wheat')
# Create an inset GeoAxes showing the location of the Solomon Islands.
sub_ax = fig.add_axes([0.7, 0.625, 0.2, 0.2],
projection=ccrs.PlateCarree())
sub_ax.set_extent([110, 180, -50, 10], geodetic)
# Make a nice border around the inset axes.
effect = Stroke(linewidth=4, foreground='wheat', alpha=0.5)
sub_ax.outline_patch.set_path_effects([effect])
# Add the land, coastlines and the extent of the Solomon Islands.
sub_ax.add_feature(cfeature.LAND)
sub_ax.coastlines()
extent_box = sgeom.box(extent[0], extent[2], extent[1], extent[3])
sub_ax.add_geometries([extent_box],
ccrs.PlateCarree(),
facecolor='none',
edgecolor='blue',
linewidth=2)
plt.show()
mec='k')
ax.axis(zoom)
leg_ax.legend(loc='upper left',
frameon=False,
bbox_to_anchor=[0, 0.90],
fontsize=9,
handles=ax.lines)
plot_wkb.plot_wkb(sfe_geom.intersection(clip_poly),
ax=overview_ax,
facecolor='#2d74ad',
edgecolor='#2d74ad',
lw=0.2)
if overview_ax2:
rect = geometry.box(zoom2[0], zoom2[2], zoom2[1], zoom2[3]).buffer(5000)
plot_wkb.plot_wkb(sfe_geom.intersection(rect),
ax=overview_ax2,
facecolor='#2d74ad',
edgecolor='#2d74ad',
lw=0.2)
for ov_ax in [overview_ax, overview_ax2]:
ov_ax.axis('equal')
ov_ax.xaxis.set_visible(0)
ov_ax.yaxis.set_visible(0)
x = 0.5 * (zoom[0] + zoom[1])
y = 0.5 * (zoom[2] + zoom[3])
# overview_ax.plot( [x], [y], 'r*',ms=7)
def search(dataset,
node,
lat=None,
lng=None,
distance=100,
ll=None,
ur=None,
start_date=None,
end_date=None,
where=None,
max_results=50000,
starting_number=1,
sort_order="DESC",
api_key=None):
"""
:param dataset:
:param node:
:param lat:
:param lng:
:param ll:
:param distance:
:param ur:
:param start_date:
:param end_date:
:param where:
Specify additional search criteria
:param max_results:
:param starting_number:
:param sort_order:
:param api_key:
API key is not required.
"""
payload = {
"datasetName": dataset,
"node": node,
"apiKey": api_key,
}
# Latitude and longitude take precedence over ll and ur
if lat and lng:
try:
import pyproj
from shapely import geometry
except ImportError:
raise USGSDependencyRequired(
"Shapely and PyProj are required for spatial searches.")
prj = pyproj.Proj(proj='aeqd', lat_0=lat, lon_0=lng)
half_distance = 0.5 * distance
box = geometry.box(-half_distance, -half_distance, half_distance,
half_distance)
lngs, lats = prj(*box.exterior.xy, inverse=True)
ll = {"longitude": min(*lngs), "latitude": min(*lats)}
ur = {"longitude": max(*lngs), "latitude": max(*lats)}
if ll and ur:
payload["lowerLeft"] = ll
payload["upperRight"] = ur
if start_date:
payload["startDate"] = start_date
if end_date:
payload["endDate"] = end_date
if where:
# TODO: Support more than AND key/value equality queries
# usgs search --node EE LANDSAT_8_C1 --start-date 20170410 --end-date 20170411 --where wrs-row 032 | jq ""
# LC81810322017101LGN00
payload["additionalCriteria"] = {
"filterType":
"and",
"childFilters": [{
"filterType": "value",
"fieldId": field_id,
"value": value,
"operand": "="
} for field_id, value in where.items()]
}
if max_results:
payload["maxResults"] = max_results
if starting_number:
payload["startingNumber"] = starting_number
if sort_order:
payload["sortOrder"] = sort_order
return json.dumps(payload)
'''
p_coll_circ = circ_coll_prob(car, obst, obst_mu, obst_cov)
if p_coll_circ > circ_thresh:
return total_coll_prob(car, obst,
obst_mu, obst_cov,
gamma_mu, gamma_sd, delta, n_intervals)
else:
return p_coll_circ
if __name__ == "__main__":
# Test it
plt.close('all')
obstacle = geom.box(0,0,2,1)
obstacle = affinity.translate(obstacle, -0.5, -0.5) # shift so back axle at origin
car = geom.box(0,0,2,1)
car = affinity.translate(car, -0.5, -0.5) # shift so back axle at origin
plt.figure()
# Make a rotated convariance matrix
obst_cov = np.array([[2e-1,0],[0,1e-1]])
theta = -np.pi/4
R = lambda theta: np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
obst_cov = np.dot(np.dot(R(theta), obst_cov), R(theta).T)
def load_oh_sections(gdb_path, layer, datastore):
dml = """
INSERT INTO landgrid.ohio_section (SUBDIV_NM, TWP, TNS, RGE, REW, SEC,
QTR_TWP, ALLOTMENT, TRACT, LOT, DIVISION,
FRACTION, COUNTY, TOWNSHIP, SURVEY_TYP,
ObjectID, VMSLOT, OTHER_SUB, Shape_Length,
Shape_Area, State_Name, geobounds, shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
%s, %s, %s, %s, %s, ST_GeomFromText(%s, 4326));
"""
try:
with fiona.open(gdb_path, layer=layer) as src:
datastore.execute_dml('TRUNCATE TABLE landgrid.ohio_section;')
# restart
total = len(src)
logger.info(f"Starting load of {layer}: {total} sections...")
# logger.info(f"Schema: {src.schema}")
for i, rec in enumerate(src):
poly = shape(rec['geometry'])
if not poly.is_valid:
logger.warning(
f"Cleaning {rec['properties']['TOWNSHIP']}...")
clean = poly.buffer(0.0)
assert clean.is_valid, 'Invalid Polygon!'
assert clean.geom_type == 'Polygon' or clean.geom_type == 'MultiPolygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml,
(
rec['properties']['SUBDIV_NM'],
rec['properties']['TWP'],
rec['properties']['TNS'],
rec['properties']['RGE'],
rec['properties']['REW'],
rec['properties']['SEC'],
rec['properties']['QTR_TWP'],
rec['properties']['ALLOTMENT'],
rec['properties']['TRACT'],
rec['properties']['LOT'],
rec['properties']['DIVISION'],
rec['properties']['FRACTION'],
rec['properties']['COUNTY'],
rec['properties']['TOWNSHIP'],
rec['properties']['SURVEY_TYP'],
rec['properties']['ObjectID'],
rec['properties']['VMSLOT'],
rec['properties']['OTHER_SUB'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'],
'Ohio', # TMP
g_json,
poly.wkt))
if i % 5000 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total}: {rec['properties']['TOWNSHIP']}"
)
datastore.batch_commit()
logger.info(f"Completed {layer} load.")
except Exception as e:
# datastore.rollback()
logger.exception("Error processing OH Sections.", e)
raise e
def grid():
raw_query_params = request.args.copy()
buff = request.args.get('buffer', 100)
resolution = request.args.get('resolution')
if not resolution:
resolution = 500
else:
del raw_query_params['resolution']
center = request.args.getlist('center[]')
if not center:
center = [41.880517,-87.644061]
else:
del raw_query_params['center[]']
location_geom = request.args.get('location_geom__within')
if raw_query_params.get('buffer'):
del raw_query_params['buffer']
agg, datatype, queries = parse_join_query(raw_query_params)
size_x, size_y = getSizeInDegrees(float(resolution), float(center[0]))
if location_geom:
location_geom = json.loads(location_geom)['geometry']
if location_geom['type'] == 'LineString':
shape = asShape(location_geom)
lat = shape.centroid.y
# 100 meters by default
x, y = getSizeInDegrees(int(buff), lat)
size_x, size_y = getSizeInDegrees(50, lat)
location_geom = shape.buffer(y).__geo_interface__
location_geom['crs'] = {"type":"name","properties":{"name":"EPSG:4326"}}
mt = MasterTable.__table__
valid_query, base_clauses, resp, status_code = make_query(mt, queries['base'])
if valid_query:
base_query = session.query(func.count(mt.c.dataset_row_id),
func.ST_SnapToGrid(mt.c.location_geom, size_x, size_y))
dname = raw_query_params['dataset_name']
dataset = Table('dat_%s' % dname, Base.metadata,
autoload=True, autoload_with=engine,
extend_existing=True)
valid_query, detail_clauses, resp, status_code = make_query(dataset, queries['detail'])
if valid_query:
pk = [p.name for p in dataset.primary_key][0]
base_query = base_query.join(dataset, mt.c.dataset_row_id == dataset.c[pk])
for clause in base_clauses:
base_query = base_query.filter(clause)
for clause in detail_clauses:
base_query = base_query.filter(clause)
base_query = base_query.group_by(func.ST_SnapToGrid(mt.c.location_geom, size_x, size_y))
values = [d for d in base_query.all()]
resp = {'type': 'FeatureCollection', 'features': []}
for value in values:
d = {
'type': 'Feature',
'properties': {
'count': value[0],
},
}
if value[1]:
pt = loads(value[1].decode('hex'))
south, west = (pt.x - (size_x / 2)), (pt.y - (size_y /2))
north, east = (pt.x + (size_x / 2)), (pt.y + (size_y / 2))
d['geometry'] = box(south, west, north, east).__geo_interface__
resp['features'].append(d)
resp = make_response(json.dumps(resp, default=dthandler), status_code)
resp.headers['Content-Type'] = 'application/json'
return resp
def load_sections(gdb_path, layer_queue, datastore):
dml = """
INSERT INTO landgrid.plss_section (StateID, StateAPI, TWPCODE,
SECCODE, MER, MST, TWP, THALF,
TNS, RGE, RHALF, REW, SEC,
Shape_Length, Shape_Area,
State_Name, geobounds, shape)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s,
%s, %s, ST_GeomFromText(%s, 4326));
"""
try:
datastore.execute_dml('TRUNCATE TABLE landgrid.plss_section;')
# restart
for ts in layer_queue:
with fiona.open(gdb_path, layer=ts.layer) as src:
total = len(src)
logger.info(
f"Starting load of {ts.layer}: {total} sections...")
# logger.info(f"Schema: {src.schema}")
for i, rec in enumerate(src):
try:
poly = shape(rec['geometry'])
except Exception as e:
logger.error(
f"Error reading {rec['properties']['SECCODE']}: {rec['geometry']}"
)
logger.exception("Unknown error", e)
continue
if not poly.is_valid:
logger.warning(
f"Cleaning {rec['properties']['SECCODE']} [{ts.layer}]..."
)
clean = poly.buffer(0.0)
assert clean.is_valid, 'Invalid Polygon!'
assert clean.geom_type == 'MultiPolygon' or clean.geom_type == 'Polygon', \
f'{clean.geom_type} is not a Polygon!'
poly = clean
bbox = box(*poly.bounds)
g_json = geojson.dumps(mapping(bbox), sort_keys=True)
datastore.write_record(
dml,
(
rec['properties']['StateID'],
rec['properties']['StateAPI'],
rec['properties']['TWPCODE'],
rec['properties']['SECCODE'],
rec['properties']['MER'],
rec['properties']['MST'],
rec['properties']['TWP'],
rec['properties']['THALF'],
rec['properties']['TNS'],
rec['properties']['RGE'],
rec['properties']['RHALF'],
rec['properties']['REW'],
rec['properties']['SEC'],
rec['properties']['Shape_Length'],
rec['properties']['Shape_Area'],
ts.state, # TMP
g_json,
poly.wkt))
if i % 5000 == 0:
logger.info(
f"{round(i / total * 100, 2)}%: {i} of {total} [{ts.layer}]"
)
datastore.batch_commit()
logger.info(f"Completed {ts.layer} load.")
except Exception as e:
# datastore.rollback()
logger.exception(f"Error processing {rec['properties']['SECCODE']}", e)
raise e
def save_patches(slice_no, slice_length, pickled_files_path, dg_path, struct_locs_landuse_path, output_path, buff_radius):
# print("Reading building locations file appended with land cover information", flush=True)
# dll = pd.read_pickle(struct_locs_landuse_path + s_l_file)
# print("Read the locs + landuse file", flush=True)
print("***********************************************************", flush=True)
print("Figuring which folders to process based on slicing input", flush=True)
list_of_folders = glob.glob(pickled_files_path + "*")
total_length = len(list_of_folders)
assert slice_length<=total_length, "Choose smaller slice length"
print("Total number of available folders: {}".format(total_length), flush=True)
folders_at_a_time = total_length//slice_length
print("Number of folders to be read at a time: {}".format(folders_at_a_time), flush=True)
if slice_no == slice_length-1:
# maybe here +1 is not needed. ***** CHECK ********
folders_to_process = list_of_folders[slice_no*folders_at_a_time : total_length+1]
else:
folders_to_process = list_of_folders[slice_no*folders_at_a_time : (slice_no+1)*folders_at_a_time]
print("Folders to be processed on one machine: {}".format(folders_to_process), flush=True)
print("Number of folders: {}".format(len(folders_to_process)), flush=True)
for pck_file in folders_to_process:
print("##############################################################################", flush=True)
foldername = pck_file.split('/')[6].split('_pickle_file')[0] #6 for laptop, 5 for gypsum?
print("Reading pickle file for folder {}".format(foldername), flush=True)
df = pd.read_pickle(pck_file)
print("Pickle file read!", flush=True)
# df['ct_array'] = df.struct_locs.apply(lambda x: len(x))
# print("Created struct_locs array length column", flush=True)
# df = df[(df.ct_array != 0)]
# print("Filtered dataframe by removing image names without structures i.e. with count=0", flush=True)
# print("Checking if dataframe is empty after filteration", flush=True)
# if len(df)==0:
# print("Dataframe is empty. Moving to next folder.", flush=True)
# continue
print("Dataframe is not empty. Continuing the process!", flush=True)
df = df.groupby(['file_name']).struct_locs.apply(lambda x: itertools.chain(*x)).reset_index()
print("Created iter objects for locations in every image", flush=True)
print("###################################", flush=True)
print("Folder Name = {}".format(foldername), flush=True)
print("Total number of files = {}".format(df.file_name.nunique()), flush=True)
print("###################################", flush=True)
file_counter = 0
for fname in df.file_name.unique():
print("------------------------------------------------------------------", flush=True)
print("{} - Processing satellite image: {}".format(file_counter, fname), flush=True)
print('{} : Processing satellite image'.format(time.strftime('%m/%d/%Y %H:%M:%S',time.localtime())))
filename = fname.split("/")[1]
sat_img_path = glob.glob(dg_path + foldername + "/" + filename)[0]
print("Sat image path extracted", flush=True)
sat_img = cv2.imread(sat_img_path)
print("Cv2 read!", flush=True)
sat_img_rast = rasterio.open(sat_img_path)
print("rasterio read!", flush=True)
db = df[(df.file_name == fname)]
# An itertool chain object can only be used once and so
# we save it in list instead and iterate through the list
iter_list = list(db.struct_locs.values[0])
print("Total buildings in image: {}".format(len(iter_list)), flush=True)
print("Beginning patch extraction", flush=True)
# create a hollow box representing boundary of raster
bound = sat_img_rast.bounds
img_box = box(bound.left, bound.bottom, bound.right, bound.top)
# reduce the boundary of the box in order to avoid
# errors when the points are selected near the edge of the image
# and the negative patch length crosses the image.
# boundary buffer radius will be equal to radius of
# patch (16m or 32pixels)
boundary_buff = buff_radius/2
img_box = box(bound.left+boundary_buff, bound.bottom+boundary_buff, bound.right-boundary_buff, bound.top-boundary_buff)
print("image box cropped at the edges for buffering purposes")
# if an image contains no buildings, then the complete image can
# be used for extracting negative patches.
# In that case, we won't need cascaded union
if len(iter_list) !=0:
print("Proceeding with cascaded union formation")
# create point geoms for using in cascaded_union
geom = []
for k in iter_list:
if k != []: #avoids "not enough values to unpack" error
geom.append(Point(k[0],k[1]))
else:
None
# geom = [Point(k[0],k[1]) for k in iter_list]
# create buffers around each point
buff = [x.buffer(buff_radius, cap_style=3) for x in geom]
# create cascaded_union object
casc_union = cascaded_union(buff)
print("Cascaded union is ready")
# extract portion of box that doesn't intersect with cascaded union
# this shape should not contain buildings
if casc_union.intersects(img_box) == True:
nobuild_poly = img_box.difference(casc_union)
else:
print("No buildings in image. Using complete image for extracting negative patches")
nobuild_poly = img_box
# create a list of random geo-spatial points that lie
# Approx 1500 patches per image (127 folders x 64 images per folder)
# will give us 12 million negative patches
print('{} : Negative Poly is ready'.format(time.strftime('%m/%d/%Y %H:%M:%S',time.localtime())))
print("Extracting 1500 random non-building points inside the image file")
nobuild_list = generate_random_spatialpoints(1500, nobuild_poly)
print('{} : Negative Points extracted'.format(time.strftime('%m/%d/%Y %H:%M:%S',time.localtime())))
locs_counter = 0
for p in nobuild_list:
print("=================================", flush=True)
x, y = p
print("{} - Obtained (x,y) coordinates: {}".format(locs_counter, p), flush=True)
img_name = foldername + "_z_" + filename.split('.')[0] + "_z_" + str(x) + "_z_" + str(y) + "_z_" +"nobuild"
print("Sending locs for a point:{} for patch extraction".format(p), flush=True)
patch = read_and_extract(sat_img, sat_img_rast, x, y, img_name, output_path)
locs_counter = locs_counter + 1
file_counter = file_counter + 1
class CommonObsSpatialTestCase(baseDafTestCase.DafTestCase):
"""Test DAF support for common_obs_spatial data"""
datatype = "common_obs_spatial"
envelope = box(-97.0, 41.0, -96.0, 42.0)
"""Default request area (box around KOAX)"""
def testGetAvailableParameters(self):
req = DAL.newDataRequest(self.datatype)
self.runParametersTest(req)
def testGetAvailableLocations(self):
req = DAL.newDataRequest(self.datatype)
req.addIdentifier("country", ["US", "CN"])
self.runLocationsTest(req)
def testGetIdentifierValues(self):
self.runGetIdValuesTest(['country'])
@unittest.skip('avoid EDEX error')
def testGetInvalidIdentifierValuesThrowsException(self):
self.runInvalidIdValuesTest()
@unittest.skip('avoid EDEX error')
def testGetNonexistentIdentifierValuesThrowsException(self):
self.runNonexistentIdValuesTest()
def testGetGeometryData(self):
req = DAL.newDataRequest(self.datatype)
req.setEnvelope(self.envelope)
req.setParameters("name", "stationid")
self.runGeometryDataTest(req)
def testRequestingTimesThrowsTimeAgnosticDataException(self):
req = DAL.newDataRequest(self.datatype)
self.runTimeAgnosticTest(req)
def _runConstraintTest(self, key, operator, value):
req = DAL.newDataRequest(self.datatype)
constraint = RequestConstraint.new(operator, value)
req.addIdentifier(key, constraint)
req.setParameters('catalogtype', 'elevation', 'state')
return self.runGeometryDataTest(req)
def testGetDataWithEqualsString(self):
geometryData = self._runConstraintTest('state', '=', 'NE')
for record in geometryData:
self.assertEqual(record.getString('state'), 'NE')
def testGetDataWithEqualsUnicode(self):
geometryData = self._runConstraintTest('state', '=', u'NE')
for record in geometryData:
self.assertEqual(record.getString('state'), 'NE')
def testGetDataWithEqualsInt(self):
geometryData = self._runConstraintTest('catalogtype', '=', 32)
for record in geometryData:
self.assertEqual(record.getNumber('catalogtype'), 32)
def testGetDataWithEqualsLong(self):
geometryData = self._runConstraintTest('elevation', '=', 0L)
for record in geometryData:
self.assertEqual(record.getNumber('elevation'), 0)
# No float test since there are no float identifiers available. Attempting
# to filter a non-float identifier on a float value raises an exception.
def testGetDataWithEqualsNone(self):
geometryData = self._runConstraintTest('state', '=', None)
for record in geometryData:
self.assertEqual(record.getType('state'), 'NULL')
def testGetDataWithNotEquals(self):
geometryData = self._runConstraintTest('state', '!=', 'NE')
for record in geometryData:
self.assertNotEqual(record.getString('state'), 'NE')
def testGetDataWithNotEqualsNone(self):
geometryData = self._runConstraintTest('state', '!=', None)
for record in geometryData:
self.assertNotEqual(record.getType('state'), 'NULL')
def testGetDataWithGreaterThan(self):
geometryData = self._runConstraintTest('elevation', '>', 500)
for record in geometryData:
self.assertGreater(record.getNumber('elevation'), 500)
def testGetDataWithLessThan(self):
geometryData = self._runConstraintTest('elevation', '<', 100)
for record in geometryData:
self.assertLess(record.getNumber('elevation'), 100)
def testGetDataWithGreaterThanEquals(self):
geometryData = self._runConstraintTest('elevation', '>=', 500)
for record in geometryData:
self.assertGreaterEqual(record.getNumber('elevation'), 500)
def testGetDataWithLessThanEquals(self):
geometryData = self._runConstraintTest('elevation', '<=', 100)
for record in geometryData:
self.assertLessEqual(record.getNumber('elevation'), 100)
def testGetDataWithInTuple(self):
collection = ('NE', 'TX')
geometryData = self._runConstraintTest('state', 'in', collection)
for record in geometryData:
self.assertIn(record.getString('state'), collection)
def testGetDataWithInList(self):
collection = ['NE', 'TX']
geometryData = self._runConstraintTest('state', 'in', collection)
for record in geometryData:
self.assertIn(record.getString('state'), collection)
def testGetDataWithInGenerator(self):
collection = ('NE', 'TX')
generator = (item for item in collection)
geometryData = self._runConstraintTest('state', 'in', generator)
for record in geometryData:
self.assertIn(record.getString('state'), collection)
def testGetDataWithInvalidConstraintTypeThrowsException(self):
with self.assertRaises(ValueError):
self._runConstraintTest('state', 'junk', 'NE')
def testGetDataWithInvalidConstraintValueThrowsException(self):
with self.assertRaises(TypeError):
self._runConstraintTest('state', '=', {})
def testGetDataWithEmptyInConstraintThrowsException(self):
with self.assertRaises(ValueError):
self._runConstraintTest('state', 'in', [])
def plane_cluster(self, cloud_data):
"""Clustering the exported plane data
Finding clustered points in the data extracted as planes.
Args:
cloud_data: Pointcloud data extracted by plane
Returns:
new_cloud_data: Clustered PointCloud data list
"""
max_size = cloud_data.size # The number of points included in plane
min_size = 100 # Minimum size of point included in each result of clustering
new_data_st = list()
count = 0
check_count = 0
while True:
if cloud_data.size < min_size or count > 10:
break
tree = cloud_data.make_kdtree()
segment = cloud_data.make_EuclideanClusterExtraction()
segment.set_ClusterTolerance(0.1) # distance of clustering
segment.set_MinClusterSize(min_size)
segment.set_MaxClusterSize(max_size)
segment.set_SearchMethod(tree)
cluster_indices = segment.Extract()
if len(cluster_indices) != 0:
inliers = cloud_data.extract(
cluster_indices[0],
negative=False) # Save all the inliers as a point cloudd
outliers = cloud_data.extract(
cluster_indices[0],
negative=True) # Save all the outliers as a point cloud.
if inliers.size >= min_size:
inliers_p, outliers_p, coeff_p = self.do_plane_ransac(
inliers)
bbox_info = PointCloudUtils.get_range(
inliers
) # Getting the bounding box information [[min_x, min_y, min_z], [max_x, max_y, max_z]]
# Use of information with a height of 40 cm or more
check_height = True if math.fabs(
bbox_info[1][2] - bbox_info[0][2]) > 1.0 else False
check_count += 1
m_minX = bbox_info[0][0]
m_minY = bbox_info[0][1]
m_maxX = bbox_info[1][0]
m_maxY = bbox_info[1][1]
make_box = box(m_minX, m_minY, m_maxX, m_maxY)
# Use of information with a area of 10 or more
if check_height is False:
check_height = True if math.fabs(
bbox_info[0][2]) >= 1.5 else False
check_area = True if make_box.area > 0.1 else False
if check_height:
# 4 outer points of extracted result of clustering
side_points = PointCloudUtils.make_side_line(
bbox_info, coeff_p)
# Adding the list of points, plane equation and outer points
new_data_st.append([inliers, coeff_p, side_points])
count = 0
cloud_data = outliers
else:
cloud_data = outliers
count += 1
else:
break
return new_data_st
def trend_all():
srfc = cnst.ERA5_MONTHLY_SRFC_SYNOP #cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA5_MONTHLY_PL_SYNOP #cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc' #gridsat_WA_-70_monthly_mean_5000km2.nc' #gridsat_WA_-50_monthly_count_-50base.nc' #gridsat_WA_-70_monthly_mean_5000km2.nc' gridsat_WA_-50_monthly_count
fpath = cnst.network_data + 'figs/CLOVER/months/ERA5_WA/'
box = [-18, 30, 0, 25] #[-18,30,0,25]# [-18,40,0,25] #
da = xr.open_dataset(pl) #xr.open_dataset(pl)
#da = xr.decode_cf(da)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da2 = xr.open_dataset(srfc) #xr.open_dataset(srfc)
#da2 = xr.decode_cf(da2)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da3 = xr.open_dataarray(mcs) * 100 #/30*100
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
#ipdb.set_trace()
da = da.isel(time=(da['time.hour'] == 12))
da2 = da2.isel(time=(da2['time.hour'] == 12))
lons = da.longitude
lats = da.latitude
press = da2['tcwv']
#press = press[press['time.hour'] == 12]
#press.values = press.values#*1000
low_press = 950
up_press = 650
mid_press = 700
q = da['q'].sel(level=slice(low_press - 30, low_press)).mean('level')
t2d = da2['t2m']
theta_low = u_met.theta_e(
low_press,
da['t'].sel(level=slice(low_press -
30, low_press)).mean('level').values - 273.15,
da['q'].sel(level=slice(low_press -
30, low_press)).mean('level').values)
theta_high = u_met.theta_e(
mid_press,
da['t'].sel(level=slice(up_press, mid_press)).mean('level').values -
273.15,
da['q'].sel(level=slice(up_press, mid_press)).mean('level').values)
theta_high_d = u_met.theta(
mid_press,
da['t'].sel(level=slice(up_press, mid_press)).mean('level').values -
273.15)
theta_low_d = u_met.theta(
low_press,
da['t'].sel(level=slice(low_press -
30, low_press)).mean('level').values - 273.15)
# punit = units.Quantity(mid_press, 'hPa')
# tunit = units.Quantity(da['t'].sel(level=slice(mid_press-30, mid_press)).mean('level').values, 'K')
# theta_high_d = calc.saturation_equivalent_potential_temperature(punit,tunit)
#
# punit = units.Quantity(low_press, 'hPa')
# tunit = units.Quantity(da['t'].sel(level=slice(low_press-30, low_press)).mean('level').values, 'K')
# theta_low_d = calc.saturation_equivalent_potential_temperature(punit, tunit)
theta_diff = (theta_high /
theta_low) * 100 #(np.array(theta_high)-273.15) #theta_low -
theta_diff_d = da2[
'cape'] ##np.array(theta_low_d) - np.array(theta_high_d)
#
theta_e = t2d.copy(deep=True)
theta_e.name = 'theta'
theta_e.values = theta_diff
theta_e = da['r'].sel(level=slice(mid_press - 30, mid_press)).mean(
'level') #da2['cape']
theta_d = t2d.copy(deep=True)
theta_d.name = 'theta'
theta_d.values = theta_diff_d
u600 = da['u'].sel(level=slice(up_press, mid_press)).mean('level')
v600 = da['v'].sel(level=slice(up_press, mid_press)).mean('level')
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=925)
v800 = da['v'].sel(level=925)
shear_u = u600 - u800
shear_v = v600 - v800
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
u6 = shear_u
v6 = shear_v
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=1)
t2 = t2d # grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3) #t2d.salem.lookup_transform(da3['tir']) #
grid = grid.to_dataset()
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
months = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
] #[3,4,5,6,9,10,11]#,4,5,6,9,10,11#,4,5,6,9,10,11,(3,5), (9,11)]#, 10,5,9]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'polyfit'
if type(m) == int:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir,
m,
method=method,
sig=True,
wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
shear_mean = shearmean.mean(axis=0)
presstrend, pressmean = calc_trend(press,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
press_mean = pressmean.mean(axis=0)
u6trend, u6mean = calc_trend(u6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
u6_mean = u6mean.mean(axis=0)
v6trend, v6mean = calc_trend(v6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
v6_mean = v6mean.mean(axis=0)
u8trend, u8mean = calc_trend(u800,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
u8_mean = u8mean.mean(axis=0)
v8trend, v8mean = calc_trend(v800,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
v8_mean = v8mean.mean(axis=0)
aej = np.argmin(u6_mean, axis=0)
itd = np.argmin(np.abs(v8_mean.values), axis=0)
thetatrend, thetamean = calc_trend(theta_e,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
theta_mean = thetamean.mean(axis=0)
thetatrend_d, thetamean_d = calc_trend(theta_d,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
thetad_mean = thetamean_d.mean(axis=0)
t_da = t2trend * 10. # warming over decade
q_da = qtrend * 10. # warming over decade
s_da = sheartrend * 10. # warming over decade
u6trend = u6trend * 10
v6trend = v6trend * 10
tcwv_da = presstrend * 10
theta_da = thetatrend * 10
thetad_da = thetatrend_d * 10
u8trend = u8trend * 10
v8trend = v8trend * 10
tdata = (tirtrend.values * 10. / tirm_mean.values) * 100.
#ipdb.set_trace()
tirtrend_out = xr.DataArray(tdata,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
tirtrend_out.name = 'tir'
#tirmean_out = xr.DataArray(tirm_mean, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirm_mean
if len(m) == 1:
fp = fpath + 'use/ERA5_-70_use_nosig_2003_' + str(
m[0]).zfill(2) + '.png'
else:
fp = fpath + 'use/ERA5_-70_use_nosig_2003_' + str(
m[0]).zfill(2) + '-' + str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map(countries=False)
# Change the country borders
map.set_shapefile(countries=True, color='grey', linewidths=0.5)
#map.set_lonlat_contours(interval=0)
# Change the lon-lat countour setting
map.set_lonlat_contours(add_ytick_labels=True,
interval=5,
linewidths=0.01,
linestyles='-',
colors='white')
ti_da = t2d.salem.transform(tirtrend_out)
f = plt.figure(figsize=(15, 8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xaej, yaej = map.grid.transform(u6_mean.longitude.values,
u6_mean.latitude.values[aej.values],
crs=shear.salem.grid.proj)
xitd, yitd = map.grid.transform(v8_mean.longitude.values,
v8_mean.latitude.values[itd],
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#ipdb.set_trace()
#Quiver only every 7th grid point
u = u6trend.values[1::2, 1::2]
v = v6trend.values[1::2, 1::2]
#Quiver only every 7th grid point
uu = u8trend.values[1::2, 1::2]
vv = v8trend.values[1::2, 1::2]
#Quiver only every 7th grid point
um = u8_mean.values[1::2, 1::2]
vm = v8_mean.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
# pdic = {
# 'tlin' : (t2_mean.values-273.15).astype(np.float64),
# 'tmean' : (t2_mean.values-273.15).astype(np.float64),
# 'qmean' : (q_mean.values).astype(np.float64),
# 'qlin' : q_da.values,
# 'shearlin' : s_da.values,
# 'u' : u,
# 'v' : v,
# 'xx' : xx,
# 'yy' : yy,
# 'tirmean' : tirm_mean,
#
#
# }
# pkl.dump(dicm,
# open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_WA.p',
# 'wb'))
map.set_shapefile(countries=True, linewidths=1.2, color='grey')
ax1 = f.add_subplot(221)
map.set_data(t_da.values, interp='linear') # interp='linear'
map.set_contour(s_da.values,
interp='linear',
levels=[0.4, 0.6, 0.8],
colors='k',
linewidths=1.8)
map.set_plot_params(
levels=[-0.5, -0.4, -0.3, -0.2, 0.2, 0.3, 0.4, 0.5],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
qu = ax1.quiver(xx, yy, u, v, scale=30, width=0.002, headwidth=4)
# qk = plt.quiverkey(qu, 0.4, 0.03, 1, '1 m s$^{-1}$decade$^{-1}$',
# labelpos='E', coordinates='figure')
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(
ax=ax1,
title=
'2m temperature | 925-600hPa wind shear | 650hPa wind vectors',
cbar_title=r'K decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
qk = plt.quiverkey(qu,
0.45,
0.52,
1,
'1 m s$^{-1}$decade$^{-1}$',
labelpos='E',
coordinates='figure')
ax2 = f.add_subplot(222)
map.set_data(theta_da.values - 0.2, interp='linear') # interp='linear'
map.set_contour(
(q_da.values).astype(np.float64),
interp='linear',
colors='k',
linewidths=1.8,
levels=[
-0.6, -0.4, -0.2, 0.2, 0.4, 0.6
]) #[6,8,10,12,14,16] #levels=[-0.6,-0.4,-0.2,0.2,0.4, 0.6],
map.set_plot_params(
levels=np.array([-0.4, -0.3, -0.2, -0.1, 0.1, 0.2, 0.3, 0.4]) * 10,
cmap='RdBu',
extend='both'
) # levels=np.arange(-0.5,0.51,0.1), [-0.6,-0.4,-0.2,0.2,0.4,0.6]
qu = ax2.quiver(xx, yy, um, vm, scale=100, width=0.002, headwidth=4)
qk = plt.quiverkey(qu,
0.94,
0.52,
3,
'3 m s$^{-1}$',
labelpos='E',
coordinates='figure')
dic = map.visualize(
ax=ax2,
title=r'650hPa RH | 925hPa q | 925hPa wind vectors',
cbar_title=r'% decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(tcwv_da.values - 0.05, interp='linear') # interp='linear'
map.set_contour(thetad_da.values,
interp='linear',
levels=np.array([-2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2]) *
100,
colors='k',
linewidths=1.8)
map.set_plot_params(levels=[
-1.5, -1, -0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8, 1, 1.5
],
cmap='RdBu',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
qu = ax3.quiver(xx, yy, uu, vv, scale=30, width=0.002, headwidth=4)
qk = plt.quiverkey(qu,
0.45,
0.03,
1,
'1 m s$^{-1}$decade$^{-1}$',
labelpos='E',
coordinates='figure')
dic = map.visualize(ax=ax3,
title=r'TCWV | CAPE | 925hPa wind vectors',
cbar_title=r'kg m$^{-2}$ decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.0f')
ax4 = f.add_subplot(224)
map.set_contour((tirm_mean),
interp='linear',
levels=[0.1, 1, 2, 4],
colors='k',
linewidths=1.5)
ti_da.values[ti_da.values == 0] = np.nan
map.set_data(ti_da) #
coord = [18, 25, -28, -20]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
#map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
map.set_plot_params(cmap='viridis',
extend='both',
levels=np.arange(10, 41,
10)) # levels=np.arange(10,51,10)
ax4.scatter(xaej, yaej, color='r', s=50, edgecolors='r', linewidths=1)
#ax4.scatter(xitd, yitd, color='r', s=50, edgecolors='k', linewidths=1)
dic = map.visualize(ax=ax4,
title='-70$^{\circ}$C cloud cover change ',
cbar_title='$\%$ decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
plt.tight_layout()
plt.annotate('a)',
xy=(0.02, 0.96),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('b)',
xy=(0.49, 0.96),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('c)',
xy=(0.02, 0.48),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('d)',
xy=(0.49, 0.48),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.savefig(fp)
plt.close('all')
def __call__(self, sample):
image, bb = sample['image'], sample['bb']
# img_size = image.size
if self.train:
min_x, max_y, max_x, min_y = bb[0], bb[1], bb[2], bb[3]
else:
if self.real:
## This is for Homebrew dataset
##min_x,max_y,max_x,min_y = bb[0]-25, bb[1]-25, bb[0] + bb[2] + 25 , bb[1] + bb[3] + 25
## For others, probabaly
min_x, max_y, max_x, min_y = bb[0], bb[
1], bb[0] + bb[2], bb[1] + bb[3]
else:
min_x, max_y, max_x, min_y = bb[0], bb[1], bb[2], bb[3]
center_x = (min_x + max_x) / 2
center_y = (min_y + max_y) / 2
width, height = max_x - min_x, max_y - min_y
scaleFactor = max([width, height])
min_x = int(center_x) - int(scaleFactor) // 2
min_y = int(center_y) - int(scaleFactor) // 2
max_x = int(center_x) + int(scaleFactor) // 2
max_y = int(center_y) + int(scaleFactor) // 2
## This is for Homebrew dataset
## Image crop works in a way (0, 0, 10, 10) but here the
## image coordinates are revresed on Y-axis nd so the crop.
sample['image'] = image.crop(box=(min_x, min_y, max_x, max_y))
sample['orig_image'] = image
sample['center'] = np.array([center_x, center_y], dtype=np.float32)
sample['width'] = width
sample['height'] = height
## This sclae is used for OKS calculation
sample['scaleArea'] = np.sqrt(
np.divide(
box(min_x, min_y, max_x, max_y).area, sample['scaleArea']))
#print(sample['scaleArea'])
w, h = self.out_size
## Crop and scale
sample['crop'] = np.array([min_x, min_y], dtype=np.float32)
sample['scale'] = np.array([w / scaleFactor, h / scaleFactor],
dtype=np.float32)
if width != self.out_size[0]:
sample['image'] = sample['image'].resize((w, h))
if 'mask' in sample:
sample['mask'] = sample['mask'].crop(box=(min_x, min_y, max_x,
max_y)).resize((w, h))
if 'keypoints' in sample:
keypoints = sample['keypoints']
for i in range(keypoints.shape[0]):
if keypoints[i, 0] < min_x or keypoints[
i, 0] > max_x or keypoints[i, 1] < min_y or keypoints[
i, 1] > max_y:
keypoints[i, :] = [0, 0, 0]
else:
keypoints[i, :2] = (keypoints[i, :2] -
sample['crop']) * sample['scale']
sample['keypoints'] = keypoints
if 'initial_keypoints' in sample:
initial_keypoints = sample['initial_keypoints']
for i in range(initial_keypoints.shape[0]):
if initial_keypoints[i,0] < min_x or initial_keypoints[i,0] > max_x \
or initial_keypoints[i,1] < min_y or initial_keypoints[i,1] > max_y:
initial_keypoints[i, :] = [0, 0, 0]
else:
initial_keypoints[
i, :2] = (initial_keypoints[i, :2] -
sample['crop']) * sample['scale']
sample['initial_keypoints'] = initial_keypoints
sample.pop('bb')
return sample
def __geo_interface__(self):
return box(*self.bounds).__geo_interface__
# Input raster
fp = os.path.join(data_dir, "p188r018_7t20020529_z34__LV-FIN.tif")
# Output raster
out_tif = os.path.join(data_dir, "Helsinki_masked.tif")
# Read the data
raster = rasterio.open(fp)
# Visualize NIR
show((raster, 4), cmap="terrain")
minx, miny = 24.60, 60.00
maxx, maxy = 25.22, 60.35
bbox = box(minx, miny, maxx, maxy)
# Create a GeoDataFrame
crs_code = pycrs.parser.from_epsg_code(4326).to_proj4()
geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=crs_code)
geo.plot()
# When masking the data they need ot be in same coordinate system
# Project the GeoDataFrame to the same projection as the raster
geo = geo.to_crs(crs=raster.crs)
# Convert GeoDataFrame to the same projection as the raster
coords = get_features(geo)
print(coords)
