def test_buffer_simple_polygon_large(self):
"""Ensures correct output from buffer_simple_polygon.
In this case the buffer distance is larger.
"""
this_buffer_polygon_object = polygons.buffer_simple_polygon(
EXTERIOR_VERTEX_X_METRES,
EXTERIOR_VERTEX_Y_METRES,
max_buffer_dist_metres=LARGE_BUFFER_DIST_METRES,
preserve_angles=True)
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffer_polygon_object)
this_buffer_vertex_x_metres, this_buffer_vertex_y_metres = (
polygons.merge_exterior_and_holes(
this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN],
hole_y_coords_list=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN]))
self.assertTrue(
numpy.allclose(this_buffer_vertex_x_metres,
VERTEX_X_METRES_LARGE_BUFFER,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(this_buffer_vertex_y_metres,
VERTEX_Y_METRES_LARGE_BUFFER,
atol=TOLERANCE))
def test_buffer_simple_polygon_large_inclusive(self):
"""Ensures correct output from buffer_simple_polygon.
In this case the buffer is large and inclusive (includes original
polygon).
"""
this_buffered_polygon_object = polygons.buffer_simple_polygon(
vertex_x_metres=EXTERIOR_VERTEX_X_METRES,
vertex_y_metres=EXTERIOR_VERTEX_Y_METRES,
max_buffer_dist_metres=LARGE_BUFFER_DIST_METRES,
preserve_angles=True)
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffered_polygon_object)
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
LARGE_BUFFER_VERTEX_X_METRES,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
LARGE_BUFFER_VERTEX_Y_METRES,
atol=TOLERANCE))
self.assertTrue(
len(this_buffer_vertex_dict[polygons.HOLE_X_COLUMN]) == 0)
self.assertTrue(
len(this_buffer_vertex_dict[polygons.HOLE_Y_COLUMN]) == 0)
def test_buffer_simple_polygon_exclusive(self):
"""Ensures correct output from buffer_simple_polygon.
In this case the buffer is exclusive (does not include original
polygon).
"""
this_buffer_polygon_object = polygons.buffer_simple_polygon(
EXTERIOR_VERTEX_X_METRES,
EXTERIOR_VERTEX_Y_METRES,
min_buffer_dist_metres=SMALL_BUFFER_DIST_METRES,
max_buffer_dist_metres=LARGE_BUFFER_DIST_METRES,
preserve_angles=True)
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffer_polygon_object)
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
EXCLUSIVE_BUFFER_EXTERIOR_X_METRES,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
EXCLUSIVE_BUFFER_EXTERIOR_Y_METRES,
atol=TOLERANCE))
self.assertTrue(
len(this_buffer_vertex_dict[polygons.HOLE_X_COLUMN]) == 1)
self.assertTrue(
len(this_buffer_vertex_dict[polygons.HOLE_Y_COLUMN]) == 1)
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.HOLE_X_COLUMN][0],
EXCLUSIVE_BUFFER_HOLE_X_METRES,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(this_buffer_vertex_dict[polygons.HOLE_Y_COLUMN][0],
EXCLUSIVE_BUFFER_HOLE_Y_METRES,
atol=TOLERANCE))
def test_buffer_simple_polygon_nested(self):
"""Ensures correct output from buffer_simple_polygon.
In this case the buffer is nested (large distance used to create
exterior, small distance used to create hole).
"""
this_buffer_polygon_object = polygons.buffer_simple_polygon(
EXTERIOR_VERTEX_X_METRES,
EXTERIOR_VERTEX_Y_METRES,
min_buffer_dist_metres=SMALL_BUFFER_DIST_METRES,
max_buffer_dist_metres=LARGE_BUFFER_DIST_METRES,
preserve_angles=True)
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffer_polygon_object)
this_buffer_vertex_x_metres, this_buffer_vertex_y_metres = (
polygons.merge_exterior_and_holes(
this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN],
hole_y_coords_list=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN]))
self.assertTrue(
numpy.allclose(this_buffer_vertex_x_metres,
VERTEX_X_METRES_NESTED_BUFFER,
atol=TOLERANCE,
equal_nan=True))
self.assertTrue(
numpy.allclose(this_buffer_vertex_y_metres,
VERTEX_Y_METRES_NESTED_BUFFER,
atol=TOLERANCE,
equal_nan=True))
LIVE_SELECTED_INDICES_FOR_MIN_OBS_SAMPLING = numpy.array(
[5, 6, 9, 10, 12, 13, 14, 15, 18, 20, 21])
LIVE_SELECTED_INDICES_FOR_MIN_OBS_PLUS_SAMPLING = numpy.array(
[5, 6, 9, 10, 12, 13, 14, 15, 18, 20, 21, 23, 24])
LIVE_SELECTED_INDICES_FOR_MIN_DENSITY_SAMPLING = copy.deepcopy(
LIVE_SELECTED_INDICES_FOR_MIN_OBS_SAMPLING)
LIVE_SELECTED_INDICES_FOR_MIN_DENSITY_PLUS_SAMPLING = copy.deepcopy(
LIVE_SELECTED_INDICES_FOR_MIN_OBS_PLUS_SAMPLING)
# The following constants are used to test _get_observation_densities.
POLYGON_OBJECT_XY, THIS_PROJECTION_OBJECT = polygons.project_latlng_to_xy(
POLYGON_OBJECT_LATLNG)
VERTEX_DICT_XY = polygons.polygon_object_to_vertex_arrays(POLYGON_OBJECT_XY)
BUFFERED_POLYGON_OBJECT_XY = polygons.buffer_simple_polygon(
VERTEX_DICT_XY[polygons.EXTERIOR_X_COLUMN],
VERTEX_DICT_XY[polygons.EXTERIOR_Y_COLUMN],
min_buffer_dist_metres=MIN_DISTANCE_METRES,
max_buffer_dist_metres=MAX_DISTANCE_METRES)
BUFFER_AREA_METRES2 = BUFFERED_POLYGON_OBJECT_XY.area
OBSERVATION_DENSITY_BY_STORM_OBJECT_M02 = (NUM_OBSERVATIONS_BY_STORM_OBJECT /
BUFFER_AREA_METRES2)
MIN_OBS_DENSITY_FOR_SAMPLING_M02 = (MIN_OBSERVATIONS_FOR_SAMPLING /
BUFFER_AREA_METRES2)
BUFFERED_POLYGON_OBJECT_LATLNG = polygons.project_xy_to_latlng(
BUFFERED_POLYGON_OBJECT_XY, THIS_PROJECTION_OBJECT)
BUFFERED_POLYGON_OBJECT_ARRAY_LATLNG = numpy.full(
NUM_STORM_OBJECTS, BUFFERED_POLYGON_OBJECT_LATLNG, dtype=object)
BUFFER_COLUMN_NAME = tracking_io.distance_buffer_to_column_name(
def create_distance_buffers(storm_object_table, min_distances_metres,
max_distances_metres):
"""Creates one or more distance buffers around each storm object.
K = number of buffers
:param storm_object_table: pandas DataFrame with the following columns.
Each row is one storm object.
storm_object_table.centroid_latitude_deg: Latitude (deg N) of storm-object
centroid.
storm_object_table.centroid_longitude_deg: Longitude (deg E) of storm-object
centroid.
storm_object_table.polygon_object_latlng_deg: Instance of
`shapely.geometry.Polygon`, with x-coords in longitude (deg E) and
y-coords in latitude (deg N).
:param min_distances_metres: length-K numpy array of minimum distances. If
the storm object is inside the [k]th buffer -- i.e., the [k]th buffer
has no minimum distance -- then min_distances_metres[k] should be NaN.
:param max_distances_metres: length-K numpy array of max distances.
:return: storm_object_table: Same as input but with K additional columns
(one per distance buffer). Column names are generated by
`buffer_to_column_name`, and each value in these columns is a
`shapely.geometry.Polygon` object, with x-coords in longitude (deg E) and
y-coords in latitude (deg N).
"""
num_buffers = len(min_distances_metres)
these_expected_dim = numpy.array([num_buffers], dtype=int)
error_checking.assert_is_numpy_array(max_distances_metres,
exact_dimensions=these_expected_dim)
global_centroid_lat_deg, global_centroid_lng_deg = (
geodetic_utils.get_latlng_centroid(
latitudes_deg=storm_object_table[CENTROID_LATITUDE_COLUMN].values,
longitudes_deg=storm_object_table[CENTROID_LONGITUDE_COLUMN].values
))
projection_object = projections.init_azimuthal_equidistant_projection(
central_latitude_deg=global_centroid_lat_deg,
central_longitude_deg=global_centroid_lng_deg)
num_storm_objects = len(storm_object_table.index)
object_array = numpy.full(num_storm_objects, numpy.nan, dtype=object)
buffer_column_names = [''] * num_buffers
for j in range(num_buffers):
buffer_column_names[j] = buffer_to_column_name(
min_distance_metres=min_distances_metres[j],
max_distance_metres=max_distances_metres[j])
storm_object_table = storm_object_table.assign(
**{buffer_column_names[j]: object_array})
for i in range(num_storm_objects):
this_orig_vertex_dict_latlng_deg = (
polygons.polygon_object_to_vertex_arrays(
storm_object_table[LATLNG_POLYGON_COLUMN].values[i]))
these_orig_x_metres, these_orig_y_metres = (
projections.project_latlng_to_xy(
latitudes_deg=this_orig_vertex_dict_latlng_deg[
polygons.EXTERIOR_Y_COLUMN],
longitudes_deg=this_orig_vertex_dict_latlng_deg[
polygons.EXTERIOR_X_COLUMN],
projection_object=projection_object))
for j in range(num_buffers):
this_buffer_poly_object_xy_metres = polygons.buffer_simple_polygon(
vertex_x_metres=these_orig_x_metres,
vertex_y_metres=these_orig_y_metres,
min_buffer_dist_metres=min_distances_metres[j],
max_buffer_dist_metres=max_distances_metres[j])
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffer_poly_object_xy_metres)
(this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN]
) = projections.project_xy_to_latlng(
x_coords_metres=this_buffer_vertex_dict[
polygons.EXTERIOR_X_COLUMN],
y_coords_metres=this_buffer_vertex_dict[
polygons.EXTERIOR_Y_COLUMN],
projection_object=projection_object)
this_num_holes = len(
this_buffer_vertex_dict[polygons.HOLE_X_COLUMN])
for k in range(this_num_holes):
(this_buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
this_buffer_vertex_dict[polygons.HOLE_X_COLUMN][k]
) = projections.project_xy_to_latlng(
x_coords_metres=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN][k],
y_coords_metres=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN][k],
projection_object=projection_object)
this_buffer_poly_object_latlng_deg = (
polygons.vertex_arrays_to_polygon_object(
exterior_x_coords=this_buffer_vertex_dict[
polygons.EXTERIOR_X_COLUMN],
exterior_y_coords=this_buffer_vertex_dict[
polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN],
hole_y_coords_list=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN]))
storm_object_table[buffer_column_names[j]].values[i] = (
this_buffer_poly_object_latlng_deg)
return storm_object_table
def make_buffers_around_polygons(storm_object_table,
min_buffer_dists_metres=None,
max_buffer_dists_metres=None):
"""Creates one or more buffers around each storm polygon.
N = number of buffers
V = number of vertices in a given polygon
:param storm_object_table: pandas DataFrame with the following columns.
storm_object_table.storm_id: String ID for storm cell.
storm_object_table.polygon_object_latlng: Instance of
`shapely.geometry.Polygon`, with vertices in lat-long coordinates.
:param min_buffer_dists_metres: length-N numpy array of minimum buffer
distances. If min_buffer_dists_metres[i] is NaN, the [i]th buffer
includes the original polygon. If min_buffer_dists_metres[i] is
defined, the [i]th buffer is a "nested" buffer, not including the
original polygon.
:param max_buffer_dists_metres: length-N numpy array of maximum buffer
distances. Must be all real numbers (no NaN).
:return: storm_object_table: Same as input, but with N extra columns.
storm_object_table.polygon_object_latlng_buffer_<D>m: Instance of
`shapely.geometry.Polygon` for D-metre buffer around storm.
storm_object_table.polygon_object_latlng_buffer_<d>_<D>m: Instance of
`shapely.geometry.Polygon` for d-to-D-metre buffer around storm.
"""
error_checking.assert_is_geq_numpy_array(min_buffer_dists_metres,
0.,
allow_nan=True)
error_checking.assert_is_numpy_array(min_buffer_dists_metres,
num_dimensions=1)
num_buffers = len(min_buffer_dists_metres)
error_checking.assert_is_geq_numpy_array(max_buffer_dists_metres,
0.,
allow_nan=False)
error_checking.assert_is_numpy_array(max_buffer_dists_metres,
exact_dimensions=numpy.array(
[num_buffers]))
for j in range(num_buffers):
if numpy.isnan(min_buffer_dists_metres[j]):
continue
error_checking.assert_is_greater(max_buffer_dists_metres[j],
min_buffer_dists_metres[j],
allow_nan=False)
num_storm_objects = len(storm_object_table.index)
centroid_latitudes_deg = numpy.full(num_storm_objects, numpy.nan)
centroid_longitudes_deg = numpy.full(num_storm_objects, numpy.nan)
for i in range(num_storm_objects):
this_centroid_object = storm_object_table[
POLYGON_OBJECT_LATLNG_COLUMN].values[0].centroid
centroid_latitudes_deg[i] = this_centroid_object.y
centroid_longitudes_deg[i] = this_centroid_object.x
global_centroid_lat_deg, global_centroid_lng_deg = (
polygons.get_latlng_centroid(centroid_latitudes_deg,
centroid_longitudes_deg))
projection_object = projections.init_azimuthal_equidistant_projection(
global_centroid_lat_deg, global_centroid_lng_deg)
object_array = numpy.full(num_storm_objects, numpy.nan, dtype=object)
argument_dict = {}
buffer_column_names = [''] * num_buffers
for j in range(num_buffers):
buffer_column_names[j] = distance_buffer_to_column_name(
min_buffer_dists_metres[j], max_buffer_dists_metres[j])
argument_dict.update({buffer_column_names[j]: object_array})
storm_object_table = storm_object_table.assign(**argument_dict)
for i in range(num_storm_objects):
orig_vertex_dict_latlng = polygons.polygon_object_to_vertex_arrays(
storm_object_table[POLYGON_OBJECT_LATLNG_COLUMN].values[i])
(orig_vertex_x_metres,
orig_vertex_y_metres) = projections.project_latlng_to_xy(
orig_vertex_dict_latlng[polygons.EXTERIOR_Y_COLUMN],
orig_vertex_dict_latlng[polygons.EXTERIOR_X_COLUMN],
projection_object=projection_object)
for j in range(num_buffers):
buffer_polygon_object_xy = polygons.buffer_simple_polygon(
orig_vertex_x_metres,
orig_vertex_y_metres,
min_buffer_dist_metres=min_buffer_dists_metres[j],
max_buffer_dist_metres=max_buffer_dists_metres[j])
buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
buffer_polygon_object_xy)
(buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
projection_object=projection_object))
this_num_holes = len(buffer_vertex_dict[polygons.HOLE_X_COLUMN])
for k in range(this_num_holes):
(buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
projection_object=projection_object))
buffer_polygon_object_latlng = (
polygons.vertex_arrays_to_polygon_object(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=buffer_vertex_dict[
polygons.HOLE_X_COLUMN],
hole_y_coords_list=buffer_vertex_dict[
polygons.HOLE_Y_COLUMN]))
storm_object_table[buffer_column_names[j]].values[
i] = buffer_polygon_object_latlng
return storm_object_table
def make_buffers_around_storm_objects(
storm_object_table, min_distances_metres, max_distances_metres):
"""Creates one or more distance buffers around each storm object.
N = number of storm objects
B = number of buffers around each storm object
V = number of vertices in a given buffer
:param storm_object_table: N-row pandas DataFrame with the following
columns.
storm_object_table.storm_id: String ID for storm cell.
storm_object_table.polygon_object_latlng: Instance of
`shapely.geometry.Polygon`, containing vertices of storm object in
lat-long coordinates.
:param min_distances_metres: length-B numpy array of minimum buffer
distances. If min_distances_metres[i] is NaN, the storm object is
included in the [i]th buffer, so the [i]th buffer is inclusive. If
min_distances_metres[i] is a real number, the storm object is *not*
included in the [i]th buffer, so the [i]th buffer is exclusive.
:param max_distances_metres: length-B numpy array of maximum buffer
distances. Must be all real numbers (no NaN).
:return: storm_object_table: Same as input, but with B additional columns.
Each additional column (listed below) contains a
`shapely.geometry.Polygon` instance for each storm object. Each
`shapely.geometry.Polygon` instance contains the lat-long vertices of
one distance buffer around one storm object.
storm_object_table.polygon_object_latlng_buffer_<D>m: For an inclusive
buffer of D metres around the storm.
storm_object_table.polygon_object_latlng_buffer_<d>_<D>m: For an exclusive
buffer of d...D metres around the storm.
"""
error_checking.assert_is_geq_numpy_array(
min_distances_metres, 0., allow_nan=True)
error_checking.assert_is_numpy_array(
min_distances_metres, num_dimensions=1)
num_buffers = len(min_distances_metres)
error_checking.assert_is_geq_numpy_array(
max_distances_metres, 0., allow_nan=False)
error_checking.assert_is_numpy_array(
max_distances_metres, exact_dimensions=numpy.array([num_buffers]))
for j in range(num_buffers):
if numpy.isnan(min_distances_metres[j]):
continue
error_checking.assert_is_greater(
max_distances_metres[j], min_distances_metres[j],
allow_nan=False)
num_storm_objects = len(storm_object_table.index)
centroid_latitudes_deg = numpy.full(num_storm_objects, numpy.nan)
centroid_longitudes_deg = numpy.full(num_storm_objects, numpy.nan)
for i in range(num_storm_objects):
this_centroid_object = storm_object_table[
POLYGON_OBJECT_LATLNG_COLUMN].values[0].centroid
centroid_latitudes_deg[i] = this_centroid_object.y
centroid_longitudes_deg[i] = this_centroid_object.x
(global_centroid_lat_deg, global_centroid_lng_deg
) = geodetic_utils.get_latlng_centroid(
latitudes_deg=centroid_latitudes_deg,
longitudes_deg=centroid_longitudes_deg)
projection_object = projections.init_azimuthal_equidistant_projection(
global_centroid_lat_deg, global_centroid_lng_deg)
object_array = numpy.full(num_storm_objects, numpy.nan, dtype=object)
argument_dict = {}
buffer_column_names = [''] * num_buffers
for j in range(num_buffers):
buffer_column_names[j] = distance_buffer_to_column_name(
min_distances_metres[j], max_distances_metres[j])
argument_dict.update({buffer_column_names[j]: object_array})
storm_object_table = storm_object_table.assign(**argument_dict)
for i in range(num_storm_objects):
orig_vertex_dict_latlng = polygons.polygon_object_to_vertex_arrays(
storm_object_table[POLYGON_OBJECT_LATLNG_COLUMN].values[i])
(orig_vertex_x_metres,
orig_vertex_y_metres) = projections.project_latlng_to_xy(
orig_vertex_dict_latlng[polygons.EXTERIOR_Y_COLUMN],
orig_vertex_dict_latlng[polygons.EXTERIOR_X_COLUMN],
projection_object=projection_object)
for j in range(num_buffers):
buffer_polygon_object_xy = polygons.buffer_simple_polygon(
orig_vertex_x_metres, orig_vertex_y_metres,
min_buffer_dist_metres=min_distances_metres[j],
max_buffer_dist_metres=max_distances_metres[j])
buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
buffer_polygon_object_xy)
(buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
projection_object=projection_object))
this_num_holes = len(buffer_vertex_dict[polygons.HOLE_X_COLUMN])
for k in range(this_num_holes):
(buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
projection_object=projection_object))
buffer_polygon_object_latlng = (
polygons.vertex_arrays_to_polygon_object(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=
buffer_vertex_dict[polygons.HOLE_X_COLUMN],
hole_y_coords_list=
buffer_vertex_dict[polygons.HOLE_Y_COLUMN]))
storm_object_table[buffer_column_names[j]].values[
i] = buffer_polygon_object_latlng
return storm_object_table
