def test_arrays(self):
# 1d array of vectors
vv = numpy.array([(0., 0., -0.1), (10., 0., 0.)])
nn = numpy.array([(0., 0., -1.), (1., 0., 0.)])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
# 2d array
vv = numpy.array([vv, vv * 2, vv * (-3)])
nn = numpy.array([nn, nn, -nn])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
# 3d array
vv = numpy.array([vv])
nn = numpy.array([nn])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
def test_arrays(self):
# 1d array of vectors
vv = numpy.array([(0., 0., -0.1), (10., 0., 0.)])
nn = numpy.array([(0., 0., -1.), (1., 0., 0.)])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
# 2d array
vv = numpy.array([vv, vv * 2, vv * (-3)])
nn = numpy.array([nn, nn, -nn])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
# 3d array
vv = numpy.array([vv])
nn = numpy.array([nn])
self.assertTrue(numpy.allclose(utils.normalized(vv), nn))
def _init_plane(self):
"""
Prepare everything needed for projecting arbitrary points on a plane
containing the surface.
"""
tl, tr, bl, br = geo_utils.spherical_to_cartesian(
self.corner_lons, self.corner_lats, self.corner_depths
)
# these two parameters define the plane that contains the surface
# (in 3d Cartesian space): a normal unit vector,
self.normal = geo_utils.normalized(numpy.cross(tl - tr, tl - bl))
# ... and scalar "d" parameter from the plane equation (uses
# an equation (3) from http://mathworld.wolfram.com/Plane.html)
self.d = - (self.normal * tl).sum()
# these two 3d vectors together with a zero point represent surface's
# coordinate space (the way to translate 3d Cartesian space with
# a center in earth's center to 2d space centered in surface's top
# left corner with basis vectors directed to top right and bottom left
# corners. see :meth:`_project`.
self.uv1 = geo_utils.normalized(tr - tl)
self.uv2 = numpy.cross(self.normal, self.uv1)
self.zero_zero = tl
def _init_plane(self):
"""
Prepare everything needed for projecting arbitrary points on a plane
containing the surface.
"""
tl, tr, bl, br = geo_utils.spherical_to_cartesian(
self.corner_lons, self.corner_lats, self.corner_depths
)
# these two parameters define the plane that contains the surface
# (in 3d Cartesian space): a normal unit vector,
self.normal = geo_utils.normalized(numpy.cross(tl - tr, tl - bl))
# ... and scalar "d" parameter from the plane equation (uses
# an equation (3) from http://mathworld.wolfram.com/Plane.html)
self.d = - (self.normal * tl).sum()
# these two 3d vectors together with a zero point represent surface's
# coordinate space (the way to translate 3d Cartesian space with
# a center in earth's center to 2d space centered in surface's top
# left corner with basis vectors directed to top right and bottom left
# corners. see :meth:`_project`.
self.uv1 = geo_utils.normalized(tr - tl)
self.uv2 = numpy.cross(self.normal, self.uv1)
self.zero_zero = tl
def test_one_vector(self):
v = numpy.array([0., 0., 2.])
self.assertTrue(numpy.allclose(utils.normalized(v), [0, 0, 1]))
v = numpy.array([0., -1., -1.])
n = utils.normalized(v)
self.assertTrue(numpy.allclose(n, [0, -2**0.5 / 2., -2**0.5 / 2.]))
def get_mean_inclination_and_azimuth(self):
"""
Calculate weighted average inclination and azimuth of the mesh surface.
:returns:
Tuple of two float numbers: inclination angle in a range [0, 90]
and azimuth in range [0, 360) (in decimal degrees).
The mesh is triangulated, the inclination and azimuth for each triangle
is computed and average values weighted on each triangle's area
are calculated. Azimuth is always defined in a way that inclination
angle doesn't exceed 90 degree.
"""
assert not 1 in self.lons.shape, (
"inclination and azimuth are only defined for mesh of more than "
"one row and more than one column of points"
)
if self.depths is not None:
assert ((self.depths[1:] - self.depths[:-1]) >= 0).all(), (
"get_mean_inclination_and_azimuth() requires next mesh row "
"to be not shallower than the previous one"
)
points, along_azimuth, updip, diag = self.triangulate()
# define planes that are perpendicular to each point's vector
# as normals to those planes
earth_surface_tangent_normal = geo_utils.normalized(points)
# calculating triangles' area and normals for top-left triangles
e1 = along_azimuth[:-1]
e2 = updip[:, :-1]
tl_area = geo_utils.triangle_area(e1, e2, diag)
tl_normal = geo_utils.normalized(numpy.cross(e1, e2))
# ... and bottom-right triangles
e1 = along_azimuth[1:]
e2 = updip[:, 1:]
br_area = geo_utils.triangle_area(e1, e2, diag)
br_normal = geo_utils.normalized(numpy.cross(e1, e2))
if self.depths is None:
# mesh is on earth surface, inclination is zero
inclination = 0
else:
# inclination calculation
# top-left triangles
en = earth_surface_tangent_normal[:-1, :-1]
# cosine of inclination of the triangle is scalar product
# of vector normal to triangle plane and (normalized) vector
# pointing to top left corner of a triangle from earth center
incl_cos = numpy.sum(en * tl_normal, axis=-1).clip(-1.0, 1.0)
# we calculate average angle using mean of circular quantities
# formula: define 2d vector for each triangle where length
# of the vector corresponds to triangle's weight (we use triangle
# area) and angle is equal to inclination angle. then we calculate
# the angle of vector sum of all those vectors and that angle
# is the weighted average.
xx = numpy.sum(tl_area * incl_cos)
# express sine via cosine using Pythagorean trigonometric identity,
# this is a bit faster than sin(arccos(incl_cos))
yy = numpy.sum(tl_area * numpy.sqrt(1 - incl_cos * incl_cos))
# bottom-right triangles
en = earth_surface_tangent_normal[1:, 1:]
# we need to clip scalar product values because in some cases
# they might exceed range where arccos is defined ([-1, 1])
# because of floating point imprecision
incl_cos = numpy.sum(en * br_normal, axis=-1).clip(-1.0, 1.0)
# weighted angle vectors are calculated independently for top-left
# and bottom-right triangles of each cell in a mesh. here we
# combine both and finally get the weighted mean angle
xx += numpy.sum(br_area * incl_cos)
yy += numpy.sum(br_area * numpy.sqrt(1 - incl_cos * incl_cos))
inclination = numpy.degrees(numpy.arctan2(yy, xx))
# azimuth calculation is done similar to one for inclination. we also
# do separate calculations for top-left and bottom-right triangles
# and also combine results using mean of circular quantities approach
# unit vector along z axis
z_unit = numpy.array([0.0, 0.0, 1.0])
# unit vectors pointing west from each point of the mesh, they define
# planes that contain meridian of respective point
norms_west = geo_utils.normalized(numpy.cross(points + z_unit, points))
# unit vectors parallel to planes defined by previous ones. they are
# directed from each point to a point lying on z axis on the same
# distance from earth center
norms_north = geo_utils.normalized(numpy.cross(points, norms_west))
# need to normalize triangles' azimuthal edges because we will project
# them on other normals and thus calculate an angle in between
along_azimuth = geo_utils.normalized(along_azimuth)
# process top-left triangles
# here we identify the sign of direction of the triangles' azimuthal
# edges: is edge pointing west or east? for finding that we project
# those edges to vectors directing to west by calculating scalar
# product and get the sign of resulting value: if it is negative
# than the resulting azimuth should be negative as top edge is pointing
# west.
sign = numpy.sign(numpy.sign(
numpy.sum(along_azimuth[:-1] * norms_west[:-1, :-1], axis=-1))
# we run numpy.sign(numpy.sign(...) + 0.1) to make resulting values
# be only either -1 or 1 with zero values (when edge is pointing
# strictly north or south) expressed as 1 (which means "don't
# change the sign")
+ 0.1
)
# the length of projection of azimuthal edge on norms_north is cosine
# of edge's azimuth
az_cos = numpy.sum(along_azimuth[:-1] * norms_north[:-1, :-1], axis=-1)
# use the same approach for finding the weighted mean
# as for inclination (see above)
xx = numpy.sum(tl_area * az_cos)
# the only difference is that azimuth is defined in a range
# [0, 360), so we need to have two reference planes and change
# sign of projection on one normal to sign of projection to another one
yy = numpy.sum(tl_area * numpy.sqrt(1 - az_cos * az_cos) * sign)
# bottom-right triangles
sign = numpy.sign(numpy.sign(
numpy.sum(along_azimuth[1:] * norms_west[1:, 1:], axis=-1))
+ 0.1
)
az_cos = numpy.sum(along_azimuth[1:] * norms_north[1:, 1:], axis=-1)
xx += numpy.sum(br_area * az_cos)
yy += numpy.sum(br_area * numpy.sqrt(1 - az_cos * az_cos) * sign)
azimuth = numpy.degrees(numpy.arctan2(yy, xx))
if azimuth < 0:
azimuth += 360
if inclination > 90:
# average inclination is over 90 degree, that means that we need
# to reverse azimuthal direction in order for inclination to be
# in range [0, 90]
inclination = 180 - inclination
azimuth = (azimuth + 180) % 360
return inclination, azimuth
def test_one_vector(self):
v = numpy.array([0., 0., 2.])
self.assertTrue(numpy.allclose(utils.normalized(v), [0, 0, 1]))
v = numpy.array([0., -1., -1.])
n = utils.normalized(v)
self.assertTrue(numpy.allclose(n, [0, -2 ** 0.5 / 2., -2 ** 0.5 / 2.]))
def get_mean_inclination_and_azimuth(self):
"""
Calculate weighted average inclination and azimuth of the mesh surface.
:returns:
Tuple of two float numbers: inclination angle in a range [0, 90]
and azimuth in range [0, 360) (in decimal degrees).
The mesh is triangulated, the inclination and azimuth for each triangle
is computed and average values weighted on each triangle's area
are calculated. Azimuth is always defined in a way that inclination
angle doesn't exceed 90 degree.
"""
assert not 1 in self.lons.shape, (
"inclination and azimuth are only defined for mesh of more than "
"one row and more than one column of points")
if self.depths is not None:
assert ((self.depths[1:] - self.depths[:-1]) >= 0).all(), (
"get_mean_inclination_and_azimuth() requires next mesh row "
"to be not shallower than the previous one")
points, along_azimuth, updip, diag = self.triangulate()
# define planes that are perpendicular to each point's vector
# as normals to those planes
earth_surface_tangent_normal = geo_utils.normalized(points)
# calculating triangles' area and normals for top-left triangles
e1 = along_azimuth[:-1]
e2 = updip[:, :-1]
tl_area = geo_utils.triangle_area(e1, e2, diag)
tl_normal = geo_utils.normalized(numpy.cross(e1, e2))
# ... and bottom-right triangles
e1 = along_azimuth[1:]
e2 = updip[:, 1:]
br_area = geo_utils.triangle_area(e1, e2, diag)
br_normal = geo_utils.normalized(numpy.cross(e1, e2))
if self.depths is None:
# mesh is on earth surface, inclination is zero
inclination = 0
else:
# inclination calculation
# top-left triangles
en = earth_surface_tangent_normal[:-1, :-1]
# cosine of inclination of the triangle is scalar product
# of vector normal to triangle plane and (normalized) vector
# pointing to top left corner of a triangle from earth center
incl_cos = numpy.sum(en * tl_normal, axis=-1).clip(-1.0, 1.0)
# we calculate average angle using mean of circular quantities
# formula: define 2d vector for each triangle where length
# of the vector corresponds to triangle's weight (we use triangle
# area) and angle is equal to inclination angle. then we calculate
# the angle of vector sum of all those vectors and that angle
# is the weighted average.
xx = numpy.sum(tl_area * incl_cos)
# express sine via cosine using Pythagorean trigonometric identity,
# this is a bit faster than sin(arccos(incl_cos))
yy = numpy.sum(tl_area * numpy.sqrt(1 - incl_cos * incl_cos))
# bottom-right triangles
en = earth_surface_tangent_normal[1:, 1:]
# we need to clip scalar product values because in some cases
# they might exceed range where arccos is defined ([-1, 1])
# because of floating point imprecision
incl_cos = numpy.sum(en * br_normal, axis=-1).clip(-1.0, 1.0)
# weighted angle vectors are calculated independently for top-left
# and bottom-right triangles of each cell in a mesh. here we
# combine both and finally get the weighted mean angle
xx += numpy.sum(br_area * incl_cos)
yy += numpy.sum(br_area * numpy.sqrt(1 - incl_cos * incl_cos))
inclination = numpy.degrees(numpy.arctan2(yy, xx))
# azimuth calculation is done similar to one for inclination. we also
# do separate calculations for top-left and bottom-right triangles
# and also combine results using mean of circular quantities approach
# unit vector along z axis
z_unit = numpy.array([0.0, 0.0, 1.0])
# unit vectors pointing west from each point of the mesh, they define
# planes that contain meridian of respective point
norms_west = geo_utils.normalized(numpy.cross(points + z_unit, points))
# unit vectors parallel to planes defined by previous ones. they are
# directed from each point to a point lying on z axis on the same
# distance from earth center
norms_north = geo_utils.normalized(numpy.cross(points, norms_west))
# need to normalize triangles' azimuthal edges because we will project
# them on other normals and thus calculate an angle in between
along_azimuth = geo_utils.normalized(along_azimuth)
# process top-left triangles
# here we identify the sign of direction of the triangles' azimuthal
# edges: is edge pointing west or east? for finding that we project
# those edges to vectors directing to west by calculating scalar
# product and get the sign of resulting value: if it is negative
# than the resulting azimuth should be negative as top edge is pointing
# west.
sign = numpy.sign(
numpy.sign(
numpy.sum(along_azimuth[:-1] * norms_west[:-1, :-1], axis=-1))
# we run numpy.sign(numpy.sign(...) + 0.1) to make resulting values
# be only either -1 or 1 with zero values (when edge is pointing
# strictly north or south) expressed as 1 (which means "don't
# change the sign")
+ 0.1)
# the length of projection of azimuthal edge on norms_north is cosine
# of edge's azimuth
az_cos = numpy.sum(along_azimuth[:-1] * norms_north[:-1, :-1], axis=-1)
# use the same approach for finding the weighted mean
# as for inclination (see above)
xx = numpy.sum(tl_area * az_cos)
# the only difference is that azimuth is defined in a range
# [0, 360), so we need to have two reference planes and change
# sign of projection on one normal to sign of projection to another one
yy = numpy.sum(tl_area * numpy.sqrt(1 - az_cos * az_cos) * sign)
# bottom-right triangles
sign = numpy.sign(
numpy.sign(
numpy.sum(along_azimuth[1:] * norms_west[1:, 1:], axis=-1)) +
0.1)
az_cos = numpy.sum(along_azimuth[1:] * norms_north[1:, 1:], axis=-1)
xx += numpy.sum(br_area * az_cos)
yy += numpy.sum(br_area * numpy.sqrt(1 - az_cos * az_cos) * sign)
azimuth = numpy.degrees(numpy.arctan2(yy, xx))
if azimuth < 0:
azimuth += 360
if inclination > 90:
# average inclination is over 90 degree, that means that we need
# to reverse azimuthal direction in order for inclination to be
# in range [0, 90]
inclination = 180 - inclination
azimuth = (azimuth + 180) % 360
return inclination, azimuth
