def clip_grid_by_polygons(A, geotransform, polygons):
"""Clip raster grid by polygon.
Args:
* A: MxN array of grid points
* geotransform: 6-tuple used to locate A geographically
(top left x, w-e pixel resolution, rotation,
top left y, rotation, n-s pixel resolution)
* polygons: list of polygon geometry objects or list of polygon arrays
Returns:
points_covered: List of (points, values) - one per input polygon.
Implementing algorithm suggested in
https://github.com/AIFDR/inasafe/issues/91#issuecomment-7025120
.. note:: Grid points are considered to be pixel-registered which means
that each point represents the center of its grid cell.
The required half cell shifts are taken care of by the
function :func:`geotransform_to_axes`.
If multiple polygons overlap, the one first encountered will be used.
"""
# Convert raster grid to Nx2 array of points and an N array of pixel values
ny, nx = A.shape
x, y = geotransform_to_axes(geotransform, nx, ny)
points, values = grid_to_points(A, x, y)
# Generate list of points and values that fall inside each polygon
points_covered = []
remaining_points = points
remaining_values = values
for polygon in polygons:
#print 'Remaining points', len(remaining_points)
if hasattr(polygon, 'outer_ring'):
outer_ring = polygon.outer_ring
inner_rings = polygon.inner_rings
else:
# Assume it is an array
outer_ring = polygon
inner_rings = None
inside, outside = in_and_outside_polygon(remaining_points,
outer_ring,
holes=inner_rings,
closed=True,
check_input=False)
# Add features inside this polygon
points_covered.append((remaining_points[inside],
remaining_values[inside]))
# Select remaining points to clip
remaining_points = remaining_points[outside]
remaining_values = remaining_values[outside]
return points_covered
def clip_grid_by_polygons(A, geotransform, polygons):
"""Clip raster grid by polygon.
Args:
* A: MxN array of grid points
* geotransform: 6-tuple used to locate A geographically
(top left x, w-e pixel resolution, rotation,
top left y, rotation, n-s pixel resolution)
* polygons: list of polygon geometry objects or list of polygon arrays
Returns:
points_covered: List of (points, values) - one per input polygon.
Implementing algorithm suggested in
https://github.com/AIFDR/inasafe/issues/91#issuecomment-7025120
.. note:: Grid points are considered to be pixel-registered which means
that each point represents the center of its grid cell.
The required half cell shifts are taken care of by the
function :func:`geotransform_to_axes`.
If multiple polygons overlap, the one first encountered will be used.
"""
# Convert raster grid to Nx2 array of points and an N array of pixel values
ny, nx = A.shape
x, y = geotransform_to_axes(geotransform, nx, ny)
points, values = grid_to_points(A, x, y)
# Generate list of points and values that fall inside each polygon
points_covered = []
remaining_points = points
remaining_values = values
for polygon in polygons:
#print 'Remaining points', len(remaining_points)
if hasattr(polygon, 'outer_ring'):
outer_ring = polygon.outer_ring
inner_rings = polygon.inner_rings
else:
# Assume it is an array
outer_ring = polygon
inner_rings = None
inside, outside = in_and_outside_polygon(remaining_points,
outer_ring,
holes=inner_rings,
closed=True,
check_input=False)
# Add features inside this polygon
points_covered.append(
(remaining_points[inside], remaining_values[inside]))
# Select remaining points to clip
remaining_points = remaining_points[outside]
remaining_values = remaining_values[outside]
return points_covered
def test_grid2points(self):
"""Raster grids can be converted to point data
"""
# Pixel values
A = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
A = numpy.array(A, dtype='f')
M, N = A.shape
L = M * N
# Axis
longitudes = numpy.linspace(100, 110, N, endpoint=False)
latitudes = numpy.linspace(-4, 0, M, endpoint=True)
# Call function to be tested
P, V = grid_to_points(A, longitudes, latitudes)
# Assert correctness
assert P.shape[0] == L
assert P.shape[1] == 2
assert len(V) == L
# print
# print longitudes
# print latitudes
# print A
# print P
assert numpy.allclose(P[:N, 0], longitudes)
assert numpy.allclose(P[:L:N, 1], latitudes[::-1])
assert numpy.allclose(V, A.flat[:])
def test_grid2points(self):
"""Raster grids can be converted to point data
"""
# Pixel values
A = [[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]]
A = numpy.array(A, dtype='f')
M, N = A.shape
L = M * N
# Axis
longitudes = numpy.linspace(100, 110, N, endpoint=False)
latitudes = numpy.linspace(-4, 0, M, endpoint=True)
# Call function to be tested
P, V = grid_to_points(A, longitudes, latitudes)
# Assert correctness
assert P.shape[0] == L
assert P.shape[1] == 2
assert len(V) == L
#print
#print longitudes
#print latitudes
#print A
#print P
assert numpy.allclose(P[:N, 0], longitudes)
assert numpy.allclose(P[:L:N, 1], latitudes[::-1])
assert numpy.allclose(V, A.flat[:])
def to_vector_points(self):
"""Convert raster grid to vector point data
Returns:
* coordinates: Nx2 array of x, y (lon, lat) coordinates
* values: N array of corresponding grid values
"""
# Convert grid data to point data
A = self.get_data()
x, y = self.get_geometry()
P, V = grid_to_points(A, x, y)
return P, V
def to_vector_points(self):
"""Convert raster grid to vector point data
Returns:
* coordinates: Nx2 array of x, y (lon, lat) coordinates
* values: N array of corresponding grid values
"""
# Convert grid data to point data
A = self.get_data()
x, y = self.get_geometry()
P, V = grid_to_points(A, x, y)
return P, V
