def interpolate_raster_vector_points(R, V, name=None):
"""Interpolate from raster layer to point data
Input
R: Raster data set (grid)
V: Vector data set (points)
name: Name for new attribute.
If None (default) the name of R is used
Output
I: Vector data set; points located as V with values interpolated from R
"""
msg = ('There are no data points to interpolate to. Perhaps zoom out '
'and try again')
verify(len(V) > 0, msg)
# Input checks
verify(R.is_raster)
verify(V.is_vector)
verify(V.is_point_data)
# Get raster data and corresponding x and y axes
A = R.get_data(nan=True)
longitudes, latitudes = R.get_geometry()
verify(len(longitudes) == A.shape[1])
verify(len(latitudes) == A.shape[0])
# Get vector point geometry as Nx2 array
coordinates = numpy.array(V.get_geometry(),
dtype='d',
copy=False)
# Interpolate and create new attribute
N = len(V)
attributes = []
if name is None:
name = R.get_name()
values = interpolate_raster(longitudes, latitudes, A,
coordinates, mode='linear')
# Create list of dictionaries for this attribute and return
for i in range(N):
attributes.append({name: values[i]})
return Vector(data=attributes, projection=V.get_projection(),
geometry=coordinates)
def test_interpolation_raster_data(self):
"""Interpolation library works for raster data
This shows interpolation of data arranged with
latitudes bottom - up and
longitudes left - right
"""
# Create test data
lon_ul = 100 # Longitude of upper left corner
lat_ul = 10 # Latitude of upper left corner
numlon = 8 # Number of longitudes
numlat = 5 # Number of latitudes
dlon = 1
dlat = -1
# Define array where latitudes are rows and longitude columns
A = numpy.zeros((numlat, numlon))
# Establish coordinates for lower left corner
lat_ll = lat_ul - numlat
lon_ll = lon_ul
# Define pixel centers along each direction
longitudes = numpy.linspace(lon_ll + 0.5, lon_ll + numlon - 0.5,
numlon)
latitudes = numpy.linspace(lat_ll + 0.5, lat_ll + numlat - 0.5, numlat)
# Define raster with latitudes going bottom-up (south to north).
# Longitudes go left-right (west to east)
for i in range(numlat):
for j in range(numlon):
A[numlat - 1 - i, j] = linear_function(longitudes[j],
latitudes[i])
# Then test that interpolated points are correct
xis = numpy.linspace(lon_ll + 1, lon_ll + numlon - 1, 100)
etas = numpy.linspace(lat_ll + 1, lat_ll + numlat - 1, 100)
points = combine_coordinates(xis, etas)
vals = interpolate_raster(longitudes,
latitudes,
A,
points,
mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)
def test_interpolation_raster_data(self):
"""Interpolation library works for raster data
This shows interpolation of data arranged with
latitudes bottom - up and
longitudes left - right
"""
# Create test data
lon_ul = 100 # Longitude of upper left corner
lat_ul = 10 # Latitude of upper left corner
numlon = 8 # Number of longitudes
numlat = 5 # Number of latitudes
dlon = 1
dlat = -1
# Define array where latitudes are rows and longitude columns
A = numpy.zeros((numlat, numlon))
# Establish coordinates for lower left corner
lat_ll = lat_ul - numlat
lon_ll = lon_ul
# Define pixel centers along each direction
longitudes = numpy.linspace(lon_ll + 0.5,
lon_ll + numlon - 0.5, numlon)
latitudes = numpy.linspace(lat_ll + 0.5,
lat_ll + numlat - 0.5, numlat)
# Define raster with latitudes going bottom-up (south to north).
# Longitudes go left-right (west to east)
for i in range(numlat):
for j in range(numlon):
A[numlat - 1 - i, j] = linear_function(longitudes[j],
latitudes[i])
# Then test that interpolated points are correct
xis = numpy.linspace(lon_ll + 1, lon_ll + numlon - 1, 100)
etas = numpy.linspace(lat_ll + 1, lat_ll + numlat - 1, 100)
points = combine_coordinates(xis, etas)
vals = interpolate_raster(longitudes, latitudes, A, points,
mode='linear')
refs = linear_function(points[:, 0], points[:, 1])
assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)