My aim is to interpolate meteorological data from neighboring meteorological stations into the point with exact coordinates. In SciPy docs I found information about multidimensional interpolation
( from scipy.interpolate import griddata ). But honestly, I didn't understand how to make this code sutable for my task.
I have df with coordinates of stations and values of atmospheric pressure as input (and coordinates of a place without station)
Could anyone help me with this issue, please?
(https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#id4) - Multivariate data interpolation (griddata)¶
Found solution here:
Inverse Distance Weighted (IDW) Interpolation with Python
IDW interpolation is more than enough in my case, but @user6386471, thanks for your contribution!
def linear_rbf(x, y, z, xi, yi):
dist = distance_matrix(x,y, xi,yi)
# Mutual pariwise distances between observations
internal_dist = distance_matrix(x,y, x,y)
# Now solve for the weights such that mistfit at the observations is minimized
weights = np.linalg.solve(internal_dist, z)
# Multiply the weights for each interpolated point by the distances
zi = np.dot(dist.T, weights)
return zi
(Using the distance_matrix function here:)
def distance_matrix(x0, y0, x1, y1):
obs = np.vstack((x0, y0)).T
interp = np.vstack((x1, y1)).T
# Make a distance matrix between pairwise observations
# Note: from <http://stackoverflow.com/questions/1871536>
# (Yay for ufuncs!)
d0 = np.subtract.outer(obs[:,0], interp[:,0])
d1 = np.subtract.outer(obs[:,1], interp[:,1])
return np.hypot(d0, d1)