def unif_resample(x,n,tol=1,deg=3):
x = np.atleast_2d(x)
x = remove_duplicate_rows(x)
dl = mu.norms(x[1:] - x[:-1],1)
l = np.cumsum(np.r_[0,dl])
(tck,_) = si.splprep(x.T,k=deg,s = tol**2*len(x),u=l)
newu = np.linspace(0,l[-1],n)
return np.array(si.splev(newu,tck)).T
def unif_resample(x,n,weights,tol=.001,deg=3):
x = np.atleast_2d(x)
weights = np.atleast_2d(weights)
x = mu.remove_duplicate_rows(x)
x_scaled = x * weights
dl = mu.norms(x_scaled[1:] - x_scaled[:-1],1)
l = np.cumsum(np.r_[0,dl])
(tck,_) = si.splprep(x_scaled.T,k=deg,s = tol**2*len(x),u=l)
newu = np.linspace(0,l[-1],n)
out_scaled = np.array(si.splev(newu,tck)).T
out = out_scaled/weights
return out
def unif_resample(x, n, weights, tol=.001, deg=3):
x = np.atleast_2d(x)
weights = np.atleast_2d(weights)
x = mu.remove_duplicate_rows(x)
x_scaled = x * weights
dl = mu.norms(x_scaled[1:] - x_scaled[:-1], 1)
l = np.cumsum(np.r_[0, dl])
(tck, _) = si.splprep(x_scaled.T, k=deg, s=tol**2 * len(x), u=l)
newu = np.linspace(0, l[-1], n)
out_scaled = np.array(si.splev(newu, tck)).T
out = out_scaled / weights
return out
def nearest_neighbor(x, ys):
return mu.norms(ys - x[None,:],1).argmin()
