tmdsimpy.postprocess.continuation.hermite_upsample¶

tmdsimpy.postprocess.continuation.hermite_upsample(XlamP_full, XlamP_grad_full, upsample_freq=10, new_lams=None)¶

Use cubic hermite splines to interpolate to more points.

Parameters:

XlamP_full(M, N) numpy.ndarray: Solution points calculated with continuation. First dimension corresponds to M individual solutions. Second dimension corresponds to degrees of freedom at each solution point (N). The last column corresponds to the continuation control parameter lam and must be monotonically increasing for this function.
XlamP_grad_full(M, N) numpy.ndarray: Gradients (prediction directions) at each of the continuation steps. The last column corresponds to the continuation control parameter lam and must be strictly greater than 0 for this function.
upsample_freqint, optional: Factor of how many points should be included between each step. For the default of 10, 9 points are added between each step resulting in 10 times the number of output points. This argument is ignored if new_points is not None The default is 10.
new_lams1D numpy.ndarray or None, optional: Array of new values of lam to interpolate to. If None, then the upsample_freq is used instead. The default is None.

Returns:

XlamP_interp(Minterp, N) numpy.ndarray: Solutions at interpolated points where Minterp=(M-1)*upsample_freq+1 or Minterp=new_points.shape[0].