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| ################################################################################ | |
| # Interpolation class | |
| # TODO : use a nicer n-dim method (like multilinear interpolation) | |
| from scipy.interpolate import RectBivariateSpline, UnivariateSpline | |
| from dolointerpolation.multilinear_cython import multilinear_interpolation | |
| class UnivariateSpline(UnivariateSpline): | |
| '''extended UnivariateSpline class, | |
| where spline evaluation works uses input broadcast | |
| and returns an output with a coherent shape. | |
| ''' | |
| #@profile | |
| def __call__(self, *x): | |
| # flatten the inputs after saving their shape: | |
| shape = np.array(x).shape | |
| # Evaluate the spline and reconstruct the dimension: | |
| z = super(UnivariateSpline, self).__call__(np.ravel(x)) | |
| return z.reshape(shape) | |
| #----- | |
| #----- | |
| def interp_on_state(self, A): | |
| '''returns an interpolating function of matrix A, assuming that A | |
| is expressed on the state grid `self.state_grid` | |
| the shape of A should be (len(g) for g in self.state_grid) | |
| ''' | |
| # Check the dimension of A: | |
| expect_shape = self._state_grid_shape | |
| if A.shape != expect_shape: | |
| raise ValueError('array `A` should be of shape {:s}, not {:s}'.format( | |
| str(expect_shape), str(A.shape)) ) | |
| if len(expect_shape) == 1: | |
| A_interp = UnivariateSpline(self.state_grid[0], A, ext=3) | |
| return A_interp | |
| elif len(expect_shape) <= 5: | |
| A_interp = MlinInterpolator(*self.state_grid) | |
| A_interp.set_values(A) | |
| return A_interp | |
| # if len(expect_shape) == 2: | |
| # x1_grid = self.state_grid[0] | |
| # x2_grid = self.state_grid[1] | |
| # A_interp = RectBivariateSplineBc(x1_grid, x2_grid, A, kx=1, ky=1) | |
| # return A_interp | |
| else: | |
| raise NotImplementedError('interpolation for state dimension >5' | |
| ' is not implemented.') | |
| # end interp_on_state() | |
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As to adding the UnivariateSpline interpolator, it is a very useful addition, since it removes the need to compile the multilinear_cython module at least for 1D case. However, it would be better to have a way to choose which interpolator to use. What do you think?