package scipy

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val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

module CKDTree : sig ... end
val griddata : ?method_:[ `Linear | `Nearest | `Cubic ] -> ?fill_value:float -> ?rescale:bool -> points:Py.Object.t -> values:Py.Object.t -> xi:Py.Object.t -> unit -> Py.Object.t

Interpolate unstructured D-dimensional data.

Parameters ---------- points : 2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,). Data point coordinates. values : ndarray of float or complex, shape (n,) Data values. xi : 2-D ndarray of floats with shape (m, D), or length D tuple of ndarrays broadcastable to the same shape. Points at which to interpolate data. method : 'linear', 'nearest', 'cubic', optional Method of interpolation. One of

``nearest`` return the value at the data point closest to the point of interpolation. See `NearestNDInterpolator` for more details.

``linear`` tessellate the input point set to n-dimensional simplices, and interpolate linearly on each simplex. See `LinearNDInterpolator` for more details.

``cubic`` (1-D) return the value determined from a cubic spline.

``cubic`` (2-D) return the value determined from a piecewise cubic, continuously differentiable (C1), and approximately curvature-minimizing polynomial surface. See `CloughTocher2DInterpolator` for more details. fill_value : float, optional Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is ``nan``. This option has no effect for the 'nearest' method. rescale : bool, optional Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude.

.. versionadded:: 0.14.0

Returns ------- ndarray Array of interpolated values.

Notes -----

.. versionadded:: 0.9

Examples --------

Suppose we want to interpolate the 2-D function

>>> def func(x, y): ... return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2

on a grid in 0, 1x0, 1

>>> grid_x, grid_y = np.mgrid0:1:100j, 0:1:200j

but we only know its values at 1000 data points:

>>> points = np.random.rand(1000, 2) >>> values = func(points:,0, points:,1)

This can be done with `griddata` -- below we try out all of the interpolation methods:

>>> from scipy.interpolate import griddata >>> grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest') >>> grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear') >>> grid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic')

One can see that the exact result is reproduced by all of the methods to some degree, but for this smooth function the piecewise cubic interpolant gives the best results:

>>> import matplotlib.pyplot as plt >>> plt.subplot(221) >>> plt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower') >>> plt.plot(points:,0, points:,1, 'k.', ms=1) >>> plt.title('Original') >>> plt.subplot(222) >>> plt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Nearest') >>> plt.subplot(223) >>> plt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Linear') >>> plt.subplot(224) >>> plt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Cubic') >>> plt.gcf().set_size_inches(6, 6) >>> plt.show()

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