Fast interpolation of regularly sampled 3D data with different intervals in x,y, and z

Question

I have some volumetric imaging data consisting of values sampled on a regular grid in x,y,z, but with a non-cubic voxel shape (the space between adjacent points in z is greater than in x,y). I would eventually like to be able to interpolate the values on some arbitrary 2D plane that passes through the volume, like this:

I'm aware of scipy.ndimage.map_coordinates, but in my case using it is less straightforward because it implicitly assumes that the spacing of the elements in the input array are equal across dimensions. I could first resample my input array according to the smallest voxel dimension (so that all of my voxels would then be cubes), then use map_coordinates to interpolate over my plane, but it doesn't seem like a great idea to interpolate my data twice.

I'm also aware that scipy has various interpolators for irregularly-spaced ND data (LinearNDInterpolator, NearestNDInterpolator etc.), but these are very slow and memory-intensive for my purposes. What is the best way of interpolating my data given that I know that the values are regularly spaced within each dimension?

Answer 1

You can use map_coordinates with a little bit of algebra. Lets say the spacings of your grid are dx, dy and dz. We need to map these real world coordinates to array index coordinates, so lets define three new variables:

xx = x / dx yy = y / dy zz = z / dz 

The array index input to map_coordinates is an array of shape (d, ...) where d is the number of dimensions of your original data. If you define an array such as:

scaling = np.array([dx, dy, dz]) 

you can transform your real world coordinates to array index coordinates by dividing by scaling with a little broadcasting magic:

idx = coords / scaling[(slice(None),) + (None,)*(coords.ndim-1)] 

To put it all together in an example:

dx, dy, dz = 1, 1, 2 scaling = np.array([dx, dy, dz]) data = np.random.rand(10, 15, 5) 

Lets say we want to interpolate values along the plane 2*y - z = 0. We take two vectors perpendicular to the planes normal vector:

u = np.array([1, 0 ,0]) v = np.array([0, 1, 2]) 

And get the coordinates at which we want to interpolate as:

coords = (u[:, None, None] * np.linspace(0, 9, 10)[None, :, None] +           v[:, None, None] * np.linspace(0, 2.5, 10)[None, None, :]) 

We convert them to array index coordinates and interpoalte using map_coordinates:

idx = coords / scaling[(slice(None),) + (None,)*(coords.ndim-1)] new_data = ndi.map_coordinates(data, idx) 

This last array is of shape (10, 10) and has in position [u_idx, v_idx] the value corresponding to the coordinate coords[:, u_idx, v_idx].

You could build on this idea to handle interpolation where your coordinates don't start at zero, by adding an offset before the scaling.

Answer 2

Here's a simple class Intergrid that maps / scales non-uniform to uniform grids, then does map_coordinates.
On a 4d test case it runs at about 1 μsec per query point.

pip install [--user] intergrid should work (February 2020), in python2 or python3; see intergrid on PyPi.

""" interpolate data given on an Nd rectangular grid, uniform or non-uniform.  Purpose: extend the fast N-dimensional interpolator `scipy.ndimage.map_coordinates` to non-uniform grids, using `np.interp`.  Background: please look at http://en.wikipedia.org/wiki/Bilinear_interpolation https://stackoverflow.com/questions/6238250/multivariate-spline-interpolation-in-python-scipy http://docs.scipy.org/doc/scipy-dev/reference/generated/scipy.ndimage.interpolation.map_coordinates.html  Example ------- Say we have rainfall on a 4 x 5 grid of rectangles, lat 52 .. 55 x lon -10 .. -6, and want to interpolate (estimate) rainfall at 1000 query points in between the grid points.          # define the grid --     griddata = np.loadtxt(...)  # griddata.shape == (4, 5)     lo = np.array([ 52, -10 ])  # lowest lat, lowest lon     hi = np.array([ 55, -6 ])   # highest lat, highest lon          # set up an interpolator function "interfunc()" with class Intergrid --     interfunc = Intergrid( griddata, lo=lo, hi=hi )          # generate 1000 random query points, lo <= [lat, lon] <= hi --     query_points = lo + np.random.uniform( size=(1000, 2) ) * (hi - lo)          # get rainfall at the 1000 query points --     query_values = interfunc( query_points )  # -> 1000 values  What this does:     for each [lat, lon] in query_points:         1) find the square of griddata it's in,             e.g. [52.5, -8.1] -> [0, 3] [0, 4] [1, 4] [1, 3]         2) do bilinear (multilinear) interpolation in that square,             using `scipy.ndimage.map_coordinates` . Check:     interfunc( lo ) -> griddata[0, 0],     interfunc( hi ) -> griddata[-1, -1] i.e. griddata[3, 4]  Parameters ----------     griddata: numpy array_like, 2d 3d 4d ...     lo, hi: user coordinates of the corners of griddata, 1d array-like, lo < hi     maps: a list of `dim` descriptors of piecewise-linear or nonlinear maps,         e.g. [[50, 52, 62, 63], None]  # uniformize lat, linear lon     copy: make a copy of query_points, default True;         copy=False overwrites query_points, runs in less memory     verbose: default 1: print a 1-line summary for each call, with run time     order=1: see `map_coordinates`     prefilter: 0 or False, the default: smoothing B-spline               1 or True: exact-fit interpolating spline (IIR, not C-R)               1/3: Mitchell-Netravali spline, 1/3 B + 2/3 fit         (prefilter is only for order > 1, since order = 1 interpolates)  Non-uniform rectangular grids ----------------------------- What if our griddata above is at non-uniformly-spaced latitudes, say [50, 52, 62, 63] ?  `Intergrid` can "uniformize" these before interpolation, like this:      lo = np.array([ 50, -10 ])     hi = np.array([ 63, -6 ])     maps = [[50, 52, 62, 63], None]  # uniformize lat, linear lon     interfunc = Intergrid( griddata, lo=lo, hi=hi, maps=maps )  This will map (transform, stretch, warp) the lats in query_points column 0 to array coordinates in the range 0 .. 3, using `np.interp` to do piecewise-linear (PWL) mapping:     50  51  52  53  54  55  56  57  58  59  60  61  62  63  # lo[0] .. hi[0]     0   .5  1   1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2   3  `maps[1] None` says to map the lons in query_points column 1 linearly:     -10  -9  -8  -7  -6  # lo[1] .. hi[1]     0    1   2   3   4  More doc: https://denis-bz.github.com/docs/intergrid.html  """ # split class Gridmap ?  from __future__ import division from time import time # warnings import numpy as np from scipy.ndimage import map_coordinates, spline_filter  __version__ = "2014-01-15 jan denis"  # 15jan: fix bug in linear scaling __author_email__ = "[email protected]"  # comments welcome, testcases most welcome  #............................................................................... class Intergrid:     __doc__ = globals()["__doc__"]      def __init__( self, griddata, lo, hi, maps=[], copy=True, verbose=1,             order=1, prefilter=False ):         griddata = np.asanyarray( griddata )         dim = griddata.ndim  # - (griddata.shape[-1] == 1)  # ??         assert dim >= 2, griddata.shape         self.dim = dim         if np.isscalar(lo):             lo *= np.ones(dim)         if np.isscalar(hi):             hi *= np.ones(dim)         self.loclip = lo = np.asarray_chkfinite( lo ).copy()         self.hiclip = hi = np.asarray_chkfinite( hi ).copy()         assert lo.shape == (dim,), lo.shape         assert hi.shape == (dim,), hi.shape         self.copy = copy         self.verbose = verbose         self.order = order         if order > 1  and 0 < prefilter < 1:  # 1/3: Mitchell-Netravali = 1/3 B + 2/3 fit             exactfit = spline_filter( griddata )  # see Unser             griddata += prefilter * (exactfit - griddata)             prefilter = False         self.griddata = griddata         self.prefilter = (prefilter == True)          self.maps = maps         self.nmap = 0         if len(maps) > 0:             assert len(maps) == dim, "maps must have len %d, not %d" % (                     dim, len(maps))             # linear maps (map None): Xcol -= lo *= scale -> [0, n-1]             # nonlinear: np.interp e.g. [50 52 62 63] -> [0 1 2 3]             self._lo = np.zeros(dim)             self._scale = np.ones(dim)              for j, (map, n, l, h) in enumerate( zip( maps, griddata.shape, lo, hi )):                 ## print "test: j map n l h:", j, map, n, l, h                 if map is None  or callable(map):                     self._lo[j] = l                     if h > l:                         self._scale[j] = (n - 1) / (h - l)  # _map lo -> 0, hi -> n - 1                     else:                         self._scale[j] = 0  # h <= l: X[:,j] -> 0                     continue                 self.maps[j] = map = np.asanyarray(map)                 self.nmap += 1                 assert len(map) == n, "maps[%d] must have len %d, not %d" % (                     j, n, len(map) )                 mlo, mhi = map.min(), map.max()                 if not (l <= mlo <= mhi <= h):                     print "Warning: Intergrid maps[%d] min %.3g max %.3g " \                         "are outside lo %.3g hi %.3g" % (                         j, mlo, mhi, l, h )  #...............................................................................     def _map_to_uniform_grid( self, X ):         """ clip, map X linear / nonlinear  inplace """         np.clip( X, self.loclip, self.hiclip, out=X )             # X nonlinear maps inplace --         for j, map in enumerate(self.maps):             if map is None:                 continue             if callable(map):                 X[:,j] = map( X[:,j] )  # clip again ?             else:                     # PWL e.g. [50 52 62 63] -> [0 1 2 3] --                 X[:,j] = np.interp( X[:,j], map, np.arange(len(map)) )              # linear map the rest, inplace (nonlinear _lo 0, _scale 1: noop)         if self.nmap < self.dim:             X -= self._lo             X *= self._scale  # (griddata.shape - 1) / (hi - lo)         ## print "test: _map_to_uniform_grid", X.T  #...............................................................................     def __call__( self, X, out=None ):         """ query_values = Intergrid(...) ( query_points npt x dim )         """         X = np.asanyarray(X)         assert X.shape[-1] == self.dim, ("the query array must have %d columns, "                 "but its shape is %s" % (self.dim, X.shape) )         Xdim = X.ndim         if Xdim == 1:             X = np.asarray([X])  # in a single point -> out scalar         if self.copy:             X = X.copy()         assert X.ndim == 2, X.shape         npt = X.shape[0]         if out is None:             out = np.empty( npt, dtype=self.griddata.dtype )         t0 = time()         self._map_to_uniform_grid( X )  # X inplace #...............................................................................         map_coordinates( self.griddata, X.T,             order=self.order, prefilter=self.prefilter,             mode="nearest",  # outside -> edge                 # test: mode="constant", cval=np.NaN,             output=out )         if self.verbose:             print "Intergrid: %.3g msec  %d points in a %s grid  %d maps  order %d" % (                 (time() - t0) * 1000, npt, self.griddata.shape, self.nmap, self.order )         return out if Xdim == 2  else out[0]      at = __call__  # end intergrid.py