Subversion Repositories lagranto.arpege

""" interpolate data given on an Nd rectangular grid, uniform or non-uniform.

documentation and original website:

https://denis-bz.github.io/docs/intergrid.html

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

http://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([ 60, -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]

`maps[1] None` says to map the lons in query_points column 1 linearly:

    -10  -9  -8  -7  -6  # lo[1] .. hi[1]

More doc: https://denis-bz.github.com/docs/intergrid.html

"""

# split class Gridmap ?

from time import time

# warnings

import numpy as np

from scipy.ndimage import map_coordinates, spline_filter

import collections

__version__ = "2013-07-01 jul denis"

__author_email__ = "denis-bz-py@t-online.de"  # 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)

        assert lo.shape == (dim,), lo.shape

        assert hi.shape == (dim,), hi.shape

        self.loclip = lo = np.asarray_chkfinite( lo ).copy()

        self.hiclip = hi = np.asarray_chkfinite( hi ).copy()

        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.nmap = 0

        if maps != []:  # sanity check maps --

            assert len(maps) == dim, "maps must have len %d, not %d" % (

                dim, len(maps))

            for j, (map, n, l, h) in enumerate( zip( maps, griddata.shape, lo, hi )):

                if map is None  or  isintance(map, collections.Callable):

                    lo[j], hi[j] = 0, 1

                    continue

                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 ))

        self.maps = maps

        self._lo = lo  # caller's lo for linear, 0 nonlinear maps

        shape_1 = np.array( self.griddata.shape, float ) - 1

        self._linearmap = shape_1 / np.where( hi > lo, hi - lo, np.inf )  # 25jun

#...............................................................................

    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()

        np.clip( X, self.loclip, self.hiclip, out=X )  # inplace

            # X nonlinear maps inplace --

        for j, map in enumerate(self.maps):

            if map is None:

                continue

            if isinstance(map, collections.Callable):

                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 (affine_transform ?)

        if self.nmap < self.dim:

            X -= self._lo

            X *= self._linearmap  # (griddata.shape - 1) / (hi - lo)

        ## print "test X:", X[0]

#...............................................................................

        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