Source code for discretize.utils.interputils

from __future__ import print_function
import numpy as np
import scipy.sparse as sp
from .matutils import mkvc, sub2ind

    from . import interputils_cython as pyx
    _interp_point_1D = pyx._interp_point_1D
    _interpmat1D = pyx._interpmat1D
    _interpmat2D = pyx._interpmat2D
    _interpmat3D = pyx._interpmat3D
    _interpCython = True
except ImportError as err:
    import os
    # Check if being called from non-standard location (i.e. a git repository)
    # is tree_ext.cpp here? will not be in the folder if installed to site-packages...
    file_test = os.path.dirname(os.path.abspath(__file__))+"/interputils_cython.pyx"
    if os.path.isfile(file_test):
        # Then we are being run from a repository
            Unable to import interputils_cython.

            It would appear that discretize is being imported from its repository.
            If this is intentional, you need to run:

            python build_ext --inplace

            to build the cython code.
    _interpCython = False

[docs]def interpmat(locs, x, y=None, z=None): """Local interpolation computed for each receiver point in turn :param numpy.ndarray loc: Location of points to interpolate to :param numpy.ndarray x: Tensor of 1st dimension of grid. :param numpy.ndarray y: Tensor of 2nd dimension of grid. None by default. :param numpy.ndarray z: Tensor of 3rd dimension of grid. None by default. :rtype: scipy.sparse.csr_matrix :return: Interpolation matrix .. plot:: import discretize import numpy as np import matplotlib.pyplot as plt locs = np.random.rand(50)*0.8+0.1 x = np.linspace(0, 1, 7) dense = np.linspace(0, 1, 200) fun = lambda x: np.cos(2*np.pi*x) Q = discretize.utils.interpmat(locs, x) plt.plot(x, fun(x), 'bs-') plt.plot(dense, fun(dense), 'y:') plt.plot(locs, Q*fun(x), 'mo') plt.plot(locs, fun(locs), 'rx') """ npts = locs.shape[0] locs = locs.astype(float) x = x.astype(float) if y is None and z is None: shape = [x.size] inds, vals = _interpmat1D(mkvc(locs), x) elif z is None: y = y.astype(float) shape = [x.size, y.size] inds, vals = _interpmat2D(locs, x, y) else: y = y.astype(float) z = z.astype(float) shape = [x.size, y.size, z.size] inds, vals = _interpmat3D(locs, x, y, z) I = np.repeat(range(npts), 2**len(shape)) J = sub2ind(shape, inds) Q = sp.csr_matrix((vals, (I, J)), shape=(npts, return Q