Implemented directional variograms for unstructured

gstools/variogram/estimator.pyx

@@ -10,7 +10,7 @@ import numpy as np
 cimport cython
 from cython.parallel import prange, parallel
 from libcpp.vector cimport vector
-from libc.math cimport fabs, sqrt
+from libc.math cimport fabs, sqrt, atan2, acos, asin, sin, cos, M_PI
 cimport numpy as np
 
 
@@ -47,6 +47,68 @@ cdef inline double _distance_3d(
                 (y[i] - y[j]) * (y[i] - y[j]) +
                 (z[i] - z[j]) * (z[i] - z[j]))
 
+cdef inline bint _angle_test_1d(
+    const double[:] x,
+    const double[:] y,
+    const double[:] z,
+    const double[:] angles,
+    const double angles_tol,
+    const int i,
+    const int j
+) nogil:
+    return True
+
+cdef inline bint _angle_test_2d(
+    const double[:] x,
+    const double[:] y,
+    const double[:] z,
+    const double[:] angles,
+    const double angles_tol,
+    const int i,
+    const int j
+) nogil:
+    cdef double dx = x[i] - x[j]
+    cdef double dy = y[i] - y[j]
+    # azimuth
+    cdef double phi1 = atan2(dy,dx) % (2.0 * M_PI)
+    cdef double phi2 = atan2(-dy,-dx) % (2.0 * M_PI)
+    # check both directions (+/-)
+    cdef bint dir1 = fabs(phi1 - angles[0]) <= angles_tol
+    cdef bint dir2 = fabs(phi2 - angles[0]) <= angles_tol
+    return dir1 or dir2
+
+cdef inline bint _angle_test_3d(
+    const double[:] x,
+    const double[:] y,
+    const double[:] z,
+    const double[:] angles,
+    const double angles_tol,
+    const int i,
+    const int j
+) nogil:
+    cdef double dx = x[i] - x[j]
+    cdef double dy = y[i] - y[j]
+    cdef double dz = z[i] - z[j]
+    cdef double dr = sqrt(dx**2 + dy**2 + dz**2)
+    # azimuth
+    cdef double phi1 = atan2(dy, dx) % (2.0 * M_PI)
+    cdef double phi2 = atan2(-dy, -dx) % (2.0 * M_PI)
+    # elevation
+    cdef double theta1 = acos(dz / dr)
+    cdef double theta2 = acos(-dz / dr)
+    # definitions for great-circle distance (for tolerance check)
+    cdef double dx1 = sin(theta1) * cos(phi1) - sin(angles[1]) * cos(angles[0])
+    cdef double dy1 = sin(theta1) * sin(phi1) - sin(angles[1]) * sin(angles[0])
+    cdef double dz1 = cos(theta1) - cos(angles[1])
+    cdef double dx2 = sin(theta2) * cos(phi2) - sin(angles[1]) * cos(angles[0])
+    cdef double dy2 = sin(theta2) * sin(phi2) - sin(angles[1]) * sin(angles[0])
+    cdef double dz2 = cos(theta2) - cos(angles[1])
+    cdef double dist1 = 2.0 * asin(sqrt(dx1**2 + dy1**2 + dz1**2) * 0.5)
+    cdef double dist2 = 2.0 * asin(sqrt(dx2**2 + dy2**2 + dz2**2) * 0.5)
+    # check both directions (+/-)
+    cdef bint dir1 = dist1 <= angles_tol
+    cdef bint dir2 = dist2 <= angles_tol
+    return dir1 or dir2
 
 cdef inline double estimator_matheron(const double f_diff) nogil:
     return f_diff * f_diff
@@ -110,13 +172,25 @@ ctypedef double (*_dist_func)(
     const int
 ) nogil
 
+ctypedef bint (*_angle_test_func)(
+    const double[:],
+    const double[:],
+    const double[:],
+    const double[:],
+    const double,
+    const int,
+    const int
+) nogil
+
 
 def unstructured(
     const double[:] f,
     const double[:] bin_edges,
     const double[:] x,
     const double[:] y=None,
     const double[:] z=None,
+    const double[:] angles=None,
+    const double angles_tol=0.436332,
     str estimator_type='m'
 ):
     if x.shape[0] != f.shape[0]:
@@ -126,21 +200,35 @@ def unstructured(
         raise ValueError('len(bin_edges) too small')
 
     cdef _dist_func distance
+    cdef _angle_test_func angle_test
+
     # 3d
     if z is not None:
         if z.shape[0] != f.shape[0]:
             raise ValueError('len(z) = {0} != len(f) = {1} '.
                              format(z.shape[0], f.shape[0]))
         distance = _distance_3d
+        angle_test = _angle_test_3d
     # 2d
     elif y is not None:
         if y.shape[0] != f.shape[0]:
             raise ValueError('len(y) = {0} != len(f) = {1} '.
                              format(y.shape[0], f.shape[0]))
         distance = _distance_2d
+        angle_test = _angle_test_2d
     # 1d
     else:
         distance = _distance_1d
+        angle_test = _angle_test_1d
+
+    if angles is not None:
+        if z is not None and angles.size < 2:
+            raise ValueError('3d requested but only one angle given')
+        if y is not None and angles.size < 1:
+            raise ValueError('2d with angle requested but no angle given')
+
+    if angles_tol <= 0:
+        raise ValueError('tolerance for angle search masks must be > 0')
 
     cdef _estimator_func estimator_func = choose_estimator_func(estimator_type)
     cdef _normalization_func normalization_func = (
@@ -160,11 +248,12 @@ def unstructured(
             for k in range(j+1, k_max):
                 dist = distance(x, y, z, k, j)
                 if dist >= bin_edges[i] and dist < bin_edges[i+1]:
-                    counts[i] += 1
-                    variogram[i] += estimator_func(f[k] - f[j])
+                    if angles is None or angle_test(x, y, z, angles, angles_tol, k, j):
+                        counts[i] += 1
+                        variogram[i] += estimator_func(f[k] - f[j])
 
     normalization_func(variogram, counts)
-    return np.asarray(variogram)
+    return np.asarray(variogram), np.asarray(counts)
 
 
 def structured(const double[:,:,:] f, str estimator_type='m'):

gstools/variogram/variogram.py

@@ -37,9 +37,12 @@ def vario_estimate_unstructured(
     pos,
     field,
     bin_edges,
+    angles=None,
+    angles_tol=0.436332,
     sampling_size=None,
     sampling_seed=None,
     estimator="matheron",
+    return_counts=False,
 ):
     r"""
     Estimates the variogram on a unstructured grid.
@@ -50,7 +53,8 @@ def vario_estimate_unstructured(
        \gamma(r_k) = \frac{1}{2 N(r_k)} \sum_{i=1}^{N(r_k)} (z(\mathbf x_i) -
        z(\mathbf x_i'))^2 \; ,
 
-    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}` being the bins.
+    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}`
+    being the bins.
 
     Or if the estimator "cressie" was chosen:
 
@@ -59,13 +63,10 @@ def vario_estimate_unstructured(
        \left|z(\mathbf x_i) - z(\mathbf x_i')\right|^{0.5}\right)^4}
        {0.457 + 0.494 / N(r_k) + 0.045 / N^2(r_k)} \; ,
 
-    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}` being the bins.
+    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}`
+    being the bins.
     The Cressie estimator is more robust to outliers.
 
-    Notes
-    -----
-    Internally uses double precision and also returns doubles.
-
     Parameters
     ----------
     pos : :class:`list`
@@ -75,6 +76,18 @@ def vario_estimate_unstructured(
         the spatially distributed data
     bin_edges : :class:`numpy.ndarray`
         the bins on which the variogram will be calculated
+    angles : :class:`numpy.ndarray`
+        the angles of the main axis to calculate the variogram for in radians
+        angle definitions from ISO standard 80000-2:2009
+        for 1d this parameter will have no effect at all
+        for 2d supply one angle which is azimuth φ (ccw from +x in xy plane)
+        for 3d supply two angles which are azimuth φ (ccw from +x in xy plane)
+        and inclination θ (cw from +z)
+    angles_tol : class:`float`
+        the tolerance around the variogram angle to count a point as being
+        within this direction from another point (the angular tolerance around
+        the directional vector given by angles)
+        Default: 25°≈0.436332
     sampling_size : :class:`int` or :any:`None`, optional
         for large input data, this method can take a long
         time to compute the variogram, therefore this argument specifies
@@ -90,18 +103,43 @@ def vario_estimate_unstructured(
             * "cressie": an estimator more robust to outliers
 
         Default: "matheron"
+    return_counts: class:`bool`, optional
+        if set to true, this function will also return the number of data
+        points found at each lag distance as a third return value
+        Default: False
 
     Returns
     -------
     :class:`tuple` of :class:`numpy.ndarray`
-        the estimated variogram and the bin centers
+        1. the bin centers
+        2. the estimated variogram values at bin centers
+        3. (optional) the number of points found at each bin center
+           (see argument return_counts)
+
+    Notes
+    -----
+    Internally uses double precision and also returns doubles.
     """
     # TODO check_mesh
     field = np.array(field, ndmin=1, dtype=np.double)
     bin_edges = np.array(bin_edges, ndmin=1, dtype=np.double)
     x, y, z, dim = pos2xyz(pos, calc_dim=True, dtype=np.double)
+
     bin_centres = (bin_edges[:-1] + bin_edges[1:]) / 2.0
 
+    if angles is not None and dim > 1:
+        # there are at most (dim-1) angles
+        angles = np.array(angles, ndmin=1, dtype=np.double)  # type convert
+        angles = angles.ravel()[: (dim - 1)]  # cutoff
+        angles = np.pad(
+            angles, (0, dim - angles.size - 1), "constant", constant_values=0.0
+        )  # fill with 0 if too less given
+        # correct intervalls for angles
+        angles[0] = angles[0] % (2 * np.pi)
+        angles[1:] = angles[1:] % np.pi
+    elif dim == 1:
+        angles = None
+
     if sampling_size is not None and sampling_size < len(field):
         sampled_idx = np.random.RandomState(sampling_seed).choice(
             np.arange(len(field)), sampling_size, replace=False
@@ -115,13 +153,22 @@ def vario_estimate_unstructured(
 
     cython_estimator = _set_estimator(estimator)
 
-    return (
-        bin_centres,
-        unstructured(
-            field, bin_edges, x, y, z, estimator_type=cython_estimator
-        ),
+    estimates, counts = unstructured(
+        field,
+        bin_edges,
+        x,
+        y,
+        z,
+        angles,
+        angles_tol,
+        estimator_type=cython_estimator,
     )
 
+    if return_counts:
+        return bin_centres, estimates, counts
+    else:
+        return bin_centres, estimates
+
 
 def vario_estimate_structured(field, direction="x", estimator="matheron"):
     r"""Estimates the variogram on a regular grid.
@@ -133,7 +180,8 @@ def vario_estimate_structured(field, direction="x", estimator="matheron"):
        \gamma(r_k) = \frac{1}{2 N(r_k)} \sum_{i=1}^{N(r_k)} (z(\mathbf x_i) -
        z(\mathbf x_i'))^2 \; ,
 
-    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}` being the bins.
+    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}`
+    being the bins.
 
     Or if the estimator "cressie" was chosen:
 
@@ -142,17 +190,10 @@ def vario_estimate_structured(field, direction="x", estimator="matheron"):
        \left|z(\mathbf x_i) - z(\mathbf x_i')\right|^{0.5}\right)^4}
        {0.457 + 0.494 / N(r_k) + 0.045 / N^2(r_k)} \; ,
 
-    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}` being the bins.
+    with :math:`r_k \leq \| \mathbf x_i - \mathbf x_i' \| < r_{k+1}`
+    being the bins.
     The Cressie estimator is more robust to outliers.
 
-    Warnings
-    --------
-    It is assumed that the field is defined on an equidistant Cartesian grid.
-
-    Notes
-    -----
-    Internally uses double precision and also returns doubles.
-
     Parameters
     ----------
     field : :class:`numpy.ndarray`
@@ -171,6 +212,14 @@ def vario_estimate_structured(field, direction="x", estimator="matheron"):
     -------
     :class:`numpy.ndarray`
         the estimated variogram along the given direction.
+
+    Warnings
+    --------
+    It is assumed that the field is defined on an equidistant Cartesian grid.
+
+    Notes
+    -----
+    Internally uses double precision and also returns doubles.
     """
     try:
         mask = np.array(field.mask, dtype=np.int32)