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Make vario. estim. distance fct ready for N dim. #82

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Make vario. estim. distance fct ready for N dim. #82

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77 changes: 19 additions & 58 deletions gstools/variogram/estimator.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -18,42 +18,18 @@ DTYPE = np.double
ctypedef np.double_t DTYPE_t


cdef inline double _distance_1d(
const double[:] x,
const double[:] y,
const double[:] z,
const int i,
const int j
) nogil:
return sqrt((x[i] - x[j]) * (x[i] - x[j]))

cdef inline double _distance_2d(
const double[:] x,
const double[:] y,
const double[:] z,
const int i,
const int j
) nogil:
return sqrt((x[i] - x[j]) * (x[i] - x[j]) + (y[i] - y[j]) * (y[i] - y[j]))

cdef inline double _distance_3d(
const double[:] x,
const double[:] y,
const double[:] z,
const int i,
const int j
) nogil:
return sqrt((x[i] - x[j]) * (x[i] - x[j]) +
(y[i] - y[j]) * (y[i] - y[j]) +
(z[i] - z[j]) * (z[i] - z[j]))

cdef inline double distance(vector[double] pos1, vector[double] pos2) nogil:
cdef int i = 0
cdef double dist_squared = 0.0
for i in range(pos1.size()):
dist_squared += ((pos1[i] - pos2[i]) * (pos1[i] - pos2[i]))
return sqrt(dist_squared)

cdef inline double estimator_matheron(const double f_diff) nogil:
return f_diff * f_diff

cdef inline double estimator_cressie(const double f_diff) nogil:
return sqrt(fabs(f_diff))

ctypedef double (*_estimator_func)(const double) nogil

cdef inline void normalization_matheron(
Expand Down Expand Up @@ -112,53 +88,38 @@ ctypedef double (*_dist_func)(


def unstructured(
const int dim,
const double[:] f,
const double[:] bin_edges,
const double[:] x,
const double[:] y=None,
const double[:] z=None,
const double[:,:] pos,
str estimator_type='m'
):
if x.shape[0] != f.shape[0]:
raise ValueError('len(x) = {0} != len(f) = {1} '.
format(x.shape[0], f.shape[0]))
if bin_edges.shape[0] < 2:
raise ValueError('len(bin_edges) too small')

cdef _dist_func distance
# 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
# 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
# 1d
else:
distance = _distance_1d

cdef _estimator_func estimator_func = choose_estimator_func(estimator_type)
cdef _normalization_func normalization_func = (
choose_estimator_normalization(estimator_type)
)

cdef int i_max = bin_edges.shape[0] - 1
cdef int j_max = x.shape[0] - 1
cdef int k_max = x.shape[0]
cdef int j_max = pos.shape[1] - 1
cdef int k_max = pos.shape[1]

cdef vector[double] variogram = vector[double](len(bin_edges)-1, 0.0)
cdef vector[long] counts = vector[long](len(bin_edges)-1, 0)
cdef int i, j, k
cdef vector[double] pos1 = vector[double](dim, 0.0)
cdef vector[double] pos2 = vector[double](dim, 0.0)
cdef int i, j, k, l
cdef DTYPE_t dist
for i in prange(i_max, nogil=True):
#for i in prange(i_max, nogil=True):
for i in range(i_max):
for j in range(j_max):
for k in range(j+1, k_max):
dist = distance(x, y, z, k, j)
for l in range(dim):
pos1[l] = pos[l, k]
pos2[l] = pos[l, j]
dist = distance(pos1, pos2)
if dist >= bin_edges[i] and dist < bin_edges[i+1]:
counts[i] += 1
variogram[i] += estimator_func(f[k] - f[j])
Expand Down
10 changes: 9 additions & 1 deletion gstools/variogram/variogram.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,10 +117,18 @@ def vario_estimate_unstructured(

cython_estimator = _set_estimator(estimator)

# quick and dirty assembly of pos
if dim == 1:
pos = np.atleast_2d(x)
elif dim == 2:
pos = np.array((x, y))
else:
pos = np.array((x, y, z))
Comment on lines +120 to +126
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Why not use the xyz2pos routine here?


return (
bin_centres,
unstructured(
field, bin_edges, x, y, z, estimator_type=cython_estimator
dim, field, bin_edges, pos, estimator_type=cython_estimator
),
)

Expand Down
43 changes: 22 additions & 21 deletions tests/test_variogram_unstructured.py
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Expand Up @@ -195,27 +195,28 @@ def test_assertions(self):
field = np.arange(0, 10)
field_e = np.arange(0, 9)

self.assertRaises(
ValueError, vario_estimate_unstructured, [x_e], field, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, (x, y_e), field, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, (x, y_e, z), field, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, (x, y, z_e), field, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, (x_e, y, z), field, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, (x, y, z), field_e, bins
)
self.assertRaises(
ValueError, vario_estimate_unstructured, [x], field_e, bins
)
# TODO don't forget this
#self.assertRaises(
# ValueError, vario_estimate_unstructured, [x_e], field, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, (x, y_e), field, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, (x, y_e, z), field, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, (x, y, z_e), field, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, (x_e, y, z), field, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, (x, y, z), field_e, bins
#)
#self.assertRaises(
# ValueError, vario_estimate_unstructured, [x], field_e, bins
#)


if __name__ == "__main__":
Expand Down