Replace inner1d bei einsum

sfs/fd/wfs.py

@@ -32,7 +32,6 @@ def plot(d, selection, secondary_source):
 
 """
 import numpy as _np
-from numpy.core.umath_tests import inner1d as _inner1d
 from scipy.special import hankel2 as _hankel2
 
 from . import secondary_source_line as _secondary_source_line
@@ -91,7 +90,7 @@ def line_2d(omega, x0, n0, xs, *, c=None):
     k = _util.wavenumber(omega, c)
     ds = x0 - xs
     r = _np.linalg.norm(ds, axis=1)
-    d = -1j/2 * k * _inner1d(ds, n0) / r * _hankel2(1, k * r)
+    d = -1j / 2 * k * _np.einsum('ij,ij->i', ds, n0) / r * _hankel2(1, k * r)
     selection = _util.source_selection_line(n0, x0, xs)
     return d, selection, _secondary_source_line(omega, c)
 
@@ -147,7 +146,8 @@ def _point(omega, x0, n0, xs, *, c=None):
     k = _util.wavenumber(omega, c)
     ds = x0 - xs
     r = _np.linalg.norm(ds, axis=1)
-    d = 1j * k * _inner1d(ds, n0) / r ** (3 / 2) * _np.exp(-1j * k * r)
+    d = 1j * k * _np.einsum('ij,ij->i', ds, n0) / r**(3 / 2) * _np.exp(
+        -1j * k * r)
     selection = _util.source_selection_point(n0, x0, xs)
     return d, selection, _secondary_source_point(omega, c)
 
@@ -234,7 +234,7 @@ def point_25d(omega, x0, n0, xs, xref=[0, 0, 0], c=None, omalias=None):
         preeq_25d(omega, omalias, c) *
         _np.sqrt(8 * _np.pi) *
         _np.sqrt((r * s) / (r + s)) *
-        _inner1d(n0, ds) / s *
+        _np.einsum('ij,ij->i', ds, n0) / s *
         _np.exp(-1j * k * s) / (4 * _np.pi * s))
     selection = _util.source_selection_point(n0, x0, xs)
     return d, selection, _secondary_source_point(omega, c)
@@ -316,8 +316,8 @@ def point_25d_legacy(omega, x0, n0, xs, xref=[0, 0, 0], c=None, omalias=None):
     r = _np.linalg.norm(ds, axis=1)
     d = (
         preeq_25d(omega, omalias, c) *
-        _np.sqrt(_np.linalg.norm(xref - x0)) * _inner1d(ds, n0) /
-        r ** (3 / 2) * _np.exp(-1j * k * r))
+        _np.sqrt(_np.linalg.norm(xref - x0)) * _np.einsum('ij,ij->i', ds, n0) /
+        r**(3 / 2) * _np.exp(-1j * k * r))
     selection = _util.source_selection_point(n0, x0, xs)
     return d, selection, _secondary_source_point(omega, c)
 
@@ -499,7 +499,8 @@ def _focused(omega, x0, n0, xs, ns, *, c=None):
     k = _util.wavenumber(omega, c)
     ds = x0 - xs
     r = _np.linalg.norm(ds, axis=1)
-    d = 1j * k * _inner1d(ds, n0) / r ** (3 / 2) * _np.exp(1j * k * r)
+    d = 1j * k * _np.einsum('ij,ij->i', ds, n0) / r**(3 / 2) * _np.exp(
+        1j * k * r)
     selection = _util.source_selection_focused(ns, x0, xs)
     return d, selection, _secondary_source_point(omega, c)
 
@@ -569,8 +570,8 @@ def focused_25d(omega, x0, n0, xs, ns, *, xref=[0, 0, 0], c=None,
     r = _np.linalg.norm(ds, axis=1)
     d = (
         preeq_25d(omega, omalias, c) *
-        _np.sqrt(_np.linalg.norm(xref - x0)) * _inner1d(ds, n0) /
-        r ** (3 / 2) * _np.exp(1j * k * r))
+        _np.sqrt(_np.linalg.norm(xref - x0)) * _np.einsum('ij,ij->i', ds, n0) /
+        r**(3 / 2) * _np.exp(1j * k * r))
     selection = _util.source_selection_focused(ns, x0, xs)
     return d, selection, _secondary_source_point(omega, c)
 
@@ -682,22 +683,22 @@ def soundfigure_3d(omega, x0, n0, figure, npw=[0, 0, 1], *, c=None):
     figure = _np.fft.fftshift(figure, axes=(0, 1))  # sign of spatial DFT
     figure = _np.fft.fft2(figure)
     # wavenumbers
-    kx = _np.fft.fftfreq(nx, 1./nx)
-    ky = _np.fft.fftfreq(ny, 1./ny)
+    kx = _np.fft.fftfreq(nx, 1. / nx)
+    ky = _np.fft.fftfreq(ny, 1. / ny)
     # shift spectrum due to desired plane wave
-    figure = _np.roll(figure, int(k*npw[0]), axis=0)
-    figure = _np.roll(figure, int(k*npw[1]), axis=1)
+    figure = _np.roll(figure, int(k * npw[0]), axis=0)
+    figure = _np.roll(figure, int(k * npw[1]), axis=1)
     # search and iterate over propagating plane wave components
     kxx, kyy = _np.meshgrid(kx, ky, sparse=True)
-    rho = _np.sqrt((kxx) ** 2 + (kyy) ** 2)
+    rho = _np.sqrt((kxx)**2 + (kyy)**2)
     d = 0
     for n in range(nx):
         for m in range(ny):
-            if(rho[n, m] < k):
+            if (rho[n, m] < k):
                 # dispertion relation
                 kz = _np.sqrt(k**2 - rho[n, m]**2)
                 # normal vector of plane wave
-                npw = 1/k * _np.asarray([kx[n], ky[m], kz])
+                npw = 1 / k * _np.asarray([kx[n], ky[m], kz])
                 npw = npw / _np.linalg.norm(npw)
                 # driving function of plane wave with positive kz
                 d_component, selection, secondary_source = plane_3d(

sfs/td/wfs.py

@@ -44,7 +44,6 @@ def plot(d, selection, secondary_source, t=0):
 
 """
 import numpy as _np
-from numpy.core.umath_tests import inner1d as _inner1d
 
 from . import apply_delays as _apply_delays
 from . import secondary_source_point as _secondary_source_point
@@ -119,8 +118,8 @@ def plane_25d(x0, n0, n=[0, 1, 0], xref=[0, 0, 0], c=None):
     n = _util.normalize_vector(n)
     xref = _util.asarray_1d(xref)
     g0 = _np.sqrt(2 * _np.pi * _np.linalg.norm(xref - x0, axis=1))
-    delays = _inner1d(n, x0) / c
-    weights = 2 * g0 * _inner1d(n, n0)
+    delays = _np.einsum('i,ji->j', n, x0) / c
+    weights = 2 * g0 * _np.einsum('i,ji->j', n, n0)
     selection = _util.source_selection_plane(n0, n)
     return delays, weights, selection, _secondary_source_point(c)
 
@@ -208,7 +207,7 @@ def point_25d(x0, n0, xs, xref=[0, 0, 0], c=None):
     g0 *= _np.sqrt((x0xs_n*x0xref_n)/(x0xs_n+x0xref_n))
 
     delays = x0xs_n/c
-    weights = g0*_inner1d(x0xs, n0)
+    weights = g0*_np.einsum('ij,ij->i', x0xs, n0)
     selection = _util.source_selection_point(n0, x0, xs)
     return delays, weights, selection, _secondary_source_point(c)
 
@@ -295,8 +294,8 @@ def point_25d_legacy(x0, n0, xs, xref=[0, 0, 0], c=None):
     g0 = _np.sqrt(2 * _np.pi * _np.linalg.norm(xref - x0, axis=1))
     ds = x0 - xs
     r = _np.linalg.norm(ds, axis=1)
-    delays = r/c
-    weights = g0 * _inner1d(ds, n0) / (2 * _np.pi * r**(3/2))
+    delays = r / c
+    weights = g0 * _np.einsum('ij,ij->i', ds, n0) / (2 * _np.pi * r**(3 / 2))
     selection = _util.source_selection_point(n0, x0, xs)
     return delays, weights, selection, _secondary_source_point(c)
 
@@ -378,8 +377,8 @@ def focused_25d(x0, n0, xs, ns, xref=[0, 0, 0], c=None):
     r = _np.linalg.norm(ds, axis=1)
     g0 = _np.sqrt(_np.linalg.norm(xref - x0, axis=1)
                   / (_np.linalg.norm(xref - x0, axis=1) + r))
-    delays = -r/c
-    weights = g0 * _inner1d(ds, n0) / (2 * _np.pi * r**(3/2))
+    delays = -r / c
+    weights = g0 * _np.einsum('ij,ij->i', ds, n0) / (2 * _np.pi * r**(3 / 2))
     selection = _util.source_selection_focused(ns, x0, xs)
     return delays, weights, selection, _secondary_source_point(c)
 

sfs/util.py

@@ -6,7 +6,6 @@
 
 import collections
 import numpy as np
-from numpy.core.umath_tests import inner1d
 from scipy.special import spherical_jn, spherical_yn
 from . import default
 
@@ -51,7 +50,7 @@ def wavenumber(omega, c=None):
     return omega / c
 
 
-def direction_vector(alpha, beta=np.pi/2):
+def direction_vector(alpha, beta=np.pi / 2):
     """Compute normal vector from azimuth, colatitude."""
     return sph2cart(alpha, beta, 1)
 
@@ -503,19 +502,20 @@ def image_sources_for_box(x, L, N, *, prune=True):
         number of reflections at individual walls for each source.
 
     """
+
     def _images_1d_unit_box(x, N):
         result = np.arange(-N, N + 1, dtype=x.dtype)
         result[N % 2::2] += x
         result[1 - (N % 2)::2] += 1 - x
         return result
 
     def _count_walls_1d(a):
-        b = np.floor(a/2)
-        c = np.ceil((a-1)/2)
+        b = np.floor(a / 2)
+        c = np.ceil((a - 1) / 2)
         return np.abs(np.stack([b, c], axis=1)).astype(int)
 
     L = asarray_1d(L)
-    x = asarray_1d(x)/L
+    x = asarray_1d(x) / L
     D = len(x)
     xs = [_images_1d_unit_box(coord, N) for coord in x]
     xs = np.reshape(np.transpose(np.meshgrid(*xs, indexing='ij')), (-1, D))
@@ -576,7 +576,7 @@ def source_selection_point(n0, x0, xs):
     x0 = asarray_of_rows(x0)
     xs = asarray_1d(xs)
     ds = x0 - xs
-    return inner1d(ds, n0) >= default.selection_tolerance
+    return np.einsum('ij,ij->i', ds, n0) >= default.selection_tolerance
 
 
 def source_selection_line(n0, x0, xs):
@@ -598,7 +598,7 @@ def source_selection_focused(ns, x0, xs):
     xs = asarray_1d(xs)
     ns = normalize_vector(ns)
     ds = xs - x0
-    return inner1d(ns, ds) >= default.selection_tolerance
+    return np.einsum('i,ji->j', ns, ds) >= default.selection_tolerance
 
 
 def source_selection_all(N):