File numpy125.patch of Package python-sfs
From e07d48be3f3be32edcc34192bd24044bba2f124f Mon Sep 17 00:00:00 2001
From: Frank Schultz <scf175@googlemail.com>
Date: Wed, 11 Mar 2020 13:02:24 +0100
Subject: [PATCH] inner1d -> einsum in all wfs driving functions, pep8 corr in
all wfs.py
---
sfs/fd/wfs.py | 33 +++++++++++++++++----------------
sfs/td/wfs.py | 15 +++++++--------
sfs/util.py | 14 +++++++-------
3 files changed, 31 insertions(+), 31 deletions(-)
diff --git a/sfs/fd/wfs.py b/sfs/fd/wfs.py
index 44fb2c6..d4d293c 100644
--- a/sfs/fd/wfs.py
+++ b/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(
diff --git a/sfs/td/wfs.py b/sfs/td/wfs.py
index 3b59301..05961ef 100644
--- a/sfs/td/wfs.py
+++ b/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)
diff --git a/sfs/util.py b/sfs/util.py
index c15358f..7eccd6a 100644
--- a/sfs/util.py
+++ b/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)
@@ -503,6 +502,7 @@ 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
@@ -510,12 +510,12 @@ def _images_1d_unit_box(x, N):
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):
