Replace Python 3.6, 3.7 with 3.9, 3.10 (#180)

* Replace Python 3.6, 3.7 with 3.9, 3.10

* Fix definition of Python version

* Try to fix readthedocs config

* Fix Python version in readthedocs config

* Replace removed inner1d

* Try changes from #179

* Fix remaining inner1d errors

* Fix ptp() error

* Don't limit numpy version

* Fix _np import

* Try to fix readthedocs config

* Update test.yml

* Update test.yml

check with ubuntu only, current python only

* Update test.yml

redo

* Update test.yml

redo2

* Update test.yml

go for 3.11, 3.12

there is still some code that is not working, but we merged into the main branch, such that most of the toolbox can be used out of the main branch

---------

Co-authored-by: Frank Schultz <[email protected]>

Author: hagenw

.github/workflows/test.yml

@@ -9,12 +9,12 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macOS-latest, windows-latest]
-        python-version: [3.6, 3.7, 3.8]
-
+        python-version: ['3.11', '3.12']
+        
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Prepare Ubuntu

doc/readthedocs-environment.yml

@@ -1,4 +1,11 @@
-conda:
-    file: doc/readthedocs-environment.yml
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
 python:
-    pip_install: true
+  install:
+    - requirements: requirements.txt
+    - requirements: doc/requirements.txt

readthedocs.yml

@@ -13,7 +13,7 @@
     version=__version__,
     packages=find_packages(),
     install_requires=[
-        'numpy!=1.11.0',  # https://github.com/sfstoolbox/sfs-python/issues/11
+        'numpy',
         'scipy',
     ],
     author="SFS Toolbox Developers",
@@ -31,8 +31,9 @@
         "Operating System :: OS Independent",
         "Programming Language :: Python",
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.6",
-        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3 :: Only",
         "Topic :: Scientific/Engineering",
     ],

setup.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 * _util._inner1d(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 +146,7 @@ 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 * _util._inner1d(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 +233,7 @@ 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 *
+        _util._inner1d(n0, ds) / 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)
@@ -316,7 +315,7 @@ 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) /
+        _np.sqrt(_np.linalg.norm(xref - x0)) * _util._inner1d(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 +498,7 @@ 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 * _util._inner1d(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)
 
@@ -569,7 +568,7 @@ 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) /
+        _np.sqrt(_np.linalg.norm(xref - x0)) * _util._inner1d(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)

sfs/fd/wfs.py

@@ -18,13 +18,17 @@ def _register_cmap_clip(name, original_cmap, alpha):
         cmap = LinearSegmentedColormap.from_list(name, cdata)
     cmap.set_over([alpha * c + 1 - alpha for c in cmap(1.0)[:3]])
     cmap.set_under([alpha * c + 1 - alpha for c in cmap(0.0)[:3]])
-    _plt.cm.register_cmap(cmap=cmap)
+    _plt.colormaps.register(cmap=cmap)
 
 
 # The 'coolwarm' colormap is based on the paper
 # "Diverging Color Maps for Scientific Visualization" by Kenneth Moreland
 # http://www.sandia.gov/~kmorel/documents/ColorMaps/
-_register_cmap_clip('coolwarm_clip', 'coolwarm', 0.7)
+# already registered in MPL 3.9.0
+try:
+    _register_cmap_clip('coolwarm_clip', 'coolwarm', 0.7)
+except ImportError:
+    pass
 
 
 def _register_cmap_transparent(name, color):
@@ -36,7 +40,7 @@ def _register_cmap_transparent(name, color):
              'blue': ((0, blue, blue), (1, blue, blue)),
              'alpha': ((0, 0, 0), (1, 1, 1))}
     cmap = LinearSegmentedColormap(name, cdict)
-    _plt.cm.register_cmap(cmap=cmap)
+    _plt.colormaps.register(cmap=cmap)
 
 
 _register_cmap_transparent('blacktransparent', 'black')
@@ -285,8 +289,8 @@ def amplitude(p, grid, *, xnorm=None, cmap='coolwarm_clip',
     elif plotting_plane == 'yz':
         x, y = grid[[1, 2]]
 
-    dx = 0.5 * x.ptp() / p.shape[0]
-    dy = 0.5 * y.ptp() / p.shape[1]
+    dx = 0.5 * _np.ptp(x) / p.shape[0]
+    dy = 0.5 * _np.ptp(y) / p.shape[1]
 
     if ax is None:
         ax = _plt.gca()

sfs/plot2d.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 = _util._inner1d(n, x0) / c
+    weights = 2 * g0 * _util._inner1d(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 * _util._inner1d(x0xs, n0)
     selection = _util.source_selection_point(n0, x0, xs)
     return delays, weights, selection, _secondary_source_point(c)
 
@@ -296,7 +295,7 @@ def point_25d_legacy(x0, n0, xs, xref=[0, 0, 0], c=None):
     ds = x0 - xs
     r = _np.linalg.norm(ds, axis=1)
     delays = r/c
-    weights = g0 * _inner1d(ds, n0) / (2 * _np.pi * r**(3/2))
+    weights = g0 * _util._inner1d(ds, n0) / (2 * _np.pi * r**(3/2))
     selection = _util.source_selection_point(n0, x0, xs)
     return delays, weights, selection, _secondary_source_point(c)
 
@@ -379,7 +378,7 @@ def focused_25d(x0, n0, xs, ns, xref=[0, 0, 0], c=None):
     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))
+    weights = g0 * _util._inner1d(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/td/wfs.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
 
@@ -576,7 +575,7 @@ 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 _inner1d(ds, n0) >= default.selection_tolerance
 
 
 def source_selection_line(n0, x0, xs):
@@ -598,7 +597,7 @@ 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 _inner1d(ns, ds) >= default.selection_tolerance
 
 
 def source_selection_all(N):
@@ -646,3 +645,8 @@ def max_order_spherical_harmonics(N):
 
     """
     return int(np.sqrt(N) - 1)
+
+
+def _inner1d(arr1, arr2):
+    # https://github.com/numpy/numpy/issues/10815#issuecomment-376847774
+    return (arr1 * arr2).sum(axis=1)