Reproducing xtensor-stack/xtensor#2569

Author: tdegeus

.github/workflows/check.yml

@@ -0,0 +1,53 @@
+name: check
+
+on:
+  workflow_dispatch:
+  pull_request:
+  push:
+    branches:
+    - main
+
+jobs:
+
+  standard:
+
+    strategy:
+      fail-fast: false
+      matrix:
+        runs-on: [ubuntu-latest, macos-latest, windows-latest]
+
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    name: ${{ matrix.runs-on }} • x64 ${{ matrix.args }}
+    runs-on: ${{ matrix.runs-on }}
+
+    steps:
+
+    - name: Basic GitHub action setup
+      uses: actions/checkout@v3
+
+    - name: Set conda environment
+      uses: mamba-org/provision-with-micromamba@main
+      with:
+        environment-file: environment-dev.yml
+        environment-name: myenv
+        cache-env: true
+        extra-specs: |
+          prettytable
+          setuptools_scm
+          scikit-build
+          xsimd
+
+    - name: Set dummy version
+      run: echo "SETUPTOOLS_SCM_PRETEND_VERSION=0.0" >> $GITHUB_ENV
+
+    - name: Build and install Python module
+      working-directory: python-module
+      run: |
+        SKBUILD_CONFIGURE_OPTIONS="-DUSE_XSIMD=1" python -m pip install . -v
+
+    - name: Run Python tests
+      working-directory: python-module
+      run: python -m unittest discover tests

python-module/module/main.cpp

@@ -46,6 +46,8 @@ PYBIND11_MODULE(_xt, m)
     m.doc() = "Python bindings of xtensor";
 
     m.def("mean", [](const xt::pyarray<double>& a) -> xt::pyarray<double> { return xt::mean(a); });
+    m.def("average", [](const xt::pyarray<double>& a, const xt::pyarray<double>& w) -> xt::pyarray<double> { return xt::average(a, w); });
+    m.def("average", [](const xt::pyarray<double>& a, const xt::pyarray<double>& w, const std::vector<ptrdiff_t>& axes) -> xt::pyarray<double> { return xt::average(a, w, axes); });
 
     m.def("flip", [](const xt::pyarray<double>& a, ptrdiff_t axis) -> xt::pyarray<double> { return xt::flip(a, axis); });
 

python-module/tests/test_a.py

@@ -1,6 +1,4 @@
 import unittest
-import timeit
-import warnings
 
 import xt
 import numpy as np
@@ -19,11 +17,24 @@ def test_mean(self):
 
         self.assertTrue(np.allclose(n, x))
 
-        n = timeit.timeit(lambda: np.mean(a), number=10)
-        x = timeit.timeit(lambda: xt.mean(a), number=10)
+    def test_average(self):
 
-        if x / n > 1.1:
-            warnings.warn(f"efficiency xt.mean {x / n:.2e}")
+        a = np.random.random([103, 102, 101])
+        w = np.random.random([103, 102, 101])
+        n = np.average(a, weights=w)
+        x = xt.average(a, w)
+
+        self.assertTrue(np.allclose(n, x))
+
+    def test_average_axes(self):
+
+        a = np.random.random([103, 102, 101])
+        w = np.random.random([103, 102, 101])
+        axis = int(np.random.randint(0, high=3))
+        n = np.average(a, weights=w, axis=(axis,))
+        x = xt.average(a, w, [axis])
+
+        self.assertTrue(np.allclose(n, x))
 
     def test_flip(self):
 
@@ -34,12 +45,6 @@ def test_flip(self):
 
         self.assertTrue(np.allclose(n, x))
 
-        n = timeit.timeit(lambda: np.flip(a, axis), number=10)
-        x = timeit.timeit(lambda: xt.flip(a, axis), number=10)
-
-        if x / n > 1.1:
-            warnings.warn(f"efficiency xt.flip {x / n:.2e}")
-
     def test_cos(self):
 
         a = np.random.random([103, 102, 101])
@@ -48,13 +53,6 @@ def test_cos(self):
 
         self.assertTrue(np.allclose(n, x))
 
-        n = timeit.timeit(lambda: np.cos(a), number=10)
-        x = timeit.timeit(lambda: xt.cos(a), number=10)
-
-        if x / n > 1.1:
-            warnings.warn(f"efficiency xt.cos {x / n:.2e}")
-
-
 
 if __name__ == "__main__":