Experimental PR

.github/workflows/python-package.yml

@@ -14,12 +14,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.8]
+        python-version: [3.11]
     name: "lint | Python ${{ matrix.python-version }}"
     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: Install dependencies
@@ -45,37 +45,37 @@ jobs:
       matrix:
         include:
           # fastest jobs first
-          - python-version: 3.8
+          - python-version: 3.11
             name: without JIT
             disable_jit: 1
-          - python-version: 3.8
+          - python-version: 3.11
             name: doctests
             mode: doctests
           # really slow job next, so it runs in parallel with the others
-          - python-version: 3.8
+          - python-version: 3.11
             name: slow tests
             mode: very_slow
-          - python-version: 3.5
-            name: default
           - python-version: 3.8
             name: default
-          - python-version: 3.9
+          - python-version: 3.11
             name: default
-          - python-version: 3.8
+          - python-version: 3.12
+            name: default
+          - python-version: 3.11
             name: conda
             conda: true
-          - python-version: 3.8
+          - python-version: 3.11
             name: benchmarks
             mode: bench
 
     name: "build | ${{ matrix.name }} | Python ${{matrix.python-version}}"
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
 
     # python / pip
     - name: Set up Python ${{ matrix.python-version }}
       if: "!matrix.conda"
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -89,7 +89,7 @@ jobs:
     # conda
     - name: Set up Python ${{ matrix.python-version }} (conda)
       if: matrix.conda
-      uses: conda-incubator/setup-miniconda@v2
+      uses: conda-incubator/setup-miniconda@v3
       with:
         auto-update-conda: true
         python-version: ${{ matrix.python-version }}
@@ -146,24 +146,24 @@ jobs:
     #   if: ${{ always() }}
     #   with:
     #     report_paths: 'junit/test-results.xml'
-    - uses: codecov/codecov-action@v1
+    - uses: codecov/codecov-action@v5
 
   deploy:
     needs: test
     runs-on: ubuntu-latest
     name: deploy
     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: "Install"
       run: |
         python -m pip install --upgrade pip;
         python -m pip install build
         python -m build --sdist --wheel --outdir dist/
-    - uses: actions/upload-artifact@v2
+    - uses: actions/upload-artifact@v4
       with:
         name: dist
         path: dist

.readthedocs.yml

@@ -5,6 +5,12 @@
 # Required
 version: 2
 
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.9"
+
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
   configuration: docs/conf.py
@@ -15,7 +21,6 @@ formats: all
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.8
   install:
     - method: pip
       path: .

clifford/__init__.py

@@ -81,12 +81,7 @@
 
 # Major library imports.
 import numpy as np
-import numba as _numba  # to avoid clashing with clifford.numba
 import sparse
-try:
-    from numba.np import numpy_support as _numpy_support
-except ImportError:
-    import numba.numpy_support as _numpy_support
 
 
 from clifford.io import write_ga_file, read_ga_file  # noqa: F401
@@ -152,12 +147,6 @@ def get_mult_function(mt: sparse.COO, gradeList,
         return _get_mult_function_runtime_sparse(mt)
 
 
-def _get_mult_function_result_type(a: _numba.types.Type, b: _numba.types.Type, mt: np.dtype):
-    a_dt = _numpy_support.as_dtype(getattr(a, 'dtype', a))
-    b_dt = _numpy_support.as_dtype(getattr(b, 'dtype', b))
-    return np.result_type(a_dt, mt, b_dt)
-
-
 def _get_mult_function(mt: sparse.COO):
     """
     Get a function similar to `` lambda a, b: np.einsum('i,ijk,k->j', a, mt, b)``
@@ -172,19 +161,14 @@ def _get_mult_function(mt: sparse.COO):
     k_list, l_list, m_list = mt.coords
     mult_table_vals = mt.data
 
-    @_numba_utils.generated_jit(nopython=True)
+    @_numba_utils.njit
     def mv_mult(value, other_value):
-        # this casting will be done at jit-time
-        ret_dtype = _get_mult_function_result_type(value, other_value, mult_table_vals.dtype)
-        mult_table_vals_t = mult_table_vals.astype(ret_dtype)
-
-        def mult_inner(value, other_value):
-            output = np.zeros(dims, dtype=ret_dtype)
-            for k, l, m, val in zip(k_list, l_list, m_list, mult_table_vals_t):
-                output[l] += value[k] * val * other_value[m]
-            return output
-
-        return mult_inner
+        res = value[k_list] * mult_table_vals * other_value[m_list]
+        output = np.zeros(dims, dtype=res.dtype)
+        # Can not use "np.add.at(output, l_list, res)", as ufunc.at is not supported by numba
+        for l, val in zip(l_list, res):
+            output[l] += val
+        return output
 
     return mv_mult
 
@@ -203,24 +187,16 @@ def _get_mult_function_runtime_sparse(mt: sparse.COO):
     k_list, l_list, m_list = mt.coords
     mult_table_vals = mt.data
 
-    @_numba_utils.generated_jit(nopython=True)
+    @_numba_utils.njit
     def mv_mult(value, other_value):
-        # this casting will be done at jit-time
-        ret_dtype = _get_mult_function_result_type(value, other_value, mult_table_vals.dtype)
-        mult_table_vals_t = mult_table_vals.astype(ret_dtype)
-
-        def mult_inner(value, other_value):
-            output = np.zeros(dims, dtype=ret_dtype)
-            for ind, k in enumerate(k_list):
-                v_val = value[k]
-                if v_val != 0.0:
-                    m = m_list[ind]
-                    ov_val = other_value[m]
-                    if ov_val != 0.0:
-                        l = l_list[ind]
-                        output[l] += v_val * mult_table_vals_t[ind] * ov_val
-            return output
-        return mult_inner
+        # Use mask where both operands are non-zero, to avoid zero-multiplications
+        nz_mask = (value != 0.0)[k_list] & (other_value != 0.0)[m_list]
+        res = value[k_list[nz_mask]] * mult_table_vals[nz_mask] * other_value[m_list[nz_mask]]
+        output = np.zeros(dims, dtype=res.dtype)
+        # Can not use "np.add.at(output, l_list[nz_mask], res)", as ufunc.at is not supported by numba
+        for l, val in zip(l_list[nz_mask], res):
+            output[l] += val
+        return output
 
     return mv_mult
 

clifford/_conformal_layout.py

@@ -66,7 +66,7 @@ def up(self, x: MultiVector) -> MultiVector:
                 new_val = np.zeros(self.gaDims)
                 new_val[:len(old_val)] = old_val
                 x = self.MultiVector(value=new_val)
-        except(AttributeError):
+        except AttributeError:
             # if x is a scalar it does not have layout but following
             # will still work
             pass

clifford/_layout.py

@@ -479,7 +479,7 @@ def vee(aval, bval):
     def __repr__(self):
         return "{}({!r}, ids={!r}, order={!r}, names={!r})".format(
             type(self).__name__,
-            list(self.sig), self._basis_vector_ids, self._basis_blade_order, self.names
+            self.sig.tolist(), self._basis_vector_ids, self._basis_blade_order, self.names
         )
 
     def _repr_pretty_(self, p, cycle):
@@ -489,7 +489,7 @@ def _repr_pretty_(self, p, cycle):
         prefix = '{}('.format(type(self).__name__)
 
         with p.group(len(prefix), prefix, ')'):
-            p.text('{},'.format(list(self.sig)))
+            p.text('{},'.format(self.sig.tolist()))
             p.breakable()
             p.text('ids=')
             p.pretty(self._basis_vector_ids)

clifford/_multivector.py

@@ -579,7 +579,7 @@ def _repr_pretty_(self, p, cycle):
                 p.pretty(self.layout)
                 p.text(",")
                 p.breakable()
-            p.text(repr(list(self.value)))
+            p.text(repr(self.value.tolist()))
             if self.value.dtype != np.float64:
                 p.text(",")
                 p.breakable()

clifford/_numba_utils.py

@@ -59,39 +59,11 @@ def __repr__(self):
         return "_pickleable_function({!r})".format(self.__func)
 
 
-class _fake_generated_jit:
-    def __init__(self, f):
-        self.__cache = {}
-        self.__func = pickleable_function(f)
-        functools.update_wrapper(self, self.__func)
-
-    def __getnewargs_ex__(self):
-        return (self.__func,), {}
-
-    def __getstate__(self):
-        return {}
-
-    def __call__(self, *args):
-        arg_type = tuple(numba.typeof(arg) for arg in args)
-        try:
-            func = self.__cache[arg_type]
-        except KeyError:
-            func = self.__cache[arg_type] = self.__func(*arg_type)
-        return func(*args)
-
-
 if not DISABLE_JIT:
     njit = numba.njit
-    generated_jit = numba.generated_jit
 else:
     def njit(f=None, **kwargs):
         if f is None:
             return pickleable_function
         else:
             return pickleable_function(f)
-
-    def generated_jit(f=None, **kwargs):
-        if f is None:
-            return _fake_generated_jit
-        else:
-            return _fake_generated_jit(f)

clifford/cga.py

@@ -399,7 +399,7 @@ def __init__(self, cga, *args) -> None:
                     arg = float(arg)
 
             if arg < 0:
-                raise(ValueError('dilation should be positive'))
+                raise ValueError('dilation should be positive')
 
             mv = e**((-log(arg)/2.)*(self.cga.E0))
 

clifford/test/test_clifford.py

@@ -220,7 +220,7 @@ def test_2d_mv_array(self, g3, rng):  # noqa: F811
         # check properties of the array are preserved (no need to check both a and b)
         np.testing.assert_array_equal(mv_array_a.value, value_array_a)
         assert mv_array_a.value.dtype == value_array_a.dtype
-        assert type(mv_array_a.value) == type(value_array_a)
+        assert type(mv_array_a.value) is type(value_array_a)
 
         # Check addition
         mv_array_sum = mv_array_a + mv_array_b
@@ -806,9 +806,9 @@ def check_inv(self, A):
         for m, a in enumerate(A):
             for n, b in enumerate(A.inv):
                 if m == n:
-                    assert(a | b == 1)
+                    assert (a | b == 1)
                 else:
-                    assert(a | b == 0)
+                    assert (a | b == 0)
 
     @pytest.mark.parametrize(('p', 'q'), [
         (2, 0), (3, 0), (4, 0)

clifford/test/test_function_cache.py

@@ -1,19 +1,15 @@
 import numpy as np
-from clifford._numba_utils import generated_jit
+from clifford._numba_utils import njit
 import pytest
 
 
-@generated_jit(cache=True)
-def foo(x):
-    from clifford.g3 import e3
-
-    def impl(x):
-        return (x * e3).value
-    return impl
+@njit(cache=True)
+def foo(x, y):
+    return (x * y).value
 
 
 # Make the test fail on a failed cache warning
 @pytest.mark.filterwarnings("error")
 def test_function_cache():
     from clifford.g3 import e3
-    np.testing.assert_array_equal((1.0*e3).value, foo(1.0))
+    np.testing.assert_array_equal((1.0*e3).value, foo(1.0, e3))