Fix/compat and dead code bugs

.github/workflows/ci.yml

@@ -10,8 +10,28 @@ jobs:
   test:
     strategy:
       matrix:
-        python-version: ['3.9', '3.10', '3.11', '3.12']  # Specify the Python versions you want to test against
-        os: [ubuntu-latest, windows-latest]
+        # ubuntu-latest now resolves to a newer Ubuntu release than
+        # actions/setup-python has prebuilt Python 3.9/3.10 for (Python itself
+        # dropped support for the kernel/libc these need) - pin an older
+        # Ubuntu LTS for those so "Set up Python" doesn't fail outright. See
+        # https://raw.githubusercontent.com/actions/python-versions/main/versions-manifest.json
+        include:
+          - python-version: '3.9'
+            os: ubuntu-24.04
+          - python-version: '3.9'
+            os: windows-latest
+          - python-version: '3.10'
+            os: ubuntu-24.04
+          - python-version: '3.10'
+            os: windows-latest
+          - python-version: '3.11'
+            os: ubuntu-latest
+          - python-version: '3.11'
+            os: windows-latest
+          - python-version: '3.12'
+            os: ubuntu-latest
+          - python-version: '3.12'
+            os: windows-latest
     name: Python ${{ matrix.python-version }} - ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     steps:

README.md

@@ -25,3 +25,4 @@ Requirements
 - pyNFFT3 0.2.2 or greater
 - NumPy 2.0.0 or greater
 - SciPy 1.16.0 or greater
+

pyproject.toml

@@ -16,8 +16,8 @@ readme = "README.md"
 requires-python = ">=3.9"
 dependencies = [
   "pyNFFT3>=0.2.2",
-  "numpy>=2.0.0",
-  "scipy>=1.16.0",
+  "numpy>=1.26.4",
+  "scipy>=1.11.4",
 ]
 
 [project.urls]

src/pyGroupedTransforms/CWWTtools.py

@@ -119,7 +119,7 @@ def indextoN(j, k):  # indextoN(j::Array{Int}, k::Array{Int})::Array{Int}
     s = k.shape[1]
 
     if d != d2:
-        ValueError("j and k have to have same length, k has to be column vector.")
+        raise ValueError("j and k have to have same length, k has to be column vector.")
 
     out = np.zeros(s)
 

src/pyGroupedTransforms/DeferredLinearOperator.py

@@ -7,8 +7,8 @@ def __init__(
         self,
         dtype="complex",
         shape=None,
-        mfunc=lambda x: "not yet initialized",
-        rmfunc=lambda x: "not yet initialized",
+        mfunc=None,
+        rmfunc=None,
     ):
 
         self.sh = False
@@ -19,8 +19,6 @@ def __init__(
 
         self._matvec_func = mfunc
         self._rmatvec_func = rmfunc
-        self._matvec = lambda x: self._matvec_func(x)
-        self._rmatvec = lambda x: self._rmatvec_func(x)
         super().__init__(dtype=dtype, shape=shape)
 
     def set(self, dtype=None, shape=None, mfunc=None, rmfunc=None):
@@ -38,16 +36,15 @@ def set(self, dtype=None, shape=None, mfunc=None, rmfunc=None):
             self._rmatvec_func = rmfunc
 
     def _matvec(self, x):
-        if self._matvec_func(0) == "not implemented yet" or self.sh == False:
+        if self._matvec_func is None or self.sh == False:
             raise NotImplementedError("LinearOperator is not yet initialized.")
 
-        print(self.LO @ x)
-        return self @ x
+        return self._matvec_func(x)
 
     def _rmatvec(self, x):
-        if self._matvec_func(0) == "not implemented yet" or self.sh == False:
+        if self._matvec_func is None or self.sh == False:
             raise NotImplementedError("LinearOperator is not yet initialized.")
-        if self._rmatvec_func(0) == "not implemented yet":
+        if self._rmatvec_func is None:
             raise NotImplementedError("rmatvec is not yet implemented")
 
-        return self.H @ x
+        return self._rmatvec_func(x)

src/pyGroupedTransforms/GroupedTransform.py

@@ -53,11 +53,11 @@ def get_setting(
 
         if systems[system] == NFMTtools:
             if len(basis_vect) == 0:
-                ValueError(
+                raise ValueError(
                     "please call get_setting with basis_vect for a NFMT transform."
                 )
             if len(basis_vect) < d:
-                ValueError("basis_vect must have an entry for every dimension.")
+                raise ValueError("basis_vect must have an entry for every dimension.")
             return [
                 Setting(
                     u=U[idx],
@@ -93,11 +93,11 @@ def get_setting(
 
         if systems[system] == NFMTtools:
             if len(basis_vect) == 0:
-                ValueError(
+                raise ValueError(
                     "please call get_setting with basis_vect for a NFMT transform."
                 )
             if len(basis_vect) < max(max(u) for u in U):
-                ValueError("basis_vect must have an entry for every dimension.")
+                raise ValueError("basis_vect must have an entry for every dimension.")
             return [
                 Setting(
                     u=u,
@@ -132,11 +132,11 @@ def get_setting(
 
         if systems[system] == NFMTtools:
             if len(basis_vect) == 0:
-                ValueError(
+                raise ValueError(
                     "please call get_setting with basis_vect for a NFMT transform."
                 )
             if len(basis_vect) < max(max(u) for u in U):
-                ValueError("basis_vect must have an entry for every dimension.")
+                raise ValueError("basis_vect must have an entry for every dimension.")
             return [
                 Setting(
                     u=U[idx],
@@ -197,11 +197,11 @@ def __init__(
 
         if system == "mixed":
             if len(basis_vect) == 0:
-                ValueError(
+                raise ValueError(
                     "please call GroupedTransform with basis_vect for a NFMT transform."
                 )
             if len(basis_vect) != X.shape[1]:
-                ValueError("basis_vect must have an entry for every dimension.")
+                raise ValueError("basis_vect must have an entry for every dimension.")
 
         if system in {"exp", "chui1", "chui2", "chui3", "chui4"}:
             if np.min(X) < -0.5 or np.max(X) >= 0.5:
@@ -433,7 +433,7 @@ def get_matrix(self):
                 u1 = (0,)
             else:
                 u1 = s1.u
-            F_direct = s1.mode.get_matrix(s.bdanwidths, self.X[:, u1].T, bases=s.bases)
+            F_direct = s1.mode.get_matrix(s1.bandwidths, self.X[:, u1].T, bases=s1.bases)
             for idx, s in enumerate(self.settings):
                 if idx == 0:
                     continue

src/pyGroupedTransforms/GroupedTransforms.py

@@ -47,7 +47,7 @@ def get_IndexSet(settings, d):
         elif s.mode == NFMTtools:
             index_set_u = s.mode.nfmt_index_set_without_zeros(s.bandwidths, s.bases)
         else:
-            ValueError(f"Unknown mode: {s.mode}")
+            raise ValueError(f"Unknown mode: {s.mode}")
 
         if len(s.u) == 1:
             index_set[s.u[0], idx : idx + nf_u] = index_set_u

src/pyGroupedTransforms/NFMTtools.py

@@ -1 +1,6 @@
-RuntimeError("NFMTtools is not implemented yet!")
+def __getattr__(name):
+    # NFMTtools is imported unconditionally at package import time (see
+    # GroupedCoefficients.py), so raising here directly would break importing
+    # pyGroupedTransforms entirely. Raise lazily instead, only once someone
+    # actually tries to use the (currently unimplemented) NFMT/"mixed" system.
+    raise RuntimeError("NFMTtools is not implemented yet!")