Conda forge mkl

.github/scripts/numpy-info.py

@@ -0,0 +1,14 @@
+from numpy.core._multiarray_umath import (
+        __cpu_features__, __cpu_baseline__, __cpu_dispatch__)
+
+features_found, features_not_found = [], []
+for feature in __cpu_dispatch__:
+    if __cpu_features__[feature]:
+        features_found.append(feature)
+    else:
+        features_not_found.append(feature)
+
+print("Supported SIMD extensions in this NumPy install:")
+print("    baseline = %s" % (','.join(__cpu_baseline__)))
+print("    found = %s" % (','.join(features_found)))
+print("    not found = %s" % (','.join(features_not_found)))

.github/workflows/python-package-conda.yml

@@ -10,7 +10,7 @@ jobs:
     strategy:
       max-parallel: 5
       matrix:
-        python-version: [3.6, 3.9]
+        python-version: [3.9]
         slycot: ["", "conda"]
         array-and-matrix: [0]
         include:
@@ -36,7 +36,9 @@ jobs:
         pip install coveralls
 
         # Install python-control dependencies
-        conda install numpy matplotlib scipy
+        conda install -c conda-forge 'libblas=*=*mkl' mkl
+        echo "libblas * *mkl" >> $CONDA_PREFIX/conda-meta/pinned
+        conda install -c conda-forge numpy matplotlib scipy     
         if [[ '${{matrix.slycot}}' == 'conda' ]]; then
           conda install -c conda-forge slycot
         fi
@@ -49,6 +51,9 @@ jobs:
         # Use xvfb-run instead of pytest-xvfb to get proper mpl backend
         # Use coverage instead of pytest-cov to get .coverage file
         # See https://github.com/python-control/python-control/pull/504
+
+        python3 .github/scripts/numpy-info.py
+
         xvfb-run --auto-servernum coverage run -m pytest control/tests
 
     - name: Coveralls parallel

control/statesp.py

@@ -887,6 +887,8 @@ def horner(self, x, warn_infinite=True):
         """
         # Make sure the argument is a 1D array of complex numbers
         x_arr = np.atleast_1d(x).astype(complex, copy=False)
+        if len(x_arr.shape) > 1:
+            raise ValueError("input list must be 1D")
 
         # return fast on systems with 0 or 1 state
         if not config.defaults['statesp.use_numpy_matrix']:
@@ -908,21 +910,21 @@ def horner(self, x, warn_infinite=True):
             # Fall back because either Slycot unavailable or cannot handle
             # certain cases.
 
-            # Make sure that we are operating on a simple list
-            if len(x_arr.shape) > 1:
-                raise ValueError("input list must be 1D")
-
             # Preallocate
             out = empty((self.noutputs, self.ninputs, len(x_arr)),
                         dtype=complex)
 
             # TODO: can this be vectorized?
             for idx, x_idx in enumerate(x_arr):
                 try:
-                    out[:, :, idx] = np.dot(
-                        self.C,
-                        solve(x_idx * eye(self.nstates) - self.A, self.B)) \
-                        + self.D
+                    xI_A = x_idx * eye(self.nstates) - self.A
+                    xI_A_inv = solve(xI_A, self.B)
+                    # gh-664: xI_A did not raise singular matrix error,
+                    print(f"DEBUG np.linalg.solve(\n,"
+                          f"{xI_A}\n"
+                          f"{self.B} = \n"
+                          f"{xI_A_inv}")
+                    out[:, :, idx] = np.dot(self.C, xI_A_inv) + self.D
                 except LinAlgError:
                     # Issue a warning messsage, for consistency with xferfcn
                     if warn_infinite: