feat: linalg.jack_matmul now also works with non Obs valued matrices.

pyerrors/linalg.py

@@ -205,7 +205,10 @@ def jack_matmul(*operands):
 
         r = _exp_to_jack(operands[0])
         for op in operands[1:]:
-            r = r @ _exp_to_jack(op)
+            if isinstance(op[0, 0], CObs):
+                r = r @ _exp_to_jack(op)
+            else:
+                r = r @ op
         return _imp_from_jack(r)
     else:
         name = operands[0][0, 0].names[0]
@@ -225,7 +228,10 @@ def jack_matmul(*operands):
 
         r = _exp_to_jack(operands[0])
         for op in operands[1:]:
-            r = r @ _exp_to_jack(op)
+            if isinstance(op[0, 0], Obs):
+                r = r @ _exp_to_jack(op)
+            else:
+                r = r @ op
         return _imp_from_jack(r)
 
 

tests/linalg_test.py

@@ -55,23 +55,46 @@ def test_jack_matmul():
     check1 = pe.linalg.jack_matmul(tt, tt) - pe.linalg.matmul(tt, tt)
     [o.gamma_method() for o in check1.ravel()]
     assert np.all([o.is_zero_within_error(0.1) for o in check1.ravel()])
+    assert np.all([o.dvalue < 0.001 for o in check1.ravel()])
     trace1 = np.trace(check1)
     trace1.gamma_method()
     assert trace1.dvalue < 0.001
 
-    tt2 = get_complex_matrix(8)
+    tr = np.random.rand(8, 8)
-    check2 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
+    check2 = pe.linalg.jack_matmul(tt, tr) - pe.linalg.matmul(tt, tr)
     [o.gamma_method() for o in check2.ravel()]
-    assert np.all([o.real.is_zero_within_error(0.1) for o in check2.ravel()])
+    assert np.all([o.is_zero_within_error(0.1) for o in check2.ravel()])
-    assert np.all([o.imag.is_zero_within_error(0.1) for o in check2.ravel()])
+    assert np.all([o.dvalue < 0.001 for o in check2.ravel()])
     trace2 = np.trace(check2)
     trace2.gamma_method()
-    assert trace2.real.dvalue < 0.001
+    assert trace2.dvalue < 0.001
-    assert trace2.imag.dvalue < 0.001
 
+    tt2 = get_complex_matrix(8)
+    check3 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
+    [o.gamma_method() for o in check3.ravel()]
+    assert np.all([o.real.is_zero_within_error(0.1) for o in check3.ravel()])
+    assert np.all([o.imag.is_zero_within_error(0.1) for o in check3.ravel()])
+    assert np.all([o.real.dvalue < 0.001 for o in check3.ravel()])
+    assert np.all([o.imag.dvalue < 0.001 for o in check3.ravel()])
+    trace3 = np.trace(check3)
+    trace3.gamma_method()
+    assert trace3.real.dvalue < 0.001
+    assert trace3.imag.dvalue < 0.001
+
+    tr2 = np.random.rand(8, 8) + 1j * np.random.rand(8, 8)
+    check4 = pe.linalg.jack_matmul(tt2, tr2) - pe.linalg.matmul(tt2, tr2)
+    [o.gamma_method() for o in check4.ravel()]
+    assert np.all([o.real.is_zero_within_error(0.1) for o in check4.ravel()])
+    assert np.all([o.imag.is_zero_within_error(0.1) for o in check4.ravel()])
+    assert np.all([o.real.dvalue < 0.001 for o in check4.ravel()])
+    assert np.all([o.imag.dvalue < 0.001 for o in check4.ravel()])
+    trace4 = np.trace(check4)
+    trace4.gamma_method()
+    assert trace4.real.dvalue < 0.001
+    assert trace4.imag.dvalue < 0.001
 
 def test_multi_dot():
-    for dim in [4, 8]:
+    for dim in [4, 6]:
         my_list = []
         length = 1000 + np.random.randint(200)
         for i in range(dim ** 2):
@@ -167,8 +190,8 @@ def test_complex_matrix_inverse():
     base_matrix = np.empty((dimension, dimension), dtype=object)
     matrix = np.empty((dimension, dimension), dtype=complex)
     for (n, m), entry in np.ndenumerate(base_matrix):
-        exponent_real = np.random.normal(3, 5)
+        exponent_real = np.random.normal(2, 3)
-        exponent_imag = np.random.normal(3, 5)
+        exponent_imag = np.random.normal(2, 3)
         base_matrix[n, m] = pe.CObs(pe.pseudo_Obs(2 + 10 ** exponent_real, 10 ** (exponent_real - 1), 't'),
                                     pe.pseudo_Obs(2 + 10 ** exponent_imag, 10 ** (exponent_imag - 1), 't'))