Merge branch 'dev-0.4.10' into dev-0.5.0

.github/workflows/python-app.yml

@@ -1,13 +1,15 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Python package
+name: Python App
 
 on:
   push:
-    branches: [ master ]
+    branches:
+      - '*'
   pull_request:
-    branches: [ master ]
+    branches: 
+      - '*'
 
 jobs:
   build:

.github/workflows/python-os.yml

@@ -1,13 +1,15 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Python package
+name: Python OS
 
 on:
   push:
-    branches: [ master ]
+    branches:
+      - '*'
   pull_request:
-    branches: [ master ]
+    branches: 
+      - '*'
 
 jobs:
   build:

.github/workflows/python-package.yml

@@ -5,9 +5,11 @@ name: Python package
 
 on:
   push:
-    branches: [ master ]
+    branches:
+      - '*'
   pull_request:
-    branches: [ master ]
+    branches: 
+      - '*'
 
 jobs:
   build:

fxpmath/functions.py

@@ -366,9 +366,11 @@ def _mul_raw(x, y, n_frac):
     if not isinstance(y, Fxp):
         y = Fxp(y)
 
+    is_complex = x.vdtype == complex or y.vdtype == complex
+
     signed = x.signed or y.signed
     n_frac = x.n_frac + y.n_frac
-    n_word = x.n_word + y.n_word
+    n_word = x.n_word + y.n_word + int(is_complex)
     n_int = n_word - int(signed) - n_frac
     optimal_size = (signed, n_word, n_int, n_frac)
 
@@ -405,12 +407,13 @@ def _floordiv_raw_complex(x, y, n_frac):
     if not isinstance(y, Fxp):
         y = Fxp(y)
 
-    if x.vdtype == complex or y.vdtype == complex:
+    is_complex = x.vdtype == complex or y.vdtype == complex
+    if is_complex:
         _floordiv_repr = _floordiv_repr_complex
         _floordiv_raw = _floordiv_raw_complex
 
     signed = x.signed or y.signed
-    n_int = x.n_int + y.n_frac + signed
+    n_int = x.n_int + y.n_frac + int(signed) + int(is_complex)
     n_frac = 0
     n_word = int(signed) + n_int + n_frac
     optimal_size = (signed, n_word, n_int, n_frac)
@@ -444,12 +447,13 @@ def _truediv_raw_complex(x, y, n_frac):
     if not isinstance(y, Fxp):
         y = Fxp(y)
 
-    if x.vdtype == complex or y.vdtype == complex:
+    is_complex = x.vdtype == complex or y.vdtype == complex
+    if is_complex:
         _truediv_raw = _truediv_raw_complex
 
     signed = x.signed or y.signed
-    n_int = x.n_int + y.n_frac + signed
-    n_frac = x.n_frac + y.n_int
+    n_int = x.n_int + y.n_frac + int(signed) + int(is_complex)
+    n_frac = x.n_frac + y.n_int + int(is_complex)
     n_word = int(signed) + n_int + n_frac
     optimal_size = (signed, n_word, n_int, n_frac)
 
@@ -568,7 +572,7 @@ def cumsum(x, axis=None, out=None, out_like=None, sizing='optimal', method='raw'
     """
     def _cumsum_raw(x, n_frac, **kwargs):
         precision_cast = (lambda m: np.array(m, dtype=object)) if n_frac >= _n_word_max else (lambda m: m)
-        return np.cumsum(x.val, **kwargs) * precision_cast(2**(n_frac - x.n_frac))
+        return np.cumsum(precision_cast(x.val), **kwargs) * precision_cast(2**(n_frac - x.n_frac))
 
     if not isinstance(x, Fxp):
         x = Fxp(x)
@@ -591,7 +595,7 @@ def _cumprod_raw(x, n_frac, **kwargs):
         precision_cast = (lambda m: np.array(m, dtype=object)) if n_frac >= _n_word_max else (lambda m: m)
         pow_vals = n_frac - np.cumsum(np.ones_like(np.array(x)), axis=axis).astype(int)  * x.n_frac
         conv_factors = utils.int_array([2**pow_val for pow_val in precision_cast(pow_vals)])
-        return np.cumprod(x.val, **kwargs) * conv_factors
+        return np.cumprod(precision_cast(x.val), **kwargs) * precision_cast(conv_factors)
 
     if not isinstance(x, Fxp):
         x = Fxp(x)

fxpmath/objects.py

@@ -619,8 +619,9 @@ def reshape(self, shape, order='C'):
 
         """
 
-        self.val = self.val.reshape(shape=shape, order=order)
-        return self
+        x = self.copy()
+        x.val = x.val.reshape(shape, order=order)
+        return x
 
     def flatten(self, order='C'):
         """
@@ -849,7 +850,10 @@ def set_val(self, val, raw=False, vdtype=None, index=None):
                 val = val.astype(object)
             else:
                 val = val.astype(original_vdtype)
-                val_dtype = np.int64 if self.signed else np.uint64
+                if _n_word_max_ <= 32:
+                    val_dtype = np.int32 if self.signed else np.uint32
+                else:
+                    val_dtype = np.int64 if self.signed else np.uint64
 
             # rounding and overflowing
             new_val = self._round(val * conv_factor , method=self.config.rounding)

tests/test_issues.py

@@ -422,6 +422,11 @@ def test_issue_76_v0_4_8():
     y = np.cumsum(w)
     assert np.all(y() == np.array([1, 2, 3, 4]))
 
+    # Increase word size to 96 bits
+    w = Fxp([1, 1, 1, 1], dtype='fxp-s96/0')
+    y = np.cumsum(w)
+    assert np.all(y() == np.array([1, 2, 3, 4]))
+
 def test_issue_77_v0_4_8():
     # Precision error when numpy.reshape
 
@@ -466,3 +471,34 @@ def test_issue_85_v0_4_8():
 
         x = Fxp(0.0, dtype=dt, overflow='wrap')  #  EXCEPTION
         assert x() == 0.0
+
+def test_issue_90_v0_4_9():
+    # Built-in Fxp.reshape() method passes the wrong number of arguments to underlying numpy reshape() function
+    x = Fxp([1,2,3,4], False, 8, 0)
+    y = x.reshape((2,2))
+
+    assert np.all(x.val.shape == np.array((4,)))
+    assert np.all(y.val.shape == np.array((2,2)))
+
+    z = np.reshape(x, (2,2))
+
+    assert np.all(x.val.shape == np.array((4,)))
+    assert np.all(z.val.shape == np.array((2,2)))
+
+    zz = fxp.reshape(x, (2,2))
+
+    assert np.all(x.val.shape == np.array((4,)))
+    assert np.all(zz.val.shape == np.array((2,2)))
+
+
+def test_issue_91_v0_4_10():
+    # Inaccuracy for certain complex multiplications
+
+    x = Fxp(-1-1j)
+    xa = np.array(-1-1j)
+
+    y = x * x
+    ya = xa * xa
+
+    assert np.all(y() == ya)
+