fix numpy int size for windows OS.

This fix #73 #76 and #85 in windows OS using numpy 32 bit integer type

fxpmath/functions.py

@@ -568,7 +568,7 @@ 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 +591,7 @@ 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

@@ -849,7 +849,10 @@ 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