version 0.4.3

* Fxp adds `config` as optional init parameter (kwarg).
* Functions (operators) over one and two variables copy first operator configuration by default.
* Changes above fixed wrap overflowing bug after operation (issue #42 solved).
* Wrap function of utils fix bug when data has more than n_word_max (64 bits in the most of cases) (issue #41 soved).
* Config new methods: copy, deepcopy.
* Fix warning about internal complex check (issue #39).
* Add link implementation between `functions.truediv` with `numpy.divide`.
* Add `axes` parameter to `transpose` method.

changelog.txt

@@ -1,3 +1,14 @@
+version 0.4.3
+--------------------------------------------------
+* Fxp adds `config` as optional init parameter (kwarg).
+* Functions (operators) over one and two variables copy first operator configuration by default.
+* Changes above fixed wrap overflowing bug after operation (issue #42 solved).
+* Wrap function of utils fix bug when data has more than n_word_max (64 bits in the most of cases) (issue #41 soved).
+* Config new methods: copy, deepcopy.
+* Fix warning about internal complex check (issue #39).
+* Add link implementation between `functions.truediv` with `numpy.divide`.
+* Add `axes` parameter to `transpose` method.
+
 version 0.4.2
 --------------------------------------------------
 * Add template to Config object.

fxpmath/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.4.2'
+__version__ = '0.4.3'
 
 import sys
 import os

fxpmath/functions.py

@@ -99,6 +99,8 @@ def _function_over_one_var(repr_func, raw_func, x, out=None, out_like=None, sizi
         if not out.signed and signed:
             raise ValueError('Signed addition can not be stored in unsigned `out` object!')
         n_frac = out.n_frac
+        config = None
+
     elif out_like is not None:
         if not isinstance(out_like, Fxp):
             raise TypeError('`out_like` must be a Fxp object!')
@@ -107,6 +109,10 @@ def _function_over_one_var(repr_func, raw_func, x, out=None, out_like=None, sizi
         signed = None
         n_frac = None
         n_int = None
+        config = None
+
+    else:
+        config = x.config
 
     if method == 'repr' or x.scaled or n_frac is None:
         raw = False
@@ -141,6 +147,8 @@ def _function_over_two_vars(repr_func, raw_func, x, y, out=None, out_like=None,
         if not out.signed and signed:
             raise ValueError('Signed addition can not be stored in unsigned `out` object!')
         n_frac = out.n_frac
+        config = None
+
     elif out_like is not None:
         if not isinstance(out_like, Fxp):
             raise TypeError('`out_like` must be a Fxp object!')
@@ -149,6 +157,10 @@ def _function_over_two_vars(repr_func, raw_func, x, y, out=None, out_like=None,
         signed = None
         n_frac = None
         n_int = None
+        config = None
+
+    else:
+        config = x.config
 
     if method == 'repr' or x.scaled or n_frac is None:
         raw = False
@@ -163,7 +175,7 @@ def _function_over_two_vars(repr_func, raw_func, x, y, out=None, out_like=None,
     if out is not None:
         z = out.set_val(val, raw=raw)
     else:
-        z = Fxp(val, signed=signed, n_int=n_int, n_frac=n_frac, like=out_like, raw=raw)
+        z = Fxp(val, signed=signed, n_int=n_int, n_frac=n_frac, like=out_like, raw=raw, config=config)
 
     return z   
 
@@ -374,7 +386,7 @@ def _floordiv_raw(x, y, n_frac):
 
     return _function_over_two_vars(repr_func=_floordiv_repr, raw_func=_floordiv_raw, x=x, y=y, out=out, out_like=out_like, sizing=sizing, method=method, optimal_size=optimal_size, **kwargs)
 
-@implements(np.true_divide)
+@implements(np.true_divide, np.divide)
 def truediv(x, y, out=None, out_like=None, sizing='optimal', method='raw', **kwargs):
     """
     """
@@ -572,13 +584,14 @@ def _conjugate_raw(x, n_frac, **kwargs):
     return _function_over_one_var(repr_func=np.conjugate, raw_func=_conjugate_raw, x=x, out=out, out_like=out_like, sizing=sizing, method=method, **kwargs)
 
 @implements(np.transpose)
-def transpose(x, out=None, out_like=None, sizing='optimal', method='raw', **kwargs):
+def transpose(x, axes=None, out=None, out_like=None, sizing='optimal', method='raw', **kwargs):
     """
     """
     def _transpose_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 (x.val.T) * precision_cast(2**(n_frac - x.n_frac))
 
+    kwargs['axes'] = axes
     return _function_over_one_var(repr_func=np.transpose, raw_func=_transpose_raw, x=x, out=out, out_like=out_like, sizing=sizing, method=method, **kwargs)
 
 @implements(np.clip)

fxpmath/objects.py

@@ -149,6 +149,14 @@ def __init__(self, val=None, signed=None, n_word=None, n_frac=None, n_int=None,
             'inaccuracy': False,
             'extended_prec': False}
 
+        # update config as argument
+        _config = kwargs.pop('config', None)
+        if _config is not None:
+            if isinstance(_config, Config):
+                self.config = _config.deepcopy()
+            else:
+                raise TypeError('config parameter must be Config class type!')
+
         # update config from kwargs
         self.config.update(**kwargs)
 
@@ -594,7 +602,7 @@ def set_val(self, val, raw=False, vdtype=None, index=None):
         conv_factor = self._get_conv_factor(raw)
 
         # round, saturate and store
-        if original_vdtype != complex and not np.issubdtype(original_vdtype, complex):
+        if original_vdtype != complex and not np.issubdtype(original_vdtype, np.complexfloating):
             # val_dtype determination
             _n_word_max_ = min(_n_word_max, 64)
             if np.max(val) >= 2**_n_word_max_ or np.min(val) < -2**_n_word_max_ or self.n_word >= _n_word_max_:
@@ -1522,15 +1530,15 @@ def T(self):
         x.val = x.val.T
         return x    
 
-    def transpose(self, **kwargs):
+    def transpose(self, axes=None, **kwargs):
         from .functions import transpose
 
         out = kwargs.pop('out', self.config.op_out)
         out_like = kwargs.pop('out_like', self.config.op_out_like)
         sizing = kwargs.pop('sizing', self.config.op_sizing)
         method = kwargs.pop('method', self.config.op_method)
 
-        return transpose(self, out=out, out_like=out_like, sizing=sizing, method=method, **kwargs)
+        return transpose(self, axes=axes, out=out, out_like=out_like, sizing=sizing, method=method, **kwargs)
 
     def item(self, *args):
         if len(args) > 1:
@@ -1894,7 +1902,12 @@ def update(self, **kwargs):
             if hasattr(self, k):
                 setattr(self, k, v)
 
+    # copy
+    def copy(self):
+        return copy.copy(self)
 
+    def deepcopy(self):
+        return copy.deepcopy(self)
     # endregion
 
 # ----------------------------------------------------------------------------------------

fxpmath/utils.py

@@ -34,6 +34,7 @@
 
 #%%
 import numpy as np
+from . import _n_word_max
 
 #%% 
 def array_support(func):
@@ -312,13 +313,20 @@ def int_clip(x, val_min, val_max):
     x_clipped = np.array(max(val_min, min(val_max, int(x))))
     return x_clipped
 
-def wrap(x, signed, n_word): 
+def wrap(x, signed, n_word):
+    if n_word >= _n_word_max:
+        dtype = object
+    else:
+        dtype = int
+
     m = (1 << n_word)
     if signed: 
-        x = np.array(x).astype(int) & (m - 1) 
+        x = np.array(x).astype(dtype) & (m - 1)
+        x = np.asarray(x).astype(dtype)
         x = np.where(x < (1 << (n_word-1)), x, x | (-m)) 
     else: 
-        x = np.array(x).astype(int) & (m - 1) 
+        x = np.array(x).astype(dtype) & (m - 1) 
+        x = np.asarray(x).astype(dtype)
     return x
 
 def get_sizes_from_dtype(dtype):

tests/test_issues.py

@@ -146,3 +146,15 @@ def test_issue_31_v0_4_0():
     q3 = t(0.125*2**3)
     assert q3.val.dtype == object
     assert q3() == 1.0
+
+def test_issue_41_v0_4_2():
+    x = Fxp(2, False, 63, 0, overflow='wrap')
+    y = Fxp(2, False, 64, 0, overflow='wrap')
+
+    assert x() == 2
+    assert y() == 2
+
+def test_issue_42_v0_4_2():
+    b = Fxp(2, True, 4, 0, overflow='wrap')
+    assert (b + 8)() == -6.0
+    assert (b - 8)() == -6.0