def vdot(a, b):
"""Returns the dot product of 2 vectors (or anything that can be made into
a vector). NB: this is not the same as `dot`, as it takes the conjugate
of its first argument if complex and always returns a scalar."""
a, b = _na.ravel(a), _na.ravel(b)
try:
return _dotblas.vdot(a, b)
# in case we get an integer Value
except TypeError:
return _numarray.dot(a, b)
def vdot(a, b):
"""Returns the dot product of 2 vectors (or anything that can be made into
a vector). NB: this is not the same as `dot`, as it takes the conjugate
of its first argument if complex and always returns a scalar."""
a, b = _na.ravel(a), _na.ravel(b)
try:
return _dotblas.vdot(a, b)
# in case we get an integer Value
except TypeError:
return _numarray.dot(a, b)
def eval(self):
"""eval(self) converts CharArray 'self' into a NumArray.
This is the original slow implementation based on a Python loop
and the eval() function.
>>> array([["1","2"],["3","4"]]).eval()
array([[1, 2],
[3, 4]])
>>> try:
... array([["1","2"],["3","other"]]).eval()
... except NameError:
... pass
"""
n = _na.array([eval(x, {}, {}) for x in _na.ravel(self)])
n.setshape(self._shape)
return n
def __contains__(self, str):
"""
Returns 1 if-and-only-if 'self' has an element == to 'str'
>>> s=array(["this ", "thar", "other"])
>>> int("this" in s)
1
>>> int("tjt" in s)
0
>>> x=array([""])
>>> int("this" in x)
0
>>> int("" in x)
1
"""
return _na.logical_or.reduce(_na.ravel(self.__eq__(str)))
def eval(self):
"""eval(self) converts CharArray 'self' into a NumArray.
This is the original slow implementation based on a Python loop
and the eval() function.
>>> array([["1","2"],["3","4"]]).eval()
array([[1, 2],
[3, 4]])
>>> try:
... array([["1","2"],["3","other"]]).eval()
... except NameError:
... pass
"""
n = _na.array([eval(x, {}, {}) for x in _na.ravel(self)])
n.setshape(self._shape)
return n
def __contains__(self, str):
"""
Returns 1 if-and-only-if 'self' has an element == to 'str'
>>> s=array(["this ", "thar", "other"])
>>> int("this" in s)
1
>>> int("tjt" in s)
0
>>> x=array([""])
>>> int("this" in x)
0
>>> int("" in x)
1
"""
return _na.logical_or.reduce(_na.ravel(self.__eq__(str)))
def mask(a, m):
"""mask(a, m) returns the values of 'a' satisfying category 'm'.
mask does a parallel check for values which are not classifyable
by the categorization code, raising a RuntimeError exception if
any are found.
"""
a = _na.asarray(a)
if isinstance(a.type(), _na.IntegralType):
a = a.astype('Float64')
if isinstance(a.type(), _na.ComplexType):
f = _na.ieeemask(a.real, m) | _na.ieeemask(a.imag, m)
g = _na.ieeemask(a.real, BUG) | _na.ieeemask(a.imag, BUG)
else:
f = _na.ieeemask(a, m)
g = _na.ieeemask(a, BUG)
if _na.bitwise_or.reduce(_na.ravel(g)) != 0:
raise RuntimeError("Unclassifyable floating point values.")
if f.rank == 0:
f = f[()]
return f
def mask(a, m):
"""mask(a, m) returns the values of 'a' satisfying category 'm'.
mask does a parallel check for values which are not classifyable
by the categorization code, raising a RuntimeError exception if
any are found.
"""
a = _na.asarray(a)
if isinstance(a.type(), _na.IntegralType):
a = a.astype('Float64')
if isinstance(a.type(), _na.ComplexType):
f = _na.ieeemask(a.real, m) | _na.ieeemask(a.imag, m)
g = _na.ieeemask(a.real, BUG) | _na.ieeemask(a.imag, BUG)
else:
f = _na.ieeemask(a, m)
g = _na.ieeemask(a, BUG)
if _na.bitwise_or.reduce(_na.ravel(g)) != 0:
raise RuntimeError("Unclassifyable floating point values.")
if f.rank == 0:
f = f[()]
return f
