def __new__(cls, dtype):
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError, "data type %r not inexact" % (dtype)
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([ 2., 3.])
"""
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype)
def __new__(cls, dtype):
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError, "data type %r not inexact" % (dtype)
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype,None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([ 2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([ 2., 3.])
"""
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype)
def nan_to_num(x):
"""
Replace nan with zero and inf with large numbers.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray
Array with the same shape and dtype as `x`. Nan is replaced
by zero, and inf (-inf) is replaced by the largest (smallest)
floating point value that fits in the output dtype.
Examples
--------
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x):
"""
Returns a copy of replacing NaN's with 0 and Infs with large numbers
The following mappings are applied:
NaN -> 0
Inf -> limits.double_max
-Inf -> limits.double_min
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x):
"""
Returns a copy of replacing NaN's with 0 and Infs with large numbers
The following mappings are applied:
NaN -> 0
Inf -> limits.double_max
-Inf -> limits.double_min
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def nan_to_num(x):
"""
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero,
(positive) infinity with a very large number and negative infinity
with a very small (or negative) number.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray, float
Array with the same shape as `x` and dtype of the element in `x` with
the greatest precision. NaN is replaced by zero, and infinity
(-infinity) is replaced by the largest (smallest or most negative)
floating point value that fits in the output dtype. All finite numbers
are upcast to the output dtype (default float64).
See Also
--------
isinf : Shows which elements are negative or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def asfarray(a, dtype=_nx.float_):
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return afnumpy.asarray(a, dtype=dtype)
def nan_to_num(x):
"""
Replace nan with zero and inf with finite numbers.
Returns an array or scalar replacing Not a Number (NaN) with zero,
(positive) infinity with a very large number and negative infinity
with a very small (or negative) number.
Parameters
----------
x : array_like
Input data.
Returns
-------
out : ndarray, float
Array with the same shape as `x` and dtype of the element in `x` with
the greatest precision. NaN is replaced by zero, and infinity
(-infinity) is replaced by the largest (smallest or most negative)
floating point value that fits in the output dtype. All finite numbers
are upcast to the output dtype (default float64).
See Also
--------
isinf : Shows which elements are negative or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.set_printoptions(precision=8)
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,
-1.28000000e+002, 1.28000000e+002])
"""
try:
t = x.dtype.type
except AttributeError:
t = obj2sctype(type(x))
if issubclass(t, _nx.complexfloating):
return nan_to_num(x.real) + 1j * nan_to_num(x.imag)
else:
try:
y = x.copy()
except AttributeError:
y = array(x)
if not issubclass(t, _nx.integer):
if not y.shape:
y = array([x])
scalar = True
else:
scalar = False
are_inf = isposinf(y)
are_neg_inf = isneginf(y)
are_nan = isnan(y)
maxf, minf = _getmaxmin(y.dtype.type)
y[are_nan] = 0
y[are_inf] = maxf
y[are_neg_inf] = minf
if scalar:
y = y[0]
return y
def asfarray(a, dtype=_nx.float_):
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return afnumpy.asarray(a, dtype=dtype)
def asfarray(a, dtype=_nx.float_):
"""asfarray(a,dtype=None) returns a as a float array."""
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return asanyarray(a, dtype=dtype)
def asfarray(a, dtype=_nx.float_):
"""asfarray(a,dtype=None) returns a as a float array."""
dtype = _nx.obj2sctype(dtype)
if not issubclass(dtype, _nx.inexact):
dtype = _nx.float_
return asanyarray(a,dtype=dtype)
