def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
python类all()的实例源码
def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def __init__(self):
# The tree, which should be built.
self._tree = []
# List of all open constructs
self._open_blocks = []
# Nodes to which the open blocks have to be appended when closed
self._path = []
# Nr. of open blocks when file was opened. Used for checking whether all
# blocks have been closed, when file processing finishes.
self._nr_prev_blocks = []
# Current node, to which content should be added
self._curnode = self._tree
# Current file
self._curfile = None
def __init__(self, env=None):
# Global scope
self._globals = env if env is not None else {}
# Local scope(s)
self._locals = None
self._locals_stack = []
# Variables which are references to entries in global scope
self._globalrefs = None
self._globalrefs_stack = []
# Current scope (globals + locals in all embedding and in current scope)
self._scope = self._globals
# Turn on restricted mode
self._restrict_builtins()
def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def any(items):
for item in items:
if item:
return True
return False
# ---all() from Python 2.5 ---
def all(items):
return reduce(operator.__and__, items)
def _validate_axis(axis, ndim, argname):
try:
axis = [operator.index(axis)]
except TypeError:
axis = list(axis)
axis = [a + ndim if a < 0 else a for a in axis]
if not builtins.all(0 <= a < ndim for a in axis):
raise ValueError('invalid axis for this array in `%s` argument' %
argname)
if len(set(axis)) != len(axis):
raise ValueError('repeated axis in `%s` argument' % argname)
return axis
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
def array_equal(a1, a2):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except:
return False
if a1.shape != a2.shape:
return False
return bool(asarray(a1 == a2).all())
def _validate_axis(axis, ndim, argname):
try:
axis = [operator.index(axis)]
except TypeError:
axis = list(axis)
axis = [a + ndim if a < 0 else a for a in axis]
if not builtins.all(0 <= a < ndim for a in axis):
raise ValueError('invalid axis for this array in `%s` argument' %
argname)
if len(set(axis)) != len(axis):
raise ValueError('repeated axis in `%s` argument' % argname)
return axis
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
def array_equal(a1, a2):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except:
return False
if a1.shape != a2.shape:
return False
return bool(asarray(a1 == a2).all())
def _validate_axis(axis, ndim, argname):
try:
axis = [operator.index(axis)]
except TypeError:
axis = list(axis)
axis = [a + ndim if a < 0 else a for a in axis]
if not builtins.all(0 <= a < ndim for a in axis):
raise ValueError('invalid axis for this array in `%s` argument' %
argname)
if len(set(axis)) != len(axis):
raise ValueError('repeated axis in `%s` argument' % argname)
return axis
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
def array_equal(a1, a2):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except:
return False
if a1.shape != a2.shape:
return False
return bool(asarray(a1 == a2).all())
def _validate_axis(axis, ndim, argname):
try:
axis = [operator.index(axis)]
except TypeError:
axis = list(axis)
axis = [a + ndim if a < 0 else a for a in axis]
if not builtins.all(0 <= a < ndim for a in axis):
raise ValueError('invalid axis for this array in `%s` argument' %
argname)
if len(set(axis)) != len(axis):
raise ValueError('repeated axis in `%s` argument' % argname)
return axis
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
def array_equal(a1, a2):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except:
return False
if a1.shape != a2.shape:
return False
return bool(asarray(a1 == a2).all())
def any(items):
for item in items:
if item:
return True
return False
# ---all() from Python 2.5 ---
def all(items):
return reduce(operator.__and__, items)
# --- test if interpreter supports yield keyword ---
def _validate_axis(axis, ndim, argname):
try:
axis = [operator.index(axis)]
except TypeError:
axis = list(axis)
axis = [a + ndim if a < 0 else a for a in axis]
if not builtins.all(0 <= a < ndim for a in axis):
raise ValueError('invalid axis for this array in `%s` argument' %
argname)
if len(set(axis)) != len(axis):
raise ValueError('repeated axis in `%s` argument' % argname)
return axis
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
