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ryfeus/lambda-packs | HDF4_H5_NETCDF/source2.7/numpy/distutils/tests/test_exec_command.py | 18 | 7146 | from __future__ import division, absolute_import, print_function
import os
import sys
from tempfile import TemporaryFile
from numpy.distutils import exec_command
from numpy.distutils.exec_command import get_pythonexe
from numpy.testing import tempdir, assert_
# In python 3 stdout, stderr are text (unicode compliant) devices, so to
# emulate them import StringIO from the io module.
if sys.version_info[0] >= 3:
from io import StringIO
else:
from StringIO import StringIO
class redirect_stdout(object):
"""Context manager to redirect stdout for exec_command test."""
def __init__(self, stdout=None):
self._stdout = stdout or sys.stdout
def __enter__(self):
self.old_stdout = sys.stdout
sys.stdout = self._stdout
def __exit__(self, exc_type, exc_value, traceback):
self._stdout.flush()
sys.stdout = self.old_stdout
# note: closing sys.stdout won't close it.
self._stdout.close()
class redirect_stderr(object):
"""Context manager to redirect stderr for exec_command test."""
def __init__(self, stderr=None):
self._stderr = stderr or sys.stderr
def __enter__(self):
self.old_stderr = sys.stderr
sys.stderr = self._stderr
def __exit__(self, exc_type, exc_value, traceback):
self._stderr.flush()
sys.stderr = self.old_stderr
# note: closing sys.stderr won't close it.
self._stderr.close()
class emulate_nonposix(object):
"""Context manager to emulate os.name != 'posix' """
def __init__(self, osname='non-posix'):
self._new_name = osname
def __enter__(self):
self._old_name = os.name
os.name = self._new_name
def __exit__(self, exc_type, exc_value, traceback):
os.name = self._old_name
def test_exec_command_stdout():
# Regression test for gh-2999 and gh-2915.
# There are several packages (nose, scipy.weave.inline, Sage inline
# Fortran) that replace stdout, in which case it doesn't have a fileno
# method. This is tested here, with a do-nothing command that fails if the
# presence of fileno() is assumed in exec_command.
# The code has a special case for posix systems, so if we are on posix test
# both that the special case works and that the generic code works.
# Test posix version:
with redirect_stdout(StringIO()):
with redirect_stderr(TemporaryFile()):
exec_command.exec_command("cd '.'")
if os.name == 'posix':
# Test general (non-posix) version:
with emulate_nonposix():
with redirect_stdout(StringIO()):
with redirect_stderr(TemporaryFile()):
exec_command.exec_command("cd '.'")
def test_exec_command_stderr():
# Test posix version:
with redirect_stdout(TemporaryFile(mode='w+')):
with redirect_stderr(StringIO()):
exec_command.exec_command("cd '.'")
if os.name == 'posix':
# Test general (non-posix) version:
with emulate_nonposix():
with redirect_stdout(TemporaryFile()):
with redirect_stderr(StringIO()):
exec_command.exec_command("cd '.'")
class TestExecCommand(object):
def setup(self):
self.pyexe = get_pythonexe()
def check_nt(self, **kws):
s, o = exec_command.exec_command('cmd /C echo path=%path%')
assert_(s == 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.stderr.write(sys.platform)"' % self.pyexe)
assert_(s == 0)
assert_(o == 'win32')
def check_posix(self, **kws):
s, o = exec_command.exec_command("echo Hello", **kws)
assert_(s == 0)
assert_(o == 'Hello')
s, o = exec_command.exec_command('echo $AAA', **kws)
assert_(s == 0)
assert_(o == '')
s, o = exec_command.exec_command('echo "$AAA"', AAA='Tere', **kws)
assert_(s == 0)
assert_(o == 'Tere')
s, o = exec_command.exec_command('echo "$AAA"', **kws)
assert_(s == 0)
assert_(o == '')
if 'BBB' not in os.environ:
os.environ['BBB'] = 'Hi'
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == 'Hi')
s, o = exec_command.exec_command('echo "$BBB"', BBB='Hey', **kws)
assert_(s == 0)
assert_(o == 'Hey')
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == 'Hi')
del os.environ['BBB']
s, o = exec_command.exec_command('echo "$BBB"', **kws)
assert_(s == 0)
assert_(o == '')
s, o = exec_command.exec_command('this_is_not_a_command', **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command('echo path=$PATH', **kws)
assert_(s == 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys,os;sys.stderr.write(os.name)"' %
self.pyexe, **kws)
assert_(s == 0)
assert_(o == 'posix')
def check_basic(self, *kws):
s, o = exec_command.exec_command(
'"%s" -c "raise \'Ignore me.\'"' % self.pyexe, **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.stderr.write(\'0\');'
'sys.stderr.write(\'1\');sys.stderr.write(\'2\')"' %
self.pyexe, **kws)
assert_(s == 0)
assert_(o == '012')
s, o = exec_command.exec_command(
'"%s" -c "import sys;sys.exit(15)"' % self.pyexe, **kws)
assert_(s == 15)
assert_(o == '')
s, o = exec_command.exec_command(
'"%s" -c "print(\'Heipa\'")' % self.pyexe, **kws)
assert_(s == 0)
assert_(o == 'Heipa')
def check_execute_in(self, **kws):
with tempdir() as tmpdir:
fn = "file"
tmpfile = os.path.join(tmpdir, fn)
f = open(tmpfile, 'w')
f.write('Hello')
f.close()
s, o = exec_command.exec_command(
'"%s" -c "f = open(\'%s\', \'r\'); f.close()"' %
(self.pyexe, fn), **kws)
assert_(s != 0)
assert_(o != '')
s, o = exec_command.exec_command(
'"%s" -c "f = open(\'%s\', \'r\'); print(f.read()); '
'f.close()"' % (self.pyexe, fn), execute_in=tmpdir, **kws)
assert_(s == 0)
assert_(o == 'Hello')
def test_basic(self):
with redirect_stdout(StringIO()):
with redirect_stderr(StringIO()):
if os.name == "posix":
self.check_posix(use_tee=0)
self.check_posix(use_tee=1)
elif os.name == "nt":
self.check_nt(use_tee=0)
self.check_nt(use_tee=1)
self.check_execute_in(use_tee=0)
self.check_execute_in(use_tee=1)
| mit |
ryfeus/lambda-packs | Tensorflow_OpenCV_Nightly/source/numpy/add_newdocs.py | 6 | 242489 | """
This is only meant to add docs to objects defined in C-extension modules.
The purpose is to allow easier editing of the docstrings without
requiring a re-compile.
NOTE: Many of the methods of ndarray have corresponding functions.
If you update these docstrings, please keep also the ones in
core/fromnumeric.py, core/defmatrix.py up-to-date.
"""
from __future__ import division, absolute_import, print_function
from numpy.lib import add_newdoc
###############################################################################
#
# flatiter
#
# flatiter needs a toplevel description
#
###############################################################################
add_newdoc('numpy.core', 'flatiter',
"""
Flat iterator object to iterate over arrays.
A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
It allows iterating over the array as if it were a 1-D array,
either in a for-loop or by calling its `next` method.
Iteration is done in row-major, C-style order (the last
index varying the fastest). The iterator can also be indexed using
basic slicing or advanced indexing.
See Also
--------
ndarray.flat : Return a flat iterator over an array.
ndarray.flatten : Returns a flattened copy of an array.
Notes
-----
A `flatiter` iterator can not be constructed directly from Python code
by calling the `flatiter` constructor.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> type(fl)
<type 'numpy.flatiter'>
>>> for item in fl:
... print(item)
...
0
1
2
3
4
5
>>> fl[2:4]
array([2, 3])
""")
# flatiter attributes
add_newdoc('numpy.core', 'flatiter', ('base',
"""
A reference to the array that is iterated over.
Examples
--------
>>> x = np.arange(5)
>>> fl = x.flat
>>> fl.base is x
True
"""))
add_newdoc('numpy.core', 'flatiter', ('coords',
"""
An N-dimensional tuple of current coordinates.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.coords
(0, 0)
>>> fl.next()
0
>>> fl.coords
(0, 1)
"""))
add_newdoc('numpy.core', 'flatiter', ('index',
"""
Current flat index into the array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.index
0
>>> fl.next()
0
>>> fl.index
1
"""))
# flatiter functions
add_newdoc('numpy.core', 'flatiter', ('__array__',
"""__array__(type=None) Get array from iterator
"""))
add_newdoc('numpy.core', 'flatiter', ('copy',
"""
copy()
Get a copy of the iterator as a 1-D array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> fl = x.flat
>>> fl.copy()
array([0, 1, 2, 3, 4, 5])
"""))
###############################################################################
#
# nditer
#
###############################################################################
add_newdoc('numpy.core', 'nditer',
"""
Efficient multi-dimensional iterator object to iterate over arrays.
To get started using this object, see the
:ref:`introductory guide to array iteration <arrays.nditer>`.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
flags : sequence of str, optional
Flags to control the behavior of the iterator.
* "buffered" enables buffering when required.
* "c_index" causes a C-order index to be tracked.
* "f_index" causes a Fortran-order index to be tracked.
* "multi_index" causes a multi-index, or a tuple of indices
with one per iteration dimension, to be tracked.
* "common_dtype" causes all the operands to be converted to
a common data type, with copying or buffering as necessary.
* "copy_if_overlap" causes the iterator to determine if read
operands have overlap with write operands, and make temporary
copies as necessary to avoid overlap. False positives (needless
copying) are possible in some cases.
* "delay_bufalloc" delays allocation of the buffers until
a reset() call is made. Allows "allocate" operands to
be initialized before their values are copied into the buffers.
* "external_loop" causes the `values` given to be
one-dimensional arrays with multiple values instead of
zero-dimensional arrays.
* "grow_inner" allows the `value` array sizes to be made
larger than the buffer size when both "buffered" and
"external_loop" is used.
* "ranged" allows the iterator to be restricted to a sub-range
of the iterindex values.
* "refs_ok" enables iteration of reference types, such as
object arrays.
* "reduce_ok" enables iteration of "readwrite" operands
which are broadcasted, also known as reduction operands.
* "zerosize_ok" allows `itersize` to be zero.
op_flags : list of list of str, optional
This is a list of flags for each operand. At minimum, one of
"readonly", "readwrite", or "writeonly" must be specified.
* "readonly" indicates the operand will only be read from.
* "readwrite" indicates the operand will be read from and written to.
* "writeonly" indicates the operand will only be written to.
* "no_broadcast" prevents the operand from being broadcasted.
* "contig" forces the operand data to be contiguous.
* "aligned" forces the operand data to be aligned.
* "nbo" forces the operand data to be in native byte order.
* "copy" allows a temporary read-only copy if required.
* "updateifcopy" allows a temporary read-write copy if required.
* "allocate" causes the array to be allocated if it is None
in the `op` parameter.
* "no_subtype" prevents an "allocate" operand from using a subtype.
* "arraymask" indicates that this operand is the mask to use
for selecting elements when writing to operands with the
'writemasked' flag set. The iterator does not enforce this,
but when writing from a buffer back to the array, it only
copies those elements indicated by this mask.
* 'writemasked' indicates that only elements where the chosen
'arraymask' operand is True will be written to.
* "overlap_assume_elementwise" can be used to mark operands that are
accessed only in the iterator order, to allow less conservative
copying when "copy_if_overlap" is present.
op_dtypes : dtype or tuple of dtype(s), optional
The required data type(s) of the operands. If copying or buffering
is enabled, the data will be converted to/from their original types.
order : {'C', 'F', 'A', 'K'}, optional
Controls the iteration order. 'C' means C order, 'F' means
Fortran order, 'A' means 'F' order if all the arrays are Fortran
contiguous, 'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible. This also
affects the element memory order of "allocate" operands, as they
are allocated to be compatible with iteration order.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when making a copy
or buffering. Setting this to 'unsafe' is not recommended,
as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
op_axes : list of list of ints, optional
If provided, is a list of ints or None for each operands.
The list of axes for an operand is a mapping from the dimensions
of the iterator to the dimensions of the operand. A value of
-1 can be placed for entries, causing that dimension to be
treated as "newaxis".
itershape : tuple of ints, optional
The desired shape of the iterator. This allows "allocate" operands
with a dimension mapped by op_axes not corresponding to a dimension
of a different operand to get a value not equal to 1 for that
dimension.
buffersize : int, optional
When buffering is enabled, controls the size of the temporary
buffers. Set to 0 for the default value.
Attributes
----------
dtypes : tuple of dtype(s)
The data types of the values provided in `value`. This may be
different from the operand data types if buffering is enabled.
Valid only before the iterator is closed.
finished : bool
Whether the iteration over the operands is finished or not.
has_delayed_bufalloc : bool
If True, the iterator was created with the "delay_bufalloc" flag,
and no reset() function was called on it yet.
has_index : bool
If True, the iterator was created with either the "c_index" or
the "f_index" flag, and the property `index` can be used to
retrieve it.
has_multi_index : bool
If True, the iterator was created with the "multi_index" flag,
and the property `multi_index` can be used to retrieve it.
index
When the "c_index" or "f_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
and `has_index` is False.
iterationneedsapi : bool
Whether iteration requires access to the Python API, for example
if one of the operands is an object array.
iterindex : int
An index which matches the order of iteration.
itersize : int
Size of the iterator.
itviews
Structured view(s) of `operands` in memory, matching the reordered
and optimized iterator access pattern. Valid only before the iterator
is closed.
multi_index
When the "multi_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
accessed and `has_multi_index` is False.
ndim : int
The iterator's dimension.
nop : int
The number of iterator operands.
operands : tuple of operand(s)
The array(s) to be iterated over. Valid only before the iterator is
closed.
shape : tuple of ints
Shape tuple, the shape of the iterator.
value
Value of `operands` at current iteration. Normally, this is a
tuple of array scalars, but if the flag "external_loop" is used,
it is a tuple of one dimensional arrays.
Notes
-----
`nditer` supersedes `flatiter`. The iterator implementation behind
`nditer` is also exposed by the NumPy C API.
The Python exposure supplies two iteration interfaces, one which follows
the Python iterator protocol, and another which mirrors the C-style
do-while pattern. The native Python approach is better in most cases, but
if you need the iterator's coordinates or index, use the C-style pattern.
Examples
--------
Here is how we might write an ``iter_add`` function, using the
Python iterator protocol::
def iter_add_py(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
with it:
for (a, b, c) in it:
addop(a, b, out=c)
return it.operands[2]
Here is the same function, but following the C-style pattern::
def iter_add(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
with it:
while not it.finished:
addop(it[0], it[1], out=it[2])
it.iternext()
return it.operands[2]
Here is an example outer product function::
def outer_it(x, y, out=None):
mulop = np.multiply
it = np.nditer([x, y, out], ['external_loop'],
[['readonly'], ['readonly'], ['writeonly', 'allocate']],
op_axes=[list(range(x.ndim)) + [-1] * y.ndim,
[-1] * x.ndim + list(range(y.ndim)),
None])
with it:
for (a, b, c) in it:
mulop(a, b, out=c)
return it.operands[2]
>>> a = np.arange(2)+1
>>> b = np.arange(3)+1
>>> outer_it(a,b)
array([[1, 2, 3],
[2, 4, 6]])
Here is an example function which operates like a "lambda" ufunc::
def luf(lamdaexpr, *args, **kwargs):
"luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)"
nargs = len(args)
op = (kwargs.get('out',None),) + args
it = np.nditer(op, ['buffered','external_loop'],
[['writeonly','allocate','no_broadcast']] +
[['readonly','nbo','aligned']]*nargs,
order=kwargs.get('order','K'),
casting=kwargs.get('casting','safe'),
buffersize=kwargs.get('buffersize',0))
while not it.finished:
it[0] = lamdaexpr(*it[1:])
it.iternext()
return it.operands[0]
>>> a = np.arange(5)
>>> b = np.ones(5)
>>> luf(lambda i,j:i*i + j/2, a, b)
array([ 0.5, 1.5, 4.5, 9.5, 16.5])
If operand flags `"writeonly"` or `"readwrite"` are used the operands may
be views into the original data with the `WRITEBACKIFCOPY` flag. In this case
nditer must be used as a context manager or the nditer.close
method must be called before using the result. The temporary
data will be written back to the original data when the `__exit__`
function is called but not before:
>>> a = np.arange(6, dtype='i4')[::-2]
>>> with nditer(a, [],
... [['writeonly', 'updateifcopy']],
... casting='unsafe',
... op_dtypes=[np.dtype('f4')]) as i:
... x = i.operands[0]
... x[:] = [-1, -2, -3]
... # a still unchanged here
>>> a, x
array([-1, -2, -3]), array([-1, -2, -3])
It is important to note that once the iterator is exited, dangling
references (like `x` in the example) may or may not share data with
the original data `a`. If writeback semantics were active, i.e. if
`x.base.flags.writebackifcopy` is `True`, then exiting the iterator
will sever the connection between `x` and `a`, writing to `x` will
no longer write to `a`. If writeback semantics are not active, then
`x.data` will still point at some part of `a.data`, and writing to
one will affect the other.
""")
# nditer methods
add_newdoc('numpy.core', 'nditer', ('copy',
"""
copy()
Get a copy of the iterator in its current state.
Examples
--------
>>> x = np.arange(10)
>>> y = x + 1
>>> it = np.nditer([x, y])
>>> it.next()
(array(0), array(1))
>>> it2 = it.copy()
>>> it2.next()
(array(1), array(2))
"""))
add_newdoc('numpy.core', 'nditer', ('operands',
"""
operands[`Slice`]
The array(s) to be iterated over. Valid only before the iterator is closed.
"""))
add_newdoc('numpy.core', 'nditer', ('debug_print',
"""
debug_print()
Print the current state of the `nditer` instance and debug info to stdout.
"""))
add_newdoc('numpy.core', 'nditer', ('enable_external_loop',
"""
enable_external_loop()
When the "external_loop" was not used during construction, but
is desired, this modifies the iterator to behave as if the flag
was specified.
"""))
add_newdoc('numpy.core', 'nditer', ('iternext',
"""
iternext()
Check whether iterations are left, and perform a single internal iteration
without returning the result. Used in the C-style pattern do-while
pattern. For an example, see `nditer`.
Returns
-------
iternext : bool
Whether or not there are iterations left.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_axis',
"""
remove_axis(i)
Removes axis `i` from the iterator. Requires that the flag "multi_index"
be enabled.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_multi_index',
"""
remove_multi_index()
When the "multi_index" flag was specified, this removes it, allowing
the internal iteration structure to be optimized further.
"""))
add_newdoc('numpy.core', 'nditer', ('reset',
"""
reset()
Reset the iterator to its initial state.
"""))
add_newdoc('numpy.core', 'nested_iters',
"""
Create nditers for use in nested loops
Create a tuple of `nditer` objects which iterate in nested loops over
different axes of the op argument. The first iterator is used in the
outermost loop, the last in the innermost loop. Advancing one will change
the subsequent iterators to point at its new element.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
axes : list of list of int
Each item is used as an "op_axes" argument to an nditer
flags, op_flags, op_dtypes, order, casting, buffersize (optional)
See `nditer` parameters of the same name
Returns
-------
iters : tuple of nditer
An nditer for each item in `axes`, outermost first
See Also
--------
nditer
Examples
--------
Basic usage. Note how y is the "flattened" version of
[a[:, 0, :], a[:, 1, 0], a[:, 2, :]] since we specified
the first iter's axes as [1]
>>> a = np.arange(12).reshape(2, 3, 2)
>>> i, j = np.nested_iters(a, [[1], [0, 2]], flags=["multi_index"])
>>> for x in i:
... print(i.multi_index)
... for y in j:
... print('', j.multi_index, y)
(0,)
(0, 0) 0
(0, 1) 1
(1, 0) 6
(1, 1) 7
(1,)
(0, 0) 2
(0, 1) 3
(1, 0) 8
(1, 1) 9
(2,)
(0, 0) 4
(0, 1) 5
(1, 0) 10
(1, 1) 11
""")
add_newdoc('numpy.core', 'nditer', ('close',
"""
close()
Resolve all writeback semantics in writeable operands.
See Also
--------
:ref:`nditer-context-manager`
"""))
###############################################################################
#
# broadcast
#
###############################################################################
add_newdoc('numpy.core', 'broadcast',
"""
Produce an object that mimics broadcasting.
Parameters
----------
in1, in2, ... : array_like
Input parameters.
Returns
-------
b : broadcast object
Broadcast the input parameters against one another, and
return an object that encapsulates the result.
Amongst others, it has ``shape`` and ``nd`` properties, and
may be used as an iterator.
See Also
--------
broadcast_arrays
broadcast_to
Examples
--------
Manually adding two vectors, using broadcasting:
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[ 5., 6., 7.],
[ 6., 7., 8.],
[ 7., 8., 9.]])
Compare against built-in broadcasting:
>>> x + y
array([[5, 6, 7],
[6, 7, 8],
[7, 8, 9]])
""")
# attributes
add_newdoc('numpy.core', 'broadcast', ('index',
"""
current index in broadcasted result
Examples
--------
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (1, 5), (1, 6))
>>> b.index
3
"""))
add_newdoc('numpy.core', 'broadcast', ('iters',
"""
tuple of iterators along ``self``'s "components."
Returns a tuple of `numpy.flatiter` objects, one for each "component"
of ``self``.
See Also
--------
numpy.flatiter
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> row, col = b.iters
>>> row.next(), col.next()
(1, 4)
"""))
add_newdoc('numpy.core', 'broadcast', ('ndim',
"""
Number of dimensions of broadcasted result. Alias for `nd`.
.. versionadded:: 1.12.0
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.ndim
2
"""))
add_newdoc('numpy.core', 'broadcast', ('nd',
"""
Number of dimensions of broadcasted result. For code intended for NumPy
1.12.0 and later the more consistent `ndim` is preferred.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.nd
2
"""))
add_newdoc('numpy.core', 'broadcast', ('numiter',
"""
Number of iterators possessed by the broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.numiter
2
"""))
add_newdoc('numpy.core', 'broadcast', ('shape',
"""
Shape of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.shape
(3, 3)
"""))
add_newdoc('numpy.core', 'broadcast', ('size',
"""
Total size of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.size
9
"""))
add_newdoc('numpy.core', 'broadcast', ('reset',
"""
reset()
Reset the broadcasted result's iterator(s).
Parameters
----------
None
Returns
-------
None
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]]
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (2, 4), (3, 4))
>>> b.index
3
>>> b.reset()
>>> b.index
0
"""))
###############################################################################
#
# numpy functions
#
###############################################################################
add_newdoc('numpy.core.multiarray', 'array',
"""
array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
Create an array.
Parameters
----------
object : array_like
An array, any object exposing the array interface, an object whose
__array__ method returns an array, or any (nested) sequence.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence. This argument can only be used to 'upcast' the array. For
downcasting, use the .astype(t) method.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy will
only be made if __array__ returns a copy, if obj is a nested sequence,
or if a copy is needed to satisfy any of the other requirements
(`dtype`, `order`, etc.).
order : {'K', 'A', 'C', 'F'}, optional
Specify the memory layout of the array. If object is not an array, the
newly created array will be in C order (row major) unless 'F' is
specified, in which case it will be in Fortran order (column major).
If object is an array the following holds.
===== ========= ===================================================
order no copy copy=True
===== ========= ===================================================
'K' unchanged F & C order preserved, otherwise most similar order
'A' unchanged F order if input is F and not C, otherwise C order
'C' C order C order
'F' F order F order
===== ========= ===================================================
When ``copy=False`` and a copy is made for other reasons, the result is
the same as if ``copy=True``, with some exceptions for `A`, see the
Notes section. The default order is 'K'.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned array will be forced to be a base-class array (default).
ndmin : int, optional
Specifies the minimum number of dimensions that the resulting
array should have. Ones will be pre-pended to the shape as
needed to meet this requirement.
Returns
-------
out : ndarray
An array object satisfying the specified requirements.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Notes
-----
When order is 'A' and `object` is an array in neither 'C' nor 'F' order,
and a copy is forced by a change in dtype, then the order of the result is
not necessarily 'C' as expected. This is likely a bug.
Examples
--------
>>> np.array([1, 2, 3])
array([1, 2, 3])
Upcasting:
>>> np.array([1, 2, 3.0])
array([ 1., 2., 3.])
More than one dimension:
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
Minimum dimensions 2:
>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])
Type provided:
>>> np.array([1, 2, 3], dtype=complex)
array([ 1.+0.j, 2.+0.j, 3.+0.j])
Data-type consisting of more than one element:
>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
>>> x['a']
array([1, 3])
Creating an array from sub-classes:
>>> np.array(np.mat('1 2; 3 4'))
array([[1, 2],
[3, 4]])
>>> np.array(np.mat('1 2; 3 4'), subok=True)
matrix([[1, 2],
[3, 4]])
""")
add_newdoc('numpy.core.multiarray', 'empty',
"""
empty(shape, dtype=float, order='C')
Return a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
Desired output data-type for the array, e.g, `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: 'C'
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data of the given shape, dtype, and
order. Object arrays will be initialized to None.
See Also
--------
empty_like : Return an empty array with shape and type of input.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
full : Return a new array of given shape filled with value.
Notes
-----
`empty`, unlike `zeros`, does not set the array values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> np.empty([2, 2])
array([[ -9.74499359e+001, 6.69583040e-309],
[ 2.13182611e-314, 3.06959433e-309]]) #random
>>> np.empty([2, 2], dtype=int)
array([[-1073741821, -1067949133],
[ 496041986, 19249760]]) #random
""")
add_newdoc('numpy.core.multiarray', 'empty_like',
"""
empty_like(prototype, dtype=None, order='K', subok=True)
Return a new array with the same shape and type as a given array.
Parameters
----------
prototype : array_like
The shape and data-type of `prototype` define these same attributes
of the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran
contiguous, 'C' otherwise. 'K' means match the layout of ``prototype``
as closely as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data with the same
shape and type as `prototype`.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
full_like : Return a new array with shape of input filled with value.
empty : Return a new uninitialized array.
Notes
-----
This function does *not* initialize the returned array; to do that use
`zeros_like` or `ones_like` instead. It may be marginally faster than
the functions that do set the array values.
Examples
--------
>>> a = ([1,2,3], [4,5,6]) # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821, 3], #random
[ 0, 0, -1073741821]])
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random
[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
""")
add_newdoc('numpy.core.multiarray', 'scalar',
"""
scalar(dtype, obj)
Return a new scalar array of the given type initialized with obj.
This function is meant mainly for pickle support. `dtype` must be a
valid data-type descriptor. If `dtype` corresponds to an object
descriptor, then `obj` can be any object, otherwise `obj` must be a
string. If `obj` is not given, it will be interpreted as None for object
type and as zeros for all other types.
""")
add_newdoc('numpy.core.multiarray', 'zeros',
"""
zeros(shape, dtype=float, order='C')
Return a new array of given shape and type, filled with zeros.
Parameters
----------
shape : int or tuple of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional, default: 'C'
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : ndarray
Array of zeros with the given shape, dtype, and order.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
full : Return a new array of given shape filled with value.
Examples
--------
>>> np.zeros(5)
array([ 0., 0., 0., 0., 0.])
>>> np.zeros((5,), dtype=int)
array([0, 0, 0, 0, 0])
>>> np.zeros((2, 1))
array([[ 0.],
[ 0.]])
>>> s = (2,2)
>>> np.zeros(s)
array([[ 0., 0.],
[ 0., 0.]])
>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
dtype=[('x', '<i4'), ('y', '<i4')])
""")
add_newdoc('numpy.core.multiarray', 'set_typeDict',
"""set_typeDict(dict)
Set the internal dictionary that can look up an array type using a
registered code.
""")
add_newdoc('numpy.core.multiarray', 'fromstring',
"""
fromstring(string, dtype=float, count=-1, sep='')
A new 1-D array initialized from text data in a string.
Parameters
----------
string : str
A string containing the data.
dtype : data-type, optional
The data type of the array; default: float. For binary input data,
the data must be in exactly this format.
count : int, optional
Read this number of `dtype` elements from the data. If this is
negative (the default), the count will be determined from the
length of the data.
sep : str, optional
The string separating numbers in the data; extra whitespace between
elements is also ignored.
.. deprecated:: 1.14
If this argument is not provided, `fromstring` falls back on the
behaviour of `frombuffer` after encoding unicode string inputs as
either utf-8 (python 3), or the default encoding (python 2).
Returns
-------
arr : ndarray
The constructed array.
Raises
------
ValueError
If the string is not the correct size to satisfy the requested
`dtype` and `count`.
See Also
--------
frombuffer, fromfile, fromiter
Examples
--------
>>> np.fromstring('1 2', dtype=int, sep=' ')
array([1, 2])
>>> np.fromstring('1, 2', dtype=int, sep=',')
array([1, 2])
""")
add_newdoc('numpy.core.multiarray', 'fromiter',
"""
fromiter(iterable, dtype, count=-1)
Create a new 1-dimensional array from an iterable object.
Parameters
----------
iterable : iterable object
An iterable object providing data for the array.
dtype : data-type
The data-type of the returned array.
count : int, optional
The number of items to read from *iterable*. The default is -1,
which means all data is read.
Returns
-------
out : ndarray
The output array.
Notes
-----
Specify `count` to improve performance. It allows ``fromiter`` to
pre-allocate the output array, instead of resizing it on demand.
Examples
--------
>>> iterable = (x*x for x in range(5))
>>> np.fromiter(iterable, float)
array([ 0., 1., 4., 9., 16.])
""")
add_newdoc('numpy.core.multiarray', 'fromfile',
"""
fromfile(file, dtype=float, count=-1, sep='')
Construct an array from data in a text or binary file.
A highly efficient way of reading binary data with a known data-type,
as well as parsing simply formatted text files. Data written using the
`tofile` method can be read using this function.
Parameters
----------
file : file or str
Open file object or filename.
dtype : data-type
Data type of the returned array.
For binary files, it is used to determine the size and byte-order
of the items in the file.
count : int
Number of items to read. ``-1`` means all items (i.e., the complete
file).
sep : str
Separator between items if file is a text file.
Empty ("") separator means the file should be treated as binary.
Spaces (" ") in the separator match zero or more whitespace characters.
A separator consisting only of spaces must match at least one
whitespace.
See also
--------
load, save
ndarray.tofile
loadtxt : More flexible way of loading data from a text file.
Notes
-----
Do not rely on the combination of `tofile` and `fromfile` for
data storage, as the binary files generated are are not platform
independent. In particular, no byte-order or data-type information is
saved. Data can be stored in the platform independent ``.npy`` format
using `save` and `load` instead.
Examples
--------
Construct an ndarray:
>>> dt = np.dtype([('time', [('min', int), ('sec', int)]),
... ('temp', float)])
>>> x = np.zeros((1,), dtype=dt)
>>> x['time']['min'] = 10; x['temp'] = 98.25
>>> x
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
Save the raw data to disk:
>>> import os
>>> fname = os.tmpnam()
>>> x.tofile(fname)
Read the raw data from disk:
>>> np.fromfile(fname, dtype=dt)
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
The recommended way to store and load data:
>>> np.save(fname, x)
>>> np.load(fname + '.npy')
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
""")
add_newdoc('numpy.core.multiarray', 'frombuffer',
"""
frombuffer(buffer, dtype=float, count=-1, offset=0)
Interpret a buffer as a 1-dimensional array.
Parameters
----------
buffer : buffer_like
An object that exposes the buffer interface.
dtype : data-type, optional
Data-type of the returned array; default: float.
count : int, optional
Number of items to read. ``-1`` means all data in the buffer.
offset : int, optional
Start reading the buffer from this offset (in bytes); default: 0.
Notes
-----
If the buffer has data that is not in machine byte-order, this should
be specified as part of the data-type, e.g.::
>>> dt = np.dtype(int)
>>> dt = dt.newbyteorder('>')
>>> np.frombuffer(buf, dtype=dt)
The data of the resulting array will not be byteswapped, but will be
interpreted correctly.
Examples
--------
>>> s = 'hello world'
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
array(['w', 'o', 'r', 'l', 'd'],
dtype='|S1')
>>> np.frombuffer(b'\\x01\\x02', dtype=np.uint8)
array([1, 2], dtype=uint8)
>>> np.frombuffer(b'\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)
array([1, 2, 3], dtype=uint8)
""")
add_newdoc('numpy.core.multiarray', 'concatenate',
"""
concatenate((a1, a2, ...), axis=0, out=None)
Join a sequence of arrays along an existing axis.
Parameters
----------
a1, a2, ... : sequence of array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. If axis is None,
arrays are flattened before use. Default is 0.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what concatenate would have returned if no
out argument were specified.
Returns
-------
res : ndarray
The concatenated array.
See Also
--------
ma.concatenate : Concatenate function that preserves input masks.
array_split : Split an array into multiple sub-arrays of equal or
near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
hsplit : Split array into multiple sub-arrays horizontally (column wise)
vsplit : Split array into multiple sub-arrays vertically (row wise)
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
stack : Stack a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise)
vstack : Stack arrays in sequence vertically (row wise)
dstack : Stack arrays in sequence depth wise (along third dimension)
Notes
-----
When one or more of the arrays to be concatenated is a MaskedArray,
this function will return a MaskedArray object instead of an ndarray,
but the input masks are *not* preserved. In cases where a MaskedArray
is expected as input, use the ma.concatenate function from the masked
array module instead.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])
This function will not preserve masking of MaskedArray inputs.
>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0 -- 2],
mask = [False True False],
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
mask = False,
fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0 -- 2 2 3 4],
mask = [False True False False False False],
fill_value = 999999)
""")
add_newdoc('numpy.core', 'inner',
"""
inner(a, b)
Inner product of two arrays.
Ordinary inner product of vectors for 1-D arrays (without complex
conjugation), in higher dimensions a sum product over the last axes.
Parameters
----------
a, b : array_like
If `a` and `b` are nonscalar, their last dimensions must match.
Returns
-------
out : ndarray
`out.shape = a.shape[:-1] + b.shape[:-1]`
Raises
------
ValueError
If the last dimension of `a` and `b` has different size.
See Also
--------
tensordot : Sum products over arbitrary axes.
dot : Generalised matrix product, using second last dimension of `b`.
einsum : Einstein summation convention.
Notes
-----
For vectors (1-D arrays) it computes the ordinary inner-product::
np.inner(a, b) = sum(a[:]*b[:])
More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
or explicitly::
np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
= sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
In addition `a` or `b` may be scalars, in which case::
np.inner(a,b) = a*b
Examples
--------
Ordinary inner product for vectors:
>>> a = np.array([1,2,3])
>>> b = np.array([0,1,0])
>>> np.inner(a, b)
2
A multidimensional example:
>>> a = np.arange(24).reshape((2,3,4))
>>> b = np.arange(4)
>>> np.inner(a, b)
array([[ 14, 38, 62],
[ 86, 110, 134]])
An example where `b` is a scalar:
>>> np.inner(np.eye(2), 7)
array([[ 7., 0.],
[ 0., 7.]])
""")
add_newdoc('numpy.core', 'fastCopyAndTranspose',
"""_fastCopyAndTranspose(a)""")
add_newdoc('numpy.core.multiarray', 'correlate',
"""cross_correlate(a,v, mode=0)""")
add_newdoc('numpy.core.multiarray', 'arange',
"""
arange([start,] stop[, step,], dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better to use ``linspace`` for these cases.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : number
End of interval. The interval does not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : number, optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` is specified as a position argument,
`start` must also be given.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
See Also
--------
linspace : Evenly spaced numbers with careful handling of endpoints.
ogrid: Arrays of evenly spaced numbers in N-dimensions.
mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3.0)
array([ 0., 1., 2.])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5])
""")
add_newdoc('numpy.core.multiarray', '_get_ndarray_c_version',
"""_get_ndarray_c_version()
Return the compile time NDARRAY_VERSION number.
""")
add_newdoc('numpy.core.multiarray', '_reconstruct',
"""_reconstruct(subtype, shape, dtype)
Construct an empty array. Used by Pickles.
""")
add_newdoc('numpy.core.multiarray', 'set_string_function',
"""
set_string_function(f, repr=1)
Internal method to set a function to be used when pretty printing arrays.
""")
add_newdoc('numpy.core.multiarray', 'set_numeric_ops',
"""
set_numeric_ops(op1=func1, op2=func2, ...)
Set numerical operators for array objects.
Parameters
----------
op1, op2, ... : callable
Each ``op = func`` pair describes an operator to be replaced.
For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
addition by modulus 5 addition.
Returns
-------
saved_ops : list of callables
A list of all operators, stored before making replacements.
Notes
-----
.. WARNING::
Use with care! Incorrect usage may lead to memory errors.
A function replacing an operator cannot make use of that operator.
For example, when replacing add, you may not use ``+``. Instead,
directly call ufuncs.
Examples
--------
>>> def add_mod5(x, y):
... return np.add(x, y) % 5
...
>>> old_funcs = np.set_numeric_ops(add=add_mod5)
>>> x = np.arange(12).reshape((3, 4))
>>> x + x
array([[0, 2, 4, 1],
[3, 0, 2, 4],
[1, 3, 0, 2]])
>>> ignore = np.set_numeric_ops(**old_funcs) # restore operators
""")
add_newdoc('numpy.core.multiarray', 'where',
"""
where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x`, `y` and `condition` need to be
broadcastable to some shape.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
Find the indices of elements of `x` that are in `goodvalues`.
>>> goodvalues = [3, 4, 7]
>>> ix = np.isin(x, goodvalues)
>>> ix
array([[False, False, False],
[ True, True, False],
[False, True, False]])
>>> np.where(ix)
(array([1, 1, 2]), array([0, 1, 1]))
""")
add_newdoc('numpy.core.multiarray', 'lexsort',
"""
lexsort(keys, axis=-1)
Perform an indirect stable sort using a sequence of keys.
Given multiple sorting keys, which can be interpreted as columns in a
spreadsheet, lexsort returns an array of integer indices that describes
the sort order by multiple columns. The last key in the sequence is used
for the primary sort order, the second-to-last key for the secondary sort
order, and so on. The keys argument must be a sequence of objects that
can be converted to arrays of the same shape. If a 2D array is provided
for the keys argument, it's rows are interpreted as the sorting keys and
sorting is according to the last row, second last row etc.
Parameters
----------
keys : (k, N) array or tuple containing k (N,)-shaped sequences
The `k` different "columns" to be sorted. The last column (or row if
`keys` is a 2D array) is the primary sort key.
axis : int, optional
Axis to be indirectly sorted. By default, sort over the last axis.
Returns
-------
indices : (N,) ndarray of ints
Array of indices that sort the keys along the specified axis.
See Also
--------
argsort : Indirect sort.
ndarray.sort : In-place sort.
sort : Return a sorted copy of an array.
Examples
--------
Sort names: first by surname, then by name.
>>> surnames = ('Hertz', 'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])
>>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
Sort two columns of numbers:
>>> a = [1,5,1,4,3,4,4] # First column
>>> b = [9,4,0,4,0,2,1] # Second column
>>> ind = np.lexsort((b,a)) # Sort by a, then by b
>>> print(ind)
[2 0 4 6 5 3 1]
>>> [(a[i],b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
Note that sorting is first according to the elements of ``a``.
Secondary sorting is according to the elements of ``b``.
A normal ``argsort`` would have yielded:
>>> [(a[i],b[i]) for i in np.argsort(a)]
[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
Structured arrays are sorted lexically by ``argsort``:
>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
... dtype=np.dtype([('x', int), ('y', int)]))
>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])
""")
add_newdoc('numpy.core.multiarray', 'can_cast',
"""
can_cast(from_, to, casting='safe')
Returns True if cast between data types can occur according to the
casting rule. If from is a scalar or array scalar, also returns
True if the scalar value can be cast without overflow or truncation
to an integer.
Parameters
----------
from_ : dtype, dtype specifier, scalar, or array
Data type, scalar, or array to cast from.
to : dtype or dtype specifier
Data type to cast to.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Returns
-------
out : bool
True if cast can occur according to the casting rule.
Notes
-----
Starting in NumPy 1.9, can_cast function now returns False in 'safe'
casting mode for integer/float dtype and string dtype if the string dtype
length is not long enough to store the max integer/float value converted
to a string. Previously can_cast in 'safe' mode returned True for
integer/float dtype and a string dtype of any length.
See also
--------
dtype, result_type
Examples
--------
Basic examples
>>> np.can_cast(np.int32, np.int64)
True
>>> np.can_cast(np.float64, complex)
True
>>> np.can_cast(complex, float)
False
>>> np.can_cast('i8', 'f8')
True
>>> np.can_cast('i8', 'f4')
False
>>> np.can_cast('i4', 'S4')
False
Casting scalars
>>> np.can_cast(100, 'i1')
True
>>> np.can_cast(150, 'i1')
False
>>> np.can_cast(150, 'u1')
True
>>> np.can_cast(3.5e100, np.float32)
False
>>> np.can_cast(1000.0, np.float32)
True
Array scalar checks the value, array does not
>>> np.can_cast(np.array(1000.0), np.float32)
True
>>> np.can_cast(np.array([1000.0]), np.float32)
False
Using the casting rules
>>> np.can_cast('i8', 'i8', 'no')
True
>>> np.can_cast('<i8', '>i8', 'no')
False
>>> np.can_cast('<i8', '>i8', 'equiv')
True
>>> np.can_cast('<i4', '>i8', 'equiv')
False
>>> np.can_cast('<i4', '>i8', 'safe')
True
>>> np.can_cast('<i8', '>i4', 'safe')
False
>>> np.can_cast('<i8', '>i4', 'same_kind')
True
>>> np.can_cast('<i8', '>u4', 'same_kind')
False
>>> np.can_cast('<i8', '>u4', 'unsafe')
True
""")
add_newdoc('numpy.core.multiarray', 'promote_types',
"""
promote_types(type1, type2)
Returns the data type with the smallest size and smallest scalar
kind to which both ``type1`` and ``type2`` may be safely cast.
The returned data type is always in native byte order.
This function is symmetric, but rarely associative.
Parameters
----------
type1 : dtype or dtype specifier
First data type.
type2 : dtype or dtype specifier
Second data type.
Returns
-------
out : dtype
The promoted data type.
Notes
-----
.. versionadded:: 1.6.0
Starting in NumPy 1.9, promote_types function now returns a valid string
length when given an integer or float dtype as one argument and a string
dtype as another argument. Previously it always returned the input string
dtype, even if it wasn't long enough to store the max integer/float value
converted to a string.
See Also
--------
result_type, dtype, can_cast
Examples
--------
>>> np.promote_types('f4', 'f8')
dtype('float64')
>>> np.promote_types('i8', 'f4')
dtype('float64')
>>> np.promote_types('>i8', '<c8')
dtype('complex128')
>>> np.promote_types('i4', 'S8')
dtype('S11')
An example of a non-associative case:
>>> p = np.promote_types
>>> p('S', p('i1', 'u1'))
dtype('S6')
>>> p(p('S', 'i1'), 'u1')
dtype('S4')
""")
add_newdoc('numpy.core.multiarray', 'min_scalar_type',
"""
min_scalar_type(a)
For scalar ``a``, returns the data type with the smallest size
and smallest scalar kind which can hold its value. For non-scalar
array ``a``, returns the vector's dtype unmodified.
Floating point values are not demoted to integers,
and complex values are not demoted to floats.
Parameters
----------
a : scalar or array_like
The value whose minimal data type is to be found.
Returns
-------
out : dtype
The minimal data type.
Notes
-----
.. versionadded:: 1.6.0
See Also
--------
result_type, promote_types, dtype, can_cast
Examples
--------
>>> np.min_scalar_type(10)
dtype('uint8')
>>> np.min_scalar_type(-260)
dtype('int16')
>>> np.min_scalar_type(3.1)
dtype('float16')
>>> np.min_scalar_type(1e50)
dtype('float64')
>>> np.min_scalar_type(np.arange(4,dtype='f8'))
dtype('float64')
""")
add_newdoc('numpy.core.multiarray', 'result_type',
"""
result_type(*arrays_and_dtypes)
Returns the type that results from applying the NumPy
type promotion rules to the arguments.
Type promotion in NumPy works similarly to the rules in languages
like C++, with some slight differences. When both scalars and
arrays are used, the array's type takes precedence and the actual value
of the scalar is taken into account.
For example, calculating 3*a, where a is an array of 32-bit floats,
intuitively should result in a 32-bit float output. If the 3 is a
32-bit integer, the NumPy rules indicate it can't convert losslessly
into a 32-bit float, so a 64-bit float should be the result type.
By examining the value of the constant, '3', we see that it fits in
an 8-bit integer, which can be cast losslessly into the 32-bit float.
Parameters
----------
arrays_and_dtypes : list of arrays and dtypes
The operands of some operation whose result type is needed.
Returns
-------
out : dtype
The result type.
See also
--------
dtype, promote_types, min_scalar_type, can_cast
Notes
-----
.. versionadded:: 1.6.0
The specific algorithm used is as follows.
Categories are determined by first checking which of boolean,
integer (int/uint), or floating point (float/complex) the maximum
kind of all the arrays and the scalars are.
If there are only scalars or the maximum category of the scalars
is higher than the maximum category of the arrays,
the data types are combined with :func:`promote_types`
to produce the return value.
Otherwise, `min_scalar_type` is called on each array, and
the resulting data types are all combined with :func:`promote_types`
to produce the return value.
The set of int values is not a subset of the uint values for types
with the same number of bits, something not reflected in
:func:`min_scalar_type`, but handled as a special case in `result_type`.
Examples
--------
>>> np.result_type(3, np.arange(7, dtype='i1'))
dtype('int8')
>>> np.result_type('i4', 'c8')
dtype('complex128')
>>> np.result_type(3.0, -2)
dtype('float64')
""")
add_newdoc('numpy.core.multiarray', 'newbuffer',
"""
newbuffer(size)
Return a new uninitialized buffer object.
Parameters
----------
size : int
Size in bytes of returned buffer object.
Returns
-------
newbuffer : buffer object
Returned, uninitialized buffer object of `size` bytes.
""")
add_newdoc('numpy.core.multiarray', 'getbuffer',
"""
getbuffer(obj [,offset[, size]])
Create a buffer object from the given object referencing a slice of
length size starting at offset.
Default is the entire buffer. A read-write buffer is attempted followed
by a read-only buffer.
Parameters
----------
obj : object
offset : int, optional
size : int, optional
Returns
-------
buffer_obj : buffer
Examples
--------
>>> buf = np.getbuffer(np.ones(5), 1, 3)
>>> len(buf)
3
>>> buf[0]
'\\x00'
>>> buf
<read-write buffer for 0x8af1e70, size 3, offset 1 at 0x8ba4ec0>
""")
add_newdoc('numpy.core', 'dot',
"""
dot(a, b, out=None)
Dot product of two arrays. Specifically,
- If both `a` and `b` are 1-D arrays, it is inner product of vectors
(without complex conjugation).
- If both `a` and `b` are 2-D arrays, it is matrix multiplication,
but using :func:`matmul` or ``a @ b`` is preferred.
- If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
- If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
the last axis of `a` and `b`.
- If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
sum product over the last axis of `a` and the second-to-last axis of `b`::
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned
if it was not used. In particular, it must have the right type, must be
C-contiguous, and its dtype must be the dtype that would be returned
for `dot(a,b)`. This is a performance feature. Therefore, if these
conditions are not met, an exception is raised, instead of attempting
to be flexible.
Returns
-------
output : ndarray
Returns the dot product of `a` and `b`. If `a` and `b` are both
scalars or both 1-D arrays then a scalar is returned; otherwise
an array is returned.
If `out` is given, then it is returned.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
See Also
--------
vdot : Complex-conjugating dot product.
tensordot : Sum products over arbitrary axes.
einsum : Einstein summation convention.
matmul : '@' operator as method with out parameter.
Examples
--------
>>> np.dot(3, 4)
12
Neither argument is complex-conjugated:
>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)
For 2-D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128
""")
add_newdoc('numpy.core', 'matmul',
"""
matmul(a, b, out=None)
Matrix product of two arrays.
The behavior depends on the arguments in the following way.
- If both arguments are 2-D they are multiplied like conventional
matrices.
- If either argument is N-D, N > 2, it is treated as a stack of
matrices residing in the last two indexes and broadcast accordingly.
- If the first argument is 1-D, it is promoted to a matrix by
prepending a 1 to its dimensions. After matrix multiplication
the prepended 1 is removed.
- If the second argument is 1-D, it is promoted to a matrix by
appending a 1 to its dimensions. After matrix multiplication
the appended 1 is removed.
Multiplication by a scalar is not allowed, use ``*`` instead. Note that
multiplying a stack of matrices with a vector will result in a stack of
vectors, but matmul will not recognize it as such.
``matmul`` differs from ``dot`` in two important ways.
- Multiplication by scalars is not allowed.
- Stacks of matrices are broadcast together as if the matrices
were elements.
.. warning::
This function is preliminary and included in NumPy 1.10.0 for testing
and documentation. Its semantics will not change, but the number and
order of the optional arguments will.
.. versionadded:: 1.10.0
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned
if it was not used. In particular, it must have the right type, must be
C-contiguous, and its dtype must be the dtype that would be returned
for `dot(a,b)`. This is a performance feature. Therefore, if these
conditions are not met, an exception is raised, instead of attempting
to be flexible.
Returns
-------
output : ndarray
Returns the dot product of `a` and `b`. If `a` and `b` are both
1-D arrays then a scalar is returned; otherwise an array is
returned. If `out` is given, then it is returned.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
If scalar value is passed.
See Also
--------
vdot : Complex-conjugating dot product.
tensordot : Sum products over arbitrary axes.
einsum : Einstein summation convention.
dot : alternative matrix product with different broadcasting rules.
Notes
-----
The matmul function implements the semantics of the `@` operator introduced
in Python 3.5 following PEP465.
Examples
--------
For 2-D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.matmul(a, b)
array([[4, 1],
[2, 2]])
For 2-D mixed with 1-D, the result is the usual.
>>> a = [[1, 0], [0, 1]]
>>> b = [1, 2]
>>> np.matmul(a, b)
array([1, 2])
>>> np.matmul(b, a)
array([1, 2])
Broadcasting is conventional for stacks of arrays
>>> a = np.arange(2*2*4).reshape((2,2,4))
>>> b = np.arange(2*2*4).reshape((2,4,2))
>>> np.matmul(a,b).shape
(2, 2, 2)
>>> np.matmul(a,b)[0,1,1]
98
>>> sum(a[0,1,:] * b[0,:,1])
98
Vector, vector returns the scalar inner product, but neither argument
is complex-conjugated:
>>> np.matmul([2j, 3j], [2j, 3j])
(-13+0j)
Scalar multiplication raises an error.
>>> np.matmul([1,2], 3)
Traceback (most recent call last):
...
ValueError: Scalar operands are not allowed, use '*' instead
""")
add_newdoc('numpy.core', 'c_einsum',
"""
c_einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe')
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional
array operations can be represented in a simple fashion. This function
provides a way to compute such summations. The best way to understand this
function is to try the examples below, which show how many common NumPy
functions can be implemented as calls to `einsum`.
This is the core C function.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
operands : list of array_like
These are the arrays for the operation.
out : ndarray, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum, dot, inner, outer, tensordot
Notes
-----
.. versionadded:: 1.6.0
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Repeated subscripts labels in one operand take the diagonal. For example,
``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.
Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to ``np.inner(a,b)``. If a label appears only once,
it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``
with no changes.
The order of labels in the output is by default alphabetical. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose.
The output can be controlled by specifying output subscript labels
as well. This specifies the label order, and allows summing to
be disallowed or forced when desired. The call ``np.einsum('i->', a)``
is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``
is like ``np.diag(a)``. The difference is that `einsum` does not
allow broadcasting by default.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, you can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view.
An alternative way to provide the subscripts and operands is as
``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples
below have corresponding `einsum` calls with the two parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as ``np.swapaxes(a, 0, 2)`` and
``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> c.T
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum('i...->...', a)
array([50, 55, 60, 65, 70])
>>> np.einsum(a, [0,Ellipsis], [Ellipsis])
array([50, 55, 60, 65, 70])
>>> np.sum(a, axis=0)
array([50, 55, 60, 65, 70])
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> # since version 1.10.0
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
""")
add_newdoc('numpy.core', 'vdot',
"""
vdot(a, b)
Return the dot product of two vectors.
The vdot(`a`, `b`) function handles complex numbers differently than
dot(`a`, `b`). If the first argument is complex the complex conjugate
of the first argument is used for the calculation of the dot product.
Note that `vdot` handles multidimensional arrays differently than `dot`:
it does *not* perform a matrix product, but flattens input arguments
to 1-D vectors first. Consequently, it should only be used for vectors.
Parameters
----------
a : array_like
If `a` is complex the complex conjugate is taken before calculation
of the dot product.
b : array_like
Second argument to the dot product.
Returns
-------
output : ndarray
Dot product of `a` and `b`. Can be an int, float, or
complex depending on the types of `a` and `b`.
See Also
--------
dot : Return the dot product without using the complex conjugate of the
first argument.
Examples
--------
>>> a = np.array([1+2j,3+4j])
>>> b = np.array([5+6j,7+8j])
>>> np.vdot(a, b)
(70-8j)
>>> np.vdot(b, a)
(70+8j)
Note that higher-dimensional arrays are flattened!
>>> a = np.array([[1, 4], [5, 6]])
>>> b = np.array([[4, 1], [2, 2]])
>>> np.vdot(a, b)
30
>>> np.vdot(b, a)
30
>>> 1*4 + 4*1 + 5*2 + 6*2
30
""")
##############################################################################
#
# Documentation for ndarray attributes and methods
#
##############################################################################
##############################################################################
#
# ndarray object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray',
"""
ndarray(shape, dtype=float, buffer=None, offset=0,
strides=None, order=None)
An array object represents a multidimensional, homogeneous array
of fixed-size items. An associated data-type object describes the
format of each element in the array (its byte-order, how many bytes it
occupies in memory, whether it is an integer, a floating point number,
or something else, etc.)
Arrays should be constructed using `array`, `zeros` or `empty` (refer
to the See Also section below). The parameters given here refer to
a low-level method (`ndarray(...)`) for instantiating an array.
For more information, refer to the `numpy` module and examine the
methods and attributes of an array.
Parameters
----------
(for the __new__ method; see Notes below)
shape : tuple of ints
Shape of created array.
dtype : data-type, optional
Any object that can be interpreted as a numpy data type.
buffer : object exposing buffer interface, optional
Used to fill the array with data.
offset : int, optional
Offset of array data in buffer.
strides : tuple of ints, optional
Strides of data in memory.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Attributes
----------
T : ndarray
Transpose of the array.
data : buffer
The array's elements, in memory.
dtype : dtype object
Describes the format of the elements in the array.
flags : dict
Dictionary containing information related to memory use, e.g.,
'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
flat : numpy.flatiter object
Flattened version of the array as an iterator. The iterator
allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
assignment examples; TODO).
imag : ndarray
Imaginary part of the array.
real : ndarray
Real part of the array.
size : int
Number of elements in the array.
itemsize : int
The memory use of each array element in bytes.
nbytes : int
The total number of bytes required to store the array data,
i.e., ``itemsize * size``.
ndim : int
The array's number of dimensions.
shape : tuple of ints
Shape of the array.
strides : tuple of ints
The step-size required to move from one element to the next in
memory. For example, a contiguous ``(3, 4)`` array of type
``int16`` in C-order has strides ``(8, 2)``. This implies that
to move from element to element in memory requires jumps of 2 bytes.
To move from row-to-row, one needs to jump 8 bytes at a time
(``2 * 4``).
ctypes : ctypes object
Class containing properties of the array needed for interaction
with ctypes.
base : ndarray
If the array is a view into another array, that array is its `base`
(unless that array is also a view). The `base` array is where the
array data is actually stored.
See Also
--------
array : Construct an array.
zeros : Create an array, each element of which is zero.
empty : Create an array, but leave its allocated memory unchanged (i.e.,
it contains "garbage").
dtype : Create a data-type.
Notes
-----
There are two modes of creating an array using ``__new__``:
1. If `buffer` is None, then only `shape`, `dtype`, and `order`
are used.
2. If `buffer` is an object exposing the buffer interface, then
all keywords are interpreted.
No ``__init__`` method is needed because the array is fully initialized
after the ``__new__`` method.
Examples
--------
These examples illustrate the low-level `ndarray` constructor. Refer
to the `See Also` section above for easier ways of constructing an
ndarray.
First mode, `buffer` is None:
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[ -1.13698227e+002, 4.25087011e-303],
[ 2.88528414e-306, 3.27025015e-309]]) #random
Second mode:
>>> np.ndarray((2,), buffer=np.array([1,2,3]),
... offset=np.int_().itemsize,
... dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])
""")
##############################################################################
#
# ndarray attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_interface__',
"""Array protocol: Python side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_finalize__',
"""None."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__',
"""Array priority."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__',
"""Array protocol: C-struct side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('_as_parameter_',
"""Allow the array to be interpreted as a ctypes object by returning the
data-memory location as an integer
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('base',
"""
Base object if memory is from some other object.
Examples
--------
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
"""
An object to simplify the interaction of the array with the ctypes
module.
This attribute creates an object that makes it easier to use arrays
when calling shared libraries with the ctypes module. The returned
object has, among others, data, shape, and strides attributes (see
Notes below) which themselves return ctypes objects that can be used
as arguments to a shared library.
Parameters
----------
None
Returns
-------
c : Python object
Possessing attributes data, shape, strides, etc.
See Also
--------
numpy.ctypeslib
Notes
-----
Below are the public attributes of this object which were documented
in "Guide to NumPy" (we have omitted undocumented public attributes,
as well as documented private attributes):
* data: A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as self._array_interface_['data'][0].
* shape (c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to dtype('p') on this
platform. This base-type could be c_int, c_long, or c_longlong
depending on the platform. The c_intp type is defined accordingly in
numpy.ctypeslib. The ctypes array contains the shape of the underlying
array.
* strides (c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
* data_as(obj): Return the data pointer cast to a particular c-types object.
For example, calling self._as_parameter_ is equivalent to
self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
self.data_as(ctypes.POINTER(ctypes.c_double)).
* shape_as(obj): Return the shape tuple as an array of some other c-types
type. For example: self.shape_as(ctypes.c_short).
* strides_as(obj): Return the strides tuple as an array of some other
c-types type. For example: self.strides_as(ctypes.c_longlong).
Be careful using the ctypes attribute - especially on temporary
arrays or arrays constructed on the fly. For example, calling
``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory
that is invalid because the array created as (a+b) is deallocated
before the next Python statement. You can avoid this problem using
either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will
hold a reference to the array until ct is deleted or re-assigned.
If the ctypes module is not available, then the ctypes attribute
of array objects still returns something useful, but ctypes objects
are not returned and errors may be raised instead. In particular,
the object will still have the as parameter attribute which will
return an integer equal to the data attribute.
Examples
--------
>>> import ctypes
>>> x
array([[0, 1],
[2, 3]])
>>> x.ctypes.data
30439712
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))
<ctypes.LP_c_long object at 0x01F01300>
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents
c_long(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents
c_longlong(4294967296L)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x01FFD580>
>>> x.ctypes.shape_as(ctypes.c_long)
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides_as(ctypes.c_longlong)
<numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
"""Python buffer object pointing to the start of the array's data."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dtype',
"""
Data-type of the array's elements.
Parameters
----------
None
Returns
-------
d : numpy dtype object
See Also
--------
numpy.dtype
Examples
--------
>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
"""
The imaginary part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64')
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
"""
Length of one array element in bytes.
Examples
--------
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
"""
Information about the memory layout of the array.
Attributes
----------
C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment.
F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment.
OWNDATA (O)
The array owns the memory it uses or borrows it from another object.
WRITEABLE (W)
The data area can be written to. Setting this to False locks
the data, making it read-only. A view (slice, etc.) inherits WRITEABLE
from its base array at creation time, but a view of a writeable
array may be subsequently locked while the base array remains writeable.
(The opposite is not true, in that a view of a locked array may not
be made writeable. However, currently, locking a base object does not
lock any views that already reference it, so under that circumstance it
is possible to alter the contents of a locked array via a previously
created writeable view onto it.) Attempting to change a non-writeable
array raises a RuntimeError exception.
ALIGNED (A)
The data and all elements are aligned appropriately for the hardware.
WRITEBACKIFCOPY (X)
This array is a copy of some other array. The C-API function
PyArray_ResolveWritebackIfCopy must be called before deallocating
to the base array will be updated with the contents of this array.
UPDATEIFCOPY (U)
(Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.
When this array is
deallocated, the base array will be updated with the contents of
this array.
FNC
F_CONTIGUOUS and not C_CONTIGUOUS.
FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
BEHAVED (B)
ALIGNED and WRITEABLE.
CARRAY (CA)
BEHAVED and C_CONTIGUOUS.
FARRAY (FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
-----
The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),
or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag
names are only supported in dictionary access.
Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be
changed by the user, via direct assignment to the attribute or dictionary
entry, or by calling `ndarray.setflags`.
The array flags cannot be set arbitrarily:
- UPDATEIFCOPY can only be set ``False``.
- WRITEBACKIFCOPY can only be set ``False``.
- ALIGNED can only be set ``True`` if the data is truly aligned.
- WRITEABLE can only be set ``True`` if the array owns its own memory
or the ultimate owner of the memory exposes a writeable buffer
interface or is a string.
Arrays can be both C-style and Fortran-style contiguous simultaneously.
This is clear for 1-dimensional arrays, but can also be true for higher
dimensional arrays.
Even for contiguous arrays a stride for a given dimension
``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``
or the array has no elements.
It does *not* generally hold that ``self.strides[-1] == self.itemsize``
for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for
Fortran-style contiguous arrays is true.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
"""
A 1-D iterator over the array.
This is a `numpy.flatiter` instance, which acts similarly to, but is not
a subclass of, Python's built-in iterator object.
See Also
--------
flatten : Return a copy of the array collapsed into one dimension.
flatiter
Examples
--------
>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<type 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
"""
Total bytes consumed by the elements of the array.
Notes
-----
Does not include memory consumed by non-element attributes of the
array object.
Examples
--------
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
"""
Number of array dimensions.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
"""
The real part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1. , 0.70710678])
>>> x.real.dtype
dtype('float64')
See Also
--------
numpy.real : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
"""
Tuple of array dimensions.
The shape property is usually used to get the current shape of an array,
but may also be used to reshape the array in-place by assigning a tuple of
array dimensions to it. As with `numpy.reshape`, one of the new shape
dimensions can be -1, in which case its value is inferred from the size of
the array and the remaining dimensions. Reshaping an array in-place will
fail if a copy is required.
Examples
--------
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: incompatible shape for a non-contiguous array
See Also
--------
numpy.reshape : similar function
ndarray.reshape : similar method
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
"""
Number of elements in the array.
Equal to ``np.prod(a.shape)``, i.e., the product of the array's
dimensions.
Notes
-----
`a.size` returns a standard arbitrary precision Python integer. This
may not be the case with other methods of obtaining the same value
(like the suggested ``np.prod(a.shape)``, which returns an instance
of ``np.int_``), and may be relevant if the value is used further in
calculations that may overflow a fixed size integer type.
Examples
--------
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
"""
Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
is::
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the
"ndarray.rst" file in the NumPy reference guide.
Notes
-----
Imagine an array of 32-bit integers (each 4 bytes)::
x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other
(known as a contiguous block of memory). The strides of an array tell
us how many bytes we have to skip in memory to move to the next position
along a certain axis. For example, we have to skip 4 bytes (1 value) to
move to the next column, but 20 bytes (5 values) to get to the same
position in the next row. As such, the strides for the array `x` will be
``(20, 4)``.
See Also
--------
numpy.lib.stride_tricks.as_strided
Examples
--------
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
"""
Same as self.transpose(), except that self is returned if
self.ndim < 2.
Examples
--------
>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1., 2.],
[ 3., 4.]])
>>> x.T
array([[ 1., 3.],
[ 2., 4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1., 2., 3., 4.])
>>> x.T
array([ 1., 2., 3., 4.])
"""))
##############################################################################
#
# ndarray methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
""" a.__array__(|dtype) -> reference if type unchanged, copy otherwise.
Returns either a new reference to self if dtype is not given or a new array
of provided data type if dtype is different from the current dtype of the
array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__',
"""a.__array_prepare__(obj) -> Object of same type as ndarray object obj.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__',
"""a.__array_wrap__(obj) -> Object of same type as ndarray object a.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
"""a.__copy__()
Used if :func:`copy.copy` is called on an array. Returns a copy of the array.
Equivalent to ``a.copy(order='K')``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
"""a.__deepcopy__(memo, /) -> Deep copy of array.
Used if :func:`copy.deepcopy` is called on an array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__reduce__',
"""a.__reduce__()
For pickling.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__setstate__',
"""a.__setstate__(state, /)
For unpickling.
The `state` argument must be a sequence that contains the following
elements:
Parameters
----------
version : int
optional pickle version. If omitted defaults to 0.
shape : tuple
dtype : data-type
isFortran : bool
rawdata : string or list
a binary string with the data (or a list if 'a' is an object array)
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
"""
a.all(axis=None, out=None, keepdims=False)
Returns True if all elements evaluate to True.
Refer to `numpy.all` for full documentation.
See Also
--------
numpy.all : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
"""
a.any(axis=None, out=None, keepdims=False)
Returns True if any of the elements of `a` evaluate to True.
Refer to `numpy.any` for full documentation.
See Also
--------
numpy.any : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
"""
a.argmax(axis=None, out=None)
Return indices of the maximum values along the given axis.
Refer to `numpy.argmax` for full documentation.
See Also
--------
numpy.argmax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
"""
a.argmin(axis=None, out=None)
Return indices of the minimum values along the given axis of `a`.
Refer to `numpy.argmin` for detailed documentation.
See Also
--------
numpy.argmin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
"""
a.argsort(axis=-1, kind='quicksort', order=None)
Returns the indices that would sort this array.
Refer to `numpy.argsort` for full documentation.
See Also
--------
numpy.argsort : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argpartition',
"""
a.argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to `numpy.argpartition` for full documentation.
.. versionadded:: 1.8.0
See Also
--------
numpy.argpartition : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
"""
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result.
'C' means C order, 'F' means Fortran order, 'A'
means 'F' order if all the arrays are Fortran contiguous,
'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'unsafe'
for backwards compatibility.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through (default), otherwise
the returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to false, and the `dtype`, `order`, and `subok`
requirements are satisfied, the input array is returned instead
of a copy.
Returns
-------
arr_t : ndarray
Unless `copy` is False and the other conditions for returning the input
array are satisfied (see description for `copy` input parameter), `arr_t`
is a new array of the same shape as the input array, with dtype, order
given by `dtype`, `order`.
Notes
-----
Starting in NumPy 1.9, astype method now returns an error if the string
dtype to cast to is not long enough in 'safe' casting mode to hold the max
value of integer/float array that is being casted. Previously the casting
was allowed even if the result was truncated.
Raises
------
ComplexWarning
When casting from complex to float or int. To avoid this,
one should use ``a.real.astype(t)``.
Examples
--------
>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
"""
a.byteswap(inplace=False)
Swap the bytes of the array elements
Toggle between low-endian and big-endian data representation by
returning a byteswapped array, optionally swapped in-place.
Parameters
----------
inplace : bool, optional
If ``True``, swap bytes in-place, default is ``False``.
Returns
-------
out : ndarray
The byteswapped array. If `inplace` is ``True``, this is
a view to self.
Examples
--------
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> map(hex, A)
['0x100', '0x1', '0x3322']
Arrays of strings are not swapped
>>> A = np.array(['ceg', 'fac'])
>>> A.byteswap()
array(['ceg', 'fac'],
dtype='|S3')
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
"""
a.choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices.
Refer to `numpy.choose` for full documentation.
See Also
--------
numpy.choose : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
"""
a.clip(min=None, max=None, out=None)
Return an array whose values are limited to ``[min, max]``.
One of max or min must be given.
Refer to `numpy.clip` for full documentation.
See Also
--------
numpy.clip : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
"""
a.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis.
Refer to `numpy.compress` for full documentation.
See Also
--------
numpy.compress : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
"""
a.conj()
Complex-conjugate all elements.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
"""
a.conjugate()
Return the complex conjugate, element-wise.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
"""
a.copy(order='C')
Return a copy of the array.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :func:`numpy.copy` are very
similar, but have different default values for their order=
arguments.)
See also
--------
numpy.copy
numpy.copyto
Examples
--------
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
"""
a.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis.
Refer to `numpy.cumprod` for full documentation.
See Also
--------
numpy.cumprod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
"""
a.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis.
Refer to `numpy.cumsum` for full documentation.
See Also
--------
numpy.cumsum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
"""
a.diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a
read-only view instead of a copy as in previous NumPy versions. In
a future version the read-only restriction will be removed.
Refer to :func:`numpy.diagonal` for full documentation.
See Also
--------
numpy.diagonal : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dot',
"""
a.dot(b, out=None)
Dot product of two arrays.
Refer to `numpy.dot` for full documentation.
See Also
--------
numpy.dot : equivalent function
Examples
--------
>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[ 2., 2.],
[ 2., 2.]])
This array method can be conveniently chained:
>>> a.dot(b).dot(b)
array([[ 8., 8.],
[ 8., 8.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
"""a.dump(file)
Dump a pickle of the array to the specified file.
The array can be read back with pickle.load or numpy.load.
Parameters
----------
file : str
A string naming the dump file.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
"""
a.dumps()
Returns the pickle of the array as a string.
pickle.loads or numpy.loads will convert the string back to an array.
Parameters
----------
None
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
"""
a.fill(value)
Fill the array with a scalar value.
Parameters
----------
value : scalar
All elements of `a` will be assigned this value.
Examples
--------
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1., 1.])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
"""
a.flatten(order='C')
Return a copy of the array collapsed into one dimension.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
'C' means to flatten in row-major (C-style) order.
'F' means to flatten in column-major (Fortran-
style) order. 'A' means to flatten in column-major
order if `a` is Fortran *contiguous* in memory,
row-major order otherwise. 'K' means to flatten
`a` in the order the elements occur in memory.
The default is 'C'.
Returns
-------
y : ndarray
A copy of the input array, flattened to one dimension.
See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the array.
Examples
--------
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
"""
a.getfield(dtype, offset=0)
Returns a field of the given array as a certain type.
A field is a view of the array data with a given data-type. The values in
the view are determined by the given type and the offset into the current
array in bytes. The offset needs to be such that the view dtype fits in the
array dtype; for example an array of dtype complex128 has 16-byte elements.
If taking a view with a 32-bit integer (4 bytes), the offset needs to be
between 0 and 12 bytes.
Parameters
----------
dtype : str or dtype
The data type of the view. The dtype size of the view can not be larger
than that of the array itself.
offset : int
Number of bytes to skip before beginning the element view.
Examples
--------
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[ 1.+1.j, 0.+0.j],
[ 0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[ 1., 0.],
[ 0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the
array for our view:
>>> x.getfield(np.float64, offset=8)
array([[ 1., 0.],
[ 0., 4.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
"""
a.item(*args)
Copy an element of an array to a standard Python scalar and return it.
Parameters
----------
\\*args : Arguments (variable number and type)
* none: in this case, the method only works for arrays
with one element (`a.size == 1`), which element is
copied into a standard Python scalar object and returned.
* int_type: this argument is interpreted as a flat index into
the array, specifying which element to copy and return.
* tuple of int_types: functions as does a single int_type argument,
except that the argument is interpreted as an nd-index into the
array.
Returns
-------
z : Standard Python scalar object
A copy of the specified element of the array as a suitable
Python scalar
Notes
-----
When the data type of `a` is longdouble or clongdouble, item() returns
a scalar array object because there is no available Python scalar that
would not lose information. Void arrays return a buffer object for item(),
unless fields are defined, in which case a tuple is returned.
`item` is very similar to a[args], except, instead of an array scalar,
a standard Python scalar is returned. This can be useful for speeding up
access to elements of the array and doing arithmetic on elements of the
array using Python's optimized math.
Examples
--------
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.item(3)
2
>>> x.item(7)
5
>>> x.item((0, 1))
1
>>> x.item((2, 2))
3
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemset',
"""
a.itemset(*args)
Insert scalar into an array (scalar is cast to array's dtype, if possible)
There must be at least 1 argument, and define the last argument
as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster
than ``a[args] = item``. The item should be a scalar value and `args`
must select a single item in the array `a`.
Parameters
----------
\\*args : Arguments
If one argument: a scalar, only used in case `a` is of size 1.
If two arguments: the last argument is the value to be set
and must be a scalar, the first argument specifies a single array
element location. It is either an int or a tuple.
Notes
-----
Compared to indexing syntax, `itemset` provides some speed increase
for placing a scalar into a particular location in an `ndarray`,
if you must do this. However, generally this is discouraged:
among other problems, it complicates the appearance of the code.
Also, when using `itemset` (and `item`) inside a loop, be sure
to assign the methods to a local variable to avoid the attribute
look-up at each loop iteration.
Examples
--------
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[3, 1, 7],
[2, 0, 3],
[8, 5, 9]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
"""
a.max(axis=None, out=None, keepdims=False)
Return the maximum along a given axis.
Refer to `numpy.amax` for full documentation.
See Also
--------
numpy.amax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
"""
a.mean(axis=None, dtype=None, out=None, keepdims=False)
Returns the average of the array elements along given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
"""
a.min(axis=None, out=None, keepdims=False)
Return the minimum along a given axis.
Refer to `numpy.amin` for full documentation.
See Also
--------
numpy.amin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'shares_memory',
"""
shares_memory(a, b, max_work=None)
Determine if two arrays share memory
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem (maximum number
of candidate solutions to consider). The following special
values are recognized:
max_work=MAY_SHARE_EXACT (default)
The problem is solved exactly. In this case, the function returns
True only if there is an element shared between the arrays.
max_work=MAY_SHARE_BOUNDS
Only the memory bounds of a and b are checked.
Raises
------
numpy.TooHardError
Exceeded max_work.
Returns
-------
out : bool
See Also
--------
may_share_memory
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
""")
add_newdoc('numpy.core.multiarray', 'may_share_memory',
"""
may_share_memory(a, b, max_work=None)
Determine if two arrays might share memory
A return of True does not necessarily mean that the two arrays
share any element. It just means that they *might*.
Only the memory bounds of a and b are checked by default.
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem. See
`shares_memory` for details. Default for ``may_share_memory``
is to do a bounds check.
Returns
-------
out : bool
See Also
--------
shares_memory
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
>>> x = np.zeros([3, 4])
>>> np.may_share_memory(x[:,0], x[:,1])
True
""")
add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
"""
arr.newbyteorder(new_order='S')
Return the array with the same data viewed with a different byte order.
Equivalent to::
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data
type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
The default value ('S') results in swapping the current
byte order. The code does a case-insensitive check on the first
letter of `new_order` for the alternatives above. For example,
any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
Returns
-------
new_arr : array
New array object with the dtype reflecting given change to the
byte order.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
"""
a.nonzero()
Return the indices of the elements that are non-zero.
Refer to `numpy.nonzero` for full documentation.
See Also
--------
numpy.nonzero : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
"""
a.prod(axis=None, dtype=None, out=None, keepdims=False)
Return the product of the array elements over the given axis
Refer to `numpy.prod` for full documentation.
See Also
--------
numpy.prod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
"""
a.ptp(axis=None, out=None, keepdims=False)
Peak to peak (maximum - minimum) value along a given axis.
Refer to `numpy.ptp` for full documentation.
See Also
--------
numpy.ptp : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
"""
a.put(indices, values, mode='raise')
Set ``a.flat[n] = values[n]`` for all `n` in indices.
Refer to `numpy.put` for full documentation.
See Also
--------
numpy.put : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'copyto',
"""
copyto(dst, src, casting='same_kind', where=True)
Copies values from one array to another, broadcasting as necessary.
Raises a TypeError if the `casting` rule is violated, and if
`where` is provided, it selects which elements to copy.
.. versionadded:: 1.7.0
Parameters
----------
dst : ndarray
The array into which values are copied.
src : array_like
The array from which values are copied.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when copying.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
where : array_like of bool, optional
A boolean array which is broadcasted to match the dimensions
of `dst`, and selects elements to copy from `src` to `dst`
wherever it contains the value True.
""")
add_newdoc('numpy.core.multiarray', 'putmask',
"""
putmask(a, mask, values)
Changes elements of an array based on conditional and input values.
Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
If `values` is not the same size as `a` and `mask` then it will repeat.
This gives behavior different from ``a[mask] = values``.
Parameters
----------
a : array_like
Target array.
mask : array_like
Boolean mask array. It has to be the same shape as `a`.
values : array_like
Values to put into `a` where `mask` is True. If `values` is smaller
than `a` it will be repeated.
See Also
--------
place, put, take, copyto
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> np.putmask(x, x>2, x**2)
>>> x
array([[ 0, 1, 2],
[ 9, 16, 25]])
If `values` is smaller than `a` it is repeated:
>>> x = np.arange(5)
>>> np.putmask(x, x>1, [-33, -44])
>>> x
array([ 0, 1, -33, -44, -33])
""")
add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
"""
a.ravel([order])
Return a flattened array.
Refer to `numpy.ravel` for full documentation.
See Also
--------
numpy.ravel : equivalent function
ndarray.flat : a flat iterator on the array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
"""
a.repeat(repeats, axis=None)
Repeat elements of an array.
Refer to `numpy.repeat` for full documentation.
See Also
--------
numpy.repeat : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
"""
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to `numpy.reshape` for full documentation.
See Also
--------
numpy.reshape : equivalent function
Notes
-----
Unlike the free function `numpy.reshape`, this method on `ndarray` allows
the elements of the shape parameter to be passed in as separate arguments.
For example, ``a.reshape(10, 11)`` is equivalent to
``a.reshape((10, 11))``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
"""
a.resize(new_shape, refcheck=True)
Change shape and size of array in-place.
Parameters
----------
new_shape : tuple of ints, or `n` ints
Shape of resized array.
refcheck : bool, optional
If False, reference count will not be checked. Default is True.
Returns
-------
None
Raises
------
ValueError
If `a` does not own its own data or references or views to it exist,
and the data memory must be changed.
PyPy only: will always raise if the data memory must be changed, since
there is no reliable way to determine if references or views to it
exist.
SystemError
If the `order` keyword argument is specified. This behaviour is a
bug in NumPy.
See Also
--------
resize : Return a new array with the specified shape.
Notes
-----
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be
resized.
The purpose of the reference count check is to make sure you
do not use this array as a buffer for another Python object and then
reallocate the memory. However, reference counts can increase in
other ways so if you are sure that you have not shared the memory
for this array with another Python object, then you may safely set
`refcheck` to False.
Examples
--------
Shrinking an array: array is flattened (in the order that the data are
stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing...
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...
Unless `refcheck` is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
"""
a.round(decimals=0, out=None)
Return `a` with each element rounded to the given number of decimals.
Refer to `numpy.around` for full documentation.
See Also
--------
numpy.around : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
"""
a.searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
"""
a.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place `val` into `a`'s field defined by `dtype` and beginning `offset`
bytes into the field.
Parameters
----------
val : object
Value to be placed in field.
dtype : dtype object
Data-type of the field in which to place `val`.
offset : int, optional
The number of bytes into the field at which to place `val`.
Returns
-------
None
See Also
--------
getfield
Examples
--------
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]])
>>> x
array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],
[ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],
[ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
"""
a.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),
respectively.
These Boolean-valued flags affect how numpy interprets the memory
area used by `a` (see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Parameters
----------
write : bool, optional
Describes whether or not `a` can be written to.
align : bool, optional
Describes whether or not `a` is aligned properly for its type.
uic : bool, optional
Describes whether or not `a` is a copy of another "base" array.
Notes
-----
Array flags provide information about how the memory area used
for the array is to be interpreted. There are 7 Boolean flags
in use, only four of which can be changed by the user:
WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware
(as determined by the compiler);
UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well
as the full name.
Examples
--------
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
"""
a.sort(axis=-1, kind='quicksort', order=None)
Sort an array, in-place.
Parameters
----------
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm. Default is 'quicksort'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.sort : Return a sorted copy of an array.
argsort : Indirect sort.
lexsort : Indirect stable sort on multiple keys.
searchsorted : Find elements in sorted array.
partition: Partial sort.
Notes
-----
See ``sort`` for notes on the different sorting algorithms.
Examples
--------
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the `order` keyword to specify a field to use when sorting a
structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([('c', 1), ('a', 2)],
dtype=[('x', '|S1'), ('y', '<i4')])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('partition',
"""
a.partition(kth, axis=-1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that the value of the
element in kth position is in the position it would be in a sorted array.
All elements smaller than the kth element are moved before this element and
all equal or greater are moved behind it. The ordering of the elements in
the two partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
kth : int or sequence of ints
Element index to partition by. The kth element value will be in its
final sorted position and all smaller elements will be moved before it
and all equal or greater elements behind it.
The order of all elements in the partitions is undefined.
If provided with a sequence of kth it will partition all elements
indexed by kth of them into their sorted position at once.
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need to be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.partition : Return a parititioned copy of an array.
argpartition : Indirect partition.
sort : Full sort.
Notes
-----
See ``np.partition`` for notes on the different algorithms.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
array([1, 2, 3, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
"""
a.squeeze(axis=None)
Remove single-dimensional entries from the shape of `a`.
Refer to `numpy.squeeze` for full documentation.
See Also
--------
numpy.squeeze : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
"""
a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Returns the standard deviation of the array elements along given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
"""
a.sum(axis=None, dtype=None, out=None, keepdims=False)
Return the sum of the array elements over the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
"""
a.swapaxes(axis1, axis2)
Return a view of the array with `axis1` and `axis2` interchanged.
Refer to `numpy.swapaxes` for full documentation.
See Also
--------
numpy.swapaxes : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
"""
a.take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of `a` at the given indices.
Refer to `numpy.take` for full documentation.
See Also
--------
numpy.take : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
"""
a.tofile(fid, sep="", format="%s")
Write array to a file as text or binary (default).
Data is always written in 'C' order, independent of the order of `a`.
The data produced by this method can be recovered using the function
fromfile().
Parameters
----------
fid : file or str
An open file object, or a string containing a filename.
sep : str
Separator between array items for text output.
If "" (empty), a binary file is written, equivalent to
``file.write(a.tobytes())``.
format : str
Format string for text file output.
Each entry in the array is formatted to text by first converting
it to the closest Python type, and then using "format" % item.
Notes
-----
This is a convenience function for quick storage of array data.
Information on endianness and precision is lost, so this method is not a
good choice for files intended to archive data or transport data between
machines with different endianness. Some of these problems can be overcome
by outputting the data as text files, at the expense of speed and file
size.
When fid is a file object, array contents are directly written to the
file, bypassing the file object's ``write`` method. As a result, tofile
cannot be used with files objects supporting compression (e.g., GzipFile)
or file-like objects that do not support ``fileno()`` (e.g., BytesIO).
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
"""
a.tolist()
Return the array as a (possibly nested) list.
Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible Python type.
Parameters
----------
none
Returns
-------
y : list
The possibly nested list of array elements.
Notes
-----
The array may be recreated, ``a = np.array(a.tolist())``.
Examples
--------
>>> a = np.array([1, 2])
>>> a.tolist()
[1, 2]
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
"""))
tobytesdoc = """
a.{name}(order='C')
Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of
data memory. The bytes object can be produced in either 'C' or 'Fortran',
or 'Any' order (the default is 'C'-order). 'Any' order means C-order
unless the F_CONTIGUOUS flag in the array is set, in which case it
means 'Fortran' order.
{deprecated}
Parameters
----------
order : {{'C', 'F', None}}, optional
Order of the data for multidimensional arrays:
C, Fortran, or the same as for the original array.
Returns
-------
s : bytes
Python bytes exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
"""
add_newdoc('numpy.core.multiarray', 'ndarray',
('tostring', tobytesdoc.format(name='tostring',
deprecated=
'This function is a compatibility '
'alias for tobytes. Despite its '
'name it returns bytes not '
'strings.')))
add_newdoc('numpy.core.multiarray', 'ndarray',
('tobytes', tobytesdoc.format(name='tobytes',
deprecated='.. versionadded:: 1.9.0')))
add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
"""
a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array.
Refer to `numpy.trace` for full documentation.
See Also
--------
numpy.trace : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
"""
a.transpose(*axes)
Returns a view of the array with axes transposed.
For a 1-D array, this has no effect. (To change between column and
row vectors, first cast the 1-D array into a matrix object.)
For a 2-D array, this is the usual matrix transpose.
For an n-D array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
Parameters
----------
axes : None, tuple of ints, or `n` ints
* None or no argument: reverses the order of the axes.
* tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
`i`-th axis becomes `a.transpose()`'s `j`-th axis.
* `n` ints: same as an n-tuple of the same ints (this form is
intended simply as a "convenience" alternative to the tuple form)
Returns
-------
out : ndarray
View of `a`, with axes suitably permuted.
See Also
--------
ndarray.T : Array property returning the array transposed.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('var',
"""
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Returns the variance of the array elements, along given axis.
Refer to `numpy.var` for full documentation.
See Also
--------
numpy.var : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
"""
a.view(dtype=None, type=None)
New view of array with the same data.
Parameters
----------
dtype : data-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16. The
default, None, results in the view having the same data-type as `a`.
This argument can also be specified as an ndarray sub-class, which
then specifies the type of the returned object (this is equivalent to
setting the ``type`` parameter).
type : Python type, optional
Type of the returned view, e.g., ndarray or matrix. Again, the
default None results in type preservation.
Notes
-----
``a.view()`` is used two different ways:
``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
of the array's memory with a different data-type. This can cause a
reinterpretation of the bytes of memory.
``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
returns an instance of `ndarray_subclass` that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
bytes per entry than the previous dtype (for example, converting a
regular array to a structured array), then the behavior of the view
cannot be predicted just from the superficial appearance of ``a`` (shown
by ``print(a)``). It also depends on exactly how ``a`` is stored in
memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
defined as a slice or transpose, etc., the view may give different
results.
Examples
--------
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrixlib.defmatrix.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([ 2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> print(x)
[(1, 20) (3, 4)]
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
"""))
##############################################################################
#
# umath functions
#
##############################################################################
add_newdoc('numpy.core.umath', 'frompyfunc',
"""
frompyfunc(func, nin, nout)
Takes an arbitrary Python function and returns a NumPy ufunc.
Can be used, for example, to add broadcasting to a built-in Python
function (see Examples section).
Parameters
----------
func : Python function object
An arbitrary Python function.
nin : int
The number of input arguments.
nout : int
The number of objects returned by `func`.
Returns
-------
out : ufunc
Returns a NumPy universal function (``ufunc``) object.
See Also
--------
vectorize : evaluates pyfunc over input arrays using broadcasting rules of numpy
Notes
-----
The returned ufunc always returns PyObject arrays.
Examples
--------
Use frompyfunc to add broadcasting to the Python function ``oct``:
>>> oct_array = np.frompyfunc(oct, 1, 1)
>>> oct_array(np.array((10, 30, 100)))
array([012, 036, 0144], dtype=object)
>>> np.array((oct(10), oct(30), oct(100))) # for comparison
array(['012', '036', '0144'],
dtype='|S4')
""")
add_newdoc('numpy.core.umath', 'geterrobj',
"""
geterrobj()
Return the current object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `geterrobj` is used internally by the other
functions that get and set error handling behavior (`geterr`, `seterr`,
`geterrcall`, `seterrcall`).
Returns
-------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
seterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrobj() # first get the defaults
[10000, 0, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> old_bufsize = np.setbufsize(20000)
>>> old_err = np.seterr(divide='raise')
>>> old_handler = np.seterrcall(err_handler)
>>> np.geterrobj()
[20000, 2, <function err_handler at 0x91dcaac>]
>>> old_err = np.seterr(all='ignore')
>>> np.base_repr(np.geterrobj()[1], 8)
'0'
>>> old_err = np.seterr(divide='warn', over='log', under='call',
invalid='print')
>>> np.base_repr(np.geterrobj()[1], 8)
'4351'
""")
add_newdoc('numpy.core.umath', 'seterrobj',
"""
seterrobj(errobj)
Set the object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `seterrobj` is used internally by the other
functions that set error handling behavior (`seterr`, `seterrcall`).
Parameters
----------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
geterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> old_errobj = np.geterrobj() # first get the defaults
>>> old_errobj
[10000, 0, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> new_errobj = [20000, 12, err_handler]
>>> np.seterrobj(new_errobj)
>>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn')
'14'
>>> np.geterr()
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
>>> np.geterrcall() is err_handler
True
""")
##############################################################################
#
# compiled_base functions
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'digitize',
"""
digitize(x, bins, right=False)
Return the indices of the bins to which each value in input array belongs.
========= ============= ============================
`right` order of bins returned index `i` satisfies
========= ============= ============================
``False`` increasing ``bins[i-1] <= x < bins[i]``
``True`` increasing ``bins[i-1] < x <= bins[i]``
``False`` decreasing ``bins[i-1] > x >= bins[i]``
``True`` decreasing ``bins[i-1] >= x > bins[i]``
========= ============= ============================
If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is
returned as appropriate.
Parameters
----------
x : array_like
Input array to be binned. Prior to NumPy 1.10.0, this array had to
be 1-dimensional, but can now have any shape.
bins : array_like
Array of bins. It has to be 1-dimensional and monotonic.
right : bool, optional
Indicating whether the intervals include the right or the left bin
edge. Default behavior is (right==False) indicating that the interval
does not include the right edge. The left bin end is open in this
case, i.e., bins[i-1] <= x < bins[i] is the default behavior for
monotonically increasing bins.
Returns
-------
indices : ndarray of ints
Output array of indices, of same shape as `x`.
Raises
------
ValueError
If `bins` is not monotonic.
TypeError
If the type of the input is complex.
See Also
--------
bincount, histogram, unique, searchsorted
Notes
-----
If values in `x` are such that they fall outside the bin range,
attempting to index `bins` with the indices that `digitize` returns
will result in an IndexError.
.. versionadded:: 1.10.0
`np.digitize` is implemented in terms of `np.searchsorted`. This means
that a binary search is used to bin the values, which scales much better
for larger number of bins than the previous linear search. It also removes
the requirement for the input array to be 1-dimensional.
For monotonically _increasing_ `bins`, the following are equivalent::
np.digitize(x, bins, right=True)
np.searchsorted(bins, x, side='left')
Note that as the order of the arguments are reversed, the side must be too.
The `searchsorted` call is marginally faster, as it does not do any
monotonicity checks. Perhaps more importantly, it supports all dtypes.
Examples
--------
>>> x = np.array([0.2, 6.4, 3.0, 1.6])
>>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
>>> inds = np.digitize(x, bins)
>>> inds
array([1, 4, 3, 2])
>>> for n in range(x.size):
... print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]])
...
0.0 <= 0.2 < 1.0
4.0 <= 6.4 < 10.0
2.5 <= 3.0 < 4.0
1.0 <= 1.6 < 2.5
>>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = np.array([0, 5, 10, 15, 20])
>>> np.digitize(x,bins,right=True)
array([1, 2, 3, 4, 4])
>>> np.digitize(x,bins,right=False)
array([1, 3, 3, 4, 5])
""")
add_newdoc('numpy.core.multiarray', 'bincount',
"""
bincount(x, weights=None, minlength=0)
Count number of occurrences of each value in array of non-negative ints.
The number of bins (of size 1) is one larger than the largest value in
`x`. If `minlength` is specified, there will be at least this number
of bins in the output array (though it will be longer if necessary,
depending on the contents of `x`).
Each bin gives the number of occurrences of its index value in `x`.
If `weights` is specified the input array is weighted by it, i.e. if a
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
of ``out[n] += 1``.
Parameters
----------
x : array_like, 1 dimension, nonnegative ints
Input array.
weights : array_like, optional
Weights, array of the same shape as `x`.
minlength : int, optional
A minimum number of bins for the output array.
.. versionadded:: 1.6.0
Returns
-------
out : ndarray of ints
The result of binning the input array.
The length of `out` is equal to ``np.amax(x)+1``.
Raises
------
ValueError
If the input is not 1-dimensional, or contains elements with negative
values, or if `minlength` is negative.
TypeError
If the type of the input is float or complex.
See Also
--------
histogram, digitize, unique
Examples
--------
>>> np.bincount(np.arange(5))
array([1, 1, 1, 1, 1])
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
array([1, 3, 1, 1, 0, 0, 0, 1])
>>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
>>> np.bincount(x).size == np.amax(x)+1
True
The input array needs to be of integer dtype, otherwise a
TypeError is raised:
>>> np.bincount(np.arange(5, dtype=float))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: array cannot be safely cast to required type
A possible use of ``bincount`` is to perform sums over
variable-size chunks of an array, using the ``weights`` keyword.
>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
>>> x = np.array([0, 1, 1, 2, 2, 2])
>>> np.bincount(x, weights=w)
array([ 0.3, 0.7, 1.1])
""")
add_newdoc('numpy.core.multiarray', 'ravel_multi_index',
"""
ravel_multi_index(multi_index, dims, mode='raise', order='C')
Converts a tuple of index arrays into an array of flat
indices, applying boundary modes to the multi-index.
Parameters
----------
multi_index : tuple of array_like
A tuple of integer arrays, one array for each dimension.
dims : tuple of ints
The shape of array into which the indices from ``multi_index`` apply.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices are handled. Can specify
either one mode or a tuple of modes, one mode per index.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
In 'clip' mode, a negative index which would normally
wrap will clip to 0 instead.
order : {'C', 'F'}, optional
Determines whether the multi-index should be viewed as
indexing in row-major (C-style) or column-major
(Fortran-style) order.
Returns
-------
raveled_indices : ndarray
An array of indices into the flattened version of an array
of dimensions ``dims``.
See Also
--------
unravel_index
Notes
-----
.. versionadded:: 1.6.0
Examples
--------
>>> arr = np.array([[3,6,6],[4,5,1]])
>>> np.ravel_multi_index(arr, (7,6))
array([22, 41, 37])
>>> np.ravel_multi_index(arr, (7,6), order='F')
array([31, 41, 13])
>>> np.ravel_multi_index(arr, (4,6), mode='clip')
array([22, 23, 19])
>>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
array([12, 13, 13])
>>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
1621
""")
add_newdoc('numpy.core.multiarray', 'unravel_index',
"""
unravel_index(indices, dims, order='C')
Converts a flat index or array of flat indices into a tuple
of coordinate arrays.
Parameters
----------
indices : array_like
An integer array whose elements are indices into the flattened
version of an array of dimensions ``dims``. Before version 1.6.0,
this function accepted just one index value.
dims : tuple of ints
The shape of the array to use for unraveling ``indices``.
order : {'C', 'F'}, optional
Determines whether the indices should be viewed as indexing in
row-major (C-style) or column-major (Fortran-style) order.
.. versionadded:: 1.6.0
Returns
-------
unraveled_coords : tuple of ndarray
Each array in the tuple has the same shape as the ``indices``
array.
See Also
--------
ravel_multi_index
Examples
--------
>>> np.unravel_index([22, 41, 37], (7,6))
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index([31, 41, 13], (7,6), order='F')
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index(1621, (6,7,8,9))
(3, 1, 4, 1)
""")
add_newdoc('numpy.core.multiarray', 'add_docstring',
"""
add_docstring(obj, docstring)
Add a docstring to a built-in obj if possible.
If the obj already has a docstring raise a RuntimeError
If this routine does not know how to add a docstring to the object
raise a TypeError
""")
add_newdoc('numpy.core.umath', '_add_newdoc_ufunc',
"""
add_ufunc_docstring(ufunc, new_docstring)
Replace the docstring for a ufunc with new_docstring.
This method will only work if the current docstring for
the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
Parameters
----------
ufunc : numpy.ufunc
A ufunc whose current doc is NULL.
new_docstring : string
The new docstring for the ufunc.
Notes
-----
This method allocates memory for new_docstring on
the heap. Technically this creates a mempory leak, since this
memory will not be reclaimed until the end of the program
even if the ufunc itself is removed. However this will only
be a problem if the user is repeatedly creating ufuncs with
no documentation, adding documentation via add_newdoc_ufunc,
and then throwing away the ufunc.
""")
add_newdoc('numpy.core.multiarray', 'packbits',
"""
packbits(myarray, axis=None)
Packs the elements of a binary-valued array into bits in a uint8 array.
The result is padded to full bytes by inserting zero bits at the end.
Parameters
----------
myarray : array_like
An array of integers or booleans whose elements should be packed to
bits.
axis : int, optional
The dimension over which bit-packing is done.
``None`` implies packing the flattened array.
Returns
-------
packed : ndarray
Array of type uint8 whose elements represent bits corresponding to the
logical (0 or nonzero) value of the input elements. The shape of
`packed` has the same number of dimensions as the input (unless `axis`
is None, in which case the output is 1-D).
See Also
--------
unpackbits: Unpacks elements of a uint8 array into a binary-valued output
array.
Examples
--------
>>> a = np.array([[[1,0,1],
... [0,1,0]],
... [[1,1,0],
... [0,0,1]]])
>>> b = np.packbits(a, axis=-1)
>>> b
array([[[160],[64]],[[192],[32]]], dtype=uint8)
Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
and 32 = 0010 0000.
""")
add_newdoc('numpy.core.multiarray', 'unpackbits',
"""
unpackbits(myarray, axis=None)
Unpacks elements of a uint8 array into a binary-valued output array.
Each element of `myarray` represents a bit-field that should be unpacked
into a binary-valued output array. The shape of the output array is either
1-D (if `axis` is None) or the same shape as the input array with unpacking
done along the axis specified.
Parameters
----------
myarray : ndarray, uint8 type
Input array.
axis : int, optional
The dimension over which bit-unpacking is done.
``None`` implies unpacking the flattened array.
Returns
-------
unpacked : ndarray, uint8 type
The elements are binary-valued (0 or 1).
See Also
--------
packbits : Packs the elements of a binary-valued array into bits in a uint8
array.
Examples
--------
>>> a = np.array([[2], [7], [23]], dtype=np.uint8)
>>> a
array([[ 2],
[ 7],
[23]], dtype=uint8)
>>> b = np.unpackbits(a, axis=1)
>>> b
array([[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)
""")
add_newdoc('numpy.core._multiarray_tests', 'format_float_OSprintf_g',
"""
format_float_OSprintf_g(val, precision)
Print a floating point scalar using the system's printf function,
equivalent to:
printf("%.*g", precision, val);
for half/float/double, or replacing 'g' by 'Lg' for longdouble. This
method is designed to help cross-validate the format_float_* methods.
Parameters
----------
val : python float or numpy floating scalar
Value to format.
precision : non-negative integer, optional
Precision given to printf.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
format_float_positional
""")
##############################################################################
#
# Documentation for ufunc attributes and methods
#
##############################################################################
##############################################################################
#
# ufunc object
#
##############################################################################
add_newdoc('numpy.core', 'ufunc',
"""
Functions that operate element by element on whole arrays.
To see the documentation for a specific ufunc, use `info`. For
example, ``np.info(np.sin)``. Because ufuncs are written in C
(for speed) and linked into Python with NumPy's ufunc facility,
Python's help() function finds this page whenever help() is called
on a ufunc.
A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.
Calling ufuncs:
===============
op(*x[, out], where=True, **kwargs)
Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.
The broadcasting rules are:
* Dimensions of length 1 may be prepended to either array.
* Arrays may be repeated along dimensions of length 1.
Parameters
----------
*x : array_like
Input arrays.
out : ndarray, None, or tuple of ndarray and None, optional
Alternate array object(s) in which to put the result; if provided, it
must have a shape that the inputs broadcast to. A tuple of arrays
(possible only as a keyword argument) must have length equal to the
number of outputs; use `None` for uninitialized outputs to be
allocated by the ufunc.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone. Note that if
an uninitialized return array is created via the default ``out=None``,
then the elements where the values are False will remain uninitialized.
**kwargs
For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
r : ndarray or tuple of ndarray
`r` will have the shape that the arrays in `x` broadcast to; if `out` is
provided, it will be returned. If not, `r` will be allocated and
may contain uninitialized values. If the function has more than one
output, then the result will be a tuple of arrays.
""")
##############################################################################
#
# ufunc attributes
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('identity',
"""
The identity value.
Data attribute containing the identity element for the ufunc, if it has one.
If it does not, the attribute value is None.
Examples
--------
>>> np.add.identity
0
>>> np.multiply.identity
1
>>> np.power.identity
1
>>> print(np.exp.identity)
None
"""))
add_newdoc('numpy.core', 'ufunc', ('nargs',
"""
The number of arguments.
Data attribute containing the number of arguments the ufunc takes, including
optional ones.
Notes
-----
Typically this value will be one more than what you might expect because all
ufuncs take the optional "out" argument.
Examples
--------
>>> np.add.nargs
3
>>> np.multiply.nargs
3
>>> np.power.nargs
3
>>> np.exp.nargs
2
"""))
add_newdoc('numpy.core', 'ufunc', ('nin',
"""
The number of inputs.
Data attribute containing the number of arguments the ufunc treats as input.
Examples
--------
>>> np.add.nin
2
>>> np.multiply.nin
2
>>> np.power.nin
2
>>> np.exp.nin
1
"""))
add_newdoc('numpy.core', 'ufunc', ('nout',
"""
The number of outputs.
Data attribute containing the number of arguments the ufunc treats as output.
Notes
-----
Since all ufuncs can take output arguments, this will always be (at least) 1.
Examples
--------
>>> np.add.nout
1
>>> np.multiply.nout
1
>>> np.power.nout
1
>>> np.exp.nout
1
"""))
add_newdoc('numpy.core', 'ufunc', ('ntypes',
"""
The number of types.
The number of numerical NumPy types - of which there are 18 total - on which
the ufunc can operate.
See Also
--------
numpy.ufunc.types
Examples
--------
>>> np.add.ntypes
18
>>> np.multiply.ntypes
18
>>> np.power.ntypes
17
>>> np.exp.ntypes
7
>>> np.remainder.ntypes
14
"""))
add_newdoc('numpy.core', 'ufunc', ('types',
"""
Returns a list with types grouped input->output.
Data attribute listing the data-type "Domain-Range" groupings the ufunc can
deliver. The data-types are given using the character codes.
See Also
--------
numpy.ufunc.ntypes
Examples
--------
>>> np.add.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.multiply.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.power.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
'OO->O']
>>> np.exp.types
['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
>>> np.remainder.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
"""))
add_newdoc('numpy.core', 'ufunc', ('signature',
"""
Definition of the core elements a generalized ufunc operates on.
The signature determines how the dimensions of each input/output array
are split into core and loop dimensions:
1. Each dimension in the signature is matched to a dimension of the
corresponding passed-in array, starting from the end of the shape tuple.
2. Core dimensions assigned to the same label in the signature must have
exactly matching sizes, no broadcasting is performed.
3. The core dimensions are removed from all inputs and the remaining
dimensions are broadcast together, defining the loop dimensions.
Notes
-----
Generalized ufuncs are used internally in many linalg functions, and in
the testing suite; the examples below are taken from these.
For ufuncs that operate on scalars, the signature is `None`, which is
equivalent to '()' for every argument.
Examples
--------
>>> np.core.umath_tests.matrix_multiply.signature
'(m,n),(n,p)->(m,p)'
>>> np.linalg._umath_linalg.det.signature
'(m,m)->()'
>>> np.add.signature is None
True # equivalent to '(),()->()'
"""))
##############################################################################
#
# ufunc methods
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('reduce',
"""
reduce(a, axis=0, dtype=None, out=None, keepdims=False, initial)
Reduces `a`'s dimension by one, by applying ufunc along one axis.
Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
:math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::
r = op.identity # op = ufunc
for i in range(len(A)):
r = op(r, A[i])
return r
For example, add.reduce() is equivalent to sum().
Parameters
----------
a : array_like
The array to act on.
axis : None or int or tuple of ints, optional
Axis or axes along which a reduction is performed.
The default (`axis` = 0) is perform a reduction over the first
dimension of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is `None`, a reduction is performed over all the axes.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
For operations which are either not commutative or not associative,
doing a reduction over multiple axes is not well-defined. The
ufuncs do not currently raise an exception in this case, but will
likely do so in the future.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data-type of the output array if this is provided, or
the data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
.. versionadded:: 1.7.0
initial : scalar, optional
The value with which to start the reduction.
If the ufunc has no identity or the dtype is object, this defaults
to None - otherwise it defaults to ufunc.identity.
If ``None`` is given, the first element of the reduction is used,
and an error is thrown if the reduction is empty.
.. versionadded:: 1.15.0
Returns
-------
r : ndarray
The reduced array. If `out` was supplied, `r` is a reference to it.
Examples
--------
>>> np.multiply.reduce([2,3,5])
30
A multi-dimensional array example:
>>> X = np.arange(8).reshape((2,2,2))
>>> X
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.add.reduce(X, 0)
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X, 1)
array([[ 2, 4],
[10, 12]])
>>> np.add.reduce(X, 2)
array([[ 1, 5],
[ 9, 13]])
You can use the ``initial`` keyword argument to initialize the reduction with a
different value.
>>> np.add.reduce([10], initial=5)
15
>>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initializer=10)
array([14., 14.])
Allows reductions of empty arrays where they would normally fail, i.e.
for ufuncs without an identity.
>>> np.minimum.reduce([], initial=np.inf)
inf
>>> np.minimum.reduce([])
Traceback (most recent call last):
...
ValueError: zero-size array to reduction operation minimum which has no identity
"""))
add_newdoc('numpy.core', 'ufunc', ('accumulate',
"""
accumulate(array, axis=0, dtype=None, out=None)
Accumulate the result of applying the operator to all elements.
For a one-dimensional array, accumulate produces results equivalent to::
r = np.empty(len(A))
t = op.identity # op = the ufunc being applied to A's elements
for i in range(len(A)):
t = op(t, A[i])
r[i] = t
return r
For example, add.accumulate() is equivalent to np.cumsum().
For a multi-dimensional array, accumulate is applied along only one
axis (axis zero by default; see Examples below) so repeated use is
necessary if one wants to accumulate over multiple axes.
Parameters
----------
array : array_like
The array to act on.
axis : int, optional
The axis along which to apply the accumulation; default is zero.
dtype : data-type code, optional
The data-type used to represent the intermediate results. Defaults
to the data-type of the output array if such is provided, or the
the data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The accumulated values. If `out` was supplied, `r` is a reference to
`out`.
Examples
--------
1-D array examples:
>>> np.add.accumulate([2, 3, 5])
array([ 2, 5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2, 6, 30])
2-D array examples:
>>> I = np.eye(2)
>>> I
array([[ 1., 0.],
[ 0., 1.]])
Accumulate along axis 0 (rows), down columns:
>>> np.add.accumulate(I, 0)
array([[ 1., 0.],
[ 1., 1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[ 1., 0.],
[ 1., 1.]])
Accumulate along axis 1 (columns), through rows:
>>> np.add.accumulate(I, 1)
array([[ 1., 1.],
[ 0., 1.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('reduceat',
"""
reduceat(a, indices, axis=0, dtype=None, out=None)
Performs a (local) reduce with specified slices over a single axis.
For i in ``range(len(indices))``, `reduceat` computes
``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th
generalized "row" parallel to `axis` in the final result (i.e., in a
2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
`axis = 1`, it becomes the i-th column). There are three exceptions to this:
* when ``i = len(indices) - 1`` (so for the last index),
``indices[i+1] = a.shape[axis]``.
* if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
simply ``a[indices[i]]``.
* if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.
The shape of the output depends on the size of `indices`, and may be
larger than `a` (this happens if ``len(indices) > a.shape[axis]``).
Parameters
----------
a : array_like
The array to act on.
indices : array_like
Paired indices, comma separated (not colon), specifying slices to
reduce.
axis : int, optional
The axis along which to apply the reduceat.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data type of the output array if this is provided, or
the data type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The reduced values. If `out` was supplied, `r` is a reference to
`out`.
Notes
-----
A descriptive example:
If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as
``ufunc.reduceat(a, indices)[::2]`` where `indices` is
``range(len(array) - 1)`` with a zero placed
in every other element:
``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.
Don't be fooled by this attribute's name: `reduceat(a)` is not
necessarily smaller than `a`.
Examples
--------
To take the running sum of four successive values:
>>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
array([ 6, 10, 14, 18])
A 2-D example:
>>> x = np.linspace(0, 15, 16).reshape(4,4)
>>> x
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
::
# reduce such that the result has the following five rows:
# [row1 + row2 + row3]
# [row4]
# [row2]
# [row3]
# [row1 + row2 + row3 + row4]
>>> np.add.reduceat(x, [0, 3, 1, 2, 0])
array([[ 12., 15., 18., 21.],
[ 12., 13., 14., 15.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 24., 28., 32., 36.]])
::
# reduce such that result has the following two columns:
# [col1 * col2 * col3, col4]
>>> np.multiply.reduceat(x, [0, 3], 1)
array([[ 0., 3.],
[ 120., 7.],
[ 720., 11.],
[ 2184., 15.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('outer',
"""
outer(A, B, **kwargs)
Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
``op.outer(A, B)`` is an array of dimension M + N such that:
.. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
For `A` and `B` one-dimensional, this is equivalent to::
r = empty(len(A),len(B))
for i in range(len(A)):
for j in range(len(B)):
r[i,j] = op(A[i], B[j]) # op = ufunc in question
Parameters
----------
A : array_like
First array
B : array_like
Second array
kwargs : any
Arguments to pass on to the ufunc. Typically `dtype` or `out`.
Returns
-------
r : ndarray
Output array
See Also
--------
numpy.outer
Examples
--------
>>> np.multiply.outer([1, 2, 3], [4, 5, 6])
array([[ 4, 5, 6],
[ 8, 10, 12],
[12, 15, 18]])
A multi-dimensional example:
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> A.shape
(2, 3)
>>> B = np.array([[1, 2, 3, 4]])
>>> B.shape
(1, 4)
>>> C = np.multiply.outer(A, B)
>>> C.shape; C
(2, 3, 1, 4)
array([[[[ 1, 2, 3, 4]],
[[ 2, 4, 6, 8]],
[[ 3, 6, 9, 12]]],
[[[ 4, 8, 12, 16]],
[[ 5, 10, 15, 20]],
[[ 6, 12, 18, 24]]]])
"""))
add_newdoc('numpy.core', 'ufunc', ('at',
"""
at(a, indices, b=None)
Performs unbuffered in place operation on operand 'a' for elements
specified by 'indices'. For addition ufunc, this method is equivalent to
``a[indices] += b``, except that results are accumulated for elements that
are indexed more than once. For example, ``a[[0,0]] += 1`` will only
increment the first element once because of buffering, whereas
``add.at(a, [0,0], 1)`` will increment the first element twice.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first
operand. If first operand has multiple dimensions, indices can be a
tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be
broadcastable over first operand after indexing or slicing.
Examples
--------
Set items 0 and 1 to their negative values:
>>> a = np.array([1, 2, 3, 4])
>>> np.negative.at(a, [0, 1])
>>> print(a)
array([-1, -2, 3, 4])
Increment items 0 and 1, and increment item 2 twice:
>>> a = np.array([1, 2, 3, 4])
>>> np.add.at(a, [0, 1, 2, 2], 1)
>>> print(a)
array([2, 3, 5, 4])
Add items 0 and 1 in first array to second array,
and store results in first array:
>>> a = np.array([1, 2, 3, 4])
>>> b = np.array([1, 2])
>>> np.add.at(a, [0, 1], b)
>>> print(a)
array([2, 4, 3, 4])
"""))
##############################################################################
#
# Documentation for dtype attributes and methods
#
##############################################################################
##############################################################################
#
# dtype object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype',
"""
dtype(obj, align=False, copy=False)
Create a data type object.
A numpy array is homogeneous, and contains elements described by a
dtype object. A dtype object can be constructed from different
combinations of fundamental numeric types.
Parameters
----------
obj
Object to be converted to a data type object.
align : bool, optional
Add padding to the fields to match what a C compiler would output
for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
or a comma-separated string. If a struct dtype is being created,
this also sets a sticky alignment flag ``isalignedstruct``.
copy : bool, optional
Make a new copy of the data-type object. If ``False``, the result
may just be a reference to a built-in data-type object.
See also
--------
result_type
Examples
--------
Using array-scalar type:
>>> np.dtype(np.int16)
dtype('int16')
Structured type, one field name 'f1', containing int16:
>>> np.dtype([('f1', np.int16)])
dtype([('f1', '<i2')])
Structured type, one field named 'f1', in itself containing a structured
type with one field:
>>> np.dtype([('f1', [('f1', np.int16)])])
dtype([('f1', [('f1', '<i2')])])
Structured type, two fields: the first field contains an unsigned int, the
second an int32:
>>> np.dtype([('f1', np.uint), ('f2', np.int32)])
dtype([('f1', '<u4'), ('f2', '<i4')])
Using array-protocol type strings:
>>> np.dtype([('a','f8'),('b','S10')])
dtype([('a', '<f8'), ('b', '|S10')])
Using comma-separated field formats. The shape is (2,3):
>>> np.dtype("i4, (2,3)f8")
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
Using tuples. ``int`` is a fixed type, 3 the field's shape. ``void``
is a flexible type, here of size 10:
>>> np.dtype([('hello',(int,3)),('world',np.void,10)])
dtype([('hello', '<i4', 3), ('world', '|V10')])
Subdivide ``int16`` into 2 ``int8``'s, called x and y. 0 and 1 are
the offsets in bytes:
>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
dtype(('<i2', [('x', '|i1'), ('y', '|i1')]))
Using dictionaries. Two fields named 'gender' and 'age':
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
dtype([('gender', '|S1'), ('age', '|u1')])
Offsets in bytes, here 0 and 25:
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
dtype([('surname', '|S25'), ('age', '|u1')])
""")
##############################################################################
#
# dtype attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('alignment',
"""
The required alignment (bytes) of this data-type according to the compiler.
More information is available in the C-API section of the manual.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
"""
A character indicating the byte-order of this data-type object.
One of:
=== ==============
'=' native
'<' little-endian
'>' big-endian
'|' not applicable
=== ==============
All built-in data-type objects have byteorder either '=' or '|'.
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.byteorder
'='
>>> # endian is not relevant for 8 bit numbers
>>> np.dtype('i1').byteorder
'|'
>>> # or ASCII strings
>>> np.dtype('S2').byteorder
'|'
>>> # Even if specific code is given, and it is native
>>> # '=' is the byteorder
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> dt = np.dtype(native_code + 'i2')
>>> dt.byteorder
'='
>>> # Swapped code shows up as itself
>>> dt = np.dtype(swapped_code + 'i2')
>>> dt.byteorder == swapped_code
True
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('char',
"""A unique character code for each of the 21 different built-in types."""))
add_newdoc('numpy.core.multiarray', 'dtype', ('descr',
"""
PEP3118 interface description of the data-type.
The format is that required by the 'descr' key in the
PEP3118 `__array_interface__` attribute.
Warning: This attribute exists specifically for PEP3118 compliance, and
is not a datatype description compatible with `np.dtype`.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('fields',
"""
Dictionary of named fields defined for this data type, or ``None``.
The dictionary is indexed by keys that are the names of the fields.
Each entry in the dictionary is a tuple fully describing the field::
(dtype, offset[, title])
If present, the optional title can be any object (if it is a string
or unicode then it will also be a key in the fields dictionary,
otherwise it's meta-data). Notice also that the first two elements
of the tuple can be passed directly as arguments to the ``ndarray.getfield``
and ``ndarray.setfield`` methods.
See Also
--------
ndarray.getfield, ndarray.setfield
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> print(dt.fields)
{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('flags',
"""
Bit-flags describing how this data type is to be interpreted.
Bit-masks are in `numpy.core.multiarray` as the constants
`ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
`NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
of these flags is in C-API documentation; they are largely useful
for user-defined data-types.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('hasobject',
"""
Boolean indicating whether this dtype contains any reference-counted
objects in any fields or sub-dtypes.
Recall that what is actually in the ndarray memory representing
the Python object is the memory address of that object (a pointer).
Special handling may be required, and this attribute is useful for
distinguishing data types that may contain arbitrary Python objects
and data-types that won't.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
"""
Integer indicating how this dtype relates to the built-in dtypes.
Read-only.
= ========================================================================
0 if this is a structured array type, with fields
1 if this is a dtype compiled into numpy (such as ints, floats etc)
2 if the dtype is for a user-defined numpy type
A user-defined type uses the numpy C-API machinery to extend
numpy to handle a new array type. See
:ref:`user.user-defined-data-types` in the NumPy manual.
= ========================================================================
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.isbuiltin
1
>>> dt = np.dtype('f8')
>>> dt.isbuiltin
1
>>> dt = np.dtype([('field1', 'f8')])
>>> dt.isbuiltin
0
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isnative',
"""
Boolean indicating whether the byte order of this dtype is native
to the platform.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isalignedstruct',
"""
Boolean indicating whether the dtype is a struct which maintains
field alignment. This flag is sticky, so when combining multiple
structs together, it is preserved and produces new dtypes which
are also aligned.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('itemsize',
"""
The element size of this data-type object.
For 18 of the 21 types this number is fixed by the data-type.
For the flexible data-types, this number can be anything.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('kind',
"""
A character code (one of 'biufcmMOSUV') identifying the general kind of data.
= ======================
b boolean
i signed integer
u unsigned integer
f floating-point
c complex floating-point
m timedelta
M datetime
O object
S (byte-)string
U Unicode
V void
= ======================
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('name',
"""
A bit-width name for this data-type.
Un-sized flexible data-type objects do not have this attribute.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('names',
"""
Ordered list of field names, or ``None`` if there are no fields.
The names are ordered according to increasing byte offset. This can be
used, for example, to walk through all of the named fields in offset order.
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.names
('name', 'grades')
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('num',
"""
A unique number for each of the 21 different built-in types.
These are roughly ordered from least-to-most precision.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('shape',
"""
Shape tuple of the sub-array if this data type describes a sub-array,
and ``()`` otherwise.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('ndim',
"""
Number of dimensions of the sub-array if this data type describes a
sub-array, and ``0`` otherwise.
.. versionadded:: 1.13.0
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('str',
"""The array-protocol typestring of this data-type object."""))
add_newdoc('numpy.core.multiarray', 'dtype', ('subdtype',
"""
Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
None otherwise.
The *shape* is the fixed shape of the sub-array described by this
data type, and *item_dtype* the data type of the array.
If a field whose dtype object has this attribute is retrieved,
then the extra dimensions implied by *shape* are tacked on to
the end of the retrieved array.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('type',
"""The type object used to instantiate a scalar of this data-type."""))
##############################################################################
#
# dtype methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
"""
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. The default value ('S') results in swapping the current
byte order. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
The code does a case-insensitive check on the first letter of
`new_order` for these alternatives. For example, any of '>'
or 'B' or 'b' or 'brian' are valid to specify big-endian.
Returns
-------
new_dtype : dtype
New dtype object with the given change to the byte order.
Notes
-----
Changes are also made in all fields and sub-arrays of the data type.
Examples
--------
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> native_dt = np.dtype(native_code+'i2')
>>> swapped_dt = np.dtype(swapped_code+'i2')
>>> native_dt.newbyteorder('S') == swapped_dt
True
>>> native_dt.newbyteorder() == swapped_dt
True
>>> native_dt == swapped_dt.newbyteorder('S')
True
>>> native_dt == swapped_dt.newbyteorder('=')
True
>>> native_dt == swapped_dt.newbyteorder('N')
True
>>> native_dt == native_dt.newbyteorder('|')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('<')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('L')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('>')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('B')
True
"""))
##############################################################################
#
# Datetime-related Methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'busdaycalendar',
"""
busdaycalendar(weekmask='1111100', holidays=None)
A business day calendar object that efficiently stores information
defining valid days for the busday family of functions.
The default valid days are Monday through Friday ("business days").
A busdaycalendar object can be specified with any set of weekly
valid days, plus an optional "holiday" dates that always will be invalid.
Once a busdaycalendar object is created, the weekmask and holidays
cannot be modified.
.. versionadded:: 1.7.0
Parameters
----------
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates, no matter which
weekday they fall upon. Holiday dates may be specified in any
order, and NaT (not-a-time) dates are ignored. This list is
saved in a normalized form that is suited for fast calculations
of valid days.
Returns
-------
out : busdaycalendar
A business day calendar object containing the specified
weekmask and holidays values.
See Also
--------
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Attributes
----------
Note: once a busdaycalendar object is created, you cannot modify the
weekmask or holidays. The attributes return copies of internal data.
weekmask : (copy) seven-element array of bool
holidays : (copy) sorted array of datetime64[D]
Examples
--------
>>> # Some important days in July
... bdd = np.busdaycalendar(
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
>>> # Default is Monday to Friday weekdays
... bdd.weekmask
array([ True, True, True, True, True, False, False], dtype='bool')
>>> # Any holidays already on the weekend are removed
... bdd.holidays
array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')
""")
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('weekmask',
"""A copy of the seven-element boolean mask indicating valid days."""))
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('holidays',
"""A copy of the holiday array indicating additional invalid days."""))
add_newdoc('numpy.core.multiarray', 'is_busday',
"""
is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
Calculates which of the given dates are valid days, and which are not.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of bool, optional
If provided, this array is filled with the result.
Returns
-------
out : array of bool
An array with the same shape as ``dates``, containing True for
each valid day, and False for each invalid day.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # The weekdays are Friday, Saturday, and Monday
... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
array([False, False, True], dtype='bool')
""")
add_newdoc('numpy.core.multiarray', 'busday_offset',
"""
busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)
First adjusts the date to fall on a valid day according to
the ``roll`` rule, then applies offsets to the given dates
counted in valid days.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
offsets : array_like of int
The array of offsets, which is broadcast with ``dates``.
roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
How to treat dates that do not fall on a valid day. The default
is 'raise'.
* 'raise' means to raise an exception for an invalid day.
* 'nat' means to return a NaT (not-a-time) for an invalid day.
* 'forward' and 'following' mean to take the first valid day
later in time.
* 'backward' and 'preceding' mean to take the first valid day
earlier in time.
* 'modifiedfollowing' means to take the first valid day
later in time unless it is across a Month boundary, in which
case to take the first valid day earlier in time.
* 'modifiedpreceding' means to take the first valid day
earlier in time unless it is across a Month boundary, in which
case to take the first valid day later in time.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of datetime64[D], optional
If provided, this array is filled with the result.
Returns
-------
out : array of datetime64[D]
An array with a shape from broadcasting ``dates`` and ``offsets``
together, containing the dates with offsets applied.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # First business day in October 2011 (not accounting for holidays)
... np.busday_offset('2011-10', 0, roll='forward')
numpy.datetime64('2011-10-03','D')
>>> # Last business day in February 2012 (not accounting for holidays)
... np.busday_offset('2012-03', -1, roll='forward')
numpy.datetime64('2012-02-29','D')
>>> # Third Wednesday in January 2011
... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
numpy.datetime64('2011-01-19','D')
>>> # 2012 Mother's Day in Canada and the U.S.
... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
numpy.datetime64('2012-05-13','D')
>>> # First business day on or after a date
... np.busday_offset('2011-03-20', 0, roll='forward')
numpy.datetime64('2011-03-21','D')
>>> np.busday_offset('2011-03-22', 0, roll='forward')
numpy.datetime64('2011-03-22','D')
>>> # First business day after a date
... np.busday_offset('2011-03-20', 1, roll='backward')
numpy.datetime64('2011-03-21','D')
>>> np.busday_offset('2011-03-22', 1, roll='backward')
numpy.datetime64('2011-03-23','D')
""")
add_newdoc('numpy.core.multiarray', 'busday_count',
"""
busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)
Counts the number of valid days between `begindates` and
`enddates`, not including the day of `enddates`.
If ``enddates`` specifies a date value that is earlier than the
corresponding ``begindates`` date value, the count will be negative.
.. versionadded:: 1.7.0
Parameters
----------
begindates : array_like of datetime64[D]
The array of the first dates for counting.
enddates : array_like of datetime64[D]
The array of the end dates for counting, which are excluded
from the count themselves.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of int, optional
If provided, this array is filled with the result.
Returns
-------
out : array of int
An array with a shape from broadcasting ``begindates`` and ``enddates``
together, containing the number of valid days between
the begin and end dates.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
Examples
--------
>>> # Number of weekdays in January 2011
... np.busday_count('2011-01', '2011-02')
21
>>> # Number of weekdays in 2011
... np.busday_count('2011', '2012')
260
>>> # Number of Saturdays in 2011
... np.busday_count('2011', '2012', weekmask='Sat')
53
""")
add_newdoc('numpy.core.multiarray', 'normalize_axis_index',
"""
normalize_axis_index(axis, ndim, msg_prefix=None)
Normalizes an axis index, `axis`, such that is a valid positive index into
the shape of array with `ndim` dimensions. Raises an AxisError with an
appropriate message if this is not possible.
Used internally by all axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int
The un-normalized index of the axis. Can be negative
ndim : int
The number of dimensions of the array that `axis` should be normalized
against
msg_prefix : str
A prefix to put before the message, typically the name of the argument
Returns
-------
normalized_axis : int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If the axis index is invalid, when `-ndim <= axis < ndim` is false.
Examples
--------
>>> normalize_axis_index(0, ndim=3)
0
>>> normalize_axis_index(1, ndim=3)
1
>>> normalize_axis_index(-1, ndim=3)
2
>>> normalize_axis_index(3, ndim=3)
Traceback (most recent call last):
...
AxisError: axis 3 is out of bounds for array of dimension 3
>>> normalize_axis_index(-4, ndim=3, msg_prefix='axes_arg')
Traceback (most recent call last):
...
AxisError: axes_arg: axis -4 is out of bounds for array of dimension 3
""")
add_newdoc('numpy.core.multiarray', 'datetime_as_string',
"""
datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')
Convert an array of datetimes into an array of strings.
Parameters
----------
arr : array_like of datetime64
The array of UTC timestamps to format.
unit : str
One of None, 'auto', or a :ref:`datetime unit <arrays.dtypes.dateunits>`.
timezone : {'naive', 'UTC', 'local'} or tzinfo
Timezone information to use when displaying the datetime. If 'UTC', end
with a Z to indicate UTC time. If 'local', convert to the local timezone
first, and suffix with a +-#### timezone offset. If a tzinfo object,
then do as with 'local', but use the specified timezone.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}
Casting to allow when changing between datetime units.
Returns
-------
str_arr : ndarray
An array of strings the same shape as `arr`.
Examples
--------
>>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')
>>> d
array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',
'2002-10-27T07:30'], dtype='datetime64[m]')
Setting the timezone to UTC shows the same information, but with a Z suffix
>>> np.datetime_as_string(d, timezone='UTC')
array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',
'2002-10-27T07:30Z'], dtype='<U35')
Note that we picked datetimes that cross a DST boundary. Passing in a
``pytz`` timezone object will print the appropriate offset
>>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))
array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',
'2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='<U39')
Passing in a unit will change the precision
>>> np.datetime_as_string(d, unit='h')
array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],
dtype='<U32')
>>> np.datetime_as_string(d, unit='s')
array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',
'2002-10-27T07:30:00'], dtype='<U38')
'casting' can be used to specify whether precision can be changed
>>> np.datetime_as_string(d, unit='h', casting='safe')
TypeError: Cannot create a datetime string as units 'h' from a NumPy
datetime with units 'm' according to the rule 'safe'
""")
add_newdoc('numpy.core.multiarray', 'datetime_data',
"""
datetime_data(dtype, /)
Get information about the step size of a date or time type.
The returned tuple can be passed as the second argument of `datetime64` and
`timedelta64`.
Parameters
----------
dtype : dtype
The dtype object, which must be a `datetime64` or `timedelta64` type.
Returns
-------
unit : str
The :ref:`datetime unit <arrays.dtypes.dateunits>` on which this dtype
is based.
count : int
The number of base units in a step.
Examples
--------
>>> dt_25s = np.dtype('timedelta64[25s]')
>>> np.datetime_data(dt_25s)
('s', 25)
>>> np.array(10, dt_25s).astype('timedelta64[s]')
array(250, dtype='timedelta64[s]')
The result can be used to construct a datetime that uses the same units
as a timedelta::
>>> np.datetime64('2010', np.datetime_data(dt_25s))
numpy.datetime64('2010-01-01T00:00:00','25s')
""")
##############################################################################
#
# nd_grid instances
#
##############################################################################
add_newdoc('numpy.lib.index_tricks', 'mgrid',
"""
`nd_grid` instance which returns a dense multi-dimensional "meshgrid".
An instance of `numpy.lib.index_tricks.nd_grid` which returns an dense
(or fleshed out) mesh-grid when indexed, so that each returned argument
has the same shape. The dimensions and number of the output arrays are
equal to the number of indexing dimensions. If the step length is not a
complex number, then the stop is not inclusive.
However, if the step length is a **complex number** (e.g. 5j), then
the integer part of its magnitude is interpreted as specifying the
number of points to create between the start and stop values, where
the stop value **is inclusive**.
Returns
----------
mesh-grid `ndarrays` all of the same dimensions
See Also
--------
numpy.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
ogrid : like mgrid but returns open (not fleshed out) mesh grids
r_ : array concatenator
Examples
--------
>>> np.mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
>>> np.mgrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
""")
add_newdoc('numpy.lib.index_tricks', 'ogrid',
"""
`nd_grid` instance which returns an open multi-dimensional "meshgrid".
An instance of `numpy.lib.index_tricks.nd_grid` which returns an open
(i.e. not fleshed out) mesh-grid when indexed, so that only one dimension
of each returned array is greater than 1. The dimension and number of the
output arrays are equal to the number of indexing dimensions. If the step
length is not a complex number, then the stop is not inclusive.
However, if the step length is a **complex number** (e.g. 5j), then
the integer part of its magnitude is interpreted as specifying the
number of points to create between the start and stop values, where
the stop value **is inclusive**.
Returns
----------
mesh-grid `ndarrays` with only one dimension :math:`\\neq 1`
See Also
--------
np.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
r_ : array concatenator
Examples
--------
>>> from numpy import ogrid
>>> ogrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
>>> ogrid[0:5,0:5]
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
""")
##############################################################################
#
# Documentation for `generic` attributes and methods
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'generic',
"""
Base class for numpy scalar types.
Class from which most (all?) numpy scalar types are derived. For
consistency, exposes the same API as `ndarray`, despite many
consequent attributes being either "get-only," or completely irrelevant.
This is the class from which it is strongly suggested users should derive
custom scalar types.
""")
# Attributes
add_newdoc('numpy.core.numerictypes', 'generic', ('T',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('base',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('data',
"""Pointer to start of data."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dtype',
"""Get array data-descriptor."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flags',
"""The integer value of flags."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flat',
"""A 1-D view of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('imag',
"""The imaginary part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('itemsize',
"""The length of one element in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('nbytes',
"""The length of the scalar in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ndim',
"""The number of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('real',
"""The real part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('shape',
"""Tuple of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('size',
"""The number of elements in the gentype."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('strides',
"""Tuple of bytes steps in each dimension."""))
# Methods
add_newdoc('numpy.core.numerictypes', 'generic', ('all',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('any',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argmax',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argmin',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argsort',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('astype',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('byteswap',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('choose',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('clip',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('compress',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('conjugate',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('copy',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('cumprod',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('cumsum',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('diagonal',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dump',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dumps',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('fill',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flatten',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('getfield',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('item',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('itemset',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('max',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('mean',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('min',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('newbyteorder',
"""
newbyteorder(new_order='S')
Return a new `dtype` with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The `new_order` code can be any from the following:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
Parameters
----------
new_order : str, optional
Byte order to force; a value from the byte order specifications
above. The default value ('S') results in swapping the current
byte order. The code does a case-insensitive check on the first
letter of `new_order` for the alternatives above. For example,
any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
Returns
-------
new_dtype : dtype
New `dtype` object with the given change to the byte order.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('nonzero',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('prod',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ptp',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('put',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ravel',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('repeat',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('reshape',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('resize',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('round',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('searchsorted',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('setfield',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('setflags',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('sort',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('squeeze',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('std',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('sum',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('swapaxes',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('take',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tofile',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tolist',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tostring',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('trace',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('transpose',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('var',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('view',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
##############################################################################
#
# Documentation for other scalar classes
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'bool_',
"""NumPy's Boolean type. Character code: ``?``. Alias: bool8""")
add_newdoc('numpy.core.numerictypes', 'complex64',
"""
Complex number type composed of two 32 bit floats. Character code: 'F'.
""")
add_newdoc('numpy.core.numerictypes', 'complex128',
"""
Complex number type composed of two 64 bit floats. Character code: 'D'.
Python complex compatible.
""")
add_newdoc('numpy.core.numerictypes', 'complex256',
"""
Complex number type composed of two 128-bit floats. Character code: 'G'.
""")
add_newdoc('numpy.core.numerictypes', 'float32',
"""
32-bit floating-point number. Character code 'f'. C float compatible.
""")
add_newdoc('numpy.core.numerictypes', 'float64',
"""
64-bit floating-point number. Character code 'd'. Python float compatible.
""")
add_newdoc('numpy.core.numerictypes', 'float96',
"""
""")
add_newdoc('numpy.core.numerictypes', 'float128',
"""
128-bit floating-point number. Character code: 'g'. C long float
compatible.
""")
add_newdoc('numpy.core.numerictypes', 'int8',
"""8-bit integer. Character code ``b``. C char compatible.""")
add_newdoc('numpy.core.numerictypes', 'int16',
"""16-bit integer. Character code ``h``. C short compatible.""")
add_newdoc('numpy.core.numerictypes', 'int32',
"""32-bit integer. Character code 'i'. C int compatible.""")
add_newdoc('numpy.core.numerictypes', 'int64',
"""64-bit integer. Character code 'l'. Python int compatible.""")
add_newdoc('numpy.core.numerictypes', 'object_',
"""Any Python object. Character code: 'O'.""")
| mit |
Maikflow/django_test | lib/python2.7/site-packages/Django-1.7.1-py2.7.egg/django/core/files/images.py | 66 | 2171 | """
Utility functions for handling images.
Requires Pillow (or PIL), as you might imagine.
"""
import zlib
from django.core.files import File
class ImageFile(File):
"""
A mixin for use alongside django.core.files.base.File, which provides
additional features for dealing with images.
"""
def _get_width(self):
return self._get_image_dimensions()[0]
width = property(_get_width)
def _get_height(self):
return self._get_image_dimensions()[1]
height = property(_get_height)
def _get_image_dimensions(self):
if not hasattr(self, '_dimensions_cache'):
close = self.closed
self.open()
self._dimensions_cache = get_image_dimensions(self, close=close)
return self._dimensions_cache
def get_image_dimensions(file_or_path, close=False):
"""
Returns the (width, height) of an image, given an open file or a path. Set
'close' to True to close the file at the end if it is initially in an open
state.
"""
from django.utils.image import ImageFile as PILImageFile
p = PILImageFile.Parser()
if hasattr(file_or_path, 'read'):
file = file_or_path
file_pos = file.tell()
file.seek(0)
else:
file = open(file_or_path, 'rb')
close = True
try:
# Most of the time PIL only needs a small chunk to parse the image and
# get the dimensions, but with some TIFF files PIL needs to parse the
# whole file.
chunk_size = 1024
while 1:
data = file.read(chunk_size)
if not data:
break
try:
p.feed(data)
except zlib.error as e:
# ignore zlib complaining on truncated stream, just feed more
# data to parser (ticket #19457).
if e.args[0].startswith("Error -5"):
pass
else:
raise
if p.image:
return p.image.size
chunk_size *= 2
return None
finally:
if close:
file.close()
else:
file.seek(file_pos)
| gpl-2.0 |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/click/_winconsole.py | 195 | 7790 | # -*- coding: utf-8 -*-
# This module is based on the excellent work by Adam Bartoš who
# provided a lot of what went into the implementation here in
# the discussion to issue1602 in the Python bug tracker.
#
# There are some general differences in regards to how this works
# compared to the original patches as we do not need to patch
# the entire interpreter but just work in our little world of
# echo and prmopt.
import io
import os
import sys
import zlib
import time
import ctypes
import msvcrt
from click._compat import _NonClosingTextIOWrapper, text_type, PY2
from ctypes import byref, POINTER, c_int, c_char, c_char_p, \
c_void_p, py_object, c_ssize_t, c_ulong, windll, WINFUNCTYPE
try:
from ctypes import pythonapi
PyObject_GetBuffer = pythonapi.PyObject_GetBuffer
PyBuffer_Release = pythonapi.PyBuffer_Release
except ImportError:
pythonapi = None
from ctypes.wintypes import LPWSTR, LPCWSTR
c_ssize_p = POINTER(c_ssize_t)
kernel32 = windll.kernel32
GetStdHandle = kernel32.GetStdHandle
ReadConsoleW = kernel32.ReadConsoleW
WriteConsoleW = kernel32.WriteConsoleW
GetLastError = kernel32.GetLastError
GetCommandLineW = WINFUNCTYPE(LPWSTR)(
('GetCommandLineW', windll.kernel32))
CommandLineToArgvW = WINFUNCTYPE(
POINTER(LPWSTR), LPCWSTR, POINTER(c_int))(
('CommandLineToArgvW', windll.shell32))
STDIN_HANDLE = GetStdHandle(-10)
STDOUT_HANDLE = GetStdHandle(-11)
STDERR_HANDLE = GetStdHandle(-12)
PyBUF_SIMPLE = 0
PyBUF_WRITABLE = 1
ERROR_SUCCESS = 0
ERROR_NOT_ENOUGH_MEMORY = 8
ERROR_OPERATION_ABORTED = 995
STDIN_FILENO = 0
STDOUT_FILENO = 1
STDERR_FILENO = 2
EOF = b'\x1a'
MAX_BYTES_WRITTEN = 32767
class Py_buffer(ctypes.Structure):
_fields_ = [
('buf', c_void_p),
('obj', py_object),
('len', c_ssize_t),
('itemsize', c_ssize_t),
('readonly', c_int),
('ndim', c_int),
('format', c_char_p),
('shape', c_ssize_p),
('strides', c_ssize_p),
('suboffsets', c_ssize_p),
('internal', c_void_p)
]
if PY2:
_fields_.insert(-1, ('smalltable', c_ssize_t * 2))
# On PyPy we cannot get buffers so our ability to operate here is
# serverly limited.
if pythonapi is None:
get_buffer = None
else:
def get_buffer(obj, writable=False):
buf = Py_buffer()
flags = PyBUF_WRITABLE if writable else PyBUF_SIMPLE
PyObject_GetBuffer(py_object(obj), byref(buf), flags)
try:
buffer_type = c_char * buf.len
return buffer_type.from_address(buf.buf)
finally:
PyBuffer_Release(byref(buf))
class _WindowsConsoleRawIOBase(io.RawIOBase):
def __init__(self, handle):
self.handle = handle
def isatty(self):
io.RawIOBase.isatty(self)
return True
class _WindowsConsoleReader(_WindowsConsoleRawIOBase):
def readable(self):
return True
def readinto(self, b):
bytes_to_be_read = len(b)
if not bytes_to_be_read:
return 0
elif bytes_to_be_read % 2:
raise ValueError('cannot read odd number of bytes from '
'UTF-16-LE encoded console')
buffer = get_buffer(b, writable=True)
code_units_to_be_read = bytes_to_be_read // 2
code_units_read = c_ulong()
rv = ReadConsoleW(self.handle, buffer, code_units_to_be_read,
byref(code_units_read), None)
if GetLastError() == ERROR_OPERATION_ABORTED:
# wait for KeyboardInterrupt
time.sleep(0.1)
if not rv:
raise OSError('Windows error: %s' % GetLastError())
if buffer[0] == EOF:
return 0
return 2 * code_units_read.value
class _WindowsConsoleWriter(_WindowsConsoleRawIOBase):
def writable(self):
return True
@staticmethod
def _get_error_message(errno):
if errno == ERROR_SUCCESS:
return 'ERROR_SUCCESS'
elif errno == ERROR_NOT_ENOUGH_MEMORY:
return 'ERROR_NOT_ENOUGH_MEMORY'
return 'Windows error %s' % errno
def write(self, b):
bytes_to_be_written = len(b)
buf = get_buffer(b)
code_units_to_be_written = min(bytes_to_be_written,
MAX_BYTES_WRITTEN) // 2
code_units_written = c_ulong()
WriteConsoleW(self.handle, buf, code_units_to_be_written,
byref(code_units_written), None)
bytes_written = 2 * code_units_written.value
if bytes_written == 0 and bytes_to_be_written > 0:
raise OSError(self._get_error_message(GetLastError()))
return bytes_written
class ConsoleStream(object):
def __init__(self, text_stream, byte_stream):
self._text_stream = text_stream
self.buffer = byte_stream
@property
def name(self):
return self.buffer.name
def write(self, x):
if isinstance(x, text_type):
return self._text_stream.write(x)
try:
self.flush()
except Exception:
pass
return self.buffer.write(x)
def writelines(self, lines):
for line in lines:
self.write(line)
def __getattr__(self, name):
return getattr(self._text_stream, name)
def isatty(self):
return self.buffer.isatty()
def __repr__(self):
return '<ConsoleStream name=%r encoding=%r>' % (
self.name,
self.encoding,
)
def _get_text_stdin(buffer_stream):
text_stream = _NonClosingTextIOWrapper(
io.BufferedReader(_WindowsConsoleReader(STDIN_HANDLE)),
'utf-16-le', 'strict', line_buffering=True)
return ConsoleStream(text_stream, buffer_stream)
def _get_text_stdout(buffer_stream):
text_stream = _NonClosingTextIOWrapper(
_WindowsConsoleWriter(STDOUT_HANDLE),
'utf-16-le', 'strict', line_buffering=True)
return ConsoleStream(text_stream, buffer_stream)
def _get_text_stderr(buffer_stream):
text_stream = _NonClosingTextIOWrapper(
_WindowsConsoleWriter(STDERR_HANDLE),
'utf-16-le', 'strict', line_buffering=True)
return ConsoleStream(text_stream, buffer_stream)
if PY2:
def _hash_py_argv():
return zlib.crc32('\x00'.join(sys.argv[1:]))
_initial_argv_hash = _hash_py_argv()
def _get_windows_argv():
argc = c_int(0)
argv_unicode = CommandLineToArgvW(GetCommandLineW(), byref(argc))
argv = [argv_unicode[i] for i in range(0, argc.value)]
if not hasattr(sys, 'frozen'):
argv = argv[1:]
while len(argv) > 0:
arg = argv[0]
if not arg.startswith('-') or arg == '-':
break
argv = argv[1:]
if arg.startswith(('-c', '-m')):
break
return argv[1:]
_stream_factories = {
0: _get_text_stdin,
1: _get_text_stdout,
2: _get_text_stderr,
}
def _get_windows_console_stream(f, encoding, errors):
if get_buffer is not None and \
encoding in ('utf-16-le', None) \
and errors in ('strict', None) and \
hasattr(f, 'isatty') and f.isatty():
func = _stream_factories.get(f.fileno())
if func is not None:
if not PY2:
f = getattr(f, 'buffer')
if f is None:
return None
else:
# If we are on Python 2 we need to set the stream that we
# deal with to binary mode as otherwise the exercise if a
# bit moot. The same problems apply as for
# get_binary_stdin and friends from _compat.
msvcrt.setmode(f.fileno(), os.O_BINARY)
return func(f)
| mit |
Alex-van-der-Peet/bitcoin | qa/rpc-tests/test_framework/blocktools.py | 93 | 2057 | # blocktools.py - utilities for manipulating blocks and transactions
#
# Distributed under the MIT/X11 software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
#
from mininode import *
from script import CScript, CScriptOp
# Create a block (with regtest difficulty)
def create_block(hashprev, coinbase, nTime=None):
block = CBlock()
if nTime is None:
import time
block.nTime = int(time.time()+600)
else:
block.nTime = nTime
block.hashPrevBlock = hashprev
block.nBits = 0x207fffff # Will break after a difficulty adjustment...
block.vtx.append(coinbase)
block.hashMerkleRoot = block.calc_merkle_root()
block.calc_sha256()
return block
def serialize_script_num(value):
r = bytearray(0)
if value == 0:
return r
neg = value < 0
absvalue = -value if neg else value
while (absvalue):
r.append(chr(absvalue & 0xff))
absvalue >>= 8
if r[-1] & 0x80:
r.append(0x80 if neg else 0)
elif neg:
r[-1] |= 0x80
return r
counter=1
# Create an anyone-can-spend coinbase transaction, assuming no miner fees
def create_coinbase(heightAdjust = 0):
global counter
coinbase = CTransaction()
coinbase.vin.append(CTxIn(COutPoint(0, 0xffffffff),
ser_string(serialize_script_num(counter+heightAdjust)), 0xffffffff))
counter += 1
coinbaseoutput = CTxOut()
coinbaseoutput.nValue = 50*100000000
halvings = int((counter+heightAdjust)/150) # regtest
coinbaseoutput.nValue >>= halvings
coinbaseoutput.scriptPubKey = ""
coinbase.vout = [ coinbaseoutput ]
coinbase.calc_sha256()
return coinbase
# Create a transaction with an anyone-can-spend output, that spends the
# nth output of prevtx.
def create_transaction(prevtx, n, sig, value):
tx = CTransaction()
assert(n < len(prevtx.vout))
tx.vin.append(CTxIn(COutPoint(prevtx.sha256, n), sig, 0xffffffff))
tx.vout.append(CTxOut(value, ""))
tx.calc_sha256()
return tx
| mit |
google-research/meta-dataset | meta_dataset/trainer.py | 1 | 71223 | # coding=utf-8
# Copyright 2022 The Meta-Dataset Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python2, python3
"""Interface for a learner that uses BenchmarkReaderDataSource to get data."""
# TODO(lamblinp): Update variable names to be more consistent
# - target, class_idx, label
# - support, query
# TODO(lamblinp): Simplify the logic around performing evaluation on the
# `TRAIN_SPLIT` by, for instance, recording which data is episodic, and which
# split it is coming from (independently from how it is used).
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import functools
import os
import re
from absl import logging
import gin.tf
from meta_dataset import distribute_utils
from meta_dataset import learners
from meta_dataset.data import dataset_spec as dataset_spec_lib
from meta_dataset.data import learning_spec
from meta_dataset.data import pipeline
from meta_dataset.data import providers
from meta_dataset.data import read_episodes
from meta_dataset.learners import experimental as experimental_learners
from meta_dataset.models import functional_backbones
import numpy as np
import six
from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 as tf
# Enable tf.data optimizations, which are applied to the input data pipeline.
# It may be helpful to disable them when investigating regressions due to
# changes in tf.data (see b/121130181 for instance), but they seem to be helpful
# (or at least not detrimental) in general.
ENABLE_DATA_OPTIMIZATIONS = True
DATASETS_WITH_EXAMPLE_SPLITS = ()
TF_DATA_OPTIONS = tf.data.Options()
if not ENABLE_DATA_OPTIMIZATIONS:
# The Options object can be used to control which static or dynamic
# optimizations to apply.
TF_DATA_OPTIONS.experimental_optimization.apply_default_optimizations = False
# Objective labels for hyperparameter optimization.
ACC_MEAN_FORMAT_STRING = '%s_acc/mean'
ACC_CI95_FORMAT_STRING = '%s_acc/ci95'
# TODO(eringrant): Use `learning_spec.Split.TRAIN`, `learning_spec.Split.VALID`,
# and `learning_spec.Split.TEST` instead of string constants, and replace all
# remaining string redefinitions.
TRAIN_SPLIT = 'train'
VALID_SPLIT = 'valid'
TEST_SPLIT = 'test'
FLAGS = tf.flags.FLAGS
class UnexpectedSplitError(ValueError):
def __init__(self,
unexpected_split,
expected_splits=(TRAIN_SPLIT, TEST_SPLIT, VALID_SPLIT)):
super(UnexpectedSplitError,
self).__init__('Split must be one of {}, but received `{}`. '.format(
expected_splits, unexpected_split))
@gin.configurable('benchmark')
def get_datasets_and_restrictions(train_datasets='',
eval_datasets='',
use_dumped_episodes=False,
restrict_classes=None,
restrict_num_per_class=None):
"""Gets the list of dataset names and possible restrictions on their classes.
Args:
train_datasets: A string of comma-separated dataset names for training.
eval_datasets: A string of comma-separated dataset names for evaluation.
use_dumped_episodes: bool, if True `eval_datasets` are prefixed with
`dumped` to trigger evaluation on dumped episodes instead of on the fly
sampling.
restrict_classes: If provided, a dict that maps dataset names to a dict that
specifies for each of `TRAIN_SPLIT`, `VALID_SPLIT` and `TEST_SPLIT` the
number of classes to restrict to. This can lead to some classes of a
particular split of a particular dataset never participating in episode
creation.
restrict_num_per_class: If provided, a dict that maps dataset names to a
dict that specifies for each of `meta_dataset.trainer.TRAIN_SPLIT`,
`meta_dataset.trainer.VALID_SPLIT` and `meta_dataset.trainer.TEST_SPLIT`
the number of examples per class to restrict to. For datasets / splits
that are not specified, no restriction is applied.
Returns:
Two lists of dataset names and two possibly empty dictionaries.
"""
if restrict_classes is None:
restrict_classes = {}
if restrict_num_per_class is None:
restrict_num_per_class = {}
train_datasets = [d.strip() for d in train_datasets.split(',')]
eval_datasets = [d.strip() for d in eval_datasets.split(',')]
if use_dumped_episodes:
eval_datasets = ['dumped_%s' % ds for ds in eval_datasets]
return train_datasets, eval_datasets, restrict_classes, restrict_num_per_class
def apply_dataset_options(dataset):
"""Apply the module-wide set of dataset options to dataset.
In particular, this is used to enable or disable tf.data optimizations.
This applies to the whole pipeline, so we can just set it at the end.
Args:
dataset: a tf.data.Dataset object.
Returns:
A tf.data.Dataset object with options applied.
"""
return dataset.with_options(TF_DATA_OPTIONS)
def compute_class_proportions(unique_class_ids, shots, dataset_spec):
"""Computes the proportion of the total number of examples appearing as shots.
Args:
unique_class_ids: A 1D int Tensor of unique class IDs.
shots: A 1D Tensor of the number of shots for each class in
`unique_class_ids`.
dataset_spec: A DatasetSpecification that contains informations about the
class labels in `unique_class_ids`.
Returns:
A 1D Tensor with the proportion of examples appearing as shots per class in
`unique_class_ids`, normalized by the total number of examples for each
class in the dataset according to `dataset_spec`.
"""
# Get the total number of examples of each class in the dataset.
num_dataset_classes = len(dataset_spec.images_per_class)
num_images_per_class = [
dataset_spec.get_total_images_per_class(class_id)
for class_id in range(num_dataset_classes)
]
# Make sure that `unique_class_ids` are valid indices of
# `num_images_per_class`. This is important since `tf.gather` will fail
# silently and return zeros otherwise.
num_classes = tf.shape(num_images_per_class)[0]
check_valid_inds_op = tf.assert_less(unique_class_ids, num_classes)
with tf.control_dependencies([check_valid_inds_op]):
# Get the total number of examples of each class that is in the episode.
num_images_per_class = tf.gather(num_images_per_class,
unique_class_ids) # [?, ]
# Get the proportions of examples of each class that appear in the episode.
class_props = tf.truediv(shots, num_images_per_class)
return class_props
def get_split_enum(split):
"""Returns the Enum value corresponding to the given split.
Args:
split: A string, one of TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT.
Raises:
UnexpectedSplitError: split not TRAIN_SPLIT, VALID_SPLIT, or TEST_SPLIT.
"""
# Get the int representing the chosen split.
if split == TRAIN_SPLIT:
split_enum = learning_spec.Split.TRAIN
elif split == VALID_SPLIT:
split_enum = learning_spec.Split.VALID
elif split == TEST_SPLIT:
split_enum = learning_spec.Split.TEST
else:
raise UnexpectedSplitError(split)
return split_enum
def restore_or_log_informative_error(saver, sess, checkpoint_to_restore):
"""Attempt to restore from `checkpoint_to_restore` in `sess` using `saver`."""
try:
saver.restore(sess, checkpoint_to_restore)
except tf.errors.NotFoundError as e:
logging.error('Tried to restore from checkpoint %s but failed.',
checkpoint_to_restore)
raise e
else:
logging.info('Restored from checkpoint %s.', checkpoint_to_restore)
# TODO(eringrant): Split the current `Trainer` class into `Trainer` and
# `Evaluator` classes to partition the constructor arguments into meaningful
# groups.
# TODO(eringrant): Refactor the current `Trainer` class to more transparently
# deal with operations per split, since the present logic surrounding the
# `eval_finegrainedness_split` is confusing.
# TODO(eringrant): Better organize `Trainer` Gin configurations, which are
# currently set in many configuration files.
@gin.configurable
class Trainer(object):
"""A Trainer for training a Learner on data provided by ReaderDataSource."""
def __init__(
self,
num_updates,
batch_size,
num_eval_episodes,
checkpoint_every,
validate_every,
log_every,
train_learner_class,
eval_learner_class,
is_training,
checkpoint_to_restore,
learning_rate,
decay_learning_rate,
decay_every,
decay_rate,
normalized_gradient_descent,
experiment_name,
pretrained_source,
train_dataset_list,
eval_dataset_list,
restrict_classes,
restrict_num_per_class,
checkpoint_dir,
summary_dir,
records_root_dir,
eval_finegrainedness,
eval_finegrainedness_split,
eval_imbalance_dataset,
omit_from_saving_and_reloading,
eval_split,
train_episode_config,
eval_episode_config,
data_config,
distribute,
enable_tf_optimizations):
# pyformat: disable
"""Initializes a Trainer.
Args:
num_updates: An integer, the number of training updates.
batch_size: An integer, the size of batches for non-episodic models.
num_eval_episodes: An integer, the number of episodes for evaluation.
checkpoint_every: An integer, the number of episodes between consecutive
checkpoints.
validate_every: An integer, the number of episodes between consecutive
validations.
log_every: An integer, the number of episodes between consecutive logging.
train_learner_class: A Learner to be used for meta-training.
eval_learner_class: A Learner to be used for meta-validation or
meta-testing.
is_training: Bool, whether or not to train or just evaluate.
checkpoint_to_restore: A string, the path to a checkpoint from which to
restore variables.
learning_rate: A float, the meta-learning learning rate.
decay_learning_rate: A boolean, whether to decay the learning rate.
decay_every: An integer, the learning rate is decayed for every multiple
of this value.
decay_rate: A float, the decay to apply to the learning rate.
normalized_gradient_descent: A boolean, whether to use normalized
gradient descent in addition to ADAM; improves stability for
crosstransformers.
experiment_name: A string, a name for the experiment.
pretrained_source: A string, the pretraining setup to use.
train_dataset_list: A list of names of datasets to train on. This can be
any subset of the supported datasets.
eval_dataset_list: A list of names of datasets to evaluate on either for
validation during train or for final test evaluation, depending on the
nature of the experiment, as dictated by `is_training'.
restrict_classes: A dict that maps dataset names to a dict that specifies
for each of TRAIN_SPLIT, VALID_SPLIT and TEST_SPLIT the number of
classes to restrict to. This can lead to some classes of a particular
split of a particular dataset never participating in episode creation.
restrict_num_per_class: A dict that maps dataset names to a dict that
specifies for each of TRAIN_SPLIT, VALID_SPLIT and TEST_SPLIT the number
of examples per class to restrict to. For datasets / splits that are not
mentioned, no restriction is applied. If restrict_num_per_class is the
empty dict, no restriction is applied to any split of any dataset.
checkpoint_dir: A string, the path to the checkpoint directory, or None if
no checkpointing should occur.
summary_dir: A string, the path to the checkpoint directory, or None if no
summaries should be saved.
records_root_dir: A string, the path to the dataset records directory.
eval_finegrainedness: Whether to perform binary ImageNet evaluation for
assessing the performance on fine- vs coarse- grained tasks.
eval_finegrainedness_split: The subgraph of ImageNet to perform the
aforementioned analysis on. Notably, if this is TRAIN_SPLIT, we need to
ensure that an training data is used episodically, even if the given
model is the baseline model which usually uses batches for training.
eval_imbalance_dataset: A dataset on which to perform evaluation for
assessing how class imbalance affects performance in binary episodes. By
default it is empty and no imbalance analysis is performed.
omit_from_saving_and_reloading: A list of strings that specifies
substrings of variable names that should not be reloaded.
eval_split: One of the constants TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT
or None, according to the split whose results we want to
use for the analysis.
train_episode_config: An instance of EpisodeDescriptionConfig (in
data/config.py). This is a config for setting the ways and shots of
training episodes or the parameters for sampling them, if variable.
eval_episode_config: An instance of EpisodeDescriptionConfig. Analogous to
train_episode_config but used for eval episodes (validation or testing).
data_config: A DataConfig, the data configuration.
distribute: (Experimental) use tf.distribute to distribute computation
across multiple GPUs using a MirroredStrategy. This has been tested
with CrossTransformers, and may work with other episodic learners.
It will split Episodes into multiple EpisodePieces before passing them
to the learners, where each EpisodePiece contains a portion of the
original episode's query and support-set images. Batch learners are
not currently supported.
enable_tf_optimizations: Enable TensorFlow optimizations. It can add a
few minutes to the first calls to session.run(), but decrease memory
usage.
Raises:
RuntimeError: If requested to meta-learn the initialization of the linear
layer weights but they are unexpectedly omitted from saving/restoring.
UnexpectedSplitError: If split configuration is not as expected.
"""
# pyformat: enable
self.num_updates = num_updates
self.batch_size = batch_size
self.num_eval_episodes = num_eval_episodes
self.checkpoint_every = checkpoint_every
self.validate_every = validate_every
self.log_every = log_every
self.checkpoint_to_restore = checkpoint_to_restore
self.learning_rate = learning_rate
self.decay_learning_rate = decay_learning_rate
self.decay_every = decay_every
self.decay_rate = decay_rate
self.experiment_name = experiment_name
self.pretrained_source = pretrained_source
self.train_learner_class = train_learner_class
self.eval_learner_class = eval_learner_class
self.is_training = is_training
self.train_dataset_list = train_dataset_list
self.eval_dataset_list = eval_dataset_list
self.normalized_gradient_descent = normalized_gradient_descent
self.enable_tf_optimizations = enable_tf_optimizations
# Currently we are supporting single dataset when we read from fixed
# datasets like VTAB or dumped episodes.
# Check whether we evaluate on VTAB
if (len(self.eval_dataset_list) == 1 and
self.eval_dataset_list[0].startswith('vtab')):
self._fixed_eval = 'vtab'
elif (len(self.eval_dataset_list) == 1 and
self.eval_dataset_list[0].startswith('dumped')):
self._fixed_eval = 'dumped'
else:
self._fixed_eval = None
self.restrict_classes = restrict_classes
self.restrict_num_per_class = restrict_num_per_class
self.checkpoint_dir = checkpoint_dir
self.summary_dir = summary_dir
self.records_root_dir = records_root_dir
self.eval_finegrainedness = eval_finegrainedness
self.eval_finegrainedness_split = eval_finegrainedness_split
self.eval_imbalance_dataset = eval_imbalance_dataset
self.omit_from_saving_and_reloading = omit_from_saving_and_reloading
self.data_initializeable_iterators = []
if eval_finegrainedness:
# The fine- vs coarse- grained evaluation may potentially be performed on
# the training graph as it exhibits greater variety in this aspect.
self.eval_split = eval_finegrainedness_split
elif eval_split:
if eval_split not in (TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT):
raise UnexpectedSplitError(eval_split)
self.eval_split = eval_split
elif is_training:
self.eval_split = VALID_SPLIT
else:
self.eval_split = TEST_SPLIT
if eval_finegrainedness or eval_imbalance_dataset:
# We restrict this analysis to the binary classification setting.
logging.info(
'Forcing the number of %s classes to be 2, since '
'the finegrainedness analysis is applied on binary '
'classification tasks only.', eval_finegrainedness_split)
if eval_finegrainedness and eval_finegrainedness_split == TRAIN_SPLIT:
train_episode_config.num_ways = 2
else:
eval_episode_config.num_ways = 2
self.num_classes_train = train_episode_config.num_ways
self.num_classes_eval = eval_episode_config.num_ways
self.num_support_train = train_episode_config.num_support
self.num_query_train = train_episode_config.num_query
self.num_support_eval = eval_episode_config.num_support
self.num_query_eval = eval_episode_config.num_query
self.train_episode_config = train_episode_config
self.eval_episode_config = eval_episode_config
self.data_config = data_config
# TODO(eringrant): Adapt these image-specific expectations to feature
# inputs.
self.image_shape = [data_config.image_height] * 2 + [3]
# Create the benchmark specification.
self.benchmark_spec = self.get_benchmark_specification()
# Which splits to support depends on whether we are in the meta-training
# phase or not. If we are, we need the train split, and the valid one for
# early-stopping. If not, we only need the test split.
self.required_splits = [TRAIN_SPLIT] if self.is_training else []
self.required_splits += [self.eval_split]
# Get the training, validation and testing specifications.
# Each is either an EpisodeSpecification or a BatchSpecification.
self.split_episode_or_batch_specs = dict(
zip(self.required_splits,
map(self.get_batch_or_episodic_specification,
self.required_splits)))
# Get the next data (episode or batch) for the different splits.
self.next_data = dict(
zip(self.required_splits, map(self.build_data, self.required_splits)))
self.distribute = distribute
if self.distribute:
self.strategy = tf.distribute.MirroredStrategy()
else:
self.strategy = None
# Create the global step to pass to the learners.
global_step = tf.train.get_or_create_global_step()
if len(self.required_splits) > 1:
if issubclass(self.train_learner_class,
experimental_learners.ExperimentalLearner):
assert issubclass(
self.eval_learner_class, experimental_learners.ExperimentalLearner
), ('If the `Learner` for the train split is an `ExperimentalLearner`,'
' the `Learner` for the evaluation split must be as well, since '
'otherwise parameters cannot be shared.')
else:
assert not issubclass(
self.eval_learner_class, experimental_learners.ExperimentalLearner
), ('If the `Learner` for the evaluation split is not an '
'`ExperimentalLearner`, the `Learner` for the train split must not'
' be either, since otherwise parameters cannot be shared.')
# Initialize the learners.
self.learners = {}
for split in self.required_splits:
if split == TRAIN_SPLIT:
# The learner for the training split should only be in training mode if
# the evaluation split is not the training split.
learner_is_training = self.eval_split != TRAIN_SPLIT
learner_class = self.train_learner_class
tied_learner = None
else:
learner_is_training = False
learner_class = self.eval_learner_class
# Share parameters between the training and evaluation `Learner`s.
tied_learner = (
self.learners[TRAIN_SPLIT]
if TRAIN_SPLIT in self.required_splits else None)
learner = self.create_learner(
is_training=learner_is_training,
learner_class=learner_class,
split=get_split_enum(split),
tied_learner=tied_learner)
if (isinstance(learner, learners.MAMLLearner) and
not learner.zero_fc_layer and not learner.proto_maml_fc_layer_init):
if 'linear_classifier' in FLAGS.omit_from_saving_and_reloading:
raise ValueError('The linear layer is requested to be meta-learned '
'since both `MAMLLearner.zero_fc_layer` and '
'`MAMLLearner.proto_maml_fc_layer_init` are False, '
'but the `linear_classifier` tags is found in '
'FLAGS.omit_from_saving_and_reloading so they will '
'not be properly restored. Please exclude these '
'weights from omit_from_saving_and_reloading for '
'this setting to work as expected.')
self.learners[split] = learner
# Build the data-dependent functions (run_fn returns prediction,
# un-regularized loss, accuracy, and episode statistics), the iterators
# producing data (data_fn), and regularizer, for each learner.
run_fn, data_fn, regularizer_fn = zip(
*[self.build_learner(split) for split in self.required_splits])
self.run_fns = dict(zip(self.required_splits, run_fn))
self.data_fns = dict(zip(self.required_splits, data_fn))
self.regularizer_fns = dict(zip(self.required_splits, regularizer_fn))
# Get an optimizer and the operation for meta-training.
self.train_op = None
if self.is_training:
learning_rate = self.learning_rate
if self.decay_learning_rate:
learning_rate = tf.train.exponential_decay(
self.learning_rate,
global_step,
decay_steps=self.decay_every,
decay_rate=self.decay_rate,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
if self.distribute:
self.optimizer = tf.keras.optimizers.Adam(learning_rate)
else:
self.optimizer = tf.train.AdamOptimizer(learning_rate)
self.run_fns[TRAIN_SPLIT] = self.get_run_fn_with_train_op(
self.run_fns[TRAIN_SPLIT], self.regularizer_fns[TRAIN_SPLIT],
global_step)
self.predictions = {}
self.losses = {}
self.accuracies = {}
self.episode_info = {}
for split in self.required_splits:
if self.distribute:
with self.strategy.scope():
output = self.strategy.experimental_run(self.run_fns[split],
self.data_fns[split])
if self.strategy.num_replicas_in_sync > 1:
output['predictions'] = tf.concat(
output['predictions'].values, axis=0)
output['loss'] = tf.concat(output['loss'].values, axis=0)
output['accuracy'] = tf.concat(output['accuracy'].values, axis=0)
if split == TRAIN_SPLIT and self.is_training:
output['train_op'] = tf.group(output['train_op'].values)
# The computed episode_info should be identical for all replicas.
episode_info = {}
for key, val in output['episode_info'].items():
if val is not None:
# This control_dependencies is required or the call to
# session.run() gets deadlocked.
with tf.control_dependencies(val.values):
episode_info[key] = tf.identity(val.values[0])
else:
episode_info[key] = None
output['episode_info'] = episode_info
else:
data_tensors = tf.data.make_one_shot_iterator(
self.data_fns[split]()).get_next()
output = self.run_fns[split](data_tensors)
loss = tf.reduce_mean(output['loss'])
loss += self.regularizer_fns[split]()
self.losses[split] = loss
self.accuracies[split] = tf.reduce_mean(output['accuracy'])
self.predictions[split] = output['predictions']
self.episode_info[split] = output['episode_info']
if split == TRAIN_SPLIT and self.is_training:
self.train_op = output['train_op']
if self.checkpoint_dir is not None:
if not tf.io.gfile.exists(self.checkpoint_dir):
tf.io.gfile.makedirs(self.checkpoint_dir)
# Meaningless values so that logging works even if called before evaluation.
self.valid_acc = np.nan
self.valid_ci = np.nan
self.initialize_session()
self.initialize_saver()
self.create_summary_writer()
def build_learner(self, split):
"""Return predictions, losses and accuracies for the learner on split.
Args:
split: A `learning_spec.Split` that identifies the data split for which
the learner is to be built.
Returns:
run_fn: a function which, when called on data, will run the network
forward pass in split mode `split` and return:
predictions: A `tf.Tensor`; the predictions of the learner on `split`.
losses: A `tf.Tensor`; the losses of the learner on `split`.
accuracies: A `tf.Tensor`; the accuracies of the learner on `split`.
episode_info: A map of string to `tf.Tensor`, which has statistics
about the input data.
data_fn: a function which returns a `tf.Dataset` which can be used to
provide input to run_fn
regularizer: a `tf.Tensor` which computes a data-independent regularizer
(e.g. weight decay) that is to be applied at every iteration.
"""
learner = self.learners[split]
# Build the learner and its variables outside the name scope.
learner.build()
with tf.name_scope(split):
data_src = self.next_data[split]
if self.distribute:
with self.strategy.scope():
# We need to split both support and query sets across GPUs, and
# tf.data doesn't make this straightforward, as there are few
# functions for splitting up datasets. We use unbatch to accomplish
# this, but unbatch splits the first dimension and creates one
# example for every possible index along that dimension.
#
# Therefore, the strategy is to first compute the chunk boundaries
# for the support and query sets (chunk_bounds), pad the last
# chunks to match the earlier chunks along the first axis,
# and then stack all chunks along a new first axis.
# unbatch() then correctly splits the episode across gpus. We then
# trim the padding from later chunks (trim_extra). The result is a
# single dataset with chunks for each GPU interleaved with one
# another. We use shard() to split this single dataset into one
# dataset per gpu.
def chunk_bounds(x, num_gpu, idx):
num_per_gpu = tf.cast(
tf.ceil(
tf.cast(tf.shape(x)[0], tf.float32) /
tf.cast(num_gpu, tf.float32)), tf.int32)
lb = tf.minimum(idx * num_per_gpu, tf.shape(x)[0])
ub = tf.minimum((idx + 1) * num_per_gpu, tf.shape(x)[0])
return lb, ub, tf.minimum((idx + 1) * num_per_gpu - ub, num_per_gpu)
def chunk_array(arr):
"""Deterministically chunk the arrays across devices."""
num_gpu = self.strategy.num_replicas_in_sync
chunks = []
num = []
for idx in range(num_gpu):
lb, ub, num_extra = chunk_bounds(arr, num_gpu, idx)
pad = tf.tile(arr[0:1], [num_extra] + [1] *
(len(arr.shape) - 1)) * 0 - 1
chunks.append(tf.concat([arr[lb:ub], pad], axis=0))
num.append(ub - lb)
return tf.stack(chunks), tf.stack(num)
def chunk_episode(episode):
way_tiled = episode.way + tf.zeros(
[self.strategy.num_replicas_in_sync], dtype=tf.int32)
return ((way_tiled, chunk_array(episode.support_images)[0],
chunk_array(episode.support_labels)[0]) +
chunk_array(episode.support_class_ids) +
(chunk_array(episode.query_images)[0],
chunk_array(episode.query_labels)[0]) +
chunk_array(episode.query_class_ids))
def trim_extra(way, support_images, support_labels, support_class_ids,
support_num, query_images, query_labels,
query_class_ids, query_num):
return providers.EpisodePiece(support_images[:support_num],
query_images[:query_num],
support_labels[:support_num],
query_labels[:query_num],
support_class_ids[:support_num],
query_class_ids[:query_num], way)
chunked_data = data_src.map(chunk_episode).unbatch().map(trim_extra)
def input_fn(input_context):
return chunked_data.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
data = self.strategy.make_input_fn_iterator(input_fn)
# Return a function that computes the regularizers, to defer their
# computation till after the computational graph is instantiated
# (otherwise the graph will be empty and no regularizers found).
regularizer = self.learners[split].compute_regularizer
self.data_initializeable_iterators.append(data)
else:
data = lambda: data_src
regularizer = self.learners[split].compute_regularizer
def run(data_local):
"""Run the forward pass of the model."""
predictions_dist = self.learners[split].forward_pass(data_local)
loss_dist = self.learners[split].compute_loss(
predictions=predictions_dist,
onehot_labels=data_local.onehot_labels)
accuracy_dist = self.learners[split].compute_accuracy(
predictions=predictions_dist,
onehot_labels=data_local.onehot_labels)
episode_info = self.get_episode_info(data_local)
return {
'predictions': predictions_dist,
'loss': loss_dist,
'accuracy': accuracy_dist,
'episode_info': episode_info,
}
return run, data, regularizer
def get_episode_info(self, data):
"""Sets the Tensors for the info about the learner's next episode."""
res = {}
# The batch trainer receives episodes only for the valid and test splits.
# Therefore for the train split there is no defined way and shots.
if isinstance(data, providers.Batch):
(way_, shots_, class_props_, class_ids_, query_targets_) = [None] * 5
else:
way_ = data.way
shots_ = data.support_shots
class_ids_ = data.unique_class_ids
class_props_ = None
if self.eval_imbalance_dataset:
class_props_ = compute_class_proportions(
class_ids_, shots_, self.eval_imbalance_dataset_spec)
query_targets_ = data.query_labels
res['way'] = way_
res['shots'] = shots_
res['class_props'] = class_props_
res['class_ids'] = class_ids_
res['query_targets'] = query_targets_
return res
def create_summary_writer(self):
"""Create summaries and writer."""
# Add summaries for the losses / accuracies of the different learners.
standard_summaries = []
for split in self.required_splits:
with tf.name_scope(split):
loss_summary = tf.summary.scalar('loss', self.losses[split])
acc_summary = tf.summary.scalar('acc',
tf.reduce_mean(self.accuracies[split]))
standard_summaries.append(loss_summary)
standard_summaries.append(acc_summary)
# Add summaries for the way / shot / logits / targets of the learner.
evaluation_summaries = self.add_eval_summaries()
# All summaries.
self.standard_summaries = tf.summary.merge(standard_summaries)
self.evaluation_summaries = tf.summary.merge(evaluation_summaries)
# Get a writer.
self.summary_writer = None
if self.summary_dir is not None:
self.summary_writer = tf.summary.FileWriter(self.summary_dir)
if not tf.io.gfile.exists(self.summary_dir):
tf.io.gfile.makedirs(self.summary_dir)
def create_learner(self,
is_training,
learner_class,
split,
tied_learner=None):
"""Instantiates `learner_class`, tying weights to `tied_learner`."""
if issubclass(learner_class, learners.BatchLearner):
logit_dim = self._get_logit_dim(
split, is_batch_learner=True, is_training=is_training)
elif issubclass(learner_class, learners.EpisodicLearner):
logit_dim = self._get_logit_dim(
split, is_batch_learner=False, is_training=is_training)
else:
raise ValueError('The specified `learner_class` should be a subclass of '
'`learners.BatchLearner` or `learners.EpisodicLearner`, '
'but received {}.'.format(learner_class))
if (issubclass(learner_class, experimental_learners.ExperimentalLearner) and
tied_learner is not None):
return learner_class(
is_training=is_training,
logit_dim=logit_dim,
input_shape=self.image_shape,
embedding_fn=tied_learner.embedding_fn,
)
else:
return learner_class(
is_training=is_training,
logit_dim=logit_dim,
input_shape=self.image_shape,
)
def get_benchmark_specification(self, records_root_dir=None):
"""Returns a BenchmarkSpecification.
Args:
records_root_dir: Optional. If provided, a list or string that sets the
directory in which a child directory will be searched for each dataset
to locate that dataset's records and dataset specification. If it's a
string, that path will be used for all datasets. If it's a list, its
length must be the same as the number of datasets, in order to specify a
different such directory for each. If None, self.records_root_dir will
be used for all datasets.
Raises:
RuntimeError: Incorrect file_pattern detected in a dataset specification.
"""
(data_spec_list, has_dag_ontology, has_bilevel_ontology,
splits_to_contribute) = [], [], [], []
seen_datasets = set()
eval_dataset_list = self.eval_dataset_list
# No need to read specs when specs not available and not needed.
if self._fixed_eval:
eval_dataset_list = []
if self.is_training:
benchmark_datasets = self.train_dataset_list + eval_dataset_list
else:
benchmark_datasets = eval_dataset_list
if isinstance(records_root_dir, list):
if len(records_root_dir) != len(benchmark_datasets):
raise ValueError('The given records_root_dir is a list whose length is '
'not the same as the number of benchmark datasets. '
'Found datasets {} (for the {} phase) but '
'len(records_root_dir) is {}. Expected their lengths '
'to match or records_path to be a string').format(
benchmark_datasets, len(records_root_dir))
records_roots_for_datasets = records_root_dir
elif isinstance(records_root_dir, six.text_type):
records_roots_for_datasets = [records_root_dir] * len(benchmark_datasets)
elif records_root_dir is None:
records_roots_for_datasets = [self.records_root_dir
] * len(benchmark_datasets)
for dataset_name, dataset_records_root in zip(benchmark_datasets,
records_roots_for_datasets):
# Might be seeing a dataset for a second time if it belongs to both the
# train and eval dataset lists.
if dataset_name in seen_datasets:
continue
dataset_records_path = os.path.join(dataset_records_root, dataset_name)
data_spec = dataset_spec_lib.load_dataset_spec(dataset_records_path)
# Only ImageNet has a DAG ontology.
has_dag = (dataset_name.startswith('ilsvrc_2012'))
# Only Omniglot has a bi-level ontology.
is_bilevel = (dataset_name == 'omniglot')
# The meta-splits that this dataset will contribute data to.
if not self.is_training:
# If we're meta-testing, all datasets contribute only to meta-test.
splits = {self.eval_split}
else:
splits = set()
if dataset_name in self.train_dataset_list:
splits.add(TRAIN_SPLIT)
if dataset_name in self.eval_dataset_list:
splits.add(VALID_SPLIT)
# By default, all classes of each split will eventually be used for
# episode creation. But it might be that for some datasets, it is
# requested to restrict the available number of classes of some splits.
restricted_classes_per_split = {}
if dataset_name in self.restrict_classes:
classes_per_split = self.restrict_classes[dataset_name]
for split, num_classes in classes_per_split.items():
# The option to restrict classes is not supported in conjuction with
# non-uniform (bilevel or hierarhical) class sampling.
episode_descr_config = (
self.train_episode_config
if split == TRAIN_SPLIT else self.eval_episode_config)
if has_dag and not episode_descr_config.ignore_dag_ontology:
raise ValueError('Restrictions on the class set of a dataset with '
'a DAG ontology are not supported when '
'ignore_dag_ontology is False.')
if is_bilevel and not episode_descr_config.ignore_bilevel_ontology:
raise ValueError('Restrictions on the class set of a dataset with '
'a bilevel ontology are not supported when '
'ignore_bilevel_ontology is False.')
restricted_classes_per_split[get_split_enum(split)] = num_classes
# Initialize the DatasetSpecificaton to account for this restriction.
data_spec.initialize(restricted_classes_per_split)
# Log the applied restrictions.
logging.info('Restrictions for dataset %s:', dataset_name)
for split in list(splits):
num_classes = data_spec.get_classes(get_split_enum(split))
logging.info('\t split %s is restricted to %d classes', split,
num_classes)
# Add this dataset to the benchmark.
logging.info('Adding dataset %s', data_spec.name)
data_spec_list.append(data_spec)
has_dag_ontology.append(has_dag)
has_bilevel_ontology.append(is_bilevel)
splits_to_contribute.append(splits)
# Book-keeping.
seen_datasets.add(dataset_name)
if self.eval_imbalance_dataset:
self.eval_imbalance_dataset_spec = data_spec
assert len(data_spec_list) == 1, ('Imbalance analysis is only '
'supported on one dataset at a time.')
benchmark_spec = dataset_spec_lib.BenchmarkSpecification(
'benchmark', self.image_shape, data_spec_list, has_dag_ontology,
has_bilevel_ontology, splits_to_contribute)
# Logging of which datasets will be used for the different meta-splits.
splits_to_datasets = collections.defaultdict(list)
for dataset_spec, splits_to_contribute in zip(data_spec_list,
splits_to_contribute):
for split in splits_to_contribute:
splits_to_datasets[split].append(dataset_spec.name)
for split, datasets in splits_to_datasets.items():
logging.info('Episodes for split %s will be created from %s', split,
datasets)
return benchmark_spec
def initialize_session(self):
"""Initializes a tf.Session."""
if self.enable_tf_optimizations:
self.sess = tf.Session()
else:
session_config = tf.ConfigProto()
rewrite_options = session_config.graph_options.rewrite_options
rewrite_options.disable_model_pruning = True
rewrite_options.constant_folding = rewrite_options.OFF
rewrite_options.arithmetic_optimization = rewrite_options.OFF
rewrite_options.remapping = rewrite_options.OFF
rewrite_options.shape_optimization = rewrite_options.OFF
rewrite_options.dependency_optimization = rewrite_options.OFF
rewrite_options.function_optimization = rewrite_options.OFF
rewrite_options.layout_optimizer = rewrite_options.OFF
rewrite_options.loop_optimization = rewrite_options.OFF
rewrite_options.memory_optimization = rewrite_options.NO_MEM_OPT
self.sess = tf.Session(config=session_config)
# Restore or initialize the variables.
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
for it in self.data_initializeable_iterators:
self.sess.run(it.initialize())
def initialize_saver(self):
"""Initializes a tf.train.Saver and possibly restores parameters."""
# TODO(eringrant): Implement saving and restoring for
# `ExperimentalLearner`s.
# We omit from saving and restoring any variables that contains as a
# substring anything in the list `self.omit_from_saving_and_reloading.
# For example, those that track iterator state.
logging.info(
'Omitting from saving / restoring any variable that '
'contains any of the following substrings: %s',
self.omit_from_saving_and_reloading)
def is_not_requested_to_omit(variable_name):
return all([
substring not in variable_name
for substring in self.omit_from_saving_and_reloading
])
# TODO(doersch): the replica_ variables are created by the keras
# distributed optimizer, and are copies of the optimizer (e.g. Adam)
# variables. There's probably a smarter way to avoid saving them.
var_list = list([
var for var in tf.global_variables()
if is_not_requested_to_omit(var.name) and 'replica_' not in var.name
])
if var_list:
self.saver = tf.train.Saver(var_list=var_list, max_to_keep=1200)
else:
self.saver = None
logging.info('Variables not being saved since no variables left after '
'filtering.')
if self.checkpoint_to_restore:
if not self.saver:
raise ValueError(
'Checkpoint not restored, since there is no Saver created. This is '
'likely due to no parameters being available. If you intend to run '
'parameterless training, set `checkpoint_to_restore` to None.')
if self.is_training:
# To handle pre-emption, we continue from the latest checkpoint if
# checkpoints already exist in the checkpoint directory.
latest_checkpoint = None
if self.checkpoint_dir is not None:
latest_checkpoint = tf.train.latest_checkpoint(self.checkpoint_dir)
if latest_checkpoint is not None:
if not self.saver:
raise ValueError(
'Checkpoint not restored, since there is no Saver created. This '
'is likely due to no parameters being available. ')
restore_or_log_informative_error(self.saver, self.sess,
latest_checkpoint)
elif self.checkpoint_to_restore:
logging.info('No training checkpoints found.')
# For training episodic models from a checkpoint, we restore the
# backbone weights but omit other (e.g., optimizer) parameters.
backbone_vars_to_reload = [
var for var in tf.global_variables()
if functional_backbones.is_backbone_variable(
var.name, only_if=is_not_requested_to_omit)
]
backbone_saver = tf.train.Saver(
var_list=backbone_vars_to_reload, max_to_keep=1)
restore_or_log_informative_error(backbone_saver, self.sess,
self.checkpoint_to_restore)
logging.info(
'Restored only vars %s from provided `checkpoint_to_restore`: %s',
[var.name for var in backbone_vars_to_reload],
self.checkpoint_to_restore)
else:
logging.info(
'No checkpoints found; training from random initialization.')
elif self.checkpoint_to_restore is not None:
# For evaluation, we restore more than the backbone (embedding function)
# variables from the provided checkpoint, so we use `self.saver`.
restore_or_log_informative_error(self.saver, self.sess,
self.checkpoint_to_restore)
else:
logging.info(
'No checkpoints found; evaluating with a random initialization.')
def get_batch_or_episodic_specification(self, split):
if split == TRAIN_SPLIT:
return self._create_train_specification()
else:
return self._create_eval_specification(split)
def _create_train_specification(self):
"""Returns a `BatchSpecification` or `EpisodeSpecification` for training."""
if (issubclass(self.train_learner_class, learners.EpisodicLearner) or
self.eval_split == TRAIN_SPLIT):
return learning_spec.EpisodeSpecification(learning_spec.Split.TRAIN,
self.num_classes_train,
self.num_support_train,
self.num_query_train)
elif issubclass(self.train_learner_class, learners.BatchLearner):
return learning_spec.BatchSpecification(learning_spec.Split.TRAIN,
self.batch_size)
else:
raise ValueError('The specified `learner_class` should be a subclass of '
'`learners.BatchLearner` or `learners.EpisodicLearner`, '
'but received {}.'.format(self.train_learner_class))
def _create_eval_specification(self, split=TEST_SPLIT):
"""Create an `EpisodeSpecification` for episodic evaluation.
Args:
split: The split from which to generate the `EpisodeSpecification`.
Returns:
An `EpisodeSpecification`.
Raises:
ValueError: Invalid `split`.
"""
if split not in (VALID_SPLIT, TEST_SPLIT):
raise UnexpectedSplitError(
split, expected_splits=(VALID_SPLIT, TEST_SPLIT))
split_enum = get_split_enum(split)
return learning_spec.EpisodeSpecification(split_enum, self.num_classes_eval,
self.num_support_eval,
self.num_query_eval)
def _restrict_dataset_list_for_split(self, split, splits_to_contribute,
dataset_list):
"""Returns the restricted dataset_list for the given split.
Args:
split: A string, either TRAIN_SPLIT, VALID_SPLIT or TEST_SPLIT.
splits_to_contribute: A list whose length is the number of datasets in the
benchmark. Each element is a set of strings corresponding to the splits
that the respective dataset will contribute to.
dataset_list: A list which has one element per selected dataset (same
length as splits_to_contribute), e.g. this can be one of the lists
dataset_spec_list, has_dag_ontology, has_bilevel_ontology of the
BenchmarkSpecification.
"""
updated_list = []
for dataset_num, dataset_splits in enumerate(splits_to_contribute):
if split in dataset_splits:
updated_list.append(dataset_list[dataset_num])
return updated_list
def get_num_to_take(self, dataset_name, split):
"""Return the number of examples to restrict to for a dataset/split pair."""
num_to_take = -1 # By default, no restriction.
if dataset_name in self.restrict_num_per_class:
dataset_restrict_num_per_class = self.restrict_num_per_class[dataset_name]
if split in dataset_restrict_num_per_class:
num_to_take = dataset_restrict_num_per_class[split]
return num_to_take
def build_data(self, split):
"""Builds a `tf.Dataset` of episodes or batches from `split`."""
learner_class = (
self.train_learner_class
if split == TRAIN_SPLIT else self.eval_learner_class)
if (issubclass(learner_class, learners.BatchLearner) and
split != self.eval_split):
return self._build_batch(split)
elif (issubclass(learner_class, learners.EpisodicLearner) or
split == self.eval_split):
if self._fixed_eval == 'vtab':
return self._build_vtab_episode()
elif self._fixed_eval == 'dumped':
return self._build_dumped_episode(split)
else:
return self._build_episode(split)
else:
raise ValueError('The `Learner` for `split` should be a subclass of '
'`learners.BatchLearner` or `learners.EpisodicLearner`, '
'but received {}.'.format(learner_class))
def _build_vtab_episode(self):
"""Build a `tf.Dataset` of vtab episodes."""
# There is only one dataset.
# ['vtab_cifar'] -> 'cifar'
dataset_name = re.search('^vtab_(.+)', self.eval_dataset_list[0]).group(1)
# Replace back comma.
dataset_name = dataset_name.replace(':', ',')
return_vals = read_episodes.read_vtab_as_episode(
dataset_name,
self.data_config.image_height,
query_size_limit=self.data_config.vtab_query_size_limit)
support_ds, query_ds, n_eval, n_classes = return_vals
self.vtab_test_classes = n_classes
logging.info('Using VTAB episode for eval. Dataset: %s, n_eval: %d',
dataset_name, n_eval)
episodes = tf.data.Dataset.zip((support_ds.repeat(), query_ds.repeat()))
self.num_eval_episodes = n_eval
def create_episode_struct(support_data, query_data):
return providers.Episode(
support_images=support_data['image'],
query_images=query_data['image'],
support_labels=support_data['label'],
query_labels=query_data['label'],
support_class_ids=support_data['label'],
query_class_ids=query_data['label'])
return episodes.map(create_episode_struct)
def _build_dumped_episode(self, split):
"""Builds a `tf.Dataset` of episodes through reading dumped episodes."""
dataset_name = re.search('^dumped_(.+)', self.eval_dataset_list[0]).group(1)
folder_path = os.path.join(self.data_config.eval_dumped_episodes_dir,
'valid' if split == VALID_SPLIT else 'test',
dataset_name)
dumped_episode_ds, n_eval = read_episodes.read_episodes_from_records(
folder_path)
# If we request less than the available number of episodes, we use that
# number instead.
n_eval = min(self.num_eval_episodes, n_eval)
logging.info('Using dumped episode for eval. Dataset: %s, n_eval: %d',
dataset_name, n_eval)
self.num_eval_episodes = n_eval
map_fn = functools.partial(
pipeline.process_dumped_episode,
image_size=self.data_config.image_height)
dataset = dumped_episode_ds.map(map_fn)
# Overlap episode processing and training.
data_pipeline = dataset.prefetch(1)
data_pipeline = apply_dataset_options(data_pipeline)
def create_episode_struct(support_images, support_labels, support_class_ids,
query_images, query_labels, query_class_ids):
return providers.Episode(
support_images=support_images,
query_images=query_images,
support_labels=support_labels,
query_labels=query_labels,
support_class_ids=support_class_ids,
query_class_ids=query_class_ids)
return data_pipeline.map(create_episode_struct)
def _build_episode(self, split):
"""Builds a `tf.Dataset` containing Episodes for "split".
Args:
split: A string, either TRAIN_SPLIT, VALID_SPLIT, or TEST_SPLIT.
Returns:
An `tf.Dataset` with Episodes.
Raises:
UnexpectedSplitError: If split not as expected for this episode build.
"""
shuffle_buffer_size = self.data_config.shuffle_buffer_size
read_buffer_size_bytes = self.data_config.read_buffer_size_bytes
num_prefetch = self.data_config.num_prefetch
(_, image_shape, dataset_spec_list, has_dag_ontology, has_bilevel_ontology,
splits_to_contribute) = self.benchmark_spec
# Choose only the datasets that are chosen to contribute to the given split.
dataset_spec_list = self._restrict_dataset_list_for_split(
split, splits_to_contribute, dataset_spec_list)
has_dag_ontology = self._restrict_dataset_list_for_split(
split, splits_to_contribute, has_dag_ontology)
has_bilevel_ontology = self._restrict_dataset_list_for_split(
split, splits_to_contribute, has_bilevel_ontology)
episode_spec = self.split_episode_or_batch_specs[split]
dataset_split = episode_spec[0]
# TODO(lamblinp): Support non-square shapes if necessary. For now, all
# images are resized to square, even if it changes the aspect ratio.
image_size = image_shape[0]
if image_shape[1] != image_size:
raise ValueError(
'Expected a square image shape, not {}'.format(image_shape))
if split == TRAIN_SPLIT:
episode_descr_config = self.train_episode_config
elif split in (VALID_SPLIT, TEST_SPLIT):
episode_descr_config = self.eval_episode_config
else:
raise UnexpectedSplitError(split)
# Decide how many examples per class to restrict to for each dataset for the
# given split (by default there is no restriction).
num_per_class = [] # A list whose length is the number of datasets.
for dataset_spec in dataset_spec_list:
num_per_class.append(self.get_num_to_take(dataset_spec.name, split))
if split == TRAIN_SPLIT:
# The learner for the training split should only be in training mode if
# the evaluation split is not the training split.
gin_scope_name = ('train'
if self.eval_split != TRAIN_SPLIT else 'evaluation')
else:
gin_scope_name = 'evaluation'
ignore_hierarchy_prob = episode_descr_config.ignore_hierarchy_probability
simclr_episode_fraction = episode_descr_config.simclr_episode_fraction
if simclr_episode_fraction > 0:
assert not self.enable_tf_optimizations, (
'Must set enable_tf_optimizations=False or SimCLR will fail; see '
'https://github.com/tensorflow/tensorflow/issues/22145')
# TODO(lamblinp): pass specs directly to the pipeline builder.
# TODO(lamblinp): move the special case directly in make_..._pipeline
if len(dataset_spec_list) == 1:
use_dag_ontology = has_dag_ontology[0]
if self.eval_finegrainedness or self.eval_imbalance_dataset:
use_dag_ontology = False
with gin.config_scope(gin_scope_name):
data_pipeline = pipeline.make_one_source_episode_pipeline(
dataset_spec_list[0],
use_dag_ontology=use_dag_ontology,
use_bilevel_ontology=has_bilevel_ontology[0],
split=dataset_split,
episode_descr_config=episode_descr_config,
shuffle_buffer_size=shuffle_buffer_size,
read_buffer_size_bytes=read_buffer_size_bytes,
num_prefetch=num_prefetch,
image_size=image_size,
num_to_take=num_per_class[0],
simclr_episode_fraction=simclr_episode_fraction,
ignore_hierarchy_probability=ignore_hierarchy_prob)
else:
if ignore_hierarchy_prob > 0.0:
raise ValueError(
'ignore_hierarchy_probability not supported with multisource pipelines'
)
with gin.config_scope(gin_scope_name):
data_pipeline = pipeline.make_multisource_episode_pipeline(
dataset_spec_list,
use_dag_ontology_list=has_dag_ontology,
use_bilevel_ontology_list=has_bilevel_ontology,
split=dataset_split,
episode_descr_config=episode_descr_config,
shuffle_buffer_size=shuffle_buffer_size,
read_buffer_size_bytes=read_buffer_size_bytes,
num_prefetch=num_prefetch,
image_size=image_size,
num_to_take=num_per_class,
simclr_episode_fraction=simclr_episode_fraction)
data_pipeline = apply_dataset_options(data_pipeline)
def create_episode_struct(support_images, support_labels, support_class_ids,
query_images, query_labels, query_class_ids):
return providers.Episode(
support_images=support_images,
query_images=query_images,
support_labels=support_labels,
query_labels=query_labels,
support_class_ids=support_class_ids,
query_class_ids=query_class_ids)
return data_pipeline.map(lambda x, y: x).map(create_episode_struct)
def _build_batch(self, split):
"""Builds a `tf.Dataset` of Batch objects containing data for "split".
Args:
split: A string, either TRAIN_SPLIT, VALID_SPLIT, or TEST_SPLIT.
Returns:
A `tf.Dataset` containing Batches
"""
shuffle_buffer_size = self.data_config.shuffle_buffer_size
read_buffer_size_bytes = self.data_config.read_buffer_size_bytes
num_prefetch = self.data_config.num_prefetch
(_, image_shape, dataset_spec_list, _, _,
splits_to_contribute) = self.benchmark_spec
# Choose only the datasets that are chosen to contribute to the given split.
dataset_spec_list = self._restrict_dataset_list_for_split(
split, splits_to_contribute, dataset_spec_list)
# Decide how many examples per class to restrict to for each dataset for the
# given split (by default there is no restriction).
num_per_class = [] # A list whose length is the number of datasets.
for dataset_spec in dataset_spec_list:
num_per_class.append(self.get_num_to_take(dataset_spec.name, split))
dataset_split, batch_size = self.split_episode_or_batch_specs[split]
for dataset_spec in dataset_spec_list:
if dataset_spec.name in DATASETS_WITH_EXAMPLE_SPLITS:
raise ValueError(
'Batch pipeline is used only at meta-train time, and does not '
'handle datasets with example splits, which should only be used '
'at meta-test (evaluation) time.')
# TODO(lamblinp): pass specs directly to the pipeline builder.
# TODO(lamblinp): move the special case directly in make_..._pipeline
if len(dataset_spec_list) == 1:
data_pipeline = pipeline.make_one_source_batch_pipeline(
dataset_spec_list[0],
split=dataset_split,
batch_size=batch_size,
shuffle_buffer_size=shuffle_buffer_size,
read_buffer_size_bytes=read_buffer_size_bytes,
num_prefetch=num_prefetch,
image_size=image_shape[0],
num_to_take=num_per_class[0])
else:
data_pipeline = pipeline.make_multisource_batch_pipeline(
dataset_spec_list,
split=dataset_split,
batch_size=batch_size,
shuffle_buffer_size=shuffle_buffer_size,
read_buffer_size_bytes=read_buffer_size_bytes,
num_prefetch=num_prefetch,
image_size=image_shape[0],
num_to_take=num_per_class)
data_pipeline = apply_dataset_options(data_pipeline)
def create_batch_structure(data, dataset_index):
(images, class_ids) = data
# The number of available classes for each dataset
all_n_classes = [
len(dataset_spec.get_classes(get_split_enum(split)))
for dataset_spec in dataset_spec_list
]
if len(dataset_spec_list) == 1:
n_classes = all_n_classes[0]
elif gin.query_parameter('BatchSplitReaderGetReader.add_dataset_offset'):
# The total number of classes is the sum for all datasets
n_classes = sum(all_n_classes)
else:
# The number of classes is the one of the current dataset
n_classes = tf.convert_to_tensor(all_n_classes)[dataset_index]
return providers.Batch(
images=images, labels=class_ids, n_classes=n_classes)
return data_pipeline.map(create_batch_structure)
def get_run_fn_with_train_op(self, run_fn, regularizer_fn, global_step):
"""Returns the operation that performs a training update."""
def run_fn_with_train_op(data):
"""Run and train the model."""
res = run_fn(data)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
loss = distribute_utils.aggregate(res['loss'])
# note: every worker computes the same loss. This is because the
# reduce_mean needs to be computed globally.
loss = tf.reduce_mean(loss)
loss += regularizer_fn()
replica_ctx = tf.distribute.get_replica_context()
if replica_ctx:
loss /= replica_ctx.num_replicas_in_sync
# TODO(doersch): there's probably a better way to do EMA updates. EMAs
# are MirroredVariables, which means the assign needs to happen on every
# replica. I think what's happening is that the update from replica 1
# is getting run on every replica for a different copy of the mirrored
# variable. Only running the assign ops from the first replica doesn't
# work.
with tf.control_dependencies([tf.group(update_ops)]):
if self.normalized_gradient_descent:
opt_vars = tf.trainable_variables()
if self.distribute:
grads = self.optimizer.get_gradients(loss, opt_vars)
grads_and_vars = list(zip(grads, opt_vars))
else:
grads_and_vars = self.optimizer.compute_gradients(loss, opt_vars)
global_norm = 0
# We reverse the order of grads_and_vars because they're computed in
# reverse order; this way, the network can begin aggregating the
# global norm before the backwards pass is complete.
for g, v in grads_and_vars[::-1]:
if replica_ctx:
g = replica_ctx.all_reduce('sum', g)
sumsq = tf.reduce_sum(tf.square(g))
global_norm += sumsq
nrm = tf.sqrt(tf.maximum(global_norm, 1e-5))
grads_and_vars2 = []
for g, v in grads_and_vars:
grads_and_vars2.append((g / nrm, v))
train_op = self.optimizer.apply_gradients(grads_and_vars2)
with tf.control_dependencies([train_op]):
train_op = tf.assign(global_step, global_step + 1)
else:
train_op = self.optimizer.minimize(
loss, global_step=global_step, var_list=tf.trainable_variables())
res['train_op'] = train_op
return res
return run_fn_with_train_op
def get_updated_global_step(self):
with tf.control_dependencies([self.train_op]):
global_step = tf.identity(tf.train.get_global_step())
return global_step
def train(self):
"""The training loop."""
global_step = self.sess.run(tf.train.get_global_step())
logging.info('Starting training from global_step: %d', global_step)
updated_global_step = self.get_updated_global_step()
should_save = self.checkpoint_dir is not None
if should_save and global_step == 0:
# Save the initialization weights.
save_path = self.saver.save(
self.sess, os.path.join(self.checkpoint_dir, 'model_0.ckpt'))
logging.info('Model initialization saved: %s', save_path)
# Compute the initial validation performance before starting the training,
# unless train() has already been called on this object.
if np.isnan([self.valid_acc, self.valid_ci]).any():
self.maybe_evaluate(global_step)
while global_step < self.num_updates:
# Perform the next update.
(_, train_loss, train_acc, global_step) = self.sess.run([
self.train_op, self.losses[TRAIN_SPLIT], self.accuracies[TRAIN_SPLIT],
updated_global_step
])
train_acc = np.mean(train_acc)
# Maybe validate, depending on the global step's value.
self.maybe_evaluate(global_step)
# Log training progress.
if not global_step % self.log_every:
message = (
'Update %d. Train loss: %f, Train accuracy: %f, '
'Valid accuracy %f +/- %f.\n' %
(global_step, train_loss, train_acc, self.valid_acc, self.valid_ci))
logging.info(message)
# Update summaries.
if self.summary_writer:
summaries = self.sess.run(self.standard_summaries)
self.summary_writer.add_summary(summaries, global_step)
if should_save and global_step % self.checkpoint_every == 0:
save_path = self.saver.save(
self.sess,
os.path.join(self.checkpoint_dir, 'model_%d.ckpt' % global_step))
logging.info('Model checkpoint saved: %s', save_path)
def maybe_evaluate(self, global_step):
"""Maybe perform evaluation, depending on the value of global_step."""
if not global_step % self.validate_every:
# Get the validation accuracy and confidence interval.
(valid_acc, valid_ci, valid_acc_summary,
valid_ci_summary) = self.evaluate(
VALID_SPLIT, step=global_step)
# Validation summaries are updated every time validation happens which is
# every validate_every steps instead of log_every steps.
if self.summary_writer:
self.summary_writer.add_summary(valid_acc_summary, global_step)
self.summary_writer.add_summary(valid_ci_summary, global_step)
self.valid_acc = valid_acc
self.valid_ci = valid_ci
# TODO(evcu) Improve this so that if the eval_only loads a global_step, it is
# used at logging instead of value 0.
def evaluate(self, split, step=0):
"""Returns performance metrics across num_eval_trials episodes / batches."""
num_eval_trials = self.num_eval_episodes
logging.info('Performing evaluation of the %s split using %d episodes...',
split, num_eval_trials)
accuracies = []
total_samples = 0
for eval_trial_num in range(num_eval_trials):
# Following is used to normalize accuracies.
acc, summaries = self.sess.run(
[self.accuracies[split], self.evaluation_summaries])
# Write complete summaries during evaluation, but not training.
# Otherwise, validation summaries become too big.
if not self.is_training and self.summary_writer:
self.summary_writer.add_summary(summaries, eval_trial_num)
if self._fixed_eval == 'vtab':
accuracies.append(np.sum(acc))
total_samples += np.size(acc)
continue
accuracies.append(np.mean(acc))
total_samples += 1
logging.info('Finished evaluation.')
mean_acc = np.sum(accuracies) / total_samples
ci_acc = np.std(accuracies) * 1.96 / np.sqrt(len(accuracies)) # confidence
# VTAB evaluation has 1 episode.
if self._fixed_eval == 'vtab':
ci_acc = 0
if not self.is_training:
# Logging during training is handled by self.train() instead.
logging.info('Accuracy on the meta-%s split: %f, +/- %f.\n', split,
mean_acc, ci_acc)
with tf.name_scope('trainer_metrics'):
with tf.name_scope(split):
mean_acc_summary = tf.Summary()
mean_acc_summary.value.add(tag='mean acc', simple_value=mean_acc)
ci_acc_summary = tf.Summary()
ci_acc_summary.value.add(tag='acc CI', simple_value=ci_acc)
return mean_acc, ci_acc, mean_acc_summary, ci_acc_summary
def add_eval_summaries(self):
"""Returns summaries of way / shot / classes/ logits / targets."""
evaluation_summaries = [
tf.summary.scalar('global_step', tf.train.get_global_step())
]
for split in self.required_splits:
evaluation_summaries.extend(self._add_eval_summaries_split(split))
return evaluation_summaries
def _add_eval_summaries_split(self, split):
"""Returns split's summaries of way / shot / classes / logits / targets."""
split_eval_summaries = []
episode_info = copy.copy(self.episode_info[split])
episode_info['query_logits'] = self.predictions[split]
summary_labels = [
'way', 'shots', 'class_ids', 'query_logits', 'query_targets'
]
if self.eval_imbalance_dataset:
summary_labels += ['class_props']
for label in summary_labels:
if episode_info[label] is not None:
if episode_info[label].shape:
summary_fn = tf.summary.tensor_summary
else:
summary_fn = tf.summary.scalar
summary = summary_fn('%s_%s' % (split, label), episode_info[label])
split_eval_summaries.append(summary)
return split_eval_summaries
def _get_logit_dim(self, split, is_batch_learner, is_training):
"""Returns the total number of logits needed.
Args:
split: string, one of TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT.
is_batch_learner: bool, if True the logit count is obtained from dataset
spec. If False, `max_ways` is used for episodic dataset.
is_training: bool, used to decide number of logits.
Returns:
int, total number of logits needed.
"""
if self._fixed_eval == 'vtab':
return self.vtab_test_classes
if is_batch_learner:
# Get the total number of classes in this split, across all datasets
# contributing to this split.
total_classes = 0
for dataset_spec, dataset_splits in zip(
self.benchmark_spec.dataset_spec_list,
self.benchmark_spec.splits_to_contribute):
if any(
get_split_enum(ds_split) == split for ds_split in dataset_splits):
total_classes += len(dataset_spec.get_classes(split))
else:
total_classes = (
self.train_episode_config.max_ways
if is_training else self.eval_episode_config.max_ways)
return total_classes
| apache-2.0 |
ryfeus/lambda-packs | Keras_tensorflow_nightly/source2.7/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py | 26 | 50558 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TensorFlow estimators for Linear and DNN joined training models (deprecated).
This module and all its submodules are deprecated. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for migration instructions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import six
from tensorflow.contrib import layers
from tensorflow.contrib.framework import deprecated
from tensorflow.contrib.framework import deprecated_arg_values
from tensorflow.contrib.layers.python.layers import feature_column as feature_column_lib
from tensorflow.contrib.layers.python.layers import optimizers
from tensorflow.contrib.learn.python.learn import metric_spec
from tensorflow.contrib.learn.python.learn.estimators import estimator
from tensorflow.contrib.learn.python.learn.estimators import head as head_lib
from tensorflow.contrib.learn.python.learn.estimators import model_fn
from tensorflow.contrib.learn.python.learn.estimators import prediction_key
from tensorflow.contrib.learn.python.learn.utils import export
from tensorflow.python.feature_column import feature_column as fc_core
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.summary import summary
from tensorflow.python.training import sync_replicas_optimizer
from tensorflow.python.training import training_util
# The default learning rates are a historical artifact of the initial
# implementation, but seem a reasonable choice.
_DNN_LEARNING_RATE = 0.05
_LINEAR_LEARNING_RATE = 0.2
_FIX_GLOBAL_STEP_INCREMENT_DATE = "2017-04-15"
_FIX_GLOBAL_STEP_INCREMENT_INSTRUCTIONS = (
"Please set fix_global_step_increment_bug=True and update training steps "
"in your pipeline. See pydoc for details.")
def _as_iterable(preds, output):
for pred in preds:
yield pred[output]
def _get_feature_dict(features):
if isinstance(features, dict):
return features
return {"": features}
def _get_optimizer(optimizer):
if callable(optimizer):
return optimizer()
else:
return optimizer
def _check_no_sync_replicas_optimizer(optimizer):
if isinstance(optimizer, sync_replicas_optimizer.SyncReplicasOptimizer):
raise ValueError(
"SyncReplicasOptimizer is not supported in DNNLinearCombined model. "
"If you want to use this optimizer, please use either DNN or Linear "
"model.")
def _linear_learning_rate(num_linear_feature_columns):
"""Returns the default learning rate of the linear model.
The calculation is a historical artifact of this initial implementation, but
has proven a reasonable choice.
Args:
num_linear_feature_columns: The number of feature columns of the linear
model.
Returns:
A float.
"""
default_learning_rate = 1. / math.sqrt(num_linear_feature_columns)
return min(_LINEAR_LEARNING_RATE, default_learning_rate)
def _add_hidden_layer_summary(value, tag):
summary.scalar("%s/fraction_of_zero_values" % tag, nn.zero_fraction(value))
summary.histogram("%s/activation" % tag, value)
def _add_layer_summary(value, tag):
summary.scalar("%s/fraction_of_zero_values" % tag, nn.zero_fraction(value))
summary.histogram("%s/activation" % tag, value)
def _get_embedding_variable(column, collection_key, input_layer_scope):
return ops.get_collection(collection_key,
input_layer_scope + "/" + column.name)
def _extract_embedding_lr_multipliers(embedding_lr_multipliers, collection_key,
input_layer_scope):
"""Converts embedding lr multipliers to variable based gradient multiplier."""
if not embedding_lr_multipliers:
return None
gradient_multipliers = {}
for column, lr_mult in embedding_lr_multipliers.items():
if not isinstance(column, feature_column_lib._EmbeddingColumn): # pylint: disable=protected-access
raise ValueError(
"learning rate multipler can only be defined for embedding columns. "
"It is defined for {}".format(column))
embedding = _get_embedding_variable(
column, collection_key, input_layer_scope)
if not embedding:
raise ValueError("Couldn't find a variable for column {}".format(column))
for v in embedding:
gradient_multipliers[v] = lr_mult
return gradient_multipliers
def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
"""Deep Neural Net and Linear combined model_fn.
Args:
features: `Tensor` or dict of `Tensor` (depends on data passed to `fit`).
labels: `Tensor` of shape [batch_size, 1] or [batch_size] labels of dtype
`int32` or `int64` in the range `[0, n_classes)`.
mode: Defines whether this is training, evaluation or prediction.
See `ModeKeys`.
params: A dict of hyperparameters.
The following hyperparameters are expected:
* head: A `Head` instance.
* linear_feature_columns: An iterable containing all the feature columns
used by the Linear model.
* linear_optimizer: string, `Optimizer` object, or callable that defines
the optimizer to use for training the Linear model. Defaults to the
Ftrl optimizer.
* joint_linear_weights: If True a single (possibly partitioned) variable
will be used to store the linear model weights. It's faster, but
requires all columns are sparse and have the 'sum' combiner.
* dnn_feature_columns: An iterable containing all the feature columns used
by the DNN model.
* dnn_optimizer: string, `Optimizer` object, or callable that defines the
optimizer to use for training the DNN model. Defaults to the Adagrad
optimizer.
* dnn_hidden_units: List of hidden units per DNN layer.
* dnn_activation_fn: Activation function applied to each DNN layer. If
`None`, will use `tf.nn.relu`.
* dnn_dropout: When not `None`, the probability we will drop out a given
DNN coordinate.
* gradient_clip_norm: A float > 0. If provided, gradients are
clipped to their global norm with this clipping ratio.
* embedding_lr_multipliers: Optional. A dictionary from
`EmbeddingColumn` to a `float` multiplier. Multiplier will be used to
multiply with learning rate for the embedding variables.
* input_layer_partitioner: Optional. Partitioner for input layer.
config: `RunConfig` object to configure the runtime settings.
Returns:
`ModelFnOps`
Raises:
ValueError: If both `linear_feature_columns` and `dnn_features_columns`
are empty at the same time, or `input_layer_partitioner` is missing.
"""
head = params["head"]
linear_feature_columns = params.get("linear_feature_columns")
linear_optimizer = params.get("linear_optimizer") or "Ftrl"
joint_linear_weights = params.get("joint_linear_weights")
dnn_feature_columns = params.get("dnn_feature_columns")
dnn_optimizer = params.get("dnn_optimizer") or "Adagrad"
dnn_hidden_units = params.get("dnn_hidden_units")
dnn_activation_fn = params.get("dnn_activation_fn") or nn.relu
dnn_dropout = params.get("dnn_dropout")
gradient_clip_norm = params.get("gradient_clip_norm")
num_ps_replicas = config.num_ps_replicas if config else 0
input_layer_partitioner = params.get("input_layer_partitioner") or (
partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas,
min_slice_size=64 << 20))
embedding_lr_multipliers = params.get("embedding_lr_multipliers", {})
fix_global_step_increment_bug = params.get(
"fix_global_step_increment_bug", True)
if not linear_feature_columns and not dnn_feature_columns:
raise ValueError(
"Either linear_feature_columns or dnn_feature_columns must be defined.")
features = _get_feature_dict(features)
linear_optimizer = _get_optimizer(linear_optimizer)
_check_no_sync_replicas_optimizer(linear_optimizer)
dnn_optimizer = _get_optimizer(dnn_optimizer)
_check_no_sync_replicas_optimizer(dnn_optimizer)
# Build DNN Logits.
dnn_parent_scope = "dnn"
if not dnn_feature_columns:
dnn_logits = None
else:
if not dnn_hidden_units:
raise ValueError(
"dnn_hidden_units must be defined when dnn_feature_columns is "
"specified.")
dnn_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas))
with variable_scope.variable_scope(
dnn_parent_scope,
values=tuple(six.itervalues(features)),
partitioner=dnn_partitioner):
with variable_scope.variable_scope(
"input_from_feature_columns",
values=tuple(six.itervalues(features)),
partitioner=input_layer_partitioner) as dnn_input_scope:
if all([
isinstance(fc, feature_column_lib._FeatureColumn) # pylint: disable=protected-access
for fc in dnn_feature_columns
]):
net = layers.input_from_feature_columns(
columns_to_tensors=features,
feature_columns=dnn_feature_columns,
weight_collections=[dnn_parent_scope],
scope=dnn_input_scope)
else:
net = fc_core.input_layer(
features=features,
feature_columns=dnn_feature_columns,
weight_collections=[dnn_parent_scope])
for layer_id, num_hidden_units in enumerate(dnn_hidden_units):
with variable_scope.variable_scope(
"hiddenlayer_%d" % layer_id,
values=(net,)) as dnn_hidden_layer_scope:
net = layers.fully_connected(
net,
num_hidden_units,
activation_fn=dnn_activation_fn,
variables_collections=[dnn_parent_scope],
scope=dnn_hidden_layer_scope)
if dnn_dropout is not None and mode == model_fn.ModeKeys.TRAIN:
net = layers.dropout(
net,
keep_prob=(1.0 - dnn_dropout))
# TODO(b/31209633): Consider adding summary before dropout.
_add_layer_summary(net, dnn_hidden_layer_scope.name)
with variable_scope.variable_scope(
"logits",
values=(net,)) as dnn_logits_scope:
dnn_logits = layers.fully_connected(
net,
head.logits_dimension,
activation_fn=None,
variables_collections=[dnn_parent_scope],
scope=dnn_logits_scope)
_add_layer_summary(dnn_logits, dnn_logits_scope.name)
# Build Linear logits.
linear_parent_scope = "linear"
if not linear_feature_columns:
linear_logits = None
else:
linear_partitioner = partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas,
min_slice_size=64 << 20)
with variable_scope.variable_scope(
linear_parent_scope,
values=tuple(six.itervalues(features)),
partitioner=linear_partitioner) as scope:
if all([isinstance(fc, feature_column_lib._FeatureColumn) # pylint: disable=protected-access
for fc in linear_feature_columns]):
if joint_linear_weights:
linear_logits, _, _ = layers.joint_weighted_sum_from_feature_columns(
columns_to_tensors=features,
feature_columns=linear_feature_columns,
num_outputs=head.logits_dimension,
weight_collections=[linear_parent_scope],
scope=scope)
else:
linear_logits, _, _ = layers.weighted_sum_from_feature_columns(
columns_to_tensors=features,
feature_columns=linear_feature_columns,
num_outputs=head.logits_dimension,
weight_collections=[linear_parent_scope],
scope=scope)
else:
linear_logits = fc_core.linear_model(
features=features,
feature_columns=linear_feature_columns,
units=head.logits_dimension,
weight_collections=[linear_parent_scope])
_add_layer_summary(linear_logits, scope.name)
# Combine logits and build full model.
if dnn_logits is not None and linear_logits is not None:
logits = dnn_logits + linear_logits
elif dnn_logits is not None:
logits = dnn_logits
else:
logits = linear_logits
def _make_training_op(training_loss):
"""Training op for the DNN linear combined model."""
train_ops = []
global_step = training_util.get_global_step()
if dnn_logits is not None:
train_ops.append(
optimizers.optimize_loss(
loss=training_loss,
global_step=global_step,
learning_rate=_DNN_LEARNING_RATE,
optimizer=dnn_optimizer,
gradient_multipliers=_extract_embedding_lr_multipliers( # pylint: disable=protected-access
embedding_lr_multipliers, dnn_parent_scope,
dnn_input_scope.name),
clip_gradients=gradient_clip_norm,
variables=ops.get_collection(dnn_parent_scope),
name=dnn_parent_scope,
# Empty summaries, because head already logs "loss" summary.
summaries=[],
increment_global_step=not fix_global_step_increment_bug))
if linear_logits is not None:
train_ops.append(
optimizers.optimize_loss(
loss=training_loss,
global_step=global_step,
learning_rate=_linear_learning_rate(len(linear_feature_columns)),
optimizer=linear_optimizer,
clip_gradients=gradient_clip_norm,
variables=ops.get_collection(linear_parent_scope),
name=linear_parent_scope,
# Empty summaries, because head already logs "loss" summary.
summaries=[],
increment_global_step=not fix_global_step_increment_bug))
train_op = control_flow_ops.group(*train_ops)
if fix_global_step_increment_bug:
with ops.control_dependencies([train_op]):
with ops.colocate_with(global_step):
return state_ops.assign_add(global_step, 1).op
return train_op
return head.create_model_fn_ops(
features=features,
mode=mode,
labels=labels,
train_op_fn=_make_training_op,
logits=logits)
class DNNLinearCombinedEstimator(estimator.Estimator):
"""An estimator for TensorFlow Linear and DNN joined training models.
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.
Note: New users must set `fix_global_step_increment_bug=True` when creating an
estimator.
Input of `fit`, `train`, and `evaluate` should have following features,
otherwise there will be a `KeyError`:
if `weight_column_name` is not `None`, a feature with
`key=weight_column_name` whose value is a `Tensor`.
for each `column` in `dnn_feature_columns` + `linear_feature_columns`:
- if `column` is a `SparseColumn`, a feature with `key=column.name`
whose `value` is a `SparseTensor`.
- if `column` is a `WeightedSparseColumn`, two features: the first with
`key` the id column name, the second with `key` the weight column
name. Both features' `value` must be a `SparseTensor`.
- if `column` is a `RealValuedColumn, a feature with `key=column.name`
whose `value` is a `Tensor`.
"""
@deprecated_arg_values(
_FIX_GLOBAL_STEP_INCREMENT_DATE,
_FIX_GLOBAL_STEP_INCREMENT_INSTRUCTIONS,
fix_global_step_increment_bug=False)
def __init__(self, # _joint_linear_weights pylint: disable=invalid-name
head,
model_dir=None,
linear_feature_columns=None,
linear_optimizer=None,
_joint_linear_weights=False,
dnn_feature_columns=None,
dnn_optimizer=None,
dnn_hidden_units=None,
dnn_activation_fn=None,
dnn_dropout=None,
gradient_clip_norm=None,
config=None,
feature_engineering_fn=None,
embedding_lr_multipliers=None,
fix_global_step_increment_bug=False,
input_layer_partitioner=None):
"""Initializes a DNNLinearCombinedEstimator instance.
Note: New users must set `fix_global_step_increment_bug=True` when creating
an estimator.
Args:
head: A _Head object.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set should be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. If `None`, will use a FTRL optimizer.
_joint_linear_weights: If True will use a single (possibly partitioned)
variable to store all weights for the linear model. More efficient if
there are many columns, however requires all columns are sparse and
have the 'sum' combiner.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set should be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. If `None`, will use an Adagrad optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If `None`,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
config: RunConfig object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and returns features and
labels which will be fed into the model.
embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
fix_global_step_increment_bug: If `False`, the estimator needs two fit
steps to optimize both linear and dnn parts. If `True`, this bug is
fixed. New users must set this to `True`, but the default value is
`False` for backwards compatibility.
input_layer_partitioner: Optional. Partitioner for input layer.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time.
"""
linear_feature_columns = tuple(linear_feature_columns or [])
dnn_feature_columns = tuple(dnn_feature_columns or [])
if not linear_feature_columns + dnn_feature_columns:
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
"must be defined.")
super(DNNLinearCombinedEstimator, self).__init__(
model_fn=_dnn_linear_combined_model_fn,
model_dir=model_dir,
config=config,
params={
"head": head,
"linear_feature_columns": linear_feature_columns,
"linear_optimizer": linear_optimizer,
"joint_linear_weights": _joint_linear_weights,
"dnn_feature_columns": dnn_feature_columns,
"dnn_optimizer": dnn_optimizer,
"dnn_hidden_units": dnn_hidden_units,
"dnn_activation_fn": dnn_activation_fn,
"dnn_dropout": dnn_dropout,
"gradient_clip_norm": gradient_clip_norm,
"embedding_lr_multipliers": embedding_lr_multipliers,
"fix_global_step_increment_bug": fix_global_step_increment_bug,
"input_layer_partitioner": input_layer_partitioner
},
feature_engineering_fn=feature_engineering_fn)
class DNNLinearCombinedClassifier(estimator.Estimator):
"""A classifier for TensorFlow Linear and DNN joined training models.
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.
Note: New users must set `fix_global_step_increment_bug=True` when creating an
estimator.
Example:
```python
sparse_feature_a = sparse_column_with_hash_bucket(...)
sparse_feature_b = sparse_column_with_hash_bucket(...)
sparse_feature_a_x_sparse_feature_b = crossed_column(...)
sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a,
...)
sparse_feature_b_emb = embedding_column(sparse_id_column=sparse_feature_b,
...)
estimator = DNNLinearCombinedClassifier(
# common settings
n_classes=n_classes,
weight_column_name=weight_column_name,
# wide settings
linear_feature_columns=[sparse_feature_a_x_sparse_feature_b],
linear_optimizer=tf.train.FtrlOptimizer(...),
# deep settings
dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
dnn_hidden_units=[1000, 500, 100],
dnn_optimizer=tf.train.AdagradOptimizer(...))
# Input builders
def input_fn_train: # returns x, y (where y represents label's class index).
...
def input_fn_eval: # returns x, y (where y represents label's class index).
...
def input_fn_predict: # returns x, None.
...
estimator.fit(input_fn=input_fn_train)
estimator.evaluate(input_fn=input_fn_eval)
# predict_classes returns class indices.
estimator.predict_classes(input_fn=input_fn_predict)
```
If the user specifies `label_keys` in constructor, labels must be strings from
the `label_keys` vocabulary. Example:
```python
label_keys = ['label0', 'label1', 'label2']
estimator = DNNLinearCombinedClassifier(
n_classes=n_classes,
linear_feature_columns=[sparse_feature_a_x_sparse_feature_b],
dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
dnn_hidden_units=[1000, 500, 100],
label_keys=label_keys)
def input_fn_train: # returns x, y (where y is one of label_keys).
pass
estimator.fit(input_fn=input_fn_train)
def input_fn_eval: # returns x, y (where y is one of label_keys).
pass
estimator.evaluate(input_fn=input_fn_eval)
def input_fn_predict: # returns x, None
# predict_classes returns one of label_keys.
estimator.predict_classes(input_fn=input_fn_predict)
```
Input of `fit` and `evaluate` should have following features,
otherwise there will be a `KeyError`:
* if `weight_column_name` is not `None`, a feature with
`key=weight_column_name` whose value is a `Tensor`.
* for each `column` in `dnn_feature_columns` + `linear_feature_columns`:
- if `column` is a `SparseColumn`, a feature with `key=column.name`
whose `value` is a `SparseTensor`.
- if `column` is a `WeightedSparseColumn`, two features: the first with
`key` the id column name, the second with `key` the weight column name.
Both features' `value` must be a `SparseTensor`.
- if `column` is a `RealValuedColumn, a feature with `key=column.name`
whose `value` is a `Tensor`.
"""
@deprecated_arg_values(
_FIX_GLOBAL_STEP_INCREMENT_DATE,
_FIX_GLOBAL_STEP_INCREMENT_INSTRUCTIONS,
fix_global_step_increment_bug=False)
def __init__(self, # _joint_linear_weights pylint: disable=invalid-name
model_dir=None,
n_classes=2,
weight_column_name=None,
linear_feature_columns=None,
linear_optimizer=None,
_joint_linear_weights=False,
dnn_feature_columns=None,
dnn_optimizer=None,
dnn_hidden_units=None,
dnn_activation_fn=nn.relu,
dnn_dropout=None,
gradient_clip_norm=None,
enable_centered_bias=False,
config=None,
feature_engineering_fn=None,
embedding_lr_multipliers=None,
input_layer_min_slice_size=None,
label_keys=None,
fix_global_step_increment_bug=False):
"""Constructs a DNNLinearCombinedClassifier instance.
Note: New users must set `fix_global_step_increment_bug=True` when creating
an estimator.
Args:
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
n_classes: number of label classes. Default is binary classification.
Note that class labels are integers representing the class index (i.e.
values from 0 to n_classes-1). For arbitrary label values (e.g. string
labels), convert to class indices first.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training.
It will be multiplied by the loss of the example.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set must be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. If `None`, will use a FTRL optimizer.
_joint_linear_weights: If True a single (possibly partitioned) variable
will be used to store the linear model weights. It's faster, but
requires all columns are sparse and have the 'sum' combiner.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set must be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. If `None`, will use an Adagrad optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If `None`,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: RunConfig object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and returns features and
labels which will be fed into the model.
embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
input_layer_min_slice_size: Optional. The min slice size of input layer
partitions. If not provided, will use the default of 64M.
label_keys: Optional list of strings with size `[n_classes]` defining the
label vocabulary. Only supported for `n_classes` > 2.
fix_global_step_increment_bug: If `False`, the estimator needs two fit
steps to optimize both linear and dnn parts. If `True`, this bug is
fixed. New users must set this to `True`, but it the default value is
`False` for backwards compatibility.
Raises:
ValueError: If `n_classes` < 2.
ValueError: If both `linear_feature_columns` and `dnn_features_columns`
are empty at the same time.
"""
head = head_lib.multi_class_head(
n_classes=n_classes,
weight_column_name=weight_column_name,
enable_centered_bias=enable_centered_bias,
label_keys=label_keys)
linear_feature_columns = tuple(linear_feature_columns or [])
dnn_feature_columns = tuple(dnn_feature_columns or [])
self._feature_columns = linear_feature_columns + dnn_feature_columns
if not self._feature_columns:
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
"must be defined.")
# TODO(b/35922130): Replace with `input_layer_partitioner` arg.
input_layer_partitioner = None
if input_layer_min_slice_size is not None:
input_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=config.num_ps_replicas if config else 0,
min_slice_size=input_layer_min_slice_size))
super(DNNLinearCombinedClassifier, self).__init__(
model_fn=_dnn_linear_combined_model_fn,
model_dir=model_dir,
config=config,
params={
"head": head,
"linear_feature_columns": linear_feature_columns,
"linear_optimizer": linear_optimizer,
"joint_linear_weights": _joint_linear_weights,
"dnn_feature_columns": dnn_feature_columns,
"dnn_optimizer": dnn_optimizer,
"dnn_hidden_units": dnn_hidden_units,
"dnn_activation_fn": dnn_activation_fn,
"dnn_dropout": dnn_dropout,
"gradient_clip_norm": gradient_clip_norm,
"embedding_lr_multipliers": embedding_lr_multipliers,
"input_layer_partitioner": input_layer_partitioner,
"fix_global_step_increment_bug": fix_global_step_increment_bug,
},
feature_engineering_fn=feature_engineering_fn)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE, estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
@deprecated_arg_values(
"2017-03-01",
"Please switch to predict_classes, or set `outputs` argument.",
outputs=None)
def predict(self, x=None, input_fn=None, batch_size=None, outputs=None,
as_iterable=True):
"""Returns predictions for given features.
By default, returns predicted classes. But this default will be dropped
soon. Users should either pass `outputs`, or call `predict_classes` method.
Args:
x: features.
input_fn: Input function. If set, x must be None.
batch_size: Override default batch size.
outputs: list of `str`, name of the output to predict.
If `None`, returns classes.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted classes with shape [batch_size] (or an iterable
of predicted classes if as_iterable is True). Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).
If `outputs` is set, returns a dict of predictions.
"""
if not outputs:
return self.predict_classes(
x=x,
input_fn=input_fn,
batch_size=batch_size,
as_iterable=as_iterable)
return super(DNNLinearCombinedClassifier, self).predict(
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=outputs,
as_iterable=as_iterable)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE, estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
def predict_classes(self, x=None, input_fn=None, batch_size=None,
as_iterable=True):
"""Returns predicted classes for given features.
Args:
x: features.
input_fn: Input function. If set, x must be None.
batch_size: Override default batch size.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted classes with shape [batch_size] (or an iterable
of predicted classes if as_iterable is True). Each predicted class is
represented by its class index (i.e. integer from 0 to n_classes-1).
"""
key = prediction_key.PredictionKey.CLASSES
preds = super(DNNLinearCombinedClassifier, self).predict(
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=[key],
as_iterable=as_iterable)
if as_iterable:
return _as_iterable(preds, output=key)
return preds[key].reshape(-1)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE, estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
def predict_proba(
self, x=None, input_fn=None, batch_size=None, as_iterable=True):
"""Returns prediction probabilities for given features.
Args:
x: features.
input_fn: Input function. If set, x and y must be None.
batch_size: Override default batch size.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted probabilities with shape [batch_size, n_classes]
(or an iterable of predicted probabilities if as_iterable is True).
"""
key = prediction_key.PredictionKey.PROBABILITIES
preds = super(DNNLinearCombinedClassifier, self).predict(
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=[key],
as_iterable=as_iterable)
if as_iterable:
return _as_iterable(preds, output=key)
return preds[key]
@deprecated("2017-03-25", "Please use Estimator.export_savedmodel() instead.")
def export(self,
export_dir,
input_fn=None,
input_feature_key=None,
use_deprecated_input_fn=True,
signature_fn=None,
default_batch_size=1,
exports_to_keep=None):
"""See BasEstimator.export."""
def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
return super(DNNLinearCombinedClassifier, self).export(
export_dir=export_dir,
input_fn=input_fn or default_input_fn,
input_feature_key=input_feature_key,
use_deprecated_input_fn=use_deprecated_input_fn,
signature_fn=(signature_fn or
export.classification_signature_fn_with_prob),
prediction_key=prediction_key.PredictionKey.PROBABILITIES,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep)
class DNNLinearCombinedRegressor(estimator.Estimator):
"""A regressor for TensorFlow Linear and DNN joined training models.
THIS CLASS IS DEPRECATED. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for general migration instructions.
Note: New users must set `fix_global_step_increment_bug=True` when creating an
estimator.
Example:
```python
sparse_feature_a = sparse_column_with_hash_bucket(...)
sparse_feature_b = sparse_column_with_hash_bucket(...)
sparse_feature_a_x_sparse_feature_b = crossed_column(...)
sparse_feature_a_emb = embedding_column(sparse_id_column=sparse_feature_a,
...)
sparse_feature_b_emb = embedding_column(sparse_id_column=sparse_feature_b,
...)
estimator = DNNLinearCombinedRegressor(
# common settings
weight_column_name=weight_column_name,
# wide settings
linear_feature_columns=[sparse_feature_a_x_sparse_feature_b],
linear_optimizer=tf.train.FtrlOptimizer(...),
# deep settings
dnn_feature_columns=[sparse_feature_a_emb, sparse_feature_b_emb],
dnn_hidden_units=[1000, 500, 100],
dnn_optimizer=tf.train.ProximalAdagradOptimizer(...))
# To apply L1 and L2 regularization, you can set optimizers as follows:
tf.train.ProximalAdagradOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001,
l2_regularization_strength=0.001)
# It is same for FtrlOptimizer.
# Input builders
def input_fn_train: # returns x, y
...
def input_fn_eval: # returns x, y
...
def input_fn_predict: # returns x, None
...
estimator.train(input_fn_train)
estimator.evaluate(input_fn_eval)
estimator.predict(input_fn_predict)
```
Input of `fit`, `train`, and `evaluate` should have following features,
otherwise there will be a `KeyError`:
if `weight_column_name` is not `None`, a feature with
`key=weight_column_name` whose value is a `Tensor`.
for each `column` in `dnn_feature_columns` + `linear_feature_columns`:
- if `column` is a `SparseColumn`, a feature with `key=column.name`
whose `value` is a `SparseTensor`.
- if `column` is a `WeightedSparseColumn`, two features: the first with
`key` the id column name, the second with `key` the weight column name.
Both features' `value` must be a `SparseTensor`.
- if `column` is a `RealValuedColumn, a feature with `key=column.name`
whose `value` is a `Tensor`.
"""
@deprecated_arg_values(
_FIX_GLOBAL_STEP_INCREMENT_DATE,
_FIX_GLOBAL_STEP_INCREMENT_INSTRUCTIONS,
fix_global_step_increment_bug=False)
def __init__(self, # _joint_linear_weights pylint: disable=invalid-name
model_dir=None,
weight_column_name=None,
linear_feature_columns=None,
linear_optimizer=None,
_joint_linear_weights=False,
dnn_feature_columns=None,
dnn_optimizer=None,
dnn_hidden_units=None,
dnn_activation_fn=nn.relu,
dnn_dropout=None,
gradient_clip_norm=None,
enable_centered_bias=False,
label_dimension=1,
config=None,
feature_engineering_fn=None,
embedding_lr_multipliers=None,
input_layer_min_slice_size=None,
fix_global_step_increment_bug=False):
"""Initializes a DNNLinearCombinedRegressor instance.
Note: New users must set `fix_global_step_increment_bug=True` when creating
an estimator.
Args:
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator
to continue training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
linear_feature_columns: An iterable containing all the feature columns
used by linear part of the model. All items in the set must be
instances of classes derived from `FeatureColumn`.
linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the linear part of the model. If `None`, will use a FTRL optimizer.
_joint_linear_weights: If True a single (possibly partitioned) variable
will be used to store the linear model weights. It's faster, but
requires that all columns are sparse and have the 'sum' combiner.
dnn_feature_columns: An iterable containing all the feature columns used
by deep part of the model. All items in the set must be instances of
classes derived from `FeatureColumn`.
dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to
the deep part of the model. If `None`, will use an Adagrad optimizer.
dnn_hidden_units: List of hidden units per layer. All layers are fully
connected.
dnn_activation_fn: Activation function applied to each layer. If None,
will use `tf.nn.relu`.
dnn_dropout: When not None, the probability we will drop out
a given coordinate.
gradient_clip_norm: A float > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
tf.clip_by_global_norm for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
label_dimension: Number of regression targets per example. This is the
size of the last dimension of the labels and logits `Tensor` objects
(typically, these have shape `[batch_size, label_dimension]`).
config: RunConfig object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and returns features and
labels which will be fed into the model.
embedding_lr_multipliers: Optional. A dictionary from `EmbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
input_layer_min_slice_size: Optional. The min slice size of input layer
partitions. If not provided, will use the default of 64M.
fix_global_step_increment_bug: If `False`, the estimator needs two fit
steps to optimize both linear and dnn parts. If `True`, this bug is
fixed. New users must set this to `True`, but it the default value is
`False` for backwards compatibility.
Raises:
ValueError: If both linear_feature_columns and dnn_features_columns are
empty at the same time.
"""
linear_feature_columns = linear_feature_columns or []
dnn_feature_columns = dnn_feature_columns or []
self._feature_columns = linear_feature_columns + dnn_feature_columns
if not self._feature_columns:
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
"must be defined.")
# TODO(b/35922130): Replace with `input_layer_partitioner` arg.
input_layer_partitioner = None
if input_layer_min_slice_size is not None:
input_layer_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=config.num_ps_replicas if config else 0,
min_slice_size=input_layer_min_slice_size))
head = head_lib.regression_head(
weight_column_name=weight_column_name,
label_dimension=label_dimension,
enable_centered_bias=enable_centered_bias)
super(DNNLinearCombinedRegressor, self).__init__(
model_fn=_dnn_linear_combined_model_fn,
model_dir=model_dir,
config=config,
params={
"head": head,
"linear_feature_columns": linear_feature_columns,
"linear_optimizer": linear_optimizer,
"joint_linear_weights": _joint_linear_weights,
"dnn_feature_columns": dnn_feature_columns,
"dnn_optimizer": dnn_optimizer,
"dnn_hidden_units": dnn_hidden_units,
"dnn_activation_fn": dnn_activation_fn,
"dnn_dropout": dnn_dropout,
"gradient_clip_norm": gradient_clip_norm,
"embedding_lr_multipliers": embedding_lr_multipliers,
"input_layer_partitioner": input_layer_partitioner,
"fix_global_step_increment_bug": fix_global_step_increment_bug,
},
feature_engineering_fn=feature_engineering_fn)
def evaluate(self,
x=None,
y=None,
input_fn=None,
feed_fn=None,
batch_size=None,
steps=None,
metrics=None,
name=None,
checkpoint_path=None,
hooks=None):
"""See evaluable.Evaluable."""
# TODO(zakaria): remove once deprecation is finished (b/31229024)
custom_metrics = {}
if metrics:
for key, metric in six.iteritems(metrics):
if (not isinstance(metric, metric_spec.MetricSpec) and
not isinstance(key, tuple)):
custom_metrics[(key, prediction_key.PredictionKey.SCORES)] = metric
else:
custom_metrics[key] = metric
return super(DNNLinearCombinedRegressor, self).evaluate(
x=x,
y=y,
input_fn=input_fn,
feed_fn=feed_fn,
batch_size=batch_size,
steps=steps,
metrics=custom_metrics,
name=name,
checkpoint_path=checkpoint_path,
hooks=hooks)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE, estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
@deprecated_arg_values(
"2017-03-01",
"Please switch to predict_scores, or set `outputs` argument.",
outputs=None)
def predict(self, x=None, input_fn=None, batch_size=None, outputs=None,
as_iterable=True):
"""Returns predictions for given features.
By default, returns predicted scores. But this default will be dropped
soon. Users should either pass `outputs`, or call `predict_scores` method.
Args:
x: features.
input_fn: Input function. If set, x must be None.
batch_size: Override default batch size.
outputs: list of `str`, name of the output to predict.
If `None`, returns scores.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted scores (or an iterable of predicted scores if
as_iterable is True). If `label_dimension == 1`, the shape of the output
is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`.
If `outputs` is set, returns a dict of predictions.
"""
if not outputs:
return self.predict_scores(
x=x,
input_fn=input_fn,
batch_size=batch_size,
as_iterable=as_iterable)
return super(DNNLinearCombinedRegressor, self).predict(
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=outputs,
as_iterable=as_iterable)
@deprecated_arg_values(
estimator.AS_ITERABLE_DATE, estimator.AS_ITERABLE_INSTRUCTIONS,
as_iterable=False)
def predict_scores(self, x=None, input_fn=None, batch_size=None,
as_iterable=True):
"""Returns predicted scores for given features.
Args:
x: features.
input_fn: Input function. If set, x must be None.
batch_size: Override default batch size.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
Numpy array of predicted scores (or an iterable of predicted scores if
as_iterable is True). If `label_dimension == 1`, the shape of the output
is `[batch_size]`, otherwise the shape is `[batch_size, label_dimension]`.
"""
key = prediction_key.PredictionKey.SCORES
preds = super(DNNLinearCombinedRegressor, self).predict(
x=x,
input_fn=input_fn,
batch_size=batch_size,
outputs=[key],
as_iterable=as_iterable)
if as_iterable:
return (pred[key] for pred in preds)
return preds[key]
@deprecated("2017-03-25", "Please use Estimator.export_savedmodel() instead.")
def export(self,
export_dir,
input_fn=None,
input_feature_key=None,
use_deprecated_input_fn=True,
signature_fn=None,
default_batch_size=1,
exports_to_keep=None):
"""See BaseEstimator.export."""
def default_input_fn(unused_estimator, examples):
return layers.parse_feature_columns_from_examples(
examples, self._feature_columns)
return super(DNNLinearCombinedRegressor, self).export(
export_dir=export_dir,
input_fn=input_fn or default_input_fn,
input_feature_key=input_feature_key,
use_deprecated_input_fn=use_deprecated_input_fn,
signature_fn=signature_fn or export.regression_signature_fn,
prediction_key=prediction_key.PredictionKey.SCORES,
default_batch_size=default_batch_size,
exports_to_keep=exports_to_keep)
# Aliases
# TODO(zakaria): Remove these aliases, See b/34751732
_DNNLinearCombinedEstimator = DNNLinearCombinedEstimator
| mit |
ryfeus/lambda-packs | Keras_tensorflow/source/tensorflow/core/framework/function_pb2.py | 7 | 11710 | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: tensorflow/core/framework/function.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node__def__pb2
from tensorflow.core.framework import op_def_pb2 as tensorflow_dot_core_dot_framework_dot_op__def__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='tensorflow/core/framework/function.proto',
package='tensorflow',
syntax='proto3',
serialized_pb=_b('\n(tensorflow/core/framework/function.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\x1a(tensorflow/core/framework/node_def.proto\x1a&tensorflow/core/framework/op_def.proto\"j\n\x12\x46unctionDefLibrary\x12)\n\x08\x66unction\x18\x01 \x03(\x0b\x32\x17.tensorflow.FunctionDef\x12)\n\x08gradient\x18\x02 \x03(\x0b\x32\x17.tensorflow.GradientDef\"\xaa\x02\n\x0b\x46unctionDef\x12$\n\tsignature\x18\x01 \x01(\x0b\x32\x11.tensorflow.OpDef\x12/\n\x04\x61ttr\x18\x05 \x03(\x0b\x32!.tensorflow.FunctionDef.AttrEntry\x12%\n\x08node_def\x18\x03 \x03(\x0b\x32\x13.tensorflow.NodeDef\x12-\n\x03ret\x18\x04 \x03(\x0b\x32 .tensorflow.FunctionDef.RetEntry\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1a*\n\x08RetEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\";\n\x0bGradientDef\x12\x15\n\rfunction_name\x18\x01 \x01(\t\x12\x15\n\rgradient_func\x18\x02 \x01(\tB/\n\x18org.tensorflow.frameworkB\x0e\x46unctionProtosP\x01\xf8\x01\x01\x62\x06proto3')
,
dependencies=[tensorflow_dot_core_dot_framework_dot_attr__value__pb2.DESCRIPTOR,tensorflow_dot_core_dot_framework_dot_node__def__pb2.DESCRIPTOR,tensorflow_dot_core_dot_framework_dot_op__def__pb2.DESCRIPTOR,])
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_FUNCTIONDEFLIBRARY = _descriptor.Descriptor(
name='FunctionDefLibrary',
full_name='tensorflow.FunctionDefLibrary',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='function', full_name='tensorflow.FunctionDefLibrary.function', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='gradient', full_name='tensorflow.FunctionDefLibrary.gradient', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=182,
serialized_end=288,
)
_FUNCTIONDEF_ATTRENTRY = _descriptor.Descriptor(
name='AttrEntry',
full_name='tensorflow.FunctionDef.AttrEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='tensorflow.FunctionDef.AttrEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='value', full_name='tensorflow.FunctionDef.AttrEntry.value', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=479,
serialized_end=545,
)
_FUNCTIONDEF_RETENTRY = _descriptor.Descriptor(
name='RetEntry',
full_name='tensorflow.FunctionDef.RetEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='tensorflow.FunctionDef.RetEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='value', full_name='tensorflow.FunctionDef.RetEntry.value', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=547,
serialized_end=589,
)
_FUNCTIONDEF = _descriptor.Descriptor(
name='FunctionDef',
full_name='tensorflow.FunctionDef',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='signature', full_name='tensorflow.FunctionDef.signature', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='attr', full_name='tensorflow.FunctionDef.attr', index=1,
number=5, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='node_def', full_name='tensorflow.FunctionDef.node_def', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ret', full_name='tensorflow.FunctionDef.ret', index=3,
number=4, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[_FUNCTIONDEF_ATTRENTRY, _FUNCTIONDEF_RETENTRY, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=291,
serialized_end=589,
)
_GRADIENTDEF = _descriptor.Descriptor(
name='GradientDef',
full_name='tensorflow.GradientDef',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='function_name', full_name='tensorflow.GradientDef.function_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='gradient_func', full_name='tensorflow.GradientDef.gradient_func', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=591,
serialized_end=650,
)
_FUNCTIONDEFLIBRARY.fields_by_name['function'].message_type = _FUNCTIONDEF
_FUNCTIONDEFLIBRARY.fields_by_name['gradient'].message_type = _GRADIENTDEF
_FUNCTIONDEF_ATTRENTRY.fields_by_name['value'].message_type = tensorflow_dot_core_dot_framework_dot_attr__value__pb2._ATTRVALUE
_FUNCTIONDEF_ATTRENTRY.containing_type = _FUNCTIONDEF
_FUNCTIONDEF_RETENTRY.containing_type = _FUNCTIONDEF
_FUNCTIONDEF.fields_by_name['signature'].message_type = tensorflow_dot_core_dot_framework_dot_op__def__pb2._OPDEF
_FUNCTIONDEF.fields_by_name['attr'].message_type = _FUNCTIONDEF_ATTRENTRY
_FUNCTIONDEF.fields_by_name['node_def'].message_type = tensorflow_dot_core_dot_framework_dot_node__def__pb2._NODEDEF
_FUNCTIONDEF.fields_by_name['ret'].message_type = _FUNCTIONDEF_RETENTRY
DESCRIPTOR.message_types_by_name['FunctionDefLibrary'] = _FUNCTIONDEFLIBRARY
DESCRIPTOR.message_types_by_name['FunctionDef'] = _FUNCTIONDEF
DESCRIPTOR.message_types_by_name['GradientDef'] = _GRADIENTDEF
FunctionDefLibrary = _reflection.GeneratedProtocolMessageType('FunctionDefLibrary', (_message.Message,), dict(
DESCRIPTOR = _FUNCTIONDEFLIBRARY,
__module__ = 'tensorflow.core.framework.function_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.FunctionDefLibrary)
))
_sym_db.RegisterMessage(FunctionDefLibrary)
FunctionDef = _reflection.GeneratedProtocolMessageType('FunctionDef', (_message.Message,), dict(
AttrEntry = _reflection.GeneratedProtocolMessageType('AttrEntry', (_message.Message,), dict(
DESCRIPTOR = _FUNCTIONDEF_ATTRENTRY,
__module__ = 'tensorflow.core.framework.function_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.FunctionDef.AttrEntry)
))
,
RetEntry = _reflection.GeneratedProtocolMessageType('RetEntry', (_message.Message,), dict(
DESCRIPTOR = _FUNCTIONDEF_RETENTRY,
__module__ = 'tensorflow.core.framework.function_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.FunctionDef.RetEntry)
))
,
DESCRIPTOR = _FUNCTIONDEF,
__module__ = 'tensorflow.core.framework.function_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.FunctionDef)
))
_sym_db.RegisterMessage(FunctionDef)
_sym_db.RegisterMessage(FunctionDef.AttrEntry)
_sym_db.RegisterMessage(FunctionDef.RetEntry)
GradientDef = _reflection.GeneratedProtocolMessageType('GradientDef', (_message.Message,), dict(
DESCRIPTOR = _GRADIENTDEF,
__module__ = 'tensorflow.core.framework.function_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.GradientDef)
))
_sym_db.RegisterMessage(GradientDef)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\n\030org.tensorflow.frameworkB\016FunctionProtosP\001\370\001\001'))
_FUNCTIONDEF_ATTRENTRY.has_options = True
_FUNCTIONDEF_ATTRENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
_FUNCTIONDEF_RETENTRY.has_options = True
_FUNCTIONDEF_RETENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
# @@protoc_insertion_point(module_scope)
| mit |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/setuptools/pep425tags.py | 35 | 10882 | # This file originally from pip:
# https://github.com/pypa/pip/blob/8f4f15a5a95d7d5b511ceaee9ed261176c181970/src/pip/_internal/pep425tags.py
"""Generate and work with PEP 425 Compatibility Tags."""
from __future__ import absolute_import
import distutils.util
import platform
import re
import sys
import sysconfig
import warnings
from collections import OrderedDict
from . import glibc
_osx_arch_pat = re.compile(r'(.+)_(\d+)_(\d+)_(.+)')
def get_config_var(var):
try:
return sysconfig.get_config_var(var)
except IOError as e: # Issue #1074
warnings.warn("{}".format(e), RuntimeWarning)
return None
def get_abbr_impl():
"""Return abbreviated implementation name."""
if hasattr(sys, 'pypy_version_info'):
pyimpl = 'pp'
elif sys.platform.startswith('java'):
pyimpl = 'jy'
elif sys.platform == 'cli':
pyimpl = 'ip'
else:
pyimpl = 'cp'
return pyimpl
def get_impl_ver():
"""Return implementation version."""
impl_ver = get_config_var("py_version_nodot")
if not impl_ver or get_abbr_impl() == 'pp':
impl_ver = ''.join(map(str, get_impl_version_info()))
return impl_ver
def get_impl_version_info():
"""Return sys.version_info-like tuple for use in decrementing the minor
version."""
if get_abbr_impl() == 'pp':
# as per https://github.com/pypa/pip/issues/2882
return (sys.version_info[0], sys.pypy_version_info.major,
sys.pypy_version_info.minor)
else:
return sys.version_info[0], sys.version_info[1]
def get_impl_tag():
"""
Returns the Tag for this specific implementation.
"""
return "{}{}".format(get_abbr_impl(), get_impl_ver())
def get_flag(var, fallback, expected=True, warn=True):
"""Use a fallback method for determining SOABI flags if the needed config
var is unset or unavailable."""
val = get_config_var(var)
if val is None:
if warn:
warnings.warn("Config variable '{0}' is unset, Python ABI tag may "
"be incorrect".format(var), RuntimeWarning, 2)
return fallback()
return val == expected
def get_abi_tag():
"""Return the ABI tag based on SOABI (if available) or emulate SOABI
(CPython 2, PyPy)."""
soabi = get_config_var('SOABI')
impl = get_abbr_impl()
if not soabi and impl in {'cp', 'pp'} and hasattr(sys, 'maxunicode'):
d = ''
m = ''
u = ''
if get_flag('Py_DEBUG',
lambda: hasattr(sys, 'gettotalrefcount'),
warn=(impl == 'cp')):
d = 'd'
if get_flag('WITH_PYMALLOC',
lambda: impl == 'cp',
warn=(impl == 'cp')):
m = 'm'
if get_flag('Py_UNICODE_SIZE',
lambda: sys.maxunicode == 0x10ffff,
expected=4,
warn=(impl == 'cp' and
sys.version_info < (3, 3))) \
and sys.version_info < (3, 3):
u = 'u'
abi = '%s%s%s%s%s' % (impl, get_impl_ver(), d, m, u)
elif soabi and soabi.startswith('cpython-'):
abi = 'cp' + soabi.split('-')[1]
elif soabi:
abi = soabi.replace('.', '_').replace('-', '_')
else:
abi = None
return abi
def _is_running_32bit():
return sys.maxsize == 2147483647
def get_platform():
"""Return our platform name 'win32', 'linux_x86_64'"""
if sys.platform == 'darwin':
# distutils.util.get_platform() returns the release based on the value
# of MACOSX_DEPLOYMENT_TARGET on which Python was built, which may
# be significantly older than the user's current machine.
release, _, machine = platform.mac_ver()
split_ver = release.split('.')
if machine == "x86_64" and _is_running_32bit():
machine = "i386"
elif machine == "ppc64" and _is_running_32bit():
machine = "ppc"
return 'macosx_{}_{}_{}'.format(split_ver[0], split_ver[1], machine)
# XXX remove distutils dependency
result = distutils.util.get_platform().replace('.', '_').replace('-', '_')
if result == "linux_x86_64" and _is_running_32bit():
# 32 bit Python program (running on a 64 bit Linux): pip should only
# install and run 32 bit compiled extensions in that case.
result = "linux_i686"
return result
def is_manylinux1_compatible():
# Only Linux, and only x86-64 / i686
if get_platform() not in {"linux_x86_64", "linux_i686"}:
return False
# Check for presence of _manylinux module
try:
import _manylinux
return bool(_manylinux.manylinux1_compatible)
except (ImportError, AttributeError):
# Fall through to heuristic check below
pass
# Check glibc version. CentOS 5 uses glibc 2.5.
return glibc.have_compatible_glibc(2, 5)
def get_darwin_arches(major, minor, machine):
"""Return a list of supported arches (including group arches) for
the given major, minor and machine architecture of an macOS machine.
"""
arches = []
def _supports_arch(major, minor, arch):
# Looking at the application support for macOS versions in the chart
# provided by https://en.wikipedia.org/wiki/OS_X#Versions it appears
# our timeline looks roughly like:
#
# 10.0 - Introduces ppc support.
# 10.4 - Introduces ppc64, i386, and x86_64 support, however the ppc64
# and x86_64 support is CLI only, and cannot be used for GUI
# applications.
# 10.5 - Extends ppc64 and x86_64 support to cover GUI applications.
# 10.6 - Drops support for ppc64
# 10.7 - Drops support for ppc
#
# Given that we do not know if we're installing a CLI or a GUI
# application, we must be conservative and assume it might be a GUI
# application and behave as if ppc64 and x86_64 support did not occur
# until 10.5.
#
# Note: The above information is taken from the "Application support"
# column in the chart not the "Processor support" since I believe
# that we care about what instruction sets an application can use
# not which processors the OS supports.
if arch == 'ppc':
return (major, minor) <= (10, 5)
if arch == 'ppc64':
return (major, minor) == (10, 5)
if arch == 'i386':
return (major, minor) >= (10, 4)
if arch == 'x86_64':
return (major, minor) >= (10, 5)
if arch in groups:
for garch in groups[arch]:
if _supports_arch(major, minor, garch):
return True
return False
groups = OrderedDict([
("fat", ("i386", "ppc")),
("intel", ("x86_64", "i386")),
("fat64", ("x86_64", "ppc64")),
("fat32", ("x86_64", "i386", "ppc")),
])
if _supports_arch(major, minor, machine):
arches.append(machine)
for garch in groups:
if machine in groups[garch] and _supports_arch(major, minor, garch):
arches.append(garch)
arches.append('universal')
return arches
def get_supported(versions=None, noarch=False, platform=None,
impl=None, abi=None):
"""Return a list of supported tags for each version specified in
`versions`.
:param versions: a list of string versions, of the form ["33", "32"],
or None. The first version will be assumed to support our ABI.
:param platform: specify the exact platform you want valid
tags for, or None. If None, use the local system platform.
:param impl: specify the exact implementation you want valid
tags for, or None. If None, use the local interpreter impl.
:param abi: specify the exact abi you want valid
tags for, or None. If None, use the local interpreter abi.
"""
supported = []
# Versions must be given with respect to the preference
if versions is None:
versions = []
version_info = get_impl_version_info()
major = version_info[:-1]
# Support all previous minor Python versions.
for minor in range(version_info[-1], -1, -1):
versions.append(''.join(map(str, major + (minor,))))
impl = impl or get_abbr_impl()
abis = []
abi = abi or get_abi_tag()
if abi:
abis[0:0] = [abi]
abi3s = set()
import imp
for suffix in imp.get_suffixes():
if suffix[0].startswith('.abi'):
abi3s.add(suffix[0].split('.', 2)[1])
abis.extend(sorted(list(abi3s)))
abis.append('none')
if not noarch:
arch = platform or get_platform()
if arch.startswith('macosx'):
# support macosx-10.6-intel on macosx-10.9-x86_64
match = _osx_arch_pat.match(arch)
if match:
name, major, minor, actual_arch = match.groups()
tpl = '{}_{}_%i_%s'.format(name, major)
arches = []
for m in reversed(range(int(minor) + 1)):
for a in get_darwin_arches(int(major), m, actual_arch):
arches.append(tpl % (m, a))
else:
# arch pattern didn't match (?!)
arches = [arch]
elif platform is None and is_manylinux1_compatible():
arches = [arch.replace('linux', 'manylinux1'), arch]
else:
arches = [arch]
# Current version, current API (built specifically for our Python):
for abi in abis:
for arch in arches:
supported.append(('%s%s' % (impl, versions[0]), abi, arch))
# abi3 modules compatible with older version of Python
for version in versions[1:]:
# abi3 was introduced in Python 3.2
if version in {'31', '30'}:
break
for abi in abi3s: # empty set if not Python 3
for arch in arches:
supported.append(("%s%s" % (impl, version), abi, arch))
# Has binaries, does not use the Python API:
for arch in arches:
supported.append(('py%s' % (versions[0][0]), 'none', arch))
# No abi / arch, but requires our implementation:
supported.append(('%s%s' % (impl, versions[0]), 'none', 'any'))
# Tagged specifically as being cross-version compatible
# (with just the major version specified)
supported.append(('%s%s' % (impl, versions[0][0]), 'none', 'any'))
# No abi / arch, generic Python
for i, version in enumerate(versions):
supported.append(('py%s' % (version,), 'none', 'any'))
if i == 0:
supported.append(('py%s' % (version[0]), 'none', 'any'))
return supported
implementation_tag = get_impl_tag()
| mit |
mafiya69/rollcall | rollcall/tests/test_main/test_update_json_file.py | 3 | 1312 | import unittest
import os
from rollcall.tests import helper
from datetime import date
from rollcall.main import add, update_json_file, fileExists
from rollcall.func_json import gen_dict, dict_to_json
from rollcall.exce import UnknownTag, SubjectError
class Testupdate_json_file(unittest.TestCase):
"""
tests the update_json_file function in main module
"""
def setUp(self):
self.dire = os.path.dirname(__file__)
self.dummy = os.path.join(self.dire, 'dummy.json')
helper.newFile(self.dummy)
self.new = os.path.join(self.dire, 'new.json')
self.sub = os.path.join(self.dire, 'sub.json')
json = gen_dict(date.today(), [0, 1, 2, 3, 4, 5, 6])
json = dict_to_json(json)
add(str(json), self.sub)
def test_update_json_file_file_exists(self):
self.assertTrue(fileExists(self.sub))
self.assertTrue(update_json_file('f', self.sub))
def test_update_json_file_file_does_not_exist(self):
self.assertFalse(fileExists(self.new))
self.assertRaises(SubjectError, update_json_file, 'f', self.new)
def test_update_json_file_unknow_tag(self):
self.assertRaises(UnknownTag, update_json_file, 'blahblah', self.dummy)
def tearDown(self):
os.remove(self.dummy)
os.remove(self.sub)
| mit |
aldur/RedditWallpaperChooser | setup.py | 1 | 2221 | #!/usr/bin/env python
# encoding: utf-8
"""
Python setup file.
"""
import setuptools
import os
import os.path
from RedditWallpaperChooser import __version__
__author__ = 'aldur'
_readme = "README.md"
_requirements = "requirements.txt"
_requirements_extra = "requirements_extra.txt"
def readme():
"""Open and return the _readme contents."""
if not os.path.isfile(_readme):
return ""
with open(_readme) as f:
return f.read()
def requirements():
"""Open and return the _requirements contents."""
if not os.path.isfile(_requirements):
return []
with open(_requirements) as f:
return [line.rstrip() for line in f]
def requirements_extras():
"""Open and return the other requirement files content."""
extra = dict()
if not os.path.isfile(_requirements_extra):
return extra
with open(_requirements_extra) as f:
extra.update({
'extras': [line.rstrip() for line in f]
})
return extra
base_url = 'https://github.com/aldur/RedditWallpaperChooser'
setuptools.setup(
name='RedditWallpaperChooser',
description='Automatically download trending wallpapers from subreddits of your choice.',
long_description=readme(),
version=__version__,
url=base_url,
download_url='{}/releases/'.format(base_url),
license='MIT',
author='aldur',
author_email='adrianodl@hotmail.it',
packages=[
"RedditWallpaperChooser",
],
install_requires=requirements(),
extras_require=requirements_extras(),
scripts=[os.path.join('bin', f) for f in os.listdir('bin')],
zip_safe=False,
include_package_data=True,
platforms=['any'],
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: End Users/Desktop',
'Intended Audience :: System Administrators',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3 :: Only',
'Programming Language :: Python :: 3.5',
'Topic :: Desktop Environment',
'Topic :: Multimedia',
'Topic :: Utilities',
],
keywords="Reddit subreddit wallpaper desktop"
)
| mit |
AndreasMadsen/tensorflow | tensorflow/contrib/integrate/__init__.py | 8 | 1930 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Integration and ODE solvers for TensorFlow.
## Example: Lorenz attractor
We can use `odeint` to solve the
[Lorentz system](https://en.wikipedia.org/wiki/Lorenz_system) of ordinary
differential equations, a prototypical example of chaotic dynamics:
```python
rho = 28.0
sigma = 10.0
beta = 8.0/3.0
def lorenz_equation(state, t):
x, y, z = tf.unstack(state)
dx = sigma * (y - x)
dy = x * (rho - z) - y
dz = x * y - beta * z
return tf.stack([dx, dy, dz])
init_state = tf.constant([0, 2, 20], dtype=tf.float64)
t = np.linspace(0, 50, num=5000)
tensor_state, tensor_info = tf.contrib.integrate.odeint(
lorenz_equation, init_state, t, full_output=True)
sess = tf.Session()
state, info = sess.run([tensor_state, tensor_info])
x, y, z = state.T
plt.plot(x, z)
```
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../images/lorenz_attractor.png" alt>
</div>
## Ops
@@odeint
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=wildcard-import
from tensorflow.contrib.integrate.python.ops.odes import *
from tensorflow.python.util.all_util import make_all
__all__ = make_all(__name__)
| apache-2.0 |
jnerin/ansible | lib/ansible/modules/monitoring/zabbix/zabbix_proxy.py | 58 | 12457 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# (c) 2017, Alen Komic
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: zabbix_proxy
short_description: Zabbix proxy creates/deletes/gets/updates
description:
- This module allows you to create, modify, get and delete Zabbix proxy entries.
version_added: "2.5"
author:
- "Alen Komic"
requirements:
- "python >= 2.6"
- "zabbix-api >= 0.5.3"
options:
proxy_name:
description:
- Name of the proxy in Zabbix.
required: true
description:
description:
- Description of the proxy..
required: false
status:
description:
- Type of proxy. (4 - active, 5 - passive)
required: false
choices: ['active', 'passive']
default: "active"
tls_connect:
description:
- Connections to proxy.
required: false
choices: ['no_encryption','PSK','certificate']
default: 'no_encryption'
tls_accept:
description:
- Connections from proxy.
required: false
choices: ['no_encryption','PSK','certificate']
default: 'no_encryption'
tls_issuer:
description:
- Certificate issuer.
required: false
tls_subject:
description:
- Certificate subject.
required: false
tls_psk_identity:
description:
- PSK identity. Required if either I(tls_connect) or I(tls_accept) has PSK enabled.
required: false
tls_psk:
description:
- The preshared key, at least 32 hex digits. Required if either I(tls_connect) or I(tls_accept) has PSK enabled.
required: false
state:
description:
- State of the proxy.
- On C(present), it will create if proxy does not exist or update the proxy if the associated data is different.
- On C(absent) will remove a proxy if it exists.
required: false
choices: ['present', 'absent']
default: "present"
interface:
description:
- Dictionary with params for the interface when proxy is in passive mode
- 'Available values are: dns, ip, main, port, type and useip.'
- Please review the interface documentation for more information on the supported properties
- U(https://www.zabbix.com/documentation/3.2/manual/api/reference/proxy/object#proxy_interface)
required: false
default: {}
extends_documentation_fragment:
- zabbix
'''
EXAMPLES = '''
- name: Create a new proxy or update an existing proxies info
local_action:
module: zabbix_proxy
server_url: http://monitor.example.com
login_user: username
login_password: password
proxy_name: ExampleProxy
description: ExampleProxy
status: active
state: present
interface:
type: 0
main: 1
useip: 1
ip: 10.xx.xx.xx
dns: ""
port: 10050
'''
RETURN = ''' # '''
from ansible.module_utils.basic import AnsibleModule
try:
from zabbix_api import ZabbixAPI
HAS_ZABBIX_API = True
except ImportError:
HAS_ZABBIX_API = False
class Proxy(object):
def __init__(self, module, zbx):
self._module = module
self._zapi = zbx
self.existing_data = None
def proxy_exists(self, proxy_name):
result = self._zapi.proxy.get({
'output': 'extend', 'selectInterface': 'extend',
'filter': {'host': proxy_name}})
if len(result) > 0 and 'proxyid' in result[0]:
self.existing_data = result[0]
return result[0]['proxyid']
else:
return result
def add_proxy(self, data):
try:
if self._module.check_mode:
self._module.exit_json(changed=True)
parameters = {}
for item in data:
if data[item]:
parameters[item] = data[item]
proxy_ids_list = self._zapi.proxy.create(parameters)
self._module.exit_json(changed=True,
result="Successfully added proxy %s (%s)" %
(data['host'], data['status']))
if len(proxy_ids_list) >= 1:
return proxy_ids_list['proxyids'][0]
except Exception as e:
self._module.fail_json(msg="Failed to create proxy %s: %s" %
(data['host'], e))
def delete_proxy(self, proxy_id, proxy_name):
try:
if self._module.check_mode:
self._module.exit_json(changed=True)
self._zapi.proxy.delete([proxy_id])
self._module.exit_json(changed=True,
result="Successfully deleted" +
" proxy %s" % proxy_name)
except Exception as e:
self._module.fail_json(msg="Failed to delete proxy %s: %s" %
(proxy_name, str(e)))
def compile_interface_params(self, new_interface):
old_interface = {}
if 'interface' in self.existing_data and \
len(self.existing_data['interface']) > 0:
old_interface = self.existing_data['interface']
final_interface = old_interface.copy()
final_interface.update(new_interface)
final_interface = dict((k, str(v)) for k, v in final_interface.items())
if final_interface != old_interface:
return final_interface
else:
return {}
def update_proxy(self, proxy_id, data):
try:
if self._module.check_mode:
self._module.exit_json(changed=True)
parameters = {'proxyid': proxy_id}
for item in data:
if data[item] and item in self.existing_data and \
self.existing_data[item] != data[item]:
parameters[item] = data[item]
if 'interface' in parameters:
parameters.pop('interface')
if 'interface' in data and data['status'] == '6':
new_interface = self.compile_interface_params(data['interface'])
if len(new_interface) > 0:
parameters['interface'] = new_interface
if len(parameters) > 1:
self._zapi.proxy.update(parameters)
self._module.exit_json(
changed=True,
result="Successfully updated proxy %s (%s)" %
(data['host'], proxy_id)
)
else:
self._module.exit_json(changed=False)
except Exception as e:
self._module.fail_json(msg="Failed to update proxy %s: %s" %
(data['host'], e))
def main():
module = AnsibleModule(
argument_spec=dict(
server_url=dict(type='str', required=True, aliases=['url']),
login_user=dict(type='str', required=True),
login_password=dict(type='str', required=True, no_log=True),
proxy_name=dict(type='str', required=True),
http_login_user=dict(type='str', required=False, default=None),
http_login_password=dict(type='str', required=False,
default=None, no_log=True),
validate_certs=dict(type='bool', required=False, default=True),
status=dict(default="active", choices=['active', 'passive']),
state=dict(default="present", choices=['present', 'absent']),
description=dict(type='str', required=False),
tls_connect=dict(default='no_encryption',
choices=['no_encryption', 'PSK', 'certificate']),
tls_accept=dict(default='no_encryption',
choices=['no_encryption', 'PSK', 'certificate']),
tls_issuer=dict(type='str', required=False, default=None),
tls_subject=dict(type='str', required=False, default=None),
tls_psk_identity=dict(type='str', required=False, default=None),
tls_psk=dict(type='str', required=False, default=None),
timeout=dict(type='int', default=10),
interface=dict(type='dict', required=False, default={})
),
supports_check_mode=True
)
if not HAS_ZABBIX_API:
module.fail_json(msg="Missing requried zabbix-api module" +
" (check docs or install with:" +
" pip install zabbix-api)")
server_url = module.params['server_url']
login_user = module.params['login_user']
login_password = module.params['login_password']
http_login_user = module.params['http_login_user']
http_login_password = module.params['http_login_password']
validate_certs = module.params['validate_certs']
proxy_name = module.params['proxy_name']
description = module.params['description']
status = module.params['status']
tls_connect = module.params['tls_connect']
tls_accept = module.params['tls_accept']
tls_issuer = module.params['tls_issuer']
tls_subject = module.params['tls_subject']
tls_psk_identity = module.params['tls_psk_identity']
tls_psk = module.params['tls_psk']
state = module.params['state']
timeout = module.params['timeout']
interface = module.params['interface']
# convert enabled to 0; disabled to 1
status = 6 if status == "passive" else 5
if tls_connect == 'certificate':
tls_connect = 4
elif tls_connect == 'PSK':
tls_connect = 2
else:
tls_connect = 1
if tls_accept == 'certificate':
tls_accept = 4
elif tls_accept == 'PSK':
tls_accept = 2
else:
tls_accept = 1
zbx = None
# login to zabbix
try:
zbx = ZabbixAPI(server_url, timeout=timeout,
user=http_login_user,
passwd=http_login_password,
validate_certs=validate_certs)
zbx.login(login_user, login_password)
except Exception as e:
module.fail_json(msg="Failed to connect to Zabbix server: %s" % e)
proxy = Proxy(module, zbx)
# check if proxy already exists
proxy_id = proxy.proxy_exists(proxy_name)
if proxy_id:
if state == "absent":
# remove proxy
proxy.delete_proxy(proxy_id, proxy_name)
else:
proxy.update_proxy(proxy_id, {
'host': proxy_name,
'description': description,
'status': str(status),
'tls_connect': str(tls_connect),
'tls_accept': str(tls_accept),
'tls_issuer': tls_issuer,
'tls_subject': tls_subject,
'tls_psk_identity': tls_psk_identity,
'tls_psk': tls_psk,
'interface': interface
})
else:
if state == "absent":
# the proxy is already deleted.
module.exit_json(changed=False)
proxy_id = proxy.add_proxy(data={
'host': proxy_name,
'description': description,
'status': str(status),
'tls_connect': str(tls_connect),
'tls_accept': str(tls_accept),
'tls_issuer': tls_issuer,
'tls_subject': tls_subject,
'tls_psk_identity': tls_psk_identity,
'tls_psk': tls_psk,
'interface': interface
})
if __name__ == '__main__':
main()
| gpl-3.0 |