_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_25400 | A generic version of collections.abc.Iterable. Deprecated since version 3.9: collections.abc.Iterable now supports []. See PEP 585 and Generic Alias Type. | |
doc_25401 |
Return the artist's zorder. | |
doc_25402 |
Set the artist's visibility. Parameters
bbool | |
doc_25403 | Resizes self sparse tensor to the desired size and the number of sparse and dense dimensions. Note If the number of specified elements in self is zero, then size, sparse_dim, and dense_dim can be any size and positive integers such that len(size) == sparse_dim +
dense_dim. If self specifies one or more elements, however, then each dimension in size must not be smaller than the corresponding dimension of self, sparse_dim must equal the number of sparse dimensions in self, and dense_dim must equal the number of dense dimensions in self. Warning Throws an error if self is not a sparse tensor. Parameters
size (torch.Size) – the desired size. If self is non-empty sparse tensor, the desired size cannot be smaller than the original size.
sparse_dim (int) – the number of sparse dimensions
dense_dim (int) – the number of dense dimensions | |
doc_25404 | Search for the object on multiple scales of input image. The function takes the input image, the scale factor by which the searching window is multiplied on each step, minimum window size and maximum window size that specify the interval for the search windows that are applied to the input image to detect objects. Parameters
img2-D or 3-D ndarray
Ndarray that represents the input image.
scale_factorcnp.float32_t
The scale by which searching window is multiplied on each step.
step_ratiocnp.float32_t
The ratio by which the search step in multiplied on each scale of the image. 1 represents the exaustive search and usually is slow. By setting this parameter to higher values the results will be worse but the computation will be much faster. Usually, values in the interval [1, 1.5] give good results.
min_sizetyple (int, int)
Minimum size of the search window.
max_sizetyple (int, int)
Maximum size of the search window.
min_neighbour_numberint
Minimum amount of intersecting detections in order for detection to be approved by the function.
intersection_score_thresholdcnp.float32_t
The minimum value of value of ratio (intersection area) / (small rectangle ratio) in order to merge two detections into one. Returns
outputlist of dicts
Dict have form {‘r’: int, ‘c’: int, ‘width’: int, ‘height’: int}, where ‘r’ represents row position of top left corner of detected window, ‘c’ - col position, ‘width’ - width of detected window, ‘height’ - height of detected window. | |
doc_25405 |
Find edges in an image using the Sobel filter. Parameters
imagearray
The input image.
maskarray of bool, optional
Clip the output image to this mask. (Values where mask=0 will be set to 0.)
axisint or sequence of int, optional
Compute the edge filter along this axis. If not provided, the edge magnitude is computed. This is defined as: sobel_mag = np.sqrt(sum([sobel(image, axis=i)**2
for i in range(image.ndim)]) / image.ndim)
The magnitude is also computed if axis is a sequence.
modestr or sequence of str, optional
The boundary mode for the convolution. See scipy.ndimage.convolve for a description of the modes. This can be either a single boundary mode or one boundary mode per axis.
cvalfloat, optional
When mode is 'constant', this is the constant used in values outside the boundary of the image data. Returns
outputarray of float
The Sobel edge map. See also
scharr, prewitt, canny
References
1
D. Kroon, 2009, Short Paper University Twente, Numerical Optimization of Kernel Based Image Derivatives.
2
https://en.wikipedia.org/wiki/Sobel_operator Examples >>> from skimage import data
>>> from skimage import filters
>>> camera = data.camera()
>>> edges = filters.sobel(camera) | |
doc_25406 | Token value for "**=". | |
doc_25407 |
Multiply a Hermite series by x. Multiply the Hermite series c by x, where x is the independent variable. Parameters
carray_like
1-D array of Hermite series coefficients ordered from low to high. Returns
outndarray
Array representing the result of the multiplication. Notes The multiplication uses the recursion relationship for Hermite polynomials in the form \[xP_i(x) = (P_{i + 1}(x) + iP_{i - 1}(x)))\] Examples >>> from numpy.polynomial.hermite_e import hermemulx
>>> hermemulx([1, 2, 3])
array([2., 7., 2., 3.]) | |
doc_25408 | If the value has a name attribute, it is returned to unmodified. Otherwise the name, and the value with any CR or LF characters removed, are passed to the header_factory, and the resulting header object is returned. Any surrogateescaped bytes get turned into the unicode unknown-character glyph. | |
doc_25409 |
The hours of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="h")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 01:00:00
2 2000-01-01 02:00:00
dtype: datetime64[ns]
>>> datetime_series.dt.hour
0 0
1 1
2 2
dtype: int64 | |
doc_25410 | Returns a copy of the compression object. This can be used to efficiently compress a set of data that share a common initial prefix. | |
doc_25411 |
Set the label for the x-axis. Parameters
xlabelstr
The label text.
labelpadfloat, default: rcParams["axes.labelpad"] (default: 4.0)
Spacing in points from the Axes bounding box including ticks and tick labels. If None, the previous value is left as is.
loc{'left', 'center', 'right'}, default: rcParams["xaxis.labellocation"] (default: 'center')
The label position. This is a high-level alternative for passing parameters x and horizontalalignment. Other Parameters
**kwargsText properties
Text properties control the appearance of the label. See also text
Documents the properties supported by Text.
Examples using matplotlib.pyplot.xlabel
Scatter Symbol
Multiple subplots
Controlling style of text and labels using a dictionary
Infinite lines
Pyplot Mathtext
Pyplot Text
Solarized Light stylesheet
Findobj Demo
Custom scale
Basic Usage
Pyplot tutorial | |
doc_25412 |
A scatter plot of y vs. x with varying marker size and/or color. Parameters
x, yfloat or array-like, shape (n, )
The data positions.
sfloat or array-like, shape (n, ), optional
The marker size in points**2. Default is rcParams['lines.markersize'] ** 2.
carray-like or list of colors or color, optional
The marker colors. Possible values: A scalar or sequence of n numbers to be mapped to colors using cmap and norm. A 2D array in which the rows are RGB or RGBA. A sequence of colors of length n. A single color format string. Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. Otherwise, value- matching will have precedence in case of a size matching with x and y. If you wish to specify a single color for all points prefer the color keyword argument. Defaults to None. In that case the marker color is determined by the value of color, facecolor or facecolors. In case those are not specified or None, the marker color is determined by the next color of the Axes' current "shape and fill" color cycle. This cycle defaults to rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])).
markerMarkerStyle, default: rcParams["scatter.marker"] (default: 'o')
The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See matplotlib.markers for more information about marker styles.
cmapstr or Colormap, default: rcParams["image.cmap"] (default: 'viridis')
A Colormap instance or registered colormap name. cmap is only used if c is an array of floats.
normNormalize, default: None
If c is an array of floats, norm is used to scale the color data, c, in the range 0 to 1, in order to map into the colormap cmap. If None, use the default colors.Normalize.
vmin, vmaxfloat, default: None
vmin and vmax are used in conjunction with the default norm to map the color array c to the colormap cmap. If None, the respective min and max of the color array is used. It is an error to use vmin/vmax when norm is given.
alphafloat, default: None
The alpha blending value, between 0 (transparent) and 1 (opaque).
linewidthsfloat or array-like, default: rcParams["lines.linewidth"] (default: 1.5)
The linewidth of the marker edges. Note: The default edgecolors is 'face'. You may want to change this as well.
edgecolors{'face', 'none', None} or color or sequence of color, default: rcParams["scatter.edgecolors"] (default: 'face')
The edge color of the marker. Possible values: 'face': The edge color will always be the same as the face color. 'none': No patch boundary will be drawn. A color or sequence of colors. For non-filled markers, edgecolors is ignored. Instead, the color is determined like with 'face', i.e. from c, colors, or facecolors.
plotnonfinitebool, default: False
Whether to plot points with nonfinite c (i.e. inf, -inf or nan). If True the points are drawn with the bad colormap color (see Colormap.set_bad). Returns
PathCollection
Other Parameters
dataindexable object, optional
If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception): x, y, s, linewidths, edgecolors, c, facecolor, facecolors, color
**kwargsCollection properties
See also plot
To plot scatter plots when markers are identical in size and color. Notes The plot function will be faster for scatterplots where markers don't vary in size or color. Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Fundamentally, scatter works with 1D arrays; x, y, s, and c may be input as N-D arrays, but within scatter they will be flattened. The exception is c, which will be flattened only if its size matches the size of x and y.
Examples using matplotlib.pyplot.scatter
Scatter Masked
Scatter Symbol
Scatter plot
Hyperlinks
Pyplot tutorial | |
doc_25413 | See Migration guide for more details. tf.compat.v1.deserialize_many_sparse, tf.compat.v1.io.deserialize_many_sparse
tf.io.deserialize_many_sparse(
serialized_sparse, dtype, rank=None, name=None
)
The input serialized_sparse must be a string matrix of shape [N x 3] where N is the minibatch size and the rows correspond to packed outputs of serialize_sparse. The ranks of the original SparseTensor objects must all match. When the final SparseTensor is created, it has rank one higher than the ranks of the incoming SparseTensor objects (they have been concatenated along a new row dimension). The output SparseTensor object's shape values for all dimensions but the first are the max across the input SparseTensor objects' shape values for the corresponding dimensions. Its first shape value is N, the minibatch size. The input SparseTensor objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run sparse.reorder to restore index ordering. For example, if the serialized input is a [2, 3] matrix representing two original SparseTensor objects: index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
and index = [ 2]
[10]
values = [4, 5]
shape = [30]
then the final deserialized SparseTensor will be: index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
Args
serialized_sparse 2-D Tensor of type string of shape [N, 3]. The serialized and packed SparseTensor objects.
dtype The dtype of the serialized SparseTensor objects.
rank (optional) Python int, the rank of the SparseTensor objects.
name A name prefix for the returned tensors (optional)
Returns A SparseTensor representing the deserialized SparseTensors, concatenated along the SparseTensors' first dimension. All of the serialized SparseTensors must have had the same rank and type. | |
doc_25414 |
Bases: matplotlib.backend_bases.RendererBase Implements a Renderer which contains another renderer. This proxy then intercepts draw calls, calling the appropriate AbstractPathEffect draw method. Note Not all methods have been overridden on this RendererBase subclass. It may be necessary to add further methods to extend the PathEffects capabilities further. Parameters
path_effectsiterable of AbstractPathEffect
The path effects which this renderer represents.
renderermatplotlib.backend_bases.RendererBase subclass
copy_with_path_effect(path_effects)[source]
draw_markers(gc, marker_path, marker_trans, path, *args, **kwargs)[source]
Draw a marker at each of path's vertices (excluding control points). This provides a fallback implementation of draw_markers that makes multiple calls to draw_path(). Some backends may want to override this method in order to draw the marker only once and reuse it multiple times. Parameters
gcGraphicsContextBase
The graphics context.
marker_transmatplotlib.transforms.Transform
An affine transform applied to the marker.
transmatplotlib.transforms.Transform
An affine transform applied to the path.
draw_path(gc, tpath, affine, rgbFace=None)[source]
Draw a Path instance using the given affine transform.
draw_path_collection(gc, master_transform, paths, *args, **kwargs)[source]
Draw a collection of paths selecting drawing properties from the lists facecolors, edgecolors, linewidths, linestyles and antialiaseds. offsets is a list of offsets to apply to each of the paths. The offsets in offsets are first transformed by offsetTrans before being applied. offset_position is unused now, but the argument is kept for backwards compatibility. This provides a fallback implementation of draw_path_collection() that makes multiple calls to draw_path(). Some backends may want to override this in order to render each set of path data only once, and then reference that path multiple times with the different offsets, colors, styles etc. The generator methods _iter_collection_raw_paths() and _iter_collection() are provided to help with (and standardize) the implementation across backends. It is highly recommended to use those generators, so that changes to the behavior of draw_path_collection() can be made globally. | |
doc_25415 | >>> np.nonzero(myarr == np.nan)
(array([], dtype=int64),)
>>> np.nan == np.nan # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([ 1., 0., NaN, 3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1., 0., 0., 3.])
Other related special value functions: isinf(): True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
The following corresponds to the usual functions except that nans are excluded from the results: nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()
>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
How numpy handles numerical exceptions The default is to 'warn' for invalid, divide, and overflow and 'ignore' for underflow. But this can be changed, and it can be set individually for different kinds of exceptions. The different behaviors are: ‘ignore’ : Take no action when the exception occurs. ‘warn’ : Print a RuntimeWarning (via the Python warnings module). ‘raise’ : Raise a FloatingPointError. ‘call’ : Call a function specified using the seterrcall function. ‘print’ : Print a warning directly to stdout. ‘log’ : Record error in a Log object specified by seterrcall. These behaviors can be set for all kinds of errors or specific ones: all : apply to all numeric exceptions invalid : when NaNs are generated divide : divide by zero (for integers as well!) overflow : floating point overflows underflow : floating point underflows Note that integer divide-by-zero is handled by the same machinery. These behaviors are set on a per-thread basis. Examples >>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
... print("saw stupid error!")
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
... # error-handling settings
Interfacing to C Only a survey of the choices. Little detail on how each works. Bare metal, wrap your own C-code manually.
Plusses: Efficient No dependencies on other tools
Minuses:
Lots of learning overhead: need to learn basics of Python C API need to learn basics of numpy C API need to learn how to handle reference counting and love it.
Reference counting often difficult to get right. getting it wrong leads to memory leaks, and worse, segfaults API will change for Python 3.0! Cython
Plusses: avoid learning C API’s no dealing with reference counting can code in pseudo python and generate C code can also interface to existing C code should shield you from changes to Python C api has become the de-facto standard within the scientific Python community fast indexing support for arrays
Minuses: Can write code in non-standard form which may become obsolete Not as flexible as manual wrapping ctypes
Plusses: part of Python standard library good for interfacing to existing shareable libraries, particularly Windows DLLs avoids API/reference counting issues
good numpy support: arrays have all these in their ctypes attribute: a.ctypes.data
a.ctypes.data_as
a.ctypes.shape
a.ctypes.shape_as
a.ctypes.strides
a.ctypes.strides_as
Minuses: can’t use for writing code to be turned into C extensions, only a wrapper tool. SWIG (automatic wrapper generator)
Plusses: around a long time multiple scripting language support C++ support Good for wrapping large (many functions) existing C libraries
Minuses: generates lots of code between Python and the C code can cause performance problems that are nearly impossible to optimize out interface files can be hard to write doesn’t necessarily avoid reference counting issues or needing to know API’s scipy.weave
Plusses: can turn many numpy expressions into C code dynamic compiling and loading of generated C code can embed pure C code in Python module and have weave extract, generate interfaces and compile, etc.
Minuses: Future very uncertain: it’s the only part of Scipy not ported to Python 3 and is effectively deprecated in favor of Cython. Psyco
Plusses: Turns pure python into efficient machine code through jit-like optimizations very fast when it optimizes well
Minuses: Only on intel (windows?) Doesn’t do much for numpy? Interfacing to Fortran: The clear choice to wrap Fortran code is f2py. Pyfort is an older alternative, but not supported any longer. Fwrap is a newer project that looked promising but isn’t being developed any longer. Interfacing to C++: Cython CXX Boost.python SWIG SIP (used mainly in PyQT) | |
doc_25416 |
Computes log probabilities for all n_classes\texttt{n\_classes} Parameters
input (Tensor) – a minibatch of examples Returns
log-probabilities of for each class cc in range 0<=c<=n_classes0 <= c <= \texttt{n\_classes} , where n_classes\texttt{n\_classes} is a parameter passed to AdaptiveLogSoftmaxWithLoss constructor. Shape:
Input: (N,in_features)(N, \texttt{in\_features})
Output: (N,n_classes)(N, \texttt{n\_classes}) | |
doc_25417 | Creates a Tensor from a numpy.ndarray. The returned tensor and ndarray share the same memory. Modifications to the tensor will be reflected in the ndarray and vice versa. The returned tensor is not resizable. It currently accepts ndarray with dtypes of numpy.float64, numpy.float32, numpy.float16, numpy.complex64, numpy.complex128, numpy.int64, numpy.int32, numpy.int16, numpy.int8, numpy.uint8, and numpy.bool. Example: >>> a = numpy.array([1, 2, 3])
>>> t = torch.from_numpy(a)
>>> t
tensor([ 1, 2, 3])
>>> t[0] = -1
>>> a
array([-1, 2, 3]) | |
doc_25418 | Computes the eigenvalues of a complex Hermitian (or real symmetric) matrix input, or of each such matrix in a batched input. The eigenvalues are returned in ascending order. Since the matrix or matrices in input are assumed to be Hermitian, the imaginary part of their diagonals is always treated as zero. When UPLO is “L”, its default value, only the lower triangular part of each matrix is used in the computation. When UPLO is “U” only the upper triangular part of each matrix is used. Supports input of float, double, cfloat and cdouble dtypes. Note When given inputs on a CUDA device, this function synchronizes that device with the CPU. Note The eigenvalues are computed using LAPACK’s syevd and heevd routines for CPU inputs, and MAGMA’s syevd and heevd routines for CUDA inputs. Note The eigenvalues of real symmetric or complex Hermitian matrices are always real. Note This function doesn’t support backpropagation, please use torch.linalg.eigh() instead, which also computes the eigenvectors. Note See torch.linalg.eigh() for a related function that computes both eigenvalues and eigenvectors. Parameters
input (Tensor) – the Hermitian n times n matrix or the batch of such matrices of size (*, n, n) where * is one or more batch dimensions.
UPLO ('L', 'U', optional) – controls whether to use the upper-triangular or the lower-triangular part of input in the computations. Default is 'L'. Keyword Arguments
out (Tensor, optional) – tensor to write the output to. Default is None. Examples: >>> a = torch.randn(2, 2, dtype=torch.complex128)
>>> a = a + a.t().conj() # creates a Hermitian matrix
>>> a
tensor([[2.9228+0.0000j, 0.2029-0.0862j],
[0.2029+0.0862j, 0.3464+0.0000j]], dtype=torch.complex128)
>>> w = torch.linalg.eigvalsh(a)
>>> w
tensor([0.3277, 2.9415], dtype=torch.float64)
>>> a = torch.randn(3, 2, 2, dtype=torch.float64)
>>> a = a + a.transpose(-2, -1) # creates a symmetric matrix
>>> a
tensor([[[ 2.8050, -0.3850],
[-0.3850, 3.2376]],
[[-1.0307, -2.7457],
[-2.7457, -1.7517]],
[[ 1.7166, 2.2207],
[ 2.2207, -2.0898]]], dtype=torch.float64)
>>> w = torch.linalg.eigvalsh(a)
>>> w
tensor([[ 2.5797, 3.4629],
[-4.1605, 1.3780],
[-3.1113, 2.7381]], dtype=torch.float64) | |
doc_25419 |
Return a backend-specific tuple to return to the backend after all processing is done. | |
doc_25420 | Wrapper around a torch._C.Future which encapsulates an asynchronous execution of a callable, e.g. rpc_async(). It also exposes a set of APIs to add callback functions and set results.
add_done_callback(self: torch._C.Future, arg0: function) → None
done() [source]
Return True if this Future is done. A Future is done if it has a result or an exception.
set_exception(result) [source]
Set an exception for this Future, which will mark this Future as completed with an error and trigger all attached callbacks. Note that when calling wait()/value() on this Future, the exception set here will be raised inline. Parameters
result (BaseException) – the exception for this Future. Example::
>>> import torch
>>>
>>> fut = torch.futures.Future()
>>> fut.set_exception(ValueError("foo"))
>>> fut.wait()
>>>
>>> # Output:
>>> # This will run after the future has finished.
>>> ValueError: foo
set_result(result) [source]
Set the result for this Future, which will mark this Future as completed and trigger all attached callbacks. Note that a Future cannot be marked completed twice. Parameters
result (object) – the result object of this Future. Example::
>>> import threading
>>> import time
>>> import torch
>>>
>>> def slow_set_future(fut, value):
>>> time.sleep(0.5)
>>> fut.set_result(value)
>>>
>>> fut = torch.futures.Future()
>>> t = threading.Thread(
>>> target=slow_set_future,
>>> args=(fut, torch.ones(2) * 3)
>>> )
>>> t.start()
>>>
>>> print(fut.wait()) # tensor([3., 3.])
>>> t.join()
then(callback) [source]
Append the given callback function to this Future, which will be run when the Future is completed. Multiple callbacks can be added to the same Future, and will be invoked in the same order as they were added. The callback must take one argument, which is the reference to this Future. The callback function can use the Future.wait() API to get the value. Note that if this Future is already completed, the given callback will be run immediately inline. Parameters
callback (Callable) – a Callable that takes this Future as the only argument. Returns
A new Future object that holds the return value of the callback and will be marked as completed when the given callback finishes. Example::
>>> import torch
>>>
>>> def callback(fut):
>>> print(f"RPC return value is {fut.wait()}.")
>>>
>>> fut = torch.futures.Future()
>>> # The inserted callback will print the return value when
>>> # receiving the response from "worker1"
>>> cb_fut = fut.then(callback)
>>> chain_cb_fut = cb_fut.then(
>>> lambda x : print(f"Chained cb done. {x.wait()}")
>>> )
>>> fut.set_result(5)
>>>
>>> # Outputs are:
>>> # RPC return value is 5.
>>> # Chained cb done. None
value(self: torch._C.Future) → object
wait() [source]
Block until the value of this Future is ready. Returns
The value held by this Future. If the function (callback or RPC) creating the value has thrown an error, this wait method will also throw an error. | |
doc_25421 |
A decorator for a function indicating that the return value of the function is guaranteed to be a Future object and this function can run asynchronously on the RPC callee. More specifically, the callee extracts the Future returned by the wrapped function and installs subsequent processing steps as a callback to that Future. The installed callback will read the value from the Future when completed and send the value back as the RPC response. That also means the returned Future only exists on the callee side and is never sent through RPC. This decorator is useful when the wrapped function’s (fn) execution needs to pause and resume due to, e.g., containing rpc_async() or waiting for other signals. Note To enable asynchronous execution, applications must pass the function object returned by this decorator to RPC APIs. If RPC detected attributes installed by this decorator, it knows that this function returns a Future object and will handle that accordingly. However, this does not mean this decorator has to be outmost one when defining a function. For example, when combined with @staticmethod or @classmethod, @rpc.functions.async_execution needs to be the inner decorator to allow the target function be recognized as a static or class function. This target function can still execute asynchronously because, when accessed, the static or class method preserves attributes installed by @rpc.functions.async_execution. Example::
The returned Future object can come from rpc_async(), then(), or Future constructor. The example below shows directly using the Future returned by then(). >>> from torch.distributed import rpc
>>>
>>> # omitting setup and shutdown RPC
>>>
>>> # On all workers
>>> @rpc.functions.async_execution
>>> def async_add_chained(to, x, y, z):
>>> # This function runs on "worker1" and returns immediately when
>>> # the callback is installed through the `then(cb)` API. In the
>>> # mean time, the `rpc_async` to "worker2" can run concurrently.
>>> # When the return value of that `rpc_async` arrives at
>>> # "worker1", "worker1" will run the lambda function accordingly
>>> # and set the value for the previously returned `Future`, which
>>> # will then trigger RPC to send the result back to "worker0".
>>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(
>>> lambda fut: fut.wait() + z
>>> )
>>>
>>> # On worker0
>>> ret = rpc.rpc_sync(
>>> "worker1",
>>> async_add_chained,
>>> args=("worker2", torch.ones(2), 1, 1)
>>> )
>>> print(ret) # prints tensor([3., 3.])
When combined with TorchScript decorators, this decorator must be the outmost one. >>> from torch import Tensor
>>> from torch.futures import Future
>>> from torch.distributed import rpc
>>>
>>> # omitting setup and shutdown RPC
>>>
>>> # On all workers
>>> @torch.jit.script
>>> def script_add(x: Tensor, y: Tensor) -> Tensor:
>>> return x + y
>>>
>>> @rpc.functions.async_execution
>>> @torch.jit.script
>>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
>>> return rpc.rpc_async(to, script_add, (x, y))
>>>
>>> # On worker0
>>> ret = rpc.rpc_sync(
>>> "worker1",
>>> async_add,
>>> args=("worker2", torch.ones(2), 1)
>>> )
>>> print(ret) # prints tensor([2., 2.])
When combined with static or class method, this decorator must be the inner one. >>> from torch.distributed import rpc
>>>
>>> # omitting setup and shutdown RPC
>>>
>>> # On all workers
>>> class AsyncExecutionClass:
>>>
>>> @staticmethod
>>> @rpc.functions.async_execution
>>> def static_async_add(to, x, y, z):
>>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(
>>> lambda fut: fut.wait() + z
>>> )
>>>
>>> @classmethod
>>> @rpc.functions.async_execution
>>> def class_async_add(cls, to, x, y, z):
>>> ret_fut = torch.futures.Future()
>>> rpc.rpc_async(to, torch.add, args=(x, y)).then(
>>> lambda fut: ret_fut.set_result(fut.wait() + z)
>>> )
>>> return ret_fut
>>>
>>> @rpc.functions.async_execution
>>> def bound_async_add(self, to, x, y, z):
>>> return rpc.rpc_async(to, torch.add, args=(x, y)).then(
>>> lambda fut: fut.wait() + z
>>> )
>>>
>>> # On worker0
>>> ret = rpc.rpc_sync(
>>> "worker1",
>>> AsyncExecutionClass.static_async_add,
>>> args=("worker2", torch.ones(2), 1, 2)
>>> )
>>> print(ret) # prints tensor([4., 4.])
>>>
>>> ret = rpc.rpc_sync(
>>> "worker1",
>>> AsyncExecutionClass.class_async_add,
>>> args=("worker2", torch.ones(2), 1, 2)
>>> )
>>> print(ret) # prints tensor([4., 4.])
This decorator also works with RRef helpers, i.e., . torch.distributed.rpc.RRef.rpc_sync(), torch.distributed.rpc.RRef.rpc_async(), and torch.distributed.rpc.RRef.remote(). >>> from torch.distributed import rpc
>>>
>>> # reuse the AsyncExecutionClass class above
>>> rref = rpc.remote("worker1", AsyncExecutionClass)
>>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
>>> print(ret) # prints tensor([4., 4.])
>>>
>>> rref = rpc.remote("worker1", AsyncExecutionClass)
>>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
>>> print(ret) # prints tensor([4., 4.])
>>>
>>> rref = rpc.remote("worker1", AsyncExecutionClass)
>>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
>>> print(ret) # prints tensor([4., 4.]) | |
doc_25422 | bytearray.expandtabs(tabsize=8)
Return a copy of the sequence where all ASCII tab characters are replaced by one or more ASCII spaces, depending on the current column and the given tab size. Tab positions occur every tabsize bytes (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the sequence, the current column is set to zero and the sequence is examined byte by byte. If the byte is an ASCII tab character (b'\t'), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the current byte is an ASCII newline (b'\n') or carriage return (b'\r'), it is copied and the current column is reset to zero. Any other byte value is copied unchanged and the current column is incremented by one regardless of how the byte value is represented when printed: >>> b'01\t012\t0123\t01234'.expandtabs()
b'01 012 0123 01234'
>>> b'01\t012\t0123\t01234'.expandtabs(4)
b'01 012 0123 01234'
Note The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made. | |
doc_25423 | Under Windows, this gives you the native Windows error code. The errno attribute is then an approximate translation, in POSIX terms, of that native error code. Under Windows, if the winerror constructor argument is an integer, the errno attribute is determined from the Windows error code, and the errno argument is ignored. On other platforms, the winerror argument is ignored, and the winerror attribute does not exist. | |
doc_25424 | Return True if the Future was cancelled. The method is usually used to check if a Future is not cancelled before setting a result or an exception for it: if not fut.cancelled():
fut.set_result(42) | |
doc_25425 | By default, a ManyToManyField is displayed in the admin site with a <select multiple>. However, multiple-select boxes can be difficult to use when selecting many items. Adding a ManyToManyField to this list will instead use a nifty unobtrusive JavaScript “filter” interface that allows searching within the options. The unselected and selected options appear in two boxes side by side. See filter_vertical to use a vertical interface. | |
doc_25426 | Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | |
doc_25427 | readline.get_completer_delims()
Set or get the word delimiters for completion. These determine the start of the word to be considered for completion (the completion scope). These functions access the rl_completer_word_break_characters variable in the underlying library. | |
doc_25428 |
Bases: mpl_toolkits.axisartist.axislines.AxisArtistHelper.Fixed Helper class for a fixed axis. nth_coord = along which coordinate value varies.
nth_coord = 0 -> x axis, nth_coord = 1 -> y axis change_tick_coord(coord_number=None)[source]
[Deprecated] Notes Deprecated since version 3.5:
get_tick_iterators(axes)[source]
tick_loc, tick_angle, tick_label
get_tick_transform(axes)[source]
update_lim(axes)[source] | |
doc_25429 |
Performs inductive inference across the model. Parameters
Xarray-like of shape (n_samples, n_features)
The data matrix. Returns
yndarray of shape (n_samples,)
Predictions for input data. | |
doc_25430 | See Migration guide for more details. tf.compat.v1.math.segment_prod, tf.compat.v1.segment_prod
tf.math.segment_prod(
data, segment_ids, name=None
)
Read the section on segmentation for an explanation of segments. Computes a tensor such that \(output_i = \prod_j data_j\) where the product is over j such that segment_ids[j] == i. If the product is empty for a given segment ID i, output[i] = 1. For example: c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
tf.segment_prod(c, tf.constant([0, 0, 1]))
# ==> [[4, 6, 6, 4],
# [5, 6, 7, 8]]
Args
data A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
segment_ids A Tensor. Must be one of the following types: int32, int64. A 1-D tensor whose size is equal to the size of data's first dimension. Values should be sorted and can be repeated.
name A name for the operation (optional).
Returns A Tensor. Has the same type as data. | |
doc_25431 | A Popen creationflags parameter to specify that a new process group will be created. This flag is necessary for using os.kill() on the subprocess. This flag is ignored if CREATE_NEW_CONSOLE is specified. | |
doc_25432 | See Migration guide for more details. tf.compat.v1.keras.models.model_from_config
tf.keras.models.model_from_config(
config, custom_objects=None
)
Usage: # for a Functional API model
tf.keras.Model().from_config(model.get_config())
# for a Sequential model
tf.keras.Sequential().from_config(model.get_config())
Arguments
config Configuration dictionary.
custom_objects Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
Returns A Keras model instance (uncompiled).
Raises
TypeError if config is not a dictionary. | |
doc_25433 | See Migration guide for more details. tf.compat.v1.linalg.lu_matrix_inverse
tf.linalg.lu_matrix_inverse(
lower_upper, perm, validate_args=False, name=None
)
This op is conceptually identical to, inv_X = tf.lu_matrix_inverse(*tf.linalg.lu(X))
tf.assert_near(tf.matrix_inverse(X), inv_X)
# ==> True
Note: this function does not verify the implied matrix is actually invertible nor is this condition checked even when validate_args=True.
Args
lower_upper lu as returned by tf.linalg.lu, i.e., if matmul(P, matmul(L, U)) = X then lower_upper = L + U - eye.
perm p as returned by tf.linag.lu, i.e., if matmul(P, matmul(L, U)) = X then perm = argmax(P).
validate_args Python bool indicating whether arguments should be checked for correctness. Note: this function does not verify the implied matrix is actually invertible, even when validate_args=True. Default value: False (i.e., don't validate arguments).
name Python str name given to ops managed by this object. Default value: None (i.e., 'lu_matrix_inverse').
Returns
inv_x The matrix_inv, i.e., tf.matrix_inverse(tf.linalg.lu_reconstruct(lu, perm)). Examples import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
x = [[[3., 4], [1, 2]],
[[7., 8], [3, 4]]]
inv_x = tf.linalg.lu_matrix_inverse(*tf.linalg.lu(x))
tf.assert_near(tf.matrix_inverse(x), inv_x)
# ==> True | |
doc_25434 |
Attach the plugin to an ImageViewer. Note that the ImageViewer will automatically call this method when the plugin is added to the ImageViewer. For example: viewer += Plugin(...)
Also note that attach automatically calls the filter function so that the image matches the filtered value specified by attached widgets. | |
doc_25435 | Abstract. Returns the complex conjugate. For example, (1+3j).conjugate()
== (1-3j). | |
doc_25436 |
Quadratic Discriminant Analysis A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. The model fits a Gaussian density to each class. New in version 0.17: QuadraticDiscriminantAnalysis Read more in the User Guide. Parameters
priorsndarray of shape (n_classes,), default=None
Class priors. By default, the class proportions are inferred from the training data.
reg_paramfloat, default=0.0
Regularizes the per-class covariance estimates by transforming S2 as S2 = (1 - reg_param) * S2 + reg_param * np.eye(n_features), where S2 corresponds to the scaling_ attribute of a given class.
store_covariancebool, default=False
If True, the class covariance matrices are explicitely computed and stored in the self.covariance_ attribute. New in version 0.17.
tolfloat, default=1.0e-4
Absolute threshold for a singular value to be considered significant, used to estimate the rank of Xk where Xk is the centered matrix of samples in class k. This parameter does not affect the predictions. It only controls a warning that is raised when features are considered to be colinear. New in version 0.17. Attributes
covariance_list of len n_classes of ndarray of shape (n_features, n_features)
For each class, gives the covariance matrix estimated using the samples of that class. The estimations are unbiased. Only present if store_covariance is True.
means_array-like of shape (n_classes, n_features)
Class-wise means.
priors_array-like of shape (n_classes,)
Class priors (sum to 1).
rotations_list of len n_classes of ndarray of shape (n_features, n_k)
For each class k an array of shape (n_features, n_k), where n_k = min(n_features, number of elements in class k) It is the rotation of the Gaussian distribution, i.e. its principal axis. It corresponds to V, the matrix of eigenvectors coming from the SVD of Xk = U S Vt where Xk is the centered matrix of samples from class k.
scalings_list of len n_classes of ndarray of shape (n_k,)
For each class, contains the scaling of the Gaussian distributions along its principal axes, i.e. the variance in the rotated coordinate system. It corresponds to S^2 /
(n_samples - 1), where S is the diagonal matrix of singular values from the SVD of Xk, where Xk is the centered matrix of samples from class k.
classes_ndarray of shape (n_classes,)
Unique class labels. See also
LinearDiscriminantAnalysis
Linear Discriminant Analysis. Examples >>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = QuadraticDiscriminantAnalysis()
>>> clf.fit(X, y)
QuadraticDiscriminantAnalysis()
>>> print(clf.predict([[-0.8, -1]]))
[1]
Methods
decision_function(X) Apply decision function to an array of samples.
fit(X, y) Fit the model according to the given training data and parameters.
get_params([deep]) Get parameters for this estimator.
predict(X) Perform classification on an array of test vectors X.
predict_log_proba(X) Return log of posterior probabilities of classification.
predict_proba(X) Return posterior probabilities of classification.
score(X, y[, sample_weight]) Return the mean accuracy on the given test data and labels.
set_params(**params) Set the parameters of this estimator.
decision_function(X) [source]
Apply decision function to an array of samples. The decision function is equal (up to a constant factor) to the log-posterior of the model, i.e. log p(y = k | x). In a binary classification setting this instead corresponds to the difference log p(y = 1 | x) - log p(y = 0 | x). See Mathematical formulation of the LDA and QDA classifiers. Parameters
Xarray-like of shape (n_samples, n_features)
Array of samples (test vectors). Returns
Cndarray of shape (n_samples,) or (n_samples, n_classes)
Decision function values related to each class, per sample. In the two-class case, the shape is (n_samples,), giving the log likelihood ratio of the positive class.
fit(X, y) [source]
Fit the model according to the given training data and parameters. Changed in version 0.19: store_covariances has been moved to main constructor as store_covariance Changed in version 0.19: tol has been moved to main constructor. Parameters
Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values (integers)
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
predict(X) [source]
Perform classification on an array of test vectors X. The predicted class C for each sample in X is returned. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Cndarray of shape (n_samples,)
predict_log_proba(X) [source]
Return log of posterior probabilities of classification. Parameters
Xarray-like of shape (n_samples, n_features)
Array of samples/test vectors. Returns
Cndarray of shape (n_samples, n_classes)
Posterior log-probabilities of classification per class.
predict_proba(X) [source]
Return posterior probabilities of classification. Parameters
Xarray-like of shape (n_samples, n_features)
Array of samples/test vectors. Returns
Cndarray of shape (n_samples, n_classes)
Posterior probabilities of classification per class.
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
Mean accuracy of self.predict(X) wrt. y.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_25437 |
Bases: list A list with a short repr(). This is meant to be used for a homogeneous list of artists, so that they don't cause long, meaningless output. Instead of [<matplotlib.lines.Line2D object at 0x7f5749fed3c8>,
<matplotlib.lines.Line2D object at 0x7f5749fed4e0>,
<matplotlib.lines.Line2D object at 0x7f5758016550>]
one will get <a list of 3 Line2D objects>
If self.type is None, the type name is obtained from the first item in the list (if any). | |
doc_25438 |
Pickle (serialize) object to file. Parameters
path:str
File path where the pickled object will be stored.
compression:str or dict, default ‘infer’
For on-the-fly compression of the output data. If ‘infer’ and ‘path’ path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, or ‘.zst’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, or zstandard.ZstdDecompressor, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.
protocol:int
Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1] paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. 1
https://docs.python.org/3/library/pickle.html.
storage_options:dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details. New in version 1.2.0. See also read_pickle
Load pickled pandas object (or any object) from file. DataFrame.to_hdf
Write DataFrame to an HDF5 file. DataFrame.to_sql
Write DataFrame to a SQL database. DataFrame.to_parquet
Write a DataFrame to the binary parquet format. Examples
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9 | |
doc_25439 | In-place version of renorm() | |
doc_25440 |
Return a components namedtuple-like. | |
doc_25441 | A dictionary-like object containing all given HTTP POST parameters, providing that the request contains form data. See the QueryDict documentation below. If you need to access raw or non-form data posted in the request, access this through the HttpRequest.body attribute instead. It’s possible that a request can come in via POST with an empty POST dictionary – if, say, a form is requested via the POST HTTP method but does not include form data. Therefore, you shouldn’t use if request.POST to check for use of the POST method; instead, use if request.method ==
"POST" (see HttpRequest.method). POST does not include file-upload information. See FILES. | |
doc_25442 | tf.compat.v1.initializers.lecun_normal(
seed=None
)
It draws samples from a truncated normal distribution centered on 0 with standard deviation (after truncation) given by stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.
Arguments
seed A Python integer. Used to seed the random generator.
Returns An initializer.
References: Self-Normalizing Neural Networks, Klambauer et al., 2017
(pdf) Efficient Backprop, Lecun et al., 1998 | |
doc_25443 | tf.losses.deserialize Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.deserialize
tf.keras.losses.deserialize(
name, custom_objects=None
)
Arguments
name Loss configuration.
custom_objects Optional dictionary mapping names (strings) to custom objects (classes and functions) to be considered during deserialization.
Returns A Keras Loss instance or a loss function. | |
doc_25444 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_25445 | tf.compat.v1.keras.experimental.export_saved_model(
model, saved_model_path, custom_objects=None, as_text=False,
input_signature=None, serving_only=False
)
Note that at this time, subclassed models can only be saved using serving_only=True. The exported SavedModel is a standalone serialization of Tensorflow objects, and is supported by TF language APIs and the Tensorflow Serving system. To load the model, use the function tf.keras.experimental.load_from_saved_model. The SavedModel contains: a checkpoint containing the model weights. a SavedModel proto containing the Tensorflow backend graph. Separate graphs are saved for prediction (serving), train, and evaluation. If the model has not been compiled, then only the graph computing predictions will be exported. the model's json config. If the model is subclassed, this will only be included if the model's get_config() method is overwritten. Example: import tensorflow as tf
# Create a tf.keras model.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_shape=[10]))
model.summary()
# Save the tf.keras model in the SavedModel format.
path = '/tmp/simple_keras_model'
tf.keras.experimental.export_saved_model(model, path)
# Load the saved keras model back.
new_model = tf.keras.experimental.load_from_saved_model(path)
new_model.summary()
Args
model A tf.keras.Model to be saved. If the model is subclassed, the flag serving_only must be set to True.
saved_model_path a string specifying the path to the SavedModel directory.
custom_objects Optional dictionary mapping string names to custom classes or functions (e.g. custom loss functions).
as_text bool, False by default. Whether to write the SavedModel proto in text format. Currently unavailable in serving-only mode.
input_signature A possibly nested sequence of tf.TensorSpec objects, used to specify the expected model inputs. See tf.function for more details.
serving_only bool, False by default. When this is true, only the prediction graph is saved.
Raises
NotImplementedError If the model is a subclassed model, and serving_only is False.
ValueError If the input signature cannot be inferred from the model.
AssertionError If the SavedModel directory already exists and isn't empty. | |
doc_25446 | Exception raised for programming errors, e.g. table not found or already exists, syntax error in the SQL statement, wrong number of parameters specified, etc. It is a subclass of DatabaseError. | |
doc_25447 | Write audio frames and make sure nframes is correct. It will raise an error if the output stream is not seekable and the total number of frames that have been written after data has been written does not match the previously set value for nframes. Changed in version 3.4: Any bytes-like object is now accepted. | |
doc_25448 |
Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Returns
pndarray of shape (n_samples, n_classes), or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_. | |
doc_25449 |
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array. | |
doc_25450 | See Migration guide for more details. tf.compat.v1.image.rot90
tf.image.rot90(
image, k=1, name=None
)
For example:
a=tf.constant([[[1],[2]],
[[3],[4]]])
# rotating `a` counter clockwise by 90 degrees
a_rot=tf.image.rot90(a)
print(a_rot[...,0].numpy())
[[2 4]
[1 3]]
# rotating `a` counter clockwise by 270 degrees
a_rot=tf.image.rot90(a, k=3)
print(a_rot[...,0].numpy())
[[3 1]
[4 2]]
Args
image 4-D Tensor of shape [batch, height, width, channels] or 3-D Tensor of shape [height, width, channels].
k A scalar integer. The number of times the image is rotated by 90 degrees.
name A name for this operation (optional).
Returns A rotated tensor of the same type and shape as image.
Raises
ValueError if the shape of image not supported. | |
doc_25451 |
USERNAME_FIELD
A string describing the name of the field on the user model that is used as the unique identifier. This will usually be a username of some kind, but it can also be an email address, or any other unique identifier. The field must be unique (i.e., have unique=True set in its definition), unless you use a custom authentication backend that can support non-unique usernames. In the following example, the field identifier is used as the identifying field: class MyUser(AbstractBaseUser):
identifier = models.CharField(max_length=40, unique=True)
...
USERNAME_FIELD = 'identifier'
EMAIL_FIELD
A string describing the name of the email field on the User model. This value is returned by get_email_field_name().
REQUIRED_FIELDS
A list of the field names that will be prompted for when creating a user via the createsuperuser management command. The user will be prompted to supply a value for each of these fields. It must include any field for which blank is False or undefined and may include additional fields you want prompted for when a user is created interactively. REQUIRED_FIELDS has no effect in other parts of Django, like creating a user in the admin. For example, here is the partial definition for a user model that defines two required fields - a date of birth and height: class MyUser(AbstractBaseUser):
...
date_of_birth = models.DateField()
height = models.FloatField()
...
REQUIRED_FIELDS = ['date_of_birth', 'height']
Note REQUIRED_FIELDS must contain all required fields on your user model, but should not contain the USERNAME_FIELD or password as these fields will always be prompted for.
is_active
A boolean attribute that indicates whether the user is considered “active”. This attribute is provided as an attribute on AbstractBaseUser defaulting to True. How you choose to implement it will depend on the details of your chosen auth backends. See the documentation of the is_active attribute on the built-in
user model for details.
get_full_name()
Optional. A longer formal identifier for the user such as their full name. If implemented, this appears alongside the username in an object’s history in django.contrib.admin.
get_short_name()
Optional. A short, informal identifier for the user such as their first name. If implemented, this replaces the username in the greeting to the user in the header of django.contrib.admin.
Importing AbstractBaseUser AbstractBaseUser and BaseUserManager are importable from django.contrib.auth.base_user so that they can be imported without including django.contrib.auth in INSTALLED_APPS. | |
doc_25452 | Computes and returns a Point guaranteed to be on the interior of this geometry. | |
doc_25453 | Gets the data for the specified type from the clipboard. get(type) -> bytes or str or None Retrieves the data for the specified type from the clipboard. In python 3 the data is returned as a byte string and might need further processing (such as decoding to Unicode).
Parameters:
type (string) -- data type to retrieve from the clipboard
Returns:
data (byte string in python 3 or str in python 2) for the given type identifier or None if no data for the given type is available
Return type:
bytes or str or None text = pygame.scrap.get(pygame.SCRAP_TEXT)
if text:
print("There is text in the clipboard.")
else:
print("There does not seem to be text in the clipboard.") | |
doc_25454 |
Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters
imagendarray, 2D or 3D
A binary image containing the objects to be skeletonized. Zeros represent background, nonzero values are foreground. Returns
skeletonndarray
The thinned image. See also
skeletonize, medial_axis
Notes The method of [Lee94] uses an octree data structure to examine a 3x3x3 neighborhood of a pixel. The algorithm proceeds by iteratively sweeping over the image, and removing pixels at each iteration until the image stops changing. Each iteration consists of two steps: first, a list of candidates for removal is assembled; then pixels from this list are rechecked sequentially, to better preserve connectivity of the image. The algorithm this function implements is different from the algorithms used by either skeletonize or medial_axis, thus for 2D images the results produced by this function are generally different. References
Lee94
T.-C. Lee, R.L. Kashyap and C.-N. Chu, Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462-478, 1994. | |
doc_25455 | A class attribute that denotes the number of arguments the function accepts. If this attribute is set and the function is called with a different number of expressions, TypeError will be raised. Defaults to None. | |
doc_25456 | Return True if the argument is a signaling NaN and False otherwise. | |
doc_25457 | See torch.tan() | |
doc_25458 | Unpacks and returns a fixed length string. n is the number of characters expected. Padding with null bytes to guaranteed 4 byte alignment is assumed. | |
doc_25459 | Creates a StreamReaderWriter instance. stream must be a file-like object. Reader and Writer must be factory functions or classes providing the StreamReader and StreamWriter interface resp. Error handling is done in the same way as defined for the stream readers and writers. | |
doc_25460 | Return a string representing the time, controlled by an explicit format string. For a complete list of formatting directives, see strftime() and strptime() Behavior. | |
doc_25461 |
Make a large circle containing a smaller circle in 2d. A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide. Parameters
n_samplesint or tuple of shape (2,), dtype=int, default=100
If int, it is the total number of points generated. For odd numbers, the inner circle will have one point more than the outer circle. If two-element tuple, number of points in outer circle and inner circle. Changed in version 0.23: Added two-element tuple.
shufflebool, default=True
Whether to shuffle the samples.
noisefloat, default=None
Standard deviation of Gaussian noise added to the data.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary.
factorfloat, default=.8
Scale factor between inner and outer circle in the range (0, 1). Returns
Xndarray of shape (n_samples, 2)
The generated samples.
yndarray of shape (n_samples,)
The integer labels (0 or 1) for class membership of each sample. | |
doc_25462 | User ID of the user who originally stored this member. | |
doc_25463 | Overridable interface to open unknown URL types. | |
doc_25464 | Wait until process pid completes and check that the process exit code is exitcode. Raise an AssertionError if the process exit code is not equal to exitcode. If the process runs longer than timeout seconds (SHORT_TIMEOUT by default), kill the process and raise an AssertionError. The timeout feature is not available on Windows. New in version 3.9. | |
doc_25465 | See Migration guide for more details. tf.compat.v1.raw_ops.BatchNormWithGlobalNormalization
tf.raw_ops.BatchNormWithGlobalNormalization(
t, m, v, beta, gamma, variance_epsilon, scale_after_normalization, name=None
)
This op is deprecated. Prefer tf.nn.batch_normalization.
Args
t A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A 4D input Tensor.
m A Tensor. Must have the same type as t. A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.
v A Tensor. Must have the same type as t. A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.
beta A Tensor. Must have the same type as t. A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor.
gamma A Tensor. Must have the same type as t. A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor.
variance_epsilon A float. A small float number to avoid dividing by 0.
scale_after_normalization A bool. A bool indicating whether the resulted tensor needs to be multiplied with gamma.
name A name for the operation (optional).
Returns A Tensor. Has the same type as t. | |
doc_25466 | sklearn.metrics.dcg_score(y_true, y_score, *, k=None, log_base=2, sample_weight=None, ignore_ties=False) [source]
Compute Discounted Cumulative Gain. Sum the true scores ranked in the order induced by the predicted scores, after applying a logarithmic discount. This ranking metric yields a high value if true labels are ranked high by y_score. Usually the Normalized Discounted Cumulative Gain (NDCG, computed by ndcg_score) is preferred. Parameters
y_truendarray of shape (n_samples, n_labels)
True targets of multilabel classification, or true scores of entities to be ranked.
y_scorendarray of shape (n_samples, n_labels)
Target scores, can either be probability estimates, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).
kint, default=None
Only consider the highest k scores in the ranking. If None, use all outputs.
log_basefloat, default=2
Base of the logarithm used for the discount. A low value means a sharper discount (top results are more important).
sample_weightndarray of shape (n_samples,), default=None
Sample weights. If None, all samples are given the same weight.
ignore_tiesbool, default=False
Assume that there are no ties in y_score (which is likely to be the case if y_score is continuous) for efficiency gains. Returns
discounted_cumulative_gainfloat
The averaged sample DCG scores. See also
ndcg_score
The Discounted Cumulative Gain divided by the Ideal Discounted Cumulative Gain (the DCG obtained for a perfect ranking), in order to have a score between 0 and 1. References Wikipedia entry for Discounted Cumulative Gain. Jarvelin, K., & Kekalainen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20(4), 422-446. Wang, Y., Wang, L., Li, Y., He, D., Chen, W., & Liu, T. Y. (2013, May). A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th Annual Conference on Learning Theory (COLT 2013). McSherry, F., & Najork, M. (2008, March). Computing information retrieval performance measures efficiently in the presence of tied scores. In European conference on information retrieval (pp. 414-421). Springer, Berlin, Heidelberg. Examples >>> from sklearn.metrics import dcg_score
>>> # we have groud-truth relevance of some answers to a query:
>>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
>>> # we predict scores for the answers
>>> scores = np.asarray([[.1, .2, .3, 4, 70]])
>>> dcg_score(true_relevance, scores)
9.49...
>>> # we can set k to truncate the sum; only top k answers contribute
>>> dcg_score(true_relevance, scores, k=2)
5.63...
>>> # now we have some ties in our prediction
>>> scores = np.asarray([[1, 0, 0, 0, 1]])
>>> # by default ties are averaged, so here we get the average true
>>> # relevance of our top predictions: (10 + 5) / 2 = 7.5
>>> dcg_score(true_relevance, scores, k=1)
7.5
>>> # we can choose to ignore ties for faster results, but only
>>> # if we know there aren't ties in our scores, otherwise we get
>>> # wrong results:
>>> dcg_score(true_relevance,
... scores, k=1, ignore_ties=True)
5.0 | |
doc_25467 | See Migration guide for more details. tf.compat.v1.raw_ops.BatchMatrixDiag
tf.raw_ops.BatchMatrixDiag(
diagonal, name=None
)
Args
diagonal A Tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as diagonal. | |
doc_25468 | Call the Template object’s render() method with a Context to “fill” the template: >>> from django.template import Context, Template
>>> template = Template("My name is {{ my_name }}.")
>>> context = Context({"my_name": "Adrian"})
>>> template.render(context)
"My name is Adrian."
>>> context = Context({"my_name": "Dolores"})
>>> template.render(context)
"My name is Dolores." | |
doc_25469 | Iterator Arguments Results Example
count() start, [step] start, start+step, start+2*step, … count(10) --> 10 11 12 13 14 ...
cycle() p p0, p1, … plast, p0, p1, … cycle('ABCD') --> A B C D A B C D ...
repeat() elem [,n] elem, elem, elem, … endlessly or up to n times repeat(10, 3) --> 10 10 10 Iterators terminating on the shortest input sequence:
Iterator Arguments Results Example
accumulate() p [,func] p0, p0+p1, p0+p1+p2, … accumulate([1,2,3,4,5]) --> 1 3 6 10 15
chain() p, q, … p0, p1, … plast, q0, q1, … chain('ABC', 'DEF') --> A B C D E F
chain.from_iterable() iterable p0, p1, … plast, q0, q1, … chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
compress() data, selectors (d[0] if s[0]), (d[1] if s[1]), … compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
dropwhile() pred, seq seq[n], seq[n+1], starting when pred fails dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
filterfalse() pred, seq elements of seq where pred(elem) is false filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
groupby() iterable[, key] sub-iterators grouped by value of key(v)
islice() seq, [start,] stop [, step] elements from seq[start:stop:step] islice('ABCDEFG', 2, None) --> C D E F G
starmap() func, seq func(*seq[0]), func(*seq[1]), … starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
takewhile() pred, seq seq[0], seq[1], until pred fails takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
tee() it, n it1, it2, … itn splits one iterator into n
zip_longest() p, q, … (p[0], q[0]), (p[1], q[1]), … zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D- Combinatoric iterators:
Iterator Arguments Results
product() p, q, … [repeat=1] cartesian product, equivalent to a nested for-loop
permutations() p[, r] r-length tuples, all possible orderings, no repeated elements
combinations() p, r r-length tuples, in sorted order, no repeated elements
combinations_with_replacement() p, r r-length tuples, in sorted order, with repeated elements
Examples Results
product('ABCD', repeat=2) AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD
permutations('ABCD', 2) AB AC AD BA BC BD CA CB CD DA DB DC
combinations('ABCD', 2) AB AC AD BC BD CD
combinations_with_replacement('ABCD', 2) AA AB AC AD BB BC BD CC CD DD Itertool functions The following module functions all construct and return iterators. Some provide streams of infinite length, so they should only be accessed by functions or loops that truncate the stream.
itertools.accumulate(iterable[, func, *, initial=None])
Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument). If func is supplied, it should be a function of two arguments. Elements of the input iterable may be any type that can be accepted as arguments to func. (For example, with the default operation of addition, elements may be any addable type including Decimal or Fraction.) Usually, the number of elements output matches the input iterable. However, if the keyword argument initial is provided, the accumulation leads off with the initial value so that the output has one more element than the input iterable. Roughly equivalent to: def accumulate(iterable, func=operator.add, *, initial=None):
'Return running totals'
# accumulate([1,2,3,4,5]) --> 1 3 6 10 15
# accumulate([1,2,3,4,5], initial=100) --> 100 101 103 106 110 115
# accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
it = iter(iterable)
total = initial
if initial is None:
try:
total = next(it)
except StopIteration:
return
yield total
for element in it:
total = func(total, element)
yield total
There are a number of uses for the func argument. It can be set to min() for a running minimum, max() for a running maximum, or operator.mul() for a running product. Amortization tables can be built by accumulating interest and applying payments. First-order recurrence relations can be modeled by supplying the initial value in the iterable and using only the accumulated total in func argument: >>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
>>> list(accumulate(data, operator.mul)) # running product
[3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]
>>> list(accumulate(data, max)) # running maximum
[3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
# Amortize a 5% loan of 1000 with 4 annual payments of 90
>>> cashflows = [1000, -90, -90, -90, -90]
>>> list(accumulate(cashflows, lambda bal, pmt: bal*1.05 + pmt))
[1000, 960.0, 918.0, 873.9000000000001, 827.5950000000001]
# Chaotic recurrence relation https://en.wikipedia.org/wiki/Logistic_map
>>> logistic_map = lambda x, _: r * x * (1 - x)
>>> r = 3.8
>>> x0 = 0.4
>>> inputs = repeat(x0, 36) # only the initial value is used
>>> [format(x, '.2f') for x in accumulate(inputs, logistic_map)]
['0.40', '0.91', '0.30', '0.81', '0.60', '0.92', '0.29', '0.79', '0.63',
'0.88', '0.39', '0.90', '0.33', '0.84', '0.52', '0.95', '0.18', '0.57',
'0.93', '0.25', '0.71', '0.79', '0.63', '0.88', '0.39', '0.91', '0.32',
'0.83', '0.54', '0.95', '0.20', '0.60', '0.91', '0.30', '0.80', '0.60']
See functools.reduce() for a similar function that returns only the final accumulated value. New in version 3.2. Changed in version 3.3: Added the optional func parameter. Changed in version 3.8: Added the optional initial parameter.
itertools.chain(*iterables)
Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Roughly equivalent to: def chain(*iterables):
# chain('ABC', 'DEF') --> A B C D E F
for it in iterables:
for element in it:
yield element
classmethod chain.from_iterable(iterable)
Alternate constructor for chain(). Gets chained inputs from a single iterable argument that is evaluated lazily. Roughly equivalent to: def from_iterable(iterables):
# chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
for it in iterables:
for element in it:
yield element
itertools.combinations(iterable, r)
Return r length subsequences of elements from the input iterable. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each combination. Roughly equivalent to: def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
# combinations(range(4), 3) --> 012 013 023 123
pool = tuple(iterable)
n = len(pool)
if r > n:
return
indices = list(range(r))
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != i + n - r:
break
else:
return
indices[i] += 1
for j in range(i+1, r):
indices[j] = indices[j-1] + 1
yield tuple(pool[i] for i in indices)
The code for combinations() can be also expressed as a subsequence of permutations() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in permutations(range(n), r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is n! / r! / (n-r)! when 0 <= r <= n or zero when r > n.
itertools.combinations_with_replacement(iterable, r)
Return r length subsequences of elements from the input iterable allowing individual elements to be repeated more than once. The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique. Roughly equivalent to: def combinations_with_replacement(iterable, r):
# combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
pool = tuple(iterable)
n = len(pool)
if not n and r:
return
indices = [0] * r
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != n - 1:
break
else:
return
indices[i:] = [indices[i] + 1] * (r - i)
yield tuple(pool[i] for i in indices)
The code for combinations_with_replacement() can be also expressed as a subsequence of product() after filtering entries where the elements are not in sorted order (according to their position in the input pool): def combinations_with_replacement(iterable, r):
pool = tuple(iterable)
n = len(pool)
for indices in product(range(n), repeat=r):
if sorted(indices) == list(indices):
yield tuple(pool[i] for i in indices)
The number of items returned is (n+r-1)! / r! / (n-1)! when n > 0. New in version 3.1.
itertools.compress(data, selectors)
Make an iterator that filters elements from data returning only those that have a corresponding element in selectors that evaluates to True. Stops when either the data or selectors iterables has been exhausted. Roughly equivalent to: def compress(data, selectors):
# compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
return (d for d, s in zip(data, selectors) if s)
New in version 3.1.
itertools.count(start=0, step=1)
Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Roughly equivalent to: def count(start=0, step=1):
# count(10) --> 10 11 12 13 14 ...
# count(2.5, 0.5) -> 2.5 3.0 3.5 ...
n = start
while True:
yield n
n += step
When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: (start + step * i
for i in count()). Changed in version 3.1: Added step argument and allowed non-integer arguments.
itertools.cycle(iterable)
Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Roughly equivalent to: def cycle(iterable):
# cycle('ABCD') --> A B C D A B C D A B C D ...
saved = []
for element in iterable:
yield element
saved.append(element)
while saved:
for element in saved:
yield element
Note, this member of the toolkit may require significant auxiliary storage (depending on the length of the iterable).
itertools.dropwhile(predicate, iterable)
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note, the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Roughly equivalent to: def dropwhile(predicate, iterable):
# dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
iterable = iter(iterable)
for x in iterable:
if not predicate(x):
yield x
break
for x in iterable:
yield x
itertools.filterfalse(predicate, iterable)
Make an iterator that filters elements from iterable returning only those for which the predicate is False. If predicate is None, return the items that are false. Roughly equivalent to: def filterfalse(predicate, iterable):
# filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
if predicate is None:
predicate = bool
for x in iterable:
if not predicate(x):
yield x
itertools.groupby(iterable, key=None)
Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function. The operation of groupby() is similar to the uniq filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order. The returned group is itself an iterator that shares the underlying iterable with groupby(). Because the source is shared, when the groupby() object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list: groups = []
uniquekeys = []
data = sorted(data, key=keyfunc)
for k, g in groupby(data, keyfunc):
groups.append(list(g)) # Store group iterator as a list
uniquekeys.append(k)
groupby() is roughly equivalent to: class groupby:
# [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
# [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
def __init__(self, iterable, key=None):
if key is None:
key = lambda x: x
self.keyfunc = key
self.it = iter(iterable)
self.tgtkey = self.currkey = self.currvalue = object()
def __iter__(self):
return self
def __next__(self):
self.id = object()
while self.currkey == self.tgtkey:
self.currvalue = next(self.it) # Exit on StopIteration
self.currkey = self.keyfunc(self.currvalue)
self.tgtkey = self.currkey
return (self.currkey, self._grouper(self.tgtkey, self.id))
def _grouper(self, tgtkey, id):
while self.id is id and self.currkey == tgtkey:
yield self.currvalue
try:
self.currvalue = next(self.it)
except StopIteration:
return
self.currkey = self.keyfunc(self.currvalue)
itertools.islice(iterable, stop)
itertools.islice(iterable, start, stop[, step])
Make an iterator that returns selected elements from the iterable. If start is non-zero, then elements from the iterable are skipped until start is reached. Afterward, elements are returned consecutively unless step is set higher than one which results in items being skipped. If stop is None, then iteration continues until the iterator is exhausted, if at all; otherwise, it stops at the specified position. Unlike regular slicing, islice() does not support negative values for start, stop, or step. Can be used to extract related fields from data where the internal structure has been flattened (for example, a multi-line report may list a name field on every third line). Roughly equivalent to: def islice(iterable, *args):
# islice('ABCDEFG', 2) --> A B
# islice('ABCDEFG', 2, 4) --> C D
# islice('ABCDEFG', 2, None) --> C D E F G
# islice('ABCDEFG', 0, None, 2) --> A C E G
s = slice(*args)
start, stop, step = s.start or 0, s.stop or sys.maxsize, s.step or 1
it = iter(range(start, stop, step))
try:
nexti = next(it)
except StopIteration:
# Consume *iterable* up to the *start* position.
for i, element in zip(range(start), iterable):
pass
return
try:
for i, element in enumerate(iterable):
if i == nexti:
yield element
nexti = next(it)
except StopIteration:
# Consume to *stop*.
for i, element in zip(range(i + 1, stop), iterable):
pass
If start is None, then iteration starts at zero. If step is None, then the step defaults to one.
itertools.permutations(iterable, r=None)
Return successive r length permutations of elements in the iterable. If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated. The permutation tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each permutation. Roughly equivalent to: def permutations(iterable, r=None):
# permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
# permutations(range(3)) --> 012 021 102 120 201 210
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
if r > n:
return
indices = list(range(n))
cycles = list(range(n, n-r, -1))
yield tuple(pool[i] for i in indices[:r])
while n:
for i in reversed(range(r)):
cycles[i] -= 1
if cycles[i] == 0:
indices[i:] = indices[i+1:] + indices[i:i+1]
cycles[i] = n - i
else:
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool): def permutations(iterable, r=None):
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
for indices in product(range(n), repeat=r):
if len(set(indices)) == r:
yield tuple(pool[i] for i in indices)
The number of items returned is n! / (n-r)! when 0 <= r <= n or zero when r > n.
itertools.product(*iterables, repeat=1)
Cartesian product of input iterables. Roughly equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B). The nested loops cycle like an odometer with the rightmost element advancing on every iteration. This pattern creates a lexicographic ordering so that if the input’s iterables are sorted, the product tuples are emitted in sorted order. To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A). This function is roughly equivalent to the following code, except that the actual implementation does not build up intermediate results in memory: def product(*args, repeat=1):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = [tuple(pool) for pool in args] * repeat
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
Before product() runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs.
itertools.repeat(object[, times])
Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified. Used as argument to map() for invariant parameters to the called function. Also used with zip() to create an invariant part of a tuple record. Roughly equivalent to: def repeat(object, times=None):
# repeat(10, 3) --> 10 10 10
if times is None:
while True:
yield object
else:
for i in range(times):
yield object
A common use for repeat is to supply a stream of constant values to map or zip: >>> list(map(pow, range(10), repeat(2)))
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
itertools.starmap(function, iterable)
Make an iterator that computes the function using arguments obtained from the iterable. Used instead of map() when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”). The difference between map() and starmap() parallels the distinction between function(a,b) and function(*c). Roughly equivalent to: def starmap(function, iterable):
# starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
for args in iterable:
yield function(*args)
itertools.takewhile(predicate, iterable)
Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to: def takewhile(predicate, iterable):
# takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
for x in iterable:
if predicate(x):
yield x
else:
break
itertools.tee(iterable, n=2)
Return n independent iterators from a single iterable. The following Python code helps explain what tee does (although the actual implementation is more complex and uses only a single underlying FIFO queue). Roughly equivalent to: def tee(iterable, n=2):
it = iter(iterable)
deques = [collections.deque() for i in range(n)]
def gen(mydeque):
while True:
if not mydeque: # when the local deque is empty
try:
newval = next(it) # fetch a new value and
except StopIteration:
return
for d in deques: # load it to all the deques
d.append(newval)
yield mydeque.popleft()
return tuple(gen(d) for d in deques)
Once tee() has made a split, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed. tee iterators are not threadsafe. A RuntimeError may be raised when using simultaneously iterators returned by the same tee() call, even if the original iterable is threadsafe. This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee().
itertools.zip_longest(*iterables, fillvalue=None)
Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted. Roughly equivalent to: def zip_longest(*args, fillvalue=None):
# zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
iterators = [iter(it) for it in args]
num_active = len(iterators)
if not num_active:
return
while True:
values = []
for i, it in enumerate(iterators):
try:
value = next(it)
except StopIteration:
num_active -= 1
if not num_active:
return
iterators[i] = repeat(fillvalue)
value = fillvalue
values.append(value)
yield tuple(values)
If one of the iterables is potentially infinite, then the zip_longest() function should be wrapped with something that limits the number of calls (for example islice() or takewhile()). If not specified, fillvalue defaults to None.
Itertools Recipes This section shows recipes for creating an extended toolset using the existing itertools as building blocks. Substantially all of these recipes and many, many others can be installed from the more-itertools project found on the Python Package Index: pip install more-itertools
The extended tools offer the same high performance as the underlying toolset. The superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style which helps eliminate temporary variables. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead. def take(n, iterable):
"Return first n items of the iterable as a list"
return list(islice(iterable, n))
def prepend(value, iterator):
"Prepend a single value in front of an iterator"
# prepend(1, [2, 3, 4]) -> 1 2 3 4
return chain([value], iterator)
def tabulate(function, start=0):
"Return function(0), function(1), ..."
return map(function, count(start))
def tail(n, iterable):
"Return an iterator over the last n items"
# tail(3, 'ABCDEFG') --> E F G
return iter(collections.deque(iterable, maxlen=n))
def consume(iterator, n=None):
"Advance the iterator n-steps ahead. If n is None, consume entirely."
# Use functions that consume iterators at C speed.
if n is None:
# feed the entire iterator into a zero-length deque
collections.deque(iterator, maxlen=0)
else:
# advance to the empty slice starting at position n
next(islice(iterator, n, n), None)
def nth(iterable, n, default=None):
"Returns the nth item or a default value"
return next(islice(iterable, n, None), default)
def all_equal(iterable):
"Returns True if all the elements are equal to each other"
g = groupby(iterable)
return next(g, True) and not next(g, False)
def quantify(iterable, pred=bool):
"Count how many times the predicate is true"
return sum(map(pred, iterable))
def pad_none(iterable):
"""Returns the sequence elements and then returns None indefinitely.
Useful for emulating the behavior of the built-in map() function.
"""
return chain(iterable, repeat(None))
def ncycles(iterable, n):
"Returns the sequence elements n times"
return chain.from_iterable(repeat(tuple(iterable), n))
def dotproduct(vec1, vec2):
return sum(map(operator.mul, vec1, vec2))
def convolve(signal, kernel):
# See: https://betterexplained.com/articles/intuitive-convolution/
# convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur)
# convolve(data, [1, -1]) --> 1st finite difference (1st derivative)
# convolve(data, [1, -2, 1]) --> 2nd finite difference (2nd derivative)
kernel = tuple(kernel)[::-1]
n = len(kernel)
window = collections.deque([0], maxlen=n) * n
for x in chain(signal, repeat(0, n-1)):
window.append(x)
yield sum(map(operator.mul, kernel, window))
def flatten(list_of_lists):
"Flatten one level of nesting"
return chain.from_iterable(list_of_lists)
def repeatfunc(func, times=None, *args):
"""Repeat calls to func with specified arguments.
Example: repeatfunc(random.random)
"""
if times is None:
return starmap(func, repeat(args))
return starmap(func, repeat(args, times))
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
def roundrobin(*iterables):
"roundrobin('ABC', 'D', 'EF') --> A D E B F C"
# Recipe credited to George Sakkis
num_active = len(iterables)
nexts = cycle(iter(it).__next__ for it in iterables)
while num_active:
try:
for next in nexts:
yield next()
except StopIteration:
# Remove the iterator we just exhausted from the cycle.
num_active -= 1
nexts = cycle(islice(nexts, num_active))
def partition(pred, iterable):
"Use a predicate to partition entries into false entries and true entries"
# partition(is_odd, range(10)) --> 0 2 4 6 8 and 1 3 5 7 9
t1, t2 = tee(iterable)
return filterfalse(pred, t1), filter(pred, t2)
def powerset(iterable):
"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
def unique_everseen(iterable, key=None):
"List unique elements, preserving order. Remember all elements ever seen."
# unique_everseen('AAAABBBCCDAABBB') --> A B C D
# unique_everseen('ABBCcAD', str.lower) --> A B C D
seen = set()
seen_add = seen.add
if key is None:
for element in filterfalse(seen.__contains__, iterable):
seen_add(element)
yield element
else:
for element in iterable:
k = key(element)
if k not in seen:
seen_add(k)
yield element
def unique_justseen(iterable, key=None):
"List unique elements, preserving order. Remember only the element just seen."
# unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
# unique_justseen('ABBCcAD', str.lower) --> A B C A D
return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
def iter_except(func, exception, first=None):
""" Call a function repeatedly until an exception is raised.
Converts a call-until-exception interface to an iterator interface.
Like builtins.iter(func, sentinel) but uses an exception instead
of a sentinel to end the loop.
Examples:
iter_except(functools.partial(heappop, h), IndexError) # priority queue iterator
iter_except(d.popitem, KeyError) # non-blocking dict iterator
iter_except(d.popleft, IndexError) # non-blocking deque iterator
iter_except(q.get_nowait, Queue.Empty) # loop over a producer Queue
iter_except(s.pop, KeyError) # non-blocking set iterator
"""
try:
if first is not None:
yield first() # For database APIs needing an initial cast to db.first()
while True:
yield func()
except exception:
pass
def first_true(iterable, default=False, pred=None):
"""Returns the first true value in the iterable.
If no true value is found, returns *default*
If *pred* is not None, returns the first item
for which pred(item) is true.
"""
# first_true([a,b,c], x) --> a or b or c or x
# first_true([a,b], x, f) --> a if f(a) else b if f(b) else x
return next(filter(pred, iterable), default)
def random_product(*args, repeat=1):
"Random selection from itertools.product(*args, **kwds)"
pools = [tuple(pool) for pool in args] * repeat
return tuple(map(random.choice, pools))
def random_permutation(iterable, r=None):
"Random selection from itertools.permutations(iterable, r)"
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def random_combination(iterable, r):
"Random selection from itertools.combinations(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.sample(range(n), r))
return tuple(pool[i] for i in indices)
def random_combination_with_replacement(iterable, r):
"Random selection from itertools.combinations_with_replacement(iterable, r)"
pool = tuple(iterable)
n = len(pool)
indices = sorted(random.choices(range(n), k=r))
return tuple(pool[i] for i in indices)
def nth_combination(iterable, r, index):
"Equivalent to list(combinations(iterable, r))[index]"
pool = tuple(iterable)
n = len(pool)
if r < 0 or r > n:
raise ValueError
c = 1
k = min(r, n-r)
for i in range(1, k+1):
c = c * (n - k + i) // i
if index < 0:
index += c
if index < 0 or index >= c:
raise IndexError
result = []
while r:
c, n, r = c*r//n, n-1, r-1
while index >= c:
index -= c
c, n = c*(n-r)//n, n-1
result.append(pool[-1-n])
return tuple(result) | |
doc_25470 | Determine whether code is in tableC.1.2 (Non-ASCII space characters). | |
doc_25471 |
Holds parameters in a dictionary. ParameterDict can be indexed like a regular Python dictionary, but parameters it contains are properly registered, and will be visible by all Module methods. ParameterDict is an ordered dictionary that respects the order of insertion, and in update(), the order of the merged OrderedDict or another ParameterDict (the argument to update()). Note that update() with other unordered mapping types (e.g., Python’s plain dict) does not preserve the order of the merged mapping. Parameters
parameters (iterable, optional) – a mapping (dictionary) of (string : Parameter) or an iterable of key-value pairs of type (string, Parameter) Example: class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
self.params = nn.ParameterDict({
'left': nn.Parameter(torch.randn(5, 10)),
'right': nn.Parameter(torch.randn(5, 10))
})
def forward(self, x, choice):
x = self.params[choice].mm(x)
return x
clear() [source]
Remove all items from the ParameterDict.
items() [source]
Return an iterable of the ParameterDict key/value pairs.
keys() [source]
Return an iterable of the ParameterDict keys.
pop(key) [source]
Remove key from the ParameterDict and return its parameter. Parameters
key (string) – key to pop from the ParameterDict
update(parameters) [source]
Update the ParameterDict with the key-value pairs from a mapping or an iterable, overwriting existing keys. Note If parameters is an OrderedDict, a ParameterDict, or an iterable of key-value pairs, the order of new elements in it is preserved. Parameters
parameters (iterable) – a mapping (dictionary) from string to Parameter, or an iterable of key-value pairs of type (string, Parameter)
values() [source]
Return an iterable of the ParameterDict values. | |
doc_25472 |
Return the largest n elements. Parameters
n:int, default 5
Return this many descending sorted values.
keep:{‘first’, ‘last’, ‘all’}, default ‘first’
When there are duplicate values that cannot all fit in a Series of n elements: first : return the first n occurrences in order of appearance. last : return the last n occurrences in reverse order of appearance. all : keep all occurrences. This can result in a Series of size larger than n. Returns
Series
The n largest values in the Series, sorted in decreasing order. See also Series.nsmallest
Get the n smallest elements. Series.sort_values
Sort Series by values. Series.head
Return the first n rows. Notes Faster than .sort_values(ascending=False).head(n) for small n relative to the size of the Series object. Examples
>>> countries_population = {"Italy": 59000000, "France": 65000000,
... "Malta": 434000, "Maldives": 434000,
... "Brunei": 434000, "Iceland": 337000,
... "Nauru": 11300, "Tuvalu": 11300,
... "Anguilla": 11300, "Montserrat": 5200}
>>> s = pd.Series(countries_population)
>>> s
Italy 59000000
France 65000000
Malta 434000
Maldives 434000
Brunei 434000
Iceland 337000
Nauru 11300
Tuvalu 11300
Anguilla 11300
Montserrat 5200
dtype: int64
The n largest elements where n=5 by default.
>>> s.nlargest()
France 65000000
Italy 59000000
Malta 434000
Maldives 434000
Brunei 434000
dtype: int64
The n largest elements where n=3. Default keep value is ‘first’ so Malta will be kept.
>>> s.nlargest(3)
France 65000000
Italy 59000000
Malta 434000
dtype: int64
The n largest elements where n=3 and keeping the last duplicates. Brunei will be kept since it is the last with value 434000 based on the index order.
>>> s.nlargest(3, keep='last')
France 65000000
Italy 59000000
Brunei 434000
dtype: int64
The n largest elements where n=3 with all duplicates kept. Note that the returned Series has five elements due to the three duplicates.
>>> s.nlargest(3, keep='all')
France 65000000
Italy 59000000
Malta 434000
Maldives 434000
Brunei 434000
dtype: int64 | |
doc_25473 | This method allows you to compare two Header instances for equality. | |
doc_25474 |
Return the group id. | |
doc_25475 | Total size of memory blocks in bytes in the new snapshot (int): 0 if the memory blocks have been released in the new snapshot. | |
doc_25476 |
Set multiple properties at once. Supported properties are
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
latitude_grid unknown
longitude_grid unknown
longitude_grid_ends unknown
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim unknown
xmargin float greater than -0.5
xscale unknown
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim unknown
ymargin float greater than -0.5
yscale unknown
yticklabels unknown
yticks unknown
zorder float | |
doc_25477 | See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdNonAliasingAdd
tf.raw_ops.ScatterNdNonAliasingAdd(
input, indices, updates, name=None
)
from updates according to indices indices. The updates are non-aliasing: input is only modified in-place if no other operations will use it. Otherwise, a copy of input is made. This operation has a gradient with respect to both input and updates. input is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into input. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or (P-K)-dimensional slices (if K < P) along the Kth dimension of input. updates is Tensor of rank Q-1+P-K with shape: $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$ For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:
print(sess.run(output))
The resulting value output would look like this: [1, 13, 3, 14, 14, 6, 7, 20]
See tf.scatter_nd for more details about how to make updates to slices.
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool. A Tensor.
indices A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into input.
updates A Tensor. Must have the same type as input. A Tensor. Must have the same type as ref. A tensor of updated values to add to input.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_25478 |
Return the day of the year. This attribute returns the day of the year on which the particular date occurs. The return value ranges between 1 to 365 for regular years and 1 to 366 for leap years. Returns
int
The day of year. See also Period.day
Return the day of the month. Period.day_of_week
Return the day of week. PeriodIndex.day_of_year
Return the day of year of all indexes. Examples
>>> period = pd.Period("2015-10-23", freq='H')
>>> period.day_of_year
296
>>> period = pd.Period("2012-12-31", freq='D')
>>> period.day_of_year
366
>>> period = pd.Period("2013-01-01", freq='D')
>>> period.day_of_year
1 | |
doc_25479 | Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself. Yields
(string, Module) – Tuple containing a name and child module Example: >>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module) | |
doc_25480 | Reverse the elements of the deque in-place and then return None. New in version 3.2. | |
doc_25481 | Availability: BSD, OSX. New in version 3.4. | |
doc_25482 | tf.nn.max_pool_with_argmax(
input, ksize, strides, padding, data_format='NHWC',
output_dtype=tf.dtypes.int64, include_batch_in_index=False, name=None
)
The indices in argmax are flattened, so that a maximum value at position [b, y, x, c] becomes flattened index: (y * width + x) * channels + c if include_batch_in_index is False; ((b * height + y) * width + x) * channels + c if include_batch_in_index is True. The indices returned are always in [0, height) x [0, width) before flattening, even if padding is involved and the mathematically correct answer is outside (either negative or too large). This is a bug, but fixing it is difficult to do in a safe backwards compatible way, especially due to flattening.
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D with shape [batch, height, width, channels]. Input to pool over.
ksize An int or list of ints that has length 1, 2 or 4. The size of the window for each dimension of the input tensor.
strides An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of the input tensor.
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
data_format An optional string, must be set to "NHWC". Defaults to "NHWC". Specify the data format of the input and output data.
output_dtype An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64. The dtype of the returned argmax tensor.
include_batch_in_index An optional boolean. Defaults to False. Whether to include batch dimension in flattened index of argmax.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output, argmax). output A Tensor. Has the same type as input.
argmax A Tensor of type output_dtype. | |
doc_25483 | The maximum number of headers of this type that can have the same name. A value of None means unlimited. The BaseHeader value for this attribute is None; it is expected that specialized header classes will override this value as needed. | |
doc_25484 | converts midi events to pygame events midis2events(midi_events, device_id) -> [Event, ...] Takes a sequence of midi events and returns list of pygame events. The midi_events data is expected to be a sequence of ((status, data1, data2, data3), timestamp) midi events (all values required).
Returns:
a list of pygame events of event type MIDIIN
Return type:
list | |
doc_25485 | See Migration guide for more details. tf.compat.v1.raw_ops.AccumulatorSetGlobalStep
tf.raw_ops.AccumulatorSetGlobalStep(
handle, new_global_step, name=None
)
Logs warning if the accumulator's value is already higher than new_global_step.
Args
handle A Tensor of type mutable string. The handle to an accumulator.
new_global_step A Tensor of type int64. The new global_step value to set.
name A name for the operation (optional).
Returns The created Operation. | |
doc_25486 | class sklearn.exceptions.DataConversionWarning [source]
Warning used to notify implicit data conversions happening in the code. This warning occurs when some input data needs to be converted or interpreted in a way that may not match the user’s expectations. For example, this warning may occur when the user
passes an integer array to a function which expects float input and will convert the input requests a non-copying operation, but a copy is required to meet the implementation’s data-type expectations; passes an input whose shape can be interpreted ambiguously. Changed in version 0.18: Moved from sklearn.utils.validation. Attributes
args
Methods
with_traceback Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.
with_traceback()
Exception.with_traceback(tb) – set self.__traceback__ to tb and return self. | |
doc_25487 | See Migration guide for more details. tf.compat.v1.raw_ops.ResourceSparseApplyAdagradDA
tf.raw_ops.ResourceSparseApplyAdagradDA(
var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1,
l2, global_step, use_locking=False, name=None
)
Args
var A Tensor of type resource. Should be from a Variable().
gradient_accumulator A Tensor of type resource. Should be from a Variable().
gradient_squared_accumulator A Tensor of type resource. Should be from a Variable().
grad A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The gradient.
indices A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
lr A Tensor. Must have the same type as grad. Learning rate. Must be a scalar.
l1 A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
l2 A Tensor. Must have the same type as grad. L2 regularization. Must be a scalar.
global_step A Tensor of type int64. Training step number. Must be a scalar.
use_locking An optional bool. Defaults to False. If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).
Returns The created Operation. | |
doc_25488 |
Align the xlabels and ylabels of subplots with the same subplots row or column (respectively) if label alignment is being done automatically (i.e. the label position is not manually set). Alignment persists for draw events after this is called. Parameters
axslist of Axes
Optional list (or ndarray) of Axes to align the labels. Default is to align all Axes on the figure. See also matplotlib.figure.Figure.align_xlabels
matplotlib.figure.Figure.align_ylabels | |
doc_25489 |
Enable interactive mode. See pyplot.isinteractive for more details. See also ioff
Disable interactive mode. isinteractive
Whether interactive mode is enabled. show
Show all figures (and maybe block). pause
Show all figures, and block for a time. Notes For a temporary change, this can be used as a context manager: # if interactive mode is off
# then figures will not be shown on creation
plt.ioff()
# This figure will not be shown immediately
fig = plt.figure()
with plt.ion():
# interactive mode will be on
# figures will automatically be shown
fig2 = plt.figure()
# ...
To enable usage as a context manager, this function returns an _IonContext object. The return value is not intended to be stored or accessed by the user. | |
doc_25490 | Raised when a generator or coroutine is closed; see generator.close() and coroutine.close(). It directly inherits from BaseException instead of Exception since it is technically not an error. | |
doc_25491 | True if the address is allocated for private networks. See iana-ipv4-special-registry (for IPv4) or iana-ipv6-special-registry (for IPv6). | |
doc_25492 |
Evaluate a 3-D polynomial on the Cartesian product of x, y and z. This function returns the values: \[p(a,b,c) = \sum_{i,j,k} c_{i,j,k} * a^i * b^j * c^k\] where the points (a, b, c) consist of all triples formed by taking a from x, b from y, and c from z. The resulting points form a grid with x in the first dimension, y in the second, and z in the third. The parameters x, y, and z are converted to arrays only if they are tuples or a lists, otherwise they are treated as a scalars. In either case, either x, y, and z or their elements must support multiplication and addition both with themselves and with the elements of c. If c has fewer than three dimensions, ones are implicitly appended to its shape to make it 3-D. The shape of the result will be c.shape[3:] + x.shape + y.shape + z.shape. Parameters
x, y, zarray_like, compatible objects
The three dimensional series is evaluated at the points in the Cartesian product of x, y, and z. If x,`y`, or z is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and, if it isn’t an ndarray, it is treated as a scalar.
carray_like
Array of coefficients ordered so that the coefficients for terms of degree i,j are contained in c[i,j]. If c has dimension greater than two the remaining indices enumerate multiple sets of coefficients. Returns
valuesndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian product of x and y. See also
polyval, polyval2d, polygrid2d, polyval3d
Notes New in version 1.7.0. | |
doc_25493 |
Predict using the linear model. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape (n_samples,)
Returns predicted values. | |
doc_25494 |
Get the artist's bounding box in display space. The bounding box' width and height are nonnegative. Subclasses should override for inclusion in the bounding box "tight" calculation. Default is to return an empty bounding box at 0, 0. Be careful when using this function, the results will not update if the artist window extent of the artist changes. The extent can change due to any changes in the transform stack, such as changing the axes limits, the figure size, or the canvas used (as is done when saving a figure). This can lead to unexpected behavior where interactive figures will look fine on the screen, but will save incorrectly. | |
doc_25495 | See Migration guide for more details. tf.compat.v1.raw_ops.Mod
tf.raw_ops.Mod(
x, y, name=None
)
the result here is consistent with a truncating divide. E.g. tf.truncatediv(x, y) * y + truncate_mod(x, y) = x.
Note: Mod supports broadcasting. More about broadcasting here
Args
x A Tensor. Must be one of the following types: int32, int64, half, half, bfloat16, float32, float64.
y A Tensor. Must have the same type as x.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_25496 | Calls get() on a given model manager, but it raises Http404 instead of the model’s DoesNotExist exception. | |
doc_25497 |
Roll provided date backward to next offset only if not on offset. | |
doc_25498 | Saves a new file with the file name and contents provided. This will not replace the existing file, but will create a new file and update the object to point to it. If save is True, the model’s save() method will be called once the file is saved. That is, these two lines: >>> car.photo.save('myphoto.jpg', content, save=False)
>>> car.save()
are equivalent to: >>> car.photo.save('myphoto.jpg', content, save=True)
Note that the content argument must be an instance of either File or of a subclass of File, such as ContentFile. | |
doc_25499 | Control whether the cookie is sent with every response when session.permanent is true. Sending the cookie every time (the default) can more reliably keep the session from expiring, but uses more bandwidth. Non-permanent sessions are not affected. Default: True |
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