_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_27100 | Put the SMTP connection in TLS (Transport Layer Security) mode. All SMTP commands that follow will be encrypted. You should then call ehlo() again. If keyfile and certfile are provided, they are used to create an ssl.SSLContext. Optional context parameter is an ssl.SSLContext object; This is an alternative to using a keyfile and a certfile and if specified both keyfile and certfile should be None. If there has been no previous EHLO or HELO command this session, this method tries ESMTP EHLO first. Deprecated since version 3.6: keyfile and certfile are deprecated in favor of context. Please use ssl.SSLContext.load_cert_chain() instead, or let ssl.create_default_context() select the system’s trusted CA certificates for you.
SMTPHeloError
The server didn’t reply properly to the HELO greeting.
SMTPNotSupportedError
The server does not support the STARTTLS extension.
RuntimeError
SSL/TLS support is not available to your Python interpreter. Changed in version 3.3: context was added. Changed in version 3.4: The method now supports hostname check with SSLContext.check_hostname and Server Name Indicator (see HAS_SNI). Changed in version 3.5: The error raised for lack of STARTTLS support is now the SMTPNotSupportedError subclass instead of the base SMTPException. | |
doc_27101 | class sklearn.cluster.AffinityPropagation(*, damping=0.5, max_iter=200, convergence_iter=15, copy=True, preference=None, affinity='euclidean', verbose=False, random_state='warn') [source]
Perform Affinity Propagation Clustering of data. Read more in the User Guide. Parameters
dampingfloat, default=0.5
Damping factor (between 0.5 and 1) is the extent to which the current value is maintained relative to incoming values (weighted 1 - damping). This in order to avoid numerical oscillations when updating these values (messages).
max_iterint, default=200
Maximum number of iterations.
convergence_iterint, default=15
Number of iterations with no change in the number of estimated clusters that stops the convergence.
copybool, default=True
Make a copy of input data.
preferencearray-like of shape (n_samples,) or float, default=None
Preferences for each point - points with larger values of preferences are more likely to be chosen as exemplars. The number of exemplars, ie of clusters, is influenced by the input preferences value. If the preferences are not passed as arguments, they will be set to the median of the input similarities.
affinity{‘euclidean’, ‘precomputed’}, default=’euclidean’
Which affinity to use. At the moment ‘precomputed’ and euclidean are supported. ‘euclidean’ uses the negative squared euclidean distance between points.
verbosebool, default=False
Whether to be verbose.
random_stateint, RandomState instance or None, default=0
Pseudo-random number generator to control the starting state. Use an int for reproducible results across function calls. See the Glossary. New in version 0.23: this parameter was previously hardcoded as 0. Attributes
cluster_centers_indices_ndarray of shape (n_clusters,)
Indices of cluster centers.
cluster_centers_ndarray of shape (n_clusters, n_features)
Cluster centers (if affinity != precomputed).
labels_ndarray of shape (n_samples,)
Labels of each point.
affinity_matrix_ndarray of shape (n_samples, n_samples)
Stores the affinity matrix used in fit.
n_iter_int
Number of iterations taken to converge. Notes For an example, see examples/cluster/plot_affinity_propagation.py. The algorithmic complexity of affinity propagation is quadratic in the number of points. When fit does not converge, cluster_centers_ becomes an empty array and all training samples will be labelled as -1. In addition, predict will then label every sample as -1. When all training samples have equal similarities and equal preferences, the assignment of cluster centers and labels depends on the preference. If the preference is smaller than the similarities, fit will result in a single cluster center and label 0 for every sample. Otherwise, every training sample becomes its own cluster center and is assigned a unique label. References Brendan J. Frey and Delbert Dueck, “Clustering by Passing Messages Between Data Points”, Science Feb. 2007 Examples >>> from sklearn.cluster import AffinityPropagation
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [4, 2], [4, 4], [4, 0]])
>>> clustering = AffinityPropagation(random_state=5).fit(X)
>>> clustering
AffinityPropagation(random_state=5)
>>> clustering.labels_
array([0, 0, 0, 1, 1, 1])
>>> clustering.predict([[0, 0], [4, 4]])
array([0, 1])
>>> clustering.cluster_centers_
array([[1, 2],
[4, 2]])
Methods
fit(X[, y]) Fit the clustering from features, or affinity matrix.
fit_predict(X[, y]) Fit the clustering from features or affinity matrix, and return cluster labels.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict the closest cluster each sample in X belongs to.
set_params(**params) Set the parameters of this estimator.
fit(X, y=None) [source]
Fit the clustering from features, or affinity matrix. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention. Returns
self
fit_predict(X, y=None) [source]
Fit the clustering from features or affinity matrix, and return cluster labels. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse feature matrix is provided, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention. Returns
labelsndarray of shape (n_samples,)
Cluster labels.
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]
Predict the closest cluster each sample in X belongs to. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Returns
labelsndarray of shape (n_samples,)
Cluster labels.
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.
Examples using sklearn.cluster.AffinityPropagation
Demo of affinity propagation clustering algorithm
Comparing different clustering algorithms on toy datasets | |
doc_27102 | True if this cookie was received as an RFC 2109 cookie (ie. the cookie arrived in a Set-Cookie header, and the value of the Version cookie-attribute in that header was 1). This attribute is provided because http.cookiejar may ‘downgrade’ RFC 2109 cookies to Netscape cookies, in which case version is 0. | |
doc_27103 | Returns a list of the supported start methods, the first of which is the default. The possible start methods are 'fork', 'spawn' and 'forkserver'. On Windows only 'spawn' is available. On Unix 'fork' and 'spawn' are always supported, with 'fork' being the default. New in version 3.4. | |
doc_27104 |
sc.__array_wrap__(obj) return scalar from array | |
doc_27105 |
Bases: skimage.io.collection.ImageCollection A class containing all frames from multi-frame images. Parameters
load_patternstr or list of str
Pattern glob or filenames to load. The path can be absolute or relative.
conserve_memorybool, optional
Whether to conserve memory by only caching a single frame. Default is True. Other Parameters
load_funccallable
imread by default. See notes below. Notes If conserve_memory=True the memory footprint can be reduced, however the performance can be affected because frames have to be read from file more often. The last accessed frame is cached, all other frames will have to be read from file. The current implementation makes use of tifffile for Tiff files and PIL otherwise. Examples >>> from skimage import data_dir
>>> img = MultiImage(data_dir + '/multipage.tif')
>>> len(img)
2
>>> for frame in img:
... print(frame.shape)
(15, 10)
(15, 10)
__init__(filename, conserve_memory=True, dtype=None, **imread_kwargs) [source]
Load a multi-img.
property filename | |
doc_27106 |
Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and NaN is returned for that slice. Parameters
aarray_like
Array containing numbers whose maximum is desired. If a is not an array, a conversion is attempted.
axis{int, tuple of int, None}, optional
Axis or axes along which the maximum is computed. The default is to compute the maximum of the flattened array.
outndarray, optional
Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See Output type determination for more details. New in version 1.8.0.
keepdimsbool, optional
If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a. If the value is anything but the default, then keepdims will be passed through to the max method of sub-classes of ndarray. If the sub-classes methods does not implement keepdims any exceptions will be raised. New in version 1.8.0.
initialscalar, optional
The minimum value of an output element. Must be present to allow computation on empty slice. See reduce for details. New in version 1.22.0.
wherearray_like of bool, optional
Elements to compare for the maximum. See reduce for details. New in version 1.22.0. Returns
nanmaxndarray
An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned. See also nanmin
The minimum value of an array along a given axis, ignoring any NaNs. amax
The maximum value of an array along a given axis, propagating any NaNs. fmax
Element-wise maximum of two arrays, ignoring any NaNs. maximum
Element-wise maximum of two arrays, propagating any NaNs. isnan
Shows which elements are Not a Number (NaN). isfinite
Shows which elements are neither NaN nor infinity.
amin, fmin, minimum
Notes NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. If the input has a integer type the function is equivalent to np.max. Examples >>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
array([3., 2.])
>>> np.nanmax(a, axis=1)
array([2., 3.])
When positive infinity and negative infinity are present: >>> np.nanmax([1, 2, np.nan, np.NINF])
2.0
>>> np.nanmax([1, 2, np.nan, np.inf])
inf | |
doc_27107 | If the stderr argument was PIPE, this attribute is a readable stream object as returned by open(). Reading from the stream provides error output from the child process. If the encoding or errors arguments were specified or the universal_newlines argument was True, the stream is a text stream, otherwise it is a byte stream. If the stderr argument was not PIPE, this attribute is None. | |
doc_27108 | Read and parse configuration data from f which must be an iterable yielding Unicode strings (for example files opened in text mode). Optional argument source specifies the name of the file being read. If not given and f has a name attribute, that is used for source; the default is '<???>'. New in version 3.2: Replaces readfp(). | |
doc_27109 | A function that takes one argument and returns a coerced value. Examples include the built-in int, float, bool and other types. Defaults to an identity function. Note that coercion happens after input validation, so it is possible to coerce to a value not present in choices. | |
doc_27110 |
Return boolean flag, True if artist is included in layout calculations. E.g. Constrained Layout Guide, Figure.tight_layout(), and fig.savefig(fname, bbox_inches='tight'). | |
doc_27111 |
Description of the Tool. str: Tooltip used if the Tool is included in a Toolbar. | |
doc_27112 | sklearn.metrics.pairwise.pairwise_kernels(X, Y=None, metric='linear', *, filter_params=False, n_jobs=None, **kwds) [source]
Compute the kernel between arrays X and optional array Y. This method takes either a vector array or a kernel matrix, and returns a kernel matrix. If the input is a vector array, the kernels are computed. If the input is a kernel matrix, it is returned instead. This method provides a safe way to take a kernel matrix as input, while preserving compatibility with many other algorithms that take a vector array. If Y is given (default is None), then the returned matrix is the pairwise kernel between the arrays from both X and Y. Valid values for metric are:
[‘additive_chi2’, ‘chi2’, ‘linear’, ‘poly’, ‘polynomial’, ‘rbf’, ‘laplacian’, ‘sigmoid’, ‘cosine’] Read more in the User Guide. Parameters
Xndarray of shape (n_samples_X, n_samples_X) or (n_samples_X, n_features)
Array of pairwise kernels between samples, or a feature array. The shape of the array should be (n_samples_X, n_samples_X) if metric == “precomputed” and (n_samples_X, n_features) otherwise.
Yndarray of shape (n_samples_Y, n_features), default=None
A second feature array only if X has shape (n_samples_X, n_features).
metricstr or callable, default=”linear”
The metric to use when calculating kernel between instances in a feature array. If metric is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If metric is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from sklearn.metrics.pairwise are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.
filter_paramsbool, default=False
Whether to filter invalid parameters or not.
n_jobsint, default=None
The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
**kwdsoptional keyword parameters
Any further parameters are passed directly to the kernel function. Returns
Kndarray of shape (n_samples_X, n_samples_X) or (n_samples_X, n_samples_Y)
A kernel matrix K such that K_{i, j} is the kernel between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then K_{i, j} is the kernel between the ith array from X and the jth array from Y. Notes If metric is ‘precomputed’, Y is ignored and X is returned. | |
doc_27113 | Creates a gin index. To use this index on data types not in the built-in operator classes, you need to activate the btree_gin extension on PostgreSQL. You can install it using the BtreeGinExtension migration operation. Set the fastupdate parameter to False to disable the GIN Fast Update Technique that’s enabled by default in PostgreSQL. Provide an integer number of bytes to the gin_pending_list_limit parameter to tune the maximum size of the GIN pending list which is used when fastupdate is enabled. Changed in Django 3.2: Positional argument *expressions was added in order to support functional indexes. | |
doc_27114 |
Get or set the y-limits of the current axes. Call signatures: bottom, top = ylim() # return the current ylim
ylim((bottom, top)) # set the ylim to bottom, top
ylim(bottom, top) # set the ylim to bottom, top
If you do not specify args, you can alternatively pass bottom or top as kwargs, i.e.: ylim(top=3) # adjust the top leaving bottom unchanged
ylim(bottom=1) # adjust the bottom leaving top unchanged
Setting limits turns autoscaling off for the y-axis. Returns
bottom, top
A tuple of the new y-axis limits. Notes Calling this function with no arguments (e.g. ylim()) is the pyplot equivalent of calling get_ylim on the current axes. Calling this function with arguments is the pyplot equivalent of calling set_ylim on the current axes. All arguments are passed though.
Examples using matplotlib.pyplot.ylim
Infinite lines
Pyplot Text
Frame grabbing
Interactive functions
Findobj Demo
Pyplot tutorial | |
doc_27115 | os.O_DIRECT
os.O_DIRECTORY
os.O_NOFOLLOW
os.O_NOATIME
os.O_PATH
os.O_TMPFILE
os.O_SHLOCK
os.O_EXLOCK
The above constants are extensions and not present if they are not defined by the C library. Changed in version 3.4: Add O_PATH on systems that support it. Add O_TMPFILE, only available on Linux Kernel 3.11 or newer. | |
doc_27116 | See Migration guide for more details. tf.compat.v1.raw_ops.CheckNumerics
tf.raw_ops.CheckNumerics(
tensor, message, name=None
)
When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.
Args
tensor A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
message A string. Prefix of the error message.
name A name for the operation (optional).
Returns A Tensor. Has the same type as tensor. | |
doc_27117 |
Predict the classes of X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Array representing the data. Returns
yndarray of shape (n_samples,)
Array with predicted labels. | |
doc_27118 |
Number of iterators possessed by the broadcasted result. Examples >>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.numiter
2 | |
doc_27119 |
Numerical negative, element-wise. Parameters
xarray_like or scalar
Input array.
outndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized. **kwargs
For other keyword-only arguments, see the ufunc docs. Returns
yndarray or scalar
Returned array or scalar: y = -x. This is a scalar if x is a scalar. Examples >>> np.negative([1.,-1.])
array([-1., 1.])
The unary - operator can be used as a shorthand for np.negative on ndarrays. >>> x1 = np.array(([1., -1.]))
>>> -x1
array([-1., 1.]) | |
doc_27120 | Rethinking the Inception Architecture for Computer Vision (CVPR 2016) Functions InceptionV3(...): Instantiates the Inception v3 architecture. decode_predictions(...): Decodes the prediction of an ImageNet model. preprocess_input(...): Preprocesses a tensor or Numpy array encoding a batch of images. | |
doc_27121 |
Helper decorator for forward methods of custom autograd functions (subclasses of torch.autograd.Function). See the example page for more detail. Parameters
cast_inputs (torch.dtype or None, optional, default=None) – If not None, when forward runs in an autocast-enabled region, casts incoming floating-point CUDA Tensors to the target dtype (non-floating-point Tensors are not affected), then executes forward with autocast disabled. If None, forward’s internal ops execute with the current autocast state. Note If the decorated forward is called outside an autocast-enabled region, custom_fwd is a no-op and cast_inputs has no effect. | |
doc_27122 | A form for allowing a user to change their password. | |
doc_27123 |
Make a horizontal bar plot. The bars are positioned at y with the given alignment. Their dimensions are given by width and height. The horizontal baseline is left (default 0). Many parameters can take either a single value applying to all bars or a sequence of values, one for each bar. Parameters
yfloat or array-like
The y coordinates of the bars. See also align for the alignment of the bars to the coordinates.
widthfloat or array-like
The width(s) of the bars.
heightfloat or array-like, default: 0.8
The heights of the bars.
leftfloat or array-like, default: 0
The x coordinates of the left sides of the bars.
align{'center', 'edge'}, default: 'center'
Alignment of the base to the y coordinates*: 'center': Center the bars on the y positions. 'edge': Align the bottom edges of the bars with the y positions. To align the bars on the top edge pass a negative height and align='edge'. Returns
BarContainer
Container with all the bars and optionally errorbars. Other Parameters
colorcolor or list of color, optional
The colors of the bar faces.
edgecolorcolor or list of color, optional
The colors of the bar edges.
linewidthfloat or array-like, optional
Width of the bar edge(s). If 0, don't draw edges.
tick_labelstr or list of str, optional
The tick labels of the bars. Default: None (Use default numeric labels.)
xerr, yerrfloat or array-like of shape(N,) or shape(2, N), optional
If not None, add horizontal / vertical errorbars to the bar tips. The values are +/- sizes relative to the data: scalar: symmetric +/- values for all bars shape(N,): symmetric +/- values for each bar shape(2, N): Separate - and + values for each bar. First row contains the lower errors, the second row contains the upper errors.
None: No errorbar. (default) See Different ways of specifying error bars for an example on the usage of xerr and yerr.
ecolorcolor or list of color, default: 'black'
The line color of the errorbars.
capsizefloat, default: rcParams["errorbar.capsize"] (default: 0.0)
The length of the error bar caps in points.
error_kwdict, optional
Dictionary of kwargs to be passed to the errorbar method. Values of ecolor or capsize defined here take precedence over the independent kwargs.
logbool, default: False
If True, set the x-axis to be log scale.
**kwargsRectangle properties
Property Description
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
angle unknown
animated bool
antialiased or aa bool or None
bounds (left, bottom, width, height)
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
height unknown
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
width unknown
x unknown
xy (float, float)
y unknown
zorder float See also bar
Plot a vertical bar plot. Notes Stacked bars can be achieved by passing individual left values per bar. See Discrete distribution as horizontal bar chart .
Examples using matplotlib.axes.Axes.barh
Bar Label Demo
Horizontal bar chart
Producing multiple histograms side by side
The Lifecycle of a Plot | |
doc_27124 | Sets the given key to list_ (unlike __setitem__()). | |
doc_27125 | Return the exception of the Task. If the wrapped coroutine raised an exception that exception is returned. If the wrapped coroutine returned normally this method returns None. If the Task has been cancelled, this method raises a CancelledError exception. If the Task isn’t done yet, this method raises an InvalidStateError exception. | |
doc_27126 | true if the font module is initialized get_init() -> bool Test if the font module is initialized or not. | |
doc_27127 |
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_27128 | The usage is simple: you just pass the match method the current path info as well as the method (which defaults to GET). The following things can then happen: you receive a NotFound exception that indicates that no URL is matching. A NotFound exception is also a WSGI application you can call to get a default page not found page (happens to be the same object as werkzeug.exceptions.NotFound) you receive a MethodNotAllowed exception that indicates that there is a match for this URL but not for the current request method. This is useful for RESTful applications. you receive a RequestRedirect exception with a new_url attribute. This exception is used to notify you about a request Werkzeug requests from your WSGI application. This is for example the case if you request /foo although the correct URL is /foo/ You can use the RequestRedirect instance as response-like object similar to all other subclasses of HTTPException. you receive a WebsocketMismatch exception if the only match is a WebSocket rule but the bind is an HTTP request, or if the match is an HTTP rule but the bind is a WebSocket request. you get a tuple in the form (endpoint, arguments) if there is a match (unless return_rule is True, in which case you get a tuple in the form (rule, arguments)) If the path info is not passed to the match method the default path info of the map is used (defaults to the root URL if not defined explicitly). All of the exceptions raised are subclasses of HTTPException so they can be used as WSGI responses. They will all render generic error or redirect pages. Here is a small example for matching: >>> m = Map([
... Rule('/', endpoint='index'),
... Rule('/downloads/', endpoint='downloads/index'),
... Rule('/downloads/<int:id>', endpoint='downloads/show')
... ])
>>> urls = m.bind("example.com", "/")
>>> urls.match("/", "GET")
('index', {})
>>> urls.match("/downloads/42")
('downloads/show', {'id': 42})
And here is what happens on redirect and missing URLs: >>> urls.match("/downloads")
Traceback (most recent call last):
...
RequestRedirect: http://example.com/downloads/
>>> urls.match("/missing")
Traceback (most recent call last):
...
NotFound: 404 Not Found
Parameters
path_info (Optional[str]) – the path info to use for matching. Overrides the path info specified on binding.
method (Optional[str]) – the HTTP method used for matching. Overrides the method specified on binding.
return_rule (bool) – return the rule that matched instead of just the endpoint (defaults to False).
query_args (Optional[Union[Mapping[str, Any], str]]) – optional query arguments that are used for automatic redirects as string or dictionary. It’s currently not possible to use the query arguments for URL matching.
websocket (Optional[bool]) – Match WebSocket instead of HTTP requests. A websocket request has a ws or wss url_scheme. This overrides that detection. Return type
Tuple[Union[str, werkzeug.routing.Rule], Mapping[str, Any]] Changelog New in version 1.0: Added websocket. Changed in version 0.8: query_args can be a string. New in version 0.7: Added query_args. New in version 0.6: Added return_rule. | |
doc_27129 |
Call self as a function. | |
doc_27130 |
Check whether an array-like is of a pandas extension class instance. Deprecated since version 1.0.0: Use is_extension_array_dtype instead. Extension classes include categoricals, pandas sparse objects (i.e. classes represented within the pandas library and not ones external to it like scipy sparse matrices), and datetime-like arrays. Parameters
arr:array-like, scalar
The array-like to check. Returns
boolean
Whether or not the array-like is of a pandas extension class instance. Examples
>>> is_extension_type([1, 2, 3])
False
>>> is_extension_type(np.array([1, 2, 3]))
False
>>>
>>> cat = pd.Categorical([1, 2, 3])
>>>
>>> is_extension_type(cat)
True
>>> is_extension_type(pd.Series(cat))
True
>>> is_extension_type(pd.arrays.SparseArray([1, 2, 3]))
True
>>> from scipy.sparse import bsr_matrix
>>> is_extension_type(bsr_matrix([1, 2, 3]))
False
>>> is_extension_type(pd.DatetimeIndex([1, 2, 3]))
False
>>> is_extension_type(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
True
>>>
>>> dtype = DatetimeTZDtype("ns", tz="US/Eastern")
>>> s = pd.Series([], dtype=dtype)
>>> is_extension_type(s)
True | |
doc_27131 | Computes the one dimensional discrete Fourier transform of input. Note The Fourier domain representation of any real signal satisfies the Hermitian property: X[i] = conj(X[-i]). This function always returns both the positive and negative frequency terms even though, for real inputs, the negative frequencies are redundant. rfft() returns the more compact one-sided representation where only the positive frequencies are returned. Parameters
input (Tensor) – the input tensor
n (int, optional) – Signal length. If given, the input will either be zero-padded or trimmed to this length before computing the FFT.
dim (int, optional) – The dimension along which to take the one dimensional FFT.
norm (str, optional) –
Normalization mode. For the forward transform (fft()), these correspond to:
"forward" - normalize by 1/n
"backward" - no normalization
"ortho" - normalize by 1/sqrt(n) (making the FFT orthonormal) Calling the backward transform (ifft()) with the same normalization mode will apply an overall normalization of 1/n between the two transforms. This is required to make ifft() the exact inverse. Default is "backward" (no normalization). Example >>> t = torch.arange(4)
>>> t
tensor([0, 1, 2, 3])
>>> torch.fft.fft(t)
tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])
>>> t = tensor([0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j])
>>> torch.fft.fft(t)
tensor([12.+16.j, -8.+0.j, -4.-4.j, 0.-8.j]) | |
doc_27132 |
Bases: matplotlib.transforms.Transform Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. has_inverse=True
True if this transform has a corresponding inverse transform.
input_dims=1
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
is_separable=True
True if this transform is separable in the x- and y- dimensions.
output_dims=1
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
transform_non_affine(a)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input. | |
doc_27133 | Exit code that means the input data was incorrect. Availability: Unix. | |
doc_27134 |
Set the alternate marker face color. Parameters
fccolor | |
doc_27135 |
Return whether the given object is a scalar or string like. | |
doc_27136 |
Join a sequence of arrays along a new axis. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension. New in version 1.10.0. Parameters
arrayssequence of array_like
Each array must have the same shape.
axisint, optional
The axis in the result array along which the input arrays are stacked.
outndarray, optional
If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified. Returns
stackedndarray
The stacked array has one more dimension than the input arrays. See also concatenate
Join a sequence of arrays along an existing axis. block
Assemble an nd-array from nested lists of blocks. split
Split array into a list of multiple sub-arrays of equal size. Examples >>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
[4, 5, 6]])
>>> np.stack((a, b), axis=-1)
array([[1, 4],
[2, 5],
[3, 6]]) | |
doc_27137 | Return the smallest i such that i is the index of the first occurrence of x in the array. | |
doc_27138 | Clamp all elements in input into the range [ min, max ]. Let min_value and max_value be min and max, respectively, this returns: yi=min(max(xi,min_value),max_value)y_i = \min(\max(x_i, \text{min\_value}), \text{max\_value})
Parameters
input (Tensor) – the input tensor.
min (Number) – lower-bound of the range to be clamped to
max (Number) – upper-bound of the range to be clamped to Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> a = torch.randn(4)
>>> a
tensor([-1.7120, 0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000, 0.1734, -0.0478, -0.0922])
torch.clamp(input, *, min, out=None) → Tensor
Clamps all elements in input to be larger or equal min. Parameters
input (Tensor) – the input tensor. Keyword Arguments
min (Number) – minimal value of each element in the output
out (Tensor, optional) – the output tensor. Example: >>> a = torch.randn(4)
>>> a
tensor([-0.0299, -2.3184, 2.1593, -0.8883])
>>> torch.clamp(a, min=0.5)
tensor([ 0.5000, 0.5000, 2.1593, 0.5000])
torch.clamp(input, *, max, out=None) → Tensor
Clamps all elements in input to be smaller or equal max. Parameters
input (Tensor) – the input tensor. Keyword Arguments
max (Number) – maximal value of each element in the output
out (Tensor, optional) – the output tensor. Example: >>> a = torch.randn(4)
>>> a
tensor([ 0.7753, -0.4702, -0.4599, 1.1899])
>>> torch.clamp(a, max=0.5)
tensor([ 0.5000, -0.4702, -0.4599, 0.5000]) | |
doc_27139 | See Migration guide for more details. tf.compat.v1.saved_model.signature_def_utils.classification_signature_def
tf.compat.v1.saved_model.classification_signature_def(
examples, classes, scores
)
This function produces signatures intended for use with the TensorFlow Serving Classify API (tensorflow_serving/apis/prediction_service.proto), and so constrains the input and output types to those allowed by TensorFlow Serving.
Args
examples A string Tensor, expected to accept serialized tf.Examples.
classes A string Tensor. Note that the ClassificationResponse message requires that class labels are strings, not integers or anything else.
scores a float Tensor.
Returns A classification-flavored signature_def.
Raises
ValueError If examples is None. | |
doc_27140 | See Migration guide for more details. tf.compat.v1.raw_ops.LogSoftmax
tf.raw_ops.LogSoftmax(
logits, name=None
)
For each batch i and class j we have logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
Args
logits A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 2-D with shape [batch_size, num_classes].
name A name for the operation (optional).
Returns A Tensor. Has the same type as logits. | |
doc_27141 |
Export a decision tree in DOT format. This function generates a GraphViz representation of the decision tree, which is then written into out_file. Once exported, graphical renderings can be generated using, for example: $ dot -Tps tree.dot -o tree.ps (PostScript format)
$ dot -Tpng tree.dot -o tree.png (PNG format)
The sample counts that are shown are weighted with any sample_weights that might be present. Read more in the User Guide. Parameters
decision_treedecision tree classifier
The decision tree to be exported to GraphViz.
out_fileobject or str, default=None
Handle or name of the output file. If None, the result is returned as a string. Changed in version 0.20: Default of out_file changed from “tree.dot” to None.
max_depthint, default=None
The maximum depth of the representation. If None, the tree is fully generated.
feature_nameslist of str, default=None
Names of each of the features. If None generic names will be used (“feature_0”, “feature_1”, …).
class_nameslist of str or bool, default=None
Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output. If True, shows a symbolic representation of the class name.
label{‘all’, ‘root’, ‘none’}, default=’all’
Whether to show informative labels for impurity, etc. Options include ‘all’ to show at every node, ‘root’ to show only at the top root node, or ‘none’ to not show at any node.
filledbool, default=False
When set to True, paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.
leaves_parallelbool, default=False
When set to True, draw all leaf nodes at the bottom of the tree.
impuritybool, default=True
When set to True, show the impurity at each node.
node_idsbool, default=False
When set to True, show the ID number on each node.
proportionbool, default=False
When set to True, change the display of ‘values’ and/or ‘samples’ to be proportions and percentages respectively.
rotatebool, default=False
When set to True, orient tree left to right rather than top-down.
roundedbool, default=False
When set to True, draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.
special_charactersbool, default=False
When set to False, ignore special characters for PostScript compatibility.
precisionint, default=3
Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node. Returns
dot_datastring
String representation of the input tree in GraphViz dot format. Only returned if out_file is None. New in version 0.18. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> clf = tree.DecisionTreeClassifier()
>>> iris = load_iris()
>>> clf = clf.fit(iris.data, iris.target)
>>> tree.export_graphviz(clf)
'digraph Tree {... | |
doc_27142 | See Migration guide for more details. tf.compat.v1.initializers.variables
tf.compat.v1.variables_initializer(
var_list, name='init'
)
After you launch the graph in a session, you can run the returned Op to initialize all the variables in var_list. This Op runs all the initializers of the variables in var_list in parallel. Calling initialize_variables() is equivalent to passing the list of initializers to Group(). If var_list is empty, however, the function still returns an Op that can be run. That Op just has no effect.
Args
var_list List of Variable objects to initialize.
name Optional name for the returned operation.
Returns An Op that run the initializers of all the specified variables. | |
doc_27143 |
Run fit with all sets of parameters. Parameters
Xarray-like, 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, shape (n_samples,) or (n_samples, n_output), optional
Target relative to X for classification or regression; None for unsupervised learning.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).
**fit_paramsdict of string -> object
Parameters passed to the fit method of the estimator | |
doc_27144 | """
May be applied as a `default=...` value on a serializer field.
Returns the current user.
"""
requires_context = True
def __call__(self, serializer_field):
return serializer_field.context['request'].user
When serializing the instance, default will be used if the object attribute or dictionary key is not present in the instance. Note that setting a default value implies that the field is not required. Including both the default and required keyword arguments is invalid and will raise an error. allow_null Normally an error will be raised if None is passed to a serializer field. Set this keyword argument to True if None should be considered a valid value. Note that, without an explicit default, setting this argument to True will imply a default value of null for serialization output, but does not imply a default for input deserialization. Defaults to False source The name of the attribute that will be used to populate the field. May be a method that only takes a self argument, such as URLField(source='get_absolute_url'), or may use dotted notation to traverse attributes, such as EmailField(source='user.email'). When serializing fields with dotted notation, it may be necessary to provide a default value if any object is not present or is empty during attribute traversal. The value source='*' has a special meaning, and is used to indicate that the entire object should be passed through to the field. This can be useful for creating nested representations, or for fields which require access to the complete object in order to determine the output representation. Defaults to the name of the field. validators A list of validator functions which should be applied to the incoming field input, and which either raise a validation error or simply return. Validator functions should typically raise serializers.ValidationError, but Django's built-in ValidationError is also supported for compatibility with validators defined in the Django codebase or third party Django packages. error_messages A dictionary of error codes to error messages. label A short text string that may be used as the name of the field in HTML form fields or other descriptive elements. help_text A text string that may be used as a description of the field in HTML form fields or other descriptive elements. initial A value that should be used for pre-populating the value of HTML form fields. You may pass a callable to it, just as you may do with any regular Django Field: import datetime
from rest_framework import serializers
class ExampleSerializer(serializers.Serializer):
day = serializers.DateField(initial=datetime.date.today)
style A dictionary of key-value pairs that can be used to control how renderers should render the field. Two examples here are 'input_type' and 'base_template': # Use <input type="password"> for the input.
password = serializers.CharField(
style={'input_type': 'password'}
)
# Use a radio input instead of a select input.
color_channel = serializers.ChoiceField(
choices=['red', 'green', 'blue'],
style={'base_template': 'radio.html'}
)
For more details see the HTML & Forms documentation. Boolean fields BooleanField A boolean representation. When using HTML encoded form input be aware that omitting a value will always be treated as setting a field to False, even if it has a default=True option specified. This is because HTML checkbox inputs represent the unchecked state by omitting the value, so REST framework treats omission as if it is an empty checkbox input. Note that Django 2.1 removed the blank kwarg from models.BooleanField. Prior to Django 2.1 models.BooleanField fields were always blank=True. Thus since Django 2.1 default serializers.BooleanField instances will be generated without the required kwarg (i.e. equivalent to required=True) whereas with previous versions of Django, default BooleanField instances will be generated with a required=False option. If you want to control this behaviour manually, explicitly declare the BooleanField on the serializer class, or use the extra_kwargs option to set the required flag. Corresponds to django.db.models.fields.BooleanField. Signature: BooleanField() NullBooleanField A boolean representation that also accepts None as a valid value. Corresponds to django.db.models.fields.NullBooleanField. Signature: NullBooleanField() String fields CharField A text representation. Optionally validates the text to be shorter than max_length and longer than min_length. Corresponds to django.db.models.fields.CharField or django.db.models.fields.TextField. Signature: CharField(max_length=None, min_length=None, allow_blank=False, trim_whitespace=True)
max_length - Validates that the input contains no more than this number of characters.
min_length - Validates that the input contains no fewer than this number of characters.
allow_blank - If set to True then the empty string should be considered a valid value. If set to False then the empty string is considered invalid and will raise a validation error. Defaults to False.
trim_whitespace - If set to True then leading and trailing whitespace is trimmed. Defaults to True. The allow_null option is also available for string fields, although its usage is discouraged in favor of allow_blank. It is valid to set both allow_blank=True and allow_null=True, but doing so means that there will be two differing types of empty value permissible for string representations, which can lead to data inconsistencies and subtle application bugs. EmailField A text representation, validates the text to be a valid e-mail address. Corresponds to django.db.models.fields.EmailField Signature: EmailField(max_length=None, min_length=None, allow_blank=False) RegexField A text representation, that validates the given value matches against a certain regular expression. Corresponds to django.forms.fields.RegexField. Signature: RegexField(regex, max_length=None, min_length=None, allow_blank=False) The mandatory regex argument may either be a string, or a compiled python regular expression object. Uses Django's django.core.validators.RegexValidator for validation. SlugField A RegexField that validates the input against the pattern [a-zA-Z0-9_-]+. Corresponds to django.db.models.fields.SlugField. Signature: SlugField(max_length=50, min_length=None, allow_blank=False) URLField A RegexField that validates the input against a URL matching pattern. Expects fully qualified URLs of the form http://<host>/<path>. Corresponds to django.db.models.fields.URLField. Uses Django's django.core.validators.URLValidator for validation. Signature: URLField(max_length=200, min_length=None, allow_blank=False) UUIDField A field that ensures the input is a valid UUID string. The to_internal_value method will return a uuid.UUID instance. On output the field will return a string in the canonical hyphenated format, for example: "de305d54-75b4-431b-adb2-eb6b9e546013"
Signature: UUIDField(format='hex_verbose')
format: Determines the representation format of the uuid value
'hex_verbose' - The canonical hex representation, including hyphens: "5ce0e9a5-5ffa-654b-cee0-1238041fb31a"
'hex' - The compact hex representation of the UUID, not including hyphens: "5ce0e9a55ffa654bcee01238041fb31a"
'int' - A 128 bit integer representation of the UUID: "123456789012312313134124512351145145114"
'urn' - RFC 4122 URN representation of the UUID: "urn:uuid:5ce0e9a5-5ffa-654b-cee0-1238041fb31a" Changing the format parameters only affects representation values. All formats are accepted by to_internal_value
FilePathField A field whose choices are limited to the filenames in a certain directory on the filesystem Corresponds to django.forms.fields.FilePathField. Signature: FilePathField(path, match=None, recursive=False, allow_files=True, allow_folders=False, required=None, **kwargs)
path - The absolute filesystem path to a directory from which this FilePathField should get its choice.
match - A regular expression, as a string, that FilePathField will use to filter filenames.
recursive - Specifies whether all subdirectories of path should be included. Default is False.
allow_files - Specifies whether files in the specified location should be included. Default is True. Either this or allow_folders must be True.
allow_folders - Specifies whether folders in the specified location should be included. Default is False. Either this or allow_files must be True. IPAddressField A field that ensures the input is a valid IPv4 or IPv6 string. Corresponds to django.forms.fields.IPAddressField and django.forms.fields.GenericIPAddressField. Signature: IPAddressField(protocol='both', unpack_ipv4=False, **options)
protocol Limits valid inputs to the specified protocol. Accepted values are 'both' (default), 'IPv4' or 'IPv6'. Matching is case insensitive.
unpack_ipv4 Unpacks IPv4 mapped addresses like ::ffff:192.0.2.1. If this option is enabled that address would be unpacked to 192.0.2.1. Default is disabled. Can only be used when protocol is set to 'both'. Numeric fields IntegerField An integer representation. Corresponds to django.db.models.fields.IntegerField, django.db.models.fields.SmallIntegerField, django.db.models.fields.PositiveIntegerField and django.db.models.fields.PositiveSmallIntegerField. Signature: IntegerField(max_value=None, min_value=None)
max_value Validate that the number provided is no greater than this value.
min_value Validate that the number provided is no less than this value. FloatField A floating point representation. Corresponds to django.db.models.fields.FloatField. Signature: FloatField(max_value=None, min_value=None)
max_value Validate that the number provided is no greater than this value.
min_value Validate that the number provided is no less than this value. DecimalField A decimal representation, represented in Python by a Decimal instance. Corresponds to django.db.models.fields.DecimalField. Signature: DecimalField(max_digits, decimal_places, coerce_to_string=None, max_value=None, min_value=None)
max_digits The maximum number of digits allowed in the number. It must be either None or an integer greater than or equal to decimal_places.
decimal_places The number of decimal places to store with the number.
coerce_to_string Set to True if string values should be returned for the representation, or False if Decimal objects should be returned. Defaults to the same value as the COERCE_DECIMAL_TO_STRING settings key, which will be True unless overridden. If Decimal objects are returned by the serializer, then the final output format will be determined by the renderer. Note that setting localize will force the value to True.
max_value Validate that the number provided is no greater than this value.
min_value Validate that the number provided is no less than this value.
localize Set to True to enable localization of input and output based on the current locale. This will also force coerce_to_string to True. Defaults to False. Note that data formatting is enabled if you have set USE_L10N=True in your settings file.
rounding Sets the rounding mode used when quantising to the configured precision. Valid values are decimal module rounding modes. Defaults to None. Example usage To validate numbers up to 999 with a resolution of 2 decimal places, you would use: serializers.DecimalField(max_digits=5, decimal_places=2)
And to validate numbers up to anything less than one billion with a resolution of 10 decimal places: serializers.DecimalField(max_digits=19, decimal_places=10)
This field also takes an optional argument, coerce_to_string. If set to True the representation will be output as a string. If set to False the representation will be left as a Decimal instance and the final representation will be determined by the renderer. If unset, this will default to the same value as the COERCE_DECIMAL_TO_STRING setting, which is True unless set otherwise. Date and time fields DateTimeField A date and time representation. Corresponds to django.db.models.fields.DateTimeField. Signature: DateTimeField(format=api_settings.DATETIME_FORMAT, input_formats=None, default_timezone=None)
format - A string representing the output format. If not specified, this defaults to the same value as the DATETIME_FORMAT settings key, which will be 'iso-8601' unless set. Setting to a format string indicates that to_representation return values should be coerced to string output. Format strings are described below. Setting this value to None indicates that Python datetime objects should be returned by to_representation. In this case the datetime encoding will be determined by the renderer.
input_formats - A list of strings representing the input formats which may be used to parse the date. If not specified, the DATETIME_INPUT_FORMATS setting will be used, which defaults to ['iso-8601'].
default_timezone - A pytz.timezone representing the timezone. If not specified and the USE_TZ setting is enabled, this defaults to the current timezone. If USE_TZ is disabled, then datetime objects will be naive. DateTimeField format strings. Format strings may either be Python strftime formats which explicitly specify the format, or the special string 'iso-8601', which indicates that ISO 8601 style datetimes should be used. (eg '2013-01-29T12:34:56.000000Z') When a value of None is used for the format datetime objects will be returned by to_representation and the final output representation will determined by the renderer class. auto_now and auto_now_add model fields. When using ModelSerializer or HyperlinkedModelSerializer, note that any model fields with auto_now=True or auto_now_add=True will use serializer fields that are read_only=True by default. If you want to override this behavior, you'll need to declare the DateTimeField explicitly on the serializer. For example: class CommentSerializer(serializers.ModelSerializer):
created = serializers.DateTimeField()
class Meta:
model = Comment
DateField A date representation. Corresponds to django.db.models.fields.DateField Signature: DateField(format=api_settings.DATE_FORMAT, input_formats=None)
format - A string representing the output format. If not specified, this defaults to the same value as the DATE_FORMAT settings key, which will be 'iso-8601' unless set. Setting to a format string indicates that to_representation return values should be coerced to string output. Format strings are described below. Setting this value to None indicates that Python date objects should be returned by to_representation. In this case the date encoding will be determined by the renderer.
input_formats - A list of strings representing the input formats which may be used to parse the date. If not specified, the DATE_INPUT_FORMATS setting will be used, which defaults to ['iso-8601']. DateField format strings Format strings may either be Python strftime formats which explicitly specify the format, or the special string 'iso-8601', which indicates that ISO 8601 style dates should be used. (eg '2013-01-29') TimeField A time representation. Corresponds to django.db.models.fields.TimeField Signature: TimeField(format=api_settings.TIME_FORMAT, input_formats=None)
format - A string representing the output format. If not specified, this defaults to the same value as the TIME_FORMAT settings key, which will be 'iso-8601' unless set. Setting to a format string indicates that to_representation return values should be coerced to string output. Format strings are described below. Setting this value to None indicates that Python time objects should be returned by to_representation. In this case the time encoding will be determined by the renderer.
input_formats - A list of strings representing the input formats which may be used to parse the date. If not specified, the TIME_INPUT_FORMATS setting will be used, which defaults to ['iso-8601']. TimeField format strings Format strings may either be Python strftime formats which explicitly specify the format, or the special string 'iso-8601', which indicates that ISO 8601 style times should be used. (eg '12:34:56.000000') DurationField A Duration representation. Corresponds to django.db.models.fields.DurationField The validated_data for these fields will contain a datetime.timedelta instance. The representation is a string following this format '[DD] [HH:[MM:]]ss[.uuuuuu]'. Signature: DurationField(max_value=None, min_value=None)
max_value Validate that the duration provided is no greater than this value.
min_value Validate that the duration provided is no less than this value. Choice selection fields ChoiceField A field that can accept a value out of a limited set of choices. Used by ModelSerializer to automatically generate fields if the corresponding model field includes a choices=… argument. Signature: ChoiceField(choices)
choices - A list of valid values, or a list of (key, display_name) tuples.
allow_blank - If set to True then the empty string should be considered a valid value. If set to False then the empty string is considered invalid and will raise a validation error. Defaults to False.
html_cutoff - If set this will be the maximum number of choices that will be displayed by a HTML select drop down. Can be used to ensure that automatically generated ChoiceFields with very large possible selections do not prevent a template from rendering. Defaults to None.
html_cutoff_text - If set this will display a textual indicator if the maximum number of items have been cutoff in an HTML select drop down. Defaults to "More than {count} items…"
Both the allow_blank and allow_null are valid options on ChoiceField, although it is highly recommended that you only use one and not both. allow_blank should be preferred for textual choices, and allow_null should be preferred for numeric or other non-textual choices. MultipleChoiceField A field that can accept a set of zero, one or many values, chosen from a limited set of choices. Takes a single mandatory argument. to_internal_value returns a set containing the selected values. Signature: MultipleChoiceField(choices)
choices - A list of valid values, or a list of (key, display_name) tuples.
allow_blank - If set to True then the empty string should be considered a valid value. If set to False then the empty string is considered invalid and will raise a validation error. Defaults to False.
html_cutoff - If set this will be the maximum number of choices that will be displayed by a HTML select drop down. Can be used to ensure that automatically generated ChoiceFields with very large possible selections do not prevent a template from rendering. Defaults to None.
html_cutoff_text - If set this will display a textual indicator if the maximum number of items have been cutoff in an HTML select drop down. Defaults to "More than {count} items…"
As with ChoiceField, both the allow_blank and allow_null options are valid, although it is highly recommended that you only use one and not both. allow_blank should be preferred for textual choices, and allow_null should be preferred for numeric or other non-textual choices. File upload fields Parsers and file uploads. The FileField and ImageField classes are only suitable for use with MultiPartParser or FileUploadParser. Most parsers, such as e.g. JSON don't support file uploads. Django's regular FILE_UPLOAD_HANDLERS are used for handling uploaded files. FileField A file representation. Performs Django's standard FileField validation. Corresponds to django.forms.fields.FileField. Signature: FileField(max_length=None, allow_empty_file=False, use_url=UPLOADED_FILES_USE_URL)
max_length - Designates the maximum length for the file name.
allow_empty_file - Designates if empty files are allowed.
use_url - If set to True then URL string values will be used for the output representation. If set to False then filename string values will be used for the output representation. Defaults to the value of the UPLOADED_FILES_USE_URL settings key, which is True unless set otherwise. ImageField An image representation. Validates the uploaded file content as matching a known image format. Corresponds to django.forms.fields.ImageField. Signature: ImageField(max_length=None, allow_empty_file=False, use_url=UPLOADED_FILES_USE_URL)
max_length - Designates the maximum length for the file name.
allow_empty_file - Designates if empty files are allowed.
use_url - If set to True then URL string values will be used for the output representation. If set to False then filename string values will be used for the output representation. Defaults to the value of the UPLOADED_FILES_USE_URL settings key, which is True unless set otherwise. Requires either the Pillow package or PIL package. The Pillow package is recommended, as PIL is no longer actively maintained. Composite fields ListField A field class that validates a list of objects. Signature: ListField(child=<A_FIELD_INSTANCE>, allow_empty=True, min_length=None, max_length=None)
child - A field instance that should be used for validating the objects in the list. If this argument is not provided then objects in the list will not be validated.
allow_empty - Designates if empty lists are allowed.
min_length - Validates that the list contains no fewer than this number of elements.
max_length - Validates that the list contains no more than this number of elements. For example, to validate a list of integers you might use something like the following: scores = serializers.ListField(
child=serializers.IntegerField(min_value=0, max_value=100)
)
The ListField class also supports a declarative style that allows you to write reusable list field classes. class StringListField(serializers.ListField):
child = serializers.CharField()
We can now reuse our custom StringListField class throughout our application, without having to provide a child argument to it. DictField A field class that validates a dictionary of objects. The keys in DictField are always assumed to be string values. Signature: DictField(child=<A_FIELD_INSTANCE>, allow_empty=True)
child - A field instance that should be used for validating the values in the dictionary. If this argument is not provided then values in the mapping will not be validated.
allow_empty - Designates if empty dictionaries are allowed. For example, to create a field that validates a mapping of strings to strings, you would write something like this: document = DictField(child=CharField())
You can also use the declarative style, as with ListField. For example: class DocumentField(DictField):
child = CharField()
HStoreField A preconfigured DictField that is compatible with Django's postgres HStoreField. Signature: HStoreField(child=<A_FIELD_INSTANCE>, allow_empty=True)
child - A field instance that is used for validating the values in the dictionary. The default child field accepts both empty strings and null values.
allow_empty - Designates if empty dictionaries are allowed. Note that the child field must be an instance of CharField, as the hstore extension stores values as strings. JSONField A field class that validates that the incoming data structure consists of valid JSON primitives. In its alternate binary mode, it will represent and validate JSON-encoded binary strings. Signature: JSONField(binary, encoder)
binary - If set to True then the field will output and validate a JSON encoded string, rather than a primitive data structure. Defaults to False.
encoder - Use this JSON encoder to serialize input object. Defaults to None. Miscellaneous fields ReadOnlyField A field class that simply returns the value of the field without modification. This field is used by default with ModelSerializer when including field names that relate to an attribute rather than a model field. Signature: ReadOnlyField() For example, if has_expired was a property on the Account model, then the following serializer would automatically generate it as a ReadOnlyField: class AccountSerializer(serializers.ModelSerializer):
class Meta:
model = Account
fields = ['id', 'account_name', 'has_expired']
HiddenField A field class that does not take a value based on user input, but instead takes its value from a default value or callable. Signature: HiddenField() For example, to include a field that always provides the current time as part of the serializer validated data, you would use the following: modified = serializers.HiddenField(default=timezone.now)
The HiddenField class is usually only needed if you have some validation that needs to run based on some pre-provided field values, but you do not want to expose all of those fields to the end user. For further examples on HiddenField see the validators documentation. ModelField A generic field that can be tied to any arbitrary model field. The ModelField class delegates the task of serialization/deserialization to its associated model field. This field can be used to create serializer fields for custom model fields, without having to create a new custom serializer field. This field is used by ModelSerializer to correspond to custom model field classes. Signature: ModelField(model_field=<Django ModelField instance>) The ModelField class is generally intended for internal use, but can be used by your API if needed. In order to properly instantiate a ModelField, it must be passed a field that is attached to an instantiated model. For example: ModelField(model_field=MyModel()._meta.get_field('custom_field')) SerializerMethodField This is a read-only field. It gets its value by calling a method on the serializer class it is attached to. It can be used to add any sort of data to the serialized representation of your object. Signature: SerializerMethodField(method_name=None)
method_name - The name of the method on the serializer to be called. If not included this defaults to get_<field_name>. The serializer method referred to by the method_name argument should accept a single argument (in addition to self), which is the object being serialized. It should return whatever you want to be included in the serialized representation of the object. For example: from django.contrib.auth.models import User
from django.utils.timezone import now
from rest_framework import serializers
class UserSerializer(serializers.ModelSerializer):
days_since_joined = serializers.SerializerMethodField()
class Meta:
model = User
fields = '__all__'
def get_days_since_joined(self, obj):
return (now() - obj.date_joined).days
Custom fields If you want to create a custom field, you'll need to subclass Field and then override either one or both of the .to_representation() and .to_internal_value() methods. These two methods are used to convert between the initial datatype, and a primitive, serializable datatype. Primitive datatypes will typically be any of a number, string, boolean, date/time/datetime or None. They may also be any list or dictionary like object that only contains other primitive objects. Other types might be supported, depending on the renderer that you are using. The .to_representation() method is called to convert the initial datatype into a primitive, serializable datatype. The .to_internal_value() method is called to restore a primitive datatype into its internal python representation. This method should raise a serializers.ValidationError if the data is invalid. Examples A Basic Custom Field Let's look at an example of serializing a class that represents an RGB color value: class Color:
"""
A color represented in the RGB colorspace.
"""
def __init__(self, red, green, blue):
assert(red >= 0 and green >= 0 and blue >= 0)
assert(red < 256 and green < 256 and blue < 256)
self.red, self.green, self.blue = red, green, blue
class ColorField(serializers.Field):
"""
Color objects are serialized into 'rgb(#, #, #)' notation.
"""
def to_representation(self, value):
return "rgb(%d, %d, %d)" % (value.red, value.green, value.blue)
def to_internal_value(self, data):
data = data.strip('rgb(').rstrip(')')
red, green, blue = [int(col) for col in data.split(',')]
return Color(red, green, blue)
By default field values are treated as mapping to an attribute on the object. If you need to customize how the field value is accessed and set you need to override .get_attribute() and/or .get_value(). As an example, let's create a field that can be used to represent the class name of the object being serialized: class ClassNameField(serializers.Field):
def get_attribute(self, instance):
# We pass the object instance onto `to_representation`,
# not just the field attribute.
return instance
def to_representation(self, value):
"""
Serialize the value's class name.
"""
return value.__class__.__name__
Raising validation errors Our ColorField class above currently does not perform any data validation. To indicate invalid data, we should raise a serializers.ValidationError, like so: def to_internal_value(self, data):
if not isinstance(data, str):
msg = 'Incorrect type. Expected a string, but got %s'
raise ValidationError(msg % type(data).__name__)
if not re.match(r'^rgb\([0-9]+,[0-9]+,[0-9]+\)$', data):
raise ValidationError('Incorrect format. Expected `rgb(#,#,#)`.')
data = data.strip('rgb(').rstrip(')')
red, green, blue = [int(col) for col in data.split(',')]
if any([col > 255 or col < 0 for col in (red, green, blue)]):
raise ValidationError('Value out of range. Must be between 0 and 255.')
return Color(red, green, blue)
The .fail() method is a shortcut for raising ValidationError that takes a message string from the error_messages dictionary. For example: default_error_messages = {
'incorrect_type': 'Incorrect type. Expected a string, but got {input_type}',
'incorrect_format': 'Incorrect format. Expected `rgb(#,#,#)`.',
'out_of_range': 'Value out of range. Must be between 0 and 255.'
}
def to_internal_value(self, data):
if not isinstance(data, str):
self.fail('incorrect_type', input_type=type(data).__name__)
if not re.match(r'^rgb\([0-9]+,[0-9]+,[0-9]+\)$', data):
self.fail('incorrect_format')
data = data.strip('rgb(').rstrip(')')
red, green, blue = [int(col) for col in data.split(',')]
if any([col > 255 or col < 0 for col in (red, green, blue)]):
self.fail('out_of_range')
return Color(red, green, blue)
This style keeps your error messages cleaner and more separated from your code, and should be preferred. Using source='*' Here we'll take an example of a flat DataPoint model with x_coordinate and y_coordinate attributes. class DataPoint(models.Model):
label = models.CharField(max_length=50)
x_coordinate = models.SmallIntegerField()
y_coordinate = models.SmallIntegerField()
Using a custom field and source='*' we can provide a nested representation of the coordinate pair: class CoordinateField(serializers.Field):
def to_representation(self, value):
ret = {
"x": value.x_coordinate,
"y": value.y_coordinate
}
return ret
def to_internal_value(self, data):
ret = {
"x_coordinate": data["x"],
"y_coordinate": data["y"],
}
return ret
class DataPointSerializer(serializers.ModelSerializer):
coordinates = CoordinateField(source='*')
class Meta:
model = DataPoint
fields = ['label', 'coordinates']
Note that this example doesn't handle validation. Partly for that reason, in a real project, the coordinate nesting might be better handled with a nested serializer using source='*', with two IntegerField instances, each with their own source pointing to the relevant field. The key points from the example, though, are: to_representation is passed the entire DataPoint object and must map from that to the desired output. >>> instance = DataPoint(label='Example', x_coordinate=1, y_coordinate=2)
>>> out_serializer = DataPointSerializer(instance)
>>> out_serializer.data
ReturnDict([('label', 'Example'), ('coordinates', {'x': 1, 'y': 2})])
Unless our field is to be read-only, to_internal_value must map back to a dict suitable for updating our target object. With source='*', the return from to_internal_value will update the root validated data dictionary, rather than a single key. >>> data = {
... "label": "Second Example",
... "coordinates": {
... "x": 3,
... "y": 4,
... }
... }
>>> in_serializer = DataPointSerializer(data=data)
>>> in_serializer.is_valid()
True
>>> in_serializer.validated_data
OrderedDict([('label', 'Second Example'),
('y_coordinate', 4),
('x_coordinate', 3)])
For completeness lets do the same thing again but with the nested serializer approach suggested above: class NestedCoordinateSerializer(serializers.Serializer):
x = serializers.IntegerField(source='x_coordinate')
y = serializers.IntegerField(source='y_coordinate')
class DataPointSerializer(serializers.ModelSerializer):
coordinates = NestedCoordinateSerializer(source='*')
class Meta:
model = DataPoint
fields = ['label', 'coordinates']
Here the mapping between the target and source attribute pairs (x and x_coordinate, y and y_coordinate) is handled in the IntegerField declarations. It's our NestedCoordinateSerializer that takes source='*'. Our new DataPointSerializer exhibits the same behaviour as the custom field approach. Serializing: >>> out_serializer = DataPointSerializer(instance)
>>> out_serializer.data
ReturnDict([('label', 'testing'),
('coordinates', OrderedDict([('x', 1), ('y', 2)]))])
Deserializing: >>> in_serializer = DataPointSerializer(data=data)
>>> in_serializer.is_valid()
True
>>> in_serializer.validated_data
OrderedDict([('label', 'still testing'),
('x_coordinate', 3),
('y_coordinate', 4)])
But we also get the built-in validation for free: >>> invalid_data = {
... "label": "still testing",
... "coordinates": {
... "x": 'a',
... "y": 'b',
... }
... }
>>> invalid_serializer = DataPointSerializer(data=invalid_data)
>>> invalid_serializer.is_valid()
False
>>> invalid_serializer.errors
ReturnDict([('coordinates',
{'x': ['A valid integer is required.'],
'y': ['A valid integer is required.']})])
For this reason, the nested serializer approach would be the first to try. You would use the custom field approach when the nested serializer becomes infeasible or overly complex. Third party packages The following third party packages are also available. DRF Compound Fields The drf-compound-fields package provides "compound" serializer fields, such as lists of simple values, which can be described by other fields rather than serializers with the many=True option. Also provided are fields for typed dictionaries and values that can be either a specific type or a list of items of that type. DRF Extra Fields The drf-extra-fields package provides extra serializer fields for REST framework, including Base64ImageField and PointField classes. djangorestframework-recursive the djangorestframework-recursive package provides a RecursiveField for serializing and deserializing recursive structures django-rest-framework-gis The django-rest-framework-gis package provides geographic addons for django rest framework like a GeometryField field and a GeoJSON serializer. django-rest-framework-hstore The django-rest-framework-hstore package provides an HStoreField to support django-hstore DictionaryField model field. fields.py | |
doc_27145 | sys.__stdout__
sys.__stderr__
These objects contain the original values of stdin, stderr and stdout at the start of the program. They are used during finalization, and could be useful to print to the actual standard stream no matter if the sys.std* object has been redirected. It can also be used to restore the actual files to known working file objects in case they have been overwritten with a broken object. However, the preferred way to do this is to explicitly save the previous stream before replacing it, and restore the saved object. Note Under some conditions stdin, stdout and stderr as well as the original values __stdin__, __stdout__ and __stderr__ can be None. It is usually the case for Windows GUI apps that aren’t connected to a console and Python apps started with pythonw. | |
doc_27146 | This factory function creates a function that can be used as a callable for copytree()’s ignore argument, ignoring files and directories that match one of the glob-style patterns provided. See the example below. | |
doc_27147 |
Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters
degnon-negative int
The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-negative integer. Returns
new_seriesseries
New instance of series with reduced degree. | |
doc_27148 |
Sort Series by index labels. Returns a new Series sorted by label if inplace argument is False, otherwise updates the original series and returns None. Parameters
axis:int, default 0
Axis to direct sorting. This can only be 0 for Series.
level:int, optional
If not None, sort on values in specified index level(s).
ascending:bool or list-like of bools, default True
Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
inplace:bool, default False
If True, perform operation in-place.
kind:{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’
Choice of sorting algorithm. See also numpy.sort() for more information. ‘mergesort’ and ‘stable’ are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
na_position:{‘first’, ‘last’}, default ‘last’
If ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end. Not implemented for MultiIndex.
sort_remaining:bool, default True
If True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level.
ignore_index:bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1. New in version 1.0.0.
key:callable, optional
If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. New in version 1.1.0. Returns
Series or None
The original Series sorted by the labels or None if inplace=True. See also DataFrame.sort_index
Sort DataFrame by the index. DataFrame.sort_values
Sort DataFrame by the value. Series.sort_values
Sort Series by the value. Examples
>>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
>>> s.sort_index()
1 c
2 b
3 a
4 d
dtype: object
Sort Descending
>>> s.sort_index(ascending=False)
4 d
3 a
2 b
1 c
dtype: object
Sort Inplace
>>> s.sort_index(inplace=True)
>>> s
1 c
2 b
3 a
4 d
dtype: object
By default NaNs are put at the end, but use na_position to place them at the beginning
>>> s = pd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
>>> s.sort_index(na_position='first')
NaN d
1.0 c
2.0 b
3.0 a
dtype: object
Specify index level to sort
>>> arrays = [np.array(['qux', 'qux', 'foo', 'foo',
... 'baz', 'baz', 'bar', 'bar']),
... np.array(['two', 'one', 'two', 'one',
... 'two', 'one', 'two', 'one'])]
>>> s = pd.Series([1, 2, 3, 4, 5, 6, 7, 8], index=arrays)
>>> s.sort_index(level=1)
bar one 8
baz one 6
foo one 4
qux one 2
bar two 7
baz two 5
foo two 3
qux two 1
dtype: int64
Does not sort by remaining levels when sorting by levels
>>> s.sort_index(level=1, sort_remaining=False)
qux one 2
foo one 4
baz one 6
bar one 8
qux two 1
foo two 3
baz two 5
bar two 7
dtype: int64
Apply a key function before sorting
>>> s = pd.Series([1, 2, 3, 4], index=['A', 'b', 'C', 'd'])
>>> s.sort_index(key=lambda x : x.str.lower())
A 1
b 2
C 3
d 4
dtype: int64 | |
doc_27149 | Return the local date and time corresponding to the POSIX timestamp, such as is returned by time.time(). If optional argument tz is None or not specified, the timestamp is converted to the platform’s local date and time, and the returned datetime object is naive. If tz is not None, it must be an instance of a tzinfo subclass, and the timestamp is converted to tz’s time zone. fromtimestamp() may raise OverflowError, if the timestamp is out of the range of values supported by the platform C localtime() or gmtime() functions, and OSError on localtime() or gmtime() failure. It’s common for this to be restricted to years in 1970 through 2038. Note that on non-POSIX systems that include leap seconds in their notion of a timestamp, leap seconds are ignored by fromtimestamp(), and then it’s possible to have two timestamps differing by a second that yield identical datetime objects. This method is preferred over utcfromtimestamp(). Changed in version 3.3: Raise OverflowError instead of ValueError if the timestamp is out of the range of values supported by the platform C localtime() or gmtime() functions. Raise OSError instead of ValueError on localtime() or gmtime() failure. Changed in version 3.6: fromtimestamp() may return instances with fold set to 1. | |
doc_27150 | Extends this array with data from the given unicode string. The array must be a type 'u' array; otherwise a ValueError is raised. Use array.frombytes(unicodestring.encode(enc)) to append Unicode data to an array of some other type. | |
doc_27151 | See Migration guide for more details. tf.compat.v1.raw_ops.SeluGrad
tf.raw_ops.SeluGrad(
gradients, outputs, name=None
)
Args
gradients A Tensor. Must be one of the following types: half, bfloat16, float32, float64. The backpropagated gradients to the corresponding Selu operation.
outputs A Tensor. Must have the same type as gradients. The outputs of the corresponding Selu operation.
name A name for the operation (optional).
Returns A Tensor. Has the same type as gradients. | |
doc_27152 | tkinter.WRITABLE
tkinter.EXCEPTION
Constants used in the mask arguments. | |
doc_27153 | '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_27154 | Should return True if adding an inline object is permitted, False otherwise. obj is the parent object being edited or None when adding a new parent. | |
doc_27155 |
Return x times the hanning window of len(x). See also window_none
Another window algorithm. | |
doc_27156 | See Migration guide for more details. tf.compat.v1.raw_ops.TensorListElementShape
tf.raw_ops.TensorListElementShape(
input_handle, shape_type, name=None
)
input_handle: the list element_shape: the shape of elements of the list
Args
input_handle A Tensor of type variant.
shape_type A tf.DType from: tf.int32, tf.int64.
name A name for the operation (optional).
Returns A Tensor of type shape_type. | |
doc_27157 | Get data and node arrays. Returns
arrays: tuple of array
Arrays for storing tree data, index, node data and node bounds. | |
doc_27158 | tf.optimizers.deserialize Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.optimizers.deserialize
tf.keras.optimizers.deserialize(
config, custom_objects=None
)
Arguments
config Optimizer configuration dictionary.
custom_objects Optional dictionary mapping names (strings) to custom objects (classes and functions) to be considered during deserialization.
Returns A Keras Optimizer instance. | |
doc_27159 | Move turtle to the origin – coordinates (0,0) – and set its heading to its start-orientation (which depends on the mode, see mode()). >>> turtle.heading()
90.0
>>> turtle.position()
(0.00,-10.00)
>>> turtle.home()
>>> turtle.position()
(0.00,0.00)
>>> turtle.heading()
0.0 | |
doc_27160 | class sklearn.decomposition.TruncatedSVD(n_components=2, *, algorithm='randomized', n_iter=5, random_state=None, tol=0.0) [source]
Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimator does not center the data before computing the singular value decomposition. This means it can work with sparse matrices efficiently. In particular, truncated SVD works on term count/tf-idf matrices as returned by the vectorizers in sklearn.feature_extraction.text. In that context, it is known as latent semantic analysis (LSA). This estimator supports two algorithms: a fast randomized SVD solver, and a “naive” algorithm that uses ARPACK as an eigensolver on X * X.T or X.T * X, whichever is more efficient. Read more in the User Guide. Parameters
n_componentsint, default=2
Desired dimensionality of output data. Must be strictly less than the number of features. The default value is useful for visualisation. For LSA, a value of 100 is recommended.
algorithm{‘arpack’, ‘randomized’}, default=’randomized’
SVD solver to use. Either “arpack” for the ARPACK wrapper in SciPy (scipy.sparse.linalg.svds), or “randomized” for the randomized algorithm due to Halko (2009).
n_iterint, default=5
Number of iterations for randomized SVD solver. Not used by ARPACK. The default is larger than the default in randomized_svd to handle sparse matrices that may have large slowly decaying spectrum.
random_stateint, RandomState instance or None, default=None
Used during randomized svd. Pass an int for reproducible results across multiple function calls. See Glossary.
tolfloat, default=0.
Tolerance for ARPACK. 0 means machine precision. Ignored by randomized SVD solver. Attributes
components_ndarray of shape (n_components, n_features)
explained_variance_ndarray of shape (n_components,)
The variance of the training samples transformed by a projection to each component.
explained_variance_ratio_ndarray of shape (n_components,)
Percentage of variance explained by each of the selected components.
singular_values_ndarray od shape (n_components,)
The singular values corresponding to each of the selected components. The singular values are equal to the 2-norms of the n_components variables in the lower-dimensional space. See also
PCA
Notes SVD suffers from a problem called “sign indeterminacy”, which means the sign of the components_ and the output from transform depend on the algorithm and random state. To work around this, fit instances of this class to data once, then keep the instance around to do transformations. References Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf Examples >>> from sklearn.decomposition import TruncatedSVD
>>> from scipy.sparse import random as sparse_random
>>> X = sparse_random(100, 100, density=0.01, format='csr',
... random_state=42)
>>> svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
>>> svd.fit(X)
TruncatedSVD(n_components=5, n_iter=7, random_state=42)
>>> print(svd.explained_variance_ratio_)
[0.0646... 0.0633... 0.0639... 0.0535... 0.0406...]
>>> print(svd.explained_variance_ratio_.sum())
0.286...
>>> print(svd.singular_values_)
[1.553... 1.512... 1.510... 1.370... 1.199...]
Methods
fit(X[, y]) Fit model on training data X.
fit_transform(X[, y]) Fit model to X and perform dimensionality reduction on X.
get_params([deep]) Get parameters for this estimator.
inverse_transform(X) Transform X back to its original space.
set_params(**params) Set the parameters of this estimator.
transform(X) Perform dimensionality reduction on X.
fit(X, y=None) [source]
Fit model on training data X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yIgnored
Returns
selfobject
Returns the transformer object.
fit_transform(X, y=None) [source]
Fit model to X and perform dimensionality reduction on X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yIgnored
Returns
X_newndarray of shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
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.
inverse_transform(X) [source]
Transform X back to its original space. Returns an array X_original whose transform would be X. Parameters
Xarray-like of shape (n_samples, n_components)
New data. Returns
X_originalndarray of shape (n_samples, n_features)
Note that this is always a dense array.
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.
transform(X) [source]
Perform dimensionality reduction on X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data. Returns
X_newndarray of shape (n_samples, n_components)
Reduced version of X. This will always be a dense array.
Examples using sklearn.decomposition.TruncatedSVD
Hashing feature transformation using Totally Random Trees
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
Column Transformer with Heterogeneous Data Sources
Clustering text documents using k-means | |
doc_27161 |
Test whether the mouse event occurred in the collection. Returns bool, dict(ind=itemlist), where every item in itemlist contains the event. | |
doc_27162 | Remove the first occurrence of value. If not found, raises a ValueError. | |
doc_27163 | See Migration guide for more details. tf.compat.v1.raw_ops.TensorArrayGradV3
tf.raw_ops.TensorArrayGradV3(
handle, flow_in, source, name=None
)
If the given TensorArray gradient already exists, returns a reference to it. Locks the size of the original TensorArray by disabling its dynamic size flag. A note about the input flow_in: The handle flow_in forces the execution of the gradient lookup to occur only after certain other operations have occurred. For example, when the forward TensorArray is dynamically sized, writes to this TensorArray may resize the object. The gradient TensorArray is statically sized based on the size of the forward TensorArray when this operation executes. Furthermore, the size of the forward TensorArray is frozen by this call. As a result, the flow is used to ensure that the call to generate the gradient TensorArray only happens after all writes are executed. In the case of dynamically sized TensorArrays, gradient computation should only be performed on read operations that have themselves been chained via flow to occur only after all writes have executed. That way the final size of the forward TensorArray is known when this operation is called. A note about the source attribute: TensorArray gradient calls use an accumulator TensorArray object. If multiple gradients are calculated and run in the same session, the multiple gradient nodes may accidentally flow through the same accumulator TensorArray. This double counts and generally breaks the TensorArray gradient flow. The solution is to identify which gradient call this particular TensorArray gradient is being called in. This is performed by identifying a unique string (e.g. "gradients", "gradients_1", ...) from the input gradient Tensor's name. This string is used as a suffix when creating the TensorArray gradient object here (the attribute source). The attribute source is added as a suffix to the forward TensorArray's name when performing the creation / lookup, so that each separate gradient calculation gets its own TensorArray accumulator.
Args
handle A Tensor of type resource. The handle to the forward TensorArray.
flow_in A Tensor of type float32. A float scalar that enforces proper chaining of operations.
source A string. The gradient source string, used to decide which gradient TensorArray to return.
name A name for the operation (optional).
Returns A tuple of Tensor objects (grad_handle, flow_out). grad_handle A Tensor of type resource.
flow_out A Tensor of type float32. | |
doc_27164 |
Differentiate. Return a series instance of that is the derivative of the current series. Parameters
mnon-negative int
Find the derivative of order m. Returns
new_seriesseries
A new series representing the derivative. The domain is the same as the domain of the differentiated series. | |
doc_27165 |
Set the arrow style. Old attributes are forgotten. Without arguments (or with arrowstyle=None) returns available box styles as a list of strings. Parameters
arrowstyleNone or ArrowStyle or str, default: None
Can be a string with arrowstyle name with optional comma-separated attributes, e.g.: set_arrowstyle("Fancy,head_length=0.2")
Alternatively attributes can be provided as keywords, e.g.: set_arrowstyle("fancy", head_length=0.2) | |
doc_27166 | The record is formatted, and then sent to the syslog server. If exception information is present, it is not sent to the server. Changed in version 3.2.1: (See: bpo-12168.) In earlier versions, the message sent to the syslog daemons was always terminated with a NUL byte, because early versions of these daemons expected a NUL terminated message - even though it’s not in the relevant specification (RFC 5424). More recent versions of these daemons don’t expect the NUL byte but strip it off if it’s there, and even more recent daemons (which adhere more closely to RFC 5424) pass the NUL byte on as part of the message. To enable easier handling of syslog messages in the face of all these differing daemon behaviours, the appending of the NUL byte has been made configurable, through the use of a class-level attribute, append_nul. This defaults to True (preserving the existing behaviour) but can be set to False on a SysLogHandler instance in order for that instance to not append the NUL terminator. Changed in version 3.3: (See: bpo-12419.) In earlier versions, there was no facility for an “ident” or “tag” prefix to identify the source of the message. This can now be specified using a class-level attribute, defaulting to "" to preserve existing behaviour, but which can be overridden on a SysLogHandler instance in order for that instance to prepend the ident to every message handled. Note that the provided ident must be text, not bytes, and is prepended to the message exactly as is. | |
doc_27167 | class enum.Enum
Base class for creating enumerated constants. See section Functional API for an alternate construction syntax.
class enum.IntEnum
Base class for creating enumerated constants that are also subclasses of int.
class enum.IntFlag
Base class for creating enumerated constants that can be combined using the bitwise operators without losing their IntFlag membership. IntFlag members are also subclasses of int.
class enum.Flag
Base class for creating enumerated constants that can be combined using the bitwise operations without losing their Flag membership.
enum.unique()
Enum class decorator that ensures only one name is bound to any one value.
class enum.auto
Instances are replaced with an appropriate value for Enum members. By default, the initial value starts at 1.
New in version 3.6: Flag, IntFlag, auto Creating an Enum Enumerations are created using the class syntax, which makes them easy to read and write. An alternative creation method is described in Functional API. To define an enumeration, subclass Enum as follows: >>> from enum import Enum
>>> class Color(Enum):
... RED = 1
... GREEN = 2
... BLUE = 3
...
Note Enum member values Member values can be anything: int, str, etc.. If the exact value is unimportant you may use auto instances and an appropriate value will be chosen for you. Care must be taken if you mix auto with other values. Note Nomenclature The class Color is an enumeration (or enum) The attributes Color.RED, Color.GREEN, etc., are enumeration members (or enum members) and are functionally constants. The enum members have names and values (the name of Color.RED is RED, the value of Color.BLUE is 3, etc.) Note Even though we use the class syntax to create Enums, Enums are not normal Python classes. See How are Enums different? for more details. Enumeration members have human readable string representations: >>> print(Color.RED)
Color.RED
…while their repr has more information: >>> print(repr(Color.RED))
<Color.RED: 1>
The type of an enumeration member is the enumeration it belongs to: >>> type(Color.RED)
<enum 'Color'>
>>> isinstance(Color.GREEN, Color)
True
>>>
Enum members also have a property that contains just their item name: >>> print(Color.RED.name)
RED
Enumerations support iteration, in definition order: >>> class Shake(Enum):
... VANILLA = 7
... CHOCOLATE = 4
... COOKIES = 9
... MINT = 3
...
>>> for shake in Shake:
... print(shake)
...
Shake.VANILLA
Shake.CHOCOLATE
Shake.COOKIES
Shake.MINT
Enumeration members are hashable, so they can be used in dictionaries and sets: >>> apples = {}
>>> apples[Color.RED] = 'red delicious'
>>> apples[Color.GREEN] = 'granny smith'
>>> apples == {Color.RED: 'red delicious', Color.GREEN: 'granny smith'}
True
Programmatic access to enumeration members and their attributes Sometimes it’s useful to access members in enumerations programmatically (i.e. situations where Color.RED won’t do because the exact color is not known at program-writing time). Enum allows such access: >>> Color(1)
<Color.RED: 1>
>>> Color(3)
<Color.BLUE: 3>
If you want to access enum members by name, use item access: >>> Color['RED']
<Color.RED: 1>
>>> Color['GREEN']
<Color.GREEN: 2>
If you have an enum member and need its name or value: >>> member = Color.RED
>>> member.name
'RED'
>>> member.value
1
Duplicating enum members and values Having two enum members with the same name is invalid: >>> class Shape(Enum):
... SQUARE = 2
... SQUARE = 3
...
Traceback (most recent call last):
...
TypeError: Attempted to reuse key: 'SQUARE'
However, two enum members are allowed to have the same value. Given two members A and B with the same value (and A defined first), B is an alias to A. By-value lookup of the value of A and B will return A. By-name lookup of B will also return A: >>> class Shape(Enum):
... SQUARE = 2
... DIAMOND = 1
... CIRCLE = 3
... ALIAS_FOR_SQUARE = 2
...
>>> Shape.SQUARE
<Shape.SQUARE: 2>
>>> Shape.ALIAS_FOR_SQUARE
<Shape.SQUARE: 2>
>>> Shape(2)
<Shape.SQUARE: 2>
Note Attempting to create a member with the same name as an already defined attribute (another member, a method, etc.) or attempting to create an attribute with the same name as a member is not allowed. Ensuring unique enumeration values By default, enumerations allow multiple names as aliases for the same value. When this behavior isn’t desired, the following decorator can be used to ensure each value is used only once in the enumeration:
@enum.unique
A class decorator specifically for enumerations. It searches an enumeration’s __members__ gathering any aliases it finds; if any are found ValueError is raised with the details: >>> from enum import Enum, unique
>>> @unique
... class Mistake(Enum):
... ONE = 1
... TWO = 2
... THREE = 3
... FOUR = 3
...
Traceback (most recent call last):
...
ValueError: duplicate values found in <enum 'Mistake'>: FOUR -> THREE
Using automatic values If the exact value is unimportant you can use auto: >>> from enum import Enum, auto
>>> class Color(Enum):
... RED = auto()
... BLUE = auto()
... GREEN = auto()
...
>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]
The values are chosen by _generate_next_value_(), which can be overridden: >>> class AutoName(Enum):
... def _generate_next_value_(name, start, count, last_values):
... return name
...
>>> class Ordinal(AutoName):
... NORTH = auto()
... SOUTH = auto()
... EAST = auto()
... WEST = auto()
...
>>> list(Ordinal)
[<Ordinal.NORTH: 'NORTH'>, <Ordinal.SOUTH: 'SOUTH'>, <Ordinal.EAST: 'EAST'>, <Ordinal.WEST: 'WEST'>]
Note The goal of the default _generate_next_value_() method is to provide the next int in sequence with the last int provided, but the way it does this is an implementation detail and may change. Note The _generate_next_value_() method must be defined before any members. Iteration Iterating over the members of an enum does not provide the aliases: >>> list(Shape)
[<Shape.SQUARE: 2>, <Shape.DIAMOND: 1>, <Shape.CIRCLE: 3>]
The special attribute __members__ is a read-only ordered mapping of names to members. It includes all names defined in the enumeration, including the aliases: >>> for name, member in Shape.__members__.items():
... name, member
...
('SQUARE', <Shape.SQUARE: 2>)
('DIAMOND', <Shape.DIAMOND: 1>)
('CIRCLE', <Shape.CIRCLE: 3>)
('ALIAS_FOR_SQUARE', <Shape.SQUARE: 2>)
The __members__ attribute can be used for detailed programmatic access to the enumeration members. For example, finding all the aliases: >>> [name for name, member in Shape.__members__.items() if member.name != name]
['ALIAS_FOR_SQUARE']
Comparisons Enumeration members are compared by identity: >>> Color.RED is Color.RED
True
>>> Color.RED is Color.BLUE
False
>>> Color.RED is not Color.BLUE
True
Ordered comparisons between enumeration values are not supported. Enum members are not integers (but see IntEnum below): >>> Color.RED < Color.BLUE
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'Color' and 'Color'
Equality comparisons are defined though: >>> Color.BLUE == Color.RED
False
>>> Color.BLUE != Color.RED
True
>>> Color.BLUE == Color.BLUE
True
Comparisons against non-enumeration values will always compare not equal (again, IntEnum was explicitly designed to behave differently, see below): >>> Color.BLUE == 2
False
Allowed members and attributes of enumerations The examples above use integers for enumeration values. Using integers is short and handy (and provided by default by the Functional API), but not strictly enforced. In the vast majority of use-cases, one doesn’t care what the actual value of an enumeration is. But if the value is important, enumerations can have arbitrary values. Enumerations are Python classes, and can have methods and special methods as usual. If we have this enumeration: >>> class Mood(Enum):
... FUNKY = 1
... HAPPY = 3
...
... def describe(self):
... # self is the member here
... return self.name, self.value
...
... def __str__(self):
... return 'my custom str! {0}'.format(self.value)
...
... @classmethod
... def favorite_mood(cls):
... # cls here is the enumeration
... return cls.HAPPY
...
Then: >>> Mood.favorite_mood()
<Mood.HAPPY: 3>
>>> Mood.HAPPY.describe()
('HAPPY', 3)
>>> str(Mood.FUNKY)
'my custom str! 1'
The rules for what is allowed are as follows: names that start and end with a single underscore are reserved by enum and cannot be used; all other attributes defined within an enumeration will become members of this enumeration, with the exception of special methods (__str__(), __add__(), etc.), descriptors (methods are also descriptors), and variable names listed in _ignore_. Note: if your enumeration defines __new__() and/or __init__() then any value(s) given to the enum member will be passed into those methods. See Planet for an example. Restricted Enum subclassing A new Enum class must have one base Enum class, up to one concrete data type, and as many object-based mixin classes as needed. The order of these base classes is: class EnumName([mix-in, ...,] [data-type,] base-enum):
pass
Also, subclassing an enumeration is allowed only if the enumeration does not define any members. So this is forbidden: >>> class MoreColor(Color):
... PINK = 17
...
Traceback (most recent call last):
...
TypeError: Cannot extend enumerations
But this is allowed: >>> class Foo(Enum):
... def some_behavior(self):
... pass
...
>>> class Bar(Foo):
... HAPPY = 1
... SAD = 2
...
Allowing subclassing of enums that define members would lead to a violation of some important invariants of types and instances. On the other hand, it makes sense to allow sharing some common behavior between a group of enumerations. (See OrderedEnum for an example.) Pickling Enumerations can be pickled and unpickled: >>> from test.test_enum import Fruit
>>> from pickle import dumps, loads
>>> Fruit.TOMATO is loads(dumps(Fruit.TOMATO))
True
The usual restrictions for pickling apply: picklable enums must be defined in the top level of a module, since unpickling requires them to be importable from that module. Note With pickle protocol version 4 it is possible to easily pickle enums nested in other classes. It is possible to modify how Enum members are pickled/unpickled by defining __reduce_ex__() in the enumeration class. Functional API The Enum class is callable, providing the following functional API: >>> Animal = Enum('Animal', 'ANT BEE CAT DOG')
>>> Animal
<enum 'Animal'>
>>> Animal.ANT
<Animal.ANT: 1>
>>> Animal.ANT.value
1
>>> list(Animal)
[<Animal.ANT: 1>, <Animal.BEE: 2>, <Animal.CAT: 3>, <Animal.DOG: 4>]
The semantics of this API resemble namedtuple. The first argument of the call to Enum is the name of the enumeration. The second argument is the source of enumeration member names. It can be a whitespace-separated string of names, a sequence of names, a sequence of 2-tuples with key/value pairs, or a mapping (e.g. dictionary) of names to values. The last two options enable assigning arbitrary values to enumerations; the others auto-assign increasing integers starting with 1 (use the start parameter to specify a different starting value). A new class derived from Enum is returned. In other words, the above assignment to Animal is equivalent to: >>> class Animal(Enum):
... ANT = 1
... BEE = 2
... CAT = 3
... DOG = 4
...
The reason for defaulting to 1 as the starting number and not 0 is that 0 is False in a boolean sense, but enum members all evaluate to True. Pickling enums created with the functional API can be tricky as frame stack implementation details are used to try and figure out which module the enumeration is being created in (e.g. it will fail if you use a utility function in separate module, and also may not work on IronPython or Jython). The solution is to specify the module name explicitly as follows: >>> Animal = Enum('Animal', 'ANT BEE CAT DOG', module=__name__)
Warning If module is not supplied, and Enum cannot determine what it is, the new Enum members will not be unpicklable; to keep errors closer to the source, pickling will be disabled. The new pickle protocol 4 also, in some circumstances, relies on __qualname__ being set to the location where pickle will be able to find the class. For example, if the class was made available in class SomeData in the global scope: >>> Animal = Enum('Animal', 'ANT BEE CAT DOG', qualname='SomeData.Animal')
The complete signature is: Enum(value='NewEnumName', names=<...>, *, module='...', qualname='...', type=<mixed-in class>, start=1)
value
What the new Enum class will record as its name. names
The Enum members. This can be a whitespace or comma separated string (values will start at 1 unless otherwise specified): 'RED GREEN BLUE' | 'RED,GREEN,BLUE' | 'RED, GREEN, BLUE'
or an iterator of names: ['RED', 'GREEN', 'BLUE']
or an iterator of (name, value) pairs: [('CYAN', 4), ('MAGENTA', 5), ('YELLOW', 6)]
or a mapping: {'CHARTREUSE': 7, 'SEA_GREEN': 11, 'ROSEMARY': 42}
module
name of module where new Enum class can be found. qualname
where in module new Enum class can be found. type
type to mix in to new Enum class. start
number to start counting at if only names are passed in. Changed in version 3.5: The start parameter was added. Derived Enumerations IntEnum The first variation of Enum that is provided is also a subclass of int. Members of an IntEnum can be compared to integers; by extension, integer enumerations of different types can also be compared to each other: >>> from enum import IntEnum
>>> class Shape(IntEnum):
... CIRCLE = 1
... SQUARE = 2
...
>>> class Request(IntEnum):
... POST = 1
... GET = 2
...
>>> Shape == 1
False
>>> Shape.CIRCLE == 1
True
>>> Shape.CIRCLE == Request.POST
True
However, they still can’t be compared to standard Enum enumerations: >>> class Shape(IntEnum):
... CIRCLE = 1
... SQUARE = 2
...
>>> class Color(Enum):
... RED = 1
... GREEN = 2
...
>>> Shape.CIRCLE == Color.RED
False
IntEnum values behave like integers in other ways you’d expect: >>> int(Shape.CIRCLE)
1
>>> ['a', 'b', 'c'][Shape.CIRCLE]
'b'
>>> [i for i in range(Shape.SQUARE)]
[0, 1]
IntFlag The next variation of Enum provided, IntFlag, is also based on int. The difference being IntFlag members can be combined using the bitwise operators (&, |, ^, ~) and the result is still an IntFlag member. However, as the name implies, IntFlag members also subclass int and can be used wherever an int is used. Any operation on an IntFlag member besides the bit-wise operations will lose the IntFlag membership. New in version 3.6. Sample IntFlag class: >>> from enum import IntFlag
>>> class Perm(IntFlag):
... R = 4
... W = 2
... X = 1
...
>>> Perm.R | Perm.W
<Perm.R|W: 6>
>>> Perm.R + Perm.W
6
>>> RW = Perm.R | Perm.W
>>> Perm.R in RW
True
It is also possible to name the combinations: >>> class Perm(IntFlag):
... R = 4
... W = 2
... X = 1
... RWX = 7
>>> Perm.RWX
<Perm.RWX: 7>
>>> ~Perm.RWX
<Perm.-8: -8>
Another important difference between IntFlag and Enum is that if no flags are set (the value is 0), its boolean evaluation is False: >>> Perm.R & Perm.X
<Perm.0: 0>
>>> bool(Perm.R & Perm.X)
False
Because IntFlag members are also subclasses of int they can be combined with them: >>> Perm.X | 8
<Perm.8|X: 9>
Flag The last variation is Flag. Like IntFlag, Flag members can be combined using the bitwise operators (&, |, ^, ~). Unlike IntFlag, they cannot be combined with, nor compared against, any other Flag enumeration, nor int. While it is possible to specify the values directly it is recommended to use auto as the value and let Flag select an appropriate value. New in version 3.6. Like IntFlag, if a combination of Flag members results in no flags being set, the boolean evaluation is False: >>> from enum import Flag, auto
>>> class Color(Flag):
... RED = auto()
... BLUE = auto()
... GREEN = auto()
...
>>> Color.RED & Color.GREEN
<Color.0: 0>
>>> bool(Color.RED & Color.GREEN)
False
Individual flags should have values that are powers of two (1, 2, 4, 8, …), while combinations of flags won’t: >>> class Color(Flag):
... RED = auto()
... BLUE = auto()
... GREEN = auto()
... WHITE = RED | BLUE | GREEN
...
>>> Color.WHITE
<Color.WHITE: 7>
Giving a name to the “no flags set” condition does not change its boolean value: >>> class Color(Flag):
... BLACK = 0
... RED = auto()
... BLUE = auto()
... GREEN = auto()
...
>>> Color.BLACK
<Color.BLACK: 0>
>>> bool(Color.BLACK)
False
Note For the majority of new code, Enum and Flag are strongly recommended, since IntEnum and IntFlag break some semantic promises of an enumeration (by being comparable to integers, and thus by transitivity to other unrelated enumerations). IntEnum and IntFlag should be used only in cases where Enum and Flag will not do; for example, when integer constants are replaced with enumerations, or for interoperability with other systems. Others While IntEnum is part of the enum module, it would be very simple to implement independently: class IntEnum(int, Enum):
pass
This demonstrates how similar derived enumerations can be defined; for example a StrEnum that mixes in str instead of int. Some rules: When subclassing Enum, mix-in types must appear before Enum itself in the sequence of bases, as in the IntEnum example above. While Enum can have members of any type, once you mix in an additional type, all the members must have values of that type, e.g. int above. This restriction does not apply to mix-ins which only add methods and don’t specify another type. When another data type is mixed in, the value attribute is not the same as the enum member itself, although it is equivalent and will compare equal. %-style formatting: %s and %r call the Enum class’s __str__() and __repr__() respectively; other codes (such as %i or %h for IntEnum) treat the enum member as its mixed-in type.
Formatted string literals, str.format(), and format() will use the mixed-in type’s __format__() unless __str__() or __format__() is overridden in the subclass, in which case the overridden methods or Enum methods will be used. Use the !s and !r format codes to force usage of the Enum class’s __str__() and __repr__() methods. When to use __new__() vs. __init__() __new__() must be used whenever you want to customize the actual value of the Enum member. Any other modifications may go in either __new__() or __init__(), with __init__() being preferred. For example, if you want to pass several items to the constructor, but only want one of them to be the value: >>> class Coordinate(bytes, Enum):
... """
... Coordinate with binary codes that can be indexed by the int code.
... """
... def __new__(cls, value, label, unit):
... obj = bytes.__new__(cls, [value])
... obj._value_ = value
... obj.label = label
... obj.unit = unit
... return obj
... PX = (0, 'P.X', 'km')
... PY = (1, 'P.Y', 'km')
... VX = (2, 'V.X', 'km/s')
... VY = (3, 'V.Y', 'km/s')
...
>>> print(Coordinate['PY'])
Coordinate.PY
>>> print(Coordinate(3))
Coordinate.VY
Interesting examples While Enum, IntEnum, IntFlag, and Flag are expected to cover the majority of use-cases, they cannot cover them all. Here are recipes for some different types of enumerations that can be used directly, or as examples for creating one’s own. Omitting values In many use-cases one doesn’t care what the actual value of an enumeration is. There are several ways to define this type of simple enumeration: use instances of auto for the value use instances of object as the value use a descriptive string as the value use a tuple as the value and a custom __new__() to replace the tuple with an int value Using any of these methods signifies to the user that these values are not important, and also enables one to add, remove, or reorder members without having to renumber the remaining members. Whichever method you choose, you should provide a repr() that also hides the (unimportant) value: >>> class NoValue(Enum):
... def __repr__(self):
... return '<%s.%s>' % (self.__class__.__name__, self.name)
...
Using auto
Using auto would look like: >>> class Color(NoValue):
... RED = auto()
... BLUE = auto()
... GREEN = auto()
...
>>> Color.GREEN
<Color.GREEN>
Using object
Using object would look like: >>> class Color(NoValue):
... RED = object()
... GREEN = object()
... BLUE = object()
...
>>> Color.GREEN
<Color.GREEN>
Using a descriptive string Using a string as the value would look like: >>> class Color(NoValue):
... RED = 'stop'
... GREEN = 'go'
... BLUE = 'too fast!'
...
>>> Color.GREEN
<Color.GREEN>
>>> Color.GREEN.value
'go'
Using a custom __new__()
Using an auto-numbering __new__() would look like: >>> class AutoNumber(NoValue):
... def __new__(cls):
... value = len(cls.__members__) + 1
... obj = object.__new__(cls)
... obj._value_ = value
... return obj
...
>>> class Color(AutoNumber):
... RED = ()
... GREEN = ()
... BLUE = ()
...
>>> Color.GREEN
<Color.GREEN>
>>> Color.GREEN.value
2
To make a more general purpose AutoNumber, add *args to the signature: >>> class AutoNumber(NoValue):
... def __new__(cls, *args): # this is the only change from above
... value = len(cls.__members__) + 1
... obj = object.__new__(cls)
... obj._value_ = value
... return obj
...
Then when you inherit from AutoNumber you can write your own __init__ to handle any extra arguments: >>> class Swatch(AutoNumber):
... def __init__(self, pantone='unknown'):
... self.pantone = pantone
... AUBURN = '3497'
... SEA_GREEN = '1246'
... BLEACHED_CORAL = () # New color, no Pantone code yet!
...
>>> Swatch.SEA_GREEN
<Swatch.SEA_GREEN: 2>
>>> Swatch.SEA_GREEN.pantone
'1246'
>>> Swatch.BLEACHED_CORAL.pantone
'unknown'
Note The __new__() method, if defined, is used during creation of the Enum members; it is then replaced by Enum’s __new__() which is used after class creation for lookup of existing members. OrderedEnum An ordered enumeration that is not based on IntEnum and so maintains the normal Enum invariants (such as not being comparable to other enumerations): >>> class OrderedEnum(Enum):
... def __ge__(self, other):
... if self.__class__ is other.__class__:
... return self.value >= other.value
... return NotImplemented
... def __gt__(self, other):
... if self.__class__ is other.__class__:
... return self.value > other.value
... return NotImplemented
... def __le__(self, other):
... if self.__class__ is other.__class__:
... return self.value <= other.value
... return NotImplemented
... def __lt__(self, other):
... if self.__class__ is other.__class__:
... return self.value < other.value
... return NotImplemented
...
>>> class Grade(OrderedEnum):
... A = 5
... B = 4
... C = 3
... D = 2
... F = 1
...
>>> Grade.C < Grade.A
True
DuplicateFreeEnum Raises an error if a duplicate member name is found instead of creating an alias: >>> class DuplicateFreeEnum(Enum):
... def __init__(self, *args):
... cls = self.__class__
... if any(self.value == e.value for e in cls):
... a = self.name
... e = cls(self.value).name
... raise ValueError(
... "aliases not allowed in DuplicateFreeEnum: %r --> %r"
... % (a, e))
...
>>> class Color(DuplicateFreeEnum):
... RED = 1
... GREEN = 2
... BLUE = 3
... GRENE = 2
...
Traceback (most recent call last):
...
ValueError: aliases not allowed in DuplicateFreeEnum: 'GRENE' --> 'GREEN'
Note This is a useful example for subclassing Enum to add or change other behaviors as well as disallowing aliases. If the only desired change is disallowing aliases, the unique() decorator can be used instead. Planet If __new__() or __init__() is defined the value of the enum member will be passed to those methods: >>> class Planet(Enum):
... MERCURY = (3.303e+23, 2.4397e6)
... VENUS = (4.869e+24, 6.0518e6)
... EARTH = (5.976e+24, 6.37814e6)
... MARS = (6.421e+23, 3.3972e6)
... JUPITER = (1.9e+27, 7.1492e7)
... SATURN = (5.688e+26, 6.0268e7)
... URANUS = (8.686e+25, 2.5559e7)
... NEPTUNE = (1.024e+26, 2.4746e7)
... def __init__(self, mass, radius):
... self.mass = mass # in kilograms
... self.radius = radius # in meters
... @property
... def surface_gravity(self):
... # universal gravitational constant (m3 kg-1 s-2)
... G = 6.67300E-11
... return G * self.mass / (self.radius * self.radius)
...
>>> Planet.EARTH.value
(5.976e+24, 6378140.0)
>>> Planet.EARTH.surface_gravity
9.802652743337129
TimePeriod An example to show the _ignore_ attribute in use: >>> from datetime import timedelta
>>> class Period(timedelta, Enum):
... "different lengths of time"
... _ignore_ = 'Period i'
... Period = vars()
... for i in range(367):
... Period['day_%d' % i] = i
...
>>> list(Period)[:2]
[<Period.day_0: datetime.timedelta(0)>, <Period.day_1: datetime.timedelta(days=1)>]
>>> list(Period)[-2:]
[<Period.day_365: datetime.timedelta(days=365)>, <Period.day_366: datetime.timedelta(days=366)>]
How are Enums different? Enums have a custom metaclass that affects many aspects of both derived Enum classes and their instances (members). Enum Classes The EnumMeta metaclass is responsible for providing the __contains__(), __dir__(), __iter__() and other methods that allow one to do things with an Enum class that fail on a typical class, such as list(Color) or some_enum_var in Color. EnumMeta is responsible for ensuring that various other methods on the final Enum class are correct (such as __new__(), __getnewargs__(), __str__() and __repr__()). Enum Members (aka instances) The most interesting thing about Enum members is that they are singletons. EnumMeta creates them all while it is creating the Enum class itself, and then puts a custom __new__() in place to ensure that no new ones are ever instantiated by returning only the existing member instances. Finer Points Supported __dunder__ names __members__ is a read-only ordered mapping of member_name:member items. It is only available on the class. __new__(), if specified, must create and return the enum members; it is also a very good idea to set the member’s _value_ appropriately. Once all the members are created it is no longer used. Supported _sunder_ names
_name_ – name of the member
_value_ – value of the member; can be set / modified in __new__
_missing_ – a lookup function used when a value is not found; may be overridden
_ignore_ – a list of names, either as a list or a str, that will not be transformed into members, and will be removed from the final class
_order_ – used in Python 2/3 code to ensure member order is consistent (class attribute, removed during class creation)
_generate_next_value_ – used by the Functional API and by auto to get an appropriate value for an enum member; may be overridden New in version 3.6: _missing_, _order_, _generate_next_value_ New in version 3.7: _ignore_ To help keep Python 2 / Python 3 code in sync an _order_ attribute can be provided. It will be checked against the actual order of the enumeration and raise an error if the two do not match: >>> class Color(Enum):
... _order_ = 'RED GREEN BLUE'
... RED = 1
... BLUE = 3
... GREEN = 2
...
Traceback (most recent call last):
...
TypeError: member order does not match _order_
Note In Python 2 code the _order_ attribute is necessary as definition order is lost before it can be recorded. _Private__names Private names will be normal attributes in Python 3.10 instead of either an error or a member (depending on if the name ends with an underscore). Using these names in 3.9 will issue a DeprecationWarning.
Enum member type Enum members are instances of their Enum class, and are normally accessed as EnumClass.member. Under certain circumstances they can also be accessed as EnumClass.member.member, but you should never do this as that lookup may fail or, worse, return something besides the Enum member you are looking for (this is another good reason to use all-uppercase names for members): >>> class FieldTypes(Enum):
... name = 0
... value = 1
... size = 2
...
>>> FieldTypes.value.size
<FieldTypes.size: 2>
>>> FieldTypes.size.value
2
Changed in version 3.5. Boolean value of Enum classes and members Enum members that are mixed with non-Enum types (such as int, str, etc.) are evaluated according to the mixed-in type’s rules; otherwise, all members evaluate as True. To make your own Enum’s boolean evaluation depend on the member’s value add the following to your class: def __bool__(self):
return bool(self.value)
Enum classes always evaluate as True.
Enum classes with methods If you give your Enum subclass extra methods, like the Planet class above, those methods will show up in a dir() of the member, but not of the class: >>> dir(Planet)
['EARTH', 'JUPITER', 'MARS', 'MERCURY', 'NEPTUNE', 'SATURN', 'URANUS', 'VENUS', '__class__', '__doc__', '__members__', '__module__']
>>> dir(Planet.EARTH)
['__class__', '__doc__', '__module__', 'name', 'surface_gravity', 'value']
Combining members of Flag
If a combination of Flag members is not named, the repr() will include all named flags and all named combinations of flags that are in the value: >>> class Color(Flag):
... RED = auto()
... GREEN = auto()
... BLUE = auto()
... MAGENTA = RED | BLUE
... YELLOW = RED | GREEN
... CYAN = GREEN | BLUE
...
>>> Color(3) # named combination
<Color.YELLOW: 3>
>>> Color(7) # not named combination
<Color.CYAN|MAGENTA|BLUE|YELLOW|GREEN|RED: 7> | |
doc_27168 |
Returns an iterator over immediate children modules. Yields
Module – a child module | |
doc_27169 |
Open and return file-like object. If path is an URL, it will be downloaded, stored in the DataSource directory and opened from there. Parameters
pathstr
Local file path or URL to open.
mode{‘r’, ‘w’, ‘a’}, optional
Mode to open path. Mode ‘r’ for reading, ‘w’ for writing, ‘a’ to append. Available modes depend on the type of object specified by path. Default is ‘r’.
encoding{None, str}, optional
Open text file with given encoding. The default encoding will be what io.open uses.
newline{None, str}, optional
Newline to use when reading text file. Returns
outfile object
File object. | |
doc_27170 | See Migration guide for more details. tf.compat.v1.raw_ops.Bitcast
tf.raw_ops.Bitcast(
input, type, name=None
)
Given a tensor input, this operation returns a tensor that has the same buffer data as input with datatype type. If the input datatype T is larger than the output datatype type then the shape changes from [...] to [..., sizeof(T)/sizeof(type)]. If T is smaller than type, the operator requires that the rightmost dimension be equal to sizeof(type)/sizeof(T). The shape then goes from [..., sizeof(type)/sizeof(T)] to [...]. tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() gives module error. For example, Example 1:
a = [1., 2., 3.]
equality_bitcast = tf.bitcast(a, tf.complex128)
Traceback (most recent call last):
InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast]
equality_cast = tf.cast(a, tf.complex128)
print(equality_cast)
tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)
Example 2:
tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
<tf.Tensor: shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>
Example 3:
x = [1., 2., 3.]
y = [0., 2., 3.]
equality= tf.equal(x,y)
equality_cast = tf.cast(equality,tf.float32)
equality_bitcast = tf.bitcast(equality_cast,tf.uint8)
print(equality)
tf.Tensor([False True True], shape=(3,), dtype=bool)
print(equality_cast)
tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
print(equality_bitcast)
tf.Tensor(
[[ 0 0 0 0]
[ 0 0 128 63]
[ 0 0 128 63]], shape=(3, 4), dtype=uint8)
Note: Bitcast is implemented as a low-level cast, so machines with different endian orderings will give different results.
Args
input A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int64, int32, uint8, uint16, uint32, uint64, int8, int16, complex64, complex128, qint8, quint8, qint16, quint16, qint32.
type A tf.DType from: tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32.
name A name for the operation (optional).
Returns A Tensor of type type. | |
doc_27171 |
Delete self[key]. | |
doc_27172 | class ast.AsyncWith(items, body, type_comment)
async for loops and async with context managers. They have the same fields as For and With, respectively. Only valid in the body of an AsyncFunctionDef. | |
doc_27173 |
Set the edgecolor(s) of the collection. Parameters
ccolor or list of colors or 'face'
The collection edgecolor(s). If a sequence, the patches cycle through it. If 'face', match the facecolor. | |
doc_27174 | Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan, posinf, and neginf, respectively. By default, NaN`s are replaced with zero, positive infinity is replaced with the
greatest finite value representable by :attr:`input’s dtype, and negative infinity is replaced with the least finite value representable by input’s dtype. Parameters
input (Tensor) – the input tensor.
nan (Number, optional) – the value to replace NaNs with. Default is zero.
posinf (Number, optional) – if a Number, the value to replace positive infinity values with. If None, positive infinity values are replaced with the greatest finite value representable by input’s dtype. Default is None.
neginf (Number, optional) – if a Number, the value to replace negative infinity values with. If None, negative infinity values are replaced with the lowest finite value representable by input’s dtype. Default is None. Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> x = torch.tensor([float('nan'), float('inf'), -float('inf'), 3.14])
>>> torch.nan_to_num(x)
tensor([ 0.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00])
>>> torch.nan_to_num(x, nan=2.0)
tensor([ 2.0000e+00, 3.4028e+38, -3.4028e+38, 3.1400e+00])
>>> torch.nan_to_num(x, nan=2.0, posinf=1.0)
tensor([ 2.0000e+00, 1.0000e+00, -3.4028e+38, 3.1400e+00]) | |
doc_27175 | tf.lite.TFLiteConverter(
funcs, trackable_obj=None
)
Example usage: # Converting a SavedModel to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
# Converting a tf.Keras model to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Converting ConcreteFunctions to a TensorFlow Lite model.
converter = tf.lite.TFLiteConverter.from_concrete_functions([func])
tflite_model = converter.convert()
Args
funcs List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements.
trackable_obj tf.AutoTrackable object associated with funcs. A reference to this object needs to be maintained so that Variables do not get garbage collected since functions have a weak reference to Variables. This is only required when the tf.AutoTrackable object is not maintained by the user (e.g. from_saved_model).
Attributes
allow_custom_ops Boolean indicating whether to allow custom operations. When False, any unknown operation is an error. When True, custom ops are created for any op that is unknown. The developer needs to provide these to the TensorFlow Lite runtime with a custom resolver. (default False)
optimizations Experimental flag, subject to change. A list of optimizations to apply when converting the model. E.g. [Optimize.DEFAULT]
representative_dataset A representative dataset that can be used to generate input and output samples for the model. The converter can use the dataset to evaluate different optimizations. Note that this is an optional attribute but it is necessary if INT8 is the only support builtin ops in target ops.
target_spec Experimental flag, subject to change. Specification of target device.
inference_input_type Data type of the input layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8})
inference_output_type Data type of the output layer. Note that integer types (tf.int8 and tf.uint8) are currently only supported for post training integer quantization and quantization aware training. (default tf.float32, must be in {tf.float32, tf.int8, tf.uint8})
experimental_new_converter Experimental flag, subject to change. Enables MLIR-based conversion instead of TOCO conversion. (default True) Methods convert View source
convert()
Converts a TensorFlow GraphDef based on instance variables.
Returns The converted data in serialized format.
Raises
ValueError No concrete functions is specified. Multiple concrete functions are specified. Input shape is not specified. Invalid quantization parameters. from_concrete_functions View source
@classmethod
from_concrete_functions(
funcs
)
Creates a TFLiteConverter object from ConcreteFunctions.
Args
funcs List of TensorFlow ConcreteFunctions. The list should not contain duplicate elements. Currently converter can only convert a single ConcreteFunction. Converting multiple functions is under development.
Returns TFLiteConverter object.
Raises Invalid input type.
from_keras_model View source
@classmethod
from_keras_model(
model
)
Creates a TFLiteConverter object from a Keras model.
Args
model tf.Keras.Model
Returns TFLiteConverter object.
from_saved_model View source
@classmethod
from_saved_model(
saved_model_dir, signature_keys=None, tags=None
)
Creates a TFLiteConverter object from a SavedModel directory.
Args
saved_model_dir SavedModel directory to convert.
signature_keys List of keys identifying SignatureDef containing inputs and outputs. Elements should not be duplicated. By default the signatures attribute of the MetaGraphdef is used. (default saved_model.signatures)
tags Set of tags identifying the MetaGraphDef within the SavedModel to analyze. All tags in the tag set must be present. (default set(SERVING))
Returns TFLiteConverter object.
Raises Invalid signature keys. | |
doc_27176 | test if one rectangle is inside another contains(Rect) -> bool Returns true when the argument is completely inside the Rect. | |
doc_27177 | Contains the output stream for writing a response back to the client. Proper adherence to the HTTP protocol must be used when writing to this stream in order to achieve successful interoperation with HTTP clients. Changed in version 3.6: This is an io.BufferedIOBase stream. | |
doc_27178 | If a fallback has been set, forward pgettext() to the fallback. Otherwise, return the translated message. Overridden in derived classes. New in version 3.8. | |
doc_27179 |
Introspect data for units converter and update the axis.converter instance if necessary. Return True if data is registered for unit conversion. | |
doc_27180 |
Perform one epoch of stochastic gradient descent on given samples. Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data.
yndarray of shape (n_samples,)
Subset of the target values.
classesndarray of shape (n_classes,), default=None
Classes across all calls to partial_fit. Can be obtained by via np.unique(y_all), where y_all is the target vector of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
sample_weightarray-like, shape (n_samples,), default=None
Weights applied to individual samples. If not provided, uniform weights are assumed. Returns
self :
Returns an instance of self. | |
doc_27181 |
Set the uuid applied to id attributes of HTML elements. Parameters
uuid:str
Returns
self:Styler
Notes Almost all HTML elements within the table, and including the <table> element are assigned id attributes. The format is T_uuid_<extra> where <extra> is typically a more specific identifier, such as row1_col2. | |
doc_27182 | class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
Repeated K-Fold cross validator. Repeats K-Fold n times with different randomization in each repetition. Read more in the User Guide. Parameters
n_splitsint, default=5
Number of folds. Must be at least 2.
n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None
Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary. See also
RepeatedStratifiedKFold
Repeats Stratified K-Fold n times. Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer. Examples >>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generates indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generates indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split.
Examples using sklearn.model_selection.RepeatedKFold
Common pitfalls in interpretation of coefficients of linear models | |
doc_27183 | Returns a dictionary containing the WSGI environment for a request. The default implementation copies the contents of the WSGIServer object’s base_environ dictionary attribute and then adds various headers derived from the HTTP request. Each call to this method should return a new dictionary containing all of the relevant CGI environment variables as specified in PEP 3333. | |
doc_27184 |
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | |
doc_27185 | A constant that is likely larger than the underlying OS socket buffer size, to make writes blocking. | |
doc_27186 |
Transform data back to its original space. In other words, return an input X_original whose transform would be X. Parameters
Xarray-like, shape (n_samples, n_components)
New data, where n_samples is the number of samples and n_components is the number of components. Returns
X_original array-like, shape (n_samples, n_features)
Notes If whitening is enabled, inverse_transform will compute the exact inverse operation, which includes reversing whitening. | |
doc_27187 | Returns a URL tuple that holds a IRI. This will try to decode as much information as possible in the URL without losing information similar to how a web browser does it for the URL bar. It’s usually more interesting to directly call uri_to_iri() which will return a string. Return type
werkzeug.urls.BaseURL | |
doc_27188 | This signal is sent when the app context is tearing down. This is always called, even if an exception is caused. Currently functions listening to this signal are called after the regular teardown handlers, but this is not something you can rely on. Example subscriber: def close_db_connection(sender, **extra):
session.close()
from flask import appcontext_tearing_down
appcontext_tearing_down.connect(close_db_connection, app)
This will also be passed an exc keyword argument that has a reference to the exception that caused the teardown if there was one. | |
doc_27189 | This attribute provides a way of setting the upload directory and file name, and can be set in two ways. In both cases, the value is passed to the Storage.save() method. If you specify a string value or a Path, it may contain strftime() formatting, which will be replaced by the date/time of the file upload (so that uploaded files don’t fill up the given directory). For example: class MyModel(models.Model):
# file will be uploaded to MEDIA_ROOT/uploads
upload = models.FileField(upload_to='uploads/')
# or...
# file will be saved to MEDIA_ROOT/uploads/2015/01/30
upload = models.FileField(upload_to='uploads/%Y/%m/%d/')
If you are using the default FileSystemStorage, the string value will be appended to your MEDIA_ROOT path to form the location on the local filesystem where uploaded files will be stored. If you are using a different storage, check that storage’s documentation to see how it handles upload_to. upload_to may also be a callable, such as a function. This will be called to obtain the upload path, including the filename. This callable must accept two arguments and return a Unix-style path (with forward slashes) to be passed along to the storage system. The two arguments are:
Argument Description
instance
An instance of the model where the FileField is defined. More specifically, this is the particular instance where the current file is being attached. In most cases, this object will not have been saved to the database yet, so if it uses the default AutoField, it might not yet have a value for its primary key field.
filename The filename that was originally given to the file. This may or may not be taken into account when determining the final destination path. For example: def user_directory_path(instance, filename):
# file will be uploaded to MEDIA_ROOT/user_<id>/<filename>
return 'user_{0}/{1}'.format(instance.user.id, filename)
class MyModel(models.Model):
upload = models.FileField(upload_to=user_directory_path) | |
doc_27190 | Call self as a function. | |
doc_27191 | Exception raised when attempting to parse a file which has no section headers. | |
doc_27192 |
Describe an instantaneous event that occurred at some point. Parameters
msg (string) – ASCII message to associate with the event. | |
doc_27193 |
Return Subtraction of series and other, element-wise (binary operator rsub). Equivalent to other - series, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar value
fill_value:None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing.
level:int or name
Broadcast across a level, matching Index values on the passed MultiIndex level. Returns
Series
The result of the operation. See also Series.sub
Element-wise Subtraction, see Python documentation for more details. Examples
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a 1.0
b 1.0
c 1.0
d NaN
dtype: float64
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a 1.0
b NaN
d 1.0
e NaN
dtype: float64
>>> a.subtract(b, fill_value=0)
a 0.0
b 1.0
c 1.0
d -1.0
e NaN
dtype: float64 | |
doc_27194 | Redirects to the same URL but with a slash appended. The behavior of this function is undefined if the path ends with a slash already. Parameters
environ (WSGIEnvironment) – the WSGI environment for the request that triggers the redirect.
code (int) – the status code for the redirect. Return type
Response | |
doc_27195 | A class which defines a default handler for HTTP error responses; all responses are turned into HTTPError exceptions. | |
doc_27196 |
Call self as a function. | |
doc_27197 | The same as SMTP except that utf8 is True. Useful for serializing messages to a message store without using encoded words in the headers. Should only be used for SMTP transmission if the sender or recipient addresses have non-ASCII characters (the smtplib.SMTP.send_message() method handles this automatically). | |
doc_27198 | tf.keras.layers.GlobalMaxPooling1D Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.layers.GlobalMaxPool1D, tf.compat.v1.keras.layers.GlobalMaxPooling1D
tf.keras.layers.GlobalMaxPool1D(
data_format='channels_last', **kwargs
)
Downsamples the input representation by taking the maximum value over the time dimension. For example:
x = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
x = tf.reshape(x, [3, 3, 1])
x
<tf.Tensor: shape=(3, 3, 1), dtype=float32, numpy=
array([[[1.], [2.], [3.]],
[[4.], [5.], [6.]],
[[7.], [8.], [9.]]], dtype=float32)>
max_pool_1d = tf.keras.layers.GlobalMaxPooling1D()
max_pool_1d(x)
<tf.Tensor: shape=(3, 1), dtype=float32, numpy=
array([[3.],
[6.],
[9.], dtype=float32)>
Arguments
data_format A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, steps, features) while channels_first corresponds to inputs with shape (batch, features, steps). Input shape: If data_format='channels_last': 3D tensor with shape: (batch_size, steps, features)
If data_format='channels_first': 3D tensor with shape: (batch_size, features, steps)
Output shape: 2D tensor with shape (batch_size, features). | |
doc_27199 | See Migration guide for more details. tf.compat.v1.raw_ops.StackCloseV2
tf.raw_ops.StackCloseV2(
handle, name=None
)
Args
handle A Tensor of type resource. The handle to a stack.
name A name for the operation (optional).
Returns The created Operation. |
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