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doc_2500 | tf.compat.v1.train.sdca_fprint(
input, name=None
)
Args
input A Tensor of type string. vector of strings to compute fingerprints on.
name A name for the operation (optional).
Returns A Tensor of type int64. | |
doc_2501 | A transport for datagram (UDP) connections. Instances of the DatagramTransport class are returned from the loop.create_datagram_endpoint() event loop method. | |
doc_2502 |
Return the string representation of an array. Parameters
arrndarray
Input array.
max_line_widthint, optional
Inserts newlines if text is longer than max_line_width. Defaults to numpy.get_printoptions()['linewidth'].
precisionint, optional
Floating point precision. Defaults to numpy.get_printoptions()['precision'].
suppress_smallbool, optional
Represent numbers “very close” to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to numpy.get_printoptions()['suppress']. Returns
stringstr
The string representation of an array. See also
array_str, array2string, set_printoptions
Examples >>> np.array_repr(np.array([1,2]))
'array([1, 2])'
>>> np.array_repr(np.ma.array([0.]))
'MaskedArray([0.])'
>>> np.array_repr(np.array([], np.int32))
'array([], dtype=int32)'
>>> x = np.array([1e-6, 4e-7, 2, 3])
>>> np.array_repr(x, precision=6, suppress_small=True)
'array([0.000001, 0. , 2. , 3. ])' | |
doc_2503 | Parses the lines argument. | |
doc_2504 | Return True if the queue is empty, False otherwise. | |
doc_2505 | See Migration guide for more details. tf.compat.v1.raw_ops.Cast
tf.raw_ops.Cast(
x, DstT, Truncate=False, name=None
)
Args
x A Tensor.
DstT A tf.DType.
Truncate An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type DstT. | |
doc_2506 |
Attributes
attr repeated AttrEntry attr
name string name Child Classes class AttrEntry | |
doc_2507 | Raised when a given process doesn’t exist. Corresponds to errno ESRCH. | |
doc_2508 |
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
\(R^2\) of self.predict(X) wrt. y. Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). | |
doc_2509 | Save the file to a destination path or file object. If the destination is a file object you have to close it yourself after the call. The buffer size is the number of bytes held in memory during the copy process. It defaults to 16KB. For secure file saving also have a look at secure_filename(). Parameters
dst – a filename, os.PathLike, or open file object to write to.
buffer_size – Passed as the length parameter of shutil.copyfileobj(). Changelog Changed in version 1.0: Supports pathlib. | |
doc_2510 | os.PRIO_PGRP
os.PRIO_USER
Parameters for the getpriority() and setpriority() functions. Availability: Unix. New in version 3.3. | |
doc_2511 | Returns the URL where the contents of the file referenced by name can be accessed. For storage systems that don’t support access by URL this will raise NotImplementedError instead. | |
doc_2512 | Receive normal data and ancillary data from the socket, behaving as recvmsg() would, but scatter the non-ancillary data into a series of buffers instead of returning a new bytes object. The buffers argument must be an iterable of objects that export writable buffers (e.g. bytearray objects); these will be filled with successive chunks of the non-ancillary data until it has all been written or there are no more buffers. The operating system may set a limit (sysconf() value SC_IOV_MAX) on the number of buffers that can be used. The ancbufsize and flags arguments have the same meaning as for recvmsg(). The return value is a 4-tuple: (nbytes, ancdata, msg_flags,
address), where nbytes is the total number of bytes of non-ancillary data written into the buffers, and ancdata, msg_flags and address are the same as for recvmsg(). Example: >>> import socket
>>> s1, s2 = socket.socketpair()
>>> b1 = bytearray(b'----')
>>> b2 = bytearray(b'0123456789')
>>> b3 = bytearray(b'--------------')
>>> s1.send(b'Mary had a little lamb')
22
>>> s2.recvmsg_into([b1, memoryview(b2)[2:9], b3])
(22, [], 0, None)
>>> [b1, b2, b3]
[bytearray(b'Mary'), bytearray(b'01 had a 9'), bytearray(b'little lamb---')]
Availability: most Unix platforms, possibly others. New in version 3.3. | |
doc_2513 |
alias of matplotlib.backends.backend_pdf.FigureCanvasPdf | |
doc_2514 | A deprecated alias of OSError. Changed in version 3.3: Following PEP 3151, this class was made an alias of OSError. | |
doc_2515 | See Migration guide for more details. tf.compat.v1.tensor_scatter_add, tf.compat.v1.tensor_scatter_nd_add
tf.tensor_scatter_nd_add(
tensor, indices, updates, name=None
)
This operation creates a new tensor by adding sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd_add, except that the updates are added onto an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated. indices is an integer tensor containing indices into a new tensor of shape tensor.shape. The last dimension of indices can be at most the rank of tensor.shape: indices.shape[-1] <= tensor.shape.rank
The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = tensor.shape.rank) or slices (if indices.shape[-1] < tensor.shape.rank) along dimension indices.shape[-1] of tensor.shape. updates is a tensor with shape indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
The simplest form of tensor_scatter_add is to add individual elements to a tensor by index. For example, say we want to add 4 elements in a rank-1 tensor with 8 elements. In Python, this scatter add operation would look like this: indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
The resulting tensor would look like this: [1, 12, 1, 11, 10, 1, 1, 13]
We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values. In Python, this scatter add operation would look like this: indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4],dtype=tf.int32)
updated = tf.tensor_scatter_nd_add(tensor, indices, updates)
print(updated)
The resulting tensor would look like this: [[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.
Args
tensor A Tensor. Tensor to copy/update.
indices A Tensor. Must be one of the following types: int32, int64. Index tensor.
updates A Tensor. Must have the same type as tensor. Updates to scatter into output.
name A name for the operation (optional).
Returns A Tensor. Has the same type as tensor. | |
doc_2516 | tf.distribute.ReplicaContext(
strategy, replica_id_in_sync_group
)
You can use tf.distribute.get_replica_context to get an instance of ReplicaContext, which can only be called inside the function passed to tf.distribute.Strategy.run.
strategy = tf.distribute.MirroredStrategy(['GPU:0', 'GPU:1'])
def func():
replica_context = tf.distribute.get_replica_context()
return replica_context.replica_id_in_sync_group
strategy.run(func)
PerReplica:{
0: <tf.Tensor: shape=(), dtype=int32, numpy=0>,
1: <tf.Tensor: shape=(), dtype=int32, numpy=1>
}
Args
strategy A tf.distribute.Strategy.
replica_id_in_sync_group An integer, a Tensor or None. Prefer an integer whenever possible to avoid issues with nested tf.function. It accepts a Tensor only to be compatible with tpu.replicate.
Attributes
devices Returns the devices this replica is to be executed on, as a tuple of strings. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please avoid relying on devices property.
Note: For tf.distribute.MirroredStrategy and tf.distribute.experimental.MultiWorkerMirroredStrategy, this returns a nested list of device strings, e.g, [["GPU:0"]].
num_replicas_in_sync Returns number of replicas that are kept in sync.
replica_id_in_sync_group Returns the id of the replica. This identifies the replica among all replicas that are kept in sync. The value of the replica id can range from 0 to tf.distribute.ReplicaContext.num_replicas_in_sync - 1.
Note: This is not guaranteed to be the same ID as the XLA replica ID use for low-level operations such as collective_permute.
strategy The current tf.distribute.Strategy object. Methods all_gather View source
all_gather(
value, axis, options=None
)
All-gathers value across all replicas along axis.
Note: An all_gather method can only be called in replica context. For a cross-replica context counterpart, see tf.distribute.Strategy.gather. All replicas need to participate in the all-gather, otherwise this operation hangs. So if all_gather is called in any replica, it must be called in all replicas.
Note: If there are multiple all_gather calls, they need to be executed in the same order on all replicas. Dispatching all_gather based on conditions is usually error-prone.
For all strategies except tf.distribute.TPUStrategy, the input value on different replicas must have the same rank, and their shapes must be the same in all dimensions except the axis-th dimension. In other words, their shapes cannot be different in a dimension d where d does not equal to the axis argument. For example, given a tf.distribute.DistributedValues with component tensors of shape (1, 2, 3) and (1, 3, 3) on two replicas, you can call all_gather(..., axis=1, ...) on it, but not all_gather(..., axis=0, ...) or all_gather(..., axis=2, ...). However, with tf.distribute.TPUStrategy, all tensors must have exactly the same rank and same shape.
Note: The input value must have a non-zero rank. Otherwise, consider using tf.expand_dims before gathering them.
You can pass in a single tensor to all-gather:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
@tf.function
def gather_value():
ctx = tf.distribute.get_replica_context()
local_value = tf.constant([1, 2, 3])
return ctx.all_gather(local_value, axis=0)
result = strategy.run(gather_value)
result
PerReplica:{
0: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>,
1: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>
}
strategy.experimental_local_results(result)
(<tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3],
dtype=int32)>,
<tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3],
dtype=int32)>)
You can also pass in a nested structure of tensors to all-gather, say, a list:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
@tf.function
def gather_nest():
ctx = tf.distribute.get_replica_context()
value_1 = tf.constant([1, 2, 3])
value_2 = tf.constant([[1, 2], [3, 4]])
# all_gather a nest of `tf.distribute.DistributedValues`
return ctx.all_gather([value_1, value_2], axis=0)
result = strategy.run(gather_nest)
result
[PerReplica:{
0: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>,
1: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>
}, PerReplica:{
0: <tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]], dtype=int32)>,
1: <tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]], dtype=int32)>
}]
strategy.experimental_local_results(result)
([PerReplica:{
0: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>,
1: <tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 1, 2, 3], dtype=int32)>
}, PerReplica:{
0: <tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]], dtype=int32)>,
1: <tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]], dtype=int32)>
}],)
What if you are all-gathering tensors with different shapes on different replicas? Consider the following example with two replicas, where you have value as a nested structure consisting of two items to all-gather, a and b. On Replica 0, value is {'a': [0], 'b': [[0, 1]]} On Replica 1, value is {'a': [1], 'b': [[2, 3], [4, 5]]} Result for all_gather with axis=0: (on each of the replicas): {'a': [1, 2], 'b': [[0, 1], [2, 3], [4, 5]]}
Args
value a nested structure of tf.Tensor which tf.nest.flatten accepts, or a tf.distribute.DistributedValues instance. The structure of the tf.Tensor need to be same on all replicas. The underlying tensor constructs can only be dense tensors with non-zero rank, NOT tf.IndexedSlices.
axis 0-D int32 Tensor. Dimension along which to gather.
options a tf.distribute.experimental.CommunicationOptions. Options to perform collective operations. This overrides the default options if the tf.distribute.Strategy takes one in the constructor. See tf.distribute.experimental.CommunicationOptions for details of the options.
Returns A nested structure of tf.Tensor with the gathered values. The structure is the same as value.
all_reduce View source
all_reduce(
reduce_op, value, options=None
)
All-reduces value across all replicas.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def step_fn():
ctx = tf.distribute.get_replica_context()
value = tf.identity(1.)
return ctx.all_reduce(tf.distribute.ReduceOp.SUM, value)
strategy.experimental_local_results(strategy.run(step_fn))
(<tf.Tensor: shape=(), dtype=float32, numpy=2.0>,
<tf.Tensor: shape=(), dtype=float32, numpy=2.0>)
It supports batched operations. You can pass a list of values and it attempts to batch them when possible. You can also specify options to indicate the desired batching behavior, e.g. batch the values into multiple packs so that they can better overlap with computations.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def step_fn():
ctx = tf.distribute.get_replica_context()
value1 = tf.identity(1.)
value2 = tf.identity(2.)
return ctx.all_reduce(tf.distribute.ReduceOp.SUM, [value1, value2])
strategy.experimental_local_results(strategy.run(step_fn))
([PerReplica:{
0: <tf.Tensor: shape=(), dtype=float32, numpy=2.0>,
1: <tf.Tensor: shape=(), dtype=float32, numpy=2.0>
}, PerReplica:{
0: <tf.Tensor: shape=(), dtype=float32, numpy=4.0>,
1: <tf.Tensor: shape=(), dtype=float32, numpy=4.0>
}],)
Note that all replicas need to participate in the all-reduce, otherwise this operation hangs. Note that if there're multiple all-reduces, they need to execute in the same order on all replicas. Dispatching all-reduce based on conditions is usually error-prone. This API currently can only be called in the replica context. Other variants to reduce values across replicas are:
tf.distribute.StrategyExtended.reduce_to: the reduce and all-reduce API in the cross-replica context.
tf.distribute.StrategyExtended.batch_reduce_to: the batched reduce and all-reduce API in the cross-replica context.
tf.distribute.Strategy.reduce: a more convenient method to reduce to the host in cross-replica context.
Args
reduce_op a tf.distribute.ReduceOp value specifying how values should be combined. Allows using string representation of the enum such as "SUM", "MEAN".
value a nested structure of tf.Tensor which tf.nest.flatten accepts. The structure and the shapes of the tf.Tensor need to be same on all replicas.
options a tf.distribute.experimental.CommunicationOptions. Options to perform collective operations. This overrides the default options if the tf.distribute.Strategy takes one in the constructor. See tf.distribute.experimental.CommunicationOptions for details of the options.
Returns A nested structure of tf.Tensor with the reduced values. The structure is the same as value.
merge_call View source
merge_call(
merge_fn, args=(), kwargs=None
)
Merge args across replicas and run merge_fn in a cross-replica context. This allows communication and coordination when there are multiple calls to the step_fn triggered by a call to strategy.run(step_fn, ...). See tf.distribute.Strategy.run for an explanation. If not inside a distributed scope, this is equivalent to: strategy = tf.distribute.get_strategy()
with cross-replica-context(strategy):
return merge_fn(strategy, *args, **kwargs)
Args
merge_fn Function that joins arguments from threads that are given as PerReplica. It accepts tf.distribute.Strategy object as the first argument.
args List or tuple with positional per-thread arguments for merge_fn.
kwargs Dict with keyword per-thread arguments for merge_fn.
Returns The return value of merge_fn, except for PerReplica values which are unpacked. | |
doc_2517 |
Construct a Bbox by statically transforming this one by transform. | |
doc_2518 | This method can be called to signal that the set of tests being run should be aborted by setting the shouldStop attribute to True. TestRunner objects should respect this flag and return without running any additional tests. For example, this feature is used by the TextTestRunner class to stop the test framework when the user signals an interrupt from the keyboard. Interactive tools which provide TestRunner implementations can use this in a similar manner. | |
doc_2519 |
Return the linewidth in points. See also set_linewidth. | |
doc_2520 | Version interface for Jython. Returns a tuple (release, vendor, vminfo, osinfo) with vminfo being a tuple (vm_name, vm_release, vm_vendor) and osinfo being a tuple (os_name, os_version, os_arch). Values which cannot be determined are set to the defaults given as parameters (which all default to ''). | |
doc_2521 |
Return the yaxis' tick locations in data coordinates. | |
doc_2522 | Run command with arguments and return its output. If the return code was non-zero it raises a CalledProcessError. The CalledProcessError object will have the return code in the returncode attribute and any output in the output attribute. This is equivalent to: run(..., check=True, stdout=PIPE).stdout
The arguments shown above are merely some common ones. The full function signature is largely the same as that of run() - most arguments are passed directly through to that interface. One API deviation from run() behavior exists: passing input=None will behave the same as input=b'' (or input='', depending on other arguments) rather than using the parent’s standard input file handle. By default, this function will return the data as encoded bytes. The actual encoding of the output data may depend on the command being invoked, so the decoding to text will often need to be handled at the application level. This behaviour may be overridden by setting text, encoding, errors, or universal_newlines to True as described in Frequently Used Arguments and run(). To also capture standard error in the result, use stderr=subprocess.STDOUT: >>> subprocess.check_output(
... "ls non_existent_file; exit 0",
... stderr=subprocess.STDOUT,
... shell=True)
'ls: non_existent_file: No such file or directory\n'
New in version 3.1. Changed in version 3.3: timeout was added. Changed in version 3.4: Support for the input keyword argument was added. Changed in version 3.6: encoding and errors were added. See run() for details. New in version 3.7: text was added as a more readable alias for universal_newlines. | |
doc_2523 | '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_2524 | Read and return the next nframes frames from the audio file. The returned data is a string containing for each frame the uncompressed samples of all channels. | |
doc_2525 | The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method). The @overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition, while the latter is used at runtime but should be ignored by a type checker. At runtime, calling a @overload-decorated function directly will raise NotImplementedError. An example of overload that gives a more precise type than can be expressed using a union or a type variable: @overload
def process(response: None) -> None:
...
@overload
def process(response: int) -> tuple[int, str]:
...
@overload
def process(response: bytes) -> str:
...
def process(response):
<actual implementation>
See PEP 484 for details and comparison with other typing semantics. | |
doc_2526 | sklearn.utils.parallel_backend(backend, n_jobs=- 1, inner_max_num_threads=None, **backend_params) [source]
Change the default backend used by Parallel inside a with block. If backend is a string it must match a previously registered implementation using the register_parallel_backend function. By default the following backends are available: ‘loky’: single-host, process-based parallelism (used by default), ‘threading’: single-host, thread-based parallelism, ‘multiprocessing’: legacy single-host, process-based parallelism. ‘loky’ is recommended to run functions that manipulate Python objects. ‘threading’ is a low-overhead alternative that is most efficient for functions that release the Global Interpreter Lock: e.g. I/O-bound code or CPU-bound code in a few calls to native code that explicitly releases the GIL. In addition, if the dask and distributed Python packages are installed, it is possible to use the ‘dask’ backend for better scheduling of nested parallel calls without over-subscription and potentially distribute parallel calls over a networked cluster of several hosts. It is also possible to use the distributed ‘ray’ backend for distributing the workload to a cluster of nodes. To use the ‘ray’ joblib backend add the following lines: >>> from ray.util.joblib import register_ray
>>> register_ray()
>>> with parallel_backend("ray"):
... print(Parallel()(delayed(neg)(i + 1) for i in range(5)))
[-1, -2, -3, -4, -5]
Alternatively the backend can be passed directly as an instance. By default all available workers will be used (n_jobs=-1) unless the caller passes an explicit value for the n_jobs parameter. This is an alternative to passing a backend='backend_name' argument to the Parallel class constructor. It is particularly useful when calling into library code that uses joblib internally but does not expose the backend argument in its own API. >>> from operator import neg
>>> with parallel_backend('threading'):
... print(Parallel()(delayed(neg)(i + 1) for i in range(5)))
...
[-1, -2, -3, -4, -5]
Warning: this function is experimental and subject to change in a future version of joblib. Joblib also tries to limit the oversubscription by limiting the number of threads usable in some third-party library threadpools like OpenBLAS, MKL or OpenMP. The default limit in each worker is set to max(cpu_count() // effective_n_jobs, 1) but this limit can be overwritten with the inner_max_num_threads argument which will be used to set this limit in the child processes. New in version 0.10. | |
doc_2527 |
Encode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, i.e. y, and not the input X. Read more in the User Guide. New in version 0.12. Attributes
classes_ndarray of shape (n_classes,)
Holds the label for each class. See also
OrdinalEncoder
Encode categorical features using an ordinal encoding scheme.
OneHotEncoder
Encode categorical features as a one-hot numeric array. Examples LabelEncoder can be used to normalize labels. >>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6])
array([0, 0, 1, 2]...)
>>> le.inverse_transform([0, 0, 1, 2])
array([1, 1, 2, 6])
It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. >>> le = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"])
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']
Methods
fit(y) Fit label encoder.
fit_transform(y) Fit label encoder and return encoded labels.
get_params([deep]) Get parameters for this estimator.
inverse_transform(y) Transform labels back to original encoding.
set_params(**params) Set the parameters of this estimator.
transform(y) Transform labels to normalized encoding.
fit(y) [source]
Fit label encoder. Parameters
yarray-like of shape (n_samples,)
Target values. Returns
selfreturns an instance of self.
fit_transform(y) [source]
Fit label encoder and return encoded labels. Parameters
yarray-like of shape (n_samples,)
Target values. Returns
yarray-like of shape (n_samples,)
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(y) [source]
Transform labels back to original encoding. Parameters
yndarray of shape (n_samples,)
Target values. Returns
yndarray of shape (n_samples,)
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(y) [source]
Transform labels to normalized encoding. Parameters
yarray-like of shape (n_samples,)
Target values. Returns
yarray-like of shape (n_samples,) | |
doc_2528 |
Initialize an Affine transform from a 3x3 numpy float array: a c e
b d f
0 0 1
If matrix is None, initialize with the identity transform. | |
doc_2529 |
Inplace row scaling of a CSR or CSC matrix. Scale each row of the data matrix by multiplying with specific scale provided by the caller assuming a (n_samples, n_features) shape. Parameters
Xsparse matrix of shape (n_samples, n_features)
Matrix to be scaled. It should be of CSR or CSC format.
scalendarray of shape (n_features,), dtype={np.float32, np.float64}
Array of precomputed sample-wise values to use for scaling. | |
doc_2530 |
Scikit-image logo, a RGBA image. Returns
logo(500, 500, 4) uint8 ndarray
Logo image. | |
doc_2531 |
Compute the peak signal to noise ratio (PSNR) for an image. Parameters
image_truendarray
Ground-truth image, same shape as im_test.
image_testndarray
Test image.
data_rangeint, optional
The data range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns
psnrfloat
The PSNR metric. Notes Changed in version 0.16: This function was renamed from skimage.measure.compare_psnr to skimage.metrics.peak_signal_noise_ratio. References
1
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio | |
doc_2532 |
Return the current hatching pattern. | |
doc_2533 | See Migration guide for more details. tf.compat.v1.linalg.svd, tf.compat.v1.svd
tf.linalg.svd(
tensor, full_matrices=False, compute_uv=True, name=None
)
Computes the SVD of each inner matrix in tensor such that tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(conj(v[..., :, :])) # a is a tensor.
# s is a tensor of singular values.
# u is a tensor of left singular vectors.
# v is a tensor of right singular vectors.
s, u, v = svd(a)
s = svd(a, compute_uv=False)
Args
tensor Tensor of shape [..., M, N]. Let P be the minimum of M and N.
full_matrices If true, compute full-sized u and v. If false (the default), compute only the leading P singular vectors. Ignored if compute_uv is False.
compute_uv If True then left and right singular vectors will be computed and returned in u and v, respectively. Otherwise, only the singular values will be computed, which can be significantly faster.
name string, optional name of the operation.
Returns
s Singular values. Shape is [..., P]. The values are sorted in reverse order of magnitude, so s[..., 0] is the largest value, s[..., 1] is the second largest, etc.
u Left singular vectors. If full_matrices is False (default) then shape is [..., M, P]; if full_matrices is True then shape is [..., M, M]. Not returned if compute_uv is False.
v Right singular vectors. If full_matrices is False (default) then shape is [..., N, P]. If full_matrices is True then shape is [..., N, N]. Not returned if compute_uv is False. Numpy Compatibility Mostly equivalent to numpy.linalg.svd, except that The order of output arguments here is s, u, v when compute_uv is True, as opposed to u, s, v for numpy.linalg.svd. full_matrices is False by default as opposed to True for numpy.linalg.svd. tf.linalg.svd uses the standard definition of the SVD \(A = U \Sigma V^H\), such that the left singular vectors of a are the columns of u, while the right singular vectors of a are the columns of v. On the other hand, numpy.linalg.svd returns the adjoint \(V^H\) as the third output argument. import tensorflow as tf
import numpy as np
s, u, v = tf.linalg.svd(a)
tf_a_approx = tf.matmul(u, tf.matmul(tf.linalg.diag(s), v, adjoint_b=True))
u, s, v_adj = np.linalg.svd(a, full_matrices=False)
np_a_approx = np.dot(u, np.dot(np.diag(s), v_adj))
# tf_a_approx and np_a_approx should be numerically close. | |
doc_2534 | Divides each element of the input input by the corresponding element of other. outi=inputiotheri\text{out}_i = \frac{\text{input}_i}{\text{other}_i}
Note By default, this performs a “true” division like Python 3. See the rounding_mode argument for floor division. Supports broadcasting to a common shape, type promotion, and integer, float, and complex inputs. Always promotes integer types to the default scalar type. Parameters
input (Tensor) – the dividend
other (Tensor or Number) – the divisor Keyword Arguments
rounding_mode (str, optional) –
Type of rounding applied to the result: None - default behavior. Performs no rounding and, if both input and other are integer types, promotes the inputs to the default scalar type. Equivalent to true division in Python (the / operator) and NumPy’s np.true_divide.
"trunc" - rounds the results of the division towards zero. Equivalent to C-style integer division.
"floor" - rounds the results of the division down. Equivalent to floor division in Python (the // operator) and NumPy’s np.floor_divide.
out (Tensor, optional) – the output tensor. Examples: >>> x = torch.tensor([ 0.3810, 1.2774, -0.2972, -0.3719, 0.4637])
>>> torch.div(x, 0.5)
tensor([ 0.7620, 2.5548, -0.5944, -0.7438, 0.9274])
>>> a = torch.tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
... [ 0.1815, -1.0111, 0.9805, -1.5923],
... [ 0.1062, 1.4581, 0.7759, -1.2344],
... [-0.1830, -0.0313, 1.1908, -1.4757]])
>>> b = torch.tensor([ 0.8032, 0.2930, -0.8113, -0.2308])
>>> torch.div(a, b)
tensor([[-0.4620, -6.6051, 0.5676, 1.2639],
[ 0.2260, -3.4509, -1.2086, 6.8990],
[ 0.1322, 4.9764, -0.9564, 5.3484],
[-0.2278, -0.1068, -1.4678, 6.3938]])
>>> torch.div(a, b, rounding_mode='trunc')
tensor([[-0., -6., 0., 1.],
[ 0., -3., -1., 6.],
[ 0., 4., -0., 5.],
[-0., -0., -1., 6.]])
>>> torch.div(a, b, rounding_mode='floor')
tensor([[-1., -7., 0., 1.],
[ 0., -4., -2., 6.],
[ 0., 4., -1., 5.],
[-1., -1., -2., 6.]]) | |
doc_2535 |
Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype. Parameters
other:Series or DataFrame
The other Series or DataFrame to be compared with the first. Returns
bool
True if all elements are the same in both objects, False otherwise. See also Series.eq
Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq
Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal
Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal
Like assert_series_equal, but targets DataFrames. numpy.array_equal
Return True if two arrays have the same shape and elements, False otherwise. Examples
>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
1 2
0 10 20
DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.
>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
1 2
0 10 20
>>> df.equals(exactly_equal)
True
DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.
>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
1.0 2.0
0 10 20
>>> df.equals(different_column_type)
True
DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.
>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
1 2
0 10.0 20.0
>>> df.equals(different_data_type)
False | |
doc_2536 | tf.experimental.numpy.hsplit(
ary, indices_or_sections
)
See the NumPy documentation for numpy.hsplit. | |
doc_2537 |
Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields “required” by the required_dtype. If a field name in the required_dtype does not exist in the input array, that field is created and set to 0 in the output array. Parameters
andarray
array to cast
required_dtypedtype
datatype for output array Returns
outndarray
array with the new dtype, with field values copied from the fields in the input array with the same name Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
array([(1., 1), (1., 1), (1., 1), (1., 1)],
dtype=[('b', '<f4'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
array([(1., 0), (1., 0), (1., 0), (1., 0)],
dtype=[('b', '<f4'), ('newf', 'u1')]) | |
doc_2538 | In-place version of addcdiv() | |
doc_2539 | See Migration guide for more details. tf.compat.v1.raw_ops.Cosh
tf.raw_ops.Cosh(
x, name=None
)
Given an input tensor, this function computes hyperbolic cosine of every element in the tensor. Input range is [-inf, inf] and output range is [1, inf]. x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_2540 | Return a file object associated with the socket. The exact returned type depends on the arguments given to makefile(). These arguments are interpreted the same way as by the built-in open() function, except the only supported mode values are 'r' (default), 'w' and 'b'. The socket must be in blocking mode; it can have a timeout, but the file object’s internal buffer may end up in an inconsistent state if a timeout occurs. Closing the file object returned by makefile() won’t close the original socket unless all other file objects have been closed and socket.close() has been called on the socket object. Note On Windows, the file-like object created by makefile() cannot be used where a file object with a file descriptor is expected, such as the stream arguments of subprocess.Popen(). | |
doc_2541 | The get_search_fields method is given the HttpRequest and is expected to return the same kind of sequence type as for the search_fields attribute. | |
doc_2542 | Return a view object, by calling MSIDatabaseOpenView(). sql is the SQL statement to execute. | |
doc_2543 | The method attempts to write the data to the underlying socket immediately. If that fails, the data is queued in an internal write buffer until it can be sent. The method should be used along with the drain() method: stream.write(data)
await stream.drain() | |
doc_2544 | temporarily stop audio playback pause() -> None Temporarily stop audio playback on the CD. The playback can be resumed at the same point with the CD.resume() method. If the CD is not playing this method does nothing. Note, track 0 is the first track on the CD. Track numbers start at zero. | |
doc_2545 | tf.compat.v1.keras.layers.GRU(
units, activation='tanh',
recurrent_activation='hard_sigmoid', use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros', kernel_regularizer=None,
recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, recurrent_constraint=None, bias_constraint=None,
dropout=0.0, recurrent_dropout=0.0, return_sequences=False, return_state=False,
go_backwards=False, stateful=False, unroll=False, reset_after=False, **kwargs
)
There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed. The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel and recurrent_kernel. Use 'reset_after'=True and recurrent_activation='sigmoid'.
Arguments
units Positive integer, dimensionality of the output space.
activation Activation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
recurrent_activation Activation function to use for the recurrent step. Default: hard sigmoid (hard_sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
use_bias Boolean, whether the layer uses a bias vector.
kernel_initializer Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
recurrent_initializer Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.
bias_initializer Initializer for the bias vector.
kernel_regularizer Regularizer function applied to the kernel weights matrix.
recurrent_regularizer Regularizer function applied to the recurrent_kernel weights matrix.
bias_regularizer Regularizer function applied to the bias vector.
activity_regularizer Regularizer function applied to the output of the layer (its "activation")..
kernel_constraint Constraint function applied to the kernel weights matrix.
recurrent_constraint Constraint function applied to the recurrent_kernel weights matrix.
bias_constraint Constraint function applied to the bias vector.
dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
return_sequences Boolean. Whether to return the last output in the output sequence, or the full sequence.
return_state Boolean. Whether to return the last state in addition to the output.
go_backwards Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
stateful Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
unroll Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
time_major The shape format of the inputs and outputs tensors. If True, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.
reset_after GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible). Call arguments:
inputs: A 3D tensor.
mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked.
training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if dropout or recurrent_dropout is used.
initial_state: List of initial state tensors to be passed to the first call of the cell.
Attributes
activation
bias_constraint
bias_initializer
bias_regularizer
dropout
implementation
kernel_constraint
kernel_initializer
kernel_regularizer
recurrent_activation
recurrent_constraint
recurrent_dropout
recurrent_initializer
recurrent_regularizer
reset_after
states
units
use_bias
Methods reset_states View source
reset_states(
states=None
)
Reset the recorded states for the stateful RNN layer. Can only be used when RNN layer is constructed with stateful = True. Args: states: Numpy arrays that contains the value for the initial state, which will be feed to cell at the first time step. When the value is None, zero filled numpy array will be created based on the cell state size.
Raises
AttributeError When the RNN layer is not stateful.
ValueError When the batch size of the RNN layer is unknown.
ValueError When the input numpy array is not compatible with the RNN layer state, either size wise or dtype wise. | |
doc_2546 |
Set the dimension of the drawing canvas. | |
doc_2547 | Flushes changes made to the in-memory copy of a file back to disk. Without use of this call there is no guarantee that changes are written back before the object is destroyed. If offset and size are specified, only changes to the given range of bytes will be flushed to disk; otherwise, the whole extent of the mapping is flushed. offset must be a multiple of the PAGESIZE or ALLOCATIONGRANULARITY. None is returned to indicate success. An exception is raised when the call failed. Changed in version 3.8: Previously, a nonzero value was returned on success; zero was returned on error under Windows. A zero value was returned on success; an exception was raised on error under Unix. | |
doc_2548 | Render text into an array of ints render_raw_to(array, text, dest=None, style=STYLE_DEFAULT, rotation=0, size=0, invert=False) -> Rect Render to an array object exposing an array struct interface. The array must be two dimensional with integer items. The default dest value, None, is equivalent to position (0, 0). See render_to(). As with the other render methods, text can be None to render a text string passed previously to another method. The return value is a pygame.Rect() giving the size and position of the rendered text. | |
doc_2549 |
Alias for get_fontproperties. | |
doc_2550 |
Return the Colormap instance. | |
doc_2551 |
Return a format string formatting the x, y coordinates. | |
doc_2552 |
Estimate the best class label for each sample in X. This is implemented as argmax(decision_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data. Returns
ynumpy array of shape [n_samples]
Predicted multi-class targets. | |
doc_2553 | Like rfind() but raises ValueError when the substring sub is not found. | |
doc_2554 |
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | |
doc_2555 |
Return the underlying artist that actually defines some properties (e.g., color) of this artist. | |
doc_2556 |
Determine common type following standard coercion rules. Parameters
array_typessequence
A list of dtypes or dtype convertible objects representing arrays.
scalar_typessequence
A list of dtypes or dtype convertible objects representing scalars. Returns
datatypedtype
The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind). If the kind is not understood, then None is returned. See also
dtype, common_type, can_cast, mintypecode
Examples >>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
The standard casting rules ensure that a scalar cannot up-cast an array unless the scalar is of a fundamentally different kind of data (i.e. under a different hierarchy in the data type hierarchy) then the array: >>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
Complex is of a different type, so it up-casts the float in the array_types argument: >>> np.find_common_type([np.float32], [complex])
dtype('complex128')
Type specifier strings are convertible to dtypes and can therefore be used instead of dtypes: >>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128') | |
doc_2557 | See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNV3
tf.raw_ops.CudnnRNNV3(
input, input_h, input_c, params, sequence_lengths, rnn_mode='lstm',
input_mode='linear_input', direction='unidirectional',
dropout=0, seed=0, seed2=0, num_proj=0, is_training=True, time_major=True,
name=None
)
Computes the RNN from the input and initial states, with respect to the params buffer. Accepts one extra input "sequence_lengths" than CudnnRNN. rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, input_size]. If time_major is false, the shape is [batch_size, seq_length, input_size]. input_h: If time_major is true, this is a 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. If time_major is false, the shape is [batch_size, num_layer * dir, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore sequence_lengths: a vector of lengths of each input sequence. output: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. If time_major is false, the shape is [batch_size, seq_length, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. time_major: Indicates whether the input/output format is time major or batch major. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true.
Args
input A Tensor. Must be one of the following types: half, float32, float64.
input_h A Tensor. Must have the same type as input.
input_c A Tensor. Must have the same type as input.
params A Tensor. Must have the same type as input.
sequence_lengths A Tensor of type int32.
rnn_mode An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
input_mode An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
direction An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
dropout An optional float. Defaults to 0.
seed An optional int. Defaults to 0.
seed2 An optional int. Defaults to 0.
num_proj An optional int. Defaults to 0.
is_training An optional bool. Defaults to True.
time_major An optional bool. Defaults to True.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output, output_h, output_c, reserve_space, host_reserved). output A Tensor. Has the same type as input.
output_h A Tensor. Has the same type as input.
output_c A Tensor. Has the same type as input.
reserve_space A Tensor. Has the same type as input.
host_reserved A Tensor of type int8. | |
doc_2558 | Return _testcapi.WITH_PYMALLOC. | |
doc_2559 | See Migration guide for more details. tf.compat.v1.keras.preprocessing.image.random_brightness
tf.keras.preprocessing.image.random_brightness(
x, brightness_range
)
Arguments
x Input tensor. Must be 3D.
brightness_range Tuple of floats; brightness range.
channel_axis Index of axis for channels in the input tensor.
Returns Numpy image tensor.
Raises ValueError if brightness_range isn't a tuple. | |
doc_2560 | sklearn.datasets.make_regression(n_samples=100, n_features=100, *, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None) [source]
Generate a random regression problem. The input set can either be well conditioned (by default) or have a low rank-fat tail singular profile. See make_low_rank_matrix for more details. The output is generated by applying a (potentially biased) random linear regression model with n_informative nonzero regressors to the previously generated input and some gaussian centered noise with some adjustable scale. Read more in the User Guide. Parameters
n_samplesint, default=100
The number of samples.
n_featuresint, default=100
The number of features.
n_informativeint, default=10
The number of informative features, i.e., the number of features used to build the linear model used to generate the output.
n_targetsint, default=1
The number of regression targets, i.e., the dimension of the y output vector associated with a sample. By default, the output is a scalar.
biasfloat, default=0.0
The bias term in the underlying linear model.
effective_rankint, default=None
if not None:
The approximate number of singular vectors required to explain most of the input data by linear combinations. Using this kind of singular spectrum in the input allows the generator to reproduce the correlations often observed in practice. if None:
The input set is well conditioned, centered and gaussian with unit variance.
tail_strengthfloat, default=0.5
The relative importance of the fat noisy tail of the singular values profile if effective_rank is not None. When a float, it should be between 0 and 1.
noisefloat, default=0.0
The standard deviation of the gaussian noise applied to the output.
shufflebool, default=True
Shuffle the samples and the features.
coefbool, default=False
If True, the coefficients of the underlying linear model are returned.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary. Returns
Xndarray of shape (n_samples, n_features)
The input samples.
yndarray of shape (n_samples,) or (n_samples, n_targets)
The output values.
coefndarray of shape (n_features,) or (n_features, n_targets)
The coefficient of the underlying linear model. It is returned only if coef is True.
Examples using sklearn.datasets.make_regression
Release Highlights for scikit-learn 0.23
Prediction Latency
Plot Ridge coefficients as a function of the L2 regularization
Robust linear model estimation using RANSAC
HuberRegressor vs Ridge on dataset with strong outliers
Lasso on dense and sparse data
Effect of transforming the targets in regression model | |
doc_2561 | Token value for "...". | |
doc_2562 | See Migration guide for more details. tf.compat.v1.raw_ops.PaddedBatchDataset
tf.raw_ops.PaddedBatchDataset(
input_dataset, batch_size, padded_shapes, padding_values, output_shapes,
name=None
)
Args
input_dataset A Tensor of type variant.
batch_size A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.
padded_shapes A list of at least 1 Tensor objects with type int64. A list of int64 tensors representing the desired padded shapes of the corresponding output components. These shapes may be partially specified, using -1 to indicate that a particular dimension should be padded to the maximum size of all batch elements.
padding_values A list of Tensor objects. A list of scalars containing the padding value to use for each of the outputs.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_2563 |
Parameters
urlslist of str or None
Notes URLs are currently only implemented by the SVG backend. They are ignored by all other backends. | |
doc_2564 |
Toggle zoom to rect mode. | |
doc_2565 | Holds a string containing the address identified in the “MAIL FROM:” line from the client. | |
doc_2566 |
Return the (min, max) view limits of this axis. | |
doc_2567 |
Subtract self from other, and return a new masked array. | |
doc_2568 |
Calculate the expanding standard deviation. Parameters
ddof:int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. *args
For NumPy compatibility and will not have an effect on the result.
engine:str, default None
'cython' : Runs the operation through C-extensions from cython. 'numba' : Runs the operation through JIT compiled code from numba.
None : Defaults to 'cython' or globally setting compute.use_numba New in version 1.4.0.
engine_kwargs:dict, default None
For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} New in version 1.4.0. **kwargs
For NumPy compatibility and will not have an effect on the result. Returns
Series or DataFrame
Return type is the same as the original object with np.float64 dtype. See also numpy.std
Equivalent method for NumPy array. pandas.Series.expanding
Calling expanding with Series data. pandas.DataFrame.expanding
Calling expanding with DataFrames. pandas.Series.std
Aggregating std for Series. pandas.DataFrame.std
Aggregating std for DataFrame. Notes The default ddof of 1 used in Series.std() is different than the default ddof of 0 in numpy.std(). A minimum of one period is required for the rolling calculation. Examples
>>> s = pd.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.expanding(3).std()
0 NaN
1 NaN
2 0.577350
3 0.957427
4 0.894427
5 0.836660
6 0.786796
dtype: float64 | |
doc_2569 |
Tuple of bytes steps in each dimension. | |
doc_2570 |
Set the DataFrame of strings on Styler generating :hover tooltips. These string based tooltips are only applicable to <td> HTML elements, and cannot be used for column or index headers. New in version 1.3.0. Parameters
ttips:DataFrame
DataFrame containing strings that will be translated to tooltips, mapped by identical column and index values that must exist on the underlying Styler data. None, NaN values, and empty strings will be ignored and not affect the rendered HTML.
props:list-like or str, optional
List of (attr, value) tuples or a valid CSS string. If None adopts the internal default values described in notes.
css_class:str, optional
Name of the tooltip class used in CSS, should conform to HTML standards. Only useful if integrating tooltips with external CSS. If None uses the internal default value ‘pd-t’. Returns
self:Styler
Notes Tooltips are created by adding <span class=”pd-t”></span> to each data cell and then manipulating the table level CSS to attach pseudo hover and pseudo after selectors to produce the required the results. The default properties for the tooltip CSS class are: visibility: hidden position: absolute z-index: 1 background-color: black color: white transform: translate(-20px, -20px) The property ‘visibility: hidden;’ is a key prerequisite to the hover functionality, and should always be included in any manual properties specification, using the props argument. Tooltips are not designed to be efficient, and can add large amounts of additional HTML for larger tables, since they also require that cell_ids is forced to True. Examples Basic application
>>> df = pd.DataFrame(data=[[0, 1], [2, 3]])
>>> ttips = pd.DataFrame(
... data=[["Min", ""], [np.nan, "Max"]], columns=df.columns, index=df.index
... )
>>> s = df.style.set_tooltips(ttips).to_html()
Optionally controlling the tooltip visual display
>>> df.style.set_tooltips(ttips, css_class='tt-add', props=[
... ('visibility', 'hidden'),
... ('position', 'absolute'),
... ('z-index', 1)])
>>> df.style.set_tooltips(ttips, css_class='tt-add',
... props='visibility:hidden; position:absolute; z-index:1;')
... | |
doc_2571 |
Removes the pruning reparameterization from a module. The pruned parameter named name remains permanently pruned, and the parameter named name+'_orig' is removed from the parameter list. Similarly, the buffer named name+'_mask' is removed from the buffers. Note Pruning itself is NOT undone or reversed! | |
doc_2572 | See Migration guide for more details. tf.compat.v1.raw_ops.AnonymousSeedGenerator
tf.raw_ops.AnonymousSeedGenerator(
seed, seed2, reshuffle, name=None
)
Args
seed A Tensor of type int64.
seed2 A Tensor of type int64.
reshuffle A Tensor of type bool.
name A name for the operation (optional).
Returns A tuple of Tensor objects (handle, deleter). handle A Tensor of type resource.
deleter A Tensor of type variant. | |
doc_2573 |
Return the url. | |
doc_2574 | See Migration guide for more details. tf.compat.v1.sysconfig.get_build_info
tf.sysconfig.get_build_info()
Values are generated when TensorFlow is compiled, and are static for each TensorFlow package. The return value is a dictionary with string keys such as: cuda_version cudnn_version is_cuda_build is_rocm_build msvcp_dll_names nvcuda_dll_name cudart_dll_name cudnn_dll_name Note that the actual keys and values returned by this function is subject to change across different versions of TensorFlow or across platforms.
Returns A Dictionary describing TensorFlow's build environment. | |
doc_2575 |
Add a Container to the axes' containers; return the container. | |
doc_2576 |
Learn a list of feature name -> indices mappings and transform X. Like fit(X) followed by transform(X), but does not require materializing X in memory. Parameters
XMapping or iterable over Mappings
Dict(s) or Mapping(s) from feature names (arbitrary Python objects) to feature values (strings or convertible to dtype). Changed in version 0.24: Accepts multiple string values for one categorical feature.
y(ignored)
Returns
Xa{array, sparse matrix}
Feature vectors; always 2-d. | |
doc_2577 | Returns the message for this LogRecord instance after merging any user-supplied arguments with the message. If the user-supplied message argument to the logging call is not a string, str() is called on it to convert it to a string. This allows use of user-defined classes as messages, whose __str__ method can return the actual format string to be used. | |
doc_2578 | Similar to the parse() method, except it takes a bytes-like object instead of a file-like object. Calling this method on a bytes-like object is equivalent to wrapping bytes in a BytesIO instance first and calling parse(). Optional headersonly is as with the parse() method. | |
doc_2579 | If flag is True, escape sequences generated by some keys (keypad, function keys) will be interpreted by curses. If flag is False, escape sequences will be left as is in the input stream. | |
doc_2580 |
Return the x-axis view limits. Returns
left, right(float, float)
The current x-axis limits in data coordinates. See also set_xlim
set_xbound, get_xbound
invert_xaxis, xaxis_inverted
Notes The x-axis may be inverted, in which case the left value will be greater than the right value.
Examples using matplotlib.axes.Axes.get_xlim
Decay | |
doc_2581 |
Remove o from the stack. Raises
ValueError
If o is not in the stack. | |
doc_2582 | Return x raised to the power y. Exceptional cases follow Annex ‘F’ of the C99 standard as far as possible. In particular, pow(1.0, x) and pow(x, 0.0) always return 1.0, even when x is a zero or a NaN. If both x and y are finite, x is negative, and y is not an integer then pow(x, y) is undefined, and raises ValueError. Unlike the built-in ** operator, math.pow() converts both its arguments to type float. Use ** or the built-in pow() function for computing exact integer powers. | |
doc_2583 |
Sparse coding Finds a sparse representation of data against a fixed, precomputed dictionary. Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that: X ~= code * dictionary
Read more in the User Guide. Parameters
dictionaryndarray of shape (n_components, n_features)
The dictionary atoms used for sparse coding. Lines are assumed to be normalized to unit norm.
transform_algorithm{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’omp’
Algorithm used to transform the data:
'lars': uses the least angle regression method (linear_model.lars_path);
'lasso_lars': uses Lars to compute the Lasso solution;
'lasso_cd': uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). 'lasso_lars' will be faster if the estimated components are sparse;
'omp': uses orthogonal matching pursuit to estimate the sparse solution;
'threshold': squashes to zero all coefficients less than alpha from the projection dictionary * X'.
transform_n_nonzero_coefsint, default=None
Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm='lars' and algorithm='omp' and is overridden by alpha in the omp case. If None, then transform_n_nonzero_coefs=int(n_features / 10).
transform_alphafloat, default=None
If algorithm='lasso_lars' or algorithm='lasso_cd', alpha is the penalty applied to the L1 norm. If algorithm='threshold', alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm='omp', alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overrides n_nonzero_coefs. If None, default to 1.
split_signbool, default=False
Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.
n_jobsint, default=None
Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
positive_codebool, default=False
Whether to enforce positivity when finding the code. New in version 0.20.
transform_max_iterint, default=1000
Maximum number of iterations to perform if algorithm='lasso_cd' or lasso_lars. New in version 0.22. Attributes
components_ndarray of shape (n_components, n_features)
The unchanged dictionary atoms. Deprecated since version 0.24: This attribute is deprecated in 0.24 and will be removed in 1.1 (renaming of 0.26). Use dictionary instead. See also
DictionaryLearning
MiniBatchDictionaryLearning
SparsePCA
MiniBatchSparsePCA
sparse_encode
Examples >>> import numpy as np
>>> from sklearn.decomposition import SparseCoder
>>> X = np.array([[-1, -1, -1], [0, 0, 3]])
>>> dictionary = np.array(
... [[0, 1, 0],
... [-1, -1, 2],
... [1, 1, 1],
... [0, 1, 1],
... [0, 2, 1]],
... dtype=np.float64
... )
>>> coder = SparseCoder(
... dictionary=dictionary, transform_algorithm='lasso_lars',
... transform_alpha=1e-10,
... )
>>> coder.transform(X)
array([[ 0., 0., -1., 0., 0.],
[ 0., 1., 1., 0., 0.]])
Methods
fit(X[, y]) Do nothing and return the estimator unchanged.
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X[, y]) Encode the data as a sparse combination of the dictionary atoms.
fit(X, y=None) [source]
Do nothing and return the estimator unchanged. This method is just there to implement the usual API and hence work in pipelines. Parameters
XIgnored
yIgnored
Returns
selfobject
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
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.
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, y=None) [source]
Encode the data as a sparse combination of the dictionary atoms. Coding method is determined by the object parameter transform_algorithm. Parameters
Xndarray of shape (n_samples, n_features)
Test data to be transformed, must have the same number of features as the data used to train the model. Returns
X_newndarray of shape (n_samples, n_components)
Transformed data. | |
doc_2584 | See Migration guide for more details. tf.compat.v1.app.flags.CantOpenFlagFileError E.g. the file doesn't exist, or has wrong permissions. | |
doc_2585 | Safely join zero or more untrusted path components to a base directory to avoid escaping the base directory. Parameters
directory (str) – The trusted base directory.
pathnames (str) – The untrusted path components relative to the base directory. Returns
A safe path, otherwise None. Return type
str | |
doc_2586 |
Expand the shape of an array. Insert a new axis that will appear at the axis position in the expanded array shape. Parameters
aarray_like
Input array.
axisint or tuple of ints
Position in the expanded axes where the new axis (or axes) is placed. Deprecated since version 1.13.0: Passing an axis where axis > a.ndim will be treated as axis == a.ndim, and passing axis < -a.ndim - 1 will be treated as axis == 0. This behavior is deprecated. Changed in version 1.18.0: A tuple of axes is now supported. Out of range axes as described above are now forbidden and raise an AxisError. Returns
resultndarray
View of a with the number of dimensions increased. See also squeeze
The inverse operation, removing singleton dimensions reshape
Insert, remove, and combine dimensions, and resize existing ones
doc.indexing, atleast_1d, atleast_2d, atleast_3d
Examples >>> x = np.array([1, 2])
>>> x.shape
(2,)
The following is equivalent to x[np.newaxis, :] or x[np.newaxis]: >>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)
The following is equivalent to x[:, np.newaxis]: >>> y = np.expand_dims(x, axis=1)
>>> y
array([[1],
[2]])
>>> y.shape
(2, 1)
axis may also be a tuple: >>> y = np.expand_dims(x, axis=(0, 1))
>>> y
array([[[1, 2]]])
>>> y = np.expand_dims(x, axis=(2, 0))
>>> y
array([[[1],
[2]]])
Note that some examples may use None instead of np.newaxis. These are the same objects: >>> np.newaxis is None
True | |
doc_2587 | Return an iterator over all of the immediate sub-parts of the message that are not candidate “body” parts. That is, skip the first occurrence of each of text/plain, text/html, multipart/related, or multipart/alternative (unless they are explicitly marked as attachments via Content-Disposition: attachment), and return all remaining parts. When applied directly to a multipart/related, return an iterator over the all the related parts except the root part (ie: the part pointed to by the start parameter, or the first part if there is no start parameter or the start parameter doesn’t match the Content-ID of any of the parts). When applied directly to a multipart/alternative or a non-multipart, return an empty iterator. | |
doc_2588 |
Apply the non-affine part of this transform to Path path, returning a new Path. transform_path(path) is equivalent to transform_path_affine(transform_path_non_affine(values)). | |
doc_2589 | See Migration guide for more details. tf.compat.v1.raw_ops.MatrixSetDiag
tf.raw_ops.MatrixSetDiag(
input, diagonal, name=None
)
Given input and diagonal, this operation returns a tensor with the same shape and values as input, except for the main diagonal of the innermost matrices. These will be overwritten by the values in diagonal. The output is computed as follows: Assume input has k+1 dimensions [I, J, K, ..., M, N] and diagonal has k dimensions [I, J, K, ..., min(M, N)]. Then the output is a tensor of rank k+1 with dimensions [I, J, K, ..., M, N] where:
output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n] for m == n.
output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n] for m != n.
Args
input A Tensor. Rank k+1, where k >= 1.
diagonal A Tensor. Must have the same type as input. Rank k, where k >= 1.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_2590 |
Alias for get_facecolor. | |
doc_2591 |
Add an Axes to the figure as part of a subplot arrangement. Call signatures: add_subplot(nrows, ncols, index, **kwargs)
add_subplot(pos, **kwargs)
add_subplot(ax)
add_subplot()
Parameters
*argsint, (int, int, index), or SubplotSpec, default: (1, 1, 1)
The position of the subplot described by one of Three integers (nrows, ncols, index). The subplot will take the index position on a grid with nrows rows and ncols columns. index starts at 1 in the upper left corner and increases to the right. index can also be a two-tuple specifying the (first, last) indices (1-based, and including last) of the subplot, e.g., fig.add_subplot(3, 1, (1, 2)) makes a subplot that spans the upper 2/3 of the figure. A 3-digit integer. The digits are interpreted as if given separately as three single-digit integers, i.e. fig.add_subplot(235) is the same as fig.add_subplot(2, 3, 5). Note that this can only be used if there are no more than 9 subplots. A SubplotSpec. In rare circumstances, add_subplot may be called with a single argument, a subplot Axes instance already created in the present figure but not in the figure's list of Axes.
projection{None, 'aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear', str}, optional
The projection type of the subplot (Axes). str is the name of a custom projection, see projections. The default None results in a 'rectilinear' projection.
polarbool, default: False
If True, equivalent to projection='polar'.
axes_classsubclass type of Axes, optional
The axes.Axes subclass that is instantiated. This parameter is incompatible with projection and polar. See axisartist for examples.
sharex, shareyAxes, optional
Share the x or y axis with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes.
labelstr
A label for the returned Axes. Returns
axes.SubplotBase, or another subclass of Axes
The Axes of the subplot. The returned Axes base class depends on the projection used. It is Axes if rectilinear projection is used and projections.polar.PolarAxes if polar projection is used. The returned Axes is then a subplot subclass of the base class. Other Parameters
**kwargs
This method also takes the keyword arguments for the returned Axes base class; except for the figure argument. The keyword arguments for the rectilinear base class Axes can be found in the following table but there might also be other keyword arguments if another projection is used.
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float See also Figure.add_axes
pyplot.subplot
pyplot.axes
Figure.subplots
pyplot.subplots
Examples fig = plt.figure()
fig.add_subplot(231)
ax1 = fig.add_subplot(2, 3, 1) # equivalent but more general
fig.add_subplot(232, frameon=False) # subplot with no frame
fig.add_subplot(233, projection='polar') # polar subplot
fig.add_subplot(234, sharex=ax1) # subplot sharing x-axis with ax1
fig.add_subplot(235, facecolor="red") # red subplot
ax1.remove() # delete ax1 from the figure
fig.add_subplot(ax1) # add ax1 back to the figure | |
doc_2592 | Returns whether the kernel is stationary. | |
doc_2593 | When shlex detects a source request (see source below) this method is given the following token as argument, and expected to return a tuple consisting of a filename and an open file-like object. Normally, this method first strips any quotes off the argument. If the result is an absolute pathname, or there was no previous source request in effect, or the previous source was a stream (such as sys.stdin), the result is left alone. Otherwise, if the result is a relative pathname, the directory part of the name of the file immediately before it on the source inclusion stack is prepended (this behavior is like the way the C preprocessor handles #include
"file.h"). The result of the manipulations is treated as a filename, and returned as the first component of the tuple, with open() called on it to yield the second component. (Note: this is the reverse of the order of arguments in instance initialization!) This hook is exposed so that you can use it to implement directory search paths, addition of file extensions, and other namespace hacks. There is no corresponding ‘close’ hook, but a shlex instance will call the close() method of the sourced input stream when it returns EOF. For more explicit control of source stacking, use the push_source() and pop_source() methods. | |
doc_2594 |
A torch.nn.ConvTranspose3d module with lazy initialization of the in_channels argument of the ConvTranspose3d that is inferred from the input.size(1). Parameters
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple, optional) – Stride of the convolution. Default: 1
padding (int or tuple, optional) – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Default: 0
output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0
groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional) – If True, adds a learnable bias to the output. Default: True
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1 See also torch.nn.ConvTranspose3d and torch.nn.modules.lazy.LazyModuleMixin
cls_to_become
alias of ConvTranspose3d | |
doc_2595 |
Shrink font size until the text fits into the cell width. | |
doc_2596 | if self.request.version == 'v1':
return AccountSerializerVersion1
return AccountSerializer
Reversing URLs for versioned APIs The reverse function included by REST framework ties in with the versioning scheme. You need to make sure to include the current request as a keyword argument, like so. from rest_framework.reverse import reverse
reverse('bookings-list', request=request)
The above function will apply any URL transformations appropriate to the request version. For example: If NamespaceVersioning was being used, and the API version was 'v1', then the URL lookup used would be 'v1:bookings-list', which might resolve to a URL like http://example.org/v1/bookings/. If QueryParameterVersioning was being used, and the API version was 1.0, then the returned URL might be something like http://example.org/bookings/?version=1.0
Versioned APIs and hyperlinked serializers When using hyperlinked serialization styles together with a URL based versioning scheme make sure to include the request as context to the serializer. def get(self, request):
queryset = Booking.objects.all()
serializer = BookingsSerializer(queryset, many=True, context={'request': request})
return Response({'all_bookings': serializer.data})
Doing so will allow any returned URLs to include the appropriate versioning. Configuring the versioning scheme The versioning scheme is defined by the DEFAULT_VERSIONING_CLASS settings key. REST_FRAMEWORK = {
'DEFAULT_VERSIONING_CLASS': 'rest_framework.versioning.NamespaceVersioning'
}
Unless it is explicitly set, the value for DEFAULT_VERSIONING_CLASS will be None. In this case the request.version attribute will always return None. You can also set the versioning scheme on an individual view. Typically you won't need to do this, as it makes more sense to have a single versioning scheme used globally. If you do need to do so, use the versioning_class attribute. class ProfileList(APIView):
versioning_class = versioning.QueryParameterVersioning
Other versioning settings The following settings keys are also used to control versioning:
DEFAULT_VERSION. The value that should be used for request.version when no versioning information is present. Defaults to None.
ALLOWED_VERSIONS. If set, this value will restrict the set of versions that may be returned by the versioning scheme, and will raise an error if the provided version is not in this set. Note that the value used for the DEFAULT_VERSION setting is always considered to be part of the ALLOWED_VERSIONS set (unless it is None). Defaults to None.
VERSION_PARAM. The string that should be used for any versioning parameters, such as in the media type or URL query parameters. Defaults to 'version'. You can also set your versioning class plus those three values on a per-view or a per-viewset basis by defining your own versioning scheme and using the default_version, allowed_versions and version_param class variables. For example, if you want to use URLPathVersioning: from rest_framework.versioning import URLPathVersioning
from rest_framework.views import APIView
class ExampleVersioning(URLPathVersioning):
default_version = ...
allowed_versions = ...
version_param = ...
class ExampleView(APIVIew):
versioning_class = ExampleVersioning
API Reference AcceptHeaderVersioning This scheme requires the client to specify the version as part of the media type in the Accept header. The version is included as a media type parameter, that supplements the main media type. Here's an example HTTP request using the accept header versioning style. GET /bookings/ HTTP/1.1
Host: example.com
Accept: application/json; version=1.0
In the example request above request.version attribute would return the string '1.0'. Versioning based on accept headers is generally considered as best practice, although other styles may be suitable depending on your client requirements. Using accept headers with vendor media types Strictly speaking the json media type is not specified as including additional parameters. If you are building a well-specified public API you might consider using a vendor media type. To do so, configure your renderers to use a JSON based renderer with a custom media type: class BookingsAPIRenderer(JSONRenderer):
media_type = 'application/vnd.megacorp.bookings+json'
Your client requests would now look like this: GET /bookings/ HTTP/1.1
Host: example.com
Accept: application/vnd.megacorp.bookings+json; version=1.0
URLPathVersioning This scheme requires the client to specify the version as part of the URL path. GET /v1/bookings/ HTTP/1.1
Host: example.com
Accept: application/json
Your URL conf must include a pattern that matches the version with a 'version' keyword argument, so that this information is available to the versioning scheme. urlpatterns = [
re_path(
r'^(?P<version>(v1|v2))/bookings/$',
bookings_list,
name='bookings-list'
),
re_path(
r'^(?P<version>(v1|v2))/bookings/(?P<pk>[0-9]+)/$',
bookings_detail,
name='bookings-detail'
)
]
NamespaceVersioning To the client, this scheme is the same as URLPathVersioning. The only difference is how it is configured in your Django application, as it uses URL namespacing, instead of URL keyword arguments. GET /v1/something/ HTTP/1.1
Host: example.com
Accept: application/json
With this scheme the request.version attribute is determined based on the namespace that matches the incoming request path. In the following example we're giving a set of views two different possible URL prefixes, each under a different namespace: # bookings/urls.py
urlpatterns = [
re_path(r'^$', bookings_list, name='bookings-list'),
re_path(r'^(?P<pk>[0-9]+)/$', bookings_detail, name='bookings-detail')
]
# urls.py
urlpatterns = [
re_path(r'^v1/bookings/', include('bookings.urls', namespace='v1')),
re_path(r'^v2/bookings/', include('bookings.urls', namespace='v2'))
]
Both URLPathVersioning and NamespaceVersioning are reasonable if you just need a simple versioning scheme. The URLPathVersioning approach might be better suitable for small ad-hoc projects, and the NamespaceVersioning is probably easier to manage for larger projects. HostNameVersioning The hostname versioning scheme requires the client to specify the requested version as part of the hostname in the URL. For example the following is an HTTP request to the http://v1.example.com/bookings/ URL: GET /bookings/ HTTP/1.1
Host: v1.example.com
Accept: application/json
By default this implementation expects the hostname to match this simple regular expression: ^([a-zA-Z0-9]+)\.[a-zA-Z0-9]+\.[a-zA-Z0-9]+$
Note that the first group is enclosed in brackets, indicating that this is the matched portion of the hostname. The HostNameVersioning scheme can be awkward to use in debug mode as you will typically be accessing a raw IP address such as 127.0.0.1. There are various online tutorials on how to access localhost with a custom subdomain which you may find helpful in this case. Hostname based versioning can be particularly useful if you have requirements to route incoming requests to different servers based on the version, as you can configure different DNS records for different API versions. QueryParameterVersioning This scheme is a simple style that includes the version as a query parameter in the URL. For example: GET /something/?version=0.1 HTTP/1.1
Host: example.com
Accept: application/json
Custom versioning schemes To implement a custom versioning scheme, subclass BaseVersioning and override the .determine_version method. Example The following example uses a custom X-API-Version header to determine the requested version. class XAPIVersionScheme(versioning.BaseVersioning):
def determine_version(self, request, *args, **kwargs):
return request.META.get('HTTP_X_API_VERSION', None)
If your versioning scheme is based on the request URL, you will also want to alter how versioned URLs are determined. In order to do so you should override the .reverse() method on the class. See the source code for examples. versioning.py | |
doc_2597 | Returns a tensor filled with random numbers from a uniform distribution on the interval [0,1)[0, 1) The shape of the tensor is defined by the variable argument size. Parameters
size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple. Keyword Arguments
out (Tensor, optional) – the output tensor.
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()).
layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False. Example: >>> torch.rand(4)
tensor([ 0.5204, 0.2503, 0.3525, 0.5673])
>>> torch.rand(2, 3)
tensor([[ 0.8237, 0.5781, 0.6879],
[ 0.3816, 0.7249, 0.0998]]) | |
doc_2598 |
Change the appearance of ticks, tick labels, and gridlines. Tick properties that are not explicitly set using the keyword arguments remain unchanged unless reset is True. Parameters
axis{'x', 'y', 'both'}, default: 'both'
The axis to which the parameters are applied.
which{'major', 'minor', 'both'}, default: 'major'
The group of ticks to which the parameters are applied.
resetbool, default: False
Whether to reset the ticks to defaults before updating them. Other Parameters
direction{'in', 'out', 'inout'}
Puts ticks inside the axes, outside the axes, or both.
lengthfloat
Tick length in points.
widthfloat
Tick width in points.
colorcolor
Tick color.
padfloat
Distance in points between tick and label.
labelsizefloat or str
Tick label font size in points or as a string (e.g., 'large').
labelcolorcolor
Tick label color.
colorscolor
Tick color and label color.
zorderfloat
Tick and label zorder.
bottom, top, left, rightbool
Whether to draw the respective ticks.
labelbottom, labeltop, labelleft, labelrightbool
Whether to draw the respective tick labels.
labelrotationfloat
Tick label rotation
grid_colorcolor
Gridline color.
grid_alphafloat
Transparency of gridlines: 0 (transparent) to 1 (opaque).
grid_linewidthfloat
Width of gridlines in points.
grid_linestylestr
Any valid Line2D line style spec. Examples ax.tick_params(direction='out', length=6, width=2, colors='r',
grid_color='r', grid_alpha=0.5)
This will make all major ticks be red, pointing out of the box, and with dimensions 6 points by 2 points. Tick labels will also be red. Gridlines will be red and translucent.
Examples using matplotlib.axes.Axes.tick_params
Scatter plot with histograms
Creating annotated heatmaps
Axes Props
Broken Axis
Plots with different scales
Polar Legend
Color Demo
Inset Locator Demo
Inset Locator Demo2
Make Room For Ylabel Using Axesgrid
Simple Axes Divider 3
Anatomy of a figure
Bachelor's degrees by gender
Anscombe's quartet
Multiple Yaxis With Spines
Major and minor ticks
Text in Matplotlib Plots | |
doc_2599 |
Test whether any array element along a given axis evaluates to True. Returns single boolean unless axis is not None Parameters
aarray_like
Input array or object that can be converted to an array.
axisNone or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed. The default (axis=None) is to perform a logical OR over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis. New in version 1.7.0. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before.
outndarray, optional
Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of a). See Output type determination for more details.
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 input array. If the default value is passed, then keepdims will not be passed through to the any method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.
wherearray_like of bool, optional
Elements to include in checking for any True values. See reduce for details. New in version 1.20.0. Returns
anybool or ndarray
A new boolean or ndarray is returned unless out is specified, in which case a reference to out is returned. See also ndarray.any
equivalent method all
Test whether all elements along a given axis evaluate to True. Notes Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero. Examples >>> np.any([[True, False], [True, True]])
True
>>> np.any([[True, False], [False, False]], axis=0)
array([ True, False])
>>> np.any([-1, 0, 5])
True
>>> np.any(np.nan)
True
>>> np.any([[True, False], [False, False]], where=[[False], [True]])
False
>>> o=np.array(False)
>>> z=np.any([-1, 4, 5], out=o)
>>> z, o
(array(True), array(True))
>>> # Check now that z is a reference to o
>>> z is o
True
>>> id(z), id(o) # identity of z and o
(191614240, 191614240) |
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