_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_400 | Format a pretty argument spec from the values returned by getfullargspec(). The first seven arguments are (args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations). The other six arguments are functions that are called to turn argument names, * argument name, ** argument name, default values, return annotation and individual annotations into strings, respectively. For example: >>> from inspect import formatargspec, getfullargspec
>>> def f(a: int, b: float):
... pass
...
>>> formatargspec(*getfullargspec(f))
'(a: int, b: float)'
Deprecated since version 3.5: Use signature() and Signature Object, which provide a better introspecting API for callables. | |
doc_401 | Parameters
filename – a string, used as filename Create and write docstring-dictionary to a Python script with the given filename. This function has to be called explicitly (it is not used by the turtle graphics classes). The docstring dictionary will be written to the Python script filename.py. It is intended to serve as a template for translation of the docstrings into different languages. | |
doc_402 | Renders the formset with the template_name_table template. | |
doc_403 | A datetime designating when the account was created. Is set to the current date/time by default when the account is created. | |
doc_404 |
Bases: torch.distributions.gamma.Gamma Creates a Chi2 distribution parameterized by shape parameter df. This is exactly equivalent to Gamma(alpha=0.5*df, beta=0.5) Example: >>> m = Chi2(torch.tensor([1.0]))
>>> m.sample() # Chi2 distributed with shape df=1
tensor([ 0.1046])
Parameters
df (float or Tensor) – shape parameter of the distribution
arg_constraints = {'df': GreaterThan(lower_bound=0.0)}
property df
expand(batch_shape, _instance=None) [source] | |
doc_405 | A variant of the map() method which returns a AsyncResult object. If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead. If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance. Callbacks should complete immediately since otherwise the thread which handles the results will get blocked. | |
doc_406 | This class is designed to sit between an XMLReader and the client application’s event handlers. By default, it does nothing but pass requests up to the reader and events on to the handlers unmodified, but subclasses can override specific methods to modify the event stream or the configuration requests as they pass through. | |
doc_407 | class sklearn.feature_extraction.FeatureHasher(n_features=1048576, *, input_type='dict', dtype=<class 'numpy.float64'>, alternate_sign=True) [source]
Implements feature hashing, aka the hashing trick. This class turns sequences of symbolic feature names (strings) into scipy.sparse matrices, using a hash function to compute the matrix column corresponding to a name. The hash function employed is the signed 32-bit version of Murmurhash3. Feature names of type byte string are used as-is. Unicode strings are converted to UTF-8 first, but no Unicode normalization is done. Feature values must be (finite) numbers. This class is a low-memory alternative to DictVectorizer and CountVectorizer, intended for large-scale (online) learning and situations where memory is tight, e.g. when running prediction code on embedded devices. Read more in the User Guide. New in version 0.13. Parameters
n_featuresint, default=2**20
The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners.
input_type{“dict”, “pair”, “string”}, default=”dict”
Either “dict” (the default) to accept dictionaries over (feature_name, value); “pair” to accept pairs of (feature_name, value); or “string” to accept single strings. feature_name should be a string, while value should be a number. In the case of “string”, a value of 1 is implied. The feature_name is hashed to find the appropriate column for the feature. The value’s sign might be flipped in the output (but see non_negative, below).
dtypenumpy dtype, default=np.float64
The type of feature values. Passed to scipy.sparse matrix constructors as the dtype argument. Do not set this to bool, np.boolean or any unsigned integer type.
alternate_signbool, default=True
When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection. .. versionchanged:: 0.19
alternate_sign replaces the now deprecated non_negative parameter. See also
DictVectorizer
Vectorizes string-valued features using a hash table.
sklearn.preprocessing.OneHotEncoder
Handles nominal/categorical features. Examples >>> from sklearn.feature_extraction import FeatureHasher
>>> h = FeatureHasher(n_features=10)
>>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
>>> f = h.transform(D)
>>> f.toarray()
array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.],
[ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])
Methods
fit([X, y]) No-op.
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(raw_X) Transform a sequence of instances to a scipy.sparse matrix.
fit(X=None, y=None) [source]
No-op. This method doesn’t do anything. It exists purely for compatibility with the scikit-learn transformer API. Parameters
Xndarray
Returns
selfFeatureHasher
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(raw_X) [source]
Transform a sequence of instances to a scipy.sparse matrix. Parameters
raw_Xiterable over iterable over raw features, length = n_samples
Samples. Each sample must be iterable an (e.g., a list or tuple) containing/generating feature names (and optionally values, see the input_type constructor argument) which will be hashed. raw_X need not support the len function, so it can be the result of a generator; n_samples is determined on the fly. Returns
Xsparse matrix of shape (n_samples, n_features)
Feature matrix, for use with estimators or further transformers.
Examples using sklearn.feature_extraction.FeatureHasher
FeatureHasher and DictVectorizer Comparison | |
doc_408 | acosh() -> Tensor See torch.arccosh() | |
doc_409 |
Returns whether the kernel is stationary. | |
doc_410 | Return a ctypes object allocated from shared memory. By default the return value is actually a synchronized wrapper for the object. The object itself can be accessed via the value attribute of a Value. typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array module. *args is passed on to the constructor for the type. If lock is True (the default) then a new recursive lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”. Operations like += which involve a read and write are not atomic. So if, for instance, you want to atomically increment a shared value it is insufficient to just do counter.value += 1
Assuming the associated lock is recursive (which it is by default) you can instead do with counter.get_lock():
counter.value += 1
Note that lock is a keyword-only argument. | |
doc_411 | bytearray.startswith(prefix[, start[, end]])
Return True if the binary data starts with the specified prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position. The prefix(es) to search for may be any bytes-like object. | |
doc_412 | Whether this is a multilabel classifier | |
doc_413 | Raised when a payload is added to a Message object using add_payload(), but the payload is already a scalar and the message’s Content-Type main type is not either multipart or missing. MultipartConversionError multiply inherits from MessageError and the built-in TypeError. Since Message.add_payload() is deprecated, this exception is rarely raised in practice. However the exception may also be raised if the attach() method is called on an instance of a class derived from MIMENonMultipart (e.g. MIMEImage). | |
doc_414 |
Fit linear model with coordinate descent. Fit is on grid of alphas and best alpha estimated by cross-validation. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output, X can be sparse.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values. | |
doc_415 | See Migration guide for more details. tf.compat.v1.random.categorical
tf.random.categorical(
logits, num_samples, dtype=None, seed=None, name=None
)
Example: # samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.categorical(tf.math.log([[0.5, 0.5]]), 5)
Args
logits 2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :] represents the unnormalized log-probabilities for all classes.
num_samples 0-D. Number of independent samples to draw for each row slice.
dtype integer type to use for the output. Defaults to int64.
seed A Python integer. Used to create a random seed for the distribution. See tf.random.set_seed for behavior.
name Optional name for the operation.
Returns The drawn samples of shape [batch_size, num_samples]. | |
doc_416 | This method is called whenever an exception occurs that should be handled. A special case is HTTPException which is forwarded to the handle_http_exception() method. This function will either return a response value or reraise the exception with the same traceback. Changelog Changed in version 1.0: Key errors raised from request data like form show the bad key in debug mode rather than a generic bad request message. New in version 0.7. Parameters
e (Exception) – Return type
Union[werkzeug.exceptions.HTTPException, Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], Union[Headers, Dict[str, Union[str, List[str], Tuple[str, …]]], List[Tuple[str, Union[str, List[str], Tuple[str, …]]]]]], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], int], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], int, Union[Headers, Dict[str, Union[str, List[str], Tuple[str, …]]], List[Tuple[str, Union[str, List[str], Tuple[str, …]]]]]], WSGIApplication] | |
doc_417 |
Scalar method identical to the corresponding array attribute. Please see ndarray.setfield. | |
doc_418 |
Set padding of Z data limits prior to autoscaling. m times the data interval will be added to each end of that interval before it is used in autoscaling. accepts: float in range 0 to 1 | |
doc_419 | tf.keras.layers.Input Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.Input, tf.compat.v1.keras.layers.Input
tf.keras.Input(
shape=None, batch_size=None, name=None, dtype=None, sparse=False, tensor=None,
ragged=False, **kwargs
)
A Keras tensor is a TensorFlow symbolic tensor object, which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. For instance, if a, b and c are Keras tensors, it becomes possible to do: model = Model(input=[a, b], output=c)
Arguments
shape A shape tuple (integers), not including the batch size. For instance, shape=(32,) indicates that the expected input will be batches of 32-dimensional vectors. Elements of this tuple can be None; 'None' elements represent dimensions where the shape is not known.
batch_size optional static batch size (integer).
name An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
dtype The data type expected by the input, as a string (float32, float64, int32...)
sparse A boolean specifying whether the placeholder to be created is sparse. Only one of 'ragged' and 'sparse' can be True. Note that, if sparse is False, sparse tensors can still be passed into the input - they will be densified with a default value of 0.
tensor Optional existing tensor to wrap into the Input layer. If set, the layer will use the tf.TypeSpec of this tensor rather than creating a new placeholder tensor.
ragged A boolean specifying whether the placeholder to be created is ragged. Only one of 'ragged' and 'sparse' can be True. In this case, values of 'None' in the 'shape' argument represent ragged dimensions. For more information about RaggedTensors, see this guide.
**kwargs deprecated arguments support. Supports batch_shape and batch_input_shape.
Returns A tensor.
Example: # this is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)
Note that even if eager execution is enabled, Input produces a symbolic tensor (i.e. a placeholder). This symbolic tensor can be used with other TensorFlow ops, as such: x = Input(shape=(32,))
y = tf.square(x)
Raises
ValueError If both sparse and ragged are provided.
ValueError If both shape and (batch_input_shape or batch_shape) are provided.
ValueError If both shape and tensor are None.
ValueError if any unrecognized parameters are provided. | |
doc_420 | '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_421 |
Backward fill the new missing values in the resampled data. In statistics, imputation is the process of replacing missing data with substituted values [1]. When resampling data, missing values may appear (e.g., when the resampling frequency is higher than the original frequency). The backward fill will replace NaN values that appeared in the resampled data with the next value in the original sequence. Missing values that existed in the original data will not be modified. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series, DataFrame
An upsampled Series or DataFrame with backward filled NaN values. See also bfill
Alias of backfill. fillna
Fill NaN values using the specified method, which can be ‘backfill’. nearest
Fill NaN values with nearest neighbor starting from center. ffill
Forward fill NaN values. Series.fillna
Fill NaN values in the Series using the specified method, which can be ‘backfill’. DataFrame.fillna
Fill NaN values in the DataFrame using the specified method, which can be ‘backfill’. References 1
https://en.wikipedia.org/wiki/Imputation_(statistics) Examples Resampling a Series:
>>> s = pd.Series([1, 2, 3],
... index=pd.date_range('20180101', periods=3, freq='h'))
>>> s
2018-01-01 00:00:00 1
2018-01-01 01:00:00 2
2018-01-01 02:00:00 3
Freq: H, dtype: int64
>>> s.resample('30min').bfill()
2018-01-01 00:00:00 1
2018-01-01 00:30:00 2
2018-01-01 01:00:00 2
2018-01-01 01:30:00 3
2018-01-01 02:00:00 3
Freq: 30T, dtype: int64
>>> s.resample('15min').bfill(limit=2)
2018-01-01 00:00:00 1.0
2018-01-01 00:15:00 NaN
2018-01-01 00:30:00 2.0
2018-01-01 00:45:00 2.0
2018-01-01 01:00:00 2.0
2018-01-01 01:15:00 NaN
2018-01-01 01:30:00 3.0
2018-01-01 01:45:00 3.0
2018-01-01 02:00:00 3.0
Freq: 15T, dtype: float64
Resampling a DataFrame that has missing values:
>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
... index=pd.date_range('20180101', periods=3,
... freq='h'))
>>> df
a b
2018-01-01 00:00:00 2.0 1
2018-01-01 01:00:00 NaN 3
2018-01-01 02:00:00 6.0 5
>>> df.resample('30min').bfill()
a b
2018-01-01 00:00:00 2.0 1
2018-01-01 00:30:00 NaN 3
2018-01-01 01:00:00 NaN 3
2018-01-01 01:30:00 6.0 5
2018-01-01 02:00:00 6.0 5
>>> df.resample('15min').bfill(limit=2)
a b
2018-01-01 00:00:00 2.0 1.0
2018-01-01 00:15:00 NaN NaN
2018-01-01 00:30:00 NaN 3.0
2018-01-01 00:45:00 NaN 3.0
2018-01-01 01:00:00 NaN 3.0
2018-01-01 01:15:00 NaN NaN
2018-01-01 01:30:00 6.0 5.0
2018-01-01 01:45:00 6.0 5.0
2018-01-01 02:00:00 6.0 5.0 | |
doc_422 | See Migration guide for more details. tf.compat.v1.image.convert_image_dtype
tf.image.convert_image_dtype(
image, dtype, saturate=False, name=None
)
Images that are represented using floating point values are expected to have values in the range [0,1). Image data stored in integer data types are expected to have values in the range [0,MAX], where MAX is the largest positive representable number for the data type. This op converts between data types, scaling the values appropriately before casting. Note that converting from floating point inputs to integer types may lead to over/underflow problems. Set saturate to True to avoid such problem in problematic conversions. If enabled, saturation will clip the output into the allowed range before performing a potentially dangerous cast (and only before performing such a cast, i.e., when casting from a floating point to an integer type, and when casting from a signed to an unsigned type; saturate has no effect on casts between floats, or on casts that increase the type's range). Usage Example:
x = [[[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0]],
[[7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]]]
tf.image.convert_image_dtype(x, dtype=tf.float16, saturate=False)
<tf.Tensor: shape=(2, 2, 3), dtype=float16, numpy=
array([[[ 1., 2., 3.],
[ 4., 5., 6.]],
[[ 7., 8., 9.],
[10., 11., 12.]]], dtype=float16)>
Args
image An image.
dtype A DType to convert image to.
saturate If True, clip the input before casting (if necessary).
name A name for this operation (optional).
Returns image, converted to dtype.
Raises
AttributeError Raises an attribute error when dtype is neither float nor integer | |
doc_423 | tf.compat.v1.nn.depthwise_conv2d_native(
input, filter, strides, padding, data_format='NHWC', dilations=[1, 1,
1, 1], name=None
)
Given an input tensor of shape [batch, in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, channel_multiplier], containing in_channels convolutional filters of depth 1, depthwise_conv2d applies a different filter to each input channel (expanding from 1 channel to channel_multiplier channels for each), then concatenates the results together. Thus, the output has in_channels * channel_multiplier channels. for k in 0..in_channels-1
for q in 0..channel_multiplier-1
output[b, i, j, k * channel_multiplier + q] =
sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
filter[di, dj, k, q]
Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].
Args
input A Tensor. Must be one of the following types: half, bfloat16, float32, float64.
filter A Tensor. Must have the same type as input.
strides A list of ints. 1-D of length 4. The stride of the sliding window for each dimension of input.
padding Controls how to pad the image before applying the convolution. Can be the string "SAME" or "VALID" indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is "NHWC", this should be in the form [[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is "NCHW", this should be in the form [[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]].
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].
dilations An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_424 |
Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Parameters
percentiles:list-like of numbers, optional
The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
include:‘all’, list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored for Series. Here are the options: ‘all’ : All columns of the input will be included in the output. A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category' None (default) : The result will include all numeric columns.
exclude:list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored for Series. Here are the options: A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category' None (default) : The result will exclude nothing.
datetime_is_numeric:bool, default False
Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default. New in version 1.1.0. Returns
Series or DataFrame
Summary statistics of the Series or Dataframe provided. See also DataFrame.count
Count number of non-NA/null observations. DataFrame.max
Maximum of the values in the object. DataFrame.min
Minimum of the values in the object. DataFrame.mean
Mean of the values. DataFrame.std
Standard deviation of the observations. DataFrame.select_dtypes
Subset of a DataFrame including/excluding columns based on their dtype. Notes For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median. For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items. If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type. The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series. Examples Describing a numeric Series.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical Series.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp Series.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count 3
mean 2006-09-01 08:00:00
min 2000-01-01 00:00:00
25% 2004-12-31 12:00:00
50% 2010-01-01 00:00:00
75% 2010-01-01 00:00:00
max 2010-01-01 00:00:00
dtype: object
Describing a DataFrame. By default only numeric fields are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a DataFrame regardless of data type.
>>> df.describe(include='all')
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN a
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a DataFrame by accessing it as an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a DataFrame description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a DataFrame description.
>>> df.describe(include=[object])
object
count 3
unique 3
top a
freq 1
Including only categorical columns from a DataFrame description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top d
freq 1
Excluding numeric columns from a DataFrame description.
>>> df.describe(exclude=[np.number])
categorical object
count 3 3
unique 3 3
top f a
freq 1 1
Excluding object columns from a DataFrame description.
>>> df.describe(exclude=[object])
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0 | |
doc_425 |
Close the file. | |
doc_426 | Compare the files in the two directories dir1 and dir2 whose names are given by common. Returns three lists of file names: match, mismatch, errors. match contains the list of files that match, mismatch contains the names of those that don’t, and errors lists the names of files which could not be compared. Files are listed in errors if they don’t exist in one of the directories, the user lacks permission to read them or if the comparison could not be done for some other reason. The shallow parameter has the same meaning and default value as for filecmp.cmp(). For example, cmpfiles('a', 'b', ['c', 'd/e']) will compare a/c with b/c and a/d/e with b/d/e. 'c' and 'd/e' will each be in one of the three returned lists. | |
doc_427 | See Migration guide for more details. tf.compat.v1.raw_ops.Dilation2DBackpropInput
tf.raw_ops.Dilation2DBackpropInput(
input, filter, out_backprop, strides, rates, padding, name=None
)
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D with shape [batch, in_height, in_width, depth].
filter A Tensor. Must have the same type as input. 3-D with shape [filter_height, filter_width, depth].
out_backprop A Tensor. Must have the same type as input. 4-D with shape [batch, out_height, out_width, depth].
strides A list of ints that has length >= 4. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: [1, stride_height, stride_width, 1].
rates A list of ints that has length >= 4. 1-D of length 4. The input stride for atrous morphological dilation. Must be: [1, rate_height, rate_width, 1].
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_428 | logical_invert() is a logical operation. The result is the digit-wise inversion of the operand. | |
doc_429 |
Create a new file editing window. Open…
Open an existing file with an Open dialog. Recent Files
Open a list of recent files. Click one to open it. Open Module…
Open an existing module (searches sys.path). Class Browser
Show functions, classes, and methods in the current Editor file in a tree structure. In the shell, open a module first. Path Browser
Show sys.path directories, modules, functions, classes and methods in a tree structure. Save
Save the current window to the associated file, if there is one. Windows that have been changed since being opened or last saved have a * before and after the window title. If there is no associated file, do Save As instead. Save As…
Save the current window with a Save As dialog. The file saved becomes the new associated file for the window. Save Copy As…
Save the current window to different file without changing the associated file. Print Window
Print the current window to the default printer. Close
Close the current window (ask to save if unsaved). Exit
Close all windows and quit IDLE (ask to save unsaved windows). Edit menu (Shell and Editor) Undo
Undo the last change to the current window. A maximum of 1000 changes may be undone. Redo
Redo the last undone change to the current window. Cut
Copy selection into the system-wide clipboard; then delete the selection. Copy
Copy selection into the system-wide clipboard. Paste
Insert contents of the system-wide clipboard into the current window. The clipboard functions are also available in context menus. Select All
Select the entire contents of the current window. Find…
Open a search dialog with many options Find Again
Repeat the last search, if there is one. Find Selection
Search for the currently selected string, if there is one. Find in Files…
Open a file search dialog. Put results in a new output window. Replace…
Open a search-and-replace dialog. Go to Line
Move the cursor to the beginning of the line requested and make that line visible. A request past the end of the file goes to the end. Clear any selection and update the line and column status. Show Completions
Open a scrollable list allowing selection of existing names. See Completions in the Editing and navigation section below. Expand Word
Expand a prefix you have typed to match a full word in the same window; repeat to get a different expansion. Show call tip
After an unclosed parenthesis for a function, open a small window with function parameter hints. See Calltips in the Editing and navigation section below. Show surrounding parens
Highlight the surrounding parenthesis. Format menu (Editor window only) Indent Region
Shift selected lines right by the indent width (default 4 spaces). Dedent Region
Shift selected lines left by the indent width (default 4 spaces). Comment Out Region
Insert ## in front of selected lines. Uncomment Region
Remove leading # or ## from selected lines. Tabify Region
Turn leading stretches of spaces into tabs. (Note: We recommend using 4 space blocks to indent Python code.) Untabify Region
Turn all tabs into the correct number of spaces. Toggle Tabs
Open a dialog to switch between indenting with spaces and tabs. New Indent Width
Open a dialog to change indent width. The accepted default by the Python community is 4 spaces. Format Paragraph
Reformat the current blank-line-delimited paragraph in comment block or multiline string or selected line in a string. All lines in the paragraph will be formatted to less than N columns, where N defaults to 72. Strip trailing whitespace
Remove trailing space and other whitespace characters after the last non-whitespace character of a line by applying str.rstrip to each line, including lines within multiline strings. Except for Shell windows, remove extra newlines at the end of the file. Run menu (Editor window only) Run Module
Do Check Module. If no error, restart the shell to clean the environment, then execute the module. Output is displayed in the Shell window. Note that output requires use of print or write. When execution is complete, the Shell retains focus and displays a prompt. At this point, one may interactively explore the result of execution. This is similar to executing a file with python -i file at a command line. Run… Customized
Same as Run Module, but run the module with customized settings. Command Line Arguments extend sys.argv as if passed on a command line. The module can be run in the Shell without restarting. Check Module
Check the syntax of the module currently open in the Editor window. If the module has not been saved IDLE will either prompt the user to save or autosave, as selected in the General tab of the Idle Settings dialog. If there is a syntax error, the approximate location is indicated in the Editor window. Python Shell
Open or wake up the Python Shell window. Shell menu (Shell window only) View Last Restart
Scroll the shell window to the last Shell restart. Restart Shell
Restart the shell to clean the environment and reset display and exception handling. Previous History
Cycle through earlier commands in history which match the current entry. Next History
Cycle through later commands in history which match the current entry. Interrupt Execution
Stop a running program. Debug menu (Shell window only) Go to File/Line
Look on the current line. with the cursor, and the line above for a filename and line number. If found, open the file if not already open, and show the line. Use this to view source lines referenced in an exception traceback and lines found by Find in Files. Also available in the context menu of the Shell window and Output windows. Debugger (toggle)
When activated, code entered in the Shell or run from an Editor will run under the debugger. In the Editor, breakpoints can be set with the context menu. This feature is still incomplete and somewhat experimental. Stack Viewer
Show the stack traceback of the last exception in a tree widget, with access to locals and globals. Auto-open Stack Viewer
Toggle automatically opening the stack viewer on an unhandled exception. Options menu (Shell and Editor) Configure IDLE
Open a configuration dialog and change preferences for the following: fonts, indentation, keybindings, text color themes, startup windows and size, additional help sources, and extensions. On macOS, open the configuration dialog by selecting Preferences in the application menu. For more details, see Setting preferences under Help and preferences. Most configuration options apply to all windows or all future windows. The option items below only apply to the active window. Show/Hide Code Context (Editor Window only)
Open a pane at the top of the edit window which shows the block context of the code which has scrolled above the top of the window. See Code Context in the Editing and Navigation section below. Show/Hide Line Numbers (Editor Window only)
Open a column to the left of the edit window which shows the number of each line of text. The default is off, which may be changed in the preferences (see Setting preferences). Zoom/Restore Height
Toggles the window between normal size and maximum height. The initial size defaults to 40 lines by 80 chars unless changed on the General tab of the Configure IDLE dialog. The maximum height for a screen is determined by momentarily maximizing a window the first time one is zoomed on the screen. Changing screen settings may invalidate the saved height. This toggle has no effect when a window is maximized. Window menu (Shell and Editor) Lists the names of all open windows; select one to bring it to the foreground (deiconifying it if necessary). Help menu (Shell and Editor) About IDLE
Display version, copyright, license, credits, and more. IDLE Help
Display this IDLE document, detailing the menu options, basic editing and navigation, and other tips. Python Docs
Access local Python documentation, if installed, or start a web browser and open docs.python.org showing the latest Python documentation. Turtle Demo
Run the turtledemo module with example Python code and turtle drawings. Additional help sources may be added here with the Configure IDLE dialog under the General tab. See the Help sources subsection below for more on Help menu choices. Context Menus Open a context menu by right-clicking in a window (Control-click on macOS). Context menus have the standard clipboard functions also on the Edit menu. Cut
Copy selection into the system-wide clipboard; then delete the selection. Copy
Copy selection into the system-wide clipboard. Paste
Insert contents of the system-wide clipboard into the current window. Editor windows also have breakpoint functions. Lines with a breakpoint set are specially marked. Breakpoints only have an effect when running under the debugger. Breakpoints for a file are saved in the user’s .idlerc directory. Set Breakpoint
Set a breakpoint on the current line. Clear Breakpoint
Clear the breakpoint on that line. Shell and Output windows also have the following. Go to file/line
Same as in Debug menu. The Shell window also has an output squeezing facility explained in the Python Shell window subsection below. Squeeze
If the cursor is over an output line, squeeze all the output between the code above and the prompt below down to a ‘Squeezed text’ label. Editing and navigation Editor windows IDLE may open editor windows when it starts, depending on settings and how you start IDLE. Thereafter, use the File menu. There can be only one open editor window for a given file. The title bar contains the name of the file, the full path, and the version of Python and IDLE running the window. The status bar contains the line number (‘Ln’) and column number (‘Col’). Line numbers start with 1; column numbers with 0. IDLE assumes that files with a known .py* extension contain Python code and that other files do not. Run Python code with the Run menu. Key bindings In this section, ‘C’ refers to the Control key on Windows and Unix and the Command key on macOS.
Backspace deletes to the left; Del deletes to the right
C-Backspace delete word left; C-Del delete word to the right Arrow keys and Page Up/Page Down to move around
C-LeftArrow and C-RightArrow moves by words
Home/End go to begin/end of line
C-Home/C-End go to begin/end of file
Some useful Emacs bindings are inherited from Tcl/Tk:
C-a beginning of line
C-e end of line
C-k kill line (but doesn’t put it in clipboard)
C-l center window around the insertion point
C-b go backward one character without deleting (usually you can also use the cursor key for this)
C-f go forward one character without deleting (usually you can also use the cursor key for this)
C-p go up one line (usually you can also use the cursor key for this)
C-d delete next character Standard keybindings (like C-c to copy and C-v to paste) may work. Keybindings are selected in the Configure IDLE dialog. Automatic indentation After a block-opening statement, the next line is indented by 4 spaces (in the Python Shell window by one tab). After certain keywords (break, return etc.) the next line is dedented. In leading indentation, Backspace deletes up to 4 spaces if they are there. Tab inserts spaces (in the Python Shell window one tab), number depends on Indent width. Currently, tabs are restricted to four spaces due to Tcl/Tk limitations. See also the indent/dedent region commands on the Format menu. Completions Completions are supplied, when requested and available, for module names, attributes of classes or functions, or filenames. Each request method displays a completion box with existing names. (See tab completions below for an exception.) For any box, change the name being completed and the item highlighted in the box by typing and deleting characters; by hitting Up, Down, PageUp, PageDown, Home, and End keys; and by a single click within the box. Close the box with Escape, Enter, and double Tab keys or clicks outside the box. A double click within the box selects and closes. One way to open a box is to type a key character and wait for a predefined interval. This defaults to 2 seconds; customize it in the settings dialog. (To prevent auto popups, set the delay to a large number of milliseconds, such as 100000000.) For imported module names or class or function attributes, type ‘.’. For filenames in the root directory, type os.sep or os.altsep immediately after an opening quote. (On Windows, one can specify a drive first.) Move into subdirectories by typing a directory name and a separator. Instead of waiting, or after a box is closed, open a completion box immediately with Show Completions on the Edit menu. The default hot key is C-space. If one types a prefix for the desired name before opening the box, the first match or near miss is made visible. The result is the same as if one enters a prefix after the box is displayed. Show Completions after a quote completes filenames in the current directory instead of a root directory. Hitting Tab after a prefix usually has the same effect as Show Completions. (With no prefix, it indents.) However, if there is only one match to the prefix, that match is immediately added to the editor text without opening a box. Invoking ‘Show Completions’, or hitting Tab after a prefix, outside of a string and without a preceding ‘.’ opens a box with keywords, builtin names, and available module-level names. When editing code in an editor (as oppose to Shell), increase the available module-level names by running your code and not restarting the Shell thereafter. This is especially useful after adding imports at the top of a file. This also increases possible attribute completions. Completion boxes intially exclude names beginning with ‘_’ or, for modules, not included in ‘__all__’. The hidden names can be accessed by typing ‘_’ after ‘.’, either before or after the box is opened. Calltips A calltip is shown automatically when one types ( after the name of an accessible function. A function name expression may include dots and subscripts. A calltip remains until it is clicked, the cursor is moved out of the argument area, or ) is typed. Whenever the cursor is in the argument part of a definition, select Edit and “Show Call Tip” on the menu or enter its shortcut to display a calltip. The calltip consists of the function’s signature and docstring up to the latter’s first blank line or the fifth non-blank line. (Some builtin functions lack an accessible signature.) A ‘/’ or ‘*’ in the signature indicates that the preceding or following arguments are passed by position or name (keyword) only. Details are subject to change. In Shell, the accessible functions depends on what modules have been imported into the user process, including those imported by Idle itself, and which definitions have been run, all since the last restart. For example, restart the Shell and enter itertools.count(. A calltip appears because Idle imports itertools into the user process for its own use. (This could change.) Enter turtle.write( and nothing appears. Idle does not itself import turtle. The menu entry and shortcut also do nothing. Enter import turtle. Thereafter, turtle.write( will display a calltip. In an editor, import statements have no effect until one runs the file. One might want to run a file after writing import statements, after adding function definitions, or after opening an existing file. Code Context Within an editor window containing Python code, code context can be toggled in order to show or hide a pane at the top of the window. When shown, this pane freezes the opening lines for block code, such as those beginning with class, def, or if keywords, that would have otherwise scrolled out of view. The size of the pane will be expanded and contracted as needed to show the all current levels of context, up to the maximum number of lines defined in the Configure IDLE dialog (which defaults to 15). If there are no current context lines and the feature is toggled on, a single blank line will display. Clicking on a line in the context pane will move that line to the top of the editor. The text and background colors for the context pane can be configured under the Highlights tab in the Configure IDLE dialog. Python Shell window With IDLE’s Shell, one enters, edits, and recalls complete statements. Most consoles and terminals only work with a single physical line at a time. When one pastes code into Shell, it is not compiled and possibly executed until one hits Return. One may edit pasted code first. If one pastes more that one statement into Shell, the result will be a SyntaxError when multiple statements are compiled as if they were one. The editing features described in previous subsections work when entering code interactively. IDLE’s Shell window also responds to the following keys.
C-c interrupts executing command
C-d sends end-of-file; closes window if typed at a >>> prompt
Alt-/ (Expand word) is also useful to reduce typing Command history
Alt-p retrieves previous command matching what you have typed. On macOS use C-p.
Alt-n retrieves next. On macOS use C-n.
Return while on any previous command retrieves that command Text colors Idle defaults to black on white text, but colors text with special meanings. For the shell, these are shell output, shell error, user output, and user error. For Python code, at the shell prompt or in an editor, these are keywords, builtin class and function names, names following class and def, strings, and comments. For any text window, these are the cursor (when present), found text (when possible), and selected text. Text coloring is done in the background, so uncolorized text is occasionally visible. To change the color scheme, use the Configure IDLE dialog Highlighting tab. The marking of debugger breakpoint lines in the editor and text in popups and dialogs is not user-configurable. Startup and code execution Upon startup with the -s option, IDLE will execute the file referenced by the environment variables IDLESTARTUP or PYTHONSTARTUP. IDLE first checks for IDLESTARTUP; if IDLESTARTUP is present the file referenced is run. If IDLESTARTUP is not present, IDLE checks for PYTHONSTARTUP. Files referenced by these environment variables are convenient places to store functions that are used frequently from the IDLE shell, or for executing import statements to import common modules. In addition, Tk also loads a startup file if it is present. Note that the Tk file is loaded unconditionally. This additional file is .Idle.py and is looked for in the user’s home directory. Statements in this file will be executed in the Tk namespace, so this file is not useful for importing functions to be used from IDLE’s Python shell. Command line usage idle.py [-c command] [-d] [-e] [-h] [-i] [-r file] [-s] [-t title] [-] [arg] ...
-c command run command in the shell window
-d enable debugger and open shell window
-e open editor window
-h print help message with legal combinations and exit
-i open shell window
-r file run file in shell window
-s run $IDLESTARTUP or $PYTHONSTARTUP first, in shell window
-t title set title of shell window
- run stdin in shell (- must be last option before args)
If there are arguments: If -, -c, or r is used, all arguments are placed in sys.argv[1:...] and sys.argv[0] is set to '', '-c', or '-r'. No editor window is opened, even if that is the default set in the Options dialog. Otherwise, arguments are files opened for editing and sys.argv reflects the arguments passed to IDLE itself. Startup failure IDLE uses a socket to communicate between the IDLE GUI process and the user code execution process. A connection must be established whenever the Shell starts or restarts. (The latter is indicated by a divider line that says ‘RESTART’). If the user process fails to connect to the GUI process, it usually displays a Tk error box with a ‘cannot connect’ message that directs the user here. It then exits. One specific connection failure on Unix systems results from misconfigured masquerading rules somewhere in a system’s network setup. When IDLE is started from a terminal, one will see a message starting with ** Invalid host:. The valid value is 127.0.0.1 (idlelib.rpc.LOCALHOST). One can diagnose with tcpconnect -irv 127.0.0.1 6543 in one terminal window and tcplisten <same args> in another. A common cause of failure is a user-written file with the same name as a standard library module, such as random.py and tkinter.py. When such a file is located in the same directory as a file that is about to be run, IDLE cannot import the stdlib file. The current fix is to rename the user file. Though less common than in the past, an antivirus or firewall program may stop the connection. If the program cannot be taught to allow the connection, then it must be turned off for IDLE to work. It is safe to allow this internal connection because no data is visible on external ports. A similar problem is a network mis-configuration that blocks connections. Python installation issues occasionally stop IDLE: multiple versions can clash, or a single installation might need admin access. If one undo the clash, or cannot or does not want to run as admin, it might be easiest to completely remove Python and start over. A zombie pythonw.exe process could be a problem. On Windows, use Task Manager to check for one and stop it if there is. Sometimes a restart initiated by a program crash or Keyboard Interrupt (control-C) may fail to connect. Dismissing the error box or using Restart Shell on the Shell menu may fix a temporary problem. When IDLE first starts, it attempts to read user configuration files in ~/.idlerc/ (~ is one’s home directory). If there is a problem, an error message should be displayed. Leaving aside random disk glitches, this can be prevented by never editing the files by hand. Instead, use the configuration dialog, under Options. Once there is an error in a user configuration file, the best solution may be to delete it and start over with the settings dialog. If IDLE quits with no message, and it was not started from a console, try starting it from a console or terminal (python -m idlelib) and see if this results in an error message. On Unix-based systems with tcl/tk older than 8.6.11 (see About IDLE) certain characters of certain fonts can cause a tk failure with a message to the terminal. This can happen either if one starts IDLE to edit a file with such a character or later when entering such a character. If one cannot upgrade tcl/tk, then re-configure IDLE to use a font that works better. Running user code With rare exceptions, the result of executing Python code with IDLE is intended to be the same as executing the same code by the default method, directly with Python in a text-mode system console or terminal window. However, the different interface and operation occasionally affect visible results. For instance, sys.modules starts with more entries, and threading.active_count() returns 2 instead of 1. By default, IDLE runs user code in a separate OS process rather than in the user interface process that runs the shell and editor. In the execution process, it replaces sys.stdin, sys.stdout, and sys.stderr with objects that get input from and send output to the Shell window. The original values stored in sys.__stdin__, sys.__stdout__, and sys.__stderr__ are not touched, but may be None. Sending print output from one process to a text widget in another is slower than printing to a system terminal in the same process. This has the most effect when printing multiple arguments, as the string for each argument, each separator, the newline are sent separately. For development, this is usually not a problem, but if one wants to print faster in IDLE, format and join together everything one wants displayed together and then print a single string. Both format strings and str.join() can help combine fields and lines. IDLE’s standard stream replacements are not inherited by subprocesses created in the execution process, whether directly by user code or by modules such as multiprocessing. If such subprocess use input from sys.stdin or print or write to sys.stdout or sys.stderr, IDLE should be started in a command line window. The secondary subprocess will then be attached to that window for input and output. If sys is reset by user code, such as with importlib.reload(sys), IDLE’s changes are lost and input from the keyboard and output to the screen will not work correctly. When Shell has the focus, it controls the keyboard and screen. This is normally transparent, but functions that directly access the keyboard and screen will not work. These include system-specific functions that determine whether a key has been pressed and if so, which. The IDLE code running in the execution process adds frames to the call stack that would not be there otherwise. IDLE wraps sys.getrecursionlimit and sys.setrecursionlimit to reduce the effect of the additional stack frames. When user code raises SystemExit either directly or by calling sys.exit, IDLE returns to a Shell prompt instead of exiting. User output in Shell When a program outputs text, the result is determined by the corresponding output device. When IDLE executes user code, sys.stdout and sys.stderr are connected to the display area of IDLE’s Shell. Some of its features are inherited from the underlying Tk Text widget. Others are programmed additions. Where it matters, Shell is designed for development rather than production runs. For instance, Shell never throws away output. A program that sends unlimited output to Shell will eventually fill memory, resulting in a memory error. In contrast, some system text windows only keep the last n lines of output. A Windows console, for instance, keeps a user-settable 1 to 9999 lines, with 300 the default. A Tk Text widget, and hence IDLE’s Shell, displays characters (codepoints) in the BMP (Basic Multilingual Plane) subset of Unicode. Which characters are displayed with a proper glyph and which with a replacement box depends on the operating system and installed fonts. Tab characters cause the following text to begin after the next tab stop. (They occur every 8 ‘characters’). Newline characters cause following text to appear on a new line. Other control characters are ignored or displayed as a space, box, or something else, depending on the operating system and font. (Moving the text cursor through such output with arrow keys may exhibit some surprising spacing behavior.) >>> s = 'a\tb\a<\x02><\r>\bc\nd' # Enter 22 chars.
>>> len(s)
14
>>> s # Display repr(s)
'a\tb\x07<\x02><\r>\x08c\nd'
>>> print(s, end='') # Display s as is.
# Result varies by OS and font. Try it.
The repr function is used for interactive echo of expression values. It returns an altered version of the input string in which control codes, some BMP codepoints, and all non-BMP codepoints are replaced with escape codes. As demonstrated above, it allows one to identify the characters in a string, regardless of how they are displayed. Normal and error output are generally kept separate (on separate lines) from code input and each other. They each get different highlight colors. For SyntaxError tracebacks, the normal ‘^’ marking where the error was detected is replaced by coloring the text with an error highlight. When code run from a file causes other exceptions, one may right click on a traceback line to jump to the corresponding line in an IDLE editor. The file will be opened if necessary. Shell has a special facility for squeezing output lines down to a ‘Squeezed text’ label. This is done automatically for output over N lines (N = 50 by default). N can be changed in the PyShell section of the General page of the Settings dialog. Output with fewer lines can be squeezed by right clicking on the output. This can be useful lines long enough to slow down scrolling. Squeezed output is expanded in place by double-clicking the label. It can also be sent to the clipboard or a separate view window by right-clicking the label. Developing tkinter applications IDLE is intentionally different from standard Python in order to facilitate development of tkinter programs. Enter import tkinter as tk;
root = tk.Tk() in standard Python and nothing appears. Enter the same in IDLE and a tk window appears. In standard Python, one must also enter root.update() to see the window. IDLE does the equivalent in the background, about 20 times a second, which is about every 50 milliseconds. Next enter b = tk.Button(root, text='button'); b.pack(). Again, nothing visibly changes in standard Python until one enters root.update(). Most tkinter programs run root.mainloop(), which usually does not return until the tk app is destroyed. If the program is run with python -i or from an IDLE editor, a >>> shell prompt does not appear until mainloop() returns, at which time there is nothing left to interact with. When running a tkinter program from an IDLE editor, one can comment out the mainloop call. One then gets a shell prompt immediately and can interact with the live application. One just has to remember to re-enable the mainloop call when running in standard Python. Running without a subprocess By default, IDLE executes user code in a separate subprocess via a socket, which uses the internal loopback interface. This connection is not externally visible and no data is sent to or received from the Internet. If firewall software complains anyway, you can ignore it. If the attempt to make the socket connection fails, Idle will notify you. Such failures are sometimes transient, but if persistent, the problem may be either a firewall blocking the connection or misconfiguration of a particular system. Until the problem is fixed, one can run Idle with the -n command line switch. If IDLE is started with the -n command line switch it will run in a single process and will not create the subprocess which runs the RPC Python execution server. This can be useful if Python cannot create the subprocess or the RPC socket interface on your platform. However, in this mode user code is not isolated from IDLE itself. Also, the environment is not restarted when Run/Run Module (F5) is selected. If your code has been modified, you must reload() the affected modules and re-import any specific items (e.g. from foo import baz) if the changes are to take effect. For these reasons, it is preferable to run IDLE with the default subprocess if at all possible. Deprecated since version 3.4. Help and preferences Help sources Help menu entry “IDLE Help” displays a formatted html version of the IDLE chapter of the Library Reference. The result, in a read-only tkinter text window, is close to what one sees in a web browser. Navigate through the text with a mousewheel, the scrollbar, or up and down arrow keys held down. Or click the TOC (Table of Contents) button and select a section header in the opened box. Help menu entry “Python Docs” opens the extensive sources of help, including tutorials, available at docs.python.org/x.y, where ‘x.y’ is the currently running Python version. If your system has an off-line copy of the docs (this may be an installation option), that will be opened instead. Selected URLs can be added or removed from the help menu at any time using the General tab of the Configure IDLE dialog. Setting preferences The font preferences, highlighting, keys, and general preferences can be changed via Configure IDLE on the Option menu. Non-default user settings are saved in a .idlerc directory in the user’s home directory. Problems caused by bad user configuration files are solved by editing or deleting one or more of the files in .idlerc. On the Font tab, see the text sample for the effect of font face and size on multiple characters in multiple languages. Edit the sample to add other characters of personal interest. Use the sample to select monospaced fonts. If particular characters have problems in Shell or an editor, add them to the top of the sample and try changing first size and then font. On the Highlights and Keys tab, select a built-in or custom color theme and key set. To use a newer built-in color theme or key set with older IDLEs, save it as a new custom theme or key set and it well be accessible to older IDLEs. IDLE on macOS Under System Preferences: Dock, one can set “Prefer tabs when opening documents” to “Always”. This setting is not compatible with the tk/tkinter GUI framework used by IDLE, and it breaks a few IDLE features. Extensions IDLE contains an extension facility. Preferences for extensions can be changed with the Extensions tab of the preferences dialog. See the beginning of config-extensions.def in the idlelib directory for further information. The only current default extension is zzdummy, an example also used for testing. | |
doc_430 | See Migration guide for more details. tf.compat.v1.raw_ops.BiasAddV1
tf.raw_ops.BiasAddV1(
value, bias, name=None
)
This is a deprecated version of BiasAdd and will be soon removed. This is a special case of tf.add where bias is restricted to be 1-D. Broadcasting is supported, so value may have any number of dimensions.
Args
value A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Any number of dimensions.
bias A Tensor. Must have the same type as value. 1-D with size the last dimension of value.
name A name for the operation (optional).
Returns A Tensor. Has the same type as value. | |
doc_431 |
Return whether derived is out-of-date relative to original or any of the RST files included in it using the RST include directive (includes). derived and original are full paths, and includes is optionally a list of full paths which may have been included in the original. | |
doc_432 | This class derives from BaseCookie and overrides value_decode() and value_encode(). SimpleCookie supports strings as cookie values. When setting the value, SimpleCookie calls the builtin str() to convert the value to a string. Values received from HTTP are kept as strings. | |
doc_433 | Enter post-mortem debugging of the traceback found in sys.last_traceback. | |
doc_434 |
Swap levels i and j in a MultiIndex. Default is to swap the two innermost levels of the index. Parameters
i, j:int or str
Levels of the indices to be swapped. Can pass level name as string.
copy:bool, default True
Whether to copy underlying data. Returns
Series
Series with levels swapped in MultiIndex. Examples
>>> s = pd.Series(
... ["A", "B", "A", "C"],
... index=[
... ["Final exam", "Final exam", "Coursework", "Coursework"],
... ["History", "Geography", "History", "Geography"],
... ["January", "February", "March", "April"],
... ],
... )
>>> s
Final exam History January A
Geography February B
Coursework History March A
Geography April C
dtype: object
In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.
>>> s.swaplevel()
Final exam January History A
February Geography B
Coursework March History A
April Geography C
dtype: object
By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.
>>> s.swaplevel(0)
January History Final exam A
February Geography Final exam B
March History Coursework A
April Geography Coursework C
dtype: object
We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.
>>> s.swaplevel(0, 1)
History Final exam January A
Geography Final exam February B
History Coursework March A
Geography Coursework April C
dtype: object | |
doc_435 | See Migration guide for more details. tf.compat.v1.raw_ops.ParallelInterleaveDatasetV2
tf.raw_ops.ParallelInterleaveDatasetV2(
input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls,
f, output_types, output_shapes, sloppy=False, name=None
)
The resulting dataset is similar to the InterleaveDataset, except that the dataset will fetch records from the interleaved datasets in parallel. The tf.data Python API creates instances of this op from Dataset.interleave() when the num_parallel_calls parameter of that method is set to any value other than None. By default, the output of this dataset will be deterministic, which may result in the dataset blocking if the next data item to be returned isn't available. In order to avoid head-of-line blocking, one can set the experimental_deterministic parameter of tf.data.Options to False, which can improve performance at the expense of non-determinism.
Args
input_dataset A Tensor of type variant. Dataset that produces a stream of arguments for the function f.
other_arguments A list of Tensor objects. Additional arguments to pass to f beyond those produced by input_dataset. Evaluated once when the dataset is instantiated.
cycle_length A Tensor of type int64. Number of datasets (each created by applying f to the elements of input_dataset) among which the ParallelInterleaveDatasetV2 will cycle in a round-robin fashion.
block_length A Tensor of type int64. Number of elements at a time to produce from each interleaved invocation of a dataset returned by f.
num_parallel_calls A Tensor of type int64. Determines the number of threads that should be used for fetching data from input datasets in parallel. The Python API tf.data.experimental.AUTOTUNE constant can be used to indicate that the level of parallelism should be autotuned.
f A function decorated with @Defun. A function mapping elements of input_dataset, concatenated with other_arguments, to a Dataset variant that contains elements matching output_types and output_shapes.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
sloppy An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_436 |
See torch.lu() | |
doc_437 | See Migration guide for more details. tf.compat.v1.erfc, tf.compat.v1.math.erfc
tf.math.erfc(
x, name=None
)
Args
x A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
name A name for the operation (optional).
Returns A Tensor. Has the same type as x. | |
doc_438 |
Bases: matplotlib.backends.backend_webagg_core.NavigationToolbar2WebAgg toolitems=[('Home', 'Reset original view', 'fa fa-home icon-home', 'home'), ('Back', 'Back to previous view', 'fa fa-arrow-left icon-arrow-left', 'back'), ('Forward', 'Forward to next view', 'fa fa-arrow-right icon-arrow-right', 'forward'), (None, None, None, None), ('Pan', 'Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect', 'fa fa-arrows icon-move', 'pan'), ('Zoom', 'Zoom to rectangle\nx/y fixes axis', 'fa fa-square-o icon-check-empty', 'zoom'), (None, None, None, None), ('Download', 'Download plot', 'fa fa-floppy-o icon-save', 'download')] | |
doc_439 | find the parent of a subsurface get_parent() -> Surface Returns the parent Surface of a subsurface. If this is not a subsurface then None will be returned. | |
doc_440 |
Apply clustering to a projection of the normalized Laplacian. In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non-convex or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance, when clusters are nested circles on the 2D plane. If affinity is the adjacency matrix of a graph, this method can be used to find normalized graph cuts. Read more in the User Guide. Parameters
affinity{array-like, sparse matrix} of shape (n_samples, n_samples)
The affinity matrix describing the relationship of the samples to embed. Must be symmetric. Possible examples:
adjacency matrix of a graph, heat kernel of the pairwise distance matrix of the samples, symmetric k-nearest neighbours connectivity matrix of the samples.
n_clustersint, default=None
Number of clusters to extract.
n_componentsint, default=n_clusters
Number of eigen vectors to use for the spectral embedding
eigen_solver{None, ‘arpack’, ‘lobpcg’, or ‘amg’}
The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities. If None, then 'arpack' is used.
random_stateint, RandomState instance, default=None
A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when eigen_solver == ‘amg’ and by the K-Means initialization. Use an int to make the randomness deterministic. See Glossary.
n_initint, default=10
Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
eigen_tolfloat, default=0.0
Stopping criterion for eigendecomposition of the Laplacian matrix when using arpack eigen_solver.
assign_labels{‘kmeans’, ‘discretize’}, default=’kmeans’
The strategy to use to assign labels in the embedding space. There are two ways to assign labels after the laplacian embedding. k-means can be applied and is a popular choice. But it can also be sensitive to initialization. Discretization is another approach which is less sensitive to random initialization. See the ‘Multiclass spectral clustering’ paper referenced below for more details on the discretization approach.
verbosebool, default=False
Verbosity mode. New in version 0.24. Returns
labelsarray of integers, shape: n_samples
The labels of the clusters. Notes The graph should contain only one connect component, elsewhere the results make little sense. This algorithm solves the normalized cut for k=2: it is a normalized spectral clustering. References Normalized cuts and image segmentation, 2000 Jianbo Shi, Jitendra Malik http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
A Tutorial on Spectral Clustering, 2007 Ulrike von Luxburg http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
Multiclass spectral clustering, 2003 Stella X. Yu, Jianbo Shi https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf | |
doc_441 | See Migration guide for more details. tf.compat.v1.keras.backend.get_uid
tf.keras.backend.get_uid(
prefix=''
)
Arguments
prefix String prefix to index.
Returns Unique integer ID.
Example:
get_uid('dense')
1
get_uid('dense')
2 | |
doc_442 |
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_443 |
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_444 | class sklearn.neighbors.BallTree(X, leaf_size=40, metric='minkowski', **kwargs)
BallTree for fast generalized N-point problems Read more in the User Guide. Parameters
Xarray-like of shape (n_samples, n_features)
n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made.
leaf_sizepositive int, default=40
Number of points at which to switch to brute-force. Changing leaf_size will not affect the results of a query, but can significantly impact the speed of a query and the memory required to store the constructed tree. The amount of memory needed to store the tree scales as approximately n_samples / leaf_size. For a specified leaf_size, a leaf node is guaranteed to satisfy leaf_size <= n_points <= 2 * leaf_size, except in the case that n_samples < leaf_size.
metricstr or DistanceMetric object
the distance metric to use for the tree. Default=’minkowski’ with p=2 (that is, a euclidean metric). See the documentation of the DistanceMetric class for a list of available metrics. ball_tree.valid_metrics gives a list of the metrics which are valid for BallTree. Additional keywords are passed to the distance metric class.
Note: Callable functions in the metric parameter are NOT supported for KDTree
and Ball Tree. Function call overhead will result in very poor performance.
Attributes
datamemory view
The training data Examples Query for k-nearest neighbors >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> dist, ind = tree.query(X[:1], k=3)
>>> print(ind) # indices of 3 closest neighbors
[0 3 1]
>>> print(dist) # distances to 3 closest neighbors
[ 0. 0.19662693 0.29473397]
Pickle and Unpickle a tree. Note that the state of the tree is saved in the pickle operation: the tree needs not be rebuilt upon unpickling. >>> import numpy as np
>>> import pickle
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> s = pickle.dumps(tree)
>>> tree_copy = pickle.loads(s)
>>> dist, ind = tree_copy.query(X[:1], k=3)
>>> print(ind) # indices of 3 closest neighbors
[0 3 1]
>>> print(dist) # distances to 3 closest neighbors
[ 0. 0.19662693 0.29473397]
Query for neighbors within a given radius >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
>>> tree = BallTree(X, leaf_size=2)
>>> print(tree.query_radius(X[:1], r=0.3, count_only=True))
3
>>> ind = tree.query_radius(X[:1], r=0.3)
>>> print(ind) # indices of neighbors within distance 0.3
[3 0 1]
Compute a gaussian kernel density estimate: >>> import numpy as np
>>> rng = np.random.RandomState(42)
>>> X = rng.random_sample((100, 3))
>>> tree = BallTree(X)
>>> tree.kernel_density(X[:3], h=0.1, kernel='gaussian')
array([ 6.94114649, 7.83281226, 7.2071716 ])
Compute a two-point auto-correlation function >>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> X = rng.random_sample((30, 3))
>>> r = np.linspace(0, 1, 5)
>>> tree = BallTree(X)
>>> tree.two_point_correlation(X, r)
array([ 30, 62, 278, 580, 820])
Methods
get_arrays(self) Get data and node arrays.
get_n_calls(self) Get number of calls.
get_tree_stats(self) Get tree status.
kernel_density(self, X, h[, kernel, atol, …]) Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation.
query(X[, k, return_distance, dualtree, …]) query the tree for the k nearest neighbors
query_radius(X, r[, return_distance, …]) query the tree for neighbors within a radius r
reset_n_calls(self) Reset number of calls to 0.
two_point_correlation(X, r[, dualtree]) Compute the two-point correlation function
get_arrays(self)
Get data and node arrays. Returns
arrays: tuple of array
Arrays for storing tree data, index, node data and node bounds.
get_n_calls(self)
Get number of calls. Returns
n_calls: int
number of distance computation calls
get_tree_stats(self)
Get tree status. Returns
tree_stats: tuple of int
(number of trims, number of leaves, number of splits)
kernel_density(self, X, h, kernel='gaussian', atol=0, rtol=1e-08, breadth_first=True, return_log=False)
Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation. Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query. Last dimension should match dimension of training data.
hfloat
the bandwidth of the kernel
kernelstr, default=”gaussian”
specify the kernel to use. Options are - ‘gaussian’ - ‘tophat’ - ‘epanechnikov’ - ‘exponential’ - ‘linear’ - ‘cosine’ Default is kernel = ‘gaussian’
atol, rtolfloat, default=0, 1e-8
Specify the desired relative and absolute tolerance of the result. If the true result is K_true, then the returned result K_ret satisfies abs(K_true - K_ret) < atol + rtol * K_ret The default is zero (i.e. machine precision) for both.
breadth_firstbool, default=False
If True, use a breadth-first search. If False (default) use a depth-first search. Breadth-first is generally faster for compact kernels and/or high tolerances.
return_logbool, default=False
Return the logarithm of the result. This can be more accurate than returning the result itself for narrow kernels. Returns
densityndarray of shape X.shape[:-1]
The array of (log)-density evaluations
query(X, k=1, return_distance=True, dualtree=False, breadth_first=False)
query the tree for the k nearest neighbors Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query
kint, default=1
The number of nearest neighbors to return
return_distancebool, default=True
if True, return a tuple (d, i) of distances and indices if False, return array i
dualtreebool, default=False
if True, use the dual tree formalism for the query: a tree is built for the query points, and the pair of trees is used to efficiently search this space. This can lead to better performance as the number of points grows large.
breadth_firstbool, default=False
if True, then query the nodes in a breadth-first manner. Otherwise, query the nodes in a depth-first manner.
sort_resultsbool, default=True
if True, then distances and indices of each point are sorted on return, so that the first column contains the closest points. Otherwise, neighbors are returned in an arbitrary order. Returns
iif return_distance == False
(d,i)if return_distance == True
dndarray of shape X.shape[:-1] + (k,), dtype=double
Each entry gives the list of distances to the neighbors of the corresponding point.
indarray of shape X.shape[:-1] + (k,), dtype=int
Each entry gives the list of indices of neighbors of the corresponding point.
query_radius(X, r, return_distance=False, count_only=False, sort_results=False)
query the tree for neighbors within a radius r Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query
rdistance within which neighbors are returned
r can be a single value, or an array of values of shape x.shape[:-1] if different radii are desired for each point.
return_distancebool, default=False
if True, return distances to neighbors of each point if False, return only neighbors Note that unlike the query() method, setting return_distance=True here adds to the computation time. Not all distances need to be calculated explicitly for return_distance=False. Results are not sorted by default: see sort_results keyword.
count_onlybool, default=False
if True, return only the count of points within distance r if False, return the indices of all points within distance r If return_distance==True, setting count_only=True will result in an error.
sort_resultsbool, default=False
if True, the distances and indices will be sorted before being returned. If False, the results will not be sorted. If return_distance == False, setting sort_results = True will result in an error. Returns
countif count_only == True
indif count_only == False and return_distance == False
(ind, dist)if count_only == False and return_distance == True
countndarray of shape X.shape[:-1], dtype=int
Each entry gives the number of neighbors within a distance r of the corresponding point.
indndarray of shape X.shape[:-1], dtype=object
Each element is a numpy integer array listing the indices of neighbors of the corresponding point. Note that unlike the results of a k-neighbors query, the returned neighbors are not sorted by distance by default.
distndarray of shape X.shape[:-1], dtype=object
Each element is a numpy double array listing the distances corresponding to indices in i.
reset_n_calls(self)
Reset number of calls to 0.
two_point_correlation(X, r, dualtree=False)
Compute the two-point correlation function Parameters
Xarray-like of shape (n_samples, n_features)
An array of points to query. Last dimension should match dimension of training data.
rarray-like
A one-dimensional array of distances
dualtreebool, default=False
If True, use a dualtree algorithm. Otherwise, use a single-tree algorithm. Dual tree algorithms can have better scaling for large N. Returns
countsndarray
counts[i] contains the number of pairs of points with distance less than or equal to r[i] | |
doc_445 | See Migration guide for more details. tf.compat.v1.raw_ops.AvgPoolGrad
tf.raw_ops.AvgPoolGrad(
orig_input_shape, grad, ksize, strides, padding, data_format='NHWC',
name=None
)
Args
orig_input_shape A Tensor of type int32. 1-D. Shape of the original input to avg_pool.
grad A Tensor. Must be one of the following types: half, bfloat16, float32, float64. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of avg_pool.
ksize A list of ints that has length >= 4. The size of the sliding window for each dimension of the input.
strides A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input.
padding A string from: "SAME", "VALID". The type of padding algorithm to use.
data_format An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
name A name for the operation (optional).
Returns A Tensor. Has the same type as grad. | |
doc_446 | An optional dict of months to use in the “months” select box. The keys of the dict correspond to the month number (1-indexed) and the values are the displayed months: MONTHS = {
1:_('jan'), 2:_('feb'), 3:_('mar'), 4:_('apr'),
5:_('may'), 6:_('jun'), 7:_('jul'), 8:_('aug'),
9:_('sep'), 10:_('oct'), 11:_('nov'), 12:_('dec')
} | |
doc_447 |
Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. New in version 1.8.0. Parameters
kthint or sequence of ints
Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once. Deprecated since version 1.22.0: Passing booleans as index is deprecated.
axisint, optional
Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘introselect’}, optional
Selection algorithm. Default is ‘introselect’.
orderstr or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. See also numpy.partition
Return a parititioned copy of an array. argpartition
Indirect partition. sort
Full sort. Notes See np.partition for notes on the different algorithms. Examples >>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4]) | |
doc_448 | When the database has been opened in fast mode, this method forces any unwritten data to be written to the disk. | |
doc_449 | The type of parameterized generics such as list[int]. t_origin should be a non-parameterized generic class, such as list, tuple or dict. t_args should be a tuple (possibly of length 1) of types which parameterize t_origin: >>> from types import GenericAlias
>>> list[int] == GenericAlias(list, (int,))
True
>>> dict[str, int] == GenericAlias(dict, (str, int))
True
New in version 3.9. Changed in version 3.9.2: This type can now be subclassed. | |
doc_450 | Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError). For example, to support arbitrary iterators, you could implement default() like this: def default(self, o):
try:
iterable = iter(o)
except TypeError:
pass
else:
return list(iterable)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, o) | |
doc_451 | Casts this storage to bfloat16 type | |
doc_452 |
Return the Unicode normal form for the strings in the Series/Index. For more information on the forms, see the unicodedata.normalize(). Parameters
form:{‘NFC’, ‘NFKC’, ‘NFD’, ‘NFKD’}
Unicode form. Returns
normalized:Series/Index of objects | |
doc_453 |
Create a 3D stem plot. A stem plot draws lines perpendicular to a baseline, and places markers at the heads. By default, the baseline is defined by x and y, and stems are drawn vertically from bottom to z. Parameters
x, y, zarray-like
The positions of the heads of the stems. The stems are drawn along the orientation-direction from the baseline at bottom (in the orientation-coordinate) to the heads. By default, the x and y positions are used for the baseline and z for the head position, but this can be changed by orientation.
linefmtstr, default: 'C0-'
A string defining the properties of the vertical lines. Usually, this will be a color or a color and a linestyle:
Character Line Style
'-' solid line
'--' dashed line
'-.' dash-dot line
':' dotted line Note: While it is technically possible to specify valid formats other than color or color and linestyle (e.g. 'rx' or '-.'), this is beyond the intention of the method and will most likely not result in a reasonable plot.
markerfmtstr, default: 'C0o'
A string defining the properties of the markers at the stem heads.
basefmtstr, default: 'C3-'
A format string defining the properties of the baseline.
bottomfloat, default: 0
The position of the baseline, in orientation-coordinates.
labelstr, default: None
The label to use for the stems in legends.
orientation{'x', 'y', 'z'}, default: 'z'
The direction along which stems are drawn.
dataindexable object, optional
If given, all parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception). Returns
StemContainer
The container may be treated like a tuple (markerline, stemlines, baseline) Examples (Source code, png, pdf) (png, pdf) (png, pdf) | |
doc_454 | globally enables swizzling for vectors. enable_swizzling() -> None DEPRECATED: Not needed anymore. Will be removed in a later version. Enables swizzling for all vectors until disable_swizzling() is called. By default swizzling is disabled. Lets you get or set multiple coordinates as one attribute, eg vec.xyz = 1, 2, 3. | |
doc_455 | from myapp.serializers import UserSerializer
from rest_framework import generics
from rest_framework.permissions import IsAdminUser
class UserList(generics.ListCreateAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
permission_classes = [IsAdminUser]
For more complex cases you might also want to override various methods on the view class. For example. class UserList(generics.ListCreateAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
permission_classes = [IsAdminUser]
def list(self, request):
# Note the use of `get_queryset()` instead of `self.queryset`
queryset = self.get_queryset()
serializer = UserSerializer(queryset, many=True)
return Response(serializer.data)
For very simple cases you might want to pass through any class attributes using the .as_view() method. For example, your URLconf might include something like the following entry: path('users/', ListCreateAPIView.as_view(queryset=User.objects.all(), serializer_class=UserSerializer), name='user-list')
API Reference GenericAPIView This class extends REST framework's APIView class, adding commonly required behavior for standard list and detail views. Each of the concrete generic views provided is built by combining GenericAPIView, with one or more mixin classes. Attributes Basic settings: The following attributes control the basic view behavior.
queryset - The queryset that should be used for returning objects from this view. Typically, you must either set this attribute, or override the get_queryset() method. If you are overriding a view method, it is important that you call get_queryset() instead of accessing this property directly, as queryset will get evaluated once, and those results will be cached for all subsequent requests.
serializer_class - The serializer class that should be used for validating and deserializing input, and for serializing output. Typically, you must either set this attribute, or override the get_serializer_class() method.
lookup_field - The model field that should be used to for performing object lookup of individual model instances. Defaults to 'pk'. Note that when using hyperlinked APIs you'll need to ensure that both the API views and the serializer classes set the lookup fields if you need to use a custom value.
lookup_url_kwarg - The URL keyword argument that should be used for object lookup. The URL conf should include a keyword argument corresponding to this value. If unset this defaults to using the same value as lookup_field. Pagination: The following attributes are used to control pagination when used with list views.
pagination_class - The pagination class that should be used when paginating list results. Defaults to the same value as the DEFAULT_PAGINATION_CLASS setting, which is 'rest_framework.pagination.PageNumberPagination'. Setting pagination_class=None will disable pagination on this view. Filtering:
filter_backends - A list of filter backend classes that should be used for filtering the queryset. Defaults to the same value as the DEFAULT_FILTER_BACKENDS setting. Methods Base methods: get_queryset(self) Returns the queryset that should be used for list views, and that should be used as the base for lookups in detail views. Defaults to returning the queryset specified by the queryset attribute. This method should always be used rather than accessing self.queryset directly, as self.queryset gets evaluated only once, and those results are cached for all subsequent requests. May be overridden to provide dynamic behavior, such as returning a queryset, that is specific to the user making the request. For example: def get_queryset(self):
user = self.request.user
return user.accounts.all()
get_object(self) Returns an object instance that should be used for detail views. Defaults to using the lookup_field parameter to filter the base queryset. May be overridden to provide more complex behavior, such as object lookups based on more than one URL kwarg. For example: def get_object(self):
queryset = self.get_queryset()
filter = {}
for field in self.multiple_lookup_fields:
filter[field] = self.kwargs[field]
obj = get_object_or_404(queryset, **filter)
self.check_object_permissions(self.request, obj)
return obj
Note that if your API doesn't include any object level permissions, you may optionally exclude the self.check_object_permissions, and simply return the object from the get_object_or_404 lookup. filter_queryset(self, queryset) Given a queryset, filter it with whichever filter backends are in use, returning a new queryset. For example: def filter_queryset(self, queryset):
filter_backends = [CategoryFilter]
if 'geo_route' in self.request.query_params:
filter_backends = [GeoRouteFilter, CategoryFilter]
elif 'geo_point' in self.request.query_params:
filter_backends = [GeoPointFilter, CategoryFilter]
for backend in list(filter_backends):
queryset = backend().filter_queryset(self.request, queryset, view=self)
return queryset
get_serializer_class(self) Returns the class that should be used for the serializer. Defaults to returning the serializer_class attribute. May be overridden to provide dynamic behavior, such as using different serializers for read and write operations, or providing different serializers to different types of users. For example: def get_serializer_class(self):
if self.request.user.is_staff:
return FullAccountSerializer
return BasicAccountSerializer
Save and deletion hooks: The following methods are provided by the mixin classes, and provide easy overriding of the object save or deletion behavior.
perform_create(self, serializer) - Called by CreateModelMixin when saving a new object instance.
perform_update(self, serializer) - Called by UpdateModelMixin when saving an existing object instance.
perform_destroy(self, instance) - Called by DestroyModelMixin when deleting an object instance. These hooks are particularly useful for setting attributes that are implicit in the request, but are not part of the request data. For instance, you might set an attribute on the object based on the request user, or based on a URL keyword argument. def perform_create(self, serializer):
serializer.save(user=self.request.user)
These override points are also particularly useful for adding behavior that occurs before or after saving an object, such as emailing a confirmation, or logging the update. def perform_update(self, serializer):
instance = serializer.save()
send_email_confirmation(user=self.request.user, modified=instance)
You can also use these hooks to provide additional validation, by raising a ValidationError(). This can be useful if you need some validation logic to apply at the point of database save. For example: def perform_create(self, serializer):
queryset = SignupRequest.objects.filter(user=self.request.user)
if queryset.exists():
raise ValidationError('You have already signed up')
serializer.save(user=self.request.user)
Other methods: You won't typically need to override the following methods, although you might need to call into them if you're writing custom views using GenericAPIView.
get_serializer_context(self) - Returns a dictionary containing any extra context that should be supplied to the serializer. Defaults to including 'request', 'view' and 'format' keys.
get_serializer(self, instance=None, data=None, many=False, partial=False) - Returns a serializer instance.
get_paginated_response(self, data) - Returns a paginated style Response object.
paginate_queryset(self, queryset) - Paginate a queryset if required, either returning a page object, or None if pagination is not configured for this view.
filter_queryset(self, queryset) - Given a queryset, filter it with whichever filter backends are in use, returning a new queryset. Mixins The mixin classes provide the actions that are used to provide the basic view behavior. Note that the mixin classes provide action methods rather than defining the handler methods, such as .get() and .post(), directly. This allows for more flexible composition of behavior. The mixin classes can be imported from rest_framework.mixins. ListModelMixin Provides a .list(request, *args, **kwargs) method, that implements listing a queryset. If the queryset is populated, this returns a 200 OK response, with a serialized representation of the queryset as the body of the response. The response data may optionally be paginated. CreateModelMixin Provides a .create(request, *args, **kwargs) method, that implements creating and saving a new model instance. If an object is created this returns a 201 Created response, with a serialized representation of the object as the body of the response. If the representation contains a key named url, then the Location header of the response will be populated with that value. If the request data provided for creating the object was invalid, a 400 Bad Request response will be returned, with the error details as the body of the response. RetrieveModelMixin Provides a .retrieve(request, *args, **kwargs) method, that implements returning an existing model instance in a response. If an object can be retrieved this returns a 200 OK response, with a serialized representation of the object as the body of the response. Otherwise it will return a 404 Not Found. UpdateModelMixin Provides a .update(request, *args, **kwargs) method, that implements updating and saving an existing model instance. Also provides a .partial_update(request, *args, **kwargs) method, which is similar to the update method, except that all fields for the update will be optional. This allows support for HTTP PATCH requests. If an object is updated this returns a 200 OK response, with a serialized representation of the object as the body of the response. If the request data provided for updating the object was invalid, a 400 Bad Request response will be returned, with the error details as the body of the response. DestroyModelMixin Provides a .destroy(request, *args, **kwargs) method, that implements deletion of an existing model instance. If an object is deleted this returns a 204 No Content response, otherwise it will return a 404 Not Found. Concrete View Classes The following classes are the concrete generic views. If you're using generic views this is normally the level you'll be working at unless you need heavily customized behavior. The view classes can be imported from rest_framework.generics. CreateAPIView Used for create-only endpoints. Provides a post method handler. Extends: GenericAPIView, CreateModelMixin ListAPIView Used for read-only endpoints to represent a collection of model instances. Provides a get method handler. Extends: GenericAPIView, ListModelMixin RetrieveAPIView Used for read-only endpoints to represent a single model instance. Provides a get method handler. Extends: GenericAPIView, RetrieveModelMixin DestroyAPIView Used for delete-only endpoints for a single model instance. Provides a delete method handler. Extends: GenericAPIView, DestroyModelMixin UpdateAPIView Used for update-only endpoints for a single model instance. Provides put and patch method handlers. Extends: GenericAPIView, UpdateModelMixin ListCreateAPIView Used for read-write endpoints to represent a collection of model instances. Provides get and post method handlers. Extends: GenericAPIView, ListModelMixin, CreateModelMixin RetrieveUpdateAPIView Used for read or update endpoints to represent a single model instance. Provides get, put and patch method handlers. Extends: GenericAPIView, RetrieveModelMixin, UpdateModelMixin RetrieveDestroyAPIView Used for read or delete endpoints to represent a single model instance. Provides get and delete method handlers. Extends: GenericAPIView, RetrieveModelMixin, DestroyModelMixin RetrieveUpdateDestroyAPIView Used for read-write-delete endpoints to represent a single model instance. Provides get, put, patch and delete method handlers. Extends: GenericAPIView, RetrieveModelMixin, UpdateModelMixin, DestroyModelMixin Customizing the generic views Often you'll want to use the existing generic views, but use some slightly customized behavior. If you find yourself reusing some bit of customized behavior in multiple places, you might want to refactor the behavior into a common class that you can then just apply to any view or viewset as needed. Creating custom mixins For example, if you need to lookup objects based on multiple fields in the URL conf, you could create a mixin class like the following: class MultipleFieldLookupMixin:
"""
Apply this mixin to any view or viewset to get multiple field filtering
based on a `lookup_fields` attribute, instead of the default single field filtering.
"""
def get_object(self):
queryset = self.get_queryset() # Get the base queryset
queryset = self.filter_queryset(queryset) # Apply any filter backends
filter = {}
for field in self.lookup_fields:
if self.kwargs[field]: # Ignore empty fields.
filter[field] = self.kwargs[field]
obj = get_object_or_404(queryset, **filter) # Lookup the object
self.check_object_permissions(self.request, obj)
return obj
You can then simply apply this mixin to a view or viewset anytime you need to apply the custom behavior. class RetrieveUserView(MultipleFieldLookupMixin, generics.RetrieveAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
lookup_fields = ['account', 'username']
Using custom mixins is a good option if you have custom behavior that needs to be used. Creating custom base classes If you are using a mixin across multiple views, you can take this a step further and create your own set of base views that can then be used throughout your project. For example: class BaseRetrieveView(MultipleFieldLookupMixin,
generics.RetrieveAPIView):
pass
class BaseRetrieveUpdateDestroyView(MultipleFieldLookupMixin,
generics.RetrieveUpdateDestroyAPIView):
pass
Using custom base classes is a good option if you have custom behavior that consistently needs to be repeated across a large number of views throughout your project. PUT as create Prior to version 3.0 the REST framework mixins treated PUT as either an update or a create operation, depending on if the object already existed or not. Allowing PUT as create operations is problematic, as it necessarily exposes information about the existence or non-existence of objects. It's also not obvious that transparently allowing re-creating of previously deleted instances is necessarily a better default behavior than simply returning 404 responses. Both styles "PUT as 404" and "PUT as create" can be valid in different circumstances, but from version 3.0 onwards we now use 404 behavior as the default, due to it being simpler and more obvious. If you need to generic PUT-as-create behavior you may want to include something like this AllowPUTAsCreateMixin class as a mixin to your views. Third party packages The following third party packages provide additional generic view implementations. Django Rest Multiple Models Django Rest Multiple Models provides a generic view (and mixin) for sending multiple serialized models and/or querysets via a single API request. mixins.pygenerics.py | |
doc_456 | Return True iff the actual output from an example (got) matches the expected output (want). These strings are always considered to match if they are identical; but depending on what option flags the test runner is using, several non-exact match types are also possible. See section Option Flags for more information about option flags. | |
doc_457 |
Generates a flat, diamond-shaped structuring element. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater than radius. Parameters
radiusint
The radius of the diamond-shaped structuring element. Returns
selemndarray
The structuring element where elements of the neighborhood are 1 and 0 otherwise. Other Parameters
dtypedata-type
The data type of the structuring element. | |
doc_458 | Convert binary data to a line of ASCII characters in base64 coding. The return value is the converted line, including a newline char if newline is true. The output of this function conforms to RFC 3548. Changed in version 3.6: Added the newline parameter. | |
doc_459 | An Operation subclass which installs a PostgreSQL extension. For common extensions, use one of the more specific subclasses below.
name
This is a required argument. The name of the extension to be installed. | |
doc_460 |
Set the sketch parameters. Parameters
scalefloat, optional
The amplitude of the wiggle perpendicular to the source line, in pixels. If scale is None, or not provided, no sketch filter will be provided.
lengthfloat, default: 128
The length of the wiggle along the line, in pixels.
randomnessfloat, default: 16
The scale factor by which the length is shrunken or expanded. | |
doc_461 | tf.experimental.numpy.tensordot(
a, b, axes=2
)
See the NumPy documentation for numpy.tensordot. | |
doc_462 |
Return a BrokenBarHCollection that plots horizontal bars from over the regions in x where where is True. The bars range on the y-axis from ymin to ymax kwargs are passed on to the collection. | |
doc_463 | policy is an object implementing the CookiePolicy interface. For the other arguments, see the documentation for the corresponding attributes. A CookieJar which can load cookies from, and perhaps save cookies to, a file on disk. Cookies are NOT loaded from the named file until either the load() or revert() method is called. Subclasses of this class are documented in section FileCookieJar subclasses and co-operation with web browsers. Changed in version 3.8: The filename parameter supports a path-like object. | |
doc_464 | Create a barrier object for parties number of threads. An action, when provided, is a callable to be called by one of the threads when they are released. timeout is the default timeout value if none is specified for the wait() method.
wait(timeout=None)
Pass the barrier. When all the threads party to the barrier have called this function, they are all released simultaneously. If a timeout is provided, it is used in preference to any that was supplied to the class constructor. The return value is an integer in the range 0 to parties – 1, different for each thread. This can be used to select a thread to do some special housekeeping, e.g.: i = barrier.wait()
if i == 0:
# Only one thread needs to print this
print("passed the barrier")
If an action was provided to the constructor, one of the threads will have called it prior to being released. Should this call raise an error, the barrier is put into the broken state. If the call times out, the barrier is put into the broken state. This method may raise a BrokenBarrierError exception if the barrier is broken or reset while a thread is waiting.
reset()
Return the barrier to the default, empty state. Any threads waiting on it will receive the BrokenBarrierError exception. Note that using this function may require some external synchronization if there are other threads whose state is unknown. If a barrier is broken it may be better to just leave it and create a new one.
abort()
Put the barrier into a broken state. This causes any active or future calls to wait() to fail with the BrokenBarrierError. Use this for example if one of the threads needs to abort, to avoid deadlocking the application. It may be preferable to simply create the barrier with a sensible timeout value to automatically guard against one of the threads going awry.
parties
The number of threads required to pass the barrier.
n_waiting
The number of threads currently waiting in the barrier.
broken
A boolean that is True if the barrier is in the broken state. | |
doc_465 | Create an object to trace execution of a single statement or expression. All parameters are optional. count enables counting of line numbers. trace enables line execution tracing. countfuncs enables listing of the functions called during the run. countcallers enables call relationship tracking. ignoremods is a list of modules or packages to ignore. ignoredirs is a list of directories whose modules or packages should be ignored. infile is the name of the file from which to read stored count information. outfile is the name of the file in which to write updated count information. timing enables a timestamp relative to when tracing was started to be displayed.
run(cmd)
Execute the command and gather statistics from the execution with the current tracing parameters. cmd must be a string or code object, suitable for passing into exec().
runctx(cmd, globals=None, locals=None)
Execute the command and gather statistics from the execution with the current tracing parameters, in the defined global and local environments. If not defined, globals and locals default to empty dictionaries.
runfunc(func, /, *args, **kwds)
Call func with the given arguments under control of the Trace object with the current tracing parameters.
results()
Return a CoverageResults object that contains the cumulative results of all previous calls to run, runctx and runfunc for the given Trace instance. Does not reset the accumulated trace results. | |
doc_466 |
Leave-One-Out cross-validator Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set. Note: LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples. Due to the high number of test sets (which is the same as the number of samples) this cross-validation method can be very costly. For large datasets one should favor KFold, ShuffleSplit or StratifiedKFold. Read more in the User Guide. See also
LeaveOneGroupOut
For splitting the data according to explicit, domain-specific stratification of the dataset.
GroupKFold
K-fold iterator variant with non-overlapping groups. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneOut
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = LeaveOneOut()
>>> loo.get_n_splits(X)
2
>>> print(loo)
LeaveOneOut()
>>> for train_index, test_index in loo.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
... print(X_train, X_test, y_train, y_test)
TRAIN: [1] TEST: [0]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [0] TEST: [1]
[[1 2]] [[3 4]] [1] [2]
Methods
get_n_splits(X[, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | |
doc_467 | Default widget: ClearableFileInput
Empty value: None
Normalizes to: An UploadedFile object that wraps the file content and file name into a single object. Can validate that non-empty file data has been bound to the form. Error message keys: required, invalid, missing, empty, max_length
Has two optional arguments for validation, max_length and allow_empty_file. If provided, these ensure that the file name is at most the given length, and that validation will succeed even if the file content is empty. To learn more about the UploadedFile object, see the file uploads documentation. When you use a FileField in a form, you must also remember to bind the file data to the form. The max_length error refers to the length of the filename. In the error message for that key, %(max)d will be replaced with the maximum filename length and %(length)d will be replaced with the current filename length. | |
doc_468 | A message with Babyl-specific behaviors. Parameter message has the same meaning as with the Message constructor. Certain message labels, called attributes, are defined by convention to have special meanings. The attributes are as follows:
Label Explanation
unseen Not read, but previously detected by MUA
deleted Marked for subsequent deletion
filed Copied to another file or mailbox
answered Replied to
forwarded Forwarded
edited Modified by the user
resent Resent By default, Rmail displays only visible headers. The BabylMessage class, though, uses the original headers because they are more complete. Visible headers may be accessed explicitly if desired. BabylMessage instances offer the following methods:
get_labels()
Return a list of labels on the message.
set_labels(labels)
Set the list of labels on the message to labels.
add_label(label)
Add label to the list of labels on the message.
remove_label(label)
Remove label from the list of labels on the message.
get_visible()
Return an Message instance whose headers are the message’s visible headers and whose body is empty.
set_visible(visible)
Set the message’s visible headers to be the same as the headers in message. Parameter visible should be a Message instance, an email.message.Message instance, a string, or a file-like object (which should be open in text mode).
update_visible()
When a BabylMessage instance’s original headers are modified, the visible headers are not automatically modified to correspond. This method updates the visible headers as follows: each visible header with a corresponding original header is set to the value of the original header, each visible header without a corresponding original header is removed, and any of Date, From, Reply-To, To, CC, and Subject that are present in the original headers but not the visible headers are added to the visible headers. | |
doc_469 |
Plot a 2D field of arrows. Call signature: quiver([X, Y], U, V, [C], **kw)
X, Y define the arrow locations, U, V define the arrow directions, and C optionally sets the color. Each arrow is internally represented by a filled polygon with a default edge linewidth of 0. As a result, an arrow is rather a filled area, not a line with a head, and PolyCollection properties like linewidth, linestyle, facecolor, etc. act accordingly. Arrow size The default settings auto-scales the length of the arrows to a reasonable size. To change this behavior see the scale and scale_units parameters. Arrow shape The defaults give a slightly swept-back arrow; to make the head a triangle, make headaxislength the same as headlength. To make the arrow more pointed, reduce headwidth or increase headlength and headaxislength. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone. Arrow outline linewidths and edgecolors can be used to customize the arrow outlines. Parameters
X, Y1D or 2D array-like, optional
The x and y coordinates of the arrow locations. If not given, they will be generated as a uniform integer meshgrid based on the dimensions of U and V. If X and Y are 1D but U, V are 2D, X, Y are expanded to 2D using X, Y = np.meshgrid(X, Y). In this case len(X) and len(Y) must match the column and row dimensions of U and V.
U, V1D or 2D array-like
The x and y direction components of the arrow vectors. They must have the same number of elements, matching the number of arrow locations. U and V may be masked. Only locations unmasked in U, V, and C will be drawn.
C1D or 2D array-like, optional
Numeric data that defines the arrow colors by colormapping via norm and cmap. This does not support explicit colors. If you want to set colors directly, use color instead. The size of C must match the number of arrow locations.
units{'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, default: 'width'
The arrow dimensions (except for length) are measured in multiples of this unit. The following values are supported: 'width', 'height': The width or height of the axis. 'dots', 'inches': Pixels or inches based on the figure dpi. 'x', 'y', 'xy': X, Y or \(\sqrt{X^2 + Y^2}\) in data units. The arrows scale differently depending on the units. For 'x' or 'y', the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For 'width or 'height', the arrow size increases with the width and height of the axes, respectively, when the window is resized; for 'dots' or 'inches', resizing does not change the arrows.
angles{'uv', 'xy'} or array-like, default: 'uv'
Method for determining the angle of the arrows.
'uv': The arrow axis aspect ratio is 1 so that if U == V the orientation of the arrow on the plot is 45 degrees counter-clockwise from the horizontal axis (positive to the right). Use this if the arrows symbolize a quantity that is not based on X, Y data coordinates. 'xy': Arrows point from (x, y) to (x+u, y+v). Use this for plotting a gradient field, for example.
Alternatively, arbitrary angles may be specified explicitly as an array of values in degrees, counter-clockwise from the horizontal axis. In this case U, V is only used to determine the length of the arrows. Note: inverting a data axis will correspondingly invert the arrows only with angles='xy'.
scalefloat, optional
Number of data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. Default is None. If None, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. The arrow length unit is given by the scale_units parameter.
scale_units{'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, optional
If the scale kwarg is None, the arrow length unit. Default is None. e.g. scale_units is 'inches', scale is 2.0, and (u, v) = (1, 0), then the vector will be 0.5 inches long. If scale_units is 'width' or 'height', then the vector will be half the width/height of the axes. If scale_units is 'x' then the vector will be 0.5 x-axis units. To plot vectors in the x-y plane, with u and v having the same units as x and y, use angles='xy', scale_units='xy', scale=1.
widthfloat, optional
Shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot.
headwidthfloat, default: 3
Head width as multiple of shaft width.
headlengthfloat, default: 5
Head length as multiple of shaft width.
headaxislengthfloat, default: 4.5
Head length at shaft intersection.
minshaftfloat, default: 1
Length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible!
minlengthfloat, default: 1
Minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead.
pivot{'tail', 'mid', 'middle', 'tip'}, default: 'tail'
The part of the arrow that is anchored to the X, Y grid. The arrow rotates about this point. 'mid' is a synonym for 'middle'.
colorcolor or color sequence, optional
Explicit color(s) for the arrows. If C has been set, color has no effect. This is a synonym for the PolyCollection facecolor parameter. Returns
Quiver
Other Parameters
dataindexable object, optional
If given, all parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception).
**kwargsPolyCollection properties, optional
All other keyword arguments are passed on to PolyCollection:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha array-like or scalar or None
animated bool
antialiased or aa or antialiaseds bool or list of bools
array array-like or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clim (vmin: float, vmax: float)
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
cmap Colormap or str or None
color color or list of rgba tuples
edgecolor or ec or edgecolors color or list of colors or 'face'
facecolor or facecolors or fc color or list of colors
figure Figure
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or dashes or linestyles or ls str or tuple or list thereof
linewidth or linewidths or lw float or list of floats
norm Normalize or None
offset_transform Transform
offsets (N, 2) or (2,) array-like
path_effects AbstractPathEffect
paths list of array-like
picker None or bool or float or callable
pickradius float
rasterized bool
sizes ndarray or None
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
urls list of str or None
verts list of array-like
verts_and_codes unknown
visible bool
zorder float See also Axes.quiverkey
Add a key to a quiver plot. | |
doc_470 |
Return the clip path. | |
doc_471 |
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
bbox_to_anchor unknown
child unknown
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
figure Figure
gid str
height float
in_layout bool
label object
offset (float, float) or callable
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
width float
zorder float | |
doc_472 |
ctypes interface Returns
interfacenamedtuple
Named tuple containing ctypes wrapper state_address - Memory address of the state struct state - pointer to the state struct next_uint64 - function pointer to produce 64 bit integers next_uint32 - function pointer to produce 32 bit integers next_double - function pointer to produce doubles bitgen - pointer to the bit generator struct | |
doc_473 |
Set the visibility state of the handles artist. | |
doc_474 |
Attributes
library FunctionDefLibrary library
node repeated NodeDef node
version int32 version
versions VersionDef versions | |
doc_475 | tf.experimental.numpy.ndarray.ravel
tf.experimental.numpy.ravel(
a
)
Unsupported arguments: order. See the NumPy documentation for numpy.ravel. | |
doc_476 |
Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame’s values as coordinates. Parameters
x:label or position, optional
Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
y:label or position, optional
Allows plotting of one column versus another. If not specified, all numerical columns are used.
color:str, array-like, or dict, optional
The color for each of the DataFrame’s columns. Possible values are:
A single color string referred to by name, RGB or RGBA code,
for instance ‘red’ or ‘#a98d19’.
A sequence of color strings referred to by name, RGB or RGBA
code, which will be used for each column recursively. For instance [‘green’,’yellow’] each column’s line will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used.
A dict of the form {column name:color}, so that each column will be
colored accordingly. For example, if your columns are called a and b, then passing {‘a’: ‘green’, ‘b’: ‘red’} will color lines for column a in green and lines for column b in red. New in version 1.1.0. **kwargs
Additional keyword arguments are documented in DataFrame.plot(). Returns
matplotlib.axes.Axes or np.ndarray of them
An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True. See also matplotlib.pyplot.plot
Plot y versus x as lines and/or markers. Examples
>>> s = pd.Series([1, 3, 2])
>>> s.plot.line()
<AxesSubplot:ylabel='Density'>
The following example shows the populations for some animals over the years.
>>> df = pd.DataFrame({
... 'pig': [20, 18, 489, 675, 1776],
... 'horse': [4, 25, 281, 600, 1900]
... }, index=[1990, 1997, 2003, 2009, 2014])
>>> lines = df.plot.line()
An example with subplots, so an array of axes is returned.
>>> axes = df.plot.line(subplots=True)
>>> type(axes)
<class 'numpy.ndarray'>
Let’s repeat the same example, but specifying colors for each column (in this case, for each animal).
>>> axes = df.plot.line(
... subplots=True, color={"pig": "pink", "horse": "#742802"}
... )
The following example shows the relationship between both populations.
>>> lines = df.plot.line(x='pig', y='horse') | |
doc_477 | Form that will be used to get the email of the user to reset the password for. Defaults to PasswordResetForm. | |
doc_478 |
Set the aspect ratio of the axes scaling, i.e. y/x-scale. Parameters
aspect{'auto', 'equal'} or float
Possible values: 'auto': fill the position rectangle with data. 'equal': same as aspect=1, i.e. same scaling for x and y.
float: The displayed size of 1 unit in y-data coordinates will be aspect times the displayed size of 1 unit in x-data coordinates; e.g. for aspect=2 a square in data coordinates will be rendered with a height of twice its width.
adjustableNone or {'box', 'datalim'}, optional
If not None, this defines which parameter will be adjusted to meet the required aspect. See set_adjustable for further details.
anchorNone or str or (float, float), optional
If not None, this defines where the Axes will be drawn if there is extra space due to aspect constraints. The most common way to to specify the anchor are abbreviations of cardinal directions:
value description
'C' centered
'SW' lower left corner
'S' middle of bottom edge
'SE' lower right corner
etc. See set_anchor for further details.
sharebool, default: False
If True, apply the settings to all shared Axes. See also matplotlib.axes.Axes.set_adjustable
Set how the Axes adjusts to achieve the required aspect ratio. matplotlib.axes.Axes.set_anchor
Set the position in case of extra space.
Examples using matplotlib.axes.Axes.set_aspect
Bar chart with gradients
Streamplot
Tricontour Demo
Tricontour Smooth Delaunay
Tricontour Smooth User
Trigradient Demo
Tripcolor Demo
Triplot Demo
Axes box aspect
Controlling view limits using margins and sticky_edges
Placing Colorbars
Multiline
Mmh Donuts!!!
Inset Locator Demo2
Scatter Histogram (Locatable Axes)
Simple Anchored Artists
axis_direction demo
Simple Axis Pad
The double pendulum problem
Anchored Artists
Rasterization for vector graphics
Loglog Aspect
Annotate Text Arrow
Transformations Tutorial
Colormap Normalization | |
doc_479 | Returns a boolean indicating whether the geometry is valid. | |
doc_480 | Return True if it is a symbolic link. | |
doc_481 |
Solve the isotonic regression model. Read more in the User Guide. Parameters
yarray-like of shape (n_samples,)
The data.
sample_weightarray-like of shape (n_samples,), default=None
Weights on each point of the regression. If None, weight is set to 1 (equal weights).
y_minfloat, default=None
Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf.
y_maxfloat, default=None
Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf.
increasingbool, default=True
Whether to compute y_ is increasing (if set to True) or decreasing (if set to False) Returns
y_list of floats
Isotonic fit of y. References “Active set algorithms for isotonic regression; A unifying framework” by Michael J. Best and Nilotpal Chakravarti, section 3. | |
doc_482 |
Return local minimum of an image. Parameters
image([P,] M, N) ndarray (uint8, uint16)
Input image.
selemndarray
The neighborhood expressed as an ndarray of 1’s and 0’s.
out([P,] M, N) array (same dtype as input)
If None, a new array is allocated.
maskndarray (integer or float), optional
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
shift_x, shift_y, shift_zint
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element). Returns
out([P,] M, N) ndarray (same dtype as input image)
Output image. See also
skimage.morphology.erosion
Notes The lower algorithm complexity makes skimage.filters.rank.minimum more efficient for larger images and structuring elements. Examples >>> from skimage import data
>>> from skimage.morphology import disk, ball
>>> from skimage.filters.rank import minimum
>>> import numpy as np
>>> img = data.camera()
>>> volume = np.random.randint(0, 255, size=(10,10,10), dtype=np.uint8)
>>> out = minimum(img, disk(5))
>>> out_vol = minimum(volume, ball(5)) | |
doc_483 | See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceFlush
tf.raw_ops.BoostedTreesQuantileStreamResourceFlush(
quantile_stream_resource_handle, num_buckets, generate_quantiles=False,
name=None
)
An op that flushes the summaries for a quantile stream resource.
Args
quantile_stream_resource_handle A Tensor of type resource. resource handle referring to a QuantileStreamResource.
num_buckets A Tensor of type int64. int; approximate number of buckets unless using generate_quantiles.
generate_quantiles An optional bool. Defaults to False. bool; If True, the output will be the num_quantiles for each stream where the ith entry is the ith quantile of the input with an approximation error of epsilon. Duplicate values may be present. If False, the output will be the points in the histogram that we got which roughly translates to 1/epsilon boundaries and without any duplicates. Default to False.
name A name for the operation (optional).
Returns The created Operation. | |
doc_484 |
Returns
transformTransform
The transform used for drawing x-axis labels, which will add pad_points of padding (in points) between the axis and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates
valign{'center', 'top', 'bottom', 'baseline', 'center_baseline'}
The text vertical alignment.
halign{'center', 'left', 'right'}
The text horizontal alignment. Notes This transformation is primarily used by the Axis class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. | |
doc_485 | tf.compat.v1.InteractiveSession(
target='', graph=None, config=None
)
The only difference with a regular Session is that an InteractiveSession installs itself as the default session on construction. The methods tf.Tensor.eval and tf.Operation.run will use that session to run ops. This is convenient in interactive shells and IPython notebooks, as it avoids having to pass an explicit Session object to run ops. For example: sess = tf.compat.v1.InteractiveSession()
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
# We can just use 'c.eval()' without passing 'sess'
print(c.eval())
sess.close()
Note that a regular session installs itself as the default session when it is created in a with statement. The common usage in non-interactive programs is to follow that pattern: a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
with tf.compat.v1.Session():
# We can also use 'c.eval()' here.
print(c.eval())
Args
target (Optional.) The execution engine to connect to. Defaults to using an in-process engine.
graph (Optional.) The Graph to be launched (described above).
config (Optional) ConfigProto proto used to configure the session.
Attributes
graph The graph that was launched in this session.
graph_def A serializable version of the underlying TensorFlow graph.
sess_str The TensorFlow process to which this session will connect. Methods as_default View source
as_default()
Returns a context manager that makes this object the default session. Use with the with keyword to specify that calls to tf.Operation.run or tf.Tensor.eval should be executed in this session. c = tf.constant(..)
sess = tf.compat.v1.Session()
with sess.as_default():
assert tf.compat.v1.get_default_session() is sess
print(c.eval())
To get the current default session, use tf.compat.v1.get_default_session.
Note: The as_default context manager does not close the session when you exit the context, and you must close the session explicitly.
c = tf.constant(...)
sess = tf.compat.v1.Session()
with sess.as_default():
print(c.eval())
# ...
with sess.as_default():
print(c.eval())
sess.close()
Alternatively, you can use with tf.compat.v1.Session(): to create a session that is automatically closed on exiting the context, including when an uncaught exception is raised.
Note: The default session is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a with sess.as_default(): in that thread's function.
Note: Entering a with sess.as_default(): block does not affect the current default graph. If you are using multiple graphs, and sess.graph is different from the value of tf.compat.v1.get_default_graph, you must explicitly enter a with sess.graph.as_default(): block to make sess.graph the default graph.
Returns A context manager using this session as the default session.
close View source
close()
Closes an InteractiveSession. list_devices View source
list_devices()
Lists available devices in this session. devices = sess.list_devices()
for d in devices:
print(d.name)
Where: Each element in the list has the following properties
name: A string with the full name of the device. ex: /job:worker/replica:0/task:3/device:CPU:0
device_type: The type of the device (e.g. CPU, GPU, TPU.)
memory_limit: The maximum amount of memory available on the device. Note: depending on the device, it is possible the usable memory could be substantially less.
Raises
tf.errors.OpError If it encounters an error (e.g. session is in an invalid state, or network errors occur).
Returns A list of devices in the session.
make_callable View source
make_callable(
fetches, feed_list=None, accept_options=False
)
Returns a Python callable that runs a particular step. The returned callable will take len(feed_list) arguments whose types must be compatible feed values for the respective elements of feed_list. For example, if element i of feed_list is a tf.Tensor, the ith argument to the returned callable must be a numpy ndarray (or something convertible to an ndarray) with matching element type and shape. See tf.Session.run for details of the allowable feed key and value types. The returned callable will have the same return type as tf.Session.run(fetches, ...). For example, if fetches is a tf.Tensor, the callable will return a numpy ndarray; if fetches is a tf.Operation, it will return None.
Args
fetches A value or list of values to fetch. See tf.Session.run for details of the allowable fetch types.
feed_list (Optional.) A list of feed_dict keys. See tf.Session.run for details of the allowable feed key types.
accept_options (Optional.) If True, the returned Callable will be able to accept tf.compat.v1.RunOptions and tf.compat.v1.RunMetadata as optional keyword arguments options and run_metadata, respectively, with the same syntax and semantics as tf.Session.run, which is useful for certain use cases (profiling and debugging) but will result in measurable slowdown of the Callable's performance. Default: False.
Returns A function that when called will execute the step defined by feed_list and fetches in this session.
Raises
TypeError If fetches or feed_list cannot be interpreted as arguments to tf.Session.run. partial_run View source
partial_run(
handle, fetches, feed_dict=None
)
Continues the execution with more feeds and fetches. This is EXPERIMENTAL and subject to change. To use partial execution, a user first calls partial_run_setup() and then a sequence of partial_run(). partial_run_setup specifies the list of feeds and fetches that will be used in the subsequent partial_run calls. The optional feed_dict argument allows the caller to override the value of tensors in the graph. See run() for more information. Below is a simple example: a = array_ops.placeholder(dtypes.float32, shape=[])
b = array_ops.placeholder(dtypes.float32, shape=[])
c = array_ops.placeholder(dtypes.float32, shape=[])
r1 = math_ops.add(a, b)
r2 = math_ops.multiply(r1, c)
h = sess.partial_run_setup([r1, r2], [a, b, c])
res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
res = sess.partial_run(h, r2, feed_dict={c: res})
Args
handle A handle for a sequence of partial runs.
fetches A single graph element, a list of graph elements, or a dictionary whose values are graph elements or lists of graph elements (see documentation for run).
feed_dict A dictionary that maps graph elements to values (described above).
Returns Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a dictionary with the same keys as fetches if that is a dictionary (see documentation for run).
Raises
tf.errors.OpError Or one of its subclasses on error. partial_run_setup View source
partial_run_setup(
fetches, feeds=None
)
Sets up a graph with feeds and fetches for partial run. This is EXPERIMENTAL and subject to change. Note that contrary to run, feeds only specifies the graph elements. The tensors will be supplied by the subsequent partial_run calls.
Args
fetches A single graph element, or a list of graph elements.
feeds A single graph element, or a list of graph elements.
Returns A handle for partial run.
Raises
RuntimeError If this Session is in an invalid state (e.g. has been closed).
TypeError If fetches or feed_dict keys are of an inappropriate type.
tf.errors.OpError Or one of its subclasses if a TensorFlow error happens. run View source
run(
fetches, feed_dict=None, options=None, run_metadata=None
)
Runs operations and evaluates tensors in fetches. This method runs one "step" of TensorFlow computation, by running the necessary graph fragment to execute every Operation and evaluate every Tensor in fetches, substituting the values in feed_dict for the corresponding input values. The fetches argument may be a single graph element, or an arbitrarily nested list, tuple, namedtuple, dict, or OrderedDict containing graph elements at its leaves. A graph element can be one of the following types: A tf.Operation. The corresponding fetched value will be None. A tf.Tensor. The corresponding fetched value will be a numpy ndarray containing the value of that tensor. A tf.sparse.SparseTensor. The corresponding fetched value will be a tf.compat.v1.SparseTensorValue containing the value of that sparse tensor. A get_tensor_handle op. The corresponding fetched value will be a numpy ndarray containing the handle of that tensor. A string which is the name of a tensor or operation in the graph. The value returned by run() has the same shape as the fetches argument, where the leaves are replaced by the corresponding values returned by TensorFlow. Example: a = tf.constant([10, 20])
b = tf.constant([1.0, 2.0])
# 'fetches' can be a singleton
v = session.run(a)
# v is the numpy array [10, 20]
# 'fetches' can be a list.
v = session.run([a, b])
# v is a Python list with 2 numpy arrays: the 1-D array [10, 20] and the
# 1-D array [1.0, 2.0]
# 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:
MyData = collections.namedtuple('MyData', ['a', 'b'])
v = session.run({'k1': MyData(a, b), 'k2': [b, a]})
# v is a dict with
# v['k1'] is a MyData namedtuple with 'a' (the numpy array [10, 20]) and
# 'b' (the numpy array [1.0, 2.0])
# v['k2'] is a list with the numpy array [1.0, 2.0] and the numpy array
# [10, 20].
The optional feed_dict argument allows the caller to override the value of tensors in the graph. Each key in feed_dict can be one of the following types: If the key is a tf.Tensor, the value may be a Python scalar, string, list, or numpy ndarray that can be converted to the same dtype as that tensor. Additionally, if the key is a tf.compat.v1.placeholder, the shape of the value will be checked for compatibility with the placeholder. If the key is a tf.sparse.SparseTensor, the value should be a tf.compat.v1.SparseTensorValue. If the key is a nested tuple of Tensors or SparseTensors, the value should be a nested tuple with the same structure that maps to their corresponding values as above. Each value in feed_dict must be convertible to a numpy array of the dtype of the corresponding key. The optional options argument expects a [RunOptions] proto. The options allow controlling the behavior of this particular step (e.g. turning tracing on). The optional run_metadata argument expects a [RunMetadata] proto. When appropriate, the non-Tensor output of this step will be collected there. For example, when users turn on tracing in options, the profiled info will be collected into this argument and passed back.
Args
fetches A single graph element, a list of graph elements, or a dictionary whose values are graph elements or lists of graph elements (described above).
feed_dict A dictionary that maps graph elements to values (described above).
options A [RunOptions] protocol buffer
run_metadata A [RunMetadata] protocol buffer
Returns Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a dictionary with the same keys as fetches if that is a dictionary (described above). Order in which fetches operations are evaluated inside the call is undefined.
Raises
RuntimeError If this Session is in an invalid state (e.g. has been closed).
TypeError If fetches or feed_dict keys are of an inappropriate type.
ValueError If fetches or feed_dict keys are invalid or refer to a Tensor that doesn't exist. | |
doc_486 |
Return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation. | |
doc_487 | Print objects to the text stream file, separated by sep and followed by end. sep, end, file and flush, if present, must be given as keyword arguments. All non-keyword arguments are converted to strings like str() does and written to the stream, separated by sep and followed by end. Both sep and end must be strings; they can also be None, which means to use the default values. If no objects are given, print() will just write end. The file argument must be an object with a write(string) method; if it is not present or None, sys.stdout will be used. Since printed arguments are converted to text strings, print() cannot be used with binary mode file objects. For these, use file.write(...) instead. Whether output is buffered is usually determined by file, but if the flush keyword argument is true, the stream is forcibly flushed. Changed in version 3.3: Added the flush keyword argument. | |
doc_488 | If True, any defects encountered will be raised as errors. If False (the default), defects will be passed to the register_defect() method. | |
doc_489 |
Mask corresponding to a flood fill. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found. Parameters
imagendarray
An n-dimensional array.
seed_pointtuple or int
The point in image used as the starting point for the flood fill. If the image is 1D, this point may be given as an integer.
selemndarray, optional
A structuring element used to determine the neighborhood of each evaluated pixel. It must contain only 1’s and 0’s, have the same number of dimensions as image. If not given, all adjacent pixels are considered as part of the neighborhood (fully connected).
connectivityint, optional
A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is larger or equal to connectivity are considered neighbors. Ignored if selem is not None.
tolerancefloat or int, optional
If None (default), adjacent values must be strictly equal to the initial value of image at seed_point. This is fastest. If a value is given, a comparison will be done at every point and if within tolerance of the initial value will also be filled (inclusive). Returns
maskndarray
A Boolean array with the same shape as image is returned, with True values for areas connected to and equal (or within tolerance of) the seed point. All other values are False. Notes The conceptual analogy of this operation is the ‘paint bucket’ tool in many raster graphics programs. This function returns just the mask representing the fill. If indices are desired rather than masks for memory reasons, the user can simply run numpy.nonzero on the result, save the indices, and discard this mask. Examples >>> from skimage.morphology import flood
>>> image = np.zeros((4, 7), dtype=int)
>>> image[1:3, 1:3] = 1
>>> image[3, 0] = 1
>>> image[1:3, 4:6] = 2
>>> image[3, 6] = 3
>>> image
array([[0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 2, 2, 0],
[0, 1, 1, 0, 2, 2, 0],
[1, 0, 0, 0, 0, 0, 3]])
Fill connected ones with 5, with full connectivity (diagonals included): >>> mask = flood(image, (1, 1))
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
[0, 5, 5, 0, 2, 2, 0],
[0, 5, 5, 0, 2, 2, 0],
[5, 0, 0, 0, 0, 0, 3]])
Fill connected ones with 5, excluding diagonal points (connectivity 1): >>> mask = flood(image, (1, 1), connectivity=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
[0, 5, 5, 0, 2, 2, 0],
[0, 5, 5, 0, 2, 2, 0],
[1, 0, 0, 0, 0, 0, 3]])
Fill with a tolerance: >>> mask = flood(image, (0, 0), tolerance=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[5, 5, 5, 5, 5, 5, 5],
[5, 5, 5, 5, 2, 2, 5],
[5, 5, 5, 5, 2, 2, 5],
[5, 5, 5, 5, 5, 5, 3]]) | |
doc_490 |
Return a numpy.timedelta64 object with ‘ns’ precision. | |
doc_491 |
Context-manager that changes the selected device. Parameters
device (torch.device or int) – device index to select. It’s a no-op if this argument is a negative integer or None. | |
doc_492 |
Return the locations of the ticks. Note Because the values are Null, vmin and vmax are not used in this method. | |
doc_493 |
Return the cumulative product of the array elements over the given axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Refer to numpy.cumprod for full documentation. See also numpy.ndarray.cumprod
corresponding function for ndarrays numpy.cumprod
equivalent function Notes The mask is lost if out is not a valid MaskedArray ! Arithmetic is modular when using integer types, and no error is raised on overflow. | |
doc_494 | Simple test if a sprite intersects anything in a group.
spritecollideany(sprite, group, collided = None) -> Sprite Collision with the returned sprite.
spritecollideany(sprite, group, collided = None) -> None No collision If the sprite collides with any single sprite in the group, a single sprite from the group is returned. On no collision None is returned. If you don't need all the features of the pygame.sprite.spritecollide() function, this function will be a bit quicker. The collided argument is a callback function used to calculate if two sprites are colliding. It should take two sprites as values and return a bool value indicating if they are colliding. If collided is not passed, then all sprites must have a "rect" value, which is a rectangle of the sprite area, which will be used to calculate the collision. | |
doc_495 | This method returns a bitmask indicating which control(s) are currently being used as a recording source. | |
doc_496 |
Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees deg and sample points (x, y). The pseudo-Vandermonde matrix is defined by \[V[..., (deg[1] + 1)*i + j] = L_i(x) * L_j(y),\] where 0 <= i <= deg[0] and 0 <= j <= deg[1]. The leading indices of V index the points (x, y) and the last index encodes the degrees of the Laguerre polynomials. If V = lagvander2d(x, y, [xdeg, ydeg]), then the columns of V correspond to the elements of a 2-D coefficient array c of shape (xdeg + 1, ydeg + 1) in the order \[c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...\] and np.dot(V, c.flat) and lagval2d(x, y, c) will be the same up to roundoff. This equivalence is useful both for least squares fitting and for the evaluation of a large number of 2-D Laguerre series of the same degrees and sample points. Parameters
x, yarray_like
Arrays of point coordinates, all of the same shape. The dtypes will be converted to either float64 or complex128 depending on whether any of the elements are complex. Scalars are converted to 1-D arrays.
deglist of ints
List of maximum degrees of the form [x_deg, y_deg]. Returns
vander2dndarray
The shape of the returned matrix is x.shape + (order,), where \(order = (deg[0]+1)*(deg[1]+1)\). The dtype will be the same as the converted x and y. See also
lagvander, lagvander3d, lagval2d, lagval3d
Notes New in version 1.7.0. | |
doc_497 |
Convert a polynomial to a Chebyshev series. Convert an array representing the coefficients of a polynomial (relative to the “standard” basis) ordered from lowest degree to highest, to an array of the coefficients of the equivalent Chebyshev series, ordered from lowest to highest degree. Parameters
polarray_like
1-D array containing the polynomial coefficients Returns
cndarray
1-D array containing the coefficients of the equivalent Chebyshev series. See also cheb2poly
Notes The easy way to do conversions between polynomial basis sets is to use the convert method of a class instance. Examples >>> from numpy import polynomial as P
>>> p = P.Polynomial(range(4))
>>> p
Polynomial([0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
>>> c = p.convert(kind=P.Chebyshev)
>>> c
Chebyshev([1. , 3.25, 1. , 0.75], domain=[-1., 1.], window=[-1., 1.])
>>> P.chebyshev.poly2cheb(range(4))
array([1. , 3.25, 1. , 0.75]) | |
doc_498 | Called when the transport’s buffer goes over the high watermark. | |
doc_499 | Set the mouse events to be reported, and return a tuple (availmask,
oldmask). availmask indicates which of the specified mouse events can be reported; on complete failure it returns 0. oldmask is the previous value of the given window’s mouse event mask. If this function is never called, no mouse events are ever reported. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.