_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_24000 |
Number of days. | |
doc_24001 |
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array. | |
doc_24002 | Applies 3D average-pooling operation in kT×kH×kWkT \times kH \times kW regions by step size sT×sH×sWsT \times sH \times sW steps. The number of output features is equal to ⌊input planessT⌋\lfloor\frac{\text{input planes}}{sT}\rfloor . See AvgPool3d for details and output shape. Parameters
input – input tensor (minibatch,in_channels,iT×iH,iW)(\text{minibatch} , \text{in\_channels} , iT \times iH , iW)
kernel_size – size of the pooling region. Can be a single number or a tuple (kT, kH, kW)
stride – stride of the pooling operation. Can be a single number or a tuple (sT, sH, sW). Default: kernel_size
padding – implicit zero paddings on both sides of the input. Can be a single number or a tuple (padT, padH, padW), Default: 0
ceil_mode – when True, will use ceil instead of floor in the formula to compute the output shape
count_include_pad – when True, will include the zero-padding in the averaging calculation
divisor_override – if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: None | |
doc_24003 |
Other Members
AUTO_REUSE
COMPILER_VERSION '7.3.1 20180303'
CXX11_ABI_FLAG 0
GIT_VERSION 'v2.4.0-rc4-71-g582c8d236cb'
GRAPH_DEF_VERSION 561
GRAPH_DEF_VERSION_MIN_CONSUMER 0
GRAPH_DEF_VERSION_MIN_PRODUCER 0
MONOLITHIC_BUILD 0
QUANTIZED_DTYPES
VERSION '2.4.0'
version '2.4.0'
bfloat16 tf.dtypes.DType
bool tf.dtypes.DType
complex128 tf.dtypes.DType
complex64 tf.dtypes.DType
double tf.dtypes.DType
float16 tf.dtypes.DType
float32 tf.dtypes.DType
float64 tf.dtypes.DType
half tf.dtypes.DType
int16 tf.dtypes.DType
int32 tf.dtypes.DType
int64 tf.dtypes.DType
int8 tf.dtypes.DType
newaxis None
qint16 tf.dtypes.DType
qint32 tf.dtypes.DType
qint8 tf.dtypes.DType
quint16 tf.dtypes.DType
quint8 tf.dtypes.DType
resource tf.dtypes.DType
string tf.dtypes.DType
uint16 tf.dtypes.DType
uint32 tf.dtypes.DType
uint64 tf.dtypes.DType
uint8 tf.dtypes.DType
variant tf.dtypes.DType | |
doc_24004 | See Migration guide for more details. tf.compat.v1.raw_ops.HashTable
tf.raw_ops.HashTable(
key_dtype, value_dtype, container='', shared_name='',
use_node_name_sharing=False, name=None
)
This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable.
Args
key_dtype A tf.DType. Type of the table keys.
value_dtype A tf.DType. Type of the table values.
container An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
use_node_name_sharing An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.
name A name for the operation (optional).
Returns A Tensor of type mutable string. | |
doc_24005 |
Return name of an image XObject representing the given image. | |
doc_24006 |
Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG. | |
doc_24007 |
[Deprecated] Notes Deprecated since version 3.4: | |
doc_24008 | The Expires entity-header field gives the date/time after which the response is considered stale. A stale cache entry may not normally be returned by a cache. Changed in version 2.0: The datetime object is timezone-aware. | |
doc_24009 |
CIE-LAB to XYZcolor space conversion. Parameters
lab(…, 3) array_like
The image in Lab format. Final dimension denotes channels.
illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
observer{“2”, “10”}, optional
The aperture angle of the observer. Returns
out(…, 3) ndarray
The image in XYZ format. Same dimensions as input. Raises
ValueError
If lab is not at least 2-D with shape (…, 3). ValueError
If either the illuminant or the observer angle are not supported or unknown. UserWarning
If any of the pixels are invalid (Z < 0). Notes By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883. See function ‘get_xyz_coords’ for a list of supported illuminants. References
1
http://www.easyrgb.com/index.php?X=MATH&H=07
2
https://en.wikipedia.org/wiki/Lab_color_space | |
doc_24010 |
Generate a new DataFrame or Series with the index reset. This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation. Parameters
level:int, str, tuple, or list, default optional
For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default.
drop:bool, default False
Just reset the index, without inserting it as a column in the new DataFrame.
name:object, optional
The name to use for the column containing the original Series values. Uses self.name by default. This argument is ignored when drop is True.
inplace:bool, default False
Modify the Series in place (do not create a new object). Returns
Series or DataFrame or None
When drop is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When drop is True, a Series is returned. In either case, if inplace=True, no value is returned. See also DataFrame.reset_index
Analogous function for DataFrame. Examples
>>> s = pd.Series([1, 2, 3, 4], name='foo',
... index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))
Generate a DataFrame with default index.
>>> s.reset_index()
idx foo
0 a 1
1 b 2
2 c 3
3 d 4
To specify the name of the new column use name.
>>> s.reset_index(name='values')
idx values
0 a 1
1 b 2
2 c 3
3 d 4
To generate a new Series with the default set drop to True.
>>> s.reset_index(drop=True)
0 1
1 2
2 3
3 4
Name: foo, dtype: int64
To update the Series in place, without generating a new one set inplace to True. Note that it also requires drop=True.
>>> s.reset_index(inplace=True, drop=True)
>>> s
0 1
1 2
2 3
3 4
Name: foo, dtype: int64
The level parameter is interesting for Series with a multi-level index.
>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
... np.array(['one', 'two', 'one', 'two'])]
>>> s2 = pd.Series(
... range(4), name='foo',
... index=pd.MultiIndex.from_arrays(arrays,
... names=['a', 'b']))
To remove a specific level from the Index, use level.
>>> s2.reset_index(level='a')
a foo
b
one bar 0
two bar 1
one baz 2
two baz 3
If level is not set, all levels are removed from the Index.
>>> s2.reset_index()
a b foo
0 bar one 0
1 bar two 1
2 baz one 2
3 baz two 3 | |
doc_24011 | The unix_dialect class defines the usual properties of a CSV file generated on UNIX systems, i.e. using '\n' as line terminator and quoting all fields. It is registered with the dialect name 'unix'. New in version 3.2. | |
doc_24012 | Provides a mechanism to construct a TemplateResponse, given suitable context. The template to use is configurable and can be further customized by subclasses. Attributes
template_name
The full name of a template to use as defined by a string. Not defining a template_name will raise a django.core.exceptions.ImproperlyConfigured exception.
template_engine
The NAME of a template engine to use for loading the template. template_engine is passed as the using keyword argument to response_class. Default is None, which tells Django to search for the template in all configured engines.
response_class
The response class to be returned by render_to_response method. Default is TemplateResponse. The template and context of TemplateResponse instances can be altered later (e.g. in template response middleware). If you need custom template loading or custom context object instantiation, create a TemplateResponse subclass and assign it to response_class.
content_type
The content type to use for the response. content_type is passed as a keyword argument to response_class. Default is None – meaning that Django uses 'text/html'.
Methods
render_to_response(context, **response_kwargs)
Returns a self.response_class instance. If any keyword arguments are provided, they will be passed to the constructor of the response class. Calls get_template_names() to obtain the list of template names that will be searched looking for an existent template.
get_template_names()
Returns a list of template names to search for when rendering the template. The first template that is found will be used. The default implementation will return a list containing template_name (if it is specified). | |
doc_24013 |
Set if artist is to be included in layout calculations, E.g. Constrained Layout Guide, Figure.tight_layout(), and fig.savefig(fname, bbox_inches='tight'). Parameters
in_layoutbool | |
doc_24014 |
Alias for set_antialiased. | |
doc_24015 | See torch.true_divide() | |
doc_24016 |
Make a compound path object to draw a number of polygons with equal numbers of sides XY is a (numpolys x numsides x 2) numpy array of vertices. Return object is a Path. (Source code, png, pdf) | |
doc_24017 | Path to a custom template, used by change_view(). | |
doc_24018 |
Construct a Bbox by expanding this one around its center by the factors sw and sh. | |
doc_24019 |
Mixin defining all operator special methods using __array_ufunc__. This class implements the special methods for almost all of Python’s builtin operators defined in the operator module, including comparisons (==, >, etc.) and arithmetic (+, *, -, etc.), by deferring to the __array_ufunc__ method, which subclasses must implement. It is useful for writing classes that do not inherit from numpy.ndarray, but that should support arithmetic and numpy universal functions like arrays as described in A Mechanism for Overriding Ufuncs. As an trivial example, consider this implementation of an ArrayLike class that simply wraps a NumPy array and ensures that the result of any arithmetic operation is also an ArrayLike object: class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
def __init__(self, value):
self.value = np.asarray(value)
# One might also consider adding the built-in list type to this
# list, to support operations like np.add(array_like, list)
_HANDLED_TYPES = (np.ndarray, numbers.Number)
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
out = kwargs.get('out', ())
for x in inputs + out:
# Only support operations with instances of _HANDLED_TYPES.
# Use ArrayLike instead of type(self) for isinstance to
# allow subclasses that don't override __array_ufunc__ to
# handle ArrayLike objects.
if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
return NotImplemented
# Defer to the implementation of the ufunc on unwrapped values.
inputs = tuple(x.value if isinstance(x, ArrayLike) else x
for x in inputs)
if out:
kwargs['out'] = tuple(
x.value if isinstance(x, ArrayLike) else x
for x in out)
result = getattr(ufunc, method)(*inputs, **kwargs)
if type(result) is tuple:
# multiple return values
return tuple(type(self)(x) for x in result)
elif method == 'at':
# no return value
return None
else:
# one return value
return type(self)(result)
def __repr__(self):
return '%s(%r)' % (type(self).__name__, self.value)
In interactions between ArrayLike objects and numbers or numpy arrays, the result is always another ArrayLike: >>> x = ArrayLike([1, 2, 3])
>>> x - 1
ArrayLike(array([0, 1, 2]))
>>> 1 - x
ArrayLike(array([ 0, -1, -2]))
>>> np.arange(3) - x
ArrayLike(array([-1, -1, -1]))
>>> x - np.arange(3)
ArrayLike(array([1, 1, 1]))
Note that unlike numpy.ndarray, ArrayLike does not allow operations with arbitrary, unrecognized types. This ensures that interactions with ArrayLike preserve a well-defined casting hierarchy. New in version 1.13. | |
doc_24020 | tf.compat.v1.layers.Conv1D(
filters, kernel_size, strides=1, padding='valid',
data_format='channels_last', dilation_rate=1, activation=None,
use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(),
kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, bias_constraint=None, trainable=True, name=None,
**kwargs
)
This layer creates a convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. If use_bias is True (and a bias_initializer is provided), a bias vector is created and added to the outputs. Finally, if activation is not None, it is applied to the outputs as well.
Arguments
filters Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides An integer or tuple/list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding One of "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
data_format A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length).
dilation_rate An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.
activation Activation function. Set it to None to maintain a linear activation.
use_bias Boolean, whether the layer uses a bias.
kernel_initializer An initializer for the convolution kernel.
bias_initializer An initializer for the bias vector. If None, the default initializer will be used.
kernel_regularizer Optional regularizer for the convolution kernel.
bias_regularizer Optional regularizer for the bias vector.
activity_regularizer Optional regularizer function for the output.
kernel_constraint Optional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
bias_constraint Optional projection function to be applied to the bias after being updated by an Optimizer.
trainable Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
name A string, the name of the layer.
Attributes
graph
scope_name | |
doc_24021 |
Transform the sources back to the mixed data (apply mixing matrix). Parameters
Xarray-like of shape (n_samples, n_components)
Sources, where n_samples is the number of samples and n_components is the number of components.
copybool, default=True
If False, data passed to fit are overwritten. Defaults to True. Returns
X_newndarray of shape (n_samples, n_features) | |
doc_24022 | This converter only accepts UUID strings: Rule('/object/<uuid:identifier>')
Changelog New in version 0.10. Parameters
map (Map) – the Map.
args (Any) –
kwargs (Any) – Return type
None | |
doc_24023 | Number of 512-byte blocks allocated for file. This may be smaller than st_size/512 when the file has holes. | |
doc_24024 |
Return the aspect ratio of the scaled data. Notes This method is intended to be overridden by new projection types. | |
doc_24025 | Return a string containing the hostname of the machine where the Python interpreter is currently executing. Raises an auditing event socket.gethostname with no arguments. Note: gethostname() doesn’t always return the fully qualified domain name; use getfqdn() for that. | |
doc_24026 | An abstract method for finding a spec for the specified module. The finder will search for the module only within the path entry to which it is assigned. If a spec cannot be found, None is returned. When passed in, target is a module object that the finder may use to make a more educated guess about what spec to return. importlib.util.spec_from_loader() may be useful for implementing concrete PathEntryFinders. New in version 3.4. | |
doc_24027 | sklearn.utils.safe_mask(X, mask) [source]
Return a mask which is safe to use on X. Parameters
X{array-like, sparse matrix}
Data on which to apply mask.
maskndarray
Mask to be used on X. Returns
mask | |
doc_24028 | True if this is a loopback address. See RFC 3330 (for IPv4) or RFC 2373 (for IPv6). | |
doc_24029 | tf.compat.v1.train.init_from_checkpoint(
ckpt_dir_or_file, assignment_map
)
Values are not loaded immediately, but when the initializer is run (typically by running a tf.compat.v1.global_variables_initializer op).
Note: This overrides default initialization ops of specified variables and redefines dtype.
Assignment map supports following syntax:
'checkpoint_scope_name/': 'scope_name/' - will load all variables in current scope_name from checkpoint_scope_name with matching tensor names.
'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name' - will initialize scope_name/variable_name variable from checkpoint_scope_name/some_other_variable.
'scope_variable_name': variable - will initialize given tf.Variable object with tensor 'scope_variable_name' from the checkpoint.
'scope_variable_name': list(variable) - will initialize list of partitioned variables with tensor 'scope_variable_name' from the checkpoint.
'/': 'scope_name/' - will load all variables in current scope_name from checkpoint's root (e.g. no scope). Supports loading into partitioned variables, which are represented as '<variable>/part_<part #>'. Example:
# Say, '/tmp/model.ckpt' has the following tensors:
# -- name='old_scope_1/var1', shape=[20, 2]
# -- name='old_scope_1/var2', shape=[50, 4]
# -- name='old_scope_2/var3', shape=[100, 100]
# Create new model's variables
with tf.compat.v1.variable_scope('new_scope_1'):
var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
initializer=tf.compat.v1.zeros_initializer())
with tf.compat.v1.variable_scope('new_scope_2'):
var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
initializer=tf.compat.v1.zeros_initializer())
# Partition into 5 variables along the first axis.
var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
initializer=tf.compat.v1.zeros_initializer(),
partitioner=lambda shape, dtype: [5, 1])
# Initialize all variables in `new_scope_1` from `old_scope_1`.
init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})
# Use names to specify which variables to initialize from checkpoint.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': 'new_scope_1/var1',
'old_scope_1/var2': 'new_scope_2/var2'})
# Or use tf.Variable objects to identify what to initialize.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': var1,
'old_scope_1/var2': var2})
# Initialize partitioned variables using variable's name
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': 'new_scope_2/var3'})
# Or specify the list of tf.Variable objects.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': var3._get_variable_list()})
Args
ckpt_dir_or_file Directory with checkpoints file or path to checkpoint.
assignment_map Dict, where keys are names of the variables in the checkpoint and values are current variables or names of current variables (in default graph).
Raises
ValueError If missing variables in current graph, or if missing checkpoints or tensors in checkpoints. | |
doc_24030 |
Remove a callback based on its observer id. See also add_callback | |
doc_24031 |
Generate HTML representation of the animation. Parameters
fpsint, optional
Movie frame rate (per second). If not set, the frame rate from the animation's frame interval.
embed_framesbool, optional
default_modestr, optional
What to do when the animation ends. Must be one of {'loop',
'once', 'reflect'}. Defaults to 'loop' if self.repeat is True, otherwise 'once'. | |
doc_24032 | Text is an alias for str. It is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode. Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3: def add_unicode_checkmark(text: Text) -> Text:
return text + u' \u2713'
New in version 3.5.2. | |
doc_24033 |
Shift the time index, using the index’s frequency if available. Deprecated since version 1.1.0: Use shift instead. Parameters
periods:int
Number of periods to move, can be positive or negative.
freq:DateOffset, timedelta, or str, default None
Increment to use from the tseries module or time rule expressed as a string (e.g. ‘EOM’).
axis:{0 or ‘index’, 1 or ‘columns’, None}, default 0
Corresponds to the axis that contains the Index. Returns
shifted:Series/DataFrame
Notes If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown | |
doc_24034 |
Set the agg filter. Parameters
filter_funccallable
A filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array. | |
doc_24035 | See Migration guide for more details. tf.compat.v1.raw_ops.MatrixDiagPartV2
tf.raw_ops.MatrixDiagPartV2(
input, k, padding_value, name=None
)
Returns a tensor with the k[0]-th to k[1]-th diagonals of the batched input. Assume input has r dimensions [I, J, ..., L, M, N]. Let max_diag_len be the maximum length among all diagonals to be extracted, max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0)) Let num_diags be the number of diagonals to extract, num_diags = k[1] - k[0] + 1. If num_diags == 1, the output tensor is of rank r - 1 with shape [I, J, ..., L, max_diag_len] and values: diagonal[i, j, ..., l, n]
= input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
padding_value ; otherwise.
where y = max(-k[1], 0), x = max(k[1], 0). Otherwise, the output tensor has rank r with dimensions [I, J, ..., L, num_diags, max_diag_len] with values: diagonal[i, j, ..., l, m, n]
= input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
padding_value ; otherwise.
where d = k[1] - m, y = max(-d, 0), and x = max(d, 0). The input must be at least a matrix. For example: input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4)
[5, 6, 7, 8],
[9, 8, 7, 6]],
[[5, 4, 3, 2],
[1, 2, 3, 4],
[5, 6, 7, 8]]])
# A main diagonal from each batch.
tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3)
[5, 2, 7]]
# A superdiagonal from each batch.
tf.matrix_diag_part(input, k = 1)
==> [[2, 7, 6], # Output shape: (2, 3)
[4, 3, 8]]
# A tridiagonal band from each batch.
tf.matrix_diag_part(input, k = (-1, 1))
==> [[[2, 7, 6], # Output shape: (2, 3, 3)
[1, 6, 7],
[5, 8, 0]],
[[4, 3, 8],
[5, 2, 7],
[1, 6, 0]]]
# Padding value = 9
tf.matrix_diag_part(input, k = (1, 3), padding_value = 9)
==> [[[4, 9, 9], # Output shape: (2, 3, 3)
[3, 8, 9],
[2, 7, 6]],
[[2, 9, 9],
[3, 4, 9],
[4, 3, 8]]]
Args
input A Tensor. Rank r tensor where r >= 2.
k A Tensor of type int32. Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. k can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. k[0] must not be larger than k[1].
padding_value A Tensor. Must have the same type as input. The value to fill the area outside the specified diagonal band with. Default is 0.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_24036 |
Return the position of the rectangle. | |
doc_24037 |
Return an array of native datetime.timedelta objects. Python’s standard datetime library uses a different representation timedelta’s. This method converts a Series of pandas Timedeltas to datetime.timedelta format with the same length as the original Series. Returns
numpy.ndarray
Array of 1D containing data with datetime.timedelta type. See also datetime.timedelta
A duration expressing the difference between two date, time, or datetime. Examples
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
>>> s
0 0 days
1 1 days
2 2 days
3 3 days
4 4 days
dtype: timedelta64[ns]
>>> s.dt.to_pytimedelta()
array([datetime.timedelta(0), datetime.timedelta(days=1),
datetime.timedelta(days=2), datetime.timedelta(days=3),
datetime.timedelta(days=4)], dtype=object) | |
doc_24038 | tf.distribute.StrategyExtended(
container_strategy
)
Note: For most usage of tf.distribute.Strategy, there should be no need to call these methods, since TensorFlow libraries (such as optimizers) already call these methods when needed on your behalf.
Some common use cases of functions on this page: Locality tf.distribute.DistributedValues can have the same locality as a distributed variable, which leads to a mirrored value residing on the same devices as the variable (as opposed to the compute devices). Such values may be passed to a call to tf.distribute.StrategyExtended.update to update the value of a variable. You may use tf.distribute.StrategyExtended.colocate_vars_with to give a variable the same locality as another variable. You may convert a "PerReplica" value to a variable's locality by using tf.distribute.StrategyExtended.reduce_to or tf.distribute.StrategyExtended.batch_reduce_to. How to update a distributed variable A distributed variable is variables created on multiple devices. As discussed in the glossary, mirrored variable and SyncOnRead variable are two examples. The standard pattern for updating distributed variables is to: In your function passed to tf.distribute.Strategy.run, compute a list of (update, variable) pairs. For example, the update might be a gradient of the loss with respect to the variable. Switch to cross-replica mode by calling tf.distribute.get_replica_context().merge_call() with the updates and variables as arguments. Call tf.distribute.StrategyExtended.reduce_to(VariableAggregation.SUM, t, v) (for one variable) or tf.distribute.StrategyExtended.batch_reduce_to (for a list of variables) to sum the updates. Call tf.distribute.StrategyExtended.update(v) for each variable to update its value. Steps 2 through 4 are done automatically by class tf.keras.optimizers.Optimizer if you call its tf.keras.optimizers.Optimizer.apply_gradients method in a replica context. In fact, a higher-level solution to update a distributed variable is by calling assign on the variable as you would do to a regular tf.Variable. You can call the method in both replica context and cross-replica context. For a mirrored variable, calling assign in replica context requires you to specify the aggregation type in the variable constructor. In that case, the context switching and sync described in steps 2 through 4 are handled for you. If you call assign on mirrored variable in cross-replica context, you can only assign a single value or assign values from another mirrored variable or a mirrored tf.distribute.DistributedValues. For a SyncOnRead variable, in replica context, you can simply call assign on it and no aggregation happens under the hood. In cross-replica context, you can only assign a single value to a SyncOnRead variable. One example case is restoring from a checkpoint: if the aggregation type of the variable is tf.VariableAggregation.SUM, it is assumed that replica values were added before checkpointing, so at the time of restoring, the value is divided by the number of replicas and then assigned to each replica; if the aggregation type is tf.VariableAggregation.MEAN, the value is assigned to each replica directly.
Attributes
experimental_require_static_shapes Returns True if static shape is required; False otherwise.
parameter_devices Returns the tuple of all devices used to place variables.
worker_devices Returns the tuple of all devices used to for compute replica execution. Methods batch_reduce_to View source
batch_reduce_to(
reduce_op, value_destination_pairs, options=None
)
Combine multiple reduce_to calls into one for faster execution. Similar to reduce_to, but accepts a list of (value, destinations) pairs. It's more efficient than reduce each value separately. This API currently can only be called in cross-replica context. Other variants to reduce values across replicas are:
tf.distribute.StrategyExtended.reduce_to: the non-batch version of this API.
tf.distribute.ReplicaContext.all_reduce: the counterpart of this API in replica context. It supports both batched and non-batched all-reduce.
tf.distribute.Strategy.reduce: a more convenient method to reduce to the host in cross-replica context. See reduce_to for more information.
@tf.function
def step_fn(var):
def merge_fn(strategy, value, var):
# All-reduce the value. Note that `value` here is a
# `tf.distribute.DistributedValues`.
reduced = strategy.extended.batch_reduce_to(
tf.distribute.ReduceOp.SUM, [(value, var)])[0]
strategy.extended.update(var, lambda var, value: var.assign(value),
args=(reduced,))
value = tf.identity(1.)
tf.distribute.get_replica_context().merge_call(merge_fn,
args=(value, var))
def run(strategy):
with strategy.scope():
v = tf.Variable(0.)
strategy.run(step_fn, args=(v,))
return v
run(tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]))
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>,
1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=2.0>
}
run(tf.distribute.experimental.CentralStorageStrategy(
compute_devices=["GPU:0", "GPU:1"], parameter_device="CPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>
run(tf.distribute.OneDeviceStrategy("GPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
Args
reduce_op a tf.distribute.ReduceOp value specifying how values should be combined. Allows using string representation of the enum such as "SUM", "MEAN".
value_destination_pairs a sequence of (value, destinations) pairs. See tf.distribute.Strategy.reduce_to for descriptions.
options a tf.distribute.experimental.CommunicationOptions. Options to perform collective operations. This overrides the default options if the tf.distribute.Strategy takes one in the constructor. See tf.distribute.experimental.CommunicationOptions for details of the options.
Returns A list of reduced values, one per pair in value_destination_pairs.
colocate_vars_with View source
colocate_vars_with(
colocate_with_variable
)
Scope that controls which devices variables will be created on. No operations should be added to the graph inside this scope, it should only be used when creating variables (some implementations work by changing variable creation, others work by using a tf.compat.v1.colocate_with() scope). This may only be used inside self.scope(). Example usage: with strategy.scope():
var1 = tf.Variable(...)
with strategy.extended.colocate_vars_with(var1):
# var2 and var3 will be created on the same device(s) as var1
var2 = tf.Variable(...)
var3 = tf.Variable(...)
def fn(v1, v2, v3):
# operates on v1 from var1, v2 from var2, and v3 from var3
# `fn` runs on every device `var1` is on, `var2` and `var3` will be there
# too.
strategy.extended.update(var1, fn, args=(var2, var3))
Args
colocate_with_variable A variable created in this strategy's scope(). Variables created while in the returned context manager will be on the same set of devices as colocate_with_variable.
Returns A context manager.
reduce_to View source
reduce_to(
reduce_op, value, destinations, options=None
)
Combine (via e.g. sum or mean) values across replicas. reduce_to aggregates tf.distribute.DistributedValues and distributed variables. It supports both dense values and tf.IndexedSlices. This API currently can only be called in cross-replica context. Other variants to reduce values across replicas are:
tf.distribute.StrategyExtended.batch_reduce_to: the batch version of this API.
tf.distribute.ReplicaContext.all_reduce: the counterpart of this API in replica context. It supports both batched and non-batched all-reduce.
tf.distribute.Strategy.reduce: a more convenient method to reduce to the host in cross-replica context. destinations specifies where to reduce the value to, e.g. "GPU:0". You can also pass in a Tensor, and the destinations will be the device of that tensor. For all-reduce, pass the same to value and destinations. It can be used in tf.distribute.ReplicaContext.merge_call to write code that works for all tf.distribute.Strategy.
@tf.function
def step_fn(var):
def merge_fn(strategy, value, var):
# All-reduce the value. Note that `value` here is a
# `tf.distribute.DistributedValues`.
reduced = strategy.extended.reduce_to(tf.distribute.ReduceOp.SUM,
value, destinations=var)
strategy.extended.update(var, lambda var, value: var.assign(value),
args=(reduced,))
value = tf.identity(1.)
tf.distribute.get_replica_context().merge_call(merge_fn,
args=(value, var))
def run(strategy):
with strategy.scope():
v = tf.Variable(0.)
strategy.run(step_fn, args=(v,))
return v
run(tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]))
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>,
1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=2.0>
}
run(tf.distribute.experimental.CentralStorageStrategy(
compute_devices=["GPU:0", "GPU:1"], parameter_device="CPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=2.0>
run(tf.distribute.OneDeviceStrategy("GPU:0"))
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
Args
reduce_op a tf.distribute.ReduceOp value specifying how values should be combined. Allows using string representation of the enum such as "SUM", "MEAN".
value a tf.distribute.DistributedValues, or a tf.Tensor like object.
destinations a tf.distribute.DistributedValues, a tf.Variable, a tf.Tensor alike object, or a device string. It specifies the devices to reduce to. To perform an all-reduce, pass the same to value and destinations. Note that if it's a tf.Variable, the value is reduced to the devices of that variable, and this method doesn't update the variable.
options a tf.distribute.experimental.CommunicationOptions. Options to perform collective operations. This overrides the default options if the tf.distribute.Strategy takes one in the constructor. See tf.distribute.experimental.CommunicationOptions for details of the options.
Returns A tensor or value reduced to destinations.
update View source
update(
var, fn, args=(), kwargs=None, group=True
)
Run fn to update var using inputs mirrored to the same devices. tf.distribute.StrategyExtended.update takes a distributed variable var to be updated, an update function fn, and args and kwargs for fn. It applies fn to each component variable of var and passes corresponding values from args and kwargs. Neither args nor kwargs may contain per-replica values. If they contain mirrored values, they will be unwrapped before calling fn. For example, fn can be assign_add and args can be a mirrored DistributedValues where each component contains the value to be added to this mirrored variable var. Calling update will call assign_add on each component variable of var with the corresponding tensor value on that device. Example usage: strategy = tf.distribute.MirroredStrategy(['GPU:0', 'GPU:1']) # With 2
devices
with strategy.scope():
v = tf.Variable(5.0, aggregation=tf.VariableAggregation.SUM)
def update_fn(v):
return v.assign(1.0)
result = strategy.extended.update(v, update_fn)
# result is
# Mirrored:{
# 0: tf.Tensor(1.0, shape=(), dtype=float32),
# 1: tf.Tensor(1.0, shape=(), dtype=float32)
# }
If var is mirrored across multiple devices, then this method implements logic as following: results = {}
for device, v in var:
with tf.device(device):
# args and kwargs will be unwrapped if they are mirrored.
results[device] = fn(v, *args, **kwargs)
return merged(results)
Otherwise, this method returns fn(var, *args, **kwargs) colocated with var.
Args
var Variable, possibly mirrored to multiple devices, to operate on.
fn Function to call. Should take the variable as the first argument.
args Tuple or list. Additional positional arguments to pass to fn().
kwargs Dict with keyword arguments to pass to fn().
group Boolean. Defaults to True. If False, the return value will be unwrapped.
Returns By default, the merged return value of fn across all replicas. The merged result has dependencies to make sure that if it is evaluated at all, the side effects (updates) will happen on every replica. If instead "group=False" is specified, this function will return a nest of lists where each list has an element per replica, and the caller is responsible for ensuring all elements are executed.
value_container View source
value_container(
value
)
Returns the container that this per-replica value belongs to.
Args
value A value returned by run() or a variable created in scope().
Returns A container that value belongs to. If value does not belong to any container (including the case of container having been destroyed), returns the value itself. value in experimental_local_results(value_container(value)) will always be true.
variable_created_in_scope View source
variable_created_in_scope(
v
)
Tests whether v was created while this strategy scope was active. Variables created inside the strategy scope are "owned" by it:
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
v = tf.Variable(1.)
strategy.extended.variable_created_in_scope(v)
True
Variables created outside the strategy are not owned by it:
strategy = tf.distribute.MirroredStrategy()
v = tf.Variable(1.)
strategy.extended.variable_created_in_scope(v)
False
Args
v A tf.Variable instance.
Returns True if v was created inside the scope, False if not. | |
doc_24039 | A read-only property for the standard deviation of a normal distribution. | |
doc_24040 | tf.experimental.numpy.isnan(
x
)
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.isnan. | |
doc_24041 | Python has a more primitive serialization module called marshal, but in general pickle should always be the preferred way to serialize Python objects. marshal exists primarily to support Python’s .pyc files. The pickle module differs from marshal in several significant ways:
The pickle module keeps track of the objects it has already serialized, so that later references to the same object won’t be serialized again. marshal doesn’t do this. This has implications both for recursive objects and object sharing. Recursive objects are objects that contain references to themselves. These are not handled by marshal, and in fact, attempting to marshal recursive objects will crash your Python interpreter. Object sharing happens when there are multiple references to the same object in different places in the object hierarchy being serialized. pickle stores such objects only once, and ensures that all other references point to the master copy. Shared objects remain shared, which can be very important for mutable objects.
marshal cannot be used to serialize user-defined classes and their instances. pickle can save and restore class instances transparently, however the class definition must be importable and live in the same module as when the object was stored. The marshal serialization format is not guaranteed to be portable across Python versions. Because its primary job in life is to support .pyc files, the Python implementers reserve the right to change the serialization format in non-backwards compatible ways should the need arise. The pickle serialization format is guaranteed to be backwards compatible across Python releases provided a compatible pickle protocol is chosen and pickling and unpickling code deals with Python 2 to Python 3 type differences if your data is crossing that unique breaking change language boundary. Comparison with json
There are fundamental differences between the pickle protocols and JSON (JavaScript Object Notation): JSON is a text serialization format (it outputs unicode text, although most of the time it is then encoded to utf-8), while pickle is a binary serialization format; JSON is human-readable, while pickle is not; JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific; JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python’s introspection facilities; complex cases can be tackled by implementing specific object APIs); Unlike pickle, deserializing untrusted JSON does not in itself create an arbitrary code execution vulnerability. See also The json module: a standard library module allowing JSON serialization and deserialization. Data stream format The data format used by pickle is Python-specific. This has the advantage that there are no restrictions imposed by external standards such as JSON or XDR (which can’t represent pointer sharing); however it means that non-Python programs may not be able to reconstruct pickled Python objects. By default, the pickle data format uses a relatively compact binary representation. If you need optimal size characteristics, you can efficiently compress pickled data. The module pickletools contains tools for analyzing data streams generated by pickle. pickletools source code has extensive comments about opcodes used by pickle protocols. There are currently 6 different protocols which can be used for pickling. The higher the protocol used, the more recent the version of Python needed to read the pickle produced. Protocol version 0 is the original “human-readable” protocol and is backwards compatible with earlier versions of Python. Protocol version 1 is an old binary format which is also compatible with earlier versions of Python. Protocol version 2 was introduced in Python 2.3. It provides much more efficient pickling of new-style classes. Refer to PEP 307 for information about improvements brought by protocol 2. Protocol version 3 was added in Python 3.0. It has explicit support for bytes objects and cannot be unpickled by Python 2.x. This was the default protocol in Python 3.0–3.7. Protocol version 4 was added in Python 3.4. It adds support for very large objects, pickling more kinds of objects, and some data format optimizations. It is the default protocol starting with Python 3.8. Refer to PEP 3154 for information about improvements brought by protocol 4. Protocol version 5 was added in Python 3.8. It adds support for out-of-band data and speedup for in-band data. Refer to PEP 574 for information about improvements brought by protocol 5. Note Serialization is a more primitive notion than persistence; although pickle reads and writes file objects, it does not handle the issue of naming persistent objects, nor the (even more complicated) issue of concurrent access to persistent objects. The pickle module can transform a complex object into a byte stream and it can transform the byte stream into an object with the same internal structure. Perhaps the most obvious thing to do with these byte streams is to write them onto a file, but it is also conceivable to send them across a network or store them in a database. The shelve module provides a simple interface to pickle and unpickle objects on DBM-style database files. Module Interface To serialize an object hierarchy, you simply call the dumps() function. Similarly, to de-serialize a data stream, you call the loads() function. However, if you want more control over serialization and de-serialization, you can create a Pickler or an Unpickler object, respectively. The pickle module provides the following constants:
pickle.HIGHEST_PROTOCOL
An integer, the highest protocol version available. This value can be passed as a protocol value to functions dump() and dumps() as well as the Pickler constructor.
pickle.DEFAULT_PROTOCOL
An integer, the default protocol version used for pickling. May be less than HIGHEST_PROTOCOL. Currently the default protocol is 4, first introduced in Python 3.4 and incompatible with previous versions. Changed in version 3.0: The default protocol is 3. Changed in version 3.8: The default protocol is 4.
The pickle module provides the following functions to make the pickling process more convenient:
pickle.dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None)
Write the pickled representation of the object obj to the open file object file. This is equivalent to Pickler(file, protocol).dump(obj). Arguments file, protocol, fix_imports and buffer_callback have the same meaning as in the Pickler constructor. Changed in version 3.8: The buffer_callback argument was added.
pickle.dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None)
Return the pickled representation of the object obj as a bytes object, instead of writing it to a file. Arguments protocol, fix_imports and buffer_callback have the same meaning as in the Pickler constructor. Changed in version 3.8: The buffer_callback argument was added.
pickle.load(file, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)
Read the pickled representation of an object from the open file object file and return the reconstituted object hierarchy specified therein. This is equivalent to Unpickler(file).load(). The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled representation of the object are ignored. Arguments file, fix_imports, encoding, errors, strict and buffers have the same meaning as in the Unpickler constructor. Changed in version 3.8: The buffers argument was added.
pickle.loads(data, /, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)
Return the reconstituted object hierarchy of the pickled representation data of an object. data must be a bytes-like object. The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled representation of the object are ignored. Arguments file, fix_imports, encoding, errors, strict and buffers have the same meaning as in the Unpickler constructor. Changed in version 3.8: The buffers argument was added.
The pickle module defines three exceptions:
exception pickle.PickleError
Common base class for the other pickling exceptions. It inherits Exception.
exception pickle.PicklingError
Error raised when an unpicklable object is encountered by Pickler. It inherits PickleError. Refer to What can be pickled and unpickled? to learn what kinds of objects can be pickled.
exception pickle.UnpicklingError
Error raised when there is a problem unpickling an object, such as a data corruption or a security violation. It inherits PickleError. Note that other exceptions may also be raised during unpickling, including (but not necessarily limited to) AttributeError, EOFError, ImportError, and IndexError.
The pickle module exports three classes, Pickler, Unpickler and PickleBuffer:
class pickle.Pickler(file, protocol=None, *, fix_imports=True, buffer_callback=None)
This takes a binary file for writing a pickle data stream. The optional protocol argument, an integer, tells the pickler to use the given protocol; supported protocols are 0 to HIGHEST_PROTOCOL. If not specified, the default is DEFAULT_PROTOCOL. If a negative number is specified, HIGHEST_PROTOCOL is selected. The file argument must have a write() method that accepts a single bytes argument. It can thus be an on-disk file opened for binary writing, an io.BytesIO instance, or any other custom object that meets this interface. If fix_imports is true and protocol is less than 3, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. If buffer_callback is None (the default), buffer views are serialized into file as part of the pickle stream. If buffer_callback is not None, then it can be called any number of times with a buffer view. If the callback returns a false value (such as None), the given buffer is out-of-band; otherwise the buffer is serialized in-band, i.e. inside the pickle stream. It is an error if buffer_callback is not None and protocol is None or smaller than 5. Changed in version 3.8: The buffer_callback argument was added.
dump(obj)
Write the pickled representation of obj to the open file object given in the constructor.
persistent_id(obj)
Do nothing by default. This exists so a subclass can override it. If persistent_id() returns None, obj is pickled as usual. Any other value causes Pickler to emit the returned value as a persistent ID for obj. The meaning of this persistent ID should be defined by Unpickler.persistent_load(). Note that the value returned by persistent_id() cannot itself have a persistent ID. See Persistence of External Objects for details and examples of uses.
dispatch_table
A pickler object’s dispatch table is a registry of reduction functions of the kind which can be declared using copyreg.pickle(). It is a mapping whose keys are classes and whose values are reduction functions. A reduction function takes a single argument of the associated class and should conform to the same interface as a __reduce__() method. By default, a pickler object will not have a dispatch_table attribute, and it will instead use the global dispatch table managed by the copyreg module. However, to customize the pickling for a specific pickler object one can set the dispatch_table attribute to a dict-like object. Alternatively, if a subclass of Pickler has a dispatch_table attribute then this will be used as the default dispatch table for instances of that class. See Dispatch Tables for usage examples. New in version 3.3.
reducer_override(self, obj)
Special reducer that can be defined in Pickler subclasses. This method has priority over any reducer in the dispatch_table. It should conform to the same interface as a __reduce__() method, and can optionally return NotImplemented to fallback on dispatch_table-registered reducers to pickle obj. For a detailed example, see Custom Reduction for Types, Functions, and Other Objects. New in version 3.8.
fast
Deprecated. Enable fast mode if set to a true value. The fast mode disables the usage of memo, therefore speeding the pickling process by not generating superfluous PUT opcodes. It should not be used with self-referential objects, doing otherwise will cause Pickler to recurse infinitely. Use pickletools.optimize() if you need more compact pickles.
class pickle.Unpickler(file, *, fix_imports=True, encoding="ASCII", errors="strict", buffers=None)
This takes a binary file for reading a pickle data stream. The protocol version of the pickle is detected automatically, so no protocol argument is needed. The argument file must have three methods, a read() method that takes an integer argument, a readinto() method that takes a buffer argument and a readline() method that requires no arguments, as in the io.BufferedIOBase interface. Thus file can be an on-disk file opened for binary reading, an io.BytesIO object, or any other custom object that meets this interface. The optional arguments fix_imports, encoding and errors are used to control compatibility support for pickle stream generated by Python 2. If fix_imports is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The encoding and errors tell pickle how to decode 8-bit string instances pickled by Python 2; these default to ‘ASCII’ and ‘strict’, respectively. The encoding can be ‘bytes’ to read these 8-bit string instances as bytes objects. Using encoding='latin1' is required for unpickling NumPy arrays and instances of datetime, date and time pickled by Python 2. If buffers is None (the default), then all data necessary for deserialization must be contained in the pickle stream. This means that the buffer_callback argument was None when a Pickler was instantiated (or when dump() or dumps() was called). If buffers is not None, it should be an iterable of buffer-enabled objects that is consumed each time the pickle stream references an out-of-band buffer view. Such buffers have been given in order to the buffer_callback of a Pickler object. Changed in version 3.8: The buffers argument was added.
load()
Read the pickled representation of an object from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein. Bytes past the pickled representation of the object are ignored.
persistent_load(pid)
Raise an UnpicklingError by default. If defined, persistent_load() should return the object specified by the persistent ID pid. If an invalid persistent ID is encountered, an UnpicklingError should be raised. See Persistence of External Objects for details and examples of uses.
find_class(module, name)
Import module if necessary and return the object called name from it, where the module and name arguments are str objects. Note, unlike its name suggests, find_class() is also used for finding functions. Subclasses may override this to gain control over what type of objects and how they can be loaded, potentially reducing security risks. Refer to Restricting Globals for details. Raises an auditing event pickle.find_class with arguments module, name.
class pickle.PickleBuffer(buffer)
A wrapper for a buffer representing picklable data. buffer must be a buffer-providing object, such as a bytes-like object or a N-dimensional array. PickleBuffer is itself a buffer provider, therefore it is possible to pass it to other APIs expecting a buffer-providing object, such as memoryview. PickleBuffer objects can only be serialized using pickle protocol 5 or higher. They are eligible for out-of-band serialization. New in version 3.8.
raw()
Return a memoryview of the memory area underlying this buffer. The returned object is a one-dimensional, C-contiguous memoryview with format B (unsigned bytes). BufferError is raised if the buffer is neither C- nor Fortran-contiguous.
release()
Release the underlying buffer exposed by the PickleBuffer object.
What can be pickled and unpickled? The following types can be pickled:
None, True, and False
integers, floating point numbers, complex numbers strings, bytes, bytearrays tuples, lists, sets, and dictionaries containing only picklable objects functions defined at the top level of a module (using def, not lambda) built-in functions defined at the top level of a module classes that are defined at the top level of a module instances of such classes whose __dict__ or the result of calling __getstate__() is picklable (see section Pickling Class Instances for details). Attempts to pickle unpicklable objects will raise the PicklingError exception; when this happens, an unspecified number of bytes may have already been written to the underlying file. Trying to pickle a highly recursive data structure may exceed the maximum recursion depth, a RecursionError will be raised in this case. You can carefully raise this limit with sys.setrecursionlimit(). Note that functions (built-in and user-defined) are pickled by “fully qualified” name reference, not by value. 2 This means that only the function name is pickled, along with the name of the module the function is defined in. Neither the function’s code, nor any of its function attributes are pickled. Thus the defining module must be importable in the unpickling environment, and the module must contain the named object, otherwise an exception will be raised. 3 Similarly, classes are pickled by named reference, so the same restrictions in the unpickling environment apply. Note that none of the class’s code or data is pickled, so in the following example the class attribute attr is not restored in the unpickling environment: class Foo:
attr = 'A class attribute'
picklestring = pickle.dumps(Foo)
These restrictions are why picklable functions and classes must be defined in the top level of a module. Similarly, when class instances are pickled, their class’s code and data are not pickled along with them. Only the instance data are pickled. This is done on purpose, so you can fix bugs in a class or add methods to the class and still load objects that were created with an earlier version of the class. If you plan to have long-lived objects that will see many versions of a class, it may be worthwhile to put a version number in the objects so that suitable conversions can be made by the class’s __setstate__() method. Pickling Class Instances In this section, we describe the general mechanisms available to you to define, customize, and control how class instances are pickled and unpickled. In most cases, no additional code is needed to make instances picklable. By default, pickle will retrieve the class and the attributes of an instance via introspection. When a class instance is unpickled, its __init__() method is usually not invoked. The default behaviour first creates an uninitialized instance and then restores the saved attributes. The following code shows an implementation of this behaviour: def save(obj):
return (obj.__class__, obj.__dict__)
def load(cls, attributes):
obj = cls.__new__(cls)
obj.__dict__.update(attributes)
return obj
Classes can alter the default behaviour by providing one or several special methods:
object.__getnewargs_ex__()
In protocols 2 and newer, classes that implements the __getnewargs_ex__() method can dictate the values passed to the __new__() method upon unpickling. The method must return a pair (args, kwargs) where args is a tuple of positional arguments and kwargs a dictionary of named arguments for constructing the object. Those will be passed to the __new__() method upon unpickling. You should implement this method if the __new__() method of your class requires keyword-only arguments. Otherwise, it is recommended for compatibility to implement __getnewargs__(). Changed in version 3.6: __getnewargs_ex__() is now used in protocols 2 and 3.
object.__getnewargs__()
This method serves a similar purpose as __getnewargs_ex__(), but supports only positional arguments. It must return a tuple of arguments args which will be passed to the __new__() method upon unpickling. __getnewargs__() will not be called if __getnewargs_ex__() is defined. Changed in version 3.6: Before Python 3.6, __getnewargs__() was called instead of __getnewargs_ex__() in protocols 2 and 3.
object.__getstate__()
Classes can further influence how their instances are pickled; if the class defines the method __getstate__(), it is called and the returned object is pickled as the contents for the instance, instead of the contents of the instance’s dictionary. If the __getstate__() method is absent, the instance’s __dict__ is pickled as usual.
object.__setstate__(state)
Upon unpickling, if the class defines __setstate__(), it is called with the unpickled state. In that case, there is no requirement for the state object to be a dictionary. Otherwise, the pickled state must be a dictionary and its items are assigned to the new instance’s dictionary. Note If __getstate__() returns a false value, the __setstate__() method will not be called upon unpickling.
Refer to the section Handling Stateful Objects for more information about how to use the methods __getstate__() and __setstate__(). Note At unpickling time, some methods like __getattr__(), __getattribute__(), or __setattr__() may be called upon the instance. In case those methods rely on some internal invariant being true, the type should implement __new__() to establish such an invariant, as __init__() is not called when unpickling an instance. As we shall see, pickle does not use directly the methods described above. In fact, these methods are part of the copy protocol which implements the __reduce__() special method. The copy protocol provides a unified interface for retrieving the data necessary for pickling and copying objects. 4 Although powerful, implementing __reduce__() directly in your classes is error prone. For this reason, class designers should use the high-level interface (i.e., __getnewargs_ex__(), __getstate__() and __setstate__()) whenever possible. We will show, however, cases where using __reduce__() is the only option or leads to more efficient pickling or both.
object.__reduce__()
The interface is currently defined as follows. The __reduce__() method takes no argument and shall return either a string or preferably a tuple (the returned object is often referred to as the “reduce value”). If a string is returned, the string should be interpreted as the name of a global variable. It should be the object’s local name relative to its module; the pickle module searches the module namespace to determine the object’s module. This behaviour is typically useful for singletons. When a tuple is returned, it must be between two and six items long. Optional items can either be omitted, or None can be provided as their value. The semantics of each item are in order: A callable object that will be called to create the initial version of the object. A tuple of arguments for the callable object. An empty tuple must be given if the callable does not accept any argument. Optionally, the object’s state, which will be passed to the object’s __setstate__() method as previously described. If the object has no such method then, the value must be a dictionary and it will be added to the object’s __dict__ attribute. Optionally, an iterator (and not a sequence) yielding successive items. These items will be appended to the object either using obj.append(item) or, in batch, using obj.extend(list_of_items). This is primarily used for list subclasses, but may be used by other classes as long as they have append() and extend() methods with the appropriate signature. (Whether append() or extend() is used depends on which pickle protocol version is used as well as the number of items to append, so both must be supported.) Optionally, an iterator (not a sequence) yielding successive key-value pairs. These items will be stored to the object using obj[key] =
value. This is primarily used for dictionary subclasses, but may be used by other classes as long as they implement __setitem__().
Optionally, a callable with a (obj, state) signature. This callable allows the user to programmatically control the state-updating behavior of a specific object, instead of using obj’s static __setstate__() method. If not None, this callable will have priority over obj’s __setstate__(). New in version 3.8: The optional sixth tuple item, (obj, state), was added.
object.__reduce_ex__(protocol)
Alternatively, a __reduce_ex__() method may be defined. The only difference is this method should take a single integer argument, the protocol version. When defined, pickle will prefer it over the __reduce__() method. In addition, __reduce__() automatically becomes a synonym for the extended version. The main use for this method is to provide backwards-compatible reduce values for older Python releases.
Persistence of External Objects For the benefit of object persistence, the pickle module supports the notion of a reference to an object outside the pickled data stream. Such objects are referenced by a persistent ID, which should be either a string of alphanumeric characters (for protocol 0) 5 or just an arbitrary object (for any newer protocol). The resolution of such persistent IDs is not defined by the pickle module; it will delegate this resolution to the user-defined methods on the pickler and unpickler, persistent_id() and persistent_load() respectively. To pickle objects that have an external persistent ID, the pickler must have a custom persistent_id() method that takes an object as an argument and returns either None or the persistent ID for that object. When None is returned, the pickler simply pickles the object as normal. When a persistent ID string is returned, the pickler will pickle that object, along with a marker so that the unpickler will recognize it as a persistent ID. To unpickle external objects, the unpickler must have a custom persistent_load() method that takes a persistent ID object and returns the referenced object. Here is a comprehensive example presenting how persistent ID can be used to pickle external objects by reference. # Simple example presenting how persistent ID can be used to pickle
# external objects by reference.
import pickle
import sqlite3
from collections import namedtuple
# Simple class representing a record in our database.
MemoRecord = namedtuple("MemoRecord", "key, task")
class DBPickler(pickle.Pickler):
def persistent_id(self, obj):
# Instead of pickling MemoRecord as a regular class instance, we emit a
# persistent ID.
if isinstance(obj, MemoRecord):
# Here, our persistent ID is simply a tuple, containing a tag and a
# key, which refers to a specific record in the database.
return ("MemoRecord", obj.key)
else:
# If obj does not have a persistent ID, return None. This means obj
# needs to be pickled as usual.
return None
class DBUnpickler(pickle.Unpickler):
def __init__(self, file, connection):
super().__init__(file)
self.connection = connection
def persistent_load(self, pid):
# This method is invoked whenever a persistent ID is encountered.
# Here, pid is the tuple returned by DBPickler.
cursor = self.connection.cursor()
type_tag, key_id = pid
if type_tag == "MemoRecord":
# Fetch the referenced record from the database and return it.
cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
key, task = cursor.fetchone()
return MemoRecord(key, task)
else:
# Always raises an error if you cannot return the correct object.
# Otherwise, the unpickler will think None is the object referenced
# by the persistent ID.
raise pickle.UnpicklingError("unsupported persistent object")
def main():
import io
import pprint
# Initialize and populate our database.
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
tasks = (
'give food to fish',
'prepare group meeting',
'fight with a zebra',
)
for task in tasks:
cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
# Fetch the records to be pickled.
cursor.execute("SELECT * FROM memos")
memos = [MemoRecord(key, task) for key, task in cursor]
# Save the records using our custom DBPickler.
file = io.BytesIO()
DBPickler(file).dump(memos)
print("Pickled records:")
pprint.pprint(memos)
# Update a record, just for good measure.
cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
# Load the records from the pickle data stream.
file.seek(0)
memos = DBUnpickler(file, conn).load()
print("Unpickled records:")
pprint.pprint(memos)
if __name__ == '__main__':
main()
Dispatch Tables If one wants to customize pickling of some classes without disturbing any other code which depends on pickling, then one can create a pickler with a private dispatch table. The global dispatch table managed by the copyreg module is available as copyreg.dispatch_table. Therefore, one may choose to use a modified copy of copyreg.dispatch_table as a private dispatch table. For example f = io.BytesIO()
p = pickle.Pickler(f)
p.dispatch_table = copyreg.dispatch_table.copy()
p.dispatch_table[SomeClass] = reduce_SomeClass
creates an instance of pickle.Pickler with a private dispatch table which handles the SomeClass class specially. Alternatively, the code class MyPickler(pickle.Pickler):
dispatch_table = copyreg.dispatch_table.copy()
dispatch_table[SomeClass] = reduce_SomeClass
f = io.BytesIO()
p = MyPickler(f)
does the same, but all instances of MyPickler will by default share the same dispatch table. The equivalent code using the copyreg module is copyreg.pickle(SomeClass, reduce_SomeClass)
f = io.BytesIO()
p = pickle.Pickler(f)
Handling Stateful Objects Here’s an example that shows how to modify pickling behavior for a class. The TextReader class opens a text file, and returns the line number and line contents each time its readline() method is called. If a TextReader instance is pickled, all attributes except the file object member are saved. When the instance is unpickled, the file is reopened, and reading resumes from the last location. The __setstate__() and __getstate__() methods are used to implement this behavior. class TextReader:
"""Print and number lines in a text file."""
def __init__(self, filename):
self.filename = filename
self.file = open(filename)
self.lineno = 0
def readline(self):
self.lineno += 1
line = self.file.readline()
if not line:
return None
if line.endswith('\n'):
line = line[:-1]
return "%i: %s" % (self.lineno, line)
def __getstate__(self):
# Copy the object's state from self.__dict__ which contains
# all our instance attributes. Always use the dict.copy()
# method to avoid modifying the original state.
state = self.__dict__.copy()
# Remove the unpicklable entries.
del state['file']
return state
def __setstate__(self, state):
# Restore instance attributes (i.e., filename and lineno).
self.__dict__.update(state)
# Restore the previously opened file's state. To do so, we need to
# reopen it and read from it until the line count is restored.
file = open(self.filename)
for _ in range(self.lineno):
file.readline()
# Finally, save the file.
self.file = file
A sample usage might be something like this: >>> reader = TextReader("hello.txt")
>>> reader.readline()
'1: Hello world!'
>>> reader.readline()
'2: I am line number two.'
>>> new_reader = pickle.loads(pickle.dumps(reader))
>>> new_reader.readline()
'3: Goodbye!'
Custom Reduction for Types, Functions, and Other Objects New in version 3.8. Sometimes, dispatch_table may not be flexible enough. In particular we may want to customize pickling based on another criterion than the object’s type, or we may want to customize the pickling of functions and classes. For those cases, it is possible to subclass from the Pickler class and implement a reducer_override() method. This method can return an arbitrary reduction tuple (see __reduce__()). It can alternatively return NotImplemented to fallback to the traditional behavior. If both the dispatch_table and reducer_override() are defined, then reducer_override() method takes priority. Note For performance reasons, reducer_override() may not be called for the following objects: None, True, False, and exact instances of int, float, bytes, str, dict, set, frozenset, list and tuple. Here is a simple example where we allow pickling and reconstructing a given class: import io
import pickle
class MyClass:
my_attribute = 1
class MyPickler(pickle.Pickler):
def reducer_override(self, obj):
"""Custom reducer for MyClass."""
if getattr(obj, "__name__", None) == "MyClass":
return type, (obj.__name__, obj.__bases__,
{'my_attribute': obj.my_attribute})
else:
# For any other object, fallback to usual reduction
return NotImplemented
f = io.BytesIO()
p = MyPickler(f)
p.dump(MyClass)
del MyClass
unpickled_class = pickle.loads(f.getvalue())
assert isinstance(unpickled_class, type)
assert unpickled_class.__name__ == "MyClass"
assert unpickled_class.my_attribute == 1
Out-of-band Buffers New in version 3.8. In some contexts, the pickle module is used to transfer massive amounts of data. Therefore, it can be important to minimize the number of memory copies, to preserve performance and resource consumption. However, normal operation of the pickle module, as it transforms a graph-like structure of objects into a sequential stream of bytes, intrinsically involves copying data to and from the pickle stream. This constraint can be eschewed if both the provider (the implementation of the object types to be transferred) and the consumer (the implementation of the communications system) support the out-of-band transfer facilities provided by pickle protocol 5 and higher. Provider API The large data objects to be pickled must implement a __reduce_ex__() method specialized for protocol 5 and higher, which returns a PickleBuffer instance (instead of e.g. a bytes object) for any large data. A PickleBuffer object signals that the underlying buffer is eligible for out-of-band data transfer. Those objects remain compatible with normal usage of the pickle module. However, consumers can also opt-in to tell pickle that they will handle those buffers by themselves. Consumer API A communications system can enable custom handling of the PickleBuffer objects generated when serializing an object graph. On the sending side, it needs to pass a buffer_callback argument to Pickler (or to the dump() or dumps() function), which will be called with each PickleBuffer generated while pickling the object graph. Buffers accumulated by the buffer_callback will not see their data copied into the pickle stream, only a cheap marker will be inserted. On the receiving side, it needs to pass a buffers argument to Unpickler (or to the load() or loads() function), which is an iterable of the buffers which were passed to buffer_callback. That iterable should produce buffers in the same order as they were passed to buffer_callback. Those buffers will provide the data expected by the reconstructors of the objects whose pickling produced the original PickleBuffer objects. Between the sending side and the receiving side, the communications system is free to implement its own transfer mechanism for out-of-band buffers. Potential optimizations include the use of shared memory or datatype-dependent compression. Example Here is a trivial example where we implement a bytearray subclass able to participate in out-of-band buffer pickling: class ZeroCopyByteArray(bytearray):
def __reduce_ex__(self, protocol):
if protocol >= 5:
return type(self)._reconstruct, (PickleBuffer(self),), None
else:
# PickleBuffer is forbidden with pickle protocols <= 4.
return type(self)._reconstruct, (bytearray(self),)
@classmethod
def _reconstruct(cls, obj):
with memoryview(obj) as m:
# Get a handle over the original buffer object
obj = m.obj
if type(obj) is cls:
# Original buffer object is a ZeroCopyByteArray, return it
# as-is.
return obj
else:
return cls(obj)
The reconstructor (the _reconstruct class method) returns the buffer’s providing object if it has the right type. This is an easy way to simulate zero-copy behaviour on this toy example. On the consumer side, we can pickle those objects the usual way, which when unserialized will give us a copy of the original object: b = ZeroCopyByteArray(b"abc")
data = pickle.dumps(b, protocol=5)
new_b = pickle.loads(data)
print(b == new_b) # True
print(b is new_b) # False: a copy was made
But if we pass a buffer_callback and then give back the accumulated buffers when unserializing, we are able to get back the original object: b = ZeroCopyByteArray(b"abc")
buffers = []
data = pickle.dumps(b, protocol=5, buffer_callback=buffers.append)
new_b = pickle.loads(data, buffers=buffers)
print(b == new_b) # True
print(b is new_b) # True: no copy was made
This example is limited by the fact that bytearray allocates its own memory: you cannot create a bytearray instance that is backed by another object’s memory. However, third-party datatypes such as NumPy arrays do not have this limitation, and allow use of zero-copy pickling (or making as few copies as possible) when transferring between distinct processes or systems. See also PEP 574 – Pickle protocol 5 with out-of-band data Restricting Globals By default, unpickling will import any class or function that it finds in the pickle data. For many applications, this behaviour is unacceptable as it permits the unpickler to import and invoke arbitrary code. Just consider what this hand-crafted pickle data stream does when loaded: >>> import pickle
>>> pickle.loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
hello world
0
In this example, the unpickler imports the os.system() function and then apply the string argument “echo hello world”. Although this example is inoffensive, it is not difficult to imagine one that could damage your system. For this reason, you may want to control what gets unpickled by customizing Unpickler.find_class(). Unlike its name suggests, Unpickler.find_class() is called whenever a global (i.e., a class or a function) is requested. Thus it is possible to either completely forbid globals or restrict them to a safe subset. Here is an example of an unpickler allowing only few safe classes from the builtins module to be loaded: import builtins
import io
import pickle
safe_builtins = {
'range',
'complex',
'set',
'frozenset',
'slice',
}
class RestrictedUnpickler(pickle.Unpickler):
def find_class(self, module, name):
# Only allow safe classes from builtins.
if module == "builtins" and name in safe_builtins:
return getattr(builtins, name)
# Forbid everything else.
raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
(module, name))
def restricted_loads(s):
"""Helper function analogous to pickle.loads()."""
return RestrictedUnpickler(io.BytesIO(s)).load()
A sample usage of our unpickler working has intended: >>> restricted_loads(pickle.dumps([1, 2, range(15)]))
[1, 2, range(0, 15)]
>>> restricted_loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
Traceback (most recent call last):
...
pickle.UnpicklingError: global 'os.system' is forbidden
>>> restricted_loads(b'cbuiltins\neval\n'
... b'(S\'getattr(__import__("os"), "system")'
... b'("echo hello world")\'\ntR.')
Traceback (most recent call last):
...
pickle.UnpicklingError: global 'builtins.eval' is forbidden
As our examples shows, you have to be careful with what you allow to be unpickled. Therefore if security is a concern, you may want to consider alternatives such as the marshalling API in xmlrpc.client or third-party solutions. Performance Recent versions of the pickle protocol (from protocol 2 and upwards) feature efficient binary encodings for several common features and built-in types. Also, the pickle module has a transparent optimizer written in C. Examples For the simplest code, use the dump() and load() functions. import pickle
# An arbitrary collection of objects supported by pickle.
data = {
'a': [1, 2.0, 3, 4+6j],
'b': ("character string", b"byte string"),
'c': {None, True, False}
}
with open('data.pickle', 'wb') as f:
# Pickle the 'data' dictionary using the highest protocol available.
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
The following example reads the resulting pickled data. import pickle
with open('data.pickle', 'rb') as f:
# The protocol version used is detected automatically, so we do not
# have to specify it.
data = pickle.load(f)
See also
Module copyreg
Pickle interface constructor registration for extension types.
Module pickletools
Tools for working with and analyzing pickled data.
Module shelve
Indexed databases of objects; uses pickle.
Module copy
Shallow and deep object copying.
Module marshal
High-performance serialization of built-in types. Footnotes
1
Don’t confuse this with the marshal module
2
This is why lambda functions cannot be pickled: all lambda functions share the same name: <lambda>.
3
The exception raised will likely be an ImportError or an AttributeError but it could be something else.
4
The copy module uses this protocol for shallow and deep copying operations.
5
The limitation on alphanumeric characters is due to the fact the persistent IDs, in protocol 0, are delimited by the newline character. Therefore if any kind of newline characters occurs in persistent IDs, the resulting pickle will become unreadable. | |
doc_24042 | Parameters
x – a number (integer or float) Set the turtle’s first coordinate to x, leave second coordinate unchanged. >>> turtle.position()
(0.00,240.00)
>>> turtle.setx(10)
>>> turtle.position()
(10.00,240.00) | |
doc_24043 |
Bases: matplotlib.backend_tools.ToolBase Auxiliary Tool to handle changes in views and positions. Runs in the background and should get used by all the tools that need to access the figure's history of views and positions, e.g. ToolZoom ToolPan ToolHome ToolBack ToolForward add_figure(figure)[source]
Add the current figure to the stack of views and positions.
back()[source]
Back one step in the stack of views and positions.
clear(figure)[source]
Reset the axes stack.
forward()[source]
Forward one step in the stack of views and positions.
home()[source]
Recall the first view and position from the stack.
push_current(figure=None)[source]
Push the current view limits and position onto their respective stacks.
update_home_views(figure=None)[source]
Make sure that self.home_views has an entry for all axes present in the figure.
update_view()[source]
Update the view limits and position for each axes from the current stack position. If any axes are present in the figure that aren't in the current stack position, use the home view limits for those axes and don't update any positions. | |
doc_24044 | draw a polygon polygon(surface, color, points) -> Rect polygon(surface, color, points, width=0) -> Rect Draws a polygon on the given surface.
Parameters:
surface (Surface) -- surface to draw on
color (Color or int or tuple(int, int, int, [int])) -- color to draw with, the alpha value is optional if using a tuple (RGB[A])
points (tuple(coordinate) or list(coordinate)) -- a sequence of 3 or more (x, y) coordinates that make up the vertices of the polygon, each coordinate in the sequence must be a tuple/list/pygame.math.Vector2 of 2 ints/floats, e.g. [(x1, y1), (x2, y2), (x3, y3)]
width (int) -- (optional) used for line thickness or to indicate that the polygon is to be filled
if width == 0, (default) fill the polygon if width > 0, used for line thickness if width < 0, nothing will be drawn Note When using width values > 1, the edge lines will grow outside the original boundary of the polygon. For more details on how the thickness for edge lines grow, refer to the width notes of the pygame.draw.line() function.
Returns:
a rect bounding the changed pixels, if nothing is drawn the bounding rect's position will be the position of the first point in the points parameter (float values will be truncated) and its width and height will be 0
Return type:
Rect
Raises:
ValueError -- if len(points) < 3 (must have at least 3 points)
TypeError -- if points is not a sequence or points does not contain number pairs Note For an aapolygon, use aalines() with closed=True. Changed in pygame 2.0.0: Added support for keyword arguments. | |
doc_24045 | Get the byte stream for this input source. The getEncoding method will return the character encoding for this byte stream, or None if unknown. | |
doc_24046 | See Migration guide for more details. tf.compat.v1.load_library
tf.load_library(
library_location
)
"library_location" can be a path to a specific shared object, or a folder. If it is a folder, all shared objects that are named "libtfkernel*" will be loaded. When the library is loaded, kernels registered in the library via the REGISTER_* macros are made available in the TensorFlow process.
Args
library_location Path to the plugin or the folder of plugins. Relative or absolute filesystem path to a dynamic library file or folder.
Returns None
Raises
OSError When the file to be loaded is not found.
RuntimeError when unable to load the library. | |
doc_24047 | temporarily stop music playback pause() -> None Temporarily stop playback of the music stream. It can be resumed with the pygame.mixer.music.unpause() function. | |
doc_24048 | The default map zoom is 12. | |
doc_24049 | See Migration guide for more details. tf.compat.v1.raw_ops.Diag
tf.raw_ops.Diag(
diagonal, name=None
)
Given a diagonal, this operation returns a tensor with the diagonal and everything else padded with zeros. The diagonal is computed as follows: Assume diagonal has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik] and 0 everywhere else. For example: # 'diagonal' is [1, 2, 3, 4]
tf.diag(diagonal) ==> [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
Args
diagonal A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int32, int64, complex64, complex128. Rank k tensor where k is at most 1.
name A name for the operation (optional).
Returns A Tensor. Has the same type as diagonal. | |
doc_24050 |
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_24051 | Shortcut for route() with methods=["POST"]. New in version 2.0. Parameters
rule (str) –
options (Any) – Return type
Callable | |
doc_24052 |
Bases: matplotlib.backend_tools.ToolBase Change to the current cursor while inaxes. This tool, keeps track of all ToolToggleBase derived tools, and calls set_cursor when a tool gets triggered. set_cursor(cursor)[source]
[Deprecated] Set the cursor. Notes Deprecated since version 3.5.
set_figure(figure)[source] | |
doc_24053 | fill Surface with a solid color fill(color, rect=None, special_flags=0) -> Rect Fill the Surface with a solid color. If no rect argument is given the entire Surface will be filled. The rect argument will limit the fill to a specific area. The fill will also be contained by the Surface clip area. The color argument can be either a RGB sequence, a RGBA sequence or a mapped color index. If using RGBA, the Alpha (A part of RGBA) is ignored unless the surface uses per pixel alpha (Surface has the SRCALPHA flag). New in pygame 1.8: Optional special_flags: BLEND_ADD, BLEND_SUB, BLEND_MULT, BLEND_MIN, BLEND_MAX. New in pygame 1.8.1: Optional special_flags: BLEND_RGBA_ADD, BLEND_RGBA_SUB, BLEND_RGBA_MULT, BLEND_RGBA_MIN, BLEND_RGBA_MAX BLEND_RGB_ADD, BLEND_RGB_SUB, BLEND_RGB_MULT, BLEND_RGB_MIN, BLEND_RGB_MAX. This will return the affected Surface area. | |
doc_24054 | Returns the y-intercept of the least-squares-fit linear equation determined by the (x, y) pairs as a float, or default if there aren’t any matching rows. | |
doc_24055 | This function is installed as the trace function of debugged frames. Its return value is the new trace function (in most cases, that is, itself). The default implementation decides how to dispatch a frame, depending on the type of event (passed as a string) that is about to be executed. event can be one of the following:
"line": A new line of code is going to be executed.
"call": A function is about to be called, or another code block entered.
"return": A function or other code block is about to return.
"exception": An exception has occurred.
"c_call": A C function is about to be called.
"c_return": A C function has returned.
"c_exception": A C function has raised an exception. For the Python events, specialized functions (see below) are called. For the C events, no action is taken. The arg parameter depends on the previous event. See the documentation for sys.settrace() for more information on the trace function. For more information on code and frame objects, refer to The standard type hierarchy. | |
doc_24056 | Method called to prepare the test fixture. This is called after setUp(). This is called immediately before calling the test method; other than AssertionError or SkipTest, any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing. | |
doc_24057 | A paginator acts like a sequence of Page when using len() or iterating it directly. | |
doc_24058 | See Migration guide for more details. tf.compat.v1.io.serialize_tensor, tf.compat.v1.serialize_tensor
tf.io.serialize_tensor(
tensor, name=None
)
Args
tensor A Tensor. A Tensor of type T.
name A name for the operation (optional).
Returns A Tensor of type string. | |
doc_24059 |
Check whether other does not equal self elementwise. When either of the elements is masked, the result is masked as well, but the underlying boolean data are still set, with self and other considered equal if both are masked, and unequal otherwise. For structured arrays, all fields are combined, with masked values ignored. The result is masked if all fields were masked, with self and other considered equal only if both were fully masked. | |
doc_24060 | A generic version of collections.abc.Container. Deprecated since version 3.9: collections.abc.Container now supports []. See PEP 585 and Generic Alias Type. | |
doc_24061 | Whether the OpenSSL library has built-in support not checking subject common name and SSLContext.hostname_checks_common_name is writeable. New in version 3.7. | |
doc_24062 | name is the name of the header to be mapped. It will be converted to lower case in the registry. cls is the specialized class to be used, along with base_class, to create the class used to instantiate headers that match name. | |
doc_24063 | See Migration guide for more details. tf.compat.v1.raw_ops.QuantizedConv2DWithBiasAndReluAndRequantize
tf.raw_ops.QuantizedConv2DWithBiasAndReluAndRequantize(
input, filter, bias, min_input, max_input, min_filter, max_filter,
min_freezed_output, max_freezed_output, strides, padding,
out_type=tf.dtypes.quint8, dilations=[1, 1, 1, 1], padding_list=[], name=None
)
Args
input A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
filter A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
bias A Tensor. Must be one of the following types: float32, qint32.
min_input A Tensor of type float32.
max_input A Tensor of type float32.
min_filter A Tensor of type float32.
max_filter A Tensor of type float32.
min_freezed_output A Tensor of type float32.
max_freezed_output A Tensor of type float32.
strides A list of ints.
padding A string from: "SAME", "VALID".
out_type An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.
dilations An optional list of ints. Defaults to [1, 1, 1, 1].
padding_list An optional list of ints. Defaults to [].
name A name for the operation (optional).
Returns A tuple of Tensor objects (output, min_output, max_output). output A Tensor of type out_type.
min_output A Tensor of type float32.
max_output A Tensor of type float32. | |
doc_24064 | Return a context manager that returns enter_result from __enter__, but otherwise does nothing. It is intended to be used as a stand-in for an optional context manager, for example: def myfunction(arg, ignore_exceptions=False):
if ignore_exceptions:
# Use suppress to ignore all exceptions.
cm = contextlib.suppress(Exception)
else:
# Do not ignore any exceptions, cm has no effect.
cm = contextlib.nullcontext()
with cm:
# Do something
An example using enter_result: def process_file(file_or_path):
if isinstance(file_or_path, str):
# If string, open file
cm = open(file_or_path)
else:
# Caller is responsible for closing file
cm = nullcontext(file_or_path)
with cm as file:
# Perform processing on the file
New in version 3.7. | |
doc_24065 | '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_24066 | Logs a message with level WARNING on the root logger. The arguments are interpreted as for debug(). Note There is an obsolete function warn which is functionally identical to warning. As warn is deprecated, please do not use it - use warning instead. | |
doc_24067 |
Given the quadratic Bezier control points bezier2, returns control points of quadratic Bezier lines roughly parallel to given one separated by width. | |
doc_24068 | Similar to the ETags class this implements a set-like structure. Unlike ETags this is case insensitive and used for vary, allow, and content-language headers. If not constructed using the parse_set_header() function the instantiation works like this: >>> hs = HeaderSet(['foo', 'bar', 'baz'])
>>> hs
HeaderSet(['foo', 'bar', 'baz'])
add(header)
Add a new header to the set.
as_set(preserve_casing=False)
Return the set as real python set type. When calling this, all the items are converted to lowercase and the ordering is lost. Parameters
preserve_casing – if set to True the items in the set returned will have the original case like in the HeaderSet, otherwise they will be lowercase.
clear()
Clear the set.
discard(header)
Like remove() but ignores errors. Parameters
header – the header to be discarded.
find(header)
Return the index of the header in the set or return -1 if not found. Parameters
header – the header to be looked up.
index(header)
Return the index of the header in the set or raise an IndexError. Parameters
header – the header to be looked up.
remove(header)
Remove a header from the set. This raises an KeyError if the header is not in the set. Changelog Changed in version 0.5: In older versions a IndexError was raised instead of a KeyError if the object was missing. Parameters
header – the header to be removed.
to_header()
Convert the header set into an HTTP header string.
update(iterable)
Add all the headers from the iterable to the set. Parameters
iterable – updates the set with the items from the iterable. | |
doc_24069 | Get source code segment of the source that generated node. If some location information (lineno, end_lineno, col_offset, or end_col_offset) is missing, return None. If padded is True, the first line of a multi-line statement will be padded with spaces to match its original position. New in version 3.8. | |
doc_24070 | Like digest() except the digest is returned as a string object of double length, containing only hexadecimal digits. This may be used to exchange the value safely in email or other non-binary environments. | |
doc_24071 | Returns a string representing the current Windows edition. Possible values include but are not limited to 'Enterprise', 'IoTUAP', 'ServerStandard', and 'nanoserver'. New in version 3.8. | |
doc_24072 |
Bases: matplotlib.projections.geo.GeoAxes Build an Axes in a figure. Parameters
figFigure
The Axes is built in the Figure fig.
rect[left, bottom, width, height]
The Axes is built in the rectangle rect. rect is in Figure coordinates.
sharex, shareyAxes, optional
The x or y axis is shared with the x or y axis in the input Axes.
frameonbool, default: True
Whether the Axes frame is visible.
box_aspectfloat, optional
Set a fixed aspect for the Axes box, i.e. the ratio of height to width. See set_box_aspect for details. **kwargs
Other optional keyword arguments:
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float Returns
Axes
The new Axes object. classAitoffTransform(resolution)[source]
Bases: matplotlib.projections.geo._GeoTransform The base Aitoff transform. Create a new geographical transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved space. has_inverse=True
True if this transform has a corresponding inverse transform.
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
transform_non_affine(ll)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
classInvertedAitoffTransform(resolution)[source]
Bases: matplotlib.projections.geo._GeoTransform Create a new geographical transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved space. has_inverse=True
True if this transform has a corresponding inverse transform.
inverted()[source]
Return the corresponding inverse transformation. It holds x == self.inverted().transform(self.transform(x)). The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
transform_non_affine(xy)[source]
Apply only the non-affine part of this transformation. transform(values) is always equivalent to transform_affine(transform_non_affine(values)). In non-affine transformations, this is generally equivalent to transform(values). In affine transformations, this is always a no-op. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input.
name='aitoff'
set(*, adjustable=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, anchor=<UNSET>, animated=<UNSET>, aspect=<UNSET>, autoscale_on=<UNSET>, autoscalex_on=<UNSET>, autoscaley_on=<UNSET>, axes_locator=<UNSET>, axisbelow=<UNSET>, box_aspect=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, facecolor=<UNSET>, frame_on=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, latitude_grid=<UNSET>, longitude_grid=<UNSET>, longitude_grid_ends=<UNSET>, navigate=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, prop_cycle=<UNSET>, rasterization_zorder=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, title=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, xbound=<UNSET>, xlabel=<UNSET>, xlim=<UNSET>, xmargin=<UNSET>, xscale=<UNSET>, xticklabels=<UNSET>, xticks=<UNSET>, ybound=<UNSET>, ylabel=<UNSET>, ylim=<UNSET>, ymargin=<UNSET>, yscale=<UNSET>, yticklabels=<UNSET>, yticks=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
latitude_grid unknown
longitude_grid unknown
longitude_grid_ends unknown
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim unknown
xmargin float greater than -0.5
xscale unknown
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim unknown
ymargin float greater than -0.5
yscale unknown
yticklabels unknown
yticks unknown
zorder float | |
doc_24073 |
Set the default format for the string representation of polynomials. Values for style must be valid inputs to __format__, i.e. ‘ascii’ or ‘unicode’. Parameters
stylestr
Format string for default printing style. Must be either ‘ascii’ or ‘unicode’. Notes The default format depends on the platform: ‘unicode’ is used on Unix-based systems and ‘ascii’ on Windows. This determination is based on default font support for the unicode superscript and subscript ranges. Examples >>> p = np.polynomial.Polynomial([1, 2, 3])
>>> c = np.polynomial.Chebyshev([1, 2, 3])
>>> np.polynomial.set_default_printstyle('unicode')
>>> print(p)
1.0 + 2.0·x¹ + 3.0·x²
>>> print(c)
1.0 + 2.0·T₁(x) + 3.0·T₂(x)
>>> np.polynomial.set_default_printstyle('ascii')
>>> print(p)
1.0 + 2.0 x**1 + 3.0 x**2
>>> print(c)
1.0 + 2.0 T_1(x) + 3.0 T_2(x)
>>> # Formatting supersedes all class/package-level defaults
>>> print(f"{p:unicode}")
1.0 + 2.0·x¹ + 3.0·x² | |
doc_24074 |
DateOffset subclass representing possibly n business hours. Parameters
n:int, default 1
The number of months represented.
normalize:bool, default False
Normalize start/end dates to midnight before generating date range.
weekmask:str, Default ‘Mon Tue Wed Thu Fri’
Weekmask of valid business days, passed to numpy.busdaycalendar.
start:str, default “09:00”
Start time of your custom business hour in 24h format.
end:str, default: “17:00”
End time of your custom business hour in 24h format. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
next_bday Used for moving to next business day.
offset Alias for self._offset.
calendar
end
freqstr
holidays
kwds
n
name
nanos
normalize
rule_code
start
weekmask Methods
__call__(*args, **kwargs) Call self as a function.
rollback(other) Roll provided date backward to next offset only if not on offset.
rollforward(other) Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset | |
doc_24075 | See Migration guide for more details. tf.compat.v1.signal.irfft2d, tf.compat.v1.spectral.irfft2d
tf.signal.irfft2d(
input_tensor, fft_length=None, name=None
)
Computes the inverse 2-dimensional discrete Fourier transform of a real-valued signal over the inner-most 2 dimensions of input. The inner-most 2 dimensions of input are assumed to be the result of RFFT2D: The inner-most dimension contains the fft_length / 2 + 1 unique components of the DFT of a real-valued signal. If fft_length is not provided, it is computed from the size of the inner-most 2 dimensions of input. If the FFT length used to compute input is odd, it should be provided since it cannot be inferred properly. Along each axis IRFFT2D is computed on, if fft_length (or fft_length / 2 + 1 for the inner-most dimension) is smaller than the corresponding dimension of input, the dimension is cropped. If it is larger, the dimension is padded with zeros.
Args
input A Tensor. Must be one of the following types: complex64, complex128. A complex tensor.
fft_length A Tensor of type int32. An int32 tensor of shape [2]. The FFT length for each dimension.
Treal An optional tf.DType from: tf.float32, tf.float64. Defaults to tf.float32.
name A name for the operation (optional).
Returns A Tensor of type Treal. | |
doc_24076 |
Alias for set_linewidth. | |
doc_24077 | tf.io.serialize_many_sparse(
sp_input, out_type=tf.dtypes.string, name=None
)
The SparseTensor must have rank R greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the SparseTensor must be sorted in increasing order of this first dimension. The serialized SparseTensor objects going into each row of the output Tensor will have rank R-1. The minibatch size N is extracted from sparse_shape[0].
Args
sp_input The input rank R SparseTensor.
out_type The dtype to use for serialization.
name A name prefix for the returned tensors (optional).
Returns A matrix (2-D Tensor) with N rows and 3 columns. Each column represents serialized SparseTensor's indices, values, and shape (respectively).
Raises
TypeError If sp_input is not a SparseTensor. | |
doc_24078 |
Set the CapStyle for the collection (for all its elements). Parameters
csCapStyle or {'butt', 'projecting', 'round'} | |
doc_24079 |
Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to array_repr, the difference being that array_repr also returns information on the kind of array and its data type. Parameters
andarray
Input array.
max_line_widthint, optional
Inserts newlines if text is longer than max_line_width. Defaults to numpy.get_printoptions()['linewidth'].
precisionint, optional
Floating point precision. Defaults to numpy.get_printoptions()['precision'].
suppress_smallbool, optional
Represent numbers “very close” to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to numpy.get_printoptions()['suppress']. See also
array2string, array_repr, set_printoptions
Examples >>> np.array_str(np.arange(3))
'[0 1 2]' | |
doc_24080 |
Bases: matplotlib.patches.ArrowStyle._Curve An arrow with filled triangle heads at both ends. Parameters
head_lengthfloat, default: 0.4
Length of the arrow head, relative to mutation_scale.
head_widthfloat, default: 0.2
Width of the arrow head, relative to mutation_scale.
widthAfloat, default: 1.0
Width of the bracket at the beginning of the arrow
widthBfloat, default: 1.0
Width of the bracket at the end of the arrow
lengthAfloat, default: 0.2
Length of the bracket at the beginning of the arrow
lengthBfloat, default: 0.2
Length of the bracket at the end of the arrow
angleAfloat, default 0
Orientation of the bracket at the beginning, as a counterclockwise angle. 0 degrees means perpendicular to the line.
angleBfloat, default 0
Orientation of the bracket at the beginning, as a counterclockwise angle. 0 degrees means perpendicular to the line.
scaleAfloat, default mutation_size
The mutation_size for the beginning bracket
scaleBfloat, default mutation_size
The mutation_size for the end bracket arrow='<|-|>' | |
doc_24081 |
Bases: matplotlib.axes._base._AxesBase The Axes contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. The Axes instance supports callbacks through a callbacks attribute which is a CallbackRegistry instance. The events you can connect to are 'xlim_changed' and 'ylim_changed' and the callback will be called with func(ax) where ax is the Axes instance. Attributes
dataLimBbox
The bounding box enclosing all data displayed in the Axes.
viewLimBbox
The view limits in data coordinates. Build an Axes in a figure. Parameters
figFigure
The Axes is built in the Figure fig.
rect[left, bottom, width, height]
The Axes is built in the rectangle rect. rect is in Figure coordinates.
sharex, shareyAxes, optional
The x or y axis is shared with the x or y axis in the input Axes.
frameonbool, default: True
Whether the Axes frame is visible.
box_aspectfloat, optional
Set a fixed aspect for the Axes box, i.e. the ratio of height to width. See set_box_aspect for details. **kwargs
Other optional keyword arguments:
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float Returns
Axes
The new Axes object. | |
doc_24082 |
Save several arrays into a single file in compressed .npz format. Provide arrays as keyword arguments to store them under the corresponding name in the output file: savez(fn, x=x, y=y). If arrays are specified as positional arguments, i.e., savez(fn,
x, y), their names will be arr_0, arr_1, etc. Parameters
filestr or file
Either the filename (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the .npz extension will be appended to the filename if it is not already there.
argsArguments, optional
Arrays to save to the file. Please use keyword arguments (see kwds below) to assign names to arrays. Arrays specified as args will be named “arr_0”, “arr_1”, and so on.
kwdsKeyword arguments, optional
Arrays to save to the file. Each array will be saved to the output file with its corresponding keyword name. Returns
None
See also numpy.save
Save a single array to a binary file in NumPy format. numpy.savetxt
Save an array to a file as plain text. numpy.savez
Save several arrays into an uncompressed .npz file format numpy.load
Load the files created by savez_compressed. Notes The .npz file format is a zipped archive of files named after the variables they contain. The archive is compressed with zipfile.ZIP_DEFLATED and each file in the archive contains one variable in .npy format. For a description of the .npy format, see numpy.lib.format. When opening the saved .npz file with load a NpzFile object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the .files attribute), and for the arrays themselves. Examples >>> test_array = np.random.rand(3, 2)
>>> test_vector = np.random.rand(4)
>>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
>>> loaded = np.load('/tmp/123.npz')
>>> print(np.array_equal(test_array, loaded['a']))
True
>>> print(np.array_equal(test_vector, loaded['b']))
True | |
doc_24083 |
Return the index of the leaf that each sample is predicted as. New in version 0.17. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_inputbool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do. Returns
X_leavesarray-like of shape (n_samples,)
For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering. | |
doc_24084 | Return the base-10 logarithm of x. This is usually more accurate than log(x, 10). | |
doc_24085 |
Add a 3D collection object to the plot. 2D collection types are converted to a 3D version by modifying the object and adding z coordinate information. Supported are: PolyCollection LineCollection PatchCollection | |
doc_24086 |
Bases: matplotlib.backends.backend_pdf.Operator, enum.Enum An enumeration. begin_text=(b'BT',)[source]
clip=b'W'[source]
close_fill_stroke=b'b'[source]
close_stroke=b's'[source]
closepath=(b'h',)[source]
concat_matrix=b'cm'[source]
curveto=b'c'[source]
end_text=b'ET'[source]
endpath=b'n'[source]
fill=b'f'[source]
fill_stroke=b'B'[source]
grestore=b'Q'[source]
gsave=(b'q',)[source]
lineto=b'l'[source]
moveto=(b'm',)[source]
op
classmethodpaint_path(fill, stroke)[source]
Return the PDF operator to paint a path. Parameters
fillbool
Fill the path with the fill color.
strokebool
Stroke the outline of the path with the line color.
rectangle=b're'[source]
selectfont=b'Tf'[source]
setcolor_nonstroke=b'scn'[source]
setcolor_stroke=b'SCN'[source]
setcolorspace_nonstroke=(b'cs',)[source]
setcolorspace_stroke=b'CS'[source]
setdash=(b'd',)[source]
setgray_nonstroke=b'g'[source]
setgray_stroke=(b'G',)[source]
setgstate=b'gs'[source]
setlinecap=b'J'[source]
setlinejoin=b'j'[source]
setlinewidth=b'w'[source]
setrgb_nonstroke=(b'rg',)[source]
setrgb_stroke=b'RG'[source]
shading=b'sh'[source]
show=b'Tj'[source]
showkern=b'TJ'[source]
stroke=b'S'[source]
textmatrix=(b'Tm',)[source]
textpos=b'Td'[source]
use_xobject=b'Do'[source] | |
doc_24087 | See Migration guide for more details. tf.compat.v1.raw_ops.Reverse
tf.raw_ops.Reverse(
tensor, dims, name=None
)
Given a tensor, and a bool tensor dims representing the dimensions of tensor, this operation reverses each dimension i of tensor where dims[i] is True. tensor can have up to 8 dimensions. The number of dimensions of tensor must equal the number of elements in dims. In other words: rank(tensor) = size(dims) For example: # tensor 't' is [[[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]],
# [[12, 13, 14, 15],
# [16, 17, 18, 19],
# [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]
# 'dims' is [False, False, False, True]
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
# 'dims' is [False, True, False, False]
reverse(t, dims) ==> [[[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]]]
# 'dims' is [False, False, True, False]
reverse(t, dims) ==> [[[[8, 9, 10, 11],
[4, 5, 6, 7],
[0, 1, 2, 3]]
[[20, 21, 22, 23],
[16, 17, 18, 19],
[12, 13, 14, 15]]]]
Args
tensor A Tensor. Must be one of the following types: uint8, int8, uint16, int16, int32, int64, bool, half, float32, float64, complex64, complex128, string. Up to 8-D.
dims A Tensor of type bool. 1-D. The dimensions to reverse.
name A name for the operation (optional).
Returns A Tensor. Has the same type as tensor. | |
doc_24088 | Logs a message with level CRITICAL on the root logger. The arguments are interpreted as for debug(). | |
doc_24089 |
Applies the HardTanh function element-wise HardTanh is defined as: HardTanh(x)={1 if x>1−1 if x<−1x otherwise \text{HardTanh}(x) = \begin{cases} 1 & \text{ if } x > 1 \\ -1 & \text{ if } x < -1 \\ x & \text{ otherwise } \\ \end{cases}
The range of the linear region [−1,1][-1, 1] can be adjusted using min_val and max_val. Parameters
min_val – minimum value of the linear region range. Default: -1
max_val – maximum value of the linear region range. Default: 1
inplace – can optionally do the operation in-place. Default: False
Keyword arguments min_value and max_value have been deprecated in favor of min_val and max_val. Shape:
Input: (N,∗)(N, *) where * means, any number of additional dimensions Output: (N,∗)(N, *) , same shape as the input Examples: >>> m = nn.Hardtanh(-2, 2)
>>> input = torch.randn(2)
>>> output = m(input) | |
doc_24090 | Register a custom template test, available application wide. Like Flask.template_test() but for a blueprint. Changelog New in version 0.10. Parameters
name (Optional[str]) – the optional name of the test, otherwise the function name will be used. Return type
Callable | |
doc_24091 |
Get the ‘info axis’ (see Indexing for more). This is index for Series, columns for DataFrame. Returns
Index
Info axis. | |
doc_24092 | Return a dictionary mapping each thread’s identifier to the topmost stack frame currently active in that thread at the time the function is called. Note that functions in the traceback module can build the call stack given such a frame. This is most useful for debugging deadlock: this function does not require the deadlocked threads’ cooperation, and such threads’ call stacks are frozen for as long as they remain deadlocked. The frame returned for a non-deadlocked thread may bear no relationship to that thread’s current activity by the time calling code examines the frame. This function should be used for internal and specialized purposes only. Raises an auditing event sys._current_frames with no arguments. | |
doc_24093 | Usually when you’ll interact with a QuerySet you’ll use it by chaining filters. To make this work, most QuerySet methods return new querysets. These methods are covered in detail later in this section. The QuerySet class has two public attributes you can use for introspection:
ordered
True if the QuerySet is ordered — i.e. has an order_by() clause or a default ordering on the model. False otherwise.
db
The database that will be used if this query is executed now.
Note The query parameter to QuerySet exists so that specialized query subclasses can reconstruct internal query state. The value of the parameter is an opaque representation of that query state and is not part of a public API. | |
doc_24094 | URL/URI scheme list to validate against. If not provided, the default list is ['http', 'https', 'ftp', 'ftps']. As a reference, the IANA website provides a full list of valid URI schemes. | |
doc_24095 |
Return the offsets for the collection. | |
doc_24096 | Change a registered file object’s monitored events or attached data. This is equivalent to BaseSelector.unregister(fileobj)() followed by BaseSelector.register(fileobj, events, data)(), except that it can be implemented more efficiently. This returns a new SelectorKey instance, or raises a ValueError in case of invalid event mask or file descriptor, or KeyError if the file object is not registered. | |
doc_24097 |
Convert the input to an ndarray, but pass ndarray subclasses through. Parameters
aarray_like
Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.
dtypedata-type, optional
By default, the data-type is inferred from the input data.
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Memory layout. ‘A’ and ‘K’ depend on the order of input array a. ‘C’ row-major (C-style), ‘F’ column-major (Fortran-style) memory representation. ‘A’ (any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise ‘K’ (keep) preserve input order Defaults to ‘C’.
likearray_like
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. New in version 1.20.0. Returns
outndarray or an ndarray subclass
Array interpretation of a. If a is an ndarray or a subclass of ndarray, it is returned as-is and no copy is performed. See also asarray
Similar function which always returns ndarrays. ascontiguousarray
Convert input to a contiguous array. asfarray
Convert input to a floating point ndarray. asfortranarray
Convert input to an ndarray with column-major memory order. asarray_chkfinite
Similar function which checks input for NaNs and Infs. fromiter
Create an array from an iterator. fromfunction
Construct an array by executing a function on grid positions. Examples Convert a list into an array: >>> a = [1, 2]
>>> np.asanyarray(a)
array([1, 2])
Instances of ndarray subclasses are passed through as-is: >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)
>>> np.asanyarray(a) is a
True | |
doc_24098 | The mathematical constant π, as a float. | |
doc_24099 |
When the user hits enter or leaves the submission box, call this func with event. A connection id is returned which can be used to disconnect. |
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