_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_23700 |
Number of levels. | |
doc_23701 |
Test whether the artist contains the mouse event. Parameters
mouseeventmatplotlib.backend_bases.MouseEvent
Returns
containsbool
Whether any values are within the radius.
detailsdict
An artist-specific dictionary of details of the event context, such as which points are contained in the pick radius. See the individual Artist subclasses for details. | |
doc_23702 |
Alias for get_linestyle. | |
doc_23703 | tf.keras.losses.mean_squared_error, tf.keras.losses.mse, tf.keras.metrics.MSE, tf.keras.metrics.mean_squared_error, tf.keras.metrics.mse, tf.losses.MSE, tf.losses.mean_squared_error, tf.losses.mse, tf.metrics.MSE, tf.metrics.mean_squared_error, tf.metrics.mse Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.MSE, tf.compat.v1.keras.losses.mean_squared_error, tf.compat.v1.keras.losses.mse, tf.compat.v1.keras.metrics.MSE, tf.compat.v1.keras.metrics.mean_squared_error, tf.compat.v1.keras.metrics.mse
tf.keras.losses.MSE(
y_true, y_pred
)
After computing the squared distance between the inputs, the mean value over the last dimension is returned. loss = mean(square(y_true - y_pred), axis=-1) Standalone usage:
y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.mean_squared_error(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
loss.numpy(), np.mean(np.square(y_true - y_pred), axis=-1))
Args
y_true Ground truth values. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].
Returns Mean squared error values. shape = [batch_size, d0, .. dN-1]. | |
doc_23704 |
Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a uniform distribution. The resulting tensor will have values sampled from U(−bound,bound)\mathcal{U}(-\text{bound}, \text{bound}) where bound=gain×3fan_mode\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
Also known as He initialization. Parameters
tensor – an n-dimensional torch.Tensor
a – the negative slope of the rectifier used after this layer (only used with 'leaky_relu')
mode – either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves the magnitude of the variance of the weights in the forward pass. Choosing 'fan_out' preserves the magnitudes in the backwards pass.
nonlinearity – the non-linear function (nn.functional name), recommended to use only with 'relu' or 'leaky_relu' (default). Examples >>> w = torch.empty(3, 5)
>>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu') | |
doc_23705 |
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_23706 |
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_23707 | Remove callback from the callbacks list. Returns the number of callbacks removed, which is typically 1, unless a callback was added more than once. | |
doc_23708 | Prints a month’s calendar as returned by month(). | |
doc_23709 | Suspend execution of the calling thread for the given number of seconds. The argument may be a floating point number to indicate a more precise sleep time. The actual suspension time may be less than that requested because any caught signal will terminate the sleep() following execution of that signal’s catching routine. Also, the suspension time may be longer than requested by an arbitrary amount because of the scheduling of other activity in the system. Changed in version 3.5: The function now sleeps at least secs even if the sleep is interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale). | |
doc_23710 | See Migration guide for more details. tf.compat.v1.raw_ops.ShardedFilename
tf.raw_ops.ShardedFilename(
basename, shard, num_shards, name=None
)
%s-%05d-of-%05d, basename, shard, num_shards.
Args
basename A Tensor of type string.
shard A Tensor of type int32.
num_shards A Tensor of type int32.
name A name for the operation (optional).
Returns A Tensor of type string. | |
doc_23711 | The Content-Security-Policy header adds an additional layer of security to help detect and mitigate certain types of attacks. | |
doc_23712 |
The default Matplotlib button actions for extra mouse buttons. Parameters are as for key_press_handler, except that event is a MouseEvent. | |
doc_23713 | Start the thread’s activity. It must be called at most once per thread object. It arranges for the object’s run() method to be invoked in a separate thread of control. This method will raise a RuntimeError if called more than once on the same thread object. | |
doc_23714 |
Return a dict, associating each variable name a dict, associating each value its corresponding label. Returns
dict | |
doc_23715 |
Applies Layer Normalization for last certain number of dimensions. See LayerNorm for details. | |
doc_23716 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_23717 |
Turn scientific notation on or off. See also ScalarFormatter.set_powerlimits | |
doc_23718 |
Repeat elements of an array. Refer to numpy.repeat for full documentation. See also numpy.repeat
equivalent function | |
doc_23719 |
Get an Axes title. Get one of the three available Axes titles. The available titles are positioned above the Axes in the center, flush with the left edge, and flush with the right edge. Parameters
loc{'center', 'left', 'right'}, str, default: 'center'
Which title to return. Returns
str
The title text string. | |
doc_23720 |
Return the value of an Artist's property, or print all of them. Parameters
objArtist
The queried artist; e.g., a Line2D, a Text, or an Axes.
propertystr or None, default: None
If property is 'somename', this function returns obj.get_somename(). If it's None (or unset), it prints all gettable properties from obj. Many properties have aliases for shorter typing, e.g. 'lw' is an alias for 'linewidth'. In the output, aliases and full property names will be listed as: property or alias = value e.g.: linewidth or lw = 2 See also setp | |
doc_23721 | See Migration guide for more details. tf.compat.v1.estimator.experimental.stop_if_no_decrease_hook
tf.estimator.experimental.stop_if_no_decrease_hook(
estimator, metric_name, max_steps_without_decrease, eval_dir=None, min_steps=0,
run_every_secs=60, run_every_steps=None
)
Usage example: estimator = ...
# Hook to stop training if loss does not decrease in over 100000 steps.
hook = early_stopping.stop_if_no_decrease_hook(estimator, "loss", 100000)
train_spec = tf.estimator.TrainSpec(..., hooks=[hook])
tf.estimator.train_and_evaluate(estimator, train_spec, ...)
Caveat: Current implementation supports early-stopping both training and evaluation in local mode. In distributed mode, training can be stopped but evaluation (where it's a separate job) will indefinitely wait for new model checkpoints to evaluate, so you will need other means to detect and stop it. Early-stopping evaluation in distributed mode requires changes in train_and_evaluate API and will be addressed in a future revision.
Args
estimator A tf.estimator.Estimator instance.
metric_name str, metric to track. "loss", "accuracy", etc.
max_steps_without_decrease int, maximum number of training steps with no decrease in the given metric.
eval_dir If set, directory containing summary files with eval metrics. By default, estimator.eval_dir() will be used.
min_steps int, stop is never requested if global step is less than this value. Defaults to 0.
run_every_secs If specified, calls should_stop_fn at an interval of run_every_secs seconds. Defaults to 60 seconds. Either this or run_every_steps must be set.
run_every_steps If specified, calls should_stop_fn every run_every_steps steps. Either this or run_every_secs must be set.
Returns An early-stopping hook of type SessionRunHook that periodically checks if the given metric shows no decrease over given maximum number of training steps, and initiates early stopping if true. | |
doc_23722 |
Convert a JSON string to pandas object. Parameters
path_or_buf:a valid JSON str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.json. If you want to pass in a path object, pandas accepts any os.PathLike. By file-like object, we refer to objects with a read() method, such as a file handle (e.g. via builtin open function) or StringIO.
orient:str
Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is: 'split' : dict like {index -> [index], columns -> [columns], data -> [values]} 'records' : list like [{column -> value}, ... , {column -> value}] 'index' : dict like {index -> {column -> value}} 'columns' : dict like {column -> {index -> value}} 'values' : just the values array The allowed and default values depend on the value of the typ parameter.
when typ == 'series', allowed orients are {'split','records','index'} default is 'index' The Series index must be unique for orient 'index'.
when typ == 'frame', allowed orients are {'split','records','index',
'columns','values', 'table'} default is 'columns' The DataFrame index must be unique for orients 'index' and 'columns'. The DataFrame columns must be unique for orients 'index', 'columns', and 'records'.
typ:{‘frame’, ‘series’}, default ‘frame’
The type of object to recover.
dtype:bool or dict, default None
If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don’t infer dtypes at all, applies only to the data. For all orient values except 'table', default is True. Changed in version 0.25.0: Not applicable for orient='table'.
convert_axes:bool, default None
Try to convert the axes to the proper dtypes. For all orient values except 'table', default is True. Changed in version 0.25.0: Not applicable for orient='table'.
convert_dates:bool or list of str, default True
If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates).
keep_default_dates:bool, default True
If parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if it ends with '_at', it ends with '_time', it begins with 'timestamp', it is 'modified', or it is 'date'.
numpy:bool, default False
Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. Deprecated since version 1.0.0.
precise_float:bool, default False
Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality.
date_unit:str, default None
The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or ‘ns’ to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively.
encoding:str, default is ‘utf-8’
The encoding to use to decode py3 bytes.
encoding_errors:str, optional, default “strict”
How encoding errors are treated. List of possible values . New in version 1.3.0.
lines:bool, default False
Read the file as a json object per line.
chunksize:int, optional
Return JsonReader object for iteration. See the line-delimited json docs for more information on chunksize. This can only be passed if lines=True. If this is None, the file will be read into memory all at once. Changed in version 1.2: JsonReader is a context manager.
compression:str or dict, default ‘infer’
For on-the-fly decompression of on-disk data. If ‘infer’ and ‘path_or_buf’ is path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, or ‘.zst’ (otherwise no compression). If using ‘zip’, the ZIP file must contain only one data file to be read in. Set to None for no decompression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, or zstandard.ZstdDecompressor, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: compression={'method': 'zstd', 'dict_data': my_compression_dict}. Changed in version 1.4.0: Zstandard support.
nrows:int, optional
The number of lines from the line-delimited jsonfile that has to be read. This can only be passed if lines=True. If this is None, all the rows will be returned. New in version 1.1.
storage_options:dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details. New in version 1.2.0. Returns
Series or DataFrame
The type returned depends on the value of typ. See also DataFrame.to_json
Convert a DataFrame to a JSON string. Series.to_json
Convert a Series to a JSON string. json_normalize
Normalize semi-structured JSON data into a flat table. Notes Specific to orient='table', if a DataFrame with a literal Index name of index gets written with to_json(), the subsequent read operation will incorrectly set the Index name to None. This is because index is also used by DataFrame.to_json() to denote a missing Index name, and the subsequent read_json() operation cannot distinguish between the two. The same limitation is encountered with a MultiIndex and any names beginning with 'level_'. Examples
>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
Encoding/decoding a Dataframe using 'split' formatted JSON:
>>> df.to_json(orient='split')
'{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}'
>>> pd.read_json(_, orient='split')
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using 'index' formatted JSON:
>>> df.to_json(orient='index')
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'
>>> pd.read_json(_, orient='index')
col 1 col 2
row 1 a b
row 2 c d
Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.
>>> df.to_json(orient='records')
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'
>>> pd.read_json(_, orient='records')
col 1 col 2
0 a b
1 c d
Encoding with Table Schema
>>> df.to_json(orient='table')
'{"schema":{"fields":[{"name":"index","type":"string"},{"name":"col 1","type":"string"},{"name":"col 2","type":"string"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}' | |
doc_23723 | Write some data bytes to the transport. This method does not block; it buffers the data and arranges for it to be sent out asynchronously. | |
doc_23724 |
Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor: 1-connectivity 2-connectivity diagonal connection close-up
[ ] [ ] [ ] [ ] [ ]
| \ | / | <- hop 2
[ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ]
| / | \ hop 1
[ ] [ ] [ ] [ ]
Parameters
inputndarray of dtype int
Image to label.
backgroundint, optional
Consider all pixels with this value as background pixels, and label them as 0. By default, 0-valued pixels are considered as background pixels.
return_numbool, optional
Whether to return the number of assigned labels.
connectivityint, optional
Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If None, a full connectivity of input.ndim is used. Returns
labelsndarray of dtype int
Labeled array, where all connected regions are assigned the same integer value.
numint, optional
Number of labels, which equals the maximum label index and is only returned if return_num is True. See also
regionprops
regionprops_table
References
1
Christophe Fiorio and Jens Gustedt, “Two linear time Union-Find strategies for image processing”, Theoretical Computer Science 154 (1996), pp. 165-181.
2
Kensheng Wu, Ekow Otoo and Arie Shoshani, “Optimizing connected component labeling algorithms”, Paper LBNL-56864, 2005, Lawrence Berkeley National Laboratory (University of California), http://repositories.cdlib.org/lbnl/LBNL-56864 Examples >>> import numpy as np
>>> x = np.eye(3).astype(int)
>>> print(x)
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> print(label(x, connectivity=1))
[[1 0 0]
[0 2 0]
[0 0 3]]
>>> print(label(x, connectivity=2))
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> print(label(x, background=-1))
[[1 2 2]
[2 1 2]
[2 2 1]]
>>> x = np.array([[1, 0, 0],
... [1, 1, 5],
... [0, 0, 0]])
>>> print(label(x))
[[1 0 0]
[1 1 2]
[0 0 0]] | |
doc_23725 | See Migration guide for more details. tf.compat.v1.raw_ops.RandomPoisson
tf.raw_ops.RandomPoisson(
shape, rate, seed=0, seed2=0, name=None
)
Args
shape A Tensor. Must be one of the following types: int32, int64.
rate A Tensor. Must be one of the following types: half, float32, float64.
seed An optional int. Defaults to 0.
seed2 An optional int. Defaults to 0.
name A name for the operation (optional).
Returns A Tensor. Has the same type as rate. | |
doc_23726 |
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses. | |
doc_23727 | operator.__truediv__(a, b)
Return a / b where 2/3 is .66 rather than 0. This is also known as “true” division. | |
doc_23728 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_23729 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_23730 |
Alias for get_markersize. | |
doc_23731 |
Return x minus the mean(x). Parameters
xarray or sequence
Array or sequence containing the data Can have any dimensionality
axisint
The axis along which to take the mean. See numpy.mean for a description of this argument. See also detrend_linear
Another detrend algorithm. detrend_none
Another detrend algorithm. detrend
A wrapper around all the detrend algorithms. | |
doc_23732 |
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
Mean accuracy of self.predict(X) wrt. y. | |
doc_23733 |
Set the default colormap, and applies it to the current image if any. Parameters
cmapColormap or str
A colormap instance or the name of a registered colormap. See also colormaps
matplotlib.cm.register_cmap
matplotlib.cm.get_cmap | |
doc_23734 | Optional. A regular expression, as a string, that FilePathField will use to filter filenames. Note that the regex will be applied to the base filename, not the full path. Example: "foo.*\.txt$", which will match a file called foo23.txt but not bar.txt or foo23.png. | |
doc_23735 |
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
figure Figure
gid str
height float
in_layout bool
label object
minimumdescent unknown
multilinebaseline unknown
offset (float, float)
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
text unknown
transform unknown
url str
visible bool
width float
zorder float | |
doc_23736 | A Popen creationflags parameter to specify that a new process will not inherit its parent’s console. This value cannot be used with CREATE_NEW_CONSOLE. New in version 3.7. | |
doc_23737 | Return the list of values for the key if it is in the headers, otherwise set the header to the list of values given by default and return that. Unlike MultiDict.setlistdefault(), modifying the returned list will not affect the headers. Parameters
key – The header key to get.
default – An iterable of values to set for the key if it is not in the headers. Changelog New in version 1.0. | |
doc_23738 | Subclasses have to override this method to implement the actual view function code. This method is called with all the arguments from the URL rule. Return type
Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], Union[Headers, Dict[str, Union[str, List[str], Tuple[str, …]]], List[Tuple[str, Union[str, List[str], Tuple[str, …]]]]]], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], int], Tuple[Union[Response, AnyStr, Dict[str, Any], Generator[AnyStr, None, None]], int, Union[Headers, Dict[str, Union[str, List[str], Tuple[str, …]]], List[Tuple[str, Union[str, List[str], Tuple[str, …]]]]]], WSGIApplication] | |
doc_23739 | See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalIteratorGetDevice
tf.raw_ops.ExperimentalIteratorGetDevice(
resource, name=None
)
Args
resource A Tensor of type resource.
name A name for the operation (optional).
Returns A Tensor of type string. | |
doc_23740 | The address (IPv4Address) without network information. >>> interface = IPv4Interface('192.0.2.5/24')
>>> interface.ip
IPv4Address('192.0.2.5') | |
doc_23741 |
Draw samples from a Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale (sometimes designated “theta”), where both parameters are > 0. Parameters
shapefloat or array_like of floats
The shape of the gamma distribution. Must be non-negative.
scalefloat or array_like of floats, optional
The scale of the gamma distribution. Must be non-negative. Default is equal to 1.
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if shape and scale are both scalars. Otherwise, np.broadcast(shape, scale).size samples are drawn. Returns
outndarray or scalar
Drawn samples from the parameterized gamma distribution. See also scipy.stats.gamma
probability density function, distribution or cumulative density function, etc. Notes The probability density for the Gamma distribution is \[p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},\] where \(k\) is the shape and \(\theta\) the scale, and \(\Gamma\) is the Gamma function. The Gamma distribution is often used to model the times to failure of electronic components, and arises naturally in processes for which the waiting times between Poisson distributed events are relevant. References 1
Weisstein, Eric W. “Gamma Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/GammaDistribution.html 2
Wikipedia, “Gamma distribution”, https://en.wikipedia.org/wiki/Gamma_distribution Examples Draw samples from the distribution: >>> shape, scale = 2., 2. # mean=4, std=2*sqrt(2)
>>> s = np.random.default_rng().gamma(shape, scale, 1000)
Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1)*(np.exp(-bins/scale) /
... (sps.gamma(shape)*scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show() | |
doc_23742 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_23743 | Opens the file given by name. Note that although the returned file is guaranteed to be a File object, it might actually be some subclass. In the case of remote file storage this means that reading/writing could be quite slow, so be warned. | |
doc_23744 | Return a time corresponding to a time_string in one of the formats emitted by time.isoformat(). Specifically, this function supports strings in the format: HH[:MM[:SS[.fff[fff]]]][+HH:MM[:SS[.ffffff]]]
Caution This does not support parsing arbitrary ISO 8601 strings. It is only intended as the inverse operation of time.isoformat(). Examples: >>> from datetime import time
>>> time.fromisoformat('04:23:01')
datetime.time(4, 23, 1)
>>> time.fromisoformat('04:23:01.000384')
datetime.time(4, 23, 1, 384)
>>> time.fromisoformat('04:23:01+04:00')
datetime.time(4, 23, 1, tzinfo=datetime.timezone(datetime.timedelta(seconds=14400)))
New in version 3.7. | |
doc_23745 | Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function. The default values for these arguments are the module level constants WRAPPER_ASSIGNMENTS (which assigns to the wrapper function’s __module__, __name__, __qualname__, __annotations__ and __doc__, the documentation string) and WRAPPER_UPDATES (which updates the wrapper function’s __dict__, i.e. the instance dictionary). To allow access to the original function for introspection and other purposes (e.g. bypassing a caching decorator such as lru_cache()), this function automatically adds a __wrapped__ attribute to the wrapper that refers to the function being wrapped. The main intended use for this function is in decorator functions which wrap the decorated function and return the wrapper. If the wrapper function is not updated, the metadata of the returned function will reflect the wrapper definition rather than the original function definition, which is typically less than helpful. update_wrapper() may be used with callables other than functions. Any attributes named in assigned or updated that are missing from the object being wrapped are ignored (i.e. this function will not attempt to set them on the wrapper function). AttributeError is still raised if the wrapper function itself is missing any attributes named in updated. New in version 3.2: Automatic addition of the __wrapped__ attribute. New in version 3.2: Copying of the __annotations__ attribute by default. Changed in version 3.2: Missing attributes no longer trigger an AttributeError. Changed in version 3.4: The __wrapped__ attribute now always refers to the wrapped function, even if that function defined a __wrapped__ attribute. (see bpo-17482) | |
doc_23746 |
Return a possibly reshaped matrix. Refer to numpy.squeeze for more documentation. Parameters
axisNone or int or tuple of ints, optional
Selects a subset of the axes of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised. Returns
squeezedmatrix
The matrix, but as a (1, N) matrix if it had shape (N, 1). See also numpy.squeeze
related function Notes If m has a single column then that column is returned as the single row of a matrix. Otherwise m is returned. The returned matrix is always either m itself or a view into m. Supplying an axis keyword argument will not affect the returned matrix but it may cause an error to be raised. Examples >>> c = np.matrix([[1], [2]])
>>> c
matrix([[1],
[2]])
>>> c.squeeze()
matrix([[1, 2]])
>>> r = c.T
>>> r
matrix([[1, 2]])
>>> r.squeeze()
matrix([[1, 2]])
>>> m = np.matrix([[1, 2], [3, 4]])
>>> m.squeeze()
matrix([[1, 2],
[3, 4]]) | |
doc_23747 | sklearn.utils.check_array(array, accept_sparse=False, *, accept_large_sparse=True, dtype='numeric', order=None, copy=False, force_all_finite=True, ensure_2d=True, allow_nd=False, ensure_min_samples=1, ensure_min_features=1, estimator=None) [source]
Input validation on an array, list, sparse matrix or similar. By default, the input is checked to be a non-empty 2D array containing only finite values. If the dtype of the array is object, attempt converting to float, raising on failure. Parameters
arrayobject
Input object to check / convert.
accept_sparsestr, bool or list/tuple of str, default=False
String[s] representing allowed sparse matrix formats, such as ‘csc’, ‘csr’, etc. If the input is sparse but not in the allowed format, it will be converted to the first listed format. True allows the input to be any format. False means that a sparse matrix input will raise an error.
accept_large_sparsebool, default=True
If a CSR, CSC, COO or BSR sparse matrix is supplied and accepted by accept_sparse, accept_large_sparse=False will cause it to be accepted only if its indices are stored with a 32-bit dtype. New in version 0.20.
dtype‘numeric’, type, list of type or None, default=’numeric’
Data type of result. If None, the dtype of the input is preserved. If “numeric”, dtype is preserved unless array.dtype is object. If dtype is a list of types, conversion on the first type is only performed if the dtype of the input is not in the list.
order{‘F’, ‘C’} or None, default=None
Whether an array will be forced to be fortran or c-style. When order is None (default), then if copy=False, nothing is ensured about the memory layout of the output array; otherwise (copy=True) the memory layout of the returned array is kept as close as possible to the original array.
copybool, default=False
Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.
force_all_finitebool or ‘allow-nan’, default=True
Whether to raise an error on np.inf, np.nan, pd.NA in array. The possibilities are: True: Force all values of array to be finite. False: accepts np.inf, np.nan, pd.NA in array. ‘allow-nan’: accepts only np.nan and pd.NA values in array. Values cannot be infinite. New in version 0.20: force_all_finite accepts the string 'allow-nan'. Changed in version 0.23: Accepts pd.NA and converts it into np.nan
ensure_2dbool, default=True
Whether to raise a value error if array is not 2D.
allow_ndbool, default=False
Whether to allow array.ndim > 2.
ensure_min_samplesint, default=1
Make sure that the array has a minimum number of samples in its first axis (rows for a 2D array). Setting to 0 disables this check.
ensure_min_featuresint, default=1
Make sure that the 2D array has some minimum number of features (columns). The default value of 1 rejects empty datasets. This check is only enforced when the input data has effectively 2 dimensions or is originally 1D and ensure_2d is True. Setting to 0 disables this check.
estimatorstr or estimator instance, default=None
If passed, include the name of the estimator in warning messages. Returns
array_convertedobject
The converted and validated array. | |
doc_23748 | The session object works pretty much like an ordinary dict, with the difference that it keeps track of modifications. This is a proxy. See Notes On Proxies for more information. The following attributes are interesting:
new
True if the session is new, False otherwise.
modified
True if the session object detected a modification. Be advised that modifications on mutable structures are not picked up automatically, in that situation you have to explicitly set the attribute to True yourself. Here an example: # this change is not picked up because a mutable object (here
# a list) is changed.
session['objects'].append(42)
# so mark it as modified yourself
session.modified = True
permanent
If set to True the session lives for permanent_session_lifetime seconds. The default is 31 days. If set to False (which is the default) the session will be deleted when the user closes the browser. | |
doc_23749 |
Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted
equivalent function | |
doc_23750 |
Plot filled contours. Call signature: contourf([X, Y,] Z, [levels], **kwargs)
contour and contourf draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. Parameters
X, Yarray-like, optional
The coordinates of the values in Z. X and Y must both be 2D with the same shape as Z (e.g. created via numpy.meshgrid), or they must both be 1-D such that len(X) == N is the number of columns in Z and len(Y) == M is the number of rows in Z. X and Y must both be ordered monotonically. If not given, they are assumed to be integer indices, i.e. X = range(N), Y = range(M).
Z(M, N) array-like
The height values over which the contour is drawn.
levelsint or array-like, optional
Determines the number and positions of the contour lines / regions. If an int n, use MaxNLocator, which tries to automatically choose no more than n+1 "nice" contour levels between vmin and vmax. If array-like, draw contour lines at the specified levels. The values must be in increasing order. Returns
QuadContourSet
Other Parameters
corner_maskbool, default: rcParams["contour.corner_mask"] (default: True)
Enable/disable corner masking, which only has an effect if Z is a masked array. If False, any quad touching a masked point is masked out. If True, only the triangular corners of quads nearest those points are always masked out, other triangular corners comprising three unmasked points are contoured as usual.
colorscolor string or sequence of colors, optional
The colors of the levels, i.e. the lines for contour and the areas for contourf. The sequence is cycled for the levels in ascending order. If the sequence is shorter than the number of levels, it's repeated. As a shortcut, single color strings may be used in place of one-element lists, i.e. 'red' instead of ['red'] to color all levels with the same color. This shortcut does only work for color strings, not for other ways of specifying colors. By default (value None), the colormap specified by cmap will be used.
alphafloat, default: 1
The alpha blending value, between 0 (transparent) and 1 (opaque).
cmapstr or Colormap, default: rcParams["image.cmap"] (default: 'viridis')
A Colormap instance or registered colormap name. The colormap maps the level values to colors. If both colors and cmap are given, an error is raised.
normNormalize, optional
If a colormap is used, the Normalize instance scales the level values to the canonical colormap range [0, 1] for mapping to colors. If not given, the default linear scaling is used.
vmin, vmaxfloat, optional
If not None, either or both of these values will be supplied to the Normalize instance, overriding the default color scaling based on levels.
origin{None, 'upper', 'lower', 'image'}, default: None
Determines the orientation and exact position of Z by specifying the position of Z[0, 0]. This is only relevant, if X, Y are not given.
None: Z[0, 0] is at X=0, Y=0 in the lower left corner. 'lower': Z[0, 0] is at X=0.5, Y=0.5 in the lower left corner. 'upper': Z[0, 0] is at X=N+0.5, Y=0.5 in the upper left corner. 'image': Use the value from rcParams["image.origin"] (default: 'upper').
extent(x0, x1, y0, y1), optional
If origin is not None, then extent is interpreted as in imshow: it gives the outer pixel boundaries. In this case, the position of Z[0, 0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0, 0], and (x1, y1) is the position of Z[-1, -1]. This argument is ignored if X and Y are specified in the call to contour.
locatorticker.Locator subclass, optional
The locator is used to determine the contour levels if they are not given explicitly via levels. Defaults to MaxNLocator.
extend{'neither', 'both', 'min', 'max'}, default: 'neither'
Determines the contourf-coloring of values that are outside the levels range. If 'neither', values outside the levels range are not colored. If 'min', 'max' or 'both', color the values below, above or below and above the levels range. Values below min(levels) and above max(levels) are mapped to the under/over values of the Colormap. Note that most colormaps do not have dedicated colors for these by default, so that the over and under values are the edge values of the colormap. You may want to set these values explicitly using Colormap.set_under and Colormap.set_over. Note An existing QuadContourSet does not get notified if properties of its colormap are changed. Therefore, an explicit call QuadContourSet.changed() is needed after modifying the colormap. The explicit call can be left out, if a colorbar is assigned to the QuadContourSet because it internally calls QuadContourSet.changed(). Example: x = np.arange(1, 10)
y = x.reshape(-1, 1)
h = x * y
cs = plt.contourf(h, levels=[10, 30, 50],
colors=['#808080', '#A0A0A0', '#C0C0C0'], extend='both')
cs.cmap.set_over('red')
cs.cmap.set_under('blue')
cs.changed()
xunits, yunitsregistered units, optional
Override axis units by specifying an instance of a matplotlib.units.ConversionInterface.
antialiasedbool, optional
Enable antialiasing, overriding the defaults. For filled contours, the default is True. For line contours, it is taken from rcParams["lines.antialiased"] (default: True).
nchunkint >= 0, optional
If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of nchunk by nchunk quads. Chunking reduces the maximum length of polygons generated by the contouring algorithm which reduces the rendering workload passed on to the backend and also requires slightly less RAM. It can however introduce rendering artifacts at chunk boundaries depending on the backend, the antialiased flag and value of alpha.
linewidthsfloat or array-like, default: rcParams["contour.linewidth"] (default: None)
Only applies to contour. The line width of the contour lines. If a number, all levels will be plotted with this linewidth. If a sequence, the levels in ascending order will be plotted with the linewidths in the order specified. If None, this falls back to rcParams["lines.linewidth"] (default: 1.5).
linestyles{None, 'solid', 'dashed', 'dashdot', 'dotted'}, optional
Only applies to contour. If linestyles is None, the default is 'solid' unless the lines are monochrome. In that case, negative contours will take their linestyle from rcParams["contour.negative_linestyle"] (default: 'dashed') setting. linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary.
hatcheslist[str], optional
Only applies to contourf. A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only.
dataindexable object, optional
If given, all parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception). Notes
contourf differs from the MATLAB version in that it does not draw the polygon edges. To draw edges, add line contours with calls to contour.
contourf fills intervals that are closed at the top; that is, for boundaries z1 and z2, the filled region is: z1 < Z <= z2
except for the lowest interval, which is closed on both sides (i.e. it includes the lowest value).
contour and contourf use a marching squares algorithm to compute contour locations. More information can be found in the source src/_contour.h. | |
doc_23751 |
Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually. Parameters
inplacebool, optional
If True, swap bytes in-place, default is False. Returns
outndarray
The byteswapped array. If inplace is True, this is a view to self. Examples >>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped >>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
A.newbyteorder().byteswap() produces an array with the same values
but different representation in memory >>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 3], dtype=uint8) | |
doc_23752 |
Return the drawn line and the resulting scan. Returns
line_image(M, N) uint8 array, same shape as image
An array of 0s with the scanned line set to 255. If the linewidth of the line tool is greater than 1, sets the values within the profiled polygon to 128.
scan(P,) or (P, 3) array of int or float
The line scan values across the image. | |
doc_23753 | User-defined signal 1. Availability: Unix. | |
doc_23754 | tf.experimental.numpy.tile(
a, reps
)
See the NumPy documentation for numpy.tile. | |
doc_23755 | Identical to copy() except that copy2() also attempts to preserve file metadata. When follow_symlinks is false, and src is a symbolic link, copy2() attempts to copy all metadata from the src symbolic link to the newly-created dst symbolic link. However, this functionality is not available on all platforms. On platforms where some or all of this functionality is unavailable, copy2() will preserve all the metadata it can; copy2() never raises an exception because it cannot preserve file metadata. copy2() uses copystat() to copy the file metadata. Please see copystat() for more information about platform support for modifying symbolic link metadata. Raises an auditing event shutil.copyfile with arguments src, dst. Raises an auditing event shutil.copystat with arguments src, dst. Changed in version 3.3: Added follow_symlinks argument, try to copy extended file system attributes too (currently Linux only). Now returns path to the newly created file. Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section. | |
doc_23756 |
Returns the indices that would partition this array. Refer to numpy.argpartition for full documentation. New in version 1.8.0. See also numpy.argpartition
equivalent function | |
doc_23757 |
Return a bounding box that encloses the axis. It only accounts tick labels, axis label, and offsetText. If for_layout_only is True, then the width of the label (if this is an x-axis) or the height of the label (if this is a y-axis) is collapsed to near zero. This allows tight/constrained_layout to ignore too-long labels when doing their layout. | |
doc_23758 | Returns a response object (a WSGI application) that, if called, redirects the client to the target location. Supported codes are 301, 302, 303, 305, 307, and 308. 300 is not supported because it’s not a real redirect and 304 because it’s the answer for a request with a request with defined If-Modified-Since headers. Changelog New in version 0.10: The class used for the Response object can now be passed in. New in version 0.6: The location can now be a unicode string that is encoded using the iri_to_uri() function. Parameters
location (str) – the location the response should redirect to.
code (int) – the redirect status code. defaults to 302.
Response (class) – a Response class to use when instantiating a response. The default is werkzeug.wrappers.Response if unspecified. Return type
Response | |
doc_23759 | Receive data from the socket. The return value is a pair (bytes, address) where bytes is a bytes object representing the data received and address is the address of the socket sending the data. See the Unix manual page recv(2) for the meaning of the optional argument flags; it defaults to zero. (The format of address depends on the address family — see above.) Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale). Changed in version 3.7: For multicast IPv6 address, first item of address does not contain %scope_id part anymore. In order to get full IPv6 address use getnameinfo(). | |
doc_23760 | tf.metrics.Sum Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.Sum
tf.keras.metrics.Sum(
name='sum', dtype=None
)
For example, if values is [1, 3, 5, 7] then the sum is 16. If the weights were specified as [1, 1, 0, 0] then the sum would be 4. This metric creates one variable, total, that is used to compute the sum of values. This is ultimately returned as sum. If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.
Args
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result. Standalone usage:
m = tf.keras.metrics.Sum()
m.update_state([1, 3, 5, 7])
m.result().numpy()
16.0
Usage with compile() API: model.add_metric(tf.keras.metrics.Sum(name='sum_1')(outputs))
model.compile(optimizer='sgd', loss='mse')
Methods reset_states View source
reset_states()
Resets all of the metric state variables. This function is called between epochs/steps, when a metric is evaluated during training. result View source
result()
Computes and returns the metric value tensor. Result computation is an idempotent operation that simply calculates the metric value using the state variables. update_state View source
update_state(
values, sample_weight=None
)
Accumulates statistics for computing the metric.
Args
values Per-example value.
sample_weight Optional weighting of each example. Defaults to 1.
Returns Update op. | |
doc_23761 |
Apply inverse transformation. Parameters
coords(N, 2) array
Destination coordinates. Returns
coords(N, 2) array
Source coordinates. | |
doc_23762 |
Return the alpha value used for blending - not supported on all backends. | |
doc_23763 |
Apply clustering to a projection of the normalized Laplacian. In practice Spectral Clustering is very useful when the structure of the individual clusters is highly non-convex or more generally when a measure of the center and spread of the cluster is not a suitable description of the complete cluster. For instance when clusters are nested circles on the 2D plane. If affinity is the adjacency matrix of a graph, this method can be used to find normalized graph cuts. When calling fit, an affinity matrix is constructed using either kernel function such the Gaussian (aka RBF) kernel of the euclidean distanced d(X, X): np.exp(-gamma * d(X,X) ** 2)
or a k-nearest neighbors connectivity matrix. Alternatively, using precomputed, a user-provided affinity matrix can be used. Read more in the User Guide. Parameters
n_clustersint, default=8
The dimension of the projection subspace.
eigen_solver{‘arpack’, ‘lobpcg’, ‘amg’}, default=None
The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems, but may also lead to instabilities. If None, then 'arpack' is used.
n_componentsint, default=n_clusters
Number of eigen vectors to use for the spectral embedding
random_stateint, RandomState instance, default=None
A pseudo random number generator used for the initialization of the lobpcg eigen vectors decomposition when eigen_solver='amg' and by the K-Means initialization. Use an int to make the randomness deterministic. See Glossary.
n_initint, default=10
Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.
gammafloat, default=1.0
Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels. Ignored for affinity='nearest_neighbors'.
affinitystr or callable, default=’rbf’
How to construct the affinity matrix.
‘nearest_neighbors’ : construct the affinity matrix by computing a graph of nearest neighbors. ‘rbf’ : construct the affinity matrix using a radial basis function (RBF) kernel. ‘precomputed’ : interpret X as a precomputed affinity matrix. ‘precomputed_nearest_neighbors’ : interpret X as a sparse graph of precomputed nearest neighbors, and constructs the affinity matrix by selecting the n_neighbors nearest neighbors. one of the kernels supported by pairwise_kernels. Only kernels that produce similarity scores (non-negative values that increase with similarity) should be used. This property is not checked by the clustering algorithm.
n_neighborsint, default=10
Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for affinity='rbf'.
eigen_tolfloat, default=0.0
Stopping criterion for eigendecomposition of the Laplacian matrix when eigen_solver='arpack'.
assign_labels{‘kmeans’, ‘discretize’}, default=’kmeans’
The strategy to use to assign labels in the embedding space. There are two ways to assign labels after the laplacian embedding. k-means can be applied and is a popular choice. But it can also be sensitive to initialization. Discretization is another approach which is less sensitive to random initialization.
degreefloat, default=3
Degree of the polynomial kernel. Ignored by other kernels.
coef0float, default=1
Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
kernel_paramsdict of str to any, default=None
Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels.
n_jobsint, default=None
The number of parallel jobs to run when affinity='nearest_neighbors' or affinity='precomputed_nearest_neighbors'. The neighbors search will be done in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
verbosebool, default=False
Verbosity mode. New in version 0.24. Attributes
affinity_matrix_array-like of shape (n_samples, n_samples)
Affinity matrix used for clustering. Available only if after calling fit.
labels_ndarray of shape (n_samples,)
Labels of each point Notes If you have an affinity matrix, such as a distance matrix, for which 0 means identical elements, and high values means very dissimilar elements, it can be transformed in a similarity matrix that is well suited for the algorithm by applying the Gaussian (RBF, heat) kernel: np.exp(- dist_matrix ** 2 / (2. * delta ** 2))
Where delta is a free parameter representing the width of the Gaussian kernel. Another alternative is to take a symmetric version of the k nearest neighbors connectivity matrix of the points. If the pyamg package is installed, it is used: this greatly speeds up computation. References Normalized cuts and image segmentation, 2000 Jianbo Shi, Jitendra Malik http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
A Tutorial on Spectral Clustering, 2007 Ulrike von Luxburg http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
Multiclass spectral clustering, 2003 Stella X. Yu, Jianbo Shi https://www1.icsi.berkeley.edu/~stellayu/publication/doc/2003kwayICCV.pdf
Examples >>> from sklearn.cluster import SpectralClustering
>>> import numpy as np
>>> X = np.array([[1, 1], [2, 1], [1, 0],
... [4, 7], [3, 5], [3, 6]])
>>> clustering = SpectralClustering(n_clusters=2,
... assign_labels="discretize",
... random_state=0).fit(X)
>>> clustering.labels_
array([1, 1, 1, 0, 0, 0])
>>> clustering
SpectralClustering(assign_labels='discretize', n_clusters=2,
random_state=0)
Methods
fit(X[, y]) Perform spectral clustering from features, or affinity matrix.
fit_predict(X[, y]) Perform spectral clustering from features, or affinity matrix, and return cluster labels.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
fit(X, y=None) [source]
Perform spectral clustering from features, or affinity matrix. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse matrix is provided in a format other than csr_matrix, csc_matrix, or coo_matrix, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention. Returns
self
fit_predict(X, y=None) [source]
Perform spectral clustering from features, or affinity matrix, and return cluster labels. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features), or array-like of shape (n_samples, n_samples)
Training instances to cluster, or similarities / affinities between instances if affinity='precomputed'. If a sparse matrix is provided in a format other than csr_matrix, csc_matrix, or coo_matrix, it will be converted into a sparse csr_matrix.
yIgnored
Not used, present here for API consistency by convention. Returns
labelsndarray of shape (n_samples,)
Cluster labels.
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_23764 |
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float | |
doc_23765 |
Parameters
key, value (query,) – map a query and a set of key-value pairs to an output. See “Attention Is All You Need” for more details.
key_padding_mask – if provided, specified padding elements in the key will be ignored by the attention. When given a binary mask and a value is True, the corresponding value on the attention layer will be ignored. When given a byte mask and a value is non-zero, the corresponding value on the attention layer will be ignored
need_weights – output attn_output_weights.
attn_mask – 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. Shapes for inputs:
query: (L,N,E)(L, N, E) where L is the target sequence length, N is the batch size, E is the embedding dimension. key: (S,N,E)(S, N, E) , where S is the source sequence length, N is the batch size, E is the embedding dimension. value: (S,N,E)(S, N, E) where S is the source sequence length, N is the batch size, E is the embedding dimension. key_padding_mask: (N,S)(N, S) where N is the batch size, S is the source sequence length. If a ByteTensor is provided, the non-zero positions will be ignored while the position with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the value of True will be ignored while the position with the value of False will be unchanged.
attn_mask: if a 2D mask: (L,S)(L, S) where L is the target sequence length, S is the source sequence length. If a 3D mask: (N⋅num_heads,L,S)(N\cdot\text{num\_heads}, L, S) where N is the batch size, L is the target sequence length, S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend while the zero positions will be unchanged. If a BoolTensor is provided, positions with True is not allowed to attend while False values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight. Shapes for outputs:
attn_output: (L,N,E)(L, N, E) where L is the target sequence length, N is the batch size, E is the embedding dimension. attn_output_weights: (N,L,S)(N, L, S) where N is the batch size, L is the target sequence length, S is the source sequence length. | |
doc_23766 |
Return the snap setting. See set_snap for details. | |
doc_23767 | See Migration guide for more details. tf.compat.v1.io.serialize_sparse
tf.compat.v1.serialize_sparse(
sp_input, name=None, out_type=tf.dtypes.string
)
Args
sp_input The input SparseTensor.
name A name prefix for the returned tensors (optional).
out_type The dtype to use for serialization.
Returns A 3-vector (1-D Tensor), with each column representing the serialized SparseTensor's indices, values, and shape (respectively).
Raises
TypeError If sp_input is not a SparseTensor. | |
doc_23768 | Return successive r length permutations of elements in the iterable. If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated. The permutation tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the combination tuples will be produced in sorted order. Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeat values in each permutation. Roughly equivalent to: def permutations(iterable, r=None):
# permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
# permutations(range(3)) --> 012 021 102 120 201 210
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
if r > n:
return
indices = list(range(n))
cycles = list(range(n, n-r, -1))
yield tuple(pool[i] for i in indices[:r])
while n:
for i in reversed(range(r)):
cycles[i] -= 1
if cycles[i] == 0:
indices[i:] = indices[i+1:] + indices[i:i+1]
cycles[i] = n - i
else:
j = cycles[i]
indices[i], indices[-j] = indices[-j], indices[i]
yield tuple(pool[i] for i in indices[:r])
break
else:
return
The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool): def permutations(iterable, r=None):
pool = tuple(iterable)
n = len(pool)
r = n if r is None else r
for indices in product(range(n), repeat=r):
if len(set(indices)) == r:
yield tuple(pool[i] for i in indices)
The number of items returned is n! / (n-r)! when 0 <= r <= n or zero when r > n. | |
doc_23769 | A read-only description of the dialect in use by the parser. | |
doc_23770 | See Migration guide for more details. tf.compat.v1.raw_ops.QueueEnqueueManyV2
tf.raw_ops.QueueEnqueueManyV2(
handle, components, timeout_ms=-1, name=None
)
This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tuple components must have the same size in the 0th dimension. The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given elements have been enqueued (or 'timeout_ms' elapses, if specified).
Args
handle A Tensor of type resource. The handle to a queue.
components A list of Tensor objects. One or more tensors from which the enqueued tensors should be taken.
timeout_ms An optional int. Defaults to -1. If the queue is too full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet.
name A name for the operation (optional).
Returns The created Operation. | |
doc_23771 |
Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values. Returns
numpy.ndarray[bool]
Boolean array to indicate which entries are not NA. See also Index.notnull
Alias of notna. Index.isna
Inverse of notna. notna
Top-level notna. Examples Show which entries in an Index are not NA. The result is an array.
>>> idx = pd.Index([5.2, 6.0, np.NaN])
>>> idx
Float64Index([5.2, 6.0, nan], dtype='float64')
>>> idx.notna()
array([ True, True, False])
Empty strings are not considered NA values. None is considered a NA value.
>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.notna()
array([ True, True, True, False]) | |
doc_23772 | See torch.msort() | |
doc_23773 | Convert a label to ASCII, as specified in RFC 3490. UseSTD3ASCIIRules is assumed to be false. | |
doc_23774 |
Fit the model to data matrix X and target(s) y. Parameters
Xndarray or sparse matrix of shape (n_samples, n_features)
The input data.
yndarray of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression). Returns
selfreturns a trained MLP model. | |
doc_23775 | tf.keras.applications.resnet50.decode_predictions Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.applications.resnet.decode_predictions, tf.compat.v1.keras.applications.resnet50.decode_predictions
tf.keras.applications.resnet.decode_predictions(
preds, top=5
)
Arguments
preds Numpy array encoding a batch of predictions.
top Integer, how many top-guesses to return. Defaults to 5.
Returns A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.
Raises
ValueError In case of invalid shape of the pred array (must be 2D). | |
doc_23776 | Return the base-2 logarithm of x. This is usually more accurate than log(x, 2). New in version 3.3. See also int.bit_length() returns the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros. | |
doc_23777 |
Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects. Parameters
data:array-like (1-dimensional)
dtype:NumPy dtype (default: object)
If dtype is None, we find the dtype that best fits the data. If an actual dtype is provided, we coerce to that dtype if it’s safe. Otherwise, an error will be raised.
copy:bool
Make a copy of input ndarray.
name:object
Name to be stored in the index.
tupleize_cols:bool (default: True)
When True, attempt to create a MultiIndex if possible. See also RangeIndex
Index implementing a monotonic integer range. CategoricalIndex
Index of Categorical s. MultiIndex
A multi-level, or hierarchical Index. IntervalIndex
An Index of Interval s. DatetimeIndex
Index of datetime64 data. TimedeltaIndex
Index of timedelta64 data. PeriodIndex
Index of Period data. NumericIndex
Index of numpy int/uint/float data. Int64Index
Index of purely int64 labels (deprecated). UInt64Index
Index of purely uint64 labels (deprecated). Float64Index
Index of purely float64 labels (deprecated). Notes An Index instance can only contain hashable objects Examples
>>> pd.Index([1, 2, 3])
Int64Index([1, 2, 3], dtype='int64')
>>> pd.Index(list('abc'))
Index(['a', 'b', 'c'], dtype='object')
Attributes
T Return the transpose, which is by definition self.
array The ExtensionArray of the data backing this Series or Index.
asi8 Integer representation of the values.
dtype Return the dtype object of the underlying data.
has_duplicates Check if the Index has duplicate values.
hasnans Return True if there are any NaNs.
inferred_type Return a string of the type inferred from the values.
is_all_dates Whether or not the index values only consist of dates.
is_monotonic Alias for is_monotonic_increasing.
is_monotonic_decreasing Return if the index is monotonic decreasing (only equal or decreasing) values.
is_monotonic_increasing Return if the index is monotonic increasing (only equal or increasing) values.
is_unique Return if the index has unique values.
name Return Index or MultiIndex name.
nbytes Return the number of bytes in the underlying data.
ndim Number of dimensions of the underlying data, by definition 1.
nlevels Number of levels.
shape Return a tuple of the shape of the underlying data.
size Return the number of elements in the underlying data.
values Return an array representing the data in the Index.
empty
names Methods
all(*args, **kwargs) Return whether all elements are Truthy.
any(*args, **kwargs) Return whether any element is Truthy.
append(other) Append a collection of Index options together.
argmax([axis, skipna]) Return int position of the largest value in the Series.
argmin([axis, skipna]) Return int position of the smallest value in the Series.
argsort(*args, **kwargs) Return the integer indices that would sort the index.
asof(label) Return the label from the index, or, if not present, the previous one.
asof_locs(where, mask) Return the locations (indices) of labels in the index.
astype(dtype[, copy]) Create an Index with values cast to dtypes.
copy([name, deep, dtype, names]) Make a copy of this object.
delete(loc) Make new Index with passed location(-s) deleted.
difference(other[, sort]) Return a new Index with elements of index not in other.
drop(labels[, errors]) Make new Index with passed list of labels deleted.
drop_duplicates([keep]) Return Index with duplicate values removed.
droplevel([level]) Return index with requested level(s) removed.
dropna([how]) Return Index without NA/NaN values.
duplicated([keep]) Indicate duplicate index values.
equals(other) Determine if two Index object are equal.
factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable.
fillna([value, downcast]) Fill NA/NaN values with the specified value.
format([name, formatter, na_rep]) Render a string representation of the Index.
get_indexer(target[, method, limit, tolerance]) Compute indexer and mask for new index given the current index.
get_indexer_for(target) Guaranteed return of an indexer even when non-unique.
get_indexer_non_unique(target) Compute indexer and mask for new index given the current index.
get_level_values(level) Return an Index of values for requested level.
get_loc(key[, method, tolerance]) Get integer location, slice or boolean mask for requested label.
get_slice_bound(label, side[, kind]) Calculate slice bound that corresponds to given label.
get_value(series, key) Fast lookup of value from 1-dimensional ndarray.
groupby(values) Group the index labels by a given array of values.
holds_integer() Whether the type is an integer type.
identical(other) Similar to equals, but checks that object attributes and types are also equal.
insert(loc, item) Make new Index inserting new item at location.
intersection(other[, sort]) Form the intersection of two Index objects.
is_(other) More flexible, faster check like is but that works through views.
is_boolean() Check if the Index only consists of booleans.
is_categorical() Check if the Index holds categorical data.
is_floating() Check if the Index is a floating type.
is_integer() Check if the Index only consists of integers.
is_interval() Check if the Index holds Interval objects.
is_mixed() Check if the Index holds data with mixed data types.
is_numeric() Check if the Index only consists of numeric data.
is_object() Check if the Index is of the object dtype.
is_type_compatible(kind) Whether the index type is compatible with the provided type.
isin(values[, level]) Return a boolean array where the index values are in values.
isna() Detect missing values.
isnull() Detect missing values.
item() Return the first element of the underlying data as a Python scalar.
join(other[, how, level, return_indexers, sort]) Compute join_index and indexers to conform data structures to the new index.
map(mapper[, na_action]) Map values using an input mapping or function.
max([axis, skipna]) Return the maximum value of the Index.
memory_usage([deep]) Memory usage of the values.
min([axis, skipna]) Return the minimum value of the Index.
notna() Detect existing (non-missing) values.
notnull() Detect existing (non-missing) values.
nunique([dropna]) Return number of unique elements in the object.
putmask(mask, value) Return a new Index of the values set with the mask.
ravel([order]) Return an ndarray of the flattened values of the underlying data.
reindex(target[, method, level, limit, ...]) Create index with target's values.
rename(name[, inplace]) Alter Index or MultiIndex name.
repeat(repeats[, axis]) Repeat elements of a Index.
searchsorted(value[, side, sorter]) Find indices where elements should be inserted to maintain order.
set_names(names[, level, inplace]) Set Index or MultiIndex name.
set_value(arr, key, value) (DEPRECATED) Fast lookup of value from 1-dimensional ndarray.
shift([periods, freq]) Shift index by desired number of time frequency increments.
slice_indexer([start, end, step, kind]) Compute the slice indexer for input labels and step.
slice_locs([start, end, step, kind]) Compute slice locations for input labels.
sort(*args, **kwargs) Use sort_values instead.
sort_values([return_indexer, ascending, ...]) Return a sorted copy of the index.
sortlevel([level, ascending, sort_remaining]) For internal compatibility with the Index API.
str alias of pandas.core.strings.accessor.StringMethods
symmetric_difference(other[, result_name, sort]) Compute the symmetric difference of two Index objects.
take(indices[, axis, allow_fill, fill_value]) Return a new Index of the values selected by the indices.
to_flat_index() Identity method.
to_frame([index, name]) Create a DataFrame with a column containing the Index.
to_list() Return a list of the values.
to_native_types([slicer]) (DEPRECATED) Format specified values of self and return them.
to_numpy([dtype, copy, na_value]) A NumPy ndarray representing the values in this Series or Index.
to_series([index, name]) Create a Series with both index and values equal to the index keys.
tolist() Return a list of the values.
transpose(*args, **kwargs) Return the transpose, which is by definition self.
union(other[, sort]) Form the union of two Index objects.
unique([level]) Return unique values in the index.
value_counts([normalize, sort, ascending, ...]) Return a Series containing counts of unique values.
where(cond[, other]) Replace values where the condition is False.
view | |
doc_23778 | See torch.logsumexp() | |
doc_23779 | See Migration guide for more details. tf.compat.v1.keras.applications.imagenet_utils.decode_predictions
tf.keras.applications.imagenet_utils.decode_predictions(
preds, top=5
)
Arguments
preds Numpy array encoding a batch of predictions.
top Integer, how many top-guesses to return. Defaults to 5.
Returns A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.
Raises
ValueError In case of invalid shape of the pred array (must be 2D). | |
doc_23780 |
Alias for set_antialiased. | |
doc_23781 | See Migration guide for more details. tf.compat.v1.raw_ops.BoostedTreesGetEnsembleStates
tf.raw_ops.BoostedTreesGetEnsembleStates(
tree_ensemble_handle, name=None
)
Args
tree_ensemble_handle A Tensor of type resource. Handle to the tree ensemble.
name A name for the operation (optional).
Returns A tuple of Tensor objects (stamp_token, num_trees, num_finalized_trees, num_attempted_layers, last_layer_nodes_range). stamp_token A Tensor of type int64.
num_trees A Tensor of type int32.
num_finalized_trees A Tensor of type int32.
num_attempted_layers A Tensor of type int32.
last_layer_nodes_range A Tensor of type int32. | |
doc_23782 | This function performs a call to get_logger() but in addition to returning the logger created by get_logger, it adds a handler which sends output to sys.stderr using format '[%(levelname)s/%(processName)s] %(message)s'. | |
doc_23783 | RGBAxes(*args[, pad])
Parameters | |
doc_23784 | Replace %xx escapes with their single-character equivalent. The optional encoding and errors parameters specify how to decode percent-encoded sequences into Unicode characters, as accepted by the bytes.decode() method. string may be either a str or a bytes object. encoding defaults to 'utf-8'. errors defaults to 'replace', meaning invalid sequences are replaced by a placeholder character. Example: unquote('/El%20Ni%C3%B1o/') yields '/El Niño/'. Changed in version 3.9: string parameter supports bytes and str objects (previously only str). | |
doc_23785 | This is raised when an unrecognized option is found in the argument list or when an option requiring an argument is given none. The argument to the exception is a string indicating the cause of the error. For long options, an argument given to an option which does not require one will also cause this exception to be raised. The attributes msg and opt give the error message and related option; if there is no specific option to which the exception relates, opt is an empty string. | |
doc_23786 | By default the admin shows all fields as editable. Any fields in this option (which should be a list or tuple) will display its data as-is and non-editable; they are also excluded from the ModelForm used for creating and editing. Note that when specifying ModelAdmin.fields or ModelAdmin.fieldsets the read-only fields must be present to be shown (they are ignored otherwise). If readonly_fields is used without defining explicit ordering through ModelAdmin.fields or ModelAdmin.fieldsets they will be added last after all editable fields. A read-only field can not only display data from a model’s field, it can also display the output of a model’s method or a method of the ModelAdmin class itself. This is very similar to the way ModelAdmin.list_display behaves. This provides a way to use the admin interface to provide feedback on the status of the objects being edited, for example: from django.contrib import admin
from django.utils.html import format_html_join
from django.utils.safestring import mark_safe
class PersonAdmin(admin.ModelAdmin):
readonly_fields = ('address_report',)
# description functions like a model field's verbose_name
@admin.display(description='Address')
def address_report(self, instance):
# assuming get_full_address() returns a list of strings
# for each line of the address and you want to separate each
# line by a linebreak
return format_html_join(
mark_safe('<br>'),
'{}',
((line,) for line in instance.get_full_address()),
) or mark_safe("<span class='errors'>I can't determine this address.</span>") | |
doc_23787 |
Gauss-Laguerre quadrature. Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of degree \(2*deg - 1\) or less over the interval \([0, \inf]\) with the weight function \(f(x) = \exp(-x)\). Parameters
degint
Number of sample points and weights. It must be >= 1. Returns
xndarray
1-D ndarray containing the sample points.
yndarray
1-D ndarray containing the weights. Notes New in version 1.7.0. The results have only been tested up to degree 100 higher degrees may be problematic. The weights are determined by using the fact that \[w_k = c / (L'_n(x_k) * L_{n-1}(x_k))\] where \(c\) is a constant independent of \(k\) and \(x_k\) is the k’th root of \(L_n\), and then scaling the results to get the right value when integrating 1. | |
doc_23788 |
Fit the shrunk covariance model according to the given training data and parameters. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features. y: Ignored
Not used, present for API consistency by convention. Returns
selfobject | |
doc_23789 |
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_23790 |
kwargs must consist of a single key, value pair. If key is in _mapping, return _mapping[value]; else, raise an appropriate ValueError. Examples >>> _api.check_getitem({"foo": "bar"}, arg=arg) | |
doc_23791 |
Roll provided date forward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | |
doc_23792 |
Read text from clipboard and pass to read_csv. Parameters
sep:str, default ‘s+’
A string or regex delimiter. The default of ‘s+’ denotes one or more whitespace characters. **kwargs
See read_csv for the full argument list. Returns
DataFrame
A parsed DataFrame object. | |
doc_23793 | In-place version of log1p() | |
doc_23794 | See Migration guide for more details. tf.compat.v1.raw_ops.RestoreV2
tf.raw_ops.RestoreV2(
prefix, tensor_names, shape_and_slices, dtypes, name=None
)
For backward compatibility with the V1 format, this Op currently allows restoring from a V1 checkpoint as well: This Op first attempts to find the V2 index file pointed to by "prefix", and if found proceed to read it as a V2 checkpoint; Otherwise the V1 read path is invoked. Relying on this behavior is not recommended, as the ability to fall back to read V1 might be deprecated and eventually removed. By default, restores the named tensors in full. If the caller wishes to restore specific slices of stored tensors, "shape_and_slices" should be non-empty strings and correspondingly well-formed. Callers must ensure all the named tensors are indeed stored in the checkpoint.
Args
prefix A Tensor of type string. Must have a single element. The prefix of a V2 checkpoint.
tensor_names A Tensor of type string. shape {N}. The names of the tensors to be restored.
shape_and_slices A Tensor of type string. shape {N}. The slice specs of the tensors to be restored. Empty strings indicate that they are non-partitioned tensors.
dtypes A list of tf.DTypes that has length >= 1. shape {N}. The list of expected dtype for the tensors. Must match those stored in the checkpoint.
name A name for the operation (optional).
Returns A list of Tensor objects of type dtypes. | |
doc_23795 |
Provide exponentially weighted (EW) calculations. Exactly one parameter: com, span, halflife, or alpha must be provided. Parameters
com:float, optional
Specify decay in terms of center of mass \(\alpha = 1 / (1 + com)\), for \(com \geq 0\).
span:float, optional
Specify decay in terms of span \(\alpha = 2 / (span + 1)\), for \(span \geq 1\).
halflife:float, str, timedelta, optional
Specify decay in terms of half-life \(\alpha = 1 - \exp\left(-\ln(2) / halflife\right)\), for \(halflife > 0\). If times is specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to mean(), and halflife value will not apply to the other functions. New in version 1.1.0.
alpha:float, optional
Specify smoothing factor \(\alpha\) directly \(0 < \alpha \leq 1\).
min_periods:int, default 0
Minimum number of observations in window required to have a value; otherwise, result is np.nan.
adjust:bool, default True
Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average). When adjust=True (default), the EW function is calculated using weights \(w_i = (1 - \alpha)^i\). For example, the EW moving average of the series [\(x_0, x_1, ..., x_t\)] would be: \[y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}\] When adjust=False, the exponentially weighted function is calculated recursively: \[\begin{split}\begin{split} y_0 &= x_0\\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}\end{split}\]
ignore_na:bool, default False
Ignore missing values when calculating weights. When ignore_na=False (default), weights are based on absolute positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \((1-\alpha)^2\) and \(1\) if adjust=True, and \((1-\alpha)^2\) and \(\alpha\) if adjust=False. When ignore_na=True, weights are based on relative positions. For example, the weights of \(x_0\) and \(x_2\) used in calculating the final weighted average of [\(x_0\), None, \(x_2\)] are \(1-\alpha\) and \(1\) if adjust=True, and \(1-\alpha\) and \(\alpha\) if adjust=False.
axis:{0, 1}, default 0
If 0 or 'index', calculate across the rows. If 1 or 'columns', calculate across the columns.
times:str, np.ndarray, Series, default None
New in version 1.1.0. Only applicable to mean(). Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype. If 1-D array like, a sequence with the same shape as the observations. Deprecated since version 1.4.0: If str, the name of the column in the DataFrame representing the times.
method:str {‘single’, ‘table’}, default ‘single’
New in version 1.4.0. Execute the rolling operation per single column or row ('single') or over the entire object ('table'). This argument is only implemented when specifying engine='numba' in the method call. Only applicable to mean() Returns
ExponentialMovingWindow subclass
See also rolling
Provides rolling window calculations. expanding
Provides expanding transformations. Notes See Windowing Operations for further usage details and examples. Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
B
0 0.0
1 1.0
2 2.0
3 NaN
4 4.0
>>> df.ewm(com=0.5).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(alpha=2 / 3).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
adjust
>>> df.ewm(com=0.5, adjust=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
>>> df.ewm(com=0.5, adjust=False).mean()
B
0 0.000000
1 0.666667
2 1.555556
3 1.555556
4 3.650794
ignore_na
>>> df.ewm(com=0.5, ignore_na=True).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.225000
>>> df.ewm(com=0.5, ignore_na=False).mean()
B
0 0.000000
1 0.750000
2 1.615385
3 1.615385
4 3.670213
times Exponentially weighted mean with weights calculated with a timedelta halflife relative to times.
>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
B
0 0.000000
1 0.585786
2 1.523889
3 1.523889
4 3.233686 | |
doc_23796 |
Switch minor tick labeling on or off. Parameters
labelOnlyBasebool
If True, label ticks only at integer powers of base. | |
doc_23797 |
Bases: matplotlib.collections.PolyCollection Specialized PolyCollection for arrows. The only API method is set_UVC(), which can be used to change the size, orientation, and color of the arrows; their locations are fixed when the class is instantiated. Possibly this method will be useful in animations. Much of the work in this class is done in the draw() method so that as much information as possible is available about the plot. In subsequent draw() calls, recalculation is limited to things that might have changed, so there should be no performance penalty from putting the calculations in the draw() method. The constructor takes one required argument, an Axes instance, followed by the args and kwargs described by the following pyplot interface documentation: Plot a 2D field of arrows. Call signature: quiver([X, Y], U, V, [C], **kw)
X, Y define the arrow locations, U, V define the arrow directions, and C optionally sets the color. Each arrow is internally represented by a filled polygon with a default edge linewidth of 0. As a result, an arrow is rather a filled area, not a line with a head, and PolyCollection properties like linewidth, linestyle, facecolor, etc. act accordingly. Arrow size The default settings auto-scales the length of the arrows to a reasonable size. To change this behavior see the scale and scale_units parameters. Arrow shape The defaults give a slightly swept-back arrow; to make the head a triangle, make headaxislength the same as headlength. To make the arrow more pointed, reduce headwidth or increase headlength and headaxislength. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone. Arrow outline linewidths and edgecolors can be used to customize the arrow outlines. Parameters
X, Y1D or 2D array-like, optional
The x and y coordinates of the arrow locations. If not given, they will be generated as a uniform integer meshgrid based on the dimensions of U and V. If X and Y are 1D but U, V are 2D, X, Y are expanded to 2D using X, Y = np.meshgrid(X, Y). In this case len(X) and len(Y) must match the column and row dimensions of U and V.
U, V1D or 2D array-like
The x and y direction components of the arrow vectors. They must have the same number of elements, matching the number of arrow locations. U and V may be masked. Only locations unmasked in U, V, and C will be drawn.
C1D or 2D array-like, optional
Numeric data that defines the arrow colors by colormapping via norm and cmap. This does not support explicit colors. If you want to set colors directly, use color instead. The size of C must match the number of arrow locations.
units{'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, default: 'width'
The arrow dimensions (except for length) are measured in multiples of this unit. The following values are supported: 'width', 'height': The width or height of the axis. 'dots', 'inches': Pixels or inches based on the figure dpi. 'x', 'y', 'xy': X, Y or \(\sqrt{X^2 + Y^2}\) in data units. The arrows scale differently depending on the units. For 'x' or 'y', the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For 'width or 'height', the arrow size increases with the width and height of the axes, respectively, when the window is resized; for 'dots' or 'inches', resizing does not change the arrows.
angles{'uv', 'xy'} or array-like, default: 'uv'
Method for determining the angle of the arrows.
'uv': The arrow axis aspect ratio is 1 so that if U == V the orientation of the arrow on the plot is 45 degrees counter-clockwise from the horizontal axis (positive to the right). Use this if the arrows symbolize a quantity that is not based on X, Y data coordinates. 'xy': Arrows point from (x, y) to (x+u, y+v). Use this for plotting a gradient field, for example.
Alternatively, arbitrary angles may be specified explicitly as an array of values in degrees, counter-clockwise from the horizontal axis. In this case U, V is only used to determine the length of the arrows. Note: inverting a data axis will correspondingly invert the arrows only with angles='xy'.
scalefloat, optional
Number of data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. Default is None. If None, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. The arrow length unit is given by the scale_units parameter.
scale_units{'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, optional
If the scale kwarg is None, the arrow length unit. Default is None. e.g. scale_units is 'inches', scale is 2.0, and (u, v) = (1, 0), then the vector will be 0.5 inches long. If scale_units is 'width' or 'height', then the vector will be half the width/height of the axes. If scale_units is 'x' then the vector will be 0.5 x-axis units. To plot vectors in the x-y plane, with u and v having the same units as x and y, use angles='xy', scale_units='xy', scale=1.
widthfloat, optional
Shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot.
headwidthfloat, default: 3
Head width as multiple of shaft width.
headlengthfloat, default: 5
Head length as multiple of shaft width.
headaxislengthfloat, default: 4.5
Head length at shaft intersection.
minshaftfloat, default: 1
Length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible!
minlengthfloat, default: 1
Minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead.
pivot{'tail', 'mid', 'middle', 'tip'}, default: 'tail'
The part of the arrow that is anchored to the X, Y grid. The arrow rotates about this point. 'mid' is a synonym for 'middle'.
colorcolor or color sequence, optional
Explicit color(s) for the arrows. If C has been set, color has no effect. This is a synonym for the PolyCollection facecolor parameter. Returns
Quiver
Other Parameters
dataindexable object, optional
DATA_PARAMETER_PLACEHOLDER
**kwargsPolyCollection properties, optional
All other keyword arguments are passed on to PolyCollection:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha array-like or scalar or None
animated bool
antialiased or aa or antialiaseds bool or list of bools
array array-like or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clim (vmin: float, vmax: float)
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
cmap Colormap or str or None
color color or list of rgba tuples
edgecolor or ec or edgecolors color or list of colors or 'face'
facecolor or facecolors or fc color or list of colors
figure Figure
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or dashes or linestyles or ls str or tuple or list thereof
linewidth or linewidths or lw float or list of floats
norm Normalize or None
offset_transform Transform
offsets (N, 2) or (2,) array-like
path_effects AbstractPathEffect
paths list of array-like
picker None or bool or float or callable
pickradius float
rasterized bool
sizes ndarray or None
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
urls list of str or None
verts list of array-like
verts_and_codes unknown
visible bool
zorder float See also Axes.quiverkey
Add a key to a quiver plot. draw(renderer)[source]
Draw the Artist (and its children) using the given renderer. This has no effect if the artist is not visible (Artist.get_visible returns False). Parameters
rendererRendererBase subclass.
Notes This method is overridden in the Artist subclasses.
get_datalim(transData)[source]
quiver_doc="\nPlot a 2D field of arrows.\n\nCall signature::\n\n quiver([X, Y], U, V, [C], **kw)\n\n*X*, *Y* define the arrow locations, *U*, *V* define the arrow directions, and\n*C* optionally sets the color.\n\nEach arrow is internally represented by a filled polygon with a default edge\nlinewidth of 0. As a result, an arrow is rather a filled area, not a line with\na head, and `.PolyCollection` properties like *linewidth*, *linestyle*,\n*facecolor*, etc. act accordingly.\n\n**Arrow size**\n\nThe default settings auto-scales the length of the arrows to a reasonable size.\nTo change this behavior see the *scale* and *scale_units* parameters.\n\n**Arrow shape**\n\nThe defaults give a slightly swept-back arrow; to make the head a\ntriangle, make *headaxislength* the same as *headlength*. To make the\narrow more pointed, reduce *headwidth* or increase *headlength* and\n*headaxislength*. To make the head smaller relative to the shaft,\nscale down all the head parameters. You will probably do best to leave\nminshaft alone.\n\n**Arrow outline**\n\n*linewidths* and *edgecolors* can be used to customize the arrow\noutlines.\n\nParameters\n----------\nX, Y : 1D or 2D array-like, optional\n The x and y coordinates of the arrow locations.\n\n If not given, they will be generated as a uniform integer meshgrid based\n on the dimensions of *U* and *V*.\n\n If *X* and *Y* are 1D but *U*, *V* are 2D, *X*, *Y* are expanded to 2D\n using ``X, Y = np.meshgrid(X, Y)``. In this case ``len(X)`` and ``len(Y)``\n must match the column and row dimensions of *U* and *V*.\n\nU, V : 1D or 2D array-like\n The x and y direction components of the arrow vectors.\n\n They must have the same number of elements, matching the number of arrow\n locations. *U* and *V* may be masked. Only locations unmasked in\n *U*, *V*, and *C* will be drawn.\n\nC : 1D or 2D array-like, optional\n Numeric data that defines the arrow colors by colormapping via *norm* and\n *cmap*.\n\n This does not support explicit colors. If you want to set colors directly,\n use *color* instead. The size of *C* must match the number of arrow\n locations.\n\nunits : {'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, default: 'width'\n The arrow dimensions (except for *length*) are measured in multiples of\n this unit.\n\n The following values are supported:\n\n - 'width', 'height': The width or height of the axis.\n - 'dots', 'inches': Pixels or inches based on the figure dpi.\n - 'x', 'y', 'xy': *X*, *Y* or :math:`\\sqrt{X^2 + Y^2}` in data units.\n\n The arrows scale differently depending on the units. For\n 'x' or 'y', the arrows get larger as one zooms in; for other\n units, the arrow size is independent of the zoom state. For\n 'width or 'height', the arrow size increases with the width and\n height of the axes, respectively, when the window is resized;\n for 'dots' or 'inches', resizing does not change the arrows.\n\nangles : {'uv', 'xy'} or array-like, default: 'uv'\n Method for determining the angle of the arrows.\n\n - 'uv': The arrow axis aspect ratio is 1 so that\n if *U* == *V* the orientation of the arrow on the plot is 45 degrees\n counter-clockwise from the horizontal axis (positive to the right).\n\n Use this if the arrows symbolize a quantity that is not based on\n *X*, *Y* data coordinates.\n\n - 'xy': Arrows point from (x, y) to (x+u, y+v).\n Use this for plotting a gradient field, for example.\n\n - Alternatively, arbitrary angles may be specified explicitly as an array\n of values in degrees, counter-clockwise from the horizontal axis.\n\n In this case *U*, *V* is only used to determine the length of the\n arrows.\n\n Note: inverting a data axis will correspondingly invert the\n arrows only with ``angles='xy'``.\n\nscale : float, optional\n Number of data units per arrow length unit, e.g., m/s per plot width; a\n smaller scale parameter makes the arrow longer. Default is *None*.\n\n If *None*, a simple autoscaling algorithm is used, based on the average\n vector length and the number of vectors. The arrow length unit is given by\n the *scale_units* parameter.\n\nscale_units : {'width', 'height', 'dots', 'inches', 'x', 'y', 'xy'}, optional\n If the *scale* kwarg is *None*, the arrow length unit. Default is *None*.\n\n e.g. *scale_units* is 'inches', *scale* is 2.0, and ``(u, v) = (1, 0)``,\n then the vector will be 0.5 inches long.\n\n If *scale_units* is 'width' or 'height', then the vector will be half the\n width/height of the axes.\n\n If *scale_units* is 'x' then the vector will be 0.5 x-axis\n units. To plot vectors in the x-y plane, with u and v having\n the same units as x and y, use\n ``angles='xy', scale_units='xy', scale=1``.\n\nwidth : float, optional\n Shaft width in arrow units; default depends on choice of units,\n above, and number of vectors; a typical starting value is about\n 0.005 times the width of the plot.\n\nheadwidth : float, default: 3\n Head width as multiple of shaft width.\n\nheadlength : float, default: 5\n Head length as multiple of shaft width.\n\nheadaxislength : float, default: 4.5\n Head length at shaft intersection.\n\nminshaft : float, default: 1\n Length below which arrow scales, in units of head length. Do not\n set this to less than 1, or small arrows will look terrible!\n\nminlength : float, default: 1\n Minimum length as a multiple of shaft width; if an arrow length\n is less than this, plot a dot (hexagon) of this diameter instead.\n\npivot : {'tail', 'mid', 'middle', 'tip'}, default: 'tail'\n The part of the arrow that is anchored to the *X*, *Y* grid. The arrow\n rotates about this point.\n\n 'mid' is a synonym for 'middle'.\n\ncolor : color or color sequence, optional\n Explicit color(s) for the arrows. If *C* has been set, *color* has no\n effect.\n\n This is a synonym for the `.PolyCollection` *facecolor* parameter.\n\nOther Parameters\n----------------\ndata : indexable object, optional\n DATA_PARAMETER_PLACEHOLDER\n\n**kwargs : `~matplotlib.collections.PolyCollection` properties, optional\n All other keyword arguments are passed on to `.PolyCollection`:\n\n \n .. table::\n :class: property-table\n\n ================================================================================================= =====================================================================================================\n Property Description \n ================================================================================================= =====================================================================================================\n :meth:`agg_filter <matplotlib.artist.Artist.set_agg_filter>` a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array\n :meth:`alpha <matplotlib.collections.Collection.set_alpha>` array-like or scalar or None \n :meth:`animated <matplotlib.artist.Artist.set_animated>` bool \n :meth:`antialiased <matplotlib.collections.Collection.set_antialiased>` or aa or antialiaseds bool or list of bools \n :meth:`array <matplotlib.cm.ScalarMappable.set_array>` array-like or None \n :meth:`capstyle <matplotlib.collections.Collection.set_capstyle>` `.CapStyle` or {'butt', 'projecting', 'round'} \n :meth:`clim <matplotlib.cm.ScalarMappable.set_clim>` (vmin: float, vmax: float) \n :meth:`clip_box <matplotlib.artist.Artist.set_clip_box>` `.Bbox` \n :meth:`clip_on <matplotlib.artist.Artist.set_clip_on>` bool \n :meth:`clip_path <matplotlib.artist.Artist.set_clip_path>` Patch or (Path, Transform) or None \n :meth:`cmap <matplotlib.cm.ScalarMappable.set_cmap>` `.Colormap` or str or None \n :meth:`color <matplotlib.collections.Collection.set_color>` color or list of rgba tuples \n :meth:`edgecolor <matplotlib.collections.Collection.set_edgecolor>` or ec or edgecolors color or list of colors or 'face' \n :meth:`facecolor <matplotlib.collections.Collection.set_facecolor>` or facecolors or fc color or list of colors \n :meth:`figure <matplotlib.artist.Artist.set_figure>` `.Figure` \n :meth:`gid <matplotlib.artist.Artist.set_gid>` str \n :meth:`hatch <matplotlib.collections.Collection.set_hatch>` {'/', '\\\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'} \n :meth:`in_layout <matplotlib.artist.Artist.set_in_layout>` bool \n :meth:`joinstyle <matplotlib.collections.Collection.set_joinstyle>` `.JoinStyle` or {'miter', 'round', 'bevel'} \n :meth:`label <matplotlib.artist.Artist.set_label>` object \n :meth:`linestyle <matplotlib.collections.Collection.set_linestyle>` or dashes or linestyles or ls str or tuple or list thereof \n :meth:`linewidth <matplotlib.collections.Collection.set_linewidth>` or linewidths or lw float or list of floats \n :meth:`norm <matplotlib.cm.ScalarMappable.set_norm>` `.Normalize` or None \n :meth:`offset_transform <matplotlib.collections.Collection.set_offset_transform>` `.Transform` \n :meth:`offsets <matplotlib.collections.Collection.set_offsets>` (N, 2) or (2,) array-like \n :meth:`path_effects <matplotlib.artist.Artist.set_path_effects>` `.AbstractPathEffect` \n :meth:`paths <matplotlib.collections.PolyCollection.set_verts>` list of array-like \n :meth:`picker <matplotlib.artist.Artist.set_picker>` None or bool or float or callable \n :meth:`pickradius <matplotlib.collections.Collection.set_pickradius>` float \n :meth:`rasterized <matplotlib.artist.Artist.set_rasterized>` bool \n :meth:`sizes <matplotlib.collections._CollectionWithSizes.set_sizes>` ndarray or None \n :meth:`sketch_params <matplotlib.artist.Artist.set_sketch_params>` (scale: float, length: float, randomness: float) \n :meth:`snap <matplotlib.artist.Artist.set_snap>` bool or None \n :meth:`transform <matplotlib.artist.Artist.set_transform>` `.Transform` \n :meth:`url <matplotlib.artist.Artist.set_url>` str \n :meth:`urls <matplotlib.collections.Collection.set_urls>` list of str or None \n :meth:`verts <matplotlib.collections.PolyCollection.set_verts>` list of array-like \n :meth:`verts_and_codes <matplotlib.collections.PolyCollection.set_verts_and_codes>` unknown \n :meth:`visible <matplotlib.artist.Artist.set_visible>` bool \n :meth:`zorder <matplotlib.artist.Artist.set_zorder>` float \n ================================================================================================= =====================================================================================================\n\n\nReturns\n-------\n`~matplotlib.quiver.Quiver`\n\nSee Also\n--------\n.Axes.quiverkey : Add a key to a quiver plot.\n"
remove()[source]
Remove the artist from the figure if possible. The effect will not be visible until the figure is redrawn, e.g., with FigureCanvasBase.draw_idle. Call relim to update the axes limits if desired. Note: relim will not see collections even if the collection was added to the axes with autolim = True. Note: there is no support for removing the artist's legend entry.
set(*, UVC=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, array=<UNSET>, capstyle=<UNSET>, clim=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, cmap=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, norm=<UNSET>, offset_transform=<UNSET>, offsets=<UNSET>, path_effects=<UNSET>, paths=<UNSET>, picker=<UNSET>, pickradius=<UNSET>, rasterized=<UNSET>, sizes=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, urls=<UNSET>, verts=<UNSET>, verts_and_codes=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
UVC unknown
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha array-like or scalar or None
animated bool
antialiased or aa or antialiaseds bool or list of bools
array array-like or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clim (vmin: float, vmax: float)
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
cmap Colormap or str or None
color color or list of rgba tuples
edgecolor or ec or edgecolors color or list of colors or 'face'
facecolor or facecolors or fc color or list of colors
figure Figure
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or dashes or linestyles or ls str or tuple or list thereof
linewidth or linewidths or lw float or list of floats
norm Normalize or None
offset_transform Transform
offsets (N, 2) or (2,) array-like
path_effects AbstractPathEffect
paths list of array-like
picker None or bool or float or callable
pickradius float
rasterized bool
sizes ndarray or None
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
urls list of str or None
verts list of array-like
verts_and_codes unknown
visible bool
zorder float
set_UVC(U, V, C=None)[source]
Examples using matplotlib.quiver.Quiver
Advanced quiver and quiverkey functions
Quiver Simple Demo | |
doc_23798 | See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveGatherV2
tf.raw_ops.CollectiveGatherV2(
input, group_size, group_key, instance_key, communication_hint='auto',
timeout_seconds=0, name=None
)
Args
input A Tensor. Must be one of the following types: float32, half, float64, int32, int64.
group_size A Tensor of type int32.
group_key A Tensor of type int32.
instance_key A Tensor of type int32.
communication_hint An optional string. Defaults to "auto".
timeout_seconds An optional float. Defaults to 0.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_23799 | '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 |
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