_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_1500 |
Return a data hash of the Index/Series/DataFrame. Parameters
obj:Index, Series, or DataFrame
index:bool, default True
Include the index in the hash (if Series/DataFrame).
encoding:str, default ‘utf8’
Encoding for data & key when strings.
hash_key:str, default _default_hash_key
Hash_key for string key to encode.
categorize:bool, default True
Whether to first categorize object arrays before hashing. This is more efficient when the array contains duplicate values. Returns
Series of uint64, same length as the object | |
doc_1501 |
Force rasterized (bitmap) drawing for vector graphics output. Rasterized drawing is not supported by all artists. If you try to enable this on an artist that does not support it, the command has no effect and a warning will be issued. This setting is ignored for pixel-based output. See also Rasterization for vector graphics. Parameters
rasterizedbool | |
doc_1502 | Return a file descriptor referring to the process pid. This descriptor can be used to perform process management without races and signals. The flags argument is provided for future extensions; no flag values are currently defined. See the pidfd_open(2) man page for more details. Availability: Linux 5.3+ New in version 3.9. | |
doc_1503 |
Mixin class for all regression estimators in scikit-learn. Methods
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
\(R^2\) of self.predict(X) wrt. y. Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). | |
doc_1504 |
Bases: skimage.viewer.plugins.base.Plugin Plugin for ImageViewer that contains a plot canvas. Base class for plugins that contain a Matplotlib plot canvas, which can, for example, display an image histogram. See base Plugin class for additional details.
__init__(image_filter=None, height=150, width=400, **kwargs) [source]
Initialize self. See help(type(self)) for accurate signature.
add_plot() [source]
add_tool(tool) [source]
attach(image_viewer) [source]
Attach the plugin to an ImageViewer. Note that the ImageViewer will automatically call this method when the plugin is added to the ImageViewer. For example: viewer += Plugin(...)
Also note that attach automatically calls the filter function so that the image matches the filtered value specified by attached widgets.
redraw() [source]
Redraw plot.
remove_tool(tool) [source] | |
doc_1505 |
Alias for get_linewidth. | |
doc_1506 | Return True if the symbol is nonlocal. | |
doc_1507 | Returns a list of subcommand names in the order they should appear. | |
doc_1508 | The name of the header (the portion of the field before the ‘:’). This is exactly the value passed in the header_factory call for name; that is, case is preserved. | |
doc_1509 |
Bases: mpl_toolkits.axisartist.axislines.Axes Build an Axes in a figure. Parameters
figFigure
The Axes is built in the Figure fig.
rect[left, bottom, width, height]
The Axes is built in the rectangle rect. rect is in Figure coordinates.
sharex, shareyAxes, optional
The x or y axis is shared with the x or y axis in the input Axes.
frameonbool, default: True
Whether the Axes frame is visible.
box_aspectfloat, optional
Set a fixed aspect for the Axes box, i.e. the ratio of height to width. See set_box_aspect for details. **kwargs
Other optional keyword arguments:
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float Returns
Axes
The new Axes object. set(*, adjustable=<UNSET>, agg_filter=<UNSET>, alpha=<UNSET>, anchor=<UNSET>, animated=<UNSET>, aspect=<UNSET>, autoscale_on=<UNSET>, autoscalex_on=<UNSET>, autoscaley_on=<UNSET>, axes_locator=<UNSET>, axisbelow=<UNSET>, box_aspect=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, facecolor=<UNSET>, frame_on=<UNSET>, gid=<UNSET>, in_layout=<UNSET>, label=<UNSET>, navigate=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, position=<UNSET>, prop_cycle=<UNSET>, rasterization_zorder=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, title=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, xbound=<UNSET>, xlabel=<UNSET>, xlim=<UNSET>, xmargin=<UNSET>, xscale=<UNSET>, xticklabels=<UNSET>, xticks=<UNSET>, ybound=<UNSET>, ylabel=<UNSET>, ylim=<UNSET>, ymargin=<UNSET>, yscale=<UNSET>, yticklabels=<UNSET>, yticks=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float | |
doc_1510 | A single context manager in a with block. context_expr is the context manager, often a Call node. optional_vars is a Name, Tuple or List for the as foo part, or None if that isn’t used. >>> print(ast.dump(ast.parse("""\
... with a as b, c as d:
... something(b, d)
... """), indent=4))
Module(
body=[
With(
items=[
withitem(
context_expr=Name(id='a', ctx=Load()),
optional_vars=Name(id='b', ctx=Store())),
withitem(
context_expr=Name(id='c', ctx=Load()),
optional_vars=Name(id='d', ctx=Store()))],
body=[
Expr(
value=Call(
func=Name(id='something', ctx=Load()),
args=[
Name(id='b', ctx=Load()),
Name(id='d', ctx=Load())],
keywords=[]))])],
type_ignores=[]) | |
doc_1511 | Writes all values from the tensor src into self at the indices specified in the index tensor. For each value in src, its output index is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim. For a 3-D tensor, self is updated as: self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2
This is the reverse operation of the manner described in gather(). self, index and src (if it is a Tensor) should all have the same number of dimensions. It is also required that index.size(d) <= src.size(d) for all dimensions d, and that index.size(d) <= self.size(d) for all dimensions d != dim. Note that index and src do not broadcast. Moreover, as for gather(), the values of index must be between 0 and self.size(dim) - 1 inclusive. Warning When indices are not unique, the behavior is non-deterministic (one of the values from src will be picked arbitrarily) and the gradient will be incorrect (it will be propagated to all locations in the source that correspond to the same index)! Note The backward pass is implemented only for src.shape == index.shape. Additionally accepts an optional reduce argument that allows specification of an optional reduction operation, which is applied to all values in the tensor src into self at the indicies specified in the index. For each value in src, the reduction operation is applied to an index in self which is specified by its index in src for dimension != dim and by the corresponding value in index for dimension = dim. Given a 3-D tensor and reduction using the multiplication operation, self is updated as: self[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0
self[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1
self[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2
Reducing with the addition operation is the same as using scatter_add_(). Parameters
dim (int) – the axis along which to index
index (LongTensor) – the indices of elements to scatter, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged.
src (Tensor or float) – the source element(s) to scatter.
reduce (str, optional) – reduction operation to apply, can be either 'add' or 'multiply'. Example: >>> src = torch.arange(1, 11).reshape((2, 5))
>>> src
tensor([[ 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10]])
>>> index = torch.tensor([[0, 1, 2, 0]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_(0, index, src)
tensor([[1, 0, 0, 4, 0],
[0, 2, 0, 0, 0],
[0, 0, 3, 0, 0]])
>>> index = torch.tensor([[0, 1, 2], [0, 1, 4]])
>>> torch.zeros(3, 5, dtype=src.dtype).scatter_(1, index, src)
tensor([[1, 2, 3, 0, 0],
[6, 7, 0, 0, 8],
[0, 0, 0, 0, 0]])
>>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]),
... 1.23, reduce='multiply')
tensor([[2.0000, 2.0000, 2.4600, 2.0000],
[2.0000, 2.0000, 2.0000, 2.4600]])
>>> torch.full((2, 4), 2.).scatter_(1, torch.tensor([[2], [3]]),
... 1.23, reduce='add')
tensor([[2.0000, 2.0000, 3.2300, 2.0000],
[2.0000, 2.0000, 2.0000, 3.2300]]) | |
doc_1512 |
Set multiple properties at once. Supported properties are
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float | |
doc_1513 | Return a list of namespaces bound to this name. | |
doc_1514 | The type of bound methods of some built-in data types and base classes. For example it is the type of object().__str__. New in version 3.7. | |
doc_1515 | Warns about common errors in the WSGI and HTTP behavior of the server and wrapped application. Some of the issues it checks are: invalid status codes non-bytes sent to the WSGI server strings returned from the WSGI application non-empty conditional responses unquoted etags relative URLs in the Location header unsafe calls to wsgi.input unclosed iterators Error information is emitted using the warnings module. Parameters
app (WSGIApplication) – The WSGI application to wrap. Return type
None from werkzeug.middleware.lint import LintMiddleware
app = LintMiddleware(app) | |
doc_1516 | The arguments are an object and a string. The result is True if the string is the name of one of the object’s attributes, False if not. (This is implemented by calling getattr(object, name) and seeing whether it raises an AttributeError or not.) | |
doc_1517 | Decodes obj using the codec registered for encoding. Errors may be given to set the desired error handling scheme. The default error handler is 'strict' meaning that decoding errors raise ValueError (or a more codec specific subclass, such as UnicodeDecodeError). Refer to Codec Base Classes for more information on codec error handling. | |
doc_1518 | New in Django 4.0. Set search_help_text to specify a descriptive text for the search box which will be displayed below it. | |
doc_1519 |
Aggregate using one or more operations over the specified axis. Parameters
func:function, str, list or dict
Function to use for aggregating the data. If a function, must either work when passed a Series/Dataframe or when passed to Series/Dataframe.apply. Accepted combinations are: function string function name list of functions and/or function names, e.g. [np.sum, 'mean'] dict of axis labels -> functions, function names or list of such. *args
Positional arguments to pass to func. **kwargs
Keyword arguments to pass to func. Returns
scalar, Series or DataFrame
The return can be: scalar : when Series.agg is called with single function Series : when DataFrame.agg is called with a single function DataFrame : when DataFrame.agg is called with several functions Return scalar, Series or DataFrame. See also pandas.DataFrame.aggregate
Similar DataFrame method. pandas.Series.aggregate
Similar Series method. Notes agg is an alias for aggregate. Use the alias. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details. A passed user-defined-function will be passed a Series for evaluation. Examples
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
>>> df
A B C
0 1 4 7
1 2 5 8
2 3 6 9
>>> df.ewm(alpha=0.5).mean()
A B C
0 1.000000 4.000000 7.000000
1 1.666667 4.666667 7.666667
2 2.428571 5.428571 8.428571 | |
doc_1520 |
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_1521 | Flushes the builder buffers, and returns the toplevel document element. Returns an Element instance. | |
doc_1522 | The lowercase letters 'abcdefghijklmnopqrstuvwxyz'. This value is not locale-dependent and will not change. | |
doc_1523 |
Return the YAxis instance. The use of this function is discouraged. You should instead directly access the attribute ax.yaxis. | |
doc_1524 |
Attributes
allocator_type string allocator_type
allow_growth bool allow_growth
deferred_deletion_bytes int64 deferred_deletion_bytes
experimental Experimental experimental
force_gpu_compatible bool force_gpu_compatible
per_process_gpu_memory_fraction double per_process_gpu_memory_fraction
polling_active_delay_usecs int32 polling_active_delay_usecs
polling_inactive_delay_msecs int32 polling_inactive_delay_msecs
visible_device_list string visible_device_list Child Classes class Experimental | |
doc_1525 |
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. | |
doc_1526 | A lazier version of map(). The chunksize argument is the same as the one used by the map() method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of 1. Also if chunksize is 1 then the next() method of the iterator returned by the imap() method has an optional timeout parameter: next(timeout) will raise multiprocessing.TimeoutError if the result cannot be returned within timeout seconds. | |
doc_1527 | mmap.MADV_RANDOM
mmap.MADV_SEQUENTIAL
mmap.MADV_WILLNEED
mmap.MADV_DONTNEED
mmap.MADV_REMOVE
mmap.MADV_DONTFORK
mmap.MADV_DOFORK
mmap.MADV_HWPOISON
mmap.MADV_MERGEABLE
mmap.MADV_UNMERGEABLE
mmap.MADV_SOFT_OFFLINE
mmap.MADV_HUGEPAGE
mmap.MADV_NOHUGEPAGE
mmap.MADV_DONTDUMP
mmap.MADV_DODUMP
mmap.MADV_FREE
mmap.MADV_NOSYNC
mmap.MADV_AUTOSYNC
mmap.MADV_NOCORE
mmap.MADV_CORE
mmap.MADV_PROTECT
These options can be passed to mmap.madvise(). Not every option will be present on every system. Availability: Systems with the madvise() system call. New in version 3.8. | |
doc_1528 | Execute command args with messages identified by UID, rather than message number. Returns response appropriate to command. At least one argument must be supplied; if none are provided, the server will return an error and an exception will be raised. | |
doc_1529 |
Set whether the z-axis is autoscaled on the next draw or call to Axes.autoscale_view. Parameters
bbool | |
doc_1530 | Allows specifying that the module named oldname is in fact the package named newname. | |
doc_1531 |
Fit a Generalized Linear Model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
selfreturns an instance of self. | |
doc_1532 | tf.compat.v1.Print(
input_, data, message=None, first_n=None, summarize=None, name=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2018-08-20. Instructions for updating: Use tf.print instead of tf.Print. Note that tf.print returns a no-output operator that directly prints the output. Outside of defuns or eager mode, this operator will not be executed unless it is directly specified in session.run or used as a control dependency for other operators. This is only a concern in graph mode. Below is an example of how to ensure tf.print executes in graph mode: This is an identity op (behaves like tf.identity) with the side effect of printing data when evaluating.
Note: This op prints to the standard error. It is not currently compatible with jupyter notebook (printing to the notebook server's output, not into the notebook).
Args
input_ A tensor passed through this op.
data A list of tensors to print out when op is evaluated.
message A string, prefix of the error message.
first_n Only log first_n number of times. Negative numbers log always; this is the default.
summarize Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type and contents as input_. sess = tf.compat.v1.Session()
with sess.as_default():
tensor = tf.range(10)
print_op = tf.print(tensor)
with tf.control_dependencies([print_op]):
out = tf.add(tensor, tensor)
sess.run(out) | |
doc_1533 |
Return the cap height as float. | |
doc_1534 | See Migration guide for more details. tf.compat.v1.keras.constraints.get
tf.keras.constraints.get(
identifier
) | |
doc_1535 |
Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2], is often called the bell curve because of its characteristic shape (see the example below). The normal distributions occurs often in nature. For example, it describes the commonly occurring distribution of samples influenced by a large number of tiny, random disturbances, each with its own unique distribution [2]. Note New code should use the normal method of a default_rng() instance instead; please see the Quick Start. Parameters
locfloat or array_like of floats
Mean (“centre”) of the distribution.
scalefloat or array_like of floats
Standard deviation (spread or “width”) of the distribution. Must be non-negative.
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 loc and scale are both scalars. Otherwise, np.broadcast(loc, scale).size samples are drawn. Returns
outndarray or scalar
Drawn samples from the parameterized normal distribution. See also scipy.stats.norm
probability density function, distribution or cumulative density function, etc. Generator.normal
which should be used for new code. Notes The probability density for the Gaussian distribution is \[p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }} e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },\] where \(\mu\) is the mean and \(\sigma\) the standard deviation. The square of the standard deviation, \(\sigma^2\), is called the variance. The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at \(x + \sigma\) and \(x - \sigma\) [2]). This implies that normal is more likely to return samples lying close to the mean, rather than those far away. References 1
Wikipedia, “Normal distribution”, https://en.wikipedia.org/wiki/Normal_distribution
2(1,2,3)
P. R. Peebles Jr., “Central Limit Theorem” in “Probability, Random Variables and Random Signal Principles”, 4th ed., 2001, pp. 51, 51, 125. Examples Draw samples from the distribution: >>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)
Verify the mean and the variance: >>> abs(mu - np.mean(s))
0.0 # may vary
>>> abs(sigma - np.std(s, ddof=1))
0.1 # may vary
Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, density=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
... linewidth=2, color='r')
>>> plt.show()
Two-by-four array of samples from N(3, 6.25): >>> np.random.normal(3, 2.5, size=(2, 4))
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random | |
doc_1536 |
Alias for get_edgecolor. | |
doc_1537 |
Draw samples from the standard exponential distribution. standard_exponential is identical to the exponential distribution with a scale parameter of 1. Note New code should use the standard_exponential method of a default_rng() instance instead; please see the Quick Start. Parameters
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. Default is None, in which case a single value is returned. Returns
outfloat or ndarray
Drawn samples. See also Generator.standard_exponential
which should be used for new code. Examples Output a 3x8000 array: >>> n = np.random.standard_exponential((3, 8000)) | |
doc_1538 |
Replaces specified elements of an array with given values. The indexing works on the flattened target array. put is roughly equivalent to: a.flat[ind] = v
Parameters
andarray
Target array.
indarray_like
Target indices, interpreted as integers.
varray_like
Values to place in a at target indices. If v is shorter than ind it will be repeated as necessary.
mode{‘raise’, ‘wrap’, ‘clip’}, optional
Specifies how out-of-bounds indices will behave. ‘raise’ – raise an error (default) ‘wrap’ – wrap around ‘clip’ – clip to the range ‘clip’ mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. In ‘raise’ mode, if an exception occurs the target array may still be modified. See also
putmask, place
put_along_axis
Put elements by matching the array and the index arrays Examples >>> a = np.arange(5)
>>> np.put(a, [0, 2], [-44, -55])
>>> a
array([-44, 1, -55, 3, 4])
>>> a = np.arange(5)
>>> np.put(a, 22, -5, mode='clip')
>>> a
array([ 0, 1, 2, 3, -5]) | |
doc_1539 | See torch.logaddexp2() | |
doc_1540 | tf.experimental.numpy.greater_equal(
x1, x2
)
Unsupported arguments: out, where, casting, order, dtype, subok, signature, extobj. See the NumPy documentation for numpy.greater_equal. | |
doc_1541 |
Spectral embedding for non-linear dimensionality reduction. Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point. Note : Laplacian Eigenmaps is the actual algorithm implemented here. Read more in the User Guide. Parameters
n_componentsint, default=2
The dimension of the projected subspace.
affinity{‘nearest_neighbors’, ‘rbf’, ‘precomputed’, ‘precomputed_nearest_neighbors’} or callable, default=’nearest_neighbors’
How to construct the affinity matrix.
‘nearest_neighbors’ : construct the affinity matrix by computing a graph of nearest neighbors. ‘rbf’ : construct the affinity matrix by computing 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. callable : use passed in function as affinity the function takes in data matrix (n_samples, n_features) and return affinity matrix (n_samples, n_samples).
gammafloat, default=None
Kernel coefficient for rbf kernel. If None, gamma will be set to 1/n_features.
random_stateint, RandomState instance or None, default=None
Determines the random number generator used for the initialization of the lobpcg eigenvectors when solver == ‘amg’. Pass an int for reproducible results across multiple function calls. See :term: Glossary <random_state>.
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. If None, then 'arpack' is used.
n_neighborsint, default=None
Number of nearest neighbors for nearest_neighbors graph building. If None, n_neighbors will be set to max(n_samples/10, 1).
n_jobsint, default=None
The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. Attributes
embedding_ndarray of shape (n_samples, n_components)
Spectral embedding of the training matrix.
affinity_matrix_ndarray of shape (n_samples, n_samples)
Affinity_matrix constructed from samples or precomputed.
n_neighbors_int
Number of nearest neighbors effectively used. References A Tutorial on Spectral Clustering, 2007 Ulrike von Luxburg http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
On Spectral Clustering: Analysis and an algorithm, 2001 Andrew Y. Ng, Michael I. Jordan, Yair Weiss http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100
Normalized cuts and image segmentation, 2000 Jianbo Shi, Jitendra Malik http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
Examples >>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import SpectralEmbedding
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = SpectralEmbedding(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)
Methods
fit(X[, y]) Fit the model from data in X.
fit_transform(X[, y]) Fit the model from data in X and transform X.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
fit(X, y=None) [source]
Fit the model from data in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix}, shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.
yIgnored
Returns
selfobject
Returns the instance itself.
fit_transform(X, y=None) [source]
Fit the model from data in X and transform X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix} of shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.
yIgnored
Returns
X_newarray-like of shape (n_samples, n_components)
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_1542 | Call os.unlink() on filename. On Windows platforms, this is wrapped with a wait loop that checks for the existence fo the file. | |
doc_1543 | play the full aliens example aliens.main() -> None This started off as a port of the SDL demonstration, Aliens. Now it has evolved into something sort of resembling fun. This demonstrates a lot of different uses of sprites and optimized blitting. Also transparency, colorkeys, fonts, sound, music, joystick, and more. (PS, my high score is 117! goodluck) | |
doc_1544 | stat.FILE_ATTRIBUTE_COMPRESSED
stat.FILE_ATTRIBUTE_DEVICE
stat.FILE_ATTRIBUTE_DIRECTORY
stat.FILE_ATTRIBUTE_ENCRYPTED
stat.FILE_ATTRIBUTE_HIDDEN
stat.FILE_ATTRIBUTE_INTEGRITY_STREAM
stat.FILE_ATTRIBUTE_NORMAL
stat.FILE_ATTRIBUTE_NOT_CONTENT_INDEXED
stat.FILE_ATTRIBUTE_NO_SCRUB_DATA
stat.FILE_ATTRIBUTE_OFFLINE
stat.FILE_ATTRIBUTE_READONLY
stat.FILE_ATTRIBUTE_REPARSE_POINT
stat.FILE_ATTRIBUTE_SPARSE_FILE
stat.FILE_ATTRIBUTE_SYSTEM
stat.FILE_ATTRIBUTE_TEMPORARY
stat.FILE_ATTRIBUTE_VIRTUAL
New in version 3.5. | |
doc_1545 | Write the contents of buffers to file descriptor fd. buffers must be a sequence of bytes-like objects. Buffers are processed in array order. Entire contents of the first buffer is written before proceeding to the second, and so on. Returns the total number of bytes actually written. The operating system may set a limit (sysconf() value 'SC_IOV_MAX') on the number of buffers that can be used. Availability: Unix. New in version 3.3. | |
doc_1546 | Finish the compression process, returning a bytes object containing any data stored in the compressor’s internal buffers. The compressor cannot be used after this method has been called. | |
doc_1547 | tf.compat.v1.logging.warning(
msg, *args, **kwargs
) | |
doc_1548 |
Process a pick event. Each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set. See also
set_picker, get_picker, pickable | |
doc_1549 | This mixin, just like the permission_required decorator, checks whether the user accessing a view has all given permissions. You should specify the permission (or an iterable of permissions) using the permission_required parameter: from django.contrib.auth.mixins import PermissionRequiredMixin
class MyView(PermissionRequiredMixin, View):
permission_required = 'polls.add_choice'
# Or multiple of permissions:
permission_required = ('polls.view_choice', 'polls.change_choice')
You can set any of the parameters of AccessMixin to customize the handling of unauthorized users. You may also override these methods:
get_permission_required()
Returns an iterable of permission names used by the mixin. Defaults to the permission_required attribute, converted to a tuple if necessary.
has_permission()
Returns a boolean denoting whether the current user has permission to execute the decorated view. By default, this returns the result of calling has_perms() with the list of permissions returned by get_permission_required(). | |
doc_1550 | Address space of a memory block (int or None). tracemalloc uses the domain 0 to trace memory allocations made by Python. C extensions can use other domains to trace other resources. | |
doc_1551 | See Migration guide for more details. tf.compat.v1.estimator.EvalSpec
tf.estimator.EvalSpec(
input_fn, steps=100, name=None, hooks=None, exporters=None,
start_delay_secs=120, throttle_secs=600
)
EvalSpec combines details of evaluation of the trained model as well as its export. Evaluation consists of computing metrics to judge the performance of the trained model. Export writes out the trained model on to external storage.
Args
input_fn A function that constructs the input data for evaluation. See Premade Estimators for more information. The function should construct and return one of the following: A 'tf.data.Dataset' object: Outputs of Dataset object must be a tuple (features, labels) with same constraints as below. A tuple (features, labels): Where features is a Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor.
steps Int. Positive number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception. See Estimator.evaluate for details.
name String. Name of the evaluation if user needs to run multiple evaluations on different data sets. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
hooks Iterable of tf.train.SessionRunHook objects to run during evaluation.
exporters Iterable of Exporters, or a single one, or None. exporters will be invoked after each evaluation.
start_delay_secs Int. Start evaluating after waiting for this many seconds.
throttle_secs Int. Do not re-evaluate unless the last evaluation was started at least this many seconds ago. Of course, evaluation does not occur if no new checkpoints are available, hence, this is the minimum.
Raises
ValueError If any of the input arguments is invalid.
TypeError If any of the arguments is not of the expected type.
Attributes
input_fn
steps
name
hooks
exporters
start_delay_secs
throttle_secs | |
doc_1552 |
Other Members
ABORTED 10
ALREADY_EXISTS 6
CANCELLED 1
DATA_LOSS 15
DEADLINE_EXCEEDED 4
FAILED_PRECONDITION 9
INTERNAL 13
INVALID_ARGUMENT 3
NOT_FOUND 5
OK 0
OUT_OF_RANGE 11
PERMISSION_DENIED 7
RESOURCE_EXHAUSTED 8
UNAUTHENTICATED 16
UNAVAILABLE 14
UNIMPLEMENTED 12
UNKNOWN 2 | |
doc_1553 |
Expresses to what extent the local structure is retained. The trustworthiness is within [0, 1]. It is defined as \[T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1} \sum_{j \in \mathcal{N}_{i}^{k}} \max(0, (r(i, j) - k))\] where for each sample i, \(\mathcal{N}_{i}^{k}\) are its k nearest neighbors in the output space, and every sample j is its \(r(i, j)\)-th nearest neighbor in the input space. In other words, any unexpected nearest neighbors in the output space are penalised in proportion to their rank in the input space. “Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study” J. Venna, S. Kaski “Learning a Parametric Embedding by Preserving Local Structure” L.J.P. van der Maaten Parameters
Xndarray of shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is ‘precomputed’ X must be a square distance matrix. Otherwise it contains a sample per row.
X_embeddedndarray of shape (n_samples, n_components)
Embedding of the training data in low-dimensional space.
n_neighborsint, default=5
Number of neighbors k that will be considered.
metricstr or callable, default=’euclidean’
Which metric to use for computing pairwise distances between samples from the original input space. If metric is ‘precomputed’, X must be a matrix of pairwise distances or squared distances. Otherwise, see the documentation of argument metric in sklearn.pairwise.pairwise_distances for a list of available metrics. New in version 0.20. Returns
trustworthinessfloat
Trustworthiness of the low-dimensional embedding. | |
doc_1554 | Wraps a callback so that it’s guaranteed to be executed with the script’s application context. If callbacks are registered directly to the app.cli object then they are wrapped with this function by default unless it’s disabled. | |
doc_1555 | Return the password database entry for the given numeric user ID. | |
doc_1556 | Set the debug mode of the event loop. Changed in version 3.7: The new Python Development Mode can now also be used to enable the debug mode. | |
doc_1557 | See Migration guide for more details. tf.compat.v1.raw_ops.Print
tf.raw_ops.Print(
input, data, message='', first_n=-1, summarize=3, name=None
)
Passes input through to output and prints data when evaluating.
Args
input A Tensor. The tensor passed to output
data A list of Tensor objects. A list of tensors to print out when op is evaluated.
message An optional string. Defaults to "". A string, prefix of the error message.
first_n An optional int. Defaults to -1. Only log first_n number of times. -1 disables logging.
summarize An optional int. Defaults to 3. Only print this many entries of each tensor.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_1558 |
Set the font variant. Values are: 'normal' or 'small-caps'. | |
doc_1559 | Add the file name to the archive. name may be any type of file (directory, fifo, symbolic link, etc.). If given, arcname specifies an alternative name for the file in the archive. Directories are added recursively by default. This can be avoided by setting recursive to False. Recursion adds entries in sorted order. If filter is given, it should be a function that takes a TarInfo object argument and returns the changed TarInfo object. If it instead returns None the TarInfo object will be excluded from the archive. See Examples for an example. Changed in version 3.2: Added the filter parameter. Changed in version 3.7: Recursion adds entries in sorted order. | |
doc_1560 |
Transform the given label sets. Parameters
yiterable of iterables
A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated. Returns
y_indicatorarray or CSR matrix, shape (n_samples, n_classes)
A matrix such that y_indicator[i, j] = 1 iff classes_[j] is in y[i], and 0 otherwise. | |
doc_1561 |
Make a horizontal bar plot. A horizontal bar plot is a plot that presents quantitative data with rectangular bars with lengths proportional to the values that they represent. A bar plot shows comparisons among discrete categories. One axis of the plot shows the specific categories being compared, and the other axis represents a measured value. Parameters
x:label or position, optional
Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
y:label or position, optional
Allows plotting of one column versus another. If not specified, all numerical columns are used.
color:str, array-like, or dict, optional
The color for each of the DataFrame’s columns. Possible values are:
A single color string referred to by name, RGB or RGBA code,
for instance ‘red’ or ‘#a98d19’.
A sequence of color strings referred to by name, RGB or RGBA
code, which will be used for each column recursively. For instance [‘green’,’yellow’] each column’s bar will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used.
A dict of the form {column name:color}, so that each column will be
colored accordingly. For example, if your columns are called a and b, then passing {‘a’: ‘green’, ‘b’: ‘red’} will color bars for column a in green and bars for column b in red. New in version 1.1.0. **kwargs
Additional keyword arguments are documented in DataFrame.plot(). Returns
matplotlib.axes.Axes or np.ndarray of them
An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True. See also DataFrame.plot.bar
Vertical bar plot. DataFrame.plot
Make plots of DataFrame using matplotlib. matplotlib.axes.Axes.bar
Plot a vertical bar plot using matplotlib. Examples Basic example
>>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})
>>> ax = df.plot.barh(x='lab', y='val')
Plot a whole DataFrame to a horizontal bar plot
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh()
Plot stacked barh charts for the DataFrame
>>> ax = df.plot.barh(stacked=True)
We can specify colors for each column
>>> ax = df.plot.barh(color={"speed": "red", "lifespan": "green"})
Plot a column of the DataFrame to a horizontal bar plot
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh(y='speed')
Plot DataFrame versus the desired column
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh(x='lifespan') | |
doc_1562 |
Return the alpha value used for blending - not supported on all backends. | |
doc_1563 | class sklearn.linear_model.RANSACRegressor(base_estimator=None, *, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, max_skips=inf, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, loss='absolute_loss', random_state=None) [source]
RANSAC (RANdom SAmple Consensus) algorithm. RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set. Read more in the User Guide. Parameters
base_estimatorobject, default=None
Base estimator object which implements the following methods:
fit(X, y): Fit model to given training data and target values.
score(X, y): Returns the mean accuracy on the given test data, which is used for the stop criterion defined by stop_score. Additionally, the score is used to decide which of two equally large consensus sets is chosen as the better one.
predict(X): Returns predicted values using the linear model, which is used to compute residual error using loss function. If base_estimator is None, then LinearRegression is used for target values of dtype float. Note that the current implementation only supports regression estimators.
min_samplesint (>= 1) or float ([0, 1]), default=None
Minimum number of samples chosen randomly from original data. Treated as an absolute number of samples for min_samples >= 1, treated as a relative number ceil(min_samples * X.shape[0]) for min_samples < 1. This is typically chosen as the minimal number of samples necessary to estimate the given base_estimator. By default a sklearn.linear_model.LinearRegression() estimator is assumed and min_samples is chosen as X.shape[1] + 1.
residual_thresholdfloat, default=None
Maximum residual for a data sample to be classified as an inlier. By default the threshold is chosen as the MAD (median absolute deviation) of the target values y.
is_data_validcallable, default=None
This function is called with the randomly selected data before the model is fitted to it: is_data_valid(X, y). If its return value is False the current randomly chosen sub-sample is skipped.
is_model_validcallable, default=None
This function is called with the estimated model and the randomly selected data: is_model_valid(model, X, y). If its return value is False the current randomly chosen sub-sample is skipped. Rejecting samples with this function is computationally costlier than with is_data_valid. is_model_valid should therefore only be used if the estimated model is needed for making the rejection decision.
max_trialsint, default=100
Maximum number of iterations for random sample selection.
max_skipsint, default=np.inf
Maximum number of iterations that can be skipped due to finding zero inliers or invalid data defined by is_data_valid or invalid models defined by is_model_valid. New in version 0.19.
stop_n_inliersint, default=np.inf
Stop iteration if at least this number of inliers are found.
stop_scorefloat, default=np.inf
Stop iteration if score is greater equal than this threshold.
stop_probabilityfloat in range [0, 1], default=0.99
RANSAC iteration stops if at least one outlier-free set of the training data is sampled in RANSAC. This requires to generate at least N samples (iterations): N >= log(1 - probability) / log(1 - e**m)
where the probability (confidence) is typically set to high value such as 0.99 (the default) and e is the current fraction of inliers w.r.t. the total number of samples.
lossstring, callable, default=’absolute_loss’
String inputs, “absolute_loss” and “squared_loss” are supported which find the absolute loss and squared loss per sample respectively. If loss is a callable, then it should be a function that takes two arrays as inputs, the true and predicted value and returns a 1-D array with the i-th value of the array corresponding to the loss on X[i]. If the loss on a sample is greater than the residual_threshold, then this sample is classified as an outlier. New in version 0.18.
random_stateint, RandomState instance, default=None
The generator used to initialize the centers. Pass an int for reproducible output across multiple function calls. See Glossary. Attributes
estimator_object
Best fitted model (copy of the base_estimator object).
n_trials_int
Number of random selection trials until one of the stop criteria is met. It is always <= max_trials.
inlier_mask_bool array of shape [n_samples]
Boolean mask of inliers classified as True.
n_skips_no_inliers_int
Number of iterations skipped due to finding zero inliers. New in version 0.19.
n_skips_invalid_data_int
Number of iterations skipped due to invalid data defined by is_data_valid. New in version 0.19.
n_skips_invalid_model_int
Number of iterations skipped due to an invalid model defined by is_model_valid. New in version 0.19. References
1
https://en.wikipedia.org/wiki/RANSAC
2
https://www.sri.com/sites/default/files/publications/ransac-publication.pdf
3
http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf Examples >>> from sklearn.linear_model import RANSACRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(
... n_samples=200, n_features=2, noise=4.0, random_state=0)
>>> reg = RANSACRegressor(random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9885...
>>> reg.predict(X[:1,])
array([-31.9417...])
Methods
fit(X, y[, sample_weight]) Fit estimator using RANSAC algorithm.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict using the estimated model.
score(X, y) Returns the score of the prediction.
set_params(**params) Set the parameters of this estimator.
fit(X, y, sample_weight=None) [source]
Fit estimator using RANSAC algorithm. Parameters
Xarray-like or sparse matrix, shape [n_samples, n_features]
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it. New in version 0.18. Raises
ValueError
If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples.
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values.
predict(X) [source]
Predict using the estimated model. This is a wrapper for estimator_.predict(X). Parameters
Xnumpy array of shape [n_samples, n_features]
Returns
yarray, shape = [n_samples] or [n_samples, n_targets]
Returns predicted values.
score(X, y) [source]
Returns the score of the prediction. This is a wrapper for estimator_.score(X, y). Parameters
Xnumpy array or sparse matrix of shape [n_samples, n_features]
Training data.
yarray, shape = [n_samples] or [n_samples, n_targets]
Target values. Returns
zfloat
Score of the prediction.
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance.
Examples using sklearn.linear_model.RANSACRegressor
Robust linear model estimation using RANSAC
Theil-Sen Regression
Robust linear estimator fitting | |
doc_1564 |
Draw a filled black rectangle from (x1, y1) to (x2, y2). | |
doc_1565 |
DateOffset increments between the last business day of the month. Examples
>>> from pandas.tseries.offsets import BMonthEnd
>>> ts = pd.Timestamp('2020-05-24 05:01:15')
>>> ts + BMonthEnd()
Timestamp('2020-05-29 05:01:15')
>>> ts + BMonthEnd(2)
Timestamp('2020-06-30 05:01:15')
>>> ts + BMonthEnd(-2)
Timestamp('2020-03-31 05:01:15')
Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset | |
doc_1566 | Loads the Flask app (if not yet loaded) and returns it. Calling this multiple times will just result in the already loaded app to be returned. | |
doc_1567 | Iterate over all rules or the rules of an endpoint. Parameters
endpoint (Optional[str]) – if provided only the rules for that endpoint are returned. Returns
an iterator Return type
Iterator[werkzeug.routing.Rule] | |
doc_1568 |
Return the sketch parameters for the artist. Returns
tuple or None
A 3-tuple with the following elements:
scale: The amplitude of the wiggle perpendicular to the source line.
length: The length of the wiggle along the line.
randomness: The scale factor by which the length is shrunken or expanded. Returns None if no sketch parameters were set. | |
doc_1569 | tf.compat.v1.nn.sufficient_statistics(
x, axes, shift=None, keep_dims=None, name=None, keepdims=None
)
These sufficient statistics are computed using the one pass algorithm on an input that's optionally shifted. See: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data For example:
t = [[1, 2, 3], [4, 5, 6]]
sufficient_statistics(t, [1])
(<tf.Tensor: shape=(), dtype=int32, numpy=3>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([ 6, 15], dtype=int32)>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([14, 77], dtype=int32)>, None)
sufficient_statistics(t, [-1])
(<tf.Tensor: shape=(), dtype=int32, numpy=3>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([ 6, 15], dtype=int32)>, <tf.Tensor: shape=(2,),
dtype=int32, numpy=array([14, 77], dtype=int32)>, None)
Args
x A Tensor.
axes Array of ints. Axes along which to compute mean and variance. As in Python, the axes can also be negative numbers. A negative axis is interpreted as counting from the end of the rank, i.e., axis + rank(values)-th dimension.
shift A Tensor containing the value by which to shift the data for numerical stability, or None if no shift is to be performed. A shift close to the true mean provides the most numerically stable results.
keep_dims produce statistics with the same dimensionality as the input.
name Name used to scope the operations that compute the sufficient stats.
keepdims Alias for keep_dims.
Returns Four Tensor objects of the same type as x: the count (number of elements to average over). the (possibly shifted) sum of the elements in the array. the (possibly shifted) sum of squares of the elements in the array. the shift by which the mean must be corrected or None if shift is None. | |
doc_1570 | Called when a file has finished uploading. The handler should return an UploadedFile object that will be stored in request.FILES. Handlers may also return None to indicate that the UploadedFile object should come from subsequent upload handlers. | |
doc_1571 |
The number of elements in the gentype. | |
doc_1572 |
Cast a pandas object to a specified dtype dtype. Parameters
dtype:data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.
copy:bool, default True
Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).
errors:{‘raise’, ‘ignore’}, default ‘raise’
Control raising of exceptions on invalid data for provided dtype. raise : allow exceptions to be raised ignore : suppress exceptions. On error return original object. Returns
casted:same type as caller
See also to_datetime
Convert argument to datetime. to_timedelta
Convert argument to timedelta. to_numeric
Convert argument to a numeric type. numpy.ndarray.astype
Cast a numpy array to a specified type. Notes Deprecated since version 1.3.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. Use Series.dt.tz_localize() instead. Examples Create a DataFrame:
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1 int64
col2 int64
dtype: object
Cast all columns to int32:
>>> df.astype('int32').dtypes
col1 int32
col2 int32
dtype: object
Cast col1 to int32 using a dictionary:
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object
Create a series:
>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
dtype: int32
>>> ser.astype('int64')
0 1
1 2
dtype: int64
Convert to categorical type:
>>> ser.astype('category')
0 1
1 2
dtype: category
Categories (2, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
... categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0 1
1 2
dtype: category
Categories (2, int64): [2 < 1]
Note that using copy=False and changing data on a new pandas object may propagate changes:
>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1 # note that s1[0] has changed too
0 10
1 2
dtype: int64
Create a series of dates:
>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0 2020-01-01
1 2020-01-02
2 2020-01-03
dtype: datetime64[ns] | |
doc_1573 |
Execute a call_method node and return the result. Parameters
target (Target) – The call target for this node. See Node for details on semantics
args (Tuple) – Tuple of positional args for this invocation
kwargs (Dict) – Dict of keyword arguments for this invocation Return
Any: The value returned by the method invocation | |
doc_1574 | sklearn.datasets.make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None) [source]
Generate a swiss roll dataset. Read more in the User Guide. Parameters
n_samplesint, default=100
The number of sample points on the S curve.
noisefloat, default=0.0
The standard deviation of the gaussian noise.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary. Returns
Xndarray of shape (n_samples, 3)
The points.
tndarray of shape (n_samples,)
The univariate position of the sample according to the main dimension of the points in the manifold. Notes The algorithm is from Marsland [1]. References
1
S. Marsland, “Machine Learning: An Algorithmic Perspective”, Chapter 10, 2009. http://seat.massey.ac.nz/personal/s.r.marsland/Code/10/lle.py
Examples using sklearn.datasets.make_swiss_roll
Hierarchical clustering: structured vs unstructured ward
Swiss Roll reduction with LLE | |
doc_1575 |
Blocking call to interact with the figure. Wait for user input and return True if a key was pressed, False if a mouse button was pressed and None if no input was given within timeout seconds. Negative values deactivate timeout. | |
doc_1576 | tf.compat.v1.distributions.Distribution(
dtype, reparameterization_type, validate_args, allow_nan_stats, parameters=None,
graph_parents=None, name=None
)
Distribution is a base class for constructing and organizing properties (e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian). Subclassing Subclasses are expected to implement a leading-underscore version of the same-named function. The argument signature should be identical except for the omission of name="...". For example, to enable log_prob(value, name="log_prob") a subclass should implement _log_prob(value). Subclasses can append to public-level docstrings by providing docstrings for their method specializations. For example: @util.AppendDocstring("Some other details.")
def _log_prob(self, value):
...
would add the string "Some other details." to the log_prob function docstring. This is implemented as a simple decorator to avoid python linter complaining about missing Args/Returns/Raises sections in the partial docstrings. Broadcasting, batching, and shapes All distributions support batches of independent distributions of that type. The batch shape is determined by broadcasting together the parameters. The shape of arguments to __init__, cdf, log_cdf, prob, and log_prob reflect this broadcasting, as does the return value of sample and sample_n. sample_n_shape = [n] + batch_shape + event_shape, where sample_n_shape is the shape of the Tensor returned from sample_n, n is the number of samples, batch_shape defines how many independent distributions there are, and event_shape defines the shape of samples from each of those independent distributions. Samples are independent along the batch_shape dimensions, but not necessarily so along the event_shape dimensions (depending on the particulars of the underlying distribution). Using the Uniform distribution as an example: minval = 3.0
maxval = [[4.0, 6.0],
[10.0, 12.0]]
# Broadcasting:
# This instance represents 4 Uniform distributions. Each has a lower bound at
# 3.0 as the `minval` parameter was broadcasted to match `maxval`'s shape.
u = Uniform(minval, maxval)
# `event_shape` is `TensorShape([])`.
event_shape = u.event_shape
# `event_shape_t` is a `Tensor` which will evaluate to [].
event_shape_t = u.event_shape_tensor()
# Sampling returns a sample per distribution. `samples` has shape
# [5, 2, 2], which is [n] + batch_shape + event_shape, where n=5,
# batch_shape=[2, 2], and event_shape=[].
samples = u.sample_n(5)
# The broadcasting holds across methods. Here we use `cdf` as an example. The
# same holds for `log_cdf` and the likelihood functions.
# `cum_prob` has shape [2, 2] as the `value` argument was broadcasted to the
# shape of the `Uniform` instance.
cum_prob_broadcast = u.cdf(4.0)
# `cum_prob`'s shape is [2, 2], one per distribution. No broadcasting
# occurred.
cum_prob_per_dist = u.cdf([[4.0, 5.0],
[6.0, 7.0]])
# INVALID as the `value` argument is not broadcastable to the distribution's
# shape.
cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
Shapes There are three important concepts associated with TensorFlow Distributions shapes: Event shape describes the shape of a single draw from the distribution; it may be dependent across dimensions. For scalar distributions, the event shape is []. For a 5-dimensional MultivariateNormal, the event shape is [5]. Batch shape describes independent, not identically distributed draws, aka a "collection" or "bunch" of distributions. Sample shape describes independent, identically distributed draws of batches from the distribution family. The event shape and the batch shape are properties of a Distribution object, whereas the sample shape is associated with a specific call to sample or log_prob. For detailed usage examples of TensorFlow Distributions shapes, see this tutorial Parameter values leading to undefined statistics or distributions. Some distributions do not have well-defined statistics for all initialization parameter values. For example, the beta distribution is parameterized by positive real numbers concentration1 and concentration0, and does not have well-defined mode if concentration1 < 1 or concentration0 < 1. The user is given the option of raising an exception or returning NaN. a = tf.exp(tf.matmul(logits, weights_a))
b = tf.exp(tf.matmul(logits, weights_b))
# Will raise exception if ANY batch member has a < 1 or b < 1.
dist = distributions.beta(a, b, allow_nan_stats=False)
mode = dist.mode().eval()
# Will return NaN for batch members with either a < 1 or b < 1.
dist = distributions.beta(a, b, allow_nan_stats=True) # Default behavior
mode = dist.mode().eval()
In all cases, an exception is raised if invalid parameters are passed, e.g. # Will raise an exception if any Op is run.
negative_a = -1.0 * a # beta distribution by definition has a > 0.
dist = distributions.beta(negative_a, b, allow_nan_stats=True)
dist.mean().eval()
Args
dtype The type of the event samples. None implies no type-enforcement.
reparameterization_type Instance of ReparameterizationType. If distributions.FULLY_REPARAMETERIZED, this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution. If distributions.NOT_REPARAMETERIZED, then no such reparameterization is available.
validate_args Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs.
allow_nan_stats Python bool, default True. When True, statistics (e.g., mean, mode, variance) use the value "NaN" to indicate the result is undefined. When False, an exception is raised if one or more of the statistic's batch members are undefined.
parameters Python dict of parameters used to instantiate this Distribution.
graph_parents Python list of graph prerequisites of this Distribution.
name Python str name prefixed to Ops created by this class. Default: subclass name.
Raises
ValueError if any member of graph_parents is None or not a Tensor.
Attributes
allow_nan_stats Python bool describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.
batch_shape Shape of a single sample from a single event index as a TensorShape. May be partially defined or unknown. The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.
dtype The DType of Tensors handled by this Distribution.
event_shape Shape of a single sample from a single batch as a TensorShape. May be partially defined or unknown.
name Name prepended to all ops created by this Distribution.
parameters Dictionary of parameters used to instantiate this Distribution.
reparameterization_type Describes how samples from the distribution are reparameterized. Currently this is one of the static instances distributions.FULLY_REPARAMETERIZED or distributions.NOT_REPARAMETERIZED.
validate_args Python bool indicating possibly expensive checks are enabled. Methods batch_shape_tensor View source
batch_shape_tensor(
name='batch_shape_tensor'
)
Shape of a single sample from a single event index as a 1-D Tensor. The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.
Args
name name to give to the op
Returns
batch_shape Tensor. cdf View source
cdf(
value, name='cdf'
)
Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x]
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns
cdf a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. copy View source
copy(
**override_parameters_kwargs
)
Creates a deep copy of the distribution.
Note: the copy distribution may continue to depend on the original initialization arguments.
Args
**override_parameters_kwargs String/value dictionary of initialization arguments to override with new values.
Returns
distribution A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs). covariance View source
covariance(
name='covariance'
)
Covariance. Covariance is (possibly) defined only for non-scalar-event distributions. For example, for a length-k, vector-valued distribution, it is calculated as, Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]
where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E denotes expectation. Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), Covariance shall return a (batch of) matrices under some vectorization of the events, i.e., Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]
where Cov is a (batch of) k' x k' matrices, 0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function mapping indices of this distribution's event dimensions to indices of a length-k' vector.
Args
name Python str prepended to names of ops created by this function.
Returns
covariance Floating-point Tensor with shape [B1, ..., Bn, k', k'] where the first n dimensions are batch coordinates and k' = reduce_prod(self.event_shape). cross_entropy View source
cross_entropy(
other, name='cross_entropy'
)
Computes the (Shannon) cross entropy. Denote this distribution (self) by P and the other distribution by Q. Assuming P, Q are absolutely continuous with respect to one another and permit densities p(x) dr(x) and q(x) dr(x), (Shanon) cross entropy is defined as: H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)
where F denotes the support of the random variable X ~ P.
Args
other tfp.distributions.Distribution instance.
name Python str prepended to names of ops created by this function.
Returns
cross_entropy self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of (Shanon) cross entropy. entropy View source
entropy(
name='entropy'
)
Shannon entropy in nats. event_shape_tensor View source
event_shape_tensor(
name='event_shape_tensor'
)
Shape of a single sample from a single batch as a 1-D int32 Tensor.
Args
name name to give to the op
Returns
event_shape Tensor. is_scalar_batch View source
is_scalar_batch(
name='is_scalar_batch'
)
Indicates that batch_shape == [].
Args
name Python str prepended to names of ops created by this function.
Returns
is_scalar_batch bool scalar Tensor. is_scalar_event View source
is_scalar_event(
name='is_scalar_event'
)
Indicates that event_shape == [].
Args
name Python str prepended to names of ops created by this function.
Returns
is_scalar_event bool scalar Tensor. kl_divergence View source
kl_divergence(
other, name='kl_divergence'
)
Computes the Kullback--Leibler divergence. Denote this distribution (self) by p and the other distribution by q. Assuming p, q are absolutely continuous with respect to reference measure r, the KL divergence is defined as: KL[p, q] = E_p[log(p(X)/q(X))]
= -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
= H[p, q] - H[p]
where F denotes the support of the random variable X ~ p, H[., .] denotes (Shanon) cross entropy, and H[.] denotes (Shanon) entropy.
Args
other tfp.distributions.Distribution instance.
name Python str prepended to names of ops created by this function.
Returns
kl_divergence self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of the Kullback-Leibler divergence. log_cdf View source
log_cdf(
value, name='log_cdf'
)
Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ]
Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns
logcdf a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. log_prob View source
log_prob(
value, name='log_prob'
)
Log probability density/mass function.
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns
log_prob a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. log_survival_function View source
log_survival_function(
value, name='log_survival_function'
)
Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ]
= Log[ 1 - P[X <= x] ]
= Log[ 1 - cdf(x) ]
Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
mean View source
mean(
name='mean'
)
Mean. mode View source
mode(
name='mode'
)
Mode. param_shapes View source
@classmethod
param_shapes(
sample_shape, name='DistributionParamShapes'
)
Shapes of parameters given the desired shape of a call to sample(). This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Subclasses should override class method _param_shapes.
Args
sample_shape Tensor or python list/tuple. Desired shape of a call to sample().
name name to prepend ops with.
Returns dict of parameter name to Tensor shapes.
param_static_shapes View source
@classmethod
param_static_shapes(
sample_shape
)
param_shapes with static (i.e. TensorShape) shapes. This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Assumes that the sample's shape is known statically. Subclasses should override class method _param_shapes to return constant-valued tensors when constant values are fed.
Args
sample_shape TensorShape or python list/tuple. Desired shape of a call to sample().
Returns dict of parameter name to TensorShape.
Raises
ValueError if sample_shape is a TensorShape and is not fully defined. prob View source
prob(
value, name='prob'
)
Probability density/mass function.
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns
prob a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. quantile View source
quantile(
value, name='quantile'
)
Quantile function. Aka "inverse cdf" or "percent point function". Given random variable X and p in [0, 1], the quantile is: quantile(p) := x such that P[X <= x] == p
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns
quantile a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. sample View source
sample(
sample_shape=(), seed=None, name='sample'
)
Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample.
Args
sample_shape 0D or 1D int32 Tensor. Shape of the generated samples.
seed Python integer seed for RNG
name name to give to the op.
Returns
samples a Tensor with prepended dimensions sample_shape. stddev View source
stddev(
name='stddev'
)
Standard deviation. Standard deviation is defined as, stddev = E[(X - E[X])**2]**0.5
where X is the random variable associated with this distribution, E denotes expectation, and stddev.shape = batch_shape + event_shape.
Args
name Python str prepended to names of ops created by this function.
Returns
stddev Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean(). survival_function View source
survival_function(
value, name='survival_function'
)
Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x]
= 1 - P[X <= x]
= 1 - cdf(x).
Args
value float or double Tensor.
name Python str prepended to names of ops created by this function.
Returns Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.
variance View source
variance(
name='variance'
)
Variance. Variance is defined as, Var = E[(X - E[X])**2]
where X is the random variable associated with this distribution, E denotes expectation, and Var.shape = batch_shape + event_shape.
Args
name Python str prepended to names of ops created by this function.
Returns
variance Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean(). | |
doc_1577 | By default, ArgumentParser groups command-line arguments into “positional arguments” and “optional arguments” when displaying help messages. When there is a better conceptual grouping of arguments than this default one, appropriate groups can be created using the add_argument_group() method: >>> parser = argparse.ArgumentParser(prog='PROG', add_help=False)
>>> group = parser.add_argument_group('group')
>>> group.add_argument('--foo', help='foo help')
>>> group.add_argument('bar', help='bar help')
>>> parser.print_help()
usage: PROG [--foo FOO] bar
group:
bar bar help
--foo FOO foo help
The add_argument_group() method returns an argument group object which has an add_argument() method just like a regular ArgumentParser. When an argument is added to the group, the parser treats it just like a normal argument, but displays the argument in a separate group for help messages. The add_argument_group() method accepts title and description arguments which can be used to customize this display: >>> parser = argparse.ArgumentParser(prog='PROG', add_help=False)
>>> group1 = parser.add_argument_group('group1', 'group1 description')
>>> group1.add_argument('foo', help='foo help')
>>> group2 = parser.add_argument_group('group2', 'group2 description')
>>> group2.add_argument('--bar', help='bar help')
>>> parser.print_help()
usage: PROG [--bar BAR] foo
group1:
group1 description
foo foo help
group2:
group2 description
--bar BAR bar help
Note that any arguments not in your user-defined groups will end up back in the usual “positional arguments” and “optional arguments” sections. | |
doc_1578 |
Returns whether the kernel is stationary. | |
doc_1579 |
Number of array dimensions. Examples >>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3 | |
doc_1580 |
Set the pick radius used for containment tests. Parameters
prfloat
Pick radius, in points. | |
doc_1581 |
alias of mpl_toolkits.axes_grid1.axes_size.Scaled | |
doc_1582 |
Fit the model with X. Samples random projection according to n_features. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where n_samples in the number of samples and n_features is the number of features. Returns
selfobject
Returns the transformer. | |
doc_1583 | Return the name of start method used for starting processes. If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then None is returned. The return value can be 'fork', 'spawn', 'forkserver' or None. 'fork' is the default on Unix, while 'spawn' is the default on Windows. New in version 3.4. | |
doc_1584 |
Update this artist's properties from the dict props. Parameters
propsdict | |
doc_1585 | Hann window function. w[n]=12[1−cos(2πnN−1)]=sin2(πnN−1),w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = \sin^2 \left( \frac{\pi n}{N - 1} \right),
where NN is the full window size. The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch.stft(). Therefore, if periodic is true, the NN in above formula is in fact window_length+1\text{window\_length} + 1 . Also, we always have torch.hann_window(L, periodic=True) equal to torch.hann_window(L + 1, periodic=False)[:-1]). Note If window_length =1=1 , the returned window contains a single value 1. Parameters
window_length (int) – the size of returned window
periodic (bool, optional) – If True, returns a window to be used as periodic function. If False, return a symmetric window. Keyword Arguments
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_tensor_type()). Only floating point types are supported.
layout (torch.layout, optional) – the desired layout of returned window tensor. Only torch.strided (dense layout) is supported.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False. Returns
A 1-D tensor of size (window_length,)(\text{window\_length},) containing the window Return type
Tensor | |
doc_1586 | Creates and returns a generator object that manages the state of the algorithm which produces pseudo random numbers. Used as a keyword argument in many In-place random sampling functions. Parameters
device (torch.device, optional) – the desired device for the generator. Returns
An torch.Generator object. Return type
Generator Example: >>> g_cpu = torch.Generator()
>>> g_cuda = torch.Generator(device='cuda')
device
Generator.device -> device Gets the current device of the generator. Example: >>> g_cpu = torch.Generator()
>>> g_cpu.device
device(type='cpu')
get_state() → Tensor
Returns the Generator state as a torch.ByteTensor. Returns
A torch.ByteTensor which contains all the necessary bits to restore a Generator to a specific point in time. Return type
Tensor Example: >>> g_cpu = torch.Generator()
>>> g_cpu.get_state()
initial_seed() → int
Returns the initial seed for generating random numbers. Example: >>> g_cpu = torch.Generator()
>>> g_cpu.initial_seed()
2147483647
manual_seed(seed) → Generator
Sets the seed for generating random numbers. Returns a torch.Generator object. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed. Parameters
seed (int) – The desired seed. Value must be within the inclusive range [-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]. Otherwise, a RuntimeError is raised. Negative inputs are remapped to positive values with the formula 0xffff_ffff_ffff_ffff + seed. Returns
An torch.Generator object. Return type
Generator Example: >>> g_cpu = torch.Generator()
>>> g_cpu.manual_seed(2147483647)
seed() → int
Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator. Example: >>> g_cpu = torch.Generator()
>>> g_cpu.seed()
1516516984916
set_state(new_state) → void
Sets the Generator state. Parameters
new_state (torch.ByteTensor) – The desired state. Example: >>> g_cpu = torch.Generator()
>>> g_cpu_other = torch.Generator()
>>> g_cpu.set_state(g_cpu_other.get_state()) | |
doc_1587 | Performs global post-test teardown, such as removing instrumentation from the template system and restoring normal email services. | |
doc_1588 |
Bases: matplotlib.patches.FancyArrowPatch A patch that connects two points (possibly in different axes). Connect point xyA in coordsA with point xyB in coordsB. Valid keys are
Key Description
arrowstyle the arrow style
connectionstyle the connection style
relpos default is (0.5, 0.5)
patchA default is bounding box of the text
patchB default is None
shrinkA default is 2 points
shrinkB default is 2 points
mutation_scale default is text size (in points)
mutation_aspect default is 1.
? any key for matplotlib.patches.PathPatch coordsA and coordsB are strings that indicate the coordinates of xyA and xyB.
Property Description
'figure points' points from the lower left corner of the figure
'figure pixels' pixels from the lower left corner of the figure
'figure fraction' 0, 0 is lower left of figure and 1, 1 is upper right
'subfigure points' points from the lower left corner of the subfigure
'subfigure pixels' pixels from the lower left corner of the subfigure
'subfigure fraction' fraction of the subfigure, 0, 0 is lower left.
'axes points' points from lower left corner of axes
'axes pixels' pixels from lower left corner of axes
'axes fraction' 0, 0 is lower left of axes and 1, 1 is upper right
'data' use the coordinate system of the object being annotated (default)
'offset points' offset (in points) from the xy value
'polar' you can specify theta, r for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native "data" coordinate system. Alternatively they can be set to any valid Transform. Note that 'subfigure pixels' and 'figure pixels' are the same for the parent figure, so users who want code that is usable in a subfigure can use 'subfigure pixels'. Note Using ConnectionPatch across two Axes instances is not directly compatible with constrained layout. Add the artist directly to the Figure instead of adding it to a specific Axes, or exclude it from the layout using con.set_in_layout(False). fig, ax = plt.subplots(1, 2, constrained_layout=True)
con = ConnectionPatch(..., axesA=ax[0], axesB=ax[1])
fig.add_artist(con)
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_annotation_clip()[source]
Return the clipping behavior. See set_annotation_clip for the meaning of the return value.
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, annotation_clip=<UNSET>, antialiased=<UNSET>, arrowstyle=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, connectionstyle=<UNSET>, dpi_cor=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, mutation_aspect=<UNSET>, mutation_scale=<UNSET>, patchA=<UNSET>, patchB=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, positions=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
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
annotation_clip bool or None
antialiased or aa bool or None
arrowstyle None or ArrowStyle or str, default: None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
connectionstyle str or ConnectionStyle or None, optional
dpi_cor unknown
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
mutation_aspect float
mutation_scale float
patchA patches.Patch
patchB patches.Patch
path_effects AbstractPathEffect
picker None or bool or float or callable
positions unknown
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float
set_annotation_clip(b)[source]
Set the clipping behavior. Parameters
bbool or None
False: The annotation will always be drawn regardless of its position.
True: The annotation will only be drawn if self.xy is inside the axes.
None: The annotation will only be drawn if self.xy is inside the axes and self.xycoords == "data".
Examples using matplotlib.patches.ConnectionPatch
Bar of pie
Connect Simple01
Constrained Layout Guide | |
doc_1589 | If the range is for bytes, the length is not None and there is exactly one range and it is satisfiable it returns a (start, stop) tuple, otherwise None. | |
doc_1590 | tf.losses.SquaredHinge Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.SquaredHinge
tf.keras.losses.SquaredHinge(
reduction=losses_utils.ReductionV2.AUTO, name='squared_hinge'
)
loss = square(maximum(1 - y_true * y_pred, 0)) y_true values are expected to be -1 or 1. If binary (0 or 1) labels are provided we will convert them to -1 or 1. Standalone usage:
y_true = [[0., 1.], [0., 0.]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
# Using 'auto'/'sum_over_batch_size' reduction type.
h = tf.keras.losses.SquaredHinge()
h(y_true, y_pred).numpy()
1.86
# Calling with 'sample_weight'.
h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.73
# Using 'sum' reduction type.
h = tf.keras.losses.SquaredHinge(
reduction=tf.keras.losses.Reduction.SUM)
h(y_true, y_pred).numpy()
3.72
# Using 'none' reduction type.
h = tf.keras.losses.SquaredHinge(
reduction=tf.keras.losses.Reduction.NONE)
h(y_true, y_pred).numpy()
array([1.46, 2.26], dtype=float32)
Usage with the compile() API: model.compile(optimizer='sgd', loss=tf.keras.losses.SquaredHinge())
Args
reduction (Optional) Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.
name Optional name for the op. Defaults to 'squared_hinge'. Methods from_config View source
@classmethod
from_config(
config
)
Instantiates a Loss from its config (output of get_config()).
Args
config Output of get_config().
Returns A Loss instance.
get_config View source
get_config()
Returns the config dictionary for a Loss instance. __call__ View source
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss instance.
Args
y_true Ground truth values. shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]
y_pred The predicted values. shape = [batch_size, d0, .. dN]
sample_weight Optional sample_weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each loss element of y_pred is scaled by the corresponding value of sample_weight. (Note ondN-1: all loss functions reduce by 1 dimension, usually axis=-1.)
Returns Weighted loss float Tensor. If reduction is NONE, this has shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1 because all loss functions reduce by 1 dimension, usually axis=-1.)
Raises
ValueError If the shape of sample_weight is invalid. | |
doc_1591 | Register callables to be executed when a new child process is forked using os.fork() or similar process cloning APIs. The parameters are optional and keyword-only. Each specifies a different call point.
before is a function called before forking a child process.
after_in_parent is a function called from the parent process after forking a child process.
after_in_child is a function called from the child process. These calls are only made if control is expected to return to the Python interpreter. A typical subprocess launch will not trigger them as the child is not going to re-enter the interpreter. Functions registered for execution before forking are called in reverse registration order. Functions registered for execution after forking (either in the parent or in the child) are called in registration order. Note that fork() calls made by third-party C code may not call those functions, unless it explicitly calls PyOS_BeforeFork(), PyOS_AfterFork_Parent() and PyOS_AfterFork_Child(). There is no way to unregister a function. Availability: Unix. New in version 3.7. | |
doc_1592 | See Migration guide for more details. tf.compat.v1.raw_ops.RefSelect
tf.raw_ops.RefSelect(
index, inputs, name=None
)
Args
index A Tensor of type int32. A scalar that determines the input that gets selected.
inputs A list of at least 1 mutable Tensor objects with the same type. A list of ref tensors, one of which will be forwarded to output.
name A name for the operation (optional).
Returns A mutable Tensor. Has the same type as inputs. | |
doc_1593 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_1594 |
Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | |
doc_1595 | This method makes an HttpResponse instance a file-like object. | |
doc_1596 | The code object has a variable positional parameter (*args-like). | |
doc_1597 |
Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created. Parameters
batch_shape (torch.Size) – the desired expanded size.
_instance – new instance provided by subclasses that need to override .expand. Returns
New distribution instance with batch dimensions expanded to batch_size. | |
doc_1598 |
Fit estimator. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use dtype=np.float32 for maximum efficiency. Sparse matrices are also supported, use sparse csc_matrix for maximum efficiency.
yIgnored
Not used, present for API consistency by convention.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node. Returns
selfobject | |
doc_1599 |
Check if coefficients match. New in version 1.6.0. Parameters
otherclass instance
The other class must have the coef attribute. Returns
boolboolean
True if the coefficients are the same, False otherwise. |
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