_id stringlengths 5 9 | text stringlengths 5 385k | title stringclasses 1
value |
|---|---|---|
doc_26700 |
Get Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv. Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **. Parameters
other:scalar, sequence, Series, or DataFrame
Any single or multiple element data structure, or list-like object.
axis:{0 or ‘index’, 1 or ‘columns’}
Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’). For Series input, axis to match Series index on.
level:int or label
Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_value:float or None, default None
Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing. Returns
DataFrame
Result of the arithmetic operation. See also DataFrame.add
Add DataFrames. DataFrame.sub
Subtract DataFrames. DataFrame.mul
Multiply DataFrames. DataFrame.div
Divide DataFrames (float division). DataFrame.truediv
Divide DataFrames (float division). DataFrame.floordiv
Divide DataFrames (integer division). DataFrame.mod
Calculate modulo (remainder after division). DataFrame.pow
Calculate exponential power. Notes Mismatched indices will be unioned together. Examples
>>> df = pd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df
angles degrees
circle 0 360
triangle 3 180
rectangle 4 360
Add a scalar with operator version which return the same results.
>>> df + 1
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
>>> df.add(1)
angles degrees
circle 1 361
triangle 4 181
rectangle 5 361
Divide by constant with reverse version.
>>> df.div(10)
angles degrees
circle 0.0 36.0
triangle 0.3 18.0
rectangle 0.4 36.0
>>> df.rdiv(10)
angles degrees
circle inf 0.027778
triangle 3.333333 0.055556
rectangle 2.500000 0.027778
Subtract a list and Series by axis with operator version.
>>> df - [1, 2]
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub([1, 2], axis='columns')
angles degrees
circle -1 358
triangle 2 178
rectangle 3 358
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
... axis='index')
angles degrees
circle -1 359
triangle 2 179
rectangle 3 359
Multiply a DataFrame of different shape with operator version.
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
... index=['circle', 'triangle', 'rectangle'])
>>> other
angles
circle 0
triangle 3
rectangle 4
>>> df * other
angles degrees
circle 0 NaN
triangle 9 NaN
rectangle 16 NaN
>>> df.mul(other, fill_value=0)
angles degrees
circle 0 0.0
triangle 9 0.0
rectangle 16 0.0
Divide by a MultiIndex by level.
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
... 'degrees': [360, 180, 360, 360, 540, 720]},
... index=[['A', 'A', 'A', 'B', 'B', 'B'],
... ['circle', 'triangle', 'rectangle',
... 'square', 'pentagon', 'hexagon']])
>>> df_multindex
angles degrees
A circle 0 360
triangle 3 180
rectangle 4 360
B square 4 360
pentagon 5 540
hexagon 6 720
>>> df.div(df_multindex, level=1, fill_value=0)
angles degrees
A circle NaN 1.0
triangle 1.0 1.0
rectangle 1.0 1.0
B square 0.0 0.0
pentagon 0.0 0.0
hexagon 0.0 0.0 | |
doc_26701 |
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_26702 |
Alias for set_linestyle. | |
doc_26703 |
Return a tuple of the status (True/False) of all of the check buttons. | |
doc_26704 | Return the debugging flags currently set. | |
doc_26705 | tf.experimental.numpy.uint16(
*args, **kwargs
)
Character code: 'H'. Canonical name: np.ushort. Alias on this platform: np.uint16: 16-bit unsigned integer (0 to 65535). Methods all
all()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. any
any()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmax
argmax()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argmin
argmin()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. argsort
argsort()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. astype
astype()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. byteswap
byteswap()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. choose
choose()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. clip
clip()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. compress
compress()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. conj
conj()
conjugate
conjugate()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. copy
copy()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumprod
cumprod()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. cumsum
cumsum()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. diagonal
diagonal()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dump
dump()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. dumps
dumps()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. fill
fill()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. flatten
flatten()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. getfield
getfield()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. item
item()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. itemset
itemset()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. max
max()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. mean
mean()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. min
min()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. newbyteorder
newbyteorder()
newbyteorder(new_order='S') Return a new dtype with a different byte order. Changes are also made in all fields and sub-arrays of the data type. The new_order code can be any from the following: 'S' - swap dtype from current to opposite endian '<', 'L'- little endian '>', 'B'- big endian '=', 'N'- native order '|', 'I'- ignore (no change to byte order) Parameters new_order : str, optional Byte order to force; a value from the byte order specifications above. The default value ('S') results in swapping the current byte order. The code does a case-insensitive check on the first letter of new_order for the alternatives above. For example, any of 'B' or 'b' or 'biggish' are valid to specify big-endian. Returns new_dtype : dtype New dtype object with the given change to the byte order. nonzero
nonzero()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. prod
prod()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ptp
ptp()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. put
put()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. ravel
ravel()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. repeat
repeat()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. reshape
reshape()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. resize
resize()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. round
round()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. searchsorted
searchsorted()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setfield
setfield()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. setflags
setflags()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sort
sort()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. squeeze
squeeze()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. std
std()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. sum
sum()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. swapaxes
swapaxes()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. take
take()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tobytes
tobytes()
tofile
tofile()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tolist
tolist()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. tostring
tostring()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. trace
trace()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. transpose
transpose()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. var
var()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. view
view()
Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also the corresponding attribute of the derived class of interest. __abs__
__abs__()
abs(self) __add__
__add__(
value, /
)
Return self+value. __and__
__and__(
value, /
)
Return self&value. __bool__
__bool__()
self != 0 __eq__
__eq__(
value, /
)
Return self==value. __floordiv__
__floordiv__(
value, /
)
Return self//value. __ge__
__ge__(
value, /
)
Return self>=value. __getitem__
__getitem__(
key, /
)
Return self[key]. __gt__
__gt__(
value, /
)
Return self>value. __invert__
__invert__()
~self __le__
__le__(
value, /
)
Return self<=value. __lt__
__lt__(
value, /
)
Return self<value. __mod__
__mod__(
value, /
)
Return self%value. __mul__
__mul__(
value, /
)
Return self*value. __ne__
__ne__(
value, /
)
Return self!=value. __neg__
__neg__()
-self __or__
__or__(
value, /
)
Return self|value. __pos__
__pos__()
+self __pow__
__pow__(
value, mod, /
)
Return pow(self, value, mod). __radd__
__radd__(
value, /
)
Return value+self. __rand__
__rand__(
value, /
)
Return value&self. __rfloordiv__
__rfloordiv__(
value, /
)
Return value//self. __rmod__
__rmod__(
value, /
)
Return value%self. __rmul__
__rmul__(
value, /
)
Return value*self. __ror__
__ror__(
value, /
)
Return value|self. __rpow__
__rpow__(
value, mod, /
)
Return pow(value, self, mod). __rsub__
__rsub__(
value, /
)
Return value-self. __rtruediv__
__rtruediv__(
value, /
)
Return value/self. __rxor__
__rxor__(
value, /
)
Return value^self. __sub__
__sub__(
value, /
)
Return self-value. __truediv__
__truediv__(
value, /
)
Return self/value. __xor__
__xor__(
value, /
)
Return self^value.
Class Variables
T
base
data
denominator
dtype
flags
flat
imag
itemsize
nbytes
ndim
numerator
real
shape
size
strides | |
doc_26706 | See Migration guide for more details. tf.compat.v1.keras.applications.mobilenet.decode_predictions
tf.keras.applications.mobilenet.decode_predictions(
preds, top=5
)
Arguments
preds Numpy array encoding a batch of predictions.
top Integer, how many top-guesses to return. Defaults to 5.
Returns A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.
Raises
ValueError In case of invalid shape of the pred array (must be 2D). | |
doc_26707 | See Migration guide for more details. tf.compat.v1.strings.unicode_encode
tf.strings.unicode_encode(
input, output_encoding, errors='replace', replacement_char=65533,
name=None
)
result[i1...iN] is the string formed by concatenating the Unicode codepoints input[1...iN, :], encoded using output_encoding.
Args
input An N+1 dimensional potentially ragged integer tensor with shape [D1...DN, num_chars].
output_encoding Unicode encoding that should be used to encode each codepoint sequence. Can be "UTF-8", "UTF-16-BE", or "UTF-32-BE".
errors Specifies the response when an invalid codepoint is encountered (optional). One of:
'replace': Replace invalid codepoint with the replacement_char. (default)
'ignore': Skip invalid codepoints.
'strict': Raise an exception for any invalid codepoint.
replacement_char The replacement character codepoint to be used in place of any invalid input when errors='replace'. Any valid unicode codepoint may be used. The default value is the default unicode replacement character which is 0xFFFD (U+65533).
name A name for the operation (optional).
Returns A N dimensional string tensor with shape [D1...DN].
Example:
input = tf.ragged.constant(
[[71, 246, 246, 100, 110, 105, 103, 104, 116], [128522]])
print(unicode_encode(input, 'UTF-8'))
tf.Tensor([b'G\xc3\xb6\xc3\xb6dnight' b'\xf0\x9f\x98\x8a'],
shape=(2,), dtype=string) | |
doc_26708 |
Pad a list of variable length Tensors with padding_value pad_sequence stacks a list of Tensors along a new dimension, and pads them to equal length. For example, if the input is list of sequences with size L x * and if batch_first is False, and T x B x * otherwise. B is batch size. It is equal to the number of elements in sequences. T is length of the longest sequence. L is length of the sequence. * is any number of trailing dimensions, including none. Example >>> from torch.nn.utils.rnn import pad_sequence
>>> a = torch.ones(25, 300)
>>> b = torch.ones(22, 300)
>>> c = torch.ones(15, 300)
>>> pad_sequence([a, b, c]).size()
torch.Size([25, 3, 300])
Note This function returns a Tensor of size T x B x * or B x T x * where T is the length of the longest sequence. This function assumes trailing dimensions and type of all the Tensors in sequences are same. Parameters
sequences (list[Tensor]) – list of variable length sequences.
batch_first (bool, optional) – output will be in B x T x * if True, or in T x B x * otherwise
padding_value (float, optional) – value for padded elements. Default: 0. Returns
Tensor of size T x B x * if batch_first is False. Tensor of size B x T x * otherwise | |
doc_26709 |
logit transform (base 10), masked or clipped | |
doc_26710 |
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples,) or (n_samples, 1)
Training data. Changed in version 0.24: Also accepts 2d array with 1 feature.
yarray-like of shape (n_samples,)
Training target.
sample_weightarray-like of shape (n_samples,), default=None
Weights. If set to None, all weights will be set to 1 (equal weights). Returns
selfobject
Returns an instance of self. Notes X is stored for future use, as transform needs X to interpolate new input data. | |
doc_26711 |
Return the depth of the axis used by the picker. | |
doc_26712 | Path to a custom template, used by the delete_selected action method for displaying a confirmation page when deleting one or more objects. See the actions documentation. | |
doc_26713 | time_raised = models.DateTimeField(default=timezone.now, editable=False)
reference = models.CharField(unique=True, max_length=20)
description = models.TextField()
Here's a basic ModelSerializer that we can use for creating or updating instances of CustomerReportRecord: class CustomerReportSerializer(serializers.ModelSerializer):
class Meta:
model = CustomerReportRecord
If we open up the Django shell using manage.py shell we can now >>> from project.example.serializers import CustomerReportSerializer
>>> serializer = CustomerReportSerializer()
>>> print(repr(serializer))
CustomerReportSerializer():
id = IntegerField(label='ID', read_only=True)
time_raised = DateTimeField(read_only=True)
reference = CharField(max_length=20, validators=[<UniqueValidator(queryset=CustomerReportRecord.objects.all())>])
description = CharField(style={'type': 'textarea'})
The interesting bit here is the reference field. We can see that the uniqueness constraint is being explicitly enforced by a validator on the serializer field. Because of this more explicit style REST framework includes a few validator classes that are not available in core Django. These classes are detailed below. UniqueValidator This validator can be used to enforce the unique=True constraint on model fields. It takes a single required argument, and an optional messages argument:
queryset required - This is the queryset against which uniqueness should be enforced.
message - The error message that should be used when validation fails.
lookup - The lookup used to find an existing instance with the value being validated. Defaults to 'exact'. This validator should be applied to serializer fields, like so: from rest_framework.validators import UniqueValidator
slug = SlugField(
max_length=100,
validators=[UniqueValidator(queryset=BlogPost.objects.all())]
)
UniqueTogetherValidator This validator can be used to enforce unique_together constraints on model instances. It has two required arguments, and a single optional messages argument:
queryset required - This is the queryset against which uniqueness should be enforced.
fields required - A list or tuple of field names which should make a unique set. These must exist as fields on the serializer class.
message - The error message that should be used when validation fails. The validator should be applied to serializer classes, like so: from rest_framework.validators import UniqueTogetherValidator
class ExampleSerializer(serializers.Serializer):
# ...
class Meta:
# ToDo items belong to a parent list, and have an ordering defined
# by the 'position' field. No two items in a given list may share
# the same position.
validators = [
UniqueTogetherValidator(
queryset=ToDoItem.objects.all(),
fields=['list', 'position']
)
]
Note: The UniqueTogetherValidator class always imposes an implicit constraint that all the fields it applies to are always treated as required. Fields with default values are an exception to this as they always supply a value even when omitted from user input. UniqueForDateValidator UniqueForMonthValidator UniqueForYearValidator These validators can be used to enforce the unique_for_date, unique_for_month and unique_for_year constraints on model instances. They take the following arguments:
queryset required - This is the queryset against which uniqueness should be enforced.
field required - A field name against which uniqueness in the given date range will be validated. This must exist as a field on the serializer class.
date_field required - A field name which will be used to determine date range for the uniqueness constrain. This must exist as a field on the serializer class.
message - The error message that should be used when validation fails. The validator should be applied to serializer classes, like so: from rest_framework.validators import UniqueForYearValidator
class ExampleSerializer(serializers.Serializer):
# ...
class Meta:
# Blog posts should have a slug that is unique for the current year.
validators = [
UniqueForYearValidator(
queryset=BlogPostItem.objects.all(),
field='slug',
date_field='published'
)
]
The date field that is used for the validation is always required to be present on the serializer class. You can't simply rely on a model class default=..., because the value being used for the default wouldn't be generated until after the validation has run. There are a couple of styles you may want to use for this depending on how you want your API to behave. If you're using ModelSerializer you'll probably simply rely on the defaults that REST framework generates for you, but if you are using Serializer or simply want more explicit control, use on of the styles demonstrated below. Using with a writable date field. If you want the date field to be writable the only thing worth noting is that you should ensure that it is always available in the input data, either by setting a default argument, or by setting required=True. published = serializers.DateTimeField(required=True)
Using with a read-only date field. If you want the date field to be visible, but not editable by the user, then set read_only=True and additionally set a default=... argument. published = serializers.DateTimeField(read_only=True, default=timezone.now)
Using with a hidden date field. If you want the date field to be entirely hidden from the user, then use HiddenField. This field type does not accept user input, but instead always returns its default value to the validated_data in the serializer. published = serializers.HiddenField(default=timezone.now)
Note: The UniqueFor<Range>Validator classes impose an implicit constraint that the fields they are applied to are always treated as required. Fields with default values are an exception to this as they always supply a value even when omitted from user input. Advanced field defaults Validators that are applied across multiple fields in the serializer can sometimes require a field input that should not be provided by the API client, but that is available as input to the validator. Two patterns that you may want to use for this sort of validation include: Using HiddenField. This field will be present in validated_data but will not be used in the serializer output representation. Using a standard field with read_only=True, but that also includes a default=… argument. This field will be used in the serializer output representation, but cannot be set directly by the user. REST framework includes a couple of defaults that may be useful in this context. CurrentUserDefault A default class that can be used to represent the current user. In order to use this, the 'request' must have been provided as part of the context dictionary when instantiating the serializer. owner = serializers.HiddenField(
default=serializers.CurrentUserDefault()
)
CreateOnlyDefault A default class that can be used to only set a default argument during create operations. During updates the field is omitted. It takes a single argument, which is the default value or callable that should be used during create operations. created_at = serializers.DateTimeField(
default=serializers.CreateOnlyDefault(timezone.now)
)
Limitations of validators There are some ambiguous cases where you'll need to instead handle validation explicitly, rather than relying on the default serializer classes that ModelSerializer generates. In these cases you may want to disable the automatically generated validators, by specifying an empty list for the serializer Meta.validators attribute. Optional fields By default "unique together" validation enforces that all fields be required=True. In some cases, you might want to explicit apply required=False to one of the fields, in which case the desired behaviour of the validation is ambiguous. In this case you will typically need to exclude the validator from the serializer class, and instead write any validation logic explicitly, either in the .validate() method, or else in the view. For example: class BillingRecordSerializer(serializers.ModelSerializer):
def validate(self, attrs):
# Apply custom validation either here, or in the view.
class Meta:
fields = ['client', 'date', 'amount']
extra_kwargs = {'client': {'required': False}}
validators = [] # Remove a default "unique together" constraint.
Updating nested serializers When applying an update to an existing instance, uniqueness validators will exclude the current instance from the uniqueness check. The current instance is available in the context of the uniqueness check, because it exists as an attribute on the serializer, having initially been passed using instance=... when instantiating the serializer. In the case of update operations on nested serializers there's no way of applying this exclusion, because the instance is not available. Again, you'll probably want to explicitly remove the validator from the serializer class, and write the code for the validation constraint explicitly, in a .validate() method, or in the view. Debugging complex cases If you're not sure exactly what behavior a ModelSerializer class will generate it is usually a good idea to run manage.py shell, and print an instance of the serializer, so that you can inspect the fields and validators that it automatically generates for you. >>> serializer = MyComplexModelSerializer()
>>> print(serializer)
class MyComplexModelSerializer:
my_fields = ...
Also keep in mind that with complex cases it can often be better to explicitly define your serializer classes, rather than relying on the default ModelSerializer behavior. This involves a little more code, but ensures that the resulting behavior is more transparent. Writing custom validators You can use any of Django's existing validators, or write your own custom validators. Function based A validator may be any callable that raises a serializers.ValidationError on failure. def even_number(value):
if value % 2 != 0:
raise serializers.ValidationError('This field must be an even number.')
Field-level validation You can specify custom field-level validation by adding .validate_<field_name> methods to your Serializer subclass. This is documented in the Serializer docs Class-based To write a class-based validator, use the __call__ method. Class-based validators are useful as they allow you to parameterize and reuse behavior. class MultipleOf:
def __init__(self, base):
self.base = base
def __call__(self, value):
if value % self.base != 0:
message = 'This field must be a multiple of %d.' % self.base
raise serializers.ValidationError(message)
Accessing the context In some advanced cases you might want a validator to be passed the serializer field it is being used with as additional context. You can do so by setting a requires_context = True attribute on the validator. The __call__ method will then be called with the serializer_field or serializer as an additional argument. requires_context = True
def __call__(self, value, serializer_field):
...
validators.py | |
doc_26714 |
Fit the model Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator. Parameters
Xiterable
Training data. Must fulfill input requirements of first step of the pipeline.
yiterable, default=None
Training targets. Must fulfill label requirements for all steps of the pipeline.
**fit_paramsdict of string -> object
Parameters passed to the fit method of each step, where each parameter name is prefixed such that parameter p for step s has key s__p. Returns
selfPipeline
This estimator | |
doc_26715 | Exception raised when something can’t be encoded using the binhex format (for example, a filename is too long to fit in the filename field), or when input is not properly encoded binhex data. | |
doc_26716 |
The transmute method is the very core of the ArrowStyle class and must be overridden in the subclasses. It receives the path object along which the arrow will be drawn, and the mutation_size, with which the arrow head etc. will be scaled. The linewidth may be used to adjust the path so that it does not pass beyond the given points. It returns a tuple of a Path instance and a boolean. The boolean value indicate whether the path can be filled or not. The return value can also be a list of paths and list of booleans of a same length. | |
doc_26717 |
Define the anchor location. The actual drawing area (active position) of the Axes may be smaller than the Bbox (original position) when a fixed aspect is required. The anchor defines where the drawing area will be located within the available space. Parameters
anchor(float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
Either an (x, y) pair of relative coordinates (0 is left or bottom, 1 is right or top), 'C' (center), or a cardinal direction ('SW', southwest, is bottom left, etc.). str inputs are shorthands for (x, y) coordinates, as shown in the following table: .. code-block:: none
'NW' (0.0, 1.0) 'N' (0.5, 1.0) 'NE' (1.0, 1.0)
'W' (0.0, 0.5) 'C' (0.5, 0.5) 'E' (1.0, 0.5)
'SW' (0.0, 0.0) 'S' (0.5, 0.0) 'SE' (1.0, 0.0)
sharebool, default: False
If True, apply the settings to all shared Axes. See also matplotlib.axes.Axes.set_aspect
for a description of aspect handling. | |
doc_26718 |
Number of dimensions of the underlying data, by definition 1. | |
doc_26719 |
pygame object for direct pixel access of surfaces PixelArray(Surface) -> PixelArray The PixelArray wraps a Surface and provides direct access to the surface's pixels. A pixel array can be one or two dimensional. A two dimensional array, like its surface, is indexed [column, row]. Pixel arrays support slicing, both for returning a subarray or for assignment. A pixel array sliced on a single column or row returns a one dimensional pixel array. Arithmetic and other operations are not supported. A pixel array can be safely assigned to itself. Finally, pixel arrays export an array struct interface, allowing them to interact with pygame.pixelcopy methods and NumPy arrays. A PixelArray pixel item can be assigned a raw integer values, a pygame.Color instance, or a (r, g, b[, a]) tuple. pxarray[x, y] = 0xFF00FF
pxarray[x, y] = pygame.Color(255, 0, 255)
pxarray[x, y] = (255, 0, 255) However, only a pixel's integer value is returned. So, to compare a pixel to a particular color the color needs to be first mapped using the Surface.map_rgb() method of the Surface object for which the PixelArray was created. pxarray = pygame.PixelArray(surface)
# Check, if the first pixel at the topleft corner is blue
if pxarray[0, 0] == surface.map_rgb((0, 0, 255)):
... When assigning to a range of of pixels, a non tuple sequence of colors or a PixelArray can be used as the value. For a sequence, the length must match the PixelArray width. pxarray[a:b] = 0xFF00FF # set all pixels to 0xFF00FF
pxarray[a:b] = (0xFF00FF, 0xAACCEE, ... ) # first pixel = 0xFF00FF,
# second pixel = 0xAACCEE, ...
pxarray[a:b] = [(255, 0, 255), (170, 204, 238), ...] # same as above
pxarray[a:b] = [(255, 0, 255), 0xAACCEE, ...] # same as above
pxarray[a:b] = otherarray[x:y] # slice sizes must match For PixelArray assignment, if the right hand side array has a row length of 1, then the column is broadcast over the target array's rows. An array of height 1 is broadcast over the target's columns, and is equivalent to assigning a 1D PixelArray. Subscript slices can also be used to assign to a rectangular subview of the target PixelArray. # Create some new PixelArray objects providing a different view
# of the original array/surface.
newarray = pxarray[2:4, 3:5]
otherarray = pxarray[::2, ::2] Subscript slices can also be used to do fast rectangular pixel manipulations instead of iterating over the x or y axis. The pxarray[::2, :] = (0, 0, 0) # Make even columns black.
pxarray[::2] = (0, 0, 0) # Same as [::2, :] During its lifetime, the PixelArray locks the surface, thus you explicitly have to close() it once its not used any more and the surface should perform operations in the same scope. It is best to use it as a context manager using the with PixelArray(surf) as pixel_array: style. So it works on pypy too. A simple : slice index for the column can be omitted. pxarray[::2, ...] = (0, 0, 0) # Same as pxarray[::2, :]
pxarray[...] = (255, 0, 0) # Same as pxarray[:] A note about PixelArray to PixelArray assignment, for arrays with an item size of 3 (created from 24 bit surfaces) pixel values are translated from the source to the destinations format. The red, green, and blue color elements of each pixel are shifted to match the format of the target surface. For all other pixel sizes no such remapping occurs. This should change in later pygame releases, where format conversions are performed for all pixel sizes. To avoid code breakage when full mapped copying is implemented it is suggested PixelArray to PixelArray copies be only between surfaces of identical format. New in pygame 1.9.4:
close() method was added. For explicitly cleaning up.
being able to use PixelArray as a context manager for cleanup.
both of these are useful for when working without reference counting (pypy). New in pygame 1.9.2:
array struct interface
transpose method
broadcasting for a length 1 dimension Changed in pygame 1.9.2:
A 2D PixelArray can have a length 1 dimension. Only an integer index on a 2D PixelArray returns a 1D array.
For assignment, a tuple can only be a color. Any other sequence type is a sequence of colors. surface
Gets the Surface the PixelArray uses. surface -> Surface The Surface the PixelArray was created for.
itemsize
Returns the byte size of a pixel array item itemsize -> int This is the same as Surface.get_bytesize() for the pixel array's surface. New in pygame 1.9.2.
ndim
Returns the number of dimensions. ndim -> int A pixel array can be 1 or 2 dimensional. New in pygame 1.9.2.
shape
Returns the array size. shape -> tuple of int's A tuple or length ndim giving the length of each dimension. Analogous to Surface.get_size(). New in pygame 1.9.2.
strides
Returns byte offsets for each array dimension. strides -> tuple of int's A tuple or length ndim byte counts. When a stride is multiplied by the corresponding index it gives the offset of that index from the start of the array. A stride is negative for an array that has is inverted (has a negative step). New in pygame 1.9.2.
make_surface()
Creates a new Surface from the current PixelArray. make_surface() -> Surface Creates a new Surface from the current PixelArray. Depending on the current PixelArray the size, pixel order etc. will be different from the original Surface. # Create a new surface flipped around the vertical axis.
sf = pxarray[:,::-1].make_surface () New in pygame 1.8.1.
replace()
Replaces the passed color in the PixelArray with another one. replace(color, repcolor, distance=0, weights=(0.299, 0.587, 0.114)) -> None Replaces the pixels with the passed color in the PixelArray by changing them them to the passed replacement color. It uses a simple weighted Euclidean distance formula to calculate the distance between the colors. The distance space ranges from 0.0 to 1.0 and is used as threshold for the color detection. This causes the replacement to take pixels with a similar, but not exactly identical color, into account as well. This is an in place operation that directly affects the pixels of the PixelArray. New in pygame 1.8.1.
extract()
Extracts the passed color from the PixelArray. extract(color, distance=0, weights=(0.299, 0.587, 0.114)) -> PixelArray Extracts the passed color by changing all matching pixels to white, while non-matching pixels are changed to black. This returns a new PixelArray with the black/white color mask. It uses a simple weighted Euclidean distance formula to calculate the distance between the colors. The distance space ranges from 0.0 to 1.0 and is used as threshold for the color detection. This causes the extraction to take pixels with a similar, but not exactly identical color, into account as well. New in pygame 1.8.1.
compare()
Compares the PixelArray with another one. compare(array, distance=0, weights=(0.299, 0.587, 0.114)) -> PixelArray Compares the contents of the PixelArray with those from the passed in PixelArray. It returns a new PixelArray with a black/white color mask that indicates the differences (black) of both arrays. Both PixelArray objects must have identical bit depths and dimensions. It uses a simple weighted Euclidean distance formula to calculate the distance between the colors. The distance space ranges from 0.0 to 1.0 and is used as a threshold for the color detection. This causes the comparison to mark pixels with a similar, but not exactly identical color, as white. New in pygame 1.8.1.
transpose()
Exchanges the x and y axis. transpose() -> PixelArray This method returns a new view of the pixel array with the rows and columns swapped. So for a (w, h) sized array a (h, w) slice is returned. If an array is one dimensional, then a length 1 x dimension is added, resulting in a 2D pixel array. New in pygame 1.9.2.
close()
Closes the PixelArray, and releases Surface lock. transpose() -> PixelArray This method is for explicitly closing the PixelArray, and releasing a lock on the Suface. New in pygame 1.9.4. | |
doc_26720 |
Filter an image with the Frangi vesselness filter. This filter can be used to detect continuous ridges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects. Defined only for 2-D and 3-D images. Calculates the eigenvectors of the Hessian to compute the similarity of an image region to vessels, according to the method described in [1]. Parameters
image(N, M[, P]) ndarray
Array with input image data.
sigmasiterable of floats, optional
Sigmas used as scales of filter, i.e., np.arange(scale_range[0], scale_range[1], scale_step)
scale_range2-tuple of floats, optional
The range of sigmas used.
scale_stepfloat, optional
Step size between sigmas.
alphafloat, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a plate-like structure.
betafloat, optional
Frangi correction constant that adjusts the filter’s sensitivity to deviation from a blob-like structure.
gammafloat, optional
Frangi correction constant that adjusts the filter’s sensitivity to areas of high variance/texture/structure.
black_ridgesboolean, optional
When True (the default), the filter detects black ridges; when False, it detects white ridges.
mode{‘constant’, ‘reflect’, ‘wrap’, ‘nearest’, ‘mirror’}, optional
How to handle values outside the image borders.
cvalfloat, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries. Returns
out(N, M[, P]) ndarray
Filtered image (maximum of pixels across all scales). See also
meijering
sato
hessian
Notes Written by Marc Schrijver, November 2001 Re-Written by D. J. Kroon, University of Twente, May 2009, [2] Adoption of 3D version from D. G. Ellis, Januar 20017, [3] References
1
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998,). Multiscale vessel enhancement filtering. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 130-137). Springer Berlin Heidelberg. DOI:10.1007/BFb0056195
2
Kroon, D. J.: Hessian based Frangi vesselness filter.
3
Ellis, D. G.: https://github.com/ellisdg/frangi3d/tree/master/frangi | |
doc_26721 | Some character sets must be converted before they can be used in email headers or bodies. If the input_charset is one of them, this attribute will contain the name of the character set output will be converted to. Otherwise, it will be None. | |
doc_26722 | This method is not defined in BaseHandler, but subclasses should define it if they want to catch all URLs with no specific registered handler to open it. This method, if implemented, will be called by the parent OpenerDirector. Return values should be the same as for default_open(). | |
doc_26723 |
Convert a PolyCollection to a Poly3DCollection object. | |
doc_26724 |
Adjust a range as needed to avoid singularities. This method gets called during autoscaling, with (v0, v1) set to the data limits on the axes if the axes contains any data, or (-inf, +inf) if not. If v0 == v1 (possibly up to some floating point slop), this method returns an expanded interval around this value. If (v0, v1) == (-inf, +inf), this method returns appropriate default view limits. Otherwise, (v0, v1) is returned without modification. | |
doc_26725 | '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_26726 | Return the number of CPUs in the system. Returns None if undetermined. This number is not equivalent to the number of CPUs the current process can use. The number of usable CPUs can be obtained with len(os.sched_getaffinity(0)) New in version 3.4. | |
doc_26727 | tf.compat.v1.estimator.LinearRegressor(
feature_columns, model_dir=None, label_dimension=1, weight_column=None,
optimizer='Ftrl', config=None, partitioner=None, warm_start_from=None,
loss_reduction=tf.compat.v1.losses.Reduction.SUM,
sparse_combiner='sum'
)
Train a linear regression model to predict label value given observation of feature values. Example: categorical_column_a = categorical_column_with_hash_bucket(...)
categorical_column_b = categorical_column_with_hash_bucket(...)
categorical_feature_a_x_categorical_feature_b = crossed_column(...)
# Estimator using the default optimizer.
estimator = tf.estimator.LinearRegressor(
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b])
# Or estimator using the FTRL optimizer with regularization.
estimator = tf.estimator.LinearRegressor(
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b],
optimizer=tf.keras.optimizers.Ftrl(
learning_rate=0.1,
l1_regularization_strength=0.001
))
# Or estimator using an optimizer with a learning rate decay.
estimator = tf.estimator.LinearRegressor(
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b],
optimizer=lambda: tf.keras.optimizers.Ftrl(
learning_rate=tf.compat.v1.train.exponential_decay(
learning_rate=0.1,
global_step=tf.compat.v1.train.get_global_step(),
decay_steps=10000,
decay_rate=0.96))
# Or estimator with warm-starting from a previous checkpoint.
estimator = tf.estimator.LinearRegressor(
feature_columns=[categorical_column_a,
categorical_feature_a_x_categorical_feature_b],
warm_start_from="/path/to/checkpoint/dir")
# Input builders
def input_fn_train:
# Returns tf.data.Dataset of (x, y) tuple where y represents label's class
# index.
pass
def input_fn_eval:
# Returns tf.data.Dataset of (x, y) tuple where y represents label's class
# index.
pass
def input_fn_predict:
# Returns tf.data.Dataset of (x, None) tuple.
pass
estimator.train(input_fn=input_fn_train)
metrics = estimator.evaluate(input_fn=input_fn_eval)
predictions = estimator.predict(input_fn=input_fn_predict)
Input of train and evaluate should have following features, otherwise there will be a KeyError: if weight_column is not None, a feature with key=weight_column whose value is a Tensor. for each column in feature_columns: if column is a SparseColumn, a feature with key=column.name whose value is a SparseTensor. if column is a WeightedSparseColumn, two features: the first with key the id column name, the second with key the weight column name. Both features' value must be a SparseTensor. if column is a RealValuedColumn, a feature with key=column.name whose value is a Tensor.
Loss is calculated by using mean squared error.
Args
model_fn Model function. Follows the signature:
features -- This is the first item returned from the input_fn passed to train, evaluate, and predict. This should be a single tf.Tensor or dict of same.
labels -- This is the second item returned from the input_fn passed to train, evaluate, and predict. This should be a single tf.Tensor or dict of same (for multi-head models). If mode is tf.estimator.ModeKeys.PREDICT, labels=None will be passed. If the model_fn's signature does not accept mode, the model_fn must still be able to handle labels=None.
mode -- Optional. Specifies if this is training, evaluation or prediction. See tf.estimator.ModeKeys. params -- Optional dict of hyperparameters. Will receive what is passed to Estimator in params parameter. This allows to configure Estimators from hyper parameter tuning.
config -- Optional estimator.RunConfig object. Will receive what is passed to Estimator as its config parameter, or a default value. Allows setting up things in your model_fn based on configuration such as num_ps_replicas, or model_dir. Returns -- tf.estimator.EstimatorSpec
model_dir Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into an estimator to continue training a previously saved model. If PathLike object, the path will be resolved. If None, the model_dir in config will be used if set. If both are set, they must be same. If both are None, a temporary directory will be used.
config estimator.RunConfig configuration object.
params dict of hyper parameters that will be passed into model_fn. Keys are names of parameters, values are basic python types.
warm_start_from Optional string filepath to a checkpoint or SavedModel to warm-start from, or a tf.estimator.WarmStartSettings object to fully configure warm-starting. If None, only TRAINABLE variables are warm-started. If the string filepath is provided instead of a tf.estimator.WarmStartSettings, then all variables are warm-started, and it is assumed that vocabularies and tf.Tensor names are unchanged.
Raises
ValueError parameters of model_fn don't match params.
ValueError if this is called via a subclass and if that class overrides a member of Estimator. Eager Compatibility Estimators can be used while eager execution is enabled. Note that input_fn and all hooks are executed inside a graph context, so they have to be written to be compatible with graph mode. Note that input_fn code using tf.data generally works in both graph and eager modes.
Attributes
config
model_dir
model_fn Returns the model_fn which is bound to self.params.
params
Methods eval_dir View source
eval_dir(
name=None
)
Shows the directory name where evaluation metrics are dumped.
Args
name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
Returns A string which is the path of directory contains evaluation metrics.
evaluate View source
evaluate(
input_fn, steps=None, hooks=None, checkpoint_path=None, name=None
)
Evaluates the model given evaluation data input_fn. For each step, calls input_fn, which returns one batch of data. Evaluates until:
steps batches are processed, or
input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration).
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 tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
steps Number of steps for which to evaluate model. If None, evaluates until input_fn raises an end-of-input exception.
hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the evaluation call.
checkpoint_path Path of a specific checkpoint to evaluate. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, evaluation is run with newly initialized Variables instead of ones restored from checkpoint.
name Name of the evaluation if user needs to run multiple evaluations on different data sets, such as on training data vs test data. Metrics for different evaluations are saved in separate folders, and appear separately in tensorboard.
Returns A dict containing the evaluation metrics specified in model_fn keyed by name, as well as an entry global_step which contains the value of the global step for which this evaluation was performed. For canned estimators, the dict contains the loss (mean loss per mini-batch) and the average_loss (mean loss per sample). Canned classifiers also return the accuracy. Canned regressors also return the label/mean and the prediction/mean.
Raises
ValueError If steps <= 0. experimental_export_all_saved_models View source
experimental_export_all_saved_models(
export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False,
checkpoint_path=None
)
Exports a SavedModel with tf.MetaGraphDefs for each requested mode. For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, then tf.estimator.ModeKeys.PREDICT), such that up to three tf.MetaGraphDefs are saved with a single set of variables in a single SavedModel directory. For the variables and tf.MetaGraphDefs, a timestamped export directory below export_dir_base, and writes a SavedModel into it containing the tf.MetaGraphDef for the given mode and its associated signatures. For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn. For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question. Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.
Args
export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
input_receiver_fn_map dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no arguments and returns the appropriate subclass of InputReceiver.
assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
as_text whether to write the SavedModel proto in text format.
checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
Returns The path to the exported directory as a bytes object.
Raises
ValueError if any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found. export_saved_model View source
export_saved_model(
export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
checkpoint_path=None, experimental_mode=ModeKeys.PREDICT
)
Exports inference graph as a SavedModel into the given dir. For a detailed guide, see SavedModel from Estimators. This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session. The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn. Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}. The experimental_mode parameter can be used to export a single train/eval/predict graph as a SavedModel. See experimental_export_all_saved_models for full docs.
Args
export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
serving_input_receiver_fn A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
as_text whether to write the SavedModel proto in text format.
checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
experimental_mode tf.estimator.ModeKeys value indicating with mode will be exported. Note that this feature is experimental.
Returns The path to the exported directory as a bytes object.
Raises
ValueError if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found. export_savedmodel View source
export_savedmodel(
export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False,
checkpoint_path=None, strip_default_attrs=False
)
Exports inference graph as a SavedModel into the given dir. (deprecated) Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This function has been renamed, use export_saved_model instead. For a detailed guide, see SavedModel from Estimators. This method builds a new graph by first calling the serving_input_receiver_fn to obtain feature Tensors, and then calling this Estimator's model_fn to generate the model graph based on those features. It restores the given checkpoint (or, lacking that, the most recent checkpoint) into this graph in a fresh session. Finally it creates a timestamped export directory below the given export_dir_base, and writes a SavedModel into it containing a single tf.MetaGraphDef saved from this session. The exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding tf.estimator.export.ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn. Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.
Args
export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
serving_input_receiver_fn A function that takes no argument and returns a tf.estimator.export.ServingInputReceiver or tf.estimator.export.TensorServingInputReceiver.
assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
as_text whether to write the SavedModel proto in text format.
checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.
strip_default_attrs Boolean. If True, default-valued attributes will be removed from the NodeDefs. For a detailed guide, see Stripping Default-Valued Attributes.
Returns The path to the exported directory as a bytes object.
Raises
ValueError if no serving_input_receiver_fn is provided, no export_outputs are provided, or no checkpoint can be found. get_variable_names View source
get_variable_names()
Returns list of all variable names in this model.
Returns List of names.
Raises
ValueError If the Estimator has not produced a checkpoint yet. get_variable_value View source
get_variable_value(
name
)
Returns value of the variable given by name.
Args
name string or a list of string, name of the tensor.
Returns Numpy array - value of the tensor.
Raises
ValueError If the Estimator has not produced a checkpoint yet. latest_checkpoint View source
latest_checkpoint()
Finds the filename of the latest saved checkpoint file in model_dir.
Returns The full path to the latest checkpoint or None if no checkpoint was found.
predict View source
predict(
input_fn, predict_keys=None, hooks=None, checkpoint_path=None,
yield_single_examples=True
)
Yields predictions for given features. Please note that interleaving two predict outputs does not work. See: issue/20506
Args
input_fn A function that constructs the features. Prediction continues until input_fn raises an end-of-input exception (tf.errors.OutOfRangeError or StopIteration). See Premade Estimators for more information. The function should construct and return one of the following:
tf.data.Dataset object -- Outputs of Dataset object must have same constraints as below. features -- A tf.Tensor or a dictionary of string feature name to Tensor. features are consumed by model_fn. They should satisfy the expectation of model_fn from inputs. A tuple, in which case the first item is extracted as features.
predict_keys list of str, name of the keys to predict. It is used if the tf.estimator.EstimatorSpec.predictions is a dict. If predict_keys is used then rest of the predictions will be filtered from the dictionary. If None, returns all.
hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the prediction call.
checkpoint_path Path of a specific checkpoint to predict. If None, the latest checkpoint in model_dir is used. If there are no checkpoints in model_dir, prediction is run with newly initialized Variables instead of ones restored from checkpoint.
yield_single_examples If False, yields the whole batch as returned by the model_fn instead of decomposing the batch into individual elements. This is useful if model_fn returns some tensors whose first dimension is not equal to the batch size.
Yields Evaluated values of predictions tensors.
Raises
ValueError If batch length of predictions is not the same and yield_single_examples is True.
ValueError If there is a conflict between predict_keys and predictions. For example if predict_keys is not None but tf.estimator.EstimatorSpec.predictions is not a dict. train View source
train(
input_fn, hooks=None, steps=None, max_steps=None, saving_listeners=None
)
Trains a model given training data input_fn.
Args
input_fn A function that provides input data for training as minibatches. 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 tf.Tensor or a dictionary of string feature name to Tensor and labels is a Tensor or a dictionary of string label name to Tensor. Both features and labels are consumed by model_fn. They should satisfy the expectation of model_fn from inputs.
hooks List of tf.train.SessionRunHook subclass instances. Used for callbacks inside the training loop.
steps Number of steps for which to train the model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. steps works incrementally. If you call two times train(steps=10) then training occurs in total 20 steps. If OutOfRange or StopIteration occurs in the middle, training stops before 20 steps. If you don't want to have incremental behavior please set max_steps instead. If set, max_steps must be None.
max_steps Number of total steps for which to train model. If None, train forever or train until input_fn generates the tf.errors.OutOfRange error or StopIteration exception. If set, steps must be None. If OutOfRange or StopIteration occurs in the middle, training stops before max_steps steps. Two calls to train(steps=100) means 200 training iterations. On the other hand, two calls to train(max_steps=100) means that the second call will not do any iteration since first call did all 100 steps.
saving_listeners list of CheckpointSaverListener objects. Used for callbacks that run immediately before or after checkpoint savings.
Returns self, for chaining.
Raises
ValueError If both steps and max_steps are not None.
ValueError If either steps or max_steps <= 0. | |
doc_26728 | Load MIME type information from the Windows registry. Availability: Windows. If strict is True, information will be added to the list of standard types, else to the list of non-standard types. New in version 3.2. | |
doc_26729 |
login_url
Default return value for get_login_url(). Defaults to None in which case get_login_url() falls back to settings.LOGIN_URL.
permission_denied_message
Default return value for get_permission_denied_message(). Defaults to an empty string.
redirect_field_name
Default return value for get_redirect_field_name(). Defaults to "next".
raise_exception
If this attribute is set to True, a PermissionDenied exception is raised when the conditions are not met. When False (the default), anonymous users are redirected to the login page.
get_login_url()
Returns the URL that users who don’t pass the test will be redirected to. Returns login_url if set, or settings.LOGIN_URL otherwise.
get_permission_denied_message()
When raise_exception is True, this method can be used to control the error message passed to the error handler for display to the user. Returns the permission_denied_message attribute by default.
get_redirect_field_name()
Returns the name of the query parameter that will contain the URL the user should be redirected to after a successful login. If you set this to None, a query parameter won’t be added. Returns the redirect_field_name attribute by default.
handle_no_permission()
Depending on the value of raise_exception, the method either raises a PermissionDenied exception or redirects the user to the login_url, optionally including the redirect_field_name if it is set. | |
doc_26730 | Redirects are represented by a standard Django model, which lives in django/contrib/redirects/models.py. You can access redirect objects via the Django database API. For example: >>> from django.conf import settings
>>> from django.contrib.redirects.models import Redirect
>>> # Add a new redirect.
>>> redirect = Redirect.objects.create(
... site_id=1,
... old_path='/contact-us/',
... new_path='/contact/',
... )
>>> # Change a redirect.
>>> redirect.new_path = '/contact-details/'
>>> redirect.save()
>>> redirect
<Redirect: /contact-us/ ---> /contact-details/>
>>> # Delete a redirect.
>>> Redirect.objects.filter(site_id=1, old_path='/contact-us/').delete()
(1, {'redirects.Redirect': 1}) | |
doc_26731 | Asserts that the strings html1 and html2 are equal. The comparison is based on HTML semantics. The comparison takes following things into account: Whitespace before and after HTML tags is ignored. All types of whitespace are considered equivalent. All open tags are closed implicitly, e.g. when a surrounding tag is closed or the HTML document ends. Empty tags are equivalent to their self-closing version. The ordering of attributes of an HTML element is not significant. Boolean attributes (like checked) without an argument are equal to attributes that equal in name and value (see the examples). Text, character references, and entity references that refer to the same character are equivalent. The following examples are valid tests and don’t raise any AssertionError: self.assertHTMLEqual(
'<p>Hello <b>'world'!</p>',
'''<p>
Hello <b>'world'! </b>
</p>'''
)
self.assertHTMLEqual(
'<input type="checkbox" checked="checked" id="id_accept_terms" />',
'<input id="id_accept_terms" type="checkbox" checked>'
)
html1 and html2 must contain HTML. An AssertionError will be raised if one of them cannot be parsed. Output in case of error can be customized with the msg argument. Changed in Django 4.0: In older versions, any attribute (not only boolean attributes) without a value was considered equal to an attribute with the same name and value. | |
doc_26732 | See Migration guide for more details. tf.compat.v1.raw_ops.SqlDataset
tf.raw_ops.SqlDataset(
driver_name, data_source_name, query, output_types, output_shapes, name=None
)
Args
driver_name A Tensor of type string. The database type. Currently, the only supported type is 'sqlite'.
data_source_name A Tensor of type string. A connection string to connect to the database.
query A Tensor of type string. A SQL query to execute.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_26733 | Return a tuple consisting of the minimum and maximum values of all samples in the sound fragment. | |
doc_26734 | See torch.addmv() | |
doc_26735 |
Draw samples from a Rayleigh distribution. The \(\chi\) and Weibull distributions are generalizations of the Rayleigh. Note New code should use the rayleigh method of a default_rng() instance instead; please see the Quick Start. Parameters
scalefloat or array_like of floats, optional
Scale, also equals the mode. Must be non-negative. Default is 1.
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if scale is a scalar. Otherwise, np.array(scale).size samples are drawn. Returns
outndarray or scalar
Drawn samples from the parameterized Rayleigh distribution. See also Generator.rayleigh
which should be used for new code. Notes The probability density function for the Rayleigh distribution is \[P(x;scale) = \frac{x}{scale^2}e^{\frac{-x^2}{2 \cdotp scale^2}}\] The Rayleigh distribution would arise, for example, if the East and North components of the wind velocity had identical zero-mean Gaussian distributions. Then the wind speed would have a Rayleigh distribution. References 1
Brighton Webs Ltd., “Rayleigh Distribution,” https://web.archive.org/web/20090514091424/http://brighton-webs.co.uk:80/distributions/rayleigh.asp 2
Wikipedia, “Rayleigh distribution” https://en.wikipedia.org/wiki/Rayleigh_distribution Examples Draw values from the distribution and plot the histogram >>> from matplotlib.pyplot import hist
>>> values = hist(np.random.rayleigh(3, 100000), bins=200, density=True)
Wave heights tend to follow a Rayleigh distribution. If the mean wave height is 1 meter, what fraction of waves are likely to be larger than 3 meters? >>> meanvalue = 1
>>> modevalue = np.sqrt(2 / np.pi) * meanvalue
>>> s = np.random.rayleigh(modevalue, 1000000)
The percentage of waves larger than 3 meters is: >>> 100.*sum(s>3)/1000000.
0.087300000000000003 # random | |
doc_26736 |
Bases: matplotlib.ticker.Formatter Format numbers as a percentage. Parameters
xmaxfloat
Determines how the number is converted into a percentage. xmax is the data value that corresponds to 100%. Percentages are computed as x / xmax * 100. So if the data is already scaled to be percentages, xmax will be 100. Another common situation is where xmax is 1.0.
decimalsNone or int
The number of decimal places to place after the point. If None (the default), the number will be computed automatically.
symbolstr or None
A string that will be appended to the label. It may be None or empty to indicate that no symbol should be used. LaTeX special characters are escaped in symbol whenever latex mode is enabled, unless is_latex is True.
is_latexbool
If False, reserved LaTeX characters in symbol will be escaped. convert_to_pct(x)[source]
format_pct(x, display_range)[source]
Format the number as a percentage number with the correct number of decimals and adds the percent symbol, if any. If self.decimals is None, the number of digits after the decimal point is set based on the display_range of the axis as follows:
display_range decimals sample
>50 0 x = 34.5 => 35%
>5 1 x = 34.5 => 34.5%
>0.5 2 x = 34.5 => 34.50%
... ... ... This method will not be very good for tiny axis ranges or extremely large ones. It assumes that the values on the chart are percentages displayed on a reasonable scale.
propertysymbol
The configured percent symbol as a string. If LaTeX is enabled via rcParams["text.usetex"] (default: False), the special characters {'#', '$', '%', '&', '~', '_', '^', '\', '{', '}'} are automatically escaped in the string. | |
doc_26737 |
Applies the learned transformation to the given data. Parameters
Xarray-like of shape (n_samples, n_features)
Data samples. Returns
X_embedded: ndarray of shape (n_samples, n_components)
The data samples transformed. Raises
NotFittedError
If fit has not been called before. | |
doc_26738 | Return the system’s ctime which, on some systems (like Unix) is the time of the last metadata change, and, on others (like Windows), is the creation time for path. The return value is a number giving the number of seconds since the epoch (see the time module). Raise OSError if the file does not exist or is inaccessible. Changed in version 3.6: Accepts a path-like object. | |
doc_26739 | See Migration guide for more details. tf.compat.v1.raw_ops.SaveDataset
tf.raw_ops.SaveDataset(
input_dataset, path, shard_func_other_args, shard_func,
compression='', use_shard_func=True, name=None
)
Args
input_dataset A Tensor of type variant.
path A Tensor of type string.
shard_func_other_args A list of Tensor objects.
shard_func A function decorated with @Defun.
compression An optional string. Defaults to "".
use_shard_func An optional bool. Defaults to True.
name A name for the operation (optional).
Returns The created Operation. | |
doc_26740 |
Extension array for string data. New in version 1.0.0. Warning StringArray is considered experimental. The implementation and parts of the API may change without warning. Parameters
values:array-like
The array of data. Warning Currently, this expects an object-dtype ndarray where the elements are Python strings or pandas.NA. This may change without warning in the future. Use pandas.array() with dtype="string" for a stable way of creating a StringArray from any sequence.
copy:bool, default False
Whether to copy the array of data. See also array
The recommended function for creating a StringArray. Series.str
The string methods are available on Series backed by a StringArray. Notes StringArray returns a BooleanArray for comparison methods. Examples
>>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
<StringArray>
['This is', 'some text', <NA>, 'data.']
Length: 4, dtype: string
Unlike arrays instantiated with dtype="object", StringArray will convert the values to strings.
>>> pd.array(['1', 1], dtype="object")
<PandasArray>
['1', 1]
Length: 2, dtype: object
>>> pd.array(['1', 1], dtype="string")
<StringArray>
['1', '1']
Length: 2, dtype: string
However, instantiating StringArrays directly with non-strings will raise an error. For comparison methods, StringArray returns a pandas.BooleanArray:
>>> pd.array(["a", None, "c"], dtype="string") == "a"
<BooleanArray>
[True, <NA>, False]
Length: 3, dtype: boolean
Attributes
None Methods
None | |
doc_26741 |
Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay). The Laplace distribution is similar to the Gaussian/normal distribution, but is sharper at the peak and has fatter tails. It represents the difference between two independent, identically distributed exponential random variables. Note New code should use the laplace method of a default_rng() instance instead; please see the Quick Start. Parameters
locfloat or array_like of floats, optional
The position, \(\mu\), of the distribution peak. Default is 0.
scalefloat or array_like of floats, optional
\(\lambda\), the exponential decay. Default is 1. 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 Laplace distribution. See also Generator.laplace
which should be used for new code. Notes It has the probability density function \[f(x; \mu, \lambda) = \frac{1}{2\lambda} \exp\left(-\frac{|x - \mu|}{\lambda}\right).\] The first law of Laplace, from 1774, states that the frequency of an error can be expressed as an exponential function of the absolute magnitude of the error, which leads to the Laplace distribution. For many problems in economics and health sciences, this distribution seems to model the data better than the standard Gaussian distribution. References 1
Abramowitz, M. and Stegun, I. A. (Eds.). “Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 9th printing,” New York: Dover, 1972. 2
Kotz, Samuel, et. al. “The Laplace Distribution and Generalizations, ” Birkhauser, 2001. 3
Weisstein, Eric W. “Laplace Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/LaplaceDistribution.html 4
Wikipedia, “Laplace distribution”, https://en.wikipedia.org/wiki/Laplace_distribution Examples Draw samples from the distribution >>> loc, scale = 0., 1.
>>> s = np.random.laplace(loc, scale, 1000)
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)
>>> x = np.arange(-8., 8., .01)
>>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)
>>> plt.plot(x, pdf)
Plot Gaussian for comparison: >>> g = (1/(scale * np.sqrt(2 * np.pi)) *
... np.exp(-(x - loc)**2 / (2 * scale**2)))
>>> plt.plot(x,g) | |
doc_26742 |
Bases: matplotlib.ticker.Formatter Use a new-style format string (as used by str.format) to format the tick. The field used for the tick value must be labeled x and the field used for the tick position must be labeled pos. | |
doc_26743 | The object`s repr() after the modification. | |
doc_26744 | Return (exitcode, output) of executing cmd in a shell. Execute the string cmd in a shell with Popen.check_output() and return a 2-tuple (exitcode, output). The locale encoding is used; see the notes on Frequently Used Arguments for more details. A trailing newline is stripped from the output. The exit code for the command can be interpreted as the return code of subprocess. Example: >>> subprocess.getstatusoutput('ls /bin/ls')
(0, '/bin/ls')
>>> subprocess.getstatusoutput('cat /bin/junk')
(1, 'cat: /bin/junk: No such file or directory')
>>> subprocess.getstatusoutput('/bin/junk')
(127, 'sh: /bin/junk: not found')
>>> subprocess.getstatusoutput('/bin/kill $$')
(-15, '')
Availability: POSIX & Windows. Changed in version 3.3.4: Windows support was added. The function now returns (exitcode, output) instead of (status, output) as it did in Python 3.3.3 and earlier. exitcode has the same value as returncode. | |
doc_26745 | Gets or sets the green value of the Color. g -> int The green value of the Color. | |
doc_26746 |
Dot product of two arrays. Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred. If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
If a is an N-D array and b is an M-D array (where M>=2), it is a sum product over the last axis of a and the second-to-last axis of b: dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters
aarray_like
First argument.
barray_like
Second argument.
outndarray, optional
Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for dot(a,b). This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. Returns
outputndarray
Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned. Raises
ValueError
If the last dimension of a is not the same size as the second-to-last dimension of b. See also vdot
Complex-conjugating dot product. tensordot
Sum products over arbitrary axes. einsum
Einstein summation convention. matmul
‘@’ operator as method with out parameter. linalg.multi_dot
Chained dot product. Examples >>> np.dot(3, 4)
12
Neither argument is complex-conjugated: >>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)
For 2-D arrays it is the matrix product: >>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128 | |
doc_26747 | Exit code that means a temporary failure occurred. This indicates something that may not really be an error, such as a network connection that couldn’t be made during a retryable operation. Availability: Unix. | |
doc_26748 | Send the signal sig to the process group pgid. Raises an auditing event os.killpg with arguments pgid, sig. Availability: Unix. | |
doc_26749 | Returns the Python version as tuple (major, minor, patchlevel) of strings. Note that unlike the Python sys.version, the returned value will always include the patchlevel (it defaults to '0'). | |
doc_26750 | socket.IOCTL_VM_SOCKETS_GET_LOCAL_CID
VMADDR*
SO_VM*
Constants for Linux host/guest communication. Availability: Linux >= 4.8. New in version 3.7. | |
doc_26751 | See Migration guide for more details. tf.compat.v1.raw_ops.Max
tf.raw_ops.Max(
input, axis, keep_dims=False, name=None
)
Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64, qint8, quint8, qint32, qint16, quint16. The tensor to reduce.
axis A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
keep_dims An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_26752 | Unconditionally skip the decorated test. reason should describe why the test is being skipped. | |
doc_26753 | class numbers.Number
The root of the numeric hierarchy. If you just want to check if an argument x is a number, without caring what kind, use isinstance(x, Number).
The numeric tower
class numbers.Complex
Subclasses of this type describe complex numbers and include the operations that work on the built-in complex type. These are: conversions to complex and bool, real, imag, +, -, *, /, abs(), conjugate(), ==, and !=. All except - and != are abstract.
real
Abstract. Retrieves the real component of this number.
imag
Abstract. Retrieves the imaginary component of this number.
abstractmethod conjugate()
Abstract. Returns the complex conjugate. For example, (1+3j).conjugate()
== (1-3j).
class numbers.Real
To Complex, Real adds the operations that work on real numbers. In short, those are: a conversion to float, math.trunc(), round(), math.floor(), math.ceil(), divmod(), //, %, <, <=, >, and >=. Real also provides defaults for complex(), real, imag, and conjugate().
class numbers.Rational
Subtypes Real and adds numerator and denominator properties, which should be in lowest terms. With these, it provides a default for float().
numerator
Abstract.
denominator
Abstract.
class numbers.Integral
Subtypes Rational and adds a conversion to int. Provides defaults for float(), numerator, and denominator. Adds abstract methods for ** and bit-string operations: <<, >>, &, ^, |, ~.
Notes for type implementors Implementors should be careful to make equal numbers equal and hash them to the same values. This may be subtle if there are two different extensions of the real numbers. For example, fractions.Fraction implements hash() as follows: def __hash__(self):
if self.denominator == 1:
# Get integers right.
return hash(self.numerator)
# Expensive check, but definitely correct.
if self == float(self):
return hash(float(self))
else:
# Use tuple's hash to avoid a high collision rate on
# simple fractions.
return hash((self.numerator, self.denominator))
Adding More Numeric ABCs There are, of course, more possible ABCs for numbers, and this would be a poor hierarchy if it precluded the possibility of adding those. You can add MyFoo between Complex and Real with: class MyFoo(Complex): ...
MyFoo.register(Real)
Implementing the arithmetic operations We want to implement the arithmetic operations so that mixed-mode operations either call an implementation whose author knew about the types of both arguments, or convert both to the nearest built in type and do the operation there. For subtypes of Integral, this means that __add__() and __radd__() should be defined as: class MyIntegral(Integral):
def __add__(self, other):
if isinstance(other, MyIntegral):
return do_my_adding_stuff(self, other)
elif isinstance(other, OtherTypeIKnowAbout):
return do_my_other_adding_stuff(self, other)
else:
return NotImplemented
def __radd__(self, other):
if isinstance(other, MyIntegral):
return do_my_adding_stuff(other, self)
elif isinstance(other, OtherTypeIKnowAbout):
return do_my_other_adding_stuff(other, self)
elif isinstance(other, Integral):
return int(other) + int(self)
elif isinstance(other, Real):
return float(other) + float(self)
elif isinstance(other, Complex):
return complex(other) + complex(self)
else:
return NotImplemented
There are 5 different cases for a mixed-type operation on subclasses of Complex. I’ll refer to all of the above code that doesn’t refer to MyIntegral and OtherTypeIKnowAbout as “boilerplate”. a will be an instance of A, which is a subtype of Complex (a : A <: Complex), and b : B <:
Complex. I’ll consider a + b: If A defines an __add__() which accepts b, all is well. If A falls back to the boilerplate code, and it were to return a value from __add__(), we’d miss the possibility that B defines a more intelligent __radd__(), so the boilerplate should return NotImplemented from __add__(). (Or A may not implement __add__() at all.) Then B’s __radd__() gets a chance. If it accepts a, all is well. If it falls back to the boilerplate, there are no more possible methods to try, so this is where the default implementation should live. If B <: A, Python tries B.__radd__ before A.__add__. This is ok, because it was implemented with knowledge of A, so it can handle those instances before delegating to Complex. If A <: Complex and B <: Real without sharing any other knowledge, then the appropriate shared operation is the one involving the built in complex, and both __radd__() s land there, so a+b
== b+a. Because most of the operations on any given type will be very similar, it can be useful to define a helper function which generates the forward and reverse instances of any given operator. For example, fractions.Fraction uses: def _operator_fallbacks(monomorphic_operator, fallback_operator):
def forward(a, b):
if isinstance(b, (int, Fraction)):
return monomorphic_operator(a, b)
elif isinstance(b, float):
return fallback_operator(float(a), b)
elif isinstance(b, complex):
return fallback_operator(complex(a), b)
else:
return NotImplemented
forward.__name__ = '__' + fallback_operator.__name__ + '__'
forward.__doc__ = monomorphic_operator.__doc__
def reverse(b, a):
if isinstance(a, Rational):
# Includes ints.
return monomorphic_operator(a, b)
elif isinstance(a, numbers.Real):
return fallback_operator(float(a), float(b))
elif isinstance(a, numbers.Complex):
return fallback_operator(complex(a), complex(b))
else:
return NotImplemented
reverse.__name__ = '__r' + fallback_operator.__name__ + '__'
reverse.__doc__ = monomorphic_operator.__doc__
return forward, reverse
def _add(a, b):
"""a + b"""
return Fraction(a.numerator * b.denominator +
b.numerator * a.denominator,
a.denominator * b.denominator)
__add__, __radd__ = _operator_fallbacks(_add, operator.add)
# ... | |
doc_26754 |
Called when a pan operation completes (when the mouse button is up.) Notes This is intended to be overridden by new projection types. | |
doc_26755 | Handle an exception that did not have an error handler associated with it, or that was raised from an error handler. This always causes a 500 InternalServerError. Always sends the got_request_exception signal. If propagate_exceptions is True, such as in debug mode, the error will be re-raised so that the debugger can display it. Otherwise, the original exception is logged, and an InternalServerError is returned. If an error handler is registered for InternalServerError or 500, it will be used. For consistency, the handler will always receive the InternalServerError. The original unhandled exception is available as e.original_exception. Changelog Changed in version 1.1.0: Always passes the InternalServerError instance to the handler, setting original_exception to the unhandled error. Changed in version 1.1.0: after_request functions and other finalization is done even for the default 500 response when there is no handler. New in version 0.3. Parameters
e (Exception) – Return type
flask.wrappers.Response | |
doc_26756 |
Add an axes to the current figure and make it the current axes. Call signatures: plt.axes()
plt.axes(rect, projection=None, polar=False, **kwargs)
plt.axes(ax)
Parameters
argNone or 4-tuple
The exact behavior of this function depends on the type:
None: A new full window axes is added using subplot(**kwargs). 4-tuple of floats rect = [left, bottom, width, height]. A new axes is added with dimensions rect in normalized (0, 1) units using add_axes on the current figure.
projection{None, 'aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear', str}, optional
The projection type of the Axes. str is the name of a custom projection, see projections. The default None results in a 'rectilinear' projection.
polarbool, default: False
If True, equivalent to projection='polar'.
sharex, shareyAxes, optional
Share the x or y axis with sharex and/or sharey. The axis will have the same limits, ticks, and scale as the axis of the shared axes.
labelstr
A label for the returned axes. Returns
Axes, or a subclass of Axes
The returned axes class depends on the projection used. It is Axes if rectilinear projection is used and projections.polar.PolarAxes if polar projection is used. Other Parameters
**kwargs
This method also takes the keyword arguments for the returned axes class. The keyword arguments for the rectilinear axes class Axes can be found in the following table but there might also be other keyword arguments if another projection is used, see the actual axes class.
Property Description
adjustable {'box', 'datalim'}
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
anchor (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
animated bool
aspect {'auto', 'equal'} or float
autoscale_on bool
autoscalex_on bool
autoscaley_on bool
axes_locator Callable[[Axes, Renderer], Bbox]
axisbelow bool or 'line'
box_aspect float or None
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
facecolor or fc color
figure Figure
frame_on bool
gid str
in_layout bool
label object
navigate bool
navigate_mode unknown
path_effects AbstractPathEffect
picker None or bool or float or callable
position [left, bottom, width, height] or Bbox
prop_cycle unknown
rasterization_zorder float or None
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
title str
transform Transform
url str
visible bool
xbound unknown
xlabel str
xlim (bottom: float, top: float)
xmargin float greater than -0.5
xscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
xticklabels unknown
xticks unknown
ybound unknown
ylabel str
ylim (bottom: float, top: float)
ymargin float greater than -0.5
yscale {"linear", "log", "symlog", "logit", ...} or ScaleBase
yticklabels unknown
yticks unknown
zorder float See also Figure.add_axes
pyplot.subplot
Figure.add_subplot
Figure.subplots
pyplot.subplots
Notes If the figure already has a axes with key (args, kwargs) then it will simply make that axes current and return it. This behavior is deprecated. Meanwhile, if you do not want this behavior (i.e., you want to force the creation of a new axes), you must use a unique set of args and kwargs. The axes label attribute has been exposed for this purpose: if you want two axes that are otherwise identical to be added to the figure, make sure you give them unique labels. Examples # Creating a new full window axes
plt.axes()
# Creating a new axes with specified dimensions and some kwargs
plt.axes((left, bottom, width, height), facecolor='w')
Examples using matplotlib.pyplot.axes
Subplots spacings and margins
Make Room For Ylabel Using Axesgrid
Lasso Demo
Buttons
Check Buttons
Radio Buttons
Thresholding an Image with RangeSlider
Slider
Snapping Sliders to Discrete Values | |
doc_26757 | Returns an int of the bitwise AND of all non-null input values, or default if all values are null. | |
doc_26758 |
Bases: matplotlib.ticker.Formatter String representation of the data at every tick. Parameters
units_mappingdict
Mapping of category names (str) to indices (int). format_ticks(values)[source]
Return the tick labels for all the ticks at once. | |
doc_26759 | See Migration guide for more details. tf.compat.v1.raw_ops.CudnnRNNBackprop
tf.raw_ops.CudnnRNNBackprop(
input, input_h, input_c, params, output, output_h, output_c, output_backprop,
output_h_backprop, output_c_backprop, reserve_space, rnn_mode='lstm',
input_mode='linear_input', direction='unidirectional',
dropout=0, seed=0, seed2=0, name=None
)
Compute the backprop of both data and weights in a RNN. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in for forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.
Args
input A Tensor. Must be one of the following types: half, float32, float64.
input_h A Tensor. Must have the same type as input.
input_c A Tensor. Must have the same type as input.
params A Tensor. Must have the same type as input.
output A Tensor. Must have the same type as input.
output_h A Tensor. Must have the same type as input.
output_c A Tensor. Must have the same type as input.
output_backprop A Tensor. Must have the same type as input.
output_h_backprop A Tensor. Must have the same type as input.
output_c_backprop A Tensor. Must have the same type as input.
reserve_space A Tensor. Must have the same type as input.
rnn_mode An optional string from: "rnn_relu", "rnn_tanh", "lstm", "gru". Defaults to "lstm".
input_mode An optional string from: "linear_input", "skip_input", "auto_select". Defaults to "linear_input".
direction An optional string from: "unidirectional", "bidirectional". Defaults to "unidirectional".
dropout An optional float. Defaults to 0.
seed An optional int. Defaults to 0.
seed2 An optional int. Defaults to 0.
name A name for the operation (optional).
Returns A tuple of Tensor objects (input_backprop, input_h_backprop, input_c_backprop, params_backprop). input_backprop A Tensor. Has the same type as input.
input_h_backprop A Tensor. Has the same type as input.
input_c_backprop A Tensor. Has the same type as input.
params_backprop A Tensor. Has the same type as input. | |
doc_26760 |
Returns a boolean array which is True where the string element in self starts with prefix, otherwise False. See also char.startswith | |
doc_26761 | List of supported TLS channel binding types. Strings in this list can be used as arguments to SSLSocket.get_channel_binding(). New in version 3.3. | |
doc_26762 |
Predict class probabilities for X. Parameters
X{array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
parray of shape (n_samples, n_classes)
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_. | |
doc_26763 |
Initialize self. See help(type(self)) for accurate signature. | |
doc_26764 | See Migration guide for more details. tf.compat.v1.raw_ops.MutableDenseHashTableV2
tf.raw_ops.MutableDenseHashTableV2(
empty_key, deleted_key, value_dtype, container='',
shared_name='', use_node_name_sharing=False, value_shape=[],
initial_num_buckets=131072, max_load_factor=0.8, name=None
)
It uses "open addressing" with quadratic reprobing to resolve collisions. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation.
Args
empty_key A Tensor. The key used to represent empty key buckets internally. Must not be used in insert or lookup operations.
deleted_key A Tensor. Must have the same type as empty_key.
value_dtype A tf.DType. Type of the table values.
container An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
shared_name An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
use_node_name_sharing An optional bool. Defaults to False.
value_shape An optional tf.TensorShape or list of ints. Defaults to []. The shape of each value.
initial_num_buckets An optional int. Defaults to 131072. The initial number of hash table buckets. Must be a power to 2.
max_load_factor An optional float. Defaults to 0.8. The maximum ratio between number of entries and number of buckets before growing the table. Must be between 0 and 1.
name A name for the operation (optional).
Returns A Tensor of type resource. | |
doc_26765 | See torch.floor() | |
doc_26766 |
Set the snapping behavior. Snapping aligns positions with the pixel grid, which results in clearer images. For example, if a black line of 1px width was defined at a position in between two pixels, the resulting image would contain the interpolated value of that line in the pixel grid, which would be a grey value on both adjacent pixel positions. In contrast, snapping will move the line to the nearest integer pixel value, so that the resulting image will really contain a 1px wide black line. Snapping is currently only supported by the Agg and MacOSX backends. Parameters
snapbool or None
Possible values:
True: Snap vertices to the nearest pixel center.
False: Do not modify vertex positions.
None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center. | |
doc_26767 |
Convert coefficient matrix to sparse format. Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The intercept_ member is not converted. Returns
self
Fitted estimator. Notes For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits. After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify. | |
doc_26768 | When data is changed, this is set to True. Only the session dictionary itself is tracked; if the session contains mutable data (for example a nested dict) then this must be set to True manually when modifying that data. The session cookie will only be written to the response if this is True. | |
doc_26769 | Add a header to the message with field name name and value val. The field is appended to the end of the message’s existing headers. Note that this does not overwrite or delete any existing header with the same name. If you want to ensure that the new header is the only one present in the message with field name name, delete the field first, e.g.: del msg['subject']
msg['subject'] = 'Python roolz!'
If the policy defines certain headers to be unique (as the standard policies do), this method may raise a ValueError when an attempt is made to assign a value to such a header when one already exists. This behavior is intentional for consistency’s sake, but do not depend on it as we may choose to make such assignments do an automatic deletion of the existing header in the future. | |
doc_26770 | Called for comments. data is the text of the comment, excluding the leading '<!--' and trailing '-->'. | |
doc_26771 |
Return True if date is first day of month. Examples
>>> ts = pd.Timestamp(2020, 3, 14)
>>> ts.is_month_start
False
>>> ts = pd.Timestamp(2020, 1, 1)
>>> ts.is_month_start
True | |
doc_26772 | Prompt server for an update. Returned data is None if no new messages, else value of RECENT response. | |
doc_26773 | When specified, failures that involve multi-line expected and actual outputs will be displayed using a context diff. | |
doc_26774 |
Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. For each element in the input sequence, each layer computes the following function: rt=σ(Wirxt+bir+Whrh(t−1)+bhr)zt=σ(Wizxt+biz+Whzh(t−1)+bhz)nt=tanh(Winxt+bin+rt∗(Whnh(t−1)+bhn))ht=(1−zt)∗nt+zt∗h(t−1)\begin{array}{ll} r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) * n_t + z_t * h_{(t-1)} \end{array}
where hth_t is the hidden state at time t, xtx_t is the input at time t, h(t−1)h_{(t-1)} is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and rtr_t , ztz_t , ntn_t are the reset, update, and new gates, respectively. σ\sigma is the sigmoid function, and ∗* is the Hadamard product. In a multilayer GRU, the input xt(l)x^{(l)}_t of the ll -th layer (l>=2l >= 2 ) is the hidden state ht(l−1)h^{(l-1)}_t of the previous layer multiplied by dropout δt(l−1)\delta^{(l-1)}_t where each δt(l−1)\delta^{(l-1)}_t is a Bernoulli random variable which is 00 with probability dropout. Parameters
input_size – The number of expected features in the input x
hidden_size – The number of features in the hidden state h
num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two GRUs together to form a stacked GRU, with the second GRU taking in outputs of the first GRU and computing the final results. Default: 1
bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True
batch_first – If True, then the input and output tensors are provided as (batch, seq, feature). Default: False
dropout – If non-zero, introduces a Dropout layer on the outputs of each GRU layer except the last layer, with dropout probability equal to dropout. Default: 0
bidirectional – If True, becomes a bidirectional GRU. Default: False
Inputs: input, h_0
input of shape (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See torch.nn.utils.rnn.pack_padded_sequence() for details.
h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1. Outputs: output, h_n
output of shape (seq_len, batch, num_directions * hidden_size): tensor containing the output features h_t from the last layer of the GRU, for each t. If a torch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated using output.view(seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively. Similarly, the directions can be separated in the packed case.
h_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len Like output, the layers can be separated using h_n.view(num_layers, num_directions, batch, hidden_size). Shape:
Input1: (L,N,Hin)(L, N, H_{in}) tensor containing input features where Hin=input_sizeH_{in}=\text{input\_size} and L represents a sequence length. Input2: (S,N,Hout)(S, N, H_{out}) tensor containing the initial hidden state for each element in the batch. Hout=hidden_sizeH_{out}=\text{hidden\_size} Defaults to zero if not provided. where S=num_layers∗num_directionsS=\text{num\_layers} * \text{num\_directions} If the RNN is bidirectional, num_directions should be 2, else it should be 1. Output1: (L,N,Hall)(L, N, H_{all}) where Hall=num_directions∗hidden_sizeH_{all}=\text{num\_directions} * \text{hidden\_size}
Output2: (S,N,Hout)(S, N, H_{out}) tensor containing the next hidden state for each element in the batch Variables
~GRU.weight_ih_l[k] – the learnable input-hidden weights of the kth\text{k}^{th} layer (W_ir|W_iz|W_in), of shape (3*hidden_size, input_size) for k = 0. Otherwise, the shape is (3*hidden_size, num_directions * hidden_size)
~GRU.weight_hh_l[k] – the learnable hidden-hidden weights of the kth\text{k}^{th} layer (W_hr|W_hz|W_hn), of shape (3*hidden_size, hidden_size)
~GRU.bias_ih_l[k] – the learnable input-hidden bias of the kth\text{k}^{th} layer (b_ir|b_iz|b_in), of shape (3*hidden_size)
~GRU.bias_hh_l[k] – the learnable hidden-hidden bias of the kth\text{k}^{th} layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)
Note All the weights and biases are initialized from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k}) where k=1hidden_sizek = \frac{1}{\text{hidden\_size}} Orphan Note If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype torch.float16 4) V100 GPU is used, 5) input data is not in PackedSequence format persistent algorithm can be selected to improve performance. Examples: >>> rnn = nn.GRU(10, 20, 2)
>>> input = torch.randn(5, 3, 10)
>>> h0 = torch.randn(2, 3, 20)
>>> output, hn = rnn(input, h0) | |
doc_26775 |
Return the line marker. See also set_marker. | |
doc_26776 |
Limit the domain to values between 0 and 1 (excluded). | |
doc_26777 | used to set background clear(surface, bgd) -> None | |
doc_26778 |
Set the DataFrame of strings added to the class attribute of <td> HTML elements. Parameters
classes:DataFrame
DataFrame containing strings that will be translated to CSS classes, mapped by identical column and index key values that must exist on the underlying Styler data. None, NaN values, and empty strings will be ignored and not affect the rendered HTML. Returns
self:Styler
See also Styler.set_table_styles
Set the table styles included within the <style> HTML element. Styler.set_table_attributes
Set the table attributes added to the <table> HTML element. Notes Can be used in combination with Styler.set_table_styles to define an internal CSS solution without reference to external CSS files. Examples
>>> df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
>>> classes = pd.DataFrame([
... ["min-val red", "", "blue"],
... ["red", None, "blue max-val"]
... ], index=df.index, columns=df.columns)
>>> df.style.set_td_classes(classes)
Using MultiIndex columns and a classes DataFrame as a subset of the underlying,
>>> df = pd.DataFrame([[1,2],[3,4]], index=["a", "b"],
... columns=[["level0", "level0"], ["level1a", "level1b"]])
>>> classes = pd.DataFrame(["min-val"], index=["a"],
... columns=[["level0"],["level1a"]])
>>> df.style.set_td_classes(classes)
Form of the output with new additional css classes,
>>> df = pd.DataFrame([[1]])
>>> css = pd.DataFrame([["other-class"]])
>>> s = Styler(df, uuid="_", cell_ids=False).set_td_classes(css)
>>> s.hide(axis=0).to_html()
'<style type="text/css"></style>'
'<table id="T__">'
' <thead>'
' <tr><th class="col_heading level0 col0" >0</th></tr>'
' </thead>'
' <tbody>'
' <tr><td class="data row0 col0 other-class" >1</td></tr>'
' </tbody>'
'</table>' | |
doc_26779 |
Find artist objects. Recursively find all Artist instances contained in the artist. Parameters
match
A filter criterion for the matches. This can be
None: Return all objects contained in artist. A function with signature def match(artist: Artist) -> bool. The result will only contain artists for which the function returns True. A class instance: e.g., Line2D. The result will only contain artists of this class or its subclasses (isinstance check).
include_selfbool
Include self in the list to be checked for a match. Returns
list of Artist | |
doc_26780 | set the current clipping area of the Surface set_clip(rect) -> None set_clip(None) -> None Each Surface has an active clipping area. This is a rectangle that represents the only pixels on the Surface that can be modified. If None is passed for the rectangle the full Surface will be available for changes. The clipping area is always restricted to the area of the Surface itself. If the clip rectangle is too large it will be shrunk to fit inside the Surface. | |
doc_26781 | Open a streaming transport connection to a given address specified by host and port. The socket family can be either AF_INET or AF_INET6 depending on host (or the family argument, if provided). The socket type will be SOCK_STREAM. protocol_factory must be a callable returning an asyncio protocol implementation. This method will try to establish the connection in the background. When successful, it returns a (transport, protocol) pair. The chronological synopsis of the underlying operation is as follows: The connection is established and a transport is created for it.
protocol_factory is called without arguments and is expected to return a protocol instance. The protocol instance is coupled with the transport by calling its connection_made() method. A (transport, protocol) tuple is returned on success. The created transport is an implementation-dependent bidirectional stream. Other arguments:
ssl: if given and not false, a SSL/TLS transport is created (by default a plain TCP transport is created). If ssl is a ssl.SSLContext object, this context is used to create the transport; if ssl is True, a default context returned from ssl.create_default_context() is used. See also SSL/TLS security considerations
server_hostname sets or overrides the hostname that the target server’s certificate will be matched against. Should only be passed if ssl is not None. By default the value of the host argument is used. If host is empty, there is no default and you must pass a value for server_hostname. If server_hostname is an empty string, hostname matching is disabled (which is a serious security risk, allowing for potential man-in-the-middle attacks).
family, proto, flags are the optional address family, protocol and flags to be passed through to getaddrinfo() for host resolution. If given, these should all be integers from the corresponding socket module constants.
happy_eyeballs_delay, if given, enables Happy Eyeballs for this connection. It should be a floating-point number representing the amount of time in seconds to wait for a connection attempt to complete, before starting the next attempt in parallel. This is the “Connection Attempt Delay” as defined in RFC 8305. A sensible default value recommended by the RFC is 0.25 (250 milliseconds).
interleave controls address reordering when a host name resolves to multiple IP addresses. If 0 or unspecified, no reordering is done, and addresses are tried in the order returned by getaddrinfo(). If a positive integer is specified, the addresses are interleaved by address family, and the given integer is interpreted as “First Address Family Count” as defined in RFC 8305. The default is 0 if happy_eyeballs_delay is not specified, and 1 if it is.
sock, if given, should be an existing, already connected socket.socket object to be used by the transport. If sock is given, none of host, port, family, proto, flags, happy_eyeballs_delay, interleave and local_addr should be specified.
local_addr, if given, is a (local_host, local_port) tuple used to bind the socket to locally. The local_host and local_port are looked up using getaddrinfo(), similarly to host and port.
ssl_handshake_timeout is (for a TLS connection) the time in seconds to wait for the TLS handshake to complete before aborting the connection. 60.0 seconds if None (default). New in version 3.8: Added the happy_eyeballs_delay and interleave parameters. Happy Eyeballs Algorithm: Success with Dual-Stack Hosts. When a server’s IPv4 path and protocol are working, but the server’s IPv6 path and protocol are not working, a dual-stack client application experiences significant connection delay compared to an IPv4-only client. This is undesirable because it causes the dual- stack client to have a worse user experience. This document specifies requirements for algorithms that reduce this user-visible delay and provides an algorithm. For more information: https://tools.ietf.org/html/rfc6555 New in version 3.7: The ssl_handshake_timeout parameter. Changed in version 3.6: The socket option TCP_NODELAY is set by default for all TCP connections. Changed in version 3.5: Added support for SSL/TLS in ProactorEventLoop. See also The open_connection() function is a high-level alternative API. It returns a pair of (StreamReader, StreamWriter) that can be used directly in async/await code. | |
doc_26782 |
Generate the “Friedman #3” regression problem. This dataset is described in Friedman [1] and Breiman [2]. Inputs X are 4 independent features uniformly distributed on the intervals: 0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= 11.
The output y is created according to the formula: y(X) = arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0]) + noise * N(0, 1).
Read more in the User Guide. Parameters
n_samplesint, default=100
The number of samples.
noisefloat, default=0.0
The standard deviation of the gaussian noise applied to the output.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary. Returns
Xndarray of shape (n_samples, 4)
The input samples.
yndarray of shape (n_samples,)
The output values. References
1
J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.
2
L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996. | |
doc_26783 | tf.compat.v1.keras.layers.GRUCell(
units, activation='tanh',
recurrent_activation='hard_sigmoid', use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros', kernel_regularizer=None,
recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None,
recurrent_constraint=None, bias_constraint=None, dropout=0.0,
recurrent_dropout=0.0, reset_after=False, **kwargs
)
Arguments
units Positive integer, dimensionality of the output space.
activation Activation function to use. Default: hyperbolic tangent (tanh). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
recurrent_activation Activation function to use for the recurrent step. Default: hard sigmoid (hard_sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
use_bias Boolean, whether the layer uses a bias vector.
kernel_initializer Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
recurrent_initializer Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.
bias_initializer Initializer for the bias vector.
kernel_regularizer Regularizer function applied to the kernel weights matrix.
recurrent_regularizer Regularizer function applied to the recurrent_kernel weights matrix.
bias_regularizer Regularizer function applied to the bias vector.
kernel_constraint Constraint function applied to the kernel weights matrix.
recurrent_constraint Constraint function applied to the recurrent_kernel weights matrix.
bias_constraint Constraint function applied to the bias vector.
dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
reset_after GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before" (default), True = "after" (CuDNN compatible). Call arguments:
inputs: A 2D tensor.
states: List of state tensors corresponding to the previous timestep.
training: Python boolean indicating whether the layer should behave in training mode or in inference mode. Only relevant when dropout or recurrent_dropout is used. Methods get_dropout_mask_for_cell View source
get_dropout_mask_for_cell(
inputs, training, count=1
)
Get the dropout mask for RNN cell's input. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
Args
inputs The input tensor whose shape will be used to generate dropout mask.
training Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.
Returns List of mask tensor, generated or cached mask based on context.
get_initial_state View source
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
get_recurrent_dropout_mask_for_cell View source
get_recurrent_dropout_mask_for_cell(
inputs, training, count=1
)
Get the recurrent dropout mask for RNN cell. It will create mask based on context if there isn't any existing cached mask. If a new mask is generated, it will update the cache in the cell.
Args
inputs The input tensor whose shape will be used to generate dropout mask.
training Boolean tensor, whether its in training mode, dropout will be ignored in non-training mode.
count Int, how many dropout mask will be generated. It is useful for cell that has internal weights fused together.
Returns List of mask tensor, generated or cached mask based on context.
reset_dropout_mask View source
reset_dropout_mask()
Reset the cached dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. reset_recurrent_dropout_mask View source
reset_recurrent_dropout_mask()
Reset the cached recurrent dropout masks if any. This is important for the RNN layer to invoke this in it call() method so that the cached mask is cleared before calling the cell.call(). The mask should be cached across the timestep within the same batch, but shouldn't be cached between batches. Otherwise it will introduce unreasonable bias against certain index of data within the batch. | |
doc_26784 | New in Django 3.2. A boolean value that determines whether to add a final catch-all view to the admin that redirects unauthenticated users to the login page. By default, it is set to True. Warning Setting this to False is not recommended as the view protects against a potential model enumeration privacy issue. | |
doc_26785 |
Calculate any free parameters based on the current cmap and norm, and do all the drawing. | |
doc_26786 |
Construct a new ExtensionArray from a sequence of strings. Parameters
strings:Sequence
Each element will be an instance of the scalar type for this array, cls.dtype.type.
dtype:dtype, optional
Construct for this particular dtype. This should be a Dtype compatible with the ExtensionArray.
copy:bool, default False
If True, copy the underlying data. Returns
ExtensionArray | |
doc_26787 |
Return the intersection of bbox1 and bbox2 if they intersect, or None if they don't. | |
doc_26788 | boolean that is True if the application is served by a WSGI server that spawns multiple processes. | |
doc_26789 |
Set both the edgecolor and the facecolor. Parameters
ccolor or list of rgba tuples
See also
Collection.set_facecolor, Collection.set_edgecolor
For setting the edge or face color individually. | |
doc_26790 |
Alias for set_linewidth. | |
doc_26791 |
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters. Returns
X_newndarray array of shape (n_samples, n_features_new)
Transformed array. | |
doc_26792 | readline.set_history_length(length)
Set or return the desired number of lines to save in the history file. The write_history_file() function uses this value to truncate the history file, by calling history_truncate_file() in the underlying library. Negative values imply unlimited history file size. | |
doc_26793 | Return the shadow password database entry for the given user name. Changed in version 3.6: Raises a PermissionError instead of KeyError if the user doesn’t have privileges. | |
doc_26794 | tf.sparse.reduce_sum(
sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None
)
This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_sum(). In particular, this Op also returns a dense Tensor if output_is_sparse is False, or a SparseTensor if output_is_sparse is True.
Note: if output_is_sparse is True, a gradient is not defined for this function, so it can't be used in training models that need gradient descent.
Reduces sp_input along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1. If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, similar to the indexing rules in Python. For example: # 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_sum(x) ==> 3
tf.sparse.reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse.reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis.
tf.sparse.reduce_sum(x, 1, keepdims=True) ==> [[2], [1]]
tf.sparse.reduce_sum(x, [0, 1]) ==> 3
Args
sp_input The SparseTensor to reduce. Should have numeric type.
axis The dimensions to reduce; list or scalar. If None (the default), reduces all dimensions.
keepdims If true, retain reduced dimensions with length 1.
output_is_sparse If true, returns a SparseTensor instead of a dense Tensor (the default).
name A name for the operation (optional).
Returns The reduced Tensor or the reduced SparseTensor if output_is_sparse is True. | |
doc_26795 | This returns the file descriptor used by the underlying select.kqueue() object. | |
doc_26796 | A dictionary-like object containing all given HTTP GET parameters. See the QueryDict documentation below. | |
doc_26797 |
Compute the precision. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0. Read more in the User Guide. Parameters
y_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labelsarray-like, default=None
The set of labels to include when average != 'binary', and their order if average is None. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Changed in version 0.17: Parameter labels improved for multiclass problem.
pos_labelstr or int, default=1
The class to report if average='binary' and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only.
average{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} default=’binary’
This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
'binary':
Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred}) are binary.
'micro':
Calculate metrics globally by counting the total true positives, false negatives and false positives.
'macro':
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
'weighted':
Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.
'samples':
Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score).
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
zero_division“warn”, 0 or 1, default=”warn”
Sets the value to return when there is a zero division. If set to “warn”, this acts as 0, but warnings are also raised. Returns
precisionfloat (if average is not None) or array of float of shape
(n_unique_labels,) Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task. See also
precision_recall_fscore_support, multilabel_confusion_matrix
Notes When true positive + false positive == 0, precision returns 0 and raises UndefinedMetricWarning. This behavior can be modified with zero_division. Examples >>> from sklearn.metrics import precision_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> precision_score(y_true, y_pred, average='macro')
0.22...
>>> precision_score(y_true, y_pred, average='micro')
0.33...
>>> precision_score(y_true, y_pred, average='weighted')
0.22...
>>> precision_score(y_true, y_pred, average=None)
array([0.66..., 0. , 0. ])
>>> y_pred = [0, 0, 0, 0, 0, 0]
>>> precision_score(y_true, y_pred, average=None)
array([0.33..., 0. , 0. ])
>>> precision_score(y_true, y_pred, average=None, zero_division=1)
array([0.33..., 1. , 1. ]) | |
doc_26798 |
Return a list of dictionaries of statistics used to draw a series of box and whisker plots using bxp. Parameters
Xarray-like
Data that will be represented in the boxplots. Should have 2 or fewer dimensions.
whisfloat or (float, float), default: 1.5
The position of the whiskers. If a float, the lower whisker is at the lowest datum above Q1 - whis*(Q3-Q1), and the upper whisker at the highest datum below Q3 + whis*(Q3-Q1), where Q1 and Q3 are the first and third quartiles. The default value of whis = 1.5 corresponds to Tukey's original definition of boxplots. If a pair of floats, they indicate the percentiles at which to draw the whiskers (e.g., (5, 95)). In particular, setting this to (0, 100) results in whiskers covering the whole range of the data. In the edge case where Q1 == Q3, whis is automatically set to (0, 100) (cover the whole range of the data) if autorange is True. Beyond the whiskers, data are considered outliers and are plotted as individual points.
bootstrapint, optional
Number of times the confidence intervals around the median should be bootstrapped (percentile method).
labelsarray-like, optional
Labels for each dataset. Length must be compatible with dimensions of X.
autorangebool, optional (False)
When True and the data are distributed such that the 25th and 75th percentiles are equal, whis is set to (0, 100) such that the whisker ends are at the minimum and maximum of the data. Returns
list of dict
A list of dictionaries containing the results for each column of data. Keys of each dictionary are the following:
Key Value Description
label tick label for the boxplot
mean arithmetic mean value
med 50th percentile
q1 first quartile (25th percentile)
q3 third quartile (75th percentile)
cilo lower notch around the median
cihi upper notch around the median
whislo end of the lower whisker
whishi end of the upper whisker
fliers outliers Notes Non-bootstrapping approach to confidence interval uses Gaussian-based asymptotic approximation: \[\mathrm{med} \pm 1.57 \times \frac{\mathrm{iqr}}{\sqrt{N}}\] General approach from: McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of Boxplots", The American Statistician, 32:12-16. | |
doc_26799 | display an animated liquid effect liquid.main() -> None This example was created in a quick comparison with the BlitzBasic gaming language. Nonetheless, it demonstrates a quick 8-bit setup (with colormap). |
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