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doc_28100 | Imports an object based on a string. This is useful if you want to use import paths as endpoints or something similar. An import path can be specified either in dotted notation (xml.sax.saxutils.escape) or with a colon as object delimiter (xml.sax.saxutils:escape). If silent is True the return value will be None if the import fails. Parameters
import_name (str) – the dotted name for the object to import.
silent (bool) – if set to True import errors are ignored and None is returned instead. Returns
imported object Return type
Any | |
doc_28101 | See Migration guide for more details. tf.compat.v1.keras.preprocessing.image.array_to_img
tf.keras.preprocessing.image.array_to_img(
x, data_format=None, scale=True, dtype=None
)
Usage: from PIL import Image
img = np.random.random(size=(100, 100, 3))
pil_img = tf.keras.preprocessing.image.array_to_img(img)
Arguments
x Input Numpy array.
data_format Image data format, can be either "channels_first" or "channels_last". Defaults to None, in which case the global setting tf.keras.backend.image_data_format() is used (unless you changed it, it defaults to "channels_last").
scale Whether to rescale image values to be within [0, 255]. Defaults to True.
dtype Dtype to use. Default to None, in which case the global setting tf.keras.backend.floatx() is used (unless you changed it, it defaults to "float32")
Returns A PIL Image instance.
Raises
ImportError if PIL is not available.
ValueError if invalid x or data_format is passed. | |
doc_28102 | Return the arc tangent of x. There are two branch cuts: One extends from 1j along the imaginary axis to ∞j, continuous from the right. The other extends from -1j along the imaginary axis to -∞j, continuous from the left. | |
doc_28103 | skimage.draw.bezier_curve(r0, c0, r1, c1, …) Generate Bezier curve coordinates.
skimage.draw.circle(r, c, radius[, shape]) Generate coordinates of pixels within circle.
skimage.draw.circle_perimeter(r, c, radius) Generate circle perimeter coordinates.
skimage.draw.circle_perimeter_aa(r, c, radius) Generate anti-aliased circle perimeter coordinates.
skimage.draw.disk(center, radius, *[, shape]) Generate coordinates of pixels within circle.
skimage.draw.ellipse(r, c, r_radius, c_radius) Generate coordinates of pixels within ellipse.
skimage.draw.ellipse_perimeter(r, c, …[, …]) Generate ellipse perimeter coordinates.
skimage.draw.ellipsoid(a, b, c[, spacing, …]) Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified spacing.
skimage.draw.ellipsoid_stats(a, b, c) Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified spacing.
skimage.draw.line(r0, c0, r1, c1) Generate line pixel coordinates.
skimage.draw.line_aa(r0, c0, r1, c1) Generate anti-aliased line pixel coordinates.
skimage.draw.line_nd(start, stop, *[, …]) Draw a single-pixel thick line in n dimensions.
skimage.draw.polygon(r, c[, shape]) Generate coordinates of pixels within polygon.
skimage.draw.polygon2mask(image_shape, polygon) Compute a mask from polygon.
skimage.draw.polygon_perimeter(r, c[, …]) Generate polygon perimeter coordinates.
skimage.draw.random_shapes(image_shape, …) Generate an image with random shapes, labeled with bounding boxes.
skimage.draw.rectangle(start[, end, extent, …]) Generate coordinates of pixels within a rectangle.
skimage.draw.rectangle_perimeter(start[, …]) Generate coordinates of pixels that are exactly around a rectangle.
skimage.draw.set_color(image, coords, color) Set pixel color in the image at the given coordinates. bezier_curve
skimage.draw.bezier_curve(r0, c0, r1, c1, r2, c2, weight, shape=None) [source]
Generate Bezier curve coordinates. Parameters
r0, c0int
Coordinates of the first control point.
r1, c1int
Coordinates of the middle control point.
r2, c2int
Coordinates of the last control point.
weightdouble
Middle control point weight, it describes the line tension.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for curves that exceed the image size. If None, the full extent of the curve is used. Returns
rr, cc(N,) ndarray of int
Indices of pixels that belong to the Bezier curve. May be used to directly index into an array, e.g. img[rr, cc] = 1. Notes The algorithm is the rational quadratic algorithm presented in reference [1]. References
1
A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples >>> import numpy as np
>>> from skimage.draw import bezier_curve
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = bezier_curve(1, 5, 5, -2, 8, 8, 2)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
circle
skimage.draw.circle(r, c, radius, shape=None) [source]
Generate coordinates of pixels within circle. Parameters
r, cdouble
Center coordinate of disk.
radiusdouble
Radius of disk.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for disks that exceed the image size. If None, the full extent of the disk is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, ccndarray of int
Pixel coordinates of disk. May be used to directly index into an array, e.g. img[rr, cc] = 1. Warns
Deprecated:
New in version 0.17: This function is deprecated and will be removed in scikit-image 0.19. Please use the function named disk instead.
circle_perimeter
skimage.draw.circle_perimeter(r, c, radius, method='bresenham', shape=None) [source]
Generate circle perimeter coordinates. Parameters
r, cint
Centre coordinate of circle.
radiusint
Radius of circle.
method{‘bresenham’, ‘andres’}, optional
bresenham : Bresenham method (default) andres : Andres method
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, cc(N,) ndarray of int
Bresenham and Andres’ method: Indices of pixels that belong to the circle perimeter. May be used to directly index into an array, e.g. img[rr, cc] = 1. Notes Andres method presents the advantage that concentric circles create a disc whereas Bresenham can make holes. There is also less distortions when Andres circles are rotated. Bresenham method is also known as midpoint circle algorithm. Anti-aliased circle generator is available with circle_perimeter_aa. References
1
J.E. Bresenham, “Algorithm for computer control of a digital plotter”, IBM Systems journal, 4 (1965) 25-30.
2
E. Andres, “Discrete circles, rings and spheres”, Computers & Graphics, 18 (1994) 695-706. Examples >>> from skimage.draw import circle_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = circle_perimeter(4, 4, 3)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
circle_perimeter_aa
skimage.draw.circle_perimeter_aa(r, c, radius, shape=None) [source]
Generate anti-aliased circle perimeter coordinates. Parameters
r, cint
Centre coordinate of circle.
radiusint
Radius of circle.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles that exceed the image size. If None, the full extent of the circle is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, cc, val(N,) ndarray (int, int, float)
Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val. Notes Wu’s method draws anti-aliased circle. This implementation doesn’t use lookup table optimization. Use the function draw.set_color to apply circle_perimeter_aa results to color images. References
1
X. Wu, “An efficient antialiasing technique”, In ACM SIGGRAPH Computer Graphics, 25 (1991) 143-152. Examples >>> from skimage.draw import circle_perimeter_aa
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc, val = circle_perimeter_aa(4, 4, 3)
>>> img[rr, cc] = val * 255
>>> img
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 60, 211, 255, 211, 60, 0, 0, 0],
[ 0, 60, 194, 43, 0, 43, 194, 60, 0, 0],
[ 0, 211, 43, 0, 0, 0, 43, 211, 0, 0],
[ 0, 255, 0, 0, 0, 0, 0, 255, 0, 0],
[ 0, 211, 43, 0, 0, 0, 43, 211, 0, 0],
[ 0, 60, 194, 43, 0, 43, 194, 60, 0, 0],
[ 0, 0, 60, 211, 255, 211, 60, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> from skimage import data, draw
>>> image = data.chelsea()
>>> rr, cc, val = draw.circle_perimeter_aa(r=100, c=100, radius=75)
>>> draw.set_color(image, (rr, cc), [1, 0, 0], alpha=val)
disk
skimage.draw.disk(center, radius, *, shape=None) [source]
Generate coordinates of pixels within circle. Parameters
centertuple
Center coordinate of disk.
radiusdouble
Radius of disk.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for disks that exceed the image size. If None, the full extent of the disk is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, ccndarray of int
Pixel coordinates of disk. May be used to directly index into an array, e.g. img[rr, cc] = 1. Examples >>> from skimage.draw import disk
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = disk((4, 4), 5)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
ellipse
skimage.draw.ellipse(r, c, r_radius, c_radius, shape=None, rotation=0.0) [source]
Generate coordinates of pixels within ellipse. Parameters
r, cdouble
Centre coordinate of ellipse.
r_radius, c_radiusdouble
Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses which exceed the image size. By default the full extent of the ellipse are used. Must be at least length 2. Only the first two values are used to determine the extent.
rotationfloat, optional (default 0.)
Set the ellipse rotation (rotation) in range (-PI, PI) in contra clock wise direction, so PI/2 degree means swap ellipse axis Returns
rr, ccndarray of int
Pixel coordinates of ellipse. May be used to directly index into an array, e.g. img[rr, cc] = 1. Notes The ellipse equation: ((x * cos(alpha) + y * sin(alpha)) / x_radius) ** 2 +
((x * sin(alpha) - y * cos(alpha)) / y_radius) ** 2 = 1
Note that the positions of ellipse without specified shape can have also, negative values, as this is correct on the plane. On the other hand using these ellipse positions for an image afterwards may lead to appearing on the other side of image, because image[-1, -1] = image[end-1, end-1] >>> rr, cc = ellipse(1, 2, 3, 6)
>>> img = np.zeros((6, 12), dtype=np.uint8)
>>> img[rr, cc] = 1
>>> img
array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1]], dtype=uint8)
Examples >>> from skimage.draw import ellipse
>>> img = np.zeros((10, 12), dtype=np.uint8)
>>> rr, cc = ellipse(5, 6, 3, 5, rotation=np.deg2rad(30))
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
Examples using skimage.draw.ellipse
Masked Normalized Cross-Correlation
Measure region properties ellipse_perimeter
skimage.draw.ellipse_perimeter(r, c, r_radius, c_radius, orientation=0, shape=None) [source]
Generate ellipse perimeter coordinates. Parameters
r, cint
Centre coordinate of ellipse.
r_radius, c_radiusint
Minor and major semi-axes. (r/r_radius)**2 + (c/c_radius)**2 = 1.
orientationdouble, optional
Major axis orientation in clockwise direction as radians.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for ellipses that exceed the image size. If None, the full extent of the ellipse is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, cc(N,) ndarray of int
Indices of pixels that belong to the ellipse perimeter. May be used to directly index into an array, e.g. img[rr, cc] = 1. References
1
A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples >>> from skimage.draw import ellipse_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = ellipse_perimeter(5, 5, 3, 4)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
Note that the positions of ellipse without specified shape can have also, negative values, as this is correct on the plane. On the other hand using these ellipse positions for an image afterwards may lead to appearing on the other side of image, because image[-1, -1] = image[end-1, end-1] >>> rr, cc = ellipse_perimeter(2, 3, 4, 5)
>>> img = np.zeros((9, 12), dtype=np.uint8)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]], dtype=uint8)
ellipsoid
skimage.draw.ellipsoid(a, b, c, spacing=(1.0, 1.0, 1.0), levelset=False) [source]
Generates ellipsoid with semimajor axes aligned with grid dimensions on grid with specified spacing. Parameters
afloat
Length of semimajor axis aligned with x-axis.
bfloat
Length of semimajor axis aligned with y-axis.
cfloat
Length of semimajor axis aligned with z-axis.
spacingtuple of floats, length 3
Spacing in (x, y, z) spatial dimensions.
levelsetbool
If True, returns the level set for this ellipsoid (signed level set about zero, with positive denoting interior) as np.float64. False returns a binarized version of said level set. Returns
ellip(N, M, P) array
Ellipsoid centered in a correctly sized array for given spacing. Boolean dtype unless levelset=True, in which case a float array is returned with the level set above 0.0 representing the ellipsoid.
ellipsoid_stats
skimage.draw.ellipsoid_stats(a, b, c) [source]
Calculates analytical surface area and volume for ellipsoid with semimajor axes aligned with grid dimensions of specified spacing. Parameters
afloat
Length of semimajor axis aligned with x-axis.
bfloat
Length of semimajor axis aligned with y-axis.
cfloat
Length of semimajor axis aligned with z-axis. Returns
volfloat
Calculated volume of ellipsoid.
surffloat
Calculated surface area of ellipsoid.
line
skimage.draw.line(r0, c0, r1, c1) [source]
Generate line pixel coordinates. Parameters
r0, c0int
Starting position (row, column).
r1, c1int
End position (row, column). Returns
rr, cc(N,) ndarray of int
Indices of pixels that belong to the line. May be used to directly index into an array, e.g. img[rr, cc] = 1. Notes Anti-aliased line generator is available with line_aa. Examples >>> from skimage.draw import line
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = line(1, 1, 8, 8)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
line_aa
skimage.draw.line_aa(r0, c0, r1, c1) [source]
Generate anti-aliased line pixel coordinates. Parameters
r0, c0int
Starting position (row, column).
r1, c1int
End position (row, column). Returns
rr, cc, val(N,) ndarray (int, int, float)
Indices of pixels (rr, cc) and intensity values (val). img[rr, cc] = val. References
1
A Rasterizing Algorithm for Drawing Curves, A. Zingl, 2012 http://members.chello.at/easyfilter/Bresenham.pdf Examples >>> from skimage.draw import line_aa
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc, val = line_aa(1, 1, 8, 8)
>>> img[rr, cc] = val * 255
>>> img
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 255, 74, 0, 0, 0, 0, 0, 0, 0],
[ 0, 74, 255, 74, 0, 0, 0, 0, 0, 0],
[ 0, 0, 74, 255, 74, 0, 0, 0, 0, 0],
[ 0, 0, 0, 74, 255, 74, 0, 0, 0, 0],
[ 0, 0, 0, 0, 74, 255, 74, 0, 0, 0],
[ 0, 0, 0, 0, 0, 74, 255, 74, 0, 0],
[ 0, 0, 0, 0, 0, 0, 74, 255, 74, 0],
[ 0, 0, 0, 0, 0, 0, 0, 74, 255, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
line_nd
skimage.draw.line_nd(start, stop, *, endpoint=False, integer=True) [source]
Draw a single-pixel thick line in n dimensions. The line produced will be ndim-connected. That is, two subsequent pixels in the line will be either direct or diagonal neighbours in n dimensions. Parameters
startarray-like, shape (N,)
The start coordinates of the line.
stoparray-like, shape (N,)
The end coordinates of the line.
endpointbool, optional
Whether to include the endpoint in the returned line. Defaults to False, which allows for easy drawing of multi-point paths.
integerbool, optional
Whether to round the coordinates to integer. If True (default), the returned coordinates can be used to directly index into an array. False could be used for e.g. vector drawing. Returns
coordstuple of arrays
The coordinates of points on the line. Examples >>> lin = line_nd((1, 1), (5, 2.5), endpoint=False)
>>> lin
(array([1, 2, 3, 4]), array([1, 1, 2, 2]))
>>> im = np.zeros((6, 5), dtype=int)
>>> im[lin] = 1
>>> im
array([[0, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 0, 0]])
>>> line_nd([2, 1, 1], [5, 5, 2.5], endpoint=True)
(array([2, 3, 4, 4, 5]), array([1, 2, 3, 4, 5]), array([1, 1, 2, 2, 2]))
polygon
skimage.draw.polygon(r, c, shape=None) [source]
Generate coordinates of pixels within polygon. Parameters
r(N,) ndarray
Row coordinates of vertices of polygon.
c(N,) ndarray
Column coordinates of vertices of polygon.
shapetuple, optional
Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extent of the polygon is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image. Returns
rr, ccndarray of int
Pixel coordinates of polygon. May be used to directly index into an array, e.g. img[rr, cc] = 1. Examples >>> from skimage.draw import polygon
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> r = np.array([1, 2, 8])
>>> c = np.array([1, 7, 4])
>>> rr, cc = polygon(r, c)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
polygon2mask
skimage.draw.polygon2mask(image_shape, polygon) [source]
Compute a mask from polygon. Parameters
image_shapetuple of size 2.
The shape of the mask.
polygonarray_like.
The polygon coordinates of shape (N, 2) where N is the number of points. Returns
mask2-D ndarray of type ‘bool’.
The mask that corresponds to the input polygon. Notes This function does not do any border checking, so that all the vertices need to be within the given shape. Examples >>> image_shape = (128, 128)
>>> polygon = np.array([[60, 100], [100, 40], [40, 40]])
>>> mask = polygon2mask(image_shape, polygon)
>>> mask.shape
(128, 128)
polygon_perimeter
skimage.draw.polygon_perimeter(r, c, shape=None, clip=False) [source]
Generate polygon perimeter coordinates. Parameters
r(N,) ndarray
Row coordinates of vertices of polygon.
c(N,) ndarray
Column coordinates of vertices of polygon.
shapetuple, optional
Image shape which is used to determine maximum extents of output pixel coordinates. This is useful for polygons that exceed the image size. If None, the full extents of the polygon is used. Must be at least length 2. Only the first two values are used to determine the extent of the input image.
clipbool, optional
Whether to clip the polygon to the provided shape. If this is set to True, the drawn figure will always be a closed polygon with all edges visible. Returns
rr, ccndarray of int
Pixel coordinates of polygon. May be used to directly index into an array, e.g. img[rr, cc] = 1. Examples >>> from skimage.draw import polygon_perimeter
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = polygon_perimeter([5, -1, 5, 10],
... [-1, 5, 11, 5],
... shape=img.shape, clip=True)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0, 0]], dtype=uint8)
random_shapes
skimage.draw.random_shapes(image_shape, max_shapes, min_shapes=1, min_size=2, max_size=None, multichannel=True, num_channels=3, shape=None, intensity_range=None, allow_overlap=False, num_trials=100, random_seed=None) [source]
Generate an image with random shapes, labeled with bounding boxes. The image is populated with random shapes with random sizes, random locations, and random colors, with or without overlap. Shapes have random (row, col) starting coordinates and random sizes bounded by min_size and max_size. It can occur that a randomly generated shape will not fit the image at all. In that case, the algorithm will try again with new starting coordinates a certain number of times. However, it also means that some shapes may be skipped altogether. In that case, this function will generate fewer shapes than requested. Parameters
image_shapetuple
The number of rows and columns of the image to generate.
max_shapesint
The maximum number of shapes to (attempt to) fit into the shape.
min_shapesint, optional
The minimum number of shapes to (attempt to) fit into the shape.
min_sizeint, optional
The minimum dimension of each shape to fit into the image.
max_sizeint, optional
The maximum dimension of each shape to fit into the image.
multichannelbool, optional
If True, the generated image has num_channels color channels, otherwise generates grayscale image.
num_channelsint, optional
Number of channels in the generated image. If 1, generate monochrome images, else color images with multiple channels. Ignored if multichannel is set to False.
shape{rectangle, circle, triangle, ellipse, None} str, optional
The name of the shape to generate or None to pick random ones.
intensity_range{tuple of tuples of uint8, tuple of uint8}, optional
The range of values to sample pixel values from. For grayscale images the format is (min, max). For multichannel - ((min, max),) if the ranges are equal across the channels, and ((min_0, max_0), … (min_N, max_N)) if they differ. As the function supports generation of uint8 arrays only, the maximum range is (0, 255). If None, set to (0, 254) for each channel reserving color of intensity = 255 for background.
allow_overlapbool, optional
If True, allow shapes to overlap.
num_trialsint, optional
How often to attempt to fit a shape into the image before skipping it.
random_seedint, optional
Seed to initialize the random number generator. If None, a random seed from the operating system is used. Returns
imageuint8 array
An image with the fitted shapes.
labelslist
A list of labels, one per shape in the image. Each label is a (category, ((r0, r1), (c0, c1))) tuple specifying the category and bounding box coordinates of the shape. Examples >>> import skimage.draw
>>> image, labels = skimage.draw.random_shapes((32, 32), max_shapes=3)
>>> image
array([
[[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
...,
[255, 255, 255],
[255, 255, 255],
[255, 255, 255]]], dtype=uint8)
>>> labels
[('circle', ((22, 18), (25, 21))),
('triangle', ((5, 6), (13, 13)))]
rectangle
skimage.draw.rectangle(start, end=None, extent=None, shape=None) [source]
Generate coordinates of pixels within a rectangle. Parameters
starttuple
Origin point of the rectangle, e.g., ([plane,] row, column).
endtuple
End point of the rectangle ([plane,] row, column). For a 2D matrix, the slice defined by the rectangle is [start:(end+1)]. Either end or extent must be specified.
extenttuple
The extent (size) of the drawn rectangle. E.g., ([num_planes,] num_rows, num_cols). Either end or extent must be specified. A negative extent is valid, and will result in a rectangle going along the opposite direction. If extent is negative, the start point is not included.
shapetuple, optional
Image shape used to determine the maximum bounds of the output coordinates. This is useful for clipping rectangles that exceed the image size. By default, no clipping is done. Returns
coordsarray of int, shape (Ndim, Npoints)
The coordinates of all pixels in the rectangle. Notes This function can be applied to N-dimensional images, by passing start and end or extent as tuples of length N. Examples >>> import numpy as np
>>> from skimage.draw import rectangle
>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> start = (1, 1)
>>> extent = (3, 3)
>>> rr, cc = rectangle(start, extent=extent, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> start = (0, 1)
>>> end = (3, 3)
>>> rr, cc = rectangle(start, end=end, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
array([[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype=uint8)
>>> import numpy as np
>>> from skimage.draw import rectangle
>>> img = np.zeros((6, 6), dtype=np.uint8)
>>> start = (3, 3)
>>>
>>> rr, cc = rectangle(start, extent=(2, 2))
>>> img[rr, cc] = 1
>>> rr, cc = rectangle(start, extent=(-2, 2))
>>> img[rr, cc] = 2
>>> rr, cc = rectangle(start, extent=(-2, -2))
>>> img[rr, cc] = 3
>>> rr, cc = rectangle(start, extent=(2, -2))
>>> img[rr, cc] = 4
>>> print(img)
[[0 0 0 0 0 0]
[0 3 3 2 2 0]
[0 3 3 2 2 0]
[0 4 4 1 1 0]
[0 4 4 1 1 0]
[0 0 0 0 0 0]]
rectangle_perimeter
skimage.draw.rectangle_perimeter(start, end=None, extent=None, shape=None, clip=False) [source]
Generate coordinates of pixels that are exactly around a rectangle. Parameters
starttuple
Origin point of the inner rectangle, e.g., (row, column).
endtuple
End point of the inner rectangle (row, column). For a 2D matrix, the slice defined by inner the rectangle is [start:(end+1)]. Either end or extent must be specified.
extenttuple
The extent (size) of the inner rectangle. E.g., (num_rows, num_cols). Either end or extent must be specified. Negative extents are permitted. See rectangle to better understand how they behave.
shapetuple, optional
Image shape used to determine the maximum bounds of the output coordinates. This is useful for clipping perimeters that exceed the image size. By default, no clipping is done. Must be at least length 2. Only the first two values are used to determine the extent of the input image.
clipbool, optional
Whether to clip the perimeter to the provided shape. If this is set to True, the drawn figure will always be a closed polygon with all edges visible. Returns
coordsarray of int, shape (2, Npoints)
The coordinates of all pixels in the rectangle. Examples >>> import numpy as np
>>> from skimage.draw import rectangle_perimeter
>>> img = np.zeros((5, 6), dtype=np.uint8)
>>> start = (2, 3)
>>> end = (3, 4)
>>> rr, cc = rectangle_perimeter(start, end=end, shape=img.shape)
>>> img[rr, cc] = 1
>>> img
array([[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 0, 0, 1],
[0, 0, 1, 0, 0, 1],
[0, 0, 1, 1, 1, 1]], dtype=uint8)
>>> img = np.zeros((5, 5), dtype=np.uint8)
>>> r, c = rectangle_perimeter(start, (10, 10), shape=img.shape, clip=True)
>>> img[r, c] = 1
>>> img
array([[0, 0, 0, 0, 0],
[0, 0, 1, 1, 1],
[0, 0, 1, 0, 1],
[0, 0, 1, 0, 1],
[0, 0, 1, 1, 1]], dtype=uint8)
set_color
skimage.draw.set_color(image, coords, color, alpha=1) [source]
Set pixel color in the image at the given coordinates. Note that this function modifies the color of the image in-place. Coordinates that exceed the shape of the image will be ignored. Parameters
image(M, N, D) ndarray
Image
coordstuple of ((P,) ndarray, (P,) ndarray)
Row and column coordinates of pixels to be colored.
color(D,) ndarray
Color to be assigned to coordinates in the image.
alphascalar or (N,) ndarray
Alpha values used to blend color with image. 0 is transparent, 1 is opaque. Examples >>> from skimage.draw import line, set_color
>>> img = np.zeros((10, 10), dtype=np.uint8)
>>> rr, cc = line(1, 1, 20, 20)
>>> set_color(img, (rr, cc), 1)
>>> img
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]], dtype=uint8) | |
doc_28104 | Returns an alternative filename based on the file_root and file_ext parameters, an underscore plus a random 7 character alphanumeric string is appended to the filename before the extension. | |
doc_28105 | Match.end([group])
Return the indices of the start and end of the substring matched by group; group defaults to zero (meaning the whole matched substring). Return -1 if group exists but did not contribute to the match. For a match object m, and a group g that did contribute to the match, the substring matched by group g (equivalent to m.group(g)) is m.string[m.start(g):m.end(g)]
Note that m.start(group) will equal m.end(group) if group matched a null string. For example, after m = re.search('b(c?)', 'cba'), m.start(0) is 1, m.end(0) is 2, m.start(1) and m.end(1) are both 2, and m.start(2) raises an IndexError exception. An example that will remove remove_this from email addresses: >>> email = "tony@tiremove_thisger.net"
>>> m = re.search("remove_this", email)
>>> email[:m.start()] + email[m.end():]
'tony@tiger.net' | |
doc_28106 | Returns sum(x^2) - sum(x)^2/N (“sum of squares” of the independent variable) as a float, or default if there aren’t any matching rows. | |
doc_28107 | Defaults to True since most aggregate functions can be used as the source expression in Window. | |
doc_28108 |
Factory function for creating a subclass of Styler. Uses custom templates and Jinja environment. Changed in version 1.3.0. Parameters
searchpath:str or list
Path or paths of directories containing the templates.
html_table:str
Name of your custom template to replace the html_table template. New in version 1.3.0.
html_style:str
Name of your custom template to replace the html_style template. New in version 1.3.0. Returns
MyStyler:subclass of Styler
Has the correct env,``template_html``, template_html_table and template_html_style class attributes set. | |
doc_28109 | See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingAdagradParameters
tf.raw_ops.LoadTPUEmbeddingAdagradParameters(
parameters, accumulators, num_shards, shard_id, table_id=-1,
table_name='', config='', name=None
)
An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
Args
parameters A Tensor of type float32. Value of parameters used in the Adagrad optimization algorithm.
accumulators A Tensor of type float32. Value of accumulators used in the Adagrad optimization algorithm.
num_shards An int.
shard_id An int.
table_id An optional int. Defaults to -1.
table_name An optional string. Defaults to "".
config An optional string. Defaults to "".
name A name for the operation (optional).
Returns The created Operation. | |
doc_28110 |
Stain to RGB color space conversion. Parameters
stains(…, 3) array_like
The image in stain color space. Final dimension denotes channels. conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1]. Returns
out(…, 3) ndarray
The image in RGB format. Same dimensions as input. Raises
ValueError
If stains is not at least 2-D with shape (…, 3). Notes Stain combination matrices available in the color module and their respective colorspace:
rgb_from_hed: Hematoxylin + Eosin + DAB
rgb_from_hdx: Hematoxylin + DAB
rgb_from_fgx: Feulgen + Light Green
rgb_from_bex: Giemsa stain : Methyl Blue + Eosin
rgb_from_rbd: FastRed + FastBlue + DAB
rgb_from_gdx: Methyl Green + DAB
rgb_from_hax: Hematoxylin + AEC
rgb_from_bro: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G
rgb_from_bpx: Methyl Blue + Ponceau Fuchsin
rgb_from_ahx: Alcian Blue + Hematoxylin
rgb_from_hpx: Hematoxylin + PAS References
1
https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html
2
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol. 23, no. 4, pp. 291–299, Aug. 2001. Examples >>> from skimage import data
>>> from skimage.color import (separate_stains, combine_stains,
... hdx_from_rgb, rgb_from_hdx)
>>> ihc = data.immunohistochemistry()
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
>>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx) | |
doc_28111 | Scans through the table and validates the given check constraint on existing rows. | |
doc_28112 |
Change the font family. May be either an alias (generic name is CSS parlance), such as: 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace', a real font name or a list of real font names. Real font names are not supported when rcParams["text.usetex"] (default: False) is True. | |
doc_28113 |
Return the name of the Series. The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter. Returns
label (hashable object)
The name of the Series, also the column name if part of a DataFrame. See also Series.rename
Sets the Series name when given a scalar input. Index.name
Corresponding Index property. Examples The Series name can be set initially when calling the constructor.
>>> s = pd.Series([1, 2, 3], dtype=np.int64, name='Numbers')
>>> s
0 1
1 2
2 3
Name: Numbers, dtype: int64
>>> s.name = "Integers"
>>> s
0 1
1 2
2 3
Name: Integers, dtype: int64
The name of a Series within a DataFrame is its column name.
>>> df = pd.DataFrame([[1, 2], [3, 4], [5, 6]],
... columns=["Odd Numbers", "Even Numbers"])
>>> df
Odd Numbers Even Numbers
0 1 2
1 3 4
2 5 6
>>> df["Even Numbers"].name
'Even Numbers' | |
doc_28114 | Removes the specified filter filter from this logger. | |
doc_28115 |
Applies a 1D adaptive average pooling over an input signal composed of several input planes. The output size is H, for any input size. The number of output features is equal to the number of input planes. Parameters
output_size – the target output size H Examples >>> # target output size of 5
>>> m = nn.AdaptiveAvgPool1d(5)
>>> input = torch.randn(1, 64, 8)
>>> output = m(input) | |
doc_28116 | The I/O mode for the file, either "r", "rw", or "w". | |
doc_28117 | tf.experimental.numpy.diagflat(
v, k=0
)
See the NumPy documentation for numpy.diagflat. | |
doc_28118 |
Set the points of the bounding box directly from a numpy array of the form: [[x0, y0], [x1, y1]]. No error checking is performed, as this method is mainly for internal use. | |
doc_28119 | The path to the source file. | |
doc_28120 | This is a relative of setattr(). The arguments are an object and a string. The string must be the name of one of the object’s attributes. The function deletes the named attribute, provided the object allows it. For example, delattr(x, 'foobar') is equivalent to del x.foobar. | |
doc_28121 |
Fit estimator using RANSAC algorithm. Parameters
Xarray-like or sparse matrix, shape [n_samples, n_features]
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it. New in version 0.18. Raises
ValueError
If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples. | |
doc_28122 | The UUID version number (1 through 5, meaningful only when the variant is RFC_4122). | |
doc_28123 | Similar to waitpid(), except no process id argument is given and a 3-element tuple containing the child’s process id, exit status indication, and resource usage information is returned. Refer to resource.getrusage() for details on resource usage information. The option argument is the same as that provided to waitpid() and wait4(). waitstatus_to_exitcode() can be used to convert the exit status into an exitcode. Availability: Unix. | |
doc_28124 | In-place version of ceil() | |
doc_28125 |
Load the covertype dataset (classification). Download it if necessary.
Classes 7
Samples total 581012
Dimensionality 54
Features int Read more in the User Guide. Parameters
data_homestr, default=None
Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
download_if_missingbool, default=True
If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See Glossary.
shufflebool, default=False
Whether to shuffle dataset.
return_X_ybool, default=False
If True, returns (data.data, data.target) instead of a Bunch object. New in version 0.20.
as_framebool, default=False
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below. New in version 0.24. Returns
datasetBunch
Dictionary-like object, with the following attributes.
datandarray of shape (581012, 54)
Each row corresponds to the 54 features in the dataset.
targetndarray of shape (581012,)
Each value corresponds to one of the 7 forest covertypes with values ranging between 1 to 7.
framedataframe of shape (581012, 53)
Only present when as_frame=True. Contains data and target.
DESCRstr
Description of the forest covertype dataset.
feature_nameslist
The names of the dataset columns. target_names: list
The names of the target columns.
(data, target)tuple if return_X_y is True
New in version 0.20. | |
doc_28126 | enum.IntEnum collection of CERT_* constants. New in version 3.6. | |
doc_28127 |
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
\(R^2\) of self.predict(X) wrt. y. Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). | |
doc_28128 | Boolean. Designates whether this user account should be considered active. We recommend that you set this flag to False instead of deleting accounts; that way, if your applications have any foreign keys to users, the foreign keys won’t break. This doesn’t necessarily control whether or not the user can log in. Authentication backends aren’t required to check for the is_active flag but the default backend (ModelBackend) and the RemoteUserBackend do. You can use AllowAllUsersModelBackend or AllowAllUsersRemoteUserBackend if you want to allow inactive users to login. In this case, you’ll also want to customize the AuthenticationForm used by the LoginView as it rejects inactive users. Be aware that the permission-checking methods such as has_perm() and the authentication in the Django admin all return False for inactive users. | |
doc_28129 |
GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a graph attribute, as well as code and forward attributes generated from that graph. Warning When graph is reassigned, code and forward will be automatically regenerated. However, if you edit the contents of the graph without reassigning the graph attribute itself, you must call recompile() to update the generated code.
__init__(root, graph, class_name='GraphModule') [source]
Construct a GraphModule. Parameters
root (Union[torch.nn.Module, Dict[str, Any]) – root can either be an nn.Module instance or a Dict mapping strings to any attribute type. In the case that root is a Module, any references to Module-based objects (via qualified name) in the Graph’s Nodes’ target field will be copied over from the respective place within root’s Module hierarchy into the GraphModule’s module hierarchy. In the case that root is a dict, the qualified name found in a Node’s target will be looked up directly in the dict’s keys. The object mapped to by the Dict will be copied over into the appropriate place within the GraphModule’s module hierarchy.
graph (Graph) – graph contains the nodes this GraphModule should use for code generation
name (str) – name denotes the name of this GraphModule for debugging purposes. If it’s unset, all error messages will report as originating from GraphModule. It may be helpful to set this to root’s original name or a name that makes sense within the context of your transform.
property code
Return the Python code generated from the Graph underlying this GraphModule.
property graph
Return the Graph underlying this GraphModule
recompile() [source]
Recompile this GraphModule from its graph attribute. This should be called after editing the contained graph, otherwise the generated code of this GraphModule will be out of date.
to_folder(folder, module_name='FxModule') [source]
Dumps out module to folder with module_name so that it can be imported with from <folder> import <module_name> Parameters
folder (Union[str, os.PathLike]) – The folder to write the code out to
module_name (str) – Top-level name to use for the Module while writing out the code | |
doc_28130 |
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_28131 |
Bases: matplotlib.transforms.BboxBase A Bbox that is automatically transformed by a given transform. When either the child bounding box or transform changes, the bounds of this bbox will update accordingly. Parameters
bboxBbox
transformTransform
__init__(bbox, transform, **kwargs)[source]
Parameters
bboxBbox
transformTransform
__module__='matplotlib.transforms'
__str__()[source]
Return str(self).
get_points()[source] | |
doc_28132 | See Migration guide for more details. tf.compat.v1.raw_ops.SparseCountSparseOutput
tf.raw_ops.SparseCountSparseOutput(
indices, values, dense_shape, weights, binary_output, minlength=-1,
maxlength=-1, name=None
)
Counts the number of times each value occurs in the input.
Args
indices A Tensor of type int64. Tensor containing the indices of the sparse tensor to count.
values A Tensor. Must be one of the following types: int32, int64. Tensor containing values of the sparse tensor to count.
dense_shape A Tensor of type int64. Tensor containing the dense shape of the sparse tensor to count.
weights A Tensor. Must be one of the following types: int32, int64, float32, float64. A Tensor of the same shape as indices containing per-index weight values. May also be the empty tensor if no weights are used.
binary_output A bool. Whether to output the number of occurrences of each value or 1.
minlength An optional int that is >= -1. Defaults to -1. Minimum value to count. Can be set to -1 for no minimum.
maxlength An optional int that is >= -1. Defaults to -1. Maximum value to count. Can be set to -1 for no maximum.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output_indices, output_values, output_dense_shape). output_indices A Tensor of type int64.
output_values A Tensor. Has the same type as weights.
output_dense_shape A Tensor of type int64. | |
doc_28133 |
Set the artist's visibility. Parameters
bbool | |
doc_28134 | tf.optimizers.serialize Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.optimizers.serialize
tf.keras.optimizers.serialize(
optimizer
) | |
doc_28135 | '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_28136 | from myapp.serializers import PurchaseSerializer
from rest_framework import generics
class PurchaseList(generics.ListAPIView):
serializer_class = PurchaseSerializer
def get_queryset(self):
"""
This view should return a list of all the purchases
for the currently authenticated user.
"""
user = self.request.user
return Purchase.objects.filter(purchaser=user)
Filtering against the URL Another style of filtering might involve restricting the queryset based on some part of the URL. For example if your URL config contained an entry like this: re_path('^purchases/(?P<username>.+)/$', PurchaseList.as_view()),
You could then write a view that returned a purchase queryset filtered by the username portion of the URL: class PurchaseList(generics.ListAPIView):
serializer_class = PurchaseSerializer
def get_queryset(self):
"""
This view should return a list of all the purchases for
the user as determined by the username portion of the URL.
"""
username = self.kwargs['username']
return Purchase.objects.filter(purchaser__username=username)
Filtering against query parameters A final example of filtering the initial queryset would be to determine the initial queryset based on query parameters in the url. We can override .get_queryset() to deal with URLs such as http://example.com/api/purchases?username=denvercoder9, and filter the queryset only if the username parameter is included in the URL: class PurchaseList(generics.ListAPIView):
serializer_class = PurchaseSerializer
def get_queryset(self):
"""
Optionally restricts the returned purchases to a given user,
by filtering against a `username` query parameter in the URL.
"""
queryset = Purchase.objects.all()
username = self.request.query_params.get('username')
if username is not None:
queryset = queryset.filter(purchaser__username=username)
return queryset
Generic Filtering As well as being able to override the default queryset, REST framework also includes support for generic filtering backends that allow you to easily construct complex searches and filters. Generic filters can also present themselves as HTML controls in the browsable API and admin API. Setting filter backends The default filter backends may be set globally, using the DEFAULT_FILTER_BACKENDS setting. For example. REST_FRAMEWORK = {
'DEFAULT_FILTER_BACKENDS': ['django_filters.rest_framework.DjangoFilterBackend']
}
You can also set the filter backends on a per-view, or per-viewset basis, using the GenericAPIView class-based views. import django_filters.rest_framework
from django.contrib.auth.models import User
from myapp.serializers import UserSerializer
from rest_framework import generics
class UserListView(generics.ListAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [django_filters.rest_framework.DjangoFilterBackend]
Filtering and object lookups Note that if a filter backend is configured for a view, then as well as being used to filter list views, it will also be used to filter the querysets used for returning a single object. For instance, given the previous example, and a product with an id of 4675, the following URL would either return the corresponding object, or return a 404 response, depending on if the filtering conditions were met by the given product instance: http://example.com/api/products/4675/?category=clothing&max_price=10.00
Overriding the initial queryset Note that you can use both an overridden .get_queryset() and generic filtering together, and everything will work as expected. For example, if Product had a many-to-many relationship with User, named purchase, you might want to write a view like this: class PurchasedProductsList(generics.ListAPIView):
"""
Return a list of all the products that the authenticated
user has ever purchased, with optional filtering.
"""
model = Product
serializer_class = ProductSerializer
filterset_class = ProductFilter
def get_queryset(self):
user = self.request.user
return user.purchase_set.all()
API Guide DjangoFilterBackend The django-filter library includes a DjangoFilterBackend class which supports highly customizable field filtering for REST framework. To use DjangoFilterBackend, first install django-filter. pip install django-filter
Then add 'django_filters' to Django's INSTALLED_APPS: INSTALLED_APPS = [
...
'django_filters',
...
]
You should now either add the filter backend to your settings: REST_FRAMEWORK = {
'DEFAULT_FILTER_BACKENDS': ['django_filters.rest_framework.DjangoFilterBackend']
}
Or add the filter backend to an individual View or ViewSet. from django_filters.rest_framework import DjangoFilterBackend
class UserListView(generics.ListAPIView):
...
filter_backends = [DjangoFilterBackend]
If all you need is simple equality-based filtering, you can set a filterset_fields attribute on the view, or viewset, listing the set of fields you wish to filter against. class ProductList(generics.ListAPIView):
queryset = Product.objects.all()
serializer_class = ProductSerializer
filter_backends = [DjangoFilterBackend]
filterset_fields = ['category', 'in_stock']
This will automatically create a FilterSet class for the given fields, and will allow you to make requests such as: http://example.com/api/products?category=clothing&in_stock=True
For more advanced filtering requirements you can specify a FilterSet class that should be used by the view. You can read more about FilterSets in the django-filter documentation. It's also recommended that you read the section on DRF integration. SearchFilter The SearchFilter class supports simple single query parameter based searching, and is based on the Django admin's search functionality. When in use, the browsable API will include a SearchFilter control: The SearchFilter class will only be applied if the view has a search_fields attribute set. The search_fields attribute should be a list of names of text type fields on the model, such as CharField or TextField. from rest_framework import filters
class UserListView(generics.ListAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [filters.SearchFilter]
search_fields = ['username', 'email']
This will allow the client to filter the items in the list by making queries such as: http://example.com/api/users?search=russell
You can also perform a related lookup on a ForeignKey or ManyToManyField with the lookup API double-underscore notation: search_fields = ['username', 'email', 'profile__profession']
For JSONField and HStoreField fields you can filter based on nested values within the data structure using the same double-underscore notation: search_fields = ['data__breed', 'data__owner__other_pets__0__name']
By default, searches will use case-insensitive partial matches. The search parameter may contain multiple search terms, which should be whitespace and/or comma separated. If multiple search terms are used then objects will be returned in the list only if all the provided terms are matched. The search behavior may be restricted by prepending various characters to the search_fields. '^' Starts-with search. '=' Exact matches. '@' Full-text search. (Currently only supported Django's PostgreSQL backend.) '$' Regex search. For example: search_fields = ['=username', '=email']
By default, the search parameter is named 'search', but this may be overridden with the SEARCH_PARAM setting. To dynamically change search fields based on request content, it's possible to subclass the SearchFilter and override the get_search_fields() function. For example, the following subclass will only search on title if the query parameter title_only is in the request: from rest_framework import filters
class CustomSearchFilter(filters.SearchFilter):
def get_search_fields(self, view, request):
if request.query_params.get('title_only'):
return ['title']
return super(CustomSearchFilter, self).get_search_fields(view, request)
For more details, see the Django documentation. OrderingFilter The OrderingFilter class supports simple query parameter controlled ordering of results. By default, the query parameter is named 'ordering', but this may by overridden with the ORDERING_PARAM setting. For example, to order users by username: http://example.com/api/users?ordering=username
The client may also specify reverse orderings by prefixing the field name with '-', like so: http://example.com/api/users?ordering=-username
Multiple orderings may also be specified: http://example.com/api/users?ordering=account,username
Specifying which fields may be ordered against It's recommended that you explicitly specify which fields the API should allowing in the ordering filter. You can do this by setting an ordering_fields attribute on the view, like so: class UserListView(generics.ListAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [filters.OrderingFilter]
ordering_fields = ['username', 'email']
This helps prevent unexpected data leakage, such as allowing users to order against a password hash field or other sensitive data. If you don't specify an ordering_fields attribute on the view, the filter class will default to allowing the user to filter on any readable fields on the serializer specified by the serializer_class attribute. If you are confident that the queryset being used by the view doesn't contain any sensitive data, you can also explicitly specify that a view should allow ordering on any model field or queryset aggregate, by using the special value '__all__'. class BookingsListView(generics.ListAPIView):
queryset = Booking.objects.all()
serializer_class = BookingSerializer
filter_backends = [filters.OrderingFilter]
ordering_fields = '__all__'
Specifying a default ordering If an ordering attribute is set on the view, this will be used as the default ordering. Typically you'd instead control this by setting order_by on the initial queryset, but using the ordering parameter on the view allows you to specify the ordering in a way that it can then be passed automatically as context to a rendered template. This makes it possible to automatically render column headers differently if they are being used to order the results. class UserListView(generics.ListAPIView):
queryset = User.objects.all()
serializer_class = UserSerializer
filter_backends = [filters.OrderingFilter]
ordering_fields = ['username', 'email']
ordering = ['username']
The ordering attribute may be either a string or a list/tuple of strings. Custom generic filtering You can also provide your own generic filtering backend, or write an installable app for other developers to use. To do so override BaseFilterBackend, and override the .filter_queryset(self, request, queryset, view) method. The method should return a new, filtered queryset. As well as allowing clients to perform searches and filtering, generic filter backends can be useful for restricting which objects should be visible to any given request or user. Example For example, you might need to restrict users to only being able to see objects they created. class IsOwnerFilterBackend(filters.BaseFilterBackend):
"""
Filter that only allows users to see their own objects.
"""
def filter_queryset(self, request, queryset, view):
return queryset.filter(owner=request.user)
We could achieve the same behavior by overriding get_queryset() on the views, but using a filter backend allows you to more easily add this restriction to multiple views, or to apply it across the entire API. Customizing the interface Generic filters may also present an interface in the browsable API. To do so you should implement a to_html() method which returns a rendered HTML representation of the filter. This method should have the following signature: to_html(self, request, queryset, view) The method should return a rendered HTML string. Filtering & schemas You can also make the filter controls available to the schema autogeneration that REST framework provides, by implementing a get_schema_fields() method. This method should have the following signature: get_schema_fields(self, view) The method should return a list of coreapi.Field instances. Third party packages The following third party packages provide additional filter implementations. Django REST framework filters package The django-rest-framework-filters package works together with the DjangoFilterBackend class, and allows you to easily create filters across relationships, or create multiple filter lookup types for a given field. Django REST framework full word search filter The djangorestframework-word-filter developed as alternative to filters.SearchFilter which will search full word in text, or exact match. Django URL Filter django-url-filter provides a safe way to filter data via human-friendly URLs. It works very similar to DRF serializers and fields in a sense that they can be nested except they are called filtersets and filters. That provides easy way to filter related data. Also this library is generic-purpose so it can be used to filter other sources of data and not only Django QuerySets. drf-url-filters drf-url-filter is a simple Django app to apply filters on drf ModelViewSet's Queryset in a clean, simple and configurable way. It also supports validations on incoming query params and their values. A beautiful python package Voluptuous is being used for validations on the incoming query parameters. The best part about voluptuous is you can define your own validations as per your query params requirements. filters.py | |
doc_28137 |
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | |
doc_28138 |
Apply a function repeatedly over multiple axes. func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes, with res as the first argument. Parameters
funcfunction
This function must take two arguments, func(a, axis).
aarray_like
Input array.
axesarray_like
Axes over which func is applied; the elements must be integers. Returns
apply_over_axisndarray
The output array. The number of dimensions is the same as a, but the shape can be different. This depends on whether func changes the shape of its output with respect to its input. See also apply_along_axis
Apply a function to 1-D slices of an array along the given axis. Notes This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been available since version 1.7.0. Examples >>> a = np.arange(24).reshape(2,3,4)
>>> a
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
Sum over axes 0 and 2. The result has same number of dimensions as the original array: >>> np.apply_over_axes(np.sum, a, [0,2])
array([[[ 60],
[ 92],
[124]]])
Tuple axis arguments to ufuncs are equivalent: >>> np.sum(a, axis=(0,2), keepdims=True)
array([[[ 60],
[ 92],
[124]]]) | |
doc_28139 | Stamp a copy of the turtle shape onto the canvas at the current turtle position. Return a stamp_id for that stamp, which can be used to delete it by calling clearstamp(stamp_id). >>> turtle.color("blue")
>>> turtle.stamp()
11
>>> turtle.fd(50) | |
doc_28140 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_28141 | class socketserver.ForkingUDPServer
class socketserver.ThreadingTCPServer
class socketserver.ThreadingUDPServer
These classes are pre-defined using the mix-in classes. | |
doc_28142 |
Abstract base class for objects that render into a FigureCanvas. Typically, all visible elements in a figure are subclasses of Artist. | |
doc_28143 | Computes the inverse cosine of each element in input. outi=cos−1(inputi)\text{out}_{i} = \cos^{-1}(\text{input}_{i})
Parameters
input (Tensor) – the input tensor. Keyword Arguments
out (Tensor, optional) – the output tensor. Example: >>> a = torch.randn(4)
>>> a
tensor([ 0.3348, -0.5889, 0.2005, -0.1584])
>>> torch.acos(a)
tensor([ 1.2294, 2.2004, 1.3690, 1.7298]) | |
doc_28144 | Return the number of processes in the system run queue averaged over the last 1, 5, and 15 minutes or raises OSError if the load average was unobtainable. Availability: Unix. | |
doc_28145 |
Turn this Graph into valid Python code. Parameters
root_module (str) – The name of the root module on which to look-up qualified name targets. This is usually ‘self’. Returns
The string source code generated from this Graph. | |
doc_28146 | See Migration guide for more details. tf.compat.v1.raw_ops.Bincount
tf.raw_ops.Bincount(
arr, size, weights, name=None
)
Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i. Values in arr outside of the range [0, size) are ignored.
Args
arr A Tensor of type int32. int32 Tensor.
size A Tensor of type int32. non-negative int32 scalar Tensor.
weights A Tensor. Must be one of the following types: int32, int64, float32, float64. is an int32, int64, float32, or float64 Tensor with the same shape as arr, or a length-0 Tensor, in which case it acts as all weights equal to 1.
name A name for the operation (optional).
Returns A Tensor. Has the same type as weights. | |
doc_28147 | Close the transport immediately, without waiting for pending operations to complete. Buffered data will be lost. No more data will be received. The protocol’s protocol.connection_lost() method will eventually be called with None as its argument. | |
doc_28148 | Subscribe to new mailbox. | |
doc_28149 | String of ASCII characters which are considered punctuation characters in the C locale: !"#$%&'()*+,-./:;<=>?@[\]^_`{|}~. | |
doc_28150 | Decompress the data, returning a bytes object containing the uncompressed data. New in version 3.2. | |
doc_28151 |
Apply the affine part of this transform to Path path, returning a new Path. transform_path(path) is equivalent to transform_path_affine(transform_path_non_affine(values)). | |
doc_28152 |
Draw a filled black rectangle from (x1, y1) to (x2, y2). | |
doc_28153 | Called with data holding an arbitrary amount of received data. The default method, which must be overridden, raises a NotImplementedError exception. | |
doc_28154 | tf.compat.v1.train.MonitoredSession(
session_creator=None, hooks=None, stop_grace_period_secs=120
)
Example usage: saver_hook = CheckpointSaverHook(...)
summary_hook = SummarySaverHook(...)
with MonitoredSession(session_creator=ChiefSessionCreator(...),
hooks=[saver_hook, summary_hook]) as sess:
while not sess.should_stop():
sess.run(train_op)
Initialization: At creation time the monitored session does following things in given order: calls hook.begin() for each given hook finalizes the graph via scaffold.finalize()
create session initializes the model via initialization ops provided by Scaffold
restores variables if a checkpoint exists launches queue runners calls hook.after_create_session()
Run: When run() is called, the monitored session does following things: calls hook.before_run()
calls TensorFlow session.run() with merged fetches and feed_dict calls hook.after_run()
returns result of session.run() asked by user if AbortedError or UnavailableError occurs, it recovers or reinitializes the session before executing the run() call again Exit: At the close(), the monitored session does following things in order: calls hook.end()
closes the queue runners and the session suppresses OutOfRange error which indicates that all inputs have been processed if the monitored_session is used as a context How to set tf.compat.v1.Session arguments: In most cases you can set session arguments as follows: MonitoredSession(
session_creator=ChiefSessionCreator(master=..., config=...))
In distributed setting for a non-chief worker, you can use following: MonitoredSession(
session_creator=WorkerSessionCreator(master=..., config=...))
See MonitoredTrainingSession for an example usage based on chief or worker.
Note: This is not a tf.compat.v1.Session. For example, it cannot do following:
it cannot be set as default session. it cannot be sent to saver.save. it cannot be sent to tf.train.start_queue_runners.
Args
session_creator A factory object to create session. Typically a ChiefSessionCreator which is the default one.
hooks An iterable of `SessionRunHook' objects.
Returns A MonitoredSession object.
Attributes
graph The graph that was launched in this session. Child Classes class StepContext Methods close View source
close()
run View source
run(
fetches, feed_dict=None, options=None, run_metadata=None
)
Run ops in the monitored session. This method is completely compatible with the tf.Session.run() method.
Args
fetches Same as tf.Session.run().
feed_dict Same as tf.Session.run().
options Same as tf.Session.run().
run_metadata Same as tf.Session.run().
Returns Same as tf.Session.run().
run_step_fn View source
run_step_fn(
step_fn
)
Run ops using a step function.
Args
step_fn A function or a method with a single argument of type StepContext. The function may use methods of the argument to perform computations with access to a raw session. The returned value of the step_fn will be returned from run_step_fn, unless a stop is requested. In that case, the next should_stop call will return True. Example usage: with tf.Graph().as_default():
c = tf.compat.v1.placeholder(dtypes.float32)
v = tf.add(c, 4.0)
w = tf.add(c, 0.5)
def step_fn(step_context):
a = step_context.session.run(fetches=v, feed_dict={c: 0.5})
if a <= 4.5:
step_context.request_stop()
return step_context.run_with_hooks(fetches=w,
feed_dict={c: 0.1})
with tf.MonitoredSession() as session:
while not session.should_stop():
a = session.run_step_fn(step_fn)
Hooks interact with the run_with_hooks() call inside the step_fn as they do with a MonitoredSession.run call.
Returns Returns the returned value of step_fn.
Raises
StopIteration if step_fn has called request_stop(). It may be caught by with tf.MonitoredSession() to close the session.
ValueError if step_fn doesn't have a single argument called step_context. It may also optionally have self for cases when it belongs to an object. should_stop View source
should_stop()
__enter__ View source
__enter__()
__exit__ View source
__exit__(
exception_type, exception_value, traceback
) | |
doc_28155 | tf.compat.v1.train.warm_start(
ckpt_to_initialize_from, vars_to_warm_start='.*',
var_name_to_vocab_info=None, var_name_to_prev_var_name=None
)
If you are using a tf.estimator.Estimator, this will automatically be called during training.
Args
ckpt_to_initialize_from [Required] A string specifying the directory with checkpoint file(s) or path to checkpoint from which to warm-start the model parameters.
vars_to_warm_start [Optional] One of the following: A regular expression (string) that captures which variables to warm-start (see tf.compat.v1.get_collection). This expression will only consider variables in the TRAINABLE_VARIABLES collection -- if you need to warm-start non_TRAINABLE vars (such as optimizer accumulators or batch norm statistics), please use the below option. A list of strings, each a regex scope provided to tf.compat.v1.get_collection with GLOBAL_VARIABLES (please see tf.compat.v1.get_collection). For backwards compatibility reasons, this is separate from the single-string argument type. A list of Variables to warm-start. If you do not have access to the Variable objects at the call site, please use the above option.
None, in which case only TRAINABLE variables specified in var_name_to_vocab_info will be warm-started. Defaults to '.*', which warm-starts all variables in the TRAINABLE_VARIABLES collection. Note that this excludes variables such as accumulators and moving statistics from batch norm.
var_name_to_vocab_info [Optional] Dict of variable names (strings) to tf.estimator.VocabInfo. The variable names should be "full" variables, not the names of the partitions. If not explicitly provided, the variable is assumed to have no (changes to) vocabulary.
var_name_to_prev_var_name [Optional] Dict of variable names (strings) to name of the previously-trained variable in ckpt_to_initialize_from. If not explicitly provided, the name of the variable is assumed to be same between previous checkpoint and current model. Note that this has no effect on the set of variables that is warm-started, and only controls name mapping (use vars_to_warm_start for controlling what variables to warm-start).
Raises
ValueError If the WarmStartSettings contains prev_var_name or VocabInfo configuration for variable names that are not used. This is to ensure a stronger check for variable configuration than relying on users to examine the logs. | |
doc_28156 | Returns a list of all hyperparameter specifications. | |
doc_28157 | tupled integers of the version vernum = (1, 5, 3) This version information can easily be compared with other version numbers of the same format. An example of checking pygame version numbers would look like this: if pygame.version.vernum < (1, 5):
print('Warning, older version of pygame (%s)' % pygame.version.ver)
disable_advanced_features = True New in pygame 1.9.6: Attributes major, minor, and patch. vernum.major == vernum[0]
vernum.minor == vernum[1]
vernum.patch == vernum[2] Changed in pygame 1.9.6: str(pygame.version.vernum) returns a string like "2.0.0" instead of "(2, 0, 0)". Changed in pygame 1.9.6: repr(pygame.version.vernum) returns a string like "PygameVersion(major=2, minor=0, patch=0)" instead of "(2, 0, 0)". | |
doc_28158 | See Migration guide for more details. tf.compat.v1.raw_ops.EnqueueTPUEmbeddingSparseBatch
tf.raw_ops.EnqueueTPUEmbeddingSparseBatch(
sample_indices, embedding_indices, aggregation_weights, mode_override,
device_ordinal=-1, combiners=[], name=None
)
This Op eases the porting of code that uses embedding_lookup_sparse(), although some Python preprocessing of the SparseTensor arguments to embedding_lookup_sparse() is required to produce the arguments to this Op, since only a single EnqueueTPUEmbeddingSparseBatch Op is allowed per training step. The tensors at corresponding positions in the three input lists must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding table_id.
Args
sample_indices A list of at least 1 Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors specifying the training example and feature to which the corresponding embedding_indices and aggregation_weights values belong. sample_indices[i] must equal b * nf + f, where nf is the number of features from the corresponding table, f is in [0, nf), and b is in [0, batch size).
embedding_indices A list with the same length as sample_indices of Tensor objects with the same type in: int32, int64. A list of rank 1 Tensors, indices into the embedding tables.
aggregation_weights A list with the same length as sample_indices of Tensor objects with the same type in: float32, float64. A list of rank 1 Tensors containing per sample -- i.e. per (training example, feature) -- aggregation weights.
mode_override A Tensor of type string. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used.
device_ordinal An optional int. Defaults to -1. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed.
combiners An optional list of strings. Defaults to []. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables.
name A name for the operation (optional).
Returns The created Operation. | |
doc_28159 |
Convert the input to a masked array, conserving subclasses. If a is a subclass of MaskedArray, its class is conserved. No copy is performed if the input is already an ndarray. Parameters
aarray_like
Input data, in any form that can be converted to an array.
dtypedtype, optional
By default, the data-type is inferred from the input data.
order{‘C’, ‘F’}, optional
Whether to use row-major (‘C’) or column-major (‘FORTRAN’) memory representation. Default is ‘C’. Returns
outMaskedArray
MaskedArray interpretation of a. See also asarray
Similar to asanyarray, but does not conserve subclass. Examples >>> x = np.arange(10.).reshape(2, 5)
>>> x
array([[0., 1., 2., 3., 4.],
[5., 6., 7., 8., 9.]])
>>> np.ma.asanyarray(x)
masked_array(
data=[[0., 1., 2., 3., 4.],
[5., 6., 7., 8., 9.]],
mask=False,
fill_value=1e+20)
>>> type(np.ma.asanyarray(x))
<class 'numpy.ma.core.MaskedArray'> | |
doc_28160 |
Evaluate the significance of a cross-validated score with permutations Permutes targets to generate ‘randomized data’ and compute the empirical p-value against the null hypothesis that features and targets are independent. The p-value represents the fraction of randomized data sets where the estimator performed as well or better than in the original data. A small p-value suggests that there is a real dependency between features and targets which has been used by the estimator to give good predictions. A large p-value may be due to lack of real dependency between features and targets or the estimator was not able to use the dependency to give good predictions. Read more in the User Guide. Parameters
estimatorestimator object implementing ‘fit’
The object to use to fit the data.
Xarray-like of shape at least 2D
The data to fit.
yarray-like of shape (n_samples,) or (n_samples, n_outputs) or None
The target variable to try to predict in the case of supervised learning.
groupsarray-like of shape (n_samples,), default=None
Labels to constrain permutation within groups, i.e. y values are permuted among samples with the same group identifier. When not specified, y values are permuted among all samples. When a grouped cross-validator is used, the group labels are also passed on to the split method of the cross-validator. The cross-validator uses them for grouping the samples while splitting the dataset into train/test set.
scoringstr or callable, default=None
A single str (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. If None the estimator’s score method is used.
cvint, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 5-fold cross validation, int, to specify the number of folds in a (Stratified)KFold,
CV splitter, An iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. Refer User Guide for the various cross-validation strategies that can be used here. Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.
n_permutationsint, default=100
Number of times to permute y.
n_jobsint, default=None
Number of jobs to run in parallel. Training the estimator and computing the cross-validated score are parallelized over the permutations. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
random_stateint, RandomState instance or None, default=0
Pass an int for reproducible output for permutation of y values among samples. See Glossary.
verboseint, default=0
The verbosity level.
fit_paramsdict, default=None
Parameters to pass to the fit method of the estimator. New in version 0.24. Returns
scorefloat
The true score without permuting targets.
permutation_scoresarray of shape (n_permutations,)
The scores obtained for each permutations.
pvaluefloat
The p-value, which approximates the probability that the score would be obtained by chance. This is calculated as: (C + 1) / (n_permutations + 1) Where C is the number of permutations whose score >= the true score. The best possible p-value is 1/(n_permutations + 1), the worst is 1.0. Notes This function implements Test 1 in: Ojala and Garriga. Permutation Tests for Studying Classifier Performance. The Journal of Machine Learning Research (2010) vol. 11 | |
doc_28161 |
Return the values (min, max) that are mapped to the colormap limits. | |
doc_28162 |
Autocorrelation plot for time series. Parameters
series:Time series
ax:Matplotlib axis object, optional
**kwargs
Options to pass to matplotlib plotting method. Returns
class:matplotlib.axis.Axes
Examples The horizontal lines in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band.
>>> spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
>>> s = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
>>> pd.plotting.autocorrelation_plot(s)
<AxesSubplot:title={'center':'width'}, xlabel='Lag', ylabel='Autocorrelation'> | |
doc_28163 | Return the data in the buffer as a bytestring. This is equivalent to calling the bytes constructor on the memoryview. >>> m = memoryview(b"abc")
>>> m.tobytes()
b'abc'
>>> bytes(m)
b'abc'
For non-contiguous arrays the result is equal to the flattened list representation with all elements converted to bytes. tobytes() supports all format strings, including those that are not in struct module syntax. New in version 3.8: order can be {‘C’, ‘F’, ‘A’}. When order is ‘C’ or ‘F’, the data of the original array is converted to C or Fortran order. For contiguous views, ‘A’ returns an exact copy of the physical memory. In particular, in-memory Fortran order is preserved. For non-contiguous views, the data is converted to C first. order=None is the same as order=’C’. | |
doc_28164 | See Migration guide for more details. tf.compat.v1.linalg.experimental.conjugate_gradient
tf.linalg.experimental.conjugate_gradient(
operator, rhs, preconditioner=None, x=None, tol=1e-05, max_iter=20,
name='conjugate_gradient'
)
Solves a linear system of equations A*x = rhs for self-adjoint, positive definite matrix A and right-hand side vector rhs, using an iterative, matrix-free algorithm where the action of the matrix A is represented by operator. The iteration terminates when either the number of iterations exceeds max_iter or when the residual norm has been reduced to tol times its initial value, i.e. \(||rhs - A x_k|| <= tol ||rhs||\).
Args
operator A LinearOperator that is self-adjoint and positive definite.
rhs A possibly batched vector of shape [..., N] containing the right-hand size vector.
preconditioner A LinearOperator that approximates the inverse of A. An efficient preconditioner could dramatically improve the rate of convergence. If preconditioner represents matrix M(M approximates A^{-1}), the algorithm uses preconditioner.apply(x) to estimate A^{-1}x. For this to be useful, the cost of applying M should be much lower than computing A^{-1} directly.
x A possibly batched vector of shape [..., N] containing the initial guess for the solution.
tol A float scalar convergence tolerance.
max_iter An integer giving the maximum number of iterations.
name A name scope for the operation.
Returns
output A namedtuple representing the final state with fields: i: A scalar int32 Tensor. Number of iterations executed. x: A rank-1 Tensor of shape [..., N] containing the computed solution. r: A rank-1 Tensor of shape [.., M] containing the residual vector. p: A rank-1 Tensor of shape [..., N]. A-conjugate basis vector. gamma: \(r \dot M \dot r\), equivalent to \(||r||_2^2\) when preconditioner=None. | |
doc_28165 | Reset the instance. Loses all unprocessed data. This is called implicitly at instantiation time. | |
doc_28166 | See Migration guide for more details. tf.compat.v1.raw_ops.ExperimentalParseExampleDataset
tf.raw_ops.ExperimentalParseExampleDataset(
input_dataset, num_parallel_calls, dense_defaults, sparse_keys, dense_keys,
sparse_types, dense_shapes, output_types, output_shapes, sloppy=False, name=None
)
Args
input_dataset A Tensor of type variant.
num_parallel_calls A Tensor of type int64.
dense_defaults A list of Tensor objects with types from: float32, int64, string. A dict mapping string keys to Tensors. The keys of the dict must match the dense_keys of the feature.
sparse_keys A list of strings. A list of string keys in the examples features. The results for these keys will be returned as SparseTensor objects.
dense_keys A list of strings. A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values.
sparse_types A list of tf.DTypes from: tf.float32, tf.int64, tf.string. A list of DTypes of the same length as sparse_keys. Only tf.float32 (FloatList), tf.int64 (Int64List), and tf.string (BytesList) are supported.
dense_shapes A list of shapes (each a tf.TensorShape or list of ints). List of tuples with the same length as dense_keys. The shape of the data for each dense feature referenced by dense_keys. Required for any input tensors identified by dense_keys. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension.
output_types A list of tf.DTypes that has length >= 1. The type list for the return values.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
sloppy An optional bool. Defaults to False.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_28167 | The Content-Length entity-header field indicates the size of the entity-body, in decimal number of OCTETs, sent to the recipient or, in the case of the HEAD method, the size of the entity-body that would have been sent had the request been a GET. | |
doc_28168 | Register a custom template global, available application wide. Like Flask.add_template_global() but for a blueprint. Works exactly like the app_template_global() decorator. Changelog New in version 0.10. Parameters
name (Optional[str]) – the optional name of the global, otherwise the function name will be used.
f (Callable[[], Any]) – Return type
None | |
doc_28169 | Converts tokens back into Python source code. The iterable must return sequences with at least two elements, the token type and the token string. Any additional sequence elements are ignored. The reconstructed script is returned as a single string. The result is guaranteed to tokenize back to match the input so that the conversion is lossless and round-trips are assured. The guarantee applies only to the token type and token string as the spacing between tokens (column positions) may change. It returns bytes, encoded using the ENCODING token, which is the first token sequence output by tokenize(). If there is no encoding token in the input, it returns a str instead. | |
doc_28170 | operator.itemgetter(*items)
Return a callable object that fetches item from its operand using the operand’s __getitem__() method. If multiple items are specified, returns a tuple of lookup values. For example: After f = itemgetter(2), the call f(r) returns r[2]. After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3]). Equivalent to: def itemgetter(*items):
if len(items) == 1:
item = items[0]
def g(obj):
return obj[item]
else:
def g(obj):
return tuple(obj[item] for item in items)
return g
The items can be any type accepted by the operand’s __getitem__() method. Dictionaries accept any hashable value. Lists, tuples, and strings accept an index or a slice: >>> itemgetter(1)('ABCDEFG')
'B'
>>> itemgetter(1, 3, 5)('ABCDEFG')
('B', 'D', 'F')
>>> itemgetter(slice(2, None))('ABCDEFG')
'CDEFG'
>>> soldier = dict(rank='captain', name='dotterbart')
>>> itemgetter('rank')(soldier)
'captain'
Example of using itemgetter() to retrieve specific fields from a tuple record: >>> inventory = [('apple', 3), ('banana', 2), ('pear', 5), ('orange', 1)]
>>> getcount = itemgetter(1)
>>> list(map(getcount, inventory))
[3, 2, 5, 1]
>>> sorted(inventory, key=getcount)
[('orange', 1), ('banana', 2), ('apple', 3), ('pear', 5)] | |
doc_28171 | Return the default content type. Most messages have a default content type of text/plain, except for messages that are subparts of multipart/digest containers. Such subparts have a default content type of message/rfc822. | |
doc_28172 |
Yield images of the Gaussian pyramid formed by the input image. Recursively applies the pyramid_reduce function to the image, and yields the downscaled images. Note that the first image of the pyramid will be the original, unscaled image. The total number of images is max_layer + 1. In case all layers are computed, the last image is either a one-pixel image or the image where the reduction does not change its shape. Parameters
imagendarray
Input image.
max_layerint, optional
Number of layers for the pyramid. 0th layer is the original image. Default is -1 which builds all possible layers.
downscalefloat, optional
Downscale factor.
sigmafloat, optional
Sigma for Gaussian filter. Default is 2 * downscale / 6.0 which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution.
orderint, optional
Order of splines used in interpolation of downsampling. See skimage.transform.warp for detail.
mode{‘reflect’, ‘constant’, ‘edge’, ‘symmetric’, ‘wrap’}, optional
The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’.
cvalfloat, optional
Value to fill past edges of input if mode is ‘constant’.
multichannelbool, optional
Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension.
preserve_rangebool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. Also see https://scikit-image.org/docs/dev/user_guide/data_types.html Returns
pyramidgenerator
Generator yielding pyramid layers as float images. References
1
http://persci.mit.edu/pub_pdfs/pyramid83.pdf | |
doc_28173 |
Initialize self. See help(type(self)) for accurate signature. | |
doc_28174 | The host mask, as an IPv4Address object. | |
doc_28175 |
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | |
doc_28176 | Retrieve the next item from the iterator by calling its __next__() method. If default is given, it is returned if the iterator is exhausted, otherwise StopIteration is raised. | |
doc_28177 | See Migration guide for more details. tf.compat.v1.raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug
tf.raw_ops.LoadTPUEmbeddingFTRLParametersGradAccumDebug(
parameters, accumulators, linears, gradient_accumulators, num_shards, shard_id,
table_id=-1, table_name='', config='', name=None
)
An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed.
Args
parameters A Tensor of type float32. Value of parameters used in the FTRL optimization algorithm.
accumulators A Tensor of type float32. Value of accumulators used in the FTRL optimization algorithm.
linears A Tensor of type float32. Value of linears used in the FTRL optimization algorithm.
gradient_accumulators A Tensor of type float32. Value of gradient_accumulators used in the FTRL optimization algorithm.
num_shards An int.
shard_id An int.
table_id An optional int. Defaults to -1.
table_name An optional string. Defaults to "".
config An optional string. Defaults to "".
name A name for the operation (optional).
Returns The created Operation. | |
doc_28178 | This function expands XInclude directives. elem is the root element. loader is an optional resource loader. If omitted, it defaults to default_loader(). If given, it should be a callable that implements the same interface as default_loader(). base_url is base URL of the original file, to resolve relative include file references. max_depth is the maximum number of recursive inclusions. Limited to reduce the risk of malicious content explosion. Pass a negative value to disable the limitation. Returns the expanded resource. If the parse mode is "xml", this is an ElementTree instance. If the parse mode is “text”, this is a Unicode string. If the loader fails, it can return None or raise an exception. New in version 3.9: The base_url and max_depth parameters. | |
doc_28179 | tf.compat.v1.train.LooperThread(
coord, timer_interval_secs, target=None, args=None, kwargs=None
)
This thread class is intended to be used with a Coordinator. It repeatedly runs code specified either as target and args or by the run_loop() method. Before each run the thread checks if the coordinator has requested stop. In that case the looper thread terminates immediately. If the code being run raises an exception, that exception is reported to the coordinator and the thread terminates. The coordinator will then request all the other threads it coordinates to stop. You typically pass looper threads to the supervisor Join() method.
Args
coord A Coordinator.
timer_interval_secs Time boundaries at which to call Run(), or None if it should be called back to back.
target Optional callable object that will be executed in the thread.
args Optional arguments to pass to target when calling it.
kwargs Optional keyword arguments to pass to target when calling it.
Raises
ValueError If one of the arguments is invalid.
Attributes
daemon A boolean value indicating whether this thread is a daemon thread. This must be set before start() is called, otherwise RuntimeError is raised. Its initial value is inherited from the creating thread; the main thread is not a daemon thread and therefore all threads created in the main thread default to daemon = False. The entire Python program exits when only daemon threads are left.
ident Thread identifier of this thread or None if it has not been started. This is a nonzero integer. See the get_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited.
name A string used for identification purposes only. It has no semantics. Multiple threads may be given the same name. The initial name is set by the constructor.
Methods getName
getName()
isAlive
isAlive()
Return whether the thread is alive. This method is deprecated, use is_alive() instead. isDaemon
isDaemon()
is_alive
is_alive()
Return whether the thread is alive. This method returns True just before the run() method starts until just after the run() method terminates. The module function enumerate() returns a list of all alive threads. join
join(
timeout=None
)
Wait until the thread terminates. This blocks the calling thread until the thread whose join() method is called terminates -- either normally or through an unhandled exception or until the optional timeout occurs. When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof). As join() always returns None, you must call is_alive() after join() to decide whether a timeout happened -- if the thread is still alive, the join() call timed out. When the timeout argument is not present or None, the operation will block until the thread terminates. A thread can be join()ed many times. join() raises a RuntimeError if an attempt is made to join the current thread as that would cause a deadlock. It is also an error to join() a thread before it has been started and attempts to do so raises the same exception. loop View source
@staticmethod
loop(
coord, timer_interval_secs, target, args=None, kwargs=None
)
Start a LooperThread that calls a function periodically. If timer_interval_secs is None the thread calls target(args) repeatedly. Otherwise target(args) is called every timer_interval_secs seconds. The thread terminates when a stop of the coordinator is requested.
Args
coord A Coordinator.
timer_interval_secs Number. Time boundaries at which to call target.
target A callable object.
args Optional arguments to pass to target when calling it.
kwargs Optional keyword arguments to pass to target when calling it.
Returns The started thread.
run View source
run()
Method representing the thread's activity. You may override this method in a subclass. The standard run() method invokes the callable object passed to the object's constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively. run_loop View source
run_loop()
Called at 'timer_interval_secs' boundaries. setDaemon
setDaemon(
daemonic
)
setName
setName(
name
)
start
start()
Start the thread's activity. It must be called at most once per thread object. It arranges for the object's run() method to be invoked in a separate thread of control. This method will raise a RuntimeError if called more than once on the same thread object. start_loop View source
start_loop()
Called when the thread starts. stop_loop View source
stop_loop()
Called when the thread stops. | |
doc_28180 | Deprecated enum-like class for reduction operations: SUM, PRODUCT, MIN, and MAX. ReduceOp is recommended to use instead. | |
doc_28181 | See Migration guide for more details. tf.compat.v1.sets.difference, tf.compat.v1.sets.set_difference
tf.sets.difference(
a, b, aminusb=True, validate_indices=True
)
All but the last dimension of a and b must match. Example: import tensorflow as tf
import collections
# Represent the following array of sets as a sparse tensor:
# a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]])
a = collections.OrderedDict([
((0, 0, 0), 1),
((0, 0, 1), 2),
((0, 1, 0), 3),
((1, 0, 0), 4),
((1, 1, 0), 5),
((1, 1, 1), 6),
])
a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()),
dense_shape=[2, 2, 2])
# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]])
b = collections.OrderedDict([
((0, 0, 0), 1),
((0, 0, 1), 3),
((0, 1, 0), 2),
((1, 0, 0), 4),
((1, 0, 1), 5),
((1, 1, 0), 5),
((1, 1, 1), 6),
((1, 1, 2), 7),
((1, 1, 3), 8),
])
b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()),
dense_shape=[2, 2, 4])
# `set_difference` is applied to each aligned pair of sets.
tf.sets.difference(a, b)
# The result will be equivalent to either of:
#
# np.array([[{2}, {3}], [{}, {}]])
#
# collections.OrderedDict([
# ((0, 0, 0), 2),
# ((0, 1, 0), 3),
# ])
Args
a Tensor or SparseTensor of the same type as b. If sparse, indices must be sorted in row-major order.
b Tensor or SparseTensor of the same type as a. If sparse, indices must be sorted in row-major order.
aminusb Whether to subtract b from a, vs vice versa.
validate_indices Whether to validate the order and range of sparse indices in a and b.
Returns A SparseTensor whose shape is the same rank as a and b, and all but the last dimension the same. Elements along the last dimension contain the differences.
Raises
TypeError If inputs are invalid types, or if a and b have different types.
ValueError If a is sparse and b is dense.
errors_impl.InvalidArgumentError If the shapes of a and b do not match in any dimension other than the last dimension. | |
doc_28182 | See Migration guide for more details. tf.compat.v1.app.flags.IllegalFlagValueError | |
doc_28183 | Retrieve a given field value. The key argument will be either an integer or a string. If it is an integer, it represents the index of the positional argument in args; if it is a string, then it represents a named argument in kwargs. The args parameter is set to the list of positional arguments to vformat(), and the kwargs parameter is set to the dictionary of keyword arguments. For compound field names, these functions are only called for the first component of the field name; subsequent components are handled through normal attribute and indexing operations. So for example, the field expression ‘0.name’ would cause get_value() to be called with a key argument of 0. The name attribute will be looked up after get_value() returns by calling the built-in getattr() function. If the index or keyword refers to an item that does not exist, then an IndexError or KeyError should be raised. | |
doc_28184 | The earliest representable time, time(0, 0, 0, 0). | |
doc_28185 |
Standard kind of date increment used for a date range. Works exactly like the keyword argument form of relativedelta. Note that the positional argument form of relativedelata is not supported. Use of the keyword n is discouraged– you would be better off specifying n in the keywords you use, but regardless it is there for you. n is needed for DateOffset subclasses. DateOffset works as follows. Each offset specify a set of dates that conform to the DateOffset. For example, Bday defines this set to be the set of dates that are weekdays (M-F). To test if a date is in the set of a DateOffset dateOffset we can use the is_on_offset method: dateOffset.is_on_offset(date). If a date is not on a valid date, the rollback and rollforward methods can be used to roll the date to the nearest valid date before/after the date. DateOffsets can be created to move dates forward a given number of valid dates. For example, Bday(2) can be added to a date to move it two business days forward. If the date does not start on a valid date, first it is moved to a valid date. Thus pseudo code is: def __add__(date):
date = rollback(date) # does nothing if date is valid return date + <n number of periods> When a date offset is created for a negative number of periods, the date is first rolled forward. The pseudo code is: def __add__(date):
date = rollforward(date) # does nothing is date is valid return date + <n number of periods> Zero presents a problem. Should it roll forward or back? We arbitrarily have it rollforward: date + BDay(0) == BDay.rollforward(date) Since 0 is a bit weird, we suggest avoiding its use. Parameters
n:int, default 1
The number of time periods the offset represents.
normalize:bool, default False
Whether to round the result of a DateOffset addition down to the previous midnight. **kwds
Temporal parameter that add to or replace the offset value. Parameters that add to the offset (like Timedelta): years months weeks days hours minutes seconds microseconds nanoseconds Parameters that replace the offset value: year month day weekday hour minute second microsecond nanosecond. See also dateutil.relativedelta.relativedelta
The relativedelta type is designed to be applied to an existing datetime an can replace specific components of that datetime, or represents an interval of time. Examples
>>> from pandas.tseries.offsets import DateOffset
>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=3)
Timestamp('2017-04-01 09:10:11')
>>> ts = pd.Timestamp('2017-01-01 09:10:11')
>>> ts + DateOffset(months=2)
Timestamp('2017-03-01 09:10:11')
Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
n
name
nanos
normalize
rule_code Methods
__call__(*args, **kwargs) Call self as a function.
rollback Roll provided date backward to next offset only if not on offset.
rollforward Roll provided date forward to next offset only if not on offset.
apply
apply_index
copy
isAnchored
is_anchored
is_month_end
is_month_start
is_on_offset
is_quarter_end
is_quarter_start
is_year_end
is_year_start
onOffset | |
doc_28186 |
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Estimator parameters. Returns
selfestimator instance
Estimator instance. | |
doc_28187 |
Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape
equivalent function Notes Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)). | |
doc_28188 |
Set the normalization instance. Parameters
normNormalize or None
Notes If there are any colorbars using the mappable for this norm, setting the norm of the mappable will reset the norm, locator, and formatters on the colorbar to default. | |
doc_28189 |
Bases: matplotlib.patches.Patch Connect two bboxes with a straight line. Parameters
bbox1, bbox2matplotlib.transforms.Bbox
Bounding boxes to connect.
loc1{1, 2, 3, 4}
Corner of bbox1 to draw the line. Valid values are: 'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
loc2{1, 2, 3, 4}, optional
Corner of bbox2 to draw the line. If None, defaults to loc1. Valid values are: 'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
**kwargs
Patch properties for the line drawn. Valid arguments include:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha unknown
animated bool
antialiased or aa bool or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float staticconnect_bbox(bbox1, bbox2, loc1, loc2=None)[source]
Helper function to obtain a Path from one bbox to another. Parameters
bbox1, bbox2matplotlib.transforms.Bbox
Bounding boxes to connect.
loc1{1, 2, 3, 4}
Corner of bbox1 to use. Valid values are: 'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
loc2{1, 2, 3, 4}, optional
Corner of bbox2 to use. If None, defaults to loc1. Valid values are: 'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
Returns
pathmatplotlib.path.Path
A line segment from the loc1 corner of bbox1 to the loc2 corner of bbox2.
staticget_bbox_edge_pos(bbox, loc)[source]
Helper function to obtain the location of a corner of a bbox Parameters
bboxmatplotlib.transforms.Bbox
loc{1, 2, 3, 4}
Corner of bbox. Valid values are: 'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4
Returns
x, yfloat
Coordinates of the corner specified by loc.
get_path()[source]
Return the path of this patch.
set(*, agg_filter=<UNSET>, alpha=<UNSET>, animated=<UNSET>, antialiased=<UNSET>, capstyle=<UNSET>, clip_box=<UNSET>, clip_on=<UNSET>, clip_path=<UNSET>, color=<UNSET>, edgecolor=<UNSET>, facecolor=<UNSET>, fill=<UNSET>, gid=<UNSET>, hatch=<UNSET>, in_layout=<UNSET>, joinstyle=<UNSET>, label=<UNSET>, linestyle=<UNSET>, linewidth=<UNSET>, path_effects=<UNSET>, picker=<UNSET>, rasterized=<UNSET>, sketch_params=<UNSET>, snap=<UNSET>, transform=<UNSET>, url=<UNSET>, visible=<UNSET>, zorder=<UNSET>)[source]
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color color
edgecolor or ec color or None
facecolor or fc color or None
figure Figure
fill bool
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float or None
path_effects AbstractPathEffect
picker None or bool or float or callable
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
visible bool
zorder float
Examples using mpl_toolkits.axes_grid1.inset_locator.BboxConnector
Axes Zoom Effect | |
doc_28190 | Returns a list of all hyperparameter specifications. | |
doc_28191 | Yields ModuleInfo for all modules recursively on path, or, if path is None, all accessible modules. path should be either None or a list of paths to look for modules in. prefix is a string to output on the front of every module name on output. Note that this function must import all packages (not all modules!) on the given path, in order to access the __path__ attribute to find submodules. onerror is a function which gets called with one argument (the name of the package which was being imported) if any exception occurs while trying to import a package. If no onerror function is supplied, ImportErrors are caught and ignored, while all other exceptions are propagated, terminating the search. Examples: # list all modules python can access
walk_packages()
# list all submodules of ctypes
walk_packages(ctypes.__path__, ctypes.__name__ + '.')
Note Only works for a finder which defines an iter_modules() method. This interface is non-standard, so the module also provides implementations for importlib.machinery.FileFinder and zipimport.zipimporter. Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation. | |
doc_28192 |
An abstract class representing a Dataset. All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader. Note DataLoader by default constructs a index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided. | |
doc_28193 | See Migration guide for more details. tf.compat.v1.raw_ops.Assign
tf.raw_ops.Assign(
ref, value, validate_shape=True, use_locking=True, name=None
)
This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value.
Args
ref A mutable Tensor. Should be from a Variable node. May be uninitialized.
value A Tensor. Must have the same type as ref. The value to be assigned to the variable.
validate_shape An optional bool. Defaults to True. If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'.
use_locking An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).
Returns A mutable Tensor. Has the same type as ref. | |
doc_28194 | tf.compat.v1.batch_gather(
params, indices, name=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2017-10-25. Instructions for updating: tf.batch_gather is deprecated, please use tf.gather with batch_dims=-1 instead. | |
doc_28195 |
Truncate series to length size. Reduce the series to length size by discarding the high degree terms. The value of size must be a positive integer. This can be useful in least squares where the coefficients of the high degree terms may be very small. Parameters
sizepositive int
The series is reduced to length size by discarding the high degree terms. The value of size must be a positive integer. Returns
new_seriesseries
New instance of series with truncated coefficients. | |
doc_28196 | The Form instance this BoundField is bound to. | |
doc_28197 | Mark the Future as done and set its result. Raises a InvalidStateError error if the Future is already done. | |
doc_28198 |
Create a montage of several single- or multichannel images. Create a rectangular montage from an input array representing an ensemble of equally shaped single- (gray) or multichannel (color) images. For example, montage(arr_in) called with the following arr_in
1 2 3 will return
1 2
3
where the ‘*’ patch will be determined by the fill parameter. Parameters
arr_in(K, M, N[, C]) ndarray
An array representing an ensemble of K images of equal shape.
fillfloat or array-like of floats or ‘mean’, optional
Value to fill the padding areas and/or the extra tiles in the output array. Has to be float for single channel collections. For multichannel collections has to be an array-like of shape of number of channels. If mean, uses the mean value over all images.
rescale_intensitybool, optional
Whether to rescale the intensity of each image to [0, 1].
grid_shapetuple, optional
The desired grid shape for the montage (ntiles_row, ntiles_column). The default aspect ratio is square.
padding_widthint, optional
The size of the spacing between the tiles and between the tiles and the borders. If non-zero, makes the boundaries of individual images easier to perceive.
multichannelboolean, optional
If True, the last arr_in dimension is threated as a color channel, otherwise as spatial. Returns
arr_out(K*(M+p)+p, K*(N+p)+p[, C]) ndarray
Output array with input images glued together (including padding p). Examples >>> import numpy as np
>>> from skimage.util import montage
>>> arr_in = np.arange(3 * 2 * 2).reshape(3, 2, 2)
>>> arr_in
array([[[ 0, 1],
[ 2, 3]],
[[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11]]])
>>> arr_out = montage(arr_in)
>>> arr_out.shape
(4, 4)
>>> arr_out
array([[ 0, 1, 4, 5],
[ 2, 3, 6, 7],
[ 8, 9, 5, 5],
[10, 11, 5, 5]])
>>> arr_in.mean()
5.5
>>> arr_out_nonsquare = montage(arr_in, grid_shape=(1, 3))
>>> arr_out_nonsquare
array([[ 0, 1, 4, 5, 8, 9],
[ 2, 3, 6, 7, 10, 11]])
>>> arr_out_nonsquare.shape
(2, 6) | |
doc_28199 | Close file descriptor fd. Note This function is intended for low-level I/O and must be applied to a file descriptor as returned by os.open() or pipe(). To close a “file object” returned by the built-in function open() or by popen() or fdopen(), use its close() method. |
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