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object --+
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base.parameterizedobject.ParameterizedObject --+
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base.patterngenerator.PatternGenerator --+
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Gaussian
2D Gaussian pattern generator.
The sigmas of the Gaussian are calculated from the size and aspect_ratio parameters:
ysigma=size/2 xsigma=ysigma*aspect_ratio
The Gaussian is then computed for the given (x,y) values as:
exp(-x^2/(2*xsigma^2) - y^2/(2*ysigma^2)
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aspect_ratio = <topo.base.parameterclasses.Number object at 0xRatio of the width to the height. |
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size = Number(default= 0.5, doc= Overall size of the Gaussian, defined by: exp(-x^2/(2*xsigma^2) - y^2/(2*ysigma^2) where ysigma=size/2 and xsigma=size/2*aspect_ratio. |
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__params = dict() -> new empty dictionary. |
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Function to draw a pattern that will then be scaled and rotated. Instead of implementing __call__ directly, PatternGenerator subclasses will typically implement this helper function used by __call__, because that way they can let __call__ handle the scaling and rotation for them. Alternatively, __call__ itself can be reimplemented entirely by a subclass (e.g. if it does not need to do any scaling or rotation), in which case this function will be ignored.
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aspect_ratioRatio of the width to the height. Specifically, xsigma=ysigma*aspect_ratio (see size).
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sizeOverall size of the Gaussian, defined by: exp(-x^2/(2*xsigma^2) - y^2/(2*ysigma^2) where ysigma=size/2 and xsigma=size/2*aspect_ratio.
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__params
dict() -> new empty dictionary.
dict(mapping) -> new dictionary initialized from a mapping object's
(key, value) pairs.
dict(seq) -> new dictionary initialized as if via:
d = {}
for k, v in seq:
d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
in the keyword argument list. For example: dict(one=1, two=2)
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