microstructpy.geometry.Square¶

class
microstructpy.geometry.
Square
(**kwargs)[source]¶ A square.
This class contains a generic, 2D square. It is derived from the
microstructpy.geometry.Rectangle
class and contains theside_length
property, rather than multiple side lengths. Parameters

approximate
(x1=None)[source]¶ Approximate square with a set of circles
This method approximates a square with a set of circles. These circles are spaced uniformly along the edges of the square with distance
x1
between them.Example
For a square with side_length=1, and x1=0.2, the approximation would look like Fig. 30.

classmethod
area_expectation
(**kwargs)[source]¶ Expected area of square
This method computes the expected area of a square with distributed side length. The expectation is:
\[\mathbb{E}[A] = \mathbb{E}[S^2] = \mu_S^2 + \sigma_S^2\]Example
>>> import scipy.stats >>> import microstructpy as msp >>> S = scipy.stats.expon(scale=2) >>> S.mean()^2 + S.var() 8.0 >>> msp.geometry.Square.area_expectation(side_length=S) 8.0
 Parameters
**kwargs – Keyword arguments, same as
Square
but the inputs can be from thescipy.stats
module. Returns
Expected/average area of the square.
 Return type

best_fit
(points)¶ Find rectangle of best fit for points

plot
(**kwargs)¶ Plot the rectangle.
This function adds a
matplotlib.patches.Rectangle
patch to the current axes. The keyword arguments are passed through to the patch. Parameters
**kwargs (dict) – Keyword arguments for the patch.

within
(points)¶ Test if points are within nbox.
This function tests whether a point or set of points are within the nbox. For the set of points, a list of booleans is returned to indicate which points are within the nbox.
 Parameters
points (list or numpy.ndarray) – Point or list of points.
 Returns
Flags set to True for points in geometry.
 Return type