Source code for microstructpy.geometry.rectangle

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from __future__ import division

import numpy as np
from matplotlib import patches
from matplotlib import pyplot as plt

from microstructpy.geometry.n_box import NBox

__author__ = 'Kenneth (Kip) Hart'


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# Rectangle Class                                                             #
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[docs]class Rectangle(NBox): """Rectangle This class contains a generic, 2D rectangle. The position and dimensions of the box can be specified using any of the parameters below. Without parameters, this returns a unit square centered on the origin. Args: length (float): *(optional)* Length of the rectangle. width (float): *(optional)* Width of the rectangle. *(optional)* side_lengths (list): *(optional)* Side lengths. Defaults to (1, 1). center (list): *(optional)* Center of rectangle. Defaults to (0, 0). corner (list): *(optional)* Bottom-left corner. bounds (list): *(optional)* Bounds of rectangle. Expected to be in the format [(xmin, xmax), (ymin, ymax)]. limits : Alias for *bounds*. angle (float): *(optional)* The rotation angle, in degrees. angle_deg (float): *(optional)* The rotation angle, in degrees. angle_rad (float): *(optional)* The rotation angle, in radians. matrix (numpy.ndarray): *(optional)* The 2x2 rotation matrix. """ def __init__(self, **kwargs): if 'length' in kwargs and 'width' in kwargs: kwargs['side_lengths'] = [kwargs['length'], kwargs['width']] if 'angle' in kwargs: cp = np.cos(np.radians(kwargs['angle'])) sp = np.sin(np.radians(kwargs['angle'])) kwargs['matrix'] = np.array([[cp, -sp], [sp, cp]]) elif 'angle_deg' in kwargs: cp = np.cos(np.radians(kwargs['angle_deg'])) sp = np.sin(np.radians(kwargs['angle_deg'])) kwargs['matrix'] = np.array([[cp, -sp], [sp, cp]]) elif 'angle_rad' in kwargs: cp = np.cos(kwargs['angle_rad']) sp = np.sin(kwargs['angle_rad']) kwargs['matrix'] = np.array([[cp, -sp], [sp, cp]]) NBox.__init__(self, **kwargs) try: self.center except AttributeError: self.center = [0, 0] try: self.side_lengths except AttributeError: self.side_lengths = [1, 1] # ----------------------------------------------------------------------- # # Best Fit Function # # ----------------------------------------------------------------------- #
[docs] def best_fit(self, points): """Find rectangle of best fit for points Args: points (list): List of points to fit. Returns: Rectangle: an instance of the class that best fits the points. """ # Unpack the input points pts = np.array(points, dtype='float') x, y = pts.T # Find the most likely orientation for the rectangle A = np.vstack([x, np.ones(len(x))]).T m, _ = np.linalg.lstsq(A, y, rcond=None)[0] theta = np.arctan(m) # Rotate the points to an axis-aligned frame s = np.sin(theta) c = np.cos(theta) rot = np.array([[c, -s], [s, c]]) pts_prin = pts.dot(rot) # Translate points to center of bounding box mins = pts_prin.min(axis=0) maxs = pts_prin.max(axis=0) bb_cen = 0.5 * (mins + maxs) pts_bb = pts_prin - bb_cen x_min, y_min = pts_bb.min(axis=0) x_max, y_max = pts_bb.max(axis=0) # Find nearest edge x1_dist = np.abs(pts_bb[:, 0] - x_min) x2_dist = np.abs(pts_bb[:, 0] - x_max) y1_dist = np.abs(pts_bb[:, 1] - y_min) y2_dist = np.abs(pts_bb[:, 1] - y_max) dists = np.array([x1_dist, x2_dist, y1_dist, y2_dist]).T min_dist = dists.min(axis=1) mask_x1 = np.isclose(x1_dist, min_dist) mask_x2 = np.isclose(x2_dist, min_dist) mask_y1 = np.isclose(y1_dist, min_dist) mask_y2 = np.isclose(y2_dist, min_dist) # Group points by edge x_x1 = pts_bb[mask_x1, 0].T y_y1 = pts_bb[mask_y1, 1].T x_x2 = pts_bb[mask_x2, 0].T y_y2 = pts_bb[mask_y2, 1].T # Get lines of best fit for each edge x1_fit = np.mean(x_x1) y1_fit = np.mean(y_y1) x2_fit = np.mean(x_x2) y2_fit = np.mean(y_y2) x_cen = 0.5 * (x1_fit + x2_fit) y_cen = 0.5 * (y1_fit + y2_fit) fit_cen_bb = np.array([x_cen, y_cen]) x_len = x2_fit - x1_fit y_len = y2_fit - y1_fit # Translate center to rotated frame fit_cen_prin = fit_cen_bb + bb_cen fit_cen = rot.dot(fit_cen_prin.reshape(-1, 1)).flatten() # Disambiguate the orientation and axes x_ax_seed = np.array(self.matrix)[:, 0] x_dot, y_dot = rot.T.dot(x_ax_seed) if np.abs(x_dot) > np.abs(y_dot): if x_dot > 0: x_ax_fit = rot[:, 0] else: x_ax_fit = - rot[:, 0] length = x_len width = y_len else: if y_dot > 0: x_ax_fit = rot[:, 1] else: x_ax_fit = - rot[:, 1] length = y_len width = x_len ang_diff = np.arcsin(np.cross(x_ax_seed, x_ax_fit)) ang_rad = self.angle_rad + ang_diff rect_fit = Rectangle(center=fit_cen, length=length, width=width, angle_rad=ang_rad) return rect_fit
# ----------------------------------------------------------------------- # # Representation Function # # ----------------------------------------------------------------------- # def __repr__(self): repr_str = 'Rectangle(' repr_str += 'center=' + repr(tuple(self.center)) + ', ' repr_str += 'side_lengths=' + repr(tuple(self.side_lengths)) + ', ' repr_str += 'angle=' + repr(self.angle) + ')' return repr_str # ----------------------------------------------------------------------- # # Number of Dimensions # # ----------------------------------------------------------------------- # @property def n_dim(self): """int: Number of dimensions, 2""" return 2 # ----------------------------------------------------------------------- # # Area # # ----------------------------------------------------------------------- # @property def area(self): """float: Area of rectangle""" return self.n_vol
[docs] @classmethod def area_expectation(cls, **kwargs): r"""Expected area of rectangle This method computes the expected area of a rectangle. There are two main ways to define the size of a rectangle: by the length and width and by the bounds. If the input rectangle is defined by length and width, the expected area is: .. math:: \mathbb{E}[A] = \mathbb{E}[L W] = \mu_L \mu_W For the case where it is defined by upper and lower bounds: .. math:: \mathbb{E}[A] = \mathbb{E}[(X_U - X_L) (Y_U - Y_L)] .. math:: \mathbb{E}[A] = \mu_{X_U}\mu_{Y_U} - \mu_{X_U} \mu_{Y_L} - \mu_{X_L}\mu_{Y_U} + \mu_{X_L}\mu_{Y_L} Example: >>> import scipy.stats >>> import microstructpy as msp >>> L = scipy.stats.uniform(loc=1, scale=2) >>> W = scipy.stats.norm(loc=3.2, scale=5.1) >>> L.mean() * W.mean() 6.4 >>> msp.geometry.Rectangle.area_expectation(length=L, width=W) 6.4 Args: **kwargs: Keyword arguments, same as :class:`.Rectangle` but the inputs can be from the :mod:`scipy.stats` module. Returns: float: Expected/average area of rectangle. """ if 'length' in kwargs or 'width' in kwargs: len_dist = kwargs.get('length', 1) width_dist = kwargs.get('width', 1) return _prod_exp(*[len_dist, width_dist]) if 'side_lengths' in kwargs: return _prod_exp(*kwargs['side_lengths']) if 'bounds' in kwargs: x_bnds, y_bnds = kwargs['bounds'] x_lb, x_ub = x_bnds y_lb, y_ub = y_bnds p1 = _prod_exp(*[x_ub, y_ub]) p2 = _prod_exp(*[x_ub, y_lb]) p3 = _prod_exp(*[x_lb, y_ub]) p4 = _prod_exp(*[x_lb, y_lb]) return p1 - p2 - p3 + p4 raise ValueError('Cannot find the expected area of rectangle')
# ----------------------------------------------------------------------- # # Properties # # ----------------------------------------------------------------------- # @property def length(self): """float: Length of rectangle, side length along 1st axis""" return self.side_lengths[0] @property def width(self): """float: Width of rectangle, side length along 2nd axis""" return self.side_lengths[1] @property def angle(self): """float: Rotation angle of rectangle - degrees""" return self.angle_deg @property def angle_deg(self): """float: Rotation angle of rectangle - degrees""" return 180 * self.angle_rad / np.pi @property def angle_rad(self): """float: Rotation angle of rectangle - radians""" return np.arctan2(self.matrix[1][0], self.matrix[0][0]) # ----------------------------------------------------------------------- # # Circle Approximation # # ----------------------------------------------------------------------- #
[docs] def approximate(self, x1=None): """Approximate rectangle with a set of circles. This method approximates a rectangle with a set of circles. These circles are spaced uniformly along the long axis of the rectangle with distance ``x1`` between them. Example ------- For a rectangle with length=2.5, width=1, and x1=0.3, the approximation would look like :numref:`f_api_rectangle_approx`. .. _f_api_rectangle_approx: .. figure:: ../../auto_examples/geometry/images/sphx_glr_plot_rectangle_001.png Circular approximation of rectangle. Args: x1 (float or None): *(optional)* Spacing between the circles. If not specified, the spacing is 0.25x the length of the shortest side. Returns: numpy.ndarray: An Nx3 array, where each row is a circle and the columns are x, y, and r. """ # NOQA: E501 if x1 is None: x1 = 0.25 * min(self.side_lengths) if self.side_lengths[0] >= self.side_lengths[1]: length = self.side_lengths[0] width = self.side_lengths[1] inds = [0, 1] else: length = self.side_lengths[1] width = self.side_lengths[0] inds = [1, 0] # Centerline circles xc = 0 half_len = 0.5 * length r = 0.5 * width circs = [] while xc < half_len: circ = [xc, 0, r] circs.append(circ) if np.isclose(xc + r, half_len): xc = half_len elif xc + x1 > half_len - r: xc = half_len - r else: xc = xc + x1 # Corner circle while r > 0: x_init = circs[-1][0] y_init = circs[-1][1] x = x_init + x1 y = y_init + x1 dx = half_len - x dy = 0.5 * width - y r = min(dx, dy) if r <= 0: break else: circs.append([x, y, r]) # Reflect circles circs = np.array(circs) for dim in range(self.n_dim): mask = circs[:, dim] > 0 new_circs = np.copy(circs[mask]) new_circs[:, dim] *= -1 circs = np.concatenate((circs, new_circs)) circs[:, inds] = circs[:, [0, 1]] # Rotate and translate circles pts = circs[:, :-1] circs[:, :-1] = pts.dot(np.array(self.matrix).T) circs[:, :-1] += self.center return circs
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[docs] def plot(self, **kwargs): """Plot the rectangle. This function adds a :class:`matplotlib.patches.Rectangle` patch to the current axes. The keyword arguments are passed through to the patch. Args: **kwargs (dict): Keyword arguments for the patch. """ # NOQA: E501 pt = self.corner w = self.length h = self.width ang = self.angle c = patches.Rectangle(pt, w, h, angle=ang, **kwargs) plt.gca().add_patch(c)
def _prod_exp(*args): prod = 1 for arg in args: try: arg_mu = arg.moment(1) except AttributeError: arg_mu = arg prod *= arg_mu return prod # --------------------------------------------------------------------------- # # # # Square Class # # # # --------------------------------------------------------------------------- #
[docs]class Square(Rectangle): """A square. This class contains a generic, 2D square. It is derived from the :class:`microstructpy.geometry.Rectangle` class and contains the ``side_length`` property, rather than multiple side lengths. Args: side_length (float): *(optional)* Side length. Defaults to 1. center (list): *(optional)* Center of rectangle. Defaults to (0, 0). corner (list): *(optional)* Bottom-left corner. """ def __init__(self, **kwargs): if 'side_length' in kwargs: kwargs['side_lengths'] = 2 * [kwargs['side_length']] Rectangle.__init__(self, **kwargs) # ----------------------------------------------------------------------- # # Side Length Property # # ----------------------------------------------------------------------- # @property def side_length(self): """float: length of the side of the square.""" return self.side_lengths[0] # ----------------------------------------------------------------------- # # Area # # ----------------------------------------------------------------------- #
[docs] @classmethod def area_expectation(cls, **kwargs): r"""Expected area of square This method computes the expected area of a square with distributed side length. The expectation is: .. math:: \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 Args: **kwargs: Keyword arguments, same as :class:`.Square` but the inputs can be from the :mod:`scipy.stats` module. Returns: float: Expected/average area of the square. """ if 'side_length' in kwargs: len_dist = kwargs['side_length'] try: area_exp = len_dist.moment(2) except AttributeError: area_exp = len_dist * len_dist return area_exp Rectangle.area_expectation(**kwargs)
# ----------------------------------------------------------------------- # # Circle Approximation # # ----------------------------------------------------------------------- #
[docs] def approximate(self, x1=None): """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 :numref:`f_api_square_approx`. .. _f_api_square_approx: .. figure:: ../../auto_examples/geometry/images/sphx_glr_plot_rectangle_002.png Circular approximation of square. Args: x1 (float or None): *(optional)* Spacing between the circles. If not specified, the spacing is 0.25x the side length. Returns: numpy.ndarray: An Nx3 array, where each row is a circle and the columns are x, y, and r. """ # NOQA: E501 return Rectangle.approximate(self, x1)