RBF class and documentation

Author: mtezzele

docs/source/code.rst

@@ -8,6 +8,7 @@ Code Documentation
 
    affine
    freeform
+   radial
    params
    filehandler
    openfhandler

pygem/__init__.py

@@ -1,8 +1,9 @@
 
-__all__ = ['affine', 'filehandler', 'freeform', 'openfhandler', 'params', 'stlhandler', 'unvhandler', 'vtkhandler', 'igeshandler', 'utils', 'gui']
+__all__ = ['affine', 'filehandler', 'freeform', 'radial', 'openfhandler', 'params', 'stlhandler', 'unvhandler', 'vtkhandler', 'igeshandler', 'utils', 'gui']
 
 from . import affine
 from . import freeform
+from . import radial
 from . import filehandler
 from . import openfhandler
 from . import params

pygem/radial.py

@@ -0,0 +1,276 @@
+"""
+Module focused on the implementation of the Radial Basis Functions interpolation technique.
+This technique is still based on the use of a set of parameters, the so-called control points,
+as for FFD, but RBF is interpolatory. Another important key point of RBF strategy relies in the
+way we can locate the control points: in fact, instead of FFD where control points need to be
+placed inside a regular lattice, with RBF we hano no more limitations. So we have the possibility
+to perform localized control points refiniments.
+The module is analogous to the freeform one.
+
+:Theoretical Insight:
+
+	As reference please consult M. D. Buhmann. Radial Basis Functions, volume 12 of Cambridge
+	monographs on applied and computational mathematics. Cambridge University Press, UK, 2003.
+	RBF shape parametrization technique is based on the definition of a map, 
+	:math:`\\mathcal{M}(\\boldsymbol{x}) : \\mathbb{R}^n \\rightarrow \\mathbb{R}^n`, that allows the
+	possibility of transferring data across non-matching grids and facing the dynamic mesh handling. 
+	The map	introduced is defines as follows
+
+	.. math::
+		\\mathcal{M}(\\boldsymbol{x}) = p(\\boldsymbol{x}) + \\sum_{i=1}^{\\mathcal{N}_C} \\gamma_i
+		\\varphi(\\| \\boldsymbol{x} - \\boldsymbol{x_{C_i}} \\|)
+
+	where :math:`p(\\boldsymbol{x})` is a low_degree polynomial term, :math:`\\gamma_i` is the weight,
+	corresponding to the a-priori selected :math:`\\mathcal{N}_C` control points, associated to the
+	:math:`i`-th basis function, and :math:`\\varphi(\\| \\boldsymbol{x} - \\boldsymbol{x_{C_i}} \\|)`
+	a radial function based on the Euclidean distance between the control points position 
+	:math:`\\boldsymbol{x_{C_i}}` and :math:`\\boldsymbol{x}`. A radial basis function, generally, is
+	a real-valued function whose value depends only on the distance from the origin, so that 
+	:math:`\\varphi(\\boldsymbol{x}) = \\tilde{\\varphi}(\\| \\boldsymbol{x} \\|)`. 
+
+	The matrix version of the formula above is:
+
+	.. math::
+		\\mathcal{M}(\\boldsymbol{x}) = \\boldsymbol{c} + \\boldsymbol{Q}\\boldsymbol{x} +
+		\\boldsymbol{W^T}\\boldsymbol{d}(\\boldsymbol{x})
+
+	The idea is that after the computation of the weights and the polynomial terms from the coordinates
+	of the control points before and after the deformation, we can deform all the points of the mesh
+	accordingly.
+	Among the most common used radial basis functions for modelling 2D and 3D shapes, we consider
+	Gaussian splines, Multi-quadratic biharmonic splines, Inverted multi-quadratic biharmonic splines,
+	Thin-plate splines and Beckert and Wendland :math:`C^2` basis all defined and implemented below.
+"""
+import os
+import params as rbfp
+import numpy as np
+from mpl_toolkits.mplot3d import axes3d
+import matplotlib.pyplot as plt
+
+
+class RBF(object):
+	"""
+	Class that handles the Radial Basis Functions interpolation on the mesh points.
+
+	:param RBFParameters rbf_parameters: parameters of the RBF.
+	:param numpy.ndarray original_mesh_points: coordinates of the original points of the mesh.
+
+	:cvar RBFParameters parameters: parameters of the RBF.
+	:cvar numpy.ndarray original_mesh_points: coordinates of the original points of the mesh.
+		The shape is `n_points`-by-3.
+	:cvar numpy.ndarray modified_mesh_points: coordinates of the points of the deformed mesh.
+		The shape is `n_points`-by-3.
+	:cvar dict bases: a dictionary that associates the names of the basis functions
+		implemented to the actual implementation.
+	:cvar numpy.matrix weights: the matrix formed by the weights corresponding to the a-priori
+		selected N control points, associated to the basis functions and c and Q terms that
+		describe the polynomial of order one p(x) = c + Qx. The shape is 
+		(n_control_points+1+3)-by-3. It is computed internally.
+
+	:Example:
+
+	>>> import pygem.radial as rbf
+	>>> import pygem.params as rbfp
+	>>> import numpy as np
+
+	>>> rbf_parameters = rbfp.FFDParameters()
+	>>> rbf_parameters.read_parameters('tests/test_datasets/parameters_rbf_cube.prm')
+
+	>>> nx, ny, nz = (20, 20, 20)
+	>>> mesh = np.zeros((nx * ny * nz, 3))
+	>>> xv = np.linspace(0, 1, nx)
+	>>> yv = np.linspace(0, 1, ny)
+	>>> zv = np.linspace(0, 1, nz)
+	>>> z, y, x = np.meshgrid(zv, yv, xv)
+	>>> mesh = np.array([x.ravel(), y.ravel(), z.ravel()])
+	>>> original_mesh_points = mesh.T
+	
+	>>> radial_trans = rbf.RBF(rbf_parameters, original_mesh_points)
+	>>> radial_trans.perform()
+	>>> new_mesh_points = radial_trans.modified_mesh_points
+	"""
+	def __init__(self, rbf_parameters, original_mesh_points):
+		self.parameters = rbf_parameters
+		self.original_mesh_points = original_mesh_points
+		self.modified_mesh_points = None
+		
+		self.bases = {
+			'gaussian_spline': self.gaussian_spline,
+			'multi_quadratic_biharmonic_spline': self.multi_quadratic_biharmonic_spline,
+			'inv_multi_quadratic_biharmonic_spline': self.inv_multi_quadratic_biharmonic_spline,
+			'thin_plate_spline': self.thin_plate_spline,
+			'beckert_wendland_c2_basis': self.beckert_wendland_c2_basis
+		}
+
+		# to make the str callable we have to use a dictionary with all the implemented radial basis functions
+		if params.basis in self.bases:
+			self.basis = self.bases[params.basis]
+		else:
+			raise NameError('The name of the basis function in the parameters file is not correct ' + \
+					'or not implemented. Check the documentation for all the available functions.')
+
+		self.weights = self._get_weights(self.parameters.original_control_points, \
+			self.parameters.deformed_control_points)
+
+	
+	@staticmethod
+	def gaussian_spline(X, r):
+		"""
+		It implements the following formula:
+
+		.. math::
+			\\varphi(\\| \\boldsymbol{x} \\|) = e^{-\\frac{\\| \\boldsymbol{x} \\|^2}{r^2}}
+
+		:param numpy.ndarray X: the vector x in the formula above.
+		:param float r: the parameter r in the formula above.
+
+		:return: result: the result of the formula above.
+		:rtype: float
+		"""
+		norm = np.linalg.norm(X)
+		result = np.exp( -(norm * norm) / (r * r) )
+		return result
+	
+	
+	@staticmethod
+	def multi_quadratic_biharmonic_spline(X, r):
+		"""
+		It implements the following formula:
+
+		.. math::
+			\\varphi(\\| \\boldsymbol{x} \\|) = \\sqrt{\\| \\boldsymbol{x} \\|^2 + r^2}
+
+		:param numpy.ndarray X: the vector x in the formula above.
+		:param float r: the parameter r in the formula above.
+
+		:return: result: the result of the formula above.
+		:rtype: float
+		"""
+		norm = np.linalg.norm(X)
+		result = np.sqrt( (norm * norm) + (r * r) )
+		return result
+	
+	
+	@staticmethod
+	def inv_multi_quadratic_biharmonic_spline(X, r):
+		"""
+		It implements the following formula:
+
+		.. math::
+			\\varphi(\\| \\boldsymbol{x} \\|) = (\\| \\boldsymbol{x} \\|^2 + r^2 )^{-\\frac{1}{2}}
+
+		:param numpy.ndarray X: the vector x in the formula above.
+		:param float r: the parameter r in the formula above.
+
+		:return: result: the result of the formula above.
+		:rtype: float
+		"""
+		result = 1.0/multi_quadratic_biharmonic_spline(X, r)
+		return result
+	
+	
+	@staticmethod
+	def thin_plate_spline(X, r):
+		"""
+		It implements the following formula:
+
+		.. math::
+			\\varphi(\\| \\boldsymbol{x} \\|) = \\left\\| \\frac{\\boldsymbol{x} }{r} \\right\\|^2 
+			\\ln \\left\\| \\frac{\\boldsymbol{x} }{r} \\right\\|
+
+		:param numpy.ndarray X: the vector x in the formula above.
+		:param float r: the parameter r in the formula above.
+
+		:return: result: the result of the formula above.
+		:rtype: float
+		"""
+		arg = X/r
+		norm = np.linalg.norm(arg)
+		result = norm * norm
+		if norm > 0:
+			result *= np.log(norm)
+		return result
+	
+	
+	@staticmethod
+	def beckert_wendland_c2_basis(X, r):
+		"""
+		It implements the following formula:
+
+		.. math::
+			\\varphi(\\| \\boldsymbol{x} \\|) = \\left( 1 - \\frac{\\| \\boldsymbol{x} \\|}{r} \\right)^4_+
+			\\left( 4 \\frac{\\| \\boldsymbol{x} \\|}{r} + 1 \\right)
+
+		:param numpy.ndarray X: the vector x in the formula above.
+		:param float r: the parameter r in the formula above.
+
+		:return: result: the result of the formula above.
+		:rtype: float
+		"""
+		norm = np.linalg.norm(X)
+		arg = norm / r
+		first = 0
+		if (1 - arg) > 0:
+			first = np.power((1 - arg), 4)
+		second = (4 * arg) + 1
+		result = first * second
+		return result
+	
+
+	def _distance_matrix(self, X1, X2):
+		"""
+		This private method returns the following matrix: 
+		:math:`\\boldsymbol{D_{ij}} = \\varphi(\\| \\boldsymbol{x_i} - \\boldsymbol{y_j} \\|)`
+
+		:param numpy.ndarray X1: the vector x in the formula above.
+		:param numpy.ndarray X2: the vector y in the formula above.
+
+		:return: matrix: the matrix D.
+		:rtype: numpy.ndarray
+		"""
+		m, n = X1.shape[0], X2.shape[0]
+		matrix = np.zeros(shape=(m, n))
+		for i in range(0, m):
+			for j in range(0, n):
+				matrix[i][j] = self.basis(X1[i] - X2[j], self.parameters.radius)
+		return matrix
+	
+	
+	def _get_weights(self, X, Y):
+		"""
+		This private method, given the original control points and the deformed ones, returns the matrix
+		with the weights and the polynomial terms, that is :math:`W`, :math:`c^T` and :math:`Q^T`.
+		The shape is (n_control_points+1+3)-by-3.
+
+		:param numpy.ndarray X: it is an n_control_points-by-3 array with the
+			coordinates of the original interpolation control points before the deformation.
+		:param numpy.ndarray Y: it is an n_control_points-by-3 array with the
+			coordinates of the interpolation control points after the deformation.
+
+		:return: weights: the matrix with the weights and the polynomial terms.
+		:rtype: numpy.matrix
+		"""
+		n_points = X.shape[0]
+		dim = X.shape[1]
+		identity = np.ones(n_points).reshape(n_points, 1)
+		dist = self._distance_matrix(X, X)
+		H = np.bmat([[dist, identity, X], [identity.T, np.zeros((1, 1)), np.zeros((1, dim))], \
+			[X.T, np.zeros((dim, 1)), np.zeros((dim, dim))]])
+		rhs = np.bmat([[Y], [np.zeros((1, dim))], [np.zeros((dim, dim))]])
+		inv_H = np.linalg.inv(H)
+		weights = np.dot(inv_H, rhs)
+		return weights
+	
+	
+	def perform(self):
+		"""
+		This method performs the deformation of the mesh points. After the execution
+		it sets `self.modified_mesh_points`.
+		"""
+		n_points = self.original_mesh_points.shape[0]
+		dim = self.original_mesh_points.shape[1]
+		dist = self._distance_matrix(self.original_mesh_points, self.parameters.original_control_points)
+		identity = np.ones(n_points).reshape(n_points, 1)
+		H = np.bmat([[dist, identity, self.original_mesh_points]])
+		self.modified_mesh_points = np.asarray(np.dot(H, self.weights))
+