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https://github.com/wassname/simpeg.git
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TensorMesh now inherits BaseMesh (worked with Luz!)
tests for tensorMesh and utils (e.g. ndgrid) are included and pass Split the TensorMesh into Grid and View
This commit is contained in:
Rowan Cockett
@@ -0,0 +1,144 @@
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import numpy as np
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from utils import ndgrid
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class TensorGrid(object):
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"""
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Define nodal, cell-centered and staggered tensor grids for 1, 2 and 3
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dimensions.
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This class is inherited by TensorMesh
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"""
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def __init__(self):
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pass
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def vectorNx():
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doc = "Nodal grid vector (1D) in the x direction."
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fget = lambda self: np.r_[0., self.hx.cumsum()] + self.x0[0]
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return locals()
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vectorNx = property(**vectorNx())
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def vectorNy():
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doc = "Nodal grid vector (1D) in the y direction."
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fget = lambda self: None if self.dim < 2 else np.r_[0., self.hy.cumsum()] + self.x0[1]
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return locals()
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vectorNy = property(**vectorNy())
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def vectorNz():
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doc = "Nodal grid vector (1D) in the z direction."
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fget = lambda self: None if self.dim < 3 else np.r_[0., self.hz.cumsum()] + self.x0[2]
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return locals()
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vectorNz = property(**vectorNz())
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def vectorCCx():
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doc = "Cell-centered grid vector (1D) in the x direction."
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fget = lambda self: np.r_[0, self.hx[:-1].cumsum()] + self.hx*0.5 + self.x0[0]
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return locals()
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vectorCCx = property(**vectorCCx())
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def vectorCCy():
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doc = "Cell-centered grid vector (1D) in the y direction."
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fget = lambda self: None if self.dim < 2 else np.r_[0, self.hy[:-1].cumsum()] + self.hy*0.5 + self.x0[1]
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return locals()
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vectorCCy = property(**vectorCCy())
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def vectorCCz():
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doc = "Cell-centered grid vector (1D) in the z direction."
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fget = lambda self: None if self.dim < 3 else np.r_[0, self.hz[:-1].cumsum()] + self.hz*0.5 + self.x0[2]
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return locals()
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vectorCCz = property(**vectorCCz())
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def gridCC():
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doc = "Cell-centered grid."
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def fget(self):
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if self._gridCC is None:
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self._gridCC = ndgrid([x for x in [self.vectorCCx, self.vectorCCy, self.vectorCCz] if not x is None])
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return self._gridCC
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return locals()
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_gridCC = None # Store grid by default
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gridCC = property(**gridCC())
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def gridN():
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doc = "Nodal grid."
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def fget(self):
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if self._gridN is None:
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self._gridN = ndgrid([x for x in [self.vectorNx, self.vectorNy, self.vectorNz] if not x is None])
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return self._gridN
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return locals()
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_gridN = None # Store grid by default
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gridN = property(**gridN())
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def gridFx():
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doc = "Face staggered grid in the x direction."
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def fget(self):
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if self._gridFx is None:
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self._gridFx = ndgrid([x for x in [self.vectorNx, self.vectorCCy, self.vectorCCz] if not x is None])
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return self._gridFx
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return locals()
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_gridFx = None # Store grid by default
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gridFx = property(**gridFx())
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def gridFy():
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doc = "Face staggered grid in the y direction."
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def fget(self):
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if self._gridFy is None:
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self._gridFy = ndgrid([x for x in [self.vectorCCx, self.vectorNy, self.vectorCCz] if not x is None])
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return self._gridFy
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return locals()
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_gridFy = None # Store grid by default
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gridFy = property(**gridFy())
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def gridFz():
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doc = "Face staggered grid in the z direction."
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def fget(self):
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if self._gridFz is None:
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self._gridFz = ndgrid([x for x in [self.vectorCCx, self.vectorCCy, self.vectorNz] if not x is None])
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return self._gridFz
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return locals()
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_gridFz = None # Store grid by default
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gridFz = property(**gridFz())
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def gridEx():
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doc = "Edge staggered grid in the x direction."
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def fget(self):
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if self._gridEx is None:
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self._gridEx = ndgrid([x for x in [self.vectorCCx, self.vectorNy, self.vectorNz] if not x is None])
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return self._gridEx
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return locals()
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_gridEx = None # Store grid by default
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gridEx = property(**gridEx())
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def gridEy():
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doc = "Edge staggered grid in the y direction."
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def fget(self):
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if self._gridEy is None:
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self._gridEy = ndgrid([x for x in [self.vectorNx, self.vectorCCy, self.vectorNz] if not x is None])
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return self._gridEy
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return locals()
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_gridEy = None # Store grid by default
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gridEy = property(**gridEy())
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def gridEz():
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doc = "Edge staggered grid in the z direction."
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def fget(self):
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if self._gridEz is None:
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self._gridEz = ndgrid([x for x in [self.vectorNx, self.vectorNy, self.vectorCCz] if not x is None])
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return self._gridEz
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return locals()
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_gridEz = None # Store grid by default
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gridEz = property(**gridEz())
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def getBoundaryIndex(self, gridType):
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"""Needed for faces edges and cells"""
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pass
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def getCellNumbering(self):
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pass
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+69
-244
@@ -1,257 +1,82 @@
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#-------------------------------------------------------------------------------
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# Packages
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#-------------------------------------------------------------------------------
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from BaseMesh import BaseMesh
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from TensorGrid import TensorGrid
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from TensorView import TensorView
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#-------------------------------------------------------------------------------
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# Class definition
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#-------------------------------------------------------------------------------
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class TensorMesh:
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"""
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Define nodal, cell-centered and staggered tensor meshes for 1, 2 and 3
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dimensions.
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class TensorMesh(BaseMesh, TensorGrid, TensorView):
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"""
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# ---------------------- Properties --------------------------------------
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h = None # Array Spacing or cell-sizes in each direction
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x0 = None # Array Origin (x1,x2,x3)
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dim = None # Int Dimension
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n = None # Array Number of cells in each direction
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nC = None # Int Total number of cells
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nE = None # Int Total number of edges
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nF = None # Int Total number of faces
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# ----------------------- Methods ----------------------------------------
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def __init__(self,h,x0):
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"""Compute number of edges,faces and cell-centers """
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# Assign values to properties
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self.h = h
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self.x0 = x0
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#Compute derived properties
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self.dim = np.size(x0)
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#Compute the num of cells in each direction
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self.n = np.zeros((self.dim,1))
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for d in range(self.dim):
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self.n[d] = np.size(h[d])
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# Compute the number of cell-centers
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self.nC = np.prod(self.n)
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# Compute the number of edges (makes sense only for 3D)
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# Equivalent to:
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# nEdges = n[0] * (n[1]+1) * (n[2]+1)
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# + (n[0]+1) * ny[1] * (nz[2]+1)
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# + (n[0]+1) * (ny[1]+1)* (nz[2])
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if self.dim == 3:
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self.nE = np.prod(np.kron(np.ones((3,1)),self.n.T)+np.ones((3,3))-np.eye(3),1)
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print self.nE
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# Compute the number of faces (makes sense only for 2 and 3D)
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# Equivalent to
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# nFaces = (n[0]+1) * n[1] * n[2]
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# + n[0] * (ny[1]+1) * nz[2]
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# + n[0] * ny[1] * (nz[2]+1)
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if self.dim >=2:
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self.nF = np.prod(np.kron(np.ones((self.dim,1)),self.n.T)+np.eye(self.dim),1)
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print self.nF
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TensorMesh is a mesh class that deals with tensor product meshes.
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def xin(self,i):
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"""Construct the 1D nodal mesh from the ith-component of h. Return an array."""
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return np.insert(np.cumsum(self.h[i-1]),0,0.0) + self.x0[i-1]
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def xic(self,i):
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"""Construct the 1D cell-centerd mesh from the ith-component of h. Return an array."""
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return .5*( np.insert(np.cumsum(self.h[i-1][:,0:-1]),0,0.0) + np.cumsum(self.h[i-1]))
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Any Mesh that has a constant width along the entire axis
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such that it can defined by a single width vector, called 'h'.
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e.g.
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hx = np.array([1,1,1])
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hy = np.array([1,2])
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hz = np.array([1,1,1,1])
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mesh = TensorMesh([hx, hy, hz])
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"""
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def __init__(self, h, x0=None):
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super(TensorMesh, self).__init__(np.array([len(x) for x in h]), x0)
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assert len(h) == len(x0), "Dimension mismatch. x0 != len(h)"
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for i, h_i in enumerate(h):
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assert type(h_i) == np.ndarray, ("h[%i] is not a numpy array." % i)
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# Ensure h contains 1D vectors
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self._h = [x.ravel() for x in h]
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def h():
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doc = "h is a list containing the cell widths of the tensor mesh in each dimension."
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fget = lambda self: self._h
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return locals()
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h = property(**h())
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def hx():
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doc = "Width of cells in the x direction"
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fget = lambda self: self._h[0]
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return locals()
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hx = property(**hx())
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def hy():
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doc = "Width of cells in the y direction"
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fget = lambda self: None if self.dim < 2 else self._h[1]
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return locals()
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hy = property(**hy())
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def hz():
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doc = "Width of cells in the z direction"
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fget = lambda self: None if self.dim < 3 else self._h[2]
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return locals()
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hz = property(**hz())
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def getNodalGrid(self):
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"""Construct nodal grid for 1, 2 and 3 dimensions"""
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if self.dim==1:
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return [self.xin(1)]
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elif self.dim==2:
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return self.ndgrid([self.xin(1),self.xin(2)])
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elif self.dim==3:
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return self.ndgrid([self.xin(1),self.xin(2),self.xin(3)])
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def ndgrid(self, xin):
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"""Form tensorial grid for 1, 2 and 3 dimensions. Return X1,X2,X3 arrays depending on the dimension"""
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ei = lambda i : np.ones((np.size(xin[i-1]),1))
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if self.dim==1:
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return [xin]
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elif self.dim==2:
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X1 = np.kron(ei(2),xin[0]).reshape(-1,1)
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X2 = np.kron(xin[1],ei(1).T).reshape(-1,1)
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return X1,X2
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elif self.dim==3:
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X1 = np.kron(ei(3),np.kron(ei(2),xin[0])).reshape(-1,1)
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X2 = np.kron(ei(3).T,np.kron(xin[1],ei(1).T)).reshape(-1,1)
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X3 = np.kron(xin[2],np.kron(ei(2),ei(1))).T.reshape(-1,1)
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return X1,X2,X3
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def getCellCenteredGrid(self):
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"""Construct cell-centered grid for 1, 2 and 3 dimensions."""
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if self.dim==1:
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return [self.xic(1)]
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elif self.dim==2:
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return self.ndgrid([self.xic(1),self.xic(2)])
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elif self.dim==3:
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return self.ndgrid([self.xic(1),self.xic(2),self.xic(3)])
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def getFaceStgGrid(self,direction):
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"""Construct the face staggered grids for 2 and 3 dimensions."""
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if self.dim==1:
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print 'Error: dimension must be larger than 1'
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elif self.dim==2:
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if direction == 1:
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return self.ndgrid([self.xin(1),self.xic(2)])
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elif direction == 2:
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return self.ndgrid([self.xic(1),self.xin(2)])
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else:
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print 'Error: direction must be equal to 1 or 2'
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elif self.dim==3:
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if direction == 1:
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return self.ndgrid([self.xin(1),self.xic(2),self.xic(3)])
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elif direction == 2:
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return self.ndgrid([self.xic(1),self.xin(2),self.xic(3)])
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elif direction == 3:
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return self.ndgrid([self.xic(1),self.xic(2),self.xin(3)])
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else:
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print 'Error: direction must be equal to 1, 2 or 3'
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def getEdgeStgGrid(self,direction):
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"""Construct the edge staggered grids for 3 dimension case."""
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if self.dim != 3:
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print 'Error: dimension must be equal to 3'
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else:
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if direction == 1:
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return self.ndgrid([self.xic(1),self.xin(2),self.xin(3)])
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elif direction == 2:
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return self.ndgrid([self.xin(1),self.xic(2),self.xin(3)])
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elif direction == 3:
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return self.ndgrid([self.xin(1),self.xin(2),self.xic(3)])
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else:
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print 'Error: direction must be equal to 1, 2 or 3'
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def plotImage(self,I):
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if self.dim==1:
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fig = plt.figure(1)
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fig.clf()
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ax=plt.subplot(111)
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if np.size(I)==self.n[0]:
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print 'cell-centered image'
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xx = self.getCellCenteredGrid()
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ax.plot(xx[0],I,'ro')
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elif np.size(I)==self.n[0]+1:
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print 'nodal image'
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xx = self.getNodalGrid()
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ax.plot(xx[0],I,'bs')
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fig.show()
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def plotGrid(self):
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"""Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions."""
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if self.dim == 1:
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fig = plt.figure(1)
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fig.clf()
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ax = plt.subplot(111)
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xn = self.getNodalGrid()
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xc = self.getCellCenteredGrid()
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print xn
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ax.hold(True)
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ax.plot(xn,np.ones(np.shape(xn)),'bs')
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ax.plot(xc,np.ones(np.shape(xc)),'ro')
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ax.plot(xn,np.ones(np.shape(xn)),'k--')
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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fig.show()
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elif self.dim == 2:
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fig = plt.figure(2)
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fig.clf()
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ax = plt.subplot(111)
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xn = self.getNodalGrid()
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xc = self.getCellCenteredGrid()
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xs1 = self.getFaceStgGrid(1)
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xs2 = self.getFaceStgGrid(2)
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ax.hold(True)
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ax.plot(xn[0],xn[1],'bs')
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ax.plot(xc[0],xc[1],'ro')
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ax.plot(xs1[0],xs1[1],'g>')
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ax.plot(xs2[0],xs2[1],'g^')
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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ax.set_ylabel('x2')
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fig.show()
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elif self.dim == 3:
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fig = plt.figure(3)
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fig.clf()
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ax = fig.add_subplot(111, projection='3d')
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xn = self.getNodalGrid()
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xc = self.getCellCenteredGrid()
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xfs1 = self.getFaceStgGrid(1)
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xfs2 = self.getFaceStgGrid(2)
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xfs3 = self.getFaceStgGrid(3)
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xes1 = self.getEdgeStgGrid(1)
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xes2 = self.getEdgeStgGrid(2)
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xes3 = self.getEdgeStgGrid(3)
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ax.hold(True)
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ax.plot(xn[0],xn[1],'bs',zs=xn[2])
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ax.plot(xc[0],xc[1],'ro',zs=xc[2])
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ax.plot(xfs1[0],xfs1[1],'g>',zs=xfs1[2])
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ax.plot(xfs2[0],xfs2[1],'g<',zs=xfs2[2])
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ax.plot(xfs3[0],xfs3[1],'g^',zs=xfs3[2])
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ax.plot(xes1[0],xes1[1],'k>',zs=xes1[2])
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ax.plot(xes2[0],xes2[1],'k<',zs=xes2[2])
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ax.plot(xes3[0],xes3[1],'k^',zs=xes3[2])
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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ax.set_ylabel('x2')
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ax.set_zlabel('x3')
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fig.show()
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if __name__ == '__main__':
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print('Welcome to tensor mesh!')
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testDim = 1
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h1 = 0.3*np.ones((1,7))
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h1[:,0] = 0.5
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h1[:,-1] = 0.6
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h2 = .5* np.ones((1,4))
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h3 = .4* np.ones((1,6))
|
||||
x0 = np.zeros((3,1))
|
||||
|
||||
h1 = 0.3*np.ones((1, 7))
|
||||
h1[:, 0] = 0.5
|
||||
h1[:, -1] = 0.6
|
||||
h2 = .5 * np.ones((1, 4))
|
||||
h3 = .4 * np.ones((1, 6))
|
||||
x0 = np.zeros((3, 1))
|
||||
|
||||
if testDim == 1:
|
||||
h = [h1]
|
||||
x0 = x0[0]
|
||||
elif testDim==2:
|
||||
h = [h1,h2]
|
||||
x0 = x0[0]
|
||||
elif testDim == 2:
|
||||
h = [h1, h2]
|
||||
x0 = x0[0:2]
|
||||
else:
|
||||
h = [h1,h2,h3]
|
||||
|
||||
I = np.linspace(0,1,8)
|
||||
M = TensorMesh(h,x0)
|
||||
|
||||
xn = M.plotGrid()
|
||||
|
||||
|
||||
|
||||
h = [h1, h2, h3]
|
||||
|
||||
I = np.linspace(0, 1, 8)
|
||||
M = TensorMesh(h, x0)
|
||||
|
||||
xn = M.plotGrid()
|
||||
|
||||
@@ -0,0 +1,96 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
|
||||
|
||||
class TensorView(object):
|
||||
"""
|
||||
Provides viewing functions for TensorMesh
|
||||
|
||||
This class is inherited by TensorMesh
|
||||
"""
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def plotImage(self, I):
|
||||
|
||||
if self.dim == 1:
|
||||
fig = plt.figure(1)
|
||||
fig.clf()
|
||||
ax = plt.subplot(111)
|
||||
if np.size(I) == self.n[0]:
|
||||
print 'cell-centered image'
|
||||
xx = self.gridCC
|
||||
ax.plot(xx[0], I, 'ro')
|
||||
elif np.size(I) == self.n[0]+1:
|
||||
print 'nodal image'
|
||||
xx = self.gridN
|
||||
ax.plot(xx[0], I, 'bs')
|
||||
|
||||
fig.show()
|
||||
|
||||
def plotGrid(self):
|
||||
"""Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions."""
|
||||
if self.dim == 1:
|
||||
fig = plt.figure(1)
|
||||
fig.clf()
|
||||
ax = plt.subplot(111)
|
||||
xn = self.gridN
|
||||
xc = self.gridCC
|
||||
print xn
|
||||
ax.hold(True)
|
||||
ax.plot(xn, np.ones(np.shape(xn)), 'bs')
|
||||
ax.plot(xc, np.ones(np.shape(xc)), 'ro')
|
||||
ax.plot(xn, np.ones(np.shape(xn)), 'k--')
|
||||
ax.grid(True)
|
||||
ax.hold(False)
|
||||
ax.set_xlabel('x1')
|
||||
fig.show()
|
||||
elif self.dim == 2:
|
||||
fig = plt.figure(2)
|
||||
fig.clf()
|
||||
ax = plt.subplot(111)
|
||||
xn = self.gridN
|
||||
xc = self.gridCC
|
||||
xs1 = self.gridFx
|
||||
xs2 = self.gridFy
|
||||
|
||||
ax.hold(True)
|
||||
ax.plot(xn[:, 0], xn[:, 1], 'bs')
|
||||
ax.plot(xc[:, 0], xc[:, 1], 'ro')
|
||||
ax.plot(xs1[:, 0], xs1[:, 1], 'g>')
|
||||
ax.plot(xs2[:, 0], xs2[:, 1], 'g^')
|
||||
ax.grid(True)
|
||||
ax.hold(False)
|
||||
ax.set_xlabel('x1')
|
||||
ax.set_ylabel('x2')
|
||||
fig.show()
|
||||
elif self.dim == 3:
|
||||
fig = plt.figure(3)
|
||||
fig.clf()
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
xn = self.gridN
|
||||
xc = self.gridCC
|
||||
xfs1 = self.gridFx
|
||||
xfs2 = self.gridFy
|
||||
xfs3 = self.gridFz
|
||||
|
||||
xes1 = self.gridEx
|
||||
xes2 = self.gridEy
|
||||
xes3 = self.gridEz
|
||||
|
||||
ax.hold(True)
|
||||
ax.plot(xn[:, 0], xn[:, 1], 'bs', zs=xn[:, 2])
|
||||
ax.plot(xc[:, 0], xc[:, 1], 'ro', zs=xc[:, 2])
|
||||
ax.plot(xfs1[:, 0], xfs1[:, 1], 'g>', zs=xfs1[:, 2])
|
||||
ax.plot(xfs2[:, 0], xfs2[:, 1], 'g<', zs=xfs2[:, 2])
|
||||
ax.plot(xfs3[:, 0], xfs3[:, 1], 'g^', zs=xfs3[:, 2])
|
||||
ax.plot(xes1[:, 0], xes1[:, 1], 'k>', zs=xes1[:, 2])
|
||||
ax.plot(xes2[:, 0], xes2[:, 1], 'k<', zs=xes2[:, 2])
|
||||
ax.plot(xes3[:, 0], xes3[:, 1], 'k^', zs=xes3[:, 2])
|
||||
ax.grid(True)
|
||||
ax.hold(False)
|
||||
ax.set_xlabel('x1')
|
||||
ax.set_ylabel('x2')
|
||||
ax.set_zlabel('x3')
|
||||
fig.show()
|
||||
@@ -0,0 +1,34 @@
|
||||
import numpy as np
|
||||
import unittest
|
||||
import sys
|
||||
sys.path.append('../')
|
||||
from TensorMesh import TensorMesh
|
||||
|
||||
|
||||
class TestSequenceFunctions(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
a = np.array([1, 1, 1])
|
||||
b = np.array([1, 2])
|
||||
x0 = np.array([3, 5])
|
||||
self.mesh2 = TensorMesh([a, b], x0)
|
||||
|
||||
def test_vectorN_2D(self):
|
||||
testNx = np.array([3, 4, 5, 6])
|
||||
testNy = np.array([5, 6, 8])
|
||||
|
||||
xtest = np.all(self.mesh2.vectorNx == testNx)
|
||||
ytest = np.all(self.mesh2.vectorNy == testNy)
|
||||
self.assertTrue(xtest and ytest)
|
||||
|
||||
def test_vectorCC_2D(self):
|
||||
testNx = np.array([3.5, 4.5, 5.5])
|
||||
testNy = np.array([5.5, 7])
|
||||
|
||||
xtest = np.all(self.mesh2.vectorCCx == testNx)
|
||||
ytest = np.all(self.mesh2.vectorCCy == testNy)
|
||||
self.assertTrue(xtest and ytest)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@@ -0,0 +1,53 @@
|
||||
import numpy as np
|
||||
import unittest
|
||||
import sys
|
||||
sys.path.append('../')
|
||||
from utils import mkvc, ndgrid
|
||||
|
||||
|
||||
class TestSequenceFunctions(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.a = np.array([1, 2, 3])
|
||||
self.b = np.array([1, 2])
|
||||
self.c = np.array([1, 2, 3, 4])
|
||||
|
||||
def test_mkvc1(self):
|
||||
x = mkvc(self.a)
|
||||
self.assertTrue(x.shape, (3,))
|
||||
|
||||
def test_mkvc2(self):
|
||||
x = mkvc(self.a, 2)
|
||||
self.assertTrue(x.shape, (3, 1))
|
||||
|
||||
def test_mkvc3(self):
|
||||
x = mkvc(self.a, 3)
|
||||
self.assertTrue(x.shape, (3, 1, 1))
|
||||
|
||||
def test_ndgrid_2D(self):
|
||||
XY = ndgrid([self.a, self.b])
|
||||
|
||||
X1_test = np.array([1, 2, 3, 1, 2, 3])
|
||||
X2_test = np.array([1, 1, 1, 2, 2, 2])
|
||||
|
||||
xtest = np.all(XY[:, 0] == X1_test)
|
||||
ytest = np.all(XY[:, 1] == X2_test)
|
||||
|
||||
self.assertTrue(xtest and ytest)
|
||||
|
||||
def test_ndgrid_3D(self):
|
||||
XYZ = ndgrid([self.a, self.b, self.c])
|
||||
|
||||
X1_test = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])
|
||||
X2_test = np.array([1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2])
|
||||
X3_test = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4])
|
||||
|
||||
xtest = np.all(XYZ[:, 0] == X1_test)
|
||||
ytest = np.all(XYZ[:, 1] == X2_test)
|
||||
ztest = np.all(XYZ[:, 2] == X3_test)
|
||||
|
||||
self.assertTrue(xtest and ytest and ztest)
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
+43
-21
@@ -1,4 +1,5 @@
|
||||
from numpy import *
|
||||
import numpy as np
|
||||
|
||||
|
||||
def diff(A, d):
|
||||
@@ -43,35 +44,56 @@ def ave(A, d):
|
||||
print('d must be 1,2 or 3')
|
||||
|
||||
|
||||
def reshapeF(sp, d):
|
||||
return reshape(sp, d, 'F')
|
||||
def reshapeF(x, size):
|
||||
return np.reshape(x, size, order='F')
|
||||
|
||||
|
||||
def mkvc(A):
|
||||
return reshape(A, [size(A), 1], 'F').flatten()
|
||||
def mkvc(x, numDims=1):
|
||||
"""Creates a vector with the number of dimension specified
|
||||
|
||||
e.g.:
|
||||
|
||||
a = np.array(1,2,3)
|
||||
|
||||
mkvc(a, 1).shape
|
||||
> (3, )
|
||||
|
||||
mkvc(a, 2).shape
|
||||
> (3, 1)
|
||||
|
||||
mkvc(a, 3).shape
|
||||
> (3, 1, 1)
|
||||
|
||||
"""
|
||||
assert type(x) == np.ndarray, "Vector must be a numpy array"
|
||||
|
||||
if numDims == 1:
|
||||
return x.flatten(order='F')
|
||||
elif numDims == 2:
|
||||
return x.flatten(order='F')[:, np.newaxis]
|
||||
elif numDims == 3:
|
||||
return x.flatten(order='F')[:, np.newaxis, np.newaxis]
|
||||
|
||||
|
||||
def ndgrid(x, y, z):
|
||||
def ndgrid(xin):
|
||||
"""Form tensorial grid for 1, 2 and 3 dimensions. Return X1,X2,X3 arrays depending on the dimension"""
|
||||
|
||||
n1 = size(x)
|
||||
n2 = size(y)
|
||||
n3 = size(z)
|
||||
X = zeros([n1, n2, n3])
|
||||
Y = zeros([n1, n2, n3])
|
||||
Z = zeros([n1, n2, n3])
|
||||
for i in range(0, n2):
|
||||
for j in range(0, n3):
|
||||
X[:, i, j] = x
|
||||
if len(xin) == 1:
|
||||
return xin
|
||||
elif len(xin) == 2:
|
||||
X2, X1 = [mkvc(x) for x in np.broadcast_arrays(mkvc(xin[1], 1), mkvc(xin[0], 2))]
|
||||
return np.c_[X1, X2]
|
||||
elif len(xin) == 3:
|
||||
X3, X2, X1 = [mkvc(x) for x in np.broadcast_arrays(mkvc(xin[2], 1), mkvc(xin[1], 2), mkvc(xin[0], 3))]
|
||||
return np.c_[X1, X2, X3]
|
||||
|
||||
for i in range(0, n1):
|
||||
for j in range(0, n3):
|
||||
Y[i, :, j] = y
|
||||
|
||||
for i in range(0, n1):
|
||||
for j in range(0, n2):
|
||||
Z[i, j, :] = z
|
||||
def flattenF(x):
|
||||
return np.flatten(x, order='F')
|
||||
|
||||
return (X, Y, Z)
|
||||
|
||||
def printF(x):
|
||||
pass
|
||||
|
||||
|
||||
def ind2sub(shape, ind):
|
||||
|
||||
Reference in New Issue
Block a user