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https://github.com/wassname/simpeg.git
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some updates for examples.
This commit is contained in:
Rowan Cockett
@@ -1,336 +0,0 @@
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import numpy as np
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from scipy import sparse as sp
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from SimPEG.utils import mkvc, sdiag, speye, kron3, spzeros
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def ddx(n):
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"""Define 1D derivatives, inner, this means we go from n+1 to n+1"""
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return sp.spdiags((np.ones((n+1, 1))*[-1, 1]).T, [0, 1], n, n+1, format="csr")
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def checkBC(bc):
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""" Checks if boundary condition 'bc' is valid. """
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if(type(bc) is str):
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bc = [bc, bc]
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assert type(bc) is list, 'bc must be a list'
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assert len(bc) == 2, 'bc must have two elements'
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for bc_i in bc:
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assert type(bc_i) is str, "each bc must be a string"
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assert bc_i in ['dirichlet', 'neumann'], "each bc must be either, 'dirichlet' or 'neumann'"
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return bc
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def ddxCellGrad(n, bc):
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"""Create 1D derivative operator from cell-centres to nodes this means we go from n to n+1"""
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bc = checkBC(bc)
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D = sp.spdiags((np.ones((n+1, 1))*[-1, 1]).T, [-1, 0], n+1, n, format="csr")
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# Set the first side
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if(bc[0] == 'dirichlet'):
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D[0, 0] = 2
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elif(bc[0] == 'neumann'):
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D[0, 0] = 0
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# Set the second side
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if(bc[1] == 'dirichlet'):
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D[-1, -1] = -2
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elif(bc[1] == 'neumann'):
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D[-1, -1] = 0
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return D
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def av(n):
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"""Define 1D averaging operator from cell-centres to nodes."""
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return sp.spdiags((0.5*np.ones((n+1, 1))*[1, 1]).T, [0, 1], n, n+1, format="csr")
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class DiffOperators(object):
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"""
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Class creates the differential operators that you need!
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"""
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def __init__(self):
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raise Exception('DiffOperators is a base class providing differential operators on meshes and cannot run on its own. Inherit to your favorite Mesh class.')
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def faceDiv():
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doc = "Construct divergence operator (face-stg to cell-centres)."
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def fget(self):
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if(self._faceDiv is None):
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# The number of cell centers in each direction
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n = self.n
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# Compute faceDivergence operator on faces
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if(self.dim == 1):
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D = ddx(n[0])
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elif(self.dim == 2):
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D1 = sp.kron(speye(n[1]), ddx(n[0]))
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D2 = sp.kron(ddx(n[1]), speye(n[0]))
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D = sp.hstack((D1, D2), format="csr")
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elif(self.dim == 3):
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D1 = kron3(speye(n[2]), speye(n[1]), ddx(n[0]))
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D2 = kron3(speye(n[2]), ddx(n[1]), speye(n[0]))
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D3 = kron3(ddx(n[2]), speye(n[1]), speye(n[0]))
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D = sp.hstack((D1, D2, D3), format="csr")
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# Compute areas of cell faces & volumes
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S = self.area
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V = self.vol
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self._faceDiv = sdiag(1/V)*D*sdiag(S)
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return self._faceDiv
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return locals()
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_faceDiv = None
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faceDiv = property(**faceDiv())
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def nodalGrad():
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doc = "Construct gradient operator (nodes to edges)."
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def fget(self):
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if(self._nodalGrad is None):
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# The number of cell centers in each direction
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n = self.n
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# Compute divergence operator on faces
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if(self.dim == 1):
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G = ddx(n[0])
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elif(self.dim == 2):
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D1 = sp.kron(speye(n[1]+1), ddx(n[0]))
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D2 = sp.kron(ddx(n[1]), speye(n[0]+1))
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G = sp.vstack((D1, D2), format="csr")
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elif(self.dim == 3):
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D1 = kron3(speye(n[2]+1), speye(n[1]+1), ddx(n[0]))
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D2 = kron3(speye(n[2]+1), ddx(n[1]), speye(n[0]+1))
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D3 = kron3(ddx(n[2]), speye(n[1]+1), speye(n[0]+1))
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G = sp.vstack((D1, D2, D3), format="csr")
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# Compute lengths of cell edges
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L = self.edge
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self._nodalGrad = sdiag(1/L)*G
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return self._nodalGrad
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return locals()
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_nodalGrad = None
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nodalGrad = property(**nodalGrad())
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def setCellGradBC(self, BC):
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"""
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Function that sets the boundary conditions for cell-centred derivative operators.
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Examples::
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BC = 'neumann' # Neumann in all directions
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BC = ['neumann', 'dirichlet', 'neumann'] # 3D, Dirichlet in y Neumann else
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BC = [['neumann', 'dirichlet'], 'dirichlet', 'dirichlet'] # 3D, Neumann in x on bottom of domain,
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# Dirichlet else
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"""
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if(type(BC) is str):
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BC = [BC for _ in self.n] # Repeat the str self.dim times
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elif(type(BC) is list):
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assert len(BC) == self.dim, 'BC list must be the size of your mesh'
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else:
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raise Exception("BC must be a str or a list.")
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for i, bc_i in enumerate(BC):
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BC[i] = checkBC(bc_i)
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self._cellGrad = None # ensure we create a new gradient next time we call it
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self._cellGradBC = BC
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return BC
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_cellGradBC = 'neumann'
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def cellGrad():
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doc = "The cell centered Gradient, takes you to cell faces."
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def fget(self):
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if(self._cellGrad is None):
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BC = self.setCellGradBC(self._cellGradBC)
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n = self.n
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if(self.dim == 1):
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G = ddxCellGrad(n[0], BC[0])
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elif(self.dim == 2):
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G1 = sp.kron(speye(n[1]), ddxCellGrad(n[0], BC[0]))
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G2 = sp.kron(ddxCellGrad(n[1], BC[1]), speye(n[0]))
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G = sp.vstack((G1, G2), format="csr")
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elif(self.dim == 3):
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G1 = kron3(speye(n[2]), speye(n[1]), ddxCellGrad(n[0], BC[0]))
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G2 = kron3(speye(n[2]), ddxCellGrad(n[1], BC[1]), speye(n[0]))
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G3 = kron3(ddxCellGrad(n[2], BC[2]), speye(n[1]), speye(n[0]))
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G = sp.vstack((G1, G2, G3), format="csr")
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# Compute areas of cell faces & volumes
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S = self.area
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V = self.vol
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self._cellGrad = sdiag(S)*G*sdiag(1/V)
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return self._cellGrad
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return locals()
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_cellGrad = None
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cellGrad = property(**cellGrad())
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def edgeCurl():
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doc = "Construct the 3D curl operator."
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def fget(self):
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if(self._edgeCurl is None):
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# The number of cell centers in each direction
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n1 = self.nCx
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n2 = self.nCy
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n3 = self.nCz
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# Compute lengths of cell edges
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L = self.edge
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# Compute areas of cell faces
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S = self.area
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# Compute divergence operator on faces
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d1 = ddx(n1)
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d2 = ddx(n2)
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d3 = ddx(n3)
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D32 = kron3(d3, speye(n2), speye(n1+1))
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D23 = kron3(speye(n3), d2, speye(n1+1))
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D31 = kron3(d3, speye(n2+1), speye(n1))
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D13 = kron3(speye(n3), speye(n2+1), d1)
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D21 = kron3(speye(n3+1), d2, speye(n1))
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D12 = kron3(speye(n3+1), speye(n2), d1)
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O1 = spzeros(np.shape(D32)[0], np.shape(D31)[1])
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O2 = spzeros(np.shape(D31)[0], np.shape(D32)[1])
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O3 = spzeros(np.shape(D21)[0], np.shape(D13)[1])
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C = sp.vstack((sp.hstack((O1, -D32, D23)),
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sp.hstack((D31, O2, -D13)),
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sp.hstack((-D21, D12, O3))), format="csr")
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self._edgeCurl = sdiag(1/S)*(C*sdiag(L))
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return self._edgeCurl
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return locals()
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_edgeCurl = None
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edgeCurl = property(**edgeCurl())
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def aveF2CC():
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doc = "Construct the averaging operator on cell faces to cell centers."
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def fget(self):
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if(self._aveF2CC is None):
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n = self.n
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if(self.dim == 1):
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self._aveF2CC = av(n[0])
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elif(self.dim == 2):
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self._aveF2CC = sp.hstack((sp.kron(speye(n[1]), av(n[0])),
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sp.kron(av(n[1]), speye(n[0]))), format="csr")
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elif(self.dim == 3):
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self._aveF2CC = sp.hstack((kron3(speye(n[2]), speye(n[1]), av(n[0])),
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kron3(speye(n[2]), av(n[1]), speye(n[0])),
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kron3(av(n[2]), speye(n[1]), speye(n[0]))), format="csr")
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return self._aveF2CC
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return locals()
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_aveF2CC = None
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aveF2CC = property(**aveF2CC())
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def aveE2CC():
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doc = "Construct the averaging operator on cell edges to cell centers."
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def fget(self):
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if(self._aveE2CC is None):
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# The number of cell centers in each direction
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n = self.n
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if(self.dim == 1):
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raise Exception('Edge Averaging does not make sense in 1D: Use Identity?')
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elif(self.dim == 2):
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self._aveE2CC = sp.hstack((sp.kron(av(n[1]), speye(n[0])),
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sp.kron(speye(n[1]), av(n[0]))), format="csr")
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elif(self.dim == 3):
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self._aveE2CC = sp.hstack((kron3(av(n[2]), av(n[1]), speye(n[0])),
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kron3(av(n[2]), speye(n[1]), av(n[0])),
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kron3(speye(n[2]), av(n[1]), av(n[0]))), format="csr")
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return self._aveE2CC
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return locals()
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_aveE2CC = None
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aveE2CC = property(**aveE2CC())
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def aveN2CC():
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doc = "Construct the averaging operator on cell nodes to cell centers."
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def fget(self):
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if(self._aveN2CC is None):
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# The number of cell centers in each direction
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n = self.n
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if(self.dim == 1):
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self._aveN2CC = av(n[0])
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elif(self.dim == 2):
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self._aveN2CC = sp.hstack((sp.kron(av(n[1]), av(n[0])),
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sp.kron(av(n[1]), av(n[0]))), format="csr")
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elif(self.dim == 3):
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self._aveN2CC = sp.hstack((kron3(av(n[2]), av(n[1]), av(n[0])),
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kron3(av(n[2]), av(n[1]), av(n[0])),
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kron3(av(n[2]), av(n[1]), av(n[0]))), format="csr")
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return self._aveN2CC
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return locals()
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_aveN2CC = None
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aveN2CC = property(**aveN2CC())
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def aveN2CCv():
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doc = "Construct the averaging operator on cell nodes to cell centers, keeping each dimension separate."
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def fget(self):
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if(self._aveN2CCv is None):
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# The number of cell centers in each direction
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n = self.n
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if(self.dim == 1):
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self._aveN2CCv = av(n[0])
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elif(self.dim == 2):
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self._aveN2CCv = sp.block_diag((sp.kron(av(n[1]), av(n[0])),
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sp.kron(av(n[1]), av(n[0]))), format="csr")
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elif(self.dim == 3):
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self._aveN2CCv = sp.block_diag((kron3(av(n[2]), av(n[1]), av(n[0])),
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kron3(av(n[2]), av(n[1]), av(n[0])),
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kron3(av(n[2]), av(n[1]), av(n[0]))), format="csr")
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return self._aveN2CCv
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return locals()
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_aveN2CCv = None
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aveN2CCv = property(**aveN2CCv())
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def getMass(self, materialProp=None, loc='e'):
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""" Produces mass matricies.
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:param str loc: Average to location: 'e'-edges, 'f'-faces
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:param None,float,numpy.ndarray materialProp: property to be averaged (see below)
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:rtype: scipy.sparse.csr.csr_matrix
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:return: M, the mass matrix
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materialProp can be::
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None -> takes materialProp = 1 (default)
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float -> a constant value for entire domain
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numpy.ndarray -> if materialProp.size == self.nC
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3D property model
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if materialProp.size = self.nCz
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1D (layered eath) property model
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"""
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if materialProp is None:
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materialProp = np.ones(self.nC)
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elif type(materialProp) is float:
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materialProp = np.ones(self.nC)*materialProp
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elif materialProp.shape == (self.nCz,):
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materialProp = materialProp.repeat(self.nCx*self.nCy)
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materialProp = mkvc(materialProp)
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assert materialProp.shape == (self.nC,), "materialProp incorrect shape"
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if loc=='e':
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Av = self.aveE2CC
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elif loc=='f':
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Av = self.aveF2CC
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else:
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raise ValueError('Invalid loc')
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diag = Av.T * (self.vol * mkvc(materialProp))
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return sdiag(diag)
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def getEdgeMass(self, materialProp=None):
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"""mass matrix for products of edge functions w'*M(materialProp)*e"""
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return self.getMass(loc='e', materialProp=materialProp)
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def getFaceMass(self, materialProp=None):
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"""mass matrix for products of face functions w'*M(materialProp)*f"""
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return self.getMass(loc='f', materialProp=materialProp)
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def getFaceMassDeriv(self):
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Av = self.aveF2CC
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return Av.T * sdiag(self.vol)
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@@ -1,335 +0,0 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib
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from mpl_toolkits.mplot3d import Axes3D
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from SimPEG.utils import mkvc
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class TensorView(object):
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"""
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Provides viewing functions for TensorMesh
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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 plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True,annotationColor='w',showIt=False):
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"""
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Mesh.plotImage(I)
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Plots scalar fields on the given mesh.
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Input:
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:param numpy.array I: scalar field
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Optional Input:
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:param str imageType: type of image ('CC','N','F','Fx','Fy','Fz','E','Ex','Ey','Ez') or combinations, e.g. ExEy or FxFz
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:param int figNum: number of figure to plot to
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:param matplotlib.axes.Axes ax: axis to plot to
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:param str direction: slice dimensions, 3D only ('x', 'y', 'z')
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:param bool numbering: show numbering of slices, 3D only
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:param str annotationColor: color of annotation, e.g. 'w', 'k', 'b'
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:param bool showIt: call plt.show()
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.. plot:: examples/mesh/plot_image_2D.py
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:include-source:
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.. plot:: examples/mesh/plot_image_3D.py
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:include-source:
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"""
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assert type(I) == np.ndarray, "I must be a numpy array"
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assert type(numbering) == bool, "numbering must be a bool"
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assert direction in ["x", "y","z"], "direction must be either x,y, or z"
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if imageType == 'CC':
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assert I.size == self.nC, "Incorrect dimensions for CC."
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elif imageType == 'N':
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assert I.size == self.nN, "Incorrect dimensions for N."
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elif imageType == 'Fx':
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if I.size != np.prod(self.nFx): I, fy, fz = self.r(I,'F','F','M')
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elif imageType == 'Fy':
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if I.size != np.prod(self.nFy): fx, I, fz = self.r(I,'F','F','M')
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elif imageType == 'Fz':
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if I.size != np.prod(self.nFz): fx, fy, I = self.r(I,'F','F','M')
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elif imageType == 'Ex':
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if I.size != np.prod(self.nEx): I, ey, ez = self.r(I,'E','E','M')
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elif imageType == 'Ey':
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if I.size != np.prod(self.nEy): ex, I, ez = self.r(I,'E','E','M')
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elif imageType == 'Ez':
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if I.size != np.prod(self.nEz): ex, ey, I = self.r(I,'E','E','M')
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elif imageType[0] == 'E':
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plotAll = len(imageType) == 1
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options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":showIt}
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fig = plt.figure(figNum)
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# Determine the subplot number: 131, 121
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numPlots = 130 if plotAll else len(imageType)/2*10+100
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pltNum = 1
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ex, ey, ez = self.r(I,'E','E','M')
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if plotAll or 'Ex' in imageType:
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ax_x = plt.subplot(numPlots+pltNum)
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self.plotImage(ex, imageType='Ex', ax=ax_x, **options)
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pltNum +=1
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if plotAll or 'Ey' in imageType:
|
||||
ax_y = plt.subplot(numPlots+pltNum)
|
||||
self.plotImage(ey, imageType='Ey', ax=ax_y, **options)
|
||||
pltNum +=1
|
||||
if plotAll or 'Ez' in imageType:
|
||||
ax_z = plt.subplot(numPlots+pltNum)
|
||||
self.plotImage(ez, imageType='Ez', ax=ax_z, **options)
|
||||
pltNum +=1
|
||||
return
|
||||
elif imageType[0] == 'F':
|
||||
plotAll = len(imageType) == 1
|
||||
options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":showIt}
|
||||
fig = plt.figure(figNum)
|
||||
# Determine the subplot number: 131, 121
|
||||
numPlots = 130 if plotAll else len(imageType)/2*10+100
|
||||
pltNum = 1
|
||||
fx, fy, fz = self.r(I,'F','F','M')
|
||||
if plotAll or 'Fx' in imageType:
|
||||
ax_x = plt.subplot(numPlots+pltNum)
|
||||
self.plotImage(fx, imageType='Fx', ax=ax_x, **options)
|
||||
pltNum +=1
|
||||
if plotAll or 'Fy' in imageType:
|
||||
ax_y = plt.subplot(numPlots+pltNum)
|
||||
self.plotImage(fy, imageType='Fy', ax=ax_y, **options)
|
||||
pltNum +=1
|
||||
if plotAll or 'Fz' in imageType:
|
||||
ax_z = plt.subplot(numPlots+pltNum)
|
||||
self.plotImage(fz, imageType='Fz', ax=ax_z, **options)
|
||||
pltNum +=1
|
||||
return
|
||||
else:
|
||||
raise Exception("imageType must be 'CC', 'N','Fx','Fy','Fz','Ex','Ey','Ez'")
|
||||
|
||||
|
||||
if ax is None:
|
||||
fig = plt.figure(figNum)
|
||||
fig.clf()
|
||||
ax = plt.subplot(111)
|
||||
else:
|
||||
assert isinstance(ax,matplotlib.axes.Axes), "ax must be an Axes!"
|
||||
fig = ax.figure
|
||||
|
||||
if self.dim == 1:
|
||||
if imageType == 'CC':
|
||||
ph = ax.plot(self.vectorCCx, I, '-ro')
|
||||
elif imageType == 'N':
|
||||
ph = ax.plot(self.vectorNx, I, '-bs')
|
||||
ax.set_xlabel("x")
|
||||
ax.axis('tight')
|
||||
elif self.dim == 2:
|
||||
if imageType == 'CC':
|
||||
C = I[:].reshape(self.n, order='F')
|
||||
elif imageType == 'N':
|
||||
C = I[:].reshape(self.n+1, order='F')
|
||||
C = 0.25*(C[:-1, :-1] + C[1:, :-1] + C[:-1, 1:] + C[1:, 1:])
|
||||
elif imageType == 'Fx':
|
||||
C = I[:].reshape(self.nFx, order='F')
|
||||
C = 0.5*(C[:-1, :] + C[1:, :] )
|
||||
elif imageType == 'Fy':
|
||||
C = I[:].reshape(self.nFy, order='F')
|
||||
C = 0.5*(C[:, :-1] + C[:, 1:] )
|
||||
elif imageType == 'Ex':
|
||||
C = I[:].reshape(self.nEx, order='F')
|
||||
C = 0.5*(C[:,:-1] + C[:,1:] )
|
||||
elif imageType == 'Ey':
|
||||
C = I[:].reshape(self.nEy, order='F')
|
||||
C = 0.5*(C[:-1,:] + C[1:,:] )
|
||||
|
||||
ph = ax.pcolormesh(self.vectorNx, self.vectorNy, C.T)
|
||||
ax.axis('tight')
|
||||
ax.set_xlabel("x")
|
||||
ax.set_ylabel("y")
|
||||
|
||||
elif self.dim == 3:
|
||||
if direction == 'z':
|
||||
|
||||
# get copy of image and average to cell-centres is necessary
|
||||
if imageType == 'CC':
|
||||
Ic = I[:].reshape(self.n, order='F')
|
||||
elif imageType == 'N':
|
||||
Ic = I[:].reshape(self.n+1, order='F')
|
||||
Ic = .125*(Ic[:-1,:-1,:-1]+Ic[1:,:-1,:-1] + Ic[:-1,1:,:-1]+ Ic[1:,1:,:-1]+ Ic[:-1,:-1,1:]+Ic[1:,:-1,1:] + Ic[:-1,1:,1:]+ Ic[1:,1:,1:] )
|
||||
elif imageType == 'Fx':
|
||||
Ic = I[:].reshape(self.nFx, order='F')
|
||||
Ic = .5*(Ic[:-1,:,:]+Ic[1:,:,:])
|
||||
elif imageType == 'Fy':
|
||||
Ic = I[:].reshape(self.nFy, order='F')
|
||||
Ic = .5*(Ic[:,:-1,:]+Ic[:,1:,:])
|
||||
elif imageType == 'Fz':
|
||||
Ic = I[:].reshape(self.nFz, order='F')
|
||||
Ic = .5*(Ic[:,:,:-1]+Ic[:,:,1:])
|
||||
elif imageType == 'Ex':
|
||||
Ic = I[:].reshape(self.nEx, order='F')
|
||||
Ic = .25*(Ic[:,:-1,:-1]+Ic[:,1:,:-1]+Ic[:,:-1,1:]+Ic[:,1:,:1])
|
||||
elif imageType == 'Ey':
|
||||
Ic = I[:].reshape(self.nEy, order='F')
|
||||
Ic = .25*(Ic[:-1,:,:-1]+Ic[1:,:,:-1]+Ic[:-1,:,1:]+Ic[1:,:,:1])
|
||||
elif imageType == 'Ez':
|
||||
Ic = I[:].reshape(self.nEz, order='F')
|
||||
Ic = .25*(Ic[:-1,:-1,:]+Ic[1:,:-1,:]+Ic[:-1,1:,:]+Ic[1:,:1,:])
|
||||
|
||||
# determine number oE slices in x and y dimension
|
||||
nX = np.ceil(np.sqrt(self.nCz))
|
||||
nY = np.ceil(self.nCz/nX)
|
||||
|
||||
# allocate space for montage
|
||||
nCx = self.nCx
|
||||
nCy = self.nCy
|
||||
|
||||
C = np.zeros((nX*nCx,nY*nCy))
|
||||
|
||||
for iy in range(int(nY)):
|
||||
for ix in range(int(nX)):
|
||||
iz = ix + iy*nX
|
||||
if iz < self.nCz:
|
||||
C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = Ic[:, :, iz]
|
||||
else:
|
||||
C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = np.nan
|
||||
|
||||
C = np.ma.masked_where(np.isnan(C), C)
|
||||
xx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), self.hx).ravel())]
|
||||
yy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), self.hy).ravel())]
|
||||
# Plot the mesh
|
||||
ph = ax.pcolormesh(xx, yy, C.T)
|
||||
# Plot the lines
|
||||
gx = np.arange(nX+1)*(self.vectorNx[-1]-self.x0[0])
|
||||
gy = np.arange(nY+1)*(self.vectorNy[-1]-self.x0[1])
|
||||
# Repeat and seperate with NaN
|
||||
gxX = np.c_[gx, gx, gx+np.nan].ravel()
|
||||
gxY = np.kron(np.ones((nX+1, 1)), np.array([0, sum(self.hy)*nY, np.nan])).ravel()
|
||||
gyX = np.kron(np.ones((nY+1, 1)), np.array([0, sum(self.hx)*nX, np.nan])).ravel()
|
||||
gyY = np.c_[gy, gy, gy+np.nan].ravel()
|
||||
ax.plot(gxX, gxY, annotationColor+'-', linewidth=2)
|
||||
ax.plot(gyX, gyY, annotationColor+'-', linewidth=2)
|
||||
ax.axis('tight')
|
||||
|
||||
if numbering:
|
||||
pad = np.sum(self.hx)*0.04
|
||||
for iy in range(int(nY)):
|
||||
for ix in range(int(nX)):
|
||||
iz = ix + iy*nX
|
||||
if iz < self.nCz:
|
||||
ax.text((ix+1)*(self.vectorNx[-1]-self.x0[0])-pad,(iy)*(self.vectorNy[-1]-self.x0[1])+pad,
|
||||
'#%i'%iz,color=annotationColor,verticalalignment='bottom',horizontalalignment='right',size='x-large')
|
||||
|
||||
ax.set_title(imageType)
|
||||
if showIt: plt.show()
|
||||
return ph
|
||||
|
||||
def plotGrid(self, nodes=False, faces=False, centers=False, edges=False, lines=True, showIt=False):
|
||||
"""Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions.
|
||||
|
||||
:param bool nodes: plot nodes
|
||||
:param bool faces: plot faces
|
||||
:param bool centers: plot centers
|
||||
:param bool edges: plot edges
|
||||
:param bool lines: plot lines connecting nodes
|
||||
:param bool showIt: call plt.show()
|
||||
|
||||
.. plot:: examples/mesh/plot_grid_2D.py
|
||||
:include-source:
|
||||
|
||||
.. plot:: examples/mesh/plot_grid_3D.py
|
||||
:include-source:
|
||||
"""
|
||||
if self.dim == 1:
|
||||
fig = plt.figure(1)
|
||||
fig.clf()
|
||||
ax = plt.subplot(111)
|
||||
xn = self.gridN
|
||||
xc = self.gridCC
|
||||
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')
|
||||
if showIt: plt.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)
|
||||
if nodes: ax.plot(xn[:, 0], xn[:, 1], 'bs')
|
||||
if centers: ax.plot(xc[:, 0], xc[:, 1], 'ro')
|
||||
if faces:
|
||||
ax.plot(xs1[:, 0], xs1[:, 1], 'g>')
|
||||
ax.plot(xs2[:, 0], xs2[:, 1], 'g^')
|
||||
|
||||
# Plot the grid lines
|
||||
if lines:
|
||||
NN = self.r(self.gridN, 'N', 'N', 'M')
|
||||
X1 = np.c_[mkvc(NN[0][0, :]), mkvc(NN[0][self.nCx, :]), mkvc(NN[0][0, :])*np.nan].flatten()
|
||||
Y1 = np.c_[mkvc(NN[1][0, :]), mkvc(NN[1][self.nCx, :]), mkvc(NN[1][0, :])*np.nan].flatten()
|
||||
X2 = np.c_[mkvc(NN[0][:, 0]), mkvc(NN[0][:, self.nCy]), mkvc(NN[0][:, 0])*np.nan].flatten()
|
||||
Y2 = np.c_[mkvc(NN[1][:, 0]), mkvc(NN[1][:, self.nCy]), mkvc(NN[1][:, 0])*np.nan].flatten()
|
||||
X = np.r_[X1, X2]
|
||||
Y = np.r_[Y1, Y2]
|
||||
plt.plot(X, Y)
|
||||
|
||||
ax.grid(True)
|
||||
ax.hold(False)
|
||||
ax.set_xlabel('x1')
|
||||
ax.set_ylabel('x2')
|
||||
if showIt: plt.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)
|
||||
if nodes: ax.plot(xn[:, 0], xn[:, 1], 'bs', zs=xn[:, 2])
|
||||
if centers: ax.plot(xc[:, 0], xc[:, 1], 'ro', zs=xc[:, 2])
|
||||
if faces:
|
||||
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])
|
||||
if edges:
|
||||
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])
|
||||
|
||||
# Plot the grid lines
|
||||
if lines:
|
||||
NN = self.r(self.gridN, 'N', 'N', 'M')
|
||||
X1 = np.c_[mkvc(NN[0][0, :, :]), mkvc(NN[0][self.nCx, :, :]), mkvc(NN[0][0, :, :])*np.nan].flatten()
|
||||
Y1 = np.c_[mkvc(NN[1][0, :, :]), mkvc(NN[1][self.nCx, :, :]), mkvc(NN[1][0, :, :])*np.nan].flatten()
|
||||
Z1 = np.c_[mkvc(NN[2][0, :, :]), mkvc(NN[2][self.nCx, :, :]), mkvc(NN[2][0, :, :])*np.nan].flatten()
|
||||
X2 = np.c_[mkvc(NN[0][:, 0, :]), mkvc(NN[0][:, self.nCy, :]), mkvc(NN[0][:, 0, :])*np.nan].flatten()
|
||||
Y2 = np.c_[mkvc(NN[1][:, 0, :]), mkvc(NN[1][:, self.nCy, :]), mkvc(NN[1][:, 0, :])*np.nan].flatten()
|
||||
Z2 = np.c_[mkvc(NN[2][:, 0, :]), mkvc(NN[2][:, self.nCy, :]), mkvc(NN[2][:, 0, :])*np.nan].flatten()
|
||||
X3 = np.c_[mkvc(NN[0][:, :, 0]), mkvc(NN[0][:, :, self.nCz]), mkvc(NN[0][:, :, 0])*np.nan].flatten()
|
||||
Y3 = np.c_[mkvc(NN[1][:, :, 0]), mkvc(NN[1][:, :, self.nCz]), mkvc(NN[1][:, :, 0])*np.nan].flatten()
|
||||
Z3 = np.c_[mkvc(NN[2][:, :, 0]), mkvc(NN[2][:, :, self.nCz]), mkvc(NN[2][:, :, 0])*np.nan].flatten()
|
||||
X = np.r_[X1, X2, X3]
|
||||
Y = np.r_[Y1, Y2, Y3]
|
||||
Z = np.r_[Z1, Z2, Z3]
|
||||
plt.plot(X, Y, 'b-', zs=Z)
|
||||
|
||||
ax.grid(True)
|
||||
ax.hold(False)
|
||||
ax.set_xlabel('x1')
|
||||
ax.set_ylabel('x2')
|
||||
ax.set_zlabel('x3')
|
||||
if showIt: plt.show()
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
pad = 7
|
||||
padfactor = 1.4
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
h1 = np.linspace(.1,.5,3)
|
||||
h2 = np.linspace(.1,.5,5)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
h1 = np.linspace(.1,.5,3)
|
||||
h2 = np.linspace(.1,.5,5)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
n = 20
|
||||
h = np.ones(n)/n
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
n = 20
|
||||
h = np.ones(n)/n
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from SimPEG import TensorMesh
|
||||
from SimPEG.mesh import TensorMesh
|
||||
|
||||
x0 = np.zeros(2)
|
||||
h1 = np.linspace(.1,.5,3)
|
||||
|
||||
Reference in New Issue
Block a user