mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 23:23:37 +08:00
Rowan Cockett
715 lines
29 KiB
Python
715 lines
29 KiB
Python
import numpy as np
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from SimPEG.Utils import mkvc
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try:
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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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except ImportError, e:
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print 'Trouble importing matplotlib.'
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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 components(self):
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# plotAll = len(imageType) == 1
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# options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":False}
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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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# fxyz = self.r(I,'F','F','M')
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# if plotAll or 'Fx' in imageType:
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# ax_x = plt.subplot(numPlots+pltNum)
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# self.plotImage(fxyz[0], imageType='Fx', ax=ax_x, **options)
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# pltNum +=1
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# if plotAll or 'Fy' in imageType:
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# ax_y = plt.subplot(numPlots+pltNum)
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# self.plotImage(fxyz[1], imageType='Fy', ax=ax_y, **options)
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# pltNum +=1
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# if plotAll or 'Fz' in imageType:
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# ax_z = plt.subplot(numPlots+pltNum)
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# self.plotImage(fxyz[2], imageType='Fz', ax=ax_z, **options)
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# pltNum +=1
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# if showIt: plt.show()
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def plotImage(self, v, vType='CC', grid=False, view='real',
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ax=None, clim=None, showIt=False,
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pcolorOpts=None,
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streamOpts=None,
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gridOpts=None,
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numbering=True, annotationColor='w'
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):
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"""
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Mesh.plotImage(v)
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Plots scalar fields on the given mesh.
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Input:
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:param numpy.array v: vector
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Optional Inputs:
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:param str vType: type of vector ('CC','N','F','Fx','Fy','Fz','E','Ex','Ey','Ez')
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:param matplotlib.axes.Axes ax: axis to plot to
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:param bool showIt: call plt.show()
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3D Inputs:
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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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.. plot::
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:include-source:
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from SimPEG import Mesh, np
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M = Mesh.TensorMesh([20, 20])
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v = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi)
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M.plotImage(v, showIt=True)
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.. plot::
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:include-source:
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from SimPEG import Mesh, np
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M = Mesh.TensorMesh([20,20,20])
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v = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi)*np.sin(M.gridCC[:,2]*2*np.pi)
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M.plotImage(v, annotationColor='k', showIt=True)
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"""
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if pcolorOpts is None:
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pcolorOpts = {}
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if streamOpts is None:
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streamOpts = {'color':'k'}
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if gridOpts is None:
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gridOpts = {'color':'k'}
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if ax is None:
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fig = plt.figure()
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ax = plt.subplot(111)
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else:
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assert isinstance(ax,matplotlib.axes.Axes), "ax must be an Axes!"
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fig = ax.figure
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if self.dim == 1:
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if vType == 'CC':
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ph = ax.plot(self.vectorCCx, v, '-ro')
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elif vType == 'N':
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ph = ax.plot(self.vectorNx, v, '-bs')
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ax.set_xlabel("x")
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ax.axis('tight')
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elif self.dim == 2:
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return self._plotImage2D(v, vType=vType, grid=grid, view=view,
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ax=ax, clim=clim, showIt=showIt,
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pcolorOpts=pcolorOpts, streamOpts=streamOpts,
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gridOpts=gridOpts)
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elif self.dim == 3:
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# get copy of image and average to cell-centers is necessary
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if vType == 'CC':
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vc = v.reshape(self.vnC, order='F')
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elif vType == 'N':
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vc = (self.aveN2CC*v).reshape(self.vnC, order='F')
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elif vType in ['Fx', 'Fy', 'Fz', 'Ex', 'Ey', 'Ez']:
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aveOp = 'ave' + vType[0] + '2CCV'
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# n = getattr(self,'vn'+vType[0])
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# if 'x' in vType: v = np.r_[v,np.zeros(n[1]),np.zeros(n[2])]
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# if 'y' in vType: v = np.r_[np.zeros(n[0]),v,np.zeros(n[2])]
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# if 'z' in vType: v = np.r_[np.zeros(n[0]),np.zeros(n[1]),v]
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v = getattr(self,aveOp)*v # average to cell centers
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ind_xyz = {'x':0,'y':1,'z':2}[vType[1]]
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vc = self.r(v.reshape((self.nC,-1),order='F'), 'CC','CC','M')[ind_xyz]
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# determine number oE slices in x and y dimension
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nX = np.ceil(np.sqrt(self.nCz))
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nY = np.ceil(self.nCz/nX)
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# allocate space for montage
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nCx = self.nCx
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nCy = self.nCy
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C = np.zeros((nX*nCx,nY*nCy))
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for iy in range(int(nY)):
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for ix in range(int(nX)):
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iz = ix + iy*nX
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if iz < self.nCz:
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C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = vc[:, :, iz]
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else:
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C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = np.nan
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C = np.ma.masked_where(np.isnan(C), C)
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xx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), self.hx).ravel())]
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yy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), self.hy).ravel())]
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# Plot the mesh
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if clim is None:
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clim = [C.min(),C.max()]
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ph = ax.pcolormesh(xx, yy, C.T, vmin=clim[0], vmax=clim[1])
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# Plot the lines
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gx = np.arange(nX+1)*(self.vectorNx[-1]-self.x0[0])
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gy = np.arange(nY+1)*(self.vectorNy[-1]-self.x0[1])
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# Repeat and seperate with NaN
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gxX = np.c_[gx, gx, gx+np.nan].ravel()
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gxY = np.kron(np.ones((nX+1, 1)), np.array([0, sum(self.hy)*nY, np.nan])).ravel()
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gyX = np.kron(np.ones((nY+1, 1)), np.array([0, sum(self.hx)*nX, np.nan])).ravel()
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gyY = np.c_[gy, gy, gy+np.nan].ravel()
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ax.plot(gxX, gxY, annotationColor+'-', linewidth=2)
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ax.plot(gyX, gyY, annotationColor+'-', linewidth=2)
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ax.axis('tight')
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if numbering:
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pad = np.sum(self.hx)*0.04
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for iy in range(int(nY)):
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for ix in range(int(nX)):
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iz = ix + iy*nX
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if iz < self.nCz:
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ax.text((ix+1)*(self.vectorNx[-1]-self.x0[0])-pad,(iy)*(self.vectorNy[-1]-self.x0[1])+pad,
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'#%i'%iz,color=annotationColor,verticalalignment='bottom',horizontalalignment='right',size='x-large')
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ax.set_title(vType)
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if showIt: plt.show()
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return ph
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def plotSlice(self, v, vType='CC',
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normal='Z', ind=None, grid=False, view='real',
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ax=None, clim=None, showIt=False,
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pcolorOpts=None,
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streamOpts=None,
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gridOpts=None
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):
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"""
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Plots a slice of a 3D mesh.
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.. plot::
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from SimPEG import *
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hx = [(5,2,-1.3),(2,4),(5,2,1.3)]
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hy = [(2,2,-1.3),(2,6),(2,2,1.3)]
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hz = [(2,2,-1.3),(2,6),(2,2,1.3)]
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M = Mesh.TensorMesh([hx,hy,hz])
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q = np.zeros(M.vnC)
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q[[4,4],[4,4],[2,6]]=[-1,1]
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q = Utils.mkvc(q)
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A = M.faceDiv*M.cellGrad
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b = Solver(A) * (q)
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M.plotSlice(M.cellGrad*b, 'F', view='vec', grid=True, showIt=True, pcolorOpts={'alpha':0.8})
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"""
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if pcolorOpts is None:
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pcolorOpts = {}
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if streamOpts is None:
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streamOpts = {'color':'k'}
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if gridOpts is None:
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gridOpts = {'color':'k', 'alpha':0.5}
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if type(vType) in [list, tuple]:
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assert ax is None, "cannot specify an axis to plot on with this function."
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fig, axs = plt.subplots(1,len(vType))
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out = []
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for vTypeI, ax in zip(vType, axs):
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out += [self.plotSlice(v,vType=vTypeI, normal=normal, ind=ind, grid=grid, view=view, ax=ax, clim=clim, showIt=False, pcolorOpts=pcolorOpts, streamOpts=streamOpts, gridOpts=gridOpts)]
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return out
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viewOpts = ['real','imag','abs','vec']
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normalOpts = ['X', 'Y', 'Z']
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vTypeOpts = ['CC', 'CCv','N','F','E','Fx','Fy','Fz','E','Ex','Ey','Ez']
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# Some user error checking
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assert vType in vTypeOpts, "vType must be in ['%s']" % "','".join(vTypeOpts)
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assert self.dim == 3, 'Must be a 3D mesh. Use plotImage.'
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assert view in viewOpts, "view must be in ['%s']" % "','".join(viewOpts)
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assert normal in normalOpts, "normal must be in ['%s']" % "','".join(normalOpts)
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assert type(grid) is bool, 'grid must be a boolean'
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szSliceDim = getattr(self, 'nC'+normal.lower()) #: Size of the sliced dimension
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if ind is None: ind = int(szSliceDim/2)
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assert type(ind) in [int, long], 'ind must be an integer'
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assert not (v.dtype == complex and view == 'vec'), 'Can not plot a complex vector.'
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# The slicing and plotting code!!
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def getIndSlice(v):
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if normal == 'X': v = v[ind,:,:]
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elif normal == 'Y': v = v[:,ind,:]
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elif normal == 'Z': v = v[:,:,ind]
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return v
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def doSlice(v):
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if vType == 'CC':
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return getIndSlice(self.r(v,'CC','CC','M'))
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elif vType == 'CCv':
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assert view == 'vec', 'Other types for CCv not supported'
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else:
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# Now just deal with 'F' and 'E' (x,y,z, maybe...)
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aveOp = 'ave' + vType + ('2CCV' if view == 'vec' else '2CC')
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Av = getattr(self,aveOp)
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if v.size == Av.shape[1]:
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v = Av * v
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else:
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v = self.r(v,vType[0],vType) # get specific component
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v = Av * v
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# we should now be averaged to cell centers (might be a vector)
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v = self.r(v.reshape((self.nC,-1),order='F'),'CC','CC','M')
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if view == 'vec':
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outSlice = []
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if 'X' not in normal: outSlice.append(getIndSlice(v[0]))
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if 'Y' not in normal: outSlice.append(getIndSlice(v[1]))
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if 'Z' not in normal: outSlice.append(getIndSlice(v[2]))
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return np.r_[mkvc(outSlice[0]), mkvc(outSlice[1])]
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else:
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return getIndSlice(self.r(v,'CC','CC','M'))
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h2d = []
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x2d = []
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if 'X' not in normal:
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h2d.append(self.hx)
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x2d.append(self.x0[0])
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if 'Y' not in normal:
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h2d.append(self.hy)
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x2d.append(self.x0[1])
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if 'Z' not in normal:
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h2d.append(self.hz)
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x2d.append(self.x0[2])
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tM = self.__class__(h2d, x2d) #: Temp Mesh
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v2d = doSlice(v)
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if ax is None:
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fig = plt.figure()
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ax = plt.subplot(111)
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else:
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assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes"
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fig = ax.figure
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out = tM._plotImage2D(v2d, vType=('CCv' if view == 'vec' else 'CC'), grid=grid, view=view,
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ax=ax, clim=clim, showIt=showIt,
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pcolorOpts=pcolorOpts, streamOpts=streamOpts,
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gridOpts=gridOpts)
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ax.set_xlabel('y' if normal == 'X' else 'x')
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ax.set_ylabel('y' if normal == 'Z' else 'z')
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ax.set_title('Slice %d' % ind)
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return out
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def _plotImage2D(self, v, vType='CC', grid=False, view='real',
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ax=None, clim=None, showIt=False,
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pcolorOpts=None,
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streamOpts=None,
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gridOpts=None
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):
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if pcolorOpts is None:
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pcolorOpts = {}
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if streamOpts is None:
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streamOpts = {'color':'k'}
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if gridOpts is None:
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gridOpts = {'color':'k'}
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vTypeOptsCC = ['N','CC','Fx','Fy','Ex','Ey']
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vTypeOptsV = ['CCv','F','E']
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vTypeOpts = vTypeOptsCC + vTypeOptsV
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if view == 'vec':
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assert vType in vTypeOptsV, "vType must be in ['%s'] when view='vec'" % "','".join(vTypeOptsV)
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assert vType in vTypeOpts, "vType must be in ['%s']" % "','".join(vTypeOpts)
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viewOpts = ['real','imag','abs','vec']
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assert view in viewOpts, "view must be in ['%s']" % "','".join(viewOpts)
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if ax is None:
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fig = plt.figure()
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ax = plt.subplot(111)
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else:
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assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes"
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fig = ax.figure
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# Reshape to a cell centered variable
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if vType == 'CC':
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pass
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elif vType == 'CCv':
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assert view == 'vec', 'Other types for CCv not supported'
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elif vType in ['F', 'E', 'N']:
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aveOp = 'ave' + vType + ('2CCV' if view == 'vec' else '2CC')
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v = getattr(self,aveOp)*v # average to cell centers (might be a vector)
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elif vType in ['Fx','Fy','Ex','Ey']:
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aveOp = 'ave' + vType[0] + '2CCV'
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v = getattr(self,aveOp)*v # average to cell centers (might be a vector)
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xORy = {'x':0,'y':1}[vType[1]]
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v = v.reshape((self.nC,-1), order='F')[:,xORy]
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out = ()
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if view in ['real','imag','abs']:
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v = self.r(v, 'CC', 'CC', 'M')
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v = getattr(np,view)(v) # e.g. np.real(v)
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if clim is None:
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clim = [v.min(),v.max()]
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v = np.ma.masked_where(np.isnan(v), v)
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out += (ax.pcolormesh(self.vectorNx, self.vectorNy, v.T, vmin=clim[0], vmax=clim[1], **pcolorOpts),)
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elif view in ['vec']:
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U, V = self.r(v.reshape((self.nC,-1), order='F'), 'CC', 'CC', 'M')
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if clim is None:
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uv = np.sqrt(U**2 + V**2)
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clim = [uv.min(),uv.max()]
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# Matplotlib seems to not support irregular
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# spaced vectors at the moment. So we will
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# Interpolate down to a regular mesh at the
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# smallest mesh size in this 2D slice.
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nxi = int(self.hx.sum()/self.hx.min())
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nyi = int(self.hy.sum()/self.hy.min())
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tMi = self.__class__([np.ones(nxi)*self.hx.sum()/nxi,
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np.ones(nyi)*self.hy.sum()/nyi], self.x0)
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P = self.getInterpolationMat(tMi.gridCC,'CC',zerosOutside=True)
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Ui = tMi.r(P*mkvc(U), 'CC', 'CC', 'M')
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Vi = tMi.r(P*mkvc(V), 'CC', 'CC', 'M')
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# End Interpolation
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out += (ax.pcolormesh(self.vectorNx, self.vectorNy, np.sqrt(U**2+V**2).T, vmin=clim[0], vmax=clim[1], **pcolorOpts),)
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out += (ax.streamplot(tMi.vectorCCx, tMi.vectorCCy, Ui.T, Vi.T, **streamOpts),)
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if grid:
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xXGrid = np.c_[self.vectorNx,self.vectorNx,np.nan*np.ones(self.nNx)].flatten()
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xYGrid = np.c_[self.vectorNy[0]*np.ones(self.nNx),self.vectorNy[-1]*np.ones(self.nNx),np.nan*np.ones(self.nNx)].flatten()
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yXGrid = np.c_[self.vectorNx[0]*np.ones(self.nNy),self.vectorNx[-1]*np.ones(self.nNy),np.nan*np.ones(self.nNy)].flatten()
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yYGrid = np.c_[self.vectorNy,self.vectorNy,np.nan*np.ones(self.nNy)].flatten()
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out += (ax.plot(np.r_[xXGrid,yXGrid],np.r_[xYGrid,yYGrid],**gridOpts)[0],)
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_xlim(*self.vectorNx[[0,-1]])
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ax.set_ylim(*self.vectorNy[[0,-1]])
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if showIt: plt.show()
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return out
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def plotGrid(self, ax=None, nodes=False, faces=False, centers=False, edges=False, lines=True, showIt=False):
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"""Plot the nodal, cell-centered and staggered grids for 1,2 and 3 dimensions.
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:param bool nodes: plot nodes
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:param bool faces: plot faces
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:param bool centers: plot centers
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:param bool edges: plot edges
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:param bool lines: plot lines connecting nodes
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:param bool showIt: call plt.show()
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.. plot::
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:include-source:
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from SimPEG import Mesh, np
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h1 = np.linspace(.1,.5,3)
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h2 = np.linspace(.1,.5,5)
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|
mesh = Mesh.TensorMesh([h1, h2])
|
|
mesh.plotGrid(nodes=True, faces=True, centers=True, lines=True, showIt=True)
|
|
|
|
.. plot::
|
|
:include-source:
|
|
|
|
from SimPEG import Mesh, np
|
|
h1 = np.linspace(.1,.5,3)
|
|
h2 = np.linspace(.1,.5,5)
|
|
h3 = np.linspace(.1,.5,3)
|
|
mesh = Mesh.TensorMesh([h1,h2,h3])
|
|
mesh.plotGrid(nodes=True, faces=True, centers=True, lines=True, showIt=True)
|
|
|
|
"""
|
|
|
|
axOpts = {'projection':'3d'} if self.dim == 3 else {}
|
|
if ax is None:
|
|
fig = plt.figure()
|
|
ax = plt.subplot(111, **axOpts)
|
|
else:
|
|
assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes"
|
|
fig = ax.figure
|
|
|
|
if self.dim == 1:
|
|
if nodes:
|
|
ax.plot(self.gridN, np.ones(self.nN), 'bs')
|
|
if centers:
|
|
ax.plot(self.gridCC, np.ones(self.nC), 'ro')
|
|
if lines:
|
|
ax.plot(self.gridN, np.ones(self.nN), 'b.-')
|
|
ax.set_xlabel('x1')
|
|
elif self.dim == 2:
|
|
if nodes:
|
|
ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bs')
|
|
if centers:
|
|
ax.plot(self.gridCC[:, 0], self.gridCC[:, 1], 'ro')
|
|
if faces:
|
|
ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'g>')
|
|
ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'g^')
|
|
if edges:
|
|
ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'c>')
|
|
ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'c^')
|
|
|
|
# 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]
|
|
ax.plot(X, Y, 'b-')
|
|
|
|
ax.set_xlabel('x1')
|
|
ax.set_ylabel('x2')
|
|
elif self.dim == 3:
|
|
if nodes:
|
|
ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bs', zs=self.gridN[:, 2])
|
|
if centers:
|
|
ax.plot(self.gridCC[:, 0], self.gridCC[:, 1], 'ro', zs=self.gridCC[:, 2])
|
|
if faces:
|
|
ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'g>', zs=self.gridFx[:, 2])
|
|
ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'g<', zs=self.gridFy[:, 2])
|
|
ax.plot(self.gridFz[:, 0], self.gridFz[:, 1], 'g^', zs=self.gridFz[:, 2])
|
|
if edges:
|
|
ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'k>', zs=self.gridEx[:, 2])
|
|
ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'k<', zs=self.gridEy[:, 2])
|
|
ax.plot(self.gridEz[:, 0], self.gridEz[:, 1], 'k^', zs=self.gridEz[:, 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]
|
|
ax.plot(X, Y, 'b-', zs=Z)
|
|
ax.set_xlabel('x1')
|
|
ax.set_ylabel('x2')
|
|
ax.set_zlabel('x3')
|
|
|
|
ax.grid(True)
|
|
if showIt: plt.show()
|
|
|
|
|
|
class CylView(object):
|
|
|
|
def _plotCylTensorMesh(self, plotType, *args, **kwargs):
|
|
|
|
if not self.isSymmetric:
|
|
raise Exception('We have not yet implemented this type of view.')
|
|
assert plotType in ['plotImage', 'plotGrid']
|
|
# Hackity Hack:
|
|
# Just create a TM and use its view.
|
|
from SimPEG.Mesh import TensorMesh
|
|
M = TensorMesh([self.hx, self.hz], x0=[self.x0[0], self.x0[2]])
|
|
|
|
ax = kwargs.get('ax', None)
|
|
if ax is None:
|
|
fig = plt.figure()
|
|
ax = plt.subplot(111)
|
|
kwargs['ax'] = ax
|
|
else:
|
|
assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes"
|
|
fig = ax.figure
|
|
|
|
# Don't show things in the TM.plotImage
|
|
showIt = kwargs.get('showIt', False)
|
|
kwargs['showIt'] = False
|
|
|
|
out = getattr(M, plotType)(*args, **kwargs)
|
|
|
|
ax.set_xlabel('x')
|
|
ax.set_ylabel('z')
|
|
|
|
if showIt: plt.show()
|
|
|
|
return out
|
|
|
|
|
|
def plotGrid(self, *args, **kwargs):
|
|
return self._plotCylTensorMesh('plotGrid', *args, **kwargs)
|
|
|
|
def plotImage(self, *args, **kwargs):
|
|
return self._plotCylTensorMesh('plotImage', *args, **kwargs)
|
|
|
|
class CurvView(object):
|
|
"""
|
|
Provides viewing functions for CurvilinearMesh
|
|
|
|
This class is inherited by CurvilinearMesh
|
|
|
|
"""
|
|
def __init__(self):
|
|
pass
|
|
|
|
|
|
def plotGrid(self, ax=None, 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.
|
|
|
|
|
|
.. plot::
|
|
:include-source:
|
|
|
|
from SimPEG import Mesh, Utils
|
|
X, Y = Utils.exampleLrmGrid([3,3],'rotate')
|
|
M = Mesh.CurvilinearMesh([X, Y])
|
|
M.plotGrid(showIt=True)
|
|
|
|
"""
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib
|
|
from mpl_toolkits.mplot3d import Axes3D
|
|
|
|
axOpts = {'projection':'3d'} if self.dim == 3 else {}
|
|
if ax is None: ax = plt.subplot(111, **axOpts)
|
|
|
|
NN = self.r(self.gridN, 'N', 'N', 'M')
|
|
if self.dim == 2:
|
|
|
|
if lines:
|
|
X1 = np.c_[mkvc(NN[0][:-1, :]), mkvc(NN[0][1:, :]), mkvc(NN[0][:-1, :])*np.nan].flatten()
|
|
Y1 = np.c_[mkvc(NN[1][:-1, :]), mkvc(NN[1][1:, :]), mkvc(NN[1][:-1, :])*np.nan].flatten()
|
|
|
|
X2 = np.c_[mkvc(NN[0][:, :-1]), mkvc(NN[0][:, 1:]), mkvc(NN[0][:, :-1])*np.nan].flatten()
|
|
Y2 = np.c_[mkvc(NN[1][:, :-1]), mkvc(NN[1][:, 1:]), mkvc(NN[1][:, :-1])*np.nan].flatten()
|
|
|
|
X = np.r_[X1, X2]
|
|
Y = np.r_[Y1, Y2]
|
|
|
|
ax.plot(X, Y, 'b-')
|
|
if centers:
|
|
ax.plot(self.gridCC[:,0],self.gridCC[:,1],'ro')
|
|
|
|
# Nx = self.r(self.normals, 'F', 'Fx', 'V')
|
|
# Ny = self.r(self.normals, 'F', 'Fy', 'V')
|
|
# Tx = self.r(self.tangents, 'E', 'Ex', 'V')
|
|
# Ty = self.r(self.tangents, 'E', 'Ey', 'V')
|
|
|
|
# ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bo')
|
|
|
|
# nX = np.c_[self.gridFx[:, 0], self.gridFx[:, 0] + Nx[0]*length, self.gridFx[:, 0]*np.nan].flatten()
|
|
# nY = np.c_[self.gridFx[:, 1], self.gridFx[:, 1] + Nx[1]*length, self.gridFx[:, 1]*np.nan].flatten()
|
|
# ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'rs')
|
|
# ax.plot(nX, nY, 'r-')
|
|
|
|
# nX = np.c_[self.gridFy[:, 0], self.gridFy[:, 0] + Ny[0]*length, self.gridFy[:, 0]*np.nan].flatten()
|
|
# nY = np.c_[self.gridFy[:, 1], self.gridFy[:, 1] + Ny[1]*length, self.gridFy[:, 1]*np.nan].flatten()
|
|
# #ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'gs')
|
|
# ax.plot(nX, nY, 'g-')
|
|
|
|
# tX = np.c_[self.gridEx[:, 0], self.gridEx[:, 0] + Tx[0]*length, self.gridEx[:, 0]*np.nan].flatten()
|
|
# tY = np.c_[self.gridEx[:, 1], self.gridEx[:, 1] + Tx[1]*length, self.gridEx[:, 1]*np.nan].flatten()
|
|
# ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'r^')
|
|
# ax.plot(tX, tY, 'r-')
|
|
|
|
# nX = np.c_[self.gridEy[:, 0], self.gridEy[:, 0] + Ty[0]*length, self.gridEy[:, 0]*np.nan].flatten()
|
|
# nY = np.c_[self.gridEy[:, 1], self.gridEy[:, 1] + Ty[1]*length, self.gridEy[:, 1]*np.nan].flatten()
|
|
# #ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'g^')
|
|
# ax.plot(nX, nY, 'g-')
|
|
|
|
elif self.dim == 3:
|
|
X1 = np.c_[mkvc(NN[0][:-1, :, :]), mkvc(NN[0][1:, :, :]), mkvc(NN[0][:-1, :, :])*np.nan].flatten()
|
|
Y1 = np.c_[mkvc(NN[1][:-1, :, :]), mkvc(NN[1][1:, :, :]), mkvc(NN[1][:-1, :, :])*np.nan].flatten()
|
|
Z1 = np.c_[mkvc(NN[2][:-1, :, :]), mkvc(NN[2][1:, :, :]), mkvc(NN[2][:-1, :, :])*np.nan].flatten()
|
|
|
|
X2 = np.c_[mkvc(NN[0][:, :-1, :]), mkvc(NN[0][:, 1:, :]), mkvc(NN[0][:, :-1, :])*np.nan].flatten()
|
|
Y2 = np.c_[mkvc(NN[1][:, :-1, :]), mkvc(NN[1][:, 1:, :]), mkvc(NN[1][:, :-1, :])*np.nan].flatten()
|
|
Z2 = np.c_[mkvc(NN[2][:, :-1, :]), mkvc(NN[2][:, 1:, :]), mkvc(NN[2][:, :-1, :])*np.nan].flatten()
|
|
|
|
X3 = np.c_[mkvc(NN[0][:, :, :-1]), mkvc(NN[0][:, :, 1:]), mkvc(NN[0][:, :, :-1])*np.nan].flatten()
|
|
Y3 = np.c_[mkvc(NN[1][:, :, :-1]), mkvc(NN[1][:, :, 1:]), mkvc(NN[1][:, :, :-1])*np.nan].flatten()
|
|
Z3 = np.c_[mkvc(NN[2][:, :, :-1]), mkvc(NN[2][:, :, 1:]), mkvc(NN[2][:, :, :-1])*np.nan].flatten()
|
|
|
|
X = np.r_[X1, X2, X3]
|
|
Y = np.r_[Y1, Y2, Y3]
|
|
Z = np.r_[Z1, Z2, Z3]
|
|
|
|
ax.plot(X, Y, 'b', zs=Z)
|
|
ax.set_zlabel('x3')
|
|
|
|
ax.grid(True)
|
|
ax.set_xlabel('x1')
|
|
ax.set_ylabel('x2')
|
|
|
|
if showIt: plt.show()
|
|
|
|
def plotImage(self, I, ax=None, showIt=False, grid=False, clim=None):
|
|
if self.dim == 3: raise NotImplementedError('This is not yet done!')
|
|
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib
|
|
from mpl_toolkits.mplot3d import Axes3D
|
|
import matplotlib.colors as colors
|
|
import matplotlib.cm as cmx
|
|
|
|
if ax is None: ax = plt.subplot(111)
|
|
jet = cm = plt.get_cmap('jet')
|
|
cNorm = colors.Normalize(
|
|
vmin=I.min() if clim is None else clim[0],
|
|
vmax=I.max() if clim is None else clim[1])
|
|
|
|
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
|
|
# ax.set_xlim((self.x0[0], self.h[0].sum()))
|
|
# ax.set_ylim((self.x0[1], self.h[1].sum()))
|
|
|
|
Nx = self.r(self.gridN[:,0],'N','N','M')
|
|
Ny = self.r(self.gridN[:,1],'N','N','M')
|
|
cell = self.r(I,'CC','CC','M')
|
|
|
|
for ii in range(self.nCx):
|
|
for jj in range(self.nCy):
|
|
I = [ii,ii+1,ii+1,ii]
|
|
J = [jj,jj,jj+1,jj+1]
|
|
ax.add_patch(plt.Polygon(np.c_[Nx[I,J],Ny[I,J]], facecolor=scalarMap.to_rgba(cell[ii,jj]), edgecolor='k' if grid else 'none'))
|
|
|
|
scalarMap._A = [] # http://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots
|
|
ax.set_xlabel('x')
|
|
ax.set_ylabel('y')
|
|
if showIt: plt.show()
|
|
return [scalarMap]
|
|
|
|
|
|
if __name__ == '__main__':
|
|
from SimPEG import *
|
|
hx = [(5,2,-1.3),(2,4),(5,2,1.3)]
|
|
hy = [(2,2,-1.3),(2,6),(2,2,1.3)]
|
|
hz = [(2,2,-1.3),(2,6),(2,2,1.3)]
|
|
M = Mesh.TensorMesh([hx,hy,hz], x0=[10,20,14])
|
|
q = np.zeros(M.vnC)
|
|
q[[4,4],[4,4],[2,6]]=[-1,1]
|
|
q = Utils.mkvc(q)
|
|
A = M.faceDiv*M.cellGrad
|
|
b = Solver(A) * (q)
|
|
|
|
M.plotSlice(M.cellGrad*b, 'F', view='vec', grid=True, pcolorOpts={'alpha':0.8})
|
|
M2 = Mesh.TensorMesh([10,20],x0=[10,5])
|
|
f = np.r_[np.sin(M2.gridFx[:,0]*2*np.pi), np.sin(M2.gridFy[:,1]*2*np.pi)]
|
|
M2.plotImage(f, 'F', view='vec', grid=True, pcolorOpts={'alpha':0.8})
|
|
M2.plotImage(f, 'Fx')
|
|
|
|
f = np.r_[np.sin(M2.gridEx[:,0]*2*np.pi), np.sin(M2.gridEy[:,1]*2*np.pi)]
|
|
M2.plotImage(f, 'E', view='vec', grid=True, pcolorOpts={'alpha':0.8})
|
|
|
|
c = np.r_[np.sin(M2.gridCC[:,0]*2*np.pi)]
|
|
M2.plotImage(c, 'CC', view='real')
|
|
|
|
from SimPEG import Mesh, np
|
|
M = Mesh.TensorMesh([20,20,20])
|
|
v = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi)*np.sin(M.gridCC[:,2]*2*np.pi)
|
|
M.plotImage(v, annotationColor='k')
|
|
|
|
|
|
Mesh.TensorMesh([10]).plotGrid()
|
|
|
|
plt.show()
|