mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-28 20:56:57 +08:00
rowanc1
546 lines
23 KiB
Python
546 lines
23 KiB
Python
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, animate
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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,clim=None):
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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::
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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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I = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi)
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M.plotImage(I, 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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I = 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(I, annotationColor='k', showIt=True)
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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.vnFx): 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.vnFy): 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.vnFz): 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.vnEx): 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.vnEy): 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.vnEz): 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":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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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:
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ax_y = plt.subplot(numPlots+pltNum)
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self.plotImage(ey, imageType='Ey', ax=ax_y, **options)
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pltNum +=1
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if plotAll or 'Ez' in imageType:
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ax_z = plt.subplot(numPlots+pltNum)
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self.plotImage(ez, imageType='Ez', ax=ax_z, **options)
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pltNum +=1
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if showIt: plt.show()
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return
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elif imageType[0] == 'F':
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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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return
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else:
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raise Exception("imageType must be 'CC', 'N','Fx','Fy','Fz','Ex','Ey','Ez'")
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if ax is None:
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fig = plt.figure(figNum)
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fig.clf()
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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 imageType == 'CC':
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ph = ax.plot(self.vectorCCx, I, '-ro')
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elif imageType == 'N':
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ph = ax.plot(self.vectorNx, I, '-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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if imageType == 'CC':
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C = I[:].reshape(self.vnC, order='F')
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elif imageType == 'N':
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C = I[:].reshape(self.vnN, order='F')
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C = 0.25*(C[:-1, :-1] + C[1:, :-1] + C[:-1, 1:] + C[1:, 1:])
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elif imageType == 'Fx':
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C = I[:].reshape(self.vnFx, order='F')
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C = 0.5*(C[:-1, :] + C[1:, :] )
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elif imageType == 'Fy':
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C = I[:].reshape(self.vnFy, order='F')
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C = 0.5*(C[:, :-1] + C[:, 1:] )
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elif imageType == 'Ex':
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C = I[:].reshape(self.vnEx, order='F')
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C = 0.5*(C[:,:-1] + C[:,1:] )
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elif imageType == 'Ey':
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C = I[:].reshape(self.vnEy, order='F')
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C = 0.5*(C[:-1,:] + C[1:,:] )
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if clim is None:
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clim = [C.min(),C.max()]
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ph = ax.pcolormesh(self.vectorNx, self.vectorNy, C.T, vmin=clim[0], vmax=clim[1])
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ax.axis('tight')
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ax.set_xlabel("x")
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ax.set_ylabel("y")
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elif self.dim == 3:
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if direction == 'z':
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# get copy of image and average to cell-centres is necessary
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if imageType == 'CC':
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Ic = I[:].reshape(self.vnC, order='F')
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elif imageType == 'N':
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Ic = I[:].reshape(self.vnN, order='F')
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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:] )
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elif imageType == 'Fx':
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Ic = I[:].reshape(self.vnFx, order='F')
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Ic = .5*(Ic[:-1,:,:]+Ic[1:,:,:])
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elif imageType == 'Fy':
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Ic = I[:].reshape(self.vnFy, order='F')
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Ic = .5*(Ic[:,:-1,:]+Ic[:,1:,:])
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elif imageType == 'Fz':
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Ic = I[:].reshape(self.vnFz, order='F')
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Ic = .5*(Ic[:,:,:-1]+Ic[:,:,1:])
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elif imageType == 'Ex':
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Ic = I[:].reshape(self.vnEx, order='F')
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Ic = .25*(Ic[:,:-1,:-1]+Ic[:,1:,:-1]+Ic[:,:-1,1:]+Ic[:,1:,:1])
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elif imageType == 'Ey':
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Ic = I[:].reshape(self.vnEy, order='F')
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Ic = .25*(Ic[:-1,:,:-1]+Ic[1:,:,:-1]+Ic[:-1,:,1:]+Ic[1:,:,:1])
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elif imageType == 'Ez':
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Ic = I[:].reshape(self.vnEz, order='F')
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Ic = .25*(Ic[:-1,:-1,:]+Ic[1:,:-1,:]+Ic[:-1,1:,:]+Ic[1:,:1,:])
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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] = Ic[:, :, 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(imageType)
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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={},
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streamOpts={'color':'k'},
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gridOpts={'color':'k'}
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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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mT = Utils.meshTensors(((2,5),(4,2),(2,5)),((2,2),(6,2),(2,2)),((2,2),(6,2),(2,2)))
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M = Mesh.TensorMesh(mT)
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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).solve(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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viewOpts = ['real','imag','abs','vec']
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normalOpts = ['X', 'Y', 'Z']
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vTypeOpts = ['CC', 'CCv','F','E']
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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.'
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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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if ax is None:
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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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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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# 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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v = self.r(v.reshape((self.nC,3),order='F'),'CC','CC','M')
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assert view == 'vec', 'Other types for CCv not yet supported'
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else:
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# Now just deal with 'F' and 'E'
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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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v = self.r(v.reshape((self.nC,3),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 outSlice
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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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if 'X' not in normal: h2d.append(self.hx)
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if 'Y' not in normal: h2d.append(self.hy)
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if 'Z' not in normal: h2d.append(self.hz)
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tM = self.__class__(h2d) #: Temp Mesh
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out = ()
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if view in ['real','imag','abs']:
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v = getattr(np,view)(v) # e.g. np.real(v)
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v = doSlice(v)
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if clim is None:
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clim = [v.min(),v.max()]
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out += (ax.pcolormesh(tM.vectorNx, tM.vectorNy, v.T, vmin=clim[0], vmax=clim[1], **pcolorOpts),)
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elif view in ['vec']:
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U, V = doSlice(v)
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if clim is None:
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uv = np.r_[mkvc(U), mkvc(V)]
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uv = np.sqrt(uv**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(tM.hx.sum()/tM.hx.min())
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nyi = int(tM.hy.sum()/tM.hy.min())
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tMi = self.__class__([np.ones(nxi)*tM.hx.sum()/nxi,
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np.ones(nyi)*tM.hy.sum()/nyi])
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P = tM.getInterpolationMat(tMi.gridCC,'CC',zerosOutside=True)
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Ui = P*mkvc(U)
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Vi = P*mkvc(V)
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Ui = tMi.r(Ui, 'CC', 'CC', 'M')
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Vi = tMi.r(Vi, 'CC', 'CC', 'M')
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# End Interpolation
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out += (ax.pcolormesh(tM.vectorNx, tM.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_[tM.vectorNx,tM.vectorNx,np.nan*np.ones(tM.nNx)].flatten()
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xYGrid = np.c_[tM.vectorNy[0]*np.ones(tM.nNx),tM.vectorNy[-1]*np.ones(tM.nNx),np.nan*np.ones(tM.nNx)].flatten()
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yXGrid = np.c_[tM.vectorNx[0]*np.ones(tM.nNy),tM.vectorNx[-1]*np.ones(tM.nNy),np.nan*np.ones(tM.nNy)].flatten()
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yYGrid = np.c_[tM.vectorNy,tM.vectorNy,np.nan*np.ones(tM.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('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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ax.set_xlim(*tM.vectorNx[[0,-1]])
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ax.set_ylim(*tM.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])
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mesh.plotGrid(nodes=True, faces=True, centers=True, lines=True, 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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h1 = np.linspace(.1,.5,3)
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h2 = np.linspace(.1,.5,5)
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h3 = np.linspace(.1,.5,3)
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mesh = Mesh.TensorMesh([h1,h2,h3])
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mesh.plotGrid(nodes=True, faces=True, centers=True, lines=True, showIt=True)
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"""
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axOpts = {'projection':'3d'} if self.dim == 3 else {}
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if ax is None: ax = plt.subplot(111, **axOpts)
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if self.dim == 1:
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if nodes:
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ax.plot(xn, np.ones(self.nN), 'bs')
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if centers:
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ax.plot(xc, np.ones(self.nC), 'ro')
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if lines:
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ax.plot(xn, np.ones(self.nN), 'b-')
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ax.set_xlabel('x1')
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elif self.dim == 2:
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if nodes:
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ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bs')
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if centers:
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ax.plot(self.gridCC[:, 0], self.gridCC[:, 1], 'ro')
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if faces:
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ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'g>')
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ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'g^')
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if edges:
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ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'c>')
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ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'c^')
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# Plot the grid lines
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if lines:
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NN = self.r(self.gridN, 'N', 'N', 'M')
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X1 = np.c_[mkvc(NN[0][0, :]), mkvc(NN[0][self.nCx, :]), mkvc(NN[0][0, :])*np.nan].flatten()
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Y1 = np.c_[mkvc(NN[1][0, :]), mkvc(NN[1][self.nCx, :]), mkvc(NN[1][0, :])*np.nan].flatten()
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X2 = np.c_[mkvc(NN[0][:, 0]), mkvc(NN[0][:, self.nCy]), mkvc(NN[0][:, 0])*np.nan].flatten()
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Y2 = np.c_[mkvc(NN[1][:, 0]), mkvc(NN[1][:, self.nCy]), mkvc(NN[1][:, 0])*np.nan].flatten()
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X = np.r_[X1, X2]
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Y = np.r_[Y1, Y2]
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ax.plot(X, Y, 'b-')
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ax.set_xlabel('x1')
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ax.set_ylabel('x2')
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elif self.dim == 3:
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if nodes:
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ax.plot(self.gridN[:, 0], self.gridN[:, 1], 'bs', zs=self.gridN[:, 2])
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if centers:
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ax.plot(self.gridCC[:, 0], self.gridCC[:, 1], 'ro', zs=self.gridCC[:, 2])
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if faces:
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ax.plot(self.gridFx[:, 0], self.gridFx[:, 1], 'g>', zs=self.gridFx[:, 2])
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ax.plot(self.gridFy[:, 0], self.gridFy[:, 1], 'g<', zs=self.gridFy[:, 2])
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ax.plot(self.gridFz[:, 0], self.gridFz[:, 1], 'g^', zs=self.gridFz[:, 2])
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if edges:
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ax.plot(self.gridEx[:, 0], self.gridEx[:, 1], 'k>', zs=self.gridEx[:, 2])
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ax.plot(self.gridEy[:, 0], self.gridEy[:, 1], 'k<', zs=self.gridEy[:, 2])
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ax.plot(self.gridEz[:, 0], self.gridEz[:, 1], 'k^', zs=self.gridEz[:, 2])
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# Plot the grid lines
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if lines:
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NN = self.r(self.gridN, 'N', 'N', 'M')
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X1 = np.c_[mkvc(NN[0][0, :, :]), mkvc(NN[0][self.nCx, :, :]), mkvc(NN[0][0, :, :])*np.nan].flatten()
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Y1 = np.c_[mkvc(NN[1][0, :, :]), mkvc(NN[1][self.nCx, :, :]), mkvc(NN[1][0, :, :])*np.nan].flatten()
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Z1 = np.c_[mkvc(NN[2][0, :, :]), mkvc(NN[2][self.nCx, :, :]), mkvc(NN[2][0, :, :])*np.nan].flatten()
|
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X2 = np.c_[mkvc(NN[0][:, 0, :]), mkvc(NN[0][:, self.nCy, :]), mkvc(NN[0][:, 0, :])*np.nan].flatten()
|
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Y2 = np.c_[mkvc(NN[1][:, 0, :]), mkvc(NN[1][:, self.nCy, :]), mkvc(NN[1][:, 0, :])*np.nan].flatten()
|
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Z2 = np.c_[mkvc(NN[2][:, 0, :]), mkvc(NN[2][:, self.nCy, :]), mkvc(NN[2][:, 0, :])*np.nan].flatten()
|
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X3 = np.c_[mkvc(NN[0][:, :, 0]), mkvc(NN[0][:, :, self.nCz]), mkvc(NN[0][:, :, 0])*np.nan].flatten()
|
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Y3 = np.c_[mkvc(NN[1][:, :, 0]), mkvc(NN[1][:, :, self.nCz]), mkvc(NN[1][:, :, 0])*np.nan].flatten()
|
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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)
|
|
ax.hold(False)
|
|
if showIt: plt.show()
|
|
|
|
def slicer(mesh, var, imageType='CC', normal='z', index=0, ax=None, clim=None):
|
|
assert normal in 'xyz', 'normal must be x, y, or z'
|
|
if ax is None: ax = plt.subplot(111)
|
|
I = mesh.r(var,'CC','CC','M')
|
|
axes = [p for p in 'xyz' if p not in normal.lower()]
|
|
if normal is 'x': I = I[index,:,:]
|
|
if normal is 'y': I = I[:,index,:]
|
|
if normal is 'z': I = I[:,:,index]
|
|
if clim is None: clim = [I.min(),I.max()]
|
|
p = ax.pcolormesh(getattr(mesh,'vectorN'+axes[0]),getattr(mesh,'vectorN'+axes[1]),I.T,vmin=clim[0],vmax=clim[1])
|
|
ax.axis('tight')
|
|
ax.set_xlabel(axes[0])
|
|
ax.set_ylabel(axes[1])
|
|
return p
|
|
|
|
def videoSlicer(mesh,var,imageType='CC',normal='z',figsize=(10,8)):
|
|
assert mesh.dim > 2, 'This is for 3D meshes only.'
|
|
# First set up the figure, the axis, and the plot element we want to animate
|
|
fig = plt.figure(figsize=figsize)
|
|
ax = plt.axes()
|
|
clim = [var.min(),var.max()]
|
|
plt.colorbar(mesh.slicer(var, imageType=imageType, normal=normal, index=0, ax=ax, clim=clim))
|
|
tlt = plt.title(normal)
|
|
|
|
def animateFrame(i):
|
|
mesh.slicer(var, imageType=imageType, normal=normal, index=i, ax=ax, clim=clim)
|
|
tlt.set_text(normal.upper()+('-Slice: %d, %4.4f' % (i,getattr(mesh,'vectorCC'+normal)[i])))
|
|
|
|
return animate(fig, animateFrame, frames=mesh.vnC['xyz'.index(normal)])
|
|
|
|
def video(mesh, var, function, figsize=(10, 8), colorbar=True, skip=1):
|
|
"""
|
|
Call a function for a list of models to create a video.
|
|
|
|
::
|
|
|
|
def function(var, ax, clim, tlt, i):
|
|
tlt.set_text('%d'%i)
|
|
return mesh.plotImage(var, imageType='CC', ax=ax, clim=clim)
|
|
|
|
mesh.video([model1, model2, ..., modeln],function)
|
|
"""
|
|
# First set up the figure, the axis, and the plot element we want to animate
|
|
fig = plt.figure(figsize=figsize)
|
|
ax = plt.axes()
|
|
VAR = np.concatenate(var)
|
|
clim = [VAR.min(),VAR.max()]
|
|
tlt = plt.title('')
|
|
if colorbar:
|
|
plt.colorbar(function(var[0],ax,clim,tlt,0))
|
|
|
|
frames = np.arange(0,len(var),skip)
|
|
def animateFrame(j):
|
|
i = frames[j]
|
|
function(var[i],ax,clim,tlt,i)
|
|
|
|
return animate(fig, animateFrame, frames=len(frames))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
from SimPEG import *
|
|
mT = Utils.meshTensors(((2,5),(4,2),(2,5)),((2,2),(6,2),(2,2)),((2,2),(6,2),(2,2)))
|
|
M = Mesh.TensorMesh(mT)
|
|
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).solve(q)
|
|
M.plotSlice(M.cellGrad*b, 'F', view='vec', grid=True, showIt=True, pcolorOpts={'alpha':0.8})
|