mirror of
https://github.com/wassname/simpeg.git
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rowanc1
431 lines
18 KiB
Python
431 lines
18 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 plotGrid(self, 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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if self.dim == 1:
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fig = plt.figure(1)
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fig.clf()
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ax = plt.subplot(111)
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xn = self.gridN
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xc = self.gridCC
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ax.hold(True)
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ax.plot(xn, np.ones(np.shape(xn)), 'bs')
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ax.plot(xc, np.ones(np.shape(xc)), 'ro')
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ax.plot(xn, np.ones(np.shape(xn)), 'k--')
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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if showIt: plt.show()
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elif self.dim == 2:
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fig = plt.figure(2)
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fig.clf()
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ax = plt.subplot(111)
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xn = self.gridN
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xc = self.gridCC
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xs1 = self.gridFx
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xs2 = self.gridFy
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ax.hold(True)
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if nodes: ax.plot(xn[:, 0], xn[:, 1], 'bs')
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if centers: ax.plot(xc[:, 0], xc[:, 1], 'ro')
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if faces:
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ax.plot(xs1[:, 0], xs1[:, 1], 'g>')
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ax.plot(xs2[:, 0], xs2[:, 1], 'g^')
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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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plt.plot(X, Y)
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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ax.set_ylabel('x2')
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if showIt: plt.show()
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elif self.dim == 3:
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fig = plt.figure(3)
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fig.clf()
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ax = fig.add_subplot(111, projection='3d')
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xn = self.gridN
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xc = self.gridCC
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xfs1 = self.gridFx
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xfs2 = self.gridFy
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xfs3 = self.gridFz
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xes1 = self.gridEx
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xes2 = self.gridEy
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xes3 = self.gridEz
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ax.hold(True)
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if nodes: ax.plot(xn[:, 0], xn[:, 1], 'bs', zs=xn[:, 2])
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if centers: ax.plot(xc[:, 0], xc[:, 1], 'ro', zs=xc[:, 2])
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if faces:
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ax.plot(xfs1[:, 0], xfs1[:, 1], 'g>', zs=xfs1[:, 2])
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ax.plot(xfs2[:, 0], xfs2[:, 1], 'g<', zs=xfs2[:, 2])
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ax.plot(xfs3[:, 0], xfs3[:, 1], 'g^', zs=xfs3[:, 2])
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if edges:
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ax.plot(xes1[:, 0], xes1[:, 1], 'k>', zs=xes1[:, 2])
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ax.plot(xes2[:, 0], xes2[:, 1], 'k<', zs=xes2[:, 2])
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ax.plot(xes3[:, 0], xes3[:, 1], 'k^', zs=xes3[:, 2])
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# 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()
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X = np.r_[X1, X2, X3]
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Y = np.r_[Y1, Y2, Y3]
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Z = np.r_[Z1, Z2, Z3]
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plt.plot(X, Y, 'b-', zs=Z)
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ax.grid(True)
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ax.hold(False)
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ax.set_xlabel('x1')
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ax.set_ylabel('x2')
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ax.set_zlabel('x3')
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if showIt: plt.show()
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def slicer(mesh, var, imageType='CC', normal='z', index=0, ax=None, clim=None):
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assert normal in 'xyz', 'normal must be x, y, or z'
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if ax is None: ax = plt.subplot(111)
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I = mesh.r(var,'CC','CC','M')
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axes = [p for p in 'xyz' if p not in normal.lower()]
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if normal is 'x': I = I[index,:,:]
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|
if normal is 'y': I = I[:,index,:]
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if normal is 'z': I = I[:,:,index]
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if clim is None: clim = [I.min(),I.max()]
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p = ax.pcolormesh(getattr(mesh,'vectorN'+axes[0]),getattr(mesh,'vectorN'+axes[1]),I.T,vmin=clim[0],vmax=clim[1])
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|
ax.axis('tight')
|
|
ax.set_xlabel(axes[0])
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|
ax.set_ylabel(axes[1])
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|
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))
|
|
|
|
|