diff --git a/SimPEG/Mesh/TensorView.py b/SimPEG/Mesh/TensorView.py index 4c40e537..7daf733a 100644 --- a/SimPEG/Mesh/TensorView.py +++ b/SimPEG/Mesh/TensorView.py @@ -14,229 +14,153 @@ class TensorView(object): def __init__(self): pass - def plotImage(self, I, imageType='CC', figNum=1,ax=None,direction='z',numbering=True,annotationColor='w',showIt=False,clim=None): + # def components(self): + + # plotAll = len(imageType) == 1 + # options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":False} + # fig = plt.figure(figNum) + # # Determine the subplot number: 131, 121 + # numPlots = 130 if plotAll else len(imageType)/2*10+100 + # pltNum = 1 + # fxyz = self.r(I,'F','F','M') + # if plotAll or 'Fx' in imageType: + # ax_x = plt.subplot(numPlots+pltNum) + # self.plotImage(fxyz[0], imageType='Fx', ax=ax_x, **options) + # pltNum +=1 + # if plotAll or 'Fy' in imageType: + # ax_y = plt.subplot(numPlots+pltNum) + # self.plotImage(fxyz[1], imageType='Fy', ax=ax_y, **options) + # pltNum +=1 + # if plotAll or 'Fz' in imageType: + # ax_z = plt.subplot(numPlots+pltNum) + # self.plotImage(fxyz[2], imageType='Fz', ax=ax_z, **options) + # pltNum +=1 + # if showIt: plt.show() + + def plotImage(self, v, vType='CC', grid=False, view='real', + ax=None, clim=None, showIt=False, + pcolorOpts={}, + streamOpts={'color':'k'}, + gridOpts={'color':'k'}, + numbering=True, annotationColor='w' + ): """ - Mesh.plotImage(I) + Mesh.plotImage(v) Plots scalar fields on the given mesh. Input: - :param numpy.array I: scalar field + :param numpy.array v: vector - Optional Input: + Optional Inputs: - :param str imageType: type of image ('CC','N','F','Fx','Fy','Fz','E','Ex','Ey','Ez') or combinations, e.g. ExEy or FxFz - :param int figNum: number of figure to plot to + :param str vType: type of vector ('CC','N','F','Fx','Fy','Fz','E','Ex','Ey','Ez') :param matplotlib.axes.Axes ax: axis to plot to - :param str direction: slice dimensions, 3D only ('x', 'y', 'z') + :param bool showIt: call plt.show() + + 3D Inputs: + :param bool numbering: show numbering of slices, 3D only :param str annotationColor: color of annotation, e.g. 'w', 'k', 'b' - :param bool showIt: call plt.show() .. plot:: :include-source: from SimPEG import Mesh, np M = Mesh.TensorMesh([20, 20]) - I = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi) - M.plotImage(I, showIt=True) + v = np.sin(M.gridCC[:,0]*2*np.pi)*np.sin(M.gridCC[:,1]*2*np.pi) + M.plotImage(v, showIt=True) .. plot:: :include-source: from SimPEG import Mesh, np M = Mesh.TensorMesh([20,20,20]) - 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) - M.plotImage(I, annotationColor='k', showIt=True) + 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', showIt=True) """ - assert type(I) == np.ndarray, "I must be a numpy array" - assert type(numbering) == bool, "numbering must be a bool" - assert direction in ["x", "y","z"], "direction must be either x,y, or z" - - - if imageType == 'CC': - assert I.size == self.nC, "Incorrect dimensions for CC." - elif imageType == 'N': - assert I.size == self.nN, "Incorrect dimensions for N." - elif imageType == 'Fx': - if I.size != np.prod(self.vnFx): I, fy, fz = self.r(I,'F','F','M') - elif imageType == 'Fy': - if I.size != np.prod(self.vnFy): fx, I, fz = self.r(I,'F','F','M') - elif imageType == 'Fz': - if I.size != np.prod(self.vnFz): fx, fy, I = self.r(I,'F','F','M') - elif imageType == 'Ex': - if I.size != np.prod(self.vnEx): I, ey, ez = self.r(I,'E','E','M') - elif imageType == 'Ey': - if I.size != np.prod(self.vnEy): ex, I, ez = self.r(I,'E','E','M') - elif imageType == 'Ez': - if I.size != np.prod(self.vnEz): ex, ey, I = self.r(I,'E','E','M') - elif imageType[0] == 'E': - plotAll = len(imageType) == 1 - options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":False} - fig = plt.figure(figNum) - # Determine the subplot number: 131, 121 - numPlots = 130 if plotAll else len(imageType)/2*10+100 - pltNum = 1 - ex, ey, ez = self.r(I,'E','E','M') - if plotAll or 'Ex' in imageType: - ax_x = plt.subplot(numPlots+pltNum) - self.plotImage(ex, imageType='Ex', ax=ax_x, **options) - pltNum +=1 - if plotAll or 'Ey' in imageType: - ax_y = plt.subplot(numPlots+pltNum) - self.plotImage(ey, imageType='Ey', ax=ax_y, **options) - pltNum +=1 - if plotAll or 'Ez' in imageType: - ax_z = plt.subplot(numPlots+pltNum) - self.plotImage(ez, imageType='Ez', ax=ax_z, **options) - pltNum +=1 - if showIt: plt.show() - return - elif imageType[0] == 'F': - plotAll = len(imageType) == 1 - options = {"direction":direction,"numbering":numbering,"annotationColor":annotationColor,"showIt":False} - fig = plt.figure(figNum) - # Determine the subplot number: 131, 121 - numPlots = 130 if plotAll else len(imageType)/2*10+100 - pltNum = 1 - fxyz = self.r(I,'F','F','M') - if plotAll or 'Fx' in imageType: - ax_x = plt.subplot(numPlots+pltNum) - self.plotImage(fxyz[0], imageType='Fx', ax=ax_x, **options) - pltNum +=1 - if plotAll or 'Fy' in imageType: - ax_y = plt.subplot(numPlots+pltNum) - self.plotImage(fxyz[1], imageType='Fy', ax=ax_y, **options) - pltNum +=1 - if plotAll or 'Fz' in imageType: - ax_z = plt.subplot(numPlots+pltNum) - self.plotImage(fxyz[2], imageType='Fz', ax=ax_z, **options) - pltNum +=1 - if showIt: plt.show() - return - else: - raise Exception("imageType must be 'CC', 'N','Fx','Fy','Fz','Ex','Ey','Ez'") - if ax is None: - fig = plt.figure(figNum) - fig.clf() + fig = plt.figure() ax = plt.subplot(111) else: assert isinstance(ax,matplotlib.axes.Axes), "ax must be an Axes!" fig = ax.figure if self.dim == 1: - if imageType == 'CC': - ph = ax.plot(self.vectorCCx, I, '-ro') - elif imageType == 'N': - ph = ax.plot(self.vectorNx, I, '-bs') + if vType == 'CC': + ph = ax.plot(self.vectorCCx, v, '-ro') + elif vType == 'N': + ph = ax.plot(self.vectorNx, v, '-bs') ax.set_xlabel("x") ax.axis('tight') elif self.dim == 2: - if imageType == 'CC': - C = I[:].reshape(self.vnC, order='F') - elif imageType == 'N': - C = I[:].reshape(self.vnN, order='F') - C = 0.25*(C[:-1, :-1] + C[1:, :-1] + C[:-1, 1:] + C[1:, 1:]) - elif imageType == 'Fx': - C = I[:].reshape(self.vnFx, order='F') - C = 0.5*(C[:-1, :] + C[1:, :] ) - elif imageType == 'Fy': - C = I[:].reshape(self.vnFy, order='F') - C = 0.5*(C[:, :-1] + C[:, 1:] ) - elif imageType == 'Ex': - C = I[:].reshape(self.vnEx, order='F') - C = 0.5*(C[:,:-1] + C[:,1:] ) - elif imageType == 'Ey': - C = I[:].reshape(self.vnEy, order='F') - C = 0.5*(C[:-1,:] + C[1:,:] ) + return self._plotImage2D(v, vType=vType, grid=grid, view=view, + ax=ax, clim=clim, showIt=showIt, + pcolorOpts=pcolorOpts, streamOpts=streamOpts, + gridOpts=gridOpts) + elif self.dim == 3: + # get copy of image and average to cell-centers is necessary + if vType == 'CC': + vc = v.reshape(self.vnC, order='F') + elif vType == 'N': + vc = (self.aveN2CC*v).reshape(self.vnC, order='F') + elif vType in ['Fx', 'Fy', 'Fz', 'Ex', 'Ey', 'Ez']: + aveOp = 'ave' + vType[0] + '2CCV' + v = getattr(self,aveOp)*v # average to cell centers + ind_xyz = {'x':0,'y':1,'z':2}[vType[1]] + vc = self.r(v.reshape((self.nC,-1),order='F'), 'CC','CC','M')[ind_xyz] + + # determine number oE slices in x and y dimension + nX = np.ceil(np.sqrt(self.nCz)) + nY = np.ceil(self.nCz/nX) + + # allocate space for montage + nCx = self.nCx + nCy = self.nCy + + C = np.zeros((nX*nCx,nY*nCy)) + + for iy in range(int(nY)): + for ix in range(int(nX)): + iz = ix + iy*nX + if iz < self.nCz: + C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = vc[:, :, iz] + else: + C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = np.nan + + C = np.ma.masked_where(np.isnan(C), C) + xx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), self.hx).ravel())] + yy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), self.hy).ravel())] + # Plot the mesh if clim is None: clim = [C.min(),C.max()] - ph = ax.pcolormesh(self.vectorNx, self.vectorNy, C.T, vmin=clim[0], vmax=clim[1]) + ph = ax.pcolormesh(xx, yy, C.T, vmin=clim[0], vmax=clim[1]) + # Plot the lines + gx = np.arange(nX+1)*(self.vectorNx[-1]-self.x0[0]) + gy = np.arange(nY+1)*(self.vectorNy[-1]-self.x0[1]) + # Repeat and seperate with NaN + gxX = np.c_[gx, gx, gx+np.nan].ravel() + gxY = np.kron(np.ones((nX+1, 1)), np.array([0, sum(self.hy)*nY, np.nan])).ravel() + gyX = np.kron(np.ones((nY+1, 1)), np.array([0, sum(self.hx)*nX, np.nan])).ravel() + gyY = np.c_[gy, gy, gy+np.nan].ravel() + ax.plot(gxX, gxY, annotationColor+'-', linewidth=2) + ax.plot(gyX, gyY, annotationColor+'-', linewidth=2) ax.axis('tight') - ax.set_xlabel("x") - ax.set_ylabel("y") - - elif self.dim == 3: - if direction == 'z': - - # get copy of image and average to cell-centres is necessary - if imageType == 'CC': - Ic = I[:].reshape(self.vnC, order='F') - elif imageType == 'N': - Ic = I[:].reshape(self.vnN, order='F') - Ic = .125*(Ic[:-1,:-1,:-1]+Ic[1:,:-1,:-1] + Ic[:-1,1:,:-1]+ Ic[1:,1:,:-1]+ Ic[:-1,:-1,1:]+Ic[1:,:-1,1:] + Ic[:-1,1:,1:]+ Ic[1:,1:,1:] ) - elif imageType == 'Fx': - Ic = I[:].reshape(self.vnFx, order='F') - Ic = .5*(Ic[:-1,:,:]+Ic[1:,:,:]) - elif imageType == 'Fy': - Ic = I[:].reshape(self.vnFy, order='F') - Ic = .5*(Ic[:,:-1,:]+Ic[:,1:,:]) - elif imageType == 'Fz': - Ic = I[:].reshape(self.vnFz, order='F') - Ic = .5*(Ic[:,:,:-1]+Ic[:,:,1:]) - elif imageType == 'Ex': - Ic = I[:].reshape(self.vnEx, order='F') - Ic = .25*(Ic[:,:-1,:-1]+Ic[:,1:,:-1]+Ic[:,:-1,1:]+Ic[:,1:,:1]) - elif imageType == 'Ey': - Ic = I[:].reshape(self.vnEy, order='F') - Ic = .25*(Ic[:-1,:,:-1]+Ic[1:,:,:-1]+Ic[:-1,:,1:]+Ic[1:,:,:1]) - elif imageType == 'Ez': - Ic = I[:].reshape(self.vnEz, order='F') - Ic = .25*(Ic[:-1,:-1,:]+Ic[1:,:-1,:]+Ic[:-1,1:,:]+Ic[1:,:1,:]) - - # determine number oE slices in x and y dimension - nX = np.ceil(np.sqrt(self.nCz)) - nY = np.ceil(self.nCz/nX) - - # allocate space for montage - nCx = self.nCx - nCy = self.nCy - - C = np.zeros((nX*nCx,nY*nCy)) + if numbering: + pad = np.sum(self.hx)*0.04 for iy in range(int(nY)): for ix in range(int(nX)): iz = ix + iy*nX if iz < self.nCz: - C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = Ic[:, :, iz] - else: - C[ix*nCx:(ix+1)*nCx, iy*nCy:(iy+1)*nCy] = np.nan + ax.text((ix+1)*(self.vectorNx[-1]-self.x0[0])-pad,(iy)*(self.vectorNy[-1]-self.x0[1])+pad, + '#%i'%iz,color=annotationColor,verticalalignment='bottom',horizontalalignment='right',size='x-large') - C = np.ma.masked_where(np.isnan(C), C) - xx = np.r_[0, np.cumsum(np.kron(np.ones((nX, 1)), self.hx).ravel())] - yy = np.r_[0, np.cumsum(np.kron(np.ones((nY, 1)), self.hy).ravel())] - # Plot the mesh - - if clim is None: - clim = [C.min(),C.max()] - ph = ax.pcolormesh(xx, yy, C.T, vmin=clim[0], vmax=clim[1]) - # Plot the lines - gx = np.arange(nX+1)*(self.vectorNx[-1]-self.x0[0]) - gy = np.arange(nY+1)*(self.vectorNy[-1]-self.x0[1]) - # Repeat and seperate with NaN - gxX = np.c_[gx, gx, gx+np.nan].ravel() - gxY = np.kron(np.ones((nX+1, 1)), np.array([0, sum(self.hy)*nY, np.nan])).ravel() - gyX = np.kron(np.ones((nY+1, 1)), np.array([0, sum(self.hx)*nX, np.nan])).ravel() - gyY = np.c_[gy, gy, gy+np.nan].ravel() - ax.plot(gxX, gxY, annotationColor+'-', linewidth=2) - ax.plot(gyX, gyY, annotationColor+'-', linewidth=2) - ax.axis('tight') - - if numbering: - pad = np.sum(self.hx)*0.04 - for iy in range(int(nY)): - for ix in range(int(nX)): - iz = ix + iy*nX - if iz < self.nCz: - ax.text((ix+1)*(self.vectorNx[-1]-self.x0[0])-pad,(iy)*(self.vectorNy[-1]-self.x0[1])+pad, - '#%i'%iz,color=annotationColor,verticalalignment='bottom',horizontalalignment='right',size='x-large') - - ax.set_title(imageType) + ax.set_title(vType) if showIt: plt.show() return ph @@ -270,7 +194,7 @@ class TensorView(object): # Some user error checking assert vType in vTypeOpts, "vType must be in ['%s']" % "','".join(vTypeOpts) - assert self.dim == 3, 'Must be a 3D mesh.' + assert self.dim == 3, 'Must be a 3D mesh. Use plotImage.' assert view in viewOpts, "view must be in ['%s']" % "','".join(viewOpts) assert normal in normalOpts, "normal must be in ['%s']" % "','".join(normalOpts) assert type(grid) is bool, 'grid must be a boolean' @@ -291,19 +215,18 @@ class TensorView(object): if vType == 'CC': return getIndSlice(self.r(v,'CC','CC','M')) elif vType == 'CCv': - v = self.r(v.reshape((self.nC,3),order='F'),'CC','CC','M') - assert view == 'vec', 'Other types for CCv not yet supported' + assert view == 'vec', 'Other types for CCv not supported' else: # Now just deal with 'F' and 'E' aveOp = 'ave' + vType + ('2CCV' if view == 'vec' else '2CC') v = getattr(self,aveOp)*v # average to cell centers (might be a vector) - v = self.r(v.reshape((self.nC,-1),order='F'),'CC','CC','M') + v = self.r(v.reshape((self.nC,-1),order='F'),'CC','CC','M') if view == 'vec': outSlice = [] if 'X' not in normal: outSlice.append(getIndSlice(v[0])) if 'Y' not in normal: outSlice.append(getIndSlice(v[1])) if 'Z' not in normal: outSlice.append(getIndSlice(v[2])) - return outSlice + return np.r_[mkvc(outSlice[0]), mkvc(outSlice[1])] else: return getIndSlice(self.r(v,'CC','CC','M')) @@ -319,70 +242,111 @@ class TensorView(object): h2d.append(self.hz) x2d.append(self.x0[2]) tM = self.__class__(h2d, x2d) #: Temp Mesh + v2d = doSlice(v) if ax is None: - fig = plt.figure(1) - fig.clf() + fig = plt.figure() ax = plt.subplot(111) else: assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes" fig = ax.figure + tM._plotImage2D(v2d, vType=('CCv' if view == 'vec' else 'CC'), grid=grid, view=view, + ax=ax, clim=clim, showIt=showIt, + pcolorOpts=pcolorOpts, streamOpts=streamOpts, + gridOpts=gridOpts) + + + ax.set_xlabel('y' if normal == 'X' else 'x') + ax.set_ylabel('y' if normal == 'Z' else 'z') + ax.set_title('Slice %d' % ind) + + + def _plotImage2D(self, v, vType='CC', grid=False, view='real', + ax=None, clim=None, showIt=False, + pcolorOpts={}, + streamOpts={'color':'k'}, + gridOpts={'color':'k'} + ): + + vTypeOptsCC = ['N','CC','Fx','Fy','Ex','Ey'] + vTypeOptsV = ['CCv','F','E'] + vTypeOpts = vTypeOptsCC + vTypeOptsV + if view == 'vec': + assert vType in vTypeOptsV, "vType must be in ['%s'] when view='vec'" % "','".join(vTypeOptsV) + assert vType in vTypeOpts, "vType must be in ['%s']" % "','".join(vTypeOpts) + + viewOpts = ['real','imag','abs','vec'] + assert view in viewOpts, "view must be in ['%s']" % "','".join(viewOpts) + + + if ax is None: + fig = plt.figure() + ax = plt.subplot(111) + else: + assert isinstance(ax, matplotlib.axes.Axes), "ax must be an matplotlib.axes.Axes" + fig = ax.figure + + # Reshape to a cell centered variable + if vType == 'CC': + pass + elif vType == 'CCv': + assert view == 'vec', 'Other types for CCv not supported' + elif vType in ['F', 'E', 'N']: + aveOp = 'ave' + vType + ('2CCV' if view == 'vec' else '2CC') + v = getattr(self,aveOp)*v # average to cell centers (might be a vector) + elif vType in ['Fx','Fy','Ex','Ey']: + aveOp = 'ave' + vType[0] + '2CCV' + v = getattr(self,aveOp)*v # average to cell centers (might be a vector) + xORy = {'x':0,'y':1}[vType[1]] + v = v.reshape((self.nC,-1), order='F')[:,xORy] + out = () if view in ['real','imag','abs']: + v = self.r(v, 'CC', 'CC', 'M') v = getattr(np,view)(v) # e.g. np.real(v) - v = doSlice(v) if clim is None: clim = [v.min(),v.max()] - out += (ax.pcolormesh(tM.vectorNx, tM.vectorNy, v.T, vmin=clim[0], vmax=clim[1], **pcolorOpts),) + out += (ax.pcolormesh(self.vectorNx, self.vectorNy, v.T, vmin=clim[0], vmax=clim[1], **pcolorOpts),) elif view in ['vec']: - U, V = doSlice(v) + U, V = self.r(v.reshape((self.nC,-1), order='F'), 'CC', 'CC', 'M') if clim is None: - uv = np.r_[mkvc(U), mkvc(V)] - uv = np.sqrt(uv**2) + uv = np.sqrt(U**2 + V**2) clim = [uv.min(),uv.max()] # Matplotlib seems to not support irregular # spaced vectors at the moment. So we will # Interpolate down to a regular mesh at the # smallest mesh size in this 2D slice. - nxi = int(tM.hx.sum()/tM.hx.min()) - nyi = int(tM.hy.sum()/tM.hy.min()) - tMi = self.__class__([np.ones(nxi)*tM.hx.sum()/nxi, - np.ones(nyi)*tM.hy.sum()/nyi], x2d) - P = tM.getInterpolationMat(tMi.gridCC,'CC',zerosOutside=True) + nxi = int(self.hx.sum()/self.hx.min()) + nyi = int(self.hy.sum()/self.hy.min()) + tMi = self.__class__([np.ones(nxi)*self.hx.sum()/nxi, + np.ones(nyi)*self.hy.sum()/nyi], self.x0) + P = self.getInterpolationMat(tMi.gridCC,'CC',zerosOutside=True) Ui = tMi.r(P*mkvc(U), 'CC', 'CC', 'M') Vi = tMi.r(P*mkvc(V), 'CC', 'CC', 'M') # End Interpolation - out += (ax.pcolormesh(tM.vectorNx, tM.vectorNy, np.sqrt(U**2+V**2).T, vmin=clim[0], vmax=clim[1], **pcolorOpts),) + out += (ax.pcolormesh(self.vectorNx, self.vectorNy, np.sqrt(U**2+V**2).T, vmin=clim[0], vmax=clim[1], **pcolorOpts),) out += (ax.streamplot(tMi.vectorCCx, tMi.vectorCCy, Ui.T, Vi.T, **streamOpts),) if grid: - xXGrid = np.c_[tM.vectorNx,tM.vectorNx,np.nan*np.ones(tM.nNx)].flatten() - xYGrid = np.c_[tM.vectorNy[0]*np.ones(tM.nNx),tM.vectorNy[-1]*np.ones(tM.nNx),np.nan*np.ones(tM.nNx)].flatten() - yXGrid = np.c_[tM.vectorNx[0]*np.ones(tM.nNy),tM.vectorNx[-1]*np.ones(tM.nNy),np.nan*np.ones(tM.nNy)].flatten() - yYGrid = np.c_[tM.vectorNy,tM.vectorNy,np.nan*np.ones(tM.nNy)].flatten() + xXGrid = np.c_[self.vectorNx,self.vectorNx,np.nan*np.ones(self.nNx)].flatten() + xYGrid = np.c_[self.vectorNy[0]*np.ones(self.nNx),self.vectorNy[-1]*np.ones(self.nNx),np.nan*np.ones(self.nNx)].flatten() + yXGrid = np.c_[self.vectorNx[0]*np.ones(self.nNy),self.vectorNx[-1]*np.ones(self.nNy),np.nan*np.ones(self.nNy)].flatten() + yYGrid = np.c_[self.vectorNy,self.vectorNy,np.nan*np.ones(self.nNy)].flatten() out += (ax.plot(np.r_[xXGrid,yXGrid],np.r_[xYGrid,yYGrid],**gridOpts)[0],) - ax.set_xlabel('y' if normal == 'X' else 'x') - ax.set_ylabel('y' if normal == 'Z' else 'z') - ax.set_title('Slice %d' % ind) - ax.set_xlim(*tM.vectorNx[[0,-1]]) - ax.set_ylim(*tM.vectorNy[[0,-1]]) + + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_xlim(*self.vectorNx[[0,-1]]) + ax.set_ylim(*self.vectorNy[[0,-1]]) if showIt: plt.show() return out - # def _plotImage2D(self, v, vType='CC', - # normal='Z', ind=None, grid=False, view='real', - # ax=None, clim=None, showIt=False, - # pcolorOpts={}, - # streamOpts={'color':'k'}, - # gridOpts={'color':'k'} - # ): - 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. @@ -416,7 +380,12 @@ class TensorView(object): """ axOpts = {'projection':'3d'} if self.dim == 3 else {} - if ax is None: ax = plt.subplot(111, **axOpts) + 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: @@ -557,6 +526,24 @@ if __name__ == '__main__': 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}) + 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') - Mesh.TensorMesh([10]).plotGrid(showIt=True) + 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()