diff --git a/simpegPF/BaseGrav.py b/simpegPF/BaseGrav.py new file mode 100644 index 00000000..43a9fd90 --- /dev/null +++ b/simpegPF/BaseGrav.py @@ -0,0 +1,52 @@ +from SimPEG import Maps, Survey, Utils, np, sp +from scipy.constants import mu_0 +import re + +class LinearSurvey(Survey.BaseSurvey): + """Base Magnetics Survey""" + + rxLoc = None #: receiver locations + rxType = None #: receiver type + + def __init__(self, srcField, **kwargs): + self.srcField = srcField + Survey.BaseSurvey.__init__(self, **kwargs) + + def eval(self, u): + return u + + @property + def nD(self): + return self.prob.G.shape[0] + + @property + def nRx(self): + return self.srcField.rxList[0].locs.shape[0] + # def setBackgroundField(self, SrcField): + + # if getattr(self, 'B0', None) is None: + # self._B0 = SrcField.param[0] * dipazm_2_xyz(SrcField.param[1],SrcField.param[2]) + + # return self._B0 + +class SrcField(Survey.BaseSrc): + """ Define the inducing field """ + + param = None #: Inducing field param (Amp, Incl, Decl) + + def __init__(self, rxList, **kwargs): + super(SrcField, self).__init__(rxList, **kwargs) + +class RxObs(Survey.BaseRx): + """A station location must have be located in 3-D""" + def __init__(self, locsXYZ, **kwargs): + locs = locsXYZ + assert locsXYZ.shape[1] == 3, 'locs must in 3-D (x,y,z).' + super(RxObs, self).__init__(locs, 'tmi', storeProjections=False, **kwargs) + + @property + def nD(self): + """Number of data in the receiver.""" + return self.locs[0].shape[0] + + diff --git a/simpegPF/BaseMag.py b/simpegPF/BaseMag.py index 2aacc9c4..7c996550 100644 --- a/simpegPF/BaseMag.py +++ b/simpegPF/BaseMag.py @@ -105,6 +105,54 @@ class BaseMagSurvey(Survey.BaseSurvey): return np.r_[bfx, bfy, bfz] +class LinearSurvey(Survey.BaseSurvey): + """Base Magnetics Survey""" + + rxLoc = None #: receiver locations + rxType = None #: receiver type + + def __init__(self, srcField, **kwargs): + self.srcField = srcField + Survey.BaseSurvey.__init__(self, **kwargs) + + def eval(self, u): + return u + + @property + def nD(self): + return self.prob.G.shape[0] + + @property + def nRx(self): + return self.srcField.rxList[0].locs.shape[0] + # def setBackgroundField(self, SrcField): + + # if getattr(self, 'B0', None) is None: + # self._B0 = SrcField.param[0] * dipazm_2_xyz(SrcField.param[1],SrcField.param[2]) + + # return self._B0 + +class SrcField(Survey.BaseSrc): + """ Define the inducing field """ + + param = None #: Inducing field param (Amp, Incl, Decl) + + def __init__(self, rxList, param = None, **kwargs): + self.param = param + super(SrcField, self).__init__(rxList, **kwargs) + +class RxObs(Survey.BaseRx): + """A station location must have be located in 3-D""" + def __init__(self, locsXYZ, **kwargs): + locs = locsXYZ + assert locsXYZ.shape[1] == 3, 'locs must in 3-D (x,y,z).' + super(RxObs, self).__init__(locs, 'tmi', storeProjections=False, **kwargs) + + @property + def nD(self): + """Number of data in the receiver.""" + return self.locs[0].shape[0] + class MagSurveyBx(object): """docstring for MagSurveyBx""" def __init__(self, **kwargs): @@ -144,352 +192,3 @@ class WeightMap(Maps.IdentityMap): return Utils.sdiag(self.weight) - - -def readUBCmagObs(obs_file): - - """ - Read and write UBC mag file format - - INPUT: - :param fileName, path to the UBC obs mag file - - OUTPUT: - :param dobs, observation in (x y z [data] [wd]) - :param B, primary field information (BI, BD, B0) - :param M, magnetization orentiaton (MI, MD) - - """ - - fid = open(obs_file,'r') - - # First line has the inclination,declination and amplitude of B0 - line = fid.readline() - B = np.array(line.split(),dtype=float) - - # Second line has the magnetization orientation and a flag - line = fid.readline() - M = np.array(line.split(),dtype=float) - - # Third line has the number of rows - line = fid.readline() - ndat = np.array(line.split(),dtype=int) - - # Pre-allocate space for obsx, obsy, obsz, data, uncert - line = fid.readline() - temp = np.array(line.split(),dtype=float) - - dobs = np.zeros((ndat,len(temp)), dtype=float) - - - for ii in range(ndat): - - dobs[ii,:] = np.array(line.split(),dtype=float) - line = fid.readline() - - return B, M, dobs - -def read_MAGfwr_inp(input_file): - - """Read input files for forward modeling MAG data with integral form - INPUT: - input_file: File name containing the forward parameter - - OUTPUT: - mshfile - obsfile - modfile - magfile - topofile - # All files should be in the working directory, otherwise the path must - # be specified. - - Created on Jul 17, 2013 - - @author: dominiquef - """ - - - fid = open(input_file,'r') - - line = fid.readline() - l_input = line.split('!') - mshfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - obsfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - modfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - if l_input=='null': - magfile = [] - - else: - magfile = l_input[0].rstrip() - - - line = fid.readline() - l_input = line.split('!') - if l_input=='null': - topofile = [] - - else: - topofile = l_input[0].rstrip() - - return mshfile, obsfile, modfile, magfile, topofile - -def read_MAGinv_inp(input_file): - """Read input files for forward modeling MAG data with integral form - INPUT: - input_file: File name containing the forward parameter - - OUTPUT: - mshfile - obsfile - topofile - start model - ref model - mag model - weightfile - chi_target - as, ax ,ay, az - upper, lower bounds - lp, lqx, lqy, lqz - - # All files should be in the working directory, otherwise the path must - # be specified. - - Created on Dec 21th, 2015 - - @author: dominiquef - """ - - - fid = open(input_file,'r') - - # Line 1 - line = fid.readline() - l_input = line.split('!') - mshfile = l_input[0].rstrip() - - # Line 2 - line = fid.readline() - l_input = line.split('!') - obsfile = l_input[0].rstrip() - - # Line 3 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input=='null': - topofile = [] - - else: - topofile = l_input[0].rstrip() - - - # Line 4 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input[0]=='VALUE': - mstart = float(l_input[1]) - - else: - mstart = l_input[0].rstrip() - - # Line 5 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input[0]=='VALUE': - mref = float(l_input[1]) - - else: - mref = l_input[0].rstrip() - - - # Line 6 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input=='DEFAULT': - magfile = [] - - else: - magfile = l_input[0].rstrip() - - # Line 7 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input=='DEFAULT': - wgtfile = [] - - else: - wgtfile = l_input[0].rstrip() - - # Line 8 - line = fid.readline() - l_input = re.split('[!\s]',line) - chi = float(l_input[0]) - - # Line 9 - line = fid.readline() - l_input = re.split('[!\s]',line) - val = np.array(l_input[0:4]) - alphas = val.astype(np.float) - - # Line 10 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input[0]=='VALUE': - val = np.array(l_input[1:3]) - bounds = val.astype(np.float) - - else: - bounds = l_input[0].rstrip() - - # Line 11 - line = fid.readline() - l_input = re.split('[!\s]',line) - if l_input[0]=='VALUE': - val = np.array(l_input[1:6]) - lpnorms = val.astype(np.float) - - else: - lpnorms = l_input[0].rstrip() - - return mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms - -def read_GOCAD_ts(tsfile): - """Read GOCAD triangulated surface (*.ts) file - INPUT: - tsfile: Triangulated surface - - OUTPUT: - vrts : Array of vertices in XYZ coordinates [n x 3] - trgl : Array of index for triangles [m x 3]. The order of the vertices - is important and describes the normal - n = cross( (P2 - P1 ) , (P3 - P1) ) - - - Created on Jan 13th, 2016 - - Author: @fourndo - """ - - - fid = open(tsfile,'r') - line = fid.readline() - - # Skip all the lines until the vertices - while re.match('TFACE',line)==None: - line = fid.readline() - - line = fid.readline() - vrtx = [] - - # Run down all the vertices and save in array - while re.match('VRTX',line): - l_input = re.split('[\s*]',line) - temp = np.array(l_input[2:5]) - vrtx.append(temp.astype(np.float)) - - # Read next line - line = fid.readline() - - vrtx = np.asarray(vrtx) - - # Skip lines to the triangles - while re.match('TRGL',line)==None: - line = fid.readline() - - # Run down the list of triangles - trgl = [] - - # Run down all the vertices and save in array - while re.match('TRGL',line): - l_input = re.split('[\s*]',line) - temp = np.array(l_input[1:4]) - trgl.append(temp.astype(np.int)) - - # Read next line - line = fid.readline() - - trgl = np.asarray(trgl) - - return vrtx, trgl - -def gocad2vtk(gcFile,mesh,bcflag,inflag): - """" - Function to read gocad polystructure file and output indexes of mesh with in the structure. - - """ - import vtk, vtk.util.numpy_support as npsup - - print "Reading GOCAD ts file..." - vrtx, trgl = read_GOCAD_ts(gcFile) - # Adjust the index - trgl = trgl - 1 - - # Make vtk pts - ptsvtk = vtk.vtkPoints() - ptsvtk.SetData(npsup.numpy_to_vtk(vrtx,deep=1)) - - # Make the polygon connection - polys = vtk.vtkCellArray() - for face in trgl: - poly = vtk.vtkPolygon() - poly.GetPointIds().SetNumberOfIds(len(face)) - for nrv, vert in enumerate(face): - poly.GetPointIds().SetId(nrv,vert) - polys.InsertNextCell(poly) - - # Make the polydata, structure of connections and vrtx - polyData = vtk.vtkPolyData() - polyData.SetPoints(ptsvtk) - polyData.SetPolys(polys) - - # Make implicit func - ImpDistFunc = vtk.vtkImplicitPolyDataDistance() - ImpDistFunc.SetInput(polyData) - - # Convert the mesh - vtkMesh = vtk.vtkRectilinearGrid() - vtkMesh.SetDimensions(mesh.nNx,mesh.nNy,mesh.nNz) - vtkMesh.SetXCoordinates(npsup.numpy_to_vtk(mesh.vectorNx,deep=1)) - vtkMesh.SetYCoordinates(npsup.numpy_to_vtk(mesh.vectorNy,deep=1)) - vtkMesh.SetZCoordinates(npsup.numpy_to_vtk(mesh.vectorNz,deep=1)) - # Add indexes - vtkInd = npsup.numpy_to_vtk(np.arange(mesh.nC),deep=1) - vtkInd.SetName('Index') - vtkMesh.GetCellData().AddArray(vtkInd) - - extractImpDistRectGridFilt = vtk.vtkExtractGeometry() # Object constructor - extractImpDistRectGridFilt.SetImplicitFunction(ImpDistFunc) # - extractImpDistRectGridFilt.SetInputData(vtkMesh) - - if bcflag is True: - extractImpDistRectGridFilt.ExtractBoundaryCellsOn() - - else: - extractImpDistRectGridFilt.ExtractBoundaryCellsOff() - - if inflag is True: - extractImpDistRectGridFilt.ExtractInsideOn() - - else: - extractImpDistRectGridFilt.ExtractInsideOff() - - print "Extracting indices from grid..." - # Executing the pipe - extractImpDistRectGridFilt.Update() - - # Get index inside - insideGrid = extractImpDistRectGridFilt.GetOutput() - insideGrid = npsup.vtk_to_numpy(insideGrid.GetCellData().GetArray('Index')) - - - # Return the indexes inside - return insideGrid \ No newline at end of file diff --git a/simpegPF/Dev/Archives/fwr_MAG_data.py b/simpegPF/Dev/Archives/fwr_MAG_data.py deleted file mode 100644 index 6bd3dbe1..00000000 --- a/simpegPF/Dev/Archives/fwr_MAG_data.py +++ /dev/null @@ -1,95 +0,0 @@ -def fwr_MAG_data(mesh,B,M,rxLoc,model,flag): - """ - Forward model magnetic data using integral equation - - INPUT: - xn, yn, zn = Mesh nodes location - B = Inducing field parameter [Binc, Bdecl, B0] - M = Magnetization matrix [Minc, Mdecl] - rxLox = Observation location informat [obsx, obsy, obsz] - model = Model associated with mesh - - OUTPUT: - dobs =Observation array in format [obsx, obsy, obsz, data] - - Created on Oct 7, 2015 - - @author: dominiquef - """ - - #%% - from SimPEG import np, Utils, sp, mkvc - from get_T_mat import get_T_mat - - - xn = mesh.vectorNx; - yn = mesh.vectorNy; - zn = mesh.vectorNz; - - mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) - - ndata = rxLoc.shape[0] - - # Convert declination from north to cartesian - Md = (450.-float(M[1]))%360. - - # Create magnetization matrix - mx = np.cos(np.deg2rad(M[0])) * np.cos(np.deg2rad(Md)) - my = np.cos(np.deg2rad(M[0])) * np.sin(np.deg2rad(Md)) - mz = np.sin(np.deg2rad(M[0])) - - Mx = Utils.sdiag(np.ones([mcell])*mx*B[2]) - My = Utils.sdiag(np.ones([mcell])*my*B[2]) - Mz = Utils.sdiag(np.ones([mcell])*mz*B[2]) - - #matplotlib.pyplot.spy(scipy.sparse.csr_matrix(Mx)) - #plt.show() - Mxyz = sp.vstack((Mx,My,Mz)); - - #%% Create TMI projector - - # Convert Bdecination from north to cartesian - D = (450.-float(B[1]))%360. - - Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)),np.cos(np.deg2rad(B[0]))*np.sin(np.deg2rad(D)),np.sin(np.deg2rad(B[0]))],2).T; - - if flag=='tmi': - d = np.zeros(ndata) - - elif flag=='xyz': - d = np.zeros(int(3*ndata)) - - # Loop through all observations and create forward operator (ndata-by-mcell) - print "Begin forward modeling " +str(int(ndata)) + " data points..." - - # Add counter to dsiplay progress. Good for large problems - progress = -1; - for ii in range(ndata): - - tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) - Gxyz = np.vstack((tx,ty,tz))*Mxyz - - if flag=='xyz': - d[ii:ndata:] = mkvc(Gxyz.dot(model)) - - elif flag=='tmi': - d[ii] = Ptmi.dot(Gxyz.dot(model)) - - #%% - # Forward operator - - - d_iter = np.floor(float(ii)/float(ndata)*10.); - - if d_iter > progress: - - arg = "Done " + str(d_iter*10) + " %" - print arg - progress = d_iter; - - - print "Done 100% ...forward modeling completed!!\n" - - return d - - diff --git a/simpegPF/Dev/Archives/get_MAG_F.py b/simpegPF/Dev/Archives/get_MAG_F.py deleted file mode 100644 index 54172d71..00000000 --- a/simpegPF/Dev/Archives/get_MAG_F.py +++ /dev/null @@ -1,132 +0,0 @@ -def fwr_MAG_F(mesh,B,M,rxLoc,flag): - """ - Forward model magnetic data using integral equation - - INPUT: - mesh = Mesh in SimPEG format - B = Inducing field parameter [Binc, Bdecl, B0] - M = Magnetization information - [OPTIONS] - 1- [Minc, Mdecl] : Assumes uniform magnetization orientation - 2- [mx1,mx2,..., my1,...,mz1] : cell-based defined magnetization direction - 3- diag(M): Block diagonal matrix with [Mx, My, Mz] along the diagonal - - rxLox = Observation location informat [obsx, obsy, obsz] - - flag = 'tmi' | 'xyz' | 'full' - [OPTIONS] - 1- tmi : Magnetization direction used and data are projected onto the - inducing field direction F.shape([ndata, nc]) - - 2- xyz : Magnetization direction used and data are given in 3-components - F.shape([3*ndata, nc]) - - 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) - - OUTPUT: - F = Linear forward modeling operation - - Created on Dec, 20th 2015 - - @author: dominiquef - """ - - #%% - from SimPEG import np, Utils, sp, mkvc - from get_T_mat import get_T_mat - - - xn = mesh.vectorNx; - yn = mesh.vectorNy; - zn = mesh.vectorNz; - - mcell = (len(xn)-1) * (len(yn)-1) * (len(zn)-1) - - ndata = rxLoc.shape[0] - - #%% Create TMI projector - - # Convert Bdecination from north to cartesian - D = (450.-float(B[1]))%360. - - Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)), - np.cos(np.deg2rad(B[0]))*np.sin(np.deg2rad(D)), - np.sin(np.deg2rad(B[0]))],2).T; - - # Pre-allocate space - if flag=='tmi' | flag == 'xyz': - - # If assumes uniform magnetization direction - if len(M) == 2: - - # Convert declination from north to cartesian - Md = (450.-float(M[1]))%360. - - # Create magnetization matrix - mx = np.cos(np.deg2rad(M[0])) * np.cos(np.deg2rad(Md)) - my = np.cos(np.deg2rad(M[0])) * np.sin(np.deg2rad(Md)) - mz = np.sin(np.deg2rad(M[0])) - - Mx = Utils.sdiag(np.ones([mcell])*mx*B[2]) - My = Utils.sdiag(np.ones([mcell])*my*B[2]) - Mz = Utils.sdiag(np.ones([mcell])*mz*B[2]) - - Mxyz = sp.vstack((Mx,My,Mz)); - - # Otherwise if given a vector 3*ncells - elif len(M) == mesh.nC * 3: - - Mxyz = sp.spdiags(M,0,mesh.nC * 3,mesh.nC * 3) - - if flag == 'tmi': - F = np.zeros((ndata, mesh.nC)) - - elif flag == 'xyz': - F = np.zeros((int(3*ndata), mesh.nC)) - - elif flag == 'full': - F = np.zeros((int(3*ndata), int(3*mesh.nC))) - - else: - print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" - return - - - # Loop through all observations and create forward operator (ndata-by-mcell) - print "Begin calculation of forward operator: " + flag - - # Add counter to dsiplay progress. Good for large problems - progress = -1; - for ii in range(ndata): - - tx, ty, tz = get_T_mat(xn,yn,zn,rxLoc[ii,:]) - - if flag=='tmi': - F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz - - elif flag == 'xyz': - F[ii,:] = tx*Mxyz - F[ii+ndata,:] = ty*Mxyz - F[ii+2*ndata,:] = tz*Mxyz - - elif flag == 'full': - F[ii,:] = tx - F[ii+ndata,:] = ty - F[ii+2*ndata,:] = tz - - - # Display progress - counter = np.floor(float(ii)/float(ndata)*10.); - - if counter > progress: - - arg = "Done " + str(counter*10) + " %" - print arg - progress = counter; - - - print "Done 100% ...forward modeling completed!!\n" - - return F - - diff --git a/simpegPF/Dev/Archives/get_T_mat.py b/simpegPF/Dev/Archives/get_T_mat.py deleted file mode 100644 index 3b710892..00000000 --- a/simpegPF/Dev/Archives/get_T_mat.py +++ /dev/null @@ -1,133 +0,0 @@ -''' -Created on Sep 27, 2015 - -@author: dominiquef -''' -def get_T_mat(xn,yn,zn,rxLoc): - """ - Load in the nodes of a tensor mesh and computes the magnetic tensor - for a given observation location [obsx, obsy, obsz] - OUTPUT: - Tx = [Txx Txy Txz] - Ty = [Tyx Tyy Tyz] - Tz = [Tzx Tzy Tzz] - - where each elements have dimension 1-by-mcell. - Only the upper half 5 elements have to be computed since symetric. - Currently done as for-loops but will eventually be changed to vector - indexing, once the topography has been figured out. - """ - - from SimPEG import np, mkvc - - ncx = len(xn)-1 - ncy = len(yn)-1 - ncz = len(zn)-1 - - mcell = ncx*ncy*ncz - - # Pre-allocate space for 1D array - Tx = np.zeros((1,3*mcell)) - Ty = np.zeros((1,3*mcell)) - Tz = np.zeros((1,3*mcell)) - - yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) - yn1,xn1,zn1 = np.meshgrid(yn[0:ncy], xn[0:ncx], zn[0:ncz]) - - yn2 = mkvc(yn2) - yn1 = mkvc(yn1) - - zn2 = mkvc(zn2) - zn1 = mkvc(zn1) - - xn2 = mkvc(xn2) - xn1 = mkvc(xn1) - #%% - #============================================================================== - - - dz2 = rxLoc[2] - zn1; - dz1 = rxLoc[2] - zn2; - - - dy2 = yn2 - rxLoc[1]; - dy1 = yn1 - rxLoc[1]; - - - dx2 = xn2 - rxLoc[0]; - dx1 = xn1 - rxLoc[0]; - - R1 = ( dy2**2 + dx2**2 ); - R2 = ( dy2**2 + dx1**2 ); - R3 = ( dy1**2 + dx2**2 ); - R4 = ( dy1**2 + dx1**2 ); - - - arg1 = np.sqrt( dz2**2 + R2 ); - arg2 = np.sqrt( dz2**2 + R1 ); - arg3 = np.sqrt( dz1**2 + R1 ); - arg4 = np.sqrt( dz1**2 + R2 ); - arg5 = np.sqrt( dz2**2 + R3 ); - arg6 = np.sqrt( dz2**2 + R4 ); - arg7 = np.sqrt( dz1**2 + R4 ); - arg8 = np.sqrt( dz1**2 + R3 ); - - - - Tx[0,0:mcell] = np.arctan2( dy1 * dz2 , ( dx2 * arg5 ) ) +\ - - np.arctan2( dy2 * dz2 , ( dx2 * arg2 ) ) +\ - np.arctan2( dy2 * dz1 , ( dx2 * arg3 ) ) +\ - - np.arctan2( dy1 * dz1 , ( dx2 * arg8 ) ) +\ - np.arctan2( dy2 * dz2 , ( dx1 * arg1 ) ) +\ - - np.arctan2( dy1 * dz2 , ( dx1 * arg6 ) ) +\ - np.arctan2( dy1 * dz1 , ( dx1 * arg7 ) ) +\ - - np.arctan2( dy2 * dz1 , ( dx1 * arg4 ) ); - - - Ty[0,0:mcell] = np.log( ( dz2 + arg2 ) / (dz1 + arg3 ) ) +\ - -np.log( ( dz2 + arg1 ) / (dz1 + arg4 ) ) +\ - np.log( ( dz2 + arg6 ) / (dz1 + arg7 ) ) +\ - -np.log( ( dz2 + arg5 ) / (dz1 + arg8 ) ); - - Ty[0,mcell:2*mcell] = np.arctan2( dx1 * dz2 , ( dy2 * arg1 ) ) +\ - - np.arctan2( dx2 * dz2 , ( dy2 * arg2 ) ) +\ - np.arctan2( dx2 * dz1 , ( dy2 * arg3 ) ) +\ - - np.arctan2( dx1 * dz1 , ( dy2 * arg4 ) ) +\ - np.arctan2( dx2 * dz2 , ( dy1 * arg5 ) ) +\ - - np.arctan2( dx1 * dz2 , ( dy1 * arg6 ) ) +\ - np.arctan2( dx1 * dz1 , ( dy1 * arg7 ) ) +\ - - np.arctan2( dx2 * dz1 , ( dy1 * arg8 ) ); - - R1 = (dy2**2 + dz1**2); - R2 = (dy2**2 + dz2**2); - R3 = (dy1**2 + dz1**2); - R4 = (dy1**2 + dz2**2); - - Ty[0,2*mcell:] = np.log( ( dx1 + np.sqrt( dx1**2 + R1 ) ) / (dx2 + np.sqrt( dx2**2 + R1 ) ) ) +\ - -np.log( ( dx1 + np.sqrt( dx1**2 + R2 ) ) / (dx2 + np.sqrt( dx2**2 + R2 ) ) ) +\ - np.log( ( dx1 + np.sqrt( dx1**2 + R4 ) ) / (dx2 + np.sqrt( dx2**2 + R4 ) ) ) +\ - -np.log( ( dx1 + np.sqrt( dx1**2 + R3 ) ) / (dx2 + np.sqrt( dx2**2 + R3 ) ) ); - - R1 = (dx2**2 + dz1**2); - R2 = (dx2**2 + dz2**2); - R3 = (dx1**2 + dz1**2); - R4 = (dx1**2 + dz2**2); - - Tx[0,2*mcell:] = np.log( ( dy1 + np.sqrt( dy1**2 + R1 ) ) / (dy2 + np.sqrt( dy2**2 + R1 ) ) ) +\ - -np.log( ( dy1 + np.sqrt( dy1**2 + R2 ) ) / (dy2 + np.sqrt( dy2**2 + R2 ) ) ) +\ - np.log( ( dy1 + np.sqrt( dy1**2 + R4 ) ) / (dy2 + np.sqrt( dy2**2 + R4 ) ) ) +\ - -np.log( ( dy1 + np.sqrt( dy1**2 + R3 ) ) / (dy2 + np.sqrt( dy2**2 + R3 ) ) ); - - Tz[0,2*mcell:] = -( Ty[0,mcell:2*mcell] + Tx[0,0:mcell] ); - Tz[0,mcell:2*mcell] = Ty[0,2*mcell:]; - Tx[0,mcell:2*mcell] = Ty[0,0:mcell]; - Tz[0,0:mcell] = Tx[0,2*mcell:]; - - - - Tx = Tx/(4*np.pi); - Ty = Ty/(4*np.pi); - Tz = Tz/(4*np.pi); - - - return Tx,Ty,Tz diff --git a/simpegPF/Dev/Archives/get_UBC_mesh.py b/simpegPF/Dev/Archives/get_UBC_mesh.py deleted file mode 100644 index ebdbbbac..00000000 --- a/simpegPF/Dev/Archives/get_UBC_mesh.py +++ /dev/null @@ -1,117 +0,0 @@ -''' -Created on Jul 17, 2013 - -@author: dominiquef -''' -def get_UBC_mesh(meshfile): - """ Read UBC mesh file and extract parameters - Works for the condenced version (20 * 3) --> [20 20 20] """ - - fid = open(meshfile,'r') - from numpy import zeros - - # Go through the log file and extract data and the last achieved misfit - for ii in range (1, 6): - - line = fid.readline() - line = line.split(' ') - - # First line: number of cells in i, j, k - if ii == 1: - - numcell=[] - - for jj in range(len(line)): - t = int(line[jj]) - numcell.append(t) - - nX = numcell[0] - nY = numcell[1] - nZ = numcell[2] - # Second line: origin coordinate (X,Y,Z) - elif ii==2: - - origin = [] - - for jj in range(len(line)): - t = float(line[jj]) - origin.append(t) - - - # Other lines for the xn, yn, zn (nodes location) - elif ii==3: - - xn=zeros((nX+1,1), dtype=float) - xn[0] = origin[0] - - count_entry = 0; - count = 0; - while (count3: - - data[ii] = line[3] - - if len(line)>4: - - unct[ii] = line[4] - - - - return Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct \ No newline at end of file diff --git a/simpegPF/Dev/Archives/read_MAGfwr_inp.py b/simpegPF/Dev/Archives/read_MAGfwr_inp.py deleted file mode 100644 index 3f2453a6..00000000 --- a/simpegPF/Dev/Archives/read_MAGfwr_inp.py +++ /dev/null @@ -1,52 +0,0 @@ -def read_MAGfwr_inp(input_file): - """Read input files for forward modeling MAG data with integral form - INPUT: - input_file: File name containing the forward parameter - - OUTPUT: - mshfile - obsfile - modfile - magfile - topofile - # All files should be in the working directory, otherwise the path must - # be specified. - - Created on Jul 17, 2013 - - @author: dominiquef - """ - - - fid = open(input_file,'r') - - line = fid.readline() - l_input = line.split('!') - mshfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - obsfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - modfile = l_input[0].rstrip() - - line = fid.readline() - l_input = line.split('!') - if l_input=='null': - magfile = [] - - else: - magfile = l_input[0].rstrip() - - - line = fid.readline() - l_input = line.split('!') - if l_input=='null': - topofile = [] - - else: - topofile = l_input[0].rstrip() - - return mshfile, obsfile, modfile, magfile, topofile \ No newline at end of file diff --git a/simpegPF/Dev/FWR_data.dat b/simpegPF/Dev/FWR_data.dat deleted file mode 100644 index 40994f4a..00000000 --- a/simpegPF/Dev/FWR_data.dat +++ /dev/null @@ -1,628 +0,0 @@ - 45.00 315.00 50000.00 --45.00 135.00 1.00 -625 --5.000000e-01 -5.000000e-01 5.500000e-01 1.659610e+02 0.000000e+00 --5.000000e-01 -4.583330e-01 5.500000e-01 1.925379e+02 0.000000e+00 --5.000000e-01 -4.166670e-01 5.500000e-01 2.226649e+02 0.000000e+00 --5.000000e-01 -3.750000e-01 5.500000e-01 2.563686e+02 0.000000e+00 --5.000000e-01 -3.333330e-01 5.500000e-01 2.934733e+02 0.000000e+00 --5.000000e-01 -2.916670e-01 5.500000e-01 3.335430e+02 0.000000e+00 --5.000000e-01 -2.500000e-01 5.500000e-01 3.758360e+02 0.000000e+00 --5.000000e-01 -2.083330e-01 5.500000e-01 4.192739e+02 0.000000e+00 --5.000000e-01 -1.666670e-01 5.500000e-01 4.624569e+02 0.000000e+00 --5.000000e-01 -1.250000e-01 5.500000e-01 5.037297e+02 0.000000e+00 --5.000000e-01 -8.333330e-02 5.500000e-01 5.412899e+02 0.000000e+00 --5.000000e-01 -4.166670e-02 5.500000e-01 5.733527e+02 0.000000e+00 --5.000000e-01 0.000000e+00 5.500000e-01 5.983312e+02 0.000000e+00 --5.000000e-01 4.166670e-02 5.500000e-01 6.150115e+02 0.000000e+00 --5.000000e-01 8.333330e-02 5.500000e-01 6.226869e+02 0.000000e+00 --5.000000e-01 1.250000e-01 5.500000e-01 6.212256e+02 0.000000e+00 --5.000000e-01 1.666670e-01 5.500000e-01 6.110607e+02 0.000000e+00 --5.000000e-01 2.083330e-01 5.500000e-01 5.931098e+02 0.000000e+00 --5.000000e-01 2.500000e-01 5.500000e-01 5.686407e+02 0.000000e+00 --5.000000e-01 2.916670e-01 5.500000e-01 5.391186e+02 0.000000e+00 --5.000000e-01 3.333330e-01 5.500000e-01 5.060557e+02 0.000000e+00 --5.000000e-01 3.750000e-01 5.500000e-01 4.708825e+02 0.000000e+00 --5.000000e-01 4.166670e-01 5.500000e-01 4.348657e+02 0.000000e+00 --5.000000e-01 4.583330e-01 5.500000e-01 3.990553e+02 0.000000e+00 --5.000000e-01 5.000000e-01 5.500000e-01 3.642665e+02 0.000000e+00 --4.583330e-01 -5.000000e-01 5.500000e-01 1.399730e+02 0.000000e+00 --4.583330e-01 -4.583330e-01 5.500000e-01 1.662941e+02 0.000000e+00 --4.583330e-01 -4.166670e-01 5.500000e-01 1.969125e+02 0.000000e+00 --4.583330e-01 -3.750000e-01 5.500000e-01 2.320584e+02 0.000000e+00 --4.583330e-01 -3.333330e-01 5.500000e-01 2.717511e+02 0.000000e+00 --4.583330e-01 -2.916670e-01 5.500000e-01 3.157134e+02 0.000000e+00 --4.583330e-01 -2.500000e-01 5.500000e-01 3.632921e+02 0.000000e+00 --4.583330e-01 -2.083330e-01 5.500000e-01 4.133901e+02 0.000000e+00 --4.583330e-01 -1.666670e-01 5.500000e-01 4.644542e+02 0.000000e+00 --4.583330e-01 -1.250000e-01 5.500000e-01 5.145281e+02 0.000000e+00 --4.583330e-01 -8.333330e-02 5.500000e-01 5.613715e+02 0.000000e+00 --4.583330e-01 -4.166670e-02 5.500000e-01 6.026655e+02 0.000000e+00 --4.583330e-01 0.000000e+00 5.500000e-01 6.362552e+02 0.000000e+00 --4.583330e-01 4.166670e-02 5.500000e-01 6.604000e+02 0.000000e+00 --4.583330e-01 8.333330e-02 5.500000e-01 6.739783e+02 0.000000e+00 --4.583330e-01 1.250000e-01 5.500000e-01 6.766057e+02 0.000000e+00 --4.583330e-01 1.666670e-01 5.500000e-01 6.686444e+02 0.000000e+00 --4.583330e-01 2.083330e-01 5.500000e-01 6.511099e+02 0.000000e+00 --4.583330e-01 2.500000e-01 5.500000e-01 6.254994e+02 0.000000e+00 --4.583330e-01 2.916670e-01 5.500000e-01 5.935890e+02 0.000000e+00 --4.583330e-01 3.333330e-01 5.500000e-01 5.572309e+02 0.000000e+00 --4.583330e-01 3.750000e-01 5.500000e-01 5.181837e+02 0.000000e+00 --4.583330e-01 4.166670e-01 5.500000e-01 4.780006e+02 0.000000e+00 --4.583330e-01 4.583330e-01 5.500000e-01 4.379623e+02 0.000000e+00 --4.583330e-01 5.000000e-01 5.500000e-01 3.990553e+02 0.000000e+00 --4.166670e-01 -5.000000e-01 5.500000e-01 1.038361e+02 0.000000e+00 --4.166670e-01 -4.583330e-01 5.500000e-01 1.284713e+02 0.000000e+00 --4.166670e-01 -4.166670e-01 5.500000e-01 1.581213e+02 0.000000e+00 --4.166670e-01 -3.750000e-01 5.500000e-01 1.932949e+02 0.000000e+00 --4.166670e-01 -3.333330e-01 5.500000e-01 2.343043e+02 0.000000e+00 --4.166670e-01 -2.916670e-01 5.500000e-01 2.811460e+02 0.000000e+00 --4.166670e-01 -2.500000e-01 5.500000e-01 3.333759e+02 0.000000e+00 --4.166670e-01 -2.083330e-01 5.500000e-01 3.899878e+02 0.000000e+00 --4.166670e-01 -1.666670e-01 5.500000e-01 4.493508e+02 0.000000e+00 --4.166670e-01 -1.250000e-01 5.500000e-01 5.092308e+02 0.000000e+00 --4.166670e-01 -8.333330e-02 5.500000e-01 5.669068e+02 0.000000e+00 --4.166670e-01 -4.166670e-02 5.500000e-01 6.194160e+02 0.000000e+00 --4.166670e-01 0.000000e+00 5.500000e-01 6.638734e+02 0.000000e+00 --4.166670e-01 4.166670e-02 5.500000e-01 6.978235e+02 0.000000e+00 --4.166670e-01 8.333330e-02 5.500000e-01 7.195477e+02 0.000000e+00 --4.166670e-01 1.250000e-01 5.500000e-01 7.282611e+02 0.000000e+00 --4.166670e-01 1.666670e-01 5.500000e-01 7.241600e+02 0.000000e+00 --4.166670e-01 2.083330e-01 5.500000e-01 7.083199e+02 0.000000e+00 --4.166670e-01 2.500000e-01 5.500000e-01 6.824792e+02 0.000000e+00 --4.166670e-01 2.916670e-01 5.500000e-01 6.487701e+02 0.000000e+00 --4.166670e-01 3.333330e-01 5.500000e-01 6.094492e+02 0.000000e+00 --4.166670e-01 3.750000e-01 5.500000e-01 5.666701e+02 0.000000e+00 --4.166670e-01 4.166670e-01 5.500000e-01 5.223335e+02 0.000000e+00 --4.166670e-01 4.583330e-01 5.500000e-01 4.780006e+02 0.000000e+00 --4.166670e-01 5.000000e-01 5.500000e-01 4.348657e+02 0.000000e+00 --3.750000e-01 -5.000000e-01 5.500000e-01 5.615031e+01 0.000000e+00 --3.750000e-01 -4.583330e-01 5.500000e-01 7.722713e+01 0.000000e+00 --3.750000e-01 -4.166670e-01 5.500000e-01 1.039189e+02 0.000000e+00 --3.750000e-01 -3.750000e-01 5.500000e-01 1.370930e+02 0.000000e+00 --3.750000e-01 -3.333330e-01 5.500000e-01 1.774689e+02 0.000000e+00 --3.750000e-01 -2.916670e-01 5.500000e-01 2.254638e+02 0.000000e+00 --3.750000e-01 -2.500000e-01 5.500000e-01 2.810098e+02 0.000000e+00 --3.750000e-01 -2.083330e-01 5.500000e-01 3.433596e+02 0.000000e+00 --3.750000e-01 -1.666670e-01 5.500000e-01 4.109431e+02 0.000000e+00 --3.750000e-01 -1.250000e-01 5.500000e-01 4.813258e+02 0.000000e+00 --3.750000e-01 -8.333330e-02 5.500000e-01 5.512986e+02 0.000000e+00 --3.750000e-01 -4.166670e-02 5.500000e-01 6.171524e+02 0.000000e+00 --3.750000e-01 0.000000e+00 5.500000e-01 6.750879e+02 0.000000e+00 --3.750000e-01 4.166670e-02 5.500000e-01 7.216991e+02 0.000000e+00 --3.750000e-01 8.333330e-02 5.500000e-01 7.544267e+02 0.000000e+00 --3.750000e-01 1.250000e-01 5.500000e-01 7.718762e+02 0.000000e+00 --3.750000e-01 1.666670e-01 5.500000e-01 7.739304e+02 0.000000e+00 --3.750000e-01 2.083330e-01 5.500000e-01 7.616511e+02 0.000000e+00 --3.750000e-01 2.500000e-01 5.500000e-01 7.370097e+02 0.000000e+00 --3.750000e-01 2.916670e-01 5.500000e-01 7.025352e+02 0.000000e+00 --3.750000e-01 3.333330e-01 5.500000e-01 6.609554e+02 0.000000e+00 --3.750000e-01 3.750000e-01 5.500000e-01 6.148941e+02 0.000000e+00 --3.750000e-01 4.166670e-01 5.500000e-01 5.666701e+02 0.000000e+00 --3.750000e-01 4.583330e-01 5.500000e-01 5.181837e+02 0.000000e+00 --3.750000e-01 5.000000e-01 5.500000e-01 4.708825e+02 0.000000e+00 --3.333330e-01 -5.000000e-01 5.500000e-01 -4.169920e+00 0.000000e+00 --3.333330e-01 -4.583330e-01 5.500000e-01 1.103644e+01 0.000000e+00 --3.333330e-01 -4.166670e-01 5.500000e-01 3.222871e+01 0.000000e+00 --3.333330e-01 -3.750000e-01 5.500000e-01 6.070809e+01 0.000000e+00 --3.333330e-01 -3.333330e-01 5.500000e-01 9.772515e+01 0.000000e+00 --3.333330e-01 -2.916670e-01 5.500000e-01 1.442910e+02 0.000000e+00 --3.333330e-01 -2.500000e-01 5.500000e-01 2.009307e+02 0.000000e+00 --3.333330e-01 -2.083330e-01 5.500000e-01 2.673914e+02 0.000000e+00 --3.333330e-01 -1.666670e-01 5.500000e-01 3.423827e+02 0.000000e+00 --3.333330e-01 -1.250000e-01 5.500000e-01 4.234266e+02 0.000000e+00 --3.333330e-01 -8.333330e-02 5.500000e-01 5.068778e+02 0.000000e+00 --3.333330e-01 -4.166670e-02 5.500000e-01 5.882076e+02 0.000000e+00 --3.333330e-01 0.000000e+00 5.500000e-01 6.625068e+02 0.000000e+00 --3.333330e-01 4.166670e-02 5.500000e-01 7.251376e+02 0.000000e+00 --3.333330e-01 8.333330e-02 5.500000e-01 7.723872e+02 0.000000e+00 --3.333330e-01 1.250000e-01 5.500000e-01 8.019647e+02 0.000000e+00 --3.333330e-01 1.666670e-01 5.500000e-01 8.132252e+02 0.000000e+00 --3.333330e-01 2.083330e-01 5.500000e-01 8.070913e+02 0.000000e+00 --3.333330e-01 2.500000e-01 5.500000e-01 7.857293e+02 0.000000e+00 --3.333330e-01 2.916670e-01 5.500000e-01 7.520911e+02 0.000000e+00 --3.333330e-01 3.333330e-01 5.500000e-01 7.094415e+02 0.000000e+00 --3.333330e-01 3.750000e-01 5.500000e-01 6.609554e+02 0.000000e+00 --3.333330e-01 4.166670e-01 5.500000e-01 6.094492e+02 0.000000e+00 --3.333330e-01 4.583330e-01 5.500000e-01 5.572309e+02 0.000000e+00 --3.333330e-01 5.000000e-01 5.500000e-01 5.060557e+02 0.000000e+00 --2.916670e-01 -5.000000e-01 5.500000e-01 -7.767438e+01 0.000000e+00 --2.916670e-01 -4.583330e-01 5.500000e-01 -7.102407e+01 0.000000e+00 --2.916670e-01 -4.166670e-01 5.500000e-01 -5.837249e+01 0.000000e+00 --2.916670e-01 -3.750000e-01 5.500000e-01 -3.792517e+01 0.000000e+00 --2.916670e-01 -3.333330e-01 5.500000e-01 -7.781858e+00 0.000000e+00 --2.916670e-01 -2.916670e-01 5.500000e-01 3.385111e+01 0.000000e+00 --2.916670e-01 -2.500000e-01 5.500000e-01 8.834796e+01 0.000000e+00 --2.916670e-01 -2.083330e-01 5.500000e-01 1.562561e+02 0.000000e+00 --2.916670e-01 -1.666670e-01 5.500000e-01 2.368767e+02 0.000000e+00 --2.916670e-01 -1.250000e-01 5.500000e-01 3.279459e+02 0.000000e+00 --2.916670e-01 -8.333330e-02 5.500000e-01 4.255242e+02 0.000000e+00 --2.916670e-01 -4.166670e-02 5.500000e-01 5.242448e+02 0.000000e+00 --2.916670e-01 0.000000e+00 5.500000e-01 6.179042e+02 0.000000e+00 --2.916670e-01 4.166670e-02 5.500000e-01 7.003158e+02 0.000000e+00 --2.916670e-01 8.333330e-02 5.500000e-01 7.662304e+02 0.000000e+00 --2.916670e-01 1.250000e-01 5.500000e-01 8.120893e+02 0.000000e+00 --2.916670e-01 1.666670e-01 5.500000e-01 8.364217e+02 0.000000e+00 --2.916670e-01 2.083330e-01 5.500000e-01 8.398215e+02 0.000000e+00 --2.916670e-01 2.500000e-01 5.500000e-01 8.245677e+02 0.000000e+00 --2.916670e-01 2.916670e-01 5.500000e-01 7.940381e+02 0.000000e+00 --2.916670e-01 3.333330e-01 5.500000e-01 7.520911e+02 0.000000e+00 --2.916670e-01 3.750000e-01 5.500000e-01 7.025352e+02 0.000000e+00 --2.916670e-01 4.166670e-01 5.500000e-01 6.487701e+02 0.000000e+00 --2.916670e-01 4.583330e-01 5.500000e-01 5.935890e+02 0.000000e+00 --2.916670e-01 5.000000e-01 5.500000e-01 5.391186e+02 0.000000e+00 --2.500000e-01 -5.000000e-01 5.500000e-01 -1.641466e+02 0.000000e+00 --2.500000e-01 -4.583330e-01 5.500000e-01 -1.689640e+02 0.000000e+00 --2.500000e-01 -4.166670e-01 5.500000e-01 -1.682414e+02 0.000000e+00 --2.500000e-01 -3.750000e-01 5.500000e-01 -1.596604e+02 0.000000e+00 --2.500000e-01 -3.333330e-01 5.500000e-01 -1.405745e+02 0.000000e+00 --2.500000e-01 -2.916670e-01 5.500000e-01 -1.082258e+02 0.000000e+00 --2.500000e-01 -2.500000e-01 5.500000e-01 -6.011465e+01 0.000000e+00 --2.500000e-01 -2.083330e-01 5.500000e-01 5.466449e+00 0.000000e+00 --2.500000e-01 -1.666670e-01 5.500000e-01 8.881283e+01 0.000000e+00 --2.500000e-01 -1.250000e-01 5.500000e-01 1.882470e+02 0.000000e+00 --2.500000e-01 -8.333330e-02 5.500000e-01 2.997921e+02 0.000000e+00 --2.500000e-01 -4.166670e-02 5.500000e-01 4.173135e+02 0.000000e+00 --2.500000e-01 0.000000e+00 5.500000e-01 5.331703e+02 0.000000e+00 --2.500000e-01 4.166670e-02 5.500000e-01 6.392994e+02 0.000000e+00 --2.500000e-01 8.333330e-02 5.500000e-01 7.284767e+02 0.000000e+00 --2.500000e-01 1.250000e-01 5.500000e-01 7.954200e+02 0.000000e+00 --2.500000e-01 1.666670e-01 5.500000e-01 8.374473e+02 0.000000e+00 --2.500000e-01 2.083330e-01 5.500000e-01 8.545583e+02 0.000000e+00 --2.500000e-01 2.500000e-01 5.500000e-01 8.490077e+02 0.000000e+00 --2.500000e-01 2.916670e-01 5.500000e-01 8.245677e+02 0.000000e+00 --2.500000e-01 3.333330e-01 5.500000e-01 7.857293e+02 0.000000e+00 --2.500000e-01 3.750000e-01 5.500000e-01 7.370097e+02 0.000000e+00 --2.500000e-01 4.166670e-01 5.500000e-01 6.824792e+02 0.000000e+00 --2.500000e-01 4.583330e-01 5.500000e-01 6.254994e+02 0.000000e+00 --2.500000e-01 5.000000e-01 5.500000e-01 5.686407e+02 0.000000e+00 --2.083330e-01 -5.000000e-01 5.500000e-01 -2.623794e+02 0.000000e+00 --2.083330e-01 -4.583330e-01 5.500000e-01 -2.815633e+02 0.000000e+00 --2.083330e-01 -4.166670e-01 5.500000e-01 -2.962328e+02 0.000000e+00 --2.083330e-01 -3.750000e-01 5.500000e-01 -3.035530e+02 0.000000e+00 --2.083330e-01 -3.333330e-01 5.500000e-01 -3.000746e+02 0.000000e+00 --2.083330e-01 -2.916670e-01 5.500000e-01 -2.819276e+02 0.000000e+00 --2.083330e-01 -2.500000e-01 5.500000e-01 -2.452270e+02 0.000000e+00 --2.083330e-01 -2.083330e-01 5.500000e-01 -1.867276e+02 0.000000e+00 --2.083330e-01 -1.666670e-01 5.500000e-01 -1.046762e+02 0.000000e+00 --2.083330e-01 -1.250000e-01 5.500000e-01 3.133342e-01 0.000000e+00 --2.083330e-01 -8.333330e-02 5.500000e-01 1.246108e+02 0.000000e+00 --2.083330e-01 -4.166670e-02 5.500000e-01 2.615022e+02 0.000000e+00 --2.083330e-01 0.000000e+00 5.500000e-01 4.018488e+02 0.000000e+00 --2.083330e-01 4.166670e-02 5.500000e-01 5.354153e+02 0.000000e+00 --2.083330e-01 8.333330e-02 5.500000e-01 6.525430e+02 0.000000e+00 --2.083330e-01 1.250000e-01 5.500000e-01 7.457119e+02 0.000000e+00 --2.083330e-01 1.666670e-01 5.500000e-01 8.105656e+02 0.000000e+00 --2.083330e-01 2.083330e-01 5.500000e-01 8.461699e+02 0.000000e+00 --2.083330e-01 2.500000e-01 5.500000e-01 8.545583e+02 0.000000e+00 --2.083330e-01 2.916670e-01 5.500000e-01 8.398215e+02 0.000000e+00 --2.083330e-01 3.333330e-01 5.500000e-01 8.070913e+02 0.000000e+00 --2.083330e-01 3.750000e-01 5.500000e-01 7.616511e+02 0.000000e+00 --2.083330e-01 4.166670e-01 5.500000e-01 7.083199e+02 0.000000e+00 --2.083330e-01 4.583330e-01 5.500000e-01 6.511099e+02 0.000000e+00 --2.083330e-01 5.000000e-01 5.500000e-01 5.931098e+02 0.000000e+00 --1.666670e-01 -5.000000e-01 5.500000e-01 -3.700392e+02 0.000000e+00 --1.666670e-01 -4.583330e-01 5.500000e-01 -4.061587e+02 0.000000e+00 --1.666670e-01 -4.166670e-01 5.500000e-01 -4.393757e+02 0.000000e+00 --1.666670e-01 -3.750000e-01 5.500000e-01 -4.663861e+02 0.000000e+00 --1.666670e-01 -3.333330e-01 5.500000e-01 -4.829339e+02 0.000000e+00 --1.666670e-01 -2.916670e-01 5.500000e-01 -4.839507e+02 0.000000e+00 --1.666670e-01 -2.500000e-01 5.500000e-01 -4.639656e+02 0.000000e+00 --1.666670e-01 -2.083330e-01 5.500000e-01 -4.178600e+02 0.000000e+00 --1.666670e-01 -1.666670e-01 5.500000e-01 -3.419595e+02 0.000000e+00 --1.666670e-01 -1.250000e-01 5.500000e-01 -2.352828e+02 0.000000e+00 --1.666670e-01 -8.333330e-02 5.500000e-01 -1.006223e+02 0.000000e+00 --1.666670e-01 -4.166670e-02 5.500000e-01 5.505002e+01 0.000000e+00 --1.666670e-01 0.000000e+00 5.500000e-01 2.211632e+02 0.000000e+00 --1.666670e-01 4.166670e-02 5.500000e-01 3.851058e+02 0.000000e+00 --1.666670e-01 8.333330e-02 5.500000e-01 5.343693e+02 0.000000e+00 --1.666670e-01 1.250000e-01 5.500000e-01 6.586763e+02 0.000000e+00 --1.666670e-01 1.666670e-01 5.500000e-01 7.514953e+02 0.000000e+00 --1.666670e-01 2.083330e-01 5.500000e-01 8.105656e+02 0.000000e+00 --1.666670e-01 2.500000e-01 5.500000e-01 8.374473e+02 0.000000e+00 --1.666670e-01 2.916670e-01 5.500000e-01 8.364217e+02 0.000000e+00 --1.666670e-01 3.333330e-01 5.500000e-01 8.132252e+02 0.000000e+00 --1.666670e-01 3.750000e-01 5.500000e-01 7.739304e+02 0.000000e+00 --1.666670e-01 4.166670e-01 5.500000e-01 7.241600e+02 0.000000e+00 --1.666670e-01 4.583330e-01 5.500000e-01 6.686444e+02 0.000000e+00 --1.666670e-01 5.000000e-01 5.500000e-01 6.110607e+02 0.000000e+00 --1.250000e-01 -5.000000e-01 5.500000e-01 -4.836628e+02 0.000000e+00 --1.250000e-01 -4.583330e-01 5.500000e-01 -5.386001e+02 0.000000e+00 --1.250000e-01 -4.166670e-01 5.500000e-01 -5.927629e+02 0.000000e+00 --1.250000e-01 -3.750000e-01 5.500000e-01 -6.424575e+02 0.000000e+00 --1.250000e-01 -3.333330e-01 5.500000e-01 -6.826713e+02 0.000000e+00 --1.250000e-01 -2.916670e-01 5.500000e-01 -7.071303e+02 0.000000e+00 --1.250000e-01 -2.500000e-01 5.500000e-01 -7.086786e+02 0.000000e+00 --1.250000e-01 -2.083330e-01 5.500000e-01 -6.800979e+02 0.000000e+00 --1.250000e-01 -1.666670e-01 5.500000e-01 -6.154219e+02 0.000000e+00 --1.250000e-01 -1.250000e-01 5.500000e-01 -5.115825e+02 0.000000e+00 --1.250000e-01 -8.333330e-02 5.500000e-01 -3.700087e+02 0.000000e+00 --1.250000e-01 -4.166670e-02 5.500000e-01 -1.975002e+02 0.000000e+00 --1.250000e-01 0.000000e+00 5.500000e-01 -5.840902e+00 0.000000e+00 --1.250000e-01 4.166670e-02 5.500000e-01 1.899420e+02 0.000000e+00 --1.250000e-01 8.333330e-02 5.500000e-01 3.742149e+02 0.000000e+00 --1.250000e-01 1.250000e-01 5.500000e-01 5.335157e+02 0.000000e+00 --1.250000e-01 1.666670e-01 5.500000e-01 6.586763e+02 0.000000e+00 --1.250000e-01 2.083330e-01 5.500000e-01 7.457119e+02 0.000000e+00 --1.250000e-01 2.500000e-01 5.500000e-01 7.954200e+02 0.000000e+00 --1.250000e-01 2.916670e-01 5.500000e-01 8.120893e+02 0.000000e+00 --1.250000e-01 3.333330e-01 5.500000e-01 8.019647e+02 0.000000e+00 --1.250000e-01 3.750000e-01 5.500000e-01 7.718762e+02 0.000000e+00 --1.250000e-01 4.166670e-01 5.500000e-01 7.282611e+02 0.000000e+00 --1.250000e-01 4.583330e-01 5.500000e-01 6.766057e+02 0.000000e+00 --1.250000e-01 5.000000e-01 5.500000e-01 6.212256e+02 0.000000e+00 --8.333330e-02 -5.000000e-01 5.500000e-01 -5.987970e+02 0.000000e+00 --8.333330e-02 -4.583330e-01 5.500000e-01 -6.734053e+02 0.000000e+00 --8.333330e-02 -4.166670e-01 5.500000e-01 -7.497117e+02 0.000000e+00 --8.333330e-02 -3.750000e-01 5.500000e-01 -8.237273e+02 0.000000e+00 --8.333330e-02 -3.333330e-01 5.500000e-01 -8.897772e+02 0.000000e+00 --8.333330e-02 -2.916670e-01 5.500000e-01 -9.404575e+02 0.000000e+00 --8.333330e-02 -2.500000e-01 5.500000e-01 -9.669552e+02 0.000000e+00 --8.333330e-02 -2.083330e-01 5.500000e-01 -9.598952e+02 0.000000e+00 --8.333330e-02 -1.666670e-01 5.500000e-01 -9.108369e+02 0.000000e+00 --8.333330e-02 -1.250000e-01 5.500000e-01 -8.143000e+02 0.000000e+00 --8.333330e-02 -8.333330e-02 5.500000e-01 -6.699127e+02 0.000000e+00 --8.333330e-02 -4.166670e-02 5.500000e-01 -4.838447e+02 0.000000e+00 --8.333330e-02 0.000000e+00 5.500000e-01 -2.687263e+02 0.000000e+00 --8.333330e-02 4.166670e-02 5.500000e-01 -4.181249e+01 0.000000e+00 --8.333330e-02 8.333330e-02 5.500000e-01 1.781171e+02 0.000000e+00 --8.333330e-02 1.250000e-01 5.500000e-01 3.742149e+02 0.000000e+00 --8.333330e-02 1.666670e-01 5.500000e-01 5.343693e+02 0.000000e+00 --8.333330e-02 2.083330e-01 5.500000e-01 6.525430e+02 0.000000e+00 --8.333330e-02 2.500000e-01 5.500000e-01 7.284767e+02 0.000000e+00 --8.333330e-02 2.916670e-01 5.500000e-01 7.662304e+02 0.000000e+00 --8.333330e-02 3.333330e-01 5.500000e-01 7.723872e+02 0.000000e+00 --8.333330e-02 3.750000e-01 5.500000e-01 7.544267e+02 0.000000e+00 --8.333330e-02 4.166670e-01 5.500000e-01 7.195477e+02 0.000000e+00 --8.333330e-02 4.583330e-01 5.500000e-01 6.739783e+02 0.000000e+00 --8.333330e-02 5.000000e-01 5.500000e-01 6.226869e+02 0.000000e+00 --4.166670e-02 -5.000000e-01 5.500000e-01 -7.103443e+02 0.000000e+00 --4.166670e-02 -4.583330e-01 5.500000e-01 -8.041995e+02 0.000000e+00 --4.166670e-02 -4.166670e-01 5.500000e-01 -9.023120e+02 0.000000e+00 --4.166670e-02 -3.750000e-01 5.500000e-01 -1.000488e+03 0.000000e+00 --4.166670e-02 -3.333330e-01 5.500000e-01 -1.092510e+03 0.000000e+00 --4.166670e-02 -2.916670e-01 5.500000e-01 -1.169994e+03 0.000000e+00 --4.166670e-02 -2.500000e-01 5.500000e-01 -1.222630e+03 0.000000e+00 --4.166670e-02 -2.083330e-01 5.500000e-01 -1.239032e+03 0.000000e+00 --4.166670e-02 -1.666670e-01 5.500000e-01 -1.208360e+03 0.000000e+00 --4.166670e-02 -1.250000e-01 5.500000e-01 -1.122677e+03 0.000000e+00 --4.166670e-02 -8.333330e-02 5.500000e-01 -9.795960e+02 0.000000e+00 --4.166670e-02 -4.166670e-02 5.500000e-01 -7.842856e+02 0.000000e+00 --4.166670e-02 0.000000e+00 5.500000e-01 -5.497648e+02 0.000000e+00 --4.166670e-02 4.166670e-02 5.500000e-01 -2.950744e+02 0.000000e+00 --4.166670e-02 8.333330e-02 5.500000e-01 -4.181249e+01 0.000000e+00 --4.166670e-02 1.250000e-01 5.500000e-01 1.899420e+02 0.000000e+00 --4.166670e-02 1.666670e-01 5.500000e-01 3.851058e+02 0.000000e+00 --4.166670e-02 2.083330e-01 5.500000e-01 5.354153e+02 0.000000e+00 --4.166670e-02 2.500000e-01 5.500000e-01 6.392994e+02 0.000000e+00 --4.166670e-02 2.916670e-01 5.500000e-01 7.003158e+02 0.000000e+00 --4.166670e-02 3.333330e-01 5.500000e-01 7.251376e+02 0.000000e+00 --4.166670e-02 3.750000e-01 5.500000e-01 7.216991e+02 0.000000e+00 --4.166670e-02 4.166670e-01 5.500000e-01 6.978235e+02 0.000000e+00 --4.166670e-02 4.583330e-01 5.500000e-01 6.604000e+02 0.000000e+00 --4.166670e-02 5.000000e-01 5.500000e-01 6.150115e+02 0.000000e+00 -0.000000e+00 -5.000000e-01 5.500000e-01 -8.130400e+02 0.000000e+00 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0.01331717 0.002 + 225 365 259.0291 0.0135785 0.002 + 235 365 259.6237 0.0137874 0.002 + 245 365 259.9228 0.01396726 0.002 + 255 365 259.9228 0.01412991 0.002 + 265 365 259.6237 0.01427331 0.002 + 275 365 259.0291 0.01438129 0.002 + 285 365 258.1461 0.01442539 0.002 + 295 365 256.985 0.01436882 0.002 + 305 365 255.5596 0.01417239 0.002 + 315 365 253.8864 0.01380193 0.002 + 325 365 251.9843 0.01323605 0.002 + 335 365 249.8748 0.01247224 0.002 + 345 365 247.5808 0.01152964 0.002 + 355 365 245.127 0.01044711 0.002 + 365 365 242.5387 0.009277147 0.002 + 375 365 239.8421 0.008077218 0.002 diff --git a/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver.py b/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver.py new file mode 100644 index 00000000..a6d9399d --- /dev/null +++ b/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver.py @@ -0,0 +1,275 @@ +#%% +from SimPEG import * +import simpegPF as PF +import pylab as plt +import os +home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\CraigModel\\GRAV\\checkerboard_tests' +inpfile = 'PYGRAV3D_inv.inp' +dsep = '\\' +os.chdir(home_dir) +plt.close('all') + +#%% User input +# Initial beta +beta_in = 1e-2 + +# Treshold values for compact norm +eps_p = 1e-3 # Small model values +eps_q = 1e-3 # Small model gradient + +# Plotting parameter +vmin = -0.3 +vmax = 0.3 +#%% +# Read input file +[mshfile, obsfile, topofile, mstart, mref, wgtfile, chi, alphas, bounds, lpnorms] = PF.Gravity.read_GRAVinv_inp(home_dir + dsep + inpfile) + +# Load mesh file +mesh = Mesh.TensorMesh.readUBC(mshfile) + +# Load in observation file +survey = PF.Gravity.readUBCgravObs(obsfile) + +# Get obs location and data +rxLoc = survey.srcField.rxList[0].locs +d = survey.dobs +wd = survey.std + +ndata = survey.srcField.rxList[0].locs.shape[0] + +# Load in topofile or create flat surface +if topofile == 'null': + + # All active + actv = np.asarray(range(mesh.nC)) + +else: + + topo = np.genfromtxt(topofile,skip_header=1) + # Find the active cells + actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + +nC = len(actv) + +# Create active map to go from reduce set to full +actvMap = Maps.InjectActiveCells(mesh, actv, -100) + +# Creat reduced identity map +idenMap = Maps.IdentityMap(nP = nC) + +# Load starting model file +if isinstance(mstart, float): + + mstart = np.ones(nC) * mstart +else: + mstart = Mesh.TensorMesh.readModelUBC(mesh,mstart) + mstart = mstart[actv] + +# Load reference file +if isinstance(mref, float): + mref = np.ones(nC) * mref +else: + mref = Mesh.TensorMesh.readModelUBC(mesh,mref) + mref = mref[actv] + +# Get index of the center for plotting +midx = int(mesh.nCx/2) +midy = int(mesh.nCy/2) + + +#%% Plot obs data +PF.Gravity.plot_obs_2D(survey,'Observed Data') + +#%% Run inversion +prob = PF.Gravity.GravityIntegral(mesh, mapping = idenMap, actInd = actv) +prob.solverOpts['accuracyTol'] = 1e-4 + +survey.pair(prob) + +# Write out the predicted file and generate the forward operator +pred = prob.fields(mstart) + +PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred0.dat',survey,pred) + +# +# Make depth weighting +#wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv] +#wr = ( wr/np.max(wr) ) +#wr_out = actvMap * wr + +# Load weighting file +if wgtfile is None: + wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, actv, 3., np.min(mesh.hx)/4.) + wr = wr**2. +else: + wr = Mesh.TensorMesh.readModelUBC(mesh, home_dir + dsep + wgtfile) + wr = wr[actv] + wr = wr**2. + +#%% Plot depth weighting +plt.figure() +ax = plt.subplot() +mesh.plotSlice(actvMap*wr, ax = ax, normal = 'Y', ind=midx+1 ,clim = (0, wr.max())) +plt.title('Distance weighting') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +#%% Create inversion objects + +# First start with an l2 regularization +reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap) +reg.mref = mref +reg.wght = wr + + +# Create pre-conditioner +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() +PC = Utils.sdiag(diagA**-1.) + +# Data misfit function +dmis = DataMisfit.l2_DataMisfit(survey) +dmis.Wd = 1./wd +opt = Optimization.ProjectedGNCG(maxIter=20,lower=bounds[0],upper=bounds[1], maxIterCG= 50, tolCG = 1e-4) +opt.approxHinv = PC + + +invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in) +beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1) +target = Directives.TargetMisfit() + +inv = Inversion.BaseInversion(invProb, directiveList=[beta,target]) + +m0 = mstart + +# Run inversion +mrec = inv.run(m0) + +m_out = actvMap*mrec + +# Write result +Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l2l2.sus',m_out) +#Utils.meshutils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr_out) + +# Plot predicted +pred = prob.fields(mrec) +#PF.Magnetics.plot_obs_2D(rxLoc,pred,wd,'Predicted Data') +#PF.Magnetics.plot_obs_2D(rxLoc,(d-pred),wd,'Residual Data') + +print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) + +#%% Plot out sections of the smooth model + +yslice = midx+1 +m_out[m_out==-100] = np.nan + +plt.figure() +ax = plt.subplot(221) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (vmin,vmax), pcolorOpts = {'cmap':'bwr'}) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +ax = plt.subplot(222) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (vmin,vmax), pcolorOpts = {'cmap':'bwr'}) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +ax = plt.subplot(212) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax), pcolorOpts = {'cmap':'bwr'}) +plt.title('Cross Section') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +plt.figure() +ax = plt.subplot(121) +plt.hist(mrec,100) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model values - Smooth') +ax = plt.subplot(122) +plt.hist(reg.regmesh.cellDiffxStencil*mrec,100) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model gradient values - Smooth') +#%% Run one more round for sparsity +phim = invProb.phi_m_last +phid = invProb.phi_d + +reg = Regularization.Sparse(mesh, indActive = actv, mapping = idenMap) +reg.recModel = mrec +reg.mref = mref +reg.wght = wr +reg.eps_p = eps_p +reg.eps_q = eps_q +reg.norms = lpnorms + +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() +PC = Utils.sdiag(diagA**-1.) + +#reg.alpha_s = 1. + +dmis = DataMisfit.l2_DataMisfit(survey) +dmis.Wd = 1./wd +opt = Optimization.ProjectedGNCG(maxIter=20 , maxIterLS = 20,lower=bounds[0],upper=bounds[1], maxIterCG= 50, tolCG = 1e-4) +opt.approxHinv = PC +#opt.phim_last = reg.eval(mrec) + +# opt = Optimization.InexactGaussNewton(maxIter=6) +invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta) +beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1) +#betaest = Directives.BetaEstimate_ByEig() +target = Directives.TargetMisfit() +IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid ) + +inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS]) + +m0 = mrec + +# Run inversion +mrec = inv.run(m0) + +m_out = actvMap*mrec + +Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l0l2.sus',m_out) + +pred = prob.fields(mrec) + +#%% Plot obs data +PF.Magnetics.plot_obs_2D(rxLoc,pred,'Predicted Data', vmin = np.min(d), vmax = np.max(d)) +PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data') +print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) +#%% Plot out a section of the model + +yslice = midx +m_out[m_out==-100] = np.nan +plt.figure() +ax = plt.subplot(221) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-18, clim = (vmin,vmax) , pcolorOpts = {'cmap':'bwr'}) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.title('Z: ' + str(mesh.vectorCCz[-18]) + ' m') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +ax = plt.subplot(222) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-23, clim = (vmin,vmax), pcolorOpts = {'cmap':'bwr'}) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.title('Z: ' + str(mesh.vectorCCz[-23]) + ' m') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +ax = plt.subplot(212) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax), pcolorOpts = {'cmap':'bwr'}) +plt.title('Cross Section') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') + +plt.figure() +ax = plt.subplot(121) +plt.hist(mrec,100) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model values - Sparse lp:'+str(lpnorms[0])) +ax = plt.subplot(122) +plt.hist(reg.regmesh.cellDiffxStencil*mrec,100) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model gradient values - Sparse lqx: ' + str(lpnorms[1]) + ' lqy:'+ str(lpnorms[2]) + ' lqz:' + str(lpnorms[3])) \ No newline at end of file diff --git a/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver_LdM_Craig.py b/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver_LdM_Craig.py new file mode 100644 index 00000000..eba9dc4c --- /dev/null +++ b/simpegPF/Dev/GRAV/Intgrl_GRAV_Inv_Driver_LdM_Craig.py @@ -0,0 +1,323 @@ +#%% +from SimPEG import * +import simpegPF as PF +import pylab as plt +import os +import numpy as np + +#home_dir = 'C:\Egnyte\Private\craigm\PHD\LdM\Gravity\Bouguer\SIMPEG\models' +home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev\GRAV' +#inpfile = 'PYGRAV3D_inv_LdM_Craig.inp' +inpfile = 'PYGRAV3D_inv_checkerboard.inp' +dsep = '\\' +os.chdir(home_dir) +plt.close('all') + +#%% User input +# Initial beta +beta_in = 1e-2 + +# Treshold values for compact norm +eps_p = 0.01 # Compact model values +eps_q = 0.01 # ompact model gradient + +# Plotting parameter +vmin = -0.1 +vmax = 0.2 +#%% +# Read input file +[mshfile, obsfile, topofile, mstart, mref, wgtfile, chi, alphas, bounds, lpnorms] = PF.Gravity.read_GRAVinv_inp(home_dir + dsep + inpfile) + +# Load mesh file +mesh = Mesh.TensorMesh.readUBC(mshfile) + +# Load in observation file +survey = PF.Gravity.readUBCgravObs(obsfile) + +# Get obs location and data +rxLoc = survey.srcField.rxList[0].locs +d = survey.dobs +wd = survey.std + +ndata = survey.srcField.rxList[0].locs.shape[0] + +# Load in topofile or create flat surface +if topofile == 'null': + + # All active + actv = np.asarray(range(mesh.nC)) + +else: + + topo = np.genfromtxt(topofile,skip_header=1) + # Find the active cells + actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') + +nC = len(actv) + +# Create active map to go from reduce set to full +actvMap = Maps.InjectActiveCells(mesh, actv, -100) + +# Creat reduced identity map +idenMap = Maps.IdentityMap(nP = nC) + +# Load starting model file +if isinstance(mstart, float): + + mstart = np.ones(nC) * mstart +else: + mstart = Mesh.TensorMesh.readModelUBC(mesh,mstart) + mstart = mstart[actv] + +# Load reference file +if isinstance(mref, float): + mref = np.ones(nC) * mref +else: + mref = Mesh.TensorMesh.readModelUBC(mesh,mref) + mref = mref[actv] + +# Get index of the center for plotting +midx = int(mesh.nCx/2) +midy = int(mesh.nCy/2) + + +#%% Plot obs data +#PF.Gravity.plot_obs_2D(survey,'Observed Data') + +#%% Run inversion +prob = PF.Gravity.GravityIntegral(mesh, mapping = idenMap, actInd = actv) +prob.solverOpts['accuracyTol'] = 1e-4 + +survey.pair(prob) + +# Write out the predicted file and generate the forward operator +pred = prob.fields(mstart) + +PF.Gravity.writeUBCobs(home_dir + dsep + 'Pred0.dat',survey,pred) + +# Make depth weighting +#wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv] +#wr = ( wr/np.max(wr) ) +#wr_out = actvMap * wr + + +#A different weighting function from Dominic +#wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, actv, 2., np.min(mesh.hx)/4.) +#wr = wr**2. + + +# Load weighting file +if wgtfile is None: + wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, actv, 2., np.min(mesh.hx)/4.) + wr = wr**2. +else: + wr = Mesh.TensorMesh.readModelUBC(mesh, home_dir + dsep + wgtfile) + wr = wr[actv] + wr = wr**2. + + + +#%% Plot depth weighting +plt.figure() +ax = plt.subplot() +datwgt=mesh.plotSlice(actvMap*wr, ax = ax, normal = 'Y', ind=midx+1 ,clim = (-1e-1, wr.max())) +plt.title('Distance weighting') +plt.xlabel('x');plt.ylabel('z') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(datwgt[0],orientation="vertical") +cb.set_label('Weighting') +plt.savefig(home_dir + dsep + 'Weighting.png', dpi=300) + +#%% Create inversion objects + +print '\nRun smooth inversion \n' +# First start with an l2 (smooth model) regularization +reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap) +reg.mref = mref +reg.wght = wr + + + +# Create pre-conditioner +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() +PC = Utils.sdiag(diagA**-1.) + +# Data misfit function +dmis = DataMisfit.l2_DataMisfit(survey) +dmis.Wd = 1./wd +opt = Optimization.ProjectedGNCG(maxIter=20,lower=bounds[0],upper=bounds[1], maxIterCG= 20, tolCG = 1e-3) +opt.approxHinv = PC + + +invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in) +beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1) +target = Directives.TargetMisfit() + +inv = Inversion.BaseInversion(invProb, directiveList=[beta,target]) + +m0 = mstart + +# Run inversion +mrec = inv.run(m0) + +m_out = actvMap*mrec + +# Write result +Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l2l2.den',m_out) +#Utils.meshutils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr_out) + +# Plot predicted +pred = prob.fields(mrec) +#PF.Magnetics.plot_obs_2D(rxLoc,pred,wd,'Predicted Data') +#PF.Magnetics.plot_obs_2D(rxLoc,(d-pred),wd,'Residual Data') + +print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) + +#%% Plot out sections of the smooth model + +yslice = midx+1 + +plt.figure(figsize=(15,10)) +plt.suptitle('Smooth Inversion') +ax = plt.subplot(221) +dat1=mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (vmin,vmax)) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='w',s=1) +plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') +plt.xlabel('Easting (m)');plt.ylabel('Northing (m)') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') + +ax = plt.subplot(222) +dat = mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (vmin,vmax)) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='w',s=1) +plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') +plt.xlabel('Easting (m)');plt.ylabel('Northing (m)') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') + +ax = plt.subplot(212) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax)) +plt.title('Cross Section') +plt.xlabel('Easting(m)');plt.ylabel('Elevation') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') +plt.savefig(home_dir + str('\Figure1.png'), dpi=300, bb_inches='tight') + +#plot histograms +plt.figure(figsize=(15,10)) +ax = plt.subplot(121) +plt.hist(mrec,100) +plt.yscale('log', nonposy='clip') +plt.xlim(mrec.mean() - 4.*(mrec.std()), mrec.mean() + 4.*(mrec.std())) +plt.title('Histogram of model values - Smooth') + +ax = plt.subplot(122) +plt.hist(reg.regmesh.cellDiffxStencil*mrec,100) +plt.yscale('log', nonposy='clip') +plt.xlim(mrec.mean() - 4.*(mrec.std()), mrec.mean() + 4.*(mrec.std())) +plt.title('Histogram of model gradient values - Smooth') +plt.savefig(home_dir + str('\Figure2.png'), dpi=300, bb_inches='tight') +#%% Run one more round for sparsity (Compact model) +print '\nRun compact inversion \n' +phim = invProb.phi_m_last +phid = invProb.phi_d + +reg = Regularization.Sparse(mesh, indActive = actv, mapping = idenMap) +reg.recModel = mrec +reg.mref = mref +reg.wght = wr +reg.eps_p = eps_p +reg.eps_q = eps_q +reg.norms = lpnorms + +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() +PC = Utils.sdiag(diagA**-1.) + +#reg.alpha_s = 1. + +dmis = DataMisfit.l2_DataMisfit(survey) +dmis.Wd = 1./wd +opt = Optimization.ProjectedGNCG(maxIter=10 ,lower=bounds[0],upper=bounds[1], maxIterCG= 25, tolCG = 1e-4) +opt.approxHinv = PC +#opt.phim_last = reg.eval(mrec) + +# opt = Optimization.InexactGaussNewton(maxIter=6) +invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta) +beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1) +#betaest = Directives.BetaEstimate_ByEig() +target = Directives.TargetMisfit() +IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid ) + +inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS]) + +m0 = mrec + +# Run inversion +mrec = inv.run(m0) + +m_out = actvMap*mrec + +Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l0l2.den',m_out) + +pred = prob.fields(mrec) + +#%% Plot obs data +PF.Magnetics.plot_obs_2D(rxLoc,pred,'Predicted Data', vmin = np.min(d), vmax = np.max(d)) +plt.savefig(home_dir + str('\Figure3.png'), dpi=300, bb_inches='tight') +PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data') +plt.savefig(home_dir + str('\Figure4.png'), dpi=300, bb_inches='tight') +print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) +#%% Plot out a section of the compact model + +yslice = midx +plt.figure(figsize=(15,10)) +plt.suptitle('Compact Inversion') +ax = plt.subplot(221) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (vmin,vmax)) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='w',s=1) +plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') +plt.xlabel('Easting (m)');plt.ylabel('Northing (m)') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') + +ax = plt.subplot(222) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (vmin,vmax)) +plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') +plt.scatter(rxLoc[0:,0], rxLoc[0:,1], color='w',s=1) +plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') +plt.xlabel('Easting (m)');plt.ylabel('Northing (m)') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') + +ax = plt.subplot(212) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (vmin,vmax)) +plt.title('Cross Section') +plt.xlabel('Easting (m)');plt.ylabel('Elevation (m)') +plt.gca().set_aspect('equal', adjustable='box') +cb = plt.colorbar(dat1[0],orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) +cb.set_label('Density (kg/m3)') + +plt.savefig(home_dir + str('\Figure5.png'), dpi=300, bb_inches='tight') + + +plt.figure(figsize=(15,10)) +ax = plt.subplot(121) +plt.hist(mrec,100) +plt.xlim(mrec.mean() - 4.*(mrec.std()), mrec.mean() + 4.*(mrec.std())) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model values - Sparse lp:'+str(lpnorms[0])) + +ax = plt.subplot(122) +plt.hist(reg.regmesh.cellDiffxStencil*mrec,100) +plt.xlim(mrec.mean() - 4.*(mrec.std()), mrec.mean() + 4.*(mrec.std())) +plt.yscale('log', nonposy='clip') +plt.title('Histogram of model gradient values - Sparse lqx: ' + str(lpnorms[1]) + ' lqy:'+ str(lpnorms[2]) + ' lqz:' + str(lpnorms[3])) +plt.savefig(home_dir + str('\Figure6.png'), dpi=300, bb_inches='tight') \ No newline at end of file diff --git a/simpegPF/Dev/GRAV/Mesh_10m.msh b/simpegPF/Dev/GRAV/Mesh_10m.msh new file mode 100644 index 00000000..b49f8209 --- /dev/null +++ b/simpegPF/Dev/GRAV/Mesh_10m.msh @@ -0,0 +1,5 @@ +47 47 20 +-60.00 -60.00 280.00 +50.00 30.00 20.00 41*10.00 20.00 30.00 50.00 +50.00 30.00 20.00 41*10.00 20.00 30.00 50.00 +17*10.00 20.00 30.00 50 diff --git a/simpegPF/Dev/GRAV/PYGRAV3D_inv.inp b/simpegPF/Dev/GRAV/PYGRAV3D_inv.inp new file mode 100644 index 00000000..828f95d7 --- /dev/null +++ b/simpegPF/Dev/GRAV/PYGRAV3D_inv.inp @@ -0,0 +1,10 @@ +Mesh_10m.msh ! Mesh file +Grav.dat ! Obsfile +Gaussian.topo ! Topofile | null +VALUE 1e-4 ! Starting model +VALUE 0.0 ! Reference model +DEFAULT ! Cell based weight file +1 ! target chi factor | DEFAULT=1 +1 1 1 1 ! alpha s, x ,y ,z +VALUE -1 1 ! Lower and Upper Bounds for p-component +VALUE 0 1 1 1 1 ! lp-norm for amplitude inversion FILE pqxqyqzr.dat ! Norms VALUE p, qx, qy, qz, r | FILE m-by-5 matrix \ No newline at end of file diff --git a/simpegPF/Dev/GRAV/Pred0.dat b/simpegPF/Dev/GRAV/Pred0.dat new file mode 100644 index 00000000..2db7a0eb --- /dev/null +++ b/simpegPF/Dev/GRAV/Pred0.dat @@ -0,0 +1,192 @@ +191 +3.624724e+05 6.008600e+06 2.185513e+03 2.950277e-02 5.000000e-02 +3.630474e+05 6.007742e+06 2.182354e+03 2.958855e-02 5.000000e-02 +3.658851e+05 6.010287e+06 2.181814e+03 2.909199e-02 5.000000e-02 +3.634466e+05 6.005839e+06 2.190788e+03 2.947114e-02 5.000000e-02 +3.641340e+05 6.005157e+06 2.173695e+03 2.936905e-02 5.000000e-02 +3.644170e+05 6.003660e+06 2.189776e+03 2.896865e-02 5.000000e-02 +3.673445e+05 6.005950e+06 2.194062e+03 2.885706e-02 5.000000e-02 +3.605751e+05 6.011702e+06 2.267910e+03 2.855588e-02 5.000000e-02 +3.687754e+05 6.014567e+06 2.230211e+03 2.611820e-02 5.000000e-02 +3.600941e+05 6.011233e+06 2.205465e+03 2.849530e-02 5.000000e-02 +3.662178e+05 6.013153e+06 2.310513e+03 2.835436e-02 5.000000e-02 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b/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py deleted file mode 100644 index d2b2102d..00000000 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_Driver.py +++ /dev/null @@ -1,63 +0,0 @@ -import os - -home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\ownCloud\Research\Modelling\Synthetic\Block_Gaussian_topo' - -inpfile = 'PYMAG3C_fwr.inp' - -dsep = '\\' - -os.chdir(home_dir) - -#%% -from SimPEG import np, sp, Utils, mkvc, Maps -import simpegPF as PF -import pylab as plt - -## New scripts to be added to basecode -#from fwr_MAG_data import fwr_MAG_data -#from read_MAGfwr_inp import read_MAGfwr_inp - -#%% -# Read input file -[mshfile, obsfile, modfile, magfile, topofile] = PF.BaseMag.read_MAGfwr_inp(inpfile) - -# Load mesh file -mesh = Utils.meshutils.readUBCTensorMesh(mshfile) - -# Load model file -model = Utils.meshutils.readUBCTensorModel(modfile,mesh) - -# Load in topofile or create flat surface -if topofile == 'null': - - actv = np.ones(mesh.nC) - -else: - topo = np.genfromtxt(topofile,skip_header=1) - actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') - - -Utils.writeUBCTensorModel('nullcell.dat',mesh,actv) - -# Load in observation file -[B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile) - -rxLoc = dobs[:,0:3] -#rxLoc[:,2] += 5 # Temporary change for test -ndata = rxLoc.shape[0] - -# Load GOCAD surf -tsfile = 'SphereA.ts' -[vrtx, trgl] = PF.BaseMag.read_GOCAD_ts(tsfile) - -#%% Run forward modeling -# Compute forward model using integral equation -d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') - -# Form data object with coordinates and write to file -wd = np.zeros((ndata,1)) - -# Save forward data to file -PF.Magnetics.writeUBCobs(home_dir + dsep + 'FWR_data.dat',B,M,rxLoc,d,wd) - - diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py b/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py deleted file mode 100644 index f0355000..00000000 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_DualSphereTopo_Test.py +++ /dev/null @@ -1,187 +0,0 @@ -import os - -home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG' - -os.chdir(home_dir) - -#%% -from SimPEG import * -import matplotlib.pyplot as plt -import simpegPF as PF -import scipy.interpolate as interpolation -import time - -#from fwr_MAG_data import fwr_MAG_data - -plt.close('all') - -topofile = 'Gaussian.topo' - -zoffset = 2 -#%% Create survey -# Load in topofile or create flat surface -if not topofile: - - actv = np.ones(mesh.nC) - -else: - topo = np.genfromtxt(topofile,skip_header=1) - - -B = np.array(([90.,0.,50000.])) - -M = np.array(([90.,0.,315.])) - -# Sphere radius -R = 25. - -# # Or create juste a plane grid -xr = np.linspace(-99., 99., 40) -yr = np.linspace(-49., 49., 20) -X, Y = np.meshgrid(xr, yr) - - - -sclx = 100. -dx = np.asarray([15., 10., 5., 2.5]) - -d_iter = len(dx) -l1_r = np.zeros(d_iter) -l2_r = np.zeros(d_iter) -linf_r = np.zeros(d_iter) -timer = np.zeros(d_iter) -mcell = np.zeros(d_iter) -#%% Loop through decreasing meshes and measure the residual -# Create mesh using simpeg and write out in GIF format - -for ii in range(d_iter): - - - nc = int(sclx/dx[ii]) - - hxind = [(dx[ii], 2*nc)] - hyind = [(dx[ii], nc)] - hzind = [(dx[ii], nc)] - - mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') - - mcell[ii] = mesh.nC - - actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') - - # Drape observations on topo + offset - if not topofile: - Z = np.ones((xr.size, yr.size)) * 2.5 - - else: - F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) - Z = F(X,Y) + zoffset - - rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] - - ndata = rxLoc.shape[0] - - xn = mesh.vectorNx - yn = mesh.vectorNy - zn = mesh.vectorNz - - print 'Mesh size: ' + str(mcell[ii]) - - #%% Create model - chibkg = 0. - chiblk = 0.01 - model = np.ones(mcell[ii])*chibkg - - # Do a three sphere problem for more frequencies - sph_ind = PF.MagAnalytics.spheremodel(mesh, 0., 0., -sclx/3, R) - model[sph_ind] = 0.5*chiblk - - sph_ind = PF.MagAnalytics.spheremodel(mesh, -sclx/2., 0., -sclx/3., R/3.) - model[sph_ind] = 4.*chiblk - - sph_ind = PF.MagAnalytics.spheremodel(mesh, sclx/2., 0., -sclx/2.5, R/2.5) - model[sph_ind] = 2.5*chiblk - - Utils.writeUBCTensorMesh('Mesh.msh',mesh) - Utils.writeUBCTensorModel('Model.sus',mesh,model) - #actv = np.ones(mesh.nC) - #%% Forward mode ldata - - start_time = time.time() - - d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'tmi') - - timer[ii] = (time.time() - start_time) - - #fwr_tmi = d[0:ndata] - #fwr_y = d[ndata:2*ndata] - #fwr_z = d[2*ndata:] - - #%% Get the analystical answer and compute the residual - #bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') - Bd = (450.-float(B[1]))%360. - Bi = B[0]; # Convert dip to horizontal to cartesian - - Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] - By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] - Bz = np.sin(np.deg2rad(Bi)) * B[2] - - Bo = np.c_[Bx, By, Bz] - - Ptmi = mkvc(np.r_[np.cos(np.deg2rad(Bi))*np.cos(np.deg2rad(Bd)),np.cos(np.deg2rad(Bi))*np.sin(np.deg2rad(Bd)),np.sin(np.deg2rad(Bi))],2).T; - - bxa,bya,bza = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0., 0., -sclx/3, 0.5*chiblk, Bo) - bxb,byb,bzb = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R/3., -sclx/2., 0., -sclx/3.,4.*chiblk, Bo) - bxc,byc,bzc = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R/2.5, sclx/2., 0., -sclx/2.5,2.5*chiblk, Bo) - - bx = bxa + bxb + bxc - by = bya + byb + byc - bz = bza + bzb + bzc - - b_tmi = mkvc(Ptmi.dot(np.c_[bx,by,bz].T)) - - r_tmi = d - b_tmi - #r_By = fwr_y - bya - #r_Bz = fwr_z - bza - - l2_r[ii] = np.sum( r_tmi**2 ) **0.5 - l1_r[ii] = np.sum( np.abs( r_tmi ) ) - linf_r[ii] = np.max( np.abs( r_tmi ) ) - -#%% Write predicted to file - -PF.Magnetics.writeUBCobs('Obsloc.loc',B,M,rxLoc,d,np.ones(len(d))) - -#%% Plot results -print 'Residual between analytical sphere and integral forward' -print "dx \t nc \t l1 \t l2 \t linf \t Runtime" -for ii in range(d_iter): - - print str(dx[ii]) + "\t" + str(mcell[ii]) + "\t" + str(l1_r[ii]) + "\t" + str(l2_r[ii]) + "\t" + str(linf_r[ii]) + "\t" + str(timer[ii]) - -#%% Plot fields -plt.figure(1) -ax = plt.subplot() -plt.imshow(np.reshape(b_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.02) -plt.contour(X,Y, np.reshape(b_tmi,X.shape),10) -plt.scatter(X,Y, c=np.reshape(b_tmi,X.shape), s=20) -ax.set_title('Analytical') - -#%% Plot the forward solution from integral -plt.figure(2) -ax = plt.subplot() -plt.imshow(np.reshape(d,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') -plt.colorbar(fraction=0.02) -plt.contour(X,Y, np.reshape(d,X.shape),10) -plt.scatter(X,Y, c=np.reshape(d,X.shape), s=20) -ax.set_title('Numerical') - -#%% Plot residual data -plt.figure(3) -ax = plt.subplot() -plt.imshow(np.reshape(r_tmi,X.shape), interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.02) -plt.contour(X,Y, np.reshape(r_tmi,X.shape),10) -plt.scatter(X,Y, c=np.reshape(r_tmi,X.shape), s=20) -ax.set_title('Sphere Ana Bx') \ No newline at end of file diff --git a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py b/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py deleted file mode 100644 index 4aed811b..00000000 --- a/simpegPF/Dev/Intgrl_MAG_Fwr_SphereTest.py +++ /dev/null @@ -1,173 +0,0 @@ -import os - -# home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' - -# os.chdir(home_dir) - -#%% -from SimPEG import * -import matplotlib.pyplot as plt -import simpegPF as PF - -#from fwr_MAG_data import fwr_MAG_data - -plt.close('all') - -#%% Create survey - -B = np.array(([-45.,315.,50000.])) - -M = np.array(([-45.,315.])) - -# Sphere radius -R = 0.25 - -# # Or create juste a plane grid -xr = np.linspace(-2., 2., 5) -yr = np.linspace(-2., 2., 5) -X, Y = np.meshgrid(xr, yr) -Z = np.ones((xr.size, yr.size)) * 2.5 -rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)] - -ndata = rxLoc.shape[0] - -d_iter = 4 -lrl = np.zeros(d_iter) -#%% Loop through decreasing meshes and measure the residual -# Create mesh using simpeg and write out in GIF format - -for ii in range(d_iter): - - nc = 3**(ii+1) - - hxind = [(1./nc, nc)] - hyind = [(1./nc, nc)] - hzind = [(1./nc, nc)] - - mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') - - xn = mesh.vectorNx - yn = mesh.vectorNy - zn = mesh.vectorNz - - mcell = mesh.nC - - print 'Mesh size: ' + str(mcell) - - sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 0, R) - - chibkg = 0. - chiblk = 0.01 - model = np.ones(mcell)*chibkg - model[sph_ind] = chiblk - - actv = np.ones(mcell) - - #%% Forward mode ldata - d = PF.Magnetics.Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,'xyz') - fwr_x = d[0:ndata] - fwr_y = d[ndata:2*ndata] - fwr_z = d[2*ndata:] - - #%% Get the analystical answer and compute the residual - bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') - Bd = (450.-float(B[1]))%360. - Bi = B[0]; # Convert dip to horizontal to cartesian - - Bx = np.cos(np.deg2rad(Bi)) * np.cos(np.deg2rad(Bd)) * B[2] - By = np.cos(np.deg2rad(Bi)) * np.sin(np.deg2rad(Bd)) * B[2] - Bz = np.sin(np.deg2rad(Bi)) * B[2] - - Bo = np.c_[Bx, By, Bz] - - bxa,bya,bza = PF.MagAnalytics.MagSphereFreeSpace(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, Bo) - #bxa,bya,bza = PF.MagAnalytics.MagSphereAnaFunA(rxLoc[:,0],rxLoc[:,1],rxLoc[:,2],R,0.,0.,0.,chiblk, np.array(([0.,0.,B[2]])),'secondary') - - r_Bx = fwr_x - bxa - r_By = fwr_y - bya - r_Bz = fwr_z - bza - - lrl[ii] = sum( r_Bx**2 + r_By**2 + r_Bz**2 ) **0.5 - - - -#%% Plot results -print 'Residual between analytical sphere and integral forward' -for ii in range(d_iter): - nc = 3**(ii+1) - - print "||r||= " + str(lrl[ii]) + "\t dx= " + str(1./nc) - -#%% Plot fields - -plt.figure(1) -ax = plt.subplot(221) -plt.imshow(np.reshape(bxa,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(bxa,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(bxa,X.shape).T, s=20) -ax.set_title('Sphere Ana Bx') - -ax = plt.subplot(222) -plt.imshow(np.reshape(bya,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(bya,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(bya,X.shape).T, s=20) -ax.set_title('Sphere Ana By') - -ax = plt.subplot(212) -plt.imshow(np.reshape(bza,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(bza,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(bza,X.shape).T, s=20) -ax.set_title('Sphere Ana Bz') - -#%% Plot the forward solution from integral - -plt.figure(2) -ax = plt.subplot(221) -plt.imshow(np.reshape(fwr_x,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max() ], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(fwr_x,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(fwr_x,X.shape).T, s=20) -ax.set_title('Sphere Ana Bx') - -ax = plt.subplot(222) -plt.imshow(np.reshape(fwr_y,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(fwr_y,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(fwr_y,X.shape).T, s=20) -ax.set_title('Sphere Ana By') - -ax = plt.subplot(212) -plt.imshow(np.reshape(fwr_z,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(fwr_z,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(fwr_z,X.shape).T, s=20) -ax.set_title('Sphere Ana Bz') - - -#%% Plot foward data -plt.figure(3) -ax = plt.subplot(221) -plt.imshow(np.reshape(r_Bx,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(r_Bx,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(r_Bx,X.shape).T, s=20) -ax.set_title('Sphere Ana Bx') - -ax = plt.subplot(222) -plt.imshow(np.reshape(r_By,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, np.reshape(r_By,X.shape).T,10) -plt.scatter(X,Y, c=np.reshape(r_By,X.shape).T, s=20) -ax.set_title('Sphere Ana By') - -ax = plt.subplot(212) -plt.imshow(np.reshape(r_Bz,X.shape).T, interpolation="bicubic", extent=[xr.min(), xr.max(), yr.min(), yr.max()], origin = 'lower') -plt.colorbar(fraction=0.04) -plt.contour(X,Y, 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177.486206055 +610 470 177.418701172 +580 310 184.604858398 +575 305 185.426452637 +630 110 178.678527832 +630 90 178.289123535 +630 70 177.894317627 +630 -10 176.479064941 +630 -30 176.201568604 +630 -50 175.960662842 +585 -105 175.918029785 +260 -90 186.29486084 +-95 -105 175.918029785 +-100 -105 175.855712891 +-100 -100 175.918029785 +-100 40 179.157714844 +-85 95 182.45098877 +-85 345 184.100646973 +-90 350 183.317932129 +-90 610 175.687225342 +630 610 175.334533691 +630 590 175.446044922 +630 570 175.585296631 +630 550 175.755859375 +615 505 176.59185791 +630 450 177.136230469 +630 445 177.225921631 +625 445 177.408630371 +630 440 177.318603516 diff --git a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py b/simpegPF/Dev/MAG/Intgrl_MAG_Inv_Driver.py similarity index 59% rename from simpegPF/Dev/Intgrl_MAG_Inv_Driver.py rename to simpegPF/Dev/MAG/Intgrl_MAG_Inv_Driver.py index de9f4827..f0315b6a 100644 --- a/simpegPF/Dev/Intgrl_MAG_Inv_Driver.py +++ b/simpegPF/Dev/MAG/Intgrl_MAG_Inv_Driver.py @@ -6,8 +6,9 @@ import pylab as plt import os #home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' -#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Block_Gaussian_topo' +#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Nut_Cracker\\Induced_MAG3C' home_dir = '.\\' +#home_dir = '.\\' inpfile = 'PYMAG3D_inv.inp' @@ -16,31 +17,32 @@ os.chdir(home_dir) ## New scripts to be added to basecode #from fwr_MAG_data import fwr_MAG_data #from read_MAGfwr_inp import read_MAGfwr_inp - +plt.close('all') #%% # Read input file -[mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms] = PF.BaseMag.read_MAGinv_inp(home_dir + dsep + inpfile) +[mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms] = PF.Magnetics.read_MAGinv_inp(home_dir + dsep + inpfile) # Load mesh file mesh = Mesh.TensorMesh.readUBC(mshfile) #mesh = Utils.meshutils.readUBCTensorMesh(mshfile) # Load in observation file -[B,M,dobs] = PF.BaseMag.readUBCmagObs(obsfile) +survey = PF.Magnetics.readUBCmagObs(obsfile) -rxLoc = dobs[:,0:3] -d = dobs[:,3] -wd = dobs[:,4] +rxLoc = survey.srcField.rxList[0].locs +d = survey.dobs +wd = survey.std -ndata = rxLoc.shape[0] - -beta_in = 1e+2 +ndata = survey.srcField.rxList[0].locs.shape[0] +beta_in = 1e+5 +eps_p = 1e-4 +eps_q = 1e-4 # Load in topofile or create flat surface if topofile == 'null': # All active - actv = np.ones(mesh.nC) + actv = np.asarray(range(mesh.nC)) else: @@ -48,26 +50,33 @@ else: # Find the active cells actv = PF.Magnetics.getActiveTopo(mesh,topo,'N') -nC = int(sum(actv)) +nC = len(actv) + +# Create active map to go from reduce set to full +actvMap = Maps.ActiveCells(mesh, actv, -100) + +# Creat reduced identity map +idenMap = Maps.IdentityMap(nP = nC) # Load starting model file if isinstance(mstart, float): + mstart = np.ones(nC) * mstart else: mstart = Utils.meshutils.readUBCTensorModel(mstart,mesh) - mstart = mstart[actv==1] + mstart = mstart[actv] # Load reference file if isinstance(mref, float): mref = np.ones(nC) * mref else: mref = Utils.meshutils.readUBCTensorModel(mref,mesh) - mref = mref[actv==1] + mref = mref[actv] # Get magnetization vector for MOF if magfile=='DEFAULT': - M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(nC) * M[0], np.ones(nC) * M[1]) + M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(nC) * survey.srcField.param[1], np.ones(nC) * survey.srcField.param[2]) else: M_xyz = np.genfromtxt(magfile,delimiter=' \n',dtype=np.str,comments='!') @@ -76,54 +85,52 @@ else: midx = int(mesh.nCx/2) midy = int(mesh.nCy/2) -# Create forward operator -F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,actv,'tmi') - # Get distance weighting function -wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,actv,3.,np.min(mesh.hx)/4) -wrMap = PF.BaseMag.WeightMap(mesh, wr) +#============================================================================== +# wr = PF.Magnetics.get_dist_wgt(mesh,rxLoc,actv,3.,np.min(mesh.hx)/4) +# #wrMap = PF.BaseMag.WeightMap(nC, wr) +#============================================================================== -wr_out = np.zeros(mesh.nC) -wr_out[actv==1] = wr -Mesh.TensorMesh.writeModelUBC(mesh,home_dir+dsep+'wr.dat',wr_out) -#Utils.meshutils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr_out) +#%% Plot obs data +PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data') + +#%% Run inversion +prob = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap, actInd = actv) +prob.solverOpts['accuracyTol'] = 1e-4 + +#survey = Survey.LinearSurvey() +survey.pair(prob) +#survey.makeSyntheticData(data, std=0.01) +#survey.dobs=d +#survey.mtrue = model # Write out the predicted -pred = F.dot(mstart) -PF.Magnetics.writeUBCobs(home_dir + dsep + 'Pred.dat',B,M,rxLoc,pred,wd) +pred = prob.fields(mstart) +PF.Magnetics.writeUBCobs(home_dir + dsep + 'Pred.dat',survey,pred) + +wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv] +wr = ( wr/np.max(wr) ) +wr_out = actvMap * wr -#%% plt.figure() ax = plt.subplot() -mesh.plotSlice(wr_out, ax = ax, normal = 'Y', ind=midx ) +mesh.plotSlice(wr_out, ax = ax, normal = 'Y', ind=midx ,clim = (-1e-3, wr.max())) plt.title('Distance weighting') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') -#%% Plot obs data -PF.Magnetics.plot_obs_2D(rxLoc,d,wd,'Observed Data') - -#%% Run inversion -prob = PF.Magnetics.MagneticIntegral(mesh, F) -prob.solverOpts['accuracyTol'] = 1e-4 -survey = Survey.LinearSurvey() -survey.pair(prob) -#survey.makeSyntheticData(data, std=0.01) -survey.dobs=d -#survey.mtrue = model - - -reg = Regularization.Simple(mesh, mapping=wrMap) +reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap) reg.mref = mref +reg.wght = wr #reg.alpha_s = 1. # Create pre-conditioner -diagA = np.sum(F**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*(wr**2.0) +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() PC = Utils.sdiag(diagA**-1.) dmis = DataMisfit.l2_DataMisfit(survey) -dmis.Wd = wd -opt = Optimization.ProjectedGNCG(maxIter=10,lower=0.,upper=1.) +dmis.Wd = 1/wd +opt = Optimization.ProjectedGNCG(maxIter=10,lower=0.,upper=1., maxIterCG= 20, tolCG = 1e-3) opt.approxHinv = PC # opt = Optimization.InexactGaussNewton(maxIter=6) @@ -139,16 +146,14 @@ m0 = mstart # Run inversion mrec = inv.run(m0) - -m_out = np.ones(mesh.nC) -m_out[actv==1] = mrec +m_out = actvMap*mrec # Write result Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l2l2.sus',m_out) #Utils.meshutils.writeUBCTensorModel(home_dir+dsep+'wr.dat',mesh,wr_out) # Plot predicted -pred = F.dot(mrec) +pred = prob.fields(mrec) #PF.Magnetics.plot_obs_2D(rxLoc,pred,wd,'Predicted Data') #PF.Magnetics.plot_obs_2D(rxLoc,(d-pred),wd,'Residual Data') @@ -156,87 +161,103 @@ print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) #%% Plot out a section of the model -yslice = midx-7 +yslice = midx +m_out[m_out==-100] = np.nan + plt.figure() ax = plt.subplot(221) -mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (mrec.min(), mrec.max())) plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') -plt.title('Z Section') +plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') ax = plt.subplot(222) -mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-1, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (mrec.min(), mrec.max())) plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') -plt.title('Top') +plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') ax = plt.subplot(212) -mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (mrec.min(), mrec.max())) plt.title('Cross Section') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') #%% Run one more round for sparsity phim = invProb.phi_m_last +phid = invProb.phi_d -reg = Regularization.SparseRegularization(mesh, mapping=wrMap, eps=1e-4) -reg.m = mrec +reg = Regularization.Sparse(mesh, indActive = actv, mapping = idenMap) +reg.recModel = mrec reg.mref = mref +reg.wght = wr +reg.eps_p = eps_p +reg.eps_q = eps_q +reg.norms = lpnorms - -diagA = np.sum(F**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*(wr**2.0) +diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() PC = Utils.sdiag(diagA**-1.) #reg.alpha_s = 1. dmis = DataMisfit.l2_DataMisfit(survey) dmis.Wd = wd -opt = Optimization.ProjectedGNCG(maxIter=8 ,maxIterLS=10, maxIterCG = 20,tolCG = 1e-4,lower=0.,upper=1.) +opt = Optimization.ProjectedGNCG(maxIter=20 ,lower=0.,upper=1., maxIterCG= 10, tolCG = 1e-4) opt.approxHinv = PC #opt.phim_last = reg.eval(mrec) # opt = Optimization.InexactGaussNewton(maxIter=6) invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = invProb.beta) beta = Directives.BetaSchedule(coolingFactor=1, coolingRate=1) +update_beta = Directives.Scale_Beta(tol = 0.05) #betaest = Directives.BetaEstimate_ByEig() target = Directives.TargetMisfit() -IRLS =Directives.update_IRLS( eps_min=1e-3, phi_m_last = phim ) +IRLS =Directives.Update_IRLS( phi_m_last = phim, phi_d_last = phid ) -inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS]) +inv = Inversion.BaseInversion(invProb, directiveList=[beta,IRLS,update_beta]) m0 = mrec # Run inversion mrec = inv.run(m0) -m_out[actv==1] = mrec +m_out = actvMap*mrec Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_inv_l0l2.sus',m_out) + +pred = prob.fields(mrec) + +#%% Plot obs data +PF.Magnetics.plot_obs_2D(rxLoc,pred,'Predicted Data') +PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data') +print "Final misfit:" + str(np.sum( ((d-pred)/wd)**2. ) ) #%% Plot out a section of the model -yslice = midx-7 +yslice = midx + +m_out[m_out==-100] = np.nan + plt.figure() ax = plt.subplot(221) -mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (mrec.min(), mrec.max())) plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') -plt.title('Z Section') +plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') ax = plt.subplot(222) -mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-1, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = (mrec.min(), mrec.max())) plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') -plt.title('Top') +plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') - ax = plt.subplot(212) -mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (-mrec.min(), mrec.max())) +mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = (mrec.min(), mrec.max())) plt.title('Cross Section') plt.xlabel('x');plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') \ No newline at end of file diff --git a/simpegPF/Dev/MAG/Mag_Inv_Param.py b/simpegPF/Dev/MAG/Mag_Inv_Param.py new file mode 100644 index 00000000..5613f76e --- /dev/null +++ b/simpegPF/Dev/MAG/Mag_Inv_Param.py @@ -0,0 +1,464 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Feb 03 21:34:50 2016 + +@author: dominiquef +""" +from SimPEG import * +import simpegPF as PF +from simpegPF import BaseMag as MAG +from numpy.polynomial import polynomial + +import pylab as plt + +import os + +#home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\Documents\GIT\SimPEG\simpegpf\simpegPF\Dev' +#home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Parametric_plane' +#home_dir = 'C:\\LC\\Private\\dominiquef\\Projects\\4414_Minsim\\Modeling\\MAG\\Lalor' +#home_dir = 'C:\Users\dominiquef.MIRAGEOSCIENCE\ownCloud\Research\Nate\Modeling' +home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\Google Drive\\DevDomNateDBE\\DomNate\\Fault_synthetic\\NE' +#home_dir = '.\\' +plt.close('all') + +inpfile = 'PYMAG3D_inv.inp' + +dsep = '\\' +os.chdir(home_dir) +## New scripts to be added to basecode +#from fwr_MAG_data import fwr_MAG_data +#from read_MAGfwr_inp import read_MAGfwr_inp +beta_in = 1e+0 +ndv = -100 +#%% +# Read input file +[mshfile, obsfile, topofile, m0_val, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms] = PF.Magnetics.read_MAGinv_inp(home_dir + dsep + inpfile) + +#obsfile = 'Synthetic.obs' +#obsfile ='Lalor_rtp_2pc_10nT_RegRem.obs' +#obsfile = 'Obs_ALL.obs' + +# Discretization for new mesh +dx = 50. + +# Load mesh file +mesh = Mesh.TensorMesh.readUBC(mshfile) +z0 = mesh.x0[2] + np.sum(mesh.hz) +#mesh = Utils.meshutils.readUBCTensorMesh(mshfile) + +#V2D = polynomial.polyvander2d(mesh.vectorCCx,mesh.vectorCCy,[1,1]) +# Load in observation file +survey = PF.Magnetics.readUBCmagObs(obsfile) +rxLoc_full = survey.srcField.rxList[0].locs +data = survey.dobs +wd = survey.std + +npad = 10 +#%% Pick points from dats and generate local mesh +PF.Magnetics.plot_obs_2D(rxLoc_full,data, levels = [0.]) + +#PF.Magnetics.plot_obs_2D(dobs[:,:3],dobs[:,3],dobs[:,4],'Observed Data') +gin = np.asarray(plt.ginput(100, timeout = 0)) +#gin = np.asarray([[ -81.82517326, -167.83403552], +# [ -21.0157401 , 133.78075295]]) +for ii in range(gin.shape[0]-1): + + dl_len = np.sqrt( np.sum((gin[ii,:] - gin[ii+1,:])**2) ) + dl_x = ( gin[ii,0] - gin[ii+1,0] ) / dl_len + dl_y = ( gin[ii+1,1] - gin[ii,1] ) / dl_len + azm = -np.arctan(dl_x/dl_y) + + # Create rotation matrix + Rz = np.array([[np.cos(azm), -np.sin(azm)], + [np.sin(azm), np.cos(azm)]]) + + # Re-center the experiment + midx = np.median([gin[ii,0],gin[ii+1,0]]) + midy = np.median([gin[ii+1,1],gin[ii,1]]) + + temp = np.vstack([rxLoc_full[:,0].T- midx, rxLoc_full[:,1].T- midy]) + + # Rotate + ROTxy = Rz.dot(temp) + + # Grab data points within a box + indx = (np.abs(ROTxy[0,:]) < dl_len) & (np.abs(ROTxy[1,:]) < dl_len/2) + + subrx = MAG.RxObs(np.c_[ROTxy[0,indx].T, ROTxy[1,indx].T, rxLoc_full[indx,2]]) + d = data[indx] + uncert = wd[indx] + + srcParam = np.asarray(survey.srcField.param) + srcParam[2] = srcParam[2] - np.rad2deg(azm) + srcField = MAG.SrcField([subrx],srcParam) + survey = MAG.LinearSurvey(srcField) + survey.dobs = d + survey.std = uncert + PF.Magnetics.writeUBCobs(home_dir+'\\Obsloc_local.dat',survey,survey.dobs) + rxLoc = survey.srcField.rxList[0].locs + PF.Magnetics.plot_obs_2D(rxLoc,d,'Observed Data') + + ndata = rxLoc.shape[0] + + # Get extent limits + xlim = np.max(np.abs(rxLoc[:,0])) + ylim = np.max(np.abs(rxLoc[:,1])) + + ncx = int(4*xlim/dx) + ncy = int(2*ylim/dx) + ncz = int(np.min([ncx,ncy])) + + hxind = [(dx,npad,-1.3),(dx, ncx),(dx,npad,1.3)] + hyind = [(dx,npad,-1.3),(dx, ncy),(dx,npad,1.3)] + hzind = [(dx,npad,-1.3),(dx, ncz)] + + mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + + mesh.x0[2] = np.max(rxLoc[:,2]) - np.sum(mesh.hz) # Keep top of mesh at same location as before + Mesh.TensorMesh.writeUBC(mesh,home_dir+dsep+'Mesh_local.msh') + + + # Load in topofile or create flat surface + if topofile == 'null': + + # All active + actv = np.asarray(range(mesh.nC)) + + else: + + topo = np.genfromtxt(topofile,skip_header=1) + temp = np.vstack([topo[:,0].T- midx, topo[:,1].T- midy]) + + # Rotate + ROTxy = Rz.dot(temp) + ROT_topo = np.c_[ROTxy[0,:].T, ROTxy[1,:].T, topo[:,2]] + + # Find the active cells + actv = PF.Magnetics.getActiveTopo(mesh,ROT_topo,'N') + + nC = len(actv) + idenMap = Maps.IdentityMap(nP = nC) + # Create active map to go from reduce set to full + actvMap = Maps.InjectActiveCells(mesh, actv, ndv) + + # Load starting model file +# if isinstance(mstart, float): + + mstart = np.ones(nC) * m0_val +# else: +# mstart = Utils.meshutils.readUBCTensorModel(mstart,mesh) +# mstart = mstart[actv] + + + + # Get magnetization vector for MOF + if magfile=='DEFAULT': + + M_xyz = PF.Magnetics.dipazm_2_xyz(np.ones(nC) * survey.srcField.param[1], np.ones(nC) * survey.srcField.param[2]) + + else: + M_xyz = np.genfromtxt(magfile,delimiter=' \n',dtype=np.str,comments='!') + + + # Get index of the center + #============================================================================== + # midx = int(mesh.nCx/2) + # midy = int(mesh.nCy/2) + #============================================================================== + + # Create forward operator + #F = PF.Magnetics.Intrgl_Fwr_Op(mesh,B,M_xyz,rxLoc,actv,'tmi') + + #%% Run inversion + # First start with regular inversion for regional removal + prob = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap, actInd = actv) + prob.solverOpts['accuracyTol'] = 1e-4 + + survey.pair(prob) + + #%% + pred = prob.fields(mstart) + + wr = np.sum(prob.G**2.,axis=0)**0.5 / mesh.vol[actv] + wr = ( wr/np.max(wr) ) + #IWr = Utils.sdiag(1/wr) + #wrMap = PF.BaseMag.WeightMap(mesh, wr) + + #prob.mapping = wrMap + #prob._G = prob._G * IWr + + reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap) + reg.mref = 0. + reg.wght = wr + #reg.alpha_s = 1. + + # Create pre-conditioner + diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal() + PC = Utils.sdiag(diagA**-1.) + + + dmis = DataMisfit.l2_DataMisfit(survey) + dmis.Wd = 1./survey.std + opt = Optimization.ProjectedGNCG(maxIter=10,lower=0.,upper=1., maxIterCG= 20, tolCG = 1e-3) + opt.approxHinv = PC + + # opt = Optimization.InexactGaussNewton(maxIter=6) + invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in) + beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1) + #betaest = Directives.BetaEstimate_ByEig() + target = Directives.TargetMisfit() + + inv = Inversion.BaseInversion(invProb, directiveList=[beta,target]) + + m0 = mstart + + # Run inversion + mrec = inv.run(m0) + + m_out = actvMap*mrec + + #%% Temporary plotting scipts + yslice = 14 + plt.figure() + ax = plt.subplot(221) + mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-5, clim = (mrec.min(), mrec.max())) + plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') + plt.title('Z: ' + str(mesh.vectorCCz[-5]) + ' m') + plt.xlabel('x');plt.ylabel('z') + plt.gca().set_aspect('equal', adjustable='box') + + ax = plt.subplot(222) + mesh.plotSlice(m_out, ax = ax, normal = 'Z', ind=-8, clim = ( mrec.min(), mrec.max())) + plt.plot(np.array([mesh.vectorCCx[0],mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice],mesh.vectorCCy[yslice]]),c='w',linestyle = '--') + plt.title('Z: ' + str(mesh.vectorCCz[-8]) + ' m') + plt.xlabel('x');plt.ylabel('z') + plt.gca().set_aspect('equal', adjustable='box') + + ax = plt.subplot(212) + mesh.plotSlice(m_out, ax = ax, normal = 'Y', ind=yslice, clim = ( mrec.min(), mrec.max())) + plt.title('Cross Section') + plt.xlabel('x');plt.ylabel('z') + plt.gca().set_aspect('equal', adjustable='box') + + #%% Remove core cells and forward model + m_out = np.reshape(m_out,(mesh.nCx,mesh.nCy,mesh.nCz), order = 'F') + m_out[npad:-npad,npad:-npad,npad:] = m_out[npad:-npad,npad:-npad,npad:]*0. + + m_out = mkvc(m_out) + m_pad = m_out[actv] + + Mesh.TensorMesh.writeModelUBC(mesh,home_dir+'\\SimPEG_Inv_l2l2.sus',actvMap*mrec) + Mesh.TensorMesh.writeModelUBC(mesh,home_dir+'\\SimPEG_Scooped.sus',actvMap*m_pad) + # Forward model the fields and substract from data + fwr = prob.fields(m_pad) + + d_res = survey.dobs - fwr + + PF.Magnetics.plot_obs_2D(rxLoc,fwr,'Forward Scoop Data', levels = [0.]) + + PF.Magnetics.plot_obs_2D(rxLoc,d_res,'RegRem Data', levels = [0.]) + + survey.dobs = d_res + #%% Reduce the space to only the core region + + # Create new mesh for local inversion + hxind = [(dx, ncx)] + hyind = [(dx, ncy)] + hzind = [(dx, ncz)] + + x0 = mesh.x0 + z0 = x0[2] + np.sum(mesh.hz) + + mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN') + + mesh.x0[2] = z0 - np.sum(mesh.hz) + + core = m_out==0 + grnd = core[actv] + + inds = np.asarray([inds for inds, elem in enumerate(grnd, 1) if elem], dtype = int) - 1 + + #%% Re-run with poly map + # Load in topofile or create flat surface + if topofile == 'null': + + # All active + actv = np.asarray(range(mesh.nC)) + + else: + + topo = np.genfromtxt(topofile,skip_header=1) + temp = np.vstack([topo[:,0].T- midx, topo[:,1].T- midy]) + + # Rotate + ROTxy = Rz.dot(temp) + ROT_topo = np.c_[ROTxy[0,:].T, ROTxy[1,:].T, topo[:,2]] + + # Find the active cells + actv = PF.Magnetics.getActiveTopo(mesh,ROT_topo,'N') + + nC = len(actv) + idenMap = Maps.IdentityMap(nP = nC) + # Create active map to go from reduce set to full + actvMap = Maps.InjectActiveCells(mesh, actv, ndv) + + # Load starting model file + mstart = np.ones(nC) * m0_val + + + + + #%% Invert with polymap for plane + # + # + # Create active map to go from reduce set to full + #actvMap = Maps.ActiveCells(mesh, actv, -100) + # + ## Creat reduced identity map + #idenMap = Maps.IdentityMap(nP = nC) + # + XYZ = mesh.gridCC + + order = [1,1] + + YZ = Utils.ndgrid(mesh.vectorCCy, mesh.vectorCCz) + + V = polynomial.polyvander2d(YZ[:,0], YZ[:,1], order) + + #f = polynomial.polyval2d(XYZ[:,1], XYZ[:,2], c.reshape((order[0]+1,order[1]+1))) - XYZ[:,0] + + polymap = Maps.PolyMap(mesh, order, normal='X', logSigma=False, actInd = actv) + polymap.slope = 1.0 + #polymap.actInd = actv + + #m0 = np.r_[1e-2, 0., 0.0, -0.5, 0.2, 0.] + m0 = np.r_[1e-4, 0, 1., 0., 0., 0.] + + + + #Mesh.TensorMesh.writeModelUBC(mesh,home_dir+dsep+'True_m.sus',polymap*m0) + #Mesh.TensorMesh.writeModelUBC(mesh,home_dir+dsep+'Starting_m.sus',actvMap*polymap*m0) + + m1D = Mesh.TensorMesh([(order[0]+1)*(order[1]+1)+2]) + + weight = ((V**2).sum(axis=0))**0.5 + weight = weight / weight.max() + prob_core = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap*polymap, actInd = actv) + prob_core.solverOpts['accuracyTol'] = 1e-4 + + survey.pair(prob_core) + + #prob_core._G = prob.G[:,inds] + + #%% + pred = prob_core.fields(m0) + + wr = np.sum(prob_core.G**2.,axis=0)**0.5 / mesh.vol[actv] + wr = ( wr/np.max(wr) ) + IWr = Utils.sdiag(1/wr) + wrMap = PF.BaseMag.WeightMap(mesh, wr) + + prob_core.mapping = wrMap * polymap + prob_core._G = prob_core.G * IWr + + reg = Regularization.Simple(m1D) + reg.alpha_x = 0. + reg.alpha_y = 0. + reg.alpha_z = 0. + reg.norms = [2., 2., 2., 2.] + #reg.mref = np.r_[0., 1., 421500., 1000., 10., 0.5] + reg.mref = np.zeros(6) + #============================================================================== + # reg.wght = np.asarray([ 1.00000000e+00 ,6.50176844e-01 ,4.20328370e-06 ,1.52004209e-03, + # 4.82475226e-05 ,1.37620903e-02])**2. + #============================================================================== + lower = np.r_[0.,0.,-1e+8,-1e+8,-1e+8,-1e+8] + upper = np.r_[1.,1.,1e+8,1e+8,1e+8,1e+8] + #reg.mref = mref + #reg.alpha_s = 1. + + + dmis = DataMisfit.l2_DataMisfit(survey) + dmis.Wd = 1./survey.std + opt = Optimization.ProjectedGNCG(maxIter=15,maxIterLS=50, maxIterCG = 10,tolCG = 1e-3, lower = lower,upper=upper) + #opt.approxHinv = sp.eye(6) + + beta_in = 1e+2 + + # opt = Optimization.InexactGaussNewton(maxIter=6) + invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in) + beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1) + #betaest = Directives.BetaEstimate_ByEig() + target = Directives.TargetMisfit() + up_Wj = Directives.Update_Wj() + up_Wj.itr = 2 + + inv = Inversion.BaseInversion(invProb, directiveList=[beta,target,up_Wj]) + + #m0 = mstart + #Utils.diagEst(prob.Jtvec,2) + + # Run inversion + mrec = inv.run(m0) + + sus = polymap*mrec + m_out = actvMap * sus + + # Write result + Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_Param.sus',m_out) + Mesh.TensorMesh.writeUBC(mesh,'Working_mesh.msh') + + m_out = actvMap *polymap*m0 + Mesh.TensorMesh.writeModelUBC(mesh,'SimPEG_Param_m0.sus',m_out) + # Plot predicted + pred = prob_core.fields(mrec) + + PF.Magnetics.plot_obs_2D(rxLoc,pred,'Predicted Data', vmin = np.min(survey.dobs), vmax = np.max(survey.dobs)) + + PF.Magnetics.plot_obs_2D(rxLoc,survey.dobs-pred,'Residual Data') + + #PF.Magnetics.writeUBCobs(home_dir + dsep + 'Pred_Final.pre',B,M,rxLoc,pred,np.ones(len(d))) + + + print "Final misfit:" + str(np.sum( ((d-pred)/uncert)**2. ) ) + + #%% Write parametric surface + yz = Utils.ndgrid(np.r_[-ncy/2*dx,0,ncy/2*dx],np.r_[mesh.vectorCCz[-1],mesh.vectorCCz[0]]) + + xout = polynomial.polyval2d(yz[:,0],yz[:,1],np.reshape(mrec[2:],(2,2))) + + + xyz = np.c_[xout,yz] + + # Rotate back to global coordinates + # Create rotation matrix + Rz = np.array([[np.cos(-azm), -np.sin(-azm)], + [np.sin(-azm), np.cos(-azm)]]) + + + temp = Rz.dot( np.vstack([xyz[:,0].T, xyz[:,1].T]) ) + + if ii == 0: + # Rotate + ROTxyz = np.c_[temp[0,:].T + midx, temp[1,:].T + midy, xyz[:,2]] + else: + ROTxyz = np.vstack([ROTxyz,np.c_[temp[0,:].T + midx, temp[1,:].T + midy, xyz[:,2]]]) + +#Write out the surface +with file(home_dir + dsep + 'Surf.dat','w') as fid: + np.savetxt(fid, ROTxyz, fmt='%e',delimiter=' ',newline='\n') + +#%% Right GOCAD ts file +with file(home_dir + dsep + 'Surf.ts','w') as fid: + + fid.write('GOCAD TSurf 1\n') + fid.write('HEADER {name:Mag_Param}\n') + fid.write('TFACE\n') + for ii in range(ROTxyz.shape[0]): + fid.write('VRTX %i %6.2f %6.2f %6.2f\n' %(ii+1, ROTxyz[ii,0],ROTxyz[ii,1],ROTxyz[ii,2]) ) + + for ii in range(ROTxyz.shape[0]/3): + fid.write('TRGL %i %i %i\n' %(ii+1, ii+2, np.mod(ii+3,6)+1 )) + fid.write('TRGL %i %i %i\n' %(ii+2, np.mod(ii+3,6)+1, np.mod(ii+4,6)+1 )) + + fid.write('END\n') \ No newline at end of file diff --git a/simpegPF/Dev/MAG/Mesh_10m.msh b/simpegPF/Dev/MAG/Mesh_10m.msh new file mode 100644 index 00000000..b49f8209 --- /dev/null +++ b/simpegPF/Dev/MAG/Mesh_10m.msh @@ -0,0 +1,5 @@ +47 47 20 +-60.00 -60.00 280.00 +50.00 30.00 20.00 41*10.00 20.00 30.00 50.00 +50.00 30.00 20.00 41*10.00 20.00 30.00 50.00 +17*10.00 20.00 30.00 50 diff --git a/simpegPF/Dev/MAG/Obs_loc_TMI.obs b/simpegPF/Dev/MAG/Obs_loc_TMI.obs new file mode 100644 index 00000000..1b3a927c --- /dev/null +++ b/simpegPF/Dev/MAG/Obs_loc_TMI.obs @@ -0,0 +1,619 @@ +90 0 50000 +90 0 1 +616 + 105 155 240.831177 -0.7588417 1 + 115 155 244.046936 -0.7915504 1 + 125 155 247.198578 -0.805861 1 + 135 155 250.256927 -0.798687 1 + 145 155 253.192322 -0.7701055 1 + 155 155 255.975342 -0.7241221 1 + 165 155 258.576965 -0.6681905 1 + 175 155 260.969513 -0.6113366 1 + 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simpegPF/Dev/PYMAG3D_inv.inp rename to simpegPF/Dev/MAG/PYMAG3D_inv.inp index cf2efd55..1813776c 100644 --- a/simpegPF/Dev/PYMAG3D_inv.inp +++ b/simpegPF/Dev/MAG/PYMAG3D_inv.inp @@ -1,6 +1,6 @@ -Mesh_40m.msh ! Mesh file -Obs_IND_GRID_TMI.obs ! Obsfile -null ! Topofile | null +Mesh_10m.msh ! Mesh file +Obs_loc_TMI.obs ! Obsfile +Gaussian.topo ! Topofile | null VALUE 1e-4 ! Starting model VALUE 0 ! Reference model DEFAULT !..\AzmDip.dat ! Magnetization vector model @@ -8,4 +8,4 @@ DEFAULT ! Cell based weight file 1 ! target chi factor | DEFAULT=1 1 1 1 1 ! alpha s, x ,y ,z VALUE 0 1 ! Lower and Upper Bounds for p-component -VALUE 0 2 2 2 1 ! lp-norm for amplitude inversion FILE pqxqyqzr.dat ! Norms VALUE p, qx, qy, qz, r | FILE m-by-5 matrix \ No newline at end of file +VALUE 0 1 1 1 1 ! lp-norm for amplitude inversion FILE pqxqyqzr.dat ! 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Mesh file -Obs_loc.dat ! Obsfile -Model.dat ! Susceptibility model -null ! M_azm_dip.dat ! Magnetization model | null -null ! Topography file | null \ No newline at end of file diff --git a/simpegPF/Dev/MAG3Csen.inp b/simpegPF/Dev/MAG3Csen.inp deleted file mode 100644 index 210a1da1..00000000 --- a/simpegPF/Dev/MAG3Csen.inp +++ /dev/null @@ -1,5 +0,0 @@ -Mesh.msh ! Mesh file -Obs_loc.dat ! Obsfile -null ! Topofile | null -DISTANCE ! weighting flag DISTANCE | DEPTH -G ! Define output TxTyTz | G diff --git a/simpegPF/Dev/MAG3Csen.log b/simpegPF/Dev/MAG3Csen.log deleted file mode 100644 index fc67eff0..00000000 --- a/simpegPF/Dev/MAG3Csen.log +++ /dev/null @@ -1,35 +0,0 @@ -MAG3Csen -Generates sparse matrices for magnetostatic forward modeling: Tx, Ty, Tz -Topographic model: nullcell.dat -DISTANCE | DEPTH weighting: wr.dat - -Written by: Dominique Fournier -Last update: July 14th, 2014 - -INPUT FILES: -Mesh: Mesh.msh -Obsfile: Obs_loc.dat -Topography: -Weighting: DISTANCE -Computed 0 pct of data in 0.04128 sec -Computed 5 pct of data in 0.04634 sec -Computed 10 pct of data in 0.05386 sec -Computed 15 pct of data in 0.06035 sec -Computed 20 pct of data in 0.06507 sec -Computed 25 pct of data in 0.07160 sec -Computed 30 pct of data in 0.08211 sec -Computed 35 pct of data in 0.08901 sec -Computed 40 pct of data in 0.09365 sec -Computed 45 pct of data in 0.10179 sec -Computed 50 pct of data in 0.10963 sec -Computed 55 pct of data in 0.11704 sec -Computed 60 pct of data in 0.12449 sec -Computed 65 pct of data in 0.13205 sec -Computed 70 pct of data in 0.13838 sec -Computed 75 pct of data in 0.14311 sec -Computed 80 pct of data in 0.14761 sec -Computed 85 pct of data in 0.15447 sec -Computed 90 pct of data in 0.15941 sec -Computed 95 pct of data in 0.16424 sec -Computed 100 pct of data in 0.17136 sec -Sensitivity calculation completed in: 0.002860 min diff --git a/simpegPF/Dev/MAG3D_inv.inp b/simpegPF/Dev/MAG3D_inv.inp deleted file mode 100644 index 9023ae01..00000000 --- a/simpegPF/Dev/MAG3D_inv.inp +++ /dev/null @@ -1,11 +0,0 @@ -Mesh.msh ! Mesh file -FWR_data.dat ! Obsfile -null ! Topofile -Model.dat ! Starting model -VALUE 0.0 ! Reference model -DEFAULT !..\AzmDip.dat ! Magnetization vector model -DEFAULT ! Cell based weight file -1 ! target chi factor | DEFAULT=1 -1 1 1 1 ! alpha s, x ,y ,z -VALUE 0 1 ! Lower and Upper Bounds for p-component -VALUE 0 2 2 2 1 ! lp-norm for amplitude inversion FILE pqxqyqzr.dat ! Norms VALUE p, qx, qy, qz, r | FILE m-by-5 matrix \ No newline at end of file diff --git a/simpegPF/Dev/MagForward_Intergral.ipynb b/simpegPF/Dev/MagForward_Intergral.ipynb deleted file mode 100644 index 4e7f6242..00000000 --- a/simpegPF/Dev/MagForward_Intergral.ipynb +++ /dev/null @@ -1,536 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Efficiency Warning: Interpolation will be slow, use setup.py!\n", - "\n", - " python setup.py build_ext --inplace\n", - " \n" - ] - }, - { - "ename": "SyntaxError", - "evalue": "'return' outside function (get_T_mat.py, line 131)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"get_T_mat.py\"\u001b[1;36m, line \u001b[1;32m131\u001b[0m\n\u001b[1;33m return Tx,Ty,Tz\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'return' outside function\n" - ] - } - ], - "source": [ - "from SimPEG import *\n", - "import matplotlib.pyplot as plt\n", - "import simpegPF as PF\n", - "import matplotlib\n", - "#from get_UBC_mesh import get_UBC_mesh\n", - "from read_MAG_obs import read_MAG_obs\n", - "from get_T_mat import get_T_mat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Writing magnetostatic problem with integral formulation\n", - "\n", - "## - Create tensor matrix TxTyTz" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step:1 Generating mesh and operators" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#mesh = Utils.meshutils.readUBCTensorMesh(\"Tile1.msh\")\n", - "cs = 25.\n", - "hxind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n", - "hyind = [(cs,5,-1.3), (cs/2.0, 21),(cs,5,1.3)]\n", - "hzind = [(cs,5,-1.3),(cs/2.0, 20)]\n", - "\n", - "mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')\n", - "\n", - "xn = mesh.vectorNx\n", - "yn = mesh.vectorNy\n", - "zn = mesh.vectorNz\n", - "\n", - "\n", - "mcell = (xn.size-1) * (yn.size-1) * (zn.size-1)\n", - "\n", - "N = mesh.gridN\n", - "\n", - "Utils.meshutils.writeUBCTensorMesh('Mesh.msh',mesh)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sph_ind = PF.MagAnalytics.spheremodel(mesh, 0, 0, 175, 50)\n", - "\n", - "Utils.meshutils.writeUBCTensorModel('Mesh.dat',mesh,sph_ind)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 0 0]\n", - " [1 1 1]]\n" - ] - } - ], - "source": [ - "# Load in obsfile\n", - "#Decl, Incl, B0, Mdec, Minc, obsx, obsy, obsz, data, unct = read_MAG_obs('Obs_RAW_REM_GRID_TMI.obs')\n", - "\n", - "Incl = 90.\n", - "Decl = 00.\n", - "B0 = 50000\n", - " \n", - "# Or create juste a plane grid\n", - "xr = np.linspace(-125, 125, 25)\n", - "yr = np.linspace(-125, 125, 25)\n", - "X, Y = np.meshgrid(xr, yr)\n", - "Z = np.ones((xr.size, yr.size))*280\n", - "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", - " \n", - "aa = np.array([[0,0,0],[0,0,0]])\n", - "\n", - "aa[1,0:3] = 1\n", - "print aa\n", - "ndata = rxLoc.shape[0]\n", - "\n", - "# Write obsfile in UBC format\n", - "with file('Obs_loc.dat','w') as fid:\n", - " fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, B0) )\n", - " fid.write('%6.2f %6.2f %6.2f\\n' %(Incl, Decl, 1) ) \n", - " np.savetxt(fid, rxLoc, fmt='%-7.2f',delimiter=' ',newline='\\n')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create magnetization matrix\n", - "mx = np.cos(np.deg2rad(Incl)) * np.cos(np.deg2rad(Decl))\n", - "my = np.cos(np.deg2rad(Incl)) * np.sin(np.deg2rad(Decl))\n", - "mz = np.sin(np.deg2rad(Incl))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "Mx = Utils.sdiag(np.ones([mcell])*mx*B0)\n", - "My = Utils.sdiag(np.ones([mcell])*my*B0)\n", - "Mz = Utils.sdiag(np.ones([mcell])*mz*B0)\n", - "\n", - "#matplotlib.pyplot.spy(scipy.sparse.csr_matrix(Mx))\n", - "#plt.show()\n", - "M = sp.vstack((Mx,My,Mz));\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'empty' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Call the function to build tensor matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mTx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mTy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mTz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mndata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmcell\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'empty' is not defined" - ] - } - ], - "source": [ - "# Call the function to build tensor matrix\n", - "\n", - "Tx = empty([ndata,3*mcell], dtype=float)\n", - "Ty = empty([ndata,3*mcell], dtype=float)\n", - "Tz = empty([ndata,3*mcell], dtype=float)\n", - " \n", - "Tx, Ty, Tz = get_T_mat(xn,yn,zn,rxLoc)\n", - "\n", - "print Tx[0,0],Ty[0,0],Tz[0,0]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For sparse matrix\n", - "b = A*x\n", - "\n", - "For dense matrix \n", - "b = A.dot(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x = np.ones(10)\n", - "y = np.ones(10)*3" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "30.0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.dot(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Gx = Tx*M" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cs = 25.\n", - "hxind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n", - "hyind = [(cs,5,-1.3), (cs/2.0, 41),(cs,5,1.3)]\n", - "hzind = [(cs,5,-1.3), (cs/2.0, 40),(cs,5,1.3)]\n", - "M3 = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step4: Compute analytic function (sphere in whole space)\n", - "Outside of the sphere $(r>R)$\n", - "\n", - "$$\\mathbf{H}_1 = H_0 \\hat{x} + H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}[(2x^2-y^2-z^2)\\hat{x}+(3xy)\\hat{y}+(3xz)\\hat{z}]$$\n", - "\n", - "$$H_{x1} = H_0 + H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(2x^2-y^2-z^2)$$\n", - "\n", - "$$H_{y1} = H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(3xy)$$\n", - "\n", - "$$H_{z1} = H_0\\frac{R}{r^5}\\frac{\\mu_2-\\mu_1}{\\mu_2+2\\mu_1}(3xz)$$\n", - "\n", - "Inside of the sphere $(r\\le R)$\n", - "\n", - "$$\\mathbf{H}_2 = H_0\\frac{3\\mu_1}{\\mu_2+2\\mu_1}\\hat{x}$$\n", - "\n", - "$$H_{x2} = H_0\\frac{3\\mu_1}{\\mu_2+2\\mu_1}$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Step5: Projection to receiver plane" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]\n", - "Qfx = M3.getInterpolationMat(rxLoc,'Fx')\n", - "Qfy = M3.getInterpolationMat(rxLoc,'Fy')\n", - "Qfz = M3.getInterpolationMat(rxLoc,'Fz')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100.0 kang\n" - ] - } - ], - "source": [ - "Bxr = np.reshape(Qfx*B, (size(xr), size(yr)), order='F')\n", - "Byr = np.reshape(Qfy*B, (size(xr), size(yr)), order='F')\n", - "Bzr = np.reshape(Qfz*B, (size(xr), size(yr)), order='F')\n", - "H0 = Box/mu0\n", - "flag = 'secondary'\n", - "if flag=='secondary':\n", - " Bxr = Bxr-Box\n", - "\n", - "# Bxra, Byra, Bzra = MagSphereAnalFun(X, Y, Z, 100, 0., 0., 0., mu0, mu0*(1+chiblk), H0, flag)\n", - "Bxra, Byra, Bzra = MagSphereAnalFunA(X, Y, Z, 100., 0., 0., 0., chiblk, np.array([1., 0., 0.]), flag)\n", - "\n", - "Bxra = np.reshape(Bxra, (size(xr), size(yr)), order='F')\n", - "Byra = np.reshape(Byra, (size(xr), size(yr)), order='F')\n", - "Bzra = np.reshape(Bzra, (size(xr), size(yr)), order='F')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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T76W8/B0qKvT1mvV/TSd7cloikZwcZPAmkZxkJvvg8/nAaITk5OeJiIjWtN+8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\nX3xxwDVfj3vHjh2sWrWKlJQUAMaNGwf4ftyRkZE0NTXR09NDU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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figsize(10, 4)\n", - "plot(Utils.mkvc(Bxra))\n", - "plot(Utils.mkvc(Bxr), 'k:')\n", - "plot(Utils.mkvc(Byra))\n", - "plot(Utils.mkvc(Byr), 'k:')\n", - "plot(Utils.mkvc(Bzra))\n", - "plot(Utils.mkvc(Bzr), 'k:')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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WSzHN2p8FWxkN2gMQ7PdmUvw17PEmyV8wMn8dPHgQCHsJvvbaazz11FMsLy8n\n4iwvL/PlL3+ZlZUVnnjiCY4fPw7AoUOHrGl/7Md+jEuXLvHqq6/y6quvcuONN/JP//RP3HDDDSNd\nZip+5md+htdeey2x7eLFizzyyCNUq1U+8pGP8Id/+IfpB/j7s931m0+FwcQoanmcD2PWOUzHSZu1\nxuY0aTOz5xVaeuhnaldkZWtCHMTKpMzsAclmQtNMbxN8aT168hK6bT3rw2BuA3tZjoo8jqNp500z\nt7/6dBhGwQwLp0EwEof9l7Pd9Z88FQYTwz5/o3z8BsGw/JXmj2XLrzDWs/hL5zBTbNmsTlkiyOQQ\nl/C9yBJPKr6Nv0zYrjONu7KEli2tuc12vnFgFH+sNP7656fDMCrGwGGPPvooZ86codPp8MADD3D4\n8GHOnTsHwJkzZ7jzzju5++67OXHiBEtLSzz++OOZaU2kNS0OAiH7NDi+9tpr/PzP/zz/8i//AsDR\no0f513/9V2q1Gtvb2xw/fpz/+I//sGfu9/tUDcyHy3xZ+6Ud5AEdpqxsvgA2HydzW5rPlM3x02yW\nM83mtiY/tbSZ3VOb8mTymuKyE701Kd23oaQt9VCOQsmy1INrHMfVjm3+1s+t57EfMY1CUv1qr7Z7\nnfd4ph9cWtpPioHa/YUQyG/ljh6muXt034JpxTAcJoSApy3lZeOo/chfaeIoTQwNw19mGvO3OoeZ\nxzSxo3OVjbNcklw1LH+ZXGaGQYXVNPKXvn5qMP6CwTlsmvlrYC050IXmeWj6EVTaMdIe1H75yPsg\n2ywY/chJGunM45lEgWVpni+tBmgLth6GCYFl3DsRVVGE6K2NOSRFmp4vPY5riWfCRohpwTXi2tJj\nWceynoW0WlpW/Kxj246n1vXluFBYsobG0GQ9jMCaVv4KUo6nW6xtTvJm/mwWLV2kZTXvKaFly6vJ\nCW4UV1mySpY0kOS2NN6y8ZXOW/14bD/z16DnzcKMcNjAl3ny5Elefvllbr/9dl5++WVOnjyZHnmY\nj0oWeaXFy0tSg+THJnrUer/aYNrDp5NNvxpMFjnZfBh0QrL2KrQppUhgqZ8mqZgCSidatS+v47u+\nrgsq1/id5tdg5nEYYjLzNShJ5BFKJhnp5xsnZoSgJoGBOExhGF7KG28a+MvkI/Uep/WitqVJ4zCb\npcsUWaYgsnGVzZpvi6+4Jo/AShNatt9ZfqW2Y+vnHBS7zV9p8QbBjHBYno6rCSwvL/P5z3+eRqPB\n5z//+R4VfzwUAAAgAElEQVRv/oHQj2BGJah+L8kowWYuThMN+jY9L2l5N8lJLdPIKc1xvaeXjQQv\nCr7M53hqHjOv46h5Xf3uQ1ozYlZ5jxLy3AeM/Xk+hFnpx4kxOL5Pw5QUk8DIHDYI/4yafjf5K+1d\nsVl0sq4jS2ilObm3tNDUlnrQ96lgc4Y3RVvWEDe2crcJqDShtd/5a5w8NiMddzJF1unTp/npn/5p\n/u3f/o0f+ZEf4a//+q/56Ec/yve+9z3e85738Prrr/Mbv/Eb6QdIqxXkueH90vWLk1Ub6Rcni5Bs\nQiptmymwzBc1q0xMoaX7ciUEUSSaOhI6AXha8APw/WQIomBu94y0Hdk9tifTex3axrLJuo9mmWWV\nVZ6yzfKLGPVZGCRgrNsw6HuQBVsZZgUL1LQS3/zmN/nzP/9zrly5ktivT0nx0EMP8dBDD+VKO84p\nKUbFyBymI+/HZj/xV94PftrHuJ/A0odrMAWWTUw1ZRhM8dXCLrZ0oTVI5XCQsrWt71f+yhMvL0bk\nr2lBpkb84he/aN3+1a9+dby5EMZ6HjLLijfM8WxpbTBN7Pq6+q37JmR9TNOaD237A+ziKpAQBFFz\noFraTGFmJjPeWClACJAOBAJ8J/xNtN0hvRePjZR0J9S8AsvMonn/8t6nrP3CknczbR4Te7+4o5jU\n0zBi7W5apqQYFSNxWBqn9Is3bv7CiJuVp0nxl285vkkzaRYtFcc21IwuqmwWdJ+Q06SMziOjcwht\nKbpO7aal3dZcqPdCtDWL2YRSmuP7TvJXGo8oHttp/hpVaE25hSovdv8ybQSV9ttMZyOgtPRZYict\nP2lI83FQv/VzqdHRzbi6eDJrL/qxs2qAfmStCnyQAciIWaSti6HNqcFi/5YOiGgpXQgc8CMGkW5X\nYJlElubbYKvVZYks3fFdP07eD55edv3260Vh+20eK+95TbIzjzcO9Hlzn74QhjRMy5QUu4o0UdOv\nQpf22zxWHv4aZJstzzYMy19mepvAcoz4NktWmsgyx7vqELo1oESWjLiNkKOU0AIoCfs4WKpMFA8p\ncWXjLmGJb3KXraI4af6y8Yh+n6Sxb5L8ZZ5vFKG1++pjR7C7l2lT93lFUVq8tGP2E1iD1DRsJKWv\nOyTJqZ/I8uk1/6bFi4WWsmL5EHihwJKaTVzabONm1U5nxkhECTdc4oIsQRDZaqWIapEifGo8wpqj\neVh16LwCy2ZOt93TvCQlU5YmzDLWt40Kk3xMEhwH+pjQT/10GBR+1+5ylYm9MCXFriJvhWzS/NXv\nnD1LOSB/CW2ddP6y5UOvJKb5CNk4TPfDUk1+VpEVHUAG2lIdX3TFVge7JUvnIpeuwFPclWXJMjnK\nFFglkgIrlb+M+9H33oheMZX1GtmE1ijIy1+jCCyY+mbAvHD6R9khmA/hoASlB0M72Aw2maGfP0K/\nl0/fltZEZooL2/XqIstmyfIin6vAgyBiLNkC2QQawLYWtoywrS23o/jNMK2MqpVBR/PfCtJ9skxB\no9+LQdrds8rRVmbD3Lus50EY+/IKdPOa8wj1UTGi4/u0TEmxZ5CXkwaJm4e/0uKk8Zwjh+Av2Z+/\n8vJYmiUrq7lQF1pW53YJLQmtANp+GFpBuK0p7el0Py3TN8vm+G67H1mVxLxcZSvbvvwlB+OlvM9M\nFgbhr7zH7IcZcXzf/ezbbuygNzqLtPIe0xY/L0xLliIUQbq/lYqvW7JMgtKPGdDtEegTCix8kB2Q\nkbiy9oW2WbOAHoZ2SYzKJ6M2wSAIa2IyiuOK6JAiacUyico0oSsSShvsT48/ilixWRZtcQS9+Tbj\nD2IOt8W1md7HhRHfXH1KiptuuomnnnqKT3/604k4y8vLPPjgg/zyL/8y58+ft05Joac9fPgwly5d\nitPffPPN/OM//iNLS0ujZXa3kOfeT5K/0vLSk0bat+WFJGyKQ6TzVxAd2MyfohaXXtcH/fhmc2Fs\nyZKao7vUOtnIyFpvDqylnMMM5eI4oUVOipCbApGMVhJJS5fNXytLWNl8Sksy25KFtN/LLEih3Q+6\nlkZbc53aj7EPbd+k+WsUobX76mNHsPOXaSOLvOImT9qsgGVpO14eQabDtOTopONH67qQ0onItFKZ\nQiNxEknsm0BHs1ip0CJ98CyzXc9mzjGHPK4C9TDIqKlQmbOJCE1lUpDUbTo5lYGK5fCKtGw+DViW\nZnnbtklLMOMJY5kmxvT4aftsMD8waQJ7FIzhzZ2GKSn2HHaKvxLHk8n0urBKpO1+1IWZJoak57b0\n8JcIvQeCaN0l9D5wBPgipBVH9L4/SpyZVhv9Q50QWlLrvSzB88H3NMu8OQy8WoLV1CbL4JdAROTi\nuNARUI7y7In+DvFmZbBMcnT42DIlwZWIWGBJhBNt78NfUto2qn2i+7uf4Io5TCR/m8fdi/wFMyOy\n+k6rM/SBhYDPGofOS1C2h9O2L42g0qwhaeLJlr5fPsx18yNvDrsQ0DsMQge7mVu19MX6ScK2hO2g\nG9ptYBPkZrTcihLbhoG3qQ7zom0MUwOxACxEy3moVGDO0YIIQ6TFEqFGqNNqUaiQLrJslqy0Mk5b\ntwksm3XKjKv/thWXGcc8ju3YWecw4//nwafVCVZyRwfAuZ7p9o/aYQgh4Fu7xF+JbZpYikWU7I0T\niTFhpBPxv+i3WlfxVf7Uh1/V5QIRiywZiIjHBNKLhJYvQtGimuP0ZYK7CL0SNrWwAWwpPtN4re2B\n34SgFS79Jsg2vZwm6CESUQG3Cm4NnGhZLfVy1byABWCRcLkAzBHylVrWCXmrSshZlWi9JLviSq0r\ncRUthauL267giu9GVM7dVzHcFv+MRJRM3A9hcImxHSMOJOOPk79sy7uHm1ZnEA6bZv7aOS2Zl2RG\nSWsTWGkEZh7HNOykEaZtad57vUanL9WxVYBu5UwXHOZ1AHGvQal6EiqFtg1yA1gnZDSz5mc6TZki\nS60rlaPXEHULVmTZkrq3onYMYRxGb4HsZ8mymdt16GXcb2mKpEDbH9f8tKUpfBwtjQ69Rmc7jpnf\nrPTmMYaAPyO1wD2DneIv27ZYSGkiyyLARGJb12Il9G3GMgH9XVJNblHzm/QFOE7Y8VhZswRJLwQ1\nzIxZeXL046L5ZcloXD4/HOOv0wbZgGAbAuU7qiqOuisE9E44WAM5H1rCpAw77DgiGu9Pnc+1W7LM\n5kHTkqW4qxzuF64SWwHClQgnElsiWjfvt1qVkX0xtjyFSylFSAWacBJEwkUK4uErNBHcDaKXQ0yu\nMbeNwl/90g6AWeGw3blMYazbBFBW2kwywi620s5lS2OzpmSt2x5yXWCZflc68eiklOUILzUmkxHp\nSFVt3ABWo3W97VFvKsyCOpnpdNAOMyOVVasTxtFrVPGFGNemk5UutPo1F9r80nRBRJ91JZJUcdnE\nlKk1TVGWV2iZsIkoW/oxVMhmhaD2JCbFX7ZtSjg5KSIrFlbJbSL6LWy/I4uXWu/JoAQZCAIpkIED\ngUC4Iuqw7CA8iVTNhWavQdsYUvo7k3B+l5G4CsDzwFMuEJsg10CqiqM5LLwg2Y5XBjkXNjOKSGCJ\nSthcqJoJO8Je77Q5npvNhBp3CVdGFq0A4QaRk7rEcQJDZCXLVSI0QdRdl4jQ5VVGy1hIaUuruEKr\nRFoeujxCy8QO8hfMDoft7GXmET150uYRWf2Cmd70A8+TxhRCOnR/c7VuOnlD0vDUoVdsKVN0RSIC\nHyE8hNtBeC0cuYUr13HkKq5cQchNYkuXGsYhr4lVCEIHA5fI7o0UTXwhCEQJX1QJRJ2g5EKtjKyB\nLDtIV4Y1xiyBpczt/ZoL9XIxYSOYtO2K/NXStt88TlwOxrIf0opXnWNQossJz3X6R0rAphoLDIRx\n8Zf5O3O7jD/kXbEFuqiKhZSydhGu69tFz3bZFV5Ex4sgpQg/+oFASidqOnTAkcgoL9KX4DhRqkgw\nSELrV1QX67HOq/fNB+EHCK+D47dw/DbCayH8LcLK4pq23KZ3XAfoVUHzoUCTqiuhh/TnCLwq0qsS\n+FWk5yB9t8sJiu9twqoiEaqpUK2XImHlBohSAK4fCysRiSzHMUlFWa+6QkuVb6KsI6HVK8Sktl0S\nW8ECkut5YXLSLvEXDMph08tfkxVZwljP+p0nbT/BlLe7qxlMgWVLP0gzJPR26NO7LpfpOrmr/ea8\ngRXisV9EXVKa93FFB9dpUXLaVOQmc94ac94Kc94l5rxLVPx1bTodNVCp7BUlel7j6xPgOmEohct2\n6QDbJZ9tV7BdKrNdqtESDn4Q4AcSXzp4QQlp1vh0Pwblj1UnW2Tp5a+QJaRUuaWJLCWw0tKnQZWL\nbs3KEmUmskhLj5NXxKXALw366rZHO+EsYhL8Zf42BVUPpyUFViykdFEVCSfVXCi0kPwdiSwMoUUs\nBWLIQIDTFQQyEEjXRfoOBA4ycJCeQyAEUjgEwgFXIIWIrFUi4jPRbdFT73YAju9RletU5CpVuUqV\nVVzWCa3yugOXzc8UeomkRtLRagGPA7TldbTkIVrBIdrBIfzATYqruBIouzw1JxE1iagGOJVoWZWI\nUoBwIpHl+KGwcoyydiwWLLVUZYlWrpq4kroA0/YJbb8SWtIRYc9LJ7qYHq4S6VyUxT87xF8wKIdN\nL3/tjsiyCRMzrrm0CR2M33mEVB6B1U9oZYk5BVMI6EJKBdWkaI4do0JETo4rced9KvMdKvMtKvMN\n5t0NllqrLLWvsNS6xFLrdeZaa9AOQj+EdhB2j/akXWCY+S4JqAioOPFyq3KIq1XBtWqZlWqNa9V5\ntrwy7S1Je0vQ3irhb8rQTUsJK11sKedR5VRqE1lpTbQ2gWWOqWoTWfqQGSpkCTKzgqSLK8dSZmnp\nVF6FJb6+NOMOCd9NM/kVGBvGzV/6eg8vSS1O10qlBJawCC2h/daFlNrmiMDYF8QCq7uEUAJ014FI\nYBkCIHCQpUhgBQ5BycUXLoHjgOsSuC4SRxtwVHR5TFWmolM4QYdqsM6CvMSCfIMF3qDCVeLx+mKv\n+Ta5hnCggtnzpiWX2ORdbMl3IQIXL1jAl9Xo+uharxRPVSXUJdRB1AKcqo9b8XGrPk4lwHGVsArC\nsoysV6bIVTdaokSWJqxAE1pOLKoCqVkNY+d3TXQp/7hIfInIrUEGgKN+qNsnwnwoodXP9cHctgP8\nBbPDYTsrstTSFCS2NP0Elk3c9BNSWeu2QeJs8c19PeeUyY+x/lH2RFJkOdhHQDYs4cKVlJZ8Koc7\n1I+0qB1ucKi6yTu3V3nX9go/tH2Jd22/wWLjWjjWTEN2J1LtkG/A9zJQE1AX8XK9vs0bc2XenKtR\nnlsgqB+EZhXnioDLJYIrFTpedEDTkqWIS9UOVY+dhMiS9vul18T08jOtg4GxX1qOhbbdjK+eGZvQ\n0gWbzYplpjPJyKwR2p73EYjKT21XLTA25OWvcQmsxHp3qQusWHSJpBXFvh7giLApy9FFlia0nFgK\ndAMQfuQR8Uc7FAJOJAwcAukQ+C7CKeG7pXCaU9VpxRfJgUbLIimyAnADj1qwwWJwievkqxziFepc\npjtyaLfZr/flV4WpE7LppV6hwQ1UZBshXTpykW3/huR7H1uyZGTJikJdQt3HqXi4FY9SxceteDiu\nHwnXIF7Gfm0iklVCFZ/oDdL87cR+b0I64QwLjoiFVmxBlJGFMHC6U9NG5ShioSU13owEliaYd6Qz\nz4CYFQ7bGZ+sNILqd4P6CaY08koLaZYo3R8oS2iZvkM9cWQyX/qDGhD1phG9liydkCr0iC1RDUVW\n9cY29ZuazN+0zXVzm7xzY5X/YXOFmzcucWzjda7bvBp2jd6iu2zRK0ywXGMVmCfs4hwtVxYaLC7W\nqCzO4y8eYntxCW9jDr5fIqhU6AQ+YlN2m0DNZkOzuTAhsmSyCUF/FsLqXnfAU5V33c/NFFxK9NgE\nvNnB0nRuT3sOldhScUzRZxNaJszrMs9lS5MD3owQ1J7AqPxlpjUrZT3WrEhU6Q7viR5sgSamumLL\n0Xq4OZGlxRH6eldkOYnPvSa+dKEVZUXZuAIZWqoCQpHlB27YfObI0Ne8HRFhzGci5B/VXKjxohN4\nVIM1FuRbLPHv3CBfZJ436X3BB+kdnQyb/DBIl06wwHZwA07gdZObuqxKV2DNydCSFYmrUrlDudLB\ncUKR5RAgCNfDs8vEs9ArsBxrSQfSwQkcAkciAtkVsiIStYEAISIejMhGRmJLE6whD2n3TGhCS0b7\n8gotExPiLxgPhz3zzDOcOXMGz/N44IEH+O3f/u2eOA8//DBf+tKXuO666/jCF74Qz7ualvZTn/oU\nX/va1xBC8N73vpdHH32U66+/fug87q5PVla6QUVUWrwsoWR7N81BMRP7ZbxPxHGjWoyTJMvu5YVP\noZD0jq3nguNLhC8RMkBIiSOj3+0ApxWSZlm0mCtvMF9bZ25+g7mD6xxYWGHJXeGws8I7WeFdcoXr\n5dXuKZUoEfTOF03yuoRDKITmojAPLEBtEbYPHGLzwEFWDxxiZfEgLcfFvdqmVOtQLnWoCI+OqCBd\nQVARyLpDMC+QcXCQ84JgTiRFlho/MC4o0S0tzcwt45HlRXdKRnVtUiSvK+0jKLTr1mqBCWGWBl1c\nmdv1/SYZZR1LTz8k/FkZyW83MSn+irfZLLkyEUSiuTASVJG4cjSH666wkqGwcroWFycSWEpkOdHn\n3oklgPr0B/E+83LC19HRYociq00FR3Sb0fzAC3vhCQmBRHiE098Y8xLWvavM+5dZDN7iQPAGB/ke\ni7w58C3KQkn6NOQSjeAIW/47qXtXkaoJs62FTjQmmBDIkkBWoVTzKZfbVMqdaNnGdfy4jMJSCBKv\ntIJeRslSNUpbhr5sInAQIhSuIpChn1vghPkJIp+3ICIqJbYCgep4IAKQTpC0/usPaCzcRXYzof7b\nhjHyF4yHwz72sY9x7tw5jh49yn333cfp06cTAyNfvHiRZ599lhdeeIHz58/z0EMP8eSTT2am/Z3f\n+R1+7/d+D4DPfOYzfO5zn+Mzn/nM0HncG0xtIzOToMx9gwixNOtUlsAyRJVaF66MA67USE8jP+Xz\noAcZWW4CEFEToVMOKDsdyuUO5VqH0lyHSr1DWXSotNtUNjqU2x3KzRbOpS0cZxu3vYWzscVc7Rql\nrTcItldobm2xseXhRIMAym2QkUuDbBOP/hCPAkF4fUJ0l6ICzIGoh4E6rM95NOc3CeZXKM3XmJ8H\nf+sah96cx39zjuDteYL1OTpulc6BCu1amfbBMu0jZToHynTmynhz4bIzVyYoO+Ho0ZorhVlSAZqv\ngjKVqwER/XC8HhXCCbJFd5ofTVgitHVdgJnrCmmdV9JM4rq4sgkt8xhjxqyY2qcCefgrk6v0JkJN\nYMV+V0pcaU2EqgnQkRaLlYxFlbK8OPjRMrZFJQRDclsQZU1vSux9V33h0nHKdNwyHVmhTZnAAbfT\nxN1s4lxp4b7VRFzuwDXCsAZsQb21ylLn/2PRe4NasIorVa/B8cGRber+Kove67Tb8wA0tw6GHRcd\nwopuE2SjjO/X8Ms1goUavlvFLUO51KbsdkKOFh1c/B5eT5aQbq3SS9WxbBMEuARIAiHDJTKyakXW\nLRmlkyAQkfiKzhetEx1XBBk0o4urNK7aYf6CbA77b0+3ee7p7GdibW0NgHvuuQeAe++9lwsXLvD+\n978/jnPhwgXuv/9+lpaWOH36NI888kjftIuLiwB4nsfW1lY8Bdmw2H2RZbM6ZAknc7stXpofVb9g\nm+LFVUIrElXRAHROKVo6XVO8o5Od8Yo5RDU7n3AZgFvxqJeb1GoN6nNNaq0G9WqDuXaDufUGc6LJ\nXKuB22jQcVt0Ok3aG006l5vUKhuUmivI5gqt5hbrTQ/ZgqBFPJWhbIGMxuaLQyQm1OT1cSiBqHaD\nU4GNmkertoWsrlCuCeZqLdzWIuVrVSrXapSv1ais1+jM12iIOtu1OtsH62zfUKdxoEajVqdZrdOo\n1mhW6/glN6yIOQLpEvof2EpK+XwEUW0v7s0kCHwnEltONPI0mm+bSIqrtGcoS1BJy35bTc98/mzx\n9G1jJqpCZO0RDMJfiW3RA2ERWLEPliawnB6hFWjNg0pQGeJKBDjCxxX62+Xj9rxxvdt0+4ujCQp1\nIeFr4uKJUldoUUa6PqXOGuWNNUor65RfX8O91Eh2GtyGSnOTRe91Fr03qPqruHL8vcdc2aYWrLLo\nvQEIynKbjjPftWBHQ3L5nTk65QN4iwfpeAfx3IOIshOKq0hglemgLFdEJaCEZ9JCpZeqEwkpc5va\nLglEuE3gdsWbElgSUMIqajYMfd+jTggEBNHFSJx4uDSp85cU0bMUkZAaoTav0JoQf0E2h915qs6d\np+rx70d/d6snzvPPPx83/QHcdtttPPfccwmRdfHiRT784Q/Hv48cOcIrr7zCq6++mpn2k5/8JOfO\nneM973kP//AP/zDcBUbYXZGVl6DM36MILJvfVarAUiGIp1IQboBTCnDcAKfkh0vh4zoBrvBx1BIf\nNyIvFx9HhqYkEYCQoWWr7HdYqG2y0NlkwdtgobPJgcoGBzY2OVDb4AAbHGht4K412Gh3WF/vsPF2\nh426h3CalLwNgs4mzc4m6x2PTicUVYEWpN9tlo+nPSR81xwlsADhglPqBlGGRsmjVd4kKEOp3GK+\nvMa8V2OxWWaxUWKxUWaxUcYvz7HGAuu1RdYPLrJ+wyIbhxbYLC2yWVpgoxyue24pJAghQrElBL5w\nohJyY1LyAxdfOgQyXA+dbB0C1wVf4ntO1OQqwHG64sr2LOmWLf25sSHLGV4nIH2ZJsD09QkQVYvK\n+A5WYDgMLbBU6PXB0gWWspLHFTldZOmWrEhQuYbAclE+RF2hFb5lGi8Z25KCqysT9L5yISQSh45T\nwqNMR5TwnDI4bSqdt6luvk3lyttUX38b943NUNC0iKcOK3Wa1P1r1IJrE7NkubJNzb8GQDnYZs6/\nghdUYwsWG0AVvGCR9vwNtA7fQLtzAy3HQ5TKlJ0OJccLl3gIzbdACc5udbq31MwS9XFx4vVIYEkX\nEVmyfOHG3wYR14pdpAhCS75wkEGgNeg6OLHQkokKdCy0RPRP9TZM4yrbtgnyF+wMh0kpe6bjyTOn\n6u///u/zyU9+kk9+8pN84hOf4M/+7M+GzsPuW7IU+gmqvJYuU2ylCax+6y7duapKMh7ll5KPKEUC\nq+Tjuj6u8Ck5Hq7wcR2fEh4uyWVcz5Hd5sOKbHEoWOWQXOVQsMrBYJXryqssXbnG9dVVlrjGUnsV\nZ32LKxsBl0UYSkjaeJQCjyDo0Aw6bAQeLRkNjRUNjxUv6Q6XlXDJEloRRXpFLV0HWsKj6WwinRZl\nZ505p0wVlyM4HJGCw9LhiHTwF+ZZEddxtXYdVw8eYuWG61g9dIhVJwwlp40QQei7JaKan+hasTxK\n+FGt2MfFC0r40sWXJUTg4ksXfBfpSoTnhlY4D6TjhJ0J4ndGdEWVzZplPm86JP2d4W0m96x4an0C\nKHyy9hgGFVjxNmkPpg+WE1XwdB8sbd1FCS2/K7BQFb6koBok6Gl7msqEwKOE54ShI0sIt0G98wa1\nje9Tu/J96q//gPIPrvWMwiACH1e2KMk2rmzhTGAcJJcOtWCVsmxQFyv4XgXZcWMLlqpEt8USzetv\npLm5TaPj0XRdKNcoCY8SXrgUndhylXRv0KvTTmZpKhuWH1mtApxIYDmxVUcIJbCIfatCvywRunxE\n3IkgFlqCSEjY+CvmKtGfq2zbJsRfMDqHnTx5ko9//OPx7xdffJH3ve99iTjLy8u89NJL3HfffQBc\nvnyZY8eOsbS01Dft3NwcH/nIR/j1X//1kfK580xtfphyk1GfbWkCy2bRsk3lYo7G7oTCSpSCSGSF\no/y6SliVPNyST8mNXkJHvYxe/HKWCWtAKpikVZNNrvOvcZ1/lSX/GtdxjSWushRc5XrvGkudqyw1\nryK2t3E88DvQ8aDhQSMIO8ZAWDFTI8qYnfDUxDr9OkDbisfHx8MHWmomQ+YdOFSG60vwjhK8swR+\na55aZ526t0Et2KDGFnVni5rboOo2KbttSm6Hlqj21O5U6XQod0tKRiEo4ciIvJ0SQgT4Ihx1Wjql\n7vxpIjTTx+xgszDYILWlIqh+zvC2psJBnEnHRF5Fc+EuwSbWR+Eqbb8wrFnd3oORK4JjF1mm5coV\nvSJJWatK0briI7MymLU0G8SQ0PHcMPjhUqxtUV99k/q116lf/T71lf+gvHJ1cvcjA470cKRHme3M\neO35dZpXJdvXHCqrLpW1CrJUp1zyw+D6lEtBJGx0BxARVgpVJTHHUvfQCiKB5ONGtCWT77Ug9EmN\nhnXAiURWPPdhRFWSsPNS4IRpIOpgKIbjKrUNbbvOV2Oyao3KYcpX6plnnuGmm27iqaee4tOf/nQi\nzvLyMg8++CC//Mu/zPnz5zl+/DgAhw4dSk373e9+l1tuuQXP8/jiF7/IL/7iL46Uz53vXThOMWUT\nV1m9CNOEl/K9ikVWV2A5JR9RDuL1kuvhul4oriJhVRadhKgq0yF0A+0k1qu0qNCOQ91vcHBjjYNb\n6/Fy8e015l7dwHlzm861NhuNgMCDLS+c4kv5z6txPZ2omKQWbLdAj6d+m0WNFkcFVVTqnC6hhazt\nw5YIfUjFdkDnagv3zS3m51xwoXzYo7bQZm5hm4WFTQ4trNIo1WlTiUqhGpdEJyopfd2jREeEJepS\nxnN8PLeER4AQJRwhCYSLFA5hPV5V/ETYfKjWTZjCSi0VAu13mu+WKlibZcs8V55tQ6AQWTsAk7/y\nxM/DaUCP5QppCC29J2FyqQYZdeiKLBc/FldZFiu9wqdXAPUKoVk5VL9NUSa9gNa6oLUOrXVBcx3k\nWw2q332L8htXcVe3EG2PvQ6n5eFe26Ly+jXkXDhYqXNDjeoBqB6QVA9A7YCEctfqrkSTGfTKolp3\n8fG67+cAACAASURBVGOB5eHiUIoFlW4X099pIWTMFQHhsA5AOPBrQPeZkNEyqkrHTvBCkJhAXLdm\nDcJLWWJqRB4bB4c9+uijnDlzhk6nwwMPPMDhw4c5d+4cAGfOnOHOO+/k7rvv5sSJEywtLfH4449n\npoVwyId//dd/pV6vc+rUqZEtWUKaDZZjghAC/lQ79CgCK00opa3ry7SmQHMSU9U0GAmuUFQFiLKP\nU46aBkseZdejpLfVO5EsEB0qoruuC6oqLaq0mGM7DnW2mW9us3h5k4Urmyxe3mLh8ib1t7Zwf7CN\n+4NtnGjpXWuz5sN6EIY1PxzMXR9gvUxXM9jG6NS1hVncpBRjQHKM1A5QFXDAhYMOHHDgoAvuUgX/\nxjn8H5kjuHEO/8Z5Gu+YZ+PIPJtHFtg4ssDm4Xm2qvNsU6ehlUQrKp1QfEXCS0byVFboyHIUSnhB\nGc8v0QnKeEEZ33MJOi6BWnbcyBledH221Loy66lgM/np425lFWRWPLT1vNseFD1+A1kQQnBR/lju\n+AB3im8PdI5ZhxACLhrlJSxLk4NycZpqDoyWiaZBfRn5fkZWK8dNDscQd7YhSAos0WutUvYUUxaU\n42pN/qDS0fDYfiug8VbA9puS7bcC/DcalN+4RvkHV8Pl61dxV7MtSbsN/7p5Oj98HZ0fXqLzw0u0\nf/g6yj9Uo/5Oh7kfcph7p6D+TgdqXeE0SIm1qRhCrBTfjeTdie6W1JofZeTrFUSdgeJOQZGfaqDW\nRTwSv5prUgYitG7pI8bHI8dHF5+Hq9I+Imp552D8BYNz2DTz1+6O+J53my1OHoGVtZ7WfOiGTYOU\nZCyw3LKHW/YplSKrlWh3e52I0FJViZZKYKlQoxkLrEU2OMA6i6yzyAaL/iZzmw3mLjeZ+16Due83\nKL3epHOlHYZrbbYbAa2oebAtQyuSumn6rDRZlixB73thfi90mHGVJUtERSSDUHRtydCyVmkElK+1\nKbtQa3uUN1p01rZYaNZDOVmvs31dnU3mWQ/d+VlnkQoHaFCnSY0WVcp0aFKjJCq0pUdJfTiEjyPL\noYFe1fBkWJvzIwtAWBGL/BVUqZiFYZKHbt7TrVdm0yHGul54tmbBHbBoFT5ZO4C81it9PY/ASuzr\nWrHiYRt6rFjdqVxikUXvUrdmmVYnNxZVuo0lXOrWdTMoS7weVAVSeh021zpsvunhvuLh/HuH9g9a\nuNe2cVe3cK9NhyVLRJYsAGerRenyOpUrVRa2SyxQYmGuzMKRcOTSlmaBN4MSVCrYmliTw2LI+DdA\nPAisRtjSMraVFCKyYAXdR8qJqtgy4kPVozC2ZunHiVb68ZL+sdC5NM0aNiBmhcN2TmSp34MKrDyh\nn8BKE1cJoSUTAkv5YDklPxRYZY9SqUMpElRl2lRE+GqFIqvbCKbEVZ0GNZrUaLLAJtdxLQpXuY5r\nLPob1Dbb1C63qX2vTfXf2vADj43NgI0Nn85mwEbDp+GF0xF2In1RkslLhG6FQ62bxW97b2y3yqzM\nqKKLpx+L8tDyw0pRO4C5bZ+Fa21qHY/5jRYLV7Zgs0xTVGjWK7SWKjT9CussRiWwTYU2LkFM4g3q\ncQeB7sdC606uPkDIOJPd3sjh3F9hX2enexUSMKfnUQVlCqw8QssmrtJU6oSFVtFcuAPod2/yVgjj\nbdLYpgsuacyDFw3JEA8uqo3orvywNGd3WzNhKLCSFiybrUVVCHWruy6mzEqjqjhKr0VlvUXprRbO\nKy34dhvne22ctodoeYi2Nx0iq+3hXtvG2W5TurxBUC1RX62wKKocnKty4IYKB7xwnjBVIWxGJaEs\n8Ep8mf63yQ4DpklIPQrd33H/TduzJ0BKEfZUdyB00oosoTIcbwsnQMrQoT4WWhK6wzh0z9RXaAnj\ntym0RsSscNjOSUnbBylrmy1k7c9rtbJZseIxsLpCKxZYJT/ywepQcj3NAKw3Berk1KKmNQ2qxrEF\nNlkKrrIkr3K9XGEpuMqB5gblTY/KqkflikflLQ/vzYB2C7ab4ZhX7TY0/eSg5qqVM6sI+4mpNJj7\ndUuZGgRfyKgpUYatbKWORG55uD5Um7CwAU7Job5Uon2kRHuzRLtZolZtUnL8SDCFBy9FNWn1Qeh+\nKDwcfIQIiSmcuiK8qnjwPymQJeIJVgPpaCpRzfOlkUmaBUstTX8s0wneJq7SanU2Quq3bQDMCkFN\nDQYRWLEvVnddd3qP/W3iiYi74iqe+BnZ4+TeK7R6vYbKsXWq6y+qRFPPUmqSQraoBU2qQZNa0KQW\ntJCbTVhtElxu0XmzSev7Lfzvj38ohklDeD7C82G7FW+riTJzh2ssvLPKgWs1Dm1WEeUaTUeFJi2n\nRlNopSaqcTknLVfEIkt0ZRRdS1b4y8bOEhFvllLgiHAIh8Q8lTIIe1xDKLCcsLdhV2hFx+8ZxqGP\n0JowZoXDnP5R9gCGsYD1bSKUiSA0R/e496ByblcO7hE5KetVNQ5NajSo02A+ElQLbLDIOgdZ4xCr\nXBdc44C3zkJzi7nNJrW1NuVVj/Kmj9sIcNoytpioLJpTnqqgZqXRXcpMtxDbtqyQdQzThU3lS+VN\n5Ue3rIkAnI7EbQSUN30qax619TZzm00Wm5sc8NY5JFe5jmscZC1qQNxkni3m2GIubkhsav5t3Y9D\nmQ4l0en27HTD+xYOrRGKZGHc4+7AsinPhK03QN5nbocxWEd8O5k988wzHD9+nFtuuYXHHnvMGufh\nhx/m2LFj3HHHHXznO9/pm/ZTn/oUP/ETP8FP/uRP8uEPf5iVlZXxXvhuI63ylxlft2ApM6xMfigN\nJ3dH365NRiw0gWWOKd7r4O7HTYQmd1U0i7uyuof8tcU8WywGGxwINjjor7HkrbLUXuXQ1joH1zZZ\nvLLF/Fvb1N9sUr3Sprzu4TZ9hLcDX+YdgvAlpYZPec2jeqXN3Jst5t9qsLiyxcG1DQ5tr3Nde5Xr\nvFUO+escCNZZDDZZYDNRwVb8VdUaGvUuPmbvc1MwJ5ob1dyT+rMjkk3LjmN5ppyuiE82T9N9Nq2F\noC0HeeZzYlT+mhbs3UbRYYRVlnpIqAWZXEaDjMbO7iUfx/XDD7camkEztXdb3Vta02BXaM3R0IRW\n+OItyk0OdNZZaG5SbzSoNttUVj3cjYBSI8DpBOGchSStVbpTu/LN1i1V0FvpGIbqRMq6uU3PmypS\nXfjFOiWQOO1QZLEB4pqkJtrM1Rp4tVLYfdmVYS9NzbyO5r+grkRqN1SNTRN7NDhhCNzQmiVwwxbB\niEv07s5hrU2ScPxU+3QLVT+hZbNm6cfaAYxjctVpmPdrT8F2v22/1baEwJLa9mTTIKq3oEVoqZBo\nQtSElTlfgq0nofK9Kkcf+KphiddFVp0Gc3KbOdlgPthmPthmLtim3mlR2upQ3vIobXUobXl0LnvU\nrnQor3mUGiF/7RcIT+I2fMprHWpXoP5GQKntUZ3v0Jlv4c2HX4RGucaW06bitCk5Ho7UJ5HWmwV1\nU3ovuYR7wm2K29Q9VfYugUAIgZBCm63DwdHGVgv03oYy3C7jZy7y1VI8GDcfaqYs8xbqHxsz+oi3\ne1Ymud+bIqufgs5rxcpqInR0oRU2FTpqNPdEM2F3mAbdj6FbIwybB2uRuKqzHdYEI0vWgWi5KDdZ\n6GyHlqytJrXNUGQ5mxKnEQoSoVmylMVIWY1U1vXp+KDXSX3Y59/8fqQtbZYtXWQlLFntgFIDnE2J\nuxbgu23m/AYBDpQkrvR7BFaAiKkmKar0meyduFuzFALpdl1LBaHYklGIh7hXwsrFLrDMAlRBpqxj\nbJPGtgmjn9Pof396lf/+9Grq/mmZ92vPwPZCpNXq4zhpPlgyEXqGarCsx2ILLQiJmnswzQZQ1ixZ\n5T6WLL3384LcYjHYYsHfZsHfotZu4m77uGs+7qqPsxbQftunesWnsh7gNnyc/SSy/EhkrUP1imRu\nwaPccfEPtQk8B1+4+GWXmqhRdkOLVGxp0poBFV8ZR7dQkEhwXDdE20TYg08SNQHSfX4Cy3AOQvlq\nEfqpSv3Z008cO7ZGRGbjL7MSaeO9IVA4vu8Wsr7yeaxYZo/DvL5Yyg/LDZub3JIXjt7ueJQNa1aX\npMya4HbUXLjFQtSH7mDUZLgoN5jrtKg3m9Sj5sLKqhdaeRoS0QmtP3rWlcDytGyr/boLpakRhrVk\npRluzOJVAlBvOuxptvQlThtEw8fZEHANpNsOa2uuxK16VGQbl05c60sSkvodTqRqCiwlsgKnO8Oa\nK8Kzh/yiRJZjFJTo+mHZgto3rDVrTLW8fuhnQv/RU9fzo6euj39/4XdfS+yflnm/9iTyCqz4t9k8\n07Vidf2tjDlQo3WhB7pLs6nQtGTZhmowBZYKsQVLCSw2OSCjJkN/iwPeBvVWE7ElEWsScSUMjUuS\n6hVJeQ3chtxXzYWOJyk1Aiprktpln3oFqr5AepHlqSyQdcG2G3bYcYWPcAKkxsCKl3rf1eSE0kpM\nKQt9gKPd03BmQ0k4QXT8J4X23ISVWuFE/j8ScASBVBtDq1Zi7Cz1oCqLlnpOlc+W7VbqHDcGTHsz\nYF7sDZFlElaa0DK3ZYktmzroCZHaj4SWcAKEG4Q+Wa5PSURT5YjenjlZQmuOLeaDrbA2KDdZlBss\ntjapbneorrWpXG3jXvURlwPkVQjWQW6D7EDHB8/vzjWoRI2pAcCuEUa5BXmK0ubSpCpWQRAOmNoK\nIOhE4nEdxFWJqEBJelQCJ3yPywFuzQub9kQ4pY7nlOiIUmy56rrvlnvq6OGwpA6BCIPrhD0SkdE0\nFEGACCInUAfC+YK0TEeVvFzPjmmlMi1bacJqzKSkYycIai/M+7WnkCWqsuInxBXay2RYsxKiy1jX\netd2P8W9AssUWrrg6uWuVk9z4TxbzMtt5vwt5jvbzLe2mGs3qG02CdYgWIHgMviXwLsE/goEGyCb\nIP0xl/cuQvqSoCHxN8C7Cp4L5ehz4TjgVMCpAUj8ioMvHXwh8F19yjAR85VZSez2nQ6DumcBAtci\ntsLmQyXGlG8eCBFtjZzdpdYEjePETYbdYJKS6F8xtPHYGCqShcjaDaTVDvOk6Se6EtvV2wLxQICJ\nGqQfD/YXz1+VMoiflaxkg7rfpO41qXktal6b6laH8koH55IPlwKCtyWdy+BfAW8FvFXwt6DdglYH\nml44qrone8eCy1MkYO80kqco+0EXdQFhHlsBCC8c1sEDyltQWgXXDUfFcFsgtyU0fZyOE85q74I3\n16ZValEvNWmXqrRKlWgOQ72k2z0iy6MUk5Xew0oKB0cEBNHYQsn7rC5SJC84LZgFk+cGmNauCWFU\nf4Zpmfdr/6Hb1KM7MVt5isjIIHR7h+6VmC601HoWb6mehT3NhTJsHqxst3G3fJztgGANWm9H4VIY\nNi/DyjVY34BGE/x9JLJ8H5pN2NiASgmQMC+hSjggc82BqgNOS1Ka96jMtakJF6+sPOVEwqZo3jFd\nZPm4mshSUz4nfe4CBN3poJ3Izh89P7Lb8hf6XWnGqeh7J3Wnd9SO3UXhk7WTSPuwjXKcHEIr0W1a\nm83ecYLQMqLGaxL2cWb0oehqmgN8XUYCqxWGeqtFdaONc8XHveTB6wH+GxL5NrTXtbAdDtnQ8aIQ\n9IqsvGJr2OLLAzMvvuyOm9UBmhIqW1BxoeJDtQWVDWBbQifAlT6UwK1KvKBFvdqiXW2GHm5uORZZ\n+ojJpsAq4YUWLeElxFbgODhOJLREaJ0M7enKXE72s4Fl3yDq1sZhE7FkjfbqTsu8X/sGiWdKsyxE\n+3p7jUlNXJEisKT2IU6Kq7TpdLIqh3ORFX4u2KbWblHZalNa9xBrEnk1ElaXYOMSbL4FG1dgYzMM\nzX0oshrNUEBKoN2BhQAWBSyIkFLKDjhegOuFw9D4ZYGsKzeHbql3KCcElc5fyiqvhJaPg5pK2okF\nVpAQWEH0LEj1bOiC3XiOlKN77PSuHN9hR3xHs1D4ZO0UbBaDvF/7YT6YURAJi5bmE+EEuPr4M9qy\n2yW6k0pWNWXJ8hrU2y3qjRa17TbVtTasBHApgNcDgu9J/Leh1YBGFJoNaLVDs3sQREF2LUZ5xdWo\ndZWsdGZTpbJkBQF4HrQkOD5UJNQDqLdAboKog9sMUzglD6cWwLyPL0q0/BYtmrTdCm1ZSbjqtilT\nodozP1gJL3Q+lW5iMEZlgVSjZOtTmAhHxnOA9bV+2uIMWnhmM+MYMQ5T+zTM+7V/0LVgYVgZEt3r\njS728dhYFktWb+/CpNCyWbPMyqHNp3ROblPtdChvt3HXPMRKQHA5tGJtvg3XLsHVS7B+NRRXzVYY\n/Ky5PqcMXiSyADod2N6GAz54kbtBxYV5B4SUlPCpVNrIukRIP7ZiKa7SK4k6j+kyuHu/lKhS99Mx\nhJZqiBQJYQUkBbyylmpWrHBGDIg7A6mBSncJRXPhTiDrA5c3rZ4+7VhWoaV9fHWnU+HjOH5i0tXu\nOCbpvg2xFYsmc6q5sNWk1mhR22xRW23jX4HgLYn/A0nwmqTzdtgsuO3Blg+bXjiCeg/nMnkr1iDQ\nBRbRUvqEA66HblFUO+ApgeVCqQSiGfbgdCsSZz7APQBBqUSbJu1ShU6lEQ2V2CWnKlVatBMCq0yH\njvLTEl2ScvEJHJfACRLjDMn4XpP9fJCy34yTB3r8fj4PQ2AcBPWzP/uzvPzyy4ltZ86cSfz+7Gc/\ny2c/+9lcaQH+9m//duR87Tv0VPw0XyzdCR6pdc2XiWZCs8nQNnyDbsVKH87BS1QOVXOh6h0dWrK2\nKLd9ytsBpXUfZyXkqtbboRXr6iV4+y1YvRYKq8APLT/7SWSp5sJOB7a2wXWg2QnvTVmEAitwoOQG\nlMoeck4iFn1c2SZAaALLLrL0yaP1oGRV9697b7viK7RjOSIadDRhxRKJ50eSFFoJvyzl9L5LQqsQ\nWZNCWrNgmkUrbX/Wep+Pp4g+uOGo40mnU2UJMUdSthGV3RG+RVW2qPptKl6bSrtDuelRaviwDWxB\nsAlsgFwPhYkvwQu6U+fYxsfcK7DlRUZNmoFaEvqZex74TtiMiAPMgdiUOFsSdxtKDSg3PcoVLyyr\noB2WHZWe+dOyBu0L5zfs1v6EcOMeWer+EpnLRTxuTEYA+zOkftssVGbBZAmrMQiuWfFnmAqkcVqM\nSOBrlizdmiUS6TR/LZTVq1dodcWW7BFagzQZVmhTkW2qMnz3arSoddq4zQB3Kxx6RVyVyJXQAbx9\nDRqrsLEeNhPuV6iWhI42K5BbgsU5aNfAr4KsgKhKnLpPaVEiIn9Tz23REtH3QITTr7WpJHhMyS1d\naClLlp+4l8l7HWjPQvh4RM+I7uBOV0gpH63/n713jbXluO47f1XdvV/ncc998SWGtIXIEqUEI1Kk\nrzIxDCpwokyEQAKSIDAixRPRGEUfpBgIDYwADUBnjMk4MSADhiw5GRMOoAgIEsMIJkjkWLZpGgmo\nKw3kxLihx1QgxxlZtsnLS97z2K/uqvlQXd3Vtasf+3HuOYfnLLJu9+ld1V1dXf3vf61atZb5wybt\nHPQsd31sOgYtvJXzgmEnp8laVmvVpH3w8/plXM/vVl1vpwmdJdTSOgUMjBLr/NCEDEsL309aGbcM\nOfsQAmQEUQ8YQjyCJIV+mq8qTEuFi3srdU3QJKu+F03XbEr+qseBgEFsjEZ7uSYrGpp7l3GOARrj\ntFQrpFaF3yy3HUPJ94jsTpWUz1EVIUiUEGir1bLMVTkVd1cZ+o3gN4a7r739tkZ1cW1NOS/2DGdK\n2l7Oglx5H0T3Z4d8FQQrV2f7HpRChGtRu+W+K0aXUsEtnZKolDjLiFVGlCmisSFYcl8j3gTeAO4A\n+8AYY3j5FtJadZYMmGHaYB9jBT8EuQ16R8MlYKyIUcRRZlz/5FFDEjH38GtxitclVlWvgA65yucR\nKiRcl0oD24eqUmqzBI4rhzotltuPj1HLdV4w7OQ0WY0jPy9fiDh1vUaIDeSpXCqtjLdlb0VH3Yqd\nELlaIAI6LciDNagSAkRslv8ygGQIyRx6MxjMjNrddeHkGrwvQ7JWnZ2qU+IsQ7Tsfl9CP4JeAkkP\n4h7EA3PvIsqBIFd/CWUdkxpz9zZyFQo9UQWofLrQCTlReD52VYQhctWlb2kWy1nxG919EE35lpTz\nomo/E9IJ03R+yGqqqr9V9j1DZnM6l4y5BMtft6Y97HJxK2SjlRJnKXGaEqeKeK6IjrRxkryvEXc1\n4g6GaFmSNcMQjvMmCnPvR5i2iECMQOxq5B5waH6LpCKOM5NERiLtwDv1sMuSLTeEjrtdJNPgwk5V\n2+kalNR9UrXQhpRVepSbwdm3ckxE67xg2L0lWXVqkrp8oS956HjbNf2Pq+uHpqLJyrcBDckyWixD\nsjIinRWaLKExfuFiEH2QQ2AEvYmZLswyM8cvM4Nfbgq5c6qTdc1/ViFYIS/wfWE0Wf0eJAOI+0aT\nJXqGaBaDKYWjyarXYtWRqyrRKrVZ1iWHUAohZbHAQTvTNUGiVdcodf3Qb/BQ44cAa52HxPkBqFMv\nHTGpWGgjAvuFOB/I4sNJ5UNaDThc1WSFtFh1GFa8Z3pOrFKSNCOeZcQzE+ZLHIKwmqw7utRkHWGI\nxnnUZFmSNcZ8OTUw0ohLAnlXIw5AjjVxrIh7ihhHmyWqWOY6cYgqWBZ7ZNkaugcIl+0TuSG7KPqT\nT9pF3i+r+9YBdEWOAavq5LxgmO/v//ikK0EKqVPayi3JDETuN6lw2yDL4KvFCrUl7RwWiVZW1WTZ\n6cLYTJlFdrpwAL2eISRDAQOMFtqGqlkmyHMXztlFlr2e652+j7mHgXA0WX1zr9EQor4zXagoSGjk\nTRd21Wb5KvfiQ2MD6joOHZHls29t2FUbB+rLd83XQcKT1/XpQo5B6jCqLh9QWc1iv1whUmY/oqJK\nsMLThKqyv4hb9h1ZXOOWkE8XpjnBmiiisXamCzXCny48ryTLThdaTdYbIN4A8abR/EWHmmisiSfK\nENY0MwQ2X8pTXU9YPpPQM5MOsbIaLShJNjgk3OKbT66KfOXflXxdca4t74qyCfw6jiD3P/7jP85j\njz3GE088wY/92I8xHo/Xus97R7JcETX7dflC+X1AqwO3OqJl2bw7beiv6Knt9PWreIoRSU6whPZG\nGpJiylD2jDFlHBmfKz0BPcqYhX4onVVSV1nmfCGS5RKtHuZektwWK05yWyyrxZJUHA+b0ViYyNYB\nkCVUfmTDglx5iSISfcfGCh1v649tjbtBsZMPXdOFHLMsEKn8mPdRE06ehS7h4JL7o6j7uNZOIfoE\nrEq47Ac+wmjbpVLITCFThZxr5FQjJrpYqKMPQB2CHoOegk45nyRLg56bNlBHZgGTPsC00xFm9fRU\nmzZMlbFvUyWpigpSlS3gWEmUF7VWYJ8vweNWglAknI0ov0MLmd3+egyEKiSbwC8bqP6rX/0qn//8\n53nttdcqv7tB7p999lmeffbZ2rK3b98GTCzWW7du8Y1vfIPDw0O+/OUvr3Wfx0uy1vnqr3ON1muV\navly2XSdUanNv9jxYRHcyo9/AETtRa1NmE2ijPpip9vWXVnYpTk29XhcolWE3cnvR/o/LlwsNO3R\nnMrnQfG3+7wqYFQQ5xVvbNN9N8RUVxBrwdY1XchJiKMxWNA0LC+hj2vAsqaSN5w8ex9ttMlkmHAN\nqSETag7ZzLhiyaaQzUGluauWYzSIPq2itbl3lZq2yKZ528xMW+k5pv0yjchMATvIdrWN9hn4mFb3\nLJ0aAItEa8W7KfvkBvrmKrIufrmB6h999NEiUL0rfpB763YmVPall14C4C/+xb9oHFpLyQc/+EF+\n8zd/c637bCRZ//2//3c+8IEP8J73vIenn366YHT7+/t8+MMf5pFHHuEjH/kIBwc1a3mbtAEnLYXP\nECgNTUPf1SrZCo0yoNrx69S7lRM7REvKxZiA62im6r7jdQqYujxN5w+lSuxtDMEq7AQqKzxD52wi\nVaHru6SqJF3FM7LXdiuc/975xo5LNvA+XEwXtsva+LWSlJgSJlcdPmaV2Ry9kNrfiZBGy/cY73zs\ntTYBhnOipXOSVSFas5xk5Y6Sz6VYkpWZdklnOcGa5e2V5lq+XNNX2OJ6xCo8kDQSxryqUmCjhMj7\nDrrXO25pwqvfe+G7fOW5bxQpJHVB7l25efMm7373u4u/bZD7LmUB/uk//af81b/6V9e6z0aSlSQJ\nn/vc57h16xb/6l/9Kz772c+yv7/PF77wBR555BFeeeUVHn74Yb74xS+GT3DcmqwVmUgle67lMB9k\nXcxZ+y+Ar0UJjSpaCYLPRnLCYQmBFIHgy94ttd3eMuRpHaLllwvG37b31qDJKtsWXOPdtnZ1P0BV\nQkzxYQs7dFzh5o67764gFySrXdbGr5XFsiTvA7b0s9aBblhHtkIfxmYNlsQxZ7CarFybpVJDHrJZ\nrsVyNVnZOdZkZZBZTZZtG0+TJTIQWU5cFwbfrv1c6PtipLoPVZJd7QfLkyK3f1L2U3vsHkkTXn3P\n04/yF577H4u0qqwa5B7gH/yDf8DOzg5/42/8jZWvDy0k64EHHuC9730vANeuXeM973kPX//617l5\n8ybPPPMM/X6fj3/84wsqukKOUyvgM49lruGwiZJoWYJVBbPqx7xmSsoHLockUJT36pozk9B0YZ02\na5nb61p2WS7hEzGfaLkrDKVwpgtb50CrWkBLtMw1QgQ3TLyKrcjPKXTVBqGL+DfnH9+krHjOaRHM\nqVs6j7I2fq0iRd8JTcV0JFo174dPusJFw4NDX4NSGcjYaS2lTYwsZ7pQ5VqsNCcSdrrwHik7Tpfo\n3ElpZkhWOs3bxWqzrBbLLglXOp8qDD+D0IC8mWg5FenQFxrF1VpV+uqqJ1xe1sWvp556qmLIfuvW\nLd7//vdX8tgg91ZskPsnn3yysewv/uIv8iu/8iuVUGKrSmdjjW9961vcunWL7//+7+fv/J2/aTaE\n4gAAIABJREFUU6ja3vWud3Hz5s1woX/7XLn/jqfh+55evaZd5B51jm7SgkKBui6rSTorclL3c2rb\n8fdeMGkNOa/aqVVlJfz6J8+V++97Gp56+rirebJSN1ukg7vnVzTdwWX5z8BG8h6rfOMF+H9eWLsz\nrIthxxXk/itf+Qr/+B//Y1588UUGg8FadYSOJGt/f5+/+Tf/Jp/73OfY3t5eUL/Vyv/0XLm/6R7i\na8qX0Zo6YKIBrfOxhBZOllKftai3qtel+P56Kcp7dc3dOlgjUu1o6+1AaFWFcKhpuuS10vSofBx2\n62dD6th7iPJZCG1/0M42cNVSme5PClb1h/V6RGcSRVM8U71qA/rlNvWF+b6nzaDDnv/f/sTSp7gg\nWd1lZfz6X54r95f5qApA52ry4oOc7+gO2qya98OfJAoXDb0Pi+9WZeJQ5CkSEAmIQSQgE9CJcbsS\n90Aq48dPuAB1nkTk9rMRRJGJZxjnq6ZlAsIuCy+09nm71jwD/28o504W993mdg0kVnwMbqwdG2fM\nHms74ZNPm2Qv/k+Wxy84vUHuP/WpTzGbzfihH/ohAP7cn/tz/NzP/dzKdWwlWfP5nL/21/4aH/vY\nx/jwhz8MGDXdyy+/zOOPP87LL7/MU089FS687Bd8WVmcQerU4yrZdQ5GPuEKgFVoYq1e+SsrL0nl\nwspJOcFSujSL8PnIMt/5EAmqK1d8D5xjle9Ch2uFCFZxe/m9afcH7zm5xKj2I9CgXHc/PAUpdkiz\n1s4z0iLYDq03tzKSdTz/CnJBsrrJWvi1suRvUfHRyve7ECxPFrth1TqrrR4hY4bKO5YTrMJeIScK\nIjbEgR6IniFaUZrbWep7arpzakQIEJHx8yfjPFRYv3THI3LnhjoCHQm0BC1KIuWavftGJdVB+SLB\nwnvuiwPPFUSLvFO5BOve6cs2gWHHEeT+lVdeWbterjTaZGmteeaZZ/gzf+bP8GM/9mPF8Rs3bvD8\n888zHo95/vnnF+ZByxOw+CE5LS+n7Vh5J3M/wtVqh0eFoZfCH53g5QmxEq3KwMoVguJlX+Z7HNIy\nuce75mk6fyj5mqyCZAWI5eI53Q9BlWCFr79IgCvPy14b4TVeA4gs2xCrygbehws/We2yNn6tJO6H\nK99W+lyHj1ihUAgN7MIa8qYBiaL6Ua9qU3KiJck1WYZgicSQK9krNVlRbLQ48nx2JxCY1eARREne\nJlaLlWuyhKPJMgS2im0hbVY5aFwkW8WF8Un2mjfi7lf6qv/78cl5wa9GkvUf/sN/4Etf+hK//uu/\nzuOPP87jjz/OV77yFT75yU/yB3/wB7zzne/kO9/5Dn/37/7d8AmOUxNQd41O16t2Wu0SrUaNiftR\nt5ev12IFq+ARrML3ivYICutp5Ls0x6aUNT6HsgTLarNqmSPgj7a7rSusjugap3L1khose0N+A21K\nNnTuCz9Z7bI2fq0tzvTLAtlaXkLTSqFBiJs3jE2ellgItF3aHAG5FkvmToSLlBiiVXEmfI5EYO49\nivO26JdtY4mW0QQKdIVohQiuP5AMDygXa4BXbo27cTVY94hYuXJe8Kux9j/wAz+AqnGK8q//9b9e\n/mqaxfkod3+Z/DpwPFS+JmkNQouc6AiUdj/Iiyp25ax7s6r2MjhCwE+5MKlQx+NodFLQ+YqUbA5p\nBnMFM22iNjiuVmo1Wl1TV+ma1236kAZrbvNpiJSZYshSs8yZGQjrQ0aXJ7RAH/L5Xu/vvfT+Uxml\na1k+Ryeh8m3Xxgqp+EJari6ar2MYZFxMF7bLxvGrSSpY5E7CW3Kl7f8mmxYmWK9/DlcLVhwOTyW1\nD/bKKarFaIb5OyYkSkpUJFGxQPUEqi8QfWCoYQRy2yQh87txX/TzJHm0Dht7Vg5Nu+gtYAh6INA9\nUIlAxZIskmSy6u+9Gq0wFKVw0UwC7PN1+0KoD1S37h8aCkK1YKNawcWzNV14FuTeUUSfMLkY1JbP\n/bK7eUJluiSF+egqAVqilURL8xFWSJT2ww9XyZSN/FWNAmbS3MS1JyUqiJZ1XaA1xmHdDPQE0iOY\nT2A2h2kGEw1T2g3gN0Ws6qSNL9it25xpvm+dRittCJWYQzQxA2QmIHKiJbUBbS3NKNoA/mJ7umkx\n8pcfYtWJ3mbJlnKJFrkRvmhv2KYGCBGw0LapbFtDt8h5AahTLctgWrGfaw+EU8jBs7JriPyn/MMo\n6kmU+2EOh7avBtVJHdyak5DKmDSOSHsRqZZkI4HYBrED4hKwp03MwiivZ4YBqvMmEhMzbAjsmKT3\nQO8K9A7oLYEeQjqQpi3j2LQtSfFdyAocWyRbXvRVjyhXtV2WcAGFYqBuWlq7f7sa1Tqsa8K4Dcp5\nwbB7G7sw9IVe5oO2bHk/r52msxqOnGgZLVbesbXf6eu0VU2EICETEUpEBYnQgiIkg5pBNoZ0DLMp\nTGcwSWGsDJZNMQNFq81almCt8z7UNXEbZ7WD2ynmHsbaEMfZzBDJ9Mjcs8pjnymdf28khoxKSSbi\nPJRqEiRYzUTLiTCpZTWp8llXRm3B6cuaRmnrg02NH7rGmqC1CZussxBc9cxICyZZkwR0YL+Q8gNa\nIVgO4apqNhbJVgi7LLnyw9rPiUlFzNwlWQNJNpRkWwK1I9CXgMsCLmOIxRBDNE4m6u3JSgT0gRGm\nLS4Dl00bqR1BtiVIR5KsL0l70rSpjJiLuCBYKb5mqxqhVS+QLX9q0ekH2iVYlPavPtkq+qUotVhO\nuYocA1bVyYVN1nFJl4fYhVx1vYb/QXXsg0qyJVHKEq3SbqEcEUaV/TqC5ZKDTOSqeKvJEpQeg6eG\nYM09TdZYw4SSZK2jyVrlvViFYNkmTSlJ1gSYKEMcp3OYTw3JSse5d+QsL2iXOUuRa7G6a7LaphO1\nFijlECxLrprsw5btW6HGqmvUUNk1ZBM2WWchuOqpl7rnv/CMS3tBFmwEFw2RXXtCc7r6qUE/tHDo\nfXCnCcv3LNewRDnJSqTRwgwFqiBZAn2ZkmSNMFHgzyPJkph7HwK7wB7oy6AvmbZSW4JsKEwbJhFp\nHDGPTDuXGBY72LWoiW8OFV3VVvlkq5ia1g1QpEuyFs7g7G8Iq+rkvNhkndyrsswDbCJcobyhMi7R\n0hithsKZTrJTTOGPdp37/zqilREbm6ycRNjpQpXbJ2XjfLrQarLy6UKrybK2WavaZK0qq1zLkqwZ\nYU3WzNVkzXJtnsbggRTFtKohWotarBDZCo/ao+I52ulCVUwViuKZ1/WJRobaRKyWJWYbkHXD6pyV\n4KpnStqesaYkWIXRcXVaR3t9zJItc6jOkL1Ok1WdUq9qsnLsyjVZ8zgm7UVkfUk28jVZwB5VTdbZ\nVi6sJiFN1h4oq8naFmQjSdo304XzOCKVMXNRNSPxB+2hfddAvmkxkO0jhmjVdUE7yCzL+HZ/FTlm\ncmXlvIQFu/cU0T68kCF7nRF70++h/aaPYT5NKHL2LxxNh/komw90pq1haD2pmucjwTkJM3pFmooe\nU9ljFveYJwnzfkw8iMiGGjUCvaVhWyMOjRFplBkD8TiDLDcKl/p0+qNxH4MVQTUYtBQQS+OwT+bL\nvomBbVBbArElDFgPBWk/Yt6LmUUJM5m3XR5KYUavaF9fm7UwdeiQY0uatZaQa7OKZ6872mO19bHQ\n/rJkbQ1pA54/euH/5Y9f+L3a3+sCpH7oQx8qjt28eZOPfexjxd82uOq3v/3t1rJggqv+6I/+aOd7\nOrNSh2mF5GpsYdXZZqsx73eRPT+Pu/gGTWmPpUXFNkt55KpN+27fG/M+OZglTJrSY0KfOElJhhl6\nO0PsCuRRhphp4hn0pjAcw/YRZFmelNkq9dYJHi2lSdbpaBTB9g4Md6G3B9EVEFdBXxGo3Yh0SzIf\nRKRJxDS234J+0cYlji1iWbNWvvqs3VXT4BAsrClEqc0CqkbuzjewFA/NQ5h2THLWyVNXOVk9nE+w\nfLBqK+vu1xGr0DHb0YpppNz4XeVaEBWhZFYFJ1ElVzN6xKSGVOWkYMLAvEpiThKn9HozeiOTQ6YZ\n+pqCIwVThVSKZKgZTECPQYwhnhgbLQtWRcR3B4iXbZpVpO0ath5uIGgZ5T5kcj8yvR4MhzAcQH8I\nyRDk/QIelmT3S7JrkvSSZLzdZzLsM+kPGMdDxgwZM2DCoEK2XKCq2GzpHKwsKVaReX6OzZ191o2E\nqk1LtSzgdCFca0gbQF1/+t1cf7qMPv+ff+L/XvoapyG46ltKNDnRgmJaUAuzyrkyRahz+xoRJFiW\nWMnAlKD5LyIiK0iVRTH77sSkTOkTkTGhT48BPWbWSgspNf3enP5ojrg0J54rpIJ+BtsKZvkgZdiD\nyQQm0zxN3jokK4pg0M/TwGx3r8LlB2D7fujfB+I+UFcl6aWI2ajHNEmYioRDtjhiVGBZl0FjlWy5\nU4lNK6mrpMtsKftOTrisFqvqdBRcMnYSckGy7pX4BAu6u+zooomoyWNdOJRkyxIsSaYlUpfGoqkw\nQOWSLEuw7NYSrDgnWb14Tr9vxjJ92UeqjOhqipxmyBSj5B9q+ndB3IXoLvTu5tOEGczTPOW+tCTd\n3oc6RUsXqR2IB/K4RCvONVdJbFIcQW8L+rvQ24HeLiS7wH0C9ZBE3R+hrsZklyIm233G/QHj3oBx\nNGAshkwYMnGCg3YiWDoHJTtVqHJNVkGwHLsse5PLkPOuEipzDEC2rjHoU089xY//+I8Xf9+6dYu/\n/Jf/ciWPDa76wQ9+ECiDq165cqWxrA2u+mu/9mtr1fEtI5Zc2X10jj9gP4Ki8hG0+7r8iDoEy5Ar\nVeyXGqwoP66CZg2WaJWTVllOtOY5dpnPvhSgkwliJIjmGqUzpMgMycrM4C/RMIxg/8AkgPncYNZb\nQaLIDBB3tsu0fQ127oPt+wzJkveBuiLIdmNmox6TXp+x6HPEiKOCYA0qA8UZvVpb0/qFC1VbrcqU\noYtrVnng9CGXYOVdb0HbdVJy1g3au8rJkyxYJFjLaLT88zSlSp58pYUq7XWMJitCaUWmjOF6BaRE\nCVJzksK6YUq/oAARKbFI6cVz80pFU/pJj0jOSaaCOBNINFGiEFsgXoO4Dz2Zk6sMJnNjPC5VzhGo\narJ8TtrUFMtKlyYXXooE9CMYxNBPYJBAsgXxHsRXTYqugrpPoO6XZPfFpFdj5nsJk1GPSdxnHA8Y\nxwOOhNFmTQptVi+gcnfASUeGbFmipaLCts4+0wJomqYJIdBHlmy8kHb1mIBsXWPQsxJc9S0jFYxz\nNQtGTe1+MCtJmI+jyt82kROsUoulPYJlMEs6RCslLqyyLG65TmoswTLHM4TUiJ4gGmkSMnQ8Q8TQ\nz9+fRMMWsJVrrIUw5OroLbSQNI6MBmtnB65ehit7sHUf9B+Awf3Qy0lWekmSjnKSlQw4EiOO2GLM\niImnle+qyco8sqUdgrVoFE+1v3j9qOoby/Y5TpxgwfoYdlbkdN2lffjLkKs2DUQNwaIIrpdPF+Za\nLKnsdKEqbLJckLKaLPs62OnCOCdYERmxSOlHM3pyQj/pMxn0iZM5pIZgEWfIoSAemdAMWpppQT02\nhvAJOcGKDHhJTHWXVfBB93dpGW7rEiyryepJGMawlcBWH+IRiD2Q10E8COIBSK8J0qsSdTVifjVh\neilh0jejv4noMxZGk1UFpnpwKva1o83Kn6PKp38LTZaiCjJuQ3Ui5R0b8pjJlZVNqNrPQnDVt6TY\nQZ5jRyOs1XLxe75rCVhAkyUrJEtWCJa/EtrHLpdkWZ1KcVxo4p4m0SmDeIbqC2TPkKxEGx+lCtjO\nqzufw+GR0f68VcRqsna3Dcm6/zoM7zfEKsoJlrgOeluQ9mLm/R6TniVZo2K60NdkLWjjg9qsRb9/\nYYJliVUu3kwNUEwdVhdaLKu9OB65mC68l+J/3S3R8rf+b/5+U7IsxTIW65Qyo1jlpqX5OJNJhIjI\nZEyKQkpVTBm6BMsFKUOwSqhL5Lzy+gBkWxN0KgwxiRWRVMY555Emuguip5ERzHJjS2u+oWpSEyfw\n97uIryHztWf2EWTO35a7CGHqnERGqxX3QI/y1UlXBPp+SK9GzPZ6TC4NGG8NOOr32U+2OWCLA7Yc\nW4ZRoc3yVe0L04ZWi5XbYmVZhM6sjZ1AZ6LQVrY2YBPZgmrDNh1zfzs2Tdb5CK56qiQ0AGwaFLr4\ntGBgbAd45bRO4btPlPtIz+ZGuETLfGp9gmW3dnBoLLiS4hPtr1ezeFYcE5oo0kQ9g1FRrNBCIiYa\nMdeITBMLTSI0SaaJJhq5rxGRvemzLyISyAFEu4L4qiB5QBDfL9DXBNlVQbon0DuCo+GQo3jIYTzi\nKNriUBgcM/ZYw5xohW1LmwzgQ9OHFYLlaTyVEgsarNIvoJsoFQ3+gLNOQji2gcd8QbJOSuoIls8e\nQsdcMuXuu8kyBAVkojB/QEq00MavVU6L0kghiM0suNBIUfXLW4JWVnR/sFxOOdWQpCJhnhySDWLI\nBAIFqUbe0ciRQiYaKUFjVh1Zz+nWNULmJWtf6r8jddtVmp7A1tqGueQvdupUcJcoD9ExFKgdidoT\nTC/1ONoacjgYcZBscSBG7LPDPjvcZZcDdjhkm8OCaLkG8D7RMgQrVSZlWUyaRahUorIInUp0Ks0z\ndlMdY3X9qHXVbDXlOWY5L/YMp1Lc51tHurRwlg6KUrVgC2hhog/YqUMli2+fcjBM5qdwFiWi0cYi\nSwgEvpf3qCBLlmjlOjNrLu1MNFGQLFkgijkqhBl0ZpHRhA17E6KtDJkqIp0RJRnTSDGZZswPFNnr\nGTpaJ9rq6RIVC9KhZL4bMb0mGT8UkV2PyPYk2aUINYrIepJxPOQgGrEvtzgQZrDoarImNYt4ZgGN\nVutKQ10awGtcZ8vlTIxSVZtUN+rFArmy5N8lXlD/CDc8aDwvGHb6SBZ4I0Bv20a47IfSik+yCqKV\nFxKWaBl/TYio0KpKYqTQpCIUBcwdDZYEy8KX/deqf1MRkyURemCsKuIoRaYZ8Y4iGgjoGVsIKD2o\nWy/qrmNSe7xpYFG330X8pve/IS4HsdzEeqd366UlqJ7xHp3uSLI9yeRSj6PBkP3BFnfjXfbFNvvs\ncMA2+2znJGsrNxp1DeDtcujEAaeEuU4M0coJVpZFqNQQLLVAsLwGXEU92IVU3aNvzHmxZzg1Ehr0\nWWkiWjZj5W/nQ6csBcoxSAmkyD+MFmFsyC+dk58cibTWZmCIXiBYAl0QLLfDllej2HNJlz2uhUBJ\naaYYRcK4NyHZyv3ERynJMGUezZnup8xfT8kGoOK3BsECM0jMhhGzSzGTazHjhxLm12LmWzHpVky6\nlTDvxcaOVI44lCOOxIhDhs7KwmEjwbKDxXmtbZZDtrTrssP0hUq8XeVsbRST3CY1rM3KP3Iu2YIw\nftXh3ZqP+7xg2Om9S//DtYw2yydc/jS0EJDlJ7B9LY9+qtDFNJ1AIazTqnwk6pkcOqAG1tuyRmIJ\nVuGbRiQQC+RQEcdzev0pUTZHb2foIYhEI6XRY9lqWy/qLsmyPEGziO1tzdhVmgYyVpOlKGdegwGt\npUAlZjSYbkfM9yJDspIBB8k2d5Nd3hCX2M+JlUkjDtnKydWASe4Wo6pq90aBKibLNVlZGqFzkqUz\nk0gdkpVRarN8otWmoepKvO6RnBdV+6mSuhFI6GUs+oJDrJyyZkUhhQsZtDIEC8f+SlQ/jtISLSRC\nVN1SWoLlEy2/U7pV9klW4S1eSJQwjjRnuc+/ARPzBsYz+sMZvd2Z8Qn1+ozZH0M2VOioCyKdDdGx\nIBtFzC8lTK/3OHooQV7pMev3mPXK7SRfET0WQ46EXR09qGixJgw8gtXLk+8NfpFcZUQlqbbaK0ot\nlXKTR7Qqxu+VfVjQYNXh16ZG7wE5Lxh2ekmWK13IVZ02y04NuvNeWb5T7OeAI2RO8I0aXgpttEtK\nI/Si/12/ir7RoiVYM3qkIkYmOcHqTRnoMT01NYFYh2a1oe1zlgNYkjVzbsnlBHVTem6dukrTd8Jt\nakuubLKaLF/DZjRZgmwome9EzPdiM10ohhzILd4Uu9yRlzlguxj5WTX71HHfYI3fp4FRYKrLKcMs\ni8jS2EwR5kkXBEuENVghjVYdmcL7LfT7PZTzAlCnTkKDv9b8rkYr37erm+0iHAQ4tlhKCKTVZgnp\n+D/CaK+0QAhZS7TKi+uFapu/7Ye7vIEKbgkzMJzSY6L7DOSEfjxhMJgyUBP6KkInkskfwXxXkw4z\ndPQW8d8A6MhOF8ZMrvUYP9iHKwMmss9UDpjIARPRZyoGTIQZEE5FdWDoa7HmjiZ+VhPZonHKUOfu\nGwoNlhebNaDRCtplwSLBamwMjgXnzguG3TuS5Y/+2o6FyofIld3ar39o372GP21oDwqR9z+BjTak\nREQmNUKCyOMPCrRrVlGMCJXwQ1eYZB2XpiI2WjFARYIMyXTYp78zY7A3Y3BtRv/+OXo+Z3agyPYV\n8lDRUxmp0oZw6VxzpKtkp6zxYhN2fS/qpgj9kI8SSHJ+GguzEjJJBGIUkW1LptuSw20J9ydMrvaY\n7vWYbPeYDBLu9na5w2XucIU7XOYN9jhge2HkV7HB0mZ/TsJM5yNAnfuayZJCg5U5GqwFO6y66UFd\ns22yz2KJY3i/tR1bQs6LPcOpFxeX/GlE3GOBN8waWkEZfSInWiiHXClVGsNro20SQqN0FY8MpGnn\n0qEOJih1V85Upad5T4UZHPbpMREz+gwYRFa/PGDABD2bsn95yuH1KeMHpkwfnjFXM8QsLdM8RaSn\n20OpjiW6F1fS9OEe4wf6JNd6RHt9xE4fRv0KRrladpdMhfbnlSnCquf3NDBFWNnPV00bB6SSitF7\n7uPROtMuFkx4WqyCUEGYYNVh2jHKJjDsxRdf5BOf+ARpmvLpT3+aT33qUwt5PvOZz/Av/sW/4PLl\ny/zzf/7Pi2gVdWX/5b/8lzz33HP87u/+Ll//+td54okn1qrj8ZIs9yE1AlDHYyFyBdW5K7x9W155\nWz8VKw1FcRItJFpEJXBpDFHKWY0SBghd53F+GB73xVJIZvQ4Ysg+u2xHB4y2x4yuTxiNjal3vD1l\n9toM9dqU5LU5O9mMXppypE08wFTl03PaPDwbscafEa17X3wiVZlF9Zskz2+VQCmGYPWEcdkwEjAU\n0B9GJJd7ZNd6HF3rMb/WY/7QgKM/NeDo2pCjrSFH0YADtrnLLvvs5tudQnPljvqm9JnrPOxHvp1r\nY4M1V9YWKyFNY7J5aYdlNFjCmSIUzYbuXQhW3VSi38irErEVgey82DOcqNRxFPd3F6+WIVoCCkt2\nKD6KlmihJcpawIsqAbOEqHoJbXBK1H24XPN3q8laJFmuw+UeM2b06DEr3tEyYMwUHc852J1z8EDK\nwWHKIXNml6dEbxwRvXFotncOEemspaFPVnQvIdsbkV3eMtu9LdTDPcTbE/QDMdluwjSKwVl8YwlU\nqaHqNSY7NVjdlgGjF1YZaod0ufFYc6KllJketNOEyiFdC1ODLqFahmA14dQGCNgmMMwGqn/00Uf5\n4Ac/yA//8A8X7mSgGuT+V37lV3j22Wf5N//m3zSW/bN/9s/yy7/8ywsrrVeVe0eyYHWiVQdilly5\n+z7R8gmWPb+fCq1WHskZlRMojaUxWuc/y7xzS2lSQbAW1b/+yOaIEXfZ5Q5HjKJDdrYP2b6er6sb\nHjC4dEj0/x0RJRFJNmZwlDIYg1AmRtiE3Gmp11RWm+UrSOreGZeU+TMewvvdTgnO7XFpSNaWhN0I\n4pEku9wje2jE4dtGZH9qxOT+Lfavb3FwfZv97S0OIrNq8AjXj8yosFdwR35znavULbEiIVWGYKUq\nNtssRqUxKo1Q8wg9t0TL0161abG6EqyuZMv/u8uxFeS8qNpPVOpwKUS03PxdiFbRB3K1cK7V0vlK\nQwtaWtjpQ1mPX36dvRmgKqFy1x6WKxPthz5hXgkdlng6GEsREuboOGW8qzh6UHGEYjxSpNcmJN+5\nQ/Kd1wGQR1MYn3KS1Y/JLm8xf9tl5g9dMdsHB6gHJfMHBdMdyTiWQOxpo8q/ffcyobQ4PRgVJKtC\ntLRr8O5sncD3Ve2VO2UoWbS98glVR4KF8zeBv9ckWutimBuoHiiC3LsxVP0g95/97Gdby7pxWTch\n95ZkwWpEyyVXfj5LoCwuNREtnP0QQDkHtLOo2caJkkpAJIx7gshODxojUdc5ZipKkLJWRBMGHDGq\nWBeNoiN2t+9yibtcGrzJpctDdvb6jJKYUQb9w4yt1ydksQnAOgaENjxipsvbtpos93brlCxu0win\nHFRx2yVsrn1YZAbWRpMVwV4MDCOOLveYPzji8Ht3OfrTO+xf3+XN7Uvc3TbbN6NdxgwrhMp1yVDd\nxqS6XD0414ZcmVWE1l1DXGiw9FyWBCsVpVayjmj5RCpEppoM4lnieJdjK8gFyboH4j4bl2CFiJYv\nbURLaw/ThEO0oHCSrPJdlXvFElVNltVsFefH4FUUmBb0Q7K4NqQuftmoFqW+ZXFiKyZFx4rprmAK\nTIaC6TVQ18eoUR8AeTQjfnV/paa/l6J7MdneFvO3XWH6px9g+o4HkPcPyHZhtquZ7MJhpBG5ps8l\nRG6bte0vOFFuWFHoarNUYeQuWTBs98jVQpzCCvaECBfU4lIIn3yCtQbRasKw/Re+yf4L32wsfy+C\n3G9C7j3JguWIlj1P20N1CVZoutBufe1W8XuBUEWHLEhJDniysIkQREgzNSiML+VUxEW4HR+g7L5r\ntSVRDOIJl7df58rwDkeXh0yyPvOrESgYHKb0bk/ZGUpIjAarp41Gy/rOslOFtvZ1A1ufM7jNYsmU\n8I7bZMvaayaYgND9CLZiuBSDGkrmV3pkD444+t5dXn/3ZV6/fIU78WXuRJd5PTLbKYMKiNjpCXdU\nWIF0HQeN27MsIs1XEtopQp1KmOej/ZAmy259IuU2TheC1TTqW4d0LSlTeqsVvJDuUocF/r1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CFGNTUEhky5woF4GwfifqZiBxXF1e9ARo5XwmCV5WixeQYqi8lymyelImSckyyZJ2ETxoZOaHcN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VZWVlYfFy3zW3X3/MTpHrpzf0jmA5yzZfdLoPPbXr1sCodM+ckPxXxp+QxquvTL\n9K3l1duKlbdYmrp9OXa1a90BKysrK6uZtMwhK1E+NJ5ZPHTn0jIlwOp2Fy4HLUd3IZRnmiZriqCV\n33Ih9pbttY/P7Qr5y7WysrJayVqxt+prBRTlFBCLOsNszsr2fi0BsTr7f7cDeOG0NPnVllQr9i/X\nysrKauXI3qoXSFUJTPsPsIrZ82cOGF88TQdaye/571Yl2dmFVlZWVn0vea07sFi6Vo/mBK6mA6yl\nBodiRqdyJNnS2vyqX0ZTnFKw8H1agaDmzrFU6M0332THjh1s3bqVZ555pnKbw4cPs2XLFm699VbO\nnj07Y92JiQnuueceNm3axL333svk5GT629NPP83WrVvZuXMn3//+99P1Z86cYdeuXWzZsoWvfe1r\n6fqf//znHDhwgM985jPceeedXLx4cU5DZGVlZXWttWIhq9eLpxdnX9VAVbWcDrAWrn9VKRySaLJi\nj66FqhNgTL+cn7rbWPbYtQCQ9eijj3LkyBFef/11nn322S6IOX78OG+99RYnT57kscce47HHHutZ\nd3x8HIDnnnuOTZs28c4773DjjTfy/PPPA3D+/Hm+/e1v873vfY/nnnuOQ4cOpW195Stf4fHHH+fE\niRO88cYbnDx5EoBnnnmGrVu38o//+I986Utf4pvf/Oa8h83KyspqKbViIetaqRdYTW+9Wmjk6dVS\nErqvp9lmsfuQqWy5WliwWuFypi/HLsAT/19Wyrp8+TIAn/vc59i8eTN33nknY2NjhW3Gxsa4//77\nGRkZ4aGHHuLMmTM967799tuAAbNHHnmEWq3Gww8/nLY5NjbGXXfdxaZNm/j85z+P1jq1cv3gBz/g\ngQceYN26ddx3331pnb/5m7/hN37jNwD4jd/4Df7bf/tvCzV6VlZWVkuiFQBZeYiZ/vHcL4/uDHXK\nsxRndxyzUX4O5ExpOmGhXJjF3hf7sFRwV6XZpXAoj8Zsxu+aaQbL1b4b4YndWSnrxIkTbN++Pf2+\nc+fOFJQSHT9+nJ07d6bfN2zYwLvvvjtt3fxv27dv5/jx44CBrB07dqR1tm3bxtjYGD/60Y+4/vrr\nK9v6V//qX/Hnf/7ndDod/uzP/oz//t//O51OZ85DZWVlZXWttKwhqxi9U06p0JePxi6nXXWCU5hP\n//O2tGpHpSiUhQrQL+63HJlWBK9+VO8EDosRJzZPLYC7cCZprdG6eNxCVF8byfry9tOpqq18/d/9\n3d8lDENuu+02fvjDH7Ju3bqe+7eysrLqRy1jyMoniyxHHPWn8pFQvQGrO3JqLuqGme6c9NWJOOf3\n8Kqyx3XPscxbspbyLM1lX1VWONJ1faN5QtaePXsKgeynTp3itttuK2wzOjrK6dOn0+8XLlxgy5Yt\n7N69u6vu6Oho2m7iVjxz5gx79uypbOvs2bPs2bOHT3/605w7dy5df/r06bQf69ev58knn+Tv/u7v\nOHjwILfeeiue581pmKysrKyupZYtZFW98iafLLRfVU5sWgVb83VSFVuCKoTKj1e5LMx+u6PS+iWZ\nxXTKw2ny/dqA4QyaISarq5S0Zs0awMwSfO+993jttddSUEo0OjrKyy+/zPj4OC+++GLq7lu7dm3P\nuqOjoxw9epRms8nRo0dTYNq7dy+vvvoq77//PseOHUNKyfDwMGDcii+99BIXL17klVdeSdsaHx9H\nKcVHH33Ef/gP/4G77rprgQbPysrKamm0zPNk5YFqcR/b1Y/X6ffZq04RN5J1Cx/9k+2hl7Nrccas\nmIEr/07J3i/vnl2bCyfB9DnCskzz+TzzfaQF+Mt96qmnOHDgAEEQcOjQIdavX8+RI0cAOHDgAHv3\n7uX2229n9+7djIyM8MILL0xbF+DgwYPs37+fbdu2sWvXLp588kkANm7cyMGDB7njjjvwfT/dD8C3\nvvUt9u/fz+HDh3nwwQfZvdsEkR07dozDhw+jtebXfu3X+Df/5t/M/6CtrKysllBCzyWIYi4NC8H/\npYv/8qxK2Fm1LKscr1QMdK+u2x1R0zudQm/nVvd+q9171Y6xqm2ni8Ga7qE/U7+rjrt8/N2WK9nT\nglXV5nR96NXn6kxY+c/VY1a1nyqLX9W5mW6cpltXdTzTX3vF/lYdf9Wy/Pt3xX1zjmXSj896c1Pn\nybnFS33cZWK//jXtqcIAACAASURBVPBad8PKahnoG3O+t5i/ryfmUOOJZXv/WlJLVvJYWkxnUfVD\ncvYP27L7cSZQqm5vLrBFuv1clVhX8laZ+Y7xTCB8dX3UiK5eZi1qKPRe0Ot1O8U2MttSspfkU3fd\n2azLerC01+hVyWZ8t7Kysup7LSpkFZ1UvVxWi6NeVoTZA1a11Wo2gDVTYHuVBeTqHrxlPCnCwlzb\nnH9/ispAJutrtxsxcyRm0DXdew17wdr8QWuhjnu2mte+lrmj38rKyurjoEW/VWf2hgwIFkvTgVWy\nnN5dVOWGStbPxiLWG7CqXEzzTdWQ9Tg7/quBq7Lma73KK4OaPA4WjzyL2CrC4lxAi7iN+YBW1XLx\nNM+2LWRZWVlZ9b2WxJKVf+AtzQNs7oCVh6FucJpN3TKYlAGLEmwt/PEvxPguJGDllY1A8q3bipTs\nd7p107kOE1ibDWjBwozXXKVLI3HV+7WQZWVlZdX3+tjeqnXuUV62NkEZsMitqwas6cCtykKWb69K\n0zlXlyJuqKzZ7mu6fusUe8yWmX0rq5MHnmq4ujrQKu992cvGZFlZWVn1vVYsZBUf3b236nbnzS0G\nqxdkVQe2V8djVals0amyuiwVbOXb7/U5359emj6myrQ4nRtttqBF7nN31NoK0Yr9y7WysrJaOfpY\n36p1Wq42yL0olUuJUN7PXOKxqqw3vX5Pvi8WaE0HVVV9Kn8ut9UNRqZG3k6YjGEynvl2ZxujVbZu\nZftZIfpY/+VaWVlZLQ+t2Fv1dFaLfEB7UuYX5N5duvfZ7UKsilTqFauU9CzfXm+4WniYmG2sVjc+\n9orDqnYXVrWV78PVBcMn9fN2rdlYOvtY1l1oZWVl1fdasZA1vYpg1W3NmkuwfFJ6J/ZMNJMlrJzQ\nYKa4rDJ0ZG3ObSbndP2tWk7Xp/xypr3M5hh7qbru9FascqRWeYyWMgB+3vqY/uVaWVlZLSetuFt1\nlXtrJrjJti+20+0+7A1aKl1Wvw6yav9VfZkZsOaGAcWt87Fh+Vi0MjBm2/Tqe6/+TQdaZSicCWok\nqjCevZyCxfHKbFxFiMpC4svWwqpYt76HrRX3l2tlZWW18rQib9UzAVHmGqyq2xuwerWrSlasmR7Q\n04NeGbASq0sRz5Jfsu9560w5JqwIdnl3ZbXLs+gSrU7AIMiPUF4zAdhM6/KwmoDWTJa6DJSylrtV\n/LXa7bhMZiBad6GVlZVV32tFQlai3hYsQRWMdK8rWqnyLsV8+5k1qzrwvapfczuKIlBVRxkVP2XH\nk1lmqqAxXyfvOu0+juJeq0CrbAubj/JWLIkqAFaGU0n/Ze6XTGVUpfRbVXzXstGK/su1srKyWhla\nobfq6YPXe9lZzDa9g9mnA62yNWs26uUunLlGtb1GAKrwrQwPZctU9zFlRfbsyWzSd/ayUPX6rUq9\n3IV5tExa7v4kciNQ1YvegfTLQvVr3QErKysrq5m0rCCrV9oDnS67IarKvVeOSSq6EKutVHnwqIaS\n6pis8kN7JsDodheWj7x4hGX7koz7qFBQSoFQPPZexylLlruqPic2o9m7Bcvry9vk+ynQXVasrD9V\nQf1ltMpC3svh7smep4vHKvanlwN3bpMLFlzWXWhlZWXV9+pbyCrHAuVjhIqQQ/q5d1vZQ7IcdzQd\nYGSuQpl+7gVXZciajUWkCjqKlpWynamMP9lIJa0oNBpJGWPKqFDuc3HZbcnKW9Cq7Fz5/lcty597\nqXhkebjVue/E32VsuTNAmYwASCTkfis7VItWtektikUEy/CqG7V6/SNgUdS3f7lWVlZWVon68lZd\nRoj5AFYmUXpITwdYZdgQXfDVbeEqAkvV8ZQhowhWxbl/xdrl3hRtb1kbApHafmTqOqy2jhUhRsV9\nVxTHomw1M99VDmtMS/ltu8/O9AHyvcYrO/Jk71AM7lddljvZ9bl8zJRam/7ayexe04NW0Ya2BKDV\nl3+5VlZWVlZ5VecbiPXwww+zceNGfvmXfzldNzExwT333MOmTZu49957mZycXNAO6dyyaMeZHrCK\nLqVql2J3YLdMSx6wVCVgydL2sysRTuXnpBQBLusj5MPzi+hX3otDlFtGXb+XkTA/XskW5daSvlb3\nOT9mSU+LqkLYqj6XP+cxr/y5uPcifFbbHcvblgG1t1tz+qus1xWZv3oXWc4cS4XefPNNduzYwdat\nW3nmmWcqtzl8+DBbtmzh1ltv5ezZszPWne7+8PTTT7N161Z27tzJ97///XT9mTNn2LVrF1u2bOFr\nX/taYf9//dd/zS233MLNN9/Mb/3Wb816eBJdi3uYlZWVVaJpIetLX/oS3/3udwvrnnvuOTZt2sQ7\n77zDjTfeyPPPP7/AXeoO0p4tYCUq2oHybpzprFFla1a1/SgBEl3CgukAK8KZBbQUAVEXel+2meVh\npQpaunuTtyJ1W4rKaFbuq0P5WKuQJTuD5lsv9MxDYREQZ8bWPFR12w972x97J9motq5lV9jsQKs4\nvWAJ5M6xVOjRRx/lyJEjvP766zz77LNcvHix8Pvx48d56623OHnyJI899hiPPfZYz7rj4+NA7/vD\n+fPn+fa3v833vvc9nnvuOQ4dOpS29ZWvfIXHH3+cEydO8MYbb3Dy5EkALl26xFe/+lX+7M/+jFOn\nTvHUU0/NeZiuzT3MysrKymhap8O/+Bf/gvfee6+w7vjx43z961+nVqvx8MMP881vfrNn/ZeeeDf9\nfPO+ET6zb2RWneqGpOKMvvIjL9u26nP+cZj9Np3Lr/zYLq9TldtIykCW70fiTiu70TIwkBhHX/mt\nftXWoDLiJA4tQTl6icL2+fEpo0+V/SsPFMka44TTyBIYdjsgq9E1f1y9xinvds2PvwQ0qvA9ib2S\nJDFxeXdh9yQB027SeuLy652QND+25ZD5vLswu2Knx6wPj/0j48dOlXo1R83gLjz293DsH3r/fvny\nZQA+97nPAXDnnXcyNjbG3XffnW4zNjbG/fffz8jICA899BBf//rXe9Z9++23ufvuu3veH8bGxrjr\nrrvYtGkTmzZtQmvN5OQkq1at4gc/+AEPPPAAAPfddx9jY2Ps3r2b1157jXvuuYdbb70VgA0bNsxx\nkOZ7DzuW+3xTXKysPu56Ly5Ws9GcIztOnDjB9u3bAdi+fTvHjx/vue2DT/wv6Wd9tQ+TOaoKtMpY\nUwSvMqqUgasIT0VLlqhcliEr/7AuLxNUyM+iyx7wWb28bSyx+lRBVt5WJGI/kYrbL0NWZsVyStY2\n04Ymw9n8aDhEaTuCfMaqakdqGbTydctjVB6D/HEl8JVMLugOcjfzKVXcUhGJsqiprA9Z73sB1mJo\nZN9nuG7fL6fH++43Xpp7IzPMLty325RE3/hPxd/zf8cAO3fuTEEp0fHjx/niF7+Yft+wYQPvvvsu\nP/7xj3vW7XV/GBsbY8eOHWmdbdu2MTY2xubNm7n++usLbX3nO9/hy1/+Mq+++iqO43Dbbbdx0003\n8Xu/93vcdtttMw7NTJr9PWzfvPdlZbXydBPFf3C8cW26sUw0Z8jSegniTeapbsdQ2X6SWcfIfa6y\nbpUjfqpcheUIorJTqmyZyn82cJWEqpvtEijShe00DgqXMEWiMhqaPjpEaARObBcTlZakvCUrwiHE\nLTnx8pCVjZBpPeuXJh9eXwSx6VyWZeArn7c8ZCVQlR/HzIpVBq2iTSyzUkFxNqDI/VK8ppfqHwTz\n0hIEvmutu/7ehagem2T9XO4PVW3l67daLc6dO8d3v/td/uf//J/863/9rwtxYVer5XAPs7KyWhma\nNiarSnv27OHMmTOACVjds2fPgndqIVVEhGorVn67bstU2UpVFWFULAZYutcnv+VL+XdjQZJkKEC6\n12QrjyCuHeDlik+Am/6WwVgZcvIwU9y/G7fgdZVin930+JMxzVuyqo7cLR159ehE0/5W5S7tngda\nLDAzWi9JoPpCa54xWXv27CkAy6lTp7qsRKOjo5w+fTr9fuHCBbZs2cLu3bu76o6OjqbtVt0fym2d\nPXuWPXv28OlPf5pz586l60+fPp3247Of/Sz33nsva9euZdeuXaxZs4Yf/vCHcxqmKi23e5iVldXy\n1Zwha3R0lKNHj9JsNjl69OiCmO/nr+4UAL1cdkWX4UzxWcWZh4rMmpW41cqA1Au4ekFVeV0ZXszR\n6QLSuYQp+njxZz/FozAFrXKrZetR2ZIVxi1n+Obn9pDBVr7V/NjLUj/zcNUNUxlQ9QKrJFKsDIrd\nCJyBVnU0XdXUh95TKpaF5glZa9asAcwswffee4/XXnstBaVEo6OjvPzyy4yPj/Piiy+m7r61a9f2\nrNvr/rB3715effVV3n//fY4dO4aUkuHhYcC47F566SUuXrzIK6+8krb12c9+ltdee40gCPjJT37C\nRx99xC/90i/Ne+j68x5mZWW1EjWt0+Ghhx7ijTfeYHx8nE996lP823/7bzl48CD79+9n27Zt7Nq1\niyeffHKp+tpT5YD24m+9Qavquy49qsuP7jxgqQIKyAIgVdlSysrcbyJd5tdlNQ1UuDnccQm7YEMj\nCFFIXMLCOrdru+RYelnYQty0P/n9JK675OjzAepVlqz89yqIqToPElUY46p/C+S3I95Cx67DYo74\nYg6vpG52ZjKXYnIM+etmseOzrloLkPH9qaee4sCBAwRBwKFDh1i/fj1HjhwB4MCBA+zdu5fbb7+d\n3bt3MzIywgsvvDBtXaDn/WHjxo0cPHiQO+64A9/30/0AfOtb32L//v0cPnyYBx98kN27TTDZrl27\nuOWWW7jlllvYtGkTf/qnfzrnY1wu9zArK6uVKaEXKUBBCMFf6DvT7+X4pKqYpW4wqXL3zQxJvSf0\nd0/6L8NTd8KAzJpVtNFkn/NAUAYtlet/1xjFQJUvHgF1WtRoF5bl4hEUjjoBjrKbr02NJgO0qNNk\nIP1cLh38rJ42SzPnUSNEdjR+3D/TtxYDFX2t0cYj6Dq2asiCsqOvDLLF4PziuOe373Ueq853N0hX\nJ4TofZ31ukaT9b2v7/K6/1fcO+dYJn1m1pubOjtsLNJcZOLF/vBad8PKahnoG3O+t5i/ryfmUOOJ\nZXv/WvZ5o6usUGZ9OepmeqtVPgtT3i1YjsFKHvTdLsJiuHcxtqvqHYJmdlzmTDTbC3QcdG76bVow\nLjWfTgw2TXw6XUigkHTw014k6wK8grstb8VKXIMdfFO0n35OZvRJrVLQCmkDiSUrTPuarEv6mkFW\n5jCteuV01QzNBGYz+55Dr0Sf+XFN+lsFn0UsyqxZ1Znbqy1ufaVl/5drZWVltfK1om7Vxbl25Yib\n6iSc5UdyMUd4GbC647CqY7LyVpQMb8oy6QYMYOnYwSdjyMqnKTCzCgN8OtRpM0CDGu2S/cUAVR6w\nACKcNOYpDw9lyGpTo61rdPDTzwqZWbFi0IpwUsDyCIhop+NqLHMqtaMlgJU4IJMX5BTPWbfl0Bx/\n5jw08xkT/5hDlgufdJyS85qctfxxlm1PvQArP9Ow76CqLPuCaCsrK6u+17KHrKIFK4EpqJpT1g1h\nvcKku183UwzDnn4+XNVLY6otWboAGAYpVM46lEBDYh0ylqxBmtRodTm4EgDKw1SE02XdSmolkJVY\nrtrUaFGnrc0ywkFohcy5Cw3IhfgEBLRRaV+zeKwshD6bBZnEkFWdv/Krh8yxJPss04QAoq428nA1\nk0WrGrCglzWrL4Fr2f/lWllZWa18LbtbdfmlJ92/5WNjoGjF6oapIlBlpSo2qBdc5WfclWffFWfi\nFWUe/Pm5fzKFrMySZeK2XBXi6w413aauTWRV3v4m0ETCSY08Wgi0EAR4aU+ndRdqn7YyFqyWGqCl\nYshKRiMGLQT4okNHtgiFSyQzK52ILVyJ1c3LJYRwCQsWpkTFeCsHh4gIt3A2qqc05M97PjNWN1wl\n+ylatEyNLBVp1kI5i3v+Vwp7uoZadn+5VlZWVh8/LeqtuhuCupNzzr6tRPlH5VzqTb/fvLuwO3i6\naGnJJybI544KcQqfiwkLSpCljdUqJCAUDj4OiiBe56aQJVG4KsILQrwgxA9D/CDEV4HhKaHTIqUm\ncsO4BCjXoSODAmQl6oIs5RN0PILAJ+x4hIFHFDlmtHVmE/SciND3CX2PyHcJfRclTV+TZKk1OtRo\np6DlxVFfVWetGlwjZDyueTtjpu4YvKrz2wvNRAmiknzwMynrQ4Zdc30tTtFdOQ9Us5BlZWVl1fda\nslv1Qrhc8q7AuSl7lFVZsopWre5YrLK1JQ9XmTOsukTpMs4ppTPwkyh8XCLtoERmySlDlqNC3CDE\na4b4rYBaM6AWhghp4AphYCtyNVE9JKoFqJqDkhJP+oWYrLIlKwl6D5RP0PEJmx5Bwyds+kSBg9Aa\ntEZoEEoT+BHhkGeKdolcJ4UsicKLA/TrtGLIMvMWTaC+6jofUQFMwziCzE0j2kLKqNQNV/nzmnye\n/izPF3My2JqPkh5cjbSNybKysrLqey2pJavXutmpOlv77NvrvW3Rldgd7N4NWnnAyifvzEci5dOC\nekQ4OcAysCVRRKJj9qElSkg8gm5Llo5wg8hA1mSIPxlQ6wQgQUidLkNPE0UBSksiRxL5Ap9OpSUr\nH5OVWbJ8A1gTHuEVj6jjIpQGRbp06yFhECcpdT2igVJf01mQ7TjKK4v4kqkFKbsOojSE3sXBjSPa\nVMmCpSvOU++SZMqaHp/mBlhlSxow67pVbSXHf7WWrMhasqysrKz6XksOWfNvc+7tll2G5VK1PnMT\n9srg7hRAa/piLDXJfgxsxUHu2kGJbEajT4egy5IV4XaMJcubDKldMhYtITGzzOKlqEFESCQdIl+i\nlEyRb6aYrEB5GWRd8Qk/8lFNx8SYKyDSEIE3FJpM8J4BrFA7cbwTaYB+zcxP7CpZmlBI8MKMoZkD\nGeAVItR6WbAS6HVyAfBVdqqZLVnJ1TH99VSGomIa06vTfCO6LGRZWVlZ9b/69lZddAvm7RndcV06\nt11WN7N8zMb6kX9498qPlQeTsBBt5BNorzKhZ6A9Au0T4oCOH61aoGNLlpYiDVLXUuCLTmr5SlBA\nKoXTUcgphXMpQl6IcJoRwiF9bYpwgUFwnQjXC3EHJJ6WXYCVjEv+uEJcAuURdlyipkt0xUV95BJN\nORBqCImLJho2VjJVl0TDEq2KAfpemmqiRU23TcEsE8hK+qARRCIkIMIRBrACUYSs4jVRPFdV1qv8\n7zOd8cSalVwleegS6acyYFVn1SrPU1xshc5c34jVPenAysrKympx1aeQVXYLZuvyqgKtvEvJfO9V\ninFY5Yd2PkN4kvuqHG+VJvHUxRIon1DF4BK5hMojUiaIRmsRP80FWihCRyNdnQJTW9boCD+1Pykh\nUZEgagrCSxCcg85PwZ0CWTNF1M2SEISvEQMaGWiEytJ7TgctCmP10oFANwV6CvQlYEJDW0FHQ0eZ\nsraD8ANYFSGui9J9OLGDNAUs1aamOtSiAF8F+FGYugvzlqxIKqQ0QfuOVDhOlmlMiN79TV4clKw3\nMzUFIoarcsoGUTj7pD3QhSsiW5vgUxmw8leo2bfOAdr8rFtzUeTO9U+3syj9sLKysrLqrT6FrESC\nKrjKq9r1Z+oW15etWvm6xdfglFM4dAe+uwWLVZL+oKOyZRS6aQkDFxWVIQukUAhfgw/aN9aslujQ\niV/MHAnjNtRKEDUE0SVBcF7Q+QDcK+AMZUUMYYLUB0Cs0ohQIVURH5NRyY9beoxKogKJbgqYEHAZ\n+EhDS0EzglZkls0ODAWI60JEK0IoFcdiZVasGm3quoUfBfhBkM6IFPFrEbRIzi1EjsZxNY6rCN0Q\nV7o4Ik6cqimAVq9X5mTfEtTqDVpFu6YoXRFFW1X5Out1hRa3WxpFjo18t7Kysup39S1k9Yqdmk0d\n6AVWVS7CbrjKv94lyfCuSoDVZclSPi1Vpx3V6IQ+KnCJOg6q45oSytSQookD3x1lYp60ASzlCHzH\nhIoHwrgMlc5bsoSxZH0A7iXw1gCrQawGp40JgF+lES2NCLRJJFo64qrxSkBOBwLdEujJGLI+BKYU\nNCKYCmAqhE6AuC5AbAwRLZVClpkOkIesNl4Y4gcBXifEa0dIpQxgifhcCohcE3Pm4OAIiesESKIC\nvVTBVfndhdkZlLmlLgTBly1aZdiqcgbOfMXlv82mzsIosinfraysrPpefQlZ1fFWvWcrFq1Y3Z97\nuQpVaZkHrLwbMQGtsqswjclKEnlGNVpRnU7oo9suqu2gWg665aADmQ8WMzFZjkareKafI4h8SVu1\n6Ug/BTklHFQkUY0Yss4LOu+D+xEwAlwHsg06AnxgDYiWcRcmlqzkCKvGrWDJ6sTuwkngEvChhgkF\nEyFMBKaEbbg+QFwxliwZ6TQ/VsFdqNt4UYgXRPitEC+2euUBCwHKE0Ra4ApJKCWREgY+4/AmrYVx\nmZYsii5hzn4VIVOo0jFWZaDVq2i6YavK8Vd13WWWr6L9K3MgLq7a1OZYY3JR+mFlZWVl1Vt9A1nF\nmKre66rqVH0v/lblUqx67OYBq5iMtOwuLMCW8giUTyeMS6eGbkt004Gmg25KdEcmHcv66CqE6xJ5\nChEpImUSkXZU8oqbARoM0egM4bVb+M0p6g2HaEoSTUDoYmYXOqCkmV0YrNFEExo9pZANhVOLkDJ+\nwXOc7kEjjOVKSQNwyrSpJkFPRHAlgEsaLmuYDGAyNGUqQjYj3FaA324z0GkyFE4xFDYYEC3qoo0v\nOjgiwtERjlK4YYQTKJy2AbL4dKRSvkBqgRQKKUG5hr6UcIjypZRBP8sOL+P/VAxYiduwly2024Gc\nTzFaFYOVd7GWlQFWVYD84gGXtWRZWVlZ9b+uCWRVxU6V1y/Uw6kY6pztb3rYykNXd0xWIS5Lu4TK\nJQocVMdBtx10W0JTQsPEOOmGMHHHcT5STL4Dk6U9MnmwPB3gijauDgkjl8lwmIvhBlTk0L5Sox16\nSD9iYE0T+YlLuDWIahB4xpOnLgNSI+sa6SscL8JxMFakWoRTV8iaQtRMrJNuSaKmQ9R0CZse0QUH\n9dOQ6Bcd1MUIfUnBFIbefAmrJQy6uOslQ8MdRrwrfEJd4JNTv2CDf5ER90NWuZO4TkTounS0DypA\nKo0TKYgEBLpoUlQgApODS2iQWoDWOJ4JgHedCNcJcYUpSarSLFmpSbIR5RyHZesVpWVm0esOhJ+v\nkpD5fNB8HtCmg7W5ykKWlZWVVf9rrvPA56Xu2KrqR+HCxrVUB7yX47bKcFX9eh0Z58fKWbO0mUEY\nhS6q7Rr3YMNBTznoKQGTwnhqpoAG0MIAVwCEmPQMysQz1eLEoWHoMtlaxcWpDfz08qf4YGIzF8Ib\nuOKNEKwZwrnBxf0ERGug6cGlAM5dhnMX4NJ5RfMXivDnEc7PQvyfR7jjEc4VhWyZhKI6EuiGRH3k\nEJ3zCN/3CX8iiX4aos410BcvwaVz0DwP6hJ4TVitYEMMWasCrvMmuCE6z+bGB9w49VPWN8dZ1Z7C\njUJC7dLGJ9QekXLNzMUkDUQnHoNGXKY0TIFsaGRD4zTBaWmcjrGCuSpKbYZuF+pGqQWr96u+88he\ndebN+oW92qaDO7q+X426R2L6UqU333yTHTt2sHXrVp555pnKbQ4fPsyWLVu49dZbOXv27Ix1JyYm\nuOeee9i0aRP33nsvk5OZm/Lpp59m69at7Ny5k+9///vp+jNnzrBr1y62bNnC1772tXT9888/zy23\n3MKv/MqvcP/99/Puu+9e9XhZWVlZXQstKWQl6g7FLj6WFmefyX6yx2DRqtUdEF9+xU72HsLSewuV\nSxS4xpLVcqDhoBsSpqSBq8m4NIAm0AYCEKGxZDnaZN3yRQJZHpPtYS5ObuCfLn2KD65s5kL4CSb8\n6+isGUR+wsWJIavhG8g6n0DWOU3jFxHRzyOcn4Z4Pw/xxiOcKxGypRGRhlCgUshyCd/3CH/iEP1T\niPrFFPriRwayps4bE5nfhNURXO/ibshZsqIL3DT1AZ+a/CkbmhdZ1ZnEDUMC7dLRNQLtESoTU2Ys\nWWSQ1cSMzRSIBsgGOA2N09Q4bYXbUTiheTG2p5MIteSNkXnAys8z1JQzxVdPe6jKvLawV1s33HXD\n1nyUwf7sSpUeffRRjhw5wuuvv86zzz7LxYsXC78fP36ct956i5MnT/LYY4/x2GOP9aw7Pj4OwHPP\nPcemTZt45513uPHGG3n++ecBOH/+PN/+9rf53ve+x3PPPcehQ4fStr7yla/w+OOPc+LECd544w1O\nnjwJwG/91m/xP/7H/+Af/uEf+PVf/3W+8Y1vLMjYWVlZWS2Vlgyyul2CyaOt+O/94uNvofad7Ycu\nqMr6V3xMZ9asiOp3Fwa4xl0YuUShY2YTtuIYrIZEN4SxZk2JaktWZCxZrjLuQl+0cVJL1nCXJWvC\nvy6zZP2zoiXr/GU4f0Hz0XlN45xKIcv/RWgsWRMqhiyzX90QqEsxZH3gE74viX4WW7LGP0JfOgeN\n2JLlN2FYGcgquAuNJetTsSVruDOFG0aESWJW7RLFkKUjMshqk0KWmAIxpRENjWya4rRUZsmKitnJ\n8oBVBq3yFIYqK1Ye5RcDrvK22OL+i1dgfnk1SmB/tqWsy5cvA/C5z32OzZs3c+eddzI2NlbYZmxs\njPvvv5+RkREeeughzpw507Pu22+/DRgwe+SRR6jVajz88MNpm2NjY9x1111s2rSJz3/+82itUyvX\nD37wAx544AHWrVvHfffdl9YZHh4GQCnFxMQE9Xr9qsfLysrK6lromga+Vz3uFuLxVwVyybIa8nrH\naSXZ3zPrVjkQPgaJ0EEHJh5Lt2RmrWli4CqGKhRZKJCD2a6lEU0TuI4jCRo+zcYgzcYQjeYQqu0y\nodbQclYR1msIR+Jo0JcgkNAMYaIBOoRVQ5rOAKi6Rvog6gpnUCPXaEQnjsdSwvRzSqIuOaiLDuqC\ngnENlzowMyfnZAAAIABJREFU0YTGBPgORMPgBFDTsMrBGRLUaiFDTos1eoL1wTir2hNMuQNMuYMo\nCR3pGqhqCmjH8Wj50o5LB5NANfd6IOFopKORrsKJFI5WKdYmuJtBVX6qgi65C7sdxDMBz/TB6rO7\nJvMJILI5ijpeLhzWzRSTdeJYg5PHmr1/P3GC7du3p9937tzJ22+/zd13352uO378OF/84hfT7xs2\nbODdd9/lxz/+cc+6+Xa3b9/O8ePHAQNZO3bsSOts27aNsbExNm/ezPXXX19o6zvf+Q5f/vKXAXj2\n2Wf59//+3zM0NMSJEyemPWYrKyurflPfzC5caPW2nM1cr7pOKYJHx49wLcxnJQxERfEyoGi56VAI\n+DawZV7SHAQ+7UYEl0G4inZ7gKBTI+y4aClNagYVt5cs2yADcCJwFXhx064GR4NUIKI4tUO6v1JJ\n1if9VgK0A9oFPPNZuRA6EIjsOKK4vjSbSaFxOxG+6jDQNDm2nEhRb7bxWwFOSyFaujgWndyYeJhj\nDJO+gFCA0gitkbrK1Zc/M7r0eW620MVzUi+eZoKsXfuG2bVvOP3+/DfG57wPrTVal8ZaVP8dJevL\n20+nqrbK9b/85S/zO7/zOzz99NN86Utf4pVXXpl1+1ZWVlbXWtckJmsxlQej+VgOynXzc9JSwEKi\nY8DSedBKgrzzkNWmaN1qgp4SRJccwgs+7Z8N0PzJIM33h2ifrxNc9ok6LloIAyFOvPcY4EQAIgQZ\nxpClTXE1OCqBFLoBK39AKWDFRUlQMWRpzxTlQCiNyawtMoscpJAlpMINIvzJgIEP26z6eZPBnzWp\nX+jgfRTgXokQSTxaKzceCXAlQBpDVgZYJot9twuwW8UIv9mpVzB69T76C8TmG5O1Z8+eQiD7qVOn\nuO222wrbjI6Ocvr06fT7hQsX2LJlC7t37+6qOzo6mrabuBXPnDnDnj17Kts6e/Yse/bs4dOf/jTn\nzp1L158+fbqrH57n8du//dtd7kwrKyurfteKgyzotkbNBbaq56f1yLmlBVob0CoATQJaCWQloNXK\nlQboSUH0kUtwwaf9szrNnwzR/MkQ7XMDdK7EkCWlgazkTPWyZGmzWWrJikjjrwqglR1oCbSEgayC\nJcvrbcmCzJIlNW4QUpsMGBhvM/SzBkM/a1I/38YfD3GuRIiGziArKXnICnMlBi2hdQmyZncGzVma\nnarO7nLQfGOy1qxZA5hZgu+99x6vvfZaCkqJRkdHefnllxkfH+fFF19M3X1r167tWXd0dJSjR4/S\nbDY5evRoCkx79+7l1Vdf5f333+fYsWNIKdOYq+3bt/PSSy9x8eJFXnnllbStH/3oR4Cxbr344ovc\ne++9Cz2MVlZWVouqFesuhLlbsma1ffwM1joPWrE1KxJFwMpbslpkwBNvoyWo0CGIBGHoIiKF8EBd\nJ9EjAu1I9JBIE44CmSWrY6xZTmgsV17ctBu7CmXeSlXlKuzlLlSOAS0808HIhUhCR2bAmLgL45da\nC61xgwg5qfAuSdRHAiITWyXceOnEObICipYrSRG0UijUcf4snYuK656TWo3DcwWlrP5Cxk0tphYi\nT9ZTTz3FgQMHCIKAQ4cOsX79eo4cOQLAgQMH2Lt3L7fffju7d+9mZGSEF154Ydq6AAcPHmT//v1s\n27aNXbt28eSTTwKwceNGDh48yB133IHv++l+AL71rW+xf/9+Dh8+zIMPPsju3bsB+NM//VNef/11\narUan/3sZ/n93//9eR+zlZWV1VJqRUPWQqn6YR4XkVtKDTI2JTmAzAGSxMBDB5OUM/6uOhgrUQfo\nOAg/ntNY18jVGulqBmoNao0WnhPgEiJCM0swicWqSxiUoAXUXfBdcFyQLkTJvkVFkXFxAFfElTyQ\ndRCDcUR6DZRvGgoNSIbKJRAeLadGwx9gIKwjQoWYUogPFfKcQoTGtCZ8DK/5pOnVRQJ5Irf/2IiG\nY9hOS4EWphRfc109QWG5wNFCaSEg6/Of/3zq2kt04MCBwvc/+qM/4o/+6I9mVRfMjMD/+l//a+X+\nHn30UR599NGu9Tt37uTv/u7vutY/9dRT0/bfysrKqt9lISunMkCVZ7HlX7LjCpO/KRIuuALtS1Td\nQQQOIlLGhYg2oCWFCexu6zgWKV52FAQRhJFZBhFyMKK2JqAehtRkSK0WsnHwAuumxhn2rlB3Wjgi\nQmASsQ+6EPjAAGgNqwdhcAj8VSCHgVXAIFDDnO0EsLx43RAwjMnp1fGhNWSmK7qAlCBXx8BlLFsh\nLk1Z57K7moveOn5W+wRt7eJGbdxWC+9KG/dCG6cd4XgUivSBelZEvH89DHqV6YceEkQDgqgmCT2H\n0DFAl+Ykq8iS1Q1by8Xpd/WyGd+trKys+l8WskrKJ2/IkjjkkjeICEfHL9URLpFw0a5A+ZJowEEo\nY5YRQoAQxjrjAp4wL11uamho827AqQiiAMLALKMOMuhQb7ZZFbYZli2G6202Dl5kXf0Cq73L1GUz\nRgyTYWEwnpnnDhhL1tAQDK0qQdYARciS8ec6BsBWYyCrVYPmIPgavHhjMWQgCx/ifGFNOcBlZziG\nrBtohZK6mqTWnKB2GeoXOvgN8DzTjOfG+1sF4jrzWXjAmhiwhgxc6SEDW6oeQ5bvEDjZy7jzoJUH\nrKqc74uRBauf1CvBqJWVlZVV/8hCVqxyrE93StJczncR4WrzyPdEgHIkke8iVYTQEQjHuNokBq4M\nnxjA0hqm4pxUlxWoAFQbohaoNjJqUms0GI6mGJENRuoNbhi8yEj9YmzJauKI0ECWNDHqbg1qdbMP\nfwhqQ+APlyxZPrF1ijh4CwNegxhL1qQ06eOntPE3unUToyVrsc/PBy0IcWjKOldiS9Yq/wZaLcFQ\n5DHY1Axe7jB0YYr6JNTjDBA44DggRgxcyWHTnFiNAa1BU1S8jGqCyCtashLIKlqxqgFrcdKN9he0\nVQWzW1lZWVn1l+ydOqfyK1iyLOKKfP53hwhPhEQEKOkQuQ6hHyJxEVIhXBXHjkuEr9F+7Dr8CFCx\nJWtcwcUIdAA6zu2gG0gxRb0xwXB4hRE5wcb6FT4xOM662kVW+5cNZCWWLGnCqOo+RAOAC84QyFVx\nGcYAVJUlK3EXJpA15cCkb4K6agPgRiYYXkoDjMJY6CJhIOuys5oL/jr8WpOWaxLCr24FDF9uEF2Q\nRJdNDD3SAFbkmNmOcrXJ3SU80KuBtcCAQA+AHgA1IFCuIHQkoZO3ZGU531XOklUVrzX/fOr9L+su\ntLKysup/LTvImmty0bKqMn0XM4MXAatoxZK4xHCVuKuEQElhUkthgralVCAEOBJcCb5EK2EsNTUD\nH0qDDjQoBSoyEfCqjRhsIYMmnmriiwZ1t0G91qRWb+PWA5zBCAY1DGXx9WnwuAesBb3WLFkjjDsu\njsnSHjnI0jCgEas0NDWiBbrhQNOBRvwaoAjjSkwsYkMaPSiIBhw6dY+WX6PhDVCXdVzl4wUuXkvi\nTwrcSXBdCD0zQVF7JtN8JCWRJ2FQwLBEDQt0HVQNVB10HTqOR1vU0tIRPgF+bM3y0pfrJBFy3W8w\n7BWjNR/wyrK298MsRAtZVlZWVv2vZQNZC5NktAhSVYHu+TUOCkVUSCCQPMiTR7dE4cgITwb4Toc6\nLULtmRl0QhgjkCtQoaRznUewzqNzyaNzxSPU8Wtncsk5NSbepkWNBoNMoLkiA9bUr9AartEZcYlC\nia6BngQxCUzESxfUBoHeAHq9QI9AtEYSrRKoAYH2hAEsrRGeRgwoxHCEDCNEFCLCOOeXAO3G6ShW\nawNZw2YpNoR46zrUVzcZqk+xWl5hmAkGaOBj3r0I2ljyBuKY+UGQg6A/4dDZ4BOO+HRW+wRDPnpA\noH0NflxHaALh0hF+ViiWAL8UCF98VXQ2XaEYXZcHrquFrTxgXUvYsjFZVlZWVv2vZQNZUMSjuUrE\naFR2CVbBVVIS65UmKqw3xiCdzjT0RIAvO+bBL1wi4SCFRjqYPFG+JlIOjesGaawbhCtDhFNxgNSE\ngEkDNoQ6hiyXNj4NNBNIVsmQqdolmsN1OpFHJI1VTEwCCWhNxakP1oFeJ1DrBWodRKsFqm6KjlNf\nCQ34GjGgTW4uQgSRiReTEu3Gr/LRIgdYGlZp5EiEuz6BrMkUsmo0qOUhy8W4IteAXAtyDUT/zKFz\nfY3GyCCN1UM0hgaJ6hLhKoSnwNUIqYiEQyjcQrB7EvyeBcF7RIVXRueD4cull3Vr7patfI1rac2y\nMVlWVlZW/a9le6eeC3BlFoeya1DF4NXbkmUAK7N8JIAlcqkcIuGY4HfhEEkHLQWOVOYlx55CKkWE\ny+W1axEjmnDSo92sg/JMYLzAJOJsmn6GOLSp0cDBx2NIhkzVB2kN1wkcj6gu0cNADFfEsIUU6OsE\n6jpBFC/DIUnkCZQfW7IEIDXC1zCoECJCuhHSidACc0X4oAccA10xXKWQtTrCW9OhPtxiqD7FsHOF\nVUzg0cCljRtDFoklaw2I9SA3QPgJh86GGlMjQ1xevYbLg6sJBxykVEgnMksZoYUwYxmDU4STwla5\nRF0xWkVrVtXro8sv1JnN9ZO/7vpB1l1oZWVl1f9aVpA1v8STCWZlLkNJkvY8azsPWk5swTIquQiJ\nCInwCNKkmVrEli6tcdwIR0e42ixD6SGu00RTDq3WAE5Hx2YlYQLMm4CTvDXHpY1DAw+HOoNOxGR9\niJas0am7RMPSxMrHgCWm4s8C9BpQqwVqjSBaLYjqAiWlSe6ZJB8F8GLIdBWiHiH8CFwTM8aAQKyK\nwXIVOchSyMEIb6BDvV50F0oaCNqIvCVrAFhtIEv8M9A3SNrX15gcWcVHa9ZycWiEsO7iiBBHmFmb\njgiz8yGy8x3lLFbF4nZZsrLYrCprVt5pnFwZs1dW69oG1lvIsrKysup/LRvIKoNV8Xtv6BIxVon4\noZh3GukebsP8Zyd9UV8GWAlkpRCWcVjalpNzZrmEBMqnuWqAqbWr8JoBsqMQCvMqnkBC04VJF+35\nRD50XGg5Jo/plAiZcoeYkkM0/EEaaoBmvY70NbKmcQY0ctAcXTgsCFdJolWScEjS8ZOZeRl8CKmN\nlU1EuE6A53fwZRuFi3JclKfQdY0WIIYUYihKlwO1BgNeg0GvwaA3xSoxyZBogNsCrwM1E5jvCXDW\ngDMCYoOAjRBd7xCMeLTW1JkaHORKbTWB5xbGySGqdOSVpyBk8zyrAatqxmE+Ii87o9NfP93XUrJt\nkrL+2sjGZFlZWVn1v5YNZF2NZgpMjhMrpNsmywSwkmW+PYVEolDxTEOFrIj2UWk4doIBSIHrBzhD\nAc7aEBmFCK0QkURHHgR1dEeDU0MNK8IBTcfXOFLTpMMUQ1wRw3wk1jIuR5CuwvUjPBW/AliGxus4\nIIl8Seg6hELSYJAWNQJ8IpwUIh0R4ssOdd1iiAbCg6jmoiLXuN9c12w3EOLUQ5yBAMcPGfHGuc69\nZNyEcooB0WLAbSPqAWI4Qowo5EaN78DADQL3BhAbBep6CNdJwtUO4aBD4HsEwqODR4QTWwbdFJWS\n8U5KHp6qgKv3zML82S6q+L7s/nADzlY2JsvKysqq/7VC7tRFq0JmqSpOui9uk9TqPdswsUhVPeiL\ngfBJyofMIlZ+JY+WEq8W4g6FOGGIFCFSROhQQOAZwOo4aBGiVkeEg4rAM9ampm4zxSquiNVckmv5\nUK9DOhrfM3PtajLAdzsItMmUXnMIXRMf1mSQNvUUZpL+usIE7NedNoOiYWYm1mP3m+OifAM8nt82\nlq5aB8/vsM79kLXOJVbLKwyJSeo0qTttnHqAHA5xRiLk9eB6Am8jeNcLxPWC6HpBdJ0kHHIIBl0C\n3yWQJojdTC8o2qHyY5mcy2y2YDVwFc9LeR4pVFueMhi7elf00su6C62srKz6XysEsjKVp9hXAVZ5\n27wVK/9whwzRqgLj8+30TiAQA59ILFmhydjuRwhXmZdDBy50HGjX0DoiWh0RDYYEfoSQIU06TIoh\nrujVXBLXMS4mAM2A36Iu29TdFnW/hSOUsWDFJRJObMmqp2kPNAIhDDwmlqxB0TCAhkvkOES+SzTg\n4hBRc1vU3SZ1p0XNbTHijLNWxJAlpxigyYDTxq13cIZD3BGFs1Hj+CBvEIiNBrLU9YJwWBLUHMKa\nQ+C5dIRHB79r3KtANTkXybIbuPIvQiqCVnaWimfftEWh7fLnfpWFLCsrK6v+14qDrER5S1byQM27\nB5P1edAqlzxU5V2H5YdwVaam4vw1jZIS1w9wRYD0QuRgiPQVqiOg46FbEloCQlDDAeFAAH6AliFN\n3WFKDDEhVnOJtQyJSRCaIdlgwG0wpF0iJY3LTZr0B5F0CYVDk4HYXeh1uQs9OtRFiwAPKRSh4xIp\nl1A7RMrESg3KBgOiwaBoMCgbrOND1oqPGBYTDDHFgEggK8QbjvBGFN4kJvnpRlAbhSkbTIxYKE0W\n91C6sbvQ7xqr8su4E+DNzlwetsrQVXTcdp+v8ud8AHz/w1WiNv617oKVlZWV1QxakZBVBCeRPm7z\naRh0afteZXpXk6mbxF2VoSBfzxUhrhviygDPDfCiAJeAaI2HmhBwxSGadNGBQK+SRHWBcE3PO9qn\nRZ2GGGBSDDEhhvFEJ8U54r25hGlag2TZjuOxQtxcIlWNg8ITIR4BNdrg0FXXI2CQKYZyZZgrDDHF\nIA3qtPDo4MkQz4/wByO8YY13nUbXIbxOotdI1LAkHHYI6iYIP6CYA6s8VvlYtiReq2yHIj3qvB2S\nHFwVLY7l2mWrVi+A7lfZmCwrKyur/pe9U/dQLydj8ZFO12xDAwSKshUlyamVJC6t0abmtgk9TViH\ncFCihjQ6FOg6aN+8rkcgiZQkFA6BNJafFjXaop5LXpAkMwgLs+4UxZl3WYxa0VJkXhVk4qISqFFI\nPALqtAqlRhufTjwT0LxJ0OS5UuCbV/UwBLomieqS0HcJHJcOLm1qccb2/MuenQJgaUDmYquS2Z3l\ncc+7EKt/Wdmy7kIrKyur/pec7scPPviAf/kv/yU333wz+/bt48UXXwRgYmKCe+65h02bNnHvvfcy\nOTm5JJ3trYW0PlSHTWdRP6oLrLJXF4eFtJlObBdKt8tDljSQ5fsBbi3EGYwQq4AhzAuTPYF2zGtu\nlHKItEugXDrap02thD6mtA26pSCTvFQ5cRNmo5WAVta/JJe6TwyAaWmVSttYrwhwY6udEArhaoSv\nEXVzDHpQoOoOoefScXzzHsK4b2EJsqJ0pMojma0rp2momlE4F9BaHvaq3uqdN6y6VOnNN99kx44d\nbN26lWeeeaZym8OHD7NlyxZuvfVWzp49O2Pd6e4NTz/9NFu3bmXnzp18//vfT9efOXOGXbt2sWXL\nFr72ta+l64Mg4JFHHmHz5s3s27ePX/ziF3Mao+Vz/7KyslqpmhayPM/jT/7kTzh16hT/5b/8F77+\n9a8zMTHBc889x6ZNm3jnnXe48cYbef7555eqv4usqtDqLJVlltqyV1rMKkyIfxcxZMkYspw2dcfM\n3HPrIXJQmZc1rwI9gIEsGaOQkoTKIdAeHe31hKwEtPLWojygaPIJVXVq60r6nIescqvdlqwAhxBJ\nhJTaJDWtxS+dHgI9JIjqJlVDx/Vpi+6+RbmR7IaqpHSP9HTzQeea+2q5qgiiM5cqPfrooxw5coTX\nX3+dZ599losXLxZ+P378OG+99RYnT57kscce47HHHutZd3x8HKDnveH8+fN8+9vf5nvf+x7PPfcc\nhw4dStv6yle+wuOPP86JEyd44403OHnyJACvvPIKly9f5syZM9x11138u3/37+Y0Rh+/+5eVlVW/\naVrIuuGGG/iVX/kVANavX8/NN9/MiRMnOH78OI888gi1Wo2HH36YsbGxJelsby2Mg6gbsPKRPuWk\nAVU2mKiACOVYrYIly0ksWQaynIEIMWRcbdRF6i5UWqAiSahcA1n4tHW3fSkPWJkly8tZgIoOtrK7\nMG/JKoJWftnGjyHLI0iPrWDJStyFgwJVk4SeS+B4tGJLVpCDrCSCLcvcXhzFEqb2tGKVo+pmA1jL\n3aUYFUB05lLW5cuXAfjc5z7H5s2bufPOO7v+jsfGxrj//vsZGRnhoYce4syZMz3rvv322wA97w1j\nY2PcddddbNq0ic9//vNorVML0g9+8AMeeOAB1q1bx3333Veos3//fgYHB/md3/mdOd9nls/9y8rK\naqVq1jFZP/rRjzh16hR79+7lS1/6Etu3bwdg+/btHD9+vLLO//7EO+nnm/et4+Z96+bZ3aVWMgOx\nHChfVHWaS5ELSc+niDBrijPqAJErvRuu+KFYaa6B270yR+lc7/OlPBOwZ6OF4yn2r3s2Xzmbf3Xv\n8uk2qref3fprrfFjpxg/dhq4+j7OFJP13rGf8JNj7/f8/cSJE+nfMMDOnTt5++23ufvuu9N1x48f\n54tf/GL6fcOGDbz77rv8+Mc/7lk3327+3jA2NsaOHTvSOtu2bWNsbIzNmzdz/fXXF9r6zne+w5e/\n/GWOHz/OgQMHABgZGeHcuXO0221qtdq0x16lq7l/wbHc55viYmX1cdd7cbGajWYFWRMTEzzwwAP8\nyZ/8CatWrULr2T0aHnhia/p5OczaKmfLIv6e/02h0Mg4z7vOYVNW8qkfkvYiHEIdW6O0sUa1wxpB\nxydsuUQNBz0pIMC43AKNiDRSaKSjcGVsBSOgJtqpZSnv1vMIKl1o+bD3LCIrH5VlZvtV1XULtqQo\nN1sxsUZJtJboUKM7At0SMKURnkbWFW4nxIs61LQoWMGydlU8jsR9FTkLW9EaWM6lNdN5LMNxv2jd\nvpsZ2fcZwBzzj77xf8y5jZkg61P7tvCpfVvS729+460570Nr3fW3LkSPWbbx+tneG3q1pbUutJVv\nby5t53W19y/Yd1X7s7Ja2bqJ4j843rg23VgmmtZdCCb49Atf+AJf/OIXueeeewDYs2dP6jo4c+YM\ne/bsWdxeLpnygdPdJR/2Xv2i4rLDMIco2iHQLoHy6CifVlSjFdXodDyCtotqSANZUyCaIDoaqcxe\nHalwZYQnQnzRSQPSq6KyaiWQSXpg0kpkMGOOIYOsBLQ6dAfWt0slcUdGMWQpJdChRLcFNAV6CkRD\n47Qi3E6IH3Wo6Xbct3YJsqpGryosvhu2qsrywPn5a76B73v27CkEsp86dYrbbrutsM3o6CinT59O\nv1+4cIEtW7awe/furrqjo6Npu1X3hnJbZ8+eZc+ePXz605/m3Llz6frTp0+nbeXrfPjhh2zcuHHO\nVqyP1/3Lysqq3zQtZGmteeSRR/jMZz7D7/3e76XrR0dHOXr0KM1mk6NHj3bdnJe3quN88mHviuIL\niotzCvOglaVRSCxZHW0gq62MJasTxJaspoOeAiaBVmLJUsaSJSOTPFQGKWTV05l/7RiuMsAqW4sk\nUXpUkI/KKkaVJZBVBVrdgfVunMDBMZasSEBHoJsYUGxoZEvhBgFeaDLLlwGweuJAGbay7fJ5yGQh\nx3sZsIpuzZWo+Qa+r1mzBjCzBN977z1ee+21FG4SjY6O8vLLLzM+Ps6LL76YuvvWrl3bs26ve8Pe\nvXt59dVXef/99zl27BhSSoaHhwHjsnvppZe4ePEir7zySqGtF154gampKf7jf/yPc77PfDzvX1ZW\nVv2kad2Ff/u3f8sLL7zALbfcwq/+6q8C8M1vfpODBw+yf/9+tm3bxq5du3jyySeXpLNLqXJyyl7J\nKhUSBwFEBfdcuZ2Cu1DVaEcGslTHJWrlLFlB0ZIlE0tW/K5BX3Rid2EWlJ5Al0dQSCia5b3Kh74X\nc6rnrVj5RKTJsgxC+TQMmSVLokKN7iiTtX4KRJhYsjREZkZmL8gqzwbMw1SWe6wbmqrcgcUXJa1c\nLUQy0qeeeooDBw4QBAGHDh1i/fr1HDlyBIADBw6wd+9ebr/9dnbv3s3IyAgvvPDCtHWBnveGjRs3\ncvDgQe644w5830/3A/Ctb32L/fv3c/jwYR588EF2794NwG/+5m/y3e9+lx07drBlyxZeeumlOR3f\nx/n+ZWVl1R8S+moDHWZqWAhe1v9r+r0KVqqydXfnO8rm9XVP16ewffkFzpm9I4lKyr7nkzNU5V8q\nzl6jYp+Zqqws6TjER9JSdX4efIJfdG4wy+AGLl9Zi/pAoP9Joj6Q6A8kItK4/zzA2xLg/vMQ958H\nrFl3mU+Kn/FJ/olP8lM+KX7Kej3OoJ5iUDcZ1A0GdcNkfBdu+mqdULi0RJ0rrOYKw0ywmgmGucJq\nJlnFhB5mAlNauk6kDAiaV+vEr9URDQbFFIPSvFpnvbjIJ/kpN4qsL8MfTuB9EOK9H+B+EOJ9ECJq\nGvVJgfqkTJeXhtbyc/kJfiFvSJdtUSuME9A1lklC0qprSZeuneL5q3rtdK+3I8rK66f3dVt1Tc7u\nWi639/+IL8w5lul39R/PenuAZ8RXrzqm6eMoExf2h9e6G1ZWy0DfmPO9xfx9PTGHGk8s2/vXCsn4\n3p2bvWrmWvULgqsyY3U/RLsTX2Z1ux/V3XFDgfYI2x5Rw0U1XFTDQV2S6PMKfT6ECwrGFSiFXBfi\nNCK8ToivQuqixRCTrNZXWMsl1ulx1qlx6lGLetRiIGxRj9pIIvNiaNdNXxIthIqzxNdTi5BGxK5L\nn5au0dCDtKI6UegSBi5R6BKFceZ31yF0PNpunaYzgHT+//bePUiO6rz//pzT3XPZ1e4KJKRA8Mom\nlnUBlxFhEcYFVlSAFVM2F+f3ElO4nEBiLDkFcWEXlUDqh6tM/MahiltA4CrLoYxTVEKKX/w6Nlhg\nC0ISrcTr4PiVllvKsmxAElqB9jqX7j7vH6d7+jI9szPa24w4H/swsz19Tp8+PdP91fM85zk+vXKS\nfjHGgDzONEUcr0K1JLAnFNY7PvZhkDmQNkhbIRywcj52ycfOe9g5DydfxclVUaJ+3cgs+RO/RvH3\nSUFF+F4sAAAgAElEQVSddPLGZ3XGnaUk3keWr+R+nY3J+G4wGAydz0kisiLiD8m4dSF732SdLIGV\nZd1KC660qMqyi1V9B7di4046eMdt/DEbNSpRhz3U21XU0SrqaAUpXKxxH3vKI1fxyfs+RabpVZP0\nM85S9S7L1DGWeaPkKxVy1Sq54FXi4+YtvJxOE+paFqAoUcChgoWrhYQSeMqi4juUVIEpv4fpahGv\nbOOVbfyyhVeykcKnmstRzhVwcmVyuV6k7dNnjbPUepdJeilZRRyvhCwprDEfecxFHhY4jsKxFY4N\nTk6LLqviY/d6OL2uXjfRqdbGL2uB7fh4Nro2AoVfCy2UhMIplEuRcEq7EZPpOeICqxvEVqMEowaD\nwWDoHE46kQVJi0ejDODNZhHGH+ZhEsx0Msz4wz5rTmI6WFuLLAdv0tYia9TGPyLhiEK9XYW3p+Fo\nCWFVkGMKe1rhVH3ySlEUWmT1qTFO8QNLljeKU/WwSy5OycMuayuV60kd7GxJPEeiLMEU5SBUPbJk\n6dmOOcp+gSmvyJTbi1/WFjZvUlvbhFBYhSpW0cX2dIZ3S7ksVe8yTh+TopdpCtheHlHykRMu4phE\nHIGcpSg6AhyFnQcrp7B9H8cLkrI6LrliFSzqxqp+xmByxcXo2kgEVu1qhrIpe+qCIHs1yvSC4p0v\nsMAsEG0wGAzdwEl9p57pYakS7yNrVtqKFQ/9zhJckIwpEqhaYHf4WlU5qmVHW7LetfHftlGHLTji\no45U4e0SHJ0Ap4QcE9hT4FQg70ORaZaE7kL1Lqf6oyz3RpEVhVVSyCmFNaVQAjwkriVxHYGndP/C\nHFp17kLfoeRpS9ZktRe/ZKMmLfxxG3/cAgSi10N4PkJ5COlj4XIq7zAm+pmUvUyrIo6Xh5IL4zYc\ns1CHBQUBwlHYDqi8tmTZwscWPo7j4fS4OKoKgSCNj1d6LIHEeIfjLxLRWnGJlBRo0dVOfh+0FSuU\nZt0hrkKMu9BgMBg6n64RWemHYKsPxWTwcTIgOW3rSMdgJdM0NMrcZKFUmDwxaFuBrTws5WIpnZKg\nOu0wNdFL+XiB6qiDf0SiDgNHFbzjw7gLk1VEoYJVUTiuouApepRiiZqk152itxqWaYqlaeQkiEmQ\nE/pVCYGsCKQnsZTAk4KyX8GWLpb0kMJHSIXy9VI9XsXGdR0q1TyVyTzquIX/roV610Idt/WYLbFh\niQ8lBWWf6Z5epgo9TBV7mWQJk3Yvlj+FcD1EpYoolRGTAkuB+y54vaCKCnJgCQ9HVik4JXqKk/T1\nFXClrUdX6DQVltCySYlk/FV8vMNZk3GZnLZChi7GuCUrfsWTLsRsC1f2dyn6Bi4mRmQZDAZD59M1\nIguS1qL2rQ714sqvFVlnvUrnwYon7UwXX0k8ZeH5Fr6SKF8gPR/L9/Wr5+NOOIy9O8DEaC+Vt3N4\nhwQc8uEdBeMKpgFPRxXZeBSo0otLHy59/hi9pUmKUyWcySrWlK8FViCumECnTRAgTgFZUqgK4ILV\nq7ByPtLRAeg4gE+Q00riT0tUyUKNWahRCcckHBOoY2gN0wcsEcGrxO+3qPbnKA0UmaSXsXw/glKQ\n26qCwzQ2AuGhz+m4tmQpAUL55GSJXmcSt2gj+xSeshDSQ1o+0vKQ0kcJkbQaClk35tF3Qb+zMty9\n0VXOFlvRf1vPDJ9lI1sMjMgyGAyGzqerRFaaeLxOM5LxWfVz2dLWq6TQkonUmNXaUspBUQ6usnF9\nG9fTGd19TyKrOleUrCqkq/DGLKbe6WVytIfy2zm8wxKO+FpgTQBlwNMCwMElT4UeSvRRpt8bo7c8\nSWG8RO7dKta7CjEWiKyJSGwpqRAlkIHAEj5Yro9VVMhiIA1sQAlURcC0RI1b+BMW6h0L9bZEHZWo\ntyUcBfxAXMWKf4qFW81REgUmc72M9fUjmSZPhQLTCGxsBLjANKgxUBL8Kgg88rkyS4qTiD5FbmkF\nT1oI29dF+WArfClri0i7wq69r+LE8n2F1zYUPn5dgHxU/EBYhfFz8Ui6ma1Y8bY6BRP4bjAYDJ1P\nV4mstP2gnYeeqj1eqRNYjfJkxV1ULhbVwFZTreVVD4rKUfGi4rk2lEFUBKICoizwj0sq7zhURh0q\nR3L4hwQcUlAOSolAZCnsIHFnD1P0M0W/P05vaYrCWJncqIv1to98BwgEFuPBextEBXAVUoEQClsp\npBecqQUiD8oHqgI1JVDjEvWujX/UgiMCdVhAWDygH+gLxZZATVtUyVHKFZla0suY14/FFD7TCCaw\nsSFuyRJaYPmTWmTlimXEEi2wlkxO4dsSlVOgFEookFAVth5XEY1xWmAlr2U6v5WI/U9myq4odqs9\nq1SnCC4T+G4wGAydT9fcqedmin3Wcjn1cVnJknQVxtf30yvxFSirPCW/QMkr1BZ9piz1YsklqcuY\nwH8X/GPgvy3wDwOHPfCVdt/5gKd7aOPWRFYf4/SH7sKJErljVaxDPuIoWlgFAotxwAHpKlRonLHA\nkj4WSueryofH0pYsFViy1DsW6qiNOgK8Bbwl9KsL9AcCqx9tyXJtqvkc5b4Ck6Vexv1+bCYDgZWn\nEIqs0JJVBTUFvgO28Mj3lcktrcLyaZiSqHwwBAKUFPg2VJRTW8KnRAFLRGsvAjUhFS1zpIvEx0cg\nkShUYNcKEz1EMjt0Gp+Iyy/+vVtMoWXchQaDwdD5dI3IColPtz+Rh1w8mDp8bSa2vKBEli2bak1s\n5amoXLBEToFypUCpUqBazqGmJUxJ7ZKbknAcmFRQ8sELVJUlQEpQFhBMwytU8Z0CnqxQVVXKbpVy\nWbfpTjv4UxZMCpgAfwz8cfDG9HvsIAloHqyCQhZA5LSFiwKIXrSyCCxZlARqUqCOC9S7wLs+HPf0\n67u+tmSpoH9CgpCISYU15WOXPHKVKgW3TNEvU5BVCrZLvuCT71XkXHAssCVIC21FQyE9D1nxkNOB\nQLTBdwW+zsOqRZITBKdZoCyhJxSI6Lq4WMGCPx4SiQyC4dNSS6SE1Wxl0dy0MjcYkWUwGAydT9eJ\nrPkkS2ilrSW1pYxVYNlSDq7r4FUs/JKFKkktsCYFTAq96PMkcFxot6AU0CNgmQRLahOPnw8sTBJv\nqUWpx2bcyuP4PVDqQ0zlKZQqDFQnmPbeCYLCoeJDuQqVMlSmtRbKT0JuEvLjkOsBeoAiOubLJRJZ\nLtpFOYm2go37MFmG6Ypu0K1o85KfCyLXc0AOS3kU/WkG3DFOq45yRvktTvUO0yuP0VOcoHegTM8K\nn2IR8hbkbS22LAvkMhB9WnBRAXE8sCaVFaKqrV/SVzqnluNjOx4OLp6s1ty2NnZiyZ1osaS0I2+x\nQ9PnFxOTZTAYDJ2PEVkBycdzXGjFXYiB61BZURC87+B6Nl7F1iJr0oJJCRMiVoAxoCy0n6pHwqlK\nqxDP0RYjV4Jn4/flKBdzjNs94C+hUi7DVI7+0gTLK+9Q8gt4KhJZ0y5MVmCqpGfw9U5BzwTQC3aP\nfmUJEATD17xuVbTwmiIQWZ5uaHoKypPgTgECVC+oIBuVcLDxKPolBrwxlldGOaN8iFPdIxSscfLF\ncQr9JfLLFbkeyDnojO92sMTOEm1NE1YQOzam+yKq6JmVno4XswoKq+Bj4+FJF8eWeEKPeZStLCmw\nsoTW4s8BnD9MTJbBYDB0PuZOnaI+Z1ZyVUJPychlqByqKhdYsgKRNWXBhESNCS1exgSMC20xKqMt\nWb1oV2EfWuy4Eqo2uB5+0aXc0wN2larvMllyQeRYVn6H8ephSl4RD53DquLDpAtjZTg+rfvvBikd\n7B4oFtECa4psS1ZcZI2FlqxJqByH6ph2ZfpeYBTSuR8sIkvW8qoWWcvct7FlCadQxh4oY6/wsUtg\nObFiazcmxcB9WQXG0AHyLuArhK/HXyqFhY8tPXzbxUendKji1BJqpNc2TDt8T3ZLlnEXGgwGQ+dj\nRFaM7LisSGAlrVmxFA6hJWvaRk1K1LjUwuU42k14nGCmndCxRqGFSVlQkVCxoaKgovDzilJRUbF9\npnylc14pm9NLhxmr9gciK7BkeTBdhbEKHCsFswYnwemBwgT4RbSQmyYpskJLVomYJcvXlqzSFJTH\nwD0WuDMFKBtUEUQoskr0e5G7cJk3irB8RNFDDviIaR/hgnCCmLDgNYw2F6AtaxXdD+GB0BMM9VI+\nKCyp3YV+TuArcLGDqQdeypoVCaxo9uF8RE0tfhxWnDK5xe6CwWAwGGbAiKwZaT4bUalY8YUWJV6Q\nJ8pFuwI9tPUoTAbqCP0qQXpKCxJXITyl499Plfg9Fp4lUa5gil5K1QJVL4+LjZICZYEntU4p+TDl\naa9erwsVFzwX/HgfwhmMKlaCGY26r0pX8irgl0BNBgH5vSCrYHlgK6TlYwuXnF+l4JXorU7R40+j\nbIHfK1CnCHxs3a6lELYCWyEsPwi4D45Xjb0PxkiErz4IX+miVGbcVdaVia7YyY9xFxoMBkPnc1Lf\nqduNyWlp/9AaI7QAECIoUiEsVZsVVxNUuaCo2Ptgu7AVUugFk62gCAfcHguvx8bL2Xi+rZWUj24k\nnIiY0zHznh14G4NDuIHG88N+WOg+ScIMFlEJt1uApUAqHRglAhUkfJCuFliOH/XfCup7ejclBa5j\n4S6xcKWFW7QRrgqywGvrk1CBiLTR5xMeOxwPOyi1/gotJkWUmT9rrmC6wInZsWbOfdXa0jsLhXEX\nGgwGQ+cjF7sD80GWtaPduo2sJIlHu1AI4SOCJJo1wRIKhlBA5IFCrBR1Eb0Ka6mLfVqF/Bkliqum\nKA5OUFhRwumvYOVdhO9rBRWuhBwKkxz4NnhWILJEYBgS2sLlh5kh0gIrOtFIaNVElh/47kKRVQXL\nBduDnNLnEQoiqFmmfF+LrMqSHNPLCkyeUWTqjCKlFTkqpzp4/RaqV+jzjo9DPihxoRUIQyWD9QtF\nvf2qGe3OLUyLq05Iz9AKjdbRbFTaYXx8nCuvvJLBwUGuuuoqJiYmMvd7/vnnWbduHatXr+aBBx5o\nqf7999/P6tWrWb9+PS+88EJt+8jICOeddx5nnXUWt99+e217tVrlxhtvZNWqVWzatIlDhw4B8Ktf\n/Yrf/d3f5dxzz+Xiiy/msccea+scDQaDYSE46URW0m104sHP6bqJR7yIzT0U2gIUvtasRzaRJSsU\nE5kiy8M5rULujBLFVZMUByfJr5gm11/ByrkIpZIiK2bJUg74cZElIktWTWClhVZ0QjGBFe7nR5Ys\nEYgs6YHta0tWXkWWLKiJLOULXMemvMShtCzP5OlFJs8oUFqRpxqKrB6aC6yYNUuFlixBSmQ1tjad\nSPKGtLjqpDxYMzGfImv79u0MDg7y2muvceaZZ/Lwww9n7nfLLbfwyCOP8Mwzz/Dggw8yOjratP6R\nI0d46KGHePbZZ9m+fTs333xzra1bb72V2267jb179/Lcc8/x4osvAvDkk09y/PhxRkZG2LJlC1//\n+tcBOOOMM9i9ezcvvfQS3//+9/mrv/orxsbG2h5Hg8FgmE9OWndhPGlp+Her9RrXyRBaCXch2iIU\nWrLiIksSCYuw9AQia6BKfuk0xaWTOnZpDPzjEnfMplJS2r0Wt2QF7TZyF/pxS1YostKuwkbuQuE3\ncBeqendhaMnKS23J6nWY7s0z2dOD5XmICbAmfJyJKkqK6HhxwRe2GXcXxl2GQuCLZFKGtAiqt0Sl\nzXbN6QZRlWamPFmTu/5fJnf97ITa3rNnD3fccQf5fJ4bbriBb3zjG3X7HD9+HIBLLrkEgMsvv5zd\nu3dzxRVXNKw/PDzMli1bGBwcZHBwEKUUExMTLFmyhFdeeYVrr70WgGuuuYbh4WHOP/98hoeHuf76\n6+np6eELX/gCn/jEJwBwHKfWl7GxMXzfJ5/Pn9D5GgwGw3yxqJasLNvDXCSSrBdJ9dat5GeNA6qT\ns9dqiRySdgLpIcNFjnMKkfehoHQpqprlquYuiwut0LLVo1NSySU+Tm+FYu8UfcUxTimMckpulCVy\njLw3hV0qo8Z9vHEQZW1gKtqwpAeW9EGxT2APCORSiX+KxFsq8foEqihQjs7TJaTS/ezxkQMecpmH\nXOYjTxWIAQexpAg9SyC3BGRB+yUrwJSHN6Uol22mvALHRR+j9qkcy53CWKGPqWKRSjGH3yuiJKgF\nFVms0la9YB9VCEoe/LzAzwk8W+JZEk8kRzs9pzBKtZGO24pbpxp/I9LWq9kKrrRLM/6Nmku8IOKt\nUSls2siyO7fWSjvs3buXtWvXArB27Vr27NnTdB+A9evXs3v37qb1h4eHWbduXa3OmjVrGB4e5vXX\nX2fFihWZbe3Zs4f169cDcOqpp3L48GHK5TIAv/nNbzj77LM566yzeOCBB4zIMhgMHceCWbLilqX4\nK8Ff0bp0BH/PXaYjUXuNHr3h35HdI/6olkTh1n4tL1P4qI8eZS6OqKIsC992sfIWnmvj+TI4BwFC\n6IWPqyJyiYXuMQt8KXGlRVU4WEpPy7dtl3y+ilwyjiUUp4pjnFY9RH/lHZzjU3ijLt47YI1DsQoD\nDtAPyhEsXSnoWSmwTpd4ZwiqK2zcZRZev8Qv6FmJgBZYp3hYVZ1W1c5JVM7Glz0otVQvIO1JkANQ\nLcKYBb6Lq3wmnRzH+vo4JE/jV73vY+mSdxFFD5HzEbYO5M9RwRFVLUAtX+fGCo0PMhiHapBIvihQ\nPehSFHh5iZeTeLaFa9m4OEEJR9+qXY34OpOKKLdZ8sqSej2RVKWtiKSktS19nLkUdLMNfL/ssstq\n8U1x7rrrLpQ6sV+eEMF5tVE/rBNHKZVoK95e/P2ZZ57Jvn372LdvH5deeikf/ehHOe20006o7waD\nwTAfLIq7MPl4i2RXyHykkUwKLJV6FCbfR1arKENWOsolfPR7uHjSxXIsZM5G+C4i8IkpbTbS+bEq\nJN1hFjrtgRVmM9drIUp8euxpegtT9Ihpeu0plvlHWXH8Lfqqx8gdn8R/y8V9R8ekFz3AgcKAtoQV\nVwiKp0us0y2835ZUl9tU+yzcfolfFDoJqlCRyBIudr6CXXDwLRvowXcFqlSAKQGyCJUijEmYcnHx\nmVzicGx5P29Zp1HsmeLU3l568lP05CYpWlP0iElyQossW3pI6Wt3ZJC2Ahvt/gysW6qoc3qpHoFf\nBC8n8ByJa1u40qYq7NoC3W6mVStu2aqfspA1jSESYfPxfcuyoM2tNWu2Imvnzp0NP3v00UcZGRlh\nw4YNjIyMMDQ0VLfP0NAQX/3qV2t/79u3jy1bttQ+y6q/ceNGnnnmmVqdl19+maGhIfr6+jh8+HBt\n+/79+9m4cWOtzv79+1mzZg3Hjh1j5cqVdRars88+m4997GP87Gc/q7kTDQaDoRNYUHdhVoxTMtw4\n7uyZKxo5j7IcS3FrVnJBnbjYij/ybeFiWy7S9pB5D1nwED2+Lr0KelW0tE3oLgyDvQNLlieDBadV\nTifdtKssKYyzvPdtfnvg17yv/1ecZh+iL7Bk+Yc83LfAOg7FCix1YMUArDwNlq6Q9Ky0sE+38M6w\nqZxuaUtWn6UtWRKEpZA9PnKph7XSxR6sYq/ykWfYyJU9iGVLYelK6FmhLVmVwJL1tot71GdqPMc7\n1X4OWSv4Ve/7+M2S3+bt4jLGc0twbbtmybKFG1iylLZkhTMuQxdpL9AbWrEEflHgFQVeQeLmQpGl\nBWg14Q4LhdZMed8buQvjV17/3dr3qNVvW2QbzRZYsxdcbpD7vtXSDhs3bmTHjh1MT0+zY8cOLrzw\nwrp9BgYGAD3D8MCBA+zcuTMhjLLqX3DBBTz99NMcPHiQXbt2IaWkr68P0G7Fxx9/nKNHj/Lkk08m\n2nrssceYnJzkW9/6Vq2tN954g+lpvczBb37zG1566SU2bdrU/kAaDAbDPLLglqy0FSt6eOn3kU1L\nzfpBFLZaHyadHX9Vb82K1seLW7Ci7ONVHGHhSQvLsZG4CGkjpKddhCJwh0iFqmS4RQJLlissEA6+\ngpwoY9suS+xxlvM2Z/AmK6qHGLCP1ixZ3lsu7ig4p0B+KdhLwB4AcZrAXSlwf0vinmHh/rZF9RQb\nV1p4UuJLqQPQCdyFBQ+rv4rtV7B6HFTVRpULqAkL/7iNqiiYcPUCiRNVmHRxpc/kb+c4Vu1HSJ9q\nT47SkgKekOREmX5xHFu45EUZR3pY0kVagSUrfWEIZkkWAoFVAL8QxGMJC1dYuMLWBacmbSMrVlbe\n9yyBlRQ7yQjAubMuJa1jyX86xLfNBfOZjHTr1q1cf/31rFmzhvPOO4+/+Zu/AeDNN9/kT//0T/nX\nf/1XAO69915uuukmqtUqN998M8uXL29af+XKlWzdupXNmzeTy+V45JFHase8++67uf766/mLv/gL\n/vAP/5Dzzz8fgKuvvpqnnnqKdevWcdZZZ/H4448DOuXDrbfeihCCwcFB7r33XhOTZTAYOo6OmV1Y\nH6uVvS2rTtbfyc/SzqLsfFgSP/bfsHjI4H9xoeVh4WDhU8WTWmj5tsQXeuqcryyU8vGlj7IsVFVC\nsHSMzk0A0vaReQ/L8ZCWhyV0+46skBdlimKKXjFBb36CYn6aQr6CU3Sxij5WjxZXTh/k+iG3FFgK\n5QGB6hd4SyR+r4VX1Baf9DgJO75YkIdV8vH7BLLfwhvIwUAuWGuxot2dSoHv4xcsqgWHcj7PdK6H\nCcdjwp5kiiIl8lRx9CgJC0sqfNvCcxQyL1GeQtVCpXR//JzAywsdg+VogVWxHCo4VEWwdFFKYCVL\n0tYYxWVllfSVT9qn4mOUft9IIGUJqPkQVWnmMxlpX18f//Iv/1K3/YwzzqgJLICPf/zjjIyMtFwf\ndNqHW265pW77+vXr+dnP6mdDOo7Djh076rZfeuml/PznP296HgaDwbDYdIzIShJl106LpbTlIW6X\nCAVZ/L0I3iWzF0RxVyp49WOuQonET7gIfXw8/JjASjgRRWAlCvomlY+f064tX1p4lo1ytWdWBQIL\nhV6iplDBLlRxnAq2VaUgS+TDeCbhapFnKawehX2KwlmhyA1qYWUPgN0Psh/EAKh+oDewDDkCX8Yt\nPOHoJd2itbOQPtLxEQWFWAIsRS95U5DQY8MSBf0CTnNhmYPqs/ELFr7UrWi3lEOZHGXy5EQV37Lw\nnSrKFygB0tfWPQU1keXZOsjdtSWeZeEKSYUcZZGnTJ4KOSrkamIrLbhCa1YorqLs8M3EVmTdSrvz\nGr2v/4ZmBbQvXDoIk/HdYDAYOp+OFFnxcOSkrGo+IywtsKIWmjmSkoLLjwmtuMCykCjcwHHoxiwn\nQifMlJEolELLMU/auLaNzPn4ngQV9DCwZgnp4+QqujgVHEtbsBxRwaaq7TRCCyCrqLAGFM5KyE1A\nbgCsXl1kLzq+qR9Ur0DlBcoRKBH1MRrFcDz8miyR+FFah6LSgmoAvQ5jj4ReSy8mPS1hWRW1zEH1\n26iCRFna1ucRxJWRo0QhmHUp9TqNAJZCKlW7fioUWZbEtW0df2XpGKyK0EKtTJ6KyMXEVbZFKy20\n4gLLzxBbaXGVjMxrLq7qv3HpdhcGzzciy2AwGDqdjhRZEfrR1Sw4OS274gIr/ihVqUdpUlyFaRuy\nxJZ+hCs8FCKw18RaCBNlShBCaYuQ7+NKG9d29Hvf1w9FJbS7EP0qhY9jV8jZFXJWmZxVJi/KemZe\nYBuS+AhLYRUDS9aUIlfVQksWQOZBFEDkwe8XNUuWsiW+DM8ow10YO//QkiUcH1FUiCVKW7KkgIrU\nbsOKhIoFS6uBJctCFSxUzJJVDWZIhpYsZWsLFlKBo5BKj6q2Zun+uDKYQShtPWZCuwor5KiItBXL\nToms9LSEmQVW/ZSHrCi9elne+BuaLAuF6xqRZTAYDJ1Oh4qs8HEXSqzILpW2a2XHb4Vh9Gk3Yb3L\nMC60/EBohfFZMnAkWoHAUghs3LoHuP6/0iIrECwuDlUVPPqVj1A6TXoksoS2/4gKjiyTE2XyskxB\nlHSOqdCShY+0FLJHYS8Fuwo5AbkpwKY2a0/o7AuoHqFFliNQMmZti41ZY3ehqrkLxQBgC51O3pXg\nKu0+7NOWLPptVN6qHUNbsmzt6iOPI6uBwALhKD0G+KTndnrYOvYqjL+KxWGFAisutEKB5WKncmW1\nbsGKvkdZ34joO9YoRiv8htZnW1s4oeW5HfrTNRgMBkONjr1Th3MN43Ipma40K8g9SmUaSbNGaRrq\ni6zZr3QUV/i3tmB5CVmixVbMqSli4kz4VAMnWuRMs3Q8FtReJb62+4jAOSYqKYGlz8KXApWT+L0S\nvxq4x0pWtPyMBUilUx/0WrgFi6qjxUt8Nl4oBkRw7DClp42LLV38nIvX42L1uUjXRRUUwkfntAqK\n1etjneIje32svI+QfjACAg+LKrYWWVS1xSqIwfJr4xlesXidyB1YbVLcOktWPDYunZh0pquddhXG\nv3nR31lCKxJi8bisqO5C4BlLlsFgMHQ88yqysiKoZhMYHH/ctdtOXHjVP2azLVvaRRhJNAsvacGK\n9SppHYvN2osFy9cexMFkPYlfE1a5Wph3GTtwEwK6FWlr0VS0qXo2FWysiqMXpg7WAhRS4eYsqj02\n1YJD1XaoyqT1xw9kG8E5RklVq+RkRQu5Honv6hQKouwjfKXXKPS14LILFeylFezeKlbexRZeTTz5\nhElVdVxW0lZmJSxZEIkst9YLO/U+La6SiUizl9tpVWDVf6saUS+0Zm+1ms3vAIzIMhgMhm5gwURW\nvdWpXZL5tbLabtyPUJY1TgURWbpCp2E8AFoQrtAcz8cU9ao+xskKJIcb5NXysWriKhRaEj+wXFXI\niSo5KuSp1IksV1haQBW1wCrbDrLqBsnklZ6oJxWebVHJO1QKDhXHqQWNh8cPBUh4nnGR5cgKfk7i\nFwPhYluIqoVQKjDU6fwTTq6M06NFlp1zdR6seF8DS1bc8ucFgkiiUKlRD0VS3EpVX0Kh2Czje2B+\narcAACAASURBVCSw6oVw9reiHZGUZf06UZq5IVvFrRqRZTAYDJ3Ogq9dOLs2QtptK3I3zuwy1FYs\nSdxqET1Y7ZglI+2GlAlJ4WLXZIwVtEiQc0rX1CKriiPiUUg66D1pybJwnSDmybapFByk7yIDF6UQ\nWmy50qJq21Qdh4qdo5KyZCmiBP9R9not7qrSwc9p8WI7Eq9gITwtKVGhE1VhWxXsXBU7V8XKB2sT\npkRWhVzN6ufVso7bwX5JcRPWSS5YZCVEVeQiTGd8byy0WrNktSe0IuZmJuFsfg++17GefoPBYDAE\nLKglKxmcfmLuvqRNrJ16zYWefmz6BLHasSOlXUte4qzCdiV+TUhYeDERUK2JgUQz6NUN7VREUi7L\nkiVtLbJs7QosKxupnJooFEKLIA+bitClKhwqMpRsobtQ1MY/vkSQdhdW8XKWzlvlW7i+XmBRtx/Z\n7xxRwZYVbFHFlm6Qy0v31UPWgt8FKmal0kIp64rF3X7x93HhFb2PLFlxh2yW0Ipi0Jp9M7LeN+ZE\nXI1ZbWS9to1xFxoMBkPH03X/HE6LrfhDKgqWjzsUtayIwuiz7Bp+IKdkYntoeYmjIEgD2SglQrTG\nYVwExK1IUT0/EXEUhniHswpBuyc9LKpS25zK5ClRCM4wOafOw6JEIcgxFaY/yAXr/lkJ4aHrxUSW\nKOsAeylQQT6v2mzLQGRJoSgwTZ5yLWoqHekV5suKJhEk3YVp/No+VmzkkvFWofXKTWxrLRarcRb4\nbBEdnzUYXfO06/FELWBziBFZBoPB0PF0nchKk7SWhXPn4g7FuMAKZVlaSEnqMx6FxU8cLxJYXu2o\naXdjPF98PFg+jbZkpR1iWkqEj/tQKIRuuBJ5bHpqoiU8h1BkReHz+VrqAzfIuuVj1fqhA999bPRi\nzl7sMyF0TisfiRBx+aJnQxYCCafdmmFfRSAIZc2iF8rXUEg1mghRb4WKlsypX0anscBqJKiyMsDH\nI/FIbU9L8Y7E7dB+GQwGg6FG14usOOkJ9lkCKwr8jsRQtlVLlyz7U1xggVV7LEtUIKms2oM9/oCv\nb8mvc4SFr6ElS9Ximpwg1qkQuCatOungI2vL0EQlChqPW9QE4exCDydwaSJ0gLvExxZucmwCsZWj\nQoFSza0ZnmnYVz9I4xD2x44Jp8YiKzlWXsyilSW4mrkHQ0GXFlz1ExayhFaW1apDcRe7AwaDwWCY\nia4XWekHdzyNaSOBFXcmRtaskKRVy69FU6lae/GjCQhsNjovfLjuYfRQTyYDjROmUUg6xiLrUGh3\n8wNbV4VcIEWI5Z2PLE0+MiO/VC6RIT10noZSJHQXKnQeK0t42MqlipNwF4bHcKjWnJE21ZglK4qv\n0n0jZs3TSxFlj4KoG6so3irKf5UVt+Wn9s+yYDVyI0ZXLym0wu1Z+3cURmQZDAZDx9PVIiseyB4F\n1UMytWRaYCXnJmZbs5IxWekor+hRbOEROZfiVpVwj2YBzqE1KW6zieKc6pN8SnK1bdXk4j6180+n\nPwjFVrjGX9JdGMVkhWNh4WGLSHilZYsTuBdDR2RoyQr3CAPVgZhYsrCaWLLSY5W2UMUtWnGRlZSY\njUs6pUO20KoXVI3edwTTi90Bg8FgMMxEV4uskEhgJecfNvosGceVFhLRoxkkEmoyzA/sVOlg+LQw\ni8dgZYmstBUt7RwLHWdhvbg1Kx5/FXeyxUVWfEaeG7gJ00IjrBsKrbQbNYypSssUK3AvhjMgRaqv\noQiKC6+wrfT5Z41P2jLVaBZhlsCayWrVXGjRcL+OxJt5F4PBYDAsLieFyEqTJaLq3+s944In+bjW\ncknPOQzD4+uD4dN2kPTxood0PJlE1M+4sIqXyGWZFB5hKoQscRhZkqIZeWnXWvLY0fFVrH/pc4mX\nKGYsKQYhmikYiqKwVth+trswuk5pS1b8vJsJrHjcm1/X47hgqhdXWe+7AuMuNBgMho6nPq47RqlU\nYuPGjZx77rlceOGF3HPPPQCMj49z5ZVXMjg4yFVXXcXExMSCdPZECB+z4fvoUVv/WdoeUr/csJ/x\n2G8+By7K7OQmtsVLPLRbxzjFrUOheBG1VqsxN2CUxjS+xp9Tl08qbcWK7GPJRAl2XauVxJHSmarC\nMQ1HNZ6SoVE29/QoZefEajyTsLnAkmQFuUdXPC62ulBchbhtljZo9ff9/PPPs27dOlavXs0DDzzQ\nUv3777+f1atXs379el544YXa9pGREc477zzOOussbr/99tr2arXKjTfeyKpVq9i0aROHDh2qfXbk\nyBE+/elP86EPfYizzz6bX/3qV4n+nQz3L4PB0N00FVmFQoGf/vSnvPTSSzz33HN8+9vf5rXXXmP7\n9u0MDg7y2muvceaZZ/Lwww8vVH8b0Nh9lxZY9Y/bpPtOZD6+I9GVHZrdSFg1Fl1ZAiuyaCUf/ZEb\nTgYJH5otpBxPBmHTTGjFXZXJBWyqKcmWbt2tE4PERinrzOMCK1tIZYureBxWdk6sZhasuBuykbSu\nD3TvCuZRZLX6+77lllt45JFHeOaZZ3jwwQcZHR1tWv/IkSM89NBDPPvss2zfvp2bb7651tatt97K\nbbfdxt69e3nuued48cUXAXjyySc5fvw4IyMjbNmyha9//eu1Otu2beP3f//3GRkZYe/evaxYsSLR\nv+65fxkMhpOVGd2FPT09AExMTOC6Lvl8nj179nDHHXeQz+e54YYb+MY3vpFZ9/E7/6f2/uxNp3LO\nplPnqNuNCd1c8feCcJZhtFW/S7rIqO3jI4JorDAuK5xnmBUMr2pRR35ClkFyBltazAExu0vSWhYX\ngeGnUf+jwPi4TIjmDMbtbEmRFR+nuLsy3nLoKo2St5Lob1xghp+rRD/T8WQy0d/4OKRjn+rtifGR\nyY7B8hPb4mIp/T7pMkxPYwiZb6F1bNf/x+iufcFfJ5rxfYbP/3sX/GLXCTXdyu/7+PHjAFxyySUA\nXH755ezevZsrrriiYf3h4WG2bNnC4OAgg4ODKKWYmJhgyZIlvPLKK1x77bUAXHPNNQwPD3P++ecz\nPDzM9ddfT09PD1/4whf4xCc+oU/fddm/fz9PPPEEEN2n0szm/gXx8Xt/UAyG9zoHgmJohRlFlu/7\nbNiwgX379nHvvfcyODjI3r17Wbt2LQBr165lz549mXX/8M7fqb1v58EVj15KpmKI2kkLqEjwJD/P\nSuoQf5xGx0zm+Q4D2EOhFW3TMVqqJrbCI8WdZ9lB70n7SXPLWXzc4qIiOZ5JaRLJprCITIEVHjuU\nhtERk0cLxzcpF9MZqCIxGJd8UVSbTFyrrHHIGq9GwirbxljvJmxs3YrHyGVfr0bvk1cmXjc+tjN/\nz0/ddA6nbPpwrc3/+drjM9apYyaRtX6TLiH/8LWWm27l9x3fB2D9+vU1kdWo/vDwMOvWravVWbNm\nDcPDw6xatSphhVq/fj3f+973+NKXvsSePXu46aabADj11FM5fPgw5XKZvXv3smzZMq6++mqOHj3K\nZz/7WbZt21bXz9ncv2BTy2NmMLx3eD/Jf3A8tzjd6BJmFFlSSn7+859z4MABPvnJT/Kxj30MpeZz\nUZG4nIoez/qTuAstuRfExUxapEUZrpKPwPgjMxnkDjrQXcdExYVWaNWKL8KjRZcIWpnJKlJv0Ura\nvqJ+CiIxIAjjs6hZi5J1gJoEynat1Yu9SGzpI/m19iJBFxeg2fai+LiK2jXJSo0Rby95JZJjlmXJ\nyirNBVez9A7JkW7Uj3qBlbxq9ZIxPb1hnphl4Ptll12WiG8Kueuuu0749y1EMFZt1A/rxFFKJdqK\ntxe+L5VKDA8Ps3v3bs466yz+4A/+gHPOOadmWQtZ+PuXwWAwRDSNyYrz/ve/n09+8pMMDw8zNDTE\nyMgIoANWh4aG5qxDaTtDlgjJevSR2j+r3foSX/Ql65GeDquO719fGi0Q0+zveikQF4ZJ91YU2F0f\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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = subplots(3,2, figsize = (10,15))\n", - "dat1 = ax[0,0].imshow(Bxr); fig.colorbar(dat1, ax=ax[0,0])\n", - "dat2 = ax[0,1].imshow(Bxra); fig.colorbar(dat2, ax=ax[0,1])\n", - "dat3 = ax[1,0].imshow(Byr); fig.colorbar(dat3, ax=ax[1,0])\n", - "dat4 = ax[1,1].imshow(Byra); fig.colorbar(dat4, ax=ax[1,1])\n", - "dat5 = ax[2,0].imshow(Bzr); fig.colorbar(dat5, ax=ax[2,0])\n", - "dat6 = ax[2,1].imshow(Bzra); fig.colorbar(dat6, ax=ax[2,1])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n", - " -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n", - " -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285. -285.\n", - " -285. -285. -285. -285. 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fmLyNrfO3N0fPfwDYunUrYmJiEBcXhyNHjsjbzWYzZs6ciejoaKxbt07e3tnZ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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "id = 21\n", - "fig, axes = subplots(1,3, figsize=(14,5))\n", - "epsx = np.linalg.norm(Utils.mkvc(Bxr))*1e-6\n", - "epsy = np.linalg.norm(Utils.mkvc(Byr))*1e-6\n", - "epsz = np.linalg.norm(Utils.mkvc(Bzr))*1e-6\n", - "axes[0].plot(Y[:,id], Bxra[:,id], 'b', Y[:,id], Bxr[:,id], 'r.')\n", - "axes[1].plot(Y[:,id], Byra[:,id], 'b', Y[:,id], Byr[:,id], 'r.')\n", - "axes[2].plot(Y[:,id], Bzra[:,id], 'b', Y[:,id], Bzr[:,id], 'r.')\n", - "print X[:,1]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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kpAT5+fk4d+4cGhoacP36dXR3d+Ppp5/GBx984DZhkyAI4qRPwkMTQj287iDFTO7Ez8Xg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\n5XHiY7LMS8g3biIl8zQv2U2OiEhqrFb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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = subplots(1,3, figsize=(14,5))\n", - "epsx = np.linalg.norm(Utils.mkvc(Bxr))*1e-6\n", - "epsy = np.linalg.norm(Utils.mkvc(Byr))*1e-6\n", - "epsz = np.linalg.norm(Utils.mkvc(Bzr))*1e-6\n", - "axes[0].plot(Y[:,1], abs((Bxr[:,1]-Bxra[:,1])/(Bxra[:,1]+epsx)), 'r.')\n", - "axes[1].plot(Y[:,1], abs((Byr[:,1]-Byra[:,1])/(Byra[:,1]+epsy)), 'r.')\n", - "axes[2].plot(Y[:,1], abs((Bzr[:,1]-Bzra[:,1])/(Bzra[:,1]+epsz)), 'r.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Thoughts\n", - "\n", - "- It works well with non-uniform mesh!!\n", - "- Actual accuray is ~10% relative error in secondary fields. As we pad more we can get better accuracy, since we did not consider secondary field at the boudnary ($\\partial\\Omega$). \n", - "- Here, we can try primary secondary field approach to get better accuracy. \n", - "- In addition, we can use the congruous sphere method to handle this secondary fields at boundaries" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/simpegPF/Dev/Mesh.msh b/simpegPF/Dev/Mesh.msh deleted file mode 100644 index 24664d59..00000000 --- a/simpegPF/Dev/Mesh.msh +++ /dev/null @@ -1,5 +0,0 @@ -20 20 20 --0.50 -0.50 0.50 -0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 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-MAG3D - Version 5.0 : MAGFOR3D - -Developed by University of British Columbia -Geophysical Inversion Facility (UBC-GIF) - -(C) Copyright 1992 - 2013, UBC-GIF, -Department of Earth and Ocean Sciences, UBC -http://gif.eos.ubc.ca/ - -This program is licensed to: - - For internal use within UBC-GIF. - - -MAGFOR3D started on: 12/21/2015 08:35:38 - - -magfor3d Mesh.msh Obs_loc.dat Model.dat - - # of surface data: 625 - # of borehole data: 0 - - model was read from file: Model.dat - - TOTAL cpu time: 0:00:00.16 - -MAGFOR3D ended on: 12/21/2015 08:35:39 - diff --git a/simpegPF/Dev/magfor3d.mag b/simpegPF/Dev/magfor3d.mag deleted file mode 100644 index 4fb32d9f..00000000 --- a/simpegPF/Dev/magfor3d.mag +++ /dev/null @@ -1,628 +0,0 @@ - 45.00 315.00 50000.00 !! incl, decl, geomag - -45.00 135.00 1 !! aincl, adecl, idir - 625 !! # of data - -5.000000000E-01 -5.000000000E-01 5.500000000E-01 1.659610E+02 - -5.000000000E-01 -4.583330000E-01 5.500000000E-01 1.925379E+02 - -5.000000000E-01 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-3.222276496743199659e-02 -3.120581428319695386e-02 diff --git a/simpegPF/Gravity.py b/simpegPF/Gravity.py new file mode 100644 index 00000000..49e63049 --- /dev/null +++ b/simpegPF/Gravity.py @@ -0,0 +1,501 @@ +from SimPEG import * +import BaseGrav as GRAV +import re + +class GravityIntegral(Problem.BaseProblem): + + #surveyPair = Survey.LinearSurvey + + storeG = True #: Store the forward matrix by default, otherwise just compute d + actInd = None #: Active cell indices provided + + def __init__(self, mesh, mapping=None, **kwargs): + Problem.BaseProblem.__init__(self, mesh, mapping=mapping, **kwargs) + + + def fwr_op(self): + # Add forward function + # kappa = self.curModel.kappa TODO + sus = self.mapping*self.curModel + return self.G.dot(sus) + + + def fields(self, m): + self.curModel = m + total = np.zeros(self.survey.nRx) + induced = self.fwr_op() + # rem = self.rem + + if induced is not None: + total += induced + + return total + + + # return self.G.dot(self.mapping*(m)) + + def Jvec(self, m, v, f=None): + dmudm = self.mapping.deriv(m) + return self.G.dot(dmudm*v) + + def Jtvec(self, m, v, f=None): + dmudm = self.mapping.deriv(m) + return dmudm.T * (self.G.T.dot(v)) + + @property + def G(self): + if not self.ispaired: + raise Exception('Need to pair!') + + if getattr(self,'_G', None) is None: + self._G = self.Intrgl_Fwr_Op( 'z' ) + + + return self._G + + + def Intrgl_Fwr_Op(self, flag): + + """ + + Gravity forward operator in integral form + + flag = 'z' | 'xyz' + + Return + _G = Linear forward modeling operation + + Created on March, 15th 2016 + + @author: dominiquef + + """ + # Find non-zero cells + #inds = np.nonzero(actv)[0] + if getattr(self, 'actInd', None) is not None: + if self.actInd.dtype=='bool': + inds = np.asarray([inds for inds, elem in enumerate(self.actInd, 1) if elem], dtype = int) - 1 + else: + inds = self.actInd + + + + else: + + inds = np.asarray(range(self.mesh.nC)) + + nC = len(inds) + + # Create active cell projector + P = sp.csr_matrix((np.ones(nC),(inds, range(nC))), + shape=(self.mesh.nC, nC)) + + + + + # Create vectors of nodal location (lower and upper coners for each cell) + xn = self.mesh.vectorNx; + yn = self.mesh.vectorNy; + zn = self.mesh.vectorNz; + + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) + yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) + + Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] + Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] + Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] + + rxLoc = self.survey.srcField.rxList[0].locs + ndata = rxLoc.shape[0] + + # Pre-allocate space and create magnetization matrix if required + # Pre-allocate space + if flag == 'z': + G = np.zeros((ndata, nC)) + + + elif flag == 'xyz': + + G = np.zeros((int(3*ndata), nC)) + + + + else: + print """Flag must be either 'z' | 'xyz', please revised""" + return + + + # Loop through all observations and create forward operator (ndata-by-nC) + print "Begin calculation of forward operator: " + flag + + # Add counter to dsiplay progress. Good for large problems + count = -1; + for ii in range(ndata): + + if flag=='z': + tt = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) + G[ii,:] = tt + + elif flag == 'xyz': + print "Sorry 3-component not implemented yet" + + + # Display progress + count = progress(ii,count,ndata) + + print "Done 100% ...forward operator completed!!\n" + + return G + + +def get_T_mat(Xn,Yn,Zn,rxLoc): + """ + Load in the active nodes of a tensor mesh and computes the gravity tensor + for a given observation location rxLoc[obsx, obsy, obsz] + + INPUT: + Xn, Yn, Zn: Node location matrix for the lower and upper most corners of + all cells in the mesh shape[nC,2] + M + OUTPUT: + Tx = [Txx Txy Txz] + Ty = [Tyx Tyy Tyz] + Tz = [Tzx Tzy Tzz] + + where each elements have dimension 1-by-nC. + Only the upper half 5 elements have to be computed since symetric. + Currently done as for-loops but will eventually be changed to vector + indexing, once the topography has been figured out. + + Created on Oct, 20th 2015 + + @author: dominiquef + + """ + NewtG=6.6738e-3 + eps = 1e-10 # add a small value to the locations to avoid /0 + + nC = Xn.shape[0] + + # Pre-allocate space for 1D array + T = np.zeros((1,nC)) + + dz = rxLoc[2] - Zn + eps + + dy = Yn - rxLoc[1] + eps + + dx = Xn - rxLoc[0] + eps + + + # Compute contribution from each corners + for aa in range(2): + for bb in range(2): + for cc in range(2): + + r = (dx[:,aa] ** 2 + dy[:,bb] ** 2 + dz[:,cc] ** 2) ** (0.50) + + T = T - NewtG * (-1) ** aa * (-1) ** bb * (-1) ** cc * (dx[:,aa] * np.log ( dy[:,bb] + r ) + dy[:,bb] * np.log ( dx[:,aa] + r ) - dz[:,cc] * np.arctan ( dx[:,aa] * dy[:,bb] / ( dz[:,cc] * r ))) + + return T + +def progress(iter,prog,final): + """ + progress(iter,prog,final) + + Function measuring the progress of a process and print to screen the %. + Useful to estimate the remaining runtime of a large problem. + + Created on Dec, 20th 2015 + + @author: dominiquef + """ + arg = np.floor(float(iter)/float(final)*10.); + + if arg > prog: + + strg = "Done " + str(arg*10) + " %" + print strg + prog = arg; + + return prog + + + +def writeUBCobs(filename,survey,d): + """ + writeUBCobs(filename,survey,d) + + Function writing an observation file in UBC-GRAV3D format. + + INPUT + filename : Name of out file including directory + survey + flag : dobs | dpred + + OUTPUT + Obsfile + + Created on Dec, 27th 2015 + + @author: dominiquef + """ + + rxLoc = survey.srcField.rxList[0].locs + + wd = survey.std + + data = np.c_[rxLoc , d , wd] + + with file(filename,'w') as fid: + fid.write('%i\n' %len(d) ) + np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n') + + + print "Observation file saved to: " + filename + +def getActiveTopo(mesh,topo,flag): + """ + getActiveTopo(mesh,topo) + + Function creates an active cell model from topography + + INPUT + mesh : Mesh in SimPEG format + topo : Scatter points defining topography [x,y,z] + + OUTPUT + actv : Active cell model + + Created on Dec, 27th 2015 + + @founrdo + """ + import scipy.interpolate as interpolation + + if (flag=='N'): + Zn = np.zeros((mesh.nNx,mesh.nNy)) + # wght = np.zeros((mesh.nNx,mesh.nNy)) + cx = mesh.vectorNx + cy = mesh.vectorNy + + F = interpolation.NearestNDInterpolator(topo[:,0:2],topo[:,2]) + [Y,X] = np.meshgrid(cy,cx) + + Zn = F(X,Y) + + actv = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) + + if (flag=='N'): + Nz = mesh.vectorNz[1:] + + + for jj in range(mesh.nCy): + + for ii in range(mesh.nCx): + + temp = [kk for kk in range(len(Nz)) if np.all(Zn[ii:(ii+2),jj:(jj+2)] > Nz[kk]) ] + actv[ii,jj,temp] = 1 + + + actv = mkvc(actv==1) + + inds = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype = int) - 1 + + return inds + +def plot_obs_2D(survey,varstr): + """ Function plot_obs(rxLoc,d,wd) + Generate a 2d interpolated plot from scatter points of data + + INPUT + rxLoc : Observation locations [x,y,z] + d : Data vector + wd : Uncertainty vector + + OUTPUT + figure() + + Created on Dec, 27th 2015 + + @author: dominiquef + + """ + + from scipy.interpolate import griddata + import pylab as plt + + rxLoc = survey.srcField.rxList[0].locs + d = survey.dobs + wd = survey.std + + # Create grid of points + x = np.linspace(rxLoc[:,0].min(), rxLoc[:,0].max(), 100) + y = np.linspace(rxLoc[:,1].min(), rxLoc[:,1].max(), 100) + + X, Y = np.meshgrid(x,y) + + # Interpolate + d_grid = griddata(rxLoc[:,0:2],d,(X,Y), method ='linear') + + # Plot result + plt.figure() + plt.subplot() + plt.imshow(d_grid, extent=[x.min(), x.max(), y.min(), y.max()],origin = 'lower') + plt.colorbar(fraction=0.02) + plt.contour(X,Y, d_grid,10) + plt.scatter(rxLoc[:,0],rxLoc[:,1], c=d, s=20) + plt.title(varstr) + plt.gca().set_aspect('equal', adjustable='box') + +def readUBCgravObs(obs_file): + + """ + Read UBC grav file format + + INPUT: + :param fileName, path to the UBC obs grav file + + OUTPUT: + :param survey + + """ + + fid = open(obs_file,'r') + + # First line has the number of rows + line = fid.readline() + ndat = np.array(line.split(),dtype=int) + + # Pre-allocate space for obsx, obsy, obsz, data, uncert + line = fid.readline() + temp = np.array(line.split(),dtype=float) + + d = np.zeros(ndat, dtype=float) + wd = np.zeros(ndat, dtype=float) + locXYZ = np.zeros( (ndat,3), dtype=float) + + for ii in range(ndat): + + temp = np.array(line.split(),dtype=float) + locXYZ[ii,:] = temp[:3] + d[ii] = temp[3] + wd[ii] = temp[4] + line = fid.readline() + + rxLoc = GRAV.RxObs(locXYZ) + srcField = GRAV.SrcField([rxLoc]) + survey = GRAV.LinearSurvey(srcField) + survey.dobs = d + survey.std = wd + return survey + + +def read_GRAVinv_inp(input_file): + """Read input files for forward modeling MAG data with integral form + INPUT: + input_file: File name containing the forward parameter + + OUTPUT: + mshfile + obsfile + topofile + start model + ref model + weightfile + chi_target + as, ax ,ay, az + upper, lower bounds + lp, lqx, lqy, lqz + + # All files should be in the working directory, otherwise the path must + # be specified. + + Created on Dec 21th, 2015 + + @author: dominiquef + """ + + + fid = open(input_file,'r') + + # Line 1 + line = fid.readline() + l_input = line.split('!') + mshfile = l_input[0].rstrip() + + # Line 2 + line = fid.readline() + l_input = line.split('!') + obsfile = l_input[0].rstrip() + + # Line 3 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='null': + topofile = [] + + else: + topofile = l_input[0].rstrip() + + + # Line 4 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mstart = float(l_input[1]) + + else: + mstart = l_input[0].rstrip() + + # Line 5 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mref = float(l_input[1]) + + else: + mref = l_input[0].rstrip() + + + # Line 7 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='DEFAULT': + wgtfile = None + + else: + wgtfile = l_input[0].rstrip() + + # Line 8 + line = fid.readline() + l_input = re.split('[!\s]',line) + chi = float(l_input[0]) + + # Line 9 + line = fid.readline() + l_input = re.split('[!\s]',line) + val = np.array(l_input[0:4]) + alphas = val.astype(np.float) + + # Line 10 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:3]) + bounds = val.astype(np.float) + + else: + bounds = l_input[0].rstrip() + + # Line 11 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:6]) + lpnorms = val.astype(np.float) + + else: + lpnorms = l_input[0].rstrip() + + return mshfile, obsfile, topofile, mstart, mref, wgtfile, chi, alphas, bounds, lpnorms + diff --git a/simpegPF/Magnetics.py b/simpegPF/Magnetics.py index efe91ce1..48ab2149 100644 --- a/simpegPF/Magnetics.py +++ b/simpegPF/Magnetics.py @@ -1,36 +1,217 @@ from SimPEG import * -import BaseMag +import BaseMag as MAG from scipy.constants import mu_0 from MagAnalytics import spheremodel, CongruousMagBC +import re class MagneticIntegral(Problem.BaseProblem): - surveyPair = Survey.LinearSurvey + #surveyPair = Survey.LinearSurvey - def __init__(self, mesh, G, mapping=None, **kwargs): + storeG = True #: Store the forward matrix by default, otherwise just compute d + actInd = None #: Active cell indices provided + M = None #: Magnetization matrix provided, otherwise all induced + def __init__(self, mesh, mapping=None, **kwargs): Problem.BaseProblem.__init__(self, mesh, mapping=mapping, **kwargs) - self.G = G + + + def fwr_ind(self): + # Add forward function + # kappa = self.curModel.kappa TODO + kappa = self.mapping*self.curModel + return self.G.dot(kappa) + + def fwr_rem(self): + #TODO check if we are inverting for M + return self.G.dot(self.mapping(m)) + def fields(self, m): - - return self.G.dot(self.mapping*(m)) + self.curModel = m + total = np.zeros(self.survey.nRx) + induced = self.fwr_ind() + # rem = self.rem + + if induced is not None: + total += induced + + return total + + + # return self.G.dot(self.mapping*(m)) - def Jvec(self, m, v, u=None): + def Jvec(self, m, v, f=None): dmudm = self.mapping.deriv(m) return self.G.dot(dmudm*v) - def Jtvec(self, m, v, u=None): + def Jtvec(self, m, v, f=None): dmudm = self.mapping.deriv(m) return dmudm.T * (self.G.T.dot(v)) + @property + def G(self): + if not self.ispaired: + raise Exception('Need to pair!') + if getattr(self,'_G', None) is None: + self._G = self.Intrgl_Fwr_Op( 'ind' ) + + + return self._G + + # @property + # def Grem(self): + # if not self.ispaired: + # raise Exception('Need to pair!') + + # if getattr(self,'_Grem', None) is None: + # self._Grem = Intrgl_Fwr_Op('full') + + + # return self._Grem + + def Intrgl_Fwr_Op(self, flag): + + """ + + Magnetic forward operator in integral form + + flag = 'ind' | 'full' + + 1- ind : Magnetization fixed by user + + 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) + + Return + _G = Linear forward modeling operation + + Created on March, 13th 2016 + + @author: dominiquef + + """ + # Find non-zero cells + #inds = np.nonzero(actv)[0] + if getattr(self, 'actInd', None) is not None: + if self.actInd.dtype=='bool': + inds = np.asarray([inds for inds, elem in enumerate(self.actInd, 1) if elem], dtype = int) - 1 + else: + inds = self.actInd + + + + else: + + inds = np.asarray(range(self.mesh.nC)) + + nC = len(inds) + + # Create active cell projector + P = sp.csr_matrix((np.ones(nC),(inds, range(nC))), + shape=(self.mesh.nC, nC)) + + + + + # Create vectors of nodal location (lower and upper coners for each cell) + xn = self.mesh.vectorNx; + yn = self.mesh.vectorNy; + zn = self.mesh.vectorNz; + + yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) + yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) + + Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] + Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] + Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] + + rxLoc = self.survey.srcField.rxList[0].locs + ndata = rxLoc.shape[0] + + survey = self.survey + + # Pre-allocate space and create magnetization matrix if required + if (flag=='ind'): + + # # If assumes uniform magnetization direction + # if M.shape != (nC,3): + + # print 'Magnetization vector must be Nc x 3' + # return + if getattr(self, 'M', None) is None: + M = dipazm_2_xyz(np.ones(nC) * survey.srcField.param[1],np.ones(nC) * survey.srcField.param[2]) + + + Mx = Utils.sdiag(M[:,0]*survey.srcField.param[0]) + My = Utils.sdiag(M[:,1]*survey.srcField.param[0]) + Mz = Utils.sdiag(M[:,2]*survey.srcField.param[0]) + + Mxyz = sp.vstack((Mx,My,Mz)) + + + + if survey.srcField.rxList[0].rxType == 'tmi': + G = np.zeros((ndata, nC)) + + # Convert Bdecination from north to cartesian + D = (450.-float(survey.srcField.param[2]))%360. + I = survey.srcField.param[1] + # Projection matrix + Ptmi = mkvc(np.r_[np.cos(np.deg2rad(I))*np.cos(np.deg2rad(D)), + np.cos(np.deg2rad(I))*np.sin(np.deg2rad(D)), + np.sin(np.deg2rad(I))],2).T; + + elif survey.srcField.rxList[0].rxType == 'xyz': + + G = np.zeros((int(3*ndata), nC)) + + elif flag == 'full': + G = np.zeros((int(3*ndata), int(3*nC))) + + + else: + print """Flag must be either 'ind' | 'full', please revised""" + return + + + # Loop through all observations and create forward operator (ndata-by-nC) + print "Begin calculation of forward operator: " + flag + + # Add counter to dsiplay progress. Good for large problems + count = -1; + for ii in range(ndata): + + + tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) + if flag == 'ind': + + if survey.srcField.rxList[0].rxType =='tmi': + G[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz + + elif survey.srcField.rxList[0].rxType =='xyz': + G[ii,:] = tx*Mxyz + G[ii+ndata,:] = ty*Mxyz + G[ii+2*ndata,:] = tz*Mxyz + + elif flag == 'full': + G[ii,:] = tx + G[ii+ndata,:] = ty + G[ii+2*ndata,:] = tz + + + # Display progress + count = progress(ii,count,ndata) + + print "Done 100% ...forward operator completed!!\n" + + return G class MagneticsDiffSecondary(Problem.BaseProblem): """ Secondary field approach using differential equations! """ - surveyPair = BaseMag.BaseMagSurvey - modelPair = BaseMag.BaseMagMap + surveyPair = MAG.BaseMagSurvey + modelPair = MAG.BaseMagMap def __init__(self, model, mapping=None, **kwargs): Problem.BaseProblem.__init__(self, model, mapping=mapping, **kwargs) @@ -525,139 +706,139 @@ def Intgrl_Fwr_Data(mesh,B,M,rxLoc,model,actv,flag): return d -def Intrgl_Fwr_Op(mesh,B,M,rxLoc,actv,flag): - """ - - Magnetic forward operator in integral form - - INPUT: - mesh = Mesh in SimPEG format - B = Inducing field parameter [Binc, Bdecl, B0] - M = Magnetization information - [OPTIONS] - 1- [Minc, Mdecl] : Assumes uniform magnetization orientation - 2- [mx1,mx2,..., my1,...,mz1] : cell-based defined magnetization direction - 3- diag(M): Block diagonal matrix with [Mx, My, Mz] along the diagonal - - rxLox = Observation location informat [obsx, obsy, obsz] - - flag = 'tmi' | 'xyz' | 'full' - [OPTIONS] - 1- tmi : Magnetization direction used and data are projected onto the - inducing field direction F.shape([ndata, nc]) - - 2- xyz : Magnetization direction used and data are given in 3-components - F.shape([3*ndata, nc]) - - 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) - - OUTPUT: - F = Linear forward modeling operation - - Created on Dec, 20th 2015 - - @author: dominiquef - - """ - # Find non-zero cells - #inds = np.nonzero(actv)[0] - if actv.dtype=='bool': - inds = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype = int) - 1 - else: - inds = actv - - nC = len(inds) - - # Create active cell projector - P = sp.csr_matrix((np.ones(nC),(inds, range(nC))), - shape=(mesh.nC, nC)) - - # Create vectors of nodal location (lower and upper coners for each cell) - xn = mesh.vectorNx; - yn = mesh.vectorNy; - zn = mesh.vectorNz; - - yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) - yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) - - Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] - Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] - Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] - - ndata = rxLoc.shape[0] - - # Convert Bdecination from north to cartesian - D = (450.-float(B[1]))%360. - - - # Pre-allocate space and create magnetization matrix if required - if (flag=='tmi') | (flag == 'xyz'): - # If assumes uniform magnetization direction - if M.shape != (nC,3): - - print 'Magnetization vector must be Nc x 3' - return - - - Mx = Utils.sdiag(M[:,0]*B[2]) - My = Utils.sdiag(M[:,1]*B[2]) - Mz = Utils.sdiag(M[:,2]*B[2]) - - Mxyz = sp.vstack((Mx,My,Mz)) - - - - if flag == 'tmi': - F = np.zeros((ndata, nC)) - - # Projection matrix - Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)), - np.cos(np.deg2rad(B[0]))*np.sin(np.deg2rad(D)), - np.sin(np.deg2rad(B[0]))],2).T; - - elif flag == 'xyz': - - F = np.zeros((int(3*ndata), nC)) - - elif flag == 'full': - F = np.zeros((int(3*ndata), int(3*nC))) - - - else: - print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" - return - - - # Loop through all observations and create forward operator (ndata-by-nC) - print "Begin calculation of forward operator: " + flag - - # Add counter to dsiplay progress. Good for large problems - count = -1; - for ii in range(ndata): - - - tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) - - if flag=='tmi': - F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz - - elif flag == 'xyz': - F[ii,:] = tx*Mxyz - F[ii+ndata,:] = ty*Mxyz - F[ii+2*ndata,:] = tz*Mxyz - - elif flag == 'full': - F[ii,:] = tx - F[ii+ndata,:] = ty - F[ii+2*ndata,:] = tz - - - # Display progress - count = progress(ii,count,ndata) - - print "Done 100% ...forward operator completed!!\n" - - return F +#def Intrgl_Fwr_Op(mesh,B,M,rxLoc,actv,flag): +# """ +# +# Magnetic forward operator in integral form +# +# INPUT: +# mesh = Mesh in SimPEG format +# B = Inducing field parameter [Binc, Bdecl, B0] +# M = Magnetization information +# [OPTIONS] +# 1- [Minc, Mdecl] : Assumes uniform magnetization orientation +# 2- [mx1,mx2,..., my1,...,mz1] : cell-based defined magnetization direction +# 3- diag(M): Block diagonal matrix with [Mx, My, Mz] along the diagonal +# +# rxLox = Observation location informat [obsx, obsy, obsz] +# +# flag = 'tmi' | 'xyz' | 'full' +# [OPTIONS] +# 1- tmi : Magnetization direction used and data are projected onto the +# inducing field direction F.shape([ndata, nc]) +# +# 2- xyz : Magnetization direction used and data are given in 3-components +# F.shape([3*ndata, nc]) +# +# 3- full: Full tensor matrix stored with shape([3*ndata, 3*nc]) +# +# OUTPUT: +# F = Linear forward modeling operation +# +# Created on Dec, 20th 2015 +# +# @author: dominiquef +# +# """ +# # Find non-zero cells +# #inds = np.nonzero(actv)[0] +# if actv.dtype=='bool': +# inds = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype = int) - 1 +# else: +# inds = actv +# +# nC = len(inds) +# +# # Create active cell projector +# P = sp.csr_matrix((np.ones(nC),(inds, range(nC))), +# shape=(mesh.nC, nC)) +# +# # Create vectors of nodal location (lower and upper coners for each cell) +# xn = mesh.vectorNx; +# yn = mesh.vectorNy; +# zn = mesh.vectorNz; +# +# yn2,xn2,zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:]) +# yn1,xn1,zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1]) +# +# Yn = P.T*np.c_[mkvc(yn1), mkvc(yn2)] +# Xn = P.T*np.c_[mkvc(xn1), mkvc(xn2)] +# Zn = P.T*np.c_[mkvc(zn1), mkvc(zn2)] +# +# ndata = rxLoc.shape[0] +# +# # Convert Bdecination from north to cartesian +# D = (450.-float(B[1]))%360. +# +# +# # Pre-allocate space and create magnetization matrix if required +# if (flag=='tmi') | (flag == 'xyz'): +# # If assumes uniform magnetization direction +# if M.shape != (nC,3): +# +# print 'Magnetization vector must be Nc x 3' +# return +# +# +# Mx = Utils.sdiag(M[:,0]*B[2]) +# My = Utils.sdiag(M[:,1]*B[2]) +# Mz = Utils.sdiag(M[:,2]*B[2]) +# +# Mxyz = sp.vstack((Mx,My,Mz)) +# +# +# +# if flag == 'tmi': +# F = np.zeros((ndata, nC)) +# +# # Projection matrix +# Ptmi = mkvc(np.r_[np.cos(np.deg2rad(B[0]))*np.cos(np.deg2rad(D)), +# np.cos(np.deg2rad(B[0]))*np.sin(np.deg2rad(D)), +# np.sin(np.deg2rad(B[0]))],2).T; +# +# elif flag == 'xyz': +# +# F = np.zeros((int(3*ndata), nC)) +# +# elif flag == 'full': +# F = np.zeros((int(3*ndata), int(3*nC))) +# +# +# else: +# print """Flag must be either 'tmi' | 'xyz' | 'full', please revised""" +# return +# +# +# # Loop through all observations and create forward operator (ndata-by-nC) +# print "Begin calculation of forward operator: " + flag +# +# # Add counter to dsiplay progress. Good for large problems +# count = -1; +# for ii in range(ndata): +# +# +# tx, ty, tz = get_T_mat(Xn,Yn,Zn,rxLoc[ii,:]) +# +# if flag=='tmi': +# F[ii,:] = Ptmi.dot(np.vstack((tx,ty,tz)))*Mxyz +# +# elif flag == 'xyz': +# F[ii,:] = tx*Mxyz +# F[ii+ndata,:] = ty*Mxyz +# F[ii+2*ndata,:] = tz*Mxyz +# +# elif flag == 'full': +# F[ii,:] = tx +# F[ii+ndata,:] = ty +# F[ii+2*ndata,:] = tz +# +# +# # Display progress +# count = progress(ii,count,ndata) +# +# print "Done 100% ...forward operator completed!!\n" +# +# return F def get_T_mat(Xn,Yn,Zn,rxLoc): """ @@ -823,9 +1004,9 @@ def dipazm_2_xyz(dip,azm_N): M = np.zeros((nC,3)) # Modify azimuth from North to Cartesian-X - azm_X = (450.- azm_N) % 360. + azm_X = (450.- np.asarray(azm_N)) % 360. - D = np.deg2rad(dip) + D = np.deg2rad(np.asarray(dip)) I = np.deg2rad(azm_X) M[:,0] = np.cos(D) * np.cos(I) ; @@ -913,7 +1094,7 @@ def get_dist_wgt(mesh,rxLoc,actv,R,R0): temp = (R1 + R0)**-R + (R2 + R0)**-R + (R3 + R0)**-R + (R4 + R0)**-R + (R5 + R0)**-R + (R6 + R0)**-R + (R7 + R0)**-R + (R8 + R0)**-R - wr = wr + (V*temp/8.)**2 + wr = wr + (V*temp/8.)**2. count = progress(dd,count,ndata) @@ -926,7 +1107,7 @@ def get_dist_wgt(mesh,rxLoc,actv,R,R0): return wr -def writeUBCobs(filename,B,M,rxLoc,d,wd): +def writeUBCobs(filename,survey,d): """ writeUBCobs(filename,B,M,rxLoc,d,wd) @@ -934,11 +1115,8 @@ def writeUBCobs(filename,B,M,rxLoc,d,wd): INPUT filename : Name of out file including directory - B : Inducing field parameters [Inc, Decl, Intensity] - M : Magnetization orientation [Inc, Decl, dtype] - rxLoc : Observation locations [obsx, obsy, obsz] - d : Data vector - wd : Uncertainty vector + survey + flag : dobs | dpred OUTPUT Obsfile @@ -948,11 +1126,17 @@ def writeUBCobs(filename,B,M,rxLoc,d,wd): @author: dominiquef """ + B = survey.srcField.param + + rxLoc = survey.srcField.rxList[0].locs + + wd = survey.std + data = np.c_[rxLoc , d , wd] with file(filename,'w') as fid: - fid.write('%6.2f %6.2f %6.2f\n' %(B[0], B[1], B[2]) ) - fid.write('%6.2f %6.2f %6.2f\n' %(M[0], M[1], 1) ) + fid.write('%6.2f %6.2f %6.2f\n' %(B[2], B[1], B[0]) ) + fid.write('%6.2f %6.2f %6.2f\n' %(B[2], B[1], 1) ) fid.write('%i\n' %len(d) ) np.savetxt(fid, data, fmt='%e',delimiter=' ',newline='\n') @@ -1009,8 +1193,8 @@ def getActiveTopo(mesh,topo,flag): return inds -def plot_obs_2D(rxLoc,d,wd,varstr): - """ Function plot_obs(rxLoc,d,wd) +def plot_obs_2D(rxLoc,d = None ,varstr = 'Mag Obs', vmin = None, vmax = None, levels = None): + """ Function plot_obs(rxLoc,d) Generate a 2d interpolated plot from scatter points of data INPUT @@ -1030,22 +1214,396 @@ def plot_obs_2D(rxLoc,d,wd,varstr): from scipy.interpolate import griddata import pylab as plt - # Create grid of points - x = np.linspace(rxLoc[:,0].min(), rxLoc[:,0].max(), 100) - y = np.linspace(rxLoc[:,1].min(), rxLoc[:,1].max(), 100) - - X, Y = np.meshgrid(x,y) - - # Interpolate - d_grid = griddata(rxLoc[:,0:2],d,(X,Y), method ='linear') - + # Plot result plt.figure() plt.subplot() - plt.imshow(d_grid, extent=[x.min(), x.max(), y.min(), y.max()],origin = 'lower') - plt.colorbar(fraction=0.02) - plt.contour(X,Y, d_grid,10) - plt.scatter(rxLoc[:,0],rxLoc[:,1], c=d, s=20) + plt.scatter(rxLoc[:,0],rxLoc[:,1], c='k', s=10) + + if d is not None: + + if (vmin is None): + vmin = d.min() + + if (vmax is None): + vmax = d.max() + + # Create grid of points + x = np.linspace(rxLoc[:,0].min(), rxLoc[:,0].max(), 100) + y = np.linspace(rxLoc[:,1].min(), rxLoc[:,1].max(), 100) + + X, Y = np.meshgrid(x,y) + + # Interpolate + d_grid = griddata(rxLoc[:,0:2],d,(X,Y), method ='linear') + plt.imshow(d_grid, extent=[x.min(), x.max(), y.min(), y.max()],origin = 'lower', vmin = vmin, vmax = vmax) + plt.colorbar(fraction=0.02) + + if levels is None: + plt.contour(X,Y, d_grid,10,vmin = vmin, vmax = vmax) + else: + plt.contour(X,Y, d_grid,levels = levels,colors = 'r', vmin = vmin, vmax = vmax) + plt.title(varstr) plt.gca().set_aspect('equal', adjustable='box') - \ No newline at end of file + +def readUBCmagObs(obs_file): + + """ + Read and write UBC mag file format + + INPUT: + :param fileName, path to the UBC obs mag file + + OUTPUT: + :param survey + :param M, magnetization orentiaton (MI, MD) + + """ + + fid = open(obs_file,'r') + + # First line has the inclination,declination and amplitude of B0 + line = fid.readline() + B = np.array(line.split(),dtype=float) + + # Second line has the magnetization orientation and a flag + line = fid.readline() + M = np.array(line.split(),dtype=float) + + # Third line has the number of rows + line = fid.readline() + ndat = np.array(line.split(),dtype=int) + + # Pre-allocate space for obsx, obsy, obsz, data, uncert + line = fid.readline() + temp = np.array(line.split(),dtype=float) + + d = np.zeros(ndat, dtype=float) + wd = np.zeros(ndat, dtype=float) + locXYZ = np.zeros( (ndat,3), dtype=float) + + for ii in range(ndat): + + temp = np.array(line.split(),dtype=float) + locXYZ[ii,:] = temp[:3] + + if len(temp) > 3: + d[ii] = temp[3] + + if len(temp)==5: + wd[ii] = temp[4] + + line = fid.readline() + + rxLoc = MAG.RxObs(locXYZ) + srcField = MAG.SrcField([rxLoc],(B[2],B[0],B[1])) + survey = MAG.LinearSurvey(srcField) + survey.dobs = d + survey.std = wd + return survey + +def read_MAGfwr_inp(input_file): + + """Read input files for forward modeling MAG data with integral form + INPUT: + input_file: File name containing the forward parameter + + OUTPUT: + mshfile + obsfile + modfile + magfile + topofile + # All files should be in the working directory, otherwise the path must + # be specified. + + Created on Jul 17, 2013 + + @author: dominiquef + """ + + + fid = open(input_file,'r') + + line = fid.readline() + l_input = line.split('!') + mshfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + obsfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + modfile = l_input[0].rstrip() + + line = fid.readline() + l_input = line.split('!') + if l_input=='null': + magfile = [] + + else: + magfile = l_input[0].rstrip() + + + line = fid.readline() + l_input = line.split('!') + if l_input=='null': + topofile = [] + + else: + topofile = l_input[0].rstrip() + + return mshfile, obsfile, modfile, magfile, topofile + +def read_MAGinv_inp(input_file): + """Read input files for forward modeling MAG data with integral form + INPUT: + input_file: File name containing the forward parameter + + OUTPUT: + mshfile + obsfile + topofile + start model + ref model + mag model + weightfile + chi_target + as, ax ,ay, az + upper, lower bounds + lp, lqx, lqy, lqz + + # All files should be in the working directory, otherwise the path must + # be specified. + + Created on Dec 21th, 2015 + + @author: dominiquef + """ + + + fid = open(input_file,'r') + + # Line 1 + line = fid.readline() + l_input = line.split('!') + mshfile = l_input[0].rstrip() + + # Line 2 + line = fid.readline() + l_input = line.split('!') + obsfile = l_input[0].rstrip() + + # Line 3 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='null': + topofile = [] + + else: + topofile = l_input[0].rstrip() + + + # Line 4 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mstart = float(l_input[1]) + + else: + mstart = l_input[0].rstrip() + + # Line 5 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + mref = float(l_input[1]) + + else: + mref = l_input[0].rstrip() + + + # Line 6 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='DEFAULT': + magfile = [] + + else: + magfile = l_input[0].rstrip() + + # Line 7 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input=='DEFAULT': + wgtfile = [] + + else: + wgtfile = l_input[0].rstrip() + + # Line 8 + line = fid.readline() + l_input = re.split('[!\s]',line) + chi = float(l_input[0]) + + # Line 9 + line = fid.readline() + l_input = re.split('[!\s]',line) + val = np.array(l_input[0:4]) + alphas = val.astype(np.float) + + # Line 10 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:3]) + bounds = val.astype(np.float) + + else: + bounds = l_input[0].rstrip() + + # Line 11 + line = fid.readline() + l_input = re.split('[!\s]',line) + if l_input[0]=='VALUE': + val = np.array(l_input[1:6]) + lpnorms = val.astype(np.float) + + else: + lpnorms = l_input[0].rstrip() + + return mshfile, obsfile, topofile, mstart, mref, magfile, wgtfile, chi, alphas, bounds, lpnorms + +def read_GOCAD_ts(tsfile): + """Read GOCAD triangulated surface (*.ts) file + INPUT: + tsfile: Triangulated surface + + OUTPUT: + vrts : Array of vertices in XYZ coordinates [n x 3] + trgl : Array of index for triangles [m x 3]. The order of the vertices + is important and describes the normal + n = cross( (P2 - P1 ) , (P3 - P1) ) + + + Created on Jan 13th, 2016 + + Author: @fourndo + """ + + + fid = open(tsfile,'r') + line = fid.readline() + + # Skip all the lines until the vertices + while re.match('TFACE',line)==None: + line = fid.readline() + + line = fid.readline() + vrtx = [] + + # Run down all the vertices and save in array + while re.match('VRTX',line): + l_input = re.split('[\s*]',line) + temp = np.array(l_input[2:5]) + vrtx.append(temp.astype(np.float)) + + # Read next line + line = fid.readline() + + vrtx = np.asarray(vrtx) + + # Skip lines to the triangles + while re.match('TRGL',line)==None: + line = fid.readline() + + # Run down the list of triangles + trgl = [] + + # Run down all the vertices and save in array + while re.match('TRGL',line): + l_input = re.split('[\s*]',line) + temp = np.array(l_input[1:4]) + trgl.append(temp.astype(np.int)) + + # Read next line + line = fid.readline() + + trgl = np.asarray(trgl) + + return vrtx, trgl + +def gocad2vtk(gcFile,mesh,bcflag,inflag): + """" + Function to read gocad polystructure file and output indexes of mesh with in the structure. + + """ + import vtk, vtk.util.numpy_support as npsup + + print "Reading GOCAD ts file..." + vrtx, trgl = read_GOCAD_ts(gcFile) + # Adjust the index + trgl = trgl - 1 + + # Make vtk pts + ptsvtk = vtk.vtkPoints() + ptsvtk.SetData(npsup.numpy_to_vtk(vrtx,deep=1)) + + # Make the polygon connection + polys = vtk.vtkCellArray() + for face in trgl: + poly = vtk.vtkPolygon() + poly.GetPointIds().SetNumberOfIds(len(face)) + for nrv, vert in enumerate(face): + poly.GetPointIds().SetId(nrv,vert) + polys.InsertNextCell(poly) + + # Make the polydata, structure of connections and vrtx + polyData = vtk.vtkPolyData() + polyData.SetPoints(ptsvtk) + polyData.SetPolys(polys) + + # Make implicit func + ImpDistFunc = vtk.vtkImplicitPolyDataDistance() + ImpDistFunc.SetInput(polyData) + + # Convert the mesh + vtkMesh = vtk.vtkRectilinearGrid() + vtkMesh.SetDimensions(mesh.nNx,mesh.nNy,mesh.nNz) + vtkMesh.SetXCoordinates(npsup.numpy_to_vtk(mesh.vectorNx,deep=1)) + vtkMesh.SetYCoordinates(npsup.numpy_to_vtk(mesh.vectorNy,deep=1)) + vtkMesh.SetZCoordinates(npsup.numpy_to_vtk(mesh.vectorNz,deep=1)) + # Add indexes + vtkInd = npsup.numpy_to_vtk(np.arange(mesh.nC),deep=1) + vtkInd.SetName('Index') + vtkMesh.GetCellData().AddArray(vtkInd) + + extractImpDistRectGridFilt = vtk.vtkExtractGeometry() # Object constructor + extractImpDistRectGridFilt.SetImplicitFunction(ImpDistFunc) # + extractImpDistRectGridFilt.SetInputData(vtkMesh) + + if bcflag is True: + extractImpDistRectGridFilt.ExtractBoundaryCellsOn() + + else: + extractImpDistRectGridFilt.ExtractBoundaryCellsOff() + + if inflag is True: + extractImpDistRectGridFilt.ExtractInsideOn() + + else: + extractImpDistRectGridFilt.ExtractInsideOff() + + print "Extracting indices from grid..." + # Executing the pipe + extractImpDistRectGridFilt.Update() + + # Get index inside + insideGrid = extractImpDistRectGridFilt.GetOutput() + insideGrid = npsup.vtk_to_numpy(insideGrid.GetCellData().GetArray('Index')) + + + # Return the indexes inside + return insideGrid \ No newline at end of file diff --git a/simpegPF/__init__.py b/simpegPF/__init__.py index a36f82fa..44702532 100644 --- a/simpegPF/__init__.py +++ b/simpegPF/__init__.py @@ -1,3 +1,5 @@ import MagAnalytics import BaseMag import Magnetics +import BaseGrav +import Gravity diff --git a/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb index 0abe8ef6..d504509e 100644 --- a/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb +++ b/simpegPF/notebooks/SimPEG Tutorial - MAG Linear Problem.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -53,19 +53,15 @@ "output_type": "stream", "text": [ "Using matplotlib backend: nbAgg\n", - "Populating the interactive namespace from numpy and matplotlib\n", - "Efficiency Warning: Interpolation will be slow, use setup.py!\n", - "\n", - " python setup.py build_ext --inplace\n", - " \n" + "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n", - " \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n" + "WARNING: pylab import has clobbered these variables: ['linalg']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n" ] } ], @@ -73,26 +69,34 @@ "%matplotlib notebook\n", "%pylab\n", "from SimPEG import *\n", - "import simpegPF as PF" + "import simpegPF as PF\n", + "from simpegPF import BaseMag as MAG\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dominiquef.MIRAGEOSCIENCE\\Documents\\GIT\\SimPEG\\simpeg\\SimPEG\\Maps.py:533: FutureWarning: `ActiveCells` is deprecated and will be removed in future versions. Use `InjectActiveCells` instead\n", + " FutureWarning)\n" + ] + } + ], "source": [ "# First we need to define the direction of the inducing field\n", "# As a simple case, we pick a vertical inducing field of magnitude 50,000nT. \n", "# From old convention, field orientation is given as an azimuth from North \n", "# (positive clockwise) and dip from the horizontal (positive downward).\n", - "H0 = np.array(([90.,0.,50000.]))\n", + "H0 = (50000.,90.,0.)\n", "\n", - "# Assume all induced so the magnetization M is also in the same direction\n", - "M = np.array([90,0])\n", "\n", "# Create a mesh\n", "dx = 5.\n", @@ -130,7 +134,11 @@ "# Let just put the observation above the topo\n", "Z = -np.exp( ( X**2 + Y**2 )/ 75**2 ) + mesh.vectorNz[-1] + 5. \n", "\n", - "rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]\n" + "# Create a MAGsurvey\n", + "rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)]\n", + "rxLoc = MAG.RxObs(rxLoc)\n", + "srcField = MAG.SrcField([rxLoc],H0)\n", + "survey = MAG.LinearSurvey(srcField) \n" ] }, { @@ -168,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -177,7 +185,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Begin calculation of forward operator: tmi\n", + "Begin calculation of forward operator: ind\n", "Done 0.0 %\n", "Done 10.0 %\n", "Done 20.0 %\n", @@ -193,1571 +201,6 @@ ] } ], - "source": [ - "# First, convert the magnetization direction to Cartesian\n", - "mi = np.ones(nC) * M[0]\n", - "md = np.ones(nC) * M[1]\n", - "M_xyz = PF.Magnetics.dipazm_2_xyz( mi , md ) # Ouputs an nc x 3 array\n", - "\n", - "# Create the forward model operator\n", - "F = PF.Magnetics.Intrgl_Fwr_Op(mesh,H0,M_xyz,rxLoc,actv,'tmi')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Begin calculation of distance weighting for R= 3.0\n", - "Done 0.0 %\n", - "Done 10.0 %\n", - "Done 20.0 %\n", - "Done 30.0 %\n", - "Done 40.0 %\n", - "Done 50.0 %\n", - "Done 60.0 %\n", - "Done 70.0 %\n", - "Done 80.0 %\n", - "Done 90.0 %\n", - "Done 100% ...distance weighting completed!!\n", - "\n" - ] - }, - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. 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" var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "# We can now create a susceptibility model and generate data\n", "# Lets start with a simple block in half-space\n", @@ -1766,127 +209,20 @@ "model = mkvc(model)\n", "model = model[actv]\n", "\n", - "# Plot the model\n", - "figure()\n", - "ax = subplot(212)\n", - "mesh.plotSlice(actvMap * model, ax = ax, normal = 'Y', ind=midy, grid=True, clim = (-1e-3, model.max()))\n", - "title('A simple block model.')\n", - "xlabel('x');ylabel('z')\n", - "plt.gca().set_aspect('equal', adjustable='box')\n", - "\n", - "# We can now generate data\n", - "d = F.dot(model) #: this is matrix multiplication!!\n", - "data = d + randn(len(d)) # We add some random Gaussian noise (1nT)\n", - "wd = np.ones(len(data))*1. # Assign flat uncertainties\n", - "\n", - "\n", - "subplot(221)\n", - "imshow(d.reshape(X.shape), extent=[xr.min(), xr.max(), yr.min(), yr.max()])\n", - "title('True data.')\n", - "plt.gca().set_aspect('equal', adjustable='box')\n", - "plt.colorbar()\n", - "\n", - "subplot(222)\n", - "imshow(data.reshape(X.shape), extent=[xr.min(), xr.max(), yr.min(), yr.max()])\n", - "title('Data + Noise')\n", - "plt.gca().set_aspect('equal', adjustable='box')\n", - "plt.colorbar()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "beta_in = 1e+4\n", - "\n", - "# Creat reduced identity map for topography\n", - "idenMap = Maps.IdentityMap(nP = nC)\n", - "\n", - "prob = PF.Magnetics.MagneticIntegral(mesh, F, mapping = idenMap)\n", - "prob.solverOpts['accuracyTol'] = 1e-4\n", - "survey = Survey.LinearSurvey()\n", - "survey.pair(prob)\n", - "survey.dobs=data\n", - "\n", - "# Initiate a simple Tikonov regularization\n", - "reg = Regularization.Simple(mesh, indActive = actv, mapping=wrMap)\n", - "reg.mref = np.zeros(nC)\n", - "reg.alpha_s = 1.\n", - "\n", - "# Create pre-conditioner \n", - "diagA = np.sum(F**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*(wr**2.0)\n", - "PC = Utils.sdiag(diagA**-1.)\n", + "# Create active map to go from reduce set to full\n", + "actvMap = Maps.InjectActiveCells(mesh, actv, -100)\n", "\n", "# Creat reduced identity map\n", - "idenMap = Maps.IdentityMap(nP = nC)\n", + "idenMap = Maps.IdentityMap(nP = len(actv))\n", "\n", - "# Set up the misfit function and pre-conditioner\n", - "dmis = DataMisfit.l2_DataMisfit(survey)\n", - "dmis.Wd = wd\n", - "opt = Optimization.ProjectedGNCG(maxIter=10,lower=0.,upper=1.)\n", - "opt.approxHinv = PC\n", + "# Create the forward model operator\n", + "prob = PF.Magnetics.MagneticIntegral(mesh, mapping = idenMap, actInd = actv)\n", "\n", - "# Set up directives for the inverse problem\n", - "invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in)\n", - "beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)\n", - "target = Directives.TargetMisfit()\n", + "# Pair the survey and problem\n", + "survey.pair(prob)\n", "\n", - "# The final inversion object controling all the parts above\n", - "inv = Inversion.BaseInversion(invProb, directiveList=[beta, target])\n", - "\n", - "# Define a starting model (small)\n", - "m0 = np.ones(nC) * 1e-4" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", - " ***Done using same Solver and solverOpts as the problem***\n", - "=============================== Projected GNCG ===============================\n", - " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", - "-----------------------------------------------------------------------------\n", - " 0 1.00e+04 6.72e+04 3.37e-04 6.72e+04 8.30e+01 0 \n", - " 1 5.00e+03 2.72e+03 3.17e-02 2.88e+03 6.90e+01 0 \n", - " 2 2.50e+03 3.73e+02 5.49e-02 5.10e+02 7.03e+01 0 \n", - "------------------------- STOP! -------------------------\n", - "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.7163e+03\n", - "1 : |xc-x_last| = 6.3549e-03 <= tolX*(1+|x0|) = 1.0111e-01\n", - "0 : |proj(x-g)-x| = 7.0322e+01 <= tolG = 1.0000e-01\n", - "0 : |proj(x-g)-x| = 7.0322e+01 <= 1e3*eps = 1.0000e-02\n", - "0 : maxIter = 10 <= iter = 3\n", - "------------------------- DONE! -------------------------\n" - ] - } - ], - "source": [ - "mrec = inv.run(m0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Inversion has converged. We can plot sections through the model." + "# Compute linear forward operator and compute some data\n", + "d = prob.fields(model)" ] }, { @@ -2635,7 +971,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -2643,39 +979,50 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Here is the recovered susceptibility model\n", - "plt.figure()\n", - "ax = subplot(121)\n", - "mesh.plotSlice(actvMap * mrec, ax = ax, normal = 'Z', ind=-2, clim = (-1e-3, model.max()))\n", - "title('Recovered model.')\n", - "xlabel('x');ylabel('y')\n", - "plt.gca().set_aspect('equal', adjustable='box')\n", "\n", "\n", - "# Horizontalsection\n", - "ax = subplot(122)\n", - "mesh.plotSlice(actvMap * mrec, ax = ax, normal = 'Y', ind=midx, clim = (-1e-3, model.max()))\n", - "title('Recovered model.')\n", + "# Plot the model\n", + "figure()\n", + "ax = subplot(212)\n", + "mesh.plotSlice(actvMap * model, ax = ax, normal = 'Y', ind=midy, grid=True, clim = (-1e-3, model.max()))\n", + "title('A simple block model.')\n", "xlabel('x');ylabel('z')\n", "plt.gca().set_aspect('equal', adjustable='box')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great, we have a 3D model of susceptibility, but the job is not done yet.\n", - "A VERY important step of the inversion workflow is to look at how well the model can predict the observed data.\n", - "The figure below compares the observed, predicted and normalized residual.\n" + "\n", + "# We can now generate data\n", + "data = d + randn(len(d)) # We add some random Gaussian noise (1nT)\n", + "wd = np.ones(len(data))*1. # Assign flat uncertainties\n", + "\n", + "\n", + "subplot(221)\n", + "imshow(d.reshape(X.shape), extent=[xr.min(), xr.max(), yr.min(), yr.max()])\n", + "title('True data.')\n", + "plt.gca().set_aspect('equal', adjustable='box')\n", + "plt.colorbar()\n", + "\n", + "subplot(222)\n", + "imshow(data.reshape(X.shape), extent=[xr.min(), xr.max(), yr.min(), yr.max()])\n", + "title('Data + Noise')\n", + "plt.gca().set_aspect('equal', adjustable='box')\n", + "plt.colorbar()\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -3419,7 +1766,1654 @@ { "data": { "text/html": [ - "" + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create distance weights from our linera forward operator\n", + "wr = np.sum(prob.G**2.,axis=0)**0.5\n", + "wr = ( wr/np.max(wr) )\n", + "\n", + "\n", + "wr_FULL = actvMap * wr\n", + "plt.figure()\n", + "ax = subplot()\n", + "mesh.plotSlice(wr_FULL, ax = ax, normal = 'Y', ind=midx, grid=True, clim = (0, wr.max()))\n", + "title('Distance weighting')\n", + "xlabel('x');ylabel('z')\n", + "plt.gca().set_aspect('equal', adjustable='box')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#survey.makeSyntheticData(data, std=0.01)\n", + "survey.dobs=data\n", + "survey.std = np.ones(len(data))\n", + "survey.mtrue = model\n", + "\n", + "# Create a regularization\n", + "reg = Regularization.Simple(mesh, indActive = actv, mapping = idenMap)\n", + "reg.wght = wr\n", + "\n", + "# Directive to cool the trade-off parameter beta\n", + "beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)\n", + "beta_in = 1e+4\n", + "\n", + "# Create a jacobi pre-conditioner\n", + "diagA = np.sum(prob.G**2.,axis=0) + beta_in*(reg.W.T*reg.W).diagonal()*wr\n", + "PC = Utils.sdiag(diagA**-1.)\n", + "\n", + "# Define the misfit function\n", + "dmis = DataMisfit.l2_DataMisfit(survey)\n", + "dmis.Wd = 1./survey.std\n", + "\n", + "\n", + "# Solver for the optimization problem\n", + "opt = Optimization.ProjectedGNCG(maxIter=10, lower=0.,upper=1., maxIterCG= 30, tolCG = 1e-3)\n", + "opt.approxHinv = PC\n", + "\n", + "# Put everything together\n", + "invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta = beta_in)\n", + "target = Directives.TargetMisfit()\n", + "\n", + "inv = Inversion.BaseInversion(invProb, directiveList=[beta,target])\n", + "m0 = np.ones(mesh.nC)*1e-4\n", + "m0 = m0[actv]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SimPEG.InvProblem will set Regularization.mref to m0.\n", + "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n", + " ***Done using same Solver and solverOpts as the problem***\n", + "=============================== Projected GNCG ===============================\n", + " # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n", + "-----------------------------------------------------------------------------\n", + " 0 1.00e+04 6.70e+04 0.00e+00 6.70e+04 8.30e+01 0 \n", + " 1 5.00e+03 2.94e+03 4.20e-02 3.15e+03 6.89e+01 0 \n", + " 2 2.50e+03 3.61e+02 6.08e-02 5.13e+02 7.04e+01 0 \n", + " 3 1.25e+03 2.58e+02 5.84e-02 3.31e+02 8.36e+01 0 Skip BFGS \n", + "------------------------- STOP! -------------------------\n", + "1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 6.6998e+03\n", + "1 : |xc-x_last| = 8.5475e-03 <= tolX*(1+|x0|) = 1.0111e-01\n", + "0 : |proj(x-g)-x| = 8.3598e+01 <= tolG = 1.0000e-01\n", + "0 : |proj(x-g)-x| = 8.3598e+01 <= 1e3*eps = 1.0000e-02\n", + "0 : maxIter = 10 <= iter = 4\n", + "------------------------- DONE! -------------------------\n" + ] + } + ], + "source": [ + "mrec = inv.run(m0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inversion has converged. We can plot sections through the model." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. 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