![]() #filter hexahedrons and heads for active cells Rowfinal = salto 2545 #here we insert the len of lines per layerįor item in listaitem: listaheads.append(item) #open the *.FHD file to read the heads, beware that the script runs only on static modelsīreakers=' '] Pt7 = nodesxlay*(lay) nodesxrow*(row) col Pt6 = nodesxlay*(lay) nodesxrow*(row) col 1 Pt5 = nodesxlay*(lay) nodesxrow*(row 1) col 1 Pt4 = nodesxlay*(lay) nodesxrow*(row 1) col Pt3 = nodesxlay*(lay 1) nodesxrow*(row) col Pt2 = nodesxlay*(lay 1) nodesxrow*(row) col 1 Pt0 = nodesxlay*(lay 1) nodesxrow*(row 1) col Nodesxlay, nodesxrow = (ncols 1)*(nrows 1),ncols 1 #complete the extreme right column and lowest row as a duplicate of the previous one Zmatrix=np.ones((nrows 1,ncols 1))*bottom Matrixarray = matrixarray.reshape(nrows,ncols) split()]įor item in listaitem: matrixlist.append(item) Rowfinal = salto interval #here we insert the len of lines per layerįor linea in range(rowinicio,rowfinal,1): #loop to read all the heads in one layer, reshape them to the grid dimensions and add to the numpy array Zmatrix = np.zeros((nlay 1,nrows 1,ncols 1)) #counter plus a empty numpy array for model results Linebottom='CONSTANT']īottom= float(dis].split())īreakers='INTERNAL'] Northings = np.linspace(ymin, ymax, nrows 1, endpoint=True, dtype='float32') Xres, yres = (xmax-xmin)/ncols, (ymax-ymin)/nrowsĮastings = np.linspace(xmin, xmax, ncols 1, endpoint=True, dtype='float32') Nlay, nrows, ncols = int(dis.split()), int(dis.split()), int(dis.split()) Xmax, ymax = float(upperright), float(upperright) Xmin, ymin = float(lowerleft), float(lowerleft) Lowerleft, upperright = lowerleft.split(','), upperright.split(',') #open the *.DIS file and get the model boundaries and discretization ![]() Os.chdir('C:/Users\Saul\Documents\Ih_PlayaroundwithVTK\Model') #change directory to the model files path This is the complete Python code used in this tutorial: #import the required libraries Model input files, output files and project files in Model Muse are available at the end of this article. The scripting was done in Python 3 on a Jupyter Notebook. This tutorial shows the complete procedure to create a Paraview compatible geometry data format called *.vtk, and the representation on Paraview. This visual application was designed to analyze extremely large datasets using distributed memory computing resources, in fact the term "para" in Paraview comes from the parallelization of computer cores. ![]() There is a particular open source software for data representation that is of our interest, it is called Paraview (). Despite the fact the capabilities of these softwares, there are some gaps in data processing and representation isometric views, animation and custom cross sections are still difficult to achieve under the existing tools, specially on multilayered models with transient conditions over series of time steps and stress periods. Free and commercial software is available for the MODFLOW model construction and MODFLOW output representation. MODFLOW computes the groundwater heads over a porous / fractured media upon a series of boundary conditions as recharge, evapotranspiration, drains, well and others on steady and transient conditions.
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