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From: Andrew Straw <strawman@as...>  20040414 20:44:32

On Apr 14, 2004, at 11:37 AM, Peter Groszkowski wrote: > Hello Everyone: > > Before I reinvent the wheel, I'd thought I'd ask: > > I have a set of x,y points in a plane, and to each a corresponding z. > They are spread out in circular concentric orbits. > I need to do a flat surface plot (via pcolor) of these z values. A > simple algorithm would be: > > #get data for x, y, z.. this would be some fixed points x,y inside > #a box 4<=x<=4 and 4<=y<=4 with a z value for each. > > #Create a grid > xi=linspace(4,4,200) > yi=linspace(4,4,200) > [Xi,Yi]=meshgrid(xi,yi) > > # This is the key step; find a z value for each grid point. > Zi=griddata(x,y,z,Xi,Yi) > > pcolor(Xi, Yi, Zi, shading='flat') > > show() > > The key here is this griddata function. It interpolates the surface at > the grid points based on the few data points I provide it with > (x,y,z). It uses Delaunay triangulation, and is part of the standard > MATLAB distribution (more about it here: > http://www.mathworks.com/access/helpdesk/help/techdoc/ref/ > griddata.html#1007265). My question is whether anyone has come across > its implementation in python. I could not find any information in any > of the "standard" math packages. How do people do this? VTK, including the Python API, does Delaunay triangulation, and I've used it to do what you're talking about. IIRC, there are other Delaunay triangulation codes with Python bindings around. However, the Matlab function is a little more complicated; they use interpolation in addition to a straight Delaunay triangulation, and it often produces nicelooking results. They cite a reference in either the help file or the .m file; one can presumably read the reference and reimplement it for use in Python. FYI, here's some relevant Python VTK code: profile = vtkPolyData() profile.SetPoints(points) delny = vtk.vtkDelaunay2D() delny.SetInput(profile) delny.SetTolerance(0.01) delny.BoundingTriangulationOff() normals = vtk.vtkPolyDataNormals() normals.SetInput(delny.GetOutput()) lo,hi = zrange elevation = vtk.vtkElevationFilter() elevation.SetInput(delny.GetOutput()) elevation.SetLowPoint(0, 0, lo) elevation.SetHighPoint(0, 0, hi) elevation.SetScalarRange(lo, hi) elevation.ReleaseDataFlagOn() delMesh = vtk.vtkPolyDataMapper() delMesh.SetInput(elevation.GetOutput()) delMesh.SetLookupTable(lutLand) # doesn't work delActor = vtk.vtkActor() delActor.SetMapper(delMesh) ren1.AddActor( delActor ) Cheers! Andrew 
From: Perry Greenfield <perry@st...>  20040414 19:10:19

Eugeni Doljenko writes: > > John Hunter wrote: > > > Best is to use the binary string operations tostring and fromstring > > > > > > from Numeric import fromstring, Float > > > # write > > > file('fname.out', 'wb').write(x.tostring()) > > > > > > # read > > > x = fromstring(file('fname.out', 'rb').read(), Float) > > > > > > #If data is MxN you'll need to reshape > > > x.shape = M,N > > > > > > Hope this help, > > > JDH > > > > > > > Note that numarray has a tofile method and a fromfile function > > to do this without going through the copying required by tostring > > and fromstring. Like those, it also doesn't save any shape or > Moreover tostring() and fromstring() are deprecated iirc, because you can > convert arrays from and to strings by StringIO. > I just noticed this recently. It isn't the case that tostring and fromstring are deprecated for numarray. I just wanted to clarify. Perry 
From: Peter Groszkowski <pgroszko@ge...>  20040414 18:45:35

Hello Everyone: Before I reinvent the wheel, I'd thought I'd ask: I have a set of x,y points in a plane, and to each a corresponding z. They are spread out in circular concentric orbits. I need to do a flat surface plot (via pcolor) of these z values. A simple algorithm would be: #get data for x, y, z.. this would be some fixed points x,y inside #a box 4<=x<=4 and 4<=y<=4 with a z value for each. #Create a grid xi=linspace(4,4,200) yi=linspace(4,4,200) [Xi,Yi]=meshgrid(xi,yi) # This is the key step; find a z value for each grid point. Zi=griddata(x,y,z,Xi,Yi) pcolor(Xi, Yi, Zi, shading='flat') show() The key here is this griddata function. It interpolates the surface at the grid points based on the few data points I provide it with (x,y,z). It uses Delaunay triangulation, and is part of the standard MATLAB distribution (more about it here: http://www.mathworks.com/access/helpdesk/help/techdoc/ref/griddata.html#1007265). My question is whether anyone has come across its implementation in python. I could not find any information in any of the "standard" math packages. How do people do this? Thanks, Peter 