[Qgis-developer] visualizing 3D data (Z values) or data with z attribute: a solution

[gene]

After the post of Etienne Tourigny about "Using/visualizing 3D data (Z
values)", I realized that it was relatively easy from the Python console
with matplotlib or visvis (new pure Python library for visualization of 1D
to 4D data,  http://code.google.com/p/visvis/
<http://code.google.com/p/visvis/>  ).

*1) 3D shapefile*
 *a) with matplotlib.meshgrid*
With a 3d point shapefile, the *shapely* module makes it very easy to
extract the z coordinate:

from mpl_toolkits.mplot3d.axes3d import *
import pylab as plt
from matplotlib import cm
from matplotlib.mlab import griddata
import numpy as np
from shapely.wkb import loads 
x=[]
y=[]
z=[]
for elem in layer.selectedFeatures(): 
           geom= elem.geometry() 
           wkb = geom.asWkb() 
           x.append(loads(wkb).x)
           y.append(loads(wkb).y)
           z.append(loads(wkb).z)

now, with the x,y,z coordinates, we can build a grid/surface with the
matplotlib.meshgrid function

xi = np.linspace(min(x), max(x))
yi = np.linspace(min(y), max(y))
X, Y = np.meshgrid(xi, yi)
Z = griddata(x, y, z, xi, yi) 

and draw it in a 3D matplotlib window
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(X, Y, Z, rstride=5, cstride=5, cmap=cm.jet,linewidth=1,
antialiased=True)
plt.show()
<http://osgeo-org.1560.n6.nabble.com/file/n5005360/splinematplotlib.jpg> 

It is also possible to plot the points or the contours in 3D
<http://osgeo-org.1560.n6.nabble.com/file/n5005360/contourpts.jpg> 


*with visvis*
import visvis
visvis.plot(x,y,z, lc='k', ls='', mc='g', mw=2, lw=2, ms='.')
visvis.surf(xi, yi, Z)
<http://osgeo-org.1560.n6.nabble.com/file/n5005360/splinevisvis.jpg> 

*b) with splines algorithms from scipy*
It is also possible to use other interpolation algorithms like the spline
interpolation class  BivariateSpline
<http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BivariateSpline.html#scipy.interpolate.BivariateSpline>  
of scipy

*with matplotlib*

import scipy as sp
from scipy.interpolate import SmoothBivariateSpline
spl = SmoothBivariateSpline(x,y,z)
# construction of the grid/surface
x = np.array(x)
y = np.array(y)
xn, yn = sp.mgrid[x.min():x.max():50j,y.min():y.max():50j]
zspline = spl( xn[:,0], yn[0,:])
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(xn, yn, zspline)
plt.show()

*with visvis*

import visvis
visvis.surf(xn,yn,zspline)

*2) shapefile with z attribute*
This is the same thing, but we must use the values ​​of the attribute

*Conclusions and a plugin ?*
Build a plugin seems a priori to me very complex and time consuming 
- plot 3 d points is easy.
- but in other cases, all the scenarios (3D shapefile points, lines or
polygons, shapefile with z attribute, interpolation algorithm, ...)  should
be considered before
- you need modules like shapely or matplotlib

I do not have much time to write a plugin and personally I'm very happy with
the Python console, sorry.

<http://osgeo-org.1560.n6.nabble.com/file/n5005360/pythonconsole3.jpg> 

Then, If somebody else wants to go,I can help 






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