I think scatter3D does what you want:
from matplotlib import axes3d
import pylab as pl
fig = pl.figure()
ax = axes3d.Axes3D(fig)
ax.scatter3D(data[:,0],data[:,1],data[:,2])
ax.set_xlabel('X value')
ax.set_ylabel('Y value')
ax.set_zlabel('Z value')
pl.show()
You could also change the colour and size of each point based on other
array values:
col = ax.scatter3D(data[:,0], data[:,1], data[:,2], c=data[:,3],
cmap=pl.cm.jet, s=data[:,4])
cbar = fig.colorbar(col,shrink=0.9,extend='both')
cbar.ax.set_ylabel('axis 3 data values')
Pretty nifty.
Neil
> hello,
> I would like to plot in 3D a dataset organized as 1000 x,y,z points in a
> numpy array, so it would be smthg like
> plot3d(data[:,0],data[:,1],data[:,2]). I looked at the plot3D cookbook
> page, but it all seems to expect some sort of binning on a grid.....
>
> best,
> Johann
>
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