Masked arrays seem to do the trick. Is there a reason why the nan thing won't work?
On 7/16/06, PGM <[EMAIL PROTECTED]> wrote: > On Sunday 16 July 2006 19:38, Webb Sprague wrote: > > I have data with missing values represented by nans (like array([1.0, > > nan, 3.0]) that I am plotting with pylab.semilogy(). > > Please transform your array in a MaskedArray. > import numpy as N > masked_x=N.ma.masked_where(N.isnan(x),x) > > That should do the trick. > > Cf http://www.scipy.org/Cookbook/Matplotlib/Plotting_values_with_masked_arrays > > > ------------------------------------------------------------------------- > Using Tomcat but need to do more? Need to support web services, security? > Get stuff done quickly with pre-integrated technology to make your job easier > Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo > http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
