Eric Firing wrote:
> This is not doing what you think it is,
Indeed, I guess I was seeing nans being treated as missing values rather
than being masked...
> You should use numpy.masked_where(numpy.isnan(aa), aa).
I am now ;-)
However, I'm still running into problems when I try and plot the gappy
data on a filled line as follows:
dates = *an array of datetimes*
values = *an array containing data values and a few nans*
values = numpy.ma.masked_where(numpy.isnan(values),values)
xs,ys = mlab.poly_between(dates,0,values)
pylab.fill(xs,ys,'r')
For starters, I get this warning:
numpy\core\ma.py:609: UserWarning: Cannot automatically convert masked
array to numeric because data is masked in one or more locations.
...and wherever a NaN occurs in the data, the line is plotted off the
top of the axes. I want it to appear at 0 if there's no data. Well,
ideally just not appear at all, but I'd settle for appearing at 0...
Any ideas?
cheers,
Chris
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