[SciPy-user] timeseries - mov_average_expw alters it's input

josef.pktd@gmai... josef.pktd@gmai...
Fri May 8 09:30:09 CDT 2009


On Fri, May 8, 2009 at 9:21 AM, Dave Hirschfeld
<dave.hirschfeld@gmail.com> wrote:
> As demonstrated below the mov_average_expw function changes its input series.
> Is this known or expected behaviour or a bug? I'd venture to suggest it's a
> little surprising, especially for new users.
>
> -Dave
>
>  from copy import deepcopy
>  import numpy.ma as ma
>  from numpy.random import rand
>  import scikits.timeseries as ts
>  from scikits.timeseries.lib.moving_funcs import mov_average_expw
>
>  N = 256
>  series = ts.time_series(rand(N),
>                 ts.date_array(start_date=ts.Date('D','2008-01-01'),length=N))
>  series[96:128] = ma.masked
>  original_series = deepcopy(series)
>  filtered_series = mov_average_expw(series,16)
>
>  assert (series.mask == original_series.mask).all()
> Traceback (most recent call last):
>  File "<stdin>", line 1, in <module>
> AssertionError
>
>  assert (filtered_series.mask == series.mask).all()
>
>  ts.__version__
> '0.91.1'
>  import numpy as np; np.__version__
> '1.4.0.dev6882'
>

I wouldn't be surprised. What would be the moving average of your
observations starting at 129, when the previous observations are
masked?

Maybe you can try to change the ``tol`` parameter, to get the result you want?


tol : {1e-6, float}, optional

    Tolerance for the definition of the mask. When data contains
masked values, this parameter determines what points in the result
should be masked. Values in the result that would not be
“significantly” impacted (as determined by this parameter) by the
masked values are left unmasked.

Josef


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