[Numpy-discussion] sum of array for masked area

[questions anon]

Thank you Scott for your prompt response.
Your suggestion has fixed the problem and thank you for your clear
explanation of how it works.

thanks!!


On Thu, Nov 28, 2013 at 7:20 PM, Scott Sinclair <scott.sinclair.za at gmail.com
> wrote:

> On 28 November 2013 09:06, questions anon <questions.anon at gmail.com>
> wrote:
> > I have a separate text file for daily rainfall data that covers the whole
> > country. I would like to calculate the monthly mean, min, max and the
> mean
> > of the sum for one state.
> >
> > I can get the max, min and mean for the state, but the mean of the sum
> keeps
> > giving me a result for the whole country rather than just the state, even
>
> > def accumulate_month(year, month):
> >     files = glob.glob(GLOBTEMPLATE.format(year=year, month=month))
> >     monthlyrain=[]
> >      for ifile in files:
> >         try:
> >             f=np.genfromtxt(ifile,skip_header=6)
> >         except:
> >             print "ERROR with file:", ifile
> >             errors.append(ifile)
> >         f=np.flipud(f)
> >
> >         stateonly_f=np.ma.masked_array(f, mask=newmask.mask) # this masks
> > data to state
> >
> >
> >         print "stateonly_f:", stateonly_f.max(), stateonly_f.mean(),
> > stateonly_f.sum()
> >
> >         monthlyrain.append(stateonly_f)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> At this point monthlyrain is a list of masked arrays
>
> >     r_sum=np.sum(monthlyrain, axis=0)
>                               ^^^^^^^^^^^
> Passing a list of masked arrays to np.sum returns an np.ndarray object
> (*not* a masked array)
>
> >     r_mean_of_sum=MA.mean(r_sum)
>
> Therefore this call to MA.mean returns the mean of all values in the
> ndarray r_sum.
>
> To fix: convert your monthlyrain list to a 3D maksed array before
> calling np.sum(monthlyrain, axis=0). In this case np.sum will call the
> masked array's .sum() method which knows about the mask.
>
> monthlyrain = np.ma.asarray(monthlyrain)
> r_sum=np.sum(monthlyrain, axis=0)
>
> Consider the following simplified example:
>
> alist = []
> for k in range(2):
>     a = np.arange(4).reshape((2,2))
>
>     alist.append(np.ma.masked_array(a, mask=[[0,1],[0,0]]))
>
> print(alist)
> print(type(alist))
>
> alist = np.ma.asarray(alist)
> print(alist)
> print(type(alist))
>
> asum = np.sum(alist, axis=0)
>
> print(asum)
> print(type(asum))
>
> print(asum.mean())
>
> Cheers,
> Scott
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