The difference appears to be that the boolean selection pulls out all data values <= 0.5 whether or not they are masked, and then carries over the appropriate masks to the new array.  So r2010 and bt contain identical unmasked values but different numbers of masked values.  Because the initial fill value for your masked values was a large negative number, in r2010 those masked values are carried over.  In bt, you've taken the absolute value of the data array, so those fill values are now positive and they are no longer carried over into the indexed array.

Because the final arrays are still masked, you are observing no difference in the statistical properties of the arrays, only their sizes, because one contains many more masked values than the other.  I don't think this should be a problem for your computations. If you're concerned, you could always explicitly demask them before your computations.  See the example problem below.

~Brett

In [61]: import numpy as np

In [62]: import numpy.ma as ma

In [65]: a = np.arange(-8, 8).reshape((4, 4))

In [66]: a
Out[66]:
array([[-8, -7, -6, -5],
[-4, -3, -2, -1],
[ 0,  1,  2,  3],
[ 4,  5,  6,  7]])

In [69]: b
Out[69]:
[[-- -- -- --]
[-- -- -- --]
[0 1 2 3]
[4 5 6 7]],
[[ True  True  True  True]
[ True  True  True  True]
[False False False False]
[False False False False]],
fill_value = 999999)

In [70]: b.data
Out[70]:
array([[-8, -7, -6, -5],
[-4, -3, -2, -1],
[ 0,  1,  2,  3],
[ 4,  5,  6,  7]])

In [71]: c = abs(b)

In [72]: c[c <= 4].shape
Out[72]: (9L,)

In [73]: b[b <= 4].shape
Out[73]: (13L,)

In [74]: b[b <= 4]
Out[74]:
masked_array(data = [-- -- -- -- -- -- -- -- 0 1 2 3 4],
mask = [ True  True  True  True  True  True  True  True False False False False
False],
fill_value = 999999)

In [75]: c[c <= 4]
Out[75]:
masked_array(data = [-- -- -- -- 0 1 2 3 4],
mask = [ True  True  True  True False False False False False],
fill_value = 999999)

On Thu, Mar 13, 2014 at 8:14 PM, Sudheer Joseph wrote:
Sorry,
The below solution I thoght working was not working but was just giving array size.

--------------------------------------------
On Fri, 14/3/14, Sudheer Joseph <sudheer.joseph@yahoo.com> wrote:

Subject: Re: [Numpy-discussion] python array
To: "Discussion of Numerical Python" <numpy-discussion@scipy.org>
Date: Friday, 14 March, 2014, 1:09 AM

Thank you very much Nicolas and
Chris,

The
hint was helpful and from that I treid below steps ( a crude
way I would say) and getting same result now

I have been using abs available by default and it is the
same with numpy.absolute( i checked).

nr= ((r2010>r2010.min()) & (r2010<r2010.max()))
nr[nr<.5].shape
Out[25]: (33868,)
anr=numpy.absolute(nr)
anr[anr<.5].shape
Out[27]: (33868,)

This way I used may have problem when mask used has values
which can affect the min max operation.

So I would like to know if there is a standard formal (
python/numpy) way to handle masked array when they need to
be subjected to boolean operations.

with best regards,
Sudheer

***************************************************************
Sudheer Joseph
Indian National Centre for Ocean Information Services
Ministry of Earth Sciences, Govt. of India
POST BOX NO: 21, IDA Jeedeemetla P.O.
Via Pragathi Nagar,Kukatpally, Hyderabad; Pin:5000 55
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Tel:+91-40-23044600(R),Tel:+91-40-9440832534(Mobile)
E-mail:sjo.India@gmail.com;sudheer.joseph@yahoo.com
***************************************************************

--------------------------------------------
On Thu, 13/3/14, Chris Barker - NOAA Federal <chris.barker@noaa.gov>
wrote:

Subject: Re: [Numpy-discussion] python array
To: "Discussion of Numerical Python" <numpy-discussion@scipy.org>
Date: Thursday, 13 March, 2014, 11:53 PM

On Mar 13, 2014, at 9:39 AM, Nicolas
Rougier <Nicolas.Rougier@inria.fr>
wrote:

>
> Seems to be related to the masked values:

Good hint -- a masked array keeps the "junk" values in the
main array.

What "abs" are you using -- it may not be mask-aware. (
you
want a
numpy abs anyway)

Also -- I'm not sure I know what happens with Boolean
operators on
masked arrays when you use them to index. I'd investigate
that.
(sorry, not at a machine I can play with now)

Chris

> print r2010[:3,:3]
> [[-- -- --]
> [-- -- --]
> [-- -- --]]
>
> print abs(r2010)[:3,:3]
> [[-- -- --]
> [-- -- --]
> [-- -- --]]
>
>
> print r2010[ r2010[:3,:3] <0 ]
> [-- -- -- -- -- -- -- -- --]
>
> print r2010[ abs(r2010)[:3,:3] < 0]
> []
>
> Nicolas
>
>
>
> On 13 Mar 2014, at 16:52, Sudheer Joseph <sudheer.joseph@yahoo.com>
wrote:
>
>> Dear experts,
>>
I am encountering a strange
behaviour of python data array as below. I have been
trying
to use the data from a netcdf file(attached herewith) to
do
certain calculation using below code. If I take absolute
value of the same array and look for values <.5  I
get a different value than the original array. But the
fact
is that this particular case do not have any negative
values
in the array( but there are other files where it can have
negative values so the condition is put). I do not see any
reason for getting different numbers for values <.5 in
case of bt and expected it to be same as that of r2010. If
any one has a guess on what is behind this behaviour
help.
>>
>>
>> In [14]: from netCDF4 import Dataset as nc
>>
>> In [15]: nf=nc('r2010.nc')
>> In [16]: r2010=nf.variables['R2010'][:]
>> In [17]: bt=abs(r2010)
>> In [18]: bt[bt<=.5].shape
>> Out[18]: (2872,)
>> In [19]: r2010[r2010<.5].shape
>> Out[19]: (36738,)
>>
>>
>> bt.min()
>> Out[20]: 0.0027588337040836768
>>
>> In [21]: bt.max()
>> Out[21]: 3.5078965479057089
>> In [22]: r2010.max()
>> Out[22]: 3.5078965479057089
>> In [23]: r2010.min()
>> Out[23]: 0.0027588337040836768
>>
>>
>>
>>
***************************************************************
>> Sudheer Joseph
>> Indian National Centre for Ocean Information
Services
>> Ministry of Earth Sciences, Govt. of India
>> POST BOX NO: 21, IDA Jeedeemetla P.O.
Pin:5000
55
>> Tel:+91-40-23886047(O),Fax:+91-40-23895011(O),
>>
Tel:+91-40-23044600(R),Tel:+91-40-9440832534(Mobile)
>> E-mail:sjo.India@gmail.com;sudheer.joseph@yahoo.com
>>
***************************************************************<r2010.nc>_______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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