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.in...@gmail.com;sudheer.jos...@yahoo.com
Web- http://oppamthadathil.tripod.com
***************************************************************
--------------------------------------------
On Thu, 13/3/14, Chris Barker - NOAA Federal <chris.bar...@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.roug...@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.jos...@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 please
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.
>> Via Pragathi Nagar,Kukatpally, Hyderabad; 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.in...@gmail.com;sudheer.jos...@yahoo.com
>> Web- http://oppamthadathil.tripod.com
>>
***************************************************************<r2010.nc>_______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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