[Numpy-discussion] python array

[Sudheer Joseph]

Thank you  Eric, 
                          The compress is the option which is gets the correct numbers.
                                   
a = np.arange(-8, 8).reshape((4, 4))
In [67]:  b = ma.masked_array(a, mask=a < 0)
In [68]: bb=b.compressed()
In [69]: b[b<4].size
Out[69]: 12
In [70]: bb=b.compressed()
In [71]: bb[bb<=4].size
Out[71]: 5

with best regards,
Sudheer

         
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--------------------------------------------
On Fri, 14/3/14, Eric Firing <efiring at hawaii.edu> wrote:

 Subject: Re: [Numpy-discussion] python array
 To: numpy-discussion at scipy.org
 Date: Friday, 14 March, 2014, 7:20 AM
 
 On 2014/03/13 9:09 PM, Sudheer Joseph
 wrote:
 > Dear Oslen,
 >
 > I had  a detailed look at the example you send and
 points I got were below
 >
 > a = np.arange(-8, 8).reshape((4, 4))
 > b = ma.masked_array(a, mask=a < 0)
 >
 >
 > Out[33]: b[b<4]
 > masked_array(data = [-- -- -- -- -- -- -- -- 0 1 2 3],
 >           
    mask = [ True  True  True 
 True  True  True  True  True False False
 False False],
 >         fill_value =
 999999)
 > In [34]: b[b<4].shape
 > Out[34]: (12,)
 > In [35]: b[b<4].data
 > Out[35]: array([-8, -7, -6, -5, -4, -3, -2, -1, 
 0,  1,  2,  3])
 >
 > This shows while numpy can do the bolean operation and
 list the data meeting the criteria( by masking the data
 further), it do not actually allow us get the count of data
 that meets the crieteria. I was interested in count. Because
 my objective was to find out how many numbers in the grid
 fall under different catagory.( <=4 , >4 & <=8
 , >8<=10) etc. and find the percentage of them.
 >
 >   Is there a way to get the counts
 correctly ? that is my botheration now !!
 
 Certainly.  If all you need are statistics of the type
 you describe, 
 where you are working with a 1-D array, then extract the
 unmasked values 
 into an ordinary ndarray, and work with that:
 
 a = np.random.randn(100)
 am = np.ma.masked_less(a, -0.2)
 print am.count()  # number of masked values
 a_nomask = am.compressed()
 print type(a_nomask)
 print a_nomask.shape
 
 # number of points with value less than 0.5:
 print (a_nomask < 0.5).sum()
 # (Boolean True is 1)
 
 # Or if you want the actual array of values, not just the
 count:
 a_nomask[a_nomask < 0.5]
 
 Eric
 
 
 
 >
 > with best regards,
 > Sudheer
 
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