On Sat, May 8, 2010 at 9:29 PM, Eric Firing <efir...@hawaii.edu> wrote:
> On 05/08/2010 04:16 PM, Ryan May wrote: > > On Sat, May 8, 2010 at 7:52 PM, Gökhan Sever<gokhanse...@gmail.com> > wrote: > >> Hello, > >> > >> Consider my masked arrays: > >> > >> I[28]: type basic.data['Air_Temp'] > >> -----> type(basic.data['Air_Temp']) > >> O[28]: numpy.ma.core.MaskedArray > >> > >> I[29]: basic.data['Air_Temp'] > >> O[29]: > >> masked_array(data = [-- -- -- ..., -- -- --], > >> mask = [ True True True ..., True True True], > >> fill_value = 999999.9999) > >> > >> > >> I[17]: basic.data['Air_Temp'].data = > np.ones(len(basic.data['Air_Temp']))*30 > >> > --------------------------------------------------------------------------- > >> AttributeError Traceback (most recent call > last) > >> > >> ----> 1 > >> 2 > >> 3 > >> 4 > >> 5 > >> > >> AttributeError: can't set attribute > >> > >> Why this assignment fails? I want to set each element in the original > >> basic.data['Air_Temp'].data to another value. (Because the main > instrument > >> was forgotten to turn on for that day, and I am using a secondary > >> measurement data for Air Temperature for my another calculation. However > it > >> fails. Although single assignment works: > >> > >> I[13]: basic.data['Air_Temp'].data[0] = 30 > >> > >> Shouldn't this be working like the regular NumPy arrays do? > > > > Based on the traceback, I'd say it's because you're trying to replace > > the object pointed to by the .data attribute. Instead, try to just > > change the bits contained in .data: > > > > basic.data['Air_Temp'].data[:] = np.ones(len(basic.data['Air_Temp']))*30 > > Also, you since you are setting all elements to a single value, you > don't need to generate an array on the right-hand side. And, you don't > need to manipulate ".data" directly--I think it is best to avoid doing > so. Consider: > > In [1]:x = np.ma.array([1,2,3], mask=[True, True, True], dtype=float) > > In [2]:x > Out[2]: > masked_array(data = [-- -- --], > mask = [ True True True], > fill_value = 1e+20) > > > In [3]:x[:] = 30 > > In [4]:x > Out[4]: > masked_array(data = [30.0 30.0 30.0], > mask = [False False False], > fill_value = 1e+20) > > > In [5]:x[:] = np.ma.masked > > In [6]:x > Out[6]: > masked_array(data = [-- -- --], > mask = [ True True True], > fill_value = 1e+20) > > > In [7]:x.data > Out[7]:array([ 30., 30., 30.]) > > > Eric > > > > > Ryan > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > Good to see this :) I[45]: x = np.ma.array([1,2,3], mask=[True, True, True], dtype=float) I[46]: x O[46]: masked_array(data = [-- -- --], mask = [ True True True], fill_value = 1e+20) I[47]: x.data[:] = 25 I[48]: x O[48]: masked_array(data = [-- -- --], mask = [ True True True], fill_value = 1e+20) I[49]: x[:] = 25 I[50]: x O[50]: masked_array(data = [25.0 25.0 25.0], mask = [False False False], fill_value = 1e+20) I was also updating mask values after updating data attribute. Now setting the masked array itself to a number automatically flips the masks for me which is very useful. I check if a valid temperature exists, otherwise assign my calculation to another missing value. -- Gökhan
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