On Sat, May 8, 2010 at 9:29 PM, Eric Firing efiring@hawaii.edu wrote:

On 05/08/2010 04:16 PM, Ryan May wrote:

On Sat, May 8, 2010 at 7:52 PM, Gökhan Severgokhansever@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.