Hello,
Consider these two sets of container arrays --one defined as usual np array
the others as ma arrays:
all_measured = np.ma.zeros((16, 18))
all_predicted = np.ma.zeros((16, 18))
all_measured2 = np.zeros((16, 18))
all_predicted2 = np.zeros((16, 18))
I do a computation within a for loop to assign 16 set of measurements into a
length of 18 arrays (thus constructing a 2D array to perform overall
statistics and plotting.) For the simplicity I only show a portion of
all_measured and all_measured2 as:
all_measured
masked_array(data =
[[512.632175 527.33373 565.36541 567.53967 593.86833 570.31319 574.40965
582.72649 588.21336 618.48789 593.09007 620.33474 591.10203 611.06443
655.60614 638.13193 626.71769 625.63584]
[626.6435 -- -- 1183.67671 1206.82453 1183.13248 1162.5514 1180.70062
1086.53246 1078.78711 997.1642 856.57159 645.35167 696.86947 778.40914
816.03059 862.88297 901.7237] ...
all_measured2
array([[ 512.632175 , 527.33373 , 565.36541 , 567.53967 ,
593.86833 , 570.31319 , 574.40965 , 582.72649 ,
588.21336 , 618.48789 , 593.09007 , 620.33474 ,
591.10203 , 611.06443 , 655.60614 , 638.13193 ,
626.71769 , 625.63584 ],
[ 626.6435 , 0. , 0. , 1183.67671 ,
1206.82453 , 1183.13248 , 1162.5514 , 1180.70062 ,
1086.53246 , 1078.78711 , 997.1642 , 856.57159 ,
645.35167 , 696.86947 , 778.40914 , 816.03059 ,
862.88297 , 901.7237 ],...
The issue is why masked arrays casted to regular numpy arrays as in
all_measured2 case? whereas a simple numpy function np.mean and ma
equivalent np.ma.mean yields same results on all_measured? Because the
former requires a priori knowledge about the type of arrays, however the
latter doesn't necessitate such restriction.
Hope this is clear. Thanks.
--
Gökhan
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