What I want to do is "mask" a subset of an array of j elements, from range 0 to k. Eg. For this array:
[0.2, 0.1, 0.3, 0.4, 0.5]
Masking the first 2 elements it becomes
[NaN, NaN, 0.3, 0.4, 0.5]
Does masked_array support this operation?
In [51]: arr=np.ma.array([0.2, 0.1, 0.3, 0.4, 0.5],mask=[True,True,False,False,False])
In [52]: print(arr)
[-- -- 0.3 0.4 0.5]
Or, if you already have a numpy array, you could use np.ma.masked_less_equal (see the link for a variety of other operations for masking particular elements):
In [53]: arr=np.array([0.2, 0.1, 0.3, 0.4, 0.5])
In [56]: np.ma.masked_less_equal(arr,0.2)
Out[57]:
masked_array(data = [-- -- 0.3 0.4 0.5],
mask = [ True True False False False],
fill_value = 1e+20)
Or, if you wish to mask the first two elements:
In [67]: arr=np.array([0.2, 0.1, 0.3, 0.4, 0.5])
In [68]: arr=np.ma.array(arr,mask=False)
In [69]: arr.mask[:2]=True
In [70]: arr
Out[70]:
masked_array(data = [-- -- 0.3 0.4 0.5],
mask = [ True True False False False],
fill_value = 1e+20)
I found this:
ma.array([1,2,3,4], mask=[1,1,0,0]) masked_array(data = [-- -- 3 4], mask = [ True True False False], fill_value = 999999)
