Applying multiple masks to arrays

Question

I'm explaining what I'm actually hoping to do in case there's a higher level suggestion that obviates the question entirely.

I have scientific data that I store in three arrays: wave, flux, error. These stand for wavelength, flux, and error values. The arrays are about 4000 elements long (and the index number of the arrays corresponds to the pixel number of the detector).

There are various tests that I do, but for this example let's just say I do 2 tests where I need to effectively mask out the associated arrays.

masks = []
masks.append(wave > 5500.35)
masks.append(flux / wave > 8.5)

Subquestion: I can easily do the 2-mask case like:

fullmask = [x[0] and x[1] for x in zip(masks[0], masks[1])]

but what's the way to do it for arbitrary numbers of masks?

Real question: Is there a way to apply all masks to each of the arrays (wave, flux, error), and keep the original index numbers? By "keep the original index numbers" I mean that I could, in principle, take the average pixel number of the masked wave array (the original index numbers)? That is: if wave[98:99] were the only parts not masked, the average pixel would be 98.5.

Meta question: is this the best way to be doing any of this stuff?

---

EDIT

So here's some sample data to play around with.

wave = array([5000, 5001, 5002, 5003, 5004, 5005, 5006, 5007, 5008, 5009, 5010,
   5011, 5012, 5013, 5014, 5015, 5016, 5017, 5018, 5019, 5020, 5021,
   5022, 5023, 5024, 5025, 5026, 5027, 5028, 5029, 5030, 5031, 5032,
   5033, 5034, 5035, 5036, 5037, 5038, 5039, 5040, 5041, 5042, 5043,
   5044, 5045, 5046, 5047, 5048, 5049, 5050, 5051, 5052, 5053, 5054,
   5055, 5056, 5057, 5058, 5059, 5060, 5061, 5062, 5063, 5064, 5065,
   5066, 5067, 5068, 5069, 5070, 5071, 5072, 5073, 5074, 5075, 5076,
   5077, 5078, 5079, 5080, 5081, 5082, 5083, 5084, 5085, 5086, 5087,
   5088, 5089, 5090, 5091, 5092, 5093, 5094, 5095, 5096, 5097, 5098,
   5099])

flux = array([ 112.65878609,  109.2008992 ,  113.30629929,  117.17002715,
   103.19663878,  110.42131523,  106.00841123,  100.27882741,
   103.89160905,  102.29402469,  105.58894696,  103.21314852,
    96.97242814,  106.70130478,  108.83891225,  110.60598803,
    95.10361887,  109.39734257,  103.08289878,  104.97258911,
    96.46606257,  106.75993458,   99.25386914,  105.91429417,
   105.83752232,  100.53312657,   99.74871394,  107.12735837,
   108.81187473,   96.51418895,   99.71311101,   94.08702553,
    98.81198643,   93.84567201,  103.21444519,   94.7027134 ,
    99.61842203,  103.71336458,  100.8697998 ,   92.1564786 ,
    96.56711985,   94.7728761 ,   82.65194671,   83.52280884,
    86.57960844,   73.6700194 ,   66.11794666,   61.01624627,
    63.19944529,   55.50283247,   62.09172307,   59.55436092,
    75.66399466,   70.69397378,   64.27899192,   73.80248662,
    89.17119606,   78.97024327,   82.3334254 ,  100.82581489,
   102.77937201,   99.37717696,   96.2215563 ,  104.52291339,
    93.7581944 ,   93.32154346,  103.57018896,  108.08682518,
   105.2711359 ,  100.00242988,  100.86934866,  103.20764384,
   104.19274473,  101.3314802 ,  102.75057114,   94.02347591,
    95.48758551,  106.0099397 ,   99.50733501,   97.88110415,
   107.54266965,  107.76126331,   98.14882302,  101.55654606,
   101.02418212,  106.82324958,   95.52086925,  102.65957133,
   104.93806492,  103.22762427,  108.02087993,  106.71911141,
    97.24396195,  103.3450277 ,  113.99870588,  106.4145751 ,
   110.08294674,  109.40908288,  118.61518086,  114.37341062])

error = array([ 11.72799338,  22.33423611,  16.89347382,  12.80063102,
   23.99242356,  25.15863754,  20.44765811,  14.84358628,
   19.16343785,  19.5703491 ,  18.44427035,  19.08648083,
   19.09116433,  12.22098884,  14.81280352,  11.35010222,
   18.59850136,  15.78855734,  21.85877638,  20.12179042,
   22.04894395,  21.986731  ,  13.26738352,  16.10987762,
   24.28528627,  30.11866128,  25.30220842,  25.02100014,
   29.38560916,  16.8192307 ,  29.15097205,  23.56805267,
   15.17285709,  18.27495747,  18.63750452,  18.61618504,
   11.45940025,  21.95805701,  24.22923951,  11.76824052,
   19.75465065,  14.72979889,  15.45936176,  14.73227474,
   28.91683627,  22.90534472,  16.82376093,  21.47830226,
   20.05012214,  16.74393817,  17.79456361,  20.80008233,
   19.32059989,  23.23471888,  13.77434964,  17.56121752,
   15.96716163,  18.5294016 ,  28.31005939,  13.66340359,
   10.38160267,  16.09621015,  18.25125683,  20.95954331,
   21.31996941,  24.51998489,  16.58831953,  15.25427142,
   23.93065281,  30.4552266 ,  16.94527367,  16.92730802,
   17.79659417,  18.85080572,  18.0839428 ,  23.93949481,
   26.60243553,  13.68320208,  16.74669921,  20.30238694,
   12.74773905,  19.20810456,  20.7189417 ,  20.73402554,
   17.12106905,  25.06475175,  13.0947528 ,  28.16437938,
   22.4803386 ,  13.71143627,   6.60617725,  20.41186825,
   23.54924934,  22.25930658,  20.09337438,  24.94705884,
   18.58056249,   5.58653271,  18.71242702,  17.83578444])


# How I created masks, or just jump to next comment if it's too painful to look at...
masks = []
masks.append(flux/error > 4.0) # high error
absorptionMask1 = (wave < 5060)
absorptionMask2 = (wave > 5040)
bob = [all(x) for x in zip(absorptionMask1, absorptionMask2)]
absorptionMask = ~np.array(bob)
masks.append(absorptionMask) 

# The resulting mask
masks = [array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True, False, False,
       True, False,  True, False, False,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True, False,
      False, False, False, False, False, False, False, False, False,
       True,  True,  True,  True, False,  True,  True,  True,  True,
       True,  True, False,  True,  True,  True, False,  True,  True,
       True,  True,  True, False, False,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True, False,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,  True], dtype=bool),
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True, False, False, False, False,
      False, False, False, False, False, False, False, False, False,
      False, False, False, False, False, False,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,
       True,  True,  True,  True,  True,  True,  True,  True,  True,  True], dtype=bool)]


# More in a bit, should get you a feel for what I'm looking at. 

Answer 1

otherwise you can use boolean operators, let's define en example:

d=np.arange(10)
masks = [d>5, d % 2 == 0, d<8]

you can use reduce to combine all of them:

from functools import reduce

total_mask = reduce(np.logical_and, masks)

you can also use boolean operators explicitely if you need to manually choose the masks:

total_mask = masks[0] & masks[1] & masks[2]

Answer 2

I think you're looking for the star operator:

fullmask = [all(mask) for mask in zip(*masks)]

...although I'm not sure I understand your data structure completely.