CODEWITHSUNDEEP
pythonmultidimensional-arrayscientific-computing
camzor00
camzor00

Reputation: 13

Making a multidimensional list of vectors

I am quite new to Python so bear with me. I am writing a program to calculate some physical quantity, let's call it A. A is a function of several variables, let's call them x, y, z. So I have three nested loops to calculate A for the values of x, y, z that I am interested in.

for x in xs:
 for y in ys:
  for z in zs:
   A[x, y, z] = function_calculating_value(x,y,z)

Now, the problem is that A[x,y,z] is two-dimensional array containing both the mean value and the variance so that A[x,y,z] = [mean, variance]. From other languages I am used to initializing A using function similar to np.zeros(). How do I do that here? What is the easiest way to achieve what I want, and how do I access the mean and variance easily for a given (x,y,z)?

(the end goal is to be able to plot the mean with the variance as error bars, so if there is an even more elegant way of doing this, I appreciate that as well)

thanks in advance!

Upvotes: 1

Views: 54

Answers (1)

TLousky
TLousky

Reputation: 303

You can create and manipulate your multi-dimensional array with numpy

# Generate a random 4d array that has nx = 3, ny = 3, and nz = 3, with each 3D point having 2 values
mdarray = np.random.random( size = (3,3,3,2) )

# The overall shape of the 4d array
mdarray
Out[66]: 
array([[[[ 0.80091246,  0.28476668],
         [ 0.94264747,  0.27247111],
         [ 0.64503087,  0.13722768]],

        [[ 0.21371798,  0.41006764],
         [ 0.79783723,  0.02537987],
         [ 0.80658387,  0.43464532]],

        [[ 0.04566927,  0.74836831],
         [ 0.8280196 ,  0.90288647],
         [ 0.59271082,  0.65910184]]],


       [[[ 0.82533798,  0.29075978],
         [ 0.76496127,  0.1308289 ],
         [ 0.22767752,  0.01865939]],

        [[ 0.76849458,  0.7934015 ],
         [ 0.93313128,  0.88436557],
         [ 0.06897508,  0.00307739]],

        [[ 0.15975812,  0.00792386],
         [ 0.40292818,  0.21209199],
         [ 0.48805502,  0.71974702]]],


       [[[ 0.66522525,  0.49797465],
         [ 0.29369336,  0.68743839],
         [ 0.46411967,  0.69547356]],

        [[ 0.50339875,  0.66423777],
         [ 0.80520751,  0.88115054],
         [ 0.08296022,  0.69467829]],

        [[ 0.76572574,  0.45332754],
         [ 0.87982243,  0.15773385],
         [ 0.5762041 ,  0.91268172]]]])

# Both values for this specific sample at x = 0, y = 1 and z = 2
mdarray[0,1,2] 
Out[67]: array([ 0.80658387,  0.43464532])

mdarray[0,1,2,0] # mean only at the same point
Out[68]: 0.8065838666297338

mdarray[0,1,2,1] # variance only at the same point
Out[69]: 0.43464532443865489

You can also get only the means or the variance values separately by slicing the array:

mean     = mdarray[:,:,:,0]
variance = mdarray[:,:,:,1]

mean
Out[74]: 
array([[[ 0.80091246,  0.94264747,  0.64503087],
        [ 0.21371798,  0.79783723,  0.80658387],
        [ 0.04566927,  0.8280196 ,  0.59271082]],

       [[ 0.82533798,  0.76496127,  0.22767752],
        [ 0.76849458,  0.93313128,  0.06897508],
        [ 0.15975812,  0.40292818,  0.48805502]],

       [[ 0.66522525,  0.29369336,  0.46411967],
        [ 0.50339875,  0.80520751,  0.08296022],
        [ 0.76572574,  0.87982243,  0.5762041 ]]])

I'm still unsure how I would have preferred to plot this data, will think about this a bit and update this answer.

Upvotes: 1

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