How can I average an array of arrays in python?
I have a simulation that runs many times. Every time an array is created, I insert it into a larger array, keeping track of all the data. eg
record = []
for i in range(2):
r = random.random()
array = numpy.arange(20)*r
array.shape = (10,2)
record.append(array)
record = numpy.array(record)
which produces:
[[[ 0. 0.88765927]
[ 1.77531855 2.66297782]
[ 3.55063709 4.43829637]
[ 5.32595564 6.21361492]
[ 7.10127419 7.98893346]
[ 8.87659274 9.76425201]
[ 10.65191128 11.53957056]
[ 12.42722983 13.3148891 ]
[ 14.20254838 15.09020765]
[ 15.97786693 16.8655262 ]]
[[ 0. 0.31394919]
[ 0.62789839 0.94184758]
[ 1.25579677 1.56974596]
[ 1.88369516 2.19764435]
[ 2.51159354 2.82554274]
[ 3.13949193 3.45344112]
[ 3.76739031 4.08133951]
[ 4.3952887 4.70923789]
[ 5.02318709 5.33713628]
[ 5.65108547 5.96503466]]]
Since each array
is a simulation in my program. I would like to average 2 different arrays contained in record
.
Basically, I want an array with the same dimensions as array
, but this will be the average of all the individual runs.
I could just loop over the arrays, but there is a lot of data in my real simulators, so I think it will be very expensive in time
Out put example (obviously it won't be zero):
average = [[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]
[0.0, 0.0]]
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Your array record
from the above example is 3D, with the shape:
>>> record.shape
(2, 10, 2)
The first dimension corresponds to 2 iterations of your experiment. To average them, you need to communicate np.average
how to do it:axis=0
>>> np.average(record, axis=0)
array([[ 0. , 0.45688836],
[ 0.91377672, 1.37066507],
[ 1.82755343, 2.28444179],
[ 2.74133015, 3.19821851],
[ 3.65510686, 4.11199522],
[ 4.56888358, 5.02577194],
[ 5.4826603 , 5.93954865],
[ 6.39643701, 6.85332537],
[ 7.31021373, 7.76710209],
[ 8.22399044, 8.6808788 ]])
If you know ahead of time how many simulations you are going to run, you better ditch the entire list and do something like this:
simulations, sim_rows, sim_cols = 1000000, 10, 2
record = np.empty((simulations, sim_rows, sim_cols))
for j in xrange(simulations) :
record[j] = np.random.rand(sim_rows, sim_cols)
>>> np.average(record, axis=0)
[[ 0.50021935 0.5000554 ]
[ 0.50019659 0.50009123]
[ 0.50008591 0.49973058]
[ 0.49995812 0.49973941]
[ 0.49998854 0.49989957]
[ 0.5002542 0.50027464]
[ 0.49993122 0.49989623]
[ 0.50024623 0.49981818]
[ 0.50005848 0.50016798]
[ 0.49984452 0.49999112]]
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