I have a large array full of zeros simply defined by:

I have a large array full of zeros simply defined by:

BigArray = np.zeros((100,1000,1000),np.float16)

I then have a 3D volume that I randomly rotate outside of python and each time it is rotated I want to import the file into python and add it to the next bit of the array. I currently have the following code that will do it:

n = 0while n < 99: Zaxisangle = randint(0,360) Yaxisangle = randint(0,360) Xaxisangle = randint(0,360)

`os.system('rotatevol -angles {},{},{} -input {} -output {}'.format(Zaxisangle, Yaxisangle, Xaxisangle, MRCfilewithextension, MRCforoutput)) particledata = mrcopen(MRCforoutput) if n < 10: ArtTomo[:, 0:100, (100*(n+1))-100:100*(n+1)] = particledata n = n+1 else: n = n+1`

For the purpose of this example we can simplify it down to the following:

BigArray = np.zeros((100,1000,1000),np.float16) particledata = np.random.rand(100,100,100) n = 0 while n < 99: if n < 10: ArtTomo[:, 0:100, (100(n+1))-100:100(n+1)] = particledata n = n+1 elif: 10 < n < 20 ArtTomo[:, 100:200, (100(n+1))-100:100(n+1)] = particledata n = n+1 else: n = n+1

I would then write lots of elif statements for each 'row'. Because I am iterating through the array with different files I can't simply fill it with a 'in range(0,1000,100)' statement annoyingly.

whilst I can write out all the elif statements I feel as if there must be a more efficient way to write this code I am just not good enough to see it. Could anyone else write this in a nicer way or am I just going to have to write 10 elif statments (i just don't feel like it is neat code!).

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Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.

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