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I am looking for a way to reshape the following 1d-numpy array:
# dimensions
n = 2 # int : 1 ... N
h = 2 # int : 1 ... N
m = n*(2*h+1)
input_data = np.arange(0,(n*(2*h+1))**2)
The expected output should be reshaped into (2*h+1)**2 blocks of shape (n,n) such as:
input_data.reshape(((2*h+1)**2,n,n))
>>> array([[[ 0 1]
[ 2 3]]
[[ 4 5]
[ 6 7]]
...
[[92 93]
[94 95]]
[[96 97]
[98 99]]]
These blocks finally need to be reshaped into a (m,m) matrix so that they are stacked in rows of 2*h+1 blocks:
>>> array([[ 0, 1, 4, 5, 8, 9, 12, 13, 16, 17],
[ 2, 3, 6, 7, 10, 11, 14, 15, 18, 19],
...
[80, 81, 84, 85, 88, 89, 92, 93, 96, 97],
[82, 83, 86, 87, 90, 91, 94, 95, 98, 99]])
My problem is that I can't seem to find proper axis permutations after the first reshape into (n,n) blocks. I have looked at several answers such as this one but in vain.
As the real dimensions n and h are quite bigger and this operation takes place in an iterative process, I am looking for an efficient reshaping operation.
I don't think you can do this with reshape and transpose alone (although I'd love to be proven wrong). Using np.block works, but it's a bit messy:
np.block([list(i) for i in input_data.reshape( (2*h+1), (2*h+1), n, n )])
array([[ 0, 1, 4, 5, 8, 9, 12, 13, 16, 17],
[ 2, 3, 6, 7, 10, 11, 14, 15, 18, 19],
[20, 21, 24, 25, 28, 29, 32, 33, 36, 37],
[22, 23, 26, 27, 30, 31, 34, 35, 38, 39],
[40, 41, 44, 45, 48, 49, 52, 53, 56, 57],
[42, 43, 46, 47, 50, 51, 54, 55, 58, 59],
[60, 61, 64, 65, 68, 69, 72, 73, 76, 77],
[62, 63, 66, 67, 70, 71, 74, 75, 78, 79],
[80, 81, 84, 85, 88, 89, 92, 93, 96, 97],
[82, 83, 86, 87, 90, 91, 94, 95, 98, 99]])
EDIT: Never mind, you can do without np.block:
input_data.reshape( (2*h+1), (2*h+1), n, n).transpose(0, 2, 1, 3).reshape(10, 10)
array([[ 0, 1, 4, 5, 8, 9, 12, 13, 16, 17],
[ 2, 3, 6, 7, 10, 11, 14, 15, 18, 19],
[20, 21, 24, 25, 28, 29, 32, 33, 36, 37],
[22, 23, 26, 27, 30, 31, 34, 35, 38, 39],
[40, 41, 44, 45, 48, 49, 52, 53, 56, 57],
[42, 43, 46, 47, 50, 51, 54, 55, 58, 59],
[60, 61, 64, 65, 68, 69, 72, 73, 76, 77],
[62, 63, 66, 67, 70, 71, 74, 75, 78, 79],
[80, 81, 84, 85, 88, 89, 92, 93, 96, 97],
[82, 83, 86, 87, 90, 91, 94, 95, 98, 99]])
I have an numpy array with x and y values of points. I have another array which contains pairs of start and end indices. Originally this data was in pandas DataFrame, but since it was over 60 millions items, the loc algorithm was very slow. Is there any numpy fast method to split this?
import numpy as np
xy_array = np.arange(100).reshape(2,-1)
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
split_paris = [[0, 10], [10, 13], [13, 17], [20, 22]]
expected_result = [
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]],
[[10, 11, 12], [60, 61, 62]],
[[13, 14, 15, 16], [63, 64, 65, 66]],
[[20, 21], [70, 71]]
]
Update:
It is not always the case that, next pair will start from end of previous.
This will do it:
import numpy as np
xy_array = np.array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])
split_paris = [[0, 10], [10, 13], [13, 17]]
expected_result = [xy_array[:, x:y] for x, y in split_paris]
expected_result
#[array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
# [50, 51, 52, 53, 54, 55, 56, 57, 58, 59]]), array([[10, 11, 12],
# [60, 61, 62]]), array([[13, 14, 15, 16],
# [63, 64, 65, 66]])]
It is using index slicing basically working in sense array[rows, columns] having : take all rows and x:y taking columns from x to y.
you can always use the np.array_split function provided by numpy. and use the ranges you want
x = np.arange(8.0)
>>> np.array_split(x, 3)
[array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]
sorry if this question is so basic
A=np.arange(64).reshape(2,32)
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31],
[32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]])
A.reshape(4,4,4)
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]],
[[16, 17, 18, 19],
[20, 21, 22, 23],
[24, 25, 26, 27],
[28, 29, 30, 31]],
[[32, 33, 34, 35],
[36, 37, 38, 39],
[40, 41, 42, 43],
[44, 45, 46, 47]],
[[48, 49, 50, 51],
[52, 53, 54, 55],
[56, 57, 58, 59],
[60, 61, 62, 63]]])
Now, i would have liked something like A[2] or A[2,:] or A[2,:,:] to return me the matrix
[[32, 33, 34, 35],
[36, 37, 38, 39],
[40, 41, 42, 43],
[44, 45, 46, 47]]
and A[2,2,2] to return me 42 for example
but i got this error
IndexError: too many indices for array
You have to do
A = A.reshape(4,4,4)
instead of
A.reshape(4,4,4)
Because reshape is not inplace, you need to do this. Then you can do
A[2,2,2]
Out[301]: 42
after A.reshape(4,4,4) , A does not change
I have data in a numpy array:
a = np.arange(100)
a = a.reshape((20,5))
When I type
a[:10]
it returns
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49]])
Now i decided to reshape the array into 3d array.
b = a.reshape((5,4,5))
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39]],
[[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59]],
[[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79]],
[[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99]]])
How do I slice b to that I obtain the values like a[:10]?
I tried
b[:10,0,:5]
array([[ 0, 1, 2, 3, 4],
[10, 11, 12, 13, 14],
[20, 21, 22, 23, 24],
[30, 31, 32, 33, 34],
[40, 41, 42, 43, 44],
[50, 51, 52, 53, 54],
[60, 61, 62, 63, 64],
[70, 71, 72, 73, 74],
[80, 81, 82, 83, 84],
[90, 91, 92, 93, 94]])
But its not correct.
Thank you in advance!
When you use b = a.reshape((5,4,5)) you just create a different view on the same data used by the array a. (ie changes to the elements of a will appear in b). reshape() does not copy data in this case, so it is a very fast operation. Slicing b and slicing a accesses the same memory, so there shouldn't be any need for a different syntax for the b array (just use a[:10]). If you have created a copy of the data, perhaps with np.resize(), and discarded a, just reshape b: b.reshape((20,5))[:10].
By reshaping (20,5) to (5,4,5), there's no way you can pull out the 1st half of the values. You can't split those 5 rows into 2 even groups:
In [9]: b[:2]
Out[9]:
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39]]])
In [10]: b[:3]
Out[10]:
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39]],
[[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59]]])
The last row of a[:10] is in the middle of b[3,:,:].
Note that b[:2] is (2,4,5), 8 rows of a, grouped into 2 sets of 4.
Now if you'd done c=a.reshape(4,5,5), then c[:2] would have those same 10 rows - in 2 sets of 5. And c[:2].reshape(10,-1) will look just like a[:10].
There could be a programmatic way to get what you want, but not a python slice.
It is important to understand what every component in the shape tells us about the arrangement. I like to think in terms of vectors.
Let's talk about the shape (20, 5) - this would mean, I have 20 vectors where every vector has 5 elements.
For the shape (5, 4, 5) - this would mean, I have 5 vectors, where each vector again has 4 vectors where every vector within has 5 elements.
This might sound complicated but with some deliberation, this could be understood.
Coming to your question, by a[:10] you want to retrieve the first 10 rows where each row should be a vector containing 5 elements but using a shape of (5, 4, 5).
This is only possible if you retrieve the first 4 vectors from 1st vector of the leftmost dimension (5), next 4 vectors from the next vector and next 2 from the 3rd.
Python slicing might not be the best tool to achieve this.
I'm new to python/numpy and I need to create an array containing matrices of random numbers.
What I've got so far is this:
for i in xrange(samples):
SPN[] = np.random.random((6,5)) * np.random.randint(0,100)
Which make sense for me as PHP developer but is not working for python. So how do I create a 3 dimensional array to contain this matrices/arrays?
Both np.random.randint and np.random.uniform, like most of the np.random functions, accept a size parameter, so in numpy we'd do it in one step:
>>> SPN = np.random.randint(0, 100, (3, 6, 5))
>>> SPN
array([[[45, 95, 56, 78, 90],
[87, 68, 24, 62, 12],
[11, 26, 75, 57, 12],
[95, 87, 47, 69, 90],
[58, 24, 49, 62, 85],
[38, 5, 57, 63, 16]],
[[61, 67, 73, 23, 34],
[41, 3, 69, 79, 48],
[22, 40, 22, 18, 41],
[86, 23, 58, 38, 69],
[98, 60, 70, 71, 3],
[44, 8, 33, 86, 66]],
[[62, 45, 56, 80, 22],
[27, 95, 55, 87, 22],
[42, 17, 48, 96, 65],
[36, 64, 1, 85, 31],
[10, 13, 15, 7, 92],
[27, 74, 31, 91, 60]]])
>>> SPN.shape
(3, 6, 5)
>>> SPN[0].shape
(6, 5)
.. actually, it looks like you may want np.random.uniform(0, 100, (samples, 6, 5)), because you want the elements to be floating point, not integers. Well, it works the same way. :^)
Note that what you did isn't equivalent to np.random.uniform, because you're choosing an array of values between 0 and 1 and then multiplying all of them by a fixed integer. I'm assuming that wasn't actually what you were trying to do, because it's a little unusual; please comment if that is what you actually wanted.