Suppose I have the following numpy arrays:
>>a
array([[0, 0, 2],
[2, 0, 1],
[2, 2, 1]])
>>b
array([[2, 2, 0],
[2, 0, 2],
[1, 1, 2]])
that I then vertically stack
c=np.dstack((a,b))
resulting in:
>>c
array([[[0, 2],
[0, 2],
[2, 0]],
[[2, 2],
[0, 0],
[1, 2]],
[[2, 1],
[2, 1],
[1, 2]]])
From this I wish to, for each 3rd dimension of c, check which combination is present in this subarray, and then number it accordingingly with the index of the list-match. I've tried the following, but it is not working. The algorithm is simple enough with double for-loops, but because c is very large, it is prohibitively slow.
classes=[(0,0),(2,1),(2,2)]
out=np.select( [h==c for h in classes], range(len(classes)), default=-1)
My desired output would be
out = [[-1,-1,-1],
[3, 1,-1],
[2, 2,-1]]
How about this:
(np.array([np.array(h)[...,:] == c for h in classes]).all(axis = -1) *
(2 + np.arange(len(classes)))[:, None, None]).max(axis=0) - 1
It returns, what you actually need
array([[-1, -1, -1],
[ 3, 1, -1],
[ 2, 2, -1]])
You can test the a and b arrays separately like this:
clsa = (0,2,2)
clesb = (0,1,2)
np.select ( [(ca==a) & (cb==b) for ca,cb in zip (clsa, clsb)], range (3), default = -1)
which gets your desired result (except returns 0,1,2 instead of 1,2,3).
Here is another way to get what you want, thought I would post it in case it's useful to anyone.
import numpy as np
a = np.array([[0, 0, 2],
[2, 0, 1],
[2, 2, 1]])
b = np.array([[2, 2, 0],
[2, 0, 2],
[1, 1, 2]])
classes=[(0,0),(2,1),(2,2)]
c = np.empty(a.shape, dtype=[('a', a.dtype), ('b', b.dtype)])
c['a'] = a
c['b'] = b
classes = np.array(classes, dtype=c.dtype)
classes.sort()
out = classes.searchsorted(c)
out = np.where(c == classes[out], out+1, -1)
print out
#array([[-1, -1, -1]
# [ 3, 1, -1]
# [ 2, 1, -1]])
Related
From the following array of shape (6, 3):
>>> arr
[
[1, 0, 1],
[0, 0, 2],
[1, 2, 0],
[0, 1, 3],
[2, 2, 1],
[2, 0, 2]
]
I'd like to repeat the values according to a sliding window of n=4, giving a new array of shape (6-n-1, n, 3):
>>> new_arr
[
[
[1, 0, 1],
[0, 0, 2],
[1, 2, 0],
[0, 1, 3]
],
[
[0, 0, 2],
[1, 2, 0],
[0, 1, 3],
[2, 2, 1]
],
[
[1, 2, 0],
[0, 1, 3],
[2, 2, 1],
[2, 0, 2]
]
]
It is relatively straightforward using a loop, but it gets extremely slow with several million values (instead of 6 in this example) in the initial array.
Is there a faster way to get to new_arr using Numpy primitives?
You can use NumPy, specifically this function (only NumPy >= 1.20.0):
from numpy.lib.stride_tricks import sliding_window_view
new_arr = sliding_window_view(arr, (n, arr.shape[1])).squeeze()
I have a 2D numpy array of shape [6,2] and I want to remove the subarrays with the third element containing 0.
array([[0, 2, 1], #Input
[0, 1, 1],
[1, 1, 0],
[1, 0, 2],
[0, 2, 0],
[2, 1, 2]])
array([[0, 2, 1], #Output
[0, 1, 1],
[1, 0, 2],
[2, 1, 2]])
My code is positives = gt_boxes[np.where(gt_boxes[range(gt_boxes.shape[0]),2] != 0)]
It works but is there a simplified method to this?
You can use boolean indexing.
In [413]: x[x[:, -1] != 0]
Out[413]:
array([[0, 2, 1],
[0, 1, 1],
[1, 0, 2],
[2, 1, 2]])
x[:, -1] will retrieve the last column
x[:, -1] != 0 returns a boolean mask
Use the mask to index into the original array
I am blocked on a Python problem and hope someone could help me.
The problem is prety simple actually.
Im trying to build lists with all combination possible but the elements of the list have not the same range.
here is my code, I tried to do something with for loop but it doesnt work.
for j in range(0,size):
for k, val in enumerate(self.Algo.Inputs[j].Values):
self.Commandlist[j] = k
self.Commandlist is a list with fix range, and fill with zero at first.
self.Commandlist = [0,0,0]
self.Algo.Inputs[j].Values gives me the size of each elements, for example, if self.Algo.Inputs[0].Values = 4
self.Algo.Inputs[1].Values = 1
self.Algo.Inputs[2].Values = 2
i want all the combinations, [0,0,0],[1,0,0],[2,0,0],[3,0,0],[4,0,0],[0,1,0],[1,1,0],[2,1,0],[3,1,0],[4,1,0] etc..
I think I forgot a loop but i cant figure out. I tried some stuff with itertools module as well, but i cant make it work.
Thans for your help.
As mentioned you can use itertools, for example like that:
import itertools
a = b = c = range(3) # you can specify different range for each one
[list(x) for x in list(itertools.product(a, b, c))]
Result:
[[0, 0, 0], [0, 0, 1], [0, 0, 2], [0, 1, 0], [0, 1, 1], [0, 1, 2], [0, 2, 0], [0, 2, 1], [0, 2, 2], [1, 0, 0], [1, 0, 1], [1, 0, 2], [1, 1, 0], [1, 1, 1], [1, 1, 2], [1, 2, 0], [1, 2, 1], [1, 2, 2], [2, 0, 0], [2, 0, 1], [2, 0, 2], [2, 1, 0], [2, 1, 1], [2, 1, 2], [2, 2, 0], [2, 2, 1], [2, 2, 2]]
I have a numpy array and a list of valid values in that array:
import numpy as np
arr = np.array([[1,2,0], [2,2,0], [4,1,0], [4,1,0], [3,2,0], ... ])
valid = [1,4]
Is there a nice pythonic way to set all array values to zero, that are not in the list of valid values and do it in-place? After this operation, the list should look like this:
[[1,0,0], [0,0,0], [4,1,0], [4,1,0], [0,0,0], ... ]
The following creates a copy of the array in memory, which is bad for large arrays:
arr = np.vectorize(lambda x: x if x in valid else 0)(arr)
It bugs me, that for now I loop over each array element and set it to zero if it is in the valid list.
Edit: I found an answer suggesting there is no in-place function to achieve this. Also stop changing my whitespaces. It's easier to see the changes in arr whith them.
You can use np.place for an in-situ update -
np.place(arr,~np.in1d(arr,valid),0)
Sample run -
In [66]: arr
Out[66]:
array([[1, 2, 0],
[2, 2, 0],
[4, 1, 0],
[4, 1, 0],
[3, 2, 0]])
In [67]: np.place(arr,~np.in1d(arr,valid),0)
In [68]: arr
Out[68]:
array([[1, 0, 0],
[0, 0, 0],
[4, 1, 0],
[4, 1, 0],
[0, 0, 0]])
Along the same lines, np.put could also be used -
np.put(arr,np.where(~np.in1d(arr,valid))[0],0)
Sample run -
In [70]: arr
Out[70]:
array([[1, 2, 0],
[2, 2, 0],
[4, 1, 0],
[4, 1, 0],
[3, 2, 0]])
In [71]: np.put(arr,np.where(~np.in1d(arr,valid))[0],0)
In [72]: arr
Out[72]:
array([[1, 0, 0],
[0, 0, 0],
[4, 1, 0],
[4, 1, 0],
[0, 0, 0]])
Indexing with booleans would work too:
>>> arr = np.array([[1, 2, 0], [2, 2, 0], [4, 1, 0], [4, 1, 0], [3, 2, 0]])
>>> arr[~np.in1d(arr, valid).reshape(arr.shape)] = 0
>>> arr
array([[1, 0, 0],
[0, 0, 0],
[4, 1, 0],
[4, 1, 0],
[0, 0, 0]])
I would like to compose two matrix of numbers into one matrix of formated text in python.
Is there a easy way?
I could use for, but I just want this because is better for work.
As a simple example:
array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2],
[3, 3, 3],
[4, 4, 4]])
to
array([['0:0', '1:0', '2:0'],
['0:1', '1:1', '2:1'],
['0:2', '1:2', '2:2'],
['0:3', '1:3', '2:3'],
['0:4', '1:4', '2:4']])
You can use np.dstack to combine both the arrays and use string manipulation with comprehension to manipulate each cell of the combined array
>>> arr = np.dstack((arr1, arr2))
>>> np.array([np.array([':'.join(map(str,cell)) for cell in row ]) for row in arr])
array([['0:0', '1:0', '2:0'],
['0:1', '1:1', '2:1'],
['0:2', '1:2', '2:2'],
['0:3', '1:3', '2:3'],
['0:4', '1:4', '2:4']],
dtype='|S3')
You could use nditer to iterate over the arrays, and make strings as needed: e.g.
import numpy as np
a1 = np.array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
a2 = np.array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2],
[3, 3, 3],
[4, 4, 4]])
out=np.empty(a1.shape, dtype='S5')
for x,y,o in np.nditer([a1, a2, out], op_flags=['readwrite']):
o[...] = "{}:{}".format(x,y)
print(out)
Result:
[['0:0' '1:0' '2:0']
['0:1' '1:1' '2:1']
['0:2' '1:2' '2:2']
['0:3' '1:3' '2:3']
['0:4' '1:4' '2:4']]
Use list comprehensions and zip() to form a new array:
from numpy import array
ar1 = array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
ar2 = array([[0, 0, 0],
[1, 1, 1],
[2, 2, 2],
[3, 3, 3],
[4, 4, 4]])
res = array([['%s:%s' % (j1, j2) for j1, j2 in zip(i1, i2)] for i1, i2 in zip(ar1, ar2)])
print(res)
Result:
[['0:0' '1:0' '2:0']
['0:1' '1:1' '2:1']
['0:2' '1:2' '2:2']
['0:3' '1:3' '2:3']
['0:4' '1:4' '2:4']]
This solution will also fit usual Python two-dimensional lists (just remove the 'array' functions).