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During communities' detection I am trying to remove duplicates nodes from lists of lists (aimed to calculate ARI).
What I have – few dozen lists inside one list with different dimensions:
lst_of_lts= [[5192, 32896, 34357, 34976, 36683, 43315], … ,[19, 92585, 94137, 98381, 99041, 100395, 101100, 109759]]
What I am running:
import itertools
Lst_of_lts.sort()
Lst_of_lts_2 = list(k for k,_ in itertools.groupby(Lst_of_lts))
Lst_of_lts_nodops= [list(i) for i in {tuple(sorted(i)) for i in Lst_of_lts_2}]
For some reason, it doesn’t remove duplicates.
The dimensions remain the same-
Any suggestions?
Also tried many options such as:
Remove duplicate items from lists in Python lists and
Remove duplicated lists in list of lists in Python
If you are removing duplicates just in the list itself, you can use set.
a = np.random.randint(0,5,(10,10)).tolist()
a
Out[128]:
[[0, 3, 0, 2, 4, 4, 0, 0, 3, 3],
[2, 4, 0, 2, 4, 2, 2, 4, 3, 1],
[3, 2, 0, 1, 2, 0, 2, 0, 2, 1],
[3, 1, 4, 1, 0, 1, 4, 4, 3, 4],
[2, 0, 1, 1, 0, 4, 1, 4, 2, 3],
[0, 0, 1, 3, 4, 3, 1, 3, 0, 1],
[1, 2, 0, 2, 1, 3, 4, 2, 2, 0],
[3, 3, 2, 2, 0, 4, 1, 1, 0, 0],
[0, 1, 3, 0, 4, 4, 2, 1, 1, 4],
[0, 1, 4, 4, 0, 1, 3, 2, 1, 1]]
[list(set(i)) for i in a]
Out[129]:
[[0, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3],
[0, 1, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]
Or if you want to preserve the order of the element, you can use dict.fromkeys
[list(dict.fromkeys(i)) for i in a]
Out[133]:
[[0, 3, 2, 4],
[2, 4, 0, 3, 1],
[3, 2, 0, 1],
[3, 1, 4, 0],
[2, 0, 1, 4, 3],
[0, 1, 3, 4],
[1, 2, 0, 3, 4],
[3, 2, 0, 4, 1],
[0, 1, 3, 4, 2],
[0, 1, 4, 3, 2]]
My data is as follows,
data = [[2, 1, 2, 2], [2, 2, 1, 5], [1, 2, 2, 2], [2, 1, 2, 5], [2, 5, 2, 1]]
I would like to transform this such that there is a 0 at 0, 1, 2, 3 and 4th positions of the internal lists and get it look like below,
new_Data = [[0, 2, 1, 2, 2], [2, 0, 2, 1, 5], [1, 2, 0, 2, 2], [2, 1, 2, 0, 5], [2, 5, 2, 1, 0]]
I have tried using the following method,
a = 0
for n in range(len(mRco1)-1):
mRco1[n][n] = [a]
But it does not seem to work.
Can anyone suggest how can I go about this?
Use the list.insert() method
for i in range(len(data)):
data[i].insert(i, 0)
result :
print(data)
>>> [[0, 2, 1, 2, 2], [2, 0, 2, 1, 5], [1, 2, 0, 2, 2], [2, 1, 2, 0, 5], [2, 5, 2, 1, 0]]
You'd like to iterate over the lists in data, and for the n'th list, insert a 0 at position n. You can use the insert function for that, and define the following loop:
for i in range(len(data)):
data[i].insert(i, 0)
I've been trying to solve this textbook question for a while but am a bit stuck.
Question:
We have provided a module image with a procedure
file2image(filename)
that reads in an image stored in a file in the
.png format. Import this procedure and invoke it,
providing as argument the name of a file containing an image in this format, assigning the returned
value to variable data
The value of data is a list of lists, and data[y][x] is the intensity of pixel (x,y). Pixel
(0,0) is at the bottom-left of the image, and pixel (width-1, height-1) is at the top-right.
The intensity of a pixel is a number between 0 (black) and 255 (white).
Use a comprehension to assign to a list pts the set of complex numbers x+yi such that the
image intensity of pixel (x, y) is less than 120.
Here is the relevant method that was provided
def file2image(path):
""" Reads an image into a list of lists of pixel values (tuples with
three values). This is a color image. """
(w, h, p, m) = png.Reader(filename = path).asRGBA() # force RGB and alpha
return [_flat2boxed(r) for r in p]
I'm really unsure how to parse the 3 values as a comprehension, anyone has a guess?
The way I understand the data structure goes like : [[(x,y,z)],[(x,y,z)]...etc]
So my code is obviously wrong but I tried
data = img.file2image("img01.png")
data = img.color2gray(data)
pts = [(x,y,z) for (x,y,z) in data]
plot(pts)
that's a pretty terrible description text to describe the objective, here's my take at comprehension of list of lists and list of lists of lists
>>> [[v for v in range(5)] for _ in range(5)]
[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]
>>> [[[v for v in range(5)] for _ in range(5)] for _ in range(5)]
[[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], [[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]]
>>>
the _ in my for loops throws away the variable coming out of the iterator since I don't need to store it
I have a numpy array of shape (1429,1) where each row itself is a numpy array of shape (3,100) where l may vary from row to row.
How can I reshape this array by flattening each row such that the resulting numpy array will have the shape (1429, 300)?
I guess your initial array's shape is (1429, 3, 100), if that's true, you can change it's shape as below:
import numpy as np
a = a.flatten().reshape((1429, 300)) #a is the initial numpy array
The type of your embedding structure is probably object. It's just a collection of references on 1429 numpy.ndarrays.
As an exemple :
a=np.empty((1429,1),object)
for x in a :
x[0]=np.random.rand(3,100)
In [19]: a.shape,a.dtype
Out[19]: ((1429, 1), dtype('O'))
In [20]: a[0,0].shape
Out[20]: (3, 100)
The structure is probably not contiguous. To obtain a block containing all your data, you must reconstruct it to obtain the good layout :
b=np.array([x.ravel() for x in a.ravel()])
In [21]: b.shape
Out[21]: (1429, 300)
ravel discard unwanted dimensions.
Assuming it is an object dtype array with shape (1429,1), and all elements are 2d of shape (3,100), a good way to 'flatten' is to use concatenate or stack.
np.stack(arr.ravel()).reshape(-1,300)
I use arr.ravel() so the array looks like a (1429) element list to stack. stack then concatenates the elements, creating a (1429, 3, 100) array. The reshape then converts that to (1429, 300).
In [939]: arr = np.empty((5,1),object)
In [940]: arr[:,0] = [np.arange(6).reshape(2,3) for _ in range(5)]
In [941]: arr
Out[941]:
array([[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])],
[array([[0, 1, 2],
[3, 4, 5]])]], dtype=object)
In [942]: np.stack(arr.ravel())
Out[942]:
array([[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]]])
In [943]: np.stack(arr.ravel()).reshape(-1,6)
Out[943]:
array([[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5]])
np.stack with the default axis=0 is the same as np.array(...).
Or with concatenate
In [950]: np.concatenate(arr.ravel(),axis=0)
Out[950]:
array([[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5]])
In [951]: np.concatenate(arr.ravel(),axis=0).reshape(5,6)
Out[951]:
array([[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5],
[0, 1, 2, 3, 4, 5]])
This is my current matrix:
[[0, 1, 2, 4],
[0, 3, 1, 3],
[0, 2, 3, 2],
[0, 2, 4, 1],
[0, 4, 1, 2],
[0, 3, 2, 2],
[1, 2, 2, 2]]
I want to transpose it and get this as output:
[[0, 0, 0, 0, 1],
[2, 2, 4, 3, 2],
[3, 4, 1, 2, 2],
[2, 1, 2, 2, 2]]
I used inverse = np.swapaxes(ate,0,7) but I am not sure what will be my axis2 value be. Here the axis2 is 7.
I think what you're looking for is np.transpose()
You can use np.swapaxes, however this swaps "dimensions", so for a matrix that's either 0 or 1 because you have two dimensions:
>>> np.swapaxes(arr, 0, 1) # assuming your matrix is called arr
array([[0, 0, 0, 0, 0, 0, 1],
[1, 3, 2, 2, 4, 3, 2],
[2, 1, 3, 4, 1, 2, 2],
[4, 3, 2, 1, 2, 2, 2]])
To get your desired output you'd need to remove the first two columns before the swapaxes:
>>> np.swapaxes(arr[2:], 0, 1)
array([[0, 0, 0, 0, 1],
[2, 2, 4, 3, 2],
[3, 4, 1, 2, 2],
[2, 1, 2, 2, 2]])
However generally you should use np.transpose or .T if you want to transpose the matrix/array:
>>> arr[2:].T
array([[0, 0, 0, 0, 1],
[2, 2, 4, 3, 2],
[3, 4, 1, 2, 2],
[2, 1, 2, 2, 2]])