l have an adjacency matrix of 16 by 16.
Adjacency=[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
From this adjacency matrix l applied scipy algorithm to determine the connected components as follow :
from scipy.sparse.csgraph import connected_components
supernodes=connected_components(Adjacency)
which returns 4 components :
(4, array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 3, 0], dtype=int32))
Now the algorithm returns 4 components (4 new nodes or 4 supernodes 0,1,2,3) and its associated adjacency matrix is of dim=(4,4)
My question is as follow :
Given the intial adjacency matrix of 16 by 16 and the connected components, how can l compute efficiently the new adjacency matrix ?
In other way, we need to merge all the nodes that are affected to the same connected component.
EDIT 1 :
Here a concrete example. Given the following adjacency matrix of 6 nodes, dim=-6,6) :
Adjacency_matrix=[[0,1,1,0,0,1],
[1,0,0,1,0,0],
[1,0,0,0,1,1],
[0,1,0,0,1,0],
[0,0,1,1,0,0],
[1,0,1,0,0,0]]
Given three supernodes as follow :
supernodes[0]=[0,2]# supernode 0 merges node 0 and 2
supernodes[1]=[1,4]#supernode 1 merges node 1 and 4
supernodes[2]=[3,5]#supernode 2 merges node 3 and 5
The supposed output :
Adjacency matrix of 3 supernodes dim=(3,3)
reduced_adjacency_matrix=[[0,1,1],
[1,0,1],
[1,1,0]]
What does it mean ?
For instance, consider the first supernodes[0]=[0,2]. The idea is as follow :
A) if i and j are in the same supernode then adjacency[i,j]=0
B)if i and j are in the same supernode and i or j has connection with other nodes other than i and j set 1
Thank you for your help.
Related
i am playing around with scikit image restoration package and successfully ran the unsupervised_wiener algorithm on some made up data. In this simple example it does what I expect, but on my more complicated dataset it returns a striped pattern with extreme values of -1 and 1.
I would like to fiddle with the parameters to better understand what is going on, but I get the error as stated in the question. I tried scikit image version 0.19.3 and downgraded to scikit image version 0.19.2, but the error remains.
The same goes for the "other parameters":https://scikit-image.org/docs/0.19.x/api/skimage.restoration.html#skimage.restoration.unsupervised_wiener
Can someone explain why I can't input parameters?
The example below contains a "scan" and a "point-spread-function". I convolve the scan with the point spread function and then reverse the process using the unsupervised wiener deconvolution.
import numpy as np
import matplotlib.pyplot as plt
from skimage import color, data, restoration
import pickle
rng = np.random.default_rng()
from scipy.signal import convolve2d as conv2
scan = np.array([[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, 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, 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, 0, 0, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0.5, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 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, 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, 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, 0, 0]
])
print(scan.shape)
psf = np.array([
[1, 1, 1, 1, 1],#1
[1, 0, 0, 0, 1],#2
[1, 0, 0, 0, 1],#3
[1, 0, 0, 0, 1],#4
[1, 1, 1, 1, 1]#5
])
psf = psf/(np.sum(psf))
print(psf)
scan_conv = conv2(scan, psf, 'same')
deconvolved1, _ = restoration.unsupervised_wiener(scan_conv, psf, max_num_iter=10)
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(8, 5),
sharex=True, sharey=True)
ax[0].imshow(scan, vmin=scan.min(), vmax=1)
ax[0].axis('off')
ax[0].set_title('Data')
ax[1].imshow(scan_conv)
ax[1].axis('off')
ax[1].set_title('Data_distorted')
ax[2].imshow(deconvolved1)
ax[2].axis('off')
ax[2].set_title('restoration1')
fig.tight_layout()
plt.show()
This question already has answers here:
List of lists changes reflected across sublists unexpectedly
(17 answers)
Closed 8 months ago.
What is the diference between defining with dp = [[0]*8]*8 and dp2 = [([0]*8) for i in range(8)] ? They seem to be equal but when I set one value in one case and the other they set it diferently. Why ?
Thanks
>>> dp = [[0]*8]*8
>>> dp
[[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], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
>>> dp2 = [([0]*8) for i in range(8)]
>>> dp2
[[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], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
>>> dp[1][4] = 1
>>> dp2[1][4] = 1
>>> dp
[[0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0]]
>>> dp2
[[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 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, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
>>>
They are not equal
dp = [[0]*8]*8 here there is only one inner list obejct. It's like below
a=[0]*8
dp=[a,a,a,a,a,a,a,a]
That's why when you change item in dp[1] all others are changed. There is only one inner list. dp holds multiple refereces for that same list object.
dp2 = [([0]*8) for i in range(8)] here multiple inner list objects are created. It's like below,
dp2=[[0]*8,[0]*8,[0]*8,[0]*8,[0]*8,[0]*8,[0]*8,[0]*8]
That's why you can change only 1 element without affecting others
Below I have the following code that generates a heatmap that plots each point as a block. But I want to switch the appearence to more traditional heatmaps. It currently looks like
but I want to make it appear like
though since the dataset is all 0 it would be one color but this is for future data. Below I have attached the code that generates the first heatmap, I need to rewrite the code to change its appearence into the second one. I couldnt find the code on the matplotlib examples
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
try:
temp = [
[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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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]]
temp = np.array(temp)
column = ["2-12","2-12","2-12","2-12", "2-13", "2-13","2-13","2-13","2-14","2-14","2-14","2-14", "2-15", "2-15", "2-15", "2-15", "2-16","2-16","2-16","2-16", "2-17", "2-17", "2-17", "2-17", "2-18","2-18","2-18","2-18","2-19","2-19","2-19","2-19", "2-20","2-20","2-20","2-20", "2-21", "2-21", "2-21", "2-21","2-22","2-22","2-22","2-22"]
nodes = ["0-3", "4-7", "8-11", "22-15", "26-19", "20-23", "24-27", "28-31", "32-35", "36-39"]
fig, ax = plt.subplots()
im = ax.imshow(temp)
# We want to show all ticks...
ax.set_xticks(np.arange(len(column)))
ax.set_yticks(np.arange(len(nodes)))
# ... and label them with the respective list entries
ax.set_xticklabels(column)
ax.set_yticklabels(nodes)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
for i in range(len(nodes)):
for j in range(len(column)):
text = ax.text(j, i, temp[i, j],
ha="center", va="center", color="w")
fig.tight_layout()
plt.show()
except ValueError:
pass
how to get first value (i.e index 0) of all the list and store it in another list. and second value (i.e index 1) in all list and store in another list and so on.
[[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 0, 1, 0, 0]
, [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
, [0, 0, 0, 0, 1, 1, 0, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 1, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 0, 0, 0]
, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]
This is my code:
def brujinGraph(k, strList):
vertex = [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0]]
brujinGraph = {strList[i]: strList[j][:-1] for i in range(len(vertex)) for j in range(k) and vertex[i][j] == 1}
return brujinGraph
strList = ['AAGA', 'AAGA', 'AGAT', 'ATTC', 'CTAA', 'CTCT', 'GATT', 'TAAG', 'TCTA', 'TCTC', 'TTCT']
brujinGraph(4, strList)
and it is throwing me an UnboundLocalError: local variable 'j' referenced before assignment
any idea what does it means and why am I getting this error?
Without knowing exactly what vertex and strList are :
Do you actually mean :
{strList[i]: strList[j][:-1] for i in range(len(vertex)) for j in range(len(vertex[i])) if vertex[i][j] == 1}
i.e. change that and into an if
Couple of issues:
You need an if not an and at the end
I think it is better expressed this way:
brujinGraph = {strList[i]: strList[j][:-1] for i, x in enumerate(vertex) for j, e in enumerate(x) if e == 1}