Changing the shape of a matrix using numpy - python

My question is two-fold. I have the following code to do with some matrices.
import numpy
tupleList = [(0, 122), (1, 246), (2, 157), (3, 166), (4, 315), (5, 108), (6, 172), (7, 20), (8, 173), (9, 38), (10, 28), (11, 72), (12, 102), (13, 277), (14, 318), (15, 316), (16, 283), (17, 31), (18, 160), (19, 97), (20, 26), (21, 252), (22, 105), (23, 133), (24, 162), (25, 116), (26, 284), (27, 25), (28, 80), (29, 225), (30, 107), (31, 111), (32, 208), (33, 121), (34, 249), (35, 314), (36, 163), (37, 170), (38, 48), (39, 142), (40, 95), (41, 113), (42, 285), (43, 88), (44, 184), (45, 63), (46, 129), (47, 137), (48, 87), (49, 135), (50, 207), (51, 276), (52, 174), (53, 143), (54, 92), (55, 313), (56, 85), (57, 185), (58, 96), (59, 86), (60, 222), (61, 274), (62, 0), (63, 256), (64, 27), (65, 81), (66, 219), (67, 271), (68, 115), (69, 212), (70, 83), (71, 302), (72, 69), (73, 211), (74, 139), (75, 110), (76, 2), (77, 298), (78, 244), (79, 299), (80, 248), (81, 57), (82, 293), (83, 241), (84, 188), (85, 250), (86, 29), (87, 149), (88, 51), (89, 75), (90, 264), (91, 59), (92, 33), (93, 10), (94, 210), (95, 90), (96, 262), (97, 73), (98, 138), (99, 74), (100, 89), (101, 124), (102, 118), (103, 112), (104, 295), (105, 56), (106, 100), (107, 305), (108, 273), (109, 220), (110, 66), (111, 218), (112, 141), (113, 267), (114, 47), (115, 61), (116, 224), (117, 123), (118, 136), (119, 127), (120, 126), (121, 125), (122, 292), (123, 64), (124, 84), (125, 18), (126, 134), (127, 24), (128, 279), (129, 13), (130, 1), (131, 6), (132, 282), (133, 290), (134, 151), (135, 245), (136, 307), (137, 257), (138, 187), (139, 148), (140, 234), (141, 158), (142, 161), (143, 268), (144, 209), (145, 140), (146, 35), (147, 8), (148, 291), (149, 177), (150, 7), (151, 11), (152, 194), (153, 9), (154, 195), (155, 82), (156, 186), (157, 270), (158, 280), (159, 104), (160, 101), (161, 98), (162, 50), (163, 99), (164, 216), (165, 117), (166, 215), (167, 62), (168, 297), (169, 39), (170, 176), (171, 150), (172, 60), (173, 197), (174, 183), (175, 237), (176, 192), (177, 189), (178, 23), (179, 303), (180, 272), (181, 213), (182, 37), (183, 217), (184, 236), (185, 147), (186, 199), (187, 41), (188, 55), (189, 175), (190, 67), (191, 193), (192, 46), (193, 196), (194, 278), (195, 251), (196, 204), (197, 53), (198, 258), (199, 179), (200, 247), (201, 260), (202, 238), (203, 159), (204, 114), (205, 223), (206, 308), (207, 243), (208, 45), (209, 52), (210, 269), (211, 152), (212, 154), (213, 146), (214, 198), (215, 190), (216, 203), (217, 319), (218, 242), (219, 294), (220, 130), (221, 68), (222, 311), (223, 155), (224, 36), (225, 281), (226, 17), (227, 310), (228, 296), (229, 12), (230, 153), (231, 120), (232, 4), (233, 65), (234, 180), (235, 202), (236, 226), (237, 54), (238, 289), (239, 254), (240, 109), (241, 144), (242, 205), (243, 132), (244, 240), (245, 178), (246, 263), (247, 232), (248, 58), (249, 214), (250, 275), (251, 306), (252, 309), (253, 181), (254, 231), (255, 103), (256, 227), (257, 165), (258, 286), (259, 171), (260, 32), (261, 70), (262, 312), (263, 301), (264, 287), (265, 288), (266, 206), (267, 230), (268, 16), (269, 91), (270, 182), (271, 43), (272, 191), (273, 228), (274, 317), (275, 265), (276, 145), (277, 239), (278, 259), (279, 167), (280, 34), (281, 106), (282, 131), (283, 76), (284, 266), (285, 49), (286, 300), (287, 201), (288, 93), (289, 44), (290, 42), (291, 40), (292, 3), (293, 229), (294, 304), (295, 14), (296, 94), (297, 261), (298, 221), (299, 168), (300, 255), (301, 156), (302, 233), (303, 253), (304, 77), (305, 235), (306, 79), (307, 15), (308, 19), (309, 119), (310, 78), (311, 200), (312, 5), (313, 169), (314, 128), (315, 21), (316, 22), (317, 164), (318, 30), (319, 71)]
var = 320
def binaryMatrix(list):
size = len(list)
matrix = numpy.zeros((size,size))
for tuple in list:
matrix[tuple[0],tuple[1]] = 1
#for row in matrix:
# print sum(row)
# if sum(row) > 1:
# print "Incorrect"
# break
#print matrix
return matrix
matrix = binaryMatrix(tupleList)
matrix = numpy.asarray(matrix,int)
newMatrix = numpy.eye(var)
#print newMatrix
print numpy.shape(newMatrix)
newMatrix = newMatrix[matrix]
print newMatrix
print numpy.shape(newMatrix)
The function takes a list of tuples and constructs a square binary matrix, where the entry at the location of each tuple is 1, and every other element is 0. The commented out code is simply to make sure that all rows sum to 1, which they do, so it's a valid binary matrix.
The problem I'm having is at this line: newMatrix = newMatrix[matrix]
when printing the shape after that, I'm getting that it's dimensions are 320*320*320; but what I'm looking for is 320*320.
Could someone explain to me A) Why this is happening, and B) How to reshape 'newMatrix' to be 320 by 320?


The code:
print( "numpy.eye(320) gives a matrix of shape:" , numpy.eye(320).shape )
gives:
numpy.eye(320) gives a matrix of shape: (320, 320)
This answers part B of your question. There is no need to reshape newMatrix because it is already (320, 320) .
So let's go over to part A of your question "Why is this happening that newMatrix after newMatrix = newMatrix[matrix] becomes 320x320x320 ?
The answer is: because using newMatrix[matrix] does exactly what it should do, create a 320x320x320 matrix.


You can save yourself a lot of work if you let NumPy do the indexing:
# Wrap indices in numpy array
idxarray = numpy.array(tupleList)
# Allocate zeros
data = numpy.zeros((320, 320))
# Set positions in `tupleList` to `1`
data[idxarray[:, 0], idxarray[:, 1]] = 1
# Make sure we only have one `1` per row
print numpy.all(numpy.sum(data, axis=0) <= 1) # True
# Make sure our shape is correct
print data.shape # (320, 320)


Let's experiment with small 3x3 arrays:
In [25]: mask = np.array([[1,0,0],[0,0,1],[0,1,0]])
In [26]: mask
Out[26]:
array([[1, 0, 0],
[0, 0, 1],
[0, 1, 0]])
In [27]: arr = np.arange(9).reshape(3,3)
In [28]: arr
Out[28]:
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
Using mask as you do produces a (3,3,3) array:
In [29]: arr[mask]
Out[29]:
array([[[3, 4, 5],
[0, 1, 2],
[0, 1, 2]],
[[0, 1, 2],
[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5],
[0, 1, 2]]])
Changing mask to boolean, produces a 1d array, selecting values from arr that match the location of True in mask:
In [30]: arr[mask.astype(bool)]
Out[30]: array([0, 5, 7])
Another option is multiplication, which will zero out values, returning a (3,3):
In [31]: arr*mask
Out[31]:
array([[0, 0, 0],
[0, 0, 5],
[0, 7, 0]])
Or something similar with masked array:
In [37]: np.ma.MaskedArray(arr,~mask.astype(bool))
Out[37]:
masked_array(data =
[[0 -- --]
[-- -- 5]
[-- 7 --]],
mask =
[[False True True]
[ True True False]
[ True False True]],
fill_value = 999999)


For your first question A), I suggest that you read this (Index arrays)
The use of index arrays can sometimes be non-intuitive, so see for instance the following matrix indexing:
import numpy as np
newMatrix = np.eye(2)
print newMatrix[0]
we get :
[ 1. 0.]
The result is the first element of the array along the largest dimension (dim 2), i.e. the first row of the matrix. Now, if we index using an array (notice the added brackets):
import numpy as np
newMatrix = np.eye(2)
print newMatrix[[0]]
we get :
[[ 1. 0.]]
This time, the result is still the first row of the matrix, but given in an array with a similar shape to the indexing array. From the page linked to above :
Generally speaking, what is returned when index arrays are used is an
array with the same shape as the index array, but with the type and
values of the array being indexed.
So I imagine numpy replacing the scalars in the indexing array with the matrix element given by that index scalar. If we try indexing with a matrix :
import numpy as np
indx = np.array([[1,0],[0,1]])
newMatrix = np.eye(2)
print newMatrix[indx]
gives us :
[[[ 0. 1.]
[ 1. 0.]]
[[ 1. 0.]
[ 0. 1.]]]
because every scalar in the indexing array is replaced by the corresponding row of the matrix in the result, thus also increasing the dimension of the result by 1 compared to the indexing array.
In your example, your final newMatrix is similar to your matrix, but where every scalar in matrix is replaced by either row 0 or 1 of your initial newMatrix, thus giving the final shape of your result.
This is probably not what you want, though. It might be interesting to have a look at boolean indexing. I suggest reading this (Boolean or “mask” index arrays)

Related

Problem with superpixel segmentation (opencv in python) - holes in contours of the segmentation mask

I am trying superpixel segmentation with OpenCV in Python. I have tried implementing LSC, SLIC and SEEDS algorithms https://docs.opencv.org/3.4/df/d6c/group__ximgproc__superpixel.html in order to segment cell nuclei, however, so far only SEEDS algorithm works. Countours in segmentation masks created via .getLabelContourMask() method contained holes for both LSC and SLIC algorithms, which led to flooding of the whole (LSC) or large portion (SLIC) of the mask when using floodfill() method https://docs.opencv.org/3.4/d7/d1b/group__imgproc__misc.html. The trend is - the better the segmentation (eg the lower the ratio value in LSC), the leakier the contours in the mask (which I am assuming should not happen). Only SEEDS worked as expected - no apparent holes/openings in contours of the mask, thus only areas around seedpoints flooded after using floodfill(). Anyone had the same experience? Any idea why this might be the case (may OpenCV implementation of LSC and SLIC be problematic?)? Can anything be done to close the contours/edges in the LSC/SLIC mask? Newbie in comp sci over here, any help appreciated.
Test Code for LSC:
lsc = cv2.ximgproc.createSuperpixelLSC(image, region_size=25, ratio=0.1)
lsc.iterate(100)
lsc_mask = lsc.getLabelContourMask()
cv2.imwrite("lsc_mask.tif", lsc_mask) #holes visible when zooming in
x, y = lsc_mask.shape
m = np.zeros((x+2, y+2),dtype=np.uint8)
for point in nuclei_coordinates:
retval, lsc_mask_flooded, m_flooded, rect = cv2.floodFill(image=lsc_mask, mask=m, seedPoint=point, newVal=(255,255,255), loDiff=0, upDiff=0)
cv2.imwrite("lsc_mask_flooded.tif", lsc_mask_flooded) #almost whole image flooded
cv2.imwrite("m_flooded.tif", m_flooded*255)
original image (.tif)
lsc_mask for the image (.tif)
lsc_mask zoomed in
image - lsc_mask overlay (.tif)
lsc_mask_flooded (.tif)
m_flooded (.tif)
nuclei_coordinates = [(56, 106), (32, 116), (13, 125), (18, 145), (32, 147), (13, 160), (10, 182), (46, 192), (39, 208), (33, 227), (14, 231), (27, 255), (31, 272), (38, 284), (45, 301), (82, 312), (70, 252), (56, 261), (51, 244), (63, 219), (103, 334), (121, 334), (131, 316), (148, 322), (170, 295), (219, 261), (224, 227), (220, 180), (192, 178), (196, 162), (211, 157), (215, 138), (207, 116), (190, 109), (192, 97), (186, 81), (170, 73), (174, 106), (158, 115), (147, 130), (152, 168), (156, 195), (173, 189), (131, 205), (148, 59), (125, 84), (134, 105), (150, 101), (119, 102), (124, 124), (141, 147), (122, 167), (103, 168), (112, 188), (78, 202), (104, 206), (102, 229), (116, 228), (134, 221), (146, 221), (81, 177), (110, 153), (116, 148), (111, 140), (100, 144), (91, 130), (88, 146), (74, 142), (79, 155), (54, 137), (71, 125), (82, 100), (72, 96), (76, 77), (95, 70), (104, 60), (198, 292), (207, 276), (162, 309), (148, 292), (148, 305), (131, 283), (100, 298), (232, 15), (212, 24), (225, 42), (215, 68), (247, 83), (238, 57), (264, 54), (282, 44), (317, 27), (318, 11), (309, 68), (319, 56), (306, 97), (297, 91), (233, 117), (309, 127), (314, 139), (319, 165), (326, 176), (339, 197), (350, 214), (351, 240), (364, 253), (366, 279), (378, 300), (355, 308), (332, 298), (310, 296), (314, 271), (336, 273), (283, 279), (271, 285), (258, 270), (264, 256), (250, 260), (259, 246), (245, 228), (247, 211), (255, 163), (280, 162), (293, 148), (370, 452), (368, 421), (358, 399), (358, 380), (339, 358), (337, 397), (342, 373), (328, 338), (336, 466), (325, 452), (338, 431), (324, 433), (306, 421), (317, 412), (324, 387), (323, 370), (313, 356), (298, 388), (289, 428), (265, 454), (244, 450), (241, 432), (269, 426), (275, 397), (303, 337), (283, 344), (278, 323), (264, 332), (270, 345), (242, 352), (218, 369), (225, 345), (219, 332), (208, 316), (190, 327), (183, 342), (171, 347), (136, 369), (134, 386), (152, 400), (130, 411), (154, 454), (147, 431), (183, 443), (208, 440), (194, 422), (183, 403), (232, 414), (215, 466), (296, 468), (452, 8), (464, 24), (480, 43), (431, 41), (445, 34), (427, 23), (406, 12), (380, 17), (373, 34), (351, 33), (389, 42), (409, 38), (421, 54), (414, 78), (395, 79), (403, 59), (378, 83), (363, 85), (345, 91), (342, 66), (349, 53), (375, 55), (489, 58), (492, 37), (511, 49), (536, 48), (540, 66), (544, 84), (547, 101), (548, 120), (529, 96), (520, 124), (540, 134), (554, 147), (500, 115), (482, 111), (471, 85), (459, 67), (448, 86), (449, 117), (442, 140), (427, 133), (418, 153), (405, 117), (358, 125), (355, 110), (341, 121), (353, 146), (371, 154), (387, 151), (394, 135), (384, 190), (384, 166), (368, 175), (368, 203), (383, 217), (394, 203), (389, 232), (404, 238), (409, 262), (419, 250), (419, 232), (450, 279), (456, 265), (476, 257), (433, 262), (435, 226), (451, 242), (424, 181), (320, 221), (443, 194), (455, 179), (474, 198), (488, 186), (504, 197), (475, 176), (555, 170), (551, 190), (532, 187), (527, 198), (504, 209), (481, 227), (468, 224), (456, 228), (556, 213), (539, 216), (536, 235), (520, 235), (492, 313), (509, 321), (488, 296), (491, 340), (508, 359), (501, 387), (490, 374), (480, 398), (475, 379), (470, 353), (452, 367), (449, 395), (436, 412), (431, 430), (461, 417), (447, 455), (468, 454), (488, 433), (428, 452), (419, 464), (505, 424), (493, 415), (519, 414), (539, 390), (547, 367), (522, 367), (543, 333), (518, 348), (556, 315), (569, 294), (643, 314), (628, 321), (623, 304), (611, 305), (595, 307), (669, 347), (673, 289), (661, 272), (632, 247), (630, 231), (621, 211), (608, 224), (611, 201), (605, 175), (635, 188), (644, 202), (662, 248), (688, 254), (713, 270), (729, 286), (720, 309), (739, 340), (733, 351), (724, 341), (736, 251), (729, 228), (724, 239), (714, 249), (711, 229), (699, 241), (680, 236), (675, 220), (695, 206), (727, 212), (734, 198), (737, 178), (720, 181), (709, 162), (738, 158), (739, 140), (721, 147), (702, 141), (681, 149), (662, 168), (663, 189), (675, 200), (658, 151), (652, 131), (667, 122), (648, 152), (617, 148), (619, 125), (583, 125), (581, 148), (574, 106), (573, 87), (576, 74), (711, 10), (725, 12), (736, 47), (724, 63), (733, 70), (713, 82), (710, 100), (696, 72), (691, 47), (691, 23), (677, 9), (694, 4), (649, 18), (654, 3), (635, 8), (622, 38), (612, 26), (598, 47), (619, 77), (630, 105), (653, 102), (640, 90), (676, 91), (661, 66), (670, 35), (703, 367), (712, 387), (684, 358), (656, 362), (626, 363), (611, 358), (600, 452), (633, 454), (574, 440), (589, 421), (580, 463), (335, 236), (721, 459), (733, 445), (717, 444), (728, 421), (740, 430), (718, 30), (584, 48), (589, 198)]

I think that the idea behind using LSC or LIS is to use it with watershed algorithm. That way you can get an image that has visible and continous segments contours.
Java code example:
SuperpixelLSC superpixelLSC = Ximgproc.createSuperpixelLSC(src, region_size);
superPixelLSC.iterate();
superPixelLSC.getLabels(markers);
Imgproc.watershed(src, markers);
You can then detect the contours on watershed algorithm results.

Had the same problem, when working with the SLIC-Segmentation. Used a dilate function after getting the mask, to fill the gaps on the lines:
dst = cv2.dilate(lsc_mask , kernel)
The dilation is a basic morphologic function to expand the boudries of a figure.
The kernel is the matrix you expand the boudries with. You can control the expansion of the boundries with the size of the kernel.
If you need more info I can suggest R.Gonzales' book-Digital Image Processing.
Here is also a quite good example with code:
https://cvexplained.wordpress.com/2020/05/18/dilation/

How to divide a specific number in a list?

A Mersenne Prime follows this formula 2^n-1. I have created a new type of factoring method for numbers which do not produce Mersenne primes. It is very abstract. Its premise is if a specific number is applied using modular math and the new number becomes (zero), it is not a Mersenne Prime Number. I submitted a paper to The Journal of Number Theory online, however it was rejected by the journal. I have attached it if you would like to look it over, I still feel my method is promising yet I'm no coding expert. This is a pdf I sent to the Journal of Number Theory My problem is in my new code I don't know how to divide a number in the list. The list enumerates ok yet I want to subtract z=11 from 253 which equals 242 than mod it by 121, however when I create a range from 1-254 I cannot seem to do this math. The reason I'm interested in this is 253//11=23 which is a factor of 2^11-1. I got this idea from a ratio page.
Type 1:11 and the second number is a 22 just add 1 and its 23.
Check it out
https://goodcalculators.com/ratio-calculator/
The formula will target any number in the range and what I'm looking for is a zero.
Additional details for grismar as per request:
Grismar and others,
What I have found is that Mersenne primes will produce fewer zero's below the number 11 vs. a number like 2^11-1. Also when you output the number by subtraction of z and then mod z*z you may find the number with the lowest factor in it after you divide it by z. The range must be large enough as to find that number, yet if is zero simply divide by z. Then for instance when you find 23 by dividing 11 into 253. You can divide 23 into 2047 and you should get 89. More than likely if you use a different number to check this factor you will get a fraction. So when checking using this method when you find a zero for a number which does not produce a Mersenne Prime number like. Lets pick 29. 536870911 ÷ 233 = 2304167 so you get a factor number not a fraction.
These are all the factors of 536870911
[1, 233, 1103, 256999, 2089, 486737, 2304167, 536870911]
If you would like even more details leave a comment please.
Programmer in learning looking for help here is my program:
1 should be the start range!
while True:
x = int(input("Use 1 for the start range to make this work correctly:
"))
i = int(input("End Range: "))
z = int(input("square of primes multiplied by a number plus z which
does not make a
mersenne prime, this finds its factor of z: "))
fact = [(i + 1, x) for i, x in enumerate(range(x, i))]
print([((int(i)-z) % (z*z)) if isinstance(i, str) else i for i in fact])

Maybe what you are trying is this, the int call is unnecessary since the values are integers from the start. Also, don't use the same variable i for different purposes:
calculations = [
(index + 1, (fact_tuple[0] - z) % (z*z)) for index, fact_tuple in enumerate(fact)
]
print(calculations) # with x = 1, i = 254, z = 11
>>> [(1, 111), (2, 112), (3, 113), (4, 114), (5, 115), (6, 116), (7, 117), (8, 118), (9, 119), (10, 120), (11, 0), (12, 1), (13, 2), (14, 3), (15, 4), (16, 5), (17, 6), (18, 7), (19, 8), (20, 9), (21, 10), (22, 11), (23, 12), (24, 13), (25, 14), (26, 15), (27, 16), (28, 17), (29, 18), (30, 19), (31, 20), (32, 21), (33, 22), (34, 23), (35, 24), (36, 25), (37, 26), (38, 27), (39, 28), (40, 29), (41, 30), (42, 31), (43, 32), (44, 33), (45, 34), (46, 35), (47, 36), (48, 37), (49, 38), (50, 39), (51, 40), (52, 41), (53, 42), (54, 43), (55, 44), (56, 45), (57, 46), (58, 47), (59, 48), (60, 49), (61, 50), (62, 51), (63, 52), (64, 53), (65, 54), (66, 55), (67, 56), (68, 57), (69, 58), (70, 59), (71, 60), (72, 61), (73, 62), (74, 63), (75, 64), (76, 65), (77, 66), (78, 67), (79, 68), (80, 69), (81, 70), (82, 71), (83, 72), (84, 73), (85, 74), (86, 75), (87, 76), (88, 77), (89, 78), (90, 79), (91, 80), (92, 81), (93, 82), (94, 83), (95, 84), (96, 85), (97, 86), (98, 87), (99, 88), (100, 89), (101, 90), (102, 91), (103, 92), (104, 93), (105, 94), (106, 95), (107, 96), (108, 97), (109, 98), (110, 99), (111, 100), (112, 101), (113, 102), (114, 103), (115, 104), (116, 105), (117, 106), (118, 107), (119, 108), (120, 109), (121, 110), (122, 111), (123, 112), (124, 113), (125, 114), (126, 115), (127, 116), (128, 117), (129, 118), (130, 119), (131, 120), (132, 0), (133, 1), (134, 2), (135, 3), (136, 4), (137, 5), (138, 6), (139, 7), (140, 8), (141, 9), (142, 10), (143, 11), (144, 12), (145, 13), (146, 14), (147, 15), (148, 16), (149, 17), (150, 18), (151, 19), (152, 20), (153, 21), (154, 22), (155, 23), (156, 24), (157, 25), (158, 26), (159, 27), (160, 28), (161, 29), (162, 30), (163, 31), (164, 32), (165, 33), (166, 34), (167, 35), (168, 36), (169, 37), (170, 38), (171, 39), (172, 40), (173, 41), (174, 42), (175, 43), (176, 44), (177, 45), (178, 46), (179, 47), (180, 48), (181, 49), (182, 50), (183, 51), (184, 52), (185, 53), (186, 54), (187, 55), (188, 56), (189, 57), (190, 58), (191, 59), (192, 60), (193, 61), (194, 62), (195, 63), (196, 64), (197, 65), (198, 66), (199, 67), (200, 68), (201, 69), (202, 70), (203, 71), (204, 72), (205, 73), (206, 74), (207, 75), (208, 76), (209, 77), (210, 78), (211, 79), (212, 80), (213, 81), (214, 82), (215, 83), (216, 84), (217, 85), (218, 86), (219, 87), (220, 88), (221, 89), (222, 90), (223, 91), (224, 92), (225, 93), (226, 94), (227, 95), (228, 96), (229, 97), (230, 98), (231, 99), (232, 100), (233, 101), (234, 102), (235, 103), (236, 104), (237, 105), (238, 106), (239, 107), (240, 108), (241, 109), (242, 110), (243, 111), (244, 112), (245, 113), (246, 114), (247, 115), (248, 116), (249, 117), (250, 118), (251, 119), (252, 120), (253, 0)]

How can I manually place networkx nodes using the mouse?

I have a fairly large and messy network of nodes that I wish to display as neatly as possible.
This is how it's currently being displayed:
First, I tried playing with the layout to see if it could generate a good output automatically.
I have tried many different nx layouts, but they all display similar results. I have also tried answers from all of these stack exchange questions:
How to increase node spacing for networkx.spring_layout
Drawing a huge graph with networkX and matplotlib
NetworkX - Stop Nodes from Bunching Up - Tried Scale/K parameters
Fix position of subset of nodes in NetworkX spring graph
Here is the code I am using:
import networkx as nx
import matplotlib.pyplot as plt\
def generate_plot(connections, filename):
G=nx.Graph()
G.add_edges_from(connections)
nx.draw(G)
plt.show()
#plt.savefig(filename)
and here is the data that I am trying to display:
connections = [(0, 36), (0, 113), (2, 11), (2, 12), (2, 26), (2, 27), (2, 28), (2, 29), (2, 32), (2, 33), (2, 34), (2, 35), (2, 82), (3, 41), (4, 41), (5, 3), (6, 3), (11, 7), (11, 10), (11, 13), (11, 42), (12, 10), (12, 164), (26, 10), (26, 100), (27, 10), (27, 164), (28, 10), (28, 92), (29, 56), (29, 58), (29, 79), (29, 91), (29, 99), (29, 100), (29, 101), (29, 102), (30, 59), (30, 83), (30, 99), (31, 55), (31, 56), (31, 57), (31, 74), (31, 91), (31, 96), (31, 100), (31, 113), (31, 134), (31, 164), (32, 10), (33, 10), (34, 10), (34, 164), (35, 10), (35, 91), (35, 100), (36, 64), (36, 74), (36, 82), (36, 91), (36, 107), (36, 99), (38, 41), (39, 40), (39, 41), (39, 59), (40, 41), (40, 47), (40, 91), (40, 99), (41, 3), (41, 39), (41, 40), (43, 68), (43, 69), (45, 50), (46, 51), (46, 69), (46, 99), (47, 49), (47, 91), (47, 107), (47, 100), (47, 101), (47, 113), (48, 76), (50, 68), (50, 69), (51, 68), (51, 76), (51, 114), (52, 46), (52, 47), (52, 65), (53, 42), (53, 107), (53, 99), (53, 100), (53, 101), (53, 113), (54, 76), (55, 74), (55, 96), (55, 99), (56, 99), (56, 100), (56, 109), (57, 29), (57, 64), (57, 91), (57, 96), (57, 107), (57, 100), (58, 91), (58, 99), (58, 100), (58, 101), (58, 102), (59, 30), (59, 46), (59, 47), (59, 61), (59, 83), (59, 99), (59, 100), (59, 101), (60, 3), (60, 12), (60, 26), (60, 27), (60, 29), (60, 30), (60, 31), (60, 35), (60, 36), (60, 40), (60, 42), (60, 44), (60, 49), (60, 55), (60, 56), (60, 57), (60, 58), (60, 59), (60, 61), (60, 64), (60, 74), (60, 75), (60, 79), (60, 81), (60, 82), (60, 83), (60, 86), (60, 90), (60, 91), (60, 92), (60, 93), (60, 94), (60, 96), (60, 107), (60, 99), (60, 100), (60, 101), (60, 102), (60, 111), (60, 112), (60, 113), (60, 116), (60, 126), (60, 129), (60, 134), (60, 135), (60, 136), (60, 140), (60, 144), (60, 150), (60, 152), (60, 162), (60, 164), (60, 179), (60, 195), (61, 91), (61, 107), (61, 100), (61, 101), (61, 113), (61, 135), (62, 79), (62, 102), (63, 59), (64, 29), (64, 36), (64, 91), (64, 99), (64, 100), (64, 101), (64, 140), (65, 12), (65, 27), (65, 29), (65, 30), (65, 31), (65, 34), (65, 35), (65, 43), (65, 45), (65, 50), (65, 56), (65, 57), (65, 58), (65, 59), (65, 60), (65, 61), (65, 66), (65, 67), (65, 72), (65, 75), (65, 81), (65, 82), (65, 91), (65, 92), (65, 96), (65, 107), (65, 99), (65, 100), (65, 101), (65, 102), (65, 104), (65, 109), (65, 129), (65, 140), (65, 142), (65, 156), (65, 164), (67, 68), (67, 69), (67, 195), (68, 60), (69, 51), (69, 53), (69, 68), (69, 195), (70, 12), (70, 26), (70, 29), (70, 31), (70, 35), (70, 47), (70, 56), (70, 58), (70, 59), (70, 61), (70, 66), (70, 75), (70, 79), (70, 82), (70, 83), (70, 93), (70, 96), (70, 107), (70, 99), (70, 100), (70, 101), (70, 126), (70, 129), (70, 135), (70, 136), (70, 162), (70, 164), (71, 12), (71, 26), (71, 29), (71, 31), (71, 35), (71, 47), (71, 56), (71, 58), (71, 59), (71, 61), (71, 66), (71, 75), (71, 79), (71, 83), (71, 96), (71, 107), (71, 99), (71, 100), (71, 101), (71, 126), (71, 135), (71, 162), (71, 164), (72, 29), (72, 35), (72, 36), (72, 46), (72, 47), (72, 55), (72, 57), (72, 58), (72, 60), (72, 61), (72, 64), (72, 66), (72, 70), (72, 71), (72, 74), (72, 75), (72, 76), (72, 79), (72, 86), (72, 90), (72, 91), (72, 93), (72, 94), (72, 95), (72, 96), (72, 107), (72, 99), (72, 100), (72, 101), (72, 102), (72, 111), (72, 112), (72, 116), (72, 126), (72, 129), (72, 144), (73, 83), (74, 96), (74, 100), (75, 100), (76, 29), (76, 31), (76, 42), (76, 44), (76, 49), (76, 55), (76, 56), (76, 59), (76, 61), (76, 66), (76, 74), (76, 78), (76, 83), (76, 91), (76, 93), (76, 96), (76, 107), (76, 109), (76, 113), (76, 114), (76, 116), (76, 134), (76, 140), (77, 30), (77, 44), (77, 49), (77, 61), (77, 74), (77, 78), (77, 83), (77, 96), (77, 109), (77, 140), (78, 61), (78, 91), (78, 92), (79, 44), (79, 99), (80, 42), (80, 64), (80, 65), (80, 75), (80, 83), (80, 134), (80, 135), (80, 136), (80, 144), (80, 155), (81, 35), (81, 91), (81, 100), (82, 10), (83, 84), (83, 85), (83, 86), (83, 107), (83, 100), (84, 194), (84, 195), (87, 36), (87, 88), (87, 142), (87, 144), (88, 59), (88, 83), (88, 134), (88, 135), (88, 136), (88, 144), (88, 158), (88, 162), (89, 61), (89, 135), (89, 141), (90, 36), (91, 96), (91, 113), (93, 96), (93, 107), (93, 134), (93, 135), (94, 74), (94, 96), (95, 61), (95, 134), (95, 135), (95, 162), (96, 35), (96, 74), (96, 91), (96, 99), (96, 100), (96, 101), (98, 12), (98, 26), (98, 27), (98, 29), (98, 30), (98, 31), (98, 34), (98, 35), (98, 41), (98, 56), (98, 57), (98, 58), (98, 59), (98, 60), (98, 61), (98, 70), (98, 75), (98, 79), (98, 81), (98, 82), (98, 83), (98, 84), (98, 87), (98, 91), (98, 92), (98, 95), (98, 96), (98, 107), (98, 99), (98, 100), (98, 101), (98, 106), (98, 134), (98, 135), (98, 142), (98, 147), (98, 152), (98, 159), (99, 91), (99, 113), (99, 164), (100, 91), (100, 101), (100, 113), (100, 164), (101, 57), (101, 91), (101, 113), (101, 164), (102, 101), (103, 44), (103, 61), (103, 140), (104, 56), (104, 90), (104, 101), (104, 102), (104, 104), (104, 129), (104, 140), (105, 83), (105, 135), (106, 2), (106, 29), (106, 30), (106, 31), (106, 36), (106, 41), (106, 48), (106, 56), (106, 57), (106, 58), (106, 59), (106, 60), (106, 61), (106, 62), (106, 63), (106, 65), (106, 66), (106, 68), (106, 70), (106, 71), (106, 72), (106, 74), (106, 75), (106, 76), (106, 77), (106, 78), (106, 79), (106, 80), (106, 81), (106, 82), (106, 83), (106, 84), (106, 85), (106, 87), (106, 89), (106, 90), (106, 91), (106, 92), (106, 93), (106, 94), (106, 95), (106, 96), (106, 107), (106, 99), (106, 100), (106, 101), (106, 102), (106, 103), (106, 104), (106, 105), (106, 108), (106, 110), (106, 111), (106, 112), (106, 116), (106, 119), (106, 123), (106, 124), (106, 125), (106, 126), (106, 127), (106, 128), (106, 129), (106, 130), (106, 131), (106, 134), (106, 135), (106, 136), (106, 137), (106, 138), (106, 139), (106, 140), (106, 141), (106, 142), (106, 144), (106, 146), (106, 147), (106, 148), (106, 149), (106, 151), (106, 152), (106, 153), (106, 154), (106, 157), (106, 158), (106, 159), (106, 160), (106, 161), (106, 162), (106, 164), (106, 194), (108, 42), (108, 61), (108, 76), (108, 109), (108, 110), (108, 114), (109, 42), (109, 91), (109, 113), (109, 164), (110, 76), (111, 114), (112, 114), (114, 0), (114, 26), (114, 29), (114, 30), (114, 31), (114, 35), (114, 36), (114, 40), (114, 42), (114, 44), (114, 47), (114, 49), (114, 53), (114, 55), (114, 56), (114, 57), (114, 59), (114, 61), (114, 64), (114, 74), (114, 75), (114, 81), (114, 82), (114, 86), (114, 91), (114, 93), (114, 96), (114, 107), (114, 99), (114, 100), (114, 101), (114, 102), (114, 109), (114, 129), (114, 134), (114, 135), (114, 140), (114, 164), (114, 179), (115, 10), (115, 31), (115, 164), (116, 114), (119, 150), (120, 157), (121, 137), (122, 137), (123, 163), (124, 117), (124, 125), (124, 130), (124, 150), (124, 163), (125, 126), (126, 117), (126, 135), (127, 134), (128, 135), (129, 36), (129, 91), (130, 131), (131, 134), (131, 135), (132, 157), (133, 137), (134, 36), (134, 74), (134, 109), (134, 135), (135, 36), (135, 74), (135, 136), (135, 156), (136, 36), (137, 134), (138, 139), (139, 135), (140, 107), (140, 134), (140, 136), (141, 118), (141, 135), (141, 144), (142, 36), (142, 61), (142, 64), (142, 83), (142, 118), (142, 129), (142, 134), (142, 135), (142, 136), (142, 140), (142, 141), (142, 143), (142, 144), (142, 150), (142, 152), (142, 156), (142, 158), (142, 162), (142, 164), (142, 195), (143, 135), (144, 36), (144, 134), (144, 135), (144, 136), (144, 162), (145, 134), (146, 134), (146, 135), (146, 155), (147, 29), (147, 44), (147, 46), (147, 47), (147, 118), (147, 134), (147, 135), (147, 136), (147, 140), (147, 144), (147, 156), (147, 158), (147, 164), (148, 136), (148, 150), (149, 134), (149, 135), (149, 136), (149, 141), (150, 134), (150, 164), (151, 150), (152, 134), (152, 135), (152, 156), (152, 162), (153, 134), (153, 135), (153, 162), (154, 150), (155, 134), (156, 135), (157, 134), (158, 136), (158, 140), (159, 135), (160, 134), (160, 135), (160, 155), (162, 36), (162, 61), (162, 74), (162, 96), (162, 134), (162, 135), (162, 144), (167, 171), (168, 83), (169, 99), (170, 83), (170, 85), (171, 2), (172, 195), (173, 78), (174, 78), (175, 60), (175, 66), (175, 70), (175, 142), (176, 94), (176, 120), (176, 121), (176, 122), (176, 123), (176, 132), (176, 133), (176, 137), (176, 138), (176, 145), (176, 157), (177, 12), (177, 26), (177, 27), (177, 28), (177, 29), (177, 30), (177, 31), (177, 33), (177, 34), (177, 35), (177, 36), (177, 44), (177, 55), (177, 56), (177, 57), (177, 58), (177, 59), (177, 61), (177, 62), (177, 63), (177, 64), (177, 74), (177, 75), (177, 78), (177, 79), (177, 81), (177, 82), (177, 86), (177, 89), (177, 90), (177, 91), (177, 93), (177, 94), (177, 96), (177, 107), (177, 99), (177, 100), (177, 101), (177, 102), (177, 109), (177, 113), (177, 118), (177, 128), (177, 129), (177, 134), (177, 135), (177, 136), (177, 139), (177, 140), (177, 141), (177, 144), (177, 148), (177, 149), (177, 150), (177, 151), (177, 152), (177, 153), (177, 154), (177, 159), (177, 162), (178, 35), (180, 37), (182, 31), (183, 31), (184, 185), (185, 29), (185, 30), (185, 31), (185, 44), (185, 79), (185, 166), (185, 187), (185, 190), (187, 185), (188, 164), (188, 187), (188, 190), (189, 108), (190, 185), (192, 193), (195, 164)]
I am hoping to be able to spread out the nodes more and display them in a hierarchical manner.
I would prefer to be able to move the nodes around manually , similar to this animation, but with the nodes staying where they are placed instead of snapping back. However, if you can display them neatly without the need for manual interaction, that would work too!

Here is an MWE using netgraph on a networkx graph object.
import numpy as np
import matplotlib.pyplot as plt; plt.ion()
import networkx
import netgraph # pip install netgraph
# Construct sparse, directed, weighted graph
total_nodes = 20
weights = np.random.rand(total_nodes, total_nodes)
connection_probability = 0.1
is_connected = np.random.rand(total_nodes, total_nodes) <= connection_probability
graph = np.zeros((total_nodes, total_nodes))
graph[is_connected] = weights[is_connected]
# construct a networkx graph
g = networkx.from_numpy_array(graph, networkx.DiGraph)
# decide on a layout
pos = networkx.layout.spring_layout(g)
# Create an interactive plot.
# NOTE: you must retain a reference to the object instance!
# Otherwise the whole thing will be garbage collected after the initial draw
# and you won't be able to move the plot elements around.
plot_instance = netgraph.InteractiveGraph(graph, node_positions=pos)
######## drag nodes around #########
# To access the new node positions:
node_positions = plot_instance.node_positions

How can I place networkx nodes with the most connections closer to the top?

I have a fairly large and messy network of nodes that I wish to display as neatly as possible.
This is how it's currently being displayed:
First, I tried playing with the layout to see if it could generate a good output automatically.
I have tried many different nx layouts, but they all display similar results. I have also tried answers from all of these stack exchange questions:
How to increase node spacing for networkx.spring_layout
Drawing a huge graph with networkX and matplotlib
NetworkX - Stop Nodes from Bunching Up - Tried Scale/K parameters
Fix position of subset of nodes in NetworkX spring graph
Here is the code I am using:
import networkx as nx
import matplotlib.pyplot as plt\
def generate_plot(connections, filename):
G=nx.Graph()
G.add_edges_from(connections)
nx.draw(G)
plt.show()
#plt.savefig(filename)
and here is the data that I am trying to display:
connections = [(0, 36), (0, 113), (2, 11), (2, 12), (2, 26), (2, 27), (2, 28), (2, 29), (2, 32), (2, 33), (2, 34), (2, 35), (2, 82), (3, 41), (4, 41), (5, 3), (6, 3), (11, 7), (11, 10), (11, 13), (11, 42), (12, 10), (12, 164), (26, 10), (26, 100), (27, 10), (27, 164), (28, 10), (28, 92), (29, 56), (29, 58), (29, 79), (29, 91), (29, 99), (29, 100), (29, 101), (29, 102), (30, 59), (30, 83), (30, 99), (31, 55), (31, 56), (31, 57), (31, 74), (31, 91), (31, 96), (31, 100), (31, 113), (31, 134), (31, 164), (32, 10), (33, 10), (34, 10), (34, 164), (35, 10), (35, 91), (35, 100), (36, 64), (36, 74), (36, 82), (36, 91), (36, 107), (36, 99), (38, 41), (39, 40), (39, 41), (39, 59), (40, 41), (40, 47), (40, 91), (40, 99), (41, 3), (41, 39), (41, 40), (43, 68), (43, 69), (45, 50), (46, 51), (46, 69), (46, 99), (47, 49), (47, 91), (47, 107), (47, 100), (47, 101), (47, 113), (48, 76), (50, 68), (50, 69), (51, 68), (51, 76), (51, 114), (52, 46), (52, 47), (52, 65), (53, 42), (53, 107), (53, 99), (53, 100), (53, 101), (53, 113), (54, 76), (55, 74), (55, 96), (55, 99), (56, 99), (56, 100), (56, 109), (57, 29), (57, 64), (57, 91), (57, 96), (57, 107), (57, 100), (58, 91), (58, 99), (58, 100), (58, 101), (58, 102), (59, 30), (59, 46), (59, 47), (59, 61), (59, 83), (59, 99), (59, 100), (59, 101), (60, 3), (60, 12), (60, 26), (60, 27), (60, 29), (60, 30), (60, 31), (60, 35), (60, 36), (60, 40), (60, 42), (60, 44), (60, 49), (60, 55), (60, 56), (60, 57), (60, 58), (60, 59), (60, 61), (60, 64), (60, 74), (60, 75), (60, 79), (60, 81), (60, 82), (60, 83), (60, 86), (60, 90), (60, 91), (60, 92), (60, 93), (60, 94), (60, 96), (60, 107), (60, 99), (60, 100), (60, 101), (60, 102), (60, 111), (60, 112), (60, 113), (60, 116), (60, 126), (60, 129), (60, 134), (60, 135), (60, 136), (60, 140), (60, 144), (60, 150), (60, 152), (60, 162), (60, 164), (60, 179), (60, 195), (61, 91), (61, 107), (61, 100), (61, 101), (61, 113), (61, 135), (62, 79), (62, 102), (63, 59), (64, 29), (64, 36), (64, 91), (64, 99), (64, 100), (64, 101), (64, 140), (65, 12), (65, 27), (65, 29), (65, 30), (65, 31), (65, 34), (65, 35), (65, 43), (65, 45), (65, 50), (65, 56), (65, 57), (65, 58), (65, 59), (65, 60), (65, 61), (65, 66), (65, 67), (65, 72), (65, 75), (65, 81), (65, 82), (65, 91), (65, 92), (65, 96), (65, 107), (65, 99), (65, 100), (65, 101), (65, 102), (65, 104), (65, 109), (65, 129), (65, 140), (65, 142), (65, 156), (65, 164), (67, 68), (67, 69), (67, 195), (68, 60), (69, 51), (69, 53), (69, 68), (69, 195), (70, 12), (70, 26), (70, 29), (70, 31), (70, 35), (70, 47), (70, 56), (70, 58), (70, 59), (70, 61), (70, 66), (70, 75), (70, 79), (70, 82), (70, 83), (70, 93), (70, 96), (70, 107), (70, 99), (70, 100), (70, 101), (70, 126), (70, 129), (70, 135), (70, 136), (70, 162), (70, 164), (71, 12), (71, 26), (71, 29), (71, 31), (71, 35), (71, 47), (71, 56), (71, 58), (71, 59), (71, 61), (71, 66), (71, 75), (71, 79), (71, 83), (71, 96), (71, 107), (71, 99), (71, 100), (71, 101), (71, 126), (71, 135), (71, 162), (71, 164), (72, 29), (72, 35), (72, 36), (72, 46), (72, 47), (72, 55), (72, 57), (72, 58), (72, 60), (72, 61), (72, 64), (72, 66), (72, 70), (72, 71), (72, 74), (72, 75), (72, 76), (72, 79), (72, 86), (72, 90), (72, 91), (72, 93), (72, 94), (72, 95), (72, 96), (72, 107), (72, 99), (72, 100), (72, 101), (72, 102), (72, 111), (72, 112), (72, 116), (72, 126), (72, 129), (72, 144), (73, 83), (74, 96), (74, 100), (75, 100), (76, 29), (76, 31), (76, 42), (76, 44), (76, 49), (76, 55), (76, 56), (76, 59), (76, 61), (76, 66), (76, 74), (76, 78), (76, 83), (76, 91), (76, 93), (76, 96), (76, 107), (76, 109), (76, 113), (76, 114), (76, 116), (76, 134), (76, 140), (77, 30), (77, 44), (77, 49), (77, 61), (77, 74), (77, 78), (77, 83), (77, 96), (77, 109), (77, 140), (78, 61), (78, 91), (78, 92), (79, 44), (79, 99), (80, 42), (80, 64), (80, 65), (80, 75), (80, 83), (80, 134), (80, 135), (80, 136), (80, 144), (80, 155), (81, 35), (81, 91), (81, 100), (82, 10), (83, 84), (83, 85), (83, 86), (83, 107), (83, 100), (84, 194), (84, 195), (87, 36), (87, 88), (87, 142), (87, 144), (88, 59), (88, 83), (88, 134), (88, 135), (88, 136), (88, 144), (88, 158), (88, 162), (89, 61), (89, 135), (89, 141), (90, 36), (91, 96), (91, 113), (93, 96), (93, 107), (93, 134), (93, 135), (94, 74), (94, 96), (95, 61), (95, 134), (95, 135), (95, 162), (96, 35), (96, 74), (96, 91), (96, 99), (96, 100), (96, 101), (98, 12), (98, 26), (98, 27), (98, 29), (98, 30), (98, 31), (98, 34), (98, 35), (98, 41), (98, 56), (98, 57), (98, 58), (98, 59), (98, 60), (98, 61), (98, 70), (98, 75), (98, 79), (98, 81), (98, 82), (98, 83), (98, 84), (98, 87), (98, 91), (98, 92), (98, 95), (98, 96), (98, 107), (98, 99), (98, 100), (98, 101), (98, 106), (98, 134), (98, 135), (98, 142), (98, 147), (98, 152), (98, 159), (99, 91), (99, 113), (99, 164), (100, 91), (100, 101), (100, 113), (100, 164), (101, 57), (101, 91), (101, 113), (101, 164), (102, 101), (103, 44), (103, 61), (103, 140), (104, 56), (104, 90), (104, 101), (104, 102), (104, 104), (104, 129), (104, 140), (105, 83), (105, 135), (106, 2), (106, 29), (106, 30), (106, 31), (106, 36), (106, 41), (106, 48), (106, 56), (106, 57), (106, 58), (106, 59), (106, 60), (106, 61), (106, 62), (106, 63), (106, 65), (106, 66), (106, 68), (106, 70), (106, 71), (106, 72), (106, 74), (106, 75), (106, 76), (106, 77), (106, 78), (106, 79), (106, 80), (106, 81), (106, 82), (106, 83), (106, 84), (106, 85), (106, 87), (106, 89), (106, 90), (106, 91), (106, 92), (106, 93), (106, 94), (106, 95), (106, 96), (106, 107), (106, 99), (106, 100), (106, 101), (106, 102), (106, 103), (106, 104), (106, 105), (106, 108), (106, 110), (106, 111), (106, 112), (106, 116), (106, 119), (106, 123), (106, 124), (106, 125), (106, 126), (106, 127), (106, 128), (106, 129), (106, 130), (106, 131), (106, 134), (106, 135), (106, 136), (106, 137), (106, 138), (106, 139), (106, 140), (106, 141), (106, 142), (106, 144), (106, 146), (106, 147), (106, 148), (106, 149), (106, 151), (106, 152), (106, 153), (106, 154), (106, 157), (106, 158), (106, 159), (106, 160), (106, 161), (106, 162), (106, 164), (106, 194), (108, 42), (108, 61), (108, 76), (108, 109), (108, 110), (108, 114), (109, 42), (109, 91), (109, 113), (109, 164), (110, 76), (111, 114), (112, 114), (114, 0), (114, 26), (114, 29), (114, 30), (114, 31), (114, 35), (114, 36), (114, 40), (114, 42), (114, 44), (114, 47), (114, 49), (114, 53), (114, 55), (114, 56), (114, 57), (114, 59), (114, 61), (114, 64), (114, 74), (114, 75), (114, 81), (114, 82), (114, 86), (114, 91), (114, 93), (114, 96), (114, 107), (114, 99), (114, 100), (114, 101), (114, 102), (114, 109), (114, 129), (114, 134), (114, 135), (114, 140), (114, 164), (114, 179), (115, 10), (115, 31), (115, 164), (116, 114), (119, 150), (120, 157), (121, 137), (122, 137), (123, 163), (124, 117), (124, 125), (124, 130), (124, 150), (124, 163), (125, 126), (126, 117), (126, 135), (127, 134), (128, 135), (129, 36), (129, 91), (130, 131), (131, 134), (131, 135), (132, 157), (133, 137), (134, 36), (134, 74), (134, 109), (134, 135), (135, 36), (135, 74), (135, 136), (135, 156), (136, 36), (137, 134), (138, 139), (139, 135), (140, 107), (140, 134), (140, 136), (141, 118), (141, 135), (141, 144), (142, 36), (142, 61), (142, 64), (142, 83), (142, 118), (142, 129), (142, 134), (142, 135), (142, 136), (142, 140), (142, 141), (142, 143), (142, 144), (142, 150), (142, 152), (142, 156), (142, 158), (142, 162), (142, 164), (142, 195), (143, 135), (144, 36), (144, 134), (144, 135), (144, 136), (144, 162), (145, 134), (146, 134), (146, 135), (146, 155), (147, 29), (147, 44), (147, 46), (147, 47), (147, 118), (147, 134), (147, 135), (147, 136), (147, 140), (147, 144), (147, 156), (147, 158), (147, 164), (148, 136), (148, 150), (149, 134), (149, 135), (149, 136), (149, 141), (150, 134), (150, 164), (151, 150), (152, 134), (152, 135), (152, 156), (152, 162), (153, 134), (153, 135), (153, 162), (154, 150), (155, 134), (156, 135), (157, 134), (158, 136), (158, 140), (159, 135), (160, 134), (160, 135), (160, 155), (162, 36), (162, 61), (162, 74), (162, 96), (162, 134), (162, 135), (162, 144), (167, 171), (168, 83), (169, 99), (170, 83), (170, 85), (171, 2), (172, 195), (173, 78), (174, 78), (175, 60), (175, 66), (175, 70), (175, 142), (176, 94), (176, 120), (176, 121), (176, 122), (176, 123), (176, 132), (176, 133), (176, 137), (176, 138), (176, 145), (176, 157), (177, 12), (177, 26), (177, 27), (177, 28), (177, 29), (177, 30), (177, 31), (177, 33), (177, 34), (177, 35), (177, 36), (177, 44), (177, 55), (177, 56), (177, 57), (177, 58), (177, 59), (177, 61), (177, 62), (177, 63), (177, 64), (177, 74), (177, 75), (177, 78), (177, 79), (177, 81), (177, 82), (177, 86), (177, 89), (177, 90), (177, 91), (177, 93), (177, 94), (177, 96), (177, 107), (177, 99), (177, 100), (177, 101), (177, 102), (177, 109), (177, 113), (177, 118), (177, 128), (177, 129), (177, 134), (177, 135), (177, 136), (177, 139), (177, 140), (177, 141), (177, 144), (177, 148), (177, 149), (177, 150), (177, 151), (177, 152), (177, 153), (177, 154), (177, 159), (177, 162), (178, 35), (180, 37), (182, 31), (183, 31), (184, 185), (185, 29), (185, 30), (185, 31), (185, 44), (185, 79), (185, 166), (185, 187), (185, 190), (187, 185), (188, 164), (188, 187), (188, 190), (189, 108), (190, 185), (192, 193), (195, 164)]
I am hoping to be able to spread out the nodes more and display them in a hierarchical manner, with the nodes that have the most connections closer to top, and the ones with few connections at the bottom.

This is a bit roundabout, but maybe useful:
The idea is to use the degree (i.e., its number of edges) of the node as y-coordinate, and then assign x-coords based on frequency of the given degree:
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
G=nx.Graph()
G.add_edges_from(connections)
# check how many times a given degree occurs:
degrees = [G.degree[a] for a in G.nodes]
# generate unique x-coordinates. divide by 2 for zero-centering:
degrees = {degree: [a for a in degrees.count(degree)/2. - np.arange(degrees.count(degree))] for degree in set(degrees)}
# build positioning dicitonary:
positions = {a : (degrees[G.degree[a]].pop(), G.degree[a]) for a in G.nodes}
Fixed distances in Y-direction, and slightly less compressed code:
degrees = [G.degree[a] for a in G.nodes]
degrees_unique = sorted(list(set(degrees)))
y_positions = {degrees_unique[i] : i for i in range(len(degrees_unique))}
x_positions = {}
for degree in degrees_unique:
x_positions[degree] = [a for a in degrees.count(degree) / 2. - np.arange(degrees.count(degree))]
positions = {}
for node in G.nodes:
deg = G.degree[node]
positions[node] = (x_positions[deg].pop(), y_positions[deg])
nx.draw(G, pos=positions, node_size=10)
alternatively, a mirror variable can be included in the last loop, s.t. nodes are plotted above and below the x-axis, every other iteration, somewhat decluttering everything:
mirror = 1
for node in G.nodes:
deg = G.degree[node]
positions[node] = (x_positions[deg].pop(), mirror*y_positions[deg])
mirror *= -1
nx.draw(G, pos=positions, node_size=10)

How to determine two 2 Dimensional lists are exactl same?

This is a part of a large program. I have a list like
cnfn=[(1, -3), (2, -3), (-1, -2, 3), (-1, 4), (-2, 4), (1, 2, -4), (-4, -5), (4, 5), (-3, 6), (-5, 6), (3, 5, -6), (7, -8), (6, -8), (-7, -6, 8), (-6, 9), (-7, 9), (6, 7, -9), (-9, -10), (9, 10), (-8, 11), (-10, 11), (8, 10, -11), (7, -12), (4, -12), (-7, -4, 12), (-12, 13), (-3, 13), (12, 3, -13), (14, -16), (15, -16), (-14, -15, 16), (-16, -17), (16, 17), (-14, 18), (-15, 18), (14, 15, -18), (17, -19), (18, -19), (-17, -18, 19), (13, -20), (19, -20), (-13, -19, 20), (-20, -21), (20, 21), (-19, 22), (-13, 22), (19, 13, -22), (21, -23), (22, -23), (-21, -22, 23), (13, -24), (18, -24), (-13, -18, 24), (-24, 25), (-16, 25), (24, 16, -25), (26, -28), (27, -28), (-26, -27, 28), (-28, -29), (28, 29), (-26, 30), (-27, 30), (26, 27, -30), (29, -31), (30, -31), (-29, -30, 31), (25, -32), (31, -32), (-25, -31, 32), (-32, -33), (32, 33), (-31, 34), (-25, 34), (31, 25, -34), (33, -35), (34, -35), (-33, -34, 35), (25, -36), (30, -36), (-25, -30, 36), (-36, 37), (-28, 37), (36, 28, -37), (38, -40), (39, -40), (-38, -39, 40), (-40, -41), (40, 41), (-38, 42), (-39, 42), (38, 39, -42), (41, -43), (42, -43), (-41, -42, 43), (37, -44), (43, -44), (-37, -43, 44), (-44, -45), (44, 45), (-43, 46), (-37, 46), (43, 37, -46), (45, -47), (46, -47), (-45, -46, 47), (37, -48), (42, -48), (-37, -42, 48), (-48, 49), (-40, 49), (48, 40, -49), (-50, -51), (50, 51), (-51, 53), (-52, 53), (51, 52, -53), (-52, -54), (52, 54), (-54, 55), (-50, 55), (54, 50, -55), (53, -56), (55, -56), (-53, -55, 56), (-56, -57), (56, 57), (58, -59), (57, -59), (-58, -57, 59), (52, -60), (50, -60), (-52, -50, 60), (-59, 61), (-60, 61), (59, 60, -61), (56, -62), (58, -62), (-56, -58, 62), (-58, -63), (58, 63), (57, -64), (63, -64), (-57, -63, 64), (-62, 65), (-64, 65), (62, 64, -65), (-66, -67), (66, 67), (-67, 69), (-68, 69), (67, 68, -69), (-68, -70), (68, 70), (-70, 71), (-66, 71), (70, 66, -71), (69, -72), (71, -72), (-69, -71, 72), (-72, -73), (72, 73), (61, -74), (73, -74), (-61, -73, 74), (68, -75), (66, -75), (-68, -66, 75), (-74, 76), (-75, 76), (74, 75, -76), (72, -77), (61, -77), (-72, -61, 77), (-61, -78), (61, 78), (73, -79), (78, -79), (-73, -78, 79), (-77, 80), (-79, 80), (77, 79, -80), (-81, -82), (81, 82), (-82, 84), (-83, 84), (82, 83, -84), (-83, -85), (83, 85), (-85, 86), (-81, 86), (85, 81, -86), (84, -87), (86, -87), (-84, -86, 87), (-87, -88), (87, 88), (76, -89), (88, -89), (-76, -88, 89), (83, -90), (81, -90), (-83, -81, 90), (-89, 91), (-90, 91), (89, 90, -91), (87, -92), (76, -92), (-87, -76, 92), (-76, -93), (76, 93), (88, -94), (93, -94), (-88, -93, 94), (-92, 95), (-94, 95), (92, 94, -95), (-96, -97), (96, 97), (-97, 99), (-98, 99), (97, 98, -99), (-98, -100), (98, 100), (-100, 101), (-96, 101), (100, 96, -101), (99, -102), (101, -102), (-99, -101, 102), (-102, -103), (102, 103), (91, -104), (103, -104), (-91, -103, 104), (-104, -105), (104, 105), (-104, 106), (-105, 106), (104, 105, -106), (102, -107), (91, -107), (-102, -91, 107), (-91, -108), (91, 108), (103, -109), (108, -109), (-103, -108, 109), (-107, 110), (-109, 110), (107, 109, -110), (-1, 50), (1, -50), (-2, 52), (2, -52), (-7, 58), (7, -58), (-14, 66), (14, -66), (-15, 68), (15, -68), (-26, 81), (26, -81), (-27, 83), (27, -83), (-38, 96), (38, -96), (-39, 98), (39, -98), (-11, -65, -111), (-11, 65, 111), (11, -65, 111), (11, 65, -111), (-23, -80, -112), (-23, 80, 112), (23, -80, 112), (23, 80, -112), (-35, -95, -113), (-35, 95, 113), (35, -95, 113), (35, 95, -113), (-47, -106, -114), (-47, 106, 114), (47, -106, 114), (47, 106, -114), (-49, -110, -115), (-49, 110, 115), (49, -110, 115), (49, 110, -115), (111, 112, 113, 114, 115)]
And there is another list
cnfb=[(1, -3), (2, -3), (-1, -2, 3), (-1, 4), (-2, 4), (1, 2, -4), (4, 5), (-4, -5), (-3, 6), (-5, 6), (3, 5, -6), (7, -8), (6, -8), (-7, -6, 8), (-6, 9), (-7, 9), (6, 7, -9), (9, 10), (-9, -10), (-8, 11), (-10, 11), (8, 10, -11), (7, -12), (4, -12), (-7, -4, 12), (-12, 13), (-3, 13), (12, 3, -13), (14, -16), (15, -16), (-14, -15, 16), (16, 17), (-16, -17), (-14, 18), (-15, 18), (14, 15, -18), (17, -19), (18, -19), (-17, -18, 19), (13, -20), (19, -20), (-13, -19, 20), (20, 21), (-20, -21), (-19, 22), (-13, 22), (19, 13, -22), (21, -23), (22, -23), (-21, -22, 23), (13, -24), (18, -24), (-13, -18, 24), (-24, 25), (-16, 25), (24, 16, -25), (26, -28), (27, -28), (-26, -27, 28), (28, 29), (-28, -29), (-26, 30), (-27, 30), (26, 27, -30), (29, -31), (30, -31), (-29, -30, 31), (25, -32), (31, -32), (-25, -31, 32), (32, 33), (-32, -33), (-31, 34), (-25, 34), (31, 25, -34), (33, -35), (34, -35), (-33, -34, 35), (25, -36), (30, -36), (-25, -30, 36), (-36, 37), (-28, 37), (36, 28, -37), (38, -40), (39, -40), (-38, -39, 40), (40, 41), (-40, -41), (-38, 42), (-39, 42), (38, 39, -42), (41, -43), (42, -43), (-41, -42, 43), (37, -44), (43, -44), (-37, -43, 44), (44, 45), (-44, -45), (-43, 46), (-37, 46), (43, 37, -46), (45, -47), (46, -47), (-45, -46, 47), (37, -48), (42, -48), (-37, -42, 48), (-48, 49), (-40, 49), (48, 40, -49), (50, 51), (-50, -51), (-51, 53), (-52, 53), (51, 52, -53), (52, 54), (-52, -54), (-54, 55), (-50, 55), (54, 50, -55), (53, -56), (55, -56), (-53, -55, 56), (56, 57), (-56, -57), (58, -59), (57, -59), (-58, -57, 59), (52, -60), (50, -60), (-52, -50, 60), (-59, 61), (-60, 61), (59, 60, -61), (56, -62), (58, -62), (-56, -58, 62), (58, 63), (-58, -63), (57, -64), (63, -64), (-57, -63, 64), (-62, 65), (-64, 65), (62, 64, -65), (66, 67), (-66, -67), (-67, 69), (-68, 69), (67, 68, -69), (68, 70), (-68, -70), (-70, 71), (-66, 71), (70, 66, -71), (69, -72), (71, -72), (-69, -71, 72), (72, 73), (-72, -73), (61, -74), (73, -74), (-61, -73, 74), (68, -75), (66, -75), (-68, -66, 75), (-74, 76), (-75, 76), (74, 75, -76), (72, -77), (61, -77), (-72, -61, 77), (61, 78), (-61, -78), (73, -79), (78, -79), (-73, -78, 79), (-77, 80), (-79, 80), (77, 79, -80), (81, 82), (-81, -82), (-82, 84), (-83, 84), (82, 83, -84), (83, 85), (-83, -85), (-85, 86), (-81, 86), (85, 81, -86), (84, -87), (86, -87), (-84, -86, 87), (87, 88), (-87, -88), (76, -89), (88, -89), (-76, -88, 89), (83, -90), (81, -90), (-83, -81, 90), (-89, 91), (-90, 91), (89, 90, -91), (87, -92), (76, -92), (-87, -76, 92), (76, 93), (-76, -93), (88, -94), (93, -94), (-88, -93, 94), (-92, 95), (-94, 95), (92, 94, -95), (96, 97), (-96, -97), (-97, 99), (-98, 99), (97, 98, -99), (98, 100), (-98, -100), (-100, 101), (-96, 101), (100, 96, -101), (99, -102), (101, -102), (-99, -101, 102), (102, 103), (-102, -103), (91, -104), (103, -104), (-91, -103, 104), (104, 105), (-104, -105), (-104, 106), (-105, 106), (104, 105, -106), (102, -107), (91, -107), (-102, -91, 107), (91, 108), (-91, -108), (103, -109), (108, -109), (-103, -108, 109), (-107, 110), (-109, 110), (107, 109, -110), (35, 95, -111), (-35, -95, -111), (-35, 95, 111), (35, -95, 111), (23, 80, -112), (-23, -80, -112), (-23, 80, 112), (23, -80, 112), (49, 106, -113), (-49, -106, -113), (-49, 106, 113), (49, -106, 113), (47, 110, -114), (-47, -110, -114), (-47, 110, 114), (47, -110, 114), (11, 65, -115), (-11, -65, -115), (-11, 65, 115), (11, -65, 115), [111, 112, 113, 114, 115], (-26, 83), (26, -83), (-2, 50), (2, -50), (-38, 98), (38, -98), (-27, 81), (27, -81), (-39, 96), (39, -96), (-7, 58), (7, -58), (-14, 68), (14, -68), (-15, 66), (15, -66), (-1, 52), (1, -52)]
If I check with plane eye the look like having same values but if I put them in the same function the result is different. How can I determine those two have exactly same type and same value?

The two lists are NOT the same. That is why a function may be giving you a different result for the different lists.
To check if 2 lists are identical, you can do:
list1 == list2
So to give some examples:
>>> [1, 2, 3, 4, 5] == [1, 2, 3, 4, 5]
True
>>> [1, 2, 3, 4, 5] == [1, 2, 3, 4, 3]
False
>>> [1, 2, 3, 4, 5] == [5, 4, 3, 2, 1]
False
>>> [(1, 2), (3, 4)] == [(1, 2), (3, 4)]
True
>>> [(1, 2), (3, 4)] == [(1, 2), (3, 5)]
False
If you want to find what the differences are, you can do the following:
[e for e in list1 if e not in list2] + [e for e in list2 if e not in list1]
which I think is actually very readable for what it is.
So we could put that inside a function:
def comp(list1, list2):
return [e for e in list1 if e not in list2] + [e for e in list2 if e not in list1]
and some examples:
>>> comp([1, 2, 3], [1, 2, 3]) #should be empty as no differnence
[]
>>> comp([(1, 2), (3, 4)], [(1, 2), (3, 5)])
[(3, 4), (3, 5)]
>>> comp([(1, 2), (3, 4)], [(1, 2), (3, 5), (6, 7)])
[(3, 4), (3, 5), (6, 7)]

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