CODEWITHSUNDEEP
python
Juan Ramos
Juan Ramos

Reputation: 189

I want to find the the distance between a 2D list then add it to an element

My data is as follows:

mx_ranges1 = [ 
  (848,888),
  (806,848),
  (764,806),
  (722,764),
  (680,722),
  (638,680),
  (596,638),
  (554,596),
  (512,554),
  (470,512),
  (428,470),
  (386,428),
  (344,386),
  (302,344),
  (260,302),
  (218,260),
  (176,218),
  (134,176),
]

a=((mx_ranges1[0][1]-mx_ranges1[0][0])/2)+(mx_ranges1[0][0])
b=((mx_ranges1[1][1]-mx_ranges1[1][0])/2)+(mx_ranges1[1][0])
c=((mx_ranges1[2][1]-mx_ranges1[2][0])/2)+(mx_ranges1[3][0])

print(a)
print(b)
print(c)`

That way is not really efficient, I know it can somehow be represented in a for loop, I just don't know how I might do it. Please give me some references since I'm new to python and programming in general. I then have another list with y which also need to take the distance then add it to the first element.

Not sure if it can be placed directly into a single 2D array but just doing the first part should be good enough for me. I can do the rest manually.

Upvotes: 1

Views: 297

Answers (2)

yatu
yatu

Reputation: 88236

You can use a simple list comprehension:

[(j-i)/2 + i for i,j in mx_ranges1]
# [868.0, 827.0, 785.0, 743.0, 701.0, 659.0, 617.0 ...

Which is equivalent to the following for loop:

res = []
for i,j in mx_ranges1:    
    res.append((j-i)/2 + i)

You also mention using numpy arrays. Note that this would be the most efficient and simple way to do it, as it is a matter of Basic Slicing and Indexing:

a = np.array(mx_ranges1)
(a[:,1] - a[:,0]) /2 + a[:,0]
# array([868., 827., 785., 743., ...

Upvotes: 1

duhaime
duhaime

Reputation: 27594

Numpy will be much faster!

import numpy as np

mx_ranges1 = [ 
  (848,888),
  (806,848),
  (764,806),
  (722,764),
  (680,722),
  (638,680),
  (596,638),
  (554,596),
  (512,554),
  (470,512),
  (428,470),
  (386,428),
  (344,386),
  (302,344),
  (260,302),
  (218,260),
  (176,218),
  (134,176),
]

a = np.array(mx_ranges1)

# the first index accessor : says all rows, the second specifies a column
result = (a[:,1] - a[:,0])/2 + a[:,0]

# result contains one value for each row/tuple in `mx_ranges1`
print(result)

This returns:

[868. 827. 785. 743. 701. 659. 617. 575. 533. 491. 449. 407. 365. 323.
 281. 239. 197. 155.]

Which contains one value for each row of your input 2D array. So 868 = 888-848/2 + 848.

Upvotes: 1

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