CODEWITHSUNDEEP
pythonnumpy
john22
john22

Reputation: 395

how to compute a mathematically difference in a list based on sign changing in another column

I have a big list that looks like below:

515.20320783       0.05658324
516.20360241       0.03486690
517.20399699       0.02094333
518.20439157       0.02199200
519.20478615       0.03610312
520.20518073       0.05240170
521.20557530       0.05834678
522.20596988       0.04893050
523.20636446       0.02992211
524.20675904       0.01215849
525.20715362       0.00224414
526.20754820      -0.00168170
527.20794277      -0.00608658
528.20833735      -0.01532678
529.20873193      -0.02889651
530.20912651      -0.04376070
531.20952109      -0.05776619
532.20991567      -0.07066846
533.21031024      -0.08255477
534.21070482      -0.09145077
535.21109940      -0.09284463
536.21149398      -0.08325564

I first need to separate 2 lists of column 1 based on the sign-changing of the values of column 2, meaning all values of column 1 whose corresponding values in column 2 are positive should be in a list and those for which column 2's values are negative in another list. I then want to compute the difference between the first and last items of the sub list(the lists created before). The work that I have done is below, but it does not seem right

import numpy as np

l = [f for f in sorted(os.listdir('.')) if f.startswith('config')]

maxnum = np.max([int(os.path.splitext(f)[0].split('_')[1]) for f in l])
l = ['configuration_%d.out' % i for i in range(maxnum)]
meand = []
meands=[]
for i, d in enumerate (CASES):
   a = np.loadtxt(os.path.join(root, directory,d)).T
   y = a[1]
   x = a[0]
   mig = np.abs(a[5])
   theta = a[3]
   s = a[2]
   h = min(y)+6
   t = max(y)-6
   meand = []
   for i in range(1,len(y)):
      if h <y[i]<t :
         idx = np.where(np.diff(np.sign(theta)) != 0)[0] + 1
                
         meand_length = s[idx]-s[idx-1]
                
                
         meand.append(meande_length)

    meands.append(np.mean(meand))
                
print(meands)

Upvotes: 1

Views: 60

Answers (1)

tenhjo
tenhjo

Reputation: 4537

Using numpy methods sign and nonzero:

list1 = np.arange(0, 20, 2)
# array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18]
list2 = np.linspace(-1, 1, 10)
# ~ array([-1, -0.77, -0.55, -0.33, -0.11,  0.11,  0.33,  0.55,  0.77,  1.])

s = np.sign(list2)
# array([-1., -1., -1., -1., -1.,  1.,  1.,  1.,  1.,  1.])
idx_sign_change = np.nonzero(np.diff(s) != 0)[0]
# array([4])

split_lists = np.split(list1, idx_sign_change)
# [array([0, 2, 4, 6]), array([ 8, 10, 12, 14, 16, 18])]

diff = [l[-1] - l[0] for l in split_lists]
# [6, 10]

mean_diff = np.mean(diff)

Note that this would work also if there are multiple sign changes.

Upvotes: 1

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