Find where the slope changes in my data as a parameter that can be easily indexed and extracted

Question

I have the following data:

0.8340502011561366 0.8423491600218922 0.8513456021654467 0.8458192388553084 0.8440111276014195 0.8489589671423143 0.8738088120491972 0.8845129900705279 0.8988298998926688 0.924633964692693 0.9544790734065157 0.9908034431246875 1.0236430466543138 1.061619773027915 1.1050038249835414 1.1371449802490126 1.1921182610371368 1.2752207659022576 1.344047620255176 1.4198117350668353 1.507943067143741 1.622137968203745 1.6814098429502085 1.7646810054280595 1.8485457435775694 1.919591124757554 1.9843144220593145 2.030158014640226 2.018184122476175 2.0323466012624207 2.0179200409023874 2.0316932950853723 2.013683870089898 2.03010703506514 2.0216151623726977 2.038855467786505 2.0453923522466093 2.03759031642753 2.019424996752278 2.0441806106428606 2.0607521369415136 2.059310067318373 2.0661157975162485 2.053216429539864 2.0715123971225564 2.0580473413362075 2.055814512721712 2.0808278560688964 2.0601637029377113 2.0539429365156003 2.0609648613513754 2.0585135712612646 2.087674625814453 2.062482961966647 2.066476100210777 2.0568444178944967 2.0587903943282266 2.0506399365756396

The data plotted looks like:

I want to find the point where the slope changes in sign (I circled it in black. Should be around index 26):

I need to find this point of change for several hundred files. So far I tried the recommendation from this post:

Finding the point of a slope change as a free parameter- Python

I think since my data is a bit noisey I am not getting a smooth transition in the change of the slope.

This is the code I have tried so far:

import numpy as np

#load 1-D data file
file = str(sys.argv[1])
y = np.loadtxt(file)

#create X based on file length
x = np.linspace(1,len(y), num=len(y))

Find first derivative:

m = np.diff(y)/np.diff(x)
print(m)

#Find second derivative
b = np.diff(m)
print(b)
#find Index

index = 0
for difference in b:
    index += 1
    if difference < 0: 
        print(index, difference)

Since my data is noisey I am getting some negative values before the index I want. The index I want it to retrieve in this case is around 26 (which is where my data becomes constant). Does anyone have any suggestions on what I can do to solve this issue? Thank you!

Answer 1

A gradient approach is useless in this case because you don't care about velocities or vector fields. The knowledge of the gradient don't add extra information to locate the maximum value since the run are always positive hence will not effect the sign of the gradient. A method based entirly on raise is suggested.

Detect the indices for which the data are decreasing, find the difference between them and the location of the max value. Then by index manipulation you can find the value for which data has a maximum.

data = '0.8340502011561366 0.8423491600218922 0.8513456021654467 0.8458192388553084 0.8440111276014195 0.8489589671423143 0.8738088120491972 0.8845129900705279 0.8988298998926688 0.924633964692693 0.9544790734065157 0.9908034431246875 1.0236430466543138 1.061619773027915 1.1050038249835414 1.1371449802490126 1.1921182610371368 1.2752207659022576 1.344047620255176 1.4198117350668353 1.507943067143741 1.622137968203745 1.6814098429502085 1.7646810054280595 1.8485457435775694 1.919591124757554 1.9843144220593145 2.030158014640226 2.018184122476175 2.0323466012624207 2.0179200409023874 2.0316932950853723 2.013683870089898 2.03010703506514 2.0216151623726977 2.038855467786505 2.0453923522466093 2.03759031642753 2.019424996752278 2.0441806106428606 2.0607521369415136 2.059310067318373 2.0661157975162485 2.053216429539864 2.0715123971225564 2.0580473413362075 2.055814512721712 2.0808278560688964 2.0601637029377113 2.0539429365156003 2.0609648613513754 2.0585135712612646 2.087674625814453 2.062482961966647 2.066476100210777 2.0568444178944967 2.0587903943282266 2.0506399365756396'

data = data.split()
import numpy as np

a = np.array(data, dtype=float)

diff = np.diff(a)

neg_indeces = np.where(diff<0)[0]
neg_diff = np.diff(neg_indeces)

i_max_dif = np.where(neg_diff == neg_diff.max())[0][0] + 1

i_max = neg_indeces[i_max_dif] - 1 # because aise as a difference of two consecutive values

print(i_max, a[i_max])

Output

26 1.9843144220593145

Some details

print(neg_indeces) # all indeces of the negative values in the data
# [ 2  3 27 29 31 33 36 37 40 42 44 45 47 48 50 52 54 56]
print(neg_diff) # difference between such indices
# [ 1 24  2  2  2  3  1  3  2  2  1  2  1  2  2  2  2]
print(neg_diff.max()) # value with highest difference
# 24
print(i_max_dif) # location of the max index of neg_indeces -> 27
# 2
print(i_max) # index of the max of the origonal data
# 26