Determine intercepts of noisy data with constant

Asked Dec 07 '18 at 13:04

Active Dec 07 '18 at 13:47

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Is it possible to determine all intercepts of noisy data and a constant value?

I am working on a project with a sensitive probe and need to know when my data drops below a certain limit.

You could compare my data with this random result:

import numpy as np
import matplotlib.pyplot as plt

noise = np.random.normal(1, 2, 50)
x = np.arange(0, 50)

fig, ax = plt.subplots()
ax.plot(x, noise, '-')
ax.axhline(1, color='r')

Example curve

I already tried the scipy.interpolate.interp1d function as a possible solution but this doesn't seem to be the correct approach towards this issue. Is there a way to find these intercepts?

edited Dec 07 '18 at 13:47

asked Dec 07 '18 at 13:04

Mr.Jens

1

Do you mean how to get the roots/zeros of an array like [this](https://stackoverflow.com/questions/46909373/how-to-get-values-from-a-graph)? – ImportanceOfBeingErnest Dec 07 '18 at 13:25
Indeed, this resembles my problem, but instead of the intercepts with 0 I am looking for the intercepts with a constant number (eg. 1) – Mr.Jens Dec 07 '18 at 13:48
Well, it might not be obvious, so I added a section about it in the answer over there. – ImportanceOfBeingErnest Dec 07 '18 at 13:59
Thanks a lot, it's a relatively simple solution, but works perfect! – Mr.Jens Dec 07 '18 at 14:32

Determine intercepts of noisy data with constant

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