Plotting func/peak finder #31
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…d wrote a very simple unit test for the function.
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Lines 224 228 +4
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Co-authored-by: Jenny <[email protected]>
array of numbers.
…ite/cmeutils into plotting-func/peak-finder
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| data : numpy.ndarray, shape (N,1) | ||
| Such as x, y, OR z. | ||
| max_height : int, default None |
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I think the height can be a float
| max_height : int, default None | |
| height : float, default None |
https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
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| import numpy as np | |||
| import freud | |||
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| from cmeutils.tests.base_test import BaseTest | |||
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Were the tests not working for you without this change? It might be because cmeutils is not installed in your environment or not installed in an editable way? (i.e., pip install -e .) Not sure.
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| import pytest | |||
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| import scipy.signal as signal | |||
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| import scipy.signal as signal |
Here we don't need to import this function because we want to use your find peaks function
| def test_find_peaks(self, rdf_txt): | ||
| line= np.genfromtxt(rdf_txt, names=True, delimiter=",") | ||
| y= line["rdf"] | ||
| peaks = signal.find_peaks(y) |
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| peaks = signal.find_peaks(y) | |
| peaks = find_peaks(y) |
| y= line["rdf"] | ||
| peaks = signal.find_peaks(y) | ||
| assert isinstance(y, np.ndarray) | ||
| assert isinstance(peaks, tuple) |
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Nice, now last step just check that a couple of peaks are in the right place. I do wonder if scipy.signal.find_peaks is used with no arguments (the default of your function), if any peaks will be found... I would plot this to make sure it makes sense :)
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I'm going to close this for now as its gone stale and is a bit outdated, we can make a new PR to add peak finding tools. |
Not sure if this is the type of peak finder we are looking for (mentioned in issue #27), but I gave it a try. The function calculates the average data values given, and any value greater than the average is recognized as a "peak". Thus far I have only tested it on PE and RDF data/plots and it worked. Therefore, I am not sure how well it will work for any data that we can't just steal the Y-values from (if that makes sense). Anyways, I am open to suggestions and feedback!