Comments (3)
I looked into it and it appears that the default noise reduction method (fastnl) is not optimal in your case.
If you take lee_enhanced
, the peaks are as what we would expect by eye.
Update to the latest version with pip install -U findpeaks
because I added a small function to read images from URL imread()
.
from findpeaks import findpeaks
path = r'https://user-images.githubusercontent.com/44827483/221152897-133839bb-7364-492a-921b-c9077ab9930b.png'
fp = findpeaks(method='topology', denoise='lee_enhanced', window=5, whitelist='peak')
X = fp.imread(path)
fp.fit(X)
fp.plot_persistence()
fp.plot()
fp.plot_mesh()
The persistence will give you more feeling how the peaks are detected. See more information here.
from findpeaks.
Thank you for your reply. It really helps me.
from findpeaks.
👍🏻 closing this one as it is solved.
from findpeaks.
Related Issues (18)
- iteration speed printout displayed at low verbosity. HOT 2
- Index error in findpeaks(method='caerus') using pandas 1.4.1 HOT 3
- Logging printing with verbose = 0 HOT 2
- How detect the TOPs? HOT 2
- To limit the distance between adjacent peak and valley points when finding peak & valley points HOT 3
- caerus crash error HOT 1
- Limiting z axis in plot_mesh HOT 1
- Feature request - Multiplicative Noise Lee Despeckling Filter HOT 5
- Plot Doesn't Display? HOT 1
- pip installation does not include joblib HOT 2
- Removing output messages during runtime HOT 4
- data-dependent issues with interpolate HOT 2
- findpeaks.fit applied to 2D data returns wrong coordinates HOT 2
- Extra index HOT 13
- Ascending/Descending Manifold HOT 3
- Citation improvement HOT 1
- Resizing info displayed on verbosity < 3. HOT 2
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from findpeaks.