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Merged
JulianKimmig merged 2 commits into from
Sep 4, 2025
Merged

Test #53

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feat(peak_finder): add optional re-centering of peak indices based on…
JulianKimmig Sep 4, 2025
84a6bac
Merge pull request #52 from Linkdlab/dev
JulianKimmig Sep 4, 2025
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2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[project]
name = "funcnodes-span"
version = "1.0.0"
version = "1.0.1"
description = "SPectral ANalysis (SPAN) for funcnodes"
readme = "README.md"
classifiers = [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",]
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2 changes: 1 addition & 1 deletion src/funcnodes_span/__init__.py
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Expand Up @@ -7,7 +7,7 @@
from funcnodes_lmfit import NODE_SHELF as LMFIT_NODE_SHELF
from .curves import CURVES_NODE_SHELF

__version__ = "1.0.0"
__version__ = "1.0.1"

NODE_SHELF = fn.Shelf(
name="Spectral Analysis",
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21 changes: 21 additions & 0 deletions src/funcnodes_span/peak_analysis.py
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Expand Up @@ -270,6 +270,27 @@ def peak_finder(
if y[peak] > height:
peak_lst.append([left_min, peak, right_min])

# Optionally re-center peak indices based on selected source
if len(peak_lst) > 0:
for k, (i_idx, idx, f_idx) in enumerate(peak_lst):
i = max(0, int(i_idx))
f = min(len(_y) - 1, int(f_idx))
if f <= i:
continue
if index_source == "original" and on is not None:

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The condition checks on is not None but the variable on is not defined in the visible scope. This will raise a NameError when index_source == "original".

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if index_source == "original" and on is not None:
if index_source == "original":

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# Use argmax on the original (or differently processed) signal within bounds
local_argmax = int(np.argmax(_y[i : f + 1])) + i

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Using _y for argmax calculation when the comment mentions 'original signal', but _y appears to be the processed signal. The variable name and logic don't match the intended behavior described in the comment.

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local_argmax = int(np.argmax(_y[i : f + 1])) + i
local_argmax = int(np.argmax(on[i : f + 1])) + i

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peak_lst[k] = [i_idx, local_argmax, f_idx]
elif index_source == "centroid":
# Center-of-mass within bounds on original signal
yy = _y[i : f + 1]
w = yy - np.min(yy)
w = np.maximum(w, 0)
if np.sum(w) > 0:
cm = int(np.round(np.average(np.arange(i, f + 1), weights=w)))
cm = int(np.clip(cm, i, f))
peak_lst[k] = [i_idx, cm, f_idx]

peak_properties_list = []

for peak_nr, peak in enumerate(peak_lst):
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