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Incremental de-duplication #36

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Aug 28, 2024
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Incremental de-duplication #36

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31 changes: 25 additions & 6 deletions findpeaks/stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -433,9 433,9 @@ def topology(X, limit=None, reverse=True, verbose=3):
max_peaks, min_peaks = None, None
groups0 = {}

# It is important to add random noise to the values because the method sorts the values and the unique values are processed.
# Without random noise, peaks with exactly the same height will be skipped.
X = np.maximum(X ((X>0).astype(int) * np.random.rand(X.shape[0], X.shape[1])/10), 0)
# It is important to ensure unique values because the method sorts the values and only unique values are processed.
# Without adjusting duplicated values, peaks with exactly the same height will be skipped.
X = _make_unique(X)

# Get indices orderd by value from high to low
indices = [(i, j) for i in range(h) for j in range(w) if _get_indices(X, (i, j)) is not None and _get_indices(X, (i, j)) >= limit]
Expand Down Expand Up @@ -525,10 525,10 @@ def _get_comp_birth(p):
def reverse_values(image_array):
# Clip the values to ensure they are between 0 and 255
clipped_array = np.clip(image_array, np.min(image_array), np.max(image_array))

# Reverse the values
reversed_array = np.max(image_array) - clipped_array

return reversed_array

def _get_indices(im, p):
Expand Down Expand Up @@ -564,7 564,7 @@ def _post_processing(X, Xraw, min_peaks, max_peaks, interpolate, lookahead, labx
results['labx'] = np.zeros((len(Xraw))) * np.nan
results['min_peaks'] = None
results['max_peaks'] = None

if len(min_peaks)>0 and len(max_peaks)>0 and (max_peaks[0][0] is not None):

idx_peaks, _ = zip(*max_peaks)
Expand Down Expand Up @@ -648,3 648,22 @@ def normalize(X, minscale = 0.5, maxscale = 4, scaler: str = 'zscore'):
def disable_tqdm(verbose):
"""Set the verbosity messages."""
return (True if ((verbose<4 or verbose is None) or verbose>5) else False)


def _make_unique(arr: np.ndarray):
"""Method iterates through elements of the input array to ensure all values are unique.
Duplicate values are reduced by the smallest possible increment for the given data type.
"""
res = np.empty_like(arr)
it = np.nditer([arr, res], [], [['readonly'], ['writeonly', 'allocate']])
seen = set()
with it:
while not it.finished:
a = it[0].item()
while a in seen and np.isfinite(a):
a = np.nextafter(a, -np.inf)
it[1] = a
if a not in seen:
seen.add(a)
it.iternext()
return res