added new peak finder, optimized code

[iprafols]

No description provided.

[Copilot]

Pull request overview

This PR adds a new two-power-law peak finder implementation and optimizes existing code, particularly in the statistics computation and random forest classifier modules.

• Added PeakFinderTwoPowerLaw class with fitting logic for spectra analysis
• Optimized compute_stats function by removing redundant type conversions and reorganizing calculations
• Refactored predict_proba method to extract tree probability computation into a separate function

Reviewed changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 20 comments.

Show a summary per file

File	Description
py/squeze/utils.py	Added serialization support for np.int16 type
py/squeze/stats_utils.py	Optimized compute_stats with dtype changes, added global probability constants, and removed conditional printing logic
py/squeze/random_forest_classifier.py	Extracted predict_proba_tree function and updated dtype for indexs parameter; contains unreachable code after early returns
py/squeze/peak_finder_two_power_law.py	New file implementing two-power-law peak finder with convergent fitting algorithm; contains spelling errors and incorrect NaN comparison
py/squeze/peak_finder_power_law.py	Added optional return_bestfit parameter; contains spelling error in variable name
py/squeze/model.py	Simplified probability calculation by removing NaN checks; old logic retained as comments

Comments suppressed due to low confidence (4)

py/squeze/random_forest_classifier.py:384

• Unreachable code after return statement. Lines 366-384 will never execute and should be removed.

        output = np.zeros((len(X), self.num_categories))

        for tree_index in np.arange(self.num_trees):
            proba = np.zeros((len(X), self.num_categories))
            children_left = self.trees[tree_index]["children_left"]
            children_right = self.trees[tree_index]["children_right"]
            features = self.trees[tree_index]["feature"]
            thresholds = self.trees[tree_index]["threshold"]
            tree_proba = self.trees[tree_index]["proba"]
            indexs = np.arange(X.shape[0], dtype=int)
            if len(children_left) > sys.getrecursionlimit():
                sys.setrecursionlimit(int(len(children_left) * 1.2))
            search_nodes(X, children_left, children_right, features, thresholds,
                            tree_proba, proba, indexs, 0)
            output += proba

        output /= self.num_trees

        return output

py/squeze/model.py:47

• This comment appears to contain commented-out code.

        #if np.isnan(row["PROB_CLASS3"]):
        #    prob = np.nan
        #else:
        #    prob = row["PROB_CLASS3"] + row["PROB_CLASS30"]

py/squeze/model.py:53

• This comment appears to contain commented-out code.

        #if np.isnan(row["PROB_CLASS30"]):
        #    prob = np.nan
        #else:
        #    prob = row["PROB_CLASS30"]

py/squeze/random_forest_classifier.py:362

• This statement is unreachable.

        output = np.array([
            predict_proba_tree(
                X,
                self.trees[tree_index]["children_left"],
                self.trees[tree_index]["children_right"],
                self.trees[tree_index]["feature"],
                self.trees[tree_index]["threshold"],
                self.trees[tree_index]["proba"],
                self.num_categories,
            )
            for tree_index in np.arange(self.num_trees)
        ]).sum() / self.num_trees

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Pull request overview

Copilot reviewed 25 out of 35 changed files in this pull request and generated 14 comments.

Comments suppressed due to low confidence (1)

py/squeze/tests/test_peakfinder.py:1

• Missing space after '#' in comment. Should be '# find peak indices' for consistency with Python style guidelines.

"""

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Pull request overview

Copilot reviewed 26 out of 36 changed files in this pull request and generated 3 comments.

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Pull request overview

Copilot reviewed 26 out of 36 changed files in this pull request and generated 16 comments.

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[Copilot]

Pull request overview

Copilot reviewed 26 out of 36 changed files in this pull request and generated 9 comments.

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[Copilot]

Pull request overview

Copilot reviewed 26 out of 36 changed files in this pull request and generated 5 comments.

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