Structure change

.gitignore

@@ -0,0 +1,52 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/

gfop/analysis.py

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+# analysis.py
+
+# Standard library imports
+from typing import List, Tuple, Optional
+
+# Third-party imports
+import numpy as np
+import pandas as pd
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from skbio.stats.composition import clr
+
+# Internal imports
+from foodcounts import FoodCounts
+from utils import food_counts_to_wide
+
+
+def perform_pca_food_counts(
+    food_counts_instance: FoodCounts,
+    level: int = 3,
+    n_components: int = 3,
+    apply_clr: bool = True,
+    food_types: Optional[List[str]] = None,
+) -> Tuple[pd.DataFrame, List[float]]:
+    """
+    Perform PCA on food counts data using the FoodCounts instance, with an
+    option for CLR transformation.
+
+    Parameters
+    ----------
+    food_counts_instance : FoodCounts
+        The instance of the FoodCounts class containing food counts data.
+    level : int, optional
+        Ontology level to filter food types, by default 3.
+    n_components : int, optional
+        Number of principal components to calculate, by default 3.
+    apply_clr : bool, optional
+        Whether to apply the CLR (Centered Log-Ratio) transformation before
+        PCA, by default True.
+    food_types : list of str, optional
+        List of specific food types to include in the analysis. If None, all
+        food types are included.
+
+    Returns
+    -------
+    Tuple[pd.DataFrame, List[float]]
+        A tuple containing:
+        - pd.DataFrame: DataFrame with PCA scores, filenames, and merged
+        sample metadata.
+        - List[float]: Explained variance ratios of the principal components.
+
+    Notes
+    -----
+    - The CLR transformation is applied to the numeric data after adding 1 to
+    avoid issues with zeros.
+    """
+    # Step 1: Filter counts by level
+    food_counts_filtered = food_counts_instance.filter_counts(level=level)
+
+    # Step 2: Filter food types if specified by the user
+    if food_types is not None:
+        food_counts_filtered = food_counts_filtered[
+            food_counts_filtered["food_type"].isin(food_types)
+        ]
+
+    # Step 3: Convert to wide format for PCA
+    food_counts_wide = food_counts_to_wide(food_counts_filtered, level=level)
+
+    # Step 4: Extract numeric columns (food types) for PCA
+    food_type_columns = food_counts_wide.select_dtypes(include=[np.number]).columns
+    features = food_counts_wide[food_type_columns].values
+
+    # Step 5: Apply CLR transformation if selected
+    if apply_clr:
+        features = clr(features + 1)  # Add 1 to avoid issues with zeros, then apply CLR
+
+    # Step 6: Standardize the data
+    features_scaled = StandardScaler().fit_transform(features)
+
+    # Step 7: Perform PCA
+    pca = PCA(n_components=n_components)
+    pca_scores = pca.fit_transform(features_scaled)
+    explained_variance = pca.explained_variance_ratio_
+
+    # Step 8: Create a dataframe with PCA results
+    pca_df = pd.DataFrame(pca_scores, columns=[f"PC{i+1}" for i in range(n_components)])
+    pca_df["filename"] = food_counts_wide.index
+
+    # Merge with sample metadata
+    sample_metadata = food_counts_instance.sample_metadata
+    pca_df = pd.merge(pca_df, sample_metadata, on="filename", how="left")
+
+    return pca_df, explained_variance