mpnet_efa.py

# ==============================================================================
# Semantic Factor Analysis (SFA) — Yanitski, D. and Westbury, C. (2025)
# ==============================================================================
#
# Validates psychological scales by comparing factor structures extracted from
# LLM embeddings against those extracted from human response data.
#
# PIPELINE
# --------
# 1. Load scale definition (scale_items/{SCALE}_items.csv) containing item
#    codes, text, theoretical factor labels, and scoring direction (+1/-1).
#    Optionally load empirical Likert-scale responses (scale_responses/).
#
# 2. Obtain high-dimensional embeddings for each item from a pre-trained
#    sentence-transformer (dwulff/mpnet-personality). Embeddings may be loaded
#    from cache or generated on the fly.
#
# 3. Apply atomic-reversed encoding: multiply each item's embedding by its
#    scoring direction (+1 or -1), then L2-normalize. This encodes reverse-
#    scored items as pointing in the opposite semantic direction.
#
# 4. Compute the item-by-item cosine similarity matrix from the signed
#    embeddings. This matrix serves as a pseudo-correlation matrix for EFA.
#
# 5. Run exploratory factor analysis (EFA) on the cosine similarity matrix:
#      a. Parallel analysis (95th-percentile, 100 iterations) to determine
#         the number of factors to retain.
#      b. Factor extraction (default: minres/ULS) with oblique rotation
#         (default: oblimin) to allow correlated factors.
#      c. Compute diagnostics: KMO sampling adequacy, Bartlett sphericity,
#         DAAL (Dominant Average Absolute Loading) for factor-to-construct
#         assignment, and Tucker congruence (phi) against theoretical factors.
#
# 6. Repeat step 5 on the empirical Pearson correlation matrix (traditional
#    EFA) when human response data is available. Reverse-scored items are
#    reflected before computing correlations.
#
# 7. Generate comparison visualizations between embedding-based and empirical
#    factor structures: scree plots, loading heatmaps, 2-D loading plots,
#    Tucker congruence heatmaps, within- vs between-construct similarity
#    violin plots, and t-SNE scatter plots.
#
# 8. Compute matrix-level agreement: Pearson correlation and Mantel test
#    (10 000 permutations) between the LLM cosine similarity matrix and the
#    human inter-item correlation matrix.
#
# 9. Automatic factor naming via three methods:
#      Method 1 — Feed top-loading items to an instruct LLM (Qwen3-235B-A22B).
#      Method 2 — Find nearest-neighbour words to the factor centroid in
#                 embedding space, then summarise with the instruct LLM.
#      Method 3 — (optional) Greedy token prediction from a base LLM.
#
# CONFIGURATION
# -------------
# All user-settable parameters (model, scale list, EFA settings, feature
# flags) are grouped in the first few cells. Key variables:
#   MODEL_NAMES             — sentence-transformer model(s) to use
#   SCALE_NAMES             — which scale(s) to analyse
#   N_FACTORS               — None for automatic (parallel analysis) or int
#   ROTATION_METHOD         — 'oblimin', 'promax', 'varimax', etc.
#   EXTRACTION_METHOD       — 'minres', 'ml', 'principal'
#   ENABLE_FACTOR_NAMING    — toggle LLM-based factor naming
#   ENABLE_METHOD_3         — toggle base-model token-prediction naming
#
# INPUT / OUTPUT
# --------------
# Inputs:
#   scale_items/{SCALE}_items.csv       — code, item, factor, scoring
#   scale_responses/{SCALE}_data.csv    — participants x items (optional)
#   embeddings/{SCALE}_items_{SIZE}.npz — pregenerated item embeddings
#   embeddings/{N}_constructs_{SIZE}.npz— pregenerated word embeddings
#
# Outputs (written to results/{SCALE}/):
#   analysis_log.txt                    — full console log
#   visualizations_{SIZE}.png           — 6-panel diagnostic plot
#   comparison_loadings.png             — side-by-side loading heatmaps
#   comparison_tucker.png               — Tucker congruence comparison
#   comparison_within_between.png       — violin plots
#   {SCALE}_embeddings_vs_empirical_*.png — scree, t-SNE, correlation plots
# ==============================================================================

import os
import sys
import re
import glob
import importlib
import subprocess
import warnings
from pathlib import Path
from datetime import datetime

import numpy as np
import pandas as pd
import torch

from sentence_transformers import SentenceTransformer
from openai import OpenAI

import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatable

from sklearn.metrics.pairwise import cosine_similarity
from sklearn.manifold import TSNE
from scipy.stats import ttest_ind
import scipy.stats as stats
from skbio.stats.distance import mantel

from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo, calculate_bartlett_sphericity
import factor_analyzer.factor_analyzer as fa_module

warnings.filterwarnings('ignore', message='.*Moore-Penrose.*')
warnings.filterwarnings('ignore', message='.*invalid value encountered in log.*')
warnings.filterwarnings('ignore', message=".*'force_all_finite' was renamed.*")

os.environ['HF_HOME'] = '/Users/devon7y/.cache/huggingface'
os.environ['HF_DATASETS_CACHE'] = '/Users/devon7y/.cache/huggingface'
with open(os.path.expanduser('~/.cache/huggingface/token'), 'r') as f:
    os.environ['HF_TOKEN'] = f.read().strip()

plt.rcParams.update({
    'font.size': 13,
    'axes.titlesize': 15,
    'axes.labelsize': 13,
    'xtick.labelsize': 12,
    'ytick.labelsize': 12,
    'legend.fontsize': 11,
    'figure.titlesize': 20,
})

sns.set_context("notebook", font_scale=1.25)

COMPARISON_SUBPLOT_SPACING = 1

MODEL_NAMES = ["dwulff/mpnet-personality",]

MODEL_SIZE_OVERRIDES = {
    "dwulff/mpnet-personality": "mpnet",
}

SCALE_NAMES = ["DASS"]
RUN_ALL_SCALES = True
# SCALE_NAMES = [
#     "Big5FM", "OSRI", "NIS", "RIASEC", "MACHIV", "HSNDD",
#     "ECR", "16PF", "RSE", "FBPS", "DASS", "NPAS",
#     "HEXACO", "c", "SD3", "GSE", "CFCS", "EQSQ",
#     "RWAS", "MFQ", "TMA", "FTI", "DGS", "NFC",
#     "EPQ", "AMBI", "IRI", "GCB", "CIS", "ERRI",
#     "BDI", "BAI", "HSQ", "KIMS", "LLMD12", "431PTQ"]  
# All 36 scales ordered by participant count

PREGENERATED_WORD_EMBEDDINGS = {}

N_FACTORS = None # None = auto via parallel analysis

APPLY_REVERSE_SCORING_EMBEDDINGS = False # Guenole et al.'s atomic-reversed encoding

ROTATION_METHOD = 'oblimin'
# OBLIQUE rotations (factors can correlate):
#   - 'promax': Promax rotation (power parameter defaults to 4)
#   - 'oblimin': Direct oblimin rotation (gamma parameter defaults to 0)
#   - 'quartimin': Quartimin rotation (minimizes cross-loadings)
#   - 'geomin_obl': Oblique geomin rotation (delta parameter defaults to 0.01)
#
# ORTHOGONAL rotations (factors remain uncorrelated):
#   - 'varimax': Varimax rotation (maximizes variance of squared loadings)
#   - 'quartimax': Quartimax rotation (minimizes variables' factor complexity)
#   - 'equamax': Equamax rotation (kappa parameter defaults to 0)
#   - 'oblimax': Oblimax rotation
#   - 'geomin_ort': Orthogonal geomin rotation (delta parameter defaults to 0.01)

EXTRACTION_METHOD = 'minres'
#   - 'minres' or 'uls': Minimum residual / Unweighted Least Squares (fast, stable, default)
#   - 'ml' or 'mle': Maximum Likelihood Extraction (slower, assumes multivariate normality)
#   - 'principal': Principal factor analysis (uses SVD on raw data, requires full dataset)

EIGEN_CRITERIA = 'parallel' # 'parallel' or 'eigen1'
PARALLEL_ITER = 100
RANDOM_STATE = 42

ENABLE_FACTOR_NAMING = True
ENABLE_METHOD_3 = False # Greedy token prediction for factor naming (experimental, may produce low-quality names)

# Optional manual ordering for extracted factors in EMBEDDING plots.
# Leave empty [] to use existing/default ordering logic.
# You can use either extracted names (e.g., "Factor1") or display labels
# (e.g., LLM-generated factor names).
PLOT_EXTRACTED_FACTOR_ORDER = ["Creativity", "Order and Preparedness", "Social Dynamics", "Empathy", "Anxiety and Mood Instability", "Conceptual Thinking"]

# Optional manual ordering for extracted factors in EMPIRICAL (human response)
# plots. Leave empty [] to use existing/default ordering logic.
PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL = ["Openness to Experience", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism"]

def get_model_size(model_name):
    """Return model label used for cache filenames and reporting."""
    return MODEL_SIZE_OVERRIDES.get(model_name, model_name.split('-')[-1])

def load_sentence_transformer_model(model_name, device):
    """
    Load a SentenceTransformer from local HF snapshots when available.
    If no snapshot is present, load by repo id (downloads once, then caches).
    """
    model_cache_name = model_name.replace('/', '--')
    snapshots_dir = f"/Users/devon7y/.cache/huggingface/models--{model_cache_name}/snapshots"
    snapshot_dirs = glob.glob(f"{snapshots_dir}/*")

    if snapshot_dirs:
        snapshot_path = snapshot_dirs[0]
        print(f"  Loading from local snapshot: {snapshot_path}")
        return SentenceTransformer(snapshot_path, device=device)

    print(f"  No local snapshot found for {model_name}")
    print("  Loading from Hugging Face repo ID (downloads once, then caches locally)...")
    return SentenceTransformer(model_name, device=device)

np.random.seed(RANDOM_STATE)
torch.manual_seed(RANDOM_STATE)

print("✓ Configuration loaded")

def apply_atomic_reversed(embeddings, scoring):
    """Apply atomic-reversed encoding"""
    scoring_array = np.array(scoring).reshape(-1, 1)
    embeddings_signed = embeddings * scoring_array
    norms = np.linalg.norm(embeddings_signed, axis=1)
    zero_norm_items = np.where(norms == 0)[0]
    if len(zero_norm_items) > 0:
        print(f"  WARNING: {len(zero_norm_items)} items have zero norm after signing")
        for idx in zero_norm_items:
            embeddings_signed[idx] = embeddings[idx]
    embeddings_normalized = embeddings_signed / np.linalg.norm(embeddings_signed, axis=1, keepdims=True)
    return embeddings_normalized

def compute_parallel_analysis(corr_matrix, n_iter=100, percentile=95, random_state=42, n_obs=None):
    """Parallel analysis for factor retention"""
    np.random.seed(random_state)
    n_items = corr_matrix.shape[0]
    obs_eigenvalues = np.linalg.eigvalsh(corr_matrix)
    obs_eigenvalues = np.sort(obs_eigenvalues)[::-1]
    random_eigenvalues = []
    if n_obs is None:
        n_obs = n_items * 10
    for _ in range(n_iter):
        random_data = np.random.randn(n_obs, n_items)
        random_corr = np.corrcoef(random_data, rowvar=False)
        eigs = np.linalg.eigvalsh(random_corr)
        eigs = np.sort(eigs)[::-1]
        random_eigenvalues.append(eigs)
    random_eigenvalues = np.array(random_eigenvalues)
    percentiles = np.percentile(random_eigenvalues, percentile, axis=0)
    n_factors = np.sum(obs_eigenvalues > percentiles)
    return n_factors, obs_eigenvalues, percentiles

def compute_daal(loadings_df, theoretical_factors):
    """Compute DAAL (Dominant Average Absolute Loading)"""
    theoretical_unique = sorted(set(theoretical_factors))
    extracted_factors = loadings_df.columns
    daal_matrix = []
    for ext_factor in extracted_factors:
        row = []
        for theo_factor in theoretical_unique:
            mask = [f == theo_factor for f in theoretical_factors]
            loadings_subset = loadings_df.loc[mask, ext_factor]
            daal_value = loadings_subset.abs().mean()
            row.append(daal_value)
        daal_matrix.append(row)
    daal_df = pd.DataFrame(daal_matrix, index=extracted_factors, columns=theoretical_unique)
    return daal_df

def compute_tucker_congruence(factor_loadings, reference_loadings):
    """Compute Tucker congruence coefficient (phi)"""
    numerator = np.sum(factor_loadings * reference_loadings)
    denom = np.sqrt(np.sum(factor_loadings**2)) * np.sqrt(np.sum(reference_loadings**2))
    return numerator / denom if denom != 0 else 0.0

def create_theoretical_indicators(theoretical_factors, codes):
    """Create indicator matrix for theoretical factors"""
    unique_factors = sorted(set(theoretical_factors))
    indicators = []
    for factor in unique_factors:
        indicator = [1.0 if f == factor else 0.0 for f in theoretical_factors]
        indicators.append(indicator)
    indicators_df = pd.DataFrame(np.array(indicators).T, columns=unique_factors, index=codes)
    return indicators_df

def assign_items_to_extracted_factors(loadings_df, use_abs=True):
    """Assign each item to the extracted factor with the largest loading."""
    item_to_factor = {}
    for item_code in loadings_df.index:
        loadings = loadings_df.loc[item_code].abs() if use_abs else loadings_df.loc[item_code]
        item_to_factor[item_code] = loadings.idxmax()
    return item_to_factor

def build_unique_extracted_factor_label_map(extracted_factors, tucker_best=None):
    """
    Build display labels for extracted factors.

    Each theoretical label is used at most once. If multiple extracted factors
    map to the same theoretical factor, only the best-congruent one keeps that
    label; the rest keep their original names (e.g., Factor2).
    """
    extracted_factors = list(extracted_factors)
    label_map = {factor: factor for factor in extracted_factors}

    if tucker_best is None or not isinstance(tucker_best, pd.DataFrame):
        return label_map

    required_cols = {'extracted_factor', 'best_match'}
    if not required_cols.issubset(tucker_best.columns):
        return label_map

    candidates = tucker_best[tucker_best['extracted_factor'].isin(extracted_factors)].copy()
    if candidates.empty:
        return label_map

    if 'tucker_phi' in candidates.columns:
        candidates['tucker_phi'] = pd.to_numeric(candidates['tucker_phi'], errors='coerce')
    else:
        candidates['tucker_phi'] = np.nan

    factor_order = {factor: i for i, factor in enumerate(extracted_factors)}
    candidates['_factor_order'] = candidates['extracted_factor'].map(factor_order).fillna(len(factor_order)).astype(int)

    candidates = candidates.sort_values(
        by=['best_match', 'tucker_phi', '_factor_order'],
        ascending=[True, False, True],
        kind='mergesort'
    )

    winners = candidates.drop_duplicates(subset=['best_match'], keep='first')
    for _, row in winners.iterrows():
        best_match = row['best_match']
        if pd.isna(best_match):
            continue
        label = str(best_match).strip()
        if not label:
            continue
        label_map[row['extracted_factor']] = label

    return label_map

def get_extracted_factor_display_labels(extracted_factors, tucker_best=None):
    """Return ordered display labels and factor->label mapping."""
    extracted_factors = list(extracted_factors)
    label_map = build_unique_extracted_factor_label_map(extracted_factors, tucker_best)
    labels = [label_map.get(factor, factor) for factor in extracted_factors]
    return labels, label_map

def get_embedding_factor_display_labels(extracted_factors, model_size):
    """
    Return display labels for embedding factors.

    - ENABLE_FACTOR_NAMING=True: use LLM-generated names when available.
    - ENABLE_FACTOR_NAMING=False: keep original extracted names (Factor1, Factor2, ...).
    """
    extracted_factors = list(extracted_factors)
    label_map = {factor: factor for factor in extracted_factors}

    if ENABLE_FACTOR_NAMING:
        try:
            model_mapping = factor_name_mappings_nn.get(model_size, {})
        except NameError:
            model_mapping = {}
        for factor in extracted_factors:
            label_map[factor] = model_mapping.get(factor, factor)

    labels = [label_map[factor] for factor in extracted_factors]
    return labels, label_map

def apply_manual_extracted_factor_order(factor_names, display_name_map=None, manual_order=None):
    """
    Reorder extracted factors based on a provided manual order list.

    Matches each manual entry against either raw extracted factor names
    (e.g., "Factor1") or display names (e.g., LLM labels). Unmatched factors
    keep their original order after matched factors.
    """
    factors = list(factor_names)
    manual_order = manual_order or []
    if not manual_order:
        return factors

    display_name_map = display_name_map or {}

    key_to_factor = {}
    for factor in factors:
        raw_key = str(factor).strip().lower()
        disp_key = str(display_name_map.get(factor, factor)).strip().lower()
        if raw_key:
            key_to_factor.setdefault(raw_key, factor)
        if disp_key:
            key_to_factor.setdefault(disp_key, factor)

    ordered = []
    used = set()
    for entry in manual_order:
        key = str(entry).strip().lower()
        factor = key_to_factor.get(key)
        if factor is not None and factor not in used:
            ordered.append(factor)
            used.add(factor)

    for factor in factors:
        if factor not in used:
            ordered.append(factor)

    return ordered

print("✓ Helper functions defined")

def regularize_correlation_matrix(corr_matrix, alpha=1e-6):
    """Add regularization to correlation matrix."""
    is_df = isinstance(corr_matrix, pd.DataFrame)
    corr_array = corr_matrix.values.copy() if is_df else corr_matrix.copy()

    n = corr_array.shape[0]
    regularized = corr_array + alpha * np.eye(n)
    diag = np.sqrt(np.diag(regularized))
    regularized = regularized / diag[:, None] / diag[None, :]

    if is_df:
        return pd.DataFrame(regularized, index=corr_matrix.index, columns=corr_matrix.columns)
    return regularized

def safe_calculate_kmo(corr_matrix, alpha=1e-6):
    """KMO with auto-regularization."""
    try:
        return fa_module._original_calculate_kmo(corr_matrix)
    except (np.linalg.LinAlgError, AssertionError):
        print(f"  ⚠️  Singular matrix detected, applying regularization (alpha={alpha})...")
        return fa_module._original_calculate_kmo(regularize_correlation_matrix(corr_matrix, alpha))

def safe_calculate_bartlett(corr_matrix, alpha=1e-6):
    """Bartlett with auto-regularization."""
    try:
        return fa_module._original_calculate_bartlett(corr_matrix)
    except (np.linalg.LinAlgError, AssertionError):
        print(f"  ⚠️  Singular matrix detected, applying regularization (alpha={alpha})...")
        return fa_module._original_calculate_bartlett(regularize_correlation_matrix(corr_matrix, alpha))

if not hasattr(fa_module, '_original_calculate_kmo'):
    importlib.reload(fa_module)
    fa_module._original_calculate_kmo = fa_module.calculate_kmo
    fa_module._original_calculate_bartlett = fa_module.calculate_bartlett_sphericity

fa_module.calculate_kmo = safe_calculate_kmo
fa_module.calculate_bartlett_sphericity = safe_calculate_bartlett

print("✓ Safe KMO and Bartlett calculation functions installed")

env_scale_index = os.environ.get("SFA_SCALE_INDEX")
if env_scale_index is not None:
    try:
        CURRENT_SCALE_INDEX = int(env_scale_index)
    except ValueError as exc:
        raise ValueError(f"SFA_SCALE_INDEX must be an integer, got: {env_scale_index}") from exc
else:
    CURRENT_SCALE_INDEX = 0

if CURRENT_SCALE_INDEX >= len(SCALE_NAMES):
    raise ValueError(f"CURRENT_SCALE_INDEX ({CURRENT_SCALE_INDEX}) is out of range. SCALE_NAMES has {len(SCALE_NAMES)} scales.")

SCALE_NAME = SCALE_NAMES[CURRENT_SCALE_INDEX]

if RUN_ALL_SCALES and CURRENT_SCALE_INDEX == 0 and len(SCALE_NAMES) > 1 and env_scale_index is None:
    print(f"Batch mode enabled: processing all {len(SCALE_NAMES)} scales in sequence")

print(f"{'='*80}")
print(f"PROCESSING SCALE: {SCALE_NAME}")
print(f"  (Scale {CURRENT_SCALE_INDEX + 1} of {len(SCALE_NAMES)})")
print(f"{'='*80}\n")

PREGENERATED_SCALE_EMBEDDINGS = {}

SCALE_CSV_PATH = f'scale_items/{SCALE_NAME}_items.csv'
EMPIRICAL_DATA_PATH = f"scale_responses/{SCALE_NAME}_data.csv"

SAVE_DIR = f'results/{SCALE_NAME}'
os.makedirs(SAVE_DIR, exist_ok=True)

SCALE_NAME_DISPLAY = Path(SCALE_CSV_PATH).stem.replace('_items', '')

log_file_path = f"{SAVE_DIR}/{SCALE_NAME}_analysis_log.txt"

class Logger:
    def __init__(self, filename):
        self.terminal = sys.stdout
        self.log = open(filename, 'w', encoding='utf-8')
    
    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)
    
    def flush(self):
        self.terminal.flush()
        self.log.flush()
    
    def close(self):
        self.log.close()

logger = Logger(log_file_path)
sys.stdout = logger

print(f"Loading {SCALE_CSV_PATH}...")
scale = pd.read_csv(SCALE_CSV_PATH)
print(f"Loaded {len(scale)} items")

required = ['code', 'item', 'factor']
missing = [c for c in required if c not in scale.columns]
if missing:
    raise ValueError(f"Missing required columns: {missing}")

if 'scoring' not in scale.columns:
    print("⚠ WARNING: 'scoring' column missing - defaulting to +1")
    scale['scoring'] = 1

print(f"\nScoring: {(scale['scoring']==1).sum()} normal, {(scale['scoring']==-1).sum()} reverse")
print(f"Factors: {scale['factor'].nunique()} unique")
print(scale['factor'].value_counts().sort_index())

codes = scale['code'].tolist()
items = scale['item'].tolist()
factors = scale['factor'].tolist()
scoring = scale['scoring'].tolist()

print(f"\n✓ Data validated: {len(items)} items, {len(set(factors))} factors")

empirical_data = None

if EMPIRICAL_DATA_PATH is not None:
    print(f"\n{'='*70}")
    print("Loading empirical response data...")
    print(f"{'='*70}")
    
    try:
        with open(EMPIRICAL_DATA_PATH, 'r') as f:
            first_line = f.readline()
            
        tab_count = first_line.count('\t')
        comma_count = first_line.count(',')
        
        if tab_count > comma_count:
            delimiter = '\t'
        else:
            delimiter = ','
        
        empirical_df = pd.read_csv(EMPIRICAL_DATA_PATH, sep=delimiter)
        
        print(f"✓ Loaded from: {EMPIRICAL_DATA_PATH}")
        print(f"  Shape: {empirical_df.shape} (participants × items)")
        print(f"  Participants: {len(empirical_df):,}")
        print(f"  Items: {len(empirical_df.columns)}")
        
        data_codes = list(empirical_df.columns)
        if data_codes != codes:
            print(f"\n⚠ WARNING: Column mismatch detected!")
            print(f"  Scale definition codes: {codes[:5]}...")
            print(f"  Data columns: {data_codes[:5]}...")
            
            if set(data_codes) == set(codes):
                print(f"  → Reordering columns to match scale definition...")
                empirical_df = empirical_df[codes]
                print(f"  ✓ Columns reordered successfully")
            else:
                missing = set(codes) - set(data_codes)
                extra = set(data_codes) - set(codes)
                print(f"  → Missing codes: {missing}")
                print(f"  → Extra codes: {extra}")
                raise ValueError("Column names do not match scale item codes")
        else:
            print(f"  ✓ Column names match scale item codes")
        
        empirical_data = empirical_df.values.astype(float)
        
        min_val = empirical_data.min()
        max_val = empirical_data.max()
        print(f"\n  Response range: [{min_val:.0f}, {max_val:.0f}]")
        
        print(f"\n  Sample statistics:")
        print(f"    Mean response: {empirical_data.mean():.2f}")
        print(f"    SD response: {empirical_data.std():.2f}")
        print(f"    Missing values: {np.isnan(empirical_data).sum():,}")
        
        print(f"\n✓ Empirical data ready for analysis")
        
    except FileNotFoundError:
        print(f"\n✗ ERROR: File not found: {EMPIRICAL_DATA_PATH}")
        print("  Empirical analysis will be skipped.")
        empirical_data = None
    except Exception as e:
        print(f"\n✗ ERROR loading empirical data:")
        print(f"  {type(e).__name__}: {str(e)}")
        print("  Empirical analysis will be skipped.")
        empirical_data = None
else:
    print(f"\n{'='*70}")
    print("Empirical data path not specified - skipping empirical analysis")
    print(f"{'='*70}")

if torch.cuda.is_available():
    device = 'cuda'
    print(f"✓ CUDA: {torch.cuda.get_device_name(0)}")
elif torch.backends.mps.is_available():
    device = 'mps'
    print("✓ Apple MPS")
else:
    device = 'cpu'
    print("Using CPU")

all_embeddings = {}
model_sizes = []

for model_name in MODEL_NAMES:
    model_size = get_model_size(model_name)
    model_sizes.append(model_size)
    
    print(f"\nModel: {model_name} ({model_size})")
    
    if model_size in PREGENERATED_SCALE_EMBEDDINGS:
        scale_emb_path = PREGENERATED_SCALE_EMBEDDINGS[model_size]
        if os.path.exists(scale_emb_path):
            try:
                data = np.load(scale_emb_path, allow_pickle=True)
                
                embeddings = None
                for key in ['embeddings', 'scale_embeddings', 'vectors', 'arr_0']:
                    if key in data:
                        embeddings = data[key]
                        print(f"  ✓ Loaded from key '{key}': {embeddings.shape}")
                        break
                
                if embeddings is None:
                    print(f"  ⚠ Warning: No valid embedding key found in {scale_emb_path}")
                    print(f"    Available keys: {list(data.keys())}")
                    print(f"    Falling back to cache or generation...")
                else:
                    if embeddings.shape[0] != len(items):
                        print(f"  ⚠ WARNING: Embedding count ({embeddings.shape[0]}) != item count ({len(items)})")
                        print(f"    Falling back to cache or generation...")
                    else:
                        all_embeddings[model_size] = embeddings
                        continue
                        
            except Exception as e:
                print(f"  ⚠ Warning: Error loading pregenerated embeddings:")
                print(f"    {type(e).__name__}: {str(e)}")
                print(f"    Falling back to cache or generation...")
        else:
            print(f"  ⚠ Pregenerated path specified but file not found: {scale_emb_path}")
            print(f"    Falling back to cache or generation...")
    
    scale_cache_path = f"embeddings/{SCALE_NAME}_items_{model_size}.npz"
    legacy_cache_path = f"embeddings/scale_items_{model_size}.npz"
    cache_candidates = [scale_cache_path]
    if legacy_cache_path not in cache_candidates:
        cache_candidates.append(legacy_cache_path)

    loaded_from_cache = False
    for save_path in cache_candidates:
        if not os.path.exists(save_path):
            continue

        print(f"  Loading from cache: {save_path}...")
        try:
            data = np.load(save_path, allow_pickle=True)
            embeddings = data['embeddings']

            if embeddings.shape[0] != len(items):
                print(f"  ⚠ Cached embedding count ({embeddings.shape[0]}) != item count ({len(items)})")
                print(f"    Skipping cache file: {save_path}")
                continue

            if 'codes' in data:
                cached_codes = data['codes'].tolist()
                if cached_codes != codes:
                    print(f"  ⚠ Cached codes do not match current scale codes")
                    print(f"    Skipping cache file: {save_path}")
                    continue

            print(f"  ✓ Loaded: {embeddings.shape}")
            all_embeddings[model_size] = embeddings
            loaded_from_cache = True
            break
        except Exception as e:
            print(f"  ⚠ Warning: Failed to load cache file {save_path}")
            print(f"    {type(e).__name__}: {str(e)}")

    if loaded_from_cache:
        continue
    
    print(f"  Generating embeddings...")
    model = load_sentence_transformer_model(model_name=model_name, device=device)
    
    embeddings = model.encode(items, show_progress_bar=True, batch_size=21, 
                              convert_to_numpy=True, normalize_embeddings=False)
    print(f"  ✓ Generated: {embeddings.shape}")
    all_embeddings[model_size] = embeddings
    
    os.makedirs("embeddings", exist_ok=True)
    np.savez(scale_cache_path, embeddings=embeddings, codes=codes, items=items)
    print(f"  ✓ Saved to {scale_cache_path}")

print(f"\n✓ All embeddings ready: {list(all_embeddings.keys())}")

print(f"\n{'='*70}")
print("Sample Embeddings (First 3 Items)")
print(f"{'='*70}")
for model_size in model_sizes:
    embeddings = all_embeddings[model_size]
    print(f"\nModel: {model_size}")
    for i in range(min(3, len(items))):
        print(f"\n  Item {i+1}: {codes[i]}")
        print(f"  Text: {items[i][:60]}{'...' if len(items[i]) > 60 else ''}")
        print(f"  Embedding shape: {embeddings[i].shape}")
        print(f"  First 10 values: {embeddings[i][:10]}")
        print(f"  L2 norm: {np.linalg.norm(embeddings[i]):.4f}")

def run_pfa_for_model(model_size, embeddings, codes, items, factors, scoring,
                       n_factors=None, rotation='promax', extraction_method='minres',
                       eigen_criteria='parallel', parallel_iter=100, random_state=42,
                       save_dir='results'):
    """
    Run complete Semantic Factor Analysis pipeline for one model.

    Args:
        model_size: str, e.g., "4B"
        embeddings: (n_items, dim) array
        codes: list of item codes
        items: list of item texts
        factors: list of theoretical factor labels
        scoring: list of +1/-1 scoring directions
        n_factors: int or None (None = auto via parallel analysis)
        rotation: str, rotation method (e.g., 'promax', 'oblimin', 'varimax')
        extraction_method: str, extraction method (e.g., 'minres', 'ml', 'principal')
        eigen_criteria: 'parallel' or 'eigen1'
        parallel_iter: int, iterations for parallel analysis
        random_state: int, for reproducibility
        save_dir: directory to save results

    Returns:
        results: dict with all results
    """

    print(f"SEMANTIC FACTOR ANALYSIS: {model_size}")

    results = {'model_size': model_size}

    if APPLY_REVERSE_SCORING_EMBEDDINGS:
        print("\n[1/7] Applying atomic-reversed encoding...")
        embeddings_ar = apply_atomic_reversed(embeddings, scoring)
        print(f"  ✓ Shape: {embeddings_ar.shape}")
    else:
        print("\n[1/7] Normalizing embeddings (reverse scoring disabled)...")
        # Just normalize without applying scoring direction
        embeddings_ar = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
        print(f"  ✓ Shape: {embeddings_ar.shape}")
        print(f"  ⚠ Reverse scoring disabled - all items treated as normally scored")

    print("\n[2/7] Computing cosine similarity matrix...")
    sim_matrix = cosine_similarity(embeddings_ar)
    print(f"  ✓ Shape: {sim_matrix.shape}")

    results['similarity_matrix'] = sim_matrix

    print("\n[3/7] Computing KMO and Bartlett test...")

    kmo_per_item, kmo_total = calculate_kmo(sim_matrix)
    print(f"  ✓ KMO overall: {kmo_total:.3f}")

    if kmo_total < 0.5:
        print("    ⚠ WARNING: KMO < 0.5 (unacceptable)")
    elif kmo_total < 0.6:
        print("    ⚠ WARNING: KMO < 0.6 (poor)")
    elif kmo_total < 0.7:
        print("    KMO is mediocre")
    elif kmo_total < 0.8:
        print("    KMO is middling")
    elif kmo_total < 0.9:
        print("    KMO is meritorious")
    else:
        print("    KMO is marvelous")

    chi_square, p_value = calculate_bartlett_sphericity(sim_matrix)
    print(f"  ✓ Bartlett: χ²={chi_square:.2f}, p={p_value:.4e}")

    if p_value > 0.05:
        print("    ⚠ WARNING: Not significant (p > 0.05)")
    else:
        print("    ✓ Significant (p < 0.05)")

    results['kmo_total'] = kmo_total
    results['kmo_per_item'] = kmo_per_item
    results['bartlett_chi2'] = chi_square
    results['bartlett_p'] = p_value

    print("\n[4/7] Determining number of factors...")

    eigs = np.linalg.eigvalsh(sim_matrix)
    eigs = np.sort(eigs)[::-1]
    results['observed_eigenvalues'] = eigs

    if n_factors is None:
        if eigen_criteria == 'parallel':
            print(f"  Running parallel analysis ({parallel_iter} iterations)...")
            n_factors_auto, obs_eigs, percentile_eigs = compute_parallel_analysis(
                sim_matrix, n_iter=parallel_iter, random_state=random_state
            )
            print(f"  ✓ Suggested {n_factors_auto} factors")
            n_factors = max(1, n_factors_auto)
            results['percentile_eigenvalues'] = percentile_eigs
        else:
            n_factors = np.sum(eigs > 1)
            print(f"  ✓ Kaiser rule (eigen>1): {n_factors} factors")

    print(f"  ✓ Extracting {n_factors} factors with {rotation} rotation")
    results['n_factors'] = n_factors

    print("\n[5/7] Running Exploratory Factor Analysis...")

    fa = FactorAnalyzer(
        n_factors=n_factors,
        rotation=rotation,
        method=extraction_method,
        is_corr_matrix=True,
        rotation_kwargs={'normalize': True} if rotation in ['promax', 'oblimin'] else {}
    )

    fa.fit(sim_matrix)
    print("  ✓ EFA complete")

    loadings = fa.loadings_
    communalities = fa.get_communalities()
    uniquenesses = fa.get_uniquenesses()
    variance = fa.get_factor_variance()

    factor_names = [f"Factor{i+1}" for i in range(n_factors)]
    loadings_df = pd.DataFrame(loadings, index=codes, columns=factor_names)

    print(f"  Loadings shape: {loadings.shape}")
    print(f"  Variance explained (cumulative): {variance[2][-1]:.1%}")

    variance_df = pd.DataFrame(variance, index=['SS Loadings', 'Proportion', 'Cumulative'])

    communalities_df = pd.DataFrame({
        'communality': communalities,
        'uniqueness': uniquenesses
    }, index=codes)

    results['loadings'] = loadings_df
    results['variance'] = variance
    results['communalities'] = communalities
    results['uniquenesses'] = uniquenesses

    print("\n[6/7] Computing DAAL...")

    daal_df = compute_daal(loadings_df, factors)
    print(f"  ✓ DAAL matrix: {daal_df.shape}")

    assignments = []
    for ext_factor in daal_df.index:
        best_theo = daal_df.loc[ext_factor].idxmax()
        best_daal = daal_df.loc[ext_factor, best_theo]
        assignments.append({
            'extracted_factor': ext_factor,
            'assigned_to': best_theo,
            'daal': best_daal
        })

    assignments_df = pd.DataFrame(assignments)
    print("\n  Factor assignments (DAAL):")
    for _, row in assignments_df.iterrows():
        print(f"    {row['extracted_factor']} → {row['assigned_to']} (DAAL={row['daal']:.3f})")

    results['daal'] = daal_df
    results['daal_assignments'] = assignments_df

    print("\n[7/7] Computing Tucker congruence...")

    theoretical_indicators = create_theoretical_indicators(factors, codes)

    tucker_matrix = []
    for ext_factor in factor_names:
        row = []
        for theo_factor in theoretical_indicators.columns:
            phi = compute_tucker_congruence(
                loadings_df[ext_factor].values,
                theoretical_indicators[theo_factor].values
            )
            row.append(phi)
        tucker_matrix.append(row)

    tucker_df = pd.DataFrame(
        tucker_matrix,
        index=factor_names,
        columns=theoretical_indicators.columns
    )

    print(f"  ✓ Tucker matrix: {tucker_df.shape}")
    print("\n  Interpretation guide:")
    print("    φ ≥ .95: Excellent agreement")
    print("    φ ≥ .85: Fair agreement")
    print("    φ < .85: Poor agreement")

    tucker_best = []
    for ext_factor in tucker_df.index:
        best_theo = tucker_df.loc[ext_factor].idxmax()
        best_phi = tucker_df.loc[ext_factor, best_theo]
        tucker_best.append({
            'extracted_factor': ext_factor,
            'best_match': best_theo,
            'tucker_phi': best_phi
        })

    tucker_best_df = pd.DataFrame(tucker_best)
    print("\n  Best matches (Tucker φ):")
    for _, row in tucker_best_df.iterrows():
        print(f"    {row['extracted_factor']} ↔ {row['best_match']} (φ={row['tucker_phi']:.3f})")

    results['tucker'] = tucker_df
    results['tucker_best'] = tucker_best_df

    diagnostics = {
        'model': model_size,
        'n_items': len(codes),
        'n_factors_extracted': n_factors,
        'rotation': rotation,
    }

    diagnostics['kmo'] = kmo_total
    diagnostics['bartlett_p'] = p_value
    diagnostics['variance_explained'] = variance[2][-1]

    diagnostics_df = pd.DataFrame([diagnostics])

    results['diagnostics'] = diagnostics_df

    print(f"\n{'='*70}")
    print(f"✓ SFA COMPLETE FOR {model_size}")
    print('='*70)

    return results

def run_efa_on_data(data_label, response_data, codes, items, factors, scoring,
                    n_factors=None, rotation='promax', extraction_method='minres',
                    eigen_criteria='parallel', parallel_iter=100, random_state=42,
                    save_dir='results'):
    """
    Run traditional Exploratory Factor Analysis on raw Likert scale response data.
    
    This function follows traditional factor analysis conventions:
    - Uses Pearson correlation matrix instead of cosine similarity
    - Applies reverse scoring but does NOT normalize to unit vectors
    - Otherwise identical pipeline to run_pfa_for_model()
    
    Parameters:
    -----------
    data_label : str
        Label for this dataset (e.g., "Empirical", "DASS_Study1")
    response_data : ndarray of shape (n_participants, n_items)
        Raw Likert scale responses (participants × items)
    codes : list of str
        Item codes (e.g., ["S1", "A2", "D3"])
    items : list of str
        Full item text
    factors : list of str
        Theoretical factor labels
    scoring : list of int
        +1 for normal items, -1 for reverse-scored
    n_factors : int or None
        Number of factors to extract (None = auto via parallel analysis)
    rotation : str
        Rotation method ('promax', 'oblimin', 'varimax', etc.)
    extraction_method : str
        'minres', 'ml', or 'principal'
    eigen_criteria : str
        'parallel' or 'eigen1'
    parallel_iter : int
        Iterations for parallel analysis
    random_state : int
        Random seed for reproducibility
    save_dir : str
        Output directory
        
    Returns:
    --------
    dict : Analysis results including correlation matrix, loadings, diagnostics, etc.
    """
    
    print(f"\n{'='*70}")
    print(f"TRADITIONAL EFA - {data_label}")
    print(f"{'='*70}")
    print(f"Data: {response_data.shape[0]:,} participants × {response_data.shape[1]} items")
    print(f"Rotation: {rotation}")
    print(f"Extraction: {extraction_method}")
    
    print(f"\n[1/7] Applying reverse scoring...")
    
    response_scored = response_data.copy()
    for i, score_dir in enumerate(scoring):
        if score_dir == -1:
            max_val = response_data[:, i].max()
            response_scored[:, i] = max_val - response_data[:, i]
    
    reverse_count = sum(1 for s in scoring if s == -1)
    print(f"  ✓ Applied reverse scoring to {reverse_count}/{len(scoring)} items")
    print(f"  ✓ Data shape: {response_scored.shape}")
    
    print(f"\n[2/7] Computing correlation matrix...")

    corr_matrix = np.corrcoef(response_scored.T)

    print(f"  Parity stats:")
    print(f"    Response shape: {response_scored.shape}")
    print(f"    Missing values: {np.isnan(response_scored).sum():,}")
    print(f"    First codes: {codes[:6]}")
    print(f"    Non-finite in corr: {np.sum(~np.isfinite(corr_matrix))}")
    eigs_tmp = np.linalg.eigvalsh(corr_matrix)
    eigs_tmp = np.sort(eigs_tmp)[::-1]
    print(f"    Obs eigs (top 6): {np.round(eigs_tmp[:6], 4)}")

    print(f"  ✓ Correlation matrix shape: {corr_matrix.shape}")
    print(f"  ✓ Correlation range: [{corr_matrix.min():.3f}, {corr_matrix.max():.3f}]")
    print(f"  ✓ Mean correlation: {corr_matrix[np.triu_indices_from(corr_matrix, k=1)].mean():.3f}")
    
    print(f"\n[3/7] Testing sampling adequacy...")

    kmo_per_item, kmo_total = calculate_kmo(corr_matrix)
    bartlett_chi2, bartlett_p = calculate_bartlett_sphericity(corr_matrix)
    
    print(f"  KMO Measure of Sampling Adequacy: {kmo_total:.3f}")
    if kmo_total >= 0.9:
        print(f"    → Excellent (≥0.9)")
    elif kmo_total >= 0.8:
        print(f"    → Good (≥0.8)")
    elif kmo_total >= 0.7:
        print(f"    → Adequate (≥0.7)")
    elif kmo_total >= 0.6:
        print(f"    → Mediocre (≥0.6)")
    else:
        print(f"    → Poor (<0.6)")
    
    print(f"  Bartlett's Test of Sphericity:")
    print(f"    χ²({len(codes)*(len(codes)-1)//2}) = {bartlett_chi2:.2f}, p = {bartlett_p:.2e}")
    if bartlett_p < 0.001:
        print(f"    → Correlations are factorable (p < .001)")
    else:
        print(f"    → ⚠ Warning: p ≥ .001")
    
    print(f"\n[4/7] Determining number of factors...")

    eigs = np.linalg.eigvalsh(corr_matrix)
    eigs = np.sort(eigs)[::-1]

    
    if n_factors is None:
        if eigen_criteria == 'parallel':
            n_factors_auto, obs_eigs, pct_eigs = compute_parallel_analysis(
                corr_matrix, n_iter=parallel_iter, percentile=95, random_state=random_state
            )
            print(f"  Parallel Analysis (95th percentile, {parallel_iter} iterations):")
            print(f"    → Suggested factors: {n_factors_auto}")
            n_factors = n_factors_auto
        elif eigen_criteria == 'eigen1':
            eigenvalues = np.linalg.eigvalsh(corr_matrix)[::-1]
            n_factors_auto = np.sum(eigenvalues > 1.0)
            obs_eigs = eigenvalues
            pct_eigs = None
            print(f"  Kaiser Criterion (eigenvalue > 1):")
            print(f"    → Suggested factors: {n_factors_auto}")
            n_factors = n_factors_auto
        else:
            raise ValueError(f"Unknown eigen_criteria: {eigen_criteria}")
    else:
        print(f"  Using specified n_factors: {n_factors}")
        obs_eigs = np.linalg.eigvalsh(corr_matrix)[::-1]
        pct_eigs = None
    
    print(f"\n[5/7] Running EFA extraction...")
    print(f"  Extracting {n_factors} factors with {rotation} rotation...")

    fa = FactorAnalyzer(
        n_factors=n_factors,
        rotation=rotation,
        method=extraction_method,
        is_corr_matrix=True
    )
    fa.fit(corr_matrix)
    
    loadings_array = fa.loadings_
    variance_array = fa.get_factor_variance()
    
    factor_names = [f"Factor{i+1}" for i in range(n_factors)]
    loadings_df = pd.DataFrame(loadings_array, index=codes, columns=factor_names)
    
    print(f"  ✓ Extraction complete")
    print(f"  ✓ Loadings shape: {loadings_df.shape}")
    print(f"  ✓ Total variance explained: {variance_array[2][-1]:.1%}")
    
    variance_df = pd.DataFrame(
        variance_array,
        index=['SS Loadings', 'Proportion Var', 'Cumulative Var'],
        columns=factor_names
    )
    
    communalities = fa.get_communalities()
    comm_df = pd.DataFrame({'code': codes, 'communality': communalities})
    
    print(f"\n[6/7] Computing DAAL...")
    
    daal_df = compute_daal(loadings_df, factors)
    
    daal_assignments = []
    for ext_factor in factor_names:
        theo_factor = daal_df.loc[ext_factor].idxmax()
        daal_value = daal_df.loc[ext_factor, theo_factor]
        daal_assignments.append({
            'extracted_factor': ext_factor,
            'assigned_to': theo_factor,
            'daal': daal_value
        })
    daal_assignments_df = pd.DataFrame(daal_assignments)
    
    print(f"  Factor assignments (by DAAL):")
    for _, row in daal_assignments_df.iterrows():
        print(f"    {row['extracted_factor']} → {row['assigned_to']} (DAAL = {row['daal']:.3f})")
    
    print(f"\n[7/7] Computing Tucker congruence...")
    
    theoretical_indicators = create_theoretical_indicators(factors, codes)
    
    tucker_matrix = []
    for ext_factor in factor_names:
        row = []
        for theo_factor in theoretical_indicators.columns:
            phi = compute_tucker_congruence(
                loadings_df[ext_factor].values,
                theoretical_indicators[theo_factor].values
            )
            row.append(phi)
        tucker_matrix.append(row)
    
    tucker_df = pd.DataFrame(
        tucker_matrix,
        index=factor_names,
        columns=theoretical_indicators.columns
    )
    
    tucker_best = []
    for ext_factor in factor_names:
        theo_factor = tucker_df.loc[ext_factor].idxmax()
        phi_value = tucker_df.loc[ext_factor, theo_factor]
        
        if phi_value >= 0.95:
            quality = "Excellent"
        elif phi_value >= 0.85:
            quality = "Fair"
        else:
            quality = "Poor"
        
        tucker_best.append({
            'extracted_factor': ext_factor,
            'best_match': theo_factor,
            'tucker_phi': phi_value,
            'quality': quality
        })
    tucker_best_df = pd.DataFrame(tucker_best)
    
    print(f"  Best matches (by Tucker φ):")
    for _, row in tucker_best_df.iterrows():
        print(f"    {row['extracted_factor']} → {row['best_match']} (φ = {row['tucker_phi']:.3f}, {row['quality']})")
    
    diagnostics_df = pd.DataFrame({
        'metric': ['KMO', 'Bartlett χ²', 'Bartlett p', 'n_factors', 'variance_explained'],
        'value': [kmo_total, bartlett_chi2, bartlett_p, n_factors, variance_array[2][-1]]
    })
    
    print(f"\n{'='*70}")
    print(f"✓ EFA COMPLETE - {data_label}")
    print(f"{'='*70}")
    
    return {
        'data_label': data_label,
        'similarity_matrix': corr_matrix,
        'kmo_total': kmo_total,
        'kmo_per_item': kmo_per_item,
        'bartlett_chi2': bartlett_chi2,
        'bartlett_p': bartlett_p,
        'n_factors': n_factors,
        'observed_eigenvalues': eigs,
        'percentile_eigenvalues': pct_eigs,
        'loadings': loadings_df,
        'variance': variance_array,
        'communalities': communalities,
        'uniquenesses': fa.get_uniquenesses(),
        'daal': daal_df,
        'daal_assignments': daal_assignments_df,
        'tucker': tucker_df,
        'tucker_best': tucker_best_df,
        'diagnostics': diagnostics_df
    }

def create_visualizations(results, factors, codes, model_size, save_dir='results', data_type='embeddings'):
    """
    Create all visualizations for SFA/EFA results
    
    Parameters:
    -----------
    results : dict
        Results dictionary from run_pfa_for_model() or run_efa_on_data()
    factors : list
        Theoretical factor labels
    codes : list
        Item codes
    model_size : str
        Model identifier or data label
    save_dir : str
        Output directory
    data_type : str
        'embeddings' for cosine similarity, 'empirical' for correlation
    """

    print(f"\nCreating visualizations for {model_size}...")

    scale_name = Path(save_dir).name
    manual_plot_order = (
        PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
        if data_type == 'empirical'
        else PLOT_EXTRACTED_FACTOR_ORDER
    )

    fig = plt.figure(figsize=(20, 12))

    ax1 = plt.subplot(2, 3, 1)
    if 'observed_eigenvalues' in results:
        obs_eigs = results['observed_eigenvalues']
        n_show = min(20, len(obs_eigs))
        ax1.plot(range(1, n_show + 1), obs_eigs[:n_show], 'o-', label='Observed', linewidth=2)

        if 'percentile_eigenvalues' in results and results['percentile_eigenvalues'] is not None:
            perc_eigs = results['percentile_eigenvalues']
            ax1.plot(range(1, n_show + 1), perc_eigs[:n_show], 's--',
                    label='95th percentile (random)', linewidth=2)

        ax1.axhline(1, color='red', linestyle='--', alpha=0.5, label='Eigen = 1')
        ax1.set_xlabel('Factor Number')
        ax1.set_ylabel('Eigenvalue')
        ax1.set_title(f'Scree Plot - {model_size}')
        ax1.legend()
        ax1.grid(True, alpha=0.3)

    ax2 = plt.subplot(2, 3, 2)
    if 'similarity_matrix' in results:
        sim = results['similarity_matrix']
        factor_order = sorted(range(len(factors)), key=lambda i: (factors[i], i))
        sim_ordered = sim[factor_order][:, factor_order]

        cbar_label = 'Correlation' if data_type == 'empirical' else 'Cosine Similarity'
        matrix_title = 'Correlation Matrix' if data_type == 'empirical' else 'Similarity Matrix'

        sns.heatmap(sim_ordered, cmap='RdBu_r', center=0, vmin=-1, vmax=1,
                   square=True, ax=ax2, cbar_kws={'label': cbar_label})
        ax2.set_title(f'{matrix_title} - {model_size}')
        ax2.set_xlabel('Items')
        ax2.set_ylabel('Items')

    ax3 = plt.subplot(2, 3, 3)
    if 'loadings' in results:
        loadings_df = results['loadings']
        factor_order = sorted(range(len(factors)), key=lambda i: (factors[i], i))
        ordered_loadings = loadings_df.loc[[codes[i] for i in factor_order]]
        ordered_extracted = apply_manual_extracted_factor_order(
            ordered_loadings.columns.tolist(),
            manual_order=manual_plot_order
        )
        ordered_loadings = ordered_loadings[ordered_extracted]

        sns.heatmap(ordered_loadings.values, cmap='RdBu_r', center=0,
                   vmin=-1, vmax=1, ax=ax3, cbar_kws={'label': 'Loading'},
                   xticklabels=ordered_extracted)
        ax3.set_title(f'Factor Loadings - {model_size}')
        ax3.set_xlabel('Extracted Factors')
        ax3.set_ylabel('Items')
        ax3.set_yticks([])

    ax4 = plt.subplot(2, 3, 4)
    if 'daal' in results:
        daal = results['daal']
        ordered_extracted = apply_manual_extracted_factor_order(
            daal.index.tolist(),
            manual_order=manual_plot_order
        )
        daal = daal.loc[ordered_extracted]
        sns.heatmap(daal.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlOrRd',
                   xticklabels=daal.columns, yticklabels=daal.index,
                   ax=ax4, vmin=0, vmax=1, cbar_kws={'label': 'DAAL'})
        ax4.set_title(f'DAAL Matrix - {model_size}')
        ax4.set_xlabel('Theoretical Factors')
        ax4.set_ylabel('Extracted Factors')

    ax5 = plt.subplot(2, 3, 5)
    if 'tucker' in results:
        tucker = results['tucker']
        ordered_extracted = apply_manual_extracted_factor_order(
            tucker.index.tolist(),
            manual_order=manual_plot_order
        )
        tucker = tucker.loc[ordered_extracted]
        sns.heatmap(tucker.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlGnBu',
                   xticklabels=tucker.columns, yticklabels=tucker.index,
                   ax=ax5, vmin=0, vmax=1, cbar_kws={'label': 'Tucker φ'})
        ax5.set_title(f'Tucker Congruence - {model_size}')
        ax5.set_xlabel('Theoretical Factors')
        ax5.set_ylabel('Extracted Factors')

    ax6 = plt.subplot(2, 3, 6)
    if 'tucker_best' in results:
        tucker_best = results['tucker_best'].copy()
        ordered_extracted = apply_manual_extracted_factor_order(
            tucker_best['extracted_factor'].tolist(),
            manual_order=manual_plot_order
        )
        rank = {factor: i for i, factor in enumerate(ordered_extracted)}
        tucker_best['_plot_order'] = tucker_best['extracted_factor'].map(rank).fillna(len(rank)).astype(int)
        tucker_best = tucker_best.sort_values('_plot_order').drop(columns=['_plot_order'])
        colors = ['green' if x >= 0.95 else 'orange' if x >= 0.85 else 'red'
                 for x in tucker_best['tucker_phi']]
        ax6.barh(tucker_best['extracted_factor'], tucker_best['tucker_phi'], color=colors)
        ax6.axvline(0.95, color='green', linestyle='--', alpha=0.5, label='Excellent (≥.95)')
        ax6.axvline(0.85, color='orange', linestyle='--', alpha=0.5, label='Fair (≥.85)')
        ax6.set_xlabel('Tucker φ')
        ax6.set_ylabel('Extracted Factor')
        ax6.set_title(f'Best Tucker Congruence - {model_size}')
        ax6.legend()
        ax6.set_xlim(0, 1)

    plt.tight_layout()
    plt.savefig(f'{save_dir}/{scale_name}_visualizations_{model_size}.png', dpi=150, bbox_inches='tight')
    plt.close()

all_results = {}

for model_size in model_sizes:
    embeddings = all_embeddings[model_size]
    
    results = run_pfa_for_model(
        model_size=model_size,
        embeddings=embeddings,
        codes=codes,
        items=items,
        factors=factors,
        scoring=scoring,
        n_factors=N_FACTORS,
        rotation=ROTATION_METHOD,
        extraction_method=EXTRACTION_METHOD,
        eigen_criteria=EIGEN_CRITERIA,
        parallel_iter=PARALLEL_ITER,
        random_state=RANDOM_STATE,
        save_dir=SAVE_DIR
    )
    
    all_results[model_size] = results

    # create_visualizations(results, factors, codes, model_size, save_dir=SAVE_DIR)

print(f"\n{'='*70}")
print("PREPROCESSING EMPIRICAL DATA")
print(f"{'='*70}")

if isinstance(empirical_data, pd.DataFrame):
    print(f"Original shape: {empirical_data.shape}")
    n_before = len(empirical_data)
    empirical_data = empirical_data.dropna()
    n_after = len(empirical_data)
    if n_before - n_after > 0:
        print(f"Removed {n_before - n_after} rows with missing values ({(n_before - n_after)/n_before*100:.1f}%)")
    else:
        print("No missing values found")
    print(f"Final shape: {empirical_data.shape}")
elif isinstance(empirical_data, np.ndarray):
    print(f"Original shape: {empirical_data.shape}")
    mask = ~np.isnan(empirical_data).any(axis=1)
    n_before = len(empirical_data)
    empirical_data = empirical_data[mask]
    n_after = len(empirical_data)
    if n_before - n_after > 0:
        print(f"Removed {n_before - n_after} rows with missing values ({(n_before - n_after)/n_before*100:.1f}%)")
    else:
        print("No missing values found")
    print(f"Final shape: {empirical_data.shape}")
else:
    print(f"Data type: {type(empirical_data)}")
    print("Warning: Could not clean data (unexpected type)")

print(f"{'='*70}\n")

empirical_results = None

if empirical_data is not None:
    print(f"\n{'='*70}")
    print("RUNNING TRADITIONAL EFA ON EMPIRICAL DATA")
    print(f"{'='*70}")
    
    empirical_results = run_efa_on_data(
        data_label="Empirical",
        response_data=empirical_data,
        codes=codes,
        items=items,
        factors=factors,
        scoring=scoring,
        n_factors=N_FACTORS,
        rotation=ROTATION_METHOD,
        extraction_method=EXTRACTION_METHOD,
        eigen_criteria=EIGEN_CRITERIA,
        parallel_iter=PARALLEL_ITER,
        random_state=RANDOM_STATE,
        save_dir=SAVE_DIR
    )

    # create_visualizations(empirical_results, factors, codes, "Empirical",
    #                      save_dir=SAVE_DIR, data_type='empirical')

    print(f"\n✓ TRADITIONAL EFA COMPLETE")
else:
    print(f"\n{'='*70}")
    print("Empirical data not loaded - skipping traditional EFA")
    print(f"{'='*70}")

sample_idx = 0

print("Finding nearest neighbors in original embedding space...")
print(f"\nSample item #{sample_idx}:")
print(f"  Code: {codes[sample_idx]}")
print(f"  Factor: {factors[sample_idx]}")
print(f"  Text: {items[sample_idx]}")

for model_size in model_sizes:
    embeddings = all_embeddings[model_size]
    print(f"{model_size} Model - Original {embeddings.shape[1]}D Space")
    
    similarities = cosine_similarity([embeddings[sample_idx]], embeddings)[0]
    
    most_similar_indices = np.argsort(similarities)[::-1][1:6]
    
    print(f"5 Most similar items (by cosine similarity):")
    for rank, idx in enumerate(most_similar_indices, 1):
        print(f"  {rank}. [{factors[idx]}] {items[idx][:80]}...")
        print(f"      Similarity: {similarities[idx]:.4f}")

print("✓ Nearest neighbors analysis complete")

print("Factor Separation Analysis:")

for model_size in model_sizes:
    print(f"\n{model_size} Model:")
    
    sim_matrix = all_results[model_size]['similarity_matrix']
    n_items = len(factors)
    
    within_sims = []
    between_sims = []
    
    for i in range(n_items):
        for j in range(i + 1, n_items):
            sim = sim_matrix[i, j]
            
            if factors[i] == factors[j]:
                within_sims.append(sim)
            else:
                between_sims.append(sim)
    
    within_sims = np.array(within_sims)
    between_sims = np.array(between_sims)
    
    mean_within = np.mean(within_sims)
    sd_within = np.std(within_sims, ddof=1)
    mean_between = np.mean(between_sims)
    sd_between = np.std(between_sims, ddof=1)
    mean_diff = mean_within - mean_between
    
    pooled_sd = np.sqrt((sd_within**2 + sd_between**2) / 2)
    cohens_d = mean_diff / pooled_sd if pooled_sd > 0 else 0.0
    
    t_stat, p_value = ttest_ind(within_sims, between_sims, equal_var=False)
    
    n1, n2 = len(within_sims), len(between_sims)
    s1_sq, s2_sq = sd_within**2, sd_between**2
    df = (s1_sq/n1 + s2_sq/n2)**2 / ((s1_sq/n1)**2/(n1-1) + (s2_sq/n2)**2/(n2-1))
    
    print(f"  Within-construct:  M = {mean_within:.3f}, SD = {sd_within:.3f}, n = {len(within_sims)}")
    print(f"  Between-construct: M = {mean_between:.3f}, SD = {sd_between:.3f}, n = {len(between_sims)}")
    print(f"  Mean difference: {mean_diff:.3f}")
    print(f"  Welch's t({df:.1f}) = {t_stat:.2f}, p = {p_value:.4e}")
    print(f"  Cohen's d = {cohens_d:.3f}")
    
    if p_value < 0.001:
        sig_text = "highly significant (p < .001)"
    elif p_value < 0.01:
        sig_text = "very significant (p < .01)"
    elif p_value < 0.05:
        sig_text = "significant (p < .05)"
    else:
        sig_text = "not significant (p ≥ .05)"
    
    if abs(cohens_d) >= 0.8:
        effect_text = "large effect"
    elif abs(cohens_d) >= 0.5:
        effect_text = "medium effect"
    elif abs(cohens_d) >= 0.2:
        effect_text = "small effect"
    else:
        effect_text = "negligible effect"
    
    print(f"\n  Interpretation: {sig_text}, {effect_text}")
    
    if mean_within > mean_between and p_value < 0.05:
        print(f"  ✓ Items from the same factor are significantly more similar.")
    elif mean_within < mean_between and p_value < 0.05:
        print(f"  ⚠ Items from different factors are MORE similar (unexpected!).")
    else:
        print(f"  ⚠ No significant difference in within- vs between-construct similarity.")

print("✓ Factor Separation Analysis Complete")

print("FACTOR SEPARATION ANALYSIS")
print("=" * 70)

for model_size in model_sizes:
    print(f"\n{'='*70}")
    print(f"{model_size} Model - Factor Separation Metrics")
    print(f"{'='*70}")
    
    sim_matrix = all_results[model_size]['similarity_matrix']
    
    unique_factors = sorted(set(factors))
    within_factor_sims = {factor: [] for factor in unique_factors}
    between_factor_sims = []
    
    for i in range(len(factors)):
        for j in range(i + 1, len(factors)):
            similarity = sim_matrix[i, j]
            
            if factors[i] == factors[j]:
                within_factor_sims[factors[i]].append(similarity)
            else:
                between_factor_sims.append(similarity)
    
    all_within_sims = []
    for factor_sims in within_factor_sims.values():
        all_within_sims.extend(factor_sims)
    
    within_mean = np.mean(all_within_sims)
    between_mean = np.mean(between_factor_sims)
    separation_ratio = within_mean / between_mean
    
    print(f"\nOverall Separation Metrics:")
    print(f"  Within-factor similarity:  {within_mean:.4f}")
    print(f"  Between-factor similarity: {between_mean:.4f}")
    print(f"  Separation ratio:          {separation_ratio:.4f}")
    print(f"    {'(Good separation - factors cluster together!)' if separation_ratio > 1.0 else '(Poor separation - factors overlap)'}")
    
    print(f"\nPer-Factor Within-Similarity:")
    for factor in unique_factors:
        if len(within_factor_sims[factor]) > 0:
            factor_mean = np.mean(within_factor_sims[factor])
            factor_std = np.std(within_factor_sims[factor], ddof=1)
            n_pairs = len(within_factor_sims[factor])
            print(f"  {factor:12s}: {factor_mean:.4f} ± {factor_std:.4f}  (n={n_pairs} pairs)")
    
    print(f"\nBetween-Factor Similarities:")
    factor_pairs = {}
    for i in range(len(factors)):
        for j in range(i + 1, len(factors)):
            if factors[i] != factors[j]:
                pair = tuple(sorted([factors[i], factors[j]]))
                if pair not in factor_pairs:
                    factor_pairs[pair] = []
                factor_pairs[pair].append(sim_matrix[i, j])
    
    for pair in sorted(factor_pairs.keys()):
        pair_mean = np.mean(factor_pairs[pair])
        pair_std = np.std(factor_pairs[pair], ddof=1)
        n_pairs = len(factor_pairs[pair])
        print(f"  {pair[0]:12s} vs {pair[1]:12s}: {pair_mean:.4f} ± {pair_std:.4f}  (n={n_pairs} pairs)")

if empirical_results is not None:
    print(f"\n{'='*70}")
    print(f"Empirical Data - Factor Separation Metrics")
    print(f"{'='*70}")
    
    corr_matrix = empirical_results['similarity_matrix']
    
    unique_factors = sorted(set(factors))
    within_factor_corrs = {factor: [] for factor in unique_factors}
    between_factor_corrs = []
    
    for i in range(len(factors)):
        for j in range(i + 1, len(factors)):
            correlation = corr_matrix[i, j]
            
            if factors[i] == factors[j]:
                within_factor_corrs[factors[i]].append(correlation)
            else:
                between_factor_corrs.append(correlation)
    
    all_within_corrs = []
    for factor_corrs in within_factor_corrs.values():
        all_within_corrs.extend(factor_corrs)
    
    within_mean = np.mean(all_within_corrs)
    between_mean = np.mean(between_factor_corrs)
    separation_ratio = within_mean / between_mean
    
    print(f"\nOverall Separation Metrics:")
    print(f"  Within-factor correlation:  {within_mean:.4f}")
    print(f"  Between-factor correlation: {between_mean:.4f}")
    print(f"  Separation ratio:           {separation_ratio:.4f}")
    print(f"    {'(Good separation - factors cluster together!)' if separation_ratio > 1.0 else '(Poor separation - factors overlap)'}")
    
    print(f"\nPer-Factor Within-Correlation:")
    for factor in unique_factors:
        if len(within_factor_corrs[factor]) > 0:
            factor_mean = np.mean(within_factor_corrs[factor])
            factor_std = np.std(within_factor_corrs[factor], ddof=1)
            n_pairs = len(within_factor_corrs[factor])
            print(f"  {factor:12s}: {factor_mean:.4f} ± {factor_std:.4f}  (n={n_pairs} pairs)")
    
    print(f"\nBetween-Factor Correlations:")
    factor_pairs = {}
    for i in range(len(factors)):
        for j in range(i + 1, len(factors)):
            if factors[i] != factors[j]:
                pair = tuple(sorted([factors[i], factors[j]]))
                if pair not in factor_pairs:
                    factor_pairs[pair] = []
                factor_pairs[pair].append(corr_matrix[i, j])
    
    for pair in sorted(factor_pairs.keys()):
        pair_mean = np.mean(factor_pairs[pair])
        pair_std = np.std(factor_pairs[pair], ddof=1)
        n_pairs = len(factor_pairs[pair])
        print(f"  {pair[0]:12s} vs {pair[1]:12s}: {pair_mean:.4f} ± {pair_std:.4f}  (n={n_pairs} pairs)")
else:
    print(f"\n{'='*70}")
    print("Empirical results not available - skipping empirical factor separation analysis")
    print(f"{'='*70}")

print(f"\n{'='*70}")
print("✓ Factor separation analysis complete!")
print(f"{'='*70}")

print("SUMMARY")

for model_size in model_sizes:
    results = all_results[model_size]
    print(f"\n{model_size}:")
    
    if 'diagnostics' in results:
        diag = results['diagnostics'].iloc[0]
        print(f"  Items: {diag['n_items']}")
        print(f"  Factors: {diag['n_factors_extracted']}")
        print(f"  Rotation: {diag['rotation']}")
        if 'kmo' in diag:
            print(f"  KMO: {diag['kmo']:.3f}")
        if 'bartlett_p' in diag:
            print(f"  Bartlett p: {diag['bartlett_p']:.4e}")
        print(f"  Variance: {diag['variance_explained']:.1%}")
    
    if 'daal_assignments' in results:
        print("\n  DAAL assignments:")
        for _, row in results['daal_assignments'].iterrows():
            print(f"    {row['extracted_factor']} → {row['assigned_to']} (DAAL={row['daal']:.3f})")
    
    if 'tucker_best' in results:
        print("\n  Tucker congruence:")
        for _, row in results['tucker_best'].iterrows():
            print(f"    {row['extracted_factor']} ↔ {row['best_match']} (φ={row['tucker_phi']:.3f})")

print(f"\nResults saved to: {SAVE_DIR}/")

print("Loading word list for nearest neighbor factor naming...")

word_list_path = "word_lists/constructs.csv"

configured_model_sizes = [get_model_size(m) for m in MODEL_NAMES]
has_pregenerated = any(model_size in PREGENERATED_WORD_EMBEDDINGS for model_size in configured_model_sizes)

if has_pregenerated:
    first_model_size = configured_model_sizes[0]
    if first_model_size in PREGENERATED_WORD_EMBEDDINGS:
        pregenerated_path = PREGENERATED_WORD_EMBEDDINGS[first_model_size]
        if os.path.exists(pregenerated_path):
            print(f"Loading words from pregenerated embeddings: {pregenerated_path}")
            try:
                data = np.load(pregenerated_path, allow_pickle=True)
                if 'words' in data:
                    words = data['words'].tolist()
                    print(f"✓ Loaded {len(words)} words from pregenerated embeddings")
                else:
                    print(f"✗ 'words' key not found in {pregenerated_path}")
                    print(f"  Available keys: {list(data.keys())}")
                    print(f"  Falling back to {word_list_path}")
                    words_df = pd.read_csv(word_list_path, header=None, names=['word'])
                    words = words_df['word'].tolist()
                    print(f"✓ Loaded {len(words)} words from {word_list_path}")
            except Exception as e:
                print(f"✗ Error loading words from pregenerated: {e}")
                print(f"  Falling back to {word_list_path}")
                words_df = pd.read_csv(word_list_path, header=None, names=['word'])
                words = words_df['word'].tolist()
                print(f"✓ Loaded {len(words)} words from {word_list_path}")
        else:
            print(f"✗ Pregenerated path not found: {pregenerated_path}")
            print(f"  Loading from {word_list_path}")
            words_df = pd.read_csv(word_list_path, header=None, names=['word'])
            words = words_df['word'].tolist()
            print(f"✓ Loaded {len(words)} words from {word_list_path}")
    else:
        try:
            words_df = pd.read_csv(word_list_path, header=None, names=['word'])
            words = words_df['word'].tolist()
            print(f"✓ Loaded {len(words)} words from {word_list_path}")
        except Exception as e:
            print(f"✗ Error loading word list: {e}")
            print("  Nearest neighbor method will be skipped.")
            words = None
else:
    try:
        words_df = pd.read_csv(word_list_path, header=None, names=['word'])
        words = words_df['word'].tolist()
        print(f"✓ Loaded {len(words)} words from {word_list_path}")
    except Exception as e:
        print(f"✗ Error loading word list: {e}")
        print("  Nearest neighbor method will be skipped.")
        words = None

if words is not None:
    all_word_embeddings = {}
    
    for model_size in model_sizes:
        print(f"\n{model_size} Model - Word Embeddings:")
        
        if model_size in PREGENERATED_WORD_EMBEDDINGS:
            word_emb_path = PREGENERATED_WORD_EMBEDDINGS[model_size]
            if os.path.exists(word_emb_path):
                print(f"  Loading pregenerated from {word_emb_path}...")
                try:
                    data = np.load(word_emb_path, allow_pickle=True)
                    
                    word_embeddings = None
                    for key in ['word_embeddings', 'embeddings', 'vectors', 'arr_0']:
                        if key in data:
                            word_embeddings = data[key]
                            print(f"  ✓ Loaded from key '{key}': {word_embeddings.shape}")
                            break
                    
                    if word_embeddings is None:
                        print(f"  ✗ Could not find embeddings in file")
                        print(f"  Available keys: {list(data.keys())}")
                        print(f"  Falling back to generation...")
                        word_embeddings = None
                    else:
                        if word_embeddings.shape[0] != len(words):
                            print(f"  ⚠ WARNING: Embedding count ({word_embeddings.shape[0]}) != word count ({len(words)})")
                        
                        all_word_embeddings[model_size] = word_embeddings
                        continue
                        
                except Exception as e:
                    print(f"  ✗ Error loading pregenerated embeddings: {e}")
                    print(f"  Falling back to generation...")
            else:
                print(f"  ⚠ Pregenerated path specified but not found: {word_emb_path}")
                print(f"  Falling back to generation...")
        
        word_emb_path = f"embeddings/word_embeddings_{model_size}.npz"
        
        if os.path.exists(word_emb_path):
            print(f"  Loading from cache: {word_emb_path}...")
            data = np.load(word_emb_path, allow_pickle=True)
            word_embeddings = data['word_embeddings']
            if word_embeddings.shape[0] != len(words):
                print(f"  ⚠ WARNING: Cached word embedding count ({word_embeddings.shape[0]}) != word count ({len(words)})")
                print("  Regenerating word embeddings...")
                model_name = MODEL_NAMES[model_sizes.index(model_size)]
                model = load_sentence_transformer_model(model_name=model_name, device=device)
                word_embeddings = model.encode(
                    words,
                    show_progress_bar=True,
                    batch_size=256,
                    convert_to_numpy=True,
                    normalize_embeddings=False
                )
                print(f"  ✓ Generated: {word_embeddings.shape}")
                np.savez(word_emb_path, word_embeddings=word_embeddings, words=words)
                print(f"  ✓ Saved to {word_emb_path}")
            else:
                print(f"  ✓ Loaded: {word_embeddings.shape}")
        else:
            print(f"  Generating embeddings for {len(words)} words...")
            model_name = MODEL_NAMES[model_sizes.index(model_size)]
            model = load_sentence_transformer_model(model_name=model_name, device=device)
            
            word_embeddings = model.encode(
                words, 
                show_progress_bar=True, 
                batch_size=256,
                convert_to_numpy=True, 
                normalize_embeddings=False
            )
            
            print(f"  ✓ Generated: {word_embeddings.shape}")
            
            np.savez(word_emb_path, word_embeddings=word_embeddings, words=words)
            print(f"  ✓ Saved to {word_emb_path}")
        
        all_word_embeddings[model_size] = word_embeddings
    
    print(f"\n✓ Word embeddings ready for {len(all_word_embeddings)} model(s)")
else:
    all_word_embeddings = {}
    print("\n⚠ Word embeddings not available - nearest neighbor method will be skipped")

if not ENABLE_FACTOR_NAMING:
    print("\n" + "="*70)
    print("FACTOR NAMING DISABLED")
    print("="*70)
    print("  Set ENABLE_FACTOR_NAMING = True to enable automatic factor naming")
    print("="*70 + "\n")
else:
    print("Setting up HuggingFace API client for automatic factor naming...")

    if "HF_TOKEN" not in os.environ:
        print("\n✗ Error: HF_TOKEN environment variable not set!")
        print("  Please set your HuggingFace token in the environment.")
        print("\nSkipping automatic factor naming...")
        client = None
    else:
        client = OpenAI(
            base_url="https://router.huggingface.co/v1",
            api_key=os.environ["HF_TOKEN"],
        )
        print("✓ HuggingFace API client initialized")
        print(f"  Instruct model: Qwen/Qwen3-235B-A22B-Instruct-2507:novita")
        print(f"  Base model: Qwen/Qwen3-235B-A22B:novita")

    if client is not None:
        print(f"\n{'='*70}")
        print("COMPARING THREE FACTOR NAMING APPROACHES")
        print(f"{'='*70}")
        print("\nMethod 1: Direct Scale Items (with psychological context)")
        print("Method 2: Nearest Neighbor Words (vocabulary-based, instruction-tuned)")
        print("Method 3: Base Model Token Prediction (non-instruct, greedy sampling)")
        print(f"{'='*70}\n")

        factor_name_mappings_direct = {}
        factor_name_mappings_nn = {}
        factor_name_mappings_base = {}

        factor_probabilities_base = {}

        nn_available = len(all_word_embeddings) > 0 and words is not None

        for model_size in model_sizes:
            print(f"\n{'='*70}")
            print(f"{model_size} Model - Automatic Factor Naming")
            print(f"{'='*70}")

            loadings_df = all_results[model_size]['loadings']
            embeddings = all_embeddings[model_size]

            factor_name_mappings_direct[model_size] = {}
            factor_name_mappings_nn[model_size] = {}
            factor_name_mappings_base[model_size] = {}
            factor_probabilities_base[model_size] = {}

            if nn_available:
                word_embeddings = all_word_embeddings[model_size]
                print(f"\n✓ Word embeddings available: {word_embeddings.shape}")
            else:
                print(f"\n⚠ Word embeddings not available - only Method 1 will run")

            for factor_name in loadings_df.columns:
                print(f"\n{'='*70}")
                print(f"{factor_name}:")
                print(f"{'='*70}")

                factor_loadings = loadings_df[factor_name].abs()
                top_indices = factor_loadings.nlargest(10).index

                print(f"\n[METHOD 1: Direct Scale Items]")
                top_items_text = []
                print(f"Top 10 loading items:")
                for i, code in enumerate(top_indices, 1):
                    item_idx = codes.index(code)
                    item_text = items[item_idx]
                    loading_val = loadings_df.loc[code, factor_name]
                    top_items_text.append(item_text)
                    print(f"  {i}. (λ={loading_val:.3f}): {item_text[:70]}")

                items_for_prompt = " | ".join([item[:120] for item in top_items_text])
                user_prompt_direct = f"These are items from a psychological scale that all load strongly on the same latent factor: {items_for_prompt} Provide a single word or very short phrase (max 3 words) that best describes the psychological construct. Provide ONLY the label."

                system_prompt = "You are a helpful assistant that provides concise, one or two-word summaries."

                completion = client.chat.completions.create(
                    model="Qwen/Qwen3-235B-A22B-Instruct-2507:novita",
                    messages=[
                        {"role": "system", "content": system_prompt},
                        {"role": "user", "content": user_prompt_direct}
                    ],
                    max_tokens=10,
                    temperature=0,
                    seed=42,
                )

                generated_name_direct = completion.choices[0].message.content.strip()
                generated_name_direct = re.sub(r'^["\'\s]+|["\'\s]+$', '', generated_name_direct)
                generated_name_direct = re.sub(r'[.,;:!?]+$', '', generated_name_direct)
                generated_name_direct = generated_name_direct.split('\n')[0].strip()
                generated_name_direct = re.sub(r'^\*+|\*+$', '', generated_name_direct)
                if len(generated_name_direct) < 2:
                    generated_name_direct = factor_name

                factor_name_mappings_direct[model_size][factor_name] = generated_name_direct
                print(f"  Method 1 Result: '{generated_name_direct}'")

                if nn_available:
                    print(f"\n[METHOD 2: Nearest Neighbor Words]")
                    top_item_indices = [codes.index(code) for code in top_indices]
                    factor_embeddings = embeddings[top_item_indices]
                    factor_embeddings_norm = factor_embeddings / np.linalg.norm(factor_embeddings, axis=1, keepdims=True)
                    centroid = np.mean(factor_embeddings_norm, axis=0)
                    centroid_norm = centroid / np.linalg.norm(centroid)

                    similarities = cosine_similarity([centroid_norm], word_embeddings)[0]
                    top_word_indices = np.argsort(similarities)[::-1][:10]
                    top_words = [words[idx] for idx in top_word_indices]

                    print("Top 10 nearest neighbor words:")
                    for i, (word_idx, word) in enumerate(zip(top_word_indices, top_words), 1):
                        print(f"  {i}. {word:<20} (sim={similarities[word_idx]:.4f})")

                    words_list = ", ".join(top_words)
                    user_prompt_nn = f"Give a label that best summarizes these related concepts: {words_list}. Provide ONLY the label."

                    completion = client.chat.completions.create(
                        model="Qwen/Qwen3-235B-A22B-Instruct-2507:novita",
                        messages=[
                            {"role": "system", "content": system_prompt},
                            {"role": "user", "content": user_prompt_nn}
                        ],
                        max_tokens=10,
                        temperature=0,
                        seed=42,
                    )

                    generated_name_nn = completion.choices[0].message.content.strip()
                    generated_name_nn = re.sub(r'^["\'\s]+|["\'\s]+$', '', generated_name_nn)
                    generated_name_nn = re.sub(r'[.,;:!?]+$', '', generated_name_nn)
                    generated_name_nn = generated_name_nn.split('\n')[0].strip()
                    if len(generated_name_nn) < 2:
                        generated_name_nn = factor_name

                    factor_name_mappings_nn[model_size][factor_name] = generated_name_nn
                    print(f"  Method 2 Result: '{generated_name_nn}'")

                    if ENABLE_METHOD_3:
                        print(f"\n[METHOD 3: Base Model Token Prediction]")
                        prompt_base = " ".join(top_words)
                        print(f"Prompt (words only): '{prompt_base}'")

                        try:
                            completion = client.text_generation.create(
                                model="Qwen/Qwen3-235B-A22B:novita",
                                input=prompt_base,
                                max_tokens=10,
                                temperature=0,
                                seed=42
                            )
                            top_token = completion.generations[0].text.strip()
                            factor_name_mappings_base[model_size][factor_name] = top_token
                            print(f"  Method 3 Result: '{top_token}'")
                        except Exception as e:
                            print(f"  ⚠ Method 3 failed: {str(e)}")
                            factor_name_mappings_base[model_size][factor_name] = "(error)"
                    else:
                        print(f"\n[METHOD 3: DISABLED]")
                        factor_name_mappings_base[model_size][factor_name] = "(disabled)"
                else:
                    factor_name_mappings_nn[model_size][factor_name] = "(not available)"
                    if ENABLE_METHOD_3:
                        factor_name_mappings_base[model_size][factor_name] = "(not available)"
                    else:
                        factor_name_mappings_base[model_size][factor_name] = "(disabled)"

        print(f"\n{'='*70}")
        print("FACTOR NAME COMPARISON SUMMARY")
        print(f"{'='*70}")
        for model_size in model_sizes:
            print(f"\n{model_size} Model:")
            print(f"{'-'*70}")
            print(f"{'Factor':<12} {'Method 1':<20} {'Method 2':<20} {'Method 3':<20}")
            print(f"{'-'*70}")
            for factor_name in sorted(factor_name_mappings_direct[model_size].keys()):
                print(f"{factor_name:<12} "
                      f"{factor_name_mappings_direct[model_size][factor_name]:<20} "
                      f"{factor_name_mappings_nn[model_size].get(factor_name,'N/A'):<20} "
                      f"{factor_name_mappings_base[model_size].get(factor_name,'N/A'):<20}")
            print(f"{'-'*70}")

    else:
        print("\n⚠ Skipping automatic factor naming due to missing HF_TOKEN.")

print("Creating factor assignments from EFA loadings...")

factor_assignments = {}

for model_size in model_sizes:
    loadings_df = all_results[model_size]['loadings']
    
    item_to_factor = {}
    
    for item_code in loadings_df.index:
        abs_loadings = loadings_df.loc[item_code].abs()
        assigned_factor = abs_loadings.idxmax()
        item_to_factor[item_code] = assigned_factor
    
    factor_items = {}
    for factor_name in loadings_df.columns:
        assigned_items = [
            {'index': codes.index(code), 'code': code}
            for code, assigned_f in item_to_factor.items()
            if assigned_f == factor_name
        ]
        factor_items[factor_name] = assigned_items
    
    factor_assignments[model_size] = factor_items
    
    print(f"\n{model_size}:")
    for factor_name, items_list in factor_items.items():
        display_name = factor_name
        try:
            display_name = factor_name_mappings_nn[model_size].get(factor_name, factor_name)
        except (NameError, KeyError):
            pass
        
        print(f"  {factor_name} ('{display_name}'): {len(items_list)} items")

print("✓ Factor assignments created!")

print(f"\n{'='*70}")
print("COMPUTING T-SNE FOR EMBEDDING DATA")
print(f"{'='*70}")
print(f"Number of items: {len(factors)}")
print(f"Number of models: {len(all_embeddings)}")

all_tsne_embeddings = {}

for model_size, embeddings in all_embeddings.items():
    print(f"\nProcessing {model_size}...")

    tsne = TSNE(verbose=0,
        n_components=2,
        perplexity=10,
        max_iter=1000,
        random_state=42,
    )

    embeddings_2d = tsne.fit_transform(embeddings)
    all_tsne_embeddings[model_size] = embeddings_2d

print(f"\n{'='*70}")
print(f"✓ T-SNE complete for all {len(all_tsne_embeddings)} models!")
print(f"{'='*70}")

if empirical_data is not None:
    print(f"\n{'='*70}")
    print("COMPUTING T-SNE FOR EMPIRICAL DATA")
    print(f"{'='*70}")
    print(f"Input: {empirical_data.shape[0]:,} participants × {empirical_data.shape[1]} items")
    print(f"Transposing to: {empirical_data.shape[1]} items × {empirical_data.shape[0]:,} participants")

    empirical_transposed = empirical_data.T
    print(f"  Transposed shape: {empirical_transposed.shape}")
    print(f"\n  Running T-SNE on items in participant space...")
    print(f"  This visualizes item relationships based on human response patterns")

    tsne_emp = TSNE(verbose=0, 
        n_components=2,
        perplexity=10,
        max_iter=1000,
        random_state=42,
    )

    empirical_2d = tsne_emp.fit_transform(empirical_transposed)

    print(f"\n{'='*70}")
    print("✓ EMPIRICAL T-SNE COMPUTATION COMPLETE")
    print(f"{'='*70}")

else:
    print(f"\n{'='*70}")
    print("Empirical data not available - skipping empirical t-SNE")
    print(f"{'='*70}")

if empirical_data is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("TSNE VISUALIZATION: EFA Factors Comparison")
    print(f"{'='*70}")

    model_size = list(all_tsne_embeddings.keys())[0]
    embeddings_2d = all_tsne_embeddings[model_size]

    embedding_loadings_df = all_results[model_size].get('loadings')
    empirical_loadings_df = empirical_results.get('loadings')

    if embedding_loadings_df is not None and empirical_loadings_df is not None:
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))

        custom_colors = ['#0907FF', '#00EAFF', '#0CCF14', '#FF2F00', '#C62FF4', '#F4B62F']

        empirical_item_to_factor = assign_items_to_extracted_factors(empirical_loadings_df)
        _, empirical_label_map = get_extracted_factor_display_labels(
            empirical_loadings_df.columns.tolist(),
            empirical_results.get('tucker_best')
        )
        ordered_empirical_extracted = apply_manual_extracted_factor_order(
            empirical_loadings_df.columns.tolist(),
            empirical_label_map,
            manual_order=PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
        )
        empirical_display_order = []
        for factor_name in ordered_empirical_extracted:
            label = empirical_label_map.get(factor_name, factor_name)
            if label not in empirical_display_order:
                empirical_display_order.append(label)
        empirical_item_to_display = {
            item_code: empirical_label_map.get(factor_name, factor_name)
            for item_code, factor_name in empirical_item_to_factor.items()
        }
        empirical_factor_colors = {
            label: custom_colors[i % len(custom_colors)]
            for i, label in enumerate(empirical_display_order)
        }
        empirical_handles = {}

        for label in empirical_display_order:
            indices = [i for i, code in enumerate(codes) if empirical_item_to_display.get(code) == label]
            if indices:
                handle = ax1.scatter(
                    empirical_2d[indices, 0],
                    empirical_2d[indices, 1],
                    c=[empirical_factor_colors[label]],
                    label=label,
                    alpha=0.7,
                    s=100,
                    edgecolors='black',
                    linewidths=0.5
                )
                empirical_handles[label] = handle

        for i in range(len(empirical_2d)):
            ax1.annotate(
                codes[i],
                (empirical_2d[i, 0], empirical_2d[i, 1]),
                fontsize=8,
                textcoords='offset points',
                xytext=(0, 6),
                ha='center',
                va='bottom',
                fontweight='bold'
            )

        ax1.set_xlabel('t-SNE Dimension 1', fontsize=12)
        ax1.set_ylabel('t-SNE Dimension 2', fontsize=12)
        ax1.set_title('t-SNE (Human Responses)', fontsize=14, fontweight='bold')
        empirical_legend_order = [label for label in empirical_display_order if label in empirical_handles]
        ax1.legend(
            [empirical_handles[label] for label in empirical_legend_order],
            empirical_legend_order,
            loc='upper right',
            fontsize=9,
            framealpha=0.9
        )
        ax1.grid(True, alpha=0.3)
        ax1.set_aspect('equal', adjustable='datalim')

        embedding_item_to_factor = assign_items_to_extracted_factors(embedding_loadings_df)
        _, embedding_label_map = get_embedding_factor_display_labels(
            embedding_loadings_df.columns.tolist(),
            model_size
        )
        ordered_embedding_extracted = apply_manual_extracted_factor_order(
            embedding_loadings_df.columns.tolist(),
            embedding_label_map,
            manual_order=PLOT_EXTRACTED_FACTOR_ORDER
        )
        embedding_display_order = []
        for factor_name in ordered_embedding_extracted:
            label = embedding_label_map.get(factor_name, factor_name)
            if label not in embedding_display_order:
                embedding_display_order.append(label)
        embedding_item_to_display = {
            item_code: embedding_label_map.get(factor_name, factor_name)
            for item_code, factor_name in embedding_item_to_factor.items()
        }
        embedding_factor_colors = {
            label: custom_colors[i % len(custom_colors)]
            for i, label in enumerate(embedding_display_order)
        }
        embedding_handles = {}

        for label in embedding_display_order:
            indices = [i for i, code in enumerate(codes) if embedding_item_to_display.get(code) == label]
            if indices:
                handle = ax2.scatter(
                    embeddings_2d[indices, 0],
                    embeddings_2d[indices, 1],
                    c=[embedding_factor_colors[label]],
                    label=label,
                    alpha=0.7,
                    s=100,
                    edgecolors='black',
                    linewidths=0.5
                )
                embedding_handles[label] = handle

        for i in range(len(embeddings_2d)):
            ax2.annotate(
                codes[i],
                (embeddings_2d[i, 0], embeddings_2d[i, 1]),
                fontsize=8,
                textcoords='offset points',
                xytext=(0, 6),
                ha='center',
                va='bottom',
                fontweight='bold'
            )

        ax2.set_xlabel('t-SNE Dimension 1', fontsize=12)
        ax2.set_ylabel('t-SNE Dimension 2', fontsize=12)
        ax2.set_title('t-SNE (Embeddings)', fontsize=14, fontweight='bold')
        embedding_legend_order = [label for label in embedding_display_order if label in embedding_handles]
        ax2.legend(
            [embedding_handles[label] for label in embedding_legend_order],
            embedding_legend_order,
            loc='upper right',
            fontsize=9,
            framealpha=0.9
        )
        ax2.grid(True, alpha=0.3)
        ax2.set_aspect('equal', adjustable='datalim')

        plt.tight_layout()

        filename = f'{SCALE_NAME}_comparison_tsne.png'
        filepath = f'{SAVE_DIR}/{filename}'
        plt.savefig(filepath, dpi=300, bbox_inches='tight')
        print(f"\n✓ TSNE EFA comparison saved to: {filepath}")

        plt.close()

    else:
        print("\n⚠ Factor loadings not available for comparison")
        print("  Both analyses need extracted loadings to create EFA factor visualization")

else:
    print(f"\n{'='*70}")
    print("Skipping TSNE EFA comparison (requires both empirical and embedding data)")
    print(f"{'='*70}")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Correlation Matrices")
    print(f"{'='*70}")

    model_size = list(all_results.keys())[0]

    # Get correlation/similarity matrices
    empirical_corr = empirical_results['similarity_matrix']  # Actually correlation matrix
    embedding_sim = all_results[model_size]['similarity_matrix']  # Cosine similarity

    # Create side-by-side comparison
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18 + COMPARISON_SUBPLOT_SPACING, 10))

    # Order items by theoretical factors for better visualization
    factor_order = sorted(range(len(factors)), key=lambda i: (factors[i], i))
    empirical_ordered = empirical_corr[factor_order][:, factor_order]
    embedding_ordered = embedding_sim[factor_order][:, factor_order]

    # Create dividers for proper colorbar placement
    divider1 = make_axes_locatable(ax1)
    cax1 = divider1.append_axes("right", size="5%", pad=0.5)

    divider2 = make_axes_locatable(ax2)
    cax2 = divider2.append_axes("right", size="5%", pad=0.5)

    # LEFT: Empirical correlation matrix
    sns.heatmap(empirical_ordered, cmap='RdBu_r', center=0, vmin=-1, vmax=1,
               square=True, ax=ax1, cbar_ax=cax1, cbar_kws={'label': 'Correlation'},
               xticklabels=False, yticklabels=False)
    ax1.set_title('Correlation Matrix (Human Responses)', fontweight='bold')
    ax1.set_xlabel('Items (grouped by factor)')
    ax1.set_ylabel('Items (grouped by factor)')

    # RIGHT: Embedding similarity matrix
    sns.heatmap(embedding_ordered, cmap='RdBu_r', center=0, vmin=-1, vmax=1,
               square=True, ax=ax2, cbar_ax=cax2, cbar_kws={'label': 'Cosine Similarity'},
               xticklabels=False, yticklabels=False)
    ax2.set_title('Similarity Matrix (Embeddings)', fontweight='bold')
    ax2.set_xlabel('Items (grouped by factor)')
    ax2.set_ylabel('Items (grouped by factor)')

    plt.tight_layout()

    # Save
    filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_matrices.png'
    plt.savefig(filepath, dpi=150, bbox_inches='tight')
    plt.close()

    print(f"  ✓ Saved: {filepath}")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Factor Loadings")
    print(f"{'='*70}")
    
    model_size = list(all_results.keys())[0]
    embedding_loadings = all_results[model_size]['loadings']
    empirical_loadings = empirical_results['loadings']

    embedding_factor_labels, embedding_display_label_map = get_embedding_factor_display_labels(
        embedding_loadings.columns.tolist(),
        model_size
    )
    empirical_factor_labels, empirical_display_label_map = get_extracted_factor_display_labels(
        empirical_loadings.columns.tolist(),
        empirical_results.get('tucker_best')
    )
    ordered_embedding_extracted = apply_manual_extracted_factor_order(
        embedding_loadings.columns.tolist(),
        embedding_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER
    )
    ordered_empirical_extracted = apply_manual_extracted_factor_order(
        empirical_loadings.columns.tolist(),
        empirical_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
    )
    embedding_factor_labels = [embedding_display_label_map.get(factor, factor) for factor in ordered_embedding_extracted]
    empirical_factor_labels = [empirical_display_label_map.get(factor, factor) for factor in ordered_empirical_extracted]
    
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18 + COMPARISON_SUBPLOT_SPACING, 10))
    
    factor_order = sorted(range(len(factors)), key=lambda i: (factors[i], i))
    ordered_codes = [codes[i] for i in factor_order]
    
    embedding_ordered = embedding_loadings.loc[ordered_codes, ordered_embedding_extracted]
    empirical_ordered = empirical_loadings.loc[ordered_codes, ordered_empirical_extracted]
    
    sns.heatmap(empirical_ordered.values, cmap='RdBu_r', center=0, vmin=-1, vmax=1,
               ax=ax1, cbar_kws={'label': 'Loading'},
               yticklabels=False, xticklabels=empirical_factor_labels)
    ax1.set_title('Factor Loadings (Human Responses)',
                  fontweight='bold')
    ax1.set_xlabel('Extracted Factors')
    ax1.set_ylabel('Items (ordered by theoretical factor)')
    ax1.tick_params(axis='x', rotation=0)

    sns.heatmap(embedding_ordered.values, cmap='RdBu_r', center=0, vmin=-1, vmax=1,
               ax=ax2, cbar_kws={'label': 'Loading'},
               yticklabels=False, xticklabels=embedding_factor_labels)
    ax2.set_title('Factor Loadings (Embeddings)',
                  fontweight='bold')
    ax2.set_xlabel('Extracted Factors')
    ax2.set_ylabel('Items (ordered by theoretical factor)')
    ax2.tick_params(axis='x', rotation=0)
    
    
    
    
    filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_loadings.png'
    plt.savefig(filepath, dpi=150, bbox_inches='tight')
    plt.close()
    
    print(f"  ✓ Saved: {filepath}")
    
    embedding_variance = all_results[model_size]['variance'][2][-1]
    empirical_variance = empirical_results['variance'][2][-1]
    
    print(f"\n  Variance Explained:")
    print(f"    Embeddings: {embedding_variance:.1%}")
    print(f"    Empirical:  {empirical_variance:.1%}")
    print(f"    Difference: {(empirical_variance - embedding_variance):.1%}")
    
else:
    print("\nSkipping comparison - empirical results not available")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Item Loadings on Their Theoretical Factors")
    print(f"{'='*70}")
    
    model_size = list(all_results.keys())[0]
    embedding_loadings = all_results[model_size]['loadings']
    empirical_loadings = empirical_results['loadings']
    
    embedding_tucker_best = all_results[model_size].get('tucker_best')
    empirical_tucker_best = empirical_results.get('tucker_best')
    
    if embedding_tucker_best is not None and empirical_tucker_best is not None:
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20 + COMPARISON_SUBPLOT_SPACING, 10))

        custom_colors = ['#0907FF', '#00EAFF', '#0CCF14', '#FF2F00', '#C62FF4', '#F4B62F']

        _, embedding_display_label_map = get_embedding_factor_display_labels(
            embedding_loadings.columns.tolist(),
            model_size
        )
        _, empirical_display_label_map = get_extracted_factor_display_labels(
            empirical_loadings.columns.tolist(),
            empirical_tucker_best
        )
        ordered_embedding_extracted = apply_manual_extracted_factor_order(
            embedding_loadings.columns.tolist(),
            embedding_display_label_map,
            manual_order=PLOT_EXTRACTED_FACTOR_ORDER
        )
        ordered_empirical_extracted = apply_manual_extracted_factor_order(
            empirical_loadings.columns.tolist(),
            empirical_display_label_map,
            manual_order=PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
        )
        def get_2d_factor_spec(ordered_extracted, display_label_map):
            if not ordered_extracted:
                return [None, None], ['Factor1', 'No Factor 2 (0)']
            if len(ordered_extracted) == 1:
                first = ordered_extracted[0]
                return [first, None], [display_label_map.get(first, first), 'No Factor 2 (0)']
            first, second = ordered_extracted[:2]
            return [first, second], [
                display_label_map.get(first, first),
                display_label_map.get(second, second)
            ]

        embedding_factor_names, embedding_axis_labels = get_2d_factor_spec(
            ordered_embedding_extracted,
            embedding_display_label_map
        )
        empirical_factor_names, empirical_axis_labels = get_2d_factor_spec(
            ordered_empirical_extracted,
            empirical_display_label_map
        )

        item_to_extracted_factor = assign_items_to_extracted_factors(embedding_loadings)
        embedding_extracted_factors = ordered_embedding_extracted
        embedding_display_names = {
            factor_name: embedding_display_label_map.get(factor_name, factor_name)
            for factor_name in embedding_extracted_factors
        }
        if PLOT_EXTRACTED_FACTOR_ORDER:
            sorted_embedding_factors = embedding_extracted_factors
        else:
            sorted_embedding_factors = sorted(
                embedding_extracted_factors,
                key=lambda factor_name: embedding_display_names[factor_name]
            )
        embedding_factor_colors = {
            factor_name: custom_colors[i % len(custom_colors)]
            for i, factor_name in enumerate(sorted_embedding_factors)
        }

        item_to_empirical_factor = assign_items_to_extracted_factors(empirical_loadings)
        empirical_extracted_factors = ordered_empirical_extracted
        empirical_display_names = {
            factor_name: empirical_display_label_map.get(factor_name, factor_name)
            for factor_name in empirical_extracted_factors
        }
        if PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL:
            sorted_empirical_factors = empirical_extracted_factors
        else:
            sorted_empirical_factors = sorted(
                empirical_extracted_factors,
                key=lambda factor_name: empirical_display_names[factor_name]
            )
        empirical_factor_colors = {
            factor_name: custom_colors[i % len(custom_colors)]
            for i, factor_name in enumerate(sorted_empirical_factors)
        }
        
        def plot_2d_loadings(ax, loadings_df, factor_names, axis_labels, title, 
                            item_colors_map, factor_list, display_names,
                            use_manual_order=False):
            ax.axhline(y=0, color='black', linestyle='-', linewidth=0.8, alpha=0.5)
            ax.axvline(x=0, color='black', linestyle='-', linewidth=0.8, alpha=0.5)
            
            for i, code in enumerate(codes):
                if code in loadings_df.index and code in item_colors_map:
                    x = loadings_df.loc[code, factor_names[0]] if factor_names[0] is not None else 0.0
                    y = loadings_df.loc[code, factor_names[1]] if factor_names[1] is not None else 0.0
                    
                    item_factor = item_colors_map[code]
                    
                    ax.plot([0, x], [0, y], color='gray', alpha=0.4, linewidth=1)
                    
                    ax.scatter(x, y, c=[factor_list[item_factor]], s=100, 
                              alpha=0.7, edgecolors='white', linewidth=1.5, zorder=5)
            
            ax.set_xlabel(axis_labels[0], fontweight='bold')
            ax.set_ylabel(axis_labels[1], fontweight='bold')
            ax.set_title(title, fontweight='bold')
            
            ax.set_aspect('equal')
            ax.set_xlim(-1.1, 1.1)
            ax.set_ylim(-1.1, 1.1)
            ax.grid(True, alpha=0.3, linestyle='-', linewidth=0.5)
            
            if use_manual_order:
                sorted_factors = list(factor_list.keys())
            else:
                sorted_factors = sorted(factor_list.keys(), key=lambda f: display_names[f])
            legend_elements = [plt.Line2D([0], [0], marker='o', color='w', 
                                         markerfacecolor=factor_list[f], 
                                         markersize=10, label=display_names[f], alpha=0.7)
                              for f in sorted_factors]
            ax.legend(handles=legend_elements, loc='upper right')

        plot_2d_loadings(ax1, empirical_loadings, empirical_factor_names, empirical_axis_labels,
                        'Factor Loadings Plot (Human Responses)',
                        item_to_empirical_factor, empirical_factor_colors, empirical_display_names,
                        use_manual_order=bool(PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL))

        plot_2d_loadings(ax2, embedding_loadings, embedding_factor_names, embedding_axis_labels,
                        'Factor Loadings Plot (Embeddings)',
                        item_to_extracted_factor, embedding_factor_colors, embedding_display_names,
                        use_manual_order=bool(PLOT_EXTRACTED_FACTOR_ORDER))

        filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_factor_loadings.png'
        plt.savefig(filepath, dpi=150, bbox_inches='tight')
        plt.close()
        
        print(f"  ✓ Saved: {filepath}")
        
        print(f"\n  Factor Pair Visualized:")
        print(f"    Embeddings: {embedding_axis_labels[0]} vs {embedding_axis_labels[1]}")
        print(f"    Empirical:  {empirical_axis_labels[0]} vs {empirical_axis_labels[1]}")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: DAAL Matrices")
    print(f"{'='*70}")

    model_size = list(all_results.keys())[0]
    embedding_daal = all_results[model_size]['daal']
    empirical_daal = empirical_results['daal']

    embedding_factor_labels, embedding_display_label_map = get_embedding_factor_display_labels(
        embedding_daal.index.tolist(),
        model_size
    )
    empirical_factor_labels, empirical_display_label_map = get_extracted_factor_display_labels(
        empirical_daal.index.tolist(),
        empirical_results.get('tucker_best')
    )
    ordered_embedding_extracted = apply_manual_extracted_factor_order(
        embedding_daal.index.tolist(),
        embedding_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER
    )
    ordered_empirical_extracted = apply_manual_extracted_factor_order(
        empirical_daal.index.tolist(),
        empirical_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
    )
    embedding_daal = embedding_daal.loc[ordered_embedding_extracted]
    empirical_daal = empirical_daal.loc[ordered_empirical_extracted]
    embedding_factor_labels = [embedding_display_label_map.get(factor, factor) for factor in ordered_embedding_extracted]
    empirical_factor_labels = [empirical_display_label_map.get(factor, factor) for factor in ordered_empirical_extracted]

    # Create side-by-side comparison
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20 + COMPARISON_SUBPLOT_SPACING, 8))

    # Create dividers for proper colorbar placement
    divider1 = make_axes_locatable(ax1)
    cax1 = divider1.append_axes("right", size="5%", pad=0.5)

    divider2 = make_axes_locatable(ax2)
    cax2 = divider2.append_axes("right", size="5%", pad=0.5)

    # LEFT: Empirical DAAL
    sns.heatmap(empirical_daal.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlOrRd',
               xticklabels=empirical_daal.columns, yticklabels=empirical_factor_labels,
               ax=ax1, vmin=0, vmax=1, cbar_ax=cax1, cbar_kws={'label': 'DAAL'})
    ax1.set_title('DAAL Matrix (Human Responses)', fontweight='bold')
    ax1.set_xlabel('Theoretical Factors')
    ax1.set_ylabel('Extracted Factors')

    # RIGHT: Embedding DAAL
    sns.heatmap(embedding_daal.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlOrRd',
               xticklabels=embedding_daal.columns, yticklabels=embedding_factor_labels,
               ax=ax2, vmin=0, vmax=1, cbar_ax=cax2, cbar_kws={'label': 'DAAL'})
    ax2.set_title('DAAL Matrix (Embeddings)', fontweight='bold')
    ax2.set_xlabel('Theoretical Factors')
    ax2.set_ylabel('Extracted Factors')

    plt.tight_layout()

    # Save
    filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_daal.png'
    plt.savefig(filepath, dpi=150, bbox_inches='tight')
    plt.close()

    print(f"  ✓ Saved: {filepath}")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Tucker Congruence")
    print(f"{'='*70}")
    
    model_size = list(all_results.keys())[0]
    embedding_tucker = all_results[model_size]['tucker']
    empirical_tucker = empirical_results['tucker']

    embedding_factor_labels, embedding_display_label_map = get_embedding_factor_display_labels(
        embedding_tucker.index.tolist(),
        model_size
    )
    empirical_factor_labels, empirical_display_label_map = get_extracted_factor_display_labels(
        empirical_tucker.index.tolist(),
        empirical_results.get('tucker_best')
    )
    ordered_embedding_extracted = apply_manual_extracted_factor_order(
        embedding_tucker.index.tolist(),
        embedding_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER
    )
    ordered_empirical_extracted = apply_manual_extracted_factor_order(
        empirical_tucker.index.tolist(),
        empirical_display_label_map,
        manual_order=PLOT_EXTRACTED_FACTOR_ORDER_EMPIRICAL
    )
    embedding_tucker = embedding_tucker.loc[ordered_embedding_extracted]
    empirical_tucker = empirical_tucker.loc[ordered_empirical_extracted]
    embedding_factor_labels = [embedding_display_label_map.get(factor, factor) for factor in ordered_embedding_extracted]
    empirical_factor_labels = [empirical_display_label_map.get(factor, factor) for factor in ordered_empirical_extracted]
    
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20 + COMPARISON_SUBPLOT_SPACING, 8))

    divider1 = make_axes_locatable(ax1)
    cax1 = divider1.append_axes("right", size="5%", pad=0.5)

    divider2 = make_axes_locatable(ax2)
    cax2 = divider2.append_axes("right", size="5%", pad=0.5)

    sns.heatmap(empirical_tucker.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlGnBu',
               xticklabels=empirical_tucker.columns, yticklabels=empirical_factor_labels,
               ax=ax1, vmin=0, vmax=1, cbar_ax=cax1, cbar_kws={'label': 'Tucker φ'})
    ax1.set_title('Tucker Congruence (Human Responses)',
                  fontweight='bold')
    ax1.set_xlabel('Theoretical Factors')
    ax1.set_ylabel('Extracted Factors')

    sns.heatmap(embedding_tucker.values, annot=True, annot_kws={'fontsize': 15}, fmt='.3f', cmap='YlGnBu',
               xticklabels=embedding_tucker.columns, yticklabels=embedding_factor_labels,
               ax=ax2, vmin=0, vmax=1, cbar_ax=cax2, cbar_kws={'label': 'Tucker φ'})
    ax2.set_title('Tucker Congruence (Embeddings)',
                  fontweight='bold')
    ax2.set_xlabel('Theoretical Factors')
    ax2.set_ylabel('Extracted Factors')
    
    
    
    
    filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_tucker.png'
    plt.savefig(filepath, dpi=150, bbox_inches='tight')
    plt.close()
    
    print(f"  ✓ Saved: {filepath}")
    
    embedding_best = all_results[model_size]['tucker_best']
    empirical_best = empirical_results['tucker_best']
    
    print(f"\n  Best Tucker Matches:")
    print(f"    {'Factor':<12} {'Embeddings':<25} {'Empirical':<25}")
    print(f"    {'-'*12} {'-'*25} {'-'*25}")
    
    for i in range(min(len(embedding_best), len(empirical_best))):
        emb_row = embedding_best.iloc[i]
        emp_row = empirical_best.iloc[i]
        print(f"    {emb_row['extracted_factor']:<12} {emb_row['best_match']:<15} (φ={emb_row['tucker_phi']:.3f})   "
              f"{emp_row['best_match']:<15} (φ={emp_row['tucker_phi']:.3f})")
    
    emb_matches = {row['extracted_factor']: row['best_match'] for _, row in embedding_best.iterrows()}
    emp_matches = {row['extracted_factor']: row['best_match'] for _, row in empirical_best.iterrows()}
    
    agreement_count = sum(1 for k in emb_matches if k in emp_matches and emb_matches[k] == emp_matches[k])
    total_factors = len(emb_matches)
    
    print(f"\n  Agreement: {agreement_count}/{total_factors} factors assigned to same theoretical factor")
    
else:
    print("\nSkipping comparison - empirical results not available")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Scree Plots")
    print(f"{'='*70}")

    model_size = list(all_results.keys())[0]
    emb_eigenvalues = all_results[model_size]['observed_eigenvalues']
    emp_eigenvalues = empirical_results['observed_eigenvalues']

    emb_percentile = all_results[model_size].get('percentile_eigenvalues', None)
    emp_percentile = empirical_results.get('percentile_eigenvalues', None)

    emb_n_factors = all_results[model_size]['n_factors']
    emp_n_factors = empirical_results['n_factors']

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))

    n_eigs = len(emp_eigenvalues)
    x_vals = np.arange(1, n_eigs + 1)

    ax1.plot(x_vals, emp_eigenvalues, 'o-', color='darkorange', linewidth=2,
             markersize=6, label='Observed eigenvalues')

    if emp_percentile is not None:
        ax1.plot(x_vals, emp_percentile, '--', color='darkred', linewidth=1.5,
                 alpha=0.7, label='95th percentile (parallel analysis)')

    ax1.axvline(x=emp_n_factors, color='green', linestyle=':', linewidth=2,
                alpha=0.6, label=f'Factors retained (n={emp_n_factors})')

    ax1.axhline(y=1, color='gray', linestyle='--', linewidth=1, alpha=0.5,
                label='Kaiser criterion (λ=1)')

    ax1.set_xlabel('Factor number')
    ax1.set_ylabel('Eigenvalue')
    ax1.set_title('Scree Plot (Human Responses)')
    ax1.legend(loc='upper right', fontsize=9)
    ax1.grid(True, alpha=0.3)
    ax1.set_xlim(0.5, min(20, n_eigs) + 0.5)

    ax2.plot(x_vals, emb_eigenvalues, 'o-', color='steelblue', linewidth=2,
             markersize=6, label='Observed eigenvalues')

    if emb_percentile is not None:
        ax2.plot(x_vals, emb_percentile, '--', color='darkred', linewidth=1.5,
                 alpha=0.7, label='95th percentile (parallel analysis)')

    ax2.axvline(x=emb_n_factors, color='green', linestyle=':', linewidth=2,
                alpha=0.6, label=f'Factors retained (n={emb_n_factors})')

    ax2.axhline(y=1, color='gray', linestyle='--', linewidth=1, alpha=0.5,
                label='Kaiser criterion (λ=1)')

    ax2.set_xlabel('Factor number')
    ax2.set_ylabel('Eigenvalue')
    ax2.set_title('Scree Plot (Embeddings)')
    ax2.legend(loc='upper right', fontsize=9)
    ax2.grid(True, alpha=0.3)
    ax2.set_xlim(0.5, min(20, n_eigs) + 0.5)

    plt.tight_layout()

    filename = f'{SCALE_NAME}_comparison_scree.png'
    filepath = f'{SAVE_DIR}/{filename}'
    plt.savefig(filepath, dpi=300, bbox_inches='tight')
    print(f"\n✓ Scree plots saved to: {filepath}")

    print(f"\nEigenvalue Summary:")
    print(f"  LLM Embeddings ({model_size}):")
    print(f"    First eigenvalue: {emb_eigenvalues[0]:.2f}")
    print(f"    Factors retained: {emb_n_factors}")
    print(f"  Human Responses (Empirical):")
    print(f"    First eigenvalue: {emp_eigenvalues[0]:.2f}")
    print(f"    Factors retained: {emp_n_factors}")

    plt.close()

else:
    print("\nSkipping scree plot comparison (requires both empirical and embedding results)")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Within vs Between-Construct Analysis")
    print(f"{'='*70}")
    
    model_size = list(all_results.keys())[0]
    embedding_sim = all_results[model_size]['similarity_matrix']
    empirical_corr = empirical_results['similarity_matrix']
    
    n_items = len(codes)
    emb_within = []
    emb_between = []
    emp_within = []
    emp_between = []
    
    for i in range(n_items):
        for j in range(i + 1, n_items):
            if factors[i] == factors[j]:
                emb_within.append(embedding_sim[i, j])
                emp_within.append(empirical_corr[i, j])
            else:
                emb_between.append(embedding_sim[i, j])
                emp_between.append(empirical_corr[i, j])
    
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18 + COMPARISON_SUBPLOT_SPACING, 8))
    
    emb_t, emb_p = ttest_ind(emb_within, emb_between, equal_var=False)
    emb_mean_w = np.mean(emb_within)
    emb_mean_b = np.mean(emb_between)
    emb_d = (emb_mean_w - emb_mean_b) / np.sqrt((np.std(emb_within)**2 + np.std(emb_between)**2) / 2)
    
    emp_t, emp_p = ttest_ind(emp_within, emp_between, equal_var=False)
    emp_mean_w = np.mean(emp_within)
    emp_mean_b = np.mean(emp_between)
    emp_d = (emp_mean_w - emp_mean_b) / np.sqrt((np.std(emp_within)**2 + np.std(emp_between)**2) / 2)
    
    emp_data = pd.DataFrame({
        'value': emp_within + emp_between,
        'type': ['Within'] * len(emp_within) + ['Between'] * len(emp_between)
    })
    sns.violinplot(data=emp_data, x='type', y='value', hue='type', ax=ax1, palette=['#2ecc71', '#e74c3c'], legend=False)
    ax1.plot([0], [emp_mean_w], 'D', color='darkgreen', markersize=10)
    ax1.plot([1], [emp_mean_b], 'D', color='darkred', markersize=10)
    y_offset = (ax1.get_ylim()[1] - ax1.get_ylim()[0]) * 0.07
    ax1.text(0, ax1.get_ylim()[0] - y_offset, f'n={len(emp_within)}', ha='center', va='top')
    ax1.text(1, ax1.get_ylim()[0] - y_offset, f'n={len(emp_between)}', ha='center', va='top')
    ax1.set_title(f'Within vs Between (Human Responses)\nd = {emp_d:.3f}, p < .001' if emp_p < 0.001 else f'Within vs Between (Human Responses)\nd = {emp_d:.3f}, p = {emp_p:.3f}',
                  fontweight='bold')
    ax1.set_ylabel('Correlation')
    ax1.set_xlabel('')
    ax1.grid(True, alpha=0.3, axis='y')

    emb_data = pd.DataFrame({
        'value': emb_within + emb_between,
        'type': ['Within'] * len(emb_within) + ['Between'] * len(emb_between)
    })
    sns.violinplot(data=emb_data, x='type', y='value', hue='type', ax=ax2, palette=['#2ecc71', '#e74c3c'], legend=False)
    ax2.plot([0], [emb_mean_w], 'D', color='darkgreen', markersize=10)
    ax2.plot([1], [emb_mean_b], 'D', color='darkred', markersize=10)
    y_offset = (ax2.get_ylim()[1] - ax2.get_ylim()[0]) * 0.07
    ax2.text(0, ax2.get_ylim()[0] - y_offset, f'n={len(emb_within)}', ha='center', va='top')
    ax2.text(1, ax2.get_ylim()[0] - y_offset, f'n={len(emb_between)}', ha='center', va='top')
    ax2.set_title(f'Within vs Between (Embeddings)\nd = {emb_d:.3f}, p < .001' if emb_p < 0.001 else f'Within vs Between (Embeddings)\nd = {emb_d:.3f}, p = {emb_p:.3f}',
                  fontweight='bold')
    ax2.set_ylabel('Cosine Similarity')
    ax2.set_xlabel('')
    ax2.grid(True, alpha=0.3, axis='y')

    

    plt.tight_layout()

    filepath = f'{SAVE_DIR}/{SCALE_NAME}_comparison_within_between.png'
    plt.savefig(filepath, dpi=300, bbox_inches='tight')
    plt.close()
    
    print(f"  ✓ Saved: {filepath}")
    
    print(f"\n  Summary Statistics:")
    print(f"    {'Metric':<30} {'Embeddings':<15} {'Empirical':<15}")
    print(f"    {'-'*30} {'-'*15} {'-'*15}")
    print(f"    {'Within mean':<30} {emb_mean_w:<15.3f} {emp_mean_w:<15.3f}")
    print(f"    {'Between mean':<30} {emb_mean_b:<15.3f} {emp_mean_b:<15.3f}")
    print(f"    {'Difference':<30} {emb_mean_w - emb_mean_b:<15.3f} {emp_mean_w - emp_mean_b:<15.3f}")
    cohens_d_label = "Cohen's d"
    print(f"    {cohens_d_label:<30} {emb_d:<15.3f} {emp_d:<15.3f}")
    print(f"    {'t-statistic':<30} {emb_t:<15.3f} {emp_t:<15.3f}")
    print(f"    {'p-value':<30} {emb_p:<15.2e} {emp_p:<15.2e}")
    
else:
    print("\nSkipping comparison - empirical results not available")

if empirical_results is not None and len(all_results) > 0:
    print(f"\n{'='*70}")
    print("COMPARISON: Matrix-Level Correlation Analysis")
    print(f"{'='*70}\n")
    
    model_size = list(all_results.keys())[0]
    
    llm_similarity = all_results[model_size]['similarity_matrix']
    human_correlation = empirical_results['similarity_matrix']
    
    n_items = llm_similarity.shape[0]
    mask = np.tril(np.ones((n_items, n_items), dtype=bool), k=-1)
    
    llm_cos = llm_similarity[mask]
    human_corr = human_correlation[mask]
    
    r, p_pearson = stats.pearsonr(llm_cos, human_corr)
    r_squared = r ** 2
    
    llm_dist = 1 - llm_similarity.copy()
    human_dist = 1 - human_correlation.copy()
    
    llm_dist = (llm_dist + llm_dist.T) / 2
    human_dist = (human_dist + human_dist.T) / 2
    
    np.fill_diagonal(llm_dist, 0)
    np.fill_diagonal(human_dist, 0)
    
    llm_dist = np.nan_to_num(llm_dist, nan=0.0)
    human_dist = np.nan_to_num(human_dist, nan=0.0)

    # Convert to float32 for scikit-bio compatibility
    llm_dist = llm_dist.astype(np.float32)
    human_dist = human_dist.astype(np.float32)

    r_mantel, p_mantel, n_items_compared = mantel(
        llm_dist,
        human_dist,
        method='pearson',
        permutations=10000,
        alternative='greater'
    )
    
    print(f"Pairwise Correlation Analysis:")
    print(f"  Pearson r: {r:.4f}")
    print(f"  R²: {r_squared:.4f}")
    print(f"  p-value: {p_pearson:.4e}")
    print(f"\nMantel Test Results:")
    print(f"  Mantel r: {r_mantel:.4f}")
    print(f"  p-value: {p_mantel:.4f}")
    print(f"  Items compared: {n_items_compared}")
    print(f"  Permutations: 10,000")
    
    plt.figure(figsize=(8, 6))
    plt.scatter(human_corr, llm_cos, alpha=0.3, s=10)
    plt.xlabel("Human item correlations")
    plt.ylabel("LLM cosine similarities")
    plt.title(f"r = {r:.2f}, Mantel r = {r_mantel:.2f}, p = {p_mantel:.3g}")
    
    min_val = min(human_corr.min(), llm_cos.min())
    max_val = max(human_corr.max(), llm_cos.max())
    plt.plot([min_val, max_val], [min_val, max_val], 'r--', alpha=0.5, linewidth=1, label='Perfect agreement')
    plt.legend()
    
    plt.tight_layout()
    
    filename = f'{SCALE_NAME}_comparison_correlation.png'
    filepath = f'{SAVE_DIR}/{filename}'
    plt.savefig(filepath, dpi=300, bbox_inches='tight')
    print(f"\nPlot saved to: {filepath}")

    plt.close()
    
else:
    print("\nSkipping matrix-level correlation analysis (requires both empirical and embedding results)")

print(f"\n{'='*80}")
print(f"ANALYSIS COMPLETE: {SCALE_NAME}")
print(f"{'='*80}")
print(f"\nResults saved to: {SAVE_DIR}/")
print(f"Log file: {log_file_path}")
print(f"\nAnalysis completed: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print(f"{'='*80}\n")

if 'logger' in dir() and hasattr(logger, 'close'):
    logger.close()
    sys.stdout = logger.terminal
    print(f"✓ Log saved to: {log_file_path}")

if RUN_ALL_SCALES and len(SCALE_NAMES) > 1 and CURRENT_SCALE_INDEX < len(SCALE_NAMES) - 1:
    next_scale_index = CURRENT_SCALE_INDEX + 1
    next_scale_name = SCALE_NAMES[next_scale_index]

    if "__file__" not in globals():
        print("\nBatch mode requested, but __file__ is unavailable in this runtime.")
        print("Run this file as a script to process all scales automatically.")
    else:
        print(f"\nLaunching next scale automatically: {next_scale_name} ({next_scale_index + 1}/{len(SCALE_NAMES)})")
        next_env = os.environ.copy()
        next_env["SFA_SCALE_INDEX"] = str(next_scale_index)
        result = subprocess.run([sys.executable, os.path.abspath(__file__)], env=next_env)
        if result.returncode != 0:
            raise SystemExit(result.returncode)
elif RUN_ALL_SCALES and len(SCALE_NAMES) > 1:
    print(f"\nAll scales processed: {SCALE_NAMES}")
elif len(SCALE_NAMES) > 1:
    print(f"\nTo process another scale:")
    print(f"  1. Set SFA_SCALE_INDEX to the target index")
    print(f"  2. Run all cells again")
    print(f"\nScales available: {SCALE_NAMES}")