src/data_loader.py

"""Load and normalize thematic analysis CSV data."""
import os
import pandas as pd

# Base path: one level up from src/
BASE = os.path.join(os.path.dirname(__file__), "..")
KRIUKOWTA_DIR = os.path.join(BASE, "KriukowTA")
ZAMBRANOTA_DIR = os.path.join(BASE, "ZambranoTA")
BRAUNCLARKETA_DIR = os.path.join(BASE, "BraunClarkeTA")
GOOGLEFORM_DIR = os.path.join(BASE, "GoogleForm")

# Default encoding for CSV files (handles BOM and special characters)
CSV_ENCODING = "utf-8-sig"


def load_videota_initial_coding():
    """Load KriukowTA Initial Coding data."""
    path = os.path.join(KRIUKOWTA_DIR, "VideoTA - InitialCoding.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_videota_focused_coding():
    """Load KriukowTA Focused Coding (Axial Coding) data."""
    path = os.path.join(KRIUKOWTA_DIR, "VideoTA - FocusedCoding(Axial Coding).csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_videota_mapping_evidence():
    """Load KriukowTA Mapping & Evidence data."""
    path = os.path.join(KRIUKOWTA_DIR, "VideoTA - Mapping&Evidence.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_videota_moscow():
    """Load KriukowTA MoSCoW prioritization data."""
    path = os.path.join(KRIUKOWTA_DIR, "VideoTA - MoSCoW.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_coded_dataset():
    """Load ZambranoTA Coded Dataset."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - CodedDataset.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_initial_codebook():
    """Load ZambranoTA Initial Codebook."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - InitialCodeBook.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_refined_codebook():
    """Load ZambranoTA Refined Codebook."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - RefinedCodeBook.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_theme_frequency():
    """Load ZambranoTA Theme Frequency Distribution."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - ThemeFrequencyDistribution.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_moscow():
    """Load ZambranoTA MoSCoW prioritization data."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - MoSCoW.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_zambranota_qualitative_analysis():
    """Load ZambranoTA Qualitative Analysis."""
    path = os.path.join(ZAMBRANOTA_DIR, "ZambranoTA - QualitativeAnalysis.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_braunclarke_ta():
    """Load Braun-Clarke Thematic Analysis."""
    path = os.path.join(BRAUNCLARKETA_DIR, "BraunClarkeTA - Braun_Clarke.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_braunclarke_moscow():
    """Load Braun-Clarke MoSCoW prioritization data."""
    path = os.path.join(BRAUNCLARKETA_DIR, "BraunClarkeTA - Braun_ClarkeMoSCoW.csv")
    if not os.path.exists(path):
        return pd.DataFrame()
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return pd.DataFrame()


def load_google_form():
    """Load Google Form responses CSV. Returns None if file not found (optional data)."""
    import glob
    if not os.path.exists(GOOGLEFORM_DIR):
        return None
    # Try multiple patterns to be more flexible
    patterns = [
        os.path.join(GOOGLEFORM_DIR, "*Form*Responses*.csv"),
        os.path.join(GOOGLEFORM_DIR, "*responses*.csv"),
        os.path.join(GOOGLEFORM_DIR, "*.csv"),
    ]
    files = []
    for pattern in patterns:
        files = glob.glob(pattern, recursive=False)
        if files:
            break
    if not files:
        return None
    path = files[0]
    try:
        df = pd.read_csv(path, encoding=CSV_ENCODING)
        return df.dropna(how="all")
    except Exception:
        return None


def participant_count(s):
    """Parse 'P1, P4, P6' or 'P1/P14' or 'Participant 1, Participant 2' into count."""
    if pd.isna(s) or not str(s).strip():
        return 0
    s = str(s).replace("/", ",").replace(" and ", ",")
    # Handle both "P1" and "Participant 1" formats
    parts = [x.strip() for x in s.split(",") if "P" in x or "participant" in x.lower() or x.strip().replace("#", "").isdigit()]
    # Unique participant IDs
    seen = set()
    for p in parts:
        # Extract numbers
        n = "".join(c for c in p if c.isdigit())
        if n:
            seen.add(n)
    return len(seen)