"""Extra analyses of the straight-liners"""
# %% Imports

import utils
import polars as pl
import argparse
import json
import re
from pathlib import Path
from validation import check_straight_liners


# %% Fixed Variables
RESULTS_FILE = 'data/exports/2-4-26/JPMC_Chase Brand Personality_Quant Round 1_February 4, 2026_Labels.csv'
QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'


# %% CLI argument parsing for batch automation
# When run as script: uv run XX_statistical_significance.script.py --age '["18
# Central filter configuration - add new filters here only
# Format: 'cli_arg_name': 'QualtricsSurvey.options_* attribute name'
FILTER_CONFIG = {
    'age': 'options_age',
    'gender': 'options_gender',
    'ethnicity': 'options_ethnicity',
    'income': 'options_income',
    'consumer': 'options_consumer',
    'business_owner': 'options_business_owner',
    'ai_user': 'options_ai_user',
    'investable_assets': 'options_investable_assets',
    'industry': 'options_industry',
}

def parse_cli_args():
    parser = argparse.ArgumentParser(description='Generate quant report with optional filters')
    
    # Dynamically add filter arguments from config
    for filter_name in FILTER_CONFIG:
        parser.add_argument(f'--{filter_name}', type=str, default=None, help=f'JSON list of {filter_name} values')
    
    parser.add_argument('--filter-name', type=str, default=None, help='Name for this filter combination (used for .txt description file)')
    parser.add_argument('--figures-dir', type=str, default=f'figures/straight-liner-analysis/{Path(RESULTS_FILE).parts[2]}', help='Override the default figures directory')
    
    # Only parse if running as script (not in Jupyter/interactive)
    try:
        # Check if running in Jupyter by looking for ipykernel
        get_ipython()  # noqa: F821 # type: ignore
        # Return namespace with all filters set to None
        no_filters = {f: None for f in FILTER_CONFIG}
        # Use the same default as argparse
        default_fig_dir = f'figures/straight-liner-analysis/{Path(RESULTS_FILE).parts[2]}'
        return argparse.Namespace(**no_filters, filter_name=None, figures_dir=default_fig_dir)
    except NameError:
        args = parser.parse_args()
        # Parse JSON strings to lists
        for filter_name in FILTER_CONFIG:
            val = getattr(args, filter_name)
            setattr(args, filter_name, json.loads(val) if val else None)
        return args

cli_args = parse_cli_args()


# %%
S = utils.QualtricsSurvey(RESULTS_FILE, QSF_FILE, figures_dir=cli_args.figures_dir)
data_all = S.load_data()


# %% Build filtered dataset based on CLI args

# CLI args: None means "no filter applied" - filter_data() will skip None filters

# Build filter values dict dynamically from FILTER_CONFIG
_active_filters = {filter_name: getattr(cli_args, filter_name) for filter_name in FILTER_CONFIG}

_d = S.filter_data(data_all, **_active_filters)

# Write filter description file if filter-name is provided
if cli_args.filter_name and S.fig_save_dir:
    # Get the filter slug (e.g., "All_Respondents", "Cons-Starter", etc.)
    _filter_slug = S._get_filter_slug()
    _filter_slug_dir = S.fig_save_dir / _filter_slug
    _filter_slug_dir.mkdir(parents=True, exist_ok=True)
    
    # Build filter description
    _filter_desc_lines = [
        f"Filter: {cli_args.filter_name}",
        "",
        "Applied Filters:",
    ]
    _short_desc_parts = []
    for filter_name, options_attr in FILTER_CONFIG.items():
        all_options = getattr(S, options_attr)
        values = _active_filters[filter_name]
        display_name = filter_name.replace('_', ' ').title()
        # None means no filter applied (same as "All")
        if values is not None and values != all_options:
            _short_desc_parts.append(f"{display_name}: {', '.join(values)}")
            _filter_desc_lines.append(f"  {display_name}: {', '.join(values)}")
        else:
            _filter_desc_lines.append(f"  {display_name}: All")
    
    # Write detailed description INSIDE the filter-slug directory
    # Sanitize filter name for filename usage (replace / and other chars)
    _safe_filter_name = re.sub(r'[^\w\s-]', '_', cli_args.filter_name)
    _filter_file = _filter_slug_dir / f"{_safe_filter_name}.txt"
    _filter_file.write_text('\n'.join(_filter_desc_lines))
    
    # Append to summary index file at figures/<export_date>/filter_index.txt
    _summary_file = S.fig_save_dir / "filter_index.txt"
    _short_desc = "; ".join(_short_desc_parts) if _short_desc_parts else "All Respondents"
    _summary_line = f"{_filter_slug}  |  {cli_args.filter_name}  |  {_short_desc}\n"
    
    # Append or create the summary file
    if _summary_file.exists():
        _existing = _summary_file.read_text()
        # Avoid duplicate entries for same slug
        if _filter_slug not in _existing:
            with _summary_file.open('a') as f:
                f.write(_summary_line)
    else:
        _header = "Filter Index\n" + "=" * 80 + "\n\n"
        _header += "Directory  |  Filter Name  |  Description\n"
        _header += "-" * 80 + "\n"
        _summary_file.write_text(_header + _summary_line)

# Save to logical variable name for further analysis
data = _d
data.collect()


# %% Determine straight-liner repeat offenders
# Extract question groups with renamed columns that check_straight_liners expects.
# The raw `data` has QID-based column names; the getter methods rename them to
# patterns like SS_Green_Blue__V14__Choice_1, Voice_Scale_1_10__V48, etc.

ss_or, _ = S.get_ss_orange_red(data)
ss_gb, _ = S.get_ss_green_blue(data)
vs, _ = S.get_voice_scale_1_10(data)

# Combine all question groups into one wide LazyFrame (joined on _recordId)
all_questions = ss_or.join(ss_gb, on='_recordId').join(vs, on='_recordId')

# Run straight-liner detection across all question groups
# max_score=5 catches all speaking-style straight-lining (1-5 scale)
# and voice-scale values ≤5 on the 1-10 scale
# Note: sl_threshold is NOT set on S here — this script analyses straight-liners,
# it doesn't filter them out of the dataset.
print("Running straight-liner detection across all question groups...")
sl_report, sl_df = check_straight_liners(all_questions, max_score=5)

# %% Quantify repeat offenders
# sl_df has one row per (Record ID, Question Group) that was straight-lined.
# Group by Record ID to count how many question groups each person SL'd.

if sl_df is not None and not sl_df.is_empty():
    total_respondents = data.select(pl.len()).collect().item()

    # Per-respondent count of straight-lined question groups
    respondent_sl_counts = (
        sl_df
        .group_by("Record ID")
        .agg(pl.len().alias("sl_count"))
        .sort("sl_count", descending=True)
    )

    max_sl = respondent_sl_counts["sl_count"].max()
    print(f"\nTotal respondents: {total_respondents}")
    print(f"Respondents who straight-lined at least 1 question group: "
          f"{respondent_sl_counts.height}")
    print(f"Maximum question groups straight-lined by one person: {max_sl}")
    print()

    # Build cumulative distribution: for each threshold N, count respondents
    # who straight-lined >= N question groups
    cumulative_rows = []
    for threshold in range(1, max_sl + 1):
        count = respondent_sl_counts.filter(
            pl.col("sl_count") >= threshold
        ).height
        pct = (count / total_respondents) * 100
        cumulative_rows.append({
            "threshold": threshold,
            "count": count,
            "pct": pct,
        })
        print(
            f"  ≥{threshold} question groups straight-lined: "
            f"{count} respondents ({pct:.1f}%)"
        )

    cumulative_df = pl.DataFrame(cumulative_rows)
    print(f"\n{cumulative_df}")

    # %% Save cumulative data to CSV
    _filter_slug = S._get_filter_slug()
    _csv_dir = Path(S.fig_save_dir) / _filter_slug
    _csv_dir.mkdir(parents=True, exist_ok=True)

    _csv_path = _csv_dir / "straight_liner_repeat_offenders.csv"
    cumulative_df.write_csv(_csv_path)
    print(f"Saved cumulative data to {_csv_path}")

    # %% Plot the cumulative distribution
    S.plot_straight_liner_repeat_offenders(
        cumulative_df,
        total_respondents=total_respondents,
    )

    # %% Per-question straight-lining frequency
    # Build human-readable question group names from the raw keys
    def _humanise_question_group(key: str) -> str:
        """Convert internal question group key to a readable label.

        Examples:
            SS_Green_Blue__V14  → Green/Blue – V14
            SS_Orange_Red__V48  → Orange/Red – V48
            Voice_Scale_1_10    → Voice Scale (1-10)
        """
        if key.startswith("SS_Green_Blue__"):
            voice = key.split("__")[1]
            return f"Green/Blue – {voice}"
        if key.startswith("SS_Orange_Red__"):
            voice = key.split("__")[1]
            return f"Orange/Red – {voice}"
        if key == "Voice_Scale_1_10":
            return "Voice Scale (1-10)"
        # Fallback: replace underscores
        return key.replace("_", " ")

    per_question_counts = (
        sl_df
        .group_by("Question Group")
        .agg(pl.col("Record ID").n_unique().alias("count"))
        .sort("count", descending=True)
        .with_columns(
            (pl.col("count") / total_respondents * 100).alias("pct")
        )
    )

    # Add human-readable names
    per_question_counts = per_question_counts.with_columns(
        pl.col("Question Group").map_elements(
            _humanise_question_group, return_dtype=pl.Utf8
        ).alias("question")
    )

    print("\n--- Per-Question Straight-Lining Frequency ---")
    print(per_question_counts)

    # Save per-question data to CSV
    _csv_path_pq = _csv_dir / "straight_liner_per_question.csv"
    per_question_counts.write_csv(_csv_path_pq)
    print(f"Saved per-question data to {_csv_path_pq}")

    # Plot
    S.plot_straight_liner_per_question(
        per_question_counts,
        total_respondents=total_respondents,
    )

    # %% Show the top repeat offenders (respondents with most SL'd groups)
    print("\n--- Top Repeat Offenders ---")
    print(respondent_sl_counts.head(20))

else:
    print("No straight-liners detected in the dataset.")