"""Django management command: ``python manage.py run_pipeline``

Runs the analysis pipeline from CLI: load → cluster → extract features.
Usage:  uv run python manage.py run_pipeline <csv_glob> [--algo hdbscan|kmeans]
"""
import os, sys, json, asyncio
from django.core.management.base import BaseCommand, CommandError
from django.utils import timezone

class Command(BaseCommand):
    help = 'Run the analysis pipeline from CLI (load → cluster → extract)'

    def add_arguments(self, parser):
        parser.add_argument('csv_glob', type=str, help='Glob pattern for CSV files')
        parser.add_argument('--algo', type=str, default='hdbscan', choices=['hdbscan', 'kmeans'],
                            help='Clustering algorithm (default: hdbscan)')
        parser.add_argument('--output', type=str, default=None,
                            help='Output directory for exports (default: no export)')

    def handle(self, *args, **options):
        import django; django.setup()
        from analysis.models import AnalysisRun
        from analysis.views import _run_pipeline_worker

        csv_glob = options['csv_glob']
        algo = options['algo']
        output = options['output']

        # Create AnalysisRun record first (_run_pipeline_worker needs it)
        run_id = AnalysisRun.objects.create(
            csv_glob=csv_glob, status='running',
        ).id

        # Capture self for console output inside the closure
        _self = self

        def _analysis_fn(run, ctx):
            import warnings
            warnings.filterwarnings('ignore', category=RuntimeWarning, module='sklearn')
            from analysis.session_store import SessionStore
            from analysis.data_loader import load_csv_directory

            store = SessionStore()
            store.drop_all()

            # ── Step 1: Load CSV ─────────────────────────────────────────────
            _self.stdout.write(f'[1/3] 加载 CSV: {csv_glob}')
            lf, schema, row_count, file_count, memory_mb = load_csv_directory(csv_glob)
            ds_id = 'manual_ds'
            store.store_dataset(ds_id, lf, schema=schema, metadata={
                'row_count': row_count, 'file_count': file_count, 'csv_glob': csv_glob,
            })
            _self.stdout.write(_self.style.SUCCESS(f'  → {row_count} rows, {file_count} files, {memory_mb:.1f} MB'))

            # Auto-select numeric columns for clustering
            numeric_types = {'Int8', 'Int16', 'Int32', 'Int64',
                              'UInt8', 'UInt16', 'UInt32', 'UInt64',
                              'Float32', 'Float64'}
            feature_cols = [
                c for c in schema.keys()
                if not c.startswith('_')
                and schema[c].split('(')[0].strip() in numeric_types
            ][:10]

            # ── Step 2: Clustering + Extraction + UMAP (shared pipeline) ─────
            _self.stdout.write(f'[2/3] 聚类 ({algo})')
            _self.stdout.write(f'  → 聚类特征: {feature_cols}')
            run.total_flows = row_count
            run.save(update_fields=['total_flows'])
            from analysis.services.clustering import run_clustering_pipeline
            run_clustering_pipeline(
                run=run, store=store, entity_ds_id=ds_id,
                feature_columns=feature_cols,
                algorithm=algo, min_cluster_size=5,
                run_umap=True,
            )
            if run.status == 'failed':
                _self.stderr.write(_self.style.ERROR(f'  ✗ 分析失败: {run.error_message}'))
                return
            _self.stdout.write(_self.style.SUCCESS(
                f'  → {run.cluster_count} 个簇'
            ))

            # ── Export ───────────────────────────────────────────────────────
            if output:
                from analysis.tool_registry import _handle_export_results
                os.makedirs(output, exist_ok=True)
                asyncio.run(_handle_export_results(result_id=ds_id, output_path=output, format='csv', overwrite=True))
                _self.stdout.write(_self.style.SUCCESS(f'  → 导出到 {output}'))

        _run_pipeline_worker(run_id, _analysis_fn)

        # ── Read final status and print summary ─────────────────────────────
        run = AnalysisRun.objects.get(id=run_id)
        if run.status == 'failed':
            err_msg = (run.error_message or '')[:500]
            self.stderr.write(self.style.ERROR(f'\n*** 分析失败! Run ID: #{run.display_id}'))
            if err_msg:
                self.stderr.write(self.style.ERROR(f'  {err_msg}'))
            return
        self.stdout.write(self.style.SUCCESS(
            f'\n*** 分析完成! Run ID: #{run.display_id}'
            f' 查看 Django: http://127.0.0.1:8000/runs/{run.display_id}/'
        ))