"""
Data analyzer for large datasets.

This module contains the DataAnalyzerJob class that analyzes
large datasets and returns analysis results.

Author: Vasiliy Zdanovskiy
email: vasilyvz@gmail.com
"""

import time
import statistics
from typing import Any, Dict, List

from queuemgr.jobs.base import QueueJobBase


class DataAnalyzerJob(QueueJobBase):
    """Job that analyzes large datasets and returns analysis results."""

    def __init__(self, job_id: str, params: dict):
        """
        Initialize DataAnalyzerJob.

        Args:
            job_id: Unique job identifier.
            params: Job parameters.
        """
        super().__init__(job_id, params)
        self.analysis_type = params.get("analysis_type", "basic")
        self.sample_size = params.get("sample_size", 1000)

    def execute(self) -> None:
        """Analyze large dataset and set as result."""
        print(
            f"DataAnalyzerJob {self.job_id}: Analyzing data with "
            f"{self.analysis_type} analysis..."
        )

        start_time = time.time()

        # Simulate analyzing large dataset
        analysis_result = self._perform_analysis()

        analysis_time = time.time() - start_time

        # Create result with analysis
        result = {
            "analysis_type": self.analysis_type,
            "sample_size": self.sample_size,
            "analysis_time": analysis_time,
            "analysis_result": analysis_result,
            "summary": self._generate_summary(analysis_result),
        }

        # Set result
        self.set_result(result)

        print(
            f"DataAnalyzerJob {self.job_id}: Analysis completed in "
            f"{analysis_time:.2f}s"
        )

    def _perform_analysis(self) -> Dict[str, Any]:
        """Perform data analysis based on type."""
        if self.analysis_type == "statistical":
            return self._statistical_analysis()
        elif self.analysis_type == "pattern":
            return self._pattern_analysis()
        elif self.analysis_type == "clustering":
            return self._clustering_analysis()
        else:  # basic
            return self._basic_analysis()

    def _basic_analysis(self) -> Dict[str, Any]:
        """Perform basic data analysis."""
        # Simulate analyzing large dataset
        time.sleep(2)  # Simulate processing time

        return {
            "total_records": self.sample_size * 100,
            "data_quality": 0.95,
            "completeness": 0.98,
            "consistency": 0.92,
            "anomalies_detected": 15,
            "recommendations": [
                "Data quality is good",
                "Consider data validation",
                "Monitor for anomalies",
            ],
        }

    def _statistical_analysis(self) -> Dict[str, Any]:
        """Perform statistical analysis."""
        time.sleep(3)  # Simulate processing time

        # Generate sample statistics
        sample_data = [i * 0.1 + (i % 10) for i in range(self.sample_size)]

        return {
            "mean": statistics.mean(sample_data),
            "median": statistics.median(sample_data),
            "std_dev": statistics.stdev(sample_data),
            "variance": statistics.variance(sample_data),
            "min": min(sample_data),
            "max": max(sample_data),
            "quartiles": {
                "q1": statistics.quantiles(sample_data, n=4)[0],
                "q2": statistics.quantiles(sample_data, n=4)[1],
                "q3": statistics.quantiles(sample_data, n=4)[2],
            },
            "distribution": "normal",
            "outliers": 5,
        }

    def _pattern_analysis(self) -> Dict[str, Any]:
        """Perform pattern analysis."""
        time.sleep(4)  # Simulate processing time

        return {
            "patterns_found": 12,
            "trends": {"increasing": 3, "decreasing": 2, "cyclical": 4, "stable": 3},
            "correlations": {"strong": 2, "moderate": 5, "weak": 3},
            "seasonality": True,
            "periodicity": 24,  # hours
            "anomaly_score": 0.15,
        }

    def _clustering_analysis(self) -> Dict[str, Any]:
        """Perform clustering analysis."""
        time.sleep(5)  # Simulate processing time

        return {
            "clusters_found": 6,
            "cluster_sizes": [150, 200, 180, 120, 100, 50],
            "silhouette_score": 0.75,
            "cluster_centers": [
                {"x": 1.2, "y": 2.3, "z": 0.8},
                {"x": 3.1, "y": 1.5, "z": 2.1},
                {"x": 0.9, "y": 3.2, "z": 1.7},
            ],
            "optimal_clusters": 6,
            "convergence_iterations": 15,
        }

    def _generate_summary(self, analysis_result: Dict[str, Any]) -> Dict[str, Any]:
        """Generate analysis summary."""
        return {
            "analysis_completed": True,
            "timestamp": time.time(),
            "key_findings": [
                "Data quality is acceptable",
                "Several patterns identified",
                "Recommendations generated",
            ],
            "confidence_score": 0.85,
            "next_steps": [
                "Review anomalies",
                "Validate patterns",
                "Implement recommendations",
            ],
        }

    def on_start(self) -> None:
        """Called when job starts."""
        print(f"DataAnalyzerJob {self.job_id}: Starting analysis...")

    def on_stop(self) -> None:
        """Called when job stops."""
        print(f"DataAnalyzerJob {self.job_id}: Stopping analysis...")

    def on_end(self) -> None:
        """Called when job ends."""
        print(f"DataAnalyzerJob {self.job_id}: Analysis completed!")

    def on_error(self, exc: BaseException) -> None:
        """Called when job encounters an error."""
        print(f"DataAnalyzerJob {self.job_id}: Error occurred: {exc}")
