qt-log-analyzer/statistics_widget.py at main · BaseMax/qt-log-analyzer · GitHub

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"""
Statistics Widget
Displays statistics dashboard with charts and metrics.
"""

from PyQt5.QtWidgets import QWidget, QVBoxLayout, QHBoxLayout, QLabel, QGroupBox
from PyQt5.QtCore import Qt
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure

from log_statistics import LogStatistics


# Chart colors for consistent theming
CHART_COLORS = {
    'debug': '#808080',
    'info': '#4CAF50',
    'warning': '#FFA500',
    'error': '#FF0000',
    'critical': '#8B0000',
    'unknown': '#000000',
    'primary': '#4CAF50'
}


class StatisticsWidget(QWidget):
    """Widget for displaying statistics dashboard"""

    def __init__(self):
        super().__init__()
        self.init_ui()

    def init_ui(self):
        """Initialize the UI"""
        layout = QVBoxLayout()

        # Title
        title = QLabel("Log Statistics Dashboard")
        title.setAlignment(Qt.AlignCenter)
        title.setStyleSheet("font-size: 16px; font-weight: bold; padding: 10px;")
        layout.addWidget(title)

        # Create matplotlib figure for charts
        self.figure = Figure(figsize=(12, 8))
        self.canvas = FigureCanvas(self.figure)
        layout.addWidget(self.canvas)

        # Summary metrics
        self.metrics_panel = QGroupBox("Summary Metrics")
        metrics_layout = QHBoxLayout()

        self.lbl_total = QLabel("Total: 0")
        self.lbl_errors = QLabel("Errors: 0")
        self.lbl_anomalies = QLabel("Anomalies: 0")
        self.lbl_sources = QLabel("Sources: 0")

        for lbl in [self.lbl_total, self.lbl_errors, self.lbl_anomalies, self.lbl_sources]:
            lbl.setStyleSheet("font-size: 14px; padding: 10px;")
            metrics_layout.addWidget(lbl)

        self.metrics_panel.setLayout(metrics_layout)
        layout.addWidget(self.metrics_panel)

        self.setLayout(layout)

    def update_statistics(self, statistics: LogStatistics):
        """Update statistics with new data"""
        self.figure.clear()

        summary = statistics.get_summary()

        # Update metrics
        self.lbl_total.setText(f"Total: {summary['total_entries']}")
        self.lbl_errors.setText(f"Error Rate: {summary['error_rate']:.2f}%")
        self.lbl_anomalies.setText(f"Anomalies: {summary['anomaly_count']}")
        self.lbl_sources.setText(f"Unique Sources: {summary['unique_sources']}")

        # Create subplots
        if summary['total_entries'] == 0:
            ax = self.figure.add_subplot(111)
            ax.text(0.5, 0.5, 'No data available',
                   ha='center', va='center', fontsize=14)
            self.canvas.draw()
            return

        # 1. Log Level Distribution (Pie Chart)
        ax1 = self.figure.add_subplot(2, 2, 1)
        level_dist = summary['level_distribution']
        if level_dist:
            labels = list(level_dist.keys())
            sizes = list(level_dist.values())
            colors = [CHART_COLORS['debug'], CHART_COLORS['info'], CHART_COLORS['warning'],
                     CHART_COLORS['error'], CHART_COLORS['critical'], CHART_COLORS['unknown']]

            ax1.pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors[:len(labels)],
                   startangle=90)
            ax1.set_title('Log Level Distribution', fontweight='bold')

        # 2. Top Sources (Bar Chart)
        ax2 = self.figure.add_subplot(2, 2, 2)
        top_sources = summary['top_sources']
        if top_sources:
            sources = [s[0][:20] for s in top_sources]  # Truncate long names
            counts = [s[1] for s in top_sources]

            ax2.barh(sources, counts, color=CHART_COLORS['primary'])
            ax2.set_xlabel('Count', fontweight='bold')
            ax2.set_title('Top Log Sources', fontweight='bold')
            ax2.invert_yaxis()

        # 3. Error vs Non-Error (Bar Chart)
        ax3 = self.figure.add_subplot(2, 2, 3)
        error_count = sum(count for level, count in level_dist.items()
                         if level in ['ERROR', 'CRITICAL'])
        non_error_count = summary['total_entries'] - error_count

        ax3.bar(['Non-Errors', 'Errors'], [non_error_count, error_count],
               color=[CHART_COLORS['info'], CHART_COLORS['error']])
        ax3.set_ylabel('Count', fontweight='bold')
        ax3.set_title('Error Distribution', fontweight='bold')
        ax3.grid(axis='y', alpha=0.3)

        # 4. Anomaly Count (Text Display)
        ax4 = self.figure.add_subplot(2, 2, 4)
        ax4.axis('off')

        anomaly_pct = (summary['anomaly_count'] / summary['total_entries'] * 100) if summary['total_entries'] > 0 else 0

        metrics_text = f"""
        Total Entries: {summary['total_entries']:,}

        Anomalies: {summary['anomaly_count']:,}
        Anomaly Rate: {anomaly_pct:.2f}%

        Error Rate: {summary['error_rate']:.2f}%

        Unique Sources: {summary['unique_sources']}
        """

        ax4.text(0.5, 0.5, metrics_text, ha='center', va='center',
                fontsize=12, family='monospace',
                bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))

        # Tight layout
        self.figure.tight_layout()

        self.canvas.draw()