421 lines
16 KiB
Python
421 lines
16 KiB
Python
"""
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Enhanced Flow Table Widget - TipTop-inspired with inline visualizations
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"""
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from textual.widgets import DataTable
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from textual.containers import Vertical
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from textual.reactive import reactive
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from textual.message import Message
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from typing import TYPE_CHECKING, List, Optional
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from rich.text import Text
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from rich.box import ROUNDED
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if TYPE_CHECKING:
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from ....analysis.core import EthernetAnalyzer
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from ....models import FlowStats
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class EnhancedFlowTable(Vertical):
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"""
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Enhanced flow table with TipTop-style inline visualizations
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Features:
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- Inline sparklines for packet rate
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- Bar charts for volume and quality
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- Color-coded rows based on status
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- Hierarchical sub-rows for protocol breakdown
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"""
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DEFAULT_CSS = """
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EnhancedFlowTable {
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height: 1fr;
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padding: 0;
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margin: 0;
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}
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EnhancedFlowTable DataTable {
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height: 1fr;
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scrollbar-gutter: stable;
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padding: 0;
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margin: 0;
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}
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"""
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selected_flow_index = reactive(0)
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sort_key = reactive("flows")
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def __init__(self, analyzer: 'EthernetAnalyzer', **kwargs):
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super().__init__(**kwargs)
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self.analyzer = analyzer
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self.flows_list = []
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self.row_to_flow_map = {} # Map row keys to flow indices
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self.flow_metrics = {} # Store per-flow metrics history
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def compose(self):
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"""Create the enhanced flow table"""
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# Table title with sort indicators
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yield DataTable(
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id="flows-data-table",
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cursor_type="row",
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zebra_stripes=True,
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show_header=True,
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show_row_labels=False
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)
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def on_mount(self):
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"""Initialize the table"""
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table = self.query_one("#flows-data-table", DataTable)
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# Compact columns optimized for data density
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table.add_column("#", width=2, key="num")
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table.add_column("Source", width=18, key="source")
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table.add_column("Proto", width=4, key="proto")
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table.add_column("Destination", width=18, key="dest")
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table.add_column("Extended", width=8, key="extended")
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table.add_column("Frame Type", width=10, key="frame_type")
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table.add_column("Pkts", width=6, key="rate")
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table.add_column("Size", width=8, key="volume")
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self.refresh_data()
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def refresh_data(self):
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"""Refresh flow table with enhanced visualizations"""
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table = self.query_one("#flows-data-table", DataTable)
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# Preserve cursor and scroll positions
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cursor_row = table.cursor_row
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cursor_column = table.cursor_column
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scroll_x = table.scroll_x
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scroll_y = table.scroll_y
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selected_row_key = None
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if table.rows and cursor_row < len(table.rows):
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selected_row_key = list(table.rows.keys())[cursor_row]
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table.clear()
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# Clear row mapping
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self.row_to_flow_map.clear()
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# Get and sort flows
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self.flows_list = self._get_sorted_flows()
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# Add flows with enhanced display
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for i, flow in enumerate(self.flows_list):
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# Track metrics for this flow
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flow_key = f"{flow.src_ip}:{flow.src_port}-{flow.dst_ip}:{flow.dst_port}"
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if flow_key not in self.flow_metrics:
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self.flow_metrics[flow_key] = {
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'rate_history': [],
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'last_packet_count': flow.frame_count,
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'last_update': flow.last_seen
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}
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# Calculate current rate
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metrics = self.flow_metrics[flow_key]
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time_delta = flow.last_seen - metrics['last_update'] if metrics['last_update'] else 1
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packet_delta = flow.frame_count - metrics['last_packet_count']
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current_rate = packet_delta / max(time_delta, 0.1)
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# Update metrics
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metrics['rate_history'].append(current_rate)
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if len(metrics['rate_history']) > 10:
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metrics['rate_history'].pop(0)
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metrics['last_packet_count'] = flow.frame_count
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metrics['last_update'] = flow.last_seen
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# Create row with visualizations
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row_data = self._create_enhanced_row(i + 1, flow, metrics)
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row_key = table.add_row(*row_data, key=f"flow_{i}")
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# Map row key to flow index
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self.row_to_flow_map[row_key] = i
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# Apply row styling based on status
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style = self._get_flow_style(flow)
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if style:
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# Note: DataTable doesn't have set_row_style, using CSS classes instead
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pass
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# Add sub-rows for protocol breakdown
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if self._should_show_subrows(flow):
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sub_rows = self._create_protocol_subrows(flow)
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for j, sub_row in enumerate(sub_rows):
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sub_key = table.add_row(*sub_row, key=f"flow_{i}_sub_{j}")
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# Map sub-row to parent flow
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self.row_to_flow_map[sub_key] = i
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# Restore cursor position
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if selected_row_key and selected_row_key in table.rows:
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row_index = list(table.rows.keys()).index(selected_row_key)
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table.move_cursor(row=row_index, column=cursor_column, animate=False)
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elif table.row_count > 0:
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# If original selection not found, try to maintain row position
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new_row = min(cursor_row, table.row_count - 1)
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table.move_cursor(row=new_row, column=cursor_column, animate=False)
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# Restore scroll position
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table.scroll_to(x=scroll_x, y=scroll_y, animate=False)
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def _create_enhanced_row(self, num: int, flow: 'FlowStats', metrics: dict) -> List[Text]:
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"""Create enhanced row with inline visualizations"""
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# Flow number
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num_text = Text(str(num), justify="right")
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# Source (truncated if needed)
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source = f"{flow.src_ip}:{flow.src_port}"
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source_text = Text(source[:20] + "..." if len(source) > 22 else source)
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# Protocol with color
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proto_text = Text(flow.transport_protocol, style="bold cyan")
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# Destination
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dest = f"{flow.dst_ip}:{flow.dst_port}"
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dest_text = Text(dest[:20] + "..." if len(dest) > 22 else dest)
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# Extended protocol
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extended = self._get_extended_protocol(flow)
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extended_text = Text(extended, style="yellow" if extended != "-" else "dim")
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# Frame type summary
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frame_summary = self._get_frame_summary(flow)
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frame_text = Text(frame_summary, style="blue")
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# Rate with sparkline
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rate_spark = self._create_rate_sparkline(metrics['rate_history'])
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rate_text = Text(f"{metrics['rate_history'][-1]:.0f} {rate_spark}")
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# Size with actual value
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size_value = self._format_bytes(flow.total_bytes)
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size_text = Text(f"{size_value:>8}")
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# Quality with bar chart and color
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quality_bar, quality_color = self._create_quality_bar(flow)
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quality_value = self._get_quality_score(flow)
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quality_text = Text(f"{quality_value:>3}% {quality_bar}", style=quality_color)
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# Status indicator
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status = self._get_flow_status(flow)
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status_color = {
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"Normal": "green",
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"Enhanced": "bold green",
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"Warning": "yellow",
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"Alert": "red"
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}.get(status, "white")
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status_text = Text(status, style=status_color)
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return [
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num_text, source_text, proto_text, dest_text,
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extended_text, frame_text, rate_text, size_text
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]
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def _create_rate_sparkline(self, history: List[float]) -> str:
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"""Create mini sparkline for rate"""
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if not history:
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return "─" * 4
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spark_chars = " ▁▂▃▄▅▆▇█"
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data_min = min(history) if history else 0
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data_max = max(history) if history else 1
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if data_max == data_min:
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return "▄" * 4
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result = []
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for value in history[-4:]: # Last 4 values
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normalized = (value - data_min) / (data_max - data_min)
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char_index = int(normalized * 8)
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result.append(spark_chars[char_index])
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return "".join(result)
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def _create_volume_bar(self, bytes_count: int) -> str:
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"""Create bar chart for volume"""
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# Scale to GB for comparison
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gb = bytes_count / 1_000_000_000
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# Create bar (max 5 chars)
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if gb >= 10:
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return "█████"
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elif gb >= 1:
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filled = int(gb / 2)
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return "█" * filled + "░" * (5 - filled)
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else:
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# For smaller volumes, show at least one bar
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mb = bytes_count / 1_000_000
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if mb >= 100:
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return "█░░░░"
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else:
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return "▌░░░░"
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def _create_quality_bar(self, flow: 'FlowStats') -> tuple[str, str]:
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"""Create quality bar chart with color"""
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quality = self._get_quality_score(flow)
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# Create bar (5 chars)
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filled = int(quality / 20) # 0-100 -> 0-5
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bar = "█" * filled + "░" * (5 - filled)
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# Determine color
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if quality >= 90:
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color = "green"
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elif quality >= 70:
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color = "yellow"
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else:
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color = "red"
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return bar, color
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def _get_quality_score(self, flow: 'FlowStats') -> int:
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"""Calculate quality score for flow"""
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if flow.enhanced_analysis.decoder_type != "Standard":
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return int(flow.enhanced_analysis.avg_frame_quality)
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else:
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# Base quality on outlier percentage
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outlier_pct = len(flow.outlier_frames) / flow.frame_count * 100 if flow.frame_count > 0 else 0
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return max(0, int(100 - outlier_pct * 10))
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def _get_flow_status(self, flow: 'FlowStats') -> str:
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"""Determine flow status"""
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if flow.enhanced_analysis.decoder_type != "Standard":
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return "Enhanced"
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elif len(flow.outlier_frames) > flow.frame_count * 0.1:
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return "Alert"
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elif len(flow.outlier_frames) > 0:
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return "Warning"
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else:
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return "Normal"
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def _get_flow_style(self, flow: 'FlowStats') -> Optional[str]:
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"""Get styling for flow row"""
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status = self._get_flow_status(flow)
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if status == "Enhanced":
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return "bold"
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elif status == "Alert":
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return "bold red"
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elif status == "Warning":
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return "yellow"
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return None
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def _should_show_subrows(self, flow: 'FlowStats') -> bool:
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"""Determine if flow should show protocol breakdown"""
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# Show subrows for flows with multiple frame types or enhanced analysis
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return (len(flow.frame_types) > 1 or
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flow.enhanced_analysis.decoder_type != "Standard")
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def _create_protocol_subrows(self, flow: 'FlowStats') -> List[List[Text]]:
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"""Create sub-rows for protocol/frame type breakdown"""
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subrows = []
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combinations = self._get_protocol_frame_combinations(flow)
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for extended_proto, frame_type, count, percentage in combinations[:3]: # Max 3 subrows
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subrow = [
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Text(""), # Empty flow number
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Text(""), # Empty source
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Text(""), # Empty protocol
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Text(""), # Empty destination
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Text(f" {extended_proto}", style="dim yellow"),
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Text(frame_type, style="dim blue"),
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Text(f"{count}", style="dim", justify="right"),
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Text(f"{self._format_bytes(count * (flow.total_bytes // flow.frame_count) if flow.frame_count > 0 else 0):>8}", style="dim")
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]
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subrows.append(subrow)
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return subrows
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def _get_sorted_flows(self) -> List['FlowStats']:
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"""Get flows sorted by current sort key"""
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flows = list(self.analyzer.flows.values())
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if self.sort_key == "packets":
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flows.sort(key=lambda x: x.frame_count, reverse=True)
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elif self.sort_key == "volume":
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flows.sort(key=lambda x: x.total_bytes, reverse=True)
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elif self.sort_key == "quality":
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flows.sort(key=lambda x: self._get_quality_score(x), reverse=True)
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else: # Default: sort by importance
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flows.sort(key=lambda x: (
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x.enhanced_analysis.decoder_type != "Standard",
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len(x.outlier_frames),
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x.frame_count
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), reverse=True)
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return flows
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def sort_by(self, key: str):
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"""Change sort order"""
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self.sort_key = key
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self.refresh_data()
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class FlowSelected(Message):
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"""Message sent when a flow is selected"""
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def __init__(self, flow: Optional['FlowStats']) -> None:
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self.flow = flow
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super().__init__()
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def get_selected_flow(self) -> Optional['FlowStats']:
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"""Get currently selected flow"""
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table = self.query_one("#flows-data-table", DataTable)
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if table.cursor_row is None or not table.rows:
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return None
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# Get the row key at cursor position
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row_keys = list(table.rows.keys())
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if table.cursor_row >= len(row_keys):
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return None
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row_key = row_keys[table.cursor_row]
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# Look up flow index from our mapping
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flow_idx = self.row_to_flow_map.get(row_key)
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if flow_idx is not None and 0 <= flow_idx < len(self.flows_list):
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return self.flows_list[flow_idx]
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return None
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def on_data_table_row_highlighted(self, event: DataTable.RowHighlighted) -> None:
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"""Handle row highlight to update selection"""
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selected_flow = self.get_selected_flow()
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self.post_message(self.FlowSelected(selected_flow))
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# Helper methods from original implementation
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def _get_extended_protocol(self, flow: 'FlowStats') -> str:
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"""Get extended protocol"""
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if flow.detected_protocol_types:
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enhanced_protocols = {'CHAPTER10', 'CH10', 'PTP', 'IENA'}
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found = flow.detected_protocol_types & enhanced_protocols
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if found:
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protocol = list(found)[0]
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return 'CH10' if protocol in ['CHAPTER10', 'CH10'] else protocol
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return '-'
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def _get_frame_summary(self, flow: 'FlowStats') -> str:
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"""Get frame type summary"""
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if not flow.frame_types:
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return "General"
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elif len(flow.frame_types) == 1:
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return list(flow.frame_types.keys())[0][:11]
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else:
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return f"{len(flow.frame_types)} types"
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def _get_protocol_frame_combinations(self, flow: 'FlowStats'):
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"""Get protocol/frame combinations"""
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combinations = []
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total = flow.frame_count
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for frame_type, stats in flow.frame_types.items():
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extended = self._get_extended_protocol(flow)
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percentage = (stats.count / total * 100) if total > 0 else 0
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combinations.append((extended, frame_type, stats.count, percentage))
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return sorted(combinations, key=lambda x: x[2], reverse=True)
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def _format_bytes(self, bytes_count: int) -> str:
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"""Format byte count"""
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if bytes_count >= 1_000_000_000:
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return f"{bytes_count / 1_000_000_000:.1f}G"
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elif bytes_count >= 1_000_000:
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return f"{bytes_count / 1_000_000:.1f}M"
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elif bytes_count >= 1_000:
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return f"{bytes_count / 1_000:.1f}K"
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else:
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return f"{bytes_count}B" |