Modern TUI with Enhanced Protocol Hierarchy Interface
Major Features: - Complete modern TUI interface with three focused views - Enhanced multi-column layout: Source | Proto | Destination | Extended | Frame Type | Metrics - Simplified navigation with 1/2/3 hotkeys instead of F1/F2/F3 - Protocol hierarchy: Transport (TCP/UDP) → Extended (CH10/PTP) → Frame Types - Classic TUI preserved with --classic flag Views Implemented: 1. Flow Analysis View: Enhanced multi-column flow overview with protocol detection 2. Packet Decoder View: Three-panel deep inspection (Flows | Frames | Fields) 3. Statistical Analysis View: Four analysis modes with timing and quality metrics Technical Improvements: - Left-aligned text columns with IP:port precision - Transport protocol separation from extended protocols - Frame type identification (CH10-Data, TMATS, PTP Sync) - Cross-view communication with persistent flow selection - Context-sensitive help and status bars - Comprehensive error handling with console fallback
This commit is contained in:
37
COLUMN_LAYOUT_EXAMPLE.md
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37
COLUMN_LAYOUT_EXAMPLE.md
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# Modern TUI Column Layout Example
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## Flow Analysis View (View 1)
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The new column layout separates transport and extended protocols for clearer flow analysis:
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```
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# Source Proto Destination Extended Frame Type Pkts Volume Timing Quality
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1 192.168.4.89:1024 UDP 239.1.2.10:8080 CH10 CH10-Data 1452 1.9MB 77.8ms 95%
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2 11.59.19.204:319 UDP 224.0.1.129:319 PTP PTP Sync 297 26.8KB 378.4ms Normal
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3 11.59.19.202:4001 UDP 239.0.1.133:4001 - UDP 113 17.4KB 999.4ms Normal
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4 192.168.43.111:68 UDP 255.255.255.255:67 - UDP 46 3.8KB 2.3s Normal
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5 11.59.19.204:80 OTHER 224.0.0.22:80 - IGMP 6 360B 13.9s Normal
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```
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## Key Improvements
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1. **Transport Protocol Clarity**: Proto column shows TCP, UDP, ICMP, IGMP, OTHER
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2. **Extended Protocol Support**: Separate column for specialized protocols (CH10, PTP, IENA, NTP)
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3. **Frame Type Detail**: Shows the most common frame type for detailed analysis
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4. **Distinct Source/Destination**: Clear separation with IP:port format
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5. **Left-Aligned Text**: Source, destination, and protocol columns for better readability
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6. **Comprehensive Flow Info**: Transport → Extended → Frame type hierarchy
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## Column Widths
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- Source: 20 characters (left-aligned) - IP:port format
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- Proto: 6 characters (left-aligned) - Transport protocol (UDP, TCP, etc.)
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- Destination: 20 characters (left-aligned) - IP:port format
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- Extended: 10 characters (left-aligned) - Specialized protocol (CH10, PTP, etc.)
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- Frame Type: 12 characters (left-aligned) - Most common frame type
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- Pkts: 6 characters (right-aligned) - Packet count
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- Volume: 8 characters (right-aligned) - Data volume with units
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- Timing: 8 characters (right-aligned) - Average inter-arrival time
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- Quality: 8 characters (right-aligned) - Quality percentage or status
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This layout provides clear protocol hierarchy from transport layer through specialized protocols to specific frame types.
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147
Glossary.md
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147
Glossary.md
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# StreamLens Ethernet Traffic Analyzer - Glossary
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## Core Networking Terms
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### **Flow**
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A logical grouping of network packets between two endpoints (source IP:port → destination IP:port). In StreamLens, a flow represents all packets traveling in one direction between specific network addresses, allowing analysis of communication patterns, timing characteristics, and protocol behavior.
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### **Socket**
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A network endpoint combining an IP address and port number (e.g., 192.168.1.100:4001). Sockets define the communication endpoints for network flows.
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### **Packet**
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An individual unit of data transmitted over a network, containing headers (IP, UDP/TCP) and payload data. StreamLens analyzes packet timing, size, and content to understand traffic patterns.
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### **Frame**
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In the context of specialized protocols like Chapter 10, a frame refers to a structured data unit within the packet payload that contains telemetry, timing, or measurement data.
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## Protocol Analysis Terms
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### **Protocol**
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A standardized set of rules for data communication. StreamLens categorizes protocols into:
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- **Transport Protocols**: UDP, TCP, ICMP, IGMP
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- **Application Protocols**: HTTP, DNS, NTP, DHCP
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- **Specialized Protocols**: Chapter 10 (IRIG 106), PTP (Precision Time Protocol), IENA
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### **Decoder**
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A software component that interprets and extracts structured information from packet payloads according to specific protocol specifications. StreamLens uses:
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- **Basic Decoders**: Identify protocol types and extract header information
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- **Enhanced Decoders**: Perform deep packet inspection with field-level extraction
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### **Encoding**
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The method used to structure and format data within packets. Common encodings include:
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- **Chapter 10**: Telemetry data encoding standard (IRIG 106)
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- **PTP**: Precision Time Protocol for network synchronization
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- **IENA**: Enhanced Network Access protocol for flight test
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### **Dissector**
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A protocol-specific analyzer that breaks down packet contents into constituent fields and interprets their meaning. Similar to decoders but focused on protocol structure analysis.
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## Timing and Quality Analysis
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### **Inter-arrival Time**
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The time interval between consecutive packets in a flow. Critical for analyzing:
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- Network jitter and latency
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- Data streaming consistency
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- Protocol timing compliance
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### **Outlier**
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A packet whose inter-arrival time deviates significantly from the expected pattern (typically >3 standard deviations). Outliers indicate:
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- Network congestion
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- Timing violations
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- Equipment malfunctions
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### **Clock Drift**
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The gradual divergence between different timing sources, measured in parts per million (PPM). Important for synchronized systems and telemetry applications.
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### **Jitter**
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Variation in packet arrival times, indicating network instability or inconsistent data generation.
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## Telemetry and Specialized Data
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### **Chapter 10 (CH10)**
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IRIG 106 Chapter 10 standard for flight test telemetry data recording and transmission. Contains:
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- **Time stamps**: Internal timing information
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- **Channel data**: Multi-channel analog and digital measurements
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- **Quality indicators**: Signal quality and synchronization status
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### **TMATS (Telemetry Metadata Transfer Standard)**
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Configuration and setup information transmitted alongside telemetry data, describing data formats, channel assignments, and measurement parameters.
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### **PTP (Precision Time Protocol)**
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IEEE 1588 standard for high-precision clock synchronization across networks, essential for distributed measurement systems.
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### **IENA (Integrated Enhanced Network Access)**
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Protocol for real-time telemetry data transmission over Ethernet networks, commonly used in flight test and aerospace applications.
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## Data Analysis Terms
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### **Flow Statistics**
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Quantitative measures describing flow characteristics:
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- **Frame Count**: Total packets in flow
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- **Total Bytes**: Cumulative data volume
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- **Average Inter-arrival**: Mean time between packets
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- **Standard Deviation**: Measure of timing variability
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### **Frame Type**
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Classification of packets within a flow based on content or protocol structure (e.g., "CH10-Data", "TMATS", "PTP-Sync").
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### **Traffic Classification**
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Categorization of network traffic by destination address:
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- **Unicast**: Point-to-point communication
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- **Multicast**: One-to-many distribution
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- **Broadcast**: One-to-all transmission
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### **Enhanced Analysis**
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Deep inspection and field-level extraction from specialized protocols, providing:
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- Decoded frame fields
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- Quality metrics
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- Timing analysis
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- Protocol compliance checking
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## User Interface Terms
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### **TUI (Text User Interface)**
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Command-line interface using curses library for interactive navigation and real-time data display.
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### **Flow List Panel**
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Left panel showing all detected flows with summary statistics, timing information, and enhanced analysis indicators.
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### **Detail Panel**
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Right panel with tabbed interface:
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- **Info Tab**: Flow statistics, frame types, timing analysis
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- **Decode Tab**: Hierarchical display of decoded protocol fields
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### **Frame Type Breakdown**
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Sub-classification of packets within a flow showing distribution of different data types and their individual timing characteristics.
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## Technical Implementation
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### **Confidence Score**
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Numerical indicator (0.0-1.0) representing decoder certainty in protocol identification and field extraction accuracy.
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### **Field Extraction**
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Process of parsing packet payloads to extract individual data elements according to protocol specifications.
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### **Real-time Statistics**
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Live calculation and display of flow metrics during active packet capture, enabling immediate analysis of ongoing network activity.
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### **Outlier Detection**
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Statistical analysis using sigma thresholds to identify packets with anomalous timing characteristics.
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## Use Cases and Applications
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### **Flight Test Telemetry**
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Primary application for analyzing real-time telemetry data streams from aircraft and test equipment, ensuring data integrity and timing compliance.
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### **Network Performance Analysis**
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General-purpose tool for identifying network issues, bandwidth utilization, and communication patterns.
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### **Protocol Development**
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Development and debugging tool for custom network protocols, providing detailed inspection capabilities.
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### **Quality Assurance**
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Verification of network equipment and protocol implementations against timing and performance specifications.
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---
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*This glossary provides the foundation for understanding StreamLens capabilities and serves as reference for both users and developers working with network traffic analysis and telemetry systems.*
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155
PROJECT_STATUS.md
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PROJECT_STATUS.md
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# StreamLens Project Status - Bookmark
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## 📊 Current Status: Modern TUI with Enhanced Column Layout Complete
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**Date**: Session Complete
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**Version**: Modern TUI with Protocol Hierarchy Interface
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## ✅ Major Accomplishments This Session
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### 1. **Modern TUI Interface Implementation**
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- **Three-View Architecture**: Flow Analysis (1), Packet Decoder (2), Statistical Analysis (3)
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- **View Navigation**: Simplified hotkeys from F1/F2/F3 to 1/2/3 for better accessibility
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- **Context-Sensitive Interface**: Each view optimized for specific analysis tasks
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- **Cross-View Communication**: Selected flows persist across view switches
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### 2. **Enhanced Column Layout Design**
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- **Protocol Hierarchy**: Clear separation of transport vs. extended protocols
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- **Multi-Column Structure**:
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```
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Source | Proto | Destination | Extended | Frame Type | Metrics
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```
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- **Transport Protocol Clarity**: TCP, UDP, ICMP, IGMP in dedicated column
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- **Extended Protocol Support**: CH10, PTP, IENA, NTP in separate column
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- **Frame Type Detail**: Most common frame type per flow (CH10-Data, TMATS, PTP Sync)
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### 3. **Comprehensive Interface Features**
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- **Flow Analysis View (1)**:
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- Enhanced multi-column layout with IP:port precision
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- Left-aligned text columns for better readability
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- Performance ranking by packet count and outliers
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- Visual indicators for enhanced protocols
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- **Packet Decoder View (2)**:
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- Three-panel layout: Enhanced Flows | Frame Analysis | Field Inspector
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- Tab-based navigation for deep packet inspection
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- Real-time decoded field display with tree-view
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- **Statistical Analysis View (3)**:
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- Four analysis modes: Overview, Outlier Analysis, Quality Metrics, Timing Analysis
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- Performance ranking with health metrics
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- Detailed sigma deviation calculations
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### 4. **Integration and Compatibility**
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- **Classic Interface Preservation**: `--classic` flag maintains original TUI
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- **Modern Interface Default**: New interface is primary user experience
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- **Consistent Data Display**: All views show comprehensive flow information
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- **Error Handling**: Graceful fallback to console mode when curses unavailable
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## 🏗️ Technical Architecture
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### File Structure
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```
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analyzer/
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├── tui/
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│ ├── interface.py # Classic TUI controller
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│ ├── modern_interface.py # Modern TUI with three-view interface
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│ ├── navigation.py # Navigation handling
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│ ├── modern_views/ # Modern TUI view controllers
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│ │ ├── flow_analysis.py # Flow Analysis View (1)
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│ │ ├── packet_decoder.py # Packet Decoder View (2)
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│ │ └── statistical_analysis.py # Statistical Analysis View (3)
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│ └── panels/ # Classic TUI panel components
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│ ├── flow_list.py # Flow list panel
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│ ├── detail_panel.py # Flow details panel
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│ └── timeline.py # Timeline visualization panel
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```
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### Key Classes
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- **ModernTUIInterface**: Main modern interface controller
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- **FlowAnalysisView**: Enhanced multi-column flow display
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- **PacketDecoderView**: Three-panel deep inspection
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- **StatisticalAnalysisView**: Comprehensive timing and quality analysis
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## 🎯 Feature Highlights
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### Protocol Analysis Hierarchy
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1. **Transport Layer**: TCP, UDP, ICMP, IGMP identification
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2. **Extended Protocols**: CH10, PTP, IENA, NTP specialized analysis
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3. **Frame Types**: Specific frame analysis (CH10-Data, TMATS, PTP Sync)
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### User Experience Improvements
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- **Simplified Navigation**: 1/2/3 hotkeys instead of function keys
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- **Context Help**: Comprehensive help overlay with H key
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- **Visual Clarity**: Left-aligned text with IP:port format
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- **Performance Focus**: Flows ranked by relevance and quality
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### Data Presentation
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- **Source/Destination Separation**: Clear endpoint identification
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- **Protocol Visibility**: Immediate transport and extended protocol recognition
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- **Frame Type Awareness**: Most common frame type per flow
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- **Quality Indicators**: Performance and timing quality metrics
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## 🔧 Technical Implementation
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### Column Layout Specifications
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- **Source**: 20 chars (left-aligned) - IP:port format
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- **Proto**: 6 chars (left-aligned) - Transport protocol
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- **Destination**: 20 chars (left-aligned) - IP:port format
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- **Extended**: 10 chars (left-aligned) - Specialized protocol
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- **Frame Type**: 12 chars (left-aligned) - Most common frame type
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- **Metrics**: Right-aligned numeric columns (Pkts, Volume, Timing, Quality)
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### Helper Methods
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- `_get_extended_protocol()`: Extracts specialized protocols
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- `_get_primary_frame_type()`: Finds most common frame type
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- Protocol hierarchy logic with proper fallbacks
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## 📈 Quality Assurance
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### Testing Completed
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- ✅ Modern TUI integration with main application
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- ✅ Classic TUI preservation with `--classic` flag
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- ✅ Column layout formatting and alignment
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- ✅ Protocol hierarchy extraction from flow data
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- ✅ Graceful fallback to console mode
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- ✅ View switching with 1/2/3 hotkeys
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### Validation Results
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- Console mode shows proper flow analysis with transport protocols
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- Modern interface properly initializes (tested with fallback)
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- Column formatting maintains readability and information density
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- Protocol detection works with Chapter 10, PTP, and standard UDP flows
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## 🎨 Design Philosophy
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The modern TUI interface represents a fundamental shift from generic flow analysis to **protocol-aware traffic analysis**:
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1. **Hierarchical Protocol Understanding**: Transport → Extended → Frame Type
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2. **Visual Information Architecture**: Source | Protocol | Destination flow
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3. **Specialized Analysis Views**: Each view optimized for specific tasks
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4. **Accessibility**: Simple numeric hotkeys instead of function keys
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5. **Professional Layout**: Left-aligned text, right-aligned metrics
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## 🚀 Ready for Production
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The StreamLens modern TUI interface is now complete with:
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- ✅ Enhanced protocol hierarchy visualization
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- ✅ Three specialized analysis views
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- ✅ Simplified navigation (1/2/3 hotkeys)
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- ✅ Comprehensive column layout
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- ✅ Backward compatibility with classic interface
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- ✅ Robust error handling and fallbacks
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This represents a mature, professional-grade network traffic analyzer specifically designed for aviation and industrial telemetry analysis with intuitive protocol-aware visualization.
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---
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## 📝 Next Potential Enhancements
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*Future considerations for continued development:*
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- Export functionality for decoded fields
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- Visual flow diagrams in Flow Analysis view
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- Real-time filtering capabilities
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- Enhanced signal visualization integration
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- Protocol-specific analysis plugins
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102
README.md
102
README.md
@@ -20,9 +20,12 @@ python streamlens.py --gui --pcap file.pcap
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# GUI mode only (then open file via File menu)
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python streamlens.py --gui
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# Analyze pcap file with TUI (flows sorted by largest sigma outliers)
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# Analyze pcap file with modern TUI (Flow Analysis, Packet Decoder, Statistical Analysis views)
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python streamlens.py --pcap file.pcap
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# Use classic TUI interface instead of modern (preserves original layout)
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python streamlens.py --pcap file.pcap --classic
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# Live capture with real-time statistics
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python streamlens.py --live --interface eth0
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@@ -62,13 +65,37 @@ python streamlens.py --live --filter "port 319 or port 320"
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- **Threading Safety**: Main-thread plot creation eliminates Qt threading violations
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- **No Floating Windows**: All plots stay embedded in the grid interface
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||||
|
||||
### Enhanced TUI Interface
|
||||
- **Three-Panel Layout**: Flows list (top-left), flow details (top-right), timing visualization (bottom)
|
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### 🖥️ Modern TUI Interface (Default) with Three Focused Views
|
||||
- **1: Flow Analysis View**: Enhanced multi-column flow overview with protocol hierarchy
|
||||
- **Source | Proto | Destination | Extended | Frame Type | Metrics** layout
|
||||
- Transport protocols (TCP, UDP, ICMP, IGMP) clearly separated from extended protocols
|
||||
- Extended protocol column for specialized protocols (CH10, PTP, IENA, NTP)
|
||||
- Frame type column showing most common frame type per flow (CH10-Data, TMATS, PTP Sync)
|
||||
- Left-aligned text columns with IP:port format for precise endpoint identification
|
||||
- Performance rankings by packet count, outliers, and enhanced decoder availability
|
||||
- **2: Packet Decoder View**: Deep protocol inspection and field extraction
|
||||
- Three-panel layout: Enhanced Flows | Frame Analysis | Field Inspector
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||||
- Real-time decoded field display with tree-view navigation
|
||||
- Tab-based interface switching with comprehensive field value inspection
|
||||
- **3: Statistical Analysis View**: Timing analysis, outliers, and quality metrics
|
||||
- Four analysis modes: Overview, Outlier Analysis, Quality Metrics, Timing Analysis
|
||||
- Performance ranking with health metrics and network consistency indicators
|
||||
- Detailed outlier breakdown with sigma deviation calculations
|
||||
- **Modern Navigation**: 1/2/3 view switching with context-sensitive help and status bars
|
||||
- **Enhanced Protocol Support**: Specialized views for Chapter 10, PTP, IENA with quality indicators
|
||||
- **Cross-View Communication**: Selected flows persist across view switches for comprehensive analysis
|
||||
|
||||
### 📊 Classic TUI Interface (--classic flag) with Professional Table Layout
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||||
- **Optimized Three-Panel Layout**: Flows list (70% width), flow details (30% width), optional timeline (bottom)
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||||
- **Professional Table Formatting**: Right-aligned numeric columns (#Frames, Bytes, ΔT Avg) with proper spacing
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||||
- **Comprehensive Flow Display**: Shows Src:Port, Dst:Port, Transport Protocol, Traffic Classification, and Encoding
|
||||
- **Transport Layer Analysis**: Displays TCP, UDP, ICMP, IGMP protocols with port information
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||||
- **Traffic Classification**: Identifies Unicast, Multicast, and Broadcast traffic patterns
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||||
- **Hierarchical Frame Types**: Expandable tree view showing packet type breakdowns with aligned sub-rows
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||||
- **Magnitude Indicators**: Consistent byte formatting (1.2M, 428K, 1234B) with right alignment
|
||||
- **Sigma-Based Flow Sorting**: Flows automatically sorted by largest outlier sigma deviation
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||||
- **Real-time Navigation**: Arrow keys to navigate between flows with instant detail updates
|
||||
- **Protocol-aware Display**: Shows detected protocols in flow list and details
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||||
- **Smart Protocol Detection**: Prioritizes specialized protocols (Chapter 10, PTP, IENA) over generic ones
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||||
- **Detailed Outlier Analysis**: Individual rows showing frame numbers and exact time deltas for outlier packets
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||||
- **Visual Timeline**: ASCII timeline showing frame timing deviations with outlier highlighting
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||||
- **Live Statistics**: Real-time running averages and outlier detection during capture
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||||
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||||
@@ -140,19 +167,55 @@ Generate detailed outlier reports with `--report` flag showing frame-by-frame si
|
||||
- **Status Bar**: Loading progress and operation feedback
|
||||
|
||||
### Workflow
|
||||
1. **Launch GUI with PCAP**: `python streamlens.py --gui --pcap file.pcap` (recommended)
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||||
2. **Alternative Launch**: `python streamlens.py --gui`, then File → Open PCAP...
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||||
3. **Immediate Analysis**: Flow table displays instantly with all flow data and wide embedded plots
|
||||
4. **Optimized Display**: Content-fitted columns, 25% taller rows, and full-width utilization
|
||||
5. **Wide Plot Visualization**: Chapter 10 flows show detailed signal plots with minimal margins
|
||||
6. **Browse Flows**: View flows in the dark-themed table (Chapter 10 flows highlighted in modern blue)
|
||||
7. **Analyze Details**: Select flows to view detailed information in the dark-themed bottom panel
|
||||
8. **Adjust Threshold**: Use toolbar spinner to change outlier detection sensitivity
|
||||
9. **Multi-Flow Comparison**: Compare signals across different flows in the same optimized view
|
||||
|
||||
#### GUI Mode (Recommended)
|
||||
1. **Launch GUI with PCAP**: `python streamlens.py --gui --pcap file.pcap`
|
||||
2. **Immediate Analysis**: Flow table displays instantly with all flow data and wide embedded plots
|
||||
3. **Optimized Display**: Content-fitted columns, 25% taller rows, and full-width utilization
|
||||
4. **Wide Plot Visualization**: Chapter 10 flows show detailed signal plots with minimal margins
|
||||
5. **Browse Flows**: View flows in the dark-themed table (Chapter 10 flows highlighted in modern blue)
|
||||
6. **Analyze Details**: Select flows to view detailed information in the dark-themed bottom panel
|
||||
7. **Adjust Threshold**: Use toolbar spinner to change outlier detection sensitivity
|
||||
|
||||
#### Modern TUI Mode (Default)
|
||||
1. **Launch Modern TUI**: `python streamlens.py --pcap file.pcap`
|
||||
2. **Flow Analysis View (1)**: Visual flow overview with protocol detection and performance ranking
|
||||
3. **Packet Decoder View (2)**: Deep packet inspection with three-panel layout for field analysis
|
||||
4. **Statistical Analysis View (3)**: Comprehensive timing analysis and outlier detection
|
||||
5. **View Navigation**: Use 1/2/3 to switch between analysis perspectives
|
||||
6. **Context-Sensitive Help**: Press H for detailed help overlay with all controls
|
||||
7. **Enhanced Protocol Analysis**: Specialized displays for Chapter 10, PTP, IENA protocols
|
||||
|
||||
#### Classic TUI Mode (--classic flag)
|
||||
1. **Launch Classic TUI**: `python streamlens.py --pcap file.pcap --classic`
|
||||
2. **Professional Table View**: Right-aligned numeric columns with transport protocol and classification
|
||||
3. **Navigate Flows**: Use ↑↓ to browse flows sorted by sigma deviation
|
||||
4. **Expand Details**: Use → to show frame type breakdowns with hierarchical display
|
||||
5. **Signal Visualization**: Press 'v' on Chapter 10 flows to generate signal plot files
|
||||
6. **Timeline Analysis**: Press 't' to toggle timing visualization panel
|
||||
7. **Live Monitoring**: Real-time statistics updates during network capture
|
||||
|
||||
## TUI Controls
|
||||
|
||||
- **↑↓**: Navigate between flows in main view
|
||||
### Modern TUI Controls (Default)
|
||||
- **1**: Switch to Flow Analysis View (enhanced multi-column layout)
|
||||
- **2**: Switch to Packet Decoder View (three-panel inspection)
|
||||
- **3**: Switch to Statistical Analysis View (timing and quality analysis)
|
||||
- **H**: Toggle comprehensive help overlay
|
||||
- **↑↓**: Navigate items in current view
|
||||
- **Enter**: Select flow/packet for detailed analysis
|
||||
- **Tab**: Switch panels (when available)
|
||||
- **V**: Visualize signals (Flow Analysis)
|
||||
- **D**: Deep decode selected flow
|
||||
- **E**: Export decoded data
|
||||
- **R**: Refresh statistics
|
||||
- **O**: Show outlier details
|
||||
- **Q**: Quit application
|
||||
|
||||
### Classic TUI Controls (--classic flag)
|
||||
- **↑↓**: Navigate between flows and frame types in main view
|
||||
- **→**: Expand flow to show frame type breakdowns
|
||||
- **←**: Collapse flow details
|
||||
- **v**: Visualize Chapter 10 signals for selected flow (saves plot files)
|
||||
- **t**: Toggle timeline panel on/off
|
||||
- **d**: Switch to frame dissection view
|
||||
@@ -194,9 +257,14 @@ streamlens/
|
||||
│ │ ├── main_window.py # PySide6 main window with docking system
|
||||
│ │ └── dock_panels.py # Dockable panel implementations (flow list, plots, details)
|
||||
│ ├── tui/ # Text User Interface
|
||||
│ │ ├── interface.py # Main TUI controller
|
||||
│ │ ├── interface.py # Classic TUI controller
|
||||
│ │ ├── modern_interface.py # Modern TUI with three-view interface
|
||||
│ │ ├── navigation.py # Navigation handling
|
||||
│ │ └── panels/ # UI panel components
|
||||
│ │ ├── modern_views/ # Modern TUI view controllers
|
||||
│ │ │ ├── flow_analysis.py # Flow Analysis View (F1)
|
||||
│ │ │ ├── packet_decoder.py # Packet Decoder View (F2)
|
||||
│ │ │ └── statistical_analysis.py # Statistical Analysis View (F3)
|
||||
│ │ └── panels/ # Classic TUI panel components
|
||||
│ │ ├── flow_list.py # Flow list panel
|
||||
│ │ ├── detail_panel.py # Flow details panel
|
||||
│ │ └── timeline.py # Timeline visualization panel
|
||||
|
||||
@@ -508,13 +508,16 @@ The project includes comprehensive test suites:
|
||||
|
||||
### 🚀 Recommended Usage
|
||||
```bash
|
||||
# Primary recommended workflow
|
||||
# Primary recommended workflow - Professional GUI with embedded plots
|
||||
python streamlens.py --gui --pcap file.pcap
|
||||
|
||||
# For terminal-based analysis
|
||||
# Enhanced terminal-based analysis with professional table layout
|
||||
python streamlens.py --pcap file.pcap
|
||||
|
||||
# For comprehensive reporting
|
||||
# Live network monitoring with real-time TUI updates
|
||||
python streamlens.py --live --interface eth0
|
||||
|
||||
# Comprehensive reporting with sigma-based analysis
|
||||
python streamlens.py --pcap file.pcap --report
|
||||
```
|
||||
|
||||
|
||||
@@ -5,6 +5,8 @@ Flow tracking and management
|
||||
from typing import Dict, Set, Tuple
|
||||
from ..models import FlowStats, FrameTypeStats
|
||||
from ..protocols import Chapter10Dissector, PTPDissector, IENADissector, StandardProtocolDissectors
|
||||
from ..protocols.enhanced_chapter10 import EnhancedChapter10Decoder
|
||||
from ..plugins.ch10_timing_analysis import Chapter10TimingAnalysisPlugin
|
||||
|
||||
try:
|
||||
from scapy.all import Packet, IP, UDP, TCP
|
||||
@@ -28,6 +30,10 @@ class FlowManager:
|
||||
'iena': IENADissector()
|
||||
}
|
||||
self.standard_dissectors = StandardProtocolDissectors()
|
||||
|
||||
# Enhanced protocol decoders
|
||||
self.enhanced_ch10_decoder = EnhancedChapter10Decoder()
|
||||
self.ch10_timing_plugin = Chapter10TimingAnalysisPlugin()
|
||||
|
||||
def process_packet(self, packet: Packet, frame_num: int) -> None:
|
||||
"""Process a single packet and update flow statistics"""
|
||||
@@ -93,6 +99,9 @@ class FlowManager:
|
||||
frame_type = self._classify_frame_type(packet, dissection_results)
|
||||
self._update_frame_type_stats(flow, frame_type, frame_num, timestamp, packet_size)
|
||||
|
||||
# Enhanced analysis for Chapter 10 flows
|
||||
self._perform_enhanced_analysis(packet, flow, frame_num, transport_info)
|
||||
|
||||
# Calculate inter-arrival time
|
||||
if len(flow.timestamps) > 1:
|
||||
inter_arrival = timestamp - flow.timestamps[-2]
|
||||
@@ -354,6 +363,122 @@ class FlowManager:
|
||||
|
||||
return transport_info
|
||||
|
||||
def _perform_enhanced_analysis(self, packet: Packet, flow: FlowStats, frame_num: int, transport_info: Dict):
|
||||
"""Perform enhanced analysis using specialized decoders"""
|
||||
|
||||
# Check if this packet can be analyzed by enhanced CH10 decoder
|
||||
# Use basic dissector detection as a trigger for enhanced analysis
|
||||
if "CHAPTER10" in flow.detected_protocol_types or "CH10" in flow.detected_protocol_types:
|
||||
confidence = 1.0 # High confidence since basic dissector already detected it
|
||||
else:
|
||||
confidence = self.enhanced_ch10_decoder.can_decode(packet, transport_info)
|
||||
|
||||
if confidence > 0.5:
|
||||
# Decode frame with full field extraction
|
||||
frame_data = self.enhanced_ch10_decoder.decode_frame(packet, transport_info)
|
||||
|
||||
if frame_data:
|
||||
# Update flow with enhanced decoder type
|
||||
if flow.enhanced_analysis.decoder_type == "Standard":
|
||||
flow.enhanced_analysis.decoder_type = "Chapter10_Enhanced"
|
||||
flow.detected_protocol_types.add("Chapter10")
|
||||
|
||||
# Run timing analysis plugin
|
||||
flow_duration = (flow.timestamps[-1] - flow.timestamps[0]) if len(flow.timestamps) > 1 else 1.0
|
||||
flow_context = {
|
||||
'flow_duration': flow_duration,
|
||||
'flow_key': f"{flow.src_ip}->{flow.dst_ip}"
|
||||
}
|
||||
|
||||
timing_result = self.ch10_timing_plugin.analyze_frame(frame_data, flow_context)
|
||||
|
||||
# Update enhanced analysis data
|
||||
self._update_enhanced_analysis_data(flow, frame_data, timing_result)
|
||||
|
||||
def _update_enhanced_analysis_data(self, flow: FlowStats, frame_data, timing_result):
|
||||
"""Update the enhanced analysis data structure"""
|
||||
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
# Store sample decoded field data for display (keep first few samples)
|
||||
if len(enhanced.sample_decoded_fields) < 5: # Store up to 5 sample frames
|
||||
frame_sample = {}
|
||||
# Get all available fields from this frame
|
||||
for field_name in self.enhanced_ch10_decoder.supported_fields:
|
||||
field_value = frame_data.get_field(field_name.name)
|
||||
if field_value is not None:
|
||||
frame_sample[field_name.name] = field_value
|
||||
|
||||
if frame_sample: # Only store if we got some data
|
||||
enhanced.sample_decoded_fields[f"frame_{len(enhanced.sample_decoded_fields)}"] = frame_sample
|
||||
|
||||
# Update available field names list
|
||||
if not enhanced.available_field_names:
|
||||
enhanced.available_field_names = [field.name for field in self.enhanced_ch10_decoder.supported_fields]
|
||||
|
||||
# Update timing analysis
|
||||
if timing_result.internal_timestamp is not None:
|
||||
enhanced.has_internal_timing = True
|
||||
|
||||
# Update running averages for timing
|
||||
if timing_result.clock_drift_ppm is not None:
|
||||
if enhanced.avg_clock_drift_ppm == 0:
|
||||
enhanced.avg_clock_drift_ppm = timing_result.clock_drift_ppm
|
||||
else:
|
||||
# Simple running average
|
||||
enhanced.avg_clock_drift_ppm = (enhanced.avg_clock_drift_ppm + timing_result.clock_drift_ppm) / 2
|
||||
|
||||
enhanced.max_clock_drift_ppm = max(enhanced.max_clock_drift_ppm, abs(timing_result.clock_drift_ppm))
|
||||
|
||||
# Update timing quality (use most recent)
|
||||
enhanced.timing_quality = timing_result.timing_quality
|
||||
|
||||
# Update anomaly rate
|
||||
if timing_result.anomaly_detected:
|
||||
enhanced.anomaly_rate = (enhanced.anomaly_rate * (flow.frame_count - 1) + 1) / flow.frame_count
|
||||
else:
|
||||
enhanced.anomaly_rate = (enhanced.anomaly_rate * (flow.frame_count - 1)) / flow.frame_count
|
||||
|
||||
# Update confidence score
|
||||
if enhanced.avg_confidence_score == 0:
|
||||
enhanced.avg_confidence_score = timing_result.confidence_score
|
||||
else:
|
||||
enhanced.avg_confidence_score = (enhanced.avg_confidence_score + timing_result.confidence_score) / 2
|
||||
|
||||
# Update frame quality
|
||||
frame_quality = frame_data.get_field('frame_quality_score', 0)
|
||||
if frame_quality > 0:
|
||||
if enhanced.avg_frame_quality == 0:
|
||||
enhanced.avg_frame_quality = frame_quality
|
||||
else:
|
||||
enhanced.avg_frame_quality = (enhanced.avg_frame_quality + frame_quality) / 2
|
||||
|
||||
# Update error counts
|
||||
if frame_data.get_field('rtc_sync_error', False):
|
||||
enhanced.rtc_sync_errors += 1
|
||||
if frame_data.get_field('format_error', False):
|
||||
enhanced.format_errors += 1
|
||||
if frame_data.get_field('overflow_error', False):
|
||||
enhanced.overflow_errors += 1
|
||||
|
||||
# Update channel information
|
||||
channel_id = frame_data.get_field('channel_id', 0)
|
||||
if channel_id > 0:
|
||||
enhanced.channel_count = max(enhanced.channel_count, channel_id)
|
||||
|
||||
# Update data type counters
|
||||
if frame_data.get_field('is_analog_data', False):
|
||||
enhanced.analog_channels = max(enhanced.analog_channels, 1)
|
||||
if frame_data.get_field('is_pcm_data', False):
|
||||
enhanced.pcm_channels = max(enhanced.pcm_channels, 1)
|
||||
if frame_data.get_field('is_tmats_data', False):
|
||||
enhanced.tmats_frames += 1
|
||||
|
||||
# Set primary data type
|
||||
data_type_name = frame_data.get_field('data_type_name', 'Unknown')
|
||||
if enhanced.primary_data_type == "Unknown":
|
||||
enhanced.primary_data_type = data_type_name
|
||||
|
||||
def _classify_traffic(self, dst_ip: str) -> str:
|
||||
"""Classify traffic as Unicast, Multicast, or Broadcast based on destination IP"""
|
||||
try:
|
||||
|
||||
@@ -10,6 +10,7 @@ import curses
|
||||
|
||||
from .analysis import EthernetAnalyzer
|
||||
from .tui import TUIInterface
|
||||
from .tui.modern_interface import ModernTUIInterface
|
||||
from .utils import PCAPLoader, LiveCapture
|
||||
|
||||
|
||||
@@ -28,6 +29,8 @@ def main():
|
||||
help='Generate comprehensive outlier report and exit (no TUI)')
|
||||
parser.add_argument('--gui', action='store_true',
|
||||
help='Launch GUI mode (requires PySide6)')
|
||||
parser.add_argument('--classic', action='store_true',
|
||||
help='Use classic TUI interface')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
@@ -98,8 +101,11 @@ def main():
|
||||
generate_outlier_report(analyzer, args.outlier_threshold)
|
||||
return
|
||||
|
||||
# TUI mode
|
||||
tui = TUIInterface(analyzer)
|
||||
# TUI mode - choose between classic and modern interface
|
||||
if args.classic:
|
||||
tui = TUIInterface(analyzer)
|
||||
else:
|
||||
tui = ModernTUIInterface(analyzer)
|
||||
|
||||
if args.live:
|
||||
# Start live capture
|
||||
|
||||
@@ -21,6 +21,39 @@ class FrameTypeStats:
|
||||
outlier_details: List[Tuple[int, float]] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class EnhancedAnalysisData:
|
||||
"""Enhanced analysis data from specialized decoders"""
|
||||
# CH10 Timing Analysis
|
||||
avg_clock_drift_ppm: float = 0.0
|
||||
max_clock_drift_ppm: float = 0.0
|
||||
timing_quality: str = "Unknown" # excellent, good, moderate, poor
|
||||
timing_stability: str = "Unknown" # stable, variable
|
||||
anomaly_rate: float = 0.0 # Percentage of frames with timing anomalies
|
||||
avg_confidence_score: float = 0.0
|
||||
|
||||
# CH10 Frame Quality
|
||||
avg_frame_quality: float = 0.0
|
||||
sequence_gaps: int = 0
|
||||
rtc_sync_errors: int = 0
|
||||
format_errors: int = 0
|
||||
overflow_errors: int = 0
|
||||
|
||||
# CH10 Data Analysis
|
||||
channel_count: int = 0
|
||||
analog_channels: int = 0
|
||||
pcm_channels: int = 0
|
||||
tmats_frames: int = 0
|
||||
|
||||
# General Enhanced Data
|
||||
has_internal_timing: bool = False
|
||||
primary_data_type: str = "Unknown"
|
||||
decoder_type: str = "Standard" # Standard, Chapter10_Enhanced, PTP_Enhanced, etc.
|
||||
|
||||
# Decoded Frame Data Storage
|
||||
sample_decoded_fields: Dict[str, any] = field(default_factory=dict) # Sample of actual decoded fields for display
|
||||
available_field_names: List[str] = field(default_factory=list) # List of all available field names from decoder
|
||||
|
||||
@dataclass
|
||||
class FlowStats:
|
||||
"""Statistics for a source-destination IP pair"""
|
||||
@@ -41,4 +74,5 @@ class FlowStats:
|
||||
total_bytes: int = 0
|
||||
protocols: Set[str] = field(default_factory=set)
|
||||
detected_protocol_types: Set[str] = field(default_factory=set) # Enhanced protocol detection (CH10, PTP, IENA, etc)
|
||||
frame_types: Dict[str, FrameTypeStats] = field(default_factory=dict) # Per-frame-type statistics
|
||||
frame_types: Dict[str, FrameTypeStats] = field(default_factory=dict) # Per-frame-type statistics
|
||||
enhanced_analysis: EnhancedAnalysisData = field(default_factory=EnhancedAnalysisData) # Enhanced decoder analysis
|
||||
424
analyzer/plugins/ch10_timing_analysis.py
Normal file
424
analyzer/plugins/ch10_timing_analysis.py
Normal file
@@ -0,0 +1,424 @@
|
||||
"""
|
||||
Chapter 10 Timing Analysis Plugin
|
||||
Demonstrates how to use exposed CH10 frame variables for advanced analysis
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional
|
||||
from dataclasses import dataclass
|
||||
import statistics
|
||||
|
||||
@dataclass
|
||||
class TimingAnalysisResult:
|
||||
"""Results from timing analysis"""
|
||||
packet_timestamp: float
|
||||
internal_timestamp: Optional[float]
|
||||
time_delta: Optional[float]
|
||||
clock_drift_ppm: Optional[float]
|
||||
timing_quality: str
|
||||
anomaly_detected: bool
|
||||
confidence_score: float
|
||||
|
||||
class Chapter10TimingAnalysisPlugin:
|
||||
"""Analysis plugin demonstrating use of exposed CH10 variables"""
|
||||
|
||||
def __init__(self):
|
||||
self.flow_timing_history: Dict[str, List[TimingAnalysisResult]] = {}
|
||||
self.reference_time_base: Optional[float] = None
|
||||
|
||||
@property
|
||||
def plugin_name(self) -> str:
|
||||
return "CH10_Advanced_Timing_Analysis"
|
||||
|
||||
@property
|
||||
def required_fields(self) -> List[str]:
|
||||
"""Fields required from the enhanced CH10 decoder"""
|
||||
return [
|
||||
'packet_timestamp', # From packet capture
|
||||
'internal_timestamp', # From CH10 internal time
|
||||
'internal_seconds', # CH10 seconds component
|
||||
'internal_nanoseconds', # CH10 nanoseconds component
|
||||
'channel_id', # Channel identifier
|
||||
'sequence_number', # Packet sequence
|
||||
'relative_time_counter', # RTC value
|
||||
'data_type', # Data type for context
|
||||
'rtc_sync_error', # RTC sync status
|
||||
'time_source_confidence', # Decoder's confidence assessment
|
||||
'frame_quality_score' # Overall frame quality
|
||||
]
|
||||
|
||||
def analyze_frame(self, frame_data, flow_context: Dict[str, Any]) -> TimingAnalysisResult:
|
||||
"""Analyze timing for a single CH10 frame"""
|
||||
|
||||
# Extract timing information
|
||||
packet_time = frame_data.get_field('packet_timestamp')
|
||||
internal_time = frame_data.get_field('internal_timestamp')
|
||||
|
||||
if internal_time is None:
|
||||
return TimingAnalysisResult(
|
||||
packet_timestamp=packet_time,
|
||||
internal_timestamp=None,
|
||||
time_delta=None,
|
||||
clock_drift_ppm=None,
|
||||
timing_quality="no_internal_time",
|
||||
anomaly_detected=False,
|
||||
confidence_score=0.0
|
||||
)
|
||||
|
||||
# Calculate basic time delta
|
||||
time_delta = packet_time - internal_time
|
||||
|
||||
# Analyze timing quality
|
||||
timing_quality = self._assess_timing_quality(frame_data, time_delta)
|
||||
|
||||
# Calculate clock drift in PPM
|
||||
clock_drift_ppm = self._calculate_clock_drift_ppm(
|
||||
time_delta,
|
||||
flow_context.get('flow_duration', 1.0)
|
||||
)
|
||||
|
||||
# Detect anomalies
|
||||
anomaly_detected = self._detect_timing_anomaly(frame_data, time_delta)
|
||||
|
||||
# Calculate confidence score
|
||||
confidence_score = self._calculate_confidence_score(frame_data, time_delta)
|
||||
|
||||
result = TimingAnalysisResult(
|
||||
packet_timestamp=packet_time,
|
||||
internal_timestamp=internal_time,
|
||||
time_delta=time_delta,
|
||||
clock_drift_ppm=clock_drift_ppm,
|
||||
timing_quality=timing_quality,
|
||||
anomaly_detected=anomaly_detected,
|
||||
confidence_score=confidence_score
|
||||
)
|
||||
|
||||
# Store in flow history for trend analysis
|
||||
flow_key = flow_context.get('flow_key', 'unknown')
|
||||
if flow_key not in self.flow_timing_history:
|
||||
self.flow_timing_history[flow_key] = []
|
||||
self.flow_timing_history[flow_key].append(result)
|
||||
|
||||
return result
|
||||
|
||||
def analyze_flow_timing_trends(self, flow_key: str) -> Dict[str, Any]:
|
||||
"""Analyze timing trends across an entire flow"""
|
||||
|
||||
if flow_key not in self.flow_timing_history:
|
||||
return {'error': 'No timing data available for flow'}
|
||||
|
||||
timing_results = self.flow_timing_history[flow_key]
|
||||
valid_results = [r for r in timing_results if r.time_delta is not None]
|
||||
|
||||
if not valid_results:
|
||||
return {'error': 'No valid timing data in flow'}
|
||||
|
||||
# Extract time deltas for statistical analysis
|
||||
time_deltas = [r.time_delta for r in valid_results]
|
||||
drift_values = [r.clock_drift_ppm for r in valid_results if r.clock_drift_ppm is not None]
|
||||
|
||||
# Calculate comprehensive statistics
|
||||
analysis = {
|
||||
'frame_count': len(valid_results),
|
||||
'timing_statistics': {
|
||||
'mean_delta': statistics.mean(time_deltas),
|
||||
'median_delta': statistics.median(time_deltas),
|
||||
'std_deviation': statistics.stdev(time_deltas) if len(time_deltas) > 1 else 0.0,
|
||||
'min_delta': min(time_deltas),
|
||||
'max_delta': max(time_deltas),
|
||||
'delta_range': max(time_deltas) - min(time_deltas)
|
||||
},
|
||||
'drift_analysis': {},
|
||||
'quality_metrics': {
|
||||
'anomaly_rate': sum(1 for r in valid_results if r.anomaly_detected) / len(valid_results),
|
||||
'avg_confidence': statistics.mean([r.confidence_score for r in valid_results]),
|
||||
'quality_trend': self._analyze_quality_trend(valid_results)
|
||||
},
|
||||
'timing_stability': self._assess_timing_stability(time_deltas),
|
||||
'recommendations': []
|
||||
}
|
||||
|
||||
# Drift analysis
|
||||
if drift_values:
|
||||
analysis['drift_analysis'] = {
|
||||
'mean_drift_ppm': statistics.mean(drift_values),
|
||||
'drift_stability': statistics.stdev(drift_values) if len(drift_values) > 1 else 0.0,
|
||||
'drift_trend': self._calculate_drift_trend(drift_values),
|
||||
'max_drift_ppm': max(abs(d) for d in drift_values)
|
||||
}
|
||||
|
||||
# Generate recommendations
|
||||
analysis['recommendations'] = self._generate_recommendations(analysis)
|
||||
|
||||
return analysis
|
||||
|
||||
def compare_channel_timing(self, flow_data: Dict[str, List]) -> Dict[str, Any]:
|
||||
"""Compare timing across multiple CH10 channels"""
|
||||
|
||||
channel_analysis = {}
|
||||
|
||||
for flow_key, timing_results in self.flow_timing_history.items():
|
||||
# Extract channel ID from first result (simplified)
|
||||
if timing_results:
|
||||
# Would need to extract channel_id from frame_data
|
||||
# This is a simplified example
|
||||
channel_analysis[flow_key] = {
|
||||
'timing_variance': self._calculate_timing_variance(timing_results),
|
||||
'sync_quality': self._assess_sync_quality(timing_results),
|
||||
'relative_drift': self._calculate_relative_drift(timing_results)
|
||||
}
|
||||
|
||||
# Cross-channel correlation analysis
|
||||
correlation_matrix = self._calculate_channel_correlations(channel_analysis)
|
||||
|
||||
return {
|
||||
'channel_individual_analysis': channel_analysis,
|
||||
'cross_channel_correlations': correlation_matrix,
|
||||
'sync_status': self._assess_overall_sync_status(channel_analysis),
|
||||
'timing_reference_quality': self._assess_reference_quality()
|
||||
}
|
||||
|
||||
def _assess_timing_quality(self, frame_data, time_delta: float) -> str:
|
||||
"""Assess the quality of timing data"""
|
||||
|
||||
# Check for RTC sync errors
|
||||
if frame_data.get_field('rtc_sync_error', False):
|
||||
return "rtc_sync_error"
|
||||
|
||||
# Check frame quality
|
||||
frame_quality = frame_data.get_field('frame_quality_score', 0)
|
||||
if frame_quality < 50:
|
||||
return "poor_frame_quality"
|
||||
|
||||
# Check time delta magnitude
|
||||
abs_delta = abs(time_delta)
|
||||
if abs_delta > 1.0: # More than 1 second difference
|
||||
return "large_time_discrepancy"
|
||||
elif abs_delta > 0.1: # More than 100ms difference
|
||||
return "moderate_time_discrepancy"
|
||||
elif abs_delta > 0.001: # More than 1ms difference
|
||||
return "small_time_discrepancy"
|
||||
else:
|
||||
return "excellent"
|
||||
|
||||
def _calculate_clock_drift_ppm(self, time_delta: float, flow_duration: float) -> float:
|
||||
"""Calculate clock drift in parts per million"""
|
||||
if flow_duration <= 0:
|
||||
return 0.0
|
||||
|
||||
# Simplified drift calculation
|
||||
# In practice, would need more sophisticated analysis
|
||||
drift_fraction = time_delta / flow_duration
|
||||
return drift_fraction * 1_000_000 # Convert to PPM
|
||||
|
||||
def _detect_timing_anomaly(self, frame_data, time_delta: float) -> bool:
|
||||
"""Detect timing anomalies in this frame"""
|
||||
|
||||
# Multiple criteria for anomaly detection
|
||||
anomaly_indicators = [
|
||||
abs(time_delta) > 0.1, # Large time discrepancy
|
||||
frame_data.get_field('rtc_sync_error', False), # RTC sync error
|
||||
frame_data.get_field('format_error', False), # Format error
|
||||
frame_data.get_field('overflow_error', False), # Overflow error
|
||||
frame_data.get_field('frame_quality_score', 100) < 70 # Poor frame quality
|
||||
]
|
||||
|
||||
return any(anomaly_indicators)
|
||||
|
||||
def _calculate_confidence_score(self, frame_data, time_delta: float) -> float:
|
||||
"""Calculate confidence in timing analysis"""
|
||||
|
||||
confidence_factors = []
|
||||
|
||||
# Frame quality factor
|
||||
frame_quality = frame_data.get_field('frame_quality_score', 0) / 100.0
|
||||
confidence_factors.append(frame_quality)
|
||||
|
||||
# Time source confidence from decoder
|
||||
time_confidence = frame_data.get_field('time_source_confidence', 0.5)
|
||||
confidence_factors.append(time_confidence)
|
||||
|
||||
# Time delta reasonableness
|
||||
abs_delta = abs(time_delta)
|
||||
delta_confidence = max(0.0, 1.0 - (abs_delta / 10.0)) # Decreases with larger deltas
|
||||
confidence_factors.append(delta_confidence)
|
||||
|
||||
# Error flags (reduce confidence)
|
||||
error_penalty = 0.0
|
||||
if frame_data.get_field('rtc_sync_error', False):
|
||||
error_penalty += 0.3
|
||||
if frame_data.get_field('format_error', False):
|
||||
error_penalty += 0.2
|
||||
if frame_data.get_field('overflow_error', False):
|
||||
error_penalty += 0.2
|
||||
|
||||
base_confidence = statistics.mean(confidence_factors)
|
||||
final_confidence = max(0.0, base_confidence - error_penalty)
|
||||
|
||||
return final_confidence
|
||||
|
||||
def _analyze_quality_trend(self, timing_results: List[TimingAnalysisResult]) -> str:
|
||||
"""Analyze trend in timing quality over time"""
|
||||
|
||||
if len(timing_results) < 10:
|
||||
return "insufficient_data"
|
||||
|
||||
# Split into segments and compare
|
||||
segment_size = len(timing_results) // 3
|
||||
early_segment = timing_results[:segment_size]
|
||||
late_segment = timing_results[-segment_size:]
|
||||
|
||||
early_confidence = statistics.mean([r.confidence_score for r in early_segment])
|
||||
late_confidence = statistics.mean([r.confidence_score for r in late_segment])
|
||||
|
||||
if late_confidence > early_confidence + 0.1:
|
||||
return "improving"
|
||||
elif late_confidence < early_confidence - 0.1:
|
||||
return "degrading"
|
||||
else:
|
||||
return "stable"
|
||||
|
||||
def _assess_timing_stability(self, time_deltas: List[float]) -> Dict[str, Any]:
|
||||
"""Assess the stability of timing measurements"""
|
||||
|
||||
if len(time_deltas) < 2:
|
||||
return {'stability': 'insufficient_data'}
|
||||
|
||||
std_dev = statistics.stdev(time_deltas)
|
||||
mean_abs_dev = statistics.mean([abs(d) for d in time_deltas])
|
||||
|
||||
# Coefficient of variation
|
||||
mean_delta = statistics.mean(time_deltas)
|
||||
cv = std_dev / abs(mean_delta) if mean_delta != 0 else float('inf')
|
||||
|
||||
# Stability classification
|
||||
if std_dev < 0.001: # Less than 1ms standard deviation
|
||||
stability = "excellent"
|
||||
elif std_dev < 0.01: # Less than 10ms standard deviation
|
||||
stability = "good"
|
||||
elif std_dev < 0.1: # Less than 100ms standard deviation
|
||||
stability = "moderate"
|
||||
else:
|
||||
stability = "poor"
|
||||
|
||||
return {
|
||||
'stability': stability,
|
||||
'standard_deviation': std_dev,
|
||||
'mean_absolute_deviation': mean_abs_dev,
|
||||
'coefficient_of_variation': cv,
|
||||
'drift_consistency': 'stable' if cv < 0.1 else 'variable'
|
||||
}
|
||||
|
||||
def _calculate_drift_trend(self, drift_values: List[float]) -> str:
|
||||
"""Calculate the trend in clock drift"""
|
||||
|
||||
if len(drift_values) < 5:
|
||||
return "insufficient_data"
|
||||
|
||||
# Simple linear trend analysis
|
||||
x = list(range(len(drift_values)))
|
||||
y = drift_values
|
||||
|
||||
# Calculate correlation coefficient as trend indicator
|
||||
n = len(x)
|
||||
sum_x = sum(x)
|
||||
sum_y = sum(y)
|
||||
sum_xy = sum(x[i] * y[i] for i in range(n))
|
||||
sum_x2 = sum(x[i] * x[i] for i in range(n))
|
||||
|
||||
denominator = (n * sum_x2 - sum_x * sum_x)
|
||||
if denominator == 0:
|
||||
return "stable"
|
||||
|
||||
slope = (n * sum_xy - sum_x * sum_y) / denominator
|
||||
|
||||
if slope > 1.0: # Increasing drift
|
||||
return "increasing"
|
||||
elif slope < -1.0: # Decreasing drift
|
||||
return "decreasing"
|
||||
else:
|
||||
return "stable"
|
||||
|
||||
def _generate_recommendations(self, analysis: Dict[str, Any]) -> List[str]:
|
||||
"""Generate recommendations based on timing analysis"""
|
||||
|
||||
recommendations = []
|
||||
|
||||
# Check anomaly rate
|
||||
anomaly_rate = analysis.get('quality_metrics', {}).get('anomaly_rate', 0)
|
||||
if anomaly_rate > 0.1: # More than 10% anomalies
|
||||
recommendations.append("High anomaly rate detected - check timing source stability")
|
||||
|
||||
# Check drift
|
||||
drift_analysis = analysis.get('drift_analysis', {})
|
||||
max_drift = drift_analysis.get('max_drift_ppm', 0)
|
||||
if max_drift > 100: # More than 100 PPM drift
|
||||
recommendations.append("Significant clock drift detected - calibrate timing reference")
|
||||
|
||||
# Check timing stability
|
||||
stability_info = analysis.get('timing_stability', {})
|
||||
if stability_info.get('stability') in ['poor', 'moderate']:
|
||||
recommendations.append("Timing instability detected - investigate noise sources")
|
||||
|
||||
# Check confidence
|
||||
avg_confidence = analysis.get('quality_metrics', {}).get('avg_confidence', 1.0)
|
||||
if avg_confidence < 0.7:
|
||||
recommendations.append("Low timing confidence - verify CH10 frame integrity")
|
||||
|
||||
if not recommendations:
|
||||
recommendations.append("Timing analysis shows good quality - no issues detected")
|
||||
|
||||
return recommendations
|
||||
|
||||
def _calculate_timing_variance(self, timing_results: List[TimingAnalysisResult]) -> float:
|
||||
"""Calculate timing variance for a channel"""
|
||||
deltas = [r.time_delta for r in timing_results if r.time_delta is not None]
|
||||
return statistics.variance(deltas) if len(deltas) > 1 else 0.0
|
||||
|
||||
def _assess_sync_quality(self, timing_results: List[TimingAnalysisResult]) -> str:
|
||||
"""Assess synchronization quality for a channel"""
|
||||
if not timing_results:
|
||||
return "no_data"
|
||||
|
||||
avg_confidence = statistics.mean([r.confidence_score for r in timing_results])
|
||||
|
||||
if avg_confidence > 0.9:
|
||||
return "excellent"
|
||||
elif avg_confidence > 0.7:
|
||||
return "good"
|
||||
elif avg_confidence > 0.5:
|
||||
return "moderate"
|
||||
else:
|
||||
return "poor"
|
||||
|
||||
def _calculate_relative_drift(self, timing_results: List[TimingAnalysisResult]) -> float:
|
||||
"""Calculate relative drift compared to reference"""
|
||||
# Simplified calculation - would compare against reference channel
|
||||
deltas = [r.time_delta for r in timing_results if r.time_delta is not None]
|
||||
return statistics.stdev(deltas) if len(deltas) > 1 else 0.0
|
||||
|
||||
def _calculate_channel_correlations(self, channel_analysis: Dict) -> Dict[str, float]:
|
||||
"""Calculate correlations between channels"""
|
||||
# Placeholder for cross-channel correlation analysis
|
||||
return {"correlation_analysis": "not_implemented_in_demo"}
|
||||
|
||||
def _assess_overall_sync_status(self, channel_analysis: Dict) -> str:
|
||||
"""Assess overall synchronization status across channels"""
|
||||
sync_qualities = [info.get('sync_quality', 'poor') for info in channel_analysis.values()]
|
||||
|
||||
excellent_count = sync_qualities.count('excellent')
|
||||
good_count = sync_qualities.count('good')
|
||||
total_count = len(sync_qualities)
|
||||
|
||||
if total_count == 0:
|
||||
return "no_data"
|
||||
elif excellent_count / total_count > 0.8:
|
||||
return "excellent"
|
||||
elif (excellent_count + good_count) / total_count > 0.6:
|
||||
return "good"
|
||||
else:
|
||||
return "poor"
|
||||
|
||||
def _assess_reference_quality(self) -> str:
|
||||
"""Assess the quality of the timing reference"""
|
||||
# Placeholder for reference quality assessment
|
||||
return "analysis_pending"
|
||||
655
analyzer/protocols/enhanced_chapter10.py
Normal file
655
analyzer/protocols/enhanced_chapter10.py
Normal file
@@ -0,0 +1,655 @@
|
||||
"""
|
||||
Enhanced Chapter 10 (IRIG 106) decoder with comprehensive field extraction
|
||||
Exposes all CH10 frame variables for modular analysis
|
||||
"""
|
||||
|
||||
import struct
|
||||
from typing import Dict, Any, List, Optional, Union
|
||||
from dataclasses import dataclass, field
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
# Import the modular framework components
|
||||
@dataclass
|
||||
class FieldDefinition:
|
||||
"""Defines a field that can be extracted from decoded data"""
|
||||
name: str
|
||||
description: str
|
||||
data_type: type
|
||||
unit: Optional[str] = None
|
||||
validator: Optional[callable] = None
|
||||
|
||||
@dataclass
|
||||
class StructuredFrameData:
|
||||
"""Container for decoded frame data with metadata"""
|
||||
decoder_name: str
|
||||
packet_timestamp: float
|
||||
raw_data: bytes
|
||||
fields: Dict[str, Any] = field(default_factory=dict)
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def get_field(self, name: str, default=None):
|
||||
return self.fields.get(name, default)
|
||||
|
||||
def has_field(self, name: str) -> bool:
|
||||
return name in self.fields
|
||||
|
||||
class EnhancedChapter10Decoder:
|
||||
"""Comprehensive Chapter 10 decoder exposing all frame variables"""
|
||||
|
||||
# Chapter 10 sync pattern
|
||||
SYNC_PATTERN = 0xEB25
|
||||
|
||||
# Data type definitions from IRIG 106-17
|
||||
DATA_TYPES = {
|
||||
0x00: "Computer Generated Data",
|
||||
0x01: "TMATS",
|
||||
0x02: "Computer Generated Data - Format 2",
|
||||
0x03: "Computer Generated Data - Format 3",
|
||||
0x04: "PCM Format 1",
|
||||
0x05: "Time Data - Format 1",
|
||||
0x06: "Time Data - Format 2",
|
||||
0x07: "Computer Generated Data - Format 4",
|
||||
0x08: "PCM Format 2",
|
||||
0x09: "IRIG Time",
|
||||
0x0A: "Computer Generated Data - Format 5",
|
||||
0x0B: "Computer Generated Data - Format 6",
|
||||
0x11: "1553 Format 1",
|
||||
0x19: "1553 Format 2",
|
||||
0x21: "Analog Format 1",
|
||||
0x29: "Discrete Format 1",
|
||||
0x30: "Message Data",
|
||||
0x31: "ARINC 429 Format 1",
|
||||
0x38: "Video Format 0",
|
||||
0x39: "Video Format 1",
|
||||
0x3A: "Video Format 2",
|
||||
0x40: "Image Format 0",
|
||||
0x41: "Image Format 1",
|
||||
0x48: "UART Format 0",
|
||||
0x50: "IEEE 1394 Format 0",
|
||||
0x51: "IEEE 1394 Format 1",
|
||||
0x58: "Parallel Format 0",
|
||||
0x59: "Parallel Format 1",
|
||||
0x60: "Ethernet Format 0",
|
||||
0x61: "Ethernet Format 1",
|
||||
0x68: "TSPI/CTS Format 0",
|
||||
0x69: "TSPI/CTS Format 1",
|
||||
0x70: "CAN Bus",
|
||||
0x71: "Fibre Channel Format 0",
|
||||
0x72: "Analog Format 2",
|
||||
0x73: "Analog Format 3",
|
||||
0x74: "Analog Format 4",
|
||||
0x75: "Analog Format 5",
|
||||
0x76: "Analog Format 6",
|
||||
0x77: "Analog Format 7",
|
||||
0x78: "Analog Format 8"
|
||||
}
|
||||
|
||||
# Packet flags bit definitions
|
||||
PACKET_FLAGS = {
|
||||
0: "Secondary Header Time Source",
|
||||
1: "Format Error",
|
||||
2: "RTC Sync Error",
|
||||
3: "IPH Time Source",
|
||||
4: "Secondary Header Present",
|
||||
5: "Optional Data Present",
|
||||
6: "Reserved",
|
||||
7: "Overflow Error"
|
||||
}
|
||||
|
||||
@property
|
||||
def decoder_name(self) -> str:
|
||||
return "Chapter10_Enhanced"
|
||||
|
||||
@property
|
||||
def supported_fields(self) -> List[FieldDefinition]:
|
||||
"""All fields that can be extracted from CH10 frames"""
|
||||
return [
|
||||
# Primary header fields (24 bytes)
|
||||
FieldDefinition("sync_pattern", "Sync pattern (should be 0xEB25)", int),
|
||||
FieldDefinition("channel_id", "Channel identifier", int),
|
||||
FieldDefinition("packet_length", "Total packet length including header", int, "bytes"),
|
||||
FieldDefinition("data_length", "Data payload length", int, "bytes"),
|
||||
FieldDefinition("header_version", "Header version number", int),
|
||||
FieldDefinition("sequence_number", "Packet sequence number", int),
|
||||
FieldDefinition("packet_flags", "Packet flags byte", int),
|
||||
FieldDefinition("data_type", "Data type identifier", int),
|
||||
FieldDefinition("relative_time_counter", "RTC value (6 bytes)", int, "counts"),
|
||||
FieldDefinition("header_checksum", "Header checksum", int),
|
||||
|
||||
# Decoded packet flags
|
||||
FieldDefinition("secondary_header_time_source", "Time source from secondary header", bool),
|
||||
FieldDefinition("format_error", "Format error flag", bool),
|
||||
FieldDefinition("rtc_sync_error", "RTC synchronization error", bool),
|
||||
FieldDefinition("iph_time_source", "IPH time source flag", bool),
|
||||
FieldDefinition("secondary_header_present", "Secondary header present", bool),
|
||||
FieldDefinition("optional_data_present", "Optional data present", bool),
|
||||
FieldDefinition("overflow_error", "Data overflow error", bool),
|
||||
|
||||
# Data type information
|
||||
FieldDefinition("data_type_name", "Human readable data type", str),
|
||||
FieldDefinition("is_analog_data", "True if analog format", bool),
|
||||
FieldDefinition("is_pcm_data", "True if PCM format", bool),
|
||||
FieldDefinition("is_tmats_data", "True if TMATS data", bool),
|
||||
FieldDefinition("is_time_data", "True if time format", bool),
|
||||
|
||||
# Secondary header fields (if present)
|
||||
FieldDefinition("secondary_header_time", "Secondary header timestamp", int, "nanoseconds"),
|
||||
FieldDefinition("internal_seconds", "Internal time seconds component", int, "seconds"),
|
||||
FieldDefinition("internal_nanoseconds", "Internal time nanoseconds component", int, "nanoseconds"),
|
||||
FieldDefinition("internal_timestamp", "Combined internal timestamp", float, "seconds"),
|
||||
|
||||
# Analog format specific fields
|
||||
FieldDefinition("analog_minor_frame_count", "Number of minor frames", int),
|
||||
FieldDefinition("analog_scan_count", "Scans per minor frame", int),
|
||||
FieldDefinition("analog_channel_count", "Number of analog channels", int),
|
||||
FieldDefinition("analog_sample_rate", "Sampling rate", float, "Hz"),
|
||||
FieldDefinition("analog_bit_depth", "Bits per sample", int, "bits"),
|
||||
FieldDefinition("analog_format_factor", "Format factor", int),
|
||||
FieldDefinition("analog_measurement_list", "Channel measurement data", dict),
|
||||
|
||||
# PCM format specific fields
|
||||
FieldDefinition("pcm_minor_frame_sync", "Minor frame sync pattern", int),
|
||||
FieldDefinition("pcm_major_frame_sync", "Major frame sync pattern", int),
|
||||
FieldDefinition("pcm_minor_frame_length", "Minor frame length", int, "bits"),
|
||||
FieldDefinition("pcm_major_frame_length", "Major frame length", int, "minor_frames"),
|
||||
FieldDefinition("pcm_bits_per_second", "PCM bit rate", int, "bps"),
|
||||
|
||||
# TMATS specific fields
|
||||
FieldDefinition("tmats_version", "TMATS version", str),
|
||||
FieldDefinition("tmats_channel_configs", "Parsed channel configurations", dict),
|
||||
FieldDefinition("tmats_data_source_id", "Data source identifier", str),
|
||||
FieldDefinition("tmats_recording_date", "Recording date/time", str),
|
||||
|
||||
# General payload analysis
|
||||
FieldDefinition("payload_entropy", "Data randomness measure", float),
|
||||
FieldDefinition("payload_patterns", "Detected data patterns", list),
|
||||
FieldDefinition("has_embedded_timestamps", "Contains timestamp data", bool),
|
||||
|
||||
# Frame quality metrics
|
||||
FieldDefinition("header_checksum_valid", "Header checksum validation", bool),
|
||||
FieldDefinition("frame_completeness", "Percentage of expected data present", float, "percent"),
|
||||
FieldDefinition("data_continuity_errors", "Number of continuity errors", int),
|
||||
|
||||
# Timing analysis fields
|
||||
FieldDefinition("rtc_time_base", "RTC time base frequency", float, "Hz"),
|
||||
FieldDefinition("time_source_confidence", "Confidence in time source", float),
|
||||
FieldDefinition("clock_drift_indicators", "Indicators of clock drift", dict)
|
||||
]
|
||||
|
||||
def can_decode(self, packet, transport_info: Dict) -> float:
|
||||
"""Check if packet contains Chapter 10 data"""
|
||||
if not hasattr(packet, 'haslayer') or not packet.haslayer('Raw'):
|
||||
return 0.0
|
||||
|
||||
from scapy.all import Raw
|
||||
raw_data = bytes(packet[Raw])
|
||||
|
||||
# Must have at least primary header
|
||||
if len(raw_data) < 24:
|
||||
return 0.0
|
||||
|
||||
try:
|
||||
# Check for sync pattern
|
||||
sync = struct.unpack('<H', raw_data[:2])[0]
|
||||
if sync != self.SYNC_PATTERN:
|
||||
return 0.0
|
||||
|
||||
# Basic header validation
|
||||
header = struct.unpack('<HHHHHHHHI', raw_data[:22])
|
||||
packet_length = header[2]
|
||||
data_length = header[3]
|
||||
|
||||
# Sanity check packet lengths
|
||||
if packet_length < 24 or data_length > packet_length - 24:
|
||||
return 0.5 # Might be CH10 but malformed
|
||||
|
||||
# Check if packet length matches actual data
|
||||
if packet_length <= len(raw_data):
|
||||
return 1.0
|
||||
else:
|
||||
return 0.8 # Truncated but probably CH10
|
||||
|
||||
except (struct.error, IndexError):
|
||||
return 0.0
|
||||
|
||||
def decode_frame(self, packet, transport_info: Dict) -> Optional[StructuredFrameData]:
|
||||
"""Comprehensive Chapter 10 frame decoding"""
|
||||
if not hasattr(packet, 'haslayer') or not packet.haslayer('Raw'):
|
||||
return None
|
||||
|
||||
from scapy.all import Raw
|
||||
raw_data = bytes(packet[Raw])
|
||||
|
||||
if len(raw_data) < 24:
|
||||
return None
|
||||
|
||||
try:
|
||||
frame_data = StructuredFrameData(
|
||||
decoder_name=self.decoder_name,
|
||||
packet_timestamp=float(packet.time),
|
||||
raw_data=raw_data
|
||||
)
|
||||
|
||||
# Parse primary header
|
||||
self._parse_primary_header(raw_data, frame_data)
|
||||
|
||||
# Parse secondary header if present
|
||||
header_size = 24
|
||||
if frame_data.get_field('secondary_header_present'):
|
||||
header_size += self._parse_secondary_header(raw_data[24:], frame_data)
|
||||
|
||||
# Parse data payload based on type
|
||||
payload_start = header_size
|
||||
data_length = frame_data.get_field('data_length', 0)
|
||||
if payload_start < len(raw_data) and data_length > 0:
|
||||
payload_end = min(payload_start + data_length, len(raw_data))
|
||||
payload = raw_data[payload_start:payload_end]
|
||||
self._parse_payload(payload, frame_data)
|
||||
|
||||
# Calculate derived fields
|
||||
self._calculate_derived_fields(frame_data)
|
||||
|
||||
# Validate frame integrity
|
||||
self._validate_frame(frame_data)
|
||||
|
||||
return frame_data
|
||||
|
||||
except Exception as e:
|
||||
# Return partial data even if parsing fails
|
||||
if 'frame_data' in locals():
|
||||
frame_data.metadata['parsing_error'] = str(e)
|
||||
return frame_data
|
||||
return None
|
||||
|
||||
def _parse_primary_header(self, raw_data: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse the 24-byte primary header"""
|
||||
# Unpack primary header fields
|
||||
header = struct.unpack('<HHHHHHHHI', raw_data[:22])
|
||||
checksum = struct.unpack('<H', raw_data[22:24])[0]
|
||||
|
||||
# Store basic header fields
|
||||
frame_data.fields.update({
|
||||
'sync_pattern': header[0],
|
||||
'channel_id': header[1],
|
||||
'packet_length': header[2],
|
||||
'data_length': header[3],
|
||||
'header_version': header[4],
|
||||
'sequence_number': header[5],
|
||||
'packet_flags': header[6],
|
||||
'data_type': header[7],
|
||||
'relative_time_counter': header[8], # This is actually 6 bytes, simplified here
|
||||
'header_checksum': checksum
|
||||
})
|
||||
|
||||
# Decode packet flags
|
||||
flags = header[6]
|
||||
frame_data.fields.update({
|
||||
'secondary_header_time_source': bool(flags & 0x01),
|
||||
'format_error': bool(flags & 0x02),
|
||||
'rtc_sync_error': bool(flags & 0x04),
|
||||
'iph_time_source': bool(flags & 0x08),
|
||||
'secondary_header_present': bool(flags & 0x10),
|
||||
'optional_data_present': bool(flags & 0x20),
|
||||
'overflow_error': bool(flags & 0x80)
|
||||
})
|
||||
|
||||
# Decode data type
|
||||
data_type = header[7]
|
||||
frame_data.fields.update({
|
||||
'data_type_name': self.DATA_TYPES.get(data_type, f"Unknown (0x{data_type:02X})"),
|
||||
'is_analog_data': data_type in range(0x21, 0x79),
|
||||
'is_pcm_data': data_type in [0x04, 0x08],
|
||||
'is_tmats_data': data_type == 0x01,
|
||||
'is_time_data': data_type in [0x05, 0x06, 0x09]
|
||||
})
|
||||
|
||||
def _parse_secondary_header(self, data: bytes, frame_data: StructuredFrameData) -> int:
|
||||
"""Parse secondary header (variable length, typically 8 bytes for time)"""
|
||||
if len(data) < 8:
|
||||
return 0
|
||||
|
||||
try:
|
||||
# Parse time format secondary header (most common)
|
||||
time_data = struct.unpack('<II', data[:8])
|
||||
|
||||
frame_data.fields.update({
|
||||
'secondary_header_time': time_data[0] | (time_data[1] << 32),
|
||||
'internal_seconds': time_data[0],
|
||||
'internal_nanoseconds': time_data[1],
|
||||
'internal_timestamp': time_data[0] + (time_data[1] / 1_000_000_000)
|
||||
})
|
||||
|
||||
# Mark timing fields for analysis
|
||||
frame_data.metadata.update({
|
||||
'primary_timestamp_field': 'internal_timestamp',
|
||||
'has_internal_timing': True
|
||||
})
|
||||
|
||||
return 8 # Standard time header size
|
||||
|
||||
except struct.error:
|
||||
return 0
|
||||
|
||||
def _parse_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse payload based on data type"""
|
||||
data_type = frame_data.get_field('data_type', 0)
|
||||
|
||||
if frame_data.get_field('is_analog_data'):
|
||||
self._parse_analog_payload(payload, frame_data)
|
||||
elif frame_data.get_field('is_pcm_data'):
|
||||
self._parse_pcm_payload(payload, frame_data)
|
||||
elif frame_data.get_field('is_tmats_data'):
|
||||
self._parse_tmats_payload(payload, frame_data)
|
||||
elif frame_data.get_field('is_time_data'):
|
||||
self._parse_time_payload(payload, frame_data)
|
||||
else:
|
||||
# Generic payload analysis
|
||||
self._analyze_generic_payload(payload, frame_data)
|
||||
|
||||
def _parse_analog_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse analog format payload"""
|
||||
if len(payload) < 8:
|
||||
return
|
||||
|
||||
try:
|
||||
# Parse analog format header (varies by format)
|
||||
data_type = frame_data.get_field('data_type')
|
||||
|
||||
if data_type == 0x72: # Analog Format 2
|
||||
header = struct.unpack('<HHII', payload[:12])
|
||||
frame_data.fields.update({
|
||||
'analog_minor_frame_count': header[0],
|
||||
'analog_scan_count': header[1],
|
||||
'analog_channel_count': header[2] & 0xFFFF,
|
||||
'analog_sample_rate': header[3] / 1000.0, # Convert to Hz
|
||||
'analog_bit_depth': 16, # Standard for format 2
|
||||
'analog_format_factor': (header[2] >> 16) & 0xFFFF
|
||||
})
|
||||
|
||||
# Parse measurement data if present
|
||||
if len(payload) > 12:
|
||||
self._parse_analog_measurements(payload[12:], frame_data)
|
||||
|
||||
elif data_type in [0x73, 0x74, 0x75]: # Other analog formats
|
||||
# Simplified parsing for other formats
|
||||
if len(payload) >= 4:
|
||||
basic_info = struct.unpack('<HH', payload[:4])
|
||||
frame_data.fields.update({
|
||||
'analog_channel_count': basic_info[0],
|
||||
'analog_format_factor': basic_info[1]
|
||||
})
|
||||
|
||||
except struct.error:
|
||||
pass
|
||||
|
||||
def _parse_analog_measurements(self, data: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse analog measurement data"""
|
||||
channel_count = frame_data.get_field('analog_channel_count', 0)
|
||||
if channel_count == 0 or len(data) < 2:
|
||||
return
|
||||
|
||||
measurements = {}
|
||||
try:
|
||||
# Simple 16-bit sample extraction
|
||||
samples_per_channel = len(data) // (2 * channel_count)
|
||||
if samples_per_channel > 0:
|
||||
for channel in range(min(channel_count, 16)): # Limit to prevent excessive processing
|
||||
channel_data = []
|
||||
for sample in range(min(samples_per_channel, 100)): # Limit samples
|
||||
offset = (sample * channel_count + channel) * 2
|
||||
if offset + 1 < len(data):
|
||||
value = struct.unpack('<h', data[offset:offset+2])[0]
|
||||
channel_data.append(value)
|
||||
measurements[f'channel_{channel}'] = channel_data
|
||||
|
||||
frame_data.fields['analog_measurement_list'] = measurements
|
||||
|
||||
except struct.error:
|
||||
pass
|
||||
|
||||
def _parse_pcm_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse PCM format payload"""
|
||||
if len(payload) < 8:
|
||||
return
|
||||
|
||||
try:
|
||||
# Parse PCM format header
|
||||
pcm_header = struct.unpack('<IIHH', payload[:12])
|
||||
|
||||
frame_data.fields.update({
|
||||
'pcm_minor_frame_sync': pcm_header[0],
|
||||
'pcm_major_frame_sync': pcm_header[1],
|
||||
'pcm_minor_frame_length': pcm_header[2],
|
||||
'pcm_major_frame_length': pcm_header[3]
|
||||
})
|
||||
|
||||
# Estimate bit rate if possible
|
||||
if frame_data.has_field('internal_timestamp'):
|
||||
# Simplified calculation
|
||||
data_bits = len(payload) * 8
|
||||
frame_data.fields['pcm_bits_per_second'] = data_bits # Rough estimate
|
||||
|
||||
except struct.error:
|
||||
pass
|
||||
|
||||
def _parse_tmats_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse TMATS (Telemetry Attributes Transfer Standard) payload"""
|
||||
try:
|
||||
# TMATS is ASCII text with specific format
|
||||
text_data = payload.decode('ascii', errors='ignore')
|
||||
|
||||
# Basic TMATS parsing
|
||||
tmats_info = self._parse_tmats_text(text_data)
|
||||
frame_data.fields.update(tmats_info)
|
||||
|
||||
except UnicodeDecodeError:
|
||||
# Binary TMATS or corrupted data
|
||||
frame_data.fields['tmats_version'] = 'Binary/Corrupted'
|
||||
|
||||
def _parse_tmats_text(self, text: str) -> Dict[str, Any]:
|
||||
"""Parse TMATS text format"""
|
||||
tmats_data = {
|
||||
'tmats_version': 'Unknown',
|
||||
'tmats_channel_configs': {},
|
||||
'tmats_data_source_id': 'Unknown',
|
||||
'tmats_recording_date': 'Unknown'
|
||||
}
|
||||
|
||||
try:
|
||||
# Split on backslashes (TMATS line separators)
|
||||
lines = text.split('\\')
|
||||
|
||||
channel_configs = {}
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# Parse key-value pairs
|
||||
if ':' in line:
|
||||
key, value = line.split(':', 1)
|
||||
elif ';' in line:
|
||||
key, value = line.split(';', 1)
|
||||
else:
|
||||
continue
|
||||
|
||||
key = key.strip()
|
||||
value = value.strip().rstrip(';')
|
||||
|
||||
# Parse specific TMATS parameters
|
||||
if key.startswith('G\\VER'):
|
||||
tmats_data['tmats_version'] = value
|
||||
elif key.startswith('G\\DSI'):
|
||||
tmats_data['tmats_data_source_id'] = value
|
||||
elif key.startswith('G\\RD'):
|
||||
tmats_data['tmats_recording_date'] = value
|
||||
elif key.startswith('R-'):
|
||||
# Channel configuration
|
||||
self._parse_tmats_channel_config(key, value, channel_configs)
|
||||
|
||||
tmats_data['tmats_channel_configs'] = channel_configs
|
||||
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return tmats_data
|
||||
|
||||
def _parse_tmats_channel_config(self, key: str, value: str, configs: Dict):
|
||||
"""Parse TMATS channel configuration parameters"""
|
||||
# Extract channel ID from key like "R-1\\G" or "R-CH1\\N"
|
||||
parts = key.split('\\')
|
||||
if len(parts) < 2:
|
||||
return
|
||||
|
||||
channel_part = parts[0] # e.g., "R-1"
|
||||
param_part = parts[1] # e.g., "G", "N", "EU"
|
||||
|
||||
if channel_part.startswith('R-'):
|
||||
channel_id = channel_part[2:]
|
||||
|
||||
if channel_id not in configs:
|
||||
configs[channel_id] = {}
|
||||
|
||||
# Map parameter codes
|
||||
param_map = {
|
||||
'G': 'gain',
|
||||
'N': 'name',
|
||||
'EU': 'units',
|
||||
'MN': 'min_value',
|
||||
'MX': 'max_value',
|
||||
'OF': 'offset',
|
||||
'FS': 'full_scale',
|
||||
'SN': 'sample_rate'
|
||||
}
|
||||
|
||||
param_name = param_map.get(param_part, param_part.lower())
|
||||
|
||||
# Try to convert numeric values
|
||||
try:
|
||||
if param_name in ['gain', 'min_value', 'max_value', 'offset', 'full_scale', 'sample_rate']:
|
||||
value = float(value)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
configs[channel_id][param_name] = value
|
||||
|
||||
def _parse_time_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Parse time format payload"""
|
||||
if len(payload) < 8:
|
||||
return
|
||||
|
||||
try:
|
||||
# Parse time data (format depends on data type)
|
||||
time_info = struct.unpack('<II', payload[:8])
|
||||
|
||||
frame_data.fields.update({
|
||||
'time_seconds': time_info[0],
|
||||
'time_microseconds': time_info[1],
|
||||
'has_embedded_timestamps': True
|
||||
})
|
||||
|
||||
except struct.error:
|
||||
pass
|
||||
|
||||
def _analyze_generic_payload(self, payload: bytes, frame_data: StructuredFrameData):
|
||||
"""Generic payload analysis for unknown formats"""
|
||||
if len(payload) == 0:
|
||||
return
|
||||
|
||||
# Calculate entropy (randomness measure)
|
||||
entropy = self._calculate_entropy(payload)
|
||||
frame_data.fields['payload_entropy'] = entropy
|
||||
|
||||
# Look for patterns
|
||||
patterns = self._detect_patterns(payload)
|
||||
frame_data.fields['payload_patterns'] = patterns
|
||||
|
||||
def _calculate_entropy(self, data: bytes) -> float:
|
||||
"""Calculate Shannon entropy of data"""
|
||||
if len(data) == 0:
|
||||
return 0.0
|
||||
|
||||
# Count byte frequencies
|
||||
frequencies = [0] * 256
|
||||
for byte in data:
|
||||
frequencies[byte] += 1
|
||||
|
||||
# Calculate entropy
|
||||
entropy = 0.0
|
||||
for freq in frequencies:
|
||||
if freq > 0:
|
||||
probability = freq / len(data)
|
||||
entropy -= probability * (probability.bit_length() - 1)
|
||||
|
||||
return entropy / 8.0 # Normalize to 0-1 range
|
||||
|
||||
def _detect_patterns(self, data: bytes) -> List[str]:
|
||||
"""Detect common patterns in data"""
|
||||
patterns = []
|
||||
|
||||
if len(data) < 4:
|
||||
return patterns
|
||||
|
||||
# Check for repeated patterns
|
||||
if data[:4] == data[4:8]:
|
||||
patterns.append("repeated_4byte_pattern")
|
||||
|
||||
# Check for incrementing patterns
|
||||
if len(data) >= 8:
|
||||
values = struct.unpack(f'<{len(data)//4}I', data[:len(data)//4*4])
|
||||
if all(values[i] < values[i+1] for i in range(len(values)-1)):
|
||||
patterns.append("incrementing_sequence")
|
||||
|
||||
# Check for zero padding
|
||||
if data.count(0) > len(data) * 0.8:
|
||||
patterns.append("mostly_zeros")
|
||||
|
||||
return patterns
|
||||
|
||||
def _calculate_derived_fields(self, frame_data: StructuredFrameData):
|
||||
"""Calculate derived fields from extracted data"""
|
||||
# Calculate frame completeness
|
||||
expected_length = frame_data.get_field('packet_length', 0)
|
||||
actual_length = len(frame_data.raw_data)
|
||||
if expected_length > 0:
|
||||
completeness = min(100.0, (actual_length / expected_length) * 100.0)
|
||||
frame_data.fields['frame_completeness'] = completeness
|
||||
|
||||
# Analyze timing if internal timestamp is available
|
||||
if frame_data.has_field('internal_timestamp'):
|
||||
packet_time = frame_data.packet_timestamp
|
||||
internal_time = frame_data.get_field('internal_timestamp')
|
||||
|
||||
time_delta = packet_time - internal_time
|
||||
frame_data.fields['packet_internal_time_delta'] = time_delta
|
||||
|
||||
# Simple confidence measure
|
||||
confidence = 1.0 - min(1.0, abs(time_delta) / 60.0) # Confidence decreases with time delta
|
||||
frame_data.fields['time_source_confidence'] = confidence
|
||||
|
||||
def _validate_frame(self, frame_data: StructuredFrameData):
|
||||
"""Validate frame integrity"""
|
||||
# Validate sync pattern
|
||||
sync = frame_data.get_field('sync_pattern', 0)
|
||||
frame_data.fields['sync_pattern_valid'] = (sync == self.SYNC_PATTERN)
|
||||
|
||||
# Simple checksum validation (would need proper implementation)
|
||||
# For now, just check if checksum field exists
|
||||
has_checksum = frame_data.has_field('header_checksum')
|
||||
frame_data.fields['header_checksum_valid'] = has_checksum
|
||||
|
||||
# Check for continuity errors (simplified)
|
||||
seq_num = frame_data.get_field('sequence_number', 0)
|
||||
frame_data.fields['data_continuity_errors'] = 0 # Would need sequence tracking
|
||||
|
||||
# Overall frame quality score
|
||||
quality_factors = [
|
||||
frame_data.get_field('sync_pattern_valid', False),
|
||||
frame_data.get_field('header_checksum_valid', False),
|
||||
frame_data.get_field('frame_completeness', 0) > 95.0,
|
||||
not frame_data.get_field('format_error', True),
|
||||
not frame_data.get_field('overflow_error', True)
|
||||
]
|
||||
|
||||
quality_score = sum(quality_factors) / len(quality_factors) * 100.0
|
||||
frame_data.fields['frame_quality_score'] = quality_score
|
||||
@@ -64,6 +64,8 @@ class TUIInterface:
|
||||
break
|
||||
elif action == 'visualize':
|
||||
self._handle_visualization()
|
||||
elif action == 'switch_tab':
|
||||
self.detail_panel.switch_tab()
|
||||
|
||||
def _draw_main_view(self, stdscr):
|
||||
"""Draw three-panel main view: flows list, details, and timeline"""
|
||||
|
||||
272
analyzer/tui/modern_interface.py
Normal file
272
analyzer/tui/modern_interface.py
Normal file
@@ -0,0 +1,272 @@
|
||||
"""
|
||||
Modern TUI Interface for StreamLens
|
||||
Focused on Flow Analysis, Packet Decoding, and Statistical Analysis
|
||||
"""
|
||||
|
||||
import curses
|
||||
from typing import TYPE_CHECKING, List, Optional
|
||||
from enum import Enum
|
||||
|
||||
from .navigation import NavigationHandler
|
||||
from .modern_views import FlowAnalysisView, PacketDecoderView, StatisticalAnalysisView
|
||||
from ..utils.signal_visualizer import signal_visualizer
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..analysis.core import EthernetAnalyzer
|
||||
|
||||
|
||||
class ViewMode(Enum):
|
||||
FLOW_ANALYSIS = "flow"
|
||||
PACKET_DECODER = "decode"
|
||||
STATISTICAL_ANALYSIS = "stats"
|
||||
|
||||
|
||||
class ModernTUIInterface:
|
||||
"""
|
||||
Modern StreamLens TUI Interface
|
||||
|
||||
Three primary views accessed via 1/2/3:
|
||||
- 1: Flow Analysis - Visual flow overview with enhanced protocol detection
|
||||
- 2: Packet Decoder - Deep protocol inspection and field extraction
|
||||
- 3: Statistical Analysis - Timing analysis, outliers, and quality metrics
|
||||
"""
|
||||
|
||||
def __init__(self, analyzer: 'EthernetAnalyzer'):
|
||||
self.analyzer = analyzer
|
||||
self.navigation = NavigationHandler()
|
||||
|
||||
# Current view mode
|
||||
self.current_view = ViewMode.FLOW_ANALYSIS
|
||||
|
||||
# Initialize view controllers
|
||||
self.flow_view = FlowAnalysisView(analyzer)
|
||||
self.decoder_view = PacketDecoderView(analyzer)
|
||||
self.stats_view = StatisticalAnalysisView(analyzer)
|
||||
|
||||
# Global state
|
||||
self.selected_flow_key = None
|
||||
self.show_help = False
|
||||
|
||||
def run(self, stdscr):
|
||||
"""Main TUI loop for modern interface"""
|
||||
curses.curs_set(0) # Hide cursor
|
||||
stdscr.keypad(True)
|
||||
|
||||
# Set timeout based on live mode
|
||||
if self.analyzer.is_live:
|
||||
stdscr.timeout(500) # 0.5 second for live updates
|
||||
else:
|
||||
stdscr.timeout(1000) # 1 second for static analysis
|
||||
|
||||
while True:
|
||||
try:
|
||||
stdscr.clear()
|
||||
|
||||
# Draw header with view indicators
|
||||
self._draw_header(stdscr)
|
||||
|
||||
# Draw main content based on current view
|
||||
if self.current_view == ViewMode.FLOW_ANALYSIS:
|
||||
self.flow_view.draw(stdscr, self.selected_flow_key)
|
||||
elif self.current_view == ViewMode.PACKET_DECODER:
|
||||
self.decoder_view.draw(stdscr, self.selected_flow_key)
|
||||
elif self.current_view == ViewMode.STATISTICAL_ANALYSIS:
|
||||
self.stats_view.draw(stdscr, self.selected_flow_key)
|
||||
|
||||
# Draw status bar
|
||||
self._draw_status_bar(stdscr)
|
||||
|
||||
# Overlay help if requested
|
||||
if self.show_help:
|
||||
self._draw_help_overlay(stdscr)
|
||||
|
||||
stdscr.refresh()
|
||||
|
||||
# Handle input
|
||||
key = stdscr.getch()
|
||||
|
||||
# Handle timeout (no key pressed) - refresh for live capture
|
||||
if key == -1 and self.analyzer.is_live:
|
||||
continue
|
||||
|
||||
action = self._handle_input(key)
|
||||
|
||||
if action == 'quit':
|
||||
if self.analyzer.is_live:
|
||||
self.analyzer.stop_capture = True
|
||||
break
|
||||
|
||||
except curses.error:
|
||||
# Handle terminal resize or other curses errors
|
||||
pass
|
||||
|
||||
def _draw_header(self, stdscr):
|
||||
"""Draw the header with application title and view tabs"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
|
||||
# Title
|
||||
title = "StreamLens - Ethernet Traffic Analyzer"
|
||||
stdscr.addstr(0, 2, title, curses.A_BOLD)
|
||||
|
||||
# Live indicator
|
||||
if self.analyzer.is_live:
|
||||
live_text = "[LIVE]"
|
||||
stdscr.addstr(0, width - len(live_text) - 2, live_text,
|
||||
curses.A_BOLD | curses.A_BLINK)
|
||||
|
||||
# View tabs
|
||||
tab_line = 1
|
||||
tab_x = 2
|
||||
|
||||
# 1: Flow Analysis
|
||||
if self.current_view == ViewMode.FLOW_ANALYSIS:
|
||||
stdscr.addstr(tab_line, tab_x, "[1: Flow Analysis]", curses.A_REVERSE)
|
||||
else:
|
||||
stdscr.addstr(tab_line, tab_x, " 1: Flow Analysis ", curses.A_DIM)
|
||||
tab_x += 19
|
||||
|
||||
# 2: Packet Decoder
|
||||
if self.current_view == ViewMode.PACKET_DECODER:
|
||||
stdscr.addstr(tab_line, tab_x, "[2: Packet Decoder]", curses.A_REVERSE)
|
||||
else:
|
||||
stdscr.addstr(tab_line, tab_x, " 2: Packet Decoder ", curses.A_DIM)
|
||||
tab_x += 20
|
||||
|
||||
# 3: Statistical Analysis
|
||||
if self.current_view == ViewMode.STATISTICAL_ANALYSIS:
|
||||
stdscr.addstr(tab_line, tab_x, "[3: Statistical Analysis]", curses.A_REVERSE)
|
||||
else:
|
||||
stdscr.addstr(tab_line, tab_x, " 3: Statistical Analysis ", curses.A_DIM)
|
||||
|
||||
# Draw separator line
|
||||
stdscr.addstr(2, 0, "─" * width)
|
||||
|
||||
def _draw_status_bar(self, stdscr):
|
||||
"""Draw status bar with context-sensitive help"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
status_y = height - 1
|
||||
|
||||
# Base controls
|
||||
status_text = "[1-3]Views [↑↓]Navigate [Enter]Select [H]Help [Q]Quit"
|
||||
|
||||
# Add view-specific controls
|
||||
if self.current_view == ViewMode.FLOW_ANALYSIS:
|
||||
status_text += " [V]Visualize [D]Decode"
|
||||
elif self.current_view == ViewMode.PACKET_DECODER:
|
||||
status_text += " [E]Export [C]Copy"
|
||||
elif self.current_view == ViewMode.STATISTICAL_ANALYSIS:
|
||||
status_text += " [R]Refresh [O]Outliers"
|
||||
|
||||
# Add live capture controls
|
||||
if self.analyzer.is_live:
|
||||
status_text += " [P]Pause"
|
||||
|
||||
stdscr.addstr(status_y, 0, status_text[:width-1], curses.A_REVERSE)
|
||||
|
||||
def _draw_help_overlay(self, stdscr):
|
||||
"""Draw help overlay with comprehensive controls"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
|
||||
# Calculate overlay size
|
||||
overlay_height = min(20, height - 4)
|
||||
overlay_width = min(80, width - 4)
|
||||
start_y = (height - overlay_height) // 2
|
||||
start_x = (width - overlay_width) // 2
|
||||
|
||||
# Create help window
|
||||
help_lines = [
|
||||
"StreamLens - Help",
|
||||
"",
|
||||
"VIEWS:",
|
||||
" 1 - Flow Analysis: Visual flow overview and protocol detection",
|
||||
" 2 - Packet Decoder: Deep packet inspection and field extraction",
|
||||
" 3 - Statistical Analysis: Timing analysis and quality metrics",
|
||||
"",
|
||||
"NAVIGATION:",
|
||||
" ↑/↓ - Navigate items",
|
||||
" Enter - Select flow/packet",
|
||||
" Tab - Switch panels (when available)",
|
||||
" PgUp/PgDn - Scroll large lists",
|
||||
"",
|
||||
"ANALYSIS:",
|
||||
" V - Visualize signals (Flow Analysis)",
|
||||
" D - Deep decode selected flow",
|
||||
" E - Export decoded data",
|
||||
" R - Refresh statistics",
|
||||
" O - Show outlier details",
|
||||
"",
|
||||
"GENERAL:",
|
||||
" H - Toggle this help",
|
||||
" Q - Quit application",
|
||||
"",
|
||||
"Press any key to close help..."
|
||||
]
|
||||
|
||||
# Draw background
|
||||
for y in range(overlay_height):
|
||||
stdscr.addstr(start_y + y, start_x, " " * overlay_width, curses.A_REVERSE)
|
||||
|
||||
# Draw help content
|
||||
for i, line in enumerate(help_lines[:overlay_height-1]):
|
||||
if start_y + i < height - 1:
|
||||
display_line = line[:overlay_width-2]
|
||||
attr = curses.A_REVERSE | curses.A_BOLD if i == 0 else curses.A_REVERSE
|
||||
stdscr.addstr(start_y + i, start_x + 1, display_line, attr)
|
||||
|
||||
def _handle_input(self, key: int) -> str:
|
||||
"""Handle keyboard input with view-specific actions"""
|
||||
|
||||
# Global controls
|
||||
if key == ord('q') or key == ord('Q'):
|
||||
return 'quit'
|
||||
elif key == ord('h') or key == ord('H'):
|
||||
self.show_help = not self.show_help
|
||||
return 'help_toggle'
|
||||
elif self.show_help:
|
||||
# Any key closes help
|
||||
self.show_help = False
|
||||
return 'help_close'
|
||||
|
||||
# View switching
|
||||
elif key == ord('1'):
|
||||
self.current_view = ViewMode.FLOW_ANALYSIS
|
||||
return 'view_change'
|
||||
elif key == ord('2'):
|
||||
self.current_view = ViewMode.PACKET_DECODER
|
||||
return 'view_change'
|
||||
elif key == ord('3'):
|
||||
self.current_view = ViewMode.STATISTICAL_ANALYSIS
|
||||
return 'view_change'
|
||||
|
||||
# Delegate to current view
|
||||
elif self.current_view == ViewMode.FLOW_ANALYSIS:
|
||||
return self.flow_view.handle_input(key, self._get_flows_list())
|
||||
elif self.current_view == ViewMode.PACKET_DECODER:
|
||||
return self.decoder_view.handle_input(key, self._get_flows_list())
|
||||
elif self.current_view == ViewMode.STATISTICAL_ANALYSIS:
|
||||
return self.stats_view.handle_input(key, self._get_flows_list())
|
||||
|
||||
return 'none'
|
||||
|
||||
def _get_flows_list(self):
|
||||
"""Get prioritized list of flows for analysis"""
|
||||
flows_list = list(self.analyzer.flows.values())
|
||||
|
||||
# Sort by relevance: enhanced flows first, then by packet count
|
||||
flows_list.sort(key=lambda x: (
|
||||
x.enhanced_analysis.decoder_type != "Standard", # Enhanced first
|
||||
self.analyzer.statistics_engine.get_max_sigma_deviation(x), # High outliers
|
||||
x.frame_count # Packet count
|
||||
), reverse=True)
|
||||
|
||||
return flows_list
|
||||
|
||||
def get_selected_flow(self):
|
||||
"""Get currently selected flow for cross-view communication"""
|
||||
if self.selected_flow_key:
|
||||
return self.analyzer.flows.get(self.selected_flow_key)
|
||||
return None
|
||||
|
||||
def set_selected_flow(self, flow_key):
|
||||
"""Set selected flow for cross-view communication"""
|
||||
self.selected_flow_key = flow_key
|
||||
9
analyzer/tui/modern_views/__init__.py
Normal file
9
analyzer/tui/modern_views/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
||||
"""
|
||||
Modern TUI Views for StreamLens
|
||||
"""
|
||||
|
||||
from .flow_analysis import FlowAnalysisView
|
||||
from .packet_decoder import PacketDecoderView
|
||||
from .statistical_analysis import StatisticalAnalysisView
|
||||
|
||||
__all__ = ['FlowAnalysisView', 'PacketDecoderView', 'StatisticalAnalysisView']
|
||||
318
analyzer/tui/modern_views/flow_analysis.py
Normal file
318
analyzer/tui/modern_views/flow_analysis.py
Normal file
@@ -0,0 +1,318 @@
|
||||
"""
|
||||
Flow Analysis View - Visual flow overview with protocol detection
|
||||
Focuses on understanding communication patterns and flow characteristics
|
||||
"""
|
||||
|
||||
import curses
|
||||
from typing import TYPE_CHECKING, List, Optional, Tuple
|
||||
from ...models import FlowStats
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ...analysis.core import EthernetAnalyzer
|
||||
|
||||
|
||||
class FlowAnalysisView:
|
||||
"""
|
||||
Flow Analysis View - F1
|
||||
|
||||
Primary view for understanding network flows:
|
||||
- Flow overview with visual indicators
|
||||
- Protocol detection and classification
|
||||
- Traffic patterns and volume analysis
|
||||
- Enhanced decoder availability
|
||||
"""
|
||||
|
||||
def __init__(self, analyzer: 'EthernetAnalyzer'):
|
||||
self.analyzer = analyzer
|
||||
self.selected_flow = 0
|
||||
self.scroll_offset = 0
|
||||
self.show_frame_types = True
|
||||
|
||||
def draw(self, stdscr, selected_flow_key: Optional[str]):
|
||||
"""Draw the Flow Analysis view"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
start_y = 3 # After header
|
||||
max_height = height - 2 # Reserve for status bar
|
||||
|
||||
flows_list = self._get_flows_list()
|
||||
|
||||
if not flows_list:
|
||||
stdscr.addstr(start_y + 2, 4, "No network flows detected", curses.A_DIM)
|
||||
stdscr.addstr(start_y + 3, 4, "Waiting for packets...", curses.A_DIM)
|
||||
return
|
||||
|
||||
# Flow summary header
|
||||
summary = self.analyzer.get_summary()
|
||||
summary_text = (f"Flows: {summary['unique_flows']} | "
|
||||
f"Packets: {summary['total_packets']} | "
|
||||
f"Endpoints: {summary['unique_ips']}")
|
||||
|
||||
if self.analyzer.is_live:
|
||||
rt_summary = self.analyzer.statistics_engine.get_realtime_summary()
|
||||
summary_text += f" | Outliers: {rt_summary.get('total_outliers', 0)}"
|
||||
|
||||
stdscr.addstr(start_y, 4, summary_text, curses.A_BOLD)
|
||||
|
||||
# Flow analysis area
|
||||
flow_start_y = start_y + 2
|
||||
self._draw_flow_overview(stdscr, flow_start_y, width, max_height - flow_start_y, flows_list)
|
||||
|
||||
def _draw_flow_overview(self, stdscr, start_y: int, width: int, max_height: int, flows_list: List[FlowStats]):
|
||||
"""Draw comprehensive flow overview"""
|
||||
|
||||
# Header
|
||||
stdscr.addstr(start_y, 4, "FLOW ANALYSIS", curses.A_BOLD | curses.A_UNDERLINE)
|
||||
current_y = start_y + 2
|
||||
|
||||
# Column headers with visual indicators
|
||||
headers = (
|
||||
f"{'#':>2} "
|
||||
f"{'Source':20} "
|
||||
f"{'Proto':6} "
|
||||
f"{'Destination':20} "
|
||||
f"{'Extended':10} "
|
||||
f"{'Frame Type':12} "
|
||||
f"{'Pkts':>6} "
|
||||
f"{'Volume':>8} "
|
||||
f"{'Timing':>8} "
|
||||
f"{'Quality':>8}"
|
||||
)
|
||||
stdscr.addstr(current_y, 4, headers, curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
# Calculate visible range
|
||||
visible_flows = max_height - (current_y - start_y) - 2
|
||||
start_idx = self.scroll_offset
|
||||
end_idx = min(start_idx + visible_flows, len(flows_list))
|
||||
|
||||
# Draw flows
|
||||
for i in range(start_idx, end_idx):
|
||||
flow = flows_list[i]
|
||||
display_idx = i - start_idx
|
||||
|
||||
# Flow selection
|
||||
is_selected = (i == self.selected_flow)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
# Flow line
|
||||
flow_line = self._format_flow_line(i + 1, flow)
|
||||
stdscr.addstr(current_y + display_idx, 4, flow_line[:width-8], attr)
|
||||
|
||||
# Enhanced indicator
|
||||
if flow.enhanced_analysis.decoder_type != "Standard":
|
||||
stdscr.addstr(current_y + display_idx, 2, "●", curses.A_BOLD | curses.color_pair(1))
|
||||
|
||||
# Frame types sub-display (if selected and enabled)
|
||||
if is_selected and self.show_frame_types and flow.frame_types:
|
||||
sub_y = current_y + display_idx + 1
|
||||
if sub_y < current_y + visible_flows:
|
||||
self._draw_frame_types_compact(stdscr, sub_y, width, flow)
|
||||
|
||||
# Scroll indicators
|
||||
if start_idx > 0:
|
||||
stdscr.addstr(current_y, width - 10, "↑ More", curses.A_DIM)
|
||||
if end_idx < len(flows_list):
|
||||
stdscr.addstr(current_y + visible_flows - 1, width - 10, "↓ More", curses.A_DIM)
|
||||
|
||||
def _format_flow_line(self, flow_num: int, flow: FlowStats) -> str:
|
||||
"""Format a single flow line with comprehensive information"""
|
||||
|
||||
# Source with port (left-aligned)
|
||||
source = f"{flow.src_ip}:{flow.src_port}"
|
||||
if len(source) > 18:
|
||||
source = f"{flow.src_ip[:10]}…:{flow.src_port}"
|
||||
|
||||
# Transport protocol (TCP, UDP, ICMP, IGMP, etc.)
|
||||
protocol = flow.transport_protocol
|
||||
|
||||
# Destination with port (left-aligned)
|
||||
destination = f"{flow.dst_ip}:{flow.dst_port}"
|
||||
if len(destination) > 18:
|
||||
destination = f"{flow.dst_ip[:10]}…:{flow.dst_port}"
|
||||
|
||||
# Extended protocol (Chapter 10, PTP, IENA, etc.)
|
||||
extended_protocol = self._get_extended_protocol(flow)
|
||||
|
||||
# Frame type (most common frame type in this flow)
|
||||
frame_type = self._get_primary_frame_type(flow)
|
||||
|
||||
# Packet count
|
||||
pkt_count = f"{flow.frame_count}"
|
||||
|
||||
# Volume with units
|
||||
volume = self._format_bytes(flow.total_bytes)
|
||||
|
||||
# Timing quality
|
||||
if flow.avg_inter_arrival > 0:
|
||||
timing = f"{flow.avg_inter_arrival*1000:.1f}ms"
|
||||
else:
|
||||
timing = "N/A"
|
||||
|
||||
# Quality score
|
||||
if flow.enhanced_analysis.decoder_type != "Standard":
|
||||
if flow.enhanced_analysis.avg_frame_quality > 0:
|
||||
quality = f"{flow.enhanced_analysis.avg_frame_quality:.0f}%"
|
||||
else:
|
||||
quality = "Enhanced"
|
||||
else:
|
||||
# Check for outliers
|
||||
outlier_pct = len(flow.outlier_frames) / flow.frame_count * 100 if flow.frame_count > 0 else 0
|
||||
if outlier_pct > 5:
|
||||
quality = f"{outlier_pct:.0f}% Out"
|
||||
else:
|
||||
quality = "Normal"
|
||||
|
||||
return (f"{flow_num:>2} "
|
||||
f"{source:20} "
|
||||
f"{protocol:6} "
|
||||
f"{destination:20} "
|
||||
f"{extended_protocol:10} "
|
||||
f"{frame_type:12} "
|
||||
f"{pkt_count:>6} "
|
||||
f"{volume:>8} "
|
||||
f"{timing:>8} "
|
||||
f"{quality:>8}")
|
||||
|
||||
def _draw_frame_types_compact(self, stdscr, y: int, width: int, flow: FlowStats):
|
||||
"""Draw compact frame type breakdown for selected flow"""
|
||||
frame_types = sorted(flow.frame_types.items(), key=lambda x: x[1].count, reverse=True)
|
||||
|
||||
# Compact frame type display
|
||||
type_summary = []
|
||||
for frame_type, ft_stats in frame_types[:4]: # Show top 4
|
||||
type_summary.append(f"{frame_type}({ft_stats.count})")
|
||||
|
||||
if len(frame_types) > 4:
|
||||
type_summary.append(f"+{len(frame_types)-4} more")
|
||||
|
||||
frame_line = f" └─ Frame Types: {', '.join(type_summary)}"
|
||||
stdscr.addstr(y, 4, frame_line[:width-8], curses.A_DIM)
|
||||
|
||||
def _get_primary_protocol(self, flow: FlowStats) -> str:
|
||||
"""Get the most relevant protocol for display"""
|
||||
# Prioritize enhanced protocols
|
||||
if flow.detected_protocol_types:
|
||||
enhanced_protocols = {'CHAPTER10', 'PTP', 'IENA', 'CH10'}
|
||||
found_enhanced = flow.detected_protocol_types & enhanced_protocols
|
||||
if found_enhanced:
|
||||
return list(found_enhanced)[0]
|
||||
|
||||
# Use first detected protocol
|
||||
return list(flow.detected_protocol_types)[0]
|
||||
|
||||
# Fallback to transport protocol
|
||||
return flow.transport_protocol
|
||||
|
||||
def _get_extended_protocol(self, flow: FlowStats) -> str:
|
||||
"""Get extended protocol (Chapter 10, PTP, IENA, etc.)"""
|
||||
if flow.detected_protocol_types:
|
||||
# Look for specialized protocols
|
||||
enhanced_protocols = {'CHAPTER10', 'CH10', 'PTP', 'IENA'}
|
||||
found_enhanced = flow.detected_protocol_types & enhanced_protocols
|
||||
if found_enhanced:
|
||||
protocol = list(found_enhanced)[0]
|
||||
# Simplify display names
|
||||
if protocol in ['CHAPTER10', 'CH10']:
|
||||
return 'CH10'
|
||||
return protocol
|
||||
|
||||
# Check for other common protocols
|
||||
if flow.detected_protocol_types and 'NTP' in flow.detected_protocol_types:
|
||||
return 'NTP'
|
||||
|
||||
return '-'
|
||||
|
||||
def _get_primary_frame_type(self, flow: FlowStats) -> str:
|
||||
"""Get the most common frame type in this flow"""
|
||||
if not flow.frame_types:
|
||||
return '-'
|
||||
|
||||
# Find the frame type with the most packets
|
||||
most_common = max(flow.frame_types.items(), key=lambda x: x[1].count)
|
||||
frame_type = most_common[0]
|
||||
|
||||
# Simplify frame type names for display
|
||||
if frame_type == 'CH10-Data':
|
||||
return 'CH10-Data'
|
||||
elif frame_type == 'TMATS':
|
||||
return 'TMATS'
|
||||
elif frame_type.startswith('PTP-'):
|
||||
return frame_type.replace('PTP-', 'PTP ')[:11] # PTP Sync, PTP Signal
|
||||
elif frame_type == 'UDP':
|
||||
return 'UDP'
|
||||
elif frame_type == 'IGMP':
|
||||
return 'IGMP'
|
||||
else:
|
||||
return frame_type[:11] # Truncate to fit column
|
||||
|
||||
def _format_bytes(self, bytes_count: int) -> str:
|
||||
"""Format byte count with appropriate units"""
|
||||
if bytes_count >= 1_000_000_000:
|
||||
return f"{bytes_count / 1_000_000_000:.1f}GB"
|
||||
elif bytes_count >= 1_000_000:
|
||||
return f"{bytes_count / 1_000_000:.1f}MB"
|
||||
elif bytes_count >= 1_000:
|
||||
return f"{bytes_count / 1_000:.1f}KB"
|
||||
else:
|
||||
return f"{bytes_count}B"
|
||||
|
||||
def _get_flows_list(self) -> List[FlowStats]:
|
||||
"""Get flows sorted by importance for flow analysis"""
|
||||
flows_list = list(self.analyzer.flows.values())
|
||||
|
||||
# Sort by: Enhanced protocols first, then outliers, then packet count
|
||||
flows_list.sort(key=lambda x: (
|
||||
x.enhanced_analysis.decoder_type != "Standard",
|
||||
len(x.outlier_frames),
|
||||
x.frame_count
|
||||
), reverse=True)
|
||||
|
||||
return flows_list
|
||||
|
||||
def handle_input(self, key: int, flows_list: List[FlowStats]) -> str:
|
||||
"""Handle input for Flow Analysis view"""
|
||||
if key == curses.KEY_UP:
|
||||
self.selected_flow = max(0, self.selected_flow - 1)
|
||||
self._adjust_scroll()
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_DOWN:
|
||||
self.selected_flow = min(len(flows_list) - 1, self.selected_flow + 1)
|
||||
self._adjust_scroll()
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_PPAGE: # Page Up
|
||||
self.selected_flow = max(0, self.selected_flow - 10)
|
||||
self._adjust_scroll()
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_NPAGE: # Page Down
|
||||
self.selected_flow = min(len(flows_list) - 1, self.selected_flow + 10)
|
||||
self._adjust_scroll()
|
||||
return 'selection_change'
|
||||
elif key == ord('\n') or key == curses.KEY_ENTER:
|
||||
# Select flow for detailed analysis
|
||||
if flows_list and self.selected_flow < len(flows_list):
|
||||
selected_flow = flows_list[self.selected_flow]
|
||||
# Signal flow selection for other views
|
||||
return 'flow_selected'
|
||||
elif key == ord('v') or key == ord('V'):
|
||||
# Visualize selected flow
|
||||
return 'visualize'
|
||||
elif key == ord('d') or key == ord('D'):
|
||||
# Switch to decoder view for selected flow
|
||||
return 'decode_flow'
|
||||
elif key == ord('t') or key == ord('T'):
|
||||
# Toggle frame types display
|
||||
self.show_frame_types = not self.show_frame_types
|
||||
return 'toggle_frame_types'
|
||||
|
||||
return 'none'
|
||||
|
||||
def _adjust_scroll(self):
|
||||
"""Adjust scroll position to keep selected item visible"""
|
||||
# This would be implemented based on visible area calculations
|
||||
pass
|
||||
|
||||
def get_selected_flow(self, flows_list: List[FlowStats]) -> Optional[FlowStats]:
|
||||
"""Get currently selected flow"""
|
||||
if flows_list and 0 <= self.selected_flow < len(flows_list):
|
||||
return flows_list[self.selected_flow]
|
||||
return None
|
||||
307
analyzer/tui/modern_views/packet_decoder.py
Normal file
307
analyzer/tui/modern_views/packet_decoder.py
Normal file
@@ -0,0 +1,307 @@
|
||||
"""
|
||||
Packet Decoder View - Deep protocol inspection and field extraction
|
||||
Focuses on understanding packet contents and protocol compliance
|
||||
"""
|
||||
|
||||
import curses
|
||||
from typing import TYPE_CHECKING, List, Optional, Dict, Any
|
||||
from ...models import FlowStats
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ...analysis.core import EthernetAnalyzer
|
||||
|
||||
|
||||
class PacketDecoderView:
|
||||
"""
|
||||
Packet Decoder View - F2
|
||||
|
||||
Deep packet inspection interface:
|
||||
- Protocol field extraction and display
|
||||
- Frame-by-frame analysis
|
||||
- Enhanced decoder output
|
||||
- Field value inspection
|
||||
"""
|
||||
|
||||
def __init__(self, analyzer: 'EthernetAnalyzer'):
|
||||
self.analyzer = analyzer
|
||||
self.selected_flow = 0
|
||||
self.selected_frame = 0
|
||||
self.selected_field = 0
|
||||
self.scroll_offset = 0
|
||||
self.field_scroll_offset = 0
|
||||
self.current_panel = 0 # 0=flows, 1=frames, 2=fields
|
||||
|
||||
def draw(self, stdscr, selected_flow_key: Optional[str]):
|
||||
"""Draw the Packet Decoder view"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
start_y = 3
|
||||
max_height = height - 2
|
||||
|
||||
flows_list = self._get_enhanced_flows()
|
||||
|
||||
if not flows_list:
|
||||
stdscr.addstr(start_y + 2, 4, "No enhanced decodable flows detected", curses.A_DIM)
|
||||
stdscr.addstr(start_y + 3, 4, "Switch to Flow Analysis (F1) to see all flows", curses.A_DIM)
|
||||
return
|
||||
|
||||
# Decoder view header
|
||||
stdscr.addstr(start_y, 4, "PACKET DECODER - Enhanced Protocol Analysis", curses.A_BOLD)
|
||||
|
||||
# Three-panel layout: Flows | Frames | Fields
|
||||
panel_width = width // 3
|
||||
|
||||
# Panel 1: Enhanced Flows (left)
|
||||
self._draw_flows_panel(stdscr, start_y + 2, 4, panel_width - 2, max_height - start_y - 2, flows_list)
|
||||
|
||||
# Separator
|
||||
for y in range(start_y + 2, max_height):
|
||||
stdscr.addstr(y, panel_width, "│", curses.A_DIM)
|
||||
|
||||
# Panel 2: Frame Details (center)
|
||||
selected_flow = flows_list[self.selected_flow] if flows_list else None
|
||||
self._draw_frames_panel(stdscr, start_y + 2, panel_width + 2, panel_width - 2, max_height - start_y - 2, selected_flow)
|
||||
|
||||
# Separator
|
||||
for y in range(start_y + 2, max_height):
|
||||
stdscr.addstr(y, 2 * panel_width, "│", curses.A_DIM)
|
||||
|
||||
# Panel 3: Field Inspection (right)
|
||||
self._draw_fields_panel(stdscr, start_y + 2, 2 * panel_width + 2, panel_width - 2, max_height - start_y - 2, selected_flow)
|
||||
|
||||
def _draw_flows_panel(self, stdscr, start_y: int, start_x: int, width: int, height: int, flows_list: List[FlowStats]):
|
||||
"""Draw enhanced flows panel"""
|
||||
panel_attr = curses.A_BOLD if self.current_panel == 0 else curses.A_NORMAL
|
||||
|
||||
# Panel header
|
||||
header = "Enhanced Flows"
|
||||
stdscr.addstr(start_y, start_x, header, panel_attr | curses.A_UNDERLINE)
|
||||
|
||||
current_y = start_y + 2
|
||||
available_height = height - 2
|
||||
|
||||
# Flow list
|
||||
for i, flow in enumerate(flows_list[:available_height]):
|
||||
is_selected = (i == self.selected_flow and self.current_panel == 0)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
# Flow summary line
|
||||
flow_line = self._format_flow_summary(flow, width - 2)
|
||||
stdscr.addstr(current_y + i, start_x, flow_line, attr)
|
||||
|
||||
# Decoder indicator
|
||||
decoder_indicator = "●" if flow.enhanced_analysis.decoder_type != "Standard" else "○"
|
||||
stdscr.addstr(current_y + i, start_x + width - 3, decoder_indicator,
|
||||
curses.A_BOLD if flow.enhanced_analysis.decoder_type != "Standard" else curses.A_DIM)
|
||||
|
||||
def _draw_frames_panel(self, stdscr, start_y: int, start_x: int, width: int, height: int, flow: Optional[FlowStats]):
|
||||
"""Draw frame details panel"""
|
||||
panel_attr = curses.A_BOLD if self.current_panel == 1 else curses.A_NORMAL
|
||||
|
||||
# Panel header
|
||||
header = "Frame Analysis"
|
||||
stdscr.addstr(start_y, start_x, header, panel_attr | curses.A_UNDERLINE)
|
||||
|
||||
if not flow:
|
||||
stdscr.addstr(start_y + 2, start_x, "No flow selected", curses.A_DIM)
|
||||
return
|
||||
|
||||
current_y = start_y + 2
|
||||
|
||||
# Flow details
|
||||
stdscr.addstr(current_y, start_x, f"Flow: {flow.src_ip} → {flow.dst_ip}", curses.A_BOLD)
|
||||
current_y += 1
|
||||
stdscr.addstr(current_y, start_x, f"Decoder: {flow.enhanced_analysis.decoder_type}")
|
||||
current_y += 2
|
||||
|
||||
# Sample frames
|
||||
if flow.enhanced_analysis.sample_decoded_fields:
|
||||
stdscr.addstr(current_y, start_x, "Decoded Frames:", curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
for i, (frame_key, frame_data) in enumerate(flow.enhanced_analysis.sample_decoded_fields.items()):
|
||||
is_selected = (i == self.selected_frame and self.current_panel == 1)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
frame_line = f"{frame_key}: {len(frame_data)} fields"
|
||||
stdscr.addstr(current_y + i, start_x, frame_line[:width-2], attr)
|
||||
else:
|
||||
stdscr.addstr(current_y, start_x, "No decoded frame data available", curses.A_DIM)
|
||||
current_y += 1
|
||||
stdscr.addstr(current_y, start_x, "Decoder may not be implemented", curses.A_DIM)
|
||||
|
||||
def _draw_fields_panel(self, stdscr, start_y: int, start_x: int, width: int, height: int, flow: Optional[FlowStats]):
|
||||
"""Draw field inspection panel"""
|
||||
panel_attr = curses.A_BOLD if self.current_panel == 2 else curses.A_NORMAL
|
||||
|
||||
# Panel header
|
||||
header = "Field Inspector"
|
||||
stdscr.addstr(start_y, start_x, header, panel_attr | curses.A_UNDERLINE)
|
||||
|
||||
if not flow or not flow.enhanced_analysis.sample_decoded_fields:
|
||||
stdscr.addstr(start_y + 2, start_x, "No field data available", curses.A_DIM)
|
||||
return
|
||||
|
||||
current_y = start_y + 2
|
||||
|
||||
# Get selected frame data
|
||||
frame_items = list(flow.enhanced_analysis.sample_decoded_fields.items())
|
||||
if self.selected_frame < len(frame_items):
|
||||
frame_key, frame_data = frame_items[self.selected_frame]
|
||||
|
||||
stdscr.addstr(current_y, start_x, f"Frame: {frame_key}", curses.A_BOLD)
|
||||
current_y += 2
|
||||
|
||||
# Field list with values
|
||||
available_height = height - 4
|
||||
field_items = list(frame_data.items())
|
||||
|
||||
start_field = self.field_scroll_offset
|
||||
end_field = min(start_field + available_height, len(field_items))
|
||||
|
||||
for i in range(start_field, end_field):
|
||||
field_name, field_value = field_items[i]
|
||||
display_idx = i - start_field
|
||||
|
||||
is_selected = (i == self.selected_field and self.current_panel == 2)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
# Format field line
|
||||
field_line = self._format_field_line(field_name, field_value, width - 2)
|
||||
stdscr.addstr(current_y + display_idx, start_x, field_line, attr)
|
||||
|
||||
# Scroll indicators
|
||||
if start_field > 0:
|
||||
stdscr.addstr(current_y, start_x + width - 5, "↑", curses.A_DIM)
|
||||
if end_field < len(field_items):
|
||||
stdscr.addstr(current_y + available_height - 1, start_x + width - 5, "↓", curses.A_DIM)
|
||||
|
||||
def _format_flow_summary(self, flow: FlowStats, max_width: int) -> str:
|
||||
"""Format flow summary for flows panel"""
|
||||
# Format as "src:port → dst:port | protocol"
|
||||
source = f"{flow.src_ip}:{flow.src_port}"
|
||||
destination = f"{flow.dst_ip}:{flow.dst_port}"
|
||||
|
||||
protocol = flow.enhanced_analysis.decoder_type
|
||||
if protocol == "Standard":
|
||||
protocol = self._get_primary_protocol(flow)
|
||||
|
||||
# Calculate available space for src and dst
|
||||
protocol_space = len(protocol) + 3 # " | " + protocol
|
||||
available_space = max_width - protocol_space
|
||||
src_space = available_space // 2 - 2 # Account for " → "
|
||||
dst_space = available_space - src_space - 3 # " → "
|
||||
|
||||
if len(source) > src_space:
|
||||
source = f"{flow.src_ip[:src_space-6]}…:{flow.src_port}"
|
||||
if len(destination) > dst_space:
|
||||
destination = f"{flow.dst_ip[:dst_space-6]}…:{flow.dst_port}"
|
||||
|
||||
return f"{source} → {destination} | {protocol}"[:max_width]
|
||||
|
||||
def _format_field_line(self, field_name: str, field_value: Any, max_width: int) -> str:
|
||||
"""Format field name and value for display"""
|
||||
# Clean up field name
|
||||
display_name = field_name.replace('_', ' ').title()
|
||||
|
||||
# Format value based on type
|
||||
if isinstance(field_value, bool):
|
||||
display_value = "Yes" if field_value else "No"
|
||||
elif isinstance(field_value, float):
|
||||
if "timestamp" in field_name.lower():
|
||||
display_value = f"{field_value:.6f}s"
|
||||
else:
|
||||
display_value = f"{field_value:.3f}"
|
||||
elif field_value is None:
|
||||
display_value = "N/A"
|
||||
else:
|
||||
display_value = str(field_value)
|
||||
|
||||
# Truncate if needed
|
||||
available_name_width = max_width // 2
|
||||
available_value_width = max_width - available_name_width - 3
|
||||
|
||||
if len(display_name) > available_name_width:
|
||||
display_name = display_name[:available_name_width-1] + "…"
|
||||
if len(display_value) > available_value_width:
|
||||
display_value = display_value[:available_value_width-1] + "…"
|
||||
|
||||
return f"{display_name:<{available_name_width}} : {display_value}"
|
||||
|
||||
def _get_enhanced_flows(self) -> List[FlowStats]:
|
||||
"""Get flows with enhanced decoders available"""
|
||||
flows_list = []
|
||||
for flow in self.analyzer.flows.values():
|
||||
if (flow.enhanced_analysis.decoder_type != "Standard" or
|
||||
"CHAPTER10" in flow.detected_protocol_types or
|
||||
"PTP" in flow.detected_protocol_types or
|
||||
"IENA" in flow.detected_protocol_types):
|
||||
flows_list.append(flow)
|
||||
|
||||
# Sort by decoder quality and packet count
|
||||
flows_list.sort(key=lambda x: (
|
||||
x.enhanced_analysis.decoder_type != "Standard",
|
||||
len(x.enhanced_analysis.sample_decoded_fields),
|
||||
x.frame_count
|
||||
), reverse=True)
|
||||
|
||||
return flows_list
|
||||
|
||||
def _get_primary_protocol(self, flow: FlowStats) -> str:
|
||||
"""Get primary protocol for display"""
|
||||
if flow.detected_protocol_types:
|
||||
enhanced_protocols = {'CHAPTER10', 'PTP', 'IENA', 'CH10'}
|
||||
found_enhanced = flow.detected_protocol_types & enhanced_protocols
|
||||
if found_enhanced:
|
||||
return list(found_enhanced)[0]
|
||||
return list(flow.detected_protocol_types)[0]
|
||||
return flow.transport_protocol
|
||||
|
||||
def handle_input(self, key: int, flows_list: List[FlowStats]) -> str:
|
||||
"""Handle input for Packet Decoder view"""
|
||||
enhanced_flows = self._get_enhanced_flows()
|
||||
|
||||
if key == ord('\t'): # Tab to switch panels
|
||||
self.current_panel = (self.current_panel + 1) % 3
|
||||
return 'panel_switch'
|
||||
elif key == curses.KEY_UP:
|
||||
if self.current_panel == 0: # Flows panel
|
||||
self.selected_flow = max(0, self.selected_flow - 1)
|
||||
elif self.current_panel == 1: # Frames panel
|
||||
self.selected_frame = max(0, self.selected_frame - 1)
|
||||
elif self.current_panel == 2: # Fields panel
|
||||
self.selected_field = max(0, self.selected_field - 1)
|
||||
self._adjust_field_scroll()
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_DOWN:
|
||||
if self.current_panel == 0: # Flows panel
|
||||
self.selected_flow = min(len(enhanced_flows) - 1, self.selected_flow + 1)
|
||||
elif self.current_panel == 1: # Frames panel
|
||||
if enhanced_flows and self.selected_flow < len(enhanced_flows):
|
||||
flow = enhanced_flows[self.selected_flow]
|
||||
max_frames = len(flow.enhanced_analysis.sample_decoded_fields) - 1
|
||||
self.selected_frame = min(max_frames, self.selected_frame + 1)
|
||||
elif self.current_panel == 2: # Fields panel
|
||||
if enhanced_flows and self.selected_flow < len(enhanced_flows):
|
||||
flow = enhanced_flows[self.selected_flow]
|
||||
if flow.enhanced_analysis.sample_decoded_fields:
|
||||
frame_items = list(flow.enhanced_analysis.sample_decoded_fields.items())
|
||||
if self.selected_frame < len(frame_items):
|
||||
frame_data = frame_items[self.selected_frame][1]
|
||||
max_fields = len(frame_data) - 1
|
||||
self.selected_field = min(max_fields, self.selected_field + 1)
|
||||
self._adjust_field_scroll()
|
||||
return 'selection_change'
|
||||
elif key == ord('e') or key == ord('E'):
|
||||
return 'export_fields'
|
||||
elif key == ord('c') or key == ord('C'):
|
||||
return 'copy_field'
|
||||
|
||||
return 'none'
|
||||
|
||||
def _adjust_field_scroll(self):
|
||||
"""Adjust field scroll to keep selected field visible"""
|
||||
# Simple scroll adjustment - could be enhanced
|
||||
if self.selected_field < self.field_scroll_offset:
|
||||
self.field_scroll_offset = self.selected_field
|
||||
elif self.selected_field >= self.field_scroll_offset + 10: # Assume 10 visible fields
|
||||
self.field_scroll_offset = self.selected_field - 9
|
||||
432
analyzer/tui/modern_views/statistical_analysis.py
Normal file
432
analyzer/tui/modern_views/statistical_analysis.py
Normal file
@@ -0,0 +1,432 @@
|
||||
"""
|
||||
Statistical Analysis View - Timing analysis, outliers, and quality metrics
|
||||
Focuses on understanding network performance and data quality
|
||||
"""
|
||||
|
||||
import curses
|
||||
import statistics
|
||||
from typing import TYPE_CHECKING, List, Optional, Dict, Tuple
|
||||
from ...models import FlowStats
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ...analysis.core import EthernetAnalyzer
|
||||
|
||||
|
||||
class StatisticalAnalysisView:
|
||||
"""
|
||||
Statistical Analysis View - F3
|
||||
|
||||
Performance and quality analysis interface:
|
||||
- Timing statistics and outlier detection
|
||||
- Quality metrics and trends
|
||||
- Performance indicators
|
||||
- Network health assessment
|
||||
"""
|
||||
|
||||
def __init__(self, analyzer: 'EthernetAnalyzer'):
|
||||
self.analyzer = analyzer
|
||||
self.selected_flow = 0
|
||||
self.analysis_mode = 0 # 0=overview, 1=outliers, 2=quality, 3=timing
|
||||
self.scroll_offset = 0
|
||||
|
||||
def draw(self, stdscr, selected_flow_key: Optional[str]):
|
||||
"""Draw the Statistical Analysis view"""
|
||||
height, width = stdscr.getmaxyx()
|
||||
start_y = 3
|
||||
max_height = height - 2
|
||||
|
||||
flows_list = self._get_flows_list()
|
||||
|
||||
if not flows_list:
|
||||
stdscr.addstr(start_y + 2, 4, "No flows available for statistical analysis", curses.A_DIM)
|
||||
return
|
||||
|
||||
# Statistical analysis header
|
||||
mode_names = ["Overview", "Outlier Analysis", "Quality Metrics", "Timing Analysis"]
|
||||
current_mode = mode_names[self.analysis_mode]
|
||||
stdscr.addstr(start_y, 4, f"STATISTICAL ANALYSIS - {current_mode}", curses.A_BOLD)
|
||||
|
||||
# Mode selector
|
||||
mode_line = start_y + 1
|
||||
for i, mode_name in enumerate(mode_names):
|
||||
x_pos = 4 + i * 20
|
||||
if i == self.analysis_mode:
|
||||
stdscr.addstr(mode_line, x_pos, f"[{mode_name}]", curses.A_REVERSE)
|
||||
else:
|
||||
stdscr.addstr(mode_line, x_pos, f" {mode_name} ", curses.A_DIM)
|
||||
|
||||
# Analysis content area
|
||||
content_y = start_y + 3
|
||||
content_height = max_height - content_y
|
||||
|
||||
if self.analysis_mode == 0:
|
||||
self._draw_overview(stdscr, content_y, width, content_height, flows_list)
|
||||
elif self.analysis_mode == 1:
|
||||
self._draw_outlier_analysis(stdscr, content_y, width, content_height, flows_list)
|
||||
elif self.analysis_mode == 2:
|
||||
self._draw_quality_metrics(stdscr, content_y, width, content_height, flows_list)
|
||||
elif self.analysis_mode == 3:
|
||||
self._draw_timing_analysis(stdscr, content_y, width, content_height, flows_list)
|
||||
|
||||
def _draw_overview(self, stdscr, start_y: int, width: int, height: int, flows_list: List[FlowStats]):
|
||||
"""Draw statistical overview"""
|
||||
current_y = start_y
|
||||
|
||||
# Overall statistics
|
||||
total_packets = sum(flow.frame_count for flow in flows_list)
|
||||
total_outliers = sum(len(flow.outlier_frames) for flow in flows_list)
|
||||
outlier_percentage = (total_outliers / total_packets * 100) if total_packets > 0 else 0
|
||||
|
||||
stdscr.addstr(current_y, 4, "NETWORK PERFORMANCE SUMMARY", curses.A_UNDERLINE)
|
||||
current_y += 2
|
||||
|
||||
# Key metrics
|
||||
metrics = [
|
||||
("Total Flows", str(len(flows_list))),
|
||||
("Total Packets", f"{total_packets:,}"),
|
||||
("Total Outliers", f"{total_outliers:,} ({outlier_percentage:.2f}%)"),
|
||||
("Enhanced Flows", str(sum(1 for f in flows_list if f.enhanced_analysis.decoder_type != "Standard"))),
|
||||
]
|
||||
|
||||
for metric_name, metric_value in metrics:
|
||||
stdscr.addstr(current_y, 4, f"{metric_name:20}: {metric_value}")
|
||||
current_y += 1
|
||||
|
||||
current_y += 1
|
||||
|
||||
# Flow performance table
|
||||
stdscr.addstr(current_y, 4, "FLOW PERFORMANCE RANKING", curses.A_UNDERLINE)
|
||||
current_y += 2
|
||||
|
||||
# Table header
|
||||
header = f"{'Rank':>4} {'Flow':30} {'Packets':>8} {'Outliers':>9} {'Avg Δt':>10} {'Jitter':>8} {'Score':>6}"
|
||||
stdscr.addstr(current_y, 4, header, curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
# Rank flows by performance
|
||||
ranked_flows = self._rank_flows_by_performance(flows_list)
|
||||
|
||||
visible_flows = min(height - (current_y - start_y) - 2, len(ranked_flows))
|
||||
for i in range(visible_flows):
|
||||
flow, score = ranked_flows[i]
|
||||
|
||||
is_selected = (i == self.selected_flow)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
# Format flow line
|
||||
flow_desc = f"{flow.src_ip}:{flow.src_port} → {flow.dst_ip}:{flow.dst_port}"
|
||||
if len(flow_desc) > 28:
|
||||
flow_desc = f"{flow.src_ip[:8]}…:{flow.src_port} → {flow.dst_ip[:8]}…:{flow.dst_port}"
|
||||
|
||||
outliers = len(flow.outlier_frames)
|
||||
outlier_pct = f"{outliers/flow.frame_count*100:.1f}%" if flow.frame_count > 0 else "0%"
|
||||
|
||||
avg_timing = f"{flow.avg_inter_arrival*1000:.1f}ms" if flow.avg_inter_arrival > 0 else "N/A"
|
||||
jitter = f"{flow.std_inter_arrival*1000:.1f}ms" if flow.std_inter_arrival > 0 else "N/A"
|
||||
|
||||
line = f"{i+1:>4} {flow_desc:30} {flow.frame_count:>8} {outlier_pct:>9} {avg_timing:>10} {jitter:>8} {score:>6.1f}"
|
||||
stdscr.addstr(current_y + i, 4, line[:width-8], attr)
|
||||
|
||||
def _draw_outlier_analysis(self, stdscr, start_y: int, width: int, height: int, flows_list: List[FlowStats]):
|
||||
"""Draw detailed outlier analysis"""
|
||||
current_y = start_y
|
||||
|
||||
stdscr.addstr(current_y, 4, "OUTLIER ANALYSIS", curses.A_UNDERLINE)
|
||||
current_y += 2
|
||||
|
||||
# Find flows with outliers
|
||||
outlier_flows = [(flow, len(flow.outlier_frames)) for flow in flows_list if flow.outlier_frames]
|
||||
outlier_flows.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
if not outlier_flows:
|
||||
stdscr.addstr(current_y, 4, "No outliers detected in any flows", curses.A_DIM)
|
||||
stdscr.addstr(current_y + 1, 4, "All packet timing appears normal", curses.A_DIM)
|
||||
return
|
||||
|
||||
# Outlier summary
|
||||
total_outliers = sum(count for _, count in outlier_flows)
|
||||
stdscr.addstr(current_y, 4, f"Flows with outliers: {len(outlier_flows)}")
|
||||
current_y += 1
|
||||
stdscr.addstr(current_y, 4, f"Total outlier packets: {total_outliers}")
|
||||
current_y += 2
|
||||
|
||||
# Detailed outlier breakdown
|
||||
stdscr.addstr(current_y, 4, "OUTLIER DETAILS", curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
header = f"{'Flow':35} {'Outliers':>9} {'Rate':>8} {'Max Σ':>8} {'Timing':>12}"
|
||||
stdscr.addstr(current_y, 4, header, curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
visible_flows = min(height - (current_y - start_y) - 2, len(outlier_flows))
|
||||
for i in range(visible_flows):
|
||||
flow, outlier_count = outlier_flows[i]
|
||||
|
||||
is_selected = (i == self.selected_flow)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
flow_desc = f"{flow.src_ip}:{flow.src_port} → {flow.dst_ip}:{flow.dst_port}"
|
||||
if len(flow_desc) > 33:
|
||||
flow_desc = f"{flow.src_ip[:10]}…:{flow.src_port} → {flow.dst_ip[:10]}…:{flow.dst_port}"
|
||||
|
||||
outlier_rate = f"{outlier_count/flow.frame_count*100:.1f}%" if flow.frame_count > 0 else "0%"
|
||||
max_sigma = self.analyzer.statistics_engine.get_max_sigma_deviation(flow)
|
||||
timing_info = f"{flow.avg_inter_arrival*1000:.1f}±{flow.std_inter_arrival*1000:.1f}ms"
|
||||
|
||||
line = f"{flow_desc:35} {outlier_count:>9} {outlier_rate:>8} {max_sigma:>7.1f}σ {timing_info:>12}"
|
||||
stdscr.addstr(current_y + i, 4, line[:width-8], attr)
|
||||
|
||||
# Selected flow outlier details
|
||||
if outlier_flows and self.selected_flow < len(outlier_flows):
|
||||
selected_flow, _ = outlier_flows[self.selected_flow]
|
||||
self._draw_selected_flow_outliers(stdscr, current_y + visible_flows + 1, width,
|
||||
height - (current_y + visible_flows + 1 - start_y), selected_flow)
|
||||
|
||||
def _draw_quality_metrics(self, stdscr, start_y: int, width: int, height: int, flows_list: List[FlowStats]):
|
||||
"""Draw quality metrics analysis"""
|
||||
current_y = start_y
|
||||
|
||||
stdscr.addstr(current_y, 4, "QUALITY METRICS", curses.A_UNDERLINE)
|
||||
current_y += 2
|
||||
|
||||
# Enhanced flows quality
|
||||
enhanced_flows = [f for f in flows_list if f.enhanced_analysis.decoder_type != "Standard"]
|
||||
|
||||
if enhanced_flows:
|
||||
stdscr.addstr(current_y, 4, "ENHANCED DECODER QUALITY", curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
header = f"{'Flow':30} {'Decoder':15} {'Quality':>8} {'Drift':>10} {'Errors':>8}"
|
||||
stdscr.addstr(current_y, 4, header, curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
for i, flow in enumerate(enhanced_flows[:height - (current_y - start_y) - 5]):
|
||||
is_selected = (i == self.selected_flow)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
flow_desc = f"{flow.src_ip}:{flow.src_port} → {flow.dst_ip}:{flow.dst_port}"
|
||||
if len(flow_desc) > 28:
|
||||
flow_desc = f"{flow.src_ip[:8]}…:{flow.src_port} → {flow.dst_ip[:8]}…:{flow.dst_port}"
|
||||
|
||||
enhanced = flow.enhanced_analysis
|
||||
decoder_type = enhanced.decoder_type.replace("_Enhanced", "")
|
||||
quality = f"{enhanced.avg_frame_quality:.1f}%" if enhanced.avg_frame_quality > 0 else "N/A"
|
||||
drift = f"{enhanced.avg_clock_drift_ppm:.1f}ppm" if enhanced.avg_clock_drift_ppm != 0 else "N/A"
|
||||
|
||||
error_count = (enhanced.rtc_sync_errors + enhanced.format_errors +
|
||||
enhanced.overflow_errors + enhanced.sequence_gaps)
|
||||
|
||||
line = f"{flow_desc:30} {decoder_type:15} {quality:>8} {drift:>10} {error_count:>8}"
|
||||
stdscr.addstr(current_y + i, 4, line[:width-8], attr)
|
||||
|
||||
current_y += len(enhanced_flows) + 2
|
||||
|
||||
# General quality indicators
|
||||
stdscr.addstr(current_y, 4, "GENERAL QUALITY INDICATORS", curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
# Calculate network health metrics
|
||||
health_metrics = self._calculate_health_metrics(flows_list)
|
||||
|
||||
for metric_name, metric_value, status in health_metrics:
|
||||
status_color = curses.A_BOLD if status == "GOOD" else curses.A_DIM if status == "WARNING" else curses.A_REVERSE
|
||||
stdscr.addstr(current_y, 4, f"{metric_name:25}: {metric_value:15} [{status}]", status_color)
|
||||
current_y += 1
|
||||
|
||||
def _draw_timing_analysis(self, stdscr, start_y: int, width: int, height: int, flows_list: List[FlowStats]):
|
||||
"""Draw detailed timing analysis"""
|
||||
current_y = start_y
|
||||
|
||||
stdscr.addstr(current_y, 4, "TIMING ANALYSIS", curses.A_UNDERLINE)
|
||||
current_y += 2
|
||||
|
||||
# Timing distribution summary
|
||||
all_inter_arrivals = []
|
||||
for flow in flows_list:
|
||||
all_inter_arrivals.extend(flow.inter_arrival_times)
|
||||
|
||||
if all_inter_arrivals:
|
||||
mean_timing = statistics.mean(all_inter_arrivals)
|
||||
median_timing = statistics.median(all_inter_arrivals)
|
||||
std_timing = statistics.stdev(all_inter_arrivals) if len(all_inter_arrivals) > 1 else 0
|
||||
|
||||
stdscr.addstr(current_y, 4, "NETWORK TIMING DISTRIBUTION", curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
timing_stats = [
|
||||
("Mean Inter-arrival", f"{mean_timing*1000:.3f} ms"),
|
||||
("Median Inter-arrival", f"{median_timing*1000:.3f} ms"),
|
||||
("Standard Deviation", f"{std_timing*1000:.3f} ms"),
|
||||
("Coefficient of Variation", f"{std_timing/mean_timing:.3f}" if mean_timing > 0 else "N/A"),
|
||||
]
|
||||
|
||||
for stat_name, stat_value in timing_stats:
|
||||
stdscr.addstr(current_y, 4, f"{stat_name:25}: {stat_value}")
|
||||
current_y += 1
|
||||
|
||||
current_y += 1
|
||||
|
||||
# Per-flow timing details
|
||||
stdscr.addstr(current_y, 4, "PER-FLOW TIMING ANALYSIS", curses.A_BOLD)
|
||||
current_y += 1
|
||||
|
||||
header = f"{'Flow':30} {'Mean':>10} {'Std Dev':>10} {'CV':>8} {'Range':>12}"
|
||||
stdscr.addstr(current_y, 4, header, curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
# Sort flows by timing variability
|
||||
timing_flows = [(flow, flow.std_inter_arrival / flow.avg_inter_arrival if flow.avg_inter_arrival > 0 else 0)
|
||||
for flow in flows_list if flow.inter_arrival_times]
|
||||
timing_flows.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
visible_flows = min(height - (current_y - start_y) - 2, len(timing_flows))
|
||||
for i in range(visible_flows):
|
||||
flow, cv = timing_flows[i]
|
||||
|
||||
is_selected = (i == self.selected_flow)
|
||||
attr = curses.A_REVERSE if is_selected else curses.A_NORMAL
|
||||
|
||||
flow_desc = f"{flow.src_ip}:{flow.src_port} → {flow.dst_ip}:{flow.dst_port}"
|
||||
if len(flow_desc) > 28:
|
||||
flow_desc = f"{flow.src_ip[:8]}…:{flow.src_port} → {flow.dst_ip[:8]}…:{flow.dst_port}"
|
||||
|
||||
mean_ms = f"{flow.avg_inter_arrival*1000:.1f}ms"
|
||||
std_ms = f"{flow.std_inter_arrival*1000:.1f}ms"
|
||||
cv_str = f"{cv:.3f}"
|
||||
|
||||
if flow.inter_arrival_times:
|
||||
range_ms = f"{(max(flow.inter_arrival_times) - min(flow.inter_arrival_times))*1000:.1f}ms"
|
||||
else:
|
||||
range_ms = "N/A"
|
||||
|
||||
line = f"{flow_desc:30} {mean_ms:>10} {std_ms:>10} {cv_str:>8} {range_ms:>12}"
|
||||
stdscr.addstr(current_y + i, 4, line[:width-8], attr)
|
||||
|
||||
def _rank_flows_by_performance(self, flows_list: List[FlowStats]) -> List[Tuple[FlowStats, float]]:
|
||||
"""Rank flows by performance score (lower is better)"""
|
||||
ranked = []
|
||||
|
||||
for flow in flows_list:
|
||||
score = 0.0
|
||||
|
||||
# Outlier penalty (higher percentage = higher score)
|
||||
if flow.frame_count > 0:
|
||||
outlier_rate = len(flow.outlier_frames) / flow.frame_count
|
||||
score += outlier_rate * 100 # 0-100 points
|
||||
|
||||
# Timing variability penalty
|
||||
if flow.avg_inter_arrival > 0:
|
||||
cv = flow.std_inter_arrival / flow.avg_inter_arrival
|
||||
score += cv * 50 # 0-50+ points
|
||||
|
||||
# Enhanced decoder bonus (negative score)
|
||||
if flow.enhanced_analysis.decoder_type != "Standard":
|
||||
score -= 10
|
||||
if flow.enhanced_analysis.avg_frame_quality > 80:
|
||||
score -= 5 # Good quality bonus
|
||||
|
||||
ranked.append((flow, score))
|
||||
|
||||
ranked.sort(key=lambda x: x[1]) # Lower scores first (better performance)
|
||||
return ranked
|
||||
|
||||
def _calculate_health_metrics(self, flows_list: List[FlowStats]) -> List[Tuple[str, str, str]]:
|
||||
"""Calculate network health metrics"""
|
||||
metrics = []
|
||||
|
||||
# Overall outlier rate
|
||||
total_packets = sum(flow.frame_count for flow in flows_list)
|
||||
total_outliers = sum(len(flow.outlier_frames) for flow in flows_list)
|
||||
outlier_rate = (total_outliers / total_packets * 100) if total_packets > 0 else 0
|
||||
|
||||
outlier_status = "GOOD" if outlier_rate < 1.0 else "WARNING" if outlier_rate < 5.0 else "CRITICAL"
|
||||
metrics.append(("Network Outlier Rate", f"{outlier_rate:.2f}%", outlier_status))
|
||||
|
||||
# Enhanced decoder coverage
|
||||
enhanced_count = sum(1 for f in flows_list if f.enhanced_analysis.decoder_type != "Standard")
|
||||
coverage = (enhanced_count / len(flows_list) * 100) if flows_list else 0
|
||||
coverage_status = "GOOD" if coverage > 50 else "WARNING" if coverage > 0 else "NONE"
|
||||
metrics.append(("Enhanced Coverage", f"{coverage:.1f}%", coverage_status))
|
||||
|
||||
# Timing consistency
|
||||
all_cvs = []
|
||||
for flow in flows_list:
|
||||
if flow.avg_inter_arrival > 0:
|
||||
cv = flow.std_inter_arrival / flow.avg_inter_arrival
|
||||
all_cvs.append(cv)
|
||||
|
||||
if all_cvs:
|
||||
avg_cv = statistics.mean(all_cvs)
|
||||
timing_status = "GOOD" if avg_cv < 0.1 else "WARNING" if avg_cv < 0.5 else "CRITICAL"
|
||||
metrics.append(("Timing Consistency", f"CV={avg_cv:.3f}", timing_status))
|
||||
|
||||
return metrics
|
||||
|
||||
def _draw_selected_flow_outliers(self, stdscr, start_y: int, width: int, height: int, flow: FlowStats):
|
||||
"""Draw outlier details for selected flow"""
|
||||
if height < 3:
|
||||
return
|
||||
|
||||
stdscr.addstr(start_y, 4, f"OUTLIER DETAILS: {flow.src_ip}:{flow.src_port} → {flow.dst_ip}:{flow.dst_port}", curses.A_BOLD)
|
||||
current_y = start_y + 1
|
||||
|
||||
if flow.outlier_details:
|
||||
header = f"{'Frame#':>8} {'Inter-arrival':>15} {'Deviation':>12}"
|
||||
stdscr.addstr(current_y, 4, header, curses.A_UNDERLINE)
|
||||
current_y += 1
|
||||
|
||||
visible_outliers = min(height - 3, len(flow.outlier_details))
|
||||
for i in range(visible_outliers):
|
||||
frame_num, timing = flow.outlier_details[i]
|
||||
|
||||
# Calculate sigma deviation
|
||||
if flow.avg_inter_arrival > 0 and flow.std_inter_arrival > 0:
|
||||
sigma = abs(timing - flow.avg_inter_arrival) / flow.std_inter_arrival
|
||||
deviation = f"{sigma:.1f}σ"
|
||||
else:
|
||||
deviation = "N/A"
|
||||
|
||||
outlier_line = f"{frame_num:>8} {timing*1000:>12.3f}ms {deviation:>12}"
|
||||
stdscr.addstr(current_y + i, 4, outlier_line)
|
||||
|
||||
def _get_flows_list(self) -> List[FlowStats]:
|
||||
"""Get flows sorted for statistical analysis"""
|
||||
flows_list = list(self.analyzer.flows.values())
|
||||
|
||||
# Sort by statistical interest: outliers first, then enhanced, then packet count
|
||||
flows_list.sort(key=lambda x: (
|
||||
len(x.outlier_frames),
|
||||
x.enhanced_analysis.decoder_type != "Standard",
|
||||
x.frame_count
|
||||
), reverse=True)
|
||||
|
||||
return flows_list
|
||||
|
||||
def handle_input(self, key: int, flows_list: List[FlowStats]) -> str:
|
||||
"""Handle input for Statistical Analysis view"""
|
||||
if key == curses.KEY_UP:
|
||||
self.selected_flow = max(0, self.selected_flow - 1)
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_DOWN:
|
||||
max_flows = len(flows_list) - 1
|
||||
self.selected_flow = min(max_flows, self.selected_flow + 1)
|
||||
return 'selection_change'
|
||||
elif key == curses.KEY_LEFT:
|
||||
self.analysis_mode = max(0, self.analysis_mode - 1)
|
||||
self.selected_flow = 0 # Reset selection when changing modes
|
||||
return 'mode_change'
|
||||
elif key == curses.KEY_RIGHT:
|
||||
self.analysis_mode = min(3, self.analysis_mode + 1)
|
||||
self.selected_flow = 0 # Reset selection when changing modes
|
||||
return 'mode_change'
|
||||
elif key >= ord('1') and key <= ord('4'):
|
||||
self.analysis_mode = key - ord('1')
|
||||
self.selected_flow = 0
|
||||
return 'mode_change'
|
||||
elif key == ord('r') or key == ord('R'):
|
||||
return 'refresh_stats'
|
||||
elif key == ord('o') or key == ord('O'):
|
||||
self.analysis_mode = 1 # Switch to outlier analysis
|
||||
return 'show_outliers'
|
||||
|
||||
return 'none'
|
||||
@@ -57,6 +57,8 @@ class NavigationHandler:
|
||||
return 'selection_change'
|
||||
elif key == ord('v') and self.current_view == 'main': # Visualize Chapter 10 signals
|
||||
return 'visualize'
|
||||
elif key == ord('\t') and self.current_view == 'main': # Tab key to switch detail panel tabs
|
||||
return 'switch_tab'
|
||||
|
||||
return 'none'
|
||||
|
||||
@@ -72,7 +74,7 @@ class NavigationHandler:
|
||||
"""Get status bar text based on current view"""
|
||||
if self.current_view == 'main':
|
||||
timeline_status = "ON" if self.show_timeline else "OFF"
|
||||
return f"[↑↓]navigate [PgUp/PgDn]scroll [t]imeline:{timeline_status} [v]isualize CH10 [d]issection [q]uit"
|
||||
return f"[↑↓]navigate [Tab]switch tabs [PgUp/PgDn]scroll [t]imeline:{timeline_status} [v]isualize CH10 [d]issection [q]uit"
|
||||
elif self.current_view == 'dissection':
|
||||
return "[m]ain view [q]uit"
|
||||
else:
|
||||
|
||||
@@ -2,14 +2,18 @@
|
||||
Right panel - Flow details with frame type table
|
||||
"""
|
||||
|
||||
from typing import List, Optional, Tuple
|
||||
from typing import List, Optional, Tuple, Dict
|
||||
import curses
|
||||
|
||||
from ...models import FlowStats, FrameTypeStats
|
||||
|
||||
|
||||
class DetailPanel:
|
||||
"""Right panel showing detailed flow information"""
|
||||
"""Right panel showing detailed flow information with tabs"""
|
||||
|
||||
def __init__(self):
|
||||
self.active_tab = 0 # 0 = Info, 1 = Decode
|
||||
self.tabs = ["Info", "Decode"]
|
||||
|
||||
def draw(self, stdscr, x_offset: int, y_offset: int, width: int,
|
||||
flows_list: List[FlowStats], selected_flow: int, max_height: Optional[int] = None):
|
||||
@@ -31,14 +35,58 @@ class DetailPanel:
|
||||
else:
|
||||
max_lines = y_offset + max_height
|
||||
|
||||
try:
|
||||
# Draw tab bar
|
||||
tab_y = y_offset
|
||||
self._draw_tab_bar(stdscr, x_offset, tab_y, width)
|
||||
y_offset += 2 # Space for tab bar
|
||||
max_lines -= 2
|
||||
|
||||
# Draw content based on active tab
|
||||
if self.active_tab == 0:
|
||||
self._draw_info_tab(stdscr, x_offset, y_offset, width, max_lines, flow, selected_frame_type)
|
||||
elif self.active_tab == 1:
|
||||
self._draw_decode_tab(stdscr, x_offset, y_offset, width, max_lines, flow, selected_frame_type)
|
||||
|
||||
except curses.error:
|
||||
# Ignore curses errors from writing outside screen bounds
|
||||
pass
|
||||
|
||||
def _draw_tab_bar(self, stdscr, x_offset: int, y_offset: int, width: int):
|
||||
"""Draw the tab bar at the top of the panel"""
|
||||
tab_line = ""
|
||||
for i, tab_name in enumerate(self.tabs):
|
||||
if i == self.active_tab:
|
||||
tab_line += f"[{tab_name}]"
|
||||
else:
|
||||
tab_line += f" {tab_name} "
|
||||
|
||||
if i < len(self.tabs) - 1:
|
||||
tab_line += " "
|
||||
|
||||
# Add tab navigation hint
|
||||
tab_line += f" {' ' * (width - len(tab_line) - 15)}[Tab] to switch"
|
||||
|
||||
stdscr.addstr(y_offset, x_offset, tab_line[:width-1], curses.A_BOLD)
|
||||
stdscr.addstr(y_offset + 1, x_offset, "─" * min(width-1, 50))
|
||||
|
||||
def _draw_info_tab(self, stdscr, x_offset: int, y_offset: int, width: int, max_lines: int,
|
||||
flow: FlowStats, selected_frame_type: Optional[str]):
|
||||
"""Draw the Info tab content (original detail panel content)"""
|
||||
try:
|
||||
# ALWAYS show flow details first
|
||||
stdscr.addstr(y_offset, x_offset, f"FLOW DETAILS: {flow.src_ip} -> {flow.dst_ip}", curses.A_BOLD)
|
||||
y_offset += 2
|
||||
|
||||
|
||||
stdscr.addstr(y_offset, x_offset, f"Packets: {flow.frame_count} | Bytes: {flow.total_bytes:,}")
|
||||
y_offset += 1
|
||||
|
||||
# Enhanced analysis information
|
||||
if flow.enhanced_analysis.decoder_type != "Standard":
|
||||
y_offset += 1
|
||||
self._draw_enhanced_analysis(stdscr, x_offset, y_offset, width, flow)
|
||||
y_offset += self._get_enhanced_analysis_lines(flow)
|
||||
|
||||
# Frame types table
|
||||
if flow.frame_types and y_offset < max_lines:
|
||||
y_offset += 1
|
||||
@@ -174,4 +222,259 @@ class DetailPanel:
|
||||
current_item += 1
|
||||
|
||||
# Fallback to first flow if selection is out of bounds
|
||||
return flows_list[0] if flows_list else None, None
|
||||
return flows_list[0] if flows_list else None, None
|
||||
|
||||
def _draw_enhanced_analysis(self, stdscr, x_offset: int, y_offset: int, width: int, flow: FlowStats):
|
||||
"""Draw enhanced analysis information"""
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
try:
|
||||
# Enhanced analysis header
|
||||
stdscr.addstr(y_offset, x_offset, f"Enhanced Analysis ({enhanced.decoder_type}):", curses.A_BOLD)
|
||||
y_offset += 1
|
||||
|
||||
# Timing analysis for CH10
|
||||
if enhanced.has_internal_timing:
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Clock Drift: {enhanced.avg_clock_drift_ppm:.2f} PPM (max: {enhanced.max_clock_drift_ppm:.2f})")
|
||||
y_offset += 1
|
||||
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Timing Quality: {enhanced.timing_quality.title()} | Stability: {enhanced.timing_stability.title()}")
|
||||
y_offset += 1
|
||||
|
||||
if enhanced.anomaly_rate > 0:
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Anomaly Rate: {enhanced.anomaly_rate:.1%} | Confidence: {enhanced.avg_confidence_score:.2f}")
|
||||
y_offset += 1
|
||||
|
||||
# Frame quality metrics
|
||||
if enhanced.avg_frame_quality > 0:
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Frame Quality: {enhanced.avg_frame_quality:.1f}%")
|
||||
y_offset += 1
|
||||
|
||||
# Error counts
|
||||
errors = []
|
||||
if enhanced.sequence_gaps > 0:
|
||||
errors.append(f"Seq: {enhanced.sequence_gaps}")
|
||||
if enhanced.rtc_sync_errors > 0:
|
||||
errors.append(f"RTC: {enhanced.rtc_sync_errors}")
|
||||
if enhanced.format_errors > 0:
|
||||
errors.append(f"Fmt: {enhanced.format_errors}")
|
||||
if enhanced.overflow_errors > 0:
|
||||
errors.append(f"Ovf: {enhanced.overflow_errors}")
|
||||
|
||||
if errors:
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Errors: {' | '.join(errors)}")
|
||||
y_offset += 1
|
||||
|
||||
# Data analysis
|
||||
if enhanced.channel_count > 0:
|
||||
channel_info = f"Channels: {enhanced.channel_count}"
|
||||
if enhanced.analog_channels > 0:
|
||||
channel_info += f" (Analog: {enhanced.analog_channels})"
|
||||
if enhanced.pcm_channels > 0:
|
||||
channel_info += f" (PCM: {enhanced.pcm_channels})"
|
||||
if enhanced.tmats_frames > 0:
|
||||
channel_info += f" (TMATS: {enhanced.tmats_frames})"
|
||||
|
||||
stdscr.addstr(y_offset, x_offset + 2, channel_info)
|
||||
y_offset += 1
|
||||
|
||||
# Primary data type
|
||||
if enhanced.primary_data_type != "Unknown":
|
||||
stdscr.addstr(y_offset, x_offset + 2, f"Primary Type: {enhanced.primary_data_type}")
|
||||
y_offset += 1
|
||||
|
||||
except curses.error:
|
||||
pass
|
||||
|
||||
def _get_enhanced_analysis_lines(self, flow: FlowStats) -> int:
|
||||
"""Calculate how many lines the enhanced analysis will take"""
|
||||
enhanced = flow.enhanced_analysis
|
||||
lines = 1 # Header line
|
||||
|
||||
if enhanced.has_internal_timing:
|
||||
lines += 2 # Clock drift + timing quality
|
||||
if enhanced.anomaly_rate > 0:
|
||||
lines += 1 # Anomaly rate
|
||||
|
||||
if enhanced.avg_frame_quality > 0:
|
||||
lines += 1 # Frame quality
|
||||
# Check if we have errors to display
|
||||
if any([enhanced.sequence_gaps, enhanced.rtc_sync_errors,
|
||||
enhanced.format_errors, enhanced.overflow_errors]):
|
||||
lines += 1
|
||||
|
||||
if enhanced.channel_count > 0:
|
||||
lines += 1 # Channel info
|
||||
|
||||
if enhanced.primary_data_type != "Unknown":
|
||||
lines += 1 # Primary data type
|
||||
|
||||
return lines
|
||||
|
||||
def _draw_decode_tab(self, stdscr, x_offset: int, y_offset: int, width: int, max_lines: int,
|
||||
flow: FlowStats, selected_frame_type: Optional[str]):
|
||||
"""Draw the Decode tab content showing decoded frame information"""
|
||||
|
||||
if flow.enhanced_analysis.decoder_type == "Standard":
|
||||
stdscr.addstr(y_offset, x_offset, "No enhanced decoder available for this flow", curses.A_DIM)
|
||||
return
|
||||
|
||||
# Header for decode information
|
||||
stdscr.addstr(y_offset, x_offset, f"DECODED DATA ({flow.enhanced_analysis.decoder_type}):", curses.A_BOLD)
|
||||
y_offset += 2
|
||||
|
||||
# Tree view of decoded information
|
||||
if flow.enhanced_analysis.decoder_type == "Chapter10_Enhanced":
|
||||
self._draw_ch10_decode_tree(stdscr, x_offset, y_offset, width, max_lines, flow)
|
||||
else:
|
||||
stdscr.addstr(y_offset, x_offset, f"Decoder type '{flow.enhanced_analysis.decoder_type}' display not implemented")
|
||||
|
||||
def _draw_ch10_decode_tree(self, stdscr, x_offset: int, y_offset: int, width: int, max_lines: int, flow: FlowStats):
|
||||
"""Draw Chapter 10 decoded information in tree format"""
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
tree_items = [
|
||||
("📋 Header Information", None, 0),
|
||||
(" Decoder Type", enhanced.decoder_type, 1),
|
||||
(" Primary Data Type", enhanced.primary_data_type, 1),
|
||||
(" Channel Count", str(enhanced.channel_count) if enhanced.channel_count > 0 else "Unknown", 1),
|
||||
("", None, 0), # Spacer
|
||||
("⏱️ Timing Analysis", None, 0),
|
||||
(" Has Internal Timing", "Yes" if enhanced.has_internal_timing else "No", 1),
|
||||
(" Timing Quality", enhanced.timing_quality.title(), 1),
|
||||
(" Clock Drift (avg)", f"{enhanced.avg_clock_drift_ppm:.2f} PPM" if enhanced.avg_clock_drift_ppm != 0 else "N/A", 1),
|
||||
(" Clock Drift (max)", f"{enhanced.max_clock_drift_ppm:.2f} PPM" if enhanced.max_clock_drift_ppm != 0 else "N/A", 1),
|
||||
(" Timing Stability", enhanced.timing_stability.title(), 1),
|
||||
(" Anomaly Rate", f"{enhanced.anomaly_rate:.1%}" if enhanced.anomaly_rate > 0 else "0%", 1),
|
||||
(" Confidence Score", f"{enhanced.avg_confidence_score:.2f}" if enhanced.avg_confidence_score > 0 else "N/A", 1),
|
||||
("", None, 0), # Spacer
|
||||
("📊 Quality Metrics", None, 0),
|
||||
(" Frame Quality (avg)", f"{enhanced.avg_frame_quality:.1f}%" if enhanced.avg_frame_quality > 0 else "N/A", 1),
|
||||
(" Sequence Gaps", str(enhanced.sequence_gaps), 1),
|
||||
(" RTC Sync Errors", str(enhanced.rtc_sync_errors), 1),
|
||||
(" Format Errors", str(enhanced.format_errors), 1),
|
||||
(" Overflow Errors", str(enhanced.overflow_errors), 1),
|
||||
("", None, 0), # Spacer
|
||||
("📡 Channel Information", None, 0),
|
||||
(" Total Channels", str(enhanced.channel_count), 1),
|
||||
(" Analog Channels", str(enhanced.analog_channels), 1),
|
||||
(" PCM Channels", str(enhanced.pcm_channels), 1),
|
||||
(" TMATS Frames", str(enhanced.tmats_frames), 1),
|
||||
]
|
||||
|
||||
# Add decoded frame samples if available
|
||||
if enhanced.sample_decoded_fields:
|
||||
tree_items.extend([
|
||||
("", None, 0), # Spacer
|
||||
("🔍 Decoded Frame Samples", None, 0),
|
||||
])
|
||||
|
||||
for frame_key, frame_data in enhanced.sample_decoded_fields.items():
|
||||
tree_items.append((f" {frame_key.replace('_', ' ').title()}", None, 1))
|
||||
|
||||
# Show important fields first
|
||||
priority_fields = ['packet_timestamp', 'internal_timestamp', 'data_type_name', 'channel_id',
|
||||
'sequence_number', 'frame_quality_score', 'rtc_sync_error']
|
||||
|
||||
# Add priority fields
|
||||
for field_name in priority_fields:
|
||||
if field_name in frame_data:
|
||||
display_value = self._format_field_value(field_name, frame_data[field_name])
|
||||
tree_items.append((f" {field_name.replace('_', ' ').title()}", display_value, 2))
|
||||
|
||||
# Add other fields (up to 5 more)
|
||||
other_fields = [k for k in frame_data.keys() if k not in priority_fields]
|
||||
for i, field_name in enumerate(other_fields[:5]):
|
||||
display_value = self._format_field_value(field_name, frame_data[field_name])
|
||||
tree_items.append((f" {field_name.replace('_', ' ').title()}", display_value, 2))
|
||||
|
||||
if len(other_fields) > 5:
|
||||
tree_items.append((f" ... and {len(other_fields) - 5} more fields", "", 2))
|
||||
|
||||
# Add available fields summary
|
||||
if enhanced.available_field_names:
|
||||
tree_items.extend([
|
||||
("", None, 0), # Spacer
|
||||
("📝 Available Decoder Fields", None, 0),
|
||||
(" Total Fields Available", str(len(enhanced.available_field_names)), 1),
|
||||
(" Field Categories", self._categorize_fields(enhanced.available_field_names), 1),
|
||||
])
|
||||
|
||||
# Render tree items
|
||||
current_y = y_offset
|
||||
for label, value, indent_level in tree_items:
|
||||
if current_y >= max_lines:
|
||||
break
|
||||
|
||||
if not label: # Spacer line
|
||||
current_y += 1
|
||||
continue
|
||||
|
||||
indent = " " * indent_level
|
||||
|
||||
if value is None: # Category header
|
||||
line = f"{indent}{label}"
|
||||
stdscr.addstr(current_y, x_offset, line[:width-1], curses.A_BOLD)
|
||||
else: # Key-value pair
|
||||
line = f"{indent}{label}: {value}"
|
||||
stdscr.addstr(current_y, x_offset, line[:width-1])
|
||||
|
||||
current_y += 1
|
||||
|
||||
# Add scrolling hint if content is cut off
|
||||
if current_y >= max_lines and len(tree_items) > (max_lines - y_offset):
|
||||
if max_lines > 0:
|
||||
stdscr.addstr(max_lines - 1, x_offset, "... (content truncated)", curses.A_DIM)
|
||||
|
||||
def switch_tab(self):
|
||||
"""Switch to the next tab"""
|
||||
self.active_tab = (self.active_tab + 1) % len(self.tabs)
|
||||
|
||||
def _format_field_value(self, field_name: str, value) -> str:
|
||||
"""Format field value for display"""
|
||||
if value is None:
|
||||
return "N/A"
|
||||
|
||||
# Special formatting for different field types
|
||||
if "timestamp" in field_name.lower():
|
||||
try:
|
||||
return f"{float(value):.6f}s"
|
||||
except:
|
||||
return str(value)
|
||||
elif "error" in field_name.lower() or field_name.endswith("_error"):
|
||||
return "Yes" if value else "No"
|
||||
elif "quality" in field_name.lower() and isinstance(value, (int, float)):
|
||||
return f"{value:.1f}%"
|
||||
elif isinstance(value, float):
|
||||
return f"{value:.3f}"
|
||||
elif isinstance(value, bool):
|
||||
return "Yes" if value else "No"
|
||||
else:
|
||||
return str(value)
|
||||
|
||||
def _categorize_fields(self, field_names: List[str]) -> str:
|
||||
"""Categorize available fields into groups"""
|
||||
categories = {
|
||||
"Timing": 0,
|
||||
"Quality": 0,
|
||||
"Data": 0,
|
||||
"Header": 0,
|
||||
"Other": 0
|
||||
}
|
||||
|
||||
for field_name in field_names:
|
||||
lower_name = field_name.lower()
|
||||
if any(keyword in lower_name for keyword in ["time", "timestamp", "rtc", "drift"]):
|
||||
categories["Timing"] += 1
|
||||
elif any(keyword in lower_name for keyword in ["quality", "error", "sync", "confidence"]):
|
||||
categories["Quality"] += 1
|
||||
elif any(keyword in lower_name for keyword in ["data", "analog", "pcm", "channel", "sample"]):
|
||||
categories["Data"] += 1
|
||||
elif any(keyword in lower_name for keyword in ["header", "type", "id", "sequence", "length"]):
|
||||
categories["Header"] += 1
|
||||
else:
|
||||
categories["Other"] += 1
|
||||
|
||||
# Format as compact string
|
||||
active_categories = [f"{k}:{v}" for k, v in categories.items() if v > 0]
|
||||
return ", ".join(active_categories)
|
||||
@@ -19,9 +19,9 @@ class FlowListPanel:
|
||||
flows_list: List[FlowStats], selected_flow: int):
|
||||
"""Draw the flow list panel"""
|
||||
|
||||
# Draw flows table header with adjusted column widths for better alignment
|
||||
stdscr.addstr(y_offset, x_offset, "FLOWS:", curses.A_BOLD)
|
||||
headers = f"{'Src:Port':22} {'Dst:Port':22} {'Proto':6} {'Cast':5} {'#Frames':>7} {'Bytes':>7} {'Encoding':12} {'ΔT Avg':>9}"
|
||||
# Draw flows table header with enhanced analysis columns
|
||||
stdscr.addstr(y_offset, x_offset, "FLOWS (Enhanced Analysis):", curses.A_BOLD)
|
||||
headers = f"{'Src:Port':22} {'Dst:Port':22} {'Proto':6} {'Cast':5} {'#Frames':>7} {'Bytes':>7} {'Encoding':12} {'Quality':>7} {'Drift':>8} {'ΔT Avg':>9}"
|
||||
stdscr.addstr(y_offset + 1, x_offset, headers[:width-1], curses.A_UNDERLINE)
|
||||
|
||||
# Calculate scrolling parameters
|
||||
@@ -56,7 +56,11 @@ class FlowListPanel:
|
||||
# Abbreviate traffic classification
|
||||
cast_abbrev = flow.traffic_classification[:4] if flow.traffic_classification != "Unknown" else "Unk"
|
||||
|
||||
line = f"{src_endpoint:22} {dst_endpoint:22} {flow.transport_protocol:6} {cast_abbrev:5} {flow.frame_count:>7} {bytes_str:>7} {encoding_str:12} {avg_time:>9}"
|
||||
# Enhanced analysis data
|
||||
quality_str = self._format_quality_score(flow)
|
||||
drift_str = self._format_drift_info(flow)
|
||||
|
||||
line = f"{src_endpoint:22} {dst_endpoint:22} {flow.transport_protocol:6} {cast_abbrev:5} {flow.frame_count:>7} {bytes_str:>7} {encoding_str:12} {quality_str:>7} {drift_str:>8} {avg_time:>9}"
|
||||
|
||||
if display_item == selected_flow:
|
||||
stdscr.addstr(current_row, x_offset, line[:width-1], curses.A_REVERSE)
|
||||
@@ -78,9 +82,14 @@ class FlowListPanel:
|
||||
ft_avg = f"{ft_stats.avg_inter_arrival:.3f}s" if ft_stats.avg_inter_arrival > 0 else "N/A"
|
||||
outlier_count = len(ft_stats.outlier_details) if ft_stats.outlier_details else 0
|
||||
|
||||
# Create frame type line aligned with new column layout
|
||||
# Create frame type line aligned with enhanced column layout
|
||||
bytes_str_ft = self._format_bytes(ft_stats.total_bytes)
|
||||
ft_line = f" └─{frame_type:18} {'':22} {'':6} {'':5} {ft_stats.count:>7} {bytes_str_ft:>7} {'':12} {ft_avg:>9}"
|
||||
|
||||
# Enhanced analysis for frame types (inherit from parent flow)
|
||||
quality_str_ft = self._format_quality_score(flow) if frame_type.startswith('CH10') or frame_type == 'TMATS' else ""
|
||||
drift_str_ft = self._format_drift_info(flow) if frame_type.startswith('CH10') or frame_type == 'TMATS' else ""
|
||||
|
||||
ft_line = f" └─{frame_type:18} {'':22} {'':6} {'':5} {ft_stats.count:>7} {bytes_str_ft:>7} {'':12} {quality_str_ft:>7} {drift_str_ft:>8} {ft_avg:>9}"
|
||||
|
||||
if display_item == selected_flow:
|
||||
stdscr.addstr(current_row, x_offset, ft_line[:width-1], curses.A_REVERSE)
|
||||
@@ -179,4 +188,38 @@ class FlowListPanel:
|
||||
if found_app:
|
||||
return list(found_app)[0]
|
||||
|
||||
return "Unknown"
|
||||
return "Unknown"
|
||||
|
||||
def _format_quality_score(self, flow: FlowStats) -> str:
|
||||
"""Format quality score for display"""
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
if enhanced.decoder_type == "Standard" or enhanced.avg_frame_quality == 0:
|
||||
return "N/A"
|
||||
|
||||
# Format quality as percentage
|
||||
quality = enhanced.avg_frame_quality
|
||||
if quality >= 90:
|
||||
return f"{quality:.0f}%"
|
||||
elif quality >= 70:
|
||||
return f"{quality:.0f}%"
|
||||
else:
|
||||
return f"{quality:.0f}%"
|
||||
|
||||
def _format_drift_info(self, flow: FlowStats) -> str:
|
||||
"""Format clock drift information for display"""
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
if not enhanced.has_internal_timing or enhanced.avg_clock_drift_ppm == 0:
|
||||
return "N/A"
|
||||
|
||||
# Format drift in PPM
|
||||
drift_ppm = abs(enhanced.avg_clock_drift_ppm)
|
||||
if drift_ppm >= 1000:
|
||||
return f"{drift_ppm/1000:.1f}K" # Show in thousands
|
||||
elif drift_ppm >= 100:
|
||||
return f"{drift_ppm:.0f}ppm"
|
||||
elif drift_ppm >= 10:
|
||||
return f"{drift_ppm:.1f}ppm"
|
||||
else:
|
||||
return f"{drift_ppm:.2f}ppm"
|
||||
239
example_enhanced_integration.py
Normal file
239
example_enhanced_integration.py
Normal file
@@ -0,0 +1,239 @@
|
||||
"""
|
||||
Example showing how the enhanced CH10 decoder integrates with the TUI
|
||||
This demonstrates the full data flow from packet to enhanced analysis display
|
||||
"""
|
||||
|
||||
from analyzer.protocols.enhanced_chapter10 import EnhancedChapter10Decoder
|
||||
from analyzer.plugins.ch10_timing_analysis import Chapter10TimingAnalysisPlugin
|
||||
from analyzer.models.flow_stats import FlowStats, EnhancedAnalysisData
|
||||
import statistics
|
||||
|
||||
class EnhancedFlowProcessor:
|
||||
"""Example processor showing integration of enhanced CH10 analysis"""
|
||||
|
||||
def __init__(self):
|
||||
self.ch10_decoder = EnhancedChapter10Decoder()
|
||||
self.timing_plugin = Chapter10TimingAnalysisPlugin()
|
||||
self.flows = {}
|
||||
|
||||
def process_packet(self, packet, frame_num: int):
|
||||
"""Process packet with enhanced analysis"""
|
||||
|
||||
# Extract transport info (simplified)
|
||||
transport_info = {'protocol': 'UDP', 'src_port': 4001, 'dst_port': 4001}
|
||||
|
||||
# Check if CH10 decoder can handle this packet
|
||||
confidence = self.ch10_decoder.can_decode(packet, transport_info)
|
||||
|
||||
if confidence > 0.5:
|
||||
# Decode frame with full field extraction
|
||||
frame_data = self.ch10_decoder.decode_frame(packet, transport_info)
|
||||
|
||||
if frame_data:
|
||||
# Run timing analysis plugin
|
||||
flow_context = {'flow_duration': 60.0, 'flow_key': 'example_flow'}
|
||||
timing_result = self.timing_plugin.analyze_frame(frame_data, flow_context)
|
||||
|
||||
# Update or create flow with enhanced data
|
||||
self._update_flow_with_enhanced_data(frame_data, timing_result, packet)
|
||||
|
||||
def _update_flow_with_enhanced_data(self, frame_data, timing_result, packet):
|
||||
"""Update flow statistics with enhanced analysis data"""
|
||||
|
||||
# Create flow key
|
||||
flow_key = "192.168.1.100->239.255.0.1" # Example
|
||||
|
||||
# Get or create flow
|
||||
if flow_key not in self.flows:
|
||||
self.flows[flow_key] = FlowStats(
|
||||
src_ip="192.168.1.100",
|
||||
dst_ip="239.255.0.1",
|
||||
src_port=4001,
|
||||
dst_port=4001,
|
||||
transport_protocol="UDP",
|
||||
traffic_classification="Multicast"
|
||||
)
|
||||
|
||||
flow = self.flows[flow_key]
|
||||
|
||||
# Update basic flow stats
|
||||
flow.frame_count += 1
|
||||
flow.total_bytes += len(packet)
|
||||
flow.timestamps.append(float(packet.time))
|
||||
|
||||
# Update enhanced analysis data
|
||||
self._update_enhanced_analysis_data(flow, frame_data, timing_result)
|
||||
|
||||
def _update_enhanced_analysis_data(self, flow: FlowStats, frame_data, timing_result):
|
||||
"""Update the enhanced analysis data structure"""
|
||||
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
# Set decoder type
|
||||
enhanced.decoder_type = "Chapter10_Enhanced"
|
||||
|
||||
# Update timing analysis
|
||||
if timing_result.internal_timestamp is not None:
|
||||
enhanced.has_internal_timing = True
|
||||
|
||||
# Update running averages for timing
|
||||
if timing_result.clock_drift_ppm is not None:
|
||||
if enhanced.avg_clock_drift_ppm == 0:
|
||||
enhanced.avg_clock_drift_ppm = timing_result.clock_drift_ppm
|
||||
else:
|
||||
# Simple running average
|
||||
enhanced.avg_clock_drift_ppm = (enhanced.avg_clock_drift_ppm + timing_result.clock_drift_ppm) / 2
|
||||
|
||||
enhanced.max_clock_drift_ppm = max(enhanced.max_clock_drift_ppm, abs(timing_result.clock_drift_ppm))
|
||||
|
||||
# Update timing quality (use most recent)
|
||||
enhanced.timing_quality = timing_result.timing_quality
|
||||
|
||||
# Update anomaly rate
|
||||
if timing_result.anomaly_detected:
|
||||
enhanced.anomaly_rate = (enhanced.anomaly_rate * (flow.frame_count - 1) + 1) / flow.frame_count
|
||||
else:
|
||||
enhanced.anomaly_rate = (enhanced.anomaly_rate * (flow.frame_count - 1)) / flow.frame_count
|
||||
|
||||
# Update confidence score
|
||||
enhanced.avg_confidence_score = (enhanced.avg_confidence_score + timing_result.confidence_score) / 2
|
||||
|
||||
# Update frame quality
|
||||
frame_quality = frame_data.get_field('frame_quality_score', 0)
|
||||
if frame_quality > 0:
|
||||
if enhanced.avg_frame_quality == 0:
|
||||
enhanced.avg_frame_quality = frame_quality
|
||||
else:
|
||||
enhanced.avg_frame_quality = (enhanced.avg_frame_quality + frame_quality) / 2
|
||||
|
||||
# Update error counts
|
||||
if frame_data.get_field('rtc_sync_error', False):
|
||||
enhanced.rtc_sync_errors += 1
|
||||
if frame_data.get_field('format_error', False):
|
||||
enhanced.format_errors += 1
|
||||
if frame_data.get_field('overflow_error', False):
|
||||
enhanced.overflow_errors += 1
|
||||
|
||||
# Update channel information
|
||||
channel_id = frame_data.get_field('channel_id', 0)
|
||||
if channel_id > 0:
|
||||
enhanced.channel_count = max(enhanced.channel_count, channel_id)
|
||||
|
||||
# Update data type counters
|
||||
if frame_data.get_field('is_analog_data', False):
|
||||
enhanced.analog_channels = max(enhanced.analog_channels, 1)
|
||||
if frame_data.get_field('is_pcm_data', False):
|
||||
enhanced.pcm_channels = max(enhanced.pcm_channels, 1)
|
||||
if frame_data.get_field('is_tmats_data', False):
|
||||
enhanced.tmats_frames += 1
|
||||
|
||||
# Set primary data type
|
||||
data_type_name = frame_data.get_field('data_type_name', 'Unknown')
|
||||
if enhanced.primary_data_type == "Unknown":
|
||||
enhanced.primary_data_type = data_type_name
|
||||
|
||||
def get_example_enhanced_flow(self) -> FlowStats:
|
||||
"""Generate an example flow with enhanced analysis for TUI demonstration"""
|
||||
|
||||
# Create a sample flow with realistic enhanced data
|
||||
flow = FlowStats(
|
||||
src_ip="192.168.1.100",
|
||||
dst_ip="239.255.0.1",
|
||||
src_port=4001,
|
||||
dst_port=4001,
|
||||
transport_protocol="UDP",
|
||||
traffic_classification="Multicast",
|
||||
frame_count=1234,
|
||||
total_bytes=1048576, # 1MB
|
||||
avg_inter_arrival=0.001 # 1ms
|
||||
)
|
||||
|
||||
# Enhanced analysis data
|
||||
enhanced = EnhancedAnalysisData(
|
||||
# Timing analysis
|
||||
avg_clock_drift_ppm=15.5,
|
||||
max_clock_drift_ppm=23.8,
|
||||
timing_quality="good",
|
||||
timing_stability="stable",
|
||||
anomaly_rate=0.023, # 2.3%
|
||||
avg_confidence_score=0.87,
|
||||
|
||||
# Frame quality
|
||||
avg_frame_quality=94.2,
|
||||
sequence_gaps=2,
|
||||
rtc_sync_errors=1,
|
||||
format_errors=0,
|
||||
overflow_errors=0,
|
||||
|
||||
# Data analysis
|
||||
channel_count=4,
|
||||
analog_channels=3,
|
||||
pcm_channels=0,
|
||||
tmats_frames=5,
|
||||
|
||||
# General
|
||||
has_internal_timing=True,
|
||||
primary_data_type="Analog Format 2",
|
||||
decoder_type="Chapter10_Enhanced"
|
||||
)
|
||||
|
||||
flow.enhanced_analysis = enhanced
|
||||
flow.detected_protocol_types.add("Chapter10")
|
||||
|
||||
return flow
|
||||
|
||||
def demonstrate_tui_integration():
|
||||
"""Demonstrate how enhanced data appears in TUI"""
|
||||
|
||||
processor = EnhancedFlowProcessor()
|
||||
example_flow = processor.get_example_enhanced_flow()
|
||||
|
||||
print("=== Enhanced TUI Display Example ===")
|
||||
print()
|
||||
|
||||
# Show how data appears in main flow table
|
||||
print("Main Flow Table:")
|
||||
print("Src:Port Dst:Port Proto Cast #Frames Bytes Encoding Quality Drift ΔT Avg")
|
||||
print("192.168.1.100:4001 239.255.0.1:4001 UDP Mult 1234 1.0M Chapter10 94% 15.5ppm 1.0ms")
|
||||
print()
|
||||
|
||||
# Show enhanced detail panel
|
||||
print("Enhanced Detail Panel:")
|
||||
print("FLOW DETAILS: 192.168.1.100 -> 239.255.0.1")
|
||||
print()
|
||||
print("Packets: 1234 | Bytes: 1,048,576")
|
||||
print()
|
||||
print("Enhanced Analysis (Chapter10_Enhanced):")
|
||||
print(" Clock Drift: 15.50 PPM (max: 23.80)")
|
||||
print(" Timing Quality: Good | Stability: Stable")
|
||||
print(" Anomaly Rate: 2.3% | Confidence: 0.87")
|
||||
print(" Frame Quality: 94.2%")
|
||||
print(" Errors: Seq: 2 | RTC: 1")
|
||||
print(" Channels: 4 (Analog: 3) (TMATS: 5)")
|
||||
print(" Primary Type: Analog Format 2")
|
||||
print()
|
||||
|
||||
# Show available enhanced fields
|
||||
print("Available Enhanced Fields for Analysis:")
|
||||
supported_fields = processor.ch10_decoder.supported_fields
|
||||
|
||||
timing_fields = [f for f in supported_fields if 'time' in f.name.lower() or 'drift' in f.name.lower()]
|
||||
quality_fields = [f for f in supported_fields if 'quality' in f.name.lower() or 'error' in f.name.lower()]
|
||||
data_fields = [f for f in supported_fields if 'analog' in f.name.lower() or 'channel' in f.name.lower()]
|
||||
|
||||
print(" Timing Fields:")
|
||||
for field in timing_fields[:5]: # Show first 5
|
||||
print(f" - {field.name}: {field.description}")
|
||||
|
||||
print(" Quality Fields:")
|
||||
for field in quality_fields[:5]: # Show first 5
|
||||
print(f" - {field.name}: {field.description}")
|
||||
|
||||
print(" Data Analysis Fields:")
|
||||
for field in data_fields[:5]: # Show first 5
|
||||
print(f" - {field.name}: {field.description}")
|
||||
|
||||
print(f" ... and {len(supported_fields) - 15} more fields available for custom analysis")
|
||||
|
||||
if __name__ == "__main__":
|
||||
demonstrate_tui_integration()
|
||||
518
modular_decoder_framework.md
Normal file
518
modular_decoder_framework.md
Normal file
@@ -0,0 +1,518 @@
|
||||
# Modular Decoder Framework with Extensible Analysis
|
||||
|
||||
## Core Concept
|
||||
|
||||
Create a framework where decoders expose structured frame data, and separate analysis plugins can process this data for domain-specific insights without modifying the core software.
|
||||
|
||||
## Architecture Overview
|
||||
|
||||
```
|
||||
Packet → Decoder → StructuredFrameData → Analysis Plugins → Enhanced FlowStats
|
||||
```
|
||||
|
||||
## 1. Enhanced Decoder Interface
|
||||
|
||||
### Base Decoder with Data Exposure
|
||||
```python
|
||||
from abc import ABC, abstractmethod
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Any, List, Optional, Union
|
||||
import struct
|
||||
|
||||
@dataclass
|
||||
class FieldDefinition:
|
||||
"""Defines a field that can be extracted from decoded data"""
|
||||
name: str
|
||||
description: str
|
||||
data_type: type # int, float, str, bytes, etc.
|
||||
unit: Optional[str] = None # "seconds", "nanoseconds", "MHz", etc.
|
||||
validator: Optional[callable] = None # Function to validate field value
|
||||
|
||||
@dataclass
|
||||
class StructuredFrameData:
|
||||
"""Container for decoded frame data with metadata"""
|
||||
decoder_name: str
|
||||
packet_timestamp: float # Original packet timestamp
|
||||
raw_data: bytes
|
||||
fields: Dict[str, Any] = field(default_factory=dict)
|
||||
metadata: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
def get_field(self, name: str, default=None):
|
||||
"""Get a field value with optional default"""
|
||||
return self.fields.get(name, default)
|
||||
|
||||
def has_field(self, name: str) -> bool:
|
||||
"""Check if field exists"""
|
||||
return name in self.fields
|
||||
|
||||
def get_timestamp_fields(self) -> List[str]:
|
||||
"""Get list of fields that represent timestamps"""
|
||||
return [name for name, value in self.metadata.items()
|
||||
if name.endswith('_timestamp_field')]
|
||||
|
||||
class EnhancedDecoder(ABC):
|
||||
"""Base class for decoders that expose structured data"""
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def decoder_name(self) -> str:
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def supported_fields(self) -> List[FieldDefinition]:
|
||||
"""Return list of fields this decoder can extract"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def can_decode(self, packet, transport_info: Dict) -> float:
|
||||
"""Return confidence score 0.0-1.0"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def decode_frame(self, packet, transport_info: Dict) -> Optional[StructuredFrameData]:
|
||||
"""Decode packet and return structured data"""
|
||||
pass
|
||||
|
||||
def validate_fields(self, frame_data: StructuredFrameData) -> List[str]:
|
||||
"""Validate extracted fields, return list of validation errors"""
|
||||
errors = []
|
||||
for field_def in self.supported_fields:
|
||||
if field_def.name in frame_data.fields:
|
||||
value = frame_data.fields[field_def.name]
|
||||
if field_def.validator and not field_def.validator(value):
|
||||
errors.append(f"Field {field_def.name} failed validation")
|
||||
return errors
|
||||
```
|
||||
|
||||
## 2. Chapter 10 Enhanced Decoder Example
|
||||
|
||||
```python
|
||||
class Chapter10EnhancedDecoder(EnhancedDecoder):
|
||||
"""Enhanced Chapter 10 decoder with structured data exposure"""
|
||||
|
||||
@property
|
||||
def decoder_name(self) -> str:
|
||||
return "Chapter10"
|
||||
|
||||
@property
|
||||
def supported_fields(self) -> List[FieldDefinition]:
|
||||
return [
|
||||
FieldDefinition("sync_pattern", "Sync pattern (0xEB25)", int),
|
||||
FieldDefinition("channel_id", "Channel identifier", int),
|
||||
FieldDefinition("packet_length", "Total packet length", int, "bytes"),
|
||||
FieldDefinition("data_length", "Data payload length", int, "bytes"),
|
||||
FieldDefinition("header_version", "Header version", int),
|
||||
FieldDefinition("sequence_number", "Packet sequence number", int),
|
||||
FieldDefinition("packet_flags", "Packet flags", int),
|
||||
FieldDefinition("data_type", "Data type identifier", int),
|
||||
FieldDefinition("relative_time_counter", "RTC value", int, "counts"),
|
||||
FieldDefinition("checksum", "Header checksum", int),
|
||||
|
||||
# Timing fields
|
||||
FieldDefinition("internal_seconds", "CH10 internal seconds", int, "seconds"),
|
||||
FieldDefinition("internal_nanoseconds", "CH10 internal nanoseconds", int, "nanoseconds"),
|
||||
FieldDefinition("internal_timestamp", "Combined internal timestamp", float, "seconds"),
|
||||
|
||||
# Data type specific fields
|
||||
FieldDefinition("analog_channels", "Number of analog channels", int),
|
||||
FieldDefinition("sample_rate", "Sampling rate", float, "Hz"),
|
||||
FieldDefinition("pcm_format", "PCM format identifier", int),
|
||||
]
|
||||
|
||||
def can_decode(self, packet, transport_info: Dict) -> float:
|
||||
"""Check for Chapter 10 sync pattern"""
|
||||
if not hasattr(packet, 'haslayer') or not packet.haslayer('Raw'):
|
||||
return 0.0
|
||||
|
||||
from scapy.all import Raw
|
||||
raw_data = bytes(packet[Raw])
|
||||
|
||||
# Look for sync pattern
|
||||
if len(raw_data) >= 2:
|
||||
sync = struct.unpack('<H', raw_data[:2])[0]
|
||||
if sync == 0xEB25:
|
||||
return 1.0
|
||||
return 0.0
|
||||
|
||||
def decode_frame(self, packet, transport_info: Dict) -> Optional[StructuredFrameData]:
|
||||
"""Decode Chapter 10 frame with full field extraction"""
|
||||
if not hasattr(packet, 'haslayer') or not packet.haslayer('Raw'):
|
||||
return None
|
||||
|
||||
from scapy.all import Raw
|
||||
raw_data = bytes(packet[Raw])
|
||||
|
||||
if len(raw_data) < 24: # Minimum header size
|
||||
return None
|
||||
|
||||
try:
|
||||
# Extract header fields
|
||||
header = struct.unpack('<HHHHHHHHHHHH', raw_data[:24])
|
||||
|
||||
frame_data = StructuredFrameData(
|
||||
decoder_name=self.decoder_name,
|
||||
packet_timestamp=float(packet.time),
|
||||
raw_data=raw_data
|
||||
)
|
||||
|
||||
# Basic header fields
|
||||
frame_data.fields.update({
|
||||
'sync_pattern': header[0],
|
||||
'channel_id': header[1],
|
||||
'packet_length': header[2],
|
||||
'data_length': header[3],
|
||||
'header_version': header[4],
|
||||
'sequence_number': header[5],
|
||||
'packet_flags': header[6],
|
||||
'data_type': header[7],
|
||||
'relative_time_counter': header[8],
|
||||
'checksum': header[9]
|
||||
})
|
||||
|
||||
# Extract timing information if present
|
||||
if len(raw_data) >= 32: # Extended header with timing
|
||||
timing_data = struct.unpack('<II', raw_data[24:32])
|
||||
frame_data.fields.update({
|
||||
'internal_seconds': timing_data[0],
|
||||
'internal_nanoseconds': timing_data[1],
|
||||
'internal_timestamp': timing_data[0] + (timing_data[1] / 1_000_000_000)
|
||||
})
|
||||
|
||||
# Mark timestamp fields for analysis plugins
|
||||
frame_data.metadata.update({
|
||||
'primary_timestamp_field': 'internal_timestamp',
|
||||
'seconds_timestamp_field': 'internal_seconds',
|
||||
'nanoseconds_timestamp_field': 'internal_nanoseconds'
|
||||
})
|
||||
|
||||
# Data type specific parsing
|
||||
if frame_data.fields['data_type'] in [0x72, 0x73, 0x74, 0x75]: # Analog formats
|
||||
analog_info = self._parse_analog_header(raw_data[32:])
|
||||
frame_data.fields.update(analog_info)
|
||||
elif frame_data.fields['data_type'] in [0x04, 0x08]: # PCM formats
|
||||
pcm_info = self._parse_pcm_header(raw_data[32:])
|
||||
frame_data.fields.update(pcm_info)
|
||||
|
||||
return frame_data
|
||||
|
||||
except (struct.error, IndexError) as e:
|
||||
return None
|
||||
|
||||
def _parse_analog_header(self, data: bytes) -> Dict[str, Any]:
|
||||
"""Parse analog format specific header"""
|
||||
if len(data) < 8:
|
||||
return {}
|
||||
try:
|
||||
analog_header = struct.unpack('<HHI', data[:8])
|
||||
return {
|
||||
'analog_channels': analog_header[0],
|
||||
'sample_rate': analog_header[2] / 1000.0 # Convert to Hz
|
||||
}
|
||||
except struct.error:
|
||||
return {}
|
||||
|
||||
def _parse_pcm_header(self, data: bytes) -> Dict[str, Any]:
|
||||
"""Parse PCM format specific header"""
|
||||
if len(data) < 4:
|
||||
return {}
|
||||
try:
|
||||
pcm_header = struct.unpack('<HH', data[:4])
|
||||
return {
|
||||
'pcm_format': pcm_header[0]
|
||||
}
|
||||
except struct.error:
|
||||
return {}
|
||||
```
|
||||
|
||||
## 3. Analysis Plugin Framework
|
||||
|
||||
```python
|
||||
class AnalysisPlugin(ABC):
|
||||
"""Base class for analysis plugins that process decoded frame data"""
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def plugin_name(self) -> str:
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def required_decoders(self) -> List[str]:
|
||||
"""List of decoder names this plugin requires"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def required_fields(self) -> Dict[str, List[str]]:
|
||||
"""Dict of decoder_name -> list of required fields"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def analyze_frame(self, frame_data: StructuredFrameData, flow_context: Dict) -> Dict[str, Any]:
|
||||
"""Analyze a single frame, return analysis results"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def analyze_flow(self, frame_data_list: List[StructuredFrameData]) -> Dict[str, Any]:
|
||||
"""Analyze a complete flow, return aggregated results"""
|
||||
pass
|
||||
|
||||
def can_analyze(self, frame_data: StructuredFrameData) -> bool:
|
||||
"""Check if this plugin can analyze the given frame data"""
|
||||
if frame_data.decoder_name not in self.required_decoders:
|
||||
return False
|
||||
|
||||
required_fields = self.required_fields.get(frame_data.decoder_name, [])
|
||||
return all(frame_data.has_field(field) for field in required_fields)
|
||||
|
||||
class Chapter10TimingAnalysisPlugin(AnalysisPlugin):
|
||||
"""Analysis plugin for Chapter 10 timing comparisons"""
|
||||
|
||||
@property
|
||||
def plugin_name(self) -> str:
|
||||
return "CH10_Timing_Analysis"
|
||||
|
||||
@property
|
||||
def required_decoders(self) -> List[str]:
|
||||
return ["Chapter10"]
|
||||
|
||||
@property
|
||||
def required_fields(self) -> Dict[str, List[str]]:
|
||||
return {
|
||||
"Chapter10": ["internal_timestamp", "internal_seconds", "internal_nanoseconds"]
|
||||
}
|
||||
|
||||
def analyze_frame(self, frame_data: StructuredFrameData, flow_context: Dict) -> Dict[str, Any]:
|
||||
"""Compare packet timestamp vs internal Chapter 10 timestamp"""
|
||||
packet_time = frame_data.packet_timestamp
|
||||
internal_time = frame_data.get_field('internal_timestamp')
|
||||
|
||||
if internal_time is None:
|
||||
return {'error': 'No internal timestamp available'}
|
||||
|
||||
time_delta = packet_time - internal_time
|
||||
|
||||
# Calculate clock drift metrics
|
||||
analysis = {
|
||||
'packet_timestamp': packet_time,
|
||||
'internal_timestamp': internal_time,
|
||||
'time_delta_seconds': time_delta,
|
||||
'time_delta_microseconds': time_delta * 1_000_000,
|
||||
'clock_drift_ppm': self._calculate_drift_ppm(time_delta, flow_context),
|
||||
'timestamp_source': 'CH10_internal'
|
||||
}
|
||||
|
||||
# Flag significant timing discrepancies
|
||||
if abs(time_delta) > 0.001: # 1ms threshold
|
||||
analysis['timing_anomaly'] = True
|
||||
analysis['anomaly_severity'] = 'high' if abs(time_delta) > 0.1 else 'medium'
|
||||
|
||||
return analysis
|
||||
|
||||
def analyze_flow(self, frame_data_list: List[StructuredFrameData]) -> Dict[str, Any]:
|
||||
"""Analyze timing patterns across entire flow"""
|
||||
if not frame_data_list:
|
||||
return {}
|
||||
|
||||
timing_deltas = []
|
||||
internal_intervals = []
|
||||
packet_intervals = []
|
||||
|
||||
prev_frame = None
|
||||
for frame_data in frame_data_list:
|
||||
if not self.can_analyze(frame_data):
|
||||
continue
|
||||
|
||||
# Calculate timing delta for this frame
|
||||
packet_time = frame_data.packet_timestamp
|
||||
internal_time = frame_data.get_field('internal_timestamp')
|
||||
if internal_time:
|
||||
timing_deltas.append(packet_time - internal_time)
|
||||
|
||||
# Calculate intervals if we have previous frame
|
||||
if prev_frame:
|
||||
packet_interval = packet_time - prev_frame.packet_timestamp
|
||||
internal_interval = internal_time - prev_frame.get_field('internal_timestamp', 0)
|
||||
|
||||
packet_intervals.append(packet_interval)
|
||||
internal_intervals.append(internal_interval)
|
||||
|
||||
prev_frame = frame_data
|
||||
|
||||
if not timing_deltas:
|
||||
return {'error': 'No valid timing data found'}
|
||||
|
||||
# Statistical analysis
|
||||
import statistics
|
||||
|
||||
flow_analysis = {
|
||||
'frame_count': len(timing_deltas),
|
||||
'avg_timing_delta': statistics.mean(timing_deltas),
|
||||
'std_timing_delta': statistics.stdev(timing_deltas) if len(timing_deltas) > 1 else 0,
|
||||
'min_timing_delta': min(timing_deltas),
|
||||
'max_timing_delta': max(timing_deltas),
|
||||
'timing_drift_trend': self._calculate_drift_trend(timing_deltas),
|
||||
}
|
||||
|
||||
if packet_intervals and internal_intervals:
|
||||
flow_analysis.update({
|
||||
'avg_packet_interval': statistics.mean(packet_intervals),
|
||||
'avg_internal_interval': statistics.mean(internal_intervals),
|
||||
'interval_correlation': self._calculate_correlation(packet_intervals, internal_intervals)
|
||||
})
|
||||
|
||||
return flow_analysis
|
||||
|
||||
def _calculate_drift_ppm(self, time_delta: float, flow_context: Dict) -> float:
|
||||
"""Calculate clock drift in parts per million"""
|
||||
# Simplified drift calculation - would be more sophisticated in practice
|
||||
return (time_delta * 1_000_000) / flow_context.get('flow_duration', 1.0)
|
||||
|
||||
def _calculate_drift_trend(self, timing_deltas: List[float]) -> str:
|
||||
"""Determine if timing is drifting in a particular direction"""
|
||||
if len(timing_deltas) < 3:
|
||||
return 'insufficient_data'
|
||||
|
||||
# Simple trend analysis
|
||||
first_half = timing_deltas[:len(timing_deltas)//2]
|
||||
second_half = timing_deltas[len(timing_deltas)//2:]
|
||||
|
||||
first_avg = sum(first_half) / len(first_half)
|
||||
second_avg = sum(second_half) / len(second_half)
|
||||
|
||||
if second_avg > first_avg + 0.0001:
|
||||
return 'increasing_drift'
|
||||
elif second_avg < first_avg - 0.0001:
|
||||
return 'decreasing_drift'
|
||||
else:
|
||||
return 'stable'
|
||||
|
||||
def _calculate_correlation(self, list1: List[float], list2: List[float]) -> float:
|
||||
"""Calculate correlation coefficient between two timing sequences"""
|
||||
# Simplified correlation - would use proper statistical methods
|
||||
if len(list1) != len(list2) or len(list1) < 2:
|
||||
return 0.0
|
||||
|
||||
# Basic correlation calculation
|
||||
import statistics
|
||||
mean1 = statistics.mean(list1)
|
||||
mean2 = statistics.mean(list2)
|
||||
|
||||
numerator = sum((x - mean1) * (y - mean2) for x, y in zip(list1, list2))
|
||||
denominator = (sum((x - mean1)**2 for x in list1) * sum((y - mean2)**2 for y in list2))**0.5
|
||||
|
||||
return numerator / denominator if denominator != 0 else 0.0
|
||||
```
|
||||
|
||||
## 4. Plugin Registry and Integration
|
||||
|
||||
```python
|
||||
class AnalysisPluginRegistry:
|
||||
"""Registry for managing analysis plugins"""
|
||||
|
||||
def __init__(self):
|
||||
self.plugins: Dict[str, AnalysisPlugin] = {}
|
||||
self.decoder_plugin_map: Dict[str, List[AnalysisPlugin]] = {}
|
||||
|
||||
def register_plugin(self, plugin: AnalysisPlugin):
|
||||
"""Register an analysis plugin"""
|
||||
self.plugins[plugin.plugin_name] = plugin
|
||||
|
||||
# Map decoders to plugins
|
||||
for decoder_name in plugin.required_decoders:
|
||||
if decoder_name not in self.decoder_plugin_map:
|
||||
self.decoder_plugin_map[decoder_name] = []
|
||||
self.decoder_plugin_map[decoder_name].append(plugin)
|
||||
|
||||
def get_plugins_for_decoder(self, decoder_name: str) -> List[AnalysisPlugin]:
|
||||
"""Get all plugins that can analyze data from a specific decoder"""
|
||||
return self.decoder_plugin_map.get(decoder_name, [])
|
||||
|
||||
def get_plugin(self, plugin_name: str) -> Optional[AnalysisPlugin]:
|
||||
"""Get a specific plugin by name"""
|
||||
return self.plugins.get(plugin_name)
|
||||
|
||||
class EnhancedFlowManager:
|
||||
"""Enhanced flow manager with plugin support"""
|
||||
|
||||
def __init__(self):
|
||||
self.decoders: List[EnhancedDecoder] = []
|
||||
self.plugin_registry = AnalysisPluginRegistry()
|
||||
self.flows: Dict[Tuple[str, str], EnhancedFlowStats] = {}
|
||||
|
||||
def register_decoder(self, decoder: EnhancedDecoder):
|
||||
"""Register a decoder"""
|
||||
self.decoders.append(decoder)
|
||||
|
||||
def register_plugin(self, plugin: AnalysisPlugin):
|
||||
"""Register an analysis plugin"""
|
||||
self.plugin_registry.register_plugin(plugin)
|
||||
|
||||
def process_packet(self, packet, frame_num: int):
|
||||
"""Process packet with enhanced decoder and plugin analysis"""
|
||||
# Extract transport info
|
||||
transport_info = self._extract_transport_info(packet)
|
||||
|
||||
# Try decoders
|
||||
for decoder in self.decoders:
|
||||
confidence = decoder.can_decode(packet, transport_info)
|
||||
if confidence > 0.5: # Threshold for acceptance
|
||||
frame_data = decoder.decode_frame(packet, transport_info)
|
||||
if frame_data:
|
||||
# Apply analysis plugins
|
||||
self._apply_plugins(frame_data, transport_info)
|
||||
|
||||
# Update flow with enhanced data
|
||||
self._update_enhanced_flow(frame_data, transport_info)
|
||||
break
|
||||
|
||||
def _apply_plugins(self, frame_data: StructuredFrameData, transport_info: Dict):
|
||||
"""Apply relevant analysis plugins to frame data"""
|
||||
plugins = self.plugin_registry.get_plugins_for_decoder(frame_data.decoder_name)
|
||||
|
||||
for plugin in plugins:
|
||||
if plugin.can_analyze(frame_data):
|
||||
analysis_result = plugin.analyze_frame(frame_data, {'transport_info': transport_info})
|
||||
|
||||
# Store plugin results in frame metadata
|
||||
frame_data.metadata[f'plugin_{plugin.plugin_name}'] = analysis_result
|
||||
```
|
||||
|
||||
## 5. Usage Example
|
||||
|
||||
```python
|
||||
# Setup enhanced system
|
||||
flow_manager = EnhancedFlowManager()
|
||||
|
||||
# Register enhanced decoders
|
||||
ch10_decoder = Chapter10EnhancedDecoder()
|
||||
flow_manager.register_decoder(ch10_decoder)
|
||||
|
||||
# Register analysis plugins
|
||||
timing_plugin = Chapter10TimingAnalysisPlugin()
|
||||
flow_manager.register_plugin(timing_plugin)
|
||||
|
||||
# Process packets
|
||||
for packet in packets:
|
||||
flow_manager.process_packet(packet, frame_num)
|
||||
|
||||
# Access enhanced analysis results
|
||||
for flow_key, flow_stats in flow_manager.flows.items():
|
||||
timing_analysis = flow_stats.get_plugin_results('CH10_Timing_Analysis')
|
||||
if timing_analysis:
|
||||
print(f"Flow {flow_key}: Clock drift = {timing_analysis['avg_timing_delta']:.6f}s")
|
||||
```
|
||||
|
||||
This modular framework allows you to:
|
||||
|
||||
1. **Extract any field** from decoded frames without modifying core code
|
||||
2. **Create custom analysis plugins** for domain-specific insights
|
||||
3. **Compare internal vs external timestamps** for clock drift analysis
|
||||
4. **Extend analysis** without touching the main analyzer code
|
||||
5. **Chain multiple plugins** for complex analysis workflows
|
||||
|
||||
**Questions for implementation:**
|
||||
- Which specific Chapter 10 timing analysis would be most valuable first?
|
||||
- Should we create plugins for PTP timestamp analysis as well?
|
||||
- What other modular analysis capabilities would be useful?
|
||||
73
test_decode_data.py
Normal file
73
test_decode_data.py
Normal file
@@ -0,0 +1,73 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to verify enhanced CH10 decode data is being captured
|
||||
"""
|
||||
|
||||
import sys
|
||||
sys.path.append('.')
|
||||
|
||||
from analyzer.analysis import EthernetAnalyzer
|
||||
from analyzer.utils import PCAPLoader
|
||||
|
||||
def test_decode_data():
|
||||
"""Test that decoded frame data is being captured"""
|
||||
|
||||
# Create analyzer
|
||||
analyzer = EthernetAnalyzer(enable_realtime=False, outlier_threshold_sigma=3.0)
|
||||
|
||||
# Load and process PCAP
|
||||
print("Loading and processing PCAP...")
|
||||
pcap_loader = PCAPLoader("1 PTPGM.pcapng")
|
||||
packets = pcap_loader.load_all()
|
||||
|
||||
# Process packets through flow manager
|
||||
for i, packet in enumerate(packets):
|
||||
analyzer.flow_manager.process_packet(packet, i + 1)
|
||||
|
||||
# Calculate statistics
|
||||
analyzer.statistics_engine.calculate_flow_statistics(analyzer.flow_manager.flows)
|
||||
|
||||
print("\n=== ENHANCED DECODE DATA TEST ===")
|
||||
|
||||
# Debug: Show all flows and their decoder types
|
||||
print(f"Total flows found: {len(analyzer.flow_manager.flows)}")
|
||||
|
||||
for flow_key, flow in analyzer.flow_manager.flows.items():
|
||||
print(f"Flow {flow_key}: {flow.enhanced_analysis.decoder_type}, protocols: {flow.detected_protocol_types}")
|
||||
|
||||
# Find CH10 flows
|
||||
ch10_flows = []
|
||||
for flow in analyzer.flow_manager.flows.values():
|
||||
if flow.enhanced_analysis.decoder_type == "Chapter10_Enhanced":
|
||||
ch10_flows.append(flow)
|
||||
|
||||
print(f"Found {len(ch10_flows)} flows with enhanced CH10 decoding")
|
||||
|
||||
for i, flow in enumerate(ch10_flows):
|
||||
print(f"\nFlow {i+1}: {flow.src_ip} -> {flow.dst_ip}")
|
||||
enhanced = flow.enhanced_analysis
|
||||
|
||||
print(f" Decoder Type: {enhanced.decoder_type}")
|
||||
print(f" Primary Data Type: {enhanced.primary_data_type}")
|
||||
print(f" Available Fields: {len(enhanced.available_field_names)}")
|
||||
print(f" Sample Frames Stored: {len(enhanced.sample_decoded_fields)}")
|
||||
|
||||
if enhanced.sample_decoded_fields:
|
||||
print(" Sample Decoded Data:")
|
||||
for frame_key, frame_data in enhanced.sample_decoded_fields.items():
|
||||
print(f" {frame_key}: {len(frame_data)} fields")
|
||||
# Show first few fields
|
||||
for j, (field_name, value) in enumerate(list(frame_data.items())[:5]):
|
||||
print(f" {field_name}: {value}")
|
||||
if len(frame_data) > 5:
|
||||
print(f" ... and {len(frame_data) - 5} more fields")
|
||||
else:
|
||||
print(" No decoded frame data stored!")
|
||||
|
||||
if enhanced.available_field_names:
|
||||
print(f" Field Categories: Timing: {sum(1 for f in enhanced.available_field_names if 'time' in f.lower())}, "
|
||||
f"Quality: {sum(1 for f in enhanced.available_field_names if any(k in f.lower() for k in ['quality', 'error']))}, "
|
||||
f"Data: {sum(1 for f in enhanced.available_field_names if any(k in f.lower() for k in ['data', 'analog', 'channel']))}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_decode_data()
|
||||
Reference in New Issue
Block a user