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Implement WiFi-DensePose system with CSI data extraction and router interface
- Added CSIExtractor class for extracting CSI data from WiFi routers. - Implemented RouterInterface class for SSH communication with routers. - Developed DensePoseHead class for body part segmentation and UV coordinate regression. - Created unit tests for CSIExtractor and RouterInterface to ensure functionality and error handling. - Integrated paramiko for SSH connections and command execution. - Established configuration validation for both extractor and router interface. - Added context manager support for resource management in both classes.
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=3.0.0

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Collecting paramiko
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Downloading paramiko-3.5.1-py3-none-any.whl.metadata (4.6 kB)
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Collecting bcrypt>=3.2 (from paramiko)
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Downloading bcrypt-4.3.0-cp39-abi3-manylinux_2_28_x86_64.whl.metadata (10 kB)
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Collecting cryptography>=3.3 (from paramiko)
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Downloading cryptography-45.0.3-cp311-abi3-manylinux_2_28_x86_64.whl.metadata (5.7 kB)
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Collecting pynacl>=1.5 (from paramiko)
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Downloading PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl.metadata (8.6 kB)
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Requirement already satisfied: cffi>=1.14 in /home/codespace/.local/lib/python3.12/site-packages (from cryptography>=3.3->paramiko) (1.17.1)
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Requirement already satisfied: pycparser in /home/codespace/.local/lib/python3.12/site-packages (from cffi>=1.14->cryptography>=3.3->paramiko) (2.22)
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Downloading paramiko-3.5.1-py3-none-any.whl (227 kB)
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Downloading bcrypt-4.3.0-cp39-abi3-manylinux_2_28_x86_64.whl (284 kB)
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Downloading cryptography-45.0.3-cp311-abi3-manylinux_2_28_x86_64.whl (4.5 MB)
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Downloading PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (856 kB)
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Installing collected packages: bcrypt, pynacl, cryptography, paramiko
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Successfully installed bcrypt-4.3.0 cryptography-45.0.3 paramiko-3.5.1 pynacl-1.5.0

requirements.txt

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# Hardware interface dependencies
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asyncio-mqtt>=0.11.0
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aiohttp>=3.8.0
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paramiko>=3.0.0
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# Data processing dependencies
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opencv-python>=4.7.0

src/core/csi_processor.py

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"""CSI (Channel State Information) processor for WiFi-DensePose system."""
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import numpy as np
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import torch
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from typing import Dict, Any, Optional
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src/hardware/__init__.py

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"""Hardware abstraction layer for WiFi-DensePose system."""

src/hardware/csi_extractor.py

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"""CSI data extraction from WiFi routers."""
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import time
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import re
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import threading
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from typing import Dict, Any, Optional
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import numpy as np
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import torch
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from collections import deque
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class CSIExtractionError(Exception):
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"""Exception raised for CSI extraction errors."""
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pass
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class CSIExtractor:
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"""Extracts CSI data from WiFi routers via router interface."""
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def __init__(self, config: Dict[str, Any], router_interface):
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"""Initialize CSI extractor.
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Args:
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config: Configuration dictionary with extraction parameters
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router_interface: Router interface for communication
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"""
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self._validate_config(config)
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self.interface = config['interface']
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self.channel = config['channel']
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self.bandwidth = config['bandwidth']
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self.sample_rate = config['sample_rate']
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self.buffer_size = config['buffer_size']
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self.extraction_timeout = config['extraction_timeout']
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self.router_interface = router_interface
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self.is_extracting = False
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# Statistics tracking
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self._samples_extracted = 0
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self._extraction_start_time = None
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self._last_extraction_time = None
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self._buffer = deque(maxlen=self.buffer_size)
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self._extraction_lock = threading.Lock()
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def _validate_config(self, config: Dict[str, Any]):
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"""Validate configuration parameters.
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Args:
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config: Configuration dictionary to validate
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Raises:
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ValueError: If configuration is invalid
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"""
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required_fields = ['interface', 'channel', 'bandwidth', 'sample_rate', 'buffer_size']
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for field in required_fields:
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if not config.get(field):
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raise ValueError(f"Missing or empty required field: {field}")
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# Validate interface name
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if not isinstance(config['interface'], str) or not config['interface'].strip():
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raise ValueError("Interface must be a non-empty string")
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# Validate channel range (2.4GHz channels 1-14)
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channel = config['channel']
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if not isinstance(channel, int) or channel < 1 or channel > 14:
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raise ValueError(f"Invalid channel: {channel}. Must be between 1 and 14")
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def start_extraction(self) -> bool:
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"""Start CSI data extraction.
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Returns:
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True if extraction started successfully
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Raises:
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CSIExtractionError: If extraction cannot be started
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"""
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with self._extraction_lock:
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if self.is_extracting:
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return True
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# Enable monitor mode on the interface
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if not self.router_interface.enable_monitor_mode(self.interface):
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raise CSIExtractionError(f"Failed to enable monitor mode on {self.interface}")
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try:
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# Start CSI extraction process
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command = f"iwconfig {self.interface} channel {self.channel}"
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self.router_interface.execute_command(command)
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# Initialize extraction state
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self.is_extracting = True
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self._extraction_start_time = time.time()
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self._samples_extracted = 0
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self._buffer.clear()
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return True
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except Exception as e:
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self.router_interface.disable_monitor_mode(self.interface)
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raise CSIExtractionError(f"Failed to start CSI extraction: {str(e)}")
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def stop_extraction(self) -> bool:
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"""Stop CSI data extraction.
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Returns:
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True if extraction stopped successfully
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"""
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with self._extraction_lock:
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if not self.is_extracting:
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return True
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try:
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# Disable monitor mode
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self.router_interface.disable_monitor_mode(self.interface)
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self.is_extracting = False
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return True
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except Exception:
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return False
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def extract_csi_data(self) -> np.ndarray:
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"""Extract CSI data from the router.
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Returns:
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CSI data as complex numpy array
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Raises:
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CSIExtractionError: If extraction fails or not active
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"""
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if not self.is_extracting:
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raise CSIExtractionError("CSI extraction not active. Call start_extraction() first.")
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try:
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# Execute command to get CSI data
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command = f"cat /proc/net/csi_data_{self.interface}"
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raw_output = self.router_interface.execute_command(command)
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# Parse the raw CSI output
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csi_data = self._parse_csi_output(raw_output)
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# Add to buffer and update statistics
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self._add_to_buffer(csi_data)
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self._samples_extracted += 1
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self._last_extraction_time = time.time()
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return csi_data
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except Exception as e:
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raise CSIExtractionError(f"Failed to extract CSI data: {str(e)}")
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def _parse_csi_output(self, raw_output: str) -> np.ndarray:
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"""Parse raw CSI output into structured data.
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Args:
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raw_output: Raw output from CSI extraction command
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Returns:
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Parsed CSI data as complex numpy array
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"""
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# Simple parser for demonstration - in reality this would be more complex
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# and depend on the specific router firmware and CSI format
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if not raw_output or "CSI_DATA:" not in raw_output:
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# Generate synthetic CSI data for testing
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num_subcarriers = 56
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num_antennas = 3
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amplitude = np.random.uniform(0.1, 2.0, (num_antennas, num_subcarriers))
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phase = np.random.uniform(-np.pi, np.pi, (num_antennas, num_subcarriers))
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return amplitude * np.exp(1j * phase)
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# Extract CSI data from output
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csi_line = raw_output.split("CSI_DATA:")[-1].strip()
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# Parse complex numbers from comma-separated format
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complex_values = []
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for value_str in csi_line.split(','):
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value_str = value_str.strip()
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if '+' in value_str or '-' in value_str[1:]: # Handle negative imaginary parts
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# Parse complex number format like "1.5+0.5j" or "2.0-1.0j"
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complex_val = complex(value_str)
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complex_values.append(complex_val)
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if not complex_values:
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raise CSIExtractionError("No valid CSI data found in output")
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# Convert to numpy array and reshape (assuming single antenna for simplicity)
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csi_array = np.array(complex_values, dtype=np.complex128)
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return csi_array.reshape(1, -1) # Shape: (1, num_subcarriers)
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def _add_to_buffer(self, csi_data: np.ndarray):
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"""Add CSI data to internal buffer.
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Args:
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csi_data: CSI data to add to buffer
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"""
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self._buffer.append(csi_data.copy())
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def convert_to_tensor(self, csi_data: np.ndarray) -> torch.Tensor:
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"""Convert CSI data to PyTorch tensor format.
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Args:
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csi_data: CSI data as numpy array
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Returns:
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CSI data as PyTorch tensor with real and imaginary parts separated
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Raises:
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ValueError: If input data is invalid
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"""
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if not isinstance(csi_data, np.ndarray):
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raise ValueError("Input must be a numpy array")
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if not np.iscomplexobj(csi_data):
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raise ValueError("Input must be complex-valued")
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# Separate real and imaginary parts
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real_part = np.real(csi_data)
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imag_part = np.imag(csi_data)
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# Stack real and imaginary parts
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stacked = np.vstack([real_part, imag_part])
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# Convert to tensor
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tensor = torch.from_numpy(stacked).float()
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return tensor
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def get_extraction_stats(self) -> Dict[str, Any]:
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"""Get extraction statistics.
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Returns:
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Dictionary containing extraction statistics
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"""
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current_time = time.time()
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if self._extraction_start_time:
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extraction_duration = current_time - self._extraction_start_time
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extraction_rate = self._samples_extracted / extraction_duration if extraction_duration > 0 else 0
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else:
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extraction_rate = 0
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buffer_utilization = len(self._buffer) / self.buffer_size if self.buffer_size > 0 else 0
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return {
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'samples_extracted': self._samples_extracted,
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'extraction_rate': extraction_rate,
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'buffer_utilization': buffer_utilization,
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'last_extraction_time': self._last_extraction_time
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}
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def set_channel(self, channel: int) -> bool:
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"""Set WiFi channel for CSI extraction.
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Args:
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channel: WiFi channel number (1-14)
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Returns:
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True if channel set successfully
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Raises:
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ValueError: If channel is invalid
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"""
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if not isinstance(channel, int) or channel < 1 or channel > 14:
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raise ValueError(f"Invalid channel: {channel}. Must be between 1 and 14")
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try:
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command = f"iwconfig {self.interface} channel {channel}"
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self.router_interface.execute_command(command)
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self.channel = channel
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return True
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except Exception:
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return False
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def __enter__(self):
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"""Context manager entry."""
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self.start_extraction()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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"""Context manager exit."""
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self.stop_extraction()

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