cbebdd648f
- 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.
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
"""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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class CSIProcessor:
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"""Processes raw CSI data for neural network input."""
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def __init__(self, config: Optional[Dict[str, Any]] = None):
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"""Initialize CSI processor with configuration.
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Args:
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config: Configuration dictionary with processing parameters
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"""
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self.config = config or {}
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self.sample_rate = self.config.get('sample_rate', 1000)
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self.num_subcarriers = self.config.get('num_subcarriers', 56)
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self.num_antennas = self.config.get('num_antennas', 3)
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def process_raw_csi(self, raw_data: np.ndarray) -> np.ndarray:
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"""Process raw CSI data into normalized format.
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Args:
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raw_data: Raw CSI data array
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Returns:
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Processed CSI data ready for neural network input
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"""
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if raw_data.size == 0:
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raise ValueError("Raw CSI data cannot be empty")
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# Basic processing: normalize and reshape
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processed = raw_data.astype(np.float32)
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# Handle NaN values by replacing with mean of non-NaN values
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if np.isnan(processed).any():
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nan_mask = np.isnan(processed)
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non_nan_mean = np.nanmean(processed)
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processed[nan_mask] = non_nan_mean
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# Simple normalization
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if processed.std() > 0:
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processed = (processed - processed.mean()) / processed.std()
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return processed |