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Centralized Spectrum Preprocessing Module
Single source of truth for all preprocessing operations across training, validation, testing, and live inference.
Ensures no drift between different processing stages.
"""
import hashlib
import json
import numpy as np
from pathlib import Path
from typing import Dict, Any, Tuple, Optional, List
from dataclasses import dataclass, asdict
from datetime import datetime
from backend.utils.preprocessing import (
preprocess_spectrum,
validate_spectrum_modality,
MODALITY_PARAMS,
MODALITY_RANGES,
TARGET_LENGTH
)
@dataclass
class PreprocessingConfig:
"""Immutable preprocessing configuration."""
target_length: int = TARGET_LENGTH
modality: str = "raman"
do_baseline: bool = True
baseline_degree: Optional[int] = None # Uses modality default if None
do_smooth: bool = True
smooth_window: Optional[int] = None # Uses modality default if None
smooth_polyorder: Optional[int] = None # Uses modality default if None
do_normalize: bool = True
validate_range: bool = True
version: str = "1.0.0" # Version for config compatibility
def __post_init__(self):
"""Validate configuration and set modality defaults."""
if self.modality not in MODALITY_PARAMS:
raise ValueError(f"Invalid modality: {self.modality}")
# Set modality defaults if None
modality_config = MODALITY_PARAMS[self.modality]
if self.baseline_degree is None:
object.__setattr__(self, 'baseline_degree', modality_config['baseline_degree'])
if self.smooth_window is None:
object.__setattr__(self, 'smooth_window', modality_config['smooth_window'])
if self.smooth_polyorder is None:
object.__setattr__(self, 'smooth_polyorder', modality_config['smooth_polyorder'])
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for serialization."""
return asdict(self)
def get_hash(self) -> str:
"""Get deterministic hash of configuration."""
config_str = json.dumps(self.to_dict(), sort_keys=True)
return hashlib.sha256(config_str.encode()).hexdigest()[:16]
@dataclass
class PreprocessingResult:
"""Result of preprocessing with full provenance."""
x_processed: np.ndarray
y_processed: np.ndarray
x_original: np.ndarray
y_original: np.ndarray
config: PreprocessingConfig
metadata: Dict[str, Any]
processing_time: float
timestamp: str
def get_content_hash(self) -> str:
"""Get hash of processed content for drift detection."""
# Combine config hash with data hash
config_hash = self.config.get_hash()
data_hash = hashlib.sha256(
np.concatenate([self.x_processed, self.y_processed]).tobytes()
).hexdigest()[:16]
return f"{config_hash}_{data_hash}"
class SpectrumPreprocessor:
"""
Centralized spectrum preprocessor ensuring consistent processing across all stages.
Single source of truth for preprocessing logic.
"""
def __init__(self, config: Optional[PreprocessingConfig] = None):
"""Initialize with preprocessing configuration."""
self.config = config or PreprocessingConfig()
self._processing_history: List[Dict[str, Any]] = []
def process(
self,
x: np.ndarray,
y: np.ndarray,
metadata: Optional[Dict[str, Any]] = None
) -> PreprocessingResult:
"""
Process spectrum with full provenance tracking.
Args:
x: Input wavenumber array
y: Input intensity array
metadata: Optional metadata to include
Returns:
PreprocessingResult with full provenance
"""
import time
start_time = time.time()
# Store original data
x_original = np.array(x, copy=True)
y_original = np.array(y, copy=True)
# Process using centralized function
x_processed, y_processed = preprocess_spectrum(
x, y,
target_len=self.config.target_length,
modality=self.config.modality,
do_baseline=self.config.do_baseline,
degree=self.config.baseline_degree,
do_smooth=self.config.do_smooth,
window_length=self.config.smooth_window,
polyorder=self.config.smooth_polyorder,
do_normalize=self.config.do_normalize,
validate_range=self.config.validate_range
)
processing_time = time.time() - start_time
# Create metadata
result_metadata = {
"original_length": len(x_original),
"processed_length": len(x_processed),
"wavenumber_range": [float(x_processed.min()), float(x_processed.max())],
"intensity_range": [float(y_processed.min()), float(y_processed.max())],
"modality_validated": validate_spectrum_modality(x_original, y_original, self.config.modality)[0],
**(metadata or {})
}
# Create result
result = PreprocessingResult(
x_processed=x_processed,
y_processed=y_processed,
x_original=x_original,
y_original=y_original,
config=self.config,
metadata=result_metadata,
processing_time=processing_time,
timestamp=datetime.now().isoformat()
)
# Track processing
self._processing_history.append({
"timestamp": result.timestamp,
"config_hash": self.config.get_hash(),
"content_hash": result.get_content_hash(),
"processing_time": processing_time
})
return result
def process_batch(
self,
spectra: List[Tuple[np.ndarray, np.ndarray]],
metadata_list: Optional[List[Dict[str, Any]]] = None
) -> List[PreprocessingResult]:
"""Process multiple spectra with consistent configuration."""
if metadata_list is None:
metadata_list = [None] * len(spectra)
results = []
for i, (x, y) in enumerate(spectra):
metadata = metadata_list[i] if i < len(metadata_list) else None
result = self.process(x, y, metadata)
results.append(result)
return results
def get_processing_summary(self) -> Dict[str, Any]:
"""Get summary of all processing operations."""
if not self._processing_history:
return {"total_processed": 0}
return {
"total_processed": len(self._processing_history),
"config_hash": self.config.get_hash(),
"config": self.config.to_dict(),
"processing_times": {
"min": min(h["processing_time"] for h in self._processing_history),
"max": max(h["processing_time"] for h in self._processing_history),
"mean": np.mean([h["processing_time"] for h in self._processing_history])
},
"first_processed": self._processing_history[0]["timestamp"],
"last_processed": self._processing_history[-1]["timestamp"]
}
# Global preprocessor instances for different stages
TRAINING_PREPROCESSOR = SpectrumPreprocessor(PreprocessingConfig(modality="raman"))
VALIDATION_PREPROCESSOR = SpectrumPreprocessor(PreprocessingConfig(modality="raman"))
INFERENCE_PREPROCESSOR = SpectrumPreprocessor(PreprocessingConfig(modality="raman"))
# Factory function for creating stage-specific preprocessors
def create_preprocessor(stage: str, modality: str = "raman") -> SpectrumPreprocessor:
"""
Create preprocessor for specific stage with identical configuration.
Args:
stage: 'training', 'validation', 'testing', or 'inference'
modality: 'raman' or 'ftir'
Returns:
SpectrumPreprocessor configured for the stage
"""
config = PreprocessingConfig(modality=modality)
return SpectrumPreprocessor(config)
# Utility functions for external compatibility
def preprocess_for_inference(x: np.ndarray, y: np.ndarray, modality: str = "raman") -> Tuple[np.ndarray, np.ndarray]:
"""
Process spectrum for inference using centralized preprocessor.
Maintains compatibility with existing code.
"""
preprocessor = create_preprocessor("inference", modality)
result = preprocessor.process(x, y)
return result.x_processed, result.y_processed
def preprocess_for_training(x: np.ndarray, y: np.ndarray, modality: str = "raman") -> Tuple[np.ndarray, np.ndarray]:
"""
Process spectrum for training using centralized preprocessor.
Maintains compatibility with existing code.
"""
preprocessor = create_preprocessor("training", modality)
result = preprocessor.process(x, y)
return result.x_processed, result.y_processed
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