(FEAT/DATASETS)[Demo Dataset Generation Script for ML Training]: Automate creation of synthetic polymer spectra for stable and weathered classes

- Script generates realistic synthetic spectra for both stable and weathered polymers, simulating noise, peak broadening, and chemical changes.
- Saves output in organized directory structure compatible with training pipeline, enabling quick prototyping and testing.
- Provides clear progress feedback and summary of dataset location and sample counts for user convenience.

scripts/create_demo_dataset.py

@@ -0,0 +1,141 @@
+"""
+Generate demo datasets for testing the training functionality.
+"""
+
+import numpy as np
+from pathlib import Path
+import sys
+import os
+
+# Add project root to path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+
+def generate_synthetic_spectrum(
+    wavenumbers, base_intensity=0.5, noise_level=0.05, peaks=None
+):
+    """Generate a synthetic spectrum with specified characteristics"""
+    spectrum = np.full_like(wavenumbers, base_intensity)
+
+    # Add some peaks
+    if peaks is None:
+        peaks = [
+            (1000, 0.3, 50),
+            (1500, 0.5, 80),
+            (2000, 0.2, 40),
+        ]  # (center, height, width)
+
+    for center, height, width in peaks:
+        peak = height * np.exp(-(((wavenumbers - center) / width) ** 2))
+        spectrum += peak
+
+    # Add noise
+    spectrum += np.random.normal(0, noise_level, len(wavenumbers))
+
+    # Ensure positive values
+    spectrum = np.maximum(spectrum, 0.01)
+
+    return spectrum
+
+
+def create_demo_datasets():
+    """Create demo datasets for training"""
+
+    # Define wavenumber range (typical for Raman)
+    wavenumbers = np.linspace(400, 3500, 200)
+
+    # Create stable polymer samples
+    stable_dir = Path("datasets/demo_dataset/stable")
+    stable_dir.mkdir(parents=True, exist_ok=True)
+
+    print("Generating stable polymer samples...")
+    for i in range(20):
+        # Stable polymers - higher intensity, sharper peaks
+        stable_peaks = [
+            (
+                800 + np.random.normal(0, 20),
+                0.4 + np.random.normal(0, 0.05),
+                30 + np.random.normal(0, 5),
+            ),
+            (
+                1200 + np.random.normal(0, 30),
+                0.6 + np.random.normal(0, 0.08),
+                40 + np.random.normal(0, 8),
+            ),
+            (
+                1600 + np.random.normal(0, 25),
+                0.3 + np.random.normal(0, 0.04),
+                35 + np.random.normal(0, 6),
+            ),
+            (
+                2900 + np.random.normal(0, 40),
+                0.8 + np.random.normal(0, 0.1),
+                60 + np.random.normal(0, 10),
+            ),
+        ]
+
+        spectrum = generate_synthetic_spectrum(
+            wavenumbers,
+            base_intensity=0.4 + np.random.normal(0, 0.05),
+            noise_level=0.02,
+            peaks=stable_peaks,
+        )
+
+        # Save as two-column format
+        data = np.column_stack([wavenumbers, spectrum])
+        np.savetxt(stable_dir / f"stable_sample_{i:02d}.txt", data, fmt="%.6f")
+
+    # Create weathered polymer samples
+    weathered_dir = Path("datasets/demo_dataset/weathered")
+    weathered_dir.mkdir(parents=True, exist_ok=True)
+
+    print("Generating weathered polymer samples...")
+    for i in range(20):
+        # Weathered polymers - lower intensity, broader peaks, additional oxidation peaks
+        weathered_peaks = [
+            (
+                800 + np.random.normal(0, 30),
+                0.2 + np.random.normal(0, 0.04),
+                45 + np.random.normal(0, 10),
+            ),
+            (
+                1200 + np.random.normal(0, 40),
+                0.3 + np.random.normal(0, 0.06),
+                55 + np.random.normal(0, 12),
+            ),
+            (
+                1600 + np.random.normal(0, 35),
+                0.15 + np.random.normal(0, 0.03),
+                50 + np.random.normal(0, 8),
+            ),
+            (
+                1720 + np.random.normal(0, 20),
+                0.25 + np.random.normal(0, 0.04),
+                40 + np.random.normal(0, 7),
+            ),  # Oxidation peak
+            (
+                2900 + np.random.normal(0, 50),
+                0.4 + np.random.normal(0, 0.08),
+                80 + np.random.normal(0, 15),
+            ),
+        ]
+
+        spectrum = generate_synthetic_spectrum(
+            wavenumbers,
+            base_intensity=0.25 + np.random.normal(0, 0.04),
+            noise_level=0.03,
+            peaks=weathered_peaks,
+        )
+
+        # Save as two-column format
+        data = np.column_stack([wavenumbers, spectrum])
+        np.savetxt(weathered_dir / f"weathered_sample_{i:02d}.txt", data, fmt="%.6f")
+
+    print(f"✅ Demo dataset created:")
+    print(f"   Stable samples: {len(list(stable_dir.glob('*.txt')))}")
+    print(f"   Weathered samples: {len(list(weathered_dir.glob('*.txt')))}")
+    print(f"   Location: datasets/demo_dataset/")
+
+
+if __name__ == "__main__":
+    create_demo_datasets()