Raman Fitting

Common Parsing Pitfalls

Physical Constraints for Raman Spectroscopy

Graphene/Carbon Materials

Apply these domain-specific constraints when fitting:

Fitting Parameter Bounds

Set physically reasonable bounds to avoid nonsensical fits:

# Example bounds for graphene G peak
bounds_lower = [1560, 5, 0]      # [x0, gamma, amplitude]
bounds_upper = [1600, 50, 1e6]

# Example bounds for graphene 2D peak
bounds_lower = [2650, 10, 0]
bounds_upper = [2750, 100, 1e6]

Fitting Approach

Step 1: Visual Identification of Peaks

Before fitting, identify peaks visually:

• Plot the full spectrum to see all features
• Note approximate peak positions and relative intensities
• Identify baseline behavior (flat, sloped, curved)

Step 2: Baseline Correction

Consider baseline subtraction before peak fitting:

• Linear baseline for simple cases
• Polynomial baseline for curved backgrounds
• Iterative methods for complex baselines

Step 3: Peak Function Selection

Common peak shapes for Raman:

• Lorentzian: Natural line shape, use when peaks are sharp
• Gaussian: Use when instrumental broadening dominates
• Voigt: Convolution of both, most physically accurate
• Pseudo-Voigt: Computationally efficient approximation

Lorentzian function:

I(x) = amplitude * (gamma²) / ((x - x0)² + gamma²)

Step 4: Initial Parameter Estimation

Use visual inspection or automatic peak finding to set initial guesses:

from scipy.signal import find_peaks

# Find peaks in the data
peaks, properties = find_peaks(intensity, height=threshold, distance=min_distance)

# Use found peak positions as initial guesses
for peak_idx in peaks:
    x0_guess = wavenumber[peak_idx]
    amplitude_guess = intensity[peak_idx]

Step 5: Fitting and Validation

After fitting, validate results:

1. Check R² value - Should be > 0.95 for good fits
2. Check parameter boundaries - Parameters hitting bounds indicate problems
3. Visual inspection - Plot data with fitted curve overlay
4. Physical plausibility - Do fitted parameters make physical sense?

Verification Strategies

Red Flags That Indicate Problems

• R² < 0.9: Poor fit quality, investigate cause
• Parameters at bounds: Fit is constrained, not converged
• Peak positions far from expected: Data parsing or assignment error
• Negative amplitudes: Physically impossible, check data/model
• Very large/small widths: Outside physical expectations

Systematic Debugging Checklist

1. Raw data file examined line by line
2. Parsing verified by printing first few values
3. Wavenumber range is physically reasonable (100-4000 cm⁻¹)
4. Peaks visible in plotted data at expected positions
5. Initial guesses based on actual data, not assumptions
6. Fit quality metrics (R², residuals) calculated and checked
7. Fitted parameters compared to literature values

When Fits Fail

If fitting produces poor results:

1. Go back to data parsing - Most common source of error
2. Narrow the fitting window - Fit one peak at a time
3. Improve initial guesses - Use values closer to peak maximum
4. Check for overlapping peaks - May need multi-peak model
5. Examine residuals - Systematic patterns indicate model issues

Output Format

When reporting fitted parameters, include:

• Peak position (x0) with uncertainty
• Width parameter (gamma or FWHM)
• Amplitude or integrated intensity
• Goodness of fit metric (R², chi-squared)
• Comparison to expected/literature values if available