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OPTICS & SPECTROSCOPY

Photonics Visualizations

Laser spectra, Raman analysis, photonic bandgaps, and optical transmission - create publication-ready photonics figures with AI-generated Python code.

Essential Photonics Visualizations

Photonics research spans laser characterization, spectroscopic analysis, waveguide design, and thin-film optics. Effective visualization requires precise peak fitting, spectral decomposition, and band-structure rendering.

Laser Spectra

Emission lines with Lorentzian fits, gain envelopes, and mode spacing

Raman Spectroscopy

D, G, 2D band identification with I(D)/I(G) ratios

Photonic Bandgaps

Dispersion relations and band structure plots for photonic crystals

Transmission/Absorption

UV-Vis-NIR spectra with Beer-Lambert analysis

Waveguide Modes

TE/TM mode profiles and effective index calculations

Thin-Film Interference

Reflectance spectra with Fabry-Perot fringe analysis

Laser Multi-Mode Emission Spectrum

He-Ne laser output near 632.8 nm showing six longitudinal modes with Lorentzian profiles. Includes gain envelope, lasing threshold, and free spectral range (FSR) annotation.

Live Code Editor
Code EditorPython
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Learn by Experimenting

This is a safe playground for learning! Try changing:

  • • Colors: Modify color values to see different palettes
  • • Numbers: Adjust sizes, positions, or data ranges
  • • Labels: Update titles, axis names, or legends

Edit the code, run it, then open the full data visualization tool to continue with your own dataset.

Try with your data

Raman Spectroscopy: Carbon Allotropes

Comparative Raman spectra of graphene, multi-walled carbon nanotubes (MWCNT), and amorphous carbon. D, G, and 2D bands are labeled with I(D)/I(G) ratio analysis for each material.

Live Code Editor
Code EditorPython
Loading editor...
Live Preview

Preparing preview

Running once automatically on first load

Learn by Experimenting

This is a safe playground for learning! Try changing:

  • • Colors: Modify color values to see different palettes
  • • Numbers: Adjust sizes, positions, or data ranges
  • • Labels: Update titles, axis names, or legends

Edit the code, run it, then open the full data visualization tool to continue with your own dataset.

Spectral Analysis Key Concepts

Peak Fitting

  • Lorentzian - natural line shape for lifetime-broadened transitions
  • Gaussian - Doppler-broadened or instrument-limited peaks
  • Voigt - convolution of both, used for accurate deconvolution
  • Pseudo-Voigt - linear combination approximation, faster fitting

Raman Band Ratios

  • I(D)/I(G) - disorder/defect density in sp2 carbons
  • I(2D)/I(G) - layer count: >1 = monolayer, ~1 = bilayer
  • FWHM(G) - graphitization degree indicator
  • 2D shift - strain and doping detection in graphene

Why Photonics Researchers Use Plotivy

Spectral Decomposition

Automatic peak detection, multi-peak fitting, and residual analysis with Lorentzian/Gaussian/Voigt profiles.

Band Structure

Photonic crystal dispersion relations, bandgap visualization, and density-of-states calculations.

Laser Characterization

Mode analysis, FSR calculation, linewidth extraction, and power vs current (L-I) curves.

Multi-Panel Figures

Combine emission + decomposition, Raman comparison + ratios, or transmission + absorption in one figure.

Chart gallery

Explore Optical Chart Types

Interactive examples with ready-to-run code

Browse all chart types →
Correlation heatmap with diverging color scale and coefficient annotations
Statistical•seaborn, matplotlib
From the chart gallery•Correlation analysis between variables

Heatmap

Represents data values as colors in a two-dimensional matrix format.

Sample code / prompt

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# Create correlation matrix for financial metrics
metrics = ['Revenue', 'Profit', 'Expenses', 'ROI', 'Customers', 'AOV', 'Marketing', 'Employees']
correlation_data = np.array([
    [1.00, 0.85, -0.45, 0.72, 0.88, 0.65, 0.72, 0.55],
    [0.85, 1.00, -0.78, 0.92, 0.75, 0.58, 0.63, 0.48],

Ready to Visualize Your Optical Data?

Upload laser spectra, Raman data, photonic bandgap calculations, or UV-Vis measurements. Plotivy fits peaks, labels bands, and creates publication-ready figures automatically.

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