Lära Plotting IR and UV-Vis Spectra | Spectra and Reaction Analysis

Understanding how to plot and interpret infrared (IR) and ultraviolet-visible (UV-Vis) spectra is essential for chemists. These techniques allow you to analyze molecular vibrations and electronic transitions, providing insight into the structure and composition of chemical compounds. By visualizing spectral data, you can identify functional groups, monitor reaction progress, and communicate your results clearly.


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import matplotlib.pyplot as plt
import numpy as np

# Example IR spectrum data (wavenumber in cm^-1 vs. absorbance)
wavenumbers = np.linspace(4000, 400, 1600)
absorbance = (
    np.exp(-0.5 * ((wavenumbers - 1700) / 50) ** 2) * 0.8 +  # Carbonyl stretch
    np.exp(-0.5 * ((wavenumbers - 2900) / 60) ** 2) * 0.5 +  # C-H stretch
    np.random.normal(0, 0.02, len(wavenumbers))              # Noise
)

plt.figure(figsize=(8, 4))
plt.plot(wavenumbers, absorbance, color='black')
plt.gca().invert_xaxis()  # IR spectra are plotted high-to-low wavenumber
plt.xlabel("Wavenumber (cm$^{-1}$)")
plt.ylabel("Absorbance")
plt.title("Simulated IR Spectrum")
plt.tight_layout()
plt.show()

When preparing spectra for publication or reports, customizing your plots is crucial for clarity and professionalism. You can adjust axis labels to specify units, add informative titles, and choose colors that highlight important features. Using Matplotlib, you have control over all these elements, ensuring your spectra are easy to interpret and visually appealing.

The Seaborn library is a high-level interface for creating attractive and informative statistical graphics in Python. Built on top of Matplotlib, Seaborn streamlines the process of making complex plots, offering additional features and refined aesthetics that are especially valuable for chemists working with spectral or experimental data.

Seamless Integration with Matplotlib

Seaborn works directly with Matplotlib objects, so you can combine Seaborn's advanced features with Matplotlib's full customization;
All Seaborn plots are Matplotlib figures, allowing you to use plt.xlabel, plt.ylabel, and other Matplotlib functions alongside Seaborn methods;
You can start with Seaborn for quick, beautiful plots, then fine-tune details using Matplotlib commands.

Common Plot Types in Seaborn

Seaborn provides a wide range of plot types that are useful for visualizing chemical data:

Scatterplots (sns.scatterplot): visualize relationships between two variables, such as absorbance vs. concentration;
Line plots (sns.lineplot): ideal for spectral data, showing absorbance as a function of wavelength or wavenumber;
Barplots (sns.barplot): compare averages or other statistics across categories, like reaction yields for different catalysts;
Histograms (sns.histplot): display distributions of measurements, such as peak intensities or molecular weights;
Heatmaps (sns.heatmap): visualize matrices, such as correlation between spectra or reaction conditions.

Themes and Color Palettes

Seaborn includes built-in themes and color palettes to make your plots publication-ready:

Use sns.set_theme() or sns.set_style() to apply styles like "whitegrid" or "dark";
Choose color palettes with sns.color_palette() or select predefined options like "deep", "muted", or "bright";
Consistent, attractive color schemes help highlight important trends and features in your data.

Statistical Plotting Capabilities

Seaborn simplifies statistical visualization, which is essential for interpreting experimental results:

Add confidence intervals to plots automatically (e.g., shaded regions in sns.lineplot);
Visualize regression fits using sns.regplot or sns.lmplot to explore relationships between variables;
Create violin plots, boxplots, and swarmplots to compare distributions and identify outliers.

Simplifying Complex Visualizations for Chemists

For chemists analyzing spectral data, Seaborn offers:

Fast creation of clear, informative IR, UV-Vis, or NMR spectra plots with minimal code;
Easy overlay of multiple spectra for comparison using color and style options;
Straightforward highlighting of regions of interest (such as specific peaks or bands) using built-in annotation and color features;
Compatibility with pandas DataFrames, making it simple to visualize data directly from experimental tables or results.


              1234567891011121314151617181920
            
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Example UV-Vis spectrum data (wavelength in nm vs. absorbance)
wavelengths = np.linspace(200, 800, 1000)
uvvis_abs = (
    np.exp(-0.5 * ((wavelengths - 300) / 15) ** 2) * 1.2 +  # Strong UV peak
    np.exp(-0.5 * ((wavelengths - 520) / 30) ** 2) * 0.6 +  # Visible peak
    np.random.normal(0, 0.01, len(wavelengths))             # Noise
)

sns.set(style="whitegrid", context="notebook")
plt.figure(figsize=(8, 4))
sns.lineplot(x=wavelengths, y=uvvis_abs, color="royalblue")
plt.xlabel("Wavelength (nm)")
plt.ylabel("Absorbance")
plt.title("Simulated UV-Vis Spectrum")
plt.tight_layout()
plt.show()

Interpreting spectra involves analyzing the positions and intensities of peaks. In IR spectra, sharp peaks at specific wavenumbers correspond to characteristic bond vibrations, helping you identify functional groups such as carbonyls, alcohols, or amines. In UV-Vis spectra, absorbance peaks indicate electronic transitions, often revealing conjugation or the presence of certain chromophores. Careful analysis of these features allows you to deduce important structural information about your sample.

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Avsnitt 3. Kapitel 1

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              12345678910111213141516171819
            
import matplotlib.pyplot as plt
import numpy as np

# Example IR spectrum data (wavenumber in cm^-1 vs. absorbance)
wavenumbers = np.linspace(4000, 400, 1600)
absorbance = (
    np.exp(-0.5 * ((wavenumbers - 1700) / 50) ** 2) * 0.8 +  # Carbonyl stretch
    np.exp(-0.5 * ((wavenumbers - 2900) / 60) ** 2) * 0.5 +  # C-H stretch
    np.random.normal(0, 0.02, len(wavenumbers))              # Noise
)

plt.figure(figsize=(8, 4))
plt.plot(wavenumbers, absorbance, color='black')
plt.gca().invert_xaxis()  # IR spectra are plotted high-to-low wavenumber
plt.xlabel("Wavenumber (cm$^{-1}$)")
plt.ylabel("Absorbance")
plt.title("Simulated IR Spectrum")
plt.tight_layout()
plt.show()

Seamless Integration with Matplotlib

Seaborn works directly with Matplotlib objects, so you can combine Seaborn's advanced features with Matplotlib's full customization;
All Seaborn plots are Matplotlib figures, allowing you to use plt.xlabel, plt.ylabel, and other Matplotlib functions alongside Seaborn methods;
You can start with Seaborn for quick, beautiful plots, then fine-tune details using Matplotlib commands.

Common Plot Types in Seaborn

Seaborn provides a wide range of plot types that are useful for visualizing chemical data:

Scatterplots (sns.scatterplot): visualize relationships between two variables, such as absorbance vs. concentration;
Line plots (sns.lineplot): ideal for spectral data, showing absorbance as a function of wavelength or wavenumber;
Barplots (sns.barplot): compare averages or other statistics across categories, like reaction yields for different catalysts;
Histograms (sns.histplot): display distributions of measurements, such as peak intensities or molecular weights;
Heatmaps (sns.heatmap): visualize matrices, such as correlation between spectra or reaction conditions.

Themes and Color Palettes

Seaborn includes built-in themes and color palettes to make your plots publication-ready:

Use sns.set_theme() or sns.set_style() to apply styles like "whitegrid" or "dark";
Choose color palettes with sns.color_palette() or select predefined options like "deep", "muted", or "bright";
Consistent, attractive color schemes help highlight important trends and features in your data.

Statistical Plotting Capabilities

Seaborn simplifies statistical visualization, which is essential for interpreting experimental results:

Add confidence intervals to plots automatically (e.g., shaded regions in sns.lineplot);
Visualize regression fits using sns.regplot or sns.lmplot to explore relationships between variables;
Create violin plots, boxplots, and swarmplots to compare distributions and identify outliers.

Simplifying Complex Visualizations for Chemists

For chemists analyzing spectral data, Seaborn offers:

Fast creation of clear, informative IR, UV-Vis, or NMR spectra plots with minimal code;
Easy overlay of multiple spectra for comparison using color and style options;
Straightforward highlighting of regions of interest (such as specific peaks or bands) using built-in annotation and color features;
Compatibility with pandas DataFrames, making it simple to visualize data directly from experimental tables or results.


              1234567891011121314151617181920
            
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Example UV-Vis spectrum data (wavelength in nm vs. absorbance)
wavelengths = np.linspace(200, 800, 1000)
uvvis_abs = (
    np.exp(-0.5 * ((wavelengths - 300) / 15) ** 2) * 1.2 +  # Strong UV peak
    np.exp(-0.5 * ((wavelengths - 520) / 30) ** 2) * 0.6 +  # Visible peak
    np.random.normal(0, 0.01, len(wavelengths))             # Noise
)

sns.set(style="whitegrid", context="notebook")
plt.figure(figsize=(8, 4))
sns.lineplot(x=wavelengths, y=uvvis_abs, color="royalblue")
plt.xlabel("Wavelength (nm)")
plt.ylabel("Absorbance")
plt.title("Simulated UV-Vis Spectrum")
plt.tight_layout()
plt.show()

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 1

Plotting IR and UV-Vis Spectra

Seamless Integration with Matplotlib

Common Plot Types in Seaborn

Themes and Color Palettes

Statistical Plotting Capabilities

Simplifying Complex Visualizations for Chemists

1. What does a peak in an IR spectrum indicate?

2. How can you label the axes of a spectrum plot in Matplotlib?

3. Why might you use Seaborn in addition to Matplotlib for plotting spectra?

Plotting IR and UV-Vis Spectra

Seamless Integration with Matplotlib

Common Plot Types in Seaborn

Themes and Color Palettes

Statistical Plotting Capabilities

Simplifying Complex Visualizations for Chemists

1. What does a peak in an IR spectrum indicate?

2. How can you label the axes of a spectrum plot in Matplotlib?

3. Why might you use Seaborn in addition to Matplotlib for plotting spectra?