Comparing and Presenting Experimental Results
When you conduct multiple experiments — such as measuring absorbance spectra from different samples or under varying conditions — it's essential to compare your results effectively. This allows you to identify patterns, differences, or similarities that may reveal important chemical information. By overlaying spectra on a single plot, you can visually assess how changes in conditions or sample composition affect the absorbance at different wavelengths.
1234567891011121314151617181920import numpy as np import matplotlib.pyplot as plt # Simulated wavelength range (nm) wavelength = np.linspace(200, 800, 300) # Simulated absorbance spectra for three samples abs1 = np.exp(-0.01 * (wavelength - 350) ** 2) * 1.0 abs2 = np.exp(-0.01 * (wavelength - 400) ** 2) * 0.8 abs3 = np.exp(-0.01 * (wavelength - 450) ** 2) * 0.6 plt.figure(figsize=(8, 5)) plt.plot(wavelength, abs1, label="Sample 1") plt.plot(wavelength, abs2, label="Sample 2") plt.plot(wavelength, abs3, label="Sample 3") plt.xlabel("Wavelength (nm)") plt.ylabel("Absorbance") plt.title("Comparison of Absorbance Spectra") plt.legend() plt.show()
When plotting multiple spectra together, it's important to help viewers quickly distinguish between the different experiments. Using a legend is the standard way to label each curve, so anyone reading your plot knows which line corresponds to which sample or condition. Color coding each spectrum and using distinct line styles (such as solid, dashed, or dotted lines) make the plot even clearer, especially if some viewers have color vision deficiencies or if the plot is printed in grayscale.
12345678910111213plt.figure(figsize=(8, 5)) plt.plot(wavelength, abs1, color="blue", linestyle="-", linewidth=2, label="Sample 1: Control") plt.plot(wavelength, abs2, color="green", linestyle="--", linewidth=2, label="Sample 2: Heated") plt.plot(wavelength, abs3, color="red", linestyle=":", linewidth=2, label="Sample 3: With Additive") plt.xlabel("Wavelength (nm)") plt.ylabel("Absorbance") plt.title("Absorbance Spectra Under Different Conditions") plt.legend(loc="upper right", title="Samples") plt.annotate("Peak shifts", xy=(400, 0.8), xytext=(500, 0.9), arrowprops=dict(facecolor='black', arrowstyle="->"), fontsize=10, color="black") plt.tight_layout() plt.show()
To present scientific results clearly, you should always consider the needs of your audience. Use descriptive axis labels (with units), an informative title, and a legend that explains each curve. Choose colors and line styles that remain distinguishable in both color and black-and-white formats. Annotations can highlight important features, such as peaks or shifts in the spectra, drawing attention to key findings. Consistent formatting across all your figures helps your work appear professional and makes your results easier to interpret.
In this course, we briefly touched on many Python libraries that are important for effective analysis. To deepen your knowledge and gain more hands-on practice, we recommend taking the following courses:
1. Why is it important to use legends when plotting multiple spectra?
2. How can you visually distinguish between different experiments on a plot?
3. What should you include in a plot to make it suitable for publication?
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When you conduct multiple experiments — such as measuring absorbance spectra from different samples or under varying conditions — it's essential to compare your results effectively. This allows you to identify patterns, differences, or similarities that may reveal important chemical information. By overlaying spectra on a single plot, you can visually assess how changes in conditions or sample composition affect the absorbance at different wavelengths.
1234567891011121314151617181920import numpy as np import matplotlib.pyplot as plt # Simulated wavelength range (nm) wavelength = np.linspace(200, 800, 300) # Simulated absorbance spectra for three samples abs1 = np.exp(-0.01 * (wavelength - 350) ** 2) * 1.0 abs2 = np.exp(-0.01 * (wavelength - 400) ** 2) * 0.8 abs3 = np.exp(-0.01 * (wavelength - 450) ** 2) * 0.6 plt.figure(figsize=(8, 5)) plt.plot(wavelength, abs1, label="Sample 1") plt.plot(wavelength, abs2, label="Sample 2") plt.plot(wavelength, abs3, label="Sample 3") plt.xlabel("Wavelength (nm)") plt.ylabel("Absorbance") plt.title("Comparison of Absorbance Spectra") plt.legend() plt.show()
When plotting multiple spectra together, it's important to help viewers quickly distinguish between the different experiments. Using a legend is the standard way to label each curve, so anyone reading your plot knows which line corresponds to which sample or condition. Color coding each spectrum and using distinct line styles (such as solid, dashed, or dotted lines) make the plot even clearer, especially if some viewers have color vision deficiencies or if the plot is printed in grayscale.
12345678910111213plt.figure(figsize=(8, 5)) plt.plot(wavelength, abs1, color="blue", linestyle="-", linewidth=2, label="Sample 1: Control") plt.plot(wavelength, abs2, color="green", linestyle="--", linewidth=2, label="Sample 2: Heated") plt.plot(wavelength, abs3, color="red", linestyle=":", linewidth=2, label="Sample 3: With Additive") plt.xlabel("Wavelength (nm)") plt.ylabel("Absorbance") plt.title("Absorbance Spectra Under Different Conditions") plt.legend(loc="upper right", title="Samples") plt.annotate("Peak shifts", xy=(400, 0.8), xytext=(500, 0.9), arrowprops=dict(facecolor='black', arrowstyle="->"), fontsize=10, color="black") plt.tight_layout() plt.show()
To present scientific results clearly, you should always consider the needs of your audience. Use descriptive axis labels (with units), an informative title, and a legend that explains each curve. Choose colors and line styles that remain distinguishable in both color and black-and-white formats. Annotations can highlight important features, such as peaks or shifts in the spectra, drawing attention to key findings. Consistent formatting across all your figures helps your work appear professional and makes your results easier to interpret.
In this course, we briefly touched on many Python libraries that are important for effective analysis. To deepen your knowledge and gain more hands-on practice, we recommend taking the following courses:
1. Why is it important to use legends when plotting multiple spectra?
2. How can you visually distinguish between different experiments on a plot?
3. What should you include in a plot to make it suitable for publication?
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