[Statistics question]: FRET efficiency

[haesleinhuepf]

In python, plot the Fluorescence energy transfer (given as percentage) as a function of the distance (Donor to Acceptor) between fluorophores from 0 to 14 nm. Use a black line. Use a Foersterradius of 4, 6, and 8 nm. Add two horizontal red, dashed lines at an energy transfer of 10% and 90%. Add a green dashed horizontal line at 50% FRET efficiency . Limit the x-axis between 0 and 14 nm, limit the y-axis to 0% and 100%. Use a font size of 14 pt Arial. Set all line thicknesses (including axes) to 2 pt. Make the ticks on both axes 2pt wide and 6 pt long. Remove legend, but label the three lines in the figure with 4, 6, and 8 nm at 50% FRET efficiency. Make the size of the figure 8,5 and move the labels by 0.5nm to the right from their R0 value.

git-bob think about how to achieve this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.14.1dev, model: claude-3-5-sonnet-20241022, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

I'll help you create a matplotlib visualization for FRET efficiency. Here's a Jupyter notebook that produces exactly what you need:

import numpy as np
import matplotlib.pyplot as plt

# Create distance range
r = np.linspace(0, 14, 1000)

# Calculate FRET efficiency for different Förster radii
def fret_efficiency(r, R0):
    return 100 * (1 / (1 + (r/R0)**6))

# Set up the figure
plt.figure(figsize=(8, 5))

# Plot FRET curves for different R0
for R0 in [4, 6, 8]:
    efficiency = fret_efficiency(r, R0)
    plt.plot(r, efficiency, 'k-', linewidth=2)
    # Add labels at 50% efficiency
    r_50 = R0 * (1)**(1/6)  # position where efficiency is 50%
    plt.text(r_50 + 0.5, 50, f'{R0} nm', fontsize=14, fontname='Arial')

# Add horizontal lines
plt.axhline(y=90, color='r', linestyle='--', linewidth=2)
plt.axhline(y=50, color='g', linestyle='--', linewidth=2)
plt.axhline(y=10, color='r', linestyle='--', linewidth=2)

# Customize the plot
plt.xlim(0, 14)
plt.ylim(0, 100)
plt.tick_params(width=2, length=6)
for spine in plt.gca().spines.values():
    spine.set_linewidth(2)

# Set font properties
plt.xticks(fontsize=14, fontname='Arial')
plt.yticks(fontsize=14, fontname='Arial')
plt.xlabel('Distance (nm)', fontsize=14, fontname='Arial')
plt.ylabel('FRET Efficiency (%)', fontsize=14, fontname='Arial')

plt.tight_layout()
plt.show()

This will create a plot that matches all your specifications. The FRET efficiency curves are shown in black, with appropriate labels at 50% efficiency for each Förster radius.

[haesleinhuepf]

git-bob try to plot this

[github-actions[bot]]

^{This message was generated by git-bob (version: 0.14.1dev, model: claude-3-5-sonnet-20241022, log), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

I created a visualization of FRET efficiency curves as a function of distance for three different Förster radii (4, 6, and 8 nm) using matplotlib. The implementation includes a Jupyter notebook with the plotting code and generates a figure that meets all specified formatting requirements, including black curves for FRET efficiency, red dashed lines at 10% and 90% efficiency, and a green dashed line at 50% efficiency, with appropriate labels and axis formatting.

• Generated plot showing FRET efficiency curves with specified formatting and labels for three Förster radii.
• fret_efficiency_plot.ipynb Created new Jupyter notebook containing the code for plotting FRET efficiency curves with matplotlib.
• requirements.txt No changes made as required dependencies were already present.