import numpy as np
import matplotlib.pyplot as plt

def create_s_curve_lut():
    # Define parameters for the sigmoid curve
    a = 10.0  # Adjust this parameter to control the curve's shape
    b = 127.5  # Midpoint of the curve (127.5 for 8-bit grayscale)

    # Create the S-curve LUT using the sigmoid function
    lut = np.arange(256)
    lut = 255 / (1 + np.exp(-a * (lut - b) / 255))

    # Normalize the LUT to the 0-255 range
    lut = (lut - np.min(lut)) / (np.max(lut) - np.min(lut)) * 255

    return lut.astype(np.uint8)

# Create the S-curve LUT
s_curve_lut = create_s_curve_lut()

# Plot the S-curve for visualization
plt.plot(s_curve_lut, range(256))
plt.xlabel("Input Values (0-255)")
plt.ylabel("Output Values (0-255)")
plt.title("S-curve Contrast Enhancement LUT")
plt.show()

# You can access the S-curve LUT with s_curve_lut

plt.plot(s_curve_lut, range(256)) #It's a TRAP!
plt.plot(range(256), s_curve_lut) #This is correct

lut = (lut - np.min(lut)) / (np.max(lut) - np.min(lut)) * 255 #Rounding error, fractional part is discarded
lut = np.round((lut - np.min(lut)) / (np.max(lut) - np.min(lut)) * 255) #This is correct

• LLM's are like junior developers - they can and will do unexpected mistakes, they need clear instructions and guidance. The difference though is that junior developers will learn over time and LLM's will get better only in next generation. Like junior developers - LLM's needs to be "managed" with reasonable expectations.
  
• All code from LLM's must be verified, the more niche field - the more tests. LLM's generate code that looks correct, and when it's not - errors can be very subtle and expensive to debug/fix.
  
• In case of unexpected or puzzling results it is often faster to simply ask multiple LLM's : now in addition to ChatGPT (3.5/4) we have Copilot, Bard, Replit and more. None of these gave perfect results from the first time, but some errors were different and often less subtle / easier to get it working in 20 minutes total.
  
• Some of the errors are systematic for multiple LLM's, which apparently come from training data (as LLM's currently unconditionally trust training data, unlike humans). I.e. currently LLM's cannot exceed level training data on quality, but can only approach it. It is unclear how much further work on LLM's will be needed to get perfect result consistently, I afraid it might be the case where last 10% of the work require 90% of time.