Write a Python function to analyze spring-mass oscillation data by fitting a sinusoidal model and visualizing the results. This task will help you develop practical skills in model fitting and validation, which are essential for analyzing real experimental data in physics.

• Write a function that takes two arrays as input: one for time data and one for measured displacement data from a spring-mass oscillation experiment.
• Fit the displacement versus time data to the sinusoidal model defined as A * sin(omega * t + phi) + C, where:
  • A is the amplitude;
  • omega is the angular frequency;
  • phi is the phase offset;
  • C is the vertical offset.
• Use the scipy.optimize.curve_fit function to fit the model to the data.
• Use the following initial parameter guesses for curve fitting:
  • Amplitude (A): half the range of the displacement data;
  • Angular frequency (omega): 2.0;
  • Phase offset (phi): 0;
  • Vertical offset (C): mean of the displacement data.
• Plot both the original measured data (as points) and the fitted curve (as a line) on the same graph using matplotlib.pyplot.
• Compute the residuals as the difference between the measured displacement values and the fitted values at each time point.
• Plot the residuals as a function of time on a separate graph.
• Return the fitted parameters and the residuals from your function.
• Ensure your code is clear and well-structured, and that your plots include appropriate labels and titles.