Consider the following set of data points (xi, Yi): X 0 2 1234 1037 3-1 4...

 

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Consider the following set of data points (xi, Yi): X 0 2 1234 1037 3-1 4 0 (a) (10 pts) Set up the system of equations required to compute the spline parameters g" (xi) using natural spline (aka free run-out) end conditions. Then, solve the system of equations to obtain the parameters g"(xi). You can use Python or another tool to solve the linear system. (b) (3 pts) Use your results from part (a) (i.e. not a numpy or scipy library) to compute the spline-interpolated value of the data at the coordinate x = 2.4. Hint: use Moin equation 1.6. (c) (2 pts) Check your answer to part (b) by using scipy.interpolate. CubicSpline to compute the interpolated function value at x = 2.4. Submit a screenshot of your code and its output. Hint: be sure to read the scipy documentation (see hyperlink) to implement the correct spline end conditions. Consider the following set of data points (xi, Yi): X 0 2 1234 1037 3-1 4 0 (a) (10 pts) Set up the system of equations required to compute the spline parameters g" (xi) using natural spline (aka free run-out) end conditions. Then, solve the system of equations to obtain the parameters g"(xi). You can use Python or another tool to solve the linear system. (b) (3 pts) Use your results from part (a) (i.e. not a numpy or scipy library) to compute the spline-interpolated value of the data at the coordinate x = 2.4. Hint: use Moin equation 1.6. (c) (2 pts) Check your answer to part (b) by using scipy.interpolate. CubicSpline to compute the interpolated function value at x = 2.4. Submit a screenshot of your code and its output. Hint: be sure to read the scipy documentation (see hyperlink) to implement the correct spline end conditions.