Introductory to applied physics and Jupyter notebooks with Python. Calculations and visualizations of GPS and acceleration data. Data handling, plotting and filtering.
1. Import necessary libraries
2. Read the data using pandas dataframes
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GPS Data
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3. Haversine formula
4. Calculate velocity from GPS data
5. Calculate total distance
6. Calculate total time
7. Calculate average speed
8. Distance graph
9. A map of movement
10. Velocity graph
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Acceleration Data
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11. Acceleration graph calculated from speed
12. Acceleration, observed. Three components: x, y and z.
13. Introducing filters for noisy data
14. Plottin filtered z-component acceleration data
15. Visualization of acceleration components x, y, z and acceleration from speed with Seaborn.
>Q: Does the acceleration calculated from speed match the observed acceleration. What differences do you notice? What could be their cause?
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Steps Data
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16. Calculate the steps taken
A. From filtered data
B. From unfiltered z-component data with power spectrum/FFT method.
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>Q: Is the number of steps logical?