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Scientific Python Introduction

Scientific Python Introduction. Trygve Eftestøl Karl Skretting. Aim of the course. Get to know Python What is Python? Why use Python? Basic training User interface Basic functions Visualisation Programming Analysis. What is Scientific Python?. Python

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Scientific Python Introduction

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  1. Scientific Python Introduction Trygve Eftestøl Karl Skretting

  2. Aim of the course • Get to know Python • What is Python? • Why use Python? • Basic training • User interface • Basic functions • Visualisation • Programming • Analysis

  3. What is Scientific Python? • Python • object oriented programming language • Scientific • Add numpy, matplotlib, scipy • Why use Python? • Mathematcal computations • Visualisation • Analysis • Used a lot for problem solving • Cybernetics • Signal processing • Image processing • Pattern recognition • …

  4. Basic training • User interface • Basic functions • Visualisation • Programming • Analysis

  5. User interface • Interpreter window • Editor • Help

  6. User interface • Interpreter

  7. User interface • Editor

  8. User interface • Help • Documentation • Search

  9. Basic functions • Create matrices • Matrix operations • Matrix functions • Matrix indexing • Logical operations

  10. Basic functions • Create matrices • Scalar • Vector • Matrix

  11. Basic functions • Matrix operators

  12. Basic functions • Matrix functions

  13. Basic functions • Matrix indexing

  14. Basic functions • Logical operators

  15. Visualisation • 2D-plotting • 3D-plotting

  16. Visualisation • 2D-plotting

  17. Visualisation • 3D-plotting

  18. Visualisation • 3D-plotting

  19. Programming • Function files • Control structures • if • while • for

  20. Programming • Function files

  21. Programming • Condition control using if

  22. Programming • Iterations using while

  23. Programming • Iterations using for

  24. Analysis • Estimate pulse rate • Reading from data file • Plotting of signal • Detection of peaks • Problem – false detections • Spectral analysis • Filtering

  25. Analysis • Reading from data file • Plotting of signal • Detection of peaks

  26. Analysis • Problem – false detections • Spectral analysis • Filtering

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