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Foreword (ReadME)
1. Chapter 1: Introduction
1.1. Introduction to Python as it is used in this lecture
1.2. Prerequisites
1.3. Matplotlib and Numpy for Micrographs
1.4. Open DM3 Images, Spectra, Spectrum-Images and Image-Stacks with pyNSID
1.5. Course Organization
1.6. Overview Notebook
1.7. Electron Optics
1.8. Installation on your own computer
1.9. Setting up computing resources
2. Chapter 2: Diffraction
2.2. The Electron
2.3. Atomic Form Factor
2.4. Basic Crystallography
2.5. Structure Factors
2.6. Analyzing Ring Diffraction Pattern
2.7. Kinematic Scattering Geometry
2.8. Plotting Diffraction Pattern
2.9. Analyzing Spot Diffraction Pattern
2.10. Unic Cell Determination and Stereographic Projection
2.11. Kikuchi Lines
2.12. HOLZ Lines
2.13. Lattice Determination with HOLZ
2.16. Bethe Theory
2.17. Multislice Algorithm
2.18. CBED - Multislice Algorithm
3. Chapter 3: Imaging
3.2. Resolution Limit
3.3. Contrast Transfer Function
3.4. Linear Image Approximation: Weak Phase Object
3.5. Defocus-Thickness Map with Multislice Algorithm
3.6. Image Processing
3.7. Image Analysis
3.9. Thermal-Diffuse Scattering
3.10. Z-Contrast Imaging
3.11. Ronchigram
4. Chapter 4: Spectroscopy
4.1. Introduction to Electron Energy-Loss Spectroscopy
4.2. Fit of Zero-Loss Peak
4.3. Analysing Low-Loss Spectra with Drude Theory
4.4. Introduction to Core-Loss Spectroscopy
4.5. Chemical Composition in Core-Loss Spectra
4.6. Analysis of Core-Loss Spectra
4.7. Energy-Loss Near-Edge Structure
4.8. Chapter 4
Spectroscopy
4.10. Chapter 4
Spectroscopy
4.12. Chapter 4
Spectroscopy
4.14. Chapter 4
Spectroscopy
4.18. Chapter 4
Spectroscopy
4.21. Chapter 4
Spectroscopy
5. Homework
5.1. Homework 1
5.2. Homework 2
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