plot ( energy_scale, Si_Xsection, label = 'EELS X-section' ) plt. power_law_background ( Xsection, energy_scale, , verbose = False ) plt. 8 end_bgd = edge_info - 5 M5_eds_xsection = get_eds_xsection ( Xsection, energy_scale, start_bgd, end_bgd ) background = eels. 8 end_bgd = edge_info - 5 L_eds_xsection = get_eds_xsection ( Xsection, energy_scale, start_bgd, end_bgd ) if 'M5' in edge_info : start_bgd = edge_info *. 95 end_bgd = edge_info - 5 K_eds_xsection = get_eds_xsection ( Xsection, energy_scale, start_bgd, end_bgd ) if '元' in edge_info : start_bgd = edge_info *. get_x_sections ( z ) if 'K1' in edge_info : start_bgd = edge_info *. power_law_background ( Xsection, energy_scale, , verbose = False ) cross_section_core = Xsection - background cross_section_core = 0.0 cross_section_core = 0.0 return cross_section_core energy_scale = np. Z = 14 def get_eds_xsection ( Xsection, energy_scale, start_bgd, end_bgd ): background = eels. HW6: Analyzing CBED Pattern in Two Beam Condition Chemical Composition in Core-Loss Spectraĥ.6. Introduction to Core-Loss SpectroscopyĤ.5. Analysing Low-Loss Spectra with Drude TheoryĤ.4. Introduction to Electron Energy-Loss SpectroscopyĤ.3. Defocus-Thickness Map with Multislice AlgorithmĤ.1. Linear Image Approximation: Weak Phase Objectģ.5. Unic Cell Determination and Stereographic Projectionģ.4. Open DM3 Images, Spectra, Spectrum-Images and Image-Stacks with pyNSIDĢ.10. Introduction to Python as it is used in this lectureġ.3.
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