Examples¶
This section contains examples in which pyGDM is used on specific physical problems. For more technical examples, see the tutorials. Note: The syntax of the example and tutorial notebooks is python 2 only.
Note
You can download also the source code of the example and tutorial files (these are python 2 and 3 compatible).
Comparison to Mie theory¶
Note
The spectra calculated by Mie theory used in the corresponding
examples can be downloaded here.
Comparison to selected publications¶
- Forward / backward resolved scattering
- Dipolar emitter coupled to gold split-ring resonator
- Nano Yagi-Uda: Directional Plasmonic Antenna
- Plasmonic polarization conversion
- Heat generation
- Optical forces
- Internal H-field
- Plasmonic properties of hyperdoped dielectrics
- Focused vector-beams
- Optical chirality #1
- Optical chirality #2
- Optical chirality #3
Multipole analysis¶
- Multipole Decomposition 1
- Multipole Decomposition 2
- Multipole Decomposition 3
- Generalized Polarizabilties - comparison with multipole expansion
- Generalized Polarizabilties - scattering spectra
- Generalized Polarizabilties - farfield patterns
- Generalized Polarizabilties - Mode density and multiple illuminations
- Generalized Polarizabilties - Optimum illumination
2D simulations¶
LDOS / dipole emitter decay rate¶
Fast electrons¶
Surface Green’s Dyads with retardation¶
Note
These examples require the external module pyGDM2-retard. It can be installed via pip.
Evolutionary optimization¶
Note
The examples on the evolutionary optimization module (EO) are not deterministic. If the results differ slightly from run to run, this is completely normal and actually the consequence of the main drawback of evolutionary optimization: Convergence to the global optimum can never be guaranteed!. Nevertheless, usually the optimizations converge very closely to the optimum, which can be tested by repeated runs of the same optimization.