Animations and Interactive Plots

Jupyter Notebooks can used to run code within the server or locally; however they can also be used to present the output of the code. This could include figures, animations, parameter estimations etc. along with the markdown comments and LaTeX equations. Running the code to produce these when presenting could include unwanted setup and delay. However the notebooks, along with the desired material, can be viewed in an internet browser using NBViewer without the need to run any code.

Some examples produced from the PyCav Project, click the images to view the demonstrations:

Classical Dynamics: Symmetric Spinning Top
Classical Dynamics: Symmetric Spinning Top
Electromagnetism: Cherenkov Radiation
Electromagnetism: Cherenkov Radiation
Steady State Scattering: Foldy Lax
Steady State Scattering: Foldy Lax
Fluid Dynamics: Flow onto a barrier via Lattice Boltzmann Method
Fluid Dynamics: Flow onto a barrier via Lattice Boltzmann Method
Wave Equation: 1D & 2D
Wave Equation: 1D & 2D
Time-Independent QM: Shooting Method for Finite Square Well
Time-Independent QM: Shooting Method for Finite Square Well
Time-Dependent QM: 1D Barriers & Harmonic Potentials
Time-Dependent QM: 1D Alpha Decay

A tutorial on how to use the PyCav display module can be found here.

Animations are produced using matplotlib but plots can be produced by both matplotlib and bokeh. An advantage of bokeh is that when the notebook is viewed in the browser it allows for the plot tools such as zoom to be used, while matplotlib does not. However its documentation is not as extensive.

Aidan Crilly

Aidan Crilly

Physics of Other Stuff

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