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mm2_notebooks

Requires

python3 numpy matplotlib ipywidgets scipy ipython

Available Notebook Functions

ODE

Convolution()

Performs the steps of a convolution up to maximum shift specified by the user using a slider.

NumericalMethods()

Allows the user to change the various parameters (step size, no. steps) of 3 numerical ODE solvers (Euler, Improved Euler, Runge Kutta)

Laplace()

Gives user an interactive example of a function in the t domain being transformed into the s (laplace) domain. Allows user to visualise the effect of changing different parts of the t function.

Suspension()

Produces plot of damped suspension system. Gives user sliders to change m, k, c

Pendulum()

Animation of a pendulum produced using Runge Kutta to solve system of ODEs. Could alter this to user different solvers?

Linear Algebra

CoordinateTransform()

Takes input from user to transform shapes using transformation matrix. Also option to show eigens.

IterativeMethods3D()

Solves and plots solution to 3D system of equations.

IterativeMethods2D()

Solves and plots solution to 2D system of equations.

EllipseTransform()

Plots ellipses and eigens from equation

EigenTransform()

Allows user to select transformation matrix and apply to a shape. Option to show and normalise eigens.

Fourier Series

FourierSeries()

Produces a Fourier Series approximation to a function.

RandomWave()

Does a cheat version of a Fourier analysis by producing sinusoids of random amplitude, frequency and shift. And then using these to produce a signal. Intended to give an idea for how any signal can be made from summing sinusoids and what the output of a fourier analysis actually tells you.

FourierAnalysis()

Performs the fourier and 3 specific signals and returns variables to be passed into MusicNote

MusicNote()

Plots the signal and the Fourier analysis of the signal. Also has slider to constrain frequency axis to allow zooming in on ranges of frequencies.

FilterFreqs()

Plots the signal and the Fourier analysis of the signal. Also allows user to filter out a band of frequencies and review what the signal would look like without those frequencies.

EvenOdd()

Demo of even vs. odd function multiplication

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Jupyter Notebooks developed for MM2 at UoA

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