## An improved Maxima function for inverse Laplace transform

Maxima has a fairly serviceable Laplace transform utility built-in.  Here’s an example from the popular ordinary differential equations book by Blanchard, Devaney and Hall:

Trouble arises when we look at discontinuous forcing functions, which is especially sad because it seems to me that’s what makes it worthwhile to spend time with Laplace transforms.  In particular, the inverse transform function ilt() fails on the Heaviside Function and Dirac Delta, even though the built-in laplace() treats them correctly:

So, I’ve written an alternative inverse Laplace function laplaceInv() that fixes that problem:

Here are a few differential equation solutions to show how the new function behaves:

A second-order linear equation with a constant forcing function that vanishes at $t=7$

A second-order equation with two impulsive forces:

## Popularity of CAS programming languages: Maxima, Maple, Mathematica

A few months ago, inspired by the PYPL PopularitY of Programming Language,  I compared Google trends data for the 3M of CAS software:  Maxima, Maple, and Mathematica based searches of the form “<language> tutorial”.  The result was that Maxima seems to be slowly increasing in popularity  with about 20% of the interest in the 3M.

Today I saw another popularity metric: The TIOBE index.  Using their methodology of Google trends data for the search string <language> + programming, I have these  results for the proportion of searches among the 3M:

## Legend Position in Maxima Plots

Here’s something I just learned and want to share with others and record for my use the next time I need to do this and have already forgotten!

The position and appearance of the figure legend in Maxima plots drawn with plot() (by setting gnuplot_preamble) and in draw() (by setting user_preamble),  can be manipulated to any of the gnuplot options listed here.

Here are some examples:

## logarc

In the back of my calculus book there is a table of famous integrals.  Here’s integral number 21 in that table:

From Maxima integrate(), I get

What’s going on?

Both forms give a workable antiderivative for the original integrand:

Furthermore, we believe that both forms are correct because of this helpful identity for hyperbolic sine:

${\rm asinh}(z)=\ln(z+\sqrt{1+z^2}).$

Turns out (thanks to a Barton Willis for pointing me in the right direction) there’s a variable logarc that we can set to make Maxima return the logarithmic form instead of hyperbolic sine:

I haven’t yet encountered cases where this would be a bad idea in general, but I’ll update this if I do.

## logabs

In the first week of my differential equations course, we study methods of direct integration and separation of variables.  I like to emphasize that the absolute values can lend an extra degree of generality to solutions with antiderivatives of the form

$\frac{1}{u}\;du = \ln |u|.$

As an example, for the initial value problem

$y' = \frac{x}{1-x^2}$,  $y(0)=1$,

it is convenient for treating all possible initial conditions ($x \ne \pm 1$) in one step to use the antiderivative

$y = -\frac{1}{2} \ln | 1-x^2 | + C.$

However, Maxima omits the absolute values.

For this case, we could consider only the needed interval $-1, but still…

Turns out we can set the Maxima variable logabs to make integrate() include absolute values in cases like this:

But then later in the course, I saw that logabs also impacts the Ordinary Differential Equation solver ode2().  I encountered an example for which Maxima, in particular solve() applied to expressions involving absolute value,  didn’t do what I wanted with logabs:true

For the logistic equation

$\frac{dP}{dt} = kP\left ( 1-\frac{P}{P_c} \right )$,  $P(0)=P_0$

we expect that by separating variables we can obtain the solution

$P(t) = \frac{P_0 P_c}{(P_c-P_0)e^{-kt}+ P_0}.$

Here’s what happens when we use ode2() with and without logabs:true:

## Student Projects in Maxima Vol 1, Num 4: ODE Systems for Epidemiological Models

Student Projects in Maxima is an online project to disseminate work by undergraduate students using Maxima to reproduce published results in the sciences and social sciences.

Volume 1, Number 4 (2017) is devoted to Systems of Ordinary Differential Equations for SIR models in epidemiology.

Contents:

Emenheiser, Anna, Virus Dynamics and Drug Therapy

Radermacher, Erin, A Model of the 2014 Ebola Outbreak

Rafai, Sagar, Analysis of Communicable Disease Models

## Student Projects in Maxima Vol 1, Num 3: ODE Systems for Interacting Population Models

Student Projects in Maxima is an online project to disseminate work by undergraduate students using Maxima to reproduce published results in the sciences and social sciences.

Volume 1, Number 3 (2017) is devoted to Systems of Ordinary Differential Equations for non-chaotic predator-prey and other interacting population models

Contents:

Bhullar, Abhjeet,  Lions, Wildebeest and Zebras

Goodin Aunic, Parasitic Nematodes in Grouse

DeFore, Brandon, Breeding Suppression in Predator-Prey

Jerez, Emilio, Predator-Prey with Hawk and Dove Tactics

Bontrager, Eric, Predator-Prey with Prey Switching

Beatty, Ethan, Analysis of Logistic Growth Models

Rice, Gabriel, Pharmacokinetic Mechanism of Ethanol-Benzodiazepine Interactions

Wile, Jessica, Ebola in Western Lowlands Gorillas

Bailey, John,  Kinetic Modeling for Interconnected Reactions

Piet, Joe, Elephant and Tree Population Dynamics

Kim, Judy, A Model for West Nile Virus

Kamalaldin, Kamalaldin, Infected Prey in Polluted Environment

Park, Kayla, Ratio-Dependent Predator Prey Model

Lundy, Liam, Predator Prey in a Single Species with Cannibalism

Orwin, Michael, Interactions of Model Neurons

Schultz, Pete, Photosynthetic Oscillations

Wadhwa, Raoul, Dynamics in an Inflammation Model

Del Olmo, Ricardo, Population Growth and Technological Change

Network Model of Cocaine Traffic in Spain Edited

Kill, Sean, A Logistic Model with Varying Carrying Capacity

McFadden-Keesling, Sophia,  Predator Prey Model with a Refuge

Samson, Tanush,  Lions and Wildebeest Model

Morales, Zach,  Game Theory Models