pandoc-slides

Let’s test katex

One dimensional over-dumped Lagevin’s equation:

\lambda \dfrac{dx}{dt} = -\dfrac{dV(x)}{dx} + \eta(t)

Ok it works!

Let’s test pandoc-filters

The stationary distribution relative to stochastic process x(t) described by Langevin’s equation it’s a Maxwell-Boltzmann distribution

\rho(x)\propto e^{-kV(x)}

Here the code used to create the plot:

import numpy as np
from matplotlib import pyplot as plt

x = np.linspace(0, 10)

fig, ax = plt.subplots()
for k in np.arange(1, 4):
   V = np.exp(-k*x)
   ax.plot(x, V, label=f"$V(x)=-{k}x$")

ax.set_xlabel('$x$')
ax.set_ylabel('$p(x)$')
ax.legend()

Try to drawing a simple graph with graphviz

Here the code used to create the plot:

graph G {
   bgcolor="transparent"
   fontname="Helvetica,Arial,sans-serif"
   node [fontname="Helvetica,Arial,sans-serif"]
   edge [fontname="Helvetica,Arial,sans-serif"]
   layout=neato
   run -- intr;
   intr -- runbl;
   runbl -- run;
   run -- kernel;
   kernel -- zombie;
   kernel -- sleep;
   kernel -- runmem;
   sleep -- swap;
   swap -- runswap;
   runswap -- new;
   runswap -- runmem;
   new -- runmem;
   sleep -- runmem;
}

At the end let’s try plantuml

Here the yaml file:

doe: "a deer, a female deer"
ray: "a drop of golden sun"
pi: 3.14159
xmas: true
french-hens: 3
calling-birds:
   - huey
   - dewey
   - louie
   - fred
xmas-fifth-day:
   calling-birds: four
   french-hens: 3
   golden-rings: 5
   partridges:
      count: 1
      location: "a pear tree"
   turtle-doves: two