This is the package's core function for calculating cycloids.
These are represented by a set of two-dimensional point
coordinates. Although this function provides the essential
mathematics, you may want to use the wrappers `zykloid.scaleA`

,
`zykloid.scaleAa`

, and `zykloid.scaleP`

due to their convenient scaling and positioning options.

1 |

`A` |
The Radius of the fixed circle |

`a` |
The radius of the moving circle |

`lambda` |
The distance of the tracepoint from the moving circle's ( |

`hypo` |
logical. If TRUE, the resulting figure is a hypocycloid ( |

`steps` |
positive integer. The number of steps per circuit of the moving
circle ( |

`start` |
Start angle (radians) of the moving circle's ( |

Geometrically, cycloids in the sense of this package are generated as
follows (Figure 1, 2): Imagine a circle *cfix*, with radius *A*,
which is fixed on a plane. Another circle, *cmov*, with radius
*a*, is rolling along *cfix*'s circumference at the outside
of *cfix*. The figure created by the trace of a point on
*cmov*'s circumference is called an epicycloid (Figure 1A).
If *cmov* is rolling not at the outside but at the inside of
*cfix*, the trace of a point on *cmov*'s circumference
is called an hypocycloid (Figure 2A).

If in both cases the tracepoint is not located on *cmov*'s
circumference but at a fixed distance from its midpoint
either in- or outside *cmov*, the resulting figure is an
epitrochoid (Figure 1B, C) or a hypotrochoid (Figure 2B, C),
respectively.

With the arguments of zykloid as defined above, the centre of *cfix*
in the origin, and *phi* being the counterclockwise angle of
*cmov*'s midpoint against the start position with *cfix*'
centre as the pivot, the cartesian coordinates of a point on the
cycloid are calculated as follows:

*x = (A + a) * cos(phi + start) - lambda * a * cos((A + a)/a * phi + start)*

*y = (A + a) * sin(phi + start) - lambda * a * sin((A + a)/a * phi + start)*

A dataframe with the columns *x* and *y*. Each row
represents a tracepoint position. The positions are ordered along
the trace with the last and the first point being identical in
order to warrant a closed figure when plotting the data.

Peter Biber

Bronstein IN, Semendjaev KA, Musiol G, Muehlig H (2001): Taschenbuch
der Mathematik, 5th Edition, Verlag Harri Deutsch, 1186 p.
(103 - 105)

http://en.wikipedia.org/wiki/Epicycloid

http://en.wikipedia.org/wiki/Hypocycloid

http://en.wikipedia.org/wiki/Epitrochoid

http://en.wikipedia.org/wiki/Hypotrochoid

`zykloid.scaleA`

,
`zykloid.scaleAa`

, `zykloid.scaleP`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | ```
# Very simple example
cycl <- zykloid(A = 17, a = 9, lambda = 0.9, hypo = TRUE)
plot(y ~ x, data = cycl, asp = 1, type = "l")
# More complex: Looks like a passion flower
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-23, 23), ylim = c(-23, 23))
ll <- seq(2, 0, -0.2)
ccol <- rep(c("lightblue", "lightgreen", "yellow", "yellow",
"yellow"), 2)
for (i in c(1:length(ll))) {
z <- zykloid(A = 15, a = 7, lambda = ll[i], hypo = TRUE)
lines(y ~ x, data = z, col = ccol[i])
} # for i
par(op)
# Dense hypotrochoids
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-1.5, 1.5), ylim = c(-1.5, 1.5))
m <- zykloid(A = 90, a = 89, lambda = 0.01)
lines(y ~ x, data = m, col = "grey")
m <- zykloid(A = 90, a = 89, lambda = 0.02)
lines(y ~ x, data = m, col = "red")
m <- zykloid(A = 90, a = 89, lambda = 0.015)
lines(y ~ x, data = m, col = "blue")
par(op)
# Fragile star
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-14, 14), ylim = c(-14, 14))
l.max <- 1.6
l.min <- 0.1
ll <- seq(l.max, l.min, by = -1 * (l.max - l.min)/30)
n <- length(ll)
ccol <- rainbow(n, start = 2/3, end = 1)
for (i in c(1:n)) {
m <- zykloid(A = 9, a = 8, lambda = ll[i])
lines(y ~ x, data = m, type = "l", col = ccol[i])
} # for i
par(op)
``` |

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