While `zykloid`

provides the basic functionality for
calculating cycloids, this functions allows to re-size a cycloid
by freely setting the radius of the circle its peaks are located
on. In addition, the cycloid can be re-positioned by locating
the fixed circle's midpoint. This function behaves similarly as
`zykloid.scaleAa`

. See details. See Figures 1, 2, and
`zykloid`

for the geometrical principles of cycloids.

1 2 | ```
zykloid.scaleP(A, a, lambda, hypo = TRUE, Cx = 0, Cy = 0,
RadiusP = 1, steps = 360, start = pi/2)
``` |

`A` |
The Radius of the fix circle before re-sizing. Must be an integer
Number > 0. Together with |

`a` |
The radius of the moving circle before re-sizing. Must be an
integer Number > 0. Together with |

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

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

`Cx` |
x-coordinate of the fix circle's midpoint. Default is 0. |

`Cy` |
y-coordinate of the fix circle's midpoint. Default is 0. |

`RadiusP` |
The actual radius of the circle the cycloid's peaks are located on. Default is 1. |

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

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

This function scales the radius of the circle the cycloids peaks
are located on. For hypocycloids and hypotrochoids it will thus
behave the same way as `zykloid.scaleAa`

. For
epicycloids and epitrochoids the output will be different.
While `zykloid.scaleAa`

scales the outer edge of the
figure, zykloid.scaleP always scales the circle where the
peaks of the figure are located on. In the case of epicycloids
and epitrochoids this is at the inside of the figure (see
examples below).

Figure 1 and 2 show the principle behind cycloid construction:

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

`zykloid`

,
`zykloid.scaleA`

, `zykloid.scaleAa`

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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | ```
# Epitrochoids with different lambda scaled to the same radius of
# the peak circle
cycl1 <- zykloid.scaleP(A = 21, a = 11, lambda = 1.2, hypo = FALSE,
RadiusP = 10)
cycl2 <- zykloid.scaleP(A = 21, a = 11, lambda = 1.0, hypo = FALSE,
RadiusP = 10)
cycl3 <- zykloid.scaleP(A = 21, a = 11, lambda = 0.8, hypo = FALSE,
RadiusP = 10)
plot (y ~ x, data = cycl1, col = "red", asp = 1, type = "l",
main = "A = 21, a = 11, RadiusP = 10")
lines(y ~ x, data = cycl2, col = "green")
lines(y ~ x, data = cycl3, col = "blue")
legend("topleft", c("lambda = 1.2", "lambda = 1.0", "lambda = 0.8"),
lty = rep("solid", 3), col = c("red", "green", "blue"),
bty = "n")
# Cool Disk by scaling the start angle with an
# exponential function ...
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-11, 11), ylim = c(-11, 11))
n <- 30
ccol <- topo.colors(n)
for(i in c(1:n)) {
zzz <- zykloid.scaleP(A = 3, a = 1, RadiusP = 6, lambda = 1,
start = 2*pi/3 * exp(-0.1 * (i - 1)), hypo = FALSE)
lines(y ~ x, data = zzz, col = ccol[i])
} # for i
par(op)
# ... the free space in the centre could be filled with
# the corresponding hypocycloid ...
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-11, 11), ylim = c(-11, 11))
n <- 30
ccol <- topo.colors(n)
for(i in c(1:n)) {
zzz <- zykloid.scaleP(A = 3, a = 1, RadiusP = 6, lambda = 1,
start = 2*pi/3 * exp(-0.1 * (i - 1)), hypo = FALSE)
lines(y ~ x, data = zzz, col = ccol[i])
zzz <- zykloid.scaleP(A = 3, a = 1, RadiusP = 6, lambda = 1,
start = 2*pi/3 * exp(-0.1 * (i - 1)), hypo = TRUE)
lines(y ~ x, data = zzz, col = ccol[i])
} # for i
par(op)
# ... or the same ring again and again.
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-11, 11), ylim = c(-11, 11))
n <- 30
ccol <- topo.colors(n)
rad <- 6
for(g in c(1:7)) {
for(i in c(1:n)) {
zzz <- zykloid.scaleP(A = 3, a = 1, RadiusP = rad,
lambda = 1, start = 2*pi/3 * exp(-0.1 * (i - 1)),
hypo = FALSE)
lines(y ~ x, data = zzz, col = ccol[i])
} # for i
rad <- rad * 3/5
} # for g
par(op)
# Cauliflower pattern. Here, an exponential function is used
# for scaling the radius of the circle the cycloid's loops
# are on.
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-22, 22), ylim = c(-22, 22))
n <- 15
dcol <- heat.colors(n)
for(i in c(1:n)) {
lambdax <- seq(2.0, 2.2, 0.1)
for(j in c(1:length(lambdax))) {
zzz <- zykloid.scaleP(A = 11, a = 1,
RadiusP = 15 * exp(-0.3 * (i - 1)),
lambda = lambdax[j], hypo = FALSE,
start = pi/2 + (i - 1)*pi/11)
if(j/2 == floor(j/2)) { colx <- "blue" }
else { colx <- dcol[n + 1 - i] }
lines(y ~ x, data = zzz, col = colx)
} # for j
} # for i
par(op)
# Sparkling star
op <- par(mar = c(0,0,0,0), bg = "black")
plot.new()
plot.window(asp = 1, xlim = c(-15, 15), ylim = c(-15, 15))
llam <- seq(0, 8, 0.2)
ccol <- rainbow(length(llam), start = 2/3, end = 1/3)
for(i in c(1:length(llam))) {
zzz <- zykloid.scaleP(A = 5, a = 1, RadiusP = 2.1,
hypo = FALSE, lambda = llam[i], start = pi/5)
lines(y ~ x, data = zzz, col = ccol[i])
} # for i
par(op)
``` |

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