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Force N parameters to n in most places

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Adrian Mariano
2022-03-13 21:40:55 -04:00
parent 8861464dbb
commit 7d6a2b593f
9 changed files with 132 additions and 130 deletions
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43
math.scad
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@@ -492,7 +492,7 @@ function modang(x) =
// Function: rand_int()
// Usage:
// rand_int(minval, maxval, N, [seed]);
// rand_int(minval, maxval, n, [seed]);
// Description:
// Return a list of random integers in the range of minval to maxval, inclusive.
// Arguments:
@@ -503,51 +503,51 @@ function modang(x) =
// Example:
// ints = rand_int(0,100,3);
// int = rand_int(-10,10,1)[0];
function rand_int(minval, maxval, N, seed=undef) =
assert( is_finite(minval+maxval+N) && (is_undef(seed) || is_finite(seed) ), "Input must be finite numbers.")
function rand_int(minval, maxval, n, seed=undef) =
assert( is_finite(minval+maxval+n) && (is_undef(seed) || is_finite(seed) ), "Input must be finite numbers.")
assert(maxval >= minval, "Max value cannot be smaller than minval")
let (rvect = is_def(seed) ? rands(minval,maxval+1,N,seed) : rands(minval,maxval+1,N))
let (rvect = is_def(seed) ? rands(minval,maxval+1,n,seed) : rands(minval,maxval+1,n))
[for(entry = rvect) floor(entry)];
// Function: random_points()
// Usage:
// points = random_points([N], [dim], [scale], [seed]);
// points = random_points([n], [dim], [scale], [seed]);
// See Also: random_polygon(), spherical_random_points()
// Topics: Random, Points
// Description:
// Generate `N` uniform random points of dimension `dim` with data ranging from -scale to +scale.
// Generate `n` uniform random points of dimension `dim` with data ranging from -scale to +scale.
// The `scale` may be a number, in which case the random data lies in a cube,
// or a vector with dimension `dim`, in which case each dimension has its own scale.
// Arguments:
// N = number of points to generate. Default: 1
// n = number of points to generate. Default: 1
// dim = dimension of the points. Default: 2
// scale = the scale of the point coordinates. Default: 1
// seed = an optional seed for the random generation.
function random_points(N, dim=2, scale=1, seed) =
assert( is_int(N) && N>=0, "The number of points should be a non-negative integer.")
function random_points(n, dim=2, scale=1, seed) =
assert( is_int(n) && n>=0, "The number of points should be a non-negative integer.")
assert( is_int(dim) && dim>=1, "The point dimensions should be an integer greater than 1.")
assert( is_finite(scale) || is_vector(scale,dim), "The scale should be a number or a vector with length equal to d.")
let(
rnds = is_undef(seed)
? rands(-1,1,N*dim)
: rands(-1,1,N*dim, seed) )
? rands(-1,1,n*dim)
: rands(-1,1,n*dim, seed) )
is_num(scale)
? scale*[for(i=[0:1:N-1]) [for(j=[0:dim-1]) rnds[i*dim+j] ] ]
: [for(i=[0:1:N-1]) [for(j=[0:dim-1]) scale[j]*rnds[i*dim+j] ] ];
? scale*[for(i=[0:1:n-1]) [for(j=[0:dim-1]) rnds[i*dim+j] ] ]
: [for(i=[0:1:n-1]) [for(j=[0:dim-1]) scale[j]*rnds[i*dim+j] ] ];
// Function: gaussian_rands()
// Usage:
// arr = gaussian_rands([N],[mean], [cov], [seed]);
// arr = gaussian_rands([n],[mean], [cov], [seed]);
// Description:
// Returns a random number or vector with a Gaussian/normal distribution.
// Arguments:
// N = the number of points to return. Default: 1
// n = the number of points to return. Default: 1
// mean = The average of the random value (a number or vector). Default: 0
// cov = covariance matrix of the random numbers, or variance in the 1D case. Default: 1
// seed = If given, sets the random number seed.
function gaussian_rands(N=1, mean=0, cov=1, seed=undef) =
function gaussian_rands(n=1, mean=0, cov=1, seed=undef) =
assert(is_num(mean) || is_vector(mean))
let(
dim = is_num(mean) ? 1 : len(mean)
@@ -555,8 +555,8 @@ function gaussian_rands(N=1, mean=0, cov=1, seed=undef) =
assert((dim==1 && is_num(cov)) || is_matrix(cov,dim,dim),"mean and covariance matrix not compatible")
assert(is_undef(seed) || is_finite(seed))
let(
nums = is_undef(seed)? rands(0,1,dim*N*2) : rands(0,1,dim*N*2,seed),
rdata = [for (i = count(dim*N,0,2)) sqrt(-2*ln(nums[i]))*cos(360*nums[i+1])]
nums = is_undef(seed)? rands(0,1,dim*n*2) : rands(0,1,dim*n*2,seed),
rdata = [for (i = count(dim*n,0,2)) sqrt(-2*ln(nums[i]))*cos(360*nums[i+1])]
)
dim==1 ? add_scalar(sqrt(cov)*rdata,mean) :
assert(is_matrix_symmetric(cov),"Supplied covariance matrix is not symmetric")
@@ -569,7 +569,7 @@ function gaussian_rands(N=1, mean=0, cov=1, seed=undef) =
// Function: spherical_random_points()
// Usage:
// points = spherical_random_points([N], [radius], [seed]);
// points = spherical_random_points([n], [radius], [seed]);
// See Also: random_polygon(), random_points()
// Topics: Random, Points
// Description:
@@ -580,7 +580,7 @@ function gaussian_rands(N=1, mean=0, cov=1, seed=undef) =
// seed = an optional seed for the random generation.
// See https://mathworld.wolfram.com/SpherePointPicking.html
function spherical_random_points(N=1, radius=1, seed) =
function spherical_random_points(n=1, radius=1, seed) =
assert( is_int(n) && n>=1, "The number of points should be an integer greater than zero.")
assert( is_num(radius) && radius>0, "The radius should be a non-negative number.")
let( theta = is_undef(seed)
@@ -619,9 +619,6 @@ function random_polygon(n=3,size=1, seed) =
// Section: GCD/GCF, LCM
// Function: gcd()