//////////////////////////////////////////////////////////////////////
// LibFile: math.scad
// Math helper functions.
// To use, add the following lines to the beginning of your file:
// ```
// use <BOSL2/std.scad>
// ```
//////////////////////////////////////////////////////////////////////
// Section: Math Constants
PHI = (1+sqrt(5))/2; // The golden ratio phi.
EPSILON = 1e-9; // A really small value useful in comparing FP numbers. ie: abs(a-b)<EPSILON
INF = 1/0; // The value `inf`, useful for comparisons.
NAN = acos(2); // The value `nan`, useful for comparisons.
// Section: Simple math
// Function: sqr()
// Usage:
// sqr(x);
// Description:
// Returns the square of the given number or entries in list
// Examples:
// sqr(3); // Returns: 9
// sqr(-4); // Returns: 16
// sqr([3,4]); // Returns: [9,16]
// sqr([[1,2],[3,4]]); // Returns [[1,4],[9,16]]
// sqr([[1,2],3]); // Returns [[1,4],9]
function sqr(x) = is_list(x) ? [for(val=x) sqr(val)] : x*x;
// Function: log2()
// Usage:
// foo = log2(x);
// Description:
// Returns the logarithm base 2 of the value given.
// Examples:
// log2(0.125); // Returns: -3
// log2(16); // Returns: 4
// log2(256); // Returns: 8
function log2(x) = ln(x)/ln(2);
// Function: hypot()
// Usage:
// l = hypot(x,y,[z]);
// Description:
// Calculate hypotenuse length of a 2D or 3D triangle.
// Arguments:
// x = Length on the X axis.
// y = Length on the Y axis.
// z = Length on the Z axis. Optional.
// Example:
// l = hypot(3,4); // Returns: 5
// l = hypot(3,4,5); // Returns: ~7.0710678119
function hypot(x,y,z=0) = norm([x,y,z]);
// Function: factorial()
// Usage:
// x = factorial(n,[d]);
// Description:
// Returns the factorial of the given integer value, or n!/d! if d is given.
// Arguments:
// n = The integer number to get the factorial of. (n!)
// d = If given, the returned value will be (n! / d!)
// Example:
// x = factorial(4); // Returns: 24
// y = factorial(6); // Returns: 720
// z = factorial(9); // Returns: 362880
function factorial(n,d=0) =
assert(n>=0 && d>=0, "Factorial is not defined for negative numbers")
assert(d<=n, "d cannot be larger than n")
product([1,for (i=[n:-1:d+1]) i]);
// Function: lerp()
// Usage:
// x = lerp(a, b, u);
// l = lerp(a, b, LIST);
// Description:
// Interpolate between two values or vectors.
// If `u` is given as a number, returns the single interpolated value.
// If `u` is 0.0, then the value of `a` is returned.
// If `u` is 1.0, then the value of `b` is returned.
// If `u` is a range, or list of numbers, returns a list of interpolated values.
// It is valid to use a `u` value outside the range 0 to 1. The result will be a predicted
// value along the slope formed by `a` and `b`, but not between those two values.
// Arguments:
// a = First value or vector.
// b = Second value or vector.
// u = The proportion from `a` to `b` to calculate. Standard range is 0.0 to 1.0, inclusive. If given as a list or range of values, returns a list of results.
// Example:
// x = lerp(0,20,0.3); // Returns: 6
// x = lerp(0,20,0.8); // Returns: 16
// x = lerp(0,20,-0.1); // Returns: -2
// x = lerp(0,20,1.1); // Returns: 22
// p = lerp([0,0],[20,10],0.25); // Returns [5,2.5]
// l = lerp(0,20,[0.4,0.6]); // Returns: [8,12]
// l = lerp(0,20,[0.25:0.25:0.75]); // Returns: [5,10,15]
// Example(2D):
// p1 = [-50,-20]; p2 = [50,30];
// stroke([p1,p2]);
// pts = lerp(p1, p2, [0:1/8:1]);
// // Points colored in ROYGBIV order.
// rainbow(pts) translate($item) circle(d=3,$fn=8);
function lerp(a,b,u) =
assert(same_shape(a,b), "Bad or inconsistent inputs to lerp")
is_num(u)? (1-u)*a + u*b :
assert(!is_undef(u)&&!is_bool(u)&&!is_string(u), "Input u to lerp must be a number, vector, or range.")
[for (v = u) lerp(a,b,v)];
// Section: Hyperbolic Trigonometry
// Function: sinh()
// Description: Takes a value `x`, and returns the hyperbolic sine of it.
function sinh(x) =
(exp(x)-exp(-x))/2;
// Function: cosh()
// Description: Takes a value `x`, and returns the hyperbolic cosine of it.
function cosh(x) =
(exp(x)+exp(-x))/2;
// Function: tanh()
// Description: Takes a value `x`, and returns the hyperbolic tangent of it.
function tanh(x) =
sinh(x)/cosh(x);
// Function: asinh()
// Description: Takes a value `x`, and returns the inverse hyperbolic sine of it.
function asinh(x) =
ln(x+sqrt(x*x+1));
// Function: acosh()
// Description: Takes a value `x`, and returns the inverse hyperbolic cosine of it.
function acosh(x) =
ln(x+sqrt(x*x-1));
// Function: atanh()
// Description: Takes a value `x`, and returns the inverse hyperbolic tangent of it.
function atanh(x) =
ln((1+x)/(1-x))/2;
// Section: Quantization
// Function: quant()
// Description:
// Quantize a value `x` to an integer multiple of `y`, rounding to the nearest multiple.
// If `x` is a list, then every item in that list will be recursively quantized.
// Arguments:
// x = The value to quantize.
// y = The multiple to quantize to.
// Example:
// quant(12,4); // Returns: 12
// quant(13,4); // Returns: 12
// quant(13.1,4); // Returns: 12
// quant(14,4); // Returns: 16
// quant(14.1,4); // Returns: 16
// quant(15,4); // Returns: 16
// quant(16,4); // Returns: 16
// quant(9,3); // Returns: 9
// quant(10,3); // Returns: 9
// quant(10.4,3); // Returns: 9
// quant(10.5,3); // Returns: 12
// quant(11,3); // Returns: 12
// quant(12,3); // Returns: 12
// quant([12,13,13.1,14,14.1,15,16],4); // Returns: [12,12,12,16,16,16,16]
// quant([9,10,10.4,10.5,11,12],3); // Returns: [9,9,9,12,12,12]
// quant([[9,10,10.4],[10.5,11,12]],3); // Returns: [[9,9,9],[12,12,12]]
function quant(x,y) =
is_list(x)? [for (v=x) quant(v,y)] :
floor(x/y+0.5)*y;
// Function: quantdn()
// Description:
// Quantize a value `x` to an integer multiple of `y`, rounding down to the previous multiple.
// If `x` is a list, then every item in that list will be recursively quantized down.
// Arguments:
// x = The value to quantize.
// y = The multiple to quantize to.
// Examples:
// quantdn(12,4); // Returns: 12
// quantdn(13,4); // Returns: 12
// quantdn(13.1,4); // Returns: 12
// quantdn(14,4); // Returns: 12
// quantdn(14.1,4); // Returns: 12
// quantdn(15,4); // Returns: 12
// quantdn(16,4); // Returns: 16
// quantdn(9,3); // Returns: 9
// quantdn(10,3); // Returns: 9
// quantdn(10.4,3); // Returns: 9
// quantdn(10.5,3); // Returns: 9
// quantdn(11,3); // Returns: 9
// quantdn(12,3); // Returns: 12
// quantdn([12,13,13.1,14,14.1,15,16],4); // Returns: [12,12,12,12,12,12,16]
// quantdn([9,10,10.4,10.5,11,12],3); // Returns: [9,9,9,9,9,12]
// quantdn([[9,10,10.4],[10.5,11,12]],3); // Returns: [[9,9,9],[9,9,12]]
function quantdn(x,y) =
is_list(x)? [for (v=x) quantdn(v,y)] :
floor(x/y)*y;
// Function: quantup()
// Description:
// Quantize a value `x` to an integer multiple of `y`, rounding up to the next multiple.
// If `x` is a list, then every item in that list will be recursively quantized up.
// Arguments:
// x = The value to quantize.
// y = The multiple to quantize to.
// Examples:
// quantup(12,4); // Returns: 12
// quantup(13,4); // Returns: 16
// quantup(13.1,4); // Returns: 16
// quantup(14,4); // Returns: 16
// quantup(14.1,4); // Returns: 16
// quantup(15,4); // Returns: 16
// quantup(16,4); // Returns: 16
// quantup(9,3); // Returns: 9
// quantup(10,3); // Returns: 12
// quantup(10.4,3); // Returns: 12
// quantup(10.5,3); // Returns: 12
// quantup(11,3); // Returns: 12
// quantup(12,3); // Returns: 12
// quantup([12,13,13.1,14,14.1,15,16],4); // Returns: [12,16,16,16,16,16,16]
// quantup([9,10,10.4,10.5,11,12],3); // Returns: [9,12,12,12,12,12]
// quantup([[9,10,10.4],[10.5,11,12]],3); // Returns: [[9,12,12],[12,12,12]]
function quantup(x,y) =
is_list(x)? [for (v=x) quantup(v,y)] :
ceil(x/y)*y;
// Section: Constraints and Modulos
// Function: constrain()
// Usage:
// constrain(v, minval, maxval);
// Description:
// Constrains value to a range of values between minval and maxval, inclusive.
// Arguments:
// v = value to constrain.
// minval = minimum value to return, if out of range.
// maxval = maximum value to return, if out of range.
// Example:
// constrain(-5, -1, 1); // Returns: -1
// constrain(5, -1, 1); // Returns: 1
// constrain(0.3, -1, 1); // Returns: 0.3
// constrain(9.1, 0, 9); // Returns: 9
// constrain(-0.1, 0, 9); // Returns: 0
function constrain(v, minval, maxval) = min(maxval, max(minval, v));
// Function: posmod()
// Usage:
// posmod(x,m)
// Description:
// Returns the positive modulo `m` of `x`. Value returned will be in the range 0 ... `m`-1.
// Arguments:
// x = The value to constrain.
// m = Modulo value.
// Example:
// posmod(-700,360); // Returns: 340
// posmod(-270,360); // Returns: 90
// posmod(-120,360); // Returns: 240
// posmod(120,360); // Returns: 120
// posmod(270,360); // Returns: 270
// posmod(700,360); // Returns: 340
// posmod(3,2.5); // Returns: 0.5
function posmod(x,m) = (x%m+m)%m;
// Function: modang(x)
// Usage:
// ang = modang(x)
// Description:
// Takes an angle in degrees and normalizes it to an equivalent angle value between -180 and 180.
// Example:
// modang(-700,360); // Returns: 20
// modang(-270,360); // Returns: 90
// modang(-120,360); // Returns: -120
// modang(120,360); // Returns: 120
// modang(270,360); // Returns: -90
// modang(700,360); // Returns: -20
function modang(x) =
let(xx = posmod(x,360)) xx<180? xx : xx-360;
// Function: modrange()
// Usage:
// modrange(x, y, m, [step])
// Description:
// Returns a normalized list of values from `x` to `y`, by `step`, modulo `m`. Wraps if `x` > `y`.
// Arguments:
// x = The start value to constrain.
// y = The end value to constrain.
// m = Modulo value.
// step = Step by this amount.
// Examples:
// modrange(90,270,360, step=45); // Returns: [90,135,180,225,270]
// modrange(270,90,360, step=45); // Returns: [270,315,0,45,90]
// modrange(90,270,360, step=-45); // Returns: [90,45,0,315,270]
// modrange(270,90,360, step=-45); // Returns: [270,225,180,135,90]
function modrange(x, y, m, step=1) =
let(
a = posmod(x, m),
b = posmod(y, m),
c = step>0? (a>b? b+m : b) : (a<b? b-m : b)
) [for (i=[a:step:c]) (i%m+m)%m];
// Section: Random Number Generation
// Function: rand_int()
// Usage:
// rand_int(min,max,N,[seed]);
// Description:
// Return a list of random integers in the range of min to max, inclusive.
// Arguments:
// min = Minimum integer value to return.
// max = Maximum integer value to return.
// N = Number of random integers to return.
// seed = If given, sets the random number seed.
// Example:
// ints = rand_int(0,100,3);
// int = rand_int(-10,10,1)[0];
function rand_int(min, max, N, seed=undef) =
assert(max >= min, "Max value cannot be smaller than min")
let (rvect = is_def(seed) ? rands(min,max+1,N,seed) : rands(min,max+1,N))
[for(entry = rvect) floor(entry)];
// Function: gaussian_rands()
// Usage:
// gaussian_rands(mean, stddev, [N], [seed])
// Description:
// Returns a random number with a gaussian/normal distribution.
// Arguments:
// mean = The average random number returned.
// stddev = The standard deviation of the numbers to be returned.
// N = Number of random numbers to return. Default: 1
// seed = If given, sets the random number seed.
function gaussian_rands(mean, stddev, N=1, seed=undef) =
let(nums = is_undef(seed)? rands(0,1,N*2) : rands(0,1,N*2,seed))
[for (i = list_range(N)) mean + stddev*sqrt(-2*ln(nums[i*2]))*cos(360*nums[i*2+1])];
// Function: log_rands()
// Usage:
// log_rands(minval, maxval, factor, [N], [seed]);
// Description:
// Returns a single random number, with a logarithmic distribution.
// Arguments:
// minval = Minimum value to return.
// maxval = Maximum value to return. `minval` <= X < `maxval`.
// factor = Log factor to use. Values of X are returned `factor` times more often than X+1.
// N = Number of random numbers to return. Default: 1
// seed = If given, sets the random number seed.
function log_rands(minval, maxval, factor, N=1, seed=undef) =
assert(maxval >= minval, "maxval cannot be smaller than minval")
let(
minv = 1-1/pow(factor,minval),
maxv = 1-1/pow(factor,maxval),
nums = is_undef(seed)? rands(minv, maxv, N) : rands(minv, maxv, N, seed)
) [for (num=nums) -ln(1-num)/ln(factor)];
// Section: GCD/GCF, LCM
// Function: gcd()
// Usage:
// gcd(a,b)
// Description:
// Computes the Greatest Common Divisor/Factor of `a` and `b`.
function gcd(a,b) =
assert(is_int(a) && is_int(b),"Arguments to gcd must be integers")
b==0 ? abs(a) : gcd(b,a % b);
// Computes lcm for two scalars
function _lcm(a,b) =
assert(is_int(a), "Invalid non-integer parameters to lcm")
assert(is_int(b), "Invalid non-integer parameters to lcm")
assert(a!=0 && b!=0, "Arguments to lcm must be nonzero")
abs(a*b) / gcd(a,b);
// Computes lcm for a list of values
function _lcmlist(a) =
len(a)==1 ? a[0] :
_lcmlist(concat(slice(a,0,len(a)-2),[lcm(a[len(a)-2],a[len(a)-1])]));
// Function: lcm()
// Usage:
// lcm(a,b)
// lcm(list)
// Description:
// Computes the Least Common Multiple of the two arguments or a list of arguments. Inputs should
// be non-zero integers. The output is always a positive integer. It is an error to pass zero
// as an argument.
function lcm(a,b=[]) =
!is_list(a) && !is_list(b) ? _lcm(a,b) :
let(
arglist = concat(force_list(a),force_list(b))
)
assert(len(arglist)>0,"invalid call to lcm with empty list(s)")
_lcmlist(arglist);
// Section: Sums, Products, Aggregate Functions.
// Function: sum()
// Description:
// Returns the sum of all entries in the given list.
// If passed an array of vectors, returns a vector of sums of each part.
// If passed an empty list, the value of `dflt` will be returned.
// Arguments:
// v = The list to get the sum of.
// dflt = The default value to return if `v` is an empty list. Default: 0
// Example:
// sum([1,2,3]); // returns 6.
// sum([[1,2,3], [3,4,5], [5,6,7]]); // returns [9, 12, 15]
function sum(v, dflt=0) =
is_vector(v) ? [for(i=v) 1]*v :
assert(is_consistent(v), "Input to sum is non-numeric or inconsistent")
is_vector(v[0]) ? [for(i=v) 1]*v :
len(v) == 0 ? dflt :
_sum(v,v[0]*0);
function _sum(v,_total,_i=0) = _i>=len(v) ? _total : _sum(v,_total+v[_i], _i+1);
// Function: cumsum()
// Description:
// Returns a list where each item is the cumulative sum of all items up to and including the corresponding entry in the input list.
// If passed an array of vectors, returns a list of cumulative vectors sums.
// Arguments:
// v = The list to get the sum of.
// Example:
// cumsum([1,1,1]); // returns [1,2,3]
// cumsum([2,2,2]); // returns [2,4,6]
// cumsum([1,2,3]); // returns [1,3,6]
// cumsum([[1,2,3], [3,4,5], [5,6,7]]); // returns [[1,2,3], [4,6,8], [9,12,15]]
function cumsum(v,_i=0,_acc=[]) =
_i==len(v) ? _acc :
cumsum(
v, _i+1,
concat(
_acc,
[_i==0 ? v[_i] : select(_acc,-1)+v[_i]]
)
);
// Function: sum_of_squares()
// Description:
// Returns the sum of the square of each element of a vector.
// Arguments:
// v = The vector to get the sum of.
// Example:
// sum_of_squares([1,2,3]); // Returns: 14.
// sum_of_squares([1,2,4]); // Returns: 21
// sum_of_squares([-3,-2,-1]); // Returns: 14
function sum_of_squares(v) = sum(vmul(v,v));
// Function: sum_of_sines()
// Usage:
// sum_of_sines(a,sines)
// Description:
// Gives the sum of a series of sines, at a given angle.
// Arguments:
// a = Angle to get the value for.
// sines = List of [amplitude, frequency, offset] items, where the frequency is the number of times the cycle repeats around the circle.
// Examples:
// v = sum_of_sines(30, [[10,3,0], [5,5.5,60]]);
function sum_of_sines(a, sines) =
sum([
for (s = sines) let(
ss=point3d(s),
v=ss.x*sin(a*ss.y+ss.z)
) v
]);
// Function: deltas()
// Description:
// Returns a list with the deltas of adjacent entries in the given list.
// Given [a,b,c,d], returns [b-a,c-b,d-c].
// Arguments:
// v = The list to get the deltas of.
// Example:
// deltas([2,5,9,17]); // returns [3,4,8].
// deltas([[1,2,3], [3,6,8], [4,8,11]]); // returns [[2,4,5], [1,2,3]]
function deltas(v) = [for (p=pair(v)) p.y-p.x];
// Function: product()
// Description:
// Returns the product of all entries in the given list.
// If passed an array of vectors, returns a vector of products of each part.
// If passed an array of matrices, returns a the resulting product matrix.
// Arguments:
// v = The list to get the product of.
// Example:
// product([2,3,4]); // returns 24.
// product([[1,2,3], [3,4,5], [5,6,7]]); // returns [15, 48, 105]
function product(v, i=0, tot=undef) = i>=len(v)? tot : product(v, i+1, ((tot==undef)? v[i] : is_vector(v[i])? vmul(tot,v[i]) : tot*v[i]));
// Function: outer_product()
// Description:
// Compute the outer product of two vectors, a matrix.
// Usage:
// M = outer_product(u,v);
function outer_product(u,v) =
assert(is_vector(u) && is_vector(v))
assert(len(u)==len(v))
[for(i=[0:len(u)-1]) [for(j=[0:len(u)-1]) u[i]*v[j]]];
// Function: mean()
// Description:
// Returns the arithmatic mean/average of all entries in the given array.
// If passed a list of vectors, returns a vector of the mean of each part.
// Arguments:
// v = The list of values to get the mean of.
// Example:
// mean([2,3,4]); // returns 3.
// mean([[1,2,3], [3,4,5], [5,6,7]]); // returns [3, 4, 5]
function mean(v) = sum(v)/len(v);
// Function: median()
// Usage:
// x = median(v);
// Description:
// Given a list of numbers or vectors, finds the median value or midpoint.
// If passed a list of vectors, returns the vector of the median of each part.
function median(v) =
assert(is_list(v))
assert(len(v)>0)
is_vector(v[0])? (
assert(is_consistent(v))
[
for (i=idx(v[0]))
let(vals = subindex(v,i))
(min(vals)+max(vals))/2
]
) : (min(v)+max(v))/2;
// Section: Matrix math
// Function: linear_solve()
// Usage: linear_solve(A,b)
// Description:
// Solves the linear system Ax=b. If A is square and non-singular the unique solution is returned. If A is overdetermined
// the least squares solution is returned. If A is underdetermined, the minimal norm solution is returned.
// If A is rank deficient or singular then linear_solve returns []. If b is a matrix that is compatible with A
// then the problem is solved for the matrix valued right hand side and a matrix is returned. Note that if you
// want to solve Ax=b1 and Ax=b2 that you need to form the matrix transpose([b1,b2]) for the right hand side and then
// transpose the returned value.
function linear_solve(A,b) =
assert(is_matrix(A))
let(
m = len(A),
n = len(A[0])
)
assert(is_vector(b,m) || is_matrix(b,m),"Incompatible matrix and right hand side")
let (
qr = m<n? qr_factor(transpose(A)) : qr_factor(A),
maxdim = max(n,m),
mindim = min(n,m),
Q = submatrix(qr[0],[0:maxdim-1], [0:mindim-1]),
R = submatrix(qr[1],[0:mindim-1], [0:mindim-1]),
zeros = [for(i=[0:mindim-1]) if (approx(R[i][i],0)) i]
)
zeros != [] ? [] :
m<n ? Q*back_substitute(R,b,transpose=true) :
back_substitute(R, transpose(Q)*b);
// Function: matrix_inverse()
// Usage:
// matrix_inverse(A)
// Description:
// Compute the matrix inverse of the square matrix A. If A is singular, returns undef.
// Note that if you just want to solve a linear system of equations you should NOT
// use this function. Instead use linear_solve, or use qr_factor. The computation
// will be faster and more accurate.
function matrix_inverse(A) =
assert(is_matrix(A,square=true),"Input to matrix_inverse() must be a square matrix")
linear_solve(A,ident(len(A)));
// Function: submatrix()
// Usage: submatrix(M, ind1, ind2)
// Description:
// Returns a submatrix with the specified index ranges or index sets.
function submatrix(M,ind1,ind2) =
[for(i=ind1) [for(j=ind2) M[i][j] ] ];
// Function: qr_factor()
// Usage: qr = qr_factor(A)
// Description:
// Calculates the QR factorization of the input matrix A and returns it as the list [Q,R]. This factorization can be
// used to solve linear systems of equations.
function qr_factor(A) =
assert(is_matrix(A))
let(
m = len(A),
n = len(A[0])
)
let(
qr =_qr_factor(A, column=0, m = m, n=n, Q=ident(m)),
Rzero = [
for(i=[0:m-1]) [
for(j=[0:n-1])
i>j ? 0 : qr[1][i][j]
]
]
) [qr[0],Rzero];
function _qr_factor(A,Q, column, m, n) =
column >= min(m-1,n) ? [Q,A] :
let(
x = [for(i=[column:1:m-1]) A[i][column]],
alpha = (x[0]<=0 ? 1 : -1) * norm(x),
u = x - concat([alpha],repeat(0,m-1)),
v = alpha==0 ? u : u / norm(u),
Qc = ident(len(x)) - 2*outer_product(v,v),
Qf = [for(i=[0:m-1]) [for(j=[0:m-1]) i<column || j<column ? (i==j ? 1 : 0) : Qc[i-column][j-column]]]
)
_qr_factor(Qf*A, Q*Qf, column+1, m, n);
// Function: back_substitute()
// Usage: back_substitute(R, b, [transpose])
// Description:
// Solves the problem Rx=b where R is an upper triangular square matrix. No check is made that the lower triangular entries
// are actually zero. If transpose==true then instead solve transpose(R)*x=b.
// You can supply a compatible matrix b and it will produce the solution for every column of b. Note that if you want to
// solve Rx=b1 and Rx=b2 you must set b to transpose([b1,b2]) and then take the transpose of the result. If the matrix
// is singular (e.g. has a zero on the diagonal) then it returns [].
function back_substitute(R, b, x=[],transpose = false) =
assert(is_matrix(R, square=true))
let(n=len(R))
assert(is_vector(b,n) || is_matrix(b,n),str("R and b are not compatible in back_substitute ",n, len(b)))
!is_vector(b) ? transpose([for(i=[0:len(b[0])-1]) back_substitute(R,subindex(b,i),transpose=transpose)]) :
transpose?
reverse(back_substitute(
[for(i=[0:n-1]) [for(j=[0:n-1]) R[n-1-j][n-1-i]]],
reverse(b), x, false
)) :
len(x) == n ? x :
let(
ind = n - len(x) - 1
)
R[ind][ind] == 0 ? [] :
let(
newvalue =
len(x)==0? b[ind]/R[ind][ind] :
(b[ind]-select(R[ind],ind+1,-1) * x)/R[ind][ind]
) back_substitute(R, b, concat([newvalue],x));
// Function: det2()
// Description:
// Optimized function that returns the determinant for the given 2x2 square matrix.
// Arguments:
// M = The 2x2 square matrix to get the determinant of.
// Example:
// M = [ [6,-2], [1,8] ];
// det = det2(M); // Returns: 50
function det2(M) = M[0][0] * M[1][1] - M[0][1]*M[1][0];
// Function: det3()
// Description:
// Optimized function that returns the determinant for the given 3x3 square matrix.
// Arguments:
// M = The 3x3 square matrix to get the determinant of.
// Example:
// M = [ [6,4,-2], [1,-2,8], [1,5,7] ];
// det = det3(M); // Returns: -334
function det3(M) =
M[0][0] * (M[1][1]*M[2][2]-M[2][1]*M[1][2]) -
M[1][0] * (M[0][1]*M[2][2]-M[2][1]*M[0][2]) +
M[2][0] * (M[0][1]*M[1][2]-M[1][1]*M[0][2]);
// Function: determinant()
// Description:
// Returns the determinant for the given square matrix.
// Arguments:
// M = The NxN square matrix to get the determinant of.
// Example:
// M = [ [6,4,-2,9], [1,-2,8,3], [1,5,7,6], [4,2,5,1] ];
// det = determinant(M); // Returns: 2267
function determinant(M) =
assert(len(M)==len(M[0]))
len(M)==1? M[0][0] :
len(M)==2? det2(M) :
len(M)==3? det3(M) :
sum(
[for (col=[0:1:len(M)-1])
((col%2==0)? 1 : -1) *
M[col][0] *
determinant(
[for (r=[1:1:len(M)-1])
[for (c=[0:1:len(M)-1])
if (c!=col) M[c][r]
]
]
)
]
);
// Function: is_matrix()
// Usage:
// is_matrix(A,[m],[n],[square])
// Description:
// Returns true if A is a numeric matrix of height m and width n. If m or n
// are omitted or set to undef then true is returned for any positive dimension.
// If `square` is true then the matrix is required to be square. Note if you
// specify m != n and require a square matrix then the result will always be false.
// Arguments:
// A = matrix to test
// m = optional height of matrix
// n = optional width of matrix
// square = set to true to require a square matrix. Default: false
function is_matrix(A,m,n,square=false) =
is_vector(A[0],n) && is_vector(A*(0*A[0]),m) &&
(!square || len(A)==len(A[0]));
// Section: Comparisons and Logic
// Function: approx()
// Usage:
// approx(a,b,[eps])
// Description:
// Compares two numbers or vectors, and returns true if they are closer than `eps` to each other.
// Arguments:
// a = First value.
// b = Second value.
// eps = The maximum allowed difference between `a` and `b` that will return true.
// Example:
// approx(-0.3333333333,-1/3); // Returns: true
// approx(0.3333333333,1/3); // Returns: true
// approx(0.3333,1/3); // Returns: false
// approx(0.3333,1/3,eps=1e-3); // Returns: true
// approx(PI,3.1415926536); // Returns: true
function approx(a,b,eps=EPSILON) =
a==b? true :
a*0!=b*0? false :
is_list(a)? ([for (i=idx(a)) if(!approx(a[i],b[i],eps=eps)) 1] == []) :
(abs(a-b) <= eps);
function _type_num(x) =
is_undef(x)? 0 :
is_bool(x)? 1 :
is_num(x)? 2 :
is_nan(x)? 3 :
is_string(x)? 4 :
is_list(x)? 5 : 6;
// Function: compare_vals()
// Usage:
// compare_vals(a, b);
// Description:
// Compares two values. Lists are compared recursively.
// Returns <0 if a<b. Returns >0 if a>b. Returns 0 if a==b.
// If types are not the same, then undef < bool < num < str < list < range.
// Arguments:
// a = First value to compare.
// b = Second value to compare.
function compare_vals(a, b) =
(a==b)? 0 :
let(t1=_type_num(a), t2=_type_num(b)) (t1!=t2)? (t1-t2) :
is_list(a)? compare_lists(a,b) :
is_nan(a)? 0 :
(a<b)? -1 : (a>b)? 1 : 0;
// Function: compare_lists()
// Usage:
// compare_lists(a, b)
// Description:
// Compare contents of two lists using `compare_vals()`.
// Returns <0 if `a`<`b`.
// Returns 0 if `a`==`b`.
// Returns >0 if `a`>`b`.
// Arguments:
// a = First list to compare.
// b = Second list to compare.
function compare_lists(a, b) =
a==b? 0 : let(
cmps = [
for(i=[0:1:min(len(a),len(b))-1]) let(
cmp = compare_vals(a[i],b[i])
) if(cmp!=0) cmp
]
) cmps==[]? (len(a)-len(b)) : cmps[0];
// Function: any()
// Description:
// Returns true if any item in list `l` evaluates as true.
// If `l` is a lists of lists, `any()` is applied recursively to each sublist.
// Arguments:
// l = The list to test for true items.
// Example:
// any([0,false,undef]); // Returns false.
// any([1,false,undef]); // Returns true.
// any([1,5,true]); // Returns true.
// any([[0,0], [0,0]]); // Returns false.
// any([[0,0], [1,0]]); // Returns true.
function any(l, i=0, succ=false) =
(i>=len(l) || succ)? succ :
any(
l, i=i+1, succ=(
is_list(l[i])? any(l[i]) :
!(!l[i])
)
);
// Function: all()
// Description:
// Returns true if all items in list `l` evaluate as true.
// If `l` is a lists of lists, `all()` is applied recursively to each sublist.
// Arguments:
// l = The list to test for true items.
// Example:
// all([0,false,undef]); // Returns false.
// all([1,false,undef]); // Returns false.
// all([1,5,true]); // Returns true.
// all([[0,0], [0,0]]); // Returns false.
// all([[0,0], [1,0]]); // Returns false.
// all([[1,1], [1,1]]); // Returns true.
function all(l, i=0, fail=false) =
(i>=len(l) || fail)? (!fail) :
all(
l, i=i+1, fail=(
is_list(l[i])? !all(l[i]) :
!l[i]
)
);
// Function: count_true()
// Usage:
// count_true(l)
// Description:
// Returns the number of items in `l` that evaluate as true.
// If `l` is a lists of lists, this is applied recursively to each
// sublist. Returns the total count of items that evaluate as true
// in all recursive sublists.
// Arguments:
// l = The list to test for true items.
// nmax = If given, stop counting if `nmax` items evaluate as true.
// Example:
// count_true([0,false,undef]); // Returns 0.
// count_true([1,false,undef]); // Returns 1.
// count_true([1,5,false]); // Returns 2.
// count_true([1,5,true]); // Returns 3.
// count_true([[0,0], [0,0]]); // Returns 0.
// count_true([[0,0], [1,0]]); // Returns 1.
// count_true([[1,1], [1,1]]); // Returns 4.
// count_true([[1,1], [1,1]], nmax=3); // Returns 3.
function count_true(l, nmax=undef, i=0, cnt=0) =
(i>=len(l) || (nmax!=undef && cnt>=nmax))? cnt :
count_true(
l=l, nmax=nmax, i=i+1, cnt=cnt+(
is_list(l[i])? count_true(l[i], nmax=nmax-cnt) :
(l[i]? 1 : 0)
)
);
// Section: Calculus
// Function: deriv()
// Usage: deriv(data, [h], [closed])
// Description:
// Computes a numerical derivative estimate of the data, which may be scalar or vector valued.
// The `h` parameter gives the step size of your sampling so the derivative can be scaled correctly.
// If the `closed` parameter is true the data is assumed to be defined on a loop with data[0] adjacent to
// data[len(data)-1]. This function uses a symetric derivative approximation
// for internal points, f'(t) = (f(t+h)-f(t-h))/2h. For the endpoints (when closed=false) the algorithm
// uses a two point method if sufficient points are available: f'(t) = (3*(f(t+h)-f(t)) - (f(t+2*h)-f(t+h)))/2h.
//
// If `h` is a vector then it is assumed to be nonuniform, with h[i] giving the sampling distance
// between data[i+1] and data[i], and the data values will be linearly resampled at each corner
// to produce a uniform spacing for the derivative estimate. At the endpoints a single point method
// is used: f'(t) = (f(t+h)-f(t))/h.
function deriv(data, h=1, closed=false) =
is_vector(h) ? _deriv_nonuniform(data, h, closed=closed) :
let( L = len(data) )
closed? [
for(i=[0:1:L-1])
(data[(i+1)%L]-data[(L+i-1)%L])/2/h
] :
let(
first =
L<3? data[1]-data[0] :
3*(data[1]-data[0]) - (data[2]-data[1]),
last =
L<3? data[L-1]-data[L-2]:
(data[L-3]-data[L-2])-3*(data[L-2]-data[L-1])
) [
first/2/h,
for(i=[1:1:L-2]) (data[i+1]-data[i-1])/2/h,
last/2/h
];
function _dnu_calc(f1,fc,f2,h1,h2) =
let(
f1 = h2<h1 ? lerp(fc,f1,h2/h1) : f1 ,
f2 = h1<h2 ? lerp(fc,f2,h1/h2) : f2
)
(f2-f1) / 2 / min([h1,h2]);
function _deriv_nonuniform(data, h, closed) =
assert(len(h) == len(data)-(closed?0:1),str("Vector valued h must be length ",len(data)-(closed?0:1)))
let(
L = len(data)
)
closed? [for(i=[0:1:L-1])
_dnu_calc(data[(L+i-1)%L], data[i], data[(i+1)%L], select(h,i-1), h[i]) ]
: [
(data[1]-data[0])/h[0],
for(i=[1:1:L-2]) _dnu_calc(data[i-1],data[i],data[i+1], h[i-1],h[i]),
(data[L-1]-data[L-2])/h[L-2]
];
// Function: deriv2()
// Usage: deriv2(data, [h], [closed])
// Description:
// Computes a numerical esimate of the second derivative of the data, which may be scalar or vector valued.
// The `h` parameter gives the step size of your sampling so the derivative can be scaled correctly.
// If the `closed` parameter is true the data is assumed to be defined on a loop with data[0] adjacent to
// data[len(data)-1]. For internal points this function uses the approximation
// f''(t) = (f(t-h)-2*f(t)+f(t+h))/h^2. For the endpoints (when closed=false) the algorithm
// when sufficient points are available the method is either the four point expression
// f''(t) = (2*f(t) - 5*f(t+h) + 4*f(t+2*h) - f(t+3*h))/h^2 or if five points are available
// f''(t) = (35*f(t) - 104*f(t+h) + 114*f(t+2*h) - 56*f(t+3*h) + 11*f(t+4*h)) / 12h^2
function deriv2(data, h=1, closed=false) =
let( L = len(data) )
closed? [
for(i=[0:1:L-1])
(data[(i+1)%L]-2*data[i]+data[(L+i-1)%L])/h/h
] :
let(
first = L<3? undef :
L==3? data[0] - 2*data[1] + data[2] :
L==4? 2*data[0] - 5*data[1] + 4*data[2] - data[3] :
(35*data[0] - 104*data[1] + 114*data[2] - 56*data[3] + 11*data[4])/12,
last = L<3? undef :
L==3? data[L-1] - 2*data[L-2] + data[L-3] :
L==4? -2*data[L-1] + 5*data[L-2] - 4*data[L-3] + data[L-4] :
(35*data[L-1] - 104*data[L-2] + 114*data[L-3] - 56*data[L-4] + 11*data[L-5])/12
) [
first/h/h,
for(i=[1:1:L-2]) (data[i+1]-2*data[i]+data[i-1])/h/h,
last/h/h
];
// Function: deriv3()
// Usage: deriv3(data, [h], [closed])
// Description:
// Computes a numerical third derivative estimate of the data, which may be scalar or vector valued.
// The `h` parameter gives the step size of your sampling so the derivative can be scaled correctly.
// If the `closed` parameter is true the data is assumed to be defined on a loop with data[0] adjacent to
// data[len(data)-1]. This function uses a five point derivative estimate, so the input must include five points:
// f'''(t) = (-f(t-2*h)+2*f(t-h)-2*f(t+h)+f(t+2*h)) / 2h^3. At the first and second points from the end
// the estimates are f'''(t) = (-5*f(t)+18*f(t+h)-24*f(t+2*h)+14*f(t+3*h)-3*f(t+4*h)) / 2h^3 and
// f'''(t) = (-3*f(t-h)+10*f(t)-12*f(t+h)+6*f(t+2*h)-f(t+3*h)) / 2h^3.
function deriv3(data, h=1, closed=false) =
let(
L = len(data),
h3 = h*h*h
)
assert(L>=5, "Need five points for 3rd derivative estimate")
closed? [
for(i=[0:1:L-1])
(-data[(L+i-2)%L]+2*data[(L+i-1)%L]-2*data[(i+1)%L]+data[(i+2)%L])/2/h3
] :
let(
first=(-5*data[0]+18*data[1]-24*data[2]+14*data[3]-3*data[4])/2,
second=(-3*data[0]+10*data[1]-12*data[2]+6*data[3]-data[4])/2,
last=(5*data[L-1]-18*data[L-2]+24*data[L-3]-14*data[L-4]+3*data[L-5])/2,
prelast=(3*data[L-1]-10*data[L-2]+12*data[L-3]-6*data[L-4]+data[L-5])/2
) [
first/h3,
second/h3,
for(i=[2:1:L-3]) (-data[i-2]+2*data[i-1]-2*data[i+1]+data[i+2])/2/h3,
prelast/h3,
last/h3
];
// Section: Complex Numbers
// Function: C_times()
// Usage: C_times(z1,z2)
// Description:
// Multiplies two complex numbers.
function C_times(z1,z2) = [z1.x*z2.x-z1.y*z2.y,z1.x*z2.y+z1.y*z2.x];
// Function: C_div()
// Usage: C_div(z1,z2)
// Description:
// Divides z1 by z2.
function C_div(z1,z2) = let(den = z2.x*z2.x + z2.y*z2.y)
[(z1.x*z2.x + z1.y*z2.y)/den, (z1.y*z2.x-z1.x*z2.y)/den];
// Section: Polynomials
// Function: polynomial()
// Usage:
// polynomial(p, z)
// Description:
// Evaluates specified real polynomial, p, at the complex or real input value, z.
// The polynomial is specified as p=[a_n, a_{n-1},...,a_1,a_0]
// where a_n is the z^n coefficient. Polynomial coefficients are real.
// Note: this should probably be recoded to use division by [1,-z], which is more accurate
// and avoids overflow with large coefficients, but requires poly_div to support complex coefficients.
function polynomial(p, z, k, zk, total) =
is_undef(k) ? polynomial(p, z, len(p)-1, is_num(z)? 1 : [1,0], is_num(z) ? 0 : [0,0]) :
k==-1 ? total :
polynomial(p, z, k-1, is_num(z) ? zk*z : C_times(zk,z), total+zk*p[k]);
// Function: poly_mult()
// Usage
// polymult(p,q)
// polymult([p1,p2,p3,...])
// Descriptoin:
// Given a list of polynomials represented as real coefficient lists, with the highest degree coefficient first,
// computes the coefficient list of the product polynomial.
function poly_mult(p,q) =
is_undef(q) ?
assert(is_list(p) && (is_vector(p[0]) || p[0]==[]), "Invalid arguments to poly_mult")
len(p)==2 ? poly_mult(p[0],p[1])
: poly_mult(p[0], poly_mult(select(p,1,-1)))
:
_poly_trim(
[
for(n = [len(p)+len(q)-2:-1:0])
sum( [for(i=[0:1:len(p)-1])
let(j = len(p)+len(q)- 2 - n - i)
if (j>=0 && j<len(q)) p[i]*q[j]
])
]);
// Function: poly_div()
// Usage:
// [quotient,remainder] = poly_div(n,d)
// Description:
// Computes division of the numerator polynomial by the denominator polynomial and returns
// a list of two polynomials, [quotient, remainder]. If the division has no remainder then
// the zero polynomial [] is returned for the remainder. Similarly if the quotient is zero
// the returned quotient will be [].
function poly_div(n,d,q=[]) =
assert(len(d)>0 && d[0]!=0 , "Denominator is zero or has leading zero coefficient")
len(n)<len(d) ? [q,_poly_trim(n)] :
let(
t = n[0] / d[0],
newq = concat(q,[t]),
newn = [for(i=[1:1:len(n)-1]) i<len(d) ? n[i] - t*d[i] : n[i]]
)
poly_div(newn,d,newq);
// Internal Function: _poly_trim()
// Usage:
// _poly_trim(p,[eps])
// Description:
// Removes leading zero terms of a polynomial. By default zeros must be exact,
// or give epsilon for approximate zeros.
function _poly_trim(p,eps=0) =
let( nz = [for(i=[0:1:len(p)-1]) if (!approx(p[i],0,eps)) i])
len(nz)==0 ? [] : select(p,nz[0],-1);
// Function: poly_add()
// Usage:
// sum = poly_add(p,q)
// Description:
// Computes the sum of two polynomials.
function poly_add(p,q) =
let( plen = len(p),
qlen = len(q),
long = plen>qlen ? p : q,
short = plen>qlen ? q : p
)
_poly_trim(long + concat(repeat(0,len(long)-len(short)),short));
// Function: poly_roots()
// Usage:
// poly_roots(p,[tol])
// Description:
// Returns all complex roots of the specified real polynomial p.
// The polynomial is specified as p=[a_n, a_{n-1},...,a_1,a_0]
// where a_n is the z^n coefficient. The tol parameter gives
// the stopping tolerance for the iteration. The polynomial
// must have at least one non-zero coefficient. Convergence is poor
// if the polynomial has any repeated roots other than zero.
// Arguments:
// p = polynomial coefficients with higest power coefficient first
// tol = tolerance for iteration. Default: 1e-14
// Uses the Aberth method https://en.wikipedia.org/wiki/Aberth_method
//
// Dario Bini. "Numerical computation of polynomial zeros by means of Aberth's Method", Numerical Algorithms, Feb 1996.
// https://www.researchgate.net/publication/225654837_Numerical_computation_of_polynomial_zeros_by_means_of_Aberth's_method
function poly_roots(p,tol=1e-14,error_bound=false) =
assert(p!=[], "Input polynomial must have a nonzero coefficient")
assert(is_vector(p), "Input must be a vector")
p[0] == 0 ? poly_roots(slice(p,1,-1),tol=tol,error_bound=error_bound) : // Strip leading zero coefficients
p[len(p)-1] == 0 ? // Strip trailing zero coefficients
let( solutions = poly_roots(select(p,0,-2),tol=tol, error_bound=error_bound))
(error_bound ? [ [[0,0], each solutions[0]], [0, each solutions[1]]]
: [[0,0], each solutions]) :
len(p)==1 ? (error_bound ? [[],[]] : []) : // Nonzero constant case has no solutions
len(p)==2 ? let( solution = [[-p[1]/p[0],0]]) // Linear case needs special handling
(error_bound ? [solution,[0]] : solution)
:
let(
n = len(p)-1, // polynomial degree
pderiv = [for(i=[0:n-1]) p[i]*(n-i)],
s = [for(i=[0:1:n]) abs(p[i])*(4*(n-i)+1)], // Error bound polynomial from Bini
// Using method from: http://www.kurims.kyoto-u.ac.jp/~kyodo/kokyuroku/contents/pdf/0915-24.pdf
beta = -p[1]/p[0]/n,
r = 1+pow(abs(polynomial(p,beta)/p[0]),1/n),
init = [for(i=[0:1:n-1]) // Initial guess for roots
let(angle = 360*i/n+270/n/PI)
[beta,0]+r*[cos(angle),sin(angle)]
],
roots = _poly_roots(p,pderiv,s,init,tol=tol),
error = error_bound ? [for(xi=roots) n * (norm(polynomial(p,xi))+tol*polynomial(s,norm(xi))) /
abs(norm(polynomial(pderiv,xi))-tol*polynomial(s,norm(xi)))] : 0
)
error_bound ? [roots, error] : roots;
// p = polynomial
// pderiv = derivative polynomial of p
// z = current guess for the roots
// tol = root tolerance
// i=iteration counter
function _poly_roots(p, pderiv, s, z, tol, i=0) =
assert(i<45, str("Polyroot exceeded iteration limit. Current solution:", z))
let(
n = len(z),
svals = [for(zk=z) tol*polynomial(s,norm(zk))],
p_of_z = [for(zk=z) polynomial(p,zk)],
done = [for(k=[0:n-1]) norm(p_of_z[k])<=svals[k]],
newton = [for(k=[0:n-1]) C_div(p_of_z[k], polynomial(pderiv,z[k]))],
zdiff = [for(k=[0:n-1]) sum([for(j=[0:n-1]) if (j!=k) C_div([1,0], z[k]-z[j])])],
w = [for(k=[0:n-1]) done[k] ? [0,0] : C_div( newton[k],
[1,0] - C_times(newton[k], zdiff[k]))]
)
all(done) ? z : _poly_roots(p,pderiv,s,z-w,tol,i+1);
// Function: real_roots()
// Usage:
// real_roots(p, [eps], [tol])
// Description:
// Returns the real roots of the specified real polynomial p.
// The polynomial is specified as p=[a_n, a_{n-1},...,a_1,a_0]
// where a_n is the x^n coefficient. This function works by
// computing the complex roots and returning those roots where
// the imaginary part is closed to zero. By default it uses a computed
// error bound from the polynomial solver to decide whether imaginary
// parts are zero. You can specify eps, in which case the test is
// z.y/(1+norm(z)) < eps. Because
// of poor convergence and higher error for repeated roots, such roots may
// be missed by the algorithm because their imaginary part is large.
// Arguments:
// p = polynomial to solve as coefficient list, highest power term first
// eps = used to determine whether imaginary parts of roots are zero
// tol = tolerance for the complex polynomial root finder
function real_roots(p,eps=undef,tol=1e-14) =
let(
roots_err = poly_roots(p,error_bound=true),
roots = roots_err[0],
err = roots_err[1]
)
is_def(eps) ? [for(z=roots) if (abs(z.y)/(1+norm(z))<eps) z.x]
: [for(i=idx(roots)) if (abs(roots[i].y)<=err[i]) roots[i].x];
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