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File: octave.info,  Node: Optimization,  Next: Statistics,  Prev: Differential Equations,  Up: Top

25 Optimization
***************

Octave comes with support for solving various kinds of optimization
problems.  Specifically Octave can solve problems in Linear Programming,
Quadratic Programming, Nonlinear Programming, and Linear Least Squares
Minimization.

* Menu:

* Linear Programming::
* Quadratic Programming::
* Nonlinear Programming::
* Linear Least Squares::


File: octave.info,  Node: Linear Programming,  Next: Quadratic Programming,  Up: Optimization

25.1 Linear Programming
=======================

Octave can solve Linear Programming problems using the ‘glpk’ function.
That is, Octave can solve

     min C'*x

   subject to the linear constraints A*x = b where x ≥ 0.

The ‘glpk’ function also supports variations of this problem.

 -- : [XOPT, FMIN, ERRNUM, EXTRA] = glpk (C, A, B, LB, UB, CTYPE,
          VARTYPE, SENSE, PARAM)
     Solve a linear program using the GNU GLPK library.

     Given three arguments, ‘glpk’ solves the following standard LP:

          min C'*x

     subject to

          A*x  = b
            x >= 0

     but may also solve problems of the form

          [ min | max ] C'*x

     subject to

          A*x [ "=" | "<=" | ">=" ] b
            x >= LB
            x <= UB

     Input arguments:

     C
          A column array containing the objective function coefficients.

     A
          A matrix containing the constraints coefficients.

     B
          A column array containing the right-hand side value for each
          constraint in the constraint matrix.

     LB
          An array containing the lower bound on each of the variables.
          If LB is not supplied, the default lower bound for the
          variables is zero.

     UB
          An array containing the upper bound on each of the variables.
          If UB is not supplied, the default upper bound is assumed to
          be infinite.

     CTYPE
          An array of characters containing the sense of each constraint
          in the constraint matrix.  Each element of the array may be
          one of the following values

          "F"
               A free (unbounded) constraint (the constraint is
               ignored).

          "U"
               An inequality constraint with an upper bound (‘A(i,:)*x
               <= b(i)’).

          "S"
               An equality constraint (‘A(i,:)*x = b(i)’).

          "L"
               An inequality with a lower bound (‘A(i,:)*x >= b(i)’).

          "D"
               An inequality constraint with both upper and lower bounds
               (‘A(i,:)*x >= -b(i)’) _and_ (‘A(i,:)*x <= b(i)’).

     VARTYPE
          A column array containing the types of the variables.

          "C"
               A continuous variable.

          "I"
               An integer variable.

     SENSE
          If SENSE is 1, the problem is a minimization.  If SENSE is -1,
          the problem is a maximization.  The default value is 1.

     PARAM
          A structure containing the following parameters used to define
          the behavior of solver.  Missing elements in the structure
          take on default values, so you only need to set the elements
          that you wish to change from the default.

          Integer parameters:

          ‘msglev (default: 1)’
               Level of messages output by solver routines:

               0 (‘GLP_MSG_OFF’)
                    No output.

               1 (‘GLP_MSG_ERR’)
                    Error and warning messages only.

               2 (‘GLP_MSG_ON’)
                    Normal output.

               3 (‘GLP_MSG_ALL’)
                    Full output (includes informational messages).

          ‘scale (default: 16)’
               Scaling option.  The values can be combined with the
               bitwise OR operator and may be the following:

               1 (‘GLP_SF_GM’)
                    Geometric mean scaling.

               16 (‘GLP_SF_EQ’)
                    Equilibration scaling.

               32 (‘GLP_SF_2N’)
                    Round scale factors to power of two.

               64 (‘GLP_SF_SKIP’)
                    Skip if problem is well scaled.

               Alternatively, a value of 128 (‘GLP_SF_AUTO’) may be also
               specified, in which case the routine chooses the scaling
               options automatically.

          ‘dual (default: 1)’
               Simplex method option:

               1 (‘GLP_PRIMAL’)
                    Use two-phase primal simplex.

               2 (‘GLP_DUALP’)
                    Use two-phase dual simplex, and if it fails, switch
                    to the primal simplex.

               3 (‘GLP_DUAL’)
                    Use two-phase dual simplex.

          ‘price (default: 34)’
               Pricing option (for both primal and dual simplex):

               17 (‘GLP_PT_STD’)
                    Textbook pricing.

               34 (‘GLP_PT_PSE’)
                    Steepest edge pricing.

          ‘itlim (default: intmax)’
               Simplex iterations limit.  It is decreased by one each
               time when one simplex iteration has been performed, and
               reaching zero value signals the solver to stop the
               search.

          ‘outfrq (default: 200)’
               Output frequency, in iterations.  This parameter
               specifies how frequently the solver sends information
               about the solution to the standard output.

          ‘branch (default: 4)’
               Branching technique option (for MIP only):

               1 (‘GLP_BR_FFV’)
                    First fractional variable.

               2 (‘GLP_BR_LFV’)
                    Last fractional variable.

               3 (‘GLP_BR_MFV’)
                    Most fractional variable.

               4 (‘GLP_BR_DTH’)
                    Heuristic by Driebeck and Tomlin.

               5 (‘GLP_BR_PCH’)
                    Hybrid pseudocost heuristic.

          ‘btrack (default: 4)’
               Backtracking technique option (for MIP only):

               1 (‘GLP_BT_DFS’)
                    Depth first search.

               2 (‘GLP_BT_BFS’)
                    Breadth first search.

               3 (‘GLP_BT_BLB’)
                    Best local bound.

               4 (‘GLP_BT_BPH’)
                    Best projection heuristic.

          ‘presol (default: 1)’
               If this flag is set, the simplex solver uses the built-in
               LP presolver.  Otherwise the LP presolver is not used.

          ‘lpsolver (default: 1)’
               Select which solver to use.  If the problem is a MIP
               problem this flag will be ignored.

               1
                    Revised simplex method.

               2
                    Interior point method.

          ‘rtest (default: 34)’
               Ratio test technique:

               17 (‘GLP_RT_STD’)
                    Standard ("textbook").

               34 (‘GLP_RT_HAR’)
                    Harris’ two-pass ratio test.

          ‘tmlim (default: intmax)’
               Searching time limit, in milliseconds.

          ‘outdly (default: 0)’
               Output delay, in seconds.  This parameter specifies how
               long the solver should delay sending information about
               the solution to the standard output.

          ‘save (default: 0)’
               If this parameter is nonzero, save a copy of the problem
               in CPLEX LP format to the file ‘"outpb.lp"’.  There is
               currently no way to change the name of the output file.

          Real parameters:

          ‘tolbnd (default: 1e-7)’
               Relative tolerance used to check if the current basic
               solution is primal feasible.  It is not recommended that
               you change this parameter unless you have a detailed
               understanding of its purpose.

          ‘toldj (default: 1e-7)’
               Absolute tolerance used to check if the current basic
               solution is dual feasible.  It is not recommended that
               you change this parameter unless you have a detailed
               understanding of its purpose.

          ‘tolpiv (default: 1e-10)’
               Relative tolerance used to choose eligible pivotal
               elements of the simplex table.  It is not recommended
               that you change this parameter unless you have a detailed
               understanding of its purpose.

          ‘objll (default: -DBL_MAX)’
               Lower limit of the objective function.  If the objective
               function reaches this limit and continues decreasing, the
               solver stops the search.  This parameter is used in the
               dual simplex method only.

          ‘objul (default: +DBL_MAX)’
               Upper limit of the objective function.  If the objective
               function reaches this limit and continues increasing, the
               solver stops the search.  This parameter is used in the
               dual simplex only.

          ‘tolint (default: 1e-5)’
               Relative tolerance used to check if the current basic
               solution is integer feasible.  It is not recommended that
               you change this parameter unless you have a detailed
               understanding of its purpose.

          ‘tolobj (default: 1e-7)’
               Relative tolerance used to check if the value of the
               objective function is not better than in the best known
               integer feasible solution.  It is not recommended that
               you change this parameter unless you have a detailed
               understanding of its purpose.

     Output values:

     XOPT
          The optimizer (the value of the decision variables at the
          optimum).

     FOPT
          The optimum value of the objective function.

     ERRNUM
          Error code.

          0
               No error.

          1 (‘GLP_EBADB’)
               Invalid basis.

          2 (‘GLP_ESING’)
               Singular matrix.

          3 (‘GLP_ECOND’)
               Ill-conditioned matrix.

          4 (‘GLP_EBOUND’)
               Invalid bounds.

          5 (‘GLP_EFAIL’)
               Solver failed.

          6 (‘GLP_EOBJLL’)
               Objective function lower limit reached.

          7 (‘GLP_EOBJUL’)
               Objective function upper limit reached.

          8 (‘GLP_EITLIM’)
               Iterations limit exhausted.

          9 (‘GLP_ETMLIM’)
               Time limit exhausted.

          10 (‘GLP_ENOPFS’)
               No primal feasible solution.

          11 (‘GLP_ENODFS’)
               No dual feasible solution.

          12 (‘GLP_EROOT’)
               Root LP optimum not provided.

          13 (‘GLP_ESTOP’)
               Search terminated by application.

          14 (‘GLP_EMIPGAP’)
               Relative MIP gap tolerance reached.

          15 (‘GLP_ENOFEAS’)
               No primal/dual feasible solution.

          16 (‘GLP_ENOCVG’)
               No convergence.

          17 (‘GLP_EINSTAB’)
               Numerical instability.

          18 (‘GLP_EDATA’)
               Invalid data.

          19 (‘GLP_ERANGE’)
               Result out of range.

     EXTRA
          A data structure containing the following fields:

          ‘lambda’
               Dual variables.

          ‘redcosts’
               Reduced Costs.

          ‘time’
               Time (in seconds) used for solving LP/MIP problem.

          ‘status’
               Status of the optimization.

               1 (‘GLP_UNDEF’)
                    Solution status is undefined.

               2 (‘GLP_FEAS’)
                    Solution is feasible.

               3 (‘GLP_INFEAS’)
                    Solution is infeasible.

               4 (‘GLP_NOFEAS’)
                    Problem has no feasible solution.

               5 (‘GLP_OPT’)
                    Solution is optimal.

               6 (‘GLP_UNBND’)
                    Problem has no unbounded solution.

     Example:

          c = [10, 6, 4]';
          A = [ 1, 1, 1;
               10, 4, 5;
                2, 2, 6];
          b = [100, 600, 300]';
          lb = [0, 0, 0]';
          ub = [];
          ctype = "UUU";
          vartype = "CCC";
          s = -1;

          param.msglev = 1;
          param.itlim = 100;

          [xmin, fmin, status, extra] = ...
             glpk (c, A, b, lb, ub, ctype, vartype, s, param);


File: octave.info,  Node: Quadratic Programming,  Next: Nonlinear Programming,  Prev: Linear Programming,  Up: Optimization

25.2 Quadratic Programming
==========================

Octave can also solve Quadratic Programming problems, this is

     min 0.5 x'*H*x + x'*q

   subject to

          A*x = b
          lb <= x <= ub
          A_lb <= A_in*x <= A_ub

 -- : [X, OBJ, INFO, LAMBDA] = qp (X0, H)
 -- : [X, OBJ, INFO, LAMBDA] = qp (X0, H, Q)
 -- : [X, OBJ, INFO, LAMBDA] = qp (X0, H, Q, A, B)
 -- : [X, OBJ, INFO, LAMBDA] = qp (X0, H, Q, A, B, LB, UB)
 -- : [X, OBJ, INFO, LAMBDA] = qp (X0, H, Q, A, B, LB, UB, A_LB, A_IN,
          A_UB)
 -- : [X, OBJ, INFO, LAMBDA] = qp (..., OPTIONS)
     Solve a quadratic program (QP).

     Solve the quadratic program defined by

          min 0.5 x'*H*x + x'*q
           x

     subject to

          A*x = b
          lb <= x <= ub
          A_lb <= A_in*x <= A_ub

     using a null-space active-set method.

     Any bound (A, B, LB, UB, A_IN, A_LB, A_UB) may be set to the empty
     matrix (‘[]’) if not present.  The constraints A and A_IN are
     matrices with each row representing a single constraint.  The other
     bounds are scalars or vectors depending on the number of
     constraints.  The algorithm is faster if the initial guess is
     feasible.

     OPTIONS is a structure specifying additional parameters which
     control the algorithm.  Currently, ‘qp’ recognizes these options:
     "MaxIter", "TolX".

     "MaxIter" proscribes the maximum number of algorithm iterations
     before optimization is halted.  The default value is 200.  The
     value must be a positive integer.

     "TolX" specifies the termination tolerance for the unknown
     variables X.  The default is ‘sqrt (eps)’ or approximately 1e-8.

     On return, X is the location of the minimum and FVAL contains the
     value of the objective function at X.

     INFO
          Structure containing run-time information about the algorithm.
          The following fields are defined:

          ‘solveiter’
               The number of iterations required to find the solution.

          ‘info’
               An integer indicating the status of the solution.

               0
                    The problem is feasible and convex.  Global solution
                    found.

               1
                    The problem is not convex.  Local solution found.

               2
                    The problem is not convex and unbounded.

               3
                    Maximum number of iterations reached.

               6
                    The problem is infeasible.

     See also: *note sqp: XREFsqp.

 -- : X = pqpnonneg (C, D)
 -- : X = pqpnonneg (C, D, X0)
 -- : X = pqpnonneg (C, D, X0, OPTIONS)
 -- : [X, MINVAL] = pqpnonneg (...)
 -- : [X, MINVAL, EXITFLAG] = pqpnonneg (...)
 -- : [X, MINVAL, EXITFLAG, OUTPUT] = pqpnonneg (...)
 -- : [X, MINVAL, EXITFLAG, OUTPUT, LAMBDA] = pqpnonneg (...)

     Minimize ‘ (1/2 * X' * C * X + D' * X) ’ subject to ‘X >= 0’.

     C and D must be real matrices, and C must be symmetric and positive
     definite.

     X0 is an optional initial guess for the solution X.

     OPTIONS is an options structure to change the behavior of the
     algorithm (*note ‘optimset’: XREFoptimset.).  ‘pqpnonneg’
     recognizes one option: "MaxIter".

     Outputs:

     X
          The solution matrix

     MINVAL
          The minimum attained model value, ‘1/2*XMIN'*C*XMIN + D'*XMIN’

     EXITFLAG
          An indicator of convergence.  0 indicates that the iteration
          count was exceeded, and therefore convergence was not reached;
          >0 indicates that the algorithm converged.  (The algorithm is
          stable and will converge given enough iterations.)

     OUTPUT
          A structure with two fields:

             • "algorithm": The algorithm used ("nnls")

             • "iterations": The number of iterations taken.

     LAMBDA
          Lagrange multipliers.  If these are nonzero, the corresponding
          X values should be zero, indicating the solution is pressed up
          against a coordinate plane.  The magnitude indicates how much
          the residual would improve if the ‘X >= 0’ constraints were
          relaxed in that direction.

     See also: *note lsqnonneg: XREFlsqnonneg, *note qp: XREFqp, *note
     optimset: XREFoptimset.


File: octave.info,  Node: Nonlinear Programming,  Next: Linear Least Squares,  Prev: Quadratic Programming,  Up: Optimization

25.3 Nonlinear Programming
==========================

Octave can also perform general nonlinear minimization using a
successive quadratic programming solver.

 -- : [X, OBJ, INFO, ITER, NF, LAMBDA] = sqp (X0, PHI)
 -- : [...] = sqp (X0, PHI, G)
 -- : [...] = sqp (X0, PHI, G, H)
 -- : [...] = sqp (X0, PHI, G, H, LB, UB)
 -- : [...] = sqp (X0, PHI, G, H, LB, UB, MAXITER)
 -- : [...] = sqp (X0, PHI, G, H, LB, UB, MAXITER, TOLERANCE)
     Minimize an objective function using sequential quadratic
     programming (SQP).

     Solve the nonlinear program

          min phi (x)
           x

     subject to

          g(x)  = 0
          h(x) >= 0
          lb <= x <= ub

     using a sequential quadratic programming method.

     The first argument is the initial guess for the vector X0.

     The second argument is a function handle pointing to the objective
     function PHI.  The objective function must accept one vector
     argument and return a scalar.

     The second argument may also be a 2- or 3-element cell array of
     function handles.  The first element should point to the objective
     function, the second should point to a function that computes the
     gradient of the objective function, and the third should point to a
     function that computes the Hessian of the objective function.  If
     the gradient function is not supplied, the gradient is computed by
     finite differences.  If the Hessian function is not supplied, a
     BFGS update formula is used to approximate the Hessian.

     When supplied, the gradient function ‘PHI{2}’ must accept one
     vector argument and return a vector.  When supplied, the Hessian
     function ‘PHI{3}’ must accept one vector argument and return a
     matrix.

     The third and fourth arguments G and H are function handles
     pointing to functions that compute the equality constraints and the
     inequality constraints, respectively.  If the problem does not have
     equality (or inequality) constraints, then use an empty matrix ([])
     for G (or H).  When supplied, these equality and inequality
     constraint functions must accept one vector argument and return a
     vector.

     The third and fourth arguments may also be 2-element cell arrays of
     function handles.  The first element should point to the constraint
     function and the second should point to a function that computes
     the gradient of the constraint function:

                      [ d f(x)   d f(x)        d f(x) ]
          transpose ( [ ------   -----   ...   ------ ] )
                      [  dx_1     dx_2          dx_N  ]

     The fifth and sixth arguments, LB and UB, contain lower and upper
     bounds on X.  These must be consistent with the equality and
     inequality constraints G and H.  If the arguments are vectors then
     X(i) is bound by LB(i) and UB(i).  A bound can also be a scalar in
     which case all elements of X will share the same bound.  If only
     one bound (lb, ub) is specified then the other will default to
     (-REALMAX, +REALMAX).

     The seventh argument MAXITER specifies the maximum number of
     iterations.  The default value is 100.

     The eighth argument TOLERANCE specifies the tolerance for the
     stopping criteria.  The default value is ‘sqrt (eps)’.

     The value returned in INFO may be one of the following:

     101
          The algorithm terminated normally.  All constraints meet the
          specified tolerance.

     102
          The BFGS update failed.

     103
          The maximum number of iterations was reached.

     104
          The stepsize has become too small, i.e., delta X, is less than
          ‘TOL * norm (x)’.

     An example of calling ‘sqp’:

          function r = g (x)
            r = [ sumsq(x)-10;
                  x(2)*x(3)-5*x(4)*x(5);
                  x(1)^3+x(2)^3+1 ];
          endfunction

          function obj = phi (x)
            obj = exp (prod (x)) - 0.5*(x(1)^3+x(2)^3+1)^2;
          endfunction

          x0 = [-1.8; 1.7; 1.9; -0.8; -0.8];

          [x, obj, info, iter, nf, lambda] = sqp (x0, @phi, @g, [])

          x =

            -1.71714
             1.59571
             1.82725
            -0.76364
            -0.76364

          obj = 0.053950
          info = 101
          iter = 8
          nf = 10
          lambda =

            -0.0401627
             0.0379578
            -0.0052227

     See also: *note qp: XREFqp.


File: octave.info,  Node: Linear Least Squares,  Prev: Nonlinear Programming,  Up: Optimization

25.4 Linear Least Squares
=========================

Octave also supports linear least squares minimization.  That is, Octave
can find the parameter b such that the model y = x*b fits data (x,y) as
well as possible, assuming zero-mean Gaussian noise.  If the noise is
assumed to be isotropic the problem can be solved using the ‘\’ or ‘/’
operators, or the ‘ols’ function.  In the general case where the noise
is assumed to be anisotropic the ‘gls’ is needed.

 -- : [BETA, SIGMA, R] = ols (Y, X)
     Ordinary least squares (OLS) estimation.

     OLS applies to the multivariate model Y = X*B + E where Y is a
     t-by-p matrix, X is a t-by-k matrix, B is a k-by-p matrix, and E is
     a t-by-p matrix.

     Each row of Y is a p-variate observation in which each column
     represents a variable.  Likewise, the rows of X represent k-variate
     observations or possibly designed values.  Furthermore, the
     collection of observations X must be of adequate rank, k, otherwise
     B cannot be uniquely estimated.

     The observation errors, E, are assumed to originate from an
     underlying p-variate distribution with zero mean and p-by-p
     covariance matrix S, both constant conditioned on X.  Furthermore,
     the matrix S is constant with respect to each observation such that
     ‘mean (E) = 0’ and ‘cov (vec (E)) = kron (S, I)’.  (For cases that
     don’t meet this criteria, such as autocorrelated errors, see
     generalized least squares, gls, for more efficient estimations.)

     The return values BETA, SIGMA, and R are defined as follows.

     BETA
          The OLS estimator for matrix B.  BETA is calculated directly
          via ‘inv (X'*X) * X' * Y’ if the matrix ‘X'*X’ is of full
          rank.  Otherwise, ‘BETA = pinv (X) * Y’ where ‘pinv (X)’
          denotes the pseudoinverse of X.

     SIGMA
          The OLS estimator for the matrix S,

               SIGMA = (Y-X*BETA)' * (Y-X*BETA) / (t-rank(X))

     R
          The matrix of OLS residuals, ‘R = Y - X*BETA’.

     See also: *note gls: XREFgls, *note pinv: XREFpinv.

 -- : [BETA, V, R] = gls (Y, X, O)
     Generalized least squares (GLS) model.

     Perform a generalized least squares estimation for the multivariate
     model Y = X*B + E where Y is a t-by-p matrix, X is a t-by-k matrix,
     B is a k-by-p matrix and E is a t-by-p matrix.

     Each row of Y is a p-variate observation in which each column
     represents a variable.  Likewise, the rows of X represent k-variate
     observations or possibly designed values.  Furthermore, the
     collection of observations X must be of adequate rank, k, otherwise
     B cannot be uniquely estimated.

     The observation errors, E, are assumed to originate from an
     underlying p-variate distribution with zero mean but possibly
     heteroscedastic observations.  That is, in general, ‘mean (E) = 0’
     and ‘cov (vec (E)) = (s^2)*O’ in which s is a scalar and O is a
     t*p-by-t*p matrix.

     The return values BETA, V, and R are defined as follows.

     BETA
          The GLS estimator for matrix B.

     V
          The GLS estimator for scalar s^2.

     R
          The matrix of GLS residuals, R = Y - X*BETA.

     See also: *note ols: XREFols.

 -- : X = lsqnonneg (C, D)
 -- : X = lsqnonneg (C, D, X0)
 -- : X = lsqnonneg (C, D, X0, OPTIONS)
 -- : [X, RESNORM] = lsqnonneg (...)
 -- : [X, RESNORM, RESIDUAL] = lsqnonneg (...)
 -- : [X, RESNORM, RESIDUAL, EXITFLAG] = lsqnonneg (...)
 -- : [X, RESNORM, RESIDUAL, EXITFLAG, OUTPUT] = lsqnonneg (...)
 -- : [X, RESNORM, RESIDUAL, EXITFLAG, OUTPUT, LAMBDA] = lsqnonneg (...)

     Minimize ‘norm (C*X - D)’ subject to ‘X >= 0’.

     C and D must be real matrices.

     X0 is an optional initial guess for the solution X.

     OPTIONS is an options structure to change the behavior of the
     algorithm (*note ‘optimset’: XREFoptimset.).  ‘lsqnonneg’
     recognizes these options: "MaxIter", "TolX".

     Outputs:

     RESNORM
          The squared 2-norm of the residual: ‘norm (C*X-D)^2’

     RESIDUAL
          The residual: ‘D-C*X’

     EXITFLAG
          An indicator of convergence.  0 indicates that the iteration
          count was exceeded, and therefore convergence was not reached;
          >0 indicates that the algorithm converged.  (The algorithm is
          stable and will converge given enough iterations.)

     OUTPUT
          A structure with two fields:

             • "algorithm": The algorithm used ("nnls")

             • "iterations": The number of iterations taken.

     LAMBDA
          Lagrange multipliers.  If these are nonzero, the corresponding
          X values should be zero, indicating the solution is pressed up
          against a coordinate plane.  The magnitude indicates how much
          the residual would improve if the ‘X >= 0’ constraints were
          relaxed in that direction.

     See also: *note pqpnonneg: XREFpqpnonneg, *note lscov: XREFlscov,
     *note optimset: XREFoptimset.

 -- : X = lscov (A, B)
 -- : X = lscov (A, B, V)
 -- : X = lscov (A, B, V, ALG)
 -- : [X, STDX, MSE, S] = lscov (...)

     Compute a generalized linear least squares fit.

     Estimate X under the model B = AX + W, where the noise W is assumed
     to follow a normal distribution with covariance matrix {\sigma^2}
     V.

     If the size of the coefficient matrix A is n-by-p, the size of the
     vector/array of constant terms B must be n-by-k.

     The optional input argument V may be an n-element vector of
     positive weights (inverse variances), or an n-by-n symmetric
     positive semi-definite matrix representing the covariance of B.  If
     V is not supplied, the ordinary least squares solution is returned.

     The ALG input argument, a guidance on solution method to use, is
     currently ignored.

     Besides the least-squares estimate matrix X (p-by-k), the function
     also returns STDX (p-by-k), the error standard deviation of
     estimated X; MSE (k-by-1), the estimated data error covariance
     scale factors (\sigma^2); and S (p-by-p, or p-by-p-by-k if k > 1),
     the error covariance of X.

     Reference: Golub and Van Loan (1996), ‘Matrix Computations (3rd
     Ed.)’, Johns Hopkins, Section 5.6.3

     See also: *note ols: XREFols, *note gls: XREFgls, *note lsqnonneg:
     XREFlsqnonneg.

 -- : optimset ()
 -- : OPTIONS = optimset ()
 -- : OPTIONS = optimset (PAR, VAL, ...)
 -- : OPTIONS = optimset (OLD, PAR, VAL, ...)
 -- : OPTIONS = optimset (OLD, NEW)
     Create options structure for optimization functions.

     When called without any input or output arguments, ‘optimset’
     prints a list of all valid optimization parameters.

     When called with one output and no inputs, return an options
     structure with all valid option parameters initialized to ‘[]’.

     When called with a list of parameter/value pairs, return an options
     structure with only the named parameters initialized.

     When the first input is an existing options structure OLD, the
     values are updated from either the PAR/VAL list or from the options
     structure NEW.

     Valid parameters are:

     AutoScaling

     ComplexEqn

     Display
          Request verbose display of results from optimizations.  Values
          are:

          "off" [default]
               No display.

          "iter"
               Display intermediate results for every loop iteration.

          "final"
               Display the result of the final loop iteration.

          "notify"
               Display the result of the final loop iteration if the
               function has failed to converge.

     FinDiffType

     FunValCheck
          When enabled, display an error if the objective function
          returns an invalid value (a complex number, NaN, or Inf).
          Must be set to "on" or "off" [default].  Note: the functions
          ‘fzero’ and ‘fminbnd’ correctly handle Inf values and only
          complex values or NaN will cause an error in this case.

     GradObj
          When set to "on", the function to be minimized must return a
          second argument which is the gradient, or first derivative, of
          the function at the point X.  If set to "off" [default], the
          gradient is computed via finite differences.

     Jacobian
          When set to "on", the function to be minimized must return a
          second argument which is the Jacobian, or first derivative, of
          the function at the point X.  If set to "off" [default], the
          Jacobian is computed via finite differences.

     MaxFunEvals
          Maximum number of function evaluations before optimization
          stops.  Must be a positive integer.

     MaxIter
          Maximum number of algorithm iterations before optimization
          stops.  Must be a positive integer.

     OutputFcn
          A user-defined function executed once per algorithm iteration.

     TolFun
          Termination criterion for the function output.  If the
          difference in the calculated objective function between one
          algorithm iteration and the next is less than ‘TolFun’ the
          optimization stops.  Must be a positive scalar.

     TolX
          Termination criterion for the function input.  If the
          difference in X, the current search point, between one
          algorithm iteration and the next is less than ‘TolX’ the
          optimization stops.  Must be a positive scalar.

     TypicalX

     Updating

     See also: *note optimget: XREFoptimget.

 -- : OPTVAL = optimget (OPTIONS, OPTNAME)
 -- : OPTVAL = optimget (OPTIONS, OPTNAME, DEFAULT)
     Return the specific option OPTNAME from the optimization options
     structure OPTIONS created by ‘optimset’.

     If OPTNAME is not defined then return DEFAULT if supplied,
     otherwise return an empty matrix.

     See also: *note optimset: XREFoptimset.


File: octave.info,  Node: Statistics,  Next: Sets,  Prev: Optimization,  Up: Top

26 Statistics
*************

Octave has support for various statistical methods.  The emphasis is on
basic descriptive statistics, but the Octave Forge statistics package
includes probability distributions, statistical tests, random number
generation, and much more.

   The functions that analyze data all assume that multi-dimensional
data is arranged in a matrix where each row is an observation, and each
column is a variable.  Thus, the matrix defined by

     a = [ 0.9, 0.7;
           0.1, 0.1;
           0.5, 0.4 ];

contains three observations from a two-dimensional distribution.  While
this is the default data arrangement, most functions support different
arrangements.

   It should be noted that the statistics functions don’t test for data
containing NaN, NA, or Inf.  These values need to be detected and dealt
with explicitly.  See *note isnan: XREFisnan, *note isna: XREFisna,
*note isinf: XREFisinf, *note isfinite: XREFisfinite.

* Menu:

* Descriptive Statistics::
* Statistics on Sliding Windows of Data::
* Basic Statistical Functions::
* Correlation and Regression Analysis::
* Distributions::
* Random Number Generation::


File: octave.info,  Node: Descriptive Statistics,  Next: Statistics on Sliding Windows of Data,  Up: Statistics

26.1 Descriptive Statistics
===========================

One principal goal of descriptive statistics is to represent the essence
of a large data set concisely.  Octave provides the mean, median, and
mode functions which all summarize a data set with just a single number
corresponding to the central tendency of the data.

 -- : Y = mean (X)
 -- : Y = mean (X, 'all')
 -- : Y = mean (X, DIM)
 -- : Y = mean (..., 'OUTTYPE')
 -- : Y = mean (..., 'NANFLAG')
     Compute the mean of the elements of X.

        • If X is a vector, then ‘mean (X)’ returns the mean of the
          elements in X defined as

               mean (X) = SUM_i X(i) / N

          where N is the number of elements in the X vector.

        • If X is a matrix, then ‘mean’ returns a row vector with the
          mean of each column in X.

        • If X is a multi-dimensional array, then ‘mean’ operates along
          the first non-singleton dimension of X.

     The optional input DIM forces ‘mean’ to operate over the specified
     dimension(s).  DIM can either be a scalar dimension or a vector of
     non-repeating dimensions.  Dimensions must be positive integers,
     and the mean is calculated over the array slice defined by DIM.

     Specifying dimension "all" will force ‘mean’ to operate on all
     elements of X, and is equivalent to ‘mean (X(:))’.

     The optional input OUTTYPE specifies the data type that is
     returned.  Valid values are:

     ’default’ : Output is of type double, unless the input is
          single in which case the output is of type single.

     ’double’ : Output is of type double.

     ’native’ : Output is of the same type as the input
          (‘class (X)’), unless the input is logical in which case the
          output is of type double.

     The optional input NANFLAG specifies whether to include/exclude NaN
     values from the calculation.  By default, NaN values are included
     in the calculation (NANFLAG has the value ’includenan’).  To
     exclude NaN values, set the value of NANFLAG to ’omitnan’.

     See also: *note median: XREFmedian, *note mode: XREFmode.

 -- : Y = median (X)
 -- : Y = median (X, DIM)
     Compute the median value of the elements of the vector X.

     When the elements of X are sorted, say ‘S = sort (X)’, the median
     is defined as

                       |  S(ceil(N/2))           N odd
          median (X) = |
                       | (S(N/2) + S(N/2+1))/2   N even

     If X is of a discrete type such as integer or logical, then the
     case of even N rounds up (or toward ‘true’).

     If X is a matrix, compute the median value for each column and
     return them in a row vector.

     If the optional DIM argument is given, operate along this
     dimension.

     See also: *note mean: XREFmean, *note mode: XREFmode.

 -- : M = mode (X)
 -- : M = mode (X, DIM)
 -- : [M, F, C] = mode (...)
     Compute the most frequently occurring value in a dataset (mode).

     ‘mode’ determines the frequency of values along the first
     non-singleton dimension and returns the value with the highest
     frequency.  If two, or more, values have the same frequency ‘mode’
     returns the smallest.

     If the optional argument DIM is given, operate along this
     dimension.

     The return variable F is the number of occurrences of the mode in
     the dataset.

     The cell array C contains all of the elements with the maximum
     frequency.

     See also: *note mean: XREFmean, *note median: XREFmedian.

   Using just one number, such as the mean, to represent an entire data
set may not give an accurate picture of the data.  One way to
characterize the fit is to measure the dispersion of the data.  Octave
provides several functions for measuring dispersion.

 -- : [S, L] = bounds (X)
 -- : [S, L] = bounds (X, DIM)
 -- : [S, L] = bounds (..., "nanflag")
     Return the smallest and largest values of the input data X.

     If X is a vector, the bounds are calculated over the elements of X.
     If X is a matrix, the bounds are calculated for each column.  For a
     multi-dimensional array, the bounds are calculated over the first
     non-singleton dimension.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional argument "nanflag" defaults to "omitnan" which does
     not include NaN values in the result.  If the argument "includenan"
     is given, and there is a NaN present, then the result for both
     smallest (S) and largest (L) elements will be NaN.

     The bounds are a quickly computed measure of the dispersion of a
     data set, but are less accurate than ‘iqr’ if there are outlying
     data points.

     See also: *note range: XREFrange, *note iqr: XREFiqr, *note mad:
     XREFmad, *note std: XREFstd.

 -- : Y = range (X)
 -- : Y = range (X, DIM)
     Return the range, i.e., the difference between the maximum and the
     minimum of the input data.

     If X is a vector, the range is calculated over the elements of X.
     If X is a matrix, the range is calculated over each column of X.

     If the optional argument DIM is given, operate along this
     dimension.

     The range is a quickly computed measure of the dispersion of a data
     set, but is less accurate than ‘iqr’ if there are outlying data
     points.

     See also: *note bounds: XREFbounds, *note iqr: XREFiqr, *note mad:
     XREFmad, *note std: XREFstd.

 -- : Z = iqr (X)
 -- : Z = iqr (X, DIM)
 -- : Z = iqr (X, "ALL")
     Return the interquartile range of X, defined as the distance
     between the 25th and 75th percentile values of X calculated using:
     quantile (x, [0.25 0.75])

     If X is a vector, ‘iqr (X)’ will operate on the data in X.

     If X is a matrix, ‘iqr (X)’ will operate independently on each
     column in X returning a row vector Z.

     If X is a n-dimensional array, ‘iqr (X)’ will operate independently
     on the first non-singleton dimension in X, returning an array Z the
     same shape as X with the non-singleton dimenion reduced to 1.

     The optional variable DIM can be used to force ‘iqr’ to operate
     over the specified dimension.  DIM can either be a scalar dimension
     or a vector of non-repeating dimensions over which to operate.  In
     either case DIM must be positive integers.  A vector DIM
     concatenates all specified dimensions for independent operation by
     ‘iqr’.

     Specifying dimension "ALL" will force ‘iqr’ to operate on all
     elements of X, and is equivalent to ‘iqr (X(:))’.  Similarly,
     specifying a vector dimension including all non-singleton
     dimensions of X is equivalent to ‘iqr (X, "ALL")’.

     If X is a scalar, or only singleton dimensions are specified for
     DIM, the output will be ‘zeros (size (X))’.

     As a measure of dispersion, the interquartile range is less
     affected by outliers than either ‘range’ or ‘std’.

     See also: *note bounds: XREFbounds, *note mad: XREFmad, *note
     range: XREFrange, *note std: XREFstd, *note prctile: XREFprctile,
     *note quantile: XREFquantile.

 -- : Y = mad (X)
 -- : Y = mad (X, OPT)
 -- : Y = mad (X, OPT, DIM)
     Compute the mean or median absolute deviation of the elements of X.

     The mean absolute deviation is defined as

          MAD = mean (abs (X - mean (X)))

     The median absolute deviation is defined as

          MAD = median (abs (X - median (X)))

     If X is a matrix, compute ‘mad’ for each column and return results
     in a row vector.  For a multi-dimensional array, the calculation is
     done over the first non-singleton dimension.

     The optional argument OPT determines whether mean or median
     absolute deviation is calculated.  The default is 0 which
     corresponds to mean absolute deviation; A value of 1 corresponds to
     median absolute deviation.

     If the optional argument DIM is given, operate along this
     dimension.

     As a measure of dispersion, ‘mad’ is less affected by outliers than
     ‘std’.

     See also: *note bounds: XREFbounds, *note range: XREFrange, *note
     iqr: XREFiqr, *note std: XREFstd, *note mean: XREFmean, *note
     median: XREFmedian.

 -- : Y = meansq (X)
 -- : Y = meansq (X, DIM)
     Compute the mean square of the elements of the vector X.

     The mean square is defined as

          meansq (X) = 1/N SUM_i X(i)^2

     where N is the length of the X vector.

     If X is a matrix, return a row vector containing the mean square of
     each column.

     If the optional argument DIM is given, operate along this
     dimension.

     See also: *note var: XREFvar, *note std: XREFstd, *note moment:
     XREFmoment.

 -- : Y = std (X)
 -- : Y = std (X, W)
 -- : Y = std (X, W, DIM)
 -- : Y = std (X, W, "ALL")
 -- : [Y, MU] = std (...)
     Compute the standard deviation of the elements of the vector X.

     The standard deviation is defined as

          std (X) = sqrt ( 1/(N-1) SUM_i (X(i) - mean(X))^2 )

     where N is the number of elements of the X vector.

     If X is an array, compute the standard deviation for each column
     and return them in a row vector (or for an n-D array, the result is
     returned as an array of dimension 1 x n x m x ...).

     The optional argument W determines the weighting scheme to use.
     Valid values are:

     0 [default]:
          Normalize with N-1.  This provides the square root of the best
          unbiased estimator of the variance.

     1:
          Normalize with N.  This provides the square root of the second
          moment around the mean.

     a vector:
          Compute the weighted standard deviation with non-negative
          scalar weights.  The length of W must equal the size of X
          along dimension DIM.

     If N is equal to 1 the value of W is ignored and normalization by N
     is used.

     The optional variable DIM forces ‘std’ to operate over the
     specified dimension(s).  DIM can either be a scalar dimension or a
     vector of non-repeating dimensions.  Dimensions must be positive
     integers, and the standard deviation is calculated over the array
     slice defined by DIM.

     Specifying dimension "all" will force ‘std’ to operate on all
     elements of X, and is equivalent to ‘std (X(:))’.

     When DIM is a vector or "all", W must be either 0 or 1.

     The optional second output variable MU contains the mean or
     weighted mean used to calculate Y, and will be the same size as Y.

     See also: *note var: XREFvar, *note bounds: XREFbounds, *note mad:
     XREFmad, *note range: XREFrange, *note iqr: XREFiqr, *note mean:
     XREFmean, *note median: XREFmedian.

   In addition to knowing the size of a dispersion it is useful to know
the shape of the data set.  For example, are data points massed to the
left or right of the mean?  Octave provides several common measures to
describe the shape of the data set.  Octave can also calculate moments
allowing arbitrary shape measures to be developed.

 -- : V = var (X)
 -- : V = var (X, W)
 -- : V = var (X, W, DIM)
 -- : V = var (X, W, "ALL")
 -- : [V, M] = var (...)
     Compute the variance of the elements of the vector X.

     The variance is defined as

          var (X) = 1/(N-1) SUM_i (X(i) - mean(X))^2

     where N is the length of the X vector.

     If X is an array, compute the variance for each column and return
     them in a row vector (or for an n-D array, the result is returned
     as an array of dimension 1 x n x m x ...).

     The optional argument W determines the weighting scheme to use.
     Valid values are

     0 [default]:
          Normalize with N-1.  This provides the square root of the best
          unbiased estimator of the variance.

     1:
          Normalize with N.  This provides the square root of the second
          moment around the mean.

     a vector:
          Compute the weighted variance with non-negative scalar
          weights.  The length of W must equal the size of X along
          dimension DIM.

     If N is equal to 1 the value of W is ignored and normalization by N
     is used.

     The optional variable DIM forces ‘var’ to operate over the
     specified dimension(s).  DIM can either be a scalar dimension or a
     vector of non-repeating dimensions.  Dimensions must be positive
     integers, and the variance is calculated over the array slice
     defined by DIM.

     Specifying dimension "all" will force ‘var’ to operate on all
     elements of X, and is equivalent to ‘var (X(:))’.

     When DIM is a vector or "all", W must be either 0 or 1.

     The optional second output variable MU contains the mean or
     weighted mean used to calculate V, and will be the same size as V.

     See also: *note cov: XREFcov, *note std: XREFstd, *note skewness:
     XREFskewness, *note kurtosis: XREFkurtosis, *note moment:
     XREFmoment.

 -- : Y = skewness (X)
 -- : Y = skewness (X, FLAG)
 -- : Y = skewness (X, FLAG, DIM)
     Compute the sample skewness of the elements of X.

     The sample skewness is defined as

                         mean ((X - mean (X)).^3)
          skewness (X) = ------------------------.
                                std (X).^3

     The optional argument FLAG controls which normalization is used.
     If FLAG is equal to 1 (default value, used when FLAG is omitted or
     empty), return the sample skewness as defined above.  If FLAG is
     equal to 0, return the adjusted skewness coefficient instead:

                            sqrt (N*(N-1))   mean ((X - mean (X)).^3)
          skewness (X, 0) = -------------- * ------------------------.
                                (N - 2)             std (X).^3

     where N is the length of the X vector.

     The adjusted skewness coefficient is obtained by replacing the
     sample second and third central moments by their bias-corrected
     versions.

     If X is a matrix, or more generally a multi-dimensional array,
     return the skewness along the first non-singleton dimension.  If
     the optional DIM argument is given, operate along this dimension.

     See also: *note var: XREFvar, *note kurtosis: XREFkurtosis, *note
     moment: XREFmoment.

 -- : Y = kurtosis (X)
 -- : Y = kurtosis (X, FLAG)
 -- : Y = kurtosis (X, FLAG, DIM)
     Compute the sample kurtosis of the elements of X.

     The sample kurtosis is defined as

               mean ((X - mean (X)).^4)
          k1 = ------------------------
                      std (X).^4

     The optional argument FLAG controls which normalization is used.
     If FLAG is equal to 1 (default value, used when FLAG is omitted or
     empty), return the sample kurtosis as defined above.  If FLAG is
     equal to 0, return the "bias-corrected" kurtosis coefficient
     instead:

                        N - 1
          k0 = 3 + -------------- * ((N + 1) * k1 - 3 * (N - 1))
                   (N - 2)(N - 3)

     where N is the length of the X vector.

     The bias-corrected kurtosis coefficient is obtained by replacing
     the sample second and fourth central moments by their unbiased
     versions.  It is an unbiased estimate of the population kurtosis
     for normal populations.

     If X is a matrix, or more generally a multi-dimensional array,
     return the kurtosis along the first non-singleton dimension.  If
     the optional DIM argument is given, operate along this dimension.

     See also: *note var: XREFvar, *note skewness: XREFskewness, *note
     moment: XREFmoment.

 -- : M = moment (X, P)
 -- : M = moment (X, P, TYPE)
 -- : M = moment (X, P, DIM)
 -- : M = moment (X, P, TYPE, DIM)
 -- : M = moment (X, P, DIM, TYPE)
     Compute the P-th central moment of the vector X.

     The P-th central moment of X is defined as:

          1/N SUM_i (X(i) - mean(X))^P

     where N is the length of the X vector.

     If X is a matrix, return the row vector containing the P-th central
     moment of each column.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string TYPE specifies the type of moment to be
     computed.  Valid options are:

     "c"
          Central Moment (default).

     "a"
     "ac"
          Absolute Central Moment.  The moment about the mean ignoring
          sign defined as

               1/N SUM_i (abs (X(i) - mean(X)))^P

     "r"
          Raw Moment.  The moment about zero defined as

               moment (X) = 1/N SUM_i X(i)^P

     "ar"
          Absolute Raw Moment.  The moment about zero ignoring sign
          defined as

               1/N SUM_i ( abs (X(i)) )^P

     If both TYPE and DIM are given they may appear in any order.

     See also: *note var: XREFvar, *note skewness: XREFskewness, *note
     kurtosis: XREFkurtosis.

 -- : Q = quantile (X)
 -- : Q = quantile (X, P)
 -- : Q = quantile (X, P, DIM)
 -- : Q = quantile (X, P, DIM, METHOD)
     For a sample, X, calculate the quantiles, Q, corresponding to the
     cumulative probability values in P.  All non-numeric values (NaNs)
     of X are ignored.

     If X is a matrix, compute the quantiles for each column and return
     them in a matrix, such that the i-th row of Q contains the P(i)th
     quantiles of each column of X.

     If P is unspecified, return the quantiles for ‘[0.00 0.25 0.50 0.75
     1.00]’.  The optional argument DIM determines the dimension along
     which the quantiles are calculated.  If DIM is omitted it defaults
     to the first non-singleton dimension.

     The methods available to calculate sample quantiles are the nine
     methods used by R (<https://www.r-project.org/>).  The default
     value is METHOD = 5.

     Discontinuous sample quantile methods 1, 2, and 3

       1. Method 1: Inverse of empirical distribution function.

       2. Method 2: Similar to method 1 but with averaging at
          discontinuities.

       3. Method 3: SAS definition: nearest even order statistic.

     Continuous sample quantile methods 4 through 9, where P(k) is the
     linear interpolation function respecting each method’s
     representative cdf.

       4. Method 4: P(k) = k / N. That is, linear interpolation of the
          empirical cdf, where N is the length of P.

       5. Method 5: P(k) = (k - 0.5) / N. That is, a piecewise linear
          function where the knots are the values midway through the
          steps of the empirical cdf.

       6. Method 6: P(k) = k / (N + 1).

       7. Method 7: P(k) = (k - 1) / (N - 1).

       8. Method 8: P(k) = (k - 1/3) / (N + 1/3).  The resulting
          quantile estimates are approximately median-unbiased
          regardless of the distribution of X.

       9. Method 9: P(k) = (k - 3/8) / (N + 1/4).  The resulting
          quantile estimates are approximately unbiased for the expected
          order statistics if X is normally distributed.

     Hyndman and Fan (1996) recommend method 8.  Maxima, S, and R
     (versions prior to 2.0.0) use 7 as their default.  Minitab and SPSS
     use method 6.  MATLAB uses method 5.

     References:

        • Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New
          S Language.  Wadsworth & Brooks/Cole.

        • Hyndman, R. J. and Fan, Y. (1996) Sample quantiles in
          statistical packages, American Statistician, 50, 361–365.

        • R: A Language and Environment for Statistical Computing;
          <https://cran.r-project.org/doc/manuals/fullrefman.pdf>.

     Examples:

          x = randi (1000, [10, 1]);  # Create empirical data in range 1-1000
          q = quantile (x, [0, 1]);   # Return minimum, maximum of distribution
          q = quantile (x, [0.25 0.5 0.75]); # Return quartiles of distribution

     See also: *note prctile: XREFprctile.

 -- : Q = prctile (X)
 -- : Q = prctile (X, P)
 -- : Q = prctile (X, P, DIM)
     For a sample X, compute the quantiles, Q, corresponding to the
     cumulative probability values, P, in percent.

     If X is a matrix, compute the percentiles for each column and
     return them in a matrix, such that the i-th row of Q contains the
     P(i)th percentiles of each column of X.

     If P is unspecified, return the quantiles for ‘[0 25 50 75 100]’.

     The optional argument DIM determines the dimension along which the
     percentiles are calculated.  If DIM is omitted it defaults to the
     first non-singleton dimension.

     Programming Note: All non-numeric values (NaNs) of X are ignored.

     See also: *note quantile: XREFquantile.

   A summary view of a data set can be generated quickly with the
‘statistics’ function.

 -- : STATS = statistics (X)
 -- : STATS = statistics (X, DIM)
     Return a vector with the minimum, first quartile, median, third
     quartile, maximum, mean, standard deviation, skewness, and kurtosis
     of the elements of the vector X.

     If X is a matrix, calculate statistics over the first non-singleton
     dimension.

     If the optional argument DIM is given, operate along this
     dimension.

     See also: *note min: XREFmin, *note max: XREFmax, *note median:
     XREFmedian, *note mean: XREFmean, *note std: XREFstd, *note
     skewness: XREFskewness, *note kurtosis: XREFkurtosis.


File: octave.info,  Node: Statistics on Sliding Windows of Data,  Next: Basic Statistical Functions,  Prev: Descriptive Statistics,  Up: Statistics

26.2 Statistics on Sliding Windows of Data
==========================================

It is often useful to calculate descriptive statistics over a subsection
(i.e., window) of a full dataset.  Octave provides the function ‘movfun’
which will call an arbitrary function handle with windows of data and
accumulate the results.  Many of the most commonly desired functions,
such as the moving average over a window of data (‘movmean’), are
already provided.

 -- : Y = movfun (FCN, X, WLEN)
 -- : Y = movfun (FCN, X, [NB, NA])
 -- : Y = movfun (..., "PROPERTY", VALUE)

     Apply function FCN to a moving window of length WLEN on data X.

     If WLEN is a scalar, the function FCN is applied to a moving window
     of length WLEN.  When WLEN is an odd number the window is symmetric
     and includes ‘(WLEN - 1) / 2’ elements on either side of the
     central element.  For example, when calculating the output at index
     5 with a window length of 3, ‘movfun’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movfun’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     During calculations the data input X is reshaped into a
     2-dimensional WLEN-by-N matrix and FCN is called on this new
     matrix.  Therefore, FCN must accept an array input argument and
     apply the computation along dimension 1, i.e., down the columns of
     the array.

     When applied to an array (possibly multi-dimensional) with N
     columns, FCN may return a result in either of two formats:
     Format 1) an array of size 1-by-N-by-DIM3-by-...-by-DIMN.  This is
     the typical output format from Octave core functions.  Type ‘demo
     ("movfun", 5)’ for an example of this use case.  Format 2) a row
     vector of length ‘N * NUMEL_HIGHER_DIMS’ where NUMEL_HIGHER_DIMS is
     ‘prod (size (X)(3:end))’.  The output of FCN for the i-th input
     column must be found in the output at indices
     ‘i:N:(N*NUMEL_HIGHER_DIMS)’.  This format is useful when
     concatenating functions into arrays, or when using ‘nthargout’.
     Type ‘demo ("movfun", 6)’ for an example of this case.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "dim"
          Operate along the dimension specified, rather than the default
          of the first non-singleton dimension.

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               FCN (X(1:2))’, and ‘Y(end) = FCN (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = FCN ([NaN, X(1:2)])’, and ‘Y(end) = FCN ([X(end-1:end),
               NaN])’.  This option usually results in Y having ‘NaN’
               values at the boundaries, although it is influenced by
               how FCN handles ‘NaN’, and also by the property
               "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               FCN ([USER_VALUE, X(1:2)])’, and ‘Y(end) = FCN
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = FCN ([X(1), X(1:2)])’, and
               ‘Y(end) = FCN ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = FCN ([X(end),
               X(1:2)])’, and ‘Y(end) = FCN ([X(end-1:end), X(1)])’.

          Note that for some of these choices, the window size at the
          boundaries is not the same as for the central part, and FCN
          must work in these cases.

     "nancond"
          Controls whether ‘NaN’ and ‘NA’ values should be included
          (value: "includenan"), or excluded (value: "omitnan"), from
          the data passed to FCN.  The default is "includenan".
          Caution: The "omitnan" option is not yet implemented.

     "outdim"
          A row vector that selects which dimensions of the calculation
          will appear in the output Y.  This is only useful when FCN
          returns an N-dimensional array in Format 1.  The default is to
          return all output dimensions.

     Programming Note: The property "outdim" can be used to save memory
     when the output of FCN has many dimensions, or when a wrapper to
     the base function that selects the desired outputs is too costly.
     When memory is not an issue, the easiest way to select output
     dimensions is to first calculate the complete result with ‘movfun’
     and then filter that result with indexing.  If code complexity is
     not an issue then a wrapper can be created using anonymous
     functions.  For example, if ‘basefcn’ is a function returning a
     K-dimensional row output, and only dimension D is desired, then the
     following wrapper could be used.

          FCN = @(x) basefcn (x)(:,columns(x) * (D-1) + (1:columns(x)));
          Y = movfun (@fcn, ...);

     See also: *note movslice: XREFmovslice, *note prepad: XREFprepad,
     *note postpad: XREFpostpad, *note permute: XREFpermute, *note
     reshape: XREFreshape.

 -- : SLCIDX = movslice (N, WLEN)
 -- : [SLCIDX, C, CPRE, CPOST, WIN] = movslice (...)
     Generate indices to slice a vector of length N into windows of
     length WLEN.

     The input N must be a positive integer.

     The moving window length input WLEN can either be a scalar not
     equal to 1 or a 2-element array of integers.  For scalar values, if
     odd the window is symmetric and includes ‘(WLEN - 1) / 2’ elements
     on either side of the central element.  If WLEN is even the window
     is asymmetric and has ‘WLEN/2’ elements to the left of the central
     element and ‘WLEN/2 - 1’ elements to the right of the central
     element.  When WLEN is a 2-element array, ‘[NB, NA]’, the window
     includes NB elements to the left of the current element and NA
     elements to the right of the current element.

     The output SLCIDX is an array of indices of the slices that fit
     fully within the vector, where each column is an individual slice
     as the window moves from left to right.  The slices have WLEN
     elements for scalar WLEN, or ‘NB + NA + 1’ elements for array
     valued WLEN.

     Optional output C is an row vector of window center positions where
     the window stays fully within the vector.

     Optional outputs CPRE and CPOST contain the vector elements at the
     start and end of the vector, respectively, that result in the
     window extending beyond the ends of the vector.

     Optional output WIN is a column vector with the same number of rows
     as SLCIDX that contains the moving window defined as a center
     relative position stencil.

     See also: *note movfun: XREFmovfun.

 -- : Y = movmad (X, WLEN)
 -- : Y = movmad (X, [NB, NA])
 -- : Y = movmad (..., DIM)
 -- : Y = movmad (..., "NANCOND")
 -- : Y = movmad (..., PROPERTY, VALUE)
     Calculate the moving mean absolute deviation over a sliding window
     of length WLEN on data X.

     If WLEN is a scalar, the function ‘mad’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movmad’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movmad’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘mad’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               mad (X(1:2))’, and ‘Y(end) = mad (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = mad ([NaN, X(1:2)])’, and ‘Y(end) = mad ([X(end-1:end),
               NaN])’.  This option usually results in Y having ‘NaN’
               values at the boundaries, although it is influenced by
               how ‘mad’ handles ‘NaN’, and also by the property
               "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               mad ([USER_VALUE, X(1:2)])’, and ‘Y(end) = mad
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = mad ([X(1), X(1:2)])’, and
               ‘Y(end) = mad ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = mad ([X(end),
               X(1:2)])’, and ‘Y(end) = mad ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmax: XREFmovmax, *note movmean: XREFmovmean, *note
     movmedian: XREFmovmedian, *note movmin: XREFmovmin, *note movprod:
     XREFmovprod, *note movstd: XREFmovstd, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movmax (X, WLEN)
 -- : Y = movmax (X, [NB, NA])
 -- : Y = movmax (..., DIM)
 -- : Y = movmax (..., "NANCOND")
 -- : Y = movmax (..., PROPERTY, VALUE)
     Calculate the moving maximum over a sliding window of length WLEN
     on data X.

     If WLEN is a scalar, the function ‘max’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movmax’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movmax’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘max’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               max (X(1:2))’, and ‘Y(end) = max (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = max ([NaN, X(1:2)])’, and ‘Y(end) = max ([X(end-1:end),
               NaN])’.  This option usually results in Y having ‘NaN’
               values at the boundaries, although it is influenced by
               how ‘max’ handles ‘NaN’, and also by the property
               "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               max ([USER_VALUE, X(1:2)])’, and ‘Y(end) = max
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = max ([X(1), X(1:2)])’, and
               ‘Y(end) = max ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = max ([X(end),
               X(1:2)])’, and ‘Y(end) = max ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmean: XREFmovmean, *note
     movmedian: XREFmovmedian, *note movmin: XREFmovmin, *note movprod:
     XREFmovprod, *note movstd: XREFmovstd, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movmean (X, WLEN)
 -- : Y = movmean (X, [NB, NA])
 -- : Y = movmean (..., DIM)
 -- : Y = movmean (..., "NANCOND")
 -- : Y = movmean (..., PROPERTY, VALUE)
     Calculate the moving average over a sliding window of length WLEN
     on data X.

     If WLEN is a scalar, the function ‘mean’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movmean’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movmean’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘mean’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               mean (X(1:2))’, and ‘Y(end) = mean (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = mean ([NaN, X(1:2)])’, and ‘Y(end) = mean
               ([X(end-1:end), NaN])’.  This option usually results in Y
               having ‘NaN’ values at the boundaries, although it is
               influenced by how ‘mean’ handles ‘NaN’, and also by the
               property "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               mean ([USER_VALUE, X(1:2)])’, and ‘Y(end) = mean
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = mean ([X(1), X(1:2)])’, and
               ‘Y(end) = mean ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = mean ([X(end),
               X(1:2)])’, and ‘Y(end) = mean ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note
     movmedian: XREFmovmedian, *note movmin: XREFmovmin, *note movprod:
     XREFmovprod, *note movstd: XREFmovstd, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movmedian (X, WLEN)
 -- : Y = movmedian (X, [NB, NA])
 -- : Y = movmedian (..., DIM)
 -- : Y = movmedian (..., "NANCOND")
 -- : Y = movmedian (..., PROPERTY, VALUE)
     Calculate the moving median over a sliding window of length WLEN on
     data X.

     If WLEN is a scalar, the function ‘movmedian’ is applied to a
     moving window of length WLEN.  When WLEN is an odd number the
     window is symmetric and includes ‘(WLEN - 1) / 2’ elements on
     either side of the central element.  For example, when calculating
     the output at index 5 with a window length of 3, ‘movmedian’ uses
     data elements ‘[4, 5, 6]’.  If WLEN is an even number, the window
     is asymmetric and has ‘WLEN/2’ elements to the left of the central
     element and ‘WLEN/2 - 1’ elements to the right of the central
     element.  For example, when calculating the output at index 5 with
     a window length of 4, ‘movmedian’ uses data elements
     ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘movmedian’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               movmedian (X(1:2))’, and ‘Y(end) = movmedian
               (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = movmedian ([NaN, X(1:2)])’, and ‘Y(end) = movmedian
               ([X(end-1:end), NaN])’.  This option usually results in Y
               having ‘NaN’ values at the boundaries, although it is
               influenced by how ‘movmedian’ handles ‘NaN’, and also by
               the property "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               movmedian ([USER_VALUE, X(1:2)])’, and ‘Y(end) =
               movmedian ([X(end-1:end), USER_VALUE])’.  A common choice
               for USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = movmedian ([X(1), X(1:2)])’,
               and ‘Y(end) = movmedian ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = movmedian ([X(end),
               X(1:2)])’, and ‘Y(end) = movmedian ([X(end-1:end),
               X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmin: XREFmovmin, *note movprod: XREFmovprod,
     *note movstd: XREFmovstd, *note movsum: XREFmovsum, *note movvar:
     XREFmovvar.

 -- : Y = movmin (X, WLEN)
 -- : Y = movmin (X, [NB, NA])
 -- : Y = movmin (..., DIM)
 -- : Y = movmin (..., "NANCOND")
 -- : Y = movmin (..., PROPERTY, VALUE)
     Calculate the moving minimum over a sliding window of length WLEN
     on data X.

     If WLEN is a scalar, the function ‘min’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movmin’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movmin’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘min’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               min (X(1:2))’, and ‘Y(end) = min (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = min ([NaN, X(1:2)])’, and ‘Y(end) = min ([X(end-1:end),
               NaN])’.  This option usually results in Y having ‘NaN’
               values at the boundaries, although it is influenced by
               how ‘min’ handles ‘NaN’, and also by the property
               "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               min ([USER_VALUE, X(1:2)])’, and ‘Y(end) = min
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = min ([X(1), X(1:2)])’, and
               ‘Y(end) = min ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = min ([X(end),
               X(1:2)])’, and ‘Y(end) = min ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmedian: XREFmovmedian, *note movprod:
     XREFmovprod, *note movstd: XREFmovstd, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movprod (X, WLEN)
 -- : Y = movprod (X, [NB, NA])
 -- : Y = movprod (..., DIM)
 -- : Y = movprod (..., "NANCOND")
 -- : Y = movprod (..., PROPERTY, VALUE)
     Calculate the moving product over a sliding window of length WLEN
     on data X.

     If WLEN is a scalar, the function ‘movprod’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movprod’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movprod’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘movprod’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               movprod (X(1:2))’, and ‘Y(end) = movprod (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = movprod ([NaN, X(1:2)])’, and ‘Y(end) = movprod
               ([X(end-1:end), NaN])’.  This option usually results in Y
               having ‘NaN’ values at the boundaries, although it is
               influenced by how ‘movprod’ handles ‘NaN’, and also by
               the property "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               movprod ([USER_VALUE, X(1:2)])’, and ‘Y(end) = movprod
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = movprod ([X(1), X(1:2)])’,
               and ‘Y(end) = movprod ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = movprod ([X(end),
               X(1:2)])’, and ‘Y(end) = movprod ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmedian: XREFmovmedian, *note movmin:
     XREFmovmin, *note movstd: XREFmovstd, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movstd (X, WLEN)
 -- : Y = movstd (X, [NB, NA])
 -- : Y = movstd (..., OPT)
 -- : Y = movstd (..., OPT, DIM)
 -- : Y = movstd (..., "NANCOND")
 -- : Y = movstd (..., PROPERTY, VALUE)
     Calculate the moving standard deviation over a sliding window of
     length WLEN on data X.

     If WLEN is a scalar, the function ‘movstd’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movstd’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movstd’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     The optional argument OPT determines the type of normalization to
     use.  Valid values are

     0:
          normalize with N-1, provides the square root of the best
          unbiased estimator of the variance [default]

     1:
          normalize with N, this provides the square root of the second
          moment around the mean

     If the optional argument DIM is given, operate along this
     dimension.  The normalization argument OPT must be given before the
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘movstd’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               movstd (X(1:2))’, and ‘Y(end) = movstd (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = movstd ([NaN, X(1:2)])’, and ‘Y(end) = movstd
               ([X(end-1:end), NaN])’.  This option usually results in Y
               having ‘NaN’ values at the boundaries, although it is
               influenced by how ‘movstd’ handles ‘NaN’, and also by the
               property "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               movstd ([USER_VALUE, X(1:2)])’, and ‘Y(end) = movstd
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = movstd ([X(1), X(1:2)])’, and
               ‘Y(end) = movstd ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = movstd ([X(end),
               X(1:2)])’, and ‘Y(end) = movstd ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmedian: XREFmovmedian, *note movmin:
     XREFmovmin, *note movprod: XREFmovprod, *note movsum: XREFmovsum,
     *note movvar: XREFmovvar.

 -- : Y = movsum (X, WLEN)
 -- : Y = movsum (X, [NB, NA])
 -- : Y = movsum (..., DIM)
 -- : Y = movsum (..., "NANCOND")
 -- : Y = movsum (..., PROPERTY, VALUE)
     Calculate the moving sum over a sliding window of length WLEN on
     data X.

     If WLEN is a scalar, the function ‘movsum’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movsum’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movsum’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     If the optional argument DIM is given, operate along this
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘movsum’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               movsum (X(1:2))’, and ‘Y(end) = movsum (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = movsum ([NaN, X(1:2)])’, and ‘Y(end) = movsum
               ([X(end-1:end), NaN])’.  This option usually results in Y
               having ‘NaN’ values at the boundaries, although it is
               influenced by how ‘movsum’ handles ‘NaN’, and also by the
               property "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               movsum ([USER_VALUE, X(1:2)])’, and ‘Y(end) = movsum
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = movsum ([X(1), X(1:2)])’, and
               ‘Y(end) = movsum ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = movsum ([X(end),
               X(1:2)])’, and ‘Y(end) = movsum ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmedian: XREFmovmedian, *note movmin:
     XREFmovmin, *note movprod: XREFmovprod, *note movstd: XREFmovstd,
     *note movvar: XREFmovvar.

 -- : Y = movvar (X, WLEN)
 -- : Y = movvar (X, [NB, NA])
 -- : Y = movvar (..., OPT)
 -- : Y = movvar (..., OPT, DIM)
 -- : Y = movvar (..., "NANCOND")
 -- : Y = movvar (..., PROPERTY, VALUE)
     Calculate the moving variance over a sliding window of length WLEN
     on data X.

     If WLEN is a scalar, the function ‘var’ is applied to a moving
     window of length WLEN.  When WLEN is an odd number the window is
     symmetric and includes ‘(WLEN - 1) / 2’ elements on either side of
     the central element.  For example, when calculating the output at
     index 5 with a window length of 3, ‘movvar’ uses data elements
     ‘[4, 5, 6]’.  If WLEN is an even number, the window is asymmetric
     and has ‘WLEN/2’ elements to the left of the central element and
     ‘WLEN/2 - 1’ elements to the right of the central element.  For
     example, when calculating the output at index 5 with a window
     length of 4, ‘movvar’ uses data elements ‘[3, 4, 5, 6]’.

     If WLEN is an array with two elements ‘[NB, NA]’, the function is
     applied to a moving window ‘-NB:NA’.  This window includes NB
     number of elements _before_ the current element and NA number of
     elements _after_ the current element.  The current element is
     always included.  For example, given ‘WLEN = [3, 0]’, the data used
     to calculate index 5 is ‘[2, 3, 4, 5]’.

     The optional argument OPT determines the type of normalization to
     use.  Valid values are

     0:
          normalize with N-1, provides the best unbiased estimator of
          the variance [default]

     1:
          normalizes with N, this provides the second moment around the
          mean

     If the optional argument DIM is given, operate along this
     dimension.  The normalization argument OPT must be given before the
     dimension.

     The optional string argument "NANCOND" controls whether ‘NaN’ and
     ‘NA’ values should be included ("includenan"), or excluded
     ("omitnan"), from the data passed to ‘var’.  The default is
     "includenan".  Caution: the "omitnan" option is not yet
     implemented.

     The calculation can be controlled by specifying PROPERTY/VALUE
     pairs.  Valid properties are

     "Endpoints"

          This property controls how results are calculated at the
          boundaries (endpoints) of the window.  Possible values are:

          "shrink" (default)
               The window is truncated at the beginning and end of the
               array to exclude elements for which there is no source
               data.  For example, with a window of length 3, ‘Y(1) =
               var (X(1:2))’, and ‘Y(end) = var (X(end-1:end))’.

          "discard"
               Any Y values that use a window extending beyond the
               original data array are deleted.  For example, with a
               10-element data vector and a window of length 3, the
               output will contain only 8 elements.  The first element
               would require calculating the function over indices
               ‘[0, 1, 2]’ and is therefore discarded.  The last element
               would require calculating the function over indices
               ‘[9, 10, 11]’ and is therefore discarded.

          "fill"
               Any window elements outside the data array are replaced
               by ‘NaN’.  For example, with a window of length 3, ‘Y(1)
               = var ([NaN, X(1:2)])’, and ‘Y(end) = var ([X(end-1:end),
               NaN])’.  This option usually results in Y having ‘NaN’
               values at the boundaries, although it is influenced by
               how ‘var’ handles ‘NaN’, and also by the property
               "nancond".

          USER_VALUE
               Any window elements outside the data array are replaced
               by the specified value USER_VALUE which must be a numeric
               scalar.  For example, with a window of length 3, ‘Y(1) =
               var ([USER_VALUE, X(1:2)])’, and ‘Y(end) = var
               ([X(end-1:end), USER_VALUE])’.  A common choice for
               USER_VALUE is 0.

          "same"
               Any window elements outside the data array are replaced
               by the value of X at the boundary.  For example, with a
               window of length 3, ‘Y(1) = var ([X(1), X(1:2)])’, and
               ‘Y(end) = var ([X(end-1:end), X(end)])’.

          "periodic"
               The window is wrapped so that any missing data elements
               are taken from the other side of the data.  For example,
               with a window of length 3, ‘Y(1) = var ([X(end),
               X(1:2)])’, and ‘Y(end) = var ([X(end-1:end), X(1)])’.

     "SamplePoints"
          Caution: This option is not yet implemented.

     Programming Note: This function is a wrapper which calls ‘movfun’.
     For additional options and documentation, *note ‘movfun’:
     XREFmovfun.

     See also: *note movfun: XREFmovfun, *note movslice: XREFmovslice,
     *note movmad: XREFmovmad, *note movmax: XREFmovmax, *note movmean:
     XREFmovmean, *note movmedian: XREFmovmedian, *note movmin:
     XREFmovmin, *note movprod: XREFmovprod, *note movstd: XREFmovstd,
     *note movsum: XREFmovsum.


File: octave.info,  Node: Basic Statistical Functions,  Next: Correlation and Regression Analysis,  Prev: Statistics on Sliding Windows of Data,  Up: Statistics

26.3 Basic Statistical Functions
================================

Octave supports various helpful statistical functions.  Many are useful
as initial steps to prepare a data set for further analysis.  Others
provide different measures from those of the basic descriptive
statistics.

 -- : Y = center (X)
 -- : Y = center (X, DIM)
     Center data by subtracting its mean.

     If X is a vector, subtract its mean.

     If X is a matrix, do the above for each column.

     If the optional argument DIM is given, operate along this
     dimension.

     Programming Note: ‘center’ has obvious application for normalizing
     statistical data.  It is also useful for improving the precision of
     general numerical calculations.  Whenever there is a large value
     that is common to a batch of data, the mean can be subtracted off,
     the calculation performed, and then the mean added back to obtain
     the final answer.

     See also: *note zscore: XREFzscore.

 -- : Z = zscore (X)
 -- : Z = zscore (X, OPT)
 -- : Z = zscore (X, OPT, DIM)
 -- : [Z, MU, SIGMA] = zscore (...)
     Compute the Z score of X.

     If X is a vector, subtract its mean and divide by its standard
     deviation.  If the standard deviation is zero, divide by 1 instead.

     The optional parameter OPT determines the normalization to use when
     computing the standard deviation and has the same definition as the
     corresponding parameter for ‘std’.

     If X is a matrix, calculate along the first non-singleton
     dimension.  If the third optional argument DIM is given, operate
     along this dimension.

     The optional outputs MU and SIGMA contain the mean and standard
     deviation.

     See also: *note mean: XREFmean, *note std: XREFstd, *note center:
     XREFcenter.

 -- : Z = normalize (X)
 -- : Z = normalize (X, DIM)
 -- : Z = normalize (..., METHOD)
 -- : Z = normalize (..., METHOD, OPTION)
 -- : Z = normalize (..., SCALE, SCALEOPTION, CENTER, CENTEROPTION)
 -- : [Z, C, S] = normalize (...)

     Return a normalization of the data in X using one of several
     available scaling and centering methods.

     ‘normalize’ by default will return the ‘zscore’ of X, defined as
     the number of standard deviations each element is from the mean of
     X.  This is equivalent to centering at the mean of the data and
     scaling by the standard deviation.

     The returned value Z will have the same size as X.  The optional
     return variables C and S are the centering and scaling factors used
     in the normalization such that:

            Z = (X - C) ./ S

     If X is a vector, ‘normalize’ will operate on the data in X.

     If X is a matrix, ‘normalize’ will operate independently on each
     column in X.

     If X is an N-dimensional array, ‘normalize’ will operate
     independently on the first non-singleton dimension in X.

     If the optional second argument DIM is given, operate along this
     dimension.

     The optional inputs METHOD and OPTION can be used to specify the
     type of normalization performed on X.  Note that only the ‘scale’
     and ‘center’ options may be specified together using any of the
     methods defined below.  Valid normalization methods are:

     ‘zscore’
          (Default) Normalizes the elements in X to the scaled distance
          from a central value.  Valid Options:

          ‘std’
               (Default) Data is centered at ‘mean (X)’ and scaled by
               the standard deviation.

          ‘robust’
               Data is centered at ‘median (X)’ and scaled by the median
               absolute deviation.

     ‘norm’
          Z is the general vector norm of X, with OPTION being the
          normalization factor P that determines the vector norm type
          according to:

                 Z = [sum (abs (X) .^ P)] ^ (1/P)

          P can be any positive scalar, specific values being:

          ‘P = 1’
               X is normalized by ‘sum (abs (X))’.

          ‘P = 2’
               (Default) X is normalized by the Euclidian norm, or
               vector magnitude, of the elements.

          ‘P = Inf’
               X is normalized by ‘max (abs (X))’.

     ‘scale’
          X is scaled by a factor determined by OPTION, which can be a
          numeric scalar or one of the following:

          ‘std’
               (Default) X is scaled by its standard deviation.

          ‘mad’
               X is scaled by its median absolute deviation.

          ‘first’
               X is scaled by its first element.

          ‘iqr’
               X is scaled by its interquartile range.

     ‘range’
          X is scaled to fit the range specified by OPTION as a two
          element scalar row vector.  The default range is [0, 1].

     ‘center’
          X is shifted by an amount determined by OPTION, which can be a
          numeric scalar or one of the following:

          ‘mean’
               (Default) X is shifted by ‘mean (X)’.

          ‘median’
               X is shifted by ‘median (X)’.

     ‘medianiqr’
          X is shifted by ‘median (X)’ and scaled by the interquartile
          range.

     Known MATLAB incompatibilities:

       1. The option ‘DataVariables’ is not yet implemented for Table
          class X inputs.

       2. Certain arrays containing NaN elements may not return MATLAB
          compatible output.

     See also: *note zscore: XREFzscore, *note iqr: XREFiqr, *note norm:
     XREFnorm, *note rescale: XREFrescale, *note std: XREFstd, *note
     median: XREFmedian, *note mean: XREFmean, *note mad: XREFmad.

 -- : N = histc (X, EDGES)
 -- : N = histc (X, EDGES, DIM)
 -- : [N, IDX] = histc (...)
     Compute histogram counts.

     When X is a vector, the function counts the number of elements of X
     that fall in the histogram bins defined by EDGES.  This must be a
     vector of monotonically increasing values that define the edges of
     the histogram bins.  ‘N(k)’ contains the number of elements in X
     for which ‘EDGES(k) <= X < EDGES(k+1)’.  The final element of N
     contains the number of elements of X exactly equal to the last
     element of EDGES.

     When X is an N-dimensional array, the computation is carried out
     along dimension DIM.  If not specified DIM defaults to the first
     non-singleton dimension.

     When a second output argument is requested an index matrix is also
     returned.  The IDX matrix has the same size as X.  Each element of
     IDX contains the index of the histogram bin in which the
     corresponding element of X was counted.

     See also: *note hist: XREFhist.

‘unique’ function documented at *note unique: XREFunique. is often
useful for statistics.

 -- : C = nchoosek (N, K)
 -- : C = nchoosek (SET, K)

     Compute the binomial coefficient of N or list all possible
     combinations of a SET of items.

     If N is a scalar then calculate the binomial coefficient of N and K
     which is defined as

           /   \
           | n |    n (n-1) (n-2) ... (n-k+1)       n!
           |   |  = ------------------------- =  ---------
           | k |               k!                k! (n-k)!
           \   /

     This is the number of combinations of N items taken in groups of
     size K.

     If the first argument is a vector, SET, then generate all
     combinations of the elements of SET, taken K at a time, with one
     row per combination.  The result C has K columns and
     ‘nchoosek (length (SET), K)’ rows.

     For example:

     How many ways can three items be grouped into pairs?

          nchoosek (3, 2)
             ⇒ 3

     What are the possible pairs?

          nchoosek (1:3, 2)
             ⇒  1   2
                 1   3
                 2   3

     Programming Note: When calculating the binomial coefficient
     ‘nchoosek’ works only for non-negative, integer arguments.  Use
     ‘bincoeff’ for non-integer and negative scalar arguments, or for
     computing many binomial coefficients at once with vector inputs for
     N or K.

     See also: *note bincoeff: XREFbincoeff, *note perms: XREFperms.

 -- : P = perms (V)
 -- : P = perms (V, "unique")
     Generate all permutations of vector V with one row per permutation.

     Results are returned in inverse lexicographic order.  The result
     has size ‘factorial (N) * N’, where N is the length of V.  Any
     repeated elements are included in the output.

     If the optional argument "unique" is given then only unique
     permutations are returned, using less memory and generally taking
     less time than calling ‘unique (perms (V), "rows")’.

     Example 1

          perms ([1, 2, 3])
          ⇒
            3   2   1
            3   1   2
            2   3   1
            2   1   3
            1   3   2
            1   2   3

     Example 2

          perms ([1, 1, 2, 2], "unique")
          ⇒
            1   1   2   2
            1   2   1   2
            1   2   2   1
            2   1   1   2
            2   1   2   1
            2   2   1   1

     Programming Note: If the "unique" option is not used, the length of
     V should be no more than 10-12 to limit memory consumption.  Even
     with "unique", there should be no more than 10-12 unique elements
     in V.

     See also: *note permute: XREFpermute, *note randperm: XREFrandperm,
     *note nchoosek: XREFnchoosek.

 -- : Y = ranks (X)
 -- : Y = ranks (X, DIM)
 -- : Y = ranks (X, DIM, RTYPE)
     Return the ranks (in the sense of order statistics) of X along the
     first non-singleton dimension adjusted for ties.

     If the optional DIM argument is given, operate along this
     dimension.

     The optional parameter RTYPE determines how ties are handled.  All
     examples below assume an input of ‘[ 1, 2, 2, 4 ]’.

     0 or "fractional" (default) for fractional ranking (1, 2.5,
          2.5, 4);

     1 or "competition" for competition ranking (1, 2, 2, 4);

     2 or "modified" for modified competition ranking (1, 3, 3, 4);

     3 or "ordinal" for ordinal ranking (1, 2, 3, 4);

     4 or "dense" for dense ranking (1, 2, 2, 3).

     See also: *note spearman: XREFspearman, *note kendall: XREFkendall.

 -- : CNT = run_count (X, N)
 -- : CNT = run_count (X, N, DIM)
     Count the upward runs along the first non-singleton dimension of X
     of length 1, 2, ..., N-1 and greater than or equal to N.

     If the optional argument DIM is given then operate along this
     dimension.

     See also: *note runlength: XREFrunlength.

 -- : count = runlength (X)
 -- : [count, value] = runlength (X)
     Find the lengths of all sequences of common values.

     COUNT is a vector with the lengths of each repeated value.

     The optional output VALUE contains the value that was repeated in
     the sequence.

          runlength ([2, 2, 0, 4, 4, 4, 0, 1, 1, 1, 1])
          ⇒   2   1   3   1   4

     See also: *note run_count: XREFrun_count.


File: octave.info,  Node: Correlation and Regression Analysis,  Next: Distributions,  Prev: Basic Statistical Functions,  Up: Statistics

26.4 Correlation and Regression Analysis
========================================

 -- : C = cov (X)
 -- : C = cov (X, OPT)
 -- : C = cov (X, Y)
 -- : C = cov (X, Y, OPT)
     Compute the covariance matrix.

     If each row of X and Y is an observation, and each column is a
     variable, then the (I, J)-th entry of ‘cov (X, Y)’ is the
     covariance between the I-th variable in X and the J-th variable in
     Y.

          cov (X) = 1/(N-1) * SUM_i (X(i) - mean(X)) * (Y(i) - mean(Y))

     where N is the length of the X and Y vectors.

     If called with one argument, compute ‘cov (X, X)’, the covariance
     between the columns of X.

     The argument OPT determines the type of normalization to use.
     Valid values are

     0:
          normalize with N-1, provides the best unbiased estimator of
          the covariance [default]

     1:
          normalize with N, this provides the second moment around the
          mean

     Compatibility Note:: Octave always treats rows of X and Y as
     multivariate random variables.  For two inputs, however, MATLAB
     treats X and Y as two univariate distributions regardless of their
     shapes, and will calculate ‘cov ([X(:), Y(:)])’ whenever the number
     of elements in X and Y are equal.  This will result in a 2x2
     matrix.  Code relying on MATLAB’s definition will need to be
     changed when running in Octave.

     See also: *note corr: XREFcorr.

 -- : R = corr (X)
 -- : R = corr (X, Y)
     Compute matrix of correlation coefficients.

     If each row of X and Y is an observation and each column is a
     variable, then the (I, J)-th entry of ‘corr (X, Y)’ is the
     correlation between the I-th variable in X and the J-th variable in
     Y.

          corr (X,Y) = cov (X,Y) / (std (X) * std (Y))

     If called with one argument, compute ‘corr (X, X)’, the correlation
     between the columns of X.

     See also: *note cov: XREFcov.

 -- : R = corrcoef (X)
 -- : R = corrcoef (X, Y)
 -- : R = corrcoef (..., PARAM, VALUE, ...)
 -- : [R, P] = corrcoef (...)
 -- : [R, P, LCI, HCI] = corrcoef (...)
     Compute a matrix of correlation coefficients.

     X is an array where each column contains a variable and each row is
     an observation.

     If a second input Y (of the same size as X) is given then calculate
     the correlation coefficients between X and Y.

     PARAM, VALUE are optional pairs of parameters and values which
     modify the calculation.  Valid options are:

     "alpha"
          Confidence level used for the bounds of the confidence
          interval, LCI and HCI.  Default is 0.05, i.e., 95% confidence
          interval.

     "rows"
          Determine processing of NaN values.  Acceptable values are
          "all", "complete", and "pairwise".  Default is "all".  With
          "complete", only the rows without NaN values are considered.
          With "pairwise", the selection of NaN-free rows is made for
          each pair of variables.

     Output R is a matrix of Pearson’s product moment correlation
     coefficients for each pair of variables.

     Output P is a matrix of pair-wise p-values testing for the null
     hypothesis of a correlation coefficient of zero.

     Outputs LCI and HCI are matrices containing, respectively, the
     lower and higher bounds of the 95% confidence interval of each
     correlation coefficient.

     See also: *note corr: XREFcorr, *note cov: XREFcov.

 -- : RHO = spearman (X)
 -- : RHO = spearman (X, Y)
     Compute Spearman’s rank correlation coefficient RHO.

     For two data vectors X and Y, Spearman’s RHO is the correlation
     coefficient of the ranks of X and Y.

     If X and Y are drawn from independent distributions, RHO has zero
     mean and variance ‘1 / (N - 1)’, where N is the length of the X and
     Y vectors, and is asymptotically normally distributed.

     ‘spearman (X)’ is equivalent to ‘spearman (X, X)’.

     See also: *note ranks: XREFranks, *note kendall: XREFkendall.

 -- : TAU = kendall (X)
 -- : TAU = kendall (X, Y)
     Compute Kendall’s TAU.

     For two data vectors X, Y of common length N, Kendall’s TAU is the
     correlation of the signs of all rank differences of X and Y; i.e.,
     if both X and Y have distinct entries, then

                   1
          TAU = -------   SUM sign (Q(i) - Q(j)) * sign (R(i) - R(j))
                N (N-1)   i,j

     in which the Q(i) and R(i) are the ranks of X and Y, respectively.

     If X and Y are drawn from independent distributions, Kendall’s TAU
     is asymptotically normal with mean 0 and variance ‘(2 * (2N+5)) /
     (9 * N * (N-1))’.

     ‘kendall (X)’ is equivalent to ‘kendall (X, X)’.

     See also: *note ranks: XREFranks, *note spearman: XREFspearman.


File: octave.info,  Node: Distributions,  Next: Random Number Generation,  Prev: Correlation and Regression Analysis,  Up: Statistics

26.5 Distributions
==================

Octave has functions for computing the Probability Density Function
(PDF), the Cumulative Distribution function (CDF), and the quantile (the
inverse of the CDF) for arbitrary user-defined distributions (discrete)
and for experimental data (empirical).

   The following table summarizes the supported distributions (in
alphabetical order).

Distribution           PDF               CDF               Quantile
-----------------------------------------------------------------------------
Univariate Discrete    ‘discrete_pdf’    ‘discrete_cdf’    ‘discrete_inv’
Distribution
Empirical              ‘empirical_pdf’   ‘empirical_cdf’   ‘empirical_inv’
Distribution

 -- : PDF = discrete_pdf (X, V, P)
     For each element of X, compute the probability density function
     (PDF) at X of a univariate discrete distribution which assumes the
     values in V with probabilities P.

 -- : CDF = discrete_cdf (X, V, P)
     For each element of X, compute the cumulative distribution function
     (CDF) at X of a univariate discrete distribution which assumes the
     values in V with probabilities P.

 -- : Q = discrete_inv (X, V, P)
     For each element of X, compute the quantile (the inverse of the
     CDF) at X of the univariate distribution which assumes the values
     in V with probabilities P.

 -- : PDF = empirical_pdf (X, DATA)
     For each element of X, compute the probability density function
     (PDF) at X of the empirical distribution obtained from the
     univariate sample DATA.

 -- : CDF = empirical_cdf (X, DATA)
     For each element of X, compute the cumulative distribution function
     (CDF) at X of the empirical distribution obtained from the
     univariate sample DATA.

 -- : Q = empirical_inv (X, DATA)
     For each element of X, compute the quantile (the inverse of the
     CDF) at X of the empirical distribution obtained from the
     univariate sample DATA.


File: octave.info,  Node: Random Number Generation,  Prev: Distributions,  Up: Statistics

26.6 Random Number Generation
=============================

Octave can generate random numbers from a large number of distributions.
The random number generators are based on the random number generators
described in *note Special Utility Matrices::.

   The following table summarizes the available random number generators
(in alphabetical order).

Distribution                  Function
-----------------------------------------------------
Univariate Discrete           ‘discrete_rnd’
Distribution
Empirical Distribution        ‘empirical_rnd’
Exponential Distribution      ‘rande’
Gamma Distribution            ‘randg’
Poisson Distribution          ‘randp’
Standard Normal               ‘randn’
Distribution
Uniform Distribution          ‘rand’
Uniform Distribution          ‘randi’
(integers)

 -- : RND = discrete_rnd (V, P)
 -- : RND = discrete_rnd (V, P, R)
 -- : RND = discrete_rnd (V, P, R, C, ...)
 -- : RND = discrete_rnd (V, P, [SZ])
     Return a matrix of random samples from the univariate distribution
     which assumes the values in V with probabilities P.

     When called with a single size argument, return a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The size may also be specified with a vector of
     dimensions SZ.

     If no size arguments are given then the result matrix is the common
     size of V and P.

 -- : RND = empirical_rnd (DATA)
 -- : RND = empirical_rnd (DATA, R)
 -- : RND = empirical_rnd (DATA, R, C, ...)
 -- : RND = empirical_rnd (DATA, [SZ])
     Return a matrix of random samples from the empirical distribution
     obtained from the univariate sample DATA.

     When called with a single size argument, return a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The size may also be specified with a vector of
     dimensions SZ.

     If no size arguments are given then the result matrix is a random
     ordering of the sample DATA.


File: octave.info,  Node: Sets,  Next: Polynomial Manipulations,  Prev: Statistics,  Up: Top

27 Sets
*******

Octave has a number of functions for managing sets of data.  A set is
defined as a collection of unique elements and is typically represented
by a vector of numbers sorted in ascending order.  Any vector or matrix
can be converted to a set by removing duplicates through the use of the
‘unique’ function.  However, it isn’t necessary to explicitly create a
set as all of the functions which operate on sets will convert their
input to a set before proceeding.

 -- : Y = unique (X)
 -- : Y = unique (X, "rows")
 -- : Y = unique (..., "sorted")
 -- : Y = unique (..., "stable")
 -- : [Y, I, J] = unique (...)
 -- : [Y, I, J] = unique (..., "first")
 -- : [Y, I, J] = unique (..., "last")
 -- : [Y, I, J] = unique (..., "legacy")
     Return the unique elements of X.

     If the input X is a column vector then return a column vector;
     Otherwise, return a row vector.  X may also be a cell array of
     strings.

     If the optional argument "rows" is given then return the unique
     rows of X.  The input must be a 2-D numeric matrix to use this
     option.

     The optional argument "sorted"/"stable" controls the order in which
     unique values appear in the output.  The default is "sorted" and
     values in the output are placed in ascending order.  The
     alternative "stable" preserves the order found in the input X.

     If requested, return column index vectors I and J such that ‘Y =
     X(I)’ and ‘X = Y(J)’.

     Additionally, if I is a requested output then one of the flags
     "first" or "last" may be given.  If "last" is specified, return the
     highest possible indices in I, otherwise, if "first" is specified,
     return the lowest.  The default is "first".

     Example 1 : sort order

          unique ([3, 1, 1, 2])
          ⇒ [1, 2, 3]
          unique ([3, 1, 1, 2], "stable")
          ⇒ [3, 1, 2]

     Example 2 : index selection

          [~, I] = unique ([3, 1, 1, 2], "first")
          ⇒ I = [2; 4; 1]
          [~, I] = unique ([3, 1, 1, 2], "last")
          ⇒ I = [3; 4; 1]

     Programming Notes: The input flag "legacy" changes the algorithm to
     be compatible with MATLAB releases prior to R2012b.  Specifically,
     The index ordering flag is changed to "last", and the shape of the
     outputs I, J will follow the shape of the input X rather than
     always being column vectors.

     The third output, J, has not been implemented yet when the sort
     order is "stable".

     See also: *note union: XREFunion, *note intersect: XREFintersect,
     *note setdiff: XREFsetdiff, *note setxor: XREFsetxor, *note
     ismember: XREFismember.

 -- : C = uniquetol (A)
 -- : C = uniquetol (A, TOL)
 -- : C = uniquetol (..., PROPERTY, VALUE)
 -- : [C, IA, IC] = uniquetol (...)
     Return the unique elements of A within tolerance TOL.

     Two values, X and Y, are within relative tolerance if ‘abs (X - Y)
     <= TOL * max (abs (A(:)))’.

     The input A must be a real (non-complex) floating point type
     (double or single).

     If TOL is unspecified, the default tolerance is 1e-12 for double
     precision input or 1e-6 for single precision input.

     The function may also be called with the following optional
     property/value pairs.  Property/value pairs must be passed after
     other input arguments:

     "ByRows" (default: ‘false’)
          When true, return the unique rows of A.  A must be a 2-D array
          to use this option.  For rows, the criteria for uniqueness is
          changed to ‘all (abs (X - Y) <= TOL*max (abs (A),[],1))’ which
          compares each column component of a row against a
          column-specific tolerance.

     "DataScale"
          The tolerance test is changed to ‘abs (X - Y) <= TOL*DS’ where
          DS is a scalar unless the property "ByRows" is true.  In that
          case, DS can either be a scalar or a vector with a length
          equal to the number of columns in A.  Using a value of ‘1.0’
          for DS will change the tolerance from a relative one to an
          absolute tolerance.  Using a value of ‘Inf’ will disable
          testing.

     "OutputAllIndices" (default: ‘false’)
          When true, IA is a cell array (not a vector) that contains the
          indices for _all_ elements in A that are within tolerance of a
          value in C.  That is, each cell in IA corresponds to a single
          unique value in C, and the values in each cell correspond to
          locations in A.

     The output C is a row vector if the input A is a row vector.  For
     all other cases, a column vector is returned.

     The optional output IA is a column index vector such that ‘C =
     A(IA)’.  If the "ByRows" property is true, the condition is ‘C =
     A(IA, :)’.  If the "OutputAllIndices" property is true, then the
     values ‘A(IA{I})’ are all within tolerance of the unique value
     ‘C(I)’.

     The optional output IC is a column index vector such that ‘A =
     C(IC)’ when A is a vector.  When A is a matrix, ‘A(:) = C(IC)’.  If
     the "ByRows" property is true then ‘A = C(IC,:)’.

     Example: small round-off errors require ‘uniquetol’, not ‘unique’

          x = [1:5];
          ## Inverse_Function (Function (x)) should return exactly x
          y = exp (log (x));
          D = unique ([x, y])
          ⇒ [1   2   3   3   4   5   5]
          C = uniquetol ([x, y])
          ⇒ [1   2   3   4   5]

     See also: *note unique: XREFunique, *note union: XREFunion, *note
     intersect: XREFintersect, *note setdiff: XREFsetdiff, *note setxor:
     XREFsetxor, *note ismember: XREFismember.

* Menu:

* Set Operations::


File: octave.info,  Node: Set Operations,  Up: Sets

27.1 Set Operations
===================

Octave supports several basic set operations.  Octave can compute the
union, intersection, and difference of two sets.  Octave also supports
the _Exclusive Or_ set operation.

   The functions for set operations all work in the same way by
accepting two input sets and returning a third set.  As an example,
assume that ‘a’ and ‘b’ contains two sets, then

     union (a, b)

computes the union of the two sets.

   Finally, determining whether elements belong to a set can be done
with the ‘ismember’ function.  Because sets are ordered this operation
is very efficient and is of order O(log2(n)) which is preferable to the
‘find’ function which is of order O(n).

 -- : C = intersect (A, B)
 -- : C = intersect (A, B, "rows")
 -- : C = intersect (..., "sorted")
 -- : C = intersect (..., "stable")
 -- : C = intersect (..., "legacy")
 -- : [C, IA, IB] = intersect (...)

     Return the unique elements common to both A and B.

     If A and B are both row vectors then return a row vector;
     Otherwise, return a column vector.  The inputs may also be cell
     arrays of strings.

     If the optional input "rows" is given then return the common rows
     of A and B.  The inputs must be 2-D numeric matrices to use this
     option.

     The optional argument "sorted"/"stable" controls the order in which
     unique values appear in the output.  The default is "sorted" and
     values in the output are placed in ascending order.  The
     alternative "stable" preserves the order found in the input.

     If requested, return column index vectors IA and IB such that ‘C =
     A(IA)’ and ‘C = B(IB)’.

     Programming Note: The input flag "legacy" changes the algorithm to
     be compatible with MATLAB releases prior to R2012b.

     See also: *note unique: XREFunique, *note union: XREFunion, *note
     setdiff: XREFsetdiff, *note setxor: XREFsetxor, *note ismember:
     XREFismember.

 -- : C = union (A, B)
 -- : C = union (A, B, "rows")
 -- : C = union (..., "sorted")
 -- : C = union (..., "stable")
 -- : C = union (..., "legacy")
 -- : [C, IA, IB] = union (...)

     Return the unique elements that are in either A or B.

     If A and B are both row vectors then return a row vector;
     Otherwise, return a column vector.  The inputs may also be cell
     arrays of strings.

     If the optional input "rows" is given then return rows that are in
     either A or B.  The inputs must be 2-D numeric matrices to use this
     option.

     The optional argument "sorted"/"stable" controls the order in which
     unique values appear in the output.  The default is "sorted" and
     values in the output are placed in ascending order.  The
     alternative "stable" preserves the order found in the input.

     The optional outputs IA and IB are column index vectors such that
     ‘A(IA)’ and ‘B(IB)’ are disjoint sets whose union is C.

     Programming Note: The input flag "legacy" changes the algorithm to
     be compatible with MATLAB releases prior to R2012b.

     See also: *note unique: XREFunique, *note intersect: XREFintersect,
     *note setdiff: XREFsetdiff, *note setxor: XREFsetxor, *note
     ismember: XREFismember.

 -- : C = setdiff (A, B)
 -- : C = setdiff (A, B, "rows")
 -- : C = setdiff (..., "sorted")
 -- : C = setdiff (..., "stable")
 -- : C = setdiff (..., "legacy")
 -- : [C, IA] = setdiff (...)
     Return the unique elements in A that are not in B.

     If A is a row vector return a row vector; Otherwise, return a
     column vector.  The inputs may also be cell arrays of strings.

     If the optional input "rows" is given then return the rows in A
     that are not in B.  The inputs must be 2-D numeric matrices to use
     this option.

     The optional argument "sorted"/"stable" controls the order in which
     unique values appear in the output.  The default is "sorted" and
     values in the output are placed in ascending order.  The
     alternative "stable" preserves the order found in the input.

     If requested, return the index vector IA such that ‘C = A(IA)’.

     Programming Note: The input flag "legacy" changes the algorithm to
     be compatible with MATLAB releases prior to R2012b.

     See also: *note unique: XREFunique, *note union: XREFunion, *note
     intersect: XREFintersect, *note setxor: XREFsetxor, *note ismember:
     XREFismember.

 -- : C = setxor (A, B)
 -- : C = setxor (A, B, "rows")
 -- : C = setxor (..., "sorted")
 -- : C = setxor (..., "stable")
 -- : C = setxor (..., "legacy")
 -- : [C, IA, IB] = setxor (...)

     Return the unique elements exclusive to sets A or B.

     If A and B are both row vectors then return a row vector;
     Otherwise, return a column vector.  The inputs may also be cell
     arrays of strings.

     If the optional input "rows" is given then return the rows
     exclusive to sets A and B.  The inputs must be 2-D numeric matrices
     to use this option.

     The optional argument "sorted"/"stable" controls the order in which
     unique values appear in the output.  The default is "sorted" and
     values in the output are placed in ascending order.  The
     alternative "stable" preserves the order found in the input.

     The optional outputs IA and IB are column index vectors such that
     ‘A(IA)’ and ‘B(IB)’ are disjoint sets whose union is C.

     Programming Note: The input flag "legacy" changes the algorithm to
     be compatible with MATLAB releases prior to R2012b.

     See also: *note unique: XREFunique, *note union: XREFunion, *note
     intersect: XREFintersect, *note setdiff: XREFsetdiff, *note
     ismember: XREFismember.

 -- : TF = ismember (A, S)
 -- : TF = ismember (A, S, "rows")
 -- : [TF, S_IDX] = ismember (...)

     Return a logical matrix TF with the same shape as A which is true
     (1) if the element in A is found in S and false (0) if it is not.

     If a second output argument is requested then the index into S of
     each matching element is also returned.

          a = [3, 10, 1];
          s = [0:9];
          [tf, s_idx] = ismember (a, s)
               ⇒ tf = [1, 0, 1]
               ⇒ s_idx = [4, 0, 2]

     The inputs A and S may also be cell arrays.

          a = {"abc"};
          s = {"abc", "def"};
          [tf, s_idx] = ismember (a, s)
               ⇒ tf = 1
               ⇒ s_idx = 1

     If the optional third argument "rows" is given then compare rows in
     A with rows in S.  The inputs must be 2-D matrices with the same
     number of columns to use this option.

          a = [1:3; 5:7; 4:6];
          s = [0:2; 1:3; 2:4; 3:5; 4:6];
          [tf, s_idx] = ismember (a, s, "rows")
               ⇒ tf = logical ([1; 0; 1])
               ⇒ s_idx = [2; 0; 5];

     See also: *note lookup: XREFlookup, *note unique: XREFunique, *note
     union: XREFunion, *note intersect: XREFintersect, *note setdiff:
     XREFsetdiff, *note setxor: XREFsetxor.

 -- : P = powerset (A)
 -- : P = powerset (A, "rows")
     Compute the powerset (all subsets) of the set A.

     The set A must be a numerical matrix or a cell array of strings.
     The output will always be a cell array of either vectors or
     strings.

     With the optional argument "rows", each row of the set A is
     considered one element of the set.  The input must be a 2-D numeric
     matrix to use this argument.

     See also: *note unique: XREFunique, *note union: XREFunion, *note
     intersect: XREFintersect, *note setdiff: XREFsetdiff, *note setxor:
     XREFsetxor, *note ismember: XREFismember.


File: octave.info,  Node: Polynomial Manipulations,  Next: Interpolation,  Prev: Sets,  Up: Top

28 Polynomial Manipulations
***************************

In Octave, a polynomial is represented by its coefficients (arranged in
descending order).  For example, a vector C of length N+1 corresponds to
the following polynomial of order N

     p(x) = C(1) x^N + ... + C(N) x + C(N+1).

* Menu:

* Evaluating Polynomials::
* Finding Roots::
* Products of Polynomials::
* Derivatives / Integrals / Transforms::
* Polynomial Interpolation::
* Miscellaneous Functions::


File: octave.info,  Node: Evaluating Polynomials,  Next: Finding Roots,  Up: Polynomial Manipulations

28.1 Evaluating Polynomials
===========================

The value of a polynomial represented by the vector C can be evaluated
at the point X very easily, as the following example shows:

     N = length (c) - 1;
     val = dot (x.^(N:-1:0), c);

While the above example shows how easy it is to compute the value of a
polynomial, it isn’t the most stable algorithm.  With larger polynomials
you should use more elegant algorithms, such as Horner’s Method, which
is exactly what the Octave function ‘polyval’ does.

   In the case where X is a square matrix, the polynomial given by C is
still well-defined.  As when X is a scalar the obvious implementation is
easily expressed in Octave, but also in this case more elegant
algorithms perform better.  The ‘polyvalm’ function provides such an
algorithm.

 -- : Y = polyval (P, X)
 -- : Y = polyval (P, X, [], MU)
 -- : [Y, DY] = polyval (P, X, S)
 -- : [Y, DY] = polyval (P, X, S, MU)

     Evaluate the polynomial P at the specified values of X.

     If X is a vector or matrix, the polynomial is evaluated for each of
     the elements of X.

     When MU is present, evaluate the polynomial for
     (X - MU(1)) / MU(2).

     In addition to evaluating the polynomial, the second output
     represents the prediction interval, Y +/- DY, which contains at
     least 50% of the future predictions.  To calculate the prediction
     interval, the structured variable S, originating from ‘polyfit’,
     must be supplied.

     See also: *note polyvalm: XREFpolyvalm, *note polyaffine:
     XREFpolyaffine, *note polyfit: XREFpolyfit, *note roots: XREFroots,
     *note poly: XREFpoly.

 -- : Y = polyvalm (C, X)
     Evaluate a polynomial in the matrix sense.

     ‘polyvalm (C, X)’ will evaluate the polynomial in the matrix sense,
     i.e., matrix multiplication is used instead of element by element
     multiplication as used in ‘polyval’.

     The argument X must be a square matrix.

     See also: *note polyval: XREFpolyval, *note roots: XREFroots, *note
     poly: XREFpoly.


File: octave.info,  Node: Finding Roots,  Next: Products of Polynomials,  Prev: Evaluating Polynomials,  Up: Polynomial Manipulations

28.2 Finding Roots
==================

Octave can find the roots of a given polynomial.  This is done by
computing the companion matrix of the polynomial (see the ‘compan’
function for a definition), and then finding its eigenvalues.

 -- : R = roots (C)

     Compute the roots of the polynomial C.

     For a vector C with N components, return the roots of the
     polynomial

          c(1) * x^(N-1) + ... + c(N-1) * x + c(N)

     As an example, the following code finds the roots of the quadratic
     polynomial

          p(x) = x^2 - 5.

          c = [1, 0, -5];
          roots (c)
          ⇒  2.2361
          ⇒ -2.2361

     Note that the true result is +/- sqrt(5) which is roughly +/-
     2.2361.

     See also: *note poly: XREFpoly, *note compan: XREFcompan, *note
     fzero: XREFfzero.

 -- : Z = polyeig (C0, C1, ..., CL)
 -- : [V, Z] = polyeig (C0, C1, ..., CL)

     Solve the polynomial eigenvalue problem of degree L.

     Given an NxN matrix polynomial

     ‘C(S) = C0 + C1 S + ... + CL S^L’

     ‘polyeig’ solves the eigenvalue problem

     ‘(C0 + C1 Z + ... + CL Z^L) V = 0’.

     Note that the eigenvalues Z are the zeros of the matrix polynomial.
     Z is a row vector with ‘N*L’ elements.  V is a matrix (N x N*L)
     with columns that correspond to the eigenvectors.

     See also: *note eig: XREFeig, *note eigs: XREFeigs, *note compan:
     XREFcompan.

 -- : A = compan (C)
     Compute the companion matrix corresponding to polynomial
     coefficient vector C.

     The companion matrix is

               _                                                        _
              |  -c(2)/c(1)   -c(3)/c(1)  ...  -c(N)/c(1)  -c(N+1)/c(1)  |
              |       1            0      ...       0             0      |
              |       0            1      ...       0             0      |
          A = |       .            .      .         .             .      |
              |       .            .       .        .             .      |
              |       .            .        .       .             .      |
              |_      0            0      ...       1             0     _|

     The eigenvalues of the companion matrix are equal to the roots of
     the polynomial.

     See also: *note roots: XREFroots, *note poly: XREFpoly, *note eig:
     XREFeig.

 -- : [MULTP, IDXP] = mpoles (P)
 -- : [MULTP, IDXP] = mpoles (P, TOL)
 -- : [MULTP, IDXP] = mpoles (P, TOL, REORDER)
     Identify unique poles in P and their associated multiplicity.

     The output is ordered from pole with largest magnitude to smallest
     magnitude.

     If the relative difference of two poles is less than TOL then they
     are considered to be multiples.  The default value for TOL is
     0.001.

     If the optional parameter REORDER is zero, poles are not sorted.

     The output MULTP is a vector specifying the multiplicity of the
     poles.  ‘MULTP(n)’ refers to the multiplicity of the Nth pole
     ‘P(IDXP(n))’.

     For example:

          p = [2 3 1 1 2];
          [m, n] = mpoles (p)
             ⇒ m = [1; 1; 2; 1; 2]
             ⇒ n = [2; 5; 1; 4; 3]
             ⇒ p(n) = [3, 2, 2, 1, 1]

     See also: *note residue: XREFresidue, *note poly: XREFpoly, *note
     roots: XREFroots, *note conv: XREFconv, *note deconv: XREFdeconv.


File: octave.info,  Node: Products of Polynomials,  Next: Derivatives / Integrals / Transforms,  Prev: Finding Roots,  Up: Polynomial Manipulations

28.3 Products of Polynomials
============================

 -- : Y = conv (A, B)
 -- : Y = conv (A, B, SHAPE)
     Convolve two vectors A and B.

     When A and B are the coefficient vectors of two polynomials, the
     convolution represents the coefficient vector of the product
     polynomial.

     The size of the result is determined by the optional SHAPE argument
     which takes the following values

     SHAPE = "full"
          Return the full convolution.  (default) The result is a vector
          with length equal to ‘length (A) + length (B) - 1’.

     SHAPE = "same"
          Return the central part of the convolution with the same size
          as A.

     SHAPE = "valid"
          Return only the parts which do not include zero-padded edges.
          The size of the result is ‘max (size (A) - size (B) + 1, 0)’.

     See also: *note deconv: XREFdeconv, *note conv2: XREFconv2, *note
     convn: XREFconvn, *note fftconv: XREFfftconv.

 -- : C = convn (A, B)
 -- : C = convn (A, B, SHAPE)
     Return the n-D convolution of A and B.

     The size of the result is determined by the optional SHAPE argument
     which takes the following values

     SHAPE = "full"
          Return the full convolution.  (default)

     SHAPE = "same"
          Return central part of the convolution with the same size as
          A.  The central part of the convolution begins at the indices
          ‘floor ([size(B)/2] + 1)’.

     SHAPE = "valid"
          Return only the parts which do not include zero-padded edges.
          The size of the result is ‘max (size (A) - size (B) + 1, 0)’.

     See also: *note conv2: XREFconv2, *note conv: XREFconv.

 -- : B = deconv (Y, A)
 -- : [B, R] = deconv (Y, A)
     Deconvolve two vectors (polynomial division).

     ‘[B, R] = deconv (Y, A)’ solves for B and R such that ‘Y = conv (A,
     B) + R’.

     If Y and A are polynomial coefficient vectors, B will contain the
     coefficients of the polynomial quotient and R will be a remainder
     polynomial of lowest order.

     See also: *note conv: XREFconv, *note residue: XREFresidue.

 -- : C = conv2 (A, B)
 -- : C = conv2 (V1, V2, M)
 -- : C = conv2 (..., SHAPE)
     Return the 2-D convolution of A and B.

     The size of the result is determined by the optional SHAPE argument
     which takes the following values

     SHAPE = "full"
          Return the full convolution.  (default)

     SHAPE = "same"
          Return the central part of the convolution with the same size
          as A.  The central part of the convolution begins at the
          indices ‘floor ([size(B)/2] + 1)’.

     SHAPE = "valid"
          Return only the parts which do not include zero-padded edges.
          The size of the result is ‘max (size (A) - size (B) + 1, 0)’.

     When the third argument is a matrix, return the convolution of the
     matrix M by the vector V1 in the column direction and by the vector
     V2 in the row direction.

     See also: *note conv: XREFconv, *note convn: XREFconvn.

 -- : Q = polygcd (B, A)
 -- : Q = polygcd (B, A, TOL)

     Find the greatest common divisor of two polynomials.

     This is equivalent to the polynomial found by multiplying together
     all the common roots.  Together with deconv, you can reduce a ratio
     of two polynomials.

     The tolerance TOL defaults to ‘sqrt (eps)’.

     *Caution:* This is a numerically unstable algorithm and should not
     be used on large polynomials.

     Example code:

          polygcd (poly (1:8), poly (3:12)) - poly (3:8)
          ⇒ [ 0, 0, 0, 0, 0, 0, 0 ]
          deconv (poly (1:8), polygcd (poly (1:8), poly (3:12))) - poly (1:2)
          ⇒ [ 0, 0, 0 ]

     See also: *note poly: XREFpoly, *note roots: XREFroots, *note conv:
     XREFconv, *note deconv: XREFdeconv, *note residue: XREFresidue.

 -- : [R, P, K, E] = residue (B, A)
 -- : [B, A] = residue (R, P, K)
 -- : [B, A] = residue (R, P, K, E)
     The first calling form computes the partial fraction expansion for
     the quotient of the polynomials, B and A.

     The quotient is defined as

          B(s)    M       r(m)        N
          ---- = SUM ------------- + SUM k(i)*s^(N-i)
          A(s)   m=1 (s-p(m))^e(m)   i=1

     where M is the number of poles (the length of the R, P, and E), the
     K vector is a polynomial of order N-1 representing the direct
     contribution, and the E vector specifies the multiplicity of the
     m-th residue’s pole.

     For example,

          b = [1, 1, 1];
          a = [1, -5, 8, -4];
          [r, p, k, e] = residue (b, a)
             ⇒ r = [-2; 7; 3]
             ⇒ p = [2; 2; 1]
             ⇒ k = [](0x0)
             ⇒ e = [1; 2; 1]

     which represents the following partial fraction expansion

                  s^2 + s + 1       -2        7        3
             ------------------- = ----- + ------- + -----
             s^3 - 5s^2 + 8s - 4   (s-2)   (s-2)^2   (s-1)

     The second calling form performs the inverse operation and computes
     the reconstituted quotient of polynomials, B(s)/A(s), from the
     partial fraction expansion; represented by the residues, poles, and
     a direct polynomial specified by R, P and K, and the pole
     multiplicity E.

     If the multiplicity, E, is not explicitly specified the
     multiplicity is determined by the function ‘mpoles’.

     For example:

          r = [-2; 7; 3];
          p = [2; 2; 1];
          k = [1, 0];
          [b, a] = residue (r, p, k)
             ⇒ b = [1, -5, 9, -3, 1]
             ⇒ a = [1, -5, 8, -4]

          where mpoles is used to determine e = [1; 2; 1]

     Alternatively the multiplicity may be defined explicitly, for
     example,

          r = [7; 3; -2];
          p = [2; 1; 2];
          k = [1, 0];
          e = [2; 1; 1];
          [b, a] = residue (r, p, k, e)
             ⇒ b = [1, -5, 9, -3, 1]
             ⇒ a = [1, -5, 8, -4]

     which represents the following partial fraction expansion

           -2        7        3         s^4 - 5s^3 + 9s^2 - 3s + 1
          ----- + ------- + ----- + s = --------------------------
          (s-2)   (s-2)^2   (s-1)          s^3 - 5s^2 + 8s - 4

     See also: *note mpoles: XREFmpoles, *note poly: XREFpoly, *note
     roots: XREFroots, *note conv: XREFconv, *note deconv: XREFdeconv.


File: octave.info,  Node: Derivatives / Integrals / Transforms,  Next: Polynomial Interpolation,  Prev: Products of Polynomials,  Up: Polynomial Manipulations

28.4 Derivatives / Integrals / Transforms
=========================================

Octave comes with functions for computing the derivative and the
integral of a polynomial.  The functions ‘polyder’ and ‘polyint’ both
return new polynomials describing the result.  As an example we’ll
compute the definite integral of p(x) = x^2 + 1 from 0 to 3.

     c = [1, 0, 1];
     integral = polyint (c);
     area = polyval (integral, 3) - polyval (integral, 0)
     ⇒ 12

 -- : K = polyder (P)
 -- : K = polyder (A, B)
 -- : [Q, D] = polyder (B, A)
     Return the coefficients of the derivative of the polynomial whose
     coefficients are given by the vector P.

     If a pair of polynomials is given, return the derivative of the
     product A*B.

     If two inputs and two outputs are given, return the derivative of
     the polynomial quotient B/A.  The quotient numerator is in Q and
     the denominator in D.

     See also: *note polyint: XREFpolyint, *note polyval: XREFpolyval,
     *note polyreduce: XREFpolyreduce.

 -- : Q = polyint (P)
 -- : Q = polyint (P, K)
     Return the coefficients of the integral of the polynomial whose
     coefficients are represented by the vector P.

     The variable K is the constant of integration, which by default is
     set to zero.

     See also: *note polyder: XREFpolyder, *note polyval: XREFpolyval.

 -- : G = polyaffine (F, MU)
     Return the coefficients of the polynomial vector F after an affine
     transformation.

     If F is the vector representing the polynomial f(x), then ‘G =
     polyaffine (F, MU)’ is the vector representing:

          g(x) = f( (x - MU(1)) / MU(2) )

     See also: *note polyval: XREFpolyval, *note polyfit: XREFpolyfit.


File: octave.info,  Node: Polynomial Interpolation,  Next: Miscellaneous Functions,  Prev: Derivatives / Integrals / Transforms,  Up: Polynomial Manipulations

28.5 Polynomial Interpolation
=============================

Octave comes with good support for various kinds of interpolation, most
of which are described in *note Interpolation::.  One simple alternative
to the functions described in the aforementioned chapter, is to fit a
single polynomial, or a piecewise polynomial (spline) to some given data
points.  To avoid a highly fluctuating polynomial, one most often wants
to fit a low-order polynomial to data.  This usually means that it is
necessary to fit the polynomial in a least-squares sense, which just is
what the ‘polyfit’ function does.

 -- : P = polyfit (X, Y, N)
 -- : [P, S] = polyfit (X, Y, N)
 -- : [P, S, MU] = polyfit (X, Y, N)
     Return the coefficients of a polynomial P(X) of degree N that
     minimizes the least-squares-error of the fit to the points ‘[X(:),
     Y(:)]’.

     N is typically an integer ≥ 0 specifying the degree of the
     approximating polynomial.  If N is a logical vector, it is used as
     a mask to selectively force the corresponding polynomial
     coefficients to be used or ignored.

     The polynomial coefficients are returned in the row vector P.  The
     output P may be directly used with ‘polyval’ to estimate values
     using the fitted polynomial.

     The optional output S is a structure containing the following
     fields:

     ‘yf’
          The values of the polynomial for each value of X.

     ‘X’
          The Vandermonde matrix used to compute the polynomial
          coefficients.

     ‘R’
          Triangular factor R from the QR decomposition.

     ‘C’
          The unscaled covariance matrix, formally equal to the inverse
          of X’*X, but computed in a way minimizing roundoff error
          propagation.

     ‘df’
          The degrees of freedom.

     ‘normr’
          The norm of the residuals.

     The second output may be used by ‘polyval’ to calculate the
     statistical error limits of the predicted values.  In particular,
     the standard deviation of P coefficients is given by

     ‘sqrt (diag (S.C)/S.DF) * S.NORMR’.

     When the third output, MU, is present the original data is centered
     and scaled which can improve the numerical stability of the fit.
     The coefficients P are associated with a polynomial in

     ‘XHAT = (X - MU(1)) / MU(2)’
     where MU(1) = mean (X), and MU(2) = std (X).

     Example 1 : logical N and integer N

          f = @(x) x.^2 + 5;   # data-generating function
          x = 0:5;
          y = f (x);
          ## Fit data to polynomial A*x^3 + B*x^1
          p = polyfit (x, y, logical ([1, 0, 1, 0]))
          ⇒ p = [ 0.0680, 0, 4.2444, 0 ]
          ## Fit data to polynomial using all terms up to x^3
          p = polyfit (x, y, 3)
          ⇒ p = [ -4.9608e-17, 1.0000e+00, -3.3813e-15, 5.0000e+00 ]

     See also: *note polyval: XREFpolyval, *note polyaffine:
     XREFpolyaffine, *note roots: XREFroots, *note vander: XREFvander,
     *note zscore: XREFzscore.

   In situations where a single polynomial isn’t good enough, a solution
is to use several polynomials pieced together.  The function ‘splinefit’
fits a piecewise polynomial (spline) to a set of data.

 -- : PP = splinefit (X, Y, BREAKS)
 -- : PP = splinefit (X, Y, P)
 -- : PP = splinefit (..., "periodic", PERIODIC)
 -- : PP = splinefit (..., "robust", ROBUST)
 -- : PP = splinefit (..., "beta", BETA)
 -- : PP = splinefit (..., "order", ORDER)
 -- : PP = splinefit (..., "constraints", CONSTRAINTS)

     Fit a piecewise cubic spline with breaks (knots) BREAKS to the
     noisy data, X and Y.

     X is a vector, and Y is a vector or N-D array.  If Y is an N-D
     array, then X(j) is matched to Y(:,...,:,j).

     P is a positive integer defining the number of intervals along X,
     and P+1 is the number of breaks.  The number of points in each
     interval differ by no more than 1.

     The optional property PERIODIC is a logical value which specifies
     whether a periodic boundary condition is applied to the spline.
     The length of the period is ‘max (BREAKS) - min (BREAKS)’.  The
     default value is ‘false’.

     The optional property ROBUST is a logical value which specifies if
     robust fitting is to be applied to reduce the influence of outlying
     data points.  Three iterations of weighted least squares are
     performed.  Weights are computed from previous residuals.  The
     sensitivity of outlier identification is controlled by the property
     BETA.  The value of BETA is restricted to the range, 0 < BETA < 1.
     The default value is BETA = 1/2.  Values close to 0 give all data
     equal weighting.  Increasing values of BETA reduce the influence of
     outlying data.  Values close to unity may cause instability or rank
     deficiency.

     The fitted spline is returned as a piecewise polynomial, PP, and
     may be evaluated using ‘ppval’.

     The splines are constructed of polynomials with degree ORDER.  The
     default is a cubic, ORDER=3.  A spline with P pieces has P+ORDER
     degrees of freedom.  With periodic boundary conditions the degrees
     of freedom are reduced to P.

     The optional property, CONSTRAINTS, is a structure specifying
     linear constraints on the fit.  The structure has three fields,
     "xc", "yc", and "cc".

     "xc"
          Vector of the x-locations of the constraints.

     "yc"
          Constraining values at the locations XC.  The default is an
          array of zeros.

     "cc"
          Coefficients (matrix).  The default is an array of ones.  The
          number of rows is limited to the order of the piecewise
          polynomials, ORDER.

     Constraints are linear combinations of derivatives of order 0 to
     ORDER-1 according to

          cc(1,j) * y(xc(j)) + cc(2,j) * y'(xc(j)) + ... = yc(:,...,:,j).

     See also: *note interp1: XREFinterp1, *note unmkpp: XREFunmkpp,
     *note ppval: XREFppval, *note spline: XREFspline, *note pchip:
     XREFpchip, *note ppder: XREFppder, *note ppint: XREFppint, *note
     ppjumps: XREFppjumps.

   The number of BREAKS (or knots) used to construct the piecewise
polynomial is a significant factor in suppressing the noise present in
the input data, X and Y.  This is demonstrated by the example below.

     x = 2 * pi * rand (1, 200);
     y = sin (x) + sin (2 * x) + 0.2 * randn (size (x));
     ## Uniform breaks
     breaks = linspace (0, 2 * pi, 41); % 41 breaks, 40 pieces
     pp1 = splinefit (x, y, breaks);
     ## Breaks interpolated from data
     pp2 = splinefit (x, y, 10);  % 11 breaks, 10 pieces
     ## Plot
     xx = linspace (0, 2 * pi, 400);
     y1 = ppval (pp1, xx);
     y2 = ppval (pp2, xx);
     plot (x, y, ".", xx, [y1; y2])
     axis tight
     ylim auto
     legend ({"data", "41 breaks, 40 pieces", "11 breaks, 10 pieces"})

   The piecewise polynomial fit, provided by ‘splinefit’, has continuous
derivatives up to the ORDER-1.  For example, a cubic fit has continuous
first and second derivatives.  This is demonstrated by the code

     ## Data (200 points)
     x = 2 * pi * rand (1, 200);
     y = sin (x) + sin (2 * x) + 0.1 * randn (size (x));
     ## Piecewise constant
     pp1 = splinefit (x, y, 8, "order", 0);
     ## Piecewise linear
     pp2 = splinefit (x, y, 8, "order", 1);
     ## Piecewise quadratic
     pp3 = splinefit (x, y, 8, "order", 2);
     ## Piecewise cubic
     pp4 = splinefit (x, y, 8, "order", 3);
     ## Piecewise quartic
     pp5 = splinefit (x, y, 8, "order", 4);
     ## Plot
     xx = linspace (0, 2 * pi, 400);
     y1 = ppval (pp1, xx);
     y2 = ppval (pp2, xx);
     y3 = ppval (pp3, xx);
     y4 = ppval (pp4, xx);
     y5 = ppval (pp5, xx);
     plot (x, y, ".", xx, [y1; y2; y3; y4; y5])
     axis tight
     ylim auto
     legend ({"data", "order 0", "order 1", "order 2", "order 3", "order 4"})

   When the underlying function to provide a fit to is periodic,
‘splinefit’ is able to apply the boundary conditions needed to manifest
a periodic fit.  This is demonstrated by the code below.

     ## Data (100 points)
     x = 2 * pi * [0, (rand (1, 98)), 1];
     y = sin (x) - cos (2 * x) + 0.2 * randn (size (x));
     ## No constraints
     pp1 = splinefit (x, y, 10, "order", 5);
     ## Periodic boundaries
     pp2 = splinefit (x, y, 10, "order", 5, "periodic", true);
     ## Plot
     xx = linspace (0, 2 * pi, 400);
     y1 = ppval (pp1, xx);
     y2 = ppval (pp2, xx);
     plot (x, y, ".", xx, [y1; y2])
     axis tight
     ylim auto
     legend ({"data", "no constraints", "periodic"})

   More complex constraints may be added as well.  For example, the code
below illustrates a periodic fit with values that have been clamped at
the endpoints, and a second periodic fit which is hinged at the
endpoints.

     ## Data (200 points)
     x = 2 * pi * rand (1, 200);
     y = sin (2 * x) + 0.1 * randn (size (x));
     ## Breaks
     breaks = linspace (0, 2 * pi, 10);
     ## Clamped endpoints, y = y' = 0
     xc = [0, 0, 2*pi, 2*pi];
     cc = [(eye (2)), (eye (2))];
     con = struct ("xc", xc, "cc", cc);
     pp1 = splinefit (x, y, breaks, "constraints", con);
     ## Hinged periodic endpoints, y = 0
     con = struct ("xc", 0);
     pp2 = splinefit (x, y, breaks, "constraints", con, "periodic", true);
     ## Plot
     xx = linspace (0, 2 * pi, 400);
     y1 = ppval (pp1, xx);
     y2 = ppval (pp2, xx);
     plot (x, y, ".", xx, [y1; y2])
     axis tight
     ylim auto
     legend ({"data", "clamped", "hinged periodic"})

   The ‘splinefit’ function also provides the convenience of a ROBUST
fitting, where the effect of outlying data is reduced.  In the example
below, three different fits are provided.  Two with differing levels of
outlier suppression and a third illustrating the non-robust solution.

     ## Data
     x = linspace (0, 2*pi, 200);
     y = sin (x) + sin (2 * x) + 0.05 * randn (size (x));
     ## Add outliers
     x = [x, linspace(0,2*pi,60)];
     y = [y, -ones(1,60)];
     ## Fit splines with hinged conditions
     con = struct ("xc", [0, 2*pi]);
     ## Robust fitting, beta = 0.25
     pp1 = splinefit (x, y, 8, "constraints", con, "beta", 0.25);
     ## Robust fitting, beta = 0.75
     pp2 = splinefit (x, y, 8, "constraints", con, "beta", 0.75);
     ## No robust fitting
     pp3 = splinefit (x, y, 8, "constraints", con);
     ## Plot
     xx = linspace (0, 2*pi, 400);
     y1 = ppval (pp1, xx);
     y2 = ppval (pp2, xx);
     y3 = ppval (pp3, xx);
     plot (x, y, ".", xx, [y1; y2; y3])
     legend ({"data with outliers","robust, beta = 0.25", ...
              "robust, beta = 0.75", "no robust fitting"})
     axis tight
     ylim auto

   A very specific form of polynomial interpretation is the Padé
approximant.  For control systems, a continuous-time delay can be
modeled very simply with the approximant.

 -- : [NUM, DEN] = padecoef (T)
 -- : [NUM, DEN] = padecoef (T, N)
     Compute the Nth-order Padé approximant of the continuous-time delay
     T in transfer function form.

     The Padé approximant of ‘exp (-sT)’ is defined by the following
     equation

                       Pn(s)
          exp (-sT) ~ -------
                       Qn(s)

     Where both Pn(s) and Qn(s) are Nth-order rational functions defined
     by the following expressions

                   N    (2N - k)!N!        k
          Pn(s) = SUM --------------- (-sT)
                  k=0 (2N)!k!(N - k)!

          Qn(s) = Pn(-s)

     The inputs T and N must be non-negative numeric scalars.  If N is
     unspecified it defaults to 1.

     The output row vectors NUM and DEN contain the numerator and
     denominator coefficients in descending powers of s.  Both are
     Nth-order polynomials.

     For example:

          t = 0.1;
          n = 4;
          [num, den] = padecoef (t, n)
          ⇒ num =

                1.0000e-04  -2.0000e-02   1.8000e+00  -8.4000e+01   1.6800e+03

          ⇒ den =

                1.0000e-04   2.0000e-02   1.8000e+00   8.4000e+01   1.6800e+03

   The function, ‘ppval’, evaluates the piecewise polynomials, created
by ‘mkpp’ or other means, and ‘unmkpp’ returns detailed information
about the piecewise polynomial.

   The following example shows how to combine two linear functions and a
quadratic into one function.  Each of these functions is expressed on
adjoined intervals.

     x = [-2, -1, 1, 2];
     p = [ 0,  1, 0;
           1, -2, 1;
           0, -1, 1 ];
     pp = mkpp (x, p);
     xi = linspace (-2, 2, 50);
     yi = ppval (pp, xi);
     plot (xi, yi);

 -- : PP = mkpp (BREAKS, COEFS)
 -- : PP = mkpp (BREAKS, COEFS, D)

     Construct a piecewise polynomial (pp) structure from sample points
     BREAKS and coefficients COEFS.

     BREAKS must be a vector of strictly increasing values.  The number
     of intervals is given by ‘NI = length (BREAKS) - 1’.

     When M is the polynomial order COEFS must be of size:
     NI-by-(M + 1).

     The i-th row of COEFS, ‘COEFS(I,:)’, contains the coefficients for
     the polynomial over the I-th interval, ordered from highest (M) to
     lowest (0) degree.

     COEFS may also be a multi-dimensional array, specifying a
     vector-valued or array-valued polynomial.  In that case the
     polynomial order M is defined by the length of the last dimension
     of COEFS.  The size of first dimension(s) are given by the scalar
     or vector D.  If D is not given it is set to ‘1’.  In this case
     ‘P(R, I, :)’ contains the coefficients for the R-th polynomial
     defined on interval I.  In any case COEFS is reshaped to a 2-D
     matrix of size ‘[NI*prod(D) M]’.

     Programming Note: ‘ppval’ evaluates polynomials at ‘XI -
     BREAKS(i)’, i.e., it subtracts the lower endpoint of the current
     interval from XI.  This must be taken into account when creating
     piecewise polynomials objects with ‘mkpp’.

     See also: *note unmkpp: XREFunmkpp, *note ppval: XREFppval, *note
     spline: XREFspline, *note pchip: XREFpchip, *note ppder: XREFppder,
     *note ppint: XREFppint, *note ppjumps: XREFppjumps.

 -- : [X, P, N, K, D] = unmkpp (PP)

     Extract the components of a piecewise polynomial structure PP.

     This function is the inverse of ‘mkpp’: it extracts the inputs to
     ‘mkpp’ needed to create the piecewise polynomial structure PP.  The
     code below makes this relation explicit:

          [breaks, coefs, numinter, order, dim] = unmkpp (pp);
          pp2  = mkpp (breaks, coefs, dim);

     The piecewise polynomial structure ‘pp2’ obtained in this way, is
     identical to the original ‘pp’.  The same can be obtained by
     directly accessing the fields of the structure ‘pp’.

     The components are:

     X
          Sample points or breaks.

     P
          Polynomial coefficients for points in sample interval.  ‘P(I,
          :)’ contains the coefficients for the polynomial over interval
          I ordered from highest to lowest degree.  If ‘D > 1’, then P
          is a matrix of size ‘[N*prod(D) M]’, where the ‘I + (1:D)’
          rows are the coefficients of all the D polynomials in the
          interval I.

     N
          Number of polynomial pieces or intervals, ‘N = length (X) -
          1’.

     K
          Order of the polynomial plus 1.

     D
          Number of polynomials defined for each interval.

     See also: *note mkpp: XREFmkpp, *note ppval: XREFppval, *note
     spline: XREFspline, *note pchip: XREFpchip.

 -- : YI = ppval (PP, XI)
     Evaluate the piecewise polynomial structure PP at the points XI.

     If PP describes a scalar polynomial function, the result is an
     array of the same shape as XI.  Otherwise, the size of the result
     is ‘[pp.dim, length(XI)]’ if XI is a vector, or ‘[pp.dim,
     size(XI)]’ if it is a multi-dimensional array.

     See also: *note mkpp: XREFmkpp, *note unmkpp: XREFunmkpp, *note
     spline: XREFspline, *note pchip: XREFpchip.

 -- : ppd = ppder (pp)
 -- : ppd = ppder (pp, m)
     Compute the piecewise M-th derivative of a piecewise polynomial
     struct PP.

     If M is omitted the first derivative is calculated.

     See also: *note mkpp: XREFmkpp, *note ppval: XREFppval, *note
     ppint: XREFppint.

 -- : PPI = ppint (PP)
 -- : PPI = ppint (PP, C)
     Compute the integral of the piecewise polynomial struct PP.

     C, if given, is the constant of integration.

     See also: *note mkpp: XREFmkpp, *note ppval: XREFppval, *note
     ppder: XREFppder.

 -- : JUMPS = ppjumps (PP)
     Evaluate the boundary jumps of a piecewise polynomial.

     If there are n intervals, and the dimensionality of PP is d, the
     resulting array has dimensions ‘[d, n-1]’.

     See also: *note mkpp: XREFmkpp.


File: octave.info,  Node: Miscellaneous Functions,  Prev: Polynomial Interpolation,  Up: Polynomial Manipulations

28.6 Miscellaneous Functions
============================

 -- : Y = poly (A)
 -- : Y = poly (X)
     If A is a square N-by-N matrix, ‘poly (A)’ is the row vector of the
     coefficients of ‘det (z * eye (N) - A)’, the characteristic
     polynomial of A.

     For example, the following code finds the eigenvalues of A which
     are the roots of ‘poly (A)’.

          roots (poly (eye (3)))
              ⇒ 1.00001 + 0.00001i
                 1.00001 - 0.00001i
                 0.99999 + 0.00000i

     In fact, all three eigenvalues are exactly 1 which emphasizes that
     for numerical performance the ‘eig’ function should be used to
     compute eigenvalues.

     If X is a vector, ‘poly (X)’ is a vector of the coefficients of the
     polynomial whose roots are the elements of X.  That is, if C is a
     polynomial, then the elements of ‘D = roots (poly (C))’ are
     contained in C.  The vectors C and D are not identical, however,
     due to sorting and numerical errors.

     See also: *note roots: XREFroots, *note eig: XREFeig.

 -- : polyout (C)
 -- : polyout (C, X)
 -- : STR = polyout (...)
     Display a formatted version of the polynomial C.

     The formatted polynomial

          c(x) = c(1) * x^n + ... + c(n) x + c(n+1)

     is returned as a string or written to the screen if ‘nargout’ is
     zero.

     The second argument X specifies the variable name to use for each
     term and defaults to the string "s".

     See also: *note polyreduce: XREFpolyreduce.

 -- : P = polyreduce (C)
     Reduce a polynomial coefficient vector to a minimum number of terms
     by stripping off any leading zeros.

     See also: *note polyout: XREFpolyout.


File: octave.info,  Node: Interpolation,  Next: Geometry,  Prev: Polynomial Manipulations,  Up: Top

29 Interpolation
****************

* Menu:

* One-dimensional Interpolation::
* Multi-dimensional Interpolation::


File: octave.info,  Node: One-dimensional Interpolation,  Next: Multi-dimensional Interpolation,  Up: Interpolation

29.1 One-dimensional Interpolation
==================================

Octave supports several methods for one-dimensional interpolation, most
of which are described in this section.  *note Polynomial
Interpolation:: and *note Interpolation on Scattered Data:: describe
additional methods.

 -- : YI = interp1 (X, Y, XI)
 -- : YI = interp1 (Y, XI)
 -- : YI = interp1 (..., METHOD)
 -- : YI = interp1 (..., EXTRAP)
 -- : YI = interp1 (..., "left")
 -- : YI = interp1 (..., "right")
 -- : PP = interp1 (..., "pp")

     One-dimensional interpolation.

     Interpolate input data to determine the value of YI at the points
     XI.  If not specified, X is taken to be the indices of Y (‘1:length
     (Y)’).  If Y is a matrix or an N-dimensional array, the
     interpolation is performed on each column of Y.

     The interpolation METHOD is one of:

     "nearest"
          Return the nearest neighbor.

     "previous"
          Return the previous neighbor.

     "next"
          Return the next neighbor.

     "linear" (default)
          Linear interpolation from nearest neighbors.

     "pchip"
          Piecewise cubic Hermite interpolating
          polynomial—shape-preserving interpolation with smooth first
          derivative.

     "cubic"
          Cubic interpolation (same as "pchip").

     "spline"
          Cubic spline interpolation—smooth first and second derivatives
          throughout the curve.

     Adding ’*’ to the start of any method above forces ‘interp1’ to
     assume that X is uniformly spaced, and only ‘X(1)’ and ‘X(2)’ are
     referenced.  This is usually faster, and is never slower.  The
     default method is "linear".

     If EXTRAP is the string "extrap", then extrapolate values beyond
     the endpoints using the current METHOD.  If EXTRAP is a number,
     then replace values beyond the endpoints with that number.  When
     unspecified, EXTRAP defaults to ‘NA’.

     If the string argument "pp" is specified, then XI should not be
     supplied and ‘interp1’ returns a piecewise polynomial object.  This
     object can later be used with ‘ppval’ to evaluate the
     interpolation.  There is an equivalence, such that ‘ppval (interp1
     (X, Y, METHOD, "pp"), XI) == interp1 (X, Y, XI, METHOD, "extrap")’.

     Duplicate points in X specify a discontinuous interpolant.  There
     may be at most 2 consecutive points with the same value.  If X is
     increasing, the default discontinuous interpolant is
     right-continuous.  If X is decreasing, the default discontinuous
     interpolant is left-continuous.  The continuity condition of the
     interpolant may be specified by using the options "left" or "right"
     to select a left-continuous or right-continuous interpolant,
     respectively.  Discontinuous interpolation is only allowed for
     "nearest" and "linear" methods; in all other cases, the X-values
     must be unique.

     An example of the use of ‘interp1’ is

          xf = [0:0.05:10];
          yf = sin (2*pi*xf/5);
          xp = [0:10];
          yp = sin (2*pi*xp/5);
          lin = interp1 (xp, yp, xf);
          near = interp1 (xp, yp, xf, "nearest");
          pch = interp1 (xp, yp, xf, "pchip");
          spl = interp1 (xp, yp, xf, "spline");
          plot (xf,yf,"r", xf,near,"g", xf,lin,"b", xf,pch,"c", xf,spl,"m",
                xp,yp,"r*");
          legend ("original", "nearest", "linear", "pchip", "spline");

     See also: *note pchip: XREFpchip, *note spline: XREFspline, *note
     interpft: XREFinterpft, *note interp2: XREFinterp2, *note interp3:
     XREFinterp3, *note interpn: XREFinterpn.

   There are some important differences between the various
interpolation methods.  The "spline" method enforces that both the first
and second derivatives of the interpolated values have a continuous
derivative, whereas the other methods do not.  This means that the
results of the "spline" method are generally smoother.  If the function
to be interpolated is in fact smooth, then "spline" will give excellent
results.  However, if the function to be evaluated is in some manner
discontinuous, then "pchip" interpolation might give better results.

   This can be demonstrated by the code

     t = -2:2;
     dt = 1;
     ti =-2:0.025:2;
     dti = 0.025;
     y = sign (t);
     ys = interp1 (t,y,ti,"spline");
     yp = interp1 (t,y,ti,"pchip");
     ddys = diff (diff (ys)./dti) ./ dti;
     ddyp = diff (diff (yp)./dti) ./ dti;
     figure (1);
     plot (ti,ys,"r-", ti,yp,"g-");
     legend ("spline", "pchip", 4);
     figure (2);
     plot (ti,ddys,"r+", ti,ddyp,"g*");
     legend ("spline", "pchip");

   Fourier interpolation, is a resampling technique where a signal is
converted to the frequency domain, padded with zeros and then
reconverted to the time domain.

 -- : Y = interpft (X, N)
 -- : Y = interpft (X, N, DIM)

     Fourier interpolation.

     If X is a vector then X is resampled with N points.  The data in X
     is assumed to be equispaced.  If X is a matrix or an N-dimensional
     array, the interpolation is performed on each column of X.

     If DIM is specified, then interpolate along the dimension DIM.

     ‘interpft’ assumes that the interpolated function is periodic, and
     so assumptions are made about the endpoints of the interpolation.

     See also: *note interp1: XREFinterp1.

   There are two significant limitations on Fourier interpolation.
First, the function signal is assumed to be periodic, and so
non-periodic signals will be poorly represented at the edges.  Second,
both the signal and its interpolation are required to be sampled at
equispaced points.  An example of the use of ‘interpft’ is

     t = 0 : 0.3 : pi; dt = t(2)-t(1);
     n = length (t); k = 100;
     ti = t(1) + [0 : k-1]*dt*n/k;
     y = sin (4*t + 0.3) .* cos (3*t - 0.1);
     yp = sin (4*ti + 0.3) .* cos (3*ti - 0.1);
     plot (ti, yp, "g", ti, interp1 (t, y, ti, "spline"), "b", ...
           ti, interpft (y, k), "c", t, y, "r+");
     legend ("sin(4t+0.3)cos(3t-0.1)", "spline", "interpft", "data");

which demonstrates the poor behavior of Fourier interpolation for
non-periodic functions.

   In addition, the support functions ‘spline’ and ‘lookup’ that
underlie the ‘interp1’ function can be called directly.

 -- : PP = spline (X, Y)
 -- : YI = spline (X, Y, XI)
     Return the cubic spline interpolant of points X and Y.

     When called with two arguments, return the piecewise polynomial PP
     that may be used with ‘ppval’ to evaluate the polynomial at
     specific points.

     When called with a third input argument, ‘spline’ evaluates the
     spline at the points XI.  The third calling form ‘spline (X, Y,
     XI)’ is equivalent to ‘ppval (spline (X, Y), XI)’.

     The variable X must be a vector of length N.

     Y can be either a vector or array.  If Y is a vector it must have a
     length of either N or ‘N + 2’.  If the length of Y is N, then the
     "not-a-knot" end condition is used.  If the length of Y is ‘N + 2’,
     then the first and last values of the vector Y are the values of
     the first derivative of the cubic spline at the endpoints.

     If Y is an array, then the size of Y must have the form ‘[S1, S2,
     ..., SK, N]’ or ‘[S1, S2, ..., SK, N + 2]’.  The array is reshaped
     internally to a matrix where the leading dimension is given by ‘S1
     * S2 * ... * SK’ and each row of this matrix is then treated
     separately.  Note that this is exactly the opposite of ‘interp1’
     but is done for MATLAB compatibility.

     See also: *note pchip: XREFpchip, *note ppval: XREFppval, *note
     mkpp: XREFmkpp, *note unmkpp: XREFunmkpp.


File: octave.info,  Node: Multi-dimensional Interpolation,  Prev: One-dimensional Interpolation,  Up: Interpolation

29.2 Multi-dimensional Interpolation
====================================

There are three multi-dimensional interpolation functions in Octave,
with similar capabilities.  Methods using Delaunay tessellation are
described in *note Interpolation on Scattered Data::.

 -- : ZI = interp2 (X, Y, Z, XI, YI)
 -- : ZI = interp2 (Z, XI, YI)
 -- : ZI = interp2 (Z, N)
 -- : ZI = interp2 (Z)
 -- : ZI = interp2 (..., METHOD)
 -- : ZI = interp2 (..., METHOD, EXTRAP)

     Two-dimensional interpolation.

     Interpolate reference data X, Y, Z to determine ZI at the
     coordinates XI, YI.  The reference data X, Y can be matrices, as
     returned by ‘meshgrid’, in which case the sizes of X, Y, and Z must
     be equal.  If X, Y are vectors describing a grid then ‘length (X)
     == columns (Z)’ and ‘length (Y) == rows (Z)’.  In either case the
     input data must be strictly monotonic.

     If called without X, Y, and just a single reference data matrix Z,
     the 2-D region ‘X = 1:columns (Z), Y = 1:rows (Z)’ is assumed.
     This saves memory if the grid is regular and the distance between
     points is not important.

     If called with a single reference data matrix Z and a refinement
     value N, then perform interpolation over a grid where each original
     interval has been recursively subdivided N times.  This results in
     ‘2^N-1’ additional points for every interval in the original grid.
     If N is omitted a value of 1 is used.  As an example, the interval
     [0,1] with ‘N==2’ results in a refined interval with points at [0,
     1/4, 1/2, 3/4, 1].

     The interpolation METHOD is one of:

     "nearest"
          Return the nearest neighbor.

     "linear" (default)
          Linear interpolation from nearest neighbors.

     "pchip"
          Piecewise cubic Hermite interpolating
          polynomial—shape-preserving interpolation with smooth first
          derivative.

     "cubic"
          Cubic interpolation using a convolution kernel function—third
          order method with smooth first derivative.

     "spline"
          Cubic spline interpolation—smooth first and second derivatives
          throughout the curve.

     EXTRAP is a scalar number.  It replaces values beyond the endpoints
     with EXTRAP.  Note that if EXTRAP is used, METHOD must be specified
     as well.  If EXTRAP is omitted and the METHOD is "spline", then the
     extrapolated values of the "spline" are used.  Otherwise the
     default EXTRAP value for any other METHOD is "NA".

     See also: *note interp1: XREFinterp1, *note interp3: XREFinterp3,
     *note interpn: XREFinterpn, *note meshgrid: XREFmeshgrid.

 -- : VI = interp3 (X, Y, Z, V, XI, YI, ZI)
 -- : VI = interp3 (V, XI, YI, ZI)
 -- : VI = interp3 (V, N)
 -- : VI = interp3 (V)
 -- : VI = interp3 (..., METHOD)
 -- : VI = interp3 (..., METHOD, EXTRAPVAL)

     Three-dimensional interpolation.

     Interpolate reference data X, Y, Z, V to determine VI at the
     coordinates XI, YI, ZI.  The reference data X, Y, Z can be
     matrices, as returned by ‘meshgrid’, in which case the sizes of X,
     Y, Z, and V must be equal.  If X, Y, Z are vectors describing a
     cubic grid then ‘length (X) == columns (V)’, ‘length (Y) == rows
     (V)’, and ‘length (Z) == size (V, 3)’.  In either case the input
     data must be strictly monotonic.

     If called without X, Y, Z, and just a single reference data matrix
     V, the 3-D region ‘X = 1:columns (V), Y = 1:rows (V), Z = 1:size
     (V, 3)’ is assumed.  This saves memory if the grid is regular and
     the distance between points is not important.

     If called with a single reference data matrix V and a refinement
     value N, then perform interpolation over a 3-D grid where each
     original interval has been recursively subdivided N times.  This
     results in ‘2^N-1’ additional points for every interval in the
     original grid.  If N is omitted a value of 1 is used.  As an
     example, the interval [0,1] with ‘N==2’ results in a refined
     interval with points at [0, 1/4, 1/2, 3/4, 1].

     The interpolation METHOD is one of:

     "nearest"
          Return the nearest neighbor.

     "linear" (default)
          Linear interpolation from nearest neighbors.

     "cubic"
          Piecewise cubic Hermite interpolating
          polynomial—shape-preserving interpolation with smooth first
          derivative (not implemented yet).

     "spline"
          Cubic spline interpolation—smooth first and second derivatives
          throughout the curve.

     EXTRAPVAL is a scalar number.  It replaces values beyond the
     endpoints with EXTRAPVAL.  Note that if EXTRAPVAL is used, METHOD
     must be specified as well.  If EXTRAPVAL is omitted and the METHOD
     is "spline", then the extrapolated values of the "spline" are used.
     Otherwise the default EXTRAPVAL value for any other METHOD is "NA".

     See also: *note interp1: XREFinterp1, *note interp2: XREFinterp2,
     *note interpn: XREFinterpn, *note meshgrid: XREFmeshgrid.

 -- : VI = interpn (X1, X2, ..., V, Y1, Y2, ...)
 -- : VI = interpn (V, Y1, Y2, ...)
 -- : VI = interpn (V, M)
 -- : VI = interpn (V)
 -- : VI = interpn (..., METHOD)
 -- : VI = interpn (..., METHOD, EXTRAPVAL)

     Perform N-dimensional interpolation, where N is at least two.

     Each element of the N-dimensional array V represents a value at a
     location given by the parameters X1, X2, ..., XN.  The parameters
     X1, X2, ..., XN are either N-dimensional arrays of the same size as
     the array V in the "ndgrid" format or vectors.

     The parameters Y1, Y2, ..., YN represent the points at which the
     array VI is interpolated.  They can be vectors of the same length
     and orientation in which case they are interpreted as coordinates
     of scattered points.  If they are vectors of differing orientation
     or length, they are used to form a grid in "ndgrid" format.  They
     can also be N-dimensional arrays of equal size.

     If X1, ..., XN are omitted, they are assumed to be ‘x1 = 1 : size
     (V, 1)’, etc.  If M is specified, then the interpolation adds a
     point half way between each of the interpolation points.  This
     process is performed M times.  If only V is specified, then M is
     assumed to be ‘1’.

     The interpolation METHOD is one of:

     "nearest"
          Return the nearest neighbor.

     "linear" (default)
          Linear interpolation from nearest neighbors.

     "pchip"
          Piecewise cubic Hermite interpolating
          polynomial—shape-preserving interpolation with smooth first
          derivative (not implemented yet).

     "cubic"
          Cubic interpolation (same as "pchip" [not implemented yet]).

     "spline"
          Cubic spline interpolation—smooth first and second derivatives
          throughout the curve.

     The default method is "linear".

     EXTRAPVAL is a scalar number.  It replaces values beyond the
     endpoints with EXTRAPVAL.  Note that if EXTRAPVAL is used, METHOD
     must be specified as well.  If EXTRAPVAL is omitted and the METHOD
     is "spline", then the extrapolated values of the "spline" are used.
     Otherwise the default EXTRAPVAL value for any other METHOD is ‘NA’.

     See also: *note interp1: XREFinterp1, *note interp2: XREFinterp2,
     *note interp3: XREFinterp3, *note spline: XREFspline, *note ndgrid:
     XREFndgrid.

   A significant difference between ‘interpn’ and the other two
multi-dimensional interpolation functions is the fashion in which the
dimensions are treated.  For ‘interp2’ and ‘interp3’, the y-axis is
considered to be the columns of the matrix, whereas the x-axis
corresponds to the rows of the array.  As Octave indexes arrays in
column major order, the first dimension of any array is the columns, and
so ‘interpn’ effectively reverses the ’x’ and ’y’ dimensions.  Consider
the example,

     x = y = z = -1:1;
     f = @(x,y,z) x.^2 - y - z.^2;
     [xx, yy, zz] = meshgrid (x, y, z);
     v = f (xx,yy,zz);
     xi = yi = zi = -1:0.1:1;
     [xxi, yyi, zzi] = meshgrid (xi, yi, zi);
     vi = interp3 (x, y, z, v, xxi, yyi, zzi, "spline");
     [xxi, yyi, zzi] = ndgrid (xi, yi, zi);
     vi2 = interpn (x, y, z, v, xxi, yyi, zzi, "spline");
     mesh (zi, yi, squeeze (vi2(1,:,:)));

where ‘vi’ and ‘vi2’ are identical.  The reversal of the dimensions is
treated in the ‘meshgrid’ and ‘ndgrid’ functions respectively.


File: octave.info,  Node: Geometry,  Next: Signal Processing,  Prev: Interpolation,  Up: Top

30 Geometry
***********

Much of the geometry code in Octave is based on the Qhull library(1).
Some of the documentation for Qhull, particularly for the options that
can be passed to ‘delaunay’, ‘voronoi’ and ‘convhull’, etc., is relevant
to Octave users.

* Menu:

* Delaunay Triangulation::
* Voronoi Diagrams::
* Convex Hull::
* Interpolation on Scattered Data::
* Vector Rotation Matrices::

   ---------- Footnotes ----------

   (1) Barber, C.B., Dobkin, D.P., and Huhdanpaa, H.T., ‘The Quickhull
Algorithm for Convex Hulls’, ACM Trans. on Mathematical Software,
22(4):469–483, Dec 1996, <http://www.qhull.org>


File: octave.info,  Node: Delaunay Triangulation,  Next: Voronoi Diagrams,  Up: Geometry

30.1 Delaunay Triangulation
===========================

The Delaunay triangulation is constructed from a set of circum-circles.
These circum-circles are chosen so that there are at least three of the
points in the set to triangulation on the circumference of the
circum-circle.  None of the points in the set of points falls within any
of the circum-circles.

   In general there are only three points on the circumference of any
circum-circle.  However, in some cases, and in particular for the case
of a regular grid, 4 or more points can be on a single circum-circle.
In this case the Delaunay triangulation is not unique.

 -- : TRI = delaunay (X, Y)
 -- : TETR = delaunay (X, Y, Z)
 -- : TRI = delaunay (X)
 -- : TRI = delaunay (..., OPTIONS)
     Compute the Delaunay triangulation for a 2-D or 3-D set of points.

     For 2-D sets, the return value TRI is a set of triangles which
     satisfies the Delaunay circum-circle criterion, i.e., no data point
     from [X, Y] is within the circum-circle of the defining triangle.
     The set of triangles TRI is a matrix of size [n, 3].  Each row
     defines a triangle and the three columns are the three vertices of
     the triangle.  The value of ‘TRI(i,j)’ is an index into X and Y for
     the location of the j-th vertex of the i-th triangle.

     For 3-D sets, the return value TETR is a set of tetrahedrons which
     satisfies the Delaunay circum-circle criterion, i.e., no data point
     from [X, Y, Z] is within the circum-circle of the defining
     tetrahedron.  The set of tetrahedrons is a matrix of size [n, 4].
     Each row defines a tetrahedron and the four columns are the four
     vertices of the tetrahedron.  The value of ‘TETR(i,j)’ is an index
     into X, Y, Z for the location of the j-th vertex of the i-th
     tetrahedron.

     The input X may also be a matrix with two or three columns where
     the first column contains x-data, the second y-data, and the
     optional third column contains z-data.

     An optional final argument, which must be a string or cell array of
     strings, contains options passed to the underlying qhull command.
     See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.  The default
     options are ‘{"Qt", "Qbb", "Qc"}’.  If Qhull fails for 2-D input
     the triangulation is attempted again with the options ‘{"Qt",
     "Qbb", "Qc", "Qz"}’ which may result in reduced accuracy.

     If OPTIONS is not present or ‘[]’ then the default arguments are
     used.  Otherwise, OPTIONS replaces the default argument list.  To
     append user options to the defaults it is necessary to repeat the
     default arguments in OPTIONS.  Use a null string to pass no
     arguments.

          x = rand (1, 10);
          y = rand (1, 10);
          tri = delaunay (x, y);
          triplot (tri, x, y);
          hold on;
          plot (x, y, "r*");
          axis ([0,1,0,1]);

     See also: *note delaunayn: XREFdelaunayn, *note convhull:
     XREFconvhull, *note voronoi: XREFvoronoi, *note triplot:
     XREFtriplot, *note trimesh: XREFtrimesh, *note tetramesh:
     XREFtetramesh, *note trisurf: XREFtrisurf.

   For 3-D inputs ‘delaunay’ returns a set of tetrahedra that satisfy
the Delaunay circum-circle criteria.  Similarly, ‘delaunayn’ returns the
N-dimensional simplex satisfying the Delaunay circum-circle criteria.
The N-dimensional extension of a triangulation is called a tessellation.

 -- : T = delaunayn (PTS)
 -- : T = delaunayn (PTS, OPTIONS)
     Compute the Delaunay triangulation for an N-dimensional set of
     points.

     The Delaunay triangulation is a tessellation of the convex hull of
     a set of points such that no N-sphere defined by the N-triangles
     contains any other points from the set.

     The input matrix PTS of size [n, dim] contains n points in a space
     of dimension dim.  The return matrix T has size [m, dim+1].  Each
     row of T contains a set of indices back into the original set of
     points PTS which describes a simplex of dimension dim.  For
     example, a 2-D simplex is a triangle and 3-D simplex is a
     tetrahedron.

     An optional second argument, which must be a string or cell array
     of strings, contains options passed to the underlying qhull
     command.  See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.  The default
     options depend on the dimension of the input:

        • 2-D and 3-D: OPTIONS = ‘{"Qt", "Qbb", "Qc"}’

        • 4-D and higher: OPTIONS = ‘{"Qt", "Qbb", "Qc", "Qx"}’

     If Qhull fails for 2-D input the triangulation is attempted again
     with the options ‘{"Qt", "Qbb", "Qc", "Qz"}’ which may result in
     reduced accuracy.

     If OPTIONS is not present or ‘[]’ then the default arguments are
     used.  Otherwise, OPTIONS replaces the default argument list.  To
     append user options to the defaults it is necessary to repeat the
     default arguments in OPTIONS.  Use a null string to pass no
     arguments.

     See also: *note delaunay: XREFdelaunay, *note convhulln:
     XREFconvhulln, *note voronoin: XREFvoronoin, *note trimesh:
     XREFtrimesh, *note tetramesh: XREFtetramesh.

   An example of a Delaunay triangulation of a set of points is

     rand ("state", 1);
     x = rand (1, 10);
     y = rand (1, 10);
     T = delaunay (x, y);
     X = [ x(T(:,1)); x(T(:,2)); x(T(:,3)); x(T(:,1)) ];
     Y = [ y(T(:,1)); y(T(:,2)); y(T(:,3)); y(T(:,1)) ];
     axis ([0, 1, 0, 1]);
     plot (X, Y, "b", x, y, "r*");

* Menu:

* Plotting the Triangulation::
* Identifying Points in Triangulation::


File: octave.info,  Node: Plotting the Triangulation,  Next: Identifying Points in Triangulation,  Up: Delaunay Triangulation

30.1.1 Plotting the Triangulation
---------------------------------

Octave has the functions ‘triplot’, ‘trimesh’, and ‘trisurf’ to plot the
Delaunay triangulation of a 2-dimensional set of points.  ‘tetramesh’
will plot the triangulation of a 3-dimensional set of points.

 -- : triplot (TRI, X, Y)
 -- : triplot (TRI, X, Y, LINESPEC)
 -- : H = triplot (...)
     Plot a 2-D triangular mesh.

     TRI is typically the output of a Delaunay triangulation over the
     grid of X, Y.  Every row of TRI represents one triangle and
     contains three indices into [X, Y] which are the vertices of the
     triangles in the x-y plane.

     The linestyle to use for the plot can be defined with the argument
     LINESPEC of the same format as the ‘plot’ command.

     The optional return value H is a graphics handle to the created
     patch object.

     See also: *note plot: XREFplot, *note trimesh: XREFtrimesh, *note
     trisurf: XREFtrisurf, *note delaunay: XREFdelaunay.

 -- : trimesh (TRI, X, Y, Z, C)
 -- : trimesh (TRI, X, Y, Z)
 -- : trimesh (TRI, X, Y)
 -- : trimesh (..., PROP, VAL, ...)
 -- : H = trimesh (...)
     Plot a 3-D triangular wireframe mesh.

     In contrast to ‘mesh’, which plots a mesh using rectangles,
     ‘trimesh’ plots the mesh using triangles.

     TRI is typically the output of a Delaunay triangulation over the
     grid of X, Y.  Every row of TRI represents one triangle and
     contains three indices into [X, Y] which are the vertices of the
     triangles in the x-y plane.  Z determines the height above the
     plane of each vertex.  If no Z input is given then the triangles
     are plotted as a 2-D figure.

     The color of the trimesh is computed by linearly scaling the Z
     values to fit the range of the current colormap.  Use ‘caxis’
     and/or change the colormap to control the appearance.

     Optionally, the color of the mesh can be specified independently of
     Z by supplying C, which is a vector for colormap data, or a matrix
     with three columns for RGB data.  The number of colors specified in
     C must either equal the number of vertices in Z or the number of
     triangles in TRI.

     Any property/value pairs are passed directly to the underlying
     patch object.  The full list of properties is documented at *note
     Patch Properties::.

     The optional return value H is a graphics handle to the created
     patch object.

     See also: *note mesh: XREFmesh, *note tetramesh: XREFtetramesh,
     *note triplot: XREFtriplot, *note trisurf: XREFtrisurf, *note
     delaunay: XREFdelaunay, *note patch: XREFpatch, *note hidden:
     XREFhidden.

 -- : trisurf (TRI, X, Y, Z, C)
 -- : trisurf (TRI, X, Y, Z)
 -- : trisurf (..., PROP, VAL, ...)
 -- : H = trisurf (...)
     Plot a 3-D triangular surface.

     In contrast to ‘surf’, which plots a surface mesh using rectangles,
     ‘trisurf’ plots the mesh using triangles.

     TRI is typically the output of a Delaunay triangulation over the
     grid of X, Y.  Every row of TRI represents one triangle and
     contains three indices into [X, Y] which are the vertices of the
     triangles in the x-y plane.  Z determines the height above the
     plane of each vertex.

     The color of the trisurf is computed by linearly scaling the Z
     values to fit the range of the current colormap.  Use ‘caxis’
     and/or change the colormap to control the appearance.

     Optionally, the color of the mesh can be specified independently of
     Z by supplying C, which is a vector for colormap data, or a matrix
     with three columns for RGB data.  The number of colors specified in
     C must either equal the number of vertices in Z or the number of
     triangles in TRI.  When specifying the color at each vertex the
     triangle will be colored according to the color of the first vertex
     only (see patch documentation and the "FaceColor" property when set
     to "flat").

     Any property/value pairs are passed directly to the underlying
     patch object.  The full list of properties is documented at *note
     Patch Properties::.

     The optional return value H is a graphics handle to the created
     patch object.

     See also: *note surf: XREFsurf, *note triplot: XREFtriplot, *note
     trimesh: XREFtrimesh, *note delaunay: XREFdelaunay, *note patch:
     XREFpatch, *note shading: XREFshading.

 -- : tetramesh (T, X)
 -- : tetramesh (T, X, C)
 -- : tetramesh (..., PROPERTY, VAL, ...)
 -- : H = tetramesh (...)
     Display the tetrahedrons defined in the m-by-4 matrix T as 3-D
     patches.

     T is typically the output of a Delaunay triangulation of a 3-D set
     of points.  Every row of T contains four indices into the n-by-3
     matrix X of the vertices of a tetrahedron.  Every row in X
     represents one point in 3-D space.

     The vector C specifies the color of each tetrahedron as an index
     into the current colormap.  The default value is 1:m where m is the
     number of tetrahedrons; the indices are scaled to map to the full
     range of the colormap.  If there are more tetrahedrons than colors
     in the colormap then the values in C are cyclically repeated.

     Calling ‘tetramesh (..., "property", "value", ...)’ passes all
     property/value pairs directly to the patch function as additional
     arguments.  The full list of properties is documented at *note
     Patch Properties::.

     The optional return value H is a vector of patch handles where each
     handle represents one tetrahedron in the order given by T.  A
     typical use case for H is to turn the respective patch "visible"
     property "on" or "off".

     Type ‘demo tetramesh’ to see examples on using ‘tetramesh’.

     See also: *note trimesh: XREFtrimesh, *note delaunay: XREFdelaunay,
     *note delaunayn: XREFdelaunayn, *note patch: XREFpatch.

   The difference between ‘triplot’, and ‘trimesh’ or ‘trisurf’, is that
the former only plots the 2-dimensional triangulation itself, whereas
the second two plot the value of a function ‘f (X, Y)’.  An example of
the use of the ‘triplot’ function is

     rand ("state", 2)
     x = rand (20, 1);
     y = rand (20, 1);
     tri = delaunay (x, y);
     triplot (tri, x, y);

which plots the Delaunay triangulation of a set of random points in
2-dimensions.


File: octave.info,  Node: Identifying Points in Triangulation,  Prev: Plotting the Triangulation,  Up: Delaunay Triangulation

30.1.2 Identifying Points in Triangulation
------------------------------------------

It is often necessary to identify whether a particular point in the
N-dimensional space is within the Delaunay tessellation of a set of
points in this N-dimensional space, and if so which N-simplex contains
the point and which point in the tessellation is closest to the desired
point.  The functions ‘tsearch’ and ‘dsearch’ perform this function in a
triangulation, and ‘tsearchn’ and ‘dsearchn’ in an N-dimensional
tessellation.

   To identify whether a particular point represented by a vector P
falls within one of the simplices of an N-simplex, we can write the
Cartesian coordinates of the point in a parametric form with respect to
the N-simplex.  This parametric form is called the Barycentric
Coordinates of the point.  If the points defining the N-simplex are
given by N + 1 vectors ‘T(I,:)’, then the Barycentric coordinates
defining the point P are given by

     P = BETA * T

where BETA contains N + 1 values that together as a vector represent the
Barycentric coordinates of the point P.  To ensure a unique solution for
the values of BETA an additional criteria of

     sum (BETA) == 1

is imposed, and we can therefore write the above as

     P - T(end, :) = BETA(1:end-1) * (T(1:end-1, :)
                     - ones (N, 1) * T(end, :)

Solving for BETA we can then write

     BETA(1:end-1) = (P - T(end, :)) /
                     (T(1:end-1, :) - ones (N, 1) * T(end, :))
     BETA(end) = sum (BETA(1:end-1))

which gives the formula for the conversion of the Cartesian coordinates
of the point P to the Barycentric coordinates BETA.  An important
property of the Barycentric coordinates is that for all points in the
N-simplex

     0 <= BETA(I) <= 1

Therefore, the test in ‘tsearch’ and ‘tsearchn’ essentially only needs
to express each point in terms of the Barycentric coordinates of each of
the simplices of the N-simplex and test the values of BETA.  This is
exactly the implementation used in ‘tsearchn’.  ‘tsearch’ is optimized
for 2-dimensions and the Barycentric coordinates are not explicitly
formed.

 -- : IDX = tsearch (X, Y, T, XI, YI)
     Search for the enclosing Delaunay convex hull.

     For ‘T = delaunay (X, Y)’, finds the index in T containing the
     points ‘(XI, YI)’.  For points outside the convex hull, IDX is NaN.

     See also: *note delaunay: XREFdelaunay, *note delaunayn:
     XREFdelaunayn.

 -- : IDX = tsearchn (X, T, XI)
 -- : [IDX, P] = tsearchn (X, T, XI)
     Search for the enclosing Delaunay convex hull.

     For ‘T = delaunayn (X)’, finds the index in T containing the points
     XI.  For points outside the convex hull, IDX is NaN.

     If requested ‘tsearchn’ also returns the Barycentric coordinates P
     of the enclosing triangles.

     See also: *note delaunay: XREFdelaunay, *note delaunayn:
     XREFdelaunayn.

   An example of the use of ‘tsearch’ can be seen with the simple
triangulation

     X = [-1; -1; 1; 1];
     Y = [-1; 1; -1; 1];
     TRI = [1, 2, 3; 2, 3, 4];

consisting of two triangles defined by TRI.  We can then identify which
triangle a point falls in like

     tsearch (X, Y, TRI, -0.5, -0.5)
     ⇒ 1
     tsearch (X, Y, TRI, 0.5, 0.5)
     ⇒ 2

and we can confirm that a point doesn’t lie within one of the triangles
like

     tsearch (X, Y, TRI, 2, 2)
     ⇒ NaN

   The ‘dsearch’ and ‘dsearchn’ find the closest point in a tessellation
to the desired point.  The desired point does not necessarily have to be
in the tessellation, and even if it the returned point of the
tessellation does not have to be one of the vertices of the N-simplex
within which the desired point is found.

 -- : IDX = dsearch (X, Y, TRI, XI, YI)
 -- : IDX = dsearch (X, Y, TRI, XI, YI, S)
     Return the index IDX of the closest point in ‘X, Y’ to the elements
     ‘[XI(:), YI(:)]’.

     The variable S is accepted for compatibility but is ignored.

     See also: *note dsearchn: XREFdsearchn, *note tsearch: XREFtsearch.

 -- : IDX = dsearchn (X, TRI, XI)
 -- : IDX = dsearchn (X, TRI, XI, OUTVAL)
 -- : IDX = dsearchn (X, XI)
 -- : [IDX, D] = dsearchn (...)
     Return the index IDX of the closest point in X to the elements XI.

     If OUTVAL is supplied, then the values of XI that are not contained
     within one of the simplices TRI are set to OUTVAL.  Generally, TRI
     is returned from ‘delaunayn (X)’.

     The optional output D contains a column vector of distances between
     the query points XI and the nearest simplex points X.

     See also: *note dsearch: XREFdsearch, *note tsearch: XREFtsearch.

   An example of the use of ‘dsearch’, using the above values of X, Y
and TRI is

     dsearch (X, Y, TRI, -2, -2)
     ⇒ 1

   If you wish the points that are outside the tessellation to be
flagged, then ‘dsearchn’ can be used as

     dsearchn ([X, Y], TRI, [-2, -2], NaN)
     ⇒ NaN
     dsearchn ([X, Y], TRI, [-0.5, -0.5], NaN)
     ⇒ 1

where the point outside the tessellation are then flagged with ‘NaN’.


File: octave.info,  Node: Voronoi Diagrams,  Next: Convex Hull,  Prev: Delaunay Triangulation,  Up: Geometry

30.2 Voronoi Diagrams
=====================

A Voronoi diagram or Voronoi tessellation of a set of points S in an
N-dimensional space, is the tessellation of the N-dimensional space such
that all points in ‘V(P)’, a partitions of the tessellation where P is a
member of S, are closer to P than any other point in S.  The Voronoi
diagram is related to the Delaunay triangulation of a set of points, in
that the vertices of the Voronoi tessellation are the centers of the
circum-circles of the simplices of the Delaunay tessellation.

 -- : voronoi (X, Y)
 -- : voronoi (X, Y, OPTIONS)
 -- : voronoi (..., "linespec")
 -- : voronoi (HAX, ...)
 -- : H = voronoi (...)
 -- : [VX, VY] = voronoi (...)
     Plot the Voronoi diagram of points ‘(X, Y)’.

     The Voronoi facets with points at infinity are not drawn.

     The OPTIONS argument, which must be a string or cell array of
     strings, contains options passed to the underlying qhull command.
     See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.

     If "linespec" is given it is used to set the color and line style
     of the plot.

     If an axes graphics handle HAX is supplied then the Voronoi diagram
     is drawn on the specified axes rather than in a new figure.

     If a single output argument is requested then the Voronoi diagram
     will be plotted and a graphics handle H to the plot is returned.

     [VX, VY] = voronoi (...) returns the Voronoi vertices instead of
     plotting the diagram.

          x = rand (10, 1);
          y = rand (size (x));
          h = convhull (x, y);
          [vx, vy] = voronoi (x, y);
          plot (vx, vy, "-b", x, y, "o", x(h), y(h), "-g");
          legend ("", "points", "hull");

     See also: *note voronoin: XREFvoronoin, *note delaunay:
     XREFdelaunay, *note convhull: XREFconvhull.

 -- : [C, F] = voronoin (PTS)
 -- : [C, F] = voronoin (PTS, OPTIONS)
     Compute N-dimensional Voronoi facets.

     The input matrix PTS of size [n, dim] contains n points in a space
     of dimension dim.

     C contains the points of the Voronoi facets.  The list F contains,
     for each facet, the indices of the Voronoi points.

     An optional second argument, which must be a string or cell array
     of strings, contains options passed to the underlying qhull
     command.  See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.

     The default options depend on the dimension of the input:

        • 2-D and 3-D: OPTIONS = ‘{"Qbb"}’

        • 4-D and higher: OPTIONS = ‘{"Qbb", "Qx"}’

     If OPTIONS is not present or ‘[]’ then the default arguments are
     used.  Otherwise, OPTIONS replaces the default argument list.  To
     append user options to the defaults it is necessary to repeat the
     default arguments in OPTIONS.  Use a null string to pass no
     arguments.

     See also: *note voronoi: XREFvoronoi, *note convhulln:
     XREFconvhulln, *note delaunayn: XREFdelaunayn.

   An example of the use of ‘voronoi’ is

     rand ("state",9);
     x = rand (10,1);
     y = rand (10,1);
     tri = delaunay (x, y);
     [vx, vy] = voronoi (x, y, tri);
     triplot (tri, x, y, "b");
     hold on;
     plot (vx, vy, "r");

   Additional information about the size of the facets of a Voronoi
diagram, and which points of a set of points is in a polygon can be had
with the ‘polyarea’ and ‘inpolygon’ functions respectively.

 -- : A = polyarea (X, Y)
 -- : A = polyarea (X, Y, DIM)

     Determine area of a polygon by triangle method.

     The variables X and Y define the vertex pairs, and must therefore
     have the same shape.  They can be either vectors or arrays.  If
     they are arrays then the columns of X and Y are treated separately
     and an area returned for each.

     If the optional DIM argument is given, then ‘polyarea’ works along
     this dimension of the arrays X and Y.

   An example of the use of ‘polyarea’ might be

     rand ("state", 2);
     x = rand (10, 1);
     y = rand (10, 1);
     [c, f] = voronoin ([x, y]);
     af = zeros (size (f));
     for i = 1 : length (f)
       af(i) = polyarea (c (f {i, :}, 1), c (f {i, :}, 2));
     endfor

   Facets of the Voronoi diagram with a vertex at infinity have infinity
area.  A simplified version of ‘polyarea’ for rectangles is available
with ‘rectint’

 -- : AREA = rectint (A, B)
     Compute area or volume of intersection of rectangles or N-D boxes.

     Compute the area of intersection of rectangles in A and rectangles
     in B.  N-dimensional boxes are supported in which case the volume,
     or hypervolume is computed according to the number of dimensions.

     2-dimensional rectangles are defined as ‘[xpos ypos width height]’
     where xpos and ypos are the position of the bottom left corner.
     Higher dimensions are supported where the coordinates for the
     minimum value of each dimension follow the length of the box in
     that dimension, e.g., ‘[xpos ypos zpos kpos ... width height depth
     k_length ...]’.

     Each row of A and B define a rectangle, and if both define multiple
     rectangles, then the output, AREA, is a matrix where the i-th row
     corresponds to the i-th row of a and the j-th column corresponds to
     the j-th row of b.

     See also: *note polyarea: XREFpolyarea.

 -- : IN = inpolygon (X, Y, XV, YV)
 -- : [IN, ON] = inpolygon (X, Y, XV, YV)

     For a polygon defined by vertex points ‘(XV, YV)’, return true if
     the points ‘(X, Y)’ are inside (or on the boundary) of the polygon;
     Otherwise, return false.

     The input variables X and Y, must have the same dimension.

     The optional output ON returns true if the points are exactly on
     the polygon edge, and false otherwise.

     See also: *note delaunay: XREFdelaunay.

   An example of the use of ‘inpolygon’ might be

     randn ("state", 2);
     x = randn (100, 1);
     y = randn (100, 1);
     vx = cos (pi * [-1 : 0.1: 1]);
     vy = sin (pi * [-1 : 0.1 : 1]);
     in = inpolygon (x, y, vx, vy);
     plot (vx, vy, x(in), y(in), "r+", x(!in), y(!in), "bo");
     axis ([-2, 2, -2, 2]);


File: octave.info,  Node: Convex Hull,  Next: Interpolation on Scattered Data,  Prev: Voronoi Diagrams,  Up: Geometry

30.3 Convex Hull
================

The convex hull of a set of points is the minimum convex envelope
containing all of the points.  Octave has the functions ‘convhull’ and
‘convhulln’ to calculate the convex hull of 2-dimensional and
N-dimensional sets of points.

 -- : H = convhull (X, Y)
 -- : H = convhull (X, Y, Z)
 -- : H = convhull (X)
 -- : H = convhull (..., OPTIONS)
 -- : [H, V] = convhull (...)
     Compute the convex hull of a 2-D or 3-D set of points.

     The hull H is a linear index vector into the original set of points
     that specifies which points form the enclosing hull.  For 2-D
     inputs only, the output is ordered in a counterclockwise manner
     around the hull.

     The input X may also be a matrix with two or three columns where
     the first column contains x-data, the second y-data, and the
     optional third column contains z-data.

     An optional final argument, which must be a string or cell array of
     strings, contains options passed to the underlying qhull command.
     See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.  The default
     option is ‘{"Qt"}’.

     If OPTIONS is not present or ‘[]’ then the default arguments are
     used.  Otherwise, OPTIONS replaces the default argument list.  To
     append user options to the defaults it is necessary to repeat the
     default arguments in OPTIONS.  Use a null string to pass no
     arguments.

     If the second output V is requested the volume of the enclosing
     convex hull is calculated.

     See also: *note convhulln: XREFconvhulln, *note delaunay:
     XREFdelaunay, *note voronoi: XREFvoronoi.

 -- : H = convhulln (PTS)
 -- : H = convhulln (PTS, OPTIONS)
 -- : [H, V] = convhulln (...)
     Compute the convex hull of the set of points PTS.

     PTS is a matrix of size [n, dim] containing n points in a space of
     dimension dim.

     The hull H is an index vector into the set of points and specifies
     which points form the enclosing hull.

     An optional second argument, which must be a string or cell array
     of strings, contains options passed to the underlying qhull
     command.  See the documentation for the Qhull library for details
     <http://www.qhull.org/html/qh-quick.htm#options>.  The default
     options depend on the dimension of the input:

        • 2D, 3D, 4D: OPTIONS = ‘{"Qt"}’

        • 5D and higher: OPTIONS = ‘{"Qt", "Qx"}’

     If OPTIONS is not present or ‘[]’ then the default arguments are
     used.  Otherwise, OPTIONS replaces the default argument list.  To
     append user options to the defaults it is necessary to repeat the
     default arguments in OPTIONS.  Use a null string to pass no
     arguments.

     If the second output V is requested the volume of the enclosing
     convex hull is calculated.

     See also: *note convhull: XREFconvhull, *note delaunayn:
     XREFdelaunayn, *note voronoin: XREFvoronoin.

   An example of the use of ‘convhull’ is

     x = -3:0.05:3;
     y = abs (sin (x));
     k = convhull (x, y);
     plot (x(k), y(k), "r-", x, y, "b+");
     axis ([-3.05, 3.05, -0.05, 1.05]);


File: octave.info,  Node: Interpolation on Scattered Data,  Next: Vector Rotation Matrices,  Prev: Convex Hull,  Up: Geometry

30.4 Interpolation on Scattered Data
====================================

An important use of the Delaunay tessellation is that it can be used to
interpolate from scattered data to an arbitrary set of points.  To do
this the N-simplex of the known set of points is calculated with
‘delaunay’ or ‘delaunayn’.  Then the simplices in to which the desired
points are found are identified.  Finally the vertices of the simplices
are used to interpolate to the desired points.  The functions that
perform this interpolation are ‘griddata’, ‘griddata3’ and ‘griddatan’.

 -- : ZI = griddata (X, Y, Z, XI, YI)
 -- : ZI = griddata (X, Y, Z, XI, YI, METHOD)
 -- : [XI, YI, ZI] = griddata (...)
 -- : VI = griddata (X, Y, Z, V, XI, YI, ZI)
 -- : VI = griddata (X, Y, Z, V, XI, YI, ZI, METHOD)
 -- : VI = griddata (X, Y, Z, V, XI, YI, ZI, METHOD, OPTIONS)

     Interpolate irregular 2-D and 3-D source data at specified points.

     For 2-D interpolation, the inputs X and Y define the points where
     the function ‘Z = f (X, Y)’ is evaluated.  The inputs X, Y, Z are
     either vectors of the same length, or the unequal vectors X, Y are
     expanded to a 2-D grid with ‘meshgrid’ and Z is a 2-D matrix
     matching the resulting size of the X-Y grid.

     The interpolation points are (XI, YI).  If, and only if, XI is a
     row vector and YI is a column vector, then ‘meshgrid’ will be used
     to create a mesh of interpolation points.

     For 3-D interpolation, the inputs X, Y, and Z define the points
     where the function ‘V = f (X, Y, Z)’ is evaluated.  The inputs X,
     Y, Z are either vectors of the same length, or if they are of
     unequal length, then they are expanded to a 3-D grid with
     ‘meshgrid’.  The size of the input V must match the size of the
     original data, either as a vector or a matrix.

     The optional input interpolation METHOD can be "nearest", "linear",
     or for 2-D data "v4".  When the method is "nearest", the output VI
     will be the closest point in the original data (X, Y, Z) to the
     query point (XI, YI, ZI).  When the method is "linear", the output
     VI will be a linear interpolation between the two closest points in
     the original source data in each dimension.  For 2-D cases only,
     the "v4" method is also available which implements a biharmonic
     spline interpolation.  If METHOD is omitted or empty, it defaults
     to "linear".

     For 3-D interpolation, the optional argument OPTIONS is passed
     directly to Qhull when computing the Delaunay triangulation used
     for interpolation.  For more information on the defaults and how to
     pass different values, *note ‘delaunayn’: XREFdelaunayn.

     Programming Notes: If the input is complex the real and imaginary
     parts are interpolated separately.  Interpolation is normally based
     on a Delaunay triangulation.  Any query values outside the convex
     hull of the input points will return ‘NaN’.  However, the "v4"
     method does not use the triangulation and will return values
     outside the original data (extrapolation).

     See also: *note griddata3: XREFgriddata3, *note griddatan:
     XREFgriddatan, *note delaunay: XREFdelaunay.

 -- : VI = griddata3 (X, Y, Z, V, XI, YI, ZI)
 -- : VI = griddata3 (X, Y, Z, V, XI, YI, ZI, METHOD)
 -- : VI = griddata3 (X, Y, Z, V, XI, YI, ZI, METHOD, OPTIONS)

     Interpolate irregular 3-D source data at specified points.

     The inputs X, Y, and Z define the points where the function ‘V = f
     (X, Y, Z)’ is evaluated.  The inputs X, Y, Z are either vectors of
     the same length, or if they are of unequal length, then they are
     expanded to a 3-D grid with ‘meshgrid’.  The size of the input V
     must match the size of the original data, either as a vector or a
     matrix.

     The interpolation points are specified by XI, YI, ZI.

     The optional input interpolation METHOD can be "nearest" or
     "linear".  When the method is "nearest", the output VI will be the
     closest point in the original data (X, Y, Z) to the query point
     (XI, YI, ZI).  When the method is "linear", the output VI will be a
     linear interpolation between the two closest points in the original
     source data in each dimension.  If METHOD is omitted or empty, it
     defaults to "linear".

     The optional argument OPTIONS is passed directly to Qhull when
     computing the Delaunay triangulation used for interpolation.  See
     ‘delaunayn’ for information on the defaults and how to pass
     different values.

     Programming Notes: If the input is complex the real and imaginary
     parts are interpolated separately.  Interpolation is based on a
     Delaunay triangulation and any query values outside the convex hull
     of the input points will return ‘NaN’.

     See also: *note griddata: XREFgriddata, *note griddatan:
     XREFgriddatan, *note delaunayn: XREFdelaunayn.

 -- : YI = griddatan (X, Y, XI)
 -- : YI = griddatan (X, Y, XI, METHOD)
 -- : YI = griddatan (X, Y, XI, METHOD, OPTIONS)

     Interpolate irregular source data X, Y at points specified by XI.

     The input X is an MxN matrix representing M points in an
     N-dimensional space.  The input Y is a single-valued column vector
     (Mx1) representing a function evaluated at the points X, i.e., ‘Y =
     fcn (X)’.  The input XI is a list of points for which the function
     output YI should be approximated through interpolation.  XI must
     have the same number of columns (N) as X so that the dimensionality
     matches.

     The optional input interpolation METHOD can be "nearest" or
     "linear".  When the method is "nearest", the output YI will be the
     closest point in the original data X to the query point XI.  When
     the method is "linear", the output YI will be a linear
     interpolation between the two closest points in the original source
     data.  If METHOD is omitted or empty, it defaults to "linear".

     The optional argument OPTIONS is passed directly to Qhull when
     computing the Delaunay triangulation used for interpolation.  See
     ‘delaunayn’ for information on the defaults and how to pass
     different values.

     Example

          ## Evaluate sombrero() function at irregular data points
          x = 16*gallery ("uniformdata", [200,1], 1) - 8;
          y = 16*gallery ("uniformdata", [200,1], 11) - 8;
          z = sin (sqrt (x.^2 + y.^2)) ./ sqrt (x.^2 + y.^2);
          ## Create a regular grid and interpolate data
          [xi, yi] = ndgrid (linspace (-8, 8, 50));
          zi = griddatan ([x, y], z, [xi(:), yi(:)]);
          zi = reshape (zi, size (xi));
          ## Plot results
          clf ();
          plot3 (x, y, z, "or");
          hold on
          surf (xi, yi, zi);
          legend ("Original Data", "Interpolated Data");

     Programming Notes: If the input is complex the real and imaginary
     parts are interpolated separately.  Interpolation is based on a
     Delaunay triangulation and any query values outside the convex hull
     of the input points will return ‘NaN’.  For 2-D and 3-D data
     additional interpolation methods are available by using the
     ‘griddata’ function.

     See also: *note griddata: XREFgriddata, *note griddata3:
     XREFgriddata3, *note delaunayn: XREFdelaunayn.

   An example of the use of the ‘griddata’ function is

     rand ("state", 1);
     x = 2*rand (1000,1) - 1;
     y = 2*rand (size (x)) - 1;
     z = sin (2*(x.^2+y.^2));
     [xx,yy] = meshgrid (linspace (-1,1,32));
     zz = griddata (x, y, z, xx, yy);
     mesh (xx, yy, zz);

that interpolates from a random scattering of points, to a uniform grid.


File: octave.info,  Node: Vector Rotation Matrices,  Prev: Interpolation on Scattered Data,  Up: Geometry

30.5 Vector Rotation Matrices
=============================

Also included in Octave’s geometry functions are primitive functions to
enable vector rotations in 3-dimensional space.  Separate functions are
provided for rotation about each of the principle axes, X, Y, and Z.
According to Euler’s rotation theorem, any arbitrary rotation, R, of any
vector, P, can be expressed as a product of the three principle
rotations:

     p' = Rp = Rz*Ry*Rx*p

 -- : T = rotx (ANGLE)

     ‘rotx’ returns the 3x3 transformation matrix corresponding to an
     active rotation of a vector about the x-axis by the specified
     ANGLE, given in degrees, where a positive angle corresponds to a
     counterclockwise rotation when viewing the y-z plane from the
     positive x side.

     The form of the transformation matrix is:

               | 1      0           0      |
           T = | 0  cos(ANGLE) -sin(ANGLE) |
               | 0  sin(ANGLE)  cos(ANGLE) |

     This rotation matrix is intended to be used as a left-multiplying
     matrix when acting on a column vector, using the notation ‘V =
     T*U’.  For example, a vector, U, pointing along the positive
     y-axis, rotated 90-degrees about the x-axis, will result in a
     vector pointing along the positive z-axis:

          >> u = [0 1 0]'
          u =
             0
             1
             0

          >> T = rotx (90)
          T =
             1.00000   0.00000   0.00000
             0.00000   0.00000  -1.00000
             0.00000   1.00000   0.00000

          >> v = T*u
          v =
             0.00000
             0.00000
             1.00000

     See also: *note roty: XREFroty, *note rotz: XREFrotz.

 -- : T = roty (ANGLE)

     ‘roty’ returns the 3x3 transformation matrix corresponding to an
     active rotation of a vector about the y-axis by the specified
     ANGLE, given in degrees, where a positive angle corresponds to a
     counterclockwise rotation when viewing the z-x plane from the
     positive y side.

     The form of the transformation matrix is:

               |  cos(ANGLE)  0  sin(ANGLE) |
           T = |      0       1      0      |
               | -sin(ANGLE)  0  cos(ANGLE) |

     This rotation matrix is intended to be used as a left-multiplying
     matrix when acting on a column vector, using the notation ‘V =
     T*U’.  For example, a vector, U, pointing along the positive
     z-axis, rotated 90-degrees about the y-axis, will result in a
     vector pointing along the positive x-axis:

            >> u = [0 0 1]'
             u =
                0
                0
                1

             >> T = roty (90)
             T =
                0.00000   0.00000   1.00000
                0.00000   1.00000   0.00000
               -1.00000   0.00000   0.00000

             >> v = T*u
             v =
                1.00000
                0.00000
                0.00000

     See also: *note rotx: XREFrotx, *note rotz: XREFrotz.

 -- : T = rotz (ANGLE)

     ‘rotz’ returns the 3x3 transformation matrix corresponding to an
     active rotation of a vector about the z-axis by the specified
     ANGLE, given in degrees, where a positive angle corresponds to a
     counterclockwise rotation when viewing the x-y plane from the
     positive z side.

     The form of the transformation matrix is:

               | cos(ANGLE) -sin(ANGLE) 0 |
           T = | sin(ANGLE)  cos(ANGLE) 0 |
               |     0           0      1 |

     This rotation matrix is intended to be used as a left-multiplying
     matrix when acting on a column vector, using the notation ‘V =
     T*U’.  For example, a vector, U, pointing along the positive
     x-axis, rotated 90-degrees about the z-axis, will result in a
     vector pointing along the positive y-axis:

            >> u = [1 0 0]'
             u =
                1
                0
                0

             >> T = rotz (90)
             T =
                0.00000  -1.00000   0.00000
                1.00000   0.00000   0.00000
                0.00000   0.00000   1.00000

             >> v = T*u
             v =
                0.00000
                1.00000
                0.00000

     See also: *note rotx: XREFrotx, *note roty: XREFroty.


File: octave.info,  Node: Signal Processing,  Next: Image Processing,  Prev: Geometry,  Up: Top

31 Signal Processing
********************

This chapter describes the signal processing and fast Fourier transform
functions available in Octave.  Fast Fourier transforms are computed
with the FFTW or FFTPACK libraries depending on how Octave is built.

 -- : Y = fft (X)
 -- : Y = fft (X, N)
 -- : Y = fft (X, N, DIM)
     Compute the discrete Fourier transform of X using a Fast Fourier
     Transform (FFT) algorithm.

     The FFT is calculated along the first non-singleton dimension of
     the array.  Thus if X is a matrix, ‘fft (X)’ computes the FFT for
     each column of X.

     If called with two arguments, N is expected to be an integer
     specifying the number of elements of X to use, or an empty matrix
     to specify that its value should be ignored.  If N is larger than
     the dimension along which the FFT is calculated, then X is resized
     and padded with zeros.  Otherwise, if N is smaller than the
     dimension along which the FFT is calculated, then X is truncated.

     If called with three arguments, DIM is an integer specifying the
     dimension of the matrix along which the FFT is performed.

     See also: *note ifft: XREFifft, *note fft2: XREFfft2, *note fftn:
     XREFfftn, *note fftw: XREFfftw.

 -- : X = ifft (Y)
 -- : X = ifft (Y, N)
 -- : X = ifft (Y, N, DIM)
     Compute the inverse discrete Fourier transform of Y using a Fast
     Fourier Transform (FFT) algorithm.

     The inverse FFT is calculated along the first non-singleton
     dimension of the array.  Thus if Y is a matrix, ‘ifft (Y)’ computes
     the inverse FFT for each column of Y.

     If called with two arguments, N is expected to be an integer
     specifying the number of elements of Y to use, or an empty matrix
     to specify that its value should be ignored.  If N is larger than
     the dimension along which the inverse FFT is calculated, then Y is
     resized and padded with zeros.  Otherwise, if N is smaller than the
     dimension along which the inverse FFT is calculated, then Y is
     truncated.

     If called with three arguments, DIM is an integer specifying the
     dimension of the matrix along which the inverse FFT is performed.

     See also: *note fft: XREFfft, *note ifft2: XREFifft2, *note ifftn:
     XREFifftn, *note fftw: XREFfftw.

 -- : B = fft2 (A)
 -- : B = fft2 (A, M, N)
     Compute the two-dimensional discrete Fourier transform of A using a
     Fast Fourier Transform (FFT) algorithm.

     The optional arguments M and N may be used specify the number of
     rows and columns of A to use.  If either of these is larger than
     the size of A, A is resized and padded with zeros.

     If A is a multi-dimensional matrix, each two-dimensional sub-matrix
     of A is treated separately.

     See also: *note ifft2: XREFifft2, *note fft: XREFfft, *note fftn:
     XREFfftn, *note fftw: XREFfftw.

 -- : A = ifft2 (B)
 -- : A = ifft2 (B, M, N)
     Compute the inverse two-dimensional discrete Fourier transform of B
     using a Fast Fourier Transform (FFT) algorithm.

     The optional arguments M and N may be used specify the number of
     rows and columns of B to use.  If either of these is larger than
     the size of B, B is resized and padded with zeros.

     If B is a multi-dimensional matrix, each two-dimensional sub-matrix
     of B is treated separately.

     See also: *note fft2: XREFfft2, *note ifft: XREFifft, *note ifftn:
     XREFifftn, *note fftw: XREFfftw.

 -- : B = fftn (A)
 -- : B = fftn (A, SIZE)
     Compute the N-dimensional discrete Fourier transform of A using a
     Fast Fourier Transform (FFT) algorithm.

     The optional vector argument SIZE may be used specify the
     dimensions of the array to be used.  If an element of SIZE is
     smaller than the corresponding dimension of A, then the dimension
     of A is truncated prior to performing the FFT.  Otherwise, if an
     element of SIZE is larger than the corresponding dimension then A
     is resized and padded with zeros.

     See also: *note ifftn: XREFifftn, *note fft: XREFfft, *note fft2:
     XREFfft2, *note fftw: XREFfftw.

 -- : A = ifftn (B)
 -- : A = ifftn (B, SIZE)
     Compute the inverse N-dimensional discrete Fourier transform of B
     using a Fast Fourier Transform (FFT) algorithm.

     The optional vector argument SIZE may be used specify the
     dimensions of the array to be used.  If an element of SIZE is
     smaller than the corresponding dimension of B, then the dimension
     of B is truncated prior to performing the inverse FFT.  Otherwise,
     if an element of SIZE is larger than the corresponding dimension
     then B is resized and padded with zeros.

     See also: *note fftn: XREFfftn, *note ifft: XREFifft, *note ifft2:
     XREFifft2, *note fftw: XREFfftw.

   Octave uses the FFTW libraries to perform FFT computations.  When
Octave starts up and initializes the FFTW libraries, they read a system
wide file (on a Unix system, it is typically ‘/etc/fftw/wisdom’) that
contains information useful to speed up FFT computations.  This
information is called the _wisdom_.  The system-wide file allows wisdom
to be shared between all applications using the FFTW libraries.

   Use the ‘fftw’ function to generate and save wisdom.  Using the
utilities provided together with the FFTW libraries (‘fftw-wisdom’ on
Unix systems), you can even add wisdom generated by Octave to the
system-wide wisdom file.

 -- : METHOD = fftw ("planner")
 -- : fftw ("planner", METHOD)
 -- : WISDOM = fftw ("dwisdom")
 -- : fftw ("dwisdom", WISDOM)
 -- : NTHREADS = fftw ("threads")
 -- : fftw ("threads", NTHREADS)

     Manage FFTW wisdom data.

     Wisdom data can be used to significantly accelerate the calculation
     of the FFTs, but implies an initial cost in its calculation.  When
     the FFTW libraries are initialized, they read a system wide wisdom
     file (typically in ‘/etc/fftw/wisdom’), allowing wisdom to be
     shared between applications other than Octave.  Alternatively, the
     ‘fftw’ function can be used to import wisdom.  For example,

          WISDOM = fftw ("dwisdom")

     will save the existing wisdom used by Octave to the string WISDOM.
     This string can then be saved to a file and restored using the
     ‘save’ and ‘load’ commands respectively.  This existing wisdom can
     be re-imported as follows

          fftw ("dwisdom", WISDOM)

     If WISDOM is an empty string, then the wisdom used is cleared.

     During the calculation of Fourier transforms further wisdom is
     generated.  The fashion in which this wisdom is generated is also
     controlled by the ‘fftw’ function.  There are five different
     manners in which the wisdom can be treated:

     "estimate"
          Specifies that no run-time measurement of the optimal means of
          calculating a particular is performed, and a simple heuristic
          is used to pick a (probably sub-optimal) plan.  The advantage
          of this method is that there is little or no overhead in the
          generation of the plan, which is appropriate for a Fourier
          transform that will be calculated once.

     "measure"
          In this case a range of algorithms to perform the transform is
          considered and the best is selected based on their execution
          time.

     "patient"
          Similar to "measure", but a wider range of algorithms is
          considered.

     "exhaustive"
          Like "measure", but all possible algorithms that may be used
          to treat the transform are considered.

     "hybrid"
          As run-time measurement of the algorithm can be expensive,
          this is a compromise where "measure" is used for transforms up
          to the size of 8192 and beyond that the "estimate" method is
          used.

     The default method is "estimate".  The current method can be
     queried with

          METHOD = fftw ("planner")

     or set by using

          fftw ("planner", METHOD)

     Note that calculated wisdom will be lost when restarting Octave.
     However, the wisdom data can be reloaded if it is saved to a file
     as described above.  Saved wisdom files should not be used on
     different platforms since they will not be efficient and the point
     of calculating the wisdom is lost.

     The number of threads used for computing the plans and executing
     the transforms can be set with

          fftw ("threads", NTHREADS)

     Note that Octave must be compiled with multi-threaded FFTW support
     for this feature.  By default, the number of (logical) processors
     available to the current process or 3 is used (whichever is
     smaller).

     See also: *note fft: XREFfft, *note ifft: XREFifft, *note fft2:
     XREFfft2, *note ifft2: XREFifft2, *note fftn: XREFfftn, *note
     ifftn: XREFifftn.

 -- : C = fftconv (X, Y)
 -- : C = fftconv (X, Y, N)
     Convolve two vectors using the FFT for computation.

     ‘c = fftconv (X, Y)’ returns a vector of length equal to ‘length
     (X) + length (Y) - 1’.  If X and Y are the coefficient vectors of
     two polynomials, the returned value is the coefficient vector of
     the product polynomial.

     The computation uses the FFT by calling the function ‘fftfilt’.  If
     the optional argument N is specified, an N-point FFT is used.

     See also: *note deconv: XREFdeconv, *note conv: XREFconv, *note
     conv2: XREFconv2.

 -- : Y = fftfilt (B, X)
 -- : Y = fftfilt (B, X, N)
     Filter X with the FIR filter B using the FFT.

     If X is a matrix, filter each column of the matrix.

     Given the optional third argument, N, ‘fftfilt’ uses the
     overlap-add method to filter X with B using an N-point FFT.  The
     FFT size must be an even power of 2 and must be greater than or
     equal to the length of B.  If the specified N does not meet these
     criteria, it is automatically adjusted to the nearest value that
     does.

     See also: *note filter: XREFfilter, *note filter2: XREFfilter2.

 -- : Y = filter (B, A, X)
 -- : [Y, SF] = filter (B, A, X, SI)
 -- : [Y, SF] = filter (B, A, X, [], DIM)
 -- : [Y, SF] = filter (B, A, X, SI, DIM)
     Apply a 1-D digital filter to the data X.

     ‘filter’ returns the solution to the following linear,
     time-invariant difference equation:

           N                   M
          SUM a(k+1) y(n-k) = SUM b(k+1) x(n-k)    for 1<=n<=length(x)
          k=0                 k=0

     where N=length(a)-1 and M=length(b)-1.  The result is calculated
     over the first non-singleton dimension of X or over DIM if
     supplied.

     An equivalent form of the equation is:

                    N                   M
          y(n) = - SUM c(k+1) y(n-k) + SUM d(k+1) x(n-k)  for 1<=n<=length(x)
                   k=1                 k=0

     where c = a/a(1) and d = b/a(1).

     If the fourth argument SI is provided, it is taken as the initial
     state of the system and the final state is returned as SF.  The
     state vector is a column vector whose length is equal to the length
     of the longest coefficient vector minus one.  If SI is not
     supplied, the initial state vector is set to all zeros.

     In terms of the Z Transform, Y is the result of passing the
     discrete-time signal X through a system characterized by the
     following rational system function:

                    M
                   SUM d(k+1) z^(-k)
                   k=0
          H(z) = ---------------------
                      N
                 1 + SUM c(k+1) z^(-k)
                     k=1

     See also: *note filter2: XREFfilter2, *note fftfilt: XREFfftfilt,
     *note freqz: XREFfreqz.

 -- : Y = filter2 (B, X)
 -- : Y = filter2 (B, X, SHAPE)
     Apply the 2-D FIR filter B to X.

     If the argument SHAPE is specified, return an array of the desired
     shape.  Possible values are:

     "full"
          pad X with zeros on all sides before filtering.

     "same"
          unpadded X (default)

     "valid"
          trim X after filtering so edge effects are no included.

     Note this is just a variation on convolution, with the parameters
     reversed and B rotated 180 degrees.

     See also: *note conv2: XREFconv2.

 -- : [H, W] = freqz (B, A, N, "whole")
 -- : [H, W] = freqz (B)
 -- : [H, W] = freqz (B, A)
 -- : [H, W] = freqz (B, A, N)
 -- : H = freqz (B, A, W)
 -- : [H, W] = freqz (..., FS)
 -- : freqz (...)

     Return the complex frequency response H of the rational IIR filter
     whose numerator and denominator coefficients are B and A,
     respectively.

     The response is evaluated at N angular frequencies between 0 and
     2*pi.

     The output value W is a vector of the frequencies.

     If A is omitted, the denominator is assumed to be 1 (this
     corresponds to a simple FIR filter).

     If N is omitted, a value of 512 is assumed.  For fastest
     computation, N should factor into a small number of small primes.

     If the fourth argument, "whole", is omitted the response is
     evaluated at frequencies between 0 and pi.

     ‘freqz (B, A, W)’

     Evaluate the response at the specific frequencies in the vector W.
     The values for W are measured in radians.

     ‘[...] = freqz (..., FS)’

     Return frequencies in Hz instead of radians assuming a sampling
     rate FS.  If you are evaluating the response at specific
     frequencies W, those frequencies should be requested in Hz rather
     than radians.

     ‘freqz (...)’

     Plot the magnitude and phase response of H rather than returning
     them.

     See also: *note freqz_plot: XREFfreqz_plot.

 -- : freqz_plot (W, H)
 -- : freqz_plot (W, H, FREQ_NORM)
     Plot the magnitude and phase response of H.

     If the optional FREQ_NORM argument is true, the frequency vector W
     is in units of normalized radians.  If FREQ_NORM is false, or not
     given, then W is measured in Hertz.

     See also: *note freqz: XREFfreqz.

 -- : Y = sinc (X)
     Compute the sinc function.

     Return sin (pi*x) / (pi*x).

 -- : B = unwrap (X)
 -- : B = unwrap (X, TOL)
 -- : B = unwrap (X, TOL, DIM)

     Unwrap radian phases by adding or subtracting multiples of 2*pi as
     appropriate to remove jumps greater than TOL.

     TOL defaults to pi.

     Unwrap will work along the dimension DIM.  If DIM is unspecified it
     defaults to the first non-singleton dimension.

 -- : [A, B] = arch_fit (Y, X, P, ITER, GAMMA, A0, B0)
     Fit an ARCH regression model to the time series Y using the scoring
     algorithm in Engle’s original ARCH paper.

     The model is

          y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),
          h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(p+1) * e(t-p)^2

     in which e(t) is N(0, h(t)), given a time-series vector Y up to
     time t-1 and a matrix of (ordinary) regressors X up to t.  The
     order of the regression of the residual variance is specified by P.

     If invoked as ‘arch_fit (Y, K, P)’ with a positive integer K, fit
     an ARCH(K, P) process, i.e., do the above with the t-th row of X
     given by

          [1, y(t-1), ..., y(t-k)]

     Optionally, one can specify the number of iterations ITER, the
     updating factor GAMMA, and initial values a0 and b0 for the scoring
     algorithm.

 -- : Y = arch_rnd (A, B, T)
     Simulate an ARCH sequence of length T with AR coefficients B and CH
     coefficients A.

     The result y(t) follows the model

          y(t) = b(1) + b(2) * y(t-1) + ... + b(lb) * y(t-lb+1) + e(t),

     where e(t), given Y up to time t-1, is N(0, h(t)), with

          h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(la) * e(t-la+1)^2

 -- : [PVAL, LM] = arch_test (Y, X, P)
     For a linear regression model

          y = x * b + e

     perform a Lagrange Multiplier (LM) test of the null hypothesis of
     no conditional heteroscedascity against the alternative of CH(P).

     I.e., the model is

          y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),

     given Y up to t-1 and X up to t, e(t) is N(0, h(t)) with

          h(t) = v + a(1) * e(t-1)^2 + ... + a(p) * e(t-p)^2,

     and the null is a(1) == ... == a(p) == 0.

     If the second argument is a scalar integer, k, perform the same
     test in a linear autoregression model of order k, i.e., with

          [1, y(t-1), ..., y(t-K)]

     as the t-th row of X.

     Under the null, LM approximately has a chisquare distribution with
     P degrees of freedom and PVAL is the p-value (1 minus the CDF of
     this distribution at LM) of the test.

     If no output argument is given, the p-value is displayed.

 -- : X = arma_rnd (A, B, V, T, N)
     Return a simulation of the ARMA model.

     The ARMA model is defined by

          x(n) = a(1) * x(n-1) + ... + a(k) * x(n-k)
               + e(n) + b(1) * e(n-1) + ... + b(l) * e(n-l)

     in which K is the length of vector A, L is the length of vector B
     and E is Gaussian white noise with variance V.  The function
     returns a vector of length T.

     The optional parameter N gives the number of dummy X(I) used for
     initialization, i.e., a sequence of length T+N is generated and
     X(N+1:T+N) is returned.  If N is omitted, N = 100 is used.

 -- : X = autoreg_matrix (Y, K)
     Given a time series (vector) Y, return a matrix with ones in the
     first column and the first K lagged values of Y in the other
     columns.

     In other words, for T > K, ‘[1, Y(T-1), ..., Y(T-K)]’ is the t-th
     row of the result.

     The resulting matrix may be used as a regressor matrix in
     autoregressions.

 -- : C = bartlett (M)
     Return the filter coefficients of a Bartlett (triangular) window of
     length M.

     For a definition of the Bartlett window see, e.g., A.V. Oppenheim &
     R. W. Schafer, ‘Discrete-Time Signal Processing’.

 -- : C = blackman (M)
 -- : C = blackman (M, "periodic")
 -- : C = blackman (M, "symmetric")
     Return the filter coefficients of a Blackman window of length M.

     If the optional argument "periodic" is given, the periodic form of
     the window is returned.  This is equivalent to the window of length
     M+1 with the last coefficient removed.  The optional argument
     "symmetric" is equivalent to not specifying a second argument.

     For a definition of the Blackman window, see, e.g., A.V. Oppenheim
     & R. W. Schafer, ‘Discrete-Time Signal Processing’.

 -- : Y = detrend (X, P)
     If X is a vector, ‘detrend (X, P)’ removes the best fit of a
     polynomial of order P from the data X.

     If X is a matrix, ‘detrend (X, P)’ does the same for each column in
     X.

     The second argument P is optional.  If it is not specified, a value
     of 1 is assumed.  This corresponds to removing a linear trend.

     The order of the polynomial can also be given as a string, in which
     case P must be either "constant" (corresponds to ‘P=0’) or "linear"
     (corresponds to ‘P=1’).

     See also: *note polyfit: XREFpolyfit.

 -- : [D, DD] = diffpara (X, A, B)
     Return the estimator D for the differencing parameter of an
     integrated time series.

     The frequencies from [2*pi*a/t, 2*pi*b/T] are used for the
     estimation.  If B is omitted, the interval [2*pi/T, 2*pi*a/T] is
     used.  If both B and A are omitted then a = 0.5 * sqrt (T) and b =
     1.5 * sqrt (T) is used, where T is the sample size.  If X is a
     matrix, the differencing parameter of each column is estimated.

     The estimators for all frequencies in the intervals described above
     is returned in DD.

     The value of D is simply the mean of DD.

     Reference: P.J. Brockwell & R.A. Davis.  ‘Time Series: Theory and
     Methods’.  Springer 1987.

 -- : [NEWPHI, NEWV] = durbinlevinson (C, OLDPHI, OLDV)
     Perform one step of the Durbin-Levinson algorithm.

     The vector C specifies the autocovariances ‘[gamma_0, ...,
     gamma_t]’ from lag 0 to T, OLDPHI specifies the coefficients based
     on C(T-1) and OLDV specifies the corresponding error.

     If OLDPHI and OLDV are omitted, all steps from 1 to T of the
     algorithm are performed.

 -- : Y = fftshift (X)
 -- : Y = fftshift (X, DIM)
     Perform a shift of the vector X, for use with the ‘fft’ and ‘ifft’
     functions, in order to move the frequency 0 to the center of the
     vector or matrix.

     If X is a vector of N elements corresponding to N time samples
     spaced by dt, then ‘fftshift (fft (X))’ corresponds to frequencies

          f = [ -(ceil((N-1)/2):-1:1), 0, (1:floor((N-1)/2)) ] * df

     where df = 1 / (N * dt).

     If X is a matrix, the same holds for rows and columns.  If X is an
     array, then the same holds along each dimension.

     The optional DIM argument can be used to limit the dimension along
     which the permutation occurs.

     See also: *note ifftshift: XREFifftshift.

 -- : Y = ifftshift (X)
 -- : Y = ifftshift (X, DIM)
     Undo the action of the ‘fftshift’ function.

     For even length X, ‘fftshift’ is its own inverse, but odd lengths
     differ slightly.

     See also: *note fftshift: XREFfftshift.

 -- : FD = fractdiff (X, D)
     Compute the fractional differences (1-L)^d x where L denotes the
     lag-operator and d is greater than -1.

 -- : C = hamming (M)
 -- : C = hamming (M, "periodic")
 -- : C = hamming (M, "symmetric")
     Return the filter coefficients of a Hamming window of length M.

     If the optional argument "periodic" is given, the periodic form of
     the window is returned.  This is equivalent to the window of length
     M+1 with the last coefficient removed.  The optional argument
     "symmetric" is equivalent to not specifying a second argument.

     For a definition of the Hamming window see, e.g., A.V. Oppenheim &
     R. W. Schafer, ‘Discrete-Time Signal Processing’.

 -- : C = hanning (M)
 -- : C = hanning (M, "periodic")
 -- : C = hanning (M, "symmetric")
     Return the filter coefficients of a Hanning window of length M.

     If the optional argument "periodic" is given, the periodic form of
     the window is returned.  This is equivalent to the window of length
     M+1 with the last coefficient removed.  The optional argument
     "symmetric" is equivalent to not specifying a second argument.

     For a definition of the Hanning window see, e.g., A.V. Oppenheim &
     R. W. Schafer, ‘Discrete-Time Signal Processing’.

 -- : H = hurst (X)
     Estimate the Hurst parameter of sample X via the rescaled range
     statistic.

     If X is a matrix, the parameter is estimated for every column.

 -- : PP = pchip (X, Y)
 -- : YI = pchip (X, Y, XI)
     Return the Piecewise Cubic Hermite Interpolating Polynomial (pchip)
     of points X and Y.

     If called with two arguments, return the piecewise polynomial PP
     that may be used with ‘ppval’ to evaluate the polynomial at
     specific points.

     When called with a third input argument, ‘pchip’ evaluates the
     pchip polynomial at the points XI.  The third calling form is
     equivalent to ‘ppval (pchip (X, Y), XI)’.

     The variable X must be a strictly monotonic vector (either
     increasing or decreasing) of length N.

     Y can be either a vector or array.  If Y is a vector then it must
     be the same length N as X.  If Y is an array then the size of Y
     must have the form ‘[S1, S2, ..., SK, N]’ The array is reshaped
     internally to a matrix where the leading dimension is given by ‘S1
     * S2 * ... * SK’ and each row of this matrix is then treated
     separately.  Note that this is exactly opposite to ‘interp1’ but is
     done for MATLAB compatibility.

     See also: *note spline: XREFspline, *note ppval: XREFppval, *note
     mkpp: XREFmkpp, *note unmkpp: XREFunmkpp.

 -- : [PXX, W] = periodogram (X)
 -- : [PXX, W] = periodogram (X, WIN)
 -- : [PXX, W] = periodogram (X, WIN, NFFT)
 -- : [PXX, F] = periodogram (X, WIN, NFFT, FS)
 -- : [PXX, F] = periodogram (..., "RANGE")
 -- : periodogram (...)
     Return the periodogram (Power Spectral Density) of X.

     The possible inputs are:

     X

          data vector.  If X is real-valued a one-sided spectrum is
          estimated.  If X is complex-valued, or "RANGE" specifies
          "twosided", the full spectrum is estimated.

     WIN
          window weight data.  If window is empty or unspecified a
          default rectangular window is used.  Otherwise, the window is
          applied to the signal (‘X .* WIN’) before computing the
          periodogram.  The window data must be a vector of the same
          length as X.

     NFFT
          number of frequency bins.  The default is 256 or the next
          higher power of 2 greater than the length of X (‘max (256,
          2.^nextpow2 (length (x)))’).  If NFFT is greater than the
          length of the input then X will be zero-padded to the length
          of NFFT.

     FS
          sampling rate.  The default is 1.

     RANGE
          range of spectrum.  "onesided" computes spectrum from
          [0:nfft/2+1].  "twosided" computes spectrum from [0:nfft-1].

     The optional second output W are the normalized angular
     frequencies.  For a one-sided calculation W is in the range [0, pi]
     if NFFT is even and [0, pi) if NFFT is odd.  Similarly, for a
     two-sided calculation W is in the range [0, 2*pi] or [0, 2*pi)
     depending on NFFT.

     If a sampling frequency is specified, FS, then the output
     frequencies F will be in the range [0, FS/2] or [0, FS/2) for
     one-sided calculations.  For two-sided calculations the range will
     be [0, FS).

     When called with no outputs the periodogram is immediately plotted
     in the current figure window.

     See also: *note fft: XREFfft.

 -- : Y = sinetone (FREQ, RATE, SEC, AMPL)
     Return a sinetone of frequency FREQ with a length of SEC seconds at
     sampling rate RATE and with amplitude AMPL.

     The arguments FREQ and AMPL may be vectors of common size.

     The defaults are RATE = 8000, SEC = 1, and AMPL = 64.

     See also: *note sinewave: XREFsinewave.

 -- : Y = sinewave (M, N, D)
     Return an M-element vector with I-th element given by ‘sin (2 * pi
     * (I+D-1) / N)’.

     The default value for D is 0 and the default value for N is M.

     See also: *note sinetone: XREFsinetone.

 -- : SDE = spectral_adf (C)
 -- : SDE = spectral_adf (C, WIN)
 -- : SDE = spectral_adf (C, WIN, B)
     Return the spectral density estimator given a vector of
     autocovariances C, window name WIN, and bandwidth, B.

     The window name, e.g., "triangle" or "rectangle" is used to search
     for a function called ‘WIN_lw’.

     If WIN is omitted, the triangle window is used.

     If B is omitted, ‘1 / sqrt (length (X))’ is used.

     See also: *note spectral_xdf: XREFspectral_xdf.

 -- : SDE = spectral_xdf (X)
 -- : SDE = spectral_xdf (X, WIN)
 -- : SDE = spectral_xdf (X, WIN, B)
     Return the spectral density estimator given a data vector X, window
     name WIN, and bandwidth, B.

     The window name, e.g., "triangle" or "rectangle" is used to search
     for a function called ‘WIN_sw’.

     If WIN is omitted, the triangle window is used.

     If B is omitted, ‘1 / sqrt (length (X))’ is used.

     See also: *note spectral_adf: XREFspectral_adf.

 -- : SAVG = spencer (X)
     Return Spencer’s 15-point moving average of each column of X.

 -- : Y = stft (X)
 -- : Y = stft (X, WIN_SIZE)
 -- : Y = stft (X, WIN_SIZE, INC)
 -- : Y = stft (X, WIN_SIZE, INC, NUM_COEF)
 -- : Y = stft (X, WIN_SIZE, INC, NUM_COEF, WIN_TYPE)
 -- : [Y, C] = stft (...)
     Compute the short-time Fourier transform of the vector X with
     NUM_COEF coefficients by applying a window of WIN_SIZE data points
     and an increment of INC points.

     Before computing the Fourier transform, one of the following
     windows is applied:

     "hanning"
          win_type = 1

     "hamming"
          win_type = 2

     "rectangle"
          win_type = 3

     The window names can be passed as strings or by the WIN_TYPE
     number.

     The following defaults are used for unspecified arguments: WIN_SIZE
     = 80, INC = 24, NUM_COEF = 64, and WIN_TYPE = 1.

     ‘Y = stft (X, ...)’ returns the absolute values of the Fourier
     coefficients according to the NUM_COEF positive frequencies.

     ‘[Y, C] = stft (X, ...)’ returns the entire STFT-matrix Y and a
     3-element vector C containing the window size, increment, and
     window type, which is needed by the ‘synthesis’ function.

     See also: *note synthesis: XREFsynthesis.

 -- : X = synthesis (Y, C)
     Compute a signal from its short-time Fourier transform Y and a
     3-element vector C specifying window size, increment, and window
     type.

     The values Y and C can be derived by

          [Y, C] = stft (X , ...)

     See also: *note stft: XREFstft.

 -- : [A, V] = yulewalker (C)
     Fit an AR (p)-model with Yule-Walker estimates given a vector C of
     autocovariances ‘[gamma_0, ..., gamma_p]’.

     Returns the AR coefficients, A, and the variance of white noise, V.


File: octave.info,  Node: Image Processing,  Next: Audio Processing,  Prev: Signal Processing,  Up: Top

32 Image Processing
*******************

Since an image is basically a matrix, Octave is a very powerful
environment for processing and analyzing images.  To illustrate how easy
it is to do image processing in Octave, the following example will load
an image, smooth it by a 5-by-5 averaging filter, and compute the
gradient of the smoothed image.

     I = imread ("myimage.jpg");
     S = conv2 (I, ones (5, 5) / 25, "same");
     [Dx, Dy] = gradient (S);

In this example ‘S’ contains the smoothed image, and ‘Dx’ and ‘Dy’
contains the partial spatial derivatives of the image.

* Menu:

* Loading and Saving Images::
* Displaying Images::
* Representing Images::
* Plotting on top of Images::
* Color Conversion::


File: octave.info,  Node: Loading and Saving Images,  Next: Displaying Images,  Up: Image Processing

32.1 Loading and Saving Images
==============================

The first step in most image processing tasks is to load an image into
Octave which is done with the ‘imread’ function.  The ‘imwrite’ function
is the corresponding function for writing images to the disk.

   In summary, most image processing code will follow the structure of
this code

     I = imread ("my_input_image.img");
     J = process_my_image (I);
     imwrite (J, "my_output_image.img");

 -- : [IMG, MAP, ALPHA] = imread (FILENAME)
 -- : [...] = imread (URL)
 -- : [...] = imread (..., EXT)
 -- : [...] = imread (..., IDX)
 -- : [...] = imread (..., PARAM1, VALUE1, ...)
     Read images from various file formats.

     Read an image as a matrix from the file FILENAME or from the online
     resource URL.  If neither is given, but EXT was specified, look for
     a file with the extension EXT.

     The size and class of the output depends on the format of the
     image.  A color image is returned as an MxNx3 matrix.  Grayscale
     and black-and-white images are of size MxN.  Multipage images will
     have an additional 4th dimension.

     The bit depth of the image determines the class of the output:
     "uint8", "uint16", or "single" for grayscale and color, and
     "logical" for black-and-white.  Note that indexed images always
     return the indexes for a colormap, independent of whether MAP is a
     requested output.  To obtain the actual RGB image, use ‘ind2rgb’.
     When more than one indexed image is being read, MAP is obtained
     from the first.  In some rare cases this may be incorrect and
     ‘imfinfo’ can be used to obtain the colormap of each image.

     See the Octave manual for more information in representing images.
     (*note Representing Images::)

     Some file formats, such as TIFF and GIF, are able to store multiple
     images in a single file.  IDX can be a scalar or vector specifying
     the index of the images to read.  By default, Octave will read only
     the first page.

     Depending on the file format, it is possible to configure the
     reading of images with PARAMETER, VALUE pairs.  The following
     options are supported:

     "Frames" or "Index"
          This is an alternative method to specify IDX.  When specifying
          it in this way, its value can also be the string "all".

     "Info"
          This option exists for MATLAB compatibility, but has no
          effect.  For maximum performance when reading multiple images
          from a single file, use the "Index" option.

     "PixelRegion"
          Controls the image region that is read.  The value must be a
          cell array with two arrays of 3 elements ‘{[ROWS], [COLS]}’.
          The elements in the array are the start, increment, and end
          pixel to be read.  If the increment value is omitted it
          defaults to 1.  For example, the following are all equivalent:

               imread (filename, "PixelRegion", {[200 600], [300 700]});
               imread (filename, "PixelRegion", {[200 1 600], [300 1 700]});
               imread (filename)(200:600, 300:700);

     See also: *note imwrite: XREFimwrite, *note imfinfo: XREFimfinfo,
     *note imformats: XREFimformats.

 -- : imwrite (IMG, FILENAME)
 -- : imwrite (IMG, FILENAME, EXT)
 -- : imwrite (IMG, MAP, FILENAME)
 -- : imwrite (..., PARAM1, VAL1, ...)
     Write images in various file formats.

     The image IMG can be a binary, grayscale, RGB, or multi-dimensional
     image.  The size and class of IMG should be the same as what should
     be expected when reading it with ‘imread’: the 3rd and 4th
     dimensions reserved for color space, and multiple pages
     respectively.  If it’s an indexed image, the colormap MAP must also
     be specified.

     If EXT is not supplied, the file extension of FILENAME is used to
     determine the format.  The actual supported formats are dependent
     on options made during the build of Octave.  Use ‘imformats’ to
     check the support of the different image formats.

     Depending on the file format, it is possible to configure the
     writing of images with PARAM, VAL pairs.  The following options are
     supported:

     ‘Alpha’
          Alpha (transparency) channel for the image.  This must be a
          matrix with same class, and number of rows and columns of IMG.
          In case of a multipage image, the size of the 4th dimension
          must also match and the third dimension must be a singleton.
          By default, image will be completely opaque.

     ‘Compression’
          Compression to use one the image.  Can be one of the
          following: "none" (default), "bzip", "fax3", "fax4", "jpeg",
          "lzw", "rle", or "deflate".  Note that not all compression
          types are available for all image formats in which it defaults
          to your Magick library.

     ‘DelayTime’
          For formats that accept animations (such as GIF), controls for
          how long a frame is displayed until it moves to the next one.
          The value must be scalar (which will applied to all frames in
          IMG), or a vector of length equal to the number of frames in
          IM.  The value is in seconds, must be between 0 and 655.35,
          and defaults to 0.5.

     ‘DisposalMethod’
          For formats that accept animations (such as GIF), controls
          what happens to a frame before drawing the next one.  Its
          value can be one of the following strings: "doNotSpecify"
          (default); "leaveInPlace"; "restoreBG"; and "restorePrevious",
          or a cell array of those string with length equal to the
          number of frames in IMG.

     ‘LoopCount’
          For formats that accept animations (such as GIF), controls how
          many times the sequence is repeated.  A value of Inf means an
          infinite loop (default), a value of 0 or 1 that the sequence
          is played only once (loops zero times), while a value of 2 or
          above loops that number of times (looping twice means it plays
          the complete sequence 3 times).  This option is ignored when
          there is only a single image at the end of writing the file.

     ‘Quality’
          Set the quality of the compression.  The value should be an
          integer between 0 and 100, with larger values indicating
          higher visual quality and lower compression.  Defaults to 75.

     ‘WriteMode’
          Some file formats, such as TIFF and GIF, are able to store
          multiple images in a single file.  This option specifies if
          IMG should be appended to the file (if it exists) or if a new
          file should be created for it (possibly overwriting an
          existing file).  The value should be the string "Overwrite"
          (default), or "Append".

          Despite this option, the most efficient method of writing a
          multipage image is to pass a 4 dimensional IMG to ‘imwrite’,
          the same matrix that could be expected when using ‘imread’
          with the option "Index" set to "all".

     See also: *note imread: XREFimread, *note imfinfo: XREFimfinfo,
     *note imformats: XREFimformats.

 -- : VAL = IMAGE_PATH ()
 -- : OLD_VAL = IMAGE_PATH (NEW_VAL)
 -- : OLD_VAL = IMAGE_PATH (NEW_VAL, "local")
     Query or set the internal variable that specifies a colon separated
     list of directories in which to search for image files.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note EXEC_PATH: XREFEXEC_PATH, *note OCTAVE_HOME:
     XREFOCTAVE_HOME, *note OCTAVE_EXEC_HOME: XREFOCTAVE_EXEC_HOME.

   It is possible to get information about an image file on disk,
without actually reading it into Octave.  This is done using the
‘imfinfo’ function which provides read access to many of the parameters
stored in the header of the image file.

 -- : INFO = imfinfo (FILENAME)
 -- : INFO = imfinfo (URL)
 -- : INFO = imfinfo (..., EXT)
     Read image information from a file.

     ‘imfinfo’ returns a structure containing information about the
     image stored in the file FILENAME.  If there is no file FILENAME,
     and EXT was specified, it will look for a file named FILENAME and
     extension EXT, i.e., a file named FILENAME.EXT.

     The output structure INFO contains the following fields:

     ‘Filename’
          The full name of the image file.

     ‘FileModDate’
          Date of last modification to the file.

     ‘FileSize’
          Number of bytes of the image on disk

     ‘Format’
          Image format (e.g., "jpeg").

     ‘Height’
          Image height in pixels.

     ‘Width’
          Image Width in pixels.

     ‘BitDepth’
          Number of bits per channel per pixel.

     ‘ColorType’
          Image type.  Value is "grayscale", "indexed", "truecolor",
          "CMYK", or "undefined".

     ‘XResolution’
          X resolution of the image.

     ‘YResolution’
          Y resolution of the image.

     ‘ResolutionUnit’
          Units of image resolution.  Value is "Inch", "Centimeter", or
          "undefined".

     ‘DelayTime’
          Time in 1/100ths of a second (0 to 65535) which must expire
          before displaying the next image in an animated sequence.

     ‘LoopCount’
          Number of iterations to loop an animation.

     ‘ByteOrder’
          Endian option for formats that support it.  Value is
          "little-endian", "big-endian", or "undefined".

     ‘Gamma’
          Gamma level of the image.  The same color image displayed on
          two different workstations may look different due to
          differences in the display monitor.

     ‘Quality’
          JPEG/MIFF/PNG compression level.  Value is an integer in the
          range [0 100].

     ‘DisposalMethod’
          Only valid for GIF images, control how successive frames are
          rendered (how the preceding frame is disposed of) when
          creating a GIF animation.  Values can be "doNotSpecify",
          "leaveInPlace", "restoreBG", or "restorePrevious".  For
          non-GIF files, value is an empty string.

     ‘Chromaticities’
          Value is a 1x8 Matrix with the x,y chromaticity values for
          white, red, green, and blue points, in that order.

     ‘Comment’
          Image comment.

     ‘Compression’
          Compression type.  Value can be "none", "bzip", "fax3",
          "fax4", "jpeg", "lzw", "rle", "deflate", "lzma", "jpeg2000",
          "jbig2", "jbig2", or "undefined".

     ‘Colormap’
          Colormap for each image.

     ‘Orientation’
          The orientation of the image with respect to the rows and
          columns.  Value is an integer between 1 and 8 as defined in
          the TIFF 6 specifications, and for MATLAB compatibility.

     ‘Software’
          Name and version of the software or firmware of the camera or
          image input device used to generate the image.

     ‘Make’
          The manufacturer of the recording equipment.  This is the
          manufacture of the DSC, scanner, video digitizer or other
          equipment that generated the image.

     ‘Model’
          The model name or model number of the recording equipment as
          mentioned on the field "Make".

     ‘DateTime’
          The date and time of image creation as defined by the Exif
          standard, i.e., it is the date and time the file was changed.

     ‘ImageDescription’
          The title of the image as defined by the Exif standard.

     ‘Artist’
          Name of the camera owner, photographer or image creator.

     ‘Copyright’
          Copyright notice of the person or organization claiming rights
          to the image.

     ‘DigitalCamera’
          A struct with information retrieved from the Exif tag.

     ‘GPSInfo’
          A struct with geotagging information retrieved from the Exif
          tag.

     See also: *note imread: XREFimread, *note imwrite: XREFimwrite,
     *note imshow: XREFimshow, *note imformats: XREFimformats.

   By default, Octave’s image IO functions (‘imread’, ‘imwrite’, and
‘imfinfo’) use the ‘GraphicsMagick’ library for their operations.  This
means a vast number of image formats is supported but considering the
large amount of image formats in science and its commonly closed nature,
it is impossible to have a library capable of reading them all.  Because
of this, the function ‘imformats’ keeps a configurable list of available
formats, their extensions, and what functions should the image IO
functions use.  This allows one to expand Octave’s image IO capabilities
by creating functions aimed at acting on specific file formats.

   While it would be possible to call the extra functions directly,
properly configuring Octave with ‘imformats’ allows one to keep a
consistent code that is abstracted from file formats.

   It is important to note that a file format is not actually defined by
its file extension and that ‘GraphicsMagick’ is capable to read and
write more file formats than the ones listed by ‘imformats’.  What this
means is that even with an incorrect or missing extension the image may
still be read correctly, and that even unlisted formats are not
necessarily unsupported.

 -- : imformats ()
 -- : FORMATS = imformats (EXT)
 -- : FORMATS = imformats (FORMAT)
 -- : FORMATS = imformats ("add", FORMAT)
 -- : FORMATS = imformats ("remove", EXT)
 -- : FORMATS = imformats ("update", EXT, FORMAT)
 -- : FORMATS = imformats ("factory")
     Manage supported image formats.

     FORMATS is a structure with information about each supported file
     format, or from a specific format EXT, the value displayed on the
     field EXT.  It contains the following fields:

     ext
          The name of the file format.  This may match the file
          extension but Octave will automatically detect the file
          format.

     description
          A long description of the file format.

     isa
          A function handle to confirm if a file is of the specified
          format.

     write
          A function handle to write if a file is of the specified
          format.

     read
          A function handle to open files the specified format.

     info
          A function handle to obtain image information of the specified
          format.

     alpha
          Logical value if format supports alpha channel (transparency
          or matte).

     multipage
          Logical value if format supports multipage (multiple images
          per file).

     It is possible to change the way Octave manages file formats with
     the options "add", "remove", and "update", and supplying a
     structure FORMAT with the required fields.  The option "factory"
     resets the configuration to the default.

     This can be used by Octave packages to extend the image reading
     capabilities Octave, through use of the PKG_ADD and PKG_DEL
     commands.

     See also: *note imfinfo: XREFimfinfo, *note imread: XREFimread,
     *note imwrite: XREFimwrite.


File: octave.info,  Node: Displaying Images,  Next: Representing Images,  Prev: Loading and Saving Images,  Up: Image Processing

32.2 Displaying Images
======================

A natural part of image processing is visualization of an image.  The
most basic function for this is the ‘imshow’ function that shows the
image given in the first input argument.

 -- : imshow (IM)
 -- : imshow (IM, LIMITS)
 -- : imshow (IM, MAP)
 -- : imshow (RGB, ...)
 -- : imshow (FILENAME)
 -- : imshow (..., STRING_PARAM1, VALUE1, ...)
 -- : H = imshow (...)
     Display the image IM, where IM can be a 2-dimensional (grayscale
     image) or a 3-dimensional (RGB image) matrix.

     If LIMITS is a 2-element vector ‘[LOW, HIGH]’, the image is shown
     using a display range between LOW and HIGH.  If an empty matrix is
     passed for LIMITS, the display range is computed as the range
     between the minimal and the maximal value in the image.

     If MAP is a valid color map, the image will be shown as an indexed
     image using the supplied color map.

     If a filename is given instead of an image, the file will be read
     and shown.

     If given, the parameter STRING_PARAM1 has value VALUE1.
     STRING_PARAM1 can be any of the following:

     "displayrange"
          VALUE1 is the display range as described above.

     "colormap"
          VALUE1 is the colormap to use when displaying an indexed
          image.

     "xdata"
          If VALUE1 is a 2-element vector, it must contain horizontal
          image limits in the form [xfirst, xlast], where xfirst and
          xlast are the abscissa of the centers of the corner pixels.
          Otherwise VALUE1 must be a vector and only the first and last
          elements will be used for xfirst and xlast respectively.

     "ydata"
          If VALUE1 is a 2-element vector, it must contain vertical
          image limits in the form [yfirst, ylast], where yfirst and
          ylast are the ordinates of the center of the corner pixels.
          Otherwise VALUE1 must be a vector and only the first and last
          elements will be used for yfirst and ylast respectively.

     The optional return value H is a graphics handle to the image.

     See also: *note image: XREFimage, *note imagesc: XREFimagesc, *note
     colormap: XREFcolormap, *note gray2ind: XREFgray2ind, *note
     rgb2ind: XREFrgb2ind.

 -- : image (IMG)
 -- : image (X, Y, IMG)
 -- : image (..., "PROP", VAL, ...)
 -- : image ("PROP1", VAL1, ...)
 -- : H = image (...)
     Display a matrix as an indexed color image.

     The elements of IMG are indices into the current colormap.

     X and Y are optional 2-element vectors, ‘[min, max]’, which specify
     the coordinates of the centers of the corner pixels.  If a range is
     specified as ‘[max, min]’ then the image will be reversed along
     that axis.  For convenience, X and Y may be specified as N-element
     vectors matching the length of the data in IMG.  However, only the
     first and last elements will be used to determine the axis limits.

     Multiple property/value pairs may be specified for the image
     object, but they must appear in pairs.

     The optional return value H is a graphics handle to the image.

     Implementation Note: The origin (0, 0) for images is located in the
     upper left.  For ordinary plots, the origin is located in the lower
     left.  Octave handles this inversion by plotting the data normally,
     and then reversing the direction of the y-axis by setting the
     ‘ydir’ property to "reverse".  This has implications whenever an
     image and an ordinary plot need to be overlaid.  The recommended
     solution is to display the image and then plot the reversed ydata
     using, for example, ‘flipud (ydata)’.

     Calling Forms: The ‘image’ function can be called in two forms:
     High-Level and Low-Level.  When invoked with normal options, the
     High-Level form is used which first calls ‘newplot’ to prepare the
     graphic figure and axes.  When the only inputs to ‘image’ are
     property/value pairs the Low-Level form is used which creates a new
     instance of an image object and inserts it in the current axes.

     Graphic Properties: The full list of properties is documented at
     *note Image Properties::.

     See also: *note imshow: XREFimshow, *note imagesc: XREFimagesc,
     *note colormap: XREFcolormap.

 -- : imagesc (IMG)
 -- : imagesc (X, Y, IMG)
 -- : imagesc (..., CLIMITS)
 -- : imagesc (..., "PROP", VAL, ...)
 -- : imagesc ("PROP1", VAL1, ...)
 -- : imagesc (HAX, ...)
 -- : H = imagesc (...)
     Display a scaled version of the matrix IMG as a color image.

     The colormap is scaled so that the entries of the matrix occupy the
     entire colormap.  If ‘CLIMITS = [LO, HI]’ is given, then that range
     is set to the "clim" of the current axes.

     X and Y are optional 2-element vectors, ‘[min, max]’, which specify
     the coordinates of the centers of the corner pixels.  If a range is
     specified as ‘[max, min]’ then the image will be reversed along
     that axis.  For convenience, X and Y may be specified as N-element
     vectors matching the length of the data in IMG.  However, only the
     first and last elements will be used to determine the image limits.

     The optional return value H is a graphics handle to the image.

     Calling Forms: The ‘imagesc’ function can be called in two forms:
     High-Level and Low-Level.  When invoked with normal options, the
     High-Level form is used which first calls ‘newplot’ to prepare the
     graphic figure and axes.  When the only inputs to ‘image’ are
     property/value pairs the Low-Level form is used which creates a new
     instance of an image object and inserts it in the current axes.
     The full list of properties is documented at *note Image
     Properties::.

     See also: *note image: XREFimage, *note imshow: XREFimshow, *note
     caxis: XREFcaxis.


File: octave.info,  Node: Representing Images,  Next: Plotting on top of Images,  Prev: Displaying Images,  Up: Image Processing

32.3 Representing Images
========================

In general Octave supports four different kinds of images, grayscale
images, RGB images, binary images, and indexed images.  A grayscale
image is represented with an M-by-N matrix in which each element
corresponds to the intensity of a pixel.  An RGB image is represented
with an M-by-N-by-3 array where each 3-vector corresponds to the red,
green, and blue intensities of each pixel.

   The actual meaning of the value of a pixel in a grayscale or RGB
image depends on the class of the matrix.  If the matrix is of class
‘double’ pixel intensities are between 0 and 1, if it is of class
‘uint8’ intensities are between 0 and 255, and if it is of class
‘uint16’ intensities are between 0 and 65535.

   A binary image is an M-by-N matrix of class ‘logical’.  A pixel in a
binary image is black if it is ‘false’ and white if it is ‘true’.

   An indexed image consists of an M-by-N matrix of integers and a
C-by-3 color map.  Each integer corresponds to an index in the color
map, and each row in the color map corresponds to an RGB color.  The
color map must be of class ‘double’ with values between 0 and 1.

   The following convenience functions are available for conversion
between image formats.

 -- : DIMG = im2double (IMG)
 -- : DIMG = im2double (IMG, "indexed")
     Convert image to double precision.

     The conversion of IMG to double precision, is dependent on the type
     of input image.  The following input classes are supported:

     ‘uint8, uint16, and int16’
          The range of values from the class is scaled to the interval
          [0 1].

     ‘logical’
          True and false values are assigned a value of 1 and 0
          respectively.

     ‘single’
          Values are cast to double.

     ‘double’
          Returns the same image.

     If IMG is an indexed image, then the second argument should be the
     string "indexed".  If so, then IMG must either be of floating point
     class, or unsigned integer class and it will simply be cast to
     double.  If it is an integer class, an offset of +1 is applied.

     See also: *note double: XREFdouble.

 -- : IMG = gray2ind (I)
 -- : IMG = gray2ind (I, N)
 -- : IMG = gray2ind (BW)
 -- : IMG = gray2ind (BW, N)
 -- : [IMG, MAP] = gray2ind (...)
     Convert a grayscale or binary intensity image to an indexed image.

     The indexed image will consist of N different intensity values.  If
     not given N defaults to 64 for grayscale images or 2 for binary
     black and white images.

     The output IMG is of class uint8 if N is less than or equal to 256;
     Otherwise the return class is uint16.

     See also: *note ind2gray: XREFind2gray, *note rgb2ind: XREFrgb2ind.

 -- : I = ind2gray (X, MAP)
     Convert a color indexed image to a grayscale intensity image.

     The image X must be an indexed image which will be converted using
     the colormap MAP.  If MAP does not contain enough colors for the
     image, pixels in X outside the range are mapped to the last color
     in the map before conversion to grayscale.

     The output I is of the same class as the input X and may be one of
     ‘uint8’, ‘uint16’, ‘single’, or ‘double’.

     Implementation Note: There are several ways of converting colors to
     grayscale intensities.  This functions uses the luminance value
     obtained from ‘rgb2gray’ which is ‘I = 0.299*R + 0.587*G +
     0.114*B’.  Other possibilities include the value component from
     ‘rgb2hsv’ or using a single color channel from ‘ind2rgb’.

     See also: *note gray2ind: XREFgray2ind, *note ind2rgb: XREFind2rgb.

 -- : [X, MAP] = rgb2ind (RGB)
 -- : [X, MAP] = rgb2ind (R, G, B)
     Convert an image in red-green-blue (RGB) color space to an indexed
     image.

     The input image RGB can be specified as a single matrix of size
     MxNx3, or as three separate variables, R, G, and B, its three color
     channels, red, green, and blue.

     It outputs an indexed image X and a colormap MAP to interpret an
     image exactly the same as the input.  No dithering or other form of
     color quantization is performed.  The output class of the indexed
     image X can be uint8, uint16 or double, whichever is required to
     specify the number of unique colors in the image (which will be
     equal to the number of rows in MAP) in order.

     Multi-dimensional indexed images (of size MxNx3xK) are also
     supported, both via a single input (RGB) or its three color
     channels as separate variables.

     See also: *note ind2rgb: XREFind2rgb, *note rgb2hsv: XREFrgb2hsv,
     *note rgb2gray: XREFrgb2gray.

 -- : RGB = ind2rgb (X, MAP)
 -- : [R, G, B] = ind2rgb (X, MAP)
     Convert an indexed image to red, green, and blue color components.

     The image X must be an indexed image which will be converted using
     the colormap MAP.  If MAP does not contain enough colors for the
     image, pixels in X outside the range are mapped to the last color
     in the map.

     The output may be a single RGB image (MxNx3 matrix where M and N
     are the original image X dimensions, one for each of the red, green
     and blue channels).  Alternatively, the individual red, green, and
     blue color matrices of size MxN may be returned.

     Multi-dimensional indexed images (of size MxNx1xK) are also
     supported.

     See also: *note rgb2ind: XREFrgb2ind, *note ind2gray: XREFind2gray,
     *note hsv2rgb: XREFhsv2rgb.

   Octave also provides tools to produce and work with movie frame
structures.  Those structures encapsulate the image data ("cdata" field)
together with the corresponding colormap ("colormap" field).

 -- : FRAME = getframe ()
 -- : FRAME = getframe (HAX)
 -- : FRAME = getframe (HFIG)
 -- : FRAME = getframe (..., RECT)

     Capture a figure or axes as a movie frame structure.

     Without an argument, capture the current axes excluding ticklabels,
     title, and x/y/zlabels.  The returned structure FRAME has a field
     ‘cdata’, which contains the actual image data in the form of an
     NxMx3 (RGB) uint8 matrix, and a field ‘colormap’ which is provided
     for MATLAB compatibility but is always empty.

     If the first argument HAX is an axes handle, then capture this
     axes, rather than the current axes returned by ‘gca’.

     If the first argument HFIG is a figure handle then the entire
     corresponding figure canvas is captured.

     Finally, if a second argument RECT is provided it must be a
     four-element vector ([left bottom width height]) defining the
     region inside the figure to be captured.  Regardless of the figure
     "units" property, RECT must be defined in *pixels*.

     See also: *note im2frame: XREFim2frame, *note frame2im:
     XREFframe2im, *note movie: XREFmovie.

 -- : movie (MOV)
 -- : movie (MOV, N)
 -- : movie (MOV, N, FPS)
 -- : movie (H, ...)
     Play a movie defined by an array of frame structures.

     The movie MOV must be a struct array of frames with fields "cdata"
     and "colormap", as returned by the ‘getframe’ function.  By default
     all images are displayed once, at 12 fps, in the current axes.

     The optional argument N is a scalar or vector of integers that
     controls the number of times the movie is displayed and which
     particular frames are shown:

     First element:

          N(1) > 0
               Play the movie N(1) times.

          N(1) < 0
               Play the movie ‘abs (N(1)’ times alternatively in forward
               and backward order.

     Other elements (if any):
          Indices of the frames in MOV that will be displayed.

     If the first argument is a handle to a figure or axes H, the movie
     is played in that figure or axes instead of the current axes.

     See also: *note getframe: XREFgetframe, *note im2frame:
     XREFim2frame, *note frame2im: XREFframe2im.

 -- : [X, MAP] = frame2im (FRAME)
     Convert movie frame to indexed image.

     A movie frame is simply a struct with the fields "cdata" and
     "colormap".

     Support for N-dimensional images or movies is given when FRAME is a
     struct array.  In such cases, X will be a MxNx1xK or MxNx3xK for
     indexed and RGB movies respectively, with each frame concatenated
     along the 4th dimension.

     See also: *note im2frame: XREFim2frame, *note getframe:
     XREFgetframe.

 -- : FRAME = im2frame (RGB)
 -- : FRAME = im2frame (X, MAP)
     Convert image to movie frame.

     A movie frame is simply a struct with the fields "cdata" and
     "colormap".

     Support for N-dimensional images is given when each image
     projection, matrix sizes of MxN and MxNx3 for RGB images, is
     concatenated along the fourth dimension.  In such cases, the
     returned value is a struct array.

     See also: *note frame2im: XREFframe2im.

   The ‘colormap’ function is used to change the colormap of the current
axes or figure.

 -- : CMAP = colormap ()
 -- : CMAP = colormap (MAP)
 -- : CMAP = colormap ("default")
 -- : CMAP = colormap (MAP_NAME)
 -- : CMAP = colormap (HAX, ...)
 -- : colormap MAP_NAME
     Query or set the current colormap.

     With no input arguments, ‘colormap’ returns the current color map.

     ‘colormap (MAP)’ sets the current colormap to MAP.  The colormap
     should be an N row by 3 column matrix.  The columns contain red,
     green, and blue intensities respectively.  All entries must be
     between 0 and 1 inclusive.  The new colormap is returned.

     ‘colormap ("default")’ restores the default colormap (the ‘viridis’
     map with 64 entries).  The default colormap is returned.

     The map may also be specified by a string, MAP_NAME, which is the
     name of a function that returns a colormap.

     If the first argument HAX is an axes handle, then the colormap for
     those axes is queried or set.

     For convenience, it is also possible to use this function with the
     command form, ‘colormap MAP_NAME’.

     The list of built-in colormaps is:

     Map         Description
     --------------------------------------------------------------------------
     viridis     default
     turbo       colormap traversing blue, cyan, green, yellow, red; modern
                 replacement for jet.
     jet         colormap traversing blue, cyan, green, yellow, red.
     cubehelix   colormap traversing black, blue, green, red, white with
                 increasing intensity.
     hsv         cyclic colormap traversing Hue, Saturation, Value space.
     rainbow     colormap traversing red, yellow, blue, green, violet.
     ————-       ———————————————————————————————
     hot         colormap traversing black, red, orange, yellow, white.
     cool        colormap traversing cyan, purple, magenta.
     spring      colormap traversing magenta to yellow.
     summer      colormap traversing green to yellow.
     autumn      colormap traversing red, orange, yellow.
     winter      colormap traversing blue to green.
     ————-       ———————————————————————————————
     gray        colormap traversing black to white in shades of gray.
     bone        colormap traversing black, gray-blue, white.
     copper      colormap traversing black to light copper.
     pink        colormap traversing black, gray-pink, white.
     ocean       colormap traversing black, dark-blue, white.
     ————-       ———————————————————————————————
     colorcube   equally spaced colors in RGB color space.
     flag        cyclic 4-color map of red, white, blue, black.
     lines       cyclic colormap with colors from axes "ColorOrder"
                 property.
     prism       cyclic 6-color map of red, orange, yellow, green, blue,
                 violet.
     ————-       ———————————————————————————————
     white       all white colormap (no colors).

     See also: *note viridis: XREFviridis, *note turbo: XREFturbo, *note
     jet: XREFjet, *note cubehelix: XREFcubehelix, *note hsv: XREFhsv,
     *note rainbow: XREFrainbow, *note hot: XREFhot, *note cool:
     XREFcool, *note spring: XREFspring, *note summer: XREFsummer, *note
     autumn: XREFautumn, *note winter: XREFwinter, *note gray: XREFgray,
     *note bone: XREFbone, *note copper: XREFcopper, *note pink:
     XREFpink, *note ocean: XREFocean, *note colorcube: XREFcolorcube,
     *note flag: XREFflag, *note lines: XREFlines, *note prism:
     XREFprism, *note white: XREFwhite.

 -- : TF = iscolormap (CMAP)
     Return true if CMAP is a colormap.

     A colormap is a real matrix, of class single or double, with 3
     columns.  Each row represents a single color.  The 3 columns
     contain red, green, and blue intensities respectively.

     All values in a colormap should be in the [0 1] range but this is
     not enforced.  Each function must decide what to do for values
     outside this range.

     See also: *note colormap: XREFcolormap, *note rgbplot: XREFrgbplot.

   The following functions return predefined colormaps, the same that
can be requested by name using the ‘colormap’ function.

 -- : rgbplot (CMAP)
 -- : rgbplot (CMAP, STYLE)
 -- : H = rgbplot (...)
     Plot the components of a colormap.

     Two different STYLEs are available for displaying the CMAP:

     profile (default)
          Plot the RGB line profile of the colormap for each of the
          channels (red, green and blue) with the plot lines colored
          appropriately.  Each line represents the intensity of an RGB
          component across the colormap.

     composite
          Draw the colormap across the X-axis so that the actual index
          colors are visible rather than the individual color
          components.

     The optional return value H is a graphics handle to the created
     plot.

     Run ‘demo rgbplot’ to see an example of ‘rgbplot’ and each style
     option.

     See also: *note colormap: XREFcolormap.

 -- : MAP = autumn ()
 -- : MAP = autumn (N)
     Create color colormap.  This colormap ranges from red through
     orange to yellow.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = bone ()
 -- : MAP = bone (N)
     Create color colormap.  This colormap varies from black to white
     with gray-blue shades.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = colorcube ()
 -- : MAP = colorcube (N)
     Create color colormap.  This colormap is composed of as many
     equally spaced colors (not grays) in the RGB color space as
     possible.

     If there are not a perfect number N of regularly spaced colors then
     the remaining entries in the colormap are gradients of pure red,
     green, blue, and gray.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = cool ()
 -- : MAP = cool (N)
     Create color colormap.  The colormap varies from cyan to magenta.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = copper ()
 -- : MAP = copper (N)
     Create color colormap.  This colormap varies from black to a light
     copper tone.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = cubehelix ()
 -- : MAP = cubehelix (N)
 -- : MAP = cubehelix (N, START, ROTS, HUE, GAMMA)
     Create cubehelix colormap.

     This colormap varies from black to white going though blue, green,
     and red tones while maintaining a monotonically increasing
     perception of intensity.  This is achieved by traversing a color
     cube from black to white through a helix, hence the name cubehelix,
     while taking into account the perceived brightness of each channel
     according to the NTSC specifications from 1953.

          rgbplot (cubehelix (256))

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     Reference: Green, D. A., 2011, ‘A colour scheme for the display of
     astronomical intensity images’, Bulletin of the Astronomical
     Society of India, 39, 289.

     See also: *note colormap: XREFcolormap.

 -- : MAP = flag ()
 -- : MAP = flag (N)
     Create color colormap.  This colormap cycles through red, white,
     blue, and black with each index change.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = gray ()
 -- : MAP = gray (N)
     Create gray colormap.  This colormap varies from black to white
     with shades of gray.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = hot ()
 -- : MAP = hot (N)
     Create color colormap.  This colormap ranges from black through
     dark red, red, orange, yellow, to white.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = hsv ()
 -- : MAP = hsv (N)
     Create color colormap.  This colormap begins with red, changes
     through yellow, green, cyan, blue, and magenta, before returning to
     red.

     It is useful for displaying periodic functions.  The map is
     obtained by linearly varying the hue through all possible values
     while keeping constant maximum saturation and value.  The
     equivalent code is ‘hsv2rgb ([(0:N-1)'/N, ones(N,2)])’.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = jet ()
 -- : MAP = jet (N)
     Create color colormap.  This colormap ranges from dark blue through
     blue, cyan, green, yellow, red, to dark red.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     Programming Note: The ‘jet’ colormap is not perceptually uniform.
     Try the ‘viridis’ colormap if that is important.  For a drop-in
     replacement for ‘jet’ with better perceptual characteristics try
     the ‘turbo’ colormap.

     See also: *note colormap: XREFcolormap, *note turbo: XREFturbo.

 -- : MAP = lines ()
 -- : MAP = lines (N)
     Create color colormap.  This colormap is composed of the list of
     colors in the current axes "ColorOrder" property.  The default is
     blue, orange, yellow, purple, green, light blue, and dark red.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = ocean ()
 -- : MAP = ocean (N)
     Create color colormap.  This colormap varies from black to white
     with shades of blue.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = pink ()
 -- : MAP = pink (N)
     Create color colormap.  This colormap varies from black to white
     with shades of gray-pink.

     This colormap gives a sepia tone when used on grayscale images.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = prism ()
 -- : MAP = prism (N)
     Create color colormap.  This colormap cycles through red, orange,
     yellow, green, blue and violet with each index change.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = rainbow ()
 -- : MAP = rainbow (N)
     Create color colormap.  This colormap ranges from red through
     orange, yellow, green, blue, to violet.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = spring ()
 -- : MAP = spring (N)
     Create color colormap.  This colormap varies from magenta to
     yellow.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = summer ()
 -- : MAP = summer (N)
     Create color colormap.  This colormap varies from green to yellow.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = turbo ()
 -- : MAP = turbo (N)
     Create color colormap.  This colormap ranges from dark blue through
     green to dark red; similar to the outdated ‘jet’ colormap but
     perceptually uniform.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = viridis ()
 -- : MAP = viridis (N)
     Create color colormap.  This colormap ranges from dark
     purplish-blue through blue, green, to yellow.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = white ()
 -- : MAP = white (N)
     Create color colormap.  This colormap is completely white.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : MAP = winter ()
 -- : MAP = winter (N)
     Create color colormap.  This colormap varies from blue to green.

     The argument N must be a scalar.  If unspecified, the length of the
     current colormap, or 64, is used.

     See also: *note colormap: XREFcolormap.

 -- : CMAP = contrast (X)
 -- : CMAP = contrast (X, N)
     Return a gray colormap that maximizes the contrast in an image.

     The returned colormap will have N rows.  If N is not defined then
     the size of the current colormap is used.

     See also: *note colormap: XREFcolormap, *note brighten:
     XREFbrighten.

   The following three functions modify the existing colormap rather
than replace it.

 -- : MAP_OUT = brighten (BETA)
 -- : MAP_OUT = brighten (MAP, BETA)
 -- : MAP_OUT = brighten (H, BETA)
 -- : brighten (...)
     Brighten or darken a colormap.

     The argument BETA must be a scalar between -1 and 1, where a
     negative value darkens and a positive value brightens the colormap.

     If the MAP argument is omitted, the function is applied to the
     current colormap.

     The first argument can also be a valid graphics handle H, in which
     case ‘brighten’ is applied to the colormap associated with this
     handle.

     If no output is specified then the result is written to the current
     colormap.

     See also: *note colormap: XREFcolormap, *note contrast:
     XREFcontrast.

 -- : spinmap ()
 -- : spinmap (T)
 -- : spinmap (T, INC)
 -- : spinmap ("inf")
     Cycle the colormap for T seconds with a color increment of INC.

     Both parameters are optional.  The default cycle time is 5 seconds
     and the default increment is 2.  If the option "inf" is given then
     cycle continuously until ‘Control-C’ is pressed.

     When rotating, the original color 1 becomes color 2, color 2
     becomes color 3, etc.  A positive or negative increment is allowed
     and a higher value of INC will cause faster cycling through the
     colormap.

     See also: *note colormap: XREFcolormap.

 -- : whitebg ()
 -- : whitebg (COLOR)
 -- : whitebg ("none")
 -- : whitebg (HFIG)
 -- : whitebg (HFIG, COLOR)
 -- : whitebg (HFIG, "none")
     Invert the colors in the current color scheme.

     The root properties are also inverted such that all subsequent
     plots will use the new color scheme.

     If the optional argument COLOR is present then the background color
     is set to COLOR rather than inverted.  COLOR may be a string
     representing one of the eight known colors or an RGB triplet.  The
     special string argument "none" restores the plot to the factory
     default colors.

     If the first argument HFIG is a figure handle or list of figure
     handles, then operate on these figures rather than the current
     figure returned by ‘gcf’.  The root properties will not be changed
     unless 0 is in the list of figures.

     Programming Note: ‘whitebg’ operates by changing the color
     properties of the children of the specified figures.  Only objects
     with a single color are affected.  For example, a patch with a
     single "FaceColor" will be changed, but a patch with shading
     ("interp") will not be modified.  For inversion, the new color is
     simply the inversion in RGB space: ‘CNEW = [1-R 1-G 1-B]’.  When a
     color is specified, the axes and figure are set to the new color,
     and the color of child objects are then adjusted to have some
     contrast (visibility) against the new background.

     See also: *note reset: XREFreset, *note get: XREFget, *note set:
     XREFset.

   The following functions can be used to manipulate colormaps.

 -- : [Y, NEWMAP] = cmunique (X, MAP)
 -- : [Y, NEWMAP] = cmunique (RGB)
 -- : [Y, NEWMAP] = cmunique (I)
     Convert an input image X to an output indexed image Y which uses
     the smallest colormap possible NEWMAP.

     When the input is an indexed image (X with colormap MAP) the output
     is a colormap NEWMAP from which any repeated rows have been
     eliminated.  The output image, Y, is the original input image with
     the indices adjusted to match the new, possibly smaller, colormap.

     When the input is an RGB image (an MxNx3 array), the output
     colormap will contain one entry for every unique color in the
     original image.  In the worst case the new map could have as many
     rows as the number of pixels in the original image.

     When the input is a grayscale image I, the output colormap will
     contain one entry for every unique intensity value in the original
     image.  In the worst case the new map could have as many rows as
     the number of pixels in the original image.

     Implementation Details:

     NEWMAP is always an Mx3 matrix, even if the input image is an
     intensity grayscale image I (all three RGB planes are assigned the
     same value).

     The output image is of class uint8 if the size of the new colormap
     is less than or equal to 256.  Otherwise, the output image is of
     class double.

     See also: *note rgb2ind: XREFrgb2ind, *note gray2ind: XREFgray2ind.

 -- : [Y, NEWMAP] = cmpermute (X, MAP)
 -- : [Y, NEWMAP] = cmpermute (X, MAP, INDEX)
     Reorder colors in a colormap.

     When called with only two arguments, ‘cmpermute’ randomly
     rearranges the colormap MAP and returns a new colormap NEWMAP.  It
     also returns the indexed image Y which is the equivalent of the
     original input image X when displayed using NEWMAP.

     When called with an optional third argument the order of colors in
     the new colormap is defined by INDEX.

     *Caution:* INDEX should not have repeated elements or the function
     will fail.

