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  Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770
  UPDATED VERSIONs can be found on my WEB PAGE:


  Perform Levenberg-Marquardt least-squares fit (replaces CURVEFIT)

Major Topics

  Curve and Surface Fitting

Calling Sequence

                    ITER=iter, ITMAX=itmax,
                    CHISQ=chisq, NFREE=nfree, DOF=dof,
                    NFEV=nfev, COVAR=covar, [/NOCOVAR, ] [/NODERIVATIVE, ]
                    FUNCTARGS=functargs, PARINFO=parinfo,
                    FTOL=ftol, XTOL=xtol, GTOL=gtol, TOL=tol,
                    ITERPROC=iterproc, ITERARGS=iterargs,
                    NPRINT=nprint, QUIET=quiet,
                    ERRMSG=errmsg, STATUS=status)


  MPCURVEFIT fits a user-supplied model -- in the form of an IDL
  function -- to a set of user-supplied data. MPCURVEFIT calls
  MPFIT, the MINPACK-1 least-squares minimizer, to do the main
  Given the data and their uncertainties, MPCURVEFIT finds the best
  set of model parameters which match the data (in a least-squares
  sense) and returns them in the parameter P.
  MPCURVEFIT returns the best fit function.
  The user must supply the following items:
  - An array of independent variable values ("X").
  - An array of "measured" *dependent* variable values ("Y").
  - An array of weighting values ("WEIGHTS").
  - The name of an IDL function which computes Y given X ("FUNC").
  - Starting guesses for all of the parameters ("P").
  There are very few restrictions placed on X, Y or FUNCT. Simply
  put, FUNCT must map the "X" values into "Y" values given the
  model parameters. The "X" values may represent any independent
  variable (not just Cartesian X), and indeed may be multidimensional
  themselves. For example, in the application of image fitting, X
  may be a 2xN array of image positions.
  MPCURVEFIT carefully avoids passing large arrays where possible to
  improve performance.
  See below for an example of usage.

User Function

  The user must define a function which returns the model value. For
  applications which use finite-difference derivatives -- the default
  -- the user function should be declared in the following way:
    ; The independent variable is X
    ; Parameter values are passed in "P"
    YMOD = ... computed model values at X ...
  The returned array YMOD must have the same dimensions and type as
  the "measured" Y values.
  User functions may also indicate a fatal error condition
  using the ERROR_CODE common block variable, as described
  below under the MPFIT_ERROR common block definition.
  See the discussion under "ANALYTIC DERIVATIVES" and AUTODERIVATIVE
  in MPFIT.PRO if you wish to compute the derivatives for yourself.
  AUTODERIVATIVE is accepted and passed directly to MPFIT. The user
  function must accept one additional parameter, DP, which contains
  the derivative of the user function with respect to each parameter
  at each data point, as described in MPFIT.PRO.

Constraining Parameter Values With The Parinfo Keyword

  The behavior of MPFIT can be modified with respect to each
  parameter to be fitted. A parameter value can be fixed; simple
  boundary constraints can be imposed; limitations on the parameter
  changes can be imposed; properties of the automatic derivative can
  be modified; and parameters can be tied to one another.
  These properties are governed by the PARINFO structure, which is
  passed as a keyword parameter to MPFIT.
  PARINFO should be an array of structures, one for each parameter.
  Each parameter is associated with one element of the array, in
  numerical order. The structure can have the following entries
  (none are required):
    .VALUE - the starting parameter value (but see the START_PARAMS
              parameter for more information).
    .FIXED - a boolean value, whether the parameter is to be held
              fixed or not. Fixed parameters are not varied by
              MPFIT, but are passed on to MYFUNCT for evaluation.
    .LIMITED - a two-element boolean array. If the first/second
                element is set, then the parameter is bounded on the
                lower/upper side. A parameter can be bounded on both
                sides. Both LIMITED and LIMITS must be given
    .LIMITS - a two-element float or double array. Gives the
              parameter limits on the lower and upper sides,
              respectively. Zero, one or two of these values can be
              set, depending on the values of LIMITED. Both LIMITED
              and LIMITS must be given together.
    .PARNAME - a string, giving the name of the parameter. The
                fitting code of MPFIT does not use this tag in any
                way. However, the default ITERPROC will print the
                parameter name if available.
    .STEP - the step size to be used in calculating the numerical
            derivatives. If set to zero, then the step size is
            computed automatically. Ignored when AUTODERIVATIVE=0.
            This value is superceded by the RELSTEP value.
    .RELSTEP - the *relative* step size to be used in calculating
                the numerical derivatives. This number is the
                fractional size of the step, compared to the
                parameter value. This value supercedes the STEP
                setting. If the parameter is zero, then a default
                step size is chosen.
    .MPSIDE - the sidedness of the finite difference when computing
              numerical derivatives. This field can take four
                  0 - one-sided derivative computed automatically
                  1 - one-sided derivative (f(x+h) - f(x) )/h
                -1 - one-sided derivative (f(x) - f(x-h))/h
                  2 - two-sided derivative (f(x+h) - f(x-h))/(2*h)
              Where H is the STEP parameter described above. The
              "automatic" one-sided derivative method will chose a
              direction for the finite difference which does not
              violate any constraints. The other methods do not
              perform this check. The two-sided method is in
              principle more precise, but requires twice as many
              function evaluations. Default: 0.
    .MPMAXSTEP - the maximum change to be made in the parameter
                  value. During the fitting process, the parameter
                  will never be changed by more than this value in
                  one iteration.
                  A value of 0 indicates no maximum. Default: 0.
    .TIED - a string expression which "ties" the parameter to other
            free or fixed parameters. Any expression involving
            constants and the parameter array P are permitted.
            Example: if parameter 2 is always to be twice parameter
            1 then use the following: parinfo(2).tied = '2 * P(1)'.
            Since they are totally constrained, tied parameters are
            considered to be fixed; no errors are computed for them.
            [ NOTE: the PARNAME can't be used in expressions. ]
    .MPPRINT - if set to 1, then the default ITERPROC will print the
                parameter value. If set to 0, the parameter value
                will not be printed. This tag can be used to
                selectively print only a few parameter values out of
                many. Default: 1 (all parameters printed)
  Future modifications to the PARINFO structure, if any, will involve
  adding structure tags beginning with the two letters "MP".
  Therefore programmers are urged to avoid using tags starting with
  the same letters; otherwise they are free to include their own
  fields within the PARINFO structure, and they will be ignored.
  PARINFO Example:
  parinfo = replicate({value:0.D, fixed:0, limited:[0,0], $
                      limits:[0.D,0]}, 5)
  parinfo(0).fixed = 1
  parinfo(4).limited(0) = 1
  parinfo(4).limits(0) = 50.D
  parinfo(*).value = [5.7D, 2.2, 500., 1.5, 2000.]
  A total of 5 parameters, with starting values of 5.7,
  2.2, 500, 1.5, and 2000 are given. The first parameter
  is fixed at a value of 5.7, and the last parameter is
  constrained to be above 50.


  X - Array of independent variable values.
  Y - Array of "measured" dependent variable values. Y should have
      the same data type as X. The function FUNCT should map
  WEIGHTS - Array of weights to be used in calculating the
            chi-squared value. If WEIGHTS is specified then the ERR
            parameter is ignored. The chi-squared value is computed
            as follows:
                CHISQ = TOTAL( (Y-FUNCT(X,P))^2 * ABS(WEIGHTS) )
            Here are common values of WEIGHTS:
                1D/ERR^2 - Normal weighting (ERR is the measurement error)
                1D/Y - Poisson weighting (counting statistics)
                1D - Unweighted
  P - An array of starting values for each of the parameters of the
      model. The number of parameters should be fewer than the
      number of measurements. Also, the parameters should have the
      same data type as the measurements (double is preferred).
      Upon successful completion the new parameter values are
      returned in P.
      If both START_PARAMS and PARINFO are passed, then the starting
      *value* is taken from START_PARAMS, but the *constraints* are
      taken from PARINFO.
  SIGMA - The formal 1-sigma errors in each parameter, computed from
          the covariance matrix. If a parameter is held fixed, or
          if it touches a boundary, then the error is reported as
          If the fit is unweighted (i.e. no errors were given, or
          the weights were uniformly set to unity), then SIGMA will
          probably not represent the true parameter uncertainties.
          *If* you can assume that the true reduced chi-squared
          value is unity -- meaning that the fit is implicitly
          assumed to be of good quality -- then the estimated
          parameter uncertainties can be computed by scaling SIGMA
          by the measured chi-squared value.
              DOF = N_ELEMENTS(X) - N_ELEMENTS(P) ; deg of freedom
              CSIGMA = SIGMA * SQRT(CHISQ / DOF) ; scaled uncertainties


  Returns the array containing the best-fitting function.

Keyword Parameters

  CHISQ - the value of the summed, squared, weighted residuals for
          the returned parameter values, i.e. the chi-square value.
  COVAR - the covariance matrix for the set of parameters returned
          by MPFIT. The matrix is NxN where N is the number of
          parameters. The square root of the diagonal elements
          gives the formal 1-sigma statistical errors on the
          parameters IF errors were treated "properly" in MYFUNC.
          Parameter errors are also returned in PERROR.
          To compute the correlation matrix, PCOR, use this:
          IDL> PCOR = COV * 0
          IDL> FOR i = 0, n-1 DO FOR j = 0, n-1 DO $
                PCOR(i,j) = COV(i,j)/sqrt(COV(i,i)*COV(j,j))
          If NOCOVAR is set or MPFIT terminated abnormally, then
          COVAR is set to a scalar with value !VALUES.D_NAN.
  DOF - number of degrees of freedom, computed as
        Note that this doesn't account for pegged parameters (see
  ERRMSG - a string error or warning message is returned.
  FTOL - a nonnegative input variable. Termination occurs when both
          the actual and predicted relative reductions in the sum of
          squares are at most FTOL (and STATUS is accordingly set to
          1 or 3). Therefore, FTOL measures the relative error
          desired in the sum of squares. Default: 1D-10
  FUNCTION_NAME - a scalar string containing the name of an IDL
                  procedure to compute the user model values, as
                  described above in the "USER MODEL" section.
  FUNCTARGS - A structure which contains the parameters to be passed
              to the user-supplied function specified by FUNCT via
              the _EXTRA mechanism. This is the way you can pass
              additional data to your user-supplied function without
              using common blocks.
              By default, no extra parameters are passed to the
              user-supplied function.
  GTOL - a nonnegative input variable. Termination occurs when the
          cosine of the angle between fvec and any column of the
          jacobian is at most GTOL in absolute value (and STATUS is
          accordingly set to 4). Therefore, GTOL measures the
          orthogonality desired between the function vector and the
          columns of the jacobian. Default: 1D-10
  ITER - the number of iterations completed.
  ITERARGS - The keyword arguments to be passed to ITERPROC via the
              _EXTRA mechanism. This should be a structure, and is
              similar in operation to FUNCTARGS.
              Default: no arguments are passed.
  ITERPROC - The name of a procedure to be called upon each NPRINT
              iteration of the MPFIT routine. It should be declared
              in the following way:
              PRO ITERPROC, FUNCT, p, iter, fnorm, FUNCTARGS=fcnargs, $
                PARINFO=parinfo, QUIET=quiet, ...
                ; perform custom iteration update
              ITERPROC must either accept all three keyword
              parameters (FUNCTARGS, PARINFO and QUIET), or at least
              accept them via the _EXTRA keyword.
              FUNCT is the user-supplied function to be minimized,
              P is the current set of model parameters, ITER is the
              iteration number, and FUNCTARGS are the arguments to be
              passed to FUNCT. FNORM should be the
              chi-squared value. QUIET is set when no textual output
              should be printed. See below for documentation of
              In implementation, ITERPROC can perform updates to the
              terminal or graphical user interface, to provide
              feedback while the fit proceeds. If the fit is to be
              stopped for any reason, then ITERPROC should set the
              common block variable ERROR_CODE to negative value (see
              MPFIT_ERROR common block below). In principle,
              ITERPROC should probably not modify the parameter
              values, because it may interfere with the algorithm's
              stability. In practice it is allowed.
              Default: an internal routine is used to print the
                      parameter values.
  ITMAX - The maximum number of iterations to perform. If the
            number is exceeded, then the STATUS value is set to 5
            and MPFIT returns.
            Default: 200 iterations
  NFEV - the number of FUNCT function evaluations performed.
  NFREE - the number of free parameters in the fit. This includes
          parameters which are not FIXED and not TIED, but it does
          include parameters which are pegged at LIMITS.
  NOCOVAR - set this keyword to prevent the calculation of the
            covariance matrix before returning (see COVAR)
  NODERIVATIVE - if set, then the user function will not be queried
                  for analytical derivatives, and instead the
                  derivatives will be computed by finite differences
                  (and according to the PARINFO derivative settings;
                  see above for a description).
  NPRINT - The frequency with which ITERPROC is called. A value of
            1 indicates that ITERPROC is called with every iteration,
            while 2 indicates every other iteration, etc. Note that
            several Levenberg-Marquardt attempts can be made in a
            single iteration.
            Default value: 1
  PARINFO - Provides a mechanism for more sophisticated constraints
            to be placed on parameter values. When PARINFO is not
            passed, then it is assumed that all parameters are free
            and unconstrained. Values in PARINFO are never
            modified during a call to MPFIT.
            See description above for the structure of PARINFO.
            Default value: all parameters are free and unconstrained.
  QUIET - set this keyword when no textual output should be printed
          by MPFIT
  STATUS - an integer status code is returned. All values other
            than zero can represent success. It can have one of the
            following values:
0 improper input parameters.
1 both actual and predicted relative reductions
in the sum of squares are at most FTOL.
2 relative error between two consecutive iterates
is at most XTOL
3 conditions for STATUS = 1 and STATUS = 2 both hold.
4 the cosine of the angle between fvec and any
column of the jacobian is at most GTOL in
absolute value.
5 the maximum number of iterations has been reached
6 FTOL is too small. no further reduction in
the sum of squares is possible.
7 XTOL is too small. no further improvement in
the approximate solution x is possible.
8 GTOL is too small. fvec is orthogonal to the
columns of the jacobian to machine precision.
  TOL - synonym for FTOL. Use FTOL instead.
  XTOL - a nonnegative input variable. Termination occurs when the
          relative error between two consecutive iterates is at most
          XTOL (and STATUS is accordingly set to 2 or 3). Therefore,
          XTOL measures the relative error desired in the approximate
          solution. Default: 1D-10
  YERROR - upon return, the root-mean-square variance of the


  ; First, generate some synthetic data
  npts = 200
  x = dindgen(npts) * 0.1 - 10. ; Independent variable
  yi = gauss1(x, [2.2D, 1.4, 3000.]) ; "Ideal" Y variable
  y = yi + randomn(seed, npts) * sqrt(1000. + yi); Measured, w/ noise
  sy = sqrt(1000.D + y) ; Poisson errors
  ; Now fit a Gaussian to see how well we can recover
  p0 = [1.D, 1., 1000.] ; Initial guess
  yfit = mpcurvefit(x, y, 1/sy^2, p0, $ ; Fit a function
  print, p
  Generates a synthetic data set with a Gaussian peak, and Poisson
  statistical uncertainty. Then the same function is fitted to the
  data to see how close we can get. GAUSS1 and GAUSS1P are
  available from the same web page.

Common Blocks

    User routines may stop the fitting process at any time by
    setting an error condition. This condition may be set in either
    the user's model computation routine (MYFUNCT), or in the
    iteration procedure (ITERPROC).
    To stop the fitting, the above common block must be declared,
    and ERROR_CODE must be set to a negative number. After the user
    procedure or function returns, MPFIT checks the value of this
    common block variable and exits immediately if the error
    condition has been set. By default the value of ERROR_CODE is
    zero, indicating a successful function/procedure call.


  MINPACK-1, Jorge More', available from netlib (www.netlib.org).
  "Optimization Software Guide," Jorge More' and Stephen Wright,
    SIAM, *Frontiers in Applied Mathematics*, Number 14.

Modification History

  Translated from MPFITFUN, 25 Sep 1999, CM
  Alphabetized documented keywords, 02 Oct 1999, CM
  Added QUERY keyword and query checking of MPFIT, 29 Oct 1999, CM
  Check to be sure that X and Y are present, 02 Nov 1999, CM
  Documented SIGMA for unweighted fits, 03 Nov 1999, CM
  Changed to ERROR_CODE for error condition, 28 Jan 2000, CM
  Copying permission terms have been liberalized, 26 Mar 2000, CM
  Propagated improvements from MPFIT, 17 Dec 2000, CM
  Corrected behavior of NODERIVATIVE, 13 May 2002, CM
  Documented RELSTEP field of PARINFO (!!), CM, 25 Oct 2002
  Make more consistent with comparable IDL routines, 30 Jun 2003, CM
  Minor documentation adjustment, 03 Feb 2004, CM
  Fix error in documentation, 26 Aug 2005, CM
  Convert to IDL 5 array syntax (!), 16 Jul 2006, CM
  Move STRICTARR compile option inside each function/procedure, 9 Oct 2006

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