/*
* double poisson fit function
*
* m - number of data points
* n - number of parameters (2)
* p - array of fit parameters
* dy - array of residuals to be returned
* CustomUserVariable - private data (struct vars_struct *)
*
* RETURNS: error code (0 = success)
*/
public static int DoublePossFunc(double[] p, double[] dy, IList <double>[] dvec, object vars)
{
int i;
CustomUserVariable v = (CustomUserVariable)vars;
double[] x, y, ey;
double f;
x = v.X;
y = v.Y;
ey = v.Ey;
//First I want the tallest point and then
for (i = 0; i < dy.Length; i++)
{
//Value from first poisson Note that the fact will only work for integers
double lam1 = p[0];
double y1 = Math.Pow(lam1, x[i]) * Math.Exp(-1.0 * lam1) / (factEval(x[i]));
double lam2 = p[1];
double y2 = Math.Pow(lam2, x[i]) * Math.Exp(-1.0 * lam2) / (factEval(x[i]));
//Value from second poisson
f = y1 + y2 + p[2];
dy[i] = (y[i] - f) / ey[i];
}
return(0);
}
public static int GaussianFuncAndDerivs(double[] p, double[] dy, IList <double>[] dvec, object vars)
{
CustomUserVariable v = (CustomUserVariable)vars;
double[] x, y, ey;
double sig2;
x = v.X;
y = v.Y;
ey = v.Ey;
sig2 = p[3] * p[3];
//var dvec1 = new double[dy.Length];
//var residuals = new double[dy.Length];
for (int i = 0; i < dy.Length; i++)
{
double xc = x[i] - p[2];
double exp = Math.Exp(-0.5 * xc * xc / sig2);
//residuals[i] = (y[i] - p[1] * exp - p[0]) / ey[i];
dy[i] = (y[i] - p[1] * exp - p[0]) / ey[i];
// NOTE: it would make sense to store the first 2 derivatives in vars since they don't change.
if (dvec != null)
{
if (dvec[0] != null)
{
dvec[0][i] = -1.0 / ey[i];
}
if (dvec[1] != null)
{
//dvec1[i] = -exp / ey[i];
dvec[1][i] = -exp / ey[i];
}
if (dvec[2] != null)
{
dvec[2][i] = -p[1] * xc * exp / (ey[i] * sig2);
}
if (dvec[3] != null)
{
dvec[3][i] = -p[1] * xc * xc * exp / (ey[i] * p[3] * sig2);
}
}
}
// Array assignment rather than element-wise causes failure.
//dy = residuals;
// Array mismatch exception
//if (dvec != null)
//{
// if (dvec[1] != null)
// {
// dvec[1] = dvec1;
// }
//}
return(0);
}
/* Test harness routine, which contains test gaussian-peak data */
public static double[] TestGaussFit(double[] xinc, double[] yinc, double[] p)
{
double[] x = xinc;
double[] y = yinc;
double[] ey = new double[yinc.Length];
//double[] p = { 0.0, 1.0, 1.0, 1.0 }; /* Initial conditions */
double[] pactual = { 0.0, 4.70, 0.0, 0.5 }; /* Actual values used to make data*/
//double[] perror = new double[4]; /* Returned parameter errors */
mp_par[] pars = new mp_par[4] /* Parameter constraints */
{
new mp_par(),
new mp_par(),
new mp_par(),
new mp_par()
};
int i;
int status;
mp_result result = new mp_result(4);
//result.xerror = perror;
/* No constraints */
for (i = 0; i < yinc.Length; i++)
{
ey[i] = 0.02;
}
for (i = 0; i < p.Length; i++)
{
Console.Write("P" + i + " : " + p[i]);
}
CustomUserVariable v = new CustomUserVariable {
X = x, Y = y, Ey = ey
};
/* Call fitting function for 10 data points and 4 parameters (no
* parameters fixed) */
status = MPFit.Solve(ForwardModels.GaussFunc, yinc.Length, 4, p, pars, null, v, ref result);
Console.Write("*** TestGaussFit status = {0}\n", status);
PrintResult(p, pactual, result);
double[] retval = { p[0], p[1], p[2], p[3], result.xerror[0], result.xerror[1], result.xerror[2], result.xerror[3] };
return(retval);
}
/*
* quadratic fit function
*
* m - number of data points
* n - number of parameters (2)
* p - array of fit parameters
* dy - array of residuals to be returned
* CustomUserVariable - private data (struct vars_struct *)
*
* RETURNS: error code (0 = success)
*/
public static int QuadFunc(double[] p, double[] dy, IList <double>[] dvec, object vars)
{
int i;
double[] x, y, ey;
CustomUserVariable v = (CustomUserVariable)vars;
x = v.X;
y = v.Y;
ey = v.Ey;
/* Console.Write ("QuadFunc %f %f %f\n", p[0], p[1], p[2]); */
for (i = 0; i < dy.Length; i++)
{
dy[i] = (y[i] - p[0] - p[1] * x[i] - p[2] * x[i] * x[i]) / ey[i];
}
return(0);
}
/*
* linear fit function
*
* m - number of data points
* n - number of parameters (2)
* p - array of fit parameters
* dy - array of residuals to be returned
* CustomUserVariable - private data (struct vars_struct *)
*
* RETURNS: error code (0 = success)
*/
public static int LinFunc(double[] p, double[] dy, IList <double>[] dvec, object vars)
{
int i;
double[] x, y, ey;
double f;
CustomUserVariable v = (CustomUserVariable)vars;
x = v.X;
y = v.Y;
ey = v.Ey;
for (i = 0; i < dy.Length; i++)
{
f = p[0] + p[1] * x[i]; /* Linear fit function */
dy[i] = (y[i] - f) / ey[i];
}
return(0);
}
/*
* gaussian fit function
*
* m - number of data points
* n - number of parameters (4)
* p - array of fit parameters
* dy - array of residuals to be returned
* CustomUserVariable - private data (struct vars_struct *)
*
* RETURNS: error code (0 = success)
*/
public static int GaussFunc(double[] p, double[] dy, IList <double>[] dvec, object vars)
{
int i;
CustomUserVariable v = (CustomUserVariable)vars;
double[] x, y, ey;
double xc, sig2;
x = v.X;
y = v.Y;
ey = v.Ey;
sig2 = p[3] * p[3];
for (i = 0; i < dy.Length; i++)
{
xc = x[i] - p[2];
dy[i] = (y[i] - p[1] * Math.Exp(-0.5 * xc * xc / sig2) - p[0]) / ey[i];
}
return(0);
}
