/* Test harness routine, which contains test gaussian-peak data
*
* Example of fixing two parameter
*
* Commented example of how to put boundary constraints
*/
private static int TestGaussFix()
{
double[] x = { -1.7237128E+00, 1.8712276E+00, -9.6608055E-01,
-2.8394297E-01, 1.3416969E+00, 1.3757038E+00,
-1.3703436E+00, 4.2581975E-02, -1.4970151E-01,
8.2065094E-01 };
double[] y = { -4.4494256E-02, 8.7324673E-01, 7.4443483E-01,
4.7631559E+00, 1.7187297E-01, 1.1639182E-01,
1.5646480E+00, 5.2322268E+00, 4.2543168E+00,
6.2792623E-01 };
double[] ey = new double[10];
double[] p = { 0.0, 1.0, 0.0, 0.1 }; /* 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 */
int i;
int status;
mp_result result = new mp_result(4);
//result.xerror = perror;
mp_par[] pars = new mp_par[4]/* Parameter constraints */
{
new mp_par {
isFixed = 1
}, /* Fix parameters 0 and 2 */
new mp_par(),
new mp_par {
isFixed = 1
},
new mp_par()
};
/* How to put limits on a parameter. In this case, parameter 3 is
* limited to be between -0.3 and +0.2.
* pars[3].limited[0] = 0;
* pars[3].limited[1] = 1;
* pars[3].limits[0] = -0.3;
* pars[3].limits[1] = +0.2;
*/
for (i = 0; i < 10; i++)
{
ey[i] = 0.5;
}
CustomUserVariable v = new CustomUserVariable {
X = x, Y = y, Ey = ey
};
/* Call fitting function for 10 data points and 4 parameters (2
* parameters fixed) */
status = MPFit.Solve(ForwardModels.GaussFunc, 10, 4, p, pars, null, v, ref result);
Console.Write("*** TestGaussFix status = {0}\n", status);
PrintResult(p, pactual, result);
return(0);
}
private static void TestGaussianWithDerivs()
{
double[] x =
{
-1.7237128E+00, 1.8712276E+00, -9.6608055E-01,
-2.8394297E-01, 1.3416969E+00, 1.3757038E+00,
-1.3703436E+00, 4.2581975E-02, -1.4970151E-01,
8.2065094E-01
};
double[] y =
{
-4.4494256E-02, 8.7324673E-01, 7.4443483E-01,
4.7631559E+00, 1.7187297E-01, 1.1639182E-01,
1.5646480E+00, 5.2322268E+00, 4.2543168E+00,
6.2792623E-01
};
double[] ey = new double[10];
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
mp_par[] pars =
{
new mp_par {
side = 3, deriv_debug = 0
},
new mp_par {
side = 1, deriv_debug = 0
},
new mp_par {
side = 3, deriv_debug = 0
},
new mp_par {
side = 1, deriv_debug = 0
}
};
mp_result result = new mp_result(4);
/* No constraints */
for (uint i = 0; i < 10; i++)
{
ey[i] = 0.5;
}
CustomUserVariable v = new CustomUserVariable {
X = x, Y = y, Ey = ey
};
/* Call fitting function for 10 data points and 4 parameters (no
* parameters fixed) */
var logger = new System.IO.StringWriter();
int status = MPFit.Solve(ForwardModels.GaussianFuncAndDerivs, 10, 4, p, pars, null, v, ref result, logger);
Console.Write("*** TestGaussFitWithDerivs status = {0}\n", status);
PrintResult(p, pactual, result);
Console.WriteLine(logger.ToString());
}
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 quadratic data;
* Example of how to fix a parameter
*/
private static int TestQuadFix()
{
double[] x = { -1.7237128E+00, 1.8712276E+00, -9.6608055E-01,
-2.8394297E-01, 1.3416969E+00, 1.3757038E+00,
-1.3703436E+00, 4.2581975E-02, -1.4970151E-01,
8.2065094E-01 };
double[] y = { 2.3095947E+01, 2.6449392E+01, 1.0204468E+01,
5.40507, 1.5787588E+01, 1.6520903E+01,
1.5971818E+01, 4.7668524E+00, 4.9337711E+00,
8.7348375E+00 };
double[] ey = new double[10];
double[] p = { 1.0, 0.0, 1.0 }; /* Initial conditions */
double[] pactual = { 4.7, 0.0, 6.2 }; /* Actual values used to make data */
//double[] perror = new double[3]; /* Returned parameter errors */
int i;
int status;
mp_result result = new mp_result(3);
//result.xerror = perror;
mp_par[] pars = new mp_par[3] /* Parameter constraints */
{
new mp_par(),
new mp_par()
{
isFixed = 1
}, /* Fix parameter 1 */
new mp_par()
};
for (i = 0; i < 10; i++)
{
ey[i] = 0.2;
}
CustomUserVariable v = new CustomUserVariable()
{
X = x, Y = y, Ey = ey
};
/* Call fitting function for 10 data points and 3 parameters (1
* parameter fixed) */
status = MPFit.Solve(ForwardModels.QuadFunc, 10, 3, p, pars, null, v, ref result);
Console.Write("*** TestQuadFix status = {0}\n", status);
PrintResult(p, pactual, result);
return(0);
}
/* Test harness routine, which contains test data, invokes mpfit() */
private static int TestLinFit()
{
double[] x = { -1.7237128E+00, 1.8712276E+00, -9.6608055E-01,
-2.8394297E-01, 1.3416969E+00, 1.3757038E+00,
-1.3703436E+00, 4.2581975E-02, -1.4970151E-01,
8.2065094E-01 };
double[] y = { 1.9000429E-01, 6.5807428E+00, 1.4582725E+00,
2.7270851E+00, 5.5969253E+00, 5.6249280E+00,
0.787615, 3.2599759E+00, 2.9771762E+00,
4.5936475E+00 };
double[] ey = new double[10];
/* y = a + b * x */
/* a b */
double[] p = { 1.0, 1.0 }; /* Parameter initial conditions */
double[] pactual = { 3.20, 1.78 }; /* Actual values used to make data */
//double[] perror = { 0.0, 0.0 }; /* Returned parameter errors */
int i;
int status;
mp_result result = new mp_result(2);
//result.xerror = perror;
for (i = 0; i < 10; i++)
{
ey[i] = 0.07; /* Data errors */
}
CustomUserVariable v = new CustomUserVariable();
v.X = x;
v.Y = y;
v.Ey = ey;
/* Call fitting function for 10 data points and 2 parameters */
status = MPFit.Solve(ForwardModels.LinFunc, 10, 2, p, null, null, v, ref result);
Console.Write("*** TestLinFit status = {0}\n", status);
PrintResult(p, pactual, result);
return(0);
}
/*
* 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; note f = a + b*x */
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
* p[0] = constant offset
* p[1] = peak y value
* p[2] = x centroid position
* p[3] = gaussian sigma width
* 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);
}
