public static void Main()
{
double[] xs = { 1, 2, 3, 4, 6, 9, 10, 13, 15 };
double[] ys = { 117, 100, 88, 72, 53, 29.5, 25.2, 15.2, 11.1 };
vector x = new vector(xs);
vector y = new vector(x.size);
vector dy = new vector(x.size);
for (int i = 0; i < x.size; i++)
{
y[i] = Log(ys[i]);
dy[i] = 0.05;
}
Func <double, double> f0 = (t) => 1;
Func <double, double> f1 = (t) => - t;
Func <double, double>[] fs = { f0, f1 };
ols myfit = new ols(fs, x, y, dy);
vector c = myfit.fit();
vector dc = myfit.fitUncertainty();
Write("==== FIT RESULT ====\n");
Write(" a lambda\n ");
Write($"{Exp(c[0]):f3} +/- {Exp(c[0])*dc[0]:f3} {c[1]:f3} +/- {dc[1]:f3} \n");
Write("====================\n\n");
Write($"This gives life-time of Ra-224: {Log(2)/c[1]:f3} +/- {Log(2)/c[1]/c[1] * dc[1]:f3}\n");
Write("i.e not within todays known value.\n");
//myfit.covMatrix().print("Sigma = ");
} // Main
public static void Main()
{
double[] xs = { 1, 2, 3, 4, 6, 9, 10, 13, 15 };
double[] ys = { 117, 100, 88, 72, 53, 29.5, 25.2, 15.2, 11.1 };
vector x = new vector(xs);
vector y = new vector(x.size);
vector dy = new vector(x.size);
for (int i = 0; i < x.size; i++)
{
y[i] = Log(ys[i]);
dy[i] = 0.05;
}
Func <double, double> f0 = (t) => 1;
Func <double, double> f1 = (t) => - t;
Func <double, double>[] fs = { f0, f1 };
ols myfit = new ols(fs, x, y, dy);
vector c = myfit.fit();
Write("==== FIT RESULT ====\n");
Write(" a lambda\n ");
Write($"{Exp(c[0]):f3} {c[1]:f3}\n");
Write("====================\n\n");
Write($"Found half-life: {Log(2)/c[1]:f3} days\n");
Write($"Known half-life today: 3.66 days\n");
StreamWriter fit = new StreamWriter("fit.txt");
StreamWriter data = new StreamWriter("data.txt");
for (double t = 0.75; t < 16; t += 0.25)
{
fit.Write($"{t, 10:f8} {Exp(c[0])*Exp(-c[1]*t), 15:f16} \n");
}
for (int d = 0; d < x.size; d++)
{
data.Write($"{x[d], 10:f8} {ys[d], 15:f16} {ys[d]*0.05, 15:f16} \n");
}
fit.Close();
data.Close();
} // Main
public static void Main()
{
double[] xs = { 1, 2, 3, 4, 6, 9, 10, 13, 15 };
double[] ys = { 117, 100, 88, 72, 53, 29.5, 25.2, 15.2, 11.1 };
vector x = new vector(xs);
vector y = new vector(x.size);
vector dy = new vector(x.size);
for (int i = 0; i < x.size; i++)
{
y[i] = Log(ys[i]);
dy[i] = 0.05;
}
Func <double, double> f0 = (t) => 1;
Func <double, double> f1 = (t) => - t;
Func <double, double>[] fs = { f0, f1 };
ols myfit = new ols(fs, x, y, dy);
vector c = myfit.fit();
StreamWriter data = new StreamWriter("data.txt");
for (int d = 0; d < x.size; d++)
{
data.Write($"{x[d], 10:f8} {ys[d], 15:f16} {ys[d]*0.05, 15:f16} \n");
}
data.Close();
vector dc = myfit.fitUncertainty();
StreamWriter fitwunc = new StreamWriter("fit.txt");
for (double t = 0.75; t < 16; t += 0.25)
{
fitwunc.Write($"{t, 10:f8}" +
$" {Exp(c[0])*Exp(-c[1]*t), 15:f16}" +
$" {Exp(c[0]+dc[0])*Exp(-(c[1]+dc[1])*t), 15:f16}" +
$" {Exp(c[0]-dc[0])*Exp(-(c[1]-dc[1])*t), 15:f16} \n");
}
fitwunc.Close();
} // Main
