public void ShouldDecreaseSigma()
{
var slopes = new double[]
{
Photometry2.SigmaSlopeFromDerivative(381.640843293, 1, 6, 3.0, 1500),
Photometry2.SigmaSlopeFromDerivative(381.640843293, 1, 6, 3.022, 1500),
Photometry2.SigmaSlopeFromDerivative(381.640843293, 1, 6, 3.2, 1500)
};
var residuals = new double[]
{
Photometry2.GetResidualFromEstimate(381.640843293, 1, 6, 3.022, 1500)
};
}
public static double[] GradientTowardsMinimalErrorForGaussian(double peak, double sigma, ushort[] samples, double center, GaussianFitOptions2 options)
{
var vec = new double[2];
var used = 0;
for (var x = 0; x < samples.Length; x++)
{
var distance = (x - center);
if (distance <= 5)
{
continue;
}
var distanceSquared = distance * distance;
var sample = samples[x];
var sChange = Photometry2.SigmaSlopeFromDerivative((double)sample, (double)x, center, sigma, peak);
var pChange = Photometry2.PeakSlopeFromDerivative((double)sample, (double)x, center, sigma, peak);
if (double.IsPositiveInfinity(sChange) || double.IsNegativeInfinity(sChange))
{
throw new ArgumentException();
}
if (double.IsPositiveInfinity(pChange) || double.IsNegativeInfinity(pChange))
{
throw new ArgumentException();
}
vec[0] += (1 * sChange);
vec[1] += (1 * pChange);
used++;
}
vec[0] /= used;
vec[1] /= used;
return(vec);
}
