/** Rebunches histogram data data according to n_bunch input
*
* @param xold :: old x data
* @param yold :: old y data
* @param eold :: old e data
* @param xnew :: new x data
* @param ynew :: new y data
* @param enew :: new e data
* @param n_bunch :: number of data points to bunch together for each new point
* @throw runtime_error Thrown if algorithm cannot execute
* @throw invalid_argument Thrown if input to function is incorrect
**/
void Rebunch::rebunch_hist_frequencies(const HistogramX &xold,
const HistogramY &yold,
const HistogramE &eold, HistogramX &xnew,
HistogramY &ynew, HistogramE &enew,
const size_t n_bunch) {
double width;
size_t size_x = xold.size();
size_t size_y = yold.size();
double ysum, esum;
size_t hi_index = size_x - 1;
size_t wbins = size_y / n_bunch;
size_t rem = size_y % n_bunch;
size_t i, j;
int i_in = 0;
j = 0;
while (j < wbins) {
ysum = 0.0;
esum = 0.0;
for (i = 1; i <= n_bunch; i++) {
width = xold[i_in + 1] - xold[i_in];
ysum += yold[i_in] * width;
esum += eold[i_in] * eold[i_in] * width * width;
i_in++;
}
// average contributing x values
ynew[j] = ysum;
enew[j] = sqrt(esum);
j++;
}
if (rem != 0) {
ysum = 0.0;
esum = 0.0;
for (i = 1; i <= rem; i++) {
width = xold[i_in + 1] - xold[i_in];
ysum += yold[i_in] * width;
esum += eold[i_in] * eold[i_in] * width * width;
i_in++;
}
ynew[j] = ysum;
enew[j] = sqrt(esum);
}
j = 0;
xnew[j] = xold[0];
j++;
for (i = n_bunch; i < hi_index; i += n_bunch) {
xnew[j] = xold[i];
j++;
}
xnew[j] = xold[hi_index];
for (i = 0; i < ynew.size(); i++) {
width = xnew[i + 1] - xnew[i];
ynew[i] = ynew[i] / width;
enew[i] = enew[i] / width;
}
}
void CalculateCarpenterSampleCorrection::calculate_ms_correction(
const double angle_deg, const double radius, const double coeff1,
const double coeff2, const double coeff3, const Points &wavelength,
HistogramY &y_val) {
const size_t NUM_Y = y_val.size();
bool is_histogram = false;
if (wavelength.size() == NUM_Y + 1)
is_histogram = true;
else if (wavelength.size() == NUM_Y)
is_histogram = false;
else
throw std::runtime_error("Data is neither historgram or density");
// initialize Z array for this angle
vector<double> Z = createZ(angle_deg);
const double Q2 = coeff1 * coeff2;
const double sigsct = coeff2 * coeff3;
for (size_t j = 0; j < NUM_Y; j++) {
double wl_val = wavelength[j];
if (is_histogram) // average with next value
wl_val = .5 * (wl_val + wavelength[j + 1]);
y_val[j] = calculate_ms_factor(radius, Q2, sigsct, Z, wl_val);
}
}
/** Rebunches point data data according to n_bunch input
*
* @param xold :: old x data
* @param yold :: old y data
* @param eold :: old e data
* @param xnew :: new x data
* @param ynew :: new y data
* @param enew :: new e data
* @param n_bunch :: number of data points to bunch together for each new point
* @throw runtime_error Thrown if algorithm cannot execute
* @throw invalid_argument Thrown if input to function is incorrect
**/
void Rebunch::rebunch_point(const HistogramX &xold, const HistogramY &yold,
const HistogramE &eold, HistogramX &xnew,
HistogramY &ynew, HistogramE &enew,
const size_t n_bunch) {
size_t size_y = yold.size();
double xsum, ysum, esum;
size_t wbins = size_y / n_bunch;
size_t rem = size_y % n_bunch;
size_t i, j;
int i_in = 0;
j = 0;
while (j < wbins) {
xsum = 0.0;
ysum = 0.0;
esum = 0.0;
for (i = 1; i <= n_bunch; i++) {
xsum += xold[i_in];
ysum += yold[i_in];
esum += eold[i_in] * eold[i_in];
i_in++;
}
// average contributing x values
xnew[j] = xsum / static_cast<double>(n_bunch);
ynew[j] = ysum / static_cast<double>(n_bunch);
enew[j] = sqrt(esum) / static_cast<double>(n_bunch);
j++;
}
if (rem != 0) {
xsum = 0.0;
ysum = 0.0;
esum = 0.0;
for (i = 1; i <= rem; i++) {
xsum += xold[i_in];
ysum += yold[i_in];
esum += eold[i_in] * eold[i_in];
i_in++;
}
xnew[j] = xsum / static_cast<double>(rem);
ynew[j] = ysum / static_cast<double>(rem);
enew[j] = sqrt(esum) / static_cast<double>(rem);
}
}
/** A Fourier transform of a derivative of order `n` has a factor of `i^n` where
* `i` is the imaginary unit. This code multiplies the Fourier transform of the
* input function `(re[j], im[j])` by `(2*pi*nu)^n` without using
* `std::complex`.
* @param nu :: complete real X of input histogram
* @param &re :: complete real Y of input histogram
* @param &im :: complete imaginary Y of input histogram
*/
void FFTDerivative::multiplyTransform(HistogramX &nu, HistogramY &re,
HistogramY &im) {
int dn = getProperty("Order");
bool swap_re_im = dn % 2 != 0;
int sign_re = 1;
int sign_im = -1;
switch (dn % 4) {
case 1:
sign_re = 1;
sign_im = -1;
break;
case 2:
sign_re = -1;
sign_im = -1;
break;
case 3:
sign_re = -1;
sign_im = 1;
break;
}
// Multiply the transform by (2*pi*i*w)**dn
for (size_t j = 0; j < re.size(); ++j) {
double w = 2 * M_PI * nu[j];
double ww = w;
for (int k = dn; k > 1; --k) {
ww *= w;
}
double a = sign_re * re[j] * ww;
double b = sign_im * im[j] * ww;
if (swap_re_im) {
re[j] = b;
im[j] = a;
} else {
re[j] = a;
im[j] = b;
}
}
}
