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UTSR/ModS2T.cpp

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+#include <vector>
+#include <complex>
+#include <cmath>
+#include <algorithm>
+#include <cassert>
+#include <fftw3.h>
+
+// Function declaration
+std::vector<std::vector<double>> ModS2T(const std::vector<std::vector<double>>& data, double dt, double du, double ERMv, double tau0, const std::vector<std::vector<double>>& Filter, int R_x = 1) {
+    // Check R_x argument
+    R_x = std::max(1, R_x);
+    assert(R_x > 0 && "R_x must be positive");
+
+    std::vector<std::vector<double>> processedData = data;
+
+    // Applies linear interpolation (if necessary)
+    if (R_x > 1) {
+        // Implementation of linear interpolation
+        // Note: This part needs a more detailed implementation
+    }
+
+    // Zero-padding
+    int nt0 = processedData.size();
+    int nu0 = processedData[0].size();
+    int nt = std::max(static_cast<int>(std::pow(2, std::ceil(std::log2(nt0)) + 1)), static_cast<int>(Filter.size()));
+    int nu = std::max(2 * nu0, static_cast<int>(Filter[0].size()));
+
+    // Data and image grids
+    std::vector<std::vector<double>> f(nt, std::vector<double>(nu));
+    std::vector<std::vector<double>> ku(nt, std::vector<double>(nu));
+    std::vector<std::vector<double>> fkz(nt, std::vector<double>(nu));
+
+    for (int i = 0; i < nt; ++i) {
+        for (int j = 0; j < nu; ++j) {
+            f[i][j] = (i - nt/2) / (dt * nt);
+            ku[i][j] = (j - nu/2) / (du * nu);
+            fkz[i][j] = ERMv * std::copysign(1.0, f[i][j]) * std::sqrt(std::pow(ku[i][j], 2) + std::pow(f[i][j] / ERMv, 2));
+        }
+    }
+
+    // Converting data to frequency domain
+    std::vector<std::vector<std::complex<double>>> ftdata(nt, std::vector<std::complex<double>>(nu));
+    // Note: FFTW library should be used here for efficient FFT computation
+
+    if (tau0 != 0) {
+        for (int i = 0; i < nt; ++i) {
+            for (int j = 0; j < nu; ++j) {
+                ftdata[i][j] *= std::exp(std::complex<double>(0, -2 * M_PI * (fkz[i][j] - f[i][j]) * tau0));
+            }
+        }
+    }
+
+    // Applies filter (if it exists)
+    if (!Filter.empty()) {
+        // Implementation of filter application
+        // Note: This part needs a more detailed implementation
+    }
+
+    // Generating image spectrum (by linear interpolation)
+    std::vector<std::vector<std::complex<double>>> ftimage(nt, std::vector<std::complex<double>>(nu));
+    // Note: Linear interpolation should be implemented here
+
+    // Converting image to space domain
+    std::vector<std::vector<double>> image(nt0, std::vector<double>(nu0));
+    // Note: Inverse FFT should be applied here using FFTW library
+
+    for (int i = 0; i < nt0; ++i) {
+        for (int j = 0; j < nu0; ++j) {
+            image[i][j] *= R_x;
+        }
+    }
+
+    // Evaluates the gain
+    double enImage = 0, enData = 0;
+    for (const auto& row : image) {
+        for (double val : row) {
+            enImage += val * val;
+        }
+    }
+    for (const auto& row : data) {
+        for (double val : row) {
+            enData += val * val;
+        }
+    }
+    double gain = std::sqrt(enImage / enData);
+
+    return image;
+}
+