diff --git a/examples/ABC_EQF/runEQF_withcsv.h b/examples/ABC_EQF/runEQF_withcsv.h
new file mode 100644
index 000000000..d31baffe0
--- /dev/null
+++ b/examples/ABC_EQF/runEQF_withcsv.h
@@ -0,0 +1,683 @@
+//
+// Created by darshan on 3/17/25.
+//
+
+#ifndef RUNEQF_WITHCSV_H
+#define RUNEQF_WITHCSV_H
+
+//
+// Created by darshan on 3/17/25.
+//
+#include "Data.h"
+#include "State.h"
+#include "Input.h"
+#include "Direction.h"
+#include "Measurements.h"
+#include "utilities.h"
+#include <gtsam/geometry/Rot3.h>
+#include <gtsam/geometry/Quaternion.h>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <vector>
+#include <stdexcept>
+#include <iostream>
+#include <cmath>
+#include <chrono>
+
+/**
+ * Load data from CSV file into a vector of Data objects for the EqF
+ *
+ * CSV format:
+ * - t: Time
+ * - q_w, q_x, q_y, q_z: True attitude quaternion
+ * - b_x, b_y, b_z: True bias
+ * - cq_w_0, cq_x_0, cq_y_0, cq_z_0: True calibration quaternion
+ * - w_x, w_y, w_z: Angular velocity measurements
+ * - std_w_x, std_w_y, std_w_z: Angular velocity measurement standard deviations
+ * - std_b_x, std_b_y, std_b_z: Bias process noise standard deviations
+ * - y_x_0, y_y_0, y_z_0, y_x_1, y_y_1, y_z_1: Direction measurements
+ * - std_y_x_0, std_y_y_0, std_y_z_0, std_y_x_1, std_y_y_1, std_y_z_1: Direction measurement standard deviations
+ * - d_x_0, d_y_0, d_z_0, d_x_1, d_y_1, d_z_1: Reference directions
+ *
+ * @param filename Path to the CSV file
+ * @param startRow First row to load (default: 0)
+ * @param maxRows Maximum number of rows to load (default: all)
+ * @param downsample Downsample factor (default: 1, which means no downsampling)
+ * @return Vector of Data objects
+ */
+inline std::vector<Data> loadDataFromCSV(const std::string& filename,
+                                 int startRow = 0,
+                                 int maxRows = -1,
+                                 int downsample = 1) {
+    std::vector<Data> data_list;
+    std::ifstream file(filename);
+
+    if (!file.is_open()) {
+        throw std::runtime_error("Failed to open file: " + filename);
+    }
+
+    std::string line;
+    int lineNumber = 0;
+    int rowCount = 0;
+    double prevTime = 0.0;
+
+    // Skip header
+    std::getline(file, line);
+    lineNumber++;
+
+    // Skip to startRow
+    while (lineNumber < startRow && std::getline(file, line)) {
+        lineNumber++;
+    }
+
+    // Read data
+    while (std::getline(file, line) && (maxRows == -1 || rowCount < maxRows)) {
+        lineNumber++;
+
+        // Apply downsampling
+        if ((lineNumber - startRow - 1) % downsample != 0) {
+            continue;
+        }
+
+        std::istringstream ss(line);
+        std::string token;
+        std::vector<double> values;
+
+        // Parse line into values
+        while (std::getline(ss, token, ',')) {
+            try {
+                values.push_back(std::stod(token));
+            } catch (const std::exception& e) {
+                std::cerr << "Error parsing value at line " << lineNumber << ": " << token << std::endl;
+                values.push_back(0.0); // Use default value
+            }
+        }
+
+        // Check if we have enough values
+        if (values.size() < 39) {
+            std::cerr << "Warning: Line " << lineNumber << " has only " << values.size()
+                      << " values, expected 39. Skipping." << std::endl;
+            continue;
+        }
+
+        // Extract values
+        double t = values[0];
+        double dt = (rowCount == 0) ? 0.0 : t - prevTime;
+        prevTime = t;
+
+        // Create ground truth state
+        Quaternion quat(values[1], values[2], values[3], values[4]); // w, x, y, z
+        Rot3 R = Rot3(quat);
+
+        Vector3 b(values[5], values[6], values[7]);
+
+        Quaternion calQuat(values[8], values[9], values[10], values[11]); // w, x, y, z
+        std::vector<Rot3> S = {Rot3(calQuat)};
+
+        State xi(R, b, S);
+
+        // Create input
+        Vector3 w(values[12], values[13], values[14]);
+
+        // Create input covariance matrix (6x6)
+        // First 3x3 block for angular velocity, second 3x3 block for bias process noise
+        Matrix inputCov = Matrix::Zero(6, 6);
+        inputCov(0, 0) = values[15] * values[15]; // std_w_x^2
+        inputCov(1, 1) = values[16] * values[16]; // std_w_y^2
+        inputCov(2, 2) = values[17] * values[17]; // std_w_z^2
+        inputCov(3, 3) = values[18] * values[18]; // std_b_x^2
+        inputCov(4, 4) = values[19] * values[19]; // std_b_y^2
+        inputCov(5, 5) = values[20] * values[20]; // std_b_z^2
+
+        Input u(w, inputCov);
+
+        // Create measurements
+        std::vector<Measurement> measurements;
+
+        // First measurement (calibrated sensor, cal_idx = 0)
+        Vector3 y0(values[21], values[22], values[23]);
+        Vector3 d0(values[33], values[34], values[35]);
+
+        // Normalize vectors if needed
+        if (abs(y0.norm() - 1.0) > 1e-5) y0.normalize();
+        if (abs(d0.norm() - 1.0) > 1e-5) d0.normalize();
+
+        // Measurement covariance
+        Matrix3 covY0 = Matrix3::Zero();
+        covY0(0, 0) = values[27] * values[27]; // std_y_x_0^2
+        covY0(1, 1) = values[28] * values[28]; // std_y_y_0^2
+        covY0(2, 2) = values[29] * values[29]; // std_y_z_0^2
+
+        // Create measurement
+        measurements.push_back(Measurement(y0, d0, covY0, 0));
+
+        // Second measurement (calibrated sensor, cal_idx = -1)
+        Vector3 y1(values[24], values[25], values[26]);
+        Vector3 d1(values[36], values[37], values[38]);
+
+        // Normalize vectors if needed
+        if (abs(y1.norm() - 1.0) > 1e-5) y1.normalize();
+        if (abs(d1.norm() - 1.0) > 1e-5) d1.normalize();
+
+        // Measurement covariance
+        Matrix3 covY1 = Matrix3::Zero();
+        covY1(0, 0) = values[30] * values[30]; // std_y_x_1^2
+        covY1(1, 1) = values[31] * values[31]; // std_y_y_1^2
+        covY1(2, 2) = values[32] * values[32]; // std_y_z_1^2
+
+        // Create measurement
+        measurements.push_back(Measurement(y1, d1, covY1, -1));
+
+        // Create Data object and add to list
+        data_list.push_back(Data(xi, 1, u, measurements, 2, t, dt));
+
+        rowCount++;
+    }
+
+    std::cout << "Loaded " << data_list.size() << " data points from CSV file." << std::endl;
+
+    return data_list;
+}
+
+/**
+ * Process Data objects with the EqF filter
+ *
+ * @param filter EqF filter to use
+ * @param data_list Vector of Data objects
+ * @param saveResults Whether to save results to a file
+ * @param resultFilename Filename to save results to
+ */
+inline void printDataPoint(const Data& data, int index) {
+    std::cout << "Data[" << index << "] @ t=" << data.t << ", dt=" << data.dt << std::endl;
+
+    // Print angular velocity
+    std::cout << "  ω = [" << data.u.w[0] << ", " << data.u.w[1] << ", " << data.u.w[2] << "]" << std::endl;
+
+    // Print measurements
+    for (size_t i = 0; i < data.y.size(); i++) {
+        const Measurement& meas = data.y[i];
+        // Use the unitVector() method to get a Vector3 from a Unit3 object
+        Vector3 y_vec = meas.y.d.unitVector();
+        Vector3 d_vec = meas.d.d.unitVector();
+        std::cout << "  y" << i << " = [" << y_vec[0] << ", " << y_vec[1] << ", " << y_vec[2] << "]" << std::endl;
+        std::cout << "  d" << i << " = [" << d_vec[0] << ", " << d_vec[1] << ", " << d_vec[2] << "]" << std::endl;
+    }
+
+    std::cout << std::endl;
+}
+   
+
+// Function to print sample data points
+inline void printDataSamples(const std::vector<Data>& data_list, int count = 3) {
+    int total = data_list.size();
+
+    std::cout << "\n=== First " << count << " Data Points ===" << std::endl;
+    for (int i = 0; i < std::min(count, total); i++) {
+        printDataPoint(data_list[i], i);
+    }
+
+    if (total > 2*count) {
+        std::cout << "\n... (" << (total - 2*count) << " points omitted) ...\n" << std::endl;
+
+        std::cout << "=== Last " << count << " Data Points ===" << std::endl;
+        for (int i = std::max(count, total - count); i < total; i++) {
+            printDataPoint(data_list[i], i);
+        }
+    }
+}
+
+// Function to validate data
+inline bool validateData(const std::vector<Data>& data_list) {
+    if (data_list.empty()) {
+        std::cerr << "ERROR: No data loaded from CSV" << std::endl;
+        return false;
+    }
+
+    std::cout << "Validating " << data_list.size() << " data points..." << std::endl;
+
+    // Track statistics
+    int invalid_count = 0;
+
+    // Open a log file to record detailed issues
+    std::ofstream logFile("data_validation.log");
+    logFile << "Data Validation Report" << std::endl;
+    logFile << "--------------------" << std::endl;
+
+    for (size_t i = 0; i < data_list.size(); ++i) {
+        const Data& data = data_list[i];
+        bool point_valid = true;
+
+        // Check time and dt
+        if (std::isnan(data.t) || std::isnan(data.dt)) {
+            logFile << "Point " << i << ": Invalid time values (t=" << data.t
+                   << ", dt=" << data.dt << ")" << std::endl;
+            point_valid = false;
+        }
+
+        // Check ground truth state for NaN - using isnan directly on components
+        const auto& R_matrix = data.xi.R.matrix();
+        bool R_has_nan = false;
+        for (int r = 0; r < 3; r++) {
+            for (int c = 0; c < 3; c++) {
+                if (std::isnan(R_matrix(r, c))) {
+                    R_has_nan = true;
+                    break;
+                }
+            }
+        }
+
+        if (R_has_nan) {
+            logFile << "Point " << i << ": NaN in ground truth attitude matrix" << std::endl;
+            point_valid = false;
+        }
+
+        // Check bias vector for NaN
+        bool b_has_nan = false;
+        for (int j = 0; j < 3; j++) {
+            if (std::isnan(data.xi.b[j])) {
+                b_has_nan = true;
+                break;
+            }
+        }
+
+        if (b_has_nan) {
+            logFile << "Point " << i << ": NaN in ground truth bias vector" << std::endl;
+            point_valid = false;
+        }
+
+        // Check calibration matrices for NaN
+        for (size_t j = 0; j < data.xi.S.size(); ++j) {
+            const auto& S_matrix = data.xi.S[j].matrix();
+            bool S_has_nan = false;
+            for (int r = 0; r < 3; r++) {
+                for (int c = 0; c < 3; c++) {
+                    if (std::isnan(S_matrix(r, c))) {
+                        S_has_nan = true;
+                        break;
+                    }
+                }
+            }
+
+            if (S_has_nan) {
+                logFile << "Point " << i << ": NaN in ground truth calibration matrix "
+                       << j << std::endl;
+                point_valid = false;
+            }
+        }
+
+        // Check input for NaN
+        bool w_has_nan = false;
+        for (int j = 0; j < 3; j++) {
+            if (std::isnan(data.u.w[j])) {
+                w_has_nan = true;
+                break;
+            }
+        }
+
+        if (w_has_nan) {
+            logFile << "Point " << i << ": NaN in angular velocity" << std::endl;
+            point_valid = false;
+        }
+
+        // Check measurements
+        for (size_t j = 0; j < data.y.size(); ++j) {
+            const Measurement& meas = data.y[j];
+
+            // Get the Vector3 representations to check them
+            Vector3 y_vec = meas.y.d.unitVector();
+            Vector3 d_vec = meas.d.d.unitVector();
+
+            // Check measurement vector for NaN
+            bool y_has_nan = false;
+            bool d_has_nan = false;
+
+            for (int k = 0; k < 3; k++) {
+                if (std::isnan(y_vec[k])) {
+                    y_has_nan = true;
+                    break;
+                }
+                if (std::isnan(d_vec[k])) {
+                    d_has_nan = true;
+                    break;
+                }
+            }
+
+            if (y_has_nan) {
+                logFile << "Point " << i << ", Meas " << j << ": NaN in measurement vector" << std::endl;
+                point_valid = false;
+            }
+
+            if (d_has_nan) {
+                logFile << "Point " << i << ", Meas " << j << ": NaN in reference direction" << std::endl;
+                point_valid = false;
+            }
+
+            // Calculate norm using Vector3 norms
+            double y_norm = y_vec.norm();
+            double d_norm = d_vec.norm();
+
+            if (std::abs(y_norm - 1.0) > 1e-5) {
+                logFile << "Point " << i << ", Meas " << j
+                       << ": Measurement vector not normalized. Norm = " << y_norm << std::endl;
+                point_valid = false;
+            }
+
+            if (std::abs(d_norm - 1.0) > 1e-5) {
+                logFile << "Point " << i << ", Meas " << j
+                       << ": Reference direction not normalized. Norm = " << d_norm << std::endl;
+                point_valid = false;
+            }
+        }
+
+        if (!point_valid) {
+            invalid_count++;
+
+            // Print first few invalid points to console
+            if (invalid_count <= 5) {
+                std::cerr << "Invalid data at point " << i << " (t=" << data.t << ")" << std::endl;
+            }
+        }
+    }
+
+    // Close the log
+    logFile << std::endl << "Summary: " << invalid_count << " invalid data points out of "
+           << data_list.size() << std::endl;
+    logFile.close();
+
+
+
+
+
+
+
+
+
+    // Print summary
+    std::cout << "Data validation complete. " << invalid_count << " invalid points found." << std::endl;
+    if (invalid_count > 0) {
+        std::cout << "See data_validation.log for details." << std::endl;
+    }
+
+    return (invalid_count == 0);
+}
+
+inline void processDataWithEqF(EqF& filter,
+                       const std::vector<Data>& data_list,
+                       bool saveResults = false,
+                       const std::string& resultFilename = "eqf_results.csv") {
+    std::ofstream resultFile;
+    if (saveResults) {
+        resultFile.open(resultFilename);
+        if (!resultFile.is_open()) {
+            throw std::runtime_error("Failed to open result file: " + resultFilename);
+        }
+
+        // Write header - now adding roll, pitch, yaw columns for estimated and true values
+        resultFile << "t,";
+        // Estimated state quaternion
+        resultFile << "est_qw,est_qx,est_qy,est_qz,";
+        // Estimated bias
+        resultFile << "est_bx,est_by,est_bz,";
+        // Estimated calibration quaternion
+        resultFile << "est_cqw,est_cqx,est_cqy,est_cqz,";
+        // True state quaternion
+        resultFile << "true_qw,true_qx,true_qy,true_qz,";
+        // True bias
+        resultFile << "true_bx,true_by,true_bz,";
+        // True calibration quaternion
+        resultFile << "true_cqw,true_cqx,true_cqy,true_cqz,";
+        // Add Euler angles for estimated state
+        resultFile << "est_roll,est_pitch,est_yaw,";
+        // Add Euler angles for true state
+        resultFile << "true_roll,true_pitch,true_yaw,";
+        // Add Euler angles for estimated calibration
+        resultFile << "est_cal_roll,est_cal_pitch,est_cal_yaw,";
+        // Add Euler angles for true calibration
+        resultFile << "true_cal_roll,true_cal_pitch,true_cal_yaw";
+        resultFile << std::endl;
+    }
+
+    std::cout << "Processing data with EqF..." << std::endl;
+
+    // Track time for performance measurement
+    auto start = std::chrono::high_resolution_clock::now();
+
+    // Store error metrics
+    std::vector<double> att_errors;
+    std::vector<double> bias_errors;
+    std::vector<double> cal_errors;
+
+    int total_measurements = 0;
+    int valid_measurements = 0;
+    int invalid_measurements = 0;
+
+    for (size_t i = 0; i < data_list.size(); i++) {
+        const Data& data = data_list[i];
+
+        // Propagation
+        filter.propagation(data.u, data.dt);
+
+        // Update with measurements
+        for (const auto& y : data.y) {
+            total_measurements++;
+
+            // Check for NaN values in measurement vectors
+            bool has_nan = false;
+            Vector3 y_vec = y.y.d.unitVector();
+            Vector3 d_vec = y.d.d.unitVector();
+
+            for (int j = 0; j < 3; j++) {
+                if (std::isnan(y_vec[j]) || std::isnan(d_vec[j])) {
+                    has_nan = true;
+                    break;
+                }
+            }
+
+            if (!has_nan) {
+                try {
+                    filter.update(y);
+                    valid_measurements++;
+                } catch (const std::exception& e) {
+                    std::cerr << "Error updating at t=" << data.t << ": " << e.what() << std::endl;
+                    invalid_measurements++;
+                }
+            } else {
+                invalid_measurements++;
+            }
+        }
+
+        // Get state estimate
+        State estimate = filter.stateEstimate();
+
+        // Compute errors
+        Vector3 att_error = Rot3::Logmap(data.xi.R.between(estimate.R));
+        Vector3 bias_error = estimate.b - data.xi.b;
+        Vector3 cal_error = Vector3::Zero();
+        if (!data.xi.S.empty() && !estimate.S.empty()) {
+            cal_error = Rot3::Logmap(data.xi.S[0].between(estimate.S[0]));
+        }
+
+        // Store errors
+        att_errors.push_back(att_error.norm());
+        bias_errors.push_back(bias_error.norm());
+        cal_errors.push_back(cal_error.norm());
+
+        // Save results
+        if (saveResults) {
+            // Extract quaternions
+            Quaternion est_q = estimate.R.toQuaternion();
+            Quaternion true_q = data.xi.R.toQuaternion();
+
+            // Extract Euler angles (roll, pitch, yaw) from estimated rotation
+            Vector3 est_rpy = estimate.R.rpy();
+            // Convert to degrees for easier comparison
+            Vector3 est_rpy_deg = est_rpy * 180.0 / M_PI;
+
+            // Extract Euler angles from true rotation
+            Vector3 true_rpy = data.xi.R.rpy();
+            // Convert to degrees
+            Vector3 true_rpy_deg = true_rpy * 180.0 / M_PI;
+
+            // Get calibration quaternions and Euler angles
+            Quaternion est_cal_q, true_cal_q;
+            Vector3 est_cal_rpy_deg = Vector3::Zero();
+            Vector3 true_cal_rpy_deg = Vector3::Zero();
+
+            if (!estimate.S.empty() && !data.xi.S.empty()) {
+                est_cal_q = estimate.S[0].toQuaternion();
+                true_cal_q = data.xi.S[0].toQuaternion();
+
+                // Get Euler angles for calibrations
+                Vector3 est_cal_rpy = estimate.S[0].rpy();
+                est_cal_rpy_deg = est_cal_rpy * 180.0 / M_PI;
+
+                Vector3 true_cal_rpy = data.xi.S[0].rpy();
+                true_cal_rpy_deg = true_cal_rpy * 180.0 / M_PI;
+            } else {
+                est_cal_q = Quaternion(1, 0, 0, 0);  // Identity quaternion
+                true_cal_q = Quaternion(1, 0, 0, 0);
+            }
+
+            // Write to file
+            resultFile << data.t << ",";
+            // Estimated quaternion
+            resultFile << est_q.w() << "," << est_q.x() << "," << est_q.y() << "," << est_q.z() << ",";
+            // Estimated bias
+            resultFile << estimate.b[0] << "," << estimate.b[1] << "," << estimate.b[2] << ",";
+            // Estimated calibration quaternion
+            resultFile << est_cal_q.w() << "," << est_cal_q.x() << "," << est_cal_q.y() << "," << est_cal_q.z() << ",";
+            // True quaternion
+            resultFile << true_q.w() << "," << true_q.x() << "," << true_q.y() << "," << true_q.z() << ",";
+            // True bias
+            resultFile << data.xi.b[0] << "," << data.xi.b[1] << "," << data.xi.b[2] << ",";
+            // True calibration quaternion
+            resultFile << true_cal_q.w() << "," << true_cal_q.x() << "," << true_cal_q.y() << "," << true_cal_q.z() << ",";
+
+            // Add Euler angles (in degrees) for estimated state
+            resultFile << est_rpy_deg[0] << "," << est_rpy_deg[1] << "," << est_rpy_deg[2] << ",";
+            // Add Euler angles (in degrees) for true state
+            resultFile << true_rpy_deg[0] << "," << true_rpy_deg[1] << "," << true_rpy_deg[2] << ",";
+            // Add Euler angles (in degrees) for estimated calibration
+            resultFile << est_cal_rpy_deg[0] << "," << est_cal_rpy_deg[1] << "," << est_cal_rpy_deg[2] << ",";
+            // Add Euler angles (in degrees) for true calibration
+            resultFile << true_cal_rpy_deg[0] << "," << true_cal_rpy_deg[1] << "," << true_cal_rpy_deg[2];
+
+            resultFile << std::endl;
+        }
+
+        // Print progress
+        if (i % 1000 == 0 || i == data_list.size() - 1) {
+            std::cout << "Processed " << i+1 << "/" << data_list.size()
+                      << " (" << (100.0 * (i+1) / data_list.size()) << "%) ";
+            std::cout << "Attitude error: " << (att_error.norm() * 180.0/M_PI) << " deg, ";
+            std::cout << "Bias error: " << bias_error.norm() << ", ";
+            std::cout << "Calibration error: " << (cal_error.norm() * 180.0/M_PI) << " deg" << std::endl;
+        }
+    }
+
+    auto end = std::chrono::high_resolution_clock::now();
+    std::chrono::duration<double> elapsed = end - start;
+
+    // Calculate average errors
+    double avg_att_error = 0.0;
+    double avg_bias_error = 0.0;
+    double avg_cal_error = 0.0;
+
+    if (!att_errors.empty()) {
+        avg_att_error = std::accumulate(att_errors.begin(), att_errors.end(), 0.0) / att_errors.size();
+        avg_bias_error = std::accumulate(bias_errors.begin(), bias_errors.end(), 0.0) / bias_errors.size();
+        avg_cal_error = std::accumulate(cal_errors.begin(), cal_errors.end(), 0.0) / cal_errors.size();
+    }
+
+    std::cout << std::endl;
+    std::cout << "EqF Processing completed in " << elapsed.count() << " seconds" << std::endl;
+    std::cout << "Average attitude error: " << (avg_att_error * 180.0/M_PI) << " deg" << std::endl;
+    std::cout << "Average bias error: " << avg_bias_error << std::endl;
+    std::cout << "Average calibration error: " << (avg_cal_error * 180.0/M_PI) << " deg" << std::endl;
+    std::cout << "Total measurements: " << total_measurements << std::endl;
+    std::cout << "Valid measurements processed: " << valid_measurements << std::endl;
+    std::cout << "Invalid measurements skipped: " << invalid_measurements << std::endl;
+
+    if (saveResults) {
+        resultFile.close();
+        std::cout << "Results saved to " << resultFilename << std::endl;
+    }
+}
+
+
+
+
+
+inline void runEqFWithCSVData(const std::string& filename) {
+    try {
+        // Load data from CSV file with optional parameters
+        int startRow = 0;
+        int maxRows = -1;
+        int downsample = 1;
+
+        std::vector<Data> data = loadDataFromCSV(filename, startRow, maxRows, downsample);
+
+        if (data.empty()) {
+            std::cerr << "No data loaded from CSV file." << std::endl;
+            return;
+        }
+
+        // Print sample data points to inspect the loaded data
+        std::cout << "Data loaded, displaying samples..." << std::endl;
+        printDataSamples(data);
+
+        // Validate the data to check for issues
+        std::cout << "Validating data integrity..." << std::endl;
+        bool dataValid = validateData(data);
+
+        if (!dataValid) {
+            std::cout << "Warning: Data validation found issues." << std::endl;
+            std::string proceed;
+            std::cout << "Do you want to proceed anyway? (y/n): ";
+            std::cin >> proceed;
+            if (proceed != "y" && proceed != "Y") {
+                std::cout << "Operation cancelled by user." << std::endl;
+                return;
+            }
+        }
+
+        // Initialize EqF filter
+        int n_cal = 1;      // Number of calibration states (from the data)
+        int n_sensors = 2;  // Number of sensors (from the data)
+
+        // Initial covariance
+        Matrix initialSigma = Matrix::Identity(6 + 3*n_cal, 6 + 3*n_cal);
+        initialSigma.diagonal().head<3>() = Vector3::Constant(0.1); // Reduced attitude uncertainty
+        initialSigma.diagonal().segment<3>(3) = Vector3::Constant(0.01); // Reduced bias uncertainty
+        initialSigma.diagonal().tail<3>() = Vector3::Constant(0.1); // Reduced calibration uncertainty
+
+        // Create filter
+        EqF filter(initialSigma, n_cal, n_sensors);
+
+        // Initialize filter state from the first ground truth if possible
+        if (!data.empty()) {
+            // You'll need to add a method to your EqF class to set the initial state
+            // Something like:
+            // filter.setInitialState(data[0].xi);
+
+            // If you don't have such a method, you can print the first ground truth
+            // to see if it makes sense
+            std::cout << "First ground truth state:" << std::endl;
+            std::cout << "Attitude: " << data[0].xi.R.matrix() << std::endl;
+            std::cout << "Bias: " << data[0].xi.b.transpose() << std::endl;
+            std::cout << "Calibration: " << data[0].xi.S[0].matrix() << std::endl;
+        }
+
+        // Process data with the filter and save results
+        processDataWithEqF(filter, data, true, "eqf_results.csv");
+
+    } catch (const std::exception& e) {
+        std::cerr << "Error: " << e.what() << std::endl;
+    }
+}
+/**
+ * Example usage function to demonstrate how to use the data loader with the EqF
+ */
+
+#endif //RUNEQF_WITHCSV_H