diff --git a/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py b/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
index 75cc285d352d12d4d79b5fc341ebff3b08ce6f14..058d542045b3c52f8a6ebf3190445ae825518bd5 100644
--- a/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
+++ b/dynamics_learning/dynamics_learning/preprocessing/dataset_analysis.py
@@ -1,191 +1,76 @@
-#%%
-# Import and variables
 import sys
-sys.path.append("src")
-sys.path.append("..")
-import numpy as np
-import matplotlib.pyplot as plt
-import pandas as pd
-import rwth_style
-import panda_limits 
+from typing import List, Tuple
 import os
-from scipy.optimize import curve_fit
-from sklearn.metrics import r2_score
-from rich import print
-from rich.progress import track
+import numpy as np
+import panda_limits
+import tqdm
+
+# Add the custom module path to sys.path
+sys.path.append("/root/deep-learning-based-robot-dynamics-modelling-in-robot-based-laser-material-processing/src")
+
+# Define the directory containing the data
+directory_in = "/root/deep-learning-based-robot-dynamics-modelling-in-robot-based-laser-material-processing/data/dataset_v3/test_2"
+
+def normalize_data(data: np.ndarray, min_vals: List[float], max_vals: List[float], min: float = -1, max: float = 1) -> np.ndarray:
+    """
+    Normalize the data using given min and max values for each parameter.
+
+    Args:
+        data (np.ndarray): The data to normalize.
+        min_vals (List[float]): The minimum values for each parameter.
+        max_vals (List[float]): The maximum values for each parameter.
+        min (float): The new minimum value for normalization.
+        max (float): The new maximum value for normalization.
+
+    Returns:
+        np.ndarray: The normalized data.
+    """
+    return (data - min_vals) / (max_vals - min_vals) * (max - min) + min
+
+def process_file(directory: str, filename: str) -> None:
+    """
+    Process a given file by normalizing its data and saving interpolated values.
 
-def gauss(x, *p):
-    A, mu, sigma = p
-    return A*np.exp(-(x-mu)**2/(2.*sigma**2))
+    Args:
+        directory (str): The directory containing the file.
+        filename (str): The name of the file to process.
+    """
+    # Read the command data
+    data1 = np.genfromtxt(os.path.join(directory, filename), dtype=float, delimiter=',') 
+    t_command = data1[:,0]  # Command times
+    motion_params = data1[:,1:22]  # Command parameters
 
-p0 = [1, 0, 1]
-# %%
-for directory in ["dataset_v1", "dataset_v2", "dataset_v3"]:
-    for dataset in ["train", "test"]:
-        attained_freqs = np.empty([0,1])
-        duration_meas = np.empty(0, np.float32)
-        num_samples_meas = np.empty(0, np.float32)
-        q_command = np.empty((0,7), np.float32)
-        qd_command = np.empty((0,7), np.float32)
-        qdd_command = np.empty((0,7), np.float32)
-        q_meas = np.empty((0,7), np.float32)
-        qd_meas = np.empty((0,7), np.float32)
-        tau_meas = np.empty((0,7), np.float32)
-        directory_in = "data/{}/{}".format(directory, dataset)
-        file_list = list(filter(lambda k: 'meas.csv' in k, sorted(os.listdir(directory_in))))
-        
-        for filename in track(file_list):
-                data2 = np.genfromtxt(os.path.join(directory_in, filename), dtype=float, delimiter=',') 
-                t_meas = data2[:,0]
-                time_diffs = np.diff(t_meas)
-                attained_freq = np.mean(1/time_diffs)
-                attained_freqs = np.append(attained_freqs, attained_freq)
-                num_samples_meas = np.concatenate((num_samples_meas, [data2.shape[0]]))
-                duration_meas = np.concatenate((duration_meas, [data2[-1, 0]]), axis=0)
-                q_meas = np.concatenate((q_meas, data2[:, 1:8]), axis=0)
-                qd_meas = np.concatenate((qd_meas, data2[:, 8:15]), axis=0)
-                tau_meas = np.concatenate((tau_meas, data2[:, 15:22]), axis=0)
-                filename2 = filename.replace("meas", "com")
-                data1 = np.genfromtxt(os.path.join(directory_in, filename2), dtype=float, delimiter=',') 
-                q_command = np.concatenate((q_command, data1[:,1:8]), axis=0)
-                qd_command = np.concatenate((qd_command, data1[:, 8:15]), axis=0)
-                qdd_command = np.concatenate((qdd_command, data1[:, 15:22]), axis=0)
-        
-        df = pd.DataFrame({"# trajecotries":[duration_meas.size], 
-                        "Duration Sum [s]": np.sum(duration_meas),
-                        "Duration Min [s]": np.min(duration_meas),
-                        "Duration Max [s]":np.max(duration_meas),
-                        "Duration Mean [s]":np.mean(duration_meas),
-                        "# Samples Sum":np.sum(num_samples_meas),
-                        "# Samples Min":np.min(num_samples_meas),
-                        "# Samples Max":np.max(num_samples_meas),
-                        "# Samples Mean":np.mean(num_samples_meas)
-                        })
-        df.index = ['Value']
-        df1 = df.T
-        df1.to_csv('data/{}/analysis/trajectory_analysis_{}.csv'.format(directory, dataset), float_format='%.3f')  
+    # Read the measurement data
+    filename2 = filename.replace("com", "meas")
+    data2 = np.genfromtxt(os.path.join(directory, filename2), dtype=float, delimiter=',') 
+    t_meas = data2[:,0]  # Measurement times
 
-        # Attained frequency analysis
-        freq_mu = np.mean(attained_freqs)
-        freq_sigma = np.std(attained_freqs)
-        freq_worst_case = np.max(freq_mu - attained_freqs)
-        with plt.style.context("default"):
-            num_col = 1
-            num_row = 1
-            fig, axs = plt.subplots(num_row, num_col, figsize=(10,4))
-            count, bins, ignored = axs.hist(attained_freqs, density=True, bins=50, color=rwth_style.blue)
-            plt.plot(bins, 1/(freq_sigma * np.sqrt(2 * np.pi)) *
-                    np.exp( - (bins - freq_mu)**2 / (2 * freq_sigma**2) ),linewidth=2, color=rwth_style.red)
-            axs.grid()
-            axs.set_title(r'$\mathrm{Histogram\ of\ Frequency:}\ \mu=%.3f Hz,\ \sigma=%.3f$ Hz' %(freq_mu, freq_sigma))
-            axs.set_ylabel("Probability")
-            axs.set_xlabel("Frequency")
-            fig.tight_layout()
-            fig.savefig("data/{}/analysis/hist_freq_{}.png".format(directory, dataset))
-            plt.close(fig)
+    # Normalize positions, velocities, and accelerations
+    for i in range(0, 7):
+        motion_params[:, i] = normalize_data(motion_params[:, i], panda_limits.q_lim_min_phys[i], panda_limits.q_lim_max_phys[i])
+    for i in range(7, 14):  
+        motion_params[:, i] = normalize_data(motion_params[:, i], panda_limits.qd_lim_min_phys[i-7], panda_limits.qd_lim_max_phys[i-7])
+    for i in range(14, 21):
+        motion_params[:, i] = normalize_data(motion_params[:, i], panda_limits.qdd_lim_min_phys[i-14], panda_limits.qdd_lim_max_phys[i-14])
 
-        values = [q_command, qd_command, qdd_command, tau_meas]
-        names = ["q", "qd", "qdd", "tau"]
-        labels_y = ["Position [rad]", "Velocity [rad-s]", "Acceleration [rad-s^2]", "Torque [Nm]"]
-        units = ["[rad]", "[rad-s]", "[rad-s^2]", "[Nm]"]
-        titles = ["Command Position", "Command Velocity", "Command Acceleration", "Measured Torque"]
-        labels = ['Axis 1', 'Axis 2', 'Axis 3', 'Axis 4', 'Axis 5', 'Axis 6', 'Axis 7']
-        # phyiscal limits
-        min_limits_phys = [panda_limits.q_lim_min_phys, panda_limits.qd_lim_min_phys, panda_limits.qdd_lim_min_phys, panda_limits.tau_lim_min_phys]
-        max_limits_phys = [panda_limits.q_lim_max_phys, panda_limits.qd_lim_max_phys, panda_limits.qdd_lim_max_phys, panda_limits.tau_lim_max_phys]
-        # Moveit limits
-        min_limits_moveit = [panda_limits.q_lim_min_moveit, panda_limits.qd_lim_min_moveit, panda_limits.qdd_lim_min_moveit, panda_limits.tau_lim_min_moveit]
-        max_limits_moveit = [panda_limits.q_lim_max_moveit, panda_limits.qd_lim_max_moveit, panda_limits.qdd_lim_max_moveit, panda_limits.tau_lim_max_moveit]
-        length= len(values)
+    # Interpolate command values to match measurement times
+    motion_params_interpolated = np.empty([t_meas.shape[0], 21])
+    for i in range(0, 21):
+        motion_params_interpolated[:, i] = np.interp(t_meas, t_command, motion_params[:, i])
 
-        for i in range(length):        
-            value = values[i]
-            std = np.std(value, axis=0)
-            mean = np.mean(value, axis=0)
-            min = np.min(value, axis=0)
-            max = np.max(value, axis=0)
-            phy_mean = (max_limits_phys[i]+min_limits_phys[i])/2
-            cov_max_phys  = (max-min) / (max_limits_phys[i]-min_limits_phys[i]) * 100 # Percentage of used working space
-            cov_std_phys  = (2*std) / (max_limits_phys[i]-min_limits_phys[i]) * 100 # Percentage of used working space based on standard deviaton
-            cov_max_moveit  = (max-min) / (max_limits_moveit[i]-min_limits_moveit[i]) * 100 # Percentage of used working space
-            cov_std_moveit  = (2*std) / (max_limits_moveit[i]-min_limits_moveit[i]) * 100 # Percentage of used working space based on standard deviaton
-            
-            df = pd.DataFrame({"Att. Mean {}".format(units[i]):mean, 
-                            "Phys. Mean {}".format(units[i]):phy_mean,
-                            "MoveIt Mean {}".format(units[i]):phy_mean,
-                            
-                            "Att. Min {}".format(units[i]):min,
-                            "Phys. Min {}".format(units[i]):min_limits_phys[i],
-                            "MoveIt Min {}".format(units[i]):min_limits_moveit[i],
-                                                        
-                            "Att. Max {}".format(units[i]):max,
-                            "Phys. Max {}".format(units[i]):max_limits_phys[i],
-                            "MoveIt Max {}".format(units[i]):max_limits_moveit[i],
-                            
-                            "Phy. Coverage Min/Max [\\%]":cov_max_phys ,
-                            "MoveIt Coverage Min/Max [\\%]":cov_max_moveit ,
-                            
-                            "Att. Std {}".format(units[i]):std,
-                            "Phy. Coverage Std [\\%]":cov_std_phys ,                           
-                            "MoveIt Coverage Std [\\%]":cov_std_moveit ,
-                            })
-            df.index = ['Axis_1', 'Axis_2', 'Axis_3', 'Axis_4', 'Axis_5', 'Axis_6', 'Axis_7']
-            df1 = df.T
-            df1.to_csv('data/{}/analysis/feature_analysis_{}_{}.csv'.format(directory, dataset, names[i]), float_format='%.3f')  
-            
-            x_pos = np.arange(len(labels))
-            with plt.style.context("default"):
-                f4, ax = plt.subplots(figsize=(10,5))
-                bar1_alpha = 1
-                bar1_hatch = ''
-                plt.rcParams.update({'hatch.color': rwth_style.blue_light})
-                if (i==2 or i==0):
-                    ax.bar(x_pos, max_limits_moveit[i] - min_limits_moveit[i], 0.4, bottom = min_limits_moveit[i], color=rwth_style.blue_light, label='MoveIt Limits', zorder=3)
-                    bar1_alpha = 0.5
-                    bar1_hatch = '//'
-                bar1 = ax.bar(x_pos, max_limits_phys[i] - min_limits_phys[i], 0.4, bottom = min_limits_phys[i], color=rwth_style.blue_light, alpha=bar1_alpha, hatch=bar1_hatch, label='Physical Limits', zorder=2)
-                ax.scatter(x_pos, (max_limits_phys[i] + min_limits_phys[i])/2, c=[rwth_style.blue], s=400, marker='_', label='Physical Mean', zorder=4)
-                errorbar_1 = ax.errorbar(x_pos, mean, std, fmt='o', capsize=5, label='Mean/Std Attained {}'.format(titles[i]),  c=rwth_style.blue, zorder=5)
-                scatter_min = ax.scatter(x_pos, min, c=[rwth_style.blue], s=20, marker='x', label='Min/Max Attained {}'.format(titles[i]), zorder=6)
-                ax.scatter(x_pos, max, c=[rwth_style.blue], s=20, marker='x', zorder=6)
-                ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08), ncol=2)
-                box = ax.get_position()
-                ax.set_position([box.x0, box.y0 + box.height * 0.15, box.width, box.height])
-                ax.set_ylabel(labels_y[i])
-                ax.set_ylim((-ax.get_ylim()[1], ax.get_ylim()[1]))
-                ax.set_xticks(x_pos)
-                ax.set_xticklabels(labels)
-                ax.yaxis.grid(True)
-                ax.set_axisbelow(True)
-                f4.savefig("data/{}/analysis/errorbar_{}_{}.png".format(directory, dataset, names[i]))
-                plt.close(f4)
+    # Combine measurement times with interpolated parameters
+    meas = np.hstack([t_meas[:, None], motion_params_interpolated])
+    
+    # Save the result to a new file
+    file_output = os.path.join(directory, filename.replace("com", "interp_com"))
+    np.savetxt(
+        file_output,
+        meas,
+        delimiter=",",
+        header="t_com, q1_com, q2_com, q3_com, q4_com, q5_com, q6_com, q7_com, qd1_com, qd2_com, qd3_com, qd4_com, qd5_com, qd6_com, qd7_com, qdd1_com, qdd2_com, qdd3_com, qdd4_com, qdd5_com, qdd6_com, qdd7_com",
+    )
 
-            with plt.style.context("default"):
-                num_col = 3
-                num_row = 3
-                fig, axs = plt.subplots(num_row, num_col, figsize=(20,10))
-            
-                for k, label in enumerate(labels):
-                    count, bins, ignored = axs[k//num_col, k%num_col].hist(value[:,k], density=True, bins=40, color=rwth_style.blue)
-                    bins = bins[:-1]
-                    try:
-                        coeff, var_matrix = curve_fit(gauss, bins, count, p0=p0, maxfev = 2000)
-                        # Get the fitted curve
-                        hist_fit = gauss(bins, *coeff)
-                        r_squared = r2_score(count, hist_fit)
-                        axs[k//num_col, k%num_col].plot(bins,hist_fit, color=rwth_style.red)
-                        t = axs[k//num_col, k%num_col].text(0.15,0.85, f"mean: {round(coeff[1],3)}\nstd: {round(coeff[2],3)}\nr squared: {round(round(r_squared,4)*100,2)}%", horizontalalignment='center', verticalalignment='center', transform=axs[k//num_col, k%num_col].transAxes)
-                        t.set_bbox(dict(facecolor='white', alpha=0.5, edgecolor='black'))
-                    except Exception as e:
-                        print(e)
-                    axs[k//num_col, k%num_col].grid()
-                    axs[k//num_col, k%num_col].set_title(labels[k])
-                    axs[k//num_col, k%num_col].set_ylabel("Probability Density")
-                    axs[k//num_col, k%num_col].set_xlabel(labels_y[i]) 
-                    axs[k//num_col, k%num_col].set_xlim([min_limits_phys[i][k],max_limits_phys[i][k]])
-                    
-                fig.delaxes(axs[-1,-1])
-                fig.delaxes(axs[-1,-2])
-                fig.tight_layout()
-                fig.savefig("data/{}/analysis/hist_{}_{}.png".format(directory, dataset, names[i]))
-                plt.close(fig)
+# Process all relevant files in the directory
+for filename in sorted(os.listdir(directory_in)):
+    if filename.endswith("com.csv") and "interp" not in filename:
+        process_file(directory_in, filename)