diff --git a/pipelines/steps/visualize_datatable_vars.py b/pipelines/steps/visualize_datatable_vars.py
new file mode 100644
index 0000000..75da158
--- /dev/null
+++ b/pipelines/steps/visualize_datatable_vars.py
@@ -0,0 +1,78 @@
+
+import dima.src.hdf5_ops as dataOps
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+def visualize_table_variables(data_file_path, dataset_name, flags_dataset_name, x_var, y_vars):
+
+
+
+    if not os.path.exists(data_file_path): 
+        raise ValueError(f"Path to input file {data_file_path} does not exists. The parameter 'data_file_path' must be a valid path to a suitable HDF5 file. ")
+    
+    # Create data manager object
+    dataManager = dataOps.HDF5DataOpsManager(data_file_path)
+
+    dataManager.load_file_obj()
+
+    # Specify diagnostic variables and the associated flags 
+    #dataset_name = 'ACSM_TOFWARE/2024/ACSM_JFJ_2024_meta.txt/data_table'
+    #flags_dataset_name = 'ACSM_TOFWARE_flags/2024/ACSM_JFJ_2024_meta_flags.csv/data_table'
+    dataset_df = dataManager.extract_dataset_as_dataframe(dataset_name)
+    flags_df = dataManager.extract_dataset_as_dataframe(flags_dataset_name)
+
+    if x_var not in dataset_df.columns and x_var not in flags_df.columns:
+        raise ValueError(f'Invalid x_var : {x_var}. x_var must refer to a time variable name that is both in {dataset_name} and {flags_dataset_name}')
+
+    flags_df[x_var] = pd.to_datetime(flags_df[x_var].apply(lambda x : x.decode(encoding="utf-8")))
+
+    dataManager.unload_file_obj()
+
+    if not all(var in dataset_df.columns for var in y_vars):
+        raise ValueError(f'Invalid y_vars : {y_vars}. y_vars must be a subset of {dataset_df.columns}.')
+
+    #fig, ax = plt.subplots(len(y_vars), 1, figsize=(12, 5))
+
+
+    for var_idx, var in enumerate(y_vars):
+        #y = dataset_df[var].to_numpy()
+        
+        # Plot Flow Rate
+        fig = plt.figure(var_idx,figsize=(12, 2.5))
+        ax = plt.gca()
+        #ax = fig.get_axes()
+        ax.plot(dataset_df[x_var], dataset_df[var], label=var, alpha=0.8, color='tab:blue')
+
+        # Specify flag name associated with var name in y_vars. By construction, it is assumed the name satisfy the following sufix convention.
+        var_flag_name = f"flag_{var}"        
+        if var_flag_name in flags_df.columns:   
+            # Identify valid and invalid indices         
+            ind_valid = flags_df[var_flag_name].to_numpy()
+            ind_invalid = np.logical_not(ind_valid)
+            # Detect start and end indices of invalid regions
+            # Find transition points in invalid regions
+            invalid_starts = np.diff(np.concatenate(([False], ind_invalid, [False]))).nonzero()[0][::2]
+            invalid_ends = np.diff(np.concatenate(([False], ind_invalid, [False]))).nonzero()[0][1::2]
+
+            # Fill invalid regions                
+            t_base = dataset_df[x_var].to_numpy()
+            for start, end in zip(invalid_starts, invalid_ends):
+                ax.fill_betweenx([dataset_df[var].min(), dataset_df[var].max()], t_base[start], t_base[end], 
+                                    color='red', alpha=0.3, label="Invalid Data" if start == invalid_starts[0] else "")
+
+        # Labels and Legends
+        ax.set_xlabel(x_var)
+        ax.set_ylabel(var)
+        ax.legend()
+        ax.grid(True)
+
+                #plt.tight_layout()
+                #plt.show()
+
+    return fig, ax
+    
+
+
+