diff --git a/cosipy/data_io/custom_functions.py b/cosipy/data_io/custom_functions.py
new file mode 100644
index 00000000..fa1e1155
--- /dev/null
+++ b/cosipy/data_io/custom_functions.py
@@ -0,0 +1,135 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import astropy.units as u
+from astropy.constants import h
+
+class SpectrumFileProcessor:
+    def __init__(self, input_file, reformatted_file, energy_col=0, flux_col=1, convert_data=True):
+        """
+        Initialize the SpectrumFileProcessor class with default columns for energy and flux.
+        
+        Parameters:
+        - input_file: Path to the input .dat file.
+        - reformatted_file: Path to save the reformatted data.
+        - energy_col: Index of the column containing energy or frequency (default: 0).
+        - flux_col: Index of the column containing flux (default: 1).
+        - convert_data: Boolean flag indicating if conversion is needed (default: True).
+        """
+        self.input_file = input_file
+        self.reformatted_file = reformatted_file
+        self.data = None
+        self.df_filtered = None
+        self.energy_col = energy_col  # Default to column 0 for energy
+        self.flux_col = flux_col      # Default to column 1 for flux
+        self.convert_data = convert_data  # Whether conversion is necessary
+
+    def load_data(self):
+        """Load the data from the .dat file."""
+        try:
+            self.data = np.loadtxt(self.input_file)
+            print(f"Data loaded successfully from {self.input_file}")
+        except Exception as e:
+            print(f"Error loading file {self.input_file}: {e}")
+            raise
+
+    def process_data(self):
+        """Process the data: Convert frequency to energy in keV and flux to ph/cm²/sec/keV if needed."""
+        if self.convert_data:
+            # If conversion is required
+            energy_hz = self.data[:, self.energy_col] * u.Hz   # Column defined by user
+            flux_ergs = self.data[:, self.flux_col] * u.erg / (u.cm**2 * u.s)  # Column defined by user
+            
+            # Convert frequency to energy in keV
+            energy_keV = (h * energy_hz).to(u.keV)
+
+            # Convert flux from ergs to keV
+            flux_keV = flux_ergs.to(u.keV / (u.cm**2 * u.s))
+
+            # Convert flux to ph/cm²/sec/keV
+            flux_ph = (flux_keV / energy_keV**2)
+        
+        else:
+            # If data is already in keV, assume first column is energy (keV) and second column is flux
+            energy_keV = self.data[:, self.energy_col] * u.keV  # Directly use energy in keV
+            flux_ph = self.data[:, self.flux_col] * u.ph / (u.cm**2 * u.s * u.keV)  # Directly use flux in ph/cm²/sec/keV
+
+        # Create a DataFrame to store the data
+        df = pd.DataFrame({
+            'Energy (keV)': energy_keV.value,
+            'Flux (ph/cm²/sec/keV)': flux_ph.value
+        })
+
+        # Filter out rows with energy less than 100 keV and more than 10000 keV
+        self.df_filtered = df[(df['Energy (keV)'] >= 100) & (df['Energy (keV)'] <= 10000)]
+        return self.df_filtered
+
+    def integrate_flux(self):
+        """
+        Compute the total flux using the sum of flux multiplied by energy bin widths.
+        
+        Returns:
+        - K: The total flux.
+        """
+        if self.df_filtered is None or self.df_filtered.empty:
+            raise ValueError("No data to integrate. Please run process_data() first.")
+        
+        # Get energy and flux from the processed data
+        energy = self.df_filtered['Energy (keV)'].values
+        flux = self.df_filtered['Flux (ph/cm²/sec/keV)'].values
+
+        # Calculate the energy bin widths
+        ewidths = np.diff(energy, append=energy[-1])
+
+        # Calculate the current total flux (integral of the spectrum)
+        K = np.sum(flux * ewidths)
+
+        print(f"Calculated Normalization Constant K: {K}")
+        
+        return K
+
+    def plot_spectrum(self):
+        """Generate a plot of energy vs flux with a log-log scale."""
+        if self.df_filtered is not None:
+            plt.figure(figsize=(10, 6))
+            plt.plot(self.df_filtered['Energy (keV)'], self.df_filtered['Flux (ph/cm²/sec/keV)'], marker="o", linestyle="-")
+            plt.xscale("log")
+            plt.yscale("log")
+            plt.xlabel("Energy (keV)")
+            plt.ylabel("Flux (ph/cm²/sec/keV)")
+            plt.title("Energy Flux Data")
+            plt.grid(True)
+            plt.show()
+        else:
+            print("No data available for plotting. Please run process_data first.")
+
+    def reformat_data(self):
+        """Reformat the data for the photon spectrum file."""
+        try:
+            formatted_lines = []
+            formatted_lines.append("-Ps photon spectrum file")
+            formatted_lines.append("#")
+            formatted_lines.append("# Format: DP <energy in keV> <shape of O-Ps [XX/keV]>")
+            formatted_lines.append("")
+            formatted_lines.append("IP LIN")
+            formatted_lines.append("")
+            
+            # Iterate through the DataFrame to format each line as required
+            for index, row in self.df_filtered.iterrows():
+                energy = row['Energy (keV)']
+                flux = row['Flux (ph/cm²/sec/keV)']
+                formatted_line = f"DP\t{energy:.5e}\t{flux:.5e}"  # Add DP and reformat the line
+                formatted_lines.append(formatted_line)
+            
+            # Append the closing 'EN' line
+            formatted_lines.append("EN")
+            
+            # Save the new formatted data to a file
+            with open(self.reformatted_file, 'w') as f:
+                f.write("\n".join(formatted_lines))
+            
+            print(f"Reformatted data saved to {self.reformatted_file}")
+        
+        except Exception as e:
+            print(f"Error during reformatting: {e}")
+            raise
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